from datetime import datetime
import matplotlib.pyplot as plt
from matplotlib.ticker import NullFormatter
from matplotlib import lines
import numpy as np
import angrymetalpy as amp
if __name__ == '__main__':
reviews = amp.reviews_from_json('data_20180422.txt')
print(len(reviews))
min_date, max_date = amp.date_range(reviews)
num_months = amp.months_between(min_date, max_date) + 1
scores = np.zeros(num_months)
counts = np.zeros(num_months)
perfect_albums = [[] for _ in range(num_months)]
for rev in reviews:
idx = amp.months_between(min_date, rev.date)
scores[idx] += rev.score
counts[idx] += 1
if rev.score == 5.0:
perfect_albums[idx].append(rev.album)
scores /= counts # average scores per month
score_unc = np.sqrt(counts) / counts
fig = plt.figure(figsize=(5, 4), dpi=100)
rect = (1, 1, 1, 1)
#ax2 = fig.add_axes(rect, label='axis2')
import matplotlib.pyplot as plt
import numpy as np
import angrymetalpy as amp
if __name__ == '__main__':
reviews = amp.reviews_from_json('data_20180422.txt')
amg_reviewers = amp.reviewers_from_reviews(reviews)
amg_reviewers = sorted(amg_reviewers, key=lambda x: len(x.reviews))
fig = plt.figure(figsize=(5, 8), dpi=100)
ax = fig.add_subplot(111)
min_date, max_date = amp.date_range(reviews)
n_months = amp.months_between(min_date, max_date)
# timeline of reviewer activity
xs = []
ys = []
for i, reviewer in enumerate(amg_reviewers):
for review in reviewer.reviews:
xs.append(amp.months_between(min_date, review.date))
ys.append(i)
xbins = np.arange(0, n_months + 1, 1)
ybins = np.arange(0, len(amg_reviewers) + 1, 1)
ax.hist2d(xs, ys, bins=[xbins, ybins])
ax.set_ylim(0, len(amg_reviewers) - 0.5)
# y axis should line up reviewer names with rows
import matplotlib.pyplot as plt
import numpy as np
import angrymetalpy as amp
if __name__ == '__main__':
reviews = amp.reviews_from_json('data_20180422.txt')
amg_reviewers = amp.reviewers_from_reviews(reviews)
amg_reviewers = sorted(amg_reviewers, key=lambda x: len(x.reviews))
fig = plt.figure()
ax = fig.add_subplot(111)
min_date, max_date = amp.date_range(reviews)
n_months = amp.months_between(min_date, max_date)
xs = np.arange(start=0, stop=n_months, step=1)
# timeline of reviewer activity
for i, reviewer in enumerate(amg_reviewers):
ys = np.zeros(n_months)
ys_counts = np.zeros(n_months)
for review in reviewer.reviews:
idx = amp.months_between(min_date, review.date)
ys[idx - 1] += review.score
ys_counts[idx - 1] += 1
idxs = np.where(ys_counts > 3)
ys[idxs] /= ys_counts[idxs]
if len(idxs[0]) > 1:
import datetime as dt
import numpy as np
import matplotlib.pyplot as plt
import angrymetalpy as amp
if __name__ == '__main__':
reviews = amp.reviews_from_json('data_20180422.txt')
min_date, max_date = amp.date_range(reviews)
num_months = amp.months_between(min_date, max_date) + 1
sc = []
sc_past = []
six_months_ago = dt.datetime.today() - dt.timedelta(6*365/12)
for rev in reviews:
sc.append(rev.score)
if rev.date > six_months_ago:
sc_past.append(rev.score)
print(np.mean(sc), np.median(sc))
fig_hist = plt.figure(figsize=(5,4), dpi=100)
axhist = fig_hist.add_subplot(111)
axhist.hist(sc, bins=np.arange(0, 6, step=0.5))
axhist.set_ylabel('Counts')
axhist.set_xlabel('Score')
axhist.set_xlim(0, 5.25)
axhist.set_title('All Scores')
xtickpos = 0.25 + np.arange(0, 6, step=0.5)
plt.xticks(xtickpos, np.arange(0, 6, step=0.5))