snsim¶

Init file of snsim package.

The snsim module is design to simulate supernovae lightcurve in a survey defined by an observations data base. Moreover the simulation can use a host file to simulate a velocity field.

The package use sncosmo.

Github repository : https://github.com/bcarreres/snsim

Submodules¶

Classes¶

`Simulator`	Simulation class using a config file config.yml.
`SimSample`	Class to store simulated SN sample.
`SurveyObs`	This class deals with the observations of the survey.
`SnHosts`	Class containing the SN Host parameters.

Package Contents¶

class snsim.Simulator(param_dic, print_config=False)[source]¶

Simulation class using a config file config.yml.

Parameters:: param_dic (dict / str) – The configuration yaml file / The dictionnary containing all simulation parameters.

Notes

yaml file format :

data :

write_path: ‘/PATH/TO/OUTPUT’
sim_name: ‘NAME OF SIMULATION’
write_format: ‘format’ or [‘format1’,’format2’]

survey_config:

survey_file: ‘/PATH/TO/FILE’

band_dic: {‘r’:’ztfr’,’g’:’ztfg’,’i’:’ztfi’} # Optional -> if bandname in

survey_file doesn’t match sncosmo name

gain: CCD GAIN e-/ADU # Optional, default given by survey file
sub_field: ‘sub_field_key’ # Optional
cdd_noise : sig_ccd_noise # Optional, default is 0 ADU
key_dic: {‘column_name’: ‘new_column_name’, etc}  # Optional, to change columns names
add_data: [‘keys1’, ‘keys2’, …] add db file keys to metadata
survey_cut: {‘key1’: [‘conditon1’,’conditon2’,…], ‘key2’: [‘conditon1’]}
start_day: MJD NUMBER or ‘YYYY-MM-DD’  # Optional, default given by survey file
duration: SURVEY DURATION (DAYS)  # Optional, default given by survey file
zp: INSTRUMENTAL ZEROPOINT  # Optional, default given by survey file
sig_zp: ZEROPOINT ERROR  # Optional, default given by survey file
fwhm_psf: GAUSSIAN PSF FWHM  # Otional, default given by survey file as FWHMeff
noise_key: [key, type] type can be ‘mlim5’ or ‘skysigADU’
ra_size: RA FIELD SIZE # Optional if there is geo_field_map
dec_size: DEC FIELD SIZE # Optional if there is geo_field_map
field_shape: ‘/PATH/TO/FIELD_SHAPE_FILE’ # Optional if there is geo_field_map
geo_field_map:  ‘/PATH/TO/GEO_FIELD_MAP_FILE’ # Optional

sim_par:

randseed: RANDSEED TO REPRODUCE SIMULATION  # Optional
z_range: [ZMIN, ZMAX]
nep_cut: [[nep_min1,Tmin,Tmax],[nep_min2,Tmin2,Tmax2,’filter1’],…] EP CUTS
duration_for_rate: FAKE DURATION ONLY USE TO GENERATE N OBJ  # Optional

mw_dust:

model: MOD_NAME

r_v: Rv # Optional, default Rv = 3.1

snia_gen:

n_sn: NUMBER OF SN TO GENERATE  # Optional
rate: rate of SN/Mpc^3/year # Optional, default=3e-5
Mabs: SN ABSOLUT MAGNITUDE
sigM: SN INTRINSIC COHERENT SCATTERING
sct_model: ‘G10’,’C11_i’ USE WAVELENGHT DEP MODEL FOR SN INT SCATTERING
model_name: ‘THE MODEL NAME’  Example : ‘salt2’
model_dir: ‘/PATH/TO/SALT/MODEL’
alpha: STRETCH CORRECTION = alpha*x1
beta: COLOR CORRECTION = -beta*c
dist_x1: [MEAN X1, SIGMA X1], [MEAN X1, SIGMA_X1_LOW, SIGMA_X1_HIGH] or ‘N21’
dist_c: [MEAN C, SIGMA C] or [SIGMA_C_LOW, SIGMA_C_HIGH]

cosmology:

Om0: MATTER DENSITY

H0: HUBBLE CONSTANT

cmb:

v_cmb: OUR PECULIAR VELOCITY  # Optional, default = 620 km/s
l_cmb: GAL L OF CMB DIPOLE  # Optional, default = 271.0
b_cmb: GAL B OF CMB DIPOLE  # Optional, default = 29.6

vpec_dist:

mean_vpec: MEAN SN PECULIAR VELOCITY

sig_vpec: SIGMA VPEC

hosts: (Optional)

host_file: ‘PATH/TO/HOSTFILE’
distrib: ‘rate’ or ‘random’  # Optional, default = ‘rate’
key_dic: {‘column_name’: ‘new_column_name’, etc}  # Optional, to change columns names

dask: # Optional for using dask parallelization

use: True or False

nworkers: NUMBER OF WORKERS # used to adjust work distribution

Initialise Simulator class.

simulate()[source]¶

Launch the simulation.

Return type:: None

Notes

Simulation routine : 1- Use either _cadence_sim() or _gen_n_sn() to run the simulation 2- Gen all SN parameters inside SNGen class or/and SnHosts class 3- Check if SN pass cuts and then generate the lightcurves. 4- Write LCs to parquet/pkl file(s)

property config¶: Get the whole configuration dic.

property sim_name¶: Get sim name.

property vpec_dist¶: Get vpec option.

property cmb¶: Get cmb parameters.

property samples¶: Get the list of simulated sn.

property cosmology¶: Get astropy cosmological model used in simulation.

property survey¶: Get the SurveyObs object of the simulation.

property generators¶: Get the SNGen object of the simulation.

property hosts¶: Get the SnHosts object of the simulation.

property randseed¶: Get primary random seed of the simulation.

property z_range¶: Get z_range.

property nep_cut¶: Get the list of epochs cuts.

class snsim.SimSample(sample_name, sim_lcs, header, dir_path=None)[source]¶

Class to store simulated SN sample.

Parameters:

sample_name (str) – Name of the sample.
sim_lcs (list(astropy.Table)) – The simulated lightcurves.
header (dict) – Simulation header.
model_dir (str, opt) – The path to the simulation model files.
file_path (str, opt) – Path of the sample.
class. (Initialize SimSample)

classmethod fromDFlist(sample_name, sim_lcs, header, dir_path=None)[source]¶

Initialize the class from a list of pandas.DataFrame.

Parameters:

cls (SimSample class) – The SimSample class.
sim_file (str) – The file to load.
model_dir (str, opt) – The directory of the configuration files of the sim model.
sample_name (str) – Name of the simulation.
sim_lcs (list(pandas.DataFrame)) – The sim lightcurves.
header (dict) – Simulation header.

Returns:

A SimSample class with the simulated lcs.

Return type:

SimSample class object

classmethod fromFile(sim_file, engine='pyarrow')[source]¶

Initialize the class from a fits or pickle file.

Parameters:

cls (SimSample class) – The SimSample class.
sim_file (str) – The file to load.
model_dir (str, opt) – The directory of the configuration files of the sim model.
engine (str) – Engine to use to open the file pyarrow or fastparquet.

Returns:

A SimSample class with the simulated lcs.

Return type:

SimSample class object

get_obj_sim_model(obj_ID)[source]¶

Get th sim model of one obj.

Parameters:: obj_ID (int) – ID of the obj.
Returns:: sncosmo sim model of the obj.
Return type:: sncosmo.Model

set_fit_model(model, model_dir=None, mw_dust=None)[source]¶

Set the fit with a given SNCosmo Model.

Parameters:

model (sncosmo.models.Model or str) – A sncosmo Model or a model name for utils.init_sn_model(model, model_dir).
model_dir (str, opt) – In case you want to set your model via utils.init_sn_model(model, model_dir).
mw_dust (dict, opt) – Milky Wat dust model to apply. | mw_dust | ├── model : str (model name) | └── rv : float, opt

Returns:

Set the self._fit_model attribute.

Return type:

None

fit_lc(obj_ID=None, **kwargs)[source]¶

Fit all or just one SN lightcurve(s).

Parameters:: ID (int, default is None) – The SN ID, if not specified all SN are fit.
Returns:: Directly modified the _fit_res attribute.
Return type:: None

Notes

Use snc_fitter from utils

write_mod(formats=['pkl', 'parquet'])[source]¶

Write a file containing only the modified SN epochs.

Parameters:: formats (list(str) or str, opt) – The output formats, ‘pkl’ or ‘fits’.
Returns:: Just write a file.
Return type:: None

write_fit(fit_model_name, write_path=None)[source]¶

Write fits results in fits format.

Returns:: Write an output file.
Return type:: None

Notes

Use write_fit from utils.

plot_hist(key, ax=None, **kwargs)[source]¶

Plot the histogram of the key metadata.

Parameters:

key (str) – A key of lightcurves metadata.
ax (matplotlib.axis, opt) – ax on which plot the histogram.
**kwargs (type) – matplotlib plot options.

Returns:

Description of returned object.

Return type:

type

plot_lc(obj_ID, plot_sim=True, plot_fit=False, **kwargs)[source]¶

Plot the given SN lightcurve.

Parameters:

obj_ID (int) – The Object ID.
mag (boolean, default = False) – If True plot the magnitude instead of the flux.
zp (float) – Used zeropoint for the plot.
plot_sim (boolean, default = True) – If True plot the theorical simulated lightcurve.
plot_fit (boolean, default = False) – If True plot the fitted lightcurve.
Jy (boolean, default = False) – If True plot in Jansky.
mod (boolean, default = False) – If True use the self.modified_lcs rather than self.sim_lcs

Returns:

Just plot the SN lightcurve !

Return type:

None

Notes

Use plot_lc from utils.

plot_ra_dec(plot_vpec=False, field_dic=None, field_size=None, **kwarg)[source]¶

Plot a mollweide map of ra, dec.

Parameters:

plot_vpec (boolean) – If True plot a vpec colormap.
field_dic (dict(int : dict(str : float))) – Dict of fields coordinates -> Field_ID : {‘RA’, ‘Dec’}
field_size (list(float, float)) – The size of the field [RA, Dec]

Returns:

Just plot the map.

Return type:

None

get(key, mod=False)[source]¶

Get an array of sim_lc metadata.

Parameters:: key (str) – The metadata to access.
Returns:: The array of the key metadata for all SN.
Return type:: numpy.ndarray

get_peakmags(band, magsys='ab')[source]¶

Get peak apparent magnitude in any band.

Parameters:

band (str) – The band, the name has to be in sncosmo registry
magsys (str) – The magnitude system, default: ab.

Returns:

The array of the peak apparent magnitude for all SN.

Return type:

numpy.ndarray

property name¶: Get sample name.

property header¶: Get header.

property n_obj¶: Get SN number.

property sim_lcs¶: Get sim_lcs.

property meta¶: Get lcs meta dict.

property sim_model¶: Get sim model.

property fit_model¶: Get fit model.

property fit_res¶: Get fit results list.

class snsim.SurveyObs(survey_config)[source]¶

This class deals with the observations of the survey.

Parameters:

survey_config (|) – It contains all the survey configuration.
survey_config
FILE (| ├── survey_file PATH TO SURVEY)
float (| ├── dec_size DEC FIELD SIZE IN DEG ->)
str (SUBFIELD KEY ->)
opt
str
opt
float
opt
float
opt
survey_cut (| ├──)
dict (CUT ON OBS FILE ->)
opt
add_data (| ├──)
list(str) (LIST OF KEY TO ADD METADATA ->)
opt
float
geo_field_map (opt if)
float
geo_field_map
str
opt
field_map (| ├──)
str
opt
sub_field_key (| └──)
str
opt
class. (Initialize SurveyObs)

compute_geo_field_map()[source]¶

Compute the GeoFieldMap and writte it for future use

Returns:: GeoDataFrame
Return type:: GeoFieldMap

get_observations(params, phase_cut=None, nep_cut=None, IDmin=0, use_dask=False, npartitions=None)[source]¶

Give the epochs of observations of a given SN.

Parameters:

ra (numpy.ndarray(float) or float) – Obj ra coord [rad].
dec (numpy.ndarray(float) or float) – Obj dec coord [rad].
t0 (numpy.ndarray(float) or float) – Obj sncosmo model peak time.
MinT (numpy.ndarray(float) or float) – Obj sncosmo model mintime.
MaxT (numpy.ndarray(float) or float) – Obj sncosmo model maxtime.
nep_cut (list(list(int, float, float, str)), opt) – The cut [nep, mintime, maxtime, band].
IDmin (int, opt) – ID of the first object.

Returns:

pandas dataframe containing the observations.

Return type:

pandas.DataFrame()

show_map(ax=None)[source]¶: Plot a representation of sub-fields.

property config¶: Survey configuration.

property band_dic¶

band_sncosmo.

Type:: Get the dic band_survey

property obs_table¶: Table of the observations.

property gain¶: Get CCD gain in e-/ADU.

property zp¶: Get zero point and it’s uncertainty.

property fwhm_psf¶: Get PSF width.

property duration¶: Get the survey duration in days.

property start_end_days¶: Get the survey start and ending days.

property field_size_rad¶: Get field size ra, dec in radians.

class snsim.SnHosts(config, z_range=None, geometry=None)[source]¶

Class containing the SN Host parameters.

Parameters:

config (str) –
Configuration of host.

config

├── host_file, ‘PATH/TO/HOSTFILE’

├── distrib, str, Optional, default = ‘rate’, options given by self._dist_options

├── reweight_vol, bool, Optional, default = False, reweight input host distrib to volumetric

└── key_dic: {‘column_name’: ‘new_column_name’, etc}, Optional, only use to change columns names
z_range (list(float), opt) – The redshift range.
class. (Initialize SnHost)

property config¶: Get the configuration dic of host.

property max_dz¶: Get the maximum redshift gap.

property df¶: Get pandas Dataframe of host.

compute_weights(rate=None, sn_type=None, cosmology=None)[source]¶

Compute the weights for random choice.

Parameters:: rate (function, optional) – rate function of z, by default None.
Returns:: weigths for the random draw.
Return type:: numpy.ndarray(float)

random_choice(n, seed=None, rate=None, sn_type=None, cosmology=None)[source]¶

Randomly select hosts.

Parameters:

n (int) – Number of hosts to select.
seed (int, opt) – Random seed.

Returns:

Dataframe with selected hosts properties.

Return type:

pandas.Dataframe