diff --git a/cosipy/__init__.py b/cosipy/__init__.py
index 7767e31b2..0e59cf0b0 100644
--- a/cosipy/__init__.py
+++ b/cosipy/__init__.py
@@ -23,6 +23,6 @@
 from .source_injector import SourceInjector
 
 from .background_estimation import LineBackgroundEstimation
-from .background_estimation import ContinuumEstimation
+from .background_estimation import ContinuumEstimationNN
 from .background_estimation import TransientBackgroundEstimation
 
diff --git a/cosipy/background_estimation/ContinuumEstimation.py b/cosipy/background_estimation/ContinuumEstimation.py
deleted file mode 100644
index ea90600c5..000000000
--- a/cosipy/background_estimation/ContinuumEstimation.py
+++ /dev/null
@@ -1,359 +0,0 @@
-# Imports:
-import os
-from astropy.coordinates import SkyCoord
-from astropy import units as u
-from cosipy.response import FullDetectorResponse, DetectorResponse
-from cosipy import BinnedData
-from mhealpy import HealpixMap, HealpixBase
-import matplotlib.pyplot as plt
-import numpy as np
-from scipy.stats import norm
-import numpy.ma as ma
-from tqdm import tqdm
-import logging
-logger = logging.getLogger(__name__)
-
-class ContinuumEstimation:
-    
-    def calc_psr(self, sc_orientation, detector_response, coord, nside=16):
-
-        """Calculates point source response (PSR) in Galactic coordinates.
-        
-        Parameters
-        ----------
-        ori_file : str
-            Full path to orienation file.
-        sc_orientation : cosipy.spacecraftfile.SpacecraftHistory
-            Spacecraft orientation object.  
-        detector_response : str
-            Full path to detector response file.
-        coord : astropy.coordinates.SkyCoord
-            The coordinates of the target object. 
-        nside : int, optional
-            nside of scatt map (default is 16). 
-        
-        Returns
-        -------
-        :py:class:`PointSourceResponse` 
-        """
-
-        # Detector response:
-        dr = detector_response
-
-        # Scatt map:
-        scatt_map = sc_orientation.get_scatt_map(nside = nside, target_coord = coord)
-
-        # Calculate PSR:
-        with FullDetectorResponse.open(dr) as response:
-            psr = response.get_point_source_response(coord = coord, scatt_map = scatt_map)
-
-        return psr
-
-    def load_psr_from_file(self, psr_file):
-
-        """Loads point source response from h5 file.
-            
-        Parameters
-        ----------
-        psr_file : str
-            Full path to precomputed response file (.h5 file).
-        """
-
-        logger.info("...loading the pre-computed point source response ...")
-        psr = DetectorResponse.open(psr_file)
-        logger.info("--> done")
-
-        return psr
-
-    def load_full_data(self, data_file, data_yaml):
-
-        """Loads binned data to be used as a template for the background estimate.
-        
-        Parameters
-        ----------
-        data_file : str
-            Full path to binned data (must be .h5 file). 
-        data_yaml : str
-            Full path to the dataIO yaml file used for binning the data. 
-
-        Notes
-        -----
-        In practice, the data file used for estimating the background 
-        should be the full dataset.  
-        
-        The full data binning needs to match the PSR. 
-        """
-        
-        self.full_data = BinnedData(data_yaml)
-        self.full_data.load_binned_data_from_hdf5(data_file)
-
-        return
-
-    def mask_from_cumdist(self, psichi_map, containment, make_plots=False):
-
-        """
-        Determines masked pixels from cumulative distribution of
-        the point source response.
-        
-        Parameters
-        ----------
-        psichi_map : histpy:Histogram
-            Point source response projected onto psichi. This can be 
-            either a slice of Em and Phi, or the full projection. Note
-            that psichi is a HealpixMap axis in histpy. 
-        containment : float
-            The percentage (non-inclusive) of the cumulative distribution 
-            to use for the mask, i.e. all pixels that fall below this value 
-            in the cumulative distribution will be masked. 
-        make_plots : bool
-            Option to plot cumulative distribution. 
-
-        Returns
-        -------
-        sorted_indices : array
-            Indices of sorted psichi array.
-        arm_mask : array
-            Boolean array specifying pixels in the psichi map that will be masked. 
-
-        Note
-        ----
-        The cumulative distribution is an estimate of the angular
-        resolution measure (ARM), which is a measure of the PSF
-        for Compton imaging. 
-        """
-
-        # Get healpix map:
-        h = psichi_map
-        m = HealpixMap(base = HealpixBase(npix = h.nbins), data = h.contents)
-
-        # Sort data in descending order:
-        sorted_data = np.sort(m)[::-1]
-
-        # Calculte the cummulative distribution
-        cumdist = np.cumsum(sorted_data) / sum(sorted_data)
-
-        # Get indices of sorted array
-        sorted_indices = np.argsort(h.contents.value)[::-1]
-
-        # Define mask based on fraction of total exposure (i.e. counts):
-        arm_mask = cumdist >= containment
-        arm_mask = ~arm_mask
-
-        # Plot cummulative distribution and corresponding masks:
-        if make_plots == True:
-            plt.plot(cumdist)
-            plt.title("Cumulative Distribution")
-            plt.xlabel("Pixel")
-            plt.ylabel("Fraction of Counts")
-            plt.savefig("cumdist.png")
-            plt.show()
-            plt.close()
-        
-        return sorted_indices, arm_mask
-
-    def simple_inpainting(self, m_data, sorted_indices, arm_mask):
-
-        """Highly simplistic method for inpainting masked region in CDS.
-
-        This method relies on the input healpix map having a ring 
-        ordering. For each masked pixel, it searches to the left (i.e.
-        lower pixel numbers) until reaching the first non-zero pixel. 
-        It then search to the right (i.e. higher pixel numbers) until
-        again finding the first non-zero pixel. The mean of the two 
-        values is used for filling in the masked pixel. 
-        
-        Parameters
-        ----------
-        m_data : array-like
-            HealpixMap object, containing projection of PSR onto psichi.
-        sorted_indices : array
-            Indices of sorted psichi array.
-        arm_mask : array
-            Boolean array specifying pixels in the psichi map that will be masked. 
-
-        Returns
-        -------
-        interp_list : array
-            Values for the inpainting, corresponding to the masked pixels. 
-        """
-
-        # Get mean of masked data for edge cases (simple solution for now):
-        # CK: It would be better if this were at least the mean of an 
-        # np masked array object, but a better method is anyways needed.
-        masked_mean = np.mean(m_data)
-
-        # Get interpolation values:
-        interp_list_low = []
-        interp_list_high = []
-        for i in range(0,len(sorted_indices[arm_mask])):
-            
-            this_index = sorted_indices[arm_mask][i]
-            
-            # Search left:
-            k = 1
-            search_left = True
-            while search_left == True:
-                
-                if this_index-k < 0:
-                    logger.info("Edge case!")
-                    interp_list_low.append(masked_mean)
-                    search_left = False
-                    break
-                    
-                next_value = m_data[this_index-k]
-                if next_value == 0:
-                    k += 1
-                if next_value != 0:
-                    interp_list_low.append(next_value)
-                    search_left = False
-           
-            # Search right:
-            j = 1
-            search_right = True
-            while search_right == True:
-               
-                if this_index+j >= self.psr.axes['PsiChi'].nbins-1:
-                    logger.info("Edge case!")
-                    interp_list_high.append(masked_mean)
-                    search_right = False
-                    break
-                
-                next_value = m_data[this_index+j]
-                if next_value == 0:
-                    j += 1
-                if next_value != 0:
-                    interp_list_high.append(next_value)
-                    search_right = False
-            
-        interp_list_low = np.array(interp_list_low)
-        interp_list_high = np.array(interp_list_high)
-        interp_list = (interp_list_low + interp_list_high) / 2.0 
-    
-        return interp_list
-
-    def continuum_bg_estimation(self, data_file, data_yaml, psr, \
-            containment=0.4, make_plots=False,\
-            e_loop="default", s_loop="default"):
-
-        """Estimates continuum background.
-        
-        Parameters
-        ----------
-        data_file : str
-            Full path to binned data (must be .h5 file). 
-        data_yaml : str
-            Full path to the dataIO yaml file used for binning the data.  
-        psr : py:class:`PointSourceResponse`
-            Point source response object. 
-        containment : float, optional
-            The percentage (non-inclusive) of the cumulative distribution 
-            to use for the mask, i.e. all pixels that fall below this value 
-            in the cumulative distribution will be masked. Default is 0.4. 
-        make_plots : bool, optional
-            Option to make some plots of the data, response, and masks. 
-            Default is False.
-        e_loop : tuple, optional
-            Option to pass tuple specifying which energy range to 
-            loop over. This must coincide with the energy bins. The default
-            is all bins.
-        s_loop : tuple, optional
-            Option to pass tuple specifying which Phi anlge range to
-            loop over. This must coincide with the Phi  bins. The default
-            is all bins.
-        
-        Returns
-        -------
-        estimated_bg : histpy:Histogram
-            Estimated background as histpy object. 
-        """
-
-        # Define psr attribute:
-        self.psr = psr
-
-        # Load data to be used for BG estimation:
-        self.load_full_data(data_file,data_yaml)
-        estimated_bg = self.full_data.binned_data.project('Em', 'Phi', 'PsiChi')
-
-        # Defaults for energy and scattering angle loops:
-        if e_loop == "default":
-            e_loop = (0,len(self.psr.axes['Em'].centers))
-        if s_loop == "default":
-            s_loop = (0,len(self.psr.axes['Phi'].centers))
-
-        # Progress bar:
-        e_tot = e_loop[1] - e_loop[0]
-        s_tot = s_loop[1] - s_loop[0]
-        num_lines = e_tot*s_tot
-        pbar = tqdm(total=num_lines)
-
-        # Loop through all bins of energy and phi:
-        for E in range(e_loop[0],e_loop[1]):
-            for s in range(s_loop[0],s_loop[1]):
-                
-                pbar.update(1) # update progress bar
-                logger.info("Bin %s %s" %(str(E),str(s)))
-
-                # Get PSR slice:
-                h = self.psr.slice[{'Em':E, 'Phi':s}].project('PsiChi')
-
-                # Get mask:
-                sorted_indices, arm_mask = self.mask_from_cumdist(h, containment, make_plots=make_plots)       
-
-                # Mask data:
-                h_data = self.full_data.binned_data.project('Em', 'Phi', 'PsiChi').slice[{'Em':E, 'Phi':s}].project('PsiChi')
-                m_data = HealpixMap(base = HealpixBase(npix = h_data.nbins), data = h_data.contents.todense())
-                m_data[sorted_indices[arm_mask]] = 0
-
-                # Skip this iteration if map is all zeros:
-                if len(m_data[m_data[:] > 0]) == 0:
-                    logger.info("All zeros and so skipping iteration!")
-                    continue
-
-                # Get interpolated values:
-                interp_list = self.simple_inpainting(m_data, sorted_indices, arm_mask)
-
-                # Update estimated BG:
-                for p in range(len(sorted_indices[arm_mask])):
-                    estimated_bg[E,s,sorted_indices[arm_mask][p]] = interp_list[p]
-
-                # Option to make some plots:
-                if make_plots == True:
-                    
-                    # Plot true response:
-                    m_dummy = HealpixMap(base = HealpixBase(npix = h.nbins), data = h.contents)
-                    plot,ax = m_dummy.plot('mollview')
-                    plt.title("True Response")
-                    plt.show()
-                    plt.close()
-
-                    # Plot masked response:
-                    m_dummy[sorted_indices[arm_mask]] = 0 * m_dummy.unit
-                    plot,ax = m_dummy.plot('mollview')
-                    plt.title("Masked Response")
-                    plt.show()
-                    plt.close()
-
-                    # Plot true data:
-                    m_data_dummy = HealpixMap(base = HealpixBase(npix = h_data.nbins), data = h_data.contents.todense())
-                    plot,ax = m_data_dummy.plot('mollview')
-                    plt.title("True Data")
-                    plt.show()
-                    plt.close()
-
-                    # Plot masked data:
-                    plot,ax = m_data.plot('mollview')
-                    plt.title("Masked Data")
-                    plt.show()
-                    plt.close()
-
-                    # Plot masked data with interpolated values:
-                    m_data[sorted_indices[arm_mask]] = interp_list
-                    plot,ax = m_data.plot('mollview')
-                    plt.title("Interpolated Data (Estimated BG)")
-                    plt.show()
-                    plt.close()
-      
-        # Close progress bar:
-        pbar.close()
-
-        return estimated_bg
diff --git a/cosipy/background_estimation/ContinuumEstimationNN.py b/cosipy/background_estimation/ContinuumEstimationNN.py
new file mode 100644
index 000000000..e053413b2
--- /dev/null
+++ b/cosipy/background_estimation/ContinuumEstimationNN.py
@@ -0,0 +1,1114 @@
+# Imports
+import sys
+import numpy as np
+import pandas as pd
+import healpy as hp
+import argparse
+import matplotlib.pyplot as plt
+from matplotlib.colors import LogNorm
+from histpy import Histogram, Axes
+from cosipy.response import FullDetectorResponse, DetectorResponse
+from cosipy.interfaces import BinnedBackgroundInterface
+from typing import Dict, Tuple, Union, Any, Type, Optional, Iterable
+from astropy import units as u
+from mhealpy import HealpixMap, HealpixBase
+import time
+import logging
+from tqdm.auto import tqdm
+logger = logging.getLogger(__name__)
+
+# PyTorch and torch_geometric imports:
+# These are not cosipy requirements;
+# user needs to install if they want to use this class.
+try:
+    import torch
+    import torch.nn as nn
+    from torch_geometric.data import Data
+    from torch_geometric.nn import GCNConv, GraphUNet as PyGGraphUNet
+    _TORCH_AVAILABLE = True
+    _NN_BASE = nn.Module
+
+except ImportError:
+    _TORCH_AVAILABLE = False
+    _NN_BASE = object
+
+class GCN(_NN_BASE):
+     
+    def __init__(self, in_channels=1, hidden_channels=32):
+       
+
+        """
+        Initialize a 4-layer Graph Convolutional Network (GCN) for node-level regression tasks.
+
+        This model consists of three hidden graph convolutional layers with ReLU activation functions,
+        followed by a final output layer without activation. Each convolutional layer aggregates
+        information from neighboring nodes using the graph structure.
+
+        Parameters
+        ----------
+        in_channels : int, optional
+            Number of input features per node. Default is 1.
+        hidden_channels : int, optional
+            Number of hidden features (output channels) in each intermediate convolutional layer.
+            Default is 32.
+
+        Architecture
+        ------------
+        Layer 1: GCNConv(in_channels → hidden_channels) + ReLU
+        Layer 2: GCNConv(hidden_channels → hidden_channels) + ReLU
+        Layer 3: GCNConv(hidden_channels → hidden_channels) + ReLU
+        Layer 4: GCNConv(hidden_channels → 1)  # Output layer (no activation)
+
+        Returns
+        -------
+        None
+        """
+
+        if not _TORCH_AVAILABLE:
+            raise ImportError(
+                "GCN requires PyTorch and torch-geometric.")
+
+        super().__init__()
+        self.conv1 = GCNConv(in_channels, hidden_channels)
+        self.conv2 = GCNConv(hidden_channels, hidden_channels)
+        self.conv3 = GCNConv(hidden_channels, hidden_channels)
+        self.conv4 = GCNConv(hidden_channels, 1)
+
+        return
+
+    def forward(self, x, edge_index):
+
+        """
+        Perform a forward pass through the Graph Convolutional Network (GCN).
+
+        Applies a sequence of graph convolutional layers with ReLU activations 
+        to the input node features. The final layer produces the output feature 
+        representations without an activation function.
+
+        Parameters
+        ----------
+        x : torch.Tensor: 
+            Node feature matrix of shape (N, F_in), where 
+            N is the number of nodes and F_in is the number of 
+            input features per node.
+        edge_index : torch.LongTensor
+            Graph connectivity in COO format 
+            with shape (2, E), where E is the number of edges.
+
+        Returns
+        -------
+        torch.Tensor: 
+            Output node feature matrix of shape (N, F_out) 
+            after the final convolution layer.
+        """
+
+        x = torch.relu(self.conv1(x, edge_index))
+        x = torch.relu(self.conv2(x, edge_index))
+        x = torch.relu(self.conv3(x, edge_index))
+        
+        return self.conv4(x, edge_index)
+
+class ContinuumEstimationNN():
+    
+    def __init__(self):
+        
+        """
+        COSI Background Inpainting using PyTorch Geometric (Hybrid / Self-Supervised)
+
+        Modes:
+        - supervised: uses background model as ground truth
+        - self: random masking (no model)
+        - hybrid: combination of both (falls back to self if no model provided)
+
+        Includes accuracy evaluation comparing inpainted results to true background.
+        """
+       
+        return
+
+    def select_device(self):
+
+        """Device selection with GPU compatibility check."""
+    
+        if torch.cuda.is_available():
+            
+            gpu_count = torch.cuda.device_count()
+            logger.info(f"GPUs available: {gpu_count}")
+            capability = torch.cuda.get_device_capability(0)
+            major, minor = capability
+            
+            # check capatability (may need to be generalized):
+            if major < 3 or (major == 3 and minor < 7):
+                logger.info(f"[WARNING] GPU {torch.cuda.get_device_name(0)} "
+                  f"(capability {major}.{minor}) is not supported by this PyTorch build.")
+                logger.info("Falling back to CPU.")
+                return torch.device('cpu')
+           
+            else:
+                logger.info(f"Using GPU 0: {torch.cuda.get_device_name(0)} (capability {major}.{minor})")
+                if gpu_count > 1:
+                    logger.info("Multi-GPU training not enabled; using a single GPU.")
+                return torch.device('cuda')
+        
+        # For Mac M chips:
+        elif torch.backends.mps.is_available():
+            return torch.device('mps')
+
+        else:
+            logger.info("No GPU detected. Using CPU.")
+            return torch.device('cpu')
+
+    @staticmethod
+    def load_projected_data(h5_file): 
+
+        """Loads h5 data file and projects to Em, Phi, and Psichi.
+        This is the input file with the total data used to estimate 
+        the background. 
+        
+        Parameters
+        ----------
+        h5_file : str
+            Full path to background h5 file. 
+
+        Returns
+        -------
+        projected : histogram
+            Histogram object projected onto Em, Phi, and PsiChi.
+        data : array
+            Contents of histogram given as an array. 
+        """
+        
+        hist = Histogram.open(h5_file)
+        projected = hist.project(['Em', 'Phi', 'PsiChi'])
+        data = projected.contents.todense()
+        
+        return projected, data
+
+    @staticmethod
+    def load_projected_psr(psr_file):
+        
+        """Loads precomputed point source response and projects to Em, Phi, and Psichi.
+        
+        Parameters
+        ----------
+        psr_file : str
+            Full path to precomputed point source response file. 
+
+        Returns
+        -------
+        projected : histogram
+            Histogram object projected onto Em, Phi, and PsiChi.
+        data : array
+            Contents of histogram given as an array. 
+        """
+        
+        logger.info("...loading the pre-computed point source response ...")
+        psr = DetectorResponse.open(psr_file)
+        logger.info("--> done")
+        
+        projected = psr.project(['Em', 'Phi', 'PsiChi'])
+        data = projected.contents.value
+        
+        return projected, data
+
+    @staticmethod
+    def load_projected_model(model_file):
+        
+        """Loads model file and projects to Em, Phi, and Psichi.
+        
+        Parameters
+        ----------
+        model_file : str
+            Full path to model h5 file. 
+
+        Returns
+        -------
+        projected : histogram
+            Histogram object projected onto Em, Phi, and PsiChi.
+        data : array
+            Contents of histogram object given as an array. 
+        """
+
+        hist = Histogram.open(model_file)
+        projected = hist.project(['Em', 'Phi', 'PsiChi'])
+        data = projected.contents.todense()
+        
+        return projected, data
+
+    @staticmethod
+    def mask_from_cumdist_vectorized(psr_map, containment=0.4):
+        
+        """Masks point source cone in CDS based on PSR and ARM.
+        
+        Parameters
+        ----------
+        psr_map : array
+            Point source response array, i.e. contents 
+            of histogram from load_projected_psr method. 
+        containment : float
+            Fraction of ARM to mask (default is 0.4). 
+        
+        Returns
+        -------
+        mask : array
+            Boolean array defining mask. 
+        """
+
+        psr_norm = psr_map / np.sum(psr_map, axis=-1, keepdims=True)
+        sort_idx = np.argsort(psr_norm, axis=-1)[..., ::-1]
+        sorted_vals = np.take_along_axis(psr_norm, sort_idx, axis=-1)
+        cumsum_vals = np.cumsum(sorted_vals, axis=-1)
+        mask_sorted = (cumsum_vals < containment).astype(float)
+        mask = np.empty_like(mask_sorted)
+        np.put_along_axis(mask, sort_idx, mask_sorted, axis=-1)
+        # Invert so brightest pixels = 0 (mask), background = 1 (keep)
+        mask = 1 - mask
+        
+        return mask
+
+    def build_healpix_graph(self, nside):
+        
+        """Build HEALPix graph.
+        
+        Parameters
+        ----------
+        nside : int
+            nside of healpix map.
+
+        edge_index : tensor
+            Healpix graph (nodes and edges) given as tensor object. 
+        """
+
+        edges = []
+        npix = hp.nside2npix(nside)
+        for i in range(npix):
+            neighbors = hp.get_all_neighbours(nside, i)
+            for n in neighbors:
+                if n >= 0:
+                    edges.append([i, n])
+        edge_index = torch.tensor(edges, dtype=torch.long).t().contiguous()
+        
+        return edge_index
+
+    def train_inpaint(self, input_data_map, mask_map, nside,
+                  mode='self', model_map=None,
+                  epochs=200, lr=1e-3,
+                  self_mask_fraction=0.1,
+                  lambda_sup=0.5, lambda_self=0.5,
+                  model_type="gcn",
+                  nn_model="new",
+                  nn_model_file=None,
+                  nn_model_savename="inpainting_nn_model",
+                  verbose=True):
+
+        """Training function for inpainting.
+        
+        Parameters
+        ----------
+        input_data_map : array
+            Input file with total data. Returned from 
+            load_projected_data method. 
+        mask_map : array
+            Boolean array specifying mask. Returned from
+            mask_from_cumdist_vectorized method. 
+        nside : int
+            Nside of background map.
+        mode : str, optional
+            Training mode to use (default is self). Choices are:
+            hybrid: uses both supervised and self.
+            supervised: trains from input model map.
+            self: trains from random masking. 
+        model_map : array, optional
+            Input model map array. Returned from 
+            load_projected_model method.  
+        epochs : int, optional
+            Number of training epochs. Default is 800. 
+        lr : float, optional
+            Learning rate. Default is 1e-3. 
+        self_mask_fraction : float, optional
+            Fraction of pixels to randomly mask in self training mode. 
+        lambda_sup : float, optional
+            Weight for supervised training loss function used in 
+            hybride mode. Default is 0.5. 
+        lambda_self : float, optional
+            Weight for self-supervised training loss function used in
+            hybride mode. Default is 0.5.
+        model_type : str, optional 
+            Which model to use. Options are gcn (default) or unet.
+        nn_model : str, optional
+            Train new model or load existing model. Options are 
+            new (default), load (loads model weights), and 
+            load_full (loads model weights, optimizer state, and epochs). 
+        nn_model_file : str, optional
+            Name of NN model to load. Default is None.
+        nn_model_savename : str, optional
+            Prefix of saved NN model. Default is inpainting_nn_model.  
+        verbose : bool, optional
+            Gives logger info for validation loss every 50 epochs.
+            Default is False.
+
+        Returns
+        -------
+        inpainted : array
+            Inpainted background map. 
+        """
+      
+        # Initiate device, either CPU or GPU if available. 
+        self.device = self.select_device()
+
+        npix = hp.nside2npix(nside)
+        edge_index = self.build_healpix_graph(nside).to(self.device)
+   
+        # Either start new NN model or load existing model:
+        if nn_model == "new":
+            if model_type == "unet":
+                model = PyGGraphUNet(in_channels=1, hidden_channels=32, out_channels=1, depth=3).to(self.device)
+                logger.info("Using GraphUNet model.")
+            else:
+                model = GCN(in_channels=1, hidden_channels=32).to(self.device)
+                logger.info("Using GCN model.")
+
+            optimizer = torch.optim.Adam(model.parameters(), lr=lr)
+        
+        if nn_model != "new" and nn_model_file == None:
+            raise ValueError("Must specify nn_model_file if loading NN model.")
+
+        if nn_model == "load":
+            model = self.load_NN_Model(nn_model_file, full_state=False)
+            optimizer = torch.optim.Adam(model.parameters(), lr=lr)
+            logger.info(f"Loaded NN model (weights only) from {nn_model_file}")
+
+        if nn_model == "load_full":
+            model, optimizer, start_epoch, final_loss = self.load_NN_Model(nn_model_file, full_state=True)
+            logger.info(f"Loaded full NN model from {nn_model_file}")
+
+        loss_fn = nn.MSELoss()
+
+        inpainted = np.zeros_like(input_data_map)
+        n_energy, n_phi, _ = input_data_map.shape
+
+        # training loss:
+        training_loss = np.zeros([n_energy,n_phi,epochs])
+
+        # Loss weights should only be applied in hybrid mode:
+        if mode != 'hybrid':
+            lambda_sup = 1
+            lambda_self = 1
+
+        # Progress bar:
+        total_steps = n_energy * n_phi
+        with tqdm(total=total_steps, desc="Inpainting (Em, Phi)", unit="map") as pbar:
+        
+            for e in range(n_energy):
+                for p in range(n_phi):     
+
+                    # advance progress bar:
+                    pbar.update(1)
+
+                    y = input_data_map[e, p].reshape(-1)
+                    mask = mask_map[e, p].reshape(-1)
+
+                    # Masked input
+                    x = (y * mask).reshape(-1, 1)
+
+                    # Convert to tensors
+                    data = Data(x=torch.tensor(x, dtype=torch.float32).to(self.device),
+                        edge_index=edge_index)
+                    y_tensor = torch.tensor(y, dtype=torch.float32).to(self.device)
+                    mask_tensor = torch.tensor(mask, dtype=torch.float32).to(self.device)
+
+                    target_model = None
+                    if model_map is not None:
+                        target_model = torch.tensor(model_map[e, p], dtype=torch.float32).to(self.device).reshape(-1)
+
+                    # Training loop
+                    for epoch in range(epochs):
+                        optimizer.zero_grad()
+                        pred = model(data.x, data.edge_index).squeeze()
+
+                        loss_total = 0.0
+
+                        # Supervised component
+                        if mode in ['supervised', 'hybrid'] and target_model is not None:
+                            sup_loss = loss_fn(pred * (1 - mask_tensor),
+                                       target_model * (1 - mask_tensor))
+                            loss_total += lambda_sup * sup_loss
+
+                        # Self-supervised component
+                        if mode in ['self', 'hybrid']:
+                            unmasked_idx = np.where(mask == 1)[0]
+                            num_rand = max(1, int(len(unmasked_idx) * self_mask_fraction))
+                            rand_mask_idx = np.random.choice(unmasked_idx, num_rand, replace=False)
+
+                            mask_self = mask.copy()
+                            mask_self[rand_mask_idx] = 0
+                            mask_self_tensor = torch.tensor(mask_self, dtype=torch.float32).to(self.device)
+
+                            self_loss = loss_fn(pred * (1 - mask_self_tensor),
+                                        y_tensor * (1 - mask_self_tensor))
+                            loss_total += lambda_self * self_loss
+
+                        # Save training loss:
+                        training_loss[e,p,epoch] = loss_total
+
+                        loss_total.backward()
+                        optimizer.step()
+
+                        if verbose == True: 
+                            if epoch % 50 == 0:
+                                logger.info(f"[E{e} Phi{p}] Epoch {epoch}/{epochs}: Loss {loss_total.item():.6f}")
+
+                    # Combine prediction with unmasked data
+                    pred_np = pred.detach().cpu().numpy().reshape(npix)
+                    inpainted[e, p] = y * mask + pred_np * (1 - mask)
+    
+        # write training loss array to file:
+        np.save(f"{nn_model_savename}_training_loss",training_loss)
+
+        # Save NN model:
+        torch.save({
+        'model_state_dict': model.state_dict(),
+        'optimizer_state_dict': optimizer.state_dict(),
+        'epoch': epoch,
+        'loss': loss_total}, f"{nn_model_savename}.pth")
+
+        return inpainted
+
+    def load_NN_Model(self, nn_model_file, full_state=True):
+
+        """Loads NN model saved from torch.save.
+        
+        Parameters
+        ----------
+        nn_model_file : str
+            NN model file (.pth) from torch.save.
+        full_state : bool, optional
+            Option to load full state, which includes model weights, 
+            optimizer state, and epochs. Default is False, in which 
+            case only the model weights are loaded. 
+        
+        Returns
+        -------
+        model : dict
+            State dictionary with NN model weights
+        optimizer : torch.optim.Adam
+            Optimizer and state. Only for full_state=True.
+        start_epoch : int
+            Epoch number. Only for full_state=True.
+        final_loss : float
+            Training loss. Only for full_state=True.
+        """
+
+        logger.info("loading NN model...")
+
+        if not self.device:
+            # Initiate device, either CPU or GPU if available.
+            self.device = self.select_device()
+
+        checkpoint = torch.load(nn_model_file, map_location=torch.device(self.device))
+
+        # Need to recreate the model architecture first
+        # (same layer definitions, etc.) before loading weights.
+        # For now we just use the GCN:
+        model = GCN(in_channels=1, hidden_channels=32).to(self.device)
+
+        # Load the model weights:
+        model.load_state_dict(checkpoint['model_state_dict'])
+
+        # Option to load the optimizer state and epochs:
+        if full_state == True:
+            optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
+            optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
+            start_epoch = checkpoint['epoch'] + 1
+            final_loss = checkpoint['loss']
+
+            return model, optimizer, start_epoch, final_loss
+
+        else:
+            return model
+
+    @staticmethod
+    def save_inpainted_histpy(projected_hist, inpainted_maps, output_file="inpainted.h5"):
+        
+        """Save results.
+        
+        projected_hist : histogram 
+            Histrogram object. 
+        inpainted_maps : array
+            Inpainted background map. 
+        output_file : str, optional
+            Name of output histogram file. Default is inpainted.h5.
+        """
+
+        hist = Histogram(projected_hist.axes, contents=inpainted_maps, sparse=True)
+        hist.write(output_file, overwrite=True)
+        logger.info(f"Inpainted histogram saved to {output_file}")
+
+        return
+
+    def write_csv(self, x_data, y_data, save_prefix, x_label="x", y_label="y"):
+        
+        """Save dataframe to file. 
+        
+        Parameters
+        ----------
+        x_data : array or list
+            Data for first axis.     
+        y_data : array or list
+            Data for seconds axis. 
+        save_prefix : str
+            Prefix of saved file. Will be saved as .dat file. 
+        x_label : str
+            Name of x axis in dat file. Default is 'x'. 
+        y_label : str
+            Name of y axis in dat file. Default is 'y'.
+        """
+        
+        d = {x_label:x_data,y_label:y_data}
+        df = pd.DataFrame(data=d)
+        df.to_csv(f"{save_prefix}.dat",float_format='%10.5e',index=False,sep="\t",columns=[x_label,y_label])
+
+        return
+
+    def evaluate_inpainting_accuracy(self, true_bg, estimated_bg, prefix="eval", show_plots=False):
+
+        """Evaluates accuracy of inpainted maps by making series of 
+        comparison plots, which are saved to file and can also be shown.  
+
+        Parameters
+        ---------
+        true_bg : hist
+            Histogram object projected onto Em, Phi, PsiChi with the true background. 
+        estimated_bg : hist
+            Histogram object projected onto Em, Phi, PsiChi with the estimated background.
+        prefix : str
+            Prefix of saved plots. 
+        show_plots : bool
+            Option to show plots (default is False).
+        """
+
+        # Compare projection onto measured energy axis:
+        energy = true_bg.axes['Em'].centers
+        energy_err = true_bg.axes['Em'].widths / 2.0
+
+        true_plot = true_bg.project('Em').contents.todense()
+        est_plot = estimated_bg.project('Em').contents.todense()
+
+        plt.loglog(energy,true_plot,ls="",marker="o",color="darkorange",label="BG true")
+        plt.errorbar(energy,true_plot,xerr=energy_err,color="darkorange",ls="",marker="o",label="_nolabel_")
+
+        plt.loglog(energy,est_plot,ls="",marker="s",color="cornflowerblue",label="BG estimated")
+        plt.errorbar(energy,est_plot,xerr=energy_err,color="cornflowerblue",ls="",marker="s",label="_nolabel_")
+
+        plt.xlabel("Em [keV]", fontsize=12)
+        plt.ylabel("Counts", fontsize=12)
+        plt.xlim(1e2,1e4)
+        plt.ylim(1e5,1e9)
+        plt.legend(loc=1,frameon=False)
+        if show_plots == True:
+            plt.show()
+        plt.savefig(f"{prefix}_proj_em_counts.png", dpi=150)
+        plt.close()
+
+        # Plot fractional difference:
+        diff = (est_plot - true_plot) / true_plot
+
+        plt.semilogx(energy,diff,ls="",marker="o",color="black")
+        plt.errorbar(energy,diff,xerr=energy_err,ls="",marker="o",color="black")
+
+        plt.xlabel("Em [keV]", fontsize=12)
+        plt.ylabel("(estmated - true)/true", fontsize=12)
+        plt.xlim(1e2,1e4)
+        if show_plots == True:
+            plt.show()
+        plt.savefig(f"{prefix}_proj_em_frac_diff.png", dpi=150)
+        plt.close()
+
+        # save data:
+        self.write_csv(energy,diff,"Em_diff",x_label="Em[keV]",y_label="diff")
+
+        # Compare projection onto phi axis:
+        phi = true_bg.axes['Phi'].centers
+        phi_err = true_bg.axes['Phi'].widths / 2.0
+
+        true_plot = true_bg.project('Phi').contents.todense()
+        est_plot = estimated_bg.project('Phi').contents.todense()
+
+        plt.semilogy(phi,true_plot,ls="",marker="o",color="darkorange",label="BG true")
+        plt.errorbar(phi,true_plot,xerr=phi_err,color="darkorange",ls="",marker="o",label="_nolabel_")
+
+        plt.semilogy(phi,est_plot,ls="",marker="s",color="cornflowerblue",label="BG estimated")
+        plt.errorbar(phi,est_plot,xerr=phi_err,color="cornflowerblue",ls="",marker="s",label="_nolabel_")
+
+        plt.xlabel("Phi [deg]", fontsize=12)
+        plt.ylabel("Counts", fontsize=12)
+        plt.xlim(0,200)
+        plt.ylim(1e5,1e9)
+        plt.legend(loc=1,frameon=False)
+        if show_plots == True:
+            plt.show()
+        plt.savefig(f"{prefix}_proj_phi_counts.png", dpi=150)
+        plt.close()
+
+        # Plot fractional difference:
+        diff = (est_plot - true_plot) / true_plot
+
+        plt.plot(phi,diff,ls="",marker="o",color="black")
+        plt.errorbar(phi,diff,xerr=phi_err,ls="",marker="o",color="black")
+
+        plt.xlabel("Phi [deg]", fontsize=12)
+        plt.ylabel("(estmated - true)/true", fontsize=12)
+        plt.xlim(0,200)
+        if show_plots == True:
+            plt.show()
+        plt.savefig(f"{prefix}_proj_phi_frac_diff.png", dpi=150)
+        plt.close()
+
+        # save data:
+        self.write_csv(phi,diff,"phi_diff",x_label="phi[deg]",y_label="diff")
+
+        # Compare projection onto psichi:
+        true_plot = true_bg.project('PsiChi')
+        true_plot_map = HealpixMap(base = HealpixBase(npix = true_plot.nbins), data = true_plot.contents.todense())
+        plot,ax = true_plot_map.plot('mollview')
+        plt.title("True BG")
+        if show_plots == True:
+            plt.show()
+        plt.savefig(f"{prefix}_proj_psichi_true_bg.pdf")
+        plt.close()
+
+        est_plot = estimated_bg.project('PsiChi')
+        est_plot_map = HealpixMap(base = HealpixBase(npix = est_plot.nbins), data = est_plot.contents.todense())
+        plot,ax = est_plot_map.plot('mollview')
+        plt.title("Estimated BG")
+        if show_plots == True:
+            plt.show()
+        plt.savefig(f"{prefix}_proj_psichi_estimated_bg.pdf")
+        plt.close()
+
+        # Calculate percent diff:
+        diff = (est_plot - true_plot) / true_plot
+        diff_plot_map = HealpixMap(base = HealpixBase(npix = diff.nbins), data = diff.contents.todense())
+        plot,ax = diff_plot_map.plot('mollview', **{"cmap":"bwr","vmin":-0.3,"vmax":0.3})
+        plt.title("Comparison")
+        if show_plots == True:
+            plt.show()
+        plt.savefig(f"{prefix}_proj_psichi_frac_diff.pdf")
+        plt.close()
+
+        # save data:
+        array = diff.contents.todense()
+        self.write_csv(np.arange(array.shape[0]),array,"psichi_diff",x_label="psichi",y_label="diff")
+
+        logger.info(f"Accuracy plots saved with prefix '{prefix}_...'")
+
+        return
+
+    @staticmethod
+    def visualize_and_save(input_data_map, mask_map, inpainted_maps, 
+            em_bin=2, phi_bin=4, prefix="inpainted", show_plots=False):
+        
+        """
+        Show and save Mollweide projections of true, masked, and inpainted maps.
+        
+        Parameters
+        ----------
+        input_data_map : array
+            Input file with total data. Returned from 
+            load_projected_data method. 
+        mask_map : array
+            Boolean array specifying mask. Returned from
+            mask_from_cumdist_vectorized method.  
+        inpainted_maps : array
+            Estimated background. 
+        em_bin : int
+            Which em bin to use (default is 0).
+        phi_bin : int
+            Which phi bin to use (default is 0). 
+        prefix : str
+            Prefix of saved files.
+        show_plots : bool, optional
+            Option to show plots. Default is False.
+        """
+        
+        maps = [
+            (input_data_map[em_bin, phi_bin], f"{prefix}_true_E{em_bin}_Phi{phi_bin}.pdf", 
+                f"Input Map (Em={em_bin}, Phi={phi_bin})"),
+            (input_data_map[em_bin, phi_bin] * mask_map[em_bin, phi_bin], 
+                f"{prefix}_masked_E{em_bin}_Phi{phi_bin}.pdf", f"Masked Map (Em={em_bin}, Phi={phi_bin})"),
+            (inpainted_maps[em_bin, phi_bin], f"{prefix}_inpainted_E{em_bin}_Phi{phi_bin}.pdf", 
+                f"Inpainted Map (Em={em_bin}, Phi={phi_bin})")]
+
+        for data, filename, title in maps:
+            hp.mollview(data, title=title)
+            plt.savefig(filename)
+            if show_plots == True:
+                plt.show()
+            plt.close()
+            logger.info(f"Saved visualization: {filename}")
+
+        return
+
+    def plot_training_loss(self,input_file, energy_bin, save_prefix, show_plot=True, vmax=70000):
+
+        """Plot training loss as a function of Phi and number of epochs
+        for a given energy bin.
+        
+        Parameters
+        ----------
+        input_file : str
+            File name for input training loss array.
+        energy_bin : int
+            Energy bin to plot.
+        save_prefix : str
+            Prefix of saved image file.
+        show_plot : bool, optional
+            Whether to show plot (default is True).
+        vmax : float, optional
+            Max plot value. Default is 70000.
+        """
+
+        # Load loss data
+        loss_data = np.load(input_file)  # shape: (Energy, Phi, Epochs)
+
+        loss_slice = loss_data[energy_bin]  # shape: (Phi, Epochs)
+
+        # Transpose to shape (Epochs, Phi) for plotting
+        loss_slice = loss_slice.T
+
+        # Create the plot
+        plt.figure(figsize=(8, 5))
+        plt.imshow(loss_slice, aspect='auto', origin='lower', cmap='viridis',
+           extent=[0, loss_slice.shape[1], 0, loss_slice.shape[0]],vmax=vmax)
+
+        plt.colorbar(label='Loss')
+        plt.xlabel('Phi bin')
+        plt.ylabel('Epoch')
+        plt.title(f"Training Loss Map (Energy bin {energy_bin})")
+        plt.tight_layout()
+        plt.savefig(f"{save_prefix}.png", dpi=150)
+        if show_plot:
+            plt.show()
+    
+        return
+
+    def estimate_bg(self, input_data, psr_file, background_model=None, 
+            training_mode="self", containment=0.6, epochs=200, model_type="gcn",
+            nn_model="new", nn_model_file=None, nn_model_savename="inpainting_nn_model",
+            lr=1e-3, self_mask_fraction=0.1, lambda_sup=0.5, lambda_self=0.5,  
+            prefix="inpainted", visualize=False, em_bin=2, phi_bin=4, 
+            evaluate_only=False, inpainted_file=None,
+            evaluate=False, show_plots=False, verbose=True):
+
+        """Convenience function for estimating the background. 
+        
+        Parameters
+        ----------
+        input_data : str
+            Path to HDF5 file with the input data that will be used 
+            to estimate the background.
+        psr_file : str
+            Path to point source response HDF5 file. 
+        background_model : hist, optional
+            Optional background model HDF5 file. 
+        training_mode : str, optional
+            Training mode to use. There are three options:  
+            hybrid (combination of supervised and self-supervised), 
+            supervised (training based on input model), 
+            and self-supervised (training based on randomly masking a fraction of the sky). 
+            Default is self-supervised.
+        containment : float, optional
+            Containment fraction for mask. Default is 0.6. 
+        epochs : int, optional 
+            Number of training epochs. Default is 800.
+        model_type : str, optional
+            Type of NN to use. Options are gcn or unet. Default is gcn.
+        nn_model : str, optional
+            Train new model or load existing model. Options are 
+            new (default), load (loads model weights), and 
+            load_full (loads model weights, optimizer state, and epochs). 
+        nn_model_file : str, optional
+            Name of NN model to load. Default is None.
+        nn_model_savename : str, optional
+            Prefix of saved NN model. Default is inpainting_nn_model. 
+        lr : float, optional
+            Learning rate. Default is 1e-3. 
+        self_mask_fraction : float, optional
+            Fraction of pixels to mask in self-supervised mode. Default is 0.1. 
+        lambda_sup : float, optional
+            Weight of loss function of supervised mode for hybrid 
+            training. Default is 0.5.
+        lambda_self : float, optional 
+            Weight of loss function of self-supervised mode for hybrid 
+            training. Default is 0.5.
+        prefix : str, optional
+            Prefix for output files. Default is 'inpainted'.
+        visualize : boolean, optional
+            Visualize Mollweide plots.
+        em_bin : int, optional
+            Energy bin index for visualization. Default is 2. 
+        phi_bin : int, optional
+            Phi bin index for visualization. Default is 4. 
+        evaluate_only : boolean, optional
+            Skip training and evaluate two histograms. Requires 
+            background_model file and inpainted_file. 
+        inpainted_file : hist, optional
+            Inpainted histogram file (for evaluate-only). Default is None.
+        evaluate : boolean
+            Evaluate after training (inline). Default is False. 
+        show_plots : boolean
+            Display plots to screen. Default is False. 
+        verbose : bool, optional
+            Gives logger info for validation loss every 50 epochs.
+            Default is False.
+        """
+        
+        # This is a required protection if pytorch is not available. 
+        # It can be removed in the future if pytorch becomes a dependency. 
+        if not _TORCH_AVAILABLE:
+            raise ImportError(
+            "ContinuumEstimationNN requires PyTorch and torch-geometric.")
+
+        # Record run time:
+        start_time = time.time()
+
+        # --- Evaluation-only mode ---
+        if evaluate_only == True:
+            if not background_model or not inpainted_file:
+                raise ValueError("--evaluate-only requires --background_model and --inpainted-file")
+            true_hist = Histogram.open(background_model)
+            inpainted_hist = Histogram.open(inpainted_file)
+            self.evaluate_inpainting_accuracy(true_hist, 
+                    inpainted_hist, prefix=prefix, show_plots=show_plots)
+        
+            return
+
+        # Check that training mode has needed input model:
+        if training_mode in ["hybrid","supervised"] and background_model is None:
+            raise ValueError("Hybrid and supervised modes require --background_model")
+
+        # For in-line evaluation, check that model is passed:
+        if evaluate and background_model is None:
+            raise ValueError("--evaluate requires --background_model")
+
+        # Load data
+        input_data_proj, input_data_map = self.load_projected_data(input_data)
+        _, psr_map = self.load_projected_psr(psr_file)
+
+        # Optional background model
+        model_map = None
+        if background_model:
+            model_proj, model_map = self.load_projected_model(background_model)
+
+        # Derive nside
+        npix = input_data_map.shape[-1]
+        nside = hp.npix2nside(npix)
+
+        # Mask from PSR
+        mask_map = self.mask_from_cumdist_vectorized(psr_map, containment=containment)
+
+        # Train & inpaint
+        self.inpainted_maps = self.train_inpaint(input_data_map, mask_map, nside, 
+            mode=training_mode, model_map=model_map,
+            nn_model=nn_model, nn_model_file=nn_model_file, nn_model_savename=nn_model_savename,
+            epochs=epochs, model_type=model_type,
+            lr=lr, self_mask_fraction=self_mask_fraction, 
+            lambda_sup=lambda_sup, lambda_self=lambda_self, verbose=verbose)
+
+        # Save result
+        self.save_inpainted_histpy(input_data_proj, self.inpainted_maps, output_file=f"{prefix}_estimated_bg.h5")
+
+        # Visualization
+        if visualize:
+            self.visualize_and_save(input_data_map, mask_map, self.inpainted_maps,
+                           em_bin=em_bin, phi_bin=phi_bin, prefix=prefix, show_plots=show_plots)
+
+        # Inline evaluation
+        if evaluate:
+            inpainted_hist = Histogram(input_data_proj.axes, contents=self.inpainted_maps, sparse=True)
+            self.evaluate_inpainting_accuracy(model_proj, inpainted_hist, prefix=prefix, show_plots=show_plots)
+
+        end_time = time.time()
+        logger.info(f"Total time elapsed: {end_time - start_time:.2f} seconds")
+
+        return
+
+    def load_estimated_bg(self, estimated_bg_file):
+        
+        """Loads inpainted histrogram from h5 file.
+        
+        Parameters
+        ----------
+        inpainted_file : str
+            Path to input h5 file with estimated background.
+            
+        """
+        
+        self.inpainted_map = Histogram.open(estimated_bg_file)
+
+        return
+
+class ContinuumEstimationInterp(ContinuumEstimationNN):
+    
+    """
+    Continuum background estimation using simple wrapped 1D interpolation
+    in HEALPix NESTED index space.
+
+    - Uses the same data loading + PSR-based masking as ContinuumEstimationNN.
+    - Replaces NN inpainting with:
+        For each masked pixel i:
+          * search left (wrapping) for first unmasked pixel -> L
+          * search right (wrapping) for first unmasked pixel -> R
+          * fill(i) = 0.5*(L + R)
+        Edge cases handled with fallbacks.
+
+    This class can be ran without having pytorch installed. 
+    """
+
+    def estimate_bg(self, input_data, psr_file, background_model=None,
+                    prefix="inpainted", containment=0.6, visualize=False, 
+                    em_bin=2, phi_bin=4, evaluate_only=False, inpainted_file=None,
+                    evaluate=False, show_plots=False, verbose=True):
+
+        """Convenience function for estimating the background. 
+        
+        Parameters
+        ----------
+        input_data : str
+            Path to HDF5 file with the input data that will be used 
+            to estimate the background.
+        psr_file : str
+            Path to point source response HDF5 file. 
+        background_model : hist, optional
+            Optional background model HDF5 file. 
+        containment : float, optional
+            Containment fraction for mask. Default is 0.6. 
+        prefix : str, optional
+            Prefix for output files. Default is 'inpainted'.
+        visualize : boolean, optional
+            Visualize Mollweide plots.
+        em_bin : int, optional
+            Energy bin index for visualization. Default is 2. 
+        phi_bin : int, optional
+            Phi bin index for visualization. Default is 4. 
+        evaluate_only : boolean, optional
+            Skip training and evaluate two histograms. Requires 
+            background_model file and inpainted_file. 
+        inpainted_file : hist, optional
+            Inpainted histogram file (for evaluate-only). Default is None.
+        evaluate : boolean
+            Evaluate after training (inline). Default is False. 
+        show_plots : boolean
+            Display plots to screen. Default is False. 
+        verbose : bool, optional
+            Gives logger info for validation loss every 50 epochs.
+            Default is False.
+        """
+
+        # Record run time:
+        start_time = time.time()
+
+        # --- Evaluation-only mode (kept compatible) ---
+        if evaluate_only is True:
+            if not background_model or not inpainted_file:
+                raise ValueError("--evaluate-only requires --background_model and --inpainted-file")
+            true_hist = Histogram.open(background_model)
+            inpainted_hist = Histogram.open(inpainted_file)
+            self.evaluate_inpainting_accuracy(true_hist,
+                                              inpainted_hist, prefix=prefix, show_plots=show_plots)
+            return
+
+        if evaluate and background_model is None:
+            raise ValueError("--evaluate requires --background_model")
+
+        # Load data + PSR (same as base class) 
+        input_data_proj, input_data_map = self.load_projected_data(input_data)
+        psr_proj, psr_map = self.load_projected_psr(psr_file)
+
+        # Optional background model (for evaluation)
+        model_map = None
+        model_proj = None
+        if background_model:
+            model_proj, model_map = self.load_projected_model(background_model)
+
+        npix = input_data_map.shape[-1]
+
+        # Mask from PSR:
+        mask_map = self.mask_from_cumdist_vectorized(psr_map, containment=containment)
+        # mask_map is float 0/1; treat "keep" as True
+        keep_map = (mask_map == 1)
+
+        # Interpolation inpaint:
+        inpainted = np.array(input_data_map, copy=True)
+
+        # Helper: fill a single 1D slice (length npix)
+        def _interp_fill_1d(y, keep):
+            # y: (npix,), keep: (npix,) bool, where keep==True means NOT masked
+            out = y.copy()
+            masked_idx = np.where(~keep)[0]
+            if masked_idx.size == 0:
+                return out
+
+            for i in masked_idx:
+                # search left for first unmasked
+                left_val = None
+                j = (i - 1) % npix
+                while j != i:
+                    if keep[j]:
+                        v = y[j]
+                        # accept finite; if non-finite, keep scanning
+                        if np.isfinite(v):
+                            left_val = v
+                            break
+                    j = (j - 1) % npix
+
+                # search right for first unmasked AND non-zero
+                right_val = None
+                j = (i + 1) % npix
+                while j != i:
+                    if keep[j]:
+                        v = y[j]
+                        if np.isfinite(v):
+                            right_val = v
+                            break
+                    j = (j + 1) % npix
+
+                # assign with edge-case handling
+                if (left_val is not None) and (right_val is not None):
+                    out[i] = 0.5 * (left_val + right_val)
+                elif left_val is not None:
+                    out[i] = left_val
+                elif right_val is not None:
+                    out[i] = right_val
+                else:
+                    out[i] = 0.0
+
+            return out
+
+        # Loop over (Em, Phi) planes
+        # input_data_map is dense array from Histogram.project([...]).contents.todense()
+        # shape expected: (nEm, nPhi, npix)
+        nEm = inpainted.shape[0]
+        nPhi = inpainted.shape[1]
+        for e in range(nEm):
+            for p in range(nPhi):
+                inpainted[e, p] = _interp_fill_1d(inpainted[e, p], keep_map[e, p])
+
+        self.inpainted_maps = inpainted
+
+        # Save result
+        self.save_inpainted_histpy(input_data_proj, self.inpainted_maps, output_file=f"{prefix}_estimated_bg.h5")
+        
+        #  Visualization:
+        if visualize:
+            self.visualize_and_save(input_data_map, mask_map, self.inpainted_maps,
+                                    em_bin=em_bin, phi_bin=phi_bin, prefix=prefix, show_plots=show_plots)
+
+        # Inline evaluation:
+        if evaluate:
+            inpainted_hist = Histogram(input_data_proj.axes, contents=self.inpainted_maps, sparse=True)
+            self.evaluate_inpainting_accuracy(model_proj, inpainted_hist, prefix=prefix, show_plots=show_plots)
+
+        end_time = time.time()
+        logger.info(f"Total time elapsed: {end_time - start_time:.2f} seconds")
+
+        return
diff --git a/cosipy/background_estimation/__init__.py b/cosipy/background_estimation/__init__.py
index 7bed92eec..0ccdb8531 100644
--- a/cosipy/background_estimation/__init__.py
+++ b/cosipy/background_estimation/__init__.py
@@ -1,4 +1,6 @@
 from .LineBackgroundEstimation import LineBackgroundEstimation
-from .ContinuumEstimation import ContinuumEstimation
 from .TransientBackgroundEstimation import TransientBackgroundEstimation
 from .free_norm_threeml_binned_bkg import *
+from .ContinuumEstimationNN import ContinuumEstimationNN
+from .ContinuumEstimationNN import GCN
+from .ContinuumEstimationNN import ContinuumEstimationInterp
diff --git a/docs/tutorials/background_estimation/continuum_estimation/BG_estimationNN_example.ipynb b/docs/tutorials/background_estimation/continuum_estimation/BG_estimationNN_example.ipynb
new file mode 100644
index 000000000..257d11fe0
--- /dev/null
+++ b/docs/tutorials/background_estimation/continuum_estimation/BG_estimationNN_example.ipynb
@@ -0,0 +1,30741 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e40c9735",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Neural-Network–Based Continuum Background Inpainting for COSI\n",
+    "\n",
+    "### Overview\n",
+    "\n",
+    "This notebook provides an introduction to neural-network–based background inpainting for COSI data using the `ContinuumEstimationNN` class in cosipy. The purpose of this tutorial is to demonstrate how machine learning methods can be used to estimate the continuum background. Specifically, here we use a graph-based convolutional neural network. This approach trains a neural network in order to reconstruct (inpaint) the background for regions of the Compton data space (CDS) occupied by a source of interest (currently only tested for point sources). The resulting background estimate can be used directly in COSI analysis.\n",
+    "\n",
+    "### What This Method Does\n",
+    "\n",
+    "At a high level, the inpainting procedure consists of the following steps:\n",
+    "\n",
+    "1. Project COSI data into the 3D CDS spanned by measured energy (Em), Compton scatter angle (Phi), and sky coordinates (PsiChi)\n",
+    "\n",
+    "2. Mask region in the CDS dominated by a point source using the point source response and angular resolution measure (ARM). \n",
+    "\n",
+    "3. Train a neural network on the unmasked sky pixels\n",
+    "\n",
+    "4. Predict the background in the masked region and merge the result with the observed data\n",
+    "\n",
+    "The sky is discretized using HEALPix, which naturally defines a graph where each pixel is connected to its neighbors. This allows the neural network to propagate information across the sphere while respecting the underlying sky geometry.\n",
+    "\n",
+    "### Training Modes\n",
+    "\n",
+    "The inpainting algorithm supports three complementary training modes:\n",
+    "\n",
+    "- **Self-supervised** (default)  \n",
+    "  No background model is required. The network learns by randomly masking a fraction of the sky and training itself to reconstruct those pixels.\n",
+    "\n",
+    "- **Supervised**  \n",
+    "  A background model is provided and treated as ground truth in the masked regions.\n",
+    "\n",
+    "- **Hybrid**  \n",
+    "  A weighted combination of supervised and self-supervised training. This mode leverages an existing background model while remaining robust to modeling uncertainties.\n",
+    "\n",
+    "The training mode is selected with a single keyword argument, making it easy to experiment with different strategies.\n",
+    "\n",
+    "### Neural Network Architecture\n",
+    "\n",
+    "Two graph-based neural network architectures are available:\n",
+    "\n",
+    "- **GCN (Graph Convolutional Network)**  \n",
+    "  A lightweight, four-layer architecture optimized for stability and speed.\n",
+    "\n",
+    "- **Graph U-Net**  \n",
+    "  A deeper architecture that captures larger-scale structure through pooling and unpooling operations.\n",
+    "\n",
+    "Both models operate directly on HEALPix graphs and are implemented using PyTorch Geometric. GPU acceleration is used automatically when available, but the method also runs on CPUs for smaller datasets and testing.\n",
+    "\n",
+    "### Outputs and Diagnostics\n",
+    "\n",
+    "This workflow produces:\n",
+    "\n",
+    "- **Inpainted background histogram**: compatible with cosipy\n",
+    "- **Mollweide sky maps**: optional, showing the true, masked, and inpainted data for a given bin of Em and Phi\n",
+    "- **Training-loss diagnostics**: saved during optimization\n",
+    "- **Accuracy evaluation plots**: when a reference background model is available\n",
+    "\n",
+    "These diagnostics are designed to help assess both the quality of the final background estimate and the behavior of the neural network during training.\n",
+    "\n",
+    "### Limitations of the Method\n",
+    "\n",
+    "The source region in the CDS is masked based on the ARM. However, for the COSI response, the ARM of a source has a long tail. This limits the accuracy of the estimated background, since the source photons in the tail cannot be masked.  \n",
+    "\n",
+    "In terms of training the network, most of the infrastructure is in place. However, further development is needed to optimize the network and training. \n",
+    "\n",
+    "### Tutorial Outline\n",
+    "\n",
+    "There are three main parts to this tutorial. First, we will demonstrate the basic functionality of the continuum background estimation class, and use it to get an estimate of the background using the GCN method. Then, instead of a NN-based algorithm, we'll estimate the background using a simple interpolation method (same as was used for DC3). Finally, we will then use the estimated background to perform a spectral fit. This example uses the Crab and total background from DC3. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b003b53-24da-42ed-a376-b52eb3efcd0f",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Estimating the background with NN-based inpainting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8a0f4b8b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">13:52:54 </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> The naima package is not available. Models that depend on it will not be         </span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">functions.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py#45\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">45</span></a>\n",
+       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">available                                                                         </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;5;46m13:52:54\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The naima package is not available. Models that depend on it will not be        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=197598;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py\u001b\\\u001b[2mfunctions.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=915349;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py#45\u001b\\\u001b[2m45\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mavailable                                                                        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m               \u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> The GSL library or the pygsl wrapper cannot be loaded. Models that depend on it  </span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">functions.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py#67\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">67</span></a>\n",
+       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">will not be available.                                                            </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The GSL library or the pygsl wrapper cannot be loaded. Models that depend on it \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=714807;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py\u001b\\\u001b[2mfunctions.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=357844;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py#67\u001b\\\u001b[2m67\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mwill not be available.                                                           \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m               \u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">13:52:55 </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> The ebltable package is not available. Models that depend on it will not be     </span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/functions/functions_1D/absorption.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">absorption.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/functions/functions_1D/absorption.py#34\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">34</span></a>\n",
+       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">available                                                                        </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;5;46m13:52:55\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The ebltable package is not available. Models that depend on it will not be    \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=659854;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/functions/functions_1D/absorption.py\u001b\\\u001b[2mabsorption.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=341913;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/functions/functions_1D/absorption.py#34\u001b\\\u001b[2m34\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mavailable                                                                       \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m                \u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">13:52:55 </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> Starting 3ML!                                                                     </span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py#44\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">44</span></a>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;5;46m13:52:55\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m Starting 3ML!                                                                    \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=889928;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=220172;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py#44\u001b\\\u001b[2m44\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> WARNINGs here are </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">NOT</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> errors                                                      </span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py#45\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">45</span></a>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m WARNINGs here are \u001b[0m\u001b[1;31mNOT\u001b[0m\u001b[1;38;5;251m errors                                                     \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=693707;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=878349;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py#45\u001b\\\u001b[2m45\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> but are inform you about optional packages that can be installed                  </span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py#46\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">46</span></a>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m but are inform you about optional packages that can be installed                 \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=636168;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=547946;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py#46\u001b\\\u001b[2m46\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\"> to disable these messages, turn off start_warning in your config file</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">            </span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py#47\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">47</span></a>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m \u001b[0m\u001b[1;31m to disable these messages, turn off start_warning in your config file\u001b[0m\u001b[1;38;5;251m           \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=309393;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=824037;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py#47\u001b\\\u001b[2m47\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> no display variable set. using backend for graphics without display (agg)         </span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py#53\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">53</span></a>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m no display variable set. using backend for graphics without display \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;38;5;251magg\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=432374;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=78752;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py#53\u001b\\\u001b[2m53\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">13:52:56 </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> ROOT minimizer not available                                                </span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/minimizer/minimization.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">minimization.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/minimizer/minimization.py#1208\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1208</span></a>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;5;46m13:52:56\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m ROOT minimizer not available                                               \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=933613;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/minimizer/minimization.py\u001b\\\u001b[2mminimization.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=323093;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/minimizer/minimization.py#1208\u001b\\\u001b[2m1208\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> Multinest minimizer not available                                           </span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/minimizer/minimization.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">minimization.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/minimizer/minimization.py#1218\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1218</span></a>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Multinest minimizer not available                                          \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=578630;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/minimizer/minimization.py\u001b\\\u001b[2mminimization.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=654870;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/minimizer/minimization.py#1218\u001b\\\u001b[2m1218\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> PyGMO is not available                                                      </span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/minimizer/minimization.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">minimization.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/minimizer/minimization.py#1228\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1228</span></a>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m PyGMO is not available                                                     \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=513591;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/minimizer/minimization.py\u001b\\\u001b[2mminimization.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=636269;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/minimizer/minimization.py#1228\u001b\\\u001b[2m1228\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> The cthreeML package is not installed. You will not be able to use plugins which  </span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py#95\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">95</span></a>\n",
+       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">require the C/C++ interface (currently HAWC)                                       </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">              </span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The cthreeML package is not installed. You will not be able to use plugins which \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=971687;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=848540;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py#95\u001b\\\u001b[2m95\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mrequire the C/C++ interface \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;38;5;251mcurrently HAWC\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m                                      \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m              \u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> Could not import plugin FermiLATLike.py. Do you have the relative instrument     </span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py#136\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">136</span></a>\n",
+       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">software installed and configured?                                                </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Could not import plugin FermiLATLike.py. Do you have the relative instrument    \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=73737;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=804291;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py#136\u001b\\\u001b[2m136\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251msoftware installed and configured?                                               \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m               \u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> Could not import plugin HAWCLike.py. Do you have the relative instrument         </span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py#136\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">136</span></a>\n",
+       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">software installed and configured?                                                </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Could not import plugin HAWCLike.py. Do you have the relative instrument        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=975241;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=788350;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py#136\u001b\\\u001b[2m136\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251msoftware installed and configured?                                               \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m               \u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> No fermitools installed                                              </span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/utils/data_builders/fermi/lat_transient_builder.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">lat_transient_builder.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/utils/data_builders/fermi/lat_transient_builder.py#44\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">44</span></a>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m No fermitools installed                                             \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=200886;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/utils/data_builders/fermi/lat_transient_builder.py\u001b\\\u001b[2mlat_transient_builder.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=462424;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/utils/data_builders/fermi/lat_transient_builder.py#44\u001b\\\u001b[2m44\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">13:52:57 </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> Env. variable MKL_NUM_THREADS is not set. Please set it to </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> for optimal         </span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py#345\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">345</span></a>\n",
+       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">performances in 3ML                                                               </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;5;46m13:52:57\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Env. variable MKL_NUM_THREADS is not set. Please set it to \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m for optimal        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=909389;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=639610;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py#345\u001b\\\u001b[2m345\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mperformances in 3ML                                                              \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m               \u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> Env. variable NUMEXPR_NUM_THREADS is not set. Please set it to </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> for optimal     </span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py#345\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">345</span></a>\n",
+       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">performances in 3ML                                                               </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Env. variable NUMEXPR_NUM_THREADS is not set. Please set it to \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m for optimal    \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=7735;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=877590;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py#345\u001b\\\u001b[2m345\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mperformances in 3ML                                                              \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m               \u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "WARNING UserWarning: An issue occurred while importing 'pyg-lib'. Disabling its usage. Stacktrace: /lib64/libm.so.6: version `GLIBC_2.29' not found (required by /project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/libpyg.so)\n",
+      "\n",
+      "\n",
+      "WARNING UserWarning: An issue occurred while importing 'torch-scatter'. Disabling its usage. Stacktrace: /project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/torch_scatter/_version_cuda.so: undefined symbol: _ZN5torch3jit17parseSchemaOrNameERKSs\n",
+      "\n",
+      "\n",
+      "WARNING UserWarning: An issue occurred while importing 'torch-cluster'. Disabling its usage. Stacktrace: /project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/torch_cluster/_version_cuda.so: undefined symbol: _ZN5torch3jit17parseSchemaOrNameERKSs\n",
+      "\n",
+      "\n",
+      "WARNING UserWarning: An issue occurred while importing 'torch-spline-conv'. Disabling its usage. Stacktrace: /project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/torch_spline_conv/_version_cuda.so: undefined symbol: _ZN5torch3jit17parseSchemaOrNameERKSs\n",
+      "\n",
+      "\n",
+      "WARNING UserWarning: An issue occurred while importing 'torch-sparse'. Disabling its usage. Stacktrace: /project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/torch_sparse/_version_cuda.so: undefined symbol: _ZN5torch3jit17parseSchemaOrNameERKSs\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Imports:\n",
+    "from cosipy.background_estimation import ContinuumEstimationInterp, ContinuumEstimationNN\n",
+    "from cosipy.util import fetch_wasabi_file\n",
+    "import logging\n",
+    "logging.basicConfig(level=logging.INFO)\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22076637",
+   "metadata": {},
+   "source": [
+    "The first part of the notebook requires the following files:\n",
+    "1) crab_and_bg_combined_binned_data.h5\n",
+    "2) crab_binned_data.hdf5\n",
+    "3) crab_psr.h5\n",
+    "4) Total_BG_3months_binned_for_continuum_filtered_with_SAAcut.hdf5\n",
+    "\n",
+    "They can be downloaded using the cells below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ec8e59c4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:cosipy.util.data_fetching:Downloading cosi-pipeline-public/COSI-SMEX/cosipy_tutorials/background_estimationNN/crab_and_bg_combined_binned_data.h5\n"
+     ]
+    }
+   ],
+   "source": [
+    "# crab_and_bg_combined_binned_data.h5\n",
+    "fetch_wasabi_file('COSI-SMEX/cosipy_tutorials/background_estimationNN/crab_and_bg_combined_binned_data.h5', checksum = 'f5ee6f356ebc30a3168a1697c4ec2bdc')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "475a448c-5f4b-48b8-ba7e-b26f4f2c8b02",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:cosipy.util.data_fetching:Downloading cosi-pipeline-public/COSI-SMEX/cosipy_tutorials/background_estimationNN/crab_binned_data.hdf5\n"
+     ]
+    }
+   ],
+   "source": [
+    "# crab_binned_data.hdf5\n",
+    "fetch_wasabi_file('COSI-SMEX/cosipy_tutorials/background_estimationNN/crab_binned_data.hdf5', checksum = '405862396dea2be79d7892d6d5bb50d8')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "f7966690-f4c4-4d4a-9888-9c02ccea1dca",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:cosipy.util.data_fetching:Downloading cosi-pipeline-public/COSI-SMEX/cosipy_tutorials/background_estimationNN/crab_psr.h5\n"
+     ]
+    }
+   ],
+   "source": [
+    "# crab_psr.h5\n",
+    "fetch_wasabi_file('COSI-SMEX/cosipy_tutorials/background_estimationNN/crab_psr.h5', checksum = '700ea171dd52a989deb0810b20302b0d')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b5a1eb28-5a0b-4d64-a1f4-9081a5a1f5f8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:cosipy.util.data_fetching:Downloading cosi-pipeline-public/COSI-SMEX/DC3/Data/Backgrounds/Ge/Binned_BG_Files/Total_BG_3months_binned_for_continuum_filtered_with_SAAcut.hdf5\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Total_BG_3months_binned_for_continuum_filtered_with_SAAcut.hdf5\n",
+    "fetch_wasabi_file('COSI-SMEX/DC3/Data/Backgrounds/Ge/Binned_BG_Files/Total_BG_3months_binned_for_continuum_filtered_with_SAAcut.hdf5', checksum = '270ba55f0da6bc25d4919709ae6a31c2')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42d1d040",
+   "metadata": {},
+   "source": [
+    "Define input files:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e2d285d2-7d77-4a79-bd75-06254da25cec",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "data_path = \"./\"\n",
+    "input_data = data_path + \"crab_and_bg_combined_binned_data.h5\"\n",
+    "crab_data = data_path + \"crab_binned_data.hdf5\"\n",
+    "psr_file = data_path + \"crab_psr.h5\"\n",
+    "background_model = data_path + \"Total_BG_3months_binned_for_continuum_filtered_with_SAAcut.hdf5\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8831e312-e5b8-4cb9-a6d3-fcc855f377f9",
+   "metadata": {},
+   "source": [
+    "Define instance of class:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6ade7276",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "instance = ContinuumEstimationNN()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e1c3fda-124a-4584-84cb-1a37f17516af",
+   "metadata": {},
+   "source": [
+    "There is a convenience function that allows you to run the background estimation algorithm with a single command. The command is shown below with all available options. At minimum, you need to pass the input data and point source response file. All optional inputs are shown with their default values, except for `epochs`, which we set to 1 in order to speed up the runtime. See cosipy documentation for more information on the input parameters.\n",
+    "\n",
+    "Note: this example notebook is being run with 1 GPU. The code detects this automatically and will utilize GPU acceleration. If no GPUs are detected it will default to CPU. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "36e86160-c3a9-446c-9468-324ded91bcbd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:cosipy.background_estimation.ContinuumEstimationNN:...loading the pre-computed point source response ...\n",
+      "INFO:cosipy.background_estimation.ContinuumEstimationNN:--> done\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in divide\n",
+      "\n",
+      "INFO:cosipy.background_estimation.ContinuumEstimationNN:GPUs available: 1\n",
+      "INFO:cosipy.background_estimation.ContinuumEstimationNN:Using GPU 0: NVIDIA A100-PCIE-40GB (capability 8.0)\n",
+      "INFO:cosipy.background_estimation.ContinuumEstimationNN:Using GCN model.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "98b5c64411124721a0f01eb5d2bb9510",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Inpainting (Em, Phi):   0%|          | 0/300 [00:00<?, ?map/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:cosipy.background_estimation.ContinuumEstimationNN:Inpainted histogram saved to inpainted_estimated_bg.h5\n",
+      "INFO:cosipy.background_estimation.ContinuumEstimationNN:Total time elapsed: 2.11 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "instance.estimate_bg(input_data, psr_file, background_model=None,\n",
+    "            training_mode=\"self\", containment=0.6, epochs=1, model_type=\"gcn\",\n",
+    "            nn_model=\"new\", nn_model_file=None, nn_model_savename=\"inpainting_nn_model\",\n",
+    "            lr=1e-3, self_mask_fraction=0.1, lambda_sup=0.5, lambda_self=0.5,\n",
+    "            prefix=\"inpainted\", visualize=False, em_bin=2, phi_bin=4,\n",
+    "            evaluate_only=False, inpainted_file=None,\n",
+    "            evaluate=False, show_plots=False, verbose=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "531c32cb-35f0-4418-b51f-c2e3e5877fb5",
+   "metadata": {},
+   "source": [
+    "Now let's do a full run to estimate the background. We will use the supervised mode, and correspondingly we will pass a background model. We will also make evaluation plots."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "171a4769-50f5-4254-8737-3c65d41b3322",
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:cosipy.background_estimation.ContinuumEstimationNN:...loading the pre-computed point source response ...\n",
+      "INFO:cosipy.background_estimation.ContinuumEstimationNN:--> done\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in divide\n",
+      "\n",
+      "INFO:cosipy.background_estimation.ContinuumEstimationNN:GPUs available: 1\n",
+      "INFO:cosipy.background_estimation.ContinuumEstimationNN:Using GPU 0: NVIDIA A100-PCIE-40GB (capability 8.0)\n",
+      "INFO:cosipy.background_estimation.ContinuumEstimationNN:Using GCN model.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d54ce61c6d0449adb5414de7952654f9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Inpainting (Em, Phi):   0%|          | 0/300 [00:00<?, ?map/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:cosipy.background_estimation.ContinuumEstimationNN:Inpainted histogram saved to inpainted_supervised_600_epochs_0p6containment_estimated_bg.h5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 850x540 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:cosipy.background_estimation.ContinuumEstimationNN:Saved visualization: inpainted_supervised_600_epochs_0p6containment_true_E2_Phi4.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 850x540 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:cosipy.background_estimation.ContinuumEstimationNN:Saved visualization: inpainted_supervised_600_epochs_0p6containment_masked_E2_Phi4.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 850x540 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:cosipy.background_estimation.ContinuumEstimationNN:Saved visualization: inpainted_supervised_600_epochs_0p6containment_inpainted_E2_Phi4.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmMAAAG6CAYAAABEEUEHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAABKJ0lEQVR4nO39f1iUZf7//z+GwRlFIRUVBZUCXYRqU3tpmatiSdq6kmaalqVvFSvzV6zh6u6r9l2YiS1ma1pam1m26tYmZKam64/yU62lWEogamG4wSLFL1EGZub7h1/m3TSgMAwOwv12HBxHnNd5nvO8rKlH53Vd52Ww2+12AQAAwCt8vF0AAABAc0YYAwAA8CLCGAAAgBcRxgAAALyIMAYAAOBFhDEAAAAvIowBAAB4EWHsCrlw4YIyMzN14cIFb5cCAAAaEcLYFZKdna24uDhlZ2d7uxQAANCIEMYAAAC8iDAGAADgRYQxAAAALyKMAQAAeBFhDAAAwIsIYwAAAF5EGAMAAPAiwhgAAIAXEcYAAAC8iDAGAADgRYQxAAAAL/L1dgHwLIvFolWrVunkyZMKDw/XzJkzZTKZvF0WAACoAWGsCUlISFBycrKsVqujbf78+YqPj1dSUpIXKwMAADUhjDURCQkJWrZsmUu71Wp1tBPIAABofLhnrAmwWCxKTk6+ZJ/k5GRZLJYrVBEAAKgtVsausLvuusvjoaikpMTp0mR1rFarOnbsKH9//3p9Vnx8vOLj4+s1BwAA+H8IY1fYf//7X507d84rn11cXKzi4uJ6zwEAADyHMHaFderUqUFWxmoTkgICAuq9MhYQEFCv8QAAwBlh7Ar78MMPFRER4dE5LRaL/Pz8Lnmp0mg0Kj8/n20uAABoZLiBvwkwmUyXvY8rPj6eIAYAQCPEylgTUbVtxS/3GTMajewzBgBAI0YYa0KSkpKUmJjIDvwAAFxFCGNNjMlk0rx587xdBgAAqCXuGQMAAPAiwhgAAIAXEcYAAAC8iDAGAADgRYQxAAAAL2p0T1NaLBa99tpr2rlzp0pKShQeHq7p06erX79+lx2bn5+vlStX6uDBg7LZbOrTp49mz56t4OBgp35btmzRoUOHlJ6erv/+978aMWKEFi1aVO2cJSUlevnll7V//36Vl5crMjJSM2fO9Pgu+gAAoHlqdCtjS5Ys0ebNmxUTE6M5c+bIx8dHCQkJ+uqrry45rqysTHPnzlVaWpomTZqkqVOnKisrS7Nnz1ZRUZFT37fffluHDh3SddddJ6PRWOOcNptNCxYs0K5du3TPPffokUce0U8//aS5c+fq+++/98j5AgCA5q1RrYylp6dr9+7devTRRzVx4kRJ0vDhwzVlyhStXr1aq1evrnHsli1blJOTo1deeUWRkZGSpFtuuUVTpkzRpk2bNGPGDEffF198UUFBQTIYDBo+fHiNc+7du1dHjx7V008/rejoaEnS7bffrvvvv1+vv/66nnzySQ+cNQAAaM4a1crYvn37ZDQaFRsb62gzm80aOXKkjh07pry8vBrH7t27V7169XIEMUkKDQ1V3759tWfPHqe+nTt3lsFgqFU97du31+DBgx1tbdu21dChQ/XJJ5/IYrHU5fQAAABcNKowlpWVpa5du6p169ZO7VUB68SJE9WOs9lsOnXqlHr16uVyLDIyUmfOnFFZWVmd6zl+/Lh69uwpHx/nP6bIyEhduHDhkpcqz549q8zMTMdPdnZ2nT8fAAA0fY3qMmVBQYECAwNd2qvazp49W+244uJiWSyWy47t3r17ner58ccfddNNN9U4Z0FBgcLDw6sdm5qaqnXr1tXp8wAAQPPTqMJYeXm5WrRo4dJe9aLr8vLyGsdJcmvs5eqp7iXbtZkzNjZWAwcOdPyenZ2txMTEOtcAAACatkYVxsxmsyoqKlzaq+7NMpvNNY6T5NbYy9VT3X1htZmzQ4cO6tChQ50/EwAANC+N6p6xwMBAFRQUuLRXtdUUbgICAmQymdwaeynt27e/5JzVXRYFAACoi0YVxnr06KGcnBydO3fOqT09Pd1xvDo+Pj4KCwtTRkaGy7H09HQFBwfLz8+vzvX07NlTWVlZstlsTu3ffPONWrZsqW7dutV5TgAAgJ9rVGEsOjpaVqtVqampjjaLxaJt27YpKipKQUFBkqS8vDyXpxOHDBmijIwMp0B2+vRpHT582LFHWF0NGTJEP/74o/bv3+9oKyws1J49e3TbbbdVez8ZAABAXTSqe8aioqI0dOhQrVmzRoWFhQoJCdH27duVm5urBQsWOPotXrxYaWlpTiFpzJgx2rp1qxYsWKAJEybIaDRq8+bNateunSZMmOD0OQcOHHBsk1FZWamTJ0/qjTfekCT95je/cTwhGR0drXfeeUdLlizRd999p2uuuUZbtmyRzWbT1KlTG/qPAwAANAONKoxJ0qJFixQUFKQdO3aotLRUYWFhWrp0qXr37n3JcX5+flqxYoVWrlyp9evXO95NOWvWLLVt29ap7759+7R9+3bH71lZWcrKypIkderUyRHGjEajkpKStGrVKr377rsqLy9Xr169tHDhwjpvkwEAAFAdg91ut3u7iOYgMzNTcXFxWrt2LS8ZBwAADo3qnjEAAIDmhjAGAADgRYQxAAAALyKMAQAAeBFhDAAAwIsIYwAAAF5EGAMAAPAiwhgAAIAXEcYAAAC8iDAGAADgRYQxAAAALyKMAQAAeBFhDAAAwIsIYwAAAF5EGAMAAPAiwhgAAIAXEcYAAAC8iDAGAADgRYQxAAAALyKMAQAAeBFhDAAAwIsIYwAAAF5EGAMAAPAiwhgAAIAXEcYAAAC8iDAGAADgRYQxAAAALyKMAQAAeBFhDAAAwIsIYwAAAF5EGAMAAPAiwhgAAIAXEcYAAAC8iDAGAADgRYQxAAAALyKMAQAAeBFhDAAAwIsIYwAAAF7k6+0CgOpYLBatWrVKJ0+eVHh4uGbOnCmTyeTtsgAA8DjCGBqdhIQEJScny2q1Otrmz5+v+Ph4JSUlebEyAAA8jzCGRiUhIUHLli1zabdarY52AhkAoCnhnjE0GhaLRcnJyZfsk5ycLIvFcoUqAgCg4bEyhjpJTk6+bGByV0lJidOlyepYrVZ17NhR/v7+9fqs+Ph4xcfH12sOAAA8gTCGOikuLtaZM2e8XkNxcXG95wAAoDEgjKFOAgICFBIS0iBzl5SU1CokBQQE1HtlLCAgoF7jAQDwlEYXxiwWi1577TXt3LlTJSUlCg8P1/Tp09WvX7/Ljs3Pz9fKlSt18OBB2Ww29enTR7Nnz1ZwcLBL361bt2rjxo3Kzc1Vx44dde+992rs2LEu/b744gu9+eabOnXqlKxWq7p27aqxY8dq+PDhHjnfq01DXt6zWCzy8/O75KVKo9Go/Px8trkAADQZje4G/iVLlmjz5s2KiYnRnDlz5OPjo4SEBH311VeXHFdWVqa5c+cqLS1NkyZN0tSpU5WVlaXZs2erqKjIqW9KSoqSkpJ03XXXae7cubrhhhu0YsUKbdiwwanfJ598ot///veqqKjQlClTNH36dJnNZi1evFibN2/2+Lk3dyaT6bJBLz4+niAGAGha7I3IsWPH7IMGDbK//fbbjrYLFy7YJ0yYYH/kkUcuOXbDhg32QYMG2dPT0x1t3333nT06Otr+yiuvOM33u9/9zp6QkOA0/umnn7bfeeed9uLiYkfb448/bh8zZoy9vLzc0VZRUWGfMGGCfcqUKXU6t4yMDPugQYPsGRkZdRrXHD3xxBN2o9Fol+T4MRqN9ieeeMLbpQEA4HGNamVs3759MhqNio2NdbSZzWaNHDlSx44dU15eXo1j9+7dq169eikyMtLRFhoaqr59+2rPnj2OtkOHDqmoqEijR492Gj9mzBidP39en376qaOtrKxM/v7+Tisxvr6+uuaaa2Q2m+tzqriEpKQklZWVafny5Zo1a5aWL1+usrIy9hcDADRJjeqesaysLHXt2lWtW7d2aq8KWCdOnFBQUJDLOJvNplOnTum3v/2ty7HIyEgdPHhQZWVl8vPzU1ZWliSpV69eTv0iIiLk4+Oj48eP684775Qk9e7dW2+//bZeffVVjRgxQgaDQbt27VJmZqb+/Oc/X/Jczp49q4KCAsfv2dnZl/8DgIPJZNK8efO8XUa98EonAEBtNKowVlBQoMDAQJf2qrazZ89WO664uFgWi+WyY7t3766CggIZjUa1a9fOqV+LFi0UEBDgFKAmT56sH374QW+++abWr18vSWrZsqWefvppDRo06JLnkpqaqnXr1l2yD5ouXukEAKitRhXGysvL1aJFC5f2qtWE8vLyGsdJqtXY8vJy+fpWf9omk8npM1q0aKFu3bopOjpagwcPltVq1fvvv6/ExEQlJyfr+uuvr/FcYmNjNXDgQMfv2dnZSkxMrLE/mg5e6QQAqItGFcbMZrMqKipc2qtef1PTfVpV7bUZazabVVlZWe08FovF6TNeeOEFpaen69VXX5WPz8Xb626//XY99NBDevHFF/XKK6/UeC4dOnRQhw4dajyOpqm2r3RKTEzkkiUAQFI9w5jFYtHx48f1008/6cYbb1Tbtm3rVUxgYKDy8/Nd2qsuHdYUbgICAmQymZwuMdY0NjAwUFarVT/99JPTpcqKigoVFxc7LmtWVFTogw8+0P333+8IYtLFG/hvueUWvffee6qoqKh2NQ6NX0O91olXOgEA6srtMPbOO+/o9ddf17lz5yRJf/nLX3TzzTersLBQDz74oB555BGNHDmyTnP26NFDhw8f1rlz55xu4k9PT3ccr46Pj4/CwsKUkZHhciw9PV3BwcHy8/OTJPXs2VOSlJGRoQEDBjj6ZWRkyGazOY4XFRXJarVW+x9Wq9Uqm80mm81Wp/ND4+Ht1zrxSicAQBW3wti2bdv017/+VXfccYf69eun5557znGsbdu26tu3r3bv3l3nMBYdHa2NGzcqNTVVEydOlHRx9W3btm2KiopyPEmZl5enCxcuKDQ01DF2yJAheuWVV5SRkeF4UvL06dM6fPiw7rvvPke/vn37KiAgQCkpKU5hLCUlRS1btnS0tWvXTm3atNHHH3+sadOmOVbAysrKdODAAXXv3p3tLa5iDfVaJ17pBACoK7fC2KZNm/Sb3/xGTz75pMvu9pL0q1/9Su+++26d542KitLQoUO1Zs0aFRYWKiQkRNu3b1dubq4WLFjg6Ld48WKlpaVp//79jrYxY8Zo69atWrBggSZMmCCj0ajNmzerXbt2mjBhgqOf2WzWtGnTtHz5cj355JPq37+/jhw5op07dyouLs7xHzij0agJEybo1Vdf1SOPPKLhw4fLZrPpgw8+UH5+vv70pz/V+fzQeDTUJT5e6QQAqCu3wtiZM2eqfY9jlYCAALcvoSxatEhBQUHasWOHSktLFRYWpqVLl6p3796XHOfn56cVK1Zo5cqVWr9+vePdlLNmzXK5l23MmDHy9fXVpk2bdODAAXXq1EmzZs3SuHHjnPo99NBD6tKli9555x2tW7dOFRUVCg8P19NPP63o6Gi3zg9NW9Urnap7mrIKr3QCAPycW2GsTZs21a6IVfnuu+/Uvn17twoym82aOXOmZs6cWWOfF198sdr2Tp066emnn67V54waNUqjRo26bL+YmBjFxMTUak5A+n/bVvxynzGj0cg+YwAAF269DunWW2/V+++/r5KSEpdj3377rbZu3eq0xxbQ3PBKJwBAbbm1MjZ9+nQ9/PDDmjJlim677TYZDAZt375d27Zt0759+xQYGKjJkyd7ulbgqtIUXukEAGh4boWxDh066NVXX9WaNWv0r3/9S3a7XTt37pSfn5+GDRumhx9+uN57jgEAADQHbu8z1q5dOy1YsEALFixQYWGhbDab2rZt67RBKgAAAC7NI69DYhUMAADAPW6FsXXr1l22j8Fg4L4xAACAy3ArjL3++us1HjMYDLLb7YQxAACAWnArjO3bt8+lzWazKTc3V++9956OHDlyyU0vAQAAcJHH7rb38fFRcHCwHnvsMXXt2lUrVqzw1NQAAABNVoM8+njTTTfps88+a4ipAQAAmpQGCWOZmZkyGAwNMTUAAECT4tY9Y9u3b6+2vbS0VEeOHNH+/fv1u9/9rl6FAQAANAduhbElS5bUeOyaa67RAw88wJOUAAAAteBWGNu0aZNLm8FgkL+/v/z8/OpdFAAAQHNR5zBWXl6uTz75RD169FDv3r0boCQAAIDmo8438JvNZr388sv6/vvvG6IeAACAZsWtpynDwsKUm5vr6VoAAACaHbfC2PTp05WamqovvvjC0/UAAAA0K27dwP/Pf/5T/v7+mj9/vrp06aIuXbrIZDI59TEYDJd86hIAAABuhrFTp05Jkjp16iSr1aqcnByPFgUAANBcuBXGNm/e7Ok6AAAAmiW37hlLS0tTYWFhjccLCwuVlpbmZkkAAADNh1thbN68eTp48GCNx7/88kvNmzfP3ZoAAACaDbfCmN1uv+TxiooK+fg0yDvIAQAAmpRa3zOWl5enH374wfH76dOnq70UWVpaqtTUVAUFBXmkQAAAgKas1mFs27ZtWrdunQwGgwwGg9588029+eabLv3sdrt8fHw0f/58jxYKAADQFNU6jA0dOlTXXXedJOmpp57S2LFj9etf/9qpj8FgUKtWrdSjRw+1b9/es5UCAAA0QbUOY926ddO1114rSfrDH/6gm266ScHBwQ1VFwAAQLNQ6zA2atQo9e/fXwMGDNCAAQPUtm3bBiwLAACgeah1GJs2bZo+++wzPf/886qsrNSvfvUrRzCLiIhoyBoBAACarFqHsbFjx2rs2LEqLy/XF198oc8//1zbtm3T66+/rvbt2+uWW27RgAED1K9fP/n5+TVkzQAAAE1GnV+HZDabNXDgQA0cOFDSxfdUfvrpp/r888/1f//v/5XBYNCNN96oW2+9VQMGDFBoaKjHiwYAAGgq3Ho35c+FhYUpLCxMDzzwgEpLS/Xvf/9bn332mTZu3KiXX35ZcXFxeuCBBzxRKwAAQJNT7zD2c23atNHtt9+u22+/XZL0zTffeHJ6AACAJqfeYay8vFwGg0Emk8nlWGRkZH2nBwAAaNLqHMYOHz6sTz75RF9//bWys7NVXl4u6eK9ZKGhobrhhhs0aNAg9enTx+PFAgAANDW1CmOVlZVKSUnR5s2blZubq4CAAPXs2VMxMTHy9/eX3W5XSUmJfvjhB3300Ud69913FRQUpPvuu0933323fH09ejUUAACgyahVSpo4caIqKio0YsQIDR069LL7imVmZmrPnj166623tGnTJm3evNkjxQIAADQ1tQpjkyZN0l133VXtfWHViYiIUEREhKZNm6Zt27bVq0AAAICmrFZh7O6773Zr8hYtWrg9FgAAoDnw8cQkpaWlslqtnpgKAACgWXE7jGVkZGj+/PmKiYnRqFGjlJaWJkkqLCzUwoULdfjwYU/VCAAA0GS5Fca+/vprzZo1Szk5Obrzzjtls9kcx9q2batz584pNTXVY0UCAAA0VW6FsbVr1yo0NFTr169XXFycy/E+ffooPT293sUBAAA0dW5tAJaRkaEZM2bIZDLp/PnzLsc7duyoH3/80a2CLBaLXnvtNe3cuVMlJSUKDw/X9OnT1a9fv8uOzc/P18qVK3Xw4EHZbDb16dNHs2fPVnBwsEvfrVu3auPGjcrNzVXHjh117733auzYsdXOu3v3br3zzjs6efKkfH19FRoaqunTp+vmm2926xwBAACquLUy5uvr63Rp8pfy8/PVqlUrtwpasmSJNm/erJiYGM2ZM0c+Pj5KSEjQV199dclxZWVlmjt3rtLS0jRp0iRNnTpVWVlZmj17toqKipz6pqSkKCkpSdddd53mzp2rG264QStWrNCGDRtc5v3b3/6mp59+Wp06ddJjjz2madOmKTw8XGfPnnXr/AAAAH7OrZWxqKgo7du3T+PHj3c5dv78eX344Yfq3bt3nedNT0/X7t279eijj2rixImSpOHDh2vKlClavXq1Vq9eXePYLVu2KCcnR6+88orjnZi33HKLpkyZok2bNmnGjBmSLr5L89VXX9WAAQP0zDPPSJJGjRolm82m9evXKzY2Vv7+/pKkY8eO6Y033tBjjz1W7bkCAADUl1srY1OnTlVmZqYSEhL0+eefS5JOnjyprVu3Ki4uToWFhZo8eXKd5923b5+MRqNiY2MdbWazWSNHjtSxY8eUl5dX49i9e/eqV69eTi8nDw0NVd++fbVnzx5H26FDh1RUVKTRo0c7jR8zZozOnz+vTz/91NH2j3/8Q+3bt9e9994ru92usrKyOp8TAADApbgVxqKiorR06VKdOXNGixcvliS99NJLWrZsmaxWq5KSkhQeHl7nebOystS1a1e1bt3aqb0qYJ04caLacTabTadOnVKvXr1cjkVGRurMmTOOIJWVlSVJLn0jIiLk4+Oj48ePO9q+/PJL9erVS++8845iY2M1YsQIjR49Wu++++5lz+Xs2bPKzMx0/GRnZ192DAAAaH7cfoP3zTffrA0bNigrK0s5OTmy2WwKCQlRRESEDAaDW3MWFBQoMDDQpb2qrab7tIqLi2WxWC47tnv37iooKJDRaFS7du2c+rVo0UIBAQEqKCiQJJWUlKioqEhHjx7VoUOHNGXKFAUFBenDDz/UihUr5Ovre8m3C6SmpmrdunW1Om8AANB8uR3GqvTs2VM9e/b0RC0qLy9XixYtXNqr3olZXl5e4zhJtRpbXl4uX9/qT9tkMjn6Va2kFRUV6amnntIdd9whSYqOjtaUKVO0fv36S4ax2NhYDRw40PF7dna2EhMTa+wPAACaJ7fCWNVu+5dT15v4zWazKioqXNotFovjeE3jJNVqrNlsVmVlZbXzWCwWp37SxSdHo6OjHX18fHx0++23629/+5vy8vIUFBRU7VwdOnRQhw4dqj0GAABQxa0wNnfu3Fpdity7d2+d5g0MDFR+fr5Le9Wlw5rCTUBAgEwmk6PfpcYGBgbKarXqp59+crpUWVFRoeLiYsdlzao527RpI6PR6DRn1biSkpIawxgAAEBtuBXGVqxY4dJmtVqVm5ur999/XzabTQ8//HCd5+3Ro4cOHz6sc+fOOd3EX7Wbf48ePaod5+Pjo7CwMGVkZLgcS09PV3BwsPz8/CTJcUk1IyNDAwYMcPTLyMiQzWZzHPfx8VHPnj2VkZGhiooKp0ugVfeutW3bts7nCAAA8HNuPU3Zu3dvl5+bb75ZI0eO1KpVq+Tr61vrS5k/Fx0dLavV6vReS4vFom3btikqKsqxCpWXl+fydOKQIUOUkZHhFMhOnz6tw4cPO11m7Nu3rwICApSSkuI0PiUlRS1btnQKaEOHDpXVatX27dsdbeXl5froo4907bXXchkSAADUW71v4P8lHx8f3XHHHXrrrbc0bdq0Oo2NiorS0KFDtWbNGhUWFiokJETbt29Xbm6uFixY4Oi3ePFipaWlaf/+/Y62MWPGaOvWrVqwYIEmTJggo9GozZs3q127dpowYYKjn9ls1rRp07R8+XI9+eST6t+/v44cOaKdO3cqLi5OAQEBjr533323PvjgAy1fvlzff/+9goKCtGPHDuXl5WnJkiX1+FMCAAC4yONhTLq41URpaalbYxctWuQIPaWlpQoLC9PSpUsv+zCAn5+fVqxYoZUrV2r9+vWOd1POmjXL5XLimDFj5Ovrq02bNunAgQPq1KmTZs2apXHjxjn1M5vNeuGFF7R69Wpt27ZNFy5cUI8ePbR06VL179/frfMDAAD4OYPdbrfXdVBNO+GXlpYqLS1Na9as0Q033KC//OUv9S6wqcjMzFRcXJzWrl2riIgIb5cD1JrFYtGqVat08uRJhYeHa+bMmY4tYwAA9efWytj48eNrfJrSbrcrKipK8+fPr1dhALwvISFBycnJslqtjrb58+crPj5eSUlJXqwMAJoOt8LYH/7wB5c2g8Egf39/hYSE6Nprr61vXQC8LCEhQcuWLXNpt1qtjnYCGQDUX50vU1ZWVio7O1v+/v7q1KlTQ9XV5HCZElcTi8UiPz8/pxWxXzIajSorK+OSJQDUU51XxgwGg6ZPn67HHntM9957b0PUBKAWkpOTlZyc3CBzl5SUXDKISRdXyDp27Ch/f/96fVZ8fLzi4+PrNQcAXM3qHMaMRqM6d+5c7auHAFw5xcXFOnPmjNdrKC4urvccANCcuXXP2D333KN//vOfGjlypNO+XACunICAAIWEhDTI3CUlJbUKSQEBAfVeGePfIQCaO7fCmM1mk8lk0oQJExQdHa3OnTu7vMTbYDBo/PjxHikSgKuGvLxX23vG8vPzuWcMAOrJrTC2atUqx19/8MEH1fYhjAFXL5PJpPj4+GqfpqwSHx9PEAMAD3ArjG3atMnTdQBoZKq2rfjlPmNGo5F9xgDAg9wKY507d/Z0HQAaoaSkJCUmJrIDPwA0IB93BkVHR+ujjz6q8fju3bsVHR3tbk0AGhGTyaR58+bpr3/9q+bNm0cQAwAPcyuMXW6fWJvNVuPrkgAAAPD/uBXGJNUYts6dO6d///vfuuaaa9wuCgAAoLmo9T1jr7/+ut544w1JF4NYYmKiEhMTq+1rt9s1duxYz1QIAADQhNU6jEVGRmr06NGy2+3asmWL/ud//kfdunVz6mMwGNSyZUtFRERo8ODBHi8WAACgqal1GLv11lt16623SpIuXLigu+++W1FRUQ1WGAAAQHPg1tYWCxcu9HQdAAAAzZLbN/ADAACg/ghjAAAAXkQYAwAA8CLCGAAAgBcRxgAAALzII2GssLBQ9913n44ePeqJ6QAAAJoNj4Qxm82m3NxclZeXe2I6AACAZoPLlAAAAF5EGAMAAPAij4SxVq1aacqUKQoODvbEdAAAAM2GW69D+qVWrVrp//yf/+OJqQAAAJoVLlMCAAB4EWEMAADAiwhjAAAAXkQYAwAA8CLCGAAAgBcRxgAAALyoVltbDBkyRAaDoc6T7927t85jAAAAmpNahbHJkye7hLGPP/5Y3377rfr3769u3bpJkk6fPq2DBw8qLCxMv/nNbzxfLQAAQBNTqzA2depUp99TU1P1008/6Y033lD37t2djn333XeaN2+eOnTo4LkqAQAAmii37hn7+9//rnvuuccliEnStddeq3vuuUdvv/12vYsDAABo6twKY/n5+fL1rXlRzdfXV/n5+W4XBQAA0Fy4FcbCwsL03nvvVRu4/vvf/2rLli0KCwurd3EAAABNnVsvCp81a5bmz5+vBx54QIMGDVJISIgkKScnR5988onsdrv+9Kc/ebRQAACApsitMPbrX/9aL7/8sl577TV9/PHHKi8vlySZzWb169dPU6dOVXh4uEcLBQAAaIrcCmPSxUuVixcvls1mU2FhoSSpbdu28vFhH1kAAIDacjuMVfHx8ZHJZFKrVq0IYgAAAHXkdnrKyMjQ/PnzFRMTo1GjRiktLU2SVFhYqIULF+rw4cOeqhEAAKDJciuMff3115o1a5ZycnJ05513ymazOY61bdtW586dU2pqqlsFWSwWrV69WmPGjNGwYcP08MMP6+DBg7Uam5+fr6eeekq//e1vNWLECC1cuFD/+c9/qu27detWTZo0ScOGDdPEiRP17rvvXnb++Ph4DR48WMuXL6/TOQEAANTErTC2du1ahYaGav369YqLi3M53qdPH6Wnp7tV0JIlS7R582bFxMRozpw58vHxUUJCgr766qtLjisrK9PcuXOVlpamSZMmaerUqcrKytLs2bNVVFTk1DclJUVJSUm67rrrNHfuXN1www1asWKFNmzYUOP8+/bt07Fjx9w6JwAAgJq4FcYyMjJ01113yWQyVfsC8Y4dO+rHH3+s87zp6enavXu3ZsyYoZkzZyo2NlYvvPCCOnfurNWrV19y7JYtW5STk6PnnntO999/v8aPH6+//OUv+vHHH7Vp0yZHv/Lycr366qsaMGCAnnnmGY0aNUp//OMfFRMTo/Xr16ukpMRl7vLycr300ku6//7763xOAAAAl+JWGPP19XW6NPlL+fn5atWqVZ3n3bdvn4xGo2JjYx1tZrNZI0eO1LFjx5SXl1fj2L1796pXr16KjIx0tIWGhqpv377as2ePo+3QoUMqKirS6NGjncaPGTNG58+f16effuoy99///nfZ7XZNmDChzucEAABwKW6FsaioKO3bt6/aY+fPn9eHH36o3r1713nerKwsde3aVa1bt3ZqrwpYJ06cqHaczWbTqVOn1KtXL5djkZGROnPmjMrKyhyfIcmlb0REhHx8fHT8+HGn9ry8PG3YsEGPPPKIzGZzrc/l7NmzyszMdPxkZ2fXeiwAAGg+3NraYurUqZozZ44SEhI0bNgwSdLJkyf1ww8/aOPGjSosLNTkyZPrPG9BQYECAwNd2qvazp49W+244uJiWSyWy47t3r27CgoKZDQa1a5dO6d+LVq0UEBAgAoKCpzaX3rpJfXs2VN33HFHnc4lNTVV69atq9MYAADQ/LgVxqKiorR06VIlJydr8eLFki6GFkkKDg5WUlKSWzvwl5eXq0WLFi7tJpPJcbymcZJqNba8vLzGl5ybTCanzzh06JD27dunl19+uQ5ncVFsbKwGDhzo+D07O1uJiYl1ngcAADRtbm/6evPNN2vDhg3KyspSTk6ObDabQkJCFBERUe1N/bVhNptVUVHh0m6xWBzHaxonqVZjzWazKisrq53HYrE4+lVWVmrFihW68847ne5Dq60OHTqoQ4cOdR4HAACaF7fC2Pbt23XTTTepS5cu6tmzp3r27Ol0/IcfftCRI0c0YsSIOs0bGBio/Px8l/aqS4c1hZuAgACZTCaXS4zVjQ0MDJTVatVPP/3kdKmyoqJCxcXFjsuaO3bs0Pfff6/58+frhx9+cJqzrKxMP/zwg9q1a6eWLVvW6RwBAAB+zq0b+J977jkdPXq0xuPp6el67rnn6jxvjx49lJOTo3PnzrnMV3W8Oj4+PgoLC1NGRka1tQQHB8vPz0+SHMHxl30zMjJks9kcx/Py8lRZWanHHntM9913n+NHuhjU7rvvvlpvRgsAAFATt1bG7Hb7JY9fuHBBRqOxzvNGR0dr48aNSk1N1cSJEyVdvHS4bds2RUVFKSgoSNLFoHThwgWFhoY6xg4ZMkSvvPKKMjIyHE9Knj59WocPH3aEKEnq27evAgIClJKSogEDBjjaU1JS1LJlS0fbHXfc4bLiJ0l//OMfdeutt2rUqFFuXb4EAAD4uVqHsZMnTzq2hZCkr776Slar1aVfaWmpUlJS1LVr1zoXExUVpaFDh2rNmjUqLCxUSEiItm/frtzcXC1YsMDRb/HixUpLS9P+/fsdbWPGjNHWrVu1YMECTZgwQUajUZs3b1a7du2c9gczm82aNm2ali9frieffFL9+/fXkSNHtHPnTsXFxSkgIEDSxT3Kfh72fq5Lly4aNGhQnc8PAADgl2odxvbv3+/YqsFgMCg1NbXG90+2adNGf/zjH90qaNGiRQoKCtKOHTtUWlqqsLAwLV269LL7lvn5+WnFihVauXKl1q9fL5vNpj59+mjWrFlq27atU98xY8bI19dXmzZt0oEDB9SpUyfNmjVL48aNc6tmAAAAdxnsl7vm+P939uxZFRQUyG636+GHH9bUqVN16623uvRr1aqVgoODa9w+ornKzMxUXFyc1q5dq4iICG+XAwAAGolaJ6afb9WwYsUKhYaGumycCgAAgLpxa/nKnVcdAQAAwJXb1xILCgr0wQcf6Pjx4zp37pzLi8MNBoNeeOGF+tYHAADQpLkVxk6ePKk5c+aovLxc3bt316lTpxQaGqrS0lKdPXtWwcHB6tSpk6drBQAAaHLc2vT15ZdfVqtWrbRhwwYlJyfLbrdrzpw5evfdd/XnP/9ZpaWlevjhhz1dKwAAQJPjVhg7evSoYmNjFRQUJB+fi1NUPZQ5dOhQDRs2TKtXr/ZclQAAAE2UW2HMZrOpffv2ki7uKebj46Pi4mLH8fDwcB0/ftwzFQIAADRhboWxLl26OF6e7ePjoy5duujLL790HD969KjatGnjmQoBAACaMLdu4O/Xr5/27NmjuLg4SdLo0aP10ksv6T//+Y/sdrvS0tKc3gcJAACA6rkVxh566CENGzZMlZWV8vX11bhx43T+/Hnt379fPj4+euihh/Tggw96ulYAAIAmx60w5u/v7/RKH4PBoMmTJ2vy5MkeKwwAAKA54AWSAJoFi8WiVatW6eTJkwoPD9fMmTNlMpm8XRYAuB/GcnNztX37dv3nP/9RSUmJfvm+cYPBoCVLltS7QACor4SEBCUnJ8tqtTra5s+fr/j4eCUlJXmxMgBwM4zt2rVLzz77rKxWq9q0aaPWrVu79DEYDPUuDgDqKyEhQcuWLXNpt1qtjnYCGQBvMth/uaRVC+PHj5efn5+eeeYZdevWrSHqanIyMzMVFxentWvXOt1vB6DhWCwW+fn5Oa2I/ZLRaFRZWRmXLAF4jVsrY0VFRZo4cSJBDEC9JScnKzk5uUHmLikpuWQQky6ukHXs2FH+/v71+qz4+HjFx8fXaw4AzZNbYSwyMlJ5eXmergVAM1RcXKwzZ854vYafv0XE3TkAwB1uhbHZs2crISFBvXr1UnR0tIdLAtCcBAQEKCQkpEHmLikpqVVICggIqPfKWEBAQL3GA2i+3LpnTJI+/PBDJSUlqWXLlurYsaPjheGOiQ0Gvf766x4psingnjHgyuOeMQBXA7feTfnee+9p6dKlatGihYKDg9WuXTtdc801Tj/8XyIAbzOZTJe9jys+Pp4gBsCr3LpM+dZbb+mGG27Qc889xwvBATRqVdtW/HKfMaPRyD5jABoFt8JYaWmpYmJiCGIArgpJSUlKTExkB34AjZJbYax37946efKkp2sBgAZjMpk0b948b5cBAC7cumcsPj5eR44c0dtvv62ioiJP1wQAANBsuLUy9tBDD8lut2vNmjVas2aNTCZTtU9Tfvjhhx4pEgAAoKlyK4wNGTKEd08CAAB4gFthbNGiRZ6uAwAAoFly656xdevW6dSpUzUe//bbb7Vu3Tp3awIAAGg23Apjr7/++iWfpjx16hRhDAAAoBbcCmOXU1JSIl9ft66AAgAANCu1TkxpaWlKS0tz/L5//36dOXPGpV9paan+9a9/KSwszCMFAgAANGW1DmOHDx92XHo0GAzav3+/9u/fX23fa6+9ls0VAQAAaqHWYez+++/XPffcI7vdrrvvvlu///3vNWTIEKc+BoNBZrNZZrPZ44UCAAA0RbUOYz8PWZs2bVLbtm3VsmXLBisMAACgOXDrLvvOnTu7tF24cEG7d+9WRUWFbr311mr7AAAAwJlbYey5557TN998ozfeeEOSVFFRoUceeUTffvutJKl169Z64YUX9Ktf/cpzlQIAADRBbm1tcfjwYQ0ePNjx+65du/Ttt9/qf//3f/XGG2+offv27DMGAABQC26FsR9//NHpMuTHH3+siIgIDRs2TNdee61GjRql9PR0jxUJAADQVLkVxlq2bKnS0lJJUmVlpdLS0tS/f3/HcT8/P507d84zFQIAADRhbt0z9qtf/Urvv/+++vTpowMHDqisrEy33Xab4/iZM2fUrl07jxUJAADQVLm1MhYXF6fCwkLNmDFD69at05AhQxQVFeU4/vHHH+vGG2/0WJEAAABNlVsrY7169dJbb72lr7/+Wv7+/urdu7fjWElJiUaPHu3UBgAAgOq5/Tbvtm3batCgQS7t/v7+GjduXL2KAgAAaC7cDmNWq1V79+7VoUOHVFhYqKlTpyo8PFylpaX68ssvdeONN6p9+/aerBUAAKDJcSuMlZSU6IknntA333yjVq1a6cKFC7rnnnskSa1atdKLL76o4cOHa8aMGR4tFgAAoKlxK4y98sor+vbbb/X888+rZ8+euvvuux3HjEajhgwZos8++8ytMGaxWPTaa69p586dKikpUXh4uKZPn65+/fpddmx+fr5WrlypgwcPymazqU+fPpo9e7aCg4Nd+m7dulUbN25Ubm6uOnbsqHvvvVdjx4516rNv3z7961//UkZGhn788Ud16tRJAwYM0OTJk+Xv71/ncwMAAPglt56m/OSTTzR27Fj169dPBoPB5Xi3bt2Um5vrVkFLlizR5s2bFRMTozlz5sjHx0cJCQn66quvLjmurKxMc+fOVVpamiZNmqSpU6cqKytLs2fPVlFRkVPflJQUJSUl6brrrtPcuXN1ww03aMWKFdqwYYNTv+eff17Z2dm68847NXfuXPXv31/vvfeeHn30UZWXl7t1fgAAAD/n1spYaWmpunTpUuPxyspKWa3WOs+bnp6u3bt369FHH9XEiRMlScOHD9eUKVO0evVqrV69usaxW7ZsUU5Ojl555RVFRkZKkm655RZNmTJFmzZtcqzSlZeX69VXX9WAAQP0zDPPSJJGjRolm82m9evXKzY21rHq9fTTT6tPnz5OnxMREaFnn31WH330kX73u9/V+RwBAAB+zq2VsZCQEB0/frzG4wcPHlRoaGid5923b5+MRqNiY2MdbWazWSNHjtSxY8eUl5dX49i9e/eqV69ejiAmSaGhoerbt6/27NnjaDt06JCKioo0evRop/FjxozR+fPn9emnnzrafhnEJDneyfndd9/V9fQAAABcuBXGRo4cqW3btmn37t2y2+2SJIPBIIvForVr1+rf//63U6CqraysLHXt2lWtW7d2aq8KWCdOnKh2nM1m06lTp9SrVy+XY5GRkTpz5ozKysocnyHJpW9ERIR8fHwuGTIlqaCgQNLFrT0u5ezZs8rMzHT8ZGdnX7I/AABonty6TDlu3Dh99913evrpp9WmTRtJFy/pFRcXy2q1KjY21q1LeAUFBQoMDHRpr2o7e/ZsteOKi4tlsVguO7Z79+4qKCiQ0Wh0eV1TixYtFBAQ4AhbNXn77bcdDylcSmpqqtatW3fJPgAAAG6FMYPBoISEBI0YMUJ79+5VTk6O7Ha7goODNXToULd33y8vL1eLFi1c2k0mk+N4TeMk1WpseXm5fH2rP22TyXTJG/M/+ugjffDBB5o4caK6det2iTORYmNjNXDgQMfv2dnZSkxMvOQYAADQ/Li96ask/frXv9avf/1rT9Uis9msiooKl3aLxeI4XtM4SbUaazabVVlZWe08Foulxs84cuSIli5dqv79+ysuLu4yZyJ16NBBHTp0uGw/AADQvLl1z1hDCQwMrPYyYVVbTeEmICBAJpOpVmMDAwNltVr1008/OfWrqKhQcXFxtZc6T5w4oYULFyosLExPP/10jStrAAAAdVWrMPbggw9q+/bt1a481cRisWjbtm168MEHaz2mR48eysnJ0blz55za09PTHcer4+Pjo7CwMGVkZLgcS09PV3BwsPz8/CRJPXv2lCSXvhkZGbLZbI7jVc6cOaP58+erXbt2SkpKcswDAADgCbUKY3fddZdeeukl3X333Vq8eLF27Nihb7/9VhcuXHD0OX/+vE6dOqUPP/xQzzzzjO6++26tXr1ad911V62LiY6OltVqVWpqqqOtKtRFRUUpKChIkpSXl+fydOKQIUOUkZHhFLJOnz6tw4cPKzo62tHWt29fBQQEKCUlxWl8SkqKWrZsqQEDBjjaCgoK9Pvf/14+Pj56/vnnL/sEJQAAQF0Z7FV7U1xGWVmZtm7dqu3bt+vkyZOOnfeNRqMkOTZ5tdvtuu666/Tb3/5WI0eOdNmm4nKeeuop7d+/X+PHj1dISIi2b9+ub775RsuXL3c8GDBnzhylpaVp//79TvVNmzZNZWVlmjBhgoxGozZv3iybzaa//e1vTkHqvffe0/LlyxUdHa3+/fvryJEj2rFjh+Li4pxW8qZOnaoTJ05o4sSJCg8Pd6qzXbt2tXpFU5XMzEzFxcVp7dq1ioiIqNOfCQAAaLpqffOTn5+fxo8fr/Hjx+uHH37Q0aNHdfr0acerhq655hp1795d119/fbXvgqytRYsWKSgoSDt27FBpaanCwsK0dOnSyz6h6efnpxUrVmjlypVav369492Us2bNclnRGjNmjHx9fbVp0yYdOHBAnTp10qxZszRu3DinflX7mv397393+bzevXvXKYwBAABUp9YrY6gfVsYAAEB1eCwQAK4SFotFq1at0smTJxUeHq6ZM2c69lIEcPUijAHAVSAhIUHJycmO+3Mlaf78+YqPj1dSUpIXKwNQX4QxAGjkEhIStGzZMpd2q9XqaCeQAVevRrXpKwDAmcViUXJy8iX7JCcnO942AuDqw8oYAHhAcnLyZUOTO0pKSpwuTVbHarWqY8eO8vf3r9dnxcfHKz4+vl5zAKg7whgAeEBxcbHOnDnj1c8vLi6u9xxoXngopHEgjAGABwQEBCgkJMTj85aUlNQqJAUEBNR7ZSwgIKBe43F14aGQxoMwBgAe0FCX+CwWi/z8/C55qdJoNCo/P58VjSvoal9R4qGQxoUb+AGgETOZTJcNefHx8VdVELBYLHrhhRc0e/ZsvfDCC1fdwwcJCQny8/PT448/rpUrV+rxxx+Xn5+fEhISvF1arfBQSOPDyhgANHJVKxS/vKRkNBqvuktKV/ulsSu1otRQD4RIPBTSGBHGAOAqkJSUpMTERC6NeVFtV5QSExPr/ffF2w+EVNXAQyFXBmEMAK4SJpNJ8+bN83YZbrmSQaYpbDNy8803N8gDIRIPhTRGhDEAgKSmc2ns+uuvv+q3GZk+fbpSUlI8VJEzHgppfAhjAABJTefS2PXXX882I5dQ9VBIdZeMq1xtD4Vc7QhjAABJDbdXmnRlg8zw4cO1Y8eOes1Rnaa0otSUHgppCgx2u93u7SKag8zMTMXFxWnt2rWKiIjwdjkAcEXVNsiUlZU16iBT00MIVZ544omrKshc7fulNRWsjAEAGlxTuTTW1FaUruaHQpoSwhgA4IpoKkGmKWwzgsaFy5RXCJcpAeAiLo0BzlgZAwBcUVwaA5zxbkoAAAAvIowBAAB4EWEMAADAiwhjAAAAXkQYAwAA8CLCGAAAgBcRxgAAALyIMAYAAOBFhDEAAAAvIowBAAB4EWEMAADAiwhjAAAAXkQYAwAA8CLCGAAAgBcRxgAAALyIMAYAAOBFhDEAAAAvIowBAAB4EWEMAADAiwhjAAAAXkQYAwAA8CLCGAAAgBcRxgAAALyIMAYAAOBFhDEAAAAv8vV2Ab9ksVj02muvaefOnSopKVF4eLimT5+ufv36XXZsfn6+Vq5cqYMHD8pms6lPnz6aPXu2goODXfpu3bpVGzduVG5urjp27Kh7771XY8eOrdecAAAAddXoVsaWLFmizZs3KyYmRnPmzJGPj48SEhL01VdfXXJcWVmZ5s6dq7S0NE2aNElTp05VVlaWZs+eraKiIqe+KSkpSkpK0nXXXae5c+fqhhtu0IoVK7Rhwwa35wQAAHBHo1oZS09P1+7du/Xoo49q4sSJkqThw4drypQpWr16tVavXl3j2C1btignJ0evvPKKIiMjJUm33HKLpkyZok2bNmnGjBmSpPLycr366qsaMGCAnnnmGUnSqFGjZLPZtH79esXGxsrf379OcwIAALirUa2M7du3T0ajUbGxsY42s9mskSNH6tixY8rLy6tx7N69e9WrVy9HaJKk0NBQ9e3bV3v27HG0HTp0SEVFRRo9erTT+DFjxuj8+fP69NNP6zwnAACAuxpVGMvKylLXrl3VunVrp/aqMHTixIlqx9lsNp06dUq9evVyORYZGakzZ86orKzM8RmSXPpGRETIx8dHx48fr/Oc1Tl79qwyMzMdP9nZ2TX2BQAAzVejukxZUFCgwMBAl/aqtrNnz1Y7rri4WBaL5bJju3fvroKCAhmNRrVr186pX4sWLRQQEKCCgoI6z1md1NRUrVu3roYzBQAAuKhRhbHy8nK1aNHCpd1kMjmO1zROUq3GlpeXy9e3+tM2mUxO/Wo7Z3ViY2M1cOBAx+/Z2dlKTEyssT8AAGieGlUYM5vNqqiocGm3WCyO4zWNk1SrsWazWZWVldXOY7FYnPrVds7qdOjQQR06dKjxOAAAgNTI7hkLDAx0XCb8uaq2msJNQECATCZTrcYGBgbKarXqp59+cupXUVGh4uJixyXIuswJAADgrkYVxnr06KGcnBydO3fOqT09Pd1xvDo+Pj4KCwtTRkaGy7H09HQFBwfLz89PktSzZ09JcumbkZEhm83mOF6XOQEAANzVqMJYdHS0rFarUlNTHW0Wi0Xbtm1TVFSUgoKCJEl5eXkuTycOGTJEGRkZTuHp9OnTOnz4sKKjox1tffv2VUBAgFJSUpzGp6SkqGXLlhowYECd5wQAAHBXo7pnLCoqSkOHDtWaNWtUWFiokJAQbd++Xbm5uVqwYIGj3+LFi5WWlqb9+/c72saMGaOtW7dqwYIFmjBhgoxGozZv3qx27dppwoQJjn5ms1nTpk3T8uXL9eSTT6p///46cuSIdu7cqbi4OAUEBNR5TgAAAHc1qjAmSYsWLVJQUJB27Nih0tJShYWFaenSperdu/clx/n5+WnFihVauXKl1q9f73iP5KxZs9S2bVunvmPGjJGvr682bdqkAwcOqFOnTpo1a5bGjRvn9pwAAADuMNjtdru3i2gOMjMzFRcXp7Vr1yoiIsLb5QAAgEaiUd0zBgAA0NwQxgAAALyIMAYAAOBFhDEAAAAvIowBAAB4EWEMAADAiwhjAAAAXkQYAwAA8CLCGAAAgBcRxgAAALyIMAYAAOBFje5F4QAAAHVhsVi0atUqnTx5UuHh4Zo5c6ZMJpO3y6o1whgAALhqJSQkKDk5WVar1dE2f/58xcfHKykpyYuV1R5hDAAAXJUSEhK0bNkyl3ar1epovxoCGfeMAQCAq47FYlFycvIl+yQnJ8tisVyhitzHyhgAAGgQycnJlw1M7iopKXG6NFkdq9Wqjh07yt/fv16flZOTU6/xl0MYAwAADaK4uFhnzpzxeg3FxcVereFyCGMAAKBBBAQEKCQkpEHmLikpqVXICggIqPfKWEMjjAEAgAYRHx+v+Pj4BpnbYrHIz8/vkpcqjUaj8vPzG/02F9zADwAArjomk+myQS8+Pr7RBzGJlTEAAHCVqtq24pf7jBmNRvYZAwAAuBKSkpKUmJjIDvwAAADeYjKZNG/ePG+X4TbuGQMAAPAiwhgAAIAXEcYAAAC8iDAGAADgRYQxAAAALyKMAQAAeBFhDAAAwIsIYwAAAF5EGAMAAPAiduC/QsrLyyVJ2dnZXq4EAADUVWhoqFq2bNkgcxPGrpCsrCxJUmJiopcrAQAAdbVs2TLdcsstDTI3YewKCQ0NlSQtWLBAPXr08HI1jdtf//pXzZ4929tl1Jo3623oz/b0/PWdrz7j6zq2Lv2zs7OVmJioP/3pT47vOqrH97vxfDbf77p9v1u1auVWbbVBGLtC/P39JUk9evRQRESEl6tp3Nq0aXNV/Rl5s96G/mxPz1/f+eozvq5j3fms0NDQq+qfXW/g+914Ppvvd90+y2w217WsWuMGfjQ6w4YN83YJdeLNehv6sz09f33nq8/4uo692v45vFpcbX+ufL+v3HzN+fttsNvtdm8X0RxkZmYqLi5Oa9euvar+rxDA5fH9BpquK/H9ZmXsCgkMDNSUKVMUGBjo7VIAeBjfb6DpuhLfb1bGAAAAvIiVMQAAAC8ijAEAAHgRYayRsFgseu6553TvvfdqxIgReuSRR3T06FFvlwXAQ5YtW6bRo0drxIgRmjx5sg4cOODtkgB42NGjRzVkyBC98cYbdRrHPWONxPnz57Vp0ybddddd6tixo/bs2aMXXnhBmzZtkp+fn7fLA1BP2dnZ6tKli0wmk7755hvFx8dr48aNuuaaa7xdGgAPsNlsmjlzpux2u2677TZNnjy51mNZGWskWrVqpSlTpigoKEg+Pj6644475Ovrq++//97bpQHwgNDQUJlMJkmSwWBQRUWFzp496+WqAHjK+++/r8jISLfewsEO/G4qKyvTxo0blZ6erm+++UYlJSVauHCh7rrrLpe+FotFr732mnbu3KmSkhKFh4dr+vTp6tevX43zf//99yopKVFISEhDngaAajTU9zs5OVnbtm2TxWLRrbfeqrCwsCtxOgB+piG+30VFRfrHP/6h1atX669//Wuda2JlzE1FRUVat26dsrOzL/uuySVLlmjz5s2KiYnRnDlz5OPjo4SEBH311VfV9i8vL1diYqIeeOABtWnTpiHKB3AJDfX9jo+P144dO7R8+XL169dPBoOhoU4BQA0a4vu9du1ajRs3zvHqw7oijLkpMDBQ7733nv7xj3/o0UcfrbFfenq6du/erRkzZmjmzJmKjY3VCy+8oM6dO2v16tUu/SsrK/Xkk08qJCREU6ZMacAzAFCThvp+S5LRaNTNN9+sL7/8Up9++mlDnQKAGnj6+338+HFlZGTod7/7nds1EcbcZDKZarUb7759+2Q0GhUbG+toM5vNGjlypI4dO6a8vDxHu81mU2JiogwGgxYtWsT/NQNe0hDf71+yWq06c+aMR+oFUHue/n6npaXp+++/19ixYzV69Gj961//0ttvv60lS5bUuibuGWtgWVlZ6tq1q1q3bu3UHhkZKUk6ceKEgoKCJEnPP/+8CgoK9Pzzz8vXl781QGNX2+93aWmpPv30Uw0cOFAmk0kff/yxDh8+rBkzZnijbAC1UNvvd2xsrO644w7H8RdffFFdunTRAw88UOvP4r/4DaygoKDaBF7VVvU0VW5urrZu3SqTyeSUwpOSknTTTTddmWIB1Eltv98Gg0Fbt27V8uXLZbfbFRISov/93/9Vz549r2i9AGqvtt/vli1bqmXLlo7jZrNZrVq1qtP9Y4SxBlZeXq4WLVq4tFc94l5eXi5J6ty5s/bv339FawNQP7X9frdu3VorVqy4orUBqJ/afr9/adGiRXX+LO4Za2Bms1kVFRUu7RaLxXEcwNWJ7zfQdF3J7zdhrIEFBgaqoKDApb2qrUOHDle6JAAewvcbaLqu5PebMNbAevTooZycHJ07d86pPT093XEcwNWJ7zfQdF3J7zdhrIFFR0fLarUqNTXV0WaxWLRt2zZFRUU5nqQEcPXh+w00XVfy+80N/PXw7rvvqrS01LFkeeDAAf33v/+VJI0dO1Zt2rRRVFSUhg4dqjVr1qiwsFAhISHavn27cnNztWDBAm+WD+AS+H4DTVdj+34b7Ha73aMzNiPjx49Xbm5utcc2bdqkLl26SLr4xEXVu61KS0sVFham6dOnq3///leyXAB1wPcbaLoa2/ebMAYAAOBF3DMGAADgRYQxAAAALyKMAQAAeBFhDAAAwIsIYwAAAF5EGAMAAPAiwhgAAIAXEcYAAAC8iDAGAADgRYQxAAAALyKMAUAdzZkzR4MHD9bgwYOdXhj8ww8/aPDgwfr73/9+xWv6+OOPHTUNHjxYGRkZV7wGAO7x9XYBAFBXH374oZYsWVLj8dWrV+v6669v0Bq6d++uhx56SB07dvT43JWVlRozZoy6d++ul156qdo+drtd9957r9q2bavXXntNERER+tOf/qQjR47o/fff93hNABoOYQzAVWvatGnq0qWLS3tISEiDf3b79u115513Nsjcvr6+io6OVmpqqnJzc9W5c2eXPkeOHFF+fr7Gjx8vSerUqZPuvPNOWa1WwhhwlSGMAbhq3XLLLerVq5e3y2gQMTExSklJ0a5duzRp0iSX4x999JF8fHx0xx13eKE6AJ7EPWMAmqyf38P1z3/+U/fdd59iYmIUHx+vvLw82e12vfHGGxo7dqyGDRumhQsXqri42KM12O12LVu2TLfffrv27dvnaN+5c6emT5+uYcOGaeTIkfrzn/+svLw8x/Ebb7xRnTt31q5du1zmrKys1L59+9SnTx916NDBo/UCuPIIYwCuWufOnVNhYaHTT1FRkUu/Xbt2acuWLRo7dqzuu+8+HTlyRH/+85/16quv6vPPP9f999+vUaNG6f/7//4/rVq1ymP1Wa1WPfvss9qxY4cWL16sIUOGSJLWr1+vxYsXq2vXrpo1a5bGjRunL7/8UrNnz1ZJSYkkyWAwKCYmRqdOndK3337rNO/nn3+u4uJixcTEeKxWAN7DZUoAV63HH3/cpc1kMrmsJuXn5+vtt99WmzZtJEk2m01vvfWWysvLtWbNGvn6XvxXYVFRkT766CPFx8fLZDLVq7bKykolJibqwIEDevbZZ9W/f39JUm5url5//XVNnz5dDz74oKP/4MGDNW3aNG3ZssXRHhMTozfffFMfffSRZsyY4ei7a9cumUwmR7gDcHUjjAG4aj3++OPq1q2bU5uPj+uCf3R0tCOISVJkZKSki2GnKohVte/atUtnz55VcHCw23VVVlbqqaee0hdffKGkpCT16dPHcWz//v2y2WwaOnSoCgsLHe3t27dX165ddfjwYUcYu/baa9WzZ0/t3r3bEcbOnz+vAwcO6LbbblPr1q3drhFA40EYA3DVioyMrNUN/EFBQU6/VwWzTp06VdtedanQXW+99ZbOnz+vZcuWOQUxScrJyZHdbtf9999f7difh0PpYmBctWqVvv76a9144436+OOPdeHCBS5RAk0IYQxAk1fdapkkGY3Gatvtdnu9Pq9///7697//rbffflu9e/eW2Wx2HLPZbDIYDFq2bFm1dbVq1crp92HDhunll1/Wrl27dOONN2rXrl3y9/fXrbfeWq8aATQehDEA8LCoqCjdfffd+sMf/qCnnnpKiYmJjhWvkJAQ2e12denSxeUSa3U6dOigPn36aO/evZo8ebK++OIL3XXXXWrRokVDnwaAK4SnKQGgAfzP//yPnnrqKX3++edavHixbDabpIs36huNRr3++usuK3B2u73ap0FjYmL0008/6fnnn1dlZSWXKIEmhpUxAFetzz//XKdPn3Zpv+GGG+p1A76nDBo0SAsXLtTixYvl5+enJ554QiEhIZo2bZrWrFmj3NxcDRo0SH5+fvrPf/6jjz/+WKNGjdLEiROd5hkyZIiSk5P1ySefqFOnTrrpppu8dEYAGgJhDMBV67XXXqu2feHChY0ijEnSnXfeqbKyMiUnJ6t169aaOXOmJk2apG7duukf//iH1q1bJ0nq2LGj+vXrp9/85jcuc7Ru3VoDBw7Unj17dMcdd8hgMFzhswDQkAz2+t6pCgDNzJw5c1RZWalnn31WLVq0aBRbTFRUVOjcuXPavXu3VqxYoTVr1jTZV0UBTQ0rYwDghqNHjyo2NlYDBgzQ0qVLvV2OPvvsM/3xj3/0dhkA3MDKGADUUWZmpmMvsrZt26pHjx5erkgqLCzUiRMnHL9HRUXJz8/PixUBqC3CGAAAgBextQUAAIAXEcYAAAC8iDAGAADgRYQxAAAALyKMAQAAeBFhDAAAwIsIYwAAAF5EGAMAAPAiwhgAAIAX/f8AJ8v+dSmLVswAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in divide\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: divide by zero encountered in divide\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:cosipy.background_estimation.ContinuumEstimationNN:Accuracy plots saved with prefix 'inpainted_supervised_600_epochs_0p6containment_...'\n",
+      "INFO:cosipy.background_estimation.ContinuumEstimationNN:Total time elapsed: 604.48 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "instance.estimate_bg(input_data, psr_file, background_model=background_model,\n",
+    "        training_mode=\"supervised\", containment=0.6, epochs=600, prefix=\"inpainted_supervised_600_epochs_0p6containment\", \n",
+    "        evaluate=True, show_plots=True, visualize=True, verbose=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06bc6593-fb1e-4587-9513-a0cbaef4bd59",
+   "metadata": {},
+   "source": [
+    "Producing the estimted BG map took 604.5 seconds (10.1 minutes). From the diagnostic plots, we see that the agreement with the model is generally within 1% (when projected onto a given axis).\n",
+    "\n",
+    "Let's now take a look at the training loss for one of the energy bins:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "4c26ac85-3f7e-4faa-a7a0-27e13b5497e2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 800x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for E in range(2,3):\n",
+    "    instance.plot_training_loss(\"inpainting_nn_model_training_loss.npy\",E,\"training_loss\",vmax=70000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c759b718-aec7-4dd7-b53b-2b00b6897c27",
+   "metadata": {},
+   "source": [
+    "The NN model is saved, including the weights, optimizer state, epochs, and training loss. Let's run the estimate again, but this time we'll laod the model from file. We just want to demonstrate the funcionality here, and so we'll set `epochs` to 1. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "4cfea996-3930-4eb3-89bc-2e0c05e76c73",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:cosipy.background_estimation.ContinuumEstimationNN:...loading the pre-computed point source response ...\n",
+      "INFO:cosipy.background_estimation.ContinuumEstimationNN:--> done\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in divide\n",
+      "\n",
+      "INFO:cosipy.background_estimation.ContinuumEstimationNN:GPUs available: 1\n",
+      "INFO:cosipy.background_estimation.ContinuumEstimationNN:Using GPU 0: NVIDIA A100-PCIE-40GB (capability 8.0)\n",
+      "INFO:cosipy.background_estimation.ContinuumEstimationNN:loading NN model...\n",
+      "INFO:cosipy.background_estimation.ContinuumEstimationNN:Loaded NN model (weights only) from inpainting_nn_model.pth\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d5ed3f0e15c54b7ab9f4fcf7b406930e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Inpainting (Em, Phi):   0%|          | 0/300 [00:00<?, ?map/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:cosipy.background_estimation.ContinuumEstimationNN:Inpainted histogram saved to inpainted_example_estimated_bg.h5\n",
+      "INFO:cosipy.background_estimation.ContinuumEstimationNN:Total time elapsed: 1.41 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "instance.estimate_bg(input_data, psr_file, background_model=background_model,\n",
+    "        training_mode=\"supervised\", containment=0.6, epochs=1, prefix=\"inpainted_example\", \n",
+    "        nn_model=\"load\", nn_model_file=\"inpainting_nn_model.pth\", nn_model_savename=\"inpainting_nn_model_example\", verbose=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5184e4a-693a-4266-8930-12d6371cc837",
+   "metadata": {},
+   "source": [
+    "## Estimating the background with simple interpolation inpainting\n",
+    "Instead of NN-based inpainting, we can also do a simple interpolation. This is the original method presented with DC3, and it's useful as a baseline to gauge the improvement from the NN-based method. For each masked pixel, the algorithm moves to the left till it finds the first non-masked pixel, and then moves to the right till findinging the first non-masked pixel. The inpainted value is taken as the mean of left and right. This is easy to implement because we have a healpix skymap in ring order. All other methods are inhereted from the ContinuumEstimationNN class. Let's run it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8ca09a20-c83b-445e-892d-c41c1ccf6264",
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:cosipy.background_estimation.ContinuumEstimationNN:...loading the pre-computed point source response ...\n",
+      "INFO:cosipy.background_estimation.ContinuumEstimationNN:--> done\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in divide\n",
+      "\n",
+      "INFO:cosipy.background_estimation.ContinuumEstimationNN:Inpainted histogram saved to inpainted_simple_estimated_bg.h5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 850x540 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:cosipy.background_estimation.ContinuumEstimationNN:Saved visualization: inpainted_simple_true_E2_Phi4.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 850x540 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:cosipy.background_estimation.ContinuumEstimationNN:Saved visualization: inpainted_simple_masked_E2_Phi4.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 850x540 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:cosipy.background_estimation.ContinuumEstimationNN:Saved visualization: inpainted_simple_inpainted_E2_Phi4.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in divide\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: divide by zero encountered in divide\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:cosipy.background_estimation.ContinuumEstimationNN:Accuracy plots saved with prefix 'inpainted_simple_...'\n",
+      "INFO:cosipy.background_estimation.ContinuumEstimationNN:Total time elapsed: 3.29 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "instance = ContinuumEstimationInterp()\n",
+    "instance.estimate_bg(input_data, psr_file, background_model=background_model,\n",
+    "                prefix=\"inpainted_simple\", evaluate=True, show_plots=True, visualize=True, verbose=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0d4cdffd-8afe-4265-b175-a515c7f75f61",
+   "metadata": {},
+   "source": [
+    "The agreement seems to be a bit worse compared to the NN-based method (within ~2%). "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f3db8dc1-743d-4bbd-8bda-73144f7e219d",
+   "metadata": {},
+   "source": [
+    "## Spectral fit using the estimated background\n",
+    "This example is nearly identical to the spectral fit example in docs/api/interfaces/examples/crab. The main difference is that instead of the ideal BG file, we will use the estimated BG calculated in the previous section.\n",
+    "\n",
+    "Imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "cec2141e-a22f-464b-ae40-2293bb5f6575",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from cosipy import test_data, BinnedData\n",
+    "from cosipy.spacecraftfile import SpacecraftHistory\n",
+    "from cosipy.response.FullDetectorResponse import FullDetectorResponse\n",
+    "from cosipy.util import fetch_wasabi_file\n",
+    "\n",
+    "from cosipy.statistics import PoissonLikelihood\n",
+    "from cosipy.background_estimation import FreeNormBinnedBackground\n",
+    "from cosipy.interfaces import ThreeMLPluginInterface\n",
+    "from cosipy.response import BinnedThreeMLModelFolding, BinnedInstrumentResponse, BinnedThreeMLPointSourceResponse\n",
+    "from cosipy.data_io import EmCDSBinnedData\n",
+    "\n",
+    "import sys\n",
+    "\n",
+    "from scoords import SpacecraftFrame\n",
+    "\n",
+    "from astropy.time import Time\n",
+    "import astropy.units as u\n",
+    "from astropy.coordinates import SkyCoord, Galactic\n",
+    "\n",
+    "import astromodels\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from threeML import Band, PointSource, Model, JointLikelihood, DataList\n",
+    "from astromodels import Parameter, Powerlaw\n",
+    "\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d7ea122-fb89-4e51-aac9-72643033c241",
+   "metadata": {},
+   "source": [
+    "In addition to the files above, you will need the following files:\n",
+    "\n",
+    "1. inputs_crab.yaml\n",
+    "2. crab_DC2_astromodel.yaml\n",
+    "2. DC3_final_530km_3_month_with_slew_15sbins_GalacticEarth_SAA.ori\n",
+    "3. ResponseContinuum.o3.e100_10000.b10log.s10396905069491.m2284.filtered.nonsparse.binnedimaging.imagingresponse.h5\n",
+    "\n",
+    "These can be downloaded using the cells below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "64a1136a-f49f-4f22-9d9e-41e87bbd9b6a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:cosipy.util.data_fetching:Downloading cosi-pipeline-public/COSI-SMEX/cosipy_tutorials/background_estimationNN/inputs_crab.yaml\n"
+     ]
+    }
+   ],
+   "source": [
+    "# inputs_crab.yaml\n",
+    "fetch_wasabi_file('COSI-SMEX/cosipy_tutorials/background_estimationNN/inputs_crab.yaml', checksum = 'dd30283e1809ccc51d75ad22cf20e766')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "a17f0833-2c79-44d8-9652-4ee35b4d9e35",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:cosipy.util.data_fetching:Downloading cosi-pipeline-public/COSI-SMEX/cosipy_tutorials/background_estimationNN/crab_DC2_astromodel.yaml\n"
+     ]
+    }
+   ],
+   "source": [
+    "# crab_DC2_astromodel.yaml\n",
+    "fetch_wasabi_file('COSI-SMEX/cosipy_tutorials/background_estimationNN/crab_DC2_astromodel.yaml', checksum = 'b8366b8e09ede0f29581d1ec466628c6')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "1fc66b8e-9bf6-4257-991c-827811c56dcd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:cosipy.util.data_fetching:Downloading cosi-pipeline-public/COSI-SMEX/DC3/Data/Orientation/DC3_final_530km_3_month_with_slew_15sbins_GalacticEarth_SAA.ori\n"
+     ]
+    }
+   ],
+   "source": [
+    "# DC3_final_530km_3_month_with_slew_15sbins_GalacticEarth_SAA.ori\n",
+    "fetch_wasabi_file('COSI-SMEX/DC3/Data/Orientation/DC3_final_530km_3_month_with_slew_15sbins_GalacticEarth_SAA.ori', checksum = 'e5e71e3528e39b855b0e4f74a1a2eebe')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "472b0bb0-7f47-4e6c-99a4-b599999a5cf6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:cosipy.util.data_fetching:Downloading cosi-pipeline-public/COSI-SMEX/develop/Data/Responses/ResponseContinuum.o3.e100_10000.b10log.s10396905069491.m2284.filtered.nonsparse.binnedimaging.imagingresponse.h5\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ResponseContinuum.o3.e100_10000.b10log.s10396905069491.m2284.filtered.nonsparse.binnedimaging.imagingresponse.h5 \n",
+    "fetch_wasabi_file('COSI-SMEX/develop/Data/Responses/ResponseContinuum.o3.e100_10000.b10log.s10396905069491.m2284.filtered.nonsparse.binnedimaging.imagingresponse.h5', checksum = '7121f094be50e7bfe9b31e53015b0e85')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b65cfcd-910f-43ce-a612-229bd6d0c037",
+   "metadata": {},
+   "source": [
+    "Define all input files:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "30dd030f-aadb-4085-978d-0fc5080c4441",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_path = \"./\"\n",
+    "total_data = os.path.join(data_path,\"crab_and_bg_combined_binned_data.h5\")\n",
+    "crab_data = os.path.join(data_path, \"crab_binned_data.hdf5\")\n",
+    "psr_file = os.path.join(data_path,\"crab_psr.h5\")\n",
+    "background_model = os.path.join(data_path,\"Total_BG_3months_binned_for_continuum_filtered_with_SAAcut.hdf5\")\n",
+    "input_yaml = os.path.join(data_path,\"inputs_crab.yaml\")\n",
+    "ori_file = os.path.join(data_path,\"DC3_final_530km_3_month_with_slew_15sbins_GalacticEarth_SAA.ori\")\n",
+    "dr_file = os.path.join(data_path,\"ResponseContinuum.o3.e100_10000.b10log.s10396905069491.m2284.filtered.nonsparse.binnedimaging.imagingresponse.h5\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "904c2322-78b4-468c-b97a-804e4393912c",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "The estimated BG file was calculated at the last step:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "3e378bfd-e1b2-4a01-b3a7-ef64747cda7d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "estimated_bg = \"inpainted_supervised_600_epochs_0p6containment_estimated_bg.h5\"\n",
+    "estimated_bg_simple = \"inpainted_simple_estimated_bg.h5\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c00eb73f-6abd-4ef9-bb97-8800c2586acb",
+   "metadata": {},
+   "source": [
+    "Load files:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "7edb9275-e105-4e52-8da8-e91cc60bae4c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:yayc.configurator:Using configuration file at ./inputs_crab.yaml\n",
+      "INFO:yayc.configurator:Using configuration file at ./inputs_crab.yaml\n",
+      "INFO:yayc.configurator:Using configuration file at ./inputs_crab.yaml\n",
+      "INFO:yayc.configurator:Using configuration file at ./inputs_crab.yaml\n",
+      "INFO:yayc.configurator:Using configuration file at ./inputs_crab.yaml\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Orientation file:\n",
+    "sc_orientation = SpacecraftHistory.open(ori_file)\n",
+    "\n",
+    "# Detector response:\n",
+    "dr = FullDetectorResponse.open(dr_file)\n",
+    "instrument_response = BinnedInstrumentResponse(dr)\n",
+    "\n",
+    "# Load Crab data:\n",
+    "crab = BinnedData(input_yaml)\n",
+    "crab.load_binned_data_from_hdf5(binned_data=crab_data)\n",
+    "\n",
+    "# Load BG model true:\n",
+    "bg = BinnedData(input_yaml)\n",
+    "bg.load_binned_data_from_hdf5(binned_data=background_model)\n",
+    "\n",
+    "# Load BG model estimated:\n",
+    "bg_est = BinnedData(input_yaml)\n",
+    "bg_est.load_binned_data_from_hdf5(binned_data=estimated_bg)\n",
+    "\n",
+    "# Load BG model estimated simple:\n",
+    "bg_est_simple = BinnedData(input_yaml)\n",
+    "bg_est_simple.load_binned_data_from_hdf5(binned_data=estimated_bg_simple)\n",
+    "\n",
+    "# Load total data:\n",
+    "total = BinnedData(input_yaml)\n",
+    "total.load_binned_data_from_hdf5(binned_data=total_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "016814a8-7d13-47e6-9305-03a3b66efa56",
+   "metadata": {},
+   "source": [
+    "Define interfaces below. Here we will use the FreeNormBinnedBackground interface. The only difference is that we will use the estimated BG instead of the true BG. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "fbefb778-8d3a-4395-937d-d4138160f5c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "WARNING RuntimeWarning: divide by zero encountered in divide\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ------- Interfaces ----------\n",
+    "\n",
+    "# Total data:\n",
+    "data = EmCDSBinnedData(total.binned_data.project('Em', 'Phi', 'PsiChi'))\n",
+    "\n",
+    "# BG:\n",
+    "bkg_dist = {\"total_bkg\":bg_est.binned_data.project('Em', 'Phi', 'PsiChi')}\n",
+    "\n",
+    "#for bckfile in bkg_dist.keys():\n",
+    "#        bkg_dist[bckfile] += sys.float_info.min\n",
+    "        \n",
+    "bkg = FreeNormBinnedBackground(bkg_dist,\n",
+    "                                   sc_history=sc_orientation,\n",
+    "                                   copy = False)\n",
+    "\n",
+    "psr = BinnedThreeMLPointSourceResponse(data = data,\n",
+    "                                        instrument_response = instrument_response,\n",
+    "                                        sc_history=sc_orientation,\n",
+    "                                        energy_axis = dr.axes['Ei'],\n",
+    "                                        polarization_axis = dr.axes['Pol'] if 'Pol' in dr.axes.labels else None,\n",
+    "                                        nside = 2*data.axes['PsiChi'].nside)\n",
+    "\n",
+    "response = BinnedThreeMLModelFolding(data = data, point_source_response = psr)\n",
+    "\n",
+    "like_fun = PoissonLikelihood(data, response, bkg)\n",
+    "\n",
+    "cosi = ThreeMLPluginInterface('cosi',\n",
+    "                            like_fun,\n",
+    "                            response,\n",
+    "                            bkg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a83c40ec-7378-409c-a380-79b0837120b5",
+   "metadata": {},
+   "source": [
+    "Nuisance parameters, model, and plugin:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "1add1e17-cf35-49aa-9e2a-e6e30e9a69ee",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">14:06:39 </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> The current value of the parameter beta (</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">-2.0</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">) was above the new maximum </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">-2.15</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">. </span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">parameter.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py#810\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">810</span></a>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;5;46m14:06:39\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The current value of the parameter beta \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;37m-2.0\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m was above the new maximum \u001b[0m\u001b[1;37m-2.15\u001b[0m\u001b[1;38;5;251m.\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=408782;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=842630;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py#810\u001b\\\u001b[2m810\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Nuisance parameter guess, bounds, etc.\n",
+    "for bkg_label in bkg_dist.keys():\n",
+    "    cosi.bkg_parameter[bkg_label] = Parameter(bkg_label,  # background parameter\n",
+    "                                    1,  # initial value of parameter\n",
+    "                                    min_value=0,  # minimum value of parameter\n",
+    "                                    max_value= 100,  # maximum value of parameter\n",
+    "                                    delta=0.05,  # initial step used by fitting engine\n",
+    "                                    unit = u.Hz\n",
+    "                                          )\n",
+    "    \n",
+    "# Load the astromodel for the Crab (this is the same model as \n",
+    "# used in the DC2 Crab tutorial):\n",
+    "model = astromodels.load_model('./crab_DC2_astromodel.yaml')\n",
+    "\n",
+    "\n",
+    "# Define plugin and fit:\n",
+    "plugins = DataList(cosi) # If we had multiple instruments, we would do e.g. DataList(cosi, lat, hawc, ...)  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b66d5cae-e5b6-4ad2-95e0-85f129fb780e",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "Perform likelihood fit:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "d0d9e7f8-dce5-4abf-94a5-05784352e666",
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">14:06:43 </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> set the minimizer to minuit                                              </span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/classicMLE/joint_likelihood.py#994\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">994</span></a>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;5;46m14:06:43\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m set the minimizer to minuit                                             \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=701321;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=617277;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/classicMLE/joint_likelihood.py#994\u001b\\\u001b[2m994\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:cosipy.response.threeml_point_source_response:... Calculating point source response ...\n",
+      "INFO:cosipy.response.threeml_point_source_response:--> done (source name : source)\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING IntegrationWarning: The occurrence of roundoff error is detected, which prevents \n",
+      "  the requested tolerance from being achieved.  The error may be \n",
+      "  underestimated.\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING IntegrationWarning: The occurrence of roundoff error is detected, which prevents \n",
+      "  the requested tolerance from being achieved.  The error may be \n",
+      "  underestimated.\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: overflow encountered in _pow\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">14:07:13 </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> The current value of the parameter beta (</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">-2.0</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">) was above the new maximum </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">-2.15</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">. </span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">parameter.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py#810\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">810</span></a>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;5;46m14:07:13\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The current value of the parameter beta \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;37m-2.0\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m was above the new maximum \u001b[0m\u001b[1;37m-2.15\u001b[0m\u001b[1;38;5;251m.\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=230012;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=607988;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py#810\u001b\\\u001b[2m810\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ffaf; text-decoration-color: #00ffaf; font-weight: bold; text-decoration: underline\">Best fit values:</span>\n",
+       "\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1;4;38;5;49mBest fit values:\u001b[0m\n",
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>result</th>\n",
+       "      <th>unit</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>parameter</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>source.spectrum.main.Band.K</th>\n",
+       "      <td>(3.243 +/- 0.009) x 10^-5</td>\n",
+       "      <td>1 / (keV s cm2)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_bkg</th>\n",
+       "      <td>(3.57217 +/- 0.00031) x 10</td>\n",
+       "      <td>Hz</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                 result             unit\n",
+       "parameter                                                               \n",
+       "source.spectrum.main.Band.K   (3.243 +/- 0.009) x 10^-5  1 / (keV s cm2)\n",
+       "total_bkg                    (3.57217 +/- 0.00031) x 10               Hz"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "<span style=\"color: #00ffaf; text-decoration-color: #00ffaf; font-weight: bold; text-decoration: underline\">Correlation matrix:</span>\n",
+       "\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n",
+       "\u001b[1;4;38;5;49mCorrelation matrix:\u001b[0m\n",
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><table id=\"table139936142065424\">\n",
+       "<tr><td>1.00</td><td>-0.65</td></tr>\n",
+       "<tr><td>-0.65</td><td>1.00</td></tr>\n",
+       "</table></div>"
+      ],
+      "text/plain": [
+       " 1.00 -0.65\n",
+       "-0.65  1.00"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "<span style=\"color: #00ffaf; text-decoration-color: #00ffaf; font-weight: bold; text-decoration: underline\">Values of -log(likelihood) at the minimum:</span>\n",
+       "\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n",
+       "\u001b[1;4;38;5;49mValues of -\u001b[0m\u001b[1;4;38;5;49mlog\u001b[0m\u001b[1;4;38;5;49m(\u001b[0m\u001b[1;4;38;5;49mlikelihood\u001b[0m\u001b[1;4;38;5;49m)\u001b[0m\u001b[1;4;38;5;49m at the minimum:\u001b[0m\n",
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>-log(likelihood)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>cosi</th>\n",
+       "      <td>-1591385619.4884508</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total</th>\n",
+       "      <td>-1591385619.4884508</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          -log(likelihood)\n",
+       "cosi   -1591385619.4884508\n",
+       "total  -1591385619.4884508"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "<span style=\"color: #00ffaf; text-decoration-color: #00ffaf; font-weight: bold; text-decoration: underline\">Values of statistical measures:</span>\n",
+       "\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n",
+       "\u001b[1;4;38;5;49mValues of statistical measures:\u001b[0m\n",
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>statistical measures</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>AIC</th>\n",
+       "      <td>-3182771234.9768496</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BIC</th>\n",
+       "      <td>-3182771214.281757</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    statistical measures\n",
+       "AIC  -3182771234.9768496\n",
+       "BIC   -3182771214.281757"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(                                 value  negative_error  positive_error  \\\n",
+       " source.spectrum.main.Band.K   0.000032   -8.268109e-08    8.567800e-08   \n",
+       " total_bkg                    35.721728   -3.062963e-03    3.050569e-03   \n",
+       " \n",
+       "                                     error             unit  \n",
+       " source.spectrum.main.Band.K  8.417955e-08  1 / (keV s cm2)  \n",
+       " total_bkg                    3.056766e-03               Hz  ,\n",
+       "           -log(likelihood)\n",
+       " cosi   -1591385619.4884508\n",
+       " total  -1591385619.4884508)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "like = JointLikelihood(model, plugins, verbose = False)\n",
+    "\n",
+    "like.fit()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49a925f2-683e-488b-91b9-ba931cb7ad7a",
+   "metadata": {},
+   "source": [
+    "Compare best-fit to injected source:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "ce946e23-5c79-400e-8210-80eef76a577a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">14:07:29 </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> The current value of the parameter beta (</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">-2.0</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">) was above the new maximum </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">-2.15</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">. </span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">parameter.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py#810\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">810</span></a>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;5;46m14:07:29\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The current value of the parameter beta \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;37m-2.0\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m was above the new maximum \u001b[0m\u001b[1;37m-2.15\u001b[0m\u001b[1;38;5;251m.\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=350057;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=401087;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py#810\u001b\\\u001b[2m810\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> The current value of the parameter beta (</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">-2.0</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">) was above the new maximum </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">-2.15</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">. </span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">parameter.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py#810\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">810</span></a>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The current value of the parameter beta \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;37m-2.0\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m was above the new maximum \u001b[0m\u001b[1;37m-2.15\u001b[0m\u001b[1;38;5;251m.\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=702322;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=607582;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py#810\u001b\\\u001b[2m810\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkQAAAG7CAYAAAAizIoLAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAABza0lEQVR4nO3deVhWdf7/8SeybyKCAoIbiwiuQZrlaJrkWmpZTqWTNaljmjkto9VvmnSmJlutsbLRcXJaXcrKzLQsy8ZqnEBTVgURQRYBAdk3z+8Pv94TiQrI7WF5Pa7La+Y+9+ec87rvOPL2nM9iYxiGgYiIiEg71sHsACIiIiJmU0EkIiIi7Z4KIhEREWn3VBCJiIhIu6eCSERERNo9FUQiIiLS7qkgEhERkXZPBVEDVFRUkJSUREVFhdlRRERExApUEDVAWloac+bMIS0tzewoIiIiYgUqiERERKTdU0EkIiIi7Z4KIhEREWn3VBCJiIhIu2dndgARkbaqtraW6upqs2OItAv29vbY2to2eX8VRCIiVlBSUkJGRgaGYZgdRaRdsLGxISAgADc3tybtr4JIRKSZ1dbWkpGRgYuLC126dMHGxsbsSCJtmmEY5ObmkpGRQUhISJPuFKkgEhFpZtXV1RiGQZcuXXB2djY7jki70KVLF44ePUp1dXWTCiJ1qhYRsRLdGRK5fC71elNBJCIiIu2eCiIRkXaiV69ehIaGMnjwYMLCwrjjjjsoLS1t8vHWrVtHYmLied//4YcfGDBgAFdccQU7duxg4sSJJCUlNWjflmDp0qX8/ve/b9ZjXnnllXz99ddN2jczM5MRI0ZYXi9durTOGpt33XUXL7300iUmbL9UEF3Azp07eeSRR1i5cqXZUUREmsWGDRvYv38/cXFxFBUVsW7duiYf62JFzb/+9S/uuOMO9u3bx7hx49i2bRuhoaEN2lfO1a1bN7799lvL62XLljXLouM1NTWXfIy2QAXRBURFRbF8+XIWLlxodhQRaeWqy6rJismy2p/qssbNd1RVVUVZWRmenp6Wbc8//zxDhw4lIiKC8ePHWxa0/uSTTxg4cCCDBw+mf//+fPzxx/zjH//gxx9/5IEHHmDw4MFs27atzvGXL1/Ohg0beOWVVxg8eDCFhYX06tWL/fv3X3RfgISEBMaNG8fAgQMZOHAgr7/+OgDJyclERUVZ8nz00UeWfWxsbPjrX//K0KFD6d27N2+88QYA77zzDjfccIOlnWEYBAYG8tNPPwHw3HPP0a9fPwYMGMCMGTMoKio6J0+fPn348ccfLa/XrVvHTTfdBEB2djbTp09n6NChDBgwgD/+8Y+Wdt99953le7v77rvPW3zccccdvPvuuwC89tprODg4WO7eXXfddezevZujR4/SqVMnAObNmwfAiBEjGDx4MCdOnLB8b2PGjKFPnz7cfPPNVFVV1Xs+GxsbnnjiCYYMGcKjjz5KcXExc+bMYejQoQwcOJC5c+da9n3yyScJCwtj8ODBDB482PJzYWNjwx//+EeuuOIK+vTpwzvvvGM5/o4dO4iIiGDgwIFce+21xMfHA/D111/Tv39/5s+fz6BBg+jXr5/le83NzWXs2LEMGDCAgQMHcvfdd1uOd76fzWZlyEUlJiYaI0aMMBITE82OIiKtQHl5uREfH2+Ul5dbtmVGZxpLWWq1P5nRmRfN1bNnT6NPnz7GoEGDDA8PD+O6664zqqurDcMwjHfeeceYPXu2UVNTYxiGYbz55pvGxIkTDcMwjIEDBxrfffedYRiGUVtbaxQUFBiGYRjXXnut8eGHH573fLNmzTJWrFhR5/z79u276L7V1dVGSEiI8e6771q25ebmGoZhGEOHDjVef/11wzAM49ChQ0bnzp2No0ePGoZhGIDx/PPPG4ZhGAkJCYabm5tRXV1tlJWVGV5eXkZWVpZhGIbx1VdfGREREYZhGMa2bduMvn37Wj7TnDlzjHnz5hmGYRhPPPGEsWjRIsMwDOOpp54yFixYYMkzcuRIY8uWLYZhGMbYsWONr7/+2pJ93LhxxsaNG43KykojICDA+OKLLwzDMIwdO3YYgLFr165zPvPatWuNu+++2zAMw5g6dapx9dVXG59++qlRWlpqdO7c2aiqqjJSU1MNDw8Pyz6AJffZ73vo0KFGaWmpUVNTY1xzzTV1vsOfA4xly5ZZXs+ZM8f417/+ZRiGYZw+fdq45557jGeffdY4efKk4eHhYZSVlRmGYRilpaWWn2vA+OMf/2gYhmGkpKQYnp6eRmpqqpGTk2N07tzZOHDggGEYhvH2228bYWFhxunTp41du3YZtra2xg8//GAYhmGsWrXKGDt2rGEYhvHiiy8ac+fOtWTKz883DOPCP5s/V9911xgadi8ichl49/VmbvRcqx6/ITZs2MDgwYOpqanhd7/7HUuWLOGFF17go48+4r///S+RkZHAmbmUzhozZgyLFi3illtuYezYsQwePNgaH8EiKSmJiooKbr/9dss2b29viouLiYmJYc+ePQCEhITwq1/9im+//ZaePXsCMGPGDAD69u2LnZ0d2dnZBAQEMG3aNN566y3+8Ic/sG7dOsvdh507d/LrX//acufl3nvv5dZbbz0n05133skVV1zBCy+8wPHjxzl06BATJkygtLSUL7/8kpycHEvbkpISkpKSSExMxM7OjqioKADGjh1LYGBgvZ85KiqKZcuWUVtbS3x8PE899RQ7d+7E1taWoUOHYm9v36Dv7qabbsLFxQWAoUOHkpKSct62v/3tby3//6OPPuL777/nxRdfBKC8vBxbW1s6duxISEgIM2fOZOzYsUyaNImAgADLfrNnzwYgMDCQkSNHsnv3bjw9PRkwYAADBgwAzvw3WbBgAcePHwcgODiYq666CoCrr76a559/HoBhw4axYsUKHnroIUaOHMn48eMt2c73s9mcVBBJkxQdK6Isr8zsGI3i4u2CRw8Ps2NIO2XvYo9fhJ/ZMSzs7OyYNm0af/jDH3jhhRcwDINHH32UuXPPLdpefPFF4uLi2LVrF7NmzWLGjBksXrzYhNTn+uVQaycnJ8v/t7W1tTyi+u1vf8vdd9/Nvffey9atW1mxYkWDjndWQEAAV155JR9//DFxcXHMnDkTOzs7Sx+eH374oc65AQ4cONDg4/fo0QNHR0feeecdIiMjGTNmDE899RS2traMGTPmPJ/+XOf7/PX5+YzOhmHwwQcf0KdPn3Pa/fDDD3z33Xd8/fXXDBs2jPfee69O5+6fa8jQ9/NlvPrqq9m/fz87d+5k8+bNPP744+zbt++CP5vNSQWRNFrRsSJeDXu10X0WzGbvYs+ChAUqikT+z1dffWXp5Dx16lReeOEFbrnlFjp37kx1dTWxsbFcccUVJCYm0q9fP/r164ednR2ff/45AB07dqy3v01DXGjf0NBQXFxceO+99yx3ifLy8vD29iYiIoI33niDOXPmkJyczL///W/+9re/XfR8Z+9IPPzww0RFRdG5c2fgzJ2Zhx56iAcffJCOHTvy97//nbFjx9Z7jLvvvpt//vOfJCUlWfo9ubm5MXr0aJYvX87SpUuBM6PBTp8+Td++fampqWHXrl2MHj2anTt3XvCOTVRUFH/605/405/+hKenJ/b29mzatKlOP6mfc3d3p6ioyHJ361JMnTqVZ555hr///e/Y2dlRUFBAfn4+Pj4+FBcXM2LECEaMGEFcXBz79u2zFERvvPEGS5cu5ejRo3z77be89NJLuLq6cvDgQWJjY+nfvz/r16/H398ff39/kpOTz5shNTUVf39/pk+fzvjx4+natSslJSUX/NlsTiqIpNHK8sqoLqvmprdvoktYF7PjNEhuQi4fzvyQsrwyFUTSrv3617/G2dmZmpoaevbsaemsPGPGDPLz8xk9ejRwZuTRb3/7W6644goee+wxkpKScHBwwMXFhVWrVgEwd+5cHnroIVasWMFf//pXJk6c2OAcF9rXzs6Ojz/+mIULF/LXv/6VDh06MH/+fH73u9/xzjvvMG/ePF555RVsbGz4xz/+QY8ePRp0zrvvvpvFixfz2WefWbZNmDCB2NhYrr76ajp06MDAgQN57bXX6t1/ypQp3HvvvYSEhBAWFmbZ/s477/Dggw/Sv39/bGxscHV15e9//zsBAQFs2LCB+fPnU1tby5AhQxg0aNB580VFRbFq1SrLI7aoqCjWrFlz3n0eeughrr/+elxcXCxFalOtWLGCRx55hMGDB9OhQwfs7Ox49tlncXJy4pZbbqG0tBQbGxtCQkKYNWuWZb/a2lquuOIKSktL+dvf/kavXr0s38mdd95JTU0Nnp6ebNq06aJ3j77++mtefPFFy12j5557Dg8Pjwv+bDYnm//rGCUXkJSUxJw5c1izZo3lX1PtWVZMFqsjVzM3em6LegRwIa0xs7ReFRUVpKam0rt373Meo4i0FTY2NhQUFDTLHarmcKnXnYbdS5Plncxj//79lteHDh0iNTUVOFPBx8TEUFBQcKZtXh4xMTGWtsnJyRw5cgSA06dPExMTw8mTJwE4efIkMTExlo5zR44c4fDhw5Z9Y2JiyM3NBaCwsJCYmBiqq888vjt69Khl4jeA/fv31+nsKCIiUh8VRNJkH+34iOHDh1tez507l8cffxyAU6dOERkZya5du860/egjywgBgEWLFvHwww8DZxbCjIyMtDyT37FjB5GRkZbOikuWLOG+++6z7Dt06FA2b94MwO7du4mMjKSwsBA4M3PrPffcY2k7cuRI3nnnHYpLikkhheKS4ub+GkRE2iXDMFrM3aHmoD5E0mjFJcUkk8y9v7qXG2fcaNm+evVqy9DQjh07Eh0dTe/evYEzHfYiIiIsbV9++WU6dDhTj9vb2xMdHW159jxu3Diio6MttzyfeeaZOsMs9+7dS/fu3YEzBU90dLTloly6dCmVlZWWtrt378bPz4/9O/fzFm8xI30GfTh3FIWIiLRvKoik0VLTU3mbt5lZMpNrBl9j2f7z4Zp2dnZ1CiBvb2+8vf83T0pwcLDl/3fo0KFO286dO1tGgADnzNvx87adOnWq8/psUXXW2flSQoNCWcQiQoPUB0xERM6lR2bSaH2D+/IAD9A3uK/ZURrM0cERTzxxdHA0O4qIiLRAukMkjeZg74AHHjjYO5gdpcEysjL4lE8Z9u0ws6M0iiaTFBG5PFQQSaNlZGXwCZ8wMWsifrSOIeyGk0GGTQaf/P4TfuAHs+M0mCaTFBG5PFQQSaOVV5STRRblFeVmR2mwoWOGknA0oVUtN6LJJKW5bd68maeeeora2loqKiro1q0bO3futAxwaI+WLl3KI488ovmipH31IXr33XeZNm0a48aN45577qGsrPX8cmxJQnqHMJe5hPQOMTtKo1Q5V4Ef+EX44d7HnSyy6BTWCb8IP2q71nLS8SR+EX74Rfhx0vEkp31O4xfhR6ewTmSRRcfQjmcmdfSDPPs8S9sCpwJqutTgF+GHZ7gnWWThFuKGX4QfHfw7cML2hKXtKddTVHlV4Rfhh/cAb7LIwiXIBb8IP+y625Ftk21pW+xaTCGFZn9t0kZkZWUxd+5cNm/ezP79+0lMTOT5559v0NpTDXWhdbNaqmXLllmm+JD2rd0URJs3b+Y///kPr732Gtu3b+exxx7Dzk43yNqLrKwsBg4cyAsvvADAwYMHiYyMtEwO+fLLL3PzzTdb2k+ZMoVXXnkFgMOHDxMZGUl8fDwAf//735kwYYKl7W233cZzzz0HQEZGBpGRkURHRwPw5ptvWqabB7jrrrv4y1/+ApyZrDIyMpJ///vfAGzcuJFhw/7Xx2nxk4vZyU5KSkua98sQ02RlZXHw4EHL6/j4eNLT04Ezs+zGxMRQXHxmrqycnBx++uknS9ukpCTS0tKAM3N3xcTENGodsZycHGxtbeuM4IyIiLAURD/++CPXXHMNAwcOZOjQoZYV5Y8ePVpnrpmSkpI6RZSNjQ1PPPEEQ4YM4dFHH6WoqIjZs2fTv39/Bg0aZFlRvbq6mkceeYShQ4cyePBgpk+fbpm49Zc+/fRTyzIXgwcP5j//+Q9wZo6yiIgIBg4cyLXXXmu5Jr/++mvLiFKA2NhYy4jTs/mfeOIJIiMjCQ4Otsx5Nm/ePABGjBjB4MGDOXHiBP/4xz8IDw9n8ODBDBgwwHJuaQeMdqCmpsaYOnWqkZGR0aT9ExMTjREjRhiJiYnNnKx12rl+p+GCi7Fz/U6zozRKWlqakZmZaRiGYRQXFxvR0dFGWVmZYRiGkZ6ebsTGxlraxsbGWn5eysrKjOjoaKOkpMQwDMPIzMw0Dhw4YGkbFxdnHDt2zDAMwygvLzeio6ONU6dOGYZhGNnZ2cb+/fstbRMTE42jR48ahmEYVVVVRnR0tFFYWGgYhmGcOHHCiImJsbT994f/Nv7AH4zM6Mzm/SLE6srLy434+HijvLy8zvYnnnjC8Pf3t7weMGCAsXDhQsMwDOPw4cMGYOzatcswDMN49tlnDU9PT0vbYcOGGffcc49hGGd+BgFj69atDc5UW1tr3HzzzYanp6cxdepU49lnn7X8jFdWVhrdu3c3tm/fbhiGYXz77beGj4+PUVxcbKSmphoeHh6W4xQXFxs//9UBGMuWLbO8vuuuu4x7773XqK2tNQzjzM+1YRjGU089Zfz5z3+2tPvzn/9szJ8//5ycSUlJRpcuXYyEhATDMM5cJ4WFhUZOTo7RuXNny7X39ttvG2FhYcbp06eNXbt2GYMGDbIc4+DBg0bPnj0NwzCM1NRUAzDef/99wzAM47PPPjP69OlTJ39BQYHldceOHS1/T1RVVRnFxcUN+HalJTjfdddQLbIgKi0tNdauXWs89NBDxsSJE40RI0YY27Ztq7dtZWWl8dprrxlTp041xowZY8ydO9fYu3dvnTZZWVnGhAkTjLffftuYPHmycccddxhbtmxpcB4VRHXt37HfGMMYY/+O/RdvLE2WvjfdeIRHjPS96WZHkUY631/M1iqmGyMhIcF4/fXXjSlTphgeHh7G4cOHjQMHDlgKiLMGDhxofPvttw0qiNLT//cz6u3tbaSkpJxz3iFDhhh9+vQxBg0aZAwaNMgICwszJkyYcE67V155xfjNb35zzvYtW7YY1157bZ1tHh4eRnp6+kULIicnJ+P06dOGYRhGYWGhYWtrWyf/zwuim266yRgzZozx0ksvGUlJSefkkJbrUguiFvnIrKioiHXr1pGWllZnAr/6PP3002zcuJHrr7+e+++/nw4dOrB48WIOHDhgaZObm0tJSQnp6els3LiRP//5z6xevbrO7WhpuK7eXRnBCLp6dzU7SpsWdyiO5Swn7lCc2VGkmfj5+TFgwADL6/DwcMus605OTkRERODu7g6Aj49PnVXOQ0ND6dmzJ3BmdveIiAg8PBrf2b5v37787ne/46OPPmLYsGFs2bKl3nZnH4vZ2dnVmSm+vv42bm5uFz2vYRisXLmS/fv3s3//fuLj4y2Pri7VxTI6OjpaPo+trW2dtr/0wQcfsHz5cqqrq5k4cSLr169vlozS8rXIgsjLy4sPP/yQTZs2ce+99563XXx8PF9++SVz585l/vz5TJ48mZdeeglfX19WrVplaefoeGYyvrvuugtHR0eCgoIYM2YMP/zQeoZftySlZaWkkUZpWanZUdq0Hv49uJVb6eHfw+wo0gYcP37c0i8IoKCggNTUVIKCgggNDeX06dN88cUXAHz33XdkZ2czePBgfH19MQzD0l/nzTffvOB5Jk+ezPPPP8/p06cBLAsxT506lRUrVlgGs5SVlREXd26xP27cOHbs2EFiYiJwpu9RUVERw4YN4+DBg8TGxgKwfv16/P398ff3JzAwkLS0NMu53nrrrQZ/L+7u7pa+WDU1NaSkpHDllVfy8MMPc8stt7B3794GH0tatxbZq9jBwQEvL6+Ltvvmm2+wtbVl8uTJlm2Ojo5MmjSJ1atXk5OTg4+PD927d8fe3v6cjoDSNClpKbzBG/w67dcE/+rCd/Ck6Tp17EQ/+tGpYyezo0gbUFNTw5///GdSU1NxcXGhpqaGWbNmMWXKFODMwJP777+fhx56CCcnJ95//33LnZ+VK1dyww034OXlxS233HLB86xYsYIHHniAAQMGYG9vz5AhQ1izZg1LliyhsrKSq666yvL375IlS+jXr1+d/YODg3njjTeYOXMm1dXV2Nra8vrrrzN06FDeeecd7rzzTmpqavD09GTTpk3Y2NjQrVs3Fi9ezNChQ/Hx8akz6OFiHnroIa6//npcXFzYsWMHv/3tbzl58iR2dnZ06dKFN954ozFfs7RmzfoAzwoSEhLO24fogQceMGbOnHnO9h9//NEYMWKE8e9//9uybdmyZcazzz5rVFZWGqmpqcbkyZPrPJ//udzcXCMxMdHyZ8eOHepD9DNHvjti3Md9xpHvjpgdpU2L/TLWmMIUI/bL2Is3lhblUvsyiEjjXep11yLvEDVUfn5+vXeSzm7Ly8uzbHvggQd45plnuPHGG/Hw8OCee+6p83z+57Zs2cK6deuskrktcHJ0whtvnBw1kZk1ZWRl8DEfc2/WvfSj38V3EBGRJmvVBVFlZSX29vbnbHdwcLC8f5a7uztPPvlkg447efJkhg8fbnmdlpbW4H3bg8ycTLaznRtybmg1S3e0RgP6DuBxHmdA3wEXbywiIpekVRdEjo6OVFdXn7O9qqrK8n5TeHt74+3tfUnZ2rKS0hJSSNGEgVZmY2ODLbbq79aKGYZhdgSRduNSr7dWXRB5eXlZRhX8XH5+PoCKGivpE9iHBSygT2Afs6O0aUfTj/Iu7zI2feyZJUOk1Tg7iCM3N5cuXbqoqBWxMsMwyM3NxcbGpt4nRw3Rqgui4OBg9u3bR2lpKa6urpbtZ4eHXmwOo4vZuXMnO3fupKTEundCio4VtbpFR0Xk/GxtbQkICCAjI4OjR4+aHUekXbCxsSEgIABbW9sm7d+qC6JRo0axfv16tmzZwu233w6ceVy2bds2wsPD8fHxuaTjR0VFERUVRVJSEnPmzGmOyOcoOlbEq2GvUl127qO/liqHHN6zeY+x+WPVh8iKenXvxR3cQa/uvcyOIk3g5uZGSEhIvY/1RaT52dvbN7kYghZcEH3wwQeUlJRYHn/t2bOHEydOADBt2jTc3NwIDw9n9OjRrF69msLCQvz9/dm+fTvZ2dksWbLEzPgNVpZXRnVZNTe9fRNdwrqYHadBsnOzcd3qSs9+Pc2O0qYZhkEtteqH0orZ2tpe0l/QInL5tNiCaMOGDWRnZ1te7969m927dwMwduxYy4Rhjz32GD4+PuzYsYOSkhICAwN55pln6qx83Bp0CevSavqJ+OHHFeOuMDtGm3cw8SB/4S8MTxxOt8huZscREWnTWmxBtHHjxga1c3R0ZP78+cyfP7/ZM1yuPkStTVlZGYmJifTt2xcXFxez47RZAX4BTGEKAX4BZkcREWnzWuRaZi1FVFQUy5cvZ+HChWZHaVESExOJjIy0rDUk1tG5U2eu4Ao6d+psdhQRkTZPBVELcvLkSWJiYiyLIh45coTk5GTL+zExMZbZtwsKCoiJiaGmpgaA1NRUDh06ZGm7f/9+S5+rU6dOERMTY5mf6dixY3WKmQMHDlgeT5aUlBATE2NZLTojI8Myag8gNjaWjh07Eh0dTd++fZv9O5D/KTxVSBxxFJ4qNDuKiEibp4LIZAVFBRzkIAVFBWzbto3IyEjLqJSHH36YRYsWWdpGRkby0UcfAbBr1y4iIyM5deoUAI8//jhz5861tB0+fDjr168H4PvvvycyMtJSID311FP85je/sbQdM2aMZQHD/fv3ExkZSVpaGgAvvvgit956q6XtDTfcwLp164iIiNDjMis7dvwYm9jEsePHzI4iItLmtdg+RC3B5ehDlJ6Zzgd8wJzMOUycOJHo6GjLpFLPP/+85W4RQHR0ND169ABg9OjRREdH07FjRwD+8pe/1Bneu2fPHrp1O9MR9+qrryY6OpquXbsC8P/+3/+jrOx/8x59+eWXlvcGDx5MdHQ0PXueGUH24IMPMnv2bEvbrVu30rmzHuFcDv369OMRHqFfH61jJiJibTaGxvRe1Nl5iNasWUNoaGizHjvjvxmsGrqKe/feS8AQdZ6V/8mKyWJ15OpWNSUDgIu3Cx49PMyOISLSKLpDZDJbW1sccNBcJXKOE2Un2Gy7mYKZBXjiaXacBrN3sWdBwgIVRSLSqqggMllaRhob2ci4jHGtZh4iuTxcfFzwHObJrQ/fSmlZKd18uuHl6cWp4lMczThKWHAY9vb2ZGRlUFFZQXCvM0vVxB2Ko6tXV7p4daGktIQjx47QJ7APTo5OHM8+TmlZqWUduoTDCXTu1BmfLj6UlZeRfDSZkN4hODs5k3Uii1PFpwgNOnNXNCklCXc3d7r5dKO8opzDqYcJ6hmEq4srObk55Bfm06WmCx/O/JCyvDIVRCLSqqhTtclqT9dSSSW1p2vNjiItTEhICF//+2uGTx3OjXffyDeJ3+AX4UdySTLjZ47Hvoc9fhF+/P3Dv7P4ucX4RfjhF+HH9PnT2bF/B34RfhyvPc74meOp7VKLX4Qfb25/k4V/Xmhpe+dDd/LR9x/hF+HHSYeTjJ85nhK3Evwi/Nj0zSZmPzrb0vZ3f/wd679aj1+EH+Ue5YyfOZ5c21z8IvzY+t+tzPz9zFb1aE9E5OfUh+gCft6p+sCBA1bpQ3S2n8jc6Lm6QyTntW/fPgICAujSpQtFRUWkpKQwYMAA7O3tSUtLo6KiwvKz+dNPP+Hr64uPjw/FxcUcPnyY8PBwnJycSE9Pp7i4mPDwcAAOHjyIt7c3fn5+lJaWkpSURFhYGM7Ozhw/fpzCwkL69TvTqTsuLg4PDw8CAgIoLy8nISGBPn364ObmRlZWFrm5uRQeLuTXt/yaDe9vYOS0kaZ9XyIijaWCqAGs2alaBZG0Jf/99L8suGEBr259lSGThpgdR0SkwfTIzGQHEg6wjGUcSDhgdhSRSxbgF8AkJmm5ERFpdVQQmczf159JTMLf19/sKCKXrLKqkgIKqKyqNDuKiEijqCAymZenF1dyJV6eXmZHEblkSSlJvMzLJKUkmR1FRKRRVBCZrKi4iEQSKSouMjuKyCXr3b03v+E39O7e2+woIiKNooLIZGkZaaxnPWkZaWZHEblk7m7uBBGEu5u72VFERBpFEzNewOVYyywsOIw/8AfCgsOsdg6RyyU3P5fv+I6b8m/CD42aFJHWQwXRBURFRREVFWUZdm8N9vb2uOJqWdBVpDXLycvha74mJy/H7CgiIo2iR2YmS89M50M+JD0z3ewoIpesf2h/HuMx+of2NzuKiEijqCAyWWVVJSc5qWHKIiIiJlJBZLLgXsHcwz2WhTlFWrPko8msZS3JR5PNjiIi0igqiESk2Tg6ONKZzjg6OJodRUSkUVQQmSw2KZa/8ldik2LNjiJyybp3685N3ET3bt3NjiIi0igaZXYBl2PYvY+3D6MYhY+3j9XOIXK5VFdXU0op1dXVZkcREWkU3SG6gKioKJYvX87ChQutdo4uXl24hmvo4tXFaucQuVwSkhN4judISE4wO4qISKOoIDJZcUkxKaRQXFJsdhSRS9YzoCe3cRs9A3qaHUVEpFFUEJksNT2Vt3iL1PRUs6OIXDIPdw/60hcPdw+zo4iINIoKIpOFBoWyiEWEBoWaHUXkkuUX5PMjP5JfkG92FBGRRlFBZDJHB0c88dQwZWkTjmcf51M+5Xj2cbOjiIg0igoik2VkZfApn5KRlWF2FJFLNjBsIE/wBAPDBpodRUSkUTTs3mTlFeWkk055RbnZUUSaTW5CrtkRGsXF2wWPHur3JNKeqSAyWUjvEOYxj5DeIWZHEblkWcVZvN3hbfJn5uOFl9lxGszexZ4FCQtUFIm0YyqIRKTZdOreiX7j+3Hb3NsoLimme7fueHp4UlBUQHpmOv1D+9OhQwfSMtI4bZymd/feABxIOIC/rz9enl4Unirk2PFjhIeEY2dnx7Hjx6iuqSaoZxBwZnZ33y6+eHf2prikmNT0VPoG98XB3oGMrAzKK8ot/8CIPxyPt6c3Xb27UlpWSkpaCn0C++Dk6ERmTiYlpSV4Vnry4cwPKcsrU0Ek0p4Zcl5ffPGFsWTJEmPBggXGiBEjjMTExGY/x871Ow033Iyd63c2+7FFzFJSUmIAxrvvvmsYhmG89dZbBmBUVFQYhmEYN910kzFx4kRLe8BYs2aNYRiG8cEHHxiAkZ+fbxiGYcyYMcO49tprLW2dnZ2N8ePHG5mZmcb27dsNwEhPTzcMwzDmzp1rXHnllZa23t7exl//+lfDMAzj22+/NQDLdfzAAw8Y4eHhRmZ0prGUpUZmdKaVvg0RaQ1sDMMwzCzIWoOkpCTmzJnDmjVrCA1t3uHx+3fs58HxD/Li9hcZPG5wsx5bxCy1tbX89NNP9OrVi86dO3Py5EmOHj3K4MGD6dChA0eOHOH06dMEBwcDEBMTQ48ePfD29qagoIDU1FQGDhyInZ0dqampVFdX06dPHwC++OILPv/8cx588EFcXV1JTk6mf//+ODg4cOzYMcrKyujbty8ABw4coGvXrvj6+lJSUsKhQ4cIDw/HycmJjIwMTp06RU5sDjf9+iY+3PAho6ePNu07ExFz6ZGZyXy6+HAt1+LTRWuZSdtha2tLRESE5XXnzp3p3Lmz5XVgYGCd9j9v6+npiaenp+V1796967S9/vrruf766+vdt0ePHnXaDhz4v9Fubm5uddoGBAQAUJleyUAG0smjU4M+m4i0TRp2b7LSslLSSae0rNTsKCLtkm8XX8YwBt8uvmZHERETqSAyWUpaCmtZS0paitlRRNqlsvIyMsmkrLzM7CgiYiIVRCYL6R3CfOZr2L2ISZKPJrOa1SQfTTY7ioiYSAWRyZydnOlKV5ydnM2OItIuBfcKZi5zCe4VbHYUETGRCiKTZeZk8jmfk5mTaXYUkXbJxdmFbnTDxdnF7CgiYiIVRCYrLikmiSSKS4rNjiLSLmXnZvMlX5Kdm212FBExkQoik4UGhbKQhYQGNe/8RiLSMIVFhRzgAIVFhWZHERETqSASkXatb3BfHuAB+gb3NTuKiJhIBZHJklKSeJmXSUpJMjuKiIhIu6WCyGQd3TvSj350dO9odhSRdunQkUO8yqscOnLI7CgiYiIVRCbz6+pHFFH4dfUzO4pIu+Tm6kYQQbi5upkdRURMpLXMLmDnzp3s3LmTkpISq52jvKKcbLIpryi32jlE5Py6+XRjPOPp5tPN7CgiYiLdIbqAqKgoli9fzsKFC612jsOph3md1zmcethq5xCR86uorCCPPCoqK8yOIiImUkFksuBewcxhjmbJFTHJoSOHeIVX1IdIpJ1TQWQyF2cX/PHXLLkiJgnqGcTd3E1QzyCzo4iIiVQQmSwnN4dd7CInN8fsKCLtkquLKz3piauLq9lRRMREKohMdrLwJDHEcLLwpNlRRNqlE3kn+JZvOZF3wuwoImIiFUQmCwsJ4yEeIiwkzOwoIu1SXkEe3/M9eQV5ZkcREROpIBKRdi08JJzFLCY8JNzsKCJiIhVEJjt05BCv8ZpGuIiIiJhIEzOazNXFlV70UodOEZMcTj3MalYz+MvBDGWo2XEazMXbBY8eHmbHEGkzVBCZzN/Xn4lMxN/X3+woIu1S526d8bfz5+vFX7Of/WbHaTB7F3sWJCxQUSTSTFQQmayisoJ88jVLrohJ+l/dn08TPiXmPzH07t4bdzd38k7mkZ2bTf/Q/gCkpKVgb2dPD/8e1NTUEH84nh7+PejUsRP5Bfkczz7OwLCBAKSmp9LBpgM9A3py+vRpYpNi8Xb0JvOLTLpd3428yjz69emHra0taRlp1J6uJbBHIAAHEg7g7+uPl6cXRcVFpGWkERYchr29PemZ6VRWVRLcK5jchFzWzVxHWV6ZCiKRZqI+RCY7dOQQK1mpPkQiJqp0qmT8zPGklqfiF+HHrvhdTJ09Fb8IP/wi/Pjjy39k5YaV+EX44RrsyviZ40k4mYBfhB8/pP7A+JnjLW2fWv0Uz657Fr8IP7wHeDN+5ni+z/6eaL9o/nPiP4yfOR7PcE/8Ivx44a0X+POqP1v2nTRrEnuS9+AX4cehokOMnzke50Bn/CL8eO3913j0xUfxi/Cjg28HXuVVMrIyzP7qRNoM3SEyWWCPQGYxy/IvRBG5/Lp27Up0dDTBwWeW0LntttsYOXKk5f3Vq1djb28PQMeOHYmOjqZ3794ATJ06lYiICEvbl19+mQ4dzvxb097enujoaJycnNi4cSOjRo2yvAZ45plnqK2ttey7d+9eunfvDsDIkSOJjo6mU6dOACxdupTKykoATp8+zVVchaODozW+DpF2ycYwDMPsEC1dUlISc+bMYc2aNYSGhjbrsbNislgduZq50XPxi/Br1mOLSNukvzdEml+7uUN0//33Ex8fj62tLQADBw7kueeeMzkV5Obnsoc93JR/E37oLzYRubjikmJSSKG4pFh/b4g0k3ZTEAEsXryYsWPHmh2jjhP5/7dsQL6WDRCRhklNT+Ut3mJG+gz60MfsOCJtgjpVm6xfn348wiP069PP7Cgi0kqEBoWyiEWEBjXvI3yR9qxF3iEqKytj/fr1xMfHk5CQQHFxMY8++igTJkw4p21VVRVr167l888/p7i4mKCgIGbPns2QIUPOabty5UpWrlxJSEgICxYsICgo6HJ8HBGRZuXo4IgnnupULdKMWuQdoqKiItatW0daWppl1Mf5PP3002zcuJHrr7+e+++/nw4dOrB48WIOHDhQp928efPYsGED77//PldeeSV/+MMfKCsrs+bHaJDko8n8g3+QfDTZ7Cgi0kpkZGXwKZ9q2L1IM2qRBZGXlxcffvghmzZt4t577z1vu/j4eL788kvmzp3L/PnzmTx5Mi+99BK+vr6sWrWqTtvw8HBcXFxwdHTkjjvuwMXFhbi4OGt/lItycnSiC11wcnQyO4qItBLlFeWkk055RbnZUUTajBZZEDk4OODl5XXRdt988w22trZMnjzZss3R0ZFJkyYRFxdHTk7Oefe1sbGhJcw4EOAXwBSmEOAXYHYUEWklQnqHMI95hPQOMTuKSJvRIvsQNdThw4cJCAjA1bXuwqhhYWEAJCcn4+PjQ3FxMYmJiQwaNAgbGxs+/PBDiouLCQ8Pr/e4eXl55OfnW16npaVZ7TNUV1dTTDHV1dVWO4eIiIhcWKsuiPLz8+u9k3R2W15eHgC1tbWsXr2aY8eOYWdnR3BwMM888wxubm71HnfLli2sW7fOarl/LiE5gRd4geuTr6fHVT0uyzlFpHWLPxzP8zzPrw7/ShMzijSTVl0QVVZWWqbT/zkHBwfL+wCdOnVizZo1DT7u5MmTGT58uOV1WloaTz755CWmrV+vgF7cwR30CuhlleOLSNvj1cmLIQzBq9PFuxaISMO06oLI0dGx3kdNVVVVlvebwtvbG29v70vK1lAd3TvShz50dO94Wc4nIq2fTxcfruVafLr4mB1FpM1okZ2qG8rLy6tOX5+zzm67XEXNpcgvyGcve8kvOPdziIjUp7SslHTSKS0rNTuKSJvRqgui4OBgMjIyKC2t+5dCfHy85f1LsXPnTh555BFWrlx5Sce5kMycTHawg8ycTKudQ0TalpS0FNaylpS0FLOjiLQZrbogGjVqFLW1tWzZssWyraqqim3bthEeHo6Pz6XdTo6KimL58uUsXLjwUqOe14C+A3icxxnQd4DVziEibUtI7xDmM1/D7kWaUYvtQ/TBBx9QUlJiefy1Z88eTpw4swDqtGnTcHNzIzw8nNGjR7N69WoKCwvx9/dn+/btZGdns2TJEjPji4hYjbOTM13pirOTs9lRRNqMFlsQbdiwgezsbMvr3bt3s3v3bgDGjh1rGTL/2GOP4ePjw44dOygpKSEwMJBnnnmGwYMHmxG70Y4cO8K/+BdRx6I0fFZEGiQzJ5PP+Zwbcm7AD/29IdIcWmxBtHHjxga1c3R0ZP78+cyfP7/ZM+zcuZOdO3dSUlLS7Mc+y87WDldcsbNtsf8pRKSFKS4pJokkikuKzY4i0mbot/AFREVFERUVRVJSEnPmzLHKOXr49+AWbqGHvyZlFJGGCQ0KZSELCQ0KNTuKSJvRqjtVtwW1tbVUUEFtba3ZUURERNotFUQmizsUx3KWE3cozuwoItJKJKUk8TIvk5SSZHYUkTZDBZHJevj34FZu1SMzEWmwju4d6Uc/zXAv0ozUh+gCLken6k4dO9GPfnTq2Mlq5xCRtsWvqx9RRGGXb0dWTJbZcRrMxdsFjx4eZscQqZcKogu4HJ2qTxaeZB/7OFl4UsNnRaRBbNxsyHfKZ+PMjdhz7gLXLZW9iz0LEhaoKJIWSQWRyTKyMviYj7k361760c/sOCLSCmSWZLKyYiXb396Oj7cPJwtPEhYSBsChI4dwdXHF39efisoKDh05RGCPQNxc3cjNz+VE/gn69Tnzd03y0WScHJ0I8AugurqahOQEegX0oqN7R/IL8snMybTMon/k2BHsbO3o4d+D2tpa4g7F0cO/B506duJk4UkysjIY0HcANjY2HE0/CkCv7r0wDIODiQdxLnZm1727KMsrU0EkLZMhF5WYmGiMGDHCSExMbPZjH//xuPE4jxvHfzze7McWkbappKTE2Lt3r1FSUmI88cQThr+/v+W9AQMGGAsXLjQMwzAOHz5sAMauXbsMwzCMZ5991vD09LS0HTZsmHHPPfcYhmEYmZmZBmBs3brVMAzDeOWVVwwHBwdL2zFjxhi33XabYRiGUVRUZADGxo0bDcMwjDfeeMMAjOrqasMwDOPGG280brzxRsMwDKO6utoAjBVPrDCWstTIjM60xlcicsl0h8hkNjY22GKLjY2N2VFEpJVwdXVlyJAhAPzud79j2rRplvfWr1+Pu7s7AAEBAURHRxMScmbNszvvvJOxY8da2q5btw4nJycAvL29iY6OJigoCIDp06dzzTXXWNquWrUKOzs7y/mjo6Pp3bs3ADfeeCPR0dHY2toCsGLFCst+tra2REdHU5FewUu8ROGpQnUPkBbJxjAMw+wQLdXPO1UfOHCANWvWEBravBOhff/x99w19S7WfbSOq6dc3azHFhFpKXa8s4PxM8ez/e3tjJsxzuw4IufQHaILuBydqkVE2oN+ffrxCI9Y+i+JtDSah8hkvbr34g7uoFf3XmZHERGxGltbW5xwsjxWE2lpVBCZzDAMaqlFTy5FpC07dvwY7/M+x44fMzuKSL1UEJnsYOJB/sJfOJh40OwoIiJWU1NbQyml1NTWmB1FpF4qiEwW4BfAFKYQ4BdgdhQREasJ7BHILGYR2CPQ7Cgi9VKn6gu4HEt3dO7UmSu4gs6dOlvtHCIiInJhukN0AVFRUSxfvpyFCxda7RyFpwqJI47CU4VWO4eIiNnUPUBaOhVEJjt2/Bib2KSOhiLSpnXz6cY4xtHNp5vZUUTqpYLIZJqbQ0TaAy9PL4YyFC9PL7OjiNRLBZHJNDeHiLQHp4pPcYhDnCo+ZXYUkXqpIDKZ5uYQkfbgaMZR3uVdjmYcNTuKSL1UEJlMc3OISHsQFhzGQzxEWHCY2VFE6qWCyGSam0NE2gN7e3vcccfe3t7sKCL10jxEF3A55iESEWkPMrIy+JiPmZg1ET/8zI4jcg7dIbqAyzEPkebmEJH2oKKyglxyqaisMDuKSL1UEJlMc3OISHsQ3CuY2cwmuFew2VFE6qWCyGSam0NERMR8KohMprk5RKQ9iDsUx3KWE3cozuwoIvVSQWQyzc0hIu1BV6+ujGAEXb26mh1FpF4qiEymuTlEpD3o4tWF4Qyni1cXs6OI1EsFkck0N4eItAclpSWkkkpJqaYxkZZJBZHJzs7NkZGVYXYUERGrOXLsCP/iXxw5dsTsKCL10sSMF3A5JmbU3Bwi0h70CezDQhbSJ7CP2VFE6tXkgiglJYXExERGjRqFq6srAJWVlbzyyivs2bMHR0dHbrvtNqZMmdJsYS+3qKgooqKiSEpKYs6cOVY5h+bmEJH2wMnRCS+8cHJ0MjuKSL2a/MjszTffZO3atbi4uFi2rV69mi1btlBWVsaJEydYsWIF//3vf5slqIiItF7Hs4+zjW0czz5udhSRejW5IEpISOCKK67AxsYGgJqaGj777DPCwsL4+OOP2bBhA506deL9999vtrBtkebmEJH2oLSslKMcpbSs1OwoIvVqckFUVFRE167/m08iMTGR0tJSpkyZgqOjI97e3gwfPpzk5ORmCdpWaW4OEWkP+gT2YT7z1YdIWqwmF0S2trZUV1dbXu/fvx8bGxuuuOIKyzYPDw+KioouLWEbp7k5REREzNfkgsjX15d9+/ZZXu/atQs/Pz98fX0t23Jzc/Hw8Li0hG2c5uYQkfYg4XACL/ACCYcTzI4iUq8mF0Rjx44lOTmZ3/3ud9x3332kpKQQFRVVp82RI0cICAi45JBtmebmEJH2oHOnzkQQQedOnc2OIlKvJhdEN998M6NGjSIpKYmDBw9y1VVXMXPmTMv7qampJCcnExER0SxB2yrNzSEi7YFPFx9GMxqfLj5mRxGpV5PnIXJwcGDZsmWUlpZiY2NTZ/g9gKenJ2vXrq3zCE3Opbk5RKQ9KCsv4zjHKSsvMzuKSL2afIdo//795OTk4Orqek4xBNCpUyfc3d01yuwiNDeHiLQHyUeTWcMako/qd4K0TE0uiH7/+9/z2WefXbDNjh07+P3vf9/UU7QLmptDRNqDkN4hzGMeIb1DzI4iUq8mPzIzDKNBbc5O3Cj109wcItIeODs544svJaklZDllWe08ZbllxG+OJ/zmcFy6nPv0oilcvF3w6KER022dVRd3zcjIsKxzJiIi7VdhTSFf2X3FqZmn6EhHq58vZnVMsx3L3sWeBQkLVBS1cY0qiJYvX17n9bfffkt2dvY57Wprazlx4gQHDhzgqquuurSEJrocq92fnZtjxOER+EX4We08IiJmOu16mnT/dJY9twwAdzd3uvl0o7yinMOphwnqGYSriys5uTnkF+YTHhIOwOHUwzg7ORPgF0BlVSVJKUn07t4bdzd3cvNzycnLoX9of+BMPyVHB0e6d+tOdXU1CckJ9AzoiYe7B/kF+RzPPs7AsIHAmSlPbDvY0jOgJ7W1tcQdiqN7t+54enhSUFRAemY6/UP7k5+Uz3sz36Msr0wFURvXqILo532GbGxsSE5OPm+naRsbG/r27ct99913aQlNdDlWu9fcHCLSHvTr148jR8/MtxYaGsrkyZN57rnniIuLY/zM8Xz33XcERwSz9sm1vPrqq2RlnXmsNmn2JK6++mpeffVVjh49yvirx/PFF18QFRHFey++x9KlSzl16hQAt95/K926dSM8PJxbbrmF8TPH8/HHHzP52sls+fsWFixYQE1NDQB3PXoXHTt2ZNOmTZSWljJ+6Hjeffddbh99O1++/SW/+c1vqKiooKCogI/4iJuzbsYP/aO1LWtUQbRhwwbgTN+g2267jVtvvZVbbrnlnHYdOnTA3d0dZ2fn5knZhmluDhFpbzZv3mxZxSAwMJDo6Gj69DnTj/Kee+5h8uTJlrbvvPOOpeuFn58f0dHRBAcHAzBjxgyuu+46S9u1a9dSVFTEtm3b6NixI9HR0QQGBgJn5s4bMmSIpe0rr7yCra0tAE5OTkRHR9OrVy8AJk6cSHR0NPb29pwqOUUVVVTX/G+pKmmbbIyG9I6ux2effUafPn0ICgpq7kwtztk7RGvWrCE0NLRZj52yJ4VnfvUMS/69hKDhbf+7FBFpTbJislgduZq50XPVraGNa/Kw+wkTJrSLYsjaNDeHiIiI+S55lFl8fDyJiYmUlJRw+vTpc963sbFh1qxZl3qaNktzc4iItFwHEg6wlKUMSximO0RtXJMLolOnTvHYY48RGxt7wTmJVBBd2Nm5OZyd1N9KRKSl8ff150ZuxN/X3+woYmVNLoheeeUVDh48yODBgxk/fjxdu3a1dFCThss6kcVOdnLjiRs1gkFEpIXx8vQikki8PL3MjiJW1uSC6PvvvycsLIyXXnpJs1FfglPFp4gjjlPFp8yOIiIiv1B4qpB44ik8Vah/tLZxTe5UXVlZyaBBg1QMXaLQoFAWsYjQoOYdvSYiIpfu2PFjbGQjx44fMzuKWFmTC6Lg4OB6Z6kWERFpK8JDwlnMYsvM2dJ2Nbkguuuuu9izZw9xcXHNmafdSUpJYiUrSUpJMjuKiIj8gp2dHS64YGdn1aU/pQVo8n/hkydPMmzYMO6//36uv/56QkJCzruQ6/jx45scsK1zd3MnlFDc3dzNjiIiIr9w7PgxPuADxh8fr2H3bVyTC6Knn34aGxsbDMPgs88+47PPPjunP5FhGNjY2KgguoBuPt0Yy1i6+XQzO4qIiPxCdU01pzilpTvagSYXRI888khz5mi3yivKOcEJyivKzY4iIiK/ENQziLu5m6CeWpmhrWtyQTRhwoTmzNFuHU49zGu8xuTUyQReE2h2HBERkXapyZ2qW6vY2FiuvfZa/vWvf5kdBTjzr497uEf/+hARaYFik2J5iqeITYo1O4pYWZPvEOXk5DS4rY+PT1NP06xOnz7NK6+8Qt++fc2OYuHq4kp3uuPqUn+HdBERMY9vF1/GMAbfLr5mRxEra3JBNH369AZNymhjY8OuXbuaeppm9cknnxAWFkZpaanZUSxycnP4hm+YkjtFs6CKiLQw3p29GcYwvDt7mx1FrKzJBdG4cePqLYhKSkpISUkhKyuLwYMH4+vb+Kq6rKyM9evXEx8fT0JCAsXFxTz66KP19luqqqpi7dq1fP755xQXFxMUFMTs2bMZMmRInXZFRUVs2rSJVatWsXLlykZnspb8wnz+y3/JL8w3O4qIiPxCcUkxySRTXFKsf7S2cU0uiB577LHzvmcYBuvXr+e9995jyZIljT52UVER69atw8fHh+DgYPbt23fetk8//TRff/01t956KwEBAXz22WcsXryYl19+mYEDB1rarVmzhltvvRV395Y13094SDgP87BmQRURaYFS01N5m7eZmT6TPvQxO45YkVU6VdvY2HD77bfTu3dvXnvttUbv7+XlxYcffsimTZu49957z9suPj6eL7/8krlz5zJ//nwmT57MSy+9hK+vL6tWrbK0O3ToEImJidxwww1N+jwiItI+9Q3uywM8QN/gltP3VKzDqnORh4aGsnXr1kbv5+DggJeX10XbffPNN9ja2jJ58mTLNkdHRyZNmsTq1avJycnBx8eH/fv3k56ezrRp04Azj/VsbW3JzMzk0UcfbXS+5nQ49TCv8zqjU0drFlQRkRbGwd4BDzxwsHcwO4pYmVULouPHj1NbW2u14x8+fJiAgIBzlgwJCwsDIDk5GR8fHyZPnsyYMWMs7//tb3/Dz8+PGTNm1HvcvLw88vP/16cnLS3NCunPcHZypjvdcXZytto5RESkaTKyMviET5iYNVF9iNq4Zi+ITp8+TW5uLtu3b2fPnj1EREQ09yks8vPz672TdHZbXl4eAE5OTjg5OVned3R0xNnZ+bz9ibZs2cK6deuaP3A9AvwCmMQkAvwCLsv5RESk4coryskiS6sJtANNLoiuvfbaCw67NwwDd3d3FixY0NRTXFRlZSX29vbnbHdwcLC8X58LdQgHmDx5MsOHD7e8TktL48knn7yEpOdXWVVJAQVUVtWfVUREzBPSO4S5zCWkd4jZUcTKmlwQDRo0qN6CyMbGBnd3d/r27cvEiRPx9PS8pIAX4ujoSHX1uQvuVVVVWd5vCm9vb7y9L8+cE0kpSbzMy0xImUCvYb0uyzlFRESkriYXRH/729+aM0eTeHl5kZube872s/1/LrWo2blzJzt37qSkpOSSjnMhvbv35jf8ht7de1vtHCIi0jTxh+N5lmf51eFfaeBLG9eq1zILDg4mIyPjnJmn4+PjLe9fiqioKJYvX87ChQsv6TgX4u7mThBBuLu1rPmRREQEvD29uZqr8fbUTNVtXbMURAcPHmTz5s28/fbbbN68mYMHDzbHYS9q1KhR1NbWsmXLFsu2qqoqtm3bRnh4eItZQ+1CcvNz+Y7vyM0/906XiIiYq6t3V0Ywgq7eXc2OIlZ2SaPMDh48yPLlyzl+/DhwpiP12X5FAQEBPPLII/Tv379Jx/7ggw8oKSmxPP7as2cPJ06cAGDatGm4ubkRHh7O6NGjWb16NYWFhfj7+7N9+3ays7ObNEO2GXLycviar8nJa/hiuSIicnmUlpWSRhqlZS1nDUyxjiYXRKmpqTz88MNUVFRw5ZVXcsUVV+Dl5cXJkyfZt28f//3vf3n44Yd5/fXX6dWrV6OPv2HDBrKzsy2vd+/eze7duwEYO3Ysbm5uwJkRYz4+PuzYsYOSkhICAwN55plnGDx4cFM/msXl6EPUP7Q/j/EY/UObVjiKiIj1pKSl8AZv8Ou0XxP8q0vrhiEtW5MLonXr1lFdXc2zzz7LVVddVee9GTNm8J///IdHH32UdevWsXTp0kYff+PGjQ1q5+joyPz585k/f36jz3ExUVFRREVFkZSUxJw5c5r9+CIi0rL1CezDfdxHn0CtY9bWNbkP0f79+xk1atQ5xdBZV111FaNGjbrgwqwCyUeTWctako8mmx1FRER+wcnRCW+8cXJ0unhjadWaXBCVlpbi53fhIYh+fn7njACTuhwdHOlMZxwdmjZnkoiIWE9mTibb2U5mTqbZUcTKmlwQeXl5ERcXd8E28fHxDVqktT3r3q07N3ET3bt1NzuKiIj8QklpCSmkUFJqvb6k0jI0uQ/R8OHD2bx5M//4xz/4zW9+U2dW6MrKSt555x327dtnWWG+Nbocnaqrq6sppbTeGbdFRMRcfQL7sIAF6kPUDjS5IJo1axbff/89b7/9Nlu2bCEsLAxPT08KCgpITEyksLCQbt26MWvWrObMe1ldjk7VCckJPMdzjEkeQ4+reljlHCIiInJhTX5k5uHhwapVqxg/fjzl5eX88MMPfPbZZ/zwww+UlZUxYcIEVq1aRceOHZszb5vTM6Ant3EbPQN6mh1FRER+ITE5kRWsIDE50ewoYmWXNDFjp06deOSRR3j44YdJS0ujrKwMFxcXevbsiZ3dJR263fBw96AvffFw9zA7ioiI/EInj04MZCCdPDqZHUWsrNFVy5tvvklFRQW//e1vLUWPnZ0dQUFBljbV1dWsWbMGZ2dnZs6c2Xxp26D8gnx+5EemFUzDDy0cKCLSkvh28WUMY/Dt4mt2FLGyRj0y+/HHH/nnP/9Jx44dL3gHyN7eno4dO/KPf/yDmJiYSw5plp07d/LII4+wcuVKq53jePZxPuVTjmcft9o5RESkacrKy8gkk7LyMrOjiJU1qiDasWMH7u7u3HzzzRdte9NNN+Hu7s5nn33W5HBmuxyr3Q8MG8gTPMHAsIFWO4eIiDRN8tFkVrNak+e2A40qiGJjY4mMjMTBweGibR0cHLjyyis5ePBgk8OJiIiYKbhXMHOZS3AvrWPW1jWqIMrLy6Nbt24Nbu/n52dZrV7qd+TYEd7iLY4cO2J2FBER+QUXZxe60Q0XZxezo4iVNaog6tChAzU1NQ1uX1NTQ4cOTR7Z3y7YdrDFEUdsO9iaHUVERH4hOzebL/mS7Nxss6OIlTWqWvHy8iI1NbXB7VNTU/H29m50qPakZ0BPpjNd8xCJiLRAhUWFHOAAhUWFZkcRK2vUsPuBAwfyxRdfkJWVddGFXbOysoiJiWHcuHGXFNBMl2PpjtraWqqoora21mrnEBGRpukb3JcHeIC+wX3NjiJW1qg7RDfddBM1NTX86U9/orCw8LztioqKeOKJJ6itrWXKlCmXmtE0l2OUWdyhOP7KX4k7dOGFckVERMR6GnWHKDQ0lFtvvZVNmzZx5513MmXKFK644gq6dOkCnOl0HR0dzSeffEJhYSHTp08nNDTUKsHbiu7dujONaVrtXkSkBTp05BCv8iqjjozCL0KT57ZljZ6pesGCBTg4OPDee+/x1ltv8dZbb9V53zAMOnTowMyZM5k9e3azBW2rPD08GcAAPD08zY4iIiK/4ObqRhBBuLm6mR1FrKzRBZGNjQ1z585l0qRJbNu2jdjYWE6ePAlA586dGTBgABMmTMDf37/Zw7ZFBUUF/MRPFBQVaOkOEZEWpptPN8Yznm4+DZ9yRlqnJq/A6u/vz5w5c5ozS7uUnpnOh3zI7zJ/RzjhZscREZGfqaisII88KiorzI4iVqZJgkzWP7Q/f+SP9A/tb3YUERH5hUNHDvEKr3DoyCGzo4iVqSAyWYcOHbDDThNYioi0QEE9g7ibuwnqGWR2FLGyJj8yaw8uxzxEaRlprGc94zLGaQSDiEgL4+riSk964urianYUsTIVRBcQFRVFVFQUSUlJVusvddo4TS21nDZOW+X4IiLSdCfyTvAt3zLi+xFWO0dZbhnxm+MJvzkcly7Ns2aai7cLHj08muVY7YUKIpP17t6bGcygd/feZkcREZFfKDFK+J7v2XTfJnzxteq5YlbHNNux7F3sWZCwQEVRI6ggEhEROY/h44aTmZZJWV6Z1c7R3HeIchNy+XDmh5TllakgagQVRCY7kHCApSxlWMIw9SESEWmBPHp4WL2wCBqnTttm09Amk/n7+nMjN+Lvq4ksRURamsTERIYMGUJiYqLZUcTKVBCZzMvTi0gi8fL0MjuKiIj8gouLCxEREbi4NE9nZ2m5VBCZrPBUIfHEU3iq0OwoIiLyCz169GDlypXk5eVx6tQpAE6cOMH+/fstbQ4dOkRqaioANTU1xMTEUFBQAJxZ9Dwm5n+dpZOTkzly5AgAp0+fJiYmhvj4eJYuXUp8fDwxMTHU1tYCcOTIEQ4fPmzZNyYmhtzcXAAKCwuJiYmhuroagKNHj5KUlGRpW0RRc38VbZ4KIpMdO36MjWzk2PFjZkcREZF6nDhxgsjISL7//nsA1q9fz/Dhwy3vz507l8cffxyAU6dOERkZya5duwD46KOPiIyMtLRdtGgRDz/8MADV1dVERkayefNmli1bZmlbUXFmmZAlS5Zw3333WfYdOnQomzdvBmD37t1ERkZSWFgIwNKlS7nnnnsAyC/I51VeJSMrwxpfR5ulTtUmCw8JZzGLCQ/ROmYiIi1R165diY6OJjg4GIDbbruNkSNHWt5fvXo19vb2AHTs2JHo6Gh69z4zlcrUqVOJiIiwtH355ZctKxPY29sTHR2Nk5MTNTU1TJ06lfHjx+Pk5ATAM888Y7lbBLB37166d+8OwMiRI4mOjqZTp07AmYKosrISOHPn6SquwtHB0RpfR5tlYxiGYXaIlurnM1UfOHCANWvWEBoa2qznyIrJYnXkauZGz9UoMxERuWT6vdI0emR2AVFRUSxfvpyFCxda7RzHjh/jAz7QIzMREWkWxSXFpJBCcUmx2VFaFRVEJquuqeYUp6iuqTY7ioiItAGp6am8xVukpqeaHaVVUUFkMq2kLCIizSk0KJRFLCI0qHm7eLR1KohERETaEEcHRzzxVKfqRlJBZLLYpFie4ilik2LNjiIiIm1ARlYGn/Kpht03kgoik/l28WUMY/DtYt1VlEVEpH0orygnnXTKK8rNjtKqqCAymXdnb4YxDO/O3mZHERGRNiCkdwjzmEdI7xCzo7QqKohMVlxSTDLJGh4pIiJiIhVEJktNT+Vt3tbwSBERaRbxh+N5nueJPxxvdpRWRQWRyfoG9+UBHqBvcF+zo4iISBvg1cmLIQzBq5OX2VFaFRVEJnOwd8ADDxzsHcyOIiIibYBPFx+u5Vp8uviYHaVVUUFksoysDD7hEw2PFBGRZlFaVko66ZSWlZodpVVRQWSy8opyssjS8EgREWkWKWkprGUtKWkpZkdpVezMDtCS/Xy1e2sJ6R3CXOZqeKSIiDSLkN4hzGe+fq80kgqiC4iKiiIqKoqkpCTmzJljdhwREZGLcnZypitdcXZyNjtKq6JHZiaLPxzPszyr4ZEiItIsMnMy+ZzPyczJNDtKq6KCyGTent5czdV4e2qmahERuXTFJcUkkaQJfxtJBZHJunp3ZQQj6Ord1ewoIiLSBoQGhbKQhYQGhZodpVVRQWSy0rJS0kjT8EgRERETqSAyWUpaCm/whoZHiohIs0hKSeJlXiYpJcnsKK2KCiKT9Qnsw33cR5/APmZHERGRNqCje0f60Y+O7h3NjtKqqCAymZOjE9544+ToZHYUERFpA/y6+hFFFH5d/cyO0qqoIDJZZk4m29mu4ZEiItIsyivKySZbKyA0kgoik5WUlpBCCiWl1psNW0RE2o/DqYd5ndc5nHrY7Citigoik/UJ7MMCFqgPkYiINIvgXsHMYQ7BvYLNjtKqqCASERFpQ1ycXfDHHxdnF7OjtCoqiEyWmJzIClaQmJxodhQREWkDcnJz2MUucnJzzI7SqqggMlknj04MZCCdPDqZHUVERNqAk4UniSGGk4UnzY7SqrSb1e6fe+459uzZQ0VFBT4+PsydO5fhw4ebHQvfLr6MYQy+XXzNjiIiIm1AWEgYD/EQYSFhZkdpVdpNQTR9+nQWLVqEg4MDCQkJPPjgg6xfvx4PDw9Tc5WVl5FJJmXlZabmEBERac/azSOznj174uDgAICNjQ3V1dXk5eWZnAqSjyazmtUkH002O4qIiLQBh44c4jVe49CRQ2ZHaVVa5B2isrIy1q9fT3x8PAkJCRQXF/Poo48yYcKEc9pWVVWxdu1aPv/8c4qLiwkKCmL27NkMGTLknLYvvvgi27Zto6qqimHDhhEYGHg5Ps4FBfcKZi5zNTxSRESahauLK73ohauLq9lRWpUWeYeoqKiIdevWkZaWRnDwhQuFp59+mo0bN3L99ddz//3306FDBxYvXsyBAwfOafvggw+yY8cOVqxYwZAhQ7CxsbHWR2gwF2cXutFNwyNFRKRZ+Pv6M5GJ+Pv6mx2lVWmRBZGXlxcffvghmzZt4t577z1vu/j4eL788kvmzp3L/PnzmTx5Mi+99BK+vr6sWrWq3n1sbW2JjIwkOjqa77//3lofocGyc7P5ki/Jzs02O4qIiLQBFZUV5JNPRWWF2VFalRZZEDk4OODl5XXRdt988w22trZMnjzZss3R0ZFJkyYRFxdHTs7552Cora3l+PHjzZL3UhQWFXKAAxQWFZodRURE2oBDRw6xkpXqQ9RILbIgaqjDhw8TEBCAq2vd56RhYWeGGiYnn+moXFJSwhdffEFZWRk1NTXs2rWLffv2MWjQoHqPm5eXR1JSkuVPWlqa1T5D3+C+PMAD9A3ua7VziIhI+xHYI5BZzCKwh/n9ZFuTFtmpuqHy8/PrvZN0dtvZUWQ2NjZs3bqVFStWYBgG/v7+PP7444SEhNR73C1btrBu3Tqr5RYREbEWN1c3etMbN1c3s6O0Kq26IKqsrMTe3v6c7WeH11dWVgLg6urKyy+/3ODjTp48uc6kjWlpaTz55JOXmLZ+h44c4lVeZdSRUfhF+FnlHCIi0n7k5ueyhz3clH8Tfuj3SkO16oLI0dGR6urqc7ZXVVVZ3m8Kb29vvL29LylbQ7m5uhFEkCp5ERFpFifyT/At33Ii/4TZUVqVVt2HyMvLi/z8/HO2n912uYqaS9HNpxvjGU83n25mRxERkTagX59+PMIj9OvTz+worUqrvkMUHBzMvn37KC0trdOxOj4+3vL+pdi5cyc7d+6kpKTkko5zIRWVFeSRp+GRIiIiJmrVd4hGjRpFbW0tW7ZssWyrqqpi27ZthIeH4+Pjc0nHj4qKYvny5SxcuPBSo57XoSOHeIVXNDxSRESaRfLRZP7BP7QkVCO12DtEH3zwASUlJZbHX3v27OHEiTPPQ6dNm4abmxvh4eGMHj2a1atXU1hYiL+/P9u3byc7O5slS5aYGb/BgnoGcTd3E9QzyOwoIiLSBjg5OtGFLjg5OpkdpVVpsQXRhg0byM7+3+zNu3fvZvfu3QCMHTsWN7cznZAfe+wxfHx82LFjByUlJQQGBvLMM88wePBgM2I3mquLKz3pqTVnRESkWQT4BTCFKQT4BZgdpVVpsQXRxo0bG9TO0dGR+fPnM3/+/GbPcDn6EJ3IOzMaYGreVA2PFBGRS1ZdXU0xxfWOwpbza9V9iKztcvQhyivI43u+J68gz2rnEBGR9iMhOYEXeIGE5ASzo7QqKohMFh4SzmIWEx4SbnYUERFpA3oF9OIO7qBXQC+zo7QqKohERETakI7uHelDHzq6dzQ7SqvSYvsQtQSXow/R4dTDrGY1o1NHa+kOERG5ZPkF+exlL9MKpqlvaiPoDtEFXI4+RM5Ozvjhh7OTs9XOISIi7UdmTiY72EFmTqbZUVoVFUQmC/AL4EZu1PBIERFpFgP6DuBxHmdA3wFmR2lVVBCZrKq6iiKKqKquMjuKiIhIu6WCyGSJyYmsYAWJyYlmRxERkTbgyLEj/It/ceTYEbOjtCoqiEzWu3tvZjKT3t17mx1FRETaADtbO1xxxc5W46YaQ9/WBVyOUWbubu4EE4y7m7vVziEiIu1HD/8e3MIt9PDvYXaUVkV3iC7gssxUfTKPH/iBvJOaqVpERC5dbW0tFVRQW1trdpRWRQWRybJzs/mSL8nOzb54YxERkYuIOxTHcpYTdyjO7Citigoik/UP7c//4//RP7S/2VFERKQN6OHfg1u5VY/MGkkFkYiISBvSqWMn+tGPTh07mR2lVVFBZLKUtBTe4A1S0lLMjiIiIm3AycKT7GMfJwtPmh2lVdEoswu4HKPM7O3s6UhH7O3srXYOERFpPzKyMviYj7k361760c/sOK2GCqILiIqKIioqiqSkJObMmWOVc/Tw78E0pulZr4iINAst3dE0emRmspqaGsooo6amxuwoIiLSBtjY2GCLLTY2NmZHaVVUEJks/nA8z/Is8YfjzY4iIiJtwNH0o7zLuxxNP2p2lFZFBZHJevj3YDrT9chMRERajeKsYr5e+jXFWcVmR2k26kNksk4dOxFOuIZHiohIs+jVvRd3cAeuJa5kxWRZ5Rx5CXl8s+wbvEK88A7zbpZjuni74NHDo1mO1RQqiEyWX5BPNNHkF+Tjh5/ZcUREpJVz9nKmg3MHNs/cjA3W7Ue0eebmZjuWvYs9CxIWmFYUqSC6gMsx7P549nE+4RMWZC+gP5qtWkRELs2R/CP8qfxPbH97OwPDBlrlHGW5ZcRvjif85nBcurhc8vFyE3L5cOaHlOWVqSBqiS7HsPuBYQNZylKr/dCKiEj70rNnT9544w2uHH8lXl5eVjtP0Lggqx3bDCqIRERE2hAvLy/uuusus2O0OhplZrLU9FTe4R1S01PNjiIiIm1AQUEBmzZtoqCgwOworYoKIpN1sOmALbZ0sNF/ChERuXSpqalMnz6d1FT9Q7sx9MjMZD0DenIbt9EzoKfZUUREpA0YNGgQRUVFuLq6mh2lVVFBZLLTp09TQw2nT582O4qIiLQBtra2dOzY0ewYrY6e05gsNimWJ3mS2KRYs6OIiEgbkJqayu23365HZo2kgshk3bt15yZuonu37mZHERGRNqCmpobc3FwtGt5IemRmMk8PTwYxCE8PT7OjiIhIGxASEsLOnTvNjtHqqCC6gMsxU3VBUQEHOUhBUYGW7hARETGJHpldQFRUFMuXL2fhwoVWO0d6Zjof8AHpmelWO4eIiLQf+/btw9HRkX379pkdpVVRQWSyfn368RiP0a9PP7OjiIhIGxAQEMCLL75IQECA2VFaFT0yM5mtrS0OOGBra2t2FBERaQO6dOnCggULzI7R6ugOkcnSMtLYyEbSMtLMjiIiIm1AUVERn376KUVFRWZHaVVUEJms9nQtlVRSe7rW7CgiItIGpKSkcMMNN5CSkmJ2lFZFj8xMFtgjkN/wGwJ7BJodRURE2oABAwaQmZmJt7e32VFaFRVEIiIibYi9vT1+fprGpbH0yMxkBxIOsIxlHEg4YHYUERFpA9LS0pg9ezZpaeqb2hgqiEzm7+vPJCbh7+tvdhQREWkDKioqiIuLo6KiwuworYoemZnMy9OLK7kSL08vs6OIiEgbEBoayvfff292jFZHd4hMVlRcRCKJFBVreKSIiIhZVBCZLC0jjfWs1zxEIiLSLH766Sc6d+7MTz/9ZHaUVkUFkcnCgsP4A38gLDjM7CgiItIG+Pr68uijj+Lr62t2lFZFfYgu4HKsdm9vb48rrtjb21vtHCIi0n74+Pjwhz/8wewYrY7uEF3A5Vrt/kM+1Gr3IiLSLIqLi/n6668pLi42O0qrooLIZJVVlZzkJJVVlWZHERGRNuDw4cOMHj2aw4cPmx2lVdEjM5MF9wrmHu4huFew2VFERKQNCA8P5/DhwwQEBJgdpVVRQSQiItKGODk5ERysf2Q3lh6ZmSw2KZa/8ldik2LNjiIiIm1Aeno6999/P+np6pvaGCqITObj7cMoRuHj7WN2FBERaQPUqbpp9MjMZF28unAN19DFq4vZUUREpA0IDw/nwAEtGN5YukNksuKSYlJIobhElbyIiIhZVBCZLDU9lbd4i9T0VLOjiIhIG3Dw4EECAgI4ePCg2VFaFRVEJgsNCmURiwgNCjU7ioiItAHe3t7Mnj0bb29vs6O0KupDZDJHB0c88cTRwdHsKCIi0gb4+fmxdOlSs2O0OrpDZLKMrAw+5VMysjLMjiIiIm1AaWkp//3vfyktLTU7Squigshk5RXlpJNOeUW52VFERKQNSEpKYujQoSQlJZkdpVXRIzOThfQOYR7zCOkdYnYUERFpA8LCwvjpp58ICdHvlcZQQSQiItKGODs7M3DgQLNjtDrt4pFZVVUVy5cv55ZbbmH8+PHMmzeP2NiWsVRG/OF4nud54g/Hmx1FRETagOPHj/Poo49y/Phxs6O0Ku2iIKqtrcXX15dXX32Vbdu2ceutt/Loo49SVlZmdjS8OnkxhCF4dfIyO4qIiLQBhYWFbNq0icLCQrOjtCrtoiBydnbmrrvuwsfHhw4dOjBmzBjs7OxaxMJ3Pl18uJZr8emitcxEROTS9evXj+TkZPr162d2lFalRfYhKisrY/369cTHx5OQkEBxcTGPPvooEyZMOKdtVVUVa9eu5fPPP6e4uJigoCBmz57NkCFDznv89PR0iouL8ff3t+bHaJDSslLSSae0TMMjRUREzNIi7xAVFRWxbt060tLSCA4OvmDbp59+mo0bN3L99ddz//3306FDBxYvXnzehe0qKyt58sknmTFjBm5ubtaI3ygpaSmsZS0paSlmRxERkTYgLi6O0NBQ4uLirHaOrKwsli5dSlZWltXOcbm1yILIy8uLDz/8kE2bNnHvvfeet118fDxffvklc+fOZf78+UyePJmXXnoJX19fVq1adU77mpoa/vSnP+Hv789dd91lxU/QcCG9Q5jPfA27FxGRZuHh4cHkyZPx8PCw2jmysrJYtmyZCiJrc3BwwMvr4p2Mv/nmG2xtbZk8ebJlm6OjI5MmTSIuLo6cnBzL9tOnT/Pkk09iY2PDY489ho2NjVWyN5azkzNd6Yqzk7PZUUREpA0ICAjgz3/+MydOnKCkpAQ4U8D8/MlJQkICx44dA848OYmJieHUqVMA5OTksH//fkvbpKQkjh49CkB1dTUxMTEUFxcDUFBQQExMjKXt4cOHOXLkCHBmQFNMTAwnT54E4OTJk8TExHD69GkAjhw5QnJysmXfQgqb8VtovBZZEDXU4cOHCQgIwNXVtc72sLAwgDpf9PPPP09+fj7Lli3Dzu7CXafy8vJISkqy/ElLS2v+8P8nMyeTz/mczJxMq51DRETalyNHjhAZGWlZ8X7t2rWMGzfO8v6MGTN45plngDPFUmRkJHv37gXgnXfeYeTIkZa299xzj2VttMLCQiIjIy1F0FdffcXQoUMtbe+77z6WLFkCQEVFBZGRkezYsQOAbdu2ERkZSXV1NQAPP/wwixYtAiC/IJ+XeMnUZaxaZKfqhsrPz6/3TtLZbXl5eQBkZ2ezdetWHBwc6txNevbZZxk0aNA5+2/ZsoV169ZZJ/QvFJcUk0QSxSXFl+V8IiLS9gUGBhIdHU2fPn2AM0XNz3//vfPOO5abCX5+fkRHR1v67M6YMYPrrrvO0nbt2rU4Op5ZgLxTp05ER0db7hBdd911TJs2zdL2lVdewdbWFgAnJyeio6Pp1asXABMnTiQ6Ohp7e3vgzI2Ks3eLAMYwxtSnJa26IKqsrLR8sT/n4OBgeR/A19eX3bt3N/i4kydPZvjw4ZbXaWlpPPnkk5eYtn6hQaEsZCGhQaFWOb6IiLQ/zs7OREREWF77+fnh5+dneX32SQqc6Wry87Y+Pj74+PxvKpjQ0P/9frK3tyciIsJyh8jT07POvj9fLsTW1rbOe507d6Zz586W14GBgZb/7+XpxQhG4OVp3px8rbogcnR0tNx6+7mqqirL+03h7e2Nt7f3JWUTERGRhik8VUg88RSeKsQPv4vvYAWtuiDy8vIiNzf3nO35+fkAl1zU7Ny5k507d1o6pVlDUkoSL/My16Zci1+EOT8EIiIijeHn58cTTzxR567TpTh2/Bgb2chvj/+WMMIuvoMVtOqCKDg4mH379lFaWlqnY3V8fLzl/UsRFRVFVFQUSUlJzJkz55KOdT4d3TvSj350dO9oleOLiIg0Nz8/P0tH6+YQHhLOYhYTHhLebMdsrFY9ymzUqFHU1tayZcsWy7aqqiq2bdtGeHh4nWegLZVfVz+iiMKvq+4OiYhI+2RnZ4cLLhcdBW7VDKad+SI++OADSkpKLI+/9uzZw4kTJwCYNm0abm5uhIeHM3r0aFavXk1hYSH+/v5s376d7Oxsy7C/lq68opxssimvKDc7ioiIiCmOHT/GB3zA+OPjTes+0mILog0bNpCdnW15vXv3bstIsbFjx1qW3Xjsscfw8fFhx44dlJSUEBgYyDPPPMPgwYMvOcPl6EN0OPUwr/M6U1OnEnhN4MV3EBERaWOqa6o5xSmqa84dKHW52BiGYZh29lbibB+iNWvW1Bl+2BxS9qTwzK+eYcm/lxA0PKhZjy0iItIaZMVksTpyNXOj55p2h6hV9yFqC1ycXfDHHxdnF7OjiIiItFsqiEyWk5vDLnaRk5tz8cYiIiJtUGxSLE/xFLFJsaZlUEFkspOFJ4khhpOFJ82OIiIiYgrfLr6MYQy+XXxNy9BiO1W3BJejU3VYSBgP8RBhIeZMRCUiImI2787eDGMY3p3NWyVCBdEFXI6JGUVERNq74pJikkmmuKTYtKU79MjMZIeOHOI1XuPQkUNmRxERETFFanoqb/M2qemppmVQQWQyVxdXetELVxfXizcWERFpg/oG9+UBHqBvcF/TMqggMpm/rz8TmYi/r7/ZUUREREzhYO+ABx442DuYlkF9iC7gcnSqrqisIJ98KiorrHYOERGRliwjK4NP+ISJWRPVh6glioqKYvny5SxcuNBq5zh05BArWak+RCIi0m6VV5STRZap63rqDlEDVFZWApCWltbsx7a1teU219uwtbUlKSmp2Y8vIiLS0tna/d/vQjvr/C7s2bMnTk5OF2yjtcwa4PPPP+fJJ580O4aIiIg0QUPWIlVB1ACFhYXs3buXjz76iEWLFjV4v5UrV170cVtaWhpPPvkkf/zjH+nZs+elRm0TGvK9mcWMbNY4Z3Md81KO05R9G7uPrsGmacnXIFz+fNY6X3u4Dhva1trXYUPuEOmRWQN06tSJsWPH8tVXXzVqtXs3N7cGt+/Zs2ejjt2WNeZ7u9zMyGaNczbXMS/lOE3Zt7H76BpsmpZ8DcLlz2et87WH67CxxzfzOlSn6kaIioqyans5oyV/b2Zks8Y5m+uYl3Kcpuyra/DyaOnf2+XOZ63ztYfrsKX/LP2cHpmZ7OyyIA15vikizU/XoIj5WsJ1qDtEJvPy8uKuu+7Cy8vL7Cgi7ZKuQRHztYTrUHeIREREpN3THSIRERFp91QQiYiISLungqiFq6qqYvny5dxyyy2MHz+eefPmERsba3YskXblueeeY+rUqYwfP55Zs2axZ88esyOJtFuxsbFce+21/Otf/2rW46oPUQtXXl7Ohg0bmDBhAl26dGHXrl289NJLbNiwARcXF7PjibQLaWlp+Pn54eDgQEJCAg8++CDr16/Hw8PD7Ggi7crp06eZP38+hmFwzTXXMGvWrGY7tu4QtXDOzs7cdddd+Pj40KFDB8aMGYOdnR3p6elmRxNpN3r27ImDgwMANjY2VFdXk5eXZ3Iqkfbnk08+ISwszCqzWWum6mZWVlbG+vXriY+PJyEhgeLiYh599FEmTJhwTtuqqirWrl3L559/TnFxMUFBQcyePZshQ4ac9/jp6ekUFxfj7+9vzY8h0mpZ6xp88cUX2bZtG1VVVQwbNozAwMDL8XFEWiVrXIdFRUVs2rSJVatWsXLlymbPrDtEzayoqIh169aRlpZGcHDwBds+/fTTbNy4keuvv57777+fDh06sHjxYg4cOFBv+8rKSp588klmzJiBm5ubNeKLtHrWugYffPBBduzYwYoVKxgyZAg2NjbW+ggirZ41rsM1a9Zw66234u7ubp3QhjSryspKIy8vzzAMw0hISDBGjBhhbNu27Zx2cXFxxogRI4x3333Xsq2iosK47bbbjHnz5p3Tvrq62li8eLGxbNky4/Tp09b7ACKtnLWuwZ9bsmSJ8d133zVvcJE2pLmvw6SkJOOee+4xampqDMMwjKeeespYt25ds2bWHaJm5uDg0KCZNr/55htsbW2ZPHmyZZujoyOTJk0iLi6OnJwcy/bTp0/z5JNPYmNjw2OPPaZ/mYpcgDWuwV+qra3l+PHjzZJXpC1q7utw//79pKenM23aNKZOncpXX33Fu+++y9NPP91smdWHyCSHDx8mICAAV1fXOtvDwsIASE5OxsfHB4Dnn3+e/Px8nn/+eezs9J9MpDk09BosKSnh+++/Z/jw4Tg4OPDtt9+yb98+5s6da0ZskTalodfh5MmTGTNmjOX9v/3tb/j5+TFjxoxmy6LfribJz8+vt3o+u+3sCJbs7Gy2bt2Kg4NDnQr62WefZdCgQZcnrEgb1NBr0MbGhq1bt7JixQoMw8Df35/HH3+ckJCQy5pXpC1q6HXo5OSEk5OT5X1HR0ecnZ2btT+RCiKTVFZWYm9vf872s0N7KysrAfD19WX37t2XNZtIe9DQa9DV1ZWXX375smYTaS8aeh3+0mOPPdbsWdSHyCSOjo5UV1efs72qqsryvohYj65BEfO1pOtQBZFJvLy8yM/PP2f72W3e3t6XO5JIu6JrUMR8Lek6VEFkkuDgYDIyMigtLa2zPT4+3vK+iFiPrkER87Wk61AFkUlGjRpFbW0tW7ZssWyrqqpi27ZthIeHW0aYiYh16BoUMV9Lug7VqdoKPvjgA0pKSiy3/Pbs2cOJEycAmDZtGm5uboSHhzN69GhWr15NYWEh/v7+bN++nezsbJYsWWJmfJFWT9egiPla23Wo1e6tYPr06WRnZ9f73oYNG/Dz8wPO9J4/u35LSUkJgYGBzJ49m6FDh17OuCJtjq5BEfO1tutQBZGIiIi0e+pDJCIiIu2eCiIRERFp91QQiYiISLungkhERETaPRVEIiIi0u6pIBIREZF2TwWRiIiItHsqiERERKTdU0EkIiIi7Z4KIhEREWn3VBCJiFyijRs3ct1115GVlWXZ9tlnnzFy5Eg+++wzE5P9z9atWxk1ahQpKSlmRxFpkVQQiUgdWVlZjBw58oJ/pk+fbnbMFqO4uJg333yTiRMnWhartJa9e/cycuRIHnrooYu2/fOf/8zIkSP54osvABg/fjw+Pj6sWrXKqhlFWis7swOISMvk7+/P9ddfX+97bm5ulzlNy7Vx40ZOnTrF7bffbvVzXXnllfj4+BAdHU1OTg4+Pj71tispKeHbb7/Fzc2NkSNHAmBnZ8f06dN5+eWXOXjwIAMGDLB6XpHWRAWRiNTL39+f3/72t2bHaNFqamrYunUrAwYMwN/f3+rn69ChAxMmTGDdunVs376dWbNm1dtu586dVFZWMnHiRBwdHS3bx4wZwyuvvMLHH3+sgkjkF/TITEQu2ciRI7n//vs5efIkTz31FDfeeCNRUVHMmzePffv21btPWVkZ//znP7nzzjuJiopi4sSJPPTQQxw4cOCctvfffz8jR46ksrKSNWvWcNtttzF69Gj++c9/Wtp88803zJkzh6ioKKZMmcKzzz5LcXEx06dPr/OI7y9/+QsjR44kPj6+3lxr165l5MiR7Ny586Kfe+/eveTn5zNq1KiLtj3rxIkTzJo1i6ioKL7++mvL9oKCAlauXMntt9/OmDFjuPHGG/njH//IkSNH6uw/ceJEbGxs+OyzzzAMo95zbNu2DYBJkybV2d6pUyeuuOIKvv76a8rKyhqcWaQ9UEEkIs2ipKSEBQsWcPToUcaOHcvIkSNJSkri4YcfPueX+qlTp7j33ntZt24d7u7uTJkyhZEjR3Lo0CEWLVrEt99+W+85Hn/8cbZv384VV1zBLbfcYumz8+mnn/L444+TkZHBuHHjGD9+PHFxcTz44IPU1NTUOcbkyZMt+/xSbW0t27Ztw8PDw/Ko6UKio6MB6Nev38W/IODo0aPMnz+fEydO8Nxzz1kKqePHjzN79mw2bdpEt27duPnmmxk2bBh79+7l3nvvrVO8+fr6EhkZSWZmZr3F5pEjR0hMTCQkJIQ+ffqc836/fv2oqqoiNja2QZlF2gs9MhOReh0/frzOHZif69evH1dddVWdbcnJyUydOpXf//73dOhw5t9aERERPPvss2zevJmHH37Y0vall14iNTWVxYsXc8MNN1i2FxQUMGfOHJ577jmGDh1a53EPQH5+Pm+88QYdO3a0bCsuLuZvf/sbzs7OrF69mu7duwMwZ84cHn74YZKSkvD19bW0HzRoEL169eLLL7/kvvvuw9nZ2fLe3r17yc3N5dZbb8XBweGi39HBgwfp0KEDwcHBF20bFxfHkiVLsLOzY+XKlXX2eeqppzh58iTPP/88Q4cOtWy/8847mTNnDs8++yzr1q2zbJ80aRI//vgj27ZtIyIios55znd36KzQ0FAAYmNj65xLpL3THSIRqdfx48dZt25dvX/+85//nNPe2dmZefPmWYohODOyydbWlsTERMu2wsJCdu3aRURERJ1iCMDT05Pbb7+dwsJCy92Xn7v77rvrFEMA//73vykvL2fixImWYgjOdCKePXt2vZ9t8uTJlJWV8eWXX9bZvnXrVgBuvPHG830tdeTm5uLm5nbR4un777/ngQcewN3dnddee61OMXTo0CFiY2MZN27cOQVK9+7dueGGGzhy5Eidu2wjRozAw8ODb775htLSUsv2mpoaPv/8cxwcHM7bIb5z587AmUd3IvI/ukMkIvUaOnQozz//fIPbBwQE4OLiUmebnZ0dnTt3pqSkxLItMTGR2tpaqqur670DlZGRAUBaWhrXXHNNnffCwsLOaX92Xp2BAwee8154eDi2trbnbB83bhx///vf2bp1q6UoO3nyJN999x39+/enV69eF/m0Z5w6dYouXbpcsM2uXbv473//S1BQEM899xyenp513j/7OKygoKDe7+PYsWOW/w0MDASwFDzvv/8+O3fuZMqUKQDs2bOHwsJCoqKicHd3rzfP2e1FRUUN+owi7YUKIhFpFq6urvVut7W15fTp05bXp06dAs48bjp48OB5j1dRUXHOtrN3N37u7B2SXxYacGZUloeHxznb3d3dGT16NNu3b+fIkSMEBgby2WefUVtb2+C7QwCOjo5UVVVdsE1cXBy1tbUMHDiw3oxnv4/vv/+e77///rzHKS8vr/N60qRJvP/++2zbts1SEF3scRlgyevk5HTB3CLtjQoiEbmszhZOv/71r1mwYEGj9rWxsTnv8QoKCs557/Tp0xQVFdV7F2fKlCls376dTz75hEWLFvHpp5/i6urK6NGjG5zHw8OD3NzcC7aZO3cu//73v3n//fextbU95zOfzb9o0SKmTZvW4HMHBQXRt29fEhISSE1Nxd3dnb179+Ln53dOv6KfO1uAderUqcHnEmkP1IdIRC6rvn37YmNjQ1xcXLMcLygoCKDeu00JCQnU1tbWu1+/fv0ICgriiy++YO/evWRkZHD99dc36s5JYGAgVVVV5OTknLeNg4MDTz31FFdffTUbNmzglVdeqfP+2ceATfk+zt4J+vTTT9mxYwe1tbWWYfnnc/YR3NnHbyJyhgoiEbmsvLy8GD16NLGxsbz33nv1zqUTHx9f7yOz+vzqV7/C2dmZTz/9lOPHj1u219TUsHbt2gvuO3nyZE6dOsXy5csBzunkfTGDBw+25L0QBwcHnnzySa655ho2btzIypUrLe+Fh4cTHh7Ol19+eU4nbzhzl2v//v31HjcqKgonJyc+//xztm3bRocOHRg/fvwFsyQkJNTJLiJn6JGZiNTrQsPuAWbMmHHOsPiGevDBB0lPT2fVqlXs2LGDfv364ebmRm5uLomJiWRkZPDhhx826G6Nu7s79913H8899xxz5szhuuuuw9XVlR9++AEHBwe8vb3Pe8dk7NixvP766+Tl5REaGlrvvD0X8qtf/YpXX32VH3/88aKP2uzt7fnLX/7Cn/70JzZt2oRhGNx///0A/OlPf+L3v/89y5Yt4/333yckJARHR0dOnDhBbGwsRUVF9U4U6erqyrXXXsuOHTsoLCzkqquuOu9yHgCGYRAdHU3Pnj3rjMgTERVEInIeZ4fdn8+tt97a5IKoY8eOvPbaa2zevJmvvvqKnTt3cvr0aTp37kxwcDCzZs2qtzP0+dx44424u7vz1ltvsX37dlxdXRk+fDjz5s3j1ltvPe+yGq6urowYMYLPP/+80XeHAPz8/BgyZAhff/01ixYtuujw+7NF0RNPPMH777+PYRgsWrSIbt26sXbtWjZs2MC3337LZ599RocOHfDy8mLQoEEXnAl70qRJ7NixAzgzi/WF/PTTT+Tk5LBw4cJGf1aRts7GON/c7yIirVxGRgZ33HEHo0ePZtmyZfW2mTVrFtnZ2WzevPm8I+UuJDo6mgceeIA//vGPjB079lIjW9Vf/vIX/vOf//Dee++dd1i+SHulPkQi0uoVFxefM/y9srLS0oF5xIgR9e73ww8/kJqaSlRUVJOKIYDIyEiuuuoq3nzzzTrTC7Q06enpfPXVV9x5550qhkTqoUdmItLq7d+/n2eeeYYhQ4bQtWtXioqKiImJITs7m4iICK677ro67T/66CNOnDjB1q1bcXBwYMaMGZd0/vvvv58vvviC3NzcC/bhMdOJEye46667uOmmm8yOItIi6ZGZiLR66enprF27ltjYWAoLCwHw9/fnuuuu47bbbjunr9P06dPJzc2le/fuzJs375wZsUWk/VFBJCIiIu2e+hCJiIhIu6eCSERERNo9FUQiIiLS7qkgEhERkXZPBZGIiIi0eyqIREREpN1TQSQiIiLtngoiERERaff+PzqRJBjavJ+3AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Get expectation\n",
+    "\n",
+    "results = like.results\n",
+    "\n",
+    "\n",
+    "#print(results.display())\n",
+    "\n",
+    "parameters = {par.name:results.get_variates(par.path)\n",
+    "                  for par in results.optimized_model[\"source\"].parameters.values()\n",
+    "                  if par.free}\n",
+    "\n",
+    "results_err = results.propagate(results.optimized_model[\"source\"].spectrum.main.shape.evaluate_at, **parameters)\n",
+    "\n",
+    "#print(results.optimized_model[\"source\"])\n",
+    "    \n",
+    "energy = np.geomspace(100*u.keV,10*u.MeV).to_value(u.keV)\n",
+    "\n",
+    "flux_lo = np.zeros_like(energy)\n",
+    "flux_median = np.zeros_like(energy)\n",
+    "flux_hi = np.zeros_like(energy)\n",
+    "flux_inj = np.zeros_like(energy)\n",
+    "\n",
+    "for i, e in enumerate(energy):\n",
+    "    flux = results_err(e)\n",
+    "    flux_median[i] = flux.median\n",
+    "    flux_lo[i], flux_hi[i] = flux.equal_tail_interval(cl=0.68)\n",
+    "    #flux_inj[i] = spectrum_inj.evaluate_at(e)\n",
+    "\n",
+    "binned_energy_edges = crab.binned_data.axes['Em'].edges.value\n",
+    "binned_energy = np.array([])\n",
+    "bin_sizes = np.array([])\n",
+    "\n",
+    "for i in range(len(binned_energy_edges)-1):\n",
+    "    binned_energy = np.append(binned_energy, (binned_energy_edges[i+1] + binned_energy_edges[i]) / 2)\n",
+    "    bin_sizes = np.append(bin_sizes, binned_energy_edges[i+1] - binned_energy_edges[i])\n",
+    "\n",
+    "expectation = response.expectation(data, copy = True)    \n",
+    "exp_est = expectation # for later comparison\n",
+    "\n",
+    "# Plot the fitted and injected spectra\n",
+    "fig,ax = plt.subplots()\n",
+    "\n",
+    "ax.plot(energy, energy*energy*flux_median, label = \"Best fit\")\n",
+    "ax.fill_between(energy, energy*energy*flux_lo, energy*energy*flux_hi, alpha = .5, label = \"Best fit (errors)\")\n",
+    "#ax.plot(energy, energy*energy*flux_inj, color = 'black', ls = \":\", label = \"Injected\")\n",
+    "\n",
+    "ax.set_xscale(\"log\")\n",
+    "ax.set_yscale(\"log\")\n",
+    "\n",
+    "ax.set_xlabel(\"Energy (keV)\")\n",
+    "ax.set_ylabel(r\"$E^2 \\frac{dN}{dE}$ (keV cm$^{-2}$ s$^{-1}$)\")\n",
+    "\n",
+    "ax.legend()\n",
+    "\n",
+    "ax.set_ylim(.1,100)\n",
+    "plt.show()\n",
+    "\n",
+    "# Plot the fitted spectrum convolved with the response, as well as the simulated source counts\n",
+    "fig,ax = plt.subplots()\n",
+    "\n",
+    "ax.stairs(expectation.project('Em').todense().contents, binned_energy_edges, color='purple', label = \"Best fit convolved with response\")\n",
+    "#ax.stairs(expectation_inj.project('Em').todense().contents, binned_energy_edges, color='blue', label = \"Injected spectrum convolved with response\")\n",
+    "ax.errorbar(binned_energy, expectation.project('Em').todense().contents, yerr=np.sqrt(expectation.project('Em').todense().contents), color='purple', linewidth=0, elinewidth=1)\n",
+    "ax.stairs(crab.binned_data.project('Em').todense().contents, binned_energy_edges, color = 'black', ls = \":\", label = \"Source counts\")\n",
+    "ax.errorbar(binned_energy, crab.binned_data.project('Em').todense().contents, yerr=np.sqrt(crab.binned_data.project('Em').todense().contents), color='black', linewidth=0, elinewidth=1)\n",
+    "\n",
+    "ax.set_xscale(\"log\")\n",
+    "ax.set_yscale(\"log\")\n",
+    "\n",
+    "ax.set_xlabel(\"Energy (keV)\")\n",
+    "ax.set_ylabel(\"Counts\")\n",
+    "\n",
+    "ax.legend()\n",
+    "\n",
+    "plt.show()\n",
+    "\n",
+    "# Plot the fitted spectrum convolved with the response plus the fitted background, as well as the simulated source+background counts  \n",
+    "expectation_bkg = bkg.expectation(data.axes, copy = True)\n",
+    "\n",
+    "fig,ax = plt.subplots()\n",
+    "\n",
+    "ax.stairs(expectation.project('Em').todense().contents + expectation_bkg.project('Em').todense().contents, binned_energy_edges, color='purple', label = \"Best fit convolved with response plus background\")\n",
+    "ax.errorbar(binned_energy, expectation.project('Em').todense().contents+expectation_bkg.project('Em').todense().contents, yerr=np.sqrt(expectation.project('Em').todense().contents+expectation_bkg.project('Em').todense().contents), color='purple', linewidth=0, elinewidth=1)\n",
+    "ax.stairs(data.data.project('Em').todense().contents, binned_energy_edges, color = 'black', ls = \":\", label = \"Total counts\")\n",
+    "ax.errorbar(binned_energy, data.data.project('Em').todense().contents, yerr=np.sqrt(data.data.project('Em').todense().contents), color='black', linewidth=0, elinewidth=1)\n",
+    "\n",
+    "ax.set_xscale(\"log\")\n",
+    "ax.set_yscale(\"log\")\n",
+    "\n",
+    "ax.set_xlabel(\"Energy (keV)\")\n",
+    "ax.set_ylabel(\"Counts\")\n",
+    "\n",
+    "ax.legend()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed584e47-887b-47e7-8c8b-4f48f375e71f",
+   "metadata": {},
+   "source": [
+    "Let's do the same thing for the simple inpainting. It's the same procedure, and so we'll run everything together. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "e7771329-f14c-4a9b-a5e1-e7b0e152f9be",
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:yayc.configurator:Using configuration file at ./inputs_crab.yaml\n",
+      "INFO:yayc.configurator:Using configuration file at ./inputs_crab.yaml\n",
+      "INFO:yayc.configurator:Using configuration file at ./inputs_crab.yaml\n",
+      "INFO:yayc.configurator:Using configuration file at ./inputs_crab.yaml\n",
+      "INFO:yayc.configurator:Using configuration file at ./inputs_crab.yaml\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">14:08:35 </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> The current value of the parameter beta (</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">-2.0</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">) was above the new maximum </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">-2.15</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">. </span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">parameter.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py#810\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">810</span></a>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;5;46m14:08:35\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The current value of the parameter beta \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;37m-2.0\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m was above the new maximum \u001b[0m\u001b[1;37m-2.15\u001b[0m\u001b[1;38;5;251m.\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=114718;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=208865;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py#810\u001b\\\u001b[2m810\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">14:08:35 </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> set the minimizer to minuit                                              </span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/classicMLE/joint_likelihood.py#994\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">994</span></a>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;5;46m14:08:35\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m set the minimizer to minuit                                             \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=421350;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=512278;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/classicMLE/joint_likelihood.py#994\u001b\\\u001b[2m994\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:cosipy.response.threeml_point_source_response:... Calculating point source response ...\n",
+      "INFO:cosipy.response.threeml_point_source_response:--> done (source name : source)\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n",
+      "\n",
+      "WARNING RuntimeWarning: invalid value encountered in log\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">14:09:03 </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> The current value of the parameter beta (</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">-2.0</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">) was above the new maximum </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">-2.15</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">. </span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">parameter.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py#810\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">810</span></a>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;5;46m14:09:03\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The current value of the parameter beta \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;37m-2.0\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m was above the new maximum \u001b[0m\u001b[1;37m-2.15\u001b[0m\u001b[1;38;5;251m.\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=868136;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=229769;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py#810\u001b\\\u001b[2m810\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ffaf; text-decoration-color: #00ffaf; font-weight: bold; text-decoration: underline\">Best fit values:</span>\n",
+       "\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1;4;38;5;49mBest fit values:\u001b[0m\n",
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>result</th>\n",
+       "      <th>unit</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>parameter</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>source.spectrum.main.Band.K</th>\n",
+       "      <td>(3.589 +/- 0.009) x 10^-5</td>\n",
+       "      <td>1 / (keV s cm2)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_bkg</th>\n",
+       "      <td>(3.56374 +/- 0.00031) x 10</td>\n",
+       "      <td>Hz</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                 result             unit\n",
+       "parameter                                                               \n",
+       "source.spectrum.main.Band.K   (3.589 +/- 0.009) x 10^-5  1 / (keV s cm2)\n",
+       "total_bkg                    (3.56374 +/- 0.00031) x 10               Hz"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "<span style=\"color: #00ffaf; text-decoration-color: #00ffaf; font-weight: bold; text-decoration: underline\">Correlation matrix:</span>\n",
+       "\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n",
+       "\u001b[1;4;38;5;49mCorrelation matrix:\u001b[0m\n",
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><table id=\"table139936142473152\">\n",
+       "<tr><td>1.00</td><td>-0.66</td></tr>\n",
+       "<tr><td>-0.66</td><td>1.00</td></tr>\n",
+       "</table></div>"
+      ],
+      "text/plain": [
+       " 1.00 -0.66\n",
+       "-0.66  1.00"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "<span style=\"color: #00ffaf; text-decoration-color: #00ffaf; font-weight: bold; text-decoration: underline\">Values of -log(likelihood) at the minimum:</span>\n",
+       "\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n",
+       "\u001b[1;4;38;5;49mValues of -\u001b[0m\u001b[1;4;38;5;49mlog\u001b[0m\u001b[1;4;38;5;49m(\u001b[0m\u001b[1;4;38;5;49mlikelihood\u001b[0m\u001b[1;4;38;5;49m)\u001b[0m\u001b[1;4;38;5;49m at the minimum:\u001b[0m\n",
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>-log(likelihood)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>cosi</th>\n",
+       "      <td>-1591401274.5105834</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total</th>\n",
+       "      <td>-1591401274.5105834</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          -log(likelihood)\n",
+       "cosi   -1591401274.5105834\n",
+       "total  -1591401274.5105834"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "<span style=\"color: #00ffaf; text-decoration-color: #00ffaf; font-weight: bold; text-decoration: underline\">Values of statistical measures:</span>\n",
+       "\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n",
+       "\u001b[1;4;38;5;49mValues of statistical measures:\u001b[0m\n",
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>statistical measures</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>AIC</th>\n",
+       "      <td>-3182802545.021115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BIC</th>\n",
+       "      <td>-3182802524.326022</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    statistical measures\n",
+       "AIC   -3182802545.021115\n",
+       "BIC   -3182802524.326022"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> The current value of the parameter beta (</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">-2.0</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">) was above the new maximum </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">-2.15</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">. </span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">parameter.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py#810\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">810</span></a>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The current value of the parameter beta \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;37m-2.0\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m was above the new maximum \u001b[0m\u001b[1;37m-2.15\u001b[0m\u001b[1;38;5;251m.\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=814663;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=34050;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py#810\u001b\\\u001b[2m810\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> The current value of the parameter beta (</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">-2.0</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">) was above the new maximum </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">-2.15</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">. </span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">parameter.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py#810\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">810</span></a>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The current value of the parameter beta \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;37m-2.0\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m was above the new maximum \u001b[0m\u001b[1;37m-2.15\u001b[0m\u001b[1;38;5;251m.\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=710629;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=150383;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py#810\u001b\\\u001b[2m810\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAloAAAHECAYAAADh34REAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAABSLElEQVR4nO3de1yUZd4/8M/MwMAwM5wGRQU8AEZKJWpaVpIkHjHyWcsyn13dAkstj62nX1vbZk+WtVa2uWkWm+2uWplakofRPOeapqWCeEgQVM6nGQZmmJn79wcyOA4oh7kZGD7v16tXy3WfrmGb+nhd1/29JIIgCCAiIiIip5O6ugNERERE7opBi4iIiEgkDFpEREREImHQIiIiIhIJgxYRERGRSBi0iIiIiETCoEVEREQkEgYtIiIiIpF4uLoD7Z3JZMLf/vY3HDt2DHq9Hj179sQLL7yAu+66y9VdIyIiIhfjiFYLWSwWdOnSBX//+9+RmpqKJ554AosXL4bBYHB114iIiMjFGLRaSKFQYOrUqQgODoZUKsXw4cPh4eGB7OxsV3eNiIiIXKzDTR0aDAasX78eaWlpSE9Ph06nw+LFizFmzBiHc00mE9auXYudO3dCp9MhIiICSUlJGDRoUIP3z87Ohk6nQ0hIiJgfg4iIiNqBDjeiVVZWhpSUFGRlZSEyMvKW57755pvYuHEjRowYgVmzZkEqlWLBggX49ddf6z3faDRi6dKlmDx5MlQqlRjdJyIionakwwUtjUaDb775Bl9++SWmT5/e4HlpaWnYvXs3pk2bhhkzZiAxMRHvvfceunTpglWrVjmcbzab8corryAkJARTp04V8RMQERFRe9HhgpZcLodGo7ntefv27YNMJkNiYqKtzcvLCwkJCThz5gzy8vJs7VarFUuXLoVEIsGSJUsgkUhE6TsRERG1Lx0uaDXW+fPnERoaCqVSadfep08fAMCFCxdsbe+88w6Kiorw2muvwcOjwy17IyIiogYwFTSgqKio3pGv2rbCwkIAQG5uLr777jvI5XK70a+3334b/fr1c7i+sLAQRUVFtp+NRiMqKyvRr18/eHt7O/tjEBERkQsxaDXAaDTC09PToV0ul9uOA0CXLl2wf//+Rt9369atSElJcWhfs2YNoqKimtdZIiIiapMYtBrg5eWF6upqh3aTyWQ73hyJiYl48MEHbT9nZWVh6dKlzeskERERtWkMWg3QaDQoKChwaK+d9gsKCmrWfYOCgpp9LREREbUvXAzfgMjISOTk5KCiosKuPS0tzXaciIiI6FYYtBowbNgwWCwWbN261dZmMpmQmpqKvn37Ijg42IW9IyIiovagQ04dfv3119Dr9bZpwEOHDiE/Px8AMGHCBKhUKvTt2xdxcXFYvXo1SktLERISgu3btyM3NxcLFy50ZfeJiKgNsFgs9a7lJffi6ekJmUzW7Os7ZNDasGEDcnNzbT/v37/f9ubgyJEjbdvnLFmyBMHBwdixYwf0ej3Cw8Px1ltvISYmpsV90Gq10Gq10Ov1Lb4XERG1Lr1ej5ycHAiC4OqukMgkEglCQ0ObvbWeROA/JS6VkZGB5ORklncgImonLBYLzp8/Dx8fH3Tq1Im7gbgxQRBQUFAAg8GA3r17N2tkq0OOaBERETVXdXU1BEFAp06doFAoXN0dElmnTp2QmZmJ6urqZgUtLoYnIiJqBo5kdQwt/f+ZQYuIiKid69mzJ6KiohATE4M+ffrg6aefdihP1BQpKSk4e/Zsg8ePHDmCu+++G/3798eOHTswduxYZGRkNOrajoZBi4iIyA1s2LABJ0+exJkzZ1BWVlbvdm+Ndbuw9M9//hNPP/00Tpw4gVGjRiE1NdW2zphByx6DlototVosWrQIK1eudHVXiIjIjZhMJhgMBgQEBNja3nnnHQwePBgDBgzA6NGjkZWVBQD49ttvcc899yAmJgZ33XUXtmzZgk8++QTHjh3D3LlzERMTg9TUVLv7L1u2DBs2bMCHH36ImJgYlJaWomfPnjh58uRtr+2IuBjeReLj4xEfH29765CIiNqnzf/vEAxlRtHu7+PnhfFvPHjb85588kkoFApkZmZi4MCBmDhxIgDg3//+NzIyMvDjjz9CJpNh3bp1mDFjBrZt24aXX34ZH3/8MYYMGQKr1Yry8nL4+/vjiy++wJw5czB+/HiH5yxatAhnz55FTEwM5syZY3csKSnpltd2RAxaRERELWAoM8JQLF7QaqwNGzYgJiYGZrMZzz33HBYuXIh3330Xmzdvxk8//YSBAwcCqClPUWv48OGYPXs2Hn/8cYwcOdIpdSLJHoMWERFRC/j4ebWp+3t4eGDChAn405/+hHfffReCIGDx4sWYNm2aw7l/+9vfcObMGfzwww+YMmUKJk+ejAULFjir6wQGLSIiohZpzLRea9uzZ49tcfr48ePx7rvv4vHHH0dgYCCqq6tx+vRp9O/fH2fPnkV0dDSio6Ph4eGBnTt3AgB8fX1RVlbWrGe35Fp3xMXwREREbuDJJ5+0LWpPT0/H+++/DwCYPHkypk6diri4OPTr1w8xMTHYs2cPgJqt5qKjo9G/f3+sW7cOf/nLXwAA06ZNw//93/81a0F7S651R9yCx8W4BQ8RUftSVVWFS5cuoVevXvD29nZ1d0hkLf3/m1OHLsJNpYmIiNwfg5aLsLwDERGR++MaLSIiIiKRMGgRERERiYRBi4iIiEgkDFpEREREImHQIiIiIhIJ3zokIiJqoeNfnRflvgMf792o83r27AkvLy8oFAoYjUb0798fa9asgVKpbNZzU1JScP/99+POO++s9/iRI0eQnJwMDw8PLFu2DO+//z5WrFiBqKio214LAC+99BLuvfdePPXUU83qn7O99NJLGDBgAJ5++mmn35sjWi6i1WqxaNEirFy50tVdISIiN7BhwwacPHkSZ86cQVlZGVJSUpp9r5SUFJw9e7bB4//85z/x9NNP48SJExg1ahRSU1NtRbdvd+2VK1eQmpqKJ598skl9MpvNjWpryvW1FixYgL/85S92G247C4OWi8THx2PZsmV48cUXXd0VIiJyIyaTCQaDAQEBAba2d955B4MHD8aAAQMwevRoZGVlAQC+/fZb3HPPPbate7Zs2YJPPvkEx44dw9y5c+vdRmfZsmXYsGEDPvzwQ8TExKC0tBQ9e/bEyZMnb3stAHz66aeYMGECJBIJAKC6uhqLFi3C4MGDERMTg4kTJ6KkpAQAMHXqVDzzzDOIjY3FXXfdhb179yI6OhrPPvssYmJi8M033+DYsWN44IEHcM8992Dw4ME4dOgQACAzMxP+/v5YuHAhBgwYgA8//LDezwsAnTt3RkREhG2vR2fi1CEREZEbePLJJ6FQKJCZmYmBAwdi4sSJAIB///vfyMjIwI8//giZTIZ169ZhxowZ2LZtG15++WV8/PHHGDJkCKxWK8rLy+Hv748vvvgCc+bMwfjx4x2es2jRIpw9exYxMTGYM2eO3bGkpKRbXgsAe/fuxdy5c20/L1++HEqlEkePHgUAvP7663j55Zfx97//HQBw/PhxHDx4EGq1Gnv37kV6ejo++ugjrF27FiaTCZGRkVizZg1GjRqFgwcPYsKECbhw4QIAoKysDNHR0XjrrbcAAP369XP4vLWGDBmC3bt3Y8yYMc36/TeEQYuIiMgNbNiwATExMTCbzXjuueewcOFCvPvuu9i8eTN++uknDBw4EADspseGDx+O2bNn4/HHH8fIkSMRExMjej9zcnIQHBxs+3nz5s0oKyvD119/DaBmRK5nz56240888QTUarXt5/DwcDz88MMAavYLlkqlGDVqFADgoYceQnBwME6ePInQ0FB4enrif//3f23X3urzdunSBWlpaU7/vJw6JCIiciMeHh6YMGECtm/fDgAQBAGLFy/GyZMncfLkSZw6dQqnTp0CAPztb3/DZ599Bh8fH0yZMgVvv/226P3z8fFBVVWV7WdBELBy5Upb/9LS0uymHFUqld31N/98s9opydpnSaV1UedWn7eqqgoKhaLZn6shDFpERERuZs+ePbbF6ePHj8c//vEPFBcXA6hZE3XixAkAwNmzZxEdHY0XXngB06dPx5EjRwAAvr6+KCsra9azb3ftPffcg4yMDNvP48ePx4oVK2AwGAAABoMBZ86cadSzoqKiYLVasWvXLgDA4cOHkZub2+DIXEOfFwDS09PRr1+/Rj23KTh1SERE5AZq12iZzWb06NED//jHPwAAkydPRlFREeLi4gDUvH33zDPPoH///liyZAkyMjIgl8vh4+ODVatWAQCmTZuG+fPnY8WKFfi///s/jB07ttH9uN21jz/+OD7//HMkJSUBABYuXAij0Yj77rvPNhq1cOFCREdH3/ZZcrkcmzZtwqxZszB//nx4e3vjq6++gkqlQmFhocP5DX1eQRCwe/duLFq0qNGfs7EkgiAITr8rNVpGRgaSk5OxZs0a258+iIio7aqqqsKlS5fQq1cveHt7u7o77Y7VasXgwYOxefNmhIaGuro7AIDt27fjiy++wBdffOFwrKX/f3PqkIiIiFqNVCrFxx9/jMzMTFd3xaasrEy09WmcOiQiIqJWVfsGZFvR1OKpTcGg5SJarRZarRZ6vd7VXSEiIiKRMGi5SHx8POLj421rtIiIqH3hEueOoaX/PzNoERERNYGnpyckEgkKCgrQqVMnu7pN5F4EQUBBQQEkEgk8PT2bdQ8GLSIioiaQyWQIDQ1FTk5Om1rQTeKQSCQIDQ2FTCZr1vUMWkRERE2kUqnQu3dvVFdXu7orJDJPT89mhyyAQYuIiKhZZDJZi/4DTB0D62gRERERiYRBi4iIiEgkDFpEREREImHQIiIiIhIJgxYRERGRSBi0iIiIiETC8g4uwr0OiYiI3B+Dlotwr0MiIiL3x6lDIiIiIpEwaBERERGJhEGLiIiISCQMWkREREQiYdAiIiIiEgmDFhEREZFIGLSIiIiIRMKgRURERCQSBi0iIiIikTBoEREREYmEQYuIiIhIJAxaRERERCJh0CIiIiISiYerO9BRabVaaLVa6PV6V3eFiIiIRMKg5SLx8fGIj49HRkYGkpOTXd0dIiIiEgGnDomIiIhEwqBFREREJBIGLSIiIiKRMGgRERERiYRBi4iIiEgkDFpEREREImHQIiIiIhIJgxYRERGRSBi0iIiIiETCoEVEREQkEgYtIiIiIpEwaBERERGJhEGLiIiISCQMWkREREQiYdAiIiIiEgmDFhEREZFIGLSIiIiIRMKgRURERCQSBi0iIiIikTBoEREREYnEw9Ud6Ki0Wi20Wi30er2ru0JEREQiYdBykfj4eMTHxyMjIwPJycmu7g4RERGJgFOHRERERCJx2ojWzz//jOPHj+P06dPIz89HWVkZvL294e/vj/DwcMTExGDIkCHQaDTOeiQRERFRm9aioFVZWYmvv/4a3377LfLy8iAIAgBALpfD19cXRqMRly5dwsWLF7Fr1y54eHjggQcewMSJE3H33Xc75QMQERERtVXNDlpbtmzBZ599hpKSEkRERODZZ59FdHQ07rzzTvj4+NjOEwQBOTk5SEtLw08//YSDBw/iwIEDePDBBzFz5kx069bNKR+EiIiIqK1pdtB67733EB8fj0mTJiE8PLzB8yQSCcLCwhAWFoZRo0bBaDRi165d+OKLL7Bz505MnTq1uV0gIiIiatOaHbQ+//xzhIWFNfk6Ly8vjBs3DmPGjEFeXl5zH09ERETU5jX7rcPmhKwbyWQyThsSERGRW2N5ByIiIiKRODVo6XQ6bN++3Zm3JCIiImq3nBq08vLysGzZMmfekoiIiKjdatJi+NstXi8sLGxRZ4iIiIjcSZOC1sSJEyGRSBo8LgjCLY8TERERdSRNClpqtRrPPvssYmJi6j2elZWFv/zlL07oFhEREVH716Sgdccdd0Cn06FXr171HrdYLLZteIiIiIg6uiYFrfHjx6OqqqrB48HBwVi0aFGLO0VERETkDpoUtGJjY295XK1WY8yYMS3qEBEREZG7YMFSIiIiIpEwaBERERGJpMVBa9iwYcjOznZGX4iIiIjcSouDFt8yJCIiIqofpw6JiIiIRMKgRURERCQSBi0iIiIikTBoEREREYmEQYuIiIhIJAxaRERERCJpcdB6+umn4evr64y+tEubN2/Gs88+i7i4OHz66aeu7g4RERG1IU3a67A+zz33nDP60W5pNBr88Y9/hFardXVXiIiIqI1pcdC6HUEQkJOTA7lcjuDgYLEf1+qGDh0KADhy5IiLe0JERERtjdOC1r59+3Dw4EHMmjULarUaAHDt2jUsWrQIWVlZAGq26/nzn/8MmUzmrMc2icFgwPr165GWlob09HTodDosXrwYY8aMcTjXZDJh7dq12LlzJ3Q6HSIiIpCUlIRBgwa5oOdERETUHjltMfyWLVtw/vx5W8gCgA8//BCZmZno378/IiIisHfvXqSmpjrrkU1WVlaGlJQUZGVlITIy8pbnvvnmm9i4cSNGjBiBWbNmQSqVYsGCBfj1119bqbdERETU3jktaGVmZqJPnz62nw0GA3788Uc88sgjWLFiBT7++GP06NHDpUFLo9Hgm2++wZdffonp06c3eF5aWhp2796NadOmYcaMGUhMTMR7772HLl26YNWqVa3YYyIiImrPnBa0ysvLERgYaPv5119/hcViwfDhwwEAHh4euPfee3HlyhVnPbLJ5HI5NBrNbc/bt28fZDIZEhMTbW1eXl5ISEjAmTNnkJeXJ2Y3iYiIyE04LWgplUqUl5fbfj5x4gSkUin69etna/Pw8EBVVZWzHima8+fPIzQ0FEql0q69dsTuwoULtjaz2Qyj0Qir1QqLxQKj0QiLxdKq/SUiIqK2yWmL4bt3747Dhw8jKSkJUqkUWq0Wd9xxh92ardzcXAQEBDjrkaIpKiqqd+Srtq2wsNDW9vnnnyMlJcX287p16xpcYF97bVFRke3n2hcFiIiIyP04LWhNmDABr776KiZMmGAbuUpKSrI7Jy0tDXfccYezHikao9EIT09Ph3a5XG47XuuZZ57BM8880+h7b9261S6YERERkftyWtAaNmwY5s6di23btgEAHnnkEbtRnZMnT6KiogKDBw921iNF4+Xlherqaod2k8lkO95ciYmJePDBB20/Z2VlYenSpc2+HxEREbVdTi1YOn78eIwfP77eYzExMS5947ApNBoNCgoKHNprp/yCgoKafe+goKAWXU9ERETtBzeVrkdkZCRycnJQUVFh156WlmY7TkRERHQ7DFr1GDZsGCwWC7Zu3WprM5lMSE1NRd++fd1yKyEiIiJyPtH3Omxrvv76a+j1ets04KFDh5Cfnw+gZkG/SqVC3759ERcXh9WrV6O0tBQhISHYvn07cnNzsXDhQld2n4iIiNqRDhe0NmzYgNzcXNvP+/fvx/79+wEAI0eOhEqlAgAsWbIEwcHB2LFjB/R6PcLDw/HWW28hJibGKf3QarXQarXQ6/VOuR8RERG1PRJBEARXd6Ijy8jIQHJyMtasWYOoqChXd4eIiIiciGu0iIiIiETCoEVEREQkkiYHLavVit9++81uG5paZrMZJ0+edEa/iIiIiNq9Ji2Gz83NxYIFC5CVlQWJRIL7778fixcvhp+fHwCgvLwcc+bMwd69e8XoKxEREVG70qQRrVWrViEoKAjr16/HmjVrYDQaMXPmTLvRLa6tbxytVotFixZh5cqVru4KERERiaRJQeuXX37BjBkz0LVrV/Tu3Rvvvvsu7rnnHrzwwgvIy8sDAEgkElE66m7i4+OxbNkyvPjii67uChEREYmkSUGrqqoKnp6edRdLpViwYAEGDRqEF198EVeuXHF6B4mIiIjaqyYFre7duyMjI8Ohff78+bj//vuxaNEip3WMWubENxdwensmLv33GvLOlUBXYICl2uLqbhEREXUoTVoMHxsbi127dmHkyJEOx+bNmwcA2LJli3N6Rs1mtQr4+avzqG+5nJfKE8pAb/gEeMEnwBs+/l5QaryhCvSGUqOAUuMNuY8Hp4CJiIicgJXhXUyMyvAVJVX4z8wfmn29p7cMSo03lIE1wUul8YYqSAFVJwXUnRRQBnpDKmMJNiIiotvpcHsddgReSk+M/X+DUVFSBUOJEYbimr9XlF7/uaQKVnPD+bq6yoLSKxUovVJR73GJVAJloLcteKmCav6u7qyAb2cf+AR4QyLliBgRERGDlouIuam0h1yGbtGaBo8LggCjrromeBUbUVFUCX1RFSqKq1BRVAV9USUqiqpgqbbWf71VgL6wEvrCSuSmOx6XeUqh7qyAurMPfIN94BusvP53H6g6KSDz4GgYERF1DC2eOhw2bBjWrVuHsLAwZ/WpQ2mrm0oLggCjvtoWvPSFldAVVEJfcP3vhZUw6qubfF+JBFB39oFfV2XNX92Utv/t4+/FtWFERORWWjyixSVe7kkikcBbLYe3Wg5NT996zzEZqqEvrIKuwFATxPIrUZ5nQHleBXT5lfWOiAkCrp9jQPbJArtjngoZ/LrUhC7/UBUCQtQICFVBHewDKaciiYioHeLUITWb3McTgd09Edhd7XBMsAowlFShPN9wPVhVojyvAuW5BpRdq4DZ6FhqorrSgsJL5Si8VG7XLvOUwr+bEv6hagSEqBAQqoJ/qArqzgxgRETUtjFokSgkUsn1chEKdO1jv15MEAQYiqtQlmtA6VU9yq5V1Px1tQK6wkrgpkFSS7UVRVk6FGXp7No9vGTwD1EhMEyFgDA1AsLUCAxTQ+En5xQkERG1CQxa1OokkroQdvOifbPJgvJcA0pydCjJ0aP0ih4lOXqU5xkgWO0TmNloQeFvZSj8rcyu3VvtaRe8AsNU8A9VQ67gP+5ERNS6+F8ealM85DIEdlc7TEdaqi0ovVaB0pya4FWSo0dJtg7l+QaHEbAqXTWupRXjWlqxXbuqkwKBtgBWMwrm31UJKd+CJCIikTBoUbsg85RB090Xmu72C/Orq8wovaJHcbYOJdm1f9ehsszkcA/99bcmL/+cb2uTyiTw61Y3/VgbxFRB3px+JCKiFmPQonbN09sDnSL80SnC3669stxoF7xqg9jNi/CtFgEl188BrtXdVyFDQGhd8ArsrkJAqBreankrfCoiInIXLQ5aTz/9NHx963/9nxomZsFSAhS+XlBEe9mtAasttHrz6FfptQoIFvv5x+pKC/LPlyL/fKldu4+/1/XgpUZAmAqBYWr4h6jgIZe1xsciIqJ2hnsdulhbLVjakVjMVpRdrXAY/dIXVjbqeokE8O2itAWv2ilI1v8iIiJOHVKHJ/OQ1rsA32SoRknO9ZGvyzoUX1+Af3NFfEGArURF5tG8uvvKpTV1v25YgB8YpoaCFfCJiDoMpwcti8WCgoICFBYWwmw213tOTEyMsx9L5HRyH08E3xGA4DsCbG2CIMBQarQLXsWXdSi9oneohG8xWestwOql8rQrPVEbxFh+gojI/Tjt3+xWqxXr1q3DV199BZ1Od8tz9+7d66zHErUqiUQCZYA3lAHeCO3XydZutQrQ5RlQfD14leTUTD+W51bg5sl5o74auenFyE2/qfxEkMI2/RjYXY2AUDX8uim5CTcRUTvmtKD18ccfY/369QgICMCYMWOg0Wggk3GBMHUMUqnEtjl2r8FdbO1mkwWltdOPOfqaEJatg6HU6HAPfWHNZt3ZJ+r2gKwpP6G0rf0KCFUhsLsaqiAFpx+JiNoBpwWtHTt2ICwsDKtXr4aPj4+zbkvUrnnIZQgK90NQuJ9de5XOhJIcHYov6+sW4OfoUF1ZX/kJPUqy9aiv/ERA6PXSE9enIll+goiobXFa0KqsrMSIESMYsogawVstR9c+Grt9IAVBgL6w6oY3H3Uoztaj7Koe1iaVn1DdsAbsevkJL44uExG5gtOCVnh4OIqKipx1O6IORyKRQN1JAXUnBboP6Gxrt5itKLtWYQtetUFMX+BYfsJQaoSh1Igrp274LkoA32Afh+2HfLsoWX6CiEhkTgtaf/jDH/DKK68gIyOD9aAagQVLqbFkHlLb6FTEDe215SdsJSiuL8S/ufwEBKA814DyXAMyf7qh/ISnFP4htbW/rv+9uxo+LD9BROQ0Ti1YumfPHrz//vt48MEHERERAaVSWe95o0ePdtYj2z0WLCVnEgQBlWWmutpf1xfhl+ToYDFZb38DXC8/EVoXvGq2IlJB7uMpcu+JiNyP00a0TCYTDh8+jLKyMmzbtg0AHP5ULAgCJBIJgxaRSCQSCXz8veDj74XQu4Ns7TeWn7ix+n2D5SfOliD3bIlduyrIu2YB/g3Tj/7dlJB5cv0XEVFDnBa0PvzwQ+zatQsRERF4+OGHWd6BqA25ZfmJK3pb8KoNYYaS+spPVEFfWIXsk3XlJyQyCfy6KO2nH8PUUHdSQML1X0REzgtae/fuRVRUFD766CN4eLDCNVF74CGXIaiXH4J63VR+Qm+yC161Qay60n63B8EioPSKHqVX9MCRG+7rJUNA6A1vP3avqQGm8PNqjY9FRNRmOHXqsH///gxZRG7AWyVH1z6B6Non0NYmCAIqiqrsSk+UZNdsP3Rz+Qmz0YKCi2UouFhmf19feV3wClNdrwWmgqc3/71BRO7Jaf92i4qKQk5OjrNuR0RtjEQigSpIAVWQAt3715WfsJqtKMutsCs9UXJZB1095Seqyk24eqYIV8/cVH6is49d5fuAMDX8uvhAKuP2Q0TUvjktaCUnJ2Pu3Lk4fPgwHnjgAWfdlojaOKmH1FalHkO62tpNlea69V+X64qwVunqKT+RZ0B5ngFZx24qP9FNadt0u3YTbp9Ab5afIKJ2w2lB69ixY4iJicGSJUswYMCABss7SCQSTJkyxVmPJaI2Sq7wQOdIf3SO9LdrN5QZ7YJXSbYeJVf0MBvttx+yVFtRlKVDUZb9JvVyHw/bpts3LsD3UrL8BBG1PU6ro/Xwww837oESCfbu3euMR7oF1tEiAgSrgPJ8g13pieJsHcqvOZafaIgy0Ltm5Ku76nrtLzX8Q1h+gohcy2kjWu+//76zbkVEHYxEWlMmwq+LEj0H2ZefKLtaccObj9fLTxQ7lp+oKK5CRXEVcn4psL9vVx9b8KoNYupOPiw/QUStwmlBKyYmxlm3IiICUFN+QtPTF5qevnbtRn01SnKul564fL36fbYOJsNN5SesAkqvVKD0SgUu/Te37r5eMgSEqBDQva74akCYGj4sP0FETsZ3ql2Eex0SNZ+XyhNd7gxElztvKj9RXFWz/iun7g3I0it6WM31lJ/4rQwFv9VffiIgrK4GGMtPEFFLOG2N1qlTp7Bv3z5MmjQJGo3G4XhhYSHWr1+PuLg4REdHO+ORboFrtIjEVVt+oiRHXzP6dT2A6fIdy080RN1ZURe8rr/96NdFCakHy08Q0a057Y9pGzZswMWLF/HCCy/UezwoKAiHDx9GQUEBXnvtNWc9lojolm4sPxF+f135ieoqs23K8cYF+FXlJod76PIrocuvxOXj+TfcVwL/biq7vR8Dw9RQalh+gojqOC1onT17FgMHDrzlOf369cOxY8ec9Ugiombz9K6//ERlmdGh+n1JjmP5CatZQPHlmjViF29ol/t4OJSeCAxTw0vF8hNEHZHTglZpaSmCgoJueU5gYCBKSkqc9UgiIqdT+HkhxM8LIXfV/ftMsArQFRhso161QazsmgGC1X71hclgRt65EuSds/93nU+gFwJD64JXQHcV/Lup4CFn+Qkid+a0oKVSqZCfn3/Lc/Ly8qBQKJz1SCKiViGRSuAbrIRvsBI97g22tVuqLSi9Xn6iJFuH4st6lOToUFFU5XAPQ7ERhmIjcn4trLuvBPDtorQFr9oRMHVnH0hZfoLILTgtaPXt2xf79+9HUlISgoODHY7n5eXhwIEDGDBggLMeSUTkUjJPGTQ9fKHpcVP5iYrqurVftevALtdTfkIAyq5VoOxaBS4drWv38JLBP0Rlt/YrIEwNhZ+c67+I2hmnBa2JEyfi8OHDmDlzJpKSknDvvfciKCgIhYWF+Omnn/DJJ5/AZDLhySefdNYjiYjaJC9l/eUnDCVG+70fc/QovaKHpdpqd73ZaEHhb2UovLn8hNrT4e3HgDA1y08QtWFOLVg6c+ZMfPTRR1i2bBmAmu12aqtHSCQSvPjiiyxsSkQdkkQigTLQG8pAb4T162Rrt1qsKM81OIx+lecbgJuK71TpqnEtrRjX0ort2tWdFAi4vv9jYJgKgd3VLD9B1EY49Y9BTzzxBAYMGIAtW7bg7Nmz0Ov1UKlU6NOnDx577DGEh4c783FERO2eVCaFf4gK/iEqu3az0YKSK/obRr9q3oKsLHXcfkhXUAldwU3lJ2QS+HVT2YJXQFhNEFMFsfwEUWty+nhzREQE5s2b5+zbEhF1KB5eMnQK90OncD+79qpy0w1vPtaWn9Chuuqm8hMWoeZYtg4XD1+ztXsqPOpKT4Re34Q7TA1vlbxVPhdRR8OJfSKidsTbV45u0Rp0i67bgUOwCtAXVtoVXi3J1qH0WgUEi/38Y3WlGfnnSpF/rtSu3SfA63rwul4DLFQN/1CWnyBqKQYtIqJ2TiKVQN3ZB+rOPugx8IbyE2Yrym4sP3E9iOkLHbcfMpQYYSgx4sopx/ITNxdfVQez/ARRYzFoERG5KZmHFIHda0apbmQyVNfs/Vj7BuT1RfhGfbXdeTeWn8g8mld3X7kUASE1U461bz8Ghqmh8Pfi+i+imzBoERF1MHIfTwTfEYDgOwJsbYIgwFBqtAtexZd19ZafsJisKLxUjsJL5XbtXirPmqnH0LrSEwFhasgV/E8NdVz8p5+IiGrKTwR4QxngjdAby09YBZTnVtSMgF2um4Isz3MsP2HU119+QhWksE0/1gYxv25KyFh+gjqAFgUtk8kEuZxvqjSHVquFVquFXq93dVeIiBoklUrg361mX8Zeg7vY2s1GC0qv2O/9WJKth6Ge8hP6wkroCyuRfaKg7r4yCfy6KW1vPwZcL0OhClJw+pHcikSorSjaDAkJCYiPj8fYsWMRFRXlzH51GBkZGUhOTsaaNWv4OySidq+q3ITinLrgVVsDrLrScvuLAXgqZAgIub72q3vdFkTeav6hntqnFo9obd68GVu2bEFERAQSEhIwYsQIqNXq219MRERux9tXjm59NejW94byE0JN+YkbS08UZ+tQerW+8hMW5F8oRf6FUrt2H38vBNyw92NgGMtPUPvQohEtg8GAXbt2ITU1FWfPnoVEIoGnpyeGDh2KhIQEDBw40Jl9dUsc0SKijspitqLsWoVt4f2tyk/URyIB1ME+Dns/+nZRsvwEtRktClo3yszMxLZt27Br1y6UlJRAIpGgc+fOSEhIwOjRoxEcHHz7m3RADFpERPZqy0/UjHzVjYLdXH6iITLPmm2NagJY3SiYTwDLT1Drc1rQqmWxWHD48GFs27YNR48ehcVigVQqxb333ouEhAQ89NBD8PDgy461GLSIiG5PEARUlhodqt+X5DiWn2iIl9LTLnjVjoLJfTxF7j11ZE4PWjcqLi7Gjh07kJqaisuXL0MikcDX1xdbt24V65HtDoMWEVHzWa0CdHkGh+r35bkVaOx/3ZQab4fpR/9uSsg8uf6LWk7UoaXAwEBMmjQJgwcPxooVK3Dq1CmUl5ff/kIiIqJGkEol8OuqhF9XpX35CVNd+YnazbeLs3UwlDiWn6goqkJFURWyT9aVn5DIJPDrcr38RPeavR8DwtRQd1JAwvVf1ASiBa3ahfLbtm3DuXPnIAgCvL29ERcXJ9YjiYiIAAAechmCevkhqJefXXuV3mRXeqI2iFVXmu3OEywCSq/oUXpFDxy54b5esprpx1B1XQHWMBUUvl6t8bGoHXJ60Pr555+RmpqKAwcOwGg0QhAE9O3bFwkJCXjkkUfg4+Pj7EcSERE1irdKjq59NOjax778REVRVd304+WaRfhlV/Ww3lR+wmy0oOBCGQoulNm1K/zkN6z9qlmI7x+igqc31yR3dE75JyA/Px/ff/89vv/+e+Tm5kIQBPj7+yMxMREJCQno2bOnMx5DRETkdBKJBKogBVRBCnTv39nWbjVbUZZbYQteJTk1m3DrChzLT1SWmVBZVoSrp4tuuDHg29nHbu1XQJgafl18IJVx+6GOokVBa/fu3UhNTcXPP/8Mq9UKqVSKQYMG8e1CIiJq96Qe0prtgULViLih3VRpRmnO9c23L9dtQVSlu6n8hACU5xlQnmdA1rE8W7PMUwr/bkpb8Aq8HsR8Ar1ZfsINtSgJ/fWvfwUAdO3aFWPGjMGYMWPQuXPn21xFRETUfskVHujcOwCdewfY2gRBQGW5yS54FWfXrPEyG+23H7JUW1GUpUNRls7+vj4eDtOPAWFqeClZfqI9a1HQGj58OCvAExFRhyeRSODj5wWfu70QcneQrV2wCijPN9iVnijO1qH8mmP5CZPBjLyMEuRllNi1KwO97fd+DFXDP4TlJ9qLFgWtV155xVn9ICIicjsSaU2ZCL8uSvQcdFP5iat6++Kr2XpUFFc53KOiuAoVxVXI+aXA/r5dfepGwEJrgpi6kw/LT7QxTl1EZTabsWnTJmi1Wly+fBlGoxE//PADAOD8+fP49ttv8cQTTyAsLMyZjyUiImpXPOQyBPX0Q1BP+/ITRn01SnJ0DhXwTYabyk9YBZReqUDplQpcOpJbd18vGQJCVXYL8APD1FD4sfyEqzgtaBmNRsyfPx+nT5+Gn58flEolqqrqknnXrl2RmpoKtVqN5ORkZz2WiIjIbXipPNHlzkB0uTPQ1iYIAiqKq2rWf+XUFV8tvaKH1VxP+YmLZSi4aF9+wttX7rD2KyCU5Sdag9N+w+vWrcOpU6fw3HPPYdKkSfjss8/w+eef246rVCrExMTgp59+YtAiIiJqJIlEApVGAZVGgbD6yk9cr3xfG8B0+Y7lJ6rKTbh6pghXzxTZtas7K+qC1/VRML+uSpafcCKnBa09e/agf//+ePrppwGg3ldUu3XrhvPnzzvrkURERB3WjeUnMKSrrb26yoySG0a+aqcgq8pNDvfQ5VdCl1+JrOP5N9xXAv+Q2ur310fAuquhZPmJZnFa0MrPz8fQoUNveY5CoUBFRYWzHklEREQ38fT2QOdIf3SO9Ldrrywz2pWeKLmsQ0k95SesZgHFWToUZ+lw8YZ2uY9HTbALU9VsPXR9GyIvFctP3IrTgpZCoUBpaektz7l69Sr8/PxueQ4RERE5n8LPCyF+Xgi5y778hK7AUDPqlaNDyeWa0a+yaxUQrPbrv0wGM/LOlSDvnH35CZ9Ar7o3H69vwu3fTQUPOctPAE4MWtHR0Th8+DB0Oh3UarXD8by8PBw5cuS2o14dhVarhVarhV6vd3VXiIiog5JIJfANVsI3WIke9wbb2i3VFpReragb/bo+DVlR5Fh+wlBshKHYiJxfCu3v28UHgaE1wat2HZhv545XfsJpQeupp57CnDlzMHfuXMyePRsWS81QZFVVFc6cOYP33nsPFosFTz75pLMe2a7Fx8cjPj4eGRkZfDmAiIjaFJmnDJoevtD08LVrNxmq7YJXbf0vY4X99kOCVUDZ1QqUXa3ApaN17R5eMgSEqBBwQ+mJgO5q+Lhx+QmJINxcm7b5Nm/ejA8++ABWq9XhmFQqxbx58zBu3DhnPc4t1AatNWvWICoqytXdISIiahJBEGAoub7+67IOJTl6W/kJS7VjHqiPt68cAaF1C+8D3aj8hFM/wfjx4xETE4MtW7YgPT0d5eXlUCqV6NOnD/7nf/4HvXr1cubjiIiIyMUkEgmUgd5QBnojrF8nW7vVYkV5rqEmgF0PXyWXdSjPNwA3DfFUlZtwLa0Y19KK7drVnRR2wSuwuxp+XZSQerSf8hNOj4o9e/bE7NmznX1bIiIiakekMin8Q1TwD1HZtVdXmWvWf13fgLs2iFWWGh3uoSuohK6gEpdvLj/RTWULXrVTkEpN2yw/0epjchaLBTIZ30QgIiLqiDy9PdAp3A+dwu2rEFSVm1Cco0Px5bq1XyU5OlRX1VN+4nLNeRcPX6u7r8LDVvm+tgp+QJga3ip5q3yuhjgtaG3atAm/+93vbnmOxWLBa6+9hr/+9a/OeiwRERG5AW9fObr11aBbX42tTbAK0BdW2gqvllwPYqXXKiBY7OcfqyvNyD9XivxzpXbtPgFe6N6/Mx5Kuqs1PoYDpwWtDz74AIGBgRg2bFi9x61WK1577TXs37/fWY8kIiIiNyaRSqDu7AN1Zx/0GHhD+QmzFaVX9TXh64YK+PpCx+2HDCVGGPXVDu2txWlB6+6778bSpUvh6+uLAQMG2B2rDVn79u277agXERER0a3IPKTQdPeFprtj+Qnbwvtsva0ERUCYqoE7ic9pQWvZsmV44YUX8PLLL+P9999H7969AdSErNdffx179+7F+PHjuVCeiIiIRCH38UTwHQEIviPA1iYIgsM0Y2ty2vuRSqUS77zzDlQqFf70pz/h6tWrEAQBf/3rX7Fnzx489thjmDt3rrMeR0RERHRbEonEpeUgnPpkjUaDd999F1arFfPnz8err76KH374AePGjcO8efOc+SgiIiKiNs/pES8sLAxvv/02SkpKsH//fowbNw5/+tOfnP0YIiIiojav2Wu0UlJSbnm8T58+uHDhAjQajd25EokEU6ZMae5jiYiIiNqNZgetzz77rFHn/fOf/7T7mUGLiIiIOopmB63333/fmf0gIiIicjvNDloxMTFO7AYRERGR+2k/218TERERtTPNDlovvfQS0tPTm3VtZWUlvvjiC2zatKm5jyciIiJq85o9dVhaWorp06ejX79+GDVqFGJjY6FS3brE/ZkzZ7Bz507s2bMHRqMRS5Ysae7jiYiIiNo8iSAIza5L//333yMlJQW5ubmQSqUICwtDVFQUAgICoFKpYDKZUF5ejuzsbGRkZMBgMEAqlWL48OFISkpCcHDw7R/i5jIyMpCcnIw1a9YgKirK1d0hIiIiJ2rRXodjxozB6NGjceTIEaSmpuLkyZPYuXOnw3lSqRTh4eGIjY1FQkICgoKCWvJYIiIionahxZtKSyQSDBkyBEOGDAEAZGZmoqCgAOXl5ZDL5fD390evXr1uO61IRERE5G5aHLRu1rNnT/Ts2dPZtyUiIiJqd1jegYiIiEgkDFpEREREImHQIiIiIhIJgxYRERGRSBi0Wqi0tBQLFizAyJEjMXnyZBw/ftzVXSIiIqI2gkGrhVasWIHAwEBs3boV06dPx6uvvory8nJXd4uIiIjaAAatFjAYDDhw4ACeeeYZeHt746GHHkJ4eDgOHjzo6q4RERFRG+D0Olo3GzZsGPbu3Sv2YxrFYDBg/fr1SEtLQ3p6OnQ6HRYvXowxY8Y4nGsymbB27Vrs3LkTOp0OERERSEpKwqBBg2zn5OTkQKFQoHPnzra28PBwXLp0qVU+DxEREbVtoo9otWArRacrKytDSkoKsrKyEBkZectz33zzTWzcuBEjRozArFmzIJVKsWDBAvz666+2cyorK6FUKu2uUyqVqKysFKX/RERE1L40KWjNnz8fH3/8MX744Qfk5OQ06hqJRGL731988QWqq6ub1kMn0mg0+Oabb/Dll19i+vTpDZ6XlpaG3bt3Y9q0aZgxYwYSExPx3nvvoUuXLli1apXtPIVCgYqKCrtrKyoqoFAoRPsMRERE1H40aeowJCQEv/zyCzZt2gSj0QgfHx9ERETgjjvuwB133IHevXujZ8+ekErr8tuNI1qffPIJEhISEBAQAAD417/+hd/97netFkzkcjk0Gs1tz9u3bx9kMhkSExNtbV5eXkhISMDq1auRl5eH4OBghIaGorKyEgUFBejUqRMA4NKlSxg1apRon4GIiIjajyYFrXnz5gGoCU+XL19GRkYGLly4gPPnz2PHjh3Q6/WQy+XYuXNnvdffPI24bt06xMXF2YJWSUkJnnvuOWzcuLE5n8Vpzp8/j9DQUIdpwT59+gAALly4gODgYPj4+OChhx7Cp59+ijlz5uD48eO4ePEiHnroIVd0m4iIiNqYZi2Gl0gk6NGjB3r06IGRI0fi/Pnz2L9/P7Zu3dqk0gY3By9BEJCfn9+cLjlVUVFRvSNftW2FhYW2tnnz5uGNN97AuHHj0KlTJ/zlL3+Br69vg/cuLCxEUVGR7eesrCwn9pyIiIjakma/dXj69Gns378fBw4cQEFBAQYMGICkpCS3GM0xGo3w9PR0aJfL5bbjtfz9/bF8+fJG33vr1q1ISUlpcR+JiIio7WtS0Dp+/Dj27duHAwcOoLKyEvfddx+SkpIwZMgQ+Pj4NOoehw8fRv/+/dGtW7dmdbg1eHl51bto32Qy2Y43V2JiIh588EHbz1lZWVi6dGmz70dERERtV5PXaAUFBeH3v/89Hn300XpHfW5241uHvXv3xooVK2A2m+Hj4wOTyYR//etfuPvuu9G7d2+oVKqmfwIRaDQaFBQUOLTXTvkFBQU1+95BQUEtup6IiIjajyYFrd69eyMzMxMffPABPvvsM/Tu3Ru9e/e2vXUYFhZ2y+s/+eQTmM1mZGZm4ty5czh//jzOnTsHrVYLo9FoF8pcKTIyEidOnEBFRYXdgvi0tDTbcSIiIqLbaVLQqi8onT59Gps3b4bRaIRCoUBkZCRWrlzZ8AM9PBAZGWkXVgRBQHZ2tu0tRlcbNmwY1q9fj61bt2LSpEkAaqYNU1NT0bdvXwQHB7u4h0RERNQeNHkxfHOD0vLlyxEREYHIyEhERETYjRRJJBJ0794d3bt3x4gRI5rxMRrv66+/hl6vt00DHjp0yPam44QJE6BSqdC3b1/ExcVh9erVKC0tRUhICLZv347c3FwsXLhQ1P4RERGR+2h00GppUBIEATt27MDHH38Mo9GI4OBg271qg1trLJDfsGEDcnNzbT/v378f+/fvBwCMHDnStk5syZIlCA4OttUHCw8Px1tvvYWYmBin9EOr1UKr1UKv1zvlfkRERNT2SIRGbka4fPlyXLhwAZmZmU0KSjdvKi0IAk6cOIFXX30VYWFhkMlkuHTpEvR6PRQKBXr16oWPPvrIaR+wrcvIyEBycjLWrFmDqKgoV3eHiIiInKjRI1p/+tOfANgHpZKSEpw4cQKbNm1qdFCSSCT47LPPMGPGDIwZMwYAYDabsX37dvzjH/9g2CAiIiK30eQ1Ws4ISunp6ViyZEldJzw8MG7cOHh5eWHHjh1N7RIRERFRmyS9/SmO0tPT7dYq1Qal2bNnIzs7+7bXR0ZG2k0n1oqOjsapU6ea0yUiIiKiNqdZQaulQWnmzJlISUnBBx98gKtXrwIArFYrtmzZ0maKlhIRERG1VLP2Opw5cyZeeuklFBQU4PHHH0e3bt2aFJTuvvtufPTRR1ixYgUmTZoEpVIJs9kMs9mMl156qTldanf41iEREZH7a/Rbhze7ePEiVqxYgVOnTjkEpbFjx9rOu/mtw5tdvXoV58+fh0QiQVRUVIcrBsq3DomIiNxXs0a0ACAiIgIffvjhbYPS7XJct27d2vQG00RERETN1eygVet2QWnfvn0tfQQRERFRu9TkxfBWqxW//fYbCgsLHY6ZzWacPHnSGf0iIiIiaveaNKKVm5uLBQsWICsrCxKJBPfffz8WL14MPz8/AEB5eTnmzJlzyzVZRERERB1Fk0a0Vq1ahaCgIKxfvx5r1qyB0WjEzJkz7Ua3mrm2noiIiMjtNClo/fLLL5gxYwa6du2K3r17491338U999yDF154AXl5eQBqKscTERERURODVlVVFTw9PesulkqxYMECDBo0CC+++CKuXLni9A66K61Wi0WLFmHlypWu7goRERGJpElBq3v37sjIyHBonz9/Pu6//34sWrTIaR1zd/Hx8Vi2bBlefPFFV3eFiIiIRNKkoBUbG4tdu3bVe2zevHkYPnw412gRERERXdfsyvDkHKwMT0RE5L6atal0rUuXLnEEi4iIiKgBLQpaf/zjH/H55587qy9EREREbqVFQUsQBIcRra1bt+L1119vUaeIiIiI3EGLglZ9ioqKsHv37nqPffHFF3juueec/UgiIiKiNqnFm0o3RXV1db3lIToirVYLrVYLvV7v6q4QERGRSFo1aFGd+Ph4xMfH2946JCIiIvfj9KlDIiIiIqrBoEVEREQkkhZPHX7zzTc4f/487rzzTkRFRXHNEREREdF1LQpavXv3RmZmJg4ePIiDBw9CIpHYjr366quIiIhAREQEIiMjERwc3OLOEhEREbUnLQpan3zyCcxmMy5evIjz588jIyMD586dw8WLF7F3717s3bvXFr58fHzg7e3tlE4TERERtQctnjr08PBAVFQUoqKiMG7cOACAxWJBZmYmMjIy7MJXUVGR3agXERERkTsTpbyDTCazTRuOHTsWAGC1WpGVlcU6WkRERNRhtFodLalUil69eqFXr16t9UgiIiIil2LBUhdhZXgiIiL3x6DlIqwMT0RE5P5YsJSIiIhIJAxaRERERCJh0CIiIiISCYMWERERkUgYtIiIiIhEwqBFREREJBIGLSIiIiKRMGgRERERiYRBi4iIiEgkrAzvItyCh4iIyP0xaLkIt+AhIiJyf5w6JCIiIhIJgxYRERGRSBi0iIiIiETCoEVEREQkEgYtIiIiIpEwaBERERGJhEGLiIiISCQMWkREREQiYdAiIiIiEgmDFhEREZFIGLSIiIiIRMKgRURERCQSBi0iIiIikTBoEREREYnEw9Ud6Ki0Wi20Wi30er2ru0JEREQiYdBykfj4eMTHxyMjIwPJycmu7g4RERGJgFOHRERERCJh0CIiIiISCYMWERERkUgYtIiIiIhEwqBFREREJBIGLSIiIiKRMGgRERERiYRBi4iIiEgkDFpEREREImHQIiIiIhIJgxYRERGRSBi0iIiIiETCoEVEREQkEgYtIiIiIpEwaBERERGJhEGLiIiISCQMWkREREQiYdAiIiIiEgmDFhEREZFIGLSIiIiIROLh6g50VFqtFlqtFnq93tVdISIiIpEwaLlIfHw84uPjkZGRgeTkZFd3h4iIiETAqUMiIiIikTBoEREREYmEQYuIiIhIJAxaRERERCJh0CIiIiISCYMWERERkUgYtIiIiIhEwqBFREREJBIGLSIiIiKRMGgRERERiYRBi4iIiEgkDFpEREREImHQIiIiIhIJgxYRERGRSBi0iIiIiETCoEVEREQkEgYtIiIiIpEwaBERERGJhEGLiIiISCQMWkREREQiYdAiIiIiEgmDFhEREZFIGLSIiIiIRMKgRURERCQSBi0iIiIikTBoEREREYmEQcsJNm/ejGeffRZxcXH49NNPXd0dIiIiaiMYtJxAo9Hgj3/8Ix5++GFXd4WIiIjaEA9Xd8AdDB06FABw5MgRF/eEiIiI2hK3CVoGgwHr169HWloa0tPTodPpsHjxYowZM8bhXJPJhLVr12Lnzp3Q6XSIiIhAUlISBg0a5IKeExERkbtym6nDsrIypKSkICsrC5GRkbc8980338TGjRsxYsQIzJo1C1KpFAsWLMCvv/7aSr0lIiKijsBtRrQ0Gg2++eYbaDQanD17FtOmTav3vLS0NOzevRvTp0/HpEmTAACjRo3C1KlTsWrVKqxatcp27syZM3Hq1Kl67/P73/8eycnJzv8gRERE5DbcJmjJ5XJoNJrbnrdv3z7IZDIkJiba2ry8vJCQkIDVq1cjLy8PwcHBAIC///3vovWXiIiI3J/bTB021vnz5xEaGgqlUmnX3qdPHwDAhQsXmnxPs9kMo9EIq9UKi8UCo9EIi8XilP4SERFR++U2I1qNVVRUVO/IV21bYWFhk+/5+eefIyUlxfbzunXrGlyIX1hYiKKiItvPtcEuKyuryc8lIiIi1+rRowe8vb0bPN7hgpbRaISnp6dDu1wutx1vqmeeeQbPPPNMo87dunWrXSirtXTp0iY/l4iIiFxrzZo1iIqKavB4hwtaXl5eqK6udmg3mUy242JKTEzEgw8+aPtZp9MhKysLu3fvxuzZsxt1j5UrV+LFF1+85TlZWVlYunQpXn75ZfTo0aNFfXYXjfm9uVJr90+s5znrvi25T3Oubco1jT2X30NHbfl76Iq+ifHMjvAdbOz5rfEdvN19O1zQ0mg0KCgocGivnc4LCgoS9flBQUEOz7j33nvx008/3TIR30ilUjX63B49ejT6XHfXlN+bK7R2/8R6nrPu25L7NOfaplzT1Pvze1inLX8PXdE3MZ7ZEb6DTT3fld/BDrcYPjIyEjk5OaioqLBrT0tLsx13hfj4eFHOpTpt/ffW2v0T63nOum9L7tOca/kdbB1t+Xfnir6J8cyO8B1s7jNcocMFrWHDhsFisWDr1q22NpPJhNTUVPTt29dW2qG18V/y4mvrvzcGLefdh0Gr7WrLvzsGLefdh0GrjltNHX799dfQ6/W2acBDhw4hPz8fADBhwgSoVCr07dsXcXFxWL16NUpLSxESEoLt27cjNzcXCxcudGX3nUqj0WDq1KmNqi1GROLg95DItdrCd1AiCILgsqc72cSJE5Gbm1vvsQ0bNqBr164Aat4srN3rUK/XIzw8HElJSRg8eHBrdpeIiIjcnFsFLSIiIqK2pMOt0aI6JpMJy5Ytw+OPP47Ro0fj+eefx+nTp13dLaIOZfny5Rg/fjxGjx6NKVOm4NChQ67uElGHdPr0aTz88MP45z//6dT7ckSrA6usrMSGDRswZswYdOrUCT/88APee+89bNiwAT4+Pq7uHlGHkJWVha5du0IulyM9PR3z5s3D+vXr4efn5+quEXUYVqsVM2bMgCAIeOCBBzBlyhSn3ZsjWh2YQqHA1KlTERwcDKlUiuHDh8PDwwPZ2dmu7hpRh9GjRw/bzhQSiQTV1dXN2gqMiJrv22+/RZ8+fUQpaupWbx26O4PBgPXr1yMtLQ3p6enQ6XQN7qloMplsC/51Oh0iIiKQlJSEQYMGNXj/7Oxs6HQ6hISEiPkxiNotsb6Df/vb35CamgqTyYT7778f4eHhrfFxiNodMb6DZWVl+PLLL7Fq1SqsXLnS6X3miFY7UlZWhpSUFGRlZd22sOqbb76JjRs3YsSIEZg1axakUikWLFiAX3/9td7zjUYjli5dismTJ0OlUonRfaJ2T6zv4Lx587Bjxw6sWLECgwYNgkQiEesjELVrYnwH16xZgyeeeAJqtVqcTgvUbhiNRqGwsFAQBEFIT08Xhg4dKqSmpjqcd+bMGWHo0KHCv//9b1tbVVWV8NRTTwnPP/+8w/nV1dXCggULhNdee02wWq3ifQCidk6s7+CNFi5cKBw+fNi5HSdyE87+DmZkZAjPPvusYDabBUEQhDfeeENISUlxap85otWOyOXyRhVd27dvH2QyGRITE21tXl5eSEhIwJkzZ5CXl2drt1qtWLp0KSQSCZYsWcI/SRPdghjfwZtZLBZcuXLFKf0lcjfO/g6ePHkS2dnZmDBhAsaPH489e/bg3//+N958802n9ZlrtNzQ+fPnERoaCqVSadfep08fAMCFCxdsWw298847KCoqwjvvvAMPD/7jQOQMjf0O6vV6/Pjjj3jwwQchl8tx4MABnDhxAtOmTXNFt4ncRmO/g4mJiRg+fLjt+AcffICuXbti8uTJTusL/8vqhoqKiupN/LVttW805ebm4rvvvoNcLrdL/W+//Tb69evXOp0lckON/Q5KJBJ89913WLFiBQRBQEhICP785z+jd+/erdpfInfT2O+gt7c3vL29bce9vLygUCicul6LQcsNGY1GeHp6OrTXvkJuNBoBAF26dMH+/ftbtW9EHUFjv4NKpRLvv/9+q/aNqCNo7HfwZkuWLHF6X7hGyw15eXmhurraod1kMtmOE5F4+B0kcq229B1k0HJDGo0GRUVFDu21bUFBQa3dJaIOhd9BItdqS99BBi03FBkZiZycHFRUVNi1p6Wl2Y4TkXj4HSRyrbb0HWTQckPDhg2DxWLB1q1bbW0mkwmpqano27ev7Y1DIhIHv4NErtWWvoNcDN/OfP3119Dr9bbhz0OHDiE/Px8AMGHCBKhUKvTt2xdxcXFYvXo1SktLERISgu3btyM3NxcLFy50ZfeJ2j1+B4lcq719ByWCIAit+kRqkYkTJyI3N7feYxs2bEDXrl0B1LxRUbvHk16vR3h4OJKSkjB48ODW7C6R2+F3kMi12tt3kEGLiIiISCRco0VEREQkEgYtIiIiIpEwaBERERGJhEGLiIiISCQMWkREREQiYdAiIiIiEgmDFhEREZFIGLSIiIiIRMKgRURERCQSBi0iIiIikTBoERG1URs3bsQjjzyCa9eu2dq+//57xMbG4vvvv3dhz+p89913GDZsGC5evOjqrhC1SQxaRNQqrl27htjY2Fv+NXHiRFd3s83Q6XT4/PPPMXbsWNsmuWI5evQoYmNjMX/+/Nue+9e//hWxsbHYtWsXAGD06NEIDg7GqlWrRO0jUXvl4eoOEFHHEhISghEjRtR7TKVStXJv2q6NGzeivLwckyZNEv1Z9957L4KDg3H8+HHk5eUhODi43vP0ej0OHDgAlUqF2NhYAICHhwcmTpyI999/H6dOncLdd98ten+J2hMGLSJqVSEhIXjmmWdc3Y02zWw247vvvsPdd9+NkJAQ0Z8nlUoxZswYpKSkYPv27ZgyZUq952m1WhiNRowdOxZeXl629uHDh+PDDz/Eli1bGLSIbsKpQyJqs2JjYzFr1iwUFxfjjTfewKOPPor4+Hg8//zzOHHiRL3XGAwGfPrpp/jDH/6A+Ph4jB07FvPnz8evv/7qcO6sWbMQGxsLo9GINWvW4KmnnkJcXBw+/fRT2zn79u1DcnIy4uPj8dhjj+Htt9+GTqfDxIkT7aY6X3/9dcTGxiItLa3efq1duxaxsbHQarW3/dxHjx5FUVERhg0bdttza+Xn52PKlCmIj4/H3r17be0lJSVYuXIlJk2ahOHDh+PRRx/Fyy+/jN9++83u+rFjx0IikeD777+HIAj1PiM1NRUAkJCQYNfu7++P/v37Y+/evTAYDI3uM1FHwKBFRG2aXq/HzJkzkZmZiZEjRyI2NhYZGRl46aWXHMJCeXk5pk+fjpSUFKjVajz22GOIjY3FuXPnMHv2bBw4cKDeZ/z5z3/G9u3b0b9/fzz++OO2NVHbtm3Dn//8Z+Tk5GDUqFEYPXo0zpw5g3nz5sFsNtvdIzEx0XbNzSwWC1JTU+Hn52ebcruV48ePAwCio6Nv/wsCkJmZiRkzZiA/Px/Lly+3BbQrV64gKSkJX375Jbp164bf/e53uP/++3H06FFMnz7dLhR26dIFAwcOxNWrV+sNsb/99hvOnj2L3r1744477nA4Hh0dDZPJhNOnTzeqz0QdBacOiahVXblyxW7E6EbR0dG477777NouXLiA8ePHY86cOZBKa/5sOGDAALz99tvYtGkTXnrpJdu57733Hi5duoQFCxZg3LhxtvaSkhIkJydj+fLlGDx4sN20FwAUFRXhs88+g6+vr61Np9Phgw8+gEKhwOrVqxEWFgYASE5OxksvvYSMjAx06dLFdn6/fv3Qs2dP7N69Gy+88AIUCoXt2NGjR1FQUIAnnngCcrn8tr+jU6dOQSqVIjIy8rbnnjlzBgsXLoSHhwdWrlxpd80bb7yB4uJivPPOOxg8eLCt/Q9/+AOSk5Px9ttvIyUlxdaekJCAY8eOITU1FQMGDLB7TkOjWbWioqIAAKdPn7Z7FlFHxxEtImpVV65cQUpKSr1//fe//3U4X6FQ4Pnnn7eFLKDmTTeZTIazZ8/a2kpLS/HDDz9gwIABdiELAAICAjBp0iSUlpbaRotu9Mc//tEuZAHAwYMHUVlZibFjx9pCFlCz+DspKanez5aYmAiDwYDdu3fbtX/33XcAgEcffbShX4udgoICqFSq24ayH3/8EXPnzoVarcZHH31kF7LOnTuH06dPY9SoUQ7BJywsDOPGjcNvv/1mNyo4dOhQ+Pn5Yd++faioqLC1m81m7Ny5E3K5vMEXGQIDAwHUTGESUR2OaBFRqxo8eDDeeeedRp8fGhoKHx8fuzYPDw8EBgZCr9fb2s6ePQuLxYLq6up6R8xycnIAAFlZWXjggQfsjvXp08fh/Nq6UPfcc4/Dsb59+0Imkzm0jxo1Ch9//DG+++47W9grLi7G4cOHcdddd6Fnz563+bQ1ysvL0alTp1ue88MPP+Cnn35CREQEli9fjoCAALvjtdOCJSUl9f4+Ll++bPt7eHg4ANiC1FdffQWtVovHHnsMAHDo0CGUlpYiPj4earW63v7UtpeVlTXqMxJ1FAxaRNSmKZXKettlMhmsVqvt5/LycgA1026nTp1q8H5VVVUObbWjMTeqHdG5OcAANW/p+fn5ObSr1WrExcVh+/bt+O233xAeHo7vv/8eFoul0aNZAODl5QWTyXTLc86cOQOLxYJ77rmn3j7W/j5+/PFH/Pjjjw3ep7Ky0u7nhIQEfPXVV0hNTbUFrdtNGwKw9dfb2/uW/SbqaBi0iMgt1AayJ598EjNnzmzStRKJpMH7lZSUOByzWq0oKyurd9Tpsccew/bt2/Htt99i9uzZ2LZtG5RKJeLi4hrdHz8/PxQUFNzynGnTpuHgwYP46quvIJPJHD5zbf9nz56NCRMmNPrZERERuPPOO5Geno5Lly5BrVbj6NGj6Nq1q8O6rRvVBjt/f/9GP4uoI+AaLSJyC3feeSckEgnOnDnjlPtFREQAQL2jY+np6bBYLPVeFx0djYiICOzatQtHjx5FTk4ORowY0aSRnvDwcJhMJuTl5TV4jlwuxxtvvIEhQ4Zgw4YN+PDDD+2O106HNuf3UTtytW3bNuzYsQMWi8VW/qEhtVORtdOQRFSDQYuI3IJGo0FcXBxOnz6N//znP/XWgkpLS6t36rA+Dz30EBQKBbZt24YrV67Y2s1mM9auXXvLaxMTE1FeXo5ly5YBgMPi/NuJiYmx9fdW5HI5li5digceeAAbN27EypUrbcf69u2Lvn37Yvfu3Q6L84GaUbmTJ0/We9/4+Hh4e3tj586dSE1NhVQqxejRo2/Zl/T0dLu+E1ENTh0SUau6VXkHAJg8ebJD+YXGmjdvHrKzs7Fq1Srs2LED0dHRUKlUKCgowNmzZ5GTk4NvvvmmUaNLarUaL7zwApYvX47k5GQ88sgjUCqVOHLkCORyOYKCghoc4Rk5ciT+8Y9/oLCwEFFRUfXWnbqVhx56CH//+99x7Nix2045enp64vXXX8crr7yCL7/8EoIgYNasWQCAV155BXPmzMFrr72Gr776Cr1794aXlxfy8/Nx+vRplJWV1VtAValU4uGHH8aOHTtQWlqK++67r8FteQBAEAQcP34cPXr0sHtDk4gYtIioldWWd2jIE0880eyg5evri48++gibNm3Cnj17oNVqYbVaERgYiMjISEyZMqXeRewNefTRR6FWq7Fu3Tps374dSqUSDz74IJ5//nk88cQTDW6Po1QqMXToUOzcubPJo1kA0LVrVwwaNAh79+7F7Nmzb1vmoTZsvfrqq/jqq68gCAJmz56Nbt26Ye3atdiwYQMOHDiA77//HlKpFBqNBv369btl5fmEhATs2LEDQE3V+Fv55ZdfkJeXhxdffLHJn5XI3UmEhvZaICKieuXk5ODpp59GXFwcXnvttXrPmTJlCnJzc7Fp06YG35y8lePHj2Pu3Ll4+eWXMXLkyJZ2WVSvv/46/vvf/+I///lPg+UfiDoqrtEiImqATqdzKLNgNBptC8+HDh1a73VHjhzBpUuXEB8f36yQBQADBw7Efffdh88//9yujEVbk52djT179uAPf/gDQxZRPTh1SETUgJMnT+Ktt97CoEGD0LlzZ5SVleHnn39Gbm4uBgwYgEceecTu/M2bNyM/Px/fffcd5HI5Jk+e3KLnz5o1C7t27UJBQcEt10i5Un5+PqZOnYr/+Z//cXVXiNokTh0SETUgOzsba9euxenTp1FaWgoACAkJwSOPPIKnnnrKYS3ZxIkTUVBQgLCwMDz//PMOFeiJqONh0CIiIiISCddoEREREYmEQYuIiIhIJAxaRERERCJh0CIiIiISCYMWERERkUgYtIiIiIhEwqBFREREJBIGLSIiIiKRMGgRERERieT/A0RWge0Qyam8AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Orientation file:\n",
+    "sc_orientation = SpacecraftHistory.open(ori_file)\n",
+    "\n",
+    "# Detector response:\n",
+    "dr = FullDetectorResponse.open(dr_file)\n",
+    "instrument_response = BinnedInstrumentResponse(dr)\n",
+    "\n",
+    "# Load Crab data:\n",
+    "crab = BinnedData(input_yaml)\n",
+    "crab.load_binned_data_from_hdf5(binned_data=crab_data)\n",
+    "\n",
+    "# Load BG model true:\n",
+    "bg = BinnedData(input_yaml)\n",
+    "bg.load_binned_data_from_hdf5(binned_data=background_model)\n",
+    "\n",
+    "# Load BG model estimated:\n",
+    "bg_est = BinnedData(input_yaml)\n",
+    "bg_est.load_binned_data_from_hdf5(binned_data=estimated_bg)\n",
+    "\n",
+    "# Load BG model estimated simple:\n",
+    "bg_est_simple = BinnedData(input_yaml)\n",
+    "bg_est_simple.load_binned_data_from_hdf5(binned_data=estimated_bg_simple)\n",
+    "\n",
+    "# Load total data:\n",
+    "total = BinnedData(input_yaml)\n",
+    "total.load_binned_data_from_hdf5(binned_data=total_data)\n",
+    "\n",
+    "# ------- Interfaces ----------\n",
+    "\n",
+    "# Total data:\n",
+    "data = EmCDSBinnedData(total.binned_data.project('Em', 'Phi', 'PsiChi'))\n",
+    "\n",
+    "# BG:\n",
+    "bkg_dist = {\"total_bkg\":bg_est_simple.binned_data.project('Em', 'Phi', 'PsiChi')}\n",
+    "#for bckfile in bkg_dist.keys():\n",
+    "#        bkg_dist[bckfile] += sys.float_info.min\n",
+    "        \n",
+    "bkg = FreeNormBinnedBackground(bkg_dist,\n",
+    "                                   sc_history=sc_orientation,\n",
+    "                                   copy = False)\n",
+    "\n",
+    "psr = BinnedThreeMLPointSourceResponse(data = data,\n",
+    "                                        instrument_response = instrument_response,\n",
+    "                                        sc_history=sc_orientation,\n",
+    "                                        energy_axis = dr.axes['Ei'],\n",
+    "                                        polarization_axis = dr.axes['Pol'] if 'Pol' in dr.axes.labels else None,\n",
+    "                                        nside = 2*data.axes['PsiChi'].nside)\n",
+    "\n",
+    "response = BinnedThreeMLModelFolding(data = data, point_source_response = psr)\n",
+    "\n",
+    "like_fun = PoissonLikelihood(data, response, bkg)\n",
+    "\n",
+    "cosi = ThreeMLPluginInterface('cosi',\n",
+    "                            like_fun,\n",
+    "                            response,\n",
+    "                            bkg)\n",
+    "\n",
+    "# Nuisance parameter guess, bounds, etc.\n",
+    "for bkg_label in bkg_dist.keys():\n",
+    "    cosi.bkg_parameter[bkg_label] = Parameter(bkg_label,  # background parameter\n",
+    "                                    1,  # initial value of parameter\n",
+    "                                    min_value=0,  # minimum value of parameter\n",
+    "                                    max_value= 100,  # maximum value of parameter\n",
+    "                                    delta=0.05,  # initial step used by fitting engine\n",
+    "                                    unit = u.Hz\n",
+    "                                          )\n",
+    "    \n",
+    "# Load the astromodel for the Crab (this is the same model as \n",
+    "# used in the DC2 Crab tutorial):\n",
+    "model = astromodels.load_model('./crab_DC2_astromodel.yaml')\n",
+    "\n",
+    "\n",
+    "# Define plugin and fit:\n",
+    "plugins = DataList(cosi) # If we had multiple instruments, we would do e.g. DataList(cosi, lat, hawc, ...)  \n",
+    "\n",
+    "like = JointLikelihood(model, plugins, verbose = False)\n",
+    "\n",
+    "like.fit()\n",
+    "\n",
+    "# Get expectation\n",
+    "\n",
+    "results = like.results\n",
+    "\n",
+    "\n",
+    "#print(results.display())\n",
+    "\n",
+    "parameters = {par.name:results.get_variates(par.path)\n",
+    "                  for par in results.optimized_model[\"source\"].parameters.values()\n",
+    "                  if par.free}\n",
+    "\n",
+    "results_err = results.propagate(results.optimized_model[\"source\"].spectrum.main.shape.evaluate_at, **parameters)\n",
+    "\n",
+    "#print(results.optimized_model[\"source\"])\n",
+    "    \n",
+    "energy = np.geomspace(100*u.keV,10*u.MeV).to_value(u.keV)\n",
+    "\n",
+    "flux_lo = np.zeros_like(energy)\n",
+    "flux_median = np.zeros_like(energy)\n",
+    "flux_hi = np.zeros_like(energy)\n",
+    "flux_inj = np.zeros_like(energy)\n",
+    "\n",
+    "for i, e in enumerate(energy):\n",
+    "    flux = results_err(e)\n",
+    "    flux_median[i] = flux.median\n",
+    "    flux_lo[i], flux_hi[i] = flux.equal_tail_interval(cl=0.68)\n",
+    "    #flux_inj[i] = spectrum_inj.evaluate_at(e)\n",
+    "\n",
+    "binned_energy_edges = crab.binned_data.axes['Em'].edges.value\n",
+    "binned_energy = np.array([])\n",
+    "bin_sizes = np.array([])\n",
+    "\n",
+    "for i in range(len(binned_energy_edges)-1):\n",
+    "    binned_energy = np.append(binned_energy, (binned_energy_edges[i+1] + binned_energy_edges[i]) / 2)\n",
+    "    bin_sizes = np.append(bin_sizes, binned_energy_edges[i+1] - binned_energy_edges[i])\n",
+    "\n",
+    "expectation = response.expectation(data, copy = True)    \n",
+    "exp_est_simple = expectation # for later comparison\n",
+    "\n",
+    "# Plot the fitted and injected spectra\n",
+    "fig,ax = plt.subplots()\n",
+    "\n",
+    "ax.plot(energy, energy*energy*flux_median, label = \"Best fit\")\n",
+    "ax.fill_between(energy, energy*energy*flux_lo, energy*energy*flux_hi, alpha = .5, label = \"Best fit (errors)\")\n",
+    "#ax.plot(energy, energy*energy*flux_inj, color = 'black', ls = \":\", label = \"Injected\")\n",
+    "\n",
+    "ax.set_xscale(\"log\")\n",
+    "ax.set_yscale(\"log\")\n",
+    "\n",
+    "ax.set_xlabel(\"Energy (keV)\")\n",
+    "ax.set_ylabel(r\"$E^2 \\frac{dN}{dE}$ (keV cm$^{-2}$ s$^{-1}$)\")\n",
+    "\n",
+    "ax.legend()\n",
+    "\n",
+    "ax.set_ylim(.1,100)\n",
+    "plt.show()\n",
+    "\n",
+    "# Plot the fitted spectrum convolved with the response, as well as the simulated source counts\n",
+    "fig,ax = plt.subplots()\n",
+    "\n",
+    "ax.stairs(expectation.project('Em').todense().contents, binned_energy_edges, color='purple', label = \"Best fit convolved with response\")\n",
+    "#ax.stairs(expectation_inj.project('Em').todense().contents, binned_energy_edges, color='blue', label = \"Injected spectrum convolved with response\")\n",
+    "ax.errorbar(binned_energy, expectation.project('Em').todense().contents, yerr=np.sqrt(expectation.project('Em').todense().contents), color='purple', linewidth=0, elinewidth=1)\n",
+    "ax.stairs(crab.binned_data.project('Em').todense().contents, binned_energy_edges, color = 'black', ls = \":\", label = \"Source counts\")\n",
+    "ax.errorbar(binned_energy, crab.binned_data.project('Em').todense().contents, yerr=np.sqrt(crab.binned_data.project('Em').todense().contents), color='black', linewidth=0, elinewidth=1)\n",
+    "\n",
+    "ax.set_xscale(\"log\")\n",
+    "ax.set_yscale(\"log\")\n",
+    "\n",
+    "ax.set_xlabel(\"Energy (keV)\")\n",
+    "ax.set_ylabel(\"Counts\")\n",
+    "\n",
+    "ax.legend()\n",
+    "\n",
+    "plt.show()\n",
+    "\n",
+    "# Plot the fitted spectrum convolved with the response plus the fitted background, as well as the simulated source+background counts  \n",
+    "expectation_bkg = bkg.expectation(data.axes, copy = True)\n",
+    "\n",
+    "fig,ax = plt.subplots()\n",
+    "\n",
+    "ax.stairs(expectation.project('Em').todense().contents + expectation_bkg.project('Em').todense().contents, binned_energy_edges, color='purple', label = \"Best fit convolved with response plus background\")\n",
+    "ax.errorbar(binned_energy, expectation.project('Em').todense().contents+expectation_bkg.project('Em').todense().contents, yerr=np.sqrt(expectation.project('Em').todense().contents+expectation_bkg.project('Em').todense().contents), color='purple', linewidth=0, elinewidth=1)\n",
+    "ax.stairs(data.data.project('Em').todense().contents, binned_energy_edges, color = 'black', ls = \":\", label = \"Total counts\")\n",
+    "ax.errorbar(binned_energy, data.data.project('Em').todense().contents, yerr=np.sqrt(data.data.project('Em').todense().contents), color='black', linewidth=0, elinewidth=1)\n",
+    "\n",
+    "ax.set_xscale(\"log\")\n",
+    "ax.set_yscale(\"log\")\n",
+    "\n",
+    "ax.set_xlabel(\"Energy (keV)\")\n",
+    "ax.set_ylabel(\"Counts\")\n",
+    "\n",
+    "ax.legend()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4958f127-b1f0-47fd-b05e-3c3ccdb9d6df",
+   "metadata": {},
+   "source": [
+    "Finally, let's see what we get using the ideal BG. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "323ef337-4621-47ed-b87b-09c4dd874ce1",
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:yayc.configurator:Using configuration file at ./inputs_crab.yaml\n",
+      "INFO:yayc.configurator:Using configuration file at ./inputs_crab.yaml\n",
+      "INFO:yayc.configurator:Using configuration file at ./inputs_crab.yaml\n",
+      "INFO:yayc.configurator:Using configuration file at ./inputs_crab.yaml\n",
+      "INFO:yayc.configurator:Using configuration file at ./inputs_crab.yaml\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">14:11:05 </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> The current value of the parameter beta (</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">-2.0</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">) was above the new maximum </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">-2.15</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">. </span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">parameter.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py#810\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">810</span></a>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;5;46m14:11:05\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The current value of the parameter beta \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;37m-2.0\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m was above the new maximum \u001b[0m\u001b[1;37m-2.15\u001b[0m\u001b[1;38;5;251m.\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=346398;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=32617;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py#810\u001b\\\u001b[2m810\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">14:11:05 </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> set the minimizer to minuit                                              </span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/classicMLE/joint_likelihood.py#994\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">994</span></a>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;5;46m14:11:05\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m set the minimizer to minuit                                             \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=558430;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=263220;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/classicMLE/joint_likelihood.py#994\u001b\\\u001b[2m994\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:cosipy.response.threeml_point_source_response:... Calculating point source response ...\n",
+      "INFO:cosipy.response.threeml_point_source_response:--> done (source name : source)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">14:11:35 </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> The current value of the parameter beta (</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">-2.0</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">) was above the new maximum </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">-2.15</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">. </span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">parameter.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py#810\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">810</span></a>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;5;46m14:11:35\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The current value of the parameter beta \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;37m-2.0\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m was above the new maximum \u001b[0m\u001b[1;37m-2.15\u001b[0m\u001b[1;38;5;251m.\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=573625;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=315275;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py#810\u001b\\\u001b[2m810\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ffaf; text-decoration-color: #00ffaf; font-weight: bold; text-decoration: underline\">Best fit values:</span>\n",
+       "\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1;4;38;5;49mBest fit values:\u001b[0m\n",
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>result</th>\n",
+       "      <th>unit</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>parameter</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>source.spectrum.main.Band.K</th>\n",
+       "      <td>(2.927 +/- 0.009) x 10^-5</td>\n",
+       "      <td>1 / (keV s cm2)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_bkg</th>\n",
+       "      <td>(3.57954 +/- 0.00031) x 10</td>\n",
+       "      <td>Hz</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                 result             unit\n",
+       "parameter                                                               \n",
+       "source.spectrum.main.Band.K   (2.927 +/- 0.009) x 10^-5  1 / (keV s cm2)\n",
+       "total_bkg                    (3.57954 +/- 0.00031) x 10               Hz"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "<span style=\"color: #00ffaf; text-decoration-color: #00ffaf; font-weight: bold; text-decoration: underline\">Correlation matrix:</span>\n",
+       "\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n",
+       "\u001b[1;4;38;5;49mCorrelation matrix:\u001b[0m\n",
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><table id=\"table139936205623056\">\n",
+       "<tr><td>1.00</td><td>-0.66</td></tr>\n",
+       "<tr><td>-0.66</td><td>1.00</td></tr>\n",
+       "</table></div>"
+      ],
+      "text/plain": [
+       " 1.00 -0.66\n",
+       "-0.66  1.00"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "<span style=\"color: #00ffaf; text-decoration-color: #00ffaf; font-weight: bold; text-decoration: underline\">Values of -log(likelihood) at the minimum:</span>\n",
+       "\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n",
+       "\u001b[1;4;38;5;49mValues of -\u001b[0m\u001b[1;4;38;5;49mlog\u001b[0m\u001b[1;4;38;5;49m(\u001b[0m\u001b[1;4;38;5;49mlikelihood\u001b[0m\u001b[1;4;38;5;49m)\u001b[0m\u001b[1;4;38;5;49m at the minimum:\u001b[0m\n",
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>-log(likelihood)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>cosi</th>\n",
+       "      <td>-1591705157.1250365</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total</th>\n",
+       "      <td>-1591705157.1250365</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          -log(likelihood)\n",
+       "cosi   -1591705157.1250365\n",
+       "total  -1591705157.1250365"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "<span style=\"color: #00ffaf; text-decoration-color: #00ffaf; font-weight: bold; text-decoration: underline\">Values of statistical measures:</span>\n",
+       "\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n",
+       "\u001b[1;4;38;5;49mValues of statistical measures:\u001b[0m\n",
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>statistical measures</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>AIC</th>\n",
+       "      <td>-3183410310.250021</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BIC</th>\n",
+       "      <td>-3183410289.5549283</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    statistical measures\n",
+       "AIC   -3183410310.250021\n",
+       "BIC  -3183410289.5549283"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> The current value of the parameter beta (</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">-2.0</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">) was above the new maximum </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">-2.15</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">. </span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">parameter.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py#810\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">810</span></a>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The current value of the parameter beta \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;37m-2.0\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m was above the new maximum \u001b[0m\u001b[1;37m-2.15\u001b[0m\u001b[1;38;5;251m.\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=878335;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=548787;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py#810\u001b\\\u001b[2m810\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> The current value of the parameter beta (</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">-2.0</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">) was above the new maximum </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">-2.15</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">. </span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">parameter.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py#810\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">810</span></a>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The current value of the parameter beta \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;37m-2.0\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m was above the new maximum \u001b[0m\u001b[1;37m-2.15\u001b[0m\u001b[1;38;5;251m.\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=923149;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=26314;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py#810\u001b\\\u001b[2m810\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Orientation file:\n",
+    "sc_orientation = SpacecraftHistory.open(ori_file)\n",
+    "\n",
+    "# Detector response:\n",
+    "dr = FullDetectorResponse.open(dr_file)\n",
+    "instrument_response = BinnedInstrumentResponse(dr)\n",
+    "\n",
+    "# Load Crab data:\n",
+    "crab = BinnedData(input_yaml)\n",
+    "crab.load_binned_data_from_hdf5(binned_data=crab_data)\n",
+    "\n",
+    "# Load BG model true:\n",
+    "bg = BinnedData(input_yaml)\n",
+    "bg.load_binned_data_from_hdf5(binned_data=background_model)\n",
+    "\n",
+    "# Load BG model estimated:\n",
+    "bg_est = BinnedData(input_yaml)\n",
+    "bg_est.load_binned_data_from_hdf5(binned_data=estimated_bg)\n",
+    "\n",
+    "# Load BG model estimated simple:\n",
+    "bg_est_simple = BinnedData(input_yaml)\n",
+    "bg_est_simple.load_binned_data_from_hdf5(binned_data=estimated_bg_simple)\n",
+    "\n",
+    "# Load total data:\n",
+    "total = BinnedData(input_yaml)\n",
+    "total.load_binned_data_from_hdf5(binned_data=total_data)\n",
+    "\n",
+    "# ------- Interfaces ----------\n",
+    "\n",
+    "# Total data:\n",
+    "data = EmCDSBinnedData(total.binned_data.project('Em', 'Phi', 'PsiChi'))\n",
+    "\n",
+    "# BG:\n",
+    "bkg_dist = {\"total_bkg\":bg.binned_data.project('Em', 'Phi', 'PsiChi')}\n",
+    "\n",
+    "#for bckfile in bkg_dist.keys():\n",
+    "#        bkg_dist[bckfile] += sys.float_info.min\n",
+    "        \n",
+    "bkg = FreeNormBinnedBackground(bkg_dist,\n",
+    "                                   sc_history=sc_orientation,\n",
+    "                                   copy = False)\n",
+    "\n",
+    "psr = BinnedThreeMLPointSourceResponse(data = data,\n",
+    "                                        instrument_response = instrument_response,\n",
+    "                                        sc_history=sc_orientation,\n",
+    "                                        energy_axis = dr.axes['Ei'],\n",
+    "                                        polarization_axis = dr.axes['Pol'] if 'Pol' in dr.axes.labels else None,\n",
+    "                                        nside = 2*data.axes['PsiChi'].nside)\n",
+    "\n",
+    "response = BinnedThreeMLModelFolding(data = data, point_source_response = psr)\n",
+    "\n",
+    "like_fun = PoissonLikelihood(data, response, bkg)\n",
+    "\n",
+    "cosi = ThreeMLPluginInterface('cosi',\n",
+    "                            like_fun,\n",
+    "                            response,\n",
+    "                            bkg)\n",
+    "\n",
+    "# Nuisance parameter guess, bounds, etc.\n",
+    "for bkg_label in bkg_dist.keys():\n",
+    "    cosi.bkg_parameter[bkg_label] = Parameter(bkg_label,  # background parameter\n",
+    "                                    1,  # initial value of parameter\n",
+    "                                    min_value=0,  # minimum value of parameter\n",
+    "                                    max_value= 100,  # maximum value of parameter\n",
+    "                                    delta=0.05,  # initial step used by fitting engine\n",
+    "                                    unit = u.Hz\n",
+    "                                          )\n",
+    "    \n",
+    "# Load the astromodel for the Crab (this is the same model as \n",
+    "# used in the DC2 Crab tutorial):\n",
+    "model = astromodels.load_model('/project/ckarwin/astro/chris/cosi/cosipy_development/continuum_bg_estimation/ai_inpainting/Run_4/crab_DC2_astromodel.yaml')\n",
+    "\n",
+    "\n",
+    "# Define plugin and fit:\n",
+    "plugins = DataList(cosi) # If we had multiple instruments, we would do e.g. DataList(cosi, lat, hawc, ...)  \n",
+    "\n",
+    "like = JointLikelihood(model, plugins, verbose = False)\n",
+    "\n",
+    "like.fit()\n",
+    "\n",
+    "# Get expectation\n",
+    "\n",
+    "results = like.results\n",
+    "\n",
+    "\n",
+    "#print(results.display())\n",
+    "\n",
+    "parameters = {par.name:results.get_variates(par.path)\n",
+    "                  for par in results.optimized_model[\"source\"].parameters.values()\n",
+    "                  if par.free}\n",
+    "\n",
+    "results_err = results.propagate(results.optimized_model[\"source\"].spectrum.main.shape.evaluate_at, **parameters)\n",
+    "\n",
+    "#print(results.optimized_model[\"source\"])\n",
+    "    \n",
+    "energy = np.geomspace(100*u.keV,10*u.MeV).to_value(u.keV)\n",
+    "\n",
+    "flux_lo = np.zeros_like(energy)\n",
+    "flux_median = np.zeros_like(energy)\n",
+    "flux_hi = np.zeros_like(energy)\n",
+    "flux_inj = np.zeros_like(energy)\n",
+    "\n",
+    "for i, e in enumerate(energy):\n",
+    "    flux = results_err(e)\n",
+    "    flux_median[i] = flux.median\n",
+    "    flux_lo[i], flux_hi[i] = flux.equal_tail_interval(cl=0.68)\n",
+    "    #flux_inj[i] = spectrum_inj.evaluate_at(e)\n",
+    "\n",
+    "binned_energy_edges = crab.binned_data.axes['Em'].edges.value\n",
+    "binned_energy = np.array([])\n",
+    "bin_sizes = np.array([])\n",
+    "\n",
+    "for i in range(len(binned_energy_edges)-1):\n",
+    "    binned_energy = np.append(binned_energy, (binned_energy_edges[i+1] + binned_energy_edges[i]) / 2)\n",
+    "    bin_sizes = np.append(bin_sizes, binned_energy_edges[i+1] - binned_energy_edges[i])\n",
+    "\n",
+    "expectation = response.expectation(data, copy = True)    \n",
+    "exp_true = expectation # for later comparison\n",
+    "\n",
+    "# Plot the fitted and injected spectra\n",
+    "fig,ax = plt.subplots()\n",
+    "\n",
+    "ax.plot(energy, energy*energy*flux_median, label = \"Best fit\")\n",
+    "ax.fill_between(energy, energy*energy*flux_lo, energy*energy*flux_hi, alpha = .5, label = \"Best fit (errors)\")\n",
+    "#ax.plot(energy, energy*energy*flux_inj, color = 'black', ls = \":\", label = \"Injected\")\n",
+    "\n",
+    "ax.set_xscale(\"log\")\n",
+    "ax.set_yscale(\"log\")\n",
+    "\n",
+    "ax.set_xlabel(\"Energy (keV)\")\n",
+    "ax.set_ylabel(r\"$E^2 \\frac{dN}{dE}$ (keV cm$^{-2}$ s$^{-1}$)\")\n",
+    "\n",
+    "ax.legend()\n",
+    "\n",
+    "ax.set_ylim(.1,100)\n",
+    "plt.show()\n",
+    "\n",
+    "# Plot the fitted spectrum convolved with the response, as well as the simulated source counts\n",
+    "fig,ax = plt.subplots()\n",
+    "\n",
+    "ax.stairs(expectation.project('Em').todense().contents, binned_energy_edges, color='purple', label = \"Best fit convolved with response\")\n",
+    "#ax.stairs(expectation_inj.project('Em').todense().contents, binned_energy_edges, color='blue', label = \"Injected spectrum convolved with response\")\n",
+    "ax.errorbar(binned_energy, expectation.project('Em').todense().contents, yerr=np.sqrt(expectation.project('Em').todense().contents), color='purple', linewidth=0, elinewidth=1)\n",
+    "ax.stairs(crab.binned_data.project('Em').todense().contents, binned_energy_edges, color = 'black', ls = \":\", label = \"Source counts\")\n",
+    "ax.errorbar(binned_energy, crab.binned_data.project('Em').todense().contents, yerr=np.sqrt(crab.binned_data.project('Em').todense().contents), color='black', linewidth=0, elinewidth=1)\n",
+    "\n",
+    "ax.set_xscale(\"log\")\n",
+    "ax.set_yscale(\"log\")\n",
+    "\n",
+    "ax.set_xlabel(\"Energy (keV)\")\n",
+    "ax.set_ylabel(\"Counts\")\n",
+    "\n",
+    "ax.legend()\n",
+    "\n",
+    "plt.show()\n",
+    "\n",
+    "# Plot the fitted spectrum convolved with the response plus the fitted background, as well as the simulated source+background counts  \n",
+    "expectation_bkg = bkg.expectation(data.axes, copy = True)\n",
+    "\n",
+    "fig,ax = plt.subplots()\n",
+    "\n",
+    "ax.stairs(expectation.project('Em').todense().contents + expectation_bkg.project('Em').todense().contents, binned_energy_edges, color='purple', label = \"Best fit convolved with response plus background\")\n",
+    "ax.errorbar(binned_energy, expectation.project('Em').todense().contents+expectation_bkg.project('Em').todense().contents, yerr=np.sqrt(expectation.project('Em').todense().contents+expectation_bkg.project('Em').todense().contents), color='purple', linewidth=0, elinewidth=1)\n",
+    "ax.stairs(data.data.project('Em').todense().contents, binned_energy_edges, color = 'black', ls = \":\", label = \"Total counts\")\n",
+    "ax.errorbar(binned_energy, data.data.project('Em').todense().contents, yerr=np.sqrt(data.data.project('Em').todense().contents), color='black', linewidth=0, elinewidth=1)\n",
+    "\n",
+    "ax.set_xscale(\"log\")\n",
+    "ax.set_yscale(\"log\")\n",
+    "\n",
+    "ax.set_xlabel(\"Energy (keV)\")\n",
+    "ax.set_ylabel(\"Counts\")\n",
+    "\n",
+    "ax.legend()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "474e919e-bb5d-4a2a-8f25-b527b158d1ce",
+   "metadata": {},
+   "source": [
+    "## Summary:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "8270b35f-2c62-46bf-bd05-2ff9b684b518",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the fitted spectrum convolved with the response, as well as the simulated source counts\n",
+    "fig,ax = plt.subplots()\n",
+    "\n",
+    "ax.stairs(exp_est.project('Em').todense().contents, binned_energy_edges, color='purple', label = \"Best fit convolved with response (NN)\")\n",
+    "ax.errorbar(binned_energy, exp_est.project('Em').todense().contents, yerr=np.sqrt(exp_est.project('Em').todense().contents), color='purple', linewidth=0, elinewidth=1)\n",
+    "\n",
+    "ax.stairs(exp_est_simple.project('Em').todense().contents, binned_energy_edges, color='red', label = \"Best fit convolved with response (Simple)\")\n",
+    "ax.errorbar(binned_energy, exp_est_simple.project('Em').todense().contents, yerr=np.sqrt(exp_est_simple.project('Em').todense().contents), color='red', linewidth=0, elinewidth=1)\n",
+    "\n",
+    "ax.stairs(exp_true.project('Em').todense().contents, binned_energy_edges, color='cyan', label = \"Best fit convolved with response (true)\")\n",
+    "ax.errorbar(binned_energy, exp_true.project('Em').todense().contents, yerr=np.sqrt(exp_est.project('Em').todense().contents), color='cyan', linewidth=0, elinewidth=1)\n",
+    "\n",
+    "ax.stairs(crab.binned_data.project('Em').todense().contents, binned_energy_edges, color = 'black', ls = \":\", label = \"Source counts\")\n",
+    "ax.errorbar(binned_energy, crab.binned_data.project('Em').todense().contents, yerr=np.sqrt(crab.binned_data.project('Em').todense().contents), color='black', linewidth=0, elinewidth=1)\n",
+    "\n",
+    "ax.set_xscale(\"log\")\n",
+    "ax.set_yscale(\"log\")\n",
+    "\n",
+    "ax.set_xlabel(\"Energy (keV)\")\n",
+    "ax.set_ylabel(\"Counts\")\n",
+    "\n",
+    "ax.legend()\n",
+    "plt.title(\"Crab\")\n",
+    "plt.show()\n",
+    "\n",
+    "# fractional residuals:\n",
+    "fig,ax = plt.subplots()\n",
+    "\n",
+    "xerr = crab.binned_data.axes['Em'].widths.value / 2.0\n",
+    "\n",
+    "# NN inpainted:\n",
+    "x = (crab.binned_data.project('Em').todense().contents - exp_est.project('Em').todense().contents) / crab.binned_data.project('Em').todense().contents\n",
+    "plt.semilogx(binned_energy, x, label=\"NN inpainting\", ls=\"\", marker=\"s\", color=\"purple\")\n",
+    "plt.errorbar(binned_energy, x, xerr=xerr, ls=\"\", marker=\"s\", color=\"purple\")\n",
+    "\n",
+    "# simple inpainted:\n",
+    "x = (crab.binned_data.project('Em').todense().contents - exp_est_simple.project('Em').todense().contents) / crab.binned_data.project('Em').todense().contents\n",
+    "plt.semilogx(binned_energy, x, label=\"Simple inpainting\", ls=\"\", marker=\"s\", color=\"red\")\n",
+    "plt.errorbar(binned_energy, x, xerr=xerr, ls=\"\", marker=\"s\", color=\"red\")\n",
+    "\n",
+    "# True:\n",
+    "x = (crab.binned_data.project('Em').todense().contents - exp_true.project('Em').todense().contents) / crab.binned_data.project('Em').todense().contents\n",
+    "plt.semilogx(binned_energy, x, label=\"Ideal BG\", ls=\"\", marker=\"s\", color=\"cyan\")\n",
+    "plt.errorbar(binned_energy, x, xerr=xerr, ls=\"\", marker=\"s\", color=\"cyan\")\n",
+    "\n",
+    "ax.set_xlabel(\"Energy (keV)\")\n",
+    "ax.set_ylabel(\"(true - recovered)/true\")\n",
+    "\n",
+    "plt.legend(loc=1)\n",
+    "plt.ylim(-0.5,0.5)\n",
+    "plt.xlim(1e2,1e4)\n",
+    "plt.grid(ls=\":\",color=\"grey\",alpha=0.4)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "52e9c8da-2020-4174-96fa-f9982ba6fda6",
+   "metadata": {},
+   "source": [
+    "input: norm = 3.07e-5 \n",
+    "\n",
+    "estimated BG NN: norm =  (3.243 +/- 0.009)e-5, logL = 1591385619.4884508   \n",
+    "\n",
+    "estimated BG simple: norm =  (3.589 +/- 0.009)e-5, logL = 1591401274.5105834   \n",
+    "\n",
+    "ideal BG: norm = (2.927 +/- 0.009)e-5, logL = 1591705157.1250365 \n",
+    "\n",
+    "Comparing estimated BG (simple) to true BG:\n",
+    "$2 \\Delta \\mathrm{logL}$ = 607765.2 $\\implies \\sigma = 780$ (true BG is preferred)\n",
+    "\n",
+    "Comparing estimated BG simple to GCN:\n",
+    "$2 \\Delta \\mathrm{logL}$ = 31310.0 $\\implies \\sigma = 177$ (simple inpainting is preferred)\n",
+    "\n",
+    "Thus, the ideal BG performs significanlty better than the estimated, as expected. The NN estimate gives a normalization closer to the true value. The counts also look better at most energies (except for the last 2 bins). However, the simple alogirthm gives a significantly better TS compared to the NN-based inpainting. The next step is to improve the estimation algorithm to get closer to the ideal BG."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "continuumBG",
+   "language": "python",
+   "name": "myenv"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/tutorials/background_estimation/continuum_estimation/BG_estimation_example.ipynb b/docs/tutorials/background_estimation/continuum_estimation/BG_estimation_example.ipynb
deleted file mode 100644
index 272ac18e1..000000000
--- a/docs/tutorials/background_estimation/continuum_estimation/BG_estimation_example.ipynb
+++ /dev/null
@@ -1,615 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "e40c9735",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "# Continuum Background Estimation\n",
-    "\n",
-    "This example shows how to use cosipy for estimating the continuum background. In short, the method is based on the traditional on-off analysis, where the background for a source region on the sky is estimated by performing some kind of interpolation of the nearby surrounding region. The main difference with a Compton telescope is that we are now performing the on-off analysis in the Compton data space.\n",
-    "\n",
-    "In this particular example, we want to estimate the background for the Crab. We start with the full dataset. This contains the full background, which includes instrumental + astrophysical, where the latter consists of all astrophysical sources other than the Crab. Then, for each bin of Em and Phi, we mask the Crab in the PsiChi plane based on the point source response. Finally, we interpolate over the masked region using an inpainting method.   \n",
-    "\n",
-    "The accuracy of this method depends strongly on two things:\n",
-    "1) Choosing the proper source region to mask\n",
-    "2) Making an accurate interpolation\n",
-    "\n",
-    "The current source code takes a very simple appoach for both of these, but eventually they need to be improved. For the first point, ultimately we should use the ARM measurement, and we'll need to figure out the optimal percentage of counts to mask. A major challenge here is that point sources have really long tails in the ARM distribution, extending over the entire sky. So we probably can't use a typical confidence level of 95% for the masking. For the second point, we are currently using the simplest possible inpainting algorithm. More sophisticated methods are needed. The best alrorithm will likely come from deep convolution neural networks. An alterantive to inpainting methods is to use background templates, which can be scaled outside of the masked region. The code also needs to be developed in a way that will make this easy to do. Finally, the code is super slow and needs to be vectorized. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "8a0f4b8b",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">15:24:51 </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> The naima package is not available. Models that depend on it will not be         </span><a href=\"file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">functions.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py#48\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">48</span></a>\n",
-       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">available                                                                         </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m15:24:51\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The naima package is not available. Models that depend on it will not be        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=839926;file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py\u001b\\\u001b[2mfunctions.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=940974;file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py#48\u001b\\\u001b[2m48\u001b[0m\u001b]8;;\u001b\\\n",
-       "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mavailable                                                                        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m               \u001b[0m\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> The GSL library or the pygsl wrapper cannot be loaded. Models that depend on it  </span><a href=\"file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">functions.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py#69\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">69</span></a>\n",
-       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">will not be available.                                                            </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The GSL library or the pygsl wrapper cannot be loaded. Models that depend on it \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=964256;file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py\u001b\\\u001b[2mfunctions.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=498488;file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py#69\u001b\\\u001b[2m69\u001b[0m\u001b]8;;\u001b\\\n",
-       "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mwill not be available.                                                           \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m               \u001b[0m\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">15:24:52 </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> The ebltable package is not available. Models that depend on it will not be     </span><a href=\"file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/astromodels/functions/functions_1D/absorption.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">absorption.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/astromodels/functions/functions_1D/absorption.py#33\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">33</span></a>\n",
-       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">available                                                                        </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m15:24:52\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The ebltable package is not available. Models that depend on it will not be    \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=564058;file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/astromodels/functions/functions_1D/absorption.py\u001b\\\u001b[2mabsorption.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=855455;file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/astromodels/functions/functions_1D/absorption.py#33\u001b\\\u001b[2m33\u001b[0m\u001b]8;;\u001b\\\n",
-       "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mavailable                                                                       \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m                \u001b[0m\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">15:24:53 </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> Starting 3ML!                                                                     </span><a href=\"file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/__init__.py#39\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">39</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m15:24:53\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m Starting 3ML!                                                                    \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=36093;file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=734273;file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/__init__.py#39\u001b\\\u001b[2m39\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> WARNINGs here are </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">NOT</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> errors                                                      </span><a href=\"file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/__init__.py#40\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">40</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m WARNINGs here are \u001b[0m\u001b[1;31mNOT\u001b[0m\u001b[1;38;5;251m errors                                                     \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=482312;file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=644249;file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/__init__.py#40\u001b\\\u001b[2m40\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> but are inform you about optional packages that can be installed                  </span><a href=\"file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/__init__.py#41\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">41</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m but are inform you about optional packages that can be installed                 \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=118694;file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=881199;file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/__init__.py#41\u001b\\\u001b[2m41\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\"> to disable these messages, turn off start_warning in your config file</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">            </span><a href=\"file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/__init__.py#44\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">44</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m \u001b[0m\u001b[1;31m to disable these messages, turn off start_warning in your config file\u001b[0m\u001b[1;38;5;251m           \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=133496;file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=913878;file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/__init__.py#44\u001b\\\u001b[2m44\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> no display variable set. using backend for graphics without display (agg)         </span><a href=\"file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/__init__.py#50\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">50</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m no display variable set. using backend for graphics without display \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;38;5;251magg\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=718155;file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=981750;file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/__init__.py#50\u001b\\\u001b[2m50\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">15:24:54 </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> ROOT minimizer not available                                                </span><a href=\"file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/minimizer/minimization.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">minimization.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/minimizer/minimization.py#1345\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1345</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m15:24:54\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m ROOT minimizer not available                                               \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=797190;file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/minimizer/minimization.py\u001b\\\u001b[2mminimization.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=868628;file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/minimizer/minimization.py#1345\u001b\\\u001b[2m1345\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> Multinest minimizer not available                                           </span><a href=\"file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/minimizer/minimization.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">minimization.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/minimizer/minimization.py#1357\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1357</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Multinest minimizer not available                                          \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=185828;file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/minimizer/minimization.py\u001b\\\u001b[2mminimization.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=311781;file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/minimizer/minimization.py#1357\u001b\\\u001b[2m1357\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> PyGMO is not available                                                      </span><a href=\"file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/minimizer/minimization.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">minimization.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/minimizer/minimization.py#1369\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1369</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m PyGMO is not available                                                     \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=375366;file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/minimizer/minimization.py\u001b\\\u001b[2mminimization.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=610747;file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/minimizer/minimization.py#1369\u001b\\\u001b[2m1369\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">15:24:54 </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> The cthreeML package is not installed. You will not be able to use plugins which  </span><a href=\"file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/__init__.py#94\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">94</span></a>\n",
-       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">require the C/C++ interface (currently HAWC)                                       </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">              </span>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m15:24:54\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The cthreeML package is not installed. You will not be able to use plugins which \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=713293;file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=92627;file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/__init__.py#94\u001b\\\u001b[2m94\u001b[0m\u001b]8;;\u001b\\\n",
-       "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mrequire the C/C++ interface \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;38;5;251mcurrently HAWC\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m                                      \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m              \u001b[0m\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> Could not import plugin FermiLATLike.py. Do you have the relative instrument     </span><a href=\"file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/__init__.py#144\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">144</span></a>\n",
-       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">software installed and configured?                                                </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Could not import plugin FermiLATLike.py. Do you have the relative instrument    \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=243395;file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=718997;file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/__init__.py#144\u001b\\\u001b[2m144\u001b[0m\u001b]8;;\u001b\\\n",
-       "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251msoftware installed and configured?                                               \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m               \u001b[0m\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> Could not import plugin HAWCLike.py. Do you have the relative instrument         </span><a href=\"file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/__init__.py#144\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">144</span></a>\n",
-       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">software installed and configured?                                                </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Could not import plugin HAWCLike.py. Do you have the relative instrument        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=346208;file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=220374;file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/__init__.py#144\u001b\\\u001b[2m144\u001b[0m\u001b]8;;\u001b\\\n",
-       "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251msoftware installed and configured?                                               \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m               \u001b[0m\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">15:24:57 </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> No fermitools installed                                              </span><a href=\"file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/utils/data_builders/fermi/lat_transient_builder.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">lat_transient_builder.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/utils/data_builders/fermi/lat_transient_builder.py#44\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">44</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m15:24:57\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m No fermitools installed                                             \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=34153;file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/utils/data_builders/fermi/lat_transient_builder.py\u001b\\\u001b[2mlat_transient_builder.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=174606;file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/utils/data_builders/fermi/lat_transient_builder.py#44\u001b\\\u001b[2m44\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">15:24:57 </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> Env. variable MKL_NUM_THREADS is not set. Please set it to </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> for optimal         </span><a href=\"file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/__init__.py#387\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">387</span></a>\n",
-       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">performances in 3ML                                                               </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m15:24:57\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Env. variable MKL_NUM_THREADS is not set. Please set it to \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m for optimal        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=612038;file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=220937;file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/__init__.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\n",
-       "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mperformances in 3ML                                                              \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m               \u001b[0m\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> Env. variable NUMEXPR_NUM_THREADS is not set. Please set it to </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> for optimal     </span><a href=\"file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/__init__.py#387\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">387</span></a>\n",
-       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">performances in 3ML                                                               </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Env. variable NUMEXPR_NUM_THREADS is not set. Please set it to \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m for optimal    \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=736794;file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=963635;file:///project/majello/astrohe/ckarwin/MyEnvironments/COSI/lib/python3.10/site-packages/threeML/__init__.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\n",
-       "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mperformances in 3ML                                                              \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m               \u001b[0m\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from cosipy.background_estimation import ContinuumEstimation\n",
-    "from cosipy.spacecraftfile import SpacecraftHistory\n",
-    "from cosipy.util import fetch_wasabi_file\n",
-    "import os\n",
-    "import logging\n",
-    "import astropy.units as u\n",
-    "from astropy.coordinates import SkyCoord\n",
-    "from pathlib import Path\n",
-    "logging.basicConfig()\n",
-    "logging.getLogger().setLevel(logging.INFO)\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "22076637",
-   "metadata": {},
-   "source": [
-    "The notebook requires the following files:\n",
-    "1) DC3_final_530km_3_month_with_slew_15sbins_GalacticEarth_SAA.fits\n",
-    "2) SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.nonsparse_nside8.area.good_chunks_unzip.earthocc.h5\n",
-    "3) crab_bkg_binned_data_galactic.hdf5\n",
-    "4) inputs_crab.yaml\n",
-    "\n",
-    "They can be downloaded using the cells below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a1e669ed",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Update to your desired path\n",
-    "data_path = Path(\"\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "36497975",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# DC3_final_530km_3_month_with_slew_15sbins_GalacticEarth_SAA.fits\n",
-    "fetch_wasabi_file('COSI-SMEX/develop/Data/Orientation/DC3_final_530km_3_month_with_slew_15sbins_GalacticEarth_SAA.fits', checksum = 'e86df2407eb052cf0c1db4a8e7598727')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6dafa64a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dr = data_path / \"SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.h5\"\n",
-    "\n",
-    "# download response file ~596.06 MB\n",
-    "fetch_wasabi_file(\"COSI-SMEX/develop/Data/Responses/SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.h5\", checksum = 'eb72400a1279325e9404110f909c7785')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ec8e59c4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# crab_bkg_binned_data_galactic.hdf5\n",
-    "fetch_wasabi_file('COSI-SMEX/cosipy_tutorials/background_estimation/crab_bkg_binned_data_galactic.hdf5', checksum = '7450f8ecdf6bf14bffe22d0046d47d49')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e14f1bfc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# inputs_crab.yaml\n",
-    "fetch_wasabi_file('COSI-SMEX/cosipy_tutorials/background_estimation/inputs_crab.yaml', checksum = '3b2c6ddd35d98346d9aac13ce3d59368')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "42d1d040",
-   "metadata": {},
-   "source": [
-    "Define instance of class:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "6ade7276",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "instance = ContinuumEstimation()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "78da7f08",
-   "metadata": {},
-   "source": [
-    "In order to estimate the background, we need the point source response. If you don't already have this, you can calculate it, as shown below. Note that the coordinates of the Crab need to be passed as a tuple, giving Galactic longitude and latitude in degrees. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "7cf312d4",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Orientation file:\n",
-    "ori_file = data_path/\"DC3_final_530km_3_month_with_slew_15sbins_GalacticEarth_SAA.fits\"\n",
-    "\n",
-    "# Spacecraft orientation:\n",
-    "sc_orientation = SpacecraftHistory.open(ori_file)\n",
-    "\n",
-    "crab = SkyCoord(l=184.56*u.deg,b=-5.78*u.deg,frame=\"galactic\")\n",
-    "psr = instance.calc_psr(sc_orientation, dr, crab)\n",
-    "\n",
-    "# Alternatively, you can load a PSR from file with: \n",
-    "#psr = instance.load_psr_from_file(\"psr_file_name.h5\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0f665d91",
-   "metadata": {},
-   "source": [
-    "Now let's calculate the estimated background. To make a short example, we'll only consider 1 Em bin and 2 Phi bins, as specified by the optional keywords e_loop and s_loop, respectively. We'll also make plots here for demonstrational purposes. \n",
-    "\n",
-    "Note that the current code has not yet been optimized for speed, as it uses a simple nested for loop. The time required to generate the estimated background using all bins is roughyly 4 hours. The option to use a subset of the Em bins and/or Phi bins may be useful for analyses that also use a given subset, but at this point the main motivation for this option is for demonstrational purposes, and when using this option, nothing is done with the other bins. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "71126d13",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:yayc.configurator:Using configuration file at inputs_crab.yaml\n",
-      "  0%|          | 0/2 [00:00<?, ?it/s]INFO:cosipy.background_estimation.ContinuumEstimation:Bin 2 4\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 600x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 600x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 600x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 600x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 600x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 2/2 [00:42<00:00, 21.49s/it]INFO:cosipy.background_estimation.ContinuumEstimation:Bin 2 5\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 600x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnUAAAG+CAYAAAD1MPK+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAABcSAAAXEgFnn9JSAAEAAElEQVR4nOydd5wkVbn3f6c6TN6Z3Z3ZnJcNLmnJOYhLEJQoot69gFeSCsIFrllAQDDeV8WAqIAI4kUQBclKUKILC+wCywY257wzO6m7q877R/Wprq6ucFKHma3f5zM7s91VzzlVXXXq289zzvMQSilFrFixYsWKFStWrAEto9odiBUrVqxYsWLFiqWuGOpixYoVK1asWLEGgWKoixUrVqxYsWLFGgSKoS5WrFixYsWKFWsQKIa6WLFixYoVK1asQaAY6mLFihUrVqxYsQaBYqiLFStWrFixYsUaBIqhLlasWLFixYoVaxAohrpYsWLFihUrVqxBoBjqYsWKFStWrFixBoFiqIsVK1asWLFixRoEiqEuVqxYsWLFihVrECiGulixYsWKFStWrEGgGOpixYoVK1asWLEGgWKoixVrkOjuu+8GIQSEENx9993V7g63LrzwQqffK1eurHZ3YsWKFWvAKoa6WINaDBbYzxVXXMG971VXXVWyf6zalRsOvT91dXUYOXIkjjnmGHzta1/D0qVLq93dWLFixdKuGOpi7VG6//77kclkIrfLZrO47777KtCjWJVQJpPB5s2b8eKLL+K73/0uZs2ahW9/+9vV7lasWLFiaVWy2h2IFasSSiaTyOVy2LZtGx599FGcc845odv/7W9/w9atW4v2jTVwdMUVV+CEE05w/p/JZLBy5Uo88MADeOONN5DL5XDDDTegra0NV155ZRV7GitWrFj6FHvqYu0Rmjp1KqZNmwYAXPPN2DbTp0/H1KlTy9izWOXQgQceiDPPPNP5+eQnP4kvf/nLmDdvHq655hpnu+uvvx7d3d1V7GmsWLFi6VMMdbH2GF1wwQUAgCeffBKbNm0K3G7Lli144oknivaJNThECMGtt96K0aNHAwB27dqFf/7zn1XuVaxYsWLpUQx1sfYYnX/++TAMA7lcLnS+3L333otsNgvDMHD++edz2d6yZQt+85vfYO7cudhnn30wZMgQpFIptLe34/DDD8d1112HjRs3ctl6/vnnccEFF2DGjBlobm5GOp3GqFGjsM8+++D000/HD3/4Q6xbt47Llp/mzZuHjo4OZwHBAw88ULKNZVl44IEHcN5552Hy5MlobGxES0sLZs6cic9//vNYuHAhV1u5XA4/+9nPcOSRR2Lo0KFobm7GzJkzcc0112DVqlXSx6CiVCqFww8/3Pn/kiVLQrffunUrvvOd7+CYY47BqFGjkE6n0dHRgWOOOQbf//73sXv37sg258+fj8suuwz77ruvc22MGDECs2bNwimnnIKbbrrJd/HG888/7yz2uOGGGwAACxcuxCWXXIKpU6eioaEBHR0dmDNnDu6//37uc7BmzRp89atfxYEHHohhw4ahrq4OY8eOxcc//nHcfffdME0zdP/jjz++ZPHQH//4R5x44okYNWoU6urqMHHiRFx44YV4//33I/uzdOlSXHPNNTjooIPQ1taGVCqF4cOHY8aMGTjhhBPw9a9/HW+99VaojUwmg9/+9rc4/fTTMX78eNTX16OtrQ377bcfrrnmmnhldaw9QzRWrEEsABQAnTFjBqWU0jlz5lAAdL/99gvcZ//996cA6IknnkgppXTGjBmOHT998MEHNJFIONsE/TQ1NdGHHnoosF3TNOlFF10UaQcAvfLKK0v2v+uuu5z377rrLt82nnrqKdrc3EwB0ObmZvrMM8+UbLNs2TI6e/bs0PYNw6Df+ta3Ao+FUko3bdpEDzjggEAbbW1t9JlnnqEXXHCB89qKFStCbYbJbSfo+Jk+9alPOdt+97vfDdzurrvuoi0tLaHnYuTIkfTll18OtHH99ddTQkjkZ3rGGWeU7Pvcc885719//fX0nnvuoXV1dYE2TjvtNNrb2xt67LfffjttaGgI7cu+++4b+lkcd9xxzra9vb30zDPPDLRVV1dHH3/88UBbv/71r2k6nY48P/vvv3+gjXnz5tHJkyeH7p9Op+ntt98eem5ixRroihdKxNqjdOGFF+Lvf/87FixYgPnz5+PAAw8sev/NN9/E22+/7WzLo0wmA9M0MWXKFHzkIx/BPvvsg46ODliWhdWrV+Pvf/87nn32WXR3d+NTn/oUXnrpJRxyyCEldm677Tb85je/AQC0tbVh7ty5OOCAA9Da2oqenh6sXLkSr732Gp577jmpY7///vtxwQUXIJvNoqOjA48//jgOPvjgom0++OADHH744c4ikaOPPhof+9jHMHHiRJimiTfeeAN33303duzYgZtuugmGYTgeJO85OeWUU/Dmm28CAEaMGIGLLroI++yzD7q7u/Hkk0/ioYcewrnnnovZs2dLHY+K3n33XefvCRMm+G7zk5/8BFdddRUAoLGxEZ/4xCdw5JFHYvjw4di6dSuefPJJPPLII9i0aRPmzJmDefPmYdasWUU2/vrXvzqrbBsaGvDpT38ahx9+OIYNG4a+vj6sXbsWr7/+Op555pnIPs+bNw+33HILAOC//uu/cOyxxyKRSGDevHn47W9/i+7ubjz22GOYO3cuHnzwQV8bv/rVr3DZZZc5///4xz+O0047DW1tbViyZAnuuusurFixAgsXLsTRRx+NN998Ex0dHaH9+q//+i/85S9/wUEHHYRPfepTmDBhArZu3Yr77rsPL7/8Mvr7+zF37lwsXrwY7e3tRfvOnz8fl156KSzLQjKZxDnnnINjjz0WI0aMQDabxYYNG/Dmm2/i6aefDmz/lVdewZw5c9DT0wNCCE4++WScdNJJGDt2LHp7e/HKK6/g97//PXp6enDZZZehrq6O+96OFWvAqdpUGStWOQWPp66np4cOGTKEAqBXXHFFyfZf+tKXKADa2tpKe3p6KKXRnrpt27bRl156KbQfzz77LG1qaqIA6PHHH++7zd577+20vXjx4kBbu3btom+++WbJ62Geup/+9KeOt2jixIm+9k3TpAceeKDjXXnggQd829+4caPjyTMMg77zzjsl29x0001OX2bPnk23bt1ass2DDz5Y4uGshKfu0UcfLfI4Llu2rGSbefPm0WQy6fR/1apVgbZSqRQFQA877LCS90877TQKgCYSidBrpLe3l7722mslr7s9dQBoS0sLfeWVV0q2W7JkCR0zZoyz3YMPPliyzYoVKxwPXSKRoP/3f/9Xsk1PT4/TZwD0E5/4hG9/3Z46APQb3/gGtSyraBvTNIs8eN/73vdK7Hzxi1903vfrD1Mul/M9f52dnXT8+PGO5/f555/33X/p0qV0woQJjsd8y5YtgW3FijWQFUNdrEEtL9RRSp0QZ3t7O81kMs7rmUyGtre3UwD04osvdl6PgjpeXXfddY6d1atXl7zPwmqnn366lP0gqPvGN77hvL733nvTtWvX+u7/0EMPOdv99Kc/DW1r8eLFDpC5zxWllPb399MRI0Y4Ia8lS5YE2rn22msrAnWZTIYuXryY3nTTTUWhx/POO8/X1sc//nEHooLOF9O3vvUtx54XPNi1ExbuD5MX6sLCh0888YSz3eGHH17y/n//938773/5y18OtLNr1y46evRoCoASQnw/PzfUnXDCCYG2li5dGrrdySef7HyR8UIhj370ox859h955JHQbf/xj384237nO98RbitWrIGgGOpiDWr5Qd2LL77ovO6e4+aGGvfDWRfUPfnkk6GelKFDh1IA9EMf+lARbPLKC3W5XK5ojt6RRx5Jt2/fHrg/86oMGTIkcl4WpZQeccQRjufPrWeffdZp85xzzgm1sX79emoYhnao4/k5+uijaWdnZ4md7du3O3265JJLIttdvHixY/P6668veo/NKRw2bBjduXOn8DG5oW7o0KG0v78/dPtZs2Y522/YsKHovYkTJ1IANJlM0k2bNoXacXta/Txsbqj761//GmqLedJGjRpV8t5ZZ53leEyDvKFhYh7j6dOnc23PvJnHHXeccFuxYg0ExXPqYu1xOuqoozBt2jQsXboUv/vd73D22WcDKM5Nd+SRRwrbfffdd/G73/0OL730EpYuXYpdu3YFVq9Yu3ZtyWsnnngiHnjgASxatAhz5szBtddeizlz5qChoUG4L319fTj33HPx8MMPAwBOPfVU/OlPf0JjY2PgPv/6178AAKNHj8aTTz4Z2UYikQAArFq1Cr29vU4/582b52zzkY98JNTG6NGjMWvWLLzzzjuR7elSfX09br/9dsydO9c5BrdeeuklWJYFwD7Gv/zlL6H2stms8/eiRYuK3jvxxBPx5ptvYvv27TjuuOPwla98BaeddhqGDBki3O9jjjkG6XQ6dJsTTjgB7733HgD7c/j4xz8OANi8ebOz2nj//ffHiBEjQu2cdNJJ+Na3vgUAeO2110K3da8k9tPYsWOxZs0a7Nixo+S9E088EQ8//DAsy8Lxxx+Pr3/96zjzzDNL5t75adeuXViwYAEAYOTIkZGfEwA0NzcDKP2cYsUaNKo2VcaKVU7Bx1NHKaU333wzBUBTqRTdvHkz3bRpkzOHyhuaifLUWZZFv/zlLxd5nKJ+brzxxhI7K1asKJoXBdhz24466ij65S9/mT755JOhHjy3p661tdX5e+7cuTSbzYaep66uLiEvl/dn3bp1jq0rr7zSef3pp58ObZdSWjTvSpen7oorrqAPP/wwffjhh+lDDz1Eb7vtNnruuec6n9G0adMC27rtttukzwNbMc20ffv2Iu8ZYM9nO+igg+iXvvQl+vDDDztzN/3k9tRdffXVkefgxz/+sbO9O1T71ltvOa9/+tOfjrSzY8cOZ/sjjjii5H23p840zVBb7m296u/vp8cff3zR+SGE0H322Ydecskl9A9/+EOgh3PhwoXSn1MqlYo8B7FiDUTFeepi7ZFiOeuy2Szuvfde3HvvvcjlckK56ZhuueUWfP/734dlWUgkEjj55JPx7W9/G3fddRceeOABPPzww3j44Ydx0003Ofv45QGbNGkS3nzzTVx++eVoa2sDAPT39+Oll17C97//fZxyyikYN24cfvKTn4BSGtond1mz7u7uyO137dolcMSlcnsk3XnbwjyDTE1NTUpt+8ldUeLss8/G5ZdfjgceeAAvvPAC6uvrsXTpUpx88sm+OeZUzoXXMzt06FC8+uqr+MY3voGRI0cCgLOK+Kc//SnOOussjBw5Etddd11kTWLRc+k+tq6uLt9tgsQ8Wt59/WQY8o+RdDqNp556Cj/4wQ8wadIkAAClFO+88w7uuOMOfOYzn8HIkSNx+eWXo7Ozs2hflc/J7V2NFWswKYa6WHukxo8f79QGvfvuu/G73/0OgB0uHDduHLed3t5e3HrrrQCAlpYWvPbaa3jyySdx3XXX4cILL8S5557rwMUBBxwQaW/EiBG47bbbsHnzZrz88sv44Q9/iDPPPNMJ123evBlXXXUVLr300lA7t956qxNCfvjhh3HeeeeFPsjcD/Fjjz0W1J5vy/3DHsheWz09PZHHXMkyXUcffTS+973vAbCTDn/lK18p2cbd/zvvvFPoPDz//PMl9lpaWnDzzTdj/fr1mD9/Pm677Tacd955Toixq6sLN910E04//fRQ+BY9l+7jaGlp8d0mSG4gdO9bDqXTaVx77bVYsWIF3n33Xdxxxx244IILnPuwv78fP//5z3Hssceit7fX2c99fOeff77wNRsr1mBUDHWx9lixXFULFixw5uaI5q965ZVXnIfkpZdeioMOOihwW5EKCqlUCkcccQSuueYaPPzww9iyZQvuvPNO1NXVAQB+/etfh1Z1aGlpwZNPPskNdq2trc5D0m++n4jGjBnj/L1s2bLI7Xm20akvfvGLmD59OgDgjjvuKKl4MHbsWOdv1XPhlmEYOOCAA3D55Zfjj3/8IzZt2oSHH34Yw4YNAwA89dRTeOyxxwL3Fz2X7s+BlUUD4Fu5wiv3Nm475dasWbNw8cUX4+6778aaNWvw7LPPOl8Y3n77bfz2t791ti3X5xQr1kBWDHWx9lidffbZRRPWW1tbcdZZZwnZcNeQnTp1aui2Tz31lFgHXUqn0/jsZz+LK664wnntpZdeCt2Hgd1RRx0FwAa7T37yk4Fgd+yxxwIAli9frgRahx56qPP3s88+G7rthg0bKj5pPZFI4Bvf+AYAO0x93XXXFb1/7LHHOuWvwpLeqsowDJx55pm48cYbnddefPHFwO1ffPHFyLChOzG1O8H1iBEjMHHiRADAW2+9hS1btoTacR+3+/OstD784Q/jZz/7mfN/9/lpb293kj2/+uqrJeHZWLH2RMVQF2uPVUNDA6666iocdthhOOyww3DVVVcJrzR1z3P64IMPArebP38+/va3v0n3lckd5nTPmwsSA7ujjz4aAPCXv/wlEOwuuOAC528v6IjoqKOOclZXPvLII6Hn5cc//nFkndFy6DOf+YwDOQ8++GBRhYkRI0bglFNOAWBDRDnBDuD/TLdv3+6s0PbT008/7RzHEUccgVGjRhW9f8455zht/PjHPw6009XVhV/84hcAAEKI8Bcd3Qo7P+ya7enpwXe/+91KditWrNpUGRdhxIpVdSFg9auIwla/rl271nmvra2NLl++vGSbJUuW0EmTJhWtvvPmM1u/fj295pprfPdn6u7upocccohj45///GfR+2EVJbq6uujRRx/tvH/GGWeUrKQ1TbPI/pVXXhmaF62np4fedddd9P777y95j60uBkAPPPBAum3btpJtHn74YWfFMfupVO1XSotXuZ577rlF773xxhtOpYihQ4fSJ554ItTWypUr6TXXXFOS/+3iiy+mCxcuDNwvm83SM844w+nHPffcU/S+N/nwkCFD6L///e8SO8uWLaNjx451touqKJFMJn236e3tpaeffrpjh6eiRJTCtr366qt9K2S45a464V01vnv3bif/HiGEfv/73w9djbtz5076k5/8xLfmcaxYg0GE0njGaKzBKxZGmzFjRsncKV7NnDkTixcvBgDfCdbnnHMO/vznPwOwPWOXXnop9ttvP1iWhZdffhn33HMP+vr6cP755+Oee+4BAFx//fVFNVNXrlyJyZMnA7DDZscccww+9KEPoa2tDbt27cLixYvxhz/8AevWrQNg5yx74YUXnOMD7AUfn/3sZwEAd911V8n8wN27d+PUU0918tGdccYZ+NOf/oRUKuVss2bNGhxxxBFOO6NHj8a5556L/fffH62trdi9ezdWr16N119/Hf/4xz/Q3d2Nm266Cd/85jeL2spkMjjssMPw1ltvAbDziF188cXYe++9i2q/trW1Yf/993cWGKxYsaLIMyOiCy+80Fnw4nf8XvX29mLixInYsmULCCFYsGAB9tlnH+f93/72t7j44oudz/yoo47CRz/6UUyePBmpVArbt2/H+++/jxdffBGvv/66c/7cC23Y57P33nvjwx/+MPbZZx8MGzYM3d3dWL58Of74xz8689emT5+Ot956q8hb/Pzzz+PDH/4wADvXIKsRe8EFF+CYY44pqv3KFjecc8453LVfzzjjDJx66qloa2vD0qVLceedd2L58uUA7DlrQbVfjz/+eLzwwgsA/O8J3m0nTZqEVatWYfLkyZgzZw72228/jBgxAv39/VizZg3+9Kc/OdfQ8OHD8e677zqriJneeustHHfccU74derUqTjnnHMwa9YsNDc3o7OzE8uXL8e///1vPP/888hkMvj973+PuXPnhvY7VqwBqWoSZaxY5RbK7KmjlNKtW7fSfffdNzAnlmEY9Kabbiryung9dStXruTOsfXhD3/Yt5ZqmKeOaffu3fTYY491tjv99NNLPHbr16+nH/nIR7j6kkgk6K9//WvftjZt2uRUVPD7aWtro88880yRh62SnjpKKf3Od74T6pV65JFH6MiRI7nOxfDhw0tqivJ+pvvtt5/vsXuvmd///vdOOTm/n1NPPTWyGsgvf/lLWl9fH9qfffbZJ/Sz0OWp83qwg34mTpxI58+fH9jG+++/H3qtuX/q6uoiPa+xYg1UxVAXa1CrElBHqR0avfXWW+kBBxxAGxsbaWNjI506dSr97Gc/S1999VVKaekD2qtly5bRn//85/Qzn/kM3XfffWlraytNJBK0sbGRTps2jX7605+mjz76aGAfeKCOUn+w8wuzPv/88/TSSy+le++9N21ra6OJRIIOGTKEzpo1i5533nn0l7/8JV2/fn3wiaN2zdXbbruNHn744XTIkCG0sbGRzpgxg/73f/83XblyJaWUVhXqdu7cSYcMGeKE7xYsWFCyTU9PD7399tvp6aefTsePH08bGhpoOp2mHR0d9IgjjqBXXHEFffTRR33P4bp16+hvf/tbeuGFF9IDDzyQDh06lCYSCdrQ0EAnTZpEzzrrLHrffffRXC7n2z+/a2bBggX0oosuolOmTKH19fV02LBh9IQTTqD33Xcf97lavXo1/cpXvkJnz55N29raaDqdpqNHj6annnqqU2IuTLqgbuvWrfS+++6jl112GT300ENpe3s7TaVStK6ujo4bN46eeuqp9Pbbbw9N0MxkWRb961//Si+44AI6ffp0OmTIEJpIJGhbWxvdf//96fnnn0/vvvvu0FJ5sWINdMXh11ixYsWqUbnDr96QfaxYsWJ5Fa9+jRUrVqxYsWLFGgSKoS5WrFixYsWKFWsQKIa6WLFixYoVK1asQaAY6mLFihUrVqxYsQaBYqiLFStWrFixYsUaBIpXv8aKFStWrFixYg0CxZ66WLFixYoVK1asQaAY6mLFihUrVqxYsQaBYqiLFStWrFixYsUaBIqhLlasWLFixYoVaxAoWe0OxIoVa88UpRSZTAY9PT3o6+tDJpNBJpNBf3+/87ffa9lsFqZpOj+WZRX99r4GAIQQEEJgGIbzf/a3YRjO+4lEAqlUCslkEqlUyvlJJpNIp9PO6+z/9fX1qK+vR0NDQ9HvZDIeWmPFilV5xSNPrFixpJTNZtHV1YXOzk7nt/vv7u5u9PT0hP6YplntwyiLUqlUEfA1NDSgqakJzc3Nzk9LS0vR/90/ra2tqK+vr/ZhxIoVa4ApTmkSK1YsADak7dixAzt27MD27duxfft252/2uhveent7tbWdTqeR6bcAJAAYINQAqGH/jcLfznsgxT+U5C3Z/yfOe4WXKSEAXMMdcQ991PXbAs3/tn8oQCyf1y0AJihMNLWk0NfXpxVS6+rq0NbWVvTT2tpa8tqwYcMwfPhw1NXVaWs7VqxYA1Mx1MWKNchlmiZ27NiBLVu2OD+bN2/Gli1bsHXrVgfaurq6hG0TQkCtBIAUCFJFv4EkCE3CBrUkCNx/JwCahNEHAIk8hJVRBkAT5Z1CbAMfBZADyeVAYQLEBGCCEhNADpTkAJIDRf43yRW93ja8Dl1dXcjlcsLtt7S0oL29HcOHD0d7e3vR3+7fqVRK74HHihWrZhRDXaxYA1y9vb3YuHEjNmzYgPXr12Pjxo1FALd161Z+DxIlANIgSOd/19m/adr1ugfceICMAkafOKhoUwWgLkoka3FtZ8OhCUoyAMmCkmzhb7j+zv9O1VvIZDJ8fSAE7e3tGDlyJEaNGuX7u6GhQeEoY8WKVU3FUBcrVo0rl8th8+bN2LBhgwNu7O8NGzZgx44dkTYMw4BlpkBQD6AeBHUgtB5Anf13HuJsYBP3mtGUgd6OdKG9HEV6pw1xxKRI7OaDDq0yAOQXQ9AEgdmQn0JMgURPtuLdoQkDmaGFc0RMWvR3elufnN28d5CSDCjpB4x+UNLv/J+Sfowc24ht27Yhm40+7tbWVowcORKjR4/G2LFjMW7cOIwdOxZjx45Fe3u7s8AkVqxYtacY6mLFqgFRSrFjxw6sWbOm6Gf16tVYv349RzguCUIbADSAoAGE1ue9bPV5kEvbc9MkZKUT6GsPD9m5Ic5PZQU7F7wFqQjqAjcqH+x5gc5PbsgLel8W/AAGf1lQow+U5H+Mwu+mIcDu3btDbaTT6RLQGzduHMaNG4cRI0aAkDKH0WPFihWqGOpixaqgcrkc1q1bhxUrVmDlypVFANfd3R24XzqdRn8uDRiNQKLR/m00gNBGpHYn8+FQOfFAm1dREBckLXDHAXFecUFdyU7qkMcDc0GKgjy/7VWgDwBowkRmiAnQXoD2gtJugPZg3Jg6bNiwITSM39DQgIkTJ2LixImYNGmS8/eYMWOQSCSU+hUrViw+xVAXK1YZZFkWNm7ciOXLl2PlypXO71WrVgWGwAghsEgDYDQBiebC70QTQBoAlxeE5CjSu/jhSAbcvJIFOa+EwE4C4PwkBXW+hvhBTwXovBIFPL/9hYAvQZBpK15NS6kF0D6AdgO0G5T2ALQHE8bVY926dYHAl06nMW7cOAf0pkyZgqlTp2LMmDFxKDdWLM2KoS5WLEX19PRg2bJlWLp0KZYuXeoAXF+f/0OUIpGHtZY8vDXnfzcCJNyjEQZzOsDNK10g51Uo2GkCObe0QV2R0WDA0wl0XqkCnp+9QODzgTs/2cDXA9DdAN0Natm/06m+wEUcDQ0NmDJlCvbaay/stddemDp1KqZMmYLGxkaVw4kVa49WDHWxYglo586dWLJkiQNwS5Yswbp16+B3G1EYBXgzWvIQ15KHN7G5RwzmygFuXpUL5NwqgroyQJxXZYG6kkYKkFdOqHNLN+D52U9v6+OGO68opflQbh72aBdgdYXC3tixYzF16lRMmzYN06dPx8yZMzF06FDVQ4kVa49QDHWxYgWos7MTixYtwqJFi/D+++9j6dKl2LJli++2lNQDyVYgMaTwYzQCRB5WrCRBrp4gkS3vLUqJDT2gQCJDUbejMitDiUmRqECaE0oAmjSQa07B4EwrotSeQWDWG0V5jssmAph1Bqhh50dOdpe/Qgc1iPJ5tD173QDtArU6AdqF9mEUW7du9d1+5MiRmDFjBmbOnImZM2dixowZaGlpUepDrFiDUTHUxYoFIJPJYNmyZVi0aBHee+89LFq0CGvXri3Zzp731ggkWl0Q1woY8tn8rSRBtrHYc0csaIc5B96KXgSIVdqOkUNF4K4cYEcJAKP4OKlBkG0p9nASCu2Q5wBdyRtam3Fgzr8Pnk3LBHvUKPU2q8NeP2B1AbQT1OrEhHFJrFmzxtcTPnbsWAfwZs2ahRkzZsRVNWLt8YqhLtYeqU2bNmHBggV477338N5772HZsmW+CxhosglWeihoqg1ItiJhDgGIfBjPD+Dc0gFzvvBWspE/zLlVCbDTAXV+EFeyjQ/UlfRFA+QFQl3RRkpNhAJdcV+iNgBSu9Vhzw/u3FIHvSys1G7A3AXkdmJsB7B+/fqS7ZLJJKZPn4599tnH+Wlvb1dqO1asgaYY6mINelmWhZUrV2LBggXOz+bNm0u2o0YaND3Uhrh0G2h6KGDY86KISZHsE7tVrCRBrsHjMQp5/skAHRfAFe0QDXNu1SrY8YBc0fYcUFfUJwnA4wK6kp3ENucFuqImRDaXBL0osPNKBvTMBtciIivjQB7MnRjeksH27dtL9hk1apQDePvuuy8mT56MZLLMcytjxaqiYqiLNeiUyWSwePFiLFiwAAsXLsTChQtL6pomEglkjRbQumEOyCFRuoCBF+ZEAa6oDU6YEwa4op3FYM6tcoMdL9SJglzRvoJQ5xYv4ElBXZGBqI6IA12RedldBUBPFO7c4gW9IrhzGqaA1QPkdgDmdkwbn8Ly5cthWcU2Gxsbsd9+++GAAw7A7NmzMW3atBjyYg0qxVAXa8Arl8thyZIlmD9/Pt544w0sXLiwZGUdJQnQ9FDQ9HBYdcPzXrjwwTwI6PwAzm5DrN9hMKcEcEWG5GHOrWqBnQrIFdlRgDq3ggBPGeiKjPk1rAZ0ReZ1mIkAPRW4cysI9HzBrqQTWduTl9sB5Lajua6nJMF3DHmxBptiqIs14EQpxYoVKxyIe/vtt0vKG9mh1OGw6oaB1g0HTbUKrUR1A52KFy7QvgvotAGcW5pgjqmSUKcL5NzSBXVuuQFPK9S5xT5CjVDnmNbdXQ/o6QI7t9yQxwV2blEKmJ1AbhuQ24qWuu6ScaOxsRH77rsvDjzwQBx88MGYOnVqnCA51oBSDHWxBoQ2b96Mf//735g/fz7mz59fMn+GkhRo3XBY9R2gde2gyRbhXHAFW/YDj9Di13SIUBvoyibNMOdWJcDOyJYnJUc5oK7IfrJ8NU8pIbDSZbRfTmahQLKnTBe86pchDsgbNmwYDj74YBxyyCE4+OCDMXz4cMVOx4pVXsVQF6smlc1msWDBArz22mt47bXXsGLFiqL37XDqMFh1HaD17fbqVFmIM4Bcnb0vsezku9pEbE8fs53I6E9TUmKbAJbGUpuG6XowU4pEf3ke0rqhjhICqz7h/O0GL12Q7thzw4Vu2+y6JoCV0u3BZG24rlOq+R4AYKYKttO7NII7ATIthYs9pQKQDuRtBXLb0JjsRG9vb9Eme+21Fw455BAccsgh2HfffeMUKrFqTjHUxaoZbdq0Ca+++ipee+01vPHGG0UDqmEYyKbaQOs7QNPt9py4iJJaQXJDnFtagM71cHTb1QFzfiASaFsD2BXBXFFHygN2OqDODXLe14O8aToAL9BjpGib+n1R0QR3fh466nP9AvpAz/T0WwvkecDOLTXIM4HcDljYilljDSxevLjo7bq6OsyePRtHHHEEjjzySIwaNUq+rVixNCmGulhVk2maWLhwIV566SW8+uqrWLVqVdH7NFEHs2EErIaRMBtGgBgpGBIpzYIgjkka5gIegF7bskAXmf4kyq4k2AXCnNOx2vPWBcGc+/2oEKks3HGFAGVth3mfFeAuKuQaBHdO0wqQ5wU7P9vCoJc3GQR3TLKQl200ALMfRv8WkP7NGNnYW1L9YsqUKTjqqKNw5JFHYubMmUgkNLrLY8XiVAx1sSqqvr4+zJs3Dy+++CJefvll7Nq1y3mPeeOsxpEwG0aCpludkCqhlBvooiDOLSGg44A4t11RmBNKgcJrWwDsImHOrRrw1kWBnHdbkXlvIoAnNK9LxC7vdAJBuOOdQxcFdkVdkIC8KLhz2+aGvBCvnZ9EIC/b6DpxlILkukD6NuHASUm88847RelT2traHA/eIYccgsbGRu52YsVSUQx1scquHTt24OWXX8aLL76IefPmFaUboUYKZuMomI2jYNV3AInSIug8QGclAFNwMjkX0Ak82Nx2o6BLxisk5fXjgDohmGOqItSJwJx7H5nFDDyfk9RkfR67onNEOeFOdGGECNwVdYcD9HjBzm0zEvAEwY6JB/CKwM4tMwOjfxNI3ya0JnYVLbhIpVI48MADcdxxx+Hoo49GW1ubcN9ixeJVDHWxyqJNmzbh+eefx7/+9S8sXLiwqHajlWyE1TgKZuNoWPXDI1ON+EGdDMQV2QwCOskHmNtuEHjJhveU5+QFgJ0UzLlVBrALgjoZkPPur7pCNejzU1qBGWRTctEPsxkEd6orXUUhjCkM8GRtMru+kCcJdm75QV4g1LlFLZDMNhi9mzChrQ/r1q1z3jIMA/vvvz+OP/54HHPMMXEZs1jaFUNdLG3asmULnn/+eTz33HN45513it6z0q0wG0fDbBwNmh7CvVLVDXSqIOfYdAOdIsR57brhS8cEfG0rZl1gpwxzTBXw1qnCHJMOqCvYcv2tK7+gx4wS1LlsuuFOV+oSFQhj8kKeDpvMrgN5GsCOyQ14XGDHRCmQ2w2jbwNmjTKxZMmSQl8JwT777IPjjjsOxx13HEaOHKmlr7H2bMVQF0tJW7duxQsvvIDnnnuuyCNHCEEuPQxm0xhYTaNBk+JzSgilANUDco5NCzBMqg3kmM1EhmpNk1GO9CfEokj0a77dy+StIzlLC8wx6YS6gs0yJI0mmoDOY9PknGMqIl0gBhQgT7fNdKepDeyYUj2WGNi5leuG0bsB+48H3n333aK3Zs6ciTlz5uCEE06IPXixpBVDXSxh7dq1C8899xyeffZZvP3220WhVbNuGMymsTCbxgDJBin7NAFYSYDoSmdF8vYsSK2e9VU+iTChVF8/AYDk7ZrUTlSsyTbrp8FRY5ZblCKRsaJrlnKKuK4jndUIKCF5WMq/UIZKBzq/JGgHu7zHTmeSYeZly+WrXOi0TRNw7i9dKkfCbyupOJ6YvTB61+OgSQksWLCg6AvxgQceiDlz5uC4445Dc3Ozng7H2iMUQ10sLmUyGbzyyit4+umn8corryCXK4xmZv1QmI1jYTWNkfLIAfZAbrrWSNhgo9DhPMi57SkDnedBowXoXM9uBnMF++pQx7yd7jaUwY7BXNFr8uaIzxCkA+qKwIig1JNai3CnOwzrM79OB4B558flPCXMVNugbueaJsDTDXaWp0SsGuD1wehdj9njadHUlXQ6jcMPPxxz5szBEUccESc7jhWpGOpiBYpSe4B5+umn8eyzz6Krq8t5z6xrRa51HMymsSDQA3JM0kBHSgdaZk9qwA15mCgBnV8+WQ/QFdqROxdemPO2JQV2fjDnvCduzg/mikxKQldQwt7A8HgtwZ3OhRMRq2FlwStowYMX7FTboUFRUwXIKzfYMUkDHgFyyR4ku9ZieksvVq5c6bzV1NSE4447Dqeccgr2339/EN1h+liDQjHUxSrR+vXr8dRTT+Hpp58uWrllJeuRax2P3JDxsOqHAEA+pMdvOwjkmISBjhQG/6CKC0IDbMQDQwrmIsbeIKArtCl2TsKAjrUnBHVhMFe0HZ+5KJhzzAnCVlSi3sg5j9WGO91pTjhSnIgCV1SKkiCwU2kzEO4AKcCrFNgxiQJetjH/mVEKI9OJRNdajE3uxJYtW5xtxo4di1NOOQWnnHJKvMAiVpFiqIsFAOjv78c///lP/O1vf8Obb77pvE5JArkhY5BrnQCzsb1o1Sov0EWBnGOPF+giQM5tj2tA5XwwCAEdx7M3CuaK244+N1Ew5207Eux4Yc7ZPqJNwaGGB+pEEvQKLWTRDHhccCfQZORxc+asc+xxgJZIcmEeuONtF4gAO2cjfmCrNNgxcY1HBMg2eOPwFEbfNiQ716A1twU9PT32poTgkEMOwamnnoqjjz4a6TTHQBtrUCuGuj1cK1aswKOPPoqnn34anZ2dAOyBItvYYXvlWkYDhv+IFQZ1vCDn2IoCOk6Qc9sLHUAFv+FHAp1onlgBoCv0IfgciQAdaz8U6kSBDgj3DkoMM2FQJ5OYV2p1ska4iwQ7waZ0lxALAyyZkmC8YMfbBy6wczaOvr+rBXZMYeOT463zbSiH5O71OGy0hbfeest5eciQITjxxBNx6qmnYtq0aWKdiTVoFEPdHqje3l48//zzePTRR4sm5VrJBmTbJiLXNgE0FT5Pzg/oREGuyJ4f1AmCnNuW74ApORcnEOgUkgmLAp3dj9JzJApz3n6UgJ0MzBXt72lDYXjxgzrphQOyUMdUbrhTMF9yTiSArsieD1jJ1nmVAbuwfgiBnbNTyNxYjWAnCnVulYxXft46H5FMN5JdqzA2saMoPDtz5kyceeaZOOGEE1BfXy/fsVgDTjHU7UFasWIFHn74YTzzzDPo7u4GACQSCfQ3jkC2bRLMphHgTgrsgjoVmAM8QCcJcl57ziCpuHKuBOhUFyNKAl2hP4VzpQJ07v44YKcKdIDTHxWYKzKXhykdq0C15BEsF9wpmnXOjyLQOfZcQCULdEwqYMfk7o8U2Dk7F48H1fbWeeWGu1BvnVeUItGzGafObMC//vUvZLP24Nzc3IxTTz0VZ5xxBsaPH6/WuVgDQjHUDXKZpomXXnoJDz30UNFcOSvViGzbJNsrlxT7JkdMezBUATnHlmX/qIKc256R1TNYO0Cn4TmuCnNFtlg2fh2FJizAyFjqMOfY0zucUINoq66gLTl0OebbaTJJDaIF6mxb6kDnlg64A+x+KYGdY6gw/uiUKtgxGSaft65EuX6kOldjYno7NmzY4Lx88MEH48wzz8SRRx6JZFJTJ2PVnGKoG6TatWsXHnvsMfzlL3/Bxo0bAdh1BzNNI5EdOqVk0QOPrIT90NA50BOq52GrE+bYA1bLceYBzDD966+KyjCBRJ+l5cFNTCDZa7v9dMGYTqijhOhJQkxgV37Q2DdCoa2ahE6oI6Z9P5kNmrIBU71eV50VI8w6ou16s5IEyV495fiyjURf5RYC5BoIiFRKJopEzyacMMnAK6+84iQ37ujowOmnn47TTz8dQ4cO1dPPWDWjGOoGmZYtW4aHHnoIzzzzDDKZDADASqSRa5uI7NDJkXPl/GQlUCixZKnDDnHtrgJ0DOTc/5c3VgjxENVjpDZ82X/Twt9QAzvDBBK9+YNUDLO5gQ7QAGMaw65uz5wS1BEPeGmGOiZVuNMZhmWheS1g505arfi5uj9HbWBHiksIql7D7s9BCfBIcehUCfBctgiFHNwBINluJHetxAi6BTt37gRgJzY+6aSTcO6552Ly5MnyfYxVU4qhbhCIUopXX30V999/f9FqqFxjK/pGTUVuyDgk+8UG+CKQc16Uhx3is5sM0HlBzv26uLHSydjSQOcGOec1Wvoa5MCuCOiYJMDOC3PO67IPxDLBnPOaDNR5YY6pTFDHJAt3OhdOeBfSSMOdz/HJfsZ+n6Fuj51Xstez32chBXjEf06cFOD52JIFvGyThdTO9Ti4oRuLFi1yXj/ssMPwyU9+EgcffHCc1HiAK4a6AaxsNou///3v+OMf/4gVK1YAsBc+9LSOQv+oKcg1D4eREwsr+MKc86Y48Pg9/Jh4oS4I5Nzv83coOG2CMND5gZzznj/QAWJQ5wtzTIJQFwR0zvuiD0JNobmwOXNCUBcEc0xlhjomEbgLTXMimrUl4KOVAjuN6WmCPsNygx2T6HUd9pmIjKXZpmA7QnAXAIiABNwRINtM7NBs93acNgr417/+5YRmJ0+ejPPOOw9z5syJc94NUMVQNwDV3d2NRx99FH/605+cZew0kURfxyT0j5oKq64QYjWyfANRKMwBQkAX9sBj4s41F5HcmAvoQkCuqC2e4wsDOWebYKBj4gG7UKBj4gS7KKADBB5+ZfbOFb3PA3VRMMdUIagD+MFOZ1LisDyKQmDHcZp0VQUpVyg2cDPOayDqc+GCuxAYc4sL8CJsicBdtqXYjtG/G3Wbl6Nt93r09vYCAIYNG4azzz4bZ511FlpaWvgMx6oJxVA3gLR161Y8+OCDeOSRR7B7924AgJWqQ9+ovdA/YhJosvibVRTQRYJc0cbR0MMDc0A40PGAnHf74Df5MtZHAh0PyAFcMOdWGNhxAR0QCXU8MFe0fdRDrwLeuZJtw8CAF+gAbVDHe50D0X3jLiMWsRlvpRMuuOM8Pp21e7XAHSfYAdHXuUh5t1DA4wQ7IALuBOxEAh7z1nlfzmWQ3rYSEzKbHWdBU1MTzjrrLJx77rnxoooBohjqBoA2bdqE++67D4899piTf8isb0bv6GnItI8HDH86CII6IZgDQoFO5AHH5Fuj1RSvkegLdJwg57bhe2y8IOdsLwZ0TH5gxw10TAFgJwp0QMjDroLeuZLt/eBABOaYqgB1QHg/hWrDAoFwJ1IbOBTsBI8t6HqQmQupy2sXFor1U9A1L/rZBMFdWBg2SL6AJwB2QDjceb11RaIWUjvWYR+yzZnWk06n8fGPfxyf+tSn4lqzNa4Y6mpYGzZswL333osnnngCuZx9d2abh6FvzHRk20YhKiWJF+qEYc7ZsRR8ZGAOKAY6GZBz9nUzjyDIuW0UHZcoyDn7yQEdUAp1wkAH+EKdDNA5+3ofclXwzhXt5wYEGZhjqhLUMXn7LQx0Tgc8/5X4mAPBTmYev8+1IbtquVpgx+S99mU/oyLAE4Qxt4rgTtKOH9yFQh0TpUju2oCD07vw/vvv2/1JJHDyySdj7ty5GDdunHBfYpVfMdTVoNatW4ff//73eOqpp2Ca9oidHdKO3rEzkRvSwWXDDXTSMAeUAJ3sAw0oAJ0KzAEuoJOEOWbDOS5ZmAOUgI7JSkjCnFt5sFOBOceUVTgvgDrQqSYPpgZRgzmmKkMdEzsOaahzOpL/pfBxF8Gd4nGx60Q1t2ClQ7G+u7uuFZXPyYE7BbADXHCnYKcI7gJCsL6iFMmuLTimpcdJYM/g7vzzz8eYMWOk+hOrPIqhroa0YcMG3H333Xj66addMDcCvWNnIDekXciWkQWMDJWHOaY8/Kg+yBBUj1VGqs/CfGksVRjTAXR2fygM1WSlxH6YqgId60+1vXNFNhJET6LfGoE6wD4mZagDAKIGdYAL7HScHk3rHmoB7JhkvziWw04iQ5XgECjAHZe3ztv+7m2YM7Qfr732mv3/RAKnnnoqzj///DgsWyOKoa4GtG3bNtx7773461//6oRZM60j0Td2BnItw6Vsii448JUFJLwF3wVFiT2IGBnFvmh6WAD50KQCRFFig08iS9UrHlAbMHWUEDOylhbgsD2X6qU5VIGOUApKCKy0+tOQ5JgXSdkUYBAtn5cWsKMaPIcEsNJ6qncQquGegP05mWmiDKxmmmgZO3RcNzQfWdBxj1opKEN4/1CCZK/cvond2/DhIT14/fXXAQCpVAof+9jHMHfuXHR08EWTYpVHMdRVUV1dXbj//vvx4IMPoq+vDwCQae1A77hZMJuHCdtjA4+RUwM6d5hUFurYAAbYoUUpqNMIco5JBaBzzotFkcgUwiHSDzFaHAJWAQXDpIClwaPK+qUCdWxXQx7snFCeItSRgAU+Sg/p/Oet8nk5c0sNRbBzT7lSmOdq5T1aKkDm9uyq2HHOjcvTpgJ3Rd46lVOtWqDDM11E5V61Um7D8nb6hxZOiAzgJXZvxbFNu52wbDqdxjnnnIO5c+fGqVCqpBjqqqC+vj48+OCD+MMf/uCkJsm2DEXPhL1hNot/y/EONrJQ513EIAN03oFLCujKAHOAHNB5V+oWAZ3zosRDjKJkkYYsJBju/XSAnQrUuXeTgDpvyFcF6oKAzrEt+5D2fNYyn1vRdaUCdt5LUXRlrgvonNckgazks5OwU7Iy3ieEKgN4JWFY2dOtGewAebgrAjtACu76h7lORH5/Gbij1hYcZ3ViwYIFAICWlhbMnTsXZ599Nurq6sQNxpJWDHUVlGVZeOqpp/Cb3/zGyQOUaxyC7kmzkBk2CoQS4XlnfoOMKNQFpRjhhTq/gQqQALoywRwgDnRBufR8oQ4QAzsv0LleFwEEw2/bannr/DYVhLrA9BiCYBcFcyX2RR7UAZ+xKNj5Xl+icBfQpFAePR+oc94TgDIdqU2C7rmglayicBc4v07klGsMw/p2ReCzK4E6pwF+G0VQ59pfFOwyrQAoRXrbRhywe5OTCmXEiBH43Oc+h5NOOgmJhELh61jciqGuQpo/fz5+/vOfY+nSpQAAs64R3ZNmob9jnJOahAgsJggaFESALhBcOIEubHACOKGujCDnNMEJdFFVLgKBDuCHuiCgc73PAwi+QMdUaW9d0GacUBeZxJYT6kRhrqQdngd2xGfMC3eB15oI2IU0xVvVJQjonG04rmldSYgDzwnHggdewAu1w3vay+CtK+kK56UcCHYAN9wFgR3AD3eZVve+FPUbV2PqtjWO82LKlCn4whe+gEMPPZTPYCxpxVBXZq1atQq//OUv8fLLLwMArEQSPRNmoHfM1JKkwTxQFzUY8EBdJLxEQB3PoBQJdBWAOaepCKjjK1lmL4wIHSijwC4K6PLbRIFBKNABlYM6HuaLADueFbY8UKcKdE5bUQ9sHsiJ+Hy4ah7zwB3HIUfVXo6COiAayrg+wwgbkeeEcyVrFNxxr4aNOvUVADsgGu5Coc5pLPxtX6jz7BsFd0VQx2SaaFz7AUZtXuVMMzryyCPxxS9+EePHjw83GEtaMdSVSV1dXbjzzjvxl7/8BaZpIpFIYPeIieieMBM0XTrHIAroeAeRMKjjepggGOp4ByIgBOoqCHNAMNDxnguAE+icjQMeYDxA59o2CAwigY6p3GDHG5kNgDqRdClhUKcL5kraDLrORUKSAZ8V97UXBnYCh+13HfACnbN9UNhZ5HMMsMF9PjjBztk8APCE0pwEnf4KQZ3TjYDTzAV1TqP+L4dCnWffMLjzBTsAJJvBF0Y34s9//jNM00QymcS5556L888/H01NTdFtxxJSDHWaxebN3X777dixYwcAoH/YKHRP3gdmY/BqoCCoE7nxg4BOCGB8gE50APIFugrDHOAPdCLnAhAEOsAf6kSALr+9FwgIBXcxcgDlhTqRNRQ+UCea/y4I6soFdE67fte84OR/P7ATugaDwE7w0L3XgijUAf5QJvxZ+tgQOh+CYAf4w51w/jq/j6DCYAf4w50Q2AG+1w4X2Ln29YO7IKhjSnR34VTS5eS4Gzp0KC6++GJ89KMfjefbaVQMdRq1ePFi/PjHP8a7774LAMg1NGP31P2RHToicl8v1MkMGF6oEwUYoBjqZAYdwAN1VYA5oBTopM6FKNA5O7oeXqJAx+QBO24PnVuqYOcHdaKLYl1QJ5vM2At15Ya5kvbd94DsylBT4Vr0AzuJU8CuBRmgc5p1Hb/05+myIXNfyiYV9sKdVGJi78egAHay4ytQDHfCUOd0oPAnN9R59nXDXRTUMaW3bcS+O9ZizZo1AICZM2fimmuuwYwZM8T6EMtXMdRpUGdnJ37961/jkUceAaUUNJFE94SZ+XlzfHctgzqVQYJBndRAiQLQqQw2DtBVCeaYiIngRQ08+8sCHVCAOlmgA4qgTgroAL3eOtm0dXmoU6lO4Ya6SgOd0wd2P6jkcTMLX5akxOBO4RQQqgZ1QAHKlD7TvA3pcyEJds7upoZqEwrjJJPKWAsU4E4a7ACASkCda1/AhjteqAMAWBYa1i3HqA3L0d3dDcMwcOaZZ+Kiiy5Cc3OzXF9iAYihTkmUUvz973/Hbbfdhp07dwIA+jrGoXvyPrDqGoRsESqXf8nZ31SvIEEs8dQMfjYSqtUjFEUsTyFsKRshK10F+qHaB0OloocuqMuqV5eA4sPPSmiq1aQo9bJ76sOtlVI8F8SuE6xqQ1WWYum3oFQnlewDAFDFyKGlIfJIk4r7EyDTpnAuKGClxXcz+vvwqcYs/v73vwMAhg0bhiuuuAInnHACiIYSg3uiYqiT1IYNG/C///u/zvyAXGMLdk+djWybWI1W9g1LtlwLUBhUkt1y+xMKwBKcs+W1wcLHVCzXkqO8V4saRH6QzNuQrjFLkC8BZnv5ZM8HsfIeJQL5mqVUsexXLXjp3FLgEFWgI9T+LClRL4VFKFWCKiNjKZcGs9JqdVoN0/bGm/WSSZ3z17elAFUkB3VvGy14uVTsUIXKJ3bb9m+VOtC5esUv5QTINhEk+2SnOAB9HUTZMdDXYSG9U/y6Sm3fjH23rcLatWsBAAcffDCuvvpqjBs3Tq1De6BiqBNULpfDgw8+iDvvvBN9fX2ghoGuvWagv2M6d6gVsGHOSufDaxm5GnxWsgCFiYw41BU99CWhzvbuFewJe6dcIUppoMu3SyxJoCOFMBADOuctwXPiAB2zKwN1Lu+YFJiVY5GEKthJcpAK0DGY80oW7hxbRN5blug3HYCQgbuiBSOSn7HhCgOLgl3R9Q1Igx1h96ns/DjXsbvDl7Jwx2zIwp1ZB+fzkIG7XH3hbymwIkC2uTCPVxTuGNQp9QFA74jCuCUKd9QyUbd9MUYsXoFMJoO6ujpcdNFF+MQnPhEvpBBQDHUCWrZsGb73ve9h8eLFAIC+9uHYceD+oKkhSHXyDwYO0OV3MfrFoM4Nc4A40Pk+8AWhzgtzAMS8dJ75ZsJA5wOQwlDngjl7/9J5eEpQx9oQATufcKcQoJVr1WuFoU6Xdy5IMmBXYk8C7hL9hSc+JeLlwUpWAQt+1t75maJgV3J9QxzsiPse1QB1QOm8NFGbfvPaRAHPZJmqJOHODXaAIFi5oY5JEO68YCfcBxSgDhAHO0qAvlE5JLu6cfr6XXjjjTcAALNmzcJXv/pVTJo0Sawze6hiqONQLpfDfffdh7vvvhumacJKpbBz373RPWkCEv0GUl18k5fd3jn3nBReqPPCHJMI1KkCnS/Mud6LVMDiAW6oC/AGCgGdB+bs/YMXVgidG++xiUBdwPw1bkgrx2pXx7aCXUAI6srhnQuSCNz52hUEOzfUsfZFwC4wCTPnIfstuuEFO9/rm/VLAOyI9z4VBLuga9wPzETsBi1Y4IU705t+VBDuvFDHxA1WimDnB3WifXBDndMtAbhjYAdK0fzBakx49wN0d3cjlUrhwgsvxKc//Wkkk4oTCAe5YqiL0MqVK3HLLbfg/fffBwD0jBmFHQfsD6vevgMTfSTSSxcEc0w8UBcEdAAf1IU+7DmgLgzmAER76UJWgnIBXUholxvofGCuYCN8tSzX+QlamckDdhELEiJhrZxAB1RsXl0lgY6JF+wCbXOCnZGxfFeMioBdaGUNjkMPWknNA3ah1zj4wK4E6Jw3OCtGhBxj2CpSXrgLs8EDdyVgBwjBXRDYARxg5Qd1rj5EwV0Y1HG1D3+oc7rHAXcO1OWV6OnFuZu68corrwAApk2bhq9//euYOnVqdGf2UMVQFyDTNPGnP/0Jv/nNb5DJZGClUtgxe1/0jC/UagWioc4bavUqCujCYA6IBjquB30I1EXCnGs7X4XAnLNJGNRxzNMLhboQkCvsH53+JAwWoh52kVAXAXRAFJSXGeicdhTaAEKhrtzh1ijxgF2k/Qi483rpvO0D4fPseOrfhoFdVGqcMLCLvMbzigK7QKgDuMAu6jrnSQ8S1gbP/mFw5wt1zo72rzC4C4M6pkC4CoM6Vx/C4C4K7ELbd/aXBzsv1NkvUjStXIcp736Azs5OpFIpXHLJJTj33HNhCMxj31MUQ52PNmzYgO985ztYsGABAKB35AjsOGg2zIbSNCVBUBflnWMKgroomHPaD4A6/pCd/8OKF+YA+HvpOGAOCAE6Dpiz+xkAdBwwZ+/Pn88u8Dzx5E4LAjsOoHNM+IbOKwR0QNmgrhreOT+xh3VgSSyedkLALgzq3H0IAjsuqAMCwY4n32EQ2HFf5wgGu1CgczYKhi7e65w371tQO7z7B8FdKNgBkXDHA3ZAAFzxgF2+D35wxwN1oe0j3FvHFAR3vlDH2uvtx3mbdjt11A888EB8/etfx4gR0cn99yTFUOfRP/7xD/zoRz/C7t27YSWT2LnfPuieNKHIO+eWH9RFeefc8oM6XqAD/KFObGJ96cOKAR2vnSL44oQ5Z3Mv1HHCXKFtD9Rxwpy9r3iCYve5EnnQOX1zg50A0AE+n0clgQ4oC9TVCtC5FeS1424rAOx4oI617wd23FAH+IIdbxJrP7ATvda9YMcFdM7GpcAlep2LJPT1tiWaDNgLd5FQ5+xo//LCHS/UAT5gxQt1+fa9YCcCdb7tgw/qAH+wC4M6ewN7rt3YBUvQ19eH5uZmXHPNNfjIRz7C3efBrhjq8urt7cVPfvITPP744wCA/mFDse3Qg2BGFBx2Qx2vd84tN9SJwJzTvgvqhB/wlg+k8HrnmNxeOhWgE4Q5wAN0AjBX2L+KUCcIdIDnM6k00DntKrTpeVjWItAx+YGdUHs+YMcLdax9N9gJAZ1jpPCnaFUSN9gJX+d5ucGulqGOyd2mzP5uuOMGOyB/Lxa/JAJ2gAeuRMAu3z6DO1Go82ufF+qY3HAXCXV5JTt3Y84HG7Fo0SIAwEknnYSrr74ajY2NQm0PRsVQB2DJkiW48cYbsXr1ahBCsHPGNHR+aAZX3jkGdSLeObcY1KkAnXyCWvtBJQVzeRELwjDH5ECdCtBJwJy9v3wZMeecyZSsyoMdsajU/nai6CoBHaDNWycLdOWGObe84Vjhdl1gF7RIIqp9BnZSUAe4vEES92ce7FShTgjomFxgJ3uty5bfYu2qgJ0Q1AElXjtRqGMyshCHOlcfkn1UGexEoQ4ogB0v1AEALAvX13fgnnvugWVZGD9+PG688cY9fhHFHg11lFL8+c9/xi9+8Qtks1nkGuqx/ZCD0N/BXxXCyBLp4vVGv53BW7ZuXyIDpHbL7QvYAzW7EWUGTmLK1zalBgE1xGHOaZtS6bJqKkAH2McsXYOU2McuW3rLLiencMuqAB2g7KmrZe9ckJjXTqniiuQwy8BOGuqA/Gcu237wHEMeWXVEDuoAB+xUvsDIgh0lamXIKCHiYAcUwZ0s2AGAWS/Zd2qXWJSBOiaSC18sESWrXmzfui3bse9bS7Blyxak02lceeWV+NjHPrbHlhnbY6Gut7cXP/jBD5yacz1jRmHHgQfAquMvYGdk5cuqWCl74Eh1yV14xAISfUCyT659BnSyA6YK0DHJPizshzu4UjiU7gsYWSoPkxaFoVwTVtJDSOGUH5OSKtAB8lBnAFZSvrwVoRQkawEaanVKtZ9TKw9GTCpdVYMaBLkGyYz6FEj2mcp9NxWgUrpUHgDVOrXEotJl2ZTBziBOCTHxnQGakIdSQAHsAPS1UyT6Ffaf1geyVfbgxcHO6OvHues78eqrrwIA5syZg2uvvXaPDMfukVC3evVqfOtb38KKFStACcHOfffG7r2mBC6G8MoakkPL8G50bWtCeqO4m81MA1ajBaOXINUpUabHtF3dUkBn2d/a3SFXUamEXAEbTmQ8AMSiSGTZvnLtGjkqFe5l7Vcd6AA5qNMBdEwyZhjU5fsiIkIpjIxlh9llAMECiGkBhIBKPuBJ1t5fBo4cz6psOI/kPT8ytVrzUAfIlzZjXyRkwI5d7ypQqQJ27NzL7u+EvyXy3bLPThbsnNJlsmFkBagDAfqG2+dOBu76p/eCUiIHdgSYtv8aAMDi98fy70cphiz6AO0Ll8A0TYwfPx7f+c539rhKFHsc1D3//PP47ne/i56eHlhNaWw5+FBk2odz7ctgjgDo3NGI1Ia0kKeLwRwAG+i6xDwXbhAThjqrOAQjA3Uqq1yBgldQBegK+wvtXgA6QH4OnwLQqYXufOY0iZw+nUAHiEOdG+jy/eEVAzpnV1GwY0DnGBQHO5It3l8UUIrC5TIrK0nhb2Gwc0EdIA5X3pJ3omDnve6l4U4S7JxzTyRr7boXq8iCXb59UbjzjnGiY54S1MH21jGJgh01gMxeNtiBAmSb4MEbwIdmr0LOsg9aBO7c4diGhgZ885vfxDHHHCPW/gDWHgN1pmnijjvuwP333w8AyI5rxebZh8Oqi5644IY5ps7tTUhv4PPSuWGOScRL5wdgQlBnlc6pEYU6XUAHiEOdG+gK+/O3W9JXAairpncOCAA6gB/qdAMdE69JL9AxcZ6SgQ51vvMfRfKgkdLXuMHOA3RMQv33+TIhAnZ+134lvXYl518Q7rzbioJdUbqTCoOdTqhjEoG7/umFXF00v5qN23NHgA8dsMr5ryjcGZl+nP7BFrz55psAgAsvvBAXXnjhHpGseI+Aup6eHtx4441O0sK+g8dj88TZIBGjqx/MAfxeOj+YA/i9dEHgxQ10PjDH7MLim0/nCz8CUOfXBi/UeWGueH++tn37yQl1NQt0AB/UlQvoAD6oCwI6gAvqvEDn7MoLdl6gcwzzgx3xW9AiAHaBi1p4Kxf4dJMb7AKgDuAHq6AvFLxgF3T9VwrsfM+/ANgFJoLmhDvfBMUCcBdYj5Y3wbKmEKxXPHDHvHVFrwl47mgCmLX/qqLXxODOwtX99XjooYcAAEcddRS++c1voikiTdlA16DH1g0bNuDzn/88Xn75ZZAkwbBPj0bP8VNDgc5qzaFp8i4M8QE6AAANX5FlpoFsm+ULdGx/WaDjVgDQOfZlgU5AKqvWgoBOpG2VhRw1DXRANBSVE+iA6JEjDOiASCgNAjr7PQBRqzmDgA4AKN/qZV+gy+/Ps3o5dJVyxO5BQAfY5ybRF2UgGOgAvhyJgeeIAomAz4ZXsqu/WftGVmVJrPoiL65a0yHtJzLRm4VJdWzmUtgzro7j/vH9PkRBDArangEdHn4S/PZPGhaShoUZM9dhxsx1ET0w8Nph72DoJ0chnU7jpZdewqWXXop166L2G9ga1FC3YMECXHrppVixYgWMlgQ6LhuPZWNnYPeq1sB9rNYchgwLgDkOBXnneEUs2xMnDXSWPeDJpjBgfVAZNAiVBzpiUST75YGOUCCRpYMb6KJUbqCLUhTQRSgM6LgUBnRMnGAXtr8SmABK6WG4wC5CqmAVBXZR90DVwU5xnDByCnBHgUS/GtypjtOqMusoF9z5yQ13MnLDXZheWjAdTQe3ou3SUejo6MDq1atx2WWX4Z133pFqdyBo0ELd008/jauuugo7d+5EamwdRlwxEekJDfaKHJ8bwRqS984N8ymk6lLnjkakfFa8mmkg2xrincvL6CVI7S5FxlqAOdYPpf0HgncuIHxLLPtBGQMdp/xGDxGgU4kM8XjrFBTopePdn7dvPs2EeemK2hgAYFfu9o0MDYQ7rs8g9topKwzu0stK66W7RUiI144C7709MXT/SK9d/tykxzcgeekQzJgxA7t27cJVV12F5557LtT2QNWggzpKKe677z7cfPPNyOVyaNi3GR2fn4Bkm/+ihshQa0kDxaHXIpjjmudUHHoVhTnf+XQRoVYeVdM7Z7fPD3TEZ05crYRbKwp03k0HuIcOEPPS+YIdj5eOKcBbxw10Orx1gH6PXUTo1Su/Y+C+DjWBXS147bwSWVChDHY16rWr38Z/DvzgjmvuckhIlmd/3pBsYkgSXf9p4aijjkImk8H111+P++67D4NtWcGggjrTNPHTn/4Uv/rVrwAAzccOxbD/GAMjP6l34ebR6F49BIAL5iRDrcIw5yMt8+aqHGoF1GFOJdzK2q8FoFPafyB56NxSHUHciwMrEXb1qgxhWJWKH7xeuqL2atBjJ3M/VBXsoMdjVwteO61wJ3FKdIRko+bb+ckNd9NnrHdef2nBdOdvI21g5ce34BOf+AQA4Fe/+hV+9KMfwTRVHsS1pUEDdf39/fj2t7/trHRp/XgH2j42AsS1ytKiBLRFHuZY6NWZN6cAc0qhVkCbd05p/wp654LaHwzz54ysKlhU2UMHqHnpiDzQ6Q7DSoVdXWAnDXTK92Ie7AS9dG4pg12/Vf1wbJXn2QF6wG4wh2Sj5MDd8AxXCNarpGEhlTALcOeN6hgErx66EK2nj4BhGHjkkUdw4403IpNRPOk1okGR0qS7uxtf+9rX8NZbbwEJgmHnjULj7CFF28x/fBaa11D0n7VToZ16WDvS0jBHDYrE7gTqtstPJkr0A6luRc9cThXGFL/V0kLhankbakXtiQll7wahdj9U9leCOaBq9VDdooSolYKiQKJfwzflag9l+Rqx0jLEk3KXQ75pOASlUtrL3l/R3yBZdca9f06mgodHpsp5IGr1X5lyjfrz1XGLAEMP2IIt21qkdmf57WZN2CDdhayZgHHmLgx/qrTUXs/CLnT9cQuy2SwOOeQQ3HzzzWhoCJ8HWOsa8J66rq4uXH311XjrrbdA6g20f26cL9ANWWmh+/ROqTaGNfVg/5Hr0DqkWw3oug0loKOG2kBFif2jAkOgkHLJu/ev9rdIB0oVr36q4fmr9OCpEckWqwdgA11vDlAFUx1Ap9qFJFG7pix1yFe+P6E+lYAaimMM1EOhVlIRsDWEcylR//Krw2snW5/c6YbKc4cC2xd0oGN4l9TuhFCAAuZpnTA/vlvKRiphoutPHdh2cukXx8Z9W9B64Ug0NDRg3rx5uPrqq9HZKccJtaIB/UjZuXMnrrrqKixatAhGYwIdl4xH/V6FAr7zH5uFbSdmMeF7r9thiYQ4TQxr6sH45h1ISI5S1KCgSQswqBIFKMOcAaXVhrYh9f1rAujyg7W0R4IUfmQfHKoPPSaq6h1SVT6RtZGT+2AJpc6PNNjpBDrZLqh6llxSBjtA/l7N76fs/bXUPOmgdhhUFe5U7g1CgUTI6lpeqR4DoAfsdMCdjIgJJAwLI9o7peHO6u6G1dUlDXZD6vpgdXVh24nZErirn9aE5s91oKWlBe+++y6uuOIKbNu2TaqdWtCAhbpt27bhS1/6EpYuXQqj2c5Blx5n+6rnPzYL2042MeH7r8Pq7gbNit8RzDvHgO6draOxfW2bkA1qUBvmFKTDO6cMc4C6d86sLaCTN6KhH+4uGGTgeuus4r+FwY4ChuT8r4KNMgCQqkkNn6cU2Gk+DhmwK7qWNYAdgzsVqX7pYfNeVaQDUFXBDqiu184gFAnDEgc7Aiy54xAAsMFOwWvH4HDbyWYR3NVNaEDjxcMxfPhwrFixAlddddWABbsB+TjZvHkzvvSlL2HlypVItCbRcdkEpEbV4c2/uWCuq0sLzDEPnUUBYvENDkXeOQXVjHeuFsOthHA7PokFJPrVv3FrBzpNqoq3TvUzzYdd3aFbJW+dQj+UTfh56aoFdl6JmPDZtuoeO0Ab2IncI945ojrADlD32ukKx1bLaweIe+0IoSB1BQBzvHYqcNfVVQJ3qVF1SF00BCNGjMCqVatw1VVXYfv27VL2q6kBB3Xbt2/Hf//3f2PNmjVIDE2i47LxSI1I482/zcL4H73hC3Nd5x2OrrPDL6AgmBORTpgrl3eOJgDLP2Wfz8byfWD717R3joD/Dgh6HgiEYMsBdFVR0GfK6a0jFi0BOuc9EbAr58II3i6EhV0rCXZhm1V5jl2lwS5skcaeFo4NOxe14LXTFZK1Tg8vGmAQit1PTim1kYe77afY41ZyeBrGZ5vQ0dGBVatW4corrxxwYDegoG7Xrl0eoJuAha/MxraTTYz/0Rug/f2++1nJ8Pl07nlzKnPnVGHOtqOwL6d3LtLDpcM7N0DCrVzz6srtodMQgq2Yt0419YZFYfSZaosrgMosjNDBjJUAO55+Kh4rT2Lt0Gu4Rjx2wOAKxw50r510SNYlq6sLZmdnJNi11nmz9hdkdnZi28kmtp9iITk8jcRnmx2wG2geuwEDdbt378a1115r13EdksTmqR/Grk8ZBe9cANCFyeudk9FA8M6JGeLYxACsoBQWnN45HStHw0KwWubPAcrnVDWXX02JB+hCvHW8QBfpravkStewbvAujhiIodigfqh47fJgp2MBharCwI4nPU8U2PGOb2HHwvs8GOhz7QD1hRSADWY8XrsgMTjcdrKJzrlJB+xWrlyJa6+9Fl1d8n2rpAYE1PX09ODLX/4yFi9eDCuVRnreWIz95WJtMDfQQ601M3euRrxzQvPngkKwIpAcEIIVuqxq3Vsn8rn6gV1+UQSvhy4Q7KqRusSvG6KrXcsFdhqORdSGH9hxX7sWLevKWJH8eKLz7LzSGY71hTuBrtWS166qIdk8mOmAu865SXT9qxHDhg3DsmXL8LWvfQ39ErxRadU81OVyOVx33XV45513YCVTqH9zGOiWPimYAwZPqBWokZWt+f2rDXOAvHeuJARbowsiqiYNIdegOXRCqmZy4YESiuVRtT12QHlWxkrnEK1+OBaojdQngDrYAQMnJBsms7MTdGsfel5tRlNTExYsWIDrr78euZxKyZDyq6ahjlKKH/7wh/j3v/8NWAT1C4aB7JS78AdTqBWIgc4rpXCr21sne05d3jrph1UteutkP1uPt04G6MqyGrZcq10rKAfsFD3rqjYY2Elfs5rn2VlJlTygMdgV9UMT2KmGZDuGdymHZK3Tu2Gd2SNtg27uR27BUKTTabz88sv4/ve/D8uqgYdegGoa6u666y48/vjjAAWSi1pBJOcqZpuJsncuocM7Z1AtOclUw62EhVqrDHS65tUBOrK/ixdS9+3OYPLQqdZUtQCj31TKReeAXQ1UjAA0AJ2mEVemTm6JBoPHDtA6z06l3B0DO9VxzchR5bFIWzhWg1NKFewShoX2dsW5djt3AWfIpT8BAGzIwnqrAYlEAk8++SR+/etfy9sqs2oW6v72t7/h7rvvBgAkl7UgsaNOyZ4szLWmejGtcTNa64NXzkQ3ToG0BSspbwLIe6MUc7USCwC1U5uo9UNtENW2gIBSPaExRdmeQkvds6TorSOU2p4c1ZBpTtfD1lIOuxLTUoMHan82xFQ4KQSgCUM9Px+gZ9Sl1L7eVKTB22/WGWp1f9m8Ng31ZhM6QFdRbFzTMbapRkByDdDyxT3Zo/7ZtKT70ZKWn49maDihpmL5r8T2epBFTQCA++67D0888YRyn8qhmoS6efPm4Uc/+hEAILG6CYlNjRF7lEetqV6MTu9CglgwZO+OBAVJUqUxi1j2ty4jZ6dnMevEjdnQYS9mUJEuoNMiBgtEoYC4y2MpXfLLAohkmSydcoCOSSF06kgWyPJApyzlmrCFMDChUAM7p0/qJlRGXucYVGCZ5EP1Kh5q135KYOfYky3bl/8iRIFEvyUPd5rXGFUb7Jy5wgpRmUwrHLDTBXcDWYnNDTj//PMBAD/4wQ/w1ltvVbdDPqo5qFu3bh1uuOEGmKYJY3M9EqubKt4H5p0bnd4lbyTvnSNJRQCy1F3gxNIDc4ksrT2gg0IYV9OgWwJ0VfDWlQCdgtxeMTtUL2i3HEDHkSettB+lq3ClwI556Yr6JmbCVxKjr7fvst469+IgKbAjgJkuPgBhsPNbfarBYwdaPa+dmS6tRKEqYmmatyzRF2dszYNhLXjtqq0/XvIkPvzhDyOXy+Gb3/wm1q5dW+0uFammoK6npwdf//rX0dXVBdKZQnLpEBDdX59C5Ia5hMpdFOCdo0Oy6G/ns+v2zqlIF9D5whxLp8JjQ1e4FSjr/Cru6hCWPa+pJj10bonyi48doc+tnB46GbBTlR/QOf3RYF91BJYJw/pc4rrmlGrz2PHCHQn4AlRFsPNKdOwLijpUC+y8+wt57Qgw9uD1vm8NVLAjIHjpO+9i5syZ6OzsxFe/+lXs3q0wX0+zagbqKKW45ZZbsGLFCqDfQOr9VhAtM+n55A61SivCO0c4IUiXdy4s3EoNvnl1NRduDQA6agiEYBX7wxVurZC3TquHLswOD0hXIuTKC3Y+Xjombm9dGNA5/Yk2EyneL0ZBfRYEu6AqKtxg5+OlK7LDA3Y8OeJUvXYiYFeBR00lw7G5hpA3VRfJCXrt6hLBD7OB6rUjFsHy321FR0cHVq9ejVtvvRW0mimXXKoZqLvnnnvwz3/+E7BgA11GYSa/gLSEWgHtc+dUNNjDrb5v8553Hh4IedhUdP5cBNhxA11Ud60IoANHGFYX0PEoCuxCgI4pEux4gE6nIpqKhFBesIssIRgBdhFA59jR4bEDwsEuyEtX1JHKeey8oVc/VSocy1X+sNJeuxANSLDLJrDzGROpVAr/+te/8Mc//rHaXQJQI1A3f/583HnnnQCA5AdDYHSlK9KuiHfumI5lmDJ1U+kbVZg7Z6X8F0tUZTFEgPexGuHWSG9dJTx0bpUxVCjsoQvqtgXukGbg56kT6DQujIhSTS2cAMofiiV8D/uyh2JFKzmU22NXuYCQ3VzI2Ciy4EtbODagL5lW/v3LOdfOtAwM+8wWZfvlkLE7hSuuuAIAcMcdd9TEwomqQ92OHTtw0003gVIKY2M9EpvC/MZ6JOOdSxETSe9dpME7B1RvMYRfCLamwq2A0Py5QG+dRLjB+9Cp2gpXH2+dtpCrANA58n4e1QI6P2+dANAx+YKdjJeujGAnBJ4hYMflvXFv692c00tXZCehlvut0HbpggqhxUSqK2MjxOOl86pc4djQ0GuQfPoiNPtJE9gB/l47c6diJK2M+tnZ9+LEE0+EaZq44YYbsG3btqr2p6pQZ1kWbrnlFmzbtg2kJ4Hk8iFlb1PL3DnAATpRuRdLxOHWCEnMUSjx1pVrhSuvdHjrXGCnBHRW8d8yiw6KwrDV9tC5wU4C6JiKwE4l7FoGsJPyJPqBncTztgjsJICuyBYDO5V6q+VYGVthL51X5QjHisB7kXSHY0MWSURpIIVjCQhe+PbbmDx5MrZv347vfOc7Va04UVWoe+CBB/Daa68BFpB8vxXEKs8d1nH3fLz+5321zZ1TCbeyxRKq3jkrZcNLTeSeY8ekA+jYYgjJSadF3y5VI3kGqZkcdIAmD50FaaAr9APVBzrXvtpWxOqYR1eLoVjOsKuvGU2hWEDvylilyjya59nJeOncYuOmdK5NZkdjOJYr9BqyPwO7sEUSURpIiyiIRbD2wU7U1dXh9ddfx0MPPVS1vlQN6pYsWYJf/epXAIDk8hYYPamytUX7+2H0Q9k7N2fkIuw7ba3SF0bam0B6F1H3zpkA0VVXWEv4WNeDVdcTRN0Esagy0BEavQihknZA9UAQUS0hxqQDyKwKLtLgkaZzo/w55b8YSXtumBmN9YS11iVWEdU3Zum4F2xvsbodS9P6QlVHASiQ7CZYtrFDzQwlGPYf1Q1n8sroTeILX/gCAOBXv/qVncmjGv2oRqPZbBa33nqrnWB4ax2MjeWfR6eiOiOLoclutCZ6kVagMdqbQLIzASsN5BRyKhNTU8qTnPq3Ozv0S+WS1JZB9mIR1RCyXUuSWGreBULzYKjoYSMmtWt9qp5fBnQa7Kh4fxxp8rBpOSZmS3XhBM3fC6rTGNj1ouFLjmqZNseOKnRo+sJGCdTTcsDeX3WsYFI9N870Cg1gl+inSGTU7Sg9YwjQN60P1FIHO3PHDqX9K6mfn3MfDjvsMGQyGdx8883IZBQL8EqoKlD3+9//Hh988AGQJUh+UNkEw6KqM7JoNDJIwEJCIbbCgA5UfiwiJpDoL9xsNGGHYYXtaMpQXgR0GuSETiUGf2IByb78YKYSSaa05FuqDNgxoFNVCdAplO0q6o+CnULnFMBOJ9AxKYTs3WFXFbBj94IK2JV8AZA8x1Y677ahimDHpkhp8iapqGTyvuxlzDyGebCThTu3Z0zl3LiPS8s5ppAGu1xz4W8jJw93JJH3FGsAu4EiAoI3f/oBWltbsXTpUtx1110V70PFoW7p0qX4/e9/D8BOX0KylenCmF+/hd/fdyL39sw7x4BOVrQ3gcTmtAN0svL1zkk8VP2AjhqAlZSw4wE6FW8dG9RkwM5ecEKVv737AZ0UYPoAnYy3LtBDJ1G2yxcwJexoUTmAjkmwjw7QeRdXSi5QKLKhwWNXMCZ2HTpAxyQLdt7zIgt2uqZV+EmDx07Ga+cX6iQmFT4/vimhJM6x6f2CLwl2fqteVSNDDOz2BLgj2QS6X7H//uMf/4ilS5dWtP2KQl0ul8N3v/vdQth1a13F2rZ6epDo5ds2zDv34eGLsc/kdVx2HO+chZKBx6ynyDXy9afWwq1Glmrz0FFSOoiILKUvAjoF+QKdu0+c3rowD50I2EWGXHkf0Jrm0AW2J/rFopxAx8Sb1zAA6Jw2BFOJ+N0PomAXen1oyNcmBHZB50UU7HSGXQPfFLATNK+vSuHYoOOSAcRS4/b4WNFwbD70WtIViwh57SglSHyyR6CHtaPE9nocf/zxME0TP/jBD2CaqpMU+VVRqPvzn/9sU2uWIPlBS82FXXm8c/UkGzmvjsc7R4HIBQrecKuvHY4QLFcGcg5vnds7FwR0It66SHiLeBhUCuh4H0plC7nKigfoOMt/hYoX7CoBdEwcFUjCgM5piwfsAoDOscEJdlzAz3GeS7x0bqmGYlk3aiAUWyId3eEEO54FCTznh6sUIIedEi9dUSPg9tq5Q69+4g3HstCrb3cEvHbmtu3RjdWoXv7+O2hqasL777+Pv/zlLxVrt2JQt3Xr1kLViJUtINnKlAHjlfa5cz7eORFxe+ciHqg1P39O8v2KAZ27TyHeOl6gi/LWCQFdRNkubsCMsMOlKLCrJNAxBdUJ5gQ6p80wsIsAOsdGBNgJheZDznMo0DHxgB3HueECu0p46Yo2jHibZ/VtBT12vMdVqXl2vP3REY4d7HPtSCaBSy+9FIBdbWLz5s0VabdiUPerX/0KPT09IF1JGJvqK9VskYLm1bmBjkdBIVj3YggeBYVgRcOtQd46UaAL8taJAl2Ut04oU7nPQ6EaQMf64Qd2oh66ILCT8tD5bSsTcg2wI6QgsKsG0DF5jkEU6Jy2/cCOE+gcGwFgJ7Uy2uc8cwEdUxjYiUx/CAO7SgOds0PAyyLpVELATjRtSND5Ec21F2Qn1EtX0mgw2EV56bzyfUYRoG8v/txy1CL4wAfsKCVInMc5X6qG9bOz78Xee++N3t5e/OIXv6hImxWBugULFuCpp54CKKq62tU7r052MYQ3BCu7GMIbguUJt/rK8zCt1flzgNgA7d1W5wpXIycAdM6OxR0i1E7arBpyJSZFotdUD7lSCmJa+pLxDlZpGn60Vk+phvJfRlTDsYQifz/V0AnJhxxVbaisjHXLb36cMKwG2BFWwDw7mf74hWNJUuzBY+U9dl64M7cOjPx0YSIgWHLvBhBC8Oyzz+Kdd94pe5tlhzrLsvCTn/zEbmxTA4zd5UsyzCPmrVMNtzJvnWq4lXnrVBdDMG+dariVeet45s+Fyeut81sQwW+MlHeFq6BogijPn2PeuiLvnEqaER0LItzty/bF662rppeOyUnCC6WKEUXeOsnz4/XWKSWUdp1nIS+dV26vnQLwFnntquWlKzGQ/yWb9Dg/3jCwU03uy86PUkUMlx0hL51bnnl2ol46r4wchL10Rd2xCKy8145SgsSnBkYlCR4Z3SmceuqpAICf//znoGXO51p2qHvuuefsxRE5guRKxStHg6yeHiT6oZyqpJ5k0Z1VT1VC8/+ozlFwcklpmT9XPe+c7740X5NWR39UbWhMzWADpgLMuW3p8pKowKXTmTzY1QLQuSQTdi3pi2kJh11LbOTBTkuFEELUgI6JorRerEx3JL8E+trSWP5NWYpf4twilgZYhd55djr6Y+TEvXReMbAzt2xR71AN6ZnrXkNDQwPeffddPPvss2Vtq6xQl8vl8Jvf/AYAkFjXCJKraqnZIqkA3drMMLy4axo6GnYjMXm3tB0jC6Q77fqtZlraDICCp0/1m6SRo0j2qQOd7RUhyoMFsag68LpsqYdk9MGuDhV5R3RBr4awFQBAR0kolQLwTITASmoaezQ93I2cpV69AvZ9pqM2MbuGDB01UTXW41WqSe2SlqoaFEhoWkChY0yjBkEiq24n10CQKM1AIqzekRbojjToDvmHGbUIpn9plXpnakwkm8BnPvMZAPb6gnJWmigrZT322GNYt24dkDGQWMeZlK0CGnPH2/jRvWdL7bs2MwxLu0eg30zCAEUiITcIGlkg2UsKD0DJ5xYxgUSfeh4iI0eR6raQ7FMHHwZ0dgfl7biBjhICqgCszkNGJQzMgM6yv9kqwwY7z5KlyFhftMkdhpM1647gEqIGdmxfFbDzAp3K6aKu3wqQ4IY5HWAHaAA7l4dWBeyce0sR7Nz7KoGde86ywmfmjD2qYJf39BMd0RmnP1CDO2L3J9kLJbijBgCLABZRAruBnMYkTL//3KNob2/Hxo0b8fjjj5etnbJBXX9/P373u98BABJrmkCs2vHSWd3dSEnkNGRAZym6n0qADoCVFPfWEdN2nRd5jgxxb53jnVNMw0KJPR9PpV4qk6+HTva57n24yICdC+icl1TAzgs/gucsEOgUYIw99KQffn7dkQU77z4yYBfkoZM5PO8+kmDnB3GyYFeykEgS7IrCeApgV3JPSYKd3z5SYEdQMmbUkseO5IFMBu6K7gU2P04C7HKNpYu/ZMCud5TnelEEu8EoYhH8x3/8BwDg3nvvRTarwc3qo7KR1t/+9jds3boV6DOQ2NhQrmakJeKtY+HWD3raS4DuQyM2ITGFPwTrB3SORFIJmPK1/Yr6kwc634eWgIq8c16JHJdlex39BjoZb52uagpBECUFdkHwI1C1ItRDJ3rINOBhJ2InrDui8xCDzqcI2EWFXHUcm6LHzq1qeex853RKgF3glyRBsAvbVhjsgoYj0VJyfmNOHuyE4C7gPhD12oVVxBAGOx9TMmDnu/BDEOyoRTDzv1eKNTzA9MtP3o/hw4dj8+bNeOKJJ8rSRlmgLpfL4f/+7/8AAMm1TSA6ZmFqFq+3bm1mGD7oaUe/mUTOxwWWNkzsM3pDJNix+XOBQAd+b10k0HF66wKBjolzvAoFOgHxzJ+jBj/YhT5MeL11IUDnbCJy6Irwwx1y5X3WBACdUBiWZxteGNMxD493Dp22OYh8hqLATQTswq45XrALXaSjIRTrqBpz7CIuI16wCx1rHA+ZHq+dznAsj7xeOm9/eMGuxEvnVh7souCOAd1gDb0yEUqcuXX33nsvcjlNE8ZdKgvUvfDCC9i4cSOQJTA2156XjmnMbxbiR/cFe+sY0PnBnFtpwwydW1fknYu6/8MG6/z8Oa68SRFgFwl0nOIGuqhBVmRBBIctrodIFNhxAJ2zKWem+khFVK0QmkMXtWmQh85pjwPsuMGfIwzLcw6jvHWiiyI4zhGXjQhI4AU2nu14vkREgR3XqmtOsOP6UsNxT/KCXyTY+YRdfTej6jn6APCBHecXNp4vtXz9Cd8k10g4wNcGuyi4i0zPwjnPbrADHdOvPv1/GDZsGDZu3IhnnnlGu33tUEcpxR/+8AcAQGJ9I4hVe146JqurC6nu0tfd4dYooGMKCsOGhlv9+hTgrXPPn1NZfSm8ICLUsyTooQsKhwiucA0Lw2pLgyAAdHafIgZcEfgJqFpRtkURIQoFO+EQfQjYiXjogsBOdpWroqfa2TbgfIqGVsO2F/EKh4IdL8xEgJ2QlzoE7ETv21CwE516GVROTmSqRxjYCUw/CAM70YoYoQsoeGcx0HCvXaiXzqsAsKMWwcyrB9+K1yARi+Dcc88FADz44IPa89Zph7r58+fbeelMgsSG2lnxGiSvty4q3BokP2+dKNA58k7uNSVrEXq8ddILInzngEmGXL3HJpmyxC8MKwV0ft46QaBzdgs6HTLw4zq3SkAXAGMiHgrl+Xbu3fzATnIhhfLq4zDJHJ8P2MnOlfPbT6oCgQ/YCec3CwA7qVk15Q7Fyi6m8paTk1lpX4OhWD+vXVjYNaxPfmAnnETZIqA7S8FuMFSPENFdn/0z6urqsHTpUrz99ttabWuHuocfftg2vKm+pvLSBcntreMNtwbpQyM2wZjczTV/LrRPLm8dAzrV3Gg6w61lXeEq0hcX2Ck9KBjY5asyqEBUCWiowE++coWyh44W/628ulX5GnKBnWLKE+d8q+aiowF/y9hhq4gVFz+UY/GEdLJrD9gpTZP2gJ3KvWuYtAB3nGHXILH7QiV1UgnYSSYr966MVfoC4wU7afAtBjshL51bZn6e3c70HuelYyI5AyeddBIA21unU1qpa+vWrXjppZcAAImNte+lYxrzm4X46R1nKwEdYHvrqEX458+FiFiF+XNKQGfY/VAGOqrgnfOKaPw2Cn0hV2JSG6AU7BWFYXV0S1s4GUpAp5y/ztemBiMGAU0aepILK96zbjtGRrEWXV4M7JSTeOcs9eolDOw0JDtmYKevSgNVAjrHjs45dhqqz+heQCHjpfP2J9FnOx6USp1ZBDAJsDO1x3npmJ789ssAgBdffNHOFKJJWqHu8ccfh2maIJ0pGD1JnabLKqurC2PueBtv/3EfJTvru1thUYLekWqDXiIDpLrz3/ZVPXQZINWjo3ZY/peiKVbD1dBQIolQO5SsIwmwjhJJTDoeVIRSEB0rD/O2dFQwsMua6bKjdx5JLUrbMeoAH43l7QDouX9zmkqlIT/3S4ctCzA0pIqyy29ZSGi4hwkFkv3qfTJT+S/3GvoDAHXbVAvYUtx+6p342Ls7lPs0EGX0JLHvvvvCsiw8+eST+uzqMkQpxWOPPQYANZmXLkpWd7cDUjJa392K9duHAADI8H5p13QiA6R2F7xqst/QjQxQt4si1WOpe0TcfciDlJQZS8/DAMh/o2amZHLFMWkGuqJ5Pgrze0jG0rI6TytAuUNmsnPFPP3R1TctHhZNMrzhTkVp/QxV4I4QwICTekflXnaPITUDdq4+6QI7Bnc6pAx2xO5Pspci2StvK5sv4U5MBbAjFL8+7TeYlNqJOU2L9liwW/RnO/T8xBNPaFswoQ3q3n33XWzYsAEwCYyt9brMVlQd9y/Auw98SGif9d2teGPteKzfPgRWvmoGMSiQEP+AvEDHJAp2zDvnzA9jkhnPWSRREex8gU5lUjNV9xqWFegk5QY692vSttwApTI/z2c/4VWdAXAymMDOLzRZdbDzgpwM2HmAjknbl7RaATuXtIAdoBXsZBPOF2VUyMOmDNhlm4ufBdJgR4DxyU4AQIJQzGlahNPf24aPvrtT3NYAlrG1Dg0NDVizZg3eeecdPTa1WAHwj3/8wza4ra6m05iESdRbx7xzpmk4QMdE2jJC3rogoBOVA3RBdkQ+Gj+gk1Coh04m/YAf0Il66yoFdCIpKHyAzv2eiB2DzaEqeQ/iYBeyPXf+tQgoGQxgFzbXrGpgFwRwhPDDXQDQMYmCXdCXsaqCXUCfag3sCBUHOzMN/3FIAuz8ngXCYEcobv/onUUvJQjFiU3v4+Sm9/YosCOmgeOOOw5AgaFUpQXqcrkcnn32WdvgloHppWPq+L93uLx1DOi8MMdEDModhuUBOh6wigQ6p3Mc70cBHae3jivkypso1Irw0PGCnUagIxZnMtQoOyFA596Gy05Ef4TAjmO7yEoJnDAykMGOZ/FAxcGOB9qitokAOiZesIsaM3TNsxMCu4g+GRmqdZ6dqtgcOx64CwQ6V594wS7bFNInXrDLz6OblNrp+3aCUJzc9B5OfqeTq0+DQX//0asA7KINlqV+fWiBuoULF2LHjh12BQmfHDQDSVZXF8bcuTAU7KKAjikqDJvIAHU7KLeHLgzsuIEuSi6YiwTJCLDTNYfO7Z2LBEmOJMC6gM4wBea8hXSJB+jc24ba4Xzgc4GdzmTHnBqICyi0rAYVENfnLBJijdiWt3Zv1L0uMl2iYmAn0KdaAjuA02vH89FxgF22KXq1KwO7ULgjCAQ6pgSh+GjzO3sM2Bk702hubsa2bdu0hGC1QN3LL9tLc43tdTVZ51VUdu46/4ucF+iYgsKwzDsnnAjYR1JA5/cxyYRbA8BOGOiCokQy8+eCwE4z0AnLL3QhAHShpiVCc6FgJwh0Qd46qX5p8mhVQqJAp9MbGWhLds6c32uCT4ige15m/mvZwU6iTwMJ7HjqiLv7FAZ2vOlLiBnitfMJuwZpTwI7QgmOOuooAMDzzz+vbE8L1L3yyiu2se11OszVhLxh2PXdrZi/bpwQ0AH+YViV+XNe4DIyQKpXMpcdKf1bB5NLe+i887l1LYgAqg90TO5JxpJA591eZRK9L9hJeui8YKfUrwEAdrIeurKCnerqVvffHGFXP3nvfZX7t+KhWA4NBLCLDLsG9MkP7MLCroF98oKdQfGLU++O9NK5xcDuo+/uHPRw948f2yHY1157TdmWMtStWbMGq1evtvP7DPDQq1vuMCzzzuVyCSGgY3KHYXUsiKDEk7JEsZqCMtC5vHXKIVeWD08V6DxJgGsC6FxS9dCx/XSkuygCO8WQKwM7Lf2qYbBTDbmWBex05KJjCygkgY5J16pYoExgpzgc1DLYSQGdq0/ulCc8YdfAPnnAbkpqu7CNBKE4tfndQe+1M3amkUgksGbNGqxfv17Nlmpn3njjDQAA6UyBmLVfFkxEVlcXmtebwt45P5G2DDJD+efPhSnBvHPelCUKUvbQUTuhsLbBXIeHLg+sOoCOWEBC1+IKjSFXXfPetJQjY7ZM/5W3MlIBCybdYKfLXkVCsTL2LKrlvBs5+ZyWJbY0LTAgOaoMdEw1C3bKYzkKFYRU8wubQN12A7/46N1KdpjXbs47XWodqlER08Dee+8NAJg3b56SLWUKe/PNN21DuwaPl45p28VH4Oivv4b9xqqRMwBYu1NIdhNkm9TuuEQGSPVQtXI/ZRCDS3U41PcwIJZdZk3djsb0JxSASdVqnjLlIUz1nFMCWCkDNKHpS5lBtITxWUk6LWCnoRIGUAA6Xcen49hYLV0tlUzyNlSvd5Y429B0zgmlWsAOhGj7IgyiySNJgUS/nuoTVhJI9Kt3CbDHdFYXXd4I0D+lD19+52zl/lggeO6s/ZXt1KoOPfRQAOpQR6hCGmNKKc466yxs374dqQVDYXQOHrDbdvEROO3yf2J6/UZ0W3V4eussvLVmnLAdc1cadVsSIBYBMWG7t3vyIVhBeYFO2+CUl+yDytsPaSCjGpOZuoFO4WFXAnSGWvWKouLqNHiBQaT8kgFLztGk7rqpKn0CimFVwZPsBR4l75jnXKnAa1E/auD4GNAVvSZ5ffpBoZWSmG7icyyWwjl3gyElBFZazpb3vCiBuWc+spXUtECQAKbk8Zmpwt/UIDAVprjnGkhhWo4hN68OBOjbqx9G2q6D3NCQASEUDx7wG2FTWWrgf06aC3PJBxIdGRiyhmSQ3W8Hhg0bhocffhhE8guf0lfzNWvWYPv27YAFkK5U9A4DRG6gA4Amox8ntb+H2ePXCtkxd6VRvykJI5sHOiA/10u8T34eOt0LjWUeUH77SPWrXEAHSHvFfD10lv/DL1IeoAPyQCXzsNMUJi0BOsC+PmUfwN7zLOmx8/NgSXu0NFTCcPbzwkqVj88P6ADJ6zNAoh67IDiV9dh595P12PmBrvSXYq8pjWOXbDjW9Dx+iUWlPXZuoLNtyXvsGNABQG9vGj09dfjEmxcJ2dgTgA4AyO4Ukskktm/fblfnkpQS1L3//vtOZwZDKhOgFOiYRMGOAR0xfd6rB7LN/OcrLORazdMeVrVCLC2Kpnx2FkWi3/IPuQqCXWjIVRTsfIDOeUsUokLaFTnnvkDHJAN2QedXEHzCQpLC4KOhEoazfZAnrUrHFwR0TKJgF7Y9L9hFeRtFwS5oe22hWOS95SKnKuiU1wDYeSUDdl6gK9gSBLu8l85PImC3pwAdABCLYPr06QCglK9OCeqWLFliG9mdVDFTE9p28REY82oLTvniiyVAx9Rk9KMxmYm0ZXYGAx0AgAC5Bj6w45lDpxPseAc4nqoVXP3SCHQ65s/ZtjgeZLxgFwJ0zia8EMXRHs85DwU6JhGwiwJmTvDhmWPGDT4aKmE420WFRit8fFFAx8QLdjzbRd0PvOFjXrCL2k4E7HjC0VzjXpSZKoGd10vnFgM7HrgLArqCLU6w84Rd/SQCdnsC0DGxxRKLFi2StqEF6sjugR163XrJETjliy9iztD38KGG8EURR7ctC/XWmbvSqN8YAnRMRjTYiSyKqCTYcX+zjQK7SgMd14NQIOQUBXYcQMctAc9L2DnnAjomHrDj9YBGgI/IooHI7QTOFU+JMy5V6Ph4gY4pCthEPHr60gKF2+EFPx6wE5lfqGWOcoXBLgzomIhF+bx2HKcqEuw4gI4pCuyy1MD/nPyf0Z0aRHr4O08BAJYvXy5tQxrqKKVYtmwZAIB0D1xP3dZLjsBHv/BiJMwxhYVhw0KuvgoBu2qvcg0a4IQHviCwq5aHLjRkJfHgCgI7QaAL9dZJzJHyO+dCQMcU1i8dK3jzEg2tBm4vca7CqmEIKeR86Dg+UaBjCgI3mbl3fveHzCKPIHATDdGGgZ3MgpGwKSXcqhDY8QCdW2Fgl2sQ+aIQAHYCQMfU01OHc33ALksN/M9Hz4e5eBm3rcEgxlLLly+H7BpWaajbsWMHdu/ebT+8egYm1IkCHZMf2AkDHZNRunBCFujKvXBCZWJxUd+qHXL1nVyu4Inwgp2kh84X7BQmvbvPuRTQMfn1SwboArxZLHWJqErAR2WVs8+EfBmV6/hkgY7JC3Aqiync94nKqmQvwMkupvADO+kV6vAZ56q88MsP7ESBjskP7KLCrv52/MFOBOiYuj1g5wDdoqXCtga6SG8ShmFg165d2LZtm5QNaahbs2aN/Ue/MSAXSWy7+Aic/PmXhIGOyQ120kCXl1lX8NapeujKBXbKoQnWr2oDHZNr0CeW/AOlRIoh1yKw07CKkRJFoGNy90vFQ+cBO9Vcbc6+OnK0uaphSKsMx6cKdEwM5HSsjjWy6smzgcJ9pyMRNwM7FaAr2GN/KBgpE9jJAh2TG+xkgK5gxwV2IQsjeMTALksN/M+pF+yRQAfYiyVGjx4NAFi7VizbBpP0SM8aJL0D00uXGUKwd+M6JRtNRj+29TUpAR0A+1Mg9mRWbSFXTWMJMYMLRstIC9CZFEa/Bggz7ESkhmmpZ5m3AJKj2ubQ6XgwAbAhQxXomHTAIWD3KUG0JN/VWVUDUC//ZRvRd3xaRSmM/pw2c0TjHDstiZM1roq17WkwohnstC0GoxSJDNSgFXmw6wFoQs5L51Z3Tx0+9ebnYL63RK1TA1wM6mTTmkiP0Kw+GelLyJqoqsbd/T5u+r9PKtn465bZWLlsJMx6tRst0QekuiisJJCrU33I2TcaoVAGOxvoLE3VHeys6aqDNzFp3g601BZ1HuK1VuGOaIAxg9jeNVbPU0OftNgBbBBTvRbyQCecliLInmV/odJScksjaGqxR2neuwaQrNrDFwykdX1xzFG7yoqq8mNCok/x+AD7GKm+MmdKX/rddihFqleHB58AFEjvUrNFCZBttv9OrGhQs2URTLhil5KNWlfdC6NQ98Ko0G1GjbLf37jRPwtHlKSfGizeS/pr7WnIJ3PbdqR3yT+g/rplNua9MxUkY8Csp8g1yd0ciT77xiIWYGcTB0xJsGNA5/6/7MDLgM6uMKAW6mRAx2oKyoIdAzrnmKg82BUBnU4pe56KbUmDHQO6IuMKfXPP71L1Iro+f6VEwJ6KDipgVzwnUhHsmC2L6q07Kwt2eaBjUgI7asMcOy5Vbx3Je7EIhR6wgwawo55rQeUQ87eKXZ9XwY7blqUGdtQz9UQW7CgBskMKc8KNDJEGO2oRzPzqZuTWqkXPall1L4zCCe3v44T290PB7omfPQegClC3fft2AADJDkxPHSDvrXOALg+0lEAK7IqAjonY3jpRsPMCnft1UbmBjsnIyoFdEdAxSYBdCdC5bQk+DAKBTtf3E0l48v2sZMDOD+hc9oTl3YcQebDTUOHBD+gK74l3yX/1siTYeW1VG+w8QMckBXZeoGP/lwQ74glLKoGdpwvSYOf3WcmCnfe2UQE7ry1FsCu2JQ52XqBjkgG7PQnomBjY+cEdYyrGWKJShjpkBqanDrC9dVN+shg3PsAPdl6gYxIFO1+gYxIEuyCgK3SOy4xtywfomETBzhfoXH3iTo4aBHRuW5wPg7J56EoaEofyMFvcYBcGdC573AraVgbstFZ4kHuvZNvQPIOCD80gW9UCuwCgYxICOw/QFWzIgZ0X6Nx9Ega7gKaFwS7sMxIFu6DbRgbsgmxJgF3QPUssIN3JZysI6JhEwI5aBDO/tmVQA13q+dFFQMfEvHap50cXv5G1P6POzk6p9qSJjDVIsgMX6gAb7Kb+eDGXxy4I6Jh4wS4U6JjyYJerDz+/kUAH/jBsGNAx8YJdKNAxcYBdJNC5bUU8DLiAzkDFPXZ82ew5wI4H6EQU1X8RsNNU4YF3YQTPOeX5UsHtrYuyVW2PXYC4wC4A6Ao2xMAuCOjcfeIGu6ixjxfseD4bXrCLum1EwC7KlgDYRd2rxIwGuyigY+IBOwfo1sit8hwISj0/GnM6wqtDzOlYVAR2jKl27ZKbXyj9BOjt7bX/MGtodZekzG3bMeWnS0LBLgromKLAjgvomAhCb2oeoHNvGwZGPEDHFAV2XEDHFAJ23EDHIWEPXYXATqzuZAjYiQIdB7Bx9ykK7DRVeAgLu/pvH/KegLcrEux4bVUS7CK8dG6Fgl0E0BVs8IFdFNC5+xQJdrxjn67FE0A02PHeNjxgx2uLA+x4v3xxgR1v0CAE7KhFMPMbWwc10AGIBDr3donnxtj/ydknuOKeOgZ1xBr4UAcA5tZtgWDHC3RMQWAnBHSsXyn/hRMiQOfexw+QRICOKQjshICOyQfspIAuwFsnHXItM9hJTez3AztZD11IaFW4T0EPDU0VHkSBrrCfz2sS89ICwU7UViXATgDomHzBjhPoCjbCwY4X6Nx9CgQ70bEvDOxEP48gsBO9bcLATjgZcDDYiU6TCAI75qUTkR/YOUC3ao2YsQEmB9I4ddKI95B4bozDVJlMdJ15P0k9tkzTLDQ4CDx1TH5gJwp0TF6wkwE6APCbXycDdO593aAkA3RMXrCTAjomF9gpeeg8YKc8h65MYKeUgsMNdqohV59FENJ98j48NFV4kAW6wv6uvxVWkJaAnaytcoKdBNAxFYGdINAVbPiDnSjQuftUAnayY58f2Ml+Dl6wk71t/MBOOhlwKdjJLmjygh1v2NVPbrDbk4DupBHvCe930oj3kPiTvXgim81KlQqThjpHGsemWhADuxsf+KQ00DExsKOGJNAxucBOBegcc3mwUwE6JgZ2SkDHRG17OuwQk1ZuUQSv8sCkJalpHuy0zKFjIKeag84NdpoqPKgCnWMrJMzPLTfYqdoqB9gpAB2TA3YSQFewUQx2skDn7pMDdqpjnxvsVM8/AzvV28YNdsrJgAtgp5p6iIEdJUC2RQ7omIwMQWJ5wx4BdOTZsVJAx3Rch518mVJamOYmIKmPyTAG9uKIKJlbtyGRIRhV36mch4+VDFJOYskWTigmJ3bM0cLgq2zLzHvHdD2jauyLAk0Q0KQmj7RKOhAfW9qky5buCgoagE6rKNX3+QFayn/pFlEAuoKNPNhpqvBAKGBpWgREKAXVlYlLE5gTjdcVsSgMTYVDiAkk+vWcLyNDBj3QdbzchlNGvqvNnkwIVv0uqb0xSVkbrzwSd372Nlw38nkceUjpUmQRJfoJiAVkW3Rk9Ady9QTZBnVblABWgsBKqX41hH3DE0gXLi90CvZDUxWgCJyap6oPAkry5Z4MTWCXX12rFQxUlc+er0XMg6Xx+FTrGdP8tanjnFtp++mm7CHNh80pgZ5zxRykGsq4WemEFg8woVRbGTcrbffHSqmedyDblLTvaw33s5U2tFxXZtrIj1sabKXsiI6O8o5mvQ3U9VuVTaF/rz4s+/Hh6oZqVMNfGopPdLyOMakdSnYMF0Ank+JlWJU9dVv/6xAZEzWrjVceiV9deRsOr0+gPdGEH49/TBrsEv0EyW77Js021g7YuR+SVlIB7Ii9P3ug0IQC2NFCWNIuQC/fp6KC6oY82DGgK9hSfBAYcBVorxGw82TPV5I3pFgDYMegyT7namDHgA5A/kuMWrUPdkzKYEcAp4QbUQM7K51wvhDpSo+jCnZW2iiCHWmwY0CXP9eqYGcDHfI/8nYY0AFQBjszRYqmeaiAnVmfvzapXb9WBez6p/Yhmc4hMbJ3UILd8JeG4pMj5gEA0sRUAjvDda80NIhX6JCGunQ6DQA4+Jy3sPkLR8qYqTm5gY5JFuwY0DlhV6M2wM7v4SgFdh6gc+zLgB0DOhcQSIEd8T8+GbDzAl3BluSDwCgerGsC7AKy50spaI5YFcGuCOgAJbArAjomGfDxAJ23r8JiQOf8Xx7sGNC5+1RtsPMCnfO6KNh5gI5JFuwY0Dl2JO/lIqBjkgQ7N9A5piTBzgE6JgWwY0DHOjTYwM4NdEyyYJcAhdlvQ0NdXR0SCfG4t/Qd29TUBACYRDbhiM/Ox5bPHyFrqibkB3RMDOyOPpRv8mMJ0DFpBjuzTgzswh6KQmAXAHROOyJg5wN0zlsqHjuvLQGwCwK6gi3BB4HhP0hXFewisucLKWrSfxXAzgt0hdfFwc4X6JhEwCcA6Ap9g9i58gKd87o42HmBzt2naoFdENA57/OCXQDQMYkClBfoHDuC97Iv0DEJgp0f0DmmBMGuBOiY8mAnoiKgc3VosIDd0JeGlQAdU1qwdEgCFAaxkOuz/9/Y2CjVJ+m7lTWY7QX2blqHw//rzQHrsdt4VTDQMbUnmvC/456IBLtAoGPSCHbU4Ac7nochF9hFAJ3THg/YhQCdswkv2AV46YpscYBdFNAVbHGCXQDQudurusfOT7xgx7uKs4JgFwR0hff5wS4U6Jh4wCcC6Ap9A9+5CgI65/3oe5QpCOjcfao02EUBnbNdFNhFAB0T3xhDAoHOscN5L4cCndMeH9iFAZ1jihPsAoHOJS5vHQkAOleHBjrYDX1pGD414t+h2/B66xjQAage1DU3NwMAMj32hbJ30zoc8dn5Aw7sNl51JH7xpZ+FAh0TA7ugUGwk0DFVGOxEwlahYMcJdE67YWDHAXTOplFgxwF0jq0QsOMFuoKtCLCLADp3uwMS7ETTclTg+KKArrCdfCjWV2Hgwwl0hb4h/FxFAZ1LUd66KKBz96lSYMcLdM72QWDHCXQARxiW2OMiT1qPKLDjAjqn3fAxhAfoHFMRYMcDdFxhWAL0TwkBOleHEiN7sez/DTywa31xeCTQAXxhWDfQAUB/t/0ZtbS0SPVN+i4dPnw4AKBvV+E1BnbWP8YPCLhjQHdURH1Vt4Lm2HEDHVOVPHY88gU7QaBz+uYHdgJA5+wSBHYCQOfY8gE7UaAr2Ap4GHACnbv9ioGdCIgFbSubZ03T8QWFCnmArrB9ONhxeenc8gMfQaAr9A3+50oA6KLCsLxA5+5TucFOFOic/bxgJwB0TIFgJwB0jq2Ae1kI6Jz2/ccSEaBzTAXcslxAxxQGdrxA5+pQYtTAArvWF4fjMyNf494+COwSoCVABxSYqr29Xap/0ndoR0cHAKBnR/FVsnfTOpw5+q0B4bXrHwohoGNqTzThf11gJwx0TBUAO9lVg0VgJwl0Th/cYCcBdI4dL9hJAJ1jS2FVbKktz8NAEOgcO5UAOxkQ8+6jmji3DGAnCnSF/fzBThjomNzgIwl0hb6h+FyJAJ2zjz/YiQKdu0/lAjtZoCuRBNAxlYCdBNA5tjz3shTQOf0oHlNkgI7J660TAjomP7AjQP9kAaBz9suD3f/WPti1/KtdCOiYvGDHYM4LdADQu9P+fJjjTFTSdyejyN6d/u874dgv1ibYbbzySPz2P38mvf+IPNjtM3ulHNAxlRHsVPN7MbCjAvNzAvuWsAc4WaBz7DCwUwA6x1Ye7GS9dMW2iO0tkAQ6x04e7MoiFRBz7aulEoJGsJMFuoKNYrCTBjomAqfah+o16rapUsLNDXayQMdUDrDTAXTs/pMFOiYH7BSAzrFl2OOoEtAx5fdXATqgOAwrBXRMbrBjQFcnmfWYUCRG1zbYtfyrHXNHvSq9PwM7P++cW5PpRwFUAepGjhwJAOgOia0//cJsjPnzctkmyqr+YXJeOrdGJJpw1LAP0D9MMWu6AZh1QLapuMarjKgBmGl9lSespHqfAIBViVAZIB1TJGSunqgtQ58tYlE9ngaoA2uJLR0gRmlRTdZaEU2oA4EDdhqS9wIACNHyGTrXuoYSbjAIzPqktmtLx7XOzrtqKTEAdqqnBjWgc/fLrFMDOiYzTWCJ55ANsGWoXwuwwY4aesaZXBNg9EMe6PKilGDmzzaqd6hMUgE6pi25ITAj6H79+vUAgNGjR0u1IX3JTpgwAQDQtSn4ZkzvMJDbUJsf0tQ7VmLyI5co2binsx33fXAwyPB+9LXLP+xIDjCytjfLSkMJoohl37BmmsBMqz4I7AcKTRBY2iBK3SvmzAvR7e2pAdmeC/tvXVCgqzQZyVl2uExDhQAAWj4/XR4jIO+hIQTKZeo0AZ1bWq6FZP7e03H/USqdT83pDwNpHV86iA081IB6jV8ANIm8d1TNjpnOA7mmShHaQKze9t4aWQUjBMgMKYCvsbhZ2hS1DEy7bhdyy1cqdKi8uv3TZyrtvy47FFmOemurV68GAIwfP16qHekRkTXYtxPI9pTeRP/v8dMw+c7a9NIBQG7desy6aa002N3T2Y4fvn8ienvqYBhUGewcTxaRBzti2T/MlhLYkeL5IEpgRwF3yh4VsCuZ6FsDVQsAFAq9K8gBOk8SZlkxoLONq4EdyXmu7RoAO51AV2pccr8yAB2T0rWQ9CRhVrn/3AAmCXZFQJeXkZUcP0npfDUVsKNer5rkeXeALi9LIapvh1wL/VG5FnL1BRAjVBLsGNAlCnYSfXL9YUBnLlshZ6BCoq+/I72vF+iC4C7bS7F9+3YABceZqKRHxZaWFgwbNgwA0Olxxv2/J07DjP+3uma9dEy5desx6+Z1mPzoxUL7uYGOyTAoyDBxsCM5IOGp2SsDdl6gY5ICOw/QOf2SATsP0DkvS4BdYGH3KoNduYBOpU9FQOc0Igd2JUDHpAvsJFRWoHMakdilzB5fqWshWTrfUAbsAufDCoKdH9DZb1BxsPMBOuctieuzBOhc7YjIC3S2DSIFdkVA5+qPzLXgBjrHlCjYeYDOLWFvHSWYdn1nzQMd0+2fOUt4nyAPnd9rXXlkGjZsmJM2TlRKI+OUKVMAALvWFm6e//fEaZjxv6uRW7dexXTFlFu7DrNuWs/tsfMDOiYjIQZ2DtD5VWuS8dgFLVdPC8yLCwA6pwkRsAsAOudtDaFYR1UCOx1AV2g4PAkztxk/oGMSBLtAoGPSAXaCn11FgM5pTGBbTfMpoyR0LfgAXcEOP9hFLnDiHRKCgM7ZQADsQoDO2UTg+gwEOkH5Ap3TITGw8wU6x5bYteAHdI4p3tMUAnTMW8cLdtQysNd1XTCX1m5Ezys6b6EQ2EWFXL3v7VhtfxB77bWXXAehCHUzZ84EAGxfWbgi0tuNAQN0TLm16zDr5nU48ZMXRsLddrPZF+iYeMEuDOiYeMHO8dKFyEwRZBuNcK9dBNA5/eIBuwigczbjBDuuQafCYKcL6Nzz6MLEmyQ2Ei44wS4S6JgqCHYVBTqnUY5tyhh29RPXtRACdAU70WDHnySco60woHM25AA7DqBzNuXpOw/QcbQVCnSOHT6w40ojxQF2uXoSCnRMkd66EKBzNuEEO2oZmHbDwAI6JjpvIX75H9FgxzuHzq3RO+2VrzNmzJDqG6AIdR/60IcAFKDu/z1xGibfvVLFZNWUW7sOxotv2eHYALC7r2s47lwaXeM2Cux4gI4pCuyCwq6+tgxNCygQAXacQFfoVzjYcX+LBGw4KGOCWybtQMc5UTyqnJNIYtowsOMGOqYKgF1VgM5pPOS9CgMdU+i1wAF0BTvBYCeUgigkDMsNdM4OIWAnAHS2rZC3koIeupA2uYDOsRMOdlZSYOVtSJMM5nhshYZhOYDObScM7BygW/JBtLFa1b/DwU4E6NzbLV68GEDBYSYjLZ66zvXAjx85eUCFXYPEvHZesLunsx3fW3RSqJfOrUiPnUhS/wCwEwE6t3zBjtNLV9QvP7ATBDpntwCwEwI6t8oIdtUCurA+2QYFjzkA7ISBjqmMYFdVoHM64fNalYCOybeyhgDQhUkqp6QP2AkDnbOjD9iJAh3bzW+uqmy41adtIaBz7PiDnRDQ5eV3bnm8cyVd8gM7AaBz2/FbODEogI7p3wtLXlqXHSrlocvSBLJ9FCtW2HMLq+ap6+jowKhRo0AtYMpv3h/wQMeUW7sOs75TmGd3T2c7fhQwjy5MDOx6pxTgzm9hBI+8YCcLdExF8+wkgM7pVxnTnUgDHVMZwK7aQOfXJ7//83ekGOykgY6pCnPsqqYqAx1TUWUNSaDzeuuUkoS7wE4a6JyOucBOEuhsO8Vgpzx/ztUHSyUZsGc/GaBj/XGfYxmgc0y5wU4C6NwyljQByM+fu75n8ABdXr/8z7OdvxnMiQId06alBKZpYsyYMRgxYoR0n5S//h544IEAgFxug6qpmlJuzVrMunkd9v3xF/Cj909EjyDQMRkJimSdCTKsH5k2izvs6qcSj53i89NMsQSbak8mmiD5HEpEyktXZCsPdspAx1SBUKyoVIGOifVJKOzq2yH7oaQMdEyawa4mvHRM7NBqBOiYWKUV5bxxCfWqLwAKXxRVgM7pGFUDOseOfe/pWhABgnzFCbUDtBL56j2yQOfqDyVqQOeYolAGOkKBRC9B4v1mG+YWLxtUQAcAeHUBfvmfZ0t557yatPVcAMDs2bOV7GiDuv4ZaVVTNafc2nUABY4bt0zZltWfAMkS5OrV7FACWCnA1HS6zRSBmdJgiNgDiVMvVkF2yS0Ndoi+Fbb2g07ZjF65yhmpyoYCDfBkENBUQk/9Wo0eO0IV69V6pAoqhBZ+lEUIaEpP3VSqEVZpyiiuo6oqDaacsoeKooTkkx2r27JSdsUJHdUrdAAdk5WQBzq3+sZnsPRz8p6nmterC5SBDgDefPNNAAWmkpU2qEv27MTGLx2kaq6mtOXzR+CmS+7BtSOew0env6tmzLS/AZuNFLkGeTPsIWDWEWSGECVbTFZSA9hRmi9ppAh2zEZSDeycSgrst44QsUKYukQG1L1rrM6sjkE8H4ZVAjt3AXsdVSzyHj9VGHMKx1uawI5CKbmtF+SUwC4P9jrqMwOF6QXK3ntNkMlsAYCRUfMkW2lPAmbZ/rCasEQd7q0kHDuqMJar01NxIlcPx/mQ7Ja3QwnQs1cGyYYc6DjJzMQDRE989hil/Q/LLXEWSVQd6trb2zFt2jSAAmfu/wg2XHOkqsma0JbPH4Fv/Pd9+GjjDoxM1OHaEc/h7sPvlIK7XE8SyV22z58a8mBX5C3Kw49ZV0NgB6iBnWdg0wJ2gDLYFT10FcGuCCpkwc4NdKgRsHMDHaAOdm5oUoAxB+g02CqSJNgFAZwU2LmBDlAGO2e+KLMnO8/WBXQq1SuKbOW9mrJg5wY6FbmBrvCanC0GdKp2gALQqdrK1eejG/nPnphyYOcGOtsQxfLvRmeOGLD690I88bljpXY9oel9LH+7HpRSzJgxA+3t7Upd0eKoPfroowEAuxf04SuX/N+gALveDoKPNW1z/j8yUYeD60xhr12uO4Xk9pSnTJa6x84RqQGw8zwkpcAu4JuqDNj5zTGTBTvfh60k2PnChCjYeYCOqapg5wG6Qp8gB3Z+sCQBYyVAp2DLV4JgFwVuQmDnATqnS5JgV7IASBLs/Dx0smBXYksS7IKAThTy/ICu8J6QqRKgc+xI3MNeoJPtEwM6r0TnSZcAHexLkk7oHdxg9+oC4V1OaHofw4wcFr13DgDgqKOOUu6GVqhbuLARhxur8ZVL/g9T59UPWLjb8vkjcOt/3uP7HvPacYOd5X9TMLDLtPHBXeicrmqCXcDDUUso1tUnXjtFYdeg90Sy6Ae+KfbAC4UIQbALzwXGbSZYEmAX9AARXsQRBkmiMBZhS4sUQ7FSCgA6p0uCYBe4olsUekJCrqJgF2hLcB5ilIdOJJdfENCJKgjo7HbEwC4I6EQVBHRMPN46SoDuaZkSoGMiAOj4OAwL2DDHgK6vj+D1118HUENQt9dee2HUqFHIZAy8+WYTjmtYha+P/DuuvfiBAQd27rBrkHjBLtedcsKufrIXFkR77bgm6WsGu1wdB9xFPGC5wY5jILOS9sTkMFthQFeyja45doloTxQXiPCAHQe0VQPseFancgEwDxxxgp2ubbjEAXa8IBK5XQTQOV3iBDueFD1cVWY45tDxgB1NGVy2eLx1vCHXKOhj404U0PEAXxjQue1EjYe5OsIFdDx9igI6IDoM6/bO+QFdwRDFisHsrfv3Qjx+0XGhmzCYG2bY52n+/Cb09fVh9OjRSuXBmLRAHSEEJ554IgDgpX+1OK+f0LhywIGdN+wapCiwy3WnkNyR5CyVpSkcqxHsqKFnnl0k2Al8Mw1bQMEDdCXb6qpiEeK1EwKHMLALCLv6yQE71bs7CuzYSleeh11UGFbE2xUBdoFhV0E7QgoBO9H5coHbcwKd06UQsCMm5cu5yBGGFVkUEQZ2zE6kLY4wrOgcuqDwrOiCiLDteICOx46zIIJ33AyzxQF0TEHPMr9wa6ANANaE3kENduSVtwPfY0Dn1ptv2LnuTjzxRBAN8z61JYBiULdgQRM6OwvLewcS2G257Ah8e+593NszsPvt4XeXwl1A2DVIQWAnnEpDI9gBIWAn8DAMBDvBUIO7T4FgJ9AnP7CTTjMhGI4NlB/YCQAdk/NwLBfYBcyjC+4PgsFOJnwZAGTcQBdhR0o+YCe7srVkP0Ggc7rkA3bCCbRDwE5bKhVROyFgp2NRhEq41W8fEaBz7Pjcu7LhVv+KE+JjltdbJwJ0TASANcjDsI9fXOqt8wO6rk4D//73vwEUGEpV2qBu0qRJmDFjBkyT4NVXimu+MbAb92pzTcNd7wiCM5q2Cu0zMlGHw+qyRV4792pXEXnBTjo3GilzyhOJh6DOOXasT8yWbPJdL9gp5wzzgJ00LLjBTgLo3CoL2AkCXaEvKAU7lfloHiATBroAO0pygZ3q9eTsLwl0vjZlK6L4gJ0s0Hm9ddJg6AN2KkDnXrGra/4cIAd0dj+KwU7n/DkZoAOKw7AyQFcwNLjDsOTlgrfOPX/Oq5dfboFpmpg+fTomTpyopW2tqdpPOeUUAMCz/2gtee6f0LgS3x7zJK6++MGaBDtRL51XzGt3wIQ1JatdRaQzFKs75YmVhBTQMVFXn6y0+gDlgB1n2DWoT9rm2AEO2ClDggH7wacAdExawS6VkAK6Ql9QCnYqsiiIackDnduOxuTE2iqiAHoqRRAFoGNygZ2qh46BnbKnz+VZr7aHrtiODXOyQOe2Qw09QGdXm0BRyhIZETMPdFMlgQ6uMOytgxfsHr/k+JL5c25RCvzzhUMAAB/72Me0tasV6k4++WTU19dj7do6vP++P0nMaVyOntGayhFp1Ogn1+Erz39SycbIRB0OaVuJ3BC1WlnUAKw6G+wsxcoRzoCnoTSOmVKv9GA/1GF7IDU811l5HfU+qdspsqmtPJkG7wylgEmVgNw2lPcYqSamZWCn4dgA5CFTQwhQMh2Iry1dn7+GKh+EUpCcJspkMKbhfFtJPRUZABtYtVwDGpL3MhFLjy0diYkB24ahWMYRAHL5QFyyUQ7oCh0i2Ove7eodqlGRl97yhTmm999vwKpVq9DQ0KAt9Apohrrm5manc39/ptV3m+NeuALTb9+ks1ktyq1cjVm3bMLMx74gbeOv3e34y9r9UTesF9bEXmSHyt1BJAcYWfubmZlSBLv8N1l7kYGCHcAO66YIcg0GzLTcaEWo/U3P8YqoDnoEoEkoe9pYSTErZajBHWX2dMCPRqBzjCo83A3Xb4W+ObVFCZTrutJkvh6ooi2acNUVVTntrB+GOthZqUKfVJIvawM6QIvnGPDcr7LmSN6Lma9Xm+hTcxZQw7apLUUN1QNRAJDIqvWJHZtqqcNcs12rFgCMD+RCQJQSTPtOL6bf0gPz3cVqHapxXXDhlYHvvfLS+QCAOXPmoKmpSVub2itln3nmmQCA1+c1Y9u2UoowNtTDXLZCd7NalFu5GrNu3YyZj8uB3ZbcEOzqaUAyaaG+Pou6YfJg564cIQt2xAKMbAGedIEdWxkr5bWjPg8olUE97/HRAXaAC+40eO1UwK4sQOcY1/DQkgS7kmLxCjBW4sWSPWWGj4dOxpYL6Gy78mDnBjpZDQigkxUptWOYVBrsHOhB/otnpXMPRkmhO+5jA+QB0Q10AJDskZhPSYkDc4Md6AAg+ewbvq9v2ZLECy+8AKDATLqkHeqmTZuGAw44AKZJ8PhjbUXvHffCFZh2x0bdTWpVbsUqzLplMz762c8Lee3+2t2O+1cfUvRaMmkJgx3z0hW/KAl23pWcDOzSEnDn89ATDcfagyWnfY7+uB8wOsEOkAQ7P36SADtdYcnwRgQHdr+RQhDsSoDOeUMc7ILCkjKAGHi+RT4GL9AxSYBdENCJeOsGHNAJn2v/HWRgzAs9gEawq6K3zkl74j1VEoflBTomYzm/t84NdHuSzv+vUm/di/+8FqZp4uCDD7bLrGqUdqgDgP/4j/8AADz/XGtRepNa9tK5lVuxCqmnX7e9dpxgtyU3BDt7Si9wKbDzA5882PHOsyvy0nnsUEPQaxc04AqAXUnYVaQdHwXllaLJ/IRpHV47EbALOSwRsNMFdIFeuqLGOEf3sFGCE+wCgc7ZgB/IQrcTBMSisGuAvUgFAR2TANhFeeh4kyoPKKBj4j7XwRuKhGEDocdlS4t0gR3lBzvnuAKOjddOrjkY6AAg2c05rlGCabf27nFABwCpvxd76zo7E/jb3/4GAPjMZz6jvb2yQN0hhxyCGTNmoL/fwFNP2nPrjvvnFZj269qbSxem3IpVXGDn56Vzi4Fd1Dw7Yvp46Yo2sGEsymtHLMDI0OgyVxrn2UXOsfMLuwbYU9mGAZS2cGwNhGJFxQV0TFGfCc8IEQF2kUDnbMgJZFGnkdNOJNDxKAromDjAjjfkGpV4eUACHY8igI6JJwwbBT1OkwMwDBsGqm47UWDHYC4I6JjCvHWUEuz13T5Mu7UX1jvvR3Rq8Or8i65y/v73qzeiv78fM2bMwEEHHaS9rbJAHSHE8dY983QbenoMGOvrYS5dXo7myioGdidf9AVfuPtrdzt+uvwjvl46t7jm2YWFJ92KCsd6w64hdiLBjhOywubYhYZdg9oM8Q7yVlWoGNjx8lME2JV1Hl2YgiBBZHRQXDzhKALIuL1wPHZ4uxt2LfIAHVPIdqJz6IISLw94oAs91wKh/hAY44IeZmeAhWFFji1s3ArzznkV5K2jlGDa93phLXh/jwY6AEg9bdd27ekx8NBDDwGwI5o6Kkh4VRaoA4Bjjz0WEyZMQE9PAk883lauZiqi3IpVqHtinq/XLijsGqSgcKzvXLowBYBdYNg1xE4g2AnOczFTBLl6n/qsvF46jvaFqyqUG+xE+SkA7KoGdE4HyrN4gttLV7STP5AJe9Z02cnb8rUtOIL6ffbxogiPfM+1mJ2gMKwQ9Lhs1ZQCvGwyx+ZnRwTomLzeOjfQxSroXy/cgK6uLkyYMAHHHHNMWdooG9QZhoGLLroIAPDQIyOw151ry9VUxcQbjo1SINiJLtzym2fH66Xz2JFeQOFjxx2OFfbS+dj0/ZtTZQU7WX7ygF1FFkbwyA1fsiODC+ykgI7JA2TSoVJddvK2imzKnCNPGFYF6NgXpUEFdExF51rOjjcMKwM9TndqzFvnHnui5gby2omaPxcmt7cuBjp/zf3cZfjTn/4EALjkkkuQSEicaA4RSjWmUfeIUorLLrsMixYtgrGhAakPhpSrqYoqOWUSumd2YNMhKQw/YqOQp86tXM5ALpuEuSuF+o1JNfihQKIfSO2OmEvHYYeYGgYfCiQyFMk+TZn6Kf9kcz/ZDz6oZ9WHPcAbOSoNdUV9svQkTVXy0nmlY/6TBRBTQ5JxV2qfmrADBaBzy8rDio6I9WADOo12KAFyjQkloGOyNFadkQEnPzu5Og3XEAH6hxLlPuUaKaY8uBMAYL29SLFTg08f/eER+Mtf/oK9994bv/jFL8oSegXK6KkD7Ll1n//85wEA1qheWPWKGahrRLnlK1H3+Dy0rKJIJeTpJ5m0UN+QAeot0KQqIcD2SKlO6mdeO9VBJ29HRyZ0Zk9FzGOn5YpXKEvmltbFE7qKtGh6cGnxbBACmja0VFWAQewfRWn7vDSOvNpK3OkCOh3nWcFD5xXJJ7pWla6xTJ8dPV8KcvXqQAcAZr0NczHQlYrW5/Doo48CAC699NKyAR1QZqgDgNmzZ+OII46wQ4WTd5e7uYqp98xDYZ6zDeObd6CjRf64ervTMHYm7dBnWv5BSEx7Xp6VgnIpLwCAoQZ29kpeewBT/oZLIT8vj/WH0oK3T+Gqd0KKhnotRl1VNYiOEmCAPqBjniOVgctVxJ4miFrlCXc/VOqVGqSwvwaIZtekuiENAFRjQKcDwAHAStvXjWwtbsdO/suycv3VBPR8IUzYXyxDqlBxKddgj2OJfjU7/e0WrHoL6/+n9uq614IOuXImTNPEYYcdhtmzZ5e1rbJDHQBcdtllSCQSsIb3wxyqePXUiHqGJ3DgiHWoM0yMb96BmR2bpODOyhl2STBiz2nLNVJpuCM0D1Ep+2aVLeUFsIE1X5RaAu5YaBFAoQKFBrgjVD6cy8Lb1MjnsVO4+tmDx66FKm+n0DmowV2tzMvzwo5q0XemioxUEaqVc+wn2a4Z0AJ0JUXiJW5RxzunAejMOgNmnQGaH7tkxwxWW1oVxGhCD9DZX3Dcc1bl7Jj1xAE6QB56+9stG+jqbG9o93hdkwYHj8yh/XjppZeQSCTwxS9+seztVWSonDx5Ms4991wAQG5KF6hRa8uJxNR7xqGgZ25z/l9nmBia7hX22vV2p2HsSDn/p+4FCwJgx7xijp18cmGalPDauSfgkvw3U0Gvnbc/blvCYOc9DRJeO1+PCBH32nkn/lPigjvBOymw/6Ir89zz6AiRB49aC7t6phHYHjuJ4crvfEicI18PVC156yDhrcvDnA6gU5VO7xyDOeoZs0TBhcGcF8RE73UGczqAzk+i3jozH271Hoeot47BnFXnHoOA9dfG3jomalB0zLHn3H/yk5/EpEmTyt5mxb7/Xnjhhejo6AAaTJjjuivVbFnU02576bxiXjtesGNeOq+Y104I7Hw2ZV471XCs47XjBDu3l863TzoAghPs2EMzaBEKL9iFreQUBbvIfnOensBFH6IPad1h15I3BOy7wq5eCYdhw9oVSY1jhMByLYGdSBi2EuFW7vyN0A50fiKUcoOdDu8coDfc6idRb50ZMn9OBHod71yJEaB7QuytY/rMHadiw4YN6OjowAUXXFCRNisGdY2NjbjiiisAAOa47kGzaMIr1XAsE2841s8rVmQnD3ZcodiQwYEX7KL64/SJ5+ETNVjxgl3Eg1d1nh0g77ELlPKKNk4D5QY6ZwOOdkKAjokb7Djbi1Io0DENNLCr5Py5iGOqFNAx8YwXPEDHc5+XG+jc4vHWhQEdU5S3rijcGitUVn0Of/jDHwAAl19+ORobGyvSbkVnqhx33HE49NBDAQPITesE1RV3qKB6zzgU1hnbQ7dh4dixTbsCwc4bevUTbzg2KoUJNexQbK6Br05rWH+i5tmFeem8fSq3x07kQRkGdrz51njATmhuT5ijiSd9SYXmgHGn1IjwnEUBHVMk2Al6BgPb4QE6phoDu0AN0gURPEDHFOaRqiUPnXv+XJiivHVmPeECOiD83PiGW32NABuu2bNDsBQU0y8YhUwmg0MOOQTHH398xdquKNQRQnDNNdegoaEBtDULc2xPJZvXop72BA4ayZdIuSGRxdimXb5eu6DQq59kwrElNvLz7ALDsQKhkqB5djxeOm+fAhdQiBxqGNgJPGzL7bHTVVVDKNde2ANcUz46IQXMceMFOqZAsJMBFr9QrwjQMelKK6NBvjBQLaDzmxpSJaADgu9DUaAruceJ3gURovLz1jGYU01ZIuSdI8DuiXt2CNYc24N3330XTU1N+PKXv1zWFCZeVXxN2ejRowth2Im7YTUMzjAsU0MiG+m145FfOFYUooDyzrPj9dJ57ZR47WTY1bMyVtbr4QU7maoI5QzFSiVP9htQqrkwwg+iZBYveMFOMYWKY1cG6DSpbGHYanvoPAuwqgV0TG6PlMoKV+cez49j5VwQESY/bx2vd84rdwg2DreKy2rIITEjA8AOu44cObKi7VclUcBpp52Gww47zA7DTt81IMOwomJeu46W3fncdOGhVz/5hWNlqke4055oCccm5ACzpE+a89nJVugoSnkimQrBC3bKVTUqNcdOxKRKJQPWH5+VriJSzmHntacKdLUYhq020LltaAA6SgAzbUgDHVC4H4NWuIoZKwCdqnQkXBYJt/qJAS93uNXXyJ4ZgqWg2GvuCGQyGRx22GE49dRTK96HqkAdIQRf+cpX0NLSAtqSgzlhYKyG3TX3cIw5f4X0/gzsmlv6lPrhhGPFubBgg6U9IfLww/rC5tkpJ+bUOc9O1ftP1B+EOj0SWpRPeaKjQoOWerWEPVAVbbnqzcrK9hhr8tBpCsPqKtel57NSNyFVl9pPTj1nxe4YmubP6QA6r2dVQcrhViIYbg2wkTmsS6ETA1Pn/epkLFq0CM3NzRUPuzJVLaVne3s7rr76agCAOb4bVmvtJyXuHmPgK+Mfx5nD38DezaUpTXjUkMiiIZ2FVWepVZDIAsk+ON4pJVmKg61l14qlCRQXvBdVvnqAmSZqdgD1c0IlQ51B5jQ9WJVrYSYJnBx9sjZI3obqZ5SXynnWWvNW1wBMqRLYsfOrRTrs6ADvvB1VsevWyKh95qaGqjskD6lKNbsB57yo5HikxP5irSo7CgSkOtXQoH5UNxrrM+odGkCy2vpx7733AgCuvfZaO4VbFVTVPO0f+chH8PGPfxwgQHZGJ2iq9idXthgZjE/uxCENy6XAbllnOzZtagUIYNVZMBvl4M4wCYh7OqIOsJOEO3tgs3dUBjvAgTtZO84gqxrxVEykSzxgWG2wK0noKwt2pLC/UuiU7UqpFNgxoFP1/lACvUCn1BdSBFFVh6lyAJ3kfaVasYLJTBU8srJA5sxhU/1SrfrdlQBmKg90irasNJx63bJlw+pHdaN+VDea6jNIGBTLv3+EWqcGiGjKRPMxBJRSnHHGGTjhhBOq1peqF9/50pe+hClTpgBpC9kZA2d+XYuRkQK73mwK6GO1awCapMpeO0eCJowskOh3z2CGutcOkmAXkGxWCuxowN8C+2vz0nnM6MjgrzNUIwp23u1Vwa5gSAzsah7oJLx1XqArel1VMiZ0AB0pbVtuHnCxEULFvXVmihQBnWxf/BYlGDL+CA1A58Ccy1ayT/ygGNCpiMFck8tD1zIzPP3XYBAFxT6fn4AdO3Zg6tSpuPzyy6van6pDXV1dHW688UY7zUlbtmbn1+2aezhO+dQrRa8xsFMJxwJwvHa8YGdkgGRQNhiRb45Bc88EwI6Y/kvp7fQp6mFUUbDz/eYt8W1at5eupEsVBrsw8OIFu6A+y4Cd7xwkYbATatK/D+X00AmAXRDQVU26gC5IAvdX4PUp8Pk7MOdzTCLeusCccCLXog/oFvrCZ6gI6Lw2BACThVv9gK5uCz/lMaDbE/WpO07B/Pnz0dDQgBtuuAF1dXVV7U/VoQ4AJkyYgGuuuQYAYE7ohjlcbSFBObR7rIH/HPZKyeuq4VhHAuHYktCrnyoUjnWHXku6wHLjRT3wIx4eQmAX1l+ec1JGL13J2zoemjyZ7TnOHRfYhWwiAnahk8p5S78pfkYVC7lygB0P0FXUW1duoAM/kIddl7zeOq93TrYvUUl+ubx1tRhuDWA3nhCsO9y6J8oc3od77rkHAHDNNddg4sSJVe5RjUAdAJx00kk499xzAQC56Z2wmhTyY1RBzGv38WFvysNdOcKxAWZKQq9B+1crHFtig0R6/ri+cXMcS7m9dEXd0VFMXdNdHPYA5YE+HrDjWSUYdu50hF21Ap2iRDx0FQG7CgCdo4j7TEcZsiigYwobO9iCCJ6yheGNRHYj3HxAuNVPYSHYMO+ciPzCrXuSrKYskgfY5HveeefhpJNOqnKPbNUM1AHA5z//eRx88MFAgiL7oZ2gqRpK0c6hFiODSantgV675V3DsXlza7QhAa8dlwLCBdxu+gCwCwq9+nbBUA/HsvxxgTYEws5Br1fKS1e0aRnBTjws6jO3UaTSg445dgFhWF3z6LSKZ2FEgLdujwu5ejcNOHXUINxTAsK8dbxAF9aXogURsuKAsKLNfWA3LNzqayNgbI/yzvGobmQPt3cuYVCs+O7gWyxBkxaGfzSNvr4+HHLIIbjsssuq3SVHNQV1yWQS3/72tzFu3Dig3kJ25k7QmhrB+RTktevOpEF7eQsUlsFrpyKfcGxY6LVEJCAcK1lJoFwpTyrppSvqTq147Pwe7IJdCwI7oVxeHrCr+YURPNu6wE4W6Mrmrasw0Dny3G9SK7J9PgYRoAsSl3fOo5IQbA2GW1VUP6obzQ39Qt655lmDa7EEJRQfunQMNm7ciLFjx+KGG25AIqF4YjWqpqAOAFpaWnDrrbeiubkZtDWL3LTOAbMi1q0orx23PF670EUSUVL9xllr4ViXDanUBJ5VstXw0hXtphnspD1mrt1k0554wU4qOWsJ2El1pbgP1Uxdkgc7VQ+dds9etYAOxZ+p7LXm9tZZydIVrtx2XGOIDNABnn0qGG4Nk0q4lS2WEPHODWZRUBxz3b54++230djYiFtvvRUtLS3V7laRag7qAGDixIkO/Voj+mBOkq+ZWm25vXZTWrfJGXF57WgS0YskIsQ1ny5MOsCOhWMVvuCweXY0QeRhNT94y9St9UrWS1fUHQ1gRxPqIVAdqVcY2Cll26cUJGcNnIURPKqFkCsp/K4W0DmyqHoJMmp751SqgjjJhFW//AJ6gU5SiX6qHG41MjbQiXrnBqvMSbvxj3/8A4lEAjfffDMmTZpU7S6VqCahDgAOPfRQfPWrXwUAmON6kBtTm6lOeMS8dsPSPWo3O4FT+5WqZg8nUPr0iWl/s5V+IBB74CJU0ouTF9tXuWRQPuGxmg2o548j6qXSqEG02NBR61OLdJRrY7V8ZWWgUA9YVoRBbo2cV9QAXELDPYO8V1rHdQIoz59TrTBBCbHBUmVcNPLjs8qX5nz7zQ1q1Z4a0wNr0WOQcmO6YY6zw2Rf+9rX7Pn/NaiahToAOPnkk3HppZcCAMwpu2G2116qE2ERChhU+oZlxaethBrY0fwcN6UrID/wKD0YaKE/yv3QUQtS9gHD6qoStbCnDQ7yQEUJyX+uajacOZAKYEes/DwyleuD7SsdqgSQr39KiSqU5c+NAoTogihlO/n7jmjwOqrI0gBjVtK+5o2s/LFQA+oeR3apKq3MZt9SFWywY9HwZVlFTekMmtIZJI2BteDRT2Z7H8wpdsTwsssuq5mVrn6qaagDgM985jM455xzAAC56btgDq39GrGhYjcbkQc7ploAO5o/Hi3Fx5UGIVJ9sAOUwY5BO00oeMry+6nYcDygRAHsNI/lsteH+9pUnscme60TTZU3/PokvGPA36KqFaBjBXqky48pdSHfuLoJLWOoBjjVBXQJw0JiMADd0H7QvW2gO+ecc/DpT3+6yj0KV81DHSEEl19+OT784Q8DBpD70E5YrQMc7IAC2Cl47QAb7FTCsQzsaALSV4MDdoaGeVhE8eFN7HOhFHZgc8F44a5ktagE2HlCtzJg53jp3K9J2PBejzJgV/KAlQQh7/9Frg0n7OqVyHXOwq5eMyLgX0thV7/URDLeuioDnZUkRUDHJOKtUxlrHCl6xOx+6Ji/qg50Os4H884NBpgDAKutH2R2N0zTxJw5c3D55ZeD1MJ9HKKahzoASCQS+Na3voWjjjoKMIDsrF2whgyCSZtur51iSFbFa+eAHafXjvjkuHMGBF5PBg3PwM41uFBaYsMZIBW9djYgqYVjGdjJVlqQ8tj5bCsOdv6vcecOY2HXkjfEQCjodZ5rwx12LX2PMwxr+EOuUBi2zEAnZDckP6OQVL6E5mFMFeiCJv+LlNkKBDLeroVsx5sDNOzzMzinotVCuNUNc4MG6IZkYBzUi2w2i2OOOQZf//rXayp1SZAGBNQBhRx2hx12mJ2ceO+dsFoGAdgB2kKyA85rF1XQggvsgvZVBztAfZ6dfR4ivHYeL11R+/n3rAgw8/PSFb3PAXa+AOPtCw/YhY3pWvKshYNdGNAVtokAuyCgc/rAAVQ17qFzi9tbpwh0QfVXRWyoL4qCtvlzgW9Hjm3R10XUgota8s4NJpgDAKslg+Rhfejv78fhhx+OG264Acmk6urEymjAQB0ApNNp3HzzzYWqE4MJ7ICKeO2IhdCUKKJeu0AbIl47HltS+1YA7HiOjyMcGwUpNEGiwSwK2kL2L+ROCzURCXZcnhIOGOKxEX7Oom0EbhMFdG6FXBeVAjrVL09c2yiCkO75c2EKC8EqA50GINQ6dy7CVCokG1gcavWX1ZJB+sgMent7cdBBB+Gmm25CKpWqdre4NaCgDgDq6upwyy23YPbs2UCSIrvPTlitgw3sUDavHbH4HrxavXblWEThE3r134/om2dXxQUUQHA4NspLV7RtKNgJ9CMI7HjH95DwKrd8Q6sQm+8WdN54TLBr29teFTx0gW0JhFYDvXWKh1FJoAP8xzf3F00+I5yvCarSc+f8ADf2zgXLau1H4rBedHd3Y/bs2bjllltQV1dX7W4JacBBHQDU19fje9/7nstjt6Psq2InPLoVZ/z98rK2UaQKeO149tfhtdO5iKL4Bd79NM2zKwfYkeDQq28fAsAuyktXZMOzP7dXytsPT5vCqw81wz5P2LV0f1JybQtdp94vLQMo5Mq1/QADOj+Fzp/jlQbvnA6gq6W5c4NN5tB+kIN60NfXh0MPPRTf//730dDQUO1uCWtAQh0ANDQ04NZbb8XRRx9tr4qdtRPm8PLlsTPfW4LmJemy2fdVmb12vFJNfTKYwrFFK2NlU1t4wE70eNxgJ+KlK7JRAnYSNlxgF7g4IkoeIJLZ37kmBIGOyQG7oJWuPCpauVwdoCtqVzJVSZG3rspAF7TCVUSDLtwq3YfYOxcmc3gf6H5dyGQyOOaYY3DLLbegvr6+2t2S0oCFOsAOxd54442YM2cOQIDczF0wR/ZWu1uBOqltIWbstV58R7fXzpDMxZT32lkaFlHI5nVyD7A8odNySOvKWNm7x70yVtLzVwA7CHnpimzkF2CoPyyIWk46xcnzhcTP8jbc3lzx9vP7Jw3tueikpFrfGagq0NklsoJXuPLIyFE9QKeyu6kIdO5ISRW9cw2prLR3zqIExg/b1TpQZpkje2Dt3eWkLfn2t7+NdLrCDhyNGtBQB9irYr/xjW/gYx/7mA120zqRG78bVLl4X6nSuyh+v/0IzOudJLX/pNR2TGnZBlgyHgk4MGKl5Ac7Kw3kGuVgwh2OlW2feXcsxTlukPRQ2X0gLi+ofBeUwU6xnJcNhobaoK0aUmZ9UbWhOBLpKM+mw6OjBJYC6W8CbSiWdrNrpyqZUAY6quidM+uMfB/kbeiQan1sVilH9jisJEG2WeF6zD9rUglT2ju3YccQpJ5+Xa4DhKD7ySnofGKq3P4RoqDITdiN3LQuWJaF0047Dd/4xjcGzCrXIA14qAPsPHb/8z//g7lz5wIAzIndyO3VCap8VxVr1NPr8bdHjsCLu6ZJg51JiX2zmkQO7pD3lklCkT1o5m1Ip+pQh0sGNGpwqOg5VP0WTNTAjhI4IT+lyg+CSXm9+9v9UIciaa8jC5vKnkcdUKphzhMQstggqv1kIf2NfOMFD65UHxSBzgmXKlzLDtBJ9sOsM+yIRA2kE9NRmUFWzANvSjqcnHFV8hg27ByCDTuHINsvCUiEoPuJyXhsn/tg/WGEnI0QUUKRm9YJc4JdU/6CCy7Al7/85QGRhy5KgwLqAIAQgksuuQRXX301DMOANaoP2Q/tBE3oi//nVqxCy0qKXZl6vLhrmrzXjrp+ZMEuD2eyYEUdMFP02imESEDswUcaDl1hUJVSP1UDO6MQyoVCnVVVsHNsSLTPcmnJhi6pdy5aNUYkV7+rBXZF8xtlvHWuOVNS8yM1AB111RuWmieqw0M38J/JIJRKfzmw2JQK6S9Ial+UARvoMv0pZPol0oAQgs4npmL3E5Px5D5/gEUp2n7/inxnfEQNCwdcPRnWyD4YhoFrr70Wn/vc52q+UgSvBraf0Udnnnkm2tvb8e1vfxv9w/qR3XcHUu+1gWT03u27MvXYlanH1kwz3u8djZkNG3BIw0oxI+y+NfMDKsd8OaveQi5LkOzJP8RdKUd4s5g7zbtCkNQgdroTU2wwYX2gBvL7i/XBmY9EAAOUe/8iJywhoPlzF5WwM0is/1JRe8IeihSgfH1w5qI5/ycgBgUF4V9FWrQ/QEBAQbnDPn4VLIhBAMkamoANaaLXUHEf7PPAO0dPR9i12itVi1dD5z9DEQPE57MU+Bx0AZ2M3IuGYqCT9/Q60zhUvtx6YY4AdO8u7v037BwCAPIw9/gUGITimX3uE9+fUzRtYtLcYXjttddQV1eHG264wa5UNYhEKJW8impc7733Hr761a9i586dQL+B1KI2GLvVEwgmp0zCBxeMwawPLy16vTXdhxF1XZhevzEU7pZn2/GTlR/ByqUji98grt8RcGf0JJDaVXz3EgrA4gMrwwQMnwwwrPxX1MOAMC9j4P7h7QdCBwUMkw/sfG1Qms/DF7UvDYQ3Z1+eu8LvOUbtlaCR2eADwmSE0vznyJFL0OdBan82fGAX5E2xr6VoA37HyM4tD1CEAZnq/rwKghHZh2uRbZ4EyH5AlP/8SI4nkXPw52hw7F9OoIuuqhDhneM4fLPOHgeDgE7zDBwhsQUfPPK73ighyDVG7xvmmcu0RO/Pzn+Jd44AY45ZG7l/EMxRCkz5FWD8683gnQnBrsemghCKZ/YthjkTFCd+82oMvVuPp85qzqLlBGD79u1obW3F9773PcyaNUuL7VrSoAm/ejVr1iz88pe/xKRJk4A6C9n9tsPsUF8Zm1u+EkNWlN6AuzL1WNrVgRd3TcOrvVMC95+S2orprf+/vTePjqM68/6/t6pbUmu3LBvZeMG7wdhgApjgCMxme2wMZCGQA9kgL2RCJssLSebHmSwTZpKcISSZnJMwGSDLGyCQCQQSEoYlLEmIgw0YMGBkNi/Ykhdt1tKt7q66vz9q6arq2m5VyZJaz+ccne6uqnvrVnWp6tPP3Q6Wr7BWyUZob2dUp455lWyctnaJVMfGqz4I3c7Oa33Y6liP9UYV5ESsitXSsljt60yOxp3Jp4hHZSYILyEK+/35CF2o/Y9yhC5wlpSg6taAso2n9nNxGA9VrVHul0a7Oc+qVh4sdEf+OB+Pr7izTOgAQOU8MaFTpmXBTh9ET08P5s2bh5/85CcVKXRABUsdABx77LG49dZbceaZZ2pj2S05guLcgVHpGWvQn6/B5v4FuKv3DF+58yRmezur3EUWO7Ot3Bi0tdOlKHInignQzs5Z9Vq+PoTY+aZHoBiEmQ7MT+z8IpFhxC7o2gpqXzcRql39HtaeQufYxjvzEN+h3/kfB1Wuk726NW7bOWNsxSjEkTkgfru5/j8uxJE/zneVuSTh4CjOHUBxyRHk83msXr0at956K2bOnDmq+x1LKrb61YqiKLjjjjtw5513AgCkniqkOprAlGhXtFcVrBOjSnZBzUGckXnbXO5ZBevEaPMG2KtkVQZ5QEZq2P+m6lclKxW0v8D0HGVt7byqXz33z+37D10lwvUbn0v5Q+XhUR3rV/3qxLOdXZjnmUdVbNgein5VsWEeqH5VsWEjPF5VsWHaDfpVxYaVMuMcREnrnWl4oYtbDeu2nzBCp23I3ecxFYjQuVXBHk2hc157wkLncvgiQjdeq1/DXFdu1a8ibeeKmfJxST2rWssKCKgnDGLW1D7b4q7+BnDOAmWOc2D+bYD0tCVSxxh6fr8IjHE8cdIvfdMr4Dj/q/8XLT+LHqnjsor3/N+F2LxZy+PKK6/Epz71KUhSRceyKq+jhBuyLOOaa67B/Pnz8Z3vfAf5ljzyJ/cg/XoTpCHxXxpaFewM4Bz/7YzOFAdHGvBWbropd/PTh3F88wHsQoDUGfLEUIra6QMQ87Sxwic5g3dHihDXtX16nVLDa248E0K0mYFc2u5odaIopdc7UDCYciqK2YECCC2Cpf0b8hXcxs5131E6T9jSu3eeEKmyc+s8EfZYONP37mi0LzYtmpZHkohOIxZH7Bjn9g4xghEunmL2tnWCVa5l534CR+iC2s9NBMJeS5yxsuFIRKta3YROJDJnFbqwMlfamUXoJBk9v9PazT150v8LlVzlPJbQqXUFtG6oxubNm1FVVYUvf/nLWLt2beT8JhKTQuoMzj//fMyePRtf/epX0dXVhcJJPUi91QDpQAZM8E7X+tcuvDZ/IU5Y82bgtk65W1zThbXNr2DHomOCo3VAudwJzirBGYBUSVAi9ZKVoVX5hWwE75aeM/2XsyL4C1pvaxdZ7Bi0n46ILnZm2qhiJ9Az1rZvN7ETkhJ3sRMrQ7nYhU/rLnZCSDCjdYkMMjxGiApdWU/YCG3ouMXhxkrokujhOhGrW53HKvrjwJjWMW7P1tDROQ+6+hswkgs/4B3nwLw7GCDJOPzgAkgMePLkcDIH6B0kvnE9pkJc6jg41GOykJaNoLOzBzNnzsQ3v/lNLF68WDiviUplxyFdWLJkCW6//fZSO7tFAyguOmIOiREW5c130Ph28HZWrJ0pDhYbcXzzAbEMLG3teJqjWBu+zM6OFKI3SGOasVht7WTEamuXSCeKsWhnZ7ax06MUgmVwtrGL8mBPqvNE1Iijcd6iXTtiEQpPIraji9v2jnFuidqKw/XZEcZzpwjP/Vqic5NK6CzX2li0nbPdb0XvewxQjh9EV3+DsNBpOwfkP7+Ent8twNMr/5+Q0AFalG7qHRGETtIGFC4u0uZwPfPMM3HbbbdNKqEDJqHUAUBjYyO+9a1v4ZprrtEGKj4mh8JJPVAzRaF8Wv/ahdeeWii8f6MzxWu9bWDCT2gAqnYBc1k8+mLtoapEmK/YTB9rztKIc9DqD0at11eU9JrYxZqaS+8dHImIUgFYxC7OeZeiV50ZRKkGNssQ57xLDGo63u1qLMejiz57S/yZJtQqacyqXNXqOLPGaDNUTDShA0oCG2kgav1HUJyerYX6CDKnI+WBdFrBSK5KXOgAqANpHH5wgbDMAUCBq7jgX68X32emiFlX1psDCl9zzTX41re+hYaGEGO6VBiToqOEH9u2bcO//uu/oqenB1AYUu/UQ+oKXx079KFV6P3IIBoyI5hZ3y+074FCDXYdmAqlp1p/4It9FXJWQvqIFPlXvDwCpIbE0wF654diuLGwPNMrgCTm0aX0Cocc0NHDE84hhRiPz33H2kNOK3uE9Lx0oxeVDLdOA1EwOlAIp1M45BE18rRmIh1UyhNrDzkWcgw/N6KKSZw2deYDOqaQsmK0L96YG1jOR0uvVEm26m8R1GqmD2oeTWy43mQj7FhvbkRqcmCkibBb48dqlLaY1oh2oRbRZhbRd5mbKp4U0DrPMRXIrRAf/kvpTwMM+PT7nsR1U7aLpQXHJ9++CCqXkD07fA0WB4faloV0wgjy+TymTJmCr3/96zjllFNEi18xTMpInZWVK1fi9ttvx6mnngrIHMWFAyge3w+eCncXkwoc2cFqHO6rx/7BJqF9N6RzqK0dMaNvolE7tYqjmCm11TqaPb2MeUsZj7ZfY85RHlFIucSgpiJWSTGmVyULJmalKJ8WbYwQZY1J1PNtK4ZeHRu5DAoXflAnMZgvoEcfYsyVK0KcajPA/pCOKmVxMIQuKkq1VJqXVxT9f8WIEItgFTogWjtYIML/ibVXf0ShM6LhIteNOVWg0USBRRA661BYETBGQ5AUYHiOaMNlTeiknAQpJ0UWuu3vHotX3g0/1AhPqVj1/y1BcaFW3bpq1Sr87Gc/m9RCB0yyjhJetLa24rvf/S7+53/+Bz/5yU9QnDqCfH030jsbIfVXh8pDLUjo7qvHQLY6UtROywTa/HMho3Zc1nvBZo27gX4jCylKRhWsnBMvKqA9YNWUFm0ziiv0ENHHs2MKSp0owsJKnSeg34PE0ms9Y6P0yHV9Hza55Wbv7BkZmE61fo7ZxoppHSjCRuwYt0cmzZ7QIg/shH50mL2yI/YKDkMiQ5lYTg2LEaQEINzRxCl0aooJRZaVKsn+3QpG69SqeNJtLTtTubAYCt0LnNsKFt2Mzkml+3DoXbvMmSzULIY7XgEUBGocjWGtJMUoDyA150OnV/q13rBSTjw+pIDj6nc2AQC2v3usUFq1KY+mc2T85S9/QTqdxrXXXosPfehDFT9cSRhI6nQkScJll12GlStX4pvf/Cb27NmDwvI+yO/WQt5TDxZiIGClICFbqMaI3u07ktjpv7aYxISrY615MAQ/9I1BgqNijrdmfdgLyIYZ7bPcUIw8QsG0Y4ia3ih/qIelJUpnppcEHpYem4iJXfnnoyZ2Ki87T5HELkG09kLJi13SQpcIjCGsLbhF6IyhdcKgVEllUWyRoWXUamb7Xyl1sgnxQ5W5t8FjarT2Yf47i5e8TOZ0wo5B53aNcCbQZtctMsdQNhSKF0ZkLipGdC5SWl3oXto7SygdlziUuYNQZ2Vx+DDHnDlz8LWvfW3SdYbwg7TWweLFi3H77bfjoosuAgAos4ZROLkbakP4Xy9G1K7j0HR0DjVGK4gKMJWJd6QwCFklq6aidZgwdyOVd3oQqiJk5WIpUiXr9hAInT5sNawhdG43YSlENawxEonHzT7eOGiRkwIYm6rYJEmyOjZudSswSkInsv+4Va4uQieCU+jMcoWohnVWu1oJM5+yuW3QV+hXTRmmhiNlr2oty9tv146qVieh7sU+Va1honTWqtYoKP3pyEKngOOqXRsjCZ3akMcxl1VDOXYYnHNs2rRpUvZuDYIidS7U1NTghhtuwHvf+17cfPPN6EEPCit6Ie+rhby7PpRoGVG7/EganDPPqN30hkEMT62G0u1SzWtE7XyqZI12damsR5kCqmTjDDUAuEfrDMJUyTqjdWXrEHCTdkTrxNMzqDKHhICIndcxsHARuyBZ8IvYBXWQGO2InbPqtWy9ws3yj0XULonq2MRmjhjFww+KKgcJXdA1Gih0AVWwXkIXBj+hMwhTDev5v57A7w6vyJx9/+47CiP7gVE6l6pW+879o3TOqlY3huconpGewKpWBlx93pOeeUeOzjEOZc4g+Nwc9u7tRWtrK7785S/jjDPOEMpnskBS58Pq1auxfPly/PCHP8Sjjz4KZdYw1JYRpHY2QRoMN7K2okftBnPVaMjkMKPuiG290VliwE3qDHyqZMva1fnkIdLeTgRr2zo3AuWOldrWueYfIGfaA8G7fZytfY5bHn7t61yqXd228RQ7gYeJv9gFpR1FsXOpei3fBmb1XJTqqCSIWh17tKJzUdqG2TPwrkINE6Hzq4INE6Hzq4INI3Re1bDcGEMy1Dn0roZ1/R8RbKfrRhiZ89qXSOTWM0oXJHMBhJE5AL7t6cJG5j7X8lJ5WnD8n10XAoB4dK6+gGMvacSuXQcBFVi3bh0+97nPTcqhSsJCUhdAY2Mj/uVf/gVnn302brnlFi1qd1KPFrXbU4+GFzvROGc2jpzq3dtAKUgYLlRjJJfGQLbGVe5CIdiRwhWX9nZGFWzkDhM+0TorXnLnF60r2w4uN28WfsYJrzxc29f5VLuWoYupinKxE5EGp9iJDGOSmNgB4drZuZXBq53dUaqh1QaIZjDm3fUjCdkUeWgzJabUue0/JenliJ6HUJWrS7QudoROYDw17TtlwduLfrUuxQ8tc0YWlutJtBreNUonKHNuVa9JtJsDoneEMGRu257ZgdtzAHN+pl/Pkgpl7hD47Bx27epBS0sLrr/+erS3twuXY7JBUheS9vZ2rFixAv/5n/+Jxx9/HMqsYSitOahv5dG4ZwaOnBqch1Xu/KpkfXGpklWrOJQMhxwUrbPkYY3axa2CBYKjdVZc5S4gWmfbl5uY+VTDhsrDqxpW9Fll3T6iM5SLnUja+GIH+FfHBpZhrDtQhKiOnQjVrYFliNB+zhlNFm1D54zWiQqdW7ROtAOEW8STCQqQF9b2wVF6dkdtU2mL0kU5FkfVa9jonJXh2aWq1zgyB5SELozMmXCG9OPPQ5kygqnnVuPAgWFA1ab3/PznP4+mpqZIZZlskNQJ0NTUhK997Wu44IIL8P3vfx9dXV0oLusDG3gO0vBSqLXhehw4q2QjYZU7CVDTAlJnycOQu7iEjdZZscqdOb8sxMXMyCuoGtYvD+08WMRO5ZGiD8ZgqcaDM6o8mOkijNEVV+yAUtSOqTzSIMvWdnaxry8WTRDdonZHOzqXNEY0OWqHCGsVbOROEXq0Lk6EDihF6aJgrYZlfh0fQmUmHpWzYdzHok4DZ0TpEhDTKDJnlEGakk9E5q7dvREqZ2JCB0AayqGwpA/qtBEcOAC0tbXh+uuvx6pVqyKVZbIy6WeUiEo2m8VPf/pT/OY3v4GiKFDTKfSdvhiDS+cAAv/cclp7ahePVIEVot8gpRGG9ED0zsxM1WaYkEYiZ2HOMiEXol1SnCHWLBNGGaRihPHnLOmB4Ko7XzggKRysGF8ioh4HkEzbSUnhkLMxCgHE6k0JIFy7xqAs1PEhdHFnliiVI3paQ/pjTduWivedmGWIMxezzOLJHHSZi/ijISmKNQ6pi4BSAyjV8apaB48rRpY5AFCbizht8TvCMgeVo+61vZj1wl4MDg5ClmVceuml+OQnP4lMJhO5PJMVkrqY7Ny5E9/97nfx+uuvAwBGWhvRe+YJyB8zRSgfPpyCPCRBrY5a5cWQGmKQhxl4xPirVNDa1TGOyNNRSQog53isBx8ravlEfXBpU5Dx2HIYR+yYapQhquBq3yNTtFlLouWB0vcQuSoYkEcUsIjHAUAbZDrOvKuslD5yZCfG96l9F0zrMBLH9WMOvZJIByejyWScchjDeSQRgY3TVCCmiKmpeGLLFB5zPmPttVAbR461aHixPnIWkPJAbpo+O1GkMgByjkGdJz61WFVnL858pRdvvPEGAGDJkiX40pe+RMOUxICkLgEURcEDDzyA22+/HUND2mSqg4uORd/pS6DWhpuRAgNppPsk7RdwikeSu9SQhKreUsNjUbljamkMI7NqVlDuGAekvEWootyvVJTN6yp682eqI2IoeDqN+VGjPsSNMbWiih1nTBuVX488RhE7m9QBkc4B03u+mtWXUY4lbqTOmpcUrcouqtQZQsclrY1hZEmPIXSJ9Va3XA9RJdsQuqTKFTlSx+JLpVItnoG1vS3j0ealNY6ZM0CtihbxtEadlRoGNdxgDDYkvaMrU4CBeeK/4hmH2eRHKjAUlwyH3/fwCJo270Rtx34AQH19Pa6++mpccsklkOWYDbwnOSR1CdLT04P//u//xh//+EcAgJpOof+UhRhYNheQA+5eA2lUdWsXM5d5JLlLDUmo7i5FNKLInVTQqmFNIsidVATkEUe5RaWs6F6VEPZGbkbrnHkIdjqIJHbGOTPyERQ7I0pnnXZIKojmAfdzLnj8UsH+xTOVC4tdklIHWKRCQAhEpc6MzsH6PYhLnXHs40noSnmLZW67Js084pdLOI8EhE40SmfInPX/OnTPeB2rzGmZAcUasQNxa0JQaBDLw5A544c3Z2JSZ5O5vNEoGeGkTlFRv30PZry4F8PD2vYbN27ENddcgylTxGq3CHdI6kaB1157DT/4wQ/MKtlCcx16Vy1FbvY0eI7eb5E6A1G5YwWtXV160JKHoNzZonW2woSXu7JonW1lcHpzUw+xA8I9CDzFDggtN1HEzm3kexGx4xJzHd5AROw8pU7PKwxuUgeIi13SUmfmKxC1E5E6W3TOtkJQrMdDdSvgKnRa/uF34CZ0Wh4xymXkIRKtO8pC5yZzgJjQlcmckYdAlM6rPahIlM4pc0aZctM4CvUhni9uMmeuDJA6zlGz+xBWvtKNPXv2AACWLl2KL3zhCzjhhBPCHQARCpK6UUJVVTz88MP4yU9+gr6+PgBAbkYLes9YikJreddsXpAg96eQGiy/w4nInTVaZ8tDQO7KonW2jMLJnWu0zkqYe5lLNWxZcQLy8RU7M5PgPEKLHS9/AJj5hBC7siidI+8wYucrdJa8/LBWvbquFxC70ZI6IHzULozUuUbnbBuEk7q40TmtLJGT2gm4FsKInef1aOYRoVxWwkbrjqLQeclcKZ8Q581D5rSMw0Xpgjr3hInSucmcQZgona/M6eRmFpFqdB+4OH2wH+veGsG2bdsAaKNIXHvttdiwYQMkiWYqTRqSulFmYGAAd955J+677z7k89pFP7RwJvpOWwyl3tGzZzCNqsPe7QnCyJ2X1Jl5hJA7X6kzM/KXO99onW3DgH0o4Xt0ed3wy9rX+ezPi1Bi5yN0Zj4BYucapXPsI0jsQkmdJT/XcnpE6WzbhGlnF7eTREiConZBUucZnbNtFEKox1N0zvrquT//DYKETstDoFxeBIldAkIH+Lejc7aX8yIoSucrc0YeAVG6sD21/aTOT+YM/KQujMxpG7pH6eSBLBqffQO1OzsBAFVVVbj00ktxxRVXoL4+Rs8OwheSuqNEV1cXbrvtNjz22GMAAC5LGFg2F/0nLwCv1uPnAVJn4Cd3blWwrnn4yJ1nFaxrRt5yFxitc+LRBkxE7IDym2moaJ3LfsuKFyB2oScc9xA73yido2xeYickdJb8ysoYQurMbX2idqMZpSvbl0/UzkvqAqNzto29pW4iRefK9+u+cahr0cwj/P488/CS8oSEzitKFxSVs+XhIXTWsgeW1SdKJzLsjlfVaxiZA7yrXkPLnJnALnUsV0DDtncw9dV9ZiBj7dq1+NSnPoW2trbg/IhYkNQdZV5//XXceuutZihaqU5jYMU8DCybCz6SCSV1BlzmUGrKxS4oWmfLw0PuQkXrbBlpL1bBCx2tc+IseohqWM9iGe14o4gdUCY8nhGfEFE6Wz4OsQstdNb9qVwf18+aD8SlTs/PLFtA1asbXlG7oyl15j5d5M75vQnJnAW38zJuonOAsNBp+7cnMO4Dosc0WtWwSYwh5xS6sFG58nwc5ypEVK4sD0eULur4ic4oXViZM3CL0hlCF0rmzESa1LFCEXUv78Gxr3ZicFCLKpx00km47rrrsHTp0vD5EbEgqRsDOOfYvHkz/uu//gu7du0CACiZKvQvX4jhtvlIZcP3T3eL2olInZmPQ+6EonVlmZXkLrLYAaWHU4RoXVmRWAyxMzPxiNYJCp2BVewCq119ymRE7SILnSUvQCxK58QZtRsLqTP3bamStUpdqKpWz0wt39l4is4BkYSuVA79WER+WJTlEW3fNpxil3A7OpGonBPt/mj/wSA8TZtF6OIMhm2N0onKnIFV6oSjcxZy00fQ9O7bmPfqAfT29gIA5s2bh0996lN43/vep816RBw1SOrGEEVR8Pjjj+NnP/sZ9u/Xxusp1mYwNHcJsjPnAgKNSLnMzaluGLTBIFND4mWyyp028Kx4HqXMDKkTrIb1IK7YmWVSY4idJQ9jeqI4A9IyRYu2xXmYGmLHFB5P6owyqdGlTkuvi91Rak/nh3lOGQCOSNE5e4a6QMeMzgFHv/2cH8KRYs98YiXXMMQuIaGzZR3xf1WVmW1+7Ejl0qtdk5jZpNDAIsscoFe9tmrPDQNRmYOqoubAHizq3oeDBw8CAI499lh88pOfxHnnnUfjzY0RJHXjgGKxiD/+8Y/4xS9+gUOHDmnLMnUYmrcE2RmzheQO0P5RpQJDetCnnUpQHhIApkXqrPMsRoEputjlY15qXJO6sG3XfPMpxpsdIBE51HGboDxyeSLOPmFFm2YtXj5M5a5t9aJlhth5cSlGdM6aD9OmThsX8gMtApWEJKjp+OcGSPa4Ys9fbPlxGgc1pQ/oHlcw07CJYRTkEaCY0aJ0cWbMAYDhNi4ucgCgqsh07cHSIwfMYMS0adPw8Y9/HBs2bEAqRVPKjyUkdeOIkZERPPjgg7jzzjvNYVCKNbUYmrcY2ZlzACn8HUEegRaps0TeomDMxcpZKa9I+RSBVNZS9RHxqmOqReqiRsmMSJtFDiPPHBGnOhcozcHJ4wsiZ9FnnyhloklL3CmxAMQWQ7NIki4ucbJjgFId74lqbZ8ZO58kRFVvtB9rnmIY0uLeAUAoH/30xv3RZa3mjJLWJFbksvReqYqekfVcFGujl0fOaa9SEcg3JWPOuRbBBIqCTOduLOrvwoEDBwBow5N89KMfxcUXX4zq6pCzJxGjCkndOGR4eBgPPvgg7r33XvT09AAAlOoaDB23GMPHzgXk4F9CTAFSOe1mYBUyYSnj2o2EKShVi0TIp9RGj9tlTPTqc6SNXAVqrUK1lCNy27gIQsZZKQqgtflCZLmzPoQkhZtVsuIZ6ekBwOj8EEN4k8B4wJsRqSjZxpA66wwAcarbbfkAkaXObMsnGz9yomWkOquiI7qCIXPm9+T4wSRCVKEz70dxo3uO70iVI05Lp9pflWpEmsrLkDm5oM/xmoE2fWBUOAAGFOq0MoVCKaJ23y4c17sf3d3dAICpU6fiIx/5CDZt2oRMJhOQAXE0Iakbx4yMjOD3v/89fvWrX5nVskpVNYbnLsTwsfPA0/53CXkE9tklIsqdNqm8dUFEueOAnLfIhvMBGfZKdElXJnph83FGpCzvhXuzCsoYlwDF0TkiamcOt6qhKFE7prhE6CJE7ZKsfnU+5CPJXQSpK5MwfZ+i58I1H0t+Qnk52nYB2v+naBWs6lUVLdqsSnaXsEjzHQsK3WhE5az5iAqdLfJveR9F6KwyVyonix6ls34dLFyUjhULyLz7NmZ170N/fz8AYPr06bjiiiuwYcMGisyNU0jqJgD5fB4PP/ww7rrrLnR1dQEAVDmF7KzjMDRnAdQa97i+U+oMhOXOGq2zYu2pFjIvVgRSOZdLTjR65/VwFRU8N7GzrDMIm4/IQMlGlM6JqNj5tfUREjtrlM6JYNQuqSgd4P2gF5I7AakLkjARqQvskRz2q7FE55yIRus8hQ4QkiMvodPKJBatCyt0oy1yBiJC54zK2VdqUbEwGCIHlA+WHjlK53ZZBEidlMui9t23ML17P4aGtN52M2fOxJVXXol169YhHRBMIMYWkroJRLFYxGOPPYZf/epX5lAonDHk2mZhaO5CFBuabdtbq2DdMOQOCBa8smhd2Qbhone2aljXQglE74IesGGrZ/3EzqUcftuFqUL1EzrrPsKKXVAD7rDVsa5ROicho3aJSR1DYA/aUO3tQkpd2CnWgs6Brxg68gosk0t0zop2zQVH68qqWz0zDC6Tn9CVyhUsdubQIH7TkCUkckD478UZQXfiFZUryydElM4tKudEOErnlZVP1WtqoA+1e95E/eH9UBTtxjNnzhx89KMfxXnnnUcdICYIJHUTEFVV8eyzz+Kee+4xBzEGgJGWaRg6bhHyLdMB/UHoFa1zEhi984rWOQkRvQsUO8s+faN3YaMmYaJ3YcTOsq2BX15uQhZG6Kx5B4mdSI8836idX5TOSYioXdLt6YIIjNoFSF1oCdP34XfsSUzTBvhH55wERet8o3NlmfmvDiN0pXJ5i51fdG5URC5kXn5ROt+onAM/ofOLyjkJHaULGbG2Rek4R1XPQdTueQPVvYfMxSeffDIuv/xynHHGGTQ/6wSDpG6C09HRgXvuuQdPPfWU+euqUN+I4TkLkG2bBYaUb7TOiZ/cBUbrnPhE7zyrYV0L5RO9E23j5Cd4ImLnUha3vJxC5taOzo+gDhSiwyx4iV2oKJ0Tj3M1mu3pgvCUOw+pE5I5M5H7NRcpLz2/skUB0TknftE6IaEzMyxf5OwQERbX6exchG4sRc7ATejCRuXsidyrXcNE5ZwERukE/tcK9XqUTlFQc2AvTh7pwzvvvKOVSZaxZs0aXHbZZTQDxASGpK5C6OzsxG9+8xs89NBDyGazAAA1lUb22LnITZ8PWQnZsEPHtWo2bLTOiUv0LnS0rqxgLoInKnZueVmraqMO5+EUPIfYiUTpnLhF7aKOm1VWHSsSpXPiErU7Gu3pgiiTO4fURRYwPU/r8cbKS8/PfCsQnXPijNaFrm51zcz+USQ6V14ue7TObWYGt32KElXkDKxCF0nkLFijdCJROSe+UTrRfzMG5DNDqN33Dmb0HcCRI0cAAJlMBps2bcKHPvQhmpu1AiCpqzAGBgbw0EMP4YEHHkBnZycA7X+/2HgMRqYtQLGxVDUbFlu0jUecOsyA2W++khJx6A2g7OEqLJteefFkBicGSjLG1OhCZ2AVuyQGeTWjdnGkzsAid+NB6gyskSulWo4vYIB5rSSSl55fHJkzsEpdpOhcWYbaSxyhK5WNm4OY2+ZPHWORM1Ad09nFGWtPqbak5+IiZ6UsShfpxyZHauAAqrrfRtXAQRiP/La2Nrz//e/HhRdeiIaGhshlJMYXJHUViqIo2LJlC+6//348++yzpeXVdci3zkd+6hzwVJVQnkZbIakIQI33AAK0vLRhThK4BA0pMwYnjvvQVi1CFrd4en5xhM7AWh0be2YNaDInFZKZiQIqN6OASZHELAcGSrWUiICZQpdU0Vj5RPFR4RJLZoYSBnMWhSS/A2OmmniZWP6XEsmLmbUHcZELHIUMiyVyVooZBqWaRfqfYsU80j27sVDqxb59+8zlp512Gt7//vfjve99L03lVYGQ1E0C9u7diwceeAAPP/wwBge1XhNcklFonomR1uOg1E0Vi95x2MbG4kys/Y8VayQrEYyyWfOLUS2mTUtWqspLInoXB1t1cYxBXq3IeX1asLgRO5VHGp/Mj8SEgulSF6swpfMfW9D1wzJ69sb9gWRG+qSYEWugJHRS/Km6bJH5mD+0bHnGiXjr/zOGmMedn9g+llzM79LSXKBYowudUHoOebgHVd270DB8ACMj2uTd9fX1+Id/+AdccsklmD17dowCEuMdkrpJRDabxWOPPYb7778fb7/9trlcqa5HvnUu8i1zwNM1ofJi1mpT81d9NLlLWuyM9nqASzVlhEbsxtRktkbocQQvYjq3nsBxxc4aWWMqwIo8Vvu6RKUugQeuNa/IUmermtd/yESdVsshc+YuYogAdzbu54h+TViEzswu6lfAEC+i6XYpRRQ6r/PhrHYNi1XkjGteSUf/ceuc0YYzYKQ5/IGyQg7p3j1Ykh7Anj17zOULFy7E+9//fpx//vk088MkgaRuEsI5x2uvvYaHHnoITzzxhNmxgjOGQmMb8q3Hodh4jH/0zmh3Zo0KWG64ooLH1Gij0HuVjSko73kaVfAsYmcmjSt4UapTuHs6bbm43LlVlTIV0aJ2ulzGnYfUSpLVfpGkzkXmSvkJto/0kDnrelFZsUbnylcKip31f9flvAuLXVSh87t8BM9RmDHyRH40uImclWKNaFSt9NbZ8casdvVNr+pt5XajZuigOfpBTU0NzjnnHFx44YU48cQTwZL6YURMCEjqJjnDw8N44okn8Ic//AGvvvqquVxNZ5CfOgf5ltlQa9wb0TK/Tg6Cgjda1bBeY7wJC56L2JlJnf9CYSVP4D8vzGwbolE7P4kWjtqN56pXQEzq/GTOzE9A6kJGHEV+BJVF59x2G7Ya1iU6V7Y/ka9CROjCXjIhhS7s9R9W6IJEziB0lM5D5GybBETppJFBpHt2Y5bag8OHD5vLTzjhBGzcuBHnnnsu6urERjsgKgeSOsLk7bffxh/+8Ac88sgjZnd3ACjWTkG+ZTYKU2aBpy1DkbtF69wIKXhHW+zMzcIKno/Y2ZKLRPFC/PeJTJ8WNmoXpkODUNRuokud83sKui2Gkbqg6JyzCCGkwDc6V7ZxwLUaEJ0ryy5ok7Dt50QvkwChE71fBAldWJEzCBS6ECJnbuoRpWOFEaT73kW6by9Sw73m8qamJqxbtw4bN27EvHnzAstKVD4kdUQZ+Xwef/nLX/DII49g69atZlifg6HYOF0TvOYZgJTyj9a5ESB4YyV25uZBgmd5UIYpY+gonldASEDobOl8onaiw44Eyt0oVL0CR0nqwkTlXPPzkTpBmbOm88pTSOasWXpd9yGic67l8NrcLzoX9bIwzqPHrDRR8BI6UZEz8BQ6AZGzJbNG6ZQi0kc6ke7di5rhw+Z9WJZlnHrqqdi4cSNWr15Nc7ESNkjqCF96e3vxxBNP4NFHH8WOHTvM5VxKodA8A/kpc6BmpoKpUVovuwveWIudmcztAWUsCxm1K0vuF8VzNtuKKHS29C5Ru6jDjnjK3ShE6RLtJKHnZ5O6qDJnoayzRFSZs+YpOz9Hk7lSBo5rVDA655qlyw+fMqGLezk4BDeJe4FT6KKKnEGZ0EUUOTM5A4o1HCx/GOnevWgaOWy2dwaApUuXYu3atTj33HPR0tLikxMxmSGpI0Kzd+9ePProo3j00UfNgY0BQJWrUGyYgWLjsVDqWgEWX/C08fASvDQjip0tC5dnYBSxM9O6TERv74yB+A9H2OUuiXHkbHJXUMd/lA4oSV0CMlfKk9nGXUtEQo0IWlyZs6Ifc1yZs2WpS1wSg2CXYdwL9K8n9jiRKAldXJGzUqxhsUVOK5wKNnIY0vB+tKb60dfXZ66aOXMm1q5diwsuuICGIiFCQVJHCMM5xyuvvIJHH30UTz31FPr7+811iQke0wVMBXgqqYLHFzszK/1hZgwGzFQe+6FulbzSIMPxymnFGGg4KYzOFKlsAie0LPPkI3Vq2rCaZM4Bl5g200qC5eQyQnWCiJR3koMIj4LUGYM6cykZkTNmc1DlUkQ1iR+KUoGjmJGgyjFFLncY8vB+tKT6bPfQpqYmnHvuuVi7di1OOOEE6r1KCEFSR8SiWCzixRdfxJNPPok///nPHoI3E0rdNGHBYxxgxdJnLiUgeAmKnZVSO7vSv1Ochz2zVJvGHkzWyNOYh5YnE1ljCiDnlOSrXs0daC9JRcCSmrUBjJWkJiFRMqvx9DyTkDr7FFoJCp0ZUY+XT6Lz55oZ2atVk5rJRR7R8jSmORMeFBgAuAopdwjS8H40S70YGBgwVzU1NeGss87C2WefjVNOOQWpVFK/ZInJBkkdkRjFYhEvvfSSKXjWagQupVCsb0OxoQ3F+umAHG6KMsa1gYSlojb1kSob+cUQvFESOwC+VZFR5MSQu7hix1T7dGdGFDCO3ElFjtTQKJxEJ0nIXRJSxxwzLJjRqgTa0DmyiFNW784MCVS7StFlriyqxUt5xhmg2DYlFy9F4+JGPA2RA7SINAAUayUxoVMLkLIHIWW70CT1mTP6AMCUKVNw1llnYc2aNTjppJNI5IhEIKkjRgVD8J566in8+c9/Rm9vqRs+B4NS16oLXht4lf+YSkwBUiP2y9QqeNpnQckbI7Ezdy/wgI0btXMKnX0dIsndqEfpXHdaehulZ2kkUTKm8fLr9RkhWuclc+b6CNG6ULIVUeyiRuf8OgKZ+Yrk6SNxts0iCJ3suMcwl3xHpoQYd6Y4BDnbBWm4C+lir9lrFQBaWlpw1lln4ZxzzsGKFSto7lUicUjqiFFHURTs2LEDf/3rX/HMM89g9+7d9vXVjVo1bUMb1JrmsgePNVrnRaQo3hiLXVlxAh64UeUuzADConJ31KJ0XohG70SlLkjmLPmKSF2QzFkJW17x2R4EyisgcyJD9Zh5hymKVeQ8JM62uYDQuUXjvPCM0nEOlu+FlO3S/goDttVz5szB6tWrsXr1aixbtoxEjhhVSOqIo87evXvxzDPP4JlnnsH27duhqqUun6pcBaV+Oop1x0Cpnw6e0gY7DiN2BkJRvFEWO+014pAZHg9fkSpZvyid+/YIlLsxidJ5Fqb01lfwwkidJX1oUQopdSIyZ6YJiNZFn5M1pCwGVLV6VaeGyVfLwGuDcNE416QBQhcmGudGmdApI5ByByFlD6K1eshWEyHLMpYvX47Vq1fjzDPPpF6rxFGFpI4YU/r7+/H3v/8df/3rX7FlyxbbuEwAoNQ0o1g/HUrddKiZFkhFSVgmAiXP0tN0vETt3LBKS5ionajQ2dPCU+7GPErnhV/0zk/qwkblvPbp064uisxZcStzIj1OfcTOKzoXVeLK8rbm6xQ4fZnw/7jHEDBRJc5KsVaCUsXBRnpMkZMK/bZtamtrsWrVKqxevRpnnHEGGhsbhfdDEElAUkeMGwqFAl599VU8++yz2LJlC9544w3bei6loNRNg1JzDFA1FTxVF6mNkKfkjeOoXai8Feuy6EJXlrdF7sZVlM4Lt+idU+qiROV89meN1tkGpI2ZtzVal/iYcI7/Het+RKtSg7DKVtQonGfeluhcEhKnZcoBPgxFOgwUD6Ee/WU/OBctWoTTTjsNp59+OpYvX04zOxDjApI6YtzS3d2NrVu3YsuWLdi6dattuBQA4HIGak0rlEwr1JpWIFUbaT9WyTMH/B2loaGYgvKpw5LK24g2FpMROmfeTOVIDauQ8klN9XEUsETv1KqSWSQqSAxQ08z2OUmcP0ISxdqj1xFBSwqpyG15JzlWolTkpqwb4yZGRh0GlG6g2A0ohwGes61uamoyJe60007D1KlT4xSdIEYFkjpiQqAoCnbu3IktW7bgueeew6uvvopisWjbRk3VQa1pNf+Qqom0LyO6Y3QwMMamSgrXqs2ksjeqkHm4DhIi+aZyKuScGnsoFDcY54DCS1WhcsJmxIBiTfJmxFStzFwC1KpR+iUAQB2NAYnjVDt7IOct4zTq5U0qqstU2H5Q8BQD4vy+ULOaxBkix4dtq2VZxrJly3D66adj1apVWLRoESRpFEaFJogEIakjJiS5XA7bt2/Htm3b8MILL6Cjo8M2dAAAqOl6qNVToda0gFdPBU/Vhq+u1eXIHB/P0gYvKcljSoAcxdlFwnLHVJjt6Iy5eY39JDKQMee2KIu9OjMB60hQ6qzHa5wHLjMomdF74HNmvwZjkZDMWQWutEyzrCQGUHZKHADII9pnNcVKM4SEQa9OhdILKD26xA3Z85ZlLFmyBCtXrsTKlSuxfPlyZDKZeAdBEEcZkjqiIhgaGsLLL7+MF154AS+88ALefPNNOC9tLldDrW4piV5VE3xnueCAVCyXIqfkacuiiZ7wOHGiu0hC7rjWVkkqlIdFTMGLIXdGlM6ryjgRwYspdW4iZ0NimmiMUrSO69HAWKIUQ+b8BM6KMW+zaDndBA4oSZyVUELHVUDtL0mc0gvwEfs+GcPixYtNiVuxYgXq6vzHzCSI8Q5JHVGRHDlyBC+99BJeeeUVbN++Ha+//npZdS1nMtTqKeDVLbrsNQNytT0jbrQn85eiuKIXa4aHkGN9RZI7H6GzFSFG9M4ZpfMtTlTBiyB1gSLnLNtRiNZFEjtBmQsrcM6yhZU5EYErJdKqoAG4C52aB9ReU+KqU0MYGbFLXCqVwpIlS3DiiSdixYoVOPnkk9HQ0BBcYIKYQJDUEZOCkZERdHR04OWXXzZFzzr3ooGaqgWvaoZaPQVqdTN4VTMgpTyjdn5EEb3AKtmw+IwBFlruQgpd2a4FBC8oSudbPBHBCyl1oiJnQ2JQ08zeaSJhhMQuhMxFEbiyMvlUtUYSOBds0TleBJR+QO0DlD7tvaM9HAA0NjbixBNPxIknnojly5dj6dKlqK6uLtuOICoJkjpiUqKqKvbs2WNK3o4dO7Bnz57yKlsAPN2oC94UQG4Gk+oBFq0qL4zoJTEvqyfGboLkLqLQle0uQPBEonR+OAcALpM8D6krK0/M9oejHa0DQoidi8y5yZu2PPr364zOJSVwtn1wFao8AM6OmCInYcg2YLnB7NmzTYE78cQTMWfOHOrYQEw6SOoIQmdwcBAdHR3YsWMHXn/9dezYsQOHDh0q246DAal68FQTeLoJSDWBpxsBqSrSft1EzxhSQk2x0ZE7HcZL49wZw6HIea2NXFyhc92XRfAkRY0cpQuiTPJSDMUaOXGJK+MoROuAktgBulTpIifnVdeBkOPIm9u+tf2Ur4sncHlwPgDgCDgfAOdHkEply5pNAMC0adOwdOlSHH/88Tj++OOxePFiqkolCJDUEYQvhw8ftknezp07ceTIEddtuZTRJa8RPN0EnmoA5GgDJGv5aeOTmWPEZTl4CsjXjU70gelVzHJehZwd3bHomMKRHiwAumTx9OjOh2lKXdIS58AYk02pkkY9Ygdo1fVcLg3Dk6S8AdCGsslqvZ6N6k/O4sobBzBsihtgvOZct29oaMDSpUtNiVu6dClaW1sj758gKhmSOoIQgHOOgwcP4s0338Sbb76JnTt34s0330RnZ6f79pD0qF4DeKrRfC8qe0yFNkac35RUCQkfU4GUPjI/U/moCJ5U5Ej36A9xCeCyvdxJSx5ngDIK49Q5x2AzoptcYhiZOjozDDhnDomNRdxc92ccU0rs2irJ2yCAQXA+aL73GmBuxowZWLhwIRYtWoQFCxZg0aJFOOaYY8Ai/jAiiMkGSR1BJMDAwADeeustvPHGG6bw7d69G/l83nV7TfYadNlrAFJ14HI9kKrzba9nyB1TgVTWMfiyLKFY556Wy9GEbzQEjykcqeEipGx5tRqAMslLQvCSlDqryHlVUXMGFOtSiUXrYoucn7hxQM6VfxeqLnFBMse5Ak3ehmCXtyF4yVtVVRXmzZtnitvChQuxYMEC1NfXhz8mgiDKIKkjiFFCURR0dnbinXfewa5du8w/P9kD9GrcVB0g14Gn6i2vtea4en5y50YSwpeE4AUKnROXKB4gLnpRpc5tNoSwbQ3jip2wyEUQNzfcZE4Tt6wubkPgfNjy6l5tCmjyNnfuXBx33HG2vxkzZiCVSmo0ZYIgDEjqCOIoY5W93bt3Y9euXXj33Xexd+9e12FWDDgYIGfA5VpArgWXa8GkDKRiBuA1SI2kI1VT+Qkf4C59TAVSOcvwH9xf8pjCIecUMJWHFzovIlTXhpU6r+rUqHAGKLUp7TVA7pjDx8pELqCaVETcypNyqKkCOLLgUg6awGX112FI0ohrj1OD+vp6zJo1q0zg2traIMuj216SIIgSJHUEMY7o7+83Bc/6+u677yKbzQakZgDLgPGM9ooMGKoBVgOGGgDVAFLC4sclhkJ9cFTFNiyLRfKEo3OieETzgJLsOaXOaz7SpHv8muVwidrZJI4D6aGA8xNR2jg4AAUcOYCNgGMEHDlwltVepSy0aJv/oyCTyWDWrFmYPXs2Zs2aZftramqidm8EMQ4gqSOICQDnHN3d3di3bx+6urrQ1dWFzs5OdHZ2oqurCwcPHiyb+9YdGUC1JnmsRpM+1ICxan15FYAqiMqfU/ys1bOMc8hDBbPB/VHDKnsSg1JTKt9oyVsQxYwmlsw5Zl8EYTNkDciDIw+uC5tV3GAuC742ZFnG9OnT0dbWhhkzZuCYY45BW1sbZs6ciVmzZqGlpYXEjSDGOSR1BFEBFItFdHd320TvwIEDOHToEA4fPoxDhw5hcHBQIEcGLbJXpYkeq9IF0PicBpAGQxpASnvvNyCzCkgFh1io/OiJngSoNaPTG9UV522V67Nn+CWBAqAIjoL2ygoACuDIA0wXN/N9AUDeMshgMPX19WhtbUVrayumT5+OGTNmoK2tzfybOnUqtXMjiAkOSR1BTBKy2SwOHz5sSp5V+Lq7u9HX14fe3l4MD5dPuRQOCYBD9lgaDCloEULtlSEFsBSgypCKrLQMspaHKoEVOFioSW3DFy1pqePgAFeh9fAsQqviVLRprPTPxh+HokfLiuAoAijo0laSOBFBs5LJZNDU1ISpU6di2rRppri1trbaPmcymSQOmyCIcQxJHUEQNnK5nCl4xmtPT4/5vre3F4ODgxgYGMDAwAAGBwd9G9FHhkvQRFH70yRPhjE3FeNMf6+/Avqrx3tz6jB9wGNtEjjbMpjLVNsfd1kGqN5z7EaEMYb6+no0NDSgvr4ezc3NmDJlCpqbm8331s/Nzc0kawRBmJDUEQQRC1VVMTQ0ZJM84/2RI0eQzWaRzWYxPDwc+H6i3o5kWUYmkzH/ampqbJ+Nv9raWpu0WV8bGhpQV1dH85USBBEZkjqCIMYFnHMUCgXk83nk83nbe+vnQqGAkZERFItFqKoKVVXBOYeiKOCcm8tUVbUtkyQJjDHbq/HnXJ5KpVBVVYV0Oo10Ou373vijTgQEQYw1JHUEQRAEQRAVAMX5CYIgCIIgKgCSOoIgCIIgiAqApI4gCIIgCKICIKkjCIIgCIKoAEjqCIIgCIIgKgCSOoIgCIIgiAqApI4gCIIgCKICIKkjCIIgCIKoAEjqCIIgCIIgKgCSOoIgCIIgiAqApI4gCIIgCKICIKkjCIIgCIKoAEjqCIIgCIIgKgCSOoIgCIIgiAqApI4gCIIgCKICIKkjCIIgCIKoAEjqCIIgCIIgKgCSOoIgCIIgiAqApI4gCIIgCKICIKkjCIIgCIKoAEjqCIIgCIIgKgCSOoIgCIIgiAqApI4gCIIgCKICIKkjCIIgCIKoAEjqCIIgCIIgKoDUWBeAIAw458jlcmNdDIIgCCFqamrAGBvrYhAESR0xfsjlcli3bt1YF4MgCEKIRx55BJlMZqyLQRBU/UoQBEEQBFEJUKSOGJdUbZkOxvXfHEwCkxjAJEBiAGNgkrFOX84YIDEwYxtzHTPTmH+AZZlkX68lNJdxxko/fSx5mMtZaV/WZZxp2ZjrJC1fbTkz1xlpuL7MXA+U8pD07Y31sO/DlkYvPpdc1tm2h62MpWWsbF1ZGljL4VgPj+Ue+XmVoyyNX77mcl6e3pLGXG/Ji+vLYUmnreOW8mjrmXWdua2xjpt5Muv2jJvrzEvMWG5kp2+jXQrc/GykkfTP2jrts5HOXMc4GErpJH2Z+QduppMYbMu19GopHYztVchGGv1zKS/VzE+25C9DWy4b+ZnbqpCNPGGUQy1tj1LeWp4qJGj719Zp+cn6MgYVspHekkYGtHTQ9mOcD+Ozti+uv4e+jkPSz4sMBgmArH/ZEpi+jEFmDBIkMP2bK+RlfPD/tIEgxhMkdcT4RGH67RWa1EEXMP1pWVrHAKlkMEwzJD0T4+kuoeypXTImu0kYeZY95eFYZt0HXJY506EkcxapK1tmkTDrZ2cR7du7pJF81nkdRlk5PA7bb53XqYqanyVPN+EbValzWw/nZ27mbS2HdZ9u60wJhGUb6/ZlabjLvrjtzyp1JVHU/7zWwRA/LUurABryBxhyBlOKrOs0qVNLUsSsUqS9lxjThEt/hfmemem0fKDnaaSFnk5f7rbOkkbWhVQ2y2lIHQ+UOmt+snE+YF8mwVpGy3dIEOMEqn4lCIIgCIKoAEjqCIIgCIIgKgCSOoIgCIIgiAqApI4gCIIgCKICIKkjCIIgCIKoAEjqCIIgCIIgKgCSOoIgCIIgiAqAxqkjxicyB+fagKPauGvM8socAwIbr5b3sC7jlvch1lkGLSsNEeu1vPTKbe9hS8cBgBvLS3lyMIDDTGtdb+ZhG1zNWhaXz9xWJMf58PhzbhtmLDq/daH3FXKddZe+6XhAntyjjN6DD9vHlrOsM7eNPvhwqRyWceoQfZw6jlI6zrj9D9qrtg625SrjAFNLecLYl2oZT0/fRl/PmWrmB1v++quxL/2zpG9jvAIoW6Za/q2N9yoDVJTGqVP1ZQxe49Qxc8BgGaXvzPgs6WmcY9+FH3yYoTT4sNv/JUGMLSR1xLgkf/rBsS7C6GA8MyPidBKCMLBeWupYFiQyVqumSiSCiAL95xDjhlwuN9ZFIAiCIIgJC0XqiHFDdXW1+f7BBx9ETU3NGJZm/JPL5XDxxRcDoPMVFjpnYtD5CgedF2K8QFJHjBsYK1Uo1tTUIJPJjGFpJhZ0vsShcyYGnS+CGP9Q9StBEARBEEQFQFJHEARBEARRAZDUEQRBEARBVAAkdQRBEARBEBUA45zHGDWLIAiCIAiCGA9QpI4gCIIgCKICIKkjCIIgCIKoAEjqCIIgCIIgKgCSOoIgCIIgiAqApI4gCIIgCKICIKkjCIIgCIKoAEjqCIIgCIIgKgCSOoIgCIIgiAogNdYFIAgAGB4exj333IOnn34aXV1dkCQJs2fPxrnnnosPfvCDSKfTY11Ek1wuhxdffBEdHR3YuXMndu7ciQMHDgAAPvGJT+Cqq64KzKOnpwd33303Nm/ejAMHDqC6uhrz5s3D+vXrsXHjRjDGfNPv27cPd999N7Zu3Yqenh5kMhksXrwYmzZtwpo1awL339HRgV//+td48cUX0dfXh4aGBixbtgwf+MAH8J73vCcw/QsvvID77rsPr776KgYGBtDc3IyTTz4ZH/7wh7FkyRLbtv39/XjmmWfw/PPPm+dKURQ0NzdjyZIlWL9+Pc466yzf/cW9Piba+e7o6MDf/vY3dHR04N1330VfXx+GhoZQV1eHOXPm4IwzzsAll1yCxsbGijlmJyLXGEEQGjSjBDHmdHV14XOf+xy6uroAADU1NVBVFfl8HgCwaNEi/OAHP0BDQ8NYFtNk27Zt+PznP++6LozUdXR04IYbbkB/fz8AIJPJIJ/PQ1EUAMDpp5+Ob3/7256isnnzZnz9619HLpcDANTV1SGbzUJVVQDAhg0b8JWvfMXzof3QQw/hlltuMfdXX1+PoaEhGLeCoGP46U9/ip///OcAAMYY6urqMDg4CACQZRnXX389LrzwQnP7c845x9wXAFRVVUGWZWSzWXPZqlWrcNNNN6GmpqZsf3Gvj4l4vr///e/jt7/9re2cpVIpDA8Pm8uamprw7W9/GyeeeGJFHLMV0WuMIAgdThBjSKFQ4B//+Md5e3s7v+SSS/jWrVs555wrisIff/xxvm7dOt7e3s6/9KUvjXFJS7zwwgt8w4YN/Atf+AL/8Y9/zB9//HF+8cUX8/b2dn7HHXf4ph0YGDC3veKKK/iOHTs455zn83l+33338XPOOYe3t7fzW265xTX9vn37+Nq1a3l7ezv/zGc+w/fs2cM553xoaIjfcccdvL29nbe3t/O77rrLNf327dv5mjVreHt7O7/xxhv5gQMHOOec9/X18ZtvvtlM/6c//ck1/Z/+9Cdzm5tvvpn39fVxzjk/cOAAv/HGG3l7eztfs2YN3759u5mmvb2dX3PNNfy3v/0t37dvn7l8//79/Dvf+Y6Z30033VS2v7jXx0Q93w8//DC/++67+SuvvMKPHDliLh8aGuIPP/ww37RpE29vb+cXXXQRHxgYqIhjNohyjREEoUFSR4wpv//9780buNtN+rHHHjPXP/fcc2NQwnKKxWLZsksvvTSU1N122228vb2dn3/++TbBMfjlL39pPrSMh6mVm266ibe3t/OLL77Y9rA3+I//+A/e3t7O169f77r+uuuu4+3t7fzjH/84LxQKZeuvv/563t7ezi+99NKy4ywWi+Zx3nDDDWVp8/k8/9jHPsbb29v5ddddZy5//vnn3U+GjvVB39XVZVsX9/qYyOfbj2effdY87kceeaRijjnqNUYQhAZ1lCDGlP/93/8FAKxcudK1Gum8887DjBkzbNuONbIsR077yCOPANCOa+bMmWXrP/CBDyCTyUBRFDz22GO2ddlsFk8//TQA4JJLLnGtbrzyyisBAENDQ/jLX/5iW7d//368/PLLAIDLL78cqVR5k1ojfVdXF1566SXbuhdffNGsAr3iiivK0qbTaVx++eUAgJdffhn79+8HAJxyyill21rZuHGj+b6jo8O2Lu71MZHPtx/Lli0z3x86dMi2biIfc9RrjCAIDZI6YszI5XJ45ZVXAABnnHGG6zaMMaxatQoAsHXr1qNWttFgz549ZocK45ic1NbWYsWKFQDKj3f79u0YGRnxTT9jxgzMnTvXNb31s1f65cuXo7a21jX9c889Z5Zx+fLlrumt32PY76uqqsp8b7TZAuJfHxP9fPthiBMAHHvsseb7iX7Mo3WNEcRkgaSOGDN2795tPsTnzZvnuZ2xrqenB0eOHDkqZRsN3n77bfO93/HOnz8fALBr1y7P9MY2funfeecd23Lj85QpUzBlyhTXtLIsY86cOb7p586d6xmtnDJlCpqbm13LGR0hzwAAB8RJREFU78WLL75YVnYg/vUx0c+3k3w+j87OTtx33334t3/7NwCa0J155pmuZZ6Ixzxa1xhBTBZoSBNizDh8+LD5ftq0aZ7btba22tL4DeMwnunu7jbfhzneoaEhDA8Pm1EN43w1NDSguro6ML11f9b01vPpxrRp0/D666/HSt/X12f7fr0YGBjAnXfeCQBYsWKF+bC37s/I0wuv62Oin2+D888/3+zpa2X58uX42te+Zot0TvRjHo1rjCAmExSpI8YM6/AMfg8Q6zAX1jQTjbjHawwB4jbsh1t657kyPgelN8qWdHonqqri3//939Hd3Y2qqip88YtfdN2fNU83vM7XRD/fBi0tLWhpaUEmkzGXrVy5Ev/0T/+EY445xnWf1nz9yuxMM9bHnPQ1RhCTDYrUEQQxJvzwhz/E3/72NwDAF7/4RSxYsGCMSzQ++fWvf22+7+3txSOPPIJf/vKXuPbaa/Gxj30MV1999RiWjiCI8QRF6ogxw6jyAWA2znbDGADVmWaiEfd4jUiNdb1feue5Mj4HpTfKlnR6Kz/60Y9w//33AwA++9nP2nrAOvdnzdMNr/M10c+3G1OmTMHll1+Om2++GYwx/OIXvzDF2JnHRDzm0ThnBDGZIKkjxgxruxnnsAxWrO1mgtrajGemTp1qvg9zvHV1dbaHlnHsAwMDvg9sI711f9b0Qe2QjLLFTe/1Xd1666249957AQCf+cxn8OEPf9h1u7jXx0Q/336ccMIJZu/Q3/3ud+byiX7MSV1jBDFZIakjxoy5c+dCkrRL0K/nn7GupaVlwnaSAOy9Cf2O1+iBeNxxx3mmt/ZS9Erv7P1ofO7t7UVfX59rWkVRsGfPHt/0u3fvtk37ZcWat7P8APDjH/8Yv/rVrwAA//iP/2iOOeZG3Otjop/vIIyOEPv27XMt80Q85iSuMYKYzJDUEWNGTU2NOaDss88+67oN5xxbtmwBAJx22mlHrWyjwezZs82G7V7Hm81mzTHInMe7fPlys4G4cU6cdHV1Yffu3a7prZ+99r99+3az8bkz/amnngpAa5xujB/nxJqvM/2PfvQj3HPPPQA0ofvIRz7imodB3Otjop/vIIyBd62Rtol+zHGvMYKY7JDUEWPK+vXrAQDbtm3Da6+9Vrb+ySefNB9exrYTFcYY1q1bBwB44okn0NnZWbbNb3/7W2SzWciyjAsuuMC2LpPJ4OyzzwYAPPDAA+YE51buvvtuANqDvr293bZu5syZ5qCz9957L4rFYln6u+66CwDQ1taGk046ybbu5JNPRltbm207K8Vi0axWXbFihW02gx/96Ee2KtcgoTOIc31M1POtKIo58b0Xzz//PHbs2AFA+14m+jEbxLnGCIIgqSPGmPXr12P+/PngnOOrX/0qnn/+eQDacBdPPvkkbr75ZgDa6PTvec97xrKoNgYGBtDX12f+GYPkjoyM2JY7h1y4/PLL0dLSglwuh6985SvmtFiFQgEPPPAA7rjjDgDApk2bMHv27LL9XnXVVchkMuju7sY///M/Y+/evQC06MvPf/5zPPjggwCAj33sY65TPF177bWQZRlvvvkmvvGNb5htk44cOYLvfe97ZhTk05/+dNngr7Is49Of/jQA4O9//zu+973vmYP9Hjp0CN/4xjfw1ltv2bYD7G3oPvvZz/pWuTqJe31MxPN98OBBXH311XjwwQexf/9+m+AdOHAAd955J2688UZwztHY2FjWJnEiHrNB1GuMIAgNxoN+EhLEKNPZ2YnPf/7z5pyPNTU1UFXVHHB10aJF+MEPfuD6ABkrPvzhD5vl9WP9+vW48cYbbcs6Ojpwww03oL+/H4AW8cjn82ZU47TTTsO3v/1t26CyVjZv3oyvf/3rZg/B+vp6ZLNZsw3Shg0b8JWvfAWMMdf0Dz30EG655RZz+/r6egwNDZny8IlPfAJXXXWV5zH99Kc/xc9//nMAWmSorq7OjOjIsozrr78eF154IQBNQi699FIAgCRJ5kwAXlx22WVlUby418dEO9+dnZ247LLLzM/pdNosszGOHKBN13XTTTdh8eLFE/6YnYhcYwRBlCCpI8YFw8PDuOeee/D000+jq6sLjDHMnj0b5513Hj74wQ8inU6PdRFtxJE6QJvS6u6778bf/vY3HDx4EFVVVZg/fz7Wr1+PDRs2mB0EvNi3bx/uvvtubN26FT09PchkMli0aBEuuugirFmzJrBcHR0duPfee/HSSy+hr68PDQ0NWLZsGT7wgQ+Eiog+//zzuP/++/Hqq69iYGAAzc3NOOmkk3DZZZdhyZIl5nZOQQnC62Ef9/qYSOe7UCjgmWeewbZt27Bjxw4cPnwY/f39phQvWLAA73vf+3DBBRf4DjA8kY7ZjbDXGEEQJUjqCIIgCIIgKgBqU0cQBEEQBFEBkNQRBEEQBEFUACR1BEEQBEEQFQBJHUEQBEEQRAVAUkcQBEEQBFEBkNQRBEEQBEFUACR1BEEQBEEQFQBJHUEQBEEQRAVAUkcQBEEQBFEBkNQRBEEQBEFUACR1BEEQBEEQFQBJHUEQBEEQRAVAUkcQBEEQBFEBkNQRBEEQBEFUACR1BEEQBEEQFQBJHUEQBEEQRAVAUkcQBEEQBFEBkNQRBEEQBEFUACR1BEEQBEEQFcD/D5be8CCUsnTcAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 600x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnUAAAG+CAYAAAD1MPK+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAABcSAAAXEgFnn9JSAAEAAElEQVR4nOz9eZwkR30nfn8iM+vo+5ieSzOakUbHCISQ0MFpkM0KkDEGgY3tBRbjBZs1v4fDhue3Ng/GINv4YMHYXhvDsiCDMMJGgDmNJBCXTpDQiY6RZkYajebunr7ryMx4/siMqMisyMw4snsO5ef1mtfMdFdFRldXZb3rG5ERhFJKUaVKlSpVqlSpUuWEjnOsO1ClSpUqVapUqVLFPhXqqlSpUqVKlSpVToJUqKtSpUqVKlWqVDkJUqGuSpUqVapUqVLlJEiFuipVqlSpUqVKlZMgFeqqVKlSpUqVKlVOglSoq1KlSpUqVapUOQlSoa5KlSpVqlSpUuUkSIW6KlWqVKlSpUqVkyAV6qpUqVKlSpUqVU6CVKirUqVKlSpVqlQ5CVKhrkqVKlWqVKlS5SRIhboqVapUqVKlSpWTIBXqqlSpUqVKlSpVToJUqKtSpUqVKlWqVDkJUqGuSpWTILt37wYhpJQ/V1111bH+cVY8b3rTm6Q/u+u6GB8fx5YtW3DRRRfhv//3/47//b//N3bv3r3qfbzrrrvwgQ98AB/4wAdw1113rfrxq1SpcuKlQl2VKlWqxAnDELOzs9izZw/uvPNOfOYzn8Hb3/52bNu2DS972ctw6623rlpf7rrrLnzwgx/EBz/4wQp1VapUUYp3rDtQpUoV+6xbtw5f+cpXMr//ve99D//wD/8AAPilX/olvOMd78i87YUXXlh6/47nvP3tb8eLX/xi/v+lpSUcPXoUO3fuxK233opbbrkFYRjiuuuuw3e/+128973vxZVXXnkMe1ylSpUq8lSoq1LlJMjg4CCuuOKKzO8fPXqU/3vLli25t32q5cILL8x9PB599FF84AMfwNVXX40gCPBnf/ZnqNfreN/73rd6naxSpUoVhVTDr1WqVKmSkzPOOAOf+9zn8NGPfpR/7f3vfz9uvvnmY9irKlWqVOlPhboqVargqquu6rtQ4qc//Sne8pa34Mwzz8TQ0BAIIfj+978PIHlhxpve9KbctnVuCwD/+Z//iTe96U0466yzMDIygsHBQZxxxhl405vehB//+Md2P6hF/uAP/gC/+Zu/CQCglOKDH/xg5m3vuOMO/Nmf/Rkuv/xybNmyBc1mEwMDAzj11FNxxRVX8KqfLOx38Tu/8zv8a7/zO7/Td1HHaaed1nffBx98EB/+8Ifxyle+Etu2bcPg4CAajQY2btyIyy+/HP/0T/+EVqtl90BUqVLluE01/FqlSpW+/NVf/RXe9773ZcJjJXLo0CH81m/9Fr73ve/1fW/nzp3YuXMn/uVf/gVvfvOb8fGPfxy1Wm3V+sby/ve/H1/84hcBANdddx2eeOIJbN68OXGbD37wg/jABz4gvf8TTzyBJ554Av/xH/+Bj33sY/ja176GU045pZS+/cu//Esmmvfv34/9+/fjO9/5Dj760Y/i61//Op72tKeVctwqVaocP6lQV6VKlUS++MUv4j//8z8xNjaG3/7t38ZFF10E13Vx9913Y2xsbEWOOT09jec973l49NFHAQDnn38+XvOa1+DMM8+E4zi47777cNVVV2Hv3r34v//3/8L3/WOy9MrTn/50XHDBBfxq1B/+8Id43etel7jN8vIyPM/D8573PLzgBS/AmWeeidHRUUxPT2PXrl24+uqrsXfvXtxxxx244oorcNNNNyWA+uIXvxhf+cpXEhe3pC/mAKJ5lOnjEkJw0UUX4UUvehG2b9+OiYkJzM3N4bHHHsMXv/hFPPzww3j00Ufxy7/8y7jrrrswPj5e/oNUpUqVYxdapUqVkz6f+cxnKAAKgP72b/927vcB0HPOOYfu3bs3s71du3bltqd72yuuuIICoIQQ+rGPfUx6m/n5efrSl76Ut/Xtb38797h5+e3f/m3ezmc+8xmt+771rW/l933Xu97V9/3bb7+d7tu3L/P+7XabvvOd7+RtXHXVVdLbib8TlT7ed999dOfOnZnfD4KAfvjDH+ZtfuADHyhss0qVKidWqjl1VapUSYQQgmuuuaa0YcGi3HnnnfjqV78KIJq39s53vlN6u+HhYVxzzTW8WiheuLCaEeeyHTp0qO/7l1xyCTZs2JB5/3q9jo985CM4/fTTAQCf+9znSunXueeey9uUxXEcvOc978GLXvSiUo9bpUqV4ycV6qpUqZLIC1/4Qpx//vmrdjyGC0II3v3ud+fedmJiAi9/+csBREOf7XZ7xfsn6wPLkSNHjNpwXRfPec5zAAC33347KKWl9E0lz3/+8wFES7UcPnx41Y5bpUqVlU81p65KlSqJvPCFL1zV4/3oRz8CAIyPj+P2228vvD2DXLvdxs6dO1d9wn8YhvzfhJDM23z1q1/Ftddei5/97Gd48sknMT8/n7gvy/z8PObm5kqbr3jDDTfgmmuuwU9+8hM8/vjjmJ+fz7zgZe/evZiamirluFWqVDn2qVBXpUqVRDZt2rSqx2P7qs7MzODVr3611n1nZmZWoEf5ERdynpyc7Pv+E088gSuuuAJ33HGHcptloG52dha/8Ru/geuuu07ruFWqVDl5UqGuSpUqiQwMDKzq8WZnZ43v2+l0SuyJWhhCAWDt2rWJ73W7XbzsZS/Dz3/+cwDA1NQUXvnKV+IZz3gG1q9fj2azCceJZr38/d//PW688UYAKGXpmF//9V/HDTfcAAAYGRnBr/7qr+KCCy7Axo0bMTg4yI97zTXX8GVZVnPJmipVqqx8KtRVqVJlRSMbchQzPDyMo0ePYsuWLXjsscdWqVfmue222/i/n/3sZye+94UvfIGD7iUveQm+8pWvYGhoSNrO5z//+dL69MMf/pCD7vzzz8f111/fB06Wm266qbTjVqlS5fhKdaFElSpVtNNoNPi/i6plRZPx2XDvwYMH0e127Tu3grn//vtx99138/+zK0lZGKwA4G//9m8zQQegVMCKx/2Lv/iLTNCVfdwqVaocX6lQV6VKFe2Ii9Y++eSTubcVK1uyXHrppQCAVquFH/7wh9Z9W8lceeWV/N+XX3553/zDAwcO8H+fccYZme0cPHiQL2CcFTZcCqDw6ljV43Y6HT7kW6VKlZMvFeqqVKminYGBAb5e209+8hMsLCxIb9ftdvHxj388t603vvGN/N8f/OAHj9t5Xn/7t3+Lf/u3fwMQXfX6/ve/v+824i4PbHcMWf7yL/+ysCo5PDzM/724uJh7W9XjfvzjH6+WMalS5SROhboqVaoY5fLLLwcALC0t4U//9E/7vu/7Pt761rfigQceyG3nOc95Dn7t134NQLS8yetf//rcqzJ938eXv/xl/OM//qNF79Wzc+dOvPGNb8Qf/uEf8q9deeWVeN7zntd320suuYT/+0/+5E+k8wk/+clP4u///u8LjysuJHznnXfm3lY87pVXXildv+/rX/86/uiP/qjwuFWqVDlxU10oUaVKFaO84x3vwKc//Wl0Oh189KMfxYMPPojXvOY1GBkZwSOPPILPfvazeOihh/Bbv/VbuOaaa3Lb+vSnP42HH34Y9957L774xS/iO9/5Dn7jN34DF198MSYmJrC8vIy9e/fiZz/7Ga6//nrMzMzgzW9+cyk/x5133pkYTm61Wjh69CgeffRR3Hrrrbj55ps5zlzXxR//8R/jfe97n7St3/md38GHPvQhLC4u4itf+QouvPBC/Lf/9t+wefNmHDhwAF/+8pfxgx/8ABs2bMB5552H66+/PrNf5513HtatW4eDBw/i6quvxtq1a/Hc5z6XX508MDDAh65f/epXY9OmTdi7dy9uv/12PP3pT8eb3/xmbNu2DUePHsW3vvUtfP3rX8fQ0BB+7dd+Dddee20pj12VKlWOsxzrfcqqVKmy8tHZ+1VnL9RPfepT1HGcxL6x4p+3vOUt9NFHH1XaJ3Z2dpb+5m/+ZmZb6T9/8id/ov9AxBH3flX5Qwihl112Gb3lllsK2/7qV79Km81mZlubNm2it99+e6IPu3btkrb1iU98IrOdrVu3Jm57yy230ImJiczbj4+P029+85v0T//0T/nXbrzxRuPHsEqVKsdfqkpdlSpVjPPmN78Z5513Hj7ykY/gRz/6EQ4fPow1a9bgoosuwu///u/jV37lVxLruuVldHQU11xzDf7n//yf+OxnP4sf/OAHePzxxzE7O4tms4mNGzfi3HPPxYte9CK86lWvwrZt20r/eQghGBkZwejoKNauXYvzzz8fF110EV7xilck9nzNy6te9Srceeed+Ju/+Rt897vfxf79+zE6OorTTjsNr3rVq/C2t70Na9asUWrr937v97B161b88z//M37605/i0KFDmVujPfe5z8Xdd9+Nv/mbv8G3v/1t7NmzBwMDAzj11FPxK7/yK3jb296GLVu2KO3aUaVKlRMzhNJV3HSwSpUqVapUqVKlyoqkulCiSpUqVapUqVLlJEiFuipVqlSpUqVKlZMgFeqqVKlSpUqVKlVOglSoq1KlSpUqVapUOQlSoa5KlSpVqlSpUuUkSIW6KlWqVKlSpUqVkyAV6qpUqVKlSpUqVU6CVKirUqVKlSpVqlQ5CVKhrkqVKlWqVKlS5SRIhboqVapUqVKlSpWTINXer1WqVDkmoZSi0+lgaWkJrVYLnU4HnU4H7Xab/1v2tW63iyAI+J8wDBN/p78GRHu6EkLgOA7/P/u34zj8+67rolarwfM81Go1/sfzPNTrdf519v9ms4lms4mBgYHE355XnVqrVKmy+qnOPFWqVDFKt9vF/Pw85ubm+N/ivxcXF7G0tJT7JwiCY/1jrEhqtVoCfAMDAxgaGsLw8DD/MzIykvi/+GdsbAzNZvNY/xhVqlQ5wUIopfRYd6JKlSrHPt1uFzMzM5iZmcH09DSmp6f5v9nXRbwtLy+Xdux6vY5OOwTggFAHaAcAJSAhAagDhAQICQglACUARe9vIPo6xK8JfwMg9Ro6m8eFI1LJvylAKYAw+sP+TcPk3/G/CQ1BWi3ACTE41kCr1SoVqY1GA+Pj44k/Y2NjfV+bnJzEmjVr0Gg0Sjt2lSpVTsxUqKtS5SRPEASYmZnBoUOH+J+DBw/i0KFDOHz4MEfb/Py8dtuEENDQBVADQQ2g8d+ogcADqBv9DReABwIXoNH/3TaAR/ZFWIvxtVIhw0NYfPZpK3oMhkDS6aJ5/+MACUGdEHDC6N9uCOoEgBNEf8f/j/4d/T2+fgjz8/PwfV/78CMjI5iamsKaNWswNTWV+Lf4d61WK/9nr1KlynGRCnVVqpzgWV5exv79+7Fv3z48+eST2L9/fwJwhw8fVq8gUQKgDoI6QKO/CRrCv5Nwi6BWDDKn5QM7HrP5Ma2yKqjLidMJMXDX40q3paCAE4K6fgQ+z4/+7Qbx39G/Ef+7NkTQ6XSU2iaEYGpqCuvXr8eGDRukfw8MDNj8qFWqVDmGqVBXpcpxHt/3cfDgQezbt4/Djf173759mJmZKWzDcRyEQQ0ETRDaAEETiP8WARdV2PSrZsGAh+ntveE/QgESF5sacyFGv3Wfdpu2cSbGsf/lWwAAYZ1gblt00YS3TLDh1tWfy9cddLDv0vjCjYCgccTl3/OWgFP/5RGjdjkCvW4EPq8b//FB3ejvdaeP4siRI+h2u4XtjY2NYf369di4cSM2bdqEzZs3Y9OmTdi0aROmpqb4BSZVqlQ5/lKhrkqV4yCUUszMzGDPnj2JP48//jiefPLJwuE4Sjw44QAIHYjB1kwCDnUQwxWM/MEaZs6u595GRJwsjXmK0W/ea3T8ooh4y4qIuqysJPZE0MmSRp4s7jKw5Soz+AEx/lwfodcFrXVBvU7v77qPwUkXCwsLuW3U6/U+6G3evBmbN2/GunXrQMjKDqNXqVIlPxXqqlRZxfi+j71792LXrl3YvXt3AnCLi4uZ96vX61hGHbQ+hLA2iLA2CFofhBMMYmQPiYdDDfukgLZ0ihCXlTJw50xOYP8vn6p1HxXUpVMG8oowlxUV5KVjiz4AQN3BwjPHgXAZCBZBwiUgWMSp6+vYt29f7jD+wMAAtm7diq1bt+K0007j/z7llFPguno/S5UqVcxSoa5KlRVIGIbYv38/du7cid27d/O/H3vsscwhMEIIAm8AYX0YYWM4+jv+N/UGAKEKUluiGHtkSbk//lANM2fpwa2vf4aQS0cHdiaAk8UEdbK4LYKNt6hBzxR06ZgALx23BWz5jDr4SK2G+Ys2Jb9IQ449hIsgwSIQLGHrxgb27t2bCb56vY7Nmzdz6G3btg1nnHEGTjnllGoot0qVklOhrkoVyywtLeGRRx7Bjh07sGPHDg64VqslvT0lLsLGSO8Pw1t9CHDy37zzMFcG3NIpC3Lp5MGuLMiJKQt1YvKA1x1ysO9F5R4PKAd46ThtYOun5eCT4k4WGkbYCxaAYB4k/rvhtjIv4hgYGMC2bdtw5pln4swzz8QZZ5yBbdu2YXBw0ObHqVLlKZ0KdVWqaOTo0aN4+OGHOeAefvhh7N27F7KXESVOhLXmKMLGKILGCMLGKGhtMFF1UwnD3ErALZ2VgpwYEXXO1CT2v2zzih5vJVCXjoi8lUKdmJUAXjoMfMq4S4dSIFxKYs+fQ8NdzsTepk2bcMYZZ+Css87C2WefjXPOOQcTExOWP0mVKk+NVKirUiUjc3NzeOCBB/DAAw/gwQcfxI4dO3Do0CHpbUOvibA5hqA5jrA5iqAxClofAoj58FJYJ+iMAI2ZlX2JUocgbAChCwQNYHD/KpwS4jWFVyPUBZY2Ap2tbZAjKwtiAKANis1nHsSeR9eu+LHgUmw9/RCOLA6i23VBfj6y4od0OsCa+y3Vzyt7cyD+HBDMYu0YxeHDh6U3X79+PbZv345zzjkH55xzDrZv346RkZX/WatUOdFSoa5KFQCdTgePPPIIHnjgAfz85z/HAw88gCeeeKLvdoQQBLUhBM0xAXFjoJ75av5hnaA1mfya0y0fcwxviWN7QHek/zhOm5SPu1WCHHWB1ppkJTSsU/hnJIfDQ5+UjjzaoDj7acnnzVK3Xj7wXIptZxzo+3IQOjiymBy+XDHspZ4exAem7rPEXtgGgjnAnwPxZ7F1g4s9e/ZIK+GbNm3iwHv605+O7du3V7tqVHnKp0JdladkDhw4gHvuuQc///nP8fOf/xyPPPKI9AKGoDEEf2gSwdAEwsYYqDsKuOZXmsoAJ6YMzMnw1tePDMwl+lIG7FYBcjLEpSNDXd9tSkCeDHViSgFeBujEyHCXTmnYy3mKlAE9Ch+t0Xm4yzNwl49i6xDFk08+2Xc7z/Nw9tln4xnPeAb/MzU1ZXXsKlVOtFSoq3LSJwxD7N69G/fccw//c/Dgwf7buXUEQxPwhybhD00gGJoA9aI3eScAnI7eS6UIcOmYgE4FcIk+KWAu0ScT2K0C5EKPoK3x2KqgLnF7A+AVgS4dI+ApgE6MCu7EGENP4yliAj3qECxuEJ5Ufgfu8lEOvfVeG9PT033327BhAwfeeeedh9NPPx2e52kdu0qVEykV6qqcdOl0OnjooYdwzz334N5778W9997bt6+p67poN0bhD/cAF9aH+i5gUMVc0CBoG87lVsWcLuDE6GJOjBLsjkPIJe6ribrEfRWBp4s6MS2/hsceWZd/I03QidHFnRjfd4H7FaFn+G6iCr0+3PFvUJDuErzFabhL03j6pIudO3ciDJMXqwwODuKZz3wmnvWsZ+GCCy7AWWedVSGvykmVCnVVTvj4vo+HH34Yd955J+644w7ce++9fVfWUceNKnDDa6I/Q5OAm38yzwKdDeAS7edgzgZwYmwwJyYTdiuMORvIJdqxQF2inQzg2YBOTCbuLEAnxgZ3YgqhV8K7Shb0MmGXTtCNKnmL0/CWpjFGF/sW+K6QV+VkS4W6KidcKKXYtWsXR9zdd9/dt71R6NV7gBueQjA4pnUlqgi6shCXaF8AXVmAE1MW5licDsHgvri9EwRyiTZLQl2izYCAHI6AVxbqxCSAVxLqWMrCnZg+6JV9nU0ATN0bIU8ZdmIohdOahbd4GO7iYUyEC33njcHBQZx33nm48MILcfHFF+OMM86oFkiuckKlQl2VEyIHDx7E7bffjjvvvBN33nln3/yZ0K3BH5mCP7IW3ZG1CJsj2mvBsVAP6A6V0ev+OD7gLa9M20D5mBPjdAgGDq7c6WIlMMfbXgHUsThugG3rjqxI2yzdcGXWo1sJ3InxfRe4b2WWHiEh0Dxs8XxUQN7k5CQuvvhiXHLJJbj44ouxZs0ay15XqbKyqVBX5bhMt9vFPffcg9tuuw233XYbdu3alfg+JS78kTXojqyFP7IWweC4MeLEyhP1AL9p1/dE0wHgspFgGlUAy0roAv5g7zi1eKOJsmFHAgJvSTjOQnltB02C+dNCfpz6bHkbwlMPaG2Ih+9civpoO/o6BcKgPCQ5boCzN/TWL/TDcis7AXUQ0vIeFxaHUEw0ol+sHzrYtzgKAKCUYLFd3lIvlBIszsYvqoCguafkXU/YtDkKNI+UhLyFQxj157C8nPwEduaZZ+KSSy7BJZdcgvPOO69aQqXKcZcKdVWOmxw4cAC33norbrvtNtxxxx2JE6rjOGgPjaM7tg7B4Fr4QxOFW2plJmP4sAzQJRDHG7bHnAi49PFqkl3DyoCdiLm+Y1rAToRc+ni2qAtrQHu9ZMK9gDoxZQAvjToxtsBbDdCJEXEnpgzoJXDHUhby2MwAmvyaFfLCAM7yDJzuQTxrkOChhx5KfLvRaOCCCy7A8573PDz/+c/Hhg0bzI9VpUpJqVBX5ZglCALce++9uOmmm3DrrbfiscceS3w/rDXQmViPzuR6dMbXgTh1OP3vy8UpmANmijkp4PoaNwNdFuLEY8swl2jDEHZZmEsc2wB1WZhLH9sEdpmYY8lAHYsp7vJAJ8YUdysx7JoFOjEhJdi7MJb5fVPkSWHXd3CC5uOG0EsviFwC8igh6IwDpNNG/egh1GcOYHOw1Lf7xbZt2/CCF7wAz3/+83HOOefAdVd2C7cqVWSpUFdlVdNqtfCTn/wEP/7xj3HzzTdjdnaWf89xHLSHx9GZ2IDO5Hr4Q2N8SNUJoA46jYn8OqBTQhxvWA9zRYhL96MIdLxdDdgVYa6vD4qwU8Gc2AdV1BVCTkwB6sToAE8VdSw6uFuJKp0K6FiKYCdGF3lKuOMd0URe3mLIhshjsOt9gcJdmkd9ej+eP+LivvvuSyyfMj4+zit4l1xyCQYHV27eYpUqYirUVVnxzMzM4Oabb8aPf/xj/OQnP0ksNxJ6NXQmN6A9uRHd8bWgtf6TtxLoDK7IVAGdFuR4w/mg0wFcui+qmOPHUkCdDuYSfSlAnQ7mxL4UoU4LcywaqGNRwZ0u6lhUcHesqnTpZA3JFqUIelqwY1EFnuK7GkeeAvD6YCe2022jPn0Q9el9mFqeTVxwUavVcOGFF+LSSy/FL/zCL2B8PKORKlVKSIW6KiuSAwcO4Pvf/z5+9KMf4d57703s3Rg0BtFesxGdNRvRHVtTuNSIFHWWy2pkgc4IcYmG5aAzhRzrky7mEsfOgJ0J5vr6JYGdCebEPslQZwQ5MQaoE5MFPFPUsWTh7lhX6WTRqdylkwU8I9glOpWDPN2NUAqqeHmoS/YpRG3uCOpH9uEstLB3717+LcdxcP755+MXf/EX8cIXvrDaxqxK6alQV6W0HDp0CN///vdx44034r777kt8rzs0hs6aU9BesxHB0KjylaoJ0JW0PpoIOmvEJRrugc4GcWJsQcciws4Wcyxp1NlgLtluD3bWmGOxRB2LiDtb0IlJ467sKp0t6FhsYCdGRJ417MSkkWfx7iar4inDjh+fwl1eQOPwk3hWPVoknbdPCJ7xjGfg0ksvxaWXXor169ebd7ZKlTgV6qpY5fDhw/jBD36AG2+8MVGRI4SgPTKJ9tQmdNZsRNjUF060AHC5C91SDwhqJUIOiN44COAPlNdkWZgTQwlASx7RI0G09l4ZmOu1SeAtkXIwx1IS6ljYWbMs1LH4oVN6la4s0IkpC3cslBIstBrl4Q7oAa+M3S2ENhpHoAc7Ic7yIhqHn8RzBinuv//+xPfOOeccXHbZZXjxi19cVfCqGKdCXRXtzM7O4sYbb8T3vvc93H333Ymh1e7oJNpTm9GeOgVhw0w5bjuqAHWHy3ljI2GMQxcIynrPoFG7Za5r53SB+pzQTxqBqYyELhA0SsZsDNnOWDmgIxRASEBdinCwJCS6FLWRNmjoIOi4AKGoNct5UB2HYmSwBdcJsWagHDR1Axe7D09i85qjpbTH2nziwAQu3vZY8Y0V4ocOdh+dBKUEISVwCEWz3i2l7SB0cGRmGAAQdkv6NEcJyJILUKBxyP5TDaHRByTA7vXptJfROPwkXjBMcM899yQ+EF944YW47LLLcOmll2J4eNi6z1WeOqlQV0UpnU4Ht9xyC6677jrccsst8P3e2cwfnkBrHYOc2Zij10rugxrUYYU6BjmWUkBHhYVOYQ86hjjeXrqPJaAudIEgtjUlJQ03k6jayUI9O9gxzPH2ykBdjDneJkOdcFBb3DHU8UNa4q4buNh1sLdjAXGoNe66gYs9e4U2vdAadwx1LAx3LLbIE2HHYg08Bjv+/3KAJ8bmtUo6LTQOP4nnNYPE1JV6vY7nPve5uOyyy/C85z2vWuy4SmEq1FXJDKUU9913H6677jp873vfw/z8PP+ePzCGzuSp6ExtQne0HMixmIIuDTkWY9ClEJf4liHo0pDj7WX10RB2acyJMYZdCnMspqhLYy7RpinsUpjj7aVRJ3TCBHdp0CW6YIi7NOp4Fw1xlwZdok1D3KVBx5KGHYsp8GSwE2OEvDTs+NePL+C5S4toHHoC55Il7N69m399aGgIl156KS6//HKcf/75IKY76FQ5qVOhrkpfnnzySXznO9/Bddddl7hyK6w10Zk8Fe01WxAOxFsKeRHCVJMFORZd0GVBjkUbdDmQA/Qxl4W4RJtFfdSEHQNd1rQsbdRlYE6MDuzyMMfb00WdS1Eb7qQuYRTay0Kd0Ckd3OWhjndJA3dZoEt0URN3eagD9GGXBTqWLNiJ0UVeEe4ATeBlwY5//9gDr7ftGYW7OIfGoT3Y2jmKQ4d68zc3bdqEyy+/HJdffnl1gUWVRCrUVQEAtNtt/PCHP8Q3vvEN/OxnP+Nfp46Lzvgp6KzZAn9kbeKqVRrP0ypKEeRYVEFXBLlE/1QAVgA5fjNF0KlADtAEpwLs8qpz6SjBTgFzvHsKqFPBHG9PBXUMcrzxnPaKUCdGAXgqqGNRwZ0K6nj3FHBXBLpEewq4KwKdGBXcAerAU4EdixLwimDHb3fsgNd3PqIU3twRNA8+jsn5Q1haip5PhBBccsklePnLX45f+IVfQL1e7r66VU68VKh7imfXrl34+te/juuuuw5zc5FECCHojKyNhlfHTwFcT3rfvCqdKuRYikCnCjneN4Xqlwrk+M1zQKeKuER7JkPCObArqs6lU4g6DdABxajTAR1QgLqCqpy0PR3UseTgTgd1QDHsdFAHFMNOB3VAPux0QMeiCjsxecjTgR1LLvBUYcdvv7rAyz03BT4ah5/EC4cC3HXXXfzLo6OjeMlLXoKXv/zlOOuss0rrZ5UTKxXqnoJZXl7G97//fXz9619PTMoNawNoT21Fe2oraD1/npysSue1gPrR+AouzWeVDHW6kEv0LWN+mg7k+N0koDOBHG/PYo5f+o1ApzqXjhR2mphLdE8CO13M8bZkqDPAHG/PBHUsEtzpoo5Fhjtd0CW6JsGdLugS7UlwZ4I6wAx2LDLgmcCORQo8Xdjx+60O8FTOVc7yApoHH8fW1nRiePacc87BFVdcgRe/+MVoNktcJqbKcZ8KdU+h7Nq1C1/5yldw/fXXY3FxEQDgui6WR9ahPXUa/NH1yosCi1U6hjmD91oASdCZQi7RNxFNhpDjbQmgs4GctG9GDfTeAHSrc7IkYGcBOiCJOlPMJdpjsLPAHG/LBnUsMe5MQSdGxJ0N6oAk7GxAx9sTYGcKOhYb2LGIwLOBHQsHninqxJQMPBF3WuctSlE7ehCvXt/Ej370I3S70eM1PDyMl7/85XjVq16FU089tbR+Vjl+U6HuJE8QBLjppptw7bXXJubKBfUhtNduRWfNVtCanjJotNyXcVUu0b864A8QOOUsc8UriDaQ4215EZxsIcf7VeIaeaD2oANi1HXtMMe75QHd0dAac7w9lyIcCaxBB5SEOgAgFI3BrjXqWBxCMdZoWaGOhTgU68fnrVEHRLC74LQ9VqATUwbugOjxqnmBNexYwo5rDzuWFQCeyXmMdNtoHngMZ7ansW/fPv71iy++GFdccQWe//znw/PkU2qqnPipUHeSZnZ2Ft/85jfx1a9+Ffv37wcQ7TvYGt2A9trT+y56UIoDEB+oz5fzlCFhtFtEd9D+ZE8oBSWkFDjxamFJKwaQkIKEQGesvCUIgoY96MQQyX61uqE1oD0RwumU8fuMUOecslwa6Jx6gLAk2Hn1AOOj9gsO+4GDo9PD8Br2CyKHlCCYi8vn9TI+1QDoOpg4Zda6qSB0MD83AK/uY6Bp/wmOApifHoJTL+OJC4QLNZCgnBeU0yaozZa3DY4/SNE4atA3SlGbOYBfHie45ZZb+OLGa9euxStf+Uq88pWvxMTERGn9rHJ8pELdSZZHHnkE1157La6//np0OtG4WujW0V57GtprTy+cKyeNE1WsgGi3BxvUkRAgQXT/sEasQEco5RgJPTvQ9Q372p7f4/uTgMKNl00La/awYz8vdcvbyUJs1yS0BrQng+hnDokV6ogwXB7WKJxN5nCioQO/FVUkiEPhxgCggB3uCIVTizrpEGqFOz9wMHN4BCAUhMAKdyElCGZj1BHYwY4CaMcwcWAFuyB0MD/b213GYxAj1Ap4DHYsVsCjQLhY4/+2BR6JXwOEwhp4/mDvfGuEOwBOaxHN/btxysJBHD16FEC0sPFLX/pSvPa1r8Xpp59u1ccqx08q1J0EoZTi1ltvxRe+8IXE1VBBcwydNdvQmTgVYUPzTUyAHIsp6ETIsZiCToQcb8sQdJnz90zP532L/PZAx2IDu/TPXSbsTFCXwByLIepEzPGmDFEnYo6371AOJhqXOI1xJ6COxRR3HHVC26a4S6COtwd93ImgYzGEXRp0LF4aYIbAS8OOxQh4IuyEr5kCj6ReBzbAE2HHYgI8igCNQ0/iOc4iHnjgAf715zznOfiN3/gNXHzxxdWixid4KtSdwOl2u7jhhhtwzTXXYNeuXQCiCx9aQxvQWbMNweAkqOsg1JkvJcEciy7qZJhj0UGdDHK8HU3QFV6IoXs+y1zgtx90gBnqsn72Y1mtozWgvSbrl6IOOxnmeDOaqJNhjh9HQB2/vSnuJKhj0cGdHziYOTIsHUeX9TcvUtDxxqAHOxnqWDRxl4U6QAI7Fk3gZcGORQt4MtgJ39MBXhp1/OsGuJOhjkUXd9RBvO7dNF41BvzoRz/iQ7Onn346fvM3fxOXXXZZtebdCZoKdSdgFhcX8fWvfx3//u//zi9jp46HzsRp6KzZlhhipQ5RQ10O5gB10OVBjkUVdHmYA9RBp3xFreq5sXBhXznoWHRgVwSt1a7W8eockP04KKIuD3SAOuryMMePlYMkbdzloA5Qh11flU5yHNWqXS7qAHXY5YGORRF2eaBjyYQdiyLwimDHogS8PNjF31fFXRbs+Pc1gJcHO0AddzRdhF1ewMDenVgz8ySWl5cBAJOTk3jNa16DV7/61RgZyXmOVjnuUqHuBMrhw4fxpS99CV/72tewsLAAAAi9BjprzkBn8jTATZ7UC0FXADkxRahTwRxQDLoiyCXaKkCd1vIoRedDhfNlEebEqMBOCVmrWK2TDrfKUoC6IswlmiqAnQroALXKF6WkGHYFoBNThLtC1LFDFvSdXyChciYvwp0K6oBC2KmATkwh7oBC4KnCDijAXRHqUrctAl4R7AA13BWhTkwR8NKwAwDid9DctxunLxzkxYKhoSG8+tWvxmtf+9rqoooTJBXqToAcOHAAn//85/HNb36Trz8UNIbRWXMmuuOnAo78TSgTdRqYA7JBpwo5MVmo08EckA06o3Xuss5/GqMaOqBjyYOd1lDoClfrlKpzYnJQpwM6IBt1qpjjx1Ucziys2mmgDsiHnSrq2HGzqnaFVbq+tiCHnSroWDJgpws6FiXYsWQATwd2LFLgacIOyMadCuoSt88Bng7sgGzcyVDHE4ZoHNqL8ztH+LSeer2OX/3VX8Vv/dZvVXvNHuepUHccZ9++fbj66qvx7W9/G74fncz9wUl0ps6CP7IBRUuS9KFOE3MsadSZYA7oB50u5Hg7KdBZLVicfggN5gibgA7IRp3uY7KS1Trl6lw6KdjpYo43k0KdLub48TXnqGXiThN1LGnc5c2ny0v659Cq0iUaQj/sdFEHSGFnijpAE3YsKeCZwI4lATwd2LEDsy6lgKcLO0COO13UsaRxl4s6fiOK+pF9eE44iwcffBBANGf7ZS97Gd7whjdg8+bNRn2psrKpUHccZu/evfjc5z6H73znOwiC6CTjD02hvXY7guG1Sm0kQGeIOSAJOlPMAUnQmWIOSILOevcJkvFvnSYMQcciws5qWZGSq3Xa1bl0YtSZYo43E6POFHMsuqhj6cOdIepYGO60qnTpCFU77SpdX1uIcGcCOjEx7mxAx2IEOyCBOxvYAQLudGHHIqnemcAO6MedKeyAJO6UYAfEu1Ucwi95S3wBe4a7N77xjTjllFOM+1Ol/FSoO46yb98+XHXVVbjuuusEzK1Fe912BENTWm1RhyBsmGOOxW0DjVlqjDneH5eUgw4ChG4Jl9wTGEOON2EJOpbQA7ojtltqlVitcwyrc2JCArdNrHf2CGsUZGPLCnSAOepY+Hy7rmOFOiCC3fBQyxx1cYhD4dQDO9QB0e+5FtqhLm6HDNovogxYwI73haLZ7FrBjqdj8bgIp01n2e7xZbizQR1L4yhRR50Qb+4ILm+0cdtttwGIcPfyl78cb3zjG6th2eMkFeqOgxw5cgRXX301/uM//oMPs3aH16OzbjuCQf1tehw/+hTWGTV/V3Y7QG2ewu1YPj1IXF0r4+r4EkDndii8ZYr2hPkJ1ulQ1JYo/AFivRNDWAPa40S6obdOqAv4A9AfhkvHiSoBwYA5XEhA4LZK2iqMAP5ayx0IQoC0XAxsXEC3Y45DXrWjBPa7XBCECzX7nR9CALLN6rU6A7jLjtXvnMelIE37XR5oSFBrWr4oCIXnhVies/y0Q6PnD3UtX1wUcNpO9DuzDHUBd9nuNRYMhhjYb/ap35s7gpd4S/jpT38KAKjVanjFK16BN7zhDVi7Vm00qcrKpELdMcz8/Dy+8IUv4Etf+hJarWgvye7IWnTWPk0bc47f25jdb5qDzu0A3hLlbTpdw6cHAWg854+6MEYde++kjjno3A5FYzY6kwZ1gva4Y1SFcjoUjVkaVdaGSFSZCM2HTcMa0J6IO0JhDDtepSMwR50DBM3oztSB8Rt8maAjQVTFNEZdCDjzEeKoRzG4KbpinFIY445SwnFnCjsaEoTz8ZBeGTs/sL9NcUcBdyl6TVjBjh2e2MGOBg5vpwzY1WrxTiKUmAOPAmQ5BhCBGfAY6lhsfu0e5fMxjXFHItixvpkArzZ7GL/kLPBh2Xq9jl/7tV/DG97whmoplGOUCnXHIK1WC1/60pfwr//6r3xpku7wBFqnnAta1xtmFTHHYoI6EXOsXSPQCZgDzEEnvl+agk7EHGAGOgY5FhF0vK8GsGOg4z4IzVCXAB3/omYjMeio+H+DN/eyQCc+lsaoY6BjHwpqPdQB5rBLoI53WO8BT6AOsIMdTf1bF3YMdEJfjGEnHtoQdhx0Qjtlwg6wwJ0IO8AMd2nYAca4ox57cpvjLhjqv1jGBHfu0kH8Ymce99xzDwBgZGQEb3jDG/Ca17wGjUZDu70q5qlQt4oJwxDf+c538KlPfYqvA+QPjmJpy9PRndgAp0vgttR/HTLQAXqoS2NObFsZdSnIsZiATvb+qIO6NORYdEHndCgac7QPSKEHdIclV6xqwC4NOiD+uTWqdYk5dOnu6Lyi06ATv6745l52dS4dLdgFBM5C/KYkfjBIoY5/XQN3UtCxaMCuD3W8Dejv/CD7mg7s0qgT+qKFO9khNWFHQ9J/NXAZqGPt1Pr7ogs8siRb5kYTdzLYsej8+r3UMQ1w14c6wAh2QZMClKJx+AAuPHqAL4Wybt06vPnNb8ZLX/pSuK7lBO8qSqlQt0q588478Y//+I/YsWMHACBoDGJpy9PRmdrMlyZxOlBCXRbmAHXQZWGOta8EugzMsaiiLu/9UBV0bpuiMSc/I6qCjkMOkL5hyqp0LKqok4GOt6EIO2l1ru9GxX3JBB37nsKb+kpU59JRQl0G5liyUMe/r4C7XNSxKOAuE3WAHuyyDqUKuyzQCX1Rgl3eoTRg11elE9pYSdgBGrhLV+v6jqEAvDzUsag+BdKwA/RwJw7B9rWjjjs2dSO6H8XAk4/jzINP8OLFtm3b8La3vQ3PfvazldqrYp4KdSucxx57DB//+Mdx8803AwCo62Fp83a0Np7Rt2hwEeryMMdShLo8zInHyUVdAeYANdAVvf8VgS4PcmKCOsm9MCKrKicmD3QsRbDLA53YRh7qlEAH5P4s1AXCBs2/WQHqVro6J6YQdQx0eT9zAeqAYtgpoQ7IfWLngi7RBopxV3TmLsJdEerifhTCrsiPCrCTVulSbZQBu1pdbQHqXOAVwQ4oxp0K7IBC3ElRx7+phjtptS7RTjHuEqjjXwww9PhObNz3GJ9m9PznPx//z//z/+DUU0/NP2YV41SoW6HMz8/j05/+NL761a8iCAK4rovFtVuxdOo5oLX+OQZ5oFPBHEsW6lQwJx5PijoFzLHkoU51pCoLdaqYA7KrdEVVOTEqoGPJgp0K6ID8ap0y6Pgd5G2EjYzqXDoZsFuN6pyYTNQVVOfEqKCO3zYDd8qoY5E80ZVRB+TDTvWsnQU7FdAJ/ciEnepIbw7sCkEntFH2/Lq85OFOOgwrvWEG7lRRx5L1NMhDHb9RPu4KUcfbycedFHYASLeD/8/UEL785S8jCAJ4nofXvva1eOMb34ihoRKWnKmSSIW6ksPmzf3zP/8zZmZmAACdiQ1Y3PoMhIPZVwPJUKeDOUAOOh3MsWP2gU4Dc0A26HTmk8tAp4M5QA46laqcGB3QAXLUqYJObCONOm3QAX0/oxboACnqVht0QAbqFKpzYnRQB8hhp406oO9Jr4U6IBt2OmdtGex0UBf3Qwo7nesyMmCXOeya0cZqwg7IwJ1KtS5xTAnudGEHSHGnBDsgG3d5Q7DSduS4y0Idi7s4j1f4C3yNu4mJCfzu7/4ufvmXf7mab1diKtSVmIceeggf+9jHcP/99wMA/IFhLJ1+Prrj6wrvK6JOF3MsIup0Mcf7IaJOE3MsadTprvyQBp0u5oB+0OliDtAHHYsIO13QAf3VOiPQsbCfN2/+XFZSqCsDdGbbwgmo06jOidFFHb+fgDsj1LHELwJt1AFy2Om+tEXY6YJO6EcCdiYrqKRgp1ylS7Wx2rADJLjThR2QxJ0J6liEX4My6vgd+nGnXK1LtJPEXRHqWOqH9uP8w3uxZ88eAMA555yDd7/73di+fbt+H6r0pUJdCZmbm8P/+T//B1/72tdAKY3nzZ0Tz5tTe9E6HaC2QI0wx+I3gaBJjDAHCKAzxByQBJ3p+qzUAYgPbciJCeoE3SGiPMSajinogB7qTEAntuH4lqADop/bBHQsDhDW6apX58Rw1GlW58SYog7owc4KdQBAqBnq+P3R29LLJAx2pqiL+8BhZ7rmsQA7rSpdqg1b2KnMr8sKA57yMGw6DHc2sAM47rRhByRwZ4Q63k6EO1XUAQDCEIN7dmLjE7uwuLgIx3FwxRVX4C1veQuGh4fN+1KlQp1NKKW44YYb8A//8A84evQoAKA9tRlLW5+BsKG3/6G3BNTnzH8V1Ine/GxCAvMFcHk/XPT2nDWI0wVqi/ZLrlPHDiFZS5eo35+gO6JfhGAhNLpvWIMx6KijcEFEUT8M75fsCKy2C6PCwsimP4gN6ljCkKDTNn9y0xDRLhI2IYi29LJJSODOWgx3EcPKTqINGgPV/AWyWhdOZIUhv31g0KoPxLd/lVHH4m2c7ZBi0w0K1I/q49Rpt/C6mo8bbrgBADA5OYm3v/3tePGLXwxiWFh4qqdCnWH27duHj370o3x+gD8wgsVtF8Af01w8uA3U56kVpkhIQQlBYLjGo9uJ1ncLauYvou5QhCBxoV6dBDUCfxjwFoHBg2YlndAjCBoE3rJZH6IqY1RZo270x6gdBwgaJP7boo1BCsdwE3AQIKizqqthEzRCdmixxRtFVEVw2+aVX3+AWm3HRhsUa7bOYHahibrFnqK+76A9PQB3xGyHC0qBsOPa7bFKARIS0AHDn6OMah0AUIJgxPyxdJajqRHhqMWJLyAgMXBt9vUNKUGjYfY7JQRYPzqPIHTw+MMWe5+G0e/VJtHogOmnHgKnQxA27bDuLTpwDEab6kcO4pkHHscTTzwBALj44ovxh3/4h9i8ebNVf56KqVCnGd/38aUvfQmf/vSn0Wq1QImD5U3bsbz5bOWhViDCXC0eJjWtkJGw92YXgUbv/m4H8JZDEBrf3wB13SGgtaY3j69xVO/pxDAHRNXKwQP6bxShFw21Oj6MQMcwB0QA4htdE33YMdD1/q3dHVAH8Ifj50YIfdgx0MX/Nhr+pcJxCdWGHUU0bAtEP4MJ6hjoeJ80nxq0QbF+22EAQBA6ODoXV1QI1cad7ztoHxngw9kmsKMUCPm8NqKPOxrNawQQ/V5NYFfC3DrWB+rAGHZ8c3tT2Pm9xwFeyJb6NMIdH1Yn1Ah3hAAbx+YQUmKOO9r72xR3iZ1YdHFHCdx2b6qIKe68xd7zSRt3QYDBPTswtXsnOp0OGo0G3vKWt+DXf/3XqwspNFKhTiOPPPII/vqv/xoPPfQQAKA7MoXF0y5AMDSiNb9EBB2gjzoRc0AMsjqUy+ci5hJtaKBOxBwI4Lb1QCdiDtAHHYMcD4nm4dU05hOmMQcIoIvbtEFd7/9692egAwxQJ4JO+JruhRqJY2qiTgQdoI+6BOb45r9EC3UMdE58/27g9lAXt6sDO9930D6cmlKhibsE6gB92ImoA/RhV8ZVsEj2wQR2HHSAPeriNuBFCDHBXdbWbzrAY7ADYI47mvy3Lu7kSynpXE1E+OvUFHci6gB92IV1wF1cwMvnZnDHHXcAAJ7+9Kfjj/7oj3DaaafpNfYUTYU6hfi+j89//vO46qqrEAQBQreG5VOfgfbaraAuUQZdGnMsqqhLY45Fp0rndoDaUvKFGrqE46YoaczxdhVRl8Yci+qwawJz4nldA3QyzAEp0PGG1WGXBl3va+r3F0HHu6AKOxno4q+rrpkrPY4G6tKgA/RQx0GXvspGEXVidc4R2uhDHe+cGu6kqAO0YNeHOkAPdmnUsajiToa6+OuqsJMdXxd2CdQB+rCTzUETYAfo4y7zIhhF3ImoY9HGnez0pYk7K9gJqBO+FLWhiLs06lhUcUcJQGsAKMXgnsew6dEdWFxcRK1Ww5ve9Cb81//6X+F5lpPHT/JUqCvI7t278aEPfQgPPvggAKAzsRGLWy8ArUeXtlMHhajLwhyLCuqyQAeooY5V54D+90uVKl0W5oAIdPVZmnu1a1An8Af77wuoVemyMAeogy4Lc/z7Wb9HBdjJQJf8XvH9/SH5ZGUl1GWBLv6eyqLHmcdQRJ0MdLwJBdhlgg5QQl26OicmE3WAEuwyUQcowU4KOv5NRdhloQ5Qg10W6uLvqcAu6/iqsOsDHW9YEXZ5FxVILiDRwV3R3r5FuJPBDtDEXdZpTBF3+TvaFJwjJagTvqUEuyzUsajgTjzXOMvLePXSHG655RYAwFlnnYX3vve9OOOMM4obeoqmQl1GgiDAv//7v+NTn/oUOp0OQreGpa3no7Omt1crUIw6W9DlYQ4oHnrNw1yijQzU5WGOHyOnSpeHOZa8Kl0e5ljyUCdCDsjGTSbo4uPmoS4PdL3v598/C3S8C3mwywOdcJu8veiL0ZgNuzzM8bvnoE463Np3o3zU5YEOKEBdfNw82OWiDoh+dyQbd7mo4zfKwV0e6IQ+ZMIuD3TCbfJgV3R8Fdhlog5Qg10e6lLVusS3hLtlAU91b98s3GWhjkUJd0XvxgW4U9t/OucgBbADinFnC7u+8wylGNj7BLbu3IG5uTnUajX83u/9Hl772tfC0ZjH/lRJhTpJ9u3bh7/4i7/APffcAwDojK3H4unPAq33n9SzUFeEOZYs1BVhjiWrSqeCOSB76FUFc0B2lU4Fc0B2lU4Fc0A26IqqconbqpwXMmBXBLrk7eRfLwIdkIM6FdDFt8sYXVIc2pWjTgV0QDbqcqtziRvKUZc13JpOIep4R+W4K0QdS0bVTgl1QDbsVFAHZMNOBXXx7bJgp3L8PNjlgo4fJAd2Kkt/5MCO3ySneqezv68Md0WwAwA/dEApycadyjtyDu6Ut9+T4S4HdcJNovtn4K4IdSxZuMv68Oi0W3jN4hzfR/3CCy/Ee9/7XqxbV7y4/1MpFepS+e53v4uPfOQjWFhYAHU8LG05D+21W5Mf9YTIUKcKOkCOOlXQAXLUyS6EyL1/qkrHQadwbktX6VQxx5Ku0qlijiWNOh3MAYqg4wdLwk4VdMnbJ/+vAjogA3WqoItvK5kLrn4RhgR1qqAD5KhTBh0gRV1RdU6MMuoAKeyUUQdIYaeMOkAOO1XUAXLYqaIuvq0MdqrHl8FOCXT8QBLY6azlpgA7QI47k/19RdypoI4lE3eq78gZsNPagi8NOwXUCTeVwk4VdYAcdrlTPSjF4OOPYcOOB9FqtTA8PIx3v/vd+C//5b8oH/NkT4W6OMvLy/i7v/s7fOtb3wIAdIcnsXjGxQgb+RsOi6jTwRyLiDodzLGIqFOtzvH7pqp0qtU5lnSVLqgT+Br7M4ugC2sE3UH1YwNJ0OlijuVYoU4HdPzwIux0QMcb6D02WqDj9+/BTgd0QD/qtEAH9KFOB3SAJuqAPthpoQ7og50W6oAk7HRAxyLCTgd0/PhJ2OkePw27VUUdoLVAcxp3pnv8MtzpwA6InpvGsItvm8ad9vsIw50G6uKbR/cXcKeDOhYRdyrzd92FBfzigb144IEHAAAvfelL8Yd/+IcYHLRbCPpkSIU6AA8//DCuvPJKPP744yCEYGnj2VjedA5Aip+cDHUmoAN6qDMGXR1wu3qYS9y/RrQxx8KqdLrVORaGOg46HeDEoDPFHKAJOn7gCCS6oBOPGTSQeaVr7qEZ6kxAB3DUGYEO4KjTBR2/eww7bdABHHWqw63paKOOJcadNuoADjtt0LEw2JmgDujBzgR1QAJ2Jqj0R6MTmhbohPtz2JnsuKBYrUvcRcCdDex0UQdIqnYm78oC7oy35KtRbdgByaqdCeqAHuyUl08KQ7x33SQ++9nPIgxDnHrqqbjyyiuf8hdRPKVRRynFl7/8ZfzTP/0Tut0uwloTC2dcDH90rXIbThfwWmYPIQminRzM98SM3uBVh1rTaY05WNqgjzkgAp23BCPMARHomjOhdnWOJwQcVogwOL4R6IDozaZmBjp2XH9Av0rXa8DosFHi45nvUkER1M1Ax47fW9hZsw1KQGshNpx+RAtzLMaoAwBC4ThUH3UA2AUUaJjuAEGAlmOGuvj4tBmYoQ4AKODNmy38yqp1RqgDerAz3UbLAHZAD3eO6XZshKLZ7GrDDkjhzvS1TgGna/iYIdoi0WixcKFqZwo7k9Snj+BpOx7EoUOHUK/X8c53vhOveMUrnrLbjD1lUbe8vIwPf/jDfM+5zsRGLJ72LNCa+kqxNqBjm9abVPcARCcsCjh+/lIiWeGgM3neEwBhVCE0CSVRdbI+awibEMZ7ibI9VY1R50SVQdP9bcM60B43+7lJCBCfGG8FRGj8Cd5w3zDqUviDFqBzzU81tEYxsnEeI8220f2tUAcgCByEc+b7tpKAgA4bbmUVEvON49momvH2UYC76Jrv3Usstq6KEw6Zb0dGug7ooNnjTogF7AC4XoAtUzNG9/VDB3OtBmZ2T5gd3BJ2xDd8b0A8GhAY390oTqeNK+ZmcOuttwIALrvsMrznPe95Sg7HPiWvB3788cfxP/7H/8ANN9wASggWt5yHhTOfoww66gDUM4cBdaL5bEZh87oo4Hb1QRfUCVoTDvwhc9CZ7iUKpECl206MEhPEAub343FgtT9u9HsHvCX9NkhoeZJmoDMM9Si646E5DixCaxRD6xeN3yQ6vofDh0fgd8y3GiIOhWOwNRihBMS0SgYAIYF71DOvrgLR0LXFcwew+BBkiQtQwFm02CIqBMiS2WK1lAJhQBAaVkkpBR4/bIYyh1DU3BATp5mhEAQI13QRrjHcp9ij0QcCk1ke7IOE0ZGj1OYIanPqj3tYb+DLa9Zj7pxz4boubrjhBvzu7/4udu/ebdGLEzNPuUrd97//ffzVX/0VlpaWouHWMy+BPzKldF/biyJEzLltitoy1XvmCyAiQYQ61QT13lWllABBk6Azqnls0vs38fUqdSLm3A5Qn9MdfktV53SvERBub1SpE0EXD8Gqpu+qVzceflVMGnRUs/rRBzpNlVOPojsW8uowMdiL1rRKx0EX/wIdQjGsUa3r+B6OHBlOXCHiae77GgqPFw0Iwnn1Xz6hpDd86FD9al2MOv5f3aFvisTProVyNqdO+PltKnaAyZ6kvfubVOwIu9jEgVHFjgjPW8fgOey4AR/O1anahZRgsRNNLgtCByGFftVuND5BUwJKAXdar9JM0lux6UZ4rpjcXURdd1T9sReHYwcGBvC+970PL3zhCw16cGLmKYO6IAjwyU9+El/4whcAAN3RKSyccQlorVl4X9tlS2SVObdN1VEoQYgO6hjoxPdyLdQx0LH3BgvQAQaoS4Mu/ppKMjYnUEedrDqngTrZ+nQ6qJNV6HRQl1mhU4RdAnQsIdQrPyUMuw5vWOD/N0Ld4RGhP6uHugToWHRglwId/7Iq7ETQ9TqlDrt46DUdY9gB+sOx6Ztq4o6IS8PE5zAd3JHUc1cXdo7b6ysh5rADgG7g6MFuNHWSpgTOEY0PJFnbsalG8jzRubusUqeKO6fdxi8f3o+f/exnAIA3velNeNOb3vSUWKz4KYG6paUlXHnllXzRwuWNZ2L51HNRNPpsu7Bw1jCrcpUuAx+qoEtX5/jXmwSdERS/wlLVOf5lRdRl4UkZdTLMCd8rSs7mBGqoyxpuVURd5oLDiqjLGnJVRV3ukKsC6qSgA9SrdSWATqzSsajCrq9Kx/ulDrtQ8jipwk6KOkAddhmoAxRhJ0Nd1DE12GWgDlhF2MlupgE7IlvIWaNql0Ydb0LxeS2iDoBW1S6NOkCzakcApKcMaFbtpLBjbask43midHcK1Oblt1TCXRji7QM1XHvttQCAF7zgBXjf+96HoSGNdbdOwJz0bN23bx9+//d/HzfffDMocTB39sVYOu085P3obM6c6aNDYwzkzpszBJ1qxOqc9P1bFXRE4baSWF2MAOSDTiHHw/y5ov1e85I3h44ozFMqnENX8ABlgg6InhtFb8orBDogerNbaOU/uJmgAwBKlObXyUAHRG/0RfPrMkEHRBc+LBTM88oBHaBwBXMW6AC1OXYUuVuG2b62T9R5drwJi3l2NnPtXCdUn2snBTEFcSiCScOr3PLa1rx7YRM5D7HSnDvHwSf2h5g/60LU63XcdNNNeOtb34q9e/dq9vbEykmNunvuuQdvfetbsWvXLoS1BmbPeyG6a07NxULRXq5FsboIAuAXQpieNP1GdCFEerhV6/gOEsOtOqEkuhjAZmI1CcxBR+gqgI7m79dbBDoSAN5idvvHxUURWaDjB8n/3kqBTiW5oOMHUYNdVlRglxsV2BVkxS+eKDiBrDjs8n79ZcBu0bPCXRkXUZjiruaGWHP6jPGFFMShCNd07XBneCFFugmbFMGuM0HRXrcVB895PtauXcsvkrzvvvssj3z85qRF3XXXXYd3vetdOHr0KPyhcRw9/5fgj0xG35TNs1KszmUNvSpV5yAMvaZTEub8wZzqXFGeItU5knUcJ1pyRKlClzW0u4IVOqX7rwbogOxqnSXoeDMFv8zCap3NJdrIrtKpJLdKlzhIBuwKqnRiTtirYoFyKnYLbibuSKegcxRPnaqdLHHVbkVxp/D8yLt7d6T4XKJStfNHJvDgaRdj+/btmJ2dxbve9S7ceOONxZ07AXPSzamjlOJf//Vf8YlPfAIA0F5zCubPughwoxcuCZNverqVuTTqdCtzfRdIaEJINp/ObxCOOZX0XSSRMXcusw+pOXW6mJPOqdMFXeruBmvZJvusO9yamleniznZvDpd0KXn1hmBTrwIQBV0/A6puXUlgE63SpeeX6dUpRMjmV+nAzrZ/Dpl1LGk59hpoI7fRZxjlzf0Kkt6jh3tv+q1sAnLOXaAbB9SvTbS8+yk8+myIplnlzWfLrMJye3Tc+qyIptrJ5tTl5XMuXbpiyVyQkMinWuXOa8uK+mbaz430nfXWdoE6J9vV58R7h/4eJU7jZtuugkA8Na3vhWve93rTqqFik+qSl0QBPj7v/97DrqlU87E/PZnJ0EXP8FWZd5cUSwrW+nqnOnxj3l1zmS4VbTECTh/jgTJ9epOmApd4qAKc+t0+rAaw659By13GFYbdOkYgA5YgYqd7rI3lueAk2We3bGu2h3zIVlgdebb5SRduetMCK25Hv6DrsWv//qvAwA+8YlP4CMf+QiCwOLkeZzlpEFdu93GBz/4QX6ly8Lp52Hp9PN6H4HiUGKOOValO9bz5oBkde5YDbVazZ0DShluPWagi+fVWQ23xj+/2ybHDnSEmoGO37/397GYR9c3DHsMhl0Z7IxBV8L8OiCGnW6VjqWkodjShmNNnkrHwTw7wHw4FrCfa1fWkGwpsCtpvp3KEKwsmcOyhOCf9wVYOP2ZcBwHX/va13DllVei0+mYd/Y4ykkx/Lq4uIg//uM/xl133QVKHMyfdRE6azcnbsNONo4FyJ1uBDubuB1qvLUYEGGSbXhscu72Bwg6Y/F/DM89JLCrCrltoHHU/hVvA7rQjebPUYuyO/WArs3V8URj8+q8fjgWzycP8EcNQcc7AO0hlsTdLSt0ABCGBMsLDXPUEWq1JRQAhL4DzNj9QvMuwFHuh6UPnS6scWy95Iln8eJmLrRpw0H/ciAG8ermv1BCgImRJeP7B2H0pnfkyLBZA/HyJ95Bu+d02KBwWnbPp7rmEGw63WGK+mx/G/XDe7Fm58/Q7XZxySWX4M///M8xMGCwx/NxlBMedfPz83jPe96DBx54ANT1MHfOc9EdX5u4DXUAEIsTZrx5PAmi+WSmsQEddQiCGqyGbLsjBO1JajxUE9YogmZ0/6z1g4pSBujY+40N0NmwcdAwHC5xgWAgwqFxH+LnJTV8E2bPx2DA8DnlAt2RGHSm0xAIBQiMqzy0TjG1+SgAoNU1eyDCkGB5sQGEFkAPCJx5D2Rdy6wPXQfe3gZAYLw/Lgmjq6JNf5+ggNsioI7ejiWJPlDAmycIitdkz77/Eol3rTF/nXtLBJ0xMxkSGlctCeAPmV5GH31YIqb79QKgHQcgQG3IsAJECfx29JpYt27WuB9B6NjBLt532Js3PNmRaM6nDezYe7fJFossWbCrHT2IDTvvxPLyMs4991z89V//NUZHdbZbOr5yQg+/Hj16FO9617vwwAMPIPTqOPqMFyZAR+OrGU3fNAFw0NmEUApiYWcOOsN0RwjmtgHL66jxB3AGOptw0Jm+Z5EehGxjWhSiLuAPxkOu1Py5YTVMhfiCmXZ0fHdZ/wHhoLMIJTQ6g1jOrXOdEK4TolnTfwMNQ4LWUlxJMKxY0oDAnfFAugT0oL5mGOiIxZATCQFvgUTD8Qa/z762cpbMybwfjYasSEjgmtk20ZZr+CbuLUaPQ/2og/qszfpSgLdo+mklnh85XwO1GRqnQHfRvNLF5todPDhWfOOMEEKxZs0C1qxZKL5xTvwR05NdPF2nSRGavofE04RMPzCxdMYoOmPJNrrj67DvrGdjZGQE999/P97+9rfjyJEjVsc5ljlhUXfkyBG84x3vwI4dO6I16J7xQgTD4wBKxFy396ZtWqWzxZzfsAfd8rrem69JbEHntoHBA9QadGVgzibUjTFn2ZcE6Kj+84qBznTeSuilQGcwfMpBxzulDzuxSgcUL2OSDgMdFT+paMKOgY592jGFHes6q1Rp3ZeBjvb+rw27FKJMYccfBwPYpX92U9iJjyUJcexgx/tD7GG3ULfGnS3sGO5s4o8Exrhj879NYMdHRGLYlY07f2QSj5/xbKxZswa7du3Cu971rhMWdick6g4ePIh3vOMd2L17N4J6MwLd0OiKYM40x1N1zgZz3ZHQGnQcc2WCjtgNfWr3QQSdTTslVejEx1KnWhd6gD9cUoXOpo0YdK7T64tDqHa1jspKz4qwS4Ou9w314/NhVyEkNIBd6pirXbFjVbrk/Y9txa7XjxJgt+Do4S6917Yt7OJ+lAG7E7pqh3KrdmXiLhgaxY7TLsK6devw2GOP4V3vehemp6et2j8WOeFQNz09jT/4gz/Anj17EDQGMXveixAMjvSWKJGEBArz6cKoYnI8YG4lq3PUA4JGcf9OtuFWWaIrT4s7lws6jSHYlQAd64PS8d0c0ClW63JBp1itk4GORXUYNjHsahMJComvVq1LDLum21CEHavSZX1PCXY5cNKq2Mkei1WGXVZfdWCXOWfYsmrHYHciDcfKruYus2oXGH5AFKt2q4W7WsbrDACHXdgcwoObz8fatWvx2GOP4Z3vfOcJB7sTCnWzs7NJ0D3jhQgGh0qbN2e7PIYN5oCTqzo3cPD4H25V+X2vSoWuYAg2WtMuA3Rxiqp1SnPoCmC3UhW6dAp3k5ANu6ZTUK3jVbqsPigOw+Z1VRl2BW3kwk4BTEWwk1Xpkvcn8JaRi7uiYWdV2BU9FkWwK3xNlwC742k49lhX7SihxrCL7m8+JMtT8ny7cGAYD516AYfdiVaxO2FQt7CwgPe85z3YtWsXgnoTR5/5Avijg9aYOx6qc6F7Ys2dC+s0c+0gVp0jIfJBl/M9ZdCt8BBsGaCzXbtLef5c3uNZ5kURRcmp1qmADsgfhlUCHW8oox9Zw66p5MFONuwqbSMHdnlVuvTtVnwotujxpPYXUBAKeMvEajjWeigWKIadQvNFsKNFW5TF/ciEHSXotvLf3Mq6iMK2akcJtaraRW1YVu2AUodkWxuGOOx2796N97znPZifn7dqd7VyQixpsrS0hPe85z247777ENbqmHlWNIdONX3Dr/HOEjqVOdmFErqQc7pILGliVJlLLWnSGSVoTVGtyfvEjxa8ZTEZak0va+K2gfos1bsCMD00bFKd0xj+lN5dsrQJx5ykj5mRAFMbc6nlTTKHWwv6IC6JYTSHjiDxxmZUoaNILHOiCjoxQegkljnRAp0YYakTVdCJoTWaWOokb9g1sw0nWUlIXxyh2kZiuROD+Wrp5U54lU7n8XBoYskTk4tDZEueeIvqjwc7V4jLnvClTHRCJMueaDzXadzh9NInSqhL9SOx9IkC6hJ3JwAIxbq1c4mva215F982sfyJsKSJcl8ogbuQ/Pl13l/YcyC9BIrWeT5uI/287A7rvc+5iwt42r13Ynp6Gs985jPxkY98BI2Gxebeq5DjvlLn+z7e//73R6Dzajh6wS9oga4vJ8lQKxCDbu3qVOfyolydy8mxuro1/TxYkStcDaINOkS3FX+e4/miCJWIw7DGoJN2SPMiBknFTvf8IavYmbSxIhU73cdjhebZaX3IpqtUtSvsRwnDsXE/rOfahQQHD5m/N5Y11+5kGpINhobx4NOfiaGhIdxzzz340z/9U/h+CSuEr2COa9RRSvG//tf/wu233w7quDj6zOfDHzYsNZ9EQ62AADqLc/zxcDEEcOxAx8IumLAabhUqhsagi+fWGYEuDnvjtwJdPLfOCnSpYVhd0AH9w7DGoIuHYYvm0eUm/lFUh13z2lAddpWFw86gSie2wapiupul99qIYGdSpeNtCD+D0fIr6MHOqErHYgk7oHg4VrUfNrAD7GEHoDTYnSxDsv7oOB4771mo1+u4+eab8Td/8zcIQ7sPyyuZ43r49dOf/jSuuuoqUBDMnvdcdKY2GLVDfMCz/WQZAE7X7qEiYf6EeJV0xkjvQgjTc1hcqreJ2yZoHiKoz9mBzmaHDB7LIVgACOoRlG2rc7ZbNBEaTxWweEyDOtBab/mAuACth4DFPpYAgFqIqfVzRqhj6fguZmcHrap0NCBwj9S0q1KJkGg3Fev9hjWnfvSFEjgd851ExBDb3y+hIBY7efT6YXd/6tjtYBF1AvDLmHtat3zDJ4BTD6Jt50ybiM/xU1Pm88AoJQgpML1n3LgN3p+uLZrN4c9D7d5rGgf2Yd1dP0UQBHj961+Pt771rXb9WaEct5W6b3zjG7jqqqsAAPPbzzcCHXWBoE6tfpF843qb94IQcDr2oAuaBP4QrIZb/ckuTnnmfrinmu8p6HTijZJ15s+l2wiA+iJVWlIkN9Tap6UsnUICoDYP1Ow+4ALU7uKP0Ive3KwqDw5AG5bvsgDghRifWrACXTdwMLcwYIftgIDM1UAtL6ohISnlA4g1ClHCUCwFnC6x2qOVUKBxhKA+a94GKFA/av+6IaE9DBECzSddNPeZP1FISOAdrsE7bDEUw+aUeRZVLgqMjC3DD2xgSOE6FJu2HTZug/dnxAcdsdhyjQDdzW10N1tsvk4Af3MbvmEb7fUbcfjc8wEAn//85/Htb3/bvC8rmOMSdT/5yU/wkY98BACwuHU7WqecrnV/hrnQtRuepCVUkcqozgVNgvaaCHRO1+wTiz/Zxcbz92PrlsOouQEcwy2VnA5BfdauWuEEvQtG7CoW8V+GvycOdid6XGsLZp0hAeAt03ho3qiJqB3LD/ih16veEN9iSIk9vQgFXMMfyAsxvnYBhFB0Dd9YuoGD2flBq09UNCAg81H5NFrL0lJUjgW6qf3vGLQ3n834uUYBx+89prawIwGsYYfQDnZBnSamL5j0oRZv00YC2MEuhrsV7OLYwM4hFJQS+IFjjLuhegc1N8CpZxyyw138oNjAjjgUxKFWsGNtmOJuefMWvPGNbwQAfPjDH8Zdd91l3JeVynGHur179+IDH/gAgiDA0qbNWDz9aVr3py56mLMYnmRv9qYpuzrHJo+aVMf8yS7HnGnVxOkQNA87pYIOiIYavWWDBsuqzlm2yUDH4nSp0ZtT+o1VdxiXgc76OoJ0lc4EdgLogGgYRxd2adAREp2QdcJBJzwoprBLPOdNYFcy6FibthdOsOj2jVCgPiP83wR2NHUfS9iJ7ZrCjsUIdjTauYK3USLs7Kp2hOPONK4TouYG9lW7GHa2uLOt2om4081fHZrFL/3SL8H3fbzvfe/DE088YdyPlchxhbqlpSW8973vxfz8PNqTE5h55rPiyQHFKaM6VwbmgPKrczZv1Ax0NkNgvDqXmgsUNqL18ZTaiIdbRdCxaCNxJUAHvWodCQBvUQ5Sk6sabZIFOu1qnQPQhmVnaknQsejALqtCZwI76YtHd9UL2SF1YFcG6DKiPQybqtKl29I6dupxMYVdIqH+NIagLvkFmcJOiCnsEm3EsNPBnZMx9cEGdgCsYQfArGrXTPW75KqdLe60q3aE4LMTAzjnnHMwNzeHP/qjP8LCQhmfRsrJcYM6Sik+9KEPYdeuXfCbDRx+wSWAq/aCKqs6VwbmVqQ6ZxBxuFUGug3jc0rz6oqGW5XWgE1V54yTU6VU/f0V3k6hm+Jwqyw61bq8N1KVal25FbqMGfyq1bpaNIeuaEeIvBQNuarCThx27fseUa/W5f4oNkOxuklX6YSUsdSJ2FbhbVJVusT3VGGXrtKlvldK1U4VdjR7CykSlDDP7hgMx45OyM/ttsOxgEHVLutFVFLVrswhWdVQz8MNZ23B2rVr8fjjj+Mv//Ivcbxcc3rcoO6zn/0sfvjDH4I6Dg6/4BIEA00EAzSxUGY6VXVOHpXhVofQ3Hl1bntlhlult1EZgi3DhArwK6rWpYdbM2+ngkOF83Me7FRAp1Stc3NAxxsqgJ1XDDrlal3RTg8FsJMNu/bdRmEYVul5X/Q6XYlhV1k3VGCXU6VLt5X5vRh0uU8VDdjlRgF20ipd6hiqsMuK0jy71NCrtJ1VHI51Cl6Hx6xql04JVTugvCFZlaodM0k40MS955+DWq2GH/3oR7jmmmuMj11mjgvU3Xnnnfj0pz8NAJi+8Dx01kxG3yBU+lrrw9wqVOfCGhDU+w90LKpzbkd+sURRdU41bju6ulVl142sIdi84VZZco+jCLq836dWJTbrg6Ui6IDiat1KDblKj5UHO7Z0iY3cPfmQqyx5sONVOouogI7ftowLJ0hOtW4Fh12lXcmDnSLoxLYyv6dYzc6rxCkP0650xS6nSpeOCuwK2ygYjs0aepXetqTh2CzcDdU70q+LWbW5dhPFfVm1CymEF0BncgJvf/vbAQCf/OQnj4sLJ4456mZmZvBnf/ZnoJRi4fQtWNy2Nff2x8uFEEA51TkgCTqlSIYhTS6GkA3BsuVKtLYvSvXbZLg1s1pXwvw53d+1rFqnAzp+H9lIZqj/Rp+u1pU25KoLOlm1TgN0LDLY6V7pKqvW6YCO3ycDdlrGlQ3DlgU6SqKFqBWzkkOxecOu0vsH0e2lgNN5fDNgV1ilSx0v88pYjWaksFOo0iXaWOHh2KyhV1nKrNqVAbusqp3O+WW1L6R4154deMlLXoIgCPCBD3wAR44cMT5uGTmmqAvDEB/60Idw5MgRdEeHMfOsZ2Te9nhapgQ4Podbdatz4hDsag63ZqXvuAbNiL9fq9+1cGwT0AH91TqbN3kGO1PQ9VXrTCt0IuwyLorQjenSJSLsTEDHkoad0Y+zEvPrGOh0f0Vp2GlW6dJtAWrDrtL701TVTqdKJ2YlrozVqNKJyYKddjsrNBybN/SalRUbjk1fJKGSkoZjbat2rJ3CIVlC8OmRGk4//XRMT0/jL/7iL47pjhPHFHX/9m//httuuw3UdXD4uReDehkTmy2rc0GTojtYXnXueLgYwu0AQYNi/TMPrOpwqyxsCNb2ggherZNUInVji3dWrTMFHQt7PMuo2thW6DjsyhhytbwoglXrbNeiS1TsrFYIj/+yed4x2JU57GrYH3E7MVPQiW0Bdo8Nh53Na1uAnVaVLp0U7EwiXkChU6Xra0eAnc7QqyxlDseqDL3K0jcca/qkKeEiCiBZteueagY8laod9TzctP00NBoN/PSnP8W1115r2mXrHDPUPfzww/jEJz4BAJi54Bnojkv2q3OBsITqnBPYrZzOQsSTgUUbYR3W1TkaX63Y8Hwr0PldF+6yXXWO+BGAbK9wjbbZsh86ogTWuwcAFmvoCYlQaN8X6kR7GNoOuVIC0IHA6h2a1AOs3TBbQoXOxdGjli8EADQkIEt2+7NREp1rrFPSc48EBI1pyzZCoD5r/3oiATD4pP1jQ3xg8IBlOyVV7EgINI7aY7es9xXvcA101m7fVwAglvvPsq34Fjt2fWGwGxy12J8zHo4d3mqzsnUPZZeevcO6HX9zGwOb5E9Af3QEb3vb2wAAn/jEJ7Br1y6r45nmmKCu2+3iL//yL+MFhjdgQTaPzgUoG+YxfO05PoHbIiBdu0+Zbgeoz1H7N/j4E7zbAjyLExPfeN4iew5N4IlbNsF9ZMCqKkZ8wFuiCGtAd9D8JEkdIKiVMxfI5gNAImG8D6thKCEIvehNUWdeVF87TrxThOXPFdYAf53di4HUQ6xbO4eaG6BRM39w/NDBwtwAaEgQWuw/GgbxRuoleAyEImhYVoAooosnLEbVSEDQmImnMpjv5he1FUbrKdrGtmINGlXxSWj/QclbohjaCwzus31B2L2+E7HpCgGCwRAIYQ+7kGB2zxhmnxgzboLNs7OF3exyE4TADnYAPCfE6OgyRkftPh0PuF286MxH8KIzHzFugzjRlKUs2P3BE4/gOc95DjqdDv78z/8cnY5ZxdMmxwR1n/vc5/Doo48iqNcxfdH5SCww7AK0TnugM4zjR5izPdm7HbYmmV07DHT83wZr2lAXCAZ6oKsfcrFz13rtdvYcHoezYzB6jCzeUBno2GNjWnDhoCPRY+OYVkPTw/Nl4M7wxE8JsXpj5+2IoLO4OIiBjjg0erkZvL5IPcS6qTleGXYINYIdA53ta5OBjtDouWO8pRkQvyhhDzvenhnsGOjYucIGduyDhA3sSCBU10oAGRBXpgzb8VqUD287PjWCHaFAbT6+H41e30a4I0B3hCb+b50yYBcQwCfGsPPc6MnHYGeKO1b1KwN2hFAQQkuBHcOdTRjs+nBHCK5dN4axsTHs2LEDn/nMZ6yOY9S31T7gjh078LnPfQ4AMHPheQibQslJrM7FCQdDvp+lSsTqXKIdF6Aalekyq3NOYF+mZ9U5EU4kJNELWCN7Do/DeXgogTnqAoHmG1AadEA0rKxTraMO4DcIBx1vOzSAXVnVuRIiA51JtU5aoTOAnQg63owmgtKgM40UdBTa1boE6HgnYQY7IjzGprBjVbpEu4awSz3EJrBLP9dMYMdBJ/bHBHa0f66tCeyIZD6eKezSzz+GO/XOxKBLH1r39UkAfzC9R6AZ7OjR1H0sYMfbLKlqx2BXBu5MqnYvOiuJONOq3eBQ74XlZFTtwoEmHnnG2QCAa665Bjt22A376mZVUef7Pv7qr/4qHnbdiKVTT4m+UVCdU60ArUp1TuNFK1bn+o7RirbDUUkpw62Hx/HErZv6QBd1VL0d4gO1OdoHOt5XxbbE6pz0+Dq/w7xjrnK1Lq9CpwO73CFXjeegDHS6yQOdTrUut0KnATsp6HhnoQc7kvXGrAE7GeiEtlRhx6p0sqz2UKwUdCw6sJOAjh9DA3aEAm5bflsd2CWqdJK+6sLO6HsqiWGnhbtQclDffjgWgDbsDi8M9X2NEP2qnWx5Fla1Gx5Rb2fA7Uq/tlJVu+VNG/GLv/iLCIIAH/7whxEElpPxdfqzakcC8OUvfxk7duyIh13Pi37DkuqcSTjoLLKa1TmVIdj0cKss9UMudu7MH4Ldc2gCzsNDcLrZw60q1TqxOpf1JqZSrUuALutYqsOwx3mFzqidkufQZYFOpVqnUqFTgZ3SkKsC7HJBxzsNNdjJQCe2oQK7PNAJbRU9L9LDrrKowi7vg4NWxS6vKHsshmJzbqZVsSt4DhbCLj3smnO7ou/7eaNQIZSrdn1VOjEaw7Fs6FV6DI3hWJrz+tSp2uWdd9x4rp0O7mQpC3bpqt3nR+oYGhrCgw8+iK9+9atW7Wv1ZbUOdPjwYb5rxNFnPg3hUFN57lzeEGzWcKtuVmLunE1kw63S44VE/gktzp5DE3B2DBbPnSv6tmS4NSt5fVYBXe/GBd9XRc8qVOtUQVdUrVMGXUG1TrVClwc7nSHXPNhpzaHLgZ0S6FiKYJcHOrGNPNipgE5oK7uCWww6liLYqVSCi2CXmEeXlyLY5VTpEscrgF1elU5MEexyq3RiFGGnlMLnmNrjXAi7nPcAnuNsOLasuXauE5YGuzzcDQwWX/QgVu2CgSbe+ta3Aoh2mzh48KBVH1Wzaqj7xCc+gaWlJbQnx7F45hbt6pzsPK473Jo1r46BTjkZb6i6oMsagtUdbs26YEIZdOJxJW88OqADsqt1WqBDQbVOF2orCDvdCl0W7LQrdBnPQ90hVxnsTObQyWBndFGEBHZaoGPJgp0K6MQ2ZLDTAZ3QVv9cS3XQsWTBTnfnCRnscoddZcmCnSLo+HEzYMdBp9hUFuw46FS7lAU71Spd6j6yr+VW6dLJgV1ulS6dHNjlVen6jpkDO9nQa1byYKezM0Ye7NLz6bJSNByruoSTWLV7154dOPfcc7G8vIx/+qd/Urq/bVYFdffccw++853vgAKYfvZ5WhcsyFJmde5YXgyRHoJVGW6VtpO6YGLPoYlo/pwG6KKGkv91ukBtPnv+XF7E996sCyLUGpL08TgZco0wZz/kSp1ok2jbIdewBnRO6VrPoWOxvSiCx74rAKAHutyGVvj2us2bLbpfynFrCxLc6fYnjD70lXEercna0WzW8TOWPNHtHs24Mtbk+SA7b+n+ErPm2alU6cSUPM8ujbu8oVdZsoZjdc8/WcOxsvl0eSlj6RMgrtptXsSPnjUIQgi+973v4b777rNqU+m4K32AMAzxd3/3dwCAhTO3oDM1btZOPARrezEEq9ZZD7fGL1Lb4VZWrVMdbs0Kq9ax6lze/Lm8sGqd0wXcZWq+y0RcrSu8IKIgiWqd7ZtridU6Vp0z3t0hrtb1LVmi3RB4Bchf1wVxqRHoxGodq9KZRKzW8SqdSYRqHV+LziTpap3FCve8WmdSpRP6wz4E5F0YUdhMmKzWGa+DSHtVOxIAAwcNfzCKXtVOs0qXDqvaqQ67StsQljxRHnaVhaJXtTOp0vV1DPpVOjGpeXZaVToxqXl2OlU6MenhWJ0qnZj0cKxOlS7ZTnI4VrVKl066aqcy9CqL41DQ9cN4+ctfDgD4x3/8R1CD5cy0jrmirQO48cYbsWPHDoQ1D0cvOMe4HaflwFu0r84B0TZfZUzyLWM7IELLubqVhASNfZ5+da6voegE5pYwvzD09IZbM1PSa8DtAM1Z++oTJXrL42SFP762F0R4gD8eWFfnCAHcgcB66RInvjrNei06CgQdR3/YNR0GO51hV1kbhCKs278uQCLM6w67puMEQG3RbmFr3lYXGH8ktN8xJwCG99lf6UdCoDGjPuyaFcenGHwS9ucQGj1GZXwwpIB9qZXBTrdKl04J8+yAXtVOt0qXDoOd7SgBq9rpVunSKeMiCgD43OYZDAwM4P7778f3vvc96/bysqKo830fn/rUpwAAc08/I7kmnUaclgN3iUTDjMdBQhcIGgTBALHCGHWAoA64HYq6WWGEJ9qrlCQ38jaItxR9WndKwLPtyvoAStvyy+0AzZnA+uRO49+9dRx7yAPRc9Efi95Eacd2Y+PowTm6YFhdi9PxPczMDFu/4dDAgTNbg2P7c4WAt+DAXbZrh4QE3gKx3veZ+MDgkwRO1/6NfeBQiMGDdm+AEcRCOD5FY97+E5TjUzSn7T88kZCitlRCfwKgXsLPRUKgMW3/2ne6QG3O/q23fsRF/Wg5b+GHnxi3bmNusYlu10W3a3fC3jA2j4nBZUwM2i00TAhFO/DQDuw+gdfj8XcbsNLhBl73utcBiK4vWMmdJlYUdd/85jexd+9eBI0G5s84w6gNDjoKhB4FLWHJiKAJdIfNfkHR8C2JsGHx+qZODB7WhuE5x+kKn9YtzqPeEjC8h6J52HzIlUeoYEb7uRo2I4BOtvCoahjoSEjhtqlxtS4BOpshOKc31M43XjcIB10Zn3UIheNFP5DvuzgyZzaM0vE9HJkWQGf4IqGBA2fO48NNpGv4Q4aAt+jw56S7ZHbKI0EEOjYcZwo7BjoSRlUoEpgPww1Mh9EwYxclwC7uT2gOO0KB5ky820hgATtx+DaeZ2ca9holoR3seDuBHezYlpUktIcd23u2ftSxwh0l0YUxh58Yt8KdiB4b2Hkk5H9sYPeMtfv4v01hF1CCW588jf/fBnYfPLIfU1NT2L9/P771rW8Zt1OUFUNdu93Gv/zLvwAA5refBVrTe1CdloPajMtBB6C0CcumIBNBZ5PQ7a9geUv61TqnK2AufowcPxpe1om3hAhzwsUeJDR444pPVgnsmE5lKblCR8J4vhilcFv6sJNW6Exg5/TPnSSBPuykoAuJWbVOAB0AgBKEBlW2PtAJ7ekkAToWE9gJoGMxgV0CdLyT+q+PBOjEdkxhJ9zNFHasSpfojwHsGOjEC7+MYMdAl3qsTWCXfm3awo63YwE7knou1uYcI9zVZ3onRxF32hFf9/EHKBPYzS/1DzvYVuwAWMGulprbYFq1C8Lk48rmEWqHeHj9618PALj66qvR7ZYxHNafFUPdN77xDRw+fBj+wAAWTjtN67694db+F+axqNax4VYZ6MKa3hBs1rIqupUoDrr0fTTPWRx06XNvfIGCzhtXFnB0qnWURLeVgU73MUqDrteO3lyd3CFXHdhJQMf7pAG73AqdLuzSoGPNhI5WtS4TdJqRgo53SgN2EtCx6MBOCjreWQPYhZKGdGEX9ipiYnRhx0DXd2xN2MlAx/ukAzsZ6ITvlTEUawI72eubBEDjCNHCnWw+OAOZDuzqM650LqYJ7KjshzOAXRZydGG3frR/jS8GOx3cPX3qQOb3dGAnVunSMYHd/3fXo1izZg0OHjyIb3/729r3V8mKoM73fXzxi18EAMyffRbgunA6ROmELA63SrPK1bqi4VZK1GAXutH8ubwJ9qrVukzQse8rVusyQceiCpaiC0YUf2cq1TlV2GWBjn9fcRi27Dl0uc85hZ9LachVFXYZoIv6QpSHYZVAp/BiywUdv1FhM7mgY9Gq2OUdUxF2rEqX244K7GLQZb3eVGGXCTqxP1qwy76dLuzyvqcKu7zzlg7sitrRqdoVtaUKu9ydinRgl/XaB7RgJ6vSiVGdZ7d+dB5exg+nOxzbKFg9WgV2ASV9Vbp0VGG39Nho9A/X5XPrrr76avi+5QRdSVYEdT/4wQ+wf/9+BPU6lracqtYR2XDrCidoAt2h7F+K6nBr0fdDN8ZKwe2KwJKYP1dw8iuCXSHoWJ+KhmEV4VdUrStzuHXwUJALOkBtGFYZdEWPgQroUDy/TmsOXRHs8kDHogA7rQpdzgOgBDogeqzzPhwqgI6lCHa8SleUAthJh12z2smDXQHoWIpgVwg6sT8FsBPn0eX2qQh2VHEZFAp4Be/rKucjBrvVGo5VWbVBBXbisGteO4Ww86i8SidGcZ6dKmyKYJcFuvRtimCXV6UTU8ZFFIDacKx4Vfn/fGwXJicnsX//flx//fXWx0+ndNRRSvGv//qvAICFM7aBer0HLatalzfcKktZQ7A0Xlqgr/2c4dbMPmVU61RBx5JVrZPNn8tNxm3SF0SotJM5DKsz9EiyYacLuiz8suqc49PiN1DkD8NqV+iyHgtF0PE+ZQzDrtRFEYXJgV2razDkKnkglEHHkjUMqwE6lizY5Q67ypIBO2XQie3IsKUIOpZi2Gn0JwN2ecOu0j5lwS5v2FUSEtJC2Km1k1+10yku5MGOXRyh2qcs2GUNu2a1k3cBRSHoWArm2RVV6dLJgp1s2DUrRcOxRVW6dLJgd/u+rVrtZMGOV+lYXBevfe1rAQBf+tKXSl+3rnTU3XnnndG6dK6LxdNPL+5A0XCrLCWubJKu1ple3Sq7rS7oADlYioZbs5Ku1skuiFCKDCw6oGPJGL42qdClH6ei4dasyIZhV3XIVZI07IxBJ6vW6YCORXLhRKtrsWyJ8IBog44lC3Ym58fUfbRBJ7Qjwk4bdGI7EnTprmkng13fhRGq/UnBjlCgeVQddLxPObDTSRbsTEZ5ZLAzaicDdrptZcFOe8eirAsodF//QAS7vWN9XzaZVyaDnUqVLn17WdVOtUqXThp2ASXoBvpvTLLHQ7b24/ufeByNRgM7duzA3XffrX2cvJSOuq985SsAgKWtWxA2+le7ZtU62+HWlajW2V7dyqp1fP6cJuhYxGqdKeiA5H1Uh1uzwodh4/lzpkPkYrXOdsiVwc4UdFEbvWHYaJs2Yg46EbqGoOP9imFnXaETYWcCOtaMcOGEFehYKDEHHe+UADtWpTMIob1qnTHoWGLYGYNObIfBLuPCCJWIsFMeds3qTww7DjrDny0BO9VhV0nYOnYMdzbTdkTYWbWTgp3pYvnpK2NVhl3z2hJhp1ylSycgCdjpVunEiPPsdKp06aRhp1ulE8NgF1CiXaUTIw7H9lXp2G3qdbz0pS8FEFXrykypqDt8+DBuuukmAMDC6adl3s5d0htulYYA1KGl/ARBE2iP6Q23ykIJELqkhznTN3Qa7bk6cMgCdHEcP1q53gZ0AKwxxxMjOqiVM4fOa1Nj0PEusWFYw6VuEonbsQEdb6qsIdeQgHYdY9BFnYmGYQ9Mj5aysDB/Itk8J+P7kzbRHnbt606MQivQxXE6BCO7YfWcBBBNfehQrWFXaX+6wNC+0Bx0Qn9ISFGfNwcd71MMO51h16w+OQHFyF77CedlL3miM+ya1R+GO+udi0KgPkvMqnRiAoLDT4xjqV2z3j0CAHzf0a7SpcNgZ1qlE9MOPDwyt9aoSpcOpSR3h5b/s7QAAPjxj3+Mw4cPWx+PpVTUfetb30IQBGhPTsIflQuVdAmcrn4pua+dAHbbYcVpzAJjj4YY2ROitmD3hCcB4HbttxFiV1Upz5/LibcMNI7S3v6pNv2i8lKyTpwu0JiLPvHb7A8JRDtxNI5avlEBCBoO2mPlvBTCGtAZtd9JI2wArU1d+5MwS0AQLFueqOKTuO12ZFbbdWUktHycSAh48wRuq5yO8Qq7TZ/i15vftOsToUBtKYTbthV09Dg1p314C5YnAgo0p7sY3F/OWl3Epxh+0h523jLQPGL/mvMWgYGDBM1DJTyfCBDWbF9zQGc8tH4/AcA/iLmG+8WKIQTYO9s/rKub/37qTXjFmrvxkon7rdvqBi7GLXezAIClfcO5Fwf6o6M477zzEIYh/vM//9P6eCyloY5Sim9+85sAgMXT+0uXpBudMC2qo722BNBRAuOfojELDB6IJtZHk+vt+uR240+d1BytJJ6YCkTzvUwnBHstYOAQRX3O8pMw71ivf6awc7rxkgSU/THvDgcdqxiYjig0HLTGHVAn/h12zDsV1oDuMHg10nR/2LABtE7pRvuVOjS5Ib1NbD9Zx59WiENBPNMneA90xAsRjpVzSX+0Q4vpMB74NBASAm7b/HFyugTDT9Beu4awEz9AUQfoDlrCLq76OL5NRRuoz/r8/GYMOwrU56J2HJ9i8IA57AgFGjNRP2xhx0ZFSFAC7Ch7n4IV7PhoRhmwY7MwQrstNxtT0ZsSIdQKdk784TAIHGvYDTltNJ0umk7XCnY759dEfSPUHnYBAEIRetkrP/zAi37B3/72t0u7YKI01N1///3Yt28fQtfF8imnJL5HujHm0n3WnUAaxHPyxAqd4XOzMRvNNRHxVVsK9RemDKI5IRx0FhFBx+K29GHntYD6XIxU1idxvpd2x/r7qQu7BOjY13yzal0f6Awjgo7FFHYi6GySAB1LWbCzqdalnjxGsJNU6Ejt2MJOBF3iawawY6ATXxsmsOOgE/pkCjtCkajQmcJOBJ3Ylg3sWExhx0AnngeITzG0z75PNrBzW8n/m8Kub3qKKewI0J7sf0xMYNeYWk5AzhZ2LAx2Jrh74+ZbEv+3gZ24Lh2DnQnulvYN9/5DKMddOsubTsHAwAD27NmD++67z6TLfSkNdd/97ncBAMunbEwsY8JBZ5m84Vbdah0HnXQldfV2HJ8mqnM2bclAJ7alGga6zBXwdc8JWa97jXZkoOt9Tw92uaDT6JMMdCy6sMsCnW61Tgo6lrJg5zv6sMt40mjBLmfI9VjBTga6xPc0YCcDXbIttXZkoGPRhR0DXfo8xGCnijsZ6MS2tGDHqnSp6MJOBjreVjfUhp3s92MCO7cF+eNkWbHrNaQJOwa6jPdHXdjJAGcCO0cyhSMIHKOq3ZDT/8szgR2r0olxCDWr2smefhLY0VoNl156KYCeoWxTCup838f3vvc9AMDy5s3860qgU3h+Fs6fI+qwywRdHNVqHQkgrz6KUYRdLuigPgybCzqxTyrnhIILPVTn1+WBrnebYti5HYrBg0FxhU7hZ8sDHYsK7MIa0BnPr9Cpwi4XdCzHAnYFTxYl2CnMoVtt2OWBLnEbBdjlgS7ZVn47eaBjUYVdFujE/qhU7fJAJ7alBDth2FUWx6cY2t8txF0e6HhbGrDLuxiNwU4Fd1mgS7R1WA1RuReRxbArxF0B6PjNFGHHhl2lbWjATgY6MTqwe/2mWzO/pwO7nfNrcneP0IFdokqXjgR2V88eBRBt2hCG9lXPUlB37733YmZmBkG9jta6tfrz5wpeCEoXRBTcpDELjD8a5oKO9aW+kA+7xPw5i/BlQhR+j0XDsEqgU+6Y4s0KhmFVQMeTcxOxOqc05JpzExXQqYRV56iDwserCHZKoGMpa7sVFdgpHouUdNFDmbBTeQ6r7jyQBzsV0OkcT+X1WwS7ItAlbqsyHKv4OOXCrgB0vJ2AKlXtVM4DKrBTWV2Az40rgp3K4+QXw05pVYD4Q3ch7BTPc0WwSw+7StuIYVfmcGxeXr/pVoymx7pTYbArwl3RdmCAGuyW9g3Lq3RiUvPsWuvWYnh4GEeOHCllCLYU1N18880AgNaG9SCBW1zBUozuFa5Z1TpWnXO6VO1KyZwKmzboMtoqqs5ltSWLNujyqnW6C+VmwE4LdMieX2c8f05yc7+pB7qsap3J/Lks2GmBDvExy7pwIg92Wsvp51TrNK90LQt2NOcNj1XplPuUATsd0EWdyq7W6V5ZngU7HdDx+2TAjlfpNNqRwk4RdGKyYCdeGKHUTg7sdJeLyoNdgS2S7eTATnuZp6znecY8utymcmCnCjVCaG7VrqhKJ6YIdkWgYym6gEI27JqVQtipPuTiPDvHwQte8AIAwPe//33lvmSlFNTdcks0UbE1tcF8/pzwu5ZeEKESyTBs42j+cGtW0sOwDB1GFboU7IxAB/kwrHGFTgY704pLqh1d0PXul4RdWRdEAL1lS3QrdCSI+sVic0FEGnbaoGMp84pYGewMqoHSYVjDpUtKg51kGFZl2FXapxTsnC7B0F4N0CXaSX1NYdhVljTsTEAn9kuEHaFATRNirJ0E7AxAx5KGncqwq7SdLNgZ9EkGu6JhV2k7ChU79cZSz3MCtCdCo3d3Gezyhl0z25HATgd0LFkXUOQNu2ZFBrvdC5NKVToxWRdQ5A67ZiWG3eePTgMAbrvtNv020v2zbWDPnj14/PHHQQlBd3KdXWNUvzrXF+GujaPA4CHzFdTZMGw0qRjyCyJ0u2cIOhY2DFvKkiUi7GwecqHKYAo6Fga7UkAX39V2yNXxI2CWdYUrYAE63qkVgp3F8G4CdpZr0a0E7ExBx/sUw46BzvQDrAg7U9CxMNjZgE7sl+NTDjqbZZnSsDMNg50p6Hg7KdjZrCEows4EdLydFOysFmNnsGOgszg3iEueqAy7ZrZT4pWxYtVOZdg1K2nYmS4ynL6AQmnYNSuEYnnjWriuiz179uDJJ580bCjum9W9Adxxxx0AgM7EGlDPbsXVaCjP/h2TEqA+ZwE63lCEjFKGk9nJ2/45jsZsiMEDQXLJEpuUcVFWGJ3gbEDH4rXKrdCVMYcuqBO0J+ygAkRvwv4wRWujBehYyl7DroT5esShcGphKc8pkJLOB070hmkDOt6lAKjPwvqKfhJGH8xsQMfCYVfCuYUEwMD+Vik7GNTmA+mVrrpxuyFGdi/b72LRDTH8pG+9Sw8gwM62nRh2Zeyuw2FX0jnBaTnWKGOwM6nSpcNgZwo6Fga73QuT1n3isLNdi7vu4dxzzwUA/OQnP7Hrk11XgJ/97GcAgM7klF1HOgTeEklsiG0UAlCPoj0JLE3Z/XjeUojR3S0MHrTfjsFrUQwcCVFfLAc8ZewQ4bVCDO3vonnUrrHaYoixR1sY3bWMxrTdCvG1pRCDTy6jfrRj1Q4AUIeAOuZ7S7L4AwTL62kpkxWoE+0N7CyVcRZHObBzKeqj7VI+VYMShL79VlvUd+DOeHEly37hXXe5hDdOCj79oTtcxi4PFHXLXWyA6OdrzgSlfPBsHGmD+CG8JbvXMaEU3mIX7nI5F744nQD1WftzAgmAgSMlXYwT/w5tE9SB2hxBba6ET0IU8ObKWamMhMDyEyPW7fhdF4Fv3ydCKH7rzDuwv5s9z041/7T7FzHXamK5a7+B/N69k6B1++fBs5/9bADHGHWUUtx9990AgPaUOeqcDoHbQVQZC2AOO4JoP1gA1KVWJ3FvKUT9aAekG6B5sI3BQ2bw4ZhbiCpPXsscdl6LojEbglAKp0NRWzZ/E/ZaIRozPkhA4S0ExrCrLYYYejJ6IyCB3a4ctaUQjUMtkG4At+WjNmf+xkIdgqARnSTdDjU++TLQUVdvDTR5n4CwEd3f6ZBoM/syYgO7GHSOkz/BWSkcdOyPYTMMdCVU6UiI6MMiBeBkr+yu3rnor9Azhx2h8QcNGl24ZfNBj4TAwHQQD5vaTcVoHGnD8cPosQqpMcgIje5LAhq10zJHFKEU7lJ0f+KHdrCLL9MmATAwbQc7XvkvA3YEwj6vFs/5uBsktIedE88fdbp2sOt2ei+4wHeMcUcIxevO/ikmvEUEcKxht9ipIYyenlaw27N3DeBHoxy2sPvQQw8CAO677z6r3SWsfvN79uzB9PQ0qOOgNTWhjSinQ+At9kAHIP/KzLwIoGNprwEW1+vLjoMuiN7gSBAawc5rUY458eczOfGKoAPik13LDHYcdOwkQGEEOwY6CMMibstHY0b/hMlBFz/moNQYdhx0wvnRBHYi6HjbrhnsGOj4PWnJsDN50QigA6L3PGPYiaATvqY9+V8COtNqHQedOPnbFHY0Gi4VYwI7EXT8a4awE0HXa98MdoRSOL6wUwCNznu6sEuALm7HFHYcdOJjZQq71Lo7NrDrm8phATt/MPl/Y9ilDm8DO6edfN3awi4dXdiJoONtWMDuoztfkvi/Fey6wu8qhp0J7pwOQWdsAp7nYXp6Gvv27TPrDyxR9+CDkSw74+OA6yL01Cd7suqcbF4YX79NNRLQAdHXWuv0YJcGHT+EJuwY6GQnWK+tV61Lg473icFuSf1NOA26Xlu9ix1UIgMdgOjNb0kPdn2g423pwY46BP6A0wc6sW+qkYGON6MJuz7QCf0pDXa6S52kQMebMYGdDHTC91Qf97wKnS7spKBj0YUdA53kIdGBnQx0/HuasJOBrnccTdhRoD7dDyVd2KVBJ7ajCzsZ6Pj3bCt2rJ14KFYHd5lzcw1g5w9Cep6yrtgJ7ejCLg06/nUD2IlVunRUYScDHW/DAHYf3fkSLHb6AWcCuz17JUuhxEuV6MDO6cTnTtfF2WefDQBW69VZoe7hhx8GAHTGxntfVHguisOt0ugMw2aAjjflULTWFsPOWwoxuK8lBR0/VBAqba2TB7qoU1AehvVaFI25ftDxPlEKt11csfNaIQYPdqWgY3GX1ap1tcUQg/skoGPRgF0m6HhbarBLVOcynoNuV61alwc6fjxF2GWCjt+gRNipDsNmgI5FC3Z5oBNuU4QMlSFXVdjlgo5FFXY5oGNRgV0e6PhtFGGXB7re8dRgR0LKh12z+q0CuyzQie2owi4PdPw2OrDLWR2brbOpArvCi60o4C2rvZlngU7sV21eEXY5h2SwU8FdFuj49zVglwc6FlXYyUDH24hhp4K7j+26TAo6Fh3Y7dm7JlmlS0cHdsK5k10s8cADD6jdV5JSUNcd7z2goZtfrSsEHYsK7ApAx5tyae6K/rXFuDrnh9m4iFOf7uRW6wpBxztVDDsOuoKrvgjNn8vGqnNsuYLsdoqHYRnoCq8qVoBdIeh4W/k7SciGW7NSNAyrAjp+3ALYFYKO3xClXLkIoBh2BaBjUYKdCuiE22Y9EDpz6IpgpwQ6liLYKYCOJQ92KqDjty2AnQroesfNhx0JKerTnUzQ9dpRhF3BOUEFdiqg47dVgZ3ididlzLMD4udfAeyKQCf2qbBipwR3xaqdQlsqsFMBHUse7FiVrrANOEpVu/l2vbAtFdgVgo5FAXZOJ9nOJx55BACwc+fO4vaz2jS9I6UUj8Qd6IqVOiBzGFYZdPwgObBTBB1Le0JerasthqjNZlfn+g4bhGgekg/DKoOOJQd2qqBjybpwImu4NSt5sFMGHUsO7JRBF8fpBNJqnQ7oWLJgpwM6fvwM2CmDLg7xV+HCCUXQ8T7lwU4HdCrH0hlazYCdFuhYsmCnATp+l7yzqcbE5yzY6YCO3ycDdqqg67WTDTtWpVNtJwt2OqDj98mDneb+dXmw01kSKQ92qqAT28qs2OmN9ubCzlHY45jfNgd2OqBjkcEub9g1s50c2H1s12XK7eTBThl0LDmw48OuQrqjUf937txpfLGEMepmZmawsLAACqA73L+Schp22qBjoZDufKADOiCq1qWHYXVBxw/v98NOG3S8Y+i7jy7oAGF+nQA7XdD12uqHnTboWCSw0wVd1E7/MKwJ6FjSsDMBHe9HCna6oIvutMJXxGqCjkUKO1PQSap1rEqnmzTsjEDHkoadAeiA6PeertbxKp1m0rAzAV2vD0nY6YKu104/7IqGXbPaScPOBHT8vjLYGW5ILIOdyRqXMtjpgk7sUx/szN7vpbArGnaVZSUvnjABHW9HAruP7bpMqUonJhN2OqBjkcBOBjoA6I6MwHEczM7O4siRI/rHggXq9uzZAwAIBgcBN2v/yPggpqBjzbALJ0g8lGq4kKEIO2/JDHS8TwLsvBZFbVFtDoss4oUTJqDjfRJg57VCNGYD89XzBdgZg45FGF40Ah1vpwc7G9CxMNjZgI4n7ocR6FgY7OZLhp0h6FgSsLOt0Amws126hMHOCnQsDHaGoGMRh2F1hl1lYbCzAR1vi8GOwgh0vXZ6sDMBndhOX8XOYhmHBOwMQcfbCuJ1/2AGOt5OGnYW3UrAzvxh6vUrhp0J6FjSsDOp0okJfMcKdLwdAXYmoGNhsGO4k14YoZo07LLOn66LjRs3AgCeeOIJo0MZP2XZAf2hoczbhG70BLIBHYAeCspYG9Wl8AeBsEGMQcdC/BC1+QC1xfw5X8WdApqHu5h4pGUMOt4nStGY9jH0RMtuNw1EJ97mwTZGH56zbstt+Rh6sm0OOpb4xB/W7EDHmyNAMJA/D1SpHQcIGtQcdLwhxDuPlDOsCYeCNALrFd0JifZudL3AfsiVEtBueWvRAbADHWuDAt4SrM8zoQd0h4gV6FjcDsXEQ0tWoGMhAcXg43PGoOPtUMDpBqg/cdTqvCDCzilhkWLih2gcsdttgMXxKYYO2C3ADPRgl166xKitAPAWynq9AO6iYw1EBrulmYFS+uX7jhXoWAI4eKi10Rh0LGwtuz17NIddZYlhl55Hlw5DnemyJsaoY/uT+UPZm9iSkA1NmR6FNRT95fjR3CObeIsE9TmK5QkXrfV2T0RCgdp8B42jlrsoLARoHFqCN7OMhmRpAZ14SwHqR5bgLrbhzdu15S758A7OgXTKmEBMQYLQepumsFlDe009btOuLb9B0Jp0ov1qbZcQIEBYpwhL2igCFPawIwBxowpNp21f/SMkWqDYNtQn8A7X4FieJCmr3LtAd8gSrQFQm+tNKreJEwDNmbKufAHcpS4ahyyxEgIDe+ZBugGcJcvlQCiFe2QepFvCeYEC7vQi3Fn9TeP72uoGIN0AjcP2bbntINpWbJ/tzhpAfZFieG85zwdCo/cw21Avrrpavp8CAG2EQEDQXbADFABsmJjHtw8+w7qdWX8QXeriss0PW7c1vX8M8J1S5g87reItKzds2AAA2L9/v9kxjO4F8PHeoNmUfp+dHCkBgiZFaPr7ZiM9JcDOWyRozNAeNj3zXxKhiD6FUwpvoWu8x2FtIUD98BLfkcGdb6FuiERvKUBtehkIKBACTqtrDDt3yYd3aC6CWLsLb878TUVcjoW6BHDMnnZhs4b2ZB3U6Q1DmL4BM9BR164dABHovPhnJHbDuNSjCIbYAswwhx0BCKvOUQIaECvYMcw5DoXXtNjpwyfwjtT4+pSmjzsDHR/29qgx7ETQ8a8Z9ssJgOYRYRs/i6FAJwCG9ixF/+4E5rBjoAviToWhOexi0MEPev+2iDO7FJ1HwxDOfDlVNlvYue2AD1XbwI5QwFuORl7crj3sGAZsYSeuBEFgB7tgOOxNeg+B7qJ5BWfD5BxqboBlv2YNuzA+MYy4Lbx40w7jdqb3jwHiaIIF7Jxltzf1JGeFgs8/8iiAY4C66elpAEDQbCS+TsIYXsKiwsawS4GOxQR2IuhYWuOOUbWOg47FEHYi6HjbhrBLgI5FeL3pRAQdAIBSkOW2Eexk6+uZwI6Brg/iBjBIgC6O2zGs1omg418zvODCo9FJUhwqNYGdCDrejjns0tU5zwuNYJcAHf+i/u8vDTr+dQPYyUDHv6fZrz7Q8Yb0n1cMdKTba8wIdmnQ8a8bwE4EHRCNSwWBMew46MQ+GcJOfJzY/01gx0HHYgg7DjqhLbdjDjvqIPF8L6tiB5jDLgE6/kViBDsGOhYb2M2mxrrHvGUj2PWBjsUAdiLo+AdRl0pxxwplzFjaxzK6l3DAsNGr1PGhi/hTjhht2GWAziQy0AFRtW5pSm8Ytg90vLEIdrV5tW0ZZKDjx9CEnRR0rK22XrWuD3QsBrDLWjAZ0INdJuh4Y+pvwDLQsTa8libsZKDj39NcGkUGOqFvyrCTgY63ow+7rOFWXdhJQce/aVAZy3oqeBT+oBrs8kCnGxJmgI7fQGPJCAno+Pd0YJcFOv59DdilQcfbMINdH+jEPmnCTvY4sa/rwK4PdCwx7Ib2qz3fZaDjxzCAXRp0ieNowi5rvVZd2ElBx7+pB7s06FhMYDfrD/IqnRgj2OXN99WAXQJ0LIJt0rAL6xGS5ubmlI+ROJ7RvYQDsg4kQJcRZdgpgE61WpcFOt4nDdhlgo43RlGb7xRW7PJAx4+lCLs80AHQGobNBB2LBuzyQMebU4BdIeh4Y8VvzpmgE9pQhl0e6PhtFBcxzgOd0LdC2OWBjrejfrVb0fw5Vdjlgo7fSA1XvEqXk7BWXLFTBZ1Kn5wAGDicAzreWPHzKg90/DaKsIuuVC3olArsskDH29CDXSboxD4pwi7vcWLfV4FdJuhYKOB2iit2eaDjx9KAXRboEsdThF3eAvyAOuxyQcdvpAa7LNCx6MAuC3QsOrCb3j9WfCMF2ElBJ0YCO2aq2dnZ4j7Ijml0LwDLy9ELJfQ8JdCx0KLVEDQqdEWwKwId75MC7ApBxxvLH4pVAR0/ZkCzt+KCAuhYFGBXCDoWBdipgI43lwM7ZdAppBB0vEMKsFMBHb9twXZjKqAT+pYJOxXQsWbC4mqd6gURRbBTAh2/cT6isoZdpbfNqdjpVujybpc55JrZWHbnVUDHb1sEuxBoPqFYPcuDXRHoeBtqsCsEndinAtipPE7sdoWwU3m6F1Ts2BI2Ki8dFdgVgS5x3ALYFYGOt4UC2BGoz+kpgF0R6FiW/Rq+c+jc3NsUgY5FBXaZw66yFMFO5aGKvcNgd8wqdQx1cDxl0LGEjYxqncGQaxbsVEHHkgc7ZdDxxuSw0wEdi7vYllbrlEHHkgM7ZdCx5MBOB3S8OQnsjECXgQJl0AntZMJOB3T8PnLYaYFO6Fsf7DRAF7WRPwyre4VrFuy0QMfvJP8d6oCOJaz1w850yFV2e23Q8cb6fwgd0PH7ZMGuaNhVFhnsVEHH28iHnTLoxD5lwE7ncWK3z4Kd29ZoK67YpWHHQaexHFUe7FRBlzh+BuxUQcfbQjbs+EVcqsmAnSroWBa79UzYqYKOJQ92WqBjyYCds6w5sZqweXbR/Tods4uZjFAXBEHvgE5BeVES6TCsxRy6NOx0Qcf7JYGdNuh4Y0nYmYAOkA/DaoOORQI7bdCxSGBnAjrenAA7qwpdCgXaoBPa6YOdCej4fZOwMwKd0DcOO13Q8TbksDNdsiQNOyPQ8Tsnf4cmoGMRYWc7h068nzHoeGO9H8YEdPy+adiZgI7fV4CdLuh4G3LYaYNO7FMKdiaPE7tfGnaFw66ypGBnAjp+fAnsdEHHIoOdLuh4W+iHndKwqywp2OmCjkUGO13QschgZwQ6lhTsCodds0LiNVgBdLtdo63CjFHHQg2LfQnYlXBRBIOdKeh4vwTYGYOONxbBbvDJZSPQsYiwMwYdiwA7Y9CxCLCzAR1vziUIBuv2Q64xCoxBJ7TDYWcDOpYYdlagE/qGkJiBjreRhJ3tGnQMdlag430DXxLJFHQsYY3Cb9JSLoogYQmg440RK9CxcNiFwMBeQ9CxhCGcxbYZ6HgbEeycmYWof6agE/sUw87mcWL3Z7AzAh2LMMfOFHQsIuxMQcciws4UdLwt9GBnDDqWGHbrJuaNQMciws4UdCwi7KYPjJqDjiWGnTHoWNg8O0p7I6IaMRKZkxgqs6jOkGgV/iCGnW2CQYpgwP7ETV2gPeqgO2q/mCIojRbFtF3FPaDwZpZQP7BgDjqWMHojcJfMt0njoTTC2EA5W1tRl5Qyhy6oE7THLUDHOxSdy8paVJi6FOEAtQNdmaEEYddBa7ZRfFvF9pwFr5QrShEvj1TGuQHE/rwARG3UFqg96IBoD9ajvjVUAMBp+3xxYesEoTnoWOLFxjtjdTvQsVCK7mg5z1HSDVA/2rZ74wX4HDur3YTiuB2K5hG7Dy8sJAQaM2W8aFh7xA50LCFBw7NfsHqxW8drRx60Ah3LmLeMuhdEiwuXENK136VDjMkQrP1PYvsDkHglfksTdEcouuMBWut9LG20+2W7nehF1hnz0B2zPJEEFLAEHQAgDIFOF6TVsV/J3SWgLgH1HNBB+eLRyt0aGkB73RCChmsNu2DAw/K6Oi8/G7fTIFiejFbutn0jDxpAe4KWclKjDkBrcTu2nwoJAJdav1/SkADLLtBxsHzU7rngd134R6I2wrptx6LfHQkAt2X3WBGfoDFDzBdAZ+2EiLcERPRB1KqtGHSUwh+xPMdQCtLxo/OCLTAIQWfjKDpbp+za8VwsnzGFsO6gfeqEfZ82jYF6BN1J++2oqOeA+CHcJfttwECB+lF7qPhNAscHmofsXzduJ3rdNA7bvW4owJHpzpfwqdYn2LlrvXUznzzrXzHm1PHm8Z9at3XjwbPhEIqJU8yuNBVD/GhlAWo9otNjmefpv6faV+osKg78fdISdt0Riu5EwDcwXz7FHHZuh60CHlUSrWAXxFU623feMAS6Pv9ESLq+OexizMW7tCNsesawC4cG0N4wFLUHWMEuGPCwtK6O0IuqYqaw46Bj56CCKyrz2wLak/IFInXDQUdYn4g57GLQsReQ6dOLhgRoOSCUgFBiBTsGOjZcQ10L2KV+ZzawIz5BY5qABNHvwBR2DHSg8ZCwQ4xhx0EXApQQUI+Ywy4GXQJzprAjBJ11wwg9grDhmsNOAB0ABE0L2MWgC2vxfNuaYwU7fu5D9HsoA3aOT61g5zcJ3yXH7VrALgYd4gsXHd8cdiLo2PnKCnZBdI4hHccKdp8861+x2YteK5Nuwwp2Nx48Gy0/er8aqHetYMdAx2IMu9SUr4EB/ee6Merq8WW3IL7REFdf4cMQdgnQsRjCTgQdizHsVgh0LEawE0HHGzKDXRp0LCawY6DjzyNiBrs+0LEYwG7FQJfokwHsUqDjzelerCSAjjdtCLs06PgxTGCX8bsygR0DnSO8TExgJ4KOt2MIOxF0vbYMYScDHYsu7ATQRX2CGexSoGMxgl0KdCymsOs79+HYw04EHYsR7ETQCV8zgV0CdMIXjWEXg47FFHYi6FhMYSeCjsUUdmnQsWjDjrL2oudRo9GA6+o/3sbDr0NDQ7wD1NNbPT9zJEsTdlLQsWjCTgY6Fm3YlQU6IJqbknGC1oKdDHS8IT3YZYGORQd2wYCH5bX1/uePAeyiSfVZ31SH3YqDLtEnDdhlgI43p9hdGej4ITRhlwU6fiwd2BX8jnRgJwMdP4wG7EgIeEtUftLWhJ0MdL22NGGXBzoWVdilQNfrUwS77hZF2GWAjkULdhmgY9GFXea5DyhlThygDzsZ6Fi0YCcDnfA9HdhJQSd8k+huWZgCHYsu7D5+1hf6QMeiCzsZ6Fh0YZcFOu0IbTjxe/rg4GDGjfNjjDp2QNLtRic3TdhlRhF2uaBjUYRdHuhYlGFXKuhCoOBKNiXY5YGON6QGuyLQsajAjoEu83etAbugQdCaKHg6K8Bu1UCX6JMC7ApAx5tTfR/PWTRTFXZFoON9UoGdIrpVYJcHOn44Bdgx0BUuiKwAuzzQ9dpShJ0K6FSTAbpen4CgqVCxKwAdixLsCkDHogq7wnMfUM78OqjDLg90vE8qsMsDnXAbFdjlgk7sl2q1LgN0LKqw+/hZX8BWL/8Fpgq7PNCxqMJOBXRK1brUTVilbtVRNzw8HDXQjV4MqrBTmm+uADvqQG0+XwHsVEDHj1kEu7JBJxl2lSUXdiqg4w3lw04VdCx5sCsEHe9TMewyh11lycEDuyhi1UCX6FMO7BRBx5vLuRmr0hWlCHaqoOPHzYOd7vB4zs+nAjrejCLsCtspgJ0K6HptFcBOF3R5tysAXa9PBRU7RdCx5MJOEXQsRbBTPfeVNQwLFMNOBXQsubBTAZ1w2zzYqYJOeRi2AHQsRbBTAR1LEexUQMfSqOWfQHQqdLmwk3zLia94HRkZUTtA+v5G9wKwZs0aAIDb6i0MyWAXNuS407qAMAd23REKf0zjkvsM2OmAjiUTdscIdCy5sNPYTDwLdrqgY5HBThl0vE/ZsNMCHYsEERx0tlcuQRN0iT5JYKcJOt6cbLgwZ9hVlizY6YKOH18GO4P5jiSUV+t0QMcPnwE7Puyq2k4G7HRA12srA3amFTrZ7RVB1+tTVLHrg50m6FiksNMEHUsW7JQ/zLLDrwLsdEDHIoWdDuiE+0inIwBG56pM2CmCjiULdjqgY8mCnQ7oAOReEWsy5Nr3vkKR2QYz1dSU2YVKxqhbu3Zt1IHU4nhsb9d01c5oRQgJ7JSGXWVxKZY3pmBn8IYCxLAbFWB3jEHH0gc7VqXTbigJO1PQsYiw0wYd71M/7IxAx5L+3RODia2yZk1Al+iTADtD0PHmxIn9mqBjScPOFHS8HyLsDF9/QP8wrAnoeJ9SsFMZdpW2k4KdCeh6baVgZzvkKt5PE3S9PqVg57lobdMHHUsCdoagYwnd1HxATdCxrCTsTEDHkoCdCeiEiNU6bdAJdyQhgbuQOvlqgo4lDTsT0LFMuskPQz84dJYW6Fhkw7A2c+j4CFDB/RnqWOFMN8aoY4p0l+V783HYOcbvSVEE2BmDjsXrwc7tAF7LfBEz6kSw80fqxwXoWDjsdIZdpQ0JsDPFoZCg4aI92TADHe9TD3ZWoGOJURE0gPb4MQad0CdQWIOON0fNQcciwo5SYgw63ic32uXBeneHGHY2oON9imFnCjreTgy70IMx6HptRetJljaHLqTGoOv1KQm7oGF5Xmg6aG2dsAIdAMDprWFndd7DysDOBnS9tmANOnEY1hh0QlskEGBnCDoW0nGwc/c6K9CxsGrdDw6dhaVu/96zqhFhZ31RBIHS1J7/uu1MAMcAdevXR6r2lpYybxN60QbAtgt1ggDdsdAOdCweRXsyRHuc2O884QCtNTW0NwzZNQQgHKihs34E4XgJbY0OoLVhyOrEBgAgBMFIA501ZhM2xQRNguU1HvyGbZ+A7iDB0toSdosAENaA7mg5w64kICBdy58PMaICYg06IHoKEDe0OtnyPnUc+HP2u6xQAoRNCn/I/udzurAGHQsJgcas3nQMaTsBxfCeNrwl+05Rh8AfbZZyUQSteZjfPmG9YwslgD/kYfbCDdZ9AgHaEx5aa8zfeFmCuoMjF4zbn/cQvfT8wZK2kXEImjP2O30QCgwcpOagY2HDsPYPU6+9Jdf6HAMAgxPL1qADomrdhLdoBTqWgXoX7kjXDnRxqEdBG/kNPfnkkwCAjRs3Gh3DGHVbtmwBAHjzC9k3IhRwgHDADnb+AEUwGgC1MK5emLdF2g6cDsHyOorFjXYvWuoS+A2C5bU1tDYOG7cTDtTgj0T7nXZHGwgmzdsKJoexsGUQnTEP7TWWu0XUXPhDNVCP5M++V0hUdYgrIq75LzCoE7THCMIarFEX1oHOWPRzOZbVJxYSEJCOzRO01w7adj8gIYBTC+F4FHSwBPXE/bIBMOUVyKiqaQO7qKpWEugCoHmYRtW/rnmfHJ9i5LEWnLYf7VrQsu9cWHPQXWt+TgAY6MbgDxC0Jyy373EIlqc8dAcdtMcsnqME6A46oI59FYs6BPOn1tAdIpg+12yCea9fBK2pZgRqS9iFdQeURM8v25CQwu1SNGYtdUGA5XVlXDkN0LjIQgKAtO1+h831i/DcEK966Arrrv3H4hRqJMCvnHK/VTtH5oZwZG4IjhOCTOhv2SWG7WFNCwpTjz/+OADg1FNPNTqOMerYAb1WC6TbX6qmLgBxMVkb2KWvdHXMYEfaDtwlB4QC1AOWNpjDjroEQQ1xSRVWsKMO4dChDoxhF0wOY2HrEMIaiYaHLWAX1lwEg140/IO4aGQIu6BB0BmOn2rEHHYMdAxz1DGHHQMduz8JLGEnLq5pCrv0NRIWsGOgY3HroRXsKNu9glDj+Yci6KJOmsOOga6UN8sAGDhE4fjCnCUD2HHQdXqdsoYdQTwFxTWGHQNdEFfJw5oF7GLQsddv6BEz2AmgA9g8ZcNzsUMwv9lDUI/asoIdA51H4nOVOewY6KJOUgwcMX8eiFNyHN8CdjHorEc5JDixqXA31y+i5kWvm31zo1aw+4/FKRwNohGvqdq8MeyOzA0hDByEQfTe5boW07VSxaisah3pdjE9PQ2gVzjTjTHqRkZGMDk5CQCozc8nvkddAOmTviHs/AGKcEhy5taEnQg63k9D2Img633NDHbhQA3BULJEbAI7EXRiO51RVxt2adCxmMAuaBC0R93kScQAdmnQsZjALg063i1T2MkW19Qdis24qQns0qBjMYUdTe9eYQG7vp9TcZ5J4i4rCToWA9gRigTo+NeNt+1K/tsEdmnQsRjBzommUKRft9qwS4GOt+MSbdilQcdiBDsRdEJfTWCXAB1ryqdoTus/aWXPHyPYrSDo+LcMqnWNdUscdCymsPvG4hoOOhYT2DHQpWNSrUuDDogePxnsavHI5+TkJF82TjdWs1y3bdsWdWR2jn9NCjoWTdj5AxThSM48Oh3YZcw514WdDHS97+nBjg+7SnCjCzvqOdIlP2h8olSFXRboWHRgJwUdb0gPdnm7RejALgt0vFu6sMtbXNO3HIrlfVKHXRboWHRhlwZd4usasONVOlkcKFfrVgV0LBqwc/xoHp00IdWv1smeNpqwywId75YO7BjoshYpVoVCBuh4nzRglwU6Fi3YyUAn9FlneFgGOhanG2rBLu8DgRbsVgF0QPT61IFdY90S6hlrwunC7huLa3AkkL82pmrz0q/LkgU6IKrW6cBOBjr+PQnsakePAgDOPPNM5WOkY4W6c845BwBQn57pfbFocrci7ApBx6IAO9J24C5n/6gMdkfOdQtxR+PhkMzvK8IuD3S8LUXYBZPDWDolZwHO+ES5uGUoF3dFoGNRgV0u6HhDarAL6gSd0fzbqMCuCHS8W6qwU1lcU2UoVuFQKrArAh2LKuyyQJf4vgLs+oZd01Echi0TdI5fADoWBdg5PsXI42047ezHVGsYNu/5oAi7ItCxKMGuAHRANGe2sFpXADreJwXYFYGORQl2eaATolKtywMdiyrsVCq8hbAjQGsdRWvtyoOO30wRdnmgYzkwX4zybyyuyQUdi0q1Lg90LKqwywMdv03q8fxvm6Jpbdu3by9sPytWqHva054GAKjHukzMo8tLAeyUQceSAzvZsKss1AOCQZpbtaMuUVqOowh2KqDjbRXALpgcxuKWocKTG3VJjKOcqh1BIej4TXNgFzQVQCceMwd2WcOuslAn+j1mVvRyqn193SqCnc7imnmw0xmhzYGdKuhYimBXBLrE7XJgVwg6lgLYlQ265mEF0LHkwE4FdCxKsFN5PhTAThV0LLmwUwAdbydvGFYRdLytHNipgo4lyPocSwhaaweUQKcyDKsCOpYi2GktPJ/VDAFaa6PlwIyXkRLaUgEdv3kB7FRABwAhJXj1w6/K/D7DXBHogOJhWBXQsRTBTgV0/LZCte6hhx4C0CuYmaSUSl1tbh6gfvawqywx7Loj/bijLvSXLpHAThV0iWNnDMfmDbtK28mBnXhhhFJbGbBTBV2yX3LYhbXivVrTkcEuaBK0RxRBxxuSw04HdCyUyKt2YT1aukQnmbAzWVxTBjuDkdkyroplyYKdKugSt5fAThl0LBmwO6agY5HATgd0LLmw03k+ZMBOF3T8frLzkQboWKSw0wQdb0sCO13QsXb6qnVCda4QdPw+2bDTAR1LFux052ASKqnWCaCzjibo+N0yPmOqgo5l7+yYFHYq1bl0smCnAzoW2YUT1KVaoAN6w7Ck28WuXbsAHMNK3dq1a7Fhw4boSTVzRL8BAsBNVu38AQo6aHj2TsMuYx5dUdKw0wUdb8cFWlM1zD1tnONOdmGEUlsO4I/UOexMQNfrVxJ2qsOusqRhx5Yu0W8oCTsT0IkRYac67CrtVhp2NotrihdPWEy1S8NOt0onJg07XdAl7ifATht0LDLY0WMMOmlb+qBjkcLO5PkQw86fis4JpqADotdLolpnADqWBOwMQcfbcgk6I/F52AB0LN0hgpmnx7BTHG6VRgI7E9CxpJ+LphfVJIZhjwPQ8bsL1brGuiVt0LGkYWcCOpb0/DoT0LGI1TqOOYPnAnUo6nNHEAQBTjnlFKxbt86oP4Al6gDgwgsvBAA0Dxw2bySu2nUmAr1hV1kcCrgUpJs/j64oDHbzm10j0LGEXrQ9TGuqhqXTRpWHXaVtub117KjnGJ3cWBjsljcMGoOOhdDoZBTWhKVLjBqKq5IDdqBjoQ4QDJiDjnfLdrkTsS2/pAWKY9jZgI6Fwc4UdCwJ2NmsJynAjoSAt1x8l8ImgxJAF1frbEDH+yPCzubpQKLdWjobR41Bx8KHYS1Ax9vyCNrjrhXoWKhLrEDH0hmOKnbGoGMRYGcDOgAAja6IJSG12kkIiHevmKPHDeiA3jAsw5wJ6FgY7GxAx/Irp9zP16AzBR3QG4bVrc7J8s5TogtPL7jgAqt2SkNd48Ahu4bYJ3vbHSMAuAM+BjYswJ+wW/iTekB3JPqUZ5ugFp0o2xN2K1xTB1jeMIj50+x3eaAuQdAk1lv9ANFq7u2xEnZ5INHjLttkXTdhDegOlzBBGEAZq4kDEXjC4QC0brssPKI+lbDrBAA4XljC2SBOQOxXvY8/8brL9lU6EgK1hXKeByQEGnOhFegAgLoOuiM1431TE215BAub61agYwlrBK1JO9ABiCp0AwSdkRJ+PgK0Jlwr0LGENWB5qgTxxH0qYRMFBM1yFtCmhMAfUpv3rRKnjJ3SCBAOhmjNN4pvq5DXn3KbNegA4NGltdi29ogV6BIp4Xnws5/9DEDPVKYpDXX16VmQrnwfWJVQl2YveaAZh1A0aj6G1y6iO2n+anFbBG4L8AcJuoP2JznqAJ1hB91R81edP+RiecpFd5CgO0AQSJYxUe5SEFUdogtAzNsJ6g7a4479GwGAoA50RojVwsJAdPL2h6K9h21BRp1or9IIGpaNEQBetDOKDeyoQ4FGCFCCMLCshIRAuBw/J20KrZQAPokrtwSwwBjpEjQOxwuF2/QpBh0J420LLeBDaG+/6M6E+ZsUdR10R2txBQpWe55Sl2BxfQ1BnZSCHkqi/ZWt9rIm0esYjt3vjrUVxhc/1RbtXntuJ34e1IDWuJ3w22PRElKtNXaC6o64oE60ZaXV3sUkWiEgdIHmYbvngePHfQkB12aXCBJtEwqHAiWMdPy/T7sOZ9QO4uz6fqt2bpvdhsWgjtG6uVdYgsCJpnkN2H3ydPxlfpHEMUfd1NQUzjrrLBAAA/v3ay8kCsSg88JStOsO+BgYiMa5a25gDDu3ReAtAE4AgESwa004RrijhPCTmw3s/CEXrQkXodu7ICCswQh2JIi2nAFl88/MYUddlAe60XJ2jGCI5jF8P0iALm7XFHZRlY4NuVFj2HHQERpNZwzNYcdBFwr3NzgriKDjXzOEHQMdf5NL/y5V22GgE/pgCjsGOtZWWHeMYCeCjn/NEHYMdGGt147VlIy++cgGz3MGOuH1YjzSEQOTteUE1Bh2DHQsNtWs9ljvA2xQN4cdAx2L6Y4MHHTsc1nHHHYMc7zSbgo7EXRx5mbMRpf+36ddx0EHAE3SNYYdAx3LmRvMRhiDwOGgAxDtr20Ku1qIgb0HQSnF9u3bMTU1ZdZOnFJqj7/wC78AABh8/ABQC/VhR+zHo4EIdEODbbhO79VhDLswBh3vYzQsqFu1k104YAq76KrZdPv6sBNBJ/bJBHZB3SlliCUNumS/9NoKa4A/KHkO6l7wmAYdiwHsqEcRjvjJarQB7ETQ8a8Zwk4KOhaNX6kMdPx7mrDrAx3/hh7sZKBj0YVdGnS8HU3YyUDHv6cJuzToxHaMLp6S3UUXdmnQsWZcA9ilQMdiArs06Fj7JtU6EXQsJrBLg45Ft1qXBh1vxwB2HHR9B9Hrkwx0AICuow07hjkGOpYm0R8bToMOAEbrLW3YccylLzg22UasFk17uQKjAIAXvOAF+m2kUirqmnsPgQRBBLtGoIS7soddRdCxMNg52xaUcOe2CLyljG8STdhl3IzBbml9TQl3/pCL9pj816ULOyJ5QrI+6cCuzGHXaI08+be0dowQh12ljen1KW/BXFXYSUHH29GEXcZxdWGXCzoWLdjlHUzxeZkFOn4DTdjlYFK1nSzQsejDLvuxUJ2flQU6/n1N2OUeV/X1kgE63owO7DJAx7+t8d4pBV0c3WFYGehYdD5UZ4EOgNYwbBboWByNHa0yQQdEFwipVOsIEAyHctCxdNVfwGJ1ThbVat1ts9ukoGPRGYYVq3OyKFfraiEHHen6+OlPfwrgOELdmWeeiQ0bNsAJQgw8fiB6MToorNqt1LCrLDU3wGCzU1i1Swy7ZkURduKwq/T7DvgVo3mwE4dds4+lBjs2jy6vT9FCxflYW4l5dHkpWlgYUAAdb6y4T9QBwqJ1FwniZXRyHs880PF21GBHHQrk3EYVdkqgYyl4LFmVLv82KKzWFYKO37AYZKxKlxfqFFfrikDHogI7VqUrSlG1rgh0/HaKsFOBZGG1rgB0/Fgu8s+Z8fy5PNCx/qhU6/JAx6ICu/aYkwu6qFNQqtZ1h7NBx5sqgF20e4eTCzoWlWpdLuhYioZhxepcwcWOKtW6d59zfS7oALVhWIa5LNCxqFTrikAHKA7Dxphj59aBxw+g1Wph48aNVtuDsZSCOkIIXvKSlwAAhh7dK3wD+bBbwWHXrBQOx6aHXbNSADud9dqKhmNlw67yY+bDTjbsmtUf6uZX7VZqHl1uv0h21U4ZdLyxnG9lDbtmJat6pgI63kY+7GTDrtLbMdj5ctxpgY4l4zHNG3btu23OMKwy6PgdsmGXN+yaTt4wrCroeFs5sMsbdk0elL2GMyryiqDjt8+BHSXqlcHcYVhF0PGmvAzYidU5lc8aBcOwKqBjyYMdw5zKua5oGLY77Krv2JPRd7E6pzIvsGgYVgl0/OAZX88abs1KwTDsu8+5HmfXDyg1lTcMm1edS6doGFYFdCy5w7AMdEJe2Ym2+HzJS14CQuzfU8taxICjbuCJQ3CWhY2tM2C3GsOuWcmCXe6wqywk5wIKzd9NFuzyhl2l7WTAThV06T7JYLfS8+jU+pX6oubQXNSQvG0t0AnH79MNgd5znMEute6cKuj47SlAQ9JXtTMCHUvqsdUBHb+PBHbaoON37P9964CORQY7XdDxtiSwUwYdP7gcdrqg4/eTwM5oGQ4Z7DRBx5uSvHaLqnOyZMFOB3QsMiAVVuckyYKdDuhY0q+JouHWzHYyYKcFOgCggCPZFUcLdCySYdh3n3O9FuhYZNW6n8ydrgw6FtkwbPqCCNVIq3US0DmtDm6//XYAPUPZpjTUnXbaadi+fTsIpRja9WTymwx24jy7Eqt0ecOuWZHCTrVKJ4ag7wKKomHXrKRhpzLsKm0nhp245EnWPDqVPomwW615dGr9iv4d1gB/wLAPqYtFjEDHIsAucaWrVhsU8HoVO13QiZEOx5qAjsVhXdQHHYsIO2PQ8cZ6sDMBHYsIO1PQ8bYE2GmDjiUFO1PQsYiIs1soV4CdIejYffmHYEPQsaRhZwI61g+xWmcCOpb0B2oT0AHJYVhT0LGI8+vEJUu0+xQIsDMFXRyxWscwpws6oL9a95O50zHvmy05JFbrsi6IUEmiWifMn0tn6NG9CIIAZ599NrZu3ap/IElKQx0AXH755QCA4QcfR99m7wS9eXaN4JhV6cQw2JHTF+EPhXpVunTYcOyQ3U4IIuxUh12l7RCGlOikabOSPoNdd9hd1Xl0Kv0KGoA/CKOldHoNxX8rDv3khgC0pjHsKm0jrtg1A2PQsXDYdR2ErXIWXTUFHW+CwY7arc3F+wNz0LGEXlRlsQEdC/WIOehYYtgFDQdL6zxj0LG2gjopZaFcBjubXXaAeBh2iFiBjoUhzhh0ccJatKCwDeiiDkXz67rDrjHoeFMx7GxAx9I8TPqXLDHpU7zMlw3oAPBhWJPqXDpn1/fjJ3OnW4EO6FXrTKpz6ZCBoG/+XCKU4ln7FwEAr3jFK+wOJqRU1L3sZS9Ds9lE/egCGvun5TdiuCvhyLXBLsZGluGZXErM2nCDaLjKpdobvvelYJ6daqgDtEccrWHXzLZIdDGG37RrizrRp+v2uH2fQjeqjNicgMV+WYFOaKfwwgiVdlwgbJbwgxEKUgtRGyhhWXdC4TV8OAOWgqKA03LK2Ye1S9CYLuEkQKOV760/aNDog09Qwg4PYY1g/rQBqzdzAIATVXmsdzOhdh/qEiHxhzHLh4kSoDVJsLzW/vFmF06Ucj4hJc0VrpFoizTb5wAAULt19Vi8JaB5qISfbYBGHxDK2P2pHlqDDgDOrM3hd9d93wp0ALB/cRRjI8v2rzkAg6Ot3NdJY/80HnvsMQwMDJQ29AqUjLrh4WHeuZEHH8u+IfuYb1EZqQ12MT66hLrno+YGVrALw+hh6IyHaE/YVbRCL9pvtD1hjrvoxBK/kG0/xdKoPb9J4A+Y/7q7Qw6W1jvoDgGtNQTdYbOOhS4QDBCAxKV8C7NQFwgbJUGsFl1koTWBXNZOM/70GpJouyzDEJei3uzC9ULUmhZ7jDox6NwQXi2AM2z4gDPQscKh5Sf9+owD4tsPB7rtqJpBXYvdIijgtSlI/OZp8zoJPYLlCRfdoWhbQOMQxPumRlNC3Lbh85xG+9USu4Iv71N7NKpi2S5y3BkloF5UaV+esn8bcgJqXfV1/HhWkC0O48eZOoBfwtZthAIDR+w65fiI58TZ9SUYiLfbIwBZNh+ScgZ9OIM+CKF414O/adWnzd4ymoRgm2f3w+1fHEU3dFD3fIyPLxq3MzDUwcBQB64TYnAse+Pq17Wj4efLLrsMQ0NDxsdLp1TUAcAVV1wBABjcvR/uQs5O3IQmcacZxw1Rc3slA1PYLSw1EMxHYxvUoxHsxvX7I0bEnRHs0ti1fOMD7GEXuiQaAmLz9YZgBjtxHhQ1hx11gaCpcbVrTjsMdImvG/xofZ9eDWHHQOc4FIRQuF4Ir6FfHuOgi/tECLWCnfhmZwo7DjrhxzGduM9Ax79k8VxI7zZg8joJPYLWuAMafxgLGjCDHQOdUOUxgoYAOt6OKexi0Im7vZgucsxAx9otY59nACChOezS9zOGXfrxLWPIG4Br4RUGOpbmEcO5ggPJ/ZNNHyNn0AdxKIhDQQEcOWq+lysDHcsfnvIdo3YY6FiaNbMnEsMcmxKWZRJ3fgk/+MEPAPTMVFZKR91ZZ52FZz3rWSCUYvS+nf036LtCUB92tcEuRof6r1QxgV0YOok3XupRdCYCLJyqV7WjTv+bCnX0YZdZKTLxU6r7DHbtUVfrTas75GB5Xf9VT7qwC91o8+p0H3VhVxboeHsZ7ehgg7oAbUiee5qwE0HHvxZjTLtiR5BoR2xLC3Zxla6veU3YyUDHD6Hz/JaALjqAQbUurtKlowu7BOgS/dGEnQR0QDzNUqdaJwGd2JZWUqDjhzBY5DgBOuHrZVTrosb075IFQW20yK6mJ+VU6wD9ah2/ICLVL5NqXRp0LLrVOga6dP7god/Qamezt9wHOgBG1bo06Fh0q3UMdH1fH+23yjvDSQRBgIsvvhhnnXWW1nGKUjrqAOD1r389AGD44T3J5U2yojkcm67Siam5ARo1Xwl3YpVODPUowmZoPRwLGMAu7zHQHa7OOMmEnnrVrjvkYHGDI5+orQE7NuwqA5Qu7NhFILZhVbqiYym1w4ZdZVGEnQx0/HuaFTviUHh1+buVFuyEYVdpW4qwywMdP5TKczsLdOzbOsOwbNg1a8cBRdiFXrQYbBorADjsWpMKb34ZoGNRHobNAZ12MkDHD6WxyLEMdOwYZQ3DEqperSMBSpkfCiAXk2UNw7odddhxzGX0S6laRyLMZYEO0INvFugogMMzI8rtMMylQceiWq3bvziaCTpAvVrXHOygOSgHHQDUvOSTzFlu4xvf+AYA4HWve53SMXSyIqi75JJLsH37djh+gJGf71a7k+VwrBiHUKWqXbpKlw4fji2AHZuLlff9YABoj+fPs1Oez6Xyeix4sakOx4YuyR8eUYVdAcRUYbcS8+gKb1vwoylNGlaEnQx0LAxjRbBLD7vmtZULuwLQ8bYKYEf8YtDxQxY9jRTmPCnBrgB0/GYFzw8GutyrU+PXWi7sCkDHUgg7RdApVesKQMcPWfAY5YJOONZqDsOSIPmWk92WwgEVH8syojIMK6vO9d2mUwA7Eo+GuCj8/RdV6/j8uYJzpEq1TladS0elWscwlwU61TQHO/DcsNAaYrXuTxqb0W63sX37dlx00UVWx5dlRVBHCOHVutEHdoN04jcOpaXn82GXNfQqvW0O7BaX5VW6dBjsFjfLccdBV/QeEi8vklu106nE5b0eFc1TBLvukIPltWqf6PzBbNjJhl2lzcSwc9ty3K3WsKv0thndzxx2lSUHdsSlqDWKPxkWwU4FdOm2pLBTBB1vKw92lGhVRDJhpzHROxd2iqAD4ipLxutDCXQsJKeipQg6lkzYaVbocs8TiqBjydu5ohB0wm1XYxiWgU41mY9nTiWs76YkXtevhORV61RAx2+b9VoSQKeSvOebOH8uL6xalwW7rOHWrLxr4/WZ38urzqUzNpa9zhkDnUpYtY50urj22msBRCOaZewgkc6KoA4AXvSiF2HLli1wOj5G79uld+ec4di8oVdZsoZjgyC/SieGehTBQM5wrM7cq4zhWKOrLnPe/JT7kwE7PuyqeCKiTgQ76ZWxGsOlrBIjq9qt5rCr9H6pH6tw2FUWCewY6FzFE0QW7HRAl25LBjvd4TsZ7EgA1Of0T1x9r4WCYVdpGzLYaYCORTYMqwU6dmhHMgyrCTp+t3T/DYdcpbjRBB0gH4bVAR077koPw+qCLjMGbRTt+aqarGFYHdCx9FXrNEHH7yap1qlU58RkDcMWDbfKcmatv/BTNNwqy0C9/7zIhltNLsz8/5H1mJ+fx5YtW/DCF75Q+/4qWTHUOY6Dt7zlLQCA0ft2wllSq67xSIZjdap0ib6khmNVq3TpqA7HFrYjg53ufLmM+5mcsGQXUIRewbCrrJ24GilW7VSrdOmkh2OPxbCr9P5EaEcXdCwC7HRBxyKFHckfvi1qi8Mu48IIpbYE2JEAqB+Nli4xCYedAeh4GyLsDEDHIsLOBHQA+odhDUEHIHnhhOUcusQ5wwB0LCLstEEnHL802KWGYW1Al3hsDdsos1rnynaIMLlIhLXD5s8ZgA74/7d35mGWVPXd/56qulvv3bMPMwPDvg0CAoPiIAICAVHUIGhM3HgFI8ZEjPrGGHF5Ja/ERPNE0VcTlyBi3CDigiS4AgFEdoYZ9hlmumfrvftuVee8f1SdulV1aznn1J3pnp7zeZ55pvveW6dO3/Vzv7+ztL82ZYUuCZl0LkowrctTbg2mdaLl1ji6zQl873vfAwC8+93vhmnm2KUghb0mdQDwyle+EscccwwM28HAQ0+pNRIQO9mULgpP7RiD8hpiwXJsbYipr2kWHGfX3YHV3gMfgEr9Ia0JFLOLTVQXq3eIp3b1AZI4OUKEoNh1KqXj/ct1PE9V87xpBcROVug4QbFLmxgh0xbpsqXKrrFtMXcroTxC55ND6PwmTPfLhqrQcajlJthKQsfxxK66yMq9OK3hAFaN7bNJESLw4ShKQhfoR6fG1/H3w7lM6IJQk3S0DJs1IUKE8igRHj+XBqkZMCpi4+fSuGbzpdLl1jgOL9SU0rkoPK1TTec4V+xajFqthuOOO26vpXTAXpY6Qgje8573AAB6Nm2FNTGt2JD7BCkVbfQWBWbTpmAQhqG+WVgD6gv/8HKs3c1gd2XfPrEdL9lq9AH1ofzRPP/wytWG94aa903VHYsENNWXIALgPvTM6FBKZ7mlhdwYACt0Zvl6kvOThhCGQtFGqSt+1qwM1DEAhQS7De9Dxinm/Ns8qc89yJy5SUbenQJ4ot2RLxdpY+wk+uMUCGgndi0gQH0gn9AB7vvP2Msb6kLnwTzBzAthrDNCxwCj2Qkr7Bx2heSWTMD7ktqJ0MhwKw95E7rdEz3S5dY4KNCRyRBr+0ZzC50xXsWPf/xjAMCVV165V8bS+efaay17nHjiiXjZy14GwhgWPbhRuZ3ugSoOG9qNodJsLrEzCUN3sYFlQ5O5xI7YBMQhcLqY+mby8Bby7WVoDDDUB/OlY7RAvH85+lNUXFQ4rj9Ft9yQR3xpAbB7FMYbRvtjAXaFgVlq5QUfA2AF5ooGzdEpAsCiYJSgUc8nUqZJYVmOcuIHAI5twhkrgTSJ1BjINhjcvV3BS/Fqb/CEAcRuSb3yY8bcNAvUGwqgKFJ8uz1G3DaVl8Jg3pAChtx7OztFd59pZrRvIi/VlgHUBt30Mc9rgxaA6imzWLR4CtXjUxaez+yQW16khfxix0i+fYp5fwzHTUPNnGJnOAyd2Par3k9yPeYcWnIf//KunPdzgYEZTGlYE4cAIN6yIG/efHmu/vB3wo8f/uNc7aztG0VfoYZ1y4dztXPhRguO42D9+vU48cQTc7WVRQd2lMvmqquuwn333YfyM7uw8pTnMLr8INSm5aKgUqGJnoIrc0OlWfQVa5hslDHVkNvrzeCpn2Vj2dAkqr0WJqe6YI/L9YdQd+9SRgCni4GWAKNOYEm+l/FFixmAxgCD3U1gzQClMck3j8DYOlpwN382bCa9W0OuD9CEPjlF9wPVcNz9B2X7Q/nergzKqQ0DWuVSwyubS87KbPUp0B8udkpj2dw+UBuo0wKIARRLcg9YMOlzd6CgYMybCCQBY8QVuk7Au0SQK7EJfhgzojjkNDJ5I0/KlnuIhEeoVJqjTf91qijhzPDSOdJK+FW6QwvA7EurIIRh0YBbjenpqcGB5LddFhgr5t1Hqs8fxpOQPI+ZJ3P859D/kkTbUX0u1fvdA/MKHfU+NvmXCtXtw5g34cx/T1TZQQctmeMP29Y9A0r9iX6tPbE0rtTO2j537/o+b8LFUFHyg8tjsKsKbB7DXXdthmmaeO9736vUjgx7PakDgLVr1+LSSy8FABR+8QyWdU2g3KOeklmGg7LZlE7tTMJCCwSWLBsDlZp0akfs8AcgH48mm9pFkwxqAnZZPrVztyWLTLrwkzuJ/nQypQu+GRsAK8indrQA2NEt8RTGjzALoNGyqzcmTkpgjdabWFt/ZFI7L6VrtUHAHAPUlk/t4naMMAwmldrZtgka/VKjIgqBlM6/iMindTyla+uP7JcN1r70B0+4pJrxUrpo29JfCHhKF2pbPq1L+htkPui50DnF9iEbMvczLQCzp8xi8eCUL3QAULQcVI+T+IbLhY4iLOEKZVhGiPrEs0B/eKoWfb+RTeuS2pFN63g61wmhoybanneyaR1P51jkPUgmreNCR0hL6AD3S+afbJbbEzbpHU82rePpXF/MDFoZBruqKNIGVvx6CgDwpje9CYccckiuNkXYJ1IHAG9/+9uxZMkSYKwB8+4Xsax/CsuWicldd38NB/ePtV1uGQ6GSrNY0T0pJHdGQg7PUztRseMpXRSe2omKXdzWYoD7YpMSu6Q3MCIndh1N6eKeWYac2IVSurYrBduw+BT9hANExc5ovYkl9kdE7Dyhix1SwYiU2KWJm6jY2bYJysuuMX0VFjsudNG7h8iVYYNl17ZTyDw/A2XXKDJl2FDZNeYcwmLnCV3s+4ZEGTZYdg1BxMuwQaGLa1/0fvaFbqB9rLRpUAwNzoiVYYNCl3AeUbHzhS4PQaFLuF6UtHZk0rpOlluTnmsyaV3qe6FgWhcUujhe2DMo3J+0dzrRtG5t36gvdHEcs2SHUDuDXVUMdlVRMm285cWXYHh4GEuWLMHb3vY2oePzss+krqurC+973/sAAM7vRlAYm0Z/qYZl/VOZYhcsvUaxDAddViP3WDsudkOrx/NNovDErtGfc6ydJ3Yzq0iq3LWldFEExa6jKV3auQTFLjalazuZQH+AZKHz+yQmdolvYsGTCYhd6hhZQbETmWCRJXapQuefSEDskoQu0IaI2KUJnX8qEeFIETqOiNilCl3gXJlilyJ0/k0ExC5R6DgCYpcmdNG20kgTOo5pUPT1ZkhdhtD5NxNavHgfCJ2HSFon0o5IWrcvhI6TldaxAksXOgEIAMOgqUInSiTYVUYknRMpwXKZK5k26O4abrrpJgDA1Vdfja6uHIPLJdhnUge4S5ycdtppgMNg3/oCGGUombZUapcET+3yil1WOTZaeo2Dj1FJS+1EPuSEyrEiZQbvw8kpJwtXx8fSpSEgdqkpXeiGKVfFlV0T+5QidnFl17T+JIldtOya2Ea22InOdE0VO9FxdGlilyV0gTbSnvMiQuefMu25KiB0nDSxExK6wDkTxU5A6PybpohdptBxUh4rYaHjt084l4jQcSyTJpdhBYUOyC7D7kuh47dNQ7SdtOdXvZ90ROhoSUzogPS0LqncGkdSCTap3Bp7vowSbCdkDkBqOidKMJ0DAEYZjrwTaDQaOPXUU3HWWWd1oKdi7FOpI4TgmmuuQaVSAdsyDeeenQCAkmknpnZJpdc40sqx0fF0aaSldkml1zjSUruk0msc0uXYOAjAzPjZsXttLF0antg1etvlTiilC504+eLMlC7Up2Sxk/pmGid2aWXX2DaSxU52lmuc2MWOo0sjQ+xE24gTOxmh808ZJ3YSQseJEzspoQucu03sJITOJ+acwkIXICoC0kLnfVmMnlNG6IBAGTYqdhJC5x+S8N6yz4XOw7Dbb2w4TLqduPfMvT1+ThbpdC66aw7U0rm4EqxKOvexw3/SdllWuTWOuBJsMJ3jOPfswOOPP47u7m586EMf2qtLmETZp1IHACtWrGiVYe/cBrqz9WIvmTaW9E2HUru00mscSeXYpPF0SahOoogiktqJ4JdjD2qVYzNLr3HElGP3+li6lNtGJ1DQgru2nVBKFyQ6f0EmpQv1KSJ2MildtD8RsZN+XceIneqyJUGxs20TznhRfrZrVOxiJkaItJEkdrKEnrcKQscJip2S0MV2TkHogLaJEypCFy3Dygpd8NxBsZMVOo5pUPT1BaROQeh4fxq9UQHPKXTMWyVAUsQA97ENil3ShIjMLkT6vy/LrXEES7CdKreKpnNZqKZzJ5dGQ7+rToYIlmCj6Zzfx11VmL/aBcAtuy5btkyt04rsc6kDgIsuugjr168HbAb7R8+DOa0nTMVqCo+1S0N2EkUSnR5rZ1fU1++iprvWmp/aiZRe4xAox8oildJFCZRj3XYU30ACb6jSKV2oP956dnnf0LjYiZZdY9toiV3ehYp9sWMERkN1mw9P7ETLrglt8NdA7ExXCbjYxe07K91OXqHjaZ2i0LX64oqdktBx+IQHRaHz++K9x6gKHccvwyoKHccpEnfXC0I6I3SKIsbhj7GKFAbh7537utwaBy/BypRb43CmCpmTIUTp5Ni5TpVbo+kcADCH4fD/Zmg0Gli/fj0uvPDCXOdRYU6kjhCCD3/4w+jt7QXbPgvn1+0L+/HU7rDBPcprxARTu54OjLUr5NiGCfDepCsMzV6Wa/2uTpZj7W6C+kAHSq8EYKoyBrTELkea6felEztQ8LskZzMA5MqucTAC1qEBJJQauRYI5RBHUej8BlrltLyLw8oMZUhsgwB2V2cSOuKoC50Pcdd1zJWgE8Au5d+WihE3PVcVOqCV1jkl5Pp09ve8Vf1CG4B/OclLXqFz9wDu4Pi5uSi3xuGQ3ELHGMFfbr0oXz88OrFUyVBhJjad41z23HHYuHEjenp69nnZlTMnUgcAixcvxgc+8AEAgPObYdBnJttuU7Ga6C/UsLQwpSx2AGAzAzY1YBAmXYaNwgoMThcFVSnJoZUsMIPluveZCTR7GGqL3XFpqlATbmKXUzL9vQNzvBG4268xsJwr2/tv+Hl2fAC8pM0TmDzd6cDG1hyWwzps20RtvAzQnALOAv8UIQywpr3yYN7SP2svYcl3yNtyq0P7cuaRTMKYKy4s53uV9zqwZvO145RzPl8ANB0Dk88O5NtblAHWDINh53+8WQek0K4Q2BWSb7s34m091wERy5POcZjpftkq7e7MeJw8TtNdqaO7UsfT44tz9WHQKGPQKOPvDmofVyfDUGEGZaOZKHT06UnceOONAIAPfvCD7hJuc8CcSR0AnHPOObj44osBBjR/8BzYVPxq+mWjiaWFKRzWtVtJ7mxqwAm8y+YRO0bcNc9YiamJHWmJD+PjtxQfBWYCTonB7ma5xA6EueP+SopyR+D/DXnEjhHvH9TH+TEToN6eoyRtJqoMqmJHABS9WCLPlwmDoVDyZlUxNbHzha7Jx1kxtQ9qBhg2Cf2uBHOTKLcvOcSOeXetATgVxceatJ731OyQ2Kl2xRM6Qt2/q6AoZEFpMWx1sXPKXnnbIdi1We3DtekYGH96yJV4wtBUeK/yha4DewEH75u2RaUFsSveFm1mjuduQOgA9beIvOVWDrNawxCMfFus+zQm5XZ84nRX6rBMmmu/VcAVugIxUSAmjizIzL5rMVSY8YUuCTbVRM9tu8EYw+te9zqcffbZql3OzZxKHQD8xV/8BQ499FBgxnbFjsY/s8tGE71m/tSOkze1Y4SBFRXFLghhHUntVMQutKyIJ6u0kC+14/2RFTtXMqKXKbxhRmbs+WInK3fR7iuKHQm+Kak83wyGYrkZmiChInaUEl/o/HZkxY4BRpO0l89kB5gHUrpWXxQeaxa+S2lBQew8oWNG67hOiB1fxFeqKwGhcxtxB+LLil1cCmUobIfHhQ5wy8nFMUNK7Bq2iV2bF7eEDnzcolw/QkIXQPa7Df/SGLxvVBJVLnRBnJL88y4odKp0rNxqdiDtjkNh60EudHng6VyB5LtjuMylCh1lOP5OC2NjYzjssMNw9dVX5zpnXuZc6kqlEj75yU+6y5w8NwXnV+kb5/LUbm2X+li7IKJit3u6G9Wx8IAvZriJnVNRL8cG2xJ9NAjQ9kHKxU6mHMuMmLIKkRM76qWFbW2b7oQHUbnz33Dj+ii62n4gpQtCGDqT2smIXTCli+2MYDMkfp05GbGzbTPxG7OU2KVNRhBsgjDAmiKxkiErdnF3o5TYxQhdsC95kRnr1yZ0fiMAccSfL4llRQZYVfF2gkLn95EChUmx+7Zhm5h4ZhDWFGkTeJm0LknoZNM6/36JOU4mrYsTOkDieUu8sm2C0JGEUCOOjpVbzfjnaXnnPtka3oeXW6NCRxnBByTG1QXTOVVE0jnO5c8djz/84Q+oVCq49tprUSqppZOdYs6lDgDWrFmDa665BgDg/HoYzhNjAIAllWmsKo+13b5sNNFvVjsmdyKpXdM2Y791MOLNlOxAardflmMDpde460RSu7iULny94JsmSb+dsNildVdC7Ejat00RsQuUXeMQEbto2TW2HRGxi5ZdE26TBk/o0lIjmW3JkhASuxShA+AuLdKBMqyI2CUKnX+9WBk2a5yY0RQTuzih8/siUIb1hS4qc4F+iqR1iUIXaSuLrPtF9DmXJHSczLQukM4l3r8CbwsdK7ea6c/PTpVgRcgqt4qMq+tEOheUuSShO35gu/+z8/gYvvWtbwEArrnmGhx88MHK5+4U80LqAOC8887DpZdeCgCwf/g86PAsKqZbck0iKHf7MrWLgxmu2OVO7TpYjq0tSk7tMrfq2ofl2KSULnQbb027xMWBE1K6KPsksUtK6WI7k0BM2TWOLLGLK7vGtpMmdkll14Tbpl1nCEwgF9kGLOulmip2GULnt7EPxtdlCR0AoTKs6MD/rPs/TeiA7DJsltC1GkpP60SETuTvFb1f0tI6PiEi63mZev08KrempXP7mqR0TpZOpXNZpVYAGCy4rkGHZ2H9pyt4l112Gc477zzlc3eSefCwtnjPe96DU045BWhSNL/zDOwpsYEgnSzJ5hlrxz8YE1M7ki43bW3lTe3KyaldbOk1jpRybFLpNak/MuXY2DaQktplpHShm6aJnWj3KNwt4xLkLjWla+tMzMUJZdc4ksQurewa247B4tf2Syu7xjbUfpH7IS0m01nbgIm+PNM++LKErtXG3htfRxiD2RBc/iSlDCs1kzOlDJsldJykMmzDNjHxrIDQITmtIwwozDBYsxlCF2kr8XLB+yVJcIITIpTIKLfGHpJQgt0X6dzeIOk9qBOTITqdzonCZppYdMsYarUaTj31VFx11VXK5+8080rqLMvCJz7xCaxatQqYaGD4mztAY7ZiiWNvpHa55G6epXZ7pRybVnqNbac9tcsqvcbRiR0wOpLYebITEjvRlK6tMwEyyq6xXYkRO9GULtSOGRE7kbJrbEOtHwn1yq4Sf1LSNmBSL8m4GbFEPnnuiNhFPkh9oZMYLxdXhlVZmiPucRAVOr8vkTKsL3RTEp2JpHWEubN0DVss0XUPir+4U0uWyL7PhEqwAuXWOKLP8U6XW/c5kfegTqRzQZnbF+lcENpkOOZ2gpGRERx00EG49tprYZqd2pIpP/NK6gCgt7cX1113HXp6elB7roYHv9lMnBEbx1xNpIiSmdrJtuWldnGTJDKPFyjHZrKXyrEipdfYNgIf+qKl1yhtM2NVH6aI2AmndG2d8X6USOmCBMVONqULwd8VZMqusR3yhG5GTuj8wyNip/JSDJVhBcuuSX3JCxc7FaFzG4gpw6qISyStkxU6IJzWKQkdwmldUOhU4O8hcTNcRQmWYFWEDgjcjwuk3Fra1dnJEp1K5/blRIggjDKQ/zwVDz/8MLq6unDdddehtzdPYtJ55p3UAcDBBx/s2++2+x088SO5V3p0IsVgsZp9UAodG2unIB4+XmqnOu08WI6tDWWMp8voR6fEjlo5U8jAODvVN89OzowFg3xKF+kMMal0SheEMaDZzJ4ckdqGJ/DSZdfYxiRSl7jDudjleOm4W5IRZaED0LGJEwDUhM7vSOvYPEtQGE3ArKkJHYc4BLueXKwkdK1GGJo9+YQOAaHLs9MEM8THz6UhW25NYj6UW1OGtEvRVW50NJ1TRWQiRBpP/aiG//7v/4Zpmvj0pz+NQw45RLkve4t5KXUAcNppp+EjH/kIAOCZO2w881/yr3gud0PFGXRb6vu2AkC52ARKak9IP7UzWe6SAC24uy4o72jhyR0tqgsiCPNn2eZ543LFDrmehbTAYPc7oHlkCgBMBqb4+HJIBxZ6IiZDb1e+aWeObYLM5i8HmI18fw9hbht5P5jAkPt1wwz1hWYBN10zRMe/JbbhJm15nyaEAVYt/3uJ4eR7/RIKlHcb6kIHAIygNJ5P/P09XHPeH2aDwWzkvE+8vX/ztOGUiPvFOc/rxpPb3Alz/rc0EJugYDm5hK5s2bnTuY2NWWWZA4AX/ruOLf/tesT//t//2x3/Pw+Zt1IHAOeffz6uvPJKAMDj329i6/0KK2gCKBAHfcUqlpSnleXONBjMig2jV13ugMCgaWWhcic4UNUdLaJtqfbDCHwLzCN23grmyhNCCACLAgWmLHbMZGAVB6RE1cXOAFiBum/ojtofY1gMQ/0zqBSa6CqpvfE0myac0RIMGyBNtX4Qh8CcMXLtYUoAmHWvdJvjg8H/kMzz4eK9TPLuXGE4XrKbU+yYAVBL/Q9ixNslQmLdufaOAGBAcSJHE7bbhllT+1sIJajsdP8O5ceXC50nzKqYDXcGch655M/VPM8Pp0TyfT4ArbQyr5Dl/fLRNNx/OaogZctG2bJhGup36ubmDDY3ZzBB1deP2/1ADU/9wI0tr7rqqnkz0zWOeS11APCWt7wFb3zjGwEAD32jgeHH1B7cAqGomE30FavKYmcQBtOiMCt2LrEDOjCQl2Duxc6jExMX8ogdAG8UuaLYGQCx3H2ZlMWOADC8kqWi2BHC0FVowiBMWewoNWDUiF/2VBI71tqBQFligqVbkiN1CM7ZUH1+BF8iCs91d9mR4O8qbQBGk/l9UP1b/ONY/nIlYYDRUHv/IIFzqzw/gkIX7JMUAaHjv6vAhS4PXOjy4Atdro6gI+laR4SO5hNcLnN5hW7UKWPUKSu3MfZYHY9+w33Cv/GNb8Sb3/xm5bb2BfNe6gghuPrqq/GqV70KzAEe+HIdOzZSOIrPugKhuVM7g8yf1I6W8pVjeTuqExY4bvqQM7XjYqf6rORiV6bCcsdMBlYOJMBc7LoccbnjKZ3fKKTFzrAYBvpaE3tUxK7ZNEFHA99GFcSOOATmbPj2kptgtFI63g3vcZUVOxKzLZT0h16k34zIPUcJYzBr7dtsyXxY8bJr26xGybQu+reTlOVJkjsT+ZXKp3UkRiZl0ro2ofMQfv/x7s+Q0HnIpHW83JoreXXyC51TJPmFrlPpHG9L9VA/nVNvo5PpXB6ZKxAHE0/W8chXHTiOg3PPPRdXX301COnEnbz32Lf7gChimiY+9rGPodFo4K677sL9X6rjtPeVsOhI993ZlHxFFQhFwaSwDAddVgOzdhEzttwoaIMwwGIgJgMrEtCGCdTVXpU8tSOeDEjhlWP5B5WhODjeH2yM7A9wLnCxfeEpQEalPCnd8483kDlInxUYaFfkRt7jAkZAQWE0Mh4TntJF2iAFBljEvT+yHlee0oU65/3niV3WjFhCGLqL4S8ZXOwAYLaevQS/n9JF+mHYAIURFs8kWLvAAJ7EGNkfvIQFyq7BZon7mFKI7UOa9EHJBVF0bbfYNozAOdL6kCB07nUAaLZk+kIX7S9plWFFRCT2PIzJLTcTc9NWWifWTpzQAWKPB6EElV1A4lp5Xkk4lWg6F3O9CGkyZ1UZbIGt5tJkjtgQWqopTeYMR/CL0DyROSB/Mgcgt8wByC1zALBns4NHvsLQbDaxYcMG/M3f/M28WrokiXmf1HH4Gnbr168HbQL3f7GOPU+7rygHRCm5W1Al2X2Z2mX0UzS1SzuHSDnWH08X28cc5dhAG7nG2fESZEZqF03pQtd5Ytdbqaemdm0pXaQfIoldXEoXul60nJJwG6nELuUpLJTYZXxByhoykCZ0rdukdyFR6PwbiCWPabcRTutSXmsiaR2xk4WOk5bW+encLFMfC5gldIJkpXMiZe2sdC7rdSKUzmX9nfMpnVsApdYCcXyhG3vWwaNfBur1Ok4//XRce+21sKz9IgPbf6QOAIrFIj796U/jlFNOgVMHfv8vVex5hoF6r4wksesx6xgoJK9Z18mSLOnJJ3e5SrIikyhESml85lTOMX+5J1F0qhybIHZtpdekNoopYhctvcaeCKliF5fShU5BGMqWnVqOjU3pIn1IEzs+OSIrRUsrxfopXQoiYpeVoPF2cn0gAomvMRGh82+b8p0iVegCpJVhs+WVwcza01UgXU0bW5clc/7tku6LhHJrHInvORJCl5Z8zotya4nkXwhYUOgy/9YcUri/lVofqq1JvK4QeNMZe8bBo18yUK1W8dKXvhSf+tSnUCgIbFY8T9ivpA4ASqUSPvOZz+DEE0+EXQPu/+cq9jxph8QuKndlo4lSxtevYGqnKncGYbAKTsfkbq+ldqJvSB0aa5dX7OJSu9jSaxxp4+ziSq9xTRgpiV1c6TWOHBMoOEnj7FJTukgfEsUuoewaB6EpXwxEKrwpYifzgZkmAKLHxz43Ze6LmIkkMkKXltYJf+inTZoQfO0mpXWiQpfcrrjQuQfEXCab0MXcTnb8XFx//edmXqHbh2PnUr/Az2E6F5Q5VaELypxIOrfbbl8kOJjOAcDuJ2089C8MMzMzOPHEE/GZz3wGpZL6rNm5YL+TOgAol8v4v//3/7YSuy/WsPNRV+zS5E6EJLnrLjZQqYiJXlDu5nVqJ9hO3tSOeR/gHUntgr8LCBkAr0aVb9mTzMROhBixSyu9xhEndpkpXaQPUbHLKrvGERW76OSIzG4kiZ3kU7XtA1LyQ7d914rwTFcRQveDjNAFiKZ1sh/8sWVYmeF2MWmditAFnwN8DF2upVc6UHLlMifzmEQluSOTIYrzZDLEPEjnZGXuk4fe4v8sK3NxRGUOAHY+auPhGyhqtRpOO+00fPazn0WlUlFqfy7ZL6UOACqVCq677jq84hWvALWBP3y5huEH3Fdip+Wu22qgYDooWnLr5HWyJDtfxtopY8z/cmzm4YYndnxmrEjpNUpE7AyDppZe4wiKXbNpgo5JfpOMip1EMhWEix2BN55K9q6IiLpI2TWujdYyH1D64OXPS5mya5Tgh5z0B15g0gTvjzTRtE7htepu/eX9rJjQ8ccwOIZOFuZNmEia4SrDvCi3FuX3f23vCPb7sXOqpVaDMJxRdl8Ue0PmAGD4ARsPfaWJRqOBDRs24DOf+QzKZfXJFnPJfit1gFuK/eQnP4lzzz0XjAIP/WsNW+8KJBgBucsaV5dEcLwdn4koQzC1YxVH+YXdudRO7fx+O51I7XIsfZJUjhWGi13FyR5PF3e4wUAK1JW7siNWeo3iyQcxGPp71baw42JXKDgwqgoPiCd2Rs2QTumCEOoJQJ4A08j3wem/NvIOoFcUOsBLupwcC+DyMag53pH9tE41hWHuThV5S65WNWdCRwLpnGITht2Z5Ury9IHYLaFT7wQ6I3Rzlc4RoLByJleplTKSe4mSh2prYmUOALb+rolH/q3hL1vyiU98AsVih/YEnAP2j+kcKViWhY9+9KMol8u47bbb8NiNddTGGA6/qOCvJ0OZgYJho8lMzDhFdJtyyQhfAqViNWEVHFBKQCXHRhmEAV02aJGC1kwY02qvdL7lmLsHpOSrlAC0RMFMAqNJoLRbiidUzGTy5/egRQZaoSA2gVFVXYUVgEMAU+EdlzDABEAJmG0Ijatra8JgvtAxxQV+gXzT96vNAmbHKyAFqD2WDDAc73mQY2FfqWU1oocTgFmuzCjvM8mFiKjdD77M5JTCXLsJMMCqu69r5X2VGUOh6u5zqwwDCtNAs0ftcMLcx1EloeNYtXxbqTGTuM9Lqt6HvF8SmJUyZlOmDznuB2oBebbYs7vcOyCPGJs1gqX908rvc3tm3E3K8yxR8tvZI/1wJwhjDE/d1sAzP3XfNC666CJ88IMf3C+WLUljv5c6wF3H7q//+q8xMDCAG2+8EU//pIHaGMVxbynBMFtiZzMT080SanYBZaspLXeLyjOo9hawZ9p9oqmInVmywQoO7LIJpiJ3BH7Ni0FB7AxvkoHhNqbyIcjAP0TdD0LZPjADgOXKJQVyiB0BbOJ9ksgOxiKuFFKAQUHsiJfaEYCCKokdYwQN24RDDZgG9ddpEsV2DJCaCVgMVOWxZO4xhAHMW4dOGoZcKR2AUGlKWuwC5VeSQ0yJ00qA83yIMZXjPaEzmsz7W9Q+hc2mK4XFSYZGn3wbXKTcnS/kjnfH5Hk/K6adgCfXDtzXlcJnKzNJruEq/LmUSyqt1peMPH3IK3TMVH8s7C6m/uUCreVtiK3+xXXPTBcaDQvvOvZupeN/O3skAGCk3o+lxanQddRheOzbdWy7x33Pfdvb3oZ3vvOd835hYREWhNQBACEE7373u7F06VJ8/vOfx4t326iNM5z0v8qwyu4DtaQ4hWm7iJ3VXtjMkJa7itlEV6GBUdIF06QwDKaU2pkWhWlROAUHTRSVUzsYzB9/Ii1WxE3MmAU3MZMVAj6BggGAYmpHGFiJwrGYWmrnexwBHKildowAtprY8de/YTFpsSMWQ6W7DsYIbIfA8fZHlBU7HwWxIywwBkpF7LykLw/MhD/TgBbctlQTO0YAKpla8pTO/SVXsOEdTwBDruwX2kIM3mLRku/MZpP5j6VVp2hA7j0lj8RwoePnZwSwK0S6/MqFzm1Evh++0CmSR8QA+IsN5ymhdySdg3pCyNO5vEKXp4TP07lGw+3Ehq7NUsffPXsEAFfmAGAwMuzKrjFM3nw8tt17LwzDwAc+8AG89rWvVe/wPGPBSB3nkksuweLFi/GJT3wCu5+o495/rOKlf15GecBAl9FAj9XATgA2NWDDUJI7wP1AJ4SBEJZL7tDbkErtiEMA2wDjApIjteNjeIiRT+7ypHZ8BwiZ1I6ZDCw46YN5HZkDseMYFgMzHDBKxOTOYKGJNzy1A8TEbqZRxPienvD7v4zYsfbbSYtdB1K66NRRKbEj7R+gzPR2rRC4D/gYtLbtyPKkdZ7YEeF1VdzB/P7h1LtQ4pM9KHS8TZm0LioyhIqXYKNC57cpIRVcqvMkfHtD6GTkmqdzec4PQFno8socEJ/OMYnJdcF0ToWozMnCZW5bfSB0eXAsXXWMYvs3VmHz5ntRKpVw7bXX4owzzlDr8Dxlv54okcQrXvEKfOELX8DAwAAmt1Lc/fdVjD8f/45hUwM1x8J0s4Q99W7MOOkDJIdKsxjqbpk/IYBhMJgmhVVwYGRsB8UYQGnrlWtaFKVKE4XeBtjiBmhPxjsbRfwHqTfWjilIDZ+8QIuKEyn4ODtvrJ0ShIEVKWhFZO25mPMwtEqqWbCYmZpeKZc1TDBb7WVBDFfuiOyMWN4FT+xqdvabmu0YINWYd3BL7DEMpnTRy4VErVMpXUwHhD6YYoTOR0JK2+4DPhko5zuj0PGBsmsUkV0NgBihgytlVl3sOZgkQnF9ipIkdDL45VbFNphJEoVOWMgSEjoiOCavI0KXo2TMS6150rmkcqvI32XWiJ/ORYWOHDWdefyemS6/1BoVuncc8z+Zx989ewTunj0C2+oDbUIXZPw5B4/8QxmbN29Gf38/vvCFLyw4oQMWqNQBwLHHHosbbrgBhxxyCOoTDPd+ropt9zWxpDiFpZWpttsH5S5N7HgJNkpQ7lLFLuFdVFru4iAsv9wV+fIn8qdvLc2gdn4YXjm2xxGTuyhc7GwjW+7iHgcuhTZJFztvPF0SWWLHS6+x3ZIQu0QExS4JIbHrdEoXbNoAcoyL9suwWaf2y65tV+Yrw7nHZz3/koXOXTYi+/UTJ3QydLLkGte2XU4/QajcGkPWTGQuc0l/BzXTz88rFcpj36x8QufPds4hc3lWEgjKnEq5NU3mOCsGJ1PbSJI5ziu7n0w9Pkvm+iw38t92XxMPfN7B6Ogo1q5di6985Ss49thjU9veX1mwUgcABx10EG644Qa8/OUvB7WBR75ex9Zbp9BtJK8ualNDOLWLgxAIp3ZxBOVOSeyAltztj6kdYUDBXeRXXewgntrFtpEtdlmf2aliFym9tp0+Q+x46TWVNLGLKb1GSRW7vZXSBU5OCyliJ5CkMTNZ7JLKrm1t7K20LkXogqSldZlC55VgE6/OePiINws26TqRhC5tQ/ssoXMbSLmqQ+XWXEKXYwma+ZDOqcocgEyZyyKYzqkQTOfSKKGBTbfU8cjX62g0GjjjjDNwww03YOXKlUrn3R8gjLG8E/nnPY7j4F//9V9x4403AgCGji+g602rsRsDqcdZBoVFaNt4u6pTwIvTAxj1xgAkwZj7Ad023o4wP9lL7bdtwG7GjLfzZrAykfFfjCB2IgVF5jsaX2gyOtaOWgAtCzxtPMGKjrVzSgysW+DdgLpiRpzwJApmudt+ZcJnCreVagWFzxvv1zbOjgCGoLBSu32MHSlS9Pdnr5lIiCt/0TF2E9UyJl7sFzo/YsZJEkqEJyPErglIAaOZU+oskeeP2/dQXyVLo8QJj68TFTr3/IjdBkwY5q23FrlMROgAL7Estt/PogkdM4HZJe2f+qIiwwygPhi+sUzJ1RXD9r9TSOg8YkuCEkJn1dvPL5PORe9/mckQjABOJK2UHTsXlTapsXMx2/nJTIRgBdb2d0qNmztyBiuHJkIXyYybe8cx/9OW1CWNm4uj0piF88Njcc899wAA3vrWt+KKK66AYSzoLGvhTZSIwzRNvPvd78ahhx6Kv//7v8foYw3MjjyPgbeuwvjQ4sTjkiZT+LNgkS51iZMpGEksPYX6HZglG5pMkTSuLrYTCRMpvFmzaQQnUiAwAJ9QwGgQ0GJGA3lnyBq8nKy49AmfQJFn2RMbYNR0ZzQqTKJQmRnrn15y8kQs0ckTAildkLbJE3s7pYucnKVsdi9E9G5PGEsYf37kmw0LhCddSAgdJzpYX6rkGjNhYm+WXEWQEboozCun5l0qJG86p4psqTV6LtlSa5zQySRzwfOrTIIICp3KJIig0MnIHACQ7dN45ls9GB6+B8ViER/60Idw3nnnCZ97f+aAkDrOueeei9WrV+NjH/sYRkZGsONftsK8CDBPXpS6Pk2c3A2VZjHdVcL4bPbecLFy5/3LSuuADi2Bkmf5E2+snbvukSsFTMZvIjNkjSZA6yZYSXzXdFakoMzdAUGu84FOqMyOZd5xquvZwRM7QgEGabmLil3srNcsAmKXNEEijZDY7cWxdHEwAjgluPuxKkxgCC5zkjqOLq0N1dmwwZmwCkIXnQkrO4aOT5jgy5vIygwvwTZ78gudWWdKzz3OfFiuROa55z7viH9uAPLfDrzbz/UyJUpLlBw5A0B9RuufHX0fAHmZY4xhz/9UMfzDOhqNKaxcuRKf/OQnceSRR0qdf3/mgCi/RpmcnMRnPvMZ3H23u6ihceIQCq9ZA1IUe9XysuxorSuzBBsHY4DjGGBMTOqC+CXZugnSMMRKsG0dcGd/EoX0iKd0YIIl2LZzu21QC2Il2CA0xzg5oFWONaDWDi/HFqhw+TWOUrkhvY8wL8XWm5Z46TWKTWDWxUuvsf2gMTOHJQiuSyd+EIHRAMyGelJiNIHilGJKxNS3D3NLuO6+rIUZ+TvOTcsJCFPrOy/B5km3GgNEWeiMJlAep8oy56ZT6kJn1VkuoeO7c6h8mXDKJNdECGbmmwjhlNRljhWY+z4PtXFzK0/fnmvM3LuPvQuAuMwBAG0wbPneBPbc5269+PKXvxwf/ehH0dvbq9SH/ZUDUuoAgFKKm266CV/72tdAKQVZVkbhTWthLMlO3jhNamKyVka1IT+jgIsdpQSmgiA4tgG7boF5szXlO0C8Ne/kjyXUHRPFihRG3LIamef2xmt02+qS5hBX8mTJWc2DyWCUbeXdCwyDolJpuNvGSUIIQ71poTqiuIcTBcxZw029VPBKt8olKALlJW8IJbBm1T+ciQOUR/O91YkuM9J2bspQmJVL6TjMBOyyoSSE/PjZxWaux6zZTZSkjDCgMOX+7aoQBjQr6i9as6m+5ZhdJqmTPdKgFoFTgvL7jV32EnJVoSszpR05gNZ3LtVJELQIVI4aVxa641cM4+CuUaljqiNN1L7bh2effRaGYeCKK67AW97ylgU/fi6OA1bqOA8++CA+8YlPYHR0FCgYsC44COYpi4W3C5lqlDBZLYMpvHMwBti2CeoYIAaVlrvcYudt14VmwppnaZgMrECBAgOaRFrumMHAigyk7Lhj7lTlTjW9U/2cMBmMip05+zUJw6AoVxruJGFJsas3LdR2dIM0CFjWeMY4PKkjVHGPVOomNsqz/ohbxlfZI3UupY7/rZK7CoIvLOxOzlBI6Uyg2WWAmgTlcXmrYgbQ6DVgl9Q2lfeHSliu2MkeW5hisOrqY+gIde+7RrfaB7PhsMxxw/HHuc8zla3WqDcGlBbVXiO2N+ObmfKBNtB6ffCSqyyET25TeMxoAe4ol2UNFLtkXyxAs24BBLjkmIeFj2GMYfddsxi5tYZGo4HBwUF8/OMfx8knnyx9/oXCgaexEU466SR87WtfwymnnAI0Kewfb0XzO8+Czch9TSFKyYt7nDtD1oAjKSeGSWEUHRCTAUUKiMwoDGIyWCUHRpcN1tcEq0i8kh13PQDTO5722aASx/tlPMLciRiyCQ6flWkxoEAVxsrJ3RyAO1mi6P6NKl+FCGEoFG0wJrzfQAjbNmBOGzCaBKShajfeGEnVdexYa1a07HlpgfkL+6omR4SpfdipkGsdMT6GzmYgDvM/7IUP94TOKag9zkGhU4ELXXT2sAx85qnKY01oa6u1uBmsWagIneF4ZXbmbnMmC7VIa+07yb/ZLrv/WMH9pzBCobXnreLIEl/oFEJhWoC/Pqqq0FHbkFr43Z6h6P7BEdjyvUk0Gg2sX78eX//61w9ooQN0UudDKcX3vvc9fOUrX4Ft20BvAYU3HAzzsL7U43hSF0QmtbNtA3bTi6kJc1+TEqmdYxuwG97XcEZc2aCCyZ0BkKID01tPjVIC2jTEkzuTgZUdmJ7MMQrQWUs4tWPeYsOk3No0Uiq189/EmD9OUCq5k33z81K6UBMSbRgGRanc9I5jUmkdT+nMae9Nj0uSaGLHAKNOWpNkvDdv4Q9sr/Tqf4OXlTMDcCJ9lZFDntRFuiSMbFLXJnNMIqnzhI4EljOJ7u+aenhE6IiX+ImWYGOFTmKSSVDo+LFOUTytC6Z0/mWSyY9hB+4rIp/WhY7Pum1kv9lmN5H60sOF3Z9dKzmpgstcq0FxqfNfA4GHxim1L0WShH+e4N0t8Vjx+ym4RJG1Zkb4+Gbd/eyjnswRArx+3YOZx009VcfozQZ2796NQqGAK6+8En/8x398QJZbo+h7wMMwDFx22WX48pe/jDVr1gBTTTS/+TSaP38RrCm70buiJzOinNq5J3ZTL2IysdSOAiywfp5hBJI7kdTN24fWP70BN7XrFUvtSFTAeP8tweTO+2bJjwX/u0WTO5mHKZDSqcBTOv/UXlpHBc2Ep3StBiCX2DG0LWkjk9i1zVxUTeyCTeRI7Hif9ga5NlWPETqpw2MSOkYgnNglJnSC3WkTOu9YUfmPEzreL1HidtOQSeva1gZMvF0rmWu9j4i/JqhFQukcR/RvDaZzQaSELiaZkxK64N8uiZ/OyVaIPHg6RyXSOdpg2PqjSTz1xTHs3r0ba9aswQ033IA3velNWug89L0Q4cgjj8TXvvY1vPa1rwUAOHfvROOGjaBbxb99APCXMFHCEzu7aarLnWhJ1iFwIrNgDYMJl2SJTeAEkjliAGbZEZY7YhOwWiTZk5a7sBhKyZ3IQ2QwkJJX5o4eLnA8F7roTGdZsWs/uaTYtXVsjkqxwSY6IHadkru82zbtDaGTOr6DJVfV4+OEzr1S7HEOll19mJu8iYidSNk1VuY8ml3Z912SzAEQmlgRLbWGG88+PlRqjR4ukNwHS60q0EJL6FRo1i1f6GSYfr6B3f9cwM5fzYAxhosvvhhf/epXD6jlSkTQ5dcU7rrrLlx//fXuJAoCmGcsg/WqFSCF1pMxrvwaJa0cyxjQbJqgTkLJMqMkSylxZ9EmLU8iUpItMFjl+DGEIiVZVqIwe+O/yjMK0Ibp7myQcDwtUpC05U2yyrIE7kLFCcf6b5Rppdm09/KYsmvb4SnHB8uu8ceml2LbSq9tDWSUYqOl15jrU0ux0dJrzPn5h0ziB3dM+TXURMZ4nrjya0w3k9vPKL9mylxa+ZW1JkQkCV1a+ZWZQLNiuGXOBKHLKsEKCV3K4yMidE4puQSbKnTB26W0Hyt0oRukl2HThM6IJI9J7df7k++/aJk19jYpO0v6kyBSvkSl3T9xpda2c1QyXmNAqswRmnx9XKm17TaLm6lj6rJkLq78SpsM2382hV2/rIJSisWLF+NDH/oQTj/99MR2DmR0UpfCGWecgW9961vuStQMcH63A40vPwm6rXOpXeZ2YRklWcNg6eOygiVZhckUoZJsQnIXTetC1wkkd7FpXdzfkJbcJX2i8+ROtjTLESy7Jn01IoTBKqQfn5XYtZVe2xrISOyipde2TqYndpmLxgYSu9gPZQLQjOdd3gkUvJ8qdCKd4xMiEm9GEDtZwk/niiQ1oUsrwQondEnPUcGELkn6RYWO9zW2jSyh80g9R8xVbalcyuFJKV1aMhc6fUJKl5rMhU6U0G4HJ0FkpnMpQidSak0SOtV0buaFBsb+pYwd/z0DSinOP/98fPOb39RCl4JO6gT57W9/i8997nOt1O5lS2GdvQK2VcRUvSS1Vl00uQtNlkiDeAJHWCi1C02WyDw5aV8CJTJhIo2k5C4trQudPiG5y0zron9DNLkLTpoQOD52UkX0DTOl7JpENLHLSunCx7YndpkpXaiBhMSOAqbIFmuBN//QfqmUwBBd2y5OzjJSurYmYuRQJKkLEvXjuKROaqX/uKROstwaTetky62EIrS0CTOARo+b8ImWXKNLm8iWXJ1yWH5khM4/JnIuUaFzb+xKVvTvjaZ00ckPWcRNjhBJ5kK3j6R0IslckOj9IpLMhc/fvjadbJk12geRdC5IdJJEdCJE6rkJcPTBwzimbwROnWL7T6ex+zduOjc0NIRrrrkGGzZsEOrHgYyWOgkmJibwhS98Af/1X//lXjBQROE1qzF7yNLMEmyUoNgJSx0nIneZJdi2k8eUZFNKsHG0yV1kJmxmFyJy1zYTVvDvANASvLQybNLxUbkLvoEKlF3j4GLHx9KZpvigs6jYzVSLYC90S5w8InZZpdc4IuVYKanz+hASO0mpA9rLsbJSB0SGWkakTjqdC0qdQLk1jqDUqYyfC0qd8vi5QAlWZQwdM4D6APGPlxU6t5GwtMvMVHVPHC7DBoVOVuZ4e7zsGkxTpbYF82a88m3tAHGZc0/c+j4qK3OcYOlVpNQaJVh6lZU5TlDqZJM5XnqdeLyGqVuL2LFjBwB3e8/3v//96O/vl+rLgYqWOgXuuece/NM//RNGRkYAAPSYRRg/8yiwnpJ0W4wReanjBOQOjIindf7JCRgl4NtLykgdJyh3oEQorQt1ISB3pG7Ii53fjvcOyIh8HS4qd54cyqZ0HC51Mild+HhX7Jq2KZ7ShRoIiJ1oSheFi10jYzxdSh/8cXaWvNT5zVC3dGzW1LfqYqQldcr7cAL+mm2qkyEIc1OprPFzacebDQarSnNNiOAL26o+rk7JXcdNSeh4M955pVK6QB94WseFTknmPJrdxF+XTqX8zyyAmooy50EcdZnj2BWmJHPBPqjKHMdaMyOVzgUxqzWse+JujD3k7mO4fPlyXHPNNVi/fr1SXw5UtNQpUq1W8W//9m/4/ve/D8dxwIomZjYchvoJKwFD7lVJKUmfLJGFJ3VUZVcJwH83MQpUecYupW5Z1yjQ9BHrSV3gckcAIpFstTcUSO9UjuWnNpn7tyhCDIaiZEoXOp4w1KpFsC3yewu7Dbjj2EjWeLo0mLs1ljWtOuDM7Qcz0wdwZ2HYQCFPHwCAAqVxpj52jrcxpT671V3vLX3sXGYTnhiqCh3vh2ErCF0AaqktCuzD3IROeea0d1/6x6t0hQCNXgKnSHKN5Wx4S5mqyJx7IPwv1qpQi7lj+vI8JIa6zAGA00NhdDelZQ6UoWfTFhz8yAuYnp6GaZq49NJL8Y53vAOVivi2nRoXLXU52bx5M/7hH/4BTz75JADAXtaLmbOPgL1SLiomhMFxDNRrKdOnMmA0kFipwMuGBkufvJGCYxtgsxZgUeFSbBTaNMCaRq514fwSsyKEAMTKIXUEMC0HxaLaBoq2baI2VoYxbcJoKj6mnRA7Clg1yfJrpA98Y3JaUHtADBso7XET1LTZhandcIDipPoTgjhualicUnxOED7gHqCm+ms0724cZsMr/6ruc+e3lUPI4EklZeoSQvLdj3bFHUNnl3MItgM0+gEqX6ABAFAz52sTrS9LeR4Lp8xgNInyvswAQEsMrEf+va60YwwbNu3GU089BQA46qij8Nd//dd6mZIcaKnrAI7j4JZbbsHXvvY1zMy4Ywpqxy3H7IZDwbrFXvEF00HRclBvWmjYppLcMQZ3EkQnIGpyRykBnbFAGgZYgSnJndMwQGat1puMqbbwbx7BJbwEqyi3fkm7oCZ29boFe5f7LZU0iZLYMQPuYwBPSmSfG3ziBOUfwgpy50kd43KnIHZGk6CyE54YqYldHqkjNk/IILyrQ7gBV0D4OD6VpC5aNpYNw7nQGTYSZ+LKIrrIb9txXOgA5YSNGUT6PvBLrMQtMzoKaWdo/B8Dqsvl26D8fY24r0kVIbMrzB9mYTTU2nDKnuAXGIy62vOBr4vHLAZIvM8bs3UM3v8kep7eBgDo6enBu971LlxyySUwTcWKlQaAlrqOMjo6iv/3//4ffvrTnwIAWNHE7MsOQe2kVYCZ/vW6YDroKrrjr2xqKMldx6SOv1sSpiR3dt0EmfRqEQaTljtGAVq1QOrefWbAFTxZuetAWqckdsGbK4idbZuojZdBaoHdOmTFzkvpgosWSYtdZEC7O0FAYdJEaxc797EkcnLnS53XHjUhndqpSB3xlsLgIxKkpS6QJgWlTFbq4iZ1SG2RFhA6/9icSZfbrnxaFxI6juSkBlmh4zLnBJ4v1CJtM4HT4DIX/HvrAwSORHWQGaxt0WDZL2u+zAUmQpk1uTaCMuf3Q1LqQjLHEXl/pxS9jz+PVY9tweysO/Ppoosuwrvf/W4MDg5K9UETj5a6vcATTzyBz3/+862S7FAXZl95GJprFyFpldqg1HFU5C53CdZvKNCGpNz5aV1w7TlJuWsTO0BN7uZC7KI3lRS7YEoXakZC7IIpXagNGbGLSp13mZTYBaTOb0IytQtJXaBdmdROVup4Ohe6TEbqgulc5HIZqUuapSsqNVGhCx6/r9O6WKEDxKVOUujiZA6QE7o4mQPkhC5O5gC5lK5N5gB3XKJEShcnc4D7viLaRqzMcdLe1xlDZetOnLJpF7Zs2QIAOProo/GXf/mXOPbYY8VOrhFCS91eglKKn/3sZ/jKV76C8fFxAEBz9QBmXnk4nGW9bbc3CEOpYKNotr8wZOSu42ldEAm5C6V1QSTkzmkYINMxs4Jl5W5fil3STQTFLi6lCzUjInYxKV3oahGx4wlV3N8jI3YxUgfIpXaxUue1LZraiUpdNJ0LXScidXHpXOR6mX1c0wbPZ8lNktD5x+7DtC5R6PwOZZ1ITOi4yAHtMgeIC12SzAHufSdSdk2SOf8cAl/QYmXOP4FYSpckc34/BFK6VJkD3IXtE8bkFXdP4MIts3jwwQcBAP39/bjyyitx4YUX6v1a9wJa6vYyU1NTuPHGG/GDH/wAjYa70FX9mGWYfcWhoH3hte3i0rogInK3V6WOIyB3sWldEAG5i03rQm1AeNxd3vRSSOwEPpiyxC4ppQs1kyF2SSldCG9drKytw5JPIjDOjoiJSVZqlyh1gfNkpXYiUheXzoWuz5K6pHQuchtmpMuU6LIrqZuoeBMi4oQuePy+SOsyhc7vUMLlAkKXlMpFSRtHFxwvl/bcz0rpsmQOyE7p+AQIWkp/v0mTuiyZ46RJXabMcWLew83pKgZ+vwk9z2wHABSLRVx66aX4kz/5E/T09KS3p1FGS90+YmRkBF/96ldxxx13AACYaaB28ipUT1sDVnYTrSyp42TJ3V4pwcaRIXeJaV0QLndArOBlip3fTkZ6lzOtAwTETqT9FLHLSulCzSSJXUZK13bzpNQuS+oCt0tM7RJSurYmMlK7TKnzzpWW2qVJXVo6F7pdktRlpXMxt0/b8kt0aYukMD0pnYs9fi+ndcJCB8S/flKELiuVi5KU0qWlclGShI557wlZMuefM+FLmZDMAamlV1GZA5JLr8Iyxwm8bxv1JvoeeQZLnnzRDzLOO+88XHHFFVi+fLlYexpltNTtY5588knccMMNfhRNyxaqp6xB7eRVKJQhJHUcmxqYqpbaxG6fpHVBSEB0SGsv28y0LkpCepdYho1tIyW965DYxS51ItNujNjJCJ3fjEPcmamBDwihlK6tnYjYpZVe40hK7QSlzm8mJrUjDlCcILCqgo0kyF2c1InKXKuDgNlkMIPrs4mkczF9bJM6Ii4EoS5Fhr6KCl3w+E6kdXFiJyV0foeiDbdLp2gqFyQqdKKpXKhrMWVXkVQuSjSlC67hmClz/onbUzoZmePEpXS0mL3HaxsVB6Rpo/eJ57H6ye2Ynp4GALzkJS/Be9/7Xhx99NFy7WmU0VI3BzDGcM899+DLX/4ynn/+eQAA7Sqgtv5g4JSlKIq+sBGf2nV0aROFtROIwXy5kxY7oE3uhNO6tnZi0ru9Mb5Opb2I2ImUXROb4qmdZEoXaiModqIpXZRoaicpdUBL7ABvuQaRlC6OSEk2KnVZpdbEZnlaJ5vORfoWlDrpLcsCBCeqywqdf3wH0jogXIZVEjq/U97/PKUzwuvJycgcEBY6mVQuSjClU5E5ICx0wqlclEhKpyJznKDUSadzHLOJ3qe24LBNwxgbGwMArF27FldccQVe8YpXgORcE1Ejh5a6OcRxHPzXf/0Xvv71r2P7dnfcAe0tgW04COwlSzKXQQnC5Q5wN4GvVwv7pgSbREDuaNPILsPGESzNehIlLXZAOL0zGEiRdk7s8rx6PLEzDCad0rU11XT3d1V5Y/fh4+xstXWvALTErpFfVpjFAKYodUAotWOmK3XS6Vy0SQoUZql8OhfpFx9Xl+c+4oiMn0uj02ldLqEL9ssgaHbJp3JBqEVAAi921ed1fYDA7m61o/rYG02iLnP+yd2ULo/MAe7rPLjgsLTMUYqe517EMc8NY+dO90V60EEH4R3veAfOOeccvd7cHKGlbh5g2zZ++tOf4pvf/CZ27doFAGCDJdAzVoGtWywld4AreNO1EmrVovrWYR7EYCCGK2ZqDbgfzKxqgjRyzHQy3JIA8RIp9XYAVnZgVWzY9XxvOiTP4sR+IwAYwMbVdxIB3DaITZRSurYuOW4SkKcvvtjluIv594niFGA01Nvhpc3CNFOWOQ61gOIUUxc6rz9OoTNCBwCF2Xx/FCNuEqYqhUGsOsstdIwAzS5vf9wcLwt+/+bZbQEAakMETiXnYw4vNab5ZM5oujJHHHWZA9xjSYOobQvmUPQ8txXrXtzthxFLlizB2972Nlx44YWwLIV9zDUdQ0vdPKJer+PWW2/FjTfe6C+DwvpLoC/3kjtL/F2l2rQwPVsGdQywHPvCGhaDVbBBHQOUGupyB7TSsaYBUlX7tGclisKAu+Gz3bCACfkEkFkMpNtGseyOX3RsU1nwOiF2jBGwmqmWQgb70qGSOyPuZu/K25PBFcPCtCsuecTOFUSvPznEjlAmPi4vAafoikJpIk/M2zmhY8T9zpRH6hhxZ4RS072flfHSR8NmsGr5+tPsMuAobr0VFK88Au8UeV0bqC5Vb8jw3ncZAZp9inbJWq9FQgG7R91SfRE0GSD7+nYc9DyzFcdu3YkdO3YAcJcn+dM//VO87nWvQ6mk+KBpOoqWunnI7Owsbr31Vnz3u9/F6OgoAID1FkFfthLspKVAIftT0mEEM/UiGg3LlTpFuSMGg2lRWAXHHR+nKneEwbAYDNMBdUw4DUNJ7liBwhpoYLB/Bk3bxMxsCXbTlJY7ZjEYvU309NRAGUGzaSrLXS6x48PYKAFrGoBD1OSOktwpFIcZbrpq2Ah9oMhAHKAwRUITAJTkjrlC55aF3f9V5C6P1PG0iFoEhDI1qfNKnIxAeMmSJKKTI1SkjhFPXLyxh4CX0Mqmdd7fExxfZjbUxC6P0EXvU9V0jssc88r2ze7WmDUZuMyBj5/rYmpj57jMOW5/nBJTSuhCMgcA3iQrEYhto+fpF3DECzuwZ88eAMCiRYvw5je/GRdffDEqFbWxwJq9g5a6eUy9XsePf/xjfOc732mVZbsLoOtXgJ28DCinx9zVpoWZausdUlXuDIuhWGrNylWWu4DYAWjJHSAleEGxA+DLHQApwQuKHQBf7gD59E5Z7CIPA6NqqV0nU7qQHTIindoRCphV97jWhYpy50md34yi3KlIXVDmgu1ISV2czEWulyFuiKvZbI2rE23DKRFf5lpXSKZ1gTGL0XZKU3JWpSJ0ofszKLqSQuencgg8NxWEzgi+pwZnt8oKXVTmOEQ+pWuTOY7A65k0m+jd/DwOeX4YExMTAIClS5fiT/7kT3DhhRfqZG6eoqVuP6DRaOBnP/sZvv3tb2NkZAQAwIom2MlLQU9dAfTHv7iiUseRlbtgWheEy537s5jgEZPCLLSvIyeb3kXFjiMreFGx8/vDCBoNy+ubISR40mKXcNdLi12nU7q2C+VSOz+li70SciXZiNT5zUjKnYzUxclcsB1hqQuWWlNuI0LWWuCiaV2i0PG2RNO6JKHzkEnrZIQuSeT8i0S3zIoTuUC7MkIXTeWiNAYFOhV5bZHoW6RkSpcoc5yU17E5W0XvpuewcssIZmbc99eVK1firW99K84//3wUCgqT3jT7DC11+xG2beOOO+7Ad77zHX8pFBgE9NhFoKevBJZ3h24fLMHGweXO/zlF8KJpXRTh9C6S1rW1I5HesW4bS1ZMJF4vWp5NEju/TxKCx6UuU+4yPsxlxG6vpXQxNxBJ7VKlDhBP7ZjbVtoHNWFi4+1Epc4pps8EJYzBrANWNeV+ykrnIrfNQmQCelZaF1dujb+hQFqXIXR+nzLEzp8QAaQKXZbI+VcJuFOovJrYEFBblP76TUrlomSmdEmpXEyfslK6kPAlyRyQWHotjE2gb+Mz6N86AsdxO7NmzRr86Z/+Kc455xw9AWI/QUvdfgilFPfeey9uvvlmfxFjAKBr+8FOXwl2aD/grQ2UlNZFyUrvktK69r4ROLbZajNO8DLEzm/LMeHUTcAmsXKXlNZFEUnvmMVg9jXR3R0vdn6fBAUvNbUTTWdExG5vp3RtN8pO7TKlzr9hRmqXkNK1NSOQ2mVJXVo6F9dWYlonks5Fbp+EzGpCaWldVjrX1lZaWicodJwksctM50jrdiLnShM6WmhNVshMiDNSuqxULkii0GWlcjF9SkvpMlO5KMHXLWMoD+9C38ZnUNmx27/4xBNPxOWXX47TTz9d78+6n6Glbj9n06ZNuPnmm/GrX/3K/3bFlnaB4w96NAAALs9JREFUnroc7PjFcCwrNa2LkiZ3WWldFC54cXKXVIaNbYfLHdAmeKJix0kTPFGx8/uVIXiJYifxYZ01gWKfpXQxB8SldrHj6dJIS+0Epc5vKkXukqRORuaCbbVJnUw619Zg+0Uqy0PGpXWyQuceFJPWxUyIEG0rOr4uUegkRc4/LG6bKxmRC5w/TuhEU7kobWVX0VQupl9xKZ20zAF+SkdsB10vbMOpOyfx3HPPuc2YJs466yxcdtllegeI/RgtdQuE4eFhfP/738dtt92GatX99GJlE+zEpZh9yUGYLg1ItRdXmhVN66LEpneCaV1bWzGCJyt2nDjBkxU7v18JgtcmdooOFpva7euUru2g9tROOKWLEid3klLnNxUjd1GpU5E5v/2g1OWROb/B1o+qa30D4bROuNya1FYwrZNM56IE07o2oVMUOb+fAddREjm/obDQqYocx0/pZFO5mH4FUzrhEmsC1lgVPU89j9XbdmFychIAUKlUcPHFF+OP//iP9d6sCwAtdQuMqakp3HbbbbjlllswPDwMAGAA6ocsxuwJq9A4eJFfmhUlmN4BkErrooQED3DFQFLs/LaCgkcYrJIjLXacoOBR5hqGrNj5/QoIHqMEdsP0d9fIQ0jsOih0gKLU+Qe35M6sETWp4wTlzlCTOr+pgNyZNVfq8sic3y5lKE2y/DLnN+j+l0fogJbUKaVzce05gOHkEzqO2XD3zG12GXDK7mWqIufDAGblEDmOJ3QseH/lWLDY6WLhZWfU3t68g92UTimV4zCG8rZd6N30PLq37QL/yF++fDle//rX4zWveQ16e3tzdFIzn9BSt0BxHAf33XcffvjDH+Lee+/1L7f7K5g9YRWqx64EK0mu7ebtm2oYDIwRmGa+pdopJTAMhlLBxkw1344K1DHBKDAwMIOBSg1TdfXp9k3bRLVeQFe54Qptjk9bvkwKY8RNA3PCKAGzXYswJvMPXGYGAyu6q8vnhTgExXEDppoLRxpzP6gNpzOyYzQAa5Z1ZDssEHd3itwy57XlFPPJa7AtWgCsqlo619avAnEnhHSmwg8wb0JEzvacYmsbu1wLW8Pti10GaAm5RM5vzgGafSyfyPF+dbHWVokKMmfUG+h+eivWDU9i27Zt/uWnnnoqXv/61+NlL3uZ3sprAaKl7gBg69atuOWWW/Czn/0M09PTAABmGagdvgyzx61Ec+WAdHpHA/vKEgJlwTNNinLBhmm4x9ebVi7BsyyKoZ5Z9JVcs6g7Vi7BcygBpa1Pb1XBYwCaTQuU5pM7YjAUCg4IYahOlnOLHbMY0NsEa5juWJscu1oQm6A0argfuExsNmJmm967U26xo4ClFuKG4GlOYSrn26Ync265mcBMm00r0Faz272DaBEojXWgb17SZzSQe4cILl55BCy6BEmu51bguWSXXRHOAy9TM+Le/1R1+y4uch6s4sgLMGMo7RpDz1NbsWjrTtTrdQBAT08P/uiP/giXXHIJVq9erdY/zX6BlroDiGq1ijvuuAM//OEP8eyzz/qX2wNdmD1uJWrHrADtEhOg4Jg7QhhAmLLcmSZFX6WG/lINdcfCTMOVOlXB6640cMxidxsbmxmYbrp/k6rgOZR4KWX4HVZF8BiAer2gJHZc6CzLjQEcx8gldsxgYF0OSJHHHsi1XRmXOv/3nHIXV15Wlbu8Usd3GGAGvIkETC2RDMgcHwPmTi5RF4FmN/FLmm6p2U0lVdtzSqQlOkxd7LjQqcpcSOT4U1z1+ZQwGaXZo9S18Oxg765xyopCF5A5Vgr8cRLpnFGto+eZF/GSXTPYsmWLf/nhhx+O17/+9Tj33HP1zg8HCFrqDkAYY3jiiSdw22234c4772xNrDAI6msXY/a4g9BYswgw0j9BKSVggRSLy537s5zglYtNLO+dCl0WlLCmbQoLnmVRLO2bxsruidDleQSPix0nj+Cpip1hMJQr4WmJjmOgNlsEaxowpsTlrk3oAp1TSe0IJbCmCcxa+/2gKncZS+bJtaUodSGZi7QnldbFyFzrJIDRZHIlWALYXe6YPi50/lUqYhecWBFNrhhQnJT7mFAVuliRC/RD6jmU8RyRTel4SZUw+CIXpNkrd3+HUrlS5A8TGYNLKSrbdqHnqS3o3b7bX/2gXC7jVa96FV7zmtfg+OOPB5Gswmj2b7TUHeDMzs7izjvvxE9+8hM8/vjj/uVOTwnVY1agevQKOIPdsccG07oosoIXTOvikBW8JLHjqAheVOw4cZdlSZ6s2EVTura+OQaqE2VhsWMWA/pSLEIytYumdLG3yVhIuO32YsvmibUlKXVJMhdsT1jqiDuWrE3mIu0Jp3WRdC6pPeEybDSdiyKZ1skIXVDigBiRC/RB6Lkj+HwQFboskeMIpXRZIhckJaWzJqfR8/SLOGx4HLt3t9aWO/bYY3HRRRfh7LPPRnd3/Hu2ZuGjpU7j8+yzz+InP/kJbr/9dn+6OwA0lvWhdtRy1I5cDtoVlqloWheHqOBliR1HVPCyxI4jI3hJYhdENMUTHWeXJXR+33hqZxupJdnElC6mg6KpnYjUAXKpnezs3tSttASkLrjvZ9YkCCGpS0vnYtrLlLqUdK7tpiJpXVo6F0VA7ETHz6WmcQnnTn2+SAZRaUJHGPwJE1kix0kVOhmR48SkdEa1ju7nt6P72W0o7R73L+/v78f555+Piy66CGvXrs1uW7Pg0VKnaaPRaOC3v/0tbr/9dtx///2tRY0JQWPNEKpHr0Dt0CVAwUxN6+LIEjxRseNkCZ6o2HFEBM+dERuf0MXdNkpU8tJSO1GhC5KV2mWmdG0HpMtdWuk1CRG5U12yJe5hSZO6rFQujlSpk5C5VidSSrASMhftY2Jal5XOJfUxQezS0jnhNC7mfEDCc0SxohgndCoix4kVOgI4FW+MHBEUuSBeSkeaNrq2jqD72W3oGRn134dN08Qpp5yCiy66CGeccYbei1UTQkudJpWxsTHceeed+MUvfoGNGzf6l9OCifphS1E9ejlqK4fAID9YP0nwZMWOkyR4smLHCQpetH1ALLWLkpTixYmditD5fUsYa8cMBlahICWFNRcS5E40pYsjSe46sQZfaK2wGKlTkbnWwYBZd/eBbZ1EQeaCh0fTOkWZ8w+PS+tk0rk4YsQuTuik07iY84SeEx0YFhYUujwixwkJXV6Ra7WK8shudD+7DUuGR/3xzgBw9NFH47zzzsPZZ5+NoaEhxfY1Cx0tdRphtm7dil/84hf4xS9+4S9sDABOuYDaoctQPWwZ6gcNAQp7BUYFr7tSb5s4IUNU8OpNS0nsgtjMwGTD/XRtUhNT9ZKS2HGixzmMhMqxcRMjZHEcA7UZtyRLZkx1oQsSkDujakqndHFE5a6jCyuTsNTlkrkAflqXU+ZaHfXSOjufzIX6yACz6kpYLpmL9LM4yVrlVgLYZcU0LqF9QtERkePYZa9POUUuSLOPdUbkKEV5xx50vbAdq3dPYnx83L9q5cqVOO+88/DqV79aL0WiEUJLnUYaxhgee+wx/OIXv8CvfvUrTEy0RMkpF1BbuxTVw5a7gmeqCZ5pURQKNiyD5pI7oCV4ZctGX6kGmxoYKs3majMoeLPNIkZnulC0knZBF4NLHpc7AEopXRw8uesoDMB0AZVtZmcW4oX7Ye4mVt6CsJ3CS606IXO8PcMGiMPyy1wAarmD8/PKXBDCAGu2AzLHYd5iycTbbQI5JQ5eqmrA3wGkIzDAqjI0+gmcQmdEDnCfl3YXc7fvyiNyI7vRtWUYq3ZNhN5D+/v7cfbZZ+O8887Dscceq2evaqTQUqfJhW3beOihh/DLX/4Sv/nNbzoqeIwRgDAUi64sxS17IotDDdQdy1/suGTaGCjG7PQuQc2xMFrrBmUEe6a7ALhpYx7JC+5kkWdHCw6lBPV6wd13F+hICsKaBgq7CihMEL+9TggTcYDSOAMtuGlVR+SOdWjXBtZao4xQ1jmZ84Qr904QAUjgbyaO/PIiSfhPxzyhpCdxHGK7zx0j73cYT+Q4Tomg2YGJoJR/HyJAY4CClhVkzqEoj+xC95ZhrNg5jqmp1ntZf38/zjzzTLzyla/EySefDMvq4BNBc0ChpU7TMWzbxsMPP+wLXrCMQIsWagcvRu2QpaitWSy8RRljBLRpgNkEpEhR7nLLkXkEz6EGpptF1JoWCiZFd7FV4iwYjnKKV3MsvDg1gPGpCggJp2yqksflLtdWZZSgXivCafDFotGKQ/J8MFdNdD0fHXXuXacod4S6qZJZa/WvI3KXV+qCMucExpOZJJcscZkLttGRbdGaCPUZcB+TvGKnul9rVOIAN+Xj+/Mqy1xE4sAAq+aer9GfT+iCIkcL3p66XUxK6Eijicr2neh6cQeW7Z7wd/QBgMHBQZx55pk466yz8JKXvESLnKYjaKnT7BW44P3qV7/Cb37zG4yNjfnXMYOgvnLQFbxDlsLpS1/pnFICWgt/GpECRbm7JWOykhcUuyB5JS8odqH+BiRPVvDypHZRoYv2SVXuQildHIpyR5yEGZuBEqeS4KlIXUSKgjIX1y8Z4mTOP62i1CXJXKhtRbFTSeeiIkcj51USugSJi55XRehocHQCaZ/VygjQHMrurDU9i8qLI6i8uAM9u8b8WasAMDQ0hDPPPBOvetWrcMIJJ+i9VzUdR0udZq/jOA42btyI3/3ud7jrrrvwwgsvhK5vLupBde1S1A5eiubSPkT3oQ2mdUmQIkWl252KWCrYQoKXJHZBgpInKnhJYuf3lbSPlRMRPRW5cxwD1al0A1KRu9iULrZx7/aCcpcodZE2pdM7GaljnmiwBJGL6Yso1HKPyRIrGbETkblQHyTG1onKXFwSB7SLHEdK6IIiFyNx0X7ICF00jUtrNzGlYwzF3ePo2uaKXHE8/N6zZs0anHHGGTjjjDNw3HHHaZHT7FW01Gn2OVu3bsVdd92Fu+66C48++igobb1ROpUi6qsWobZmMeqrF/l70YqIHYcUKCo9rbUm0iRPROw4MilezbGwu9qDmm0lyl2ozxLlWtGSbFpKl9QHEbnLTOliG/eOzVrUV0TqAm0Kp3dZUscCgiEiczF9SENU5vzuCNy1sjLnty2Y1qWVWpPKqVlwmQNShE4gjUvqU5bQZaVxSe1Ghc6o1VHZvgvl4Z1YPT4bqkSYpol169bhjDPOwMtf/nI9a1WzT9FSp5lTJiYm8D//8z/43e9+h/vuuy+0LhMANJb0eYK3GPWlA6DUEhK7IFmS51ADVbsAhxEhueNEJQ9oF72s1C6xzxmSl5XayQpd9Nxpciec0sU27rUR06228XSS7aamd0lSJ5PKJZA2rk5W5kLtJlW2FWUu1HaK2MWlc6oSFyQ2nYsKHL9MYolKRoBGnztZJyp0KhIXbdvpYqAFG6Xdo57I7UJpdCJ0u66uLqxfvx5nnHEGTj/9dPT19UmdR6PpFFrqNPOGZrOJxx9/HPfeey/uu+8+PPXUU6HradFC7aBFqB60BLWli2H3drWVakVIkjyZ1C6JuDSvy2pIpXaxfU4o2RZMp03u8ghdtP2o3CmldLGNt37kgieV0qW0G5veBaVONZUTOCcQmMGqKHOcqNR1QuZC7UfEjgWFW6KUmkUonbPVUrgkoulcXolrNcxgVGdRmNyB8q4dWLRnsu0L5xFHHIFTTz0Vp512GtatW6d3dtDMC7TUaeYte/bswf3334/77rsP999/f2i5FACwu8uoLV+E2orFqC1fBKdHUZgCkkepAcbcxY87QVDy6raFsZkKmin7vMoQFT3ed7tp5Ra66HlAmCt0uzsgdG0n8P5zgOJEB9+OIrLl71DQCZGLnMeuEP/nTi0dArjS0mmZC0LN8Liy4OWdwF2gGTD4/Z1T4oIwA6guIbC7+MlySBwAszqL4p5dKI7uRmnPbpi1sMT19/f7Enfqqadi0aJFOXqv0ewdtNRp9gscx8HmzZtx33334fe//z0ef/xx2Hb4E67Z2+VJ3iLUli8C7VJbwZVYDGbR8RcDNkwHvd2d+SRijKDpmLBtE/VZ95s9MRjMQmcWGQYjrtxRgNqdEzs4BOZYAcUJgswN1hUwmkBlF4NVZ6Am0OjtsDjuDYhXeiWuGHVS5ny8+9lodmANtwC8z4C3G0YHICy8FRvx+luc7txHjC/jBJhZYaAxoN62UauitGc3iqO7UNyzG1Y1PD7WNE0cd9xxOO2007B+/XocccQRMBR2y9Fo9iVa6jT7JbVaDY8++igefPBB/OEPf8CmTZtCSwcAQLO/G7VlQ6gvHUJ96aB8udYh7qefyWB2tQSyE5JnOyZmZspwZizAYCCFsCXlFr1Oy13DQHm4VV7i5bROCZ5VBQY3u/VRZhA0e9w+zzvB80QOQGubLO/yju3YELg/g9umdWLx5E7JXFTg+GVmlbnlVtqZ50U0UTUc92+YWWGgvkjio4sxmLOzKI7vQWFsFKXR3bBmpkM3MU0TRx11FE466SScdNJJWLduHSoVtfRfo5krtNRpFgQzMzN45JFH8Ic//AF/+MMf8PTTTyP61LYrJdSXDrqSt2wQjaG+7H1qudgFiUgeoCZ6tmOiWiuAOqYrd6EGOyR6nZA7h8Act1CYjj+e2Mgld0YT6NrBUNnTXlucF4KXJHIRopvaS8MFLuF+JI56WpdH5vgEltBlnsBFLyMZf0PqeWJK4tG/V1joKEVhcgLFsT0ojO1BcWwUZiM8pIIQgiOPPNKXuBNOOAHd3R3YfkKjmUO01GkWJJOTk3j44Yfx2GOP4dFHH8WTTz7ZVq6llonG4gHUlg2ivmQQjcX9oOWYqZPULTmmrjORQ/RS5S7UYA7RU5W7DKEL9UUxvQumdGnsU8ETFLnoMdJpXUwql3oKSbGTlTlRgYteLytzIgIXhBnA7DIDjCBW6EijjuL4GIrjoyiM7UHfzDTq9bDEWZaFo446CscffzxOOOEEnHjiiejt7RXrsEazn6ClTnNAUK/XsWnTJjzyyCO+6AX3XuTYPRXUFw+gvngAjcX9aCzqByt4ohWX2qUhKXqhkqwosqLHCBzHAJiA3EkIXRRRwUtL6dJgBkGj1/B+7pDgqYhctF8iaZ2kyEURETsRmVMRuLY2BEqtsgIXhRnAzPJWOkdsG4XJcRTGx1CYGEdhYqxtPBwA9PX14fjjj8fxxx+PdevW4eijj0ap1ImNhDWa+YuWOs0BCaUUW7Zs8SVv48aN2LJlS1vJlhGgOdDrSd4AGoMDaPT3AqqrwmeInu2YqFaLoNSQk7tQg+2iB0RkL0vucghd23lTBE80pUsjKHju74KSF5A4QF3kom3GpnU5Ra7tNAliFydzcfIGyAtc9Ni4dC6vwEVhhKLWOwVgDIUJ919pdia0YDln9erVvsAdf/zxWLNmjZ7YoDng0FKn0XhMT09j06ZN2LhxI5588kls3LgRu3btarsdIwTN/h40BvvRGOxDY6gfzcE+0JLiyHOTwewOiw21DbCGAbPiqMtdlDjZYwSMEhhFxxW8ugljygJx0BGhixIUPLOultJlkSh5e0PiYqAW3HXeOixyUfjsUkJdkeSSFt3hIo+8tZ2TJ3MOQGhnBQ60AdKYAGlOgjQnQJoTKLLZtmETALBkyRIcffTROOaYY3DMMcfgyCOP1KVUjQZa6jSaVHbv3h2SvM2bN2NycjL2tnZ3xZU8T/aaA72we7oBQ7E0aDKQkgNWNUHqBirDJpwyUF/RgWmQQbjsMQJWN2BOWijM7N0JCWYNWPJQE6Vds2AFE/WhvVcWYwZBfcBAbdDYO8uOeBg20D3iWo1TJJhZvndTIsIAo+7KFS2Qjsobx7CBocenwCwD40e4kwgIZfnkjTHAmQVpTsAISpxTjb15b28vjj76aF/ijj76aCxevDhHBzSahYuWOo1GAsYYdu7ciaeffhpPP/00Nm/ejKeffhrDw8Oxt6emAbuvB43+XjQHetH0/re7u6Rkj9QNVHZ4kpDwiu2Y8FECY9Y9F3HIXhG84gSw6nsvuL9YJpxFrZRlb0ieUzIweXBnjS4oce7vDJXtrpjQioUdL+38chhc5NyfGYxG+u1F4OKWiMMAk2Dy8B65hhkDnBmQ5pT7z+b/T4OweCtcsWIFDj/8cBxxxBE47LDDcMQRR2DZsmUgCjvHaDQHIlrqNJoOMDU1hWeeeQZPPfWUL3wvvPACGo34T11qGmj29fiiZ/d1o9nbA7u3G8xKlg9SN1AZMWA2gO7t4ZqeXSaYXR7/4acsfHtB8Iy6m9J1P7o9/gaWCWexu3cms4yOCF6npC4ocsQBurbFDFaD2++pgyuYWdGBMYkdEDmzCQxunI69jjQd0IeeCF9mWSDHHwlmGdkyxxwQewawpz2BmwaxJ93/EV93LhaLWLt2rS9uhx9+OA477DD09EiKo0ajCaGlTqPZSziOg+HhYTz33HN4/vnn/X9psgcAdlcZzT5X8Jq93S3h6+kCTE+wUuQuti8lgpkVOYWvA4KXKXRRAoLHURE9FamLJnHuZa00Los8YqcicrLiFnu7JJljDmDPuimbPRP+30letqdYLOLggw/GIYccEvq3YsUKWFaHxopqNBofLXUazT4mKHsvvPACnn/+ebz44ovYunVr7DIrHEYI7O4K7J4u2N1dcHoqsEvdMGvdgNGNrt1FuR0zeH9KBDMrk49zSjHSFxA8IFvyjDowuNmBNUvFhS6JiOiJSJ6I1EUlLi2JE4VZBmZWVUBNpMpdUOLc39tFLk3aAHFxi8UyQdYdDGo1MLvcG/PmVEHsWRB7Biarxc445fT09GDVqlVtArd8+XKYqjPFNRqNNFrqNJp5xMTEhC94wf9ffPFFVKvpCREjBLTYBYIuMKsLzKyAmWUwqwKYZTCzAhBLWvyypA9wZ3w2Blof+kHJk07nZIlJ84Cw7AWlLi6Bc/ucX+KSiKZ2UYkzGwyLH04WNkBd2hgYYDKwIgWKDliJgpUcsJIDVBhYrwFGam6nUqhUKli1ahVWr16NVatWhf719/frcW8azTxAS51Gsx/AGMOePXuwbds2jIyMYGRkBMPDwxgeHsbIyAh27tzZtvdtbDvEBDPLABc+LntmGcwsAUbJ/V9S/trEjwEFz1GsGsOih6dhbR+V/bPzEZA9WjAxs6oMwJW6vSVvSRCHwhydRmPNkHsBBYx6YD/hhrywcVlDgYIVKFjR/YeS4/3sAEUKVqLu7TIwTRNLly7F8uXLsWLFCixbtgzLly/HypUrsWrVKgwNDWlx02jmOVrqNJoFgG3b2LNnT0j0duzYgV27dmH37t3YtWsXpqfTk6AgDAZgFsG45IX+LwJGEcwoAEYBzCgCRgEgyWU2s8lQGQlv22Q0nH0nepYJe2n/PjkVcSjMHePhCx0H9vBI6nGMMMCiYFbgf0/YfHErUFfULPd/SAzX6+npweLFi7F48WIsXboUK1aswPLly/1/ixYt0uPcNJr9HC11Gs0BQrVaxe7du33JCwrfnj17MD4+jrGxMczOqqVYjJiu5JGI7BkWGLEAwwKI6f9sUBPFMQYCE4AJwF2117AJrBdHQdDBVGhvSJ3jwNwxBhAGZjgAoWAGBWgD9sSom46ZDDAYGP/ZjEhb4HcoDj2rVCro7+/HokWLsGTJEl/cFi9eHPq9Uun8EisajWZ+oaVOo9GEqNVqvuDx/0dHR/2fx8bGMD09jampKUxNTWF6ejp1EL06Ruw/AgOgBKTuAFz9/D15ibeOn/e7dzkhBmiZ79/lveUR1vrZ+5/BvYwwCjSbrqgR5m+l4P8M6l3W2b+YEIKenh709vaip6cHAwMDGBwcxMDAgP9z8PeBgQEtaxqNxkdLnUajyQWlFDMzMyHJ4z9PTk6iWq2iWq1idnY28+f99e3INE1UKhX/X7lcDv3O/3V1dYWkLfh/b28vuru79X6lGo1GGS11Go1mXsAYQ7PZRKPRQKPRCP0c/L3ZbKJer8O2bVBKQSkFYwyO44Ax5l9GKQ1dZhgGCCGh//m/6OWWZaFYLKJQKKBQKKT+zP/pSQQajWau0VKn0Wg0Go1GswDQOb9Go9FoNBrNAkBLnUaj0Wg0Gs0CQEudRqPRaDQazQJAS51Go9FoNBrNAkBLnUaj0Wg0Gs0CQEudRqPRaDQazQJAS51Go9FoNBrNAkBLnUaj0Wg0Gs0CQEudRqPRaDQazQJAS51Go9FoNBrNAkBLnUaj0Wg0Gs0CQEudRqPRaDQazQJAS51Go9FoNBrNAkBLnUaj0Wg0Gs0CQEudRqPRaDQazQJAS51Go9FoNBrNAkBLnUaj0Wg0Gs0CQEudRqPRaDQazQJAS51Go9FoNBrNAkBLnUaj0Wg0Gs0CQEudRqPRaDQazQJAS51Go9FoNBrNAkBLnUaj0Wg0Gs0CQEudRqPRaDQazQJAS51Go9FoNBrNAsCa6w5oNBzGGGq12lx3Q6PRaKQol8sghMx1NzQaLXWa+UOtVsP5558/193QaDQaKW6//XZUKpW57oZGo8uvGo1Go9FoNAsBndRp5iXF+5aCMO87BzFADAIQAzAIQAiIwa/zLicEMAgIv41/HfGP8f8BgcuM8PXugf5ljBD3qw8Jtxl3efAyRtxm/OsMt133cuJfx49h3mX+9UCrDcO7Pb8e4XOEjvG6z4yY60K3R6iPrctI23VtxyDYj8j1SLg8ob2kfrQdk9aufzlrPz5wjH99oC3mXY7Ace51LNAf93oSvM6/Lb+O+W2S4O0J86/zn2L8ct6cdxv3qcD83/kxhve7e537Oz/Ov44wELSOM7zL/H9g/nEGQehy93jaOg789hQmP8b7vdUW9dszA+2bcC83eXv+bSlM3iZ4P2jr9mi17bZJYcA9v3ude1t+PgIKkx8fOMYE3OPgnoffH/x391zM+xnedQyGd7+YIDAAmN6DbYB4lxGYhMCAAeI9cs2GiTf+r+XQaOYTWuo08xOHeG+vcKUOnoB5n5at6whgtAyGuIbkNcI/3Q20fWq3jClsErzN9k/5cJuJl0d/DtyGy1xA6touC0hY8PdoF8O3jznGSLku6c9r60fMn5x1HRSOybrOI0749qrUxV2P6O/MbzvYj+A5467zJRCB2wRv33YMizkXC/0LSl1LFL1/SdeBi5/bZFAAufwBXM7gS1HwOlfqaEuKSFCK3J8NQlzh8v6H/zPxj3PbgdcmPxbecfCPa7sucLnpCanp95NLHcuUumB7Jr8/EL7MQLCPgcdQo5kn6PKrRqPRaDQazQJAS51Go9FoNBrNAkBLnUaj0Wg0Gs0CQEudRqPRaDQazQJAS51Go9FoNBrNAkBLnUaj0Wg0Gs0CQEudRqPRaDQazQJAr1OnmZ+YDIy5i426666RwP+k9XUktM5b4GcEL2OBnwWuCyxaFlgiNnRd3OUs9HMQ73fGL28tlsZAAAb/2OD1fhuhxdVabcb+ztq6io6vU4eM64TPJXhd8JSpx7GMNllCH5MXHw6vLRe4zr+t+uLDrX4E1qmD+jp1DK3jGGHhf3D/d69D6HJKGEBoq03wc9HAenrebbzrGaF+ewi17/3Pz+X9bni34f8DaLuMBl7W/GdKAAr3ZU8D1xEkrVNH/AWDTbQeM/47X++O/wy02hBbfJigtfhw3OtSo5lbtNRp5iWN03bOdRf2DvwzU5Gok2g0nOBTi6bdcN4StGpdRNJoVNCvHI1Go9FoNJoFAGGM6b1ONPMCxhhqtdpcd2OfUKvV8LrXvQ4AcOutt6JcLs9xjzRzgX4eLAzK5TII0dm5Zu7R5VfNvIEQgkqlMtfd2OeUy+UD8u/WhNHPA41GkxddftVoNBqNRqNZAGip02g0Go1Go1kAaKnTaDQajUajWQBoqdNoNBqNRqNZAOjZrxqNRqPRaDQLAJ3UaTQajUaj0SwAtNRpNBqNRqPRLAC01Gk0Go1Go9EsALTUaTQajUaj0SwAtNRpNBqNRqPRLAC01Gk0Go1Go9EsALTUaTQajUaj0SwAtNRpNBqNRqPRLACsue6ARjNfmJiYwF133YUHHngAmzdvxo4dO+A4DgYGBnDUUUfhggsuwJlnnpnaxuzsLG6++Wb8+te/xsjICAzDwOrVq3H22WfjjW98IwqFQurxo6OjuOmmm3DPPfdgx44dKJVKWLt2LS644AJcdNFFIISkHr9t2zbcdNNNuP/++zE6OopKpYIjjzwSF198Mc466yzZu+SAZNOmTbj77ruxadMmvPjiixgfH8fMzAy6u7uxZs0anH766bjkkkvQ19eX2MZcP46bNm3Cf/zHf+Chhx7C+Pg4ent7cdxxx+ENb3gDXvrSl8reJRqNZj9B7yih0Xi86lWvguM4/u/FYhGmaaJarfqXrV+/Hp/61KdQLpfbjh8ZGcFf/MVfYGRkBABQLpdBKUWj0QAAHHHEEfj85z+P3t7e2PNv2rQJH/zgBzExMQEAqFQqaDQafp9OO+00XHfddYlieM899+DjH/84arUaAKC7uxvVahWUUgDAhRdeiA9/+MOZQnGg80//9E/40Y9+5P9eLBZhWRZmZ2f9y/r7+3Hdddfh+OOPbzt+rh/H2267DZ/73Of88/X09GBmZgb8rf7tb3873vnOd0rdJxqNZv9AS51G43HmmWfimGOOwR/90R/htNNOw8qVKwEAw8PD+Na3voWf/OQnAIDzzjsPf/u3fxs61rZtXHHFFXj22WexaNEifPSjH8Upp5wCSil++ctf4vrrr8fs7CxOP/10fPazn2079/T0NN761rdidHQUa9aswd/+7d/i6KOPRrPZxI9//GP8y7/8C2zbxiWXXIIPfOADbcdv374d73jHO1CtVrFu3Tp85CMfwerVq/3k8Bvf+AYA4KqrrsJb3vKWDt9zC4uf//znGBsbwwknnIA1a9b4Ej47O4vf/OY3+NKXvoTx8XEMDg7i29/+Nnp6evxj5/pxfOyxx/C+970PjuNgw4YNeP/734+lS5diYmICX/3qV/Gf//mfAIBrr70WZ5999l649zQazZzCNBoNY4yxBx54IPX666+/nm3YsIFt2LCBjYyMhK778Y9/7F/36KOPth17xx13+Nf//ve/b7v+q1/9KtuwYQM799xz2bZt29qu//d//3e2YcMGdtZZZ7EtW7a0Xf+pT32Kbdiwgb3uda9jk5OTbdd/9rOfZRs2bGAXXHBB7PUace69917/sbz99ttD18314/je976Xbdiwgb3tbW9jzWaz7fprrrmGbdiwgV166aXMtm2ZP1uj0ewH6IkSGo3HySefnHr9RRdd5P+8adOm0HU///nPAQAnnXRSbEnunHPOwYoVK0K3DXL77bf7t+MJYZA3vOENqFQqcBwHd9xxR+i6arWKX//61wCASy65JLa8+9a3vhUAMDMzg9/+9rfJf6Qmk+OOO87/edeuXaHr5vJx3L59Ox555BEAwOWXXw7Lah8yzY8fGRnBww8/nP6HajSa/Q4tdRqNIMVi0f+Zj28CgFqthsceewwAcPrpp8ceSwjB+vXrAQD3339/6LotW7Zgx44dAODfJkpXVxdOOOGE2OMfffRR1Ov11ONXrFiBgw8+OPZ4jRxcnADgoIMO8n+e68cx+HvS8evWrUNXV1fs8RqNZv9HS51GI8hDDz3k/3zooYf6P7/wwgu+5K1duzbxeH7d6OgoJicn/cufffbZttvEwc/5/PPPhy4PHh/sV9Lxzz33XOJtNPE0Gg0MDw/jBz/4AT796U8DcIXu5S9/uX+buX4c+e+Dg4MYHByMPdY0TaxZsyb2eI1Gs/+jlzTRaASYmprCjTfeCAD+AHrO7t27/Z+XLFmS2MbixYtDx/AlMfbs2SN1/MzMDGZnZ/3EhZ+/t7cXpVIp8/jg+TTpnHvuuf7s5SDr1q3D3/3d34XS27l+HPnxwedZHEuWLMGTTz6pnwcazQJEJ3UaTQaUUvyf//N/sGfPHhSLRfzVX/1V6PrgUhdpH8bBZVCCx+Q9ni+5ErfMStzxwWM16QwNDWFoaAiVSsW/7KSTTsL73vc+LFu2LHTbuX4c+e9Zx/O+6eeBRrPw0EmdRpPBP//zP+Puu+8GAPzVX/0VDjvssDnukWZf8R//8R/+z2NjY7j99tvx7//+77jyyivxZ3/2Z3jXu941h73TaDSaMDqp02hS+OIXv4gf/vCHAICrr746NAOWw8tnAPyB7nHwxWSjx+Q9nqdIwevTjg8eqxFncHAQl19+Oa6//noQQvDNb37Tl31g7h9H/nvW8bxv+nmg0Sw8tNRpNAnccMMN+O53vwsA+PM//3O86U1vir1dcAxTdImLIMGxd8FjFi1aJHV8d3d36AOZtzU1NZUqE/z44Pk08hx77LFYt24dAPiL+QJz/zjy44PPszh43/TzQKNZeGip02hi+NKXvoTvfOc7AID3vOc9uPzyyxNve/DBB8Mw3JdS2oxCft3Q0FBo39DgTMe04/nsyEMOOSR0efD44AzKpOPTZmZqxOATIbZt2+ZfNtePI/99bGwM4+Pjscc6joMtW7bEHq/RaPZ/tNRpNBG++MUv4uabbwbgCt2b3/zm1NuXy2V/weF777039jaMMdx3330AgFNPPTV03erVq/1B90nHV6tVf3206PHr1q3zB7/zc0QZGRnBCy+8EHu8Rp7t27cDCJcw5/pxDP6edP5HH33UnyChnwcazcJDS51GE+CLX/xiqOSaJXScCy64AADw4IMP4oknnmi7/pe//KUvAvy2HEIIzj//fADAnXfeieHh4bbjf/SjH6FarcI0Tbz61a8OXVepVPDKV74SAHDLLbdgenq67fibbroJgCshGzZsEPqbDkQcx/E3vk/igQcewMaNGwEAJ554on/5XD+OK1eu9Bc2/u53vwvbttuO//a3vw0AWL58OV7ykpek/p0ajWb/Q0udRuMRHEN39dVXp5Zco1xwwQU49NBDwRjDxz72MTzwwAMA3OVQfvnLX+L6668H4K70/9KXvrTt+MsvvxxDQ0Oo1Wr48Ic/7G9D1mw2ccstt+Bf//VfAQAXX3wxVq9e3Xb8O9/5TlQqFezZswcf+chHsHXrVgBuMvSNb3wDt956KwDgz/7sz2K3n9K47Ny5E+9617tw6623Yvv27SHB27FjB2688Ub8zd/8DRhj6OvraxtnOdeP45VXXgnTNPH000/j2muv9cfPTU5O4h//8R/9BO+qq66CaZp57y6NRjPPICzra6lGcwCwY8cOXHrppQAAwzAwMDCQevvLLrusLcUbHh7G+9//foyMjABwy7KUUn/x2iOOOAKf//znE6Vq06ZN+OAHP4iJiQkAbhrTaDT8xOXUU0/FddddF1rwNsg999yDj3/84/7sx56eHlSrVTiOAwC48MIL8eEPfxiEkKy744BleHgYl112mf97oVDwHwe+jhzgbtf1qU99CkceeWRbG3P9ON5222343Oc+59++p6cHMzMzvqC+/e1vxzvf+U6p+0Wj0ewfaKnTaND+YZ5F0gfj7Owsbr75Zvz617/GyMgICCFYvXo1zjnnHLzxjW9EoVBIbXd0dBQ33XQT7r77buzcuRPFYhGHHnooLrjgAlx44YX+hIwktm3bhptuugn3338/RkdHUalUcMQRR+C1r30tzjrrLOG/70Cl2WzirrvuwoMPPoiNGzdi9+7dmJiY8EX/sMMOwyte8Qq8+tWvTl1geK4fx02bNuG73/0uHn74YYyPj6O3txfHHXcc3vCGN8QmxRqNZmGgpU6j0Wg0Go1mAaDH1Gk0Go1Go9EsALTUaTQajUaj0SwAtNRpNBqNRqPRLAC01Gk0Go1Go9EsALTUaTQajUaj0SwAtNRpNBqNRqPRLAC01Gk0Go1Go9EsALTUaTQajUaj0SwAtNRpNBqNRqPRLAC01Gk0Go1Go9EsALTUaTQajUaj0SwAtNRpNBqNRqPRLAC01Gk0Go1Go9EsALTUaTQajUaj0SwAtNRpNBqNRqPRLAC01Gk0Go1Go9EsALTUaTQajUaj0SwAtNRpNBqNRqPRLAC01Gk0Go1Go9EsAP4/bC49zzA858YAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 600x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 600x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 600x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 2/2 [01:25<00:00, 42.71s/it]\n"
-     ]
-    }
-   ],
-   "source": [
-    "data_file = \"crab_bkg_binned_data_galactic.hdf5\"\n",
-    "data_yaml = \"inputs_crab.yaml\"\n",
-    "estimated_bg = instance.continuum_bg_estimation(data_file, data_yaml, psr, make_plots=True, e_loop=(2,3), s_loop=(4,6))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ca992944",
-   "metadata": {},
-   "source": [
-    "Finaly, let's save the estimated background to file:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "5deeee04",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "estimated_bg.write(\"estimated_background\",overwrite=True)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python [conda env:base] *",
-   "language": "python",
-   "name": "conda-base-py"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/tutorials/run_tutorials.yml b/docs/tutorials/run_tutorials.yml
index 8b28b456e..b5df904c9 100644
--- a/docs/tutorials/run_tutorials.yml
+++ b/docs/tutorials/run_tutorials.yml
@@ -225,16 +225,24 @@ tutorials:
           checksum: 57aa483c4fd42d8964ad71d57ef646b3
 
     background_continuum:
-      notebook: background_estimation/continuum_estimation/BG_estimation_example.ipynb
+      notebook: background_estimation/continuum_estimation/BG_estimationNN_example.ipynb
       wasabi_files:
-        COSI-SMEX/develop/Data/Orientation/DC3_final_530km_3_month_with_slew_15sbins_GalacticEarth_SAA.fits:
-          checksum: ca94ff1d7a73c1f41479aaf598807673
-        COSI-SMEX/cosipy_tutorials/background_estimation/crab_bkg_binned_data_galactic.hdf5:
-          checksum: 7450f8ecdf6bf14bffe22d0046d47d49
-        COSI-SMEX/cosipy_tutorials/background_estimation/inputs_crab.yaml:
-          checksum: 3b2c6ddd35d98346d9aac13ce3d59368
-        COSI-SMEX/develop/Data/Responses/SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.h5:
-          checksum: eb72400a1279325e9404110f909c7785
+        COSI-SMEX/cosipy_tutorials/background_estimationNN/crab_and_bg_combined_binned_data.h5:
+          checksum: f5ee6f356ebc30a3168a1697c4ec2bdc
+        COSI-SMEX/cosipy_tutorials/background_estimationNN/crab_binned_data.hdf5:
+          checksum: 405862396dea2be79d7892d6d5bb50d8
+        COSI-SMEX/cosipy_tutorials/background_estimationNN/crab_psr.h5:
+          checksum: 700ea171dd52a989deb0810b20302b0d
+        COSI-SMEX/DC3/Data/Backgrounds/Ge/Binned_BG_Files/Total_BG_3months_binned_for_continuum_filtered_with_SAAcut.hdf5:
+          checksum: 270ba55f0da6bc25d4919709ae6a31c2
+        COSI-SMEX/cosipy_tutorials/background_estimationNN/inputs_crab.yaml:
+          checksum: dd30283e1809ccc51d75ad22cf20e766
+        COSI-SMEX/cosipy_tutorials/background_estimationNN/crab_DC2_astromodel.yaml:
+          checksum: b8366b8e09ede0f29581d1ec466628c6
+        COSI-SMEX/DC3/Data/Orientation/DC3_final_530km_3_month_with_slew_15sbins_GalacticEarth_SAA.ori:
+          checksum: e5e71e3528e39b855b0e4f74a1a2eebe
+        COSI-SMEX/develop/Data/Responses/ResponseContinuum.o3.e100_10000.b10log.s10396905069491.m2284.filtered.nonsparse.binnedimaging.imagingresponse.h5:
+          checksum: 7121f094be50e7bfe9b31e53015b0e85
 
     background_line:
       notebook: background_estimation/line_background/line_background_estimation_example_notebook.ipynb
diff --git a/tests/background_estimation/test_continuum_background_estimation.py b/tests/background_estimation/test_continuum_background_estimation.py
deleted file mode 100644
index 34951ed4c..000000000
--- a/tests/background_estimation/test_continuum_background_estimation.py
+++ /dev/null
@@ -1,16 +0,0 @@
-import pytest
-from cosipy.background_estimation import ContinuumEstimation
-from cosipy import test_data
-
-def test_continuum_background_estimation():
-   
-    
-    instance = ContinuumEstimation() 
-     
-    # Test main method:
-    data_yaml = test_data.path / "inputs_crab_continuum_bg_estimation_testing.yaml"
-    data_file = test_data.path / "crab_bkg_binned_data_for_continuum_bg_testing.hdf5"
-    psr_file = test_data.path / "test_precomputed_response.h5"
-    psr = instance.load_psr_from_file(psr_file)
-    
-    instance.continuum_bg_estimation(data_file, data_yaml, psr, e_loop=(1,2), s_loop=(1,2))
diff --git a/tests/background_estimation/test_continuum_background_estimationNN.py b/tests/background_estimation/test_continuum_background_estimationNN.py
new file mode 100644
index 000000000..f4bb37f29
--- /dev/null
+++ b/tests/background_estimation/test_continuum_background_estimationNN.py
@@ -0,0 +1,47 @@
+import pytest
+
+
+pytest.importorskip("torch", reason="Optional torch dependencies not installed")
+
+from cosipy.background_estimation import ContinuumEstimationInterp, ContinuumEstimationNN
+from cosipy import test_data
+
+def test_continuum_background_estimation(tmp_path,monkeypatch):
+   
+    monkeypatch.chdir(tmp_path)
+
+    instance = ContinuumEstimationNN() 
+     
+    # Test main method:
+    data_yaml = test_data.path / "inputs_crab_continuum_bg_estimation_testing.yaml"
+    input_data = test_data.path / "crab_bkg_binned_data_for_continuum_bg_testing.hdf5"
+    psr_file = test_data.path / "test_precomputed_response.h5"
+   
+    instance.estimate_bg(input_data, psr_file, background_model=None,
+            training_mode="self", containment=0.6, epochs=1, model_type="gcn",
+            nn_model="new", nn_model_file=None, nn_model_savename="inpainting_nn_model",
+            lr=1e-3, self_mask_fraction=0.1, lambda_sup=0.5, lambda_self=0.5,
+            prefix="inpainted", visualize=False, em_bin=1, phi_bin=1,
+            evaluate_only=False, inpainted_file=None,
+            evaluate=False, show_plots=False)
+
+    instance.estimate_bg(input_data, psr_file, background_model=input_data,
+        training_mode="supervised", containment=0.6, epochs=1, em_bin=1, phi_bin=1,
+        evaluate=True,visualize=True)
+
+    instance.estimate_bg(input_data, psr_file, background_model=input_data,
+        training_mode="supervised", containment=0.6, epochs=1, em_bin=1, phi_bin=1,
+        nn_model="load", nn_model_file="inpainting_nn_model.pth", nn_model_savename="inpainting_nn_model_new")
+
+    instance.estimate_bg(input_data, psr_file, background_model=input_data,
+        training_mode="supervised", containment=0.6, epochs=1, em_bin=1, phi_bin=1,
+        nn_model="load_full", nn_model_file="inpainting_nn_model.pth", nn_model_savename="inpainting_nn_model_new")
+
+    instance.estimate_bg(input_data, psr_file, background_model=input_data,
+        training_mode="hybrid", containment=0.6, epochs=1, em_bin=1, phi_bin=1)
+    
+    instance.plot_training_loss("inpainting_nn_model_training_loss.npy",1,"training_loss",show_plot=False)
+
+    # Test simple inpainging method:
+    instance = ContinuumEstimationInterp()
+    instance.estimate_bg(input_data, psr_file)