os_bias_overplots_diff_dbs

rubin_sim.maf.maf_contrib.os_bias_overplots_diff_dbs(out_dir, data_path, run_names, legend_labels, fsky_dict, fsky_best, mock_data_path, theory_lim_mag, lim_mag_i, ell_min=100, ell_max=300, lmax=500, specified_dith_only=None, filters=['u', 'g', 'r', 'i'], nside=256, pixel_radius=14, yr_cutoff=None, zbin='0.66<z<1.0', poisson_noise=False, zero_pt=True, plot_interms=False, color_dict=None, ylim_min=None, ylim_max=None, show_plot=False, suptitle=None, file_append=None)

Calculate/plot the OS bias uncertainty and the statistical floor for the specified redshift bin.

Could vary the dither strategies, but the data should be for the same magnitude cut. The title of of each panel in final plot will be “<dither strategy>, and each panel can have OS bias uncertainity from many different cadences. Panel legends will specify the redshift bin and OpSim output tag.

Parameters:
* out_dir: str: main directory where the output plots should be saved; a folder named

‘os_bias_overplots’ will be created in the directory, if its not there already.

* data_path: str: path to the artificialStructure data.
* run_names: list of str: list for run name tags to identify the output of specified OpSim outputs.
* legend_labels: list of strings: list of the ‘tags’ for each case; will be used in the legends. e.g. if

run_names=[‘enigma1189’, ‘minion1016’], legend_labels could be [‘enigma_1189’, ‘minion_1016’].

* fsky_dict: dict: dictionary of the dictionaries containing the fraction of sky covered for each of the

cadences. The keys should match the identifier; fsky_dict[indentifiers[:]] should have the dither strategies as the keys.

* fsky_best: float: best fsky for the survey to compare everything relative to.
* mock_data_path: str: path to the mock data to consider
* theory_lim_mag: float: magnitude cut as the identifer in the filename from Hu.

Allowed options: 24.0, 25.6, 27.5

* lim_mag_i: float: i-band magnitude cut to get the data for.
* specified_dith_only: list of string: list of the names (strings) of the dither strategies to consider, e.g.

if want to plot only NoDither, specified_dith_only=[‘NoDither’]. If nothing is specified, all the dither strategies will be considered (based on the npy files available for the runs). Default: None

* filters: list of strings: list containing the bands (in strings) to be used to calculate the OS bias

and its error. should contain AT LEAST two bands. e.g. if filters=[‘g’, ‘r’], OS bias (at every ell) will be calculated as the mean across g and r c_ells, while the bias error (at every ell) will be calculated as the std. dev. across g and r c_ells. Default: [‘u’, ‘g’, ‘r’, ‘i’]

* nside: int: HEALpix resolution parameter. Default: 256
* pixel_radius: int: number of pixels to mask along the shallow border. Default: 14
* yr_cutoff: int: year cut to restrict analysis to only a subset of the survey.

Must range from 1 to 9, or None for the full survey analysis (10 yrs). Default: None

* zbin: str: options include ‘0.15<z<0.37’, ‘0.37<z<0.66, ‘0.66<z<1.0’, ‘1.0<z<1.5’, ‘1.5<z<2.0’

Default: ‘0.66<z<1.0’

* poisson_noise: bool: set to True to consider the case where poisson noise is added to galaxy counts

after border masking (and the incorporation of calibration errors). Default: False

* zero_pt: bool: set to True to consider the case where 0pt calibration errors were incorporated.

Default: True

* plot_interms: bool: set to True to plot intermediate plots, e.g. BAO data. Default: False
* color_dict: dict: color dictionary; keys should be the indentifiers provided. Default: None
**** Please note that in-built colors are for only a few indentifiers:

minion1016, minion1020, kraken1043 **

* ylim_min: float: lower y-lim for the final plots. Defaut: None
* ylim_max: float: upper y-lim for the final plots. Defaut: None
* show_plot: bool: set to True if want to display the plot (aside from saving it). Default: False
* suptitle: str: title to the plot. Default: None
* file_append: str: optional string to append to the saved plot