scifysim.utilities module

scifysim.utilities.Cauchy(x, loc, scale)

Shortcut to scipy.stats.cauchy.pdf

scifysim.utilities.Gauss(x, loc, scale)

Shortcut to scipy.stats.norm.pdf

scifysim.utilities.coeff(dit, lead_transmission, context, eta=1.0, spectrum='flat', ds_sr=1.0, include_transmission=False, reverse_coeff=False)

Obtains the coefficients including detector integration time, transmission, spectral channels, pixel solid angle, and quantum efficiency (default: 1.).

Parameters:

• dit : detector integration time [s]

• lead_transmission : The transmission of the leading element (often sky) of the transmission-emission chain

• context : The spectral context of the instrument (defining the vega magnitudes)

• eta : (default=1.) The quantum efficiency of the detector

• spectrum : The type of spectrum of the object defaults to flat as opposed to the temperature of Vega.

• ds_sr : When working with a map the solid angle of a pixel [sr] Can be found in simulator.vigneting_map.ds_sr after maps have been computed

• Include transmission: whether to multiply by the transimission spectrum of the instrument The maps are now computed including the transmission of the instrument, so that is not necessary.

• reverse_coeff: if True, compute the pure theoretical map, removing the effects from the instrument transmission and efficiency

class scifysim.utilities.ee(expression)

Bases: object

Additional functionnality needed: see comments starting 2021/02/24 https://github.com/sympy/sympy/issues/5642

__call__(*args)

Call self as a function.

__dict__ = mappingproxy({'__module__': 'scifysim.utilities', '__doc__': '\n    Additional functionnality needed:\n    see comments starting 2021/02/24 `<https://github.com/sympy/sympy/issues/5642>`_\n    ', '__init__': <function ee.__init__>, 'lambdify': <function ee.lambdify>, 'numexpr_call': <function ee.numexpr_call>, 'numpy_call': <function ee.numpy_call>, '__call__': <function ee.__call__>, 'fprint': <function ee.fprint>, '__dict__': <attribute '__dict__' of 'ee' objects>, '__weakref__': <attribute '__weakref__' of 'ee' objects>, '__annotations__': {}})

__init__(expression)

ee is for Executable expression Encapsulates an expression for flexible lambda evaluation. expression : a sympy expression to lambdify

__module__ = 'scifysim.utilities'

__weakref__

list of weak references to the object (if defined)

fprint()

lambdify(args, modules='numexpr')

Creates the lambda function. Currently, lambdification to numexpr does not support multiple outputs, so we create a list of lambda functions.

Parameters:

• args : a tuple of sympy for symbols to use as inputs

• modules : The module to use for computation.

numexpr_call(*args)

The evaluation call for funcions for the numexpr case Evaluating the list of functions and returning it as an array

numpy_call(*args)

Just the normal call of the lambda function

scifysim.utilities.eval_perf(afile, t_exp, n_frames=100, thetarget=None, update_params=False, instrumental_errors=True, seed=None, crop=1.0, target_coords=None, compensate_chromatic=True, modificators=None, plotall=False, use_tqdm=False)

Evaluate the noise profile for a given parameter file.

Parameters:

• afile : Config file

• t_exp : Exposure time

• n_frames : The number of frames for MC simulation to evaluate the instrumental noise

• instrumental_errors : Bool. Whether to include instrumental errors

• seed : seed to pass when generating the time series of instrumental errors

• crop : factor to pass to the injector

• target_coords : Coordinates of the target default:None : finds the target from catalog

• compensate_chromatic : Bool Whether to run the optimization of the actuators to compensate for chromaticity in the combiner

• plotall : Whether to plot a bunch of charts

• use_tqdm : Whether tqdm should be used at the step of the MC simulations. (use false for parameter exploration)

Returns:

• asim : The simulator object

• prof : The noise profile object

• characteristics : A dictionary containing a collection of performance statistics

scifysim.utilities.extract_diffobs_map(maps, simulator, mod=<module 'numpy' from '/lhome/romain/miniconda3/envs/scify/lib/python3.8/site-packages/numpy/__init__.py'>, dit=1.0, mag=None, postprod=None, eta=1.0, K=None, reverse_coeff=False, normalize_collector=False)

Here in photons per dit (default=1s)

Parameters:

• mapsTransmission maps for 1 ph/s/m2

  at the entrance of atmo

• simulator : a sumulator object (see comments below)

• dit : Detector integration time [s]

• mag : The magnitude of a source on the map

• postprod : A post-processing (whitening matrix)

• eta : The quantum efficiency of the detector

• KA matrix to extract the differential

  observable quantities (kernel matrix). By default, the matrix is obtained from simulator.combiner.K

Simulator object is used for:

• The head transmission (simulator.src.sky)

• The solid angle of the pisel

• The spectral context (for the spectral channels)

scifysim.utilities.extract_diffobs_map_old(maps, simulator, dit=1.0, mag=None, postprod=None, eta=1.0, K=None)

Here in photons per dit (default=1s)

Parameters:

• mapsTransmission maps for 1 ph/s/m2

  at the entrance of atmo

• simulator : a sumulator object (see comments below)

• dit : Detector integration time [s]

• mag : The magnitude of a source on the map

• postprod : A post-processing (whitening matrix)

• eta : The quantum efficiency of the detector

• KA matrix to extract the differential

  observable quantities (kernel matrix). By default, the matrix is obtained from simulator.combiner.K

Simulator object is used for:

• The head transmission (simulator.src.sky)

• The solid angle of the pisel

• The spectral context (for the spectral channels)

scifysim.utilities.find_best_night(obs, target, showplot=True, duration=None)

Parameters:

• obs : Observatory object

• showplot : Whether to show a plot of the max elevation

• duration [h] : duration of the observing run

Returns:

• if duration is None (default) : besttime an astropy.Time object

• else : (Tstart, Tend) Start and end of the observing run

scifysim.utilities.fit_distribution(hist, bincenters, mypdf, nsamples=None, p0=(0.0, 0.0))

Fits the histogram to a given pdf

Arguments:

• hist : array-like The empirical histogram given in density so that it has unity integral

• bincenters : array-like The center of bins. Same shape as hist. tip: use get_bincenters to convert edges to bin centers

• mypdf : function A function of the histogram to fit

• p0 : array-like Starting parameters for the fit. Typically a couple (loc, scale). Recommended is (mean, std).

Returns:

• parameters: The best fit parameters (typically (loc, scale).

• histerror : An estimation of the errors on the bins, based on Poissonian statistics (and set to the max for empty bins.

• covariance: The covariance of the parameters.

scifysim.utilities.get_bincenters(edges)

Quickly converts bin edges to bin centers:

Arguments:

• edges: Array of size n

Returns:

• bincenters: Centers of the intervals (size n-1)

scifysim.utilities.get_header_bl_data(header)

Pulls the baseline infomation from the header

scifysim.utilities.get_index_pos(ax, ay, thesim)

Get the coordinates on the discrete map of a relative position

Arguments: * ax : [mas] Coordinate x * ay : [mas] Coordinate y * thesim : Simulator object

scifysim.utilities.get_location(simple_map, map_extent=None, search_function=<function argmax>, mode='polar')

Get location of the max or min of the map.

scifysim.utilities.get_raw_array(config)

Get the raw array for location of apertures from config file.

Arguments: * config : a config parser object sf.parsefile.ConfigParser

return: * For ground-based: (n_tel, 2[m]) * For space-based: (t[s], n_tel, 3[m])

scifysim.utilities.get_star_params(star, verbose=True, showtable=False)

Queries GAIA DR2 catalog for distance, radius and temperature of star:

Parameters:

• star : str the name of the star

• verbose: Whether to print the details found

• showtable: Whether to show the table in list form

** returns:** * dist [pc] : Distance * T [K] : Effective temperature * R [Rsun] : Radius

scifysim.utilities.get_star_params_GAIA_JMMC(star, verbose=True, showtable=False)

Queries GAIA DR2 catalog for distance, radius and temperature of star:

Parameters:

• star : str the name of the star

• verbose: Whether to print the details found

• showtable: Whether to show the table in list form

returns:

• dist [pc] : Distance

• T [K] : Effective temperature

• R [Rsun] : Radius

scifysim.utilities.get_uv(puparray)

Computes the uv baselines for a given pupil configuration.

Parameters:

• puparray : An array representing the location of each pupil

scifysim.utilities.lambdifyz(symbols, expr, modules='numpy')

Circumvents a bug in lambdify where silent variables will be simplified and therefore aren’t broadcasted. https://github.com/sympy/sympy/issues/5642

Use an extra argument = 0 when calling

Please, keep the function synchronous with kernuller

scifysim.utilities.loadGRA4MAT(filename)

scifysim.utilities.mag2sig(mag, dit, lead_transmission, context, eta=1.0, spectrum='flat', ds_sr=1.0)

Obtains the coefficients including object magnitude, detector integration time, transmission, spectral channels, pixel solid angle, and quantum efficiency (default: 1.).

Parameters:

• mag : Magnitude of object (Vega)

• dit : detector integration time [s]

• lead_transmission : The transmission of the leading element (often sky) of the transmission-emission chain

• context : The spectral context of the instrument (defining the vega magnitudes)

• eta : (default=1.) The quantum efficiency of the detector

• spectrum : The type of spectrum of the object defaults to flat as opposed to the temperature of Vega.

• ds_sr : When working with a map the solid angle of a pixel [sr] Can be found in simulator.vigneting_map.ds_sr after maps have been computed

scifysim.utilities.matchseries(a, b, nmatch=10)

Matches series b with series a with the nmatch last samples of a and the nmatch first samples of b.

• a : 1D array the root of the series

• b : 1D array the tail of the series

• nmatch : integer The number of samples used to match the mean

Returns the concatenation.

scifysim.utilities.parameters2latex(parameters, rename=None, column_to_rename='Name', rem=True, columns=None, latex_option='AA')

This is done by capturing stdout in a call of parameters.pretty_print()

Arguments:

• parameters: a lmfit.Parameters object

scifysim.utilities.parameters2table(parameters, rename=None, column_to_rename='Name', rem=True)

Convenience function to convert a lmfit.Parameters object to an astropy table.

This is done by capturing stdout in a call of parameters.pretty_print()

Arguments:

• parameters: a lmfit.Parameters object

• rename: a dict with the new names or None to do nothing

• column_to_rename: The name of the column for substitution

• rem: if:

  • True: Substitute column_to_replace

  • A string: the name of the new column

Returns: an astropy.table.Table object

scifysim.utilities.prepare_all(afile, thetarget=None, update_params=False, instrumental_errors=True, seed=None, crop=0.7, res=100, target_coords=None, compensate_chromatic=True, modificators=None, update_start_end=True, statlocs=None, verbose=False)

A shortcut to prepare a simulator object Parameters:

• afile : Config file

• thetarget : target name to replace in config file

• update_params: (bool) default: False. When True, updates the parameters of the star based on databases such as GAIA DR3

• instrumental_errors : Bool. Whether to include instrumental errors

• seed : seed to pass when generating the time series of instrumental errors

• crop : factor to pass to the injector

• target_coords : Coordinates of the target default:None : finds the target from catalog

• compensate_chromatic : Bool Whether to run the optimization of the actuators to compensate for chromaticity in the combiner

• modificators : a dict of parameters to supersede the config file

Returns:

• asim : A simulator object

scifysim.utilities.produce_maps(asim, prof, adit=1.0, mypfa=0.01, mypdet=0.9, base_name='/tmp/R200_twinmaps_', total_time=10800, starmags=array([2., 2.42105263, 2.84210526, 3.26315789, 3.68421053, 4.10526316, 4.52631579, 4.94736842, 5.36842105, 5.78947368, 6.21052632, 6.63157895, 7.05263158, 7.47368421, 7.89473684, 8.31578947, 8.73684211, 9.15789474, 9.57894737, 10.]), decs=array([-90., -76.66666667, -63.33333333, -50., -36.66666667, -23.33333333, -10., 3.33333333, 16.66666667, 30.]), mapcrop=0.5, mapres=50)

Utility macro that packages the production of multiple sensitivity maps for a given director object and noise profile

Parameters:

• asim : Simulator director object

• prof : Noise profile

Returns:

• metamap : T

• themap : The energy detector map in magnitude

• theNPmap : The Neyman-Pearson detector map in magnitude

scifysim.utilities.profileit(func, highres=False)

scifysim.utilities.random_series_fft(ps, matchto=None, matchlength=10, keepall=False, seed=None)

Returns a new series of data based on the provided power spectrum. If matchto is provided, the new series is adjusted for continuity to that data.

Parameters:

• ps : A power spectrum

• matchto : np.ndarray of preceding series. The mean of the first n (matchlength) values of the new series are matched to the mean of the last n values of this array.

• matchlength : integer. The number of values to match

• keepall : Boolean. If true, will return the concatenation of the new array with the old. If False, will return only the new array.

Returns:

• a numpy ndarray of the time series

scifysim.utilities.range2R(spectral_range, mode='bins')

Convenience function to convert a sequence of wavelengths to the target spectral resolution.

Arguments:

• spectral_range : (array-like) The series of wavelength channels [m]

• mode : Decides what is considered a step in the series.

  • “bins” : The simulator wl bins as found in simulator.lambda_science_range: 2 pixels per bin in Hi-5

  • “chans” : The individual wavelength step

  • “pixs” : The individual pixels (2.5 pixel per spectral channel)

Returns an array of the individual spectral resolutions

scifysim.utilities.sp2mod(array, dtype=<class 'complex'>, mod=<module 'numpy' from '/lhome/romain/miniconda3/envs/scify/lib/python3.8/site-packages/numpy/__init__.py'>)

Converts a sympy array/Matrix into an array of a given module

** Arguments :**

• array : A sympy Matrix, vector or expression, with no free symbols

• dtype : data type to aim for (default: complex)

• mod : the module to convert to (default: numpy) will work with cupy, jax, etc…

scifysim.utilities.table2latex(table, latex_option='AA')

Utility to output a latex formatted table from astropy.

scifysim.utilities.test_ex()

scifysim.utilities.test_maps(fname='local_config/default_R200', target='GJ 86 A')

Testing and comparing the 2 approaches to build transmission maps.

scifysim.utilities.test_random_series(seed=None)

scifysim.utilities.trois(x, xmin, xmax, ymin=0.0, ymax=1.0)

scifysim.utilities.update_observing_night(config, time=None, duration=6.0, verbose=True, target_coords=None)

Parameters

• config : The parsed config file to modify

• time : [fits format] (if None, will optimize for elevation)

• duration : [h] Default: 6.h

• verbose : Whether to print some of the intermediate results

• target_coords : Coordinates of the object of interst.

  • None Default: Uses the target in the config file

  • astropy.SkyCoords object.

scifysim.utilities.update_star_params(config, verbose=True)

scifysim.utilities.vec2diag(vec)

Replictes in sympy the np.diag functionnality to create a diagonal matrix from a 1D vector

scifysim.utilities.wrap_phase(theta)