posydon.popsyn

posydon.popsyn.GRB

posydon.popsyn.GRB.get_GRB_properties(df, GRB_efficiency, GRB_beaming, E_GRB_iso_min=0.)

Compute GRB properties for a given binary population.

posydon.popsyn.analysis

Module for analyzing binary population simulation results.

posydon.popsyn.analysis.get_subsets(path, find_n=None, select_oneline=None, select_history=None, select_columns=None, verbose=True)

Get the oneline and history subsets for a selection of binaries.

You select binaries from an HDF5 population by providing a function that operates either on oneline or history and returns a boolean mask in- dicating which rows correspond to the binaries of interest. E.g.,

def find_no_end(df):

return df[“event_f”] != “END”

can be used to find binaries that ended abruptly, based on the information in the oneline table. Therefore, we set select_oneline=find_no_end. If no selector has been set, all binaries are selected (combine this with the select_columns argument to get a column subset of the data.)

Parameters:

• path (str) – The fullpath of the HDF5 file.

• find_n (int or None) – The number of binaries to select (at most) or None to select all.

• select_oneline (function or None) – The function to operate on chunks of the oneline table.

• select_history (function or None) – The function to operate on chunks of the oneline table.

• select_columns (None or str or array) – If None, all columns will be returned. If a string, only those columns with name containing this string will be returned. An array of strings can be used for multiple such patterns.

Returns:

The subsets of the dataframes with the data for the selected binaries.

Return type:

oneline, history

posydon.popsyn.analysis.report_columns(path)

Print the columns in the oneline and history keys in an HDF5 file.

Parameters:

path (str) – The fullpath of the HDF5 file.

posydon.popsyn.binarypopulation

posydon.popsyn.defaults

Default binary population parameters.

posydon.popsyn.independent_sample

Generate the initial parameters for a binary population.

posydon.popsyn.independent_sample.binary_fraction_value(binary_fraction_const=1, binary_fraction_scheme='const', m1=None, **kwargs)

Getting the binary fraction depending on the scheme. The two possible option are a constant binary fraction and a binary fraction based on the values given in Moe and Di Stefano (2017).

posydon.popsyn.independent_sample.generate_eccentricities(number_of_binaries=1, eccentricity_scheme='thermal', **kwargs)

Generate random eccentricities.

Use the scheme defined in this particular instance of BinaryPopulation.

Parameters:

• number_of_binaries (int) – Number of binaries that require randomly sampled orbital separations

• eccentricity_scheme (string) – Distribution from which eccentricities are randomly drawn

• **kwargs (dictionary) – kwargs from BinaryPopulation class

Returns:

eccentricities – Randomly drawn eccentricities

Return type:

ndarray of floats

posydon.popsyn.independent_sample.generate_independent_samples(orbital_scheme='period', **kwargs)

Randomly generate a population of binaries at ZAMS.

Parameters:

• orbital_scheme (str (default: 'period')) – The scheme to use to get either orbital periods or separations

• **kwargs (dictionary) – kwargs from BinaryPopulation class

Returns:

• orbital_scheme_set (ndarray of floats) – Randomly drawn orbital separations/periods depending on the scheme

• eccentricity_set (ndarray of floats) – Randomly drawn eccentricities

• m1_set (ndarray of floats) – Randomly drawn primary masses

• m2_set (ndarray of floats) – Randomly drawn secondary masses

posydon.popsyn.independent_sample.generate_orbital_periods(primary_masses, number_of_binaries=1, orbital_period_min=0.35, orbital_period_max=10**3.5, orbital_period_scheme='Sana+12_period_extended', **kwargs)

Randomaly generate orbital periods for a sample of binaries.

posydon.popsyn.independent_sample.generate_orbital_separations(number_of_binaries=1, orbital_separation_min=5, orbital_separation_max=1e5, log_orbital_separation_mean=None, log_orbital_separation_sigma=None, orbital_separation_scheme='log_uniform', **kwargs)

Generate random orbital separations.

Use the scheme defined in this particular instance of BinaryPopulation.

Parameters:

• number_of_binaries (int) – Number of binaries that require randomly sampled orbital separations

• orbital_separation_min (float) – Minimum orbital separation in solar radii

• orbital_separation_max (float) – Maximum orbital separation in solar radii

• log_orbital_separation_mean (float) – Mean of the lognormal distribution.

• log_orbital_separation_sigma (float) – Standard deviation of the lorgnormal distribution.

• orbital_separation_scheme (string) – Distribution from which the orbital separations are randomly drawn

Returns:

orbital_separations – Randomly drawn orbital separations

Return type:

ndarray of floats

posydon.popsyn.independent_sample.generate_primary_masses(number_of_binaries=1, primary_mass_min=7, primary_mass_max=120, primary_mass_scheme='Salpeter', **kwargs)

Generate random primary masses.

Use the scheme defined in this particular instance of BinaryPopulation.

Parameters:

• number_of_binaries (int) – Number of binaries that require randomly sampled orbital separations

• primary_mass_min (float) – Minimum primary mass

• primary_mass_max (float) – Maximum primary mass

• primary_mass_scheme (string) – Distribution from which the primary masses are randomly drawn

Returns:

primary_masses – Randomly drawn primary masses

Return type:

ndarray of floats

posydon.popsyn.independent_sample.generate_secondary_masses(primary_masses, number_of_binaries=1, secondary_mass_min=0.35, secondary_mass_max=120, secondary_mass_scheme='flat_mass_ratio', **kwargs)

Generate random secondary masses.

Use the scheme defined in this particular instance of BinaryPopulation.

Parameters:

• primary_masses (ndarray of floats) – Previously drawn primary masses

• number_of_binaries (int) – Number of binaries that require randomly sampled orbital separations

• secondary_mass_min (float) – Minimum secondary mass

• secondary_mass_max (float) – Maximum secondary mass

• secondary_mass_scheme (string) – Distribution from which the secondary masses are randomly drawn

Returns:

secondary_masses – Randomly drawn secondary masses

Return type:

ndarray of floats

posydon.popsyn.io

Handle I/O operations for the population synthesis code.

posydon.popsyn.io.binarypop_kwargs_from_ini(path, verbose=False)

Convert an inifile into kwargs for the BinaryPopulation class.

Parameters:

• path (str or list like) – Path to inifile. If multiple files are given, duplicate args are overwritten (stacked) first to last.

• verbose (bool) – Print helpful info.

Returns:

parser_dict – The inifile converted to the kwargs.

Return type:

<class, dict>

posydon.popsyn.io.clean_binary_history_df(binary_df, extra_binary_dtypes_user=None, extra_S1_dtypes_user=None, extra_S2_dtypes_user=None)

Take a posydon binary history DataFrame from the BinaryStar.to_df method and clean the data for saving by setting Data Types of the columns explicitly.

Parameters:

• binary_df (DataFrame) – A pandas Dataframe containing binary history

• extra_binary_dtypes_user (dict, optional) – A dictionary with extra column names as keys, and their associated data types as values.

• extra_S1_dtypes_user (dict, optional) – Same as above, but only for star 1.

• extra_S2_dtypes_user (dict, optional) – Same as above, but only for star 2.

Returns:

binary_df – A cleaned binary history ready for saving to HDF.

Return type:

DataFrame

posydon.popsyn.io.clean_binary_oneline_df(oneline_df, extra_binary_dtypes_user=None, extra_S1_dtypes_user=None, extra_S2_dtypes_user=None)

Take a posydon binary oneline DataFrame from the BinaryStar.to_oneline_df method and clean the data for saving by setting Data Types of the columns explicitly.

This method is similar to clean_binary_history_df since they have many overalapping columns, with a few extras and different naming.

Note: there may be edge cases not handed if new scalar_names are added.

Parameters:

• binary_df (DataFrame) – A pandas Dataframe containing binary history

• extra_binary_dtypes_user (dict, optional) – A dictionary with extra column names as keys, and their associated data types as values.

• extra_S1_dtypes_user (dict, optional) – Same as above, but only for star 1.

• extra_S2_dtypes_user (dict, optional) – Same as above, but only for star 2.

Returns:

binary_df – A cleaned binary history ready for saving to HDF.

Return type:

DataFrame

posydon.popsyn.io.create_merge_script_text(ini_file)

posydon.popsyn.io.create_run_script_text(ini_file)

posydon.popsyn.io.parse_inifile(path, verbose=False)

Parse an inifile for evolving binary populations.

Parameters:

• path (str or list like) – Path to inifile. If multiple files are given, duplicate args are overwritten (stacked) first to last.

• verbose (bool) – Print helpful info.

Returns:

parser – An instance of ConfigParser.

Return type:

<class, ConfigParser>

posydon.popsyn.io.simprop_kwargs_from_ini(path, verbose=False)

Convert an inifile into kwargs for the SimulationProperties class.

Parameters:

• path (str or list like) – Path to inifile. If multiple files are given, duplicate args are overwritten (stacked) first to last.

• verbose (bool) – Print helpful info.

Returns:

parser_dict – The inifile converted to the kwargs.

Return type:

<class, dict>

posydon.popsyn.normalized_pop_mass

Compute the underlying stellar population mass for a given simulation.

posydon.popsyn.normalized_pop_mass.initial_total_underlying_mass(simulated_mass=None, simulated_mass_single=None, simulated_mass_binaries=None, f_bin=0.7, **kwargs)

Compute the initial total mass of the population.

Parameters:

simulated_mass, simulated_mass_single, simulated_mass_binariesfloat

Total simulated mass, simulated mass of binary systems and simulated mass of single stars, respectively.

f_bin: float

The binary fraction of your population in “nature”. If not provided, the default value is set to 0.7

primary_mass_min: float

minimum initial mass of the primary star

primary_mass_max: float

maximum initial mass of the primary star

binary_fraction_const: float

Binary fraction used in the simulations

primary_mass_scheme: string

Kroupa2001 or Salpeter options

secondary_mass_scheme: string

mass ratio distribution

———-

:returns: * **underlying_total_mass (float) – The underlying total mass of the population: float**

• f_corr_single_stars, f_corr_binaries; float – Correction factors for singles and binaries, respectively.

posydon.popsyn.rate_calculation

posydon.popsyn.rate_calculation.get_comoving_distance_from_redshift(z)

Compute the comoving distance from redshift.

Parameters:

z (double) – Cosmological redshift.

Returns:

Comoving distance in Mpc corresponding to the redhisft z.

Return type:

double

posydon.popsyn.rate_calculation.get_cosmic_time_from_redshift(z)

Compute the cosmic time from redshift.

Parameters:

z (double) – Cosmological redshift.

Returns:

Return age of the cosmic time in Gyr given the redshift z.

Return type:

double

posydon.popsyn.rate_calculation.get_redshift_bin_centers(delta_t)

Compute the redshift bin centers.

Parameters:

delta_t (double) – Time interval in Myr.

Returns:

Redshift bin centers.

Return type:

array doubles

posydon.popsyn.rate_calculation.get_redshift_bin_edges(delta_t)

Compute the redshift bin edges.

Parameters:

delta_t (double) – Time interval in Myr.

Returns:

Redshift bin edges.

Return type:

array doubles

posydon.popsyn.rate_calculation.get_redshift_from_cosmic_time(t_cosm)

Compute the cosmological redshift given the cosmic time.

Parameters:

t_cosm (float, ndarray of floats) – Cosmic time(s) for which you want to know the redhisft.

Returns:

Cosmolgocial redshift(s) corresponding to t_cosm.

Return type:

ndarray of floats

Note

• The function uses the interpolator redshift_from_cosmic_time_interpolator,

which is created each time the function is called. z_at_value from astropy can be used for single values, but it is too slow for arrays.

posydon.popsyn.rate_calculation.get_shell_comoving_volume(z_hor_i, z_hor_f, sensitivity='infinite')

Compute comoving volume corresponding to a redshift shell.

Parameters:

• z_hor_i (double) – Cosmological redshift. Lower bound of the integration.

• z_hor_f (double) – Cosmological redshift. Upper bound of the integration.

• sensitivity (string) – hoose which GW detector sensitivity you want to use. At the moment only ‘infinite’ is available, i.e. p_det = 1.

Returns:

Retruns the comoving volume between the two shells z_hor_i and z_hor_f in Gpc^3.

Return type:

double

posydon.popsyn.rate_calculation.redshift_from_cosmic_time_interpolator()

Interpolator to compute the cosmological redshift given the cosmic time.

Returns:

Returns the trained SciPy CubicSpline interpolator object.

Return type:

CubicSpline object

posydon.popsyn.sample_from_file

Get the initial parameters for a binary population.

posydon.popsyn.sample_from_file.get_kick_samples_from_file(**kwargs)

Read a kicks for population of binaries from a file.

Parameters:

**kwargs (dictionary) – kwargs from BinaryPopulation class, which should contain read_samples_from_file

Returns:

• s1_natal_kick_array_set (ndarray of floats) – natal kick array for the primary star containing: kick velocity, azimuthal angle, polar angle, mean anomaly

• s2_natal_kick_array_set (ndarray of floats) – natal kick array for the secondary star containing: kick velocity, azimuthal angle, polar angle, mean anomaly

posydon.popsyn.sample_from_file.get_samples_from_file(orbital_scheme='', **kwargs)

Read a population of binaries at ZAMS from a file.

Parameters:

• orbital_scheme (str) – Scheme to get the orbit: ‘separation’, ‘period’

• **kwargs (dictionary) – kwargs from BinaryPopulation class, which should contain read_samples_from_file

Returns:

• orbital_scheme_set (ndarray of floats) – orbital separations/periods depending on the scheme

• eccentricity_set (ndarray of floats) – eccentricities

• m1_set (ndarray of floats) – primary masses

• m2_set (ndarray of floats) – secondary masses

posydon.popsyn.sample_from_file.infer_key(available_keys=[], allowed_keys=[])

Infer key from list of allowed keys.

Parameters:

• available_keys (iterable object of str, e.g. list of str) – Collection of available keys.

• allowed_keys (iterable object of str, e.g. list of str) – Collection of allowed keys.

Returns:

key – The first matched key.

Return type:

str

posydon.popsyn.selection_effects

Simple utility for generating detection weights

Uses grid of detection probabilities to estimate detection probabilities

Anticipates data as Pandas dataframe with series [‘m1’, ‘q’, ‘z’, ‘chieff’]

class posydon.popsyn.selection_effects.KNNmodel(grid_path, sensitivity_key, verbose=False)

Bases: object

K-nearest neighbor model that instantiates based on detection probability grid

When instantiating, must supply path to the grid, and key that represents GW network and sensitivity.

Instantiates KNNmodel class and trains the KNN.

grid_pathstring

Path to grid of detection probabilities.

sensitivity_keystring

GW detector sensitivity and network configuration you want to use,

see arXiv:1304.0670v3

detector sensitivities are taken from: https://dcc.ligo.org/LIGO-T2000012-v2/public

available sensitivity keys (for Hanford, Livingston, Virgo network):

‘O3actual_H1L1V1’ : aligo_O3actual_H1.txt, aligo_O3actual_L1.txt, avirgo_O3actual.txt ‘O4low_H1L1V1’ : aligo_O4low.txt, aligo_O4low.txt, avirgo_O4high_NEW.txt ‘O4high_H1L1V1’ : aligo_O4high.txt, aligo_O4high.txt, avirgo_O4high_NEW.txt ‘design_H1L1V1’ : AplusDesign.txt, AplusDesign.txt, avirgo_O5high_NEW.txt

detection probabilities are calculated using the IMRPhenomXHM approximant with a network SNR threshold of 10

verboseboolean

Adds verbosity.

static normalize(x, xmin, xmax, a=0, b=1)

normalizes data on range [a,b]

predict_pdet(data, verbose=False)

Gives relative weight to each system in data based on its proximity to the points on the grid. Each system in data should have a primary mass m1, mass ratio q, redshift z, and effective spin chieff This function will determine detection probabilities using nearest neighbor algorithm in [log(m1), q, log(z), chieff] space Need to specify bounds (based on the trained grid) so that the grid and data get normalized properly

dataPandas dataframe

Data you wish to predict detection probabilities for. Required series in the dataframe:

‘m1’ : primary source-frame mass ‘q’ : mass ratio (secondary mass/primary mass) ‘z’ : redshift of merger ‘chieff’ : effective inspiral spin

verboseboolean

Adds verbosity.

posydon.popsyn.star_formation_history

posydon.popsyn.synthetic_population

posydon.popsyn.transient_select_funcs