import numpy as npy
import matplotlib.pyplot as plt
import csv

names = []
values = []
with open('marketCap.txt', 'r') as infile:
    csv_reader = csv.reader(infile, delimiter=';')
    for line in csv_reader:
        names.append(line[0].strip())
        #Calculate ratio of traded value (Lakh) and M-Cap (Crore)
        # x is in % as 100 Lakh = 1 Crore
        x = float(line[3].strip()) / float(line[2].strip())
        values.append(x)
infile.close()

fig = plt.figure(1)             #Prepare the plot
ax  = fig.add_subplot(111)      #Define a sub-plot
fig.set_size_inches(9.0,6.0)    #plt.rcParams["figure.figsize"]=(W,H)
# get the current axes, creating them if necessary:
#axs = plt.gca()
#
plt.title("Free-Float M-Cap vs. Trading Volume") 
plt.xlabel("Scrip Name") 
plt.ylabel("Trade/M-Cap (%)")
#
# use keyword args: plt.setp(lines, color='r', linewidth=1.0)
# MATLAB style: plt.setp(lines, 'color', 'r', 'linewidth', 1.0)
#
plt.plot(names, values, marker='s', ms=3, linewidth=1.0, linestyle='dashed')
#
#Borders or spines of the plot and margin/padding (% of plot width/height)
#ax.spines['left'].set_position('zero')
#ax.spines['bottom'].set_position(('data',0))
#ax.spines['right'].set_visible(False)
ax.margins(x=0.01, y=0.01)
# set the locations and labels of the xticks
plt.xticks(npy.arange(len(names)), names)
#
plt.xticks(rotation=90)
plt.tick_params(axis='both', which='major', labelsize=8)
plt.tick_params(axis='both', which='minor', labelsize=7)
plt.grid(alpha=0.25) #Transparency/opaqeness of grid-lines
#
plt.subplots_adjust(bottom=0.2)
plt.grid(True)
plt.savefig('Mcap_TradeValue.png', dpi=100)
plt.show()