def theme_plots(dark=False):
plt.style.use('default')
from qbstyles import mpl_style
mpl_style(dark=dark)
if dark:
plt.rcParams['figure.facecolor'] = '#111111'
def main():
"""Main."""
fp = DR / '2018.csv'
df = pd.read_csv(str(fp))
mpl_style(dark=True)
fig = plt.figure(figsize=(12, 6), dpi=72) # figsize * dpi
ax = fig.add_subplot(
111, # 1行目1列の1番目
xlabel=df['GameID'].name,
# ylabel='number'
)
plt.ylabel('number', rotation=0, labelpad=30)
fig.suptitle('Multi Bars')
col_list = ['H', 'HR', 'K', 'BB']
w = 0.15
for i, col in enumerate(col_list):
ax.bar(df.index + w * (i - len(col_list) / 2),
df.loc[:, col],
label=col,
width=w,
align='edge',
zorder=10)
ax.tick_params(bottom=False)
ax.grid(axis='y', c='gainsboro', zorder=9)
for side in ['right', 'top']:
ax.spines[side].set_visible(False)
ax.legend()
fig.savefig(str(DR / 'bar.png'))
plt.show()
def main():
mpl_style(dark=False)
plt.style.use('./egta/experiments/style/style_heatmap.mplstyle')
evaluation_data_folder_path = os.path.join(ROOT_DATA_FOLDER, 'regen_exp', 'tragedy', '')
evaluation_data_save_file_name = 'evaluation_results_data.p'
algorithms_list, regen_rates_list, seeds_list, n_episodes, max_n_steps, n_agents = load_meta_data(
evaluation_data_folder_path, evaluation_data_save_file_name)
actions = load_actions(evaluation_data_folder_path, evaluation_data_save_file_name)
restraint_data = compute_average_restraint(actions, n_episodes, n_agents)
plot_restraint_heat_map(restraint_data, ALGO_NAMES_ORDERD, evaluation_data_folder_path, None,
letter="")
def render(df):
dataDir = os.path.dirname(os.path.abspath(__file__)) + "/../../data/"
dfGrowthDNM = pd.read_csv(dataDir + "darknetmarkets_growth.csv")
dfGrowthDNMN = pd.read_csv(dataDir + "darknetmarketsnoobs_growth.csv")
variants = [
("/r/DarkNetMarkets", "pre-February 2015"),
("/r/DarkNetMarkets", "post-February 2015"),
("/r/DarkNetMarketsNoobs", "pre-February 2015"),
("/r/DarkNetMarketsNoobs", "post-February 2015")
]
for (subreddit, period) in variants:
if subreddit == "/r/DarkNetMarkets":
dfRedditGrowth = dfGrowthDNM
else:
dfRedditGrowth = dfGrowthDNMN
dfRedditGrowth['date'] = pd.to_datetime(dfRedditGrowth['date'])
dfRevenue = df.groupby(['date'])['order_amount_usd'].sum().to_frame().reset_index()
dfRevenue['date'] = pd.to_datetime(dfRevenue['date'])
dfRevenue = dfRevenue.merge(dfRedditGrowth, on='date', how='inner')
dfRevenue['growth'] = dfRevenue['growth'].rolling(7, center=True).mean()
dfRevenue['order_amount_usd'] = dfRevenue['order_amount_usd'].rolling(7, center=True).mean()
dfRevenue = dfRevenue.dropna()
if period == "pre-February 2015":
dfRevenue = dfRevenue[dfRevenue['date'] < pd.to_datetime('February 15 2015')]
else:
dfRevenue = dfRevenue[dfRevenue['date'] > pd.to_datetime('February 15 2015')]
x = dfRevenue['order_amount_usd'].tolist()
y = dfRevenue['growth'].tolist()
corr, p_value = stats.kendalltau(x, y)
from qbstyles import mpl_style
mpl_style(dark=False)
plt.figure(figsize=(10,6))
points = plt.scatter(dfRevenue['order_amount_usd'], dfRevenue['growth'], c=[date2num(i.date()) for i in dfRevenue.date], s=20, cmap="plasma")
plt.colorbar(points, format=DateFormatter('%b %y'))
ax = sns.regplot("order_amount_usd", "growth", data=dfRevenue, scatter=False)
#ax.set(xlabel='Daily Revenue in USD', ylabel='Daily Growth in Users', title='Subscriber Growth of {} vs. Revenue ({})'.format(subreddit, period))
plt.ylim(bottom=-1)
ax.text(max(x) * 0.8, max(y) * 0.95, r'$\tau$=' + str(round(corr, 2)) + '\np-value= ' + str(round(p_value, 2)))
plt.show()
def main():
"""Main."""
fp = DR / '2018.csv'
df = pd.read_csv(str(fp))
mpl_style(dark=True)
fig = plt.figure(figsize=(10, 8), dpi=72) # figsize * dpi
ax = fig.add_subplot(111, xlabel=df['GameID'].name, ylabel='number')
ax.plot(df['H'])
ax.plot(df['HR'], 'rs:', label='HR', ms=10, mew=5, mec='green')
ax.plot(df['K'], marker='^', linestyle='-.')
fig.savefig(str(DR / 'plot.png'))
plt.show()
def __init__(self, params):
MWB.__init__(self, params)
QWidget.__init__(self)
self.setStyleSheet('''
background-color: transparent;
''')
mpl_style(dark=True)
self.setLayout(QVBoxLayout())
self.canvas = FigureCanvas(Figure(figsize=(5, 3)))
self.layout().addWidget(self.canvas)
self.setFixedWidth(550)
self.setFixedHeight(500)
self.ax = self.canvas.figure.subplots()
def main():
'''Main.'''
mpl_style(dark=True)
fig = plt.figure()
ims = []
for i in range(10):
rand = np.random.randn(100) # 100個の乱数を生成
im = plt.plot(rand) # 乱数をグラフにする
ims.append(im) # グラフを配列 ims に追加
# 10枚のプロットを 100ms ごとに表示
# aniは絶対要る
ani = animation.ArtistAnimation(fig, ims, interval=100)
ani.save(str(DR / 'anime.gif'), writer='pillow')
plt.show()
def main():
"""Main."""
# Create a random (normal distribution) dataset
observation_count = 1000
series_count = 5
random_data = np.random.randn(observation_count, series_count)
# print(random_data)
bucket_count = 10
mpl_style(dark=False)
fig = plt.figure(figsize=(14, 6), dpi=72)
fig.suptitle('Hist Random')
# Use default styling
plt.hist(random_data, bucket_count)
fig.savefig(str(DR / 'hist_rand.png'))
plt.show()
def main():
'''Main.'''
mpl_style(dark=False)
fig = plt.figure()
x = np.arange(0, 10, 0.1)
ims = []
for a in range(50):
y = np.sin(x - a)
line, = plt.plot(x, y, 'r')
ims.append([line])
ani = animation.ArtistAnimation(fig, ims)
ani.save(str(DR / 'anime_sin.gif'), writer='pillow')
# ani.save(
# 'anime_sin.mp4',
# writer='ffmpeg'
# )
plt.show()
def main():
mpl_style(dark=False)
plt.style.use('./egta/experiments/style/style.mplstyle')
results_data_folder_path = os.path.join(ROOT_DATA_FOLDER, 'regen_exp', '')
interactions_save_file_name = 'interaction_results_data.p'
rewards = load_rewards(results_data_folder_path,
interactions_save_file_name)
cooperators_ids = load_cooperator_ids(results_data_folder_path,
interactions_save_file_name)
transformed_data = transform_data(rewards, cooperators_ids)
xlim, ylim = calculate_global_xlim_and_ylim(transformed_data,
ALGO_NAMES_ORDERD)
xlim *= 1.1
plot_results_with_nsi_inset(transformed_data=transformed_data,
algo_names=ALGO_NAMES_ORDERD,
xlim=xlim,
folder_path=results_data_folder_path)
def main():
"""Main."""
fp = DR / '2018.csv'
data = pd.read_csv(str(fp))
mpl_style(dark=True)
fig = plt.figure(figsize=(8, 4), dpi=72) # figsize * dpi
ax = fig.add_subplot(
111, # 1行目1列の1番目
xticks=range(0, 20, 2))
n, bins, patches = ax.hist(data['R'],
bins=data['R'].max() - data['R'].min())
fig.suptitle('Hist')
for i in range(len(bins) - 1):
ax.text((bins[i + 1] + bins[i]) / 2,
n[i],
int(n[i]),
horizontalalignment='center')
fig.savefig(str(DR / 'hist.png'))
plt.show()
from argparse import ArgumentParser
from os import path
import pandas as pd
from sklearn.linear_model import LinearRegression
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import kendalltau
import seaborn as sns
from qbstyles import mpl_style
from scipy.stats import kendalltau, zscore, shapiro
mpl_style(dark=False)
class MediaCoverageCategories:
@staticmethod
def render(df, next=False):
mapping = {
'other - fake': 'other',
'other - guide': 'other',
'other - account': 'other',
'other - custom': 'other',
'other - pirated software': 'other',
'other - voucher/invite/codes/lottery/gift': 'other',
'RAT': 'hacking services & tools',
'exploits': 'hacking services & tools',
'botnet': 'hacking services & tools',
'e-mail': 'hacking services & tools',
'malware': 'hacking services & tools',
'website': 'development',
'app': 'development',
'hosting': 'development',
