ks_test moved to fits module

README.md

@@ -1,4 +1,4 @@
-[![flake8 Lint](https://github.com/fjosw/pyerrors/actions/workflows/flake8.yml/badge.svg)](https://github.com/fjosw/pyerrors/actions/workflows/flake8.yml) [![CI](https://github.com/fjosw/pyerrors/actions/workflows/CI.yml/badge.svg)](https://github.com/fjosw/pyerrors/actions/workflows/CI.yml) [![](https://img.shields.io/badge/python-3.6+-blue.svg)](https://www.python.org/downloads/)
+[![flake8](https://github.com/fjosw/pyerrors/actions/workflows/flake8.yml/badge.svg)](https://github.com/fjosw/pyerrors/actions/workflows/flake8.yml) [![CI](https://github.com/fjosw/pyerrors/actions/workflows/CI.yml/badge.svg)](https://github.com/fjosw/pyerrors/actions/workflows/CI.yml) [![](https://img.shields.io/badge/python-3.6+-blue.svg)](https://www.python.org/downloads/)
 # pyerrors
 `pyerrors` is a python package for error computation and propagation of Markov chain Monte Carlo data.
 It is based on the **gamma method** [arXiv:hep-lat/0306017](https://arxiv.org/abs/hep-lat/0306017). Some of its features are:

pyerrors/fits.py

@@ -1,6 +1,7 @@
 #!/usr/bin/env python
 # coding: utf-8
 
+import gc
 import warnings
 import numpy as np
 import autograd.numpy as anp
@@ -598,6 +599,52 @@ def error_band(x, func, beta):
     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.show()
+
+    print(scipy.stats.kstest(Qs, 'uniform'))
+
+
 def fit_general(x, y, func, silent=False, **kwargs):
     """Performs a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.
 

pyerrors/misc.py

@@ -1,10 +1,7 @@
 #!/usr/bin/env python
 # coding: utf-8
 
-import gc
 import numpy as np
-import scipy.stats
-import matplotlib.pyplot as plt
 from .pyerrors import Obs
 
 
@@ -38,47 +35,3 @@ def gen_correlated_data(means, cov, name, tau=0.5, samples=1000):
         data.append(np.sqrt(1 - a ** 2) * rand[i] + a * data[-1])
     corr_data = np.array(data) - np.mean(data, axis=0) + means
     return [Obs([dat], [name]) for dat in corr_data.T]
-
-
-def ks_test(obs=None):
-    """Performs a Kolmogorov–Smirnov test for the Q-values of a list of Obs.
-
-    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.
-    """
-
-    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.show()
-
-    print(scipy.stats.kstest(Qs, 'uniform'))