diff Perceptual Evaluation/webaudioevaluationtool/scripts/score_plot.py @ 0:55c282f01a30 tip

Adding files to Repo. Initial Commit

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/Perceptual Evaluation/webaudioevaluationtool/scripts/score_plot.py	Fri Oct 16 18:04:00 2015 +0100
@@ -0,0 +1,150 @@
+#!/usr/bin/python
+
+import sys
+import os
+import csv
+import matplotlib.pyplot as plt
+import numpy as np
+import scipy as sp
+import scipy.stats
+
+# CONFIGURATION
+
+# Which type(s) of plot do you want? 
+enable_boxplot    = True      # show box plot
+enable_confidence = False     # show confidence interval
+confidence        = 0.90      # confidence value (for confidence interval plot)
+enable_individual = False     # show all individual ratings
+show_individual   = []        # show specific individuals
+show_legend       = False     # show names of individuals
+#TODO: Merge, implement this functionality
+#TODO: Control by CLI arguments (plot types, save and/or show, ...) 
+
+# Enter folder where rating CSV files are (generated with score_parser.py or same format).
+rating_folder = '../saves/ratings/' # folder with rating csv files
+
+# Font settings
+font = {'weight' : 'bold',
+        'size'   : 10}
+plt.rc('font', **font)
+
+
+# CODE
+
+# get every csv file in folder
+for file in os.listdir(rating_folder): # You have to put this in folder where rating csv files are.
+    if file.endswith(".csv"):
+        page_name = file[:-4] # file name (without extension) is page ID
+
+        # get header
+        with open(rating_folder+file, 'rb') as readfile: # read this csv file
+            filereader = csv.reader(readfile, delimiter=',')
+            headerrow = filereader.next() # use headerrow as X-axis
+            headerrow = headerrow[1:]
+
+        # read ratings into matrix
+#         ratings = np.loadtxt(open(rating_folder+file,"rb"),
+#                             delimiter=",",
+#                             skiprows=1,
+#                             usecols=range(1,len(headerrow)+1)
+#                             )
+            ratings = np.genfromtxt(readfile,
+                                   delimiter=",",
+                                   #skip_header = 1,
+                                   converters = {3: lambda s: float(s or 'Nan')},
+                                   usecols=range(1,len(headerrow)+1)
+                                   )
+        
+        # assert at least 2 subjects (move on to next file if violated)
+        if ratings.shape[0]<2:
+            print "WARNING: Just one subject for " + page_name + ". Moving on to next file."
+            break
+
+        # BOXPLOT
+        if enable_boxplot:
+            plt.boxplot(ratings)
+            
+        # CONFIDENCE INTERVAL
+        if enable_confidence:
+            iterator = 0
+            for column in ratings.T: # iterate over transposed matrix
+                # remove all 'Nan's from column
+                column = column[~np.isnan(column)]
+            
+                # get number of non-Nan ratings (= #subjects)
+                n = column.size
+        
+                # get mean
+                mean_rating = np.mean(column)
+        
+                # get errors
+                err = scipy.stats.sem(column)* sp.stats.t._ppf((1+confidence)/2., n-1)
+        
+                # draw plot
+                plt.errorbar(iterator+1, 
+                            mean_rating, 
+                            yerr=err,
+                            marker="x",
+                            color ="k",
+                            markersize=12,
+                            linestyle='None')
+                            
+                iterator += 1 # increase counter
+    
+    
+        # INDIVIDUAL PLOT
+        if enable_individual or show_individual:
+            # marker list and color map to cycle through
+            markerlist = ["x", ".", "o", "*", "+", "v", ">", "<", "8", "s", "p"]
+            colormap = ['b', 'r', 'g', 'c', 'm', 'y', 'k'] 
+            increment = 0
+            linehandles = []
+            legendnames = []
+            with open(rating_folder+file, 'rb') as readfile: # read this csv file
+                filereader = csv.reader(readfile, delimiter=',')
+                headerrow = filereader.next() # use headerrow as X-axis
+                headerrow = headerrow[1:]
+                for row in filereader:
+                    subject_id = row[0][:-4] # read from beginning of line
+                    # assume plotting all individuals if no individual(s) specified
+                    if not show_individual or subject_id in show_individual:
+                        plothandle, = plt.plot(range(1,len(row)), # x-values
+                                 ratings[increment,:],#row[1:], # y-values: csv values except subject name
+                                 color=colormap[increment%len(colormap)],
+                                 marker=markerlist[increment%len(markerlist)],
+                                 markersize=10,
+                                 linestyle='None',
+                                 label=subject_id
+                                )
+                        linehandles.append(plothandle)
+                        legendnames.append(subject_id)
+                        if show_legend:
+                            plt.legend(linehandles, legendnames,
+                                       loc='upper right',
+                                       bbox_to_anchor=(1.1, 1),
+                                       borderaxespad=0.,
+                                       numpoints=1 # remove extra marker
+                                       )
+                    increment += 1 # increase counter
+
+        # TITLE, AXIS LABELS AND LIMITS
+        plt.title(page_name)
+        plt.xlabel('Fragment')
+        plt.xlim(0, len(headerrow)+1) # only show relevant region, leave space left & right)
+        plt.xticks(range(1, len(headerrow)+1), headerrow) # show fragment names
+        plt.ylabel('Rating')
+        plt.ylim(0,1)
+        
+        
+
+        # SHOW PLOT
+        #plt.show()
+        #exit()
+    
+        # SAVE PLOT
+        # automatically 
+        plot_type = ("-box" if enable_boxplot else "") + \
+                    ("-conf" if enable_confidence else "") + \
+                    ("-ind" if enable_individual else "")
+        plt.savefig(rating_folder+page_name+plot_type+".png")
+        plt.close()