deploy: 84e15cb

Author: AlexHls

.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file records the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 277ca174c19dc79e7890aad60f301f9c
+config: 99c99639a0ef86ac4a79adb425f0e14a
 tags: 645f666f9bcd5a90fca523b33c5a78b7

_sources/autoapi/sccala/gen_testdata/index.rst.txt

@@ -9,6 +9,8 @@ Functions
 
 .. autoapisummary::
 
+   sccala.gen_testdata.generate_observed_data
+   sccala.gen_testdata.detection_probability
    sccala.gen_testdata.gen_testdata
    sccala.gen_testdata.main
    sccala.gen_testdata.cli
@@ -17,7 +19,11 @@ Functions
 Module Contents
 ---------------
 
-.. py:function:: gen_testdata(zrange, save, size=250, plots=False, hubble=False, zrange_hubble=(0.001, 0.005), hubble_size=25, h0=70.0)
+.. py:function:: generate_observed_data(zrange, vel_range=(7100000.0, 725000.0), col_range=(0.5, 0.06), ae_range=(0.31, 0.13), verr=np.sqrt(200000.0**2 + 150000.0**2), cerr=0.05, aeerr=0.013, r_err=0.0001, alpha=3.4, beta=1.8, gamma=-1.5, mi=-1.6, sint=0.25, rng=None, hubble=False, calib=False, h0=70.0)
+
+.. py:function:: detection_probability(mag, m_cut=21, sigma_cut=0.5)
+
+.. py:function:: gen_testdata(zrange, save, size=250, plots=False, hubble=False, zrange_hubble=(0.001, 0.005), hubble_size=25, h0=70.0, alpha=3.4, beta=1.8, gamma=-1.5, mi=-1.6, sint=0.25, vel_range=(7100000.0, 725000.0), col_range=(0.5, 0.06), ae_range=(0.31, 0.13), verr=np.sqrt(200000.0**2 + 150000.0**2), cerr=0.05, aeerr=0.013, r_err=0.0001, m_cut=21, sigma_cut=0.5, calib_m_cut=21, calib_sigma_cut=0.5, m_cut_nom=None, sig_cut_nom=None, calib_m_cut_nom=None, calib_sig_cut_nom=None)
 
    Function generating simulated datasets for standardisation
 
@@ -45,6 +51,31 @@ Module Contents
    h0 : float
        Value of the Hubble constant used for simulating calibrator sample.
        Default: 70.0
+   alpha : float
+   beta : float
+   gamma : float
+   mi : float
+   sint : float
+       Parameters for the calculation of the magnitudes.
+   vel_range : tuple
+   col_range : tuple
+   ae_range : tuple
+       Parameters for the generation of the observed data.
+   verr : float
+   cerr : float
+   aeerr : float
+   r_err : float
+       Parameters for the calculation of the magnitude errors.
+   m_cut : float
+   sigma_cut : float
+   calib_m_cut : float
+   calib_sigma_cut : float
+       Parameters for the detection probability.
+   m_cut_nom : float
+   sig_cut_nom : float
+   calib_m_cut_nom : float
+   calib_sig_cut_nom : float
+       Parameters for the nominal detection values exported to the data.
 
    Returns
    -------

_sources/autoapi/sccala/interplib/epoch_interp/index.rst.txt

@@ -121,13 +121,13 @@ Module Contents
 
 
 
-   .. py:method:: diagnostic_plot(diagnostic, target, flux_interp=False)
+   .. py:method:: diagnostic_plot(diagnostic, target, flux_interp=False, interactive=False)
 
       Plots the output of the interpolation
 
 
 
-   .. py:method:: data_interp(target, step=0.1, date_low=None, date_high=None, diagnostic=None, no_reject=False, flux_interp=False)
+   .. py:method:: data_interp(target, step=0.1, date_low=None, date_high=None, diagnostic=None, no_reject=False, flux_interp=False, interactive=False)
 
       Interpolate dataocities using Gaussian Process regression
 
@@ -152,6 +152,8 @@ Module Contents
           If True, data is converted to flux before interpolating.
           Exported values will be converted back to magnitudes.
           Only works with 'phot' in target. Default: False
+      interactive : bool
+          If True, diagnostic plots will be shown interactively.
 
       Returns
       -------

_sources/autoapi/sccala/libio/scm_loader/index.rst.txt

@@ -15,7 +15,7 @@ Functions
 Module Contents
 ---------------
 
-.. py:function:: load_data(sne_list, date, mag='I', col=('V', 'I'), calib_sne_list=None, calib_date=None, calib_mag='I', calib_col=('V', 'I'), instrument='Bessell12', export=False, mag_sys=None, vel_sys=None, col_sys=None, ae_sys=None, rho=1.0, rho_calib=0.0, error_mode='mean')
+.. py:function:: load_data(sne_list, date, mag='I', col=('V', 'I'), calib_sne_list=None, calib_date=None, calib_mag='I', calib_col=('V', 'I'), instrument='Bessell12', export=False, mag_sys=None, vel_sys=None, col_sys=None, ae_sys=None, rho=1.0, rho_calib=0.0, error_mode='mean', m_cut_nom=18.5, sig_cut_nom=0.5, pv_red_file=None)
 
    Loads all the necessary data for the scm and bundles it
    into a pandas DataFrame
@@ -65,6 +65,12 @@ Module Contents
        Correlation between the color and magnitude uncertainties for calibrator SNe. Default: 0.0
    error_mode : str
        Mode used to calculate single value error from asymmetric errors. Default: "mean"
+   m_cut_nom : float
+   sig_cut_nom : float
+       Nominal values for the magnitude cut and its uncertainty. Default: 18.5, 0.5
+   pv_red_file : str
+       Path to a file containing the peculiar velocity corrected redshifts of the SNe.
+       If None, redshifts are taken from the info file. Default: None
 
    Returns
    -------