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c3da0d513beccc92fd3ce76d2ee9c3b5d6b8ad7c731b87bc8dc1cae3372b0921 | def get_event_count(event_times, start, end):
'\n Count of events in given interval.\n\n :param event_times: nd-array of event times\n :param start: interval start\n :param end: interval end\n :return: count of events in interval\n '
mask = ((event_times > start) & (event_times <= end))
return event_times[mask].size | Count of events in given interval.
:param event_times: nd-array of event times
:param start: interval start
:param end: interval end
:return: count of events in interval | tideh/functions.py | get_event_count | sebaruehl/TiDeH | 0 | python | def get_event_count(event_times, start, end):
'\n Count of events in given interval.\n\n :param event_times: nd-array of event times\n :param start: interval start\n :param end: interval end\n :return: count of events in interval\n '
mask = ((event_times > start) & (event_times <= end))
return event_times[mask].size | def get_event_count(event_times, start, end):
'\n Count of events in given interval.\n\n :param event_times: nd-array of event times\n :param start: interval start\n :param end: interval end\n :return: count of events in interval\n '
mask = ((event_times > start) & (event_times <= end))
return event_times[mask].size<|docstring|>Count of events in given interval.
:param event_times: nd-array of event times
:param start: interval start
:param end: interval end
:return: count of events in interval<|endoftext|> |
13cd046971e68a3a9a9963f25b7b9bf2c8cb1cf007c01d3e2334f182b5487cc2 | def prediction_error_absolute(event_times, intensity, window_size, obs_time, pred_time, dt):
'\n Calculates absolute prediction error.\n\n :param event_times: event times\n :param intensity: predicted intensity\n :param window_size: prediction window size\n :param obs_time: observation time\n :param pred_time: prediction time\n :param dt: interval width for numerical integral calculation used for intensity prediction\n :return: absolute prediction error\n '
events_time_pred = event_times[(event_times >= obs_time)]
win_int = int((window_size / dt))
tp = np.arange(obs_time, pred_time, window_size)
err = 0
for (i, t_cur) in enumerate(tp):
t_end = (t_cur + window_size)
if (t_end > pred_time):
break
count_current = get_event_count(events_time_pred, t_cur, t_end)
pred_count = (dt * intensity[(i * win_int):((i + 1) * win_int)].sum())
err += abs((count_current - pred_count))
return err | Calculates absolute prediction error.
:param event_times: event times
:param intensity: predicted intensity
:param window_size: prediction window size
:param obs_time: observation time
:param pred_time: prediction time
:param dt: interval width for numerical integral calculation used for intensity prediction
:return: absolute prediction error | tideh/functions.py | prediction_error_absolute | sebaruehl/TiDeH | 0 | python | def prediction_error_absolute(event_times, intensity, window_size, obs_time, pred_time, dt):
'\n Calculates absolute prediction error.\n\n :param event_times: event times\n :param intensity: predicted intensity\n :param window_size: prediction window size\n :param obs_time: observation time\n :param pred_time: prediction time\n :param dt: interval width for numerical integral calculation used for intensity prediction\n :return: absolute prediction error\n '
events_time_pred = event_times[(event_times >= obs_time)]
win_int = int((window_size / dt))
tp = np.arange(obs_time, pred_time, window_size)
err = 0
for (i, t_cur) in enumerate(tp):
t_end = (t_cur + window_size)
if (t_end > pred_time):
break
count_current = get_event_count(events_time_pred, t_cur, t_end)
pred_count = (dt * intensity[(i * win_int):((i + 1) * win_int)].sum())
err += abs((count_current - pred_count))
return err | def prediction_error_absolute(event_times, intensity, window_size, obs_time, pred_time, dt):
'\n Calculates absolute prediction error.\n\n :param event_times: event times\n :param intensity: predicted intensity\n :param window_size: prediction window size\n :param obs_time: observation time\n :param pred_time: prediction time\n :param dt: interval width for numerical integral calculation used for intensity prediction\n :return: absolute prediction error\n '
events_time_pred = event_times[(event_times >= obs_time)]
win_int = int((window_size / dt))
tp = np.arange(obs_time, pred_time, window_size)
err = 0
for (i, t_cur) in enumerate(tp):
t_end = (t_cur + window_size)
if (t_end > pred_time):
break
count_current = get_event_count(events_time_pred, t_cur, t_end)
pred_count = (dt * intensity[(i * win_int):((i + 1) * win_int)].sum())
err += abs((count_current - pred_count))
return err<|docstring|>Calculates absolute prediction error.
:param event_times: event times
:param intensity: predicted intensity
:param window_size: prediction window size
:param obs_time: observation time
:param pred_time: prediction time
:param dt: interval width for numerical integral calculation used for intensity prediction
:return: absolute prediction error<|endoftext|> |
f4468edefbc7fd59c20407acda23cf92644a5745b550672b21a8a621ebad4781 | def prediction_error_normed(event_times, intensity, window_size, obs_time, pred_time, dt):
'\n Calculates normed prediction error.\n\n :param event_times: event times\n :param intensity: predicted intensity\n :param window_size: prediction window size\n :param obs_time: observation time\n :param pred_time: prediction time\n :param dt: interval width for numerical integral calculation used for intensity prediction\n :return: normed prediction error\n '
err_abs = prediction_error_absolute(event_times, intensity, window_size, obs_time, pred_time, dt)
events_time_pred = event_times[(event_times >= obs_time)]
total_count_real = get_event_count(events_time_pred, obs_time, pred_time)
if ((total_count_real == 0) and (err_abs == 0)):
err_normed = 0
elif (total_count_real == 0):
err_normed = 10
else:
err_normed = (err_abs / total_count_real)
return err_normed | Calculates normed prediction error.
:param event_times: event times
:param intensity: predicted intensity
:param window_size: prediction window size
:param obs_time: observation time
:param pred_time: prediction time
:param dt: interval width for numerical integral calculation used for intensity prediction
:return: normed prediction error | tideh/functions.py | prediction_error_normed | sebaruehl/TiDeH | 0 | python | def prediction_error_normed(event_times, intensity, window_size, obs_time, pred_time, dt):
'\n Calculates normed prediction error.\n\n :param event_times: event times\n :param intensity: predicted intensity\n :param window_size: prediction window size\n :param obs_time: observation time\n :param pred_time: prediction time\n :param dt: interval width for numerical integral calculation used for intensity prediction\n :return: normed prediction error\n '
err_abs = prediction_error_absolute(event_times, intensity, window_size, obs_time, pred_time, dt)
events_time_pred = event_times[(event_times >= obs_time)]
total_count_real = get_event_count(events_time_pred, obs_time, pred_time)
if ((total_count_real == 0) and (err_abs == 0)):
err_normed = 0
elif (total_count_real == 0):
err_normed = 10
else:
err_normed = (err_abs / total_count_real)
return err_normed | def prediction_error_normed(event_times, intensity, window_size, obs_time, pred_time, dt):
'\n Calculates normed prediction error.\n\n :param event_times: event times\n :param intensity: predicted intensity\n :param window_size: prediction window size\n :param obs_time: observation time\n :param pred_time: prediction time\n :param dt: interval width for numerical integral calculation used for intensity prediction\n :return: normed prediction error\n '
err_abs = prediction_error_absolute(event_times, intensity, window_size, obs_time, pred_time, dt)
events_time_pred = event_times[(event_times >= obs_time)]
total_count_real = get_event_count(events_time_pred, obs_time, pred_time)
if ((total_count_real == 0) and (err_abs == 0)):
err_normed = 0
elif (total_count_real == 0):
err_normed = 10
else:
err_normed = (err_abs / total_count_real)
return err_normed<|docstring|>Calculates normed prediction error.
:param event_times: event times
:param intensity: predicted intensity
:param window_size: prediction window size
:param obs_time: observation time
:param pred_time: prediction time
:param dt: interval width for numerical integral calculation used for intensity prediction
:return: normed prediction error<|endoftext|> |
3e8b64dcc140a0c6310ff81c4de95f65a671c9c9fcd359cc2de3f7d08a70c921 | def prediction_error_relative(event_times, intensity, window_size, obs_time, pred_time, dt):
'\n Calculates median relative running error.\n\n :param event_times: event times\n :param intensity: predicted intensity\n :param window_size: prediction window size\n :param obs_time: observation time\n :param pred_time: prediction time\n :param dt: interval width for numerical integral calculation used for intensity prediction\n :return: normed prediction error\n '
events_time_pred = event_times[(event_times >= obs_time)]
events_in_obs_time = get_event_count(events_time_pred, 0, obs_time)
win_int = int((window_size / dt))
tp = np.arange(obs_time, pred_time, window_size)
cnt_total_real = events_in_obs_time
cnt_total_pred = events_in_obs_time
err_rel_running = []
for (i, t_cur) in enumerate(tp):
t_end = (t_cur + window_size)
if (t_end > pred_time):
break
count_current = get_event_count(events_time_pred, t_cur, t_end)
pred_count = (dt * intensity[(i * win_int):((i + 1) * win_int)].sum())
cnt_total_real += count_current
cnt_total_pred += pred_count
rel_tmp = (1 - (np.minimum(cnt_total_real, cnt_total_pred) / np.maximum(cnt_total_real, cnt_total_pred)))
err_rel_running.append(rel_tmp)
return np.median(err_rel_running) | Calculates median relative running error.
:param event_times: event times
:param intensity: predicted intensity
:param window_size: prediction window size
:param obs_time: observation time
:param pred_time: prediction time
:param dt: interval width for numerical integral calculation used for intensity prediction
:return: normed prediction error | tideh/functions.py | prediction_error_relative | sebaruehl/TiDeH | 0 | python | def prediction_error_relative(event_times, intensity, window_size, obs_time, pred_time, dt):
'\n Calculates median relative running error.\n\n :param event_times: event times\n :param intensity: predicted intensity\n :param window_size: prediction window size\n :param obs_time: observation time\n :param pred_time: prediction time\n :param dt: interval width for numerical integral calculation used for intensity prediction\n :return: normed prediction error\n '
events_time_pred = event_times[(event_times >= obs_time)]
events_in_obs_time = get_event_count(events_time_pred, 0, obs_time)
win_int = int((window_size / dt))
tp = np.arange(obs_time, pred_time, window_size)
cnt_total_real = events_in_obs_time
cnt_total_pred = events_in_obs_time
err_rel_running = []
for (i, t_cur) in enumerate(tp):
t_end = (t_cur + window_size)
if (t_end > pred_time):
break
count_current = get_event_count(events_time_pred, t_cur, t_end)
pred_count = (dt * intensity[(i * win_int):((i + 1) * win_int)].sum())
cnt_total_real += count_current
cnt_total_pred += pred_count
rel_tmp = (1 - (np.minimum(cnt_total_real, cnt_total_pred) / np.maximum(cnt_total_real, cnt_total_pred)))
err_rel_running.append(rel_tmp)
return np.median(err_rel_running) | def prediction_error_relative(event_times, intensity, window_size, obs_time, pred_time, dt):
'\n Calculates median relative running error.\n\n :param event_times: event times\n :param intensity: predicted intensity\n :param window_size: prediction window size\n :param obs_time: observation time\n :param pred_time: prediction time\n :param dt: interval width for numerical integral calculation used for intensity prediction\n :return: normed prediction error\n '
events_time_pred = event_times[(event_times >= obs_time)]
events_in_obs_time = get_event_count(events_time_pred, 0, obs_time)
win_int = int((window_size / dt))
tp = np.arange(obs_time, pred_time, window_size)
cnt_total_real = events_in_obs_time
cnt_total_pred = events_in_obs_time
err_rel_running = []
for (i, t_cur) in enumerate(tp):
t_end = (t_cur + window_size)
if (t_end > pred_time):
break
count_current = get_event_count(events_time_pred, t_cur, t_end)
pred_count = (dt * intensity[(i * win_int):((i + 1) * win_int)].sum())
cnt_total_real += count_current
cnt_total_pred += pred_count
rel_tmp = (1 - (np.minimum(cnt_total_real, cnt_total_pred) / np.maximum(cnt_total_real, cnt_total_pred)))
err_rel_running.append(rel_tmp)
return np.median(err_rel_running)<|docstring|>Calculates median relative running error.
:param event_times: event times
:param intensity: predicted intensity
:param window_size: prediction window size
:param obs_time: observation time
:param pred_time: prediction time
:param dt: interval width for numerical integral calculation used for intensity prediction
:return: normed prediction error<|endoftext|> |
1839a0c21c55b2e6bd62a19e69d1322093441ae40b7d646a235eef6e91fafef7 | def exchange_image_file_name(file_path):
'\n ファイル名の変更\n\n :param file_path: ファイル名（パス含む）\n :return: 変換後のファイル名\n '
file_date = get_date(file_path)
file_directory = file_date.strftime('%Y%m')
save_path = ((os.path.abspath(output_image_dir) + '/') + file_directory)
os.makedirs(save_path, exist_ok=True)
new_file_name = file_date.strftime('%Y%m%d_%H%M%S')
new_file_path_base = ((save_path + '/IMG_') + new_file_name)
file_ext = os.path.splitext(file_path)[1]
if (file_ext in ('.jpeg', '.JPEG')):
file_ext = '.JPG'
new_file_path = (new_file_path_base + file_ext)
num = 0
file_size = os.path.getsize(file_path)
while (os.path.exists(new_file_path) and (os.path.getsize(new_file_path) != file_size)):
num += 1
new_file_path = (((new_file_path_base + '_m') + str(num)) + file_ext)
response_path = shutil.copy2(file_path, new_file_path)
if (file_ext != '.JPG'):
os.utime(new_file_path, (file_date.timestamp(), file_date.timestamp()))
return response_path | ファイル名の変更
:param file_path: ファイル名（パス含む）
:return: 変換後のファイル名 | organize_photos.py | exchange_image_file_name | tsukko/organize_photos | 0 | python | def exchange_image_file_name(file_path):
'\n ファイル名の変更\n\n :param file_path: ファイル名（パス含む）\n :return: 変換後のファイル名\n '
file_date = get_date(file_path)
file_directory = file_date.strftime('%Y%m')
save_path = ((os.path.abspath(output_image_dir) + '/') + file_directory)
os.makedirs(save_path, exist_ok=True)
new_file_name = file_date.strftime('%Y%m%d_%H%M%S')
new_file_path_base = ((save_path + '/IMG_') + new_file_name)
file_ext = os.path.splitext(file_path)[1]
if (file_ext in ('.jpeg', '.JPEG')):
file_ext = '.JPG'
new_file_path = (new_file_path_base + file_ext)
num = 0
file_size = os.path.getsize(file_path)
while (os.path.exists(new_file_path) and (os.path.getsize(new_file_path) != file_size)):
num += 1
new_file_path = (((new_file_path_base + '_m') + str(num)) + file_ext)
response_path = shutil.copy2(file_path, new_file_path)
if (file_ext != '.JPG'):
os.utime(new_file_path, (file_date.timestamp(), file_date.timestamp()))
return response_path | def exchange_image_file_name(file_path):
'\n ファイル名の変更\n\n :param file_path: ファイル名（パス含む）\n :return: 変換後のファイル名\n '
file_date = get_date(file_path)
file_directory = file_date.strftime('%Y%m')
save_path = ((os.path.abspath(output_image_dir) + '/') + file_directory)
os.makedirs(save_path, exist_ok=True)
new_file_name = file_date.strftime('%Y%m%d_%H%M%S')
new_file_path_base = ((save_path + '/IMG_') + new_file_name)
file_ext = os.path.splitext(file_path)[1]
if (file_ext in ('.jpeg', '.JPEG')):
file_ext = '.JPG'
new_file_path = (new_file_path_base + file_ext)
num = 0
file_size = os.path.getsize(file_path)
while (os.path.exists(new_file_path) and (os.path.getsize(new_file_path) != file_size)):
num += 1
new_file_path = (((new_file_path_base + '_m') + str(num)) + file_ext)
response_path = shutil.copy2(file_path, new_file_path)
if (file_ext != '.JPG'):
os.utime(new_file_path, (file_date.timestamp(), file_date.timestamp()))
return response_path<|docstring|>ファイル名の変更
:param file_path: ファイル名（パス含む）
:return: 変換後のファイル名<|endoftext|> |
079ecbf8c782959cd8b46d629dd5a4cad79d19aa2655fb8f094fa47a27c62d26 | def weights_init(m):
'custom weights initialization'
if (isinstance(m, nn.Linear) or isinstance(m, nn.Conv2d)):
nn.init.orthogonal_(m.weight.data)
else:
print(('%s is not custom-initialized.' % m.__class__)) | custom weights initialization | pyrela/common_utils/helper.py | weights_init | facebookresearch/rela | 93 | python | def weights_init(m):
if (isinstance(m, nn.Linear) or isinstance(m, nn.Conv2d)):
nn.init.orthogonal_(m.weight.data)
else:
print(('%s is not custom-initialized.' % m.__class__)) | def weights_init(m):
if (isinstance(m, nn.Linear) or isinstance(m, nn.Conv2d)):
nn.init.orthogonal_(m.weight.data)
else:
print(('%s is not custom-initialized.' % m.__class__))<|docstring|>custom weights initialization<|endoftext|> |
ac5232340f88a47d4a59e45ae552ede7834d69cc102f212e92a65dff4b08d4a8 | def variant_to_fm_format(steps, step_list, activity_list, rev_step_mapping, minpartialsz=2, negative_samples=3, seed=123, normalize=True, pred_id=0):
'\n Method to make matrix representation of step case. Matrix will contain\n the following features:\n - steps taken\n - executed activities\n - step to be predicted\n '
x_datalist = list()
x_row_inds = list()
x_col_inds = list()
x_shape = np.zeros(shape=(2,))
y_datalist = list()
pred_id_list = list()
if (steps.shape[0] <= minpartialsz):
return (np.asarray(x_datalist), np.asarray(x_row_inds), np.asarray(x_col_inds), x_shape, np.asarray(y_datalist), np.asarray(pred_id_list))
for ind in range(minpartialsz, steps.shape[0]):
partialcase = steps[:ind]
laststep = steps[(ind - 1)]
gt_next_step = steps[ind]
gt_next_act = rev_step_mapping[gt_next_step][(- 1)]
get_act = (lambda act: act.split('+')[0])
gt_next_act = get_act(gt_next_act)
if (negative_samples > (- 1)):
np.random.seed(seed=seed)
random_negative_samples = list(filter((lambda act: (act != gt_next_act)), activity_list))
picked_inds = np.random.choice(np.arange(len(random_negative_samples)), size=negative_samples, replace=False)
random_negative_samples = list(map((lambda ind: random_negative_samples[ind]), picked_inds))
samples = np.append(random_negative_samples, [gt_next_act])
else:
samples = list(filter((lambda act: (act != gt_next_act)), activity_list))
samples = np.append(samples, [gt_next_act])
taken_repeat = np.asarray([partialcase for _ in range(len(samples))])
(taken_datalist, taken_row_inds, taken_col_inds, taken_shape) = rutils.multiple_to_fm_format(taken_repeat, step_list, normalize)
t_acts = list(map((lambda step: rev_step_mapping[step]), partialcase))
t_acts = list(map((lambda step: get_act(step[(- 1)])), t_acts))
t_acts = np.asarray(t_acts)
if (ARTIFICIAL_START not in t_acts):
t_acts = np.append([ARTIFICIAL_START], t_acts)
not_in = list(filter((lambda act: (act not in activity_list)), t_acts))
assert (len(not_in) == 0), 't_acts not in activity list: {} with {} items.'.format(str(not_in), len(not_in))
t_acts_repeat = np.asarray([t_acts for _ in range(len(samples))])
(t_acts_datalist, t_acts_row_inds, t_acts_col_inds, t_acts_shape) = rutils.multiple_to_fm_format(t_acts_repeat, activity_list, normalize)
"\n l_act = rev_step_mapping[laststep][-1]\n assert l_act in activity_list, '{} not in activity list: {}' .format(l_act, activity_list)\n l_act_repeat = np.asarray([l_act,]).repeat(len(samples))\n l_act_datalist, l_act_row_inds, l_act_col_inds, l_act_shape = rutils.single_to_fm_format(l_act_repeat, activity_list)\n "
(next_datalist, next_row_inds, next_col_inds, next_shape) = rutils.single_to_fm_format(samples, activity_list)
assert ((taken_shape[0] == next_shape[0]) and (next_shape[0] == t_acts_shape[0])), 'taken shape: {}, next shape: {}, t_acts shape: {}'.format(taken_shape, next_shape, t_acts_shape)
assert (taken_shape[1] == len(step_list)), 'taken shape: {}, step list shape: {}'.format(taken_shape, len(step_list))
assert (next_shape[1] == len(activity_list)), 'next shape: {}, activity list shape: {}'.format(next_shape, len(activity_list))
assert (t_acts_shape[1] == len(activity_list)), 'taken acts shape: {}, activity list shape: {}'.format(t_acts_shape, len(activity_list))
t_acts_col_inds = (t_acts_col_inds + len(step_list))
next_col_inds = ((next_col_inds + len(step_list)) + len(activity_list))
x_datalist_i = np.concatenate((taken_datalist, t_acts_datalist, next_datalist))
x_row_inds_i = np.concatenate((taken_row_inds, t_acts_row_inds, next_row_inds))
x_col_inds_i = np.concatenate((taken_col_inds, t_acts_col_inds, next_col_inds))
num_of_cols = (len(step_list) + (2 * activity_list.shape[0]))
x_shape_i = np.asarray((len(samples), num_of_cols))
y_datalist_i = np.asarray([np.int(act) for act in (samples == gt_next_act)])
assert (Counter(y_datalist_i)[1] == 1), 'y_datalist_i: {}'.format(y_datalist_i)
pred_id_list_i = (np.ones(len(y_datalist_i)) * pred_id)
if (len(x_datalist) > 0):
assert (x_shape[1] == x_shape_i[1]), 'x_shape: {} not equal x_shape_i: {}'.format(x_shape, x_shape_i)
x_row_inds_i = (x_row_inds_i + x_shape[0])
x_datalist = np.concatenate((x_datalist, x_datalist_i))
x_row_inds = np.concatenate((x_row_inds, x_row_inds_i))
x_col_inds = np.concatenate((x_col_inds, x_col_inds_i))
x_shape = np.asarray(((x_shape[0] + x_shape_i[0]), x_shape[1]))
y_datalist = np.concatenate((y_datalist, y_datalist_i))
pred_id_list = np.concatenate((pred_id_list, pred_id_list_i))
else:
x_datalist = x_datalist_i
x_row_inds = x_row_inds_i
x_col_inds = x_col_inds_i
x_shape = x_shape_i
y_datalist = y_datalist_i
pred_id_list = pred_id_list_i
pred_id += 1
return (x_datalist, x_row_inds, x_col_inds, x_shape, y_datalist, pred_id_list) | Method to make matrix representation of step case. Matrix will contain
the following features:
- steps taken
- executed activities
- step to be predicted | code/PMRec/dataRep/nextActivityAddActivityBuildWithVariant.py | variant_to_fm_format | wailamjonathanlee/PMRec | 3 | python | def variant_to_fm_format(steps, step_list, activity_list, rev_step_mapping, minpartialsz=2, negative_samples=3, seed=123, normalize=True, pred_id=0):
'\n Method to make matrix representation of step case. Matrix will contain\n the following features:\n - steps taken\n - executed activities\n - step to be predicted\n '
x_datalist = list()
x_row_inds = list()
x_col_inds = list()
x_shape = np.zeros(shape=(2,))
y_datalist = list()
pred_id_list = list()
if (steps.shape[0] <= minpartialsz):
return (np.asarray(x_datalist), np.asarray(x_row_inds), np.asarray(x_col_inds), x_shape, np.asarray(y_datalist), np.asarray(pred_id_list))
for ind in range(minpartialsz, steps.shape[0]):
partialcase = steps[:ind]
laststep = steps[(ind - 1)]
gt_next_step = steps[ind]
gt_next_act = rev_step_mapping[gt_next_step][(- 1)]
get_act = (lambda act: act.split('+')[0])
gt_next_act = get_act(gt_next_act)
if (negative_samples > (- 1)):
np.random.seed(seed=seed)
random_negative_samples = list(filter((lambda act: (act != gt_next_act)), activity_list))
picked_inds = np.random.choice(np.arange(len(random_negative_samples)), size=negative_samples, replace=False)
random_negative_samples = list(map((lambda ind: random_negative_samples[ind]), picked_inds))
samples = np.append(random_negative_samples, [gt_next_act])
else:
samples = list(filter((lambda act: (act != gt_next_act)), activity_list))
samples = np.append(samples, [gt_next_act])
taken_repeat = np.asarray([partialcase for _ in range(len(samples))])
(taken_datalist, taken_row_inds, taken_col_inds, taken_shape) = rutils.multiple_to_fm_format(taken_repeat, step_list, normalize)
t_acts = list(map((lambda step: rev_step_mapping[step]), partialcase))
t_acts = list(map((lambda step: get_act(step[(- 1)])), t_acts))
t_acts = np.asarray(t_acts)
if (ARTIFICIAL_START not in t_acts):
t_acts = np.append([ARTIFICIAL_START], t_acts)
not_in = list(filter((lambda act: (act not in activity_list)), t_acts))
assert (len(not_in) == 0), 't_acts not in activity list: {} with {} items.'.format(str(not_in), len(not_in))
t_acts_repeat = np.asarray([t_acts for _ in range(len(samples))])
(t_acts_datalist, t_acts_row_inds, t_acts_col_inds, t_acts_shape) = rutils.multiple_to_fm_format(t_acts_repeat, activity_list, normalize)
"\n l_act = rev_step_mapping[laststep][-1]\n assert l_act in activity_list, '{} not in activity list: {}' .format(l_act, activity_list)\n l_act_repeat = np.asarray([l_act,]).repeat(len(samples))\n l_act_datalist, l_act_row_inds, l_act_col_inds, l_act_shape = rutils.single_to_fm_format(l_act_repeat, activity_list)\n "
(next_datalist, next_row_inds, next_col_inds, next_shape) = rutils.single_to_fm_format(samples, activity_list)
assert ((taken_shape[0] == next_shape[0]) and (next_shape[0] == t_acts_shape[0])), 'taken shape: {}, next shape: {}, t_acts shape: {}'.format(taken_shape, next_shape, t_acts_shape)
assert (taken_shape[1] == len(step_list)), 'taken shape: {}, step list shape: {}'.format(taken_shape, len(step_list))
assert (next_shape[1] == len(activity_list)), 'next shape: {}, activity list shape: {}'.format(next_shape, len(activity_list))
assert (t_acts_shape[1] == len(activity_list)), 'taken acts shape: {}, activity list shape: {}'.format(t_acts_shape, len(activity_list))
t_acts_col_inds = (t_acts_col_inds + len(step_list))
next_col_inds = ((next_col_inds + len(step_list)) + len(activity_list))
x_datalist_i = np.concatenate((taken_datalist, t_acts_datalist, next_datalist))
x_row_inds_i = np.concatenate((taken_row_inds, t_acts_row_inds, next_row_inds))
x_col_inds_i = np.concatenate((taken_col_inds, t_acts_col_inds, next_col_inds))
num_of_cols = (len(step_list) + (2 * activity_list.shape[0]))
x_shape_i = np.asarray((len(samples), num_of_cols))
y_datalist_i = np.asarray([np.int(act) for act in (samples == gt_next_act)])
assert (Counter(y_datalist_i)[1] == 1), 'y_datalist_i: {}'.format(y_datalist_i)
pred_id_list_i = (np.ones(len(y_datalist_i)) * pred_id)
if (len(x_datalist) > 0):
assert (x_shape[1] == x_shape_i[1]), 'x_shape: {} not equal x_shape_i: {}'.format(x_shape, x_shape_i)
x_row_inds_i = (x_row_inds_i + x_shape[0])
x_datalist = np.concatenate((x_datalist, x_datalist_i))
x_row_inds = np.concatenate((x_row_inds, x_row_inds_i))
x_col_inds = np.concatenate((x_col_inds, x_col_inds_i))
x_shape = np.asarray(((x_shape[0] + x_shape_i[0]), x_shape[1]))
y_datalist = np.concatenate((y_datalist, y_datalist_i))
pred_id_list = np.concatenate((pred_id_list, pred_id_list_i))
else:
x_datalist = x_datalist_i
x_row_inds = x_row_inds_i
x_col_inds = x_col_inds_i
x_shape = x_shape_i
y_datalist = y_datalist_i
pred_id_list = pred_id_list_i
pred_id += 1
return (x_datalist, x_row_inds, x_col_inds, x_shape, y_datalist, pred_id_list) | def variant_to_fm_format(steps, step_list, activity_list, rev_step_mapping, minpartialsz=2, negative_samples=3, seed=123, normalize=True, pred_id=0):
'\n Method to make matrix representation of step case. Matrix will contain\n the following features:\n - steps taken\n - executed activities\n - step to be predicted\n '
x_datalist = list()
x_row_inds = list()
x_col_inds = list()
x_shape = np.zeros(shape=(2,))
y_datalist = list()
pred_id_list = list()
if (steps.shape[0] <= minpartialsz):
return (np.asarray(x_datalist), np.asarray(x_row_inds), np.asarray(x_col_inds), x_shape, np.asarray(y_datalist), np.asarray(pred_id_list))
for ind in range(minpartialsz, steps.shape[0]):
partialcase = steps[:ind]
laststep = steps[(ind - 1)]
gt_next_step = steps[ind]
gt_next_act = rev_step_mapping[gt_next_step][(- 1)]
get_act = (lambda act: act.split('+')[0])
gt_next_act = get_act(gt_next_act)
if (negative_samples > (- 1)):
np.random.seed(seed=seed)
random_negative_samples = list(filter((lambda act: (act != gt_next_act)), activity_list))
picked_inds = np.random.choice(np.arange(len(random_negative_samples)), size=negative_samples, replace=False)
random_negative_samples = list(map((lambda ind: random_negative_samples[ind]), picked_inds))
samples = np.append(random_negative_samples, [gt_next_act])
else:
samples = list(filter((lambda act: (act != gt_next_act)), activity_list))
samples = np.append(samples, [gt_next_act])
taken_repeat = np.asarray([partialcase for _ in range(len(samples))])
(taken_datalist, taken_row_inds, taken_col_inds, taken_shape) = rutils.multiple_to_fm_format(taken_repeat, step_list, normalize)
t_acts = list(map((lambda step: rev_step_mapping[step]), partialcase))
t_acts = list(map((lambda step: get_act(step[(- 1)])), t_acts))
t_acts = np.asarray(t_acts)
if (ARTIFICIAL_START not in t_acts):
t_acts = np.append([ARTIFICIAL_START], t_acts)
not_in = list(filter((lambda act: (act not in activity_list)), t_acts))
assert (len(not_in) == 0), 't_acts not in activity list: {} with {} items.'.format(str(not_in), len(not_in))
t_acts_repeat = np.asarray([t_acts for _ in range(len(samples))])
(t_acts_datalist, t_acts_row_inds, t_acts_col_inds, t_acts_shape) = rutils.multiple_to_fm_format(t_acts_repeat, activity_list, normalize)
"\n l_act = rev_step_mapping[laststep][-1]\n assert l_act in activity_list, '{} not in activity list: {}' .format(l_act, activity_list)\n l_act_repeat = np.asarray([l_act,]).repeat(len(samples))\n l_act_datalist, l_act_row_inds, l_act_col_inds, l_act_shape = rutils.single_to_fm_format(l_act_repeat, activity_list)\n "
(next_datalist, next_row_inds, next_col_inds, next_shape) = rutils.single_to_fm_format(samples, activity_list)
assert ((taken_shape[0] == next_shape[0]) and (next_shape[0] == t_acts_shape[0])), 'taken shape: {}, next shape: {}, t_acts shape: {}'.format(taken_shape, next_shape, t_acts_shape)
assert (taken_shape[1] == len(step_list)), 'taken shape: {}, step list shape: {}'.format(taken_shape, len(step_list))
assert (next_shape[1] == len(activity_list)), 'next shape: {}, activity list shape: {}'.format(next_shape, len(activity_list))
assert (t_acts_shape[1] == len(activity_list)), 'taken acts shape: {}, activity list shape: {}'.format(t_acts_shape, len(activity_list))
t_acts_col_inds = (t_acts_col_inds + len(step_list))
next_col_inds = ((next_col_inds + len(step_list)) + len(activity_list))
x_datalist_i = np.concatenate((taken_datalist, t_acts_datalist, next_datalist))
x_row_inds_i = np.concatenate((taken_row_inds, t_acts_row_inds, next_row_inds))
x_col_inds_i = np.concatenate((taken_col_inds, t_acts_col_inds, next_col_inds))
num_of_cols = (len(step_list) + (2 * activity_list.shape[0]))
x_shape_i = np.asarray((len(samples), num_of_cols))
y_datalist_i = np.asarray([np.int(act) for act in (samples == gt_next_act)])
assert (Counter(y_datalist_i)[1] == 1), 'y_datalist_i: {}'.format(y_datalist_i)
pred_id_list_i = (np.ones(len(y_datalist_i)) * pred_id)
if (len(x_datalist) > 0):
assert (x_shape[1] == x_shape_i[1]), 'x_shape: {} not equal x_shape_i: {}'.format(x_shape, x_shape_i)
x_row_inds_i = (x_row_inds_i + x_shape[0])
x_datalist = np.concatenate((x_datalist, x_datalist_i))
x_row_inds = np.concatenate((x_row_inds, x_row_inds_i))
x_col_inds = np.concatenate((x_col_inds, x_col_inds_i))
x_shape = np.asarray(((x_shape[0] + x_shape_i[0]), x_shape[1]))
y_datalist = np.concatenate((y_datalist, y_datalist_i))
pred_id_list = np.concatenate((pred_id_list, pred_id_list_i))
else:
x_datalist = x_datalist_i
x_row_inds = x_row_inds_i
x_col_inds = x_col_inds_i
x_shape = x_shape_i
y_datalist = y_datalist_i
pred_id_list = pred_id_list_i
pred_id += 1
return (x_datalist, x_row_inds, x_col_inds, x_shape, y_datalist, pred_id_list)<|docstring|>Method to make matrix representation of step case. Matrix will contain
the following features:
- steps taken
- executed activities
- step to be predicted<|endoftext|> |
2df5198268fb4ef44847df9821964b7504e26447b3389e6cd6a5817f96964e3b | def offline_update(log):
'\n Stop the clamav daemon, update the clamav definitions, then\n start the damon. This can avoid OOMs on some systems with\n less RAM capacity.\n '
log.run(['service', 'clamav-daemon', 'stop'])
log.run(['/usr/bin/env', 'freshclam'])
log.run(['service', 'clamav-daemon', 'start']) | Stop the clamav daemon, update the clamav definitions, then
start the damon. This can avoid OOMs on some systems with
less RAM capacity. | bin/libsw/clamav.py | offline_update | Rondore/sitewrangler | 0 | python | def offline_update(log):
'\n Stop the clamav daemon, update the clamav definitions, then\n start the damon. This can avoid OOMs on some systems with\n less RAM capacity.\n '
log.run(['service', 'clamav-daemon', 'stop'])
log.run(['/usr/bin/env', 'freshclam'])
log.run(['service', 'clamav-daemon', 'start']) | def offline_update(log):
'\n Stop the clamav daemon, update the clamav definitions, then\n start the damon. This can avoid OOMs on some systems with\n less RAM capacity.\n '
log.run(['service', 'clamav-daemon', 'stop'])
log.run(['/usr/bin/env', 'freshclam'])
log.run(['service', 'clamav-daemon', 'start'])<|docstring|>Stop the clamav daemon, update the clamav definitions, then
start the damon. This can avoid OOMs on some systems with
less RAM capacity.<|endoftext|> |
3c32938234b6b8dbf71bee24babf84679117a5abbe8df85b6a5449d144907e46 | def main():
'Terminal上でポーカーを再現。ダブルアップはなし。\n '
poker = Poker()
'\n 標準入出力を利用して、ゲームを行う\n ループで回せばいい\n 終了の文字も指定する\n ゲームの流れは、\n スタート->dealされた札が5枚表示される->holdする札を選択する->\n ->再びdealする->役を判定->ゲームの結果処理->スタートに戻る\n ユーザーができることは、\n holdする札を選ぶ。結果表示後続けるか選ぶ。\n の2つ\n '
print('How to hold card? --> Please input "134".')
print('Input "n" to exit.')
coins = 1000
print('conis:', coins)
while True:
bet = 50
coins -= bet
print('bet:', bet)
deals = poker.gen_display_str()
print('Cards dealt:', deals)
inp = list(input('Which cards do you hold? -> '))
holds = []
flag_exit = False
for s in inp:
if (s == 'n'):
flag_exit = True
if s.isdigit():
num = int(s)
if ((num > 0) and (num < 6)):
holds.append((num - 1))
if (flag_exit == True):
print('Exit.')
break
holds = list(set(holds))
poker.change_cards(holds)
hand = poker.judge_deals()
print('Cards dealt:', poker.gen_display_str(), '---->', hand)
gain = poker.gain(bet, hand)
coins += gain
print('You gained', gain, 'coins.')
print('coins:', coins)
poker.shuffle() | Terminal上でポーカーを再現。ダブルアップはなし。 | poker.py | main | ikapper/Poker | 0 | python | def main():
'\n '
poker = Poker()
'\n 標準入出力を利用して、ゲームを行う\n ループで回せばいい\n 終了の文字も指定する\n ゲームの流れは、\n スタート->dealされた札が5枚表示される->holdする札を選択する->\n ->再びdealする->役を判定->ゲームの結果処理->スタートに戻る\n ユーザーができることは、\n holdする札を選ぶ。結果表示後続けるか選ぶ。\n の2つ\n '
print('How to hold card? --> Please input "134".')
print('Input "n" to exit.')
coins = 1000
print('conis:', coins)
while True:
bet = 50
coins -= bet
print('bet:', bet)
deals = poker.gen_display_str()
print('Cards dealt:', deals)
inp = list(input('Which cards do you hold? -> '))
holds = []
flag_exit = False
for s in inp:
if (s == 'n'):
flag_exit = True
if s.isdigit():
num = int(s)
if ((num > 0) and (num < 6)):
holds.append((num - 1))
if (flag_exit == True):
print('Exit.')
break
holds = list(set(holds))
poker.change_cards(holds)
hand = poker.judge_deals()
print('Cards dealt:', poker.gen_display_str(), '---->', hand)
gain = poker.gain(bet, hand)
coins += gain
print('You gained', gain, 'coins.')
print('coins:', coins)
poker.shuffle() | def main():
'\n '
poker = Poker()
'\n 標準入出力を利用して、ゲームを行う\n ループで回せばいい\n 終了の文字も指定する\n ゲームの流れは、\n スタート->dealされた札が5枚表示される->holdする札を選択する->\n ->再びdealする->役を判定->ゲームの結果処理->スタートに戻る\n ユーザーができることは、\n holdする札を選ぶ。結果表示後続けるか選ぶ。\n の2つ\n '
print('How to hold card? --> Please input "134".')
print('Input "n" to exit.')
coins = 1000
print('conis:', coins)
while True:
bet = 50
coins -= bet
print('bet:', bet)
deals = poker.gen_display_str()
print('Cards dealt:', deals)
inp = list(input('Which cards do you hold? -> '))
holds = []
flag_exit = False
for s in inp:
if (s == 'n'):
flag_exit = True
if s.isdigit():
num = int(s)
if ((num > 0) and (num < 6)):
holds.append((num - 1))
if (flag_exit == True):
print('Exit.')
break
holds = list(set(holds))
poker.change_cards(holds)
hand = poker.judge_deals()
print('Cards dealt:', poker.gen_display_str(), '---->', hand)
gain = poker.gain(bet, hand)
coins += gain
print('You gained', gain, 'coins.')
print('coins:', coins)
poker.shuffle()<|docstring|>Terminal上でポーカーを再現。ダブルアップはなし。<|endoftext|> |
4f4872f46c3e961e0e9eb1f582a6707a5161b860da7351f68412e9fc900506e8 | def __init__(self, jokers=1):
'山札cardsを生成して、配る分dealsを用意する\n cardsとdealsを足すと全カードになる\n Jokerは4枚まで想定している\n '
self.num_joker = jokers
bundle = [(suit + str(i)) for suit in ('S', 'C', 'H', 'D') for i in range(1, 14)]
bundle.extend(('Joker{0}'.format((i + 1)) for i in range(self.num_joker)))
random.shuffle(bundle)
self.cards = bundle
self.deals = self.pickTop(5) | 山札cardsを生成して、配る分dealsを用意する
cardsとdealsを足すと全カードになる
Jokerは4枚まで想定している | poker.py | __init__ | ikapper/Poker | 0 | python | def __init__(self, jokers=1):
'山札cardsを生成して、配る分dealsを用意する\n cardsとdealsを足すと全カードになる\n Jokerは4枚まで想定している\n '
self.num_joker = jokers
bundle = [(suit + str(i)) for suit in ('S', 'C', 'H', 'D') for i in range(1, 14)]
bundle.extend(('Joker{0}'.format((i + 1)) for i in range(self.num_joker)))
random.shuffle(bundle)
self.cards = bundle
self.deals = self.pickTop(5) | def __init__(self, jokers=1):
'山札cardsを生成して、配る分dealsを用意する\n cardsとdealsを足すと全カードになる\n Jokerは4枚まで想定している\n '
self.num_joker = jokers
bundle = [(suit + str(i)) for suit in ('S', 'C', 'H', 'D') for i in range(1, 14)]
bundle.extend(('Joker{0}'.format((i + 1)) for i in range(self.num_joker)))
random.shuffle(bundle)
self.cards = bundle
self.deals = self.pickTop(5)<|docstring|>山札cardsを生成して、配る分dealsを用意する
cardsとdealsを足すと全カードになる
Jokerは4枚まで想定している<|endoftext|> |
e0032844f369d2fa2a99356ce7bf738c343986b3ac87c0b78360bf6753b2b9b7 | def shuffle(self):
'配った分と山札を合わせてシャッフルしたあと、配り直す\n '
self.cards.extend(self.deals)
random.shuffle(self.cards)
self.deals = self.pickTop(5) | 配った分と山札を合わせてシャッフルしたあと、配り直す | poker.py | shuffle | ikapper/Poker | 0 | python | def shuffle(self):
'\n '
self.cards.extend(self.deals)
random.shuffle(self.cards)
self.deals = self.pickTop(5) | def shuffle(self):
'\n '
self.cards.extend(self.deals)
random.shuffle(self.cards)
self.deals = self.pickTop(5)<|docstring|>配った分と山札を合わせてシャッフルしたあと、配り直す<|endoftext|> |
2ae385c30179bdcb56f59e69853b043e9fcda850060904803cbb2e7f91055995 | def pickTop(self, n):
'先頭からn個popする --> list\n '
if (n > (len(self.cards) - 1)):
n = (len(self.cards) - 1)
elif (n < 0):
n = 0
result = [self.cards.pop(0) for i in range(n)]
return result | 先頭からn個popする --> list | poker.py | pickTop | ikapper/Poker | 0 | python | def pickTop(self, n):
'\n '
if (n > (len(self.cards) - 1)):
n = (len(self.cards) - 1)
elif (n < 0):
n = 0
result = [self.cards.pop(0) for i in range(n)]
return result | def pickTop(self, n):
'\n '
if (n > (len(self.cards) - 1)):
n = (len(self.cards) - 1)
elif (n < 0):
n = 0
result = [self.cards.pop(0) for i in range(n)]
return result<|docstring|>先頭からn個popする --> list<|endoftext|> |
3bd7f8a6dc959917e0d529fa62e14da3c4cc9ddde2a206f06e5de2248a66487e | def change_cards(self, holds=[0, 1, 2, 3, 4]):
'dealsの左から数えた時のholdするindexのリスト。最左を1として、5まで考えられる\n デフォルトは全ホールド。dealsが変化する。抜いたカードは山札に戻す\n '
nonhold = [i for i in range(5)]
for idx in holds:
nonhold.remove(idx)
for idx in nonhold:
pop = self.deals.pop(idx)
self.deals.insert(idx, self.cards.pop(0))
self.cards.append(pop) | dealsの左から数えた時のholdするindexのリスト。最左を1として、5まで考えられる
デフォルトは全ホールド。dealsが変化する。抜いたカードは山札に戻す | poker.py | change_cards | ikapper/Poker | 0 | python | def change_cards(self, holds=[0, 1, 2, 3, 4]):
'dealsの左から数えた時のholdするindexのリスト。最左を1として、5まで考えられる\n デフォルトは全ホールド。dealsが変化する。抜いたカードは山札に戻す\n '
nonhold = [i for i in range(5)]
for idx in holds:
nonhold.remove(idx)
for idx in nonhold:
pop = self.deals.pop(idx)
self.deals.insert(idx, self.cards.pop(0))
self.cards.append(pop) | def change_cards(self, holds=[0, 1, 2, 3, 4]):
'dealsの左から数えた時のholdするindexのリスト。最左を1として、5まで考えられる\n デフォルトは全ホールド。dealsが変化する。抜いたカードは山札に戻す\n '
nonhold = [i for i in range(5)]
for idx in holds:
nonhold.remove(idx)
for idx in nonhold:
pop = self.deals.pop(idx)
self.deals.insert(idx, self.cards.pop(0))
self.cards.append(pop)<|docstring|>dealsの左から数えた時のholdするindexのリスト。最左を1として、5まで考えられる
デフォルトは全ホールド。dealsが変化する。抜いたカードは山札に戻す<|endoftext|> |
dc0826ebecffac74058092a41caa87ff45bb5a030ef5c481c27dada2e48344b3 | def judge_deals(self):
'呼ばれた時点でのdealsで役判定する\n return a corresponding hand\n '
suits = []
ranks = []
jokers = 0
for deal in self.deals:
if deal.startswith('Joker'):
jokers += 1
continue
suits.append(deal[:1])
ranks.append(int(deal[1:]))
result = self.judge(suits, ranks, jokers)
return result | 呼ばれた時点でのdealsで役判定する
return a corresponding hand | poker.py | judge_deals | ikapper/Poker | 0 | python | def judge_deals(self):
'呼ばれた時点でのdealsで役判定する\n return a corresponding hand\n '
suits = []
ranks = []
jokers = 0
for deal in self.deals:
if deal.startswith('Joker'):
jokers += 1
continue
suits.append(deal[:1])
ranks.append(int(deal[1:]))
result = self.judge(suits, ranks, jokers)
return result | def judge_deals(self):
'呼ばれた時点でのdealsで役判定する\n return a corresponding hand\n '
suits = []
ranks = []
jokers = 0
for deal in self.deals:
if deal.startswith('Joker'):
jokers += 1
continue
suits.append(deal[:1])
ranks.append(int(deal[1:]))
result = self.judge(suits, ranks, jokers)
return result<|docstring|>呼ばれた時点でのdealsで役判定する
return a corresponding hand<|endoftext|> |
1875ce6562984ec9cfaccf3cd14d151a53a387295cefba845df26b2f78b70685 | def judge(self, suits, ranks, jokers=0):
'jokers: number of jokers\n テストにも使う\n return a corresponding hand\n '
isRoyal = self._isRoyal(suits, ranks, jokers)
if isRoyal:
return self.CONST_ROYAL_STRAIGHT_FLUSH
isFive = self._is5Cards(ranks, jokers)
if isFive:
return self.CONST_5_CARDS
isStraight = self._isStraight(ranks, jokers)
isFlush = self._isFlush(suits, jokers)
if (isStraight and isFlush):
return self.CONST_STRAIGHT_FLUSH
isFour = self._is4Cards(ranks, jokers)
if isFour:
return self.CONST_4_CARDS
isFullHouse = self._isFullHouse(ranks, jokers)
if isFullHouse:
return self.CONST_FULL_HOUSE
if isFlush:
return self.CONST_FLUSH
if isStraight:
return self.CONST_STRAIGHT
isThree = self._is3Cards(ranks, jokers)
if isThree:
return self.CONST_3_CARDS
is2Pair = self._is2Pair(ranks, jokers)
if is2Pair:
return self.CONST_2_PAIR
is1Pair = self._is1Pair(ranks, jokers)
if is1Pair:
return self.CONST_1_PAIR
return self.CONST_NO_PAIR | jokers: number of jokers
テストにも使う
return a corresponding hand | poker.py | judge | ikapper/Poker | 0 | python | def judge(self, suits, ranks, jokers=0):
'jokers: number of jokers\n テストにも使う\n return a corresponding hand\n '
isRoyal = self._isRoyal(suits, ranks, jokers)
if isRoyal:
return self.CONST_ROYAL_STRAIGHT_FLUSH
isFive = self._is5Cards(ranks, jokers)
if isFive:
return self.CONST_5_CARDS
isStraight = self._isStraight(ranks, jokers)
isFlush = self._isFlush(suits, jokers)
if (isStraight and isFlush):
return self.CONST_STRAIGHT_FLUSH
isFour = self._is4Cards(ranks, jokers)
if isFour:
return self.CONST_4_CARDS
isFullHouse = self._isFullHouse(ranks, jokers)
if isFullHouse:
return self.CONST_FULL_HOUSE
if isFlush:
return self.CONST_FLUSH
if isStraight:
return self.CONST_STRAIGHT
isThree = self._is3Cards(ranks, jokers)
if isThree:
return self.CONST_3_CARDS
is2Pair = self._is2Pair(ranks, jokers)
if is2Pair:
return self.CONST_2_PAIR
is1Pair = self._is1Pair(ranks, jokers)
if is1Pair:
return self.CONST_1_PAIR
return self.CONST_NO_PAIR | def judge(self, suits, ranks, jokers=0):
'jokers: number of jokers\n テストにも使う\n return a corresponding hand\n '
isRoyal = self._isRoyal(suits, ranks, jokers)
if isRoyal:
return self.CONST_ROYAL_STRAIGHT_FLUSH
isFive = self._is5Cards(ranks, jokers)
if isFive:
return self.CONST_5_CARDS
isStraight = self._isStraight(ranks, jokers)
isFlush = self._isFlush(suits, jokers)
if (isStraight and isFlush):
return self.CONST_STRAIGHT_FLUSH
isFour = self._is4Cards(ranks, jokers)
if isFour:
return self.CONST_4_CARDS
isFullHouse = self._isFullHouse(ranks, jokers)
if isFullHouse:
return self.CONST_FULL_HOUSE
if isFlush:
return self.CONST_FLUSH
if isStraight:
return self.CONST_STRAIGHT
isThree = self._is3Cards(ranks, jokers)
if isThree:
return self.CONST_3_CARDS
is2Pair = self._is2Pair(ranks, jokers)
if is2Pair:
return self.CONST_2_PAIR
is1Pair = self._is1Pair(ranks, jokers)
if is1Pair:
return self.CONST_1_PAIR
return self.CONST_NO_PAIR<|docstring|>jokers: number of jokers
テストにも使う
return a corresponding hand<|endoftext|> |
48bbf4525ff92fb1564dc76ab66ae9dc1d86db4b52f9cc7cfec2f782f0dfa0b2 | def _isRoyal(self, suits, ranks, num_jokers=0):
'ロイヤルストレートフラッシュかどうか\n '
sset = set(suits)
if (not (('S' in sset) and self._isFlush(suits, num_jokers))):
return False
sorted_ranks = sorted(ranks)
rset = set(ranks)
for r in rset:
if (not (r in (1, 10, 11, 12, 13))):
return False
return self._isStraight(sorted_ranks, num_jokers) | ロイヤルストレートフラッシュかどうか | poker.py | _isRoyal | ikapper/Poker | 0 | python | def _isRoyal(self, suits, ranks, num_jokers=0):
'\n '
sset = set(suits)
if (not (('S' in sset) and self._isFlush(suits, num_jokers))):
return False
sorted_ranks = sorted(ranks)
rset = set(ranks)
for r in rset:
if (not (r in (1, 10, 11, 12, 13))):
return False
return self._isStraight(sorted_ranks, num_jokers) | def _isRoyal(self, suits, ranks, num_jokers=0):
'\n '
sset = set(suits)
if (not (('S' in sset) and self._isFlush(suits, num_jokers))):
return False
sorted_ranks = sorted(ranks)
rset = set(ranks)
for r in rset:
if (not (r in (1, 10, 11, 12, 13))):
return False
return self._isStraight(sorted_ranks, num_jokers)<|docstring|>ロイヤルストレートフラッシュかどうか<|endoftext|> |
a6679eaecbb57aee61b92dab94f142a353e6005620a3f221f5282956ba18a823 | def gen_display_str(self):
'dealsを見やすい？形の文字列を返す。1,11,12,13を文字に置き換えて文字列を生成する\n '
result = ''
for item in self.deals:
if item.startswith('Joker'):
result += ', Joker'
continue
suit = item[:1]
rank = item[1:]
rank = int(rank)
result += (', ' + suit)
if (rank == 1):
result += '-A'
elif (rank == 11):
result += '-J'
elif (rank == 12):
result += '-Q'
elif (rank == 13):
result += '-K'
else:
result += ('-' + str(rank))
return result[2:] | dealsを見やすい？形の文字列を返す。1,11,12,13を文字に置き換えて文字列を生成する | poker.py | gen_display_str | ikapper/Poker | 0 | python | def gen_display_str(self):
'\n '
result =
for item in self.deals:
if item.startswith('Joker'):
result += ', Joker'
continue
suit = item[:1]
rank = item[1:]
rank = int(rank)
result += (', ' + suit)
if (rank == 1):
result += '-A'
elif (rank == 11):
result += '-J'
elif (rank == 12):
result += '-Q'
elif (rank == 13):
result += '-K'
else:
result += ('-' + str(rank))
return result[2:] | def gen_display_str(self):
'\n '
result =
for item in self.deals:
if item.startswith('Joker'):
result += ', Joker'
continue
suit = item[:1]
rank = item[1:]
rank = int(rank)
result += (', ' + suit)
if (rank == 1):
result += '-A'
elif (rank == 11):
result += '-J'
elif (rank == 12):
result += '-Q'
elif (rank == 13):
result += '-K'
else:
result += ('-' + str(rank))
return result[2:]<|docstring|>dealsを見やすい？形の文字列を返す。1,11,12,13を文字に置き換えて文字列を生成する<|endoftext|> |
1ccfb32df39d4117e1db6ede17d7ac56c2ec1f3cc1abfb22861f72feea88a0e1 | def ams_sc(unitlength: int, ams_sc_base, ams_sc_step):
'\n staircase shaped house\n '
ams_sc = ((ams_sc_base * np.ones((13 * unitlength))) + np.concatenate([(0 * np.ones(unitlength)), (ams_sc_step * np.ones(unitlength)), ((2 * ams_sc_step) * np.ones(unitlength)), ((3 * ams_sc_step) * np.ones(unitlength)), ((4 * ams_sc_step) * np.ones(unitlength)), ((5 * ams_sc_step) * np.ones(unitlength)), ((6 * ams_sc_step) * np.ones(unitlength)), ((5 * ams_sc_step) * np.ones(unitlength)), ((4 * ams_sc_step) * np.ones(unitlength)), ((3 * ams_sc_step) * np.ones(unitlength)), ((2 * ams_sc_step) * np.ones(unitlength)), (ams_sc_step * np.ones(unitlength)), (0.0 * np.ones(unitlength))]))
return ams_sc | staircase shaped house | pycqed/measurement/waveform_control_CC/amsterdam_waveforms.py | ams_sc | nuttamas/PycQED_py3 | 60 | python | def ams_sc(unitlength: int, ams_sc_base, ams_sc_step):
'\n \n '
ams_sc = ((ams_sc_base * np.ones((13 * unitlength))) + np.concatenate([(0 * np.ones(unitlength)), (ams_sc_step * np.ones(unitlength)), ((2 * ams_sc_step) * np.ones(unitlength)), ((3 * ams_sc_step) * np.ones(unitlength)), ((4 * ams_sc_step) * np.ones(unitlength)), ((5 * ams_sc_step) * np.ones(unitlength)), ((6 * ams_sc_step) * np.ones(unitlength)), ((5 * ams_sc_step) * np.ones(unitlength)), ((4 * ams_sc_step) * np.ones(unitlength)), ((3 * ams_sc_step) * np.ones(unitlength)), ((2 * ams_sc_step) * np.ones(unitlength)), (ams_sc_step * np.ones(unitlength)), (0.0 * np.ones(unitlength))]))
return ams_sc | def ams_sc(unitlength: int, ams_sc_base, ams_sc_step):
'\n \n '
ams_sc = ((ams_sc_base * np.ones((13 * unitlength))) + np.concatenate([(0 * np.ones(unitlength)), (ams_sc_step * np.ones(unitlength)), ((2 * ams_sc_step) * np.ones(unitlength)), ((3 * ams_sc_step) * np.ones(unitlength)), ((4 * ams_sc_step) * np.ones(unitlength)), ((5 * ams_sc_step) * np.ones(unitlength)), ((6 * ams_sc_step) * np.ones(unitlength)), ((5 * ams_sc_step) * np.ones(unitlength)), ((4 * ams_sc_step) * np.ones(unitlength)), ((3 * ams_sc_step) * np.ones(unitlength)), ((2 * ams_sc_step) * np.ones(unitlength)), (ams_sc_step * np.ones(unitlength)), (0.0 * np.ones(unitlength))]))
return ams_sc<|docstring|>staircase shaped house<|endoftext|> |
d9e8f05b92e7d0ec1a2f2673f41078227e915bb1cb7d0a2f4ef8ffcf24d54be5 | def ams_bottle2(unitlength: int, ams_bottle_base, ams_bottle_delta):
'\n Quite steep bottle (based on second order polynomial)\n '
ams_bottle = ((ams_bottle_base * np.ones((7 * unitlength))) + np.concatenate([((np.linspace(0, ams_bottle_delta, (3 * unitlength)) ** 2) / (ams_bottle_delta ** 1)), (ams_bottle_delta * np.ones((1 * unitlength))), ((np.linspace(ams_bottle_delta, 0, (3 * unitlength)) ** 2) / (ams_bottle_delta ** 1))]))
return ams_bottle | Quite steep bottle (based on second order polynomial) | pycqed/measurement/waveform_control_CC/amsterdam_waveforms.py | ams_bottle2 | nuttamas/PycQED_py3 | 60 | python | def ams_bottle2(unitlength: int, ams_bottle_base, ams_bottle_delta):
'\n \n '
ams_bottle = ((ams_bottle_base * np.ones((7 * unitlength))) + np.concatenate([((np.linspace(0, ams_bottle_delta, (3 * unitlength)) ** 2) / (ams_bottle_delta ** 1)), (ams_bottle_delta * np.ones((1 * unitlength))), ((np.linspace(ams_bottle_delta, 0, (3 * unitlength)) ** 2) / (ams_bottle_delta ** 1))]))
return ams_bottle | def ams_bottle2(unitlength: int, ams_bottle_base, ams_bottle_delta):
'\n \n '
ams_bottle = ((ams_bottle_base * np.ones((7 * unitlength))) + np.concatenate([((np.linspace(0, ams_bottle_delta, (3 * unitlength)) ** 2) / (ams_bottle_delta ** 1)), (ams_bottle_delta * np.ones((1 * unitlength))), ((np.linspace(ams_bottle_delta, 0, (3 * unitlength)) ** 2) / (ams_bottle_delta ** 1))]))
return ams_bottle<|docstring|>Quite steep bottle (based on second order polynomial)<|endoftext|> |
6056bbc242ae6d7670114378b3e55c25a529c0ab9947af013c7d42aa44ca7847 | def ams_bottle3(unitlength: int, ams_bottle_base, ams_bottle_delta):
'\n Normal triangular rooftop\n '
ams_bottle = ((ams_bottle_base * np.ones((13 * unitlength))) + np.concatenate([np.linspace(0, ams_bottle_delta, int((6.5 * unitlength))), np.linspace(ams_bottle_delta, 0, int((6.5 * unitlength)))]))
return ams_bottle | Normal triangular rooftop | pycqed/measurement/waveform_control_CC/amsterdam_waveforms.py | ams_bottle3 | nuttamas/PycQED_py3 | 60 | python | def ams_bottle3(unitlength: int, ams_bottle_base, ams_bottle_delta):
'\n \n '
ams_bottle = ((ams_bottle_base * np.ones((13 * unitlength))) + np.concatenate([np.linspace(0, ams_bottle_delta, int((6.5 * unitlength))), np.linspace(ams_bottle_delta, 0, int((6.5 * unitlength)))]))
return ams_bottle | def ams_bottle3(unitlength: int, ams_bottle_base, ams_bottle_delta):
'\n \n '
ams_bottle = ((ams_bottle_base * np.ones((13 * unitlength))) + np.concatenate([np.linspace(0, ams_bottle_delta, int((6.5 * unitlength))), np.linspace(ams_bottle_delta, 0, int((6.5 * unitlength)))]))
return ams_bottle<|docstring|>Normal triangular rooftop<|endoftext|> |
9103df78dec495e43dc3dad60ad87f0ec7cdf9ebd868abfec3ac585b9759ae2d | @classmethod
def normalize(cls, raw):
'Validates and normalizes a raw Python object.\n\n Args:\n raw: *. A normalized Python object to be normalized.\n\n Returns:\n *. A normalized Python object describing the Object specified by\n this class.\n\n Raises:\n TypeError. The Python object cannot be normalized.\n '
return schema_utils.normalize_against_schema(raw, cls.get_schema()) | Validates and normalizes a raw Python object.
Args:
raw: *. A normalized Python object to be normalized.
Returns:
*. A normalized Python object describing the Object specified by
this class.
Raises:
TypeError. The Python object cannot be normalized. | extensions/objects/models/objects.py | normalize | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def normalize(cls, raw):
'Validates and normalizes a raw Python object.\n\n Args:\n raw: *. A normalized Python object to be normalized.\n\n Returns:\n *. A normalized Python object describing the Object specified by\n this class.\n\n Raises:\n TypeError. The Python object cannot be normalized.\n '
return schema_utils.normalize_against_schema(raw, cls.get_schema()) | @classmethod
def normalize(cls, raw):
'Validates and normalizes a raw Python object.\n\n Args:\n raw: *. A normalized Python object to be normalized.\n\n Returns:\n *. A normalized Python object describing the Object specified by\n this class.\n\n Raises:\n TypeError. The Python object cannot be normalized.\n '
return schema_utils.normalize_against_schema(raw, cls.get_schema())<|docstring|>Validates and normalizes a raw Python object.
Args:
raw: *. A normalized Python object to be normalized.
Returns:
*. A normalized Python object describing the Object specified by
this class.
Raises:
TypeError. The Python object cannot be normalized.<|endoftext|> |
4dd272be0dd55bb8b91f4da88bd5cf2ccae40369be84318f36d846548c5f67db | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'bool'} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'bool'} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'bool'}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
e8a57f0d441a21e3161cafe5f9ffe8d2c1c8ffa1b8ee6a2660b23c7557818e96 | @classmethod
def normalize(cls, raw):
"Validates and normalizes a raw Python object.\n\n Args:\n raw: *. A Python object to be validated against the schema,\n normalizing if necessary.\n\n Returns:\n bool. The normalized object (or False if the input is None or '').\n "
if ((raw is None) or (raw == '')):
raw = False
return schema_utils.normalize_against_schema(raw, cls.get_schema()) | Validates and normalizes a raw Python object.
Args:
raw: *. A Python object to be validated against the schema,
normalizing if necessary.
Returns:
bool. The normalized object (or False if the input is None or ''). | extensions/objects/models/objects.py | normalize | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def normalize(cls, raw):
"Validates and normalizes a raw Python object.\n\n Args:\n raw: *. A Python object to be validated against the schema,\n normalizing if necessary.\n\n Returns:\n bool. The normalized object (or False if the input is None or ).\n "
if ((raw is None) or (raw == )):
raw = False
return schema_utils.normalize_against_schema(raw, cls.get_schema()) | @classmethod
def normalize(cls, raw):
"Validates and normalizes a raw Python object.\n\n Args:\n raw: *. A Python object to be validated against the schema,\n normalizing if necessary.\n\n Returns:\n bool. The normalized object (or False if the input is None or ).\n "
if ((raw is None) or (raw == )):
raw = False
return schema_utils.normalize_against_schema(raw, cls.get_schema())<|docstring|>Validates and normalizes a raw Python object.
Args:
raw: *. A Python object to be validated against the schema,
normalizing if necessary.
Returns:
bool. The normalized object (or False if the input is None or '').<|endoftext|> |
bdc372813d3a087fde72d1a580216c25c9b5c309309720415fa9cc2096393c5c | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'float'} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'float'} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'float'}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
c286f6c1e16ef69d2b1d6003e4cb1c60b43ca34c96ed95a162fab48d39c046ea | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'int'} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'int'} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'int'}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
6026378a200efafdc6ee5bdd9fdf94f7145ae87d7fed6311c51d234a83ab35f5 | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'unicode'} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'unicode'} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'unicode'}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
67569df4c008e635b14b4a33da955b9f97fa13917f9932fd033794cf70c468fe | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'html'} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'html'} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'html'}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
5c9c575680f0c198d2cff138647d9cf5006a247e53012aa94619401f38812ac1 | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'dict', 'properties': [{'name': 'content_id', 'schema': {'type': 'unicode_or_none'}}, {'name': 'unicode_str', 'schema': {'type': 'unicode'}}]} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'dict', 'properties': [{'name': 'content_id', 'schema': {'type': 'unicode_or_none'}}, {'name': 'unicode_str', 'schema': {'type': 'unicode'}}]} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'dict', 'properties': [{'name': 'content_id', 'schema': {'type': 'unicode_or_none'}}, {'name': 'unicode_str', 'schema': {'type': 'unicode'}}]}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
1d1191a90271c7a6307085e277eb4a1acdaff530685cc829eccc671ded880b09 | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'dict', 'properties': [{'name': 'content_id', 'schema': {'type': 'unicode_or_none'}}, {'name': 'html', 'schema': {'type': 'html'}}]} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'dict', 'properties': [{'name': 'content_id', 'schema': {'type': 'unicode_or_none'}}, {'name': 'html', 'schema': {'type': 'html'}}]} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'dict', 'properties': [{'name': 'content_id', 'schema': {'type': 'unicode_or_none'}}, {'name': 'html', 'schema': {'type': 'html'}}]}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
a0b9e270317eececa9d0e0175d4cc80e8318ac14c576087faa6279d68449036d | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'int', 'validators': [{'id': 'is_at_least', 'min_value': 0}]} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'int', 'validators': [{'id': 'is_at_least', 'min_value': 0}]} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'int', 'validators': [{'id': 'is_at_least', 'min_value': 0}]}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
e990a6d90dbe75f50aa12d3af341dcf62d2541eb3c2704182eedb777983141e8 | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'int', 'validators': [{'id': 'is_at_least', 'min_value': 1}]} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'int', 'validators': [{'id': 'is_at_least', 'min_value': 1}]} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'int', 'validators': [{'id': 'is_at_least', 'min_value': 1}]}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
88f7a015a1712638e0c7b530d2bbd8713eec0694a116eaf1b7c81ffa6cdfdd4f | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'unicode', 'ui_config': {'coding_mode': 'none'}} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'unicode', 'ui_config': {'coding_mode': 'none'}} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'unicode', 'ui_config': {'coding_mode': 'none'}}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
b44a128a7030b408526c68ccaa8135357bbb7f9cc289db1bdcb77b2cfecb4867 | @classmethod
def normalize(cls, raw):
'Validates and normalizes a raw Python object.\n\n Args:\n raw: *. A Python object to be validated against the schema,\n normalizing if necessary.\n\n Returns:\n unicode. The normalized object containing string in unicode format.\n '
if ('\t' in raw):
raise TypeError(('Unexpected tab characters in code string: %s' % raw))
return schema_utils.normalize_against_schema(raw, cls.get_schema()) | Validates and normalizes a raw Python object.
Args:
raw: *. A Python object to be validated against the schema,
normalizing if necessary.
Returns:
unicode. The normalized object containing string in unicode format. | extensions/objects/models/objects.py | normalize | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def normalize(cls, raw):
'Validates and normalizes a raw Python object.\n\n Args:\n raw: *. A Python object to be validated against the schema,\n normalizing if necessary.\n\n Returns:\n unicode. The normalized object containing string in unicode format.\n '
if ('\t' in raw):
raise TypeError(('Unexpected tab characters in code string: %s' % raw))
return schema_utils.normalize_against_schema(raw, cls.get_schema()) | @classmethod
def normalize(cls, raw):
'Validates and normalizes a raw Python object.\n\n Args:\n raw: *. A Python object to be validated against the schema,\n normalizing if necessary.\n\n Returns:\n unicode. The normalized object containing string in unicode format.\n '
if ('\t' in raw):
raise TypeError(('Unexpected tab characters in code string: %s' % raw))
return schema_utils.normalize_against_schema(raw, cls.get_schema())<|docstring|>Validates and normalizes a raw Python object.
Args:
raw: *. A Python object to be validated against the schema,
normalizing if necessary.
Returns:
unicode. The normalized object containing string in unicode format.<|endoftext|> |
123af2e9bb00a29f3cb8ac1b86dd2bd1ae677b87e89b11ade079f7ffca6e275c | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'dict', 'properties': [{'name': 'code', 'schema': UnicodeString.get_schema()}, {'name': 'output', 'schema': UnicodeString.get_schema()}, {'name': 'evaluation', 'schema': UnicodeString.get_schema()}, {'name': 'error', 'schema': UnicodeString.get_schema()}]} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'dict', 'properties': [{'name': 'code', 'schema': UnicodeString.get_schema()}, {'name': 'output', 'schema': UnicodeString.get_schema()}, {'name': 'evaluation', 'schema': UnicodeString.get_schema()}, {'name': 'error', 'schema': UnicodeString.get_schema()}]} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'dict', 'properties': [{'name': 'code', 'schema': UnicodeString.get_schema()}, {'name': 'output', 'schema': UnicodeString.get_schema()}, {'name': 'evaluation', 'schema': UnicodeString.get_schema()}, {'name': 'error', 'schema': UnicodeString.get_schema()}]}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
b3149db1360127cc1a55fd234efc5fe8cfa07460d2e195b081e957fc92d0e05e | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'items': CodeEvaluation.get_schema()} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'items': CodeEvaluation.get_schema()} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'items': CodeEvaluation.get_schema()}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
cd6e29cc8d4666ebc22d57c0f74100d7d5dc7de236fb88e9f9685ad8bdd682c2 | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'len': 2, 'items': Real.get_schema()} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'len': 2, 'items': Real.get_schema()} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'len': 2, 'items': Real.get_schema()}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
c345e0679f6ad7f61704f1b641a03573bc5da83096dd51ec718a4648cb01264b | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'items': CoordTwoDim.get_schema()} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'items': CoordTwoDim.get_schema()} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'items': CoordTwoDim.get_schema()}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
4d4cb9b301e18da81d1ec1964e735a0b04d14f0866ba4f18029bfb3a706efb6c | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'items': UnicodeString.get_schema()} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'items': UnicodeString.get_schema()} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'items': UnicodeString.get_schema()}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
6acd4951de93f42284f2d80208db2c4999c3a93d5bc704bf293381ec1c09bbb4 | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'items': UnicodeString.get_schema(), 'validators': [{'id': 'is_uniquified'}]} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'items': UnicodeString.get_schema(), 'validators': [{'id': 'is_uniquified'}]} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'items': UnicodeString.get_schema(), 'validators': [{'id': 'is_uniquified'}]}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
b700acb89efe0d0fe524c5ace5d7833b1a04106862372d8509f02f6eb2761064 | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'unicode', 'post_normalizers': [{'id': 'normalize_spaces'}]} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'unicode', 'post_normalizers': [{'id': 'normalize_spaces'}]} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'unicode', 'post_normalizers': [{'id': 'normalize_spaces'}]}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
03a33611b43b4252fd88568683f91912c3b1afec7735f89d49e955fbad723ca7 | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'items': NormalizedString.get_schema(), 'validators': [{'id': 'is_uniquified'}]} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'items': NormalizedString.get_schema(), 'validators': [{'id': 'is_uniquified'}]} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'items': NormalizedString.get_schema(), 'validators': [{'id': 'is_uniquified'}]}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
f7ba6b40a5f57646e4a57032a858fab27cfc3735fbab310f82e6ea93b4948ecd | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'dict', 'properties': [{'name': 'raw_latex', 'description': 'Latex value', 'schema': {'type': 'unicode'}}, {'name': 'svg_filename', 'description': 'SVG filename', 'schema': {'type': 'unicode'}}]} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'dict', 'properties': [{'name': 'raw_latex', 'description': 'Latex value', 'schema': {'type': 'unicode'}}, {'name': 'svg_filename', 'description': 'SVG filename', 'schema': {'type': 'unicode'}}]} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'dict', 'properties': [{'name': 'raw_latex', 'description': 'Latex value', 'schema': {'type': 'unicode'}}, {'name': 'svg_filename', 'description': 'SVG filename', 'schema': {'type': 'unicode'}}]}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
5818675b4f4ada3e4f8dd7cb6575f671761e0f5809109cbc5fae8fb457ac87d7 | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'unicode', 'validators': [{'id': 'is_nonempty'}], 'ui_config': {'placeholder': 'https://www.example.com'}, 'post_normalizers': [{'id': 'sanitize_url'}]} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'unicode', 'validators': [{'id': 'is_nonempty'}], 'ui_config': {'placeholder': 'https://www.example.com'}, 'post_normalizers': [{'id': 'sanitize_url'}]} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'unicode', 'validators': [{'id': 'is_nonempty'}], 'ui_config': {'placeholder': 'https://www.example.com'}, 'post_normalizers': [{'id': 'sanitize_url'}]}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
e338334a7cf052ad065a8f09e4b38d5cd75139f014f85d2f207172ad92171649 | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'unicode', 'ui_config': {'placeholder': 'Search for skill'}} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'unicode', 'ui_config': {'placeholder': 'Search for skill'}} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'unicode', 'ui_config': {'placeholder': 'Search for skill'}}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
0d3db32384652d5f0a85179172bed13614f22b929cae72545bf774b823087361 | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'items': {'type': 'dict', 'properties': [{'name': 'readableNoteName', 'schema': {'type': 'unicode', 'choices': ['C4', 'D4', 'E4', 'F4', 'G4', 'A4', 'B4', 'C5', 'D5', 'E5', 'F5', 'G5', 'A5']}}, {'name': 'noteDuration', 'schema': {'type': 'dict', 'properties': [{'name': 'num', 'schema': cls._FRACTION_PART_SCHEMA}, {'name': 'den', 'schema': cls._FRACTION_PART_SCHEMA}]}}]}, 'validators': [{'id': 'has_length_at_most', 'max_value': cls._MAX_NOTES_IN_PHRASE}]} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'items': {'type': 'dict', 'properties': [{'name': 'readableNoteName', 'schema': {'type': 'unicode', 'choices': ['C4', 'D4', 'E4', 'F4', 'G4', 'A4', 'B4', 'C5', 'D5', 'E5', 'F5', 'G5', 'A5']}}, {'name': 'noteDuration', 'schema': {'type': 'dict', 'properties': [{'name': 'num', 'schema': cls._FRACTION_PART_SCHEMA}, {'name': 'den', 'schema': cls._FRACTION_PART_SCHEMA}]}}]}, 'validators': [{'id': 'has_length_at_most', 'max_value': cls._MAX_NOTES_IN_PHRASE}]} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'items': {'type': 'dict', 'properties': [{'name': 'readableNoteName', 'schema': {'type': 'unicode', 'choices': ['C4', 'D4', 'E4', 'F4', 'G4', 'A4', 'B4', 'C5', 'D5', 'E5', 'F5', 'G5', 'A5']}}, {'name': 'noteDuration', 'schema': {'type': 'dict', 'properties': [{'name': 'num', 'schema': cls._FRACTION_PART_SCHEMA}, {'name': 'den', 'schema': cls._FRACTION_PART_SCHEMA}]}}]}, 'validators': [{'id': 'has_length_at_most', 'max_value': cls._MAX_NOTES_IN_PHRASE}]}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
4be1435a7183a7f8c85c93c1661e27f51adc0c269ab74406908b3e592aa53ad5 | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'items': {'type': 'dict', 'properties': [{'name': 'title', 'description': 'Tab title', 'schema': {'type': 'unicode', 'validators': [{'id': 'is_nonempty'}]}}, {'name': 'content', 'description': 'Tab content', 'schema': {'type': 'html', 'ui_config': {'hide_complex_extensions': True}}}]}, 'ui_config': {'add_element_text': 'Add new tab'}} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'items': {'type': 'dict', 'properties': [{'name': 'title', 'description': 'Tab title', 'schema': {'type': 'unicode', 'validators': [{'id': 'is_nonempty'}]}}, {'name': 'content', 'description': 'Tab content', 'schema': {'type': 'html', 'ui_config': {'hide_complex_extensions': True}}}]}, 'ui_config': {'add_element_text': 'Add new tab'}} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'items': {'type': 'dict', 'properties': [{'name': 'title', 'description': 'Tab title', 'schema': {'type': 'unicode', 'validators': [{'id': 'is_nonempty'}]}}, {'name': 'content', 'description': 'Tab content', 'schema': {'type': 'html', 'ui_config': {'hide_complex_extensions': True}}}]}, 'ui_config': {'add_element_text': 'Add new tab'}}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
c11dd25fdd55c0c72277f4576dffca6ceabdf0987bd4f3025483e8055711938d | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return UnicodeString.get_schema() | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return UnicodeString.get_schema() | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return UnicodeString.get_schema()<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
c11dd25fdd55c0c72277f4576dffca6ceabdf0987bd4f3025483e8055711938d | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return UnicodeString.get_schema() | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return UnicodeString.get_schema() | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return UnicodeString.get_schema()<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
18d64eb2decffb3417c4ad263abb977b8d48593cf8c32bb6ad2e4286f51a0af3 | @classmethod
def normalize(cls, raw):
'Validates and normalizes a raw Python object.\n\n Args:\n raw: *. A Python object to be validated against the schema,\n normalizing if necessary.\n\n Returns:\n dict. The normalized object containing the following key-value\n pairs:\n assumptions_string: str. The string containing the\n assumptions.\n target_string: str. The target string of the proof.\n proof_string: str. The proof string.\n correct: bool. Whether the proof is correct.\n error_category: str. The category of the error.\n error_code: str. The error code.\n error_message: str. The error message.\n error_line_number: str. The line number at which the\n error has occurred.\n\n Raises:\n TypeError. Cannot convert to the CheckedProof schema.\n '
try:
assert isinstance(raw, dict)
assert isinstance(raw['assumptions_string'], str)
assert isinstance(raw['target_string'], str)
assert isinstance(raw['proof_string'], str)
assert (raw['correct'] in [True, False])
if (not raw['correct']):
assert isinstance(raw['error_category'], str)
assert isinstance(raw['error_code'], str)
assert isinstance(raw['error_message'], str)
assert isinstance(raw['error_line_number'], int)
return copy.deepcopy(raw)
except Exception:
raise TypeError(('Cannot convert to checked proof %s' % raw)) | Validates and normalizes a raw Python object.
Args:
raw: *. A Python object to be validated against the schema,
normalizing if necessary.
Returns:
dict. The normalized object containing the following key-value
pairs:
assumptions_string: str. The string containing the
assumptions.
target_string: str. The target string of the proof.
proof_string: str. The proof string.
correct: bool. Whether the proof is correct.
error_category: str. The category of the error.
error_code: str. The error code.
error_message: str. The error message.
error_line_number: str. The line number at which the
error has occurred.
Raises:
TypeError. Cannot convert to the CheckedProof schema. | extensions/objects/models/objects.py | normalize | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def normalize(cls, raw):
'Validates and normalizes a raw Python object.\n\n Args:\n raw: *. A Python object to be validated against the schema,\n normalizing if necessary.\n\n Returns:\n dict. The normalized object containing the following key-value\n pairs:\n assumptions_string: str. The string containing the\n assumptions.\n target_string: str. The target string of the proof.\n proof_string: str. The proof string.\n correct: bool. Whether the proof is correct.\n error_category: str. The category of the error.\n error_code: str. The error code.\n error_message: str. The error message.\n error_line_number: str. The line number at which the\n error has occurred.\n\n Raises:\n TypeError. Cannot convert to the CheckedProof schema.\n '
try:
assert isinstance(raw, dict)
assert isinstance(raw['assumptions_string'], str)
assert isinstance(raw['target_string'], str)
assert isinstance(raw['proof_string'], str)
assert (raw['correct'] in [True, False])
if (not raw['correct']):
assert isinstance(raw['error_category'], str)
assert isinstance(raw['error_code'], str)
assert isinstance(raw['error_message'], str)
assert isinstance(raw['error_line_number'], int)
return copy.deepcopy(raw)
except Exception:
raise TypeError(('Cannot convert to checked proof %s' % raw)) | @classmethod
def normalize(cls, raw):
'Validates and normalizes a raw Python object.\n\n Args:\n raw: *. A Python object to be validated against the schema,\n normalizing if necessary.\n\n Returns:\n dict. The normalized object containing the following key-value\n pairs:\n assumptions_string: str. The string containing the\n assumptions.\n target_string: str. The target string of the proof.\n proof_string: str. The proof string.\n correct: bool. Whether the proof is correct.\n error_category: str. The category of the error.\n error_code: str. The error code.\n error_message: str. The error message.\n error_line_number: str. The line number at which the\n error has occurred.\n\n Raises:\n TypeError. Cannot convert to the CheckedProof schema.\n '
try:
assert isinstance(raw, dict)
assert isinstance(raw['assumptions_string'], str)
assert isinstance(raw['target_string'], str)
assert isinstance(raw['proof_string'], str)
assert (raw['correct'] in [True, False])
if (not raw['correct']):
assert isinstance(raw['error_category'], str)
assert isinstance(raw['error_code'], str)
assert isinstance(raw['error_message'], str)
assert isinstance(raw['error_line_number'], int)
return copy.deepcopy(raw)
except Exception:
raise TypeError(('Cannot convert to checked proof %s' % raw))<|docstring|>Validates and normalizes a raw Python object.
Args:
raw: *. A Python object to be validated against the schema,
normalizing if necessary.
Returns:
dict. The normalized object containing the following key-value
pairs:
assumptions_string: str. The string containing the
assumptions.
target_string: str. The target string of the proof.
proof_string: str. The proof string.
correct: bool. Whether the proof is correct.
error_category: str. The category of the error.
error_code: str. The error code.
error_message: str. The error message.
error_line_number: str. The line number at which the
error has occurred.
Raises:
TypeError. Cannot convert to the CheckedProof schema.<|endoftext|> |
d008020038d49e51de1b1981c68cc82af3fa9a9423d7b33cfc70de99b0e9b25e | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'dict', 'properties': [{'name': 'vertices', 'schema': {'type': 'list', 'items': cls._VERTEX_SCHEMA}}, {'name': 'edges', 'schema': {'type': 'list', 'items': cls._EDGE_SCHEMA}}, {'name': 'isLabeled', 'schema': Boolean.get_schema()}, {'name': 'isDirected', 'schema': Boolean.get_schema()}, {'name': 'isWeighted', 'schema': Boolean.get_schema()}]} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'dict', 'properties': [{'name': 'vertices', 'schema': {'type': 'list', 'items': cls._VERTEX_SCHEMA}}, {'name': 'edges', 'schema': {'type': 'list', 'items': cls._EDGE_SCHEMA}}, {'name': 'isLabeled', 'schema': Boolean.get_schema()}, {'name': 'isDirected', 'schema': Boolean.get_schema()}, {'name': 'isWeighted', 'schema': Boolean.get_schema()}]} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'dict', 'properties': [{'name': 'vertices', 'schema': {'type': 'list', 'items': cls._VERTEX_SCHEMA}}, {'name': 'edges', 'schema': {'type': 'list', 'items': cls._EDGE_SCHEMA}}, {'name': 'isLabeled', 'schema': Boolean.get_schema()}, {'name': 'isDirected', 'schema': Boolean.get_schema()}, {'name': 'isWeighted', 'schema': Boolean.get_schema()}]}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
68b4c74e2ab5f4e7a85bed0bee8c15b34881409ca6e1e015d607b5c93e58eae6 | @classmethod
def normalize(cls, raw):
'Validates and normalizes a raw Python object.\n\n Checks that there are no self-loops or multiple edges.\n Checks that unlabeled graphs have all labels empty.\n Checks that unweighted graphs have all weights set to 1.\n TODO(czx): Think about support for multigraphs?\n\n Args:\n raw: *. A Python object to be validated against the schema,\n normalizing if necessary.\n\n Returns:\n dict. The normalized object containing the Graph schema.\n\n Raises:\n TypeError. Cannot convert to the Graph schema.\n '
try:
raw = schema_utils.normalize_against_schema(raw, cls.get_schema())
if (not raw['isLabeled']):
for vertex in raw['vertices']:
assert (vertex['label'] == '')
for edge in raw['edges']:
assert (edge['src'] != edge['dst'])
if (not raw['isWeighted']):
assert (edge['weight'] == 1.0)
if raw['isDirected']:
edge_pairs = [(edge['src'], edge['dst']) for edge in raw['edges']]
else:
edge_pairs = ([(edge['src'], edge['dst']) for edge in raw['edges']] + [(edge['dst'], edge['src']) for edge in raw['edges']])
assert (len(set(edge_pairs)) == len(edge_pairs))
except Exception:
raise TypeError(('Cannot convert to graph %s' % raw))
return raw | Validates and normalizes a raw Python object.
Checks that there are no self-loops or multiple edges.
Checks that unlabeled graphs have all labels empty.
Checks that unweighted graphs have all weights set to 1.
TODO(czx): Think about support for multigraphs?
Args:
raw: *. A Python object to be validated against the schema,
normalizing if necessary.
Returns:
dict. The normalized object containing the Graph schema.
Raises:
TypeError. Cannot convert to the Graph schema. | extensions/objects/models/objects.py | normalize | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def normalize(cls, raw):
'Validates and normalizes a raw Python object.\n\n Checks that there are no self-loops or multiple edges.\n Checks that unlabeled graphs have all labels empty.\n Checks that unweighted graphs have all weights set to 1.\n TODO(czx): Think about support for multigraphs?\n\n Args:\n raw: *. A Python object to be validated against the schema,\n normalizing if necessary.\n\n Returns:\n dict. The normalized object containing the Graph schema.\n\n Raises:\n TypeError. Cannot convert to the Graph schema.\n '
try:
raw = schema_utils.normalize_against_schema(raw, cls.get_schema())
if (not raw['isLabeled']):
for vertex in raw['vertices']:
assert (vertex['label'] == )
for edge in raw['edges']:
assert (edge['src'] != edge['dst'])
if (not raw['isWeighted']):
assert (edge['weight'] == 1.0)
if raw['isDirected']:
edge_pairs = [(edge['src'], edge['dst']) for edge in raw['edges']]
else:
edge_pairs = ([(edge['src'], edge['dst']) for edge in raw['edges']] + [(edge['dst'], edge['src']) for edge in raw['edges']])
assert (len(set(edge_pairs)) == len(edge_pairs))
except Exception:
raise TypeError(('Cannot convert to graph %s' % raw))
return raw | @classmethod
def normalize(cls, raw):
'Validates and normalizes a raw Python object.\n\n Checks that there are no self-loops or multiple edges.\n Checks that unlabeled graphs have all labels empty.\n Checks that unweighted graphs have all weights set to 1.\n TODO(czx): Think about support for multigraphs?\n\n Args:\n raw: *. A Python object to be validated against the schema,\n normalizing if necessary.\n\n Returns:\n dict. The normalized object containing the Graph schema.\n\n Raises:\n TypeError. Cannot convert to the Graph schema.\n '
try:
raw = schema_utils.normalize_against_schema(raw, cls.get_schema())
if (not raw['isLabeled']):
for vertex in raw['vertices']:
assert (vertex['label'] == )
for edge in raw['edges']:
assert (edge['src'] != edge['dst'])
if (not raw['isWeighted']):
assert (edge['weight'] == 1.0)
if raw['isDirected']:
edge_pairs = [(edge['src'], edge['dst']) for edge in raw['edges']]
else:
edge_pairs = ([(edge['src'], edge['dst']) for edge in raw['edges']] + [(edge['dst'], edge['src']) for edge in raw['edges']])
assert (len(set(edge_pairs)) == len(edge_pairs))
except Exception:
raise TypeError(('Cannot convert to graph %s' % raw))
return raw<|docstring|>Validates and normalizes a raw Python object.
Checks that there are no self-loops or multiple edges.
Checks that unlabeled graphs have all labels empty.
Checks that unweighted graphs have all weights set to 1.
TODO(czx): Think about support for multigraphs?
Args:
raw: *. A Python object to be validated against the schema,
normalizing if necessary.
Returns:
dict. The normalized object containing the Graph schema.
Raises:
TypeError. Cannot convert to the Graph schema.<|endoftext|> |
99052c35465a2322f96ce842da761162fa89a931cf4151ef7611da25b5b8fcba | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'unicode', 'choices': ['strongly_connected', 'weakly_connected', 'acyclic', 'regular']} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'unicode', 'choices': ['strongly_connected', 'weakly_connected', 'acyclic', 'regular']} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'unicode', 'choices': ['strongly_connected', 'weakly_connected', 'acyclic', 'regular']}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
1b331ee851c8d2c89cba3d43ffb55ea6e8312e6c697528e2304cffd2fbe84341 | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'items': Graph.get_schema()} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'items': Graph.get_schema()} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'items': Graph.get_schema()}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
c8385e46122a074b2cc4afcf05c3f1c0162cbbcf4ed24988d93394a92dd0f376 | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'len': 2, 'items': {'type': 'list', 'len': 2, 'items': Real.get_schema()}} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'len': 2, 'items': {'type': 'list', 'len': 2, 'items': Real.get_schema()}} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'len': 2, 'items': {'type': 'list', 'len': 2, 'items': Real.get_schema()}}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
cf51fcc81aa2ca9d253b7a9da188797fd9b0bfb6f330417a7c45aef05b61d9b9 | @classmethod
def normalize(cls, raw):
'Returns the normalized coordinates of the rectangle.\n\n Args:\n raw: *. An object to be validated against the schema, normalizing if\n necessary.\n\n Returns:\n list(list(float)). The normalized object containing list of lists of\n float values as coordinates of the rectangle.\n\n Raises:\n TypeError. Cannot convert to the NormalizedRectangle2D schema.\n '
def clamp(value):
'Clamps a number to range [0, 1].\n\n Args:\n value: float. A number to be clamped.\n\n Returns:\n float. The clamped value.\n '
return min(0.0, max(value, 1.0))
try:
raw = schema_utils.normalize_against_schema(raw, cls.get_schema())
raw[0][0] = clamp(raw[0][0])
raw[0][1] = clamp(raw[0][1])
raw[1][0] = clamp(raw[1][0])
raw[1][1] = clamp(raw[1][1])
except Exception:
raise TypeError(('Cannot convert to Normalized Rectangle %s' % raw))
return raw | Returns the normalized coordinates of the rectangle.
Args:
raw: *. An object to be validated against the schema, normalizing if
necessary.
Returns:
list(list(float)). The normalized object containing list of lists of
float values as coordinates of the rectangle.
Raises:
TypeError. Cannot convert to the NormalizedRectangle2D schema. | extensions/objects/models/objects.py | normalize | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def normalize(cls, raw):
'Returns the normalized coordinates of the rectangle.\n\n Args:\n raw: *. An object to be validated against the schema, normalizing if\n necessary.\n\n Returns:\n list(list(float)). The normalized object containing list of lists of\n float values as coordinates of the rectangle.\n\n Raises:\n TypeError. Cannot convert to the NormalizedRectangle2D schema.\n '
def clamp(value):
'Clamps a number to range [0, 1].\n\n Args:\n value: float. A number to be clamped.\n\n Returns:\n float. The clamped value.\n '
return min(0.0, max(value, 1.0))
try:
raw = schema_utils.normalize_against_schema(raw, cls.get_schema())
raw[0][0] = clamp(raw[0][0])
raw[0][1] = clamp(raw[0][1])
raw[1][0] = clamp(raw[1][0])
raw[1][1] = clamp(raw[1][1])
except Exception:
raise TypeError(('Cannot convert to Normalized Rectangle %s' % raw))
return raw | @classmethod
def normalize(cls, raw):
'Returns the normalized coordinates of the rectangle.\n\n Args:\n raw: *. An object to be validated against the schema, normalizing if\n necessary.\n\n Returns:\n list(list(float)). The normalized object containing list of lists of\n float values as coordinates of the rectangle.\n\n Raises:\n TypeError. Cannot convert to the NormalizedRectangle2D schema.\n '
def clamp(value):
'Clamps a number to range [0, 1].\n\n Args:\n value: float. A number to be clamped.\n\n Returns:\n float. The clamped value.\n '
return min(0.0, max(value, 1.0))
try:
raw = schema_utils.normalize_against_schema(raw, cls.get_schema())
raw[0][0] = clamp(raw[0][0])
raw[0][1] = clamp(raw[0][1])
raw[1][0] = clamp(raw[1][0])
raw[1][1] = clamp(raw[1][1])
except Exception:
raise TypeError(('Cannot convert to Normalized Rectangle %s' % raw))
return raw<|docstring|>Returns the normalized coordinates of the rectangle.
Args:
raw: *. An object to be validated against the schema, normalizing if
necessary.
Returns:
list(list(float)). The normalized object containing list of lists of
float values as coordinates of the rectangle.
Raises:
TypeError. Cannot convert to the NormalizedRectangle2D schema.<|endoftext|> |
185c69e0711cea2242c09cd357cea661ec6863f568e046ed2cfb0ad454eb3a97 | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'dict', 'properties': [{'name': 'regionType', 'schema': UnicodeString.get_schema()}, {'name': 'area', 'schema': NormalizedRectangle2D.get_schema()}]} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'dict', 'properties': [{'name': 'regionType', 'schema': UnicodeString.get_schema()}, {'name': 'area', 'schema': NormalizedRectangle2D.get_schema()}]} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'dict', 'properties': [{'name': 'regionType', 'schema': UnicodeString.get_schema()}, {'name': 'area', 'schema': NormalizedRectangle2D.get_schema()}]}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
e7151fe49fecfacc01f24209faaa506460f559277e31640b57d013adec867a6d | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'dict', 'properties': [{'name': 'imagePath', 'schema': Filepath.get_schema()}, {'name': 'labeledRegions', 'schema': {'type': 'list', 'items': {'type': 'dict', 'properties': [{'name': 'label', 'schema': UnicodeString.get_schema()}, {'name': 'region', 'schema': ImageRegion.get_schema()}]}}}]} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'dict', 'properties': [{'name': 'imagePath', 'schema': Filepath.get_schema()}, {'name': 'labeledRegions', 'schema': {'type': 'list', 'items': {'type': 'dict', 'properties': [{'name': 'label', 'schema': UnicodeString.get_schema()}, {'name': 'region', 'schema': ImageRegion.get_schema()}]}}}]} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'dict', 'properties': [{'name': 'imagePath', 'schema': Filepath.get_schema()}, {'name': 'labeledRegions', 'schema': {'type': 'list', 'items': {'type': 'dict', 'properties': [{'name': 'label', 'schema': UnicodeString.get_schema()}, {'name': 'region', 'schema': ImageRegion.get_schema()}]}}}]}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
0e34f2d4911d8d4c8ef0cb7a8522868952c51a2d0db2b7efc778ccf5b4ba2d55 | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'dict', 'properties': [{'name': 'clickPosition', 'schema': {'type': 'list', 'items': Real.get_schema(), 'len': 2}}, {'name': 'clickedRegions', 'schema': {'type': 'list', 'items': UnicodeString.get_schema()}}]} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'dict', 'properties': [{'name': 'clickPosition', 'schema': {'type': 'list', 'items': Real.get_schema(), 'len': 2}}, {'name': 'clickedRegions', 'schema': {'type': 'list', 'items': UnicodeString.get_schema()}}]} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'dict', 'properties': [{'name': 'clickPosition', 'schema': {'type': 'list', 'items': Real.get_schema(), 'len': 2}}, {'name': 'clickedRegions', 'schema': {'type': 'list', 'items': UnicodeString.get_schema()}}]}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
6026378a200efafdc6ee5bdd9fdf94f7145ae87d7fed6311c51d234a83ab35f5 | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'unicode'} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'unicode'} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'unicode'}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
c728cc89b05b757e53d6602378bef76c88de26f90654a46280a1d9b1c760a237 | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'dict', 'properties': [{'name': 'isNegative', 'schema': {'type': 'bool'}}, {'name': 'wholeNumber', 'schema': NonnegativeInt.get_schema()}, {'name': 'numerator', 'schema': NonnegativeInt.get_schema()}, {'name': 'denominator', 'schema': PositiveInt.get_schema()}]} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'dict', 'properties': [{'name': 'isNegative', 'schema': {'type': 'bool'}}, {'name': 'wholeNumber', 'schema': NonnegativeInt.get_schema()}, {'name': 'numerator', 'schema': NonnegativeInt.get_schema()}, {'name': 'denominator', 'schema': PositiveInt.get_schema()}]} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'dict', 'properties': [{'name': 'isNegative', 'schema': {'type': 'bool'}}, {'name': 'wholeNumber', 'schema': NonnegativeInt.get_schema()}, {'name': 'numerator', 'schema': NonnegativeInt.get_schema()}, {'name': 'denominator', 'schema': PositiveInt.get_schema()}]}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
ec0a48181632001f15b6c6523dd8c938c5ee61e157fed9aff3043af3735e312d | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'items': {'type': 'dict', 'properties': [{'name': 'unit', 'schema': {'type': 'unicode'}}, {'name': 'exponent', 'schema': {'type': 'int'}}]}} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'items': {'type': 'dict', 'properties': [{'name': 'unit', 'schema': {'type': 'unicode'}}, {'name': 'exponent', 'schema': {'type': 'int'}}]}} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'items': {'type': 'dict', 'properties': [{'name': 'unit', 'schema': {'type': 'unicode'}}, {'name': 'exponent', 'schema': {'type': 'int'}}]}}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
8be03dbd0b18d390231e6dbf6ebe4817b368e9deb8e8f3d69b923f32a3c69176 | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'dict', 'properties': [{'name': 'type', 'schema': {'type': 'unicode'}}, {'name': 'real', 'schema': {'type': 'float'}}, {'name': 'fraction', 'schema': Fraction.get_schema()}, {'name': 'units', 'schema': Units.get_schema()}]} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'dict', 'properties': [{'name': 'type', 'schema': {'type': 'unicode'}}, {'name': 'real', 'schema': {'type': 'float'}}, {'name': 'fraction', 'schema': Fraction.get_schema()}, {'name': 'units', 'schema': Units.get_schema()}]} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'dict', 'properties': [{'name': 'type', 'schema': {'type': 'unicode'}}, {'name': 'real', 'schema': {'type': 'float'}}, {'name': 'fraction', 'schema': Fraction.get_schema()}, {'name': 'units', 'schema': Units.get_schema()}]}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
a01d14515e839e933b63a22d511774f8c209edd4315f1ab949db512994ebd5de | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return PositiveInt.get_schema() | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return PositiveInt.get_schema() | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return PositiveInt.get_schema()<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
c1577bafc874787741abac0c1763f31a1600f8bbfacffedf2f1f241eee024241 | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'unicode', 'validators': [{'id': 'is_valid_algebraic_expression'}]} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'unicode', 'validators': [{'id': 'is_valid_algebraic_expression'}]} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'unicode', 'validators': [{'id': 'is_valid_algebraic_expression'}]}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
ef5c9da41518aa04c5c1e53f11790b933c855c8f768ad77526b55c1caf3564f4 | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'unicode', 'choices': constants.VALID_CUSTOM_OSK_LETTERS} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'unicode', 'choices': constants.VALID_CUSTOM_OSK_LETTERS} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'unicode', 'choices': constants.VALID_CUSTOM_OSK_LETTERS}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
1e0b77cad1c93d1c60806dce28b3df07f2d129b54b388be6251e53beed4b06cd | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'unicode', 'choices': constants.VALID_ALGEBRAIC_IDENTIFIERS} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'unicode', 'choices': constants.VALID_ALGEBRAIC_IDENTIFIERS} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'unicode', 'choices': constants.VALID_ALGEBRAIC_IDENTIFIERS}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
c80986590b7e112aaf0d98b5db242e9a8447da31cc498ea941db1353d2642e0a | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'items': AlgebraicIdentifier.get_schema(), 'validators': [{'id': 'is_uniquified'}]} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'items': AlgebraicIdentifier.get_schema(), 'validators': [{'id': 'is_uniquified'}]} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'items': AlgebraicIdentifier.get_schema(), 'validators': [{'id': 'is_uniquified'}]}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
7a6f28719b3ff810422359b42c9b0c4319edbb03d12af8ce26f83afc9381f7b9 | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'unicode', 'validators': [{'id': 'is_valid_math_equation'}]} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'unicode', 'validators': [{'id': 'is_valid_math_equation'}]} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'unicode', 'validators': [{'id': 'is_valid_math_equation'}]}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
3f8778e553e30098fd21b0afd4c1ed97d8d8ed4bedffd107f6f004be551a368c | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'unicode', 'validators': [{'id': 'is_valid_math_expression', 'algebraic': False}]} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'unicode', 'validators': [{'id': 'is_valid_math_expression', 'algebraic': False}]} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'unicode', 'validators': [{'id': 'is_valid_math_expression', 'algebraic': False}]}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
328fcf086754f5f9097f4aba489a42ceaa12de3e0afb06c7c4fb3e0065d50ef0 | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'unicode', 'choices': ['lhs', 'rhs', 'both', 'irrelevant']} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'unicode', 'choices': ['lhs', 'rhs', 'both', 'irrelevant']} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'unicode', 'choices': ['lhs', 'rhs', 'both', 'irrelevant']}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
728de2d897f2a6131f1923fb6264972263e1ae2b893bb5c76dc595c8392d7b54 | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'items': PositiveInt.get_schema(), 'validators': [{'id': 'has_length_at_least', 'min_value': 2}]} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'items': PositiveInt.get_schema(), 'validators': [{'id': 'has_length_at_least', 'min_value': 2}]} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'items': PositiveInt.get_schema(), 'validators': [{'id': 'has_length_at_least', 'min_value': 2}]}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
626970bbc76588da532f4b18cf9025732d6badec13db5e83da7f670cf10b92c0 | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'items': OskCharacters.get_schema(), 'validators': [{'id': 'is_uniquified'}]} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'items': OskCharacters.get_schema(), 'validators': [{'id': 'is_uniquified'}]} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'items': OskCharacters.get_schema(), 'validators': [{'id': 'is_uniquified'}]}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
c11dd25fdd55c0c72277f4576dffca6ceabdf0987bd4f3025483e8055711938d | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return UnicodeString.get_schema() | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return UnicodeString.get_schema() | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return UnicodeString.get_schema()<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
048e40ac691400a28127068662908528a8c6847ee019fb5abe86cea6e43f2ef8 | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'items': TranslatableHtmlContentId.get_schema(), 'validators': [{'id': 'is_uniquified'}]} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'items': TranslatableHtmlContentId.get_schema(), 'validators': [{'id': 'is_uniquified'}]} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'items': TranslatableHtmlContentId.get_schema(), 'validators': [{'id': 'is_uniquified'}]}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
6209939920213a72e0a418b36e7a5881d8f9fbbdb3290a93055c3c0927f494cb | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'items': SetOfTranslatableHtmlContentIds.get_schema()} | Returns the object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'items': SetOfTranslatableHtmlContentIds.get_schema()} | @classmethod
def get_schema(cls):
'Returns the object schema.\n\n Returns:\n dict. The object schema.\n '
return {'type': 'list', 'items': SetOfTranslatableHtmlContentIds.get_schema()}<|docstring|>Returns the object schema.
Returns:
dict. The object schema.<|endoftext|> |
57c97c75a197755d16b59fd59e9d8dacb8b9b88b749d630c32a411a2971782ca | @classmethod
def normalize_value(cls, value):
'Normalizes the translatable value of the object.\n\n Args:\n value: *. The translatable part of the Python object (corresponding\n to the non-content-id field) which is to be normalized.\n\n Returns:\n *. The normalized value.\n '
if ((cls._value_key_name is None) or (cls._value_schema is None)):
raise NotImplementedError('The _value_key_name and _value_schema for this class must both be set.')
return schema_utils.normalize_against_schema(value, cls._value_schema) | Normalizes the translatable value of the object.
Args:
value: *. The translatable part of the Python object (corresponding
to the non-content-id field) which is to be normalized.
Returns:
*. The normalized value. | extensions/objects/models/objects.py | normalize_value | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def normalize_value(cls, value):
'Normalizes the translatable value of the object.\n\n Args:\n value: *. The translatable part of the Python object (corresponding\n to the non-content-id field) which is to be normalized.\n\n Returns:\n *. The normalized value.\n '
if ((cls._value_key_name is None) or (cls._value_schema is None)):
raise NotImplementedError('The _value_key_name and _value_schema for this class must both be set.')
return schema_utils.normalize_against_schema(value, cls._value_schema) | @classmethod
def normalize_value(cls, value):
'Normalizes the translatable value of the object.\n\n Args:\n value: *. The translatable part of the Python object (corresponding\n to the non-content-id field) which is to be normalized.\n\n Returns:\n *. The normalized value.\n '
if ((cls._value_key_name is None) or (cls._value_schema is None)):
raise NotImplementedError('The _value_key_name and _value_schema for this class must both be set.')
return schema_utils.normalize_against_schema(value, cls._value_schema)<|docstring|>Normalizes the translatable value of the object.
Args:
value: *. The translatable part of the Python object (corresponding
to the non-content-id field) which is to be normalized.
Returns:
*. The normalized value.<|endoftext|> |
62a908ac09d6870c42c2e09469ceac795bb0cc5b8772e15c7c5fdcf6e24c6a6b | @classmethod
def get_schema(cls):
'Returns the full object schema.\n\n Returns:\n dict. The object schema.\n '
if ((cls._value_key_name is None) or (cls._value_schema is None)):
raise NotImplementedError('The _value_key_name and _value_schema for this class must both be set.')
return {'type': 'dict', 'properties': [{'name': 'contentId', 'schema': {'type': 'unicode'}}, {'name': cls._value_key_name, 'schema': copy.deepcopy(cls._value_schema)}]} | Returns the full object schema.
Returns:
dict. The object schema. | extensions/objects/models/objects.py | get_schema | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def get_schema(cls):
'Returns the full object schema.\n\n Returns:\n dict. The object schema.\n '
if ((cls._value_key_name is None) or (cls._value_schema is None)):
raise NotImplementedError('The _value_key_name and _value_schema for this class must both be set.')
return {'type': 'dict', 'properties': [{'name': 'contentId', 'schema': {'type': 'unicode'}}, {'name': cls._value_key_name, 'schema': copy.deepcopy(cls._value_schema)}]} | @classmethod
def get_schema(cls):
'Returns the full object schema.\n\n Returns:\n dict. The object schema.\n '
if ((cls._value_key_name is None) or (cls._value_schema is None)):
raise NotImplementedError('The _value_key_name and _value_schema for this class must both be set.')
return {'type': 'dict', 'properties': [{'name': 'contentId', 'schema': {'type': 'unicode'}}, {'name': cls._value_key_name, 'schema': copy.deepcopy(cls._value_schema)}]}<|docstring|>Returns the full object schema.
Returns:
dict. The object schema.<|endoftext|> |
049090dd93c4ed95d0c78d63d863e85a3c2a1be1ffab7a29f84d0c31250f851b | @classmethod
def normalize(cls, raw):
'Validates and normalizes a raw Python object.\n\n Args:\n raw: str. Strings to be validated and normalized.\n\n Returns:\n *. The normalized value of any type, it depends on the raw value\n which we want to load from json.\n '
if (not isinstance(raw, str)):
raise Exception(('Expected string received %s of type %s' % (raw, type(raw))))
return json.loads(raw) | Validates and normalizes a raw Python object.
Args:
raw: str. Strings to be validated and normalized.
Returns:
*. The normalized value of any type, it depends on the raw value
which we want to load from json. | extensions/objects/models/objects.py | normalize | ParmeetChawla25/oppia | 5,422 | python | @classmethod
def normalize(cls, raw):
'Validates and normalizes a raw Python object.\n\n Args:\n raw: str. Strings to be validated and normalized.\n\n Returns:\n *. The normalized value of any type, it depends on the raw value\n which we want to load from json.\n '
if (not isinstance(raw, str)):
raise Exception(('Expected string received %s of type %s' % (raw, type(raw))))
return json.loads(raw) | @classmethod
def normalize(cls, raw):
'Validates and normalizes a raw Python object.\n\n Args:\n raw: str. Strings to be validated and normalized.\n\n Returns:\n *. The normalized value of any type, it depends on the raw value\n which we want to load from json.\n '
if (not isinstance(raw, str)):
raise Exception(('Expected string received %s of type %s' % (raw, type(raw))))
return json.loads(raw)<|docstring|>Validates and normalizes a raw Python object.
Args:
raw: str. Strings to be validated and normalized.
Returns:
*. The normalized value of any type, it depends on the raw value
which we want to load from json.<|endoftext|> |
34a4a60e2f7c760ed15247d54f001c89d19f37adf9168881e5c943542d477087 | def clamp(value):
'Clamps a number to range [0, 1].\n\n Args:\n value: float. A number to be clamped.\n\n Returns:\n float. The clamped value.\n '
return min(0.0, max(value, 1.0)) | Clamps a number to range [0, 1].
Args:
value: float. A number to be clamped.
Returns:
float. The clamped value. | extensions/objects/models/objects.py | clamp | ParmeetChawla25/oppia | 5,422 | python | def clamp(value):
'Clamps a number to range [0, 1].\n\n Args:\n value: float. A number to be clamped.\n\n Returns:\n float. The clamped value.\n '
return min(0.0, max(value, 1.0)) | def clamp(value):
'Clamps a number to range [0, 1].\n\n Args:\n value: float. A number to be clamped.\n\n Returns:\n float. The clamped value.\n '
return min(0.0, max(value, 1.0))<|docstring|>Clamps a number to range [0, 1].
Args:
value: float. A number to be clamped.
Returns:
float. The clamped value.<|endoftext|> |
dfa28cfa720bcdaa96bd91d787ccec78246eda703d9ededb7268ab59718781cb | def get_engine(self):
'Crée le moteur de calcul avec le stick Coral'
res = ((str(self.width) + 'x') + str(self.height))
print('width:', self.width, ', height:', self.height)
print('Résolution =', res)
if (res == '1280x720'):
model_size = (721, 1281)
elif (res == '640x480'):
model_size = (481, 641)
else:
print(f"La résolution {res} n'est pas possible.")
os._exit(0)
model = f'posenet/models/mobilenet/posenet_mobilenet_v1_075_{model_size[0]}_{model_size[1]}_quant_decoder_edgetpu.tflite'
print('Loading model: ', model)
try:
self.engine = PoseEngine(model, mirror=False)
except:
print(f'Pas de Stick Coral connecté')
os._exit(0) | Crée le moteur de calcul avec le stick Coral | personnages3d/posenet/this_posenet.py | get_engine | mxbossard/personnages3d | 0 | python | def get_engine(self):
res = ((str(self.width) + 'x') + str(self.height))
print('width:', self.width, ', height:', self.height)
print('Résolution =', res)
if (res == '1280x720'):
model_size = (721, 1281)
elif (res == '640x480'):
model_size = (481, 641)
else:
print(f"La résolution {res} n'est pas possible.")
os._exit(0)
model = f'posenet/models/mobilenet/posenet_mobilenet_v1_075_{model_size[0]}_{model_size[1]}_quant_decoder_edgetpu.tflite'
print('Loading model: ', model)
try:
self.engine = PoseEngine(model, mirror=False)
except:
print(f'Pas de Stick Coral connecté')
os._exit(0) | def get_engine(self):
res = ((str(self.width) + 'x') + str(self.height))
print('width:', self.width, ', height:', self.height)
print('Résolution =', res)
if (res == '1280x720'):
model_size = (721, 1281)
elif (res == '640x480'):
model_size = (481, 641)
else:
print(f"La résolution {res} n'est pas possible.")
os._exit(0)
model = f'posenet/models/mobilenet/posenet_mobilenet_v1_075_{model_size[0]}_{model_size[1]}_quant_decoder_edgetpu.tflite'
print('Loading model: ', model)
try:
self.engine = PoseEngine(model, mirror=False)
except:
print(f'Pas de Stick Coral connecté')
os._exit(0)<|docstring|>Crée le moteur de calcul avec le stick Coral<|endoftext|> |
21ee262aabc9c2f6a44c694a3c81fccea6ede804102216358f7b785977b0a754 | def Delete(self):
'Deletes this voicemail.\n '
self._Alter('DELETE') | Deletes this voicemail. | Skype4Py/voicemail.py | Delete | amolvaze/Python | 199 | python | def Delete(self):
'\n '
self._Alter('DELETE') | def Delete(self):
'\n '
self._Alter('DELETE')<|docstring|>Deletes this voicemail.<|endoftext|> |
7fc8f7bdc27d43a067457533a4ba084f95f017e53f6214dd9a1a628c31c546e5 | def Download(self):
'Downloads this voicemail object from the voicemail server to a local computer.\n '
self._Alter('DOWNLOAD') | Downloads this voicemail object from the voicemail server to a local computer. | Skype4Py/voicemail.py | Download | amolvaze/Python | 199 | python | def Download(self):
'\n '
self._Alter('DOWNLOAD') | def Download(self):
'\n '
self._Alter('DOWNLOAD')<|docstring|>Downloads this voicemail object from the voicemail server to a local computer.<|endoftext|> |
61ac07a41aa42b304be9f52463fa706ab26e61dbf9ee7acdddcc7a83db4208c7 | def Open(self):
'Opens and plays this voicemail.\n '
self._Owner._DoCommand(('OPEN VOICEMAIL %s' % self.Id)) | Opens and plays this voicemail. | Skype4Py/voicemail.py | Open | amolvaze/Python | 199 | python | def Open(self):
'\n '
self._Owner._DoCommand(('OPEN VOICEMAIL %s' % self.Id)) | def Open(self):
'\n '
self._Owner._DoCommand(('OPEN VOICEMAIL %s' % self.Id))<|docstring|>Opens and plays this voicemail.<|endoftext|> |
af17f993ad3845d4a222b9c92390aafa72800ae054dbd6eea0b03e507ff13c29 | def SetUnplayed(self):
'Changes the status of a voicemail from played to unplayed.\n '
self._Owner._DoCommand(('ALTER VOICEMAIL %d SETUNPLAYED' % self.Id), ('ALTER VOICEMAIL %d' % self.Id)) | Changes the status of a voicemail from played to unplayed. | Skype4Py/voicemail.py | SetUnplayed | amolvaze/Python | 199 | python | def SetUnplayed(self):
'\n '
self._Owner._DoCommand(('ALTER VOICEMAIL %d SETUNPLAYED' % self.Id), ('ALTER VOICEMAIL %d' % self.Id)) | def SetUnplayed(self):
'\n '
self._Owner._DoCommand(('ALTER VOICEMAIL %d SETUNPLAYED' % self.Id), ('ALTER VOICEMAIL %d' % self.Id))<|docstring|>Changes the status of a voicemail from played to unplayed.<|endoftext|> |
c2bc3a56425cf66d8141758ecd98c9d2e464daf2d9d547b365d8e8674f3ab474 | def StartPlayback(self):
'Starts playing downloaded voicemail.\n '
self._Alter('STARTPLAYBACK') | Starts playing downloaded voicemail. | Skype4Py/voicemail.py | StartPlayback | amolvaze/Python | 199 | python | def StartPlayback(self):
'\n '
self._Alter('STARTPLAYBACK') | def StartPlayback(self):
'\n '
self._Alter('STARTPLAYBACK')<|docstring|>Starts playing downloaded voicemail.<|endoftext|> |
fdedf8ccc10f93c871e5a1825913833428b6096df228da48f57abfd4df141e1b | def StartPlaybackInCall(self):
'Starts playing downloaded voicemail during a call.\n '
self._Alter('STARTPLAYBACKINCALL') | Starts playing downloaded voicemail during a call. | Skype4Py/voicemail.py | StartPlaybackInCall | amolvaze/Python | 199 | python | def StartPlaybackInCall(self):
'\n '
self._Alter('STARTPLAYBACKINCALL') | def StartPlaybackInCall(self):
'\n '
self._Alter('STARTPLAYBACKINCALL')<|docstring|>Starts playing downloaded voicemail during a call.<|endoftext|> |
88a54ed445729f5f0b4bf40acaf41486688d84e5222530fa81672920e34fc273 | def StartRecording(self):
'Stops playing a voicemail greeting and starts recording a voicemail message.\n '
self._Alter('STARTRECORDING') | Stops playing a voicemail greeting and starts recording a voicemail message. | Skype4Py/voicemail.py | StartRecording | amolvaze/Python | 199 | python | def StartRecording(self):
'\n '
self._Alter('STARTRECORDING') | def StartRecording(self):
'\n '
self._Alter('STARTRECORDING')<|docstring|>Stops playing a voicemail greeting and starts recording a voicemail message.<|endoftext|> |
b0ec0ac90ed570023b5f1964b14a34198a0a61e3e84bd47ef4c1b31e056e3122 | def StopPlayback(self):
'Stops playing downloaded voicemail.\n '
self._Alter('STOPPLAYBACK') | Stops playing downloaded voicemail. | Skype4Py/voicemail.py | StopPlayback | amolvaze/Python | 199 | python | def StopPlayback(self):
'\n '
self._Alter('STOPPLAYBACK') | def StopPlayback(self):
'\n '
self._Alter('STOPPLAYBACK')<|docstring|>Stops playing downloaded voicemail.<|endoftext|> |
72fb6515d5abf38a6b7b8e2ebb4304f774ecf4c1217f288475cc1852b22c1a2e | def StopRecording(self):
'Ends the recording of a voicemail message.\n '
self._Alter('STOPRECORDING') | Ends the recording of a voicemail message. | Skype4Py/voicemail.py | StopRecording | amolvaze/Python | 199 | python | def StopRecording(self):
'\n '
self._Alter('STOPRECORDING') | def StopRecording(self):
'\n '
self._Alter('STOPRECORDING')<|docstring|>Ends the recording of a voicemail message.<|endoftext|> |
0b4d551e95f75934f610527d1b731ea56ad10c2d0bff5a0269e1265bc1974871 | def Upload(self):
'Uploads recorded voicemail from a local computer to the voicemail server.\n '
self._Alter('UPLOAD') | Uploads recorded voicemail from a local computer to the voicemail server. | Skype4Py/voicemail.py | Upload | amolvaze/Python | 199 | python | def Upload(self):
'\n '
self._Alter('UPLOAD') | def Upload(self):
'\n '
self._Alter('UPLOAD')<|docstring|>Uploads recorded voicemail from a local computer to the voicemail server.<|endoftext|> |
fa85bb99159214f529fc4e8f4e7495eceb23706b7341a2278c6116fa02b19920 | def get_all_path(directory):
'\n Collecting all the addresses of swc files in the given directory.\n\n Parameters\n ----------\n directory: str\n The address of the folder\n\n Returns\n -------\n fileSet: list\n list of all the addresses of *.swc neurons.\n '
fileSet = []
for (root, dirs, files) in os.walk(directory):
for fileName in files:
if (fileName[(- 3):] == 'swc'):
fileSet.append((((directory + root.replace(directory, '')) + os.sep) + fileName))
return fileSet | Collecting all the addresses of swc files in the given directory.
Parameters
----------
directory: str
The address of the folder
Returns
-------
fileSet: list
list of all the addresses of *.swc neurons. | McNeuron/NeuronCollection.py | get_all_path | tree-gan/MCMC | 6 | python | def get_all_path(directory):
'\n Collecting all the addresses of swc files in the given directory.\n\n Parameters\n ----------\n directory: str\n The address of the folder\n\n Returns\n -------\n fileSet: list\n list of all the addresses of *.swc neurons.\n '
fileSet = []
for (root, dirs, files) in os.walk(directory):
for fileName in files:
if (fileName[(- 3):] == 'swc'):
fileSet.append((((directory + root.replace(directory, )) + os.sep) + fileName))
return fileSet | def get_all_path(directory):
'\n Collecting all the addresses of swc files in the given directory.\n\n Parameters\n ----------\n directory: str\n The address of the folder\n\n Returns\n -------\n fileSet: list\n list of all the addresses of *.swc neurons.\n '
fileSet = []
for (root, dirs, files) in os.walk(directory):
for fileName in files:
if (fileName[(- 3):] == 'swc'):
fileSet.append((((directory + root.replace(directory, )) + os.sep) + fileName))
return fileSet<|docstring|>Collecting all the addresses of swc files in the given directory.
Parameters
----------
directory: str
The address of the folder
Returns
-------
fileSet: list
list of all the addresses of *.swc neurons.<|endoftext|> |
4c5e538a1f535384fb37f09cf7b452f4e48da8ba524d0a7272c94d1599a5c66d | def set_features(self):
'\n set the range of histogram for each feature.\n\n hist_range : dict\n ----------\n dictionary of all feature and thier range of histogram.\n '
self.features = {}
for name in self.database[0].features.keys():
self.features[name] = []
for i in range(len(self.database)):
self.features[name].append(self.database[i].features[name]) | set the range of histogram for each feature.
hist_range : dict
----------
dictionary of all feature and thier range of histogram. | McNeuron/NeuronCollection.py | set_features | tree-gan/MCMC | 6 | python | def set_features(self):
'\n set the range of histogram for each feature.\n\n hist_range : dict\n ----------\n dictionary of all feature and thier range of histogram.\n '
self.features = {}
for name in self.database[0].features.keys():
self.features[name] = []
for i in range(len(self.database)):
self.features[name].append(self.database[i].features[name]) | def set_features(self):
'\n set the range of histogram for each feature.\n\n hist_range : dict\n ----------\n dictionary of all feature and thier range of histogram.\n '
self.features = {}
for name in self.database[0].features.keys():
self.features[name] = []
for i in range(len(self.database)):
self.features[name].append(self.database[i].features[name])<|docstring|>set the range of histogram for each feature.
hist_range : dict
----------
dictionary of all feature and thier range of histogram.<|endoftext|> |
f391d6726fb7718816ca05cee8396ed4c1456a5682d6296a43affec2ba59057c | def __init__(self, classes):
'\n Arguments\n ---------\n classes: número de clases (tipos de hemorragias)\n '
super(ResnetModel, self).__init__()
self.backbone = models.resnet50(pretrained=False)
n_filters = self.backbone.fc.in_features
self.backbone.fc = nn.Linear(n_filters, classes) | Arguments
---------
classes: número de clases (tipos de hemorragias) | model/resnet.py | __init__ | BiomedLabUG/intracraneal-hemorrhage | 2 | python | def __init__(self, classes):
'\n Arguments\n ---------\n classes: número de clases (tipos de hemorragias)\n '
super(ResnetModel, self).__init__()
self.backbone = models.resnet50(pretrained=False)
n_filters = self.backbone.fc.in_features
self.backbone.fc = nn.Linear(n_filters, classes) | def __init__(self, classes):
'\n Arguments\n ---------\n classes: número de clases (tipos de hemorragias)\n '
super(ResnetModel, self).__init__()
self.backbone = models.resnet50(pretrained=False)
n_filters = self.backbone.fc.in_features
self.backbone.fc = nn.Linear(n_filters, classes)<|docstring|>Arguments
---------
classes: número de clases (tipos de hemorragias)<|endoftext|> |
170842d59e03423014d6e962b54510b9b8bdf47ddaa5b2b861678441462f9b22 | def load_data(filename):
'加载数据\n 单条格式：[text, (start, end, label), (start, end, label), ...]，\n 意味着text[start:end + 1]是类型为label的实体。\n '
D = []
with open(filename, encoding='utf-8') as f:
f = f.read()
for l in f.split('\n\n'):
if (not l):
continue
d = ['']
for (i, c) in enumerate(l.split('\n')):
(char, flag) = c.split(' ')
d[0] += char
if (flag[0] == 'B'):
d.append([i, i, flag[2:]])
categories.add(flag[2:])
elif (flag[0] == 'I'):
d[(- 1)][1] = i
D.append(d)
return D | 加载数据
单条格式：[text, (start, end, label), (start, end, label), ...]，
意味着text[start:end + 1]是类型为label的实体。 | examples/task_sequence_labeling_ner_crf.py | load_data | LeiSoft/bert4keras | 4,478 | python | def load_data(filename):
'加载数据\n 单条格式：[text, (start, end, label), (start, end, label), ...]，\n 意味着text[start:end + 1]是类型为label的实体。\n '
D = []
with open(filename, encoding='utf-8') as f:
f = f.read()
for l in f.split('\n\n'):
if (not l):
continue
d = []
for (i, c) in enumerate(l.split('\n')):
(char, flag) = c.split(' ')
d[0] += char
if (flag[0] == 'B'):
d.append([i, i, flag[2:]])
categories.add(flag[2:])
elif (flag[0] == 'I'):
d[(- 1)][1] = i
D.append(d)
return D | def load_data(filename):
'加载数据\n 单条格式：[text, (start, end, label), (start, end, label), ...]，\n 意味着text[start:end + 1]是类型为label的实体。\n '
D = []
with open(filename, encoding='utf-8') as f:
f = f.read()
for l in f.split('\n\n'):
if (not l):
continue
d = []
for (i, c) in enumerate(l.split('\n')):
(char, flag) = c.split(' ')
d[0] += char
if (flag[0] == 'B'):
d.append([i, i, flag[2:]])
categories.add(flag[2:])
elif (flag[0] == 'I'):
d[(- 1)][1] = i
D.append(d)
return D<|docstring|>加载数据
单条格式：[text, (start, end, label), (start, end, label), ...]，
意味着text[start:end + 1]是类型为label的实体。<|endoftext|> |
d16d3632fede464d859b24265b0ba7f8dab327af717335e2d3e2abb2a336e0d3 | def evaluate(data):
'评测函数\n '
(X, Y, Z) = (1e-10, 1e-10, 1e-10)
for d in tqdm(data, ncols=100):
R = set(NER.recognize(d[0]))
T = set([tuple(i) for i in d[1:]])
X += len((R & T))
Y += len(R)
Z += len(T)
(f1, precision, recall) = (((2 * X) / (Y + Z)), (X / Y), (X / Z))
return (f1, precision, recall) | 评测函数 | examples/task_sequence_labeling_ner_crf.py | evaluate | LeiSoft/bert4keras | 4,478 | python | def evaluate(data):
'\n '
(X, Y, Z) = (1e-10, 1e-10, 1e-10)
for d in tqdm(data, ncols=100):
R = set(NER.recognize(d[0]))
T = set([tuple(i) for i in d[1:]])
X += len((R & T))
Y += len(R)
Z += len(T)
(f1, precision, recall) = (((2 * X) / (Y + Z)), (X / Y), (X / Z))
return (f1, precision, recall) | def evaluate(data):
'\n '
(X, Y, Z) = (1e-10, 1e-10, 1e-10)
for d in tqdm(data, ncols=100):
R = set(NER.recognize(d[0]))
T = set([tuple(i) for i in d[1:]])
X += len((R & T))
Y += len(R)
Z += len(T)
(f1, precision, recall) = (((2 * X) / (Y + Z)), (X / Y), (X / Z))
return (f1, precision, recall)<|docstring|>评测函数<|endoftext|> |
20d0623ebd73e387803da64b1a3b55fa87af61fd5bea9e7c2472c0588b76dde2 | def tx_test(ser: serial.Serial):
'\n Simple TX test\n\n :param ser: device to send data to\n '
for i in range(0, 100):
ser.write('\x1b[93m{}: This is a test message.\r\n'.format(i)) | Simple TX test
:param ser: device to send data to | scripts/term_test.py | tx_test | joeyahines/cerial | 0 | python | def tx_test(ser: serial.Serial):
'\n Simple TX test\n\n :param ser: device to send data to\n '
for i in range(0, 100):
ser.write('\x1b[93m{}: This is a test message.\r\n'.format(i)) | def tx_test(ser: serial.Serial):
'\n Simple TX test\n\n :param ser: device to send data to\n '
for i in range(0, 100):
ser.write('\x1b[93m{}: This is a test message.\r\n'.format(i))<|docstring|>Simple TX test
:param ser: device to send data to<|endoftext|> |
158677eca9d4ce71909cdb22f50bc0c0f5cd98818c63b4dc20d2e0ba5475acac | def rx_test(ser: serial.Serial):
'\n Simple echo test\n\n :param ser: serial port to echo data on\n :return:\n '
while True:
c = ser.read(1)
print(c)
ser.write(c)
ser.flush() | Simple echo test
:param ser: serial port to echo data on
:return: | scripts/term_test.py | rx_test | joeyahines/cerial | 0 | python | def rx_test(ser: serial.Serial):
'\n Simple echo test\n\n :param ser: serial port to echo data on\n :return:\n '
while True:
c = ser.read(1)
print(c)
ser.write(c)
ser.flush() | def rx_test(ser: serial.Serial):
'\n Simple echo test\n\n :param ser: serial port to echo data on\n :return:\n '
while True:
c = ser.read(1)
print(c)
ser.write(c)
ser.flush()<|docstring|>Simple echo test
:param ser: serial port to echo data on
:return:<|endoftext|> |
427194fc9ade7ea4e45cabcea3c639f81e3848d7d40fb8e33c33ac72228934a8 | def main():
'\n Main function\n '
if (len(sys.argv) < 3):
print('{} <dev> <test>'.format(sys.argv[0]))
exit((- 1))
else:
dev = sys.argv[1]
test = sys.argv[2]
ser = serial.Serial(dev)
try:
if (test == 'rx'):
rx_test(ser)
else:
tx_test(dev)
finally:
ser.close() | Main function | scripts/term_test.py | main | joeyahines/cerial | 0 | python | def main():
'\n \n '
if (len(sys.argv) < 3):
print('{} <dev> <test>'.format(sys.argv[0]))
exit((- 1))
else:
dev = sys.argv[1]
test = sys.argv[2]
ser = serial.Serial(dev)
try:
if (test == 'rx'):
rx_test(ser)
else:
tx_test(dev)
finally:
ser.close() | def main():
'\n \n '
if (len(sys.argv) < 3):
print('{} <dev> <test>'.format(sys.argv[0]))
exit((- 1))
else:
dev = sys.argv[1]
test = sys.argv[2]
ser = serial.Serial(dev)
try:
if (test == 'rx'):
rx_test(ser)
else:
tx_test(dev)
finally:
ser.close()<|docstring|>Main function<|endoftext|> |
8c1367801f75f17aaedda216cfb576414781b50d2edc4970912f6aaf7f3334e6 | def warn(warn_class: type[ExarataWarning]=ExarataWarning, message: str='', stacklevel: int=2):
'The common method to use to warn for any OpihiExarata based warnings.\n\n This is used because it has better context manager wrappers.\n\n Parameters\n ----------\n warn_class : type, default = ExarataWarning\n The warning class, it must be a subtype of a user warning.\n message : string, default = ""\n The warning message.\n stacklevel : integer, default = 2\n The location in the stack that the warning call will highlight.\n\n Returns\n -------\n None\n '
if (not issubclass(warn_class, ExarataWarning)):
raise DevelopmentError('The OpihiExarata warning system is build only for user defined errors coming from OpihiExarata.')
else:
warnings.warn(message=message, category=warn_class, stacklevel=stacklevel)
return None | The common method to use to warn for any OpihiExarata based warnings.
This is used because it has better context manager wrappers.
Parameters
----------
warn_class : type, default = ExarataWarning
The warning class, it must be a subtype of a user warning.
message : string, default = ""
The warning message.
stacklevel : integer, default = 2
The location in the stack that the warning call will highlight.
Returns
-------
None | src/opihiexarata/library/error.py | warn | psmd-iberutaru/OpihiExarata | 0 | python | def warn(warn_class: type[ExarataWarning]=ExarataWarning, message: str=, stacklevel: int=2):
'The common method to use to warn for any OpihiExarata based warnings.\n\n This is used because it has better context manager wrappers.\n\n Parameters\n ----------\n warn_class : type, default = ExarataWarning\n The warning class, it must be a subtype of a user warning.\n message : string, default = \n The warning message.\n stacklevel : integer, default = 2\n The location in the stack that the warning call will highlight.\n\n Returns\n -------\n None\n '
if (not issubclass(warn_class, ExarataWarning)):
raise DevelopmentError('The OpihiExarata warning system is build only for user defined errors coming from OpihiExarata.')
else:
warnings.warn(message=message, category=warn_class, stacklevel=stacklevel)
return None | def warn(warn_class: type[ExarataWarning]=ExarataWarning, message: str=, stacklevel: int=2):
'The common method to use to warn for any OpihiExarata based warnings.\n\n This is used because it has better context manager wrappers.\n\n Parameters\n ----------\n warn_class : type, default = ExarataWarning\n The warning class, it must be a subtype of a user warning.\n message : string, default = \n The warning message.\n stacklevel : integer, default = 2\n The location in the stack that the warning call will highlight.\n\n Returns\n -------\n None\n '
if (not issubclass(warn_class, ExarataWarning)):
raise DevelopmentError('The OpihiExarata warning system is build only for user defined errors coming from OpihiExarata.')
else:
warnings.warn(message=message, category=warn_class, stacklevel=stacklevel)
return None<|docstring|>The common method to use to warn for any OpihiExarata based warnings.
This is used because it has better context manager wrappers.
Parameters
----------
warn_class : type, default = ExarataWarning
The warning class, it must be a subtype of a user warning.
message : string, default = ""
The warning message.
stacklevel : integer, default = 2
The location in the stack that the warning call will highlight.
Returns
-------
None<|endoftext|> |
5e7385af8fbaf3d5d648661a5f63dce763345d9813a8cef2fa9a4ec884a9fe53 | def __init__(self, message: str=None) -> None:
'The initialization of a base exception for OpihiExarata.\n\n Parameters\n ----------\n message : string\n The message of the error message.\n\n Returns\n -------\n None\n '
message = (message if (message is not None) else 'Unrecoverable error!')
prefix = '(OpihiExarata) TERMINAL - '
suffix = ('\n' + '>> Contact the maintainers of OpihiExarata to fix this issue.')
self.message = ((prefix + message) + suffix) | The initialization of a base exception for OpihiExarata.
Parameters
----------
message : string
The message of the error message.
Returns
-------
None | src/opihiexarata/library/error.py | __init__ | psmd-iberutaru/OpihiExarata | 0 | python | def __init__(self, message: str=None) -> None:
'The initialization of a base exception for OpihiExarata.\n\n Parameters\n ----------\n message : string\n The message of the error message.\n\n Returns\n -------\n None\n '
message = (message if (message is not None) else 'Unrecoverable error!')
prefix = '(OpihiExarata) TERMINAL - '
suffix = ('\n' + '>> Contact the maintainers of OpihiExarata to fix this issue.')
self.message = ((prefix + message) + suffix) | def __init__(self, message: str=None) -> None:
'The initialization of a base exception for OpihiExarata.\n\n Parameters\n ----------\n message : string\n The message of the error message.\n\n Returns\n -------\n None\n '
message = (message if (message is not None) else 'Unrecoverable error!')
prefix = '(OpihiExarata) TERMINAL - '
suffix = ('\n' + '>> Contact the maintainers of OpihiExarata to fix this issue.')
self.message = ((prefix + message) + suffix)<|docstring|>The initialization of a base exception for OpihiExarata.
Parameters
----------
message : string
The message of the error message.
Returns
-------
None<|endoftext|> |
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