body_hash stringlengths 64 64 | body stringlengths 23 109k | docstring stringlengths 1 57k | path stringlengths 4 198 | name stringlengths 1 115 | repository_name stringlengths 7 111 | repository_stars float64 0 191k | lang stringclasses 1 value | body_without_docstring stringlengths 14 108k | unified stringlengths 45 133k |
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5eef1271b9ba01e9fcb76086cd060ab988e39c0b686493f6b0ec207282577d29 | def __init__(self, mean, tiny, grayscale, enc_add_res_block=0, dec_add_res_block=0, num_task_channel=3, num_pos_channel=1, num_gn_channel=32, num_mlr=0, num_unfrozen_encoder=0, full_size_output=False):
'\n Constructor.\n @param mean Mean offset for task output.\n @param tiny Flag for tiny network.\n @param grayscale Flag for grayscale image input.\n @param enc_add_res_block Number of additional DSAC* style residual block for encoder.\n @param dec_add_res_block Number of additional DSAC* style residual block for decoder.\n @param num_task_channel Number of channels for underlying task.\n @param num_pos_channel Number of channels for additional task w/ positive values, e.g., uncertainty.\n @param num_gn_channel Number of group normalization channels, a hyper-parameter.\n @param num_mlr Number of homogeneous mid-level representations encoders.\n @param num_unfrozen_encoder Number of encoders that are not frozen.\n @param full_size_output Flag for full-size network output (by using DUC-style layers).\n\n Note: if enc_add_res_block == dec_add_res_block == 0 && num_task_channel == 3 && num_pos_channel = 0,\n the model become DSAC* net + group normalization only.\n '
super(TransPoseNet, self).__init__()
'Init'
self.register_buffer('mean', torch.tensor(mean.size()).cuda())
self.mean = mean.clone()
self.tiny = tiny
self.grayscale = grayscale
self.enc_add_res_block = enc_add_res_block
self.dec_add_res_block = dec_add_res_block
self.num_task_channel = num_task_channel
self.num_pos_channel = num_pos_channel
self.num_gn_channel = num_gn_channel
self.num_mlr = num_mlr
self.full_size_output = full_size_output
self.OUTPUT_SUBSAMPLE = (1 if full_size_output else 8)
'Vanilla encoder'
if (num_mlr == 0):
self.encoder = TransPoseNetEncoder(tiny, grayscale, enc_add_res_block, num_gn_channel)
self.encoder_ls = [self.encoder]
else:
self.encoder = nn.Identity()
self.encoder_ls = [self.encoder]
'MLR encoders'
if ((num_mlr > 0) and isinstance(num_mlr, int)):
assert (0 <= num_unfrozen_encoder <= num_mlr)
self.mlr_encoder_ls = [TransPoseNetEncoder(tiny, grayscale, enc_add_res_block, num_gn_channel) for _ in range(num_mlr)]
for (i, block) in enumerate(self.mlr_encoder_ls):
if (i >= num_unfrozen_encoder):
for param in block.parameters():
param.requires_grad = False
self.add_module('mlr_encoder_{:d}'.format((i + 1)), block)
self.mlr_norm = nn.GroupNorm(num_gn_channel, ((512, 128)[tiny] * num_mlr))
self.mlr_forward = _create_mlr_concatenator(num_mlr, tiny, num_gn_channel)
self.mlr_skip = _create_mlr_skip_layer(num_mlr, tiny, num_gn_channel)
else:
self.mlr_encoder_ls = [nn.Identity()]
self.mlr_norm = nn.Identity()
self.mlr_forward = nn.Identity()
self.mlr_skip = nn.Identity()
self.mlr_ls = (self.mlr_encoder_ls + [self.mlr_norm, self.mlr_forward, self.mlr_skip])
'Decoder'
self.decoder = TransPoseNetDecoder(mean, tiny, dec_add_res_block, num_task_channel, num_pos_channel, num_gn_channel, full_size_output)
self.decoder_ls = [self.decoder]
'Print out'
safe_printout('Initialized network w/ group normalization, Tiny net: {}, Grayscale input: {}, Fullsize output: {}.'.format(self.tiny, self.grayscale, self.full_size_output))
safe_printout('#Aadditional residual blocks: Encoder: {:d}, Decoder: {:d}'.format(self.enc_add_res_block, self.dec_add_res_block))
safe_printout('#Task output channel {:d}, #Positive-value output channel {:d}, #Group normalization channel: {:d}.'.format(self.num_task_channel, self.num_pos_channel, self.num_gn_channel))
safe_printout('#MLR: {:d}, #Unfrozen encoder: {:d}'.format(num_mlr, num_unfrozen_encoder))
ttl_num_param = 0
param_info = 'Separation of #trainable parameters: '
for (name, struct) in zip(['Vanilla encoder', 'MLR encoder', 'Decoder'], [self.encoder_ls, self.mlr_ls, self.decoder_ls]):
num_param = sum([param.numel() for layer in struct for param in layer.parameters() if param.requires_grad])
ttl_num_param += num_param
param_info += '{:s}: {:,d}, '.format(name, num_param)
param_info += 'Total: {:,d}.'.format(ttl_num_param)
safe_printout(param_info) | Constructor.
@param mean Mean offset for task output.
@param tiny Flag for tiny network.
@param grayscale Flag for grayscale image input.
@param enc_add_res_block Number of additional DSAC* style residual block for encoder.
@param dec_add_res_block Number of additional DSAC* style residual block for decoder.
@param num_task_channel Number of channels for underlying task.
@param num_pos_channel Number of channels for additional task w/ positive values, e.g., uncertainty.
@param num_gn_channel Number of group normalization channels, a hyper-parameter.
@param num_mlr Number of homogeneous mid-level representations encoders.
@param num_unfrozen_encoder Number of encoders that are not frozen.
@param full_size_output Flag for full-size network output (by using DUC-style layers).
Note: if enc_add_res_block == dec_add_res_block == 0 && num_task_channel == 3 && num_pos_channel = 0,
the model become DSAC* net + group normalization only. | networks/networks.py | __init__ | TOPO-EPFL/CrossLoc | 16 | python | def __init__(self, mean, tiny, grayscale, enc_add_res_block=0, dec_add_res_block=0, num_task_channel=3, num_pos_channel=1, num_gn_channel=32, num_mlr=0, num_unfrozen_encoder=0, full_size_output=False):
'\n Constructor.\n @param mean Mean offset for task output.\n @param tiny Flag for tiny network.\n @param grayscale Flag for grayscale image input.\n @param enc_add_res_block Number of additional DSAC* style residual block for encoder.\n @param dec_add_res_block Number of additional DSAC* style residual block for decoder.\n @param num_task_channel Number of channels for underlying task.\n @param num_pos_channel Number of channels for additional task w/ positive values, e.g., uncertainty.\n @param num_gn_channel Number of group normalization channels, a hyper-parameter.\n @param num_mlr Number of homogeneous mid-level representations encoders.\n @param num_unfrozen_encoder Number of encoders that are not frozen.\n @param full_size_output Flag for full-size network output (by using DUC-style layers).\n\n Note: if enc_add_res_block == dec_add_res_block == 0 && num_task_channel == 3 && num_pos_channel = 0,\n the model become DSAC* net + group normalization only.\n '
super(TransPoseNet, self).__init__()
'Init'
self.register_buffer('mean', torch.tensor(mean.size()).cuda())
self.mean = mean.clone()
self.tiny = tiny
self.grayscale = grayscale
self.enc_add_res_block = enc_add_res_block
self.dec_add_res_block = dec_add_res_block
self.num_task_channel = num_task_channel
self.num_pos_channel = num_pos_channel
self.num_gn_channel = num_gn_channel
self.num_mlr = num_mlr
self.full_size_output = full_size_output
self.OUTPUT_SUBSAMPLE = (1 if full_size_output else 8)
'Vanilla encoder'
if (num_mlr == 0):
self.encoder = TransPoseNetEncoder(tiny, grayscale, enc_add_res_block, num_gn_channel)
self.encoder_ls = [self.encoder]
else:
self.encoder = nn.Identity()
self.encoder_ls = [self.encoder]
'MLR encoders'
if ((num_mlr > 0) and isinstance(num_mlr, int)):
assert (0 <= num_unfrozen_encoder <= num_mlr)
self.mlr_encoder_ls = [TransPoseNetEncoder(tiny, grayscale, enc_add_res_block, num_gn_channel) for _ in range(num_mlr)]
for (i, block) in enumerate(self.mlr_encoder_ls):
if (i >= num_unfrozen_encoder):
for param in block.parameters():
param.requires_grad = False
self.add_module('mlr_encoder_{:d}'.format((i + 1)), block)
self.mlr_norm = nn.GroupNorm(num_gn_channel, ((512, 128)[tiny] * num_mlr))
self.mlr_forward = _create_mlr_concatenator(num_mlr, tiny, num_gn_channel)
self.mlr_skip = _create_mlr_skip_layer(num_mlr, tiny, num_gn_channel)
else:
self.mlr_encoder_ls = [nn.Identity()]
self.mlr_norm = nn.Identity()
self.mlr_forward = nn.Identity()
self.mlr_skip = nn.Identity()
self.mlr_ls = (self.mlr_encoder_ls + [self.mlr_norm, self.mlr_forward, self.mlr_skip])
'Decoder'
self.decoder = TransPoseNetDecoder(mean, tiny, dec_add_res_block, num_task_channel, num_pos_channel, num_gn_channel, full_size_output)
self.decoder_ls = [self.decoder]
'Print out'
safe_printout('Initialized network w/ group normalization, Tiny net: {}, Grayscale input: {}, Fullsize output: {}.'.format(self.tiny, self.grayscale, self.full_size_output))
safe_printout('#Aadditional residual blocks: Encoder: {:d}, Decoder: {:d}'.format(self.enc_add_res_block, self.dec_add_res_block))
safe_printout('#Task output channel {:d}, #Positive-value output channel {:d}, #Group normalization channel: {:d}.'.format(self.num_task_channel, self.num_pos_channel, self.num_gn_channel))
safe_printout('#MLR: {:d}, #Unfrozen encoder: {:d}'.format(num_mlr, num_unfrozen_encoder))
ttl_num_param = 0
param_info = 'Separation of #trainable parameters: '
for (name, struct) in zip(['Vanilla encoder', 'MLR encoder', 'Decoder'], [self.encoder_ls, self.mlr_ls, self.decoder_ls]):
num_param = sum([param.numel() for layer in struct for param in layer.parameters() if param.requires_grad])
ttl_num_param += num_param
param_info += '{:s}: {:,d}, '.format(name, num_param)
param_info += 'Total: {:,d}.'.format(ttl_num_param)
safe_printout(param_info) | def __init__(self, mean, tiny, grayscale, enc_add_res_block=0, dec_add_res_block=0, num_task_channel=3, num_pos_channel=1, num_gn_channel=32, num_mlr=0, num_unfrozen_encoder=0, full_size_output=False):
'\n Constructor.\n @param mean Mean offset for task output.\n @param tiny Flag for tiny network.\n @param grayscale Flag for grayscale image input.\n @param enc_add_res_block Number of additional DSAC* style residual block for encoder.\n @param dec_add_res_block Number of additional DSAC* style residual block for decoder.\n @param num_task_channel Number of channels for underlying task.\n @param num_pos_channel Number of channels for additional task w/ positive values, e.g., uncertainty.\n @param num_gn_channel Number of group normalization channels, a hyper-parameter.\n @param num_mlr Number of homogeneous mid-level representations encoders.\n @param num_unfrozen_encoder Number of encoders that are not frozen.\n @param full_size_output Flag for full-size network output (by using DUC-style layers).\n\n Note: if enc_add_res_block == dec_add_res_block == 0 && num_task_channel == 3 && num_pos_channel = 0,\n the model become DSAC* net + group normalization only.\n '
super(TransPoseNet, self).__init__()
'Init'
self.register_buffer('mean', torch.tensor(mean.size()).cuda())
self.mean = mean.clone()
self.tiny = tiny
self.grayscale = grayscale
self.enc_add_res_block = enc_add_res_block
self.dec_add_res_block = dec_add_res_block
self.num_task_channel = num_task_channel
self.num_pos_channel = num_pos_channel
self.num_gn_channel = num_gn_channel
self.num_mlr = num_mlr
self.full_size_output = full_size_output
self.OUTPUT_SUBSAMPLE = (1 if full_size_output else 8)
'Vanilla encoder'
if (num_mlr == 0):
self.encoder = TransPoseNetEncoder(tiny, grayscale, enc_add_res_block, num_gn_channel)
self.encoder_ls = [self.encoder]
else:
self.encoder = nn.Identity()
self.encoder_ls = [self.encoder]
'MLR encoders'
if ((num_mlr > 0) and isinstance(num_mlr, int)):
assert (0 <= num_unfrozen_encoder <= num_mlr)
self.mlr_encoder_ls = [TransPoseNetEncoder(tiny, grayscale, enc_add_res_block, num_gn_channel) for _ in range(num_mlr)]
for (i, block) in enumerate(self.mlr_encoder_ls):
if (i >= num_unfrozen_encoder):
for param in block.parameters():
param.requires_grad = False
self.add_module('mlr_encoder_{:d}'.format((i + 1)), block)
self.mlr_norm = nn.GroupNorm(num_gn_channel, ((512, 128)[tiny] * num_mlr))
self.mlr_forward = _create_mlr_concatenator(num_mlr, tiny, num_gn_channel)
self.mlr_skip = _create_mlr_skip_layer(num_mlr, tiny, num_gn_channel)
else:
self.mlr_encoder_ls = [nn.Identity()]
self.mlr_norm = nn.Identity()
self.mlr_forward = nn.Identity()
self.mlr_skip = nn.Identity()
self.mlr_ls = (self.mlr_encoder_ls + [self.mlr_norm, self.mlr_forward, self.mlr_skip])
'Decoder'
self.decoder = TransPoseNetDecoder(mean, tiny, dec_add_res_block, num_task_channel, num_pos_channel, num_gn_channel, full_size_output)
self.decoder_ls = [self.decoder]
'Print out'
safe_printout('Initialized network w/ group normalization, Tiny net: {}, Grayscale input: {}, Fullsize output: {}.'.format(self.tiny, self.grayscale, self.full_size_output))
safe_printout('#Aadditional residual blocks: Encoder: {:d}, Decoder: {:d}'.format(self.enc_add_res_block, self.dec_add_res_block))
safe_printout('#Task output channel {:d}, #Positive-value output channel {:d}, #Group normalization channel: {:d}.'.format(self.num_task_channel, self.num_pos_channel, self.num_gn_channel))
safe_printout('#MLR: {:d}, #Unfrozen encoder: {:d}'.format(num_mlr, num_unfrozen_encoder))
ttl_num_param = 0
param_info = 'Separation of #trainable parameters: '
for (name, struct) in zip(['Vanilla encoder', 'MLR encoder', 'Decoder'], [self.encoder_ls, self.mlr_ls, self.decoder_ls]):
num_param = sum([param.numel() for layer in struct for param in layer.parameters() if param.requires_grad])
ttl_num_param += num_param
param_info += '{:s}: {:,d}, '.format(name, num_param)
param_info += 'Total: {:,d}.'.format(ttl_num_param)
safe_printout(param_info)<|docstring|>Constructor.
@param mean Mean offset for task output.
@param tiny Flag for tiny network.
@param grayscale Flag for grayscale image input.
@param enc_add_res_block Number of additional DSAC* style residual block for encoder.
@param dec_add_res_block Number of additional DSAC* style residual block for decoder.
@param num_task_channel Number of channels for underlying task.
@param num_pos_channel Number of channels for additional task w/ positive values, e.g., uncertainty.
@param num_gn_channel Number of group normalization channels, a hyper-parameter.
@param num_mlr Number of homogeneous mid-level representations encoders.
@param num_unfrozen_encoder Number of encoders that are not frozen.
@param full_size_output Flag for full-size network output (by using DUC-style layers).
Note: if enc_add_res_block == dec_add_res_block == 0 && num_task_channel == 3 && num_pos_channel = 0,
the model become DSAC* net + group normalization only.<|endoftext|> |
4276527c0c872664403e8efc1ffeb77dc24621a056f4a3dab166f457a7ffdbde | def forward(self, inputs):
'\n Forward pass.\n\n @param inputs 4D data tensor (BxCxHxW)\n '
x = inputs
(up_height, up_width) = inputs.size()[2:4]
'Vanilla encoder'
if (self.num_mlr == 0):
res = self.encoder(x)
else:
res = None
'MLR encoder'
if self.num_mlr:
mlr_activation_ls = [mlr_enc(inputs) for mlr_enc in self.mlr_encoder_ls]
mlr = torch.cat(mlr_activation_ls, dim=1)
res = self.mlr_skip(mlr)
mlr = self.mlr_norm(mlr)
mlr = self.mlr_forward(mlr)
res = F.relu((res + mlr))
'Decoder'
if self.full_size_output:
sc = self.decoder(res, up_height, up_width)
else:
sc = self.decoder(res)
return sc | Forward pass.
@param inputs 4D data tensor (BxCxHxW) | networks/networks.py | forward | TOPO-EPFL/CrossLoc | 16 | python | def forward(self, inputs):
'\n Forward pass.\n\n @param inputs 4D data tensor (BxCxHxW)\n '
x = inputs
(up_height, up_width) = inputs.size()[2:4]
'Vanilla encoder'
if (self.num_mlr == 0):
res = self.encoder(x)
else:
res = None
'MLR encoder'
if self.num_mlr:
mlr_activation_ls = [mlr_enc(inputs) for mlr_enc in self.mlr_encoder_ls]
mlr = torch.cat(mlr_activation_ls, dim=1)
res = self.mlr_skip(mlr)
mlr = self.mlr_norm(mlr)
mlr = self.mlr_forward(mlr)
res = F.relu((res + mlr))
'Decoder'
if self.full_size_output:
sc = self.decoder(res, up_height, up_width)
else:
sc = self.decoder(res)
return sc | def forward(self, inputs):
'\n Forward pass.\n\n @param inputs 4D data tensor (BxCxHxW)\n '
x = inputs
(up_height, up_width) = inputs.size()[2:4]
'Vanilla encoder'
if (self.num_mlr == 0):
res = self.encoder(x)
else:
res = None
'MLR encoder'
if self.num_mlr:
mlr_activation_ls = [mlr_enc(inputs) for mlr_enc in self.mlr_encoder_ls]
mlr = torch.cat(mlr_activation_ls, dim=1)
res = self.mlr_skip(mlr)
mlr = self.mlr_norm(mlr)
mlr = self.mlr_forward(mlr)
res = F.relu((res + mlr))
'Decoder'
if self.full_size_output:
sc = self.decoder(res, up_height, up_width)
else:
sc = self.decoder(res)
return sc<|docstring|>Forward pass.
@param inputs 4D data tensor (BxCxHxW)<|endoftext|> |
23520102a861245018c6829bf2c55c1cba432731ad6f3ed7e13545cdfcae9950 | def forward(self, inputs):
'\n @inputs [B, C, H, W], H=60, W=90 if no data augmentation.\n '
x = F.relu(self.norm1(self.conv1(inputs)))
x = F.relu(self.norm2(self.conv2(x)))
x = F.relu(self.norm3(self.conv3(x)))
x = F.relu(self.norm4(self.conv4(x)))
x = self.avgpool(x)
x = torch.flatten(x, 1)
return x | @inputs [B, C, H, W], H=60, W=90 if no data augmentation. | networks/networks.py | forward | TOPO-EPFL/CrossLoc | 16 | python | def forward(self, inputs):
'\n \n '
x = F.relu(self.norm1(self.conv1(inputs)))
x = F.relu(self.norm2(self.conv2(x)))
x = F.relu(self.norm3(self.conv3(x)))
x = F.relu(self.norm4(self.conv4(x)))
x = self.avgpool(x)
x = torch.flatten(x, 1)
return x | def forward(self, inputs):
'\n \n '
x = F.relu(self.norm1(self.conv1(inputs)))
x = F.relu(self.norm2(self.conv2(x)))
x = F.relu(self.norm3(self.conv3(x)))
x = F.relu(self.norm4(self.conv4(x)))
x = self.avgpool(x)
x = torch.flatten(x, 1)
return x<|docstring|>@inputs [B, C, H, W], H=60, W=90 if no data augmentation.<|endoftext|> |
dff5f0924117287b8a9d996b0d00f28f55c01912c2c95fa39a2fe596adc4d7d8 | def tabulate_results(results, csv_path, method='stillness'):
"\n Tabulate the results as calculated by the sequential pipeline.\n\n Parameters\n ----------\n results : {dict, str}\n Either a dictionary of the results, or the path to the h5 file where the results were stored.\n csv_path : str\n Path to save the tabular data at\n method : {'stillness', 'displacement'}, optional\n Which method to tabulate results for. Default is 'stillness'.\n "
(days, times, duration, vdisp, mxa, mna, sparc) = ([], [], [], [], [], [], [])
mtd = f'{method.capitalize()} Method'
if isinstance(results, dict):
day_list = [i for i in results['Processed']['Sit2Stand'] if ('Day' in i)]
for day in day_list:
days.extend(([int(day[4:])] * results['Processed']['Sit2Stand'][day][mtd]['STS Times'].shape[0]))
times.extend(results['Processed']['Sit2Stand'][day][mtd]['STS Times'])
duration.extend(results['Processed']['Sit2Stand'][day][mtd]['Duration'])
vdisp.extend(results['Processed']['Sit2Stand'][day][mtd]['Vertical Displacement'])
mxa.extend(results['Processed']['Sit2Stand'][day][mtd]['Max. Accel.'])
mna.extend(results['Processed']['Sit2Stand'][day][mtd]['Min. Accel.'])
sparc.extend(results['Processed']['Sit2Stand'][day][mtd]['SPARC'])
else:
with h5py.File(results, 'r') as f:
day_list = [i for i in f['Processed/Sit2Stand'] if ('Day' in i)]
for day in day_list:
days.extend(([int(day[4:])] * f[f'Processed/Sit2Stand/{day}/{mtd}/STS Times'].shape[0]))
times.extend(f[f'Processed/Sit2Stand/{day}/{mtd}/STS Times'])
duration.extend(f[f'Processed/Sit2Stand/{day}/{mtd}/Duration'])
vdisp.extend(f[f'Processed/Sit2Stand/{day}/{mtd}/Vertical Displacement'])
mxa.extend(f[f'Processed/Sit2Stand/{day}/{mtd}/Max. Accel.'])
mna.extend(f[f'Processed/Sit2Stand/{day}/{mtd}/Min. Accel.'])
sparc.extend(f[f'Processed/Sit2Stand/{day}/{mtd}/SPARC'])
table = zeros((len(days), 12), dtype='object')
table[(:, 0)] = days
table[(:, 1:3)] = array(times)
table[(:, 7)] = duration
table[(:, 9)] = mxa
table[(:, 10)] = mna
table[(:, 11)] = sparc
for (i, ts) in enumerate(table[(:, 1)]):
dt = udt.utcfromtimestamp(ts)
table[(i, 3)] = dt.strftime('%Y-%m-%d %H:%M:%S.%f')
table[(i, 4)] = dt.hour
table[(i, 5)] = dt.minute
table[(i, 6)] = (dt.weekday() >= 5)
hdr = 'Day,Start Unix Time,End Unix Time,Start Time,Hour,Minute,Weekend,Duration,Vertical Displacement,Max. Accel.,Min. Accel., SPARC'
fmt = '%d, %f, %f, %s, %i, %i, %s, %f, %f, %f, %f, %f'
savetxt(csv_path, table, header=hdr, fmt=fmt) | Tabulate the results as calculated by the sequential pipeline.
Parameters
----------
results : {dict, str}
Either a dictionary of the results, or the path to the h5 file where the results were stored.
csv_path : str
Path to save the tabular data at
method : {'stillness', 'displacement'}, optional
Which method to tabulate results for. Default is 'stillness'. | sit2standpy/v2/utility.py | tabulate_results | PfizerRD/pysit2stand | 1 | python | def tabulate_results(results, csv_path, method='stillness'):
"\n Tabulate the results as calculated by the sequential pipeline.\n\n Parameters\n ----------\n results : {dict, str}\n Either a dictionary of the results, or the path to the h5 file where the results were stored.\n csv_path : str\n Path to save the tabular data at\n method : {'stillness', 'displacement'}, optional\n Which method to tabulate results for. Default is 'stillness'.\n "
(days, times, duration, vdisp, mxa, mna, sparc) = ([], [], [], [], [], [], [])
mtd = f'{method.capitalize()} Method'
if isinstance(results, dict):
day_list = [i for i in results['Processed']['Sit2Stand'] if ('Day' in i)]
for day in day_list:
days.extend(([int(day[4:])] * results['Processed']['Sit2Stand'][day][mtd]['STS Times'].shape[0]))
times.extend(results['Processed']['Sit2Stand'][day][mtd]['STS Times'])
duration.extend(results['Processed']['Sit2Stand'][day][mtd]['Duration'])
vdisp.extend(results['Processed']['Sit2Stand'][day][mtd]['Vertical Displacement'])
mxa.extend(results['Processed']['Sit2Stand'][day][mtd]['Max. Accel.'])
mna.extend(results['Processed']['Sit2Stand'][day][mtd]['Min. Accel.'])
sparc.extend(results['Processed']['Sit2Stand'][day][mtd]['SPARC'])
else:
with h5py.File(results, 'r') as f:
day_list = [i for i in f['Processed/Sit2Stand'] if ('Day' in i)]
for day in day_list:
days.extend(([int(day[4:])] * f[f'Processed/Sit2Stand/{day}/{mtd}/STS Times'].shape[0]))
times.extend(f[f'Processed/Sit2Stand/{day}/{mtd}/STS Times'])
duration.extend(f[f'Processed/Sit2Stand/{day}/{mtd}/Duration'])
vdisp.extend(f[f'Processed/Sit2Stand/{day}/{mtd}/Vertical Displacement'])
mxa.extend(f[f'Processed/Sit2Stand/{day}/{mtd}/Max. Accel.'])
mna.extend(f[f'Processed/Sit2Stand/{day}/{mtd}/Min. Accel.'])
sparc.extend(f[f'Processed/Sit2Stand/{day}/{mtd}/SPARC'])
table = zeros((len(days), 12), dtype='object')
table[(:, 0)] = days
table[(:, 1:3)] = array(times)
table[(:, 7)] = duration
table[(:, 9)] = mxa
table[(:, 10)] = mna
table[(:, 11)] = sparc
for (i, ts) in enumerate(table[(:, 1)]):
dt = udt.utcfromtimestamp(ts)
table[(i, 3)] = dt.strftime('%Y-%m-%d %H:%M:%S.%f')
table[(i, 4)] = dt.hour
table[(i, 5)] = dt.minute
table[(i, 6)] = (dt.weekday() >= 5)
hdr = 'Day,Start Unix Time,End Unix Time,Start Time,Hour,Minute,Weekend,Duration,Vertical Displacement,Max. Accel.,Min. Accel., SPARC'
fmt = '%d, %f, %f, %s, %i, %i, %s, %f, %f, %f, %f, %f'
savetxt(csv_path, table, header=hdr, fmt=fmt) | def tabulate_results(results, csv_path, method='stillness'):
"\n Tabulate the results as calculated by the sequential pipeline.\n\n Parameters\n ----------\n results : {dict, str}\n Either a dictionary of the results, or the path to the h5 file where the results were stored.\n csv_path : str\n Path to save the tabular data at\n method : {'stillness', 'displacement'}, optional\n Which method to tabulate results for. Default is 'stillness'.\n "
(days, times, duration, vdisp, mxa, mna, sparc) = ([], [], [], [], [], [], [])
mtd = f'{method.capitalize()} Method'
if isinstance(results, dict):
day_list = [i for i in results['Processed']['Sit2Stand'] if ('Day' in i)]
for day in day_list:
days.extend(([int(day[4:])] * results['Processed']['Sit2Stand'][day][mtd]['STS Times'].shape[0]))
times.extend(results['Processed']['Sit2Stand'][day][mtd]['STS Times'])
duration.extend(results['Processed']['Sit2Stand'][day][mtd]['Duration'])
vdisp.extend(results['Processed']['Sit2Stand'][day][mtd]['Vertical Displacement'])
mxa.extend(results['Processed']['Sit2Stand'][day][mtd]['Max. Accel.'])
mna.extend(results['Processed']['Sit2Stand'][day][mtd]['Min. Accel.'])
sparc.extend(results['Processed']['Sit2Stand'][day][mtd]['SPARC'])
else:
with h5py.File(results, 'r') as f:
day_list = [i for i in f['Processed/Sit2Stand'] if ('Day' in i)]
for day in day_list:
days.extend(([int(day[4:])] * f[f'Processed/Sit2Stand/{day}/{mtd}/STS Times'].shape[0]))
times.extend(f[f'Processed/Sit2Stand/{day}/{mtd}/STS Times'])
duration.extend(f[f'Processed/Sit2Stand/{day}/{mtd}/Duration'])
vdisp.extend(f[f'Processed/Sit2Stand/{day}/{mtd}/Vertical Displacement'])
mxa.extend(f[f'Processed/Sit2Stand/{day}/{mtd}/Max. Accel.'])
mna.extend(f[f'Processed/Sit2Stand/{day}/{mtd}/Min. Accel.'])
sparc.extend(f[f'Processed/Sit2Stand/{day}/{mtd}/SPARC'])
table = zeros((len(days), 12), dtype='object')
table[(:, 0)] = days
table[(:, 1:3)] = array(times)
table[(:, 7)] = duration
table[(:, 9)] = mxa
table[(:, 10)] = mna
table[(:, 11)] = sparc
for (i, ts) in enumerate(table[(:, 1)]):
dt = udt.utcfromtimestamp(ts)
table[(i, 3)] = dt.strftime('%Y-%m-%d %H:%M:%S.%f')
table[(i, 4)] = dt.hour
table[(i, 5)] = dt.minute
table[(i, 6)] = (dt.weekday() >= 5)
hdr = 'Day,Start Unix Time,End Unix Time,Start Time,Hour,Minute,Weekend,Duration,Vertical Displacement,Max. Accel.,Min. Accel., SPARC'
fmt = '%d, %f, %f, %s, %i, %i, %s, %f, %f, %f, %f, %f'
savetxt(csv_path, table, header=hdr, fmt=fmt)<|docstring|>Tabulate the results as calculated by the sequential pipeline.
Parameters
----------
results : {dict, str}
Either a dictionary of the results, or the path to the h5 file where the results were stored.
csv_path : str
Path to save the tabular data at
method : {'stillness', 'displacement'}, optional
Which method to tabulate results for. Default is 'stillness'.<|endoftext|> |
ad906f8b0d492b6e28f773e3f04fc79780d51052e51f1e74232f34c4669cbfaf | def mov_stats(seq, window):
'\n Compute the centered moving average and standard deviation.\n\n Parameters\n ----------\n seq : numpy.ndarray\n Data to take the moving average and standard deviation on.\n window : int\n Window size for the moving average/standard deviation.\n\n Returns\n -------\n m_mn : numpy.ndarray\n Moving average\n m_st : numpy.ndarray\n Moving standard deviation\n pad : int\n Padding at beginning of the moving average and standard deviation\n '
def rolling_window(x, wind):
if (not x.flags['C_CONTIGUOUS']):
raise ValueError('Data must be C-contiguous to be able to window for moving statistics')
shape = (x.shape[:(- 1)] + (((x.shape[(- 1)] - wind) + 1), wind))
strides = (x.strides + (x.strides[(- 1)],))
return stride_tricks.as_strided(x, shape=shape, strides=strides)
m_mn = zeros(seq.shape)
m_st = zeros(seq.shape)
if (window < 2):
window = 2
pad = int(ceil((window / 2)))
rw_seq = rolling_window(seq, window)
n = rw_seq.shape[0]
m_mn[pad:(pad + n)] = mean(rw_seq, axis=(- 1))
m_st[pad:(pad + n)] = std(rw_seq, axis=(- 1), ddof=1)
(m_mn[:pad], m_mn[(pad + n):]) = (m_mn[pad], m_mn[((- pad) - 1)])
(m_st[:pad], m_st[(pad + n):]) = (m_st[pad], m_st[((- pad) - 1)])
return (m_mn, m_st, pad) | Compute the centered moving average and standard deviation.
Parameters
----------
seq : numpy.ndarray
Data to take the moving average and standard deviation on.
window : int
Window size for the moving average/standard deviation.
Returns
-------
m_mn : numpy.ndarray
Moving average
m_st : numpy.ndarray
Moving standard deviation
pad : int
Padding at beginning of the moving average and standard deviation | sit2standpy/v2/utility.py | mov_stats | PfizerRD/pysit2stand | 1 | python | def mov_stats(seq, window):
'\n Compute the centered moving average and standard deviation.\n\n Parameters\n ----------\n seq : numpy.ndarray\n Data to take the moving average and standard deviation on.\n window : int\n Window size for the moving average/standard deviation.\n\n Returns\n -------\n m_mn : numpy.ndarray\n Moving average\n m_st : numpy.ndarray\n Moving standard deviation\n pad : int\n Padding at beginning of the moving average and standard deviation\n '
def rolling_window(x, wind):
if (not x.flags['C_CONTIGUOUS']):
raise ValueError('Data must be C-contiguous to be able to window for moving statistics')
shape = (x.shape[:(- 1)] + (((x.shape[(- 1)] - wind) + 1), wind))
strides = (x.strides + (x.strides[(- 1)],))
return stride_tricks.as_strided(x, shape=shape, strides=strides)
m_mn = zeros(seq.shape)
m_st = zeros(seq.shape)
if (window < 2):
window = 2
pad = int(ceil((window / 2)))
rw_seq = rolling_window(seq, window)
n = rw_seq.shape[0]
m_mn[pad:(pad + n)] = mean(rw_seq, axis=(- 1))
m_st[pad:(pad + n)] = std(rw_seq, axis=(- 1), ddof=1)
(m_mn[:pad], m_mn[(pad + n):]) = (m_mn[pad], m_mn[((- pad) - 1)])
(m_st[:pad], m_st[(pad + n):]) = (m_st[pad], m_st[((- pad) - 1)])
return (m_mn, m_st, pad) | def mov_stats(seq, window):
'\n Compute the centered moving average and standard deviation.\n\n Parameters\n ----------\n seq : numpy.ndarray\n Data to take the moving average and standard deviation on.\n window : int\n Window size for the moving average/standard deviation.\n\n Returns\n -------\n m_mn : numpy.ndarray\n Moving average\n m_st : numpy.ndarray\n Moving standard deviation\n pad : int\n Padding at beginning of the moving average and standard deviation\n '
def rolling_window(x, wind):
if (not x.flags['C_CONTIGUOUS']):
raise ValueError('Data must be C-contiguous to be able to window for moving statistics')
shape = (x.shape[:(- 1)] + (((x.shape[(- 1)] - wind) + 1), wind))
strides = (x.strides + (x.strides[(- 1)],))
return stride_tricks.as_strided(x, shape=shape, strides=strides)
m_mn = zeros(seq.shape)
m_st = zeros(seq.shape)
if (window < 2):
window = 2
pad = int(ceil((window / 2)))
rw_seq = rolling_window(seq, window)
n = rw_seq.shape[0]
m_mn[pad:(pad + n)] = mean(rw_seq, axis=(- 1))
m_st[pad:(pad + n)] = std(rw_seq, axis=(- 1), ddof=1)
(m_mn[:pad], m_mn[(pad + n):]) = (m_mn[pad], m_mn[((- pad) - 1)])
(m_st[:pad], m_st[(pad + n):]) = (m_st[pad], m_st[((- pad) - 1)])
return (m_mn, m_st, pad)<|docstring|>Compute the centered moving average and standard deviation.
Parameters
----------
seq : numpy.ndarray
Data to take the moving average and standard deviation on.
window : int
Window size for the moving average/standard deviation.
Returns
-------
m_mn : numpy.ndarray
Moving average
m_st : numpy.ndarray
Moving standard deviation
pad : int
Padding at beginning of the moving average and standard deviation<|endoftext|> |
ea1ad910ae9ed98bf2996f3b2d07e5ebf69b01d34ee6d7208795dfd691c43850 | def get_stillness(filt_accel, dt, window, gravity, thresholds):
'\n Stillness determination based on filtered acceleration magnitude and jerk magnitude\n\n Parameters\n ----------\n filt_accel : numpy.ndarray\n 1D array of filtered magnitude of acceleration data, units of m/s^2\n dt : float\n Sampling time, in seconds\n window : float\n Moving statistics window length, in seconds\n gravity : float\n Gravitational acceleration, as measured by the sensor during static periods.\n thresholds : dict\n Dictionary of the 4 thresholds to be used - accel moving avg, accel moving std, \n jerk moving avg, and jerk moving std. \n Acceleration average thresholds should be for difference from gravitional acceleration.\n\n Returns\n -------\n still : numpy.ndarray\n (N, ) boolean array of stillness (True)\n starts : numpy.ndarray\n (Q, ) array of indices where stillness starts. Includes index 0 if still[0] is True. Q < (N/2)\n stops : numpy.ndarray\n (Q, ) array of indices where stillness ends. Includes index N-1 if still[-1] is True. Q < (N/2)\n '
n_window = int(around((window / dt)))
(acc_rm, acc_rsd, _) = mov_stats(filt_accel, n_window)
jerk = gradient(filt_accel, dt, edge_order=2)
(jerk_rm, jerk_rsd, _) = mov_stats(jerk, n_window)
arm_mask = (abs((acc_rm - gravity)) < thresholds['accel moving avg'])
arsd_mask = (acc_rsd < thresholds['accel moving std'])
jrm_mask = (abs(jerk_rm) < thresholds['jerk moving avg'])
jrsd_mask = (jerk_rsd < thresholds['jerk moving std'])
still = (((arm_mask & arsd_mask) & jrm_mask) & jrsd_mask)
starts = where((diff(still.astype(int)) == 1))[0]
stops = where((diff(still.astype(int)) == (- 1)))[0]
if still[0]:
starts = insert(starts, 0, 0)
if still[(- 1)]:
stops = append(stops, (len(still) - 1))
return (still, starts, stops) | Stillness determination based on filtered acceleration magnitude and jerk magnitude
Parameters
----------
filt_accel : numpy.ndarray
1D array of filtered magnitude of acceleration data, units of m/s^2
dt : float
Sampling time, in seconds
window : float
Moving statistics window length, in seconds
gravity : float
Gravitational acceleration, as measured by the sensor during static periods.
thresholds : dict
Dictionary of the 4 thresholds to be used - accel moving avg, accel moving std,
jerk moving avg, and jerk moving std.
Acceleration average thresholds should be for difference from gravitional acceleration.
Returns
-------
still : numpy.ndarray
(N, ) boolean array of stillness (True)
starts : numpy.ndarray
(Q, ) array of indices where stillness starts. Includes index 0 if still[0] is True. Q < (N/2)
stops : numpy.ndarray
(Q, ) array of indices where stillness ends. Includes index N-1 if still[-1] is True. Q < (N/2) | sit2standpy/v2/utility.py | get_stillness | PfizerRD/pysit2stand | 1 | python | def get_stillness(filt_accel, dt, window, gravity, thresholds):
'\n Stillness determination based on filtered acceleration magnitude and jerk magnitude\n\n Parameters\n ----------\n filt_accel : numpy.ndarray\n 1D array of filtered magnitude of acceleration data, units of m/s^2\n dt : float\n Sampling time, in seconds\n window : float\n Moving statistics window length, in seconds\n gravity : float\n Gravitational acceleration, as measured by the sensor during static periods.\n thresholds : dict\n Dictionary of the 4 thresholds to be used - accel moving avg, accel moving std, \n jerk moving avg, and jerk moving std. \n Acceleration average thresholds should be for difference from gravitional acceleration.\n\n Returns\n -------\n still : numpy.ndarray\n (N, ) boolean array of stillness (True)\n starts : numpy.ndarray\n (Q, ) array of indices where stillness starts. Includes index 0 if still[0] is True. Q < (N/2)\n stops : numpy.ndarray\n (Q, ) array of indices where stillness ends. Includes index N-1 if still[-1] is True. Q < (N/2)\n '
n_window = int(around((window / dt)))
(acc_rm, acc_rsd, _) = mov_stats(filt_accel, n_window)
jerk = gradient(filt_accel, dt, edge_order=2)
(jerk_rm, jerk_rsd, _) = mov_stats(jerk, n_window)
arm_mask = (abs((acc_rm - gravity)) < thresholds['accel moving avg'])
arsd_mask = (acc_rsd < thresholds['accel moving std'])
jrm_mask = (abs(jerk_rm) < thresholds['jerk moving avg'])
jrsd_mask = (jerk_rsd < thresholds['jerk moving std'])
still = (((arm_mask & arsd_mask) & jrm_mask) & jrsd_mask)
starts = where((diff(still.astype(int)) == 1))[0]
stops = where((diff(still.astype(int)) == (- 1)))[0]
if still[0]:
starts = insert(starts, 0, 0)
if still[(- 1)]:
stops = append(stops, (len(still) - 1))
return (still, starts, stops) | def get_stillness(filt_accel, dt, window, gravity, thresholds):
'\n Stillness determination based on filtered acceleration magnitude and jerk magnitude\n\n Parameters\n ----------\n filt_accel : numpy.ndarray\n 1D array of filtered magnitude of acceleration data, units of m/s^2\n dt : float\n Sampling time, in seconds\n window : float\n Moving statistics window length, in seconds\n gravity : float\n Gravitational acceleration, as measured by the sensor during static periods.\n thresholds : dict\n Dictionary of the 4 thresholds to be used - accel moving avg, accel moving std, \n jerk moving avg, and jerk moving std. \n Acceleration average thresholds should be for difference from gravitional acceleration.\n\n Returns\n -------\n still : numpy.ndarray\n (N, ) boolean array of stillness (True)\n starts : numpy.ndarray\n (Q, ) array of indices where stillness starts. Includes index 0 if still[0] is True. Q < (N/2)\n stops : numpy.ndarray\n (Q, ) array of indices where stillness ends. Includes index N-1 if still[-1] is True. Q < (N/2)\n '
n_window = int(around((window / dt)))
(acc_rm, acc_rsd, _) = mov_stats(filt_accel, n_window)
jerk = gradient(filt_accel, dt, edge_order=2)
(jerk_rm, jerk_rsd, _) = mov_stats(jerk, n_window)
arm_mask = (abs((acc_rm - gravity)) < thresholds['accel moving avg'])
arsd_mask = (acc_rsd < thresholds['accel moving std'])
jrm_mask = (abs(jerk_rm) < thresholds['jerk moving avg'])
jrsd_mask = (jerk_rsd < thresholds['jerk moving std'])
still = (((arm_mask & arsd_mask) & jrm_mask) & jrsd_mask)
starts = where((diff(still.astype(int)) == 1))[0]
stops = where((diff(still.astype(int)) == (- 1)))[0]
if still[0]:
starts = insert(starts, 0, 0)
if still[(- 1)]:
stops = append(stops, (len(still) - 1))
return (still, starts, stops)<|docstring|>Stillness determination based on filtered acceleration magnitude and jerk magnitude
Parameters
----------
filt_accel : numpy.ndarray
1D array of filtered magnitude of acceleration data, units of m/s^2
dt : float
Sampling time, in seconds
window : float
Moving statistics window length, in seconds
gravity : float
Gravitational acceleration, as measured by the sensor during static periods.
thresholds : dict
Dictionary of the 4 thresholds to be used - accel moving avg, accel moving std,
jerk moving avg, and jerk moving std.
Acceleration average thresholds should be for difference from gravitional acceleration.
Returns
-------
still : numpy.ndarray
(N, ) boolean array of stillness (True)
starts : numpy.ndarray
(Q, ) array of indices where stillness starts. Includes index 0 if still[0] is True. Q < (N/2)
stops : numpy.ndarray
(Q, ) array of indices where stillness ends. Includes index N-1 if still[-1] is True. Q < (N/2)<|endoftext|> |
8f137d6d7cc904229f474f415fdb6524466048e0a51a9edee6156bf100376040 | def remove_primers(workflow, fwd_primer, rev_primer, input_folder, output_folder, pair_id, threads):
' Identifies primers and N filters samples\n Args:\n workflow (anadama2.workflow): an instance of the workflow class\n input_folder (string): path to input folder\n output_folder (string): path to output folder\n fwd_primer (string): forward primer\n rev_primer (string): reverse primer\n pair_id (string): pair identifier\n threads (string): number of threads\n\n Requires:\n dada2, Biostrings, ShortRead, tools r packages\n\n Returns:\n string: path to folder with primers removed files\n '
script_path = utilities.get_package_file('identify_primers', 'Rscript')
filtN_folder = os.path.join(output_folder, 'filtN')
primers_folder = os.path.join(output_folder, 'primers')
fwd_primer_file = os.path.join(primers_folder, 'fwd_primer_file.txt')
rev_primer_file = os.path.join(primers_folder, 'rev_primer_file.txt')
cutadapt_folder = os.path.join(output_folder, 'cutadapt')
workflow.add_task('[vars[0]] --input_dir=[args[3]] --filtn_dir=[vars[1]] --primers_dir=[vars[2]] --threads=[args[4]] --fwd_primer_file=[targets[0]] --rev_primer_file=[targets[1]] --fwd_primer=[args[0]] --rev_primer=[args[1]] --pair_id=[args[2]]', targets=[fwd_primer_file, rev_primer_file, TrackedDirectory(filtN_folder)], args=[fwd_primer, rev_primer, pair_id, input_folder, threads], vars=[script_path, filtN_folder, primers_folder, output_folder], name='identify_primers')
pair_id2 = pair_id.replace('1', '2', 1)
fwd_files = sorted(fnmatch.filter(os.listdir(input_folder), (('*' + pair_id) + '*.fastq*')))
rev_files = sorted(fnmatch.filter(os.listdir(input_folder), (('*' + pair_id2) + '*.fastq*')))
for i in range(0, len(fwd_files)):
fwd_file = os.path.join(input_folder, fwd_files[i])
rev_file = os.path.join(input_folder, rev_files[i])
workflow.add_task(cutadapt_do, depends=[fwd_primer_file, rev_primer_file, fwd_file, rev_file, TrackedDirectory(filtN_folder), TrackedExecutable('cutadapt', version_command="echo 'cutadapt' `cutadapt --version`")], targets=[TrackedDirectory(cutadapt_folder)], name='remove_primers')
return cutadapt_folder | Identifies primers and N filters samples
Args:
workflow (anadama2.workflow): an instance of the workflow class
input_folder (string): path to input folder
output_folder (string): path to output folder
fwd_primer (string): forward primer
rev_primer (string): reverse primer
pair_id (string): pair identifier
threads (string): number of threads
Requires:
dada2, Biostrings, ShortRead, tools r packages
Returns:
string: path to folder with primers removed files | biobakery_workflows/tasks/dadatwo.py | remove_primers | tkuntz-hsph/biobakery_workflows | 47 | python | def remove_primers(workflow, fwd_primer, rev_primer, input_folder, output_folder, pair_id, threads):
' Identifies primers and N filters samples\n Args:\n workflow (anadama2.workflow): an instance of the workflow class\n input_folder (string): path to input folder\n output_folder (string): path to output folder\n fwd_primer (string): forward primer\n rev_primer (string): reverse primer\n pair_id (string): pair identifier\n threads (string): number of threads\n\n Requires:\n dada2, Biostrings, ShortRead, tools r packages\n\n Returns:\n string: path to folder with primers removed files\n '
script_path = utilities.get_package_file('identify_primers', 'Rscript')
filtN_folder = os.path.join(output_folder, 'filtN')
primers_folder = os.path.join(output_folder, 'primers')
fwd_primer_file = os.path.join(primers_folder, 'fwd_primer_file.txt')
rev_primer_file = os.path.join(primers_folder, 'rev_primer_file.txt')
cutadapt_folder = os.path.join(output_folder, 'cutadapt')
workflow.add_task('[vars[0]] --input_dir=[args[3]] --filtn_dir=[vars[1]] --primers_dir=[vars[2]] --threads=[args[4]] --fwd_primer_file=[targets[0]] --rev_primer_file=[targets[1]] --fwd_primer=[args[0]] --rev_primer=[args[1]] --pair_id=[args[2]]', targets=[fwd_primer_file, rev_primer_file, TrackedDirectory(filtN_folder)], args=[fwd_primer, rev_primer, pair_id, input_folder, threads], vars=[script_path, filtN_folder, primers_folder, output_folder], name='identify_primers')
pair_id2 = pair_id.replace('1', '2', 1)
fwd_files = sorted(fnmatch.filter(os.listdir(input_folder), (('*' + pair_id) + '*.fastq*')))
rev_files = sorted(fnmatch.filter(os.listdir(input_folder), (('*' + pair_id2) + '*.fastq*')))
for i in range(0, len(fwd_files)):
fwd_file = os.path.join(input_folder, fwd_files[i])
rev_file = os.path.join(input_folder, rev_files[i])
workflow.add_task(cutadapt_do, depends=[fwd_primer_file, rev_primer_file, fwd_file, rev_file, TrackedDirectory(filtN_folder), TrackedExecutable('cutadapt', version_command="echo 'cutadapt' `cutadapt --version`")], targets=[TrackedDirectory(cutadapt_folder)], name='remove_primers')
return cutadapt_folder | def remove_primers(workflow, fwd_primer, rev_primer, input_folder, output_folder, pair_id, threads):
' Identifies primers and N filters samples\n Args:\n workflow (anadama2.workflow): an instance of the workflow class\n input_folder (string): path to input folder\n output_folder (string): path to output folder\n fwd_primer (string): forward primer\n rev_primer (string): reverse primer\n pair_id (string): pair identifier\n threads (string): number of threads\n\n Requires:\n dada2, Biostrings, ShortRead, tools r packages\n\n Returns:\n string: path to folder with primers removed files\n '
script_path = utilities.get_package_file('identify_primers', 'Rscript')
filtN_folder = os.path.join(output_folder, 'filtN')
primers_folder = os.path.join(output_folder, 'primers')
fwd_primer_file = os.path.join(primers_folder, 'fwd_primer_file.txt')
rev_primer_file = os.path.join(primers_folder, 'rev_primer_file.txt')
cutadapt_folder = os.path.join(output_folder, 'cutadapt')
workflow.add_task('[vars[0]] --input_dir=[args[3]] --filtn_dir=[vars[1]] --primers_dir=[vars[2]] --threads=[args[4]] --fwd_primer_file=[targets[0]] --rev_primer_file=[targets[1]] --fwd_primer=[args[0]] --rev_primer=[args[1]] --pair_id=[args[2]]', targets=[fwd_primer_file, rev_primer_file, TrackedDirectory(filtN_folder)], args=[fwd_primer, rev_primer, pair_id, input_folder, threads], vars=[script_path, filtN_folder, primers_folder, output_folder], name='identify_primers')
pair_id2 = pair_id.replace('1', '2', 1)
fwd_files = sorted(fnmatch.filter(os.listdir(input_folder), (('*' + pair_id) + '*.fastq*')))
rev_files = sorted(fnmatch.filter(os.listdir(input_folder), (('*' + pair_id2) + '*.fastq*')))
for i in range(0, len(fwd_files)):
fwd_file = os.path.join(input_folder, fwd_files[i])
rev_file = os.path.join(input_folder, rev_files[i])
workflow.add_task(cutadapt_do, depends=[fwd_primer_file, rev_primer_file, fwd_file, rev_file, TrackedDirectory(filtN_folder), TrackedExecutable('cutadapt', version_command="echo 'cutadapt' `cutadapt --version`")], targets=[TrackedDirectory(cutadapt_folder)], name='remove_primers')
return cutadapt_folder<|docstring|>Identifies primers and N filters samples
Args:
workflow (anadama2.workflow): an instance of the workflow class
input_folder (string): path to input folder
output_folder (string): path to output folder
fwd_primer (string): forward primer
rev_primer (string): reverse primer
pair_id (string): pair identifier
threads (string): number of threads
Requires:
dada2, Biostrings, ShortRead, tools r packages
Returns:
string: path to folder with primers removed files<|endoftext|> |
9ce2db5d8810b7a08ad5010f77937b6b22c02a1a1bae5ee11f92caef28b0e181 | def cutadapt_do(task):
'Reads primers from the files and runs cutadapt task\n Args:\n task (anadama2.task): an instance of the task class'
from anadama2.util import get_name
with open(get_name(task.depends[0])) as f:
FWD = f.read().splitlines()
with open(get_name(task.depends[1])) as f:
REV = f.read().splitlines()
cutadapt_folder = get_name(task.targets[0])
filtN_folder = get_name(task.depends[4])
fwd_filename = os.path.basename(get_name(task.depends[2]))
rev_filename = os.path.basename(get_name(task.depends[3]))
fwd_reads_out = os.path.join(cutadapt_folder, fwd_filename)
rev_reads_out = os.path.join(cutadapt_folder, rev_filename)
fwd_reads_in = os.path.join(filtN_folder, fwd_filename)
rev_reads_in = os.path.join(filtN_folder, rev_filename)
if (not os.path.exists(cutadapt_folder)):
os.mkdir(cutadapt_folder)
command = (((((((((((((((('cutadapt -g ' + FWD[0]) + ' -a ') + REV[1]) + ' -G ') + REV[0]) + ' -A ') + FWD[1]) + ' -n 2 -o ') + fwd_reads_out) + ' -p ') + rev_reads_out) + ' ') + fwd_reads_in) + ' ') + rev_reads_in) + ' --minimum-length 10')
utilities.run_task(command, depends=task.depends, targets=task.targets) | Reads primers from the files and runs cutadapt task
Args:
task (anadama2.task): an instance of the task class | biobakery_workflows/tasks/dadatwo.py | cutadapt_do | tkuntz-hsph/biobakery_workflows | 47 | python | def cutadapt_do(task):
'Reads primers from the files and runs cutadapt task\n Args:\n task (anadama2.task): an instance of the task class'
from anadama2.util import get_name
with open(get_name(task.depends[0])) as f:
FWD = f.read().splitlines()
with open(get_name(task.depends[1])) as f:
REV = f.read().splitlines()
cutadapt_folder = get_name(task.targets[0])
filtN_folder = get_name(task.depends[4])
fwd_filename = os.path.basename(get_name(task.depends[2]))
rev_filename = os.path.basename(get_name(task.depends[3]))
fwd_reads_out = os.path.join(cutadapt_folder, fwd_filename)
rev_reads_out = os.path.join(cutadapt_folder, rev_filename)
fwd_reads_in = os.path.join(filtN_folder, fwd_filename)
rev_reads_in = os.path.join(filtN_folder, rev_filename)
if (not os.path.exists(cutadapt_folder)):
os.mkdir(cutadapt_folder)
command = (((((((((((((((('cutadapt -g ' + FWD[0]) + ' -a ') + REV[1]) + ' -G ') + REV[0]) + ' -A ') + FWD[1]) + ' -n 2 -o ') + fwd_reads_out) + ' -p ') + rev_reads_out) + ' ') + fwd_reads_in) + ' ') + rev_reads_in) + ' --minimum-length 10')
utilities.run_task(command, depends=task.depends, targets=task.targets) | def cutadapt_do(task):
'Reads primers from the files and runs cutadapt task\n Args:\n task (anadama2.task): an instance of the task class'
from anadama2.util import get_name
with open(get_name(task.depends[0])) as f:
FWD = f.read().splitlines()
with open(get_name(task.depends[1])) as f:
REV = f.read().splitlines()
cutadapt_folder = get_name(task.targets[0])
filtN_folder = get_name(task.depends[4])
fwd_filename = os.path.basename(get_name(task.depends[2]))
rev_filename = os.path.basename(get_name(task.depends[3]))
fwd_reads_out = os.path.join(cutadapt_folder, fwd_filename)
rev_reads_out = os.path.join(cutadapt_folder, rev_filename)
fwd_reads_in = os.path.join(filtN_folder, fwd_filename)
rev_reads_in = os.path.join(filtN_folder, rev_filename)
if (not os.path.exists(cutadapt_folder)):
os.mkdir(cutadapt_folder)
command = (((((((((((((((('cutadapt -g ' + FWD[0]) + ' -a ') + REV[1]) + ' -G ') + REV[0]) + ' -A ') + FWD[1]) + ' -n 2 -o ') + fwd_reads_out) + ' -p ') + rev_reads_out) + ' ') + fwd_reads_in) + ' ') + rev_reads_in) + ' --minimum-length 10')
utilities.run_task(command, depends=task.depends, targets=task.targets)<|docstring|>Reads primers from the files and runs cutadapt task
Args:
task (anadama2.task): an instance of the task class<|endoftext|> |
fc301987072b613d46acdcce4f82f47fb669bc57902c49d85098d80b1f02f69e | def filter_trim(workflow, input_folder, output_folder, maxee, trunc_len_max, pair_id, threads, trunc_len_rev_offset):
' Filters samples by maxee and trims them, renders quality control plots\n of forward and reverse reads for each sample, creates read counts tsv and rds files.\n \n Args:\n workflow (anadama2.workflow): an instance of the workflow class\n input_folder (string): path to input folder\n output_folder (string): path to output folder\n maxee (string): maxee value to use for filtering\n trunc_len_max (string): max length for truncating reads\n pair_id (string): pair identifier\n threads (int): number of threads\n \n Requires:\n dada2, gridExtra,tools r packages\n \n Returns:\n string: path to file that contains read counts before and after filtering\n string: path to folder with filtered and trimmed sample files\n '
reads_plotF_png = files.SixteenS.path('readF_qc', output_folder)
reads_plotR_png = files.SixteenS.path('readR_qc', output_folder)
readcounts_tsv_path = os.path.join(output_folder, 'Read_counts_after_filtering.tsv')
readcounts_rds_path = os.path.join(output_folder, 'Read_counts_filt.rds')
filtered_dir = 'filtered_input'
script_path = utilities.get_package_file('filter_and_trim', 'Rscript')
workflow.add_task("[vars[0]] --input_dir=[args[0]] --output_dir=[args[1]] --filtered_dir=[vars[1]] --maxee=[args[2]] --trunc_len_max=[args[3]] --readcounts_tsv_path=[targets[0]] --readcounts_rds_path=[targets[1]] --reads_plotF=[targets[2]] --reads_plotR=[args[7]] --pair_id=[args[4]] --threads=[args[5]] --trunc_len_rev_offset='[args[6]]'", depends=[TrackedDirectory(input_folder)], targets=[readcounts_tsv_path, readcounts_rds_path, reads_plotF_png], args=[input_folder, output_folder, maxee, trunc_len_max, pair_id, threads, trunc_len_rev_offset, reads_plotR_png], vars=[script_path, filtered_dir], name='filter_and_trim')
return (readcounts_tsv_path, filtered_dir) | Filters samples by maxee and trims them, renders quality control plots
of forward and reverse reads for each sample, creates read counts tsv and rds files.
Args:
workflow (anadama2.workflow): an instance of the workflow class
input_folder (string): path to input folder
output_folder (string): path to output folder
maxee (string): maxee value to use for filtering
trunc_len_max (string): max length for truncating reads
pair_id (string): pair identifier
threads (int): number of threads
Requires:
dada2, gridExtra,tools r packages
Returns:
string: path to file that contains read counts before and after filtering
string: path to folder with filtered and trimmed sample files | biobakery_workflows/tasks/dadatwo.py | filter_trim | tkuntz-hsph/biobakery_workflows | 47 | python | def filter_trim(workflow, input_folder, output_folder, maxee, trunc_len_max, pair_id, threads, trunc_len_rev_offset):
' Filters samples by maxee and trims them, renders quality control plots\n of forward and reverse reads for each sample, creates read counts tsv and rds files.\n \n Args:\n workflow (anadama2.workflow): an instance of the workflow class\n input_folder (string): path to input folder\n output_folder (string): path to output folder\n maxee (string): maxee value to use for filtering\n trunc_len_max (string): max length for truncating reads\n pair_id (string): pair identifier\n threads (int): number of threads\n \n Requires:\n dada2, gridExtra,tools r packages\n \n Returns:\n string: path to file that contains read counts before and after filtering\n string: path to folder with filtered and trimmed sample files\n '
reads_plotF_png = files.SixteenS.path('readF_qc', output_folder)
reads_plotR_png = files.SixteenS.path('readR_qc', output_folder)
readcounts_tsv_path = os.path.join(output_folder, 'Read_counts_after_filtering.tsv')
readcounts_rds_path = os.path.join(output_folder, 'Read_counts_filt.rds')
filtered_dir = 'filtered_input'
script_path = utilities.get_package_file('filter_and_trim', 'Rscript')
workflow.add_task("[vars[0]] --input_dir=[args[0]] --output_dir=[args[1]] --filtered_dir=[vars[1]] --maxee=[args[2]] --trunc_len_max=[args[3]] --readcounts_tsv_path=[targets[0]] --readcounts_rds_path=[targets[1]] --reads_plotF=[targets[2]] --reads_plotR=[args[7]] --pair_id=[args[4]] --threads=[args[5]] --trunc_len_rev_offset='[args[6]]'", depends=[TrackedDirectory(input_folder)], targets=[readcounts_tsv_path, readcounts_rds_path, reads_plotF_png], args=[input_folder, output_folder, maxee, trunc_len_max, pair_id, threads, trunc_len_rev_offset, reads_plotR_png], vars=[script_path, filtered_dir], name='filter_and_trim')
return (readcounts_tsv_path, filtered_dir) | def filter_trim(workflow, input_folder, output_folder, maxee, trunc_len_max, pair_id, threads, trunc_len_rev_offset):
' Filters samples by maxee and trims them, renders quality control plots\n of forward and reverse reads for each sample, creates read counts tsv and rds files.\n \n Args:\n workflow (anadama2.workflow): an instance of the workflow class\n input_folder (string): path to input folder\n output_folder (string): path to output folder\n maxee (string): maxee value to use for filtering\n trunc_len_max (string): max length for truncating reads\n pair_id (string): pair identifier\n threads (int): number of threads\n \n Requires:\n dada2, gridExtra,tools r packages\n \n Returns:\n string: path to file that contains read counts before and after filtering\n string: path to folder with filtered and trimmed sample files\n '
reads_plotF_png = files.SixteenS.path('readF_qc', output_folder)
reads_plotR_png = files.SixteenS.path('readR_qc', output_folder)
readcounts_tsv_path = os.path.join(output_folder, 'Read_counts_after_filtering.tsv')
readcounts_rds_path = os.path.join(output_folder, 'Read_counts_filt.rds')
filtered_dir = 'filtered_input'
script_path = utilities.get_package_file('filter_and_trim', 'Rscript')
workflow.add_task("[vars[0]] --input_dir=[args[0]] --output_dir=[args[1]] --filtered_dir=[vars[1]] --maxee=[args[2]] --trunc_len_max=[args[3]] --readcounts_tsv_path=[targets[0]] --readcounts_rds_path=[targets[1]] --reads_plotF=[targets[2]] --reads_plotR=[args[7]] --pair_id=[args[4]] --threads=[args[5]] --trunc_len_rev_offset='[args[6]]'", depends=[TrackedDirectory(input_folder)], targets=[readcounts_tsv_path, readcounts_rds_path, reads_plotF_png], args=[input_folder, output_folder, maxee, trunc_len_max, pair_id, threads, trunc_len_rev_offset, reads_plotR_png], vars=[script_path, filtered_dir], name='filter_and_trim')
return (readcounts_tsv_path, filtered_dir)<|docstring|>Filters samples by maxee and trims them, renders quality control plots
of forward and reverse reads for each sample, creates read counts tsv and rds files.
Args:
workflow (anadama2.workflow): an instance of the workflow class
input_folder (string): path to input folder
output_folder (string): path to output folder
maxee (string): maxee value to use for filtering
trunc_len_max (string): max length for truncating reads
pair_id (string): pair identifier
threads (int): number of threads
Requires:
dada2, gridExtra,tools r packages
Returns:
string: path to file that contains read counts before and after filtering
string: path to folder with filtered and trimmed sample files<|endoftext|> |
7e1ba373c9d0083e1b47db7d52a95d7d3939d41b1a8ed70342f76f40fa56cc83 | def learn_error(workflow, output_folder, filtered_dir, readcounts_tsv_path, threads):
' Learns error rates for each sample, renders error rates plots for forward and reverse reads\n \n Args:\n workflow (anadama2.workflow): an instance of the workflow class\n output_folder (string): path to output folder\n filtered_dir (string): path to directory with filtered files\n readcounts_tsv_path (string): path to read counts after filtering tsv file\n threads (int): number of threads\n\n Requires:\n dada2, ggplot2 r packages\n\n Returns:\n string: path to file that contains error rates of forward reads\n string: path to file that contains error rates of reverse reads\n '
error_ratesF_png = files.SixteenS.path('error_ratesF', output_folder)
error_ratesR_png = files.SixteenS.path('error_ratesR', output_folder)
error_ratesF_path = os.path.join(output_folder, 'error_ratesFWD.rds')
error_ratesR_path = os.path.join(output_folder, 'error_ratesREV.rds')
script_path = utilities.get_package_file('learn_error_rates', 'Rscript')
workflow.add_task('[vars[0]] --output_dir=[args[0]] --filtered_dir=[args[1]] --error_ratesF_png=[targets[0]] --error_ratesR_png=[args[2]] --error_ratesF_path=[targets[1]] --error_ratesR_path=[args[3]] --threads=[vars[1]]', depends=[readcounts_tsv_path], targets=[error_ratesF_png, error_ratesF_path], args=[output_folder, filtered_dir, error_ratesR_png, error_ratesR_path], vars=[script_path, threads], name='learn_error_rates')
return (error_ratesF_path, error_ratesR_path) | Learns error rates for each sample, renders error rates plots for forward and reverse reads
Args:
workflow (anadama2.workflow): an instance of the workflow class
output_folder (string): path to output folder
filtered_dir (string): path to directory with filtered files
readcounts_tsv_path (string): path to read counts after filtering tsv file
threads (int): number of threads
Requires:
dada2, ggplot2 r packages
Returns:
string: path to file that contains error rates of forward reads
string: path to file that contains error rates of reverse reads | biobakery_workflows/tasks/dadatwo.py | learn_error | tkuntz-hsph/biobakery_workflows | 47 | python | def learn_error(workflow, output_folder, filtered_dir, readcounts_tsv_path, threads):
' Learns error rates for each sample, renders error rates plots for forward and reverse reads\n \n Args:\n workflow (anadama2.workflow): an instance of the workflow class\n output_folder (string): path to output folder\n filtered_dir (string): path to directory with filtered files\n readcounts_tsv_path (string): path to read counts after filtering tsv file\n threads (int): number of threads\n\n Requires:\n dada2, ggplot2 r packages\n\n Returns:\n string: path to file that contains error rates of forward reads\n string: path to file that contains error rates of reverse reads\n '
error_ratesF_png = files.SixteenS.path('error_ratesF', output_folder)
error_ratesR_png = files.SixteenS.path('error_ratesR', output_folder)
error_ratesF_path = os.path.join(output_folder, 'error_ratesFWD.rds')
error_ratesR_path = os.path.join(output_folder, 'error_ratesREV.rds')
script_path = utilities.get_package_file('learn_error_rates', 'Rscript')
workflow.add_task('[vars[0]] --output_dir=[args[0]] --filtered_dir=[args[1]] --error_ratesF_png=[targets[0]] --error_ratesR_png=[args[2]] --error_ratesF_path=[targets[1]] --error_ratesR_path=[args[3]] --threads=[vars[1]]', depends=[readcounts_tsv_path], targets=[error_ratesF_png, error_ratesF_path], args=[output_folder, filtered_dir, error_ratesR_png, error_ratesR_path], vars=[script_path, threads], name='learn_error_rates')
return (error_ratesF_path, error_ratesR_path) | def learn_error(workflow, output_folder, filtered_dir, readcounts_tsv_path, threads):
' Learns error rates for each sample, renders error rates plots for forward and reverse reads\n \n Args:\n workflow (anadama2.workflow): an instance of the workflow class\n output_folder (string): path to output folder\n filtered_dir (string): path to directory with filtered files\n readcounts_tsv_path (string): path to read counts after filtering tsv file\n threads (int): number of threads\n\n Requires:\n dada2, ggplot2 r packages\n\n Returns:\n string: path to file that contains error rates of forward reads\n string: path to file that contains error rates of reverse reads\n '
error_ratesF_png = files.SixteenS.path('error_ratesF', output_folder)
error_ratesR_png = files.SixteenS.path('error_ratesR', output_folder)
error_ratesF_path = os.path.join(output_folder, 'error_ratesFWD.rds')
error_ratesR_path = os.path.join(output_folder, 'error_ratesREV.rds')
script_path = utilities.get_package_file('learn_error_rates', 'Rscript')
workflow.add_task('[vars[0]] --output_dir=[args[0]] --filtered_dir=[args[1]] --error_ratesF_png=[targets[0]] --error_ratesR_png=[args[2]] --error_ratesF_path=[targets[1]] --error_ratesR_path=[args[3]] --threads=[vars[1]]', depends=[readcounts_tsv_path], targets=[error_ratesF_png, error_ratesF_path], args=[output_folder, filtered_dir, error_ratesR_png, error_ratesR_path], vars=[script_path, threads], name='learn_error_rates')
return (error_ratesF_path, error_ratesR_path)<|docstring|>Learns error rates for each sample, renders error rates plots for forward and reverse reads
Args:
workflow (anadama2.workflow): an instance of the workflow class
output_folder (string): path to output folder
filtered_dir (string): path to directory with filtered files
readcounts_tsv_path (string): path to read counts after filtering tsv file
threads (int): number of threads
Requires:
dada2, ggplot2 r packages
Returns:
string: path to file that contains error rates of forward reads
string: path to file that contains error rates of reverse reads<|endoftext|> |
2299ab290e6523e07579a3bfda345a84c929eacba7aa1b31d605d7f46ae1addb | def merge_paired_ends(workflow, output_dir, filtered_dir, error_ratesF_path, error_ratesR_path, threads, minoverlap, maxmismatch):
' Dereplicates and merges paired reads\n \n Args:\n workflow (anadama2.workflow): an instance of the workflow class\n output_folder (string): path to output folder\n filtered_dir (string): path to directory with filtered files\n error_ratesF_path (string): path to rds file that contains error rates of forward reads\n error_ratesR_path (string): path to rds file that contains error rates of reverse reads\n threads (int): number of threads\n minoverlap (int): the min number of pairs for overlap for the merge step\n maxmismatch (int): the max number of mismatch for pairs to merge\n Requires:\n dada2, tools r packages\n \n Returns:\n string: path to rds file that contains merged and dereplicated reads\n '
mergers_file_path = os.path.join(output_dir, 'mergers.rds')
script_path = utilities.get_package_file('merge_paired_ends', 'Rscript')
workflow.add_task('[vars[0]] --output_dir=[args[0]] --filtered_dir=[args[1]] --error_ratesF_path=[depends[0]] --error_ratesR_path=[args[4]] --mergers_file_path=[targets[0]] --threads=[vars[1]] --minoverlap=[args[2]] --maxmismatch=[args[3]]', depends=[error_ratesF_path], targets=[mergers_file_path], args=[output_dir, filtered_dir, minoverlap, maxmismatch, error_ratesR_path], vars=[script_path, threads], name='dereplicate_and_merge')
return mergers_file_path | Dereplicates and merges paired reads
Args:
workflow (anadama2.workflow): an instance of the workflow class
output_folder (string): path to output folder
filtered_dir (string): path to directory with filtered files
error_ratesF_path (string): path to rds file that contains error rates of forward reads
error_ratesR_path (string): path to rds file that contains error rates of reverse reads
threads (int): number of threads
minoverlap (int): the min number of pairs for overlap for the merge step
maxmismatch (int): the max number of mismatch for pairs to merge
Requires:
dada2, tools r packages
Returns:
string: path to rds file that contains merged and dereplicated reads | biobakery_workflows/tasks/dadatwo.py | merge_paired_ends | tkuntz-hsph/biobakery_workflows | 47 | python | def merge_paired_ends(workflow, output_dir, filtered_dir, error_ratesF_path, error_ratesR_path, threads, minoverlap, maxmismatch):
' Dereplicates and merges paired reads\n \n Args:\n workflow (anadama2.workflow): an instance of the workflow class\n output_folder (string): path to output folder\n filtered_dir (string): path to directory with filtered files\n error_ratesF_path (string): path to rds file that contains error rates of forward reads\n error_ratesR_path (string): path to rds file that contains error rates of reverse reads\n threads (int): number of threads\n minoverlap (int): the min number of pairs for overlap for the merge step\n maxmismatch (int): the max number of mismatch for pairs to merge\n Requires:\n dada2, tools r packages\n \n Returns:\n string: path to rds file that contains merged and dereplicated reads\n '
mergers_file_path = os.path.join(output_dir, 'mergers.rds')
script_path = utilities.get_package_file('merge_paired_ends', 'Rscript')
workflow.add_task('[vars[0]] --output_dir=[args[0]] --filtered_dir=[args[1]] --error_ratesF_path=[depends[0]] --error_ratesR_path=[args[4]] --mergers_file_path=[targets[0]] --threads=[vars[1]] --minoverlap=[args[2]] --maxmismatch=[args[3]]', depends=[error_ratesF_path], targets=[mergers_file_path], args=[output_dir, filtered_dir, minoverlap, maxmismatch, error_ratesR_path], vars=[script_path, threads], name='dereplicate_and_merge')
return mergers_file_path | def merge_paired_ends(workflow, output_dir, filtered_dir, error_ratesF_path, error_ratesR_path, threads, minoverlap, maxmismatch):
' Dereplicates and merges paired reads\n \n Args:\n workflow (anadama2.workflow): an instance of the workflow class\n output_folder (string): path to output folder\n filtered_dir (string): path to directory with filtered files\n error_ratesF_path (string): path to rds file that contains error rates of forward reads\n error_ratesR_path (string): path to rds file that contains error rates of reverse reads\n threads (int): number of threads\n minoverlap (int): the min number of pairs for overlap for the merge step\n maxmismatch (int): the max number of mismatch for pairs to merge\n Requires:\n dada2, tools r packages\n \n Returns:\n string: path to rds file that contains merged and dereplicated reads\n '
mergers_file_path = os.path.join(output_dir, 'mergers.rds')
script_path = utilities.get_package_file('merge_paired_ends', 'Rscript')
workflow.add_task('[vars[0]] --output_dir=[args[0]] --filtered_dir=[args[1]] --error_ratesF_path=[depends[0]] --error_ratesR_path=[args[4]] --mergers_file_path=[targets[0]] --threads=[vars[1]] --minoverlap=[args[2]] --maxmismatch=[args[3]]', depends=[error_ratesF_path], targets=[mergers_file_path], args=[output_dir, filtered_dir, minoverlap, maxmismatch, error_ratesR_path], vars=[script_path, threads], name='dereplicate_and_merge')
return mergers_file_path<|docstring|>Dereplicates and merges paired reads
Args:
workflow (anadama2.workflow): an instance of the workflow class
output_folder (string): path to output folder
filtered_dir (string): path to directory with filtered files
error_ratesF_path (string): path to rds file that contains error rates of forward reads
error_ratesR_path (string): path to rds file that contains error rates of reverse reads
threads (int): number of threads
minoverlap (int): the min number of pairs for overlap for the merge step
maxmismatch (int): the max number of mismatch for pairs to merge
Requires:
dada2, tools r packages
Returns:
string: path to rds file that contains merged and dereplicated reads<|endoftext|> |
ec96af9c78223d3e999dde1e0fc3a266ad77827ce8bc4b5e46b3e7ca4f682ed0 | def const_seq_table(workflow, output_folder, filtered_dir, mergers_file_path, threads):
' Builds ASV table, removes chimeras, creates read counts at each step, and fasta file with all sequences\n \n Args:\n workflow (anadama2.workflow): an instance of the workflow class\n output_folder (string): path to output folder\n filtered_dir (string): path to directory with filtered files\n mergers_file_path (string): path to rds file that contains merged reads\n threads (int): number of threads\n \n Requires:\n dada2, tools, seqinr r packages\n\n Returns:\n string: path to rds file that contains ASV data\n string: path to read counts at each step tsv file\n string: path to fasta file with all sequences\n '
read_counts_steps_path = files.SixteenS.path('counts_each_step', output_folder)
seqtab_file_path = os.path.join(output_folder, 'seqtab_final.rds')
seqs_fasta_path = os.path.join(output_folder, 'sequences.fasta')
readcounts_rds = 'Read_counts_filt.rds'
asv_tsv = 'all_samples_SV_counts.tsv'
script_path = utilities.get_package_file('const_seq_table', 'Rscript')
version_script = utilities.get_package_file('dada2_version', 'Rscript')
version_command = 'echo \'r\' `r -e \'packageVersion("dada2")\' | grep -C 1 dada2`'
workflow.add_task('[vars[0]] --output_dir=[args[0]] --filtered_dir=[args[1]] --merged_file_path=[depends[0]] --read_counts_steps_path=[targets[0]] --readcounts_rds=[vars[2]] --asv_tsv=[vars[3]] --seqtab_file_path=[targets[1]] --seqs_fasta_path=[targets[2]] --threads=[vars[1]]', depends=[mergers_file_path, TrackedExecutable('R', version_command=(((("echo '" + version_script) + "' `") + version_script) + '`'))], targets=[read_counts_steps_path, seqtab_file_path, seqs_fasta_path], args=[output_folder, filtered_dir], vars=[script_path, threads, readcounts_rds, asv_tsv], name='construct_sequence_table')
return (seqtab_file_path, read_counts_steps_path, seqs_fasta_path) | Builds ASV table, removes chimeras, creates read counts at each step, and fasta file with all sequences
Args:
workflow (anadama2.workflow): an instance of the workflow class
output_folder (string): path to output folder
filtered_dir (string): path to directory with filtered files
mergers_file_path (string): path to rds file that contains merged reads
threads (int): number of threads
Requires:
dada2, tools, seqinr r packages
Returns:
string: path to rds file that contains ASV data
string: path to read counts at each step tsv file
string: path to fasta file with all sequences | biobakery_workflows/tasks/dadatwo.py | const_seq_table | tkuntz-hsph/biobakery_workflows | 47 | python | def const_seq_table(workflow, output_folder, filtered_dir, mergers_file_path, threads):
' Builds ASV table, removes chimeras, creates read counts at each step, and fasta file with all sequences\n \n Args:\n workflow (anadama2.workflow): an instance of the workflow class\n output_folder (string): path to output folder\n filtered_dir (string): path to directory with filtered files\n mergers_file_path (string): path to rds file that contains merged reads\n threads (int): number of threads\n \n Requires:\n dada2, tools, seqinr r packages\n\n Returns:\n string: path to rds file that contains ASV data\n string: path to read counts at each step tsv file\n string: path to fasta file with all sequences\n '
read_counts_steps_path = files.SixteenS.path('counts_each_step', output_folder)
seqtab_file_path = os.path.join(output_folder, 'seqtab_final.rds')
seqs_fasta_path = os.path.join(output_folder, 'sequences.fasta')
readcounts_rds = 'Read_counts_filt.rds'
asv_tsv = 'all_samples_SV_counts.tsv'
script_path = utilities.get_package_file('const_seq_table', 'Rscript')
version_script = utilities.get_package_file('dada2_version', 'Rscript')
version_command = 'echo \'r\' `r -e \'packageVersion("dada2")\' | grep -C 1 dada2`'
workflow.add_task('[vars[0]] --output_dir=[args[0]] --filtered_dir=[args[1]] --merged_file_path=[depends[0]] --read_counts_steps_path=[targets[0]] --readcounts_rds=[vars[2]] --asv_tsv=[vars[3]] --seqtab_file_path=[targets[1]] --seqs_fasta_path=[targets[2]] --threads=[vars[1]]', depends=[mergers_file_path, TrackedExecutable('R', version_command=(((("echo '" + version_script) + "' `") + version_script) + '`'))], targets=[read_counts_steps_path, seqtab_file_path, seqs_fasta_path], args=[output_folder, filtered_dir], vars=[script_path, threads, readcounts_rds, asv_tsv], name='construct_sequence_table')
return (seqtab_file_path, read_counts_steps_path, seqs_fasta_path) | def const_seq_table(workflow, output_folder, filtered_dir, mergers_file_path, threads):
' Builds ASV table, removes chimeras, creates read counts at each step, and fasta file with all sequences\n \n Args:\n workflow (anadama2.workflow): an instance of the workflow class\n output_folder (string): path to output folder\n filtered_dir (string): path to directory with filtered files\n mergers_file_path (string): path to rds file that contains merged reads\n threads (int): number of threads\n \n Requires:\n dada2, tools, seqinr r packages\n\n Returns:\n string: path to rds file that contains ASV data\n string: path to read counts at each step tsv file\n string: path to fasta file with all sequences\n '
read_counts_steps_path = files.SixteenS.path('counts_each_step', output_folder)
seqtab_file_path = os.path.join(output_folder, 'seqtab_final.rds')
seqs_fasta_path = os.path.join(output_folder, 'sequences.fasta')
readcounts_rds = 'Read_counts_filt.rds'
asv_tsv = 'all_samples_SV_counts.tsv'
script_path = utilities.get_package_file('const_seq_table', 'Rscript')
version_script = utilities.get_package_file('dada2_version', 'Rscript')
version_command = 'echo \'r\' `r -e \'packageVersion("dada2")\' | grep -C 1 dada2`'
workflow.add_task('[vars[0]] --output_dir=[args[0]] --filtered_dir=[args[1]] --merged_file_path=[depends[0]] --read_counts_steps_path=[targets[0]] --readcounts_rds=[vars[2]] --asv_tsv=[vars[3]] --seqtab_file_path=[targets[1]] --seqs_fasta_path=[targets[2]] --threads=[vars[1]]', depends=[mergers_file_path, TrackedExecutable('R', version_command=(((("echo '" + version_script) + "' `") + version_script) + '`'))], targets=[read_counts_steps_path, seqtab_file_path, seqs_fasta_path], args=[output_folder, filtered_dir], vars=[script_path, threads, readcounts_rds, asv_tsv], name='construct_sequence_table')
return (seqtab_file_path, read_counts_steps_path, seqs_fasta_path)<|docstring|>Builds ASV table, removes chimeras, creates read counts at each step, and fasta file with all sequences
Args:
workflow (anadama2.workflow): an instance of the workflow class
output_folder (string): path to output folder
filtered_dir (string): path to directory with filtered files
mergers_file_path (string): path to rds file that contains merged reads
threads (int): number of threads
Requires:
dada2, tools, seqinr r packages
Returns:
string: path to rds file that contains ASV data
string: path to read counts at each step tsv file
string: path to fasta file with all sequences<|endoftext|> |
b1ae30f0d9b1e45b2abece8a49fa8b7339ad50027599d84bdbb37ffcab108309 | def assign_taxonomy(workflow, output_folder, seqtab_file_path, ref_path, threads, tryRC):
' Assigns taxonomy using green genes, silva, or rdp database, creates closed reference file\n \n Args:\n workflow (anadama2.workflow): an instance of the workflow class\n output_folder (string): path to output folder\n seqtab_file_path (string): path to rds file that contains ASV data\n ref_path (string): reference database name\n threads (int):\n \n Requires:\n dada2 r package\n \n Returns:\n string: path to closed reference file\n '
otu_closed_ref_path = files.SixteenS.path('otu_table_closed_reference', output_folder)
if (ref_path == 'unite'):
refdb_path = config.SixteenS().unite
refdb_species_path = 'None'
elif (ref_path == 'silva'):
refdb_path = config.SixteenS().silva_dada2
refdb_species_path = config.SixteenS().silva_species_dada2
elif (ref_path == 'rdp'):
refdb_path = config.SixteenS().rdp_dada2
refdb_species_path = config.SixteenS().rdp_species_dada2
else:
refdb_path = config.SixteenS().greengenes_dada2
refdb_species_path = 'None'
script_path = utilities.get_package_file('assign_taxonomy', 'Rscript')
workflow.add_task('[vars[2]] --output_dir=[args[0]] --refdb_path=[vars[0]] --refdb_species_path=[vars[1]] --seqtab_file_path=[depends[0]] --otu_closed_ref_path=[targets[0]] --threads=[vars[3]] --tryRC=[vars[4]] && check_for_reverse_reads.py --input [targets[0]]', depends=[seqtab_file_path], targets=[otu_closed_ref_path], args=[output_folder], vars=[refdb_path, refdb_species_path, script_path, threads, tryRC], name='assign_taxonomy')
return otu_closed_ref_path | Assigns taxonomy using green genes, silva, or rdp database, creates closed reference file
Args:
workflow (anadama2.workflow): an instance of the workflow class
output_folder (string): path to output folder
seqtab_file_path (string): path to rds file that contains ASV data
ref_path (string): reference database name
threads (int):
Requires:
dada2 r package
Returns:
string: path to closed reference file | biobakery_workflows/tasks/dadatwo.py | assign_taxonomy | tkuntz-hsph/biobakery_workflows | 47 | python | def assign_taxonomy(workflow, output_folder, seqtab_file_path, ref_path, threads, tryRC):
' Assigns taxonomy using green genes, silva, or rdp database, creates closed reference file\n \n Args:\n workflow (anadama2.workflow): an instance of the workflow class\n output_folder (string): path to output folder\n seqtab_file_path (string): path to rds file that contains ASV data\n ref_path (string): reference database name\n threads (int):\n \n Requires:\n dada2 r package\n \n Returns:\n string: path to closed reference file\n '
otu_closed_ref_path = files.SixteenS.path('otu_table_closed_reference', output_folder)
if (ref_path == 'unite'):
refdb_path = config.SixteenS().unite
refdb_species_path = 'None'
elif (ref_path == 'silva'):
refdb_path = config.SixteenS().silva_dada2
refdb_species_path = config.SixteenS().silva_species_dada2
elif (ref_path == 'rdp'):
refdb_path = config.SixteenS().rdp_dada2
refdb_species_path = config.SixteenS().rdp_species_dada2
else:
refdb_path = config.SixteenS().greengenes_dada2
refdb_species_path = 'None'
script_path = utilities.get_package_file('assign_taxonomy', 'Rscript')
workflow.add_task('[vars[2]] --output_dir=[args[0]] --refdb_path=[vars[0]] --refdb_species_path=[vars[1]] --seqtab_file_path=[depends[0]] --otu_closed_ref_path=[targets[0]] --threads=[vars[3]] --tryRC=[vars[4]] && check_for_reverse_reads.py --input [targets[0]]', depends=[seqtab_file_path], targets=[otu_closed_ref_path], args=[output_folder], vars=[refdb_path, refdb_species_path, script_path, threads, tryRC], name='assign_taxonomy')
return otu_closed_ref_path | def assign_taxonomy(workflow, output_folder, seqtab_file_path, ref_path, threads, tryRC):
' Assigns taxonomy using green genes, silva, or rdp database, creates closed reference file\n \n Args:\n workflow (anadama2.workflow): an instance of the workflow class\n output_folder (string): path to output folder\n seqtab_file_path (string): path to rds file that contains ASV data\n ref_path (string): reference database name\n threads (int):\n \n Requires:\n dada2 r package\n \n Returns:\n string: path to closed reference file\n '
otu_closed_ref_path = files.SixteenS.path('otu_table_closed_reference', output_folder)
if (ref_path == 'unite'):
refdb_path = config.SixteenS().unite
refdb_species_path = 'None'
elif (ref_path == 'silva'):
refdb_path = config.SixteenS().silva_dada2
refdb_species_path = config.SixteenS().silva_species_dada2
elif (ref_path == 'rdp'):
refdb_path = config.SixteenS().rdp_dada2
refdb_species_path = config.SixteenS().rdp_species_dada2
else:
refdb_path = config.SixteenS().greengenes_dada2
refdb_species_path = 'None'
script_path = utilities.get_package_file('assign_taxonomy', 'Rscript')
workflow.add_task('[vars[2]] --output_dir=[args[0]] --refdb_path=[vars[0]] --refdb_species_path=[vars[1]] --seqtab_file_path=[depends[0]] --otu_closed_ref_path=[targets[0]] --threads=[vars[3]] --tryRC=[vars[4]] && check_for_reverse_reads.py --input [targets[0]]', depends=[seqtab_file_path], targets=[otu_closed_ref_path], args=[output_folder], vars=[refdb_path, refdb_species_path, script_path, threads, tryRC], name='assign_taxonomy')
return otu_closed_ref_path<|docstring|>Assigns taxonomy using green genes, silva, or rdp database, creates closed reference file
Args:
workflow (anadama2.workflow): an instance of the workflow class
output_folder (string): path to output folder
seqtab_file_path (string): path to rds file that contains ASV data
ref_path (string): reference database name
threads (int):
Requires:
dada2 r package
Returns:
string: path to closed reference file<|endoftext|> |
22c3c2c21fb709488e3ee25c254af2d5b1ecb1d79c9fc6a731fc262693631d30 | def remove_tmp_files(workflow, output_folder, otu_closed_ref_path, msa_fasta_path, fasttree_file_path):
' Removes temporary rds files\n \n Args:\n workflow (anadama2.workflow): an instance of the workflow class\n output_folder (string): path to output folder.\n otu_closed_ref_path (string): path to closed reference file\n msa_fasta_path (string): path to msa file\n fasttree_file_path (string): path to phylogenetic tree file\n\n Requires:\n None\n \n Returns:\n None\n '
rm_out_file = os.path.join(output_folder, 'tmp_rm.txt')
workflow.add_task('rm [args[0]]/*.rds &>[targets[0]] ', depends=[otu_closed_ref_path, msa_fasta_path, fasttree_file_path], args=[output_folder], targets=[rm_out_file], name='rm_tmp_files') | Removes temporary rds files
Args:
workflow (anadama2.workflow): an instance of the workflow class
output_folder (string): path to output folder.
otu_closed_ref_path (string): path to closed reference file
msa_fasta_path (string): path to msa file
fasttree_file_path (string): path to phylogenetic tree file
Requires:
None
Returns:
None | biobakery_workflows/tasks/dadatwo.py | remove_tmp_files | tkuntz-hsph/biobakery_workflows | 47 | python | def remove_tmp_files(workflow, output_folder, otu_closed_ref_path, msa_fasta_path, fasttree_file_path):
' Removes temporary rds files\n \n Args:\n workflow (anadama2.workflow): an instance of the workflow class\n output_folder (string): path to output folder.\n otu_closed_ref_path (string): path to closed reference file\n msa_fasta_path (string): path to msa file\n fasttree_file_path (string): path to phylogenetic tree file\n\n Requires:\n None\n \n Returns:\n None\n '
rm_out_file = os.path.join(output_folder, 'tmp_rm.txt')
workflow.add_task('rm [args[0]]/*.rds &>[targets[0]] ', depends=[otu_closed_ref_path, msa_fasta_path, fasttree_file_path], args=[output_folder], targets=[rm_out_file], name='rm_tmp_files') | def remove_tmp_files(workflow, output_folder, otu_closed_ref_path, msa_fasta_path, fasttree_file_path):
' Removes temporary rds files\n \n Args:\n workflow (anadama2.workflow): an instance of the workflow class\n output_folder (string): path to output folder.\n otu_closed_ref_path (string): path to closed reference file\n msa_fasta_path (string): path to msa file\n fasttree_file_path (string): path to phylogenetic tree file\n\n Requires:\n None\n \n Returns:\n None\n '
rm_out_file = os.path.join(output_folder, 'tmp_rm.txt')
workflow.add_task('rm [args[0]]/*.rds &>[targets[0]] ', depends=[otu_closed_ref_path, msa_fasta_path, fasttree_file_path], args=[output_folder], targets=[rm_out_file], name='rm_tmp_files')<|docstring|>Removes temporary rds files
Args:
workflow (anadama2.workflow): an instance of the workflow class
output_folder (string): path to output folder.
otu_closed_ref_path (string): path to closed reference file
msa_fasta_path (string): path to msa file
fasttree_file_path (string): path to phylogenetic tree file
Requires:
None
Returns:
None<|endoftext|> |
8f5bbf0be0384edfdfb96757032078b41a996c66d5233493da4790a8dda7e76b | def invalidate_all_tokens(self, user=None):
'\n To invalidate all tokens the user uuid is changed\n '
if (user is None):
user = self._user
user.uuid = getUUID()
user.save()
log.warning('User uuid changed to: {}', user.uuid)
return True | To invalidate all tokens the user uuid is changed | restapi/services/authentication/mongo.py | invalidate_all_tokens | fossabot/http-api | 0 | python | def invalidate_all_tokens(self, user=None):
'\n \n '
if (user is None):
user = self._user
user.uuid = getUUID()
user.save()
log.warning('User uuid changed to: {}', user.uuid)
return True | def invalidate_all_tokens(self, user=None):
'\n \n '
if (user is None):
user = self._user
user.uuid = getUUID()
user.save()
log.warning('User uuid changed to: {}', user.uuid)
return True<|docstring|>To invalidate all tokens the user uuid is changed<|endoftext|> |
46fba7fdac2ac30b098a2797cf886eff3b88106dee0869e84e06b0d090804123 | def build_url_string(uri_scheme=None, uri_authority=None, uri_path=None, uri_query=None, uri_fragment=None, query_string_parameters=None):
'\n https://tools.ietf.org/html/rfc3986#section-3\n URI Components: scheme, authority, path, query, fragment\n uri_query and query_parameter_string are mutually-exclusive.\n :param uri_scheme: [optional] (Default: "https")\n :type uri_scheme: str\n :param uri_authority: [required]\n :type uri_authority: str\n :param uri_path: [required]\n :type uri_path: str\n :param uri_query: [optional] (Default: None)\n :type uri_query: str\n :param uri_fragment: [optional] (Default: None)\n :type uri_fragment: str\n :param query_string_parameters:\n :type query_string_parameters: dict\n :return:\n '
if (uri_scheme is None):
pass
if (uri_authority is None):
raise ValueError('uri_authority? Nein danke!')
if (uri_path is None):
raise ValueError('uri_path? Nein danke!')
if (uri_query is None):
pass
if (uri_fragment is None):
pass
if (query_string_parameters is None):
pass
if (uri_query and query_string_parameters):
print('Are you trying to get a rise out of me, Agent Kujan')
print('PS. What shall we do if you pass a query /and/ query_string_parameters?')
if (query_string_parameters is not None):
urlencode(query_string_parameters)
uri_scheme = (DEFAULT_URI_SCHEME if (uri_scheme is None) else uri_scheme)
url_string = '{uri_scheme}://{uri_authority}{uri_path}'.format(uri_scheme=uri_scheme, uri_authority=uri_authority, uri_path=uri_path)
if uri_query:
if (not uri_query.startswith('?')):
uri_query = ('?' + uri_query)
url_string = (url_string + uri_query)
if uri_fragment:
if (not uri_fragment.startswith('#')):
uri_fragment = ('#' + uri_fragment)
url_string = (url_string + uri_fragment)
return url_string | https://tools.ietf.org/html/rfc3986#section-3
URI Components: scheme, authority, path, query, fragment
uri_query and query_parameter_string are mutually-exclusive.
:param uri_scheme: [optional] (Default: "https")
:type uri_scheme: str
:param uri_authority: [required]
:type uri_authority: str
:param uri_path: [required]
:type uri_path: str
:param uri_query: [optional] (Default: None)
:type uri_query: str
:param uri_fragment: [optional] (Default: None)
:type uri_fragment: str
:param query_string_parameters:
:type query_string_parameters: dict
:return: | google_places_api_cacher/ats_utilities/ats_url.py | build_url_string | james-w-balcomb/google-places-api-cacher | 1 | python | def build_url_string(uri_scheme=None, uri_authority=None, uri_path=None, uri_query=None, uri_fragment=None, query_string_parameters=None):
'\n https://tools.ietf.org/html/rfc3986#section-3\n URI Components: scheme, authority, path, query, fragment\n uri_query and query_parameter_string are mutually-exclusive.\n :param uri_scheme: [optional] (Default: "https")\n :type uri_scheme: str\n :param uri_authority: [required]\n :type uri_authority: str\n :param uri_path: [required]\n :type uri_path: str\n :param uri_query: [optional] (Default: None)\n :type uri_query: str\n :param uri_fragment: [optional] (Default: None)\n :type uri_fragment: str\n :param query_string_parameters:\n :type query_string_parameters: dict\n :return:\n '
if (uri_scheme is None):
pass
if (uri_authority is None):
raise ValueError('uri_authority? Nein danke!')
if (uri_path is None):
raise ValueError('uri_path? Nein danke!')
if (uri_query is None):
pass
if (uri_fragment is None):
pass
if (query_string_parameters is None):
pass
if (uri_query and query_string_parameters):
print('Are you trying to get a rise out of me, Agent Kujan')
print('PS. What shall we do if you pass a query /and/ query_string_parameters?')
if (query_string_parameters is not None):
urlencode(query_string_parameters)
uri_scheme = (DEFAULT_URI_SCHEME if (uri_scheme is None) else uri_scheme)
url_string = '{uri_scheme}://{uri_authority}{uri_path}'.format(uri_scheme=uri_scheme, uri_authority=uri_authority, uri_path=uri_path)
if uri_query:
if (not uri_query.startswith('?')):
uri_query = ('?' + uri_query)
url_string = (url_string + uri_query)
if uri_fragment:
if (not uri_fragment.startswith('#')):
uri_fragment = ('#' + uri_fragment)
url_string = (url_string + uri_fragment)
return url_string | def build_url_string(uri_scheme=None, uri_authority=None, uri_path=None, uri_query=None, uri_fragment=None, query_string_parameters=None):
'\n https://tools.ietf.org/html/rfc3986#section-3\n URI Components: scheme, authority, path, query, fragment\n uri_query and query_parameter_string are mutually-exclusive.\n :param uri_scheme: [optional] (Default: "https")\n :type uri_scheme: str\n :param uri_authority: [required]\n :type uri_authority: str\n :param uri_path: [required]\n :type uri_path: str\n :param uri_query: [optional] (Default: None)\n :type uri_query: str\n :param uri_fragment: [optional] (Default: None)\n :type uri_fragment: str\n :param query_string_parameters:\n :type query_string_parameters: dict\n :return:\n '
if (uri_scheme is None):
pass
if (uri_authority is None):
raise ValueError('uri_authority? Nein danke!')
if (uri_path is None):
raise ValueError('uri_path? Nein danke!')
if (uri_query is None):
pass
if (uri_fragment is None):
pass
if (query_string_parameters is None):
pass
if (uri_query and query_string_parameters):
print('Are you trying to get a rise out of me, Agent Kujan')
print('PS. What shall we do if you pass a query /and/ query_string_parameters?')
if (query_string_parameters is not None):
urlencode(query_string_parameters)
uri_scheme = (DEFAULT_URI_SCHEME if (uri_scheme is None) else uri_scheme)
url_string = '{uri_scheme}://{uri_authority}{uri_path}'.format(uri_scheme=uri_scheme, uri_authority=uri_authority, uri_path=uri_path)
if uri_query:
if (not uri_query.startswith('?')):
uri_query = ('?' + uri_query)
url_string = (url_string + uri_query)
if uri_fragment:
if (not uri_fragment.startswith('#')):
uri_fragment = ('#' + uri_fragment)
url_string = (url_string + uri_fragment)
return url_string<|docstring|>https://tools.ietf.org/html/rfc3986#section-3
URI Components: scheme, authority, path, query, fragment
uri_query and query_parameter_string are mutually-exclusive.
:param uri_scheme: [optional] (Default: "https")
:type uri_scheme: str
:param uri_authority: [required]
:type uri_authority: str
:param uri_path: [required]
:type uri_path: str
:param uri_query: [optional] (Default: None)
:type uri_query: str
:param uri_fragment: [optional] (Default: None)
:type uri_fragment: str
:param query_string_parameters:
:type query_string_parameters: dict
:return:<|endoftext|> |
ee571aac756235e61e86a338ea8d0d1296cb5cda85ea0dc03d9374862871591e | async def async_setup_entry(hass: HomeAssistant, entry: ConfigEntry, async_add_entities: AddEntitiesCallback) -> None:
'Set up device tracker for Freebox component.'
router: FreeboxRouter = hass.data[DOMAIN][entry.unique_id]
tracked: set[str] = set()
@callback
def update_router() -> None:
'Update the values of the router.'
add_entities(router, async_add_entities, tracked)
entry.async_on_unload(async_dispatcher_connect(hass, router.signal_device_new, update_router))
update_router() | Set up device tracker for Freebox component. | homeassistant/components/freebox/device_tracker.py | async_setup_entry | GrandMoff100/homeassistant-core | 30,023 | python | async def async_setup_entry(hass: HomeAssistant, entry: ConfigEntry, async_add_entities: AddEntitiesCallback) -> None:
router: FreeboxRouter = hass.data[DOMAIN][entry.unique_id]
tracked: set[str] = set()
@callback
def update_router() -> None:
'Update the values of the router.'
add_entities(router, async_add_entities, tracked)
entry.async_on_unload(async_dispatcher_connect(hass, router.signal_device_new, update_router))
update_router() | async def async_setup_entry(hass: HomeAssistant, entry: ConfigEntry, async_add_entities: AddEntitiesCallback) -> None:
router: FreeboxRouter = hass.data[DOMAIN][entry.unique_id]
tracked: set[str] = set()
@callback
def update_router() -> None:
'Update the values of the router.'
add_entities(router, async_add_entities, tracked)
entry.async_on_unload(async_dispatcher_connect(hass, router.signal_device_new, update_router))
update_router()<|docstring|>Set up device tracker for Freebox component.<|endoftext|> |
28750987f52c6816a35011df36550cd98a94a3fe78a5a011c2fc67800c243de1 | @callback
def add_entities(router: FreeboxRouter, async_add_entities: AddEntitiesCallback, tracked: set[str]) -> None:
'Add new tracker entities from the router.'
new_tracked = []
for (mac, device) in router.devices.items():
if (mac in tracked):
continue
new_tracked.append(FreeboxDevice(router, device))
tracked.add(mac)
if new_tracked:
async_add_entities(new_tracked, True) | Add new tracker entities from the router. | homeassistant/components/freebox/device_tracker.py | add_entities | GrandMoff100/homeassistant-core | 30,023 | python | @callback
def add_entities(router: FreeboxRouter, async_add_entities: AddEntitiesCallback, tracked: set[str]) -> None:
new_tracked = []
for (mac, device) in router.devices.items():
if (mac in tracked):
continue
new_tracked.append(FreeboxDevice(router, device))
tracked.add(mac)
if new_tracked:
async_add_entities(new_tracked, True) | @callback
def add_entities(router: FreeboxRouter, async_add_entities: AddEntitiesCallback, tracked: set[str]) -> None:
new_tracked = []
for (mac, device) in router.devices.items():
if (mac in tracked):
continue
new_tracked.append(FreeboxDevice(router, device))
tracked.add(mac)
if new_tracked:
async_add_entities(new_tracked, True)<|docstring|>Add new tracker entities from the router.<|endoftext|> |
0c9a430cd83103f94462838570bcb58a553d92a339f942bf3c561be3e59e979d | def icon_for_freebox_device(device) -> str:
'Return a device icon from its type.'
return DEVICE_ICONS.get(device['host_type'], 'mdi:help-network') | Return a device icon from its type. | homeassistant/components/freebox/device_tracker.py | icon_for_freebox_device | GrandMoff100/homeassistant-core | 30,023 | python | def icon_for_freebox_device(device) -> str:
return DEVICE_ICONS.get(device['host_type'], 'mdi:help-network') | def icon_for_freebox_device(device) -> str:
return DEVICE_ICONS.get(device['host_type'], 'mdi:help-network')<|docstring|>Return a device icon from its type.<|endoftext|> |
2671b2c751969dd1fcbeace75482830fd5491f0b22a241f2290d7342f40ae2a7 | @callback
def update_router() -> None:
'Update the values of the router.'
add_entities(router, async_add_entities, tracked) | Update the values of the router. | homeassistant/components/freebox/device_tracker.py | update_router | GrandMoff100/homeassistant-core | 30,023 | python | @callback
def update_router() -> None:
add_entities(router, async_add_entities, tracked) | @callback
def update_router() -> None:
add_entities(router, async_add_entities, tracked)<|docstring|>Update the values of the router.<|endoftext|> |
23930a3b90294df4393762645c87bcd128e1626fa31706710a9d2359c6ffc7a1 | def __init__(self, router: FreeboxRouter, device: dict[(str, Any)]) -> None:
'Initialize a Freebox device.'
self._router = router
self._name = (device['primary_name'].strip() or DEFAULT_DEVICE_NAME)
self._mac = device['l2ident']['id']
self._manufacturer = device['vendor_name']
self._icon = icon_for_freebox_device(device)
self._active = False
self._attrs: dict[(str, Any)] = {} | Initialize a Freebox device. | homeassistant/components/freebox/device_tracker.py | __init__ | GrandMoff100/homeassistant-core | 30,023 | python | def __init__(self, router: FreeboxRouter, device: dict[(str, Any)]) -> None:
self._router = router
self._name = (device['primary_name'].strip() or DEFAULT_DEVICE_NAME)
self._mac = device['l2ident']['id']
self._manufacturer = device['vendor_name']
self._icon = icon_for_freebox_device(device)
self._active = False
self._attrs: dict[(str, Any)] = {} | def __init__(self, router: FreeboxRouter, device: dict[(str, Any)]) -> None:
self._router = router
self._name = (device['primary_name'].strip() or DEFAULT_DEVICE_NAME)
self._mac = device['l2ident']['id']
self._manufacturer = device['vendor_name']
self._icon = icon_for_freebox_device(device)
self._active = False
self._attrs: dict[(str, Any)] = {}<|docstring|>Initialize a Freebox device.<|endoftext|> |
8f9f1838e83633be6fd5001ff6301560ce93c5d8caf65d0c3fd05da935d73b88 | @callback
def async_update_state(self) -> None:
'Update the Freebox device.'
device = self._router.devices[self._mac]
self._active = device['active']
if (device.get('attrs') is None):
self._attrs = {'last_time_reachable': datetime.fromtimestamp(device['last_time_reachable']), 'last_time_activity': datetime.fromtimestamp(device['last_activity'])}
else:
self._attrs = device['attrs'] | Update the Freebox device. | homeassistant/components/freebox/device_tracker.py | async_update_state | GrandMoff100/homeassistant-core | 30,023 | python | @callback
def async_update_state(self) -> None:
device = self._router.devices[self._mac]
self._active = device['active']
if (device.get('attrs') is None):
self._attrs = {'last_time_reachable': datetime.fromtimestamp(device['last_time_reachable']), 'last_time_activity': datetime.fromtimestamp(device['last_activity'])}
else:
self._attrs = device['attrs'] | @callback
def async_update_state(self) -> None:
device = self._router.devices[self._mac]
self._active = device['active']
if (device.get('attrs') is None):
self._attrs = {'last_time_reachable': datetime.fromtimestamp(device['last_time_reachable']), 'last_time_activity': datetime.fromtimestamp(device['last_activity'])}
else:
self._attrs = device['attrs']<|docstring|>Update the Freebox device.<|endoftext|> |
9fe0c6e2d94931e1186f599d3a7f0c3f4887a435c556fe7fd759afa2708b8cb2 | @property
def mac_address(self) -> str:
'Return a unique ID.'
return self._mac | Return a unique ID. | homeassistant/components/freebox/device_tracker.py | mac_address | GrandMoff100/homeassistant-core | 30,023 | python | @property
def mac_address(self) -> str:
return self._mac | @property
def mac_address(self) -> str:
return self._mac<|docstring|>Return a unique ID.<|endoftext|> |
6461a800075af75cf8e501085571d402bafd8cc18e72608adbf1e1cee60aa916 | @property
def name(self) -> str:
'Return the name.'
return self._name | Return the name. | homeassistant/components/freebox/device_tracker.py | name | GrandMoff100/homeassistant-core | 30,023 | python | @property
def name(self) -> str:
return self._name | @property
def name(self) -> str:
return self._name<|docstring|>Return the name.<|endoftext|> |
48241ed7ec4118214370c7fcb068b9f9677c729f15dd9d8b35119f2ebd99abc4 | @property
def is_connected(self):
'Return true if the device is connected to the network.'
return self._active | Return true if the device is connected to the network. | homeassistant/components/freebox/device_tracker.py | is_connected | GrandMoff100/homeassistant-core | 30,023 | python | @property
def is_connected(self):
return self._active | @property
def is_connected(self):
return self._active<|docstring|>Return true if the device is connected to the network.<|endoftext|> |
46f58a1645c6343dbb1258cb8ee12371687f459946be74f83fe6e94e57f78b1f | @property
def source_type(self) -> str:
'Return the source type.'
return SOURCE_TYPE_ROUTER | Return the source type. | homeassistant/components/freebox/device_tracker.py | source_type | GrandMoff100/homeassistant-core | 30,023 | python | @property
def source_type(self) -> str:
return SOURCE_TYPE_ROUTER | @property
def source_type(self) -> str:
return SOURCE_TYPE_ROUTER<|docstring|>Return the source type.<|endoftext|> |
05dab95798aa59b72c55641def3865cbb29c704573ab7fbebf793e753b0ff8bb | @property
def icon(self) -> str:
'Return the icon.'
return self._icon | Return the icon. | homeassistant/components/freebox/device_tracker.py | icon | GrandMoff100/homeassistant-core | 30,023 | python | @property
def icon(self) -> str:
return self._icon | @property
def icon(self) -> str:
return self._icon<|docstring|>Return the icon.<|endoftext|> |
b7adc6c3a39d1f5b956797a5d2ff5b51927419d8e51d04930116cc3dd0897edb | @property
def extra_state_attributes(self) -> dict[(str, Any)]:
'Return the attributes.'
return self._attrs | Return the attributes. | homeassistant/components/freebox/device_tracker.py | extra_state_attributes | GrandMoff100/homeassistant-core | 30,023 | python | @property
def extra_state_attributes(self) -> dict[(str, Any)]:
return self._attrs | @property
def extra_state_attributes(self) -> dict[(str, Any)]:
return self._attrs<|docstring|>Return the attributes.<|endoftext|> |
9ecb1e6c2d5a7ad8bb2880d8b6acd725a49d529d4ea0beecec22b00711318188 | @property
def should_poll(self) -> bool:
'No polling needed.'
return False | No polling needed. | homeassistant/components/freebox/device_tracker.py | should_poll | GrandMoff100/homeassistant-core | 30,023 | python | @property
def should_poll(self) -> bool:
return False | @property
def should_poll(self) -> bool:
return False<|docstring|>No polling needed.<|endoftext|> |
1c923b940e9ccfb3d7d0692d79a6561666537efa4f4562149620a1081be74581 | @callback
def async_on_demand_update(self):
'Update state.'
self.async_update_state()
self.async_write_ha_state() | Update state. | homeassistant/components/freebox/device_tracker.py | async_on_demand_update | GrandMoff100/homeassistant-core | 30,023 | python | @callback
def async_on_demand_update(self):
self.async_update_state()
self.async_write_ha_state() | @callback
def async_on_demand_update(self):
self.async_update_state()
self.async_write_ha_state()<|docstring|>Update state.<|endoftext|> |
4e1c46e5a85fe447717eabffffe33e3d0e5aaccf231ba50a1c0ccd641768aed2 | async def async_added_to_hass(self):
'Register state update callback.'
self.async_update_state()
self.async_on_remove(async_dispatcher_connect(self.hass, self._router.signal_device_update, self.async_on_demand_update)) | Register state update callback. | homeassistant/components/freebox/device_tracker.py | async_added_to_hass | GrandMoff100/homeassistant-core | 30,023 | python | async def async_added_to_hass(self):
self.async_update_state()
self.async_on_remove(async_dispatcher_connect(self.hass, self._router.signal_device_update, self.async_on_demand_update)) | async def async_added_to_hass(self):
self.async_update_state()
self.async_on_remove(async_dispatcher_connect(self.hass, self._router.signal_device_update, self.async_on_demand_update))<|docstring|>Register state update callback.<|endoftext|> |
bf86529f1020548201b338ade45db081ca7b35313f1373503086e7046e920bc5 | def __init__(self, name=None, level='info'):
' HEV logging class\n\n Centralizing all logging capabilities\n '
FORMAT = '%(asctime)s [%(levelname)s\t] [%(name)s] %(message)s'
self.name = name
self.level = self._level(level)
self.logging = logging
self.logging.basicConfig(format=FORMAT, level=self.level)
self.logging = self.logging.getLogger(name)
self.logging.setLevel(self.level) | HEV logging class
Centralizing all logging capabilities | core/helpers/hevlog.py | __init__ | N7SALab/HEV | 0 | python | def __init__(self, name=None, level='info'):
' HEV logging class\n\n Centralizing all logging capabilities\n '
FORMAT = '%(asctime)s [%(levelname)s\t] [%(name)s] %(message)s'
self.name = name
self.level = self._level(level)
self.logging = logging
self.logging.basicConfig(format=FORMAT, level=self.level)
self.logging = self.logging.getLogger(name)
self.logging.setLevel(self.level) | def __init__(self, name=None, level='info'):
' HEV logging class\n\n Centralizing all logging capabilities\n '
FORMAT = '%(asctime)s [%(levelname)s\t] [%(name)s] %(message)s'
self.name = name
self.level = self._level(level)
self.logging = logging
self.logging.basicConfig(format=FORMAT, level=self.level)
self.logging = self.logging.getLogger(name)
self.logging.setLevel(self.level)<|docstring|>HEV logging class
Centralizing all logging capabilities<|endoftext|> |
fe4cdd399bc3558bce5ba52c58c21093aea62398a5559947bd0285ccd3c5913e | def _level(self, level):
' a way to set the level\n '
if (level is None):
return INFO
elif ((level.lower() == 'notset') or (level.lower() == 'off')):
return NOTSET
elif ((level.lower() == 'debug') or (level.lower() == 'd')):
return DEBUG
elif ((level.lower() == 'info') or (level.lower() == 'i')):
return INFO
elif ((level.lower() == 'warning') or (level.lower() == 'w')):
return WARN
elif ((level.lower() == 'error') or (level.lower() == 'e')):
return ERROR
elif ((level.lower() == 'fatal') or (level.lower() == 'f')):
return FATAL
elif ((level.lower() == 'critical') or (level.lower() == 'c')):
return CRITICAL
else:
return INFO | a way to set the level | core/helpers/hevlog.py | _level | N7SALab/HEV | 0 | python | def _level(self, level):
' \n '
if (level is None):
return INFO
elif ((level.lower() == 'notset') or (level.lower() == 'off')):
return NOTSET
elif ((level.lower() == 'debug') or (level.lower() == 'd')):
return DEBUG
elif ((level.lower() == 'info') or (level.lower() == 'i')):
return INFO
elif ((level.lower() == 'warning') or (level.lower() == 'w')):
return WARN
elif ((level.lower() == 'error') or (level.lower() == 'e')):
return ERROR
elif ((level.lower() == 'fatal') or (level.lower() == 'f')):
return FATAL
elif ((level.lower() == 'critical') or (level.lower() == 'c')):
return CRITICAL
else:
return INFO | def _level(self, level):
' \n '
if (level is None):
return INFO
elif ((level.lower() == 'notset') or (level.lower() == 'off')):
return NOTSET
elif ((level.lower() == 'debug') or (level.lower() == 'd')):
return DEBUG
elif ((level.lower() == 'info') or (level.lower() == 'i')):
return INFO
elif ((level.lower() == 'warning') or (level.lower() == 'w')):
return WARN
elif ((level.lower() == 'error') or (level.lower() == 'e')):
return ERROR
elif ((level.lower() == 'fatal') or (level.lower() == 'f')):
return FATAL
elif ((level.lower() == 'critical') or (level.lower() == 'c')):
return CRITICAL
else:
return INFO<|docstring|>a way to set the level<|endoftext|> |
ad8e8d84d6b7be24405fae623b0f1e16310d625fd4dfc55497470b37988429da | def rotate(self, nums: List[int], k: int) -> None:
'\n Do not return anything, modify nums in-place instead.\n '
k = (k % len(nums))
def reverse(nums, left, right):
while (left <= right):
(nums[left], nums[right]) = (nums[right], nums[left])
left += 1
right -= 1
reverse(nums, 0, (len(nums) - 1))
reverse(nums, 0, (k - 1))
reverse(nums, k, (len(nums) - 1)) | Do not return anything, modify nums in-place instead. | 189_rotate.py | rotate | xinming365/LeetCode | 0 | python | def rotate(self, nums: List[int], k: int) -> None:
'\n \n '
k = (k % len(nums))
def reverse(nums, left, right):
while (left <= right):
(nums[left], nums[right]) = (nums[right], nums[left])
left += 1
right -= 1
reverse(nums, 0, (len(nums) - 1))
reverse(nums, 0, (k - 1))
reverse(nums, k, (len(nums) - 1)) | def rotate(self, nums: List[int], k: int) -> None:
'\n \n '
k = (k % len(nums))
def reverse(nums, left, right):
while (left <= right):
(nums[left], nums[right]) = (nums[right], nums[left])
left += 1
right -= 1
reverse(nums, 0, (len(nums) - 1))
reverse(nums, 0, (k - 1))
reverse(nums, k, (len(nums) - 1))<|docstring|>Do not return anything, modify nums in-place instead.<|endoftext|> |
0bb1b573e720d0886368ec3b60a0dced52160d2b1a4aa9fe7b59a4c9e22aea09 | def rotate(self, nums: List[int], k: int) -> None:
'\n Do not return anything, modify nums in-place instead.\n '
n = len(nums)
nums[:] = (nums[(n - k):] + nums[:(n - k)]) | Do not return anything, modify nums in-place instead. | 189_rotate.py | rotate | xinming365/LeetCode | 0 | python | def rotate(self, nums: List[int], k: int) -> None:
'\n \n '
n = len(nums)
nums[:] = (nums[(n - k):] + nums[:(n - k)]) | def rotate(self, nums: List[int], k: int) -> None:
'\n \n '
n = len(nums)
nums[:] = (nums[(n - k):] + nums[:(n - k)])<|docstring|>Do not return anything, modify nums in-place instead.<|endoftext|> |
f867caa28e3d6a428493db1d9f44b246fda433600f4315352a5b469e3b18e644 | @decorators.idempotent_id('319b765e-7f3d-4b3d-8b37-3ca3876ee768')
def test_basic_metadata_definition_namespaces(self):
'Test operations of image metadata definition namespaces'
body = self.resource_types_client.list_resource_types()
resource_name = body['resource_types'][0]['name']
name = [{'name': resource_name}]
namespace_name = data_utils.rand_name('namespace')
body = self.namespaces_client.create_namespace(namespace=namespace_name, visibility='public', description='Tempest', display_name=namespace_name, resource_type_associations=name, protected=True)
self.addCleanup(test_utils.call_and_ignore_notfound_exc, self._cleanup_namespace, namespace_name)
bodys = self.namespaces_client.list_namespaces()['namespaces']
body = [namespace['namespace'] for namespace in bodys]
self.assertIn(namespace_name, body)
body = self.namespaces_client.show_namespace(namespace_name)
self.assertEqual(namespace_name, body['namespace'])
self.assertEqual('public', body['visibility'])
self.assertRaises(lib_exc.Forbidden, self.namespaces_client.delete_namespace, namespace_name)
body = self.namespaces_client.update_namespace(namespace=namespace_name, description='Tempest', visibility='private', display_name=namespace_name, protected=False)
self.assertEqual('private', body['visibility'])
self.assertEqual(False, body['protected'])
self.namespaces_client.delete_namespace(namespace_name) | Test operations of image metadata definition namespaces | tempest/api/image/v2/admin/test_images_metadefs_namespaces.py | test_basic_metadata_definition_namespaces | jonringer/tempest | 254 | python | @decorators.idempotent_id('319b765e-7f3d-4b3d-8b37-3ca3876ee768')
def test_basic_metadata_definition_namespaces(self):
body = self.resource_types_client.list_resource_types()
resource_name = body['resource_types'][0]['name']
name = [{'name': resource_name}]
namespace_name = data_utils.rand_name('namespace')
body = self.namespaces_client.create_namespace(namespace=namespace_name, visibility='public', description='Tempest', display_name=namespace_name, resource_type_associations=name, protected=True)
self.addCleanup(test_utils.call_and_ignore_notfound_exc, self._cleanup_namespace, namespace_name)
bodys = self.namespaces_client.list_namespaces()['namespaces']
body = [namespace['namespace'] for namespace in bodys]
self.assertIn(namespace_name, body)
body = self.namespaces_client.show_namespace(namespace_name)
self.assertEqual(namespace_name, body['namespace'])
self.assertEqual('public', body['visibility'])
self.assertRaises(lib_exc.Forbidden, self.namespaces_client.delete_namespace, namespace_name)
body = self.namespaces_client.update_namespace(namespace=namespace_name, description='Tempest', visibility='private', display_name=namespace_name, protected=False)
self.assertEqual('private', body['visibility'])
self.assertEqual(False, body['protected'])
self.namespaces_client.delete_namespace(namespace_name) | @decorators.idempotent_id('319b765e-7f3d-4b3d-8b37-3ca3876ee768')
def test_basic_metadata_definition_namespaces(self):
body = self.resource_types_client.list_resource_types()
resource_name = body['resource_types'][0]['name']
name = [{'name': resource_name}]
namespace_name = data_utils.rand_name('namespace')
body = self.namespaces_client.create_namespace(namespace=namespace_name, visibility='public', description='Tempest', display_name=namespace_name, resource_type_associations=name, protected=True)
self.addCleanup(test_utils.call_and_ignore_notfound_exc, self._cleanup_namespace, namespace_name)
bodys = self.namespaces_client.list_namespaces()['namespaces']
body = [namespace['namespace'] for namespace in bodys]
self.assertIn(namespace_name, body)
body = self.namespaces_client.show_namespace(namespace_name)
self.assertEqual(namespace_name, body['namespace'])
self.assertEqual('public', body['visibility'])
self.assertRaises(lib_exc.Forbidden, self.namespaces_client.delete_namespace, namespace_name)
body = self.namespaces_client.update_namespace(namespace=namespace_name, description='Tempest', visibility='private', display_name=namespace_name, protected=False)
self.assertEqual('private', body['visibility'])
self.assertEqual(False, body['protected'])
self.namespaces_client.delete_namespace(namespace_name)<|docstring|>Test operations of image metadata definition namespaces<|endoftext|> |
a15bef1692c74feb7aecf1055a590b19b3227348211bb39b307ad7b2526cfd0f | def reset(self):
'\n Reset all environments\n '
obs = self.venv.reset()
self.stackedobs[...] = 0
self.stackedobs[(..., (- obs.shape[(- 1)]):)] = obs
return self.stackedobs | Reset all environments | pytorch-a2c-ppo-acktr/vec_env/vec_frame_stack.py | reset | sahandrez/gym-miniworld | 499 | python | def reset(self):
'\n \n '
obs = self.venv.reset()
self.stackedobs[...] = 0
self.stackedobs[(..., (- obs.shape[(- 1)]):)] = obs
return self.stackedobs | def reset(self):
'\n \n '
obs = self.venv.reset()
self.stackedobs[...] = 0
self.stackedobs[(..., (- obs.shape[(- 1)]):)] = obs
return self.stackedobs<|docstring|>Reset all environments<|endoftext|> |
7df925753194d2cff4191113a7f676b0915f9ac0f9e0d21904bee67a45da8474 | def categorical_sample(prob_n, np_random):
'Sample from a categorical distribution.'
prob_n = np.asarray(prob_n)
csprob_n = np.cumsum(prob_n)
return (csprob_n > np_random.rand()).argmax() | Sample from a categorical distribution. | rlsuite/envs/aliased_gridworld/aliased_gridworld.py | categorical_sample | christopher-wolff/lab | 2 | python | def categorical_sample(prob_n, np_random):
prob_n = np.asarray(prob_n)
csprob_n = np.cumsum(prob_n)
return (csprob_n > np_random.rand()).argmax() | def categorical_sample(prob_n, np_random):
prob_n = np.asarray(prob_n)
csprob_n = np.cumsum(prob_n)
return (csprob_n > np_random.rand()).argmax()<|docstring|>Sample from a categorical distribution.<|endoftext|> |
089f4fdec7f3084497c5941d7cc07177ac2dede1f214068db10afdf28eec9285 | def do_query_update(query, params={}):
'\n Execute one query statement,\n Return result and statistics\n '
url = get_neo4j_api_url('/db/data/transaction/commit')
query_request = {'statements': [{'statement': query, 'parameters': params, 'includeStats': True}]}
query_request = json.dumps(query_request).encode()
response = requests.post(url, data=query_request)
raise_for_update_errors(response)
return response | Execute one query statement,
Return result and statistics | graph_data/neo4j.py | do_query_update | andreisirghi/graph-data | 0 | python | def do_query_update(query, params={}):
'\n Execute one query statement,\n Return result and statistics\n '
url = get_neo4j_api_url('/db/data/transaction/commit')
query_request = {'statements': [{'statement': query, 'parameters': params, 'includeStats': True}]}
query_request = json.dumps(query_request).encode()
response = requests.post(url, data=query_request)
raise_for_update_errors(response)
return response | def do_query_update(query, params={}):
'\n Execute one query statement,\n Return result and statistics\n '
url = get_neo4j_api_url('/db/data/transaction/commit')
query_request = {'statements': [{'statement': query, 'parameters': params, 'includeStats': True}]}
query_request = json.dumps(query_request).encode()
response = requests.post(url, data=query_request)
raise_for_update_errors(response)
return response<|docstring|>Execute one query statement,
Return result and statistics<|endoftext|> |
f2e304901b26385e9ecc2c8d7b2eebb017e65b7d6114e4722f7ba467291ae95a | def do_query_update_batch(queries):
'\n Execute multiple query statements,\n Return result and statistics for each of the statements\n '
url = get_neo4j_api_url('/db/data/transaction/commit')
statements = []
for query in queries:
statement = {'statement': query['statement'], 'parameters': query['params'], 'includeStats': True}
statements.append(statement)
query_request = {'statements': statements}
query_request = json.dumps(query_request).encode()
response = requests.post(url, data=query_request)
raise_for_update_errors(response)
return response | Execute multiple query statements,
Return result and statistics for each of the statements | graph_data/neo4j.py | do_query_update_batch | andreisirghi/graph-data | 0 | python | def do_query_update_batch(queries):
'\n Execute multiple query statements,\n Return result and statistics for each of the statements\n '
url = get_neo4j_api_url('/db/data/transaction/commit')
statements = []
for query in queries:
statement = {'statement': query['statement'], 'parameters': query['params'], 'includeStats': True}
statements.append(statement)
query_request = {'statements': statements}
query_request = json.dumps(query_request).encode()
response = requests.post(url, data=query_request)
raise_for_update_errors(response)
return response | def do_query_update_batch(queries):
'\n Execute multiple query statements,\n Return result and statistics for each of the statements\n '
url = get_neo4j_api_url('/db/data/transaction/commit')
statements = []
for query in queries:
statement = {'statement': query['statement'], 'parameters': query['params'], 'includeStats': True}
statements.append(statement)
query_request = {'statements': statements}
query_request = json.dumps(query_request).encode()
response = requests.post(url, data=query_request)
raise_for_update_errors(response)
return response<|docstring|>Execute multiple query statements,
Return result and statistics for each of the statements<|endoftext|> |
85335c9a6ed086216b9dc5466c137d9812d15b03888e07c413c719aa892afc04 | def prepare_query(query, params):
'Replace template query {parameter}s with the\n values provided in the dictionary\n '
return query.format(**params) | Replace template query {parameter}s with the
values provided in the dictionary | graph_data/neo4j.py | prepare_query | andreisirghi/graph-data | 0 | python | def prepare_query(query, params):
'Replace template query {parameter}s with the\n values provided in the dictionary\n '
return query.format(**params) | def prepare_query(query, params):
'Replace template query {parameter}s with the\n values provided in the dictionary\n '
return query.format(**params)<|docstring|>Replace template query {parameter}s with the
values provided in the dictionary<|endoftext|> |
53ebf6d2a70341235b63c432b68d1626f72917ababc0b4ba85c253ebb498037d | def create_schema():
'\n Create database indexes and uniqueness constraints\n '
logger.info('graph.db.schema.prepare')
params = {'type': 'Student', 'property': 'idno'}
do_query_update(prepare_query(Q_CR_UNIQUE_CONSTRAINT, params))
params = {'type': 'Characteristic', 'property': 'id'}
do_query_update(prepare_query(Q_CR_UNIQUE_CONSTRAINT, params))
params['property'] = 'type'
do_query_update(prepare_query(Q_CR_INDEX, params))
params['property'] = 'value'
do_query_update(prepare_query(Q_CR_INDEX, params))
logger.info('graph.db.schema.created') | Create database indexes and uniqueness constraints | graph_data/neo4j.py | create_schema | andreisirghi/graph-data | 0 | python | def create_schema():
'\n \n '
logger.info('graph.db.schema.prepare')
params = {'type': 'Student', 'property': 'idno'}
do_query_update(prepare_query(Q_CR_UNIQUE_CONSTRAINT, params))
params = {'type': 'Characteristic', 'property': 'id'}
do_query_update(prepare_query(Q_CR_UNIQUE_CONSTRAINT, params))
params['property'] = 'type'
do_query_update(prepare_query(Q_CR_INDEX, params))
params['property'] = 'value'
do_query_update(prepare_query(Q_CR_INDEX, params))
logger.info('graph.db.schema.created') | def create_schema():
'\n \n '
logger.info('graph.db.schema.prepare')
params = {'type': 'Student', 'property': 'idno'}
do_query_update(prepare_query(Q_CR_UNIQUE_CONSTRAINT, params))
params = {'type': 'Characteristic', 'property': 'id'}
do_query_update(prepare_query(Q_CR_UNIQUE_CONSTRAINT, params))
params['property'] = 'type'
do_query_update(prepare_query(Q_CR_INDEX, params))
params['property'] = 'value'
do_query_update(prepare_query(Q_CR_INDEX, params))
logger.info('graph.db.schema.created')<|docstring|>Create database indexes and uniqueness constraints<|endoftext|> |
dab0634bdfbabab7ee9efe237dd7618df5eac33271d0a0bc767e8c0bacd26355 | def extractSelfishTranslation(item):
"\n\t'Selfish Translation'\n\t"
(vol, chp, frag, postfix) = extractVolChapterFragmentPostfix(item['title'])
if ((not (chp or vol or frag)) or ('preview' in item['title'].lower())):
return None
return False | 'Selfish Translation' | WebMirror/management/rss_parser_funcs/feed_parse_extractSelfishTranslation.py | extractSelfishTranslation | fake-name/ReadableWebProxy | 193 | python | def extractSelfishTranslation(item):
"\n\t\n\t"
(vol, chp, frag, postfix) = extractVolChapterFragmentPostfix(item['title'])
if ((not (chp or vol or frag)) or ('preview' in item['title'].lower())):
return None
return False | def extractSelfishTranslation(item):
"\n\t\n\t"
(vol, chp, frag, postfix) = extractVolChapterFragmentPostfix(item['title'])
if ((not (chp or vol or frag)) or ('preview' in item['title'].lower())):
return None
return False<|docstring|>'Selfish Translation'<|endoftext|> |
eb23890ca2a4cd549f51c80ae6767f77ce0570c8f9091c389681596e55676551 | @swagger_auto_schema(request_body=serializers.FindingNoteSerializer, responses={status.HTTP_200_OK: ''})
@action(detail=True, methods=['patch'])
def remove_note(self, request, pk=None):
'Remove Note From Finding Note'
finding = get_object_or_404(Finding.objects, id=pk)
notes = finding.notes.all()
if request.data['note_id']:
note = get_object_or_404(Notes.objects, id=request.data['note_id'])
if (note not in notes):
return Response({'error': 'Selected Note is not assigned to this Finding'}, status=status.HTTP_400_BAD_REQUEST)
else:
return Response({'error': "('note_id') parameter missing"}, status=status.HTTP_400_BAD_REQUEST)
if ((note.author.username == request.user.username) or request.user.is_staff):
finding.notes.remove(note)
note.delete()
else:
return Response({'error': "Delete Failed, You are not the Note's author"}, status=status.HTTP_400_BAD_REQUEST)
return Response({'Success': 'Selected Note has been Removed successfully'}, status=status.HTTP_200_OK) | Remove Note From Finding Note | dojo/api_v2/views.py | remove_note | satackey/django-DefectDojo | 0 | python | @swagger_auto_schema(request_body=serializers.FindingNoteSerializer, responses={status.HTTP_200_OK: })
@action(detail=True, methods=['patch'])
def remove_note(self, request, pk=None):
finding = get_object_or_404(Finding.objects, id=pk)
notes = finding.notes.all()
if request.data['note_id']:
note = get_object_or_404(Notes.objects, id=request.data['note_id'])
if (note not in notes):
return Response({'error': 'Selected Note is not assigned to this Finding'}, status=status.HTTP_400_BAD_REQUEST)
else:
return Response({'error': "('note_id') parameter missing"}, status=status.HTTP_400_BAD_REQUEST)
if ((note.author.username == request.user.username) or request.user.is_staff):
finding.notes.remove(note)
note.delete()
else:
return Response({'error': "Delete Failed, You are not the Note's author"}, status=status.HTTP_400_BAD_REQUEST)
return Response({'Success': 'Selected Note has been Removed successfully'}, status=status.HTTP_200_OK) | @swagger_auto_schema(request_body=serializers.FindingNoteSerializer, responses={status.HTTP_200_OK: })
@action(detail=True, methods=['patch'])
def remove_note(self, request, pk=None):
finding = get_object_or_404(Finding.objects, id=pk)
notes = finding.notes.all()
if request.data['note_id']:
note = get_object_or_404(Notes.objects, id=request.data['note_id'])
if (note not in notes):
return Response({'error': 'Selected Note is not assigned to this Finding'}, status=status.HTTP_400_BAD_REQUEST)
else:
return Response({'error': "('note_id') parameter missing"}, status=status.HTTP_400_BAD_REQUEST)
if ((note.author.username == request.user.username) or request.user.is_staff):
finding.notes.remove(note)
note.delete()
else:
return Response({'error': "Delete Failed, You are not the Note's author"}, status=status.HTTP_400_BAD_REQUEST)
return Response({'Success': 'Selected Note has been Removed successfully'}, status=status.HTTP_200_OK)<|docstring|>Remove Note From Finding Note<|endoftext|> |
84655a3cd2a9b80151fa58808c0e62301e18ac44673fd77995b227c196a9e485 | @swagger_auto_schema(responses={status.HTTP_200_OK: ''}, methods=['put', 'patch'], request_body=serializers.TagSerializer)
@action(detail=True, methods=['put', 'patch'])
def remove_tags(self, request, pk=None):
' Remove Tag(s) from finding list of tags '
finding = get_object_or_404(Finding.objects, id=pk)
delete_tags = serializers.TagSerializer(data=request.data)
if delete_tags.is_valid():
all_tags = finding.tags
all_tags = serializers.TagSerializer({'tags': all_tags}).data['tags']
del_tags = delete_tags.validated_data['tags']
if (len(del_tags) < 1):
return Response({'error': 'Empty Tag List Not Allowed'}, status=status.HTTP_400_BAD_REQUEST)
for tag in del_tags:
if (tag not in all_tags):
return Response({'error': "'{}' is not a valid tag in list".format(tag)}, status=status.HTTP_400_BAD_REQUEST)
all_tags.remove(tag)
t = ', '.join(all_tags)
finding.tags = t
finding.save()
return Response({'success': 'Tag(s) Removed'}, status=status.HTTP_200_OK)
else:
return Response(delete_tags.errors, status=status.HTTP_400_BAD_REQUEST) | Remove Tag(s) from finding list of tags | dojo/api_v2/views.py | remove_tags | satackey/django-DefectDojo | 0 | python | @swagger_auto_schema(responses={status.HTTP_200_OK: }, methods=['put', 'patch'], request_body=serializers.TagSerializer)
@action(detail=True, methods=['put', 'patch'])
def remove_tags(self, request, pk=None):
' '
finding = get_object_or_404(Finding.objects, id=pk)
delete_tags = serializers.TagSerializer(data=request.data)
if delete_tags.is_valid():
all_tags = finding.tags
all_tags = serializers.TagSerializer({'tags': all_tags}).data['tags']
del_tags = delete_tags.validated_data['tags']
if (len(del_tags) < 1):
return Response({'error': 'Empty Tag List Not Allowed'}, status=status.HTTP_400_BAD_REQUEST)
for tag in del_tags:
if (tag not in all_tags):
return Response({'error': "'{}' is not a valid tag in list".format(tag)}, status=status.HTTP_400_BAD_REQUEST)
all_tags.remove(tag)
t = ', '.join(all_tags)
finding.tags = t
finding.save()
return Response({'success': 'Tag(s) Removed'}, status=status.HTTP_200_OK)
else:
return Response(delete_tags.errors, status=status.HTTP_400_BAD_REQUEST) | @swagger_auto_schema(responses={status.HTTP_200_OK: }, methods=['put', 'patch'], request_body=serializers.TagSerializer)
@action(detail=True, methods=['put', 'patch'])
def remove_tags(self, request, pk=None):
' '
finding = get_object_or_404(Finding.objects, id=pk)
delete_tags = serializers.TagSerializer(data=request.data)
if delete_tags.is_valid():
all_tags = finding.tags
all_tags = serializers.TagSerializer({'tags': all_tags}).data['tags']
del_tags = delete_tags.validated_data['tags']
if (len(del_tags) < 1):
return Response({'error': 'Empty Tag List Not Allowed'}, status=status.HTTP_400_BAD_REQUEST)
for tag in del_tags:
if (tag not in all_tags):
return Response({'error': "'{}' is not a valid tag in list".format(tag)}, status=status.HTTP_400_BAD_REQUEST)
all_tags.remove(tag)
t = ', '.join(all_tags)
finding.tags = t
finding.save()
return Response({'success': 'Tag(s) Removed'}, status=status.HTTP_200_OK)
else:
return Response(delete_tags.errors, status=status.HTTP_400_BAD_REQUEST)<|docstring|>Remove Tag(s) from finding list of tags<|endoftext|> |
8a03c24c02805d37927e6a9e7db4fbe3ec6f256bace2efe9491085e454f4e3cb | def __clone_cloudbiolinux(cbl_config):
'Clone CloudBioLinux to a temporary directory.\n\n TODO: Support particular revision.\n '
cbl_url = cbl_config.get('repository', DEFAULT_CBL_URL)
cbl_dir = mkdtemp(suffix='cbl')
check_call(['git', 'clone', cbl_url, cbl_dir])
revision = cbl_config.get('revision', None)
if revision:
git_dir = os.path.join(cbl_dir, '.git')
check_call(['git', '--work-tree', cbl_dir, '--git-dir', git_dir, 'checkout', revision])
return cbl_dir | Clone CloudBioLinux to a temporary directory.
TODO: Support particular revision. | deploy/test_install_galaxy_tool.py | __clone_cloudbiolinux | bcbio/cloudbiolinux | 122 | python | def __clone_cloudbiolinux(cbl_config):
'Clone CloudBioLinux to a temporary directory.\n\n TODO: Support particular revision.\n '
cbl_url = cbl_config.get('repository', DEFAULT_CBL_URL)
cbl_dir = mkdtemp(suffix='cbl')
check_call(['git', 'clone', cbl_url, cbl_dir])
revision = cbl_config.get('revision', None)
if revision:
git_dir = os.path.join(cbl_dir, '.git')
check_call(['git', '--work-tree', cbl_dir, '--git-dir', git_dir, 'checkout', revision])
return cbl_dir | def __clone_cloudbiolinux(cbl_config):
'Clone CloudBioLinux to a temporary directory.\n\n TODO: Support particular revision.\n '
cbl_url = cbl_config.get('repository', DEFAULT_CBL_URL)
cbl_dir = mkdtemp(suffix='cbl')
check_call(['git', 'clone', cbl_url, cbl_dir])
revision = cbl_config.get('revision', None)
if revision:
git_dir = os.path.join(cbl_dir, '.git')
check_call(['git', '--work-tree', cbl_dir, '--git-dir', git_dir, 'checkout', revision])
return cbl_dir<|docstring|>Clone CloudBioLinux to a temporary directory.
TODO: Support particular revision.<|endoftext|> |
87f9698fd8ac07ced4ee449c8260c136e6bfa4009bf58c919ededbd8e429243c | def hexdump(source: bytearray, length: int=16, separator: string='.', show_raw: bool=False, base: int=0) -> string:
'\n Produces a `hexdump` command like output version of the bytearray given.\n '
result = []
for i in range(0, len(source), length):
s = source[i:(i + length)]
hexa = ' '.join([('%02X' % c) for c in s])
text = ''.join([(chr(c) if (32 <= c < 127) else separator) for c in s])
if show_raw:
result.append(hexa)
else:
result.append(('%#-.*x %-*s %s' % (16, (base + i), (3 * length), hexa, text)))
return '\n'.join(result) | Produces a `hexdump` command like output version of the bytearray given. | cemu/utils.py | hexdump | hugsy/cemu | 823 | python | def hexdump(source: bytearray, length: int=16, separator: string='.', show_raw: bool=False, base: int=0) -> string:
'\n \n '
result = []
for i in range(0, len(source), length):
s = source[i:(i + length)]
hexa = ' '.join([('%02X' % c) for c in s])
text = .join([(chr(c) if (32 <= c < 127) else separator) for c in s])
if show_raw:
result.append(hexa)
else:
result.append(('%#-.*x %-*s %s' % (16, (base + i), (3 * length), hexa, text)))
return '\n'.join(result) | def hexdump(source: bytearray, length: int=16, separator: string='.', show_raw: bool=False, base: int=0) -> string:
'\n \n '
result = []
for i in range(0, len(source), length):
s = source[i:(i + length)]
hexa = ' '.join([('%02X' % c) for c in s])
text = .join([(chr(c) if (32 <= c < 127) else separator) for c in s])
if show_raw:
result.append(hexa)
else:
result.append(('%#-.*x %-*s %s' % (16, (base + i), (3 * length), hexa, text)))
return '\n'.join(result)<|docstring|>Produces a `hexdump` command like output version of the bytearray given.<|endoftext|> |
3b06c6aad27a973e2b78b64ce46e19002ffdac4d1b4bd9453f7a24a430f472c8 | def assemble(asm_code: string, mode: int) -> Tuple[(bytearray, int)]:
"\n Helper function to assemble code receive in parameter `asm_code` using Keystone.\n\n @param asm_code : assembly code in bytes (multiple instructions must be separated by ';')\n @param mode : defines the mode to use Keystone with\n @return a tuple of bytecodes as bytearray, along with the number of instruction compiled. If failed, the\n bytearray will be empty, the count of instruction will be the negative number for the faulty line.\n "
(arch, mode, endian) = get_arch_mode('keystone', mode)
ks = keystone.Ks(arch, (mode | endian))
if (is_x86(mode) and (mode.syntax == Syntax.ATT)):
ks.syntax = keystone.KS_OPT_SYNTAX_ATT
try:
(bytecode, cnt) = ks.asm(asm_code, as_bytes=True)
except keystone.keystone.KsError as kse:
return (b'', kse.get_asm_count())
return (bytecode, cnt) | Helper function to assemble code receive in parameter `asm_code` using Keystone.
@param asm_code : assembly code in bytes (multiple instructions must be separated by ';')
@param mode : defines the mode to use Keystone with
@return a tuple of bytecodes as bytearray, along with the number of instruction compiled. If failed, the
bytearray will be empty, the count of instruction will be the negative number for the faulty line. | cemu/utils.py | assemble | hugsy/cemu | 823 | python | def assemble(asm_code: string, mode: int) -> Tuple[(bytearray, int)]:
"\n Helper function to assemble code receive in parameter `asm_code` using Keystone.\n\n @param asm_code : assembly code in bytes (multiple instructions must be separated by ';')\n @param mode : defines the mode to use Keystone with\n @return a tuple of bytecodes as bytearray, along with the number of instruction compiled. If failed, the\n bytearray will be empty, the count of instruction will be the negative number for the faulty line.\n "
(arch, mode, endian) = get_arch_mode('keystone', mode)
ks = keystone.Ks(arch, (mode | endian))
if (is_x86(mode) and (mode.syntax == Syntax.ATT)):
ks.syntax = keystone.KS_OPT_SYNTAX_ATT
try:
(bytecode, cnt) = ks.asm(asm_code, as_bytes=True)
except keystone.keystone.KsError as kse:
return (b, kse.get_asm_count())
return (bytecode, cnt) | def assemble(asm_code: string, mode: int) -> Tuple[(bytearray, int)]:
"\n Helper function to assemble code receive in parameter `asm_code` using Keystone.\n\n @param asm_code : assembly code in bytes (multiple instructions must be separated by ';')\n @param mode : defines the mode to use Keystone with\n @return a tuple of bytecodes as bytearray, along with the number of instruction compiled. If failed, the\n bytearray will be empty, the count of instruction will be the negative number for the faulty line.\n "
(arch, mode, endian) = get_arch_mode('keystone', mode)
ks = keystone.Ks(arch, (mode | endian))
if (is_x86(mode) and (mode.syntax == Syntax.ATT)):
ks.syntax = keystone.KS_OPT_SYNTAX_ATT
try:
(bytecode, cnt) = ks.asm(asm_code, as_bytes=True)
except keystone.keystone.KsError as kse:
return (b, kse.get_asm_count())
return (bytecode, cnt)<|docstring|>Helper function to assemble code receive in parameter `asm_code` using Keystone.
@param asm_code : assembly code in bytes (multiple instructions must be separated by ';')
@param mode : defines the mode to use Keystone with
@return a tuple of bytecodes as bytearray, along with the number of instruction compiled. If failed, the
bytearray will be empty, the count of instruction will be the negative number for the faulty line.<|endoftext|> |
860cab0f1dd580a080047999b23ea93901d5074c117de013765b6166581c8d85 | def get_cursor_row_number(widget: QTextEdit) -> int:
'\n Get the cursor row number from the QTextEdit widget\n '
assert isinstance(widget, QTextEdit)
pos = widget.textCursor().position()
text = widget.toPlainText()
return text[:pos].count('\n') | Get the cursor row number from the QTextEdit widget | cemu/utils.py | get_cursor_row_number | hugsy/cemu | 823 | python | def get_cursor_row_number(widget: QTextEdit) -> int:
'\n \n '
assert isinstance(widget, QTextEdit)
pos = widget.textCursor().position()
text = widget.toPlainText()
return text[:pos].count('\n') | def get_cursor_row_number(widget: QTextEdit) -> int:
'\n \n '
assert isinstance(widget, QTextEdit)
pos = widget.textCursor().position()
text = widget.toPlainText()
return text[:pos].count('\n')<|docstring|>Get the cursor row number from the QTextEdit widget<|endoftext|> |
4dc9735736835ac583bbb9c41ee85deb348c40c7a5e2b85aa13e3bac76c87753 | def get_cursor_column_number(widget: QTextEdit) -> int:
'\n Get the cursor column number from the QTextEdit widget\n '
assert isinstance(widget, QTextEdit)
pos = widget.textCursor().position()
text = widget.toPlainText()
return len(text[:pos].split('\n')[(- 1)]) | Get the cursor column number from the QTextEdit widget | cemu/utils.py | get_cursor_column_number | hugsy/cemu | 823 | python | def get_cursor_column_number(widget: QTextEdit) -> int:
'\n \n '
assert isinstance(widget, QTextEdit)
pos = widget.textCursor().position()
text = widget.toPlainText()
return len(text[:pos].split('\n')[(- 1)]) | def get_cursor_column_number(widget: QTextEdit) -> int:
'\n \n '
assert isinstance(widget, QTextEdit)
pos = widget.textCursor().position()
text = widget.toPlainText()
return len(text[:pos].split('\n')[(- 1)])<|docstring|>Get the cursor column number from the QTextEdit widget<|endoftext|> |
0ef626053e120fca6ec44ba60510a205ebd86638fab9ce863049d3f91ff0cd76 | def get_cursor_position(widget: QTextEdit) -> Tuple[(int, int)]:
'\n Returns the position of a cursor like (nb_row, nb_col) from a textedit widget\n '
return (get_cursor_row_number(widget), get_cursor_column_number(widget)) | Returns the position of a cursor like (nb_row, nb_col) from a textedit widget | cemu/utils.py | get_cursor_position | hugsy/cemu | 823 | python | def get_cursor_position(widget: QTextEdit) -> Tuple[(int, int)]:
'\n \n '
return (get_cursor_row_number(widget), get_cursor_column_number(widget)) | def get_cursor_position(widget: QTextEdit) -> Tuple[(int, int)]:
'\n \n '
return (get_cursor_row_number(widget), get_cursor_column_number(widget))<|docstring|>Returns the position of a cursor like (nb_row, nb_col) from a textedit widget<|endoftext|> |
fcab64d38b7d6ed9a9564bd705b1d2af789bcd5b746850d59d01f686f1950fe3 | def generate_random_string(length: int) -> str:
'\n Returns a random string\n '
charset = (string.ascii_letters + string.digits)
return ''.join((random.choice(charset) for i in range(length))) | Returns a random string | cemu/utils.py | generate_random_string | hugsy/cemu | 823 | python | def generate_random_string(length: int) -> str:
'\n \n '
charset = (string.ascii_letters + string.digits)
return .join((random.choice(charset) for i in range(length))) | def generate_random_string(length: int) -> str:
'\n \n '
charset = (string.ascii_letters + string.digits)
return .join((random.choice(charset) for i in range(length)))<|docstring|>Returns a random string<|endoftext|> |
17e48ff9acb1fa95ae0898cbc858d614259bfcbbaf0edb8d3e2eff70fc641629 | def get_num_lines(file: Union[(str, TextIO)]) -> int:
'\n Gets the number of lines in a file. Uses ``wc -l`` which seems to be the fastest method for larger files.\n Specifically counts the number of newline characters (thus no trailing newline may reduce count by 1)\n\n :param file: the file to get line counts for. Either a string or an open text file (returned by ``open()``)\n :raises CalledProcessError: if there was an error with the subprocess\n :return: the number of lines in the file as an int\n '
out = subprocess.run(args=['wc', '-l', (file if isinstance(file, str) else file.name)], check=True, encoding='utf-8', stdout=subprocess.PIPE, stderr=subprocess.PIPE)
return int(out.stdout.split()[0]) | Gets the number of lines in a file. Uses ``wc -l`` which seems to be the fastest method for larger files.
Specifically counts the number of newline characters (thus no trailing newline may reduce count by 1)
:param file: the file to get line counts for. Either a string or an open text file (returned by ``open()``)
:raises CalledProcessError: if there was an error with the subprocess
:return: the number of lines in the file as an int | shared/fileutils.py | get_num_lines | byu-imaal/dns-cookies-pam21 | 0 | python | def get_num_lines(file: Union[(str, TextIO)]) -> int:
'\n Gets the number of lines in a file. Uses ``wc -l`` which seems to be the fastest method for larger files.\n Specifically counts the number of newline characters (thus no trailing newline may reduce count by 1)\n\n :param file: the file to get line counts for. Either a string or an open text file (returned by ``open()``)\n :raises CalledProcessError: if there was an error with the subprocess\n :return: the number of lines in the file as an int\n '
out = subprocess.run(args=['wc', '-l', (file if isinstance(file, str) else file.name)], check=True, encoding='utf-8', stdout=subprocess.PIPE, stderr=subprocess.PIPE)
return int(out.stdout.split()[0]) | def get_num_lines(file: Union[(str, TextIO)]) -> int:
'\n Gets the number of lines in a file. Uses ``wc -l`` which seems to be the fastest method for larger files.\n Specifically counts the number of newline characters (thus no trailing newline may reduce count by 1)\n\n :param file: the file to get line counts for. Either a string or an open text file (returned by ``open()``)\n :raises CalledProcessError: if there was an error with the subprocess\n :return: the number of lines in the file as an int\n '
out = subprocess.run(args=['wc', '-l', (file if isinstance(file, str) else file.name)], check=True, encoding='utf-8', stdout=subprocess.PIPE, stderr=subprocess.PIPE)
return int(out.stdout.split()[0])<|docstring|>Gets the number of lines in a file. Uses ``wc -l`` which seems to be the fastest method for larger files.
Specifically counts the number of newline characters (thus no trailing newline may reduce count by 1)
:param file: the file to get line counts for. Either a string or an open text file (returned by ``open()``)
:raises CalledProcessError: if there was an error with the subprocess
:return: the number of lines in the file as an int<|endoftext|> |
849a256fd3de55b3a98f6a65a6419113b63d5fbf338dcedcd0e13fbe143608a5 | def get_free_filename(filename: str, format_str: str='{file}_{uniq}.{ext}'):
'\n Get a filename that\'s free so we don\'t overwrite anything. Appends a consecutive digit to the end of the name\n until it is unique. Note that this method respects file extensions.\n\n Example: if there is already ``example.txt`` in a directory,\n\n calling ``get_free_filename("example".txt, format_str="{uniq:02d}__{file}")`` will return: ``01__example.txt``\n\n calling ``get_free_filename("example".txt, format_str="{file}.{uniq}.{ext}")`` will return: ``example.1.txt``\n\n :param filename: the desired filename\n :param format_str: The string to format the filename with. Can include ``{file}`` for the filename, ``{uniq}``\n for the unique id and optionally ``{ext}`` to include the file extension separately\n :return: the orginal filename if it doesn\'t exist, otherwise a filename following the ``format_str`` pattern\n '
fmt_keys = [t[1] for t in string.Formatter().parse(format_str) if (t[1] is not None)]
if any(((key not in ['file', 'uniq', 'ext']) for key in fmt_keys)):
raise KeyError('Invalid key for file format. Allowed keys are {file, uniq, ext}')
n = 1
if (('.' in filename) and ('ext' in format_str)):
parts = filename.split('.')
(original, ext) = ('.'.join(parts[:(- 1)]), parts[(- 1)])
else:
(original, ext) = (filename, '')
while os.path.isfile(filename):
filename = format_str.format(file=original, uniq=n, ext=ext)
n += 1
return filename | Get a filename that's free so we don't overwrite anything. Appends a consecutive digit to the end of the name
until it is unique. Note that this method respects file extensions.
Example: if there is already ``example.txt`` in a directory,
calling ``get_free_filename("example".txt, format_str="{uniq:02d}__{file}")`` will return: ``01__example.txt``
calling ``get_free_filename("example".txt, format_str="{file}.{uniq}.{ext}")`` will return: ``example.1.txt``
:param filename: the desired filename
:param format_str: The string to format the filename with. Can include ``{file}`` for the filename, ``{uniq}``
for the unique id and optionally ``{ext}`` to include the file extension separately
:return: the orginal filename if it doesn't exist, otherwise a filename following the ``format_str`` pattern | shared/fileutils.py | get_free_filename | byu-imaal/dns-cookies-pam21 | 0 | python | def get_free_filename(filename: str, format_str: str='{file}_{uniq}.{ext}'):
'\n Get a filename that\'s free so we don\'t overwrite anything. Appends a consecutive digit to the end of the name\n until it is unique. Note that this method respects file extensions.\n\n Example: if there is already ``example.txt`` in a directory,\n\n calling ``get_free_filename("example".txt, format_str="{uniq:02d}__{file}")`` will return: ``01__example.txt``\n\n calling ``get_free_filename("example".txt, format_str="{file}.{uniq}.{ext}")`` will return: ``example.1.txt``\n\n :param filename: the desired filename\n :param format_str: The string to format the filename with. Can include ``{file}`` for the filename, ``{uniq}``\n for the unique id and optionally ``{ext}`` to include the file extension separately\n :return: the orginal filename if it doesn\'t exist, otherwise a filename following the ``format_str`` pattern\n '
fmt_keys = [t[1] for t in string.Formatter().parse(format_str) if (t[1] is not None)]
if any(((key not in ['file', 'uniq', 'ext']) for key in fmt_keys)):
raise KeyError('Invalid key for file format. Allowed keys are {file, uniq, ext}')
n = 1
if (('.' in filename) and ('ext' in format_str)):
parts = filename.split('.')
(original, ext) = ('.'.join(parts[:(- 1)]), parts[(- 1)])
else:
(original, ext) = (filename, )
while os.path.isfile(filename):
filename = format_str.format(file=original, uniq=n, ext=ext)
n += 1
return filename | def get_free_filename(filename: str, format_str: str='{file}_{uniq}.{ext}'):
'\n Get a filename that\'s free so we don\'t overwrite anything. Appends a consecutive digit to the end of the name\n until it is unique. Note that this method respects file extensions.\n\n Example: if there is already ``example.txt`` in a directory,\n\n calling ``get_free_filename("example".txt, format_str="{uniq:02d}__{file}")`` will return: ``01__example.txt``\n\n calling ``get_free_filename("example".txt, format_str="{file}.{uniq}.{ext}")`` will return: ``example.1.txt``\n\n :param filename: the desired filename\n :param format_str: The string to format the filename with. Can include ``{file}`` for the filename, ``{uniq}``\n for the unique id and optionally ``{ext}`` to include the file extension separately\n :return: the orginal filename if it doesn\'t exist, otherwise a filename following the ``format_str`` pattern\n '
fmt_keys = [t[1] for t in string.Formatter().parse(format_str) if (t[1] is not None)]
if any(((key not in ['file', 'uniq', 'ext']) for key in fmt_keys)):
raise KeyError('Invalid key for file format. Allowed keys are {file, uniq, ext}')
n = 1
if (('.' in filename) and ('ext' in format_str)):
parts = filename.split('.')
(original, ext) = ('.'.join(parts[:(- 1)]), parts[(- 1)])
else:
(original, ext) = (filename, )
while os.path.isfile(filename):
filename = format_str.format(file=original, uniq=n, ext=ext)
n += 1
return filename<|docstring|>Get a filename that's free so we don't overwrite anything. Appends a consecutive digit to the end of the name
until it is unique. Note that this method respects file extensions.
Example: if there is already ``example.txt`` in a directory,
calling ``get_free_filename("example".txt, format_str="{uniq:02d}__{file}")`` will return: ``01__example.txt``
calling ``get_free_filename("example".txt, format_str="{file}.{uniq}.{ext}")`` will return: ``example.1.txt``
:param filename: the desired filename
:param format_str: The string to format the filename with. Can include ``{file}`` for the filename, ``{uniq}``
for the unique id and optionally ``{ext}`` to include the file extension separately
:return: the orginal filename if it doesn't exist, otherwise a filename following the ``format_str`` pattern<|endoftext|> |
8dc2fa7d2033db42699bd3dc17054984bef133cc8d84742b223a58abad62bad3 | def jl_iter(file: TextIO, with_tqdm=True) -> iter:
"\n Create a json lines iterator from an open file.\n\n Example usage:\n with open('file.jsonl', 'r') as in_file:\n for line in jl_iter(in_file):\n print(line.keys())\n\n :param file: the open file to be used\n :param with_tqdm: if set, wraps iterator in tqdm progress bar\n :return: an iterator of dictionaries\n "
if with_tqdm:
return tqdm(map(json.loads, file), total=get_num_lines(file))
else:
return map(json.loads, file) | Create a json lines iterator from an open file.
Example usage:
with open('file.jsonl', 'r') as in_file:
for line in jl_iter(in_file):
print(line.keys())
:param file: the open file to be used
:param with_tqdm: if set, wraps iterator in tqdm progress bar
:return: an iterator of dictionaries | shared/fileutils.py | jl_iter | byu-imaal/dns-cookies-pam21 | 0 | python | def jl_iter(file: TextIO, with_tqdm=True) -> iter:
"\n Create a json lines iterator from an open file.\n\n Example usage:\n with open('file.jsonl', 'r') as in_file:\n for line in jl_iter(in_file):\n print(line.keys())\n\n :param file: the open file to be used\n :param with_tqdm: if set, wraps iterator in tqdm progress bar\n :return: an iterator of dictionaries\n "
if with_tqdm:
return tqdm(map(json.loads, file), total=get_num_lines(file))
else:
return map(json.loads, file) | def jl_iter(file: TextIO, with_tqdm=True) -> iter:
"\n Create a json lines iterator from an open file.\n\n Example usage:\n with open('file.jsonl', 'r') as in_file:\n for line in jl_iter(in_file):\n print(line.keys())\n\n :param file: the open file to be used\n :param with_tqdm: if set, wraps iterator in tqdm progress bar\n :return: an iterator of dictionaries\n "
if with_tqdm:
return tqdm(map(json.loads, file), total=get_num_lines(file))
else:
return map(json.loads, file)<|docstring|>Create a json lines iterator from an open file.
Example usage:
with open('file.jsonl', 'r') as in_file:
for line in jl_iter(in_file):
print(line.keys())
:param file: the open file to be used
:param with_tqdm: if set, wraps iterator in tqdm progress bar
:return: an iterator of dictionaries<|endoftext|> |
3b0530d90a1f2634612f86006359b562fdcc154a735286b16977203f4bd1eca3 | def __init__(self, **kwargs):
" Initializes a ExternalService instance\n\n Notes:\n You can specify all parameters while calling this methods.\n A special argument named `data` will enable you to load the\n object from a Python dictionary\n\n Examples:\n >>> externalservice = NUExternalService(id=u'xxxx-xxx-xxx-xxx', name=u'ExternalService')\n >>> externalservice = NUExternalService(data=my_dict)\n "
super(NUExternalService, self).__init__()
self._description = None
self._direction = None
self._entity_scope = None
self._external_id = None
self._last_updated_by = None
self._name = None
self._service_type = None
self._stage = None
self.expose_attribute(local_name='description', remote_name='description', attribute_type=str, is_required=False, is_unique=False)
self.expose_attribute(local_name='direction', remote_name='direction', attribute_type=str, is_required=False, is_unique=False, choices=[u'INGRESS'])
self.expose_attribute(local_name='entity_scope', remote_name='entityScope', attribute_type=str, is_required=False, is_unique=False, choices=[u'ENTERPRISE', u'GLOBAL'])
self.expose_attribute(local_name='external_id', remote_name='externalID', attribute_type=str, is_required=False, is_unique=True)
self.expose_attribute(local_name='last_updated_by', remote_name='lastUpdatedBy', attribute_type=str, is_required=False, is_unique=False)
self.expose_attribute(local_name='name', remote_name='name', attribute_type=str, is_required=True, is_unique=False)
self.expose_attribute(local_name='service_type', remote_name='serviceType', attribute_type=str, is_required=True, is_unique=False, choices=[u'L2', u'L3'])
self.expose_attribute(local_name='stage', remote_name='stage', attribute_type=str, is_required=False, is_unique=False, choices=[u'START'])
self.end_points = NUEndPointsFetcher.fetcher_with_object(parent_object=self, relationship='child')
self.event_logs = NUEventLogsFetcher.fetcher_with_object(parent_object=self, relationship='child')
self.global_metadatas = NUGlobalMetadatasFetcher.fetcher_with_object(parent_object=self, relationship='child')
self.metadatas = NUMetadatasFetcher.fetcher_with_object(parent_object=self, relationship='child')
self.metadata_tags = NUMetadataTagsFetcher.fetcher_with_object(parent_object=self, relationship='child')
self._compute_args(**kwargs) | Initializes a ExternalService instance
Notes:
You can specify all parameters while calling this methods.
A special argument named `data` will enable you to load the
object from a Python dictionary
Examples:
>>> externalservice = NUExternalService(id=u'xxxx-xxx-xxx-xxx', name=u'ExternalService')
>>> externalservice = NUExternalService(data=my_dict) | vspk/v4_0/nuexternalservice.py | __init__ | cldelcourt/vspk-python | 0 | python | def __init__(self, **kwargs):
" Initializes a ExternalService instance\n\n Notes:\n You can specify all parameters while calling this methods.\n A special argument named `data` will enable you to load the\n object from a Python dictionary\n\n Examples:\n >>> externalservice = NUExternalService(id=u'xxxx-xxx-xxx-xxx', name=u'ExternalService')\n >>> externalservice = NUExternalService(data=my_dict)\n "
super(NUExternalService, self).__init__()
self._description = None
self._direction = None
self._entity_scope = None
self._external_id = None
self._last_updated_by = None
self._name = None
self._service_type = None
self._stage = None
self.expose_attribute(local_name='description', remote_name='description', attribute_type=str, is_required=False, is_unique=False)
self.expose_attribute(local_name='direction', remote_name='direction', attribute_type=str, is_required=False, is_unique=False, choices=[u'INGRESS'])
self.expose_attribute(local_name='entity_scope', remote_name='entityScope', attribute_type=str, is_required=False, is_unique=False, choices=[u'ENTERPRISE', u'GLOBAL'])
self.expose_attribute(local_name='external_id', remote_name='externalID', attribute_type=str, is_required=False, is_unique=True)
self.expose_attribute(local_name='last_updated_by', remote_name='lastUpdatedBy', attribute_type=str, is_required=False, is_unique=False)
self.expose_attribute(local_name='name', remote_name='name', attribute_type=str, is_required=True, is_unique=False)
self.expose_attribute(local_name='service_type', remote_name='serviceType', attribute_type=str, is_required=True, is_unique=False, choices=[u'L2', u'L3'])
self.expose_attribute(local_name='stage', remote_name='stage', attribute_type=str, is_required=False, is_unique=False, choices=[u'START'])
self.end_points = NUEndPointsFetcher.fetcher_with_object(parent_object=self, relationship='child')
self.event_logs = NUEventLogsFetcher.fetcher_with_object(parent_object=self, relationship='child')
self.global_metadatas = NUGlobalMetadatasFetcher.fetcher_with_object(parent_object=self, relationship='child')
self.metadatas = NUMetadatasFetcher.fetcher_with_object(parent_object=self, relationship='child')
self.metadata_tags = NUMetadataTagsFetcher.fetcher_with_object(parent_object=self, relationship='child')
self._compute_args(**kwargs) | def __init__(self, **kwargs):
" Initializes a ExternalService instance\n\n Notes:\n You can specify all parameters while calling this methods.\n A special argument named `data` will enable you to load the\n object from a Python dictionary\n\n Examples:\n >>> externalservice = NUExternalService(id=u'xxxx-xxx-xxx-xxx', name=u'ExternalService')\n >>> externalservice = NUExternalService(data=my_dict)\n "
super(NUExternalService, self).__init__()
self._description = None
self._direction = None
self._entity_scope = None
self._external_id = None
self._last_updated_by = None
self._name = None
self._service_type = None
self._stage = None
self.expose_attribute(local_name='description', remote_name='description', attribute_type=str, is_required=False, is_unique=False)
self.expose_attribute(local_name='direction', remote_name='direction', attribute_type=str, is_required=False, is_unique=False, choices=[u'INGRESS'])
self.expose_attribute(local_name='entity_scope', remote_name='entityScope', attribute_type=str, is_required=False, is_unique=False, choices=[u'ENTERPRISE', u'GLOBAL'])
self.expose_attribute(local_name='external_id', remote_name='externalID', attribute_type=str, is_required=False, is_unique=True)
self.expose_attribute(local_name='last_updated_by', remote_name='lastUpdatedBy', attribute_type=str, is_required=False, is_unique=False)
self.expose_attribute(local_name='name', remote_name='name', attribute_type=str, is_required=True, is_unique=False)
self.expose_attribute(local_name='service_type', remote_name='serviceType', attribute_type=str, is_required=True, is_unique=False, choices=[u'L2', u'L3'])
self.expose_attribute(local_name='stage', remote_name='stage', attribute_type=str, is_required=False, is_unique=False, choices=[u'START'])
self.end_points = NUEndPointsFetcher.fetcher_with_object(parent_object=self, relationship='child')
self.event_logs = NUEventLogsFetcher.fetcher_with_object(parent_object=self, relationship='child')
self.global_metadatas = NUGlobalMetadatasFetcher.fetcher_with_object(parent_object=self, relationship='child')
self.metadatas = NUMetadatasFetcher.fetcher_with_object(parent_object=self, relationship='child')
self.metadata_tags = NUMetadataTagsFetcher.fetcher_with_object(parent_object=self, relationship='child')
self._compute_args(**kwargs)<|docstring|>Initializes a ExternalService instance
Notes:
You can specify all parameters while calling this methods.
A special argument named `data` will enable you to load the
object from a Python dictionary
Examples:
>>> externalservice = NUExternalService(id=u'xxxx-xxx-xxx-xxx', name=u'ExternalService')
>>> externalservice = NUExternalService(data=my_dict)<|endoftext|> |
02dfe5a92b4dfda89c6a1ec4d29e6934e9a90b05b76032e4cceb8e945498f532 | @property
def description(self):
' Get description value.\n\n Notes:\n Description of the External Service.\n\n \n '
return self._description | Get description value.
Notes:
Description of the External Service. | vspk/v4_0/nuexternalservice.py | description | cldelcourt/vspk-python | 0 | python | @property
def description(self):
' Get description value.\n\n Notes:\n Description of the External Service.\n\n \n '
return self._description | @property
def description(self):
' Get description value.\n\n Notes:\n Description of the External Service.\n\n \n '
return self._description<|docstring|>Get description value.
Notes:
Description of the External Service.<|endoftext|> |
348e4ae1803823fe13e9439a89d695465e61240312059675c64f23ca185d3f42 | @description.setter
def description(self, value):
' Set description value.\n\n Notes:\n Description of the External Service.\n\n \n '
self._description = value | Set description value.
Notes:
Description of the External Service. | vspk/v4_0/nuexternalservice.py | description | cldelcourt/vspk-python | 0 | python | @description.setter
def description(self, value):
' Set description value.\n\n Notes:\n Description of the External Service.\n\n \n '
self._description = value | @description.setter
def description(self, value):
' Set description value.\n\n Notes:\n Description of the External Service.\n\n \n '
self._description = value<|docstring|>Set description value.
Notes:
Description of the External Service.<|endoftext|> |
dc6ba2acd18afdeb4ef2c97d9a1c430025e170646259760fa7a15f91a9cdcd74 | @property
def direction(self):
' Get direction value.\n\n Notes:\n Direction\n\n \n '
return self._direction | Get direction value.
Notes:
Direction | vspk/v4_0/nuexternalservice.py | direction | cldelcourt/vspk-python | 0 | python | @property
def direction(self):
' Get direction value.\n\n Notes:\n Direction\n\n \n '
return self._direction | @property
def direction(self):
' Get direction value.\n\n Notes:\n Direction\n\n \n '
return self._direction<|docstring|>Get direction value.
Notes:
Direction<|endoftext|> |
28d23189071bd5b2cbf824fb8c4be02ab8fb1b7610d4c3059688a8ea723f2c3b | @direction.setter
def direction(self, value):
' Set direction value.\n\n Notes:\n Direction\n\n \n '
self._direction = value | Set direction value.
Notes:
Direction | vspk/v4_0/nuexternalservice.py | direction | cldelcourt/vspk-python | 0 | python | @direction.setter
def direction(self, value):
' Set direction value.\n\n Notes:\n Direction\n\n \n '
self._direction = value | @direction.setter
def direction(self, value):
' Set direction value.\n\n Notes:\n Direction\n\n \n '
self._direction = value<|docstring|>Set direction value.
Notes:
Direction<|endoftext|> |
086d663638fd09f513fc8a722129cf6ab6e132daed46e0aea683343015122cba | @property
def entity_scope(self):
' Get entity_scope value.\n\n Notes:\n Specify if scope of entity is Data center or Enterprise level\n\n \n This attribute is named `entityScope` in VSD API.\n \n '
return self._entity_scope | Get entity_scope value.
Notes:
Specify if scope of entity is Data center or Enterprise level
This attribute is named `entityScope` in VSD API. | vspk/v4_0/nuexternalservice.py | entity_scope | cldelcourt/vspk-python | 0 | python | @property
def entity_scope(self):
' Get entity_scope value.\n\n Notes:\n Specify if scope of entity is Data center or Enterprise level\n\n \n This attribute is named `entityScope` in VSD API.\n \n '
return self._entity_scope | @property
def entity_scope(self):
' Get entity_scope value.\n\n Notes:\n Specify if scope of entity is Data center or Enterprise level\n\n \n This attribute is named `entityScope` in VSD API.\n \n '
return self._entity_scope<|docstring|>Get entity_scope value.
Notes:
Specify if scope of entity is Data center or Enterprise level
This attribute is named `entityScope` in VSD API.<|endoftext|> |
1589f43a7c02e6c81157f9297d716e8f62ca20ff4f1c21245114f8ff0875dbbb | @entity_scope.setter
def entity_scope(self, value):
' Set entity_scope value.\n\n Notes:\n Specify if scope of entity is Data center or Enterprise level\n\n \n This attribute is named `entityScope` in VSD API.\n \n '
self._entity_scope = value | Set entity_scope value.
Notes:
Specify if scope of entity is Data center or Enterprise level
This attribute is named `entityScope` in VSD API. | vspk/v4_0/nuexternalservice.py | entity_scope | cldelcourt/vspk-python | 0 | python | @entity_scope.setter
def entity_scope(self, value):
' Set entity_scope value.\n\n Notes:\n Specify if scope of entity is Data center or Enterprise level\n\n \n This attribute is named `entityScope` in VSD API.\n \n '
self._entity_scope = value | @entity_scope.setter
def entity_scope(self, value):
' Set entity_scope value.\n\n Notes:\n Specify if scope of entity is Data center or Enterprise level\n\n \n This attribute is named `entityScope` in VSD API.\n \n '
self._entity_scope = value<|docstring|>Set entity_scope value.
Notes:
Specify if scope of entity is Data center or Enterprise level
This attribute is named `entityScope` in VSD API.<|endoftext|> |
81e1284abf6dbac3ca65e453824711d4cd8d94e4befe4635a8e5a9601cb7c453 | @property
def external_id(self):
' Get external_id value.\n\n Notes:\n External object ID. Used for integration with third party systems\n\n \n This attribute is named `externalID` in VSD API.\n \n '
return self._external_id | Get external_id value.
Notes:
External object ID. Used for integration with third party systems
This attribute is named `externalID` in VSD API. | vspk/v4_0/nuexternalservice.py | external_id | cldelcourt/vspk-python | 0 | python | @property
def external_id(self):
' Get external_id value.\n\n Notes:\n External object ID. Used for integration with third party systems\n\n \n This attribute is named `externalID` in VSD API.\n \n '
return self._external_id | @property
def external_id(self):
' Get external_id value.\n\n Notes:\n External object ID. Used for integration with third party systems\n\n \n This attribute is named `externalID` in VSD API.\n \n '
return self._external_id<|docstring|>Get external_id value.
Notes:
External object ID. Used for integration with third party systems
This attribute is named `externalID` in VSD API.<|endoftext|> |
61e254ddf0aaecbedeb7e09b0dd5fa779d32936e7edb2e664aaaa566c1fe53c4 | @external_id.setter
def external_id(self, value):
' Set external_id value.\n\n Notes:\n External object ID. Used for integration with third party systems\n\n \n This attribute is named `externalID` in VSD API.\n \n '
self._external_id = value | Set external_id value.
Notes:
External object ID. Used for integration with third party systems
This attribute is named `externalID` in VSD API. | vspk/v4_0/nuexternalservice.py | external_id | cldelcourt/vspk-python | 0 | python | @external_id.setter
def external_id(self, value):
' Set external_id value.\n\n Notes:\n External object ID. Used for integration with third party systems\n\n \n This attribute is named `externalID` in VSD API.\n \n '
self._external_id = value | @external_id.setter
def external_id(self, value):
' Set external_id value.\n\n Notes:\n External object ID. Used for integration with third party systems\n\n \n This attribute is named `externalID` in VSD API.\n \n '
self._external_id = value<|docstring|>Set external_id value.
Notes:
External object ID. Used for integration with third party systems
This attribute is named `externalID` in VSD API.<|endoftext|> |
493733db598520ef368fe6552be5863dbc3631fde735dc3c68076f99aa5ce8b2 | @property
def last_updated_by(self):
' Get last_updated_by value.\n\n Notes:\n ID of the user who last updated the object.\n\n \n This attribute is named `lastUpdatedBy` in VSD API.\n \n '
return self._last_updated_by | Get last_updated_by value.
Notes:
ID of the user who last updated the object.
This attribute is named `lastUpdatedBy` in VSD API. | vspk/v4_0/nuexternalservice.py | last_updated_by | cldelcourt/vspk-python | 0 | python | @property
def last_updated_by(self):
' Get last_updated_by value.\n\n Notes:\n ID of the user who last updated the object.\n\n \n This attribute is named `lastUpdatedBy` in VSD API.\n \n '
return self._last_updated_by | @property
def last_updated_by(self):
' Get last_updated_by value.\n\n Notes:\n ID of the user who last updated the object.\n\n \n This attribute is named `lastUpdatedBy` in VSD API.\n \n '
return self._last_updated_by<|docstring|>Get last_updated_by value.
Notes:
ID of the user who last updated the object.
This attribute is named `lastUpdatedBy` in VSD API.<|endoftext|> |
4b7c8fd8d3238dbf1ceae62c487828a281407066cc499227b53e0ddf465e9d88 | @last_updated_by.setter
def last_updated_by(self, value):
' Set last_updated_by value.\n\n Notes:\n ID of the user who last updated the object.\n\n \n This attribute is named `lastUpdatedBy` in VSD API.\n \n '
self._last_updated_by = value | Set last_updated_by value.
Notes:
ID of the user who last updated the object.
This attribute is named `lastUpdatedBy` in VSD API. | vspk/v4_0/nuexternalservice.py | last_updated_by | cldelcourt/vspk-python | 0 | python | @last_updated_by.setter
def last_updated_by(self, value):
' Set last_updated_by value.\n\n Notes:\n ID of the user who last updated the object.\n\n \n This attribute is named `lastUpdatedBy` in VSD API.\n \n '
self._last_updated_by = value | @last_updated_by.setter
def last_updated_by(self, value):
' Set last_updated_by value.\n\n Notes:\n ID of the user who last updated the object.\n\n \n This attribute is named `lastUpdatedBy` in VSD API.\n \n '
self._last_updated_by = value<|docstring|>Set last_updated_by value.
Notes:
ID of the user who last updated the object.
This attribute is named `lastUpdatedBy` in VSD API.<|endoftext|> |
11808b2c7b6006d5be294730b169c3002b45865902d72c7b2e4997579a9c2b25 | @property
def name(self):
' Get name value.\n\n Notes:\n unique name of the External Service. \n\n \n '
return self._name | Get name value.
Notes:
unique name of the External Service. | vspk/v4_0/nuexternalservice.py | name | cldelcourt/vspk-python | 0 | python | @property
def name(self):
' Get name value.\n\n Notes:\n unique name of the External Service. \n\n \n '
return self._name | @property
def name(self):
' Get name value.\n\n Notes:\n unique name of the External Service. \n\n \n '
return self._name<|docstring|>Get name value.
Notes:
unique name of the External Service.<|endoftext|> |
c0d45abb6ba0257c44dfd9cebee0276ce2d21f453aabcd93bb9b00ce3c481229 | @name.setter
def name(self, value):
' Set name value.\n\n Notes:\n unique name of the External Service. \n\n \n '
self._name = value | Set name value.
Notes:
unique name of the External Service. | vspk/v4_0/nuexternalservice.py | name | cldelcourt/vspk-python | 0 | python | @name.setter
def name(self, value):
' Set name value.\n\n Notes:\n unique name of the External Service. \n\n \n '
self._name = value | @name.setter
def name(self, value):
' Set name value.\n\n Notes:\n unique name of the External Service. \n\n \n '
self._name = value<|docstring|>Set name value.
Notes:
unique name of the External Service.<|endoftext|> |
b6a9da3e0c4ea5fc78ad975acff2b6b5e180119f76efacb7159af7b0076f48b2 | @property
def service_type(self):
' Get service_type value.\n\n Notes:\n Type of the service.\n\n \n This attribute is named `serviceType` in VSD API.\n \n '
return self._service_type | Get service_type value.
Notes:
Type of the service.
This attribute is named `serviceType` in VSD API. | vspk/v4_0/nuexternalservice.py | service_type | cldelcourt/vspk-python | 0 | python | @property
def service_type(self):
' Get service_type value.\n\n Notes:\n Type of the service.\n\n \n This attribute is named `serviceType` in VSD API.\n \n '
return self._service_type | @property
def service_type(self):
' Get service_type value.\n\n Notes:\n Type of the service.\n\n \n This attribute is named `serviceType` in VSD API.\n \n '
return self._service_type<|docstring|>Get service_type value.
Notes:
Type of the service.
This attribute is named `serviceType` in VSD API.<|endoftext|> |
968e7978e57bab8b6d2615abf0bfcb4a1dee5eca0d1141382332712c767197b0 | @service_type.setter
def service_type(self, value):
' Set service_type value.\n\n Notes:\n Type of the service.\n\n \n This attribute is named `serviceType` in VSD API.\n \n '
self._service_type = value | Set service_type value.
Notes:
Type of the service.
This attribute is named `serviceType` in VSD API. | vspk/v4_0/nuexternalservice.py | service_type | cldelcourt/vspk-python | 0 | python | @service_type.setter
def service_type(self, value):
' Set service_type value.\n\n Notes:\n Type of the service.\n\n \n This attribute is named `serviceType` in VSD API.\n \n '
self._service_type = value | @service_type.setter
def service_type(self, value):
' Set service_type value.\n\n Notes:\n Type of the service.\n\n \n This attribute is named `serviceType` in VSD API.\n \n '
self._service_type = value<|docstring|>Set service_type value.
Notes:
Type of the service.
This attribute is named `serviceType` in VSD API.<|endoftext|> |
e3f8b94bfe94e5943182b740d9107b6c5289b9a0709f6f95787274c4a6b95c5f | @property
def stage(self):
' Get stage value.\n\n Notes:\n Stage - START,END Possible values are START, .\n\n \n '
return self._stage | Get stage value.
Notes:
Stage - START,END Possible values are START, . | vspk/v4_0/nuexternalservice.py | stage | cldelcourt/vspk-python | 0 | python | @property
def stage(self):
' Get stage value.\n\n Notes:\n Stage - START,END Possible values are START, .\n\n \n '
return self._stage | @property
def stage(self):
' Get stage value.\n\n Notes:\n Stage - START,END Possible values are START, .\n\n \n '
return self._stage<|docstring|>Get stage value.
Notes:
Stage - START,END Possible values are START, .<|endoftext|> |
023604ed038a800302b046ac91a40aec4e05c5ffd5f55fc2af3d8606192ac932 | @stage.setter
def stage(self, value):
' Set stage value.\n\n Notes:\n Stage - START,END Possible values are START, .\n\n \n '
self._stage = value | Set stage value.
Notes:
Stage - START,END Possible values are START, . | vspk/v4_0/nuexternalservice.py | stage | cldelcourt/vspk-python | 0 | python | @stage.setter
def stage(self, value):
' Set stage value.\n\n Notes:\n Stage - START,END Possible values are START, .\n\n \n '
self._stage = value | @stage.setter
def stage(self, value):
' Set stage value.\n\n Notes:\n Stage - START,END Possible values are START, .\n\n \n '
self._stage = value<|docstring|>Set stage value.
Notes:
Stage - START,END Possible values are START, .<|endoftext|> |
6013000e19fbffd19e810f9441111a48a04a0ede2d468f8a1c9abcdf888f50cc | def discovery_documents(filepath, preferred=False, skip=None):
'Returns a map of API IDs to Discovery document filenames.\n\n Args:\n filepath (str): the directory to work in. Discovery documents are\n downloaded to this directory.\n preferred (bool, optional): if true, only APIs marked as preferred are\n returned.\n skip (list, optional): a list of API IDs to skip.\n\n Returns:\n dict(string, string): a map of API IDs to Discovery document\n filenames.\n '
repo = _git.clone_from_github(_repo_path(), join(filepath, _repo_name()))
filenames = glob.glob(join(repo.filepath, 'discoveries/*.json'))
filenames = [x for x in filenames if (os.path.basename(x) != 'index.json')]
ddocs = {}
for filename in filenames:
id_ = None
with open(filename) as file_:
id_ = json.load(file_)['id']
if (id_ in ddocs):
continue
ddocs[id_] = filename
if skip:
_ = [ddocs.pop(id_, None) for id_ in skip]
if (not preferred):
return ddocs
index = {}
with open(join(repo.filepath, 'discoveries/index.json')) as file_:
index = json.load(file_)
for api in index['items']:
id_ = api['id']
if (id_ in _ACTUALLY_PREFERRED):
continue
if api['preferred']:
continue
ddocs.pop(id_, None)
return ddocs | Returns a map of API IDs to Discovery document filenames.
Args:
filepath (str): the directory to work in. Discovery documents are
downloaded to this directory.
preferred (bool, optional): if true, only APIs marked as preferred are
returned.
skip (list, optional): a list of API IDs to skip.
Returns:
dict(string, string): a map of API IDs to Discovery document
filenames. | server/tasks/discovery_artifact_manager.py | discovery_documents | codyoss/discovery-artifact-manager | 38 | python | def discovery_documents(filepath, preferred=False, skip=None):
'Returns a map of API IDs to Discovery document filenames.\n\n Args:\n filepath (str): the directory to work in. Discovery documents are\n downloaded to this directory.\n preferred (bool, optional): if true, only APIs marked as preferred are\n returned.\n skip (list, optional): a list of API IDs to skip.\n\n Returns:\n dict(string, string): a map of API IDs to Discovery document\n filenames.\n '
repo = _git.clone_from_github(_repo_path(), join(filepath, _repo_name()))
filenames = glob.glob(join(repo.filepath, 'discoveries/*.json'))
filenames = [x for x in filenames if (os.path.basename(x) != 'index.json')]
ddocs = {}
for filename in filenames:
id_ = None
with open(filename) as file_:
id_ = json.load(file_)['id']
if (id_ in ddocs):
continue
ddocs[id_] = filename
if skip:
_ = [ddocs.pop(id_, None) for id_ in skip]
if (not preferred):
return ddocs
index = {}
with open(join(repo.filepath, 'discoveries/index.json')) as file_:
index = json.load(file_)
for api in index['items']:
id_ = api['id']
if (id_ in _ACTUALLY_PREFERRED):
continue
if api['preferred']:
continue
ddocs.pop(id_, None)
return ddocs | def discovery_documents(filepath, preferred=False, skip=None):
'Returns a map of API IDs to Discovery document filenames.\n\n Args:\n filepath (str): the directory to work in. Discovery documents are\n downloaded to this directory.\n preferred (bool, optional): if true, only APIs marked as preferred are\n returned.\n skip (list, optional): a list of API IDs to skip.\n\n Returns:\n dict(string, string): a map of API IDs to Discovery document\n filenames.\n '
repo = _git.clone_from_github(_repo_path(), join(filepath, _repo_name()))
filenames = glob.glob(join(repo.filepath, 'discoveries/*.json'))
filenames = [x for x in filenames if (os.path.basename(x) != 'index.json')]
ddocs = {}
for filename in filenames:
id_ = None
with open(filename) as file_:
id_ = json.load(file_)['id']
if (id_ in ddocs):
continue
ddocs[id_] = filename
if skip:
_ = [ddocs.pop(id_, None) for id_ in skip]
if (not preferred):
return ddocs
index = {}
with open(join(repo.filepath, 'discoveries/index.json')) as file_:
index = json.load(file_)
for api in index['items']:
id_ = api['id']
if (id_ in _ACTUALLY_PREFERRED):
continue
if api['preferred']:
continue
ddocs.pop(id_, None)
return ddocs<|docstring|>Returns a map of API IDs to Discovery document filenames.
Args:
filepath (str): the directory to work in. Discovery documents are
downloaded to this directory.
preferred (bool, optional): if true, only APIs marked as preferred are
returned.
skip (list, optional): a list of API IDs to skip.
Returns:
dict(string, string): a map of API IDs to Discovery document
filenames.<|endoftext|> |
f2990483d9734ec2c46808d821962e6215651557b9e7b9bee56b71284c9c228a | def update(filepath, github_account):
'Updates the discovery-artifact-manager repository.\n\n Args:\n filepath (str): the directory to work in.\n github_account (GitHubAccount): the GitHub account to commit and push\n with.\n '
repo = _git.clone_from_github(_repo_path(), join(filepath, _repo_name()), github_account=github_account)
if (_update_disco(repo, github_account) > 0):
repo.push() | Updates the discovery-artifact-manager repository.
Args:
filepath (str): the directory to work in.
github_account (GitHubAccount): the GitHub account to commit and push
with. | server/tasks/discovery_artifact_manager.py | update | codyoss/discovery-artifact-manager | 38 | python | def update(filepath, github_account):
'Updates the discovery-artifact-manager repository.\n\n Args:\n filepath (str): the directory to work in.\n github_account (GitHubAccount): the GitHub account to commit and push\n with.\n '
repo = _git.clone_from_github(_repo_path(), join(filepath, _repo_name()), github_account=github_account)
if (_update_disco(repo, github_account) > 0):
repo.push() | def update(filepath, github_account):
'Updates the discovery-artifact-manager repository.\n\n Args:\n filepath (str): the directory to work in.\n github_account (GitHubAccount): the GitHub account to commit and push\n with.\n '
repo = _git.clone_from_github(_repo_path(), join(filepath, _repo_name()), github_account=github_account)
if (_update_disco(repo, github_account) > 0):
repo.push()<|docstring|>Updates the discovery-artifact-manager repository.
Args:
filepath (str): the directory to work in.
github_account (GitHubAccount): the GitHub account to commit and push
with.<|endoftext|> |
1601e2ddc6ebb0b23257aa13ae1206aac2516de7b5f34379f46c9476e48f72b1 | def _update_disco(repo: _git.Repository, github_account: accounts.GitHubAccount) -> int:
'Invokes updatedisco on the repo. Returns the number of commits.'
with TemporaryDirectory() as gopath:
os.makedirs(join(gopath, 'src'))
check_output(['ln', '-s', join(repo.filepath, 'src'), join(gopath, 'src/discovery-artifact-manager')])
env = os.environ.copy()
env['GOPATH'] = gopath
check_output(['go', 'run', 'src/main/updatedisco/main.go'], cwd=repo.filepath, env=env)
repo.add(['discoveries'])
if (not repo.diff_name_status()):
return 0
repo.commit('Autogenerated Discovery document update', github_account.name, github_account.email)
return 1 | Invokes updatedisco on the repo. Returns the number of commits. | server/tasks/discovery_artifact_manager.py | _update_disco | codyoss/discovery-artifact-manager | 38 | python | def _update_disco(repo: _git.Repository, github_account: accounts.GitHubAccount) -> int:
with TemporaryDirectory() as gopath:
os.makedirs(join(gopath, 'src'))
check_output(['ln', '-s', join(repo.filepath, 'src'), join(gopath, 'src/discovery-artifact-manager')])
env = os.environ.copy()
env['GOPATH'] = gopath
check_output(['go', 'run', 'src/main/updatedisco/main.go'], cwd=repo.filepath, env=env)
repo.add(['discoveries'])
if (not repo.diff_name_status()):
return 0
repo.commit('Autogenerated Discovery document update', github_account.name, github_account.email)
return 1 | def _update_disco(repo: _git.Repository, github_account: accounts.GitHubAccount) -> int:
with TemporaryDirectory() as gopath:
os.makedirs(join(gopath, 'src'))
check_output(['ln', '-s', join(repo.filepath, 'src'), join(gopath, 'src/discovery-artifact-manager')])
env = os.environ.copy()
env['GOPATH'] = gopath
check_output(['go', 'run', 'src/main/updatedisco/main.go'], cwd=repo.filepath, env=env)
repo.add(['discoveries'])
if (not repo.diff_name_status()):
return 0
repo.commit('Autogenerated Discovery document update', github_account.name, github_account.email)
return 1<|docstring|>Invokes updatedisco on the repo. Returns the number of commits.<|endoftext|> |
365a1355c2c3f5c4ff6da1cc6d9b69a094eb11b86308c062b9f628357dcb7f77 | def create_pull_request(filepath, github_account):
'Creates a pull request on the discovery-artifact-manager repository.\n\n Args:\n filepath (str): the directory to work in.\n github_account (GitHubAccount): the GitHub account to commit with.\n '
repo = _git.clone_from_github(_repo_path(), join(filepath, _repo_name()), github_account=github_account)
branch = ('update-discovery-artifacts-' + datetime.datetime.now().strftime('%Y%m%d-%H%M%S'))
repo.checkout_new(branch)
if (_update_disco(repo, github_account) > 0):
repo.push(branch=branch)
gh = Github(github_account.personal_access_token)
gh_repo = gh.get_repo(_repo_path())
pr = gh_repo.create_pull(title='chore: autogenerated discovery document update', body='', base='master', head=branch) | Creates a pull request on the discovery-artifact-manager repository.
Args:
filepath (str): the directory to work in.
github_account (GitHubAccount): the GitHub account to commit with. | server/tasks/discovery_artifact_manager.py | create_pull_request | codyoss/discovery-artifact-manager | 38 | python | def create_pull_request(filepath, github_account):
'Creates a pull request on the discovery-artifact-manager repository.\n\n Args:\n filepath (str): the directory to work in.\n github_account (GitHubAccount): the GitHub account to commit with.\n '
repo = _git.clone_from_github(_repo_path(), join(filepath, _repo_name()), github_account=github_account)
branch = ('update-discovery-artifacts-' + datetime.datetime.now().strftime('%Y%m%d-%H%M%S'))
repo.checkout_new(branch)
if (_update_disco(repo, github_account) > 0):
repo.push(branch=branch)
gh = Github(github_account.personal_access_token)
gh_repo = gh.get_repo(_repo_path())
pr = gh_repo.create_pull(title='chore: autogenerated discovery document update', body=, base='master', head=branch) | def create_pull_request(filepath, github_account):
'Creates a pull request on the discovery-artifact-manager repository.\n\n Args:\n filepath (str): the directory to work in.\n github_account (GitHubAccount): the GitHub account to commit with.\n '
repo = _git.clone_from_github(_repo_path(), join(filepath, _repo_name()), github_account=github_account)
branch = ('update-discovery-artifacts-' + datetime.datetime.now().strftime('%Y%m%d-%H%M%S'))
repo.checkout_new(branch)
if (_update_disco(repo, github_account) > 0):
repo.push(branch=branch)
gh = Github(github_account.personal_access_token)
gh_repo = gh.get_repo(_repo_path())
pr = gh_repo.create_pull(title='chore: autogenerated discovery document update', body=, base='master', head=branch)<|docstring|>Creates a pull request on the discovery-artifact-manager repository.
Args:
filepath (str): the directory to work in.
github_account (GitHubAccount): the GitHub account to commit with.<|endoftext|> |
034d28cb1760ef9317b7d3b33f1c05e9f897737d94f25993b9903944205117d2 | def do_form_control_schemas(self):
'\n function returns object type control schemas used in building form controls\n :return:\n '
copo_schemas = dict()
for (k, v) in d_utils.object_type_control_map().items():
copo_schemas[k] = d_utils.get_copo_schema(v)
self.context['copo_schemas'] = copo_schemas
return self.context | function returns object type control schemas used in building form controls
:return: | dal/broker_da.py | do_form_control_schemas | collaborative-open-plant-omics/COPO | 16 | python | def do_form_control_schemas(self):
'\n function returns object type control schemas used in building form controls\n :return:\n '
copo_schemas = dict()
for (k, v) in d_utils.object_type_control_map().items():
copo_schemas[k] = d_utils.get_copo_schema(v)
self.context['copo_schemas'] = copo_schemas
return self.context | def do_form_control_schemas(self):
'\n function returns object type control schemas used in building form controls\n :return:\n '
copo_schemas = dict()
for (k, v) in d_utils.object_type_control_map().items():
copo_schemas[k] = d_utils.get_copo_schema(v)
self.context['copo_schemas'] = copo_schemas
return self.context<|docstring|>function returns object type control schemas used in building form controls
:return:<|endoftext|> |
7edf5cff024283b66c78473ea1f4ff4ad6adff762d80df09de8b7c7e3c462ff5 | def validate_and_delete(self):
'\n function handles the delete of a record for those components\n that have provided a way of first validating (dependencies checks etc.) this action\n :return:\n '
validate_delete_method = getattr(self.da_object, 'validate_and_delete', None)
if (validate_delete_method is None):
return self.context
if (not callable(validate_delete_method)):
return self.context
return self.da_object.validate_and_delete(target_id=self.param_dict.get('target_id', str())) | function handles the delete of a record for those components
that have provided a way of first validating (dependencies checks etc.) this action
:return: | dal/broker_da.py | validate_and_delete | collaborative-open-plant-omics/COPO | 16 | python | def validate_and_delete(self):
'\n function handles the delete of a record for those components\n that have provided a way of first validating (dependencies checks etc.) this action\n :return:\n '
validate_delete_method = getattr(self.da_object, 'validate_and_delete', None)
if (validate_delete_method is None):
return self.context
if (not callable(validate_delete_method)):
return self.context
return self.da_object.validate_and_delete(target_id=self.param_dict.get('target_id', str())) | def validate_and_delete(self):
'\n function handles the delete of a record for those components\n that have provided a way of first validating (dependencies checks etc.) this action\n :return:\n '
validate_delete_method = getattr(self.da_object, 'validate_and_delete', None)
if (validate_delete_method is None):
return self.context
if (not callable(validate_delete_method)):
return self.context
return self.da_object.validate_and_delete(target_id=self.param_dict.get('target_id', str()))<|docstring|>function handles the delete of a record for those components
that have provided a way of first validating (dependencies checks etc.) this action
:return:<|endoftext|> |
adc26fb930f0334f342834bbf34ee00a23994b218f07fc29c0b92018e636e7df | def do_lift_submission_embargo(self):
'\n function brokers the release of a submission\n :return:\n '
return Submission().lift_embargo(submission_id=self.param_dict.get('target_id', str())) | function brokers the release of a submission
:return: | dal/broker_da.py | do_lift_submission_embargo | collaborative-open-plant-omics/COPO | 16 | python | def do_lift_submission_embargo(self):
'\n function brokers the release of a submission\n :return:\n '
return Submission().lift_embargo(submission_id=self.param_dict.get('target_id', str())) | def do_lift_submission_embargo(self):
'\n function brokers the release of a submission\n :return:\n '
return Submission().lift_embargo(submission_id=self.param_dict.get('target_id', str()))<|docstring|>function brokers the release of a submission
:return:<|endoftext|> |
dd909ab627300f444c1977d755160ea50ccec488f8a38c05bcaad50ff2389ea7 | def do_clone_description_bundle(self):
'\n function creates a new description by cloning an existing (specified) bundle\n :return:\n '
target_id = self.param_dict.get('target_id', str())
bundle_name = self.param_dict.get('bundle_name', str())
result = dict(status='success', message='')
if (Description().get_description_handle().find({'name': {'$regex': (('^' + bundle_name) + '$'), '$options': 'i'}}).count() >= 1):
result['status'] = 'error'
result['message'] = 'Bundle name must be unique'
self.context['result'] = result
return self.context
description = Description().GET(target_id)
try:
bundle = Description().create_description(profile_id=self.profile_id, component=self.component, name=bundle_name, stages=description.get('stages', list()), attributes=description.get('attributes', dict()), meta=description.get('meta', dict()))
result['data'] = dict(id=str(bundle['_id']), name=bundle['name'])
except Exception as e:
message = ((("Couldn't create bundle: " + bundle_name) + ' ') + str(e))
result['status'] = 'error'
result['message'] = message
self.context['result'] = result
return self.context | function creates a new description by cloning an existing (specified) bundle
:return: | dal/broker_da.py | do_clone_description_bundle | collaborative-open-plant-omics/COPO | 16 | python | def do_clone_description_bundle(self):
'\n function creates a new description by cloning an existing (specified) bundle\n :return:\n '
target_id = self.param_dict.get('target_id', str())
bundle_name = self.param_dict.get('bundle_name', str())
result = dict(status='success', message=)
if (Description().get_description_handle().find({'name': {'$regex': (('^' + bundle_name) + '$'), '$options': 'i'}}).count() >= 1):
result['status'] = 'error'
result['message'] = 'Bundle name must be unique'
self.context['result'] = result
return self.context
description = Description().GET(target_id)
try:
bundle = Description().create_description(profile_id=self.profile_id, component=self.component, name=bundle_name, stages=description.get('stages', list()), attributes=description.get('attributes', dict()), meta=description.get('meta', dict()))
result['data'] = dict(id=str(bundle['_id']), name=bundle['name'])
except Exception as e:
message = ((("Couldn't create bundle: " + bundle_name) + ' ') + str(e))
result['status'] = 'error'
result['message'] = message
self.context['result'] = result
return self.context | def do_clone_description_bundle(self):
'\n function creates a new description by cloning an existing (specified) bundle\n :return:\n '
target_id = self.param_dict.get('target_id', str())
bundle_name = self.param_dict.get('bundle_name', str())
result = dict(status='success', message=)
if (Description().get_description_handle().find({'name': {'$regex': (('^' + bundle_name) + '$'), '$options': 'i'}}).count() >= 1):
result['status'] = 'error'
result['message'] = 'Bundle name must be unique'
self.context['result'] = result
return self.context
description = Description().GET(target_id)
try:
bundle = Description().create_description(profile_id=self.profile_id, component=self.component, name=bundle_name, stages=description.get('stages', list()), attributes=description.get('attributes', dict()), meta=description.get('meta', dict()))
result['data'] = dict(id=str(bundle['_id']), name=bundle['name'])
except Exception as e:
message = ((("Couldn't create bundle: " + bundle_name) + ' ') + str(e))
result['status'] = 'error'
result['message'] = message
self.context['result'] = result
return self.context<|docstring|>function creates a new description by cloning an existing (specified) bundle
:return:<|endoftext|> |
1d72bd42c9b7da966390aaa32df1feecaef27f7e250c503aad3b3d2f20273e47 | def create_rename_description_bundle(self):
'\n function creates a new description bundle or renames an existing one\n :return:\n '
target_id = self.param_dict.get('target_id', str())
bundle_name = self.param_dict.get('bundle_name', str())
result = dict(status='success', message='')
if (Description().get_description_handle().find({'name': {'$regex': (('^' + bundle_name) + '$'), '$options': 'i'}}).count() >= 1):
result['status'] = 'error'
result['message'] = 'Bundle name must be unique'
elif target_id:
Description().edit_description(target_id, {'name': bundle_name})
try:
Description().edit_description(target_id, {'name': bundle_name})
except Exception as e:
message = ((("Couldn't update bundle: " + bundle_name) + ' ') + str(e))
result['status'] = 'error'
result['message'] = message
else:
try:
bundle = Description().create_description(profile_id=self.profile_id, component=self.component, name=bundle_name)
result['data'] = dict(id=str(bundle['_id']), name=bundle['name'])
except Exception as e:
message = ((("Couldn't create bundle: " + bundle_name) + ' ') + str(e))
result['status'] = 'error'
result['message'] = message
self.context['result'] = result
return self.context | function creates a new description bundle or renames an existing one
:return: | dal/broker_da.py | create_rename_description_bundle | collaborative-open-plant-omics/COPO | 16 | python | def create_rename_description_bundle(self):
'\n function creates a new description bundle or renames an existing one\n :return:\n '
target_id = self.param_dict.get('target_id', str())
bundle_name = self.param_dict.get('bundle_name', str())
result = dict(status='success', message=)
if (Description().get_description_handle().find({'name': {'$regex': (('^' + bundle_name) + '$'), '$options': 'i'}}).count() >= 1):
result['status'] = 'error'
result['message'] = 'Bundle name must be unique'
elif target_id:
Description().edit_description(target_id, {'name': bundle_name})
try:
Description().edit_description(target_id, {'name': bundle_name})
except Exception as e:
message = ((("Couldn't update bundle: " + bundle_name) + ' ') + str(e))
result['status'] = 'error'
result['message'] = message
else:
try:
bundle = Description().create_description(profile_id=self.profile_id, component=self.component, name=bundle_name)
result['data'] = dict(id=str(bundle['_id']), name=bundle['name'])
except Exception as e:
message = ((("Couldn't create bundle: " + bundle_name) + ' ') + str(e))
result['status'] = 'error'
result['message'] = message
self.context['result'] = result
return self.context | def create_rename_description_bundle(self):
'\n function creates a new description bundle or renames an existing one\n :return:\n '
target_id = self.param_dict.get('target_id', str())
bundle_name = self.param_dict.get('bundle_name', str())
result = dict(status='success', message=)
if (Description().get_description_handle().find({'name': {'$regex': (('^' + bundle_name) + '$'), '$options': 'i'}}).count() >= 1):
result['status'] = 'error'
result['message'] = 'Bundle name must be unique'
elif target_id:
Description().edit_description(target_id, {'name': bundle_name})
try:
Description().edit_description(target_id, {'name': bundle_name})
except Exception as e:
message = ((("Couldn't update bundle: " + bundle_name) + ' ') + str(e))
result['status'] = 'error'
result['message'] = message
else:
try:
bundle = Description().create_description(profile_id=self.profile_id, component=self.component, name=bundle_name)
result['data'] = dict(id=str(bundle['_id']), name=bundle['name'])
except Exception as e:
message = ((("Couldn't create bundle: " + bundle_name) + ' ') + str(e))
result['status'] = 'error'
result['message'] = message
self.context['result'] = result
return self.context<|docstring|>function creates a new description bundle or renames an existing one
:return:<|endoftext|> |
c5de6bc02c87c495de6af9444d0470870bb8b7e4ef0b9ba6bdfef898098e97db | def do_managed_repositories(self):
'\n function returns repositories for which the request user is a manager\n :return:\n '
self.context['table_data'] = htags.generate_managed_repositories(component=self.component, user_id=self.user_id)
self.context['component'] = self.component
return self.context | function returns repositories for which the request user is a manager
:return: | dal/broker_da.py | do_managed_repositories | collaborative-open-plant-omics/COPO | 16 | python | def do_managed_repositories(self):
'\n function returns repositories for which the request user is a manager\n :return:\n '
self.context['table_data'] = htags.generate_managed_repositories(component=self.component, user_id=self.user_id)
self.context['component'] = self.component
return self.context | def do_managed_repositories(self):
'\n function returns repositories for which the request user is a manager\n :return:\n '
self.context['table_data'] = htags.generate_managed_repositories(component=self.component, user_id=self.user_id)
self.context['component'] = self.component
return self.context<|docstring|>function returns repositories for which the request user is a manager
:return:<|endoftext|> |
6489443f021d89689d19ce09a3d5348142a8ac2ae1bc1510f0abb5760496ec18 | def do_get_submission_meta_repo(self):
'\n function brokers metadata and repository details for a submission\n :return:\n '
target_id = self.param_dict.get('target_id', str())
self.context['result'] = htags.get_submission_meta_repo(submission_id=target_id, user_id=self.user_id)
return self.context | function brokers metadata and repository details for a submission
:return: | dal/broker_da.py | do_get_submission_meta_repo | collaborative-open-plant-omics/COPO | 16 | python | def do_get_submission_meta_repo(self):
'\n function brokers metadata and repository details for a submission\n :return:\n '
target_id = self.param_dict.get('target_id', str())
self.context['result'] = htags.get_submission_meta_repo(submission_id=target_id, user_id=self.user_id)
return self.context | def do_get_submission_meta_repo(self):
'\n function brokers metadata and repository details for a submission\n :return:\n '
target_id = self.param_dict.get('target_id', str())
self.context['result'] = htags.get_submission_meta_repo(submission_id=target_id, user_id=self.user_id)
return self.context<|docstring|>function brokers metadata and repository details for a submission
:return:<|endoftext|> |
89212fe861423d91291094d752692a40f081fa21159ce082554b33a730de4ce7 | def do_view_submission_remote(self):
'\n function brokers the generation of resource url/identifier to a submission in its remote location\n :return:\n '
self.context = htags.get_submission_remote_url(submission_id=self.param_dict.get('target_id', str()))
return self.context | function brokers the generation of resource url/identifier to a submission in its remote location
:return: | dal/broker_da.py | do_view_submission_remote | collaborative-open-plant-omics/COPO | 16 | python | def do_view_submission_remote(self):
'\n function brokers the generation of resource url/identifier to a submission in its remote location\n :return:\n '
self.context = htags.get_submission_remote_url(submission_id=self.param_dict.get('target_id', str()))
return self.context | def do_view_submission_remote(self):
'\n function brokers the generation of resource url/identifier to a submission in its remote location\n :return:\n '
self.context = htags.get_submission_remote_url(submission_id=self.param_dict.get('target_id', str()))
return self.context<|docstring|>function brokers the generation of resource url/identifier to a submission in its remote location
:return:<|endoftext|> |
57b41a1280a1468d87d3d79157b8955fda8fef56eaa99278a734e0f8289eb48f | def do_get_destination_repo(self):
'\n function brokers submission destination repository details\n :return:\n '
target_id = self.param_dict.get('target_id', str())
self.context['result'] = htags.get_destination_repo(submission_id=target_id)
return self.context | function brokers submission destination repository details
:return: | dal/broker_da.py | do_get_destination_repo | collaborative-open-plant-omics/COPO | 16 | python | def do_get_destination_repo(self):
'\n function brokers submission destination repository details\n :return:\n '
target_id = self.param_dict.get('target_id', str())
self.context['result'] = htags.get_destination_repo(submission_id=target_id)
return self.context | def do_get_destination_repo(self):
'\n function brokers submission destination repository details\n :return:\n '
target_id = self.param_dict.get('target_id', str())
self.context['result'] = htags.get_destination_repo(submission_id=target_id)
return self.context<|docstring|>function brokers submission destination repository details
:return:<|endoftext|> |
392b9092e42c57b9c8896fa79c01a2ad323433012e439c1e5064616031e83359 | def do_get_repo_stats(self):
'\n function brokers statistics for the target repository\n :return:\n '
self.context['result'] = htags.get_repo_stats(repository_id=self.param_dict.get('target_id', str()))
return self.context | function brokers statistics for the target repository
:return: | dal/broker_da.py | do_get_repo_stats | collaborative-open-plant-omics/COPO | 16 | python | def do_get_repo_stats(self):
'\n function brokers statistics for the target repository\n :return:\n '
self.context['result'] = htags.get_repo_stats(repository_id=self.param_dict.get('target_id', str()))
return self.context | def do_get_repo_stats(self):
'\n function brokers statistics for the target repository\n :return:\n '
self.context['result'] = htags.get_repo_stats(repository_id=self.param_dict.get('target_id', str()))
return self.context<|docstring|>function brokers statistics for the target repository
:return:<|endoftext|> |
4d0b7ee0e0d27c9f3f528cf81104fb026a3f654b20fdd9dd2c873731d61e868a | def main():
'Build the snippets from their source repos'
parser = argparse.ArgumentParser(description=main.__doc__)
parser.add_argument('-u', '--update', help="repo to update, or 'all' to update all source repos")
parser.add_argument('-p', type=int, help='Number of threads (default %(default)s)', default=5)
args = parser.parse_args()
os.makedirs(build_dir, exist_ok=True)
shutil.rmtree(snippet_dir)
os.makedirs(snippet_dir)
with open(join(here, 'sources.json'), 'r') as ifile:
sources = json.load(ifile)
lockfile_path = join(here, 'sources.lock')
if os.path.isfile(lockfile_path):
with open(lockfile_path, 'r') as ifile:
lock_data = json.load(ifile)
else:
lock_data = {}
def execute(config):
should_update = ((args.update is not None) and ((args.update == 'all') or config['url'].endswith(args.update)))
rev = (None if should_update else lock_data.get(config['url']))
return build_source(config, rev)
with concurrent.futures.ThreadPoolExecutor(max_workers=args.p) as executor:
results = executor.map(execute, sources)
all_snippets = []
packages_by_language = defaultdict(set)
lock_data = {}
for (config, (git_rev, snippets)) in zip(sources, results):
config = cast(Config, config)
all_snippets.extend(snippets)
lock_data[config['url']] = git_rev
for snip in snippets:
packages_by_language[snip['language']].add(config['url'])
package_data = {'contributes': {'snippets': all_snippets}}
with open(join(here, 'package.json'), 'w') as package_file:
json.dump(package_data, package_file, indent=2)
with open(lockfile_path, 'w') as ofile:
json.dump(lock_data, ofile, indent=2)
update_readme(packages_by_language) | Build the snippets from their source repos | build.py | main | stevearc/vim-vsnip-snippets | 5 | python | def main():
parser = argparse.ArgumentParser(description=main.__doc__)
parser.add_argument('-u', '--update', help="repo to update, or 'all' to update all source repos")
parser.add_argument('-p', type=int, help='Number of threads (default %(default)s)', default=5)
args = parser.parse_args()
os.makedirs(build_dir, exist_ok=True)
shutil.rmtree(snippet_dir)
os.makedirs(snippet_dir)
with open(join(here, 'sources.json'), 'r') as ifile:
sources = json.load(ifile)
lockfile_path = join(here, 'sources.lock')
if os.path.isfile(lockfile_path):
with open(lockfile_path, 'r') as ifile:
lock_data = json.load(ifile)
else:
lock_data = {}
def execute(config):
should_update = ((args.update is not None) and ((args.update == 'all') or config['url'].endswith(args.update)))
rev = (None if should_update else lock_data.get(config['url']))
return build_source(config, rev)
with concurrent.futures.ThreadPoolExecutor(max_workers=args.p) as executor:
results = executor.map(execute, sources)
all_snippets = []
packages_by_language = defaultdict(set)
lock_data = {}
for (config, (git_rev, snippets)) in zip(sources, results):
config = cast(Config, config)
all_snippets.extend(snippets)
lock_data[config['url']] = git_rev
for snip in snippets:
packages_by_language[snip['language']].add(config['url'])
package_data = {'contributes': {'snippets': all_snippets}}
with open(join(here, 'package.json'), 'w') as package_file:
json.dump(package_data, package_file, indent=2)
with open(lockfile_path, 'w') as ofile:
json.dump(lock_data, ofile, indent=2)
update_readme(packages_by_language) | def main():
parser = argparse.ArgumentParser(description=main.__doc__)
parser.add_argument('-u', '--update', help="repo to update, or 'all' to update all source repos")
parser.add_argument('-p', type=int, help='Number of threads (default %(default)s)', default=5)
args = parser.parse_args()
os.makedirs(build_dir, exist_ok=True)
shutil.rmtree(snippet_dir)
os.makedirs(snippet_dir)
with open(join(here, 'sources.json'), 'r') as ifile:
sources = json.load(ifile)
lockfile_path = join(here, 'sources.lock')
if os.path.isfile(lockfile_path):
with open(lockfile_path, 'r') as ifile:
lock_data = json.load(ifile)
else:
lock_data = {}
def execute(config):
should_update = ((args.update is not None) and ((args.update == 'all') or config['url'].endswith(args.update)))
rev = (None if should_update else lock_data.get(config['url']))
return build_source(config, rev)
with concurrent.futures.ThreadPoolExecutor(max_workers=args.p) as executor:
results = executor.map(execute, sources)
all_snippets = []
packages_by_language = defaultdict(set)
lock_data = {}
for (config, (git_rev, snippets)) in zip(sources, results):
config = cast(Config, config)
all_snippets.extend(snippets)
lock_data[config['url']] = git_rev
for snip in snippets:
packages_by_language[snip['language']].add(config['url'])
package_data = {'contributes': {'snippets': all_snippets}}
with open(join(here, 'package.json'), 'w') as package_file:
json.dump(package_data, package_file, indent=2)
with open(lockfile_path, 'w') as ofile:
json.dump(lock_data, ofile, indent=2)
update_readme(packages_by_language)<|docstring|>Build the snippets from their source repos<|endoftext|> |
e709ea42ada7547c6cc2279f37c63b2dccab53dcdc8f6391e49c7145ca4f49e1 | def __init__(self, factory):
'Constructor.\n\n Args:\n factory (factory.FactoryCreate)\n '
self.create = factory.create_create()
self.time = factory.create_time_helper()
self.servo = factory.create_servo()
self.sonar = factory.create_sonar()
self.virtual_create = factory.create_virtual_create()
self.map = lab10_map.Map('lab10.map')
self.pf = particle_filter.ParticleFilter()
self.odometry = odometry.Odometry() | Constructor.
Args:
factory (factory.FactoryCreate) | particle_sampler/lab10.py | __init__ | yesitsreallyme/Robotics | 0 | python | def __init__(self, factory):
'Constructor.\n\n Args:\n factory (factory.FactoryCreate)\n '
self.create = factory.create_create()
self.time = factory.create_time_helper()
self.servo = factory.create_servo()
self.sonar = factory.create_sonar()
self.virtual_create = factory.create_virtual_create()
self.map = lab10_map.Map('lab10.map')
self.pf = particle_filter.ParticleFilter()
self.odometry = odometry.Odometry() | def __init__(self, factory):
'Constructor.\n\n Args:\n factory (factory.FactoryCreate)\n '
self.create = factory.create_create()
self.time = factory.create_time_helper()
self.servo = factory.create_servo()
self.sonar = factory.create_sonar()
self.virtual_create = factory.create_virtual_create()
self.map = lab10_map.Map('lab10.map')
self.pf = particle_filter.ParticleFilter()
self.odometry = odometry.Odometry()<|docstring|>Constructor.
Args:
factory (factory.FactoryCreate)<|endoftext|> |
6bf5bde467e18c7d3a33a43eac0cac3bb1954f3e62f43a92de52fa2c2d06474c | def rolling_mean(arr, window_size=7, axis=0, weights=None, mean_type='arithmetic'):
'Calculate a rolling mean over a numpy/cupy ndarray.'
reimport_numerical_libs('util.rolling_mean.rolling_mean')
if (mean_type == 'arithmetic'):
return _rolling_arithmetic_mean(arr, window_size, axis, weights)
elif (mean_type == 'geometric'):
return _rolling_geometric_mean(arr, window_size, axis, weights)
elif (mean_type == 'harmonic'):
return _rolling_harmonic_mean(arr, window_size, axis, weights)
else:
raise RuntimeError | Calculate a rolling mean over a numpy/cupy ndarray. | bucky/util/rolling_mean.py | rolling_mean | ragram88/bucky | 1 | python | def rolling_mean(arr, window_size=7, axis=0, weights=None, mean_type='arithmetic'):
reimport_numerical_libs('util.rolling_mean.rolling_mean')
if (mean_type == 'arithmetic'):
return _rolling_arithmetic_mean(arr, window_size, axis, weights)
elif (mean_type == 'geometric'):
return _rolling_geometric_mean(arr, window_size, axis, weights)
elif (mean_type == 'harmonic'):
return _rolling_harmonic_mean(arr, window_size, axis, weights)
else:
raise RuntimeError | def rolling_mean(arr, window_size=7, axis=0, weights=None, mean_type='arithmetic'):
reimport_numerical_libs('util.rolling_mean.rolling_mean')
if (mean_type == 'arithmetic'):
return _rolling_arithmetic_mean(arr, window_size, axis, weights)
elif (mean_type == 'geometric'):
return _rolling_geometric_mean(arr, window_size, axis, weights)
elif (mean_type == 'harmonic'):
return _rolling_harmonic_mean(arr, window_size, axis, weights)
else:
raise RuntimeError<|docstring|>Calculate a rolling mean over a numpy/cupy ndarray.<|endoftext|> |
9a70b347560214a26ed7087e80a50665aa76c2a74ec45f2c4fc49b75c60e15a1 | def rolling_window(a, window_size):
'Use stride_tricks to add an extra dim on the end of an ndarray for each elements window'
reimport_numerical_libs('util.rolling_mean.rolling_window')
pad = xp.zeros(len(a.shape), dtype=xp.int32)
pad[(- 1)] = (window_size - 1)
pad = list(zip(list(xp.to_cpu(pad)), list(xp.to_cpu(xp.zeros(len(a.shape), dtype=xp.int32)))))
a = xp.pad(a, pad, mode='reflect')
shape = (a.shape[:(- 1)] + (((a.shape[(- 1)] - window_size) + 1), window_size))
strides = (a.strides + (a.strides[(- 1)],))
return xp.lib.stride_tricks.as_strided(a, shape=shape, strides=strides) | Use stride_tricks to add an extra dim on the end of an ndarray for each elements window | bucky/util/rolling_mean.py | rolling_window | ragram88/bucky | 1 | python | def rolling_window(a, window_size):
reimport_numerical_libs('util.rolling_mean.rolling_window')
pad = xp.zeros(len(a.shape), dtype=xp.int32)
pad[(- 1)] = (window_size - 1)
pad = list(zip(list(xp.to_cpu(pad)), list(xp.to_cpu(xp.zeros(len(a.shape), dtype=xp.int32)))))
a = xp.pad(a, pad, mode='reflect')
shape = (a.shape[:(- 1)] + (((a.shape[(- 1)] - window_size) + 1), window_size))
strides = (a.strides + (a.strides[(- 1)],))
return xp.lib.stride_tricks.as_strided(a, shape=shape, strides=strides) | def rolling_window(a, window_size):
reimport_numerical_libs('util.rolling_mean.rolling_window')
pad = xp.zeros(len(a.shape), dtype=xp.int32)
pad[(- 1)] = (window_size - 1)
pad = list(zip(list(xp.to_cpu(pad)), list(xp.to_cpu(xp.zeros(len(a.shape), dtype=xp.int32)))))
a = xp.pad(a, pad, mode='reflect')
shape = (a.shape[:(- 1)] + (((a.shape[(- 1)] - window_size) + 1), window_size))
strides = (a.strides + (a.strides[(- 1)],))
return xp.lib.stride_tricks.as_strided(a, shape=shape, strides=strides)<|docstring|>Use stride_tricks to add an extra dim on the end of an ndarray for each elements window<|endoftext|> |
d35c78de351def423d4841922e8fcf8d347c7966f7da2a080b406fe5d7442925 | def _rolling_arithmetic_mean(arr, window_size=7, axis=0, weights=None):
'Compute a rolling arithmetic mean'
arr = xp.swapaxes(arr, axis, (- 1))
if (weights is None):
rolling_arr = xp.mean(rolling_window(arr, window_size), axis=(- 1))
else:
window = ((weights / xp.sum(weights)) * window_size)
window = xp.broadcast_to(window, (arr.shape + (window_size,)))
rolling_arr = xp.mean((window * rolling_window(arr, window_size)), axis=(- 1))
rolling_arr = xp.swapaxes(rolling_arr, axis, (- 1))
return rolling_arr | Compute a rolling arithmetic mean | bucky/util/rolling_mean.py | _rolling_arithmetic_mean | ragram88/bucky | 1 | python | def _rolling_arithmetic_mean(arr, window_size=7, axis=0, weights=None):
arr = xp.swapaxes(arr, axis, (- 1))
if (weights is None):
rolling_arr = xp.mean(rolling_window(arr, window_size), axis=(- 1))
else:
window = ((weights / xp.sum(weights)) * window_size)
window = xp.broadcast_to(window, (arr.shape + (window_size,)))
rolling_arr = xp.mean((window * rolling_window(arr, window_size)), axis=(- 1))
rolling_arr = xp.swapaxes(rolling_arr, axis, (- 1))
return rolling_arr | def _rolling_arithmetic_mean(arr, window_size=7, axis=0, weights=None):
arr = xp.swapaxes(arr, axis, (- 1))
if (weights is None):
rolling_arr = xp.mean(rolling_window(arr, window_size), axis=(- 1))
else:
window = ((weights / xp.sum(weights)) * window_size)
window = xp.broadcast_to(window, (arr.shape + (window_size,)))
rolling_arr = xp.mean((window * rolling_window(arr, window_size)), axis=(- 1))
rolling_arr = xp.swapaxes(rolling_arr, axis, (- 1))
return rolling_arr<|docstring|>Compute a rolling arithmetic mean<|endoftext|> |
9f8413fd8f32dfd1d45bb2c556948374c53f19a70433e3c1e902904792fa4907 | def _rolling_geometric_mean(arr, window_size, axis=0, weights=None):
'Compute a rolling geometric mean'
arr = xp.swapaxes(arr, axis, (- 1))
if (weights is None):
rolling_arr = xp.exp(xp.nanmean(rolling_window(xp.log(arr), window_size), axis=(- 1)))
else:
window = ((weights / xp.sum(weights)) * window_size)
window = xp.broadcast_to(window, (arr.shape + (window_size,)))
rolling_arr = xp.exp(xp.nanmean((window * rolling_window(xp.log(arr), window_size)), axis=(- 1)))
rolling_arr = xp.swapaxes(rolling_arr, axis, (- 1))
return rolling_arr | Compute a rolling geometric mean | bucky/util/rolling_mean.py | _rolling_geometric_mean | ragram88/bucky | 1 | python | def _rolling_geometric_mean(arr, window_size, axis=0, weights=None):
arr = xp.swapaxes(arr, axis, (- 1))
if (weights is None):
rolling_arr = xp.exp(xp.nanmean(rolling_window(xp.log(arr), window_size), axis=(- 1)))
else:
window = ((weights / xp.sum(weights)) * window_size)
window = xp.broadcast_to(window, (arr.shape + (window_size,)))
rolling_arr = xp.exp(xp.nanmean((window * rolling_window(xp.log(arr), window_size)), axis=(- 1)))
rolling_arr = xp.swapaxes(rolling_arr, axis, (- 1))
return rolling_arr | def _rolling_geometric_mean(arr, window_size, axis=0, weights=None):
arr = xp.swapaxes(arr, axis, (- 1))
if (weights is None):
rolling_arr = xp.exp(xp.nanmean(rolling_window(xp.log(arr), window_size), axis=(- 1)))
else:
window = ((weights / xp.sum(weights)) * window_size)
window = xp.broadcast_to(window, (arr.shape + (window_size,)))
rolling_arr = xp.exp(xp.nanmean((window * rolling_window(xp.log(arr), window_size)), axis=(- 1)))
rolling_arr = xp.swapaxes(rolling_arr, axis, (- 1))
return rolling_arr<|docstring|>Compute a rolling geometric mean<|endoftext|> |
e8bebe7b31372ab84e1abf5f1becf683ea2614d007eebc1761ede5013ee98c1d | def _rolling_harmonic_mean(arr, window_size, axis=0, weights=None):
'Compute a rolling harmonic mean'
arr = xp.swapaxes(arr, axis, (- 1)).astype(float)
if (weights is None):
rolling_arr = xp.reciprocal(xp.nanmean(rolling_window(xp.reciprocal(arr), window_size), axis=(- 1)))
else:
window = ((weights / xp.sum(weights)) * window_size)
window = xp.broadcast_to(window, (arr.shape + (window_size,)))
rolling_arr = xp.reciprocal(xp.nanmean((window * rolling_window(xp.reciprocal(arr), window_size)), axis=(- 1)))
rolling_arr = xp.swapaxes(rolling_arr, axis, (- 1))
return rolling_arr | Compute a rolling harmonic mean | bucky/util/rolling_mean.py | _rolling_harmonic_mean | ragram88/bucky | 1 | python | def _rolling_harmonic_mean(arr, window_size, axis=0, weights=None):
arr = xp.swapaxes(arr, axis, (- 1)).astype(float)
if (weights is None):
rolling_arr = xp.reciprocal(xp.nanmean(rolling_window(xp.reciprocal(arr), window_size), axis=(- 1)))
else:
window = ((weights / xp.sum(weights)) * window_size)
window = xp.broadcast_to(window, (arr.shape + (window_size,)))
rolling_arr = xp.reciprocal(xp.nanmean((window * rolling_window(xp.reciprocal(arr), window_size)), axis=(- 1)))
rolling_arr = xp.swapaxes(rolling_arr, axis, (- 1))
return rolling_arr | def _rolling_harmonic_mean(arr, window_size, axis=0, weights=None):
arr = xp.swapaxes(arr, axis, (- 1)).astype(float)
if (weights is None):
rolling_arr = xp.reciprocal(xp.nanmean(rolling_window(xp.reciprocal(arr), window_size), axis=(- 1)))
else:
window = ((weights / xp.sum(weights)) * window_size)
window = xp.broadcast_to(window, (arr.shape + (window_size,)))
rolling_arr = xp.reciprocal(xp.nanmean((window * rolling_window(xp.reciprocal(arr), window_size)), axis=(- 1)))
rolling_arr = xp.swapaxes(rolling_arr, axis, (- 1))
return rolling_arr<|docstring|>Compute a rolling harmonic mean<|endoftext|> |
f57b84f22abd493002601ac2f3e639eb06e9b40d05ac5773883fb5548adcfd93 | @project_to_simple
@not_compatible_for('arrow_property', 'dynamic_property')
def bfs(graph, src=0):
'Breadth first search from the src on projected simple graph.\n\n Args:\n graph (:class:`Graph`): A simple graph.\n src (optional): Source vertex of breadth first search. The type should be consistent\n with the id type of the `graph`, that is, it\'s `int` or `str` depending\n on the `oid_type` is `int64_t` or `string` of the `graph`. Defaults to 0.\n\n Returns:\n :class:`graphscope.framework.context.VertexDataContextDAGNode`:\n A context with each vertex with a distance from the source, will be evaluated in eager mode.\n\n Examples:\n\n .. code:: python\n\n import graphscope as gs\n g = gs.g()\n # Load some data, then project to a simple graph (if needed).\n pg = g.project(vertices={"vlabel": []}, edges={"elabel": []})\n r = gs.bfs(pg, 6) # use 6 as source vertex\n s.close()\n\n '
return AppAssets(algo='bfs', context='vertex_data')(graph, src) | Breadth first search from the src on projected simple graph.
Args:
graph (:class:`Graph`): A simple graph.
src (optional): Source vertex of breadth first search. The type should be consistent
with the id type of the `graph`, that is, it's `int` or `str` depending
on the `oid_type` is `int64_t` or `string` of the `graph`. Defaults to 0.
Returns:
:class:`graphscope.framework.context.VertexDataContextDAGNode`:
A context with each vertex with a distance from the source, will be evaluated in eager mode.
Examples:
.. code:: python
import graphscope as gs
g = gs.g()
# Load some data, then project to a simple graph (if needed).
pg = g.project(vertices={"vlabel": []}, edges={"elabel": []})
r = gs.bfs(pg, 6) # use 6 as source vertex
s.close() | python/graphscope/analytical/app/bfs.py | bfs | haoxins/GraphScope | 2 | python | @project_to_simple
@not_compatible_for('arrow_property', 'dynamic_property')
def bfs(graph, src=0):
'Breadth first search from the src on projected simple graph.\n\n Args:\n graph (:class:`Graph`): A simple graph.\n src (optional): Source vertex of breadth first search. The type should be consistent\n with the id type of the `graph`, that is, it\'s `int` or `str` depending\n on the `oid_type` is `int64_t` or `string` of the `graph`. Defaults to 0.\n\n Returns:\n :class:`graphscope.framework.context.VertexDataContextDAGNode`:\n A context with each vertex with a distance from the source, will be evaluated in eager mode.\n\n Examples:\n\n .. code:: python\n\n import graphscope as gs\n g = gs.g()\n # Load some data, then project to a simple graph (if needed).\n pg = g.project(vertices={"vlabel": []}, edges={"elabel": []})\n r = gs.bfs(pg, 6) # use 6 as source vertex\n s.close()\n\n '
return AppAssets(algo='bfs', context='vertex_data')(graph, src) | @project_to_simple
@not_compatible_for('arrow_property', 'dynamic_property')
def bfs(graph, src=0):
'Breadth first search from the src on projected simple graph.\n\n Args:\n graph (:class:`Graph`): A simple graph.\n src (optional): Source vertex of breadth first search. The type should be consistent\n with the id type of the `graph`, that is, it\'s `int` or `str` depending\n on the `oid_type` is `int64_t` or `string` of the `graph`. Defaults to 0.\n\n Returns:\n :class:`graphscope.framework.context.VertexDataContextDAGNode`:\n A context with each vertex with a distance from the source, will be evaluated in eager mode.\n\n Examples:\n\n .. code:: python\n\n import graphscope as gs\n g = gs.g()\n # Load some data, then project to a simple graph (if needed).\n pg = g.project(vertices={"vlabel": []}, edges={"elabel": []})\n r = gs.bfs(pg, 6) # use 6 as source vertex\n s.close()\n\n '
return AppAssets(algo='bfs', context='vertex_data')(graph, src)<|docstring|>Breadth first search from the src on projected simple graph.
Args:
graph (:class:`Graph`): A simple graph.
src (optional): Source vertex of breadth first search. The type should be consistent
with the id type of the `graph`, that is, it's `int` or `str` depending
on the `oid_type` is `int64_t` or `string` of the `graph`. Defaults to 0.
Returns:
:class:`graphscope.framework.context.VertexDataContextDAGNode`:
A context with each vertex with a distance from the source, will be evaluated in eager mode.
Examples:
.. code:: python
import graphscope as gs
g = gs.g()
# Load some data, then project to a simple graph (if needed).
pg = g.project(vertices={"vlabel": []}, edges={"elabel": []})
r = gs.bfs(pg, 6) # use 6 as source vertex
s.close()<|endoftext|> |
059d016d8385aeff73902fe5542942a7570a5905a7e3963e09f0738f62bdd8d0 | def run(self):
'Run the Force module\n '
if (self.parent is None):
raise InputError('ERROR: The Force object must be in a Simulation object to run')
if (self.parent.parent is None):
raise InputError('ERROR: The Force object must be in an Output object to run')
output = self.parent.parent
self.comp_time_angle(output)
self.comp_force(output) | Run the Force module | pyleecan/Methods/Simulation/Force/run.py | run | helene-t/pyleecan | 2 | python | def run(self):
'\n '
if (self.parent is None):
raise InputError('ERROR: The Force object must be in a Simulation object to run')
if (self.parent.parent is None):
raise InputError('ERROR: The Force object must be in an Output object to run')
output = self.parent.parent
self.comp_time_angle(output)
self.comp_force(output) | def run(self):
'\n '
if (self.parent is None):
raise InputError('ERROR: The Force object must be in a Simulation object to run')
if (self.parent.parent is None):
raise InputError('ERROR: The Force object must be in an Output object to run')
output = self.parent.parent
self.comp_time_angle(output)
self.comp_force(output)<|docstring|>Run the Force module<|endoftext|> |
42125eb29bb58265e2a8076d416979fd6b8dfe4127cc15dac550d6b1f8721fda | def __init__(self, ipaddr, sshport=22, name=''):
'\n is host running the hypervisor\n '
NodeBase.__init__(self, ipaddr=ipaddr, sshport=sshport, role='host', name=name)
self.startMonitor() | is host running the hypervisor | lib/JumpScale/tools/perftesttools/NodeHost.py | __init__ | Jumpscale/jumpscale_core8 | 8 | python | def __init__(self, ipaddr, sshport=22, name=):
'\n \n '
NodeBase.__init__(self, ipaddr=ipaddr, sshport=sshport, role='host', name=name)
self.startMonitor() | def __init__(self, ipaddr, sshport=22, name=):
'\n \n '
NodeBase.__init__(self, ipaddr=ipaddr, sshport=sshport, role='host', name=name)
self.startMonitor()<|docstring|>is host running the hypervisor<|endoftext|> |
a5935fccb4045b1d610f3590121895bc4de05974481272cfc5d0102efa9bdbfc | def __init__(self, new_password=None, is_check_password=None):
'ResetServerPasswordOption - a model defined in huaweicloud sdk'
self._new_password = None
self._is_check_password = None
self.discriminator = None
self.new_password = new_password
if (is_check_password is not None):
self.is_check_password = is_check_password | ResetServerPasswordOption - a model defined in huaweicloud sdk | huaweicloud-sdk-ecs/huaweicloudsdkecs/v2/model/reset_server_password_option.py | __init__ | huaweicloud/huaweicloud-sdk-python-v3 | 64 | python | def __init__(self, new_password=None, is_check_password=None):
self._new_password = None
self._is_check_password = None
self.discriminator = None
self.new_password = new_password
if (is_check_password is not None):
self.is_check_password = is_check_password | def __init__(self, new_password=None, is_check_password=None):
self._new_password = None
self._is_check_password = None
self.discriminator = None
self.new_password = new_password
if (is_check_password is not None):
self.is_check_password = is_check_password<|docstring|>ResetServerPasswordOption - a model defined in huaweicloud sdk<|endoftext|> |
3d48ab7de8f5784ab5a33e3c8a32038ba7c70af447e0a69c2fc3c09e11d6e089 | @property
def new_password(self):
'Gets the new_password of this ResetServerPasswordOption.\n\n 弹性云服务器新密码。 该接口默认不做密码安全性校验；如需校验，请指定字段“is_check_password”为true。 新密码的校验规则： - 密码长度范围为8到26位。 - 允许输入的字符包括：!@%-_=+[]:./? - 禁止输入的字符包括：汉字及【】：；“”‘’、，。《》？￥…（）—— ·！~`#&^,{}*();"\'<>|\\ $ - 复杂度上必须包含大写字母（A-Z）、小写字母（a-z）、数字（0-9）、以及允许的特殊字符中的3种以上搭配 - 不能包含用户名 "Administrator" 和“root”及逆序字符 - 不能包含用户名 "Administrator" 中连续3个字符\n\n :return: The new_password of this ResetServerPasswordOption.\n :rtype: str\n '
return self._new_password | Gets the new_password of this ResetServerPasswordOption.
弹性云服务器新密码。 该接口默认不做密码安全性校验；如需校验，请指定字段“is_check_password”为true。 新密码的校验规则： - 密码长度范围为8到26位。 - 允许输入的字符包括：!@%-_=+[]:./? - 禁止输入的字符包括：汉字及【】：；“”‘’、，。《》？￥…（）—— ·！~`#&^,{}*();"'<>|\ $ - 复杂度上必须包含大写字母（A-Z）、小写字母（a-z）、数字（0-9）、以及允许的特殊字符中的3种以上搭配 - 不能包含用户名 "Administrator" 和“root”及逆序字符 - 不能包含用户名 "Administrator" 中连续3个字符
:return: The new_password of this ResetServerPasswordOption.
:rtype: str | huaweicloud-sdk-ecs/huaweicloudsdkecs/v2/model/reset_server_password_option.py | new_password | huaweicloud/huaweicloud-sdk-python-v3 | 64 | python | @property
def new_password(self):
'Gets the new_password of this ResetServerPasswordOption.\n\n 弹性云服务器新密码。 该接口默认不做密码安全性校验；如需校验，请指定字段“is_check_password”为true。 新密码的校验规则： - 密码长度范围为8到26位。 - 允许输入的字符包括：!@%-_=+[]:./? - 禁止输入的字符包括：汉字及【】：；“”‘’、，。《》？￥…（）—— ·！~`#&^,{}*();"\'<>|\\ $ - 复杂度上必须包含大写字母（A-Z）、小写字母（a-z）、数字（0-9）、以及允许的特殊字符中的3种以上搭配 - 不能包含用户名 "Administrator" 和“root”及逆序字符 - 不能包含用户名 "Administrator" 中连续3个字符\n\n :return: The new_password of this ResetServerPasswordOption.\n :rtype: str\n '
return self._new_password | @property
def new_password(self):
'Gets the new_password of this ResetServerPasswordOption.\n\n 弹性云服务器新密码。 该接口默认不做密码安全性校验；如需校验，请指定字段“is_check_password”为true。 新密码的校验规则： - 密码长度范围为8到26位。 - 允许输入的字符包括：!@%-_=+[]:./? - 禁止输入的字符包括：汉字及【】：；“”‘’、，。《》？￥…（）—— ·！~`#&^,{}*();"\'<>|\\ $ - 复杂度上必须包含大写字母（A-Z）、小写字母（a-z）、数字（0-9）、以及允许的特殊字符中的3种以上搭配 - 不能包含用户名 "Administrator" 和“root”及逆序字符 - 不能包含用户名 "Administrator" 中连续3个字符\n\n :return: The new_password of this ResetServerPasswordOption.\n :rtype: str\n '
return self._new_password<|docstring|>Gets the new_password of this ResetServerPasswordOption.
弹性云服务器新密码。 该接口默认不做密码安全性校验；如需校验，请指定字段“is_check_password”为true。 新密码的校验规则： - 密码长度范围为8到26位。 - 允许输入的字符包括：!@%-_=+[]:./? - 禁止输入的字符包括：汉字及【】：；“”‘’、，。《》？￥…（）—— ·！~`#&^,{}*();"'<>|\ $ - 复杂度上必须包含大写字母（A-Z）、小写字母（a-z）、数字（0-9）、以及允许的特殊字符中的3种以上搭配 - 不能包含用户名 "Administrator" 和“root”及逆序字符 - 不能包含用户名 "Administrator" 中连续3个字符
:return: The new_password of this ResetServerPasswordOption.
:rtype: str<|endoftext|> |
59dc2a4761268897e3528fa23f07e84122f0f5528537e56afb9045e0870cd90a | @new_password.setter
def new_password(self, new_password):
'Sets the new_password of this ResetServerPasswordOption.\n\n 弹性云服务器新密码。 该接口默认不做密码安全性校验；如需校验，请指定字段“is_check_password”为true。 新密码的校验规则： - 密码长度范围为8到26位。 - 允许输入的字符包括：!@%-_=+[]:./? - 禁止输入的字符包括：汉字及【】：；“”‘’、，。《》？￥…（）—— ·！~`#&^,{}*();"\'<>|\\ $ - 复杂度上必须包含大写字母（A-Z）、小写字母（a-z）、数字（0-9）、以及允许的特殊字符中的3种以上搭配 - 不能包含用户名 "Administrator" 和“root”及逆序字符 - 不能包含用户名 "Administrator" 中连续3个字符\n\n :param new_password: The new_password of this ResetServerPasswordOption.\n :type: str\n '
self._new_password = new_password | Sets the new_password of this ResetServerPasswordOption.
弹性云服务器新密码。 该接口默认不做密码安全性校验；如需校验，请指定字段“is_check_password”为true。 新密码的校验规则： - 密码长度范围为8到26位。 - 允许输入的字符包括：!@%-_=+[]:./? - 禁止输入的字符包括：汉字及【】：；“”‘’、，。《》？￥…（）—— ·！~`#&^,{}*();"'<>|\ $ - 复杂度上必须包含大写字母（A-Z）、小写字母（a-z）、数字（0-9）、以及允许的特殊字符中的3种以上搭配 - 不能包含用户名 "Administrator" 和“root”及逆序字符 - 不能包含用户名 "Administrator" 中连续3个字符
:param new_password: The new_password of this ResetServerPasswordOption.
:type: str | huaweicloud-sdk-ecs/huaweicloudsdkecs/v2/model/reset_server_password_option.py | new_password | huaweicloud/huaweicloud-sdk-python-v3 | 64 | python | @new_password.setter
def new_password(self, new_password):
'Sets the new_password of this ResetServerPasswordOption.\n\n 弹性云服务器新密码。 该接口默认不做密码安全性校验；如需校验，请指定字段“is_check_password”为true。 新密码的校验规则： - 密码长度范围为8到26位。 - 允许输入的字符包括：!@%-_=+[]:./? - 禁止输入的字符包括：汉字及【】：；“”‘’、，。《》？￥…（）—— ·！~`#&^,{}*();"\'<>|\\ $ - 复杂度上必须包含大写字母（A-Z）、小写字母（a-z）、数字（0-9）、以及允许的特殊字符中的3种以上搭配 - 不能包含用户名 "Administrator" 和“root”及逆序字符 - 不能包含用户名 "Administrator" 中连续3个字符\n\n :param new_password: The new_password of this ResetServerPasswordOption.\n :type: str\n '
self._new_password = new_password | @new_password.setter
def new_password(self, new_password):
'Sets the new_password of this ResetServerPasswordOption.\n\n 弹性云服务器新密码。 该接口默认不做密码安全性校验；如需校验，请指定字段“is_check_password”为true。 新密码的校验规则： - 密码长度范围为8到26位。 - 允许输入的字符包括：!@%-_=+[]:./? - 禁止输入的字符包括：汉字及【】：；“”‘’、，。《》？￥…（）—— ·！~`#&^,{}*();"\'<>|\\ $ - 复杂度上必须包含大写字母（A-Z）、小写字母（a-z）、数字（0-9）、以及允许的特殊字符中的3种以上搭配 - 不能包含用户名 "Administrator" 和“root”及逆序字符 - 不能包含用户名 "Administrator" 中连续3个字符\n\n :param new_password: The new_password of this ResetServerPasswordOption.\n :type: str\n '
self._new_password = new_password<|docstring|>Sets the new_password of this ResetServerPasswordOption.
弹性云服务器新密码。 该接口默认不做密码安全性校验；如需校验，请指定字段“is_check_password”为true。 新密码的校验规则： - 密码长度范围为8到26位。 - 允许输入的字符包括：!@%-_=+[]:./? - 禁止输入的字符包括：汉字及【】：；“”‘’、，。《》？￥…（）—— ·！~`#&^,{}*();"'<>|\ $ - 复杂度上必须包含大写字母（A-Z）、小写字母（a-z）、数字（0-9）、以及允许的特殊字符中的3种以上搭配 - 不能包含用户名 "Administrator" 和“root”及逆序字符 - 不能包含用户名 "Administrator" 中连续3个字符
:param new_password: The new_password of this ResetServerPasswordOption.
:type: str<|endoftext|> |
be9eec3f23db8c5d3a8298312b920bebc0c86f5b7166b150b352c00e6d94dfac | @property
def is_check_password(self):
'Gets the is_check_password of this ResetServerPasswordOption.\n\n 是否检查密码的复杂度。\n\n :return: The is_check_password of this ResetServerPasswordOption.\n :rtype: bool\n '
return self._is_check_password | Gets the is_check_password of this ResetServerPasswordOption.
是否检查密码的复杂度。
:return: The is_check_password of this ResetServerPasswordOption.
:rtype: bool | huaweicloud-sdk-ecs/huaweicloudsdkecs/v2/model/reset_server_password_option.py | is_check_password | huaweicloud/huaweicloud-sdk-python-v3 | 64 | python | @property
def is_check_password(self):
'Gets the is_check_password of this ResetServerPasswordOption.\n\n 是否检查密码的复杂度。\n\n :return: The is_check_password of this ResetServerPasswordOption.\n :rtype: bool\n '
return self._is_check_password | @property
def is_check_password(self):
'Gets the is_check_password of this ResetServerPasswordOption.\n\n 是否检查密码的复杂度。\n\n :return: The is_check_password of this ResetServerPasswordOption.\n :rtype: bool\n '
return self._is_check_password<|docstring|>Gets the is_check_password of this ResetServerPasswordOption.
是否检查密码的复杂度。
:return: The is_check_password of this ResetServerPasswordOption.
:rtype: bool<|endoftext|> |
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