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4aa1b80cadb82ba229ab290bebcc4d8986d1218fe2c95eeeb9d265c94de6bbd2 | def data_sorting(fname, keyword, limit=10, output_file=False, line_plot=False, bar_plot=False, save_fig=False):
"\n Sorting out the total sale of a certain type (keyword) video games each year.\n Only top 'limit' video games are listed in the file and picture.\n \n Args:\n :param fname: string\n :param keyword: 'Genre', 'ESRB_Rating', 'Platform', 'Publisher', 'Developer'\n :param limit: integer, only show top 'limit' number of data\n Return:\n A sorted dataframe\n "
assert isinstance(fname, str), 'fname is not a string'
assert isinstance(keyword, str), 'keyword is not a string'
assert isinstance(limit, int), 'limit is not a integer'
df = pd.read_csv(fname, delimiter=',')
(nrow, ncol) = df.shape
keyword_type = list(df[keyword].value_counts().index)
print(('There are %d keyword types' % len(keyword_type)))
year_range = list(df['Year'].value_counts().index)
print(('There are %d years' % len(year_range)))
output = pd.DataFrame(0, index=year_range, columns=keyword_type).sort_index(axis=0)
for i in range(nrow):
output.loc[(df['Year'][i], df[keyword][i])] += df['Total_Shipped'][i]
output['total'] = output.sum(axis=1)
output = output.append(pd.Series(output.sum(axis=0), name='total'))
output = output.sort_values(by='total', axis=1, ascending=False)
output = output.drop(list(output)[(limit + 2):], axis=1)
output['total'] = output.drop('total', axis=1).sum(axis=1)
output = output.round(2)
if output_file:
output.to_csv(('../../../conf/video_games/output/vgsales-%s-year.csv' % keyword))
output.drop('total', axis=1, inplace=True)
output.drop('total', axis=0, inplace=True)
output.drop(output.columns[0], axis=1, inplace=True)
ind = list(range(2004, 2019))
plt.rcParams.update({'font.size': 20})
if line_plot:
(fig, ax) = plt.subplots(figsize=(12, 6))
[plt.plot(output[i][:(- 2)], label=i, linewidth=5) for i in output.columns.values]
plt.legend(bbox_to_anchor=(1, 1), prop={'size': 15}, frameon=False)
plt.grid()
plt.ylabel('Total Sales (millions)', fontsize=25)
plt.xticks(ind, rotation=45)
plt.yticks(fontsize=25)
plt.xlim(min(ind), max(ind))
plt.ylim(0, output.max().max())
ax.xaxis.set_major_formatter(FormatStrFormatter('%d'))
if save_fig:
plt.savefig(f'../../../saved_plots/vgsales-{keyword}-year_line.png', bbox_inches='tight')
elif bar_plot:
(fig, ax) = plt.subplots(figsize=(12, 6))
axes = []
agg_sum = np.zeros(len(ind))
for i in list(output.columns.values):
axes.append(plt.bar(ind, output[i][:(- 2)], label=i, edgecolor='none', bottom=agg_sum, zorder=3))
agg_sum += output[i].values[:(- 2)]
plt.legend(bbox_to_anchor=(1, 1), prop={'size': 15}, frameon=False)
plt.grid(axis='y', zorder=0)
plt.ylabel('Total Sales (millions)', fontsize=25)
plt.xticks(ind, rotation=45)
plt.yticks(fontsize=20)
plt.xlim((min(ind) - 1), (max(ind) + 1))
ax.xaxis.set_major_formatter(FormatStrFormatter('%d'))
if save_fig:
plt.savefig(f'../../../saved_plots/vgsales-{keyword}-year_bar.png', bbox_inches='tight')
return output | Sorting out the total sale of a certain type (keyword) video games each year.
Only top 'limit' video games are listed in the file and picture.
Args:
:param fname: string
:param keyword: 'Genre', 'ESRB_Rating', 'Platform', 'Publisher', 'Developer'
:param limit: integer, only show top 'limit' number of data
Return:
A sorted dataframe | src/analytics/video_games/data_preprocessing.py | data_sorting | manjotms10/google-trends-analytics | 6 | python | def data_sorting(fname, keyword, limit=10, output_file=False, line_plot=False, bar_plot=False, save_fig=False):
"\n Sorting out the total sale of a certain type (keyword) video games each year.\n Only top 'limit' video games are listed in the file and picture.\n \n Args:\n :param fname: string\n :param keyword: 'Genre', 'ESRB_Rating', 'Platform', 'Publisher', 'Developer'\n :param limit: integer, only show top 'limit' number of data\n Return:\n A sorted dataframe\n "
assert isinstance(fname, str), 'fname is not a string'
assert isinstance(keyword, str), 'keyword is not a string'
assert isinstance(limit, int), 'limit is not a integer'
df = pd.read_csv(fname, delimiter=',')
(nrow, ncol) = df.shape
keyword_type = list(df[keyword].value_counts().index)
print(('There are %d keyword types' % len(keyword_type)))
year_range = list(df['Year'].value_counts().index)
print(('There are %d years' % len(year_range)))
output = pd.DataFrame(0, index=year_range, columns=keyword_type).sort_index(axis=0)
for i in range(nrow):
output.loc[(df['Year'][i], df[keyword][i])] += df['Total_Shipped'][i]
output['total'] = output.sum(axis=1)
output = output.append(pd.Series(output.sum(axis=0), name='total'))
output = output.sort_values(by='total', axis=1, ascending=False)
output = output.drop(list(output)[(limit + 2):], axis=1)
output['total'] = output.drop('total', axis=1).sum(axis=1)
output = output.round(2)
if output_file:
output.to_csv(('../../../conf/video_games/output/vgsales-%s-year.csv' % keyword))
output.drop('total', axis=1, inplace=True)
output.drop('total', axis=0, inplace=True)
output.drop(output.columns[0], axis=1, inplace=True)
ind = list(range(2004, 2019))
plt.rcParams.update({'font.size': 20})
if line_plot:
(fig, ax) = plt.subplots(figsize=(12, 6))
[plt.plot(output[i][:(- 2)], label=i, linewidth=5) for i in output.columns.values]
plt.legend(bbox_to_anchor=(1, 1), prop={'size': 15}, frameon=False)
plt.grid()
plt.ylabel('Total Sales (millions)', fontsize=25)
plt.xticks(ind, rotation=45)
plt.yticks(fontsize=25)
plt.xlim(min(ind), max(ind))
plt.ylim(0, output.max().max())
ax.xaxis.set_major_formatter(FormatStrFormatter('%d'))
if save_fig:
plt.savefig(f'../../../saved_plots/vgsales-{keyword}-year_line.png', bbox_inches='tight')
elif bar_plot:
(fig, ax) = plt.subplots(figsize=(12, 6))
axes = []
agg_sum = np.zeros(len(ind))
for i in list(output.columns.values):
axes.append(plt.bar(ind, output[i][:(- 2)], label=i, edgecolor='none', bottom=agg_sum, zorder=3))
agg_sum += output[i].values[:(- 2)]
plt.legend(bbox_to_anchor=(1, 1), prop={'size': 15}, frameon=False)
plt.grid(axis='y', zorder=0)
plt.ylabel('Total Sales (millions)', fontsize=25)
plt.xticks(ind, rotation=45)
plt.yticks(fontsize=20)
plt.xlim((min(ind) - 1), (max(ind) + 1))
ax.xaxis.set_major_formatter(FormatStrFormatter('%d'))
if save_fig:
plt.savefig(f'../../../saved_plots/vgsales-{keyword}-year_bar.png', bbox_inches='tight')
return output | def data_sorting(fname, keyword, limit=10, output_file=False, line_plot=False, bar_plot=False, save_fig=False):
"\n Sorting out the total sale of a certain type (keyword) video games each year.\n Only top 'limit' video games are listed in the file and picture.\n \n Args:\n :param fname: string\n :param keyword: 'Genre', 'ESRB_Rating', 'Platform', 'Publisher', 'Developer'\n :param limit: integer, only show top 'limit' number of data\n Return:\n A sorted dataframe\n "
assert isinstance(fname, str), 'fname is not a string'
assert isinstance(keyword, str), 'keyword is not a string'
assert isinstance(limit, int), 'limit is not a integer'
df = pd.read_csv(fname, delimiter=',')
(nrow, ncol) = df.shape
keyword_type = list(df[keyword].value_counts().index)
print(('There are %d keyword types' % len(keyword_type)))
year_range = list(df['Year'].value_counts().index)
print(('There are %d years' % len(year_range)))
output = pd.DataFrame(0, index=year_range, columns=keyword_type).sort_index(axis=0)
for i in range(nrow):
output.loc[(df['Year'][i], df[keyword][i])] += df['Total_Shipped'][i]
output['total'] = output.sum(axis=1)
output = output.append(pd.Series(output.sum(axis=0), name='total'))
output = output.sort_values(by='total', axis=1, ascending=False)
output = output.drop(list(output)[(limit + 2):], axis=1)
output['total'] = output.drop('total', axis=1).sum(axis=1)
output = output.round(2)
if output_file:
output.to_csv(('../../../conf/video_games/output/vgsales-%s-year.csv' % keyword))
output.drop('total', axis=1, inplace=True)
output.drop('total', axis=0, inplace=True)
output.drop(output.columns[0], axis=1, inplace=True)
ind = list(range(2004, 2019))
plt.rcParams.update({'font.size': 20})
if line_plot:
(fig, ax) = plt.subplots(figsize=(12, 6))
[plt.plot(output[i][:(- 2)], label=i, linewidth=5) for i in output.columns.values]
plt.legend(bbox_to_anchor=(1, 1), prop={'size': 15}, frameon=False)
plt.grid()
plt.ylabel('Total Sales (millions)', fontsize=25)
plt.xticks(ind, rotation=45)
plt.yticks(fontsize=25)
plt.xlim(min(ind), max(ind))
plt.ylim(0, output.max().max())
ax.xaxis.set_major_formatter(FormatStrFormatter('%d'))
if save_fig:
plt.savefig(f'../../../saved_plots/vgsales-{keyword}-year_line.png', bbox_inches='tight')
elif bar_plot:
(fig, ax) = plt.subplots(figsize=(12, 6))
axes = []
agg_sum = np.zeros(len(ind))
for i in list(output.columns.values):
axes.append(plt.bar(ind, output[i][:(- 2)], label=i, edgecolor='none', bottom=agg_sum, zorder=3))
agg_sum += output[i].values[:(- 2)]
plt.legend(bbox_to_anchor=(1, 1), prop={'size': 15}, frameon=False)
plt.grid(axis='y', zorder=0)
plt.ylabel('Total Sales (millions)', fontsize=25)
plt.xticks(ind, rotation=45)
plt.yticks(fontsize=20)
plt.xlim((min(ind) - 1), (max(ind) + 1))
ax.xaxis.set_major_formatter(FormatStrFormatter('%d'))
if save_fig:
plt.savefig(f'../../../saved_plots/vgsales-{keyword}-year_bar.png', bbox_inches='tight')
return output<|docstring|>Sorting out the total sale of a certain type (keyword) video games each year.
Only top 'limit' video games are listed in the file and picture.
Args:
:param fname: string
:param keyword: 'Genre', 'ESRB_Rating', 'Platform', 'Publisher', 'Developer'
:param limit: integer, only show top 'limit' number of data
Return:
A sorted dataframe<|endoftext|> |
64b89f36af6954de712982338449e06bdae43b3fcb6f4b37993b400d92007397 | def sale_history(fname, limit=10, month_aft=5, plot=False):
"\n Returns sale history of top number (<='limit') of games from the data file. \n The sale history of selective games will be output to csv file and plotted.\n \n Args:\n :param fname: string\n :param limit: integer, output sale history of top 'limit' number of games\n :param month_aft: the specified number of months after release, including the release month\n :param plot: bool, if True, line plot is produced and saved\n Return:\n A dataframe that contains monthly sales of games\n "
assert isinstance(fname, str), 'fname is not a string'
assert isinstance(month_aft, int), 'month_aft is not a integer'
assert isinstance(limit, int), 'limit is not a integer'
df = pd.read_csv(fname, delimiter=',')
week_aft = (month_aft * 4)
df = df.loc[(df['rank of the week'] <= 30)]
game_list = df.name.tolist()
game_list = list(set(game_list))
msale_hist = pd.DataFrame(index=list(range((month_aft + 1))))
for game in game_list:
wsale_hist = df.loc[(df['name'] == game)]
wsale_hist = wsale_hist.iloc[::(- 1)]
wsale_hist.reset_index(inplace=True, drop=True)
temp = wsale_hist['week after release']
if ((len(temp) >= week_aft) and all((temp[:20] == list(range(1, 21))))):
j = 0
msale_hist[game] = 0
for i in range((month_aft * 4)):
if ((i % 4) == 0):
j += 1
week_sale = int(wsale_hist['weekly sales'][i].replace(',', ''))
msale_hist[game][j] += week_sale
if (len(msale_hist.columns.to_list()) > limit):
msale_hist = msale_hist.iloc[(:, :limit)]
msale_hist.swapaxes('index', 'columns').to_csv('../../../conf/video_games/output/vgsales-game-sale-history.csv')
print(msale_hist)
if plot:
plt.rcParams.update({'font.size': 18})
plt.figure(figsize=(12, 6))
[plt.plot(msale_hist[game][:(month_aft + 1)], label=game) for game in msale_hist.columns.to_list()]
plt.legend(bbox_to_anchor=(1, 1), fontsize=12)
plt.grid()
plt.xlabel('Months after release')
plt.ylabel('Monthly sales')
plt.xticks(np.arange(6))
plt.savefig(f'../../../saved_plots/vgsales-game-sale-history.png', bbox_inches='tight')
return msale_hist | Returns sale history of top number (<='limit') of games from the data file.
The sale history of selective games will be output to csv file and plotted.
Args:
:param fname: string
:param limit: integer, output sale history of top 'limit' number of games
:param month_aft: the specified number of months after release, including the release month
:param plot: bool, if True, line plot is produced and saved
Return:
A dataframe that contains monthly sales of games | src/analytics/video_games/data_preprocessing.py | sale_history | manjotms10/google-trends-analytics | 6 | python | def sale_history(fname, limit=10, month_aft=5, plot=False):
"\n Returns sale history of top number (<='limit') of games from the data file. \n The sale history of selective games will be output to csv file and plotted.\n \n Args:\n :param fname: string\n :param limit: integer, output sale history of top 'limit' number of games\n :param month_aft: the specified number of months after release, including the release month\n :param plot: bool, if True, line plot is produced and saved\n Return:\n A dataframe that contains monthly sales of games\n "
assert isinstance(fname, str), 'fname is not a string'
assert isinstance(month_aft, int), 'month_aft is not a integer'
assert isinstance(limit, int), 'limit is not a integer'
df = pd.read_csv(fname, delimiter=',')
week_aft = (month_aft * 4)
df = df.loc[(df['rank of the week'] <= 30)]
game_list = df.name.tolist()
game_list = list(set(game_list))
msale_hist = pd.DataFrame(index=list(range((month_aft + 1))))
for game in game_list:
wsale_hist = df.loc[(df['name'] == game)]
wsale_hist = wsale_hist.iloc[::(- 1)]
wsale_hist.reset_index(inplace=True, drop=True)
temp = wsale_hist['week after release']
if ((len(temp) >= week_aft) and all((temp[:20] == list(range(1, 21))))):
j = 0
msale_hist[game] = 0
for i in range((month_aft * 4)):
if ((i % 4) == 0):
j += 1
week_sale = int(wsale_hist['weekly sales'][i].replace(',', ))
msale_hist[game][j] += week_sale
if (len(msale_hist.columns.to_list()) > limit):
msale_hist = msale_hist.iloc[(:, :limit)]
msale_hist.swapaxes('index', 'columns').to_csv('../../../conf/video_games/output/vgsales-game-sale-history.csv')
print(msale_hist)
if plot:
plt.rcParams.update({'font.size': 18})
plt.figure(figsize=(12, 6))
[plt.plot(msale_hist[game][:(month_aft + 1)], label=game) for game in msale_hist.columns.to_list()]
plt.legend(bbox_to_anchor=(1, 1), fontsize=12)
plt.grid()
plt.xlabel('Months after release')
plt.ylabel('Monthly sales')
plt.xticks(np.arange(6))
plt.savefig(f'../../../saved_plots/vgsales-game-sale-history.png', bbox_inches='tight')
return msale_hist | def sale_history(fname, limit=10, month_aft=5, plot=False):
"\n Returns sale history of top number (<='limit') of games from the data file. \n The sale history of selective games will be output to csv file and plotted.\n \n Args:\n :param fname: string\n :param limit: integer, output sale history of top 'limit' number of games\n :param month_aft: the specified number of months after release, including the release month\n :param plot: bool, if True, line plot is produced and saved\n Return:\n A dataframe that contains monthly sales of games\n "
assert isinstance(fname, str), 'fname is not a string'
assert isinstance(month_aft, int), 'month_aft is not a integer'
assert isinstance(limit, int), 'limit is not a integer'
df = pd.read_csv(fname, delimiter=',')
week_aft = (month_aft * 4)
df = df.loc[(df['rank of the week'] <= 30)]
game_list = df.name.tolist()
game_list = list(set(game_list))
msale_hist = pd.DataFrame(index=list(range((month_aft + 1))))
for game in game_list:
wsale_hist = df.loc[(df['name'] == game)]
wsale_hist = wsale_hist.iloc[::(- 1)]
wsale_hist.reset_index(inplace=True, drop=True)
temp = wsale_hist['week after release']
if ((len(temp) >= week_aft) and all((temp[:20] == list(range(1, 21))))):
j = 0
msale_hist[game] = 0
for i in range((month_aft * 4)):
if ((i % 4) == 0):
j += 1
week_sale = int(wsale_hist['weekly sales'][i].replace(',', ))
msale_hist[game][j] += week_sale
if (len(msale_hist.columns.to_list()) > limit):
msale_hist = msale_hist.iloc[(:, :limit)]
msale_hist.swapaxes('index', 'columns').to_csv('../../../conf/video_games/output/vgsales-game-sale-history.csv')
print(msale_hist)
if plot:
plt.rcParams.update({'font.size': 18})
plt.figure(figsize=(12, 6))
[plt.plot(msale_hist[game][:(month_aft + 1)], label=game) for game in msale_hist.columns.to_list()]
plt.legend(bbox_to_anchor=(1, 1), fontsize=12)
plt.grid()
plt.xlabel('Months after release')
plt.ylabel('Monthly sales')
plt.xticks(np.arange(6))
plt.savefig(f'../../../saved_plots/vgsales-game-sale-history.png', bbox_inches='tight')
return msale_hist<|docstring|>Returns sale history of top number (<='limit') of games from the data file.
The sale history of selective games will be output to csv file and plotted.
Args:
:param fname: string
:param limit: integer, output sale history of top 'limit' number of games
:param month_aft: the specified number of months after release, including the release month
:param plot: bool, if True, line plot is produced and saved
Return:
A dataframe that contains monthly sales of games<|endoftext|> |
cb3e830424ebfec6f82a5e218b2dc0e3941c9700f1c07e0bceecdbc7f919f7d2 | def keyword_data_sorting(fname, year=[], genre=[], esrb_rating=[], platform=[], publisher=[], developer=[], top=1):
"\n Sorting out the total sale of a certain type (keyword) video games each year.\n Only top 'top' video games are listed in the file and plots.\n \n Args:\n :param fname: string\n :param year: list of years (int)\n :param genre: list of genres (string)\n :param esrb_rating: list of esrb_rating (string)\n :param platform: list of platforms (string)\n :param publisher: list of publishers (string)\n :param developer: list of developers (string)\n :param top: integer, only show top 'limit' number of data\n Retrun:\n A dataframe sorted by specified keywords\n "
assert isinstance(fname, str), 'fname is not a string'
assert isinstance(year, list), 'year is not a list'
assert isinstance(genre, list), 'genre is not a list'
assert isinstance(esrb_rating, list), 'esrb_rating is not a list'
assert isinstance(platform, list), 'platform is not a list'
assert isinstance(publisher, list), 'publisher is not a list'
assert isinstance(developer, list), 'developer is not a list'
assert isinstance(top, int), 'top is not a int'
for (i, j) in enumerate(year):
assert isinstance(j, int), f'{i} component in year is not integer'
for (i, j) in enumerate(genre):
assert isinstance(j, str), f'{i} component in genre is not string'
for (i, j) in enumerate(esrb_rating):
assert isinstance(j, str), f'{i} component in esrb_rating is not string'
for (i, j) in enumerate(platform):
assert isinstance(j, str), f'{i} component in platform is not string'
for (i, j) in enumerate(publisher):
assert isinstance(j, str), f'{i} component in publisher is not string'
for (i, j) in enumerate(developer):
assert isinstance(j, str), f'{i} component in developer is not string'
df = pd.read_csv(fname, delimiter=',')
(nrow, ncol) = df.shape
df['Year'] = df['Year'].astype('int')
for i in range(nrow):
df.loc[(i, 'Name')] = df.loc[(i, 'Name')].translate(str.maketrans('', '', string.punctuation))
for i in range(nrow):
if (year and (df['Year'][i] not in year)):
df.drop(index=i, inplace=True)
elif (genre and (df['Genre'][i] not in genre)):
df.drop(index=i, inplace=True)
elif (esrb_rating and (df['ESRB_Rating'][i] not in esrb_rating)):
df.drop(index=i, inplace=True)
elif (platform and (df['Platform'][i] not in platform)):
df.drop(index=i, inplace=True)
elif (publisher and (df['Publisher'][i] not in publisher)):
df.drop(index=i, inplace=True)
elif (developer and (df['Developer'][i] not in developer)):
df.drop(index=i, inplace=True)
assert (not df.empty), 'No video game satisfy this criteria'
output_df = pd.DataFrame(index=list(set(df['Name'])), columns=['Normalized Sales Volume'])
output_df['Normalized Sales Volume'] = 0
(nrow, ncol) = df.shape
for i in range(nrow):
output_df.loc[(df.iloc[(i, 1)], 'Normalized Sales Volume')] += df.iloc[(i, 9)]
output_df.sort_values(by='Normalized Sales Volume', ascending=False, inplace=True)
(nrow, ncol) = output_df.shape
assert (nrow >= top), ('Only %d video game satisfy this criteria, please check input "top"' % nrow)
output_df.drop(index=output_df.index[top:], inplace=True)
max_sale = max(output_df['Normalized Sales Volume'])
for i in range(top):
output_df.iloc[(i, 0)] = ((output_df.iloc[(i, 0)] / max_sale) * 100)
return output_df | Sorting out the total sale of a certain type (keyword) video games each year.
Only top 'top' video games are listed in the file and plots.
Args:
:param fname: string
:param year: list of years (int)
:param genre: list of genres (string)
:param esrb_rating: list of esrb_rating (string)
:param platform: list of platforms (string)
:param publisher: list of publishers (string)
:param developer: list of developers (string)
:param top: integer, only show top 'limit' number of data
Retrun:
A dataframe sorted by specified keywords | src/analytics/video_games/data_preprocessing.py | keyword_data_sorting | manjotms10/google-trends-analytics | 6 | python | def keyword_data_sorting(fname, year=[], genre=[], esrb_rating=[], platform=[], publisher=[], developer=[], top=1):
"\n Sorting out the total sale of a certain type (keyword) video games each year.\n Only top 'top' video games are listed in the file and plots.\n \n Args:\n :param fname: string\n :param year: list of years (int)\n :param genre: list of genres (string)\n :param esrb_rating: list of esrb_rating (string)\n :param platform: list of platforms (string)\n :param publisher: list of publishers (string)\n :param developer: list of developers (string)\n :param top: integer, only show top 'limit' number of data\n Retrun:\n A dataframe sorted by specified keywords\n "
assert isinstance(fname, str), 'fname is not a string'
assert isinstance(year, list), 'year is not a list'
assert isinstance(genre, list), 'genre is not a list'
assert isinstance(esrb_rating, list), 'esrb_rating is not a list'
assert isinstance(platform, list), 'platform is not a list'
assert isinstance(publisher, list), 'publisher is not a list'
assert isinstance(developer, list), 'developer is not a list'
assert isinstance(top, int), 'top is not a int'
for (i, j) in enumerate(year):
assert isinstance(j, int), f'{i} component in year is not integer'
for (i, j) in enumerate(genre):
assert isinstance(j, str), f'{i} component in genre is not string'
for (i, j) in enumerate(esrb_rating):
assert isinstance(j, str), f'{i} component in esrb_rating is not string'
for (i, j) in enumerate(platform):
assert isinstance(j, str), f'{i} component in platform is not string'
for (i, j) in enumerate(publisher):
assert isinstance(j, str), f'{i} component in publisher is not string'
for (i, j) in enumerate(developer):
assert isinstance(j, str), f'{i} component in developer is not string'
df = pd.read_csv(fname, delimiter=',')
(nrow, ncol) = df.shape
df['Year'] = df['Year'].astype('int')
for i in range(nrow):
df.loc[(i, 'Name')] = df.loc[(i, 'Name')].translate(str.maketrans(, , string.punctuation))
for i in range(nrow):
if (year and (df['Year'][i] not in year)):
df.drop(index=i, inplace=True)
elif (genre and (df['Genre'][i] not in genre)):
df.drop(index=i, inplace=True)
elif (esrb_rating and (df['ESRB_Rating'][i] not in esrb_rating)):
df.drop(index=i, inplace=True)
elif (platform and (df['Platform'][i] not in platform)):
df.drop(index=i, inplace=True)
elif (publisher and (df['Publisher'][i] not in publisher)):
df.drop(index=i, inplace=True)
elif (developer and (df['Developer'][i] not in developer)):
df.drop(index=i, inplace=True)
assert (not df.empty), 'No video game satisfy this criteria'
output_df = pd.DataFrame(index=list(set(df['Name'])), columns=['Normalized Sales Volume'])
output_df['Normalized Sales Volume'] = 0
(nrow, ncol) = df.shape
for i in range(nrow):
output_df.loc[(df.iloc[(i, 1)], 'Normalized Sales Volume')] += df.iloc[(i, 9)]
output_df.sort_values(by='Normalized Sales Volume', ascending=False, inplace=True)
(nrow, ncol) = output_df.shape
assert (nrow >= top), ('Only %d video game satisfy this criteria, please check input "top"' % nrow)
output_df.drop(index=output_df.index[top:], inplace=True)
max_sale = max(output_df['Normalized Sales Volume'])
for i in range(top):
output_df.iloc[(i, 0)] = ((output_df.iloc[(i, 0)] / max_sale) * 100)
return output_df | def keyword_data_sorting(fname, year=[], genre=[], esrb_rating=[], platform=[], publisher=[], developer=[], top=1):
"\n Sorting out the total sale of a certain type (keyword) video games each year.\n Only top 'top' video games are listed in the file and plots.\n \n Args:\n :param fname: string\n :param year: list of years (int)\n :param genre: list of genres (string)\n :param esrb_rating: list of esrb_rating (string)\n :param platform: list of platforms (string)\n :param publisher: list of publishers (string)\n :param developer: list of developers (string)\n :param top: integer, only show top 'limit' number of data\n Retrun:\n A dataframe sorted by specified keywords\n "
assert isinstance(fname, str), 'fname is not a string'
assert isinstance(year, list), 'year is not a list'
assert isinstance(genre, list), 'genre is not a list'
assert isinstance(esrb_rating, list), 'esrb_rating is not a list'
assert isinstance(platform, list), 'platform is not a list'
assert isinstance(publisher, list), 'publisher is not a list'
assert isinstance(developer, list), 'developer is not a list'
assert isinstance(top, int), 'top is not a int'
for (i, j) in enumerate(year):
assert isinstance(j, int), f'{i} component in year is not integer'
for (i, j) in enumerate(genre):
assert isinstance(j, str), f'{i} component in genre is not string'
for (i, j) in enumerate(esrb_rating):
assert isinstance(j, str), f'{i} component in esrb_rating is not string'
for (i, j) in enumerate(platform):
assert isinstance(j, str), f'{i} component in platform is not string'
for (i, j) in enumerate(publisher):
assert isinstance(j, str), f'{i} component in publisher is not string'
for (i, j) in enumerate(developer):
assert isinstance(j, str), f'{i} component in developer is not string'
df = pd.read_csv(fname, delimiter=',')
(nrow, ncol) = df.shape
df['Year'] = df['Year'].astype('int')
for i in range(nrow):
df.loc[(i, 'Name')] = df.loc[(i, 'Name')].translate(str.maketrans(, , string.punctuation))
for i in range(nrow):
if (year and (df['Year'][i] not in year)):
df.drop(index=i, inplace=True)
elif (genre and (df['Genre'][i] not in genre)):
df.drop(index=i, inplace=True)
elif (esrb_rating and (df['ESRB_Rating'][i] not in esrb_rating)):
df.drop(index=i, inplace=True)
elif (platform and (df['Platform'][i] not in platform)):
df.drop(index=i, inplace=True)
elif (publisher and (df['Publisher'][i] not in publisher)):
df.drop(index=i, inplace=True)
elif (developer and (df['Developer'][i] not in developer)):
df.drop(index=i, inplace=True)
assert (not df.empty), 'No video game satisfy this criteria'
output_df = pd.DataFrame(index=list(set(df['Name'])), columns=['Normalized Sales Volume'])
output_df['Normalized Sales Volume'] = 0
(nrow, ncol) = df.shape
for i in range(nrow):
output_df.loc[(df.iloc[(i, 1)], 'Normalized Sales Volume')] += df.iloc[(i, 9)]
output_df.sort_values(by='Normalized Sales Volume', ascending=False, inplace=True)
(nrow, ncol) = output_df.shape
assert (nrow >= top), ('Only %d video game satisfy this criteria, please check input "top"' % nrow)
output_df.drop(index=output_df.index[top:], inplace=True)
max_sale = max(output_df['Normalized Sales Volume'])
for i in range(top):
output_df.iloc[(i, 0)] = ((output_df.iloc[(i, 0)] / max_sale) * 100)
return output_df<|docstring|>Sorting out the total sale of a certain type (keyword) video games each year.
Only top 'top' video games are listed in the file and plots.
Args:
:param fname: string
:param year: list of years (int)
:param genre: list of genres (string)
:param esrb_rating: list of esrb_rating (string)
:param platform: list of platforms (string)
:param publisher: list of publishers (string)
:param developer: list of developers (string)
:param top: integer, only show top 'limit' number of data
Retrun:
A dataframe sorted by specified keywords<|endoftext|> |
7c6f9e6baa4b778bd10505c1cb981fab1314b96eafe8faae0ebfcb5d426225d9 | @pytest.fixture(scope='session')
def tasks_just_a_few():
'All summaries and owners are unique.'
return (Task('Write some code', 'Brian', True), Task("Code review Brian's code", 'Katie', False), Task('Fix what Brian did', 'Anna', False)) | All summaries and owners are unique. | master/bopytest-code/code/ch5/d/tasks_proj/tests/conftest.py | tasks_just_a_few | AlexRogalskiy/DevArtifacts | 4 | python | @pytest.fixture(scope='session')
def tasks_just_a_few():
return (Task('Write some code', 'Brian', True), Task("Code review Brian's code", 'Katie', False), Task('Fix what Brian did', 'Anna', False)) | @pytest.fixture(scope='session')
def tasks_just_a_few():
return (Task('Write some code', 'Brian', True), Task("Code review Brian's code", 'Katie', False), Task('Fix what Brian did', 'Anna', False))<|docstring|>All summaries and owners are unique.<|endoftext|> |
6c09a2d98def6da9db3748071d8839262a42804f650fe11b5f59045cec0e0271 | @pytest.fixture(scope='session')
def tasks_mult_per_owner():
'Several owners with several tasks each.'
return (Task('Make a cookie', 'Raphael'), Task('Use an emoji', 'Raphael'), Task('Move to Berlin', 'Raphael'), Task('Teach people', 'Carrie'), Task('Make some videos', 'Carrie'), Task('Inspire', 'Carrie'), Task('Do a handstand', 'Daniel'), Task('Write some books', 'Daniel'), Task('Eat ice cream', 'Daniel')) | Several owners with several tasks each. | master/bopytest-code/code/ch5/d/tasks_proj/tests/conftest.py | tasks_mult_per_owner | AlexRogalskiy/DevArtifacts | 4 | python | @pytest.fixture(scope='session')
def tasks_mult_per_owner():
return (Task('Make a cookie', 'Raphael'), Task('Use an emoji', 'Raphael'), Task('Move to Berlin', 'Raphael'), Task('Teach people', 'Carrie'), Task('Make some videos', 'Carrie'), Task('Inspire', 'Carrie'), Task('Do a handstand', 'Daniel'), Task('Write some books', 'Daniel'), Task('Eat ice cream', 'Daniel')) | @pytest.fixture(scope='session')
def tasks_mult_per_owner():
return (Task('Make a cookie', 'Raphael'), Task('Use an emoji', 'Raphael'), Task('Move to Berlin', 'Raphael'), Task('Teach people', 'Carrie'), Task('Make some videos', 'Carrie'), Task('Inspire', 'Carrie'), Task('Do a handstand', 'Daniel'), Task('Write some books', 'Daniel'), Task('Eat ice cream', 'Daniel'))<|docstring|>Several owners with several tasks each.<|endoftext|> |
3e823a7031e4398575fa8d0a77e2a35be2212b9a9916bea60365e12d63fa04c7 | @pytest.fixture()
def tasks_db(tasks_db_session):
'an empty tasks db'
tasks.delete_all() | an empty tasks db | master/bopytest-code/code/ch5/d/tasks_proj/tests/conftest.py | tasks_db | AlexRogalskiy/DevArtifacts | 4 | python | @pytest.fixture()
def tasks_db(tasks_db_session):
tasks.delete_all() | @pytest.fixture()
def tasks_db(tasks_db_session):
tasks.delete_all()<|docstring|>an empty tasks db<|endoftext|> |
ea7bee143a6bbfb04be4644ec8c3dd22b013486564e8f9a36a214c0146e73006 | @pytest.fixture()
def db_with_3_tasks(tasks_db, tasks_just_a_few):
'tasks db with 3 tasks, all unique'
for t in tasks_just_a_few:
tasks.add(t) | tasks db with 3 tasks, all unique | master/bopytest-code/code/ch5/d/tasks_proj/tests/conftest.py | db_with_3_tasks | AlexRogalskiy/DevArtifacts | 4 | python | @pytest.fixture()
def db_with_3_tasks(tasks_db, tasks_just_a_few):
for t in tasks_just_a_few:
tasks.add(t) | @pytest.fixture()
def db_with_3_tasks(tasks_db, tasks_just_a_few):
for t in tasks_just_a_few:
tasks.add(t)<|docstring|>tasks db with 3 tasks, all unique<|endoftext|> |
c2f35727fa5cf3ef951b86c1fc11ac6a59c7a98c7f4a75d639eaa7585764d200 | @pytest.fixture()
def db_with_multi_per_owner(tasks_db, tasks_mult_per_owner):
'tasks db 3 owners, all with 3 tasks'
for t in tasks_mult_per_owner:
tasks.add(t) | tasks db 3 owners, all with 3 tasks | master/bopytest-code/code/ch5/d/tasks_proj/tests/conftest.py | db_with_multi_per_owner | AlexRogalskiy/DevArtifacts | 4 | python | @pytest.fixture()
def db_with_multi_per_owner(tasks_db, tasks_mult_per_owner):
for t in tasks_mult_per_owner:
tasks.add(t) | @pytest.fixture()
def db_with_multi_per_owner(tasks_db, tasks_mult_per_owner):
for t in tasks_mult_per_owner:
tasks.add(t)<|docstring|>tasks db 3 owners, all with 3 tasks<|endoftext|> |
c073bd091fac59eb185a4565f69011618d69273d5f4e371d8f63cba8689a704c | @pytest.mark.parametrize('callback_cls', get_cbs_and_marks(callbacks=True))
def test_logged_data_is_json_serializable(callback_cls: Type[Callback]):
'Test that all logged data is json serializable, which is a requirement to use wandb.'
pytest.importorskip('wandb', reason='wandb is optional')
from wandb.sdk.data_types.base_types.wb_value import WBValue
callback_kwargs = get_cb_kwargs(callback_cls)
callback = callback_cls(**callback_kwargs)
logger = InMemoryLogger()
trainer = Trainer(model=SimpleModel(), train_dataloader=DataLoader(RandomClassificationDataset()), train_subset_num_batches=2, max_duration='1ep', callbacks=callback, loggers=logger, compute_training_metrics=True)
trainer.fit()
for log_calls in logger.data.values():
for (timestamp, log_level, data) in log_calls:
del timestamp, log_level
if isinstance(data, (WBValue, torch.Tensor)):
continue
json.dumps(data) | Test that all logged data is json serializable, which is a requirement to use wandb. | tests/loggers/test_wandb_logger.py | test_logged_data_is_json_serializable | growlix/composer | 0 | python | @pytest.mark.parametrize('callback_cls', get_cbs_and_marks(callbacks=True))
def test_logged_data_is_json_serializable(callback_cls: Type[Callback]):
pytest.importorskip('wandb', reason='wandb is optional')
from wandb.sdk.data_types.base_types.wb_value import WBValue
callback_kwargs = get_cb_kwargs(callback_cls)
callback = callback_cls(**callback_kwargs)
logger = InMemoryLogger()
trainer = Trainer(model=SimpleModel(), train_dataloader=DataLoader(RandomClassificationDataset()), train_subset_num_batches=2, max_duration='1ep', callbacks=callback, loggers=logger, compute_training_metrics=True)
trainer.fit()
for log_calls in logger.data.values():
for (timestamp, log_level, data) in log_calls:
del timestamp, log_level
if isinstance(data, (WBValue, torch.Tensor)):
continue
json.dumps(data) | @pytest.mark.parametrize('callback_cls', get_cbs_and_marks(callbacks=True))
def test_logged_data_is_json_serializable(callback_cls: Type[Callback]):
pytest.importorskip('wandb', reason='wandb is optional')
from wandb.sdk.data_types.base_types.wb_value import WBValue
callback_kwargs = get_cb_kwargs(callback_cls)
callback = callback_cls(**callback_kwargs)
logger = InMemoryLogger()
trainer = Trainer(model=SimpleModel(), train_dataloader=DataLoader(RandomClassificationDataset()), train_subset_num_batches=2, max_duration='1ep', callbacks=callback, loggers=logger, compute_training_metrics=True)
trainer.fit()
for log_calls in logger.data.values():
for (timestamp, log_level, data) in log_calls:
del timestamp, log_level
if isinstance(data, (WBValue, torch.Tensor)):
continue
json.dumps(data)<|docstring|>Test that all logged data is json serializable, which is a requirement to use wandb.<|endoftext|> |
9f4381653e7c45d024a0329c0c9ca5a5538ec3deefb1e3e33a5ba1339ff3dfb7 | def testJson(self):
'Load in a json string'
s = '{"name": "test_job", "range": "1-10", "layers": [{"name": "layer_1", "module": "outline.modules.shell.Shell", "env": {"LAYER_KEY1": "LAYER_VALUE1"}, "command": ["/bin/ls"]}]}'
ol = outline.load_json(s)
self.assertEqual('test_job', ol.get_name())
self.assertEqual('1-10', ol.get_frame_range())
self.assertEqual('LAYER_VALUE1', ol.get_layer('layer_1').get_env('LAYER_KEY1'))
ol.get_layer('layer_1').set_env('LAYER_KEY2', 'LAYER_VALUE2')
l = outline.cuerun.OutlineLauncher(ol)
root = Et.fromstring(l.serialize())
env1 = root.find('job/layers/layer/env/key[@name="LAYER_KEY1"]')
self.assertEqual('LAYER_VALUE1', env1.text)
env2 = root.find('job/layers/layer/env/key[@name="LAYER_KEY2"]')
self.assertEqual('LAYER_VALUE2', env2.text) | Load in a json string | pyoutline/tests/json_test.py | testJson | jkellefiel4/OpenCue | 334 | python | def testJson(self):
s = '{"name": "test_job", "range": "1-10", "layers": [{"name": "layer_1", "module": "outline.modules.shell.Shell", "env": {"LAYER_KEY1": "LAYER_VALUE1"}, "command": ["/bin/ls"]}]}'
ol = outline.load_json(s)
self.assertEqual('test_job', ol.get_name())
self.assertEqual('1-10', ol.get_frame_range())
self.assertEqual('LAYER_VALUE1', ol.get_layer('layer_1').get_env('LAYER_KEY1'))
ol.get_layer('layer_1').set_env('LAYER_KEY2', 'LAYER_VALUE2')
l = outline.cuerun.OutlineLauncher(ol)
root = Et.fromstring(l.serialize())
env1 = root.find('job/layers/layer/env/key[@name="LAYER_KEY1"]')
self.assertEqual('LAYER_VALUE1', env1.text)
env2 = root.find('job/layers/layer/env/key[@name="LAYER_KEY2"]')
self.assertEqual('LAYER_VALUE2', env2.text) | def testJson(self):
s = '{"name": "test_job", "range": "1-10", "layers": [{"name": "layer_1", "module": "outline.modules.shell.Shell", "env": {"LAYER_KEY1": "LAYER_VALUE1"}, "command": ["/bin/ls"]}]}'
ol = outline.load_json(s)
self.assertEqual('test_job', ol.get_name())
self.assertEqual('1-10', ol.get_frame_range())
self.assertEqual('LAYER_VALUE1', ol.get_layer('layer_1').get_env('LAYER_KEY1'))
ol.get_layer('layer_1').set_env('LAYER_KEY2', 'LAYER_VALUE2')
l = outline.cuerun.OutlineLauncher(ol)
root = Et.fromstring(l.serialize())
env1 = root.find('job/layers/layer/env/key[@name="LAYER_KEY1"]')
self.assertEqual('LAYER_VALUE1', env1.text)
env2 = root.find('job/layers/layer/env/key[@name="LAYER_KEY2"]')
self.assertEqual('LAYER_VALUE2', env2.text)<|docstring|>Load in a json string<|endoftext|> |
9893a2f416853501e7af40eff4e1af49758c6da4d3f664833139662161ce519b | @mock.patch('outline.layer.Layer.system')
@mock.patch.dict(os.environ, {}, clear=True)
def testJsonFile(self, systemMock):
'Load JSON from a file'
with open(os.path.join(JSON_DIR, 'shell.outline')) as fp:
ol = outline.load_json(fp.read())
with test_utils.TemporarySessionDirectory():
ol.setup()
layer = ol.get_layer('shell_layer')
self.assertEqual('LAYER_VALUE', layer.get_env('LAYER_KEY'))
layer.execute('1000')
systemMock.assert_has_calls([mock.call(['/bin/ls'], frame=1000)])
self.assertEqual('LAYER_VALUE', os.environ['LAYER_KEY']) | Load JSON from a file | pyoutline/tests/json_test.py | testJsonFile | jkellefiel4/OpenCue | 334 | python | @mock.patch('outline.layer.Layer.system')
@mock.patch.dict(os.environ, {}, clear=True)
def testJsonFile(self, systemMock):
with open(os.path.join(JSON_DIR, 'shell.outline')) as fp:
ol = outline.load_json(fp.read())
with test_utils.TemporarySessionDirectory():
ol.setup()
layer = ol.get_layer('shell_layer')
self.assertEqual('LAYER_VALUE', layer.get_env('LAYER_KEY'))
layer.execute('1000')
systemMock.assert_has_calls([mock.call(['/bin/ls'], frame=1000)])
self.assertEqual('LAYER_VALUE', os.environ['LAYER_KEY']) | @mock.patch('outline.layer.Layer.system')
@mock.patch.dict(os.environ, {}, clear=True)
def testJsonFile(self, systemMock):
with open(os.path.join(JSON_DIR, 'shell.outline')) as fp:
ol = outline.load_json(fp.read())
with test_utils.TemporarySessionDirectory():
ol.setup()
layer = ol.get_layer('shell_layer')
self.assertEqual('LAYER_VALUE', layer.get_env('LAYER_KEY'))
layer.execute('1000')
systemMock.assert_has_calls([mock.call(['/bin/ls'], frame=1000)])
self.assertEqual('LAYER_VALUE', os.environ['LAYER_KEY'])<|docstring|>Load JSON from a file<|endoftext|> |
eb7520768fd99a428477de94988504c04444afd53ded7b940b07426fa3193851 | def testFacility(self):
'Test facility from JSON'
with open(os.path.join(JSON_DIR, 'facility.json')) as fp:
ol = outline.load_json(fp.read())
self.assertEqual('test_facility', ol.get_facility()) | Test facility from JSON | pyoutline/tests/json_test.py | testFacility | jkellefiel4/OpenCue | 334 | python | def testFacility(self):
with open(os.path.join(JSON_DIR, 'facility.json')) as fp:
ol = outline.load_json(fp.read())
self.assertEqual('test_facility', ol.get_facility()) | def testFacility(self):
with open(os.path.join(JSON_DIR, 'facility.json')) as fp:
ol = outline.load_json(fp.read())
self.assertEqual('test_facility', ol.get_facility())<|docstring|>Test facility from JSON<|endoftext|> |
fd54ea6ec722fb48c60af271a9212fd63496348fa82204e6274d1aabf5ac0020 | async def watch_config(self, data_id, group, tenant):
'监听nacos中的配置'
while 1:
res = (await self.__post_config_check(data_id, group, tenant))
if res:
(await self.__get_config(data_id, group, tenant)) | 监听nacos中的配置 | aio_nacos/nacos_config.py | watch_config | zqsc/aio-nacos | 2 | python | async def watch_config(self, data_id, group, tenant):
while 1:
res = (await self.__post_config_check(data_id, group, tenant))
if res:
(await self.__get_config(data_id, group, tenant)) | async def watch_config(self, data_id, group, tenant):
while 1:
res = (await self.__post_config_check(data_id, group, tenant))
if res:
(await self.__get_config(data_id, group, tenant))<|docstring|>监听nacos中的配置<|endoftext|> |
906d84db20fcbe9ea506651187501c94bed9e47f274503f5ee54027aac811625 | async def __post_config_check(self, data_id, group, tenant):
'检查md5, nacos又返回值 则配置有更新'
headers = {'Long-Pulling-Timeout': '3000'}
if (tenant and (tenant != 'public')):
data = {'Listening-Configs': f'{data_id}{group}{self.config_pool.get(data_id).md5}{tenant}'}
else:
data = {'Listening-Configs': f'{data_id}{group}{self.config_pool.get(data_id).md5}'}
url = (self.nacos_client.nacos_addr + '/nacos/v1/cs/configs/listener')
url = self.nacos_client.add_url_auth(url)
async with self.nacos_client.session.post(url, data=data, headers=headers, proxy=self.nacos_client.proxy) as response:
check_status(response.status)
res = (await response.read())
return res | 检查md5, nacos又返回值 则配置有更新 | aio_nacos/nacos_config.py | __post_config_check | zqsc/aio-nacos | 2 | python | async def __post_config_check(self, data_id, group, tenant):
headers = {'Long-Pulling-Timeout': '3000'}
if (tenant and (tenant != 'public')):
data = {'Listening-Configs': f'{data_id}{group}{self.config_pool.get(data_id).md5}{tenant}'}
else:
data = {'Listening-Configs': f'{data_id}{group}{self.config_pool.get(data_id).md5}'}
url = (self.nacos_client.nacos_addr + '/nacos/v1/cs/configs/listener')
url = self.nacos_client.add_url_auth(url)
async with self.nacos_client.session.post(url, data=data, headers=headers, proxy=self.nacos_client.proxy) as response:
check_status(response.status)
res = (await response.read())
return res | async def __post_config_check(self, data_id, group, tenant):
headers = {'Long-Pulling-Timeout': '3000'}
if (tenant and (tenant != 'public')):
data = {'Listening-Configs': f'{data_id}{group}{self.config_pool.get(data_id).md5}{tenant}'}
else:
data = {'Listening-Configs': f'{data_id}{group}{self.config_pool.get(data_id).md5}'}
url = (self.nacos_client.nacos_addr + '/nacos/v1/cs/configs/listener')
url = self.nacos_client.add_url_auth(url)
async with self.nacos_client.session.post(url, data=data, headers=headers, proxy=self.nacos_client.proxy) as response:
check_status(response.status)
res = (await response.read())
return res<|docstring|>检查md5, nacos又返回值 则配置有更新<|endoftext|> |
d689e1ec675c74e980f8ce7273883ff0e0a4972a76b5f9aedb50a4b36ec239ed | async def __get_config(self, data_id, group, tenant):
'获得配置配置， 并写入配置池中'
self.logger.info(('从nacos中更新配置-data_id:%s; grout:%s; tenant:%s' % (data_id, group, tenant)))
params = {'dataId': data_id, 'group': group}
if (tenant and (tenant != 'public')):
params['tenant'] = tenant
url = (self.nacos_client.nacos_addr + '/nacos/v1/cs/configs')
url = self.nacos_client.add_url_auth(url)
async with self.nacos_client.session.get(url=url, params=params, proxy=self.nacos_client.proxy) as response:
check_status(response.status)
conf_md5 = response.headers.getone('Content-MD5')
conf_type = response.headers.getone('Config-Type')
res = (await response.read())
if (conf_type == 'json'):
conf = Config(conf_md5)
conf.__dict__.update(json.loads(res))
self.config_pool[data_id] = conf | 获得配置配置， 并写入配置池中 | aio_nacos/nacos_config.py | __get_config | zqsc/aio-nacos | 2 | python | async def __get_config(self, data_id, group, tenant):
self.logger.info(('从nacos中更新配置-data_id:%s; grout:%s; tenant:%s' % (data_id, group, tenant)))
params = {'dataId': data_id, 'group': group}
if (tenant and (tenant != 'public')):
params['tenant'] = tenant
url = (self.nacos_client.nacos_addr + '/nacos/v1/cs/configs')
url = self.nacos_client.add_url_auth(url)
async with self.nacos_client.session.get(url=url, params=params, proxy=self.nacos_client.proxy) as response:
check_status(response.status)
conf_md5 = response.headers.getone('Content-MD5')
conf_type = response.headers.getone('Config-Type')
res = (await response.read())
if (conf_type == 'json'):
conf = Config(conf_md5)
conf.__dict__.update(json.loads(res))
self.config_pool[data_id] = conf | async def __get_config(self, data_id, group, tenant):
self.logger.info(('从nacos中更新配置-data_id:%s; grout:%s; tenant:%s' % (data_id, group, tenant)))
params = {'dataId': data_id, 'group': group}
if (tenant and (tenant != 'public')):
params['tenant'] = tenant
url = (self.nacos_client.nacos_addr + '/nacos/v1/cs/configs')
url = self.nacos_client.add_url_auth(url)
async with self.nacos_client.session.get(url=url, params=params, proxy=self.nacos_client.proxy) as response:
check_status(response.status)
conf_md5 = response.headers.getone('Content-MD5')
conf_type = response.headers.getone('Config-Type')
res = (await response.read())
if (conf_type == 'json'):
conf = Config(conf_md5)
conf.__dict__.update(json.loads(res))
self.config_pool[data_id] = conf<|docstring|>获得配置配置， 并写入配置池中<|endoftext|> |
d8d8e19e52e979e9779557cc60658957707c66968e90d7c8ff4ebaad33ca3add | def make_trending_cache_key(time_range, genre, version=DEFAULT_TRENDING_VERSIONS[TrendingType.TRACKS]):
'Makes a cache key resembling `generated-trending:week:electronic`'
version_name = (f':{version.name}' if (version != DEFAULT_TRENDING_VERSIONS[TrendingType.TRACKS]) else '')
return f"generated-trending{version_name}:{time_range}:{(genre.lower() if genre else '')}" | Makes a cache key resembling `generated-trending:week:electronic` | discovery-provider/src/queries/get_trending_tracks.py | make_trending_cache_key | lucylow/audius-protocol | 1 | python | def make_trending_cache_key(time_range, genre, version=DEFAULT_TRENDING_VERSIONS[TrendingType.TRACKS]):
version_name = (f':{version.name}' if (version != DEFAULT_TRENDING_VERSIONS[TrendingType.TRACKS]) else )
return f"generated-trending{version_name}:{time_range}:{(genre.lower() if genre else )}" | def make_trending_cache_key(time_range, genre, version=DEFAULT_TRENDING_VERSIONS[TrendingType.TRACKS]):
version_name = (f':{version.name}' if (version != DEFAULT_TRENDING_VERSIONS[TrendingType.TRACKS]) else )
return f"generated-trending{version_name}:{time_range}:{(genre.lower() if genre else )}"<|docstring|>Makes a cache key resembling `generated-trending:week:electronic`<|endoftext|> |
127b04b641ccca5de4073cfb5c159429cf2708824a8d933a509f67b4357c0048 | def make_generate_unpopulated_trending(session, genre, time_range, strategy):
'Wraps a call to `generate_unpopulated_trending` for use in `use_redis_cache`, which\n expects to be passed a function with no arguments.'
def wrapped():
if strategy.use_mat_view:
return generate_unpopulated_trending_from_mat_views(session, genre, time_range, strategy)
return generate_unpopulated_trending(session, genre, time_range, strategy)
return wrapped | Wraps a call to `generate_unpopulated_trending` for use in `use_redis_cache`, which
expects to be passed a function with no arguments. | discovery-provider/src/queries/get_trending_tracks.py | make_generate_unpopulated_trending | lucylow/audius-protocol | 1 | python | def make_generate_unpopulated_trending(session, genre, time_range, strategy):
'Wraps a call to `generate_unpopulated_trending` for use in `use_redis_cache`, which\n expects to be passed a function with no arguments.'
def wrapped():
if strategy.use_mat_view:
return generate_unpopulated_trending_from_mat_views(session, genre, time_range, strategy)
return generate_unpopulated_trending(session, genre, time_range, strategy)
return wrapped | def make_generate_unpopulated_trending(session, genre, time_range, strategy):
'Wraps a call to `generate_unpopulated_trending` for use in `use_redis_cache`, which\n expects to be passed a function with no arguments.'
def wrapped():
if strategy.use_mat_view:
return generate_unpopulated_trending_from_mat_views(session, genre, time_range, strategy)
return generate_unpopulated_trending(session, genre, time_range, strategy)
return wrapped<|docstring|>Wraps a call to `generate_unpopulated_trending` for use in `use_redis_cache`, which
expects to be passed a function with no arguments.<|endoftext|> |
38209f940b946b865cffd08e4846b980df178b90266c26b2d7afb347da660107 | def get_trending_tracks(args: GetTrendingTracksArgs, strategy: BaseTrendingStrategy):
'Gets trending by getting the currently cached tracks and then populating them.'
db = get_db_read_replica()
with db.scoped_session() as session:
return _get_trending_tracks_with_session(session, args, strategy) | Gets trending by getting the currently cached tracks and then populating them. | discovery-provider/src/queries/get_trending_tracks.py | get_trending_tracks | lucylow/audius-protocol | 1 | python | def get_trending_tracks(args: GetTrendingTracksArgs, strategy: BaseTrendingStrategy):
db = get_db_read_replica()
with db.scoped_session() as session:
return _get_trending_tracks_with_session(session, args, strategy) | def get_trending_tracks(args: GetTrendingTracksArgs, strategy: BaseTrendingStrategy):
db = get_db_read_replica()
with db.scoped_session() as session:
return _get_trending_tracks_with_session(session, args, strategy)<|docstring|>Gets trending by getting the currently cached tracks and then populating them.<|endoftext|> |
a71f6df6ba8dacea95c77b6ae352c69173be8bafc19570bbabf15ac233c32dcc | def create_ec2_instance(stack, name, ami, subnetid, keyname, instance_profile='', instance_type='t1.micro', security_groups=(), user_data=''):
'Add EC2 Instance Resource.'
return stack.stack.add_resource(Instance('{0}'.format(name), ImageId=ami, InstanceType=instance_type, KeyName=keyname, SecurityGroupIds=list(security_groups), SubnetId=subnetid, Tags=Tags(Name=name), UserData=Base64(user_data), IamInstanceProfile=instance_profile)) | Add EC2 Instance Resource. | tropohelper/instances.py | create_ec2_instance | devblueray/tropohelper | 0 | python | def create_ec2_instance(stack, name, ami, subnetid, keyname, instance_profile=, instance_type='t1.micro', security_groups=(), user_data=):
return stack.stack.add_resource(Instance('{0}'.format(name), ImageId=ami, InstanceType=instance_type, KeyName=keyname, SecurityGroupIds=list(security_groups), SubnetId=subnetid, Tags=Tags(Name=name), UserData=Base64(user_data), IamInstanceProfile=instance_profile)) | def create_ec2_instance(stack, name, ami, subnetid, keyname, instance_profile=, instance_type='t1.micro', security_groups=(), user_data=):
return stack.stack.add_resource(Instance('{0}'.format(name), ImageId=ami, InstanceType=instance_type, KeyName=keyname, SecurityGroupIds=list(security_groups), SubnetId=subnetid, Tags=Tags(Name=name), UserData=Base64(user_data), IamInstanceProfile=instance_profile))<|docstring|>Add EC2 Instance Resource.<|endoftext|> |
75e60cfc98ae60758f99a82c96cee80f8cfc8bc325e288bea0fc91d32018333c | def create_launch_config(stack, name, ami, security_group, instance_type, profile, block_devices=[], user_data=''):
'Add EC2 LaunchConfiguration Resource.'
return stack.stack.add_resource(LaunchConfiguration('{0}{1}LC'.format(stack.env, name.replace('_', '')), ImageId=ami, KeyName=Ref(stack.ssh_key_param), SecurityGroups=security_group, InstanceType=instance_type, IamInstanceProfile=profile, UserData=Base64(user_data), BlockDeviceMappings=block_devices)) | Add EC2 LaunchConfiguration Resource. | tropohelper/instances.py | create_launch_config | devblueray/tropohelper | 0 | python | def create_launch_config(stack, name, ami, security_group, instance_type, profile, block_devices=[], user_data=):
return stack.stack.add_resource(LaunchConfiguration('{0}{1}LC'.format(stack.env, name.replace('_', )), ImageId=ami, KeyName=Ref(stack.ssh_key_param), SecurityGroups=security_group, InstanceType=instance_type, IamInstanceProfile=profile, UserData=Base64(user_data), BlockDeviceMappings=block_devices)) | def create_launch_config(stack, name, ami, security_group, instance_type, profile, block_devices=[], user_data=):
return stack.stack.add_resource(LaunchConfiguration('{0}{1}LC'.format(stack.env, name.replace('_', )), ImageId=ami, KeyName=Ref(stack.ssh_key_param), SecurityGroups=security_group, InstanceType=instance_type, IamInstanceProfile=profile, UserData=Base64(user_data), BlockDeviceMappings=block_devices))<|docstring|>Add EC2 LaunchConfiguration Resource.<|endoftext|> |
ee0a271262af3d03ee5437363b754158deff12565b839a6cd6c6f73fa1220d05 | def create_autoscale_group(stack, name, launch_con, vpc_zones, elbs=[], target_groups=[]):
'Add EC2 AutoScalingGroup Resource.'
return stack.stack.add_resource(AutoScalingGroup('{0}{1}ASG'.format(stack.env, name.replace('_', '')), LaunchConfigurationName=Ref(launch_con), MinSize='0', MaxSize='5', HealthCheckType='EC2', VPCZoneIdentifier=vpc_zones, TerminationPolicies=['OldestInstance'], LoadBalancerNames=elbs, TargetGroupARNs=target_groups)) | Add EC2 AutoScalingGroup Resource. | tropohelper/instances.py | create_autoscale_group | devblueray/tropohelper | 0 | python | def create_autoscale_group(stack, name, launch_con, vpc_zones, elbs=[], target_groups=[]):
return stack.stack.add_resource(AutoScalingGroup('{0}{1}ASG'.format(stack.env, name.replace('_', )), LaunchConfigurationName=Ref(launch_con), MinSize='0', MaxSize='5', HealthCheckType='EC2', VPCZoneIdentifier=vpc_zones, TerminationPolicies=['OldestInstance'], LoadBalancerNames=elbs, TargetGroupARNs=target_groups)) | def create_autoscale_group(stack, name, launch_con, vpc_zones, elbs=[], target_groups=[]):
return stack.stack.add_resource(AutoScalingGroup('{0}{1}ASG'.format(stack.env, name.replace('_', )), LaunchConfigurationName=Ref(launch_con), MinSize='0', MaxSize='5', HealthCheckType='EC2', VPCZoneIdentifier=vpc_zones, TerminationPolicies=['OldestInstance'], LoadBalancerNames=elbs, TargetGroupARNs=target_groups))<|docstring|>Add EC2 AutoScalingGroup Resource.<|endoftext|> |
3dd18c1663647b79dbd1cd183c7df6842c02aacfe2aca2e05261f490d85433d1 | def create_db_param_group(stack, name, description, family, parameters={}):
'Create a DB Parameter Group'
return stack.stack.add_resource(DBParameterGroup('{0}DBParamGroup'.format(name), Description='{0} Parameter Group'.format(description), Family=family, Parameters=parameters)) | Create a DB Parameter Group | tropohelper/instances.py | create_db_param_group | devblueray/tropohelper | 0 | python | def create_db_param_group(stack, name, description, family, parameters={}):
return stack.stack.add_resource(DBParameterGroup('{0}DBParamGroup'.format(name), Description='{0} Parameter Group'.format(description), Family=family, Parameters=parameters)) | def create_db_param_group(stack, name, description, family, parameters={}):
return stack.stack.add_resource(DBParameterGroup('{0}DBParamGroup'.format(name), Description='{0} Parameter Group'.format(description), Family=family, Parameters=parameters))<|docstring|>Create a DB Parameter Group<|endoftext|> |
125561f9178de364db51c2aa9770718ab1449767fedb16f003ca16d4be66d423 | def create_rds_instance(stack, db_instance_identifier, db_name, db_instance_class, db_username, db_password, db_subnet_group, db_security_groups, vpc_security_groups, db_param_group, allocated_storage='20', engine='MySQL', engine_version='5.7.17', storage_encrypted='True', deletion_policy='Retain', multi_az=False):
'Add RDS Instance Resource.'
return stack.stack.add_resource(DBInstance('RDSInstance', DBInstanceIdentifier=db_instance_identifier, DBName=db_name, DBInstanceClass=db_instance_class, AllocatedStorage=allocated_storage, Engine=engine, EngineVersion=engine_version, MasterUsername=db_username, MasterUserPassword=db_password, DBSubnetGroupName=db_subnet_group, DBSecurityGroups=list(db_security_groups), VPCSecurityGroups=list(vpc_security_groups), DBParameterGroupName=db_param_group, StorageEncrypted=storage_encrypted, DeletionPolicy=deletion_policy, MultiAZ=multi_az)) | Add RDS Instance Resource. | tropohelper/instances.py | create_rds_instance | devblueray/tropohelper | 0 | python | def create_rds_instance(stack, db_instance_identifier, db_name, db_instance_class, db_username, db_password, db_subnet_group, db_security_groups, vpc_security_groups, db_param_group, allocated_storage='20', engine='MySQL', engine_version='5.7.17', storage_encrypted='True', deletion_policy='Retain', multi_az=False):
return stack.stack.add_resource(DBInstance('RDSInstance', DBInstanceIdentifier=db_instance_identifier, DBName=db_name, DBInstanceClass=db_instance_class, AllocatedStorage=allocated_storage, Engine=engine, EngineVersion=engine_version, MasterUsername=db_username, MasterUserPassword=db_password, DBSubnetGroupName=db_subnet_group, DBSecurityGroups=list(db_security_groups), VPCSecurityGroups=list(vpc_security_groups), DBParameterGroupName=db_param_group, StorageEncrypted=storage_encrypted, DeletionPolicy=deletion_policy, MultiAZ=multi_az)) | def create_rds_instance(stack, db_instance_identifier, db_name, db_instance_class, db_username, db_password, db_subnet_group, db_security_groups, vpc_security_groups, db_param_group, allocated_storage='20', engine='MySQL', engine_version='5.7.17', storage_encrypted='True', deletion_policy='Retain', multi_az=False):
return stack.stack.add_resource(DBInstance('RDSInstance', DBInstanceIdentifier=db_instance_identifier, DBName=db_name, DBInstanceClass=db_instance_class, AllocatedStorage=allocated_storage, Engine=engine, EngineVersion=engine_version, MasterUsername=db_username, MasterUserPassword=db_password, DBSubnetGroupName=db_subnet_group, DBSecurityGroups=list(db_security_groups), VPCSecurityGroups=list(vpc_security_groups), DBParameterGroupName=db_param_group, StorageEncrypted=storage_encrypted, DeletionPolicy=deletion_policy, MultiAZ=multi_az))<|docstring|>Add RDS Instance Resource.<|endoftext|> |
8182611ce14b8db3493b0347586e67b207f1a89d92495e93aec445b1a3616b19 | def mymake_blob_url(container_name, blob_name):
"\n Creates the url to access a blob.\n container_name: Name of container.\n blob_name: Name of blob.\n account_name:\n Name of the storage account. If not specified, uses the account\n specified when BlobService was initialized.\n protocol:\n Protocol to use: 'http' or 'https'. If not specified, uses the\n protocol specified when BlobService was initialized.\n host_base:\n Live host base url. If not specified, uses the host base specified\n when BlobService was initialized.\n "
return '{0}://{1}{2}/{3}/{4}'.format(settings.AZURE_PROTOCOL, settings.AZURE_STORAGE_ACCOUNT, settings.AZURE_HOST_BASE, container_name, blob_name) | Creates the url to access a blob.
container_name: Name of container.
blob_name: Name of blob.
account_name:
Name of the storage account. If not specified, uses the account
specified when BlobService was initialized.
protocol:
Protocol to use: 'http' or 'https'. If not specified, uses the
protocol specified when BlobService was initialized.
host_base:
Live host base url. If not specified, uses the host base specified
when BlobService was initialized. | web/survey/views.py | mymake_blob_url | sbreslav/mimic | 3 | python | def mymake_blob_url(container_name, blob_name):
"\n Creates the url to access a blob.\n container_name: Name of container.\n blob_name: Name of blob.\n account_name:\n Name of the storage account. If not specified, uses the account\n specified when BlobService was initialized.\n protocol:\n Protocol to use: 'http' or 'https'. If not specified, uses the\n protocol specified when BlobService was initialized.\n host_base:\n Live host base url. If not specified, uses the host base specified\n when BlobService was initialized.\n "
return '{0}://{1}{2}/{3}/{4}'.format(settings.AZURE_PROTOCOL, settings.AZURE_STORAGE_ACCOUNT, settings.AZURE_HOST_BASE, container_name, blob_name) | def mymake_blob_url(container_name, blob_name):
"\n Creates the url to access a blob.\n container_name: Name of container.\n blob_name: Name of blob.\n account_name:\n Name of the storage account. If not specified, uses the account\n specified when BlobService was initialized.\n protocol:\n Protocol to use: 'http' or 'https'. If not specified, uses the\n protocol specified when BlobService was initialized.\n host_base:\n Live host base url. If not specified, uses the host base specified\n when BlobService was initialized.\n "
return '{0}://{1}{2}/{3}/{4}'.format(settings.AZURE_PROTOCOL, settings.AZURE_STORAGE_ACCOUNT, settings.AZURE_HOST_BASE, container_name, blob_name)<|docstring|>Creates the url to access a blob.
container_name: Name of container.
blob_name: Name of blob.
account_name:
Name of the storage account. If not specified, uses the account
specified when BlobService was initialized.
protocol:
Protocol to use: 'http' or 'https'. If not specified, uses the
protocol specified when BlobService was initialized.
host_base:
Live host base url. If not specified, uses the host base specified
when BlobService was initialized.<|endoftext|> |
f095b52f7ef4933307c4e15a5545f1ecb02ea121de8125e6305b1b92e54e321e | def find_FWHM(max_time_index, freq_index):
'\n Find full width at half maximum in time-direction for a given time and freq\n index. The time index is assumed to be the index where the energy is maximal\n for this frequency.\n\n Returns (center time in seconds, FWMH in seconds)\n '
max_value = energy_values[(max_time_index, freq_index)]
min_time = times_in_seconds[max_time_index]
max_time = times_in_seconds[max_time_index]
for i in range((max_time_index + 1), energy_values.shape[0]):
value = energy_values[(i, freq_index)]
if (value < (max_value / 2)):
max_time = ((max_time + times_in_seconds[i]) / 2)
break
max_time = times_in_seconds[i]
for i in range((max_time_index - 1), 0, (- 1)):
value = energy_values[(i, freq_index)]
if (value < (max_value / 2)):
min_time = ((min_time + times_in_seconds[i]) / 2)
break
min_time = times_in_seconds[i]
avg = ((min_time + max_time) / 2)
return (avg, (max_time - min_time)) | Find full width at half maximum in time-direction for a given time and freq
index. The time index is assumed to be the index where the energy is maximal
for this frequency.
Returns (center time in seconds, FWMH in seconds) | exp1-ligo/analyze.py | find_FWHM | rluhtaru/jlab | 0 | python | def find_FWHM(max_time_index, freq_index):
'\n Find full width at half maximum in time-direction for a given time and freq\n index. The time index is assumed to be the index where the energy is maximal\n for this frequency.\n\n Returns (center time in seconds, FWMH in seconds)\n '
max_value = energy_values[(max_time_index, freq_index)]
min_time = times_in_seconds[max_time_index]
max_time = times_in_seconds[max_time_index]
for i in range((max_time_index + 1), energy_values.shape[0]):
value = energy_values[(i, freq_index)]
if (value < (max_value / 2)):
max_time = ((max_time + times_in_seconds[i]) / 2)
break
max_time = times_in_seconds[i]
for i in range((max_time_index - 1), 0, (- 1)):
value = energy_values[(i, freq_index)]
if (value < (max_value / 2)):
min_time = ((min_time + times_in_seconds[i]) / 2)
break
min_time = times_in_seconds[i]
avg = ((min_time + max_time) / 2)
return (avg, (max_time - min_time)) | def find_FWHM(max_time_index, freq_index):
'\n Find full width at half maximum in time-direction for a given time and freq\n index. The time index is assumed to be the index where the energy is maximal\n for this frequency.\n\n Returns (center time in seconds, FWMH in seconds)\n '
max_value = energy_values[(max_time_index, freq_index)]
min_time = times_in_seconds[max_time_index]
max_time = times_in_seconds[max_time_index]
for i in range((max_time_index + 1), energy_values.shape[0]):
value = energy_values[(i, freq_index)]
if (value < (max_value / 2)):
max_time = ((max_time + times_in_seconds[i]) / 2)
break
max_time = times_in_seconds[i]
for i in range((max_time_index - 1), 0, (- 1)):
value = energy_values[(i, freq_index)]
if (value < (max_value / 2)):
min_time = ((min_time + times_in_seconds[i]) / 2)
break
min_time = times_in_seconds[i]
avg = ((min_time + max_time) / 2)
return (avg, (max_time - min_time))<|docstring|>Find full width at half maximum in time-direction for a given time and freq
index. The time index is assumed to be the index where the energy is maximal
for this frequency.
Returns (center time in seconds, FWMH in seconds)<|endoftext|> |
bafc607b0f02c36f0077f3bbb819f9e55d5180f2210a2c5ff5572ab8b3e179d0 | def gwfreq(t, t0, M):
'\n Frequency (not angular frequency!) model for gravitational waves.\n t - time in seconds\n t0 - event time in seconds\n M - chirp mass in sun masses\n Returns f in Hz.\n '
const = (((1 / (2 * np.pi)) * 948.5) * np.power((1 / M), (5 / 8)))
TIME_CUTOFF = 1e-05
return (const * np.power(np.maximum((t0 - t), TIME_CUTOFF), ((- 3) / 8))) | Frequency (not angular frequency!) model for gravitational waves.
t - time in seconds
t0 - event time in seconds
M - chirp mass in sun masses
Returns f in Hz. | exp1-ligo/analyze.py | gwfreq | rluhtaru/jlab | 0 | python | def gwfreq(t, t0, M):
'\n Frequency (not angular frequency!) model for gravitational waves.\n t - time in seconds\n t0 - event time in seconds\n M - chirp mass in sun masses\n Returns f in Hz.\n '
const = (((1 / (2 * np.pi)) * 948.5) * np.power((1 / M), (5 / 8)))
TIME_CUTOFF = 1e-05
return (const * np.power(np.maximum((t0 - t), TIME_CUTOFF), ((- 3) / 8))) | def gwfreq(t, t0, M):
'\n Frequency (not angular frequency!) model for gravitational waves.\n t - time in seconds\n t0 - event time in seconds\n M - chirp mass in sun masses\n Returns f in Hz.\n '
const = (((1 / (2 * np.pi)) * 948.5) * np.power((1 / M), (5 / 8)))
TIME_CUTOFF = 1e-05
return (const * np.power(np.maximum((t0 - t), TIME_CUTOFF), ((- 3) / 8)))<|docstring|>Frequency (not angular frequency!) model for gravitational waves.
t - time in seconds
t0 - event time in seconds
M - chirp mass in sun masses
Returns f in Hz.<|endoftext|> |
aa396d5774ff5f6392d1e1ead36fbb0daf4474b3ddac86b362d98e0cc788770b | def inv_gwfreq(f, t0, M):
'\n Inverse of gwfreq.\n '
return (t0 - ((((948.5 / (2 * np.pi)) ** (8 / 3)) * np.power((1 / M), (5 / 3))) * np.power(f, ((- 8) / 3)))) | Inverse of gwfreq. | exp1-ligo/analyze.py | inv_gwfreq | rluhtaru/jlab | 0 | python | def inv_gwfreq(f, t0, M):
'\n \n '
return (t0 - ((((948.5 / (2 * np.pi)) ** (8 / 3)) * np.power((1 / M), (5 / 3))) * np.power(f, ((- 8) / 3)))) | def inv_gwfreq(f, t0, M):
'\n \n '
return (t0 - ((((948.5 / (2 * np.pi)) ** (8 / 3)) * np.power((1 / M), (5 / 3))) * np.power(f, ((- 8) / 3))))<|docstring|>Inverse of gwfreq.<|endoftext|> |
ac159b40dc981b71e48421be003cde88b67a0540a43e376d82f5807d81563a18 | @mock.patch('requests.post', side_effect=mocked_requests_post)
def test_report_for_unknown_app(self, mock_post):
'When the app is unknown scout will return an HTTP 404 but the report function should just act normally'
scout = Scout(app='unknown', version='0.1.0', install_id=install_id)
resp = scout.report()
logging.debug(('SR: %s' % resp))
self.assertEqual(resp, {'latest_version': '0.1.0'}) | When the app is unknown scout will return an HTTP 404 but the report function should just act normally | scout/test_scout.py | test_report_for_unknown_app | datawire/scout-py | 0 | python | @mock.patch('requests.post', side_effect=mocked_requests_post)
def test_report_for_unknown_app(self, mock_post):
scout = Scout(app='unknown', version='0.1.0', install_id=install_id)
resp = scout.report()
logging.debug(('SR: %s' % resp))
self.assertEqual(resp, {'latest_version': '0.1.0'}) | @mock.patch('requests.post', side_effect=mocked_requests_post)
def test_report_for_unknown_app(self, mock_post):
scout = Scout(app='unknown', version='0.1.0', install_id=install_id)
resp = scout.report()
logging.debug(('SR: %s' % resp))
self.assertEqual(resp, {'latest_version': '0.1.0'})<|docstring|>When the app is unknown scout will return an HTTP 404 but the report function should just act normally<|endoftext|> |
5616f40a847bb8f31e9774f5d850ee0653781830168eb0757178510d0c6bb21b | @mock.patch('requests.post', side_effect=mocked_requests_post)
def test_report(self, mock_post):
'Scout backend returns the latest version. The scout client returns this to the caller.'
scout = Scout(app='foshizzolator', version='0.1.0', install_id=install_id)
resp = scout.report()
self.assertEqual(resp, {'latest_version': '0.2.0'}) | Scout backend returns the latest version. The scout client returns this to the caller. | scout/test_scout.py | test_report | datawire/scout-py | 0 | python | @mock.patch('requests.post', side_effect=mocked_requests_post)
def test_report(self, mock_post):
scout = Scout(app='foshizzolator', version='0.1.0', install_id=install_id)
resp = scout.report()
self.assertEqual(resp, {'latest_version': '0.2.0'}) | @mock.patch('requests.post', side_effect=mocked_requests_post)
def test_report(self, mock_post):
scout = Scout(app='foshizzolator', version='0.1.0', install_id=install_id)
resp = scout.report()
self.assertEqual(resp, {'latest_version': '0.2.0'})<|docstring|>Scout backend returns the latest version. The scout client returns this to the caller.<|endoftext|> |
b3900a532c2e55607c4e78f6f8a33ed97959f360c73eba5c14975419c7509411 | @mock.patch('requests.post', side_effect=mocked_requests_post)
def test_plugin(self, mock_post):
'Scout install-id plugin should set the install_id and requisite metadata.'
scout = Scout(app='foshizzolator', version='0.1.0', id_plugin=install_id_plugin, id_plugin_args={'hello': 'world'})
self.assertEqual(scout.install_id, PLUGIN_UUID)
self.assertEqual(scout.metadata['new_install'], True)
self.assertEqual(scout.metadata['swallow_speed'], 42)
self.assertEqual(scout.metadata['hello'], 'world') | Scout install-id plugin should set the install_id and requisite metadata. | scout/test_scout.py | test_plugin | datawire/scout-py | 0 | python | @mock.patch('requests.post', side_effect=mocked_requests_post)
def test_plugin(self, mock_post):
scout = Scout(app='foshizzolator', version='0.1.0', id_plugin=install_id_plugin, id_plugin_args={'hello': 'world'})
self.assertEqual(scout.install_id, PLUGIN_UUID)
self.assertEqual(scout.metadata['new_install'], True)
self.assertEqual(scout.metadata['swallow_speed'], 42)
self.assertEqual(scout.metadata['hello'], 'world') | @mock.patch('requests.post', side_effect=mocked_requests_post)
def test_plugin(self, mock_post):
scout = Scout(app='foshizzolator', version='0.1.0', id_plugin=install_id_plugin, id_plugin_args={'hello': 'world'})
self.assertEqual(scout.install_id, PLUGIN_UUID)
self.assertEqual(scout.metadata['new_install'], True)
self.assertEqual(scout.metadata['swallow_speed'], 42)
self.assertEqual(scout.metadata['hello'], 'world')<|docstring|>Scout install-id plugin should set the install_id and requisite metadata.<|endoftext|> |
ce656cf81bd49658b22312d9d717b01515a3c9bdc07372f6e9926676ac3e0c51 | def build_mnist_mlp_net(model, input_blob_name):
'Create a feedforward neural network composed of fullyconnected layers.\n A final softmax layer is used to get probabilities for the 10 numbers.'
fc_layer_0_input_dims = MNIST_IMG_PIXEL_NUM
fc_layer_0_output_dims = (MNIST_IMG_PIXEL_NUM * 2)
fc_layer_0 = brew.fc(model, input_blob_name, 'fc_layer_0', dim_in=fc_layer_0_input_dims, dim_out=fc_layer_0_output_dims)
relu_layer_0 = brew.relu(model, fc_layer_0, 'relu_layer_0')
fc_layer_1_input_dims = fc_layer_0_output_dims
fc_layer_1_output_dims = (MNIST_IMG_PIXEL_NUM * 2)
fc_layer_1 = brew.fc(model, relu_layer_0, 'fc_layer_1', dim_in=fc_layer_1_input_dims, dim_out=fc_layer_1_output_dims)
relu_layer_1 = brew.relu(model, fc_layer_1, 'relu_layer_1')
fc_layer_2_input_dims = fc_layer_1_output_dims
fc_layer_2_output_dims = MNIST_IMG_PIXEL_NUM
fc_layer_2 = brew.fc(model, relu_layer_1, 'fc_layer_2', dim_in=fc_layer_2_input_dims, dim_out=fc_layer_2_output_dims)
relu_layer_2 = brew.relu(model, fc_layer_2, 'relu_layer_2')
softmax_layer = brew.softmax(model, relu_layer_2, 'softmax_layer')
return softmax_layer | Create a feedforward neural network composed of fullyconnected layers.
A final softmax layer is used to get probabilities for the 10 numbers. | Chapter02/infer_mnist_mlp.py | build_mnist_mlp_net | PacktPublishing/Caffe2-Quick-Start-Guide | 8 | python | def build_mnist_mlp_net(model, input_blob_name):
'Create a feedforward neural network composed of fullyconnected layers.\n A final softmax layer is used to get probabilities for the 10 numbers.'
fc_layer_0_input_dims = MNIST_IMG_PIXEL_NUM
fc_layer_0_output_dims = (MNIST_IMG_PIXEL_NUM * 2)
fc_layer_0 = brew.fc(model, input_blob_name, 'fc_layer_0', dim_in=fc_layer_0_input_dims, dim_out=fc_layer_0_output_dims)
relu_layer_0 = brew.relu(model, fc_layer_0, 'relu_layer_0')
fc_layer_1_input_dims = fc_layer_0_output_dims
fc_layer_1_output_dims = (MNIST_IMG_PIXEL_NUM * 2)
fc_layer_1 = brew.fc(model, relu_layer_0, 'fc_layer_1', dim_in=fc_layer_1_input_dims, dim_out=fc_layer_1_output_dims)
relu_layer_1 = brew.relu(model, fc_layer_1, 'relu_layer_1')
fc_layer_2_input_dims = fc_layer_1_output_dims
fc_layer_2_output_dims = MNIST_IMG_PIXEL_NUM
fc_layer_2 = brew.fc(model, relu_layer_1, 'fc_layer_2', dim_in=fc_layer_2_input_dims, dim_out=fc_layer_2_output_dims)
relu_layer_2 = brew.relu(model, fc_layer_2, 'relu_layer_2')
softmax_layer = brew.softmax(model, relu_layer_2, 'softmax_layer')
return softmax_layer | def build_mnist_mlp_net(model, input_blob_name):
'Create a feedforward neural network composed of fullyconnected layers.\n A final softmax layer is used to get probabilities for the 10 numbers.'
fc_layer_0_input_dims = MNIST_IMG_PIXEL_NUM
fc_layer_0_output_dims = (MNIST_IMG_PIXEL_NUM * 2)
fc_layer_0 = brew.fc(model, input_blob_name, 'fc_layer_0', dim_in=fc_layer_0_input_dims, dim_out=fc_layer_0_output_dims)
relu_layer_0 = brew.relu(model, fc_layer_0, 'relu_layer_0')
fc_layer_1_input_dims = fc_layer_0_output_dims
fc_layer_1_output_dims = (MNIST_IMG_PIXEL_NUM * 2)
fc_layer_1 = brew.fc(model, relu_layer_0, 'fc_layer_1', dim_in=fc_layer_1_input_dims, dim_out=fc_layer_1_output_dims)
relu_layer_1 = brew.relu(model, fc_layer_1, 'relu_layer_1')
fc_layer_2_input_dims = fc_layer_1_output_dims
fc_layer_2_output_dims = MNIST_IMG_PIXEL_NUM
fc_layer_2 = brew.fc(model, relu_layer_1, 'fc_layer_2', dim_in=fc_layer_2_input_dims, dim_out=fc_layer_2_output_dims)
relu_layer_2 = brew.relu(model, fc_layer_2, 'relu_layer_2')
softmax_layer = brew.softmax(model, relu_layer_2, 'softmax_layer')
return softmax_layer<|docstring|>Create a feedforward neural network composed of fullyconnected layers.
A final softmax layer is used to get probabilities for the 10 numbers.<|endoftext|> |
db1b8139b67e13911f0799d9081d802032693a7ca8a03daad31b0870e44f0a95 | def set_model_weights(inference_model):
'Set the weights of the fully connected layers in the inference network.\n Weights are pre-trained and are read from NumPy files on disk.'
for (i, layer_blob_name) in enumerate(inference_model.params):
layer_weights_filepath = 'mnist_mlp_weights/{}.npy'.format(str(i))
layer_weights = np.load(layer_weights_filepath, allow_pickle=False)
workspace.FeedBlob(layer_blob_name, layer_weights) | Set the weights of the fully connected layers in the inference network.
Weights are pre-trained and are read from NumPy files on disk. | Chapter02/infer_mnist_mlp.py | set_model_weights | PacktPublishing/Caffe2-Quick-Start-Guide | 8 | python | def set_model_weights(inference_model):
'Set the weights of the fully connected layers in the inference network.\n Weights are pre-trained and are read from NumPy files on disk.'
for (i, layer_blob_name) in enumerate(inference_model.params):
layer_weights_filepath = 'mnist_mlp_weights/{}.npy'.format(str(i))
layer_weights = np.load(layer_weights_filepath, allow_pickle=False)
workspace.FeedBlob(layer_blob_name, layer_weights) | def set_model_weights(inference_model):
'Set the weights of the fully connected layers in the inference network.\n Weights are pre-trained and are read from NumPy files on disk.'
for (i, layer_blob_name) in enumerate(inference_model.params):
layer_weights_filepath = 'mnist_mlp_weights/{}.npy'.format(str(i))
layer_weights = np.load(layer_weights_filepath, allow_pickle=False)
workspace.FeedBlob(layer_blob_name, layer_weights)<|docstring|>Set the weights of the fully connected layers in the inference network.
Weights are pre-trained and are read from NumPy files on disk.<|endoftext|> |
1f3cb5ac142c0a92158b9e5ad6697edb4f8e87fdaa0018adc4fde7bf691b1051 | def backoff(self, item):
'Get the backoff time for an item (in seconds)'
exp = self._failures[item]
self._failures[item] = (exp + 1)
backoff = min((self.base_delay * (2 ** exp)), self.max_delay)
return backoff | Get the backoff time for an item (in seconds) | dask-gateway-server/dask_gateway_server/workqueue.py | backoff | CrispyCrafter/dask-gateway | 69 | python | def backoff(self, item):
exp = self._failures[item]
self._failures[item] = (exp + 1)
backoff = min((self.base_delay * (2 ** exp)), self.max_delay)
return backoff | def backoff(self, item):
exp = self._failures[item]
self._failures[item] = (exp + 1)
backoff = min((self.base_delay * (2 ** exp)), self.max_delay)
return backoff<|docstring|>Get the backoff time for an item (in seconds)<|endoftext|> |
60ad761840782561d14880de2b709953ee6601d749b176c38aacfb19d4baaae7 | def failures(self, item):
'Get the number of failures seen for an item'
return self._failures.get(item, 0) | Get the number of failures seen for an item | dask-gateway-server/dask_gateway_server/workqueue.py | failures | CrispyCrafter/dask-gateway | 69 | python | def failures(self, item):
return self._failures.get(item, 0) | def failures(self, item):
return self._failures.get(item, 0)<|docstring|>Get the number of failures seen for an item<|endoftext|> |
614af9cc7e2e0a4d714b4a565827bc2b6fe926dbad39b86169a63918582c68fe | def reset(self, item):
'Reset the backoff for an item'
self._failures.pop(item, None) | Reset the backoff for an item | dask-gateway-server/dask_gateway_server/workqueue.py | reset | CrispyCrafter/dask-gateway | 69 | python | def reset(self, item):
self._failures.pop(item, None) | def reset(self, item):
self._failures.pop(item, None)<|docstring|>Reset the backoff for an item<|endoftext|> |
369139195261de49070f70b0b9245afb24bd6539657302d41705f4c7ebe613f9 | def is_empty(self):
'True if there are no items queued'
return (not self._dirty) | True if there are no items queued | dask-gateway-server/dask_gateway_server/workqueue.py | is_empty | CrispyCrafter/dask-gateway | 69 | python | def is_empty(self):
return (not self._dirty) | def is_empty(self):
return (not self._dirty)<|docstring|>True if there are no items queued<|endoftext|> |
a84614db1aa8c711d556185e69f1bcfb7a2e964609b9329bb8ffb08946962d07 | def put(self, item):
'Put an item in the queue'
self._put(item)
self._wakeup_next() | Put an item in the queue | dask-gateway-server/dask_gateway_server/workqueue.py | put | CrispyCrafter/dask-gateway | 69 | python | def put(self, item):
self._put(item)
self._wakeup_next() | def put(self, item):
self._put(item)
self._wakeup_next()<|docstring|>Put an item in the queue<|endoftext|> |
dee4a6d42c1a1e825b1e5b8cc8230fae995749c8d4def2900f1a69289bc96f01 | def put_after(self, item, delay):
'Schedule an item to be put in the queue after a delay.\n\n If the item is already scheduled, it will be rescheduled only if the\n delay would enqueue it sooner than the existing schedule.\n '
when = (self._loop.time() + delay)
existing = self._timers.get(item, None)
if ((existing is None) or (existing[1] > when)):
if (existing is not None):
existing[0].cancel()
if (delay > 0):
self._timers[item] = (self._loop.call_at(when, self._put_delayed, item), when)
else:
self._timers.pop(item, None)
self.put(item) | Schedule an item to be put in the queue after a delay.
If the item is already scheduled, it will be rescheduled only if the
delay would enqueue it sooner than the existing schedule. | dask-gateway-server/dask_gateway_server/workqueue.py | put_after | CrispyCrafter/dask-gateway | 69 | python | def put_after(self, item, delay):
'Schedule an item to be put in the queue after a delay.\n\n If the item is already scheduled, it will be rescheduled only if the\n delay would enqueue it sooner than the existing schedule.\n '
when = (self._loop.time() + delay)
existing = self._timers.get(item, None)
if ((existing is None) or (existing[1] > when)):
if (existing is not None):
existing[0].cancel()
if (delay > 0):
self._timers[item] = (self._loop.call_at(when, self._put_delayed, item), when)
else:
self._timers.pop(item, None)
self.put(item) | def put_after(self, item, delay):
'Schedule an item to be put in the queue after a delay.\n\n If the item is already scheduled, it will be rescheduled only if the\n delay would enqueue it sooner than the existing schedule.\n '
when = (self._loop.time() + delay)
existing = self._timers.get(item, None)
if ((existing is None) or (existing[1] > when)):
if (existing is not None):
existing[0].cancel()
if (delay > 0):
self._timers[item] = (self._loop.call_at(when, self._put_delayed, item), when)
else:
self._timers.pop(item, None)
self.put(item)<|docstring|>Schedule an item to be put in the queue after a delay.
If the item is already scheduled, it will be rescheduled only if the
delay would enqueue it sooner than the existing schedule.<|endoftext|> |
1f601cb60adeee6849374b66720cfa21f4db034e5bd0fc31b22bb9a69d943b14 | def put_backoff(self, item):
'Schedule an item to be put in the queue after a backoff.\n\n If the item is already scheduled, it will be rescheduled only if the\n delay would enqueue it sooner than the existing schedule.\n '
self.put_after(item, self.backoff.backoff(item)) | Schedule an item to be put in the queue after a backoff.
If the item is already scheduled, it will be rescheduled only if the
delay would enqueue it sooner than the existing schedule. | dask-gateway-server/dask_gateway_server/workqueue.py | put_backoff | CrispyCrafter/dask-gateway | 69 | python | def put_backoff(self, item):
'Schedule an item to be put in the queue after a backoff.\n\n If the item is already scheduled, it will be rescheduled only if the\n delay would enqueue it sooner than the existing schedule.\n '
self.put_after(item, self.backoff.backoff(item)) | def put_backoff(self, item):
'Schedule an item to be put in the queue after a backoff.\n\n If the item is already scheduled, it will be rescheduled only if the\n delay would enqueue it sooner than the existing schedule.\n '
self.put_after(item, self.backoff.backoff(item))<|docstring|>Schedule an item to be put in the queue after a backoff.
If the item is already scheduled, it will be rescheduled only if the
delay would enqueue it sooner than the existing schedule.<|endoftext|> |
faae193dce3aeeea95166094ff32c46d5b9fd5904ea1dbed96aa29e6f0a74b0c | def failures(self, item):
'Get the number of failures seen for this item'
return self.backoff.failures(item) | Get the number of failures seen for this item | dask-gateway-server/dask_gateway_server/workqueue.py | failures | CrispyCrafter/dask-gateway | 69 | python | def failures(self, item):
return self.backoff.failures(item) | def failures(self, item):
return self.backoff.failures(item)<|docstring|>Get the number of failures seen for this item<|endoftext|> |
eb577479d514a5af70e885e94151b40c02ddd9bb587eb94ae7bfe9bcd318f31e | def reset_backoff(self, item):
'Reset the backoff for an item'
self.backoff.reset(item) | Reset the backoff for an item | dask-gateway-server/dask_gateway_server/workqueue.py | reset_backoff | CrispyCrafter/dask-gateway | 69 | python | def reset_backoff(self, item):
self.backoff.reset(item) | def reset_backoff(self, item):
self.backoff.reset(item)<|docstring|>Reset the backoff for an item<|endoftext|> |
24e01784088d0ecd21356896cd8b7bd6f7e131586bcc83e3f3818bbe4ddb4f39 | async def get(self):
'Get an item from the queue.'
while (not self._queue):
if self.closed:
raise WorkQueueClosed
waiter = self._loop.create_future()
self._waiting.append(waiter)
try:
(await waiter)
except asyncio.CancelledError:
try:
self._waiting.remove(waiter)
except ValueError:
pass
raise
return self._get() | Get an item from the queue. | dask-gateway-server/dask_gateway_server/workqueue.py | get | CrispyCrafter/dask-gateway | 69 | python | async def get(self):
while (not self._queue):
if self.closed:
raise WorkQueueClosed
waiter = self._loop.create_future()
self._waiting.append(waiter)
try:
(await waiter)
except asyncio.CancelledError:
try:
self._waiting.remove(waiter)
except ValueError:
pass
raise
return self._get() | async def get(self):
while (not self._queue):
if self.closed:
raise WorkQueueClosed
waiter = self._loop.create_future()
self._waiting.append(waiter)
try:
(await waiter)
except asyncio.CancelledError:
try:
self._waiting.remove(waiter)
except ValueError:
pass
raise
return self._get()<|docstring|>Get an item from the queue.<|endoftext|> |
a70e0df38e8534f1be3c201842c6f73b6f3aa11a3447a80e81f5c63be48cedf4 | def task_done(self, item):
'Mark a task as done.\n\n This *must* be done before the item can be processed again.\n '
self._processing.discard(item)
if (item in self._dirty):
self._queue.append(item)
self._wakeup_next() | Mark a task as done.
This *must* be done before the item can be processed again. | dask-gateway-server/dask_gateway_server/workqueue.py | task_done | CrispyCrafter/dask-gateway | 69 | python | def task_done(self, item):
'Mark a task as done.\n\n This *must* be done before the item can be processed again.\n '
self._processing.discard(item)
if (item in self._dirty):
self._queue.append(item)
self._wakeup_next() | def task_done(self, item):
'Mark a task as done.\n\n This *must* be done before the item can be processed again.\n '
self._processing.discard(item)
if (item in self._dirty):
self._queue.append(item)
self._wakeup_next()<|docstring|>Mark a task as done.
This *must* be done before the item can be processed again.<|endoftext|> |
d5c2c903c031ce524c9cf47b4469c12ff016fce3920e5942add12c7d7f267bda | def close(self):
'Close the queue.\n\n Future calls to ``WorkQueue.get`` will raise ``WorkQueueClosed``\n '
self.closed = True
self._wakeup_all() | Close the queue.
Future calls to ``WorkQueue.get`` will raise ``WorkQueueClosed`` | dask-gateway-server/dask_gateway_server/workqueue.py | close | CrispyCrafter/dask-gateway | 69 | python | def close(self):
'Close the queue.\n\n Future calls to ``WorkQueue.get`` will raise ``WorkQueueClosed``\n '
self.closed = True
self._wakeup_all() | def close(self):
'Close the queue.\n\n Future calls to ``WorkQueue.get`` will raise ``WorkQueueClosed``\n '
self.closed = True
self._wakeup_all()<|docstring|>Close the queue.
Future calls to ``WorkQueue.get`` will raise ``WorkQueueClosed``<|endoftext|> |
5fd7f03ad715ada996cae20c5e472a598b0512abd392b7cbeff88a1e8a3fcfb1 | @functools.lru_cache()
def get_attribute_classes() -> Dict[(str, Attribute)]:
'\n Lookup all builtin Attribute subclasses, load them, and return a dict of\n attribute name -> class.\n '
attribute_children = pkgutil.iter_modules(importlib.import_module('lawu.attributes').__path__, prefix='lawu.attributes.')
result = {}
for (_, name, _) in attribute_children:
classes = inspect.getmembers(importlib.import_module(name), (lambda c: (inspect.isclass(c) and issubclass(c, Attribute) and (c is not Attribute))))
for (class_name, class_) in classes:
attribute_name = getattr(class_, 'ATTRIBUTE_NAME', class_name[:(- 9)])
result[attribute_name.lower()] = class_
return result | Lookup all builtin Attribute subclasses, load them, and return a dict of
attribute name -> class. | lawu/attribute.py | get_attribute_classes | nickelpro/Lawu | 31 | python | @functools.lru_cache()
def get_attribute_classes() -> Dict[(str, Attribute)]:
'\n Lookup all builtin Attribute subclasses, load them, and return a dict of\n attribute name -> class.\n '
attribute_children = pkgutil.iter_modules(importlib.import_module('lawu.attributes').__path__, prefix='lawu.attributes.')
result = {}
for (_, name, _) in attribute_children:
classes = inspect.getmembers(importlib.import_module(name), (lambda c: (inspect.isclass(c) and issubclass(c, Attribute) and (c is not Attribute))))
for (class_name, class_) in classes:
attribute_name = getattr(class_, 'ATTRIBUTE_NAME', class_name[:(- 9)])
result[attribute_name.lower()] = class_
return result | @functools.lru_cache()
def get_attribute_classes() -> Dict[(str, Attribute)]:
'\n Lookup all builtin Attribute subclasses, load them, and return a dict of\n attribute name -> class.\n '
attribute_children = pkgutil.iter_modules(importlib.import_module('lawu.attributes').__path__, prefix='lawu.attributes.')
result = {}
for (_, name, _) in attribute_children:
classes = inspect.getmembers(importlib.import_module(name), (lambda c: (inspect.isclass(c) and issubclass(c, Attribute) and (c is not Attribute))))
for (class_name, class_) in classes:
attribute_name = getattr(class_, 'ATTRIBUTE_NAME', class_name[:(- 9)])
result[attribute_name.lower()] = class_
return result<|docstring|>Lookup all builtin Attribute subclasses, load them, and return a dict of
attribute name -> class.<|endoftext|> |
e284d3b289d2d9e8f8cccbde03c99abe5e764bce80390ad3c14bc91b771fcdbc | @staticmethod
def from_binary(pool, source):
'Called when converting a ClassFile into an AST.'
raise NotImplementedError() | Called when converting a ClassFile into an AST. | lawu/attribute.py | from_binary | nickelpro/Lawu | 31 | python | @staticmethod
def from_binary(pool, source):
raise NotImplementedError() | @staticmethod
def from_binary(pool, source):
raise NotImplementedError()<|docstring|>Called when converting a ClassFile into an AST.<|endoftext|> |
fb3b9d3f7df7dea711129824f63de323bde393629e99030c738f7c240ef2bf93 | def update(self):
'Update ticks'
local = time.localtime(time.time())
self.timeSprite.add(Message((time.strftime('%H:%M:%S', local),), vector=(0, 0), fontsize=90, align='left', padding=0, fgcolor=(0, 0, 255)))
surfaceRect = self.image.get_rect()
self.timeSprite.sprite.rect.midbottom = surfaceRect.center
self.timeSprite.draw(self.baseImage)
self.dateSprite.add(Message((time.strftime('%Y-%m-%d', local),), vector=(0, 0), fontsize=25, align='left', padding=0, fgcolor=(0, 0, 255)))
self.dateSprite.sprite.rect.midtop = self.timeSprite.sprite.rect.midbottom
self.dateSprite.draw(self.baseImage) | Update ticks | faces/digitalclock.py | update | khan-git/pialarmclock | 1 | python | def update(self):
local = time.localtime(time.time())
self.timeSprite.add(Message((time.strftime('%H:%M:%S', local),), vector=(0, 0), fontsize=90, align='left', padding=0, fgcolor=(0, 0, 255)))
surfaceRect = self.image.get_rect()
self.timeSprite.sprite.rect.midbottom = surfaceRect.center
self.timeSprite.draw(self.baseImage)
self.dateSprite.add(Message((time.strftime('%Y-%m-%d', local),), vector=(0, 0), fontsize=25, align='left', padding=0, fgcolor=(0, 0, 255)))
self.dateSprite.sprite.rect.midtop = self.timeSprite.sprite.rect.midbottom
self.dateSprite.draw(self.baseImage) | def update(self):
local = time.localtime(time.time())
self.timeSprite.add(Message((time.strftime('%H:%M:%S', local),), vector=(0, 0), fontsize=90, align='left', padding=0, fgcolor=(0, 0, 255)))
surfaceRect = self.image.get_rect()
self.timeSprite.sprite.rect.midbottom = surfaceRect.center
self.timeSprite.draw(self.baseImage)
self.dateSprite.add(Message((time.strftime('%Y-%m-%d', local),), vector=(0, 0), fontsize=25, align='left', padding=0, fgcolor=(0, 0, 255)))
self.dateSprite.sprite.rect.midtop = self.timeSprite.sprite.rect.midbottom
self.dateSprite.draw(self.baseImage)<|docstring|>Update ticks<|endoftext|> |
42d2440e1a3a098909a5d6c5fa5e44e3ee64f3b2b92026f8faef37b04453b817 | def Diff_mat_r(Nr, r):
"\n Args:\n Nr : number of points\n r : list of r's\n Returns:\n Dr_1d : d/dr\n rDr_1d : 1/r * d/dr\n D2r_1d : d^2/dr^2\n "
Dr_1d = sp.diags([(- 1), 1], [(- 1), 1], shape=(Nr, Nr))
Dr_1d = sp.lil_matrix(Dr_1d)
Dr_1d[(0, [0, 1, 2])] = [(- 3), 4, (- 1)]
Dr_1d[((Nr - 1), [(Nr - 3), (Nr - 2), (Nr - 1)])] = [1, (- 4), 3]
rDr_1d = Dr_1d.T.multiply((1 / r)).T
D2r_1d = sp.diags([1, (- 2), 1], [(- 1), 0, 1], shape=(Nr, Nr))
D2r_1d = sp.lil_matrix(D2r_1d)
D2r_1d[(0, [0, 1, 2, 3])] = [2, (- 5), 4, (- 1)]
D2r_1d[((Nr - 1), [(Nr - 4), (Nr - 3), (Nr - 2), (Nr - 1)])] = [(- 1), 4, (- 5), 2]
return (Dr_1d, rDr_1d, D2r_1d) | Args:
Nr : number of points
r : list of r's
Returns:
Dr_1d : d/dr
rDr_1d : 1/r * d/dr
D2r_1d : d^2/dr^2 | diff_matrices_polar.py | Diff_mat_r | itrosen/hall-solver | 0 | python | def Diff_mat_r(Nr, r):
"\n Args:\n Nr : number of points\n r : list of r's\n Returns:\n Dr_1d : d/dr\n rDr_1d : 1/r * d/dr\n D2r_1d : d^2/dr^2\n "
Dr_1d = sp.diags([(- 1), 1], [(- 1), 1], shape=(Nr, Nr))
Dr_1d = sp.lil_matrix(Dr_1d)
Dr_1d[(0, [0, 1, 2])] = [(- 3), 4, (- 1)]
Dr_1d[((Nr - 1), [(Nr - 3), (Nr - 2), (Nr - 1)])] = [1, (- 4), 3]
rDr_1d = Dr_1d.T.multiply((1 / r)).T
D2r_1d = sp.diags([1, (- 2), 1], [(- 1), 0, 1], shape=(Nr, Nr))
D2r_1d = sp.lil_matrix(D2r_1d)
D2r_1d[(0, [0, 1, 2, 3])] = [2, (- 5), 4, (- 1)]
D2r_1d[((Nr - 1), [(Nr - 4), (Nr - 3), (Nr - 2), (Nr - 1)])] = [(- 1), 4, (- 5), 2]
return (Dr_1d, rDr_1d, D2r_1d) | def Diff_mat_r(Nr, r):
"\n Args:\n Nr : number of points\n r : list of r's\n Returns:\n Dr_1d : d/dr\n rDr_1d : 1/r * d/dr\n D2r_1d : d^2/dr^2\n "
Dr_1d = sp.diags([(- 1), 1], [(- 1), 1], shape=(Nr, Nr))
Dr_1d = sp.lil_matrix(Dr_1d)
Dr_1d[(0, [0, 1, 2])] = [(- 3), 4, (- 1)]
Dr_1d[((Nr - 1), [(Nr - 3), (Nr - 2), (Nr - 1)])] = [1, (- 4), 3]
rDr_1d = Dr_1d.T.multiply((1 / r)).T
D2r_1d = sp.diags([1, (- 2), 1], [(- 1), 0, 1], shape=(Nr, Nr))
D2r_1d = sp.lil_matrix(D2r_1d)
D2r_1d[(0, [0, 1, 2, 3])] = [2, (- 5), 4, (- 1)]
D2r_1d[((Nr - 1), [(Nr - 4), (Nr - 3), (Nr - 2), (Nr - 1)])] = [(- 1), 4, (- 5), 2]
return (Dr_1d, rDr_1d, D2r_1d)<|docstring|>Args:
Nr : number of points
r : list of r's
Returns:
Dr_1d : d/dr
rDr_1d : 1/r * d/dr
D2r_1d : d^2/dr^2<|endoftext|> |
e4257d9d74dffc9aaa83bbd0df131180b5df2d9df837be77cb4f07518479f63a | def Diff_mat_t(Nt):
'\n Args:\n Nr : number of points\n Returns:\n Dt_1d : d/dt\n D2t_1d : d^2/dt^2\n '
Dt_1d = sp.diags([(- 1), 1], [(- 1), 1], shape=(Nt, Nt))
Dt_1d = sp.lil_matrix(Dt_1d)
Dt_1d[(0, (- 1))] = [(- 1)]
Dt_1d[((- 1), 0)] = [1]
D2t_1d = sp.diags([1, (- 2), 1], [(- 1), 0, 1], shape=(Nt, Nt))
D2t_1d = sp.lil_matrix(D2t_1d)
D2t_1d[(0, (- 1))] = [1]
D2t_1d[((- 1), 0)] = [1]
return (Dt_1d, D2t_1d) | Args:
Nr : number of points
Returns:
Dt_1d : d/dt
D2t_1d : d^2/dt^2 | diff_matrices_polar.py | Diff_mat_t | itrosen/hall-solver | 0 | python | def Diff_mat_t(Nt):
'\n Args:\n Nr : number of points\n Returns:\n Dt_1d : d/dt\n D2t_1d : d^2/dt^2\n '
Dt_1d = sp.diags([(- 1), 1], [(- 1), 1], shape=(Nt, Nt))
Dt_1d = sp.lil_matrix(Dt_1d)
Dt_1d[(0, (- 1))] = [(- 1)]
Dt_1d[((- 1), 0)] = [1]
D2t_1d = sp.diags([1, (- 2), 1], [(- 1), 0, 1], shape=(Nt, Nt))
D2t_1d = sp.lil_matrix(D2t_1d)
D2t_1d[(0, (- 1))] = [1]
D2t_1d[((- 1), 0)] = [1]
return (Dt_1d, D2t_1d) | def Diff_mat_t(Nt):
'\n Args:\n Nr : number of points\n Returns:\n Dt_1d : d/dt\n D2t_1d : d^2/dt^2\n '
Dt_1d = sp.diags([(- 1), 1], [(- 1), 1], shape=(Nt, Nt))
Dt_1d = sp.lil_matrix(Dt_1d)
Dt_1d[(0, (- 1))] = [(- 1)]
Dt_1d[((- 1), 0)] = [1]
D2t_1d = sp.diags([1, (- 2), 1], [(- 1), 0, 1], shape=(Nt, Nt))
D2t_1d = sp.lil_matrix(D2t_1d)
D2t_1d[(0, (- 1))] = [1]
D2t_1d[((- 1), 0)] = [1]
return (Dt_1d, D2t_1d)<|docstring|>Args:
Nr : number of points
Returns:
Dt_1d : d/dt
D2t_1d : d^2/dt^2<|endoftext|> |
03ff09f717870f334a50c314f5b41ef586a1e7feeebd47bae6c661a9790f5979 | def Diff_mat_2D_polar(Nr, Nt, r):
'\n Args:\n Nr : number of points in radial coordinate\n Nt : number of points in theta coordinate\n r : radial points\n Returns: \n Finite element matrices for the 2D space, in sparse format\n Dr_2d : d/dr\n rDr_2d : 1/r * d/dr\n d2r_2d : d^2/dr^2\n rDt_2d : 1/r * d/dt\n r2D2t_2d : 1/r^2 * d^2/dt^2\n '
(Dr_1d, rDr_1d, D2r_1d) = Diff_mat_r(Nr, r)
(Dt_1d, D2t_1d) = Diff_mat_t(Nt)
Ir = sp.eye(Nr)
It = sp.eye(Nt)
Rr = sp.spdiags([(1 / r)], [0], Nr, Nr)
R2r = sp.spdiags([(1 / (r ** 2))], [0], Nr, Nr)
Dr_2d = sp.kron(It, Dr_1d)
rDr_2d = sp.kron(It, rDr_1d)
D2r_2d = sp.kron(It, D2r_1d)
rDt_2d = sp.kron(Dt_1d, Rr)
r2D2t_2d = sp.kron(D2t_1d, R2r)
return (Dr_2d.tocsr(), rDr_2d.tocsr(), D2r_2d.tocsr(), rDt_2d.tocsr(), r2D2t_2d.tocsr()) | Args:
Nr : number of points in radial coordinate
Nt : number of points in theta coordinate
r : radial points
Returns:
Finite element matrices for the 2D space, in sparse format
Dr_2d : d/dr
rDr_2d : 1/r * d/dr
d2r_2d : d^2/dr^2
rDt_2d : 1/r * d/dt
r2D2t_2d : 1/r^2 * d^2/dt^2 | diff_matrices_polar.py | Diff_mat_2D_polar | itrosen/hall-solver | 0 | python | def Diff_mat_2D_polar(Nr, Nt, r):
'\n Args:\n Nr : number of points in radial coordinate\n Nt : number of points in theta coordinate\n r : radial points\n Returns: \n Finite element matrices for the 2D space, in sparse format\n Dr_2d : d/dr\n rDr_2d : 1/r * d/dr\n d2r_2d : d^2/dr^2\n rDt_2d : 1/r * d/dt\n r2D2t_2d : 1/r^2 * d^2/dt^2\n '
(Dr_1d, rDr_1d, D2r_1d) = Diff_mat_r(Nr, r)
(Dt_1d, D2t_1d) = Diff_mat_t(Nt)
Ir = sp.eye(Nr)
It = sp.eye(Nt)
Rr = sp.spdiags([(1 / r)], [0], Nr, Nr)
R2r = sp.spdiags([(1 / (r ** 2))], [0], Nr, Nr)
Dr_2d = sp.kron(It, Dr_1d)
rDr_2d = sp.kron(It, rDr_1d)
D2r_2d = sp.kron(It, D2r_1d)
rDt_2d = sp.kron(Dt_1d, Rr)
r2D2t_2d = sp.kron(D2t_1d, R2r)
return (Dr_2d.tocsr(), rDr_2d.tocsr(), D2r_2d.tocsr(), rDt_2d.tocsr(), r2D2t_2d.tocsr()) | def Diff_mat_2D_polar(Nr, Nt, r):
'\n Args:\n Nr : number of points in radial coordinate\n Nt : number of points in theta coordinate\n r : radial points\n Returns: \n Finite element matrices for the 2D space, in sparse format\n Dr_2d : d/dr\n rDr_2d : 1/r * d/dr\n d2r_2d : d^2/dr^2\n rDt_2d : 1/r * d/dt\n r2D2t_2d : 1/r^2 * d^2/dt^2\n '
(Dr_1d, rDr_1d, D2r_1d) = Diff_mat_r(Nr, r)
(Dt_1d, D2t_1d) = Diff_mat_t(Nt)
Ir = sp.eye(Nr)
It = sp.eye(Nt)
Rr = sp.spdiags([(1 / r)], [0], Nr, Nr)
R2r = sp.spdiags([(1 / (r ** 2))], [0], Nr, Nr)
Dr_2d = sp.kron(It, Dr_1d)
rDr_2d = sp.kron(It, rDr_1d)
D2r_2d = sp.kron(It, D2r_1d)
rDt_2d = sp.kron(Dt_1d, Rr)
r2D2t_2d = sp.kron(D2t_1d, R2r)
return (Dr_2d.tocsr(), rDr_2d.tocsr(), D2r_2d.tocsr(), rDt_2d.tocsr(), r2D2t_2d.tocsr())<|docstring|>Args:
Nr : number of points in radial coordinate
Nt : number of points in theta coordinate
r : radial points
Returns:
Finite element matrices for the 2D space, in sparse format
Dr_2d : d/dr
rDr_2d : 1/r * d/dr
d2r_2d : d^2/dr^2
rDt_2d : 1/r * d/dt
r2D2t_2d : 1/r^2 * d^2/dt^2<|endoftext|> |
c37b8f94dde8de063721c631320612d425252da5d78ed04595e94df307947acc | def test_tagging_erg_sent(self):
' Test import tokens '
txt = 'In this way I am no doubt indirectly responsible for Dr. Grimesby Roylott\'s death, and I cannot say that it is likely to weigh very heavily upon my conscience." '
words = ['in', 'this', 'way', 'i', 'am', 'no', 'doubt', 'indirectly', 'responsible', 'for', 'dr.', 'Grimesby', 'Roylott', "'s", 'death', ',', 'and', 'i', 'can', 'not', 'say', 'that', 'it', 'is', 'likely', 'to', 'weigh', 'very', 'heavily', 'upon', 'my', 'conscience', '.', '"']
s = ttl.Sentence(txt)
s._import_tokens(words)
self.assertEqual(words, [x.text for x in s.tokens]) | Test import tokens | test/test_ttlib.py | test_tagging_erg_sent | letuananh/chirptext | 5 | python | def test_tagging_erg_sent(self):
' '
txt = 'In this way I am no doubt indirectly responsible for Dr. Grimesby Roylott\'s death, and I cannot say that it is likely to weigh very heavily upon my conscience." '
words = ['in', 'this', 'way', 'i', 'am', 'no', 'doubt', 'indirectly', 'responsible', 'for', 'dr.', 'Grimesby', 'Roylott', "'s", 'death', ',', 'and', 'i', 'can', 'not', 'say', 'that', 'it', 'is', 'likely', 'to', 'weigh', 'very', 'heavily', 'upon', 'my', 'conscience', '.', '"']
s = ttl.Sentence(txt)
s._import_tokens(words)
self.assertEqual(words, [x.text for x in s.tokens]) | def test_tagging_erg_sent(self):
' '
txt = 'In this way I am no doubt indirectly responsible for Dr. Grimesby Roylott\'s death, and I cannot say that it is likely to weigh very heavily upon my conscience." '
words = ['in', 'this', 'way', 'i', 'am', 'no', 'doubt', 'indirectly', 'responsible', 'for', 'dr.', 'Grimesby', 'Roylott', "'s", 'death', ',', 'and', 'i', 'can', 'not', 'say', 'that', 'it', 'is', 'likely', 'to', 'weigh', 'very', 'heavily', 'upon', 'my', 'conscience', '.', '"']
s = ttl.Sentence(txt)
s._import_tokens(words)
self.assertEqual(words, [x.text for x in s.tokens])<|docstring|>Test import tokens<|endoftext|> |
7a155b17e0fda3c9ec9e8680746ed6bfbf79141f25594f287083b3bf822279cd | def weight_path(model_path):
' Get path of weights based on path to IR\n\n Params:\n model_path: the string contains path to IR file\n\n Return:\n Path to weights file\n '
assert model_path.endswith('.xml'), 'Wrong topology path was provided'
return (model_path[:(- 3)] + 'bin') | Get path of weights based on path to IR
Params:
model_path: the string contains path to IR file
Return:
Path to weights file | modules/gapi/misc/python/samples/gaze_estimation.py | weight_path | badfilms/opencv | 56,632 | python | def weight_path(model_path):
' Get path of weights based on path to IR\n\n Params:\n model_path: the string contains path to IR file\n\n Return:\n Path to weights file\n '
assert model_path.endswith('.xml'), 'Wrong topology path was provided'
return (model_path[:(- 3)] + 'bin') | def weight_path(model_path):
' Get path of weights based on path to IR\n\n Params:\n model_path: the string contains path to IR file\n\n Return:\n Path to weights file\n '
assert model_path.endswith('.xml'), 'Wrong topology path was provided'
return (model_path[:(- 3)] + 'bin')<|docstring|>Get path of weights based on path to IR
Params:
model_path: the string contains path to IR file
Return:
Path to weights file<|endoftext|> |
07a601fda718f4b1ea94d398d259bd7e80880d83ab930d40bce6b65bc989e657 | def build_argparser():
' Parse arguments from command line\n\n Return:\n Pack of arguments from command line\n '
parser = argparse.ArgumentParser(description='This is an OpenCV-based version of Gaze Estimation example')
parser.add_argument('--input', help='Path to the input video file')
parser.add_argument('--out', help='Path to the output video file')
parser.add_argument('--facem', default='face-detection-retail-0005.xml', help='Path to OpenVINO face detection model (.xml)')
parser.add_argument('--faced', default='CPU', help=('Target device for the face detection' + '(e.g. CPU, GPU, VPU, ...)'))
parser.add_argument('--headm', default='head-pose-estimation-adas-0001.xml', help='Path to OpenVINO head pose estimation model (.xml)')
parser.add_argument('--headd', default='CPU', help=('Target device for the head pose estimation inference ' + '(e.g. CPU, GPU, VPU, ...)'))
parser.add_argument('--landm', default='facial-landmarks-35-adas-0002.xml', help='Path to OpenVINO landmarks detector model (.xml)')
parser.add_argument('--landd', default='CPU', help='Target device for the landmarks detector (e.g. CPU, GPU, VPU, ...)')
parser.add_argument('--gazem', default='gaze-estimation-adas-0002.xml', help='Path to OpenVINO gaze vector estimaiton model (.xml)')
parser.add_argument('--gazed', default='CPU', help=('Target device for the gaze vector estimation inference ' + '(e.g. CPU, GPU, VPU, ...)'))
parser.add_argument('--eyem', default='open-closed-eye-0001.xml', help='Path to OpenVINO open closed eye model (.xml)')
parser.add_argument('--eyed', default='CPU', help='Target device for the eyes state inference (e.g. CPU, GPU, VPU, ...)')
return parser | Parse arguments from command line
Return:
Pack of arguments from command line | modules/gapi/misc/python/samples/gaze_estimation.py | build_argparser | badfilms/opencv | 56,632 | python | def build_argparser():
' Parse arguments from command line\n\n Return:\n Pack of arguments from command line\n '
parser = argparse.ArgumentParser(description='This is an OpenCV-based version of Gaze Estimation example')
parser.add_argument('--input', help='Path to the input video file')
parser.add_argument('--out', help='Path to the output video file')
parser.add_argument('--facem', default='face-detection-retail-0005.xml', help='Path to OpenVINO face detection model (.xml)')
parser.add_argument('--faced', default='CPU', help=('Target device for the face detection' + '(e.g. CPU, GPU, VPU, ...)'))
parser.add_argument('--headm', default='head-pose-estimation-adas-0001.xml', help='Path to OpenVINO head pose estimation model (.xml)')
parser.add_argument('--headd', default='CPU', help=('Target device for the head pose estimation inference ' + '(e.g. CPU, GPU, VPU, ...)'))
parser.add_argument('--landm', default='facial-landmarks-35-adas-0002.xml', help='Path to OpenVINO landmarks detector model (.xml)')
parser.add_argument('--landd', default='CPU', help='Target device for the landmarks detector (e.g. CPU, GPU, VPU, ...)')
parser.add_argument('--gazem', default='gaze-estimation-adas-0002.xml', help='Path to OpenVINO gaze vector estimaiton model (.xml)')
parser.add_argument('--gazed', default='CPU', help=('Target device for the gaze vector estimation inference ' + '(e.g. CPU, GPU, VPU, ...)'))
parser.add_argument('--eyem', default='open-closed-eye-0001.xml', help='Path to OpenVINO open closed eye model (.xml)')
parser.add_argument('--eyed', default='CPU', help='Target device for the eyes state inference (e.g. CPU, GPU, VPU, ...)')
return parser | def build_argparser():
' Parse arguments from command line\n\n Return:\n Pack of arguments from command line\n '
parser = argparse.ArgumentParser(description='This is an OpenCV-based version of Gaze Estimation example')
parser.add_argument('--input', help='Path to the input video file')
parser.add_argument('--out', help='Path to the output video file')
parser.add_argument('--facem', default='face-detection-retail-0005.xml', help='Path to OpenVINO face detection model (.xml)')
parser.add_argument('--faced', default='CPU', help=('Target device for the face detection' + '(e.g. CPU, GPU, VPU, ...)'))
parser.add_argument('--headm', default='head-pose-estimation-adas-0001.xml', help='Path to OpenVINO head pose estimation model (.xml)')
parser.add_argument('--headd', default='CPU', help=('Target device for the head pose estimation inference ' + '(e.g. CPU, GPU, VPU, ...)'))
parser.add_argument('--landm', default='facial-landmarks-35-adas-0002.xml', help='Path to OpenVINO landmarks detector model (.xml)')
parser.add_argument('--landd', default='CPU', help='Target device for the landmarks detector (e.g. CPU, GPU, VPU, ...)')
parser.add_argument('--gazem', default='gaze-estimation-adas-0002.xml', help='Path to OpenVINO gaze vector estimaiton model (.xml)')
parser.add_argument('--gazed', default='CPU', help=('Target device for the gaze vector estimation inference ' + '(e.g. CPU, GPU, VPU, ...)'))
parser.add_argument('--eyem', default='open-closed-eye-0001.xml', help='Path to OpenVINO open closed eye model (.xml)')
parser.add_argument('--eyed', default='CPU', help='Target device for the eyes state inference (e.g. CPU, GPU, VPU, ...)')
return parser<|docstring|>Parse arguments from command line
Return:
Pack of arguments from command line<|endoftext|> |
24e71050e980d58a1dccf053c55487a88f563e80eb0da790858282b47ef248a8 | def intersection(surface, rect):
' Remove zone of out of bound from ROI\n\n Params:\n surface: image bounds is rect representation (top left coordinates and width and height)\n rect: region of interest is also has rect representation\n\n Return:\n Modified ROI with correct bounds\n '
l_x = max(surface[0], rect[0])
l_y = max(surface[1], rect[1])
width = (min((surface[0] + surface[2]), (rect[0] + rect[2])) - l_x)
height = (min((surface[1] + surface[3]), (rect[1] + rect[3])) - l_y)
if ((width < 0) or (height < 0)):
return (0, 0, 0, 0)
return (l_x, l_y, width, height) | Remove zone of out of bound from ROI
Params:
surface: image bounds is rect representation (top left coordinates and width and height)
rect: region of interest is also has rect representation
Return:
Modified ROI with correct bounds | modules/gapi/misc/python/samples/gaze_estimation.py | intersection | badfilms/opencv | 56,632 | python | def intersection(surface, rect):
' Remove zone of out of bound from ROI\n\n Params:\n surface: image bounds is rect representation (top left coordinates and width and height)\n rect: region of interest is also has rect representation\n\n Return:\n Modified ROI with correct bounds\n '
l_x = max(surface[0], rect[0])
l_y = max(surface[1], rect[1])
width = (min((surface[0] + surface[2]), (rect[0] + rect[2])) - l_x)
height = (min((surface[1] + surface[3]), (rect[1] + rect[3])) - l_y)
if ((width < 0) or (height < 0)):
return (0, 0, 0, 0)
return (l_x, l_y, width, height) | def intersection(surface, rect):
' Remove zone of out of bound from ROI\n\n Params:\n surface: image bounds is rect representation (top left coordinates and width and height)\n rect: region of interest is also has rect representation\n\n Return:\n Modified ROI with correct bounds\n '
l_x = max(surface[0], rect[0])
l_y = max(surface[1], rect[1])
width = (min((surface[0] + surface[2]), (rect[0] + rect[2])) - l_x)
height = (min((surface[1] + surface[3]), (rect[1] + rect[3])) - l_y)
if ((width < 0) or (height < 0)):
return (0, 0, 0, 0)
return (l_x, l_y, width, height)<|docstring|>Remove zone of out of bound from ROI
Params:
surface: image bounds is rect representation (top left coordinates and width and height)
rect: region of interest is also has rect representation
Return:
Modified ROI with correct bounds<|endoftext|> |
c21cb23a2e5bdd56e04221a8a70e837a82b6ce34c493d992a13a5500c9e5e8d6 | def process_landmarks(r_x, r_y, r_w, r_h, landmarks):
' Create points from result of inference of facial-landmarks network and size of input image\n\n Params:\n r_x: x coordinate of top left corner of input image\n r_y: y coordinate of top left corner of input image\n r_w: width of input image\n r_h: height of input image\n landmarks: result of inference of facial-landmarks network\n\n Return:\n Array of landmarks points for one face\n '
lmrks = landmarks[0]
raw_x = ((lmrks[::2] * r_w) + r_x)
raw_y = ((lmrks[1::2] * r_h) + r_y)
return np.array([[int(x), int(y)] for (x, y) in zip(raw_x, raw_y)]) | Create points from result of inference of facial-landmarks network and size of input image
Params:
r_x: x coordinate of top left corner of input image
r_y: y coordinate of top left corner of input image
r_w: width of input image
r_h: height of input image
landmarks: result of inference of facial-landmarks network
Return:
Array of landmarks points for one face | modules/gapi/misc/python/samples/gaze_estimation.py | process_landmarks | badfilms/opencv | 56,632 | python | def process_landmarks(r_x, r_y, r_w, r_h, landmarks):
' Create points from result of inference of facial-landmarks network and size of input image\n\n Params:\n r_x: x coordinate of top left corner of input image\n r_y: y coordinate of top left corner of input image\n r_w: width of input image\n r_h: height of input image\n landmarks: result of inference of facial-landmarks network\n\n Return:\n Array of landmarks points for one face\n '
lmrks = landmarks[0]
raw_x = ((lmrks[::2] * r_w) + r_x)
raw_y = ((lmrks[1::2] * r_h) + r_y)
return np.array([[int(x), int(y)] for (x, y) in zip(raw_x, raw_y)]) | def process_landmarks(r_x, r_y, r_w, r_h, landmarks):
' Create points from result of inference of facial-landmarks network and size of input image\n\n Params:\n r_x: x coordinate of top left corner of input image\n r_y: y coordinate of top left corner of input image\n r_w: width of input image\n r_h: height of input image\n landmarks: result of inference of facial-landmarks network\n\n Return:\n Array of landmarks points for one face\n '
lmrks = landmarks[0]
raw_x = ((lmrks[::2] * r_w) + r_x)
raw_y = ((lmrks[1::2] * r_h) + r_y)
return np.array([[int(x), int(y)] for (x, y) in zip(raw_x, raw_y)])<|docstring|>Create points from result of inference of facial-landmarks network and size of input image
Params:
r_x: x coordinate of top left corner of input image
r_y: y coordinate of top left corner of input image
r_w: width of input image
r_h: height of input image
landmarks: result of inference of facial-landmarks network
Return:
Array of landmarks points for one face<|endoftext|> |
d15a9977860a98db3ed710442de472f133726d1f0f72d3107865361ac8061535 | def eye_box(p_1, p_2, scale=1.8):
' Get bounding box of eye\n\n Params:\n p_1: point of left edge of eye\n p_2: point of right edge of eye\n scale: change size of box with this value\n\n Return:\n Bounding box of eye and its midpoint\n '
size = np.linalg.norm((p_1 - p_2))
midpoint = ((p_1 + p_2) / 2)
width = (scale * size)
height = width
p_x = (midpoint[0] - (width / 2))
p_y = (midpoint[1] - (height / 2))
return ((int(p_x), int(p_y), int(width), int(height)), list(map(int, midpoint))) | Get bounding box of eye
Params:
p_1: point of left edge of eye
p_2: point of right edge of eye
scale: change size of box with this value
Return:
Bounding box of eye and its midpoint | modules/gapi/misc/python/samples/gaze_estimation.py | eye_box | badfilms/opencv | 56,632 | python | def eye_box(p_1, p_2, scale=1.8):
' Get bounding box of eye\n\n Params:\n p_1: point of left edge of eye\n p_2: point of right edge of eye\n scale: change size of box with this value\n\n Return:\n Bounding box of eye and its midpoint\n '
size = np.linalg.norm((p_1 - p_2))
midpoint = ((p_1 + p_2) / 2)
width = (scale * size)
height = width
p_x = (midpoint[0] - (width / 2))
p_y = (midpoint[1] - (height / 2))
return ((int(p_x), int(p_y), int(width), int(height)), list(map(int, midpoint))) | def eye_box(p_1, p_2, scale=1.8):
' Get bounding box of eye\n\n Params:\n p_1: point of left edge of eye\n p_2: point of right edge of eye\n scale: change size of box with this value\n\n Return:\n Bounding box of eye and its midpoint\n '
size = np.linalg.norm((p_1 - p_2))
midpoint = ((p_1 + p_2) / 2)
width = (scale * size)
height = width
p_x = (midpoint[0] - (width / 2))
p_y = (midpoint[1] - (height / 2))
return ((int(p_x), int(p_y), int(width), int(height)), list(map(int, midpoint)))<|docstring|>Get bounding box of eye
Params:
p_1: point of left edge of eye
p_2: point of right edge of eye
scale: change size of box with this value
Return:
Bounding box of eye and its midpoint<|endoftext|> |
518d054721de28bc2de7213023d484a4e52098ef9396c37963eb8234edc955a5 | @staticmethod
def run(in_ys, in_ps, in_rs):
' Сustom kernel executable code\n\n Params:\n in_ys: yaw angle of head\n in_ps: pitch angle of head\n in_rs: roll angle of head\n\n Return:\n Arrays with heads poses\n '
return [np.array([ys[0], ps[0], rs[0]]).T for (ys, ps, rs) in zip(in_ys, in_ps, in_rs)] | Сustom kernel executable code
Params:
in_ys: yaw angle of head
in_ps: pitch angle of head
in_rs: roll angle of head
Return:
Arrays with heads poses | modules/gapi/misc/python/samples/gaze_estimation.py | run | badfilms/opencv | 56,632 | python | @staticmethod
def run(in_ys, in_ps, in_rs):
' Сustom kernel executable code\n\n Params:\n in_ys: yaw angle of head\n in_ps: pitch angle of head\n in_rs: roll angle of head\n\n Return:\n Arrays with heads poses\n '
return [np.array([ys[0], ps[0], rs[0]]).T for (ys, ps, rs) in zip(in_ys, in_ps, in_rs)] | @staticmethod
def run(in_ys, in_ps, in_rs):
' Сustom kernel executable code\n\n Params:\n in_ys: yaw angle of head\n in_ps: pitch angle of head\n in_rs: roll angle of head\n\n Return:\n Arrays with heads poses\n '
return [np.array([ys[0], ps[0], rs[0]]).T for (ys, ps, rs) in zip(in_ys, in_ps, in_rs)]<|docstring|>Сustom kernel executable code
Params:
in_ys: yaw angle of head
in_ps: pitch angle of head
in_rs: roll angle of head
Return:
Arrays with heads poses<|endoftext|> |
da2c85b431d473464923ba78f0e32ea34253f608127ebffe1dd36d5af6875441 | @staticmethod
def run(in_landm_per_face, in_face_rcs, frame_size):
' Сustom kernel executable code\n\n Params:\n in_landm_per_face: landmarks from inference of facial-landmarks network for each face\n in_face_rcs: bounding boxes for each face\n frame_size: size of input image\n\n Return:\n Arrays of ROI for left and right eyes, array of midpoints and\n array of landmarks points\n '
left_eyes = []
right_eyes = []
midpoints = []
lmarks = []
surface = (0, 0, *frame_size)
for (landm_face, rect) in zip(in_landm_per_face, in_face_rcs):
points = process_landmarks(*rect, landm_face)
lmarks.extend(points)
(rect, midpoint_l) = eye_box(points[0], points[1])
left_eyes.append(intersection(surface, rect))
(rect, midpoint_r) = eye_box(points[2], points[3])
right_eyes.append(intersection(surface, rect))
midpoints.append(midpoint_l)
midpoints.append(midpoint_r)
return (left_eyes, right_eyes, midpoints, lmarks) | Сustom kernel executable code
Params:
in_landm_per_face: landmarks from inference of facial-landmarks network for each face
in_face_rcs: bounding boxes for each face
frame_size: size of input image
Return:
Arrays of ROI for left and right eyes, array of midpoints and
array of landmarks points | modules/gapi/misc/python/samples/gaze_estimation.py | run | badfilms/opencv | 56,632 | python | @staticmethod
def run(in_landm_per_face, in_face_rcs, frame_size):
' Сustom kernel executable code\n\n Params:\n in_landm_per_face: landmarks from inference of facial-landmarks network for each face\n in_face_rcs: bounding boxes for each face\n frame_size: size of input image\n\n Return:\n Arrays of ROI for left and right eyes, array of midpoints and\n array of landmarks points\n '
left_eyes = []
right_eyes = []
midpoints = []
lmarks = []
surface = (0, 0, *frame_size)
for (landm_face, rect) in zip(in_landm_per_face, in_face_rcs):
points = process_landmarks(*rect, landm_face)
lmarks.extend(points)
(rect, midpoint_l) = eye_box(points[0], points[1])
left_eyes.append(intersection(surface, rect))
(rect, midpoint_r) = eye_box(points[2], points[3])
right_eyes.append(intersection(surface, rect))
midpoints.append(midpoint_l)
midpoints.append(midpoint_r)
return (left_eyes, right_eyes, midpoints, lmarks) | @staticmethod
def run(in_landm_per_face, in_face_rcs, frame_size):
' Сustom kernel executable code\n\n Params:\n in_landm_per_face: landmarks from inference of facial-landmarks network for each face\n in_face_rcs: bounding boxes for each face\n frame_size: size of input image\n\n Return:\n Arrays of ROI for left and right eyes, array of midpoints and\n array of landmarks points\n '
left_eyes = []
right_eyes = []
midpoints = []
lmarks = []
surface = (0, 0, *frame_size)
for (landm_face, rect) in zip(in_landm_per_face, in_face_rcs):
points = process_landmarks(*rect, landm_face)
lmarks.extend(points)
(rect, midpoint_l) = eye_box(points[0], points[1])
left_eyes.append(intersection(surface, rect))
(rect, midpoint_r) = eye_box(points[2], points[3])
right_eyes.append(intersection(surface, rect))
midpoints.append(midpoint_l)
midpoints.append(midpoint_r)
return (left_eyes, right_eyes, midpoints, lmarks)<|docstring|>Сustom kernel executable code
Params:
in_landm_per_face: landmarks from inference of facial-landmarks network for each face
in_face_rcs: bounding boxes for each face
frame_size: size of input image
Return:
Arrays of ROI for left and right eyes, array of midpoints and
array of landmarks points<|endoftext|> |
36472f12893f824518e1eee9bf4e236b86a40deba9ab7330482e7ed3c4bfe771 | @staticmethod
def run(eyesl, eyesr):
' Сustom kernel executable code\n\n Params:\n eyesl: result of inference of open-closed-eye network for left eye\n eyesr: result of inference of open-closed-eye network for right eye\n\n Return:\n States of left eyes and states of right eyes\n '
out_l_st = [int(st) for eye_l in eyesl for st in (eye_l[(:, 0)] < eye_l[(:, 1)]).ravel()]
out_r_st = [int(st) for eye_r in eyesr for st in (eye_r[(:, 0)] < eye_r[(:, 1)]).ravel()]
return (out_l_st, out_r_st) | Сustom kernel executable code
Params:
eyesl: result of inference of open-closed-eye network for left eye
eyesr: result of inference of open-closed-eye network for right eye
Return:
States of left eyes and states of right eyes | modules/gapi/misc/python/samples/gaze_estimation.py | run | badfilms/opencv | 56,632 | python | @staticmethod
def run(eyesl, eyesr):
' Сustom kernel executable code\n\n Params:\n eyesl: result of inference of open-closed-eye network for left eye\n eyesr: result of inference of open-closed-eye network for right eye\n\n Return:\n States of left eyes and states of right eyes\n '
out_l_st = [int(st) for eye_l in eyesl for st in (eye_l[(:, 0)] < eye_l[(:, 1)]).ravel()]
out_r_st = [int(st) for eye_r in eyesr for st in (eye_r[(:, 0)] < eye_r[(:, 1)]).ravel()]
return (out_l_st, out_r_st) | @staticmethod
def run(eyesl, eyesr):
' Сustom kernel executable code\n\n Params:\n eyesl: result of inference of open-closed-eye network for left eye\n eyesr: result of inference of open-closed-eye network for right eye\n\n Return:\n States of left eyes and states of right eyes\n '
out_l_st = [int(st) for eye_l in eyesl for st in (eye_l[(:, 0)] < eye_l[(:, 1)]).ravel()]
out_r_st = [int(st) for eye_r in eyesr for st in (eye_r[(:, 0)] < eye_r[(:, 1)]).ravel()]
return (out_l_st, out_r_st)<|docstring|>Сustom kernel executable code
Params:
eyesl: result of inference of open-closed-eye network for left eye
eyesr: result of inference of open-closed-eye network for right eye
Return:
States of left eyes and states of right eyes<|endoftext|> |
8a3bb0fda7abca0a31b1e3d237b84e5c1dd3eda107622116855d97a480c2f859 | @pytest.fixture(scope='session')
def engine():
'Create the engine.'
return create_engine('postgresql://localhost/pollbot_test') | Create the engine. | tests/conftest.py | engine | shubham-king/poll | 112 | python | @pytest.fixture(scope='session')
def engine():
return create_engine('postgresql://localhost/pollbot_test') | @pytest.fixture(scope='session')
def engine():
return create_engine('postgresql://localhost/pollbot_test')<|docstring|>Create the engine.<|endoftext|> |
fc03157f0927a3c75e178bbc812a8f85c0f35ef69a952194a8cb7fe5625de707 | @pytest.fixture(scope='session')
def tables(engine):
'Create the base schema.'
with engine.connect() as con:
con.execute('CREATE EXTENSION IF NOT EXISTS pg_trgm;')
con.execute('CREATE EXTENSION IF NOT EXISTS pgcrypto;')
base.metadata.create_all(engine)
(yield)
base.metadata.drop_all(engine) | Create the base schema. | tests/conftest.py | tables | shubham-king/poll | 112 | python | @pytest.fixture(scope='session')
def tables(engine):
with engine.connect() as con:
con.execute('CREATE EXTENSION IF NOT EXISTS pg_trgm;')
con.execute('CREATE EXTENSION IF NOT EXISTS pgcrypto;')
base.metadata.create_all(engine)
(yield)
base.metadata.drop_all(engine) | @pytest.fixture(scope='session')
def tables(engine):
with engine.connect() as con:
con.execute('CREATE EXTENSION IF NOT EXISTS pg_trgm;')
con.execute('CREATE EXTENSION IF NOT EXISTS pgcrypto;')
base.metadata.create_all(engine)
(yield)
base.metadata.drop_all(engine)<|docstring|>Create the base schema.<|endoftext|> |
6226b485d584566850a4c240f3c54215815aee78b55132dc7d812e044a559bb2 | @pytest.fixture
def connection(engine, tables):
'Create the connection for the test case.'
connection = engine.connect()
(yield connection) | Create the connection for the test case. | tests/conftest.py | connection | shubham-king/poll | 112 | python | @pytest.fixture
def connection(engine, tables):
connection = engine.connect()
(yield connection) | @pytest.fixture
def connection(engine, tables):
connection = engine.connect()
(yield connection)<|docstring|>Create the connection for the test case.<|endoftext|> |
7ed6c1ef489c1efd699a9d3a04b5eb72e6d33c04367fdb530cd119a40af377b7 | @pytest.fixture
def session(connection, monkeypatch):
'Return an sqlalchemy session, and after the test tear down everything properly.'
transaction = connection.begin()
session = Session(bind=connection)
def get_session():
return session
from pollbot import db
monkeypatch.setattr(db, 'get_session', get_session)
assert (session == db.get_session())
(yield session)
try:
connection.execute('SET CONSTRAINTS ALL IMMEDIATE')
except InternalError:
pass
session.close()
transaction.rollback()
connection.close() | Return an sqlalchemy session, and after the test tear down everything properly. | tests/conftest.py | session | shubham-king/poll | 112 | python | @pytest.fixture
def session(connection, monkeypatch):
transaction = connection.begin()
session = Session(bind=connection)
def get_session():
return session
from pollbot import db
monkeypatch.setattr(db, 'get_session', get_session)
assert (session == db.get_session())
(yield session)
try:
connection.execute('SET CONSTRAINTS ALL IMMEDIATE')
except InternalError:
pass
session.close()
transaction.rollback()
connection.close() | @pytest.fixture
def session(connection, monkeypatch):
transaction = connection.begin()
session = Session(bind=connection)
def get_session():
return session
from pollbot import db
monkeypatch.setattr(db, 'get_session', get_session)
assert (session == db.get_session())
(yield session)
try:
connection.execute('SET CONSTRAINTS ALL IMMEDIATE')
except InternalError:
pass
session.close()
transaction.rollback()
connection.close()<|docstring|>Return an sqlalchemy session, and after the test tear down everything properly.<|endoftext|> |
fce75b9ab6cea6d91af17dbe821dd08629cc4c5050eb78e0878ddb8754654b4d | def register(self, model=None, include_fields=[], exclude_fields=[], mapping_fields={}):
"\n Register a model with auditlog. Auditlog will then track mutations on this model's instances.\n\n :param model: The model to register.\n :type model: Model\n :param include_fields: The fields to include. Implicitly excludes all other fields.\n :type include_fields: list\n :param exclude_fields: The fields to exclude. Overrides the fields to include.\n :type exclude_fields: list\n "
def registrar(cls):
'Register models for a given class.'
if (not issubclass(cls, Model)):
raise TypeError('Supplied model is not a valid model.')
self._registry[cls] = {'include_fields': include_fields, 'exclude_fields': exclude_fields, 'mapping_fields': mapping_fields}
self._connect_signals(cls)
return cls
if (model is None):
return (lambda cls: registrar(cls))
else:
registrar(model) | Register a model with auditlog. Auditlog will then track mutations on this model's instances.
:param model: The model to register.
:type model: Model
:param include_fields: The fields to include. Implicitly excludes all other fields.
:type include_fields: list
:param exclude_fields: The fields to exclude. Overrides the fields to include.
:type exclude_fields: list | src/auditlog/registry.py | register | mathspace/django-auditlog | 0 | python | def register(self, model=None, include_fields=[], exclude_fields=[], mapping_fields={}):
"\n Register a model with auditlog. Auditlog will then track mutations on this model's instances.\n\n :param model: The model to register.\n :type model: Model\n :param include_fields: The fields to include. Implicitly excludes all other fields.\n :type include_fields: list\n :param exclude_fields: The fields to exclude. Overrides the fields to include.\n :type exclude_fields: list\n "
def registrar(cls):
'Register models for a given class.'
if (not issubclass(cls, Model)):
raise TypeError('Supplied model is not a valid model.')
self._registry[cls] = {'include_fields': include_fields, 'exclude_fields': exclude_fields, 'mapping_fields': mapping_fields}
self._connect_signals(cls)
return cls
if (model is None):
return (lambda cls: registrar(cls))
else:
registrar(model) | def register(self, model=None, include_fields=[], exclude_fields=[], mapping_fields={}):
"\n Register a model with auditlog. Auditlog will then track mutations on this model's instances.\n\n :param model: The model to register.\n :type model: Model\n :param include_fields: The fields to include. Implicitly excludes all other fields.\n :type include_fields: list\n :param exclude_fields: The fields to exclude. Overrides the fields to include.\n :type exclude_fields: list\n "
def registrar(cls):
'Register models for a given class.'
if (not issubclass(cls, Model)):
raise TypeError('Supplied model is not a valid model.')
self._registry[cls] = {'include_fields': include_fields, 'exclude_fields': exclude_fields, 'mapping_fields': mapping_fields}
self._connect_signals(cls)
return cls
if (model is None):
return (lambda cls: registrar(cls))
else:
registrar(model)<|docstring|>Register a model with auditlog. Auditlog will then track mutations on this model's instances.
:param model: The model to register.
:type model: Model
:param include_fields: The fields to include. Implicitly excludes all other fields.
:type include_fields: list
:param exclude_fields: The fields to exclude. Overrides the fields to include.
:type exclude_fields: list<|endoftext|> |
596c74b6eff4d47c9ca8105efd2f7ec9102cee1cc9cebb25ca85b10e060256b3 | def contains(self, model):
'\n Check if a model is registered with auditlog.\n\n :param model: The model to check.\n :type model: Model\n :return: Whether the model has been registered.\n :rtype: bool\n '
return (model in self._registry) | Check if a model is registered with auditlog.
:param model: The model to check.
:type model: Model
:return: Whether the model has been registered.
:rtype: bool | src/auditlog/registry.py | contains | mathspace/django-auditlog | 0 | python | def contains(self, model):
'\n Check if a model is registered with auditlog.\n\n :param model: The model to check.\n :type model: Model\n :return: Whether the model has been registered.\n :rtype: bool\n '
return (model in self._registry) | def contains(self, model):
'\n Check if a model is registered with auditlog.\n\n :param model: The model to check.\n :type model: Model\n :return: Whether the model has been registered.\n :rtype: bool\n '
return (model in self._registry)<|docstring|>Check if a model is registered with auditlog.
:param model: The model to check.
:type model: Model
:return: Whether the model has been registered.
:rtype: bool<|endoftext|> |
129fcb3099697693e13e9455667e39d89509e859a0de55c2f45dfba1aac68055 | def unregister(self, model):
'\n Unregister a model with auditlog. This will not affect the database.\n\n :param model: The model to unregister.\n :type model: Model\n '
try:
del self._registry[model]
except KeyError:
pass
else:
self._disconnect_signals(model) | Unregister a model with auditlog. This will not affect the database.
:param model: The model to unregister.
:type model: Model | src/auditlog/registry.py | unregister | mathspace/django-auditlog | 0 | python | def unregister(self, model):
'\n Unregister a model with auditlog. This will not affect the database.\n\n :param model: The model to unregister.\n :type model: Model\n '
try:
del self._registry[model]
except KeyError:
pass
else:
self._disconnect_signals(model) | def unregister(self, model):
'\n Unregister a model with auditlog. This will not affect the database.\n\n :param model: The model to unregister.\n :type model: Model\n '
try:
del self._registry[model]
except KeyError:
pass
else:
self._disconnect_signals(model)<|docstring|>Unregister a model with auditlog. This will not affect the database.
:param model: The model to unregister.
:type model: Model<|endoftext|> |
71b86384cbabd736d82f72ecb3e7d29f22732b6e641f442b7b515e081ce6b470 | def _connect_signals(self, model):
'\n Connect signals for the model.\n '
if user_settings.get('disable_auditlog', False):
return
for signal in self._signals:
receiver = self._signals[signal]
signal.connect(receiver, sender=model, dispatch_uid=self._dispatch_uid(signal, model)) | Connect signals for the model. | src/auditlog/registry.py | _connect_signals | mathspace/django-auditlog | 0 | python | def _connect_signals(self, model):
'\n \n '
if user_settings.get('disable_auditlog', False):
return
for signal in self._signals:
receiver = self._signals[signal]
signal.connect(receiver, sender=model, dispatch_uid=self._dispatch_uid(signal, model)) | def _connect_signals(self, model):
'\n \n '
if user_settings.get('disable_auditlog', False):
return
for signal in self._signals:
receiver = self._signals[signal]
signal.connect(receiver, sender=model, dispatch_uid=self._dispatch_uid(signal, model))<|docstring|>Connect signals for the model.<|endoftext|> |
7be4cabea09d860b3debad088e88030b2265d53d61db3b66d3b2c6be5b9a08bd | def _disconnect_signals(self, model):
'\n Disconnect signals for the model.\n '
if user_settings.get('disable_auditlog', False):
return
for (signal, receiver) in self._signals.items():
signal.disconnect(sender=model, dispatch_uid=self._dispatch_uid(signal, model)) | Disconnect signals for the model. | src/auditlog/registry.py | _disconnect_signals | mathspace/django-auditlog | 0 | python | def _disconnect_signals(self, model):
'\n \n '
if user_settings.get('disable_auditlog', False):
return
for (signal, receiver) in self._signals.items():
signal.disconnect(sender=model, dispatch_uid=self._dispatch_uid(signal, model)) | def _disconnect_signals(self, model):
'\n \n '
if user_settings.get('disable_auditlog', False):
return
for (signal, receiver) in self._signals.items():
signal.disconnect(sender=model, dispatch_uid=self._dispatch_uid(signal, model))<|docstring|>Disconnect signals for the model.<|endoftext|> |
4907bf6b94401b18d531385ff3ca9eacef6e0801d56b9e39820e8cbee32b7029 | def _dispatch_uid(self, signal, model):
'\n Generate a dispatch_uid.\n '
return (self.__class__, model, signal) | Generate a dispatch_uid. | src/auditlog/registry.py | _dispatch_uid | mathspace/django-auditlog | 0 | python | def _dispatch_uid(self, signal, model):
'\n \n '
return (self.__class__, model, signal) | def _dispatch_uid(self, signal, model):
'\n \n '
return (self.__class__, model, signal)<|docstring|>Generate a dispatch_uid.<|endoftext|> |
07d4545a2738745db51a029de56a2c6cdccaf5d790420daac076b45a2b1ddc32 | def registrar(cls):
'Register models for a given class.'
if (not issubclass(cls, Model)):
raise TypeError('Supplied model is not a valid model.')
self._registry[cls] = {'include_fields': include_fields, 'exclude_fields': exclude_fields, 'mapping_fields': mapping_fields}
self._connect_signals(cls)
return cls | Register models for a given class. | src/auditlog/registry.py | registrar | mathspace/django-auditlog | 0 | python | def registrar(cls):
if (not issubclass(cls, Model)):
raise TypeError('Supplied model is not a valid model.')
self._registry[cls] = {'include_fields': include_fields, 'exclude_fields': exclude_fields, 'mapping_fields': mapping_fields}
self._connect_signals(cls)
return cls | def registrar(cls):
if (not issubclass(cls, Model)):
raise TypeError('Supplied model is not a valid model.')
self._registry[cls] = {'include_fields': include_fields, 'exclude_fields': exclude_fields, 'mapping_fields': mapping_fields}
self._connect_signals(cls)
return cls<|docstring|>Register models for a given class.<|endoftext|> |
f2f5d2db413c8c17d9f114e8db7a209a8b2c88d9578e68e20c9bedc5f2085d38 | def registrar(cls):
'Register models for a given class.'
if (not issubclass(cls, Model)):
raise TypeError('Supplied model is not a valid model.')
if user_settings.get('disable_auditlog', False):
return
receiver = self._signals[m2m_changed]
m2m_changed.connect(receiver, sender=model, dispatch_uid=self._dispatch_uid(m2m_changed, model))
return cls | Register models for a given class. | src/auditlog/registry.py | registrar | mathspace/django-auditlog | 0 | python | def registrar(cls):
if (not issubclass(cls, Model)):
raise TypeError('Supplied model is not a valid model.')
if user_settings.get('disable_auditlog', False):
return
receiver = self._signals[m2m_changed]
m2m_changed.connect(receiver, sender=model, dispatch_uid=self._dispatch_uid(m2m_changed, model))
return cls | def registrar(cls):
if (not issubclass(cls, Model)):
raise TypeError('Supplied model is not a valid model.')
if user_settings.get('disable_auditlog', False):
return
receiver = self._signals[m2m_changed]
m2m_changed.connect(receiver, sender=model, dispatch_uid=self._dispatch_uid(m2m_changed, model))
return cls<|docstring|>Register models for a given class.<|endoftext|> |
1a3566d8b67d22818936023a2ee7c6d08e13a4516f53f7964451b116222d48c8 | def _verify_patchelf() -> None:
"This function looks for the ``patchelf`` external binary in the PATH,\n checks for the required version, and throws an exception if a proper\n version can't be found. Otherwise, silcence is golden\n "
if (not find_executable('patchelf')):
raise ValueError('Cannot find required utility `patchelf` in PATH')
try:
version = check_output(['patchelf', '--version']).decode('utf-8')
except CalledProcessError:
raise ValueError('Could not call `patchelf` binary')
m = re.match('patchelf\\s+(\\d+(.\\d+)?)', version)
if (m and (tuple((int(x) for x in m.group(1).split('.'))) >= (0, 9))):
return
raise ValueError(('patchelf %s found. auditwheel repair requires patchelf >= 0.9.' % version)) | This function looks for the ``patchelf`` external binary in the PATH,
checks for the required version, and throws an exception if a proper
version can't be found. Otherwise, silcence is golden | src/auditwheel/patcher.py | _verify_patchelf | f3flight/auditwheel | 280 | python | def _verify_patchelf() -> None:
"This function looks for the ``patchelf`` external binary in the PATH,\n checks for the required version, and throws an exception if a proper\n version can't be found. Otherwise, silcence is golden\n "
if (not find_executable('patchelf')):
raise ValueError('Cannot find required utility `patchelf` in PATH')
try:
version = check_output(['patchelf', '--version']).decode('utf-8')
except CalledProcessError:
raise ValueError('Could not call `patchelf` binary')
m = re.match('patchelf\\s+(\\d+(.\\d+)?)', version)
if (m and (tuple((int(x) for x in m.group(1).split('.'))) >= (0, 9))):
return
raise ValueError(('patchelf %s found. auditwheel repair requires patchelf >= 0.9.' % version)) | def _verify_patchelf() -> None:
"This function looks for the ``patchelf`` external binary in the PATH,\n checks for the required version, and throws an exception if a proper\n version can't be found. Otherwise, silcence is golden\n "
if (not find_executable('patchelf')):
raise ValueError('Cannot find required utility `patchelf` in PATH')
try:
version = check_output(['patchelf', '--version']).decode('utf-8')
except CalledProcessError:
raise ValueError('Could not call `patchelf` binary')
m = re.match('patchelf\\s+(\\d+(.\\d+)?)', version)
if (m and (tuple((int(x) for x in m.group(1).split('.'))) >= (0, 9))):
return
raise ValueError(('patchelf %s found. auditwheel repair requires patchelf >= 0.9.' % version))<|docstring|>This function looks for the ``patchelf`` external binary in the PATH,
checks for the required version, and throws an exception if a proper
version can't be found. Otherwise, silcence is golden<|endoftext|> |
7229fcfdf609fa22f30e349dc6cd99a9a50310f798bc4c69f0e3484d9fb4fabb | @overrides
def optimize_policy(self, itr, samples_data):
'\n Perform algorithm optimizing.\n\n Returns:\n action_loss: Loss of action predicted by the policy network.\n qval_loss: Loss of q value predicted by the q network.\n ys: y_s.\n qval: Q value predicted by the q network.\n\n '
transitions = self.replay_buffer.sample(self.buffer_batch_size)
observations = transitions['observation']
rewards = transitions['reward']
actions = transitions['action']
next_observations = transitions['next_observation']
terminals = transitions['terminal']
rewards = rewards.reshape((- 1), 1)
terminals = terminals.reshape((- 1), 1)
if self.input_include_goal:
goals = transitions['goal']
next_inputs = np.concatenate((next_observations, goals), axis=(- 1))
inputs = np.concatenate((observations, goals), axis=(- 1))
else:
next_inputs = next_observations
inputs = observations
target_actions = self.target_policy_f_prob_online(next_inputs)
target_qvals = self.target_qf_f_prob_online(next_inputs, target_actions)
clip_range = ((- self.clip_return), (0.0 if self.clip_pos_returns else self.clip_return))
ys = np.clip((rewards + (((1.0 - terminals) * self.discount) * target_qvals)), clip_range[0], clip_range[1])
(_, qval_loss, qval) = self.f_train_qf(ys, inputs, actions)
(_, action_loss) = self.f_train_policy(inputs)
self.f_update_target()
return (qval_loss, ys, qval, action_loss) | Perform algorithm optimizing.
Returns:
action_loss: Loss of action predicted by the policy network.
qval_loss: Loss of q value predicted by the q network.
ys: y_s.
qval: Q value predicted by the q network. | src/garage/tf/algos/ddpg.py | optimize_policy | lywong92/garage | 1 | python | @overrides
def optimize_policy(self, itr, samples_data):
'\n Perform algorithm optimizing.\n\n Returns:\n action_loss: Loss of action predicted by the policy network.\n qval_loss: Loss of q value predicted by the q network.\n ys: y_s.\n qval: Q value predicted by the q network.\n\n '
transitions = self.replay_buffer.sample(self.buffer_batch_size)
observations = transitions['observation']
rewards = transitions['reward']
actions = transitions['action']
next_observations = transitions['next_observation']
terminals = transitions['terminal']
rewards = rewards.reshape((- 1), 1)
terminals = terminals.reshape((- 1), 1)
if self.input_include_goal:
goals = transitions['goal']
next_inputs = np.concatenate((next_observations, goals), axis=(- 1))
inputs = np.concatenate((observations, goals), axis=(- 1))
else:
next_inputs = next_observations
inputs = observations
target_actions = self.target_policy_f_prob_online(next_inputs)
target_qvals = self.target_qf_f_prob_online(next_inputs, target_actions)
clip_range = ((- self.clip_return), (0.0 if self.clip_pos_returns else self.clip_return))
ys = np.clip((rewards + (((1.0 - terminals) * self.discount) * target_qvals)), clip_range[0], clip_range[1])
(_, qval_loss, qval) = self.f_train_qf(ys, inputs, actions)
(_, action_loss) = self.f_train_policy(inputs)
self.f_update_target()
return (qval_loss, ys, qval, action_loss) | @overrides
def optimize_policy(self, itr, samples_data):
'\n Perform algorithm optimizing.\n\n Returns:\n action_loss: Loss of action predicted by the policy network.\n qval_loss: Loss of q value predicted by the q network.\n ys: y_s.\n qval: Q value predicted by the q network.\n\n '
transitions = self.replay_buffer.sample(self.buffer_batch_size)
observations = transitions['observation']
rewards = transitions['reward']
actions = transitions['action']
next_observations = transitions['next_observation']
terminals = transitions['terminal']
rewards = rewards.reshape((- 1), 1)
terminals = terminals.reshape((- 1), 1)
if self.input_include_goal:
goals = transitions['goal']
next_inputs = np.concatenate((next_observations, goals), axis=(- 1))
inputs = np.concatenate((observations, goals), axis=(- 1))
else:
next_inputs = next_observations
inputs = observations
target_actions = self.target_policy_f_prob_online(next_inputs)
target_qvals = self.target_qf_f_prob_online(next_inputs, target_actions)
clip_range = ((- self.clip_return), (0.0 if self.clip_pos_returns else self.clip_return))
ys = np.clip((rewards + (((1.0 - terminals) * self.discount) * target_qvals)), clip_range[0], clip_range[1])
(_, qval_loss, qval) = self.f_train_qf(ys, inputs, actions)
(_, action_loss) = self.f_train_policy(inputs)
self.f_update_target()
return (qval_loss, ys, qval, action_loss)<|docstring|>Perform algorithm optimizing.
Returns:
action_loss: Loss of action predicted by the policy network.
qval_loss: Loss of q value predicted by the q network.
ys: y_s.
qval: Q value predicted by the q network.<|endoftext|> |
f31e687e982bb8a9f342517872d2ab3a2fb4950c4e8b6a719033806f35dddcad | @callback
def setup_bans(hass, app, login_threshold):
'Create IP Ban middleware for the app.'
app.middlewares.append(ban_middleware)
app[KEY_FAILED_LOGIN_ATTEMPTS] = defaultdict(int)
app[KEY_LOGIN_THRESHOLD] = login_threshold
async def ban_startup(app):
'Initialize bans when app starts up.'
app[KEY_BANNED_IPS] = (await async_load_ip_bans_config(hass, hass.config.path(IP_BANS_FILE)))
app.on_startup.append(ban_startup) | Create IP Ban middleware for the app. | homeassistant/components/http/ban.py | setup_bans | uSpike/home-assistant | 23 | python | @callback
def setup_bans(hass, app, login_threshold):
app.middlewares.append(ban_middleware)
app[KEY_FAILED_LOGIN_ATTEMPTS] = defaultdict(int)
app[KEY_LOGIN_THRESHOLD] = login_threshold
async def ban_startup(app):
'Initialize bans when app starts up.'
app[KEY_BANNED_IPS] = (await async_load_ip_bans_config(hass, hass.config.path(IP_BANS_FILE)))
app.on_startup.append(ban_startup) | @callback
def setup_bans(hass, app, login_threshold):
app.middlewares.append(ban_middleware)
app[KEY_FAILED_LOGIN_ATTEMPTS] = defaultdict(int)
app[KEY_LOGIN_THRESHOLD] = login_threshold
async def ban_startup(app):
'Initialize bans when app starts up.'
app[KEY_BANNED_IPS] = (await async_load_ip_bans_config(hass, hass.config.path(IP_BANS_FILE)))
app.on_startup.append(ban_startup)<|docstring|>Create IP Ban middleware for the app.<|endoftext|> |
275c137e4663b857a777c12b2b6ad8884119b26647a174e030114ff502323185 | @middleware
async def ban_middleware(request, handler):
'IP Ban middleware.'
if (KEY_BANNED_IPS not in request.app):
_LOGGER.error('IP Ban middleware loaded but banned IPs not loaded')
return (await handler(request))
ip_address_ = request[KEY_REAL_IP]
is_banned = any(((ip_ban.ip_address == ip_address_) for ip_ban in request.app[KEY_BANNED_IPS]))
if is_banned:
raise HTTPForbidden()
try:
return (await handler(request))
except HTTPUnauthorized:
(await process_wrong_login(request))
raise | IP Ban middleware. | homeassistant/components/http/ban.py | ban_middleware | uSpike/home-assistant | 23 | python | @middleware
async def ban_middleware(request, handler):
if (KEY_BANNED_IPS not in request.app):
_LOGGER.error('IP Ban middleware loaded but banned IPs not loaded')
return (await handler(request))
ip_address_ = request[KEY_REAL_IP]
is_banned = any(((ip_ban.ip_address == ip_address_) for ip_ban in request.app[KEY_BANNED_IPS]))
if is_banned:
raise HTTPForbidden()
try:
return (await handler(request))
except HTTPUnauthorized:
(await process_wrong_login(request))
raise | @middleware
async def ban_middleware(request, handler):
if (KEY_BANNED_IPS not in request.app):
_LOGGER.error('IP Ban middleware loaded but banned IPs not loaded')
return (await handler(request))
ip_address_ = request[KEY_REAL_IP]
is_banned = any(((ip_ban.ip_address == ip_address_) for ip_ban in request.app[KEY_BANNED_IPS]))
if is_banned:
raise HTTPForbidden()
try:
return (await handler(request))
except HTTPUnauthorized:
(await process_wrong_login(request))
raise<|docstring|>IP Ban middleware.<|endoftext|> |
172ae85c069e2f305b676747b31865c3eef93a10a27736b5fb70c63f6f981dfb | def log_invalid_auth(func):
'Decorate function to handle invalid auth or failed login attempts.'
async def handle_req(view, request, *args, **kwargs):
'Try to log failed login attempts if response status >= 400.'
resp = (await func(view, request, *args, **kwargs))
if (resp.status >= 400):
(await process_wrong_login(request))
return resp
return handle_req | Decorate function to handle invalid auth or failed login attempts. | homeassistant/components/http/ban.py | log_invalid_auth | uSpike/home-assistant | 23 | python | def log_invalid_auth(func):
async def handle_req(view, request, *args, **kwargs):
'Try to log failed login attempts if response status >= 400.'
resp = (await func(view, request, *args, **kwargs))
if (resp.status >= 400):
(await process_wrong_login(request))
return resp
return handle_req | def log_invalid_auth(func):
async def handle_req(view, request, *args, **kwargs):
'Try to log failed login attempts if response status >= 400.'
resp = (await func(view, request, *args, **kwargs))
if (resp.status >= 400):
(await process_wrong_login(request))
return resp
return handle_req<|docstring|>Decorate function to handle invalid auth or failed login attempts.<|endoftext|> |
24aaa1a7bb6680275287ed913011f6fcc1ad689ff6ce65c91cff81211d96263b | async def process_wrong_login(request):
'Process a wrong login attempt.\n\n Increase failed login attempts counter for remote IP address.\n Add ip ban entry if failed login attempts exceeds threshold.\n '
remote_addr = request[KEY_REAL_IP]
msg = 'Login attempt or request with invalid authentication from {}'.format(remote_addr)
_LOGGER.warning(msg)
hass = request.app['hass']
hass.components.persistent_notification.async_create(msg, 'Login attempt failed', NOTIFICATION_ID_LOGIN)
if ((KEY_BANNED_IPS not in request.app) or (request.app[KEY_LOGIN_THRESHOLD] < 1)):
return
request.app[KEY_FAILED_LOGIN_ATTEMPTS][remote_addr] += 1
if (request.app[KEY_FAILED_LOGIN_ATTEMPTS][remote_addr] >= request.app[KEY_LOGIN_THRESHOLD]):
new_ban = IpBan(remote_addr)
request.app[KEY_BANNED_IPS].append(new_ban)
(await hass.async_add_job(update_ip_bans_config, hass.config.path(IP_BANS_FILE), new_ban))
_LOGGER.warning('Banned IP %s for too many login attempts', remote_addr)
hass.components.persistent_notification.async_create(f'Too many login attempts from {remote_addr}', 'Banning IP address', NOTIFICATION_ID_BAN) | Process a wrong login attempt.
Increase failed login attempts counter for remote IP address.
Add ip ban entry if failed login attempts exceeds threshold. | homeassistant/components/http/ban.py | process_wrong_login | uSpike/home-assistant | 23 | python | async def process_wrong_login(request):
'Process a wrong login attempt.\n\n Increase failed login attempts counter for remote IP address.\n Add ip ban entry if failed login attempts exceeds threshold.\n '
remote_addr = request[KEY_REAL_IP]
msg = 'Login attempt or request with invalid authentication from {}'.format(remote_addr)
_LOGGER.warning(msg)
hass = request.app['hass']
hass.components.persistent_notification.async_create(msg, 'Login attempt failed', NOTIFICATION_ID_LOGIN)
if ((KEY_BANNED_IPS not in request.app) or (request.app[KEY_LOGIN_THRESHOLD] < 1)):
return
request.app[KEY_FAILED_LOGIN_ATTEMPTS][remote_addr] += 1
if (request.app[KEY_FAILED_LOGIN_ATTEMPTS][remote_addr] >= request.app[KEY_LOGIN_THRESHOLD]):
new_ban = IpBan(remote_addr)
request.app[KEY_BANNED_IPS].append(new_ban)
(await hass.async_add_job(update_ip_bans_config, hass.config.path(IP_BANS_FILE), new_ban))
_LOGGER.warning('Banned IP %s for too many login attempts', remote_addr)
hass.components.persistent_notification.async_create(f'Too many login attempts from {remote_addr}', 'Banning IP address', NOTIFICATION_ID_BAN) | async def process_wrong_login(request):
'Process a wrong login attempt.\n\n Increase failed login attempts counter for remote IP address.\n Add ip ban entry if failed login attempts exceeds threshold.\n '
remote_addr = request[KEY_REAL_IP]
msg = 'Login attempt or request with invalid authentication from {}'.format(remote_addr)
_LOGGER.warning(msg)
hass = request.app['hass']
hass.components.persistent_notification.async_create(msg, 'Login attempt failed', NOTIFICATION_ID_LOGIN)
if ((KEY_BANNED_IPS not in request.app) or (request.app[KEY_LOGIN_THRESHOLD] < 1)):
return
request.app[KEY_FAILED_LOGIN_ATTEMPTS][remote_addr] += 1
if (request.app[KEY_FAILED_LOGIN_ATTEMPTS][remote_addr] >= request.app[KEY_LOGIN_THRESHOLD]):
new_ban = IpBan(remote_addr)
request.app[KEY_BANNED_IPS].append(new_ban)
(await hass.async_add_job(update_ip_bans_config, hass.config.path(IP_BANS_FILE), new_ban))
_LOGGER.warning('Banned IP %s for too many login attempts', remote_addr)
hass.components.persistent_notification.async_create(f'Too many login attempts from {remote_addr}', 'Banning IP address', NOTIFICATION_ID_BAN)<|docstring|>Process a wrong login attempt.
Increase failed login attempts counter for remote IP address.
Add ip ban entry if failed login attempts exceeds threshold.<|endoftext|> |
6092c2c803d876ce78e21172eaec2c8e0b0209520da44ff83f2addf329976fb2 | async def process_success_login(request):
'Process a success login attempt.\n\n Reset failed login attempts counter for remote IP address.\n No release IP address from banned list function, it can only be done by\n manual modify ip bans config file.\n '
remote_addr = request[KEY_REAL_IP]
if ((KEY_BANNED_IPS not in request.app) or (request.app[KEY_LOGIN_THRESHOLD] < 1)):
return
if ((remote_addr in request.app[KEY_FAILED_LOGIN_ATTEMPTS]) and (request.app[KEY_FAILED_LOGIN_ATTEMPTS][remote_addr] > 0)):
_LOGGER.debug('Login success, reset failed login attempts counter from %s', remote_addr)
request.app[KEY_FAILED_LOGIN_ATTEMPTS].pop(remote_addr) | Process a success login attempt.
Reset failed login attempts counter for remote IP address.
No release IP address from banned list function, it can only be done by
manual modify ip bans config file. | homeassistant/components/http/ban.py | process_success_login | uSpike/home-assistant | 23 | python | async def process_success_login(request):
'Process a success login attempt.\n\n Reset failed login attempts counter for remote IP address.\n No release IP address from banned list function, it can only be done by\n manual modify ip bans config file.\n '
remote_addr = request[KEY_REAL_IP]
if ((KEY_BANNED_IPS not in request.app) or (request.app[KEY_LOGIN_THRESHOLD] < 1)):
return
if ((remote_addr in request.app[KEY_FAILED_LOGIN_ATTEMPTS]) and (request.app[KEY_FAILED_LOGIN_ATTEMPTS][remote_addr] > 0)):
_LOGGER.debug('Login success, reset failed login attempts counter from %s', remote_addr)
request.app[KEY_FAILED_LOGIN_ATTEMPTS].pop(remote_addr) | async def process_success_login(request):
'Process a success login attempt.\n\n Reset failed login attempts counter for remote IP address.\n No release IP address from banned list function, it can only be done by\n manual modify ip bans config file.\n '
remote_addr = request[KEY_REAL_IP]
if ((KEY_BANNED_IPS not in request.app) or (request.app[KEY_LOGIN_THRESHOLD] < 1)):
return
if ((remote_addr in request.app[KEY_FAILED_LOGIN_ATTEMPTS]) and (request.app[KEY_FAILED_LOGIN_ATTEMPTS][remote_addr] > 0)):
_LOGGER.debug('Login success, reset failed login attempts counter from %s', remote_addr)
request.app[KEY_FAILED_LOGIN_ATTEMPTS].pop(remote_addr)<|docstring|>Process a success login attempt.
Reset failed login attempts counter for remote IP address.
No release IP address from banned list function, it can only be done by
manual modify ip bans config file.<|endoftext|> |
616ea3b2ec3b983d52b2318031954a3e15cadafaf5c6db1eba4c464ab0f863c7 | async def async_load_ip_bans_config(hass: HomeAssistant, path: str) -> List[IpBan]:
'Load list of banned IPs from config file.'
ip_list: List[IpBan] = []
try:
list_ = (await hass.async_add_executor_job(load_yaml_config_file, path))
except FileNotFoundError:
return ip_list
except HomeAssistantError as err:
_LOGGER.error('Unable to load %s: %s', path, str(err))
return ip_list
for (ip_ban, ip_info) in list_.items():
try:
ip_info = SCHEMA_IP_BAN_ENTRY(ip_info)
ip_list.append(IpBan(ip_ban, ip_info['banned_at']))
except vol.Invalid as err:
_LOGGER.error('Failed to load IP ban %s: %s', ip_info, err)
continue
return ip_list | Load list of banned IPs from config file. | homeassistant/components/http/ban.py | async_load_ip_bans_config | uSpike/home-assistant | 23 | python | async def async_load_ip_bans_config(hass: HomeAssistant, path: str) -> List[IpBan]:
ip_list: List[IpBan] = []
try:
list_ = (await hass.async_add_executor_job(load_yaml_config_file, path))
except FileNotFoundError:
return ip_list
except HomeAssistantError as err:
_LOGGER.error('Unable to load %s: %s', path, str(err))
return ip_list
for (ip_ban, ip_info) in list_.items():
try:
ip_info = SCHEMA_IP_BAN_ENTRY(ip_info)
ip_list.append(IpBan(ip_ban, ip_info['banned_at']))
except vol.Invalid as err:
_LOGGER.error('Failed to load IP ban %s: %s', ip_info, err)
continue
return ip_list | async def async_load_ip_bans_config(hass: HomeAssistant, path: str) -> List[IpBan]:
ip_list: List[IpBan] = []
try:
list_ = (await hass.async_add_executor_job(load_yaml_config_file, path))
except FileNotFoundError:
return ip_list
except HomeAssistantError as err:
_LOGGER.error('Unable to load %s: %s', path, str(err))
return ip_list
for (ip_ban, ip_info) in list_.items():
try:
ip_info = SCHEMA_IP_BAN_ENTRY(ip_info)
ip_list.append(IpBan(ip_ban, ip_info['banned_at']))
except vol.Invalid as err:
_LOGGER.error('Failed to load IP ban %s: %s', ip_info, err)
continue
return ip_list<|docstring|>Load list of banned IPs from config file.<|endoftext|> |
28e0265c7649fff1b8d7f2ce5a52ccedd8b6ff783bcae90462a87ecfe6559421 | def update_ip_bans_config(path: str, ip_ban: IpBan) -> None:
'Update config file with new banned IP address.'
with open(path, 'a') as out:
ip_ = {str(ip_ban.ip_address): {ATTR_BANNED_AT: ip_ban.banned_at.strftime('%Y-%m-%dT%H:%M:%S')}}
out.write('\n')
out.write(dump(ip_)) | Update config file with new banned IP address. | homeassistant/components/http/ban.py | update_ip_bans_config | uSpike/home-assistant | 23 | python | def update_ip_bans_config(path: str, ip_ban: IpBan) -> None:
with open(path, 'a') as out:
ip_ = {str(ip_ban.ip_address): {ATTR_BANNED_AT: ip_ban.banned_at.strftime('%Y-%m-%dT%H:%M:%S')}}
out.write('\n')
out.write(dump(ip_)) | def update_ip_bans_config(path: str, ip_ban: IpBan) -> None:
with open(path, 'a') as out:
ip_ = {str(ip_ban.ip_address): {ATTR_BANNED_AT: ip_ban.banned_at.strftime('%Y-%m-%dT%H:%M:%S')}}
out.write('\n')
out.write(dump(ip_))<|docstring|>Update config file with new banned IP address.<|endoftext|> |
8f3d20e64e8bc020d119ce208339b00dae40e9baa9e4fd5c582b43eb335f817b | async def ban_startup(app):
'Initialize bans when app starts up.'
app[KEY_BANNED_IPS] = (await async_load_ip_bans_config(hass, hass.config.path(IP_BANS_FILE))) | Initialize bans when app starts up. | homeassistant/components/http/ban.py | ban_startup | uSpike/home-assistant | 23 | python | async def ban_startup(app):
app[KEY_BANNED_IPS] = (await async_load_ip_bans_config(hass, hass.config.path(IP_BANS_FILE))) | async def ban_startup(app):
app[KEY_BANNED_IPS] = (await async_load_ip_bans_config(hass, hass.config.path(IP_BANS_FILE)))<|docstring|>Initialize bans when app starts up.<|endoftext|> |
0459a41b3360b59ab7f84131f5fc0c077793657b0cb4def6ea9e08417d61bb60 | async def handle_req(view, request, *args, **kwargs):
'Try to log failed login attempts if response status >= 400.'
resp = (await func(view, request, *args, **kwargs))
if (resp.status >= 400):
(await process_wrong_login(request))
return resp | Try to log failed login attempts if response status >= 400. | homeassistant/components/http/ban.py | handle_req | uSpike/home-assistant | 23 | python | async def handle_req(view, request, *args, **kwargs):
resp = (await func(view, request, *args, **kwargs))
if (resp.status >= 400):
(await process_wrong_login(request))
return resp | async def handle_req(view, request, *args, **kwargs):
resp = (await func(view, request, *args, **kwargs))
if (resp.status >= 400):
(await process_wrong_login(request))
return resp<|docstring|>Try to log failed login attempts if response status >= 400.<|endoftext|> |
66464665317894d7070d2506381dbe5f7e9318e0b38d689e4045a322c3af5332 | def __init__(self, ip_ban: str, banned_at: Optional[datetime]=None) -> None:
'Initialize IP Ban object.'
self.ip_address = ip_address(ip_ban)
self.banned_at = (banned_at or datetime.utcnow()) | Initialize IP Ban object. | homeassistant/components/http/ban.py | __init__ | uSpike/home-assistant | 23 | python | def __init__(self, ip_ban: str, banned_at: Optional[datetime]=None) -> None:
self.ip_address = ip_address(ip_ban)
self.banned_at = (banned_at or datetime.utcnow()) | def __init__(self, ip_ban: str, banned_at: Optional[datetime]=None) -> None:
self.ip_address = ip_address(ip_ban)
self.banned_at = (banned_at or datetime.utcnow())<|docstring|>Initialize IP Ban object.<|endoftext|> |
f70c6a772c258a45281e7bba5050fbb27fcc730b0b06dc762c1dcc0395ff62af | def get_content(suffix, print_data=False):
'\n From the page ( \'ecolex.org\'+ suffix ) we grab the relevant metadata (eg. type, document Type, name, reference, number,\n date, source name and source link, status, subject, keywords, treaty name and link, meeting name and link, website, abstract,\n ...).\n The data is then saved into a dictionary with parameter names as keys and the grabbed results as the values.\n\n Example:\n\n data["category"] = "Treaty decision"\n data["name"] = "Decision XXIX_21 _ Membership of the Implementation Committee"\n\n In the end the dictionary is saved into a json file named (data["name"] without forbidden characters and \n length limited to 100).json\n\n Parameters:\n suffix : string\n the suffix of the url from which we are extracting the data. The suffix string is everything that comes \n after the \'ecolex.org\'\n\n print_data : boolean \n Optional parameter that is by default set to False. In case it is set to True, the function will at the end \n also print what it managed to extract from the page.\n\n Returns \n None\n '
data = dict()
data['URL'] = (BASE_URL + suffix)
get_page = requests.get((BASE_URL + suffix))
if (get_page.status_code != 200):
print('Request Denied!', suffix)
page_text = get_page.text
soup = BeautifulSoup(page_text, 'html.parser')
important_text = str(soup.find('article'))
string_parameters = {'date': '<dt>Date.*\\s*<dd>(.*?)<', 'sourceLink': 'Source.*\\s*.*\\s*.*?href="(.*?)"', 'sourceName': 'Source.*\\s*.*\\s*.*?>(.*?)<', 'sourceID': '\\(ID:.*?>(.*?)<', 'publisher': 'Publisher.*\\s*.*\\s*(.*)', 'placePublication': 'Place of publication.*\\s*.*\\s*.*\\s*\\|(.*)', 'ISBN': 'ISBN.*\\s*<dd>(.*?)<', 'ISSN': 'ISSN.*\\s*<dd>(.*?)<', 'pages': 'Pages.*\\s*<dd>(\\d*)', 'documentType': 'Document type.*\\s*<dd>(.*?)<', 'fullTextLink': 'Full text.*\\s*.*\\s*.*?href="(.*?)"', 'website': 'Website.*\\s*.*\\s*<a href="(.*?)"', 'basin': 'Basin.*\\s*<dd>(.*?)<', 'fieldOfApplication': 'Field of application.*\\s*<dd>(.*?)<', 'DOI': 'DOI.*\\s*.*\\s*<a href="(.*?)"', 'journal/series': 'Journal\\/Series.*\\s*<dd>\\s*(.*\\s*\\|.*)'}
list_parameters = {'author': 'uthor.*\\s*<dd>(.*?)<', 'language': 'Language.*\\s*<dd>(.*?)<', 'country/Territory': 'Country\\/Territory.*\\s*<dd>(.*?)<', 'subject': 'Subject.*\\s*<dd>(.*?)<', 'geographicalArea': 'Geographical area.*\\s*<dd>(.*?)<'}
for (parameter_name, regex_pattern) in string_parameters.items():
re_pat = re.compile(regex_pattern)
data[parameter_name] = get_value_or_none(re_pat, important_text)
for (parameter_name, regex_pattern) in list_parameters.items():
re_pat = re.compile(regex_pattern)
data[parameter_name] = get_list_or_none(re_pat, important_text)
data['category'] = 'literature'
re_name = re.compile('<h1>(.*?)<')
data['name'] = get_value_or_none(re_name, important_text)
if (data['name'] is not None):
data['name'] = remove_forbidden_characters(data['name'])
else:
print('Name of the file not found!', suffix)
re_keyword = re.compile('span class="tag">(.*?)<')
data['keyword'] = re.findall(re_keyword, important_text)
re_abstract = re.compile('class="abstract">(.*)')
data['abstract'] = get_value_or_none(re_abstract, important_text)
ref_section = soup.find('article').find('section', {'id': 'other-references'})
if (ref_section is not None):
data['other_references'] = list()
other_refs = ref_section.find_all('dl')
for each_reference in other_refs:
reftext = str(each_reference)
single_reference = dict()
ref_string_parameters = {'refType': '<dt>(.*?)<', 'refLink': 'result-title.*\\s*.*?href="(.*)"', 'refName': 'result-title.*\\s*.*\\s*title="(.*)"', 'refDocumentType': 'Document type">(.*?)<', 'refPlaceOfAdoption': 'Place of adoption">(.*?)<', 'refDate': 'Date:(.*?)"', 'refSourceID': 'source.*\\s*.*?ID:(.*?)<', 'refSourceLink': 'source.*\\s*.*?href="(.*?)"', 'refSourceName': 'source.*\\s*.*?href.*?>(.*?)<'}
ref_list_parameters = {'refKeywords': 'keywords">(.*?)<'}
for (parameter_name, regex_pattern) in ref_string_parameters.items():
re_pat = re.compile(regex_pattern)
single_reference[parameter_name] = get_value_or_none(re_pat, reftext)
for (parameter_name, regex_pattern) in ref_list_parameters.items():
re_pat = re.compile(regex_pattern)
single_reference[parameter_name] = get_list_or_none(re_pat, reftext)
data['other_references'].append(single_reference)
ref_section_literature = soup.find('article').find('section', {'id': 'literature-references'})
if (ref_section_literature is not None):
data['literature_references'] = []
literature_references = ref_section_literature.find('dl')
for each_reference in literature_references:
reftext = str(each_reference)
single_reference = dict()
ref_string_parameters = {'refName': 'result-title.*\\s*.*\\s*.*?>(.*?)<', 'refLink': 'result-title.*\\s*.*?href="(.*?)"', 'refAuthor': 'uthor:.*\\s*.*?>(.*?)<', 'refPublishedIn': 'details.*\\s*.*?In:.*?span>(.*?)<', 'refPublishedInWhere': 'details.*\\s*.*In.*\\s*\\|(.*)', 'refPublisher': 'Publisher.*?span>(.*)<', 'refPublicationPlace': 'Publication place">(.*)<', 'refPublicationDate': 'ublication date">(.*)<', 'refSourceLink': 'Source.*\\s*.*?href="(.*?)"', 'refSourceName': 'Source.*\\s*.*?>(.*?)<', 'refSourceID': 'result-source.*\\s*.*?ID:(.*)\\)'}
ref_list_parameters = {'refCountryTerritory': 'Territory">(.*)<', 'refKeywords': 'keywords">(.*)<'}
for (parameter_name, regex_pattern) in ref_string_parameters.items():
re_pat = re.compile(regex_pattern)
single_reference[parameter_name] = get_value_or_none(re_pat, reftext)
for (parameter_name, regex_pattern) in ref_list_parameters.items():
re_pat = re.compile(regex_pattern)
single_reference[parameter_name] = get_list_or_none(re_pat, reftext)
data['literature_references'].append(single_reference)
if print_data:
for (key, value) in data.items():
print(((key + ' : ') + str(value)))
with open((('literature\\' + data['name'][:150]) + '.json'), 'w') as outfile:
json.dump(data, outfile, indent=2) | From the page ( 'ecolex.org'+ suffix ) we grab the relevant metadata (eg. type, document Type, name, reference, number,
date, source name and source link, status, subject, keywords, treaty name and link, meeting name and link, website, abstract,
...).
The data is then saved into a dictionary with parameter names as keys and the grabbed results as the values.
Example:
data["category"] = "Treaty decision"
data["name"] = "Decision XXIX_21 _ Membership of the Implementation Committee"
In the end the dictionary is saved into a json file named (data["name"] without forbidden characters and
length limited to 100).json
Parameters:
suffix : string
the suffix of the url from which we are extracting the data. The suffix string is everything that comes
after the 'ecolex.org'
print_data : boolean
Optional parameter that is by default set to False. In case it is set to True, the function will at the end
also print what it managed to extract from the page.
Returns
None | crawlers/ecolex/get_content_literature.py | get_content | KraljSamo/text_embedding_service_entrypoint | 1 | python | def get_content(suffix, print_data=False):
'\n From the page ( \'ecolex.org\'+ suffix ) we grab the relevant metadata (eg. type, document Type, name, reference, number,\n date, source name and source link, status, subject, keywords, treaty name and link, meeting name and link, website, abstract,\n ...).\n The data is then saved into a dictionary with parameter names as keys and the grabbed results as the values.\n\n Example:\n\n data["category"] = "Treaty decision"\n data["name"] = "Decision XXIX_21 _ Membership of the Implementation Committee"\n\n In the end the dictionary is saved into a json file named (data["name"] without forbidden characters and \n length limited to 100).json\n\n Parameters:\n suffix : string\n the suffix of the url from which we are extracting the data. The suffix string is everything that comes \n after the \'ecolex.org\'\n\n print_data : boolean \n Optional parameter that is by default set to False. In case it is set to True, the function will at the end \n also print what it managed to extract from the page.\n\n Returns \n None\n '
data = dict()
data['URL'] = (BASE_URL + suffix)
get_page = requests.get((BASE_URL + suffix))
if (get_page.status_code != 200):
print('Request Denied!', suffix)
page_text = get_page.text
soup = BeautifulSoup(page_text, 'html.parser')
important_text = str(soup.find('article'))
string_parameters = {'date': '<dt>Date.*\\s*<dd>(.*?)<', 'sourceLink': 'Source.*\\s*.*\\s*.*?href="(.*?)"', 'sourceName': 'Source.*\\s*.*\\s*.*?>(.*?)<', 'sourceID': '\\(ID:.*?>(.*?)<', 'publisher': 'Publisher.*\\s*.*\\s*(.*)', 'placePublication': 'Place of publication.*\\s*.*\\s*.*\\s*\\|(.*)', 'ISBN': 'ISBN.*\\s*<dd>(.*?)<', 'ISSN': 'ISSN.*\\s*<dd>(.*?)<', 'pages': 'Pages.*\\s*<dd>(\\d*)', 'documentType': 'Document type.*\\s*<dd>(.*?)<', 'fullTextLink': 'Full text.*\\s*.*\\s*.*?href="(.*?)"', 'website': 'Website.*\\s*.*\\s*<a href="(.*?)"', 'basin': 'Basin.*\\s*<dd>(.*?)<', 'fieldOfApplication': 'Field of application.*\\s*<dd>(.*?)<', 'DOI': 'DOI.*\\s*.*\\s*<a href="(.*?)"', 'journal/series': 'Journal\\/Series.*\\s*<dd>\\s*(.*\\s*\\|.*)'}
list_parameters = {'author': 'uthor.*\\s*<dd>(.*?)<', 'language': 'Language.*\\s*<dd>(.*?)<', 'country/Territory': 'Country\\/Territory.*\\s*<dd>(.*?)<', 'subject': 'Subject.*\\s*<dd>(.*?)<', 'geographicalArea': 'Geographical area.*\\s*<dd>(.*?)<'}
for (parameter_name, regex_pattern) in string_parameters.items():
re_pat = re.compile(regex_pattern)
data[parameter_name] = get_value_or_none(re_pat, important_text)
for (parameter_name, regex_pattern) in list_parameters.items():
re_pat = re.compile(regex_pattern)
data[parameter_name] = get_list_or_none(re_pat, important_text)
data['category'] = 'literature'
re_name = re.compile('<h1>(.*?)<')
data['name'] = get_value_or_none(re_name, important_text)
if (data['name'] is not None):
data['name'] = remove_forbidden_characters(data['name'])
else:
print('Name of the file not found!', suffix)
re_keyword = re.compile('span class="tag">(.*?)<')
data['keyword'] = re.findall(re_keyword, important_text)
re_abstract = re.compile('class="abstract">(.*)')
data['abstract'] = get_value_or_none(re_abstract, important_text)
ref_section = soup.find('article').find('section', {'id': 'other-references'})
if (ref_section is not None):
data['other_references'] = list()
other_refs = ref_section.find_all('dl')
for each_reference in other_refs:
reftext = str(each_reference)
single_reference = dict()
ref_string_parameters = {'refType': '<dt>(.*?)<', 'refLink': 'result-title.*\\s*.*?href="(.*)"', 'refName': 'result-title.*\\s*.*\\s*title="(.*)"', 'refDocumentType': 'Document type">(.*?)<', 'refPlaceOfAdoption': 'Place of adoption">(.*?)<', 'refDate': 'Date:(.*?)"', 'refSourceID': 'source.*\\s*.*?ID:(.*?)<', 'refSourceLink': 'source.*\\s*.*?href="(.*?)"', 'refSourceName': 'source.*\\s*.*?href.*?>(.*?)<'}
ref_list_parameters = {'refKeywords': 'keywords">(.*?)<'}
for (parameter_name, regex_pattern) in ref_string_parameters.items():
re_pat = re.compile(regex_pattern)
single_reference[parameter_name] = get_value_or_none(re_pat, reftext)
for (parameter_name, regex_pattern) in ref_list_parameters.items():
re_pat = re.compile(regex_pattern)
single_reference[parameter_name] = get_list_or_none(re_pat, reftext)
data['other_references'].append(single_reference)
ref_section_literature = soup.find('article').find('section', {'id': 'literature-references'})
if (ref_section_literature is not None):
data['literature_references'] = []
literature_references = ref_section_literature.find('dl')
for each_reference in literature_references:
reftext = str(each_reference)
single_reference = dict()
ref_string_parameters = {'refName': 'result-title.*\\s*.*\\s*.*?>(.*?)<', 'refLink': 'result-title.*\\s*.*?href="(.*?)"', 'refAuthor': 'uthor:.*\\s*.*?>(.*?)<', 'refPublishedIn': 'details.*\\s*.*?In:.*?span>(.*?)<', 'refPublishedInWhere': 'details.*\\s*.*In.*\\s*\\|(.*)', 'refPublisher': 'Publisher.*?span>(.*)<', 'refPublicationPlace': 'Publication place">(.*)<', 'refPublicationDate': 'ublication date">(.*)<', 'refSourceLink': 'Source.*\\s*.*?href="(.*?)"', 'refSourceName': 'Source.*\\s*.*?>(.*?)<', 'refSourceID': 'result-source.*\\s*.*?ID:(.*)\\)'}
ref_list_parameters = {'refCountryTerritory': 'Territory">(.*)<', 'refKeywords': 'keywords">(.*)<'}
for (parameter_name, regex_pattern) in ref_string_parameters.items():
re_pat = re.compile(regex_pattern)
single_reference[parameter_name] = get_value_or_none(re_pat, reftext)
for (parameter_name, regex_pattern) in ref_list_parameters.items():
re_pat = re.compile(regex_pattern)
single_reference[parameter_name] = get_list_or_none(re_pat, reftext)
data['literature_references'].append(single_reference)
if print_data:
for (key, value) in data.items():
print(((key + ' : ') + str(value)))
with open((('literature\\' + data['name'][:150]) + '.json'), 'w') as outfile:
json.dump(data, outfile, indent=2) | def get_content(suffix, print_data=False):
'\n From the page ( \'ecolex.org\'+ suffix ) we grab the relevant metadata (eg. type, document Type, name, reference, number,\n date, source name and source link, status, subject, keywords, treaty name and link, meeting name and link, website, abstract,\n ...).\n The data is then saved into a dictionary with parameter names as keys and the grabbed results as the values.\n\n Example:\n\n data["category"] = "Treaty decision"\n data["name"] = "Decision XXIX_21 _ Membership of the Implementation Committee"\n\n In the end the dictionary is saved into a json file named (data["name"] without forbidden characters and \n length limited to 100).json\n\n Parameters:\n suffix : string\n the suffix of the url from which we are extracting the data. The suffix string is everything that comes \n after the \'ecolex.org\'\n\n print_data : boolean \n Optional parameter that is by default set to False. In case it is set to True, the function will at the end \n also print what it managed to extract from the page.\n\n Returns \n None\n '
data = dict()
data['URL'] = (BASE_URL + suffix)
get_page = requests.get((BASE_URL + suffix))
if (get_page.status_code != 200):
print('Request Denied!', suffix)
page_text = get_page.text
soup = BeautifulSoup(page_text, 'html.parser')
important_text = str(soup.find('article'))
string_parameters = {'date': '<dt>Date.*\\s*<dd>(.*?)<', 'sourceLink': 'Source.*\\s*.*\\s*.*?href="(.*?)"', 'sourceName': 'Source.*\\s*.*\\s*.*?>(.*?)<', 'sourceID': '\\(ID:.*?>(.*?)<', 'publisher': 'Publisher.*\\s*.*\\s*(.*)', 'placePublication': 'Place of publication.*\\s*.*\\s*.*\\s*\\|(.*)', 'ISBN': 'ISBN.*\\s*<dd>(.*?)<', 'ISSN': 'ISSN.*\\s*<dd>(.*?)<', 'pages': 'Pages.*\\s*<dd>(\\d*)', 'documentType': 'Document type.*\\s*<dd>(.*?)<', 'fullTextLink': 'Full text.*\\s*.*\\s*.*?href="(.*?)"', 'website': 'Website.*\\s*.*\\s*<a href="(.*?)"', 'basin': 'Basin.*\\s*<dd>(.*?)<', 'fieldOfApplication': 'Field of application.*\\s*<dd>(.*?)<', 'DOI': 'DOI.*\\s*.*\\s*<a href="(.*?)"', 'journal/series': 'Journal\\/Series.*\\s*<dd>\\s*(.*\\s*\\|.*)'}
list_parameters = {'author': 'uthor.*\\s*<dd>(.*?)<', 'language': 'Language.*\\s*<dd>(.*?)<', 'country/Territory': 'Country\\/Territory.*\\s*<dd>(.*?)<', 'subject': 'Subject.*\\s*<dd>(.*?)<', 'geographicalArea': 'Geographical area.*\\s*<dd>(.*?)<'}
for (parameter_name, regex_pattern) in string_parameters.items():
re_pat = re.compile(regex_pattern)
data[parameter_name] = get_value_or_none(re_pat, important_text)
for (parameter_name, regex_pattern) in list_parameters.items():
re_pat = re.compile(regex_pattern)
data[parameter_name] = get_list_or_none(re_pat, important_text)
data['category'] = 'literature'
re_name = re.compile('<h1>(.*?)<')
data['name'] = get_value_or_none(re_name, important_text)
if (data['name'] is not None):
data['name'] = remove_forbidden_characters(data['name'])
else:
print('Name of the file not found!', suffix)
re_keyword = re.compile('span class="tag">(.*?)<')
data['keyword'] = re.findall(re_keyword, important_text)
re_abstract = re.compile('class="abstract">(.*)')
data['abstract'] = get_value_or_none(re_abstract, important_text)
ref_section = soup.find('article').find('section', {'id': 'other-references'})
if (ref_section is not None):
data['other_references'] = list()
other_refs = ref_section.find_all('dl')
for each_reference in other_refs:
reftext = str(each_reference)
single_reference = dict()
ref_string_parameters = {'refType': '<dt>(.*?)<', 'refLink': 'result-title.*\\s*.*?href="(.*)"', 'refName': 'result-title.*\\s*.*\\s*title="(.*)"', 'refDocumentType': 'Document type">(.*?)<', 'refPlaceOfAdoption': 'Place of adoption">(.*?)<', 'refDate': 'Date:(.*?)"', 'refSourceID': 'source.*\\s*.*?ID:(.*?)<', 'refSourceLink': 'source.*\\s*.*?href="(.*?)"', 'refSourceName': 'source.*\\s*.*?href.*?>(.*?)<'}
ref_list_parameters = {'refKeywords': 'keywords">(.*?)<'}
for (parameter_name, regex_pattern) in ref_string_parameters.items():
re_pat = re.compile(regex_pattern)
single_reference[parameter_name] = get_value_or_none(re_pat, reftext)
for (parameter_name, regex_pattern) in ref_list_parameters.items():
re_pat = re.compile(regex_pattern)
single_reference[parameter_name] = get_list_or_none(re_pat, reftext)
data['other_references'].append(single_reference)
ref_section_literature = soup.find('article').find('section', {'id': 'literature-references'})
if (ref_section_literature is not None):
data['literature_references'] = []
literature_references = ref_section_literature.find('dl')
for each_reference in literature_references:
reftext = str(each_reference)
single_reference = dict()
ref_string_parameters = {'refName': 'result-title.*\\s*.*\\s*.*?>(.*?)<', 'refLink': 'result-title.*\\s*.*?href="(.*?)"', 'refAuthor': 'uthor:.*\\s*.*?>(.*?)<', 'refPublishedIn': 'details.*\\s*.*?In:.*?span>(.*?)<', 'refPublishedInWhere': 'details.*\\s*.*In.*\\s*\\|(.*)', 'refPublisher': 'Publisher.*?span>(.*)<', 'refPublicationPlace': 'Publication place">(.*)<', 'refPublicationDate': 'ublication date">(.*)<', 'refSourceLink': 'Source.*\\s*.*?href="(.*?)"', 'refSourceName': 'Source.*\\s*.*?>(.*?)<', 'refSourceID': 'result-source.*\\s*.*?ID:(.*)\\)'}
ref_list_parameters = {'refCountryTerritory': 'Territory">(.*)<', 'refKeywords': 'keywords">(.*)<'}
for (parameter_name, regex_pattern) in ref_string_parameters.items():
re_pat = re.compile(regex_pattern)
single_reference[parameter_name] = get_value_or_none(re_pat, reftext)
for (parameter_name, regex_pattern) in ref_list_parameters.items():
re_pat = re.compile(regex_pattern)
single_reference[parameter_name] = get_list_or_none(re_pat, reftext)
data['literature_references'].append(single_reference)
if print_data:
for (key, value) in data.items():
print(((key + ' : ') + str(value)))
with open((('literature\\' + data['name'][:150]) + '.json'), 'w') as outfile:
json.dump(data, outfile, indent=2)<|docstring|>From the page ( 'ecolex.org'+ suffix ) we grab the relevant metadata (eg. type, document Type, name, reference, number,
date, source name and source link, status, subject, keywords, treaty name and link, meeting name and link, website, abstract,
...).
The data is then saved into a dictionary with parameter names as keys and the grabbed results as the values.
Example:
data["category"] = "Treaty decision"
data["name"] = "Decision XXIX_21 _ Membership of the Implementation Committee"
In the end the dictionary is saved into a json file named (data["name"] without forbidden characters and
length limited to 100).json
Parameters:
suffix : string
the suffix of the url from which we are extracting the data. The suffix string is everything that comes
after the 'ecolex.org'
print_data : boolean
Optional parameter that is by default set to False. In case it is set to True, the function will at the end
also print what it managed to extract from the page.
Returns
None<|endoftext|> |
6c4049a53c313665ddf5770402f19081b6f6c983e90006548527544bf12a4fe9 | def create_mutual_information_matrix(sources_lagged, sinks, start_date, end_date):
'Takes in the lagged sources and calculates the mutual information between them and the\n sinks. mutual information is used'
start_date_str = start_date.strftime('%Y-%m-%d')
end_date_str = end_date.strftime('%Y-%m-%d')
sources_lagged.index = pd.to_datetime(sources_lagged.index)
sinks.index = pd.to_datetime(sinks.index)
sources_clipped = sources_lagged[start_date_str:end_date_str]
sinks_clipped = sinks[start_date_str:end_date_str]
bins = [11, 11, 11]
dfs = pd.DataFrame()
MI_array = np.zeros((sources_clipped.shape[1], sinks_clipped.shape[1]))
for (i, src_name) in enumerate(sources_clipped):
for (j, snk_name) in enumerate(sinks_clipped):
temp_src = sources_clipped[src_name]
temp_snk = sinks_clipped[snk_name]
paired = temp_src.to_frame().join(temp_snk).to_numpy()
(MI, n) = TEpython_ParallelNAN2.mutinfo_new(paired, nbins=bins)
MI_array[(i, j)] = MI
mat = pd.DataFrame(MI_array.T, columns=sources_clipped.columns)
mat = mat.set_index(sinks_clipped.columns)
dfs = dfs.append(mat)
return dfs | Takes in the lagged sources and calculates the mutual information between them and the
sinks. mutual information is used | methods_exploration/functions/data_exploration_functions.py | create_mutual_information_matrix | galengorski/drb-estuary-salinity-ml | 0 | python | def create_mutual_information_matrix(sources_lagged, sinks, start_date, end_date):
'Takes in the lagged sources and calculates the mutual information between them and the\n sinks. mutual information is used'
start_date_str = start_date.strftime('%Y-%m-%d')
end_date_str = end_date.strftime('%Y-%m-%d')
sources_lagged.index = pd.to_datetime(sources_lagged.index)
sinks.index = pd.to_datetime(sinks.index)
sources_clipped = sources_lagged[start_date_str:end_date_str]
sinks_clipped = sinks[start_date_str:end_date_str]
bins = [11, 11, 11]
dfs = pd.DataFrame()
MI_array = np.zeros((sources_clipped.shape[1], sinks_clipped.shape[1]))
for (i, src_name) in enumerate(sources_clipped):
for (j, snk_name) in enumerate(sinks_clipped):
temp_src = sources_clipped[src_name]
temp_snk = sinks_clipped[snk_name]
paired = temp_src.to_frame().join(temp_snk).to_numpy()
(MI, n) = TEpython_ParallelNAN2.mutinfo_new(paired, nbins=bins)
MI_array[(i, j)] = MI
mat = pd.DataFrame(MI_array.T, columns=sources_clipped.columns)
mat = mat.set_index(sinks_clipped.columns)
dfs = dfs.append(mat)
return dfs | def create_mutual_information_matrix(sources_lagged, sinks, start_date, end_date):
'Takes in the lagged sources and calculates the mutual information between them and the\n sinks. mutual information is used'
start_date_str = start_date.strftime('%Y-%m-%d')
end_date_str = end_date.strftime('%Y-%m-%d')
sources_lagged.index = pd.to_datetime(sources_lagged.index)
sinks.index = pd.to_datetime(sinks.index)
sources_clipped = sources_lagged[start_date_str:end_date_str]
sinks_clipped = sinks[start_date_str:end_date_str]
bins = [11, 11, 11]
dfs = pd.DataFrame()
MI_array = np.zeros((sources_clipped.shape[1], sinks_clipped.shape[1]))
for (i, src_name) in enumerate(sources_clipped):
for (j, snk_name) in enumerate(sinks_clipped):
temp_src = sources_clipped[src_name]
temp_snk = sinks_clipped[snk_name]
paired = temp_src.to_frame().join(temp_snk).to_numpy()
(MI, n) = TEpython_ParallelNAN2.mutinfo_new(paired, nbins=bins)
MI_array[(i, j)] = MI
mat = pd.DataFrame(MI_array.T, columns=sources_clipped.columns)
mat = mat.set_index(sinks_clipped.columns)
dfs = dfs.append(mat)
return dfs<|docstring|>Takes in the lagged sources and calculates the mutual information between them and the
sinks. mutual information is used<|endoftext|> |
cb56d1606d56889d300d52ec89f83dba1616af2c4a2e212525e29b58d3b8bd77 | def _get_variant(self, variant_file: Path) -> GameVariant:
'\n Return the GameVariant for the variant specified by variant_file. \n Searches through the vgdl code to find the correct type:\n {chaser, fleeing, immovable}\n '
code = variant_file.read_text()
return GameVariant(path=str(variant_file), enemy_type=re.search('enemy > (\\S+)', code)[1].lower(), message_type=re.search('message > (\\S+)', code)[1].lower(), goal_type=re.search('goal > (\\S+)', code)[1].lower(), decoy_message_type=re.search('decoy_message > (\\S+)', code)[1].lower(), decoy_goal_type=re.search('decoy_goal > (\\S+)', code)[1].lower()) | Return the GameVariant for the variant specified by variant_file.
Searches through the vgdl code to find the correct type:
{chaser, fleeing, immovable} | messenger/envs/stage_three.py | _get_variant | ahjwang/messenger-emma | 13 | python | def _get_variant(self, variant_file: Path) -> GameVariant:
'\n Return the GameVariant for the variant specified by variant_file. \n Searches through the vgdl code to find the correct type:\n {chaser, fleeing, immovable}\n '
code = variant_file.read_text()
return GameVariant(path=str(variant_file), enemy_type=re.search('enemy > (\\S+)', code)[1].lower(), message_type=re.search('message > (\\S+)', code)[1].lower(), goal_type=re.search('goal > (\\S+)', code)[1].lower(), decoy_message_type=re.search('decoy_message > (\\S+)', code)[1].lower(), decoy_goal_type=re.search('decoy_goal > (\\S+)', code)[1].lower()) | def _get_variant(self, variant_file: Path) -> GameVariant:
'\n Return the GameVariant for the variant specified by variant_file. \n Searches through the vgdl code to find the correct type:\n {chaser, fleeing, immovable}\n '
code = variant_file.read_text()
return GameVariant(path=str(variant_file), enemy_type=re.search('enemy > (\\S+)', code)[1].lower(), message_type=re.search('message > (\\S+)', code)[1].lower(), goal_type=re.search('goal > (\\S+)', code)[1].lower(), decoy_message_type=re.search('decoy_message > (\\S+)', code)[1].lower(), decoy_goal_type=re.search('decoy_goal > (\\S+)', code)[1].lower())<|docstring|>Return the GameVariant for the variant specified by variant_file.
Searches through the vgdl code to find the correct type:
{chaser, fleeing, immovable}<|endoftext|> |
bcd8aa56c28593309fd3e79af98f4b349fbd2bb2f01f8bf429edb3c2c3b71e82 | def _convert_obs(self, vgdl_obs):
'\n Return a grid built from the vgdl observation which is a\n KeyValueObservation object (see vgdl code for details).\n '
entity_locs = Grid(layers=5, shuffle=self.shuffle_obs)
avatar_locs = Grid(layers=1)
if ('enemy.1' in vgdl_obs):
entity_locs.add(self.game.enemy, Position(*vgdl_obs['enemy.1']['position']))
if ('message.1' in vgdl_obs):
entity_locs.add(self.game.message, Position(*vgdl_obs['message.1']['position']))
else:
entity_locs.entity_count += 1
if ('goal.1' in vgdl_obs):
entity_locs.add(self.game.goal, Position(*vgdl_obs['goal.1']['position']))
if ('decoy_message.1' in vgdl_obs):
entity_locs.add(self.game.message, Position(*vgdl_obs['decoy_message.1']['position']))
if ('decoy_goal.1' in vgdl_obs):
entity_locs.add(self.game.goal, Position(*vgdl_obs['decoy_goal.1']['position']))
if ('no_message.1' in vgdl_obs):
'\n Due to a quirk in VGDL, the avatar is no_message if it starts as no_message\n even if the avatar may have acquired the message at a later point.\n To check, if it has a message, check that the class vector corresponding to\n with_message is == 1.\n '
avatar_pos = Position(*vgdl_obs['no_message.1']['position'])
if (vgdl_obs['no_message.1']['class'][(- 1)] == 1):
avatar = config.WITH_MESSAGE
else:
avatar = config.NO_MESSAGE
elif ('with_message.1' in vgdl_obs):
avatar_pos = Position(*vgdl_obs['with_message.1']['position'])
avatar = config.WITH_MESSAGE
else:
return {'entities': entity_locs.grid, 'avatar': avatar_locs.grid}
avatar_locs.add(avatar, avatar_pos)
return {'entities': entity_locs.grid, 'avatar': avatar_locs.grid} | Return a grid built from the vgdl observation which is a
KeyValueObservation object (see vgdl code for details). | messenger/envs/stage_three.py | _convert_obs | ahjwang/messenger-emma | 13 | python | def _convert_obs(self, vgdl_obs):
'\n Return a grid built from the vgdl observation which is a\n KeyValueObservation object (see vgdl code for details).\n '
entity_locs = Grid(layers=5, shuffle=self.shuffle_obs)
avatar_locs = Grid(layers=1)
if ('enemy.1' in vgdl_obs):
entity_locs.add(self.game.enemy, Position(*vgdl_obs['enemy.1']['position']))
if ('message.1' in vgdl_obs):
entity_locs.add(self.game.message, Position(*vgdl_obs['message.1']['position']))
else:
entity_locs.entity_count += 1
if ('goal.1' in vgdl_obs):
entity_locs.add(self.game.goal, Position(*vgdl_obs['goal.1']['position']))
if ('decoy_message.1' in vgdl_obs):
entity_locs.add(self.game.message, Position(*vgdl_obs['decoy_message.1']['position']))
if ('decoy_goal.1' in vgdl_obs):
entity_locs.add(self.game.goal, Position(*vgdl_obs['decoy_goal.1']['position']))
if ('no_message.1' in vgdl_obs):
'\n Due to a quirk in VGDL, the avatar is no_message if it starts as no_message\n even if the avatar may have acquired the message at a later point.\n To check, if it has a message, check that the class vector corresponding to\n with_message is == 1.\n '
avatar_pos = Position(*vgdl_obs['no_message.1']['position'])
if (vgdl_obs['no_message.1']['class'][(- 1)] == 1):
avatar = config.WITH_MESSAGE
else:
avatar = config.NO_MESSAGE
elif ('with_message.1' in vgdl_obs):
avatar_pos = Position(*vgdl_obs['with_message.1']['position'])
avatar = config.WITH_MESSAGE
else:
return {'entities': entity_locs.grid, 'avatar': avatar_locs.grid}
avatar_locs.add(avatar, avatar_pos)
return {'entities': entity_locs.grid, 'avatar': avatar_locs.grid} | def _convert_obs(self, vgdl_obs):
'\n Return a grid built from the vgdl observation which is a\n KeyValueObservation object (see vgdl code for details).\n '
entity_locs = Grid(layers=5, shuffle=self.shuffle_obs)
avatar_locs = Grid(layers=1)
if ('enemy.1' in vgdl_obs):
entity_locs.add(self.game.enemy, Position(*vgdl_obs['enemy.1']['position']))
if ('message.1' in vgdl_obs):
entity_locs.add(self.game.message, Position(*vgdl_obs['message.1']['position']))
else:
entity_locs.entity_count += 1
if ('goal.1' in vgdl_obs):
entity_locs.add(self.game.goal, Position(*vgdl_obs['goal.1']['position']))
if ('decoy_message.1' in vgdl_obs):
entity_locs.add(self.game.message, Position(*vgdl_obs['decoy_message.1']['position']))
if ('decoy_goal.1' in vgdl_obs):
entity_locs.add(self.game.goal, Position(*vgdl_obs['decoy_goal.1']['position']))
if ('no_message.1' in vgdl_obs):
'\n Due to a quirk in VGDL, the avatar is no_message if it starts as no_message\n even if the avatar may have acquired the message at a later point.\n To check, if it has a message, check that the class vector corresponding to\n with_message is == 1.\n '
avatar_pos = Position(*vgdl_obs['no_message.1']['position'])
if (vgdl_obs['no_message.1']['class'][(- 1)] == 1):
avatar = config.WITH_MESSAGE
else:
avatar = config.NO_MESSAGE
elif ('with_message.1' in vgdl_obs):
avatar_pos = Position(*vgdl_obs['with_message.1']['position'])
avatar = config.WITH_MESSAGE
else:
return {'entities': entity_locs.grid, 'avatar': avatar_locs.grid}
avatar_locs.add(avatar, avatar_pos)
return {'entities': entity_locs.grid, 'avatar': avatar_locs.grid}<|docstring|>Return a grid built from the vgdl observation which is a
KeyValueObservation object (see vgdl code for details).<|endoftext|> |
2611c9ee2cac77f406386dc594007f31f6335ed6694c8628b402fa13f288c76d | def reset(self, variant_id: int=None, **kwargs):
'\n Resets the current environment. NOTE: We remake the environment each time.\n This is a workaround to a bug in py-vgdl, where env.reset() does not\n properly reset the environment. kwargs go to get_document().\n '
self.game = random.choice(self.all_games)
if (variant_id is not None):
variant = self.game_variants[variant_id]
else:
variant = random.choice(self.game_variants)
init_state = random.choice(self.init_states)
self._envargs = {'game_file': variant.path, 'level_file': init_state, 'notable_sprites': self.notable_sprites.copy(), 'obs_type': 'objects', 'block_size': 34}
self.env = VGDLEnv(**self._envargs)
vgdl_obs = self.env.reset()
all_npcs = (Descr(entity=self.game.enemy.name, role='enemy', type=variant.enemy_type), Descr(entity=self.game.message.name, role='message', type=variant.message_type), Descr(entity=self.game.goal.name, role='goal', type=variant.goal_type), Descr(entity=self.game.message.name, role='enemy', type=variant.decoy_message_type), Descr(entity=self.game.goal.name, role='enemy', type=variant.decoy_goal_type))
manual = self.text_manual.get_document_plus(*all_npcs, **kwargs)
manual.append(self.text_manual.get_decoy_descriptor(entity=self.game.enemy.name, not_of_role='enemy', not_of_type=variant.enemy_type))
if self.shuffle_obs:
random.shuffle(manual)
return (self._convert_obs(vgdl_obs), manual) | Resets the current environment. NOTE: We remake the environment each time.
This is a workaround to a bug in py-vgdl, where env.reset() does not
properly reset the environment. kwargs go to get_document(). | messenger/envs/stage_three.py | reset | ahjwang/messenger-emma | 13 | python | def reset(self, variant_id: int=None, **kwargs):
'\n Resets the current environment. NOTE: We remake the environment each time.\n This is a workaround to a bug in py-vgdl, where env.reset() does not\n properly reset the environment. kwargs go to get_document().\n '
self.game = random.choice(self.all_games)
if (variant_id is not None):
variant = self.game_variants[variant_id]
else:
variant = random.choice(self.game_variants)
init_state = random.choice(self.init_states)
self._envargs = {'game_file': variant.path, 'level_file': init_state, 'notable_sprites': self.notable_sprites.copy(), 'obs_type': 'objects', 'block_size': 34}
self.env = VGDLEnv(**self._envargs)
vgdl_obs = self.env.reset()
all_npcs = (Descr(entity=self.game.enemy.name, role='enemy', type=variant.enemy_type), Descr(entity=self.game.message.name, role='message', type=variant.message_type), Descr(entity=self.game.goal.name, role='goal', type=variant.goal_type), Descr(entity=self.game.message.name, role='enemy', type=variant.decoy_message_type), Descr(entity=self.game.goal.name, role='enemy', type=variant.decoy_goal_type))
manual = self.text_manual.get_document_plus(*all_npcs, **kwargs)
manual.append(self.text_manual.get_decoy_descriptor(entity=self.game.enemy.name, not_of_role='enemy', not_of_type=variant.enemy_type))
if self.shuffle_obs:
random.shuffle(manual)
return (self._convert_obs(vgdl_obs), manual) | def reset(self, variant_id: int=None, **kwargs):
'\n Resets the current environment. NOTE: We remake the environment each time.\n This is a workaround to a bug in py-vgdl, where env.reset() does not\n properly reset the environment. kwargs go to get_document().\n '
self.game = random.choice(self.all_games)
if (variant_id is not None):
variant = self.game_variants[variant_id]
else:
variant = random.choice(self.game_variants)
init_state = random.choice(self.init_states)
self._envargs = {'game_file': variant.path, 'level_file': init_state, 'notable_sprites': self.notable_sprites.copy(), 'obs_type': 'objects', 'block_size': 34}
self.env = VGDLEnv(**self._envargs)
vgdl_obs = self.env.reset()
all_npcs = (Descr(entity=self.game.enemy.name, role='enemy', type=variant.enemy_type), Descr(entity=self.game.message.name, role='message', type=variant.message_type), Descr(entity=self.game.goal.name, role='goal', type=variant.goal_type), Descr(entity=self.game.message.name, role='enemy', type=variant.decoy_message_type), Descr(entity=self.game.goal.name, role='enemy', type=variant.decoy_goal_type))
manual = self.text_manual.get_document_plus(*all_npcs, **kwargs)
manual.append(self.text_manual.get_decoy_descriptor(entity=self.game.enemy.name, not_of_role='enemy', not_of_type=variant.enemy_type))
if self.shuffle_obs:
random.shuffle(manual)
return (self._convert_obs(vgdl_obs), manual)<|docstring|>Resets the current environment. NOTE: We remake the environment each time.
This is a workaround to a bug in py-vgdl, where env.reset() does not
properly reset the environment. kwargs go to get_document().<|endoftext|> |
dba59fe509f157b7e00afc511640d6b1eda635d4b60d0f52e7d99c68793bfa5e | def convertBST(self, root):
'\n # 先使用递归方法实现\n Solution._convert(root, [0]) # 使用list传引用简化代码\n return root\n '
if root:
sum_ = 0
stack = []
p = root
while (p or (len(stack) > 0)):
while p:
stack.append(p)
p = p.right
p = stack[(- 1)]
stack.pop()
p.val += sum_
sum_ = p.val
p = p.left
return root | # 先使用递归方法实现
Solution._convert(root, [0]) # 使用list传引用简化代码
return root | convert_bst_to_greater_tree.py | convertBST | Jwy-jump/python_codesets | 0 | python | def convertBST(self, root):
'\n # 先使用递归方法实现\n Solution._convert(root, [0]) # 使用list传引用简化代码\n return root\n '
if root:
sum_ = 0
stack = []
p = root
while (p or (len(stack) > 0)):
while p:
stack.append(p)
p = p.right
p = stack[(- 1)]
stack.pop()
p.val += sum_
sum_ = p.val
p = p.left
return root | def convertBST(self, root):
'\n # 先使用递归方法实现\n Solution._convert(root, [0]) # 使用list传引用简化代码\n return root\n '
if root:
sum_ = 0
stack = []
p = root
while (p or (len(stack) > 0)):
while p:
stack.append(p)
p = p.right
p = stack[(- 1)]
stack.pop()
p.val += sum_
sum_ = p.val
p = p.left
return root<|docstring|># 先使用递归方法实现
Solution._convert(root, [0]) # 使用list传引用简化代码
return root<|endoftext|> |
75af1159c07020b95b94a37e75991e7796f6862b32d57a2565fccc0a8e90215c | def __init__(self, msg, app_label=None, detailed_error=None, last_sql_statement=None):
"Initialize the error.\n\n Args:\n msg (unicode):\n The error message.\n\n app_label (unicode, optional):\n The label of the app that failed evolution.\n\n detailed_error (unicode, optional):\n Detailed error information from the failure that triggered this\n exception. This might be another exception's error message.\n\n last_sql_statement (unicode, optional):\n The last SQL statement that was executed.\n "
super(EvolutionExecutionError, self).__init__(msg)
self.app_label = app_label
self.detailed_error = detailed_error
self.last_sql_statement = last_sql_statement | Initialize the error.
Args:
msg (unicode):
The error message.
app_label (unicode, optional):
The label of the app that failed evolution.
detailed_error (unicode, optional):
Detailed error information from the failure that triggered this
exception. This might be another exception's error message.
last_sql_statement (unicode, optional):
The last SQL statement that was executed. | django_evolution/errors.py | __init__ | beanbaginc/django-evolution | 18 | python | def __init__(self, msg, app_label=None, detailed_error=None, last_sql_statement=None):
"Initialize the error.\n\n Args:\n msg (unicode):\n The error message.\n\n app_label (unicode, optional):\n The label of the app that failed evolution.\n\n detailed_error (unicode, optional):\n Detailed error information from the failure that triggered this\n exception. This might be another exception's error message.\n\n last_sql_statement (unicode, optional):\n The last SQL statement that was executed.\n "
super(EvolutionExecutionError, self).__init__(msg)
self.app_label = app_label
self.detailed_error = detailed_error
self.last_sql_statement = last_sql_statement | def __init__(self, msg, app_label=None, detailed_error=None, last_sql_statement=None):
"Initialize the error.\n\n Args:\n msg (unicode):\n The error message.\n\n app_label (unicode, optional):\n The label of the app that failed evolution.\n\n detailed_error (unicode, optional):\n Detailed error information from the failure that triggered this\n exception. This might be another exception's error message.\n\n last_sql_statement (unicode, optional):\n The last SQL statement that was executed.\n "
super(EvolutionExecutionError, self).__init__(msg)
self.app_label = app_label
self.detailed_error = detailed_error
self.last_sql_statement = last_sql_statement<|docstring|>Initialize the error.
Args:
msg (unicode):
The error message.
app_label (unicode, optional):
The label of the app that failed evolution.
detailed_error (unicode, optional):
Detailed error information from the failure that triggered this
exception. This might be another exception's error message.
last_sql_statement (unicode, optional):
The last SQL statement that was executed.<|endoftext|> |
ecbf04d19ac41a0b7aa6ceef26881c5dbca9c1033653981b7b744c614dc788ef | def __init__(self, version):
'Initialize the exception.\n\n Args:\n version (int):\n The invalid signature version.\n '
super(InvalidSignatureVersion, self).__init__(('%s is not a known signature version' % version)) | Initialize the exception.
Args:
version (int):
The invalid signature version. | django_evolution/errors.py | __init__ | beanbaginc/django-evolution | 18 | python | def __init__(self, version):
'Initialize the exception.\n\n Args:\n version (int):\n The invalid signature version.\n '
super(InvalidSignatureVersion, self).__init__(('%s is not a known signature version' % version)) | def __init__(self, version):
'Initialize the exception.\n\n Args:\n version (int):\n The invalid signature version.\n '
super(InvalidSignatureVersion, self).__init__(('%s is not a known signature version' % version))<|docstring|>Initialize the exception.
Args:
version (int):
The invalid signature version.<|endoftext|> |
6f670a8c06612b5de7a6a08446765dfa8a3243d45815e640ea40318179a7d7a4 | def __init__(self, conflicts):
'Initialize the error.\n\n Args:\n conflicts (dict):\n A dictionary of conflicts, provided by the migrations system.\n '
super(MigrationConflictsError, self).__init__(("Conflicting migrations detected; multiple leaf nodes in the migration graph: (%s).\nTo fix them run 'python manage.py makemigrations --merge'" % '; '.join((('%s in %s' % (', '.join(sorted(conflict_names)), app_label)) for (app_label, conflict_names) in six.iteritems(conflicts))))) | Initialize the error.
Args:
conflicts (dict):
A dictionary of conflicts, provided by the migrations system. | django_evolution/errors.py | __init__ | beanbaginc/django-evolution | 18 | python | def __init__(self, conflicts):
'Initialize the error.\n\n Args:\n conflicts (dict):\n A dictionary of conflicts, provided by the migrations system.\n '
super(MigrationConflictsError, self).__init__(("Conflicting migrations detected; multiple leaf nodes in the migration graph: (%s).\nTo fix them run 'python manage.py makemigrations --merge'" % '; '.join((('%s in %s' % (', '.join(sorted(conflict_names)), app_label)) for (app_label, conflict_names) in six.iteritems(conflicts))))) | def __init__(self, conflicts):
'Initialize the error.\n\n Args:\n conflicts (dict):\n A dictionary of conflicts, provided by the migrations system.\n '
super(MigrationConflictsError, self).__init__(("Conflicting migrations detected; multiple leaf nodes in the migration graph: (%s).\nTo fix them run 'python manage.py makemigrations --merge'" % '; '.join((('%s in %s' % (', '.join(sorted(conflict_names)), app_label)) for (app_label, conflict_names) in six.iteritems(conflicts)))))<|docstring|>Initialize the error.
Args:
conflicts (dict):
A dictionary of conflicts, provided by the migrations system.<|endoftext|> |
93aea98df7ca27b4a4b7deea727ce9ec96f517ea7c999f48c4b13808d5c7ad7e | def __init__(self, channel):
'Constructor.\n\n Args:\n channel: A grpc.Channel.\n '
self.OpenPassWindow = channel.unary_unary('/base.FunctionalService/OpenPassWindow', request_serializer=common__pb2.ClientId.SerializeToString, response_deserializer=common__pb2.Empty.FromString)
self.ClosePassWindow = channel.unary_unary('/base.FunctionalService/ClosePassWindow', request_serializer=common__pb2.ClientId.SerializeToString, response_deserializer=common__pb2.Empty.FromString)
self.SetFanSpeed = channel.unary_unary('/base.FunctionalService/SetFanSpeed', request_serializer=functional__api__pb2.SenderInfo.SerializeToString, response_deserializer=common__pb2.Empty.FromString)
self.SubscribeToFanSpeed = channel.unary_stream('/base.FunctionalService/SubscribeToFanSpeed', request_serializer=functional__api__pb2.SubscriberRequest.SerializeToString, response_deserializer=functional__api__pb2.Value.FromString) | Constructor.
Args:
channel: A grpc.Channel. | examples/grpc/python/generated/functional_api_pb2_grpc.py | __init__ | niclaslind/signalbroker-server | 17 | python | def __init__(self, channel):
'Constructor.\n\n Args:\n channel: A grpc.Channel.\n '
self.OpenPassWindow = channel.unary_unary('/base.FunctionalService/OpenPassWindow', request_serializer=common__pb2.ClientId.SerializeToString, response_deserializer=common__pb2.Empty.FromString)
self.ClosePassWindow = channel.unary_unary('/base.FunctionalService/ClosePassWindow', request_serializer=common__pb2.ClientId.SerializeToString, response_deserializer=common__pb2.Empty.FromString)
self.SetFanSpeed = channel.unary_unary('/base.FunctionalService/SetFanSpeed', request_serializer=functional__api__pb2.SenderInfo.SerializeToString, response_deserializer=common__pb2.Empty.FromString)
self.SubscribeToFanSpeed = channel.unary_stream('/base.FunctionalService/SubscribeToFanSpeed', request_serializer=functional__api__pb2.SubscriberRequest.SerializeToString, response_deserializer=functional__api__pb2.Value.FromString) | def __init__(self, channel):
'Constructor.\n\n Args:\n channel: A grpc.Channel.\n '
self.OpenPassWindow = channel.unary_unary('/base.FunctionalService/OpenPassWindow', request_serializer=common__pb2.ClientId.SerializeToString, response_deserializer=common__pb2.Empty.FromString)
self.ClosePassWindow = channel.unary_unary('/base.FunctionalService/ClosePassWindow', request_serializer=common__pb2.ClientId.SerializeToString, response_deserializer=common__pb2.Empty.FromString)
self.SetFanSpeed = channel.unary_unary('/base.FunctionalService/SetFanSpeed', request_serializer=functional__api__pb2.SenderInfo.SerializeToString, response_deserializer=common__pb2.Empty.FromString)
self.SubscribeToFanSpeed = channel.unary_stream('/base.FunctionalService/SubscribeToFanSpeed', request_serializer=functional__api__pb2.SubscriberRequest.SerializeToString, response_deserializer=functional__api__pb2.Value.FromString)<|docstring|>Constructor.
Args:
channel: A grpc.Channel.<|endoftext|> |
5ef4075ca4c6c8e614144d827787504bf691b52e21b717a75f41d8aa1e354976 | @staticmethod
def create_output(df, colname):
'\n this function will calculate the aggregated e and m\n given colname we would like to aggregate over\n '
return df[[colname, 'e']].groupby([colname]).sum().merge(df[[colname, 'm']].groupby([colname]).agg(AggregatedGeography.agg_moe), on=colname).reset_index().rename(columns={colname: 'census_geoid'}) | this function will calculate the aggregated e and m
given colname we would like to aggregate over | factfinder/geography/2010.py | create_output | EricaMaurer/db-factfinder | 0 | python | @staticmethod
def create_output(df, colname):
'\n this function will calculate the aggregated e and m\n given colname we would like to aggregate over\n '
return df[[colname, 'e']].groupby([colname]).sum().merge(df[[colname, 'm']].groupby([colname]).agg(AggregatedGeography.agg_moe), on=colname).reset_index().rename(columns={colname: 'census_geoid'}) | @staticmethod
def create_output(df, colname):
'\n this function will calculate the aggregated e and m\n given colname we would like to aggregate over\n '
return df[[colname, 'e']].groupby([colname]).sum().merge(df[[colname, 'm']].groupby([colname]).agg(AggregatedGeography.agg_moe), on=colname).reset_index().rename(columns={colname: 'census_geoid'})<|docstring|>this function will calculate the aggregated e and m
given colname we would like to aggregate over<|endoftext|> |
5b32110cf17d07c7895a0bf24290411bd4f870f36d82bdf596cefff3dc0565d2 | def block_group_to_cd_fp500(self, df):
'\n 500 yr flood plain aggregation for block group data (ACS)\n '
df = df.merge(self.lookup_geo.loc[((~ self.lookup_geo.cd_fp_500.isna()), ['geoid_block_group', 'cd_fp_500'])].drop_duplicates(), how='right', right_on='geoid_block_group', left_on='census_geoid')
output = AggregatedGeography.create_output(df, 'cd_fp_500')
output['pff_variable'] = df['pff_variable'].to_list()[0]
output['geotype'] = 'cd_fp_500'
return output[['census_geoid', 'pff_variable', 'geotype', 'e', 'm']] | 500 yr flood plain aggregation for block group data (ACS) | factfinder/geography/2010.py | block_group_to_cd_fp500 | EricaMaurer/db-factfinder | 0 | python | def block_group_to_cd_fp500(self, df):
'\n \n '
df = df.merge(self.lookup_geo.loc[((~ self.lookup_geo.cd_fp_500.isna()), ['geoid_block_group', 'cd_fp_500'])].drop_duplicates(), how='right', right_on='geoid_block_group', left_on='census_geoid')
output = AggregatedGeography.create_output(df, 'cd_fp_500')
output['pff_variable'] = df['pff_variable'].to_list()[0]
output['geotype'] = 'cd_fp_500'
return output[['census_geoid', 'pff_variable', 'geotype', 'e', 'm']] | def block_group_to_cd_fp500(self, df):
'\n \n '
df = df.merge(self.lookup_geo.loc[((~ self.lookup_geo.cd_fp_500.isna()), ['geoid_block_group', 'cd_fp_500'])].drop_duplicates(), how='right', right_on='geoid_block_group', left_on='census_geoid')
output = AggregatedGeography.create_output(df, 'cd_fp_500')
output['pff_variable'] = df['pff_variable'].to_list()[0]
output['geotype'] = 'cd_fp_500'
return output[['census_geoid', 'pff_variable', 'geotype', 'e', 'm']]<|docstring|>500 yr flood plain aggregation for block group data (ACS)<|endoftext|> |
96ef224971d892232a17b5410e7c2d0d11673faddd1a357644149cbc50053fbf | def block_group_to_cd_fp100(self, df):
'\n 100 yr flood plain aggregation for block group data (ACS)\n '
df = df.merge(self.lookup_geo.loc[((~ self.lookup_geo.cd_fp_100.isna()), ['geoid_block_group', 'cd_fp_100'])].drop_duplicates(), how='right', right_on='geoid_block_group', left_on='census_geoid')
output = AggregatedGeography.create_output(df, 'cd_fp_100')
output['pff_variable'] = df['pff_variable'].to_list()[0]
output['geotype'] = 'cd_fp_100'
return output[['census_geoid', 'pff_variable', 'geotype', 'e', 'm']] | 100 yr flood plain aggregation for block group data (ACS) | factfinder/geography/2010.py | block_group_to_cd_fp100 | EricaMaurer/db-factfinder | 0 | python | def block_group_to_cd_fp100(self, df):
'\n \n '
df = df.merge(self.lookup_geo.loc[((~ self.lookup_geo.cd_fp_100.isna()), ['geoid_block_group', 'cd_fp_100'])].drop_duplicates(), how='right', right_on='geoid_block_group', left_on='census_geoid')
output = AggregatedGeography.create_output(df, 'cd_fp_100')
output['pff_variable'] = df['pff_variable'].to_list()[0]
output['geotype'] = 'cd_fp_100'
return output[['census_geoid', 'pff_variable', 'geotype', 'e', 'm']] | def block_group_to_cd_fp100(self, df):
'\n \n '
df = df.merge(self.lookup_geo.loc[((~ self.lookup_geo.cd_fp_100.isna()), ['geoid_block_group', 'cd_fp_100'])].drop_duplicates(), how='right', right_on='geoid_block_group', left_on='census_geoid')
output = AggregatedGeography.create_output(df, 'cd_fp_100')
output['pff_variable'] = df['pff_variable'].to_list()[0]
output['geotype'] = 'cd_fp_100'
return output[['census_geoid', 'pff_variable', 'geotype', 'e', 'm']]<|docstring|>100 yr flood plain aggregation for block group data (ACS)<|endoftext|> |
0ad69b23d9b4c230a6de3f531ce5236d36e58e46466e9b1197b74895a172b09d | def block_group_to_cd_park_access(self, df):
'\n walk-to-park access zone aggregation for block group data (acs)\n '
df = df.merge(self.lookup_geo.loc[((~ self.lookup_geo.cd_park_access.isna()), ['geoid_block_group', 'cd_park_access'])].drop_duplicates(), how='right', right_on='geoid_block_group', left_on='census_geoid')
output = AggregatedGeography.create_output(df, 'cd_park_access')
output['pff_variable'] = df['pff_variable'].to_list()[0]
output['geotype'] = 'cd_park_access'
return output[['census_geoid', 'pff_variable', 'geotype', 'e', 'm']] | walk-to-park access zone aggregation for block group data (acs) | factfinder/geography/2010.py | block_group_to_cd_park_access | EricaMaurer/db-factfinder | 0 | python | def block_group_to_cd_park_access(self, df):
'\n \n '
df = df.merge(self.lookup_geo.loc[((~ self.lookup_geo.cd_park_access.isna()), ['geoid_block_group', 'cd_park_access'])].drop_duplicates(), how='right', right_on='geoid_block_group', left_on='census_geoid')
output = AggregatedGeography.create_output(df, 'cd_park_access')
output['pff_variable'] = df['pff_variable'].to_list()[0]
output['geotype'] = 'cd_park_access'
return output[['census_geoid', 'pff_variable', 'geotype', 'e', 'm']] | def block_group_to_cd_park_access(self, df):
'\n \n '
df = df.merge(self.lookup_geo.loc[((~ self.lookup_geo.cd_park_access.isna()), ['geoid_block_group', 'cd_park_access'])].drop_duplicates(), how='right', right_on='geoid_block_group', left_on='census_geoid')
output = AggregatedGeography.create_output(df, 'cd_park_access')
output['pff_variable'] = df['pff_variable'].to_list()[0]
output['geotype'] = 'cd_park_access'
return output[['census_geoid', 'pff_variable', 'geotype', 'e', 'm']]<|docstring|>walk-to-park access zone aggregation for block group data (acs)<|endoftext|> |
2626541de7c67f14127c18e0858e5a27c27ffba1189310b96e1fcafc1ea2f950 | def block_to_cd_fp500(self, df):
'\n 500 yr flood plain aggregation for block data (decennial)\n '
df = df.merge(self.lookup_geo.loc[((~ self.lookup_geo.cd_fp_500.isna()), ['geoid_block', 'cd_fp_500'])].drop_duplicates(), how='right', right_on='geoid_block', left_on='census_geoid')
output = AggregatedGeography.create_output(df, 'cd_fp_500')
output['pff_variable'] = df['pff_variable'].to_list()[0]
output['geotype'] = 'cd_fp_500'
return output[['census_geoid', 'pff_variable', 'geotype', 'e', 'm']] | 500 yr flood plain aggregation for block data (decennial) | factfinder/geography/2010.py | block_to_cd_fp500 | EricaMaurer/db-factfinder | 0 | python | def block_to_cd_fp500(self, df):
'\n \n '
df = df.merge(self.lookup_geo.loc[((~ self.lookup_geo.cd_fp_500.isna()), ['geoid_block', 'cd_fp_500'])].drop_duplicates(), how='right', right_on='geoid_block', left_on='census_geoid')
output = AggregatedGeography.create_output(df, 'cd_fp_500')
output['pff_variable'] = df['pff_variable'].to_list()[0]
output['geotype'] = 'cd_fp_500'
return output[['census_geoid', 'pff_variable', 'geotype', 'e', 'm']] | def block_to_cd_fp500(self, df):
'\n \n '
df = df.merge(self.lookup_geo.loc[((~ self.lookup_geo.cd_fp_500.isna()), ['geoid_block', 'cd_fp_500'])].drop_duplicates(), how='right', right_on='geoid_block', left_on='census_geoid')
output = AggregatedGeography.create_output(df, 'cd_fp_500')
output['pff_variable'] = df['pff_variable'].to_list()[0]
output['geotype'] = 'cd_fp_500'
return output[['census_geoid', 'pff_variable', 'geotype', 'e', 'm']]<|docstring|>500 yr flood plain aggregation for block data (decennial)<|endoftext|> |
5039c7b63075f9f1b7c527ac19dfc9a2e0999b49ac39f312da99d9006fa029e2 | def block_to_cd_fp100(self, df):
'\n 100 yr flood plain aggregation for block data (decennial)\n '
df = df.merge(self.lookup_geo.loc[((~ self.lookup_geo.cd_fp_100.isna()), ['geoid_block', 'cd_fp_100'])].drop_duplicates(), how='right', right_on='geoid_block', left_on='census_geoid')
output = AggregatedGeography.create_output(df, 'cd_fp_100')
output['pff_variable'] = df['pff_variable'].to_list()[0]
output['geotype'] = 'cd_fp_100'
return output[['census_geoid', 'pff_variable', 'geotype', 'e', 'm']] | 100 yr flood plain aggregation for block data (decennial) | factfinder/geography/2010.py | block_to_cd_fp100 | EricaMaurer/db-factfinder | 0 | python | def block_to_cd_fp100(self, df):
'\n \n '
df = df.merge(self.lookup_geo.loc[((~ self.lookup_geo.cd_fp_100.isna()), ['geoid_block', 'cd_fp_100'])].drop_duplicates(), how='right', right_on='geoid_block', left_on='census_geoid')
output = AggregatedGeography.create_output(df, 'cd_fp_100')
output['pff_variable'] = df['pff_variable'].to_list()[0]
output['geotype'] = 'cd_fp_100'
return output[['census_geoid', 'pff_variable', 'geotype', 'e', 'm']] | def block_to_cd_fp100(self, df):
'\n \n '
df = df.merge(self.lookup_geo.loc[((~ self.lookup_geo.cd_fp_100.isna()), ['geoid_block', 'cd_fp_100'])].drop_duplicates(), how='right', right_on='geoid_block', left_on='census_geoid')
output = AggregatedGeography.create_output(df, 'cd_fp_100')
output['pff_variable'] = df['pff_variable'].to_list()[0]
output['geotype'] = 'cd_fp_100'
return output[['census_geoid', 'pff_variable', 'geotype', 'e', 'm']]<|docstring|>100 yr flood plain aggregation for block data (decennial)<|endoftext|> |
17c7234e66de2254dad7772211402560a1a9fccb10f6e52b2330065053b3242d | def block_to_cd_park_access(self, df):
'\n walk-to-park access zone aggregation for block data (decennial)\n '
df = df.merge(self.lookup_geo.loc[((~ self.lookup_geo.cd_park_access.isna()), ['geoid_block', 'cd_park_access'])].drop_duplicates(), how='right', right_on='geoid_block', left_on='census_geoid')
output = AggregatedGeography.create_output(df, 'cd_park_access')
output['pff_variable'] = df['pff_variable'].to_list()[0]
output['geotype'] = 'cd_park_access'
return output[['census_geoid', 'pff_variable', 'geotype', 'e', 'm']] | walk-to-park access zone aggregation for block data (decennial) | factfinder/geography/2010.py | block_to_cd_park_access | EricaMaurer/db-factfinder | 0 | python | def block_to_cd_park_access(self, df):
'\n \n '
df = df.merge(self.lookup_geo.loc[((~ self.lookup_geo.cd_park_access.isna()), ['geoid_block', 'cd_park_access'])].drop_duplicates(), how='right', right_on='geoid_block', left_on='census_geoid')
output = AggregatedGeography.create_output(df, 'cd_park_access')
output['pff_variable'] = df['pff_variable'].to_list()[0]
output['geotype'] = 'cd_park_access'
return output[['census_geoid', 'pff_variable', 'geotype', 'e', 'm']] | def block_to_cd_park_access(self, df):
'\n \n '
df = df.merge(self.lookup_geo.loc[((~ self.lookup_geo.cd_park_access.isna()), ['geoid_block', 'cd_park_access'])].drop_duplicates(), how='right', right_on='geoid_block', left_on='census_geoid')
output = AggregatedGeography.create_output(df, 'cd_park_access')
output['pff_variable'] = df['pff_variable'].to_list()[0]
output['geotype'] = 'cd_park_access'
return output[['census_geoid', 'pff_variable', 'geotype', 'e', 'm']]<|docstring|>walk-to-park access zone aggregation for block data (decennial)<|endoftext|> |
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