Put all the reformatting of the ensemble output in a function and fixed the normalisation (c7204da0) · Commits · binary_c / binary_c-python

binarycpython/utils/grid.py

+22 −28

Original line number	Diff line number	Diff line
		@@ -67,6 +67,7 @@ from binarycpython.utils.functions import (
		recursive_change_key_to_float,
		custom_sort_dict,
		recursive_change_key_to_string,
		multiply_values_dict,
		)


		@@ -1017,27 +1018,34 @@ class Population:
		break

		# Extra ensemble result manipulation:
		# We need to reformat and multiply by a factor

		combined_output_dict['ensemble_results']['ensemble'] = self.format_ensemble_results(combined_output_dict['ensemble_results']['ensemble'])
		gc.collect()

		# Multiply the value by a factor:
		# if self.grid_options['ensemble_factor_in_probability_weighted_mass']:


		# Take into account that we run this on multiple cores
		combined_output_dict["_total_probability_weighted_mass_run"] = combined_output_dict["_total_probability_weighted_mass_run"]

		# Put the values back as object properties
		self.grid_results = combined_output_dict["results"]

		# Put Ensemble results
		self.grid_ensemble_results = combined_output_dict[
		"ensemble_results"
		] # Ensemble results are also passed as output from that dictionary

		# Add some metadata
		self.grid_ensemble_results["population_id"] = self.grid_options[
		self.grid_ensemble_results["metadata"] = {}
		self.grid_ensemble_results["metadata"]["population_id"] = self.grid_options[
		"_population_id"
		]
		self.grid_ensemble_results["metadata"][
		"total_probability_weighted_mass"
		] = combined_output_dict["_total_probability_weighted_mass_run"]
		self.grid_ensemble_results["metadata"]["factored_in_probability_weighted_mass"] = False
		if self.grid_options['ensemble_factor_in_probability_weighted_mass']:
		multiply_values_dict(self.grid_ensemble_results['ensemble'], 1/self.grid_ensemble_results["metadata"]['total_probability_weighted_mass'])
		self.grid_ensemble_results["metadata"]["factored_in_probability_weighted_mass"] = True

		#
		self.grid_options["_failed_count"] = combined_output_dict["_failed_count"]
		self.grid_options["_failed_prob"] = combined_output_dict["_failed_prob"]
		self.grid_options["_failed_systems_error_codes"] = list(
		@@ -1359,26 +1367,8 @@ class Population:

		# Extra ensemble result manipulation:
		# We need to reformat and multiply by a factor
		original_ensemble_results = ensemble_json["ensemble"]

		float_format_ensemble_results = recursive_change_key_to_float(original_ensemble_results)
		del original_ensemble_results
		ensemble_json["ensemble"] = self.format_ensemble_results(ensemble_json["ensemble"])
		gc.collect()

		# Then sort the dictionary
		sorted_ensemble_results = custom_sort_dict(float_format_ensemble_results)
		del float_format_ensemble_results
		gc.collect()

		# Then Change the keys back to a string but with a %g format.
		reformatted_ensemble_results = recursive_change_key_to_string(sorted_ensemble_results)
		del sorted_ensemble_results
		gc.collect()

		# Put back in the dictionary
		ensemble_json["ensemble"] = reformatted_ensemble_results
		gc.collect()

		else:
		# If we do not allow this, automatically we will export this to the data_dir, in
		# some formatted way
		@@ -1779,6 +1769,7 @@ class Population:
		# Import packages
		code_string += "import math\n"
		code_string += "import numpy as np\n"
		code_string += "from collections import OrderedDict\n"
		code_string += "from binarycpython.utils.distribution_functions import *\n"
		code_string += "from binarycpython.utils.spacing_functions import *\n"
		code_string += "from binarycpython.utils.useful_funcs import *\n"
		@@ -3514,6 +3505,9 @@ class Population:
		######
		# Setting up the grid variables

		# Make a normal dict of the options again
		options = dict(options)

		# Multiplicity
		self.add_grid_variable(
		name="multiplicity",
		@@ -3558,7 +3552,7 @@ class Population:
		dphasevol="dlnm1",
		precode='M_1 = np.exp(lnm1); options["M1"]=M_1',
		probdist="Moe_de_Stefano_2017_pdf({{{}, {}, {}}}, verbosity=self.grid_options['verbosity'])['total_probdens'] if multiplicity == 1 else 1".format(
		str(options)[1:-1], "'multiplicity': multiplicity", "'M1': M_1"
		str(dict(options))[1:-1], "'multiplicity': multiplicity", "'M1': M_1"
		),
		)

		@@ -3827,7 +3821,7 @@ eccentricity3=0
		# and add some keys to it:

		probdist_addition = "Moe_de_Stefano_2017_pdf({{{}, {}, {}, {}, {}, {}, {}, {}, {}, {}, {}, {}}}, verbosity=self.grid_options['verbosity'])['total_probdens']".format(
		str(options)[1:-1],
		str(dict(options))[1:-1],
		'"multiplicity": multiplicity',
		'"M1": M_1',
		'"M2": M_2',

binarycpython/utils/grid_options_defaults.py

+3 −1

Original line number	Diff line number	Diff line
		@@ -44,6 +44,7 @@ grid_options_defaults_dict = {
		"_set_ms_grid": False, # Whether the M&S grid has been loaded
		"run_zero_probability_system": True, # Whether to run the zero probability systems
		"_zero_prob_stars_skipped": 0,
		"ensemble_factor_in_probability_weighted_mass": False, # Whether to multiply the ensemble results by 1/probability_weighted_mass
		##########################
		# Execution log:
		##########################
		@@ -484,7 +485,8 @@ grid_options_descriptions = {
		"max_queue_size": "Maximum size of the queue that is used to feed the processes. Don't make this too big! Default: 1000. Input: int",
		"_set_ms_grid": "Internal flag whether the M&S grid has been loaded",
		"run_zero_probability_system": "Whether to run the zero probability systems. Default: True. Input: boolean",
		"_zero_prob_stars_skipped": "Internal counter to track how many systems are skipped because they have 0 probability"
		"_zero_prob_stars_skipped": "Internal counter to track how many systems are skipped because they have 0 probability",
		"ensemble_factor_in_probability_weighted_mass": "Flag to multiply all the ensemble results with 1/probability_weighted_mass",
		}

		###