refactor!: ks_test removed from develop for now.

pyerrors/fits.py

@@ -1,4 +1,3 @@
-import gc
 from collections.abc import Sequence
 import warnings
 import numpy as np
@@ -739,49 +738,3 @@ def error_band(x, func, beta):
     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'))