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oscarlazoarjona/fast
build/lib/fast/symbolic.py
https://github.com/oscarlazoarjona/fast/blob/3e5400672af2a7b7cc616e7f4aa10d7672720222/build/lib/fast/symbolic.py#L153-L186
def define_laser_variables(Nl, real_amplitudes=False, variables=None): r"""Return the amplitudes and frequencies of Nl fields. >>> E0, omega_laser = define_laser_variables(2) >>> E0, omega_laser ([E_0^1, E_0^2], [varpi_1, varpi_2]) The amplitudes are complex by default: >>> conjugate(E0[0]) conjugate(E_0^1) But they can optionally be made real: >>> E0, omega_laser = define_laser_variables(2, real_amplitudes=True) >>> conjugate(E0[0]) E_0^1 They can also be made explicit functions of given variables: >>> from sympy import symbols >>> t, z = symbols("t, z", real=True) >>> E0, omega_laser = define_laser_variables(2, variables=[t, z]) >>> E0 [E_0^1(t, z), E_0^2(t, z)] """ if variables is None: E0 = [Symbol(r"E_0^"+str(l+1), real=real_amplitudes) for l in range(Nl)] else: E0 = [Function(r"E_0^"+str(l+1), real=real_amplitudes)(*variables) for l in range(Nl)] omega_laser = [Symbol(r"varpi_"+str(l+1), positive=True) for l in range(Nl)] return E0, omega_laser
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r"""Return the amplitudes and frequencies of Nl fields. >>> E0, omega_laser = define_laser_variables(2) >>> E0, omega_laser ([E_0^1, E_0^2], [varpi_1, varpi_2]) The amplitudes are complex by default: >>> conjugate(E0[0]) conjugate(E_0^1) But they can optionally be made real: >>> E0, omega_laser = define_laser_variables(2, real_amplitudes=True) >>> conjugate(E0[0]) E_0^1 They can also be made explicit functions of given variables: >>> from sympy import symbols >>> t, z = symbols("t, z", real=True) >>> E0, omega_laser = define_laser_variables(2, variables=[t, z]) >>> E0 [E_0^1(t, z), E_0^2(t, z)]
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python
train
veltzer/pypitools
pypitools/common.py
https://github.com/veltzer/pypitools/blob/5f097be21e9bc65578eed5b6b7855c1945540701/pypitools/common.py#L219-L229
def register(self): """ Register via the method configured :return: """ if self.register_method == "twine": self.register_by_twine() if self.register_method == "setup": self.register_by_setup() if self.register_method == "upload": self.upload()
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Register via the method configured :return:
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python
train
librosa/librosa
librosa/feature/utils.py
https://github.com/librosa/librosa/blob/180e8e6eb8f958fa6b20b8cba389f7945d508247/librosa/feature/utils.py#L15-L115
def delta(data, width=9, order=1, axis=-1, mode='interp', **kwargs): r'''Compute delta features: local estimate of the derivative of the input data along the selected axis. Delta features are computed Savitsky-Golay filtering. Parameters ---------- data : np.ndarray the input data matrix (eg, spectrogram) width : int, positive, odd [scalar] Number of frames over which to compute the delta features. Cannot exceed the length of `data` along the specified axis. If `mode='interp'`, then `width` must be at least `data.shape[axis]`. order : int > 0 [scalar] the order of the difference operator. 1 for first derivative, 2 for second, etc. axis : int [scalar] the axis along which to compute deltas. Default is -1 (columns). mode : str, {'interp', 'nearest', 'mirror', 'constant', 'wrap'} Padding mode for estimating differences at the boundaries. kwargs : additional keyword arguments See `scipy.signal.savgol_filter` Returns ------- delta_data : np.ndarray [shape=(d, t)] delta matrix of `data` at specified order Notes ----- This function caches at level 40. See Also -------- scipy.signal.savgol_filter Examples -------- Compute MFCC deltas, delta-deltas >>> y, sr = librosa.load(librosa.util.example_audio_file()) >>> mfcc = librosa.feature.mfcc(y=y, sr=sr) >>> mfcc_delta = librosa.feature.delta(mfcc) >>> mfcc_delta array([[ 1.666e+01, 1.666e+01, ..., 1.869e-15, 1.869e-15], [ 1.784e+01, 1.784e+01, ..., 6.085e-31, 6.085e-31], ..., [ 7.262e-01, 7.262e-01, ..., 9.259e-31, 9.259e-31], [ 6.578e-01, 6.578e-01, ..., 7.597e-31, 7.597e-31]]) >>> mfcc_delta2 = librosa.feature.delta(mfcc, order=2) >>> mfcc_delta2 array([[ -1.703e+01, -1.703e+01, ..., 3.834e-14, 3.834e-14], [ -1.108e+01, -1.108e+01, ..., -1.068e-30, -1.068e-30], ..., [ 4.075e-01, 4.075e-01, ..., -1.565e-30, -1.565e-30], [ 1.676e-01, 1.676e-01, ..., -2.104e-30, -2.104e-30]]) >>> import matplotlib.pyplot as plt >>> plt.subplot(3, 1, 1) >>> librosa.display.specshow(mfcc) >>> plt.title('MFCC') >>> plt.colorbar() >>> plt.subplot(3, 1, 2) >>> librosa.display.specshow(mfcc_delta) >>> plt.title(r'MFCC-$\Delta$') >>> plt.colorbar() >>> plt.subplot(3, 1, 3) >>> librosa.display.specshow(mfcc_delta2, x_axis='time') >>> plt.title(r'MFCC-$\Delta^2$') >>> plt.colorbar() >>> plt.tight_layout() ''' data = np.atleast_1d(data) if mode == 'interp' and width > data.shape[axis]: raise ParameterError("when mode='interp', width={} " "cannot exceed data.shape[axis]={}".format(width, data.shape[axis])) if width < 3 or np.mod(width, 2) != 1: raise ParameterError('width must be an odd integer >= 3') if order <= 0 or not isinstance(order, int): raise ParameterError('order must be a positive integer') kwargs.pop('deriv', None) kwargs.setdefault('polyorder', order) return scipy.signal.savgol_filter(data, width, deriv=order, axis=axis, mode=mode, **kwargs)
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r'''Compute delta features: local estimate of the derivative of the input data along the selected axis. Delta features are computed Savitsky-Golay filtering. Parameters ---------- data : np.ndarray the input data matrix (eg, spectrogram) width : int, positive, odd [scalar] Number of frames over which to compute the delta features. Cannot exceed the length of `data` along the specified axis. If `mode='interp'`, then `width` must be at least `data.shape[axis]`. order : int > 0 [scalar] the order of the difference operator. 1 for first derivative, 2 for second, etc. axis : int [scalar] the axis along which to compute deltas. Default is -1 (columns). mode : str, {'interp', 'nearest', 'mirror', 'constant', 'wrap'} Padding mode for estimating differences at the boundaries. kwargs : additional keyword arguments See `scipy.signal.savgol_filter` Returns ------- delta_data : np.ndarray [shape=(d, t)] delta matrix of `data` at specified order Notes ----- This function caches at level 40. See Also -------- scipy.signal.savgol_filter Examples -------- Compute MFCC deltas, delta-deltas >>> y, sr = librosa.load(librosa.util.example_audio_file()) >>> mfcc = librosa.feature.mfcc(y=y, sr=sr) >>> mfcc_delta = librosa.feature.delta(mfcc) >>> mfcc_delta array([[ 1.666e+01, 1.666e+01, ..., 1.869e-15, 1.869e-15], [ 1.784e+01, 1.784e+01, ..., 6.085e-31, 6.085e-31], ..., [ 7.262e-01, 7.262e-01, ..., 9.259e-31, 9.259e-31], [ 6.578e-01, 6.578e-01, ..., 7.597e-31, 7.597e-31]]) >>> mfcc_delta2 = librosa.feature.delta(mfcc, order=2) >>> mfcc_delta2 array([[ -1.703e+01, -1.703e+01, ..., 3.834e-14, 3.834e-14], [ -1.108e+01, -1.108e+01, ..., -1.068e-30, -1.068e-30], ..., [ 4.075e-01, 4.075e-01, ..., -1.565e-30, -1.565e-30], [ 1.676e-01, 1.676e-01, ..., -2.104e-30, -2.104e-30]]) >>> import matplotlib.pyplot as plt >>> plt.subplot(3, 1, 1) >>> librosa.display.specshow(mfcc) >>> plt.title('MFCC') >>> plt.colorbar() >>> plt.subplot(3, 1, 2) >>> librosa.display.specshow(mfcc_delta) >>> plt.title(r'MFCC-$\Delta$') >>> plt.colorbar() >>> plt.subplot(3, 1, 3) >>> librosa.display.specshow(mfcc_delta2, x_axis='time') >>> plt.title(r'MFCC-$\Delta^2$') >>> plt.colorbar() >>> plt.tight_layout()
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python
test
Azure/azure-sdk-for-python
azure-mgmt-servermanager/azure/mgmt/servermanager/operations/power_shell_operations.py
https://github.com/Azure/azure-sdk-for-python/blob/d7306fde32f60a293a7567678692bdad31e4b667/azure-mgmt-servermanager/azure/mgmt/servermanager/operations/power_shell_operations.py#L328-L381
def update_command( self, resource_group_name, node_name, session, pssession, custom_headers=None, raw=False, polling=True, **operation_config): """Updates a running PowerShell command with more data. :param resource_group_name: The resource group name uniquely identifies the resource group within the user subscriptionId. :type resource_group_name: str :param node_name: The node name (256 characters maximum). :type node_name: str :param session: The sessionId from the user. :type session: str :param pssession: The PowerShell sessionId from the user. :type pssession: str :param dict custom_headers: headers that will be added to the request :param bool raw: The poller return type is ClientRawResponse, the direct response alongside the deserialized response :param polling: True for ARMPolling, False for no polling, or a polling object for personal polling strategy :return: An instance of LROPoller that returns PowerShellCommandResults or ClientRawResponse<PowerShellCommandResults> if raw==True :rtype: ~msrestazure.azure_operation.AzureOperationPoller[~azure.mgmt.servermanager.models.PowerShellCommandResults] or ~msrestazure.azure_operation.AzureOperationPoller[~msrest.pipeline.ClientRawResponse[~azure.mgmt.servermanager.models.PowerShellCommandResults]] :raises: :class:`ErrorException<azure.mgmt.servermanager.models.ErrorException>` """ raw_result = self._update_command_initial( resource_group_name=resource_group_name, node_name=node_name, session=session, pssession=pssession, custom_headers=custom_headers, raw=True, **operation_config ) def get_long_running_output(response): deserialized = self._deserialize('PowerShellCommandResults', response) if raw: client_raw_response = ClientRawResponse(deserialized, response) return client_raw_response return deserialized lro_delay = operation_config.get( 'long_running_operation_timeout', self.config.long_running_operation_timeout) if polling is True: polling_method = ARMPolling(lro_delay, **operation_config) elif polling is False: polling_method = NoPolling() else: polling_method = polling return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
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python
test
pypa/pipenv
pipenv/vendor/pyparsing.py
https://github.com/pypa/pipenv/blob/cae8d76c210b9777e90aab76e9c4b0e53bb19cde/pipenv/vendor/pyparsing.py#L219-L227
def _xml_escape(data): """Escape &, <, >, ", ', etc. in a string of data.""" # ampersand must be replaced first from_symbols = '&><"\'' to_symbols = ('&'+s+';' for s in "amp gt lt quot apos".split()) for from_,to_ in zip(from_symbols, to_symbols): data = data.replace(from_, to_) return data
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Escape &, <, >, ", ', etc. in a string of data.
[ "Escape", "&", "<", ">", "etc", ".", "in", "a", "string", "of", "data", "." ]
python
train
KrzyHonk/bpmn-python
bpmn_python/bpmn_diagram_import.py
https://github.com/KrzyHonk/bpmn-python/blob/6e5e28e3d656dbf5bd3d85d78fe8e3f2fb462629/bpmn_python/bpmn_diagram_import.py#L381-L393
def import_task_to_graph(diagram_graph, process_id, process_attributes, task_element): """ Adds to graph the new element that represents BPMN task. In our representation tasks have only basic attributes and elements, inherited from Activity type, so this method only needs to call add_flownode_to_graph. :param diagram_graph: NetworkX graph representing a BPMN process diagram, :param process_id: string object, representing an ID of process element, :param process_attributes: dictionary that holds attribute values of 'process' element, which is parent of imported flow node, :param task_element: object representing a BPMN XML 'task' element. """ BpmnDiagramGraphImport.import_activity_to_graph(diagram_graph, process_id, process_attributes, task_element)
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Adds to graph the new element that represents BPMN task. In our representation tasks have only basic attributes and elements, inherited from Activity type, so this method only needs to call add_flownode_to_graph. :param diagram_graph: NetworkX graph representing a BPMN process diagram, :param process_id: string object, representing an ID of process element, :param process_attributes: dictionary that holds attribute values of 'process' element, which is parent of imported flow node, :param task_element: object representing a BPMN XML 'task' element.
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python
train
bububa/pyTOP
pyTOP/packages/requests/hooks.py
https://github.com/bububa/pyTOP/blob/1e48009bcfe886be392628244b370e6374e1f2b2/pyTOP/packages/requests/hooks.py#L28-L40
def dispatch_hook(key, hooks, hook_data): """Dispatches a hook dictionary on a given piece of data.""" hooks = hooks or dict() if key in hooks: try: return hooks.get(key).__call__(hook_data) or hook_data except Exception, why: warnings.warn(str(why)) return hook_data
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Dispatches a hook dictionary on a given piece of data.
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python
train
googleapis/gax-python
google/gax/api_callable.py
https://github.com/googleapis/gax-python/blob/309aedfcfd48e4c8fa22dd60e9c84c3cc71bb20e/google/gax/api_callable.py#L363-L382
def _catch_errors(a_func, to_catch): """Updates a_func to wrap exceptions with GaxError Args: a_func (callable): A callable. to_catch (list[Exception]): Configures the exceptions to wrap. Returns: Callable: A function that will wrap certain exceptions with GaxError """ def inner(*args, **kwargs): """Wraps specified exceptions""" try: return a_func(*args, **kwargs) # pylint: disable=catching-non-exception except tuple(to_catch) as exception: utils.raise_with_traceback( gax.errors.create_error('RPC failed', cause=exception)) return inner
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Updates a_func to wrap exceptions with GaxError Args: a_func (callable): A callable. to_catch (list[Exception]): Configures the exceptions to wrap. Returns: Callable: A function that will wrap certain exceptions with GaxError
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python
train
saltstack/salt
salt/modules/grafana4.py
https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/modules/grafana4.py#L930-L954
def get_datasource(name, orgname=None, profile='grafana'): ''' Show a single datasource in an organisation. name Name of the datasource. orgname Name of the organization. profile Configuration profile used to connect to the Grafana instance. Default is 'grafana'. CLI Example: .. code-block:: bash salt '*' grafana4.get_datasource <name> <orgname> ''' data = get_datasources(orgname=orgname, profile=profile) for datasource in data: if datasource['name'] == name: return datasource return None
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Show a single datasource in an organisation. name Name of the datasource. orgname Name of the organization. profile Configuration profile used to connect to the Grafana instance. Default is 'grafana'. CLI Example: .. code-block:: bash salt '*' grafana4.get_datasource <name> <orgname>
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python
train
jbloomlab/phydms
phydmslib/models.py
https://github.com/jbloomlab/phydms/blob/9cdebc10bafbe543c552d79486c7f950780ed3c0/phydmslib/models.py#L790-L808
def _update_dprx(self): """Update `dprx`.""" if 'beta' in self.freeparams: for r in range(self.nsites): self.dprx['beta'][r] = self.prx[r] * (self.ln_pi_codon[r] - scipy.dot(self.ln_pi_codon[r], self.prx[r])) if 'eta' in self.freeparams: boolterm = scipy.ndarray(N_CODON, dtype='float') with scipy.errstate(divide='raise', under='raise', over='raise', invalid='raise'): for i in range(N_NT - 1): boolterm.fill(0) for j in range(3): boolterm += ((i <= CODON_NT_INDEX[j]).astype('float') / (self.eta[i] - (i == CODON_NT_INDEX[j]).astype( 'float'))) for r in range(self.nsites): self.dprx['eta'][i][r] = self.prx[r] * (boolterm - scipy.dot(boolterm, self.prx[r]) / self.prx[r].sum())
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Update `dprx`.
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python
train
awslabs/aws-sam-cli
samcli/commands/local/lib/sam_api_provider.py
https://github.com/awslabs/aws-sam-cli/blob/c05af5e7378c6f05f7d82ad3f0bca17204177db6/samcli/commands/local/lib/sam_api_provider.py#L201-L222
def _normalize_apis(apis): """ Normalize the APIs to use standard method name Parameters ---------- apis : list of samcli.commands.local.lib.provider.Api List of APIs to replace normalize Returns ------- list of samcli.commands.local.lib.provider.Api List of normalized APIs """ result = list() for api in apis: for normalized_method in SamApiProvider._normalize_http_methods(api.method): # _replace returns a copy of the namedtuple. This is the official way of creating copies of namedtuple result.append(api._replace(method=normalized_method)) return result
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Normalize the APIs to use standard method name Parameters ---------- apis : list of samcli.commands.local.lib.provider.Api List of APIs to replace normalize Returns ------- list of samcli.commands.local.lib.provider.Api List of normalized APIs
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python
train
dnephin/PyStaticConfiguration
staticconf/config.py
https://github.com/dnephin/PyStaticConfiguration/blob/229733270bc0dc0d9690ba850dbfb470e535c212/staticconf/config.py#L436-L451
def build_compare_func(err_logger=None): """Returns a compare_func that can be passed to MTimeComparator. The returned compare_func first tries os.path.getmtime(filename), then calls err_logger(filename) if that fails. If err_logger is None, then it does nothing. err_logger is always called within the context of an OSError raised by os.path.getmtime(filename). Information on this error can be retrieved by calling sys.exc_info inside of err_logger.""" def compare_func(filename): try: return os.path.getmtime(filename) except OSError: if err_logger is not None: err_logger(filename) return -1 return compare_func
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Returns a compare_func that can be passed to MTimeComparator. The returned compare_func first tries os.path.getmtime(filename), then calls err_logger(filename) if that fails. If err_logger is None, then it does nothing. err_logger is always called within the context of an OSError raised by os.path.getmtime(filename). Information on this error can be retrieved by calling sys.exc_info inside of err_logger.
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python
train
jealous/stockstats
stockstats.py
https://github.com/jealous/stockstats/blob/a479a504ea1906955feeb8519c34ef40eb48ec9b/stockstats.py#L392-L405
def _get_tr(cls, df): """ True Range of the trading tr = max[(high - low), abs(high - close_prev), abs(low - close_prev)] :param df: data :return: None """ prev_close = df['close_-1_s'] high = df['high'] low = df['low'] c1 = high - low c2 = np.abs(high - prev_close) c3 = np.abs(low - prev_close) df['tr'] = np.max((c1, c2, c3), axis=0)
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True Range of the trading tr = max[(high - low), abs(high - close_prev), abs(low - close_prev)] :param df: data :return: None
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python
train
materialsproject/pymatgen
pymatgen/analysis/gb/grain.py
https://github.com/materialsproject/pymatgen/blob/4ca558cf72f8d5f8a1f21dfdfc0181a971c186da/pymatgen/analysis/gb/grain.py#L326-L668
def gb_from_parameters(self, rotation_axis, rotation_angle, expand_times=4, vacuum_thickness=0.0, ab_shift=[0, 0], normal=False, ratio=None, plane=None, max_search=20, tol_coi=1.e-8, rm_ratio=0.7, quick_gen=False): """ Args: rotation_axis (list): Rotation axis of GB in the form of a list of integer e.g.: [1, 1, 0] rotation_angle (float, in unit of degree): rotation angle used to generate GB. Make sure the angle is accurate enough. You can use the enum* functions in this class to extract the accurate angle. e.g.: The rotation angle of sigma 3 twist GB with the rotation axis [1, 1, 1] and GB plane (1, 1, 1) can be 60.000000000 degree. If you do not know the rotation angle, but know the sigma value, we have provide the function get_rotation_angle_from_sigma which is able to return all the rotation angles of sigma value you provided. expand_times (int): The multiple times used to expand one unit grain to larger grain. This is used to tune the grain length of GB to warrant that the two GBs in one cell do not interact with each other. Default set to 4. vacuum_thickness (float, in angstrom): The thickness of vacuum that you want to insert between two grains of the GB. Default to 0. ab_shift (list of float, in unit of a, b vectors of Gb): in plane shift of two grains normal (logic): determine if need to require the c axis of top grain (first transformation matrix) perperdicular to the surface or not. default to false. ratio (list of integers): lattice axial ratio. For cubic system, ratio is not needed. For tetragonal system, ratio = [mu, mv], list of two integers, that is, mu/mv = c2/a2. If it is irrational, set it to none. For orthorhombic system, ratio = [mu, lam, mv], list of three integers, that is, mu:lam:mv = c2:b2:a2. If irrational for one axis, set it to None. e.g. mu:lam:mv = c2,None,a2, means b2 is irrational. For rhombohedral system, ratio = [mu, mv], list of two integers, that is, mu/mv is the ratio of (1+2*cos(alpha))/cos(alpha). If irrational, set it to None. For hexagonal system, ratio = [mu, mv], list of two integers, that is, mu/mv = c2/a2. If it is irrational, set it to none. This code also supplies a class method to generate the ratio from the structure (get_ratio). User can also make their own approximation and input the ratio directly. plane (list): Grain boundary plane in the form of a list of integers e.g.: [1, 2, 3]. If none, we set it as twist GB. The plane will be perpendicular to the rotation axis. max_search (int): max search for the GB lattice vectors that give the smallest GB lattice. If normal is true, also max search the GB c vector that perpendicular to the plane. For complex GB, if you want to speed up, you can reduce this value. But too small of this value may lead to error. tol_coi (float): tolerance to find the coincidence sites. When making approximations to the ratio needed to generate the GB, you probably need to increase this tolerance to obtain the correct number of coincidence sites. To check the number of coincidence sites are correct or not, you can compare the generated Gb object's sigma_from_site_prop with enum* sigma values (what user expected by input). rm_ratio (float): the criteria to remove the atoms which are too close with each other. rm_ratio*bond_length of bulk system is the criteria of bond length, below which the atom will be removed. Default to 0.7. quick_gen (bool): whether to quickly generate a supercell, if set to true, no need to find the smallest cell. Returns: Grain boundary structure (gb object). """ lat_type = self.lat_type # if the initial structure is primitive cell in cubic system, # calculate the transformation matrix from its conventional cell # to primitive cell, basically for bcc and fcc systems. trans_cry = np.eye(3) if lat_type == 'c': analyzer = SpacegroupAnalyzer(self.initial_structure) convention_cell = analyzer.get_conventional_standard_structure() vol_ratio = self.initial_structure.volume / convention_cell.volume # bcc primitive cell, belong to cubic system if abs(vol_ratio - 0.5) < 1.e-3: trans_cry = np.array([[0.5, 0.5, -0.5], [-0.5, 0.5, 0.5], [0.5, -0.5, 0.5]]) logger.info("Make sure this is for cubic with bcc primitive cell") # fcc primitive cell, belong to cubic system elif abs(vol_ratio - 0.25) < 1.e-3: trans_cry = np.array([[0.5, 0.5, 0], [0, 0.5, 0.5], [0.5, 0, 0.5]]) logger.info("Make sure this is for cubic with fcc primitive cell") else: logger.info("Make sure this is for cubic with conventional cell") elif lat_type == 't': logger.info("Make sure this is for tetragonal system") if ratio is None: logger.info('Make sure this is for irrational c2/a2') elif len(ratio) != 2: raise RuntimeError('Tetragonal system needs correct c2/a2 ratio') elif lat_type == 'o': logger.info('Make sure this is for orthorhombic system') if ratio is None: raise RuntimeError('CSL does not exist if all axial ratios are irrational ' 'for an orthorhombic system') elif len(ratio) != 3: raise RuntimeError('Orthorhombic system needs correct c2:b2:a2 ratio') elif lat_type == 'h': logger.info('Make sure this is for hexagonal system') if ratio is None: logger.info('Make sure this is for irrational c2/a2') elif len(ratio) != 2: raise RuntimeError('Hexagonal system needs correct c2/a2 ratio') elif lat_type == 'r': logger.info('Make sure this is for rhombohedral system') if ratio is None: logger.info('Make sure this is for irrational (1+2*cos(alpha)/cos(alpha) ratio') elif len(ratio) != 2: raise RuntimeError('Rhombohedral system needs correct ' '(1+2*cos(alpha)/cos(alpha) ratio') else: raise RuntimeError('Lattice type not implemented. This code works for cubic, ' 'tetragonal, orthorhombic, rhombehedral, hexagonal systems') # transform four index notation to three index notation for hexagonal and rhombohedral if len(rotation_axis) == 4: u1 = rotation_axis[0] v1 = rotation_axis[1] w1 = rotation_axis[3] if lat_type.lower() == 'h': u = 2 * u1 + v1 v = 2 * v1 + u1 w = w1 rotation_axis = [u, v, w] elif lat_type.lower() == 'r': u = 2 * u1 + v1 + w1 v = v1 + w1 - u1 w = w1 - 2 * v1 - u1 rotation_axis = [u, v, w] # make sure gcd(rotation_axis)==1 if reduce(gcd, rotation_axis) != 1: rotation_axis = [int(round(x / reduce(gcd, rotation_axis))) for x in rotation_axis] # transform four index notation to three index notation for plane if plane is not None: if len(plane) == 4: u1 = plane[0] v1 = plane[1] w1 = plane[3] plane = [u1, v1, w1] # set the plane for grain boundary when plane is None. if plane is None: if lat_type.lower() == 'c': plane = rotation_axis else: if lat_type.lower() == 'h': if ratio is None: c2_a2_ratio = 1 else: c2_a2_ratio = ratio[0] / ratio[1] metric = np.array([[1, -0.5, 0], [-0.5, 1, 0], [0, 0, c2_a2_ratio]]) elif lat_type.lower() == 'r': if ratio is None: cos_alpha = 0.5 else: cos_alpha = 1.0 / (ratio[0] / ratio[1] - 2) metric = np.array([[1, cos_alpha, cos_alpha], [cos_alpha, 1, cos_alpha], [cos_alpha, cos_alpha, 1]]) elif lat_type.lower() == 't': if ratio is None: c2_a2_ratio = 1 else: c2_a2_ratio = ratio[0] / ratio[1] metric = np.array([[1, 0, 0], [0, 1, 0], [0, 0, c2_a2_ratio]]) elif lat_type.lower() == 'o': for i in range(3): if ratio[i] is None: ratio[i] = 1 metric = np.array([[1, 0, 0], [0, ratio[1] / ratio[2], 0], [0, 0, ratio[0] / ratio[2]]]) else: raise RuntimeError('Lattice type has not implemented.') plane = np.matmul(rotation_axis, metric) fractions = [Fraction(x).limit_denominator() for x in plane] least_mul = reduce(lcm, [f.denominator for f in fractions]) plane = [int(round(x * least_mul)) for x in plane] if reduce(gcd, plane) != 1: index = reduce(gcd, plane) plane = [int(round(x / index)) for x in plane] t1, t2 = self.get_trans_mat(r_axis=rotation_axis, angle=rotation_angle, normal=normal, trans_cry=trans_cry, lat_type=lat_type, ratio=ratio, surface=plane, max_search=max_search, quick_gen=quick_gen) # find the join_plane if lat_type.lower() != 'c': if lat_type.lower() == 'h': if ratio is None: mu, mv = [1, 1] else: mu, mv = ratio trans_cry1 = np.array([[1, 0, 0], [-0.5, np.sqrt(3.0) / 2.0, 0], [0, 0, np.sqrt(mu / mv)]]) elif lat_type.lower() == 'r': if ratio is None: c2_a2_ratio = 1 else: mu, mv = ratio c2_a2_ratio = 3.0 / (2 - 6 * mv / mu) trans_cry1 = np.array([[0.5, np.sqrt(3.0) / 6.0, 1.0 / 3 * np.sqrt(c2_a2_ratio)], [-0.5, np.sqrt(3.0) / 6.0, 1.0 / 3 * np.sqrt(c2_a2_ratio)], [0, -1 * np.sqrt(3.0) / 3.0, 1.0 / 3 * np.sqrt(c2_a2_ratio)]]) else: if lat_type.lower() == 't': if ratio is None: mu, mv = [1, 1] else: mu, mv = ratio lam = mv elif lat_type.lower() == 'o': new_ratio = [1 if v is None else v for v in ratio] mu, lam, mv = new_ratio trans_cry1 = np.array([[1, 0, 0], [0, np.sqrt(lam / mv), 0], [0, 0, np.sqrt(mu / mv)]]) else: trans_cry1 = trans_cry grain_matrix = np.dot(t2, trans_cry1) plane_init = np.cross(grain_matrix[0], grain_matrix[1]) if lat_type.lower() != 'c': plane_init = np.dot(plane_init, trans_cry1.T) join_plane = self.vec_to_surface(plane_init) parent_structure = self.initial_structure.copy() # calculate the bond_length in bulk system. if len(parent_structure) == 1: temp_str = parent_structure.copy() temp_str.make_supercell([1, 1, 2]) distance = temp_str.distance_matrix else: distance = parent_structure.distance_matrix bond_length = np.min(distance[np.nonzero(distance)]) # top grain top_grain = fix_pbc(parent_structure * t1) # obtain the smallest oriended cell if normal and not quick_gen: t_temp = self.get_trans_mat(r_axis=rotation_axis, angle=rotation_angle, normal=False, trans_cry=trans_cry, lat_type=lat_type, ratio=ratio, surface=plane, max_search=max_search) oriended_unit_cell = fix_pbc(parent_structure * t_temp[0]) t_matrix = oriended_unit_cell.lattice.matrix normal_v_plane = np.cross(t_matrix[0], t_matrix[1]) unit_normal_v = normal_v_plane / np.linalg.norm(normal_v_plane) unit_ab_adjust = (t_matrix[2] - np.dot(unit_normal_v, t_matrix[2]) * unit_normal_v) \ / np.dot(unit_normal_v, t_matrix[2]) else: oriended_unit_cell = top_grain.copy() unit_ab_adjust = 0.0 # bottom grain, using top grain's lattice matrix bottom_grain = fix_pbc(parent_structure * t2, top_grain.lattice.matrix) # label both grains with 'top','bottom','top_incident','bottom_incident' n_sites = top_grain.num_sites t_and_b = Structure(top_grain.lattice, top_grain.species + bottom_grain.species, list(top_grain.frac_coords) + list(bottom_grain.frac_coords)) t_and_b_dis = t_and_b.lattice.get_all_distances(t_and_b.frac_coords[0:n_sites], t_and_b.frac_coords[n_sites:n_sites * 2]) index_incident = np.nonzero(t_and_b_dis < np.min(t_and_b_dis) + tol_coi) top_labels = [] for i in range(n_sites): if i in index_incident[0]: top_labels.append('top_incident') else: top_labels.append('top') bottom_labels = [] for i in range(n_sites): if i in index_incident[1]: bottom_labels.append('bottom_incident') else: bottom_labels.append('bottom') top_grain = Structure(Lattice(top_grain.lattice.matrix), top_grain.species, top_grain.frac_coords, site_properties={'grain_label': top_labels}) bottom_grain = Structure(Lattice(bottom_grain.lattice.matrix), bottom_grain.species, bottom_grain.frac_coords, site_properties={'grain_label': bottom_labels}) # expand both grains top_grain.make_supercell([1, 1, expand_times]) bottom_grain.make_supercell([1, 1, expand_times]) top_grain = fix_pbc(top_grain) bottom_grain = fix_pbc(bottom_grain) # determine the top-grain location. edge_b = 1.0 - max(bottom_grain.frac_coords[:, 2]) edge_t = 1.0 - max(top_grain.frac_coords[:, 2]) c_adjust = (edge_t - edge_b) / 2.0 # construct all species all_species = [] all_species.extend([site.specie for site in bottom_grain]) all_species.extend([site.specie for site in top_grain]) half_lattice = top_grain.lattice # calculate translation vector, perpendicular to the plane normal_v_plane = np.cross(half_lattice.matrix[0], half_lattice.matrix[1]) unit_normal_v = normal_v_plane / np.linalg.norm(normal_v_plane) translation_v = unit_normal_v * vacuum_thickness # construct the final lattice whole_matrix_no_vac = np.array(half_lattice.matrix) whole_matrix_no_vac[2] = half_lattice.matrix[2] * 2 whole_matrix_with_vac = whole_matrix_no_vac.copy() whole_matrix_with_vac[2] = whole_matrix_no_vac[2] + translation_v * 2 whole_lat = Lattice(whole_matrix_with_vac) # construct the coords, move top grain with translation_v all_coords = [] grain_labels = bottom_grain.site_properties['grain_label'] \ + top_grain.site_properties['grain_label'] for site in bottom_grain: all_coords.append(site.coords) for site in top_grain: all_coords.append(site.coords + half_lattice.matrix[2] * (1 + c_adjust) + unit_ab_adjust * np.linalg.norm(half_lattice.matrix[2] * (1 + c_adjust)) + translation_v + ab_shift[0] * whole_matrix_with_vac[0] + ab_shift[1] * whole_matrix_with_vac[1]) gb_with_vac = Structure(whole_lat, all_species, all_coords, coords_are_cartesian=True, site_properties={'grain_label': grain_labels}) # merge closer atoms. extract near gb atoms. cos_c_norm_plane = np.dot(unit_normal_v, whole_matrix_with_vac[2]) / whole_lat.c range_c_len = abs(bond_length / cos_c_norm_plane / whole_lat.c) sites_near_gb = [] sites_away_gb = [] for site in gb_with_vac.sites: if site.frac_coords[2] < range_c_len or site.frac_coords[2] > 1 - range_c_len \ or (site.frac_coords[2] > 0.5 - range_c_len and site.frac_coords[2] < 0.5 + range_c_len): sites_near_gb.append(site) else: sites_away_gb.append(site) if len(sites_near_gb) >= 1: s_near_gb = Structure.from_sites(sites_near_gb) s_near_gb.merge_sites(tol=bond_length * rm_ratio, mode='d') all_sites = sites_away_gb + s_near_gb.sites gb_with_vac = Structure.from_sites(all_sites) return GrainBoundary(whole_lat, gb_with_vac.species, gb_with_vac.cart_coords, rotation_axis, rotation_angle, plane, join_plane, self.initial_structure, vacuum_thickness, ab_shift, site_properties=gb_with_vac.site_properties, oriented_unit_cell=oriended_unit_cell, coords_are_cartesian=True)
[ "def", "gb_from_parameters", "(", "self", ",", "rotation_axis", ",", "rotation_angle", ",", "expand_times", "=", "4", ",", "vacuum_thickness", "=", "0.0", ",", "ab_shift", "=", "[", "0", ",", "0", "]", ",", "normal", "=", "False", ",", "ratio", "=", "None", ",", "plane", "=", "None", ",", "max_search", "=", "20", ",", "tol_coi", "=", "1.e-8", ",", "rm_ratio", "=", "0.7", ",", "quick_gen", "=", "False", ")", ":", "lat_type", "=", "self", ".", "lat_type", "# if the initial structure is primitive cell in cubic system,", "# calculate the transformation matrix from its conventional cell", "# to primitive cell, basically for bcc and fcc systems.", "trans_cry", "=", "np", ".", "eye", "(", "3", ")", "if", "lat_type", "==", "'c'", ":", "analyzer", "=", "SpacegroupAnalyzer", "(", "self", ".", "initial_structure", ")", "convention_cell", "=", "analyzer", ".", "get_conventional_standard_structure", "(", ")", "vol_ratio", "=", "self", ".", "initial_structure", ".", "volume", "/", "convention_cell", ".", "volume", "# bcc primitive cell, belong to cubic system", "if", "abs", "(", "vol_ratio", "-", "0.5", ")", "<", "1.e-3", ":", "trans_cry", "=", "np", ".", "array", "(", "[", "[", "0.5", ",", "0.5", ",", "-", "0.5", "]", ",", "[", "-", "0.5", ",", "0.5", ",", "0.5", "]", ",", "[", "0.5", ",", "-", "0.5", ",", "0.5", "]", "]", ")", "logger", ".", "info", "(", "\"Make sure this is for cubic with bcc primitive cell\"", ")", "# fcc primitive cell, belong to cubic system", "elif", "abs", "(", "vol_ratio", "-", "0.25", ")", "<", "1.e-3", ":", "trans_cry", "=", "np", ".", "array", "(", "[", "[", "0.5", ",", "0.5", ",", "0", "]", ",", "[", "0", ",", "0.5", ",", "0.5", "]", ",", "[", "0.5", ",", "0", ",", "0.5", "]", "]", ")", "logger", ".", "info", "(", "\"Make sure this is for cubic with fcc primitive cell\"", ")", "else", ":", "logger", ".", "info", "(", "\"Make sure this is for cubic with conventional cell\"", ")", "elif", "lat_type", "==", "'t'", ":", "logger", ".", "info", "(", "\"Make sure this is for tetragonal system\"", ")", "if", "ratio", "is", "None", ":", "logger", ".", "info", "(", "'Make sure this is for irrational c2/a2'", ")", "elif", "len", "(", "ratio", ")", "!=", "2", ":", "raise", "RuntimeError", "(", "'Tetragonal system needs correct c2/a2 ratio'", ")", "elif", "lat_type", "==", "'o'", ":", "logger", ".", "info", "(", "'Make sure this is for orthorhombic system'", ")", "if", "ratio", "is", "None", ":", "raise", "RuntimeError", "(", "'CSL does not exist if all axial ratios are irrational '", "'for an orthorhombic system'", ")", "elif", "len", "(", "ratio", ")", "!=", "3", ":", "raise", "RuntimeError", "(", "'Orthorhombic system needs correct c2:b2:a2 ratio'", ")", "elif", "lat_type", "==", "'h'", ":", "logger", ".", "info", "(", "'Make sure this is for hexagonal system'", ")", "if", "ratio", "is", "None", ":", "logger", ".", "info", "(", "'Make sure this is for irrational c2/a2'", ")", "elif", "len", "(", "ratio", ")", "!=", "2", ":", "raise", "RuntimeError", "(", "'Hexagonal system needs correct c2/a2 ratio'", ")", "elif", "lat_type", "==", "'r'", ":", "logger", ".", "info", "(", "'Make sure this is for rhombohedral system'", ")", "if", "ratio", "is", "None", ":", "logger", ".", "info", "(", "'Make sure this is for irrational (1+2*cos(alpha)/cos(alpha) ratio'", ")", "elif", "len", "(", "ratio", ")", "!=", "2", ":", "raise", "RuntimeError", "(", "'Rhombohedral system needs correct '", "'(1+2*cos(alpha)/cos(alpha) ratio'", ")", "else", ":", "raise", "RuntimeError", "(", "'Lattice type not implemented. This code works for cubic, '", "'tetragonal, orthorhombic, rhombehedral, hexagonal systems'", ")", "# transform four index notation to three index notation for hexagonal and rhombohedral", "if", "len", "(", "rotation_axis", ")", "==", "4", ":", "u1", "=", "rotation_axis", "[", "0", "]", "v1", "=", "rotation_axis", "[", "1", "]", "w1", "=", "rotation_axis", "[", "3", "]", "if", "lat_type", ".", "lower", "(", ")", "==", "'h'", ":", "u", "=", "2", "*", "u1", "+", "v1", "v", "=", "2", "*", "v1", "+", "u1", "w", "=", "w1", "rotation_axis", "=", "[", "u", ",", "v", ",", "w", "]", "elif", "lat_type", ".", "lower", "(", ")", "==", "'r'", ":", "u", "=", "2", "*", "u1", "+", "v1", "+", "w1", "v", "=", "v1", "+", "w1", "-", "u1", "w", "=", "w1", "-", "2", "*", "v1", "-", "u1", "rotation_axis", "=", "[", "u", ",", "v", ",", "w", "]", "# make sure gcd(rotation_axis)==1", "if", "reduce", "(", "gcd", ",", "rotation_axis", ")", "!=", "1", ":", "rotation_axis", "=", "[", "int", "(", "round", "(", "x", "/", "reduce", "(", "gcd", ",", "rotation_axis", ")", ")", ")", "for", "x", "in", "rotation_axis", "]", "# transform four index notation to three index notation for plane", "if", "plane", "is", "not", "None", ":", "if", "len", "(", "plane", ")", "==", "4", ":", "u1", "=", "plane", "[", "0", "]", "v1", "=", "plane", "[", "1", "]", "w1", "=", "plane", "[", "3", "]", "plane", "=", "[", "u1", ",", "v1", ",", "w1", "]", "# set the plane for grain boundary when plane is None.", "if", "plane", "is", "None", ":", "if", "lat_type", ".", "lower", "(", ")", "==", "'c'", ":", "plane", "=", "rotation_axis", "else", ":", "if", "lat_type", ".", "lower", "(", ")", "==", "'h'", ":", "if", "ratio", "is", "None", ":", "c2_a2_ratio", "=", "1", "else", ":", "c2_a2_ratio", "=", "ratio", "[", "0", "]", "/", "ratio", "[", "1", "]", "metric", "=", "np", ".", "array", "(", "[", "[", "1", ",", "-", "0.5", ",", "0", "]", ",", "[", "-", "0.5", ",", "1", ",", "0", "]", ",", "[", "0", ",", "0", ",", "c2_a2_ratio", "]", "]", ")", "elif", "lat_type", ".", "lower", "(", ")", "==", "'r'", ":", "if", "ratio", "is", "None", ":", "cos_alpha", "=", "0.5", "else", ":", "cos_alpha", "=", "1.0", "/", "(", "ratio", "[", "0", "]", "/", "ratio", "[", "1", "]", "-", "2", ")", "metric", "=", "np", ".", "array", "(", "[", "[", "1", ",", "cos_alpha", ",", "cos_alpha", "]", ",", "[", "cos_alpha", ",", "1", ",", "cos_alpha", "]", ",", "[", "cos_alpha", ",", "cos_alpha", ",", "1", "]", "]", ")", "elif", "lat_type", ".", "lower", "(", ")", "==", "'t'", ":", "if", "ratio", "is", "None", ":", "c2_a2_ratio", "=", "1", "else", ":", "c2_a2_ratio", "=", "ratio", "[", "0", "]", "/", "ratio", "[", "1", "]", "metric", "=", "np", ".", "array", "(", "[", "[", "1", ",", "0", ",", "0", "]", ",", "[", "0", ",", "1", ",", "0", "]", ",", "[", "0", ",", "0", ",", "c2_a2_ratio", "]", "]", ")", "elif", "lat_type", ".", "lower", "(", ")", "==", "'o'", ":", "for", "i", "in", "range", "(", "3", ")", ":", "if", "ratio", "[", "i", "]", "is", "None", ":", "ratio", "[", "i", "]", "=", "1", "metric", "=", "np", ".", "array", "(", "[", "[", "1", ",", "0", ",", "0", "]", ",", "[", "0", ",", "ratio", "[", "1", "]", "/", "ratio", "[", "2", "]", ",", "0", "]", ",", "[", "0", ",", "0", ",", "ratio", "[", "0", "]", "/", "ratio", "[", "2", "]", "]", "]", ")", "else", ":", "raise", "RuntimeError", "(", "'Lattice type has not implemented.'", ")", "plane", "=", "np", ".", "matmul", "(", "rotation_axis", ",", "metric", ")", "fractions", "=", "[", "Fraction", "(", "x", ")", ".", "limit_denominator", "(", ")", "for", "x", "in", "plane", "]", "least_mul", "=", "reduce", "(", "lcm", ",", "[", "f", ".", "denominator", "for", "f", "in", "fractions", "]", ")", "plane", "=", "[", "int", "(", "round", "(", "x", "*", "least_mul", ")", ")", "for", "x", "in", "plane", "]", "if", "reduce", "(", "gcd", ",", "plane", ")", "!=", "1", ":", "index", "=", "reduce", "(", "gcd", ",", "plane", ")", "plane", "=", "[", "int", "(", "round", "(", "x", "/", "index", ")", ")", "for", "x", "in", "plane", "]", "t1", ",", "t2", "=", "self", ".", "get_trans_mat", "(", "r_axis", "=", "rotation_axis", ",", "angle", "=", "rotation_angle", ",", "normal", "=", "normal", ",", "trans_cry", "=", "trans_cry", ",", "lat_type", "=", "lat_type", ",", "ratio", "=", "ratio", ",", "surface", "=", "plane", ",", "max_search", "=", "max_search", ",", "quick_gen", "=", "quick_gen", ")", "# find the join_plane", "if", "lat_type", ".", "lower", "(", ")", "!=", "'c'", ":", "if", "lat_type", ".", "lower", "(", ")", "==", "'h'", ":", "if", "ratio", "is", "None", ":", "mu", ",", "mv", "=", "[", "1", ",", "1", "]", "else", ":", "mu", ",", "mv", "=", "ratio", "trans_cry1", "=", "np", ".", "array", "(", "[", "[", "1", ",", "0", ",", "0", "]", ",", "[", "-", "0.5", ",", "np", ".", "sqrt", "(", "3.0", ")", "/", "2.0", ",", "0", "]", ",", "[", "0", ",", "0", ",", "np", ".", "sqrt", "(", "mu", "/", "mv", ")", "]", "]", ")", "elif", "lat_type", ".", "lower", "(", ")", "==", "'r'", ":", "if", "ratio", "is", "None", ":", "c2_a2_ratio", "=", "1", "else", ":", "mu", ",", "mv", "=", "ratio", "c2_a2_ratio", "=", "3.0", "/", "(", "2", "-", "6", "*", "mv", "/", "mu", ")", "trans_cry1", "=", "np", ".", "array", "(", "[", "[", "0.5", ",", "np", ".", "sqrt", "(", "3.0", ")", "/", "6.0", ",", "1.0", "/", "3", "*", "np", ".", "sqrt", "(", "c2_a2_ratio", ")", "]", ",", "[", "-", "0.5", ",", "np", ".", "sqrt", "(", "3.0", ")", "/", "6.0", ",", "1.0", "/", "3", "*", "np", ".", "sqrt", "(", "c2_a2_ratio", ")", "]", ",", "[", "0", ",", "-", "1", "*", "np", ".", "sqrt", "(", "3.0", ")", "/", "3.0", ",", "1.0", "/", "3", "*", "np", ".", "sqrt", "(", "c2_a2_ratio", ")", "]", "]", ")", "else", ":", "if", "lat_type", ".", "lower", "(", ")", "==", "'t'", ":", "if", "ratio", "is", "None", ":", "mu", ",", "mv", "=", "[", "1", ",", "1", "]", "else", ":", "mu", ",", "mv", "=", "ratio", "lam", "=", "mv", "elif", "lat_type", ".", "lower", "(", ")", "==", "'o'", ":", "new_ratio", "=", "[", "1", "if", "v", "is", "None", "else", "v", "for", "v", "in", "ratio", "]", "mu", ",", "lam", ",", "mv", "=", "new_ratio", "trans_cry1", "=", "np", ".", "array", "(", "[", "[", "1", ",", "0", ",", "0", "]", ",", "[", "0", ",", "np", ".", "sqrt", "(", "lam", "/", "mv", ")", ",", "0", "]", ",", "[", "0", ",", "0", ",", "np", ".", "sqrt", "(", "mu", "/", "mv", ")", "]", "]", ")", "else", ":", "trans_cry1", "=", "trans_cry", "grain_matrix", "=", "np", ".", "dot", "(", "t2", ",", "trans_cry1", ")", "plane_init", "=", "np", ".", "cross", "(", "grain_matrix", "[", "0", "]", ",", "grain_matrix", "[", "1", "]", ")", "if", "lat_type", ".", "lower", "(", ")", "!=", "'c'", ":", "plane_init", "=", "np", ".", "dot", "(", "plane_init", ",", "trans_cry1", ".", "T", ")", "join_plane", "=", "self", ".", "vec_to_surface", "(", "plane_init", ")", "parent_structure", "=", "self", ".", "initial_structure", ".", "copy", "(", ")", "# calculate the bond_length in bulk system.", "if", "len", "(", "parent_structure", ")", "==", "1", ":", "temp_str", "=", "parent_structure", ".", "copy", "(", ")", "temp_str", ".", "make_supercell", "(", "[", "1", ",", "1", ",", "2", "]", ")", "distance", "=", "temp_str", ".", "distance_matrix", "else", ":", "distance", "=", "parent_structure", ".", "distance_matrix", "bond_length", "=", "np", ".", "min", "(", "distance", "[", "np", ".", "nonzero", "(", "distance", ")", "]", ")", "# top grain", "top_grain", "=", "fix_pbc", "(", "parent_structure", "*", "t1", ")", "# obtain the smallest oriended cell", "if", "normal", "and", "not", "quick_gen", ":", "t_temp", "=", "self", ".", "get_trans_mat", "(", "r_axis", "=", "rotation_axis", ",", "angle", "=", "rotation_angle", ",", "normal", "=", "False", ",", "trans_cry", "=", "trans_cry", ",", "lat_type", "=", "lat_type", ",", "ratio", "=", "ratio", ",", "surface", "=", "plane", ",", "max_search", "=", "max_search", ")", "oriended_unit_cell", "=", "fix_pbc", "(", "parent_structure", "*", "t_temp", "[", "0", "]", ")", "t_matrix", "=", "oriended_unit_cell", ".", "lattice", ".", "matrix", "normal_v_plane", "=", "np", ".", "cross", "(", "t_matrix", "[", "0", "]", ",", "t_matrix", "[", "1", "]", ")", "unit_normal_v", "=", "normal_v_plane", "/", "np", ".", "linalg", ".", "norm", "(", "normal_v_plane", ")", "unit_ab_adjust", "=", "(", "t_matrix", "[", "2", "]", "-", "np", ".", "dot", "(", "unit_normal_v", ",", "t_matrix", "[", "2", "]", ")", "*", "unit_normal_v", ")", "/", "np", ".", "dot", "(", "unit_normal_v", ",", "t_matrix", "[", "2", "]", ")", "else", ":", "oriended_unit_cell", "=", "top_grain", ".", "copy", "(", ")", "unit_ab_adjust", "=", "0.0", "# bottom grain, using top grain's lattice matrix", "bottom_grain", "=", "fix_pbc", "(", "parent_structure", "*", "t2", ",", "top_grain", ".", "lattice", ".", "matrix", ")", "# label both grains with 'top','bottom','top_incident','bottom_incident'", "n_sites", "=", "top_grain", ".", "num_sites", "t_and_b", "=", "Structure", "(", "top_grain", ".", "lattice", ",", "top_grain", ".", "species", "+", "bottom_grain", ".", "species", ",", "list", "(", "top_grain", ".", "frac_coords", ")", "+", "list", "(", "bottom_grain", ".", "frac_coords", ")", ")", "t_and_b_dis", "=", "t_and_b", ".", "lattice", ".", "get_all_distances", "(", "t_and_b", ".", "frac_coords", "[", "0", ":", "n_sites", "]", ",", "t_and_b", ".", "frac_coords", "[", "n_sites", ":", "n_sites", "*", "2", "]", ")", "index_incident", "=", "np", ".", "nonzero", "(", "t_and_b_dis", "<", "np", ".", "min", "(", "t_and_b_dis", ")", "+", "tol_coi", ")", "top_labels", "=", "[", "]", "for", "i", "in", "range", "(", "n_sites", ")", ":", "if", "i", "in", "index_incident", "[", "0", "]", ":", "top_labels", ".", "append", "(", "'top_incident'", ")", "else", ":", "top_labels", ".", "append", "(", "'top'", ")", "bottom_labels", "=", "[", "]", "for", "i", "in", "range", "(", "n_sites", ")", ":", "if", "i", "in", "index_incident", "[", "1", "]", ":", "bottom_labels", ".", "append", "(", "'bottom_incident'", ")", "else", ":", "bottom_labels", ".", "append", "(", "'bottom'", ")", "top_grain", "=", "Structure", "(", "Lattice", "(", "top_grain", ".", "lattice", ".", "matrix", ")", ",", "top_grain", ".", "species", ",", "top_grain", ".", "frac_coords", ",", "site_properties", "=", "{", "'grain_label'", ":", "top_labels", "}", ")", "bottom_grain", "=", "Structure", "(", "Lattice", "(", "bottom_grain", ".", "lattice", ".", "matrix", ")", ",", "bottom_grain", ".", "species", ",", "bottom_grain", ".", "frac_coords", ",", "site_properties", "=", "{", "'grain_label'", ":", "bottom_labels", "}", ")", "# expand both grains", "top_grain", ".", "make_supercell", "(", "[", "1", ",", "1", ",", "expand_times", "]", ")", "bottom_grain", ".", "make_supercell", "(", "[", "1", ",", "1", ",", "expand_times", "]", ")", "top_grain", "=", "fix_pbc", "(", "top_grain", ")", "bottom_grain", "=", "fix_pbc", "(", "bottom_grain", ")", "# determine the top-grain location.", "edge_b", "=", "1.0", "-", "max", "(", "bottom_grain", ".", "frac_coords", "[", ":", ",", "2", "]", ")", "edge_t", "=", "1.0", "-", "max", "(", "top_grain", ".", "frac_coords", "[", ":", ",", "2", "]", ")", "c_adjust", "=", "(", "edge_t", "-", "edge_b", ")", "/", "2.0", "# construct all species", "all_species", "=", "[", "]", "all_species", ".", "extend", "(", "[", "site", ".", "specie", "for", "site", "in", "bottom_grain", "]", ")", "all_species", ".", "extend", "(", "[", "site", ".", "specie", "for", "site", "in", "top_grain", "]", ")", "half_lattice", "=", "top_grain", ".", "lattice", "# calculate translation vector, perpendicular to the plane", "normal_v_plane", "=", "np", ".", "cross", "(", "half_lattice", ".", "matrix", "[", "0", "]", ",", "half_lattice", ".", "matrix", "[", "1", "]", ")", "unit_normal_v", "=", "normal_v_plane", "/", "np", ".", "linalg", ".", "norm", "(", "normal_v_plane", ")", "translation_v", "=", "unit_normal_v", "*", "vacuum_thickness", "# construct the final lattice", "whole_matrix_no_vac", "=", "np", ".", "array", "(", "half_lattice", ".", "matrix", ")", "whole_matrix_no_vac", "[", "2", "]", "=", "half_lattice", ".", "matrix", "[", "2", "]", "*", "2", "whole_matrix_with_vac", "=", "whole_matrix_no_vac", ".", "copy", "(", ")", "whole_matrix_with_vac", "[", "2", "]", "=", "whole_matrix_no_vac", "[", "2", "]", "+", "translation_v", "*", "2", "whole_lat", "=", "Lattice", "(", "whole_matrix_with_vac", ")", "# construct the coords, move top grain with translation_v", "all_coords", "=", "[", "]", "grain_labels", "=", "bottom_grain", ".", "site_properties", "[", "'grain_label'", "]", "+", "top_grain", ".", "site_properties", "[", "'grain_label'", "]", "for", "site", "in", "bottom_grain", ":", "all_coords", ".", "append", "(", "site", ".", "coords", ")", "for", "site", "in", "top_grain", ":", "all_coords", ".", "append", "(", "site", ".", "coords", "+", "half_lattice", ".", "matrix", "[", "2", "]", "*", "(", "1", "+", "c_adjust", ")", "+", "unit_ab_adjust", "*", "np", ".", "linalg", ".", "norm", "(", "half_lattice", ".", "matrix", "[", "2", "]", "*", "(", "1", "+", "c_adjust", ")", ")", "+", "translation_v", "+", "ab_shift", "[", "0", "]", "*", "whole_matrix_with_vac", "[", "0", "]", "+", "ab_shift", "[", "1", "]", "*", "whole_matrix_with_vac", "[", "1", "]", ")", "gb_with_vac", "=", "Structure", "(", "whole_lat", ",", "all_species", ",", "all_coords", ",", "coords_are_cartesian", "=", "True", ",", "site_properties", "=", "{", "'grain_label'", ":", "grain_labels", "}", ")", "# merge closer atoms. extract near gb atoms.", "cos_c_norm_plane", "=", "np", ".", "dot", "(", "unit_normal_v", ",", "whole_matrix_with_vac", "[", "2", "]", ")", "/", "whole_lat", ".", "c", "range_c_len", "=", "abs", "(", "bond_length", "/", "cos_c_norm_plane", "/", "whole_lat", ".", "c", ")", "sites_near_gb", "=", "[", "]", "sites_away_gb", "=", "[", "]", "for", "site", "in", "gb_with_vac", ".", "sites", ":", "if", "site", ".", "frac_coords", "[", "2", "]", "<", 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Args: rotation_axis (list): Rotation axis of GB in the form of a list of integer e.g.: [1, 1, 0] rotation_angle (float, in unit of degree): rotation angle used to generate GB. Make sure the angle is accurate enough. You can use the enum* functions in this class to extract the accurate angle. e.g.: The rotation angle of sigma 3 twist GB with the rotation axis [1, 1, 1] and GB plane (1, 1, 1) can be 60.000000000 degree. If you do not know the rotation angle, but know the sigma value, we have provide the function get_rotation_angle_from_sigma which is able to return all the rotation angles of sigma value you provided. expand_times (int): The multiple times used to expand one unit grain to larger grain. This is used to tune the grain length of GB to warrant that the two GBs in one cell do not interact with each other. Default set to 4. vacuum_thickness (float, in angstrom): The thickness of vacuum that you want to insert between two grains of the GB. Default to 0. ab_shift (list of float, in unit of a, b vectors of Gb): in plane shift of two grains normal (logic): determine if need to require the c axis of top grain (first transformation matrix) perperdicular to the surface or not. default to false. ratio (list of integers): lattice axial ratio. For cubic system, ratio is not needed. For tetragonal system, ratio = [mu, mv], list of two integers, that is, mu/mv = c2/a2. If it is irrational, set it to none. For orthorhombic system, ratio = [mu, lam, mv], list of three integers, that is, mu:lam:mv = c2:b2:a2. If irrational for one axis, set it to None. e.g. mu:lam:mv = c2,None,a2, means b2 is irrational. For rhombohedral system, ratio = [mu, mv], list of two integers, that is, mu/mv is the ratio of (1+2*cos(alpha))/cos(alpha). If irrational, set it to None. For hexagonal system, ratio = [mu, mv], list of two integers, that is, mu/mv = c2/a2. If it is irrational, set it to none. This code also supplies a class method to generate the ratio from the structure (get_ratio). User can also make their own approximation and input the ratio directly. plane (list): Grain boundary plane in the form of a list of integers e.g.: [1, 2, 3]. If none, we set it as twist GB. The plane will be perpendicular to the rotation axis. max_search (int): max search for the GB lattice vectors that give the smallest GB lattice. If normal is true, also max search the GB c vector that perpendicular to the plane. For complex GB, if you want to speed up, you can reduce this value. But too small of this value may lead to error. tol_coi (float): tolerance to find the coincidence sites. When making approximations to the ratio needed to generate the GB, you probably need to increase this tolerance to obtain the correct number of coincidence sites. To check the number of coincidence sites are correct or not, you can compare the generated Gb object's sigma_from_site_prop with enum* sigma values (what user expected by input). rm_ratio (float): the criteria to remove the atoms which are too close with each other. rm_ratio*bond_length of bulk system is the criteria of bond length, below which the atom will be removed. Default to 0.7. quick_gen (bool): whether to quickly generate a supercell, if set to true, no need to find the smallest cell. Returns: Grain boundary structure (gb object).
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python
train
tcalmant/ipopo
pelix/ipopo/handlers/requires.py
https://github.com/tcalmant/ipopo/blob/2f9ae0c44cd9c34ef1a9d50837b3254e75678eb1/pelix/ipopo/handlers/requires.py#L492-L500
def clear(self): """ Cleans up the manager. The manager can't be used after this method has been called """ self.services.clear() self.services = None self._future_value = None super(AggregateDependency, self).clear()
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Cleans up the manager. The manager can't be used after this method has been called
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python
train
lpantano/seqcluster
seqcluster/libs/thinkbayes.py
https://github.com/lpantano/seqcluster/blob/774e23add8cd4fdc83d626cea3bd1f458e7d060d/seqcluster/libs/thinkbayes.py#L1827-L1838
def LogBinomialCoef(n, k): """Computes the log of the binomial coefficient. http://math.stackexchange.com/questions/64716/ approximating-the-logarithm-of-the-binomial-coefficient n: number of trials k: number of successes Returns: float """ return n * log(n) - k * log(k) - (n - k) * log(n - k)
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Computes the log of the binomial coefficient. http://math.stackexchange.com/questions/64716/ approximating-the-logarithm-of-the-binomial-coefficient n: number of trials k: number of successes Returns: float
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python
train
angr/angr
angr/analyses/vfg.py
https://github.com/angr/angr/blob/4e2f97d56af5419ee73bdb30482c8dd8ff5f3e40/angr/analyses/vfg.py#L1215-L1259
def _create_graph(self, return_target_sources=None): """ Create a DiGraph out of the existing edge map. :param return_target_sources: Used for making up those missing returns :returns: A networkx.DiGraph() object """ if return_target_sources is None: # We set it to a defaultdict in order to be consistent with the # actual parameter. return_target_sources = defaultdict(list) cfg = networkx.DiGraph() # The corner case: add a node to the graph if there is only one block if len(self._nodes) == 1: cfg.add_node(self._nodes[next(iter(self._nodes.keys()))]) # Adding edges for tpl, targets in self._exit_targets.items(): basic_block = self._nodes[tpl] # Cannot fail :) for ex, jumpkind in targets: if ex in self._nodes: target_bbl = self._nodes[ex] cfg.add_edge(basic_block, target_bbl, jumpkind=jumpkind) # Add edges for possibly missing returns if basic_block.addr in return_target_sources: for src_irsb_key in \ return_target_sources[basic_block.addr]: cfg.add_edge(self._nodes[src_irsb_key], basic_block, jumpkind="Ijk_Ret") else: # Debugging output def addr_formalize(addr): if addr is None: return "None" else: return "%#08x" % addr s = "([" for addr in ex[:-1]: s += addr_formalize(addr) + ", " s += "] %s)" % addr_formalize(ex[-1]) l.warning("Key %s does not exist.", s) return cfg
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python
train
manns/pyspread
pyspread/src/lib/vlc.py
https://github.com/manns/pyspread/blob/0e2fd44c2e0f06605efc3058c20a43a8c1f9e7e0/pyspread/src/lib/vlc.py#L243-L248
def _Cobject(cls, ctype): """(INTERNAL) New instance from ctypes. """ o = object.__new__(cls) o._as_parameter_ = ctype return o
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(INTERNAL) New instance from ctypes.
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python
train
pandas-dev/pandas
pandas/core/missing.py
https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/missing.py#L445-L460
def _cast_values_for_fillna(values, dtype): """ Cast values to a dtype that algos.pad and algos.backfill can handle. """ # TODO: for int-dtypes we make a copy, but for everything else this # alters the values in-place. Is this intentional? if (is_datetime64_dtype(dtype) or is_datetime64tz_dtype(dtype) or is_timedelta64_dtype(dtype)): values = values.view(np.int64) elif is_integer_dtype(values): # NB: this check needs to come after the datetime64 check above values = ensure_float64(values) return values
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python
train
gem/oq-engine
openquake/hmtk/plotting/seismicity/occurrence/recurrence_plot.py
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hmtk/plotting/seismicity/occurrence/recurrence_plot.py#L80-L93
def _check_completeness_table(completeness, catalogue): """ Generates the completeness table according to different instances """ if isinstance(completeness, np.ndarray) and np.shape(completeness)[1] == 2: return completeness elif isinstance(completeness, float): return np.array([[float(np.min(catalogue.data['year'])), completeness]]) elif completeness is None: return np.array([[float(np.min(catalogue.data['year'])), np.min(catalogue.data['magnitude'])]]) else: raise ValueError('Completeness representation not recognised')
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Generates the completeness table according to different instances
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python
train
ssato/python-anyconfig
src/anyconfig/backend/ini.py
https://github.com/ssato/python-anyconfig/blob/f2f4fb8d8e232aadea866c202e1dd7a5967e2877/src/anyconfig/backend/ini.py#L124-L149
def _load(stream, container, sep=_SEP, dkey=DEFAULTSECT, **kwargs): """ :param stream: File or file-like object provides ini-style conf :param container: any callable to make container :param sep: Seprator string :param dkey: Default section name :return: Dict or dict-like object represents config values """ (kwargs_1, psr) = _make_parser(**kwargs) if IS_PYTHON_3: psr.read_file(stream, **kwargs_1) else: psr.readfp(stream, **kwargs_1) cnf = container() kwargs["sep"] = sep defaults = psr.defaults() if defaults: cnf[dkey] = container(_parsed_items(iteritems(defaults), **kwargs)) for sect in psr.sections(): cnf[sect] = container(_parsed_items(psr.items(sect), **kwargs)) return cnf
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:param stream: File or file-like object provides ini-style conf :param container: any callable to make container :param sep: Seprator string :param dkey: Default section name :return: Dict or dict-like object represents config values
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python
train
Asana/python-asana
asana/resources/gen/sections.py
https://github.com/Asana/python-asana/blob/6deb7a34495db23f44858e53b6bb2c9eccff7872/asana/resources/gen/sections.py#L11-L23
def create_in_project(self, project, params={}, **options): """Creates a new section in a project. Returns the full record of the newly created section. Parameters ---------- project : {Id} The project to create the section in [data] : {Object} Data for the request - name : {String} The text to be displayed as the section name. This cannot be an empty string. """ path = "/projects/%s/sections" % (project) return self.client.post(path, params, **options)
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Creates a new section in a project. Returns the full record of the newly created section. Parameters ---------- project : {Id} The project to create the section in [data] : {Object} Data for the request - name : {String} The text to be displayed as the section name. This cannot be an empty string.
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python
train
DataONEorg/d1_python
client_cli/src/d1_cli/impl/command_parser.py
https://github.com/DataONEorg/d1_python/blob/3ac4d4f3ca052d3e8641a6a329cab526c8ddcb0d/client_cli/src/d1_cli/impl/command_parser.py#L200-L204
def do_reset(self, line): """reset Set all session variables to their default values.""" self._split_args(line, 0, 0) self._command_processor.get_session().reset() self._print_info_if_verbose("Successfully reset session variables")
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reset Set all session variables to their default values.
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python
train
pytest-dev/pytest-xdist
xdist/scheduler/load.py
https://github.com/pytest-dev/pytest-xdist/blob/9fcf8fa636bc69ee6cac9348a6ec20c87f2bb5e4/xdist/scheduler/load.py#L186-L208
def remove_node(self, node): """Remove a node from the scheduler This should be called either when the node crashed or at shutdown time. In the former case any pending items assigned to the node will be re-scheduled. Called by the ``DSession.worker_workerfinished`` and ``DSession.worker_errordown`` hooks. Return the item which was being executing while the node crashed or None if the node has no more pending items. """ pending = self.node2pending.pop(node) if not pending: return # The node crashed, reassing pending items crashitem = self.collection[pending.pop(0)] self.pending.extend(pending) for node in self.node2pending: self.check_schedule(node) return crashitem
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Remove a node from the scheduler This should be called either when the node crashed or at shutdown time. In the former case any pending items assigned to the node will be re-scheduled. Called by the ``DSession.worker_workerfinished`` and ``DSession.worker_errordown`` hooks. Return the item which was being executing while the node crashed or None if the node has no more pending items.
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python
train
wonambi-python/wonambi
wonambi/attr/annotations.py
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/attr/annotations.py#L1501-L1842
def export_sleep_stats(self, filename, lights_off, lights_on): """Create CSV with sleep statistics. Parameters ---------- filename: str Filename for csv export lights_off: float Initial time when sleeper turns off the light (or their phone) to go to sleep, in seconds from recording start lights_on: float Final time when sleeper rises from bed after sleep, in seconds from recording start Returns ------- float or None If there are no epochs scored as sleep, returns None. Otherwise, returns the sleep onset latency, for testing purposes. Note ---- Total dark time and sleep efficiency does NOT subtract epochs marked as Undefined or Unknown. """ epochs = self.get_epochs() ep_starts = [i['start'] for i in epochs] hypno = [i['stage'] for i in epochs] n_ep_per_min = 60 / self.epoch_length first = {} latency = {} for stage in ['NREM1', 'NREM2', 'NREM3', 'REM']: first[stage] = next(((i, j) for i, j in enumerate(epochs) if \ j['stage'] == stage), None) if first[stage] is not None: latency[stage] = (first[stage][1]['start'] - lights_off) / 60 else: first[stage] = nan latency[stage] = nan idx_loff = asarray([abs(x - lights_off) for x in ep_starts]).argmin() idx_lon = asarray([abs(x - lights_on) for x in ep_starts]).argmin() duration = {} for stage in ['NREM1', 'NREM2', 'NREM3', 'REM', 'Wake', 'Movement', 'Artefact']: duration[stage] = hypno[idx_loff:idx_lon].count( stage) / n_ep_per_min slp_onset = sorted(first.values(), key=lambda x: x[1]['start'])[0] wake_up = next((len(epochs) - i, j) for i, j in enumerate( epochs[::-1]) if j['stage'] in ['NREM1', 'NREM2', 'NREM3', 'REM']) total_dark_time = (lights_on - lights_off) / 60 #slp_period_time = (wake_up[1]['start'] - slp_onset[1]['start']) / 60 slp_onset_lat = (slp_onset[1]['start'] - lights_off) / 60 waso = hypno[slp_onset[0]:wake_up[0]].count('Wake') / n_ep_per_min wake = waso + slp_onset_lat total_slp_period = sum((waso, duration['NREM1'], duration['NREM2'], duration['NREM3'], duration['REM'])) total_slp_time = total_slp_period - waso slp_eff = total_slp_time / total_dark_time switch = self.switch() slp_frag = self.slp_frag() dt_format = '%d/%m/%Y %H:%M:%S' loff_str = (self.start_time + timedelta(seconds=lights_off)).strftime( dt_format) lon_str = (self.start_time + timedelta(seconds=lights_on)).strftime( dt_format) slp_onset_str = (self.start_time + timedelta( seconds=slp_onset[1]['start'])).strftime(dt_format) wake_up_str = (self.start_time + timedelta( seconds=wake_up[1]['start'])).strftime(dt_format) slcnrem5 = self.latency_to_consolidated(lights_off, duration=5, stage=['NREM2', 'NREM3']) slcnrem10 = self.latency_to_consolidated(lights_off, duration=10, stage=['NREM2', 'NREM3']) slcn35 = self.latency_to_consolidated(lights_off, duration=5, stage=['NREM3']) slcn310 = self.latency_to_consolidated(lights_off, duration=10, stage=['NREM3']) cycles = self.get_cycles() if self.get_cycles() else [] cyc_stats = [] for i, cyc in enumerate(cycles): one_cyc = {} cyc_hypno = [x['stage'] for x in self.get_epochs(time=cyc)] one_cyc['duration'] = {} for stage in ['NREM1', 'NREM2', 'NREM3', 'REM', 'Wake', 'Movement', 'Artefact']: one_cyc['duration'][stage] = cyc_hypno.count(stage) # in epochs one_cyc['tst'] = sum([one_cyc['duration'][stage] for stage in [ 'NREM1', 'NREM2', 'NREM3', 'REM']]) one_cyc['tsp'] = one_cyc['tst'] + one_cyc['duration']['Wake'] one_cyc['slp_eff'] = one_cyc['tst'] / one_cyc['tsp'] one_cyc['switch'] = self.switch(time=cyc) one_cyc['slp_frag'] = self.slp_frag(time=cyc) cyc_stats.append(one_cyc) with open(filename, 'w', newline='') as f: lg.info('Writing to ' + str(filename)) cf = writer(f) cf.writerow(['Wonambi v{}'.format(__version__)]) cf.writerow(['Variable', 'Acronym', 'Unit 1', 'Value 1', 'Unit 2', 'Value 2', 'Formula']) cf.writerow(['Lights off', 'LOFF', 'dd/mm/yyyy HH:MM:SS', loff_str, 'seconds from recording start', lights_off, 'marker']) cf.writerow(['Lights on', 'LON', 'dd/mm/yyyy HH:MM:SS', lon_str, 'seconds from recording start', lights_on, 'marker']) cf.writerow(['Sleep onset', 'SO', 'dd/mm/yyyy HH:MM:SS', slp_onset_str, 'seconds from recording start', slp_onset[1]['start'], 'first sleep epoch (N1 or N2) - LOFF']) cf.writerow(['Time of last awakening', '', 'dd/mm/yyyy HH:MM:SS', wake_up_str, 'seconds from recording start', wake_up[1]['start'], 'end time of last epoch of N1, N2, N3 or REM']) cf.writerow(['Total dark time (Time in bed)', 'TDT (TIB)', 'Epochs', total_dark_time * n_ep_per_min, 'Minutes', total_dark_time, 'LON - LOFF']) cf.writerow(['Sleep latency', 'SL', 'Epochs', slp_onset_lat * n_ep_per_min, 'Minutes', slp_onset_lat, 'LON - SO']) cf.writerow(['Wake', 'W', 'Epochs', wake * n_ep_per_min, 'Minutes', wake, 'total wake duration between LOFF and LON']) cf.writerow(['Wake after sleep onset', 'WASO', 'Epochs', waso * n_ep_per_min, 'Minutes', waso, 'W - SL']) cf.writerow(['N1 duration', '', 'Epochs', duration['NREM1'] * n_ep_per_min, 'Minutes', duration['NREM1'], 'total N1 duration between LOFF and LON']) cf.writerow(['N2 duration', '', 'Epochs', duration['NREM2'] * n_ep_per_min, 'Minutes', duration['NREM2'], 'total N2 duration between LOFF and LON']) cf.writerow(['N3 duration', '', 'Epochs', duration['NREM3'] * n_ep_per_min, 'Minutes', duration['NREM3'], 'total N3 duration between LOFF and LON']) cf.writerow(['REM duration', '', 'Epochs', duration['REM'] * n_ep_per_min, 'Minutes', duration['REM'], 'total REM duration between LOFF and LON']) cf.writerow(['Artefact duration', '', 'Epochs', duration['Artefact'] * n_ep_per_min, 'Minutes', duration['Artefact'], 'total Artefact duration between LOFF and LON']) cf.writerow(['Movement duration', '', 'Epochs', duration['Movement'] * n_ep_per_min, 'Minutes', duration['Movement'], 'total Movement duration between LOFF and LON']) cf.writerow(['Total sleep period', 'TSP', 'Epochs', total_slp_period * n_ep_per_min, 'Minutes', total_slp_period, 'WASO + N1 + N2 + N3 + REM']) cf.writerow(['Total sleep time', 'TST', 'Epochs', total_slp_time * n_ep_per_min, 'Minutes', total_slp_time, 'N1 + N2 + N3 + REM']) cf.writerow(['Sleep efficiency', 'SE', '%', slp_eff * 100, '', '', 'TST / TDT']) cf.writerow(['W % TSP', '', '%', waso * 100 / total_slp_period, '', '', 'WASO / TSP']) cf.writerow(['N1 % TSP', '', '%', duration['NREM1'] * 100 / total_slp_period, '', '', 'N1 / TSP']) cf.writerow(['N2 % TSP', '', '%', duration['NREM2'] * 100 / total_slp_period, '', '', 'N2 / TSP']) cf.writerow(['N3 % TSP', '', '%', duration['NREM3'] * 100 / total_slp_period, '', '', 'N3 / TSP']) cf.writerow(['REM % TSP', '', '%', duration['REM'] * 100 / total_slp_period, '', '', 'REM / TSP']) cf.writerow(['N1 % TST', '', '%', duration['NREM1'] * 100 / total_slp_time, '', '', 'N1 / TST']) cf.writerow(['N2 % TST', '', '%', duration['NREM2'] * 100 / total_slp_time, '', '', 'N2 / TST']) cf.writerow(['N3 % TST', '', '%', duration['NREM3'] * 100 / total_slp_time, '', '', 'N3 / TST']) cf.writerow(['REM % TST', '', '%', duration['REM'] * 100 / total_slp_time, '', '', 'REM / TST']) cf.writerow(['Switch', '', 'N', switch, '', '', 'number of stage shifts']) cf.writerow(['Switch %', '', '% epochs', switch * 100 / total_slp_period / n_ep_per_min, '% minutes', switch * 100 / total_slp_period, 'switch / TSP']) cf.writerow(['Sleep fragmentation', '', 'N', slp_frag, '', '', ('number of shifts to a lighter stage ' '(W > N1 > N2 > N3; W > N1 > REM)')]) cf.writerow(['Sleep fragmentation index', 'SFI', '% epochs', slp_frag * 100 / total_slp_time / n_ep_per_min, '% minutes', slp_frag * 100 / total_slp_time, 'sleep fragmentation / TST']) cf.writerow(['Sleep latency to N1', 'SLN1', 'Epochs', latency['NREM1'] * n_ep_per_min, 'Minutes', latency['NREM1'], 'first N1 epoch - LOFF']) cf.writerow(['Sleep latency to N2', 'SLN2', 'Epochs', latency['NREM2'] * n_ep_per_min, 'Minutes', latency['NREM2'], 'first N2 epoch - LOFF']) cf.writerow(['Sleep latency to N3', 'SLN3', 'Epochs', latency['NREM3'] * n_ep_per_min, 'Minutes', latency['NREM3'], 'first N3 epoch - LOFF']) cf.writerow(['Sleep latency to REM', 'SLREM', 'Epochs', latency['REM'] * n_ep_per_min, 'Minutes', latency['REM'], 'first REM epoch - LOFF']) cf.writerow(['Sleep latency to consolidated NREM, 5 min', 'SLCNREM5', 'Epochs', slcnrem5 * n_ep_per_min, 'Minutes', slcnrem5, ('start of first uninterrupted 5-minute period of ' 'N2 and/or N3 - LOFF')]) cf.writerow(['Sleep latency to consolidated NREM, 10 min', 'SLCNREM10', 'Epochs', slcnrem10 * n_ep_per_min, 'Minutes', slcnrem10, ('start of first uninterrupted 10-minute period of ' 'N2 and/or N3 - LOFF')]) cf.writerow(['Sleep latency to consolidated N3, 5 min', 'SLCN35', 'Epochs', slcn35 * n_ep_per_min, 'Minutes', slcn35, ('start of first uninterrupted 5-minute period of ' 'N3 - LOFF')]) cf.writerow(['Sleep latency to consolidated N3, 10 min', 'SLCN310', 'Epochs', slcn310 * n_ep_per_min, 'Minutes', slcn310, ('start of first uninterrupted 10-minute period of ' 'N3 - LOFF')]) for i in range(len(cycles)): one_cyc = cyc_stats[i] cf.writerow(['']) cf.writerow([f'Cycle {i + 1}']) cf.writerow(['Cycle % duration', '', '%', (one_cyc['tsp'] * 100 / total_slp_period / n_ep_per_min), '', '', 'cycle TSP / night TSP']) for stage in ['Wake', 'NREM1', 'NREM2', 'NREM3', 'REM', 'Artefact', 'Movement']: cf.writerow([f'{stage} (c{i + 1})', '', 'Epochs', one_cyc['duration'][stage], 'Minutes', one_cyc['duration'][stage] / n_ep_per_min, f'total {stage} duration in cycle {i + 1}']) cf.writerow([f'Total sleep period (c{i + 1})', f'TSP (c{i + 1})', 'Epochs', one_cyc['tsp'], 'Minutes', one_cyc['tsp'] / n_ep_per_min, f'Wake + N1 + N2 + N3 + REM in cycle {i + 1}']) cf.writerow([f'Total sleep time (c{i + 1})', f'TST (c{i + 1})', 'Epochs', one_cyc['tst'], 'Minutes', one_cyc['tst'] / n_ep_per_min, f'N1 + N2 + N3 + REM in cycle {i + 1}']) cf.writerow([f'Sleep efficiency (c{i + 1})', f'SE (c{i + 1})', '%', one_cyc['slp_eff'] * 100, '', '', f'TST / TSP in cycle {i + 1}']) for denom in ['TSP', 'TST']: for stage in ['Wake', 'NREM1', 'NREM2', 'NREM3', 'REM']: cf.writerow([f'{stage} % {denom} (c{i + 1})', '', '%', (one_cyc['duration'][stage] / one_cyc[denom.lower()]) * 100, '', '', f'{stage} / {denom} in cycle {i + 1}']) cf.writerow([f'Switch (c{i + 1})', '', 'N', one_cyc['switch'], '', '', f'number of stage shifts in cycle {i + 1}']) cf.writerow([f'Switch % (c{i + 1})', '', '% epochs', (one_cyc['switch'] * 100 / one_cyc['tsp']), '% minutes', (one_cyc['switch'] * 100 * n_ep_per_min / one_cyc['tsp']), f'switch / TSP in cycle {i + 1}']) cf.writerow([f'Sleep fragmentation (c{i + 1})', '', 'N', one_cyc['slp_frag'], '', '', 'number of shifts to a lighter stage in cycle ' f'{i + 1}']) cf.writerow([f'Sleep fragmentation index (c{i + 1})', f'SFI (c{i + 1})', '% epochs', (one_cyc['slp_frag'] * 100 / one_cyc['tsp']), '% minutes', (one_cyc['slp_frag'] * 100 * n_ep_per_min / one_cyc['tsp']), f'sleep fragmentation / TSP in cycle {i + 1}']) return slp_onset_lat, waso, total_slp_time
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Create CSV with sleep statistics. Parameters ---------- filename: str Filename for csv export lights_off: float Initial time when sleeper turns off the light (or their phone) to go to sleep, in seconds from recording start lights_on: float Final time when sleeper rises from bed after sleep, in seconds from recording start Returns ------- float or None If there are no epochs scored as sleep, returns None. Otherwise, returns the sleep onset latency, for testing purposes. Note ---- Total dark time and sleep efficiency does NOT subtract epochs marked as Undefined or Unknown.
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python
train
davebridges/mousedb
mousedb/animal/views.py
https://github.com/davebridges/mousedb/blob/2a33f6d15d88b1540b05f7232b154fdbf8568580/mousedb/animal/views.py#L213-L215
def dispatch(self, *args, **kwargs): """This decorator sets this view to have restricted permissions.""" return super(StrainUpdate, self).dispatch(*args, **kwargs)
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This decorator sets this view to have restricted permissions.
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python
train
francois-vincent/clingon
clingon/utils.py
https://github.com/francois-vincent/clingon/blob/afc9db073dbc72b2562ce3e444152986a555dcbf/clingon/utils.py#L10-L34
def auto_update_attrs_from_kwargs(method): """ this decorator will update the attributes of an instance object with all the kwargs of the decorated method, updated with the kwargs of the actual call. This saves you from boring typing: self.xxxx = xxxx self.yyyy = yyyy ... in the decorated method (typically __init__) """ def wrapped(self, **kwargs): # method signature introspection argspec = inspect.getargspec(method) defaults = argspec.defaults or () nb_args, nb_defaults = len(argspec.args), len(defaults) # construct a dict of method's keyword arguments options = dict(zip(argspec.args[nb_args - nb_defaults:], defaults)) # update it with actual keyword arguments options.update(kwargs) # update attributes of instance self.__dict__.update(options) method(self, **kwargs) return wrapped
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this decorator will update the attributes of an instance object with all the kwargs of the decorated method, updated with the kwargs of the actual call. This saves you from boring typing: self.xxxx = xxxx self.yyyy = yyyy ... in the decorated method (typically __init__)
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python
train
saltstack/salt
salt/returners/mongo_return.py
https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/returners/mongo_return.py#L218-L227
def get_fun(fun): ''' Return the most recent jobs that have executed the named function ''' conn, mdb = _get_conn(ret=None) ret = {} rdata = mdb.saltReturns.find_one({'fun': fun}, {'_id': 0}) if rdata: ret = rdata return ret
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Return the most recent jobs that have executed the named function
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python
train
django-haystack/pysolr
pysolr.py
https://github.com/django-haystack/pysolr/blob/ee28b39324fa21a99842d297e313c1759d8adbd2/pysolr.py#L1187-L1193
def unload(self, core): """http://wiki.apache.org/solr/CoreAdmin#head-f5055a885932e2c25096a8856de840b06764d143""" params = { 'action': 'UNLOAD', 'core': core, } return self._get_url(self.url, params=params)
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http://wiki.apache.org/solr/CoreAdmin#head-f5055a885932e2c25096a8856de840b06764d143
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python
train
inveniosoftware/invenio-pidstore
invenio_pidstore/models.py
https://github.com/inveniosoftware/invenio-pidstore/blob/8bf35f4e62d5dcaf1a2cfe5803245ba5220a9b78/invenio_pidstore/models.py#L291-L318
def unassign(self): """Unassign the registered object. Note: Only registered PIDs can be redirected so we set it back to registered. :returns: `True` if the PID is successfully unassigned. """ if self.object_uuid is None and self.object_type is None: return True try: with db.session.begin_nested(): if self.is_redirected(): db.session.delete(Redirect.query.get(self.object_uuid)) # Only registered PIDs can be redirected so we set it back # to registered self.status = PIDStatus.REGISTERED self.object_type = None self.object_uuid = None db.session.add(self) except SQLAlchemyError: logger.exception("Failed to unassign object.".format(self), extra=dict(pid=self)) raise logger.info("Unassigned object from {0}.".format(self), extra=dict(pid=self)) return True
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Unassign the registered object. Note: Only registered PIDs can be redirected so we set it back to registered. :returns: `True` if the PID is successfully unassigned.
[ "Unassign", "the", "registered", "object", "." ]
python
train
tanghaibao/jcvi
jcvi/formats/vcf.py
https://github.com/tanghaibao/jcvi/blob/d2e31a77b6ade7f41f3b321febc2b4744d1cdeca/jcvi/formats/vcf.py#L488-L597
def summary(args): """ %prog summary txtfile fastafile The txtfile can be generated by: %prog mstmap --noheader --freq=0 Tabulate on all possible combinations of genotypes and provide results in a nicely-formatted table. Give a fastafile for SNP rate (average # of SNPs per Kb). Only three-column file is supported: locus_id intra- genotype inter- genotype """ from jcvi.utils.cbook import thousands from jcvi.utils.table import tabulate p = OptionParser(summary.__doc__) p.add_option("--counts", help="Print SNP counts in a txt file [default: %default]") p.add_option("--bed", help="Print SNPs locations in a bed file [default: %default]") opts, args = p.parse_args(args) if len(args) != 2: sys.exit(not p.print_help()) txtfile, fastafile = args bedfw = open(opts.bed, "w") if opts.bed else None fp = open(txtfile) header = fp.next().split() # Header snps = defaultdict(list) # contig => list of loci combinations = defaultdict(int) intraSNPs = interSNPs = 0 distinctSet = set() # set of genes that show A-B pattern ref, alt = header[1:3] snpcounts, goodsnpcounts = defaultdict(int), defaultdict(int) for row in fp: atoms = row.split() assert len(atoms) == 3, \ "Only three-column file is supported" locus, intra, inter = atoms ctg, pos = locus.rsplit(".", 1) pos = int(pos) snps[ctg].append(pos) snpcounts[ctg] += 1 if intra == 'X': intraSNPs += 1 if inter in ('B', 'X'): interSNPs += 1 if intra == 'A' and inter == 'B': distinctSet.add(ctg) goodsnpcounts[ctg] += 1 # Tabulate all possible combinations intra = ref + "-" + intra inter = alt + "-" + inter combinations[(intra, inter)] += 1 if bedfw: print("\t".join(str(x) for x in \ (ctg, pos - 1, pos, locus)), file=bedfw) if bedfw: logging.debug("SNP locations written to `{0}`.".format(opts.bed)) bedfw.close() nsites = sum(len(x) for x in snps.values()) sizes = Sizes(fastafile) bpsize = sizes.totalsize snprate = lambda a: a * 1000. / bpsize m = "Dataset `{0}` contains {1} contigs ({2} bp).\n".\ format(fastafile, len(sizes), thousands(bpsize)) m += "A total of {0} SNPs within {1} contigs ({2} bp).\n".\ format(nsites, len(snps), thousands(sum(sizes.mapping[x] for x in snps.keys()))) m += "SNP rate: {0:.1f}/Kb, ".format(snprate(nsites)) m += "IntraSNPs: {0} ({1:.1f}/Kb), InterSNPs: {2} ({3:.1f}/Kb)".\ format(intraSNPs, snprate(intraSNPs), interSNPs, snprate(interSNPs)) print(m, file=sys.stderr) print(tabulate(combinations), file=sys.stderr) leg = "Legend: A - homozygous same, B - homozygous different, X - heterozygous" print(leg, file=sys.stderr) tag = (ref + "-A", alt + "-B") distinctSNPs = combinations[tag] tag = str(tag).replace("'", "") print("A total of {0} disparate {1} SNPs in {2} contigs.".\ format(distinctSNPs, tag, len(distinctSet)), file=sys.stderr) if not opts.counts: return snpcountsfile = opts.counts fw = open(snpcountsfile, "w") header = "\t".join(("Contig", "#_SNPs", "#_AB_SNP")) print(header, file=fw) assert sum(snpcounts.values()) == nsites assert sum(goodsnpcounts.values()) == distinctSNPs for ctg in sorted(snps.keys()): snpcount = snpcounts[ctg] goodsnpcount = goodsnpcounts[ctg] print("\t".join(str(x) for x in (ctg, snpcount, goodsnpcount)), file=fw) fw.close() logging.debug("SNP counts per contig is written to `{0}`.".\ format(snpcountsfile))
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%prog summary txtfile fastafile The txtfile can be generated by: %prog mstmap --noheader --freq=0 Tabulate on all possible combinations of genotypes and provide results in a nicely-formatted table. Give a fastafile for SNP rate (average # of SNPs per Kb). Only three-column file is supported: locus_id intra- genotype inter- genotype
[ "%prog", "summary", "txtfile", "fastafile" ]
python
train
saltstack/salt
salt/utils/reactor.py
https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/utils/reactor.py#L510-L514
def caller(self, fun, **kwargs): ''' Wrap LocalCaller to execute remote exec functions locally on the Minion ''' self.client_cache['caller'].cmd(fun, *kwargs['arg'], **kwargs['kwarg'])
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Wrap LocalCaller to execute remote exec functions locally on the Minion
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python
train
CI-WATER/gsshapy
gsshapy/grid/era_to_gssha.py
https://github.com/CI-WATER/gsshapy/blob/00fd4af0fd65f1614d75a52fe950a04fb0867f4c/gsshapy/grid/era_to_gssha.py#L149-L283
def download_interim_for_gssha(main_directory, start_datetime, end_datetime, leftlon=-180, rightlon=180, toplat=90, bottomlat=-90, precip_only=False): """ Function to download ERA5 data for GSSHA .. note:: https://software.ecmwf.int/wiki/display/WEBAPI/Access+ECMWF+Public+Datasets Args: main_directory(:obj:`str`): Location of the output for the forecast data. start_datetime(:obj:`str`): Datetime for download start. end_datetime(:obj:`str`): Datetime for download end. leftlon(Optional[:obj:`float`]): Left bound for longitude. Default is -180. rightlon(Optional[:obj:`float`]): Right bound for longitude. Default is 180. toplat(Optional[:obj:`float`]): Top bound for latitude. Default is 90. bottomlat(Optional[:obj:`float`]): Bottom bound for latitude. Default is -90. precip_only(Optional[bool]): If True, will only download precipitation. Example:: from gsshapy.grid.era_to_gssha import download_era_interim_for_gssha era_interim_folder = '/era_interim' leftlon = -95 rightlon = -75 toplat = 35 bottomlat = 30 download_era_interim_for_gssha(era5_folder, leftlon, rightlon, toplat, bottomlat) """ # parameters: https://software.ecmwf.int/wiki/display/CKB/Details+of+ERA-Interim+parameters # import here to make sure it is not required to run from ecmwfapi import ECMWFDataServer server = ECMWFDataServer() try: mkdir(main_directory) except OSError: pass download_area = "{toplat}/{leftlon}/{bottomlat}/{rightlon}".format(toplat=toplat, leftlon=leftlon, bottomlat=bottomlat, rightlon=rightlon) download_datetime = start_datetime interim_request = { 'dataset': "interim", # 'oper' specifies the high resolution daily data, as opposed to monthly means, wave, eda edmm, etc. 'stream': "oper", # Surface level, as opposed to pressure level (pl) or model level (ml) 'levtype': "sfc", # The spatial resolution in ERA interim is 80 km globally on a Gaussian grid. # Here we us lat/long with 0.75 degrees, which is approximately the equivalent of 80km. 'grid': "0.5/0.5", 'area': download_area, 'format': 'netcdf', } while download_datetime <= end_datetime: interim_request['date'] = download_datetime.strftime("%Y-%m-%d") if not precip_only: download_file = path.join(main_directory, "erai_gssha_{0}_an.nc".format(download_datetime.strftime("%Y%m%d"))) if not path.exists(download_file): # We want instantaneous parameters, which are archived as type Analysis ('an') as opposed to forecast (fc) interim_request['type'] = "an" # For parameter codes see the ECMWF parameter database at http://apps.ecmwf.int/codes/grib/param-db interim_request['param'] = "2t/2d/sp/10u/10v/tcc" # step 0 is analysis, 3-12 is forecast interim_request['step'] = "0" # ERA Interim provides 6-hourly analysis interim_request['time'] = "00/06/12/18" interim_request['target'] = download_file server.retrieve(interim_request) download_file = path.join(main_directory, "erai_gssha_{0}_1_fc.nc".format(download_datetime.strftime("%Y%m%d"))) if not path.exists(download_file): interim_request['type'] = "fc" interim_request['param'] = "2t/2d/sp/10u/10v/tcc" interim_request['step'] = "3" interim_request['time'] = "00/06/12/18" interim_request['target'] = download_file server.retrieve(interim_request) download_file = path.join(main_directory, "erai_gssha_{0}_fc.nc".format(download_datetime.strftime("%Y%m%d"))) if not path.exists(download_file): interim_request['type'] = "fc" interim_request['param'] = "tp/ssrd" interim_request['step'] = "3/6/9/12" interim_request['time'] = "00/12" interim_request['target'] = download_file server.retrieve(interim_request) # TODO: READ FILE AND MODIFY VALUES SO IT IS NOT INCREMENTAL # https://software.ecmwf.int/wiki/pages/viewpage.action?pageId=56658233 # You need total precipitation for every 6 hours. # Daily total precipitation (tp) is only available with a forecast base time 00:00 and 12:00, # so to get tp for every 6 hours you will need to extract (and for the second and fourth period calculate): # tp(00-06) = (time 00, step 6) # tp(06-12) = (time 00, step 12) minus (time 00, step 6) # tp(12-18) = (time 12, step 6) # tp(18-24) = (time 12, step 12) minus (time 12, step 6) # (Note the units for total precipitation is meters.) tmp_download_file = download_file + '_tmp' with xr.open_dataset(download_file) as xd: diff_xd = xd.diff('time') xd.tp[1:4] = diff_xd.tp[:3] xd.tp[5:] = diff_xd.tp[4:] xd.ssrd[1:4] = diff_xd.ssrd[:3] xd.ssrd[5:] = diff_xd.ssrd[4:] xd.to_netcdf(tmp_download_file) remove(download_file) rename(tmp_download_file, download_file) download_file = path.join(main_directory, "erai_gssha_{0}_0_fc.nc".format(download_datetime.strftime("%Y%m%d"))) if download_datetime <= start_datetime and not path.exists(download_file): loc_download_date = (download_datetime-timedelta(1)).strftime("%Y-%m-%d") interim_request['type'] = "fc" interim_request['param'] = "tp/ssrd" interim_request['step'] = "9/12" interim_request['time'] = "12" interim_request['target'] = download_file interim_request['date'] = loc_download_date server.retrieve(interim_request) # convert to incremental (see above) tmp_download_file = download_file + '_tmp' with xr.open_dataset(download_file) as xd: inc_xd = xd.diff('time') inc_xd.to_netcdf(tmp_download_file) remove(download_file) rename(tmp_download_file, download_file) download_datetime += timedelta(1)
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monthly means, wave, eda edmm, etc.", "'stream'", ":", "\"oper\"", ",", "# Surface level, as opposed to pressure level (pl) or model level (ml)", "'levtype'", ":", "\"sfc\"", ",", "# The spatial resolution in ERA interim is 80 km globally on a Gaussian grid.", "# Here we us lat/long with 0.75 degrees, which is approximately the equivalent of 80km.", "'grid'", ":", "\"0.5/0.5\"", ",", "'area'", ":", "download_area", ",", "'format'", ":", "'netcdf'", ",", "}", "while", "download_datetime", "<=", "end_datetime", ":", "interim_request", "[", "'date'", "]", "=", "download_datetime", ".", "strftime", "(", "\"%Y-%m-%d\"", ")", "if", "not", "precip_only", ":", "download_file", "=", "path", ".", "join", "(", "main_directory", ",", "\"erai_gssha_{0}_an.nc\"", ".", "format", "(", "download_datetime", ".", "strftime", "(", "\"%Y%m%d\"", ")", ")", ")", "if", "not", "path", ".", "exists", "(", "download_file", ")", ":", "# We want instantaneous parameters, which are archived as type Analysis ('an') 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"'step'", "]", "=", "\"3\"", "interim_request", "[", "'time'", "]", "=", "\"00/06/12/18\"", "interim_request", "[", "'target'", "]", "=", "download_file", "server", ".", "retrieve", "(", "interim_request", ")", "download_file", "=", "path", ".", "join", "(", "main_directory", ",", "\"erai_gssha_{0}_fc.nc\"", ".", "format", "(", "download_datetime", ".", "strftime", "(", "\"%Y%m%d\"", ")", ")", ")", "if", "not", "path", ".", "exists", "(", "download_file", ")", ":", "interim_request", "[", "'type'", "]", "=", "\"fc\"", "interim_request", "[", "'param'", "]", "=", "\"tp/ssrd\"", "interim_request", "[", "'step'", "]", "=", "\"3/6/9/12\"", "interim_request", "[", "'time'", "]", "=", "\"00/12\"", "interim_request", "[", "'target'", "]", "=", "download_file", "server", ".", "retrieve", "(", "interim_request", ")", "# TODO: READ FILE AND MODIFY VALUES SO IT IS NOT INCREMENTAL", "# https://software.ecmwf.int/wiki/pages/viewpage.action?pageId=56658233", "# You need total precipitation for every 6 hours.", "# Daily total precipitation (tp) is only available with a forecast base time 00:00 and 12:00,", "# so to get tp for every 6 hours you will need to extract (and for the second and fourth period calculate):", "# tp(00-06) = (time 00, step 6)", "# tp(06-12) = (time 00, step 12) minus (time 00, step 6)", "# tp(12-18) = (time 12, step 6)", "# tp(18-24) = (time 12, step 12) minus (time 12, step 6)", "# (Note the units for total precipitation is meters.)", "tmp_download_file", "=", "download_file", "+", "'_tmp'", "with", "xr", ".", "open_dataset", "(", "download_file", ")", "as", "xd", ":", "diff_xd", "=", "xd", ".", "diff", "(", "'time'", ")", "xd", ".", "tp", "[", "1", ":", "4", "]", "=", "diff_xd", ".", "tp", "[", ":", "3", "]", "xd", ".", "tp", "[", "5", ":", "]", "=", "diff_xd", ".", "tp", "[", "4", ":", "]", "xd", ".", "ssrd", "[", "1", ":", "4", "]", "=", "diff_xd", ".", "ssrd", "[", ":", "3", "]", "xd", ".", "ssrd", "[", "5", ":", "]", "=", "diff_xd", ".", "ssrd", "[", "4", ":", "]", "xd", ".", "to_netcdf", "(", "tmp_download_file", ")", "remove", "(", "download_file", ")", "rename", "(", "tmp_download_file", ",", "download_file", ")", "download_file", "=", "path", ".", "join", "(", "main_directory", ",", "\"erai_gssha_{0}_0_fc.nc\"", ".", "format", "(", "download_datetime", ".", "strftime", "(", "\"%Y%m%d\"", ")", ")", ")", "if", "download_datetime", "<=", "start_datetime", "and", "not", "path", ".", "exists", "(", "download_file", ")", ":", "loc_download_date", "=", "(", "download_datetime", "-", "timedelta", "(", "1", ")", ")", ".", "strftime", "(", "\"%Y-%m-%d\"", ")", "interim_request", "[", "'type'", "]", "=", "\"fc\"", "interim_request", "[", "'param'", "]", "=", "\"tp/ssrd\"", "interim_request", "[", "'step'", "]", "=", "\"9/12\"", "interim_request", "[", "'time'", "]", "=", "\"12\"", "interim_request", "[", "'target'", "]", "=", "download_file", "interim_request", "[", "'date'", "]", "=", "loc_download_date", "server", ".", "retrieve", "(", "interim_request", ")", "# convert to incremental (see above)", "tmp_download_file", "=", "download_file", "+", "'_tmp'", "with", "xr", ".", "open_dataset", "(", "download_file", ")", "as", "xd", ":", "inc_xd", "=", "xd", ".", "diff", "(", "'time'", ")", "inc_xd", ".", "to_netcdf", "(", "tmp_download_file", ")", "remove", "(", "download_file", ")", "rename", "(", "tmp_download_file", ",", "download_file", ")", "download_datetime", "+=", "timedelta", "(", "1", ")" ]
Function to download ERA5 data for GSSHA .. note:: https://software.ecmwf.int/wiki/display/WEBAPI/Access+ECMWF+Public+Datasets Args: main_directory(:obj:`str`): Location of the output for the forecast data. start_datetime(:obj:`str`): Datetime for download start. end_datetime(:obj:`str`): Datetime for download end. leftlon(Optional[:obj:`float`]): Left bound for longitude. Default is -180. rightlon(Optional[:obj:`float`]): Right bound for longitude. Default is 180. toplat(Optional[:obj:`float`]): Top bound for latitude. Default is 90. bottomlat(Optional[:obj:`float`]): Bottom bound for latitude. Default is -90. precip_only(Optional[bool]): If True, will only download precipitation. Example:: from gsshapy.grid.era_to_gssha import download_era_interim_for_gssha era_interim_folder = '/era_interim' leftlon = -95 rightlon = -75 toplat = 35 bottomlat = 30 download_era_interim_for_gssha(era5_folder, leftlon, rightlon, toplat, bottomlat)
[ "Function", "to", "download", "ERA5", "data", "for", "GSSHA" ]
python
train
BDNYC/astrodbkit
astrodbkit/astrodb.py
https://github.com/BDNYC/astrodbkit/blob/02c03c5e91aa7c7b0f3b5fa95bcf71e33ffcee09/astrodbkit/astrodb.py#L1755-L1865
def search(self, criterion, table, columns='', fetch=False, radius=1/60., use_converters=False, sql_search=False): """ General search method for tables. For (ra,dec) input in decimal degrees, i.e. (12.3456,-65.4321), returns all sources within 1 arcminute, or the specified radius. For string input, i.e. 'vb10', returns all sources with case-insensitive partial text matches in columns with 'TEXT' data type. For integer input, i.e. 123, returns all exact matches of columns with INTEGER data type. Parameters ---------- criterion: (str, int, sequence, tuple) The text, integer, coordinate tuple, or sequence thereof to search the table with. table: str The name of the table to search columns: sequence Specific column names to search, otherwise searches all columns fetch: bool Return the results of the query as an Astropy table radius: float Radius in degrees in which to search for objects if using (ra,dec). Default: 1/60 degree use_converters: bool Apply converters to columns with custom data types sql_search: bool Perform the search by coordinates in a box defined within the SQL commands, rather than with true angular separations. Faster, but not a true radial search. """ # Get list of columns to search and format properly t = self.query("PRAGMA table_info({})".format(table), unpack=True, fmt='table') all_columns = t['name'].tolist() types = t['type'].tolist() columns = columns or all_columns columns = np.asarray([columns] if isinstance(columns, str) else columns) # Separate good and bad columns and corresponding types badcols = columns[~np.in1d(columns, all_columns)] columns = columns[np.in1d(columns, all_columns)] columns = np.array([c for c in all_columns if c in columns]) types = np.array([t for c, t in zip(all_columns, types) if c in columns])[np.in1d(columns, all_columns)] for col in badcols: print("'{}' is not a column in the {} table.".format(col, table.upper())) # Coordinate search if sys.version_info[0] == 2: str_check = (str, unicode) else: str_check = str results = '' if isinstance(criterion, (tuple, list, np.ndarray)): try: if sql_search: q = "SELECT * FROM {} WHERE ra BETWEEN ".format(table) \ + str(criterion[0] - radius) + " AND " \ + str(criterion[0] + radius) + " AND dec BETWEEN " \ + str(criterion[1] - radius) + " AND " \ + str(criterion[1] + radius) results = self.query(q, fmt='table') else: t = self.query('SELECT id,ra,dec FROM sources', fmt='table') df = t.to_pandas() df[['ra', 'dec']] = df[['ra', 'dec']].apply(pd.to_numeric) # convert everything to floats mask = df['ra'].isnull() df = df[~mask] df['theta'] = df.apply(ang_sep, axis=1, args=(criterion[0], criterion[1])) good = df['theta'] <= radius if sum(good) > 0: params = ", ".join(['{}'.format(s) for s in df[good]['id'].tolist()]) try: results = self.query('SELECT * FROM {} WHERE source_id IN ({})'.format(table, params), fmt='table') except: results = self.query('SELECT * FROM {} WHERE id IN ({})'.format(table, params), fmt='table') except: print("Could not search {} table by coordinates {}. Try again.".format(table.upper(), criterion)) # Text string search of columns with 'TEXT' data type elif isinstance(criterion, str_check) and any(columns) and 'TEXT' in types: try: q = "SELECT * FROM {} WHERE {}".format(table, ' OR '.join([r"REPLACE(" + c + r",' ','') like '%" \ + criterion.replace(' ', '') + r"%'" for c, t in zip(columns,types[np.in1d(columns, all_columns)]) \ if t == 'TEXT'])) results = self.query(q, fmt='table', use_converters=use_converters) except: print("Could not search {} table by string {}. Try again.".format(table.upper(), criterion)) # Integer search of columns with 'INTEGER' data type elif isinstance(criterion, int): try: q = "SELECT * FROM {} WHERE {}".format(table, ' OR '.join(['{}={}'.format(c, criterion) \ for c, t in zip(columns, types[np.in1d(columns, all_columns)]) if t == 'INTEGER'])) results = self.query(q, fmt='table', use_converters=use_converters) except: print("Could not search {} table by id {}. Try again.".format(table.upper(), criterion)) # Problem! else: print("Could not search {} table by '{}'. Try again.".format(table.upper(), criterion)) # Print or return the results if fetch: return results or at.Table(names=columns, dtype=[type_dict[t] for t in types], masked=True) else: if results: pprint(results, title=table.upper()) else: print("No results found for {} in the {} table.".format(criterion, table.upper()))
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General search method for tables. For (ra,dec) input in decimal degrees, i.e. (12.3456,-65.4321), returns all sources within 1 arcminute, or the specified radius. For string input, i.e. 'vb10', returns all sources with case-insensitive partial text matches in columns with 'TEXT' data type. For integer input, i.e. 123, returns all exact matches of columns with INTEGER data type. Parameters ---------- criterion: (str, int, sequence, tuple) The text, integer, coordinate tuple, or sequence thereof to search the table with. table: str The name of the table to search columns: sequence Specific column names to search, otherwise searches all columns fetch: bool Return the results of the query as an Astropy table radius: float Radius in degrees in which to search for objects if using (ra,dec). Default: 1/60 degree use_converters: bool Apply converters to columns with custom data types sql_search: bool Perform the search by coordinates in a box defined within the SQL commands, rather than with true angular separations. Faster, but not a true radial search.
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python
train
UCL-INGI/INGInious
inginious/common/entrypoints.py
https://github.com/UCL-INGI/INGInious/blob/cbda9a9c7f2b8e8eb1e6d7d51f0d18092086300c/inginious/common/entrypoints.py#L20-L48
def filesystem_from_config_dict(config_fs): """ Given a dict containing an entry "module" which contains a FSProvider identifier, parse the configuration and returns a fs_provider. Exits if there is an error. """ if "module" not in config_fs: print("Key 'module' should be defined for the filesystem provider ('fs' configuration option)", file=sys.stderr) exit(1) filesystem_providers = get_filesystems_providers() if config_fs["module"] not in filesystem_providers: print("Unknown filesystem provider "+config_fs["module"], file=sys.stderr) exit(1) fs_class = filesystem_providers[config_fs["module"]] fs_args_needed = fs_class.get_needed_args() fs_args = {} for arg_name, (arg_type, arg_required, _) in fs_args_needed.items(): if arg_name in config_fs: fs_args[arg_name] = arg_type(config_fs[arg_name]) elif arg_required: print("fs option {} is required".format(arg_name), file=sys.stderr) exit(1) try: return fs_class.init_from_args(**fs_args) except: print("Unable to load class " + config_fs["module"], file=sys.stderr) raise
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Given a dict containing an entry "module" which contains a FSProvider identifier, parse the configuration and returns a fs_provider. Exits if there is an error.
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python
train
liftoff/pyminifier
pyminifier/obfuscate.py
https://github.com/liftoff/pyminifier/blob/087ea7b0c8c964f1f907c3f350f5ce281798db86/pyminifier/obfuscate.py#L498-L517
def remap_name(name_generator, names, table=None): """ Produces a series of variable assignments in the form of:: <obfuscated name> = <some identifier> for each item in *names* using *name_generator* to come up with the replacement names. If *table* is provided, replacements will be looked up there before generating a new unique name. """ out = "" for name in names: if table and name in table[0].keys(): replacement = table[0][name] else: replacement = next(name_generator) out += "%s=%s\n" % (replacement, name) return out
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Produces a series of variable assignments in the form of:: <obfuscated name> = <some identifier> for each item in *names* using *name_generator* to come up with the replacement names. If *table* is provided, replacements will be looked up there before generating a new unique name.
[ "Produces", "a", "series", "of", "variable", "assignments", "in", "the", "form", "of", "::" ]
python
train
sorgerlab/indra
indra/sources/trips/processor.py
https://github.com/sorgerlab/indra/blob/79a70415832c5702d7a820c7c9ccc8e25010124b/indra/sources/trips/processor.py#L606-L635
def get_active_forms_state(self): """Extract ActiveForm INDRA Statements.""" for term in self._isolated_terms: act = term.find('features/active') if act is None: continue if act.text == 'TRUE': is_active = True elif act.text == 'FALSE': is_active = False else: logger.warning('Unhandled term activity feature %s' % act.text) agent = self._get_agent_by_id(term.attrib['id'], None) # Skip aggregates for now if not isinstance(agent, Agent): continue # If the Agent state is at the base state then this is not an # ActiveForm statement if _is_base_agent_state(agent): continue # Remove the activity flag since it's irrelevant here agent.activity = None text_term = term.find('text') if text_term is not None: ev_text = text_term.text else: ev_text = None ev = Evidence(source_api='trips', text=ev_text, pmid=self.doc_id) st = ActiveForm(agent, 'activity', is_active, evidence=[ev]) self.statements.append(st)
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Extract ActiveForm INDRA Statements.
[ "Extract", "ActiveForm", "INDRA", "Statements", "." ]
python
train
googleapis/google-cloud-python
firestore/google/cloud/firestore_v1beta1/client.py
https://github.com/googleapis/google-cloud-python/blob/85e80125a59cb10f8cb105f25ecc099e4b940b50/firestore/google/cloud/firestore_v1beta1/client.py#L294-L340
def get_all(self, references, field_paths=None, transaction=None): """Retrieve a batch of documents. .. note:: Documents returned by this method are not guaranteed to be returned in the same order that they are given in ``references``. .. note:: If multiple ``references`` refer to the same document, the server will only return one result. See :meth:`~.firestore_v1beta1.client.Client.field_path` for more information on **field paths**. If a ``transaction`` is used and it already has write operations added, this method cannot be used (i.e. read-after-write is not allowed). Args: references (List[.DocumentReference, ...]): Iterable of document references to be retrieved. field_paths (Optional[Iterable[str, ...]]): An iterable of field paths (``.``-delimited list of field names) to use as a projection of document fields in the returned results. If no value is provided, all fields will be returned. transaction (Optional[~.firestore_v1beta1.transaction.\ Transaction]): An existing transaction that these ``references`` will be retrieved in. Yields: .DocumentSnapshot: The next document snapshot that fulfills the query, or :data:`None` if the document does not exist. """ document_paths, reference_map = _reference_info(references) mask = _get_doc_mask(field_paths) response_iterator = self._firestore_api.batch_get_documents( self._database_string, document_paths, mask, transaction=_helpers.get_transaction_id(transaction), metadata=self._rpc_metadata, ) for get_doc_response in response_iterator: yield _parse_batch_get(get_doc_response, reference_map, self)
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Retrieve a batch of documents. .. note:: Documents returned by this method are not guaranteed to be returned in the same order that they are given in ``references``. .. note:: If multiple ``references`` refer to the same document, the server will only return one result. See :meth:`~.firestore_v1beta1.client.Client.field_path` for more information on **field paths**. If a ``transaction`` is used and it already has write operations added, this method cannot be used (i.e. read-after-write is not allowed). Args: references (List[.DocumentReference, ...]): Iterable of document references to be retrieved. field_paths (Optional[Iterable[str, ...]]): An iterable of field paths (``.``-delimited list of field names) to use as a projection of document fields in the returned results. If no value is provided, all fields will be returned. transaction (Optional[~.firestore_v1beta1.transaction.\ Transaction]): An existing transaction that these ``references`` will be retrieved in. Yields: .DocumentSnapshot: The next document snapshot that fulfills the query, or :data:`None` if the document does not exist.
[ "Retrieve", "a", "batch", "of", "documents", "." ]
python
train
astooke/gtimer
gtimer/public/timedloop.py
https://github.com/astooke/gtimer/blob/2146dab459e5d959feb291821733d3d3ba7c523c/gtimer/public/timedloop.py#L13-L69
def timed_loop(name=None, rgstr_stamps=None, save_itrs=SET['SI'], loop_end_stamp=None, end_stamp_unique=SET['UN'], keep_prev_subdivisions=SET['KS'], keep_end_subdivisions=SET['KS'], quick_print=SET['QP']): """ Instantiate a TimedLoop object for measuring loop iteration timing data. Can be used with either for or while loops. Example:: loop = timed_loop() while x > 0: # or for x in <iterable>: next(loop) # or loop.next() <body of loop, with gtimer stamps> loop.exit() Notes: Can be used as a context manager around the loop, without requiring separate call to exit(). Redundant calls to exit() do no harm. Loop functionality is implemented in the next() or __next__() methods. Each instance can only be used once, so for an inner loop, this function must be called within the outer loop. Any awaiting subdivisions kept at entrance to a loop section will go to the 'UNASSIGNED' position to indicate that they are not properly accounted for in the hierarchy. Likewise for any awaiting subdivisions kept at the end of loop iterations without a named stamp. Args: name (any, optional): Identifier (makes the loop a subdivision), passed through str(). rgstr_stamps (list, tuple, optional): Identifiers, see subdivision(). save_itrs (bool, optional): see subdivision(). loop_end_stamp (any, optional): Identifier, automatic stamp at end of every iteration. end_stamp_unique (bool, optional): see stamp(). keep_prev_subdivisions (bool, optional): Keep awaiting subdivisions on entering loop. keep_end_subdivisions (bool, optional): Keep awaiting subdivisions at end of iterations. quick_print (bool, optional): Named loop only, print at end of each iteration. Returns: TimedLoop: Custom gtimer object for measuring loops. """ return TimedLoop(name=name, rgstr_stamps=rgstr_stamps, save_itrs=save_itrs, loop_end_stamp=loop_end_stamp, end_stamp_unique=end_stamp_unique, keep_prev_subdivisions=keep_prev_subdivisions, keep_end_subdivisions=keep_end_subdivisions)
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Instantiate a TimedLoop object for measuring loop iteration timing data. Can be used with either for or while loops. Example:: loop = timed_loop() while x > 0: # or for x in <iterable>: next(loop) # or loop.next() <body of loop, with gtimer stamps> loop.exit() Notes: Can be used as a context manager around the loop, without requiring separate call to exit(). Redundant calls to exit() do no harm. Loop functionality is implemented in the next() or __next__() methods. Each instance can only be used once, so for an inner loop, this function must be called within the outer loop. Any awaiting subdivisions kept at entrance to a loop section will go to the 'UNASSIGNED' position to indicate that they are not properly accounted for in the hierarchy. Likewise for any awaiting subdivisions kept at the end of loop iterations without a named stamp. Args: name (any, optional): Identifier (makes the loop a subdivision), passed through str(). rgstr_stamps (list, tuple, optional): Identifiers, see subdivision(). save_itrs (bool, optional): see subdivision(). loop_end_stamp (any, optional): Identifier, automatic stamp at end of every iteration. end_stamp_unique (bool, optional): see stamp(). keep_prev_subdivisions (bool, optional): Keep awaiting subdivisions on entering loop. keep_end_subdivisions (bool, optional): Keep awaiting subdivisions at end of iterations. quick_print (bool, optional): Named loop only, print at end of each iteration. Returns: TimedLoop: Custom gtimer object for measuring loops.
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python
train
Knio/pynmea2
pynmea2/nmea_file.py
https://github.com/Knio/pynmea2/blob/c4fc66c6a13dd85ad862b15c516245af6e571456/pynmea2/nmea_file.py#L66-L73
def readline(self): """ Return the next NMEASentence in the file object :return: NMEASentence """ data = self._file.readline() s = self.parse(data) return s
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Return the next NMEASentence in the file object :return: NMEASentence
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python
train
alejandrobll/py-sphviewer
sphviewer/Render.py
https://github.com/alejandrobll/py-sphviewer/blob/f198bd9ed5adfb58ebdf66d169206e609fd46e42/sphviewer/Render.py#L164-L314
def histogram(self,axis=None, **kargs): """ - histogram(axis=None, **kargs): It computes and shows the histogram of the image. This is usefull for choosing a proper scale to the output, or for clipping some values. If axis is None, it selects the current axis to plot the histogram. Keyword arguments: *bins*: Either an integer number of bins or a sequence giving the bins. If *bins* is an integer, *bins* + 1 bin edges will be returned, consistent with :func:`numpy.histogram` for numpy version >= 1.3, and with the *new* = True argument in earlier versions. Unequally spaced bins are supported if *bins* is a sequence. *range*: The lower and upper range of the bins. Lower and upper outliers are ignored. If not provided, *range* is (x.min(), x.max()). Range has no effect if *bins* is a sequence. If *bins* is a sequence or *range* is specified, autoscaling is based on the specified bin range instead of the range of x. *normed*: If *True*, the first element of the return tuple will be the counts normalized to form a probability density, i.e., ``n/(len(x)*dbin)``. In a probability density, the integral of the histogram should be 1; you can verify that with a trapezoidal integration of the probability density function:: pdf, bins, patches = ax.hist(...) print(np.sum(pdf * np.diff(bins))) .. note:: Until numpy release 1.5, the underlying numpy histogram function was incorrect with *normed*=*True* if bin sizes were unequal. MPL inherited that error. It is now corrected within MPL when using earlier numpy versions *weights*: An array of weights, of the same shape as *x*. Each value in *x* only contributes its associated weight towards the bin count (instead of 1). If *normed* is True, the weights are normalized, so that the integral of the density over the range remains 1. *cumulative*: If *True*, then a histogram is computed where each bin gives the counts in that bin plus all bins for smaller values. The last bin gives the total number of datapoints. If *normed* is also *True* then the histogram is normalized such that the last bin equals 1. If *cumulative* evaluates to less than 0 (e.g. -1), the direction of accumulation is reversed. In this case, if *normed* is also *True*, then the histogram is normalized such that the first bin equals 1. *histtype*: [ 'bar' | 'barstacked' | 'step' | 'stepfilled' ] The type of histogram to draw. - 'bar' is a traditional bar-type histogram. If multiple data are given the bars are aranged side by side. - 'barstacked' is a bar-type histogram where multiple data are stacked on top of each other. - 'step' generates a lineplot that is by default unfilled. - 'stepfilled' generates a lineplot that is by default filled. *align*: ['left' | 'mid' | 'right' ] Controls how the histogram is plotted. - 'left': bars are centered on the left bin edges. - 'mid': bars are centered between the bin edges. - 'right': bars are centered on the right bin edges. *orientation*: [ 'horizontal' | 'vertical' ] If 'horizontal', :func:`~matplotlib.pyplot.barh` will be used for bar-type histograms and the *bottom* kwarg will be the left edges. *rwidth*: The relative width of the bars as a fraction of the bin width. If *None*, automatically compute the width. Ignored if *histtype* = 'step' or 'stepfilled'. *log*: If *True*, the histogram axis will be set to a log scale. If *log* is *True* and *x* is a 1D array, empty bins will be filtered out and only the non-empty (*n*, *bins*, *patches*) will be returned. *color*: Color spec or sequence of color specs, one per dataset. Default (*None*) uses the standard line color sequence. *label*: String, or sequence of strings to match multiple datasets. Bar charts yield multiple patches per dataset, but only the first gets the label, so that the legend command will work as expected:: ax.hist(10+2*np.random.randn(1000), label='men') ax.hist(12+3*np.random.randn(1000), label='women', alpha=0.5) ax.legend() kwargs are used to update the properties of the :class:`~matplotlib.patches.Patch` instances returned by *hist*: agg_filter: unknown alpha: float or None animated: [True | False] antialiased or aa: [True | False] or None for default axes: an :class:`~matplotlib.axes.Axes` instance clip_box: a :class:`matplotlib.transforms.Bbox` instance clip_on: [True | False] clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ] color: matplotlib color spec contains: a callable function edgecolor or ec: mpl color spec, or None for default, or 'none' for no color facecolor or fc: mpl color spec, or None for default, or 'none' for no color figure: a :class:`matplotlib.figure.Figure` instance fill: [True | False] gid: an id string hatch: [ '/' | '\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*' ] label: any string linestyle or ls: ['solid' | 'dashed' | 'dashdot' | 'dotted'] linewidth or lw: float or None for default lod: [True | False] path_effects: unknown picker: [None|float|boolean|callable] rasterized: [True | False | None] snap: unknown transform: :class:`~matplotlib.transforms.Transform` instance url: a url string visible: [True | False] zorder: any number """ if(axis == None): axis = plt.gca() axis.hist(self.__image.ravel(), **kargs)
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- histogram(axis=None, **kargs): It computes and shows the histogram of the image. This is usefull for choosing a proper scale to the output, or for clipping some values. If axis is None, it selects the current axis to plot the histogram. Keyword arguments: *bins*: Either an integer number of bins or a sequence giving the bins. If *bins* is an integer, *bins* + 1 bin edges will be returned, consistent with :func:`numpy.histogram` for numpy version >= 1.3, and with the *new* = True argument in earlier versions. Unequally spaced bins are supported if *bins* is a sequence. *range*: The lower and upper range of the bins. Lower and upper outliers are ignored. If not provided, *range* is (x.min(), x.max()). Range has no effect if *bins* is a sequence. If *bins* is a sequence or *range* is specified, autoscaling is based on the specified bin range instead of the range of x. *normed*: If *True*, the first element of the return tuple will be the counts normalized to form a probability density, i.e., ``n/(len(x)*dbin)``. In a probability density, the integral of the histogram should be 1; you can verify that with a trapezoidal integration of the probability density function:: pdf, bins, patches = ax.hist(...) print(np.sum(pdf * np.diff(bins))) .. note:: Until numpy release 1.5, the underlying numpy histogram function was incorrect with *normed*=*True* if bin sizes were unequal. MPL inherited that error. It is now corrected within MPL when using earlier numpy versions *weights*: An array of weights, of the same shape as *x*. Each value in *x* only contributes its associated weight towards the bin count (instead of 1). If *normed* is True, the weights are normalized, so that the integral of the density over the range remains 1. *cumulative*: If *True*, then a histogram is computed where each bin gives the counts in that bin plus all bins for smaller values. The last bin gives the total number of datapoints. If *normed* is also *True* then the histogram is normalized such that the last bin equals 1. If *cumulative* evaluates to less than 0 (e.g. -1), the direction of accumulation is reversed. In this case, if *normed* is also *True*, then the histogram is normalized such that the first bin equals 1. *histtype*: [ 'bar' | 'barstacked' | 'step' | 'stepfilled' ] The type of histogram to draw. - 'bar' is a traditional bar-type histogram. If multiple data are given the bars are aranged side by side. - 'barstacked' is a bar-type histogram where multiple data are stacked on top of each other. - 'step' generates a lineplot that is by default unfilled. - 'stepfilled' generates a lineplot that is by default filled. *align*: ['left' | 'mid' | 'right' ] Controls how the histogram is plotted. - 'left': bars are centered on the left bin edges. - 'mid': bars are centered between the bin edges. - 'right': bars are centered on the right bin edges. *orientation*: [ 'horizontal' | 'vertical' ] If 'horizontal', :func:`~matplotlib.pyplot.barh` will be used for bar-type histograms and the *bottom* kwarg will be the left edges. *rwidth*: The relative width of the bars as a fraction of the bin width. If *None*, automatically compute the width. Ignored if *histtype* = 'step' or 'stepfilled'. *log*: If *True*, the histogram axis will be set to a log scale. If *log* is *True* and *x* is a 1D array, empty bins will be filtered out and only the non-empty (*n*, *bins*, *patches*) will be returned. *color*: Color spec or sequence of color specs, one per dataset. Default (*None*) uses the standard line color sequence. *label*: String, or sequence of strings to match multiple datasets. Bar charts yield multiple patches per dataset, but only the first gets the label, so that the legend command will work as expected:: ax.hist(10+2*np.random.randn(1000), label='men') ax.hist(12+3*np.random.randn(1000), label='women', alpha=0.5) ax.legend() kwargs are used to update the properties of the :class:`~matplotlib.patches.Patch` instances returned by *hist*: agg_filter: unknown alpha: float or None animated: [True | False] antialiased or aa: [True | False] or None for default axes: an :class:`~matplotlib.axes.Axes` instance clip_box: a :class:`matplotlib.transforms.Bbox` instance clip_on: [True | False] clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ] color: matplotlib color spec contains: a callable function edgecolor or ec: mpl color spec, or None for default, or 'none' for no color facecolor or fc: mpl color spec, or None for default, or 'none' for no color figure: a :class:`matplotlib.figure.Figure` instance fill: [True | False] gid: an id string hatch: [ '/' | '\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*' ] label: any string linestyle or ls: ['solid' | 'dashed' | 'dashdot' | 'dotted'] linewidth or lw: float or None for default lod: [True | False] path_effects: unknown picker: [None|float|boolean|callable] rasterized: [True | False | None] snap: unknown transform: :class:`~matplotlib.transforms.Transform` instance url: a url string visible: [True | False] zorder: any number
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python
train
cokelaer/spectrum
src/spectrum/criteria.py
https://github.com/cokelaer/spectrum/blob/bad6c32e3f10e185098748f67bb421b378b06afe/src/spectrum/criteria.py#L219-L229
def FPE(N,rho, k=None): r"""Final prediction error criterion .. math:: FPE(k) = \frac{N + k + 1}{N - k - 1} \rho_k :validation: double checked versus octave. """ #k #todo check convention. agrees with octave fpe = rho * (N + k + 1.) / (N- k -1) return fpe
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r"""Final prediction error criterion .. math:: FPE(k) = \frac{N + k + 1}{N - k - 1} \rho_k :validation: double checked versus octave.
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python
valid
inveniosoftware-attic/invenio-utils
invenio_utils/datacite.py
https://github.com/inveniosoftware-attic/invenio-utils/blob/9a1c6db4e3f1370901f329f510480dd8df188296/invenio_utils/datacite.py#L104-L119
def get_description(self, description_type='Abstract'): """Get DataCite description.""" if 'descriptions' in self.xml: if isinstance(self.xml['descriptions']['description'], list): for description in self.xml['descriptions']['description']: if description_type in description: return description[description_type] elif isinstance(self.xml['descriptions']['description'], dict): description = self.xml['descriptions']['description'] if description_type in description: return description[description_type] elif len(description) == 1: # return the only description return description.values()[0] return None
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Get DataCite description.
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python
train
titusjan/argos
argos/config/stringcti.py
https://github.com/titusjan/argos/blob/20d0a3cae26c36ea789a5d219c02ca7df21279dd/argos/config/stringcti.py#L54-L58
def createEditor(self, delegate, parent, option): """ Creates a StringCtiEditor. For the parameters see the AbstractCti constructor documentation. """ return StringCtiEditor(self, delegate, parent=parent)
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Creates a StringCtiEditor. For the parameters see the AbstractCti constructor documentation.
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python
train
heigeo/climata
climata/acis/__init__.py
https://github.com/heigeo/climata/blob/2028bdbd40e1c8985b0b62f7cb969ce7dfa8f1bd/climata/acis/__init__.py#L80-L93
def parse(self): """ Convert ACIS 'll' value into separate latitude and longitude. """ super(AcisIO, self).parse() # This is more of a "mapping" step than a "parsing" step, but mappers # only allow one-to-one mapping from input fields to output fields. for row in self.data: if 'meta' in row: row = row['meta'] if 'll' in row: row['longitude'], row['latitude'] = row['ll'] del row['ll']
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Convert ACIS 'll' value into separate latitude and longitude.
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python
train
neovim/pynvim
pynvim/msgpack_rpc/event_loop/base.py
https://github.com/neovim/pynvim/blob/5e577188e6d7133f597ad0ce60dc6a4b1314064a/pynvim/msgpack_rpc/event_loop/base.py#L129-L149
def run(self, data_cb): """Run the event loop.""" if self._error: err = self._error if isinstance(self._error, KeyboardInterrupt): # KeyboardInterrupt is not destructive(it may be used in # the REPL). # After throwing KeyboardInterrupt, cleanup the _error field # so the loop may be started again self._error = None raise err self._on_data = data_cb if threading.current_thread() == main_thread: self._setup_signals([signal.SIGINT, signal.SIGTERM]) debug('Entering event loop') self._run() debug('Exited event loop') if threading.current_thread() == main_thread: self._teardown_signals() signal.signal(signal.SIGINT, default_int_handler) self._on_data = None
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Run the event loop.
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python
train
dhermes/bezier
src/bezier/surface.py
https://github.com/dhermes/bezier/blob/4f941f82637a8e70a5b159a9203132192e23406b/src/bezier/surface.py#L698-L748
def subdivide(self): r"""Split the surface into four sub-surfaces. Does so by taking the unit triangle (i.e. the domain of the surface) and splitting it into four sub-triangles .. image:: ../../images/surface_subdivide1.png :align: center Then the surface is re-parameterized via the map to / from the given sub-triangles and the unit triangle. For example, when a degree two surface is subdivided: .. image:: ../../images/surface_subdivide2.png :align: center .. doctest:: surface-subdivide :options: +NORMALIZE_WHITESPACE >>> nodes = np.asfortranarray([ ... [-1.0, 0.5, 2.0, 0.25, 2.0, 0.0], ... [ 0.0, 0.5, 0.0, 1.75, 3.0, 4.0], ... ]) >>> surface = bezier.Surface(nodes, degree=2) >>> _, sub_surface_b, _, _ = surface.subdivide() >>> sub_surface_b <Surface (degree=2, dimension=2)> >>> sub_surface_b.nodes array([[ 1.5 , 0.6875, -0.125 , 1.1875, 0.4375, 0.5 ], [ 2.5 , 2.3125, 1.875 , 1.3125, 1.3125, 0.25 ]]) .. testcleanup:: surface-subdivide import make_images make_images.surface_subdivide1() make_images.surface_subdivide2(surface, sub_surface_b) Returns: Tuple[Surface, Surface, Surface, Surface]: The lower left, central, lower right and upper left sub-surfaces (in that order). """ nodes_a, nodes_b, nodes_c, nodes_d = _surface_helpers.subdivide_nodes( self._nodes, self._degree ) return ( Surface(nodes_a, self._degree, _copy=False), Surface(nodes_b, self._degree, _copy=False), Surface(nodes_c, self._degree, _copy=False), Surface(nodes_d, self._degree, _copy=False), )
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r"""Split the surface into four sub-surfaces. Does so by taking the unit triangle (i.e. the domain of the surface) and splitting it into four sub-triangles .. image:: ../../images/surface_subdivide1.png :align: center Then the surface is re-parameterized via the map to / from the given sub-triangles and the unit triangle. For example, when a degree two surface is subdivided: .. image:: ../../images/surface_subdivide2.png :align: center .. doctest:: surface-subdivide :options: +NORMALIZE_WHITESPACE >>> nodes = np.asfortranarray([ ... [-1.0, 0.5, 2.0, 0.25, 2.0, 0.0], ... [ 0.0, 0.5, 0.0, 1.75, 3.0, 4.0], ... ]) >>> surface = bezier.Surface(nodes, degree=2) >>> _, sub_surface_b, _, _ = surface.subdivide() >>> sub_surface_b <Surface (degree=2, dimension=2)> >>> sub_surface_b.nodes array([[ 1.5 , 0.6875, -0.125 , 1.1875, 0.4375, 0.5 ], [ 2.5 , 2.3125, 1.875 , 1.3125, 1.3125, 0.25 ]]) .. testcleanup:: surface-subdivide import make_images make_images.surface_subdivide1() make_images.surface_subdivide2(surface, sub_surface_b) Returns: Tuple[Surface, Surface, Surface, Surface]: The lower left, central, lower right and upper left sub-surfaces (in that order).
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python
train
richq/cmake-lint
cmakelint/main.py
https://github.com/richq/cmake-lint/blob/058c6c0ed2536abd3e79a51c38ee6e686568e3b3/cmakelint/main.py#L321-L354
def CheckCommandSpaces(filename, linenumber, clean_lines, errors): """ No extra spaces between command and parenthesis """ line = clean_lines.lines[linenumber] match = ContainsCommand(line) if match and len(match.group(2)): errors(filename, linenumber, 'whitespace/extra', "Extra spaces between '%s' and its ()"%(match.group(1))) if match: spaces_after_open = len(_RE_COMMAND_START_SPACES.match(line).group(1)) initial_spaces = GetInitialSpaces(line) initial_linenumber = linenumber end = None while True: line = clean_lines.lines[linenumber] end = _RE_COMMAND_END_SPACES.search(line) if end: break linenumber += 1 if linenumber >= len(clean_lines.lines): break if linenumber == len(clean_lines.lines) and not end: errors(filename, initial_linenumber, 'syntax', 'Unable to find the end of this command') if end: spaces_before_end = len(end.group(1)) initial_spaces = GetInitialSpaces(line) if initial_linenumber != linenumber and spaces_before_end >= initial_spaces: spaces_before_end -= initial_spaces if spaces_after_open != spaces_before_end: errors(filename, initial_linenumber, 'whitespace/mismatch', 'Mismatching spaces inside () after command')
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No extra spaces between command and parenthesis
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python
train
watson-developer-cloud/python-sdk
ibm_watson/discovery_v1.py
https://github.com/watson-developer-cloud/python-sdk/blob/4c2c9df4466fcde88975da9ecd834e6ba95eb353/ibm_watson/discovery_v1.py#L9804-L9809
def _to_dict(self): """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'relations') and self.relations is not None: _dict['relations'] = [x._to_dict() for x in self.relations] return _dict
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Return a json dictionary representing this model.
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python
train
Tanganelli/CoAPthon3
coapthon/resources/resource.py
https://github.com/Tanganelli/CoAPthon3/blob/985763bfe2eb9e00f49ec100c5b8877c2ed7d531/coapthon/resources/resource.py#L110-L118
def etag(self, etag): """ Set the ETag of the resource. :param etag: the ETag """ if not isinstance(etag, bytes): etag = bytes(etag, "utf-8") self._etag.append(etag)
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Set the ETag of the resource. :param etag: the ETag
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python
train
JoelBender/bacpypes
py25/bacpypes/iocb.py
https://github.com/JoelBender/bacpypes/blob/4111b8604a16fa2b7f80d8104a43b9f3e28dfc78/py25/bacpypes/iocb.py#L474-L503
def get(self, block=1, delay=None): """Get a request from a queue, optionally block until a request is available.""" if _debug: IOQueue._debug("get block=%r delay=%r", block, delay) # if the queue is empty and we do not block return None if not block and not self.notempty.isSet(): if _debug: IOQueue._debug(" - not blocking and empty") return None # wait for something to be in the queue if delay: self.notempty.wait(delay) if not self.notempty.isSet(): return None else: self.notempty.wait() # extract the first element priority, iocb = self.queue[0] del self.queue[0] iocb.ioQueue = None # if the queue is empty, clear the event qlen = len(self.queue) if not qlen: self.notempty.clear() # return the request return iocb
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Get a request from a queue, optionally block until a request is available.
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python
train
idlesign/uwsgiconf
uwsgiconf/runtime/monitoring.py
https://github.com/idlesign/uwsgiconf/blob/475407acb44199edbf7e0a66261bfeb51de1afae/uwsgiconf/runtime/monitoring.py#L50-L73
def set(self, value, mode=None): """Sets metric value. :param int|long value: New value. :param str|unicode mode: Update mode. * None - Unconditional update. * max - Sets metric value if it is greater that the current one. * min - Sets metric value if it is less that the current one. :rtype: bool """ if mode == 'max': func = uwsgi.metric_set_max elif mode == 'min': func = uwsgi.metric_set_min else: func = uwsgi.metric_set return func(self.name, value)
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Sets metric value. :param int|long value: New value. :param str|unicode mode: Update mode. * None - Unconditional update. * max - Sets metric value if it is greater that the current one. * min - Sets metric value if it is less that the current one. :rtype: bool
[ "Sets", "metric", "value", "." ]
python
train
GNS3/gns3-server
gns3server/controller/export_project.py
https://github.com/GNS3/gns3-server/blob/a221678448fb5d24e977ef562f81d56aacc89ab1/gns3server/controller/export_project.py#L202-L227
def _export_local_images(project, image, z): """ Take a project file (.gns3) and export images to the zip :param image: Image path :param z: Zipfile instance for the export """ from ..compute import MODULES for module in MODULES: try: img_directory = module.instance().get_images_directory() except NotImplementedError: # Some modules don't have images continue directory = os.path.split(img_directory)[-1:][0] if os.path.exists(image): path = image else: path = os.path.join(img_directory, image) if os.path.exists(path): arcname = os.path.join("images", directory, os.path.basename(image)) z.write(path, arcname) return
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Take a project file (.gns3) and export images to the zip :param image: Image path :param z: Zipfile instance for the export
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python
train
wal-e/wal-e
wal_e/blobstore/__init__.py
https://github.com/wal-e/wal-e/blob/027263860e72a403bc0e1497bb3e67523138e7a2/wal_e/blobstore/__init__.py#L1-L21
def get_blobstore(layout): """Return Blobstore instance for a given storage layout Args: layout (StorageLayout): Target storage layout. """ if layout.is_s3: from wal_e.blobstore import s3 blobstore = s3 elif layout.is_wabs: from wal_e.blobstore import wabs blobstore = wabs elif layout.is_swift: from wal_e.blobstore import swift blobstore = swift elif layout.is_gs: from wal_e.blobstore import gs blobstore = gs elif layout.is_file: from wal_e.blobstore import file blobstore = file return blobstore
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Return Blobstore instance for a given storage layout Args: layout (StorageLayout): Target storage layout.
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python
train
pywbem/pywbem
try/run_central_instances.py
https://github.com/pywbem/pywbem/blob/e54ecb82c2211e289a268567443d60fdd489f1e4/try/run_central_instances.py#L1096-L1118
def show_instances(server, cim_class): """ Display the instances of the CIM_Class defined by cim_class. If the namespace is None, use the interop namespace. Search all namespaces for instances except for CIM_RegisteredProfile """ if cim_class == 'CIM_RegisteredProfile': for inst in server.profiles: print(inst.tomof()) return for ns in server.namespaces: try: insts = server.conn.EnumerateInstances(cim_class, namespace=ns) if len(insts): print('INSTANCES OF %s ns=%s' % (cim_class, ns)) for inst in insts: print(inst.tomof()) except pywbem.Error as er: if er.status_code != pywbem.CIM_ERR_INVALID_CLASS: print('%s namespace %s Enumerate failed for conn=%s\n' 'exception=%s' % (cim_class, ns, server, er))
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Display the instances of the CIM_Class defined by cim_class. If the namespace is None, use the interop namespace. Search all namespaces for instances except for CIM_RegisteredProfile
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python
train
phareous/insteonlocal
insteonlocal/Hub.py
https://github.com/phareous/insteonlocal/blob/a4544a17d143fb285852cb873e862c270d55dd00/insteonlocal/Hub.py#L155-L159
def direct_command_hub(self, command): """Send direct hub command""" self.logger.info("direct_command_hub: Command %s", command) command_url = (self.hub_url + '/3?' + command + "=I=3") return self.post_direct_command(command_url)
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Send direct hub command
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python
train
pypa/pipenv
pipenv/patched/notpip/_internal/download.py
https://github.com/pypa/pipenv/blob/cae8d76c210b9777e90aab76e9c4b0e53bb19cde/pipenv/patched/notpip/_internal/download.py#L950-L971
def _check_download_dir(link, download_dir, hashes): # type: (Link, str, Hashes) -> Optional[str] """ Check download_dir for previously downloaded file with correct hash If a correct file is found return its path else None """ download_path = os.path.join(download_dir, link.filename) if os.path.exists(download_path): # If already downloaded, does its hash match? logger.info('File was already downloaded %s', download_path) if hashes: try: hashes.check_against_path(download_path) except HashMismatch: logger.warning( 'Previously-downloaded file %s has bad hash. ' 'Re-downloading.', download_path ) os.unlink(download_path) return None return download_path return None
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Check download_dir for previously downloaded file with correct hash If a correct file is found return its path else None
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python
train
ansible/tower-cli
setup.py
https://github.com/ansible/tower-cli/blob/a2b151fed93c47725018d3034848cb3a1814bed7/setup.py#L125-L132
def combine_files(*args): """returns a string of all the strings in *args combined together, with two line breaks between them""" file_contents = [] for filename in args: with codecs.open(filename, mode='r', encoding='utf8') as f: file_contents.append(f.read()) return "\n\n".join(file_contents)
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returns a string of all the strings in *args combined together, with two line breaks between them
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python
valid
gplepage/lsqfit
src/lsqfit/__init__.py
https://github.com/gplepage/lsqfit/blob/6a57fd687632c175fccb47d8e8e943cda5e9ce9d/src/lsqfit/__init__.py#L1261-L1280
def dump_pmean(self, filename): """ Dump parameter means (``fit.pmean``) into file ``filename``. ``fit.dump_pmean(filename)`` saves the means of the best-fit parameter values (``fit.pmean``) from a ``nonlinear_fit`` called ``fit``. These values are recovered using ``p0 = nonlinear_fit.load_parameters(filename)`` where ``p0``'s layout is the same as ``fit.pmean``. The saved values can be used to initialize a later fit (``nonlinear_fit`` parameter ``p0``). """ warnings.warn( "nonlinear_fit.dump_pmean deprecated; use pickle.dump instead", DeprecationWarning, ) with open(filename, "wb") as f: if self.p0.shape is not None: pickle.dump(numpy.array(self.pmean), f) else: pickle.dump(collections.OrderedDict(self.pmean), f)
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Dump parameter means (``fit.pmean``) into file ``filename``. ``fit.dump_pmean(filename)`` saves the means of the best-fit parameter values (``fit.pmean``) from a ``nonlinear_fit`` called ``fit``. These values are recovered using ``p0 = nonlinear_fit.load_parameters(filename)`` where ``p0``'s layout is the same as ``fit.pmean``. The saved values can be used to initialize a later fit (``nonlinear_fit`` parameter ``p0``).
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python
train
orsinium/deal
deal/core.py
https://github.com/orsinium/deal/blob/e23c716216543d0080a956250fb45d9e170c3940/deal/core.py#L107-L113
def patched_function(self, *args, **kwargs): """ Step 3. Wrapped function calling. """ result = self.function(*args, **kwargs) self.validate(result) return result
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Step 3. Wrapped function calling.
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python
train
h2oai/datatable
datatable/utils/terminal.py
https://github.com/h2oai/datatable/blob/dd5fba74d2ca85b66f82ae3c1e0b6ea2fd792564/datatable/utils/terminal.py#L144-L159
def wait_for_keypresses(self, refresh_rate=1): """ Listen to user's keystrokes and return them to caller one at a time. The produced values are instances of blessed.keyboard.Keystroke class. If the user did not press anything with the last `refresh_rate` seconds the generator will yield `None`, allowing the caller to perform any updates necessary. This generator is infinite, and thus needs to be stopped explicitly. """ if not self._enable_keyboard: return with self._blessed_term.cbreak(): while True: yield self._blessed_term.inkey(timeout=refresh_rate)
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Listen to user's keystrokes and return them to caller one at a time. The produced values are instances of blessed.keyboard.Keystroke class. If the user did not press anything with the last `refresh_rate` seconds the generator will yield `None`, allowing the caller to perform any updates necessary. This generator is infinite, and thus needs to be stopped explicitly.
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python
train
draios/python-sdc-client
sdcclient/_monitor.py
https://github.com/draios/python-sdc-client/blob/47f83415842048778939b90944f64386a3bcb205/sdcclient/_monitor.py#L36-L69
def get_notifications(self, from_ts, to_ts, state=None, resolved=None): '''**Description** Returns the list of Sysdig Monitor alert notifications. **Arguments** - **from_ts**: filter events by start time. Timestamp format is in UTC (seconds). - **to_ts**: filter events by start time. Timestamp format is in UTC (seconds). - **state**: filter events by alert state. Supported values are ``OK`` and ``ACTIVE``. - **resolved**: filter events by resolution status. Supported values are ``True`` and ``False``. **Success Return Value** A dictionary containing the list of notifications. **Example** `examples/list_alert_notifications.py <https://github.com/draios/python-sdc-client/blob/master/examples/list_alert_notifications.py>`_ ''' params = {} if from_ts is not None: params['from'] = from_ts * 1000000 if to_ts is not None: params['to'] = to_ts * 1000000 if state is not None: params['state'] = state if resolved is not None: params['resolved'] = resolved res = requests.get(self.url + '/api/notifications', headers=self.hdrs, params=params, verify=self.ssl_verify) if not self._checkResponse(res): return [False, self.lasterr] return [True, res.json()]
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**Description** Returns the list of Sysdig Monitor alert notifications. **Arguments** - **from_ts**: filter events by start time. Timestamp format is in UTC (seconds). - **to_ts**: filter events by start time. Timestamp format is in UTC (seconds). - **state**: filter events by alert state. Supported values are ``OK`` and ``ACTIVE``. - **resolved**: filter events by resolution status. Supported values are ``True`` and ``False``. **Success Return Value** A dictionary containing the list of notifications. **Example** `examples/list_alert_notifications.py <https://github.com/draios/python-sdc-client/blob/master/examples/list_alert_notifications.py>`_
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python
test
h2non/pook
pook/mock.py
https://github.com/h2non/pook/blob/e64094e41e4d89d98d2d29af7608ef27dc50cf19/pook/mock.py#L496-L503
def persist(self, status=None): """ Enables persistent mode for the current mock. Returns: self: current Mock instance. """ self._persist = status if type(status) is bool else True
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Enables persistent mode for the current mock. Returns: self: current Mock instance.
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python
test
FutunnOpen/futuquant
futuquant/common/event/eventEngine.py
https://github.com/FutunnOpen/futuquant/blob/1512b321845f92ec9c578ce2689aa4e8482669e4/futuquant/common/event/eventEngine.py#L88-L101
def __process(self, event): """处理事件""" # 检查是否存在对该事件进行监听的处理函数 if event.type_ in self.__handlers: # 若存在,则按顺序将事件传递给处理函数执行 [handler(event) for handler in self.__handlers[event.type_]] # 以上语句为Python列表解析方式的写法,对应的常规循环写法为: #for handler in self.__handlers[event.type_]: #handler(event) # 调用通用处理函数进行处理 if self.__generalHandlers: [handler(event) for handler in self.__generalHandlers]
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处理事件
[ "处理事件" ]
python
train
RJT1990/pyflux
pyflux/garch/garch.py
https://github.com/RJT1990/pyflux/blob/297f2afc2095acd97c12e827dd500e8ea5da0c0f/pyflux/garch/garch.py#L173-L219
def _mean_prediction(self, sigma2, Y, scores, h, t_params): """ Creates a h-step ahead mean prediction Parameters ---------- sigma2 : np.array The past predicted values Y : np.array The past data scores : np.array The past scores h : int How many steps ahead for the prediction t_params : np.array A vector of (transformed) latent variables Returns ---------- h-length vector of mean predictions """ # Create arrays to iteratre over sigma2_exp = sigma2.copy() scores_exp = scores.copy() # Loop over h time periods for t in range(0,h): new_value = t_params[0] # ARCH if self.q != 0: for j in range(1,self.q+1): new_value += t_params[j]*scores_exp[-j] # GARCH if self.p != 0: for k in range(1,self.p+1): new_value += t_params[k+self.q]*sigma2_exp[-k] sigma2_exp = np.append(sigma2_exp,[new_value]) # For indexing consistency scores_exp = np.append(scores_exp,[0]) # expectation of score is zero return sigma2_exp
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Creates a h-step ahead mean prediction Parameters ---------- sigma2 : np.array The past predicted values Y : np.array The past data scores : np.array The past scores h : int How many steps ahead for the prediction t_params : np.array A vector of (transformed) latent variables Returns ---------- h-length vector of mean predictions
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python
train
xguse/table_enforcer
table_enforcer/main_classes.py
https://github.com/xguse/table_enforcer/blob/f3137839574bf8ea933a14ea16a8acba45e3e0c3/table_enforcer/main_classes.py#L287-L294
def recode(self, table: pd.DataFrame, validate=False) -> pd.DataFrame: """Pass the appropriate columns through each recoder function sequentially and return the final result. Args: table (pd.DataFrame): A dataframe on which to apply recoding logic. validate (bool): If ``True``, recoded table must pass validation tests. """ return self._recode_output(self._recode_input(table, validate=validate), validate=validate)
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Pass the appropriate columns through each recoder function sequentially and return the final result. Args: table (pd.DataFrame): A dataframe on which to apply recoding logic. validate (bool): If ``True``, recoded table must pass validation tests.
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python
train
synw/dataswim
dataswim/data/export.py
https://github.com/synw/dataswim/blob/4a4a53f80daa7cd8e8409d76a19ce07296269da2/dataswim/data/export.py#L122-L134
def to_records_(self) -> dict: """Returns a list of dictionary records from the main dataframe :return: a python dictionnary with the data :rtype: str :example: ``ds.to_records_()`` """ try: dic = self.df.to_dict(orient="records") return dic except Exception as e: self.err(e, "Can not convert data to records")
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Returns a list of dictionary records from the main dataframe :return: a python dictionnary with the data :rtype: str :example: ``ds.to_records_()``
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python
train
itamarst/crochet
crochet/_resultstore.py
https://github.com/itamarst/crochet/blob/ecfc22cefa90f3dfbafa71883c1470e7294f2b6d/crochet/_resultstore.py#L30-L39
def store(self, deferred_result): """ Store a EventualResult. Return an integer, a unique identifier that can be used to retrieve the object. """ self._counter += 1 self._stored[self._counter] = deferred_result return self._counter
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Store a EventualResult. Return an integer, a unique identifier that can be used to retrieve the object.
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python
train
apache/incubator-mxnet
example/deep-embedded-clustering/data.py
https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/example/deep-embedded-clustering/data.py#L25-L35
def get_mnist(): """ Gets MNIST dataset """ np.random.seed(1234) # set seed for deterministic ordering mnist_data = mx.test_utils.get_mnist() X = np.concatenate([mnist_data['train_data'], mnist_data['test_data']]) Y = np.concatenate([mnist_data['train_label'], mnist_data['test_label']]) p = np.random.permutation(X.shape[0]) X = X[p].reshape((X.shape[0], -1)).astype(np.float32)*5 Y = Y[p] return X, Y
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Gets MNIST dataset
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python
train
libyal/dtfabric
dtfabric/runtime/data_maps.py
https://github.com/libyal/dtfabric/blob/0d2b5719fa257f6e5c661a406737ebcf8c8db266/dtfabric/runtime/data_maps.py#L2050-L2065
def CreateDataTypeMapByType(cls, data_type_definition): """Creates a specific data type map by type indicator. Args: data_type_definition (DataTypeDefinition): data type definition. Returns: DataTypeMap: data type map or None if the date type definition is not available. """ data_type_map_class = cls._MAP_PER_DEFINITION.get( data_type_definition.TYPE_INDICATOR, None) if not data_type_map_class: return None return data_type_map_class(data_type_definition)
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Creates a specific data type map by type indicator. Args: data_type_definition (DataTypeDefinition): data type definition. Returns: DataTypeMap: data type map or None if the date type definition is not available.
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python
train
jssimporter/python-jss
jss/jssobject.py
https://github.com/jssimporter/python-jss/blob/b95185d74e0c0531b0b563f280d4129e21d5fe5d/jss/jssobject.py#L605-L619
def add_device(self, device, container): """Add a device to a group. Wraps JSSObject.add_object_to_path. Args: device: A JSSObject to add (as list data), to this object. location: Element or a string path argument to find() """ # There is a size tag which the JSS manages for us, so we can # ignore it. if self.findtext("is_smart") == "false": self.add_object_to_path(device, container) else: # Technically this isn't true. It will strangely accept # them, and they even show up as members of the group! raise ValueError("Devices may not be added to smart groups.")
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Add a device to a group. Wraps JSSObject.add_object_to_path. Args: device: A JSSObject to add (as list data), to this object. location: Element or a string path argument to find()
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python
train
artefactual-labs/agentarchives
agentarchives/atom/client.py
https://github.com/artefactual-labs/agentarchives/blob/af19ade56a90c64069cf46b50972fe72b6f10a45/agentarchives/atom/client.py#L370-L395
def get_resource_component_and_children( self, resource_id, resource_type="collection", level=1, sort_data={}, recurse_max_level=False, sort_by=None, **kwargs ): """ Fetch detailed metadata for the specified resource_id and all of its children. :param str resource_id: The slug for which to fetch description metadata. :param str resource_type: no-op; not required or used in this implementation. :param int recurse_max_level: The maximum depth level to fetch when fetching children. Default is to fetch all of the resource's children, descending as deeply as necessary. Pass 1 to fetch no children. :return: A dict containing detailed metadata about both the requested resource and its children. Consult ArchivistsToolkitClient.get_resource_component_and_children for the output format. :rtype dict: """ return self._get_resources( resource_id, recurse_max_level=recurse_max_level, sort_by=sort_by )
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python
train
SheffieldML/GPyOpt
GPyOpt/util/general.py
https://github.com/SheffieldML/GPyOpt/blob/255539dc5927819ca701e44fe3d76cd4864222fa/GPyOpt/util/general.py#L86-L96
def get_moments(model,x): ''' Moments (mean and sdev.) of a GP model at x ''' input_dim = model.X.shape[1] x = reshape(x,input_dim) fmin = min(model.predict(model.X)[0]) m, v = model.predict(x) s = np.sqrt(np.clip(v, 0, np.inf)) return (m,s, fmin)
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Moments (mean and sdev.) of a GP model at x
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python
train
androguard/androguard
androguard/core/analysis/analysis.py
https://github.com/androguard/androguard/blob/984c0d981be2950cf0451e484f7b0d4d53bc4911/androguard/core/analysis/analysis.py#L1213-L1318
def _create_xref(self, current_class): """ Create the xref for `current_class` There are four steps involved in getting the xrefs: * Xrefs for classes * for method calls * for string usage * for field manipulation All these information are stored in the *Analysis Objects. Note that this might be quite slow, as all instructions are parsed. :param androguard.core.bytecodes.dvm.ClassDefItem current_class: The class to create xrefs for """ cur_cls_name = current_class.get_name() log.debug("Creating XREF/DREF for %s" % cur_cls_name) for current_method in current_class.get_methods(): log.debug("Creating XREF for %s" % current_method) off = 0 for instruction in current_method.get_instructions(): op_value = instruction.get_op_value() # 1) check for class calls: const-class (0x1c), new-instance (0x22) if op_value in [0x1c, 0x22]: idx_type = instruction.get_ref_kind() # type_info is the string like 'Ljava/lang/Object;' type_info = instruction.cm.vm.get_cm_type(idx_type) # Internal xref related to class manipulation # FIXME should the xref really only set if the class is in self.classes? If an external class is added later, it will be added too! # See https://github.com/androguard/androguard/blob/d720ebf2a9c8e2a28484f1c81fdddbc57e04c157/androguard/core/analysis/analysis.py#L806 # Before the check would go for internal classes only! # FIXME: effectively ignoring calls to itself - do we want that? if type_info != cur_cls_name: if type_info not in self.classes: # Create new external class self.classes[type_info] = ClassAnalysis(ExternalClass(type_info)) cur_cls = self.classes[cur_cls_name] oth_cls = self.classes[type_info] # FIXME: xref_to does not work here! current_method is wrong, as it is not the target! cur_cls.AddXrefTo(REF_TYPE(op_value), oth_cls, current_method, off) oth_cls.AddXrefFrom(REF_TYPE(op_value), cur_cls, current_method, off) # 2) check for method calls: invoke-* (0x6e ... 0x72), invoke-xxx/range (0x74 ... 0x78) elif (0x6e <= op_value <= 0x72) or (0x74 <= op_value <= 0x78): idx_meth = instruction.get_ref_kind() method_info = instruction.cm.vm.get_cm_method(idx_meth) if method_info: class_info = method_info[0] method_item = None # TODO: should create get_method_descriptor inside Analysis for vm in self.vms: method_item = vm.get_method_descriptor(method_info[0], method_info[1], ''.join(method_info[2])) if method_item: break if not method_item: # Seems to be an external class, create it first # Beware: if not all DEX files are loaded at the time create_xref runs # you will run into problems! if method_info[0] not in self.classes: self.classes[method_info[0]] = ClassAnalysis(ExternalClass(method_info[0])) method_item = self.classes[method_info[0]].get_fake_method(method_info[1], method_info[2]) self.classes[cur_cls_name].AddMXrefTo(current_method, self.classes[class_info], method_item, off) self.classes[class_info].AddMXrefFrom(method_item, self.classes[cur_cls_name], current_method, off) # Internal xref related to class manipulation if class_info in self.classes and class_info != cur_cls_name: self.classes[cur_cls_name].AddXrefTo(REF_TYPE(op_value), self.classes[class_info], method_item, off) self.classes[class_info].AddXrefFrom(REF_TYPE(op_value), self.classes[cur_cls_name], current_method, off) # 3) check for string usage: const-string (0x1a), const-string/jumbo (0x1b) elif 0x1a <= op_value <= 0x1b: string_value = instruction.cm.vm.get_cm_string(instruction.get_ref_kind()) if string_value not in self.strings: self.strings[string_value] = StringAnalysis(string_value) # TODO: The bytecode offset is stored for classes but not here? self.strings[string_value].AddXrefFrom(self.classes[cur_cls_name], current_method) # TODO maybe we should add a step 3a) here and check for all const fields. You can then xref for integers etc! # But: This does not work, as const fields are usually optimized internally to const calls... # 4) check for field usage: i*op (0x52 ... 0x5f), s*op (0x60 ... 0x6d) elif 0x52 <= op_value <= 0x6d: idx_field = instruction.get_ref_kind() field_info = instruction.cm.vm.get_cm_field(idx_field) field_item = instruction.cm.vm.get_field_descriptor(field_info[0], field_info[2], field_info[1]) # TODO: The bytecode offset is stored for classes but not here? if field_item: if (0x52 <= op_value <= 0x58) or (0x60 <= op_value <= 0x66): # read access to a field self.classes[cur_cls_name].AddFXrefRead(current_method, self.classes[cur_cls_name], field_item) else: # write access to a field self.classes[cur_cls_name].AddFXrefWrite(current_method, self.classes[cur_cls_name], field_item) off += instruction.get_length()
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Create the xref for `current_class` There are four steps involved in getting the xrefs: * Xrefs for classes * for method calls * for string usage * for field manipulation All these information are stored in the *Analysis Objects. Note that this might be quite slow, as all instructions are parsed. :param androguard.core.bytecodes.dvm.ClassDefItem current_class: The class to create xrefs for
[ "Create", "the", "xref", "for", "current_class" ]
python
train
scidash/sciunit
sciunit/utils.py
https://github.com/scidash/sciunit/blob/41b2e38c45c0776727ab1f281a572b65be19cea1/sciunit/utils.py#L334-L339
def _do_notebook(self, name, convert_notebooks=False): """Called by do_notebook to actually run the notebook.""" if convert_notebooks: self.convert_and_execute_notebook(name) else: self.execute_notebook(name)
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Called by do_notebook to actually run the notebook.
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python
train
AtomHash/evernode
evernode/classes/app.py
https://github.com/AtomHash/evernode/blob/b2fb91555fb937a3f3eba41db56dee26f9b034be/evernode/classes/app.py#L37-L45
def __root_path(self): """ Just checks the root path if set """ if self.root_path is not None: if os.path.isdir(self.root_path): sys.path.append(self.root_path) return raise RuntimeError('EverNode requires a valid root path.' ' Directory: %s does not exist' % (self.root_path))
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Just checks the root path if set
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python
train
CivicSpleen/ambry
ambry/library/__init__.py
https://github.com/CivicSpleen/ambry/blob/d7f2be4bf1f7ffd086f3fadd4fcae60c32473e42/ambry/library/__init__.py#L411-L419
def remove(self, bundle): """ Removes a bundle from the library and deletes the configuration for it from the library database.""" from six import string_types if isinstance(bundle, string_types): bundle = self.bundle(bundle) self.database.remove_dataset(bundle.dataset)
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Removes a bundle from the library and deletes the configuration for it from the library database.
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python
train
saltstack/salt
salt/modules/yumpkg.py
https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/modules/yumpkg.py#L709-L952
def list_repo_pkgs(*args, **kwargs): ''' .. versionadded:: 2014.1.0 .. versionchanged:: 2014.7.0 All available versions of each package are now returned. This required a slight modification to the structure of the return dict. The return data shown below reflects the updated return dict structure. Note that packages which are version-locked using :py:mod:`pkg.hold <salt.modules.yumpkg.hold>` will only show the currently-installed version, as locking a package will make other versions appear unavailable to yum/dnf. .. versionchanged:: 2017.7.0 By default, the versions for each package are no longer organized by repository. To get results organized by repository, use ``byrepo=True``. Returns all available packages. Optionally, package names (and name globs) can be passed and the results will be filtered to packages matching those names. This is recommended as it speeds up the function considerably. .. warning:: Running this function on RHEL/CentOS 6 and earlier will be more resource-intensive, as the version of yum that ships with older RHEL/CentOS has no yum subcommand for listing packages from a repository. Thus, a ``yum list installed`` and ``yum list available`` are run, which generates a lot of output, which must then be analyzed to determine which package information to include in the return data. This function can be helpful in discovering the version or repo to specify in a :mod:`pkg.installed <salt.states.pkg.installed>` state. The return data will be a dictionary mapping package names to a list of version numbers, ordered from newest to oldest. If ``byrepo`` is set to ``True``, then the return dictionary will contain repository names at the top level, and each repository will map packages to lists of version numbers. For example: .. code-block:: python # With byrepo=False (default) { 'bash': ['4.1.2-15.el6_5.2', '4.1.2-15.el6_5.1', '4.1.2-15.el6_4'], 'kernel': ['2.6.32-431.29.2.el6', '2.6.32-431.23.3.el6', '2.6.32-431.20.5.el6', '2.6.32-431.20.3.el6', '2.6.32-431.17.1.el6', '2.6.32-431.11.2.el6', '2.6.32-431.5.1.el6', '2.6.32-431.3.1.el6', '2.6.32-431.1.2.0.1.el6', '2.6.32-431.el6'] } # With byrepo=True { 'base': { 'bash': ['4.1.2-15.el6_4'], 'kernel': ['2.6.32-431.el6'] }, 'updates': { 'bash': ['4.1.2-15.el6_5.2', '4.1.2-15.el6_5.1'], 'kernel': ['2.6.32-431.29.2.el6', '2.6.32-431.23.3.el6', '2.6.32-431.20.5.el6', '2.6.32-431.20.3.el6', '2.6.32-431.17.1.el6', '2.6.32-431.11.2.el6', '2.6.32-431.5.1.el6', '2.6.32-431.3.1.el6', '2.6.32-431.1.2.0.1.el6'] } } fromrepo : None Only include results from the specified repo(s). Multiple repos can be specified, comma-separated. enablerepo (ignored if ``fromrepo`` is specified) Specify a disabled package repository (or repositories) to enable. (e.g., ``yum --enablerepo='somerepo'``) .. versionadded:: 2017.7.0 disablerepo (ignored if ``fromrepo`` is specified) Specify an enabled package repository (or repositories) to disable. (e.g., ``yum --disablerepo='somerepo'``) .. versionadded:: 2017.7.0 byrepo : False When ``True``, the return data for each package will be organized by repository. .. versionadded:: 2017.7.0 cacheonly : False When ``True``, the repo information will be retrieved from the cached repo metadata. This is equivalent to passing the ``-C`` option to yum/dnf. .. versionadded:: 2017.7.0 setopt A comma-separated or Python list of key=value options. This list will be expanded and ``--setopt`` prepended to each in the yum/dnf command that is run. .. versionadded:: 2019.2.0 CLI Examples: .. code-block:: bash salt '*' pkg.list_repo_pkgs salt '*' pkg.list_repo_pkgs foo bar baz salt '*' pkg.list_repo_pkgs 'samba4*' fromrepo=base,updates salt '*' pkg.list_repo_pkgs 'python2-*' byrepo=True ''' byrepo = kwargs.pop('byrepo', False) cacheonly = kwargs.pop('cacheonly', False) fromrepo = kwargs.pop('fromrepo', '') or '' disablerepo = kwargs.pop('disablerepo', '') or '' enablerepo = kwargs.pop('enablerepo', '') or '' repo_arg = _get_options(fromrepo=fromrepo, **kwargs) if fromrepo and not isinstance(fromrepo, list): try: fromrepo = [x.strip() for x in fromrepo.split(',')] except AttributeError: fromrepo = [x.strip() for x in six.text_type(fromrepo).split(',')] if disablerepo and not isinstance(disablerepo, list): try: disablerepo = [x.strip() for x in disablerepo.split(',') if x != '*'] except AttributeError: disablerepo = [x.strip() for x in six.text_type(disablerepo).split(',') if x != '*'] if enablerepo and not isinstance(enablerepo, list): try: enablerepo = [x.strip() for x in enablerepo.split(',') if x != '*'] except AttributeError: enablerepo = [x.strip() for x in six.text_type(enablerepo).split(',') if x != '*'] if fromrepo: repos = fromrepo else: repos = [ repo_name for repo_name, repo_info in six.iteritems(list_repos()) if repo_name in enablerepo or (repo_name not in disablerepo and six.text_type(repo_info.get('enabled', '1')) == '1') ] ret = {} def _check_args(args, name): ''' Do glob matching on args and return True if a match was found. Otherwise, return False ''' for arg in args: if fnmatch.fnmatch(name, arg): return True return False def _parse_output(output, strict=False): for pkg in _yum_pkginfo(output): if strict and (pkg.repoid not in repos or not _check_args(args, pkg.name)): continue repo_dict = ret.setdefault(pkg.repoid, {}) version_list = repo_dict.setdefault(pkg.name, set()) version_list.add(pkg.version) yum_version = None if _yum() != 'yum' else _LooseVersion( __salt__['cmd.run']( ['yum', '--version'], python_shell=False ).splitlines()[0].strip() ) # Really old version of yum; does not even have --showduplicates option if yum_version and yum_version < _LooseVersion('3.2.13'): cmd_prefix = ['--quiet'] if cacheonly: cmd_prefix.append('-C') cmd_prefix.append('list') for pkg_src in ('installed', 'available'): # Check installed packages first out = _call_yum(cmd_prefix + [pkg_src], ignore_retcode=True) if out['retcode'] == 0: _parse_output(out['stdout'], strict=True) # The --showduplicates option is added in 3.2.13, but the # repository-packages subcommand is only in 3.4.3 and newer elif yum_version and yum_version < _LooseVersion('3.4.3'): cmd_prefix = ['--quiet', '--showduplicates'] if cacheonly: cmd_prefix.append('-C') cmd_prefix.append('list') for pkg_src in ('installed', 'available'): # Check installed packages first out = _call_yum(cmd_prefix + [pkg_src], ignore_retcode=True) if out['retcode'] == 0: _parse_output(out['stdout'], strict=True) else: for repo in repos: cmd = ['--quiet', '--showduplicates', 'repository-packages', repo, 'list'] if cacheonly: cmd.append('-C') # Can't concatenate because args is a tuple, using list.extend() cmd.extend(args) out = _call_yum(cmd, ignore_retcode=True) if out['retcode'] != 0 and 'Error:' in out['stdout']: continue _parse_output(out['stdout']) if byrepo: for reponame in ret: # Sort versions newest to oldest for pkgname in ret[reponame]: sorted_versions = sorted( [_LooseVersion(x) for x in ret[reponame][pkgname]], reverse=True ) ret[reponame][pkgname] = [x.vstring for x in sorted_versions] return ret else: byrepo_ret = {} for reponame in ret: for pkgname in ret[reponame]: byrepo_ret.setdefault(pkgname, []).extend(ret[reponame][pkgname]) for pkgname in byrepo_ret: sorted_versions = sorted( [_LooseVersion(x) for x in byrepo_ret[pkgname]], reverse=True ) byrepo_ret[pkgname] = [x.vstring for x in sorted_versions] return byrepo_ret
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.. versionadded:: 2014.1.0 .. versionchanged:: 2014.7.0 All available versions of each package are now returned. This required a slight modification to the structure of the return dict. The return data shown below reflects the updated return dict structure. Note that packages which are version-locked using :py:mod:`pkg.hold <salt.modules.yumpkg.hold>` will only show the currently-installed version, as locking a package will make other versions appear unavailable to yum/dnf. .. versionchanged:: 2017.7.0 By default, the versions for each package are no longer organized by repository. To get results organized by repository, use ``byrepo=True``. Returns all available packages. Optionally, package names (and name globs) can be passed and the results will be filtered to packages matching those names. This is recommended as it speeds up the function considerably. .. warning:: Running this function on RHEL/CentOS 6 and earlier will be more resource-intensive, as the version of yum that ships with older RHEL/CentOS has no yum subcommand for listing packages from a repository. Thus, a ``yum list installed`` and ``yum list available`` are run, which generates a lot of output, which must then be analyzed to determine which package information to include in the return data. This function can be helpful in discovering the version or repo to specify in a :mod:`pkg.installed <salt.states.pkg.installed>` state. The return data will be a dictionary mapping package names to a list of version numbers, ordered from newest to oldest. If ``byrepo`` is set to ``True``, then the return dictionary will contain repository names at the top level, and each repository will map packages to lists of version numbers. For example: .. code-block:: python # With byrepo=False (default) { 'bash': ['4.1.2-15.el6_5.2', '4.1.2-15.el6_5.1', '4.1.2-15.el6_4'], 'kernel': ['2.6.32-431.29.2.el6', '2.6.32-431.23.3.el6', '2.6.32-431.20.5.el6', '2.6.32-431.20.3.el6', '2.6.32-431.17.1.el6', '2.6.32-431.11.2.el6', '2.6.32-431.5.1.el6', '2.6.32-431.3.1.el6', '2.6.32-431.1.2.0.1.el6', '2.6.32-431.el6'] } # With byrepo=True { 'base': { 'bash': ['4.1.2-15.el6_4'], 'kernel': ['2.6.32-431.el6'] }, 'updates': { 'bash': ['4.1.2-15.el6_5.2', '4.1.2-15.el6_5.1'], 'kernel': ['2.6.32-431.29.2.el6', '2.6.32-431.23.3.el6', '2.6.32-431.20.5.el6', '2.6.32-431.20.3.el6', '2.6.32-431.17.1.el6', '2.6.32-431.11.2.el6', '2.6.32-431.5.1.el6', '2.6.32-431.3.1.el6', '2.6.32-431.1.2.0.1.el6'] } } fromrepo : None Only include results from the specified repo(s). Multiple repos can be specified, comma-separated. enablerepo (ignored if ``fromrepo`` is specified) Specify a disabled package repository (or repositories) to enable. (e.g., ``yum --enablerepo='somerepo'``) .. versionadded:: 2017.7.0 disablerepo (ignored if ``fromrepo`` is specified) Specify an enabled package repository (or repositories) to disable. (e.g., ``yum --disablerepo='somerepo'``) .. versionadded:: 2017.7.0 byrepo : False When ``True``, the return data for each package will be organized by repository. .. versionadded:: 2017.7.0 cacheonly : False When ``True``, the repo information will be retrieved from the cached repo metadata. This is equivalent to passing the ``-C`` option to yum/dnf. .. versionadded:: 2017.7.0 setopt A comma-separated or Python list of key=value options. This list will be expanded and ``--setopt`` prepended to each in the yum/dnf command that is run. .. versionadded:: 2019.2.0 CLI Examples: .. code-block:: bash salt '*' pkg.list_repo_pkgs salt '*' pkg.list_repo_pkgs foo bar baz salt '*' pkg.list_repo_pkgs 'samba4*' fromrepo=base,updates salt '*' pkg.list_repo_pkgs 'python2-*' byrepo=True
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python
train
chrisjrn/registrasion
registrasion/controllers/conditions.py
https://github.com/chrisjrn/registrasion/blob/461d5846c6f9f3b7099322a94f5d9911564448e4/registrasion/controllers/conditions.py#L173-L194
def pre_filter(self, queryset, user): ''' Returns all of the items from queryset where the user has a product invoking that item's condition in one of their carts. ''' in_user_carts = Q(enabling_products__productitem__cart__user=user) released = commerce.Cart.STATUS_RELEASED paid = commerce.Cart.STATUS_PAID active = commerce.Cart.STATUS_ACTIVE in_released_carts = Q( enabling_products__productitem__cart__status=released ) not_in_paid_or_active_carts = ~( Q(enabling_products__productitem__cart__status=paid) | Q(enabling_products__productitem__cart__status=active) ) queryset = queryset.filter(in_user_carts) queryset = queryset.exclude( in_released_carts & not_in_paid_or_active_carts ) return queryset
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Returns all of the items from queryset where the user has a product invoking that item's condition in one of their carts.
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python
test
UUDigitalHumanitieslab/tei_reader
tei_reader/models/element.py
https://github.com/UUDigitalHumanitieslab/tei_reader/blob/7b19c34a9d7cc941a36ecdcf6f361e26c6488697/tei_reader/models/element.py#L32-L51
def divisions(self): """ Recursively get all the text divisions directly part of this element. If an element contains parts or text without tag. Those will be returned in order and wrapped with a TextDivision. """ from .placeholder_division import PlaceholderDivision placeholder = None for item in self.__parts_and_divisions: if item.tag == 'part': if not placeholder: placeholder = PlaceholderDivision() placeholder.parts.append(item) else: if placeholder: yield placeholder placeholder = None yield item if placeholder: yield placeholder
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Recursively get all the text divisions directly part of this element. If an element contains parts or text without tag. Those will be returned in order and wrapped with a TextDivision.
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python
train
Genida/archan
src/archan/config.py
https://github.com/Genida/archan/blob/a026d3105c7e86f30e6c9507b93ceb736684bfdc/src/archan/config.py#L103-L108
def from_file(path): """Return a ``Config`` instance by reading a configuration file.""" with open(path) as stream: obj = yaml.safe_load(stream) Config.lint(obj) return Config(config_dict=obj)
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Return a ``Config`` instance by reading a configuration file.
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python
train
christophertbrown/bioscripts
ctbBio/rRNA_insertions.py
https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/rRNA_insertions.py#L104-L126
def find_orfs(fa, seqs): """ find orfs and see if they overlap with insertions # seqs[id] = [gene, model, [[i-gene_pos, i-model_pos, i-length, iseq, [orfs], [introns]], ...]] """ faa = '%s.prodigal.faa' % (fa) fna = '%s.prodigal.fna' % (fa) gbk = '%s.prodigal.gbk' % (fa) if os.path.exists(faa) is False: p = subprocess.Popen('prodigal -q -i %s -a %s -d %s -c -f gbk -m -n -o %s -p meta' \ % (fa, faa, fna, gbk), shell = True) p.communicate() for orf in parse_fasta(faa): if orf[0] == []: continue id = orf[0].split('>')[1].split('_', 1)[0] pos = sorted([int(i) for i in orf[0].split()[2:5] if i != '#']) if id not in seqs: continue for i, ins in enumerate(seqs[id][2]): if check_overlap(pos, ins, 0.90) is True: seqs[id][2][i][4].append(orf) return seqs, faa
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find orfs and see if they overlap with insertions # seqs[id] = [gene, model, [[i-gene_pos, i-model_pos, i-length, iseq, [orfs], [introns]], ...]]
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python
train
LogicalDash/LiSE
ELiDE/ELiDE/card.py
https://github.com/LogicalDash/LiSE/blob/fe6fd4f0a7c1780e065f4c9babb9bc443af6bb84/ELiDE/ELiDE/card.py#L951-L959
def upd_scroll(self, *args): """Update my own ``scroll`` property to where my deck is actually scrolled. """ att = 'deck_{}_hint_offsets'.format( 'x' if self.orientation == 'horizontal' else 'y' ) self._scroll = getattr(self.deckbuilder, att)[self.deckidx]
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Update my own ``scroll`` property to where my deck is actually scrolled.
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python
train
tensorflow/datasets
tensorflow_datasets/core/utils/gcs_utils.py
https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/utils/gcs_utils.py#L60-L66
def gcs_dataset_info_files(dataset_dir): """Return paths to GCS files in the given dataset directory.""" prefix = posixpath.join(GCS_DATASET_INFO_DIR, dataset_dir, "") # Filter for this dataset filenames = [el for el in gcs_files(prefix_filter=prefix) if el.startswith(prefix) and len(el) > len(prefix)] return filenames
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Return paths to GCS files in the given dataset directory.
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python
train
sbarham/dsrt
dsrt/models/Decoder.py
https://github.com/sbarham/dsrt/blob/bc664739f2f52839461d3e72773b71146fd56a9a/dsrt/models/Decoder.py#L34-L81
def build(self): """ The decoder computational graph consists of three components: (1) the input node `decoder_input` (2) the embedding node `decoder_embed` (3) the recurrent (RNN) part `decoder_rnn` (4) the output of the decoder RNN `decoder_output` (5) the classification output layer `decoder_dense` """ # Grab hyperparameters from self.config: hidden_dim = self.config['encoding-layer-width'] recurrent_unit = self.config['recurrent-unit-type'] bidirectional = False #self.config['encoding-layer-bidirectional'] vocab_size = self.data.properties.vocab_size embedding_dim = math.ceil(math.log(vocab_size, 2)) # self.config['embedding-dim'] input_length = self.data.properties['max-utterance-length'] + 1 # Assemble the network components: decoder_input = Input(shape=(None,)) decoder_embed = Embedding(vocab_size, embedding_dim, mask_zero=True)(decoder_input) #, input_length=input_length)(decoder_input) if recurrent_unit == 'lstm': decoder_rnn = LSTM(hidden_dim, return_sequences=True, return_state=True) decoder_output, decoder_h, decoder_c = decoder_rnn(decoder_embed, initial_state=self.encoder.encoder_hidden_state) elif recurrent_unit == 'gru': decoder_rnn = GRU(hidden_dim, return_sequences=True, return_state=True) decoder_output, _ = decoder_rnn(decoder_embed, initial_state=self.encoder.encoder_hidden_state) else: raise Exception('Invalid recurrent unit type: {}'.format(recurrent_unit)) # make the RNN component bidirectional, if desired if bidirectional: decoder_rnn = Bidirectional(decoder_rnn, merge_mode='ave') decoder_dense = Dense(vocab_size, activation='softmax') decoder_output = decoder_dense(decoder_output) # save the four Decoder components as class state self.decoder_input = decoder_input self.decoder_embed = decoder_embed self.decoder_rnn = decoder_rnn self.decoder_dense = decoder_dense self.decoder_output = decoder_output return
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python
train
MillionIntegrals/vel
vel/rl/api/replay_buffer.py
https://github.com/MillionIntegrals/vel/blob/e0726e1f63742b728966ccae0c8b825ea0ba491a/vel/rl/api/replay_buffer.py#L29-L38
def sample_forward_transitions(self, batch_size, batch_info, forward_steps: int, discount_factor: float) -> Transitions: """ Sample transitions from replay buffer with _forward steps_. That is, instead of getting a transition s_t -> s_t+1 with reward r, get a transition s_t -> s_t+n with sum of intermediate rewards. Used in a variant of Deep Q-Learning """ raise NotImplementedError
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Sample transitions from replay buffer with _forward steps_. That is, instead of getting a transition s_t -> s_t+1 with reward r, get a transition s_t -> s_t+n with sum of intermediate rewards. Used in a variant of Deep Q-Learning
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python
train
common-workflow-language/cwltool
cwltool/main.py
https://github.com/common-workflow-language/cwltool/blob/cb81b22abc52838823da9945f04d06739ab32fda/cwltool/main.py#L903-L910
def run(*args, **kwargs): # type: (...) -> None """Run cwltool.""" signal.signal(signal.SIGTERM, _signal_handler) try: sys.exit(main(*args, **kwargs)) finally: _terminate_processes()
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Run cwltool.
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python
train
hubo1016/vlcp
vlcp/event/future.py
https://github.com/hubo1016/vlcp/blob/239055229ec93a99cc7e15208075724ccf543bd1/vlcp/event/future.py#L99-L109
def set_exception(self, exception): ''' Set an exception to Future object, wake up all the waiters :param exception: exception to set ''' if hasattr(self, '_result'): raise ValueError('Cannot set the result twice') self._result = None self._exception = exception self._scheduler.emergesend(FutureEvent(self, exception = exception))
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Set an exception to Future object, wake up all the waiters :param exception: exception to set
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python
train
Microsoft/nni
examples/trials/sklearn/regression/main.py
https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/examples/trials/sklearn/regression/main.py#L55-L77
def get_model(PARAMS): '''Get model according to parameters''' model_dict = { 'LinearRegression': LinearRegression(), 'SVR': SVR(), 'KNeighborsRegressor': KNeighborsRegressor(), 'DecisionTreeRegressor': DecisionTreeRegressor() } if not model_dict.get(PARAMS['model_name']): LOG.exception('Not supported model!') exit(1) model = model_dict[PARAMS['model_name']] try: if PARAMS['model_name'] == 'SVR': model.kernel = PARAMS['svr_kernel'] elif PARAMS['model_name'] == 'KNeighborsRegressor': model.weights = PARAMS['knr_weights'] except Exception as exception: LOG.exception(exception) raise return model
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Get model according to parameters
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python
train
fishtown-analytics/dbt
core/dbt/adapters/base/impl.py
https://github.com/fishtown-analytics/dbt/blob/aa4f771df28b307af0cf9fe2fc24432f10a8236b/core/dbt/adapters/base/impl.py#L50-L65
def _catalog_filter_schemas(manifest): """Return a function that takes a row and decides if the row should be included in the catalog output. """ schemas = frozenset((d.lower(), s.lower()) for d, s in manifest.get_used_schemas()) def test(row): table_database = _expect_row_value('table_database', row) table_schema = _expect_row_value('table_schema', row) # the schema may be present but None, which is not an error and should # be filtered out if table_schema is None: return False return (table_database.lower(), table_schema.lower()) in schemas return test
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Return a function that takes a row and decides if the row should be included in the catalog output.
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python
train
axiom-data-science/pyaxiom
pyaxiom/utils.py
https://github.com/axiom-data-science/pyaxiom/blob/7ea7626695abf095df6a67f66e5b3e9ae91b16df/pyaxiom/utils.py#L167-L233
def dictify_urn(urn, combine_interval=True): """ By default, this will put the `interval` as part of the `cell_methods` attribute (NetCDF CF style). To return `interval` as its own key, use the `combine_interval=False` parameter. """ ioos_urn = IoosUrn.from_string(urn) if ioos_urn.valid() is False: return dict() if ioos_urn.asset_type != 'sensor': logger.error("This function only works on 'sensor' URNs.") return dict() if '#' in ioos_urn.component: standard_name, extras = ioos_urn.component.split('#') else: standard_name = ioos_urn.component extras = '' d = dict(standard_name=standard_name) # Discriminant if '-' in ioos_urn.component: d['discriminant'] = standard_name.split('-')[-1] d['standard_name'] = standard_name.split('-')[0] intervals = [] cell_methods = [] if extras: for section in extras.split(';'): key, values = section.split('=') if key == 'interval': # special case, intervals should be appended to the cell_methods for v in values.split(','): intervals.append(v) else: if key == 'cell_methods': value = [ x.replace('_', ' ').replace(':', ': ') for x in values.split(',') ] cell_methods = value else: value = ' '.join([x.replace('_', ' ').replace(':', ': ') for x in values.split(',')]) d[key] = value if combine_interval is True: if cell_methods and intervals: if len(cell_methods) == len(intervals): d['cell_methods'] = ' '.join([ '{} (interval: {})'.format(x[0], x[1].upper()) for x in zip(cell_methods, intervals) ]) else: d['cell_methods'] = ' '.join(cell_methods) for i in intervals: d['cell_methods'] += ' (interval: {})'.format(i.upper()) elif cell_methods: d['cell_methods'] = ' '.join(cell_methods) for i in intervals: d['cell_methods'] += ' (interval: {})'.format(i.upper()) elif intervals: raise ValueError("An interval without a cell_method is not allowed! Not possible!") else: d['cell_methods'] = ' '.join(cell_methods) d['interval'] = ','.join(intervals).upper() if 'vertical_datum' in d: d['vertical_datum'] = d['vertical_datum'].upper() return d
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By default, this will put the `interval` as part of the `cell_methods` attribute (NetCDF CF style). To return `interval` as its own key, use the `combine_interval=False` parameter.
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python
valid
CalebBell/ht
ht/conv_jacket.py
https://github.com/CalebBell/ht/blob/3097ef9524c4cf0068ad453c17b10ec9ce551eee/ht/conv_jacket.py#L152-L332
def Stein_Schmidt(m, Dtank, Djacket, H, Dinlet, rho, Cp, k, mu, muw=None, rhow=None, inlettype='tangential', inletlocation='auto', roughness=0): r'''Calculates average heat transfer coefficient for a jacket around a vessel according to [1]_ as described in [2]_. .. math:: l_{ch} = \left[\left(\frac{\pi}{2}\right)^2 D_{tank}^2+H^2\right]^{0.5} d_{ch} = 2\delta Re_j = \frac{v_{ch}d_{ch}\rho}{\mu} Gr_J = \frac{g\rho(\rho-\rho_w)d_{ch}^3}{\mu^2} Re_{J,eq} = \left[Re_J^2\pm \left(\frac{|Gr_J|\frac{H}{d_{ch}}}{50} \right)\right]^{0.5} Nu_J = (Nu_A^3 + Nu_B^3 + Nu_C^3 + Nu_D^3)^{1/3}\left(\frac{\mu} {\mu_w}\right)^{0.14} Nu_J = \frac{h d_{ch}}{k} Nu_A = 3.66 Nu_B = 1.62 Pr^{1/3}Re_{J,eq}^{1/3}\left(\frac{d_{ch}}{l_{ch}} \right)^{1/3} Nu_C = 0.664Pr^{1/3}(Re_{J,eq}\frac{d_{ch}}{l_{ch}})^{0.5} \text{if } Re_{J,eq} < 2300: Nu_D = 0 Nu_D = 0.0115Pr^{1/3}Re_{J,eq}^{0.9}\left(1 - \left(\frac{2300} {Re_{J,eq}}\right)^{2.5}\right)\left(1 + \left(\frac{d_{ch}}{l_{ch}} \right)^{2/3}\right) For Radial inlets: .. math:: v_{ch} = v_{Mit}\left(\frac{\ln\frac{b_{Mit}}{b_{Ein}}}{1 - \frac{b_{Ein}}{b_{Mit}}}\right) b_{Ein} = \frac{\pi}{8}\frac{D_{inlet}^2}{\delta} b_{Mit} = \frac{\pi}{2}D_{tank}\sqrt{1 + \frac{\pi^2}{4}\frac {D_{tank}^2}{H^2}} v_{Mit} = \frac{Q}{2\delta b_{Mit}} For Tangential inlets: .. math:: v_{ch} = (v_x^2 + v_z^2)^{0.5} v_x = v_{inlet}\left(\frac{\ln[1 + \frac{f_d D_{tank}H}{D_{inlet}^2} \frac{v_x(0)}{v_{inlet}}]}{\frac{f_d D_{tank}H}{D_{inlet}^2}}\right) v_x(0) = K_3 + (K_3^2 + K_4)^{0.5} K_3 = \frac{v_{inlet}}{4} -\frac{D_{inlet}^2v_{inlet}}{4f_d D_{tank}H} K_4 = \frac{D_{inlet}^2v_{inlet}^2}{2f_d D_{tank} H} v_z = \frac{Q}{\pi D_{tank}\delta} v_{inlet} = \frac{Q}{\frac{\pi}{4}D_{inlet}^2} Parameters ---------- m : float Mass flow rate of fluid, [kg/m^3] Dtank : float Outer diameter of tank or vessel surrounded by jacket, [m] Djacket : float Inner diameter of jacket surrounding a vessel or tank, [m] H : float Height of the vessel or tank, [m] Dinlet : float Inner diameter of inlet into the jacket, [m] rho : float Density of the fluid at Tm [kg/m^3] Cp : float Heat capacity of fluid at Tm [J/kg/K] k : float Thermal conductivity of fluid at Tm [W/m/K] mu : float Viscosity of fluid at Tm [Pa*s] muw : float, optional Viscosity of fluid at Tw [Pa*s] rhow : float, optional Density of the fluid at Tw [kg/m^3] inlettype : str, optional Either 'tangential' or 'radial' inletlocation : str, optional Either 'top' or 'bottom' or 'auto' roughness : float, optional Roughness of the tank walls [m] Returns ------- h : float Average transfer coefficient inside the jacket [W/m^2/K] Notes ----- [1]_ is in German and has not been reviewed. Multiple other formulations are considered in [1]_. If the fluid is heated and enters from the bottom, natural convection assists the heat transfer and the Grashof term is added; if it were to enter from the top, it would be subtracted. The situation is reversed if entry is from the top. Examples -------- Example as in [2]_, matches in all but friction factor: >>> Stein_Schmidt(m=2.5, Dtank=0.6, Djacket=0.65, H=0.6, Dinlet=0.025, ... rho=995.7, Cp=4178.1, k=0.615, mu=798E-6, muw=355E-6, rhow=971.8) 5695.204169808863 References ---------- .. [1] Stein, Prof Dr-Ing Werner Alexander, and Dipl-Ing (FH) Wolfgang Schmidt. "Wärmeübergang auf der Wärmeträgerseite eines Rührbehälters mit einem einfachen Mantel." Forschung im Ingenieurwesen 59, no. 5 (May 1993): 73-90. doi:10.1007/BF02561203. .. [2] Gesellschaft, V. D. I., ed. VDI Heat Atlas. 2nd edition. Berlin; New York:: Springer, 2010. ''' delta = (Djacket-Dtank)/2. Q = m/rho Pr = Cp*mu/k lch = (pi**2/4*Dtank**2 + H**2)**0.5 dch = 2*delta if inlettype == 'radial': bEin = pi/8*Dinlet**2/delta bMit = pi/2*Dtank*(1 + pi**2/4*Dtank**2/H**2)**0.5 vMit = Q/(2*delta*bMit) vch = vMit*log(bMit/bEin)/(1 - bEin/bMit) ReJ = vch*dch*rho/mu elif inlettype == 'tangential': f = friction_factor(1E5, roughness/dch) for run in range(5): vinlet = Q/(pi/4*Dinlet**2) vz = Q/(pi*Dtank*delta) K4 = Dinlet**2*vinlet**2/(2*f*Dtank*H) K3 = vinlet/4. - Dinlet**2*vinlet/(4*f*Dtank*H) vx0 = K3 + (K3**2 + K4)**0.5 vx = vinlet*log(1 + f*Dtank*H/Dinlet**2*vx0/vinlet)/(f*Dtank*H/Dinlet**2) vch = (vx**2 + vz**2)**0.5 ReJ = vch*dch*rho/mu f = friction_factor(ReJ, roughness/dch) if inletlocation and rhow: GrJ = g*rho*(rho-rhow)*dch**3/mu**2 if rhow < rho: # Heating jacket fluid if inletlocation == 'auto' or inletlocation == 'bottom': ReJeq = (ReJ**2 + GrJ*H/dch/50.)**0.5 else: ReJeq = (ReJ**2 - GrJ*H/dch/50.)**0.5 else: # Cooling jacket fluid if inletlocation == 'auto' or inletlocation == 'top': ReJeq = (ReJ**2 + GrJ*H/dch/50.)**0.5 else: ReJeq = (ReJ**2 - GrJ*H/dch/50.)**0.5 else: ReJeq = (ReJ**2)**0.5 NuA = 3.66 NuB = 1.62*Pr**(1/3.)*ReJeq**(1/3.)*(dch/lch)**(1/3.) NuC = 0.664*Pr**(1/3.)*(ReJeq*dch/lch)**0.5 if ReJeq < 2300: NuD = 0 else: NuD = 0.0115*Pr**(1/3.)*ReJeq**0.9*(1 - (2300./ReJeq)**2.5)*(1 + (dch/lch)**(2/3.)) if muw: NuJ = (NuA**3 + NuB**3 + NuC**3 + NuD**3)**(1/3.)*(mu/muw)**0.14 else: NuJ = (NuA**3 + NuB**3 + NuC**3 + NuD**3)**(1/3.) return NuJ*k/dch
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r'''Calculates average heat transfer coefficient for a jacket around a vessel according to [1]_ as described in [2]_. .. math:: l_{ch} = \left[\left(\frac{\pi}{2}\right)^2 D_{tank}^2+H^2\right]^{0.5} d_{ch} = 2\delta Re_j = \frac{v_{ch}d_{ch}\rho}{\mu} Gr_J = \frac{g\rho(\rho-\rho_w)d_{ch}^3}{\mu^2} Re_{J,eq} = \left[Re_J^2\pm \left(\frac{|Gr_J|\frac{H}{d_{ch}}}{50} \right)\right]^{0.5} Nu_J = (Nu_A^3 + Nu_B^3 + Nu_C^3 + Nu_D^3)^{1/3}\left(\frac{\mu} {\mu_w}\right)^{0.14} Nu_J = \frac{h d_{ch}}{k} Nu_A = 3.66 Nu_B = 1.62 Pr^{1/3}Re_{J,eq}^{1/3}\left(\frac{d_{ch}}{l_{ch}} \right)^{1/3} Nu_C = 0.664Pr^{1/3}(Re_{J,eq}\frac{d_{ch}}{l_{ch}})^{0.5} \text{if } Re_{J,eq} < 2300: Nu_D = 0 Nu_D = 0.0115Pr^{1/3}Re_{J,eq}^{0.9}\left(1 - \left(\frac{2300} {Re_{J,eq}}\right)^{2.5}\right)\left(1 + \left(\frac{d_{ch}}{l_{ch}} \right)^{2/3}\right) For Radial inlets: .. math:: v_{ch} = v_{Mit}\left(\frac{\ln\frac{b_{Mit}}{b_{Ein}}}{1 - \frac{b_{Ein}}{b_{Mit}}}\right) b_{Ein} = \frac{\pi}{8}\frac{D_{inlet}^2}{\delta} b_{Mit} = \frac{\pi}{2}D_{tank}\sqrt{1 + \frac{\pi^2}{4}\frac {D_{tank}^2}{H^2}} v_{Mit} = \frac{Q}{2\delta b_{Mit}} For Tangential inlets: .. math:: v_{ch} = (v_x^2 + v_z^2)^{0.5} v_x = v_{inlet}\left(\frac{\ln[1 + \frac{f_d D_{tank}H}{D_{inlet}^2} \frac{v_x(0)}{v_{inlet}}]}{\frac{f_d D_{tank}H}{D_{inlet}^2}}\right) v_x(0) = K_3 + (K_3^2 + K_4)^{0.5} K_3 = \frac{v_{inlet}}{4} -\frac{D_{inlet}^2v_{inlet}}{4f_d D_{tank}H} K_4 = \frac{D_{inlet}^2v_{inlet}^2}{2f_d D_{tank} H} v_z = \frac{Q}{\pi D_{tank}\delta} v_{inlet} = \frac{Q}{\frac{\pi}{4}D_{inlet}^2} Parameters ---------- m : float Mass flow rate of fluid, [kg/m^3] Dtank : float Outer diameter of tank or vessel surrounded by jacket, [m] Djacket : float Inner diameter of jacket surrounding a vessel or tank, [m] H : float Height of the vessel or tank, [m] Dinlet : float Inner diameter of inlet into the jacket, [m] rho : float Density of the fluid at Tm [kg/m^3] Cp : float Heat capacity of fluid at Tm [J/kg/K] k : float Thermal conductivity of fluid at Tm [W/m/K] mu : float Viscosity of fluid at Tm [Pa*s] muw : float, optional Viscosity of fluid at Tw [Pa*s] rhow : float, optional Density of the fluid at Tw [kg/m^3] inlettype : str, optional Either 'tangential' or 'radial' inletlocation : str, optional Either 'top' or 'bottom' or 'auto' roughness : float, optional Roughness of the tank walls [m] Returns ------- h : float Average transfer coefficient inside the jacket [W/m^2/K] Notes ----- [1]_ is in German and has not been reviewed. Multiple other formulations are considered in [1]_. If the fluid is heated and enters from the bottom, natural convection assists the heat transfer and the Grashof term is added; if it were to enter from the top, it would be subtracted. The situation is reversed if entry is from the top. Examples -------- Example as in [2]_, matches in all but friction factor: >>> Stein_Schmidt(m=2.5, Dtank=0.6, Djacket=0.65, H=0.6, Dinlet=0.025, ... rho=995.7, Cp=4178.1, k=0.615, mu=798E-6, muw=355E-6, rhow=971.8) 5695.204169808863 References ---------- .. [1] Stein, Prof Dr-Ing Werner Alexander, and Dipl-Ing (FH) Wolfgang Schmidt. "Wärmeübergang auf der Wärmeträgerseite eines Rührbehälters mit einem einfachen Mantel." Forschung im Ingenieurwesen 59, no. 5 (May 1993): 73-90. doi:10.1007/BF02561203. .. [2] Gesellschaft, V. D. I., ed. VDI Heat Atlas. 2nd edition. Berlin; New York:: Springer, 2010.
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python
train
bkjones/pyrabbit
pyrabbit/api.py
https://github.com/bkjones/pyrabbit/blob/e8a9f74ed5c6bba958994fb9a72c396e6a99ea0f/pyrabbit/api.py#L544-L557
def purge_queues(self, queues): """ Purge all messages from one or more queues. :param list queues: A list of ('qname', 'vhost') tuples. :returns: True on success """ for name, vhost in queues: vhost = quote(vhost, '') name = quote(name, '') path = Client.urls['purge_queue'] % (vhost, name) self._call(path, 'DELETE') return True
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Purge all messages from one or more queues. :param list queues: A list of ('qname', 'vhost') tuples. :returns: True on success
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python
train
trevisanj/f311
f311/collaboration.py
https://github.com/trevisanj/f311/blob/9e502a3d1e1f74d4290a8a0bae9a34ef8d7b29f7/f311/collaboration.py#L248-L273
def get_programs_dict(pkgname_only=None, flag_protected=False): """ Scans COLLABORATORS_S packages for scripts, eventually filtering if arguments passed Args: pkgname_only: name of single package within COLLABORATORS_S flag_protected: include scripts starting with "_"? Returns: dictionary: {"packagename0": {"exeinfo": [ExeInfo00, ...], "description": description0}, ...} """ ___ret = _get_programs_dict() __ret = ___ret if pkgname_only is None else OrderedDict(((pkgname_only, ___ret[pkgname_only]),)) if flag_protected: _ret = __ret else: _ret = copy.deepcopy(__ret) for value in _ret.values(): value["exeinfo"] = [exeinfo for exeinfo in value["exeinfo"] if not exeinfo.filename.startswith("_")] # Removes packages that may have gone out of scripts after filtering ret = _ret if pkgname_only is None and flag_protected is None else \ OrderedDict(((key, value) for key, value in _ret.items() if len(value["exeinfo"]) > 0)) return ret
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python
train
DistrictDataLabs/yellowbrick
yellowbrick/utils/nan_warnings.py
https://github.com/DistrictDataLabs/yellowbrick/blob/59b67236a3862c73363e8edad7cd86da5b69e3b2/yellowbrick/utils/nan_warnings.py#L56-L66
def warn_if_nans_exist(X): """Warn if nans exist in a numpy array.""" null_count = count_rows_with_nans(X) total = len(X) percent = 100 * null_count / total if null_count > 0: warning_message = \ 'Warning! Found {} rows of {} ({:0.2f}%) with nan values. Only ' \ 'complete rows will be plotted.'.format(null_count, total, percent) warnings.warn(warning_message, DataWarning)
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Warn if nans exist in a numpy array.
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python
train
jleclanche/fireplace
fireplace/cards/__init__.py
https://github.com/jleclanche/fireplace/blob/d0fc0e97e185c0210de86631be20638659c0609e/fireplace/cards/__init__.py#L96-L125
def filter(self, **kwargs): """ Returns a list of card IDs matching the given filters. Each filter, if not None, is matched against the registered card database. cards. Examples arguments: \a collectible: Whether the card is collectible or not. \a type: The type of the card (hearthstone.enums.CardType) \a race: The race (tribe) of the card (hearthstone.enums.Race) \a rarity: The rarity of the card (hearthstone.enums.Rarity) \a cost: The mana cost of the card """ if not self.initialized: self.initialize() cards = self.values() if "type" not in kwargs: kwargs["type"] = [CardType.SPELL, CardType.WEAPON, CardType.MINION] for attr, value in kwargs.items(): if value is not None: # What? this doesn't work? # cards = __builtins__["filter"](lambda c: getattr(c, attr) == value, cards) cards = [ card for card in cards if (isinstance(value, list) and getattr(card, attr) in value) or getattr(card, attr) == value ] return [card.id for card in cards]
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Returns a list of card IDs matching the given filters. Each filter, if not None, is matched against the registered card database. cards. Examples arguments: \a collectible: Whether the card is collectible or not. \a type: The type of the card (hearthstone.enums.CardType) \a race: The race (tribe) of the card (hearthstone.enums.Race) \a rarity: The rarity of the card (hearthstone.enums.Rarity) \a cost: The mana cost of the card
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python
train
GPflow/GPflow
gpflow/actions.py
https://github.com/GPflow/GPflow/blob/549394f0b1b0696c7b521a065e49bdae6e7acf27/gpflow/actions.py#L211-L227
def with_settings(self, stop: Optional[int] = None, start: int = 0, step: int = 1) -> 'Loop': """ Set start, stop and step loop configuration. :param stop: Looop stop iteration integer. If None then loop becomes infinite. :param start: Loop iteration start integer. :param step: Loop iteration interval integer. :return: Loop itself. """ self.start = start self.stop = stop self.step = step return self
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Set start, stop and step loop configuration. :param stop: Looop stop iteration integer. If None then loop becomes infinite. :param start: Loop iteration start integer. :param step: Loop iteration interval integer. :return: Loop itself.
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python
train
pycontribs/pyrax
pyrax/utils.py
https://github.com/pycontribs/pyrax/blob/9ddfd5064b3a292d7337906f3b2d5dce95b50b99/pyrax/utils.py#L426-L466
def _wait_until(obj, att, desired, callback, interval, attempts, verbose, verbose_atts): """ Loops until either the desired value of the attribute is reached, or the number of attempts is exceeded. """ if not isinstance(desired, (list, tuple)): desired = [desired] if verbose_atts is None: verbose_atts = [] if not isinstance(verbose_atts, (list, tuple)): verbose_atts = [verbose_atts] infinite = (attempts == 0) attempt = 0 start = time.time() while infinite or (attempt < attempts): try: # For servers: obj.get() except AttributeError: try: # For other objects that don't support .get() obj = obj.manager.get(obj.id) except AttributeError: # punt raise exc.NoReloadError("The 'wait_until' method is not " "supported for '%s' objects." % obj.__class__) attval = getattr(obj, att) if verbose: elapsed = time.time() - start msgs = ["Current value of %s: %s (elapsed: %4.1f seconds)" % ( att, attval, elapsed)] for vatt in verbose_atts: vattval = getattr(obj, vatt, None) msgs.append("%s=%s" % (vatt, vattval)) print(" ".join(msgs)) if attval in desired: return obj time.sleep(interval) attempt += 1 return obj
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Loops until either the desired value of the attribute is reached, or the number of attempts is exceeded.
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python
train
dropbox/stone
stone/backends/obj_c_types.py
https://github.com/dropbox/stone/blob/2e95cbcd1c48e05cca68c919fd8d24adec6b0f58/stone/backends/obj_c_types.py#L1265-L1376
def _generate_route_objects_m(self, route_schema, namespace): """Emits implementation files for Route objects which encapsulate information regarding each route. These objects are passed as parameters when route calls are made.""" output_path = 'Routes/RouteObjects/{}.m'.format( fmt_route_obj_class(namespace.name)) with self.output_to_relative_path(output_path): self.emit_raw(base_file_comment) import_classes = [ fmt_route_obj_class(namespace.name), 'DBStoneBase', 'DBRequestErrors', ] for auth_type in self.namespace_to_has_route_auth_list[namespace]: import_classes.append( fmt_routes_class(namespace.name, auth_type)) imports_classes_m = import_classes + \ self._get_imports_m( self._get_namespace_route_imports(namespace, include_route_args=False), []) self._generate_imports_m(imports_classes_m) with self.block_m(fmt_route_obj_class(namespace.name)): for route in namespace.routes: route_name = fmt_route_var(namespace.name, route) self.emit('static DBRoute *{};'.format(route_name)) self.emit() for route in namespace.routes: route_name = fmt_route_var(namespace.name, route) if route.version == 1: route_path = route.name else: route_path = '{}_v{}'.format(route.name, route.version) if route.deprecated: deprecated = '@{}'.format('YES') else: deprecated = '@{}'.format('NO') if not is_void_type(route.result_data_type): caller = fmt_class_type( route.result_data_type, suppress_ptr=True) result_type = fmt_func_call( caller=caller, callee='class') else: result_type = 'nil' if not is_void_type(route.error_data_type): caller = fmt_class_type( route.error_data_type, suppress_ptr=True) error_type = fmt_func_call( caller=caller, callee='class') else: error_type = 'nil' if is_list_type(route.arg_data_type) or is_map_type(route.arg_data_type): dataStructSerialBlock = '^id(id dataStruct) {{ return {}; }}'.format( self._fmt_serialization_call( route.result_data_type, 'dataStruct', True)) else: dataStructSerialBlock = 'nil' if is_list_type(route.result_data_type) or is_map_type(route.result_data_type): dataStructDeserialBlock = '^id(id dataStruct) {{ return {}; }}'.format( self._fmt_serialization_call( route.result_data_type, 'dataStruct', False)) else: dataStructDeserialBlock = 'nil' with self.block_func( func=route_name, args=[], return_type='DBRoute *', class_func=True): with self.block('if (!{})'.format(route_name)): with self.block( '{} = [[DBRoute alloc] init:'.format( route_name), delim=(None, None), after='];'): self.emit('@\"{}\"'.format(route_path)) self.emit('namespace_:@\"{}\"'.format( namespace.name)) self.emit('deprecated:{}'.format(deprecated)) self.emit('resultType:{}'.format(result_type)) self.emit('errorType:{}'.format(error_type)) attrs = [] for field in route_schema.fields: attr_key = field.name attr_val = ("@\"{}\"".format(route.attrs .get(attr_key)) if route.attrs .get(attr_key) else 'nil') attrs.append('@\"{}\": {}'.format( attr_key, attr_val)) self.generate_multiline_list( attrs, delim=('attrs:@{', '}'), compact=True) self.emit('dataStructSerialBlock:{}'.format( dataStructSerialBlock)) self.emit('dataStructDeserialBlock:{}'.format( dataStructDeserialBlock)) self.emit('return {};'.format(route_name)) self.emit()
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Emits implementation files for Route objects which encapsulate information regarding each route. These objects are passed as parameters when route calls are made.
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python
train
saltstack/salt
salt/states/postgres_schema.py
https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/states/postgres_schema.py#L31-L106
def present(dbname, name, owner=None, user=None, db_user=None, db_password=None, db_host=None, db_port=None): ''' Ensure that the named schema is present in the database. dbname The database's name will work on name The name of the schema to manage user system user all operations should be performed on behalf of db_user database username if different from config or default db_password user password if any password for a specified user db_host Database host if different from config or default db_port Database port if different from config or default ''' ret = {'dbname': dbname, 'name': name, 'changes': {}, 'result': True, 'comment': 'Schema {0} is already present in ' 'database {1}'.format(name, dbname)} db_args = { 'db_user': db_user, 'db_password': db_password, 'db_host': db_host, 'db_port': db_port, 'user': user } # check if schema exists schema_attr = __salt__['postgres.schema_get'](dbname, name, **db_args) cret = None # The schema is not present, make it! if schema_attr is None: if __opts__['test']: ret['result'] = None ret['comment'] = 'Schema {0} is set to be created' \ ' in database {1}.'.format(name, dbname) return ret cret = __salt__['postgres.schema_create'](dbname, name, owner=owner, **db_args) else: msg = 'Schema {0} already exists in database {1}' cret = None if cret: msg = 'Schema {0} has been created in database {1}' ret['result'] = True ret['changes'][name] = 'Present' elif cret is not None: msg = 'Failed to create schema {0} in database {1}' ret['result'] = False else: msg = 'Schema {0} already exists in database {1}' ret['result'] = True ret['comment'] = msg.format(name, dbname) return ret
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Ensure that the named schema is present in the database. dbname The database's name will work on name The name of the schema to manage user system user all operations should be performed on behalf of db_user database username if different from config or default db_password user password if any password for a specified user db_host Database host if different from config or default db_port Database port if different from config or default
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python
train