SourceCodeClassification-2019/boxplot.py at master · Kamel773/SourceCodeClassification-2019 · GitHub

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## numpy is used for creating fake data
import numpy as np
import matplotlib as mpl
import glob
import os
import matplotlib.pyplot as plt
from matplotlib.patches import Polygon
from sklearn.externals import joblib
import seaborn as sns
def file_len(fname):
    with open(fname) as f:
        for i, l in enumerate(f):
            pass
    return i + 1


## agg backend is used to create plot as a .png file
mpl.use('agg')

import matplotlib.pyplot as plt
sns.set()
sns.set(font_scale=1.1)

#arr = []
data_to_plot = []

np.random.seed(10)
total_400_lines = 0
code_loc='/Users/kamel/Desktop/JSSData/code25/'
#name_file=['c', 'c#', 'c++','java', 'css', 'haskell', 'html', 'javascript', 'lua', 'objective-c', 'perl', 'php', 'python','ruby', 'r', 'scala', 'sql', 'swift', 'vb.net','markdown','bash']
name_file=['bash','c', 'c#', 'c++', 'css', 'haskell', 'html','java','javascript', 'lua', 'markdown','objective-c', 'perl', 'php', 'python','r', 'ruby', 'scala', 'sql', 'swift', 'vb.net' ]

for item in name_file:
    code_loc_current=code_loc+item+'/'
    print(code_loc_current)
    file_list = glob.glob(os.path.join(code_loc_current, "*.txt"))
    Maxnumber = 0;
    arr = []
    for file_path in file_list:
        with open(file_path) as f_input:
            num_of_line = 0
            for num_of_line, l in enumerate(f_input):
                pass
            if num_of_line > 35:
               total_400_lines = total_400_lines + 1
               #print(num_of_line)
               #print(file_path)
               #print('----------------------------')
               continue
            if num_of_line == 0 or num_of_line == 1 or num_of_line == 2 or num_of_line == 3 :
                continue
            #Maxnumber = Maxnumber + 1
            #if Maxnumber == 50000:
                #print(Maxnumber)
            #    break
            #print(num_of_line)
            arr.append(num_of_line)
    #print(arr)
    collectn = np.random.normal(arr)
    data_to_plot.append(collectn)
    #print(arr)
#print(total_400_lines)
#from sklearn.externals import joblib
#joblib.dump(data_to_plot,"data_to_plot.pkl")

## Create data
#np.random.seed(10)
#collectn_1 = np.random.normal(arr)
#collectn_2 = np.random.normal(arr2)
#print(arr)
#print(arr2)
#collectn_3 = np.random.normal(90, 20, 200)
#collectn_4 = np.random.normal(70, 25, 200)

## combine these different collections into a list
#data_to_plot = [collectn_1, collectn_2]

## Custom x-axis labels
#data_to_plot=joblib.load("data_to_plot.pkl")
# Create a figure instance
fig = plt.figure(1, figsize=(8, 4))


'''
sns.plt.set_style("dark")
sns.plt.set_style("white",{"xtick.major.size": 10,"ytick.major.size": 10})
sns.plt.title('The Length of Code Snippets Extracted From Stack OverFlow Question Posts',weight='bold').set_fontsize('14')
sns.plt.ylabel('Number of Lines of Code',weight='bold').set_fontsize('12')
sns.plt.xlabel('Programming Languages',weight='bold').set_fontsize('12')
'''
# Create an axes instance
#ax = fig.add_subplot(111)
#ax.set_axisbelow(True)

# Create the boxplot
#plt.boxplot(data_to_plot)
sns.boxplot(data=data_to_plot)
plt.tight_layout()
name_file=[t.title() for t in name_file]
plt.xticks([0,1, 2, 3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21], name_file,ha='right',weight='bold')
plt.xticks(rotation=45)
plt.yticks([x for x in range(0,45,10)])
plt.ylim(-10,45)
plt.gcf().subplots_adjust(bottom=0.20)
# Save the figure
#plt.show()
plt.savefig('boxplot.png',dpi=200)