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read_FIDE.py
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read_FIDE.py
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# -*- coding: utf-8 -*-
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import scipy.stats as sc
mpl.rcParams['font.family'] = 'DejaVu Sans'
plt.rcParams['font.size'] = 12
plt.rcParams['axes.linewidth'] = 1
class read_fide:
def __init__(self):
with open('standard_rating_list.txt', 'r') as f:
data = np.empty((4, 362189), dtype=object)
k = 0
for line in f:
if k == 0:
k += 1
continue
data[0][k-1] = int(line[113:117]) # Rating
data[1][k-1] = int(line[126:130]) # Birthday
data[2][k-1] = line[76:79] # Country
data[3][k-1] = line[80:81] # Sex
k += 1
self.data = data
self.rat = data[0]
self.lab_sex = self.data[3]=='F'
self.list_cou = np.sort(list(set(data[2])))
def return_data(self):
return self.data
def global_data(self):
# Get data from top 20 countries with more players
counter = 0
data = np.empty((4, 20), dtype=object)
for cou in self.list_cou:
lab_cou = self.data[2]==cou
pob = len(self.rat[lab_cou])
if pob >= 4050:
per = (len(self.rat[lab_cou*self.lab_sex]))
data[0][counter] = cou
data[1][counter] = pob
data[2][counter] = round(per/pob, 2)
print(r'Country: %s' % cou)
print(r'Total number of players: %i' % pob)
print('Female players (%%): %.2f' % round(100*per/pob, 1))
print('\n')
print('Differences:')
diff = np.empty(3)
for k in range(3):
top = [1, 20, 100]
diff[k] = self.compare_differences(cou=cou, top=top[k])
print('%i (Top %i)' % (int(diff[k]), int(top[k])))
data[3][counter] = diff
print('\n')
counter += 1
return data
def bar_players(self):
# Bar graph of top 20 countries
data = self.global_data()
fig, ax = plt.subplots(figsize=(10,6))
ax.bar(range(20), data[1], width=.7, alpha=.6,
label='Total rated players')
ax.bar(range(20), data[2]*data[1], width=.7, alpha=.7,
color='green', label='Female rated players')
plt.ylabel('Number of players')
ax.set_xticks(range(20))
ax.set_xticklabels(data[0], fontsize=10)
ax.legend(loc='upper left', fontsize=12)
def fidedata_cou(self, cou):
# Get global information an gaussian fit of a given country
lab_bir = self.data[1]<=2000
lab_cou = self.data[2]==cou
lab_sex = self.data[3]=='F'
label = lab_bir*lab_cou
(mu, sigma) = sc.norm.fit(self.rat[label])
pop = len(self.rat[label])
ratio = len(self.rat[label*lab_sex]) / len(self.rat[label])
print(r'Normal fit for %s: %i, %i' % (cou, mu, sigma))
return int(mu), int(sigma), pop, round(ratio, 3)
def hist_country(self, cou):
# Plot ELO histogram and gaussian fit for a given country
lab_bir = self.data[1]<=2000
lab_cou = self.data[2]==cou
label = lab_bir*lab_cou
fig, ax = plt.subplots(figsize=[6, 6])
n, bins, patches = ax.hist(self.rat[label], histtype='step', bins=80,
label='FIDE data')
#plt.hist(self.rat[label*self.lab_sex], histtype='step', bins=80,
# normed=1)
(mu, sigma) = sc.norm.fit(self.rat[label])
y = mpl.mlab.normpdf(bins.astype(int), mu, sigma)
ax.plot(bins, 1.2*sum(bins)*y, 'r--', linewidth=3,
label = r'Normal fit: $\mu = %i,\ \sigma = %i$' % (int(mu), int(sigma)))
plt.xlim(mu-2.5*sigma, mu+2.5*sigma)
plt.ylim(0, 320)
plt.xlabel('ELO rating')
plt.ylabel('Number of players')
handles, labels = ax.get_legend_handles_labels()
plt.legend(handles[::-1], labels[::-1], fontsize=10, loc='upper left')
ax.xaxis.set_tick_params(which='major', size=7, width=1.5,
direction='inout', top='off')
ax.yaxis.set_tick_params(which='major', size=7, width=1.5,
direction='inout', right='off')
plt.grid()
def compare_differences(self, cou, top):
lab_bir = self.data[1] <= 2000
lab_cou = self.data[2] == cou
lab_sexf = self.data[3] == 'F'
lab_sexm = self.data[3] == 'M'
rat_men = np.sort(self.rat[lab_bir*lab_cou*lab_sexm])[::-1]
rat_wom = np.sort(self.rat[lab_bir*lab_cou*lab_sexf])[::-1]
diff = np.mean(rat_men[:top]) - np.mean(rat_wom[:top])
print('Difference (Top %i in %s): %i' % (top, cou, int(diff)))
return diff
if __name__ == '__main__':
# Example
# read_fide().hist_country(cou='ITA')