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 |
|---|---|---|---|---|---|---|---|---|---|
e311d33e2ee6dab690e5d9ac8e9714d2c754e61cf5dc0f65cff1cf0d381a8f65 | def main():
'Send the RPC command to the server and print the result.'
parser = argparse.ArgumentParser('Send electrumx an RPC command')
parser.add_argument('-p', '--port', metavar='port_num', type=int, help='RPC port number')
parser.add_argument('command', nargs=1, default=[], help='command to send')
... | Send the RPC command to the server and print the result. | electrumx_rpc.py | main | Skirmant/electrumx-trump | 0 | python | def main():
parser = argparse.ArgumentParser('Send electrumx an RPC command')
parser.add_argument('-p', '--port', metavar='port_num', type=int, help='RPC port number')
parser.add_argument('command', nargs=1, default=[], help='command to send')
parser.add_argument('param', nargs='*', default=[], hel... | def main():
parser = argparse.ArgumentParser('Send electrumx an RPC command')
parser.add_argument('-p', '--port', metavar='port_num', type=int, help='RPC port number')
parser.add_argument('command', nargs=1, default=[], help='command to send')
parser.add_argument('param', nargs='*', default=[], hel... |
7acb56f3d1695444b6d99a7ba84782b730773d46e07d620b62c76bf248cf472a | def __init__(self, n_topics, vocab_size, doc_count, batch_size, batch_steps, num_collection_passes, num_documents_passes, device, dtype, phi_smooth_sparse_tau=0.0, theta_smooth_sparse_tau=0.0, vocab_stat=None, mode='v1', dump_phi_freq=None, dump_phi_path=None, log_perplexity=False, log_matrix_norms=False):
'\n ... | :param n_topics:
:param vocab_size:
:param doc_count:
:param context_size:
:param batch_size:
:param batch_steps:
:param num_collection_passes:
:param num_documents_passes:
:param device:
:param dtype:
:param phi_smooth_sparse_tau:
:param theta_smooth_sparse_tau:
:param vocab_stat: TF for phi sparse/smooth reg.
:param ... | model_fn.py | __init__ | ilyakhov/pytorch-wntm | 3 | python | def __init__(self, n_topics, vocab_size, doc_count, batch_size, batch_steps, num_collection_passes, num_documents_passes, device, dtype, phi_smooth_sparse_tau=0.0, theta_smooth_sparse_tau=0.0, vocab_stat=None, mode='v1', dump_phi_freq=None, dump_phi_path=None, log_perplexity=False, log_matrix_norms=False):
'\n ... | def __init__(self, n_topics, vocab_size, doc_count, batch_size, batch_steps, num_collection_passes, num_documents_passes, device, dtype, phi_smooth_sparse_tau=0.0, theta_smooth_sparse_tau=0.0, vocab_stat=None, mode='v1', dump_phi_freq=None, dump_phi_path=None, log_perplexity=False, log_matrix_norms=False):
'\n ... |
b5779dd61f7e11cfd0b5268f7a968eddaed9ed3de7d26b69dcad4cc43b563c5b | def perplexity(self):
'\n Full:\n exp(-1/n_m * sum(n_dw * ln(mm(self.phi, self.theta))))\n :return:\n '
phi = self.phi.cpu()
theta = self.theta.cpu()
n_m = torch.sum(self.n_dw)
one = torch.tensor(1, dtype=self.dtype, device='cpu:0')
mm = torch.mm(phi, theta)
mm = ... | Full:
exp(-1/n_m * sum(n_dw * ln(mm(self.phi, self.theta))))
:return: | model_fn.py | perplexity | ilyakhov/pytorch-wntm | 3 | python | def perplexity(self):
'\n Full:\n exp(-1/n_m * sum(n_dw * ln(mm(self.phi, self.theta))))\n :return:\n '
phi = self.phi.cpu()
theta = self.theta.cpu()
n_m = torch.sum(self.n_dw)
one = torch.tensor(1, dtype=self.dtype, device='cpu:0')
mm = torch.mm(phi, theta)
mm = ... | def perplexity(self):
'\n Full:\n exp(-1/n_m * sum(n_dw * ln(mm(self.phi, self.theta))))\n :return:\n '
phi = self.phi.cpu()
theta = self.theta.cpu()
n_m = torch.sum(self.n_dw)
one = torch.tensor(1, dtype=self.dtype, device='cpu:0')
mm = torch.mm(phi, theta)
mm = ... |
01da978c17506836ad21b2c7c41f4cc9a1e052a0263afe9d907841fc1af609bf | def e_step(self, n_dw, doc_inxs, context_batch, gather_ndw=False):
"\n :param n_dw: freq of term 'w' occurrence in doc 'd'\n [[1, 1, 2, 1, 2] - for each word in a doc, ...] —\n [batch_size, context_size]\n :param doc_inxs: Tensor of doc inxs with shape [batch_si... | :param n_dw: freq of term 'w' occurrence in doc 'd'
[[1, 1, 2, 1, 2] - for each word in a doc, ...] —
[batch_size, context_size]
:param doc_inxs: Tensor of doc inxs with shape [batch_size]
:param context_batch: Tensor of word inxs with shape
[batch_size, context_size]
:return: | model_fn.py | e_step | ilyakhov/pytorch-wntm | 3 | python | def e_step(self, n_dw, doc_inxs, context_batch, gather_ndw=False):
"\n :param n_dw: freq of term 'w' occurrence in doc 'd'\n [[1, 1, 2, 1, 2] - for each word in a doc, ...] —\n [batch_size, context_size]\n :param doc_inxs: Tensor of doc inxs with shape [batch_si... | def e_step(self, n_dw, doc_inxs, context_batch, gather_ndw=False):
"\n :param n_dw: freq of term 'w' occurrence in doc 'd'\n [[1, 1, 2, 1, 2] - for each word in a doc, ...] —\n [batch_size, context_size]\n :param doc_inxs: Tensor of doc inxs with shape [batch_si... |
f5c59aa64191544cc1bd3079d8736988394739525aacbd76217f49bc91b628cf | def _group_by_with_index_mapping(self, true_labels, samples):
'\n TODO: implement stuff from "Notes of reproducibility"\n :param true_labels: indices for initial embedding matrix\n [100, 100, 200, 200, 0] =>\n [0, 100, 200], [1, 1, 2, 2, 0], [1, 2, 2]\n :param samples: 2D-... | TODO: implement stuff from "Notes of reproducibility"
:param true_labels: indices for initial embedding matrix
[100, 100, 200, 200, 0] =>
[0, 100, 200], [1, 1, 2, 2, 0], [1, 2, 2]
:param samples: 2D-tensor with vectors to agg(sum)
[[0.1, .0], [-0.1, 0.2], [...], [...], [...]]
:return: agg(sum): [[...], [.0,... | model_fn.py | _group_by_with_index_mapping | ilyakhov/pytorch-wntm | 3 | python | def _group_by_with_index_mapping(self, true_labels, samples):
'\n TODO: implement stuff from "Notes of reproducibility"\n :param true_labels: indices for initial embedding matrix\n [100, 100, 200, 200, 0] =>\n [0, 100, 200], [1, 1, 2, 2, 0], [1, 2, 2]\n :param samples: 2D-... | def _group_by_with_index_mapping(self, true_labels, samples):
'\n TODO: implement stuff from "Notes of reproducibility"\n :param true_labels: indices for initial embedding matrix\n [100, 100, 200, 200, 0] =>\n [0, 100, 200], [1, 1, 2, 2, 0], [1, 2, 2]\n :param samples: 2D-... |
e02a2c01f27168e013e450c577e34bf6c15f8694e152682bdd7fe34eb2b1fbca | def m_step(self):
'\n Rational EM. The same is "smoothed/sparsed" with reg_tau=0.0\n :return:\n '
with torch.cuda.device(self.device):
new_phi = (self.n_wt / self.n_t.view((- 1), self.n_topics))
phi_norm = ((torch.sum(((self.phi - new_phi) ** 2)) ** 1) / 2)
self.phi_... | Rational EM. The same is "smoothed/sparsed" with reg_tau=0.0
:return: | model_fn.py | m_step | ilyakhov/pytorch-wntm | 3 | python | def m_step(self):
'\n Rational EM. The same is "smoothed/sparsed" with reg_tau=0.0\n :return:\n '
with torch.cuda.device(self.device):
new_phi = (self.n_wt / self.n_t.view((- 1), self.n_topics))
phi_norm = ((torch.sum(((self.phi - new_phi) ** 2)) ** 1) / 2)
self.phi_... | def m_step(self):
'\n Rational EM. The same is "smoothed/sparsed" with reg_tau=0.0\n :return:\n '
with torch.cuda.device(self.device):
new_phi = (self.n_wt / self.n_t.view((- 1), self.n_topics))
phi_norm = ((torch.sum(((self.phi - new_phi) ** 2)) ** 1) / 2)
self.phi_... |
94c1725291d84daf6a5669344a0e8ea65e0acb603351766874b332a90a58860a | def run_v2(self, batch_generator):
'\n M-step after each E-step. Not tested enough!\n :param batch_generator:\n :return:\n '
for _ in tqdm(range(self.num_collection_passes), total=self.num_collection_passes, desc='Passing through collection: '):
old_phi = self.phi.cpu()
... | M-step after each E-step. Not tested enough!
:param batch_generator:
:return: | model_fn.py | run_v2 | ilyakhov/pytorch-wntm | 3 | python | def run_v2(self, batch_generator):
'\n M-step after each E-step. Not tested enough!\n :param batch_generator:\n :return:\n '
for _ in tqdm(range(self.num_collection_passes), total=self.num_collection_passes, desc='Passing through collection: '):
old_phi = self.phi.cpu()
... | def run_v2(self, batch_generator):
'\n M-step after each E-step. Not tested enough!\n :param batch_generator:\n :return:\n '
for _ in tqdm(range(self.num_collection_passes), total=self.num_collection_passes, desc='Passing through collection: '):
old_phi = self.phi.cpu()
... |
872522fff92ce50f350fb951e543f6f2a13faa14171af9037d1d807fe23dc516 | def __rectify_v2(self, t):
"\n Rectification on each step is expensive operation if\n data are being copied on cpu. For train_mode='v2' no data copy\n to 'cpu'(RAM) has being used.\n :param t:\n :return:\n "
t = torch.where((t < self.zero), self.zero, t)
t = torch.w... | Rectification on each step is expensive operation if
data are being copied on cpu. For train_mode='v2' no data copy
to 'cpu'(RAM) has being used.
:param t:
:return: | model_fn.py | __rectify_v2 | ilyakhov/pytorch-wntm | 3 | python | def __rectify_v2(self, t):
"\n Rectification on each step is expensive operation if\n data are being copied on cpu. For train_mode='v2' no data copy\n to 'cpu'(RAM) has being used.\n :param t:\n :return:\n "
t = torch.where((t < self.zero), self.zero, t)
t = torch.w... | def __rectify_v2(self, t):
"\n Rectification on each step is expensive operation if\n data are being copied on cpu. For train_mode='v2' no data copy\n to 'cpu'(RAM) has being used.\n :param t:\n :return:\n "
t = torch.where((t < self.zero), self.zero, t)
t = torch.w... |
f30fd7a798c581dfc36dd85174c3569d6a36a94825b6fff467acf153834dba2e | def __init__(self, n_topics, vocab_size, doc_count, batch_size, batch_steps, num_collection_passes, num_documents_passes, device, dtype, phi_smooth_sparse_tau=0.0, theta_smooth_sparse_tau=0.0, vocab_stat=None, mode='v1', dump_phi_freq=None, dump_phi_path=None, log_perplexity=False, log_matrix_norms=False):
'\n ... | :param n_topics:
:param vocab_size:
:param doc_count:
:param context_size:
:param batch_size:
:param batch_steps:
:param num_collection_passes:
:param num_documents_passes:
:param device:
:param dtype:
:param phi_smooth_sparse_tau:
:param theta_smooth_sparse_tau:
:param vocab_stat: TF for phi sparse/smooth reg.
:param ... | model_fn.py | __init__ | ilyakhov/pytorch-wntm | 3 | python | def __init__(self, n_topics, vocab_size, doc_count, batch_size, batch_steps, num_collection_passes, num_documents_passes, device, dtype, phi_smooth_sparse_tau=0.0, theta_smooth_sparse_tau=0.0, vocab_stat=None, mode='v1', dump_phi_freq=None, dump_phi_path=None, log_perplexity=False, log_matrix_norms=False):
'\n ... | def __init__(self, n_topics, vocab_size, doc_count, batch_size, batch_steps, num_collection_passes, num_documents_passes, device, dtype, phi_smooth_sparse_tau=0.0, theta_smooth_sparse_tau=0.0, vocab_stat=None, mode='v1', dump_phi_freq=None, dump_phi_path=None, log_perplexity=False, log_matrix_norms=False):
'\n ... |
6913eb75a0bbcd8aae093006ef21a9b0eccddc1433cf314193adea566edec713 | def e_step(self, n_dw, doc_inxs, context_batch, gather_ndw=False):
"\n :param n_dw: freq of term 'w' occurrence in doc 'd'\n [[1, 1, 2, 1, 2] - for each word in a doc, ...] —\n [batch_size, context_size]\n :param doc_inxs: Tensor of doc inxs with shape [batch_si... | :param n_dw: freq of term 'w' occurrence in doc 'd'
[[1, 1, 2, 1, 2] - for each word in a doc, ...] —
[batch_size, context_size]
:param doc_inxs: Tensor of doc inxs with shape [batch_size]
:param context_batch: Tensor of word inxs with shape
[batch_size, context_size]
:param first: 'first' ite... | model_fn.py | e_step | ilyakhov/pytorch-wntm | 3 | python | def e_step(self, n_dw, doc_inxs, context_batch, gather_ndw=False):
"\n :param n_dw: freq of term 'w' occurrence in doc 'd'\n [[1, 1, 2, 1, 2] - for each word in a doc, ...] —\n [batch_size, context_size]\n :param doc_inxs: Tensor of doc inxs with shape [batch_si... | def e_step(self, n_dw, doc_inxs, context_batch, gather_ndw=False):
"\n :param n_dw: freq of term 'w' occurrence in doc 'd'\n [[1, 1, 2, 1, 2] - for each word in a doc, ...] —\n [batch_size, context_size]\n :param doc_inxs: Tensor of doc inxs with shape [batch_si... |
88c8670d83f2cde855d0d0ab6e6b666f8b2cf79bcb84852a81fa8ceb54688bec | def __init__(self, filters, p_m, get_mask, apply_mask, path_json='src/python_code/settings.json'):
'\n CNN decoder layers (tensorflow 2 book)\n :param filters: list filters\n :param path_json: path settings\n '
settings = json.load(open(path_json))['Model']
hyperparameters = sett... | CNN decoder layers (tensorflow 2 book)
:param filters: list filters
:param path_json: path settings | src/python_code/Models/EAE_models/DecoderCNN.py | __init__ | ipmach/Thesis2021 | 0 | python | def __init__(self, filters, p_m, get_mask, apply_mask, path_json='src/python_code/settings.json'):
'\n CNN decoder layers (tensorflow 2 book)\n :param filters: list filters\n :param path_json: path settings\n '
settings = json.load(open(path_json))['Model']
hyperparameters = sett... | def __init__(self, filters, p_m, get_mask, apply_mask, path_json='src/python_code/settings.json'):
'\n CNN decoder layers (tensorflow 2 book)\n :param filters: list filters\n :param path_json: path settings\n '
settings = json.load(open(path_json))['Model']
hyperparameters = sett... |
6809d902bbacf9eac6c00c94ba9882f5b7bc3526f525fb4d5d264651951d58da | def initialize_masks(self):
'\n Initialize masks for the model\n :return:\n '
self._masks_ = []
for i in self._layers_:
self.get_mask(i.get_weights()[0].shape, p=self.p_m) | Initialize masks for the model
:return: | src/python_code/Models/EAE_models/DecoderCNN.py | initialize_masks | ipmach/Thesis2021 | 0 | python | def initialize_masks(self):
'\n Initialize masks for the model\n :return:\n '
self._masks_ = []
for i in self._layers_:
self.get_mask(i.get_weights()[0].shape, p=self.p_m) | def initialize_masks(self):
'\n Initialize masks for the model\n :return:\n '
self._masks_ = []
for i in self._layers_:
self.get_mask(i.get_weights()[0].shape, p=self.p_m)<|docstring|>Initialize masks for the model
:return:<|endoftext|> |
6939b7bdd5414e29efdb790dced6d59dadc90a6f90011df8566df9288a0bbd20 | def apply_masks(self):
'\n Apply masks to all layers of the model\n :return:\n '
for (l, m) in zip(self._layers_, self._masks_):
new_weights = self.apply_mask(m, l.get_weights())
l.set_weights(new_weights) | Apply masks to all layers of the model
:return: | src/python_code/Models/EAE_models/DecoderCNN.py | apply_masks | ipmach/Thesis2021 | 0 | python | def apply_masks(self):
'\n Apply masks to all layers of the model\n :return:\n '
for (l, m) in zip(self._layers_, self._masks_):
new_weights = self.apply_mask(m, l.get_weights())
l.set_weights(new_weights) | def apply_masks(self):
'\n Apply masks to all layers of the model\n :return:\n '
for (l, m) in zip(self._layers_, self._masks_):
new_weights = self.apply_mask(m, l.get_weights())
l.set_weights(new_weights)<|docstring|>Apply masks to all layers of the model
:return:<|endoftex... |
e863dfd88212590acb95344865d47f979802c29a18849919053f337bdcffad5a | def binary_exp(n):
'Binary exponentiation algorithm'
cur = base
res = 1
if (not n):
return res
while True:
if (n & 1):
res *= cur
if (n == 1):
return res
cur *= cur
n >>= 1 | Binary exponentiation algorithm | tests/perfomance/power.py | binary_exp | borzunov/cpmoptimize | 121 | python | def binary_exp(n):
cur = base
res = 1
if (not n):
return res
while True:
if (n & 1):
res *= cur
if (n == 1):
return res
cur *= cur
n >>= 1 | def binary_exp(n):
cur = base
res = 1
if (not n):
return res
while True:
if (n & 1):
res *= cur
if (n == 1):
return res
cur *= cur
n >>= 1<|docstring|>Binary exponentiation algorithm<|endoftext|> |
f3abeb105731c3a677d6412c50614bc26a7a908b5482a79c8e232b2344973cbe | def train(train_generator, test_generator, criterion, model, epochs, optimizer, Batch_size):
'Function to train a pytorch model\n Args:\n train_generator: pytorch train generator instance\n test_generator: pytorch test generator instance\n criterion: pytorch criterion\n model: pytorch... | Function to train a pytorch model
Args:
train_generator: pytorch train generator instance
test_generator: pytorch test generator instance
criterion: pytorch criterion
model: pytorch model
epochs: int, number of epochs to train
optmizer: pytorch optmizer
Batch_size: int, batch size for forwar... | 60_Grainsize_project/DL_functions/DL_train.py | train | htorodriguez/grainsize_measure | 0 | python | def train(train_generator, test_generator, criterion, model, epochs, optimizer, Batch_size):
'Function to train a pytorch model\n Args:\n train_generator: pytorch train generator instance\n test_generator: pytorch test generator instance\n criterion: pytorch criterion\n model: pytorch... | def train(train_generator, test_generator, criterion, model, epochs, optimizer, Batch_size):
'Function to train a pytorch model\n Args:\n train_generator: pytorch train generator instance\n test_generator: pytorch test generator instance\n criterion: pytorch criterion\n model: pytorch... |
04e79e025310e9b0232e0401f757198630acefbc7e907eef0399631aa9d9ce03 | @fitparam(param_name='flat_topP', param_latex='$P^{mie}_\\mathrm{top}$', default_mode='log', default_fit=False, default_bounds=[1e-20, 1])
def mieTopPressure(self):
'\n Pressure at top of absorbing region in Pa\n '
return self._mie_top_pressure | Pressure at top of absorbing region in Pa | taurex/contributions/flatmie.py | mieTopPressure | ucl-exoplanets/TauREx3_public | 10 | python | @fitparam(param_name='flat_topP', param_latex='$P^{mie}_\\mathrm{top}$', default_mode='log', default_fit=False, default_bounds=[1e-20, 1])
def mieTopPressure(self):
'\n \n '
return self._mie_top_pressure | @fitparam(param_name='flat_topP', param_latex='$P^{mie}_\\mathrm{top}$', default_mode='log', default_fit=False, default_bounds=[1e-20, 1])
def mieTopPressure(self):
'\n \n '
return self._mie_top_pressure<|docstring|>Pressure at top of absorbing region in Pa<|endoftext|> |
9b1a1534d195ab21458fb1320f77668ae73fce0e6fc0d659cdfe14925d54a6dd | @fitparam(param_name='flat_bottomP', param_latex='$P^{mie}_\\mathrm{bottom}$', default_mode='log', default_fit=False, default_bounds=[1e-20, 1])
def mieBottomPressure(self):
'\n Pressure at bottom of absorbing region in Pa\n '
return self._mie_bottom_pressure | Pressure at bottom of absorbing region in Pa | taurex/contributions/flatmie.py | mieBottomPressure | ucl-exoplanets/TauREx3_public | 10 | python | @fitparam(param_name='flat_bottomP', param_latex='$P^{mie}_\\mathrm{bottom}$', default_mode='log', default_fit=False, default_bounds=[1e-20, 1])
def mieBottomPressure(self):
'\n \n '
return self._mie_bottom_pressure | @fitparam(param_name='flat_bottomP', param_latex='$P^{mie}_\\mathrm{bottom}$', default_mode='log', default_fit=False, default_bounds=[1e-20, 1])
def mieBottomPressure(self):
'\n \n '
return self._mie_bottom_pressure<|docstring|>Pressure at bottom of absorbing region in Pa<|endoftext|> |
52f3d0e5a67b7806b3c1360f72c13b1bde0f0c1ff55b30e52eb6b874596a1295 | @fitparam(param_name='flat_mix_ratio', param_latex='$\\chi_\\mathrm{mie}$', default_mode='log', default_fit=False, default_bounds=[1e-20, 1])
def mieMixing(self):
'\n Opacity of absorbing region in m2\n '
return self._mie_mix | Opacity of absorbing region in m2 | taurex/contributions/flatmie.py | mieMixing | ucl-exoplanets/TauREx3_public | 10 | python | @fitparam(param_name='flat_mix_ratio', param_latex='$\\chi_\\mathrm{mie}$', default_mode='log', default_fit=False, default_bounds=[1e-20, 1])
def mieMixing(self):
'\n \n '
return self._mie_mix | @fitparam(param_name='flat_mix_ratio', param_latex='$\\chi_\\mathrm{mie}$', default_mode='log', default_fit=False, default_bounds=[1e-20, 1])
def mieMixing(self):
'\n \n '
return self._mie_mix<|docstring|>Opacity of absorbing region in m2<|endoftext|> |
5bcc51c3d54fd573aef99598ef45fe097a57863f03bf89f35d734fd152d06d1d | def prepare_each(self, model, wngrid):
'\n Computes and flat absorbing opacity for\n the pressure regions given\n\n Parameters\n ----------\n model: :class:`~taurex.model.model.ForwardModel`\n Forward model\n\n wngrid: :obj:`array`\n Wavenumber grid\n\... | Computes and flat absorbing opacity for
the pressure regions given
Parameters
----------
model: :class:`~taurex.model.model.ForwardModel`
Forward model
wngrid: :obj:`array`
Wavenumber grid
Yields
------
component: :obj:`tuple` of type (str, :obj:`array`)
``Flat`` and the weighted mie opacity. | taurex/contributions/flatmie.py | prepare_each | ucl-exoplanets/TauREx3_public | 10 | python | def prepare_each(self, model, wngrid):
'\n Computes and flat absorbing opacity for\n the pressure regions given\n\n Parameters\n ----------\n model: :class:`~taurex.model.model.ForwardModel`\n Forward model\n\n wngrid: :obj:`array`\n Wavenumber grid\n\... | def prepare_each(self, model, wngrid):
'\n Computes and flat absorbing opacity for\n the pressure regions given\n\n Parameters\n ----------\n model: :class:`~taurex.model.model.ForwardModel`\n Forward model\n\n wngrid: :obj:`array`\n Wavenumber grid\n\... |
b9d5e19d475f55978824f8d0e9c9f4915d3a46e6905ffe0d99ad4181c8cfc155 | def __init__(self, account_id=None, document_id=None, external_id=None, signer_id=None, external_signer_id=None, error=None, sign_success=None, expires=None, aborted=None, additional_properties={}):
'Constructor for the JwtPayload class'
self.account_id = account_id
self.document_id = document_id
self.e... | Constructor for the JwtPayload class | idfy_rest_client/models/jwt_payload.py | __init__ | dealflowteam/Idfy | 0 | python | def __init__(self, account_id=None, document_id=None, external_id=None, signer_id=None, external_signer_id=None, error=None, sign_success=None, expires=None, aborted=None, additional_properties={}):
self.account_id = account_id
self.document_id = document_id
self.external_id = external_id
self.sign... | def __init__(self, account_id=None, document_id=None, external_id=None, signer_id=None, external_signer_id=None, error=None, sign_success=None, expires=None, aborted=None, additional_properties={}):
self.account_id = account_id
self.document_id = document_id
self.external_id = external_id
self.sign... |
dc3c69c195d84776dc8c0d2ee75525a35d738145a857e9e6cd3464d9280c1c3b | @classmethod
def from_dictionary(cls, dictionary):
"Creates an instance of this model from a dictionary\n\n Args:\n dictionary (dictionary): A dictionary representation of the object as\n obtained from the deserialization of the server's response. The keys\n MUST match proper... | Creates an instance of this model from a dictionary
Args:
dictionary (dictionary): A dictionary representation of the object as
obtained from the deserialization of the server's response. The keys
MUST match property names in the API description.
Returns:
object: An instance of this structure class. | idfy_rest_client/models/jwt_payload.py | from_dictionary | dealflowteam/Idfy | 0 | python | @classmethod
def from_dictionary(cls, dictionary):
"Creates an instance of this model from a dictionary\n\n Args:\n dictionary (dictionary): A dictionary representation of the object as\n obtained from the deserialization of the server's response. The keys\n MUST match proper... | @classmethod
def from_dictionary(cls, dictionary):
"Creates an instance of this model from a dictionary\n\n Args:\n dictionary (dictionary): A dictionary representation of the object as\n obtained from the deserialization of the server's response. The keys\n MUST match proper... |
6a0ebe3d8c82ef380cbf357b98235e7d3b36597612a2759116ce150525e331b0 | @click.command()
@click.argument('dir', default='env')
@click.option('-n', '--name', metavar='NAME', help='Environment name (default is env parent directory name).')
@click.option('-p', '--python', metavar='VERSION', help='Version of Python to use for the environment.')
@click.option('-g', '--guild', metavar='VERSION_O... | Initialize a Guild environment.
`init` initializes a Guild environment in `DIR`, which is the
current directory by default.
`init` creates a virtual environment in `DIR` using `virtualenv`.
Use `--python` to specify the Python interpreter to use within the
generated virtual environment. By default, the default Pytho... | guild/commands/init.py | init | flamato/guildai | 1 | python | @click.command()
@click.argument('dir', default='env')
@click.option('-n', '--name', metavar='NAME', help='Environment name (default is env parent directory name).')
@click.option('-p', '--python', metavar='VERSION', help='Version of Python to use for the environment.')
@click.option('-g', '--guild', metavar='VERSION_O... | @click.command()
@click.argument('dir', default='env')
@click.option('-n', '--name', metavar='NAME', help='Environment name (default is env parent directory name).')
@click.option('-p', '--python', metavar='VERSION', help='Version of Python to use for the environment.')
@click.option('-g', '--guild', metavar='VERSION_O... |
11d30c192889d10b4d2a0c840d19812496fef21654e5734c2ff0eda9f97a8e3e | def test_invalid(self):
'Invalid pipeline param name and op_name.'
with self.assertRaises(ValueError):
p = PipelineParam(name='123_abc') | Invalid pipeline param name and op_name. | sdk/python/tests/dsl/pipeline_param_tests.py | test_invalid | awesome-archive/pipelines | 2 | python | def test_invalid(self):
with self.assertRaises(ValueError):
p = PipelineParam(name='123_abc') | def test_invalid(self):
with self.assertRaises(ValueError):
p = PipelineParam(name='123_abc')<|docstring|>Invalid pipeline param name and op_name.<|endoftext|> |
689fa98e57057a93774e13840a518d78d646fcb9db4484c98d86d77b76f7b516 | def test_str_repr(self):
'Test string representation.'
p = PipelineParam(name='param1', op_name='op1')
self.assertEqual('{{pipelineparam:op=op1;name=param1;value=}}', str(p))
p = PipelineParam(name='param2')
self.assertEqual('{{pipelineparam:op=;name=param2;value=}}', str(p))
p = PipelineParam(n... | Test string representation. | sdk/python/tests/dsl/pipeline_param_tests.py | test_str_repr | awesome-archive/pipelines | 2 | python | def test_str_repr(self):
p = PipelineParam(name='param1', op_name='op1')
self.assertEqual('{{pipelineparam:op=op1;name=param1;value=}}', str(p))
p = PipelineParam(name='param2')
self.assertEqual('{{pipelineparam:op=;name=param2;value=}}', str(p))
p = PipelineParam(name='param3', value='value3')... | def test_str_repr(self):
p = PipelineParam(name='param1', op_name='op1')
self.assertEqual('{{pipelineparam:op=op1;name=param1;value=}}', str(p))
p = PipelineParam(name='param2')
self.assertEqual('{{pipelineparam:op=;name=param2;value=}}', str(p))
p = PipelineParam(name='param3', value='value3')... |
3646d646fd9d26c6faeae5901453bfb29f56cc9c9a658ce4759d6629900c295e | def load_indicators():
'Load indicators from file.\n\n :return:\n '
ti = list()
try:
ti_file: Path = Path(__file__).with_name('json').joinpath('tv_indicators.json')
if (ti_file.exists() and ti_file.is_file()):
text = ti_file.read_text()
ti = json.loads(text)
... | Load indicators from file.
:return: | pytvc/cli.py | load_indicators | havocesp/pytvc | 12 | python | def load_indicators():
'Load indicators from file.\n\n :return:\n '
ti = list()
try:
ti_file: Path = Path(__file__).with_name('json').joinpath('tv_indicators.json')
if (ti_file.exists() and ti_file.is_file()):
text = ti_file.read_text()
ti = json.loads(text)
... | def load_indicators():
'Load indicators from file.\n\n :return:\n '
ti = list()
try:
ti_file: Path = Path(__file__).with_name('json').joinpath('tv_indicators.json')
if (ti_file.exists() and ti_file.is_file()):
text = ti_file.read_text()
ti = json.loads(text)
... |
153ad8de4b913930279e729ac841089f1650d23bd1f1ae44f8a40efc38f28022 | def list_indicators() -> int:
'List all supported indicators.\n\n :return: 0 if all was fine.\n '
ti = load_indicators()
indicators = [f'- {v:<30}' for v in ti]
indicators.sort()
for i in indicators:
print(i)
return 0 | List all supported indicators.
:return: 0 if all was fine. | pytvc/cli.py | list_indicators | havocesp/pytvc | 12 | python | def list_indicators() -> int:
'List all supported indicators.\n\n :return: 0 if all was fine.\n '
ti = load_indicators()
indicators = [f'- {v:<30}' for v in ti]
indicators.sort()
for i in indicators:
print(i)
return 0 | def list_indicators() -> int:
'List all supported indicators.\n\n :return: 0 if all was fine.\n '
ti = load_indicators()
indicators = [f'- {v:<30}' for v in ti]
indicators.sort()
for i in indicators:
print(i)
return 0<|docstring|>List all supported indicators.
:return: 0 if all wa... |
b932f3dda037f12cfb6f41ce264ad66e553213b9a2ce318e1340f7cfa29c7248 | def main(args) -> int:
'TradingView Chart parserr.\n \n :param Namespace args:\n :return:\n '
tvc = TradingViewChart()
tvc.launch(**vars(args))
return 0 | TradingView Chart parserr.
:param Namespace args:
:return: | pytvc/cli.py | main | havocesp/pytvc | 12 | python | def main(args) -> int:
'TradingView Chart parserr.\n \n :param Namespace args:\n :return:\n '
tvc = TradingViewChart()
tvc.launch(**vars(args))
return 0 | def main(args) -> int:
'TradingView Chart parserr.\n \n :param Namespace args:\n :return:\n '
tvc = TradingViewChart()
tvc.launch(**vars(args))
return 0<|docstring|>TradingView Chart parserr.
:param Namespace args:
:return:<|endoftext|> |
aacd552f635f6c6a0a64708728b14f03d00f99df080dbb827f28256341916eee | def run():
'As CLI starting point, this function dispatch argument parsing to be supplied to main function.'
base_markets = ['BTC', 'TUSD', 'USDT', 'USD', 'EUR', 'PAX', 'USDS']
exchanges = ['binance', 'hitbtc2', 'poloniex', 'kraken', 'coinbase', 'cexio']
exchanges = {e: e.strip('_12345 ') for e in excha... | As CLI starting point, this function dispatch argument parsing to be supplied to main function. | pytvc/cli.py | run | havocesp/pytvc | 12 | python | def run():
base_markets = ['BTC', 'TUSD', 'USDT', 'USD', 'EUR', 'PAX', 'USDS']
exchanges = ['binance', 'hitbtc2', 'poloniex', 'kraken', 'coinbase', 'cexio']
exchanges = {e: e.strip('_12345 ') for e in exchanges}
parser = argparse.ArgumentParser()
parser.add_argument('-l, --list-indicators', act... | def run():
base_markets = ['BTC', 'TUSD', 'USDT', 'USD', 'EUR', 'PAX', 'USDS']
exchanges = ['binance', 'hitbtc2', 'poloniex', 'kraken', 'coinbase', 'cexio']
exchanges = {e: e.strip('_12345 ') for e in exchanges}
parser = argparse.ArgumentParser()
parser.add_argument('-l, --list-indicators', act... |
360dba7075008b5182ff54dff468af1e5cd97787bff6a069d85f2f46090c21c2 | def get_location(loc):
'\n currently working only on my computer\n english Model\n english.muc.7class.distsim.crf.ser.gz\n german Models\n german.dewac_175m_600.crf.ser.gz\n german.hgc_175m_600.crf.ser.gz\n '
st = StanfordNERTagger('stanford-ner-2015-12-09/classifiers/english.mu... | currently working only on my computer
english Model
english.muc.7class.distsim.crf.ser.gz
german Models
german.dewac_175m_600.crf.ser.gz
german.hgc_175m_600.crf.ser.gz | extractor.py | get_location | phucdev/weatherbot | 0 | python | def get_location(loc):
'\n currently working only on my computer\n english Model\n english.muc.7class.distsim.crf.ser.gz\n german Models\n german.dewac_175m_600.crf.ser.gz\n german.hgc_175m_600.crf.ser.gz\n '
st = StanfordNERTagger('stanford-ner-2015-12-09/classifiers/english.mu... | def get_location(loc):
'\n currently working only on my computer\n english Model\n english.muc.7class.distsim.crf.ser.gz\n german Models\n german.dewac_175m_600.crf.ser.gz\n german.hgc_175m_600.crf.ser.gz\n '
st = StanfordNERTagger('stanford-ner-2015-12-09/classifiers/english.mu... |
6e691b4afac0e44e94ed72229fd79a3ae83be04d4f26008245f0ce566c7d973e | def flask_post_json():
'Ah the joys of frameworks! They do so much work for you\n that they get in the way of sane operation!'
if (request.json != None):
return request.json
elif ((request.data != None) and (request.data.decode('utf8') != u'')):
return json.loads(request.data.decode('u... | Ah the joys of frameworks! They do so much work for you
that they get in the way of sane operation! | server.py | flask_post_json | dcones/CMPUT404-assignment-ajax | 1 | python | def flask_post_json():
'Ah the joys of frameworks! They do so much work for you\n that they get in the way of sane operation!'
if (request.json != None):
return request.json
elif ((request.data != None) and (request.data.decode('utf8') != u)):
return json.loads(request.data.decode('utf... | def flask_post_json():
'Ah the joys of frameworks! They do so much work for you\n that they get in the way of sane operation!'
if (request.json != None):
return request.json
elif ((request.data != None) and (request.data.decode('utf8') != u)):
return json.loads(request.data.decode('utf... |
9dca322af1c95789df86925512386d1aa5155f41fb9ec3ab474d222c3cc149f8 | def __init__(self, byte: int, line: int, index: int, args: List[Token], resolved_vars: dict={}, resolved_gotos: dict={}):
'Represents a compilable line.\n\n Args:\n byte (int): The bytecode byte of the instruction associated.\n '
self.byte = byte
self.args = args
self.line = lin... | Represents a compilable line.
Args:
byte (int): The bytecode byte of the instruction associated. | assemblyish/compiler.py | __init__ | vcokltfre/assemblyish | 1 | python | def __init__(self, byte: int, line: int, index: int, args: List[Token], resolved_vars: dict={}, resolved_gotos: dict={}):
'Represents a compilable line.\n\n Args:\n byte (int): The bytecode byte of the instruction associated.\n '
self.byte = byte
self.args = args
self.line = lin... | def __init__(self, byte: int, line: int, index: int, args: List[Token], resolved_vars: dict={}, resolved_gotos: dict={}):
'Represents a compilable line.\n\n Args:\n byte (int): The bytecode byte of the instruction associated.\n '
self.byte = byte
self.args = args
self.line = lin... |
e439d61e76b4636fc0521f928161011d7c151ea40fd8f512276ad88ef749d82f | def __init__(self, filename: str, tokens: List[Token]):
"A compiler class for assemblyish.\n\n Args:\n filename (str): The filename of the file being compiled. Used for error logging.\n tokens (List[Token]): A list of Token objects representing the program's code.\n "
self.fi... | A compiler class for assemblyish.
Args:
filename (str): The filename of the file being compiled. Used for error logging.
tokens (List[Token]): A list of Token objects representing the program's code. | assemblyish/compiler.py | __init__ | vcokltfre/assemblyish | 1 | python | def __init__(self, filename: str, tokens: List[Token]):
"A compiler class for assemblyish.\n\n Args:\n filename (str): The filename of the file being compiled. Used for error logging.\n tokens (List[Token]): A list of Token objects representing the program's code.\n "
self.fi... | def __init__(self, filename: str, tokens: List[Token]):
"A compiler class for assemblyish.\n\n Args:\n filename (str): The filename of the file being compiled. Used for error logging.\n tokens (List[Token]): A list of Token objects representing the program's code.\n "
self.fi... |
d33d96144f3fc75049c4af3beafe357192a1031f88ab8e0bd8f3948b63422447 | def calc_cos(self, batch_size, n_tau=32):
'\n Calculating the cosinus values depending on the number of tau samples\n '
taus = th.rand(batch_size, n_tau).unsqueeze((- 1)).to(self.device)
cos = th.cos((taus * self.pis.to(self.device)))
assert (cos.shape == (batch_size, n_tau, self.n_cos)), ... | Calculating the cosinus values depending on the number of tau samples | custom_algos/d3pg/policies.py | calc_cos | vinerich/rl-baselines3-zoo | 0 | python | def calc_cos(self, batch_size, n_tau=32):
'\n \n '
taus = th.rand(batch_size, n_tau).unsqueeze((- 1)).to(self.device)
cos = th.cos((taus * self.pis.to(self.device)))
assert (cos.shape == (batch_size, n_tau, self.n_cos)), 'cos shape is incorrect'
return (cos, taus) | def calc_cos(self, batch_size, n_tau=32):
'\n \n '
taus = th.rand(batch_size, n_tau).unsqueeze((- 1)).to(self.device)
cos = th.cos((taus * self.pis.to(self.device)))
assert (cos.shape == (batch_size, n_tau, self.n_cos)), 'cos shape is incorrect'
return (cos, taus)<|docstring|>Calculati... |
37c18a736586dca84f0ca40fdb8086e4296534633e48f7a669439c6ac73bd86d | @property
def id(self):
'Returns the SHA1 ID of this commitish'
if (self._id is None):
self._id = self.repo.revparse(self.ref)
return self._id | Returns the SHA1 ID of this commitish | src/geogigpy/commitish.py | id | boundlessgeo/geogig-py | 7 | python | @property
def id(self):
if (self._id is None):
self._id = self.repo.revparse(self.ref)
return self._id | @property
def id(self):
if (self._id is None):
self._id = self.repo.revparse(self.ref)
return self._id<|docstring|>Returns the SHA1 ID of this commitish<|endoftext|> |
558d36ca1237fd8f9b6421157a3d73a8811139acdf1d4e24d7e249a945a71301 | def log(self):
'Return the history up to this commitish'
return self.repo.log(self.ref) | Return the history up to this commitish | src/geogigpy/commitish.py | log | boundlessgeo/geogig-py | 7 | python | def log(self):
return self.repo.log(self.ref) | def log(self):
return self.repo.log(self.ref)<|docstring|>Return the history up to this commitish<|endoftext|> |
4074d6c0af2d8f8e957fdc031e1c04d62c3215142706c5343da440cfdb17065d | @property
def root(self):
'Returns a Tree that represents the root tree at this snapshot'
return Tree(self.repo, self.ref) | Returns a Tree that represents the root tree at this snapshot | src/geogigpy/commitish.py | root | boundlessgeo/geogig-py | 7 | python | @property
def root(self):
return Tree(self.repo, self.ref) | @property
def root(self):
return Tree(self.repo, self.ref)<|docstring|>Returns a Tree that represents the root tree at this snapshot<|endoftext|> |
923927d34044c67cae9fcf8598d92d502d155fe1e4dc71269fa4fc559ff2a000 | def checkout(self):
'Checks out this commitish, and set it as the current HEAD'
self.repo.checkout(self.ref) | Checks out this commitish, and set it as the current HEAD | src/geogigpy/commitish.py | checkout | boundlessgeo/geogig-py | 7 | python | def checkout(self):
self.repo.checkout(self.ref) | def checkout(self):
self.repo.checkout(self.ref)<|docstring|>Checks out this commitish, and set it as the current HEAD<|endoftext|> |
b001b8d1de85228124517b705ad769abc10815102199ba0ffb1f14831a71b065 | def diff(self):
'Returns a list of DiffEntry with all changes introduced by this commitish'
if (self._diff is None):
self._diff = self.repo.diff((self.ref + '~1'), self.ref)
return self._diff | Returns a list of DiffEntry with all changes introduced by this commitish | src/geogigpy/commitish.py | diff | boundlessgeo/geogig-py | 7 | python | def diff(self):
if (self._diff is None):
self._diff = self.repo.diff((self.ref + '~1'), self.ref)
return self._diff | def diff(self):
if (self._diff is None):
self._diff = self.repo.diff((self.ref + '~1'), self.ref)
return self._diff<|docstring|>Returns a list of DiffEntry with all changes introduced by this commitish<|endoftext|> |
92690b4ebc00a873c04a81b7440c3be5a6bc95465999d29af322c528a5727726 | @property
def parent(self):
'Returns a commitish that represents the parent of this one'
return Commitish(self.repo, (self.ref + '~1')) | Returns a commitish that represents the parent of this one | src/geogigpy/commitish.py | parent | boundlessgeo/geogig-py | 7 | python | @property
def parent(self):
return Commitish(self.repo, (self.ref + '~1')) | @property
def parent(self):
return Commitish(self.repo, (self.ref + '~1'))<|docstring|>Returns a commitish that represents the parent of this one<|endoftext|> |
656eb7fd477a9f3a5afe33af99fa28aee264f5b1e5f7c44953c767d8d22a4396 | def humantext(self):
'Returns a nice human-readable description of the commitish'
headid = self.repo.revparse(self.repo.head.ref)
if (headid == self.id):
return 'Current branch'
return self.ref | Returns a nice human-readable description of the commitish | src/geogigpy/commitish.py | humantext | boundlessgeo/geogig-py | 7 | python | def humantext(self):
headid = self.repo.revparse(self.repo.head.ref)
if (headid == self.id):
return 'Current branch'
return self.ref | def humantext(self):
headid = self.repo.revparse(self.repo.head.ref)
if (headid == self.id):
return 'Current branch'
return self.ref<|docstring|>Returns a nice human-readable description of the commitish<|endoftext|> |
e8f1f38099c8f224dec038fca1b03ecce00136188f7c31b2776eaed0f386bb5a | def read_triformat(directory, x_filename):
'\n X - 2d numpy array with rows representing compounds and columns represnting features\n compounds - pandas DataFrame with compound information\n features - pandas DataFrame with features information\n '
features_path = os.path.join(directory, 'features.t... | X - 2d numpy array with rows representing compounds and columns represnting features
compounds - pandas DataFrame with compound information
features - pandas DataFrame with features information | projects/remyelination/feature_reader.py | read_triformat | dhimmel/serg-pycode | 0 | python | def read_triformat(directory, x_filename):
'\n X - 2d numpy array with rows representing compounds and columns represnting features\n compounds - pandas DataFrame with compound information\n features - pandas DataFrame with features information\n '
features_path = os.path.join(directory, 'features.t... | def read_triformat(directory, x_filename):
'\n X - 2d numpy array with rows representing compounds and columns represnting features\n compounds - pandas DataFrame with compound information\n features - pandas DataFrame with features information\n '
features_path = os.path.join(directory, 'features.t... |
e795c20b4fac8ca817b57c2c9d417b9a9b8111b64b2bd949aef207b998d7df8c | def whoami(string):
'Leon introduces himself'
return utils.output('end', 'introduction', utils.translate('introduction')) | Leon introduces himself | packages/leon/whoami.py | whoami | jankeromnes/leon | 4 | python | def whoami(string):
return utils.output('end', 'introduction', utils.translate('introduction')) | def whoami(string):
return utils.output('end', 'introduction', utils.translate('introduction'))<|docstring|>Leon introduces himself<|endoftext|> |
5e1981956e8cf250b39eff83e25d76ae200d81d2f4c2a215f394865d1aa8dad0 | def __get_node_label_charcnn_embeddings(self, unique_labels_as_characters: tf.Tensor, node_labels_to_unique_labels: tf.Tensor) -> tf.Tensor:
'\n Compute representation of node labels using a 2-layer character CNN.\n\n Args:\n unique_labels_as_characters: int32 tensor of shape [U, C]\n ... | Compute representation of node labels using a 2-layer character CNN.
Args:
unique_labels_as_characters: int32 tensor of shape [U, C]
representing the unique (node) labels occurring in a
batch, where U is the number of such labels and C the
maximal number of characters.
node_labels_to_un... | tf-gnn-samples/tasks/varmisuse_task.py | __get_node_label_charcnn_embeddings | yangzhou6666/adversarial-examples | 10 | python | def __get_node_label_charcnn_embeddings(self, unique_labels_as_characters: tf.Tensor, node_labels_to_unique_labels: tf.Tensor) -> tf.Tensor:
'\n Compute representation of node labels using a 2-layer character CNN.\n\n Args:\n unique_labels_as_characters: int32 tensor of shape [U, C]\n ... | def __get_node_label_charcnn_embeddings(self, unique_labels_as_characters: tf.Tensor, node_labels_to_unique_labels: tf.Tensor) -> tf.Tensor:
'\n Compute representation of node labels using a 2-layer character CNN.\n\n Args:\n unique_labels_as_characters: int32 tensor of shape [U, C]\n ... |
897ec548dd608c28f7be3f343d6a7a053c6d5f6555328aeaeea4735fde122199 | def image_entropy(im):
'\n Calculate the entropy of an image. Used for "smart cropping".\n '
if (not isinstance(im, Image.Image)):
return 0
hist = im.histogram()
hist_size = float(sum(hist))
hist = [(h / hist_size) for h in hist]
return (- sum([(p * math.log(p, 2)) for p in hist if... | Calculate the entropy of an image. Used for "smart cropping". | pressurecooker/thumbscropping.py | image_entropy | kollivier/pressurecooker | 14 | python | def image_entropy(im):
'\n \n '
if (not isinstance(im, Image.Image)):
return 0
hist = im.histogram()
hist_size = float(sum(hist))
hist = [(h / hist_size) for h in hist]
return (- sum([(p * math.log(p, 2)) for p in hist if (p != 0)])) | def image_entropy(im):
'\n \n '
if (not isinstance(im, Image.Image)):
return 0
hist = im.histogram()
hist_size = float(sum(hist))
hist = [(h / hist_size) for h in hist]
return (- sum([(p * math.log(p, 2)) for p in hist if (p != 0)]))<|docstring|>Calculate the entropy of an image. U... |
4ee07f37b37b93de7c5119bdbba3880554199858a29ab561929eace8c6180bc7 | def _compare_entropy(start_slice, end_slice, slice, difference):
'\n Calculate the entropy of two slices (from the start and end of an axis),\n returning a tuple containing the amount that should be added to the start\n and removed from the end of the axis.\n '
start_entropy = image_entropy(start_sl... | Calculate the entropy of two slices (from the start and end of an axis),
returning a tuple containing the amount that should be added to the start
and removed from the end of the axis. | pressurecooker/thumbscropping.py | _compare_entropy | kollivier/pressurecooker | 14 | python | def _compare_entropy(start_slice, end_slice, slice, difference):
'\n Calculate the entropy of two slices (from the start and end of an axis),\n returning a tuple containing the amount that should be added to the start\n and removed from the end of the axis.\n '
start_entropy = image_entropy(start_sl... | def _compare_entropy(start_slice, end_slice, slice, difference):
'\n Calculate the entropy of two slices (from the start and end of an axis),\n returning a tuple containing the amount that should be added to the start\n and removed from the end of the axis.\n '
start_entropy = image_entropy(start_sl... |
1172381e4328887fb2d06d20f7aaea9d2a3001418c1e71c419911a4a09b8f873 | def scale_and_crop(im, size, crop=False, upscale=False, zoom=None, target=None, **kwargs):
'\n Handle scaling and cropping the source image.\n Images can be scaled / cropped against a single dimension by using zero\n as the placeholder in the size. For example, ``size=(100, 0)`` will cause\n the image t... | Handle scaling and cropping the source image.
Images can be scaled / cropped against a single dimension by using zero
as the placeholder in the size. For example, ``size=(100, 0)`` will cause
the image to be resized to 100 pixels wide, keeping the aspect ratio of
the source image.
crop
Crop the source image height ... | pressurecooker/thumbscropping.py | scale_and_crop | kollivier/pressurecooker | 14 | python | def scale_and_crop(im, size, crop=False, upscale=False, zoom=None, target=None, **kwargs):
'\n Handle scaling and cropping the source image.\n Images can be scaled / cropped against a single dimension by using zero\n as the placeholder in the size. For example, ``size=(100, 0)`` will cause\n the image t... | def scale_and_crop(im, size, crop=False, upscale=False, zoom=None, target=None, **kwargs):
'\n Handle scaling and cropping the source image.\n Images can be scaled / cropped against a single dimension by using zero\n as the placeholder in the size. For example, ``size=(100, 0)`` will cause\n the image t... |
e3066797096b000bdeb5e94b93a7bb4c622e2d28cc786709e71b4a62b2588ab6 | def read_conf(self):
'MODEL'
self.model_root = self.conf['Model']
self.model_name = self.model_root.get('ModelName')
self.model_tag = '{model_name}.model'.format(model_name=self.model_name)
self.model_field_param = self.model_root.get('ModelField')
self.model_scene_param = self.model_root.get('M... | MODEL | config.py | read_conf | liuyang77886/captcha_trainer | 2,548 | python | def read_conf(self):
self.model_root = self.conf['Model']
self.model_name = self.model_root.get('ModelName')
self.model_tag = '{model_name}.model'.format(model_name=self.model_name)
self.model_field_param = self.model_root.get('ModelField')
self.model_scene_param = self.model_root.get('ModelSce... | def read_conf(self):
self.model_root = self.conf['Model']
self.model_name = self.model_root.get('ModelName')
self.model_tag = '{model_name}.model'.format(model_name=self.model_name)
self.model_field_param = self.model_root.get('ModelField')
self.model_scene_param = self.model_root.get('ModelSce... |
0dfe2eb01bfd37947fa900aec4827f80d1fb31b9d71ec5b70761b5e76aa0fc17 | def init_loader(args):
'Initialize test DataLoader'
if (args.dataset_name is not None):
datasets = load_dataset(args.dataset_name, args.dataset_config_name)
else:
data_files = {'test': args.data_path}
extension = args.data_path.split('.')[(- 1)]
datasets = load_dataset(extens... | Initialize test DataLoader | src/loaders.py | init_loader | Shreyas-21/DANCER-summ | 7 | python | def init_loader(args):
if (args.dataset_name is not None):
datasets = load_dataset(args.dataset_name, args.dataset_config_name)
else:
data_files = {'test': args.data_path}
extension = args.data_path.split('.')[(- 1)]
datasets = load_dataset(extension, data_files=data_files)
... | def init_loader(args):
if (args.dataset_name is not None):
datasets = load_dataset(args.dataset_name, args.dataset_config_name)
else:
data_files = {'test': args.data_path}
extension = args.data_path.split('.')[(- 1)]
datasets = load_dataset(extension, data_files=data_files)
... |
f06d1f68ba12ba0d86e5706514e7045670c5cb6ec29613f42b6eb1a253b4e8d4 | def load_model(args, device):
'Load model and tokenizer'
print(f'Loading tokenizer {(args.tokenizer_name if args.tokenizer_name else args.model_path)}')
tokenizer = AutoTokenizer.from_pretrained((args.tokenizer_name if args.tokenizer_name else args.model_path))
print(f'Loading model from {args.model_pat... | Load model and tokenizer | src/loaders.py | load_model | Shreyas-21/DANCER-summ | 7 | python | def load_model(args, device):
print(f'Loading tokenizer {(args.tokenizer_name if args.tokenizer_name else args.model_path)}')
tokenizer = AutoTokenizer.from_pretrained((args.tokenizer_name if args.tokenizer_name else args.model_path))
print(f'Loading model from {args.model_path}')
model = AutoModel... | def load_model(args, device):
print(f'Loading tokenizer {(args.tokenizer_name if args.tokenizer_name else args.model_path)}')
tokenizer = AutoTokenizer.from_pretrained((args.tokenizer_name if args.tokenizer_name else args.model_path))
print(f'Loading model from {args.model_path}')
model = AutoModel... |
e734b190c6b313c30cf41bd2fdc7b2c746815cd7d296c56e947880cc7a80a5ba | def __init__(self, context, scenario):
'\n Create and enter a temp directory which will be used to stored xpedite application information\n If a target application is being run remotely, TargetLauncher will create the temp directory on the remote host\n '
self.targetApp = scenario.makeTargetApp(context... | Create and enter a temp directory which will be used to stored xpedite application information
If a target application is being run remotely, TargetLauncher will create the temp directory on the remote host | test/pytest/test_xpedite/test_profiler/app.py | __init__ | mdlugajczyk/Xpedite | 99 | python | def __init__(self, context, scenario):
'\n Create and enter a temp directory which will be used to stored xpedite application information\n If a target application is being run remotely, TargetLauncher will create the temp directory on the remote host\n '
self.targetApp = scenario.makeTargetApp(context... | def __init__(self, context, scenario):
'\n Create and enter a temp directory which will be used to stored xpedite application information\n If a target application is being run remotely, TargetLauncher will create the temp directory on the remote host\n '
self.targetApp = scenario.makeTargetApp(context... |
4eed2cafe5415085ddbb6b349a4040e4890d8cc92e3128c82dac93ace78f9f2a | def __init__(self, to, subject, sender, aws_access_key, aws_secret_key, aws_region='us-east-1'):
'\n :param to:\n :param subject:\n :return:\n '
self.connection = None
self.to = to
self.subject = subject
self._html = None
self._text = None
self._format = 'html'
... | :param to:
:param subject:
:return: | datacoco_cloud/ses_interaction.py | __init__ | Phil-Ocone/datacoco-cloud | 1 | python | def __init__(self, to, subject, sender, aws_access_key, aws_secret_key, aws_region='us-east-1'):
'\n :param to:\n :param subject:\n :return:\n '
self.connection = None
self.to = to
self.subject = subject
self._html = None
self._text = None
self._format = 'html'
... | def __init__(self, to, subject, sender, aws_access_key, aws_secret_key, aws_region='us-east-1'):
'\n :param to:\n :param subject:\n :return:\n '
self.connection = None
self.to = to
self.subject = subject
self._html = None
self._text = None
self._format = 'html'
... |
9feb10e239b447f4ac2be114de26e4baea0a328cb92846118b14f4f34698505f | def html(self, html):
"\n set's email html message property\n :param html:\n :return:\n "
self._html = html | set's email html message property
:param html:
:return: | datacoco_cloud/ses_interaction.py | html | Phil-Ocone/datacoco-cloud | 1 | python | def html(self, html):
"\n set's email html message property\n :param html:\n :return:\n "
self._html = html | def html(self, html):
"\n set's email html message property\n :param html:\n :return:\n "
self._html = html<|docstring|>set's email html message property
:param html:
:return:<|endoftext|> |
670e35429a41e744d4c56e508865d063e3444969d4d358782a1d999406c03416 | def text(self, text):
"\n set's email text message property\n :param text:\n :return:\n "
self._text = text | set's email text message property
:param text:
:return: | datacoco_cloud/ses_interaction.py | text | Phil-Ocone/datacoco-cloud | 1 | python | def text(self, text):
"\n set's email text message property\n :param text:\n :return:\n "
self._text = text | def text(self, text):
"\n set's email text message property\n :param text:\n :return:\n "
self._text = text<|docstring|>set's email text message property
:param text:
:return:<|endoftext|> |
ef43abffa2c2272459b2103d2cd349888af5ec1c9c111562ec0ee50de6fcd326 | def send(self, from_addr=None):
'\n sends email\n :param from_addr:\n :return:\n '
body = self._html
if isinstance(self.to, basestring):
self.to = [self.to]
if (not from_addr):
from_addr = self.def_sender
if ((not self._html) and (not self._text)):
... | sends email
:param from_addr:
:return: | datacoco_cloud/ses_interaction.py | send | Phil-Ocone/datacoco-cloud | 1 | python | def send(self, from_addr=None):
'\n sends email\n :param from_addr:\n :return:\n '
body = self._html
if isinstance(self.to, basestring):
self.to = [self.to]
if (not from_addr):
from_addr = self.def_sender
if ((not self._html) and (not self._text)):
... | def send(self, from_addr=None):
'\n sends email\n :param from_addr:\n :return:\n '
body = self._html
if isinstance(self.to, basestring):
self.to = [self.to]
if (not from_addr):
from_addr = self.def_sender
if ((not self._html) and (not self._text)):
... |
de7931b4e1b4280b45155f897da01d4cb815badc9ae19d4a6d893a45da34dd58 | def pool_output_length(input_length, pool_size, stride, pad, ignore_border):
'\n Compute the output length of a pooling operator\n along a single dimension.\n\n Parameters\n ----------\n input_length : integer\n The length of the input in the pooling dimension\n pool_size : integer\n ... | Compute the output length of a pooling operator
along a single dimension.
Parameters
----------
input_length : integer
The length of the input in the pooling dimension
pool_size : integer
The length of the pooling region
stride : integer
The stride between successive pooling regions
pad : integer
The n... | lasagne/layers/pool.py | pool_output_length | BenjaminBossan/Lasagne | 0 | python | def pool_output_length(input_length, pool_size, stride, pad, ignore_border):
'\n Compute the output length of a pooling operator\n along a single dimension.\n\n Parameters\n ----------\n input_length : integer\n The length of the input in the pooling dimension\n pool_size : integer\n ... | def pool_output_length(input_length, pool_size, stride, pad, ignore_border):
'\n Compute the output length of a pooling operator\n along a single dimension.\n\n Parameters\n ----------\n input_length : integer\n The length of the input in the pooling dimension\n pool_size : integer\n ... |
a5df83ad447423a16cd91cf2cc2bb3a32a284fbc058de77f6f8f460ec435465b | def pool_2d(input, **kwargs):
'\n Wrapper function that calls :func:`theano.tensor.signal.pool_2d` either\n with the new or old keyword argument names expected by Theano.\n '
try:
return T.signal.pool.pool_2d(input, **kwargs)
except TypeError:
kwargs['ds'] = kwargs.pop('ws')
... | Wrapper function that calls :func:`theano.tensor.signal.pool_2d` either
with the new or old keyword argument names expected by Theano. | lasagne/layers/pool.py | pool_2d | BenjaminBossan/Lasagne | 0 | python | def pool_2d(input, **kwargs):
'\n Wrapper function that calls :func:`theano.tensor.signal.pool_2d` either\n with the new or old keyword argument names expected by Theano.\n '
try:
return T.signal.pool.pool_2d(input, **kwargs)
except TypeError:
kwargs['ds'] = kwargs.pop('ws')
... | def pool_2d(input, **kwargs):
'\n Wrapper function that calls :func:`theano.tensor.signal.pool_2d` either\n with the new or old keyword argument names expected by Theano.\n '
try:
return T.signal.pool.pool_2d(input, **kwargs)
except TypeError:
kwargs['ds'] = kwargs.pop('ws')
... |
42e9f2b53049f8378c70f7e4d1763e19d4986a779157f8d7e0511934da33cac2 | @progress.setter
def progress(self, progress):
'Should be overridden by different Task types.'
self._progress = progress | Should be overridden by different Task types. | crispy/tasks.py | progress | StephenHermes/crispy | 0 | python | @progress.setter
def progress(self, progress):
self._progress = progress | @progress.setter
def progress(self, progress):
self._progress = progress<|docstring|>Should be overridden by different Task types.<|endoftext|> |
0e7b100326078844ee1b07c18d05cb0d5bf062ed8f2b745b4005f9867e76e6df | def test_index(client):
'Check the index page loads.'
response = client.get('/')
assert (response.status_code == 200) | Check the index page loads. | test/test_app.py | test_index | j-penson/image-distortion | 0 | python | def test_index(client):
response = client.get('/')
assert (response.status_code == 200) | def test_index(client):
response = client.get('/')
assert (response.status_code == 200)<|docstring|>Check the index page loads.<|endoftext|> |
ccb176e3535e7429040a53a0fd4c7dd04937e25921183175501c08fcce3893a9 | def test_endpoint(client):
'Check the index page loads.'
response = client.get('/v1/image')
assert (response.status_code == 200) | Check the index page loads. | test/test_app.py | test_endpoint | j-penson/image-distortion | 0 | python | def test_endpoint(client):
response = client.get('/v1/image')
assert (response.status_code == 200) | def test_endpoint(client):
response = client.get('/v1/image')
assert (response.status_code == 200)<|docstring|>Check the index page loads.<|endoftext|> |
ef201c4579fe609d59eb4378906fc6f8ce8c2c622b0cae2848d704801cd05ece | @manager.option('suite', default='all', nargs='?', choices=suites.keys(), help='Specify test suite to run (default all)')
@manager.option('--spec', action='store_true', help='Output in spec style')
def test(spec, suite):
'Runs tests'
args = []
if spec:
args.extend(['--spec'])
if (not suite):
... | Runs tests | manage.py | test | crossgovernmentservices/csd_notes | 0 | python | @manager.option('suite', default='all', nargs='?', choices=suites.keys(), help='Specify test suite to run (default all)')
@manager.option('--spec', action='store_true', help='Output in spec style')
def test(spec, suite):
args = []
if spec:
args.extend(['--spec'])
if (not suite):
suite =... | @manager.option('suite', default='all', nargs='?', choices=suites.keys(), help='Specify test suite to run (default all)')
@manager.option('--spec', action='store_true', help='Output in spec style')
def test(spec, suite):
args = []
if spec:
args.extend(['--spec'])
if (not suite):
suite =... |
e812f46d995e29059073e7e24f0211bdb3ddd403b1a96b75fd7002cb2787dbb6 | def get_system_info():
'\n Get information about the system to be inserted into the User-Agent header.\n '
return 'lang={0}; arch={1}; os={2}; python.version={3}'.format('python', platform.machine(), platform.system(), platform.python_version()) | Get information about the system to be inserted into the User-Agent header. | eventstreams_sdk/common.py | get_system_info | IBM/eventstreams-python-sdk | 2 | python | def get_system_info():
'\n \n '
return 'lang={0}; arch={1}; os={2}; python.version={3}'.format('python', platform.machine(), platform.system(), platform.python_version()) | def get_system_info():
'\n \n '
return 'lang={0}; arch={1}; os={2}; python.version={3}'.format('python', platform.machine(), platform.system(), platform.python_version())<|docstring|>Get information about the system to be inserted into the User-Agent header.<|endoftext|> |
f842c6e85de8afbf9f5613362baa495f8ac084fcc9202a1fbe7e459949db72b7 | def get_user_agent():
'\n Get the value to be sent in the User-Agent header.\n '
return USER_AGENT | Get the value to be sent in the User-Agent header. | eventstreams_sdk/common.py | get_user_agent | IBM/eventstreams-python-sdk | 2 | python | def get_user_agent():
'\n \n '
return USER_AGENT | def get_user_agent():
'\n \n '
return USER_AGENT<|docstring|>Get the value to be sent in the User-Agent header.<|endoftext|> |
35f0e7ba46352d79dce1561323a3e80f67a2078519b2a22c3a8e678d751fd30e | def get_sdk_headers(service_name, service_version, operation_id):
'\n Get the request headers to be sent in requests by the SDK.\n \n If you plan to gather metrics for your SDK, the User-Agent header value must\n be a string similar to the following:\n eventstreams-python-sdk/0.0.1 (lang=python; arch... | Get the request headers to be sent in requests by the SDK.
If you plan to gather metrics for your SDK, the User-Agent header value must
be a string similar to the following:
eventstreams-python-sdk/0.0.1 (lang=python; arch=x86_64; os=Linux; python.version=3.7.4)
In the example above, the analytics tool will parse the... | eventstreams_sdk/common.py | get_sdk_headers | IBM/eventstreams-python-sdk | 2 | python | def get_sdk_headers(service_name, service_version, operation_id):
'\n Get the request headers to be sent in requests by the SDK.\n \n If you plan to gather metrics for your SDK, the User-Agent header value must\n be a string similar to the following:\n eventstreams-python-sdk/0.0.1 (lang=python; arch... | def get_sdk_headers(service_name, service_version, operation_id):
'\n Get the request headers to be sent in requests by the SDK.\n \n If you plan to gather metrics for your SDK, the User-Agent header value must\n be a string similar to the following:\n eventstreams-python-sdk/0.0.1 (lang=python; arch... |
486f4a0e64deaaca514b569fb6cdb4701dc8784f56a046d0e29ce315bfab14bd | @property
def base_api_url(self) -> str:
'The provider base REST API URL'
return self._config.api_base_url | The provider base REST API URL | jupyterlab_pullrequests/managers/manager.py | base_api_url | fcollonval/pull-requests | 32 | python | @property
def base_api_url(self) -> str:
return self._config.api_base_url | @property
def base_api_url(self) -> str:
return self._config.api_base_url<|docstring|>The provider base REST API URL<|endoftext|> |
5d4b7b93f46e3a20b0306a9dd7022ebfef5db4fb4c38774b7c1fd4f0e45156a7 | @property
def per_page_argument(self) -> Optional[Tuple[(str, int)]]:
'Returns query argument to set number of items per page.\n\n Returns\n [str, int]: (query argument name, value)\n None: the provider does not support pagination\n '
return None | Returns query argument to set number of items per page.
Returns
[str, int]: (query argument name, value)
None: the provider does not support pagination | jupyterlab_pullrequests/managers/manager.py | per_page_argument | fcollonval/pull-requests | 32 | python | @property
def per_page_argument(self) -> Optional[Tuple[(str, int)]]:
'Returns query argument to set number of items per page.\n\n Returns\n [str, int]: (query argument name, value)\n None: the provider does not support pagination\n '
return None | @property
def per_page_argument(self) -> Optional[Tuple[(str, int)]]:
'Returns query argument to set number of items per page.\n\n Returns\n [str, int]: (query argument name, value)\n None: the provider does not support pagination\n '
return None<|docstring|>Returns query arg... |
f1292826fb9ce348daa666b79df70a95992a762a6b48be559e7ff57aecef998e | @abc.abstractmethod
async def get_current_user(self) -> str:
'Get the current user ID.'
raise NotImplementedError() | Get the current user ID. | jupyterlab_pullrequests/managers/manager.py | get_current_user | fcollonval/pull-requests | 32 | python | @abc.abstractmethod
async def get_current_user(self) -> str:
raise NotImplementedError() | @abc.abstractmethod
async def get_current_user(self) -> str:
raise NotImplementedError()<|docstring|>Get the current user ID.<|endoftext|> |
81f8cc9cd628a1d16f5d5641bbc3f0eb0d8cd8e52563f5168ac9765fe1e419db | @abc.abstractmethod
async def get_file_diff(self, pr_id: str, filename: str) -> dict:
'Get the file diff for the pull request.\n\n Args:\n pr_id: pull request ID endpoint\n filename: The file name\n Returns:\n The file diff description\n '
raise NotImplement... | Get the file diff for the pull request.
Args:
pr_id: pull request ID endpoint
filename: The file name
Returns:
The file diff description | jupyterlab_pullrequests/managers/manager.py | get_file_diff | fcollonval/pull-requests | 32 | python | @abc.abstractmethod
async def get_file_diff(self, pr_id: str, filename: str) -> dict:
'Get the file diff for the pull request.\n\n Args:\n pr_id: pull request ID endpoint\n filename: The file name\n Returns:\n The file diff description\n '
raise NotImplement... | @abc.abstractmethod
async def get_file_diff(self, pr_id: str, filename: str) -> dict:
'Get the file diff for the pull request.\n\n Args:\n pr_id: pull request ID endpoint\n filename: The file name\n Returns:\n The file diff description\n '
raise NotImplement... |
87a8dd6bf03582b94cba7b5e3b5295ad0dc997361a9e89b6fa919f759320b5c2 | @abc.abstractmethod
async def get_threads(self, pr_id: str, filename: Optional[str]=None) -> List[dict]:
'Get the discussions on a file or the pull request.\n\n Args:\n pr_id: pull request ID endpoint\n filename: The file name; None to get the discussion on the pull requests\n Re... | Get the discussions on a file or the pull request.
Args:
pr_id: pull request ID endpoint
filename: The file name; None to get the discussion on the pull requests
Returns:
The discussions | jupyterlab_pullrequests/managers/manager.py | get_threads | fcollonval/pull-requests | 32 | python | @abc.abstractmethod
async def get_threads(self, pr_id: str, filename: Optional[str]=None) -> List[dict]:
'Get the discussions on a file or the pull request.\n\n Args:\n pr_id: pull request ID endpoint\n filename: The file name; None to get the discussion on the pull requests\n Re... | @abc.abstractmethod
async def get_threads(self, pr_id: str, filename: Optional[str]=None) -> List[dict]:
'Get the discussions on a file or the pull request.\n\n Args:\n pr_id: pull request ID endpoint\n filename: The file name; None to get the discussion on the pull requests\n Re... |
5e9a1b1c94335f57a67432313080d799e9e57c20f3c7153b21c3296d9f3de08e | @abc.abstractmethod
async def list_files(self, pr_id: str) -> list:
'Get the list of modified files for a pull request.\n\n Args:\n pr_id: pull request ID endpoint\n Returns:\n The list of modified files\n '
raise NotImplementedError() | Get the list of modified files for a pull request.
Args:
pr_id: pull request ID endpoint
Returns:
The list of modified files | jupyterlab_pullrequests/managers/manager.py | list_files | fcollonval/pull-requests | 32 | python | @abc.abstractmethod
async def list_files(self, pr_id: str) -> list:
'Get the list of modified files for a pull request.\n\n Args:\n pr_id: pull request ID endpoint\n Returns:\n The list of modified files\n '
raise NotImplementedError() | @abc.abstractmethod
async def list_files(self, pr_id: str) -> list:
'Get the list of modified files for a pull request.\n\n Args:\n pr_id: pull request ID endpoint\n Returns:\n The list of modified files\n '
raise NotImplementedError()<|docstring|>Get the list of modif... |
2c714dfc339b4b2c472009ce71041e4ec5d7ab40d00f0ba15e61d71c417d64a6 | @abc.abstractmethod
async def list_prs(self, username: str, pr_filter: str) -> list:
'Returns the list of pull requests for the given user.\n\n Args:\n username: User ID for the versioning service\n pr_filter: Filter to add to the pull requests requests\n Returns:\n Th... | Returns the list of pull requests for the given user.
Args:
username: User ID for the versioning service
pr_filter: Filter to add to the pull requests requests
Returns:
The list of pull requests | jupyterlab_pullrequests/managers/manager.py | list_prs | fcollonval/pull-requests | 32 | python | @abc.abstractmethod
async def list_prs(self, username: str, pr_filter: str) -> list:
'Returns the list of pull requests for the given user.\n\n Args:\n username: User ID for the versioning service\n pr_filter: Filter to add to the pull requests requests\n Returns:\n Th... | @abc.abstractmethod
async def list_prs(self, username: str, pr_filter: str) -> list:
'Returns the list of pull requests for the given user.\n\n Args:\n username: User ID for the versioning service\n pr_filter: Filter to add to the pull requests requests\n Returns:\n Th... |
5930610c6ea94fd87177154aa0abee12b364832fd6ec6fde5991e371d0949789 | @abc.abstractmethod
async def post_comment(self, pr_id: str, filename: str, body: str) -> Dict[(str, str)]:
'Create a new comment on a file or a the pull request.\n\n Args:\n pr_id: pull request ID endpoint\n filename: The file name; None to comment on the pull request\n body... | Create a new comment on a file or a the pull request.
Args:
pr_id: pull request ID endpoint
filename: The file name; None to comment on the pull request
body: Comment body
Returns:
The created comment | jupyterlab_pullrequests/managers/manager.py | post_comment | fcollonval/pull-requests | 32 | python | @abc.abstractmethod
async def post_comment(self, pr_id: str, filename: str, body: str) -> Dict[(str, str)]:
'Create a new comment on a file or a the pull request.\n\n Args:\n pr_id: pull request ID endpoint\n filename: The file name; None to comment on the pull request\n body... | @abc.abstractmethod
async def post_comment(self, pr_id: str, filename: str, body: str) -> Dict[(str, str)]:
'Create a new comment on a file or a the pull request.\n\n Args:\n pr_id: pull request ID endpoint\n filename: The file name; None to comment on the pull request\n body... |
1363baf79b4e752bea64d31b7147507cbcb4625c55a3ad58caba3d5958676e13 | async def _call_provider(self, url: str, load_json: bool=True, method: str='GET', body: Optional[dict]=None, params: Optional[Dict[(str, str)]]=None, headers: Optional[Dict[(str, str)]]=None, has_pagination: bool=True) -> Union[(dict, str)]:
'Call the third party service\n\n The request is presumed to suppor... | Call the third party service
The request is presumed to support pagination by default if
- The method is GET
- load_json is True
- The provider returns not None per_page_argument property
Args:
url: Endpoint to request
load_json: Is the response of JSON type
method: HTTP method
body: Request body; Non... | jupyterlab_pullrequests/managers/manager.py | _call_provider | fcollonval/pull-requests | 32 | python | async def _call_provider(self, url: str, load_json: bool=True, method: str='GET', body: Optional[dict]=None, params: Optional[Dict[(str, str)]]=None, headers: Optional[Dict[(str, str)]]=None, has_pagination: bool=True) -> Union[(dict, str)]:
'Call the third party service\n\n The request is presumed to suppor... | async def _call_provider(self, url: str, load_json: bool=True, method: str='GET', body: Optional[dict]=None, params: Optional[Dict[(str, str)]]=None, headers: Optional[Dict[(str, str)]]=None, has_pagination: bool=True) -> Union[(dict, str)]:
'Call the third party service\n\n The request is presumed to suppor... |
dc2dd5fc81968b55d78c1d52b972bc0834dc3c8535e3c8b8b9e8808eb9780a7f | def __init__(self, rouge_dir=None, rouge_args=None, verbose=False):
'\n ROUGE metric\n Makes use of pyrouge: https://github.com/bheinzerling/pyrouge\n\n Args:\n :param rouge_dir: directory of ROUGE-1.5.5/, by default uses environment\'s ROUGE_HOME variable\n :param... | ROUGE metric
Makes use of pyrouge: https://github.com/bheinzerling/pyrouge
Args:
:param rouge_dir: directory of ROUGE-1.5.5/, by default uses environment's ROUGE_HOME variable
:param rouge_args: arguments for ROUGE calculation; if None, defaults to "-c 95 -2 -1 -U -r 1000 -n 4 -w 1.2 -a -m"; a string o... | cal_scores/SummEval/evaluation/summ_eval/rouge_metric.py | __init__ | bzhao2718/ReliableSummEvalReg | 0 | python | def __init__(self, rouge_dir=None, rouge_args=None, verbose=False):
'\n ROUGE metric\n Makes use of pyrouge: https://github.com/bheinzerling/pyrouge\n\n Args:\n :param rouge_dir: directory of ROUGE-1.5.5/, by default uses environment\'s ROUGE_HOME variable\n :param... | def __init__(self, rouge_dir=None, rouge_args=None, verbose=False):
'\n ROUGE metric\n Makes use of pyrouge: https://github.com/bheinzerling/pyrouge\n\n Args:\n :param rouge_dir: directory of ROUGE-1.5.5/, by default uses environment\'s ROUGE_HOME variable\n :param... |
2b34bda5f373e43077868ae89ff07d978c43822401a666818eb5c4895dada364 | def get_validator() -> Type[AzPlatformValidator]:
'Returns the validator class for this module'
return AzPlatformValidator | Returns the validator class for this module | scripts/commit_validation/commit_validation/validators/az_platform_validator.py | get_validator | eerock/o3de | 11 | python | def get_validator() -> Type[AzPlatformValidator]:
return AzPlatformValidator | def get_validator() -> Type[AzPlatformValidator]:
return AzPlatformValidator<|docstring|>Returns the validator class for this module<|endoftext|> |
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