Plotting

Note

Functions for making plots, both analysis and detector plots, as well as sensitivity calculations. These are used internally in the FRA package.

Plotting functions

fast_response.plotting_utils.plot_zoom(scan, ra, dec, title, reso=3, var='pVal', range=[0, 6], cmap=None)

Make a zoomed-in skymap around a particular point (RA, decl) on the sky

Parameters:

• scan (numpy array) – Healpix map of values to be plotted (usually a skymap)

• ra (float) – Right Ascension value at the center of the plot (best-fit RA or source RA)

• dec (float) – Declination value at the center of the plot (best-fit dec or source dec)

• title (str) – Plot title to use

• reso (float) – Resolution (arcmins), default 3

• cmap (matplotlib colormap or None) – colormap to use. if not set default is Seaborn “Blues”

fast_response.plotting_utils.plot_color_bar(labels=[0.0, 2.0, 4.0, 6.0], col_label='IceCube Event Time', range=[0, 6], cmap=None, offset=-35)

Adds a color bar to an existing healpy map

Parameters:

• labels (float list) – list of points to be used (default [0., 2., 4., 6.])

• col_label (str) – label for colorbar (default IceCube Event Time)

• cmap (matplotlib colormap or None) – colormap to use. if not set default is “Blues”

• offset (int) – offset value for colorbar’s label. default is -35

fast_response.plotting_utils.plot_labels(src_dec, src_ra, reso)

Add labels to healpy zoom

Parameters:

• src_dec (float) – Declination value at the center of the plot (best-fit dec or source dec)

• src_ra (float) – Right Ascension value at the center of the plot (best-fit RA or source RA)

• reso (float) – Resolution (arcmins)

fast_response.plotting_utils.plot_events(dec, ra, sigmas, src_ra, src_dec, reso, sigma_scale=5.0, col='k', constant_sigma=False, same_marker=False, energy_size=False, with_mark=True, with_dash=False, label='')

Adds events to a healpy zoom plot. Events are expected to be from self.llh.exp

Parameters:

• dec (float array) – Array of declination values for each event

• ra (float array) – Array of Right Ascension values for each event

• sigmas (float array) – Angular error (circularized) to be plotted. Usually a 90% CL angular error

• src_ra (float) – Right Ascension value at the center of the plot (best-fit RA or source RA)

• src_dec (float) – Declination value at the center of the plot (best-fit dec or source dec)

• reso (float) – Resolution (arcmins)

• sigma_scale (float or None) – Value to rescale the sigma parameter (default 5.0). If None: no angular undertainty is plotted (sometimes used for very long time windows)

• col (str or str array) – color to use for each event plotted (default k=black)

• constant_sigma (bool) – Ignores sigma parameter and plots all markers with a size of 20.

• with_mark (bool) – Uses an x marker instead of o

• with_dash (bool) – Plot the angular error as a dashed contour. Usually used to indicated a removed event (e.g. alert event that triggered the analysis)

• same_marker (bool) – Currently unused options.

• energy_size (bool) – Currently unused options.

fast_response.plotting_utils.load_plotting_settings()

Load settings to be used as default plot settings. Includes Times New Roman font and size 12 font

fast_response.plotting_utils.contour(ra, dec, sigma, nside)

Function for plotting contours on skymaps

Parameters:

• ra (ndarray) – Array of ra for events

• dec (ndarray) – Array of dec for events

• sigma (ndarray) – Array of sigma to make contours around events

• nside – nside of healpy map

Returns:

• Theta (array) – array of theta values of contour

• Phi (array) – array of phi values of contour

fast_response.plotting_utils.plot_contours(proportions, samples)

Plot containment contour around desired level. E.g 90% containment of a PDF on a healpix map

Parameters:

• proportions (list) – list of containment level to make contours for. E.g [0.68,0.9]

• samples (array) – array of values read in from healpix map E.g samples = hp.read_map(file)

Returns:

• theta_list (list) – List of arrays containing theta values for desired contours

• phi_list (list) – List of arrays containing phi values for desired contours

Detector status plots

fast_response.make_ontime_plots.time_axis(ax, run_table, time_window)

Make an axis that is in times, formatted as ISO times

Parameters:

• ax (matplotlib.pyplot Axis) – axis to convert to times and format

• run_table (dict-like) – Table of run information for events

• time_window (tuple astropy Time object) – (start, stop) of the time window, in mjd

fast_response.make_ontime_plots.time_series(ax, run_table, time_window, t1, t2, n, scale=1, ymax=0, xerr=False, **kwargs)

Make a plot that is a rate over time

Parameters:

• ax (matplotlib.pyplot Axis) – axis to convert to times and format

• run_table (dict-like) – Table of run information for events

• time_window (tuple astropy Time object) – (start, stop) of the time window, in mjd

• t1 (astropy Time object) – start time to use in the plot, utc

• t2 (astropy Time object) – stop time to use in the plot, utc

• n (list or array-like) – value to plot on the y-axis (usually, a rate value)

• scale (float) – rescale value/unit for the y-axis (default 1, not rescaled)

• ymax (float) – maximum value to plot for the y-axis (default 0, no max)

• kwargs (optional) – passed to matplotlib.pyplot.ax.errorbar

fast_response.make_ontime_plots.make_rate_plots(time_window, run_table, query_events, dirname, season='neutrino')

Make plots of filter rates for the time window we’re interested in. Rates plotted are:

• GFU singlet rate

• Badness

• Online L2

• Muon filter

• In Ice Simple Multiplicity

Parameters:

• time_window (tuple astropy Time object) – (start, stop) of the time window, in mjd

• run_table (dict-like) – Table of run information for events

• dirname (str) – Directory events are saved to

• season (str) – Season to load (default neutrino)

Sensitivity functions

fast_response.sensitivity_utils.find_nearest_idx(array, value)

Find the index where an array is nearest to a given value. For the closest value, rather than index, see find_nearest.

Parameters:

• array (list or array) – Array to check

• value (float) – Particular value to look for in an array

Returns:

Index where array is closest to value

Return type:

int

fast_response.sensitivity_utils.find_nearest(array, value)

Find the value of an array closest to a given value. For the index, rather than value, see find_nearest_idx.

Parameters:

• array (list or array) – Array to check

• value (float) – Particular value to look for in an array

Returns:

value of the array that is closest to the value given

Return type:

float

fast_response.sensitivity_utils.deltaPsi(dec1, ra1, dec2, ra2)

Calculate angular distance between two given points Values in Right Ascension/declination

Parameters:

• dec1 (float) – Declination of first direction in radian

• ra1 (float) – Right ascension of first direction in radian

• dec2 (float) – Declination of second direction in radian

• ra2 (float) – Right ascension of second direction in radian

Returns:

angular distance in radian

Return type:

float

fast_response.sensitivity_utils.deltaPsi2(sDec1, cDec1, sRa1, cRa1, sDec2, cDec2, sRa2, cRa2)

Helper function to calculate angular distance. Should not be called directly, rather call deltaPsi which calls this helper function.

Parameters:

• sDec1 (float) – sin(Declination of first direction)

• cDec1 (float) – cos(Declination of first direction)

• sRa1 (float) – sin(Right ascension of first direction)

• cRa1 (float) – cos(Right ascension of first direction)

• sDec2 (float) – sin(Declination of second direction)

• cDec2 (float) – cos(Declination of second direction)

• sRa2 (float) – sin(Right ascension of second direction)

• cRa2 (float) – cos(Right ascension of second direction)

Returns:

angular distance in radian

Return type:

float

fast_response.sensitivity_utils.sensitivity_fit(signal_fluxes, passing, errs, fit_func, p0=None, conf_lev=0.9)

Given an array of injected fluxes (or events) and passing fraction relative to the unblinded TS, do a fit given a CDF. Built on curve_fit from scipy.optimize.

Parameters:

• signal_fluxes (float Numpy array) – set of injected flux values (or signal events).

• passing (float Numpy array) – corresponding fraction of trials with TS > threshold TS value, for each signal flux value.

• errs (float Numpy array) – binomial error for the given passing values. See also: binomial_error

• fit_func (function) – function to use in the fit. See also: Error function (erfunc), Chi squared (chi2cdf), Incomplete gamma (incomplete_gamma)

• p0 (None, scalar or N-length sequence) – initial guess for parameters, passed directly to scipy.optimize.curve_fit

• conf_lev (float) – confidence level used (default 0.9 = 90% CL)

Returns:

Fit values, with keys:

• popt, pcov: returned fit values from scipy.optimize.curve_fit

• chi2, dof, pval: chi squared, degrees of freedom, and p-value from fit

• xfit, yfit: arrays of values, using the fitted parameters of the given function

• name: name of fit used

• ls: linestyle, returns –

• sens: calculated sensitivity value, in flux or ns (whichever is passed in signal_fluxes)

Return type:

dict

fast_response.sensitivity_utils.erfunc(x, a, b)

Error function

fast_response.sensitivity_utils.chi2cdf(x, df1, loc, scale)

Chi-squared CDF

fast_response.sensitivity_utils.incomplete_gamma(x, a, scale)

Incomplete gamma function

fast_response.sensitivity_utils.poissoncdf(x, mu, loc)

Poisson CDF function

fast_response.sensitivity_utils.binomial_error(p, number)

Calculates binomial errors for given passing fraction values

Parameters:

• p (float Numpy array) – passing fraction values

• number (int Numpy array) – number of signal trials run for each injected value

Returns:

Numpy array of calculated binomial errors

Return type:

float array