1 files changed, 2 insertions, 48 deletions
diff --git a/utils/plot b/utils/plot
index 548452b..72a7754 100755
--- a/utils/plot
+++ b/utils/plot
@@ -15,15 +15,6 @@
 # this program. If not, see <https://www.gnu.org/licenses/>.
 
 from __future__ import print_function, division
-import yaml
-try:
-    from yaml import CLoader as Loader
-except ImportError:
-    from yaml.loader import SafeLoader as Loader
-
-import numpy as np
-from scipy.stats import iqr
-from matplotlib.lines import Line2D
 
 # on retina screens, the default plots are way too small
 # by using Qt5 and setting QT_AUTO_SCREEN_SCALE_FACTOR=1
@@ -31,51 +22,14 @@ from matplotlib.lines import Line2D
 import matplotlib
 matplotlib.use("Qt5Agg")
 
-IDP_E_MINUS  =    20
-IDP_MU_MINUS =    22
-
-SNOMAN_MASS = {
-    20: 0.511,
-    21: 0.511,
-    22: 105.658,
-    23: 105.658
-}
-
-def plot_hist(x, label=None):
-    # determine the bin width using the Freedman Diaconis rule
-    # see https://en.wikipedia.org/wiki/Freedman%E2%80%93Diaconis_rule
-    h = 2*iqr(x)/len(x)**(1/3)
-    n = max(int((np.max(x)-np.min(x))/h),10)
-    bins = np.linspace(np.min(x),np.max(x),n)
-    plt.hist(x, bins=bins, histtype='step', label=label)
-
-def plot_legend(n):
-    plt.figure(n)
-    ax = plt.gca()
-    handles, labels = ax.get_legend_handles_labels()
-    new_handles = [Line2D([],[],c=h.get_edgecolor()) for h in handles]
-    plt.legend(handles=new_handles,labels=labels)
-
-def get_stats(x):
-    """
-    Returns a tuple (mean, error mean, std, error std) for the values in x.
-
-    The formula for the standard error on the standard deviation comes from
-    https://stats.stackexchange.com/questions/156518.
-    """
-    mean = np.mean(x)
-    std = np.std(x)
-    n = len(x)
-    u4 = np.mean((x-mean)**4)
-    error = np.sqrt((u4-(n-3)*std**4/(n-1))/n)/(2*std)
-    return mean, std/np.sqrt(n), std, error
-
 if __name__ == '__main__':
     import argparse
     import matplotlib.pyplot as plt
     import numpy as np
     import h5py
     import pandas as pd
+    from sddm import IDP_E_MINUS, IDP_MU_MINUS, SNOMAN_MASS
+    from sddm.plot import plot_hist, plot_legend, get_stats
 
     parser = argparse.ArgumentParser("plot fit results")
     parser.add_argument("filenames", nargs='+', help="input files")