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proladder.py
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proladder.py
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# -*- coding: utf-8 -*-
"""
proladder crawler
"""
##TODO: add writer/loader
from bs4 import BeautifulSoup
import urllib
from collections import defaultdict
hdr = {'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.11 (KHTML, like Gecko) Chrome/23.0.1271.64 Safari/537.11',
'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
'Accept-Charset': 'ISO-8859-1,utf-8;q=0.7,*;q=0.3',
'Accept-Encoding': 'none',
'Accept-Language': 'en-US,en;q=0.8',
'Connection': 'keep-alive'}
import numpy as np
import matplotlib.pyplot as plt
#############
##player class
#############
def str2int(s):
return int(s.replace(",","").split(' ')[0].split('/')[0])
class pro_player(object):
classes = ["nilfgaard", "scoiatael", "northernkingdom", "skellige", "monster"]
class_colors = [(0.1, 0.1, 0.1, 1), (0.1, 0.8, 0.1, 1), (0.1, 0.3, 0.9, 1), (0.3, 0.3, 0.7, 1), (0.9, 0, 0, 1)]
def __init__(self, html_element):
divs = html_element.find_all("div")
self.rank = int(divs[0].text)
self.country = divs[1].i['class'][1].split('-')[-1]
self.id = divs[2].text
self.score, self.matches = self._get_score_matches(divs[3].findChildren())
self.matches = int(divs[3].span.text.split(' ')[0])
self.low = 0
for c in self.classes:
block = html_element.find("div", class_ = c)
if block == None:
self.low+=1
# print("{}: {} = {}".format(self.rank, self.low, c))
block = html_element.find("div", class_ = "lowest")
best = str2int(block.get_text(" ").split(" ")[0])
score, matches = self._get_score_matches(block.div.div.findChild().find_next_siblings())
setattr(self, c, {"best": best, "current": score, "matches":matches})
def _get_score_matches(self, block):
score = str2int(block[0].text)
matches = str2int(block[1].text)
return score, matches
def valid(self):
matches = []
scores = []
for c in self.classes:
matches += [self[c]['matches']]
scores += [self[c]['best']]
best_scores = sum(sorted(scores)[1:])
total_matches = sum(matches)
if self.matches != total_matches:
print("#{} {}: matches {} != total matches {}".format(self.rank, self.id, self.matches, total_matches))
if self.score != best_scores:
print("#{} {}: score {} != best scores {}".format(self.rank, self.id, self.score, best_scores))
return (self.matches == total_matches) and (self.score <= best_scores)
def __getitem__(self, key):
return getattr(self, key, None)
def to_list(self):
headers = ["rank", "country", "id", "score", "matches"]
values = [str(self[header]) for header in headers]
for c in self.classes:
for item in self[c].items():
headers.append("{}_{}".format(c, item[0]))
values.append(item[1])
return values, headers
#############
##player database
#############
class player_db(object):
def __init__(self):
self.ids = []
self.count = 0
self.player_list =[]
def append(self, player):
if isinstance(player, pro_player) and player.valid():
if player.id in self.ids:
print("{} already existed".format(player.id))
else:
self.ids.append(player.id)
self.player_list.append(player)
self.count += 1
else:
print("{} is not a valid pro player".format(player.rank))
def __iadd__(self, player):
self.append(player)
return self
def _class_stats(self, class_):
'''
all mean_score_x is normalized to 100 games
mean_score_c: mean class score of players completed 100 games
mean_score_a: mean class score of players played
mean_score_t: mean class score per game
'''
if class_ not in pro_player.classes:
raise NameError("{} is not a valid class".format(class_))
stats = defaultdict(lambda: 0)
for player in self.player_list:
class_dict = player[class_]
stats['total_matches'] += class_dict['matches']
if class_dict['matches'] >= 100:
stats['complete'] += 1
stats['mean_score_c'] += class_dict['best']
stats['mean_score_a'] += class_dict['best']
stats['mean_score_t'] += class_dict['current'] * class_dict['matches']
else:
if class_dict['matches'] > 0:
stats['incomplete'] += 1
stats['mean_score_a'] += class_dict['best'] * 100 / class_dict['matches']
stats['mean_score_t'] += class_dict['current'] * 100
if stats['complete'] > 0:
stats['mean_score_c'] /= stats['complete']
else:
stats['mean_score_c'] = 0
if (stats['complete'] + stats['incomplete']) > 0:
stats['mean_score_a'] /= (stats['complete'] + stats['incomplete'])
else:
stats['mean_score_a'] = 0
if stats['total_matches'] > 0:
stats['mean_score_t'] /= stats['total_matches']
else:
stats['mean_score_t'] = 0
return stats
def stats(self, plot = False):
all_stats = dict()
for c in pro_player.classes:
all_stats[c] = self._class_stats(c)
if plot:
pass
return all_stats
def plot(stats, radii = 'mean_score_c', width = 'total_matches', order = None, offset = 1150):
radius_list = np.array([stats[c][radii] for c in pro_player.classes])
width_list = np.array([stats[c][width] for c in pro_player.classes])
if order == 'radii':
order = np.argsort(radius_list)
elif order == 'width':
order = np.argsort(width_list)
else:
if isinstance(order, (list, np.ndarray, set)) and \
set(order) == set(np.arange(len(pro_player.classes))):
order = np.array(order)
else:
order = np.arange(len(pro_player.classes))
radius_list = radius_list[order]
width_list = width_list[order]
print(order)
width_list = width_list/np.sum(width_list) * 2 * np.pi
theta = np.cumsum(width_list)
theta -= width_list/2
ax = plt.subplot(111, projection='polar')
bars = ax.bar(theta, radius_list, width = width_list,
bottom=-offset, edgecolor = (1, 0.9, 0.1, 1),
color = np.array(pro_player.class_colors)[order],
tick_label = np.array(pro_player.classes)[order])
plt.show()
def test(a):
if isinstance(a, list) and len(a) == 1:
print(1)
else:
print(2)
############
##html
############
def get_page(index = 1):
base_site = "https://masters.playgwent.com/en/rankings/pro-ladder"
if index < 1:
index = 1
return "%s/%d" % (base_site, index)
def parse_page(index = 1):
site = get_page(index)
print("parsing: {}".format(site))
req = urllib.request.Request(site, headers=hdr)
page = urllib.request.urlopen(req).read()
soup = BeautifulSoup(page)
# page_string = soup.prettify()
players = soup.find_all("div", class_="c-ranking-mobile-table__body")
first_player = players[1].findChild() #using the second block which contains more info
other_players = first_player.find_next_siblings()
return [first_player] + other_players
def crawl(start_page = 1, crawl_pages = 50):
player_list = player_db()
for index in range(start_page, start_page + crawl_pages):
players = parse_page(index)
for player in players:
player_list.append(pro_player(player))
return player_list
def main():
db0 = crawl(1, 25) #first 25 pages, have completed ~2 classes
stats0 = db0.stats()
plot(stats0, order = 'radii')
db1 = crawl(25, 100) #first 25 pages, have completed ~2 classes
stats1 = db1.stats()
plot(stats1, order = 'radii')
if __name__ == "__main__":
main()
# pass