diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..eb0805b
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,26 @@
+# vscode
+.vscode
+
+# JBD dep
+baredSC/covarianceImportanceSampling.py
+baredSC/acf.py
+baredSC/logpriors.py
+baredSC/scaledAdaptiveMetropolis.py
+
+# pycache
+baredSC/__pycache__
+baredSC/lib/__pycache__
+
+# build
+build
+
+# egg-info
+baredSC.egg-info
+
+# sphinx
+docs/_build
+# Files for doc
+docs/savefig
+
+# Big files
+example/*.npz
diff --git a/.readthedocs.yaml b/.readthedocs.yaml
new file mode 100644
index 0000000..8c652a9
--- /dev/null
+++ b/.readthedocs.yaml
@@ -0,0 +1,20 @@
+# .readthedocs.yaml
+# Read the Docs configuration file
+# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
+
+# Required
+version: 2
+
+# Build documentation in the docs/ directory with Sphinx
+sphinx:
+   configuration: docs/conf.py
+
+# Optionally build your docs in additional formats such as PDF
+formats:
+   - pdf
+
+# Optionally set the version of Python and requirements required to build your docs
+python:
+   version: 3.7
+   install:
+   - requirements: docs/requirements.txt
\ No newline at end of file
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..f288702
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
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+
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+  If the Program specifies that a proxy can decide which future
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+copy of the Program in return for a fee.
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+                     END OF TERMS AND CONDITIONS
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+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
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+  To do so, attach the following notices to the program.  It is safest
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+    <program>  Copyright (C) <year>  <name of author>
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+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
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+might be different; for a GUI interface, you would use an "about box".
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+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
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+<https://www.gnu.org/licenses/>.
+
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+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
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+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.
diff --git a/MANIFEST.in b/MANIFEST.in
new file mode 100644
index 0000000..47046ee
--- /dev/null
+++ b/MANIFEST.in
@@ -0,0 +1 @@
+graft baredSC
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..09e701e
--- /dev/null
+++ b/README.md
@@ -0,0 +1,4 @@
+baredSC
+========
+
+baredSC (Bayesian Approach to Retreive Expression Distribution of Single Cell) is a tool that uses a Monte-Carlo Markov Chain to estimate a confidence interval on the probability density function (PDF) of expression of one or two genes from single-cell RNA-seq data. It uses the raw counts and the total number of UMI for each cell. The PDF is approximated by a number of 1d or 2d gaussians provided by the user. The likelihood is estimated using the asumption that the raw counts follow a Poisson distribution of parameter equal to the proportion of mRNA for the gene in the cell multiplied by the total number of UMI identified in this cell.
diff --git a/baredSC/__init__.py b/baredSC/__init__.py
new file mode 100644
index 0000000..d31c2a3
--- /dev/null
+++ b/baredSC/__init__.py
@@ -0,0 +1,3 @@
+# -*- coding: utf-8 -*-
+
+from . _version import __version__
diff --git a/baredSC/_version.py b/baredSC/_version.py
new file mode 100644
index 0000000..1f356cc
--- /dev/null
+++ b/baredSC/_version.py
@@ -0,0 +1 @@
+__version__ = '1.0.0'
diff --git a/baredSC/baredSC_1d.py b/baredSC/baredSC_1d.py
new file mode 100644
index 0000000..941f622
--- /dev/null
+++ b/baredSC/baredSC_1d.py
@@ -0,0 +1,385 @@
+# Copyright 2021 Jean-Baptiste Delisle and Lucille Delisle
+import numpy as np
+import sys
+import argparse
+import os
+from itertools import permutations
+import time
+import datetime
+from tempfile import NamedTemporaryFile
+from shutil import copy
+from samsam import sam
+
+# Local imports
+from . common import get_data, permuted, get_Ax, get_prefix_suffix, \
+    plot_QC, get_bins_centers
+from . oned import logprob, extract_from_npz, write_evidence, \
+    get_pdf, plots_from_pdf
+from . _version import __version__
+
+
+# Main function: running the mcmc:
+def gauss_mcmc(data, col_gene,
+               nx, osampx, osampxpdf,
+               xmin, xmax, min_scale,
+               xscale, target_sum, nnorm,
+               nsamples_mcmc, nsamples_burn,
+               nsplit_burn, T0_burn, output,
+               seed):
+
+  # 1. Calculate the constants
+  nox = nx * osampx
+  noxpdf = nx * osampxpdf
+
+  # Generate the grids using max and size
+  x = get_bins_centers(xmin, xmax, nx)
+  dx = x[1] - x[0]
+  ox = get_bins_centers(xmin, xmax, nox)
+  oxpdf = get_bins_centers(xmin, xmax, noxpdf)
+  odxpdf = oxpdf[1] - oxpdf[0]
+
+  # Evaluate p(ki|Ni,xj) * dx (independent of the pdf)
+  Ax = get_Ax(data, col_gene, nx, x, dx, ox, xscale, target_sum)
+
+  # We want to get all possible permutation of the gaussians (3 parameters for each)
+  permutx = np.array([np.concatenate(([3 * i + np.arange(3) for i in v]))
+                      for v in permutations(np.arange(nnorm))])
+
+  np.random.seed(seed)
+  # 2. Init params
+  p0 = np.empty(3 * nnorm - 1)
+  # We start with equal amplitudes for all gaussians:
+  p0[2::3] = 1 / nnorm
+  # We start loc (mean for gaussian) randomly between xmin and max
+  p0[::3] = np.random.uniform(xmin, xmax, nnorm)
+  # We start with all scales at half the range of x scale
+  p0[1::3] = max(min_scale, (xmax - xmin) / 2)
+  # MCMC
+  # Burn phase to accurately evaluate the pref
+  nsamples_split = nsamples_burn // nsplit_burn
+  # Run a first mcmc from p0 with pref at p0
+  # With a maximum temperature
+  sk, dk = sam(p0, logprob, nsamples=nsamples_split,
+               pref=p0, wdist=np.ones_like(p0), permutx=permutx,
+               xmin=xmin, xmax=xmax, nx=nx, dx=dx,
+               noxpdf=noxpdf, oxpdf=oxpdf, odxpdf=odxpdf,
+               min_scale=min_scale,
+               Ax=Ax, temp=T0_burn)
+  # Use the last set of parameters as starting_point
+  # but potentially permut it so it is the closest to
+  # the new pref (the mean of the parameters in the last mcmc)
+  starting_point = permuted(sk[-1], dk['mu'], 1 / (np.diag(dk['cov'])), permutx, 3)
+  # Repeat the process to get a more accurate pref
+  # Decreasing the temperature
+  for ksplit in range(1, nsplit_burn):
+    sk, dk = sam(starting_point, logprob, nsamples=nsamples_split,
+                 mu0=dk['mu'], cov0=dk['cov'], scale0=dk['scale'],
+                 pref=dk['mu'], wdist=1 / (np.diag(dk['cov'])), permutx=permutx,
+                 xmin=xmin, xmax=xmax, nx=nx, dx=dx,
+                 noxpdf=noxpdf, oxpdf=oxpdf, odxpdf=odxpdf,
+                 min_scale=min_scale, Ax=Ax,
+                 temp=T0_burn ** (1 - ksplit / nsplit_burn))
+    starting_point = permuted(sk[-1], dk['mu'], 1 / (np.diag(dk['cov'])), permutx, 3)
+  # Start actual MCMC with temperature at 1
+  # Run MCMC
+  samples, diags = sam(starting_point, logprob, nsamples=nsamples_mcmc,
+                       mu0=dk['mu'], cov0=dk['cov'], scale0=dk['scale'],
+                       pref=dk['mu'], wdist=1 / (np.diag(dk['cov'])), permutx=permutx,
+                       xmin=xmin, xmax=xmax, nx=nx, dx=dx,
+                       noxpdf=noxpdf, oxpdf=oxpdf, odxpdf=odxpdf,
+                       min_scale=min_scale, Ax=Ax)
+  # save data to output
+  print("Saving")
+  start = time.time()
+  np.savez_compressed(output,
+                      samples=samples,
+                      diagnostics=diags,
+                      x=x,
+                      ox=ox,
+                      oxpdf=oxpdf,
+                      nsamples_burn=nsamples_burn,
+                      nsplit_burn=nsplit_burn,
+                      T0_burn=T0_burn)
+  print(f"Saved. It took {(time.time() - start):.2f} seconds.")
+
+  return(diags['mu'], diags['cov'], ox, oxpdf, x, diags['logprob'], samples)
+
+
+# Do some plots and exports in txt
+def plot(oxpdf, x, logprob_values, samples, title, output, data, col_gene,
+         removeFirstSamples, nsampInPlot, pretty_bins, osampx, xscale, target_sum):
+  # We assume x is equally spaced
+  dx = x[1] - x[0]
+  if pretty_bins is None:
+    nx = x.size
+    noxpdf = oxpdf.size
+    # We assume oxpdf is equally spaced
+    odxpdf = oxpdf[1] - oxpdf[0]
+  else:
+    xmax = x[-1] + dx / 2
+    xmin = x[0] - dx / 2
+    nx = pretty_bins
+    x = get_bins_centers(xmin, xmax, nx)
+    dx = x[1] - x[0]
+    noxpdf = nx
+    oxpdf = x
+    odxpdf = dx
+
+  nnorm = (samples.shape[1] + 1) // 3
+  p_names = [f'{pn}{i}' for i in range(nnorm) for pn in ['amp', 'mu', 'scale']][1:]
+
+  samples, Neff = plot_QC(logprob_values, samples, title, output,
+                          removeFirstSamples, nsampInPlot,
+                          p_names)
+
+  print("Computing pdf.")
+  start = time.time()
+  pdf = np.array([get_pdf(p, nx, noxpdf, oxpdf, odxpdf) for p in samples])
+  print(f"Done. It took {datetime.timedelta(seconds=int(time.time() - start))}.")
+
+  plots_from_pdf(x, pdf, title, output, data, col_gene, osampx, xscale, target_sum)
+
+
+def parse_arguments(args=None):
+  argp = argparse.ArgumentParser(
+      description=("Run mcmc to get the pdf for a given gene using a"
+                   " normal distributions."))
+  argprequired = argp.add_argument_group('Required arguments')
+  argpopt_data = argp.add_argument_group('Optional arguments to select input data')
+  argpopt_mcmc = argp.add_argument_group('Optional arguments to run MCMC')
+  argpopt_plot = argp.add_argument_group('Optional arguments to get plots and text outputs')
+  argpopt_loge = argp.add_argument_group('Optional arguments to get logevidence')
+  # Get data:
+  argprequired.add_argument('--input', default=None, required=True,
+                            help="Input table with one line per cell"
+                            " columns with raw counts and one column"
+                            " nCount_RNA with total number of UMI per cell"
+                            " optionally other meta data to filter.")
+  argprequired.add_argument('--geneColName', default=None, required=True,
+                            help="Name of the column with gene counts.")
+  argpopt_data.add_argument('--metadata1ColName', default=None,
+                            help="Name of the column with metadata1 to filter.")
+  argpopt_data.add_argument('--metadata1Values', default=None,
+                            help="Comma separated values for metadata1.")
+  argpopt_data.add_argument('--metadata2ColName', default=None,
+                            help="Name of the column with metadata2 to filter.")
+  argpopt_data.add_argument('--metadata2Values', default=None,
+                            help="Comma separated values for metadata2.")
+  argpopt_data.add_argument('--metadata3ColName', default=None,
+                            help="Name of the column with metadata3 to filter.")
+  argpopt_data.add_argument('--metadata3Values', default=None,
+                            help="Comma separated values for metadata3.")
+  # MCMC
+  argpopt_mcmc.add_argument('--xmin', default=0, type=float,
+                            help="Minimum value to consider in x axis.")
+  argpopt_mcmc.add_argument('--xmax', default=2.5, type=float,
+                            help="Maximum value to consider in x axis.")
+  argpopt_mcmc.add_argument('--xscale', default="Seurat", choices=['Seurat', 'log'],
+                            help="scale for the x-axis: Seurat (log(1+targetSum*X)) or log (log(X))")
+  argpopt_mcmc.add_argument('--targetSum', default=10**4, type=float,
+                            help="factor when Seurat scale is used: (log(1+targetSum*X)) (default is 10^4, use 0 for the median of nRNA_Counts)")
+  argpopt_mcmc.add_argument('--nx', default=100, type=int,
+                            help="Number of values in x to check how "
+                            "your evaluated pdf is compatible with the model.")
+  argpopt_mcmc.add_argument('--osampx', default=10, type=int,
+                            help="Oversampling factor of x values when evaluating "
+                            "pdf of Poisson distribution.")
+  argpopt_mcmc.add_argument('--osampxpdf', default=5, type=int,
+                            help="Oversampling factor of x values when evaluating "
+                            "pdf at each step of the MCMC.")
+  argpopt_mcmc.add_argument('--minScale', default=0.1, type=float,
+                            help="Minimal value of the scale of gaussians"
+                            " (Default is 0.1 but cannot be smaller than "
+                            "max of twice the bin size of pdf evaluation"
+                            " and half the bin size).")
+  argpopt_mcmc.add_argument('--nnorm', default=2, type=int,
+                            help="Number of gaussian to fit.")
+  argpopt_mcmc.add_argument('--nsampMCMC', default=100000, type=int,
+                            help="Number of samplings (iteractions) of mcmc.")
+  argpopt_mcmc.add_argument('--nsampBurnMCMC', default=None, type=int,
+                            help="Number of samplings (iteractions) in the "
+                            "burning phase of mcmc (Default is nsampMCMC / 4).")
+  argpopt_mcmc.add_argument('--nsplitBurnMCMC', default=10, type=int,
+                            help="Number of steps in the "
+                            "burning phase of mcmc.")
+  argpopt_mcmc.add_argument('--T0BurnMCMC', default=100.0, type=float,
+                            help="Initial temperature in the "
+                            "burning phase of mcmc (>1).")
+  argpopt_mcmc.add_argument('--seed', default=1, type=int,
+                            help="Change seed for another output.")
+  argpopt_mcmc.add_argument('--minNeff', default=None, type=float,
+                            help="Will redo the MCMC with 10 times more samples until "
+                            "Neff is greater that this value (Default is not set so will not rerun MCMC).")
+  # To save/get MCMC
+  argpopt_mcmc.add_argument('--force', default=None, action='store_true',
+                            help="Force to redo the mcmc even if output exists.")
+  argprequired.add_argument('--output', default=None, required=True,
+                            help="Ouput file basename (will be npz)"
+                            " with results of mcmc.")
+  # Plot
+  argpopt_plot.add_argument('--figure', default=None,
+                            help="Ouput figure filename.")
+  argpopt_plot.add_argument('--title', default=None,
+                            help="Title in figures.")
+  argpopt_plot.add_argument('--removeFirstSamples', default=None, type=int,
+                            help="Number of samples to ignore before making the plots"
+                            " (default is nsampMCMC / 4).")
+  argpopt_plot.add_argument('--nsampInPlot', default=100000, type=int,
+                            help="Approximate number of samples to use in plots.")
+  argpopt_plot.add_argument('--prettyBins', default=None, type=int,
+                            help="Number of bins to use in plots (Default is nx).")
+  # Calculate evidence
+  argpopt_loge.add_argument('--logevidence', default=None,
+                            help="Ouput file to put logevidence value.")
+  argpopt_loge.add_argument('--coviscale', default=1, type=float,
+                            help="Scale factor to apply to covariance of parameters"
+                            " to get random parameters in logevidence evaluation.")
+  argpopt_loge.add_argument('--nis', default=1000, type=int,
+                            help="Size of sampling of random parameters in logevidence evaluation.")
+  # Version
+  argp.add_argument('--version', action='version',
+                    version=__version__)
+  return(argp)
+
+
+def main(args=None):
+  original_args = sys.argv[1:]
+  args = parse_arguments().parse_args(args)
+  # Put default:
+  if args.nsampBurnMCMC is None:
+    args.nsampBurnMCMC = args.nsampMCMC // 4
+  if args.removeFirstSamples is None:
+    args.removeFirstSamples = args.nsampMCMC // 4
+  # Remove potential suffix:
+  args.output = args.output.removesuffix('.npz')
+  # Check incompatibilities:
+  if args.minNeff is not None and args.figure is None:
+    raise Exception("--minNeff requires --figure to be set.")
+  if args.xscale == 'Seurat' and args.xmin < 0:
+    raise Exception("--xmin negative is not "
+                    "compatible with --xscale Seurat "
+                    "as it is log(1+targetSum*X).")
+  if args.xscale == 'Seurat' and args.targetSum < 0:
+    raise Exception("--targetSum negative is not "
+                    "compatible with --xscale Seurat "
+                    "as it is log(1+targetSum*X).")
+  if args.xscale == 'log' and args.xmax > 0:
+    raise Exception("--xmax positive is not "
+                    "compatible with --xscale log "
+                    "as it is log(X) and X < 1.")
+  # Check the minScale
+  dx = (args.xmax - args.xmin) / args.nx
+  min_minScale = max(dx / args.osampxpdf * 2, dx / 2)
+  if args.minScale < dx / 2:
+    print(f"The value of minScale ({args.minScale})"
+          f" must be above half the bin size ((xmax - xmin) / nx / 2 = {dx / 2}).\n"
+          f"Minimum value will be used ({min_minScale}).")
+    args.minScale = min_minScale
+  if args.minScale < dx / args.osampxpdf * 2:
+    print(f"The value of minScale ({args.minScale})"
+          " must be above twice the bin size "
+          "of pdf evaluation ((xmax - xmin) / (nx * osampxpdf) * 2 ="
+          f" {dx / args.osampxpdf * 2}).\n"
+          f"Minimum value will be used ({min_minScale}).")
+    args.minScale = min_minScale
+
+  # Load data
+  print("Get raw data")
+  start = time.time()
+  data = get_data(args.input,
+                  args.metadata1ColName, args.metadata1Values,
+                  args.metadata2ColName, args.metadata2Values,
+                  args.metadata3ColName, args.metadata3Values,
+                  [args.geneColName])
+  print(f"Got. It took {(time.time() - start):.2f} seconds.")
+
+  # Check the directory of args.output is writtable:
+  if os.path.dirname(args.output) != '' and not os.access(os.path.dirname(args.output), os.W_OK):
+    raise Exception("The output is not writable,"
+                    " check the directory exists.")
+  if not os.path.exists(f'{args.output}.npz') or args.force:
+    # Run mcmc:
+    print("Run MCMC")
+    start = time.time()
+    results = gauss_mcmc(data, args.geneColName,
+                         args.nx, args.osampx, args.osampxpdf,
+                         args.xmin, args.xmax, args.minScale,
+                         args.xscale, args.targetSum, args.nnorm,
+                         args.nsampMCMC, args.nsampBurnMCMC,
+                         args.nsplitBurnMCMC, args.T0BurnMCMC,
+                         args.output, args.seed)
+    print(f"MCMC + Save took {datetime.timedelta(seconds=int(time.time() - start))}.")
+  else:
+    results = extract_from_npz(f'{args.output}.npz')
+    # return(mu, cov, ox, oxpdf, x, logprob_values, samples)
+    x = results[4]
+    dx = x[1] - x[0]
+    xmin = x[0] - dx / 2
+    xmax = x[-1] + dx / 2
+    try:
+      assert (results[0].size + 1) // 3 == args.nnorm, \
+          "nnorm value do not match what is in output."
+      assert results[2].size // results[4].size == args.osampx, \
+          "osampx value do not match what is in output."
+      assert results[3].size // results[4].size == args.osampxpdf, \
+          "osampxpdf value do not match what is in output."
+      assert x.size == args.nx, \
+          "nx value do not match what is in output."
+      assert abs(xmin - args.xmin) < dx, \
+          "xmin value do not match what is in output."
+      assert abs(xmax - args.xmax) < dx, \
+          "xmax value do not match what is in output."
+      assert (results[5].size == args.nsampMCMC + 1) or (args.minNeff is not None), \
+          "nsampMCMC value do not match what is in output."
+    except Exception as e:
+      raise Exception(f"Ouput file already exists and {e}"
+                      " Use --force to rerun MCMC.")
+    if args.minNeff is not None:
+      args.nsampMCMC = results[5].size - 1
+
+  if args.figure is not None:
+    # Check the directory of args.figure is writtable:
+    if os.path.dirname(args.figure) != '' and not os.access(os.path.dirname(args.figure), os.W_OK):
+      print("The output figure is not writable no figure/export will be done,"
+            " check the directory exists.")
+    else:
+      # Make the plots:
+      plot(*results[3:], args.title, args.figure, data, args.geneColName,
+           args.removeFirstSamples, args.nsampInPlot, args.prettyBins,
+           args.osampx, args.xscale, args.targetSum)
+      if args.minNeff is not None:
+        # Process the output to get prefix and suffix
+        file_prefix, file_suffix = get_prefix_suffix(args.figure)
+        with open(f'{file_prefix}_neff.txt', 'r') as f:
+          neff = float(f.read())
+        if neff < args.minNeff:
+          print("Neff is below the minimum required.")
+          temp_file = NamedTemporaryFile(delete=False, suffix=".npz").name
+          print(f"Results are moved to {temp_file}")
+          copy(f'{args.output}.npz', temp_file)
+          os.remove(f'{args.output}.npz')
+          # Change the args to multiply by 10 the nb of samples
+          new_args = original_args
+          if '--nsampMCMC' in new_args:
+            i = [i for i, v in enumerate(new_args) if v == '--nsampMCMC'][0]
+            new_args[i + 1] += '0'
+          else:
+            new_args.append('--nsampMCMC')
+            new_args.append(f'{args.nsampMCMC}0')
+          return main(new_args)
+  if args.logevidence is not None:
+    # Check the directory of args.logevidence is writtable:
+    if os.path.dirname(args.logevidence) != '' and not os.access(os.path.dirname(args.logevidence), os.W_OK):
+      print("The output logeveidence is not writable"
+            " check the directory exists.")
+    else:
+      # Evaluate the logevid:
+      write_evidence(data, args.geneColName, *results[:5], args.minScale, args.coviscale,
+                     args.nis, logprob, args.logevidence, args.seed, args.xscale, args.targetSum)
+
+
+if __name__ == "__main__":
+    args = None
+    if len(sys.argv) == 1:
+      args = ["--help"]
+    main(args)
diff --git a/baredSC/baredSC_2d.py b/baredSC/baredSC_2d.py
new file mode 100644
index 0000000..2191ffd
--- /dev/null
+++ b/baredSC/baredSC_2d.py
@@ -0,0 +1,548 @@
+# Copyright 2021 Jean-Baptiste Delisle and Lucille Delisle
+import numpy as np
+import pandas as pd
+import sys
+import argparse
+import os
+from itertools import permutations
+import time
+import datetime
+from tempfile import NamedTemporaryFile
+from shutil import copy
+from samsam import sam
+
+# Local imports
+from . common import get_data, permuted, get_Ax, get_prefix_suffix, \
+    plot_QC, get_bins_centers
+from . twod import logprob, extract_from_npz, write_evidence, \
+    get_pdf, plots_from_pdf
+from . _version import __version__
+
+
+# Main function: running the mcmc:
+def gauss_mcmc(data, genex, geney,
+               nx, osampx, osampxpdf, xmin, xmax,
+               ny, osampy, osampypdf, ymin, ymax,
+               min_scale_x, min_scale_y,
+               scale_prior, scale, target_sum, nnorm,
+               nsamples_mcmc, nsamples_burn,
+               nsplit_burn, T0_burn, output,
+               seed):
+
+  # 1. Calculate the constants
+  nox = nx * osampx
+  noy = ny * osampy
+
+  noxpdf = nx * osampxpdf
+  noypdf = ny * osampypdf
+
+  # Define nxy
+  nxy = nx * ny
+
+  # Generate the grids using max and size
+  x = get_bins_centers(xmin, xmax, nx)
+  dx = x[1] - x[0]
+  ox = get_bins_centers(xmin, xmax, nox)
+  oxpdf = get_bins_centers(xmin, xmax, noxpdf)
+  odxpdf = oxpdf[1] - oxpdf[0]
+
+  y = get_bins_centers(ymin, ymax, ny)
+  dy = y[1] - y[0]
+  oy = get_bins_centers(ymin, ymax, noy)
+  oypdf = get_bins_centers(ymin, ymax, noypdf)
+  odypdf = oypdf[1] - oypdf[0]
+
+  odxypdf = odxpdf * odypdf
+  oxypdf = np.array(np.meshgrid(oxpdf, oypdf)).transpose(1, 2, 0)
+
+  # Evaluate p(ki|Ni,xj) * dx (independent of the pdf)
+  Ax = get_Ax(data, genex, nx, x, dx, ox, scale, target_sum)
+  Ay = get_Ax(data, geney, ny, y, dy, oy, scale, target_sum)
+
+  # Evaluate p(ki,li|Ni,xj,yk)*dx*dy
+  Axy = Ax[:, None, :] * Ay[:, :, None]
+  Axy = Axy.reshape(-1, nxy)
+
+  # We want to get all possible permutation of the gaussians (6 parameters for each)
+  permutxy = np.array([np.concatenate(([6 * i + np.arange(6) for i in v]))
+                       for v in permutations(np.arange(nnorm))])
+
+  np.random.seed(seed)
+
+  # 2. Init params
+  p0 = np.empty(6 * nnorm - 1)
+  # The first amplitude is 1 - the sum of others
+
+  # We start with equal amplitudes for all gaussians:
+  p0[5::6] = 1 / nnorm
+  # We start loc (mean for gaussian) randomy between xmin and max
+  p0[::6] = np.random.uniform(xmin, xmax, nnorm)
+  p0[1::6] = np.random.uniform(ymin, ymax, nnorm)
+  # We start with all scales at half the axis
+  p0[2::6] = max(max(xmax - xmin, ymax - ymin) / 2, min_scale_x)
+  p0[3::6] = max(max(xmax - xmin, ymax - ymin) / 2, min_scale_y)
+  # And correlation at 0
+  p0[4::6] = 0.0
+
+  # MCMC
+  # Burn phase to accuratey evaluate the pref
+  nsamples_split = nsamples_burn // nsplit_burn
+  # Run a first mcmc from p0 with pref at p0
+  sk, dk = sam(p0, logprob, nsamples=nsamples_split,
+               pref=p0, wdist=np.ones_like(p0),
+               permutxy=permutxy,
+               xmin=xmin, xmax=xmax, nx=nx, dx=dx,
+               ymax=ymax, ymin=ymin, ny=ny, dy=dy,
+               nxy=nxy, noxpdf=noxpdf, noypdf=noypdf,
+               odxpdf=odxpdf, odypdf=odypdf,
+               oxypdf=oxypdf, odxypdf=odxypdf,
+               min_scale_x=min_scale_x, min_scale_y=min_scale_y,
+               scale_prior=scale_prior,
+               Axy=Axy, temp=T0_burn)
+  # Use the last set of parameters as starting_point
+  # but potentialy permut it so it is the closest to
+  # the new pref (the mean of the parameters in the last mcmc)
+  starting_point = permuted(sk[-1], dk['mu'], 1 / (np.diag(dk['cov'])),
+                            permutxy, 6)
+  # Repeat the process to get a more accurate pref
+  for ksplit in range(1, nsplit_burn):
+    sk, dk = sam(starting_point, logprob, nsamples=nsamples_split,
+                 mu0=dk['mu'], cov0=dk['cov'], scale0=dk['scale'],
+                 pref=dk['mu'], wdist=1 / (np.diag(dk['cov'])),
+                 permutxy=permutxy,
+                 xmin=xmin, xmax=xmax, nx=nx, dx=dx,
+                 ymax=ymax, ymin=ymin, ny=ny, dy=dy,
+                 nxy=nxy, noxpdf=noxpdf, noypdf=noypdf,
+                 odxpdf=odxpdf, odypdf=odypdf,
+                 oxypdf=oxypdf, odxypdf=odxypdf,
+                 min_scale_x=min_scale_x, min_scale_y=min_scale_y,
+                 scale_prior=scale_prior,
+                 Axy=Axy, temp=T0_burn ** (1 - ksplit / nsplit_burn))
+    starting_point = permuted(sk[-1], dk['mu'], 1 / (np.diag(dk['cov'])),
+                              permutxy, 6)
+  # Start actual MCMC with temperature at 1
+  # Run MCMC
+  samples, diags = sam(starting_point, logprob, nsamples=nsamples_mcmc,
+                       mu0=dk['mu'], cov0=dk['cov'], scale0=dk['scale'],
+                       pref=dk['mu'], wdist=1 / (np.diag(dk['cov'])),
+                       permutxy=permutxy,
+                       xmin=xmin, xmax=xmax, nx=nx, dx=dx,
+                       ymax=ymax, ymin=ymin, ny=ny, dy=dy,
+                       nxy=nxy, noxpdf=noxpdf, noypdf=noypdf,
+                       odxpdf=odxpdf, odypdf=odypdf,
+                       oxypdf=oxypdf, odxypdf=odxypdf,
+                       min_scale_x=min_scale_x, min_scale_y=min_scale_y,
+                       scale_prior=scale_prior,
+                       Axy=Axy)
+  # save data to output
+  print("Saving")
+  start = time.time()
+  np.savez_compressed(output,
+                      samples=samples,
+                      diagnostics=diags,
+                      x=x,
+                      ox=ox,
+                      oxpdf=oxpdf,
+                      y=y,
+                      oy=oy,
+                      oypdf=oypdf,
+                      nsamples_burn=nsamples_burn,
+                      nsplit_burn=nsplit_burn,
+                      T0_burn=T0_burn)
+  print(f"Saved. It took {(time.time() - start):.2f} seconds.")
+
+  return(diags['mu'], diags['cov'], ox, oy,
+         oxpdf, oypdf, x, y,
+         diags['logprob'], samples)
+
+
+# Do some plots and exports in txt
+def plot(oxpdf, oypdf, x, y, logprob_values, samples,
+         title, output, genex, geney, splitys,
+         removeFirstSamples, nsampInPlot,
+         log1pColorScale, pretty_bins_x, pretty_bins_y):
+  # We assume x and y are equally spaced
+  dx = x[1] - x[0]
+  dy = y[1] - y[0]
+  if pretty_bins_x is None:
+    nx = x.size
+    noxpdf = oxpdf.size
+    # We assume oxpdf is equally spaced
+    odxpdf = oxpdf[1] - oxpdf[0]
+  else:
+    xmin = x[0] - dx / 2
+    xmax = x[-1] + dx / 2
+    nx = pretty_bins_x
+    x = get_bins_centers(xmin, xmax, nx)
+    dx = x[1] - x[0]
+    noxpdf = nx
+    oxpdf = x
+    odxpdf = dx
+  if pretty_bins_y is None:
+    ny = y.size
+    noypdf = oypdf.size
+    # We assume oypdf is equally spaced
+    odypdf = oypdf[1] - oypdf[0]
+  else:
+    ymin = y[0] - dy / 2
+    ymax = y[-1] + dy / 2
+    ny = pretty_bins_y
+    y = get_bins_centers(ymin, ymax, ny)
+    dy = y[1] - y[0]
+    noypdf = ny
+    oypdf = y
+    odypdf = dy
+
+  odxypdf = odxpdf * odypdf
+  oxypdf = np.array(np.meshgrid(oxpdf, oypdf)).transpose(1, 2, 0)
+
+  p_names = [f'{pn}{i}' for i in range((samples.shape[1] + 1) // 6)
+             for pn in ['xy_amp', 'xy_mux', 'xy_muy', 'xy_scalex', 'xy_scaley', 'xy_corr']][1:]
+
+  samples, Neff = plot_QC(logprob_values, samples, title, output,
+                          removeFirstSamples, nsampInPlot,
+                          p_names)
+  print("Computing pdf.")
+  start = time.time()
+  pdf = np.array([get_pdf(p, nx, ny, noxpdf, noypdf, oxypdf, odxypdf) for p in samples])
+  print(f"Done. It took {datetime.timedelta(seconds=int(time.time() - start))}.")
+
+  # Process the output to get prefix and suffix
+  file_prefix, file_suffix = get_prefix_suffix(output)
+
+  # 1, 2, 3 sigma:
+  qm = np.array([(1 - lvl) / 2 for lvl in [0.6827, 0.9545, 0.9973]])
+  qp = 1 - qm
+
+  # Export the parameters median and 1 sigma
+  p_summary = pd.DataFrame(columns=['name', 'low', 'median', 'high'])
+  p_summary['name'] = [f'{pn}{i}' for i in range(int((samples.shape[1] + 1) / 6)) for pn in ['xy_amp', 'xy_mux', 'xy_muy', 'xy_scalex', 'xy_scaley', 'xy_corr']][1:]
+  p_summary['low'] = np.quantile(samples, qm[0], axis=0)
+  p_summary['high'] = np.quantile(samples, qp[0], axis=0)
+  p_summary['median'] = np.median(samples, axis=0)
+  p_summary.to_csv(f'{file_prefix}_p.txt',
+                   index=False,
+                   header=True, sep='\t')
+  plots_from_pdf(x, y, pdf, title, output,
+                 genex, geney, log1pColorScale, splitys,
+                 Neff)
+
+
+def parse_arguments(args=None):
+  argp = argparse.ArgumentParser(
+      description=("Run mcmc to get the pdf in 2D for 2 given genes using a"
+                   " normal distributions."))
+  argprequired = argp.add_argument_group('Required arguments')
+  argpopt_data = argp.add_argument_group('Optional arguments to select input data')
+  argpopt_mcmc = argp.add_argument_group('Optional arguments to run MCMC')
+  argpopt_plot = argp.add_argument_group('Optional arguments to get plots and text outputs')
+  argpopt_loge = argp.add_argument_group('Optional arguments to get logevidence')
+  # To get the data
+  argprequired.add_argument('--input', default=None, required=True,
+                            help="Input table with one line per cell"
+                            " columns with raw counts and one column"
+                            " nCount_RNA with total number of UMI per cell"
+                            " optionaly other meta data to filter.")
+  argprequired.add_argument('--geneXColName', default=None, required=True,
+                            help="Name of the column with gene counts for gene in x.")
+  argprequired.add_argument('--geneYColName', default=None, required=True,
+                            help="Name of the column with gene counts for gene in y.")
+  argpopt_data.add_argument('--metadata1ColName', default=None,
+                            help="Name of the column with metadata1 to filter.")
+  argpopt_data.add_argument('--metadata1Values', default=None,
+                            help="Comma separated values for metadata1.")
+  argpopt_data.add_argument('--metadata2ColName', default=None,
+                            help="Name of the column with metadata2 to filter.")
+  argpopt_data.add_argument('--metadata2Values', default=None,
+                            help="Comma separated values for metadata2.")
+  argpopt_data.add_argument('--metadata3ColName', default=None,
+                            help="Name of the column with metadata3 to filter.")
+  argpopt_data.add_argument('--metadata3Values', default=None,
+                            help="Comma separated values for metadata3.")
+  # MCMC
+  argpopt_mcmc.add_argument('--xmin', default=0, type=float,
+                            help="Minimum value to consider in x axis.")
+  argpopt_mcmc.add_argument('--xmax', default=2.5, type=float,
+                            help="Maximum value to consider in x axis.")
+  argpopt_mcmc.add_argument('--nx', default=50, type=int,
+                            help="Number of values in x to check how "
+                            "your evaluated pdf is compatible with the model.")
+  argpopt_mcmc.add_argument('--osampx', default=10, type=int,
+                            help="Oversampling factor of x values when evaluating "
+                            "pdf of Poisson distribution.")
+  argpopt_mcmc.add_argument('--osampxpdf', default=4, type=int,
+                            help="Oversampling factor of x values when evaluating "
+                            "pdf at each step of the MCMC.")
+  argpopt_mcmc.add_argument('--minScalex', default=0.1, type=float,
+                            help="Minimal value of the scale of gaussians on x"
+                            " (Default is 0.1 but cannot be smaller than "
+                            "max of twice the bin size of pdf evaluation"
+                            " and half the bin size on x axis).")
+  argpopt_mcmc.add_argument('--ymin', default=0, type=float,
+                            help="Minimum value to consider in y axis.")
+  argpopt_mcmc.add_argument('--ymax', default=2.5, type=float,
+                            help="Maximum value to consider in y axis.")
+  argpopt_mcmc.add_argument('--ny', default=50, type=int,
+                            help="Number of values in y to check how "
+                            "your evaluated pdf is compatible with the model.")
+  argpopt_mcmc.add_argument('--osampy', default=10, type=int,
+                            help="Oversampling factor of y values when evaluating "
+                            "pdf of Poisson distribution.")
+  argpopt_mcmc.add_argument('--osampypdf', default=4, type=int,
+                            help="Oversampling factor of y values when evaluating "
+                            "pdf at each step of the MCMC.")
+  argpopt_mcmc.add_argument('--minScaley', default=0.1, type=float,
+                            help="Minimal value of the scale of gaussians on yx"
+                            " (Default is 0.1 but cannot be smaller than "
+                            "max of twice the bin size of pdf evaluation"
+                            " and half the bin size on y axis).")
+  argpopt_mcmc.add_argument('--scalePrior', default=0.3, type=float,
+                            help="Scale of the truncnorm used in the prior for "
+                            "the correlation.")
+  argpopt_mcmc.add_argument('--scale', default="Seurat", choices=['Seurat', 'log'],
+                            help="scale for the x-axis and y-axis: Seurat (log(1+targetSum*X)) or log (log(X))")
+  argpopt_mcmc.add_argument('--targetSum', default=10**4, type=float,
+                            help="factor when Seurat scale is used: (log(1+targetSum*X)) (default is 10^4, use 0 for the median of nRNA_Counts)")
+  argpopt_mcmc.add_argument('--nnorm', default=1, type=int,
+                            help="Number of gaussian 2D to fit.")
+  argpopt_mcmc.add_argument('--nsampMCMC', default=100000, type=int,
+                            help="Number of samplings (iteractions) of mcmc.")
+  argpopt_mcmc.add_argument('--nsampBurnMCMC', default=None, type=int,
+                            help="Number of samplings (iteractions) in the "
+                            "burning phase of mcmc (Default is nsampMCMC / 4).")
+  argpopt_mcmc.add_argument('--nsplitBurnMCMC', default=10, type=int,
+                            help="Number of steps in the "
+                            "burning phase of mcmc.")
+  argpopt_mcmc.add_argument('--T0BurnMCMC', default=100.0, type=float,
+                            help="Initial temperature in the "
+                            "burning phase of mcmc.")
+  argpopt_mcmc.add_argument('--seed', default=1, type=int,
+                            help="Change seed for another output.")
+  argpopt_mcmc.add_argument('--minNeff', default=None, type=float,
+                            help="Will redo the MCMC with 10 times more samples until "
+                            "Neff is greater that this value (Default is not set so will not rerun MCMC).")
+  # To save/get MCMC
+  argpopt_mcmc.add_argument('--force', default=None, action='store_true',
+                            help="Force to redo the mcmc even if output exists.")
+  argprequired.add_argument('--output', default=None, required=True,
+                            help="Ouput file basename (will be npz)"
+                            " with results of mcmc.")
+  # To display the results:
+  argpopt_plot.add_argument('--figure', default=None,
+                            help="Ouput figure basename.")
+  argpopt_plot.add_argument('--title', default=None,
+                            help="Title in figures.")
+  argpopt_plot.add_argument('--splity', default=None, nargs='+', type=float,
+                            help="Threshold value to plot the density for genex"
+                            " for 2 categories in geney values.")
+  argpopt_plot.add_argument('--removeFirstSamples', default=None, type=int,
+                            help="Number of samples to ignore before making the plots"
+                            " (default is nsampMCMC / 4).")
+  argpopt_plot.add_argument('--nsampInPlot', default=100000, type=int,
+                            help="Approximate number of samples to use in plots.")
+  argpopt_plot.add_argument('--prettyBinsx', default=None, type=int,
+                            help="Number of bins to use in x in plots (Default is nx).")
+  argpopt_plot.add_argument('--prettyBinsy', default=None, type=int,
+                            help="Number of bins to use in y in plots (Default is ny).")
+  argpopt_plot.add_argument('--log1pColorScale', action='store_true',
+                            help="Use log1p color scale instead of linear color scale.")
+  # Calculate evidence
+  argpopt_loge.add_argument('--logevidence', default=None,
+                            help="Ouput file to put logevidence value.")
+  argpopt_loge.add_argument('--coviscale', default=1, type=float,
+                            help="Scale factor to appy to covariance of parameters"
+                            " to get random parameters in logevidence evaluation.")
+  argpopt_loge.add_argument('--nis', default=1000, type=int,
+                            help="Size of sampling of random parameters in logevidence evaluation.")
+  # Version
+  argp.add_argument('--version', action='version',
+                    version=__version__)
+
+  return(argp)
+
+
+def main(args=None):
+  original_args = sys.argv[1:]
+  args = parse_arguments().parse_args(args)
+  # Put default:
+  if args.nsampBurnMCMC is None:
+    args.nsampBurnMCMC = args.nsampMCMC // 4
+  if args.removeFirstSamples is None:
+    args.removeFirstSamples = args.nsampMCMC // 4
+  # Remove potential suffix:
+  args.output = args.output.removesuffix('.npz')
+  # Check incompatibilities:
+  if args.minNeff is not None and args.figure is None:
+    raise Exception("--minNeff requires --figure to be set.")
+  if args.scale == 'Seurat' and args.targetSum < 0:
+    raise Exception("--targetSum negative is not "
+                    "compatible with --xscale Seurat "
+                    "as it is log(1+targetSum*X).")
+  if args.scale == 'Seurat' and args.xmin < 0:
+    raise Exception("--xmin negative is not "
+                    "compatible with --scale Seurat "
+                    "as it is log(1+targetSum*X).")
+  if args.scale == 'log' and args.xmax > 0:
+    raise Exception("--xmax positive is not "
+                    "compatible with --scale log "
+                    "as it is log(X) and X < 1.")
+  if args.scale == 'Seurat' and args.ymin < 0:
+    raise Exception("--ymin negative is not "
+                    "compatible with --scale Seurat "
+                    "as it is log(1+targetSum*X).")
+  if args.scale == 'log' and args.ymax > 0:
+    raise Exception("--ymax positive is not "
+                    "compatible with --scale log "
+                    "as it is log(X) and X < 1.")
+  # Check the minScales
+  dx = (args.xmax - args.xmin) / args.nx
+  min_minScalex = max(dx / args.osampxpdf * 2, dx / 2)
+  if args.minScalex < dx / 2:
+    print(f"The value of minScale ({args.minScalex})"
+          " must be above half the bin size in x"
+          f"((xmax - xmin) / nx / 2 = {dx / 2}).\n"
+          f"Minimum value will be used ({min_minScalex}).")
+    args.minScalex = min_minScalex
+  if args.minScalex < dx / args.osampxpdf * 2:
+    print(f"The value of minScale ({args.minScalex})"
+          " must be above twice the bin size "
+          "of pdf evaluation in x((xmax - xmin) / (nx * osampxpdf) * 2 ="
+          f" {dx / args.osampxpdf * 2}).\n"
+          f"Minimum value will be used ({min_minScalex}).")
+    args.minScalex = min_minScalex
+  dy = (args.ymax - args.ymin) / args.ny
+  min_minScaley = max(dy / args.osampypdf * 2, dy / 2)
+  if args.minScaley < dy / 2:
+    print(f"The value of minScale ({args.minScaley})"
+          " must be above half the bin size in y"
+          f"((ymax - ymin) / ny / 2 = {dy / 2}).\n"
+          f"Minimum value will be used ({min_minScaley}).")
+    args.minScaley = min_minScaley
+  if args.minScaley < dy / args.osampypdf * 2:
+    print(f"The value of minScale ({args.minScaley})"
+          " must be above twice the bin size "
+          "of pdf evaluation in y((ymax - ymin) / (ny * osampypdf) * 2 ="
+          f" {dy / args.osampypdf * 2}).\n"
+          f"Minimum value will be used ({min_minScaley}).")
+    args.minScaley = min_minScaley
+  # Load data
+  print("Get raw data")
+  start = time.time()
+  data = get_data(args.input,
+                  args.metadata1ColName, args.metadata1Values,
+                  args.metadata2ColName, args.metadata2Values,
+                  args.metadata3ColName, args.metadata3Values,
+                  [args.geneXColName, args.geneYColName])
+  print(f"Got. It took {(time.time() - start):.2f} seconds.")
+
+  # Check the directory of args.output is writtable:
+  if os.path.dirname(args.output) != '' and not os.access(os.path.dirname(args.output), os.W_OK):
+    raise Exception("The output is not writable,"
+                    " check the directory exists.")
+  if not os.path.exists(f'{args.output}.npz') or args.force:
+    # Run mcmc:
+    print("Run MCMC")
+    start = time.time()
+    results = gauss_mcmc(data, args.geneXColName, args.geneYColName,
+                         args.nx, args.osampx, args.osampxpdf, args.xmin, args.xmax,
+                         args.ny, args.osampy, args.osampypdf, args.ymin, args.ymax,
+                         args.minScalex, args.minScaley,
+                         args.scalePrior, args.scale, args.targetSum, args.nnorm,
+                         args.nsampMCMC, args.nsampBurnMCMC,
+                         args.nsplitBurnMCMC, args.T0BurnMCMC,
+                         args.output, args.seed)
+    print(f"MCMC + Save took {datetime.timedelta(seconds=int(time.time() - start))}.")
+  else:
+    results = extract_from_npz(f'{args.output}.npz')
+    # return(mu, cov, ox, oy, oxpdf, oypdf, x, y,
+    #        logprob_values, samples)
+    x = results[6]
+    dx = x[1] - x[0]
+    xmin = x[0] - dx / 2
+    xmax = x[-1] + dx / 2
+    y = results[7]
+    dy = y[1] - y[0]
+    ymin = y[0] - dy / 2
+    ymax = y[-1] + dy / 2
+    try:
+      assert (results[0].size + 1) // 6 == args.nnorm, \
+          "nnorm value do not match what is in output."
+      assert results[2].size // results[6].size == args.osampx, \
+          "osampx value do not match what is in output."
+      assert results[4].size // results[6].size == args.osampxpdf, \
+          "osampxpdf value do not match what is in output."
+      assert results[3].size // results[7].size == args.osampy, \
+          "osampy value do not match what is in output."
+      assert results[5].size // results[7].size == args.osampypdf, \
+          "osampypdf value do not match what is in output."
+      assert x.size == args.nx, \
+          "nx value do not match what is in output."
+      assert abs(xmin - args.xmin) < dx, \
+          "xmin value do not match what is in output."
+      assert abs(xmax - args.xmax) < dx, \
+          "xmax value do not match what is in output."
+      assert y.size == args.ny, \
+          "ny value do not match what is in output."
+      assert abs(ymin - args.ymin) < dy, \
+          "ymin value do not match what is in output."
+      assert abs(ymax - args.ymax) < dy, \
+          "ymax value do not match what is in output."
+      assert (results[8].size == args.nsampMCMC + 1) or \
+          (args.minNeff is not None), \
+          "nsampMCMC value do not match what is in output."
+    except Exception as e:
+      raise Exception(f"Ouput file already exists and {e}"
+                      " Use --force to rerun MCMC.")
+    if args.minNeff is not None:
+      args.nsampMCMC = results[8].size - 1
+
+  if args.figure is not None:
+    # Check the directory of args.figure is writtable:
+    if os.path.dirname(args.figure) != '' and not os.access(os.path.dirname(args.figure), os.W_OK):
+      print("The output figure is not writable no figure/export will be done,"
+            " check the directory exists.")
+    else:
+      # Make the plots:
+      plot(*results[4:], args.title, args.figure, args.geneXColName,
+           args.geneYColName, args.splity, args.removeFirstSamples,
+           args.nsampInPlot, args.log1pColorScale,
+           args.prettyBinsx, args.prettyBinsy)
+      if args.minNeff is not None:
+        # Process the output to get prefix and suffix
+        file_prefix, file_suffix = get_prefix_suffix(args.figure)
+        with open(f'{file_prefix}_neff.txt', 'r') as f:
+          neff = float(f.read())
+        if neff < args.minNeff:
+          print("Neff is below the minimum required.")
+          temp_file = NamedTemporaryFile(delete=False, suffix=".npz").name
+          print(f"Results are moved to {temp_file}")
+          copy(f'{args.output}.npz', temp_file)
+          os.remove(f'{args.output}.npz')
+          # Change the args to multiply by 10 the nb of samples
+          new_args = original_args
+          if '--nsampMCMC' in new_args:
+            i = [i for i, v in enumerate(new_args) if v == '--nsampMCMC'][0]
+            new_args[i + 1] += '0'
+          else:
+            new_args.append('--nsampMCMC')
+            new_args.append(f'{args.nsampMCMC}0')
+          return main(new_args)
+  if args.logevidence is not None:
+    # Check the directory of args.logevidence is writtable:
+    if os.path.dirname(args.logevidence) != '' and not os.access(os.path.dirname(args.logevidence), os.W_OK):
+      print("The output logeveidence is not writable"
+            " check the directory exists.")
+    else:
+      # Evaluate the logevid:
+      write_evidence(data, args.geneXColName, args.geneYColName,
+                     *results[:8], args.minScalex, args.minScaley,
+                     args.scalePrior, args.coviscale,
+                     args.nis, logprob, args.logevidence, args.seed,
+                     args.scale, args.targetSum)
+
+
+if __name__ == "__main__":
+    args = None
+    if len(sys.argv) == 1:
+        args = ["--help"]
+    main(args)
diff --git a/baredSC/combineMultipleModels_1d.py b/baredSC/combineMultipleModels_1d.py
new file mode 100644
index 0000000..8431046
--- /dev/null
+++ b/baredSC/combineMultipleModels_1d.py
@@ -0,0 +1,277 @@
+# Copyright 2021 Jean-Baptiste Delisle and Lucille Delisle
+import numpy as np
+import sys
+import argparse
+import os
+import time
+from tempfile import NamedTemporaryFile
+
+# Local imports
+from . common import get_data, get_bins_centers
+from . oned import logprob, extract_from_npz, write_evidence, \
+    get_pdf, plots_from_pdf
+from baredSC._version import __version__
+
+
+# Do some plots and exports in txt
+def plot_combined(all_results, all_logevid, title, output, data, col_gene,
+                  removeFirstSamples, nsampInPlot, pretty_bins, osampx,
+                  xscale, target_sum):
+  # Evaluate how many samples from each output you will get:
+  all_logevid_delta = [le - max(all_logevid) for le in all_logevid]
+  # Delog the log evid
+  delog_all_logevid = np.array([np.exp(le) for le in all_logevid_delta])
+  # Get the number of samples from each result
+  evid_prop = delog_all_logevid / sum(delog_all_logevid)
+  samples_per_results = np.array(evid_prop * nsampInPlot, dtype=int)
+  # Compute the pdf array
+  pdf = None
+  for i, n_sample in enumerate(samples_per_results):
+    if n_sample == 0:
+      print(f"Using 0 sample from output {i}.")
+    else:
+      results = all_results[i]
+      # (mu, cov, ox, oxpdf, x, logprob_values, samples)
+      x = results[4]
+      samples = results[6]
+      # We assume x is equally spaced
+      dx = x[1] - x[0]
+      if pretty_bins is None:
+        nx = x.size
+        oxpdf = results[3]
+        noxpdf = oxpdf.size
+        # We assume oxpdf is equally spaced
+        odxpdf = oxpdf[1] - oxpdf[0]
+      else:
+        xmax = x[-1] + dx / 2
+        xmin = x[0] - dx / 2
+        nx = pretty_bins
+        x = get_bins_centers(xmin, xmax, nx)
+        dx = x[1] - x[0]
+        noxpdf = nx
+        oxpdf = x
+        odxpdf = dx
+      # Remove the first samples:
+      if removeFirstSamples is None:
+        samples = samples[(samples.shape[0] // 4):]
+      else:
+        samples = samples[removeFirstSamples:]
+      print(f"Considering the last {samples.shape[0]} samples.")
+      if samples.shape[0] < n_sample:
+        new_nsampInPlot = nsampInPlot * samples.shape[0] // n_sample
+        print(f"Could not sample {n_sample} samples from output {i} will plot {new_nsampInPlot} samples.")
+        return(plot_combined(all_results, all_logevid, title, output, data, col_gene,
+                             removeFirstSamples, new_nsampInPlot, pretty_bins, osampx,
+                             xscale, target_sum))
+      # Select n_sample
+      samples_selected = np.unique(np.linspace(0, samples.shape[0] - 1, n_sample, dtype=int))
+      samples = samples[samples_selected]
+      print(f"Using {samples.shape[0]} samples from output {i}.")
+      print("Computing pdf.")
+      current_pdf = np.array([get_pdf(p, nx, noxpdf, oxpdf, odxpdf) for p in samples])
+      if pdf is None:
+        pdf = current_pdf
+      else:
+        pdf = np.concatenate((pdf, current_pdf))
+  plots_from_pdf(x, pdf, title, output, data, col_gene, osampx, xscale, target_sum)
+
+
+def parse_arguments(args=None):
+  argp = argparse.ArgumentParser(
+      description=("Combine mcmc results from multiple models to get a"
+                   " mixture using logevidence to infer weights."))
+  argprequired = argp.add_argument_group('Required arguments')
+  argpopt_mcmc = argp.add_argument_group('Optional arguments used to run MCMC')
+  argpopt_data = argp.add_argument_group('Optional arguments to select input data')
+  argpopt_plot = argp.add_argument_group('Optional arguments to customize plots and text outputs')
+  argpopt_loge = argp.add_argument_group('Optional arguments to evaluate logevidence')
+  # Get data:
+  argprequired.add_argument('--input', default=None, required=True,
+                            help="Input table with one line per cell"
+                            " columns with raw counts and one column"
+                            " nCount_RNA with total number of UMI per cell"
+                            " optionally other meta data to filter.")
+  argprequired.add_argument('--geneColName', default=None, required=True,
+                            help="Name of the column with gene counts.")
+  argpopt_data.add_argument('--metadata1ColName', default=None,
+                            help="Name of the column with metadata1 to filter.")
+  argpopt_data.add_argument('--metadata1Values', default=None,
+                            help="Comma separated values for metadata1.")
+  argpopt_data.add_argument('--metadata2ColName', default=None,
+                            help="Name of the column with metadata2 to filter.")
+  argpopt_data.add_argument('--metadata2Values', default=None,
+                            help="Comma separated values for metadata2.")
+  argpopt_data.add_argument('--metadata3ColName', default=None,
+                            help="Name of the column with metadata3 to filter.")
+  argpopt_data.add_argument('--metadata3Values', default=None,
+                            help="Comma separated values for metadata3.")
+  # MCMC
+  argprequired.add_argument('--outputs', default=None, required=True, nargs='+',
+                            help="Ouput files basename (will be npz)"
+                            " with different results of mcmc to combine.")
+  argpopt_mcmc.add_argument('--xmin', default=0, type=float,
+                            help="Minimum value to consider in x axis.")
+  argpopt_mcmc.add_argument('--xmax', default=2.5, type=float,
+                            help="Maximum value to consider in x axis.")
+  argpopt_mcmc.add_argument('--xscale', default="Seurat", choices=['Seurat', 'log'],
+                            help="scale for the x-axis: Seurat (log(1+targetSum*X)) or log (log(X))")
+  argpopt_mcmc.add_argument('--targetSum', default=10**4, type=float,
+                            help="factor when Seurat scale is used: (log(1+targetSum*X)) (default is 10^4, use 0 for the median of nRNA_Counts)")
+  argpopt_mcmc.add_argument('--nx', default=100, type=int,
+                            help="Number of values in x to check how "
+                            "your evaluated pdf is compatible with the model.")
+  argpopt_mcmc.add_argument('--osampx', default=10, type=int,
+                            help="Oversampling factor of x values when evaluating "
+                            "pdf of Poisson distribution.")
+  argpopt_mcmc.add_argument('--osampxpdf', default=5, type=int,
+                            help="Oversampling factor of x values when evaluating "
+                            "pdf at each step of the MCMC.")
+  argpopt_mcmc.add_argument('--minScale', default=0.1, type=float,
+                            help="Minimal value of the scale of gaussians"
+                            " (Default is 0.1 but cannot be smaller than "
+                            "max of twice the bin size of pdf evaluation"
+                            " and half the bin size).")
+  argpopt_mcmc.add_argument('--seed', default=1, type=int,
+                            help="Change seed for another output.")
+  # Plot
+  argprequired.add_argument('--figure', default=None, required=True,
+                            help="Ouput figure basename.")
+  argpopt_plot.add_argument('--title', default=None,
+                            help="Title in figures.")
+  argpopt_plot.add_argument('--removeFirstSamples', default=None, type=int,
+                            help="Number of samples to ignore before making the plots"
+                            " (default is nsampMCMC / 4).")
+  argpopt_plot.add_argument('--nsampInPlot', default=100000, type=int,
+                            help="Approximate number of samples to use in plots.")
+  argpopt_plot.add_argument('--prettyBins', default=None, type=int,
+                            help="Number of bins to use in plots (Default is nx).")
+  # Evidence
+  argpopt_loge.add_argument('--logevidences', default=None, nargs='+',
+                            help="Ouput files of precalculated log evidence values."
+                            "(if not provided will be calculated).")
+  argpopt_loge.add_argument('--coviscale', default=1, type=float,
+                            help="Scale factor to apply to covariance of parameters"
+                            " to get random parameters in logevidence evaluation.")
+  argpopt_loge.add_argument('--nis', default=1000, type=int,
+                            help="Size of sampling of random parameters in logevidence evaluation.")
+  # Version
+  argp.add_argument('--version', action='version',
+                    version=__version__)
+  return(argp)
+
+
+def main(args=None):
+  args = parse_arguments().parse_args(args)
+  # Check incompatibilities:
+  if args.xscale == 'Seurat' and args.xmin < 0:
+    raise Exception("--xmin negative is not "
+                    "compatible with --xscale Seurat "
+                    "as it is log(1+targetSum*X).")
+  if args.xscale == 'Seurat' and args.targetSum < 0:
+    raise Exception("--targetSum negative is not "
+                    "compatible with --xscale Seurat "
+                    "as it is log(1+targetSum*X).")
+  if args.xscale == 'log' and args.xmax > 0:
+    raise Exception("--xmax positive is not "
+                    "compatible with --xscale log "
+                    "as it is log(X) and X < 1.")
+  # Check the minScale
+  dx = (args.xmax - args.xmin) / args.nx
+  min_minScale = max(dx / args.osampxpdf * 2, dx / 2)
+  if args.minScale < dx / 2:
+    print(f"The value of minScale ({args.minScale})"
+          f" must be above half the bin size ((xmax - xmin) / nx / 2 = {dx / 2}).\n"
+          f"Minimum value will be used ({min_minScale}).")
+    args.minScale = min_minScale
+  if args.minScale < dx / args.osampxpdf * 2:
+    print(f"The value of minScale ({args.minScale})"
+          " must be above twice the bin size "
+          "of pdf evaluation ((xmax - xmin) / (nx * osampxpdf) * 2 ="
+          f" {dx / args.osampxpdf * 2}).\n"
+          f"Minimum value will be used ({min_minScale}).")
+    args.minScale = min_minScale
+  # Check the directory of args.figure is writtable:
+  if os.path.dirname(args.figure) != '' and not os.access(os.path.dirname(args.figure), os.W_OK):
+    raise Exception("The output figure is not writable no figure/export will be done,"
+                    " check the directory exists.")
+
+  # Load data
+  print("Get raw data")
+  start = time.time()
+  data = get_data(args.input,
+                  args.metadata1ColName, args.metadata1Values,
+                  args.metadata2ColName, args.metadata2Values,
+                  args.metadata3ColName, args.metadata3Values,
+                  [args.geneColName])
+  print(f"Got. It took {(time.time() - start):.2f} seconds.")
+
+  # Check that output exists:
+  for output in args.outputs:
+    # Remove potential suffix:
+    output = output.removesuffix('.npz')
+    if not os.path.exists(f'{output}.npz'):
+      raise Exception(f'{output}.npz does not exists.')
+  if args.logevidences is not None:
+    assert len(args.logevidences) == len(args.outputs), \
+        "The number of logevid does not corresponds" \
+        " to the number of outputs."
+    for logevid_file in args.logevidences:
+      if not os.path.exists(logevid_file):
+        raise Exception(f'{logevid_file} does not exists.')
+
+  # Get the results and the logevid:
+  all_results = {}
+  all_logevid_files = {}
+  all_logevid = []
+  for i, output in enumerate(args.outputs):
+    # Remove potential suffix:
+    output = output.removesuffix('.npz')
+    all_results[i] = extract_from_npz(f'{output}.npz')
+    # return(mu, cov, ox, oxpdf, x, logprob_values, samples)
+    x = all_results[i][4]
+    dx = x[1] - x[0]
+    xmin = x[0] - dx / 2
+    xmax = x[-1] + dx / 2
+    try:
+      assert all_results[i][2].size // all_results[i][4].size == args.osampx, \
+          f"osampx value do not match what is in {output}.npz."
+      assert all_results[i][3].size // all_results[i][4].size == args.osampxpdf, \
+          f"osampxpdf value do not match what is in {output}.npz."
+      assert x.size == args.nx, \
+          "nx value do not match what is in output."
+      assert abs(xmin - args.xmin) < dx, \
+          "xmin value do not match what is in output."
+      assert abs(xmax - args.xmax) < dx, \
+          "xmax value do not match what is in output."
+    except Exception as e:
+      raise Exception(f"{e}\nChange args or rerun the MCMC.")
+    # Get the logevid value if the file is provided:
+    if args.logevidences is not None:
+      all_logevid_files[i] = args.logevidences[i]
+    else:
+      # Else calculate it
+      all_logevid_files[i] = NamedTemporaryFile(delete=False).name
+      write_evidence(data, args.geneColName, *all_results[i][:5], args.minScale, args.coviscale,
+                     args.nis, logprob, all_logevid_files[i], args.seed, args.xscale, args.targetSum)
+    # Read the logevid value:
+    try:
+      with open(all_logevid_files[i], 'r') as f:
+        logevid = f.read()
+    except Exception as e:
+      raise Exception(f"Could not read the file {all_logevid_files[i]}: {e}")
+    try:
+      logevid = float(logevid)
+    except Exception:
+      raise Exception(f"The value in the file {all_logevid_files[i]} is not a float.")
+    all_logevid.append(logevid)
+  # Make the plots:
+  plot_combined(all_results, all_logevid, args.title, args.figure, data, args.geneColName,
+                args.removeFirstSamples, args.nsampInPlot, args.prettyBins,
+                args.osampx, args.xscale, args.targetSum)
+
+
+if __name__ == "__main__":
+    args = None
+    if len(sys.argv) == 1:
+      args = ["--help"]
+    main(args)
diff --git a/baredSC/combineMultipleModels_2d.py b/baredSC/combineMultipleModels_2d.py
new file mode 100644
index 0000000..8aea402
--- /dev/null
+++ b/baredSC/combineMultipleModels_2d.py
@@ -0,0 +1,393 @@
+# Copyright 2021 Jean-Baptiste Delisle and Lucille Delisle
+import numpy as np
+import sys
+import argparse
+import os
+import time
+from tempfile import NamedTemporaryFile
+
+# Local imports
+from . common import get_data, get_bins_centers, get_Neff
+from . twod import logprob, extract_from_npz, write_evidence, \
+    get_pdf, plots_from_pdf
+from baredSC._version import __version__
+
+
+# Do some plots and exports in txt
+def plot_combined(all_results, all_logevid, title, output,
+                  genex, geney, splitys,
+                  removeFirstSamples, nsampInPlot,
+                  log1pColorScale, pretty_bins_x, pretty_bins_y,
+                  getPVal):
+  # Evaluate how many samples from each output you will get:
+  all_logevid_delta = [le - max(all_logevid) for le in all_logevid]
+  # Delog the log evid
+  delog_all_logevid = np.array([np.exp(le) for le in all_logevid_delta])
+  # Get the number of samples from each result
+  evid_prop = delog_all_logevid / sum(delog_all_logevid)
+  samples_per_results = np.array(evid_prop * nsampInPlot, dtype=int)
+  # Compute the pdf array
+  pdf = None
+  for i, n_sample in enumerate(samples_per_results):
+    if n_sample == 0:
+      print(f"Using 0 sample from output {i}.")
+    else:
+      results = all_results[i]
+      # return(mu, cov, ox, oy, oxpdf, oypdf, x, y,
+      #        logprob_values, samples)
+      x = results[6]
+      y = results[7]
+      samples = results[9]
+      # We assume x and y are equally spaced
+      dx = x[1] - x[0]
+      dy = y[1] - y[0]
+      if pretty_bins_x is None:
+        nx = x.size
+        oxpdf = results[4]
+        noxpdf = oxpdf.size
+        # We assume oxpdf is equally spaced
+        odxpdf = oxpdf[1] - oxpdf[0]
+      else:
+        xmin = x[0] - dx / 2
+        xmax = x[-1] + dx / 2
+        nx = pretty_bins_x
+        x = get_bins_centers(xmin, xmax, nx)
+        dx = x[1] - x[0]
+        noxpdf = nx
+        oxpdf = x
+        odxpdf = dx
+      if pretty_bins_y is None:
+        ny = y.size
+        oypdf = results[5]
+        noypdf = oypdf.size
+        # We assume oypdf is equally spaced
+        odypdf = oypdf[1] - oypdf[0]
+      else:
+        ymin = y[0] - dy / 2
+        ymax = y[-1] + dy / 2
+        ny = pretty_bins_y
+        y = get_bins_centers(ymin, ymax, ny)
+        dy = y[1] - y[0]
+        noypdf = ny
+        oypdf = y
+        odypdf = dy
+      odxypdf = odxpdf * odypdf
+      oxypdf = np.array(np.meshgrid(oxpdf, oypdf)).transpose(1, 2, 0)
+
+      # Remove the first samples:
+      if removeFirstSamples is None:
+        samples = samples[(samples.shape[0] // 4):]
+      else:
+        samples = samples[removeFirstSamples:]
+      print(f"Considering the last {samples.shape[0]} samples.")
+      if getPVal:
+        # We need to have independent samples:
+        Neff = get_Neff(samples)
+        print(f"Sampling {round(Neff)} independent samples.")
+        # Select Neff samples
+        samples_selected = np.unique(np.linspace(0, samples.shape[0] - 1, round(Neff), dtype=int))
+        samples = samples[samples_selected]        
+      if samples.shape[0] < n_sample:
+        new_nsampInPlot = nsampInPlot * samples.shape[0] // n_sample
+        print(f"Could not sample {n_sample} samples from output {i} will plot {new_nsampInPlot} samples.")
+        return(plot_combined(all_results, all_logevid, title, output,
+                             genex, geney, splitys,
+                             removeFirstSamples, new_nsampInPlot,
+                             log1pColorScale, pretty_bins_x, pretty_bins_y,
+                             getPVal))
+      # Select n_sample
+      samples_selected = np.unique(np.linspace(0, samples.shape[0] - 1, n_sample, dtype=int))
+      samples = samples[samples_selected]
+      print(f"Using {samples.shape[0]} samples from output {i}.")
+      print("Computing pdf.")
+      current_pdf = np.array([get_pdf(p, nx, ny, noxpdf, noypdf, oxypdf, odxypdf) for p in samples])
+      if pdf is None:
+        pdf = current_pdf
+      else:
+        pdf = np.concatenate((pdf, current_pdf))
+  if getPVal:
+    Neff = pdf.shape[0]
+  else:
+    Neff = None
+  plots_from_pdf(x, y, pdf, title, output,
+                 genex, geney, log1pColorScale, splitys,
+                 Neff)
+
+
+def parse_arguments(args=None):
+  argp = argparse.ArgumentParser(
+      description=("Combine mcmc 2D results from multiple models to get a"
+                   " mixture using logevidence to infer weights."))
+  argprequired = argp.add_argument_group('Required arguments')
+  argpopt_mcmc = argp.add_argument_group('Optional arguments used to run MCMC')
+  argpopt_data = argp.add_argument_group('Optional arguments to select input data')
+  argpopt_plot = argp.add_argument_group('Optional arguments to customize plots and text outputs')
+  argpopt_loge = argp.add_argument_group('Optional arguments to evaluate logevidence')
+  # Get data:
+  argprequired.add_argument('--input', default=None, required=True,
+                            help="Input table with one line per cell"
+                            " columns with raw counts and one column"
+                            " nCount_RNA with total number of UMI per cell"
+                            " optionally other meta data to filter.")
+  argprequired.add_argument('--geneXColName', default=None, required=True,
+                            help="Name of the column with gene counts for gene in x.")
+  argprequired.add_argument('--geneYColName', default=None, required=True,
+                            help="Name of the column with gene counts for gene in y.")
+  argpopt_data.add_argument('--metadata1ColName', default=None,
+                            help="Name of the column with metadata1 to filter.")
+  argpopt_data.add_argument('--metadata1Values', default=None,
+                            help="Comma separated values for metadata1.")
+  argpopt_data.add_argument('--metadata2ColName', default=None,
+                            help="Name of the column with metadata2 to filter.")
+  argpopt_data.add_argument('--metadata2Values', default=None,
+                            help="Comma separated values for metadata2.")
+  argpopt_data.add_argument('--metadata3ColName', default=None,
+                            help="Name of the column with metadata3 to filter.")
+  argpopt_data.add_argument('--metadata3Values', default=None,
+                            help="Comma separated values for metadata3.")
+  # MCMC
+  argprequired.add_argument('--outputs', default=None, required=True, nargs='+',
+                            help="Ouput files basename (will be npz)"
+                            " with different results of mcmc to combine.")
+  argpopt_mcmc.add_argument('--xmin', default=0, type=float,
+                            help="Minimum value to consider in x axis.")
+  argpopt_mcmc.add_argument('--xmax', default=2.5, type=float,
+                            help="Maximum value to consider in x axis.")
+  argpopt_mcmc.add_argument('--nx', default=50, type=int,
+                            help="Number of values in x to check how "
+                            "your evaluated pdf is compatible with the model.")
+  argpopt_mcmc.add_argument('--osampx', default=10, type=int,
+                            help="Oversampling factor of x values when evaluating "
+                            "pdf of Poisson distribution.")
+  argpopt_mcmc.add_argument('--osampxpdf', default=4, type=int,
+                            help="Oversampling factor of x values when evaluating "
+                            "pdf at each step of the MCMC.")
+  argpopt_mcmc.add_argument('--minScalex', default=0.1, type=float,
+                            help="Minimal value of the scale of gaussians on x"
+                            " (Default is 0.1 but cannot be smaller than "
+                            "max of twice the bin size of pdf evaluation"
+                            " and half the bin size on x axis).")
+  argpopt_mcmc.add_argument('--ymin', default=0, type=float,
+                            help="Minimum value to consider in y axis.")
+  argpopt_mcmc.add_argument('--ymax', default=2.5, type=float,
+                            help="Maximum value to consider in y axis.")
+  argpopt_mcmc.add_argument('--ny', default=50, type=int,
+                            help="Number of values in y to check how "
+                            "your evaluated pdf is compatible with the model.")
+  argpopt_mcmc.add_argument('--osampy', default=10, type=int,
+                            help="Oversampling factor of y values when evaluating "
+                            "pdf of Poisson distribution.")
+  argpopt_mcmc.add_argument('--osampypdf', default=4, type=int,
+                            help="Oversampling factor of y values when evaluating "
+                            "pdf at each step of the MCMC.")
+  argpopt_mcmc.add_argument('--minScaley', default=0.1, type=float,
+                            help="Minimal value of the scale of gaussians on yx"
+                            " (Default is 0.1 but cannot be smaller than "
+                            "max of twice the bin size of pdf evaluation"
+                            " and half the bin size on y axis).")
+  argpopt_mcmc.add_argument('--scale', default="Seurat", choices=['Seurat', 'log'],
+                            help="scale for the x-axis and y-axis: Seurat (log(1+targetSum*X)) or log (log(X))")
+  argpopt_mcmc.add_argument('--scalePrior', default=0.3, type=float,
+                            help="Scale of the truncnorm used in the prior for "
+                            "the correlation.")
+  argpopt_mcmc.add_argument('--targetSum', default=10**4, type=float,
+                            help="factor when Seurat scale is used: (log(1+targetSum*X)) (default is 10^4, use 0 for the median of nRNA_Counts)")
+  argpopt_mcmc.add_argument('--seed', default=1, type=int,
+                            help="Change seed for another output.")
+  # Plot
+  argprequired.add_argument('--figure', default=None, required=True,
+                            help="Ouput figure basename.")
+  argpopt_plot.add_argument('--title', default=None,
+                            help="Title in figures.")
+  argpopt_plot.add_argument('--splity', default=None, nargs='+', type=float,
+                            help="Threshold value to plot the density for genex"
+                            " for 2 categories in geney values.")
+  argpopt_plot.add_argument('--removeFirstSamples', default=None, type=int,
+                            help="Number of samples to ignore before making the plots"
+                            " (default is nsampMCMC / 4).")
+  argpopt_plot.add_argument('--nsampInPlot', default=100000, type=int,
+                            help="Approximate number of samples to use in plots.")
+  argpopt_plot.add_argument('--prettyBins', default=None, type=int,
+                            help="Number of bins to use in plots (Default is nx).")
+  argpopt_plot.add_argument('--prettyBinsx', default=None, type=int,
+                            help="Number of bins to use in x in plots (Default is nx).")
+  argpopt_plot.add_argument('--prettyBinsy', default=None, type=int,
+                            help="Number of bins to use in y in plots (Default is ny).")
+  argpopt_plot.add_argument('--log1pColorScale', action='store_true',
+                            help="Use log1p color scale instead of linear color scale.")
+  argpopt_plot.add_argument('--getPVal', action='store_true',
+                            help="Use less samples to get an estimation of the p-value.")
+  # Evidence
+  argpopt_loge.add_argument('--logevidences', default=None, nargs='+',
+                            help="Ouput files of precalculated log evidence values."
+                            "(if not provided will be calculated).")
+  argpopt_loge.add_argument('--coviscale', default=1, type=float,
+                            help="Scale factor to apply to covariance of parameters"
+                            " to get random parameters in logevidence evaluation.")
+  argpopt_loge.add_argument('--nis', default=1000, type=int,
+                            help="Size of sampling of random parameters in logevidence evaluation.")
+  # Version
+  argp.add_argument('--version', action='version',
+                    version=__version__)
+  return(argp)
+
+
+def main(args=None):
+  args = parse_arguments().parse_args(args)
+  # Check incompatibilities:
+  if args.scale == 'Seurat' and args.targetSum < 0:
+    raise Exception("--targetSum negative is not "
+                    "compatible with --xscale Seurat "
+                    "as it is log(1+targetSum*X).")
+  if args.scale == 'Seurat' and args.xmin < 0:
+    raise Exception("--xmin negative is not "
+                    "compatible with --scale Seurat "
+                    "as it is log(1+targetSum*X).")
+  if args.scale == 'log' and args.xmax > 0:
+    raise Exception("--xmax positive is not "
+                    "compatible with --scale log "
+                    "as it is log(X) and X < 1.")
+  if args.scale == 'Seurat' and args.ymin < 0:
+    raise Exception("--ymin negative is not "
+                    "compatible with --scale Seurat "
+                    "as it is log(1+targetSum*X).")
+  if args.scale == 'log' and args.ymax > 0:
+    raise Exception("--ymax positive is not "
+                    "compatible with --scale log "
+                    "as it is log(X) and X < 1.")
+  # Check the minScales
+  dx = (args.xmax - args.xmin) / args.nx
+  min_minScalex = max(dx / args.osampxpdf * 2, dx / 2)
+  if args.minScalex < dx / 2:
+    print(f"The value of minScale ({args.minScalex})"
+          " must be above half the bin size in x"
+          f"((xmax - xmin) / nx / 2 = {dx / 2}).\n"
+          f"Minimum value will be used ({min_minScalex}).")
+    args.minScalex = min_minScalex
+  if args.minScalex < dx / args.osampxpdf * 2:
+    print(f"The value of minScale ({args.minScalex})"
+          " must be above twice the bin size "
+          "of pdf evaluation in x((xmax - xmin) / (nx * osampxpdf) * 2 ="
+          f" {dx / args.osampxpdf * 2}).\n"
+          f"Minimum value will be used ({min_minScalex}).")
+    args.minScalex = min_minScalex
+  dy = (args.ymax - args.ymin) / args.ny
+  min_minScaley = max(dy / args.osampypdf * 2, dy / 2)
+  if args.minScaley < dy / 2:
+    print(f"The value of minScale ({args.minScaley})"
+          " must be above half the bin size in y"
+          f"((ymax - ymin) / ny / 2 = {dy / 2}).\n"
+          f"Minimum value will be used ({min_minScaley}).")
+    args.minScaley = min_minScaley
+  if args.minScaley < dy / args.osampypdf * 2:
+    print(f"The value of minScale ({args.minScaley})"
+          " must be above twice the bin size "
+          "of pdf evaluation in y((ymax - ymin) / (ny * osampypdf) * 2 ="
+          f" {dy / args.osampypdf * 2}).\n"
+          f"Minimum value will be used ({min_minScaley}).")
+    args.minScaley = min_minScaley
+
+  # Check the directory of args.figure is writtable:
+  if os.path.dirname(args.figure) != '' and not os.access(os.path.dirname(args.figure), os.W_OK):
+    raise Exception("The output figure is not writable no figure/export will be done,"
+                    " check the directory exists.")
+
+  # Load data
+  print("Get raw data")
+  start = time.time()
+  data = get_data(args.input,
+                  args.metadata1ColName, args.metadata1Values,
+                  args.metadata2ColName, args.metadata2Values,
+                  args.metadata3ColName, args.metadata3Values,
+                  [args.geneXColName, args.geneYColName])
+  print(f"Got. It took {(time.time() - start):.2f} seconds.")
+
+  # Check that output exists:
+  for output in args.outputs:
+    # Remove potential suffix:
+    output = output.removesuffix('.npz')
+    if not os.path.exists(f'{output}.npz'):
+      raise Exception(f'{output}.npz does not exists.')
+  if args.logevidences is not None:
+    assert len(args.logevidences) == len(args.outputs), \
+        "The number of logevid does not corresponds" \
+        " to the number of outputs."
+    for logevid_file in args.logevidences:
+      if not os.path.exists(logevid_file):
+        raise Exception(f'{logevid_file} does not exists.')
+
+  # Get the results and the logevid:
+  all_results = {}
+  all_logevid_files = {}
+  all_logevid = []
+  for i, output in enumerate(args.outputs):
+    # Remove potential suffix:
+    output = output.removesuffix('.npz')
+    all_results[i] = extract_from_npz(f'{output}.npz')
+    # return(mu, cov, ox, oy, oxpdf, oypdf, x, y,
+    #        logprob_values, samples)
+    x = all_results[i][6]
+    dx = x[1] - x[0]
+    xmin = x[0] - dx / 2
+    xmax = x[-1] + dx / 2
+    y = all_results[i][7]
+    dy = y[1] - y[0]
+    ymin = y[0] - dy / 2
+    ymax = y[-1] + dy / 2
+    try:
+      assert all_results[i][2].size // all_results[i][6].size == args.osampx, \
+          "osampx value do not match what is in output."
+      assert all_results[i][4].size // all_results[i][6].size == args.osampxpdf, \
+          "osampxpdf value do not match what is in output."
+      assert all_results[i][3].size // all_results[i][7].size == args.osampy, \
+          "osampy value do not match what is in output."
+      assert all_results[i][5].size // all_results[i][7].size == args.osampypdf, \
+          "osampypdf value do not match what is in output."
+      assert x.size == args.nx, \
+          "nx value do not match what is in output."
+      assert abs(xmin - args.xmin) < dx, \
+          "xmin value do not match what is in output."
+      assert abs(xmax - args.xmax) < dx, \
+          "xmax value do not match what is in output."
+      assert y.size == args.ny, \
+          "ny value do not match what is in output."
+      assert abs(ymin - args.ymin) < dy, \
+          "ymin value do not match what is in output."
+      assert abs(ymax - args.ymax) < dy, \
+          "ymax value do not match what is in output."
+    except Exception as e:
+      raise Exception(f"{e}\nChange args or rerun the MCMC.")
+    # Get the logevid value if the file is provided:
+    if args.logevidences is not None:
+      all_logevid_files[i] = args.logevidences[i]
+    else:
+      # Else calculate it
+      all_logevid_files[i] = NamedTemporaryFile(delete=False).name
+      write_evidence(data, args.geneXColName, args.geneYColName,
+                     *all_results[i][:8], args.minScalex, args.minScaley,
+                     args.scalePrior, args.coviscale,
+                     args.nis, logprob, all_logevid_files[i], args.seed,
+                     args.scale, args.targetSum)
+    # Read the logevid value:
+    try:
+      with open(all_logevid_files[i], 'r') as f:
+        logevid = f.read()
+    except Exception as e:
+      raise Exception(f"Could not read the file {all_logevid_files[i]}: {e}")
+    try:
+      logevid = float(logevid)
+    except Exception:
+      raise Exception(f"The value in the file {all_logevid_files[i]} is not a float.")
+    all_logevid.append(logevid)
+  # Make the plots:
+  plot_combined(all_results, all_logevid, args.title, args.figure, args.geneXColName,
+                args.geneYColName, args.splity, args.removeFirstSamples,
+                args.nsampInPlot, args.log1pColorScale,
+                args.prettyBinsx, args.prettyBinsy, args.getPVal)
+
+
+if __name__ == "__main__":
+    args = None
+    if len(sys.argv) == 1:
+      args = ["--help"]
+    main(args)
diff --git a/baredSC/common.py b/baredSC/common.py
new file mode 100644
index 0000000..dc0adc0
--- /dev/null
+++ b/baredSC/common.py
@@ -0,0 +1,192 @@
+# Copyright 2021 Jean-Baptiste Delisle and Lucille Delisle
+import numpy as np
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+import pandas as pd
+from scipy.stats import poisson
+from samsam import acf
+
+mpl.use('agg')
+
+
+def get_data(input_file, metadata1_cn, metadata1_v,
+             metadata2_cn, metadata2_v,
+             metadata3_cn, metadata3_v,
+             genes):
+  """
+  Create a dataframe from input file
+  keep only rows matching metadata values
+  """
+  data = pd.read_csv(input_file, sep="\t")
+  filters = {metadata1_cn: metadata1_v,
+             metadata2_cn: metadata2_v,
+             metadata3_cn: metadata3_v}
+  for col_name in filters:
+    if col_name is None:
+      continue
+    data = data[data[col_name].astype(str).isin(filters[col_name].split(','))]
+  print(f"You have {data.shape[0]} cells.")
+  if data.shape[0] == 0:
+    raise Exception("No cell with selected filters.")
+  for col_name in genes + ['nCount_RNA']:
+    if col_name not in data.columns:
+      raise Exception(f"{col_name} not in the data table.")
+  return(data[genes + ['nCount_RNA']])
+
+
+# Permut p to get the permutation of p the closest to pref
+def permuted(p, pref, wdist, permut, n_param_per_gauss):
+  amp = p[(n_param_per_gauss - 1)::n_param_per_gauss]
+  # We complete the list of parameters:
+  p_full = np.concatenate(([1 - np.sum(amp)], p))
+  dp = p - pref
+  min_dist = np.sum(wdist * dp * dp)
+  min_perm = p
+  for ks in permut[1:]:
+    dpk = p_full[ks][1:] - pref
+    distk = np.sum(wdist * dpk * dpk)
+    if distk < min_dist:
+      min_dist = distk
+      min_perm = p_full[ks][1:]
+  return(min_perm)
+
+
+# Get p(ki|Ni,xj) * dx
+def get_Ax(data, col_gene, nx, x, dx, ox, xscale, target_sum):
+  # Get all UMI nbs:
+  N = data['nCount_RNA']
+  # Get the raw counts:
+  kx = data[col_gene]
+  # To get a better approximation we will use
+  # a finer grid for x (ox)
+  if ox.size % nx != 0:
+    raise Exception("The size of ox is not multiple of size of x.")
+  # The Poisson law is evaluated on the finest grid (ox)
+  # Summed up on the grid (x)
+  if xscale == 'Seurat':
+    # We assume ki follow
+    # a Poisson distribution of parameter (exp(x) - 1)*Ni / target_sum
+    # as x is expressed in the axis used in Seurat:
+    # log(1 + target_sum X)
+    if target_sum == 0:
+      target_sum = np.median(N)
+    poissx = np.array([
+        np.mean(poisson.pmf(k=kxi, mu=Ni / target_sum * (np.exp(ox) - 1)).reshape(nx, -1), axis=1)
+        for kxi, Ni in zip(kx, N)])
+  elif xscale == 'log':
+    # or a Poisson distribution of parameter exp(x)*Ni
+    # as x is expressed in the log axis:
+    # log(X)
+    poissx = np.array([
+        np.mean(poisson.pmf(k=kxi, mu=Ni * np.exp(ox)).reshape(nx, -1), axis=1)
+        for kxi, Ni in zip(kx, N)])
+  else:
+    raise Exception("Only Seurat and log xscale are implemented.")
+  # To evaluate the integral more easily multiply by the delta x
+  Ax = poissx * dx
+
+  return(Ax)
+
+
+# Process the output to get prefix and suffix
+def get_prefix_suffix(output):
+  name = output.split(".")
+  file_suffix = name[-1]
+  file_prefix = ".".join(name[:-1])
+  return(file_prefix, file_suffix)
+
+
+# Get the number of effective samples
+def get_Neff(samples):
+  # Autocorrelation (to test the convergence of MCMC)
+  R = np.array([acf.acf(pi) for pi in samples.T]).T
+  # Estimate the number of independent samples:
+  Neff = samples.shape[0] / np.max([acf.iat(R=r) for r in R.T])
+  return(Neff)
+
+
+# Do some plots and exports in txt
+def plot_QC(logprob_values, samples, title, output,
+            removeFirstSamples, nsampInPlot,
+            p_names):
+  file_prefix, file_suffix = get_prefix_suffix(output)
+  # Remove the first samples:
+  samples = samples[removeFirstSamples:]
+  print(f"Considering the last {samples.shape[0]} samples.")
+  logprob_values = logprob_values[removeFirstSamples:]
+
+  # Select nsampInPlot
+  samples_selected = np.unique(np.linspace(0, samples.shape[0] - 1, nsampInPlot, dtype=int))
+  samples = samples[samples_selected]
+  print(f"Using {samples.shape[0]} samples for plots.")
+  logprob_values = logprob_values[samples_selected]
+
+  # Autocorrelation (to test the convergence of MCMC)
+  R = np.array([acf.acf(pi) for pi in samples.T]).T
+  Rmin = np.min(R, axis=1)
+  Rmax = np.max(R, axis=1)
+  Rmed = np.median(R, axis=1)
+  x_values = samples_selected[1:]
+  plt.figure()
+  plt.fill_between(x_values, Rmin[1:], Rmax[1:], color='r', alpha=0.3, rasterized=True)
+  plt.plot(x_values, Rmed[1:], 'r', lw=2, rasterized=True)
+  plt.xscale('log')
+  plt.xlim(x_values[0], x_values[-1])
+  plt.xlabel(r'$\tau$')
+  plt.ylabel('ACF')
+  plt.title(title)
+  plt.savefig(f'{file_prefix}_convergence.{file_suffix}')
+
+  # Estimate the number of independent samples:
+  Neff = samples.shape[0] / np.max([acf.iat(R=r) for r in R.T])
+
+  print(f"Neff is {Neff}")
+  with open(f'{file_prefix}_neff.txt', 'w') as fo:
+    fo.write(f'{Neff}\n')
+
+  # Plot the value of each parameter and logprob_values along the samples:
+  fig, axs = plt.subplots(samples.shape[1] + 1, 1, sharex='col', figsize=(6, (samples.shape[1] + 1) * 1.5))
+  for i, p_name in enumerate(p_names):
+    ax = axs[i]
+    ax.plot(samples_selected, samples[:, i])
+    ax.set_ylabel(p_name)
+  ax = axs[-1]
+  ax.plot(samples_selected, logprob_values)
+  ax.set_ylabel('logprob')
+  plt.tight_layout()
+  fig.savefig(f'{file_prefix}_p.{file_suffix}')
+
+  # 1, 2, 3 sigma:
+  qm = np.array([(1 - lvl) / 2 for lvl in [0.6827, 0.9545, 0.9973]])
+  qp = 1 - qm
+
+  # Export the parameters median and 1 sigma
+  p_summary = pd.DataFrame(columns=['name', 'low', 'median', 'high'])
+  p_summary['name'] = p_names
+  p_summary['low'] = np.quantile(samples, qm[0], axis=0)
+  p_summary['high'] = np.quantile(samples, qp[0], axis=0)
+  p_summary['median'] = np.median(samples, axis=0)
+  p_summary.to_csv(f'{file_prefix}_p.txt',
+                   index=False,
+                   header=True, sep='\t')
+
+  # Try to plot corner
+  try:
+    import corner
+    plt.figure()
+    figure = corner.corner(samples,
+                           labels=p_names,
+                           quantiles=[0.15865, 0.5, 0.84135],
+                           show_titles=True, title_kwargs={"fontsize": 12})
+    figure.suptitle(title, ha='right', x=0.98)
+    plt.savefig(f'{file_prefix}_corner.{file_suffix}', dpi=100)
+  except Exception as e:
+    print("corner plot could not be plotted.")
+    print(e)
+
+  return(samples, Neff)
+
+
+def get_bins_centers(xmin, xmax, nx):
+  x_borders = np.linspace(xmin, xmax, nx + 1)
+  return((x_borders[:-1] + x_borders[1:]) / 2)
diff --git a/baredSC/oned.py b/baredSC/oned.py
new file mode 100644
index 0000000..a994d63
--- /dev/null
+++ b/baredSC/oned.py
@@ -0,0 +1,335 @@
+# Copyright 2021 Jean-Baptiste Delisle and Lucille Delisle
+import numpy as np
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+from itertools import permutations
+import time
+from scipy.stats import kde
+from scipy.special import factorial
+import pandas as pd
+import samsam
+import samsam.logprior
+
+# Local imports
+from . common import get_Ax, get_prefix_suffix, get_bins_centers
+
+mpl.use('agg')
+
+
+# Define log probability function
+def logprob(p,
+            pref, wdist, permutx,
+            xmin, xmax, nx, dx,
+            noxpdf, oxpdf, odxpdf,
+            min_scale,
+            Ax, temp=1.0):
+  # Log prior
+  lp = logprior(p, pref, wdist, permutx, xmin, xmax, dx, odxpdf, min_scale)
+  if np.isfinite(lp):
+    # If the parameters are possible
+    # Log likelihood
+    ll = loglike(p, nx, noxpdf, oxpdf, odxpdf, Ax)
+    # Return sum of log prior and log likelihood
+    return((lp + ll) / temp)
+  else:
+    # If the parameters are impossible
+    return(-np.inf)
+
+
+# Define the log prior
+def logprior(p, pref, wdist, permutx, xmin, xmax, dx, odxpdf, min_scale):
+  # Raise exception if one of the condition is not
+  # satisfied
+  try:
+    amp = p[2::3]
+    mu = p[::3]
+    sig = p[1::3]
+    # We complete the list of parameters:
+    p_full = np.concatenate(([1 - np.sum(amp)], p))
+    amp_full = np.concatenate(([1 - np.sum(amp)], amp))
+    # Amplitude
+    # Amplitudes should be positive
+    if np.any(amp_full < 0):
+      raise Exception('Negative amplitude')
+    # Because we impose that sum of amplitude is below 1
+    # We add this term:
+    lp = np.log(factorial(amp.size))
+    # Check swapping (symmetry of the problem)
+    # pref is a parameter set and we impose that pref
+    # is closer to p than any permutation of p
+    dp = p - pref
+    dist = np.sum(wdist * dp * dp)
+    for ks in permutx[1:]:
+      dpk = p_full[ks][1:] - pref
+      distk = np.sum(wdist * dpk * dpk)
+      if distk <= dist:
+        raise Exception('Swapping')
+    # Number of possible swappings
+    lp += np.log(permutx.shape[0])
+    # Finally we add the priors on other parameters
+    # Location
+    lp += sum(samsam.logprior.uniform(muk, xmin - 3 * sigk, xmax + 3 * sigk) for muk, sigk in zip(mu, sig))
+    # Scale
+    lp += sum(samsam.logprior.loguniform(sigk, np.log(min_scale), np.log(xmax - xmin)) for sigk in sig)
+  except:
+    return(-np.inf)
+  return(lp)
+
+
+# pdf of a trunc_norm as scipy.stats.truncnorm.pdf is very slow
+def trunc_norm(x, dx, mu, sigma):
+  u = (x - mu) / sigma
+  lognorm = -0.5 * u * u
+  lognorm -= np.max(lognorm)  # Avoid underflow
+  norm = np.exp(lognorm)
+  norm /= np.sum(norm * dx)
+  return(norm)
+
+
+# Log likelihood
+def loglike(p, nx, noxpdf, oxpdf, odxpdf, Ax):
+  # Get the pdf from p:
+  pdf = get_pdf(p, nx, noxpdf, oxpdf, odxpdf)
+  # The likelihood for a cell i is
+  # integral(p(ki|Ni,x)p(x|amp,mu,sig)dx)
+  # We approximate by the sum
+  # p(ki|Ni,xj)*dx is stored in Ax
+  likecell = Ax @ pdf
+  # The log likelihood for all cells is
+  # sum of log likelihood for each cell
+  ll = np.sum(np.log(likecell))
+  return(ll)
+
+
+# Get the pdf 1d using parameters
+def get_pdf(p, nx, noxpdf, oxpdf, odxpdf):
+  amp = p[2::3]
+  mu = p[::3]
+  sig = p[1::3]
+  # We complete the list of parameters:
+  amp_full = np.concatenate(([1 - np.sum(amp)], amp))
+  # Build the oversampled pdf
+  pdf_o = np.zeros(noxpdf)
+  for ampk, muk, sigk in zip(amp_full, mu, sig):
+    # Add the trunc_norm
+    pdf_o += ampk * trunc_norm(oxpdf, odxpdf, muk, sigk)
+  # I do the mean over the osampx
+  pdf = np.mean(pdf_o.reshape(nx, -1), axis=1)
+  return(pdf)
+
+
+# Return the same as gauss_mcmc but from a npz
+def extract_from_npz(output):
+  print("Reading")
+  start = time.time()
+  mcmc_res = np.load(output, allow_pickle=True)
+  diags = mcmc_res['diagnostics']
+  mu = diags.item().get('mu')
+  cov = diags.item().get('cov')
+  logprob_values = diags.item().get('logprob')
+  samples = mcmc_res['samples']
+  x = mcmc_res['x']
+  ox = mcmc_res['ox']
+  oxpdf = mcmc_res['oxpdf']
+  print(f"Read. It took {(time.time() - start):.2f} seconds.")
+  return(mu, cov, ox, oxpdf, x, logprob_values, samples)
+
+
+# In order to compare multiple models we use an estimate of the log evidence
+def write_evidence(data, col_gene, mu, cov, ox, oxpdf, x, min_scale,
+                   covis_scale, nis, logprob, output, seed,
+                   xscale, target_sum):
+  nx = x.size
+  dx = x[1] - x[0]
+  xmin = x[0] - dx / 2
+  xmax = x[-1] + dx / 2
+  noxpdf = oxpdf.size
+  odxpdf = oxpdf[1] - oxpdf[0]
+  if ox.size % nx != 0:
+    raise Exception("The size of ox is not multiple of size of x.")
+  if oxpdf.size % nx != 0:
+    raise Exception("The size of oxpdf is not multiple of size of x.")
+
+  Ax = get_Ax(data, col_gene, nx, x, dx, ox, xscale, target_sum)
+
+  nnorm = (mu.size + 1) // 3
+
+  # We want to permut the gaussians (3 parameters for each)
+  permutx = np.array([np.concatenate(([3 * i + np.arange(3) for i in v]))
+                      for v in permutations(np.arange(nnorm))])
+
+  # We calculate logevidence
+  np.random.seed(seed)
+  logevidence = samsam.covis(mu, covis_scale ** 2 * cov,
+                             logprob, nsamples=nis,
+                             pref=mu, wdist=1 / (np.diag(cov)), permutx=permutx,
+                             xmin=xmin, xmax=xmax, nx=nx, dx=dx,
+                             noxpdf=noxpdf, oxpdf=oxpdf, odxpdf=odxpdf,
+                             min_scale=min_scale,
+                             Ax=Ax)[2]['logevidence']
+
+  # We write it into a file
+  with open(output, 'w') as fo:
+    fo.write(f'{logevidence}\n')
+
+
+def plots_from_pdf(x, pdf, title, output, data, col_gene, osampx, xscale, target_sum):
+  # We assume x is equally spaced
+  dx = x[1] - x[0]
+  xmin = x[0] - dx / 2
+  xmax = x[-1] + dx / 2
+  # Process the output to get prefix and suffix
+  file_prefix, file_suffix = get_prefix_suffix(output)
+
+  # 1, 2, 3 sigma:
+  qm = np.array([(1 - lvl) / 2 for lvl in [0.6827, 0.9545, 0.9973]])
+  qp = 1 - qm
+
+  # Export the pdf mean and 1 sigma (68%) and median
+  pdf_summary = pd.DataFrame(columns=['x', 'low', 'mean', 'high', 'median'])
+  pdf_summary['x'] = x
+  # Plot PDF with 1, 2, 3 sigma
+  plt.figure()
+  # 1,2,3 sigma
+  for i, alpha in enumerate([0.2, 0.1, 0.05]):
+    pm = np.quantile(pdf, qm[i], axis=0)
+    pp = np.quantile(pdf, qp[i], axis=0)
+    if i == 0:
+      pdf_summary['low'] = pm
+      pdf_summary['high'] = pp
+    plt.fill_between(x, pm, pp, color='r', alpha=alpha, rasterized=True)
+  # Mean
+  plt.plot(x, np.mean(pdf, axis=0), 'r', lw=2, rasterized=True)
+  pdf_summary['mean'] = np.mean(pdf, axis=0)
+  # Median
+  median = np.median(pdf, axis=0)
+  pdf_summary['median'] = median
+  plt.plot(x, median, 'k--', lw=2, rasterized=True)
+  # KDE
+  ks = data[col_gene]
+  Ns = data['nCount_RNA']
+  try:
+    if xscale == 'Seurat':
+      if target_sum == 0:
+        target_sum = np.median(Ns)
+      density = kde.gaussian_kde(np.log(1 + target_sum * ks / Ns))(x)
+    elif xscale == 'log':
+      Xs = ks / Ns
+      # I artificially substitute all 0 by exp(xmin)
+      Xs[Xs == 0] = np.exp(xmin)
+      density = kde.gaussian_kde(np.log(Xs))(x)
+    else:
+      raise Exception("Only xscale Seurat and log are implemented.")
+    plt.plot(x, density, color='green')
+  except Exception:
+    print("Could not compute density from input data.")
+  plt.xlim(xmin, xmax)
+  plt.ylim(0, )
+  if xscale == 'Seurat':
+    plt.xlabel(f'log(1 + {target_sum} * expression)')
+  elif xscale == 'log':
+    plt.xlabel('log(expression)')
+  plt.ylabel('PDF')
+  plt.title(title)
+  plt.savefig(output, dpi=300)
+
+  # Compute the posterior probability of each cell
+  nx = x.size
+  nox = nx * osampx
+  ox = get_bins_centers(xmin, xmax, nox)
+  Ax = get_Ax(data, col_gene, nx, x, dx, ox, xscale, target_sum)
+  post_per_cell = Ax * np.mean(pdf, axis=0)
+  # I renormalize each pdf:
+  post_per_cell /= (np.sum(post_per_cell, axis=1)[:, None] * dx)
+  # I average:
+  post_all_cells = np.mean(post_per_cell, axis=0)
+  # I plot:
+  plt.plot(x, post_all_cells, color='orange')
+  plt.savefig(f'{file_prefix}_with_posterior.{file_suffix}', dpi=300)
+
+  # Export
+  pdf_summary.to_csv(f'{file_prefix}_pdf.txt',
+                     index=False,
+                     header=True, sep='\t')
+
+  # Plot some individual pdfs
+  colors = plt.cm.jet(np.linspace(0, 1, 100))
+
+  plt.figure()
+  plt.title(f"{title}\n100 individual samples")
+  for i, cur_pdf in enumerate(pdf[::(pdf.shape[0] // 100)][:100]):
+    plt.plot(x, cur_pdf, color=colors[i], alpha=0.3, rasterized=True)
+  plt.savefig(f'{file_prefix}_individuals.{file_suffix}')
+
+  # I plot posterior of first cells:
+  ncells = min(50, post_per_cell.shape[0])
+  colors = plt.cm.jet(np.linspace(0, 1, ncells))
+  plt.figure()
+  plt.title(f"{title}\nposterior of {ncells} cells")
+  for i, cur_pdf in enumerate(post_per_cell[:ncells, :]):
+    plt.plot(x, cur_pdf, color=colors[i], alpha=0.3, rasterized=True)
+  plt.savefig(f'{file_prefix}_posterior_individuals.{file_suffix}')
+
+  average_per_cell = np.sum(post_per_cell * x, axis=1) * dx
+  average_square_per_cell = np.sum(post_per_cell * x * x, axis=1) * dx
+  var_per_cell = average_square_per_cell - average_per_cell * average_per_cell
+  infered_pdf_per_cell = np.array([trunc_norm(x, dx, mu, np.sqrt(var)) for mu, var in zip(average_per_cell, var_per_cell)])
+  infered_pdf_global = np.mean(infered_pdf_per_cell, axis=0)
+  # Plot
+  plt.figure()
+  # Mean
+  plt.plot(x, np.mean(pdf, axis=0), 'r', lw=2, rasterized=True, label='baredSC mean pdf')
+  # Median
+  median = np.median(pdf, axis=0)
+  plt.plot(x, median, 'k--', lw=2, rasterized=True, label='baredSC median pdf')
+  # KDE
+  ks = data[col_gene]
+  Ns = data['nCount_RNA']
+  try:
+    if xscale == 'Seurat':
+      density = kde.gaussian_kde(np.log(1 + target_sum * ks / Ns))(x)
+    elif xscale == 'log':
+      Xs = ks / Ns
+      # I artificially substitute all 0 by exp(xmin)
+      Xs[Xs == 0] = np.exp(xmin)
+      density = kde.gaussian_kde(np.log(Xs))(x)
+    else:
+      raise Exception("Only xscale Seurat and log are implemented.")
+    plt.plot(x, density, color='green', label='density from data')
+  except Exception:
+    print("Could not compute density from input data.")
+  # Posterior using full pdf
+  plt.plot(x, post_all_cells, color='orange', label='mean post pdf')
+  # Only mean value per cell
+  try:
+    density = kde.gaussian_kde(average_per_cell)(x)
+    plt.hist(average_per_cell, bins=40, color='magenta', label='histogram post mean', density=True)
+    plt.plot(x, density, color='pink', label='density post mean')
+  except Exception:
+    print("Could not compute density from average per cell.")
+  plt.plot(x, infered_pdf_global, color='purple', label='post gauss each cell')
+  plt.legend()
+  if xmax > 0:
+    plt.xlim(xmin, 1.2 * xmax)
+  else:
+    plt.xlim(xmin, xmax + 1)
+  plt.ylim(0, )
+  if xscale == 'Seurat':
+    plt.xlabel(f'log(1 + {target_sum} * expression)')
+  elif xscale == 'log':
+    plt.xlabel('log(expression)')
+  plt.ylabel('PDF')
+  plt.title(title)
+  plt.savefig(f'{file_prefix}_posterior_andco.{file_suffix}', dpi=300)
+
+  # Export the predicted average and sd for each cell
+  per_cell = pd.DataFrame(columns=['mu', 'sd'])
+  per_cell['mu'] = average_per_cell
+  per_cell['sd'] = np.sqrt(var_per_cell)
+  per_cell.to_csv(f'{file_prefix}_posterior_per_cell.txt',
+                  index=False,
+                  header=True, sep='\t')
+
+  # Export values of mean found in each sample
+  means = pdf @ (x * dx)
+  np.savetxt(f'{file_prefix}_means.txt.gz', means)
diff --git a/baredSC/twod.py b/baredSC/twod.py
new file mode 100644
index 0000000..888148f
--- /dev/null
+++ b/baredSC/twod.py
@@ -0,0 +1,480 @@
+# Copyright 2021 Jean-Baptiste Delisle and Lucille Delisle
+import numpy as np
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+from itertools import permutations
+import time
+from scipy.special import factorial
+import pandas as pd
+import samsam
+import samsam.logprior
+
+# Local imports
+from . common import get_Ax, get_prefix_suffix
+
+mpl.use('agg')
+
+
+# Define log probability function
+def logprob(p,
+            pref, wdist, permutxy,
+            xmin, xmax, nx, dx,
+            ymin, ymax, ny, dy,
+            nxy,
+            noxpdf, noypdf,
+            odxpdf, odypdf,
+            oxypdf, odxypdf,
+            min_scale_x, min_scale_y,
+            scale_prior,
+            Axy,
+            temp=1.0):
+  # Log prior
+  lp = logprior(p, pref, wdist,
+                permutxy,
+                xmin, xmax, ymin, ymax, dx, dy,
+                odxpdf, odypdf,
+                min_scale_x, min_scale_y,
+                scale_prior)
+  if np.isfinite(lp):
+    # If the parameters are possible
+    # Log likelihood
+    ll = loglike(p,
+                 nx, ny, nxy, noxpdf, noypdf,
+                 oxypdf, odxypdf, Axy)
+    # Return sum of log prior and log likelihood
+    return((lp + ll) / temp)
+  else:
+    # If the parameters are impossible
+    return(-np.inf)
+
+
+# Define the log prior
+def logprior(p,
+             pref, wdist, permutxy,
+             xmin, xmax, ymin, ymax, dx, dy,
+             odxpdf, odypdf,
+             min_scale_x, min_scale_y,
+             scale_prior):
+  # Raise exception if one of the condition is not
+  # satisfied
+  amp = p[5::6]
+  mux = p[::6]
+  muy = p[1::6]
+  sigx = p[2::6]
+  sigy = p[3::6]
+  cor = p[4::6]
+  # We complete the list of parameters:
+  p_full = np.concatenate(([1 - np.sum(amp)], p))
+  amp_full = np.concatenate(([1 - np.sum(amp)], amp))
+  try:
+    # Amplitudes
+    # Amplitudes should be positive
+    # Including the first one
+    if np.any(amp_full < 0):
+      raise Exception('Negative amplitude')
+    # Because we impose that sum of amplitude is equal to 1
+    # We add this term:
+    lp = np.log(factorial(amp.size))
+    # Check swapping (symmetry of the problem)
+    # pref is a parameter set and we impose that pref
+    # is closer to p than any permutation of p
+    dp = p - pref
+    dist = np.sum(wdist * dp * dp)
+    for ks in permutxy[1:]:
+      dpk = p_full[ks][1:] - pref
+      distk = np.sum(wdist * dpk * dpk)
+      if distk <= dist:
+        raise Exception('Swapping')
+    # Number of possible swappings
+    lp += np.log(permutxy.shape[0])
+    # Finaly we add the priors on other parameters
+    # Location
+    lp += sum(samsam.logprior.uniform(muk, xmin - 3 * sigk, xmax + 3 * sigk) for muk, sigk in zip(mux, sigx))
+    lp += sum(samsam.logprior.uniform(muk, ymin - 3 * sigk, ymax + 3 * sigk) for muk, sigk in zip(muy, sigy))
+    # Correlation
+    # lp += sum(samsam.logprior.uniform(cor, -0.95, 0.95) for cor in cor)
+    lp += sum(samsam.logprior.truncnormal(cork, mu=0, sig=scale_prior, a=-0.95, b=0.95) for cork in cor)
+    # Scale
+    lp += sum(samsam.logprior.loguniform(sigk, np.log(min_scale_x), np.log(xmax - xmin)) for sigk in sigx)
+    lp += sum(samsam.logprior.loguniform(sigk, np.log(min_scale_y), np.log(ymax - ymin)) for sigk in sigy)
+  except:
+    return(-np.inf)
+  return(lp)
+
+
+# pdf of a trunc_norm in 2d:
+def trunc_norm2d(xy, dxy, mu, sigma, corr):
+  u = (xy - mu) / sigma
+  lognorm = -0.5 * (u[..., 0] * u[..., 0] + u[..., 1] * u[..., 1] -
+                    2 * corr * u[..., 0] * u[..., 1]) / (1 - corr * corr)
+  lognorm -= np.max(lognorm)  # Avoid underflow
+  norm = np.exp(lognorm)
+  norm /= np.sum(norm * dxy)
+  return(norm)
+
+
+# Log likelihood
+def loglike(p,
+            nx, ny, nxy, noxpdf, noypdf,
+            oxypdf, odxypdf, Axy):
+  # Get the pdf from p:
+  pdf = get_pdf(p, nx, ny, noxpdf, noypdf,
+                oxypdf, odxypdf)
+  # The likelihood for a cell i is
+  # integral(p(ki,li,Ni|x, y)p(x, y|amp,mu,sig)dxdy)
+  # We approximate the integral by the sum
+  # p(ki,li,Ni|xj, yj)*dx*dy is stored in Axy
+  likecell = Axy @ pdf.reshape(nxy)
+  # The log likelihood for all cells is
+  # sum of log likelihood for each cell
+  ll = np.sum(np.log(likecell))
+  return(ll)
+
+
+# Get the pdf using parameters
+def get_pdf(p,
+            nx, ny,
+            noxpdf, noypdf,
+            oxypdf, odxypdf):
+  amp = p[5::6]
+  mux = p[::6]
+  muy = p[1::6]
+  sigx = p[2::6]
+  sigy = p[3::6]
+  cor = p[4::6]
+  # We complete the list of parameters:
+  amp_full = np.concatenate(([1 - np.sum(amp)], amp))
+  # Build the oversampled pdf
+  pdf_o = np.zeros((noypdf, noxpdf))
+  for ampk, muxk, muyk, sigxk, sigyk, cork in zip(amp_full, mux, muy, sigx, sigy, cor):
+    # Add the trunc_norm 2d
+    muk = np.array([muxk, muyk])
+    sigk = np.array([sigxk, sigyk])
+    pdf_o += ampk * trunc_norm2d(oxypdf, odxypdf, muk, sigk, cork)
+  # I do the mean in each bin:
+  pdf = np.mean(pdf_o.reshape(ny, noypdf // ny, nx, noxpdf // nx), axis=(1, 3))
+  return(pdf)
+
+
+# Return the same as gauss_mcmc but from a npz
+def extract_from_npz(output):
+  print("Reading")
+  start = time.time()
+  mcmc_res = np.load(output, allow_pickle=True)
+  diags = mcmc_res['diagnostics']
+  mu = diags.item().get('mu')
+  cov = diags.item().get('cov')
+  logprob_values = diags.item().get('logprob')
+  samples = mcmc_res['samples']
+  x = mcmc_res['x']
+  y = mcmc_res['y']
+  ox = mcmc_res['ox']
+  oy = mcmc_res['oy']
+  oxpdf = mcmc_res['oxpdf']
+  oypdf = mcmc_res['oypdf']
+  print(f"Read. It took {(time.time() - start):.2f} seconds.")
+  return(mu, cov, ox, oy, oxpdf, oypdf, x, y,
+         logprob_values, samples)
+
+
+# In order to compare multiple models we use an estimate of the log evidence
+def write_evidence(data, genex, geney, mu, cov, ox, oy, oxpdf, oypdf, x, y,
+                   min_scale_x, min_scale_y, scale_prior,
+                   covis_scale, nis, logprob, output,
+                   seed, scale, target_sum):
+  nx = x.size
+  dx = x[1] - x[0]
+  xmin = x[0] - dx / 2
+  xmax = x[-1] + dx / 2
+  noxpdf = oxpdf.size
+  odxpdf = oxpdf[1] - oxpdf[0]
+  if ox.size % nx != 0:
+    raise Exception("The size of ox is not multiple of size of x.")
+  if oxpdf.size % nx != 0:
+    raise Exception("The size of oxpdf is not multiple of size of x.")
+
+  ny = y.size
+  dy = y[1] - y[0]
+  ymin = y[0] - dy / 2
+  ymax = y[-1] + dy / 2
+  noypdf = oypdf.size
+  odypdf = oypdf[1] - oypdf[0]
+  if oy.size % ny != 0:
+    raise Exception("The size of oy is not multiple of size of y.")
+  if oypdf.size % ny != 0:
+    raise Exception("The size of oypdf is not multiple of size of y.")
+
+  nxy = nx * ny
+  odxypdf = odxpdf * odypdf
+  oxypdf = np.array(np.meshgrid(oxpdf, oypdf)).transpose(1, 2, 0)
+
+  # Poisson law
+  Ax = get_Ax(data, genex, nx, x, dx, ox, scale, target_sum)
+  Ay = get_Ax(data, geney, ny, y, dy, oy, scale, target_sum)
+
+  Axy = Ax[:, None, :] * Ay[:, :, None]
+  Axy = Axy.reshape(-1, nxy)
+
+  nnorm = (mu.size + 1) // 6
+
+  # We want to get all possible permutation of the gaussians (6 parameters for each)
+  permutxy = np.array([np.concatenate(([6 * i + np.arange(6) for i in v]))
+                       for v in permutations(np.arange(nnorm))])
+
+  # We calculate logevidence
+  np.random.seed(seed)
+  logevidence = samsam.covis(mu, covis_scale ** 2 * cov,
+                             logprob, nsamples=nis,
+                             pref=mu, wdist=1 / np.diag(cov),
+                             permutxy=permutxy,
+                             xmin=xmin, xmax=xmax, nx=nx, dx=dx,
+                             ymax=ymax, ymin=ymin, ny=ny, dy=dy,
+                             nxy=nxy, noxpdf=noxpdf, noypdf=noypdf,
+                             oxypdf=oxypdf, odxypdf=odxypdf,
+                             odxpdf=odxpdf, odypdf=odypdf,
+                             min_scale_x=min_scale_x,
+                             min_scale_y=min_scale_y,
+                             scale_prior=scale_prior,
+                             Axy=Axy)[2]['logevidence']
+
+  with open(output, 'w') as fo:
+    fo.write(f'{logevidence}\n')
+
+
+def plots_from_pdf(x, y, pdf, title, output,
+                   gene_x, gene_y,
+                   log1pColorScale, splitys, Neff=None):
+  # We assume x is equally spaced
+  dx = x[1] - x[0]
+  xmin = x[0] - dx / 2
+  xmax = x[-1] + dx / 2
+  dy = y[1] - y[0]
+  ymin = y[0] - dy / 2
+  ymax = y[-1] + dy / 2
+
+  # Process the output to get prefix and suffix
+  file_prefix, file_suffix = get_prefix_suffix(output)
+
+  # 1, 2, 3 sigma:
+  qm = np.array([(1 - lvl) / 2 for lvl in [0.6827, 0.9545, 0.9973]])
+  qp = 1 - qm
+
+  # Evaluate pdf1d:
+  pdfx = np.sum(pdf, axis=1) * dy
+  pdfy = np.sum(pdf, axis=2) * dx
+
+  # Evaluate the correlation:
+  to_export = []
+  print("Evaluate mean")
+  mux = pdfx @ x * dx
+  muy = pdfy @ y * dy
+  print("Done.")
+  cx = x[None, :] - mux[:, None]
+  cy = y[None, :] - muy[:, None]
+  varx = np.sum(cx ** 2 * pdfx, axis=1) * dx
+  vary = np.sum(cy ** 2 * pdfy, axis=1) * dy
+  print("Evaluate covariance")
+  covxy = np.sum(cx[:, None, :] * cy[:, :, None] * pdf, axis=(1, 2)) * dx * dy
+  print("Done.")
+  corrxy = covxy / np.sqrt(varx * vary)
+  mucorrxy = np.mean(corrxy)
+  print('correlation:')
+  print('mean =', mucorrxy)
+  to_export.append(mucorrxy)
+  print('med =', np.median(corrxy))
+  to_export.append(np.median(corrxy))
+  for k in range(3):
+    print(f'{k+1}sig =', np.quantile(corrxy, qm[k]), np.quantile(corrxy, qp[k]))
+    if k == 0:
+      to_export.append(np.quantile(corrxy, qm[k]))
+      to_export.append(np.quantile(corrxy, qp[k]))
+
+  # Error on correlation
+  sigcorrxym = mucorrxy - np.quantile(corrxy, qm[0])
+  sigcorrxyp = np.quantile(corrxy, qp[0]) - mucorrxy
+
+  # Estimate the p-value if Neff is not None:
+  if Neff is not None:
+    # Estimate p-value for mucorrxy < 0
+    # Get the fraction
+    frac_cor_pos = np.sum(corrxy >= 0) / corrxy.size
+    # The expected value of the fraction is:
+    pval_pos = frac_cor_pos + 1 / Neff
+    # Get the error
+    pval_pos_error = np.sqrt(pval_pos / Neff)
+    # Estimate p-value for mucorrxy > 0
+    # Get the fraction
+    frac_cor_neg = np.sum(corrxy <= 0) / corrxy.size
+    # The expected value of the fraction is:
+    pval_neg = frac_cor_neg + 1 / Neff
+    # Get the error
+    pval_neg_error = np.sqrt(pval_neg / Neff)
+
+  # Plot 4 panels plot
+  fig, axs = plt.subplots(2, 2, sharex='col', sharey='row')
+  # Heatmap:
+  ax = axs[1, 0]
+  x_borders = np.linspace(xmin, xmax, len(x) + 1)
+  y_borders = np.linspace(ymin, ymax, len(y) + 1)
+  if log1pColorScale:
+    ax.pcolormesh(x_borders, y_borders, np.log1p(np.mean(pdf, axis=0)),
+                  shading='flat', rasterized=True, cmap='Greys')
+  else:
+    ax.pcolormesh(x_borders, y_borders, np.mean(pdf, axis=0),
+                  shading='flat', rasterized=True, cmap='Greys')
+  ax.set_xlabel(gene_x)
+  ax.set_ylabel(gene_y)
+
+  # PDF 1d
+  ax = axs[0, 0]
+  ax.fill_between(x, np.quantile(pdfx, qm[0], axis=0), np.quantile(pdfx, qp[0], axis=0), color='r', alpha=0.2)
+  ax.plot(x, np.mean(pdfx, axis=0), 'r')
+  ax.plot(x, np.median(pdfx, axis=0), 'k--')
+  ax.set_ylim(0,)
+  ax.set_ylabel('PDF')
+
+  ax = axs[1, 1]
+  ax.fill_betweenx(y, np.quantile(pdfy, qm[0], axis=0), np.quantile(pdfy, qp[0], axis=0), color='r', alpha=0.2)
+  ax.plot(np.mean(pdfy, axis=0), y, 'r')
+  ax.plot(np.median(pdfy, axis=0), y, 'k--')
+  ax.set_xlim(0,)
+  ax.set_xlabel('PDF')
+
+  # Write correlation
+  ax = axs[0, 1]
+  ax.axis('off')
+  ax.text(0.5, 0.5, f'Corr = ${mucorrxy:.3f}_{{-{sigcorrxym:.3f}}}^{{+{sigcorrxyp:.3f}}}$',
+          horizontalalignment='center', verticalalignment='center',
+          transform=ax.transAxes)
+  if Neff is not None:
+    if mucorrxy > 0:
+      text_pval = f'p-value = ${pval_neg:.2}^+_- {pval_neg_error:.2}$'
+      to_export.append(pval_neg)
+      to_export.append(pval_neg_error)
+    else:
+      text_pval = f'p-value = ${pval_pos:.2}^+_- {pval_pos_error:.2}$'
+      to_export.append(pval_pos)
+      to_export.append(pval_pos_error)
+    ax.text(0.5, 0.25, text_pval,
+            horizontalalignment='center', verticalalignment='center',
+            transform=ax.transAxes)
+  fig.suptitle(title)
+
+  # Save
+  plt.savefig(output)
+
+  # Use median instead of mean:
+  ax = axs[1, 0]
+  if log1pColorScale:
+    ax.pcolormesh(x_borders, y_borders, np.log1p(np.median(pdf, axis=0)),
+                  shading='flat', rasterized=True, cmap='Greys')
+  else:
+    ax.pcolormesh(x_borders, y_borders, np.median(pdf, axis=0),
+                  shading='flat', rasterized=True, cmap='Greys')
+
+  # Save
+  plt.savefig(f'{file_prefix}_median.{file_suffix}')
+
+  # Plot the projection of individual pdfs:
+  colors = plt.cm.jet(np.linspace(0, 1, 100))
+
+  # Plot 4 panels plot
+  fig, axs = plt.subplots(2, 2)
+  fig.suptitle(f"{title}\n100 individual samples")
+  axs[0, 1].axis('off')
+  axs[1, 0].axis('off')
+  # PDF 1d
+  ax = axs[0, 0]
+  for i, cur_pdf in enumerate(pdfx[::(pdfx.shape[0] // 100)][:100]):
+    ax.plot(x, cur_pdf, color=colors[i], alpha=0.3)
+  ax.set_ylim(0,)
+  ax.set_ylabel('PDF')
+  ax.set_xlabel(gene_x)
+
+  ax = axs[1, 1]
+  for i, cur_pdf in enumerate(pdfy[::(pdfy.shape[0] // 100)][:100]):
+    ax.plot(cur_pdf, y, color=colors[i], alpha=0.3)
+  ax.set_xlim(0,)
+  ax.set_xlabel('PDF')
+  ax.set_ylabel(gene_y)
+
+  # Save
+  plt.savefig(f'{file_prefix}_individuals.{file_suffix}')
+
+  # Write info of correlation to file:
+  with open(f'{file_prefix}_corr.txt', 'w') as f:
+    if Neff is None:
+      f.write('mean\tmedian\tlow\thigh\n')
+    else:
+      f.write('mean\tmedian\tlow\thigh\tpval\terror\n')
+    f.write('\t'.join([str(v) for v in to_export]) + '\n')
+
+  # Export the mean pdf with colnames and rownames
+  np.savetxt(f'{file_prefix}_pdf2d.txt',
+             np.concatenate((np.array([y]).T, np.mean(pdf, axis=0)), axis=1),
+             delimiter="\t", header="xy\t" + "\t".join([str(v) for v in x]),
+             comments="")
+
+  # Export the pdf mean and 1 sigma (68%) and median
+  pdf_summary = pd.DataFrame(columns=['x', 'y', 'low', 'mean', 'high', 'median'])
+  pdf_summary['x'] = np.tile(x, len(y))
+  pdf_summary['y'] = np.repeat(y, len(x))
+  pdf_summary['low'] = np.quantile(pdf, qm[0], axis=0).flatten()
+  pdf_summary['mean'] = np.mean(pdf, axis=0).flatten()
+  pdf_summary['high'] = np.quantile(pdf, qp[0], axis=0).flatten()
+  pdf_summary['median'] = np.median(pdf, axis=0).flatten()
+  # Export
+  pdf_summary.to_csv(f'{file_prefix}_pdf2d_flat.txt',
+                     index=False,
+                     header=True, sep='\t')
+
+  # Sum up pdf above and below threshold.
+  if splitys is not None:
+    for splity in splitys:
+      # splity renorm
+      pdfx1 = np.sum(pdf[:, y < splity], axis=1)
+      pdfx1 /= np.sum(pdfx1, axis=1)[:, None] * dx
+      pdfx2 = np.sum(pdf[:, y >= splity], axis=1)
+      pdfx2 /= np.sum(pdfx2, axis=1)[:, None] * dx
+      plt.figure()
+      plt.fill_between(x, np.quantile(pdfx1, qm[0], axis=0), np.quantile(pdfx1, qp[0], axis=0), color='r', alpha=0.2)
+      plt.plot(x, np.mean(pdfx1, axis=0), 'r', label=f'{gene_y}$<${splity}')
+      plt.plot(x, np.median(pdfx1, axis=0), 'k--')
+      plt.fill_between(x, np.quantile(pdfx2, qm[0], axis=0), np.quantile(pdfx2, qp[0], axis=0), color='g', alpha=0.2)
+      plt.plot(x, np.mean(pdfx2, axis=0), 'g', label=f'{gene_y}$\\geq${splity}')
+      plt.plot(x, np.median(pdfx2, axis=0), 'k--', label='median')
+      plt.xlim(xmin, xmax)
+      plt.ylim(0, 3)
+      plt.xlabel(gene_x)
+      plt.ylabel('PDF')
+      plt.legend()
+      plt.title(title)
+      plt.savefig(f'{file_prefix}_split{splity}_renorm.{file_suffix}')
+
+      # splity raw
+      pdfx1 = np.sum(pdf[:, y < splity], axis=1) * dy
+      pdfx2 = np.sum(pdf[:, y >= splity], axis=1) * dy
+      plt.figure()
+      plt.fill_between(x, np.quantile(pdfx, qm[0], axis=0), np.quantile(pdfx, qp[0], axis=0), color='k', alpha=0.2)
+      plt.plot(x, np.mean(pdfx, axis=0), 'k', label='all')
+      plt.fill_between(x, np.quantile(pdfx1, qm[0], axis=0), np.quantile(pdfx1, qp[0], axis=0), color='r', alpha=0.2)
+      plt.plot(x, np.mean(pdfx1, axis=0), 'r', label=f'{gene_y}$<${splity}')
+      plt.fill_between(x, np.quantile(pdfx2, qm[0], axis=0), np.quantile(pdfx2, qp[0], axis=0), color='g', alpha=0.2)
+      plt.plot(x, np.mean(pdfx2, axis=0), 'g', label=f'{gene_y}$\\geq${splity}')
+      plt.xlim(xmin, xmax)
+      plt.ylim(0, 3)
+      plt.xlabel(gene_x)
+      plt.ylabel('PDF')
+      plt.legend()
+      plt.title(title)
+      plt.savefig(f'{file_prefix}_split{splity}.{file_suffix}')
+
+      np.savetxt(f'{file_prefix}_split{splity}.txt',
+                 np.array([x, np.quantile(pdfx1, qm[0], axis=0),
+                           np.mean(pdfx1, axis=0),
+                           np.quantile(pdfx1, qp[0], axis=0),
+                           np.quantile(pdfx2, qm[0], axis=0),
+                           np.mean(pdfx2, axis=0),
+                           np.quantile(pdfx2, qp[0], axis=0)]).T,
+                 delimiter="\t",
+                 header="x\tlow1\tmean1\thigh1\tlow2\tmean2\thigh2", comments="")
diff --git a/baredSC_dev_env.yml b/baredSC_dev_env.yml
new file mode 100644
index 0000000..82bee38
--- /dev/null
+++ b/baredSC_dev_env.yml
@@ -0,0 +1,23 @@
+# create the environment using : `conda env create -f baredSC_dev_env.yml`
+# `conda activate baredSC_dev`
+# Then you need to install baredSC with `python setup.py install`
+# Then you can make the doc with cd docs && make html
+# Create the package by `python setup.py sdist`
+# Upload it with `python3 -m twine upload dist/*`
+name: baredSC_dev
+channels:
+   - conda-forge
+dependencies:
+   - numpy >=1.16
+   - matplotlib >=3.1.1
+   - pandas >=0.25.0
+   - scipy >=1.3.0
+   - corner >=2.0.0
+   - ipython # For the doc
+   - sphinx-argparse # For the doc
+   - twine # For the upload
+   - pip
+   - pip:
+      - --extra-index-url https://obswww.unige.ch/~delisle
+      - samsam >=0.1.2
+      - sphinx-autorun # For the doc
diff --git a/baredSC_env.yml b/baredSC_env.yml
new file mode 100644
index 0000000..a40b288
--- /dev/null
+++ b/baredSC_env.yml
@@ -0,0 +1,16 @@
+# create the environment using : `conda env create -f baredSC_env.yml`
+# `conda activate baredSC`
+name: baredSC
+channels:
+   - conda-forge
+dependencies:
+   - numpy >=1.16
+   - matplotlib >=3.1.1
+   - pandas >=0.25.0
+   - scipy >=1.3.0
+   - corner >=2.0.0
+   - pip
+   - pip:
+      - --extra-index-url https://obswww.unige.ch/~delisle
+      - samsam >=0.1.2
+      - baredSC
diff --git a/bin/baredSC_1d b/bin/baredSC_1d
new file mode 100644
index 0000000..85ffef0
--- /dev/null
+++ b/bin/baredSC_1d
@@ -0,0 +1,7 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from baredSC.baredSC_1d import main
+
+if __name__ == "__main__":
+    main()
diff --git a/bin/baredSC_2d b/bin/baredSC_2d
new file mode 100644
index 0000000..973aeeb
--- /dev/null
+++ b/bin/baredSC_2d
@@ -0,0 +1,7 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from baredSC.baredSC_2d import main
+
+if __name__ == "__main__":
+    main()
diff --git a/bin/combineMultipleModels_1d b/bin/combineMultipleModels_1d
new file mode 100644
index 0000000..5b7d5b7
--- /dev/null
+++ b/bin/combineMultipleModels_1d
@@ -0,0 +1,7 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from baredSC.combineMultipleModels_1d import main
+
+if __name__ == "__main__":
+    main()
diff --git a/bin/combineMultipleModels_2d b/bin/combineMultipleModels_2d
new file mode 100644
index 0000000..77636ab
--- /dev/null
+++ b/bin/combineMultipleModels_2d
@@ -0,0 +1,7 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from baredSC.combineMultipleModels_2d import main
+
+if __name__ == "__main__":
+    main()
diff --git a/docs/Makefile b/docs/Makefile
new file mode 100644
index 0000000..d4bb2cb
--- /dev/null
+++ b/docs/Makefile
@@ -0,0 +1,20 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = .
+BUILDDIR      = _build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
diff --git a/docs/conf.py b/docs/conf.py
new file mode 100644
index 0000000..3bfabf6
--- /dev/null
+++ b/docs/conf.py
@@ -0,0 +1,87 @@
+# Configuration file for the Sphinx documentation builder.
+#
+# This file only contains a selection of the most common options. For a full
+# list see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Path setup --------------------------------------------------------------
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+#
+
+import os
+import sys
+
+# from unittest.mock import Mock
+
+import warnings
+
+warnings.filterwarnings("ignore", category=DeprecationWarning)
+
+sys.path.insert(0, os.path.abspath('../'))
+
+from baredSC._version import __version__
+
+# import mock
+
+# MOCK_MODULES = ['numpy',
+#                  'matplotlib',
+#                  'pandas',
+#                  'itertools',
+#                  'scipy',
+# MOCK_MODULES = ['scaledAdaptiveMetropolis',
+#                 'covarianceImportanceSampling',
+#                 'logpriors',
+#                 'acf',
+#                 'lib.common',
+#                 'lib.oned',
+#                 'lib.twod']
+# MOCK_MODULES = ['samsam']
+# for mod_name in MOCK_MODULES:
+#     sys.modules[mod_name] = Mock()
+
+# autodoc_mock_imports = MOCK_MODULES
+
+# -- Project information -----------------------------------------------------
+
+project = 'baredSC'
+copyright = '2021, Jean-Baptiste Delisle, Lucille Lopez-Delisle'
+author = 'Jean-Baptiste Delisle, Lucille Lopez-Delisle'
+
+# The full version, including alpha/beta/rc tags
+release = __version__
+
+
+# -- General configuration ---------------------------------------------------
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = ['sphinxarg.ext',
+              'sphinx_autorun',
+              'IPython.sphinxext.ipython_console_highlighting',
+              'IPython.sphinxext.ipython_directive'
+              ]
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ['_templates']
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This pattern also affects html_static_path and html_extra_path.
+exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
+
+
+# -- Options for HTML output -------------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+#
+html_theme = 'alabaster'
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ['_static']
diff --git a/docs/content/installation.rst b/docs/content/installation.rst
new file mode 100644
index 0000000..4bf0420
--- /dev/null
+++ b/docs/content/installation.rst
@@ -0,0 +1,32 @@
+Installation
+============
+
+.. contents:: 
+    :local:
+    
+Requirements
+------------
+
+It as only been tested on linux but should work on MacOS.
+
+It requires python >= 3.7 (tested 3.7.3 and 3.9.1)
+
+Dependencies of classical python packages:
+
+* numpy (tested 1.16.4 and 1.19.5)
+* matplotlib (tested 3.1.1 and 3.3.4)
+* pandas (tested 0.25.0 and 1.2.1)
+* scipy (tested 1.3.0 and 1.6.0)
+
+Dependencies of a python package from Jean-Baptiste Delisle dedicated to mcmc:
+
+* `samsam <https://obswww.unige.ch/~delisle/samsam/doc/>`_ (above 0.1.2)
+
+Installation
+------------
+
+For the moment you can install it with pip:
+
+pip install --extra-index-url https://obswww.unige.ch/~delisle baredSC
+
+It may be accessible in conda later.
diff --git a/docs/content/outputs.rst b/docs/content/outputs.rst
new file mode 100644
index 0000000..dbc3bec
--- /dev/null
+++ b/docs/content/outputs.rst
@@ -0,0 +1,146 @@
+Outputs
+=======
+
+.. contents:: 
+    :local:
+
+We will describe here each of the output file.
+
+MCMC output
+-----------
+
+The only output by defaut is a numpy compressed ``.npz`` file. This output contains the result of the MCMC:
+
+* samples: the value of the parameter at each step of the MCMC
+* diagnostics: a dictionary with the diagnostics at each step of the MCMC, among them:
+
+  * logprob: the log probability at each step of the MCMC
+  * mu: the final estimate of the mean of each parameter
+  * cov: the final estimate of the covariance matrix of parameters
+
+* some of the input values
+
+When the tool is run while the output exists it will use it instead of rerunning it which is useful to get more plots.
+
+Plots and txt outputs
+---------------------
+
+When ``--figure name.extension`` is given then some QC and results are given.
+
+QC
+^^
+
+name_convergence.extension
+""""""""""""""""""""""""""
+
+This plot shows the autocorrelation between samples for all parameters (the solid line shows the median, the shaded area shows the min and max). If the MCMC converged, you should see a value of ACF close to 0 since a small value of T.
+
+name_neff.txt
+"""""""""""""
+
+From the autocorrelation displayed above, we can evaluate the number of independent samples, also called effective sample size. The value is printed and stored in this text file.
+
+name_p.extension
+""""""""""""""""
+
+This plot shows the value of each parameter and the log probability (y axis, one panel per parameter) for all samples (x-axis). When the MCMC did not converged, it can be helpful to see if it can be explained by the fact that the first samples considered where not around the final solution. In this case, it can be useful to rerun the plots using an increase value of ``--removeFirstSamples`` (by default it is 1/4 of the number of samples), or increase the number of samples.
+
+name_corner.extension
+"""""""""""""""""""""
+
+This plot shows the distribution of value of each parameter in relationship the one with the other. It can help to see which parameters are correlated. Also, when the MCMC did not converged, it can help to identify if 2 or more solutions were explored.
+
+
+Results
+^^^^^^^
+
+name.extension
+""""""""""""""
+
+This is the figure with the results. 
+
+- When the 1d version is used, it displays the mean pdf in solid red line, the median in black dashed lines (/!\ the integral of the median is not equal to 1) with the confidence interval of 1 sigma (68%), 2 sigma (95%) and 3 sigma (99.7%) as well as in green, the kernel density estimate of the input values, the detected expression (``log(1 + 10^4 * raw / total UMI)``).
+
+- When the 2d version is used, it displays the pdf as a heatmap as well as a projection on the x and y axis. On the projection, the confidence interval 68% is indicated as a shaded area as well as the mean with a solid red line and the median with a dashed black line. On the top right corner, the correlation is indicated with the confidence interval 68% as well as a confidence interval on the one-sided p-value (the probability that the correlation is the opposite sign of the mean, one sigma confidence interval).
+
+name_individuals.extension
+""""""""""""""""""""""""""
+
+- When the 1d version is used, it displays the pdf of 100 samples.
+
+- When the 2d version is used, it displays the projection of the pdf of 100 samples.
+
+name_p.txt
+""""""""""
+
+This is a tabulated delimited table with the 16 percentile (low), median, 84 percentile (high) value of each parameter.
+
+
+name_pdf.txt (1d only)
+""""""""""""""""""""""
+
+For each value of x, the 16 percentile (low), mean, 84 percentile (high) and median, is given in a tabulated delimited file.
+
+name_with_posterior.extension (1d only)
+"""""""""""""""""""""""""""""""""""""""
+
+Same as name.extension except that a new orange line is plotted showing the posterior density evaluated as the average of the posterior density of each cell.
+
+name_posterior_per_individuals.extension (1d only)
+""""""""""""""""""""""""""""""""""""""""""""""""""
+
+Showing posterior density probability of 50 random cells.
+
+name_posterior_per_cell.txt (1d only)
+"""""""""""""""""""""""""""""""""""""
+
+For each cell of the input, providing the posterior average and standard deviation of the density probability.
+
+name_posterior_andco.extension (1d only)
+""""""""""""""""""""""""""""""""""""""""
+Showing the mean pdf, the median pdf, the density from raw counts normalized, the average of the posterior density from all cells,  the density and a histogram using only the average value of the posterior distribution of each cell and the posterior density approximating the pdf of each cell by a Gaussian using values in the "posterior_per_cell.txt" file.
+
+
+name_median.extension (2d only)
+"""""""""""""""""""""""""""""""
+
+Same as name.extension except that the median instead of the mean is used.
+
+name_corr.txt (2d only)
+"""""""""""""""""""""""
+
+The mean, median, 16 percentile, 84 percentile, p-value and error on the p-value for the correlation (see above).
+
+
+name_pdf2d.txt (2d only)
+""""""""""""""""""""""""
+
+The mean pdf and the x and y values stored in a tabulated delimited file in a matrix format. Different x values correspond to different columns while different y values correspond to different rows.
+
+name_pdf2d_flat.txt (2d only)
+"""""""""""""""""""""""""""""
+
+The x, y, 16 percentile (low), mean, 84 percentile (high) and median of pdf in a tabulated delimited file.
+
+Results when ``--splity`` is provided in 2d
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+When ``--splity`` is provided the pdf above and below this threshold on the y axis are summed up, resulting in 2 pdf along the x axis.
+
+name_splitX.extension
+"""""""""""""""""""""
+This plot shows the 2 pdfs. The ratio between the area represent the ratio of cells above and below the threshold of the gene y. The pdf for cells below the threshold is in red (with the shaded area for the 68% confidence interval) and the pdf for cells above the threshold is in green. In black is the pdf of all cells projected on the x axis (sum of the 2).
+
+name_splitX_renorm.extension
+""""""""""""""""""""""""""""
+Same plot as above except that the pdf were renormalized so the area of each pdf is equal to 1. Also the median is added in dashed black lines.
+
+name_splitX.txt
+"""""""""""""""
+This is a tabulated delimited table with the x values, the 16 percentile (low), mean, 84 percentile (high) values of each pdf (below and above the threshold) before normalization.
+
+
+Evidence
+--------
+When ``--logevidence`` is set. The log evidence is calculated and stored in this file. This can be used to compare different models, here different number of gaussians.
+
diff --git a/docs/content/releases.rst b/docs/content/releases.rst
new file mode 100644
index 0000000..ee5bfa4
--- /dev/null
+++ b/docs/content/releases.rst
@@ -0,0 +1,7 @@
+Releases
+========
+
+1.0.0
+-----
+
+First release
diff --git a/docs/content/tuto/tutorial_sim_change_minScale.rst b/docs/content/tuto/tutorial_sim_change_minScale.rst
new file mode 100644
index 0000000..46a4146
--- /dev/null
+++ b/docs/content/tuto/tutorial_sim_change_minScale.rst
@@ -0,0 +1,78 @@
+Change minScale
+===============
+
+.. contents:: 
+    :local:
+
+Inputs
+------
+
+We took total UMI counts from a real dataset of NIH3T3.
+We generated a example where 2 genes have the same distribution (2 gaussians, one of mean 0.375, scale 0.125 and another one of mean 1 and scale 0.1).
+Half of cells goes in each gaussian.
+The gene is called "0.5_0_0_0.5_x".
+
+minScale
+--------
+
+In our model, the prior on the scale of each Gaussian is that it must be above the ``--minScale``. By default, this value is set to 0.1 because most of the time we don't have the resolution to go below.
+Here we will decrease this value to see how it affects the results
+
+Run baredSC on the subpopulation with a scale of 0.1
+----------------------------------------------------
+
+Let's focus on cells of group 3.0 (which corresponds to the second Gaussian of mean 1 and scale 0.1).
+We run baredSC but we put ``--minScale`` to 0. A minimum scale of 0 is not accepted by baredSC
+because it causes some issues so setting it to 0 will put the minimum value accepted by baredSC.
+
+.. code:: bash
+
+    $ nnorm=1
+    $ baredSC_1d \
+            --input example/nih3t3_generated_2d_2.txt \
+            --metadata1ColName 0.5_0_0_0.5_group \
+            --metadata1Values 3.0 \
+            --geneColName 0.5_0_0_0.5_x \
+            --output example/first_example_1d_group3_${nnorm}gauss_ms0 \
+            --nnorm ${nnorm} --minScale 0 \
+            --minNeff 200 \
+            --figure example/first_example_1d_group3_${nnorm}gauss_ms0.png \
+            --title "first gene ${nnorm} gauss group3 minScale 0"
+
+First let's have a look to the corner plot:
+
+.. image:: ../../../example/first_example_1d_group3_1gauss_ms0_corner.png
+
+We see that the median value for scale0 is 0.09 so very close to what was simulated.
+However, we also see some very small values of scale. As a consequence, when we look at the results:
+
+.. image:: ../../../example/first_example_1d_group3_1gauss_ms0.png
+
+We see that the confidence interval is quite large and that the mean (red) is far from the median (dashed black).
+
+In such cases, it is reasonable to use a value for the minimum scale intermediate between the minimum value accepted by baredSC (0.0125) and the default value (0.1).
+For example, we can use 0.05:
+
+.. code:: bash
+
+    $ nnorm=1
+    $ baredSC_1d \
+        --input example/nih3t3_generated_2d_2.txt \
+        --metadata1ColName 0.5_0_0_0.5_group \
+        --metadata1Values 3.0 \
+        --geneColName 0.5_0_0_0.5_x \
+        --output example/first_example_1d_group3_${nnorm}gauss_ms0.05 \
+        --nnorm ${nnorm} --minScale 0.05 \
+        --minNeff 200 \
+        --figure example/first_example_1d_group3_${nnorm}gauss_ms0.05.png \
+        --title "first gene ${nnorm} gauss group3 minScale 0.05"
+
+
+The corner plot shows that the median is still close to 0.1:
+
+.. image:: ../../../example/first_example_1d_group3_1gauss_ms0.05_corner.png
+
+We see that the confidence interval is reduced large and that the mean (red) is closer from the median (dashed black).
+
+.. image:: ../../../example/first_example_1d_group3_1gauss_ms0.05.png
+
diff --git a/docs/content/tuto/tutorial_sim_change_nx.rst b/docs/content/tuto/tutorial_sim_change_nx.rst
new file mode 100644
index 0000000..ba63f5e
--- /dev/null
+++ b/docs/content/tuto/tutorial_sim_change_nx.rst
@@ -0,0 +1,103 @@
+Change the number of bins
+=========================
+
+.. contents:: 
+    :local:
+
+baredSC can be relatively slow. A way to speed it is to decrease the number of bins.
+
+Inputs
+------
+
+We took total UMI counts from a real dataset of NIH3T3.
+We generated a example where the PDF of the 2 genes is a 2D Gaussian.
+The mean on each axis and the scale on each axis is equal to 0.5 and the correlation value is also 0.5.
+
+Run baredSC in 2D
+-----------------
+
+By default baredSC_2d uses 50 bins in x and 50 bins in y. Let's run with default parameters.
+
+..
+  python ~/mnt/home_scitas/softwares/scriptsForLopezDelisleEtAl2021/scripts/generate_2dgauss_data_columns.py --input ~/mnt/scratch/LopezDelisle2021/output1/nih3t3_nRNA.txt --colnames 1_0.5_0.5_0.5_0.5_0.5 > example/nih3t3_generated_second.txt
+
+.. code:: bash
+
+    $ nnorm=1
+    $ baredSC_2d \
+        --input example/nih3t3_generated_second.txt \
+        --geneXColName 1_0.5_0.5_0.5_0.5_0.5_x \
+        --geneYColName 1_0.5_0.5_0.5_0.5_0.5_y \
+        --metadata1ColName group \
+        --metadata1Values group1 \
+        --output example/second_example_2d_cellgroup1_${nnorm}gauss \
+        --nnorm ${nnorm} \
+        --figure example/second_example_2d_cellgroup1_${nnorm}gauss.png \
+        --title "second example 2d cell group 1 ${nnorm} gauss"
+
+It took 11 minutes to run the MCMC and 3 minutes to compute the PDF.
+
+Let's try to reduce the number of bins using ``--nx`` and ``--ny``:
+
+.. code:: bash
+
+    $ nnorm=1
+    $ baredSC_2d \
+        --input example/nih3t3_generated_second.txt \
+        --geneXColName 1_0.5_0.5_0.5_0.5_0.5_x \
+        --geneYColName 1_0.5_0.5_0.5_0.5_0.5_y \
+        --metadata1ColName group \
+        --metadata1Values group1 \
+        --output example/second_example_2d_cellgroup1_${nnorm}gauss_nx20 \
+        --nnorm ${nnorm} \
+        --nx 20 --ny 20 \
+        --figure example/second_example_2d_cellgroup1_${nnorm}gauss_nx20.png \
+        --title "second example 2d cell group 1 ${nnorm} gauss 20 bins"
+
+This time it took 3:32 minutes to compute MCMC and 30 seconds to get the PDF.
+
+The parameters found are the same:
+
+.. image:: ../../../example/second_example_2d_cellgroup1_1gauss_corner.png
+    :width: 48 %
+.. image:: ../../../example/second_example_2d_cellgroup1_1gauss_nx20_corner.png
+    :width: 48 %
+
+However, the image provided with 20 bins is much more pixelized:
+
+.. image:: ../../../example/second_example_2d_cellgroup1_1gauss.png
+    :width: 48 %
+.. image:: ../../../example/second_example_2d_cellgroup1_1gauss_nx20.png
+    :width: 48 %
+
+There is a way to render the plot prettier. However,
+you need to keep in mind that these pretty plots will not display the data
+as they have been used to compute the likelihood.
+In this example, the scale of the Gaussian is large enough that's why it gave the same results with both number of bins.
+We can set the number of bins to use in the plot with ``--prettyBinsx`` and ``--prettyBinsy``.
+
+.. code:: bash
+
+    $ nnorm=1
+    $ baredSC_2d \
+        --input example/nih3t3_generated_second.txt \
+        --geneXColName 1_0.5_0.5_0.5_0.5_0.5_x \
+        --geneYColName 1_0.5_0.5_0.5_0.5_0.5_y \
+        --metadata1ColName group \
+        --metadata1Values group1 \
+        --output example/second_example_2d_cellgroup1_${nnorm}gauss_nx20 \
+        --nnorm ${nnorm} \
+        --nx 20 --ny 20 --prettyBinsx 50 --prettyBinsy 50 \
+        --figure example/second_example_2d_cellgroup1_${nnorm}gauss_nx20_pretty.png \
+        --title "second example 2d cell group 1 ${nnorm} gauss 20 bins pretty"
+
+It will use the ``.npz`` file generated with the last run to get the MCMC results.
+
+.. image:: ../../../example/second_example_2d_cellgroup1_1gauss.png
+    :width: 48 %
+.. image:: ../../../example/second_example_2d_cellgroup1_1gauss_nx20_pretty.png
+    :width: 48 %
+
+Now they really look alike.
+
+These options also exists in 1D.
diff --git a/docs/content/tuto/tutorial_sim_change_scalePrior.rst b/docs/content/tuto/tutorial_sim_change_scalePrior.rst
new file mode 100644
index 0000000..f11376d
--- /dev/null
+++ b/docs/content/tuto/tutorial_sim_change_scalePrior.rst
@@ -0,0 +1,87 @@
+Change scalePrior
+=================
+
+.. contents:: 
+    :local:
+
+baredSC_2d uses as a prior on the correlation value of each Gaussian a normal distribution.
+In order to reduce the number of false-positive (anti-)correlation detection. The scale of the normal distribution is set to 0.3.
+We show here the influence of this prior.
+
+Inputs
+------
+
+We took total UMI counts from a real dataset of NIH3T3.
+We generated a example where the PDF of the 2 genes is a 2D Gaussian.
+The mean on each axis and the scale on each axis is equal to 0.5 and the correlation value is also 0.5.
+
+Run baredSC in 2D
+-----------------
+
+By default baredSC_2d uses 50 bins in x and 50 bins in y. But to increase the speed we use only 20 bins:
+
+.. code:: bash
+
+    $ nnorm=1
+    $ baredSC_2d \
+        --input example/nih3t3_generated_second.txt \
+        --geneXColName 1_0.5_0.5_0.5_0.5_0.5_x \
+        --geneYColName 1_0.5_0.5_0.5_0.5_0.5_y \
+        --metadata1ColName group \
+        --metadata1Values group1 \
+        --output example/second_example_2d_cellgroup1_${nnorm}gauss_nx20 \
+        --nnorm ${nnorm} \
+        --nx 20 --ny 20 \
+        --figure example/second_example_2d_cellgroup1_${nnorm}gauss_nx20.png \
+        --title "second example 2d cell group 1 ${nnorm} gauss 20 bins"
+
+We see that the correlation found is 0.44 +/- 0.11.
+
+.. image:: ../../../example/second_example_2d_cellgroup1_1gauss_nx20_corner.png
+
+Let see how this changes if we reduce the scale of the Normal distribution of the prior to 0.1
+
+.. code:: bash
+
+    $ nnorm=1
+    $ baredSC_2d \
+        --input example/nih3t3_generated_second.txt \
+        --geneXColName 1_0.5_0.5_0.5_0.5_0.5_x \
+        --geneYColName 1_0.5_0.5_0.5_0.5_0.5_y \
+        --metadata1ColName group \
+        --metadata1Values group1 \
+        --output example/second_example_2d_cellgroup1_${nnorm}gauss_nx20_smallSP \
+        --nnorm ${nnorm} \
+        --nx 20 --ny 20 \
+        --scalePrior 0.1 \
+        --figure example/second_example_2d_cellgroup1_${nnorm}gauss_nx20_smallSP.png \
+        --title "second example 2d cell group 1 ${nnorm} gauss small scalePrior"
+
+We see that the correlation drop to 0.18 +/- 0.08.
+
+.. image:: ../../../example/second_example_2d_cellgroup1_1gauss_nx20_smallSP_corner.png
+
+On the contrary, if we know that there is a correlation we can increase this value in order to remove the penalty on high correlation coefficient.
+
+.. code:: bash
+
+    $ nnorm=1
+    $ baredSC_2d \
+        --input example/nih3t3_generated_second.txt \
+        --geneXColName 1_0.5_0.5_0.5_0.5_0.5_x \
+        --geneYColName 1_0.5_0.5_0.5_0.5_0.5_y \
+        --metadata1ColName group \
+        --metadata1Values group1 \
+        --output example/second_example_2d_cellgroup1_${nnorm}gauss_nx20_largeSP \
+        --nnorm ${nnorm} \
+        --nx 20 --ny 20 \
+        --scalePrior 3 \
+        --figure example/second_example_2d_cellgroup1_${nnorm}gauss_nx20_largeSP.png \
+        --title "second example 2d cell group 1 ${nnorm} gauss large scalePrior"
+
+We see that the correlation is now at 0.51 +/- 0.11.
+
+.. image:: ../../../example/second_example_2d_cellgroup1_1gauss_nx20_largeSP_corner.png
+
+However, these settings may detect (anti-)correlations in situation where there is no,
+that's why we recommand the default value if you don't have any knowledge on the correlation you expect.
\ No newline at end of file
diff --git a/docs/content/tuto/tutorial_sim_compare_means.rst b/docs/content/tuto/tutorial_sim_compare_means.rst
new file mode 100644
index 0000000..6b24ed0
--- /dev/null
+++ b/docs/content/tuto/tutorial_sim_compare_means.rst
@@ -0,0 +1,178 @@
+Compare means from baredSC results
+==================================
+
+.. contents:: 
+    :local:
+
+baredSC outputs can be used to evaluate the fold-change of expression between 2 conditions.
+
+Inputs
+------
+
+We use the same inputs as in :ref:`previous input descriptions<Input desc>`.
+
+Run baredSC on each subpopulation
+---------------------------------
+
+Let's focus on cells of group 0.0 and group 3.0 (they correspond to each of the 2 gaussians found previously).
+To increase the speed we will use less bins (``--nx 25``):
+
+.. code:: bash
+
+    $ for group in 0 3; do
+        for nnorm in 1 2; do
+          baredSC_1d \
+            --input example/nih3t3_generated_2d_2.txt \
+            --metadata1ColName 0.5_0_0_0.5_group \
+            --metadata1Values ${group}.0 \
+            --geneColName 0.5_0_0_0.5_x \
+            --output example/first_example_1d_group${group}_${nnorm}gauss_25_neff200 \
+            --nnorm ${nnorm} --nx 25 --minNeff 200 \
+            --figure example/first_example_1d_group${group}_${nnorm}gauss_25_neff200.png \
+            --title "first gene ${nnorm} gauss group${group} 25 bins neff200" \
+            --logevidence example/first_example_1d_group${group}_${nnorm}gauss_25_neff200_logevid.txt
+        done
+        combineMultipleModels_1d \
+          --input example/nih3t3_generated_2d_2.txt \
+          --metadata1ColName 0.5_0_0_0.5_group \
+          --metadata1Values ${group}.0 \
+          --geneColName 0.5_0_0_0.5_x --nx 25 \
+          --outputs example/first_example_1d_group${group}_1gauss_25_neff200 \
+          example/first_example_1d_group${group}_2gauss_25_neff200 \
+          --figure example/first_example_1d_group${group}_1-2gauss_25.png \
+          --title "first gene group$group 25 bins 1 and 2 gauss"
+      done
+
+In one case, 100 000 samples were not sufficient.
+
+We check the QC. We can now compare the results:
+
+.. image:: ../../../example/first_example_1d_group0_1-2gauss_25.png
+    :width: 48 %
+.. image:: ../../../example/first_example_1d_group3_1-2gauss_25.png
+    :width: 48 %
+
+We can see that the tool fits relatively nicely the gaussians which were in inputs.
+
+If we use another metadata which is just 300 and 500 random cells:
+
+.. code:: bash
+
+    $ for group in 1 2; do
+        for nnorm in 1 2 3; do
+          baredSC_1d \
+            --input example/nih3t3_generated_2d_2.txt \
+            --metadata1ColName group \
+            --metadata1Values group${group} \
+            --geneColName 0.5_0_0_0.5_x \
+            --output example/first_example_1d_cellgroup${group}_${nnorm}gauss_25_neff200 \
+            --nnorm ${nnorm} --nx 25 --minNeff 200 \
+            --figure example/first_example_1d_cellgroup${group}_${nnorm}gauss_25_neff200.png \
+            --title "first gene ${nnorm} gauss cellgroup${group}" \
+            --logevidence example/first_example_1d_cellgroup${group}_${nnorm}gauss_25_neff200_logevid.txt
+        done
+        combineMultipleModels_1d \
+          --input example/nih3t3_generated_2d_2.txt \
+          --metadata1ColName group \
+          --metadata1Values group${group} \
+          --geneColName 0.5_0_0_0.5_x --nx 25 \
+          --outputs example/first_example_1d_cellgroup${group}_1gauss_25_neff200 \
+          example/first_example_1d_cellgroup${group}_2gauss_25_neff200 \
+          example/first_example_1d_cellgroup${group}_3gauss_25_neff200 \
+          --figure example/first_example_1d_cellgroup${group}_1-3gauss_25.png \
+          --title "first gene cellgroup$group 25 bins 1, 2 and 3 gauss"
+      done
+
+We see that the results are different in both groups but in both cases the confidence interval is quite large:
+
+.. image:: ../../../example/first_example_1d_cellgroup1_1-3gauss_25.png
+    :width: 48 %
+.. image:: ../../../example/first_example_1d_cellgroup2_1-3gauss_25.png
+    :width: 48 %
+
+One of the output is `*_means.txt.gz`. Each line correspond to the value of the mean expression evaluated at each sample of the MCMC.
+It can be used to estimate:
+
+* A confidence interval on the mean value (in the used axis log(1 + 10^4 * expression))
+* A confidence interval on the fold change (delogged).
+
+.. ipython::
+
+    In [1]: import numpy as np
+       ...: def delog(x):
+       ...:     return(1e-4 * (np.exp(x) - 1))
+       ...: my_quantiles = [0.5 - 0.6827 / 2, 0.5, 0.5 + 0.6827 / 2]
+       ...: # Get data
+       ...: means1 = np.genfromtxt('../example/first_example_1d_group0_1-2gauss_25_means.txt.gz')
+       ...: print(f'Values of mean in group0: {means1}')
+       ...: means2 = np.genfromtxt('../example/first_example_1d_group3_1-2gauss_25_means.txt.gz')
+       ...: print(f'Values of mean in group3: {means2}')
+       ...: # Shuffle means2
+       ...: np.random.shuffle(means2)
+       ...: print(f'Mean log expression in group0: {np.quantile(means1, my_quantiles)}')
+       ...: print(f'Mean log expression in group3: {np.quantile(means2, my_quantiles)}')
+       ...: fc = [delog(x1) / delog(x2) for x1, x2 in zip(means1, means2)]
+       ...: print(f'Estimation of fold-change: {np.quantile(fc,  my_quantiles)}')
+
+We can use matplotlib to display the results graphically:
+
+.. ipython::    
+
+    @savefig fc_group.png
+    In [2]: import matplotlib.pyplot as plt
+       ...: x1, x2 = np.random.normal(1, 0.1, len(means1)), np.random.normal(3, 0.1, len(means2))
+       ...: fig, axs = plt.subplots(1, 2)
+       ...: axs[0].scatter(x1, means1, s=1, alpha=0.01)
+       ...: axs[0].scatter(x2, means2, s=1, alpha=0.01)
+       ...: axs[0].set_ylim(0, )
+       ...: axs[0].set_ylabel('log(1 + expression * 10\'000)')
+       ...: axs[0].set_xticks([1, 3])
+       ...: axs[0].set_xticklabels(['group0', 'group3'], fontsize='x-large')
+       ...: axs[1].scatter(x1[:len(fc)], fc, s=1, alpha=0.01)
+       ...: axs[1].set_xticks([1])
+       ...: axs[1].set_xticklabels(['fold-change'], fontsize='x-large')
+       ...: axs[1].set_ylim(0, )
+       ...: axs[1].set_ylabel('Fold-change (not log transformed)')
+       ...: fig.tight_layout()
+
+
+On the first example with group0 and group3 where each is a different gaussian, we see a mean log expression around the expected 0.375 value in group 0, 1 in group 3.
+We see that the fold-change is around 25% (this is the real fold-change, not log transformed).
+
+Now if we have a look to the subsamples of cells (300 and 500 cells) and perform the same analysis:
+
+.. ipython::
+
+
+    In [1]: # Get data
+       ...: means1 = np.genfromtxt('../example/first_example_1d_cellgroup1_1-3gauss_25_means.txt.gz')
+       ...: print(f'Values of mean in cellgroup1: {means1}')
+       ...: means2 = np.genfromtxt('../example/first_example_1d_cellgroup2_1-3gauss_25_means.txt.gz')
+       ...: print(f'Values of mean in cellgroup2: {means2}')
+       ...: # Shuffle means2
+       ...: np.random.shuffle(means2)
+       ...: print(f'Mean log expression in cellgroup1: {np.quantile(means1, my_quantiles)}')
+       ...: print(f'Mean log expression in cellgroup2: {np.quantile(means2, my_quantiles)}')
+       ...: fc = [delog(x1) / delog(x2) for x1, x2 in zip(means1, means2)]
+       ...: print(f'Estimation of fold-change: {np.quantile(fc,  my_quantiles)}')
+
+.. ipython::    
+
+    @savefig fc_cellgroup.png
+    In [2]: x1, x2 = np.random.normal(1, 0.1, len(means1)), np.random.normal(3, 0.1, len(means2))
+       ...: fig, axs = plt.subplots(1, 2)
+       ...: axs[0].scatter(x1, means1, s=1, alpha=0.01)
+       ...: axs[0].scatter(x2, means2, s=1, alpha=0.01)
+       ...: axs[0].set_ylim(0, )
+       ...: axs[0].set_ylabel('log(1 + expression * 10\'000)')
+       ...: axs[0].set_xticks([1, 3])
+       ...: axs[0].set_xticklabels(['cellgroup1', 'cellgroup2'], fontsize='x-large')
+       ...: axs[1].scatter(x1[:len(fc)], fc, s=1, alpha=0.01)
+       ...: axs[1].set_xticks([1])
+       ...: axs[1].set_xticklabels(['fold-change'], fontsize='x-large')
+       ...: axs[1].set_ylim(0, )
+       ...: axs[1].set_ylabel('Fold-change (not log transformed)')
+       ...: fig.tight_layout()
+
+
+Here, we can see that the fold-change is slightly below 1.
diff --git a/docs/content/tuto/tutorial_sim_default_1d.rst b/docs/content/tuto/tutorial_sim_default_1d.rst
new file mode 100644
index 0000000..2c93871
--- /dev/null
+++ b/docs/content/tuto/tutorial_sim_default_1d.rst
@@ -0,0 +1,234 @@
+Run baredSC_1d with default parameters
+======================================
+
+.. contents:: 
+    :local:
+
+We will describe here an example step by step on simulated data where we will use default parameters and carefully check the QC.
+
+.. _Input desc:
+
+Inputs
+------
+
+We took total UMI counts from a real dataset of NIH3T3.
+We generated a example where 2 genes have the same distribution (2 gaussians, one of mean 0.375, scale 0.125 and another one of mean 1 and scale 0.1).
+Half of cells goes in each gaussian.
+The gene is called "0.5_0_0_0.5_x".
+
+Run
+---
+
+Run 1 gaussian
+^^^^^^^^^^^^^^
+
+Let say we don't know the number of gaussian, we try one.
+We keep the default parameters and we set ``--figure`` to get visual outputs:
+
+.. code:: bash
+
+    $ baredSC_1d \
+        --input example/nih3t3_generated_2d_2.txt \
+        --geneColName 0.5_0_0_0.5_x \
+        --output example/first_example_1d_1gauss \
+        --nnorm 1 \
+        --figure example/first_example_1d_1gauss.png \
+        --title "first gene 1 gauss" \
+        --logevidence example/first_example_1d_1gauss_logevid.txt
+
+Check QC
+^^^^^^^^
+
+We first check the convergence:
+
+.. image:: ../../../example/first_example_1d_1gauss_convergence.png
+
+This plot show the autocorrelation as a function of number of samples.
+The earlier the curves goes close to 0, the more it convereged.
+
+Here, this is perfect.
+
+As printed during the run (or reading the file \*neff.txt), the Neff is around 8000 which is enough to estimate the confidence interval.
+
+We have a look at the corner plot:
+
+.. image:: ../../../example/first_example_1d_1gauss_corner.png
+
+We see that the distribution of each parameter is close to gaussian, this is perfect.
+
+We have a look at the parameter plot:
+
+.. image:: ../../../example/first_example_1d_1gauss_p.png
+
+It nicely shows that the 2 parameters (mu0 and scale0) oscilate around the mean position while the log probability oscilate with a maximum value.
+
+Look at the results
+^^^^^^^^^^^^^^^^^^^
+
+.. image:: ../../../example/first_example_1d_1gauss.png
+
+The pdf is well constrained (the shaded areas indicating the 68%, 95% and 99% interval are thin).
+The mean in red is really close to the median in dashed black line.
+However, using the green curve which is the density of the detected expression, we suspect that there are 2 gaussians.
+
+Run 2 gaussians
+^^^^^^^^^^^^^^^
+
+Now let's try 2 gaussians
+
+.. code:: bash
+
+    $ baredSC_1d \
+        --input example/nih3t3_generated_2d_2.txt \
+        --geneColName 0.5_0_0_0.5_x \
+        --output example/first_example_1d_2gauss \
+        --nnorm 2 \
+        --figure example/first_example_1d_2gauss.png \
+        --title "first gene 2 gauss" \
+        --logevidence example/first_example_1d_2gauss_logevid.txt
+
+Check QC
+^^^^^^^^
+
+We first check the convergence:
+
+.. image:: ../../../example/first_example_1d_2gauss_convergence.png
+
+This is perfect.
+
+As printed during the run (or reading the file \*neff.txt), the Neff is around 1300, perfect.
+
+We have a look at the corner plot:
+
+.. image:: ../../../example/first_example_1d_2gauss_corner.png
+
+The means and amplitude are like a gaussian.
+The scale distribution is asymetric because by default,
+the minimum scale is set to 0.1 which is close to our values here.
+Some parameters are correlated: the mean of the first Gaussian with the mean of the second Gaussian.
+Some are anti-correlated: the mean of the second Gaussian with its amplitude.
+But this is not problematic, just an information we can get from this plot.
+
+We have a look at the parameter plot:
+
+.. image:: ../../../example/first_example_1d_2gauss_p.png
+
+It nicely shows that the 5 parameters oscilate around the mean position and the log probability is quite constant.
+
+Look at the results
+^^^^^^^^^^^^^^^^^^^
+
+.. image:: ../../../example/first_example_1d_2gauss.png
+
+The confidence interval is larger than in the first case but still good.
+
+Run 3 gaussians
+^^^^^^^^^^^^^^^
+
+Now let's try 3 gaussians
+
+.. code:: bash
+
+    $ baredSC_1d \
+        --input example/nih3t3_generated_2d_2.txt \
+        --geneColName 0.5_0_0_0.5_x \
+        --output example/first_example_1d_3gauss \
+        --nnorm 3 \
+        --figure example/first_example_1d_3gauss.png \
+        --title "first gene 3 gauss" \
+        --logevidence example/first_example_1d_3gauss_logevid.txt
+
+Check QC
+^^^^^^^^
+
+We first check the convergence:
+
+.. image:: ../../../example/first_example_1d_3gauss_convergence.png
+
+It is much worse than the first ones. The auto-correlation decreases later and does not stay a flat line at 0 but oscillate.
+
+As printed during the run, the Neff is around 191. This is better to get more indenpendent samples.
+We can rerun with another value of the seed but it is safer to rerun with increased number of samples.
+
+We still have a look at the corner plot:
+
+.. image:: ../../../example/first_example_1d_3gauss_corner.png
+
+The first two Gaussians are close to what was expected. The third Gaussian is a Gaussian with a reduced mean (0.17 in average). We see that this last Gaussian is not very well constrained (large error bar on each of its parameters).
+
+Look at the results
+^^^^^^^^^^^^^^^^^^^
+
+.. image:: ../../../example/first_example_1d_3gauss.png
+
+The results are very close to the one with 2 Gaussians.
+
+Rerun 3 gauss with more samples
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+.. code:: bash
+
+    $ baredSC_1d \
+        --input example/nih3t3_generated_2d_2.txt \
+        --geneColName 0.5_0_0_0.5_x \
+        --output example/first_example_1d_3gauss_1M \
+        --nnorm 3 --nsampMCMC 1000000 \
+        --figure example/first_example_1d_3gauss_1M.png \
+        --title "first gene 3 gauss 1M" \
+        --logevidence example/first_example_1d_3gauss_1M_logevid.txt
+
+It converged:
+
+.. image:: ../../../example/first_example_1d_3gauss_1M_convergence.png
+
+Automatic rerun when Neff is too small
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+While some models converge even with a small number of samples,
+some other needs a lot of sample to reach acceptable covergence.
+A way to automatically rerun the MCMC when the effectif number of samples is too low
+is to use the ``--minNeff``.
+The MCMC will be rerun with 10 times more sample until
+it reaches the value.
+This can potentially take forever
+as some model may never converge. But can be useful in other cases.
+Even with this option, we highly encourage the users to manually check the QC.
+
+Compare models
+^^^^^^^^^^^^^^
+
+In order to compare models, we will use the values of logevidence.
+
+========== =============
+model      log evidence 
+========== =============
+**1gauss** -4233.7
+**2gauss** -4221.8
+**3gauss** -4223.0
+========== =============
+
+We can see that the model with the highest log evidence is the model with 2 gaussians.
+However, we see that the model with 3 gaussians is very close.
+When you compare models, what is important is the difference between the log evidence, not its absolute value.
+
+We can either choose the best model or decide to combine them:
+
+Combine models
+^^^^^^^^^^^^^^
+
+Another way to use these models is to use samples from all models but using the log evidence to put weight on the number of sample to use from each model.
+
+.. code:: bash
+
+    $ combineMultipleModels_1d \
+        --input example/nih3t3_generated_2d_2.txt \
+        --geneColName 0.5_0_0_0.5_x \
+        --outputs example/first_example_1d_1gauss \
+        example/first_example_1d_2gauss \
+        example/first_example_1d_3gauss_1M \
+        --figure example/first_example_1d_1-3gauss.png \
+        --title "first gene 1, 2, and 3 gauss"
+
+In the standard output you will see that it only integrates samples from the 2gauss and 3gauss.
+Here is the result:
+
+.. image:: ../../../example/first_example_1d_1-3gauss.png
diff --git a/docs/content/tuto/tutorial_sim_default_2d.rst b/docs/content/tuto/tutorial_sim_default_2d.rst
new file mode 100644
index 0000000..6aad1de
--- /dev/null
+++ b/docs/content/tuto/tutorial_sim_default_2d.rst
@@ -0,0 +1,115 @@
+Run baredSC_2d with default parameters
+======================================
+
+.. contents:: 
+    :local:
+
+Inputs
+------
+
+We took total UMI counts from a real dataset of NIH3T3.
+We generated a example where 2 genes have the same distribution
+(2 gaussians, one of mean 0.375, scale 0.125 and another one of mean 1 and scale 0.1).
+For each gene, half of cells goes in each gaussian.
+The genes are called "0.5_0_0_0.5_x" and "0.5_0_0_0.5_y".
+
+2d
+--
+
+As for the 1d, you need to run it with different number of gaussian 2d to find the best model or to mix them.
+The 2d tool is much slower than the 1d.
+The time depends on the ``--nx``, ``--ny``, ``--osampxpdf``, ``--osampypdf`` parameters and the number of cells.
+To make the example quicker we will run only on the 300 random cells (group1).
+
+.. code:: bash
+
+    $ for nnorm in 1 2 3; do
+        baredSC_2d \
+          --input example/nih3t3_generated_2d_2.txt \
+          --geneXColName 0.5_0_0_0.5_x \
+          --geneYColName 0.5_0_0_0.5_y \
+          --metadata1ColName group \
+          --metadata1Values group1 \
+          --output example/first_example_2d_cellgroup1_${nnorm}gauss \
+          --nnorm ${nnorm} \
+          --figure example/first_example_2d_cellgroup1_${nnorm}gauss.png \
+          --title "first example 2d cell group 1 ${nnorm} gauss" \
+          --logevidence example/first_example_cellgroup1_2d_${nnorm}gauss_logevid.txt
+      done
+
+The QC are done the same way as the 1d. The models with 1 and 2 gaussians are converging.
+The model with 3 gaussian is not converging (picture below left). When we rerun it with 1 million samples, it now converges (picture below right).
+
+.. image:: ../../../example/first_example_2d_cellgroup1_3gauss_convergence.png
+    :width: 48 %
+    :alt: Covergence for 100'000 samples
+.. image:: ../../../example/first_example_2d_cellgroup1_1M_3gauss_convergence.png
+    :width: 48 %
+    :alt: Covergence for 1'000'000 samples
+
+The best model (using the log evidence) is the 2 gaussians model.
+
+.. image:: ../../../example/first_example_2d_cellgroup1_2gauss.png
+
+We see a very high correlation highly significant. Here, we would like to warn the users that the correlation calculated here is a Pearson correlation, so it reflects how much the data are close to a line with positive or negative slope.
+
+In order to appreciate the confidence interval it can be useful to split the 2d pdf in 2 parts: one above a threshold for y and one below the same threshold.
+This is for this purpose that we can use ``--splity``.
+For the demo we will try different values:
+
+.. code:: bash
+
+    $ baredSC_2d \
+        --input example/nih3t3_generated_2d_2.txt \
+        --geneXColName 0.5_0_0_0.5_x \
+        --geneYColName 0.5_0_0_0.5_y \
+        --metadata1ColName group \
+        --metadata1Values group1 \
+        --output example/first_example_2d_cellgroup1_2gauss \
+        --nnorm 2 \
+        --figure example/first_example_2d_cellgroup1_2gauss.png \
+        --title "first example 2d cell group 1 2 gauss" \
+        --splity 0.35 0.75
+
+As the MCMC was run previously, it will use the ``.npz`` output to generate the figures, thus this operation is really quick.
+
+When we split at 0.75 (between the 2 gaussian):
+
+.. image:: ../../../example/first_example_2d_cellgroup1_2gauss_split0.75.png
+
+We find each of the 2 gaussians in 1d and the confidence interval is quite small.
+
+When we split in the low gaussian (0.35):
+
+.. image:: ../../../example/first_example_2d_cellgroup1_2gauss_split0.35.png
+
+We see that the green curve is made of 2 gaussian. The sum of both the green and red curves is the black one.
+This can make the comparison difficult. So the output ``renorm.extension`` is sometimes better.
+
+.. image:: ../../../example/first_example_2d_cellgroup1_2gauss_split0.35_renorm.png
+
+Now we clearly see that in the cells with low expression of gene y all cells are low for gene x while for cells with relatively high expression of gene y, gene x is bimodal with a greater proportion in the second gaussian.
+
+Similarly to the 1d, the option ``--minNeff`` is also implemented.
+
+You can combine multiple models with ``combineMultipleModels_2d``. By default, no p-value will be evaluated for the correlation but you can use less samples to get a p-value with ``--getPVal``.
+
+.. code:: bash
+
+    $ combineMultipleModels_2d \
+        --input example/nih3t3_generated_2d_2.txt \
+        --geneXColName 0.5_0_0_0.5_x \
+        --geneYColName 0.5_0_0_0.5_y \
+        --metadata1ColName group \
+        --metadata1Values group1 \
+        --outputs example/first_example_2d_cellgroup1_1gauss \
+        example/first_example_2d_cellgroup1_2gauss \
+        example/first_example_2d_cellgroup1_1M_3gauss \
+        --figure example/first_example_2d_cellgroup1_1-3gauss.png \
+        --getPVal \
+        --title "first example cell group 1 1,2,3 gauss"
+
+The lines printed indicates that it uses only 282 independent samples (1 from the 1 Gaussian model, 264 from the 2 Gaussians model and 15 from the 3 Gaussians model).
+
+.. image:: ../../../example/first_example_2d_cellgroup1_1-3gauss.png
+
diff --git a/docs/content/tutorial_sim.rst b/docs/content/tutorial_sim.rst
new file mode 100644
index 0000000..843f6da
--- /dev/null
+++ b/docs/content/tutorial_sim.rst
@@ -0,0 +1,13 @@
+Tutorial on simulated data
+===========================
+
+
+.. toctree::
+   :maxdepth: 1
+
+   tuto/tutorial_sim_default_1d.rst
+   tuto/tutorial_sim_default_2d.rst
+   tuto/tutorial_sim_compare_means.rst
+   tuto/tutorial_sim_change_minScale.rst
+   tuto/tutorial_sim_change_nx.rst
+   tuto/tutorial_sim_change_scalePrior.rst
diff --git a/docs/content/usage.rst b/docs/content/usage.rst
new file mode 100644
index 0000000..8588f63
--- /dev/null
+++ b/docs/content/usage.rst
@@ -0,0 +1,41 @@
+Usage
+=====
+
+.. contents:: 
+    :local:
+
+General usage
+-------------
+
+Here is a description of all possible parameters.
+The tool take around 1 minute for 1d, 2000 cells, default parameters, independently of the number of gaussian and around 30 seconds for 300 cells.
+For the 2d 300 cells is around 3 minutes, 2000 cells around 15 minutes.
+Increasing the number of samples in the MCMC or in the burning phase will increase the time.
+
+baredSC_1d
+-----------
+
+.. argparse::
+   :ref: baredSC.baredSC_1d.parse_arguments
+   :prog: baredSC_1d
+
+combineMultipleModels_1d
+------------------------
+
+.. argparse::
+   :ref: baredSC.combineMultipleModels_1d.parse_arguments
+   :prog: combineMultipleModels_1d
+
+baredSC_2d
+-----------
+
+.. argparse::
+   :ref: baredSC.baredSC_2d.parse_arguments
+   :prog: baredSC_2d
+
+combineMultipleModels_2d
+------------------------
+
+.. argparse::
+   :ref: baredSC.combineMultipleModels_2d.parse_arguments
+   :prog: combineMultipleModels_2d
diff --git a/docs/index.rst b/docs/index.rst
new file mode 100644
index 0000000..c20c0b3
--- /dev/null
+++ b/docs/index.rst
@@ -0,0 +1,29 @@
+.. baredSC documentation master file, created by
+   sphinx-quickstart on Wed Jan 27 07:35:21 2021.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+Welcome to baredSC's documentation!
+====================================
+
+BARED (Bayesian Approach to Retreive Expression Distribution of) Single Cell
+----------------------------------------------------------------------------
+
+baredSC is a tool that uses a Monte-Carlo Markov Chain to estimate a confidence interval on the probability density function (PDF) of expression of one or two genes from single-cell RNA-seq data. It uses the raw counts and the total number of UMI for each cell. The PDF is approximated by a number of 1d or 2d gaussians provided by the user. The likelihood is estimated using the asumption that the raw counts follow a Poisson distribution of parameter equal to the proportion of mRNA for the gene in the cell multiplied by the total number of UMI identified in this cell.
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents:
+
+   content/installation.rst
+   content/usage.rst
+   content/outputs.rst
+   content/tutorial_sim.rst
+   content/releases.rst
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`
diff --git a/docs/make.bat b/docs/make.bat
new file mode 100644
index 0000000..2119f51
--- /dev/null
+++ b/docs/make.bat
@@ -0,0 +1,35 @@
+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=.
+set BUILDDIR=_build
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+	echo.installed, then set the SPHINXBUILD environment variable to point
+	echo.to the full path of the 'sphinx-build' executable. Alternatively you
+	echo.may add the Sphinx directory to PATH.
+	echo.
+	echo.If you don't have Sphinx installed, grab it from
+	echo.http://sphinx-doc.org/
+	exit /b 1
+)
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+
+:end
+popd
diff --git a/docs/requirements.txt b/docs/requirements.txt
new file mode 100644
index 0000000..68c6ff1
--- /dev/null
+++ b/docs/requirements.txt
@@ -0,0 +1,10 @@
+--extra-index-url https://obswww.unige.ch/~delisle
+numpy >=1.16
+matplotlib >=3.1.1
+pandas >=0.25.0
+scipy >=1.3.0
+corner >=2.0.0
+samsam
+sphinx-argparse
+sphinx-autorun
+IPython
diff --git a/example/first_example_1d_1-3gauss.png b/example/first_example_1d_1-3gauss.png
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diff --git a/example/first_example_1d_1-3gauss_individuals.png b/example/first_example_1d_1-3gauss_individuals.png
new file mode 100644
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diff --git a/example/first_example_1d_1-3gauss_means.txt.gz b/example/first_example_1d_1-3gauss_means.txt.gz
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diff --git a/example/first_example_1d_1-3gauss_pdf.txt b/example/first_example_1d_1-3gauss_pdf.txt
new file mode 100644
index 0000000..b2b8f94
--- /dev/null
+++ b/example/first_example_1d_1-3gauss_pdf.txt
@@ -0,0 +1,101 @@
+x	low	mean	high	median
+0.0125	0.04010168272330071	0.2210218513834667	0.42077363579025884	0.17168711667843536
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diff --git a/example/first_example_1d_1-3gauss_posterior_andco.png b/example/first_example_1d_1-3gauss_posterior_andco.png
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index 0000000..072e799
Binary files /dev/null and b/example/first_example_1d_1-3gauss_posterior_andco.png differ
diff --git a/example/first_example_1d_1-3gauss_posterior_individuals.png b/example/first_example_1d_1-3gauss_posterior_individuals.png
new file mode 100644
index 0000000..a14c9ed
Binary files /dev/null and b/example/first_example_1d_1-3gauss_posterior_individuals.png differ
diff --git a/example/first_example_1d_1-3gauss_posterior_per_cell.txt b/example/first_example_1d_1-3gauss_posterior_per_cell.txt
new file mode 100644
index 0000000..d093a89
--- /dev/null
+++ b/example/first_example_1d_1-3gauss_posterior_per_cell.txt
@@ -0,0 +1,2362 @@
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diff --git a/example/first_example_1d_1-3gauss_with_posterior.png b/example/first_example_1d_1-3gauss_with_posterior.png
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index 0000000..180bf20
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diff --git a/example/first_example_1d_1gauss_convergence.png b/example/first_example_1d_1gauss_convergence.png
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index 0000000..62a42f8
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diff --git a/example/first_example_1d_1gauss_corner.png b/example/first_example_1d_1gauss_corner.png
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index 0000000..d304a0c
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diff --git a/example/first_example_1d_1gauss_individuals.png b/example/first_example_1d_1gauss_individuals.png
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index 0000000..3136b0f
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diff --git a/example/first_example_1d_1gauss_logevid.txt b/example/first_example_1d_1gauss_logevid.txt
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index 0000000..a1a9feb
--- /dev/null
+++ b/example/first_example_1d_1gauss_logevid.txt
@@ -0,0 +1 @@
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diff --git a/example/first_example_1d_1gauss_means.txt.gz b/example/first_example_1d_1gauss_means.txt.gz
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index 0000000..359b2f9
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diff --git a/example/first_example_1d_1gauss_neff.txt b/example/first_example_1d_1gauss_neff.txt
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index 0000000..99dc05d
--- /dev/null
+++ b/example/first_example_1d_1gauss_neff.txt
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diff --git a/example/first_example_1d_1gauss_p.png b/example/first_example_1d_1gauss_p.png
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diff --git a/example/first_example_1d_1gauss_p.txt b/example/first_example_1d_1gauss_p.txt
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index 0000000..561255b
--- /dev/null
+++ b/example/first_example_1d_1gauss_p.txt
@@ -0,0 +1,3 @@
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diff --git a/example/first_example_1d_1gauss_pdf.txt b/example/first_example_1d_1gauss_pdf.txt
new file mode 100644
index 0000000..4af031c
--- /dev/null
+++ b/example/first_example_1d_1gauss_pdf.txt
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diff --git a/example/first_example_1d_1gauss_posterior_andco.png b/example/first_example_1d_1gauss_posterior_andco.png
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index 0000000..9fb141e
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diff --git a/example/first_example_1d_1gauss_posterior_individuals.png b/example/first_example_1d_1gauss_posterior_individuals.png
new file mode 100644
index 0000000..0eebd08
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diff --git a/example/first_example_1d_1gauss_posterior_per_cell.txt b/example/first_example_1d_1gauss_posterior_per_cell.txt
new file mode 100644
index 0000000..19a433e
--- /dev/null
+++ b/example/first_example_1d_1gauss_posterior_per_cell.txt
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diff --git a/example/first_example_1d_1gauss_with_posterior.png b/example/first_example_1d_1gauss_with_posterior.png
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diff --git a/example/first_example_1d_2gauss.png b/example/first_example_1d_2gauss.png
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index 0000000..9e4660b
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diff --git a/example/first_example_1d_2gauss_convergence.png b/example/first_example_1d_2gauss_convergence.png
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index 0000000..987113d
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diff --git a/example/first_example_1d_2gauss_corner.png b/example/first_example_1d_2gauss_corner.png
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index 0000000..50f49c7
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diff --git a/example/first_example_1d_2gauss_individuals.png b/example/first_example_1d_2gauss_individuals.png
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index 0000000..0ad6984
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diff --git a/example/first_example_1d_2gauss_logevid.txt b/example/first_example_1d_2gauss_logevid.txt
new file mode 100644
index 0000000..3cd2972
--- /dev/null
+++ b/example/first_example_1d_2gauss_logevid.txt
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diff --git a/example/first_example_1d_2gauss_means.txt.gz b/example/first_example_1d_2gauss_means.txt.gz
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index 0000000..af4464a
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diff --git a/example/first_example_1d_2gauss_neff.txt b/example/first_example_1d_2gauss_neff.txt
new file mode 100644
index 0000000..16049b3
--- /dev/null
+++ b/example/first_example_1d_2gauss_neff.txt
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diff --git a/example/first_example_1d_2gauss_p.png b/example/first_example_1d_2gauss_p.png
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diff --git a/example/first_example_1d_2gauss_p.txt b/example/first_example_1d_2gauss_p.txt
new file mode 100644
index 0000000..86c36de
--- /dev/null
+++ b/example/first_example_1d_2gauss_p.txt
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diff --git a/example/first_example_1d_2gauss_pdf.txt b/example/first_example_1d_2gauss_pdf.txt
new file mode 100644
index 0000000..986b56f
--- /dev/null
+++ b/example/first_example_1d_2gauss_pdf.txt
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diff --git a/example/first_example_1d_2gauss_posterior_andco.png b/example/first_example_1d_2gauss_posterior_andco.png
new file mode 100644
index 0000000..1b52c9d
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diff --git a/example/first_example_1d_2gauss_posterior_individuals.png b/example/first_example_1d_2gauss_posterior_individuals.png
new file mode 100644
index 0000000..1250b4f
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diff --git a/example/first_example_1d_2gauss_posterior_per_cell.txt b/example/first_example_1d_2gauss_posterior_per_cell.txt
new file mode 100644
index 0000000..2e9bc94
--- /dev/null
+++ b/example/first_example_1d_2gauss_posterior_per_cell.txt
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diff --git a/example/first_example_1d_3gauss_1M_logevid.txt b/example/first_example_1d_3gauss_1M_logevid.txt
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diff --git a/example/first_example_1d_3gauss_1M_p.txt b/example/first_example_1d_3gauss_1M_p.txt
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diff --git a/example/first_example_1d_3gauss_1M_posterior_andco.png b/example/first_example_1d_3gauss_1M_posterior_andco.png
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index 0000000..f708afc
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diff --git a/example/first_example_1d_3gauss_1M_posterior_individuals.png b/example/first_example_1d_3gauss_1M_posterior_individuals.png
new file mode 100644
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diff --git a/example/first_example_1d_3gauss_1M_posterior_per_cell.txt b/example/first_example_1d_3gauss_1M_posterior_per_cell.txt
new file mode 100644
index 0000000..b10814c
--- /dev/null
+++ b/example/first_example_1d_3gauss_1M_posterior_per_cell.txt
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diff --git a/example/first_example_1d_3gauss_corner.png b/example/first_example_1d_3gauss_corner.png
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diff --git a/example/first_example_1d_3gauss_individuals.png b/example/first_example_1d_3gauss_individuals.png
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diff --git a/example/first_example_1d_3gauss_logevid.txt b/example/first_example_1d_3gauss_logevid.txt
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diff --git a/example/first_example_1d_3gauss_p.txt b/example/first_example_1d_3gauss_p.txt
new file mode 100644
index 0000000..9079db1
--- /dev/null
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diff --git a/example/first_example_1d_3gauss_pdf.txt b/example/first_example_1d_3gauss_pdf.txt
new file mode 100644
index 0000000..6d43c2c
--- /dev/null
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diff --git a/example/first_example_1d_3gauss_posterior_andco.png b/example/first_example_1d_3gauss_posterior_andco.png
new file mode 100644
index 0000000..5793173
Binary files /dev/null and b/example/first_example_1d_3gauss_posterior_andco.png differ
diff --git a/example/first_example_1d_3gauss_posterior_individuals.png b/example/first_example_1d_3gauss_posterior_individuals.png
new file mode 100644
index 0000000..5212f2f
Binary files /dev/null and b/example/first_example_1d_3gauss_posterior_individuals.png differ
diff --git a/example/first_example_1d_3gauss_posterior_per_cell.txt b/example/first_example_1d_3gauss_posterior_per_cell.txt
new file mode 100644
index 0000000..d105085
--- /dev/null
+++ b/example/first_example_1d_3gauss_posterior_per_cell.txt
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diff --git a/example/first_example_1d_cellgroup1_1-3gauss_25.png b/example/first_example_1d_cellgroup1_1-3gauss_25.png
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diff --git a/example/first_example_1d_cellgroup1_1-3gauss_25_individuals.png b/example/first_example_1d_cellgroup1_1-3gauss_25_individuals.png
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diff --git a/example/first_example_1d_cellgroup1_1-3gauss_25_means.txt.gz b/example/first_example_1d_cellgroup1_1-3gauss_25_means.txt.gz
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diff --git a/example/first_example_1d_cellgroup1_1-3gauss_25_pdf.txt b/example/first_example_1d_cellgroup1_1-3gauss_25_pdf.txt
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index 0000000..17b0718
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diff --git a/example/first_example_1d_cellgroup1_1-3gauss_25_posterior_andco.png b/example/first_example_1d_cellgroup1_1-3gauss_25_posterior_andco.png
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diff --git a/example/first_example_1d_cellgroup1_1-3gauss_25_posterior_individuals.png b/example/first_example_1d_cellgroup1_1-3gauss_25_posterior_individuals.png
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diff --git a/example/first_example_1d_cellgroup1_1-3gauss_25_posterior_per_cell.txt b/example/first_example_1d_cellgroup1_1-3gauss_25_posterior_per_cell.txt
new file mode 100644
index 0000000..c261c40
--- /dev/null
+++ b/example/first_example_1d_cellgroup1_1-3gauss_25_posterior_per_cell.txt
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diff --git a/example/first_example_1d_cellgroup1_1gauss_25_neff200_posterior_andco.png b/example/first_example_1d_cellgroup1_1gauss_25_neff200_posterior_andco.png
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index 0000000..c1c7d08
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diff --git a/example/first_example_1d_cellgroup1_1gauss_25_neff200_posterior_individuals.png b/example/first_example_1d_cellgroup1_1gauss_25_neff200_posterior_individuals.png
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index 0000000..6cab4c6
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diff --git a/example/first_example_1d_cellgroup1_1gauss_25_neff200_posterior_per_cell.txt b/example/first_example_1d_cellgroup1_1gauss_25_neff200_posterior_per_cell.txt
new file mode 100644
index 0000000..9b802f1
--- /dev/null
+++ b/example/first_example_1d_cellgroup1_1gauss_25_neff200_posterior_per_cell.txt
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diff --git a/example/first_example_1d_cellgroup1_1gauss_25_neff200_with_posterior.png b/example/first_example_1d_cellgroup1_1gauss_25_neff200_with_posterior.png
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diff --git a/example/first_example_1d_cellgroup1_2gauss_25_neff200_logevid.txt b/example/first_example_1d_cellgroup1_2gauss_25_neff200_logevid.txt
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index 0000000..fd8384a
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diff --git a/example/first_example_1d_cellgroup1_2gauss_25_neff200_p.txt b/example/first_example_1d_cellgroup1_2gauss_25_neff200_p.txt
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index 0000000..b00c83a
--- /dev/null
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diff --git a/example/first_example_1d_cellgroup1_2gauss_25_neff200_pdf.txt b/example/first_example_1d_cellgroup1_2gauss_25_neff200_pdf.txt
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index 0000000..ab4804e
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diff --git a/example/first_example_1d_cellgroup1_2gauss_25_neff200_posterior_per_cell.txt b/example/first_example_1d_cellgroup1_2gauss_25_neff200_posterior_per_cell.txt
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diff --git a/example/first_example_1d_cellgroup1_2gauss_25_neff200_with_posterior.png b/example/first_example_1d_cellgroup1_2gauss_25_neff200_with_posterior.png
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diff --git a/example/first_example_1d_cellgroup1_3gauss_25_neff200_logevid.txt b/example/first_example_1d_cellgroup1_3gauss_25_neff200_logevid.txt
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index 0000000..d94019c
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diff --git a/example/first_example_1d_cellgroup1_3gauss_25_neff200_p.txt b/example/first_example_1d_cellgroup1_3gauss_25_neff200_p.txt
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index 0000000..156233a
--- /dev/null
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diff --git a/example/first_example_1d_cellgroup1_3gauss_25_neff200_pdf.txt b/example/first_example_1d_cellgroup1_3gauss_25_neff200_pdf.txt
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diff --git a/example/first_example_1d_cellgroup1_3gauss_25_neff200_posterior_individuals.png b/example/first_example_1d_cellgroup1_3gauss_25_neff200_posterior_individuals.png
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diff --git a/example/first_example_1d_cellgroup1_3gauss_25_neff200_posterior_per_cell.txt b/example/first_example_1d_cellgroup1_3gauss_25_neff200_posterior_per_cell.txt
new file mode 100644
index 0000000..b2d2a11
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diff --git a/example/first_example_1d_cellgroup2_1-3gauss_25.png b/example/first_example_1d_cellgroup2_1-3gauss_25.png
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diff --git a/example/first_example_1d_cellgroup2_1-3gauss_25_individuals.png b/example/first_example_1d_cellgroup2_1-3gauss_25_individuals.png
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diff --git a/example/first_example_1d_cellgroup2_1-3gauss_25_means.txt.gz b/example/first_example_1d_cellgroup2_1-3gauss_25_means.txt.gz
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diff --git a/example/first_example_1d_cellgroup2_1-3gauss_25_pdf.txt b/example/first_example_1d_cellgroup2_1-3gauss_25_pdf.txt
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index 0000000..8851abf
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diff --git a/example/first_example_1d_cellgroup2_1-3gauss_25_posterior_andco.png b/example/first_example_1d_cellgroup2_1-3gauss_25_posterior_andco.png
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diff --git a/example/first_example_1d_cellgroup2_1-3gauss_25_posterior_individuals.png b/example/first_example_1d_cellgroup2_1-3gauss_25_posterior_individuals.png
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diff --git a/example/first_example_1d_cellgroup2_1-3gauss_25_posterior_per_cell.txt b/example/first_example_1d_cellgroup2_1-3gauss_25_posterior_per_cell.txt
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index 0000000..aea401b
--- /dev/null
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diff --git a/example/first_example_1d_cellgroup2_1-3gauss_25_with_posterior.png b/example/first_example_1d_cellgroup2_1-3gauss_25_with_posterior.png
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diff --git a/example/first_example_1d_cellgroup2_1gauss_25_neff200_individuals.png b/example/first_example_1d_cellgroup2_1gauss_25_neff200_individuals.png
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diff --git a/example/first_example_1d_cellgroup2_1gauss_25_neff200_logevid.txt b/example/first_example_1d_cellgroup2_1gauss_25_neff200_logevid.txt
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index 0000000..6a09114
--- /dev/null
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diff --git a/example/first_example_1d_cellgroup2_1gauss_25_neff200_p.txt b/example/first_example_1d_cellgroup2_1gauss_25_neff200_p.txt
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index 0000000..c5f4588
--- /dev/null
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diff --git a/example/first_example_1d_cellgroup2_1gauss_25_neff200_pdf.txt b/example/first_example_1d_cellgroup2_1gauss_25_neff200_pdf.txt
new file mode 100644
index 0000000..707417a
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diff --git a/example/first_example_1d_cellgroup2_1gauss_25_neff200_posterior_individuals.png b/example/first_example_1d_cellgroup2_1gauss_25_neff200_posterior_individuals.png
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diff --git a/example/first_example_1d_cellgroup2_1gauss_25_neff200_posterior_per_cell.txt b/example/first_example_1d_cellgroup2_1gauss_25_neff200_posterior_per_cell.txt
new file mode 100644
index 0000000..6223f7d
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diff --git a/example/first_example_1d_cellgroup2_2gauss_25_neff200_posterior_andco.png b/example/first_example_1d_cellgroup2_2gauss_25_neff200_posterior_andco.png
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index 0000000..e5418b8
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diff --git a/example/first_example_1d_cellgroup2_2gauss_25_neff200_posterior_individuals.png b/example/first_example_1d_cellgroup2_2gauss_25_neff200_posterior_individuals.png
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index 0000000..7e779b2
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diff --git a/example/first_example_1d_cellgroup2_2gauss_25_neff200_posterior_per_cell.txt b/example/first_example_1d_cellgroup2_2gauss_25_neff200_posterior_per_cell.txt
new file mode 100644
index 0000000..299631e
--- /dev/null
+++ b/example/first_example_1d_cellgroup2_2gauss_25_neff200_posterior_per_cell.txt
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diff --git a/example/first_example_1d_cellgroup2_2gauss_25_neff200_with_posterior.png b/example/first_example_1d_cellgroup2_2gauss_25_neff200_with_posterior.png
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diff --git a/example/first_example_1d_cellgroup2_3gauss_25_neff200_convergence.png b/example/first_example_1d_cellgroup2_3gauss_25_neff200_convergence.png
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diff --git a/example/first_example_1d_cellgroup2_3gauss_25_neff200_corner.png b/example/first_example_1d_cellgroup2_3gauss_25_neff200_corner.png
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diff --git a/example/first_example_1d_cellgroup2_3gauss_25_neff200_individuals.png b/example/first_example_1d_cellgroup2_3gauss_25_neff200_individuals.png
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diff --git a/example/first_example_1d_cellgroup2_3gauss_25_neff200_logevid.txt b/example/first_example_1d_cellgroup2_3gauss_25_neff200_logevid.txt
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index 0000000..c44367a
--- /dev/null
+++ b/example/first_example_1d_cellgroup2_3gauss_25_neff200_logevid.txt
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diff --git a/example/first_example_1d_cellgroup2_3gauss_25_neff200_neff.txt b/example/first_example_1d_cellgroup2_3gauss_25_neff200_neff.txt
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diff --git a/example/first_example_1d_cellgroup2_3gauss_25_neff200_p.png b/example/first_example_1d_cellgroup2_3gauss_25_neff200_p.png
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diff --git a/example/first_example_1d_cellgroup2_3gauss_25_neff200_p.txt b/example/first_example_1d_cellgroup2_3gauss_25_neff200_p.txt
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index 0000000..510b1a5
--- /dev/null
+++ b/example/first_example_1d_cellgroup2_3gauss_25_neff200_p.txt
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diff --git a/example/first_example_1d_cellgroup2_3gauss_25_neff200_pdf.txt b/example/first_example_1d_cellgroup2_3gauss_25_neff200_pdf.txt
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index 0000000..2efcbf0
--- /dev/null
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diff --git a/example/first_example_1d_cellgroup2_3gauss_25_neff200_posterior_individuals.png b/example/first_example_1d_cellgroup2_3gauss_25_neff200_posterior_individuals.png
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diff --git a/example/first_example_1d_cellgroup2_3gauss_25_neff200_posterior_per_cell.txt b/example/first_example_1d_cellgroup2_3gauss_25_neff200_posterior_per_cell.txt
new file mode 100644
index 0000000..5bb06cc
--- /dev/null
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diff --git a/example/first_example_1d_cellgroup2_3gauss_25_neff200_with_posterior.png b/example/first_example_1d_cellgroup2_3gauss_25_neff200_with_posterior.png
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diff --git a/example/first_example_1d_group0_1-2gauss_25.png b/example/first_example_1d_group0_1-2gauss_25.png
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diff --git a/example/first_example_1d_group0_1-2gauss_25_individuals.png b/example/first_example_1d_group0_1-2gauss_25_individuals.png
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diff --git a/example/first_example_1d_group0_1-2gauss_25_means.txt.gz b/example/first_example_1d_group0_1-2gauss_25_means.txt.gz
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diff --git a/example/first_example_1d_group0_1-2gauss_25_pdf.txt b/example/first_example_1d_group0_1-2gauss_25_pdf.txt
new file mode 100644
index 0000000..969a292
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diff --git a/example/first_example_1d_group0_1-2gauss_25_posterior_individuals.png b/example/first_example_1d_group0_1-2gauss_25_posterior_individuals.png
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diff --git a/example/first_example_1d_group0_1-2gauss_25_posterior_per_cell.txt b/example/first_example_1d_group0_1-2gauss_25_posterior_per_cell.txt
new file mode 100644
index 0000000..b7740ac
--- /dev/null
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diff --git a/example/first_example_1d_group0_1gauss_25_neff200_p.png b/example/first_example_1d_group0_1gauss_25_neff200_p.png
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diff --git a/example/first_example_1d_group0_1gauss_25_neff200_p.txt b/example/first_example_1d_group0_1gauss_25_neff200_p.txt
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diff --git a/example/first_example_1d_group0_1gauss_25_neff200_posterior_per_cell.txt b/example/first_example_1d_group0_1gauss_25_neff200_posterior_per_cell.txt
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--- /dev/null
+++ b/example/first_example_1d_group0_1gauss_25_neff200_posterior_per_cell.txt
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diff --git a/example/first_example_1d_group0_1gauss_25_neff200_with_posterior.png b/example/first_example_1d_group0_1gauss_25_neff200_with_posterior.png
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diff --git a/example/first_example_1d_group0_2gauss_25_neff200_convergence.png b/example/first_example_1d_group0_2gauss_25_neff200_convergence.png
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diff --git a/example/first_example_1d_group0_2gauss_25_neff200_individuals.png b/example/first_example_1d_group0_2gauss_25_neff200_individuals.png
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diff --git a/example/first_example_1d_group0_2gauss_25_neff200_logevid.txt b/example/first_example_1d_group0_2gauss_25_neff200_logevid.txt
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index 0000000..0944c1b
--- /dev/null
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diff --git a/example/first_example_1d_group0_2gauss_25_neff200_neff.txt b/example/first_example_1d_group0_2gauss_25_neff200_neff.txt
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diff --git a/example/first_example_1d_group0_2gauss_25_neff200_p.png b/example/first_example_1d_group0_2gauss_25_neff200_p.png
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diff --git a/example/first_example_1d_group0_2gauss_25_neff200_p.txt b/example/first_example_1d_group0_2gauss_25_neff200_p.txt
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index 0000000..ebced8b
--- /dev/null
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diff --git a/example/first_example_1d_group0_2gauss_25_neff200_pdf.txt b/example/first_example_1d_group0_2gauss_25_neff200_pdf.txt
new file mode 100644
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diff --git a/example/first_example_1d_group0_2gauss_25_neff200_posterior_individuals.png b/example/first_example_1d_group0_2gauss_25_neff200_posterior_individuals.png
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diff --git a/example/first_example_1d_group0_2gauss_25_neff200_posterior_per_cell.txt b/example/first_example_1d_group0_2gauss_25_neff200_posterior_per_cell.txt
new file mode 100644
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diff --git a/example/first_example_1d_group0_2gauss_25_neff200_with_posterior.png b/example/first_example_1d_group0_2gauss_25_neff200_with_posterior.png
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diff --git a/example/first_example_1d_group3_1-2gauss_25.png b/example/first_example_1d_group3_1-2gauss_25.png
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diff --git a/example/first_example_1d_group3_1-2gauss_25_individuals.png b/example/first_example_1d_group3_1-2gauss_25_individuals.png
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diff --git a/example/first_example_1d_group3_1-2gauss_25_means.txt.gz b/example/first_example_1d_group3_1-2gauss_25_means.txt.gz
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diff --git a/example/first_example_1d_group3_1-2gauss_25_pdf.txt b/example/first_example_1d_group3_1-2gauss_25_pdf.txt
new file mode 100644
index 0000000..9f18406
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diff --git a/example/first_example_1d_group3_1-2gauss_25_posterior_andco.png b/example/first_example_1d_group3_1-2gauss_25_posterior_andco.png
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diff --git a/example/first_example_1d_group3_1-2gauss_25_posterior_individuals.png b/example/first_example_1d_group3_1-2gauss_25_posterior_individuals.png
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diff --git a/example/first_example_1d_group3_1-2gauss_25_posterior_per_cell.txt b/example/first_example_1d_group3_1-2gauss_25_posterior_per_cell.txt
new file mode 100644
index 0000000..f84a2f5
--- /dev/null
+++ b/example/first_example_1d_group3_1-2gauss_25_posterior_per_cell.txt
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diff --git a/example/first_example_1d_group3_1-2gauss_25_with_posterior.png b/example/first_example_1d_group3_1-2gauss_25_with_posterior.png
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diff --git a/example/first_example_1d_group3_1gauss_25_neff200_individuals.png b/example/first_example_1d_group3_1gauss_25_neff200_individuals.png
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diff --git a/example/first_example_1d_group3_1gauss_25_neff200_logevid.txt b/example/first_example_1d_group3_1gauss_25_neff200_logevid.txt
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diff --git a/example/first_example_1d_group3_1gauss_25_neff200_p.txt b/example/first_example_1d_group3_1gauss_25_neff200_p.txt
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index 0000000..3872bd6
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diff --git a/example/first_example_1d_group3_1gauss_25_neff200_pdf.txt b/example/first_example_1d_group3_1gauss_25_neff200_pdf.txt
new file mode 100644
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diff --git a/example/first_example_1d_group3_1gauss_25_neff200_posterior_individuals.png b/example/first_example_1d_group3_1gauss_25_neff200_posterior_individuals.png
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diff --git a/example/first_example_1d_group3_1gauss_25_neff200_posterior_per_cell.txt b/example/first_example_1d_group3_1gauss_25_neff200_posterior_per_cell.txt
new file mode 100644
index 0000000..73a3d07
--- /dev/null
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diff --git a/example/first_example_1d_group3_1gauss_ms0.05_pdf.txt b/example/first_example_1d_group3_1gauss_ms0.05_pdf.txt
new file mode 100644
index 0000000..3b65fb3
--- /dev/null
+++ b/example/first_example_1d_group3_1gauss_ms0.05_pdf.txt
@@ -0,0 +1,101 @@
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diff --git a/example/first_example_1d_group3_1gauss_ms0.05_posterior_andco.png b/example/first_example_1d_group3_1gauss_ms0.05_posterior_andco.png
new file mode 100644
index 0000000..9608532
Binary files /dev/null and b/example/first_example_1d_group3_1gauss_ms0.05_posterior_andco.png differ
diff --git a/example/first_example_1d_group3_1gauss_ms0.05_posterior_individuals.png b/example/first_example_1d_group3_1gauss_ms0.05_posterior_individuals.png
new file mode 100644
index 0000000..d33e540
Binary files /dev/null and b/example/first_example_1d_group3_1gauss_ms0.05_posterior_individuals.png differ
diff --git a/example/first_example_1d_group3_1gauss_ms0.05_posterior_per_cell.txt b/example/first_example_1d_group3_1gauss_ms0.05_posterior_per_cell.txt
new file mode 100644
index 0000000..7b45b92
--- /dev/null
+++ b/example/first_example_1d_group3_1gauss_ms0.05_posterior_per_cell.txt
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diff --git a/example/first_example_1d_group3_1gauss_ms0_means.txt.gz b/example/first_example_1d_group3_1gauss_ms0_means.txt.gz
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index 0000000..5f43cb9
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diff --git a/example/first_example_1d_group3_1gauss_ms0_p.txt b/example/first_example_1d_group3_1gauss_ms0_p.txt
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index 0000000..6b13e0c
--- /dev/null
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diff --git a/example/first_example_1d_group3_1gauss_ms0_pdf.txt b/example/first_example_1d_group3_1gauss_ms0_pdf.txt
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index 0000000..a128c41
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diff --git a/example/first_example_1d_group3_1gauss_ms0_posterior_andco.png b/example/first_example_1d_group3_1gauss_ms0_posterior_andco.png
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diff --git a/example/first_example_1d_group3_1gauss_ms0_posterior_individuals.png b/example/first_example_1d_group3_1gauss_ms0_posterior_individuals.png
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index 0000000..6596ddb
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diff --git a/example/first_example_1d_group3_1gauss_ms0_posterior_per_cell.txt b/example/first_example_1d_group3_1gauss_ms0_posterior_per_cell.txt
new file mode 100644
index 0000000..5cbc88e
--- /dev/null
+++ b/example/first_example_1d_group3_1gauss_ms0_posterior_per_cell.txt
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diff --git a/example/first_example_1d_group3_1gauss_ms0_with_posterior.png b/example/first_example_1d_group3_1gauss_ms0_with_posterior.png
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diff --git a/example/first_example_1d_group3_2gauss_25_neff200.png b/example/first_example_1d_group3_2gauss_25_neff200.png
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diff --git a/example/first_example_1d_group3_2gauss_25_neff200_convergence.png b/example/first_example_1d_group3_2gauss_25_neff200_convergence.png
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diff --git a/example/first_example_1d_group3_2gauss_25_neff200_individuals.png b/example/first_example_1d_group3_2gauss_25_neff200_individuals.png
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diff --git a/example/first_example_1d_group3_2gauss_25_neff200_logevid.txt b/example/first_example_1d_group3_2gauss_25_neff200_logevid.txt
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index 0000000..b0cd22d
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diff --git a/example/first_example_1d_group3_2gauss_25_neff200_neff.txt b/example/first_example_1d_group3_2gauss_25_neff200_neff.txt
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index 0000000..1087978
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diff --git a/example/first_example_1d_group3_2gauss_25_neff200_p.png b/example/first_example_1d_group3_2gauss_25_neff200_p.png
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diff --git a/example/first_example_1d_group3_2gauss_25_neff200_p.txt b/example/first_example_1d_group3_2gauss_25_neff200_p.txt
new file mode 100644
index 0000000..28c3a6c
--- /dev/null
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diff --git a/example/first_example_1d_group3_2gauss_25_neff200_pdf.txt b/example/first_example_1d_group3_2gauss_25_neff200_pdf.txt
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index 0000000..bb56a70
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diff --git a/example/first_example_1d_group3_2gauss_25_neff200_posterior_individuals.png b/example/first_example_1d_group3_2gauss_25_neff200_posterior_individuals.png
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diff --git a/example/first_example_1d_group3_2gauss_25_neff200_posterior_per_cell.txt b/example/first_example_1d_group3_2gauss_25_neff200_posterior_per_cell.txt
new file mode 100644
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diff --git a/example/first_example_1d_group3_2gauss_25_neff200_with_posterior.png b/example/first_example_1d_group3_2gauss_25_neff200_with_posterior.png
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index 0000000..248cc8e
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diff --git a/example/first_example_2d_cellgroup1_1-3gauss.png b/example/first_example_2d_cellgroup1_1-3gauss.png
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diff --git a/example/first_example_2d_cellgroup1_1-3gauss_corr.txt b/example/first_example_2d_cellgroup1_1-3gauss_corr.txt
new file mode 100644
index 0000000..48c1870
--- /dev/null
+++ b/example/first_example_2d_cellgroup1_1-3gauss_corr.txt
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diff --git a/example/first_example_2d_cellgroup1_1-3gauss_individuals.png b/example/first_example_2d_cellgroup1_1-3gauss_individuals.png
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diff --git a/example/first_example_2d_cellgroup1_1-3gauss_median.png b/example/first_example_2d_cellgroup1_1-3gauss_median.png
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diff --git a/example/first_example_2d_cellgroup1_1-3gauss_pdf2d.txt b/example/first_example_2d_cellgroup1_1-3gauss_pdf2d.txt
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index 0000000..5aee190
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diff --git a/example/first_example_2d_cellgroup1_1-3gauss_pdf2d_flat.txt b/example/first_example_2d_cellgroup1_1-3gauss_pdf2d_flat.txt
new file mode 100644
index 0000000..09913a4
--- /dev/null
+++ b/example/first_example_2d_cellgroup1_1-3gauss_pdf2d_flat.txt
@@ -0,0 +1,2501 @@
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diff --git a/example/first_example_2d_cellgroup1_1M_3gauss.png b/example/first_example_2d_cellgroup1_1M_3gauss.png
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index 0000000..3f3a1ed
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diff --git a/example/first_example_2d_cellgroup1_1M_3gauss_convergence.png b/example/first_example_2d_cellgroup1_1M_3gauss_convergence.png
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index 0000000..960d3e4
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diff --git a/example/first_example_2d_cellgroup1_1M_3gauss_corner.png b/example/first_example_2d_cellgroup1_1M_3gauss_corner.png
new file mode 100644
index 0000000..79f75c6
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diff --git a/example/first_example_2d_cellgroup1_1M_3gauss_corr.txt b/example/first_example_2d_cellgroup1_1M_3gauss_corr.txt
new file mode 100644
index 0000000..b9cb332
--- /dev/null
+++ b/example/first_example_2d_cellgroup1_1M_3gauss_corr.txt
@@ -0,0 +1,2 @@
+mean	median	low	high	pval	error
+0.7119945718157945	0.7185675222570048	0.6324152210018903	0.7909555401480616	0.0023053076655538394	0.0023053076655538394
diff --git a/example/first_example_2d_cellgroup1_1M_3gauss_individuals.png b/example/first_example_2d_cellgroup1_1M_3gauss_individuals.png
new file mode 100644
index 0000000..ecf0cd3
Binary files /dev/null and b/example/first_example_2d_cellgroup1_1M_3gauss_individuals.png differ
diff --git a/example/first_example_2d_cellgroup1_1M_3gauss_logevid.txt b/example/first_example_2d_cellgroup1_1M_3gauss_logevid.txt
new file mode 100644
index 0000000..5e8c008
--- /dev/null
+++ b/example/first_example_2d_cellgroup1_1M_3gauss_logevid.txt
@@ -0,0 +1 @@
+-1046.2557364060806
diff --git a/example/first_example_2d_cellgroup1_1M_3gauss_median.png b/example/first_example_2d_cellgroup1_1M_3gauss_median.png
new file mode 100644
index 0000000..0c06c25
Binary files /dev/null and b/example/first_example_2d_cellgroup1_1M_3gauss_median.png differ
diff --git a/example/first_example_2d_cellgroup1_1M_3gauss_neff.txt b/example/first_example_2d_cellgroup1_1M_3gauss_neff.txt
new file mode 100644
index 0000000..5ecff11
--- /dev/null
+++ b/example/first_example_2d_cellgroup1_1M_3gauss_neff.txt
@@ -0,0 +1 @@
+433.7815793276143
diff --git a/example/first_example_2d_cellgroup1_1M_3gauss_p.png b/example/first_example_2d_cellgroup1_1M_3gauss_p.png
new file mode 100644
index 0000000..b44e0ae
Binary files /dev/null and b/example/first_example_2d_cellgroup1_1M_3gauss_p.png differ
diff --git a/example/first_example_2d_cellgroup1_1M_3gauss_p.txt b/example/first_example_2d_cellgroup1_1M_3gauss_p.txt
new file mode 100644
index 0000000..69bd174
--- /dev/null
+++ b/example/first_example_2d_cellgroup1_1M_3gauss_p.txt
@@ -0,0 +1,18 @@
+name	low	median	high
+xy_mux0	0.932521062461425	0.9925390273722157	1.0513754638439519
+xy_muy0	0.9148182858603896	0.9643619972640047	1.0135350718787948
+xy_scalex0	0.11258077430900358	0.14687303186362283	0.2035066319347341
+xy_scaley0	0.10775611084913146	0.12930098465246	0.1676330074350814
+xy_corr0	-0.2199621407319791	0.062440647552580886	0.3563541054775712
+xy_amp1	0.011674067765843595	0.07248008344617021	0.182005807248073
+xy_mux1	-1.4207232690408864	0.025560599301804793	0.6654497516219049
+xy_muy1	-1.1459339761156155	0.19299787862338635	0.7843197231936256
+xy_scalex1	0.14659148075424494	0.34340330633420135	0.8732242606639044
+xy_scaley1	0.1387152433945414	0.30288600944574684	0.8008625267906572
+xy_corr1	-0.3308922495767569	-0.026792692902596324	0.2752234222128265
+xy_amp2	0.3324794820463551	0.44079136584411754	0.5289851398038411
+xy_mux2	0.0494080235693764	0.2792919019021486	0.3675207649197152
+xy_muy2	0.2557932102520441	0.39487037597864855	0.4835447075446739
+xy_scalex2	0.12048116080908532	0.1909653399828639	0.3705979560749332
+xy_scaley2	0.12558350495705642	0.19731554096746667	0.33887764694862904
+xy_corr2	-0.3028325334014305	-0.006474130413916328	0.2860548923637627
diff --git a/example/first_example_2d_cellgroup1_1M_3gauss_pdf2d.txt b/example/first_example_2d_cellgroup1_1M_3gauss_pdf2d.txt
new file mode 100644
index 0000000..8c9aaa3
--- /dev/null
+++ b/example/first_example_2d_cellgroup1_1M_3gauss_pdf2d.txt
@@ -0,0 +1,51 @@
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diff --git a/example/first_example_2d_cellgroup1_1M_3gauss_pdf2d_flat.txt b/example/first_example_2d_cellgroup1_1M_3gauss_pdf2d_flat.txt
new file mode 100644
index 0000000..afc19ea
--- /dev/null
+++ b/example/first_example_2d_cellgroup1_1M_3gauss_pdf2d_flat.txt
@@ -0,0 +1,2501 @@
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diff --git a/example/first_example_2d_cellgroup1_1gauss.png b/example/first_example_2d_cellgroup1_1gauss.png
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index 0000000..0ed654c
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diff --git a/example/first_example_2d_cellgroup1_1gauss_convergence.png b/example/first_example_2d_cellgroup1_1gauss_convergence.png
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index 0000000..124697d
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diff --git a/example/first_example_2d_cellgroup1_1gauss_corner.png b/example/first_example_2d_cellgroup1_1gauss_corner.png
new file mode 100644
index 0000000..1c7bb8a
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diff --git a/example/first_example_2d_cellgroup1_1gauss_corr.txt b/example/first_example_2d_cellgroup1_1gauss_corr.txt
new file mode 100644
index 0000000..6faa080
--- /dev/null
+++ b/example/first_example_2d_cellgroup1_1gauss_corr.txt
@@ -0,0 +1,2 @@
+mean	median	low	high	pval	error
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diff --git a/example/first_example_2d_cellgroup1_1gauss_individuals.png b/example/first_example_2d_cellgroup1_1gauss_individuals.png
new file mode 100644
index 0000000..ab35701
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diff --git a/example/first_example_2d_cellgroup1_1gauss_median.png b/example/first_example_2d_cellgroup1_1gauss_median.png
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index 0000000..3576211
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diff --git a/example/first_example_2d_cellgroup1_1gauss_neff.txt b/example/first_example_2d_cellgroup1_1gauss_neff.txt
new file mode 100644
index 0000000..f886e7f
--- /dev/null
+++ b/example/first_example_2d_cellgroup1_1gauss_neff.txt
@@ -0,0 +1 @@
+3357.924855068426
diff --git a/example/first_example_2d_cellgroup1_1gauss_p.png b/example/first_example_2d_cellgroup1_1gauss_p.png
new file mode 100644
index 0000000..920b271
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diff --git a/example/first_example_2d_cellgroup1_1gauss_p.txt b/example/first_example_2d_cellgroup1_1gauss_p.txt
new file mode 100644
index 0000000..ce66cdd
--- /dev/null
+++ b/example/first_example_2d_cellgroup1_1gauss_p.txt
@@ -0,0 +1,6 @@
+name	low	median	high
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+xy_muy0	0.5843562271041323	0.6311530628809803	0.6712819586211014
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+xy_corr0	0.6552232381165575	0.7647895891392685	0.8609033928860426
diff --git a/example/first_example_2d_cellgroup1_1gauss_pdf2d.txt b/example/first_example_2d_cellgroup1_1gauss_pdf2d.txt
new file mode 100644
index 0000000..e46d9fc
--- /dev/null
+++ b/example/first_example_2d_cellgroup1_1gauss_pdf2d.txt
@@ -0,0 +1,51 @@
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diff --git a/example/first_example_2d_cellgroup1_1gauss_pdf2d_flat.txt b/example/first_example_2d_cellgroup1_1gauss_pdf2d_flat.txt
new file mode 100644
index 0000000..86164d0
--- /dev/null
+++ b/example/first_example_2d_cellgroup1_1gauss_pdf2d_flat.txt
@@ -0,0 +1,2501 @@
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diff --git a/example/first_example_2d_cellgroup1_2gauss.png b/example/first_example_2d_cellgroup1_2gauss.png
new file mode 100644
index 0000000..9821a38
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diff --git a/example/first_example_2d_cellgroup1_2gauss_convergence.png b/example/first_example_2d_cellgroup1_2gauss_convergence.png
new file mode 100644
index 0000000..2fab83d
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diff --git a/example/first_example_2d_cellgroup1_2gauss_corner.png b/example/first_example_2d_cellgroup1_2gauss_corner.png
new file mode 100644
index 0000000..1d103d4
Binary files /dev/null and b/example/first_example_2d_cellgroup1_2gauss_corner.png differ
diff --git a/example/first_example_2d_cellgroup1_2gauss_corr.txt b/example/first_example_2d_cellgroup1_2gauss_corr.txt
new file mode 100644
index 0000000..e1d0801
--- /dev/null
+++ b/example/first_example_2d_cellgroup1_2gauss_corr.txt
@@ -0,0 +1,2 @@
+mean	median	low	high	pval	error
+0.7456379123610607	0.7555959669351396	0.6667477872831186	0.8222358387763793	0.0037737713639815164	0.0037737713639815164
diff --git a/example/first_example_2d_cellgroup1_2gauss_individuals.png b/example/first_example_2d_cellgroup1_2gauss_individuals.png
new file mode 100644
index 0000000..863e963
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diff --git a/example/first_example_2d_cellgroup1_2gauss_median.png b/example/first_example_2d_cellgroup1_2gauss_median.png
new file mode 100644
index 0000000..521e118
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diff --git a/example/first_example_2d_cellgroup1_2gauss_neff.txt b/example/first_example_2d_cellgroup1_2gauss_neff.txt
new file mode 100644
index 0000000..ec3a89b
--- /dev/null
+++ b/example/first_example_2d_cellgroup1_2gauss_neff.txt
@@ -0,0 +1 @@
+264.9869066113614
diff --git a/example/first_example_2d_cellgroup1_2gauss_p.png b/example/first_example_2d_cellgroup1_2gauss_p.png
new file mode 100644
index 0000000..672d777
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diff --git a/example/first_example_2d_cellgroup1_2gauss_p.txt b/example/first_example_2d_cellgroup1_2gauss_p.txt
new file mode 100644
index 0000000..ab372dd
--- /dev/null
+++ b/example/first_example_2d_cellgroup1_2gauss_p.txt
@@ -0,0 +1,12 @@
+name	low	median	high
+xy_mux0	0.010484093019590322	0.2700133521667322	0.3408493030787684
+xy_muy0	0.29843162230663933	0.3884576862065257	0.45221320359659234
+xy_scalex0	0.12250862156453521	0.1997168882504236	0.39093183569700685
+xy_scaley0	0.1248255413809003	0.18967560964490116	0.3089017694533899
+xy_corr0	-0.22111691136776246	0.031113547250021648	0.29240879811814124
+xy_amp1	0.42094219438073527	0.48607373294754264	0.554498630503803
+xy_mux1	0.9291483690030348	0.9886668526964612	1.0450064447015293
+xy_muy1	0.9159209966435211	0.9631246739991725	1.0135529858836005
+xy_scalex1	0.11399494122495102	0.1513172415523747	0.21493234479026863
+xy_scaley1	0.10797701879251341	0.13121171626438236	0.1752509307016313
+xy_corr1	-0.2715553458611735	0.008023514294676556	0.296209935565105
diff --git a/example/first_example_2d_cellgroup1_2gauss_pdf2d.txt b/example/first_example_2d_cellgroup1_2gauss_pdf2d.txt
new file mode 100644
index 0000000..5995dd0
--- /dev/null
+++ b/example/first_example_2d_cellgroup1_2gauss_pdf2d.txt
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diff --git a/example/first_example_2d_cellgroup1_2gauss_pdf2d_flat.txt b/example/first_example_2d_cellgroup1_2gauss_pdf2d_flat.txt
new file mode 100644
index 0000000..3dfb649
--- /dev/null
+++ b/example/first_example_2d_cellgroup1_2gauss_pdf2d_flat.txt
@@ -0,0 +1,2501 @@
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diff --git a/example/first_example_2d_cellgroup1_2gauss_split0.35.png b/example/first_example_2d_cellgroup1_2gauss_split0.35.png
new file mode 100644
index 0000000..c858efc
Binary files /dev/null and b/example/first_example_2d_cellgroup1_2gauss_split0.35.png differ
diff --git a/example/first_example_2d_cellgroup1_2gauss_split0.35.txt b/example/first_example_2d_cellgroup1_2gauss_split0.35.txt
new file mode 100644
index 0000000..0f6efc0
--- /dev/null
+++ b/example/first_example_2d_cellgroup1_2gauss_split0.35.txt
@@ -0,0 +1,51 @@
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diff --git a/example/first_example_2d_cellgroup1_2gauss_split0.35_renorm.png b/example/first_example_2d_cellgroup1_2gauss_split0.35_renorm.png
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index 0000000..94980b8
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diff --git a/example/first_example_2d_cellgroup1_2gauss_split0.75.png b/example/first_example_2d_cellgroup1_2gauss_split0.75.png
new file mode 100644
index 0000000..39efc1c
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diff --git a/example/first_example_2d_cellgroup1_2gauss_split0.75.txt b/example/first_example_2d_cellgroup1_2gauss_split0.75.txt
new file mode 100644
index 0000000..63ea983
--- /dev/null
+++ b/example/first_example_2d_cellgroup1_2gauss_split0.75.txt
@@ -0,0 +1,51 @@
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diff --git a/example/first_example_2d_cellgroup1_2gauss_split0.75_renorm.png b/example/first_example_2d_cellgroup1_2gauss_split0.75_renorm.png
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diff --git a/example/first_example_2d_cellgroup1_3gauss.png b/example/first_example_2d_cellgroup1_3gauss.png
new file mode 100644
index 0000000..5914d87
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diff --git a/example/first_example_2d_cellgroup1_3gauss_convergence.png b/example/first_example_2d_cellgroup1_3gauss_convergence.png
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index 0000000..b4cffdf
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diff --git a/example/first_example_2d_cellgroup1_3gauss_corner.png b/example/first_example_2d_cellgroup1_3gauss_corner.png
new file mode 100644
index 0000000..b68cf98
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diff --git a/example/first_example_2d_cellgroup1_3gauss_corr.txt b/example/first_example_2d_cellgroup1_3gauss_corr.txt
new file mode 100644
index 0000000..369c891
--- /dev/null
+++ b/example/first_example_2d_cellgroup1_3gauss_corr.txt
@@ -0,0 +1,2 @@
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diff --git a/example/first_example_2d_cellgroup1_3gauss_individuals.png b/example/first_example_2d_cellgroup1_3gauss_individuals.png
new file mode 100644
index 0000000..83da48b
Binary files /dev/null and b/example/first_example_2d_cellgroup1_3gauss_individuals.png differ
diff --git a/example/first_example_2d_cellgroup1_3gauss_median.png b/example/first_example_2d_cellgroup1_3gauss_median.png
new file mode 100644
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diff --git a/example/first_example_2d_cellgroup1_3gauss_neff.txt b/example/first_example_2d_cellgroup1_3gauss_neff.txt
new file mode 100644
index 0000000..baf79ab
--- /dev/null
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diff --git a/example/first_example_2d_cellgroup1_3gauss_p.png b/example/first_example_2d_cellgroup1_3gauss_p.png
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index 0000000..2ae5f41
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diff --git a/example/first_example_2d_cellgroup1_3gauss_p.txt b/example/first_example_2d_cellgroup1_3gauss_p.txt
new file mode 100644
index 0000000..2ed5ce0
--- /dev/null
+++ b/example/first_example_2d_cellgroup1_3gauss_p.txt
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diff --git a/example/first_example_2d_cellgroup1_3gauss_pdf2d.txt b/example/first_example_2d_cellgroup1_3gauss_pdf2d.txt
new file mode 100644
index 0000000..df4857d
--- /dev/null
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diff --git a/example/first_example_2d_cellgroup1_3gauss_pdf2d_flat.txt b/example/first_example_2d_cellgroup1_3gauss_pdf2d_flat.txt
new file mode 100644
index 0000000..7911d55
--- /dev/null
+++ b/example/first_example_2d_cellgroup1_3gauss_pdf2d_flat.txt
@@ -0,0 +1,2501 @@
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diff --git a/example/first_example_cellgroup1_2d_1gauss_logevid.txt b/example/first_example_cellgroup1_2d_1gauss_logevid.txt
new file mode 100644
index 0000000..cc74169
--- /dev/null
+++ b/example/first_example_cellgroup1_2d_1gauss_logevid.txt
@@ -0,0 +1 @@
+-1048.274356710545
diff --git a/example/first_example_cellgroup1_2d_2gauss_logevid.txt b/example/first_example_cellgroup1_2d_2gauss_logevid.txt
new file mode 100644
index 0000000..79baea7
--- /dev/null
+++ b/example/first_example_cellgroup1_2d_2gauss_logevid.txt
@@ -0,0 +1 @@
+-1043.3855167128618
diff --git a/example/first_example_cellgroup1_2d_3gauss_logevid.txt b/example/first_example_cellgroup1_2d_3gauss_logevid.txt
new file mode 100644
index 0000000..dcfae44
--- /dev/null
+++ b/example/first_example_cellgroup1_2d_3gauss_logevid.txt
@@ -0,0 +1 @@
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diff --git a/example/nih3t3_generated_2d_2.txt b/example/nih3t3_generated_2d_2.txt
new file mode 100644
index 0000000..d1ffa51
--- /dev/null
+++ b/example/nih3t3_generated_2d_2.txt
@@ -0,0 +1,2362 @@
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diff --git a/example/nih3t3_generated_second.txt b/example/nih3t3_generated_second.txt
new file mode 100644
index 0000000..fc6696f
--- /dev/null
+++ b/example/nih3t3_generated_second.txt
@@ -0,0 +1,2362 @@
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+16415	group3	2	1	0.8833770943410567	0.4940315165917649
+16653	group3	2	2	0.4100754221444672	0.9925343026078225
+6630	group1	0	0	0.11000928787804654	0.0825506461133414
+7997	group3	1	2	0.881716139399197	1.017221825839897
+16842	group2	0	6	0.5869237059660654	1.3445889733952594
+15418	group3	1	2	0.7440472375534344	0.657167016686993
+16977	group3	2	6	0.8270502816052101	1.579135995552991
+8249	group3	0	0	0.2622069846391184	0.20855807544302601
+38624	group2	13	14	1.4085473399257564	1.356555514151153
+13713	group3	1	1	0.7095381904862047	0.5511611104941236
+14578	group2	0	3	0.44194008035322435	0.9239256318773436
+12152	group3	1	3	0.574577741633639	0.7909250264354777
+28796	group1	10	3	1.5750556784812364	1.0797583826536687
+15427	group3	2	0	0.7262888094829575	0.279676030730506
+16065	group3	0	0	0.4952415138002882	0.47091307736810495
+15383	group2	0	1	0.49953684215105126	0.3349873376597454
diff --git a/example/run_first_example_compare_means.sh b/example/run_first_example_compare_means.sh
new file mode 100644
index 0000000..ab4e255
--- /dev/null
+++ b/example/run_first_example_compare_means.sh
@@ -0,0 +1,52 @@
+################
+## 1d compare ##
+################
+
+for group in 0 3; do
+  for nnorm in 1 2; do
+    baredSC_1d \
+          --input example/nih3t3_generated_2d_2.txt \
+          --metadata1ColName 0.5_0_0_0.5_group \
+          --metadata1Values ${group}.0 \
+          --geneColName 0.5_0_0_0.5_x \
+          --output example/first_example_1d_group${group}_${nnorm}gauss_25_neff200 \
+          --nnorm ${nnorm} --nx 25 --minNeff 200 \
+          --figure example/first_example_1d_group${group}_${nnorm}gauss_25_neff200.png \
+          --title "first gene ${nnorm} gauss group${group} 25 bins neff200" \
+          --logevidence example/first_example_1d_group${group}_${nnorm}gauss_25_neff200_logevid.txt
+  done
+  combineMultipleModels_1d \
+          --input example/nih3t3_generated_2d_2.txt \
+          --metadata1ColName 0.5_0_0_0.5_group \
+          --metadata1Values ${group}.0 \
+          --geneColName 0.5_0_0_0.5_x --nx 25 \
+          --outputs example/first_example_1d_group${group}_1gauss_25_neff200 \
+           example/first_example_1d_group${group}_2gauss_25_neff200 \
+          --figure example/first_example_1d_group${group}_1-2gauss_25.png \
+          --title "first gene group$group 25 bins 1 and 2 gauss"
+done
+
+for group in 1 2; do
+        for nnorm in 1 2 3; do
+          baredSC_1d \
+                --input example/nih3t3_generated_2d_2.txt \
+                --metadata1ColName group \
+                --metadata1Values group${group} \
+                --geneColName 0.5_0_0_0.5_x \
+                --output example/first_example_1d_cellgroup${group}_${nnorm}gauss_25_neff200 \
+                --nnorm ${nnorm} --nx 25 --minNeff 200 \
+            --figure example/first_example_1d_cellgroup${group}_${nnorm}gauss_25_neff200.png \
+            --title "first gene ${nnorm} gauss cellgroup${group}" \
+            --logevidence example/first_example_1d_cellgroup${group}_${nnorm}gauss_25_neff200_logevid.txt
+        done
+        combineMultipleModels_1d \
+                --input example/nih3t3_generated_2d_2.txt \
+                --metadata1ColName group \
+                --metadata1Values group${group} \
+                --geneColName 0.5_0_0_0.5_x --nx 25 \
+                --outputs example/first_example_1d_cellgroup${group}_1gauss_25_neff200 \
+                example/first_example_1d_cellgroup${group}_2gauss_25_neff200 \
+                example/first_example_1d_cellgroup${group}_3gauss_25_neff200 \
+                --figure example/first_example_1d_cellgroup${group}_1-3gauss_25.png \
+                --title "first gene cellgroup$group 25 bins 1, 2 and 3 gauss"
+      done
diff --git a/example/run_first_example_default.sh b/example/run_first_example_default.sh
new file mode 100644
index 0000000..3f76bf4
--- /dev/null
+++ b/example/run_first_example_default.sh
@@ -0,0 +1,134 @@
+########
+## 1d ##
+########
+
+# 1 gauss
+# 0:00:43 of MCMC
+# 0:00:05 of pdf
+baredSC_1d \
+      --input example/nih3t3_generated_2d_2.txt \
+      --geneColName 0.5_0_0_0.5_x \
+      --output example/first_example_1d_1gauss \
+      --nnorm 1 \
+      --figure example/first_example_1d_1gauss.png \
+      --title "first gene 1 gauss" \
+      --logevidence example/first_example_1d_1gauss_logevid.txt
+
+# 2 gauss
+# 0:00:43 of MCMC
+# 0:00:07 of pdf
+baredSC_1d \
+      --input example/nih3t3_generated_2d_2.txt \
+      --geneColName 0.5_0_0_0.5_x \
+      --output example/first_example_1d_2gauss \
+      --nnorm 2 \
+      --figure example/first_example_1d_2gauss.png \
+      --title "first gene 2 gauss" \
+      --logevidence example/first_example_1d_2gauss_logevid.txt
+
+# 3 gauss
+# 0:00:46 of MCMC
+# 0:00:08 of pdf
+baredSC_1d \
+      --input example/nih3t3_generated_2d_2.txt \
+      --geneColName 0.5_0_0_0.5_x \
+      --output example/first_example_1d_3gauss \
+      --nnorm 3 \
+      --figure example/first_example_1d_3gauss.png \
+      --title "first gene 3 gauss" \
+      --logevidence example/first_example_1d_3gauss_logevid.txt
+
+# 3 gauss 1M
+# 0:08:43 of MCMC
+# 0:00:12 of pdf
+baredSC_1d \
+      --input example/nih3t3_generated_2d_2.txt \
+      --geneColName 0.5_0_0_0.5_x \
+      --output example/first_example_1d_3gauss_1M \
+      --nnorm 3 --nsampMCMC 1000000 \
+      --figure example/first_example_1d_3gauss_1M.png \
+      --title "first gene 3 gauss 1M" \
+      --logevidence example/first_example_1d_3gauss_1M_logevid.txt
+
+# Combine the default
+combineMultipleModels_1d \
+      --input example/nih3t3_generated_2d_2.txt \
+      --geneColName 0.5_0_0_0.5_x \
+      --outputs example/first_example_1d_1gauss \
+      example/first_example_1d_2gauss \
+      example/first_example_1d_3gauss_1M \
+      --figure example/first_example_1d_1-3gauss.png \
+      --title "first gene 1, 2, and 3 gauss"
+
+########
+## 2d ##
+########
+for nnorm in 1 2 3; do
+        baredSC_2d \
+        --input example/nih3t3_generated_2d_2.txt \
+        --geneXColName 0.5_0_0_0.5_x \
+        --geneYColName 0.5_0_0_0.5_y \
+        --metadata1ColName group \
+        --metadata1Values group1 \
+        --output example/first_example_2d_cellgroup1_${nnorm}gauss \
+        --nnorm ${nnorm} \
+        --figure example/first_example_2d_cellgroup1_${nnorm}gauss.png \
+        --title "first example 2d cell group 1 ${nnorm} gauss" \
+        --logevidence example/first_example_2d_cellgroup1_${nnorm}gauss_logevid.txt
+done
+
+# Redo the plot for splity
+# pdf 0:02:23
+baredSC_2d \
+        --input example/nih3t3_generated_2d_2.txt \
+        --geneXColName 0.5_0_0_0.5_x \
+        --geneYColName 0.5_0_0_0.5_y \
+        --metadata1ColName group \
+        --metadata1Values group1 \
+        --output example/first_example_2d_cellgroup1_2gauss \
+        --nnorm 2 \
+        --figure example/first_example_2d_cellgroup1_2gauss.png \
+        --title "first example 2d cell group 1 2 gauss" \
+        --splity 0.7 1.5
+# Increase the nb of sample for 3 gauss:
+nnorm=3
+baredSC_2d \
+        --input example/nih3t3_generated_2d_2.txt \
+        --geneXColName 0.5_0_0_0.5_x \
+        --geneYColName 0.5_0_0_0.5_y \
+        --metadata1ColName group \
+        --metadata1Values group1 \
+        --nsampMCMC 1000000 \
+        --nsampInPlot 75000 \
+        --output example/first_example_2d_cellgroup1_1M_${nnorm}gauss \
+        --nnorm ${nnorm} \
+        --figure example/first_example_2d_cellgroup1_1M_${nnorm}gauss.png \
+        --title "first example 2d cell group 1 1M ${nnorm} gauss" \
+        --logevidence example/first_example_2d_cellgroup1_1M_${nnorm}gauss_logevid.txt
+
+combineMultipleModels_2d \
+          --input example/nih3t3_generated_2d_2.txt \
+          --geneXColName 0.5_0_0_0.5_x \
+          --geneYColName 0.5_0_0_0.5_y \
+          --metadata1ColName group \
+          --metadata1Values group1 \
+          --outputs example/first_example_2d_cellgroup1_1gauss \
+          example/first_example_2d_cellgroup1_2gauss \
+          example/first_example_2d_cellgroup1_1M_3gauss \
+          --figure example/first_example_2d_cellgroup1_1-3gauss.png \
+          --getPVal \
+          --title "first example cell group 1 1,2,3 gauss"
+
+# Test:
+# nnorm=1
+# baredSC_2d \
+#         --input example/nih3t3_generated_2d_2.txt \
+#         --geneXColName 0.5_0_0_0.5_x \
+#         --geneYColName 0.5_0_0_0.5_y \
+#         --metadata1ColName group \
+#         --metadata1Values group1 \
+#         --output /tmp/test \
+#         --xmax 2 --ymax 3 --nnorm $nnorm --nx 12 --ny 20 \
+#         --prettyBinsx 60 --prettyBinsy 100 \
+#         --figure /tmp/test.png \
+#         --logevidence /tmp/test.txt
diff --git a/example/run_first_example_minScale.sh b/example/run_first_example_minScale.sh
new file mode 100644
index 0000000..c8c9e82
--- /dev/null
+++ b/example/run_first_example_minScale.sh
@@ -0,0 +1,25 @@
+##############
+## minScale ##
+##############
+nnorm=1
+baredSC_1d \
+            --input example/nih3t3_generated_2d_2.txt \
+            --metadata1ColName 0.5_0_0_0.5_group \
+            --metadata1Values 3.0 \
+            --geneColName 0.5_0_0_0.5_x \
+            --output example/first_example_1d_group3_${nnorm}gauss_ms0 \
+            --nnorm ${nnorm} --minScale 0 \
+            --minNeff 200 \
+            --figure example/first_example_1d_group3_${nnorm}gauss_ms0.png \
+            --title "first gene ${nnorm} gauss group3 minScale 0"
+
+baredSC_1d \
+            --input example/nih3t3_generated_2d_2.txt \
+            --metadata1ColName 0.5_0_0_0.5_group \
+            --metadata1Values 3.0 \
+            --geneColName 0.5_0_0_0.5_x \
+            --output example/first_example_1d_group3_${nnorm}gauss_ms0.05 \
+            --nnorm ${nnorm} --minScale 0.05 \
+            --minNeff 200 \
+            --figure example/first_example_1d_group3_${nnorm}gauss_ms0.05.png \
+            --title "first gene ${nnorm} gauss group3 minScale 0.05"
diff --git a/example/run_first_example_nx.sh b/example/run_first_example_nx.sh
new file mode 100644
index 0000000..bcbb83b
--- /dev/null
+++ b/example/run_first_example_nx.sh
@@ -0,0 +1,38 @@
+##############
+##    nx    ##
+##############
+nnorm=1
+baredSC_2d \
+    --input example/nih3t3_generated_second.txt \
+    --geneXColName 1_0.5_0.5_0.5_0.5_0.5_x \
+    --geneYColName 1_0.5_0.5_0.5_0.5_0.5_y \
+    --metadata1ColName group \
+    --metadata1Values group1 \
+    --output example/second_example_2d_cellgroup1_${nnorm}gauss \
+    --nnorm ${nnorm} \
+    --figure example/second_example_2d_cellgroup1_${nnorm}gauss.png \
+    --title "second example 2d cell group 1 ${nnorm} gauss"
+
+baredSC_2d \
+    --input example/nih3t3_generated_second.txt \
+    --geneXColName 1_0.5_0.5_0.5_0.5_0.5_x \
+    --geneYColName 1_0.5_0.5_0.5_0.5_0.5_y \
+    --metadata1ColName group \
+    --metadata1Values group1 \
+    --output example/second_example_2d_cellgroup1_${nnorm}gauss_nx20 \
+    --nnorm ${nnorm} \
+    --nx 20 --ny 20 \
+    --figure example/second_example_2d_cellgroup1_${nnorm}gauss_nx20.png \
+    --title "second example 2d cell group 1 ${nnorm} gauss 20 bins"
+
+baredSC_2d \
+    --input example/nih3t3_generated_second.txt \
+    --geneXColName 1_0.5_0.5_0.5_0.5_0.5_x \
+    --geneYColName 1_0.5_0.5_0.5_0.5_0.5_y \
+    --metadata1ColName group \
+    --metadata1Values group1 \
+    --output example/second_example_2d_cellgroup1_${nnorm}gauss_nx20 \
+    --nnorm ${nnorm} \
+    --nx 20 --ny 20 --prettyBinsx 50 --prettyBinsy 50 \
+    --figure example/second_example_2d_cellgroup1_${nnorm}gauss_nx20_pretty.png \
+    --title "second example 2d cell group 1 ${nnorm} gauss 20 bins pretty"
diff --git a/example/run_first_example_scalePrior.sh b/example/run_first_example_scalePrior.sh
new file mode 100644
index 0000000..e231a93
--- /dev/null
+++ b/example/run_first_example_scalePrior.sh
@@ -0,0 +1,41 @@
+##############
+##    nx    ##
+##############
+nnorm=1
+baredSC_2d \
+    --input example/nih3t3_generated_second.txt \
+    --geneXColName 1_0.5_0.5_0.5_0.5_0.5_x \
+    --geneYColName 1_0.5_0.5_0.5_0.5_0.5_y \
+    --metadata1ColName group \
+    --metadata1Values group1 \
+    --output example/second_example_2d_cellgroup1_${nnorm}gauss_nx20 \
+    --nnorm ${nnorm} \
+    --nx 20 --ny 20 \
+    --figure example/second_example_2d_cellgroup1_${nnorm}gauss_nx20.png \
+    --title "second example 2d cell group 1 ${nnorm} gauss 20 bins"
+
+baredSC_2d \
+    --input example/nih3t3_generated_second.txt \
+    --geneXColName 1_0.5_0.5_0.5_0.5_0.5_x \
+    --geneYColName 1_0.5_0.5_0.5_0.5_0.5_y \
+    --metadata1ColName group \
+    --metadata1Values group1 \
+    --output example/second_example_2d_cellgroup1_${nnorm}gauss_nx20_smallSP \
+    --nnorm ${nnorm} \
+    --nx 20 --ny 20 \
+    --scalePrior 0.1 \
+    --figure example/second_example_2d_cellgroup1_${nnorm}gauss_nx20_smallSP.png \
+    --title "second example 2d cell group 1 ${nnorm} gauss small scalePrior"
+
+baredSC_2d \
+    --input example/nih3t3_generated_second.txt \
+    --geneXColName 1_0.5_0.5_0.5_0.5_0.5_x \
+    --geneYColName 1_0.5_0.5_0.5_0.5_0.5_y \
+    --metadata1ColName group \
+    --metadata1Values group1 \
+    --output example/second_example_2d_cellgroup1_${nnorm}gauss_nx20_largeSP \
+    --nnorm ${nnorm} \
+    --nx 20 --ny 20 \
+    --scalePrior 3 \
+    --figure example/second_example_2d_cellgroup1_${nnorm}gauss_nx20_largeSP.png \
+    --title "second example 2d cell group 1 ${nnorm} gauss large scalePrior"
diff --git a/example/second_example_2d_cellgroup1_1gauss.png b/example/second_example_2d_cellgroup1_1gauss.png
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diff --git a/example/second_example_2d_cellgroup1_1gauss_corr.txt b/example/second_example_2d_cellgroup1_1gauss_corr.txt
new file mode 100644
index 0000000..74bcdb1
--- /dev/null
+++ b/example/second_example_2d_cellgroup1_1gauss_corr.txt
@@ -0,0 +1,2 @@
+mean	median	low	high	pval	error
+0.33450129885473684	0.33475853664446686	0.2386203979175091	0.4307385989494024	0.000613539654856018	0.0004523738445563152
diff --git a/example/second_example_2d_cellgroup1_1gauss_individuals.png b/example/second_example_2d_cellgroup1_1gauss_individuals.png
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index 0000000..9d36b37
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diff --git a/example/second_example_2d_cellgroup1_1gauss_neff.txt b/example/second_example_2d_cellgroup1_1gauss_neff.txt
new file mode 100644
index 0000000..7ba498c
--- /dev/null
+++ b/example/second_example_2d_cellgroup1_1gauss_neff.txt
@@ -0,0 +1 @@
+2998.11069731516
diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20.png b/example/second_example_2d_cellgroup1_1gauss_nx20.png
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diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_corner.png b/example/second_example_2d_cellgroup1_1gauss_nx20_corner.png
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diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_corr.txt b/example/second_example_2d_cellgroup1_1gauss_nx20_corr.txt
new file mode 100644
index 0000000..99ce340
--- /dev/null
+++ b/example/second_example_2d_cellgroup1_1gauss_nx20_corr.txt
@@ -0,0 +1,2 @@
+mean	median	low	high	pval	error
+0.3361626418003792	0.33709430340111035	0.23710432048758964	0.4355111563234078	0.0004674903797985895	0.000387276275663326
diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_individuals.png b/example/second_example_2d_cellgroup1_1gauss_nx20_individuals.png
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diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_largeSP_corner.png b/example/second_example_2d_cellgroup1_1gauss_nx20_largeSP_corner.png
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diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_largeSP_corr.txt b/example/second_example_2d_cellgroup1_1gauss_nx20_largeSP_corr.txt
new file mode 100644
index 0000000..cde0757
--- /dev/null
+++ b/example/second_example_2d_cellgroup1_1gauss_nx20_largeSP_corr.txt
@@ -0,0 +1,2 @@
+mean	median	low	high	pval	error
+0.39165910961881323	0.3922805022201835	0.2922971460042615	0.4916052776125829	0.000522996457748803	0.00042763744476072063
diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_largeSP_individuals.png b/example/second_example_2d_cellgroup1_1gauss_nx20_largeSP_individuals.png
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index 0000000..fd05b81
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new file mode 100644
index 0000000..99b57f1
--- /dev/null
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diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_largeSP_p.txt b/example/second_example_2d_cellgroup1_1gauss_nx20_largeSP_p.txt
new file mode 100644
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new file mode 100644
index 0000000..899a1db
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diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_largeSP_pdf2d_flat.txt b/example/second_example_2d_cellgroup1_1gauss_nx20_largeSP_pdf2d_flat.txt
new file mode 100644
index 0000000..e1e12cf
--- /dev/null
+++ b/example/second_example_2d_cellgroup1_1gauss_nx20_largeSP_pdf2d_flat.txt
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diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_median.png b/example/second_example_2d_cellgroup1_1gauss_nx20_median.png
new file mode 100644
index 0000000..acd2b91
Binary files /dev/null and b/example/second_example_2d_cellgroup1_1gauss_nx20_median.png differ
diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_neff.txt b/example/second_example_2d_cellgroup1_1gauss_nx20_neff.txt
new file mode 100644
index 0000000..4c46f82
--- /dev/null
+++ b/example/second_example_2d_cellgroup1_1gauss_nx20_neff.txt
@@ -0,0 +1 @@
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diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_p.png b/example/second_example_2d_cellgroup1_1gauss_nx20_p.png
new file mode 100644
index 0000000..1e134aa
Binary files /dev/null and b/example/second_example_2d_cellgroup1_1gauss_nx20_p.png differ
diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_p.txt b/example/second_example_2d_cellgroup1_1gauss_nx20_p.txt
new file mode 100644
index 0000000..668af80
--- /dev/null
+++ b/example/second_example_2d_cellgroup1_1gauss_nx20_p.txt
@@ -0,0 +1,6 @@
+name	low	median	high
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diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_pdf2d.txt b/example/second_example_2d_cellgroup1_1gauss_nx20_pdf2d.txt
new file mode 100644
index 0000000..c73736b
--- /dev/null
+++ b/example/second_example_2d_cellgroup1_1gauss_nx20_pdf2d.txt
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diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_pdf2d_flat.txt b/example/second_example_2d_cellgroup1_1gauss_nx20_pdf2d_flat.txt
new file mode 100644
index 0000000..bdf50b4
--- /dev/null
+++ b/example/second_example_2d_cellgroup1_1gauss_nx20_pdf2d_flat.txt
@@ -0,0 +1,401 @@
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diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_pretty.png b/example/second_example_2d_cellgroup1_1gauss_nx20_pretty.png
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diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_pretty_convergence.png b/example/second_example_2d_cellgroup1_1gauss_nx20_pretty_convergence.png
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index 0000000..80ff63c
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diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_pretty_corner.png b/example/second_example_2d_cellgroup1_1gauss_nx20_pretty_corner.png
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index 0000000..e74c180
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diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_pretty_corr.txt b/example/second_example_2d_cellgroup1_1gauss_nx20_pretty_corr.txt
new file mode 100644
index 0000000..64547e6
--- /dev/null
+++ b/example/second_example_2d_cellgroup1_1gauss_nx20_pretty_corr.txt
@@ -0,0 +1,2 @@
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diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_pretty_individuals.png b/example/second_example_2d_cellgroup1_1gauss_nx20_pretty_individuals.png
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index 0000000..8c9e8c3
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diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_pretty_median.png b/example/second_example_2d_cellgroup1_1gauss_nx20_pretty_median.png
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index 0000000..536b583
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diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_pretty_neff.txt b/example/second_example_2d_cellgroup1_1gauss_nx20_pretty_neff.txt
new file mode 100644
index 0000000..4c46f82
--- /dev/null
+++ b/example/second_example_2d_cellgroup1_1gauss_nx20_pretty_neff.txt
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diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_pretty_p.png b/example/second_example_2d_cellgroup1_1gauss_nx20_pretty_p.png
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index 0000000..1e134aa
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diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_pretty_p.txt b/example/second_example_2d_cellgroup1_1gauss_nx20_pretty_p.txt
new file mode 100644
index 0000000..668af80
--- /dev/null
+++ b/example/second_example_2d_cellgroup1_1gauss_nx20_pretty_p.txt
@@ -0,0 +1,6 @@
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diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_pretty_pdf2d.txt b/example/second_example_2d_cellgroup1_1gauss_nx20_pretty_pdf2d.txt
new file mode 100644
index 0000000..e72524f
--- /dev/null
+++ b/example/second_example_2d_cellgroup1_1gauss_nx20_pretty_pdf2d.txt
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diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_pretty_pdf2d_flat.txt b/example/second_example_2d_cellgroup1_1gauss_nx20_pretty_pdf2d_flat.txt
new file mode 100644
index 0000000..36236d9
--- /dev/null
+++ b/example/second_example_2d_cellgroup1_1gauss_nx20_pretty_pdf2d_flat.txt
@@ -0,0 +1,2501 @@
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diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_smallSP.png b/example/second_example_2d_cellgroup1_1gauss_nx20_smallSP.png
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index 0000000..6859dab
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diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_smallSP_convergence.png b/example/second_example_2d_cellgroup1_1gauss_nx20_smallSP_convergence.png
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index 0000000..2164175
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diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_smallSP_corner.png b/example/second_example_2d_cellgroup1_1gauss_nx20_smallSP_corner.png
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index 0000000..5a2e637
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diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_smallSP_corr.txt b/example/second_example_2d_cellgroup1_1gauss_nx20_smallSP_corr.txt
new file mode 100644
index 0000000..eb7f24f
--- /dev/null
+++ b/example/second_example_2d_cellgroup1_1gauss_nx20_smallSP_corr.txt
@@ -0,0 +1,2 @@
+mean	median	low	high	pval	error
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diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_smallSP_individuals.png b/example/second_example_2d_cellgroup1_1gauss_nx20_smallSP_individuals.png
new file mode 100644
index 0000000..f69105f
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diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_smallSP_median.png b/example/second_example_2d_cellgroup1_1gauss_nx20_smallSP_median.png
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index 0000000..9690aae
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diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_smallSP_neff.txt b/example/second_example_2d_cellgroup1_1gauss_nx20_smallSP_neff.txt
new file mode 100644
index 0000000..6ae11c8
--- /dev/null
+++ b/example/second_example_2d_cellgroup1_1gauss_nx20_smallSP_neff.txt
@@ -0,0 +1 @@
+3497.002046224516
diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_smallSP_p.png b/example/second_example_2d_cellgroup1_1gauss_nx20_smallSP_p.png
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index 0000000..95749b5
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diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_smallSP_p.txt b/example/second_example_2d_cellgroup1_1gauss_nx20_smallSP_p.txt
new file mode 100644
index 0000000..6f0fb35
--- /dev/null
+++ b/example/second_example_2d_cellgroup1_1gauss_nx20_smallSP_p.txt
@@ -0,0 +1,6 @@
+name	low	median	high
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+xy_muy0	0.5561197110700096	0.6164237942672419	0.6649155056744942
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+xy_corr0	0.09825865377213258	0.1791856024686124	0.26417444647941263
diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_smallSP_pdf2d.txt b/example/second_example_2d_cellgroup1_1gauss_nx20_smallSP_pdf2d.txt
new file mode 100644
index 0000000..5d682fb
--- /dev/null
+++ b/example/second_example_2d_cellgroup1_1gauss_nx20_smallSP_pdf2d.txt
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diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_smallSP_pdf2d_flat.txt b/example/second_example_2d_cellgroup1_1gauss_nx20_smallSP_pdf2d_flat.txt
new file mode 100644
index 0000000..32e59a9
--- /dev/null
+++ b/example/second_example_2d_cellgroup1_1gauss_nx20_smallSP_pdf2d_flat.txt
@@ -0,0 +1,401 @@
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diff --git a/example/second_example_2d_cellgroup1_1gauss_p.png b/example/second_example_2d_cellgroup1_1gauss_p.png
new file mode 100644
index 0000000..ebeb9c1
Binary files /dev/null and b/example/second_example_2d_cellgroup1_1gauss_p.png differ
diff --git a/example/second_example_2d_cellgroup1_1gauss_p.txt b/example/second_example_2d_cellgroup1_1gauss_p.txt
new file mode 100644
index 0000000..8c1e28d
--- /dev/null
+++ b/example/second_example_2d_cellgroup1_1gauss_p.txt
@@ -0,0 +1,6 @@
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diff --git a/example/second_example_2d_cellgroup1_1gauss_pdf2d.txt b/example/second_example_2d_cellgroup1_1gauss_pdf2d.txt
new file mode 100644
index 0000000..3e65280
--- /dev/null
+++ b/example/second_example_2d_cellgroup1_1gauss_pdf2d.txt
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diff --git a/example/second_example_2d_cellgroup1_1gauss_pdf2d_flat.txt b/example/second_example_2d_cellgroup1_1gauss_pdf2d_flat.txt
new file mode 100644
index 0000000..ba4a429
--- /dev/null
+++ b/example/second_example_2d_cellgroup1_1gauss_pdf2d_flat.txt
@@ -0,0 +1,2501 @@
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diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..867abe8
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,7 @@
+--extra-index-url https://obswww.unige.ch/~delisle
+numpy >=1.16
+matplotlib >=3.1.1
+pandas >=0.25.0
+scipy >=1.3.0
+corner >=2.0.0
+samsam >=0.1.2
diff --git a/setup.py b/setup.py
new file mode 100644
index 0000000..b808e00
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,51 @@
+# -*- coding: utf-8 -*-
+
+import os
+import re
+from setuptools import setup
+
+
+def get_version():
+  try:
+    f = open(os.path.join("baredSC", "_version.py"))
+  except EnvironmentError:
+    return None
+  for line in f.readlines():
+    mo = re.match("__version__ = '([^']+)'", line)
+    if mo:
+      ver = mo.group(1)
+      return ver
+  return None
+
+
+install_requires_py = ["numpy >=1.16",
+                       "matplotlib >=3.1.1",
+                       "pandas >=0.25.0",
+                       "scipy >=1.3.0",
+                       "corner >=2.0.0",
+                       "samsam >=0.1.2"
+                       ]
+
+
+setup(
+    name='baredSC',
+    version=get_version(),
+    author='Lucille Lopez-Delisle, Jean-Baptiste Delisle',
+    author_email='lucille.delisle@epfl.ch',
+    packages=['baredSC'],
+    scripts=['bin/baredSC_1d', 'bin/baredSC_2d',
+             'bin/combineMultipleModels_1d',
+             'bin/combineMultipleModels_2d'],
+    # TODO: url='http://baredsc.readthedocs.io',
+    url='https://github.com/lldelisle/baredSC',
+    license='GPLv3',
+    description='baredSC: Bayesian Approach to Retreive Expression Distribution of Single Cell',
+    long_description=open('README.md').read(),
+    long_description_content_type="text/markdown",
+    classifiers=[
+                'Intended Audience :: Science/Research',
+                'Topic :: Scientific/Engineering :: Bio-Informatics'],
+    install_requires=install_requires_py,
+    zip_safe=False,
+    python_requires='>=3.7.*, <4'
+)