diff --git a/docs/pyerrors/fits.html b/docs/pyerrors/fits.html index 750d4bc3..4380c5a7 100644 --- a/docs/pyerrors/fits.html +++ b/docs/pyerrors/fits.html @@ -89,9 +89,6 @@
  • error_band
  • -
  • - ks_test -
  • @@ -111,8 +108,7 @@
    View Source -
    import gc
    -from collections.abc import Sequence
    +            
    from collections.abc import Sequence
     import warnings
     import numpy as np
     import autograd.numpy as anp
    @@ -852,52 +848,6 @@
         err = np.array(err)
     
         return err
    -
    -
    -def ks_test(obs=None):
    -    """Performs a Kolmogorov–Smirnov test for the Q-values of all fit object.
    -
    -    If no list is given all Obs in memory are used.
    -
    -    Disclaimer: The determination of the individual Q-values as well as this function have not been tested yet.
    -    """
    -
    -    raise Exception('Not yet implemented')
    -
    -    if obs is None:
    -        obs_list = []
    -        for obj in gc.get_objects():
    -            if isinstance(obj, Obs):
    -                obs_list.append(obj)
    -    else:
    -        obs_list = obs
    -
    -    # TODO: Rework to apply to Q-values of all fits in memory
    -    Qs = []
    -    for obs_i in obs_list:
    -        for ens in obs_i.e_names:
    -            if obs_i.e_Q[ens] is not None:
    -                Qs.append(obs_i.e_Q[ens])
    -
    -    bins = len(Qs)
    -    x = np.arange(0, 1.001, 0.001)
    -    plt.plot(x, x, 'k', zorder=1)
    -    plt.xlim(0, 1)
    -    plt.ylim(0, 1)
    -    plt.xlabel('Q value')
    -    plt.ylabel('Cumulative probability')
    -    plt.title(str(bins) + ' Q values')
    -
    -    n = np.arange(1, bins + 1) / np.float64(bins)
    -    Xs = np.sort(Qs)
    -    plt.step(Xs, n)
    -    diffs = n - Xs
    -    loc_max_diff = np.argmax(np.abs(diffs))
    -    loc = Xs[loc_max_diff]
    -    plt.annotate(s='', xy=(loc, loc), xytext=(loc, loc + diffs[loc_max_diff]), arrowprops=dict(arrowstyle='<->', shrinkA=0, shrinkB=0))
    -    plt.draw()
    -
    -    print(scipy.stats.kstest(Qs, 'uniform'))
     
    @@ -1660,73 +1610,6 @@ check if the residuals of the fit are gaussian distributed.

    - -
    -
    #   - - - def - ks_test(obs=None): -
    - -
    - View Source -
    def ks_test(obs=None):
    -    """Performs a Kolmogorov–Smirnov test for the Q-values of all fit object.
    -
    -    If no list is given all Obs in memory are used.
    -
    -    Disclaimer: The determination of the individual Q-values as well as this function have not been tested yet.
    -    """
    -
    -    raise Exception('Not yet implemented')
    -
    -    if obs is None:
    -        obs_list = []
    -        for obj in gc.get_objects():
    -            if isinstance(obj, Obs):
    -                obs_list.append(obj)
    -    else:
    -        obs_list = obs
    -
    -    # TODO: Rework to apply to Q-values of all fits in memory
    -    Qs = []
    -    for obs_i in obs_list:
    -        for ens in obs_i.e_names:
    -            if obs_i.e_Q[ens] is not None:
    -                Qs.append(obs_i.e_Q[ens])
    -
    -    bins = len(Qs)
    -    x = np.arange(0, 1.001, 0.001)
    -    plt.plot(x, x, 'k', zorder=1)
    -    plt.xlim(0, 1)
    -    plt.ylim(0, 1)
    -    plt.xlabel('Q value')
    -    plt.ylabel('Cumulative probability')
    -    plt.title(str(bins) + ' Q values')
    -
    -    n = np.arange(1, bins + 1) / np.float64(bins)
    -    Xs = np.sort(Qs)
    -    plt.step(Xs, n)
    -    diffs = n - Xs
    -    loc_max_diff = np.argmax(np.abs(diffs))
    -    loc = Xs[loc_max_diff]
    -    plt.annotate(s='', xy=(loc, loc), xytext=(loc, loc + diffs[loc_max_diff]), arrowprops=dict(arrowstyle='<->', shrinkA=0, shrinkB=0))
    -    plt.draw()
    -
    -    print(scipy.stats.kstest(Qs, 'uniform'))
    -
    - -
    - -

    Performs a Kolmogorov–Smirnov test for the Q-values of all fit object.

    - -

    If no list is given all Obs in memory are used.

    - -

    Disclaimer: The determination of the individual Q-values as well as this function have not been tested yet.

    -
    - -