From c7204da0eb73e455e270fddd7852d406ed63a927 Mon Sep 17 00:00:00 2001
From: David Hendriks <davidhendriks93@gmail.com>
Date: Wed, 30 Jun 2021 00:24:00 +0100
Subject: [PATCH] Put all the reformatting of the ensemble output in a function
and fixed the normalisation
---
binarycpython/utils/grid.py | 50 +++++++++-----------
binarycpython/utils/grid_options_defaults.py | 4 +-
2 files changed, 25 insertions(+), 29 deletions(-)
diff --git a/binarycpython/utils/grid.py b/binarycpython/utils/grid.py
index 577b7ba5c..4c9c1232e 100644
--- a/binarycpython/utils/grid.py
+++ b/binarycpython/utils/grid.py
@@ -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
- gc.collect()
-
- # Then sort the dictionary
- sorted_ensemble_results = custom_sort_dict(float_format_ensemble_results)
- del float_format_ensemble_results
+ ensemble_json["ensemble"] = self.format_ensemble_results(ensemble_json["ensemble"])
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',
diff --git a/binarycpython/utils/grid_options_defaults.py b/binarycpython/utils/grid_options_defaults.py
index fee8212e0..c994fae9d 100644
--- a/binarycpython/utils/grid_options_defaults.py
+++ b/binarycpython/utils/grid_options_defaults.py
@@ -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",
}
###
--
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