black format (7f8fa34f) · Commits · binary_c / binary_c-python

binarycpython/utils/custom_logging_functions.py

+2 −4

Original line number	Diff line number	Diff line
		@@ -213,9 +213,7 @@ def return_compilation_dict(verbose=0):
		libs = defaults[
		"libs"
		] # = ($ENV{BINARY_GRID2_LIBS} // $defaults{libs}).' '.($ENV{BINARY_GRID2_EXTRALIBS}//'');
		ccflags = defaults[
		"ccflags"
		] # = $ENV{BINARY_GRID2_CCFLAGS}
		ccflags = defaults["ccflags"] # = $ENV{BINARY_GRID2_CCFLAGS}
		# // ($defaults{ccflags}) . ($ENV{BINARY_GRID2_EXTRACCFLAGS} // '');

		# you must define _SEARCH_H to prevent it being loaded twice

binarycpython/utils/distribution_functions.py

+44 −28

Original line number	Diff line number	Diff line
		@@ -9,9 +9,7 @@ generate probability distributions for sampling populations
		import math
		import numpy as np

		from binarycpython.utils.useful_funcs import (
		calc_period_from_sep,
		)
		from binarycpython.utils.useful_funcs import calc_period_from_sep

		###
		# File containing probability distributions
		@@ -27,6 +25,8 @@ from binarycpython.utils.useful_funcs import (
		LOG_LN_CONVERTER = 1.0 / math.log(10.0)

		distribution_constants = {} # To store the constants in


		def prepare_dict(global_dict, list_of_sub_keys):
		"""
		Function that makes sure that the global dict is prepared to have a value set there
		@@ -44,7 +44,6 @@ def prepare_dict(global_dict, list_of_sub_keys):
		internal_dict_value = internal_dict_value[k]



		def flat():
		"""
		Dummy distribution function that returns 1
		@@ -517,6 +516,7 @@ def sana12(M1, M2, a, P, amin, amax, x0, x1, p):

		return res


		def Izzard2012_period_distribution(P, M1, log10Pmin=1):
		"""
		period distribution which interpolates between
		@@ -546,21 +546,27 @@ def Izzard2012_period_distribution(P, M1, log10Pmin=1):
		# Calculate the normalisations
		# need to normalize the distribution for this mass
		# (and perhaps secondary mass)
		prepare_dict(distribution_constants, ['Izzard2012', M1])
		if not distribution_constants['Izzard2012'][M1].get(log10Pmin):
		distribution_constants['Izzard2012'][M1][log10Pmin] = 1 # To prevent this loop from going recursive
		prepare_dict(distribution_constants, ["Izzard2012", M1])
		if not distribution_constants["Izzard2012"][M1].get(log10Pmin):
		distribution_constants["Izzard2012"][M1][
		log10Pmin
		] = 1 # To prevent this loop from going recursive
		N = 200.0 # Resolution for normalisation. I hope 1000 is enough
		dlP = (10.0 - log10Pmin) / N
		C = 0 # normalisation const.
		for lP in np.arange(log10Pmin, 10, dlP):
		C += dlP * Izzard2012_period_distribution(10 ** lP, M1, log10Pmin)

		distribution_constants["Izzard2012"][M1][log10Pmin] = 1.0 / C
		print(
		"Normalization constant for Izzard2012 M={} (log10Pmin={}) is\
		{}\n".format(
		M1, log10Pmin, distribution_constants["Izzard2012"][M1][log10Pmin]
		)
		)

		distribution_constants['Izzard2012'][M1][log10Pmin] = 1.0/C;
		print("Normalization constant for Izzard2012 M={} (log10Pmin={}) is\
		{}\n".format(M1, log10Pmin, distribution_constants['Izzard2012'][M1][log10Pmin]))

		lP = math.log10(P); # log period
		lP = math.log10(P)
		# log period

		# # fits
		mu = interpolate_in_mass_izzard2012(M1, -17.8, 5.03)
		@@ -570,16 +576,24 @@ def Izzard2012_period_distribution(P, M1, log10Pmin=1):
		g = 1.0 / (1.0 + 1e-30 ** (lP - nu))

		lPmu = lP - mu
		print("M={} ({}) P={} : mu={} sigma={} K={} nu={} norm=%g\n".format(
		Mwas, M1, P, mu, sigma, K, nu))
		print(
		"M={} ({}) P={} : mu={} sigma={} K={} nu={} norm=%g\n".format(
		Mwas, M1, P, mu, sigma, K, nu
		)
		)

		# print "FUNC $distdata{Izzard2012}{$M}{$log10Pmin} * (exp(- (x-$mu)*2/(2.0$sigma$sigma) ) + $K/MAX(0.1,$lP)) $g;\n";

		if ((lP < log10Pmin) or (lP > 10.0)):
		if (lP < log10Pmin) or (lP > 10.0):
		return 0

		else:
		return distribution_constants['Izzard2012'][M1][log10Pmin] * (math.exp(- lPmu * lPmu / (2.0 * sigma * sigma)) + K/max(0.1, lP)) * g;
		return (
		distribution_constants["Izzard2012"][M1][log10Pmin]
		* (math.exp(-lPmu * lPmu / (2.0 * sigma * sigma)) + K / max(0.1, lP))
		* g
		)


		def interpolate_in_mass_izzard2012(M, high, low):
		"""
		@@ -592,7 +606,9 @@ def interpolate_in_mass_izzard2012(M, high, low):
		log_interpolation = False

		if log_interpolation:
		return (high-low)/(math.log10(16.3)-math.log10(1.15)) * (math.log10(M)-math.log10(1.15)) + low
		return (high - low) / (math.log10(16.3) - math.log10(1.15)) * (
		math.log10(M) - math.log10(1.15)
		) + low
		else:
		return (high - low) / (16.3 - 1.15) * (M - 1.15) + low

binarycpython/utils/grid.py

+46 −48

Original line number	Diff line number	Diff line
		@@ -16,6 +16,7 @@ import argparse
		import importlib.util

		from pathos.helpers import mp as pathos_multiprocess

		# from pathos.multiprocessing import ProcessingPool as Pool
		from pathos.pools import _ProcessPool as Pool

		@@ -53,7 +54,7 @@ import binary_c_python_api
		# that is stored into a python object. rather its just written to stdout


		class Population():
		class Population:
		"""
		Population Object. Contains all the necessary functions to set up, run and process a
		population of systems
		@@ -68,9 +69,7 @@ class Population():
		self.cleaned_up_defaults = self.cleanup_defaults()

		# Different sections of options
		self.bse_options = (
		{}
		) # bse_options is just empty.
		self.bse_options = {} # bse_options is just empty.
		# Setting stuff will check against the defaults to see if the input is correct.
		self.grid_options = grid_options_defaults_dict.copy()
		self.custom_options = {}
		@@ -136,7 +135,9 @@ class Population():
		else:
		print(
		"!! Key doesnt match previously known parameter: \
		adding: {}={} to custom_options".format(key, kwargs[key])
		adding: {}={} to custom_options".format(
		key, kwargs[key]
		)
		)
		self.custom_options[key] = kwargs[key]

		@@ -680,7 +681,6 @@ class Population():
		if self.grid_options["parse_function"]:
		self.grid_options["parse_function"](self, out)


		def evolve_population(self):
		"""
		Function to evolve populations. This is the main function. Handles the setting up, evolving
		@@ -1252,10 +1252,7 @@ class Population():
		###################################################

		def write_binary_c_calls_to_file(
		self,
		output_dir=None,
		output_filename=None,
		include_defaults=False
		self, output_dir=None, output_filename=None, include_defaults=False
		):
		"""
		Function that loops over the gridcode and writes the generated parameters to a file.
		@@ -1389,4 +1386,5 @@ class Population():
		# Function to join the result dictionaries
		# """


		################################################################################################

binarycpython/utils/plot_functions.py

+2 −3

Original line number	Diff line number	Diff line
		@@ -21,9 +21,7 @@ import numpy as np

		# import matplotlib.pyplot as plt

		from binarycpython.utils.functions import (
		output_lines,
		)
		from binarycpython.utils.functions import output_lines
		from binarycpython.utils.run_system_wrapper import run_system
		from binarycpython.utils.custom_logging_functions import binary_c_log_code

		@@ -67,6 +65,7 @@ def dummy():
		"""Placeholder"""
		pass


		def parse_function_hr_diagram(output):
		"""
		Parsing function for the HR plotting routine

docs/source/conf.py

+4 −4

Original line number	Diff line number	Diff line
		@@ -40,10 +40,10 @@ author = "David Hendriks, Robert Izzard, Jeff Andrews"
		extensions = [
		"sphinx.ext.autodoc",
		"sphinx.ext.doctest",
		'sphinx.ext.todo',
		'sphinx.ext.coverage',
		'sphinx.ext.viewcode',
		'sphinx.ext.napoleon',
		"sphinx.ext.todo",
		"sphinx.ext.coverage",
		"sphinx.ext.viewcode",
		"sphinx.ext.napoleon",
		"hawkmoth",
		"m2r",
		]