Update

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2021 CandidatesClassifier
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

MLconfig_variables.py → cand_class/MLconfig_variables.py

@@ -6,6 +6,15 @@
 from scipy.stats import binned_statistic as b_s
 import matplotlib as mpl
 
+from hipe4ml import plot_utils
+
+def correlation_matrix(bgr, sign, vars_to_draw, leg_labels, output_path):
+    res_s_b = plot_utils.plot_corr([bgr, sign], vars_to_draw, leg_labels)
+    res_s_b[0].savefig(output_path+'/'+'corr_matrix_bgr.png')
+    res_s_b[1].savefig(output_path+'/'+'corr_matrix_sign.png')
+
+
+
 def calculate_correlation(df, vars_to_corr, target_var) :
     """
     Calculates correlations with target variable variable and standart errors
@@ -56,18 +65,18 @@ def plot1Dcorrelation(vars_to_draw,var_to_corr, corr_signal, corr_signal_errors,
          background covariance standart error of the mean
     output_path:
           path that contains output plot
-
     """
 
-    fig, ax = plt.subplots(figsize=(20,10))
-    plt.errorbar(vars_to_draw, corr_signal, yerr=corr_signal_errors, fmt='')
-    plt.errorbar(vars_to_draw, corr_bg, yerr=corr_bg_errors, fmt='')
+    fig, ax = plt.subplots(figsize=(10,6))
+    plt.errorbar(vars_to_draw, corr_signal, yerr=corr_signal_errors, fmt='--o')
+    plt.errorbar(vars_to_draw, corr_bg, yerr=corr_bg_errors, fmt='--o')
     ax.grid(zorder=0)
-    ax.set_xticklabels(vars_to_draw, fontsize=25, rotation =70)
-    ax.set_yticklabels([-0.5,-0.4,  -0.2,0, -0.2, 0.4], fontsize=25)
-    plt.legend(('signal','background'), fontsize = 25)
-    plt.title('Correlation of all variables with '+ var_to_corr+' along with SEM', fontsize = 25)
-    plt.ylabel('Correlation coefficient', fontsize = 25)
+    ax.set_xticklabels(vars_to_draw, fontsize=15, rotation =70)
+    # ax.set_yticklabels([-0.5,-0.4,  -0.2,0, -0.2, 0.4], fontsize=25)
+    ax.yaxis.set_tick_params(labelsize=15)
+    plt.legend(('signal','background'), fontsize = 15)
+    plt.title('Correlation of all variables with '+ var_to_corr+' along with SEM', fontsize = 18)
+    plt.ylabel('Correlation coefficient', fontsize = 15)
     fig.tight_layout()
     fig.savefig(output_path+'/all_vars_corr-'+ var_to_corr+'.png')
 
@@ -78,33 +87,24 @@ def profile_mass(df,variable_xaxis, sign, peak, edge_left, edge_right, pdf_key):
     This function takes the entries of the variables and distributes them in 25 bins.
     The function then plots the bin centers of the first variable on the x-axis and
     the mean values of the bins of the second variable on the y-axis, along with its bin stds.
-
     Parameters
     ------------------------------------------------
     df: pandas.DataFrame
         input DataFrame
-
     variable_xaxis: str
         variable to be plotted on x axis (invariant mass)
-
     x_unit: str
         x axis variable units
-
     variable_yaxis: str
         variable to be plotted on y axis
-
     sgn: int(0 or 1)
          signal definition(0 background, 1 signal)
-
     pdf_key: matplotlib.backends.backend_pdf.PdfPages
         output pdf file
-
     peak: int
         invariant mass peak position
-
     edge_left: int
         left edge of x axis variable
-
     edge_right: int
         left edge of y axis variable
     """
@@ -119,7 +119,7 @@ def profile_mass(df,variable_xaxis, sign, peak, edge_left, edge_right, pdf_key):
     for var in df.columns:
         if var != variable_xaxis:
 
-            fig, axs = plt.subplots(figsize=(20, 15))
+            fig, axs = plt.subplots(figsize=(10, 6))
 
             bin_means, bin_edges, binnumber = b_s(df[variable_xaxis],df[var], statistic='mean', bins=25)
             bin_std, bin_edges, binnumber = b_s(df[variable_xaxis],df[var], statistic='std', bins=25)
@@ -134,19 +134,21 @@ def profile_mass(df,variable_xaxis, sign, peak, edge_left, edge_right, pdf_key):
             bin_std = np.delete(bin_std , nan_ind)
 
 
-            plt.errorbar(x=bin_centers, y=bin_means, yerr=(bin_std/np.sqrt(bin_count)), linestyle='none', marker='.',mfc='red', ms=10)
-
+            plt.errorbar(x=bin_centers, y=bin_means, yerr=(bin_std/np.sqrt(bin_count)), linestyle='none', linewidth = 2, marker='.',mfc='red', ms=15)
 
+            plt.locator_params(axis='y', nbins=5)
+            plt.locator_params(axis='x', nbins=5)
 
-            plt.title('Mean of ' +var+ ' plotted versus bin centers of '+variable_xaxis+ \
-                      '('+keyword+')', fontsize=25)
-            plt.xlabel('Mass', fontsize=25)
-            plt.ylabel("Mean of each bin with the SEM ($\dfrac{bin\ std}{\sqrt{bin\ count}}$) of bin", fontsize=25)
+            plt.title('Mean of ' +var+ '  vs bin centers of '+variable_xaxis+ \
+                      '('+keyword+')', fontsize=19)
+            plt.xlabel('Mass', fontsize=17)
+            plt.ylabel(" SEM ($\dfrac{bin\ std}{\sqrt{bin\ count}}$) of bin", fontsize=17)
 
 
-            plt.vlines(x=peak,ymin=bin_means.min(),ymax=bin_means.max(), color='r', linestyle='-')
-
+            plt.vlines(x=peak,ymin=bin_means.min(),ymax=bin_means.max(), color='r', linestyle='-', linewidth = 3)
 
+            axs.xaxis.set_tick_params(labelsize=16)
+            axs.yaxis.set_tick_params(labelsize=16)
             fig.tight_layout()
             plt.savefig(pdf_key,format='pdf')
 
@@ -160,13 +162,10 @@ def plot2D_all(df, sample, sgn, pdf_key):
     ------------------------------------------------
     df: pandas.DataFrame
         input dataframe
-
     sample: str
          title of the sample
-
     sgn: int(0 or 1)
          signal definition(0 background, 1 signal)
-
     pdf_key: matplotlib.backends.backend_pdf.PdfPages
         output pdf file
     """
@@ -206,48 +205,49 @@ def plot2D_mass(df, sample, mass_var, mass_range, sgn, peak, pdf_key):
     ------------------------------------------------
     df: pandas.DataFrame
         input dataframe
-
     sample: str
          title of the sample
-
-
     mass_var: str
         name of the invariant mass variable
-
     mass_range: list
         mass range to be plotted
-
     sgn: int(0 or 1)
          signal definition(0 background, 1 signal)
-
     peak: int
         invariant mass value
-
     pdf_key: matplotlib.backends.backend_pdf.PdfPages
         output pdf file
     """
 
     for var in df.columns:
         if var != mass_var:
-            fig, axs = plt.subplots(figsize=(15, 10))
+            fig, axs = plt.subplots(figsize=(6, 4))
             cax = plt.hist2d(df[mass_var],df[var],range=[mass_range, [df[var].min(), df[var].max()]], bins=100,
                         norm=mpl.colors.LogNorm(), cmap=plt.cm.viridis)
 
 
             if sgn==1:
-                plt.title('Signal candidates ' + sample, fontsize = 25)
+                plt.title('Signal candidates ' + sample, fontsize = 15)
 
             if sgn==0:
-                plt.title('Background candidates ' + sample, fontsize = 25)
+                plt.title('Background candidates ' + sample, fontsize = 15)
 
 
-            plt.xlabel(mass_var, fontsize=25)
-            plt.ylabel(var, fontsize=25)
+            plt.xlabel(mass_var, fontsize=16)
+            plt.ylabel(var, fontsize=16)
 
-            plt.vlines(x=peak,ymin=df[var].min(),ymax=df[var].max(), color='r', linestyle='-')
+            plt.vlines(x=peak,ymin=df[var].min(),ymax=df[var].max(), color='r', linestyle='-', linewidth = 4)
 
             mpl.pyplot.colorbar()
 
+
+            axs.xaxis.set_tick_params(labelsize=11)
+            axs.yaxis.set_tick_params(labelsize=11)
+
+            plt.locator_params(axis='y', nbins=5)
+            plt.locator_params(axis='x', nbins=5)
+
+
             plt.legend(shadow=True,title =str(len(df))+ " samples")
 
             fig.tight_layout()