Delete unused methods

Author: aspeake

scout/ecm_prep.py

@@ -4529,7 +4529,7 @@ def fill_mkts(self, msegs, msegs_cpl, convert_data, tsv_data_init, opts,
                     if self.handyvars.full_dat_out[adopt_scheme]:
                         # Populate detailed breakout information for measure
                         self.breakout_mseg(
-                            self, mskeys, contrib_mseg_key, adopt_scheme, opts, brk_in_dat)
+                            mskeys, contrib_mseg_key, adopt_scheme, opts, brk_in_dat)
 
                     # Record contributing microsegment data needed for ECM
                     # competition in the analysis engine
@@ -9765,159 +9765,6 @@ def rand_list_gen(self, distrib_info, nsamples):
 
         return rand_list
 
-    def create_perf_dict(self, msegs):
-        """Create dict to fill with updated measure performance information.
-
-        Note:
-            Given a measure's applicable climate zone, building type,
-            structure type, fuel type, and end use, create a dict of zeros
-            with a hierarchy that is defined by these measure properties.
-
-        Args:
-            msegs (dict): Baseline microsegment stock and energy use
-                information to use in validating categorization of
-                measure performance information.
-
-        Returns:
-            Empty dictionary to fill with EnergyPlus-based performance
-            information broken down by climate zone, building type/vintage,
-            fuel type, and end use.
-        """
-        # Initialize performance dict
-        perf_dict_empty = {"primary": None, "secondary": None}
-        # Create primary dict structure from baseline market properties
-        perf_dict_empty["primary"] = self.create_nested_dict(
-            msegs, "primary")
-
-        # Create secondary dict structure from baseline market properties
-        # (if needed)
-        if isinstance(self.end_use, dict):
-            perf_dict_empty["secondary"] = self.create_nested_dict(
-                msegs, "secondary")
-
-        return perf_dict_empty
-
-    def create_nested_dict(self, msegs, mseg_type):
-        """Create a nested dictionary based on a pre-defined branch structure.
-
-        Note:
-            Create a nested dictionary with a structure that is defined by a
-            measure's applicable baseline market, with end leaf node values set
-            to zero.
-
-        Args:
-            msegs (dict): Baseline microsegment stock and energy use
-                information to use in validating categorization of
-                measure performance information.
-            mseg_type (string): Primary or secondary microsegment type flag.
-
-        Returns:
-            Nested dictionary of zeros with desired branch structure.
-        """
-        # Initialize output dictionary
-        output_dict = {}
-        # Establish levels of the dictionary key hierarchy from measure's
-        # applicable baseline market information
-        keylevels = [
-            self.climate_zone, self.bldg_type, self.fuel_type[mseg_type],
-            self.end_use[mseg_type], self.structure_type]
-        # Find all possible dictionary key chains from the above key level
-        # info.
-        dict_keys = list(itertools.product(*keylevels))
-        # Remove all natural gas cooling key chains (EnergyPlus output
-        # files do not include a column for natural gas cooling)
-        dict_keys = [x for x in dict_keys if not (
-            'natural gas' in x and 'cooling' in x)]
-
-        # Use an input dictionary with valid baseline microsegment information
-        # to check that each of the microsegment key chains generated above is
-        # valid for the current measure; if not, remove each invalid key chain
-        # from further operations
-
-        # Initialize a list of valid baseline microsegment key chains for the
-        # measure
-        dict_keys_fin = []
-        # Loop through the initial set of candidate key chains generated above
-        for kc in dict_keys:
-            # Copy the input dictionary containing valid key chains
-            dict_check = copy.deepcopy(msegs)
-            # Loop through all keys in the candidate key chain and move down
-            # successive levels of the input dict until either the end of the
-            # key chain is reached or a key is not found in the list of valid
-            # keys for the current input dict level. In the former case, the
-            # resultant dict will point to all technologies associated with the
-            # current key chain (e.g., ASHP, LFL, etc.) If none of these
-            # technologies are found in the list of technologies covered by the
-            # measure, the key chain is deemed invalid
-            for ind, key in enumerate(kc):
-                # If key is found in the list of valid keys for the current
-                # input microsegment dict level, move on to next level in the
-                # dict; otherwise, break the current loop
-                if key in dict_check.keys():
-                    dict_check = dict_check[key]
-                    # In the case of heating or cooling end uses, an additional
-                    # 'technology type' key must be accounted for ('supply' or
-                    # 'demand')
-                    if key in ['heating', 'cooling']:
-                        dict_check = \
-                            dict_check[self.technology_type[mseg_type]]
-                else:
-                    break
-
-            # If any of the technology types listed in the measure definition
-            # are found in the keys of the dictionary yielded by the above
-            # loop, add the key chain to the list that is used to define the
-            # final nested dictionary output (e.g., the key chain is valid)
-            if any([x in self.technology[mseg_type]
-                   for x in dict_check.keys()]):
-                dict_keys_fin.append(kc)
-
-        # Loop through each of the valid key chains and create an
-        # associated path in the dictionary, terminating with a zero value
-        # to be updated in a subsequent routine with EnergyPlus output data
-        for kc in dict_keys_fin:
-            current_level = output_dict
-            for ind, elem in enumerate(kc):
-                if elem not in current_level and (ind + 1) != len(kc):
-                    current_level[elem] = {}
-                elif elem not in current_level and (ind + 1) == len(kc):
-                    current_level[elem] = 0
-                current_level = current_level[elem]
-
-        return output_dict
-
-    def build_array(self, eplus_coltyp, files_to_build):
-        """Assemble EnergyPlus data from one or more CSVs into a record array.
-
-        Args:
-            eplus_coltypes (list): Expected EnergyPlus variable data types.
-            files_to_build (CSV objects): CSV files of EnergyPlus energy
-                consumption information under measure and baseline cases.
-
-        Returns:
-            Structured array of EnergyPlus energy savings information for the
-            Measure.
-        """
-        # Loop through CSV file objects and import/add to record array
-        for ind, f in enumerate(files_to_build):
-            # Read in CSV file to array
-            eplus_file = numpy.genfromtxt(f, names=True, dtype=eplus_coltyp,
-                                          delimiter=",", missing_values='')
-            # Find only those rows in the array that represent
-            # completed simulation runs for the measure of interest
-            eplus_file = eplus_file[(eplus_file[
-                'measure'] == self.energy_efficiency['EnergyPlus file']) |
-                (eplus_file['measure'] == 'none') &
-                (eplus_file['status'] == 'completed normal')]
-            # Initialize or add to a master array that covers all CSV data
-            if ind == 0:
-                eplus_perf_array = eplus_file
-            else:
-                eplus_perf_array = \
-                    numpy.concatenate((eplus_perf_array, eplus_file))
-
-        return eplus_perf_array
-
     def breakout_mseg(self, mskeys, contrib_mseg_key, adopt_scheme, opts, input_data):
         """Record mseg contributions to breakouts by region/bldg/end use/fuel.
 
@@ -12817,9 +12664,6 @@ def prepare_measures(measures, convert_data, msegs, msegs_cpl, handyvars,
             base_dir, handyvars, handyfiles, opts_dict, **m) for m in measures]
         logger.info("Measure initialization complete")
 
-        # Fill in EnergyPlus-based performance information for Measure objects
-        # with a 'From EnergyPlus' flag in their 'energy_efficiency' attribute
-
         # Handle a superfluous 'undefined' key in the ECM performance field that is
         # generated by the 'Add ECM' web form in certain cases *** NOTE: WILL
         # FIX IN FUTURE UI VERSION ***