annotate scripts/score_plot.py @ 1316:279930a008ca

All interfaces support comment boxes. Comment box identification matches presented tag (for instance, AB will be Comment on fragment A, rather than 1). Tighter buffer loading protocol, audioObjects register with the buffer rather than checking for buffer existence (which can be buggy depending on the buffer state). Buffers now have a state to ensure exact location in loading chain (downloading, decoding, LUFS, ready).

#!/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

# COMMAND LINE ARGUMENTS

#TODO: Merge, implement this functionality
#TODO: Control by CLI arguments (plot types, save and/or show, ...) 

assert len(sys.argv)<4, "score_plot takes at most 2 command line arguments\n"+\
                        "Use: python score_plot.py [ratings_folder_location]."+\
                        "Type 'python score_plot.py -h' for more options"

# initialise plot types (false by default) and options
enable_boxplot    = False     # 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 (empty: show all individuals found)
show_legend       = False     # show names of individuals

# DEFAULT: Looks in 'saves/ratings/' folder from 'scripts/' folder
rating_folder = "../saves/ratings/" 

# XML results files location
if len(sys.argv) == 1: # no extra arguments
    enable_boxplot    = True # show box plot
    print "Use: python score_plot.py [rating folder] [plot_type] [-l/-legend]"
    print "Type 'python score_plot.py -h' for help."
    print "Using default path: " + rating_folder + " with boxplot."
else:
    for arg in sys.argv: # go over all arguments
        if arg == '-h':
            # show help
            #TODO: replace with contents of helpfile score_plot.info (or similar)
            print "Use: python score_plot.py [rating_folder] [plot_type] [-l] [confidence]"
            print "   rating_folder:"
            print "            folder where output of 'score_parser' can be found, and"
            print "            where plots will be stored."
            print "            By default, '../saves/ratings/' is used."
            print ""
            print "PLOT TYPES"
            print " Can be used in combination."
            print "    box | boxplot | -b"
            print "            Enables the boxplot" 
            print "    conf | confidence | -c"
            print "            Enables the confidence interval plot" 
            print "    ind | individual | -i"
            print "            Enables plot of individual ratings" 
            print ""
            print "PLOT OPTIONS"
            print "    leg | legend | -l"
            print "            For individual plot: show legend with individual file names"
            print "    numeric value between 0 and 1, e.g. 0.95"
            print "            For confidence interval plot: confidence value"
            assert False, ""# stop immediately after showing help #TODO cleaner way
            
        # PLOT TYPES
        elif arg == 'box' or arg == 'boxplot' or arg == '-b':
            enable_boxplot    = True     # show box plot
        elif arg == 'conf' or arg == 'confidence' or arg == '-c':
            enable_confidence = True     # show confidence interval
            #TODO add confidence value input
        elif arg == 'ind' or arg == 'individual' or arg == '-i':
            enable_individual = True     # show all individual ratings
            
        # PLOT OPTIONS
        elif arg == 'leg' or arg == 'legend' or arg == '-l':
            if not enable_individual: 
                print "WARNING: The 'legend' option is only relevant to plots of "+\
                      "individual ratings"
            show_legend = True     # show all individual ratings
        elif arg.isdigit():
            if not enable_confidence: 
                print "WARNING: The numeric confidence value is only relevant when "+\
                      "confidence plot is enabled"
            if float(arg)>0 and float(arg)<1:
                confidence = float(arg)
            else: 
                print "WARNING: The confidence value needs to be between 0 and 1"
        
        # FOLDER NAME
        else: 
             # assume it's the folder name
             rating_folder = arg

# at least one plot type should be selected: box plot by default
if not enable_boxplot and not enable_confidence and not enable_individual:
    enable_boxplot = True

# check if folder_name exists
if not os.path.exists(rating_folder):
    #the file is not there
    print "Folder '"+rating_folder+"' does not exist."
    sys.exit() # terminate script execution
elif not os.access(os.path.dirname(rating_folder), os.W_OK):
    #the file does exist but write rating_folder are not given
    print "No write privileges in folder '"+rating_folder+"'."


# CONFIGURATION

# 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):
    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, rotation=90) # 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+".pdf", bbox_inches='tight')
        plt.close()