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shoaibrayeen/Programmers-Community
1d352fb3e6ac5e2e7d9472d90527bdcc8d5ec355
Data Structure/Array Or Vector/Sort An Array of 0s and 1s/SolutionByEnthusiastDeveloper.py
python
sortArray
(array)
return sorted_array
Sort a given array which should only include 0's and 1's Raise a value error on the first value which is not 0 or 1, otherwise returns a sorted array Logic: 1. Go over the supplied array once and: a. verify the input values b. count how many 1's are in there 2. Fill the sorted array with zeros (length of given array - number of 1's) 3. Fill the rest of the sorted array with ones 4. Return the sorted array
Sort a given array which should only include 0's and 1's Raise a value error on the first value which is not 0 or 1, otherwise returns a sorted array
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def sortArray(array): ''' Sort a given array which should only include 0's and 1's Raise a value error on the first value which is not 0 or 1, otherwise returns a sorted array Logic: 1. Go over the supplied array once and: a. verify the input values b. count how many 1's are in there 2. Fill the sorted array with zeros (length of given array - number of 1's) 3. Fill the rest of the sorted array with ones 4. Return the sorted array ''' num_of_ones = 0 for x in array: if x == 0: pass elif x == 1: num_of_ones += 1 else: raise ValueError sorted_array = [] for i in range(len(array)-num_of_ones): sorted_array.append(0) for i in range(len(array)-num_of_ones, len(array)): sorted_array.append(1) return sorted_array
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https://github.com/shoaibrayeen/Programmers-Community/blob/1d352fb3e6ac5e2e7d9472d90527bdcc8d5ec355/Data Structure/Array Or Vector/Sort An Array of 0s and 1s/SolutionByEnthusiastDeveloper.py#L5-L32
KratosMultiphysics/Kratos
0000833054ed0503424eb28205d6508d9ca6cbbc
applications/FemToDemApplication/python_scripts/MainCouplingPfemFemDemAitken.py
python
KratosPrintInfo
(message)
This function prints info on screen
This function prints info on screen
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def KratosPrintInfo(message): """This function prints info on screen """ KM.Logger.Print("", message) KM.Logger.Flush()
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https://github.com/KratosMultiphysics/Kratos/blob/0000833054ed0503424eb28205d6508d9ca6cbbc/applications/FemToDemApplication/python_scripts/MainCouplingPfemFemDemAitken.py#L13-L17
GoSSIP-SJTU/Armariris
ad5d868482956b2194a77b39c8d543c7c2318200
tools/clang/bindings/python/clang/cindex.py
python
Cursor.get_usr
(self)
return conf.lib.clang_getCursorUSR(self)
Return the Unified Symbol Resultion (USR) for the entity referenced by the given cursor (or None). A Unified Symbol Resolution (USR) is a string that identifies a particular entity (function, class, variable, etc.) within a program. USRs can be compared across translation units to determine, e.g., when references in one translation refer to an entity defined in another translation unit.
Return the Unified Symbol Resultion (USR) for the entity referenced by the given cursor (or None).
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def get_usr(self): """Return the Unified Symbol Resultion (USR) for the entity referenced by the given cursor (or None). A Unified Symbol Resolution (USR) is a string that identifies a particular entity (function, class, variable, etc.) within a program. USRs can be compared across translation units to determine, e.g., when references in one translation refer to an entity defined in another translation unit.""" return conf.lib.clang_getCursorUSR(self)
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https://github.com/GoSSIP-SJTU/Armariris/blob/ad5d868482956b2194a77b39c8d543c7c2318200/tools/clang/bindings/python/clang/cindex.py#L1257-L1266
openvinotoolkit/openvino
dedcbeafa8b84cccdc55ca64b8da516682b381c7
tools/mo/openvino/tools/mo/ops/If.py
python
If.re_numerate_internal_id_and_get_if_id
(if_node)
return if_node.node
This method is called before IR generation. This method sets internal_layer_id. :param if_node: The If node where is necessary to set internal_layer_id in bodies. :return: if_node
This method is called before IR generation. This method sets internal_layer_id.
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def re_numerate_internal_id_and_get_if_id(if_node): """ This method is called before IR generation. This method sets internal_layer_id. :param if_node: The If node where is necessary to set internal_layer_id in bodies. :return: if_node """ then_graph_nodes = if_node.then_graph.nodes() for idx in range(len(if_node.then_graph.get_op_nodes())): then_graph_nodes[idx]['internal_layer_id'] = idx else_graph_nodes = if_node.else_graph.nodes() for idx in range(len(if_node.else_graph.get_op_nodes())): else_graph_nodes[idx]['internal_layer_id'] = idx return if_node.node
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https://github.com/openvinotoolkit/openvino/blob/dedcbeafa8b84cccdc55ca64b8da516682b381c7/tools/mo/openvino/tools/mo/ops/If.py#L273-L286
rapidsai/cudf
d5b2448fc69f17509304d594f029d0df56984962
python/cudf/cudf/core/df_protocol.py
python
_CuDFBuffer.bufsize
(self)
return self._buf.nbytes
Buffer size in bytes.
Buffer size in bytes.
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def bufsize(self) -> int: """ Buffer size in bytes. """ return self._buf.nbytes
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https://github.com/rapidsai/cudf/blob/d5b2448fc69f17509304d594f029d0df56984962/python/cudf/cudf/core/df_protocol.py#L79-L83
facebookarchive/LogDevice
ce7726050edc49a1e15d9160e81c890736b779e2
logdevice/ops/ldshell/autoload/commands/safety.py
python
location_up_to_scope
( shard: ShardID, location_per_scope: Mapping[LocationScope, str], scope: LocationScope, )
return ".".join(locs)
Generates a string of the location string up to a given scope. The input scope is inclusive.
Generates a string of the location string up to a given scope. The input scope is inclusive.
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def location_up_to_scope( shard: ShardID, location_per_scope: Mapping[LocationScope, str], scope: LocationScope, ) -> str: """ Generates a string of the location string up to a given scope. The input scope is inclusive. """ if not location_per_scope: return "UNKNOWN" locs = [] # Sort scopes from bigger to smaller (ROOT > REGION > CLUSTER > ...) for loc_scope in sorted( location_per_scope.keys(), key=lambda x: x.value, reverse=True ): if loc_scope.value >= scope.value: locs.append(location_per_scope[loc_scope]) else: break if scope == LocationScope.NODE: locs.append(str(shard.node.node_index)) return ".".join(locs)
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https://github.com/facebookarchive/LogDevice/blob/ce7726050edc49a1e15d9160e81c890736b779e2/logdevice/ops/ldshell/autoload/commands/safety.py#L307-L329
metashell/metashell
f4177e4854ea00c8dbc722cadab26ef413d798ea
3rd/templight/clang/utils/check_cfc/check_cfc.py
python
get_output_file
(args)
return None
Return the output file specified by this command or None if not specified.
Return the output file specified by this command or None if not specified.
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def get_output_file(args): """Return the output file specified by this command or None if not specified.""" grabnext = False for arg in args: if grabnext: return arg if arg == '-o': # Specified as a separate arg grabnext = True elif arg.startswith('-o'): # Specified conjoined with -o return arg[2:] assert grabnext == False return None
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https://github.com/metashell/metashell/blob/f4177e4854ea00c8dbc722cadab26ef413d798ea/3rd/templight/clang/utils/check_cfc/check_cfc.py#L130-L145
apple/turicreate
cce55aa5311300e3ce6af93cb45ba791fd1bdf49
src/external/coremltools_wrap/coremltools/deps/protobuf/python/mox.py
python
MockMethod.AndReturn
(self, return_value)
return return_value
Set the value to return when this method is called. Args: # return_value can be anything.
Set the value to return when this method is called.
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def AndReturn(self, return_value): """Set the value to return when this method is called. Args: # return_value can be anything. """ self._return_value = return_value return return_value
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https://github.com/apple/turicreate/blob/cce55aa5311300e3ce6af93cb45ba791fd1bdf49/src/external/coremltools_wrap/coremltools/deps/protobuf/python/mox.py#L718-L726
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/pip/_internal/utils/misc.py
python
dist_is_editable
(dist)
return False
Return True if given Distribution is an editable install.
[]
def dist_is_editable(dist): # type: (Distribution) -> bool """ Return True if given Distribution is an editable install. """ for path_item in sys.path: egg_link = os.path.join(path_item, dist.project_name + '.egg-link') if os.path.isfile(egg_link): return True return False
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/pip/_internal/utils/misc.py#L801-L819
catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/tools/python3/src/Lib/logging/handlers.py
python
SocketHandler.makeSocket
(self, timeout=1)
return result
A factory method which allows subclasses to define the precise type of socket they want.
A factory method which allows subclasses to define the precise type of socket they want.
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def makeSocket(self, timeout=1): """ A factory method which allows subclasses to define the precise type of socket they want. """ if self.port is not None: result = socket.create_connection(self.address, timeout=timeout) else: result = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM) result.settimeout(timeout) try: result.connect(self.address) except OSError: result.close() # Issue 19182 raise return result
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/tools/python3/src/Lib/logging/handlers.py#L558-L573
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numpy/lib/histograms.py
python
histogram_bin_edges
(a, bins=10, range=None, weights=None)
return bin_edges
r""" Function to calculate only the edges of the bins used by the `histogram` function. Parameters ---------- a : array_like Input data. The histogram is computed over the flattened array. bins : int or sequence of scalars or str, optional If `bins` is an int, it defines the number of equal-width bins in the given range (10, by default). If `bins` is a sequence, it defines the bin edges, including the rightmost edge, allowing for non-uniform bin widths. If `bins` is a string from the list below, `histogram_bin_edges` will use the method chosen to calculate the optimal bin width and consequently the number of bins (see `Notes` for more detail on the estimators) from the data that falls within the requested range. While the bin width will be optimal for the actual data in the range, the number of bins will be computed to fill the entire range, including the empty portions. For visualisation, using the 'auto' option is suggested. Weighted data is not supported for automated bin size selection. 'auto' Maximum of the 'sturges' and 'fd' estimators. Provides good all around performance. 'fd' (Freedman Diaconis Estimator) Robust (resilient to outliers) estimator that takes into account data variability and data size. 'doane' An improved version of Sturges' estimator that works better with non-normal datasets. 'scott' Less robust estimator that that takes into account data variability and data size. 'stone' Estimator based on leave-one-out cross-validation estimate of the integrated squared error. Can be regarded as a generalization of Scott's rule. 'rice' Estimator does not take variability into account, only data size. Commonly overestimates number of bins required. 'sturges' R's default method, only accounts for data size. Only optimal for gaussian data and underestimates number of bins for large non-gaussian datasets. 'sqrt' Square root (of data size) estimator, used by Excel and other programs for its speed and simplicity. range : (float, float), optional The lower and upper range of the bins. If not provided, range is simply ``(a.min(), a.max())``. Values outside the range are ignored. The first element of the range must be less than or equal to the second. `range` affects the automatic bin computation as well. While bin width is computed to be optimal based on the actual data within `range`, the bin count will fill the entire range including portions containing no data. weights : array_like, optional An array of weights, of the same shape as `a`. Each value in `a` only contributes its associated weight towards the bin count (instead of 1). This is currently not used by any of the bin estimators, but may be in the future. Returns ------- bin_edges : array of dtype float The edges to pass into `histogram` See Also -------- histogram Notes ----- The methods to estimate the optimal number of bins are well founded in literature, and are inspired by the choices R provides for histogram visualisation. Note that having the number of bins proportional to :math:`n^{1/3}` is asymptotically optimal, which is why it appears in most estimators. These are simply plug-in methods that give good starting points for number of bins. In the equations below, :math:`h` is the binwidth and :math:`n_h` is the number of bins. All estimators that compute bin counts are recast to bin width using the `ptp` of the data. The final bin count is obtained from ``np.round(np.ceil(range / h))``. 'auto' (maximum of the 'sturges' and 'fd' estimators) A compromise to get a good value. For small datasets the Sturges value will usually be chosen, while larger datasets will usually default to FD. Avoids the overly conservative behaviour of FD and Sturges for small and large datasets respectively. Switchover point is usually :math:`a.size \approx 1000`. 'fd' (Freedman Diaconis Estimator) .. math:: h = 2 \frac{IQR}{n^{1/3}} The binwidth is proportional to the interquartile range (IQR) and inversely proportional to cube root of a.size. Can be too conservative for small datasets, but is quite good for large datasets. The IQR is very robust to outliers. 'scott' .. math:: h = \sigma \sqrt[3]{\frac{24 * \sqrt{\pi}}{n}} The binwidth is proportional to the standard deviation of the data and inversely proportional to cube root of ``x.size``. Can be too conservative for small datasets, but is quite good for large datasets. The standard deviation is not very robust to outliers. Values are very similar to the Freedman-Diaconis estimator in the absence of outliers. 'rice' .. math:: n_h = 2n^{1/3} The number of bins is only proportional to cube root of ``a.size``. It tends to overestimate the number of bins and it does not take into account data variability. 'sturges' .. math:: n_h = \log _{2}n+1 The number of bins is the base 2 log of ``a.size``. This estimator assumes normality of data and is too conservative for larger, non-normal datasets. This is the default method in R's ``hist`` method. 'doane' .. math:: n_h = 1 + \log_{2}(n) + \log_{2}(1 + \frac{|g_1|}{\sigma_{g_1}}) g_1 = mean[(\frac{x - \mu}{\sigma})^3] \sigma_{g_1} = \sqrt{\frac{6(n - 2)}{(n + 1)(n + 3)}} An improved version of Sturges' formula that produces better estimates for non-normal datasets. This estimator attempts to account for the skew of the data. 'sqrt' .. math:: n_h = \sqrt n The simplest and fastest estimator. Only takes into account the data size. Examples -------- >>> arr = np.array([0, 0, 0, 1, 2, 3, 3, 4, 5]) >>> np.histogram_bin_edges(arr, bins='auto', range=(0, 1)) array([0. , 0.25, 0.5 , 0.75, 1. ]) >>> np.histogram_bin_edges(arr, bins=2) array([0. , 2.5, 5. ]) For consistency with histogram, an array of pre-computed bins is passed through unmodified: >>> np.histogram_bin_edges(arr, [1, 2]) array([1, 2]) This function allows one set of bins to be computed, and reused across multiple histograms: >>> shared_bins = np.histogram_bin_edges(arr, bins='auto') >>> shared_bins array([0., 1., 2., 3., 4., 5.]) >>> group_id = np.array([0, 1, 1, 0, 1, 1, 0, 1, 1]) >>> hist_0, _ = np.histogram(arr[group_id == 0], bins=shared_bins) >>> hist_1, _ = np.histogram(arr[group_id == 1], bins=shared_bins) >>> hist_0; hist_1 array([1, 1, 0, 1, 0]) array([2, 0, 1, 1, 2]) Which gives more easily comparable results than using separate bins for each histogram: >>> hist_0, bins_0 = np.histogram(arr[group_id == 0], bins='auto') >>> hist_1, bins_1 = np.histogram(arr[group_id == 1], bins='auto') >>> hist_0; hist_1 array([1, 1, 1]) array([2, 1, 1, 2]) >>> bins_0; bins_1 array([0., 1., 2., 3.]) array([0. , 1.25, 2.5 , 3.75, 5. ])
r""" Function to calculate only the edges of the bins used by the `histogram` function.
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def histogram_bin_edges(a, bins=10, range=None, weights=None): r""" Function to calculate only the edges of the bins used by the `histogram` function. Parameters ---------- a : array_like Input data. The histogram is computed over the flattened array. bins : int or sequence of scalars or str, optional If `bins` is an int, it defines the number of equal-width bins in the given range (10, by default). If `bins` is a sequence, it defines the bin edges, including the rightmost edge, allowing for non-uniform bin widths. If `bins` is a string from the list below, `histogram_bin_edges` will use the method chosen to calculate the optimal bin width and consequently the number of bins (see `Notes` for more detail on the estimators) from the data that falls within the requested range. While the bin width will be optimal for the actual data in the range, the number of bins will be computed to fill the entire range, including the empty portions. For visualisation, using the 'auto' option is suggested. Weighted data is not supported for automated bin size selection. 'auto' Maximum of the 'sturges' and 'fd' estimators. Provides good all around performance. 'fd' (Freedman Diaconis Estimator) Robust (resilient to outliers) estimator that takes into account data variability and data size. 'doane' An improved version of Sturges' estimator that works better with non-normal datasets. 'scott' Less robust estimator that that takes into account data variability and data size. 'stone' Estimator based on leave-one-out cross-validation estimate of the integrated squared error. Can be regarded as a generalization of Scott's rule. 'rice' Estimator does not take variability into account, only data size. Commonly overestimates number of bins required. 'sturges' R's default method, only accounts for data size. Only optimal for gaussian data and underestimates number of bins for large non-gaussian datasets. 'sqrt' Square root (of data size) estimator, used by Excel and other programs for its speed and simplicity. range : (float, float), optional The lower and upper range of the bins. If not provided, range is simply ``(a.min(), a.max())``. Values outside the range are ignored. The first element of the range must be less than or equal to the second. `range` affects the automatic bin computation as well. While bin width is computed to be optimal based on the actual data within `range`, the bin count will fill the entire range including portions containing no data. weights : array_like, optional An array of weights, of the same shape as `a`. Each value in `a` only contributes its associated weight towards the bin count (instead of 1). This is currently not used by any of the bin estimators, but may be in the future. Returns ------- bin_edges : array of dtype float The edges to pass into `histogram` See Also -------- histogram Notes ----- The methods to estimate the optimal number of bins are well founded in literature, and are inspired by the choices R provides for histogram visualisation. Note that having the number of bins proportional to :math:`n^{1/3}` is asymptotically optimal, which is why it appears in most estimators. These are simply plug-in methods that give good starting points for number of bins. In the equations below, :math:`h` is the binwidth and :math:`n_h` is the number of bins. All estimators that compute bin counts are recast to bin width using the `ptp` of the data. The final bin count is obtained from ``np.round(np.ceil(range / h))``. 'auto' (maximum of the 'sturges' and 'fd' estimators) A compromise to get a good value. For small datasets the Sturges value will usually be chosen, while larger datasets will usually default to FD. Avoids the overly conservative behaviour of FD and Sturges for small and large datasets respectively. Switchover point is usually :math:`a.size \approx 1000`. 'fd' (Freedman Diaconis Estimator) .. math:: h = 2 \frac{IQR}{n^{1/3}} The binwidth is proportional to the interquartile range (IQR) and inversely proportional to cube root of a.size. Can be too conservative for small datasets, but is quite good for large datasets. The IQR is very robust to outliers. 'scott' .. math:: h = \sigma \sqrt[3]{\frac{24 * \sqrt{\pi}}{n}} The binwidth is proportional to the standard deviation of the data and inversely proportional to cube root of ``x.size``. Can be too conservative for small datasets, but is quite good for large datasets. The standard deviation is not very robust to outliers. Values are very similar to the Freedman-Diaconis estimator in the absence of outliers. 'rice' .. math:: n_h = 2n^{1/3} The number of bins is only proportional to cube root of ``a.size``. It tends to overestimate the number of bins and it does not take into account data variability. 'sturges' .. math:: n_h = \log _{2}n+1 The number of bins is the base 2 log of ``a.size``. This estimator assumes normality of data and is too conservative for larger, non-normal datasets. This is the default method in R's ``hist`` method. 'doane' .. math:: n_h = 1 + \log_{2}(n) + \log_{2}(1 + \frac{|g_1|}{\sigma_{g_1}}) g_1 = mean[(\frac{x - \mu}{\sigma})^3] \sigma_{g_1} = \sqrt{\frac{6(n - 2)}{(n + 1)(n + 3)}} An improved version of Sturges' formula that produces better estimates for non-normal datasets. This estimator attempts to account for the skew of the data. 'sqrt' .. math:: n_h = \sqrt n The simplest and fastest estimator. Only takes into account the data size. Examples -------- >>> arr = np.array([0, 0, 0, 1, 2, 3, 3, 4, 5]) >>> np.histogram_bin_edges(arr, bins='auto', range=(0, 1)) array([0. , 0.25, 0.5 , 0.75, 1. ]) >>> np.histogram_bin_edges(arr, bins=2) array([0. , 2.5, 5. ]) For consistency with histogram, an array of pre-computed bins is passed through unmodified: >>> np.histogram_bin_edges(arr, [1, 2]) array([1, 2]) This function allows one set of bins to be computed, and reused across multiple histograms: >>> shared_bins = np.histogram_bin_edges(arr, bins='auto') >>> shared_bins array([0., 1., 2., 3., 4., 5.]) >>> group_id = np.array([0, 1, 1, 0, 1, 1, 0, 1, 1]) >>> hist_0, _ = np.histogram(arr[group_id == 0], bins=shared_bins) >>> hist_1, _ = np.histogram(arr[group_id == 1], bins=shared_bins) >>> hist_0; hist_1 array([1, 1, 0, 1, 0]) array([2, 0, 1, 1, 2]) Which gives more easily comparable results than using separate bins for each histogram: >>> hist_0, bins_0 = np.histogram(arr[group_id == 0], bins='auto') >>> hist_1, bins_1 = np.histogram(arr[group_id == 1], bins='auto') >>> hist_0; hist_1 array([1, 1, 1]) array([2, 1, 1, 2]) >>> bins_0; bins_1 array([0., 1., 2., 3.]) array([0. , 1.25, 2.5 , 3.75, 5. ]) """ a, weights = _ravel_and_check_weights(a, weights) bin_edges, _ = _get_bin_edges(a, bins, range, weights) return bin_edges
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numpy/lib/histograms.py#L474-L672
catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/python/scipy/scipy/signal/_peak_finding.py
python
_boolrelextrema
(data, comparator, axis=0, order=1, mode='clip')
return results
Calculate the relative extrema of `data`. Relative extrema are calculated by finding locations where ``comparator(data[n], data[n+1:n+order+1])`` is True. Parameters ---------- data : ndarray Array in which to find the relative extrema. comparator : callable Function to use to compare two data points. Should take two arrays as arguments. axis : int, optional Axis over which to select from `data`. Default is 0. order : int, optional How many points on each side to use for the comparison to consider ``comparator(n,n+x)`` to be True. mode : str, optional How the edges of the vector are treated. 'wrap' (wrap around) or 'clip' (treat overflow as the same as the last (or first) element). Default 'clip'. See numpy.take Returns ------- extrema : ndarray Boolean array of the same shape as `data` that is True at an extrema, False otherwise. See also -------- argrelmax, argrelmin Examples -------- >>> testdata = np.array([1,2,3,2,1]) >>> _boolrelextrema(testdata, np.greater, axis=0) array([False, False, True, False, False], dtype=bool)
Calculate the relative extrema of `data`.
[ "Calculate", "the", "relative", "extrema", "of", "data", "." ]
def _boolrelextrema(data, comparator, axis=0, order=1, mode='clip'): """ Calculate the relative extrema of `data`. Relative extrema are calculated by finding locations where ``comparator(data[n], data[n+1:n+order+1])`` is True. Parameters ---------- data : ndarray Array in which to find the relative extrema. comparator : callable Function to use to compare two data points. Should take two arrays as arguments. axis : int, optional Axis over which to select from `data`. Default is 0. order : int, optional How many points on each side to use for the comparison to consider ``comparator(n,n+x)`` to be True. mode : str, optional How the edges of the vector are treated. 'wrap' (wrap around) or 'clip' (treat overflow as the same as the last (or first) element). Default 'clip'. See numpy.take Returns ------- extrema : ndarray Boolean array of the same shape as `data` that is True at an extrema, False otherwise. See also -------- argrelmax, argrelmin Examples -------- >>> testdata = np.array([1,2,3,2,1]) >>> _boolrelextrema(testdata, np.greater, axis=0) array([False, False, True, False, False], dtype=bool) """ if((int(order) != order) or (order < 1)): raise ValueError('Order must be an int >= 1') datalen = data.shape[axis] locs = np.arange(0, datalen) results = np.ones(data.shape, dtype=bool) main = data.take(locs, axis=axis, mode=mode) for shift in xrange(1, order + 1): plus = data.take(locs + shift, axis=axis, mode=mode) minus = data.take(locs - shift, axis=axis, mode=mode) results &= comparator(main, plus) results &= comparator(main, minus) if(~results.any()): return results return results
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/scipy/scipy/signal/_peak_finding.py#L16-L72
windystrife/UnrealEngine_NVIDIAGameWorks
b50e6338a7c5b26374d66306ebc7807541ff815e
Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/codecs.py
python
iterencode
(iterator, encoding, errors='strict', **kwargs)
Encoding iterator. Encodes the input strings from the iterator using a IncrementalEncoder. errors and kwargs are passed through to the IncrementalEncoder constructor.
Encoding iterator.
[ "Encoding", "iterator", "." ]
def iterencode(iterator, encoding, errors='strict', **kwargs): """ Encoding iterator. Encodes the input strings from the iterator using a IncrementalEncoder. errors and kwargs are passed through to the IncrementalEncoder constructor. """ encoder = getincrementalencoder(encoding)(errors, **kwargs) for input in iterator: output = encoder.encode(input) if output: yield output output = encoder.encode("", True) if output: yield output
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https://github.com/windystrife/UnrealEngine_NVIDIAGameWorks/blob/b50e6338a7c5b26374d66306ebc7807541ff815e/Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/codecs.py#L996-L1012
bilibili/libyuv
2e9f3e5cf5f3c71a4a34893ceb20c5d69689390f
tools/valgrind-libyuv/tsan/PRESUBMIT.py
python
CheckChange
(input_api, output_api)
return suppressions.PresubmitCheck(input_api, output_api)
Checks the TSan suppressions files for bad suppressions.
Checks the TSan suppressions files for bad suppressions.
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def CheckChange(input_api, output_api): """Checks the TSan suppressions files for bad suppressions.""" # Add the path to the Chrome valgrind dir to the import path: tools_vg_path = os.path.join(input_api.PresubmitLocalPath(), '..', '..', 'valgrind') sys.path.append(tools_vg_path) import suppressions return suppressions.PresubmitCheck(input_api, output_api)
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https://github.com/bilibili/libyuv/blob/2e9f3e5cf5f3c71a4a34893ceb20c5d69689390f/tools/valgrind-libyuv/tsan/PRESUBMIT.py#L21-L30
1989Ryan/Semantic_SLAM
0284b3f832ca431c494f9c134fe46c40ec86ee38
Third_Part/PSPNet_Keras_tensorflow/caffe-tensorflow/examples/imagenet/validate.py
python
validate
(net, model_path, image_producer, top_k=5)
Compute the top_k classification accuracy for the given network and images.
Compute the top_k classification accuracy for the given network and images.
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def validate(net, model_path, image_producer, top_k=5): '''Compute the top_k classification accuracy for the given network and images.''' # Get the data specifications for given network spec = models.get_data_spec(model_instance=net) # Get the input node for feeding in the images input_node = net.inputs['data'] # Create a placeholder for the ground truth labels label_node = tf.placeholder(tf.int32) # Get the output of the network (class probabilities) probs = net.get_output() # Create a top_k accuracy node top_k_op = tf.nn.in_top_k(probs, label_node, top_k) # The number of images processed count = 0 # The number of correctly classified images correct = 0 # The total number of images total = len(image_producer) with tf.Session() as sesh: coordinator = tf.train.Coordinator() # Load the converted parameters net.load(data_path=model_path, session=sesh) # Start the image processing workers threads = image_producer.start(session=sesh, coordinator=coordinator) # Iterate over and classify mini-batches for (labels, images) in image_producer.batches(sesh): correct += np.sum(sesh.run(top_k_op, feed_dict={input_node: images, label_node: labels})) count += len(labels) cur_accuracy = float(correct) * 100 / count print('{:>6}/{:<6} {:>6.2f}%'.format(count, total, cur_accuracy)) # Stop the worker threads coordinator.request_stop() coordinator.join(threads, stop_grace_period_secs=2) print('Top {} Accuracy: {}'.format(top_k, float(correct) / total))
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https://github.com/1989Ryan/Semantic_SLAM/blob/0284b3f832ca431c494f9c134fe46c40ec86ee38/Third_Part/PSPNet_Keras_tensorflow/caffe-tensorflow/examples/imagenet/validate.py#L37-L73
y123456yz/reading-and-annotate-mongodb-3.6
93280293672ca7586dc24af18132aa61e4ed7fcf
mongo/src/third_party/scons-2.5.0/scons-local-2.5.0/SCons/Variables/PathVariable.py
python
_PathVariableClass.PathIsFile
(self, key, val, env)
Validator to check if Path is a file
Validator to check if Path is a file
[ "Validator", "to", "check", "if", "Path", "is", "a", "file" ]
def PathIsFile(self, key, val, env): """Validator to check if Path is a file""" if not os.path.isfile(val): if os.path.isdir(val): m = 'File path for option %s is a directory: %s' else: m = 'File path for option %s does not exist: %s' raise SCons.Errors.UserError(m % (key, val))
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https://github.com/y123456yz/reading-and-annotate-mongodb-3.6/blob/93280293672ca7586dc24af18132aa61e4ed7fcf/mongo/src/third_party/scons-2.5.0/scons-local-2.5.0/SCons/Variables/PathVariable.py#L104-L111
oracle/graaljs
36a56e8e993d45fc40939a3a4d9c0c24990720f1
graal-nodejs/deps/npm/node_modules/node-gyp/gyp/pylib/gyp/MSVSProject.py
python
Writer.AddToolFile
(self, path)
Adds a tool file to the project. Args: path: Relative path from project to tool file.
Adds a tool file to the project.
[ "Adds", "a", "tool", "file", "to", "the", "project", "." ]
def AddToolFile(self, path): """Adds a tool file to the project. Args: path: Relative path from project to tool file. """ self.tool_files_section.append(["ToolFile", {"RelativePath": path}])
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https://github.com/oracle/graaljs/blob/36a56e8e993d45fc40939a3a4d9c0c24990720f1/graal-nodejs/deps/npm/node_modules/node-gyp/gyp/pylib/gyp/MSVSProject.py#L84-L90
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/idlelib/history.py
python
History.__init__
(self, text)
Initialize data attributes and bind event methods. .text - Idle wrapper of tk Text widget, with .bell(). .history - source statements, possibly with multiple lines. .prefix - source already entered at prompt; filters history list. .pointer - index into history. .cyclic - wrap around history list (or not).
Initialize data attributes and bind event methods.
[ "Initialize", "data", "attributes", "and", "bind", "event", "methods", "." ]
def __init__(self, text): '''Initialize data attributes and bind event methods. .text - Idle wrapper of tk Text widget, with .bell(). .history - source statements, possibly with multiple lines. .prefix - source already entered at prompt; filters history list. .pointer - index into history. .cyclic - wrap around history list (or not). ''' self.text = text self.history = [] self.prefix = None self.pointer = None self.cyclic = idleConf.GetOption("main", "History", "cyclic", 1, "bool") text.bind("<<history-previous>>", self.history_prev) text.bind("<<history-next>>", self.history_next)
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/idlelib/history.py#L14-L29
PrincetonUniversity/athena-public-version
9c266692b9423743d8e23509b3ab266a232a92d2
tst/style/cpplint.py
python
CheckForNonStandardConstructs
(filename, clean_lines, linenum, nesting_state, error)
r"""Logs an error if we see certain non-ANSI constructs ignored by gcc-2. Complain about several constructs which gcc-2 accepts, but which are not standard C++. Warning about these in lint is one way to ease the transition to new compilers. - put storage class first (e.g. "static const" instead of "const static"). - "%lld" instead of %qd" in printf-type functions. - "%1$d" is non-standard in printf-type functions. - "\%" is an undefined character escape sequence. - text after #endif is not allowed. - invalid inner-style forward declaration. - >? and <? operators, and their >?= and <?= cousins. Additionally, check for constructor/destructor style violations and reference members, as it is very convenient to do so while checking for gcc-2 compliance. Args: filename: The name of the current file. clean_lines: A CleansedLines instance containing the file. linenum: The number of the line to check. nesting_state: A NestingState instance which maintains information about the current stack of nested blocks being parsed. error: A callable to which errors are reported, which takes 4 arguments: filename, line number, error level, and message
r"""Logs an error if we see certain non-ANSI constructs ignored by gcc-2.
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def CheckForNonStandardConstructs(filename, clean_lines, linenum, nesting_state, error): r"""Logs an error if we see certain non-ANSI constructs ignored by gcc-2. Complain about several constructs which gcc-2 accepts, but which are not standard C++. Warning about these in lint is one way to ease the transition to new compilers. - put storage class first (e.g. "static const" instead of "const static"). - "%lld" instead of %qd" in printf-type functions. - "%1$d" is non-standard in printf-type functions. - "\%" is an undefined character escape sequence. - text after #endif is not allowed. - invalid inner-style forward declaration. - >? and <? operators, and their >?= and <?= cousins. Additionally, check for constructor/destructor style violations and reference members, as it is very convenient to do so while checking for gcc-2 compliance. Args: filename: The name of the current file. clean_lines: A CleansedLines instance containing the file. linenum: The number of the line to check. nesting_state: A NestingState instance which maintains information about the current stack of nested blocks being parsed. error: A callable to which errors are reported, which takes 4 arguments: filename, line number, error level, and message """ # Remove comments from the line, but leave in strings for now. line = clean_lines.lines[linenum] if Search(r'printf\s*\(.*".*%[-+ ]?\d*q', line): error(filename, linenum, 'runtime/printf_format', 3, '%q in format strings is deprecated. Use %ll instead.') if Search(r'printf\s*\(.*".*%\d+\$', line): error(filename, linenum, 'runtime/printf_format', 2, '%N$ formats are unconventional. Try rewriting to avoid them.') # Remove escaped backslashes before looking for undefined escapes. line = line.replace('\\\\', '') if Search(r'("|\').*\\(%|\[|\(|{)', line): error(filename, linenum, 'build/printf_format', 3, '%, [, (, and { are undefined character escapes. Unescape them.') # For the rest, work with both comments and strings removed. line = clean_lines.elided[linenum] if Search(r'\b(const|volatile|void|char|short|int|long' r'|float|double|signed|unsigned' r'|schar|u?int8|u?int16|u?int32|u?int64)' r'\s+(register|static|extern|typedef)\b', line): error(filename, linenum, 'build/storage_class', 5, 'Storage-class specifier (static, extern, typedef, etc) should be ' 'at the beginning of the declaration.') if Match(r'\s*#\s*endif\s*[^/\s]+', line): error(filename, linenum, 'build/endif_comment', 5, 'Uncommented text after #endif is non-standard. Use a comment.') if Match(r'\s*class\s+(\w+\s*::\s*)+\w+\s*;', line): error(filename, linenum, 'build/forward_decl', 5, 'Inner-style forward declarations are invalid. Remove this line.') if Search(r'(\w+|[+-]?\d+(\.\d*)?)\s*(<|>)\?=?\s*(\w+|[+-]?\d+)(\.\d*)?', line): error(filename, linenum, 'build/deprecated', 3, '>? and <? (max and min) operators are non-standard and deprecated.') if Search(r'^\s*const\s*string\s*&\s*\w+\s*;', line): # TODO(unknown): Could it be expanded safely to arbitrary references, # without triggering too many false positives? The first # attempt triggered 5 warnings for mostly benign code in the regtest, hence # the restriction. # Here's the original regexp, for the reference: # type_name = r'\w+((\s*::\s*\w+)|(\s*<\s*\w+?\s*>))?' # r'\s*const\s*' + type_name + '\s*&\s*\w+\s*;' error(filename, linenum, 'runtime/member_string_references', 2, 'const string& members are dangerous. It is much better to use ' 'alternatives, such as pointers or simple constants.') # Everything else in this function operates on class declarations. # Return early if the top of the nesting stack is not a class, or if # the class head is not completed yet. classinfo = nesting_state.InnermostClass() if not classinfo or not classinfo.seen_open_brace: return # The class may have been declared with namespace or classname qualifiers. # The constructor and destructor will not have those qualifiers. base_classname = classinfo.name.split('::')[-1] # Look for single-argument constructors that aren't marked explicit. # Technically a valid construct, but against style. explicit_constructor_match = Match( r'\s+(?:(?:inline|constexpr)\s+)*(explicit\s+)?' r'(?:(?:inline|constexpr)\s+)*%s\s*' r'\(((?:[^()]|\([^()]*\))*)\)' % re.escape(base_classname), line) if explicit_constructor_match: is_marked_explicit = explicit_constructor_match.group(1) if not explicit_constructor_match.group(2): constructor_args = [] else: constructor_args = explicit_constructor_match.group(2).split(',') # collapse arguments so that commas in template parameter lists and function # argument parameter lists don't split arguments in two i = 0 while i < len(constructor_args): constructor_arg = constructor_args[i] while (constructor_arg.count('<') > constructor_arg.count('>') or constructor_arg.count('(') > constructor_arg.count(')')): constructor_arg += ',' + constructor_args[i + 1] del constructor_args[i + 1] constructor_args[i] = constructor_arg i += 1 variadic_args = [arg for arg in constructor_args if '&&...' in arg] defaulted_args = [arg for arg in constructor_args if '=' in arg] noarg_constructor = (not constructor_args # empty arg list # 'void' arg specifier or (len(constructor_args) == 1 and constructor_args[0].strip() == 'void')) onearg_constructor = ((len(constructor_args) == 1 # exactly one arg and not noarg_constructor) # all but at most one arg defaulted or (len(constructor_args) >= 1 and not noarg_constructor and len(defaulted_args) >= len(constructor_args) - 1) # variadic arguments with zero or one argument or (len(constructor_args) <= 2 and len(variadic_args) >= 1)) initializer_list_constructor = bool( onearg_constructor and Search(r'\bstd\s*::\s*initializer_list\b', constructor_args[0])) copy_constructor = bool( onearg_constructor and Match(r'(const\s+)?%s(\s*<[^>]*>)?(\s+const)?\s*(?:<\w+>\s*)?&' % re.escape(base_classname), constructor_args[0].strip())) if (not is_marked_explicit and onearg_constructor and not initializer_list_constructor and not copy_constructor): if defaulted_args or variadic_args: error(filename, linenum, 'runtime/explicit', 5, 'Constructors callable with one argument ' 'should be marked explicit.') else: error(filename, linenum, 'runtime/explicit', 5, 'Single-parameter constructors should be marked explicit.') elif is_marked_explicit and not onearg_constructor: if noarg_constructor: error(filename, linenum, 'runtime/explicit', 5, 'Zero-parameter constructors should not be marked explicit.')
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The first", "# attempt triggered 5 warnings for mostly benign code in the regtest, hence", "# the restriction.", "# Here's the original regexp, for the reference:", "# type_name = r'\\w+((\\s*::\\s*\\w+)|(\\s*<\\s*\\w+?\\s*>))?'", "# r'\\s*const\\s*' + type_name + '\\s*&\\s*\\w+\\s*;'", "error", "(", "filename", ",", "linenum", ",", "'runtime/member_string_references'", ",", "2", ",", "'const string& members are dangerous. It is much better to use '", "'alternatives, such as pointers or simple constants.'", ")", "# Everything else in this function operates on class declarations.", "# Return early if the top of the nesting stack is not a class, or if", "# the class head is not completed yet.", "classinfo", "=", "nesting_state", ".", "InnermostClass", "(", ")", "if", "not", "classinfo", "or", "not", "classinfo", ".", "seen_open_brace", ":", "return", "# The class may have been declared with namespace or classname qualifiers.", "# The constructor and destructor will not have those qualifiers.", "base_classname", "=", "classinfo", ".", "name", ".", "split", "(", "'::'", ")", "[", "-", "1", "]", "# Look for single-argument constructors that aren't marked explicit.", "# Technically a valid construct, but against style.", "explicit_constructor_match", "=", "Match", "(", "r'\\s+(?:(?:inline|constexpr)\\s+)*(explicit\\s+)?'", "r'(?:(?:inline|constexpr)\\s+)*%s\\s*'", "r'\\(((?:[^()]|\\([^()]*\\))*)\\)'", "%", "re", ".", "escape", "(", "base_classname", ")", ",", "line", ")", "if", "explicit_constructor_match", ":", "is_marked_explicit", "=", "explicit_constructor_match", ".", "group", "(", "1", ")", "if", "not", "explicit_constructor_match", ".", "group", "(", "2", ")", ":", "constructor_args", "=", "[", "]", "else", ":", "constructor_args", "=", "explicit_constructor_match", ".", "group", "(", "2", ")", ".", "split", "(", "','", ")", "# collapse arguments so that commas in template parameter lists and function", "# argument parameter lists don't split arguments in two", "i", "=", "0", "while", "i", "<", "len", "(", "constructor_args", ")", ":", "constructor_arg", "=", "constructor_args", "[", "i", "]", "while", "(", "constructor_arg", ".", "count", "(", "'<'", ")", ">", "constructor_arg", ".", "count", "(", "'>'", ")", "or", "constructor_arg", ".", "count", "(", "'('", ")", ">", "constructor_arg", ".", "count", "(", "')'", ")", ")", ":", "constructor_arg", "+=", "','", "+", "constructor_args", "[", "i", "+", "1", "]", "del", "constructor_args", "[", "i", "+", "1", "]", "constructor_args", "[", "i", "]", "=", "constructor_arg", "i", "+=", "1", "variadic_args", "=", "[", "arg", "for", "arg", "in", "constructor_args", "if", "'&&...'", "in", "arg", "]", "defaulted_args", "=", "[", "arg", "for", "arg", "in", "constructor_args", "if", "'='", "in", "arg", "]", "noarg_constructor", "=", "(", "not", "constructor_args", "# empty arg list", "# 'void' arg specifier", "or", "(", "len", "(", "constructor_args", ")", "==", "1", "and", "constructor_args", "[", "0", "]", ".", "strip", "(", ")", "==", "'void'", ")", ")", "onearg_constructor", "=", "(", "(", "len", "(", "constructor_args", ")", "==", "1", "# exactly one arg", "and", "not", "noarg_constructor", ")", "# all but at most one arg defaulted", "or", "(", "len", "(", "constructor_args", ")", ">=", "1", "and", "not", "noarg_constructor", "and", "len", "(", "defaulted_args", ")", ">=", "len", "(", "constructor_args", ")", "-", "1", ")", "# variadic arguments with zero or one argument", "or", "(", "len", "(", "constructor_args", ")", "<=", "2", "and", "len", "(", "variadic_args", ")", ">=", "1", ")", ")", "initializer_list_constructor", "=", "bool", "(", "onearg_constructor", "and", "Search", "(", "r'\\bstd\\s*::\\s*initializer_list\\b'", ",", "constructor_args", "[", "0", "]", ")", ")", "copy_constructor", "=", "bool", "(", "onearg_constructor", "and", "Match", "(", "r'(const\\s+)?%s(\\s*<[^>]*>)?(\\s+const)?\\s*(?:<\\w+>\\s*)?&'", "%", "re", ".", "escape", "(", "base_classname", ")", ",", "constructor_args", "[", "0", "]", ".", "strip", "(", ")", ")", ")", "if", "(", "not", "is_marked_explicit", "and", "onearg_constructor", "and", "not", "initializer_list_constructor", "and", "not", "copy_constructor", ")", ":", "if", "defaulted_args", "or", "variadic_args", ":", "error", "(", "filename", ",", "linenum", ",", "'runtime/explicit'", ",", "5", ",", "'Constructors callable with one argument '", "'should be marked explicit.'", ")", "else", ":", "error", "(", "filename", ",", "linenum", ",", "'runtime/explicit'", ",", "5", ",", "'Single-parameter constructors should be marked explicit.'", ")", "elif", "is_marked_explicit", "and", "not", "onearg_constructor", ":", "if", "noarg_constructor", ":", "error", "(", "filename", ",", "linenum", ",", "'runtime/explicit'", ",", "5", ",", "'Zero-parameter constructors should not be marked explicit.'", ")" ]
https://github.com/PrincetonUniversity/athena-public-version/blob/9c266692b9423743d8e23509b3ab266a232a92d2/tst/style/cpplint.py#L3026-L3187
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/osx_carbon/_controls.py
python
Gauge.SetValue
(*args, **kwargs)
return _controls_.Gauge_SetValue(*args, **kwargs)
SetValue(self, int pos)
SetValue(self, int pos)
[ "SetValue", "(", "self", "int", "pos", ")" ]
def SetValue(*args, **kwargs): """SetValue(self, int pos)""" return _controls_.Gauge_SetValue(*args, **kwargs)
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_carbon/_controls.py#L755-L757
wyrover/book-code
7f4883d9030d553bc6bcfa3da685e34789839900
3rdparty/protobuf/python/google/protobuf/internal/decoder.py
python
_ModifiedDecoder
(wire_type, decode_value, modify_value)
return _SimpleDecoder(wire_type, InnerDecode)
Like SimpleDecoder but additionally invokes modify_value on every value before storing it. Usually modify_value is ZigZagDecode.
Like SimpleDecoder but additionally invokes modify_value on every value before storing it. Usually modify_value is ZigZagDecode.
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def _ModifiedDecoder(wire_type, decode_value, modify_value): """Like SimpleDecoder but additionally invokes modify_value on every value before storing it. Usually modify_value is ZigZagDecode. """ # Reusing _SimpleDecoder is slightly slower than copying a bunch of code, but # not enough to make a significant difference. def InnerDecode(buffer, pos): (result, new_pos) = decode_value(buffer, pos) return (modify_value(result), new_pos) return _SimpleDecoder(wire_type, InnerDecode)
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https://github.com/wyrover/book-code/blob/7f4883d9030d553bc6bcfa3da685e34789839900/3rdparty/protobuf/python/google/protobuf/internal/decoder.py#L249-L260
Komnomnomnom/swigibpy
cfd307fdbfaffabc69a2dc037538d7e34a8b8daf
swigibpy.py
python
OrderComboLegList.__getslice__
(self, i, j)
return _swigibpy.OrderComboLegList___getslice__(self, i, j)
__getslice__(OrderComboLegList self, std::vector< shared_ptr< OrderComboLeg > >::difference_type i, std::vector< shared_ptr< OrderComboLeg > >::difference_type j) -> OrderComboLegList
__getslice__(OrderComboLegList self, std::vector< shared_ptr< OrderComboLeg > >::difference_type i, std::vector< shared_ptr< OrderComboLeg > >::difference_type j) -> OrderComboLegList
[ "__getslice__", "(", "OrderComboLegList", "self", "std", "::", "vector<", "shared_ptr<", "OrderComboLeg", ">", ">", "::", "difference_type", "i", "std", "::", "vector<", "shared_ptr<", "OrderComboLeg", ">", ">", "::", "difference_type", "j", ")", "-", ">", "OrderComboLegList" ]
def __getslice__(self, i, j): """__getslice__(OrderComboLegList self, std::vector< shared_ptr< OrderComboLeg > >::difference_type i, std::vector< shared_ptr< OrderComboLeg > >::difference_type j) -> OrderComboLegList""" return _swigibpy.OrderComboLegList___getslice__(self, i, j)
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https://github.com/Komnomnomnom/swigibpy/blob/cfd307fdbfaffabc69a2dc037538d7e34a8b8daf/swigibpy.py#L495-L497
Xilinx/Vitis-AI
fc74d404563d9951b57245443c73bef389f3657f
tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/tools/compatibility/ast_edits.py
python
_PastaEditVisitor._get_applicable_dict
(self, transformer_field, full_name, name)
return transformers
Get all dict entries indexed by name that apply to full_name or name.
Get all dict entries indexed by name that apply to full_name or name.
[ "Get", "all", "dict", "entries", "indexed", "by", "name", "that", "apply", "to", "full_name", "or", "name", "." ]
def _get_applicable_dict(self, transformer_field, full_name, name): """Get all dict entries indexed by name that apply to full_name or name.""" # Transformers are indexed to full name, name, or no name # as a performance optimization. function_transformers = getattr(self._api_change_spec, transformer_field, {}) glob_name = "*." + name if name else None transformers = function_transformers.get("*", {}).copy() transformers.update(function_transformers.get(glob_name, {})) transformers.update(function_transformers.get(full_name, {})) return transformers
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https://github.com/Xilinx/Vitis-AI/blob/fc74d404563d9951b57245443c73bef389f3657f/tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/tools/compatibility/ast_edits.py#L314-L325
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/msw/_misc.py
python
ArtProvider.GetBitmap
(*args, **kwargs)
return _misc_.ArtProvider_GetBitmap(*args, **kwargs)
GetBitmap(String id, String client=ART_OTHER, Size size=DefaultSize) -> Bitmap Query the providers for bitmap with given ID and return it. Return wx.NullBitmap if no provider provides it.
GetBitmap(String id, String client=ART_OTHER, Size size=DefaultSize) -> Bitmap
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def GetBitmap(*args, **kwargs): """ GetBitmap(String id, String client=ART_OTHER, Size size=DefaultSize) -> Bitmap Query the providers for bitmap with given ID and return it. Return wx.NullBitmap if no provider provides it. """ return _misc_.ArtProvider_GetBitmap(*args, **kwargs)
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/msw/_misc.py#L2819-L2826
mantidproject/mantid
03deeb89254ec4289edb8771e0188c2090a02f32
scripts/reduction_workflow/instruments/sans/hfir_command_interface.py
python
SaveIqAscii
(reducer=None, process='')
Old command for backward compatibility
Old command for backward compatibility
[ "Old", "command", "for", "backward", "compatibility" ]
def SaveIqAscii(reducer=None, process=''): """ Old command for backward compatibility """ msg = "SaveIqAscii is not longer used:\n " msg += "Please use 'SaveIq' instead\n " Logger("CommandInterface").warning(msg) ReductionSingleton().reduction_properties["ProcessInfo"] = str(process)
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https://github.com/mantidproject/mantid/blob/03deeb89254ec4289edb8771e0188c2090a02f32/scripts/reduction_workflow/instruments/sans/hfir_command_interface.py#L490-L495
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Tools/Python/3.7.10/windows/Lib/site-packages/setuptools/__init__.py
python
PackageFinder._find_packages_iter
(cls, where, exclude, include)
All the packages found in 'where' that pass the 'include' filter, but not the 'exclude' filter.
All the packages found in 'where' that pass the 'include' filter, but not the 'exclude' filter.
[ "All", "the", "packages", "found", "in", "where", "that", "pass", "the", "include", "filter", "but", "not", "the", "exclude", "filter", "." ]
def _find_packages_iter(cls, where, exclude, include): """ All the packages found in 'where' that pass the 'include' filter, but not the 'exclude' filter. """ for root, dirs, files in os.walk(where, followlinks=True): # Copy dirs to iterate over it, then empty dirs. all_dirs = dirs[:] dirs[:] = [] for dir in all_dirs: full_path = os.path.join(root, dir) rel_path = os.path.relpath(full_path, where) package = rel_path.replace(os.path.sep, '.') # Skip directory trees that are not valid packages if ('.' in dir or not cls._looks_like_package(full_path)): continue # Should this package be included? if include(package) and not exclude(package): yield package # Keep searching subdirectories, as there may be more packages # down there, even if the parent was excluded. dirs.append(dir)
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/windows/Lib/site-packages/setuptools/__init__.py#L75-L100
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Tools/Python/3.7.10/windows/Lib/datetime.py
python
time.replace
(self, hour=None, minute=None, second=None, microsecond=None, tzinfo=True, *, fold=None)
return type(self)(hour, minute, second, microsecond, tzinfo, fold=fold)
Return a new time with new values for the specified fields.
Return a new time with new values for the specified fields.
[ "Return", "a", "new", "time", "with", "new", "values", "for", "the", "specified", "fields", "." ]
def replace(self, hour=None, minute=None, second=None, microsecond=None, tzinfo=True, *, fold=None): """Return a new time with new values for the specified fields.""" if hour is None: hour = self.hour if minute is None: minute = self.minute if second is None: second = self.second if microsecond is None: microsecond = self.microsecond if tzinfo is True: tzinfo = self.tzinfo if fold is None: fold = self._fold return type(self)(hour, minute, second, microsecond, tzinfo, fold=fold)
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/windows/Lib/datetime.py#L1452-L1467
Slicer/SlicerGitSVNArchive
65e92bb16c2b32ea47a1a66bee71f238891ee1ca
Modules/Scripted/SegmentEditor/SegmentEditor.py
python
SegmentEditorTest.test_SegmentEditor1
(self)
Add test here later.
Add test here later.
[ "Add", "test", "here", "later", "." ]
def test_SegmentEditor1(self): """Add test here later. """ self.delayDisplay("Starting the test") self.delayDisplay('Test passed!')
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https://github.com/Slicer/SlicerGitSVNArchive/blob/65e92bb16c2b32ea47a1a66bee71f238891ee1ca/Modules/Scripted/SegmentEditor/SegmentEditor.py#L183-L187
catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/python/numpy/py3/numpy/lib/utils.py
python
_lookfor_generate_cache
(module, import_modules, regenerate)
return cache
Generate docstring cache for given module. Parameters ---------- module : str, None, module Module for which to generate docstring cache import_modules : bool Whether to import sub-modules in packages. regenerate : bool Re-generate the docstring cache Returns ------- cache : dict {obj_full_name: (docstring, kind, index), ...} Docstring cache for the module, either cached one (regenerate=False) or newly generated.
Generate docstring cache for given module.
[ "Generate", "docstring", "cache", "for", "given", "module", "." ]
def _lookfor_generate_cache(module, import_modules, regenerate): """ Generate docstring cache for given module. Parameters ---------- module : str, None, module Module for which to generate docstring cache import_modules : bool Whether to import sub-modules in packages. regenerate : bool Re-generate the docstring cache Returns ------- cache : dict {obj_full_name: (docstring, kind, index), ...} Docstring cache for the module, either cached one (regenerate=False) or newly generated. """ # Local import to speed up numpy's import time. import inspect from io import StringIO if module is None: module = "numpy" if isinstance(module, str): try: __import__(module) except ImportError: return {} module = sys.modules[module] elif isinstance(module, list) or isinstance(module, tuple): cache = {} for mod in module: cache.update(_lookfor_generate_cache(mod, import_modules, regenerate)) return cache if id(module) in _lookfor_caches and not regenerate: return _lookfor_caches[id(module)] # walk items and collect docstrings cache = {} _lookfor_caches[id(module)] = cache seen = {} index = 0 stack = [(module.__name__, module)] while stack: name, item = stack.pop(0) if id(item) in seen: continue seen[id(item)] = True index += 1 kind = "object" if inspect.ismodule(item): kind = "module" try: _all = item.__all__ except AttributeError: _all = None # import sub-packages if import_modules and hasattr(item, '__path__'): for pth in item.__path__: for mod_path in os.listdir(pth): this_py = os.path.join(pth, mod_path) init_py = os.path.join(pth, mod_path, '__init__.py') if (os.path.isfile(this_py) and mod_path.endswith('.py')): to_import = mod_path[:-3] elif os.path.isfile(init_py): to_import = mod_path else: continue if to_import == '__init__': continue try: old_stdout = sys.stdout old_stderr = sys.stderr try: sys.stdout = StringIO() sys.stderr = StringIO() __import__("%s.%s" % (name, to_import)) finally: sys.stdout = old_stdout sys.stderr = old_stderr # Catch SystemExit, too except BaseException: continue for n, v in _getmembers(item): try: item_name = getattr(v, '__name__', "%s.%s" % (name, n)) mod_name = getattr(v, '__module__', None) except NameError: # ref. SWIG's global cvars # NameError: Unknown C global variable item_name = "%s.%s" % (name, n) mod_name = None if '.' not in item_name and mod_name: item_name = "%s.%s" % (mod_name, item_name) if not item_name.startswith(name + '.'): # don't crawl "foreign" objects if isinstance(v, ufunc): # ... unless they are ufuncs pass else: continue elif not (inspect.ismodule(v) or _all is None or n in _all): continue stack.append(("%s.%s" % (name, n), v)) elif inspect.isclass(item): kind = "class" for n, v in _getmembers(item): stack.append(("%s.%s" % (name, n), v)) elif hasattr(item, "__call__"): kind = "func" try: doc = inspect.getdoc(item) except NameError: # ref SWIG's NameError: Unknown C global variable doc = None if doc is not None: cache[name] = (doc, kind, index) return cache
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/numpy/py3/numpy/lib/utils.py#L814-L947
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numpy/core/fromnumeric.py
python
trace
(a, offset=0, axis1=0, axis2=1, dtype=None, out=None)
Return the sum along diagonals of the array. If `a` is 2-D, the sum along its diagonal with the given offset is returned, i.e., the sum of elements ``a[i,i+offset]`` for all i. If `a` has more than two dimensions, then the axes specified by axis1 and axis2 are used to determine the 2-D sub-arrays whose traces are returned. The shape of the resulting array is the same as that of `a` with `axis1` and `axis2` removed. Parameters ---------- a : array_like Input array, from which the diagonals are taken. offset : int, optional Offset of the diagonal from the main diagonal. Can be both positive and negative. Defaults to 0. axis1, axis2 : int, optional Axes to be used as the first and second axis of the 2-D sub-arrays from which the diagonals should be taken. Defaults are the first two axes of `a`. dtype : dtype, optional Determines the data-type of the returned array and of the accumulator where the elements are summed. If dtype has the value None and `a` is of integer type of precision less than the default integer precision, then the default integer precision is used. Otherwise, the precision is the same as that of `a`. out : ndarray, optional Array into which the output is placed. Its type is preserved and it must be of the right shape to hold the output. Returns ------- sum_along_diagonals : ndarray If `a` is 2-D, the sum along the diagonal is returned. If `a` has larger dimensions, then an array of sums along diagonals is returned. See Also -------- diag, diagonal, diagflat Examples -------- >>> np.trace(np.eye(3)) 3.0 >>> a = np.arange(8).reshape((2,2,2)) >>> np.trace(a) array([6, 8]) >>> a = np.arange(24).reshape((2,2,2,3)) >>> np.trace(a).shape (2, 3)
Return the sum along diagonals of the array.
[ "Return", "the", "sum", "along", "diagonals", "of", "the", "array", "." ]
def trace(a, offset=0, axis1=0, axis2=1, dtype=None, out=None): """ Return the sum along diagonals of the array. If `a` is 2-D, the sum along its diagonal with the given offset is returned, i.e., the sum of elements ``a[i,i+offset]`` for all i. If `a` has more than two dimensions, then the axes specified by axis1 and axis2 are used to determine the 2-D sub-arrays whose traces are returned. The shape of the resulting array is the same as that of `a` with `axis1` and `axis2` removed. Parameters ---------- a : array_like Input array, from which the diagonals are taken. offset : int, optional Offset of the diagonal from the main diagonal. Can be both positive and negative. Defaults to 0. axis1, axis2 : int, optional Axes to be used as the first and second axis of the 2-D sub-arrays from which the diagonals should be taken. Defaults are the first two axes of `a`. dtype : dtype, optional Determines the data-type of the returned array and of the accumulator where the elements are summed. If dtype has the value None and `a` is of integer type of precision less than the default integer precision, then the default integer precision is used. Otherwise, the precision is the same as that of `a`. out : ndarray, optional Array into which the output is placed. Its type is preserved and it must be of the right shape to hold the output. Returns ------- sum_along_diagonals : ndarray If `a` is 2-D, the sum along the diagonal is returned. If `a` has larger dimensions, then an array of sums along diagonals is returned. See Also -------- diag, diagonal, diagflat Examples -------- >>> np.trace(np.eye(3)) 3.0 >>> a = np.arange(8).reshape((2,2,2)) >>> np.trace(a) array([6, 8]) >>> a = np.arange(24).reshape((2,2,2,3)) >>> np.trace(a).shape (2, 3) """ if isinstance(a, np.matrix): # Get trace of matrix via an array to preserve backward compatibility. return asarray(a).trace(offset=offset, axis1=axis1, axis2=axis2, dtype=dtype, out=out) else: return asanyarray(a).trace(offset=offset, axis1=axis1, axis2=axis2, dtype=dtype, out=out)
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numpy/core/fromnumeric.py#L1626-L1686
lballabio/quantlib-old
136336947ed4fea9ecc1da6edad188700e821739
gensrc/gensrc/configuration/configuration.py
python
Configuration.prefix
(self)
return self.prefix_
Return text to be prefixed to addin function names.
Return text to be prefixed to addin function names.
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def prefix(self): """Return text to be prefixed to addin function names.""" return self.prefix_
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https://github.com/lballabio/quantlib-old/blob/136336947ed4fea9ecc1da6edad188700e821739/gensrc/gensrc/configuration/configuration.py#L55-L57
pgRouting/osm2pgrouting
8491929fc4037d308f271e84d59bb96da3c28aa2
tools/cpplint.py
python
CleansedLines.NumLines
(self)
return self.num_lines
Returns the number of lines represented.
Returns the number of lines represented.
[ "Returns", "the", "number", "of", "lines", "represented", "." ]
def NumLines(self): """Returns the number of lines represented.""" return self.num_lines
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https://github.com/pgRouting/osm2pgrouting/blob/8491929fc4037d308f271e84d59bb96da3c28aa2/tools/cpplint.py#L1311-L1313
generalized-intelligence/GAAS
29ab17d3e8a4ba18edef3a57c36d8db6329fac73
deprecated/algorithms/sfm/OpenSfM/opensfm/types.py
python
FisheyeCamera.pixel_bearing
(self, pixel)
return np.array([x / l, y / l, 1.0 / l])
Unit vector pointing to the pixel viewing direction.
Unit vector pointing to the pixel viewing direction.
[ "Unit", "vector", "pointing", "to", "the", "pixel", "viewing", "direction", "." ]
def pixel_bearing(self, pixel): """Unit vector pointing to the pixel viewing direction.""" point = np.asarray(pixel).reshape((1, 1, 2)) distortion = np.array([self.k1, self.k2, 0., 0.]) x, y = cv2.fisheye.undistortPoints(point, self.get_K(), distortion).flat l = np.sqrt(x * x + y * y + 1.0) return np.array([x / l, y / l, 1.0 / l])
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https://github.com/generalized-intelligence/GAAS/blob/29ab17d3e8a4ba18edef3a57c36d8db6329fac73/deprecated/algorithms/sfm/OpenSfM/opensfm/types.py#L479-L485
microsoft/onnxruntime
f92e47e95b13a240e37caf7b36577983544f98fc
onnxruntime/python/onnxruntime_inference_collection.py
python
OrtValue.ortvalue_from_shape_and_type
(shape=None, element_type=None, device_type='cpu', device_id=0)
return OrtValue(C.OrtValue.ortvalue_from_shape_and_type(shape, element_type, C.OrtDevice(get_ort_device_type(device_type), C.OrtDevice.default_memory(), device_id)))
Factory method to construct an OrtValue (which holds a Tensor) from given shape and element_type :param shape: List of integers indicating the shape of the OrtValue :param element_type: The data type of the elements in the OrtValue (numpy type) :param device_type: e.g. cpu, cuda, cpu by default :param device_id: device id, e.g. 0
Factory method to construct an OrtValue (which holds a Tensor) from given shape and element_type
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def ortvalue_from_shape_and_type(shape=None, element_type=None, device_type='cpu', device_id=0): ''' Factory method to construct an OrtValue (which holds a Tensor) from given shape and element_type :param shape: List of integers indicating the shape of the OrtValue :param element_type: The data type of the elements in the OrtValue (numpy type) :param device_type: e.g. cpu, cuda, cpu by default :param device_id: device id, e.g. 0 ''' if shape is None or element_type is None: raise ValueError("`element_type` and `shape` are to be provided if pre-allocated memory is provided") return OrtValue(C.OrtValue.ortvalue_from_shape_and_type(shape, element_type, C.OrtDevice(get_ort_device_type(device_type), C.OrtDevice.default_memory(), device_id)))
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https://github.com/microsoft/onnxruntime/blob/f92e47e95b13a240e37caf7b36577983544f98fc/onnxruntime/python/onnxruntime_inference_collection.py#L554-L567
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/osx_cocoa/_misc.py
python
Joystick.GetPOVCTSPosition
(*args, **kwargs)
return _misc_.Joystick_GetPOVCTSPosition(*args, **kwargs)
GetPOVCTSPosition(self) -> int
GetPOVCTSPosition(self) -> int
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def GetPOVCTSPosition(*args, **kwargs): """GetPOVCTSPosition(self) -> int""" return _misc_.Joystick_GetPOVCTSPosition(*args, **kwargs)
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_cocoa/_misc.py#L2142-L2144
Yelp/MOE
5b5a6a2c6c3cf47320126f7f5894e2a83e347f5c
moe/views/pretty_view.py
python
PrettyView.form_response
(self, response_dict)
return self.response_schema.serialize(response_dict)
Return the serialized response object from a dict. :param response_dict: a dict that can be serialized by self.response_schema :type response_dict: dict :returns: a serialized self.response_schema object
Return the serialized response object from a dict.
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def form_response(self, response_dict): """Return the serialized response object from a dict. :param response_dict: a dict that can be serialized by self.response_schema :type response_dict: dict :returns: a serialized self.response_schema object """ self._create_moe_log_line( type='response', content=response_dict, ) return self.response_schema.serialize(response_dict)
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https://github.com/Yelp/MOE/blob/5b5a6a2c6c3cf47320126f7f5894e2a83e347f5c/moe/views/pretty_view.py#L74-L85
bulletphysics/bullet3
f0f2a952e146f016096db6f85cf0c44ed75b0b9a
examples/pybullet/gym/pybullet_envs/agents/tools/mock_algorithm.py
python
MockAlgorithm.__init__
(self, envs)
Produce random actions and empty summaries. Args: envs: List of in-graph environments.
Produce random actions and empty summaries.
[ "Produce", "random", "actions", "and", "empty", "summaries", "." ]
def __init__(self, envs): """Produce random actions and empty summaries. Args: envs: List of in-graph environments. """ self._envs = envs
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https://github.com/bulletphysics/bullet3/blob/f0f2a952e146f016096db6f85cf0c44ed75b0b9a/examples/pybullet/gym/pybullet_envs/agents/tools/mock_algorithm.py#L28-L34
baidu-research/tensorflow-allreduce
66d5b855e90b0949e9fa5cca5599fd729a70e874
tensorflow/python/framework/function.py
python
_DefinedFunction._create_definition_if_needed
(self)
Creates the function definition if it's not created yet.
Creates the function definition if it's not created yet.
[ "Creates", "the", "function", "definition", "if", "it", "s", "not", "created", "yet", "." ]
def _create_definition_if_needed(self): """Creates the function definition if it's not created yet.""" if self._definition is not None: return # Create the func_def object. temp_graph = _FuncGraph() with temp_graph.as_default(): # List of placeholders for the function_def. inputs = [] for (argname, argtype) in self._args: argholder = array_ops.placeholder(argtype, name=argname) inputs.append(argholder) # Call func and gather the output tensors. with vs.variable_scope("", custom_getter=temp_graph.getvar): outputs = self._func(*inputs) # If func only returned one value, make it a tuple. if not isinstance(outputs, (list, tuple)): outputs = (outputs,) if any([_ is None for _ in outputs]): raise ValueError("Function can not return None.") # Ensures each output is a Tensor. outputs = [ops.convert_to_tensor(_) for _ in outputs] self._extra_inputs = temp_graph.extra_inputs inputs.extend(temp_graph.extra_args) # pylint: disable=protected-access self._sub_functions = temp_graph._functions # pylint: enable=protected-access # Build the FunctionDef self._definition = _graph_to_function_def( temp_graph, temp_graph.get_operations(), inputs, outputs, out_names=self._out_names) # Extra kwargs are treated as attrs on the function def. sig_pre_func_name = self._func_name or _get_func_name(self._func) kwargs_attr = _parse_kwargs_as_attrs(sig_pre_func_name, **self._extra_kwargs) for k in kwargs_attr: self._definition.attr[k].CopyFrom(kwargs_attr[k]) # Hash the definition and its dependencies. self._hash_str = self._create_hash_str( self._definition.signature.input_arg, self._definition.signature.output_arg, self._definition.node_def) # Finally, we decide the function name to use. If not specified, # make up something which is almost certainly unique (but deterministic). if not self._func_name: self._func_name = "_".join([_get_func_name(self._func), self._hash_str]) self._definition.signature.name = self._func_name if self._func.__doc__: self._definition.signature.description = self._func.__doc__
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https://github.com/baidu-research/tensorflow-allreduce/blob/66d5b855e90b0949e9fa5cca5599fd729a70e874/tensorflow/python/framework/function.py#L343-L399
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/core/frame.py
python
DataFrame.diff
(self, periods=1, axis=0)
return self._constructor(new_data)
First discrete difference of element. Calculates the difference of a DataFrame element compared with another element in the DataFrame (default is the element in the same column of the previous row). Parameters ---------- periods : int, default 1 Periods to shift for calculating difference, accepts negative values. axis : {0 or 'index', 1 or 'columns'}, default 0 Take difference over rows (0) or columns (1). Returns ------- DataFrame See Also -------- Series.diff: First discrete difference for a Series. DataFrame.pct_change: Percent change over given number of periods. DataFrame.shift: Shift index by desired number of periods with an optional time freq. Notes ----- For boolean dtypes, this uses :meth:`operator.xor` rather than :meth:`operator.sub`. Examples -------- Difference with previous row >>> df = pd.DataFrame({'a': [1, 2, 3, 4, 5, 6], ... 'b': [1, 1, 2, 3, 5, 8], ... 'c': [1, 4, 9, 16, 25, 36]}) >>> df a b c 0 1 1 1 1 2 1 4 2 3 2 9 3 4 3 16 4 5 5 25 5 6 8 36 >>> df.diff() a b c 0 NaN NaN NaN 1 1.0 0.0 3.0 2 1.0 1.0 5.0 3 1.0 1.0 7.0 4 1.0 2.0 9.0 5 1.0 3.0 11.0 Difference with previous column >>> df.diff(axis=1) a b c 0 NaN 0.0 0.0 1 NaN -1.0 3.0 2 NaN -1.0 7.0 3 NaN -1.0 13.0 4 NaN 0.0 20.0 5 NaN 2.0 28.0 Difference with 3rd previous row >>> df.diff(periods=3) a b c 0 NaN NaN NaN 1 NaN NaN NaN 2 NaN NaN NaN 3 3.0 2.0 15.0 4 3.0 4.0 21.0 5 3.0 6.0 27.0 Difference with following row >>> df.diff(periods=-1) a b c 0 -1.0 0.0 -3.0 1 -1.0 -1.0 -5.0 2 -1.0 -1.0 -7.0 3 -1.0 -2.0 -9.0 4 -1.0 -3.0 -11.0 5 NaN NaN NaN
First discrete difference of element.
[ "First", "discrete", "difference", "of", "element", "." ]
def diff(self, periods=1, axis=0) -> "DataFrame": """ First discrete difference of element. Calculates the difference of a DataFrame element compared with another element in the DataFrame (default is the element in the same column of the previous row). Parameters ---------- periods : int, default 1 Periods to shift for calculating difference, accepts negative values. axis : {0 or 'index', 1 or 'columns'}, default 0 Take difference over rows (0) or columns (1). Returns ------- DataFrame See Also -------- Series.diff: First discrete difference for a Series. DataFrame.pct_change: Percent change over given number of periods. DataFrame.shift: Shift index by desired number of periods with an optional time freq. Notes ----- For boolean dtypes, this uses :meth:`operator.xor` rather than :meth:`operator.sub`. Examples -------- Difference with previous row >>> df = pd.DataFrame({'a': [1, 2, 3, 4, 5, 6], ... 'b': [1, 1, 2, 3, 5, 8], ... 'c': [1, 4, 9, 16, 25, 36]}) >>> df a b c 0 1 1 1 1 2 1 4 2 3 2 9 3 4 3 16 4 5 5 25 5 6 8 36 >>> df.diff() a b c 0 NaN NaN NaN 1 1.0 0.0 3.0 2 1.0 1.0 5.0 3 1.0 1.0 7.0 4 1.0 2.0 9.0 5 1.0 3.0 11.0 Difference with previous column >>> df.diff(axis=1) a b c 0 NaN 0.0 0.0 1 NaN -1.0 3.0 2 NaN -1.0 7.0 3 NaN -1.0 13.0 4 NaN 0.0 20.0 5 NaN 2.0 28.0 Difference with 3rd previous row >>> df.diff(periods=3) a b c 0 NaN NaN NaN 1 NaN NaN NaN 2 NaN NaN NaN 3 3.0 2.0 15.0 4 3.0 4.0 21.0 5 3.0 6.0 27.0 Difference with following row >>> df.diff(periods=-1) a b c 0 -1.0 0.0 -3.0 1 -1.0 -1.0 -5.0 2 -1.0 -1.0 -7.0 3 -1.0 -2.0 -9.0 4 -1.0 -3.0 -11.0 5 NaN NaN NaN """ bm_axis = self._get_block_manager_axis(axis) new_data = self._data.diff(n=periods, axis=bm_axis) return self._constructor(new_data)
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/core/frame.py#L6512-L6604
RGF-team/rgf
272afb85b4c91571f576e5fc83ecfacce3672eb4
python-package/rgf/utils.py
python
RGFClassifierMixin.predict_proba
(self, X)
return y
Predict class probabilities for X. The predicted class probabilities of an input sample are computed. Parameters ---------- X : array-like or sparse matrix of shape = [n_samples, n_features] The input samples. Returns ------- p : array of shape = [n_samples, n_classes]. The class probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.
Predict class probabilities for X.
[ "Predict", "class", "probabilities", "for", "X", "." ]
def predict_proba(self, X): """ Predict class probabilities for X. The predicted class probabilities of an input sample are computed. Parameters ---------- X : array-like or sparse matrix of shape = [n_samples, n_features] The input samples. Returns ------- p : array of shape = [n_samples, n_classes]. The class probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_. """ if not hasattr(self, '_fitted') or not self._fitted: raise NotFittedError(NOT_FITTED_ERROR_DESC) X = check_array(X, accept_sparse=True) self._check_n_features(X.shape[1]) if self._n_classes == 2: y = self._estimators[0].predict(X) y = sigmoid(y) y = np.c_[y, 1 - y] else: y = np.zeros((X.shape[0], self._n_classes)) for i, clf in enumerate(self._estimators): class_proba = clf.predict(X) y[:, i] = class_proba if self.calc_prob == "sigmoid": y = sigmoid(y) normalizer = np.sum(y, axis=1)[:, np.newaxis] normalizer[normalizer == 0.0] = 1.0 y /= normalizer else: y = softmax(y) return y
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https://github.com/RGF-team/rgf/blob/272afb85b4c91571f576e5fc83ecfacce3672eb4/python-package/rgf/utils.py#L589-L628
thalium/icebox
99d147d5b9269222225443ce171b4fd46d8985d4
third_party/virtualbox/src/VBox/Devices/EFI/Firmware/AppPkg/Applications/Python/PyMod-2.7.2/Lib/pydoc.py
python
HTMLDoc.docother
(self, object, name=None, mod=None, *ignored)
return lhs + self.repr(object)
Produce HTML documentation for a data object.
Produce HTML documentation for a data object.
[ "Produce", "HTML", "documentation", "for", "a", "data", "object", "." ]
def docother(self, object, name=None, mod=None, *ignored): """Produce HTML documentation for a data object.""" lhs = name and '<strong>%s</strong> = ' % name or '' return lhs + self.repr(object)
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https://github.com/thalium/icebox/blob/99d147d5b9269222225443ce171b4fd46d8985d4/third_party/virtualbox/src/VBox/Devices/EFI/Firmware/AppPkg/Applications/Python/PyMod-2.7.2/Lib/pydoc.py#L922-L925
linyouhappy/kongkongxiyou
7a69b2913eb29f4be77f9a62fb90cdd72c4160f1
cocosjs/frameworks/cocos2d-x/tools/bindings-generator/clang/cindex.py
python
Type.element_type
(self)
return result
Retrieve the Type of elements within this Type. If accessed on a type that is not an array, complex, or vector type, an exception will be raised.
Retrieve the Type of elements within this Type.
[ "Retrieve", "the", "Type", "of", "elements", "within", "this", "Type", "." ]
def element_type(self): """Retrieve the Type of elements within this Type. If accessed on a type that is not an array, complex, or vector type, an exception will be raised. """ result = conf.lib.clang_getElementType(self) if result.kind == TypeKind.INVALID: raise Exception('Element type not available on this type.') return result
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https://github.com/linyouhappy/kongkongxiyou/blob/7a69b2913eb29f4be77f9a62fb90cdd72c4160f1/cocosjs/frameworks/cocos2d-x/tools/bindings-generator/clang/cindex.py#L1680-L1690
AngoraFuzzer/Angora
80e81c8590077bc0ac069dbd367da8ce405ff618
llvm_mode/dfsan_rt/sanitizer_common/scripts/cpplint.py
python
FindNextMultiLineCommentStart
(lines, lineix)
return len(lines)
Find the beginning marker for a multiline comment.
Find the beginning marker for a multiline comment.
[ "Find", "the", "beginning", "marker", "for", "a", "multiline", "comment", "." ]
def FindNextMultiLineCommentStart(lines, lineix): """Find the beginning marker for a multiline comment.""" while lineix < len(lines): if lines[lineix].strip().startswith('/*'): # Only return this marker if the comment goes beyond this line if lines[lineix].strip().find('*/', 2) < 0: return lineix lineix += 1 return len(lines)
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https://github.com/AngoraFuzzer/Angora/blob/80e81c8590077bc0ac069dbd367da8ce405ff618/llvm_mode/dfsan_rt/sanitizer_common/scripts/cpplint.py#L926-L934
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/tkinter/__init__.py
python
Misc.winfo_viewable
(self)
return self.tk.getint( self.tk.call('winfo', 'viewable', self._w))
Return true if the widget and all its higher ancestors are mapped.
Return true if the widget and all its higher ancestors are mapped.
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def winfo_viewable(self): """Return true if the widget and all its higher ancestors are mapped.""" return self.tk.getint( self.tk.call('winfo', 'viewable', self._w))
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/tkinter/__init__.py#L1111-L1114
Harick1/caffe-yolo
eea92bf3ddfe4d0ff6b0b3ba9b15c029a83ed9a3
python/caffe/io.py
python
Transformer.set_mean
(self, in_, mean)
Set the mean to subtract for centering the data. Parameters ---------- in_ : which input to assign this mean. mean : mean ndarray (input dimensional or broadcastable)
Set the mean to subtract for centering the data.
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def set_mean(self, in_, mean): """ Set the mean to subtract for centering the data. Parameters ---------- in_ : which input to assign this mean. mean : mean ndarray (input dimensional or broadcastable) """ self.__check_input(in_) ms = mean.shape if mean.ndim == 1: # broadcast channels if ms[0] != self.inputs[in_][1]: raise ValueError('Mean channels incompatible with input.') mean = mean[:, np.newaxis, np.newaxis] else: # elementwise mean if len(ms) == 2: ms = (1,) + ms if len(ms) != 3: raise ValueError('Mean shape invalid') if ms != self.inputs[in_][1:]: raise ValueError('Mean shape incompatible with input shape.') self.mean[in_] = mean
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https://github.com/Harick1/caffe-yolo/blob/eea92bf3ddfe4d0ff6b0b3ba9b15c029a83ed9a3/python/caffe/io.py#L236-L260
raymondlu/super-animation-samples
04234269112ff0dc32447f27a761dbbb00b8ba17
samples/cocos2d-x-3.1/CocosLuaGame2/frameworks/cocos2d-x/tools/bindings-generator/clang/cindex.py
python
CursorKind.is_invalid
(self)
return conf.lib.clang_isInvalid(self)
Test if this is an invalid kind.
Test if this is an invalid kind.
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def is_invalid(self): """Test if this is an invalid kind.""" return conf.lib.clang_isInvalid(self)
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https://github.com/raymondlu/super-animation-samples/blob/04234269112ff0dc32447f27a761dbbb00b8ba17/samples/cocos2d-x-3.1/CocosLuaGame2/frameworks/cocos2d-x/tools/bindings-generator/clang/cindex.py#L652-L654
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Gems/CloudGemFramework/v1/ResourceManager/lib/Crypto/Hash/SHA224.py
python
SHA224Hash.new
(self, data=None)
return SHA224Hash(data)
Create a fresh SHA-224 hash object.
Create a fresh SHA-224 hash object.
[ "Create", "a", "fresh", "SHA", "-", "224", "hash", "object", "." ]
def new(self, data=None): """Create a fresh SHA-224 hash object.""" return SHA224Hash(data)
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemFramework/v1/ResourceManager/lib/Crypto/Hash/SHA224.py#L143-L146
google/usd_from_gltf
6d288cce8b68744494a226574ae1d7ba6a9c46eb
tools/ufginstall/ufginstall.py
python
JpgDep.install
(self)
Installs libjpeg-turbo dependency.
Installs libjpeg-turbo dependency.
[ "Installs", "libjpeg", "-", "turbo", "dependency", "." ]
def install(self): """Installs libjpeg-turbo dependency.""" url = 'https://github.com/libjpeg-turbo/libjpeg-turbo/archive/2.0.2.zip' extra_args = ['-DCMAKE_POSITION_INDEPENDENT_CODE=1'] path = os.path.join(cfg.src_dir, 'jpg.zip') force = self.forced() dl_dir = download_archive(url, path, force) with cwd(dl_dir): run_cmake(force, extra_args)
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https://github.com/google/usd_from_gltf/blob/6d288cce8b68744494a226574ae1d7ba6a9c46eb/tools/ufginstall/ufginstall.py#L182-L190
microsoft/checkedc-clang
a173fefde5d7877b7750e7ce96dd08cf18baebf2
clang/bindings/python/clang/cindex.py
python
Cursor.linkage
(self)
return LinkageKind.from_id(self._linkage)
Return the linkage of this cursor.
Return the linkage of this cursor.
[ "Return", "the", "linkage", "of", "this", "cursor", "." ]
def linkage(self): """Return the linkage of this cursor.""" if not hasattr(self, '_linkage'): self._linkage = conf.lib.clang_getCursorLinkage(self) return LinkageKind.from_id(self._linkage)
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https://github.com/microsoft/checkedc-clang/blob/a173fefde5d7877b7750e7ce96dd08cf18baebf2/clang/bindings/python/clang/cindex.py#L1585-L1590
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/osx_carbon/stc.py
python
StyledTextCtrl.DocLineFromVisible
(*args, **kwargs)
return _stc.StyledTextCtrl_DocLineFromVisible(*args, **kwargs)
DocLineFromVisible(self, int lineDisplay) -> int Find the document line of a display line taking hidden lines into account.
DocLineFromVisible(self, int lineDisplay) -> int
[ "DocLineFromVisible", "(", "self", "int", "lineDisplay", ")", "-", ">", "int" ]
def DocLineFromVisible(*args, **kwargs): """ DocLineFromVisible(self, int lineDisplay) -> int Find the document line of a display line taking hidden lines into account. """ return _stc.StyledTextCtrl_DocLineFromVisible(*args, **kwargs)
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_carbon/stc.py#L3880-L3886
psi4/psi4
be533f7f426b6ccc263904e55122899b16663395
psi4/driver/procrouting/response/scf_products.py
python
TDRSCFEngine.reset_for_state_symm
(self, symmetry)
Reset internal quantities so the object is prepared to deal with transition to state with symmetry given
Reset internal quantities so the object is prepared to deal with transition to state with symmetry given
[ "Reset", "internal", "quantities", "so", "the", "object", "is", "prepared", "to", "deal", "with", "transition", "to", "state", "with", "symmetry", "given" ]
def reset_for_state_symm(self, symmetry): """Reset internal quantities so the object is prepared to deal with transition to state with symmetry given """ self.G_es = symmetry self._build_prec() self.product_cache.reset()
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https://github.com/psi4/psi4/blob/be533f7f426b6ccc263904e55122899b16663395/psi4/driver/procrouting/response/scf_products.py#L232-L237
ceph/ceph
959663007321a369c83218414a29bd9dbc8bda3a
qa/tasks/ceph_manager.py
python
OSDThrasher.thrash_pg_upmap_items
(self)
Install or remove random pg_upmap_items entries in OSDMap
Install or remove random pg_upmap_items entries in OSDMap
[ "Install", "or", "remove", "random", "pg_upmap_items", "entries", "in", "OSDMap" ]
def thrash_pg_upmap_items(self): """ Install or remove random pg_upmap_items entries in OSDMap """ from random import shuffle out = self.ceph_manager.raw_cluster_cmd('osd', 'dump', '-f', 'json-pretty') j = json.loads(out) self.log('j is %s' % j) try: if random.random() >= .3: pgs = self.ceph_manager.get_pg_stats() if not pgs: return pg = random.choice(pgs) pgid = str(pg['pgid']) poolid = int(pgid.split('.')[0]) sizes = [x['size'] for x in j['pools'] if x['pool'] == poolid] if len(sizes) == 0: return n = sizes[0] osds = self.in_osds + self.out_osds shuffle(osds) osds = osds[0:n*2] self.log('Setting %s to %s' % (pgid, osds)) cmd = ['osd', 'pg-upmap-items', pgid] + [str(x) for x in osds] self.log('cmd %s' % cmd) self.ceph_manager.raw_cluster_cmd(*cmd) else: m = j['pg_upmap_items'] if len(m) > 0: shuffle(m) pg = m[0]['pgid'] self.log('Clearing pg_upmap on %s' % pg) self.ceph_manager.raw_cluster_cmd( 'osd', 'rm-pg-upmap-items', pg) else: self.log('No pg_upmap entries; doing nothing') except CommandFailedError: self.log('Failed to rm-pg-upmap-items, ignoring')
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https://github.com/ceph/ceph/blob/959663007321a369c83218414a29bd9dbc8bda3a/qa/tasks/ceph_manager.py#L713-L753
windystrife/UnrealEngine_NVIDIAGameWorks
b50e6338a7c5b26374d66306ebc7807541ff815e
Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/site-packages/pip/vendor/distlib/locators.py
python
Locator.locate
(self, requirement, prereleases=False)
return result
Find the most recent distribution which matches the given requirement. :param requirement: A requirement of the form 'foo (1.0)' or perhaps 'foo (>= 1.0, < 2.0, != 1.3)' :param prereleases: If ``True``, allow pre-release versions to be located. Otherwise, pre-release versions are not returned. :return: A :class:`Distribution` instance, or ``None`` if no such distribution could be located.
Find the most recent distribution which matches the given requirement.
[ "Find", "the", "most", "recent", "distribution", "which", "matches", "the", "given", "requirement", "." ]
def locate(self, requirement, prereleases=False): """ Find the most recent distribution which matches the given requirement. :param requirement: A requirement of the form 'foo (1.0)' or perhaps 'foo (>= 1.0, < 2.0, != 1.3)' :param prereleases: If ``True``, allow pre-release versions to be located. Otherwise, pre-release versions are not returned. :return: A :class:`Distribution` instance, or ``None`` if no such distribution could be located. """ result = None scheme = get_scheme(self.scheme) r = parse_requirement(requirement) if r is None: raise DistlibException('Not a valid requirement: %r' % requirement) if r.extras: # lose the extras part of the requirement requirement = r.requirement matcher = scheme.matcher(requirement) vcls = matcher.version_class logger.debug('matcher: %s (%s)', matcher, type(matcher).__name__) versions = self.get_project(matcher.name) if versions: # sometimes, versions are invalid slist = [] for k in versions: try: if not matcher.match(k): logger.debug('%s did not match %r', matcher, k) else: if prereleases or not vcls(k).is_prerelease: slist.append(k) else: logger.debug('skipping pre-release version %s', k) except Exception: logger.warning('error matching %s with %r', matcher, k) pass # slist.append(k) if len(slist) > 1: slist = sorted(slist, key=scheme.key) if slist: logger.debug('sorted list: %s', slist) result = versions[slist[-1]] if result and r.extras: result.extras = r.extras return result
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https://github.com/windystrife/UnrealEngine_NVIDIAGameWorks/blob/b50e6338a7c5b26374d66306ebc7807541ff815e/Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/site-packages/pip/vendor/distlib/locators.py#L289-L336
NERSC/timemory
431912b360ff50d1a160d7826e2eea04fbd1037f
timemory/analyze/analyze.py
python
dump_flamegraph
(data, metric, file=None, echo_dart=False)
Dumps a flamegraph file
Dumps a flamegraph file
[ "Dumps", "a", "flamegraph", "file" ]
def dump_flamegraph(data, metric, file=None, echo_dart=False): """Dumps a flamegraph file""" from timemory.common import ( popen, get_bin_script, dart_measurement_file, ) _files = dump_entity( data, lambda x: x.to_flamegraph(_get_metric(x, metric)), file, ".flamegraph.txt", ) for itr in _files: flamegrapher = get_bin_script("flamegraph.pl") if itr is not None: if flamegrapher is None: if echo_dart is True: # write_ctest_notes(itr) dart_measurement_file( os.path.basename(itr), itr, format="string", type="text" ) else: (retc, outs, errs) = popen( [ flamegrapher, "--hash", "--inverted", "--bgcolors", "'#FFFFFF'", itr, ], shell=True, ) if outs is not None: lbl = _get_label(itr) sitr = _get_filename(itr, ".svg") pitr = _get_filename(itr, ".png") # write the SVG file print(f"[{lbl}]|0> Outputting '{sitr}'...") _create_directory(sitr) with open(sitr, "w") as fout: fout.write(f"{outs}\n") # generate png pfile = _svg_to_png(pitr, svg_code=outs) # echo svg and png if echo_dart: # write_ctest_notes(sitr) dart_measurement_file( os.path.basename(itr), itr, format="string", type="text", ) dart_measurement_file( os.path.basename(sitr), sitr, "svg" ) if pfile is not None: dart_measurement_file( os.path.basename(pitr), pitr, "png" ) else: pass
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https://github.com/NERSC/timemory/blob/431912b360ff50d1a160d7826e2eea04fbd1037f/timemory/analyze/analyze.py#L503-L570
microsoft/CNTK
e9396480025b9ca457d26b6f33dd07c474c6aa04
bindings/python/cntk/ops/functions.py
python
Function.__str__
(self)
return f_name + op_name + '(' + ", ".join([format_arg_spec(param) for param in args]) + ') -> ' + output_signature
Describes the Function and its signature as a string. Example: >>> f = C.log(C.input(1), name='f') # Function constructed as a graph >>> print(f) f: Log(Tensor[1]) -> Tensor[1] >>> d = C.layers.Dense(10) # Function constructed as a layer >>> print(d) Dense(x: Sequence[tensor]) -> Sequence[tensor] >>> @C.Function # construct a primitive Function through @Function ... def g(x,y): ... return x+y >>> print(g) Plus(x: Sequence[tensor], y: Sequence[tensor]) -> Sequence[tensor] >>> @C.Function # construct a composite through @Function ... def h(x,y): ... return C.exp(x+y) >>> print(h) Composite(x: Sequence[tensor], y: Sequence[tensor]) -> Sequence[tensor]
Describes the Function and its signature as a string.
[ "Describes", "the", "Function", "and", "its", "signature", "as", "a", "string", "." ]
def __str__(self): ''' Describes the Function and its signature as a string. Example: >>> f = C.log(C.input(1), name='f') # Function constructed as a graph >>> print(f) f: Log(Tensor[1]) -> Tensor[1] >>> d = C.layers.Dense(10) # Function constructed as a layer >>> print(d) Dense(x: Sequence[tensor]) -> Sequence[tensor] >>> @C.Function # construct a primitive Function through @Function ... def g(x,y): ... return x+y >>> print(g) Plus(x: Sequence[tensor], y: Sequence[tensor]) -> Sequence[tensor] >>> @C.Function # construct a composite through @Function ... def h(x,y): ... return C.exp(x+y) >>> print(h) Composite(x: Sequence[tensor], y: Sequence[tensor]) -> Sequence[tensor] ''' f_name = self.name op_name = self.op_name if self.is_composite: if self.root_function and all(i.uid == ri.uid for i, ri in zip(self.inputs, self.root_function.inputs)): op_name = self.root_function.op_name else: op_name = 'Composite' # (real op_name is CompositeFunctionOpName) else: op_name = self.op_name args = self.signature def format_arg_spec(v, is_output=False): s = v.name + ': ' if not is_output and v.name else '' # (suppress output names, since they duplicate the function name) return s + str(v._type) outputs = self.outputs if len(outputs) > 1: output_signature = 'Tuple[' + ', '.join(format_arg_spec(output, True) for output in outputs) + ']' else: output_signature = format_arg_spec(outputs[0], True) if self.name: f_name += ": " return f_name + op_name + '(' + ", ".join([format_arg_spec(param) for param in args]) + ') -> ' + output_signature
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https://github.com/microsoft/CNTK/blob/e9396480025b9ca457d26b6f33dd07c474c6aa04/bindings/python/cntk/ops/functions.py#L1148-L1191
arangodb/arangodb
0d658689c7d1b721b314fa3ca27d38303e1570c8
3rdParty/V8/gyp/xcode_emulation.py
python
XcodeSettings._GetTargetPostbuilds
(self, configname, output, output_binary, quiet=False)
return ( self._GetDebugInfoPostbuilds(configname, output, output_binary, quiet) + self._GetStripPostbuilds(configname, output_binary, quiet))
Returns a list of shell commands that contain the shell commands to run as postbuilds for this target, before the actual postbuilds.
Returns a list of shell commands that contain the shell commands to run as postbuilds for this target, before the actual postbuilds.
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def _GetTargetPostbuilds(self, configname, output, output_binary, quiet=False): """Returns a list of shell commands that contain the shell commands to run as postbuilds for this target, before the actual postbuilds.""" # dSYMs need to build before stripping happens. return ( self._GetDebugInfoPostbuilds(configname, output, output_binary, quiet) + self._GetStripPostbuilds(configname, output_binary, quiet))
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https://github.com/arangodb/arangodb/blob/0d658689c7d1b721b314fa3ca27d38303e1570c8/3rdParty/V8/gyp/xcode_emulation.py#L980-L987
hszhao/PSPNet
cf7e5a99ba37e46118026e96be5821a9bc63bde0
python/caffe/pycaffe.py
python
_Net_forward_backward_all
(self, blobs=None, diffs=None, **kwargs)
return all_outs, all_diffs
Run net forward + backward in batches. Parameters ---------- blobs: list of blobs to extract as in forward() diffs: list of diffs to extract as in backward() kwargs: Keys are input (for forward) and output (for backward) blob names and values are ndarrays. Refer to forward() and backward(). Prefilled variants are called for lack of input or output blobs. Returns ------- all_blobs: {blob name: blob ndarray} dict. all_diffs: {blob name: diff ndarray} dict.
Run net forward + backward in batches.
[ "Run", "net", "forward", "+", "backward", "in", "batches", "." ]
def _Net_forward_backward_all(self, blobs=None, diffs=None, **kwargs): """ Run net forward + backward in batches. Parameters ---------- blobs: list of blobs to extract as in forward() diffs: list of diffs to extract as in backward() kwargs: Keys are input (for forward) and output (for backward) blob names and values are ndarrays. Refer to forward() and backward(). Prefilled variants are called for lack of input or output blobs. Returns ------- all_blobs: {blob name: blob ndarray} dict. all_diffs: {blob name: diff ndarray} dict. """ # Batch blobs and diffs. all_outs = {out: [] for out in set(self.outputs + (blobs or []))} all_diffs = {diff: [] for diff in set(self.inputs + (diffs or []))} forward_batches = self._batch({in_: kwargs[in_] for in_ in self.inputs if in_ in kwargs}) backward_batches = self._batch({out: kwargs[out] for out in self.outputs if out in kwargs}) # Collect outputs from batches (and heed lack of forward/backward batches). for fb, bb in izip_longest(forward_batches, backward_batches, fillvalue={}): batch_blobs = self.forward(blobs=blobs, **fb) batch_diffs = self.backward(diffs=diffs, **bb) for out, out_blobs in batch_blobs.iteritems(): all_outs[out].extend(out_blobs.copy()) for diff, out_diffs in batch_diffs.iteritems(): all_diffs[diff].extend(out_diffs.copy()) # Package in ndarray. for out, diff in zip(all_outs, all_diffs): all_outs[out] = np.asarray(all_outs[out]) all_diffs[diff] = np.asarray(all_diffs[diff]) # Discard padding at the end and package in ndarray. pad = len(all_outs.itervalues().next()) - len(kwargs.itervalues().next()) if pad: for out, diff in zip(all_outs, all_diffs): all_outs[out] = all_outs[out][:-pad] all_diffs[diff] = all_diffs[diff][:-pad] return all_outs, all_diffs
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https://github.com/hszhao/PSPNet/blob/cf7e5a99ba37e46118026e96be5821a9bc63bde0/python/caffe/pycaffe.py#L190-L232
mongodb/mongo
d8ff665343ad29cf286ee2cf4a1960d29371937b
buildscripts/linter/git_base.py
python
Repository.configure
(self, parameter, value)
return self._callgito("config", ["--local", parameter, value])
Set a local configuration parameter.
Set a local configuration parameter.
[ "Set", "a", "local", "configuration", "parameter", "." ]
def configure(self, parameter, value): """Set a local configuration parameter.""" return self._callgito("config", ["--local", parameter, value])
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https://github.com/mongodb/mongo/blob/d8ff665343ad29cf286ee2cf4a1960d29371937b/buildscripts/linter/git_base.py#L100-L102
kamyu104/LeetCode-Solutions
77605708a927ea3b85aee5a479db733938c7c211
Python/split-a-string-in-balanced-strings.py
python
Solution.balancedStringSplit
(self, s)
return result
:type s: str :rtype: int
:type s: str :rtype: int
[ ":", "type", "s", ":", "str", ":", "rtype", ":", "int" ]
def balancedStringSplit(self, s): """ :type s: str :rtype: int """ result, count = 0, 0 for c in s: count += 1 if c == 'L' else -1 if count == 0: result += 1 return result
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https://github.com/kamyu104/LeetCode-Solutions/blob/77605708a927ea3b85aee5a479db733938c7c211/Python/split-a-string-in-balanced-strings.py#L5-L15
alibaba/graph-learn
54cafee9db3054dc310a28b856be7f97c7d5aee9
graphlearn/python/data/values.py
python
Layers.set_layer_nodes
(self, layer_id, nodes)
Set `Nodes` of the given `Layer`.
Set `Nodes` of the given `Layer`.
[ "Set", "Nodes", "of", "the", "given", "Layer", "." ]
def set_layer_nodes(self, layer_id, nodes): """ Set `Nodes` of the given `Layer`. """ layer_id -= 1 if isinstance(self.layers, list) and layer_id < len(self.layers): if isinstance(self.layers[layer_id], Layer): self.layers[layer_id].set_nodes(nodes) else: raise ValueError("layer {} is not a SingleLayer".format(layer_id)) else: raise ValueError("layer id beyond the layers length.")
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https://github.com/alibaba/graph-learn/blob/54cafee9db3054dc310a28b856be7f97c7d5aee9/graphlearn/python/data/values.py#L726-L736
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Tools/AWSPythonSDK/1.5.8/docutils/io.py
python
StringInput.read
(self)
return self.decode(self.source)
Decode and return the source string.
Decode and return the source string.
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def read(self): """Decode and return the source string.""" return self.decode(self.source)
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/AWSPythonSDK/1.5.8/docutils/io.py#L433-L435
catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/python/numpy/py3/numpy/polynomial/polynomial.py
python
polygrid2d
(x, y, c)
return pu._gridnd(polyval, c, x, y)
Evaluate a 2-D polynomial on the Cartesian product of x and y. This function returns the values: .. math:: p(a,b) = \\sum_{i,j} c_{i,j} * a^i * b^j where the points `(a, b)` consist of all pairs formed by taking `a` from `x` and `b` from `y`. The resulting points form a grid with `x` in the first dimension and `y` in the second. The parameters `x` and `y` are converted to arrays only if they are tuples or a lists, otherwise they are treated as a scalars. In either case, either `x` and `y` or their elements must support multiplication and addition both with themselves and with the elements of `c`. If `c` has fewer than two dimensions, ones are implicitly appended to its shape to make it 2-D. The shape of the result will be c.shape[2:] + x.shape + y.shape. Parameters ---------- x, y : array_like, compatible objects The two dimensional series is evaluated at the points in the Cartesian product of `x` and `y`. If `x` or `y` is a list or tuple, it is first converted to an ndarray, otherwise it is left unchanged and, if it isn't an ndarray, it is treated as a scalar. c : array_like Array of coefficients ordered so that the coefficients for terms of degree i,j are contained in ``c[i,j]``. If `c` has dimension greater than two the remaining indices enumerate multiple sets of coefficients. Returns ------- values : ndarray, compatible object The values of the two dimensional polynomial at points in the Cartesian product of `x` and `y`. See Also -------- polyval, polyval2d, polyval3d, polygrid3d Notes ----- .. versionadded:: 1.7.0
Evaluate a 2-D polynomial on the Cartesian product of x and y.
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def polygrid2d(x, y, c): """ Evaluate a 2-D polynomial on the Cartesian product of x and y. This function returns the values: .. math:: p(a,b) = \\sum_{i,j} c_{i,j} * a^i * b^j where the points `(a, b)` consist of all pairs formed by taking `a` from `x` and `b` from `y`. The resulting points form a grid with `x` in the first dimension and `y` in the second. The parameters `x` and `y` are converted to arrays only if they are tuples or a lists, otherwise they are treated as a scalars. In either case, either `x` and `y` or their elements must support multiplication and addition both with themselves and with the elements of `c`. If `c` has fewer than two dimensions, ones are implicitly appended to its shape to make it 2-D. The shape of the result will be c.shape[2:] + x.shape + y.shape. Parameters ---------- x, y : array_like, compatible objects The two dimensional series is evaluated at the points in the Cartesian product of `x` and `y`. If `x` or `y` is a list or tuple, it is first converted to an ndarray, otherwise it is left unchanged and, if it isn't an ndarray, it is treated as a scalar. c : array_like Array of coefficients ordered so that the coefficients for terms of degree i,j are contained in ``c[i,j]``. If `c` has dimension greater than two the remaining indices enumerate multiple sets of coefficients. Returns ------- values : ndarray, compatible object The values of the two dimensional polynomial at points in the Cartesian product of `x` and `y`. See Also -------- polyval, polyval2d, polyval3d, polygrid3d Notes ----- .. versionadded:: 1.7.0 """ return pu._gridnd(polyval, c, x, y)
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/numpy/py3/numpy/polynomial/polynomial.py#L898-L948
NASA-SW-VnV/ikos
71325dfb94737332542caa708d7537752021522d
analyzer/python/ikos/scan.py
python
run
(cmd)
return rc
Run the given command and return the exit code
Run the given command and return the exit code
[ "Run", "the", "given", "command", "and", "return", "the", "exit", "code" ]
def run(cmd): ''' Run the given command and return the exit code ''' log.debug('Running %s' % command_string(cmd)) try: proc = subprocess.Popen(cmd) rc = proc.wait() except OSError as e: printf('error: %s: %s\n', cmd[0], e.strerror, file=sys.stderr) sys.exit(e.errno) if rc != 0: sys.exit(rc) return rc
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https://github.com/NASA-SW-VnV/ikos/blob/71325dfb94737332542caa708d7537752021522d/analyzer/python/ikos/scan.py#L419-L433
ricardoquesada/Spidermonkey
4a75ea2543408bd1b2c515aa95901523eeef7858
python/configobj/configobj.py
python
ConfigObj.reload
(self)
Reload a ConfigObj from file. This method raises a ``ReloadError`` if the ConfigObj doesn't have a filename attribute pointing to a file.
Reload a ConfigObj from file. This method raises a ``ReloadError`` if the ConfigObj doesn't have a filename attribute pointing to a file.
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def reload(self): """ Reload a ConfigObj from file. This method raises a ``ReloadError`` if the ConfigObj doesn't have a filename attribute pointing to a file. """ if not isinstance(self.filename, basestring): raise ReloadError() filename = self.filename current_options = {} for entry in OPTION_DEFAULTS: if entry == 'configspec': continue current_options[entry] = getattr(self, entry) configspec = self._original_configspec current_options['configspec'] = configspec self.clear() self._initialise(current_options) self._load(filename, configspec)
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https://github.com/ricardoquesada/Spidermonkey/blob/4a75ea2543408bd1b2c515aa95901523eeef7858/python/configobj/configobj.py#L2334-L2356
windystrife/UnrealEngine_NVIDIAGameWorks
b50e6338a7c5b26374d66306ebc7807541ff815e
Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/decimal.py
python
Decimal.fma
(self, other, third, context=None)
return product.__add__(third, context)
Fused multiply-add. Returns self*other+third with no rounding of the intermediate product self*other. self and other are multiplied together, with no rounding of the result. The third operand is then added to the result, and a single final rounding is performed.
Fused multiply-add.
[ "Fused", "multiply", "-", "add", "." ]
def fma(self, other, third, context=None): """Fused multiply-add. Returns self*other+third with no rounding of the intermediate product self*other. self and other are multiplied together, with no rounding of the result. The third operand is then added to the result, and a single final rounding is performed. """ other = _convert_other(other, raiseit=True) # compute product; raise InvalidOperation if either operand is # a signaling NaN or if the product is zero times infinity. if self._is_special or other._is_special: if context is None: context = getcontext() if self._exp == 'N': return context._raise_error(InvalidOperation, 'sNaN', self) if other._exp == 'N': return context._raise_error(InvalidOperation, 'sNaN', other) if self._exp == 'n': product = self elif other._exp == 'n': product = other elif self._exp == 'F': if not other: return context._raise_error(InvalidOperation, 'INF * 0 in fma') product = _SignedInfinity[self._sign ^ other._sign] elif other._exp == 'F': if not self: return context._raise_error(InvalidOperation, '0 * INF in fma') product = _SignedInfinity[self._sign ^ other._sign] else: product = _dec_from_triple(self._sign ^ other._sign, str(int(self._int) * int(other._int)), self._exp + other._exp) third = _convert_other(third, raiseit=True) return product.__add__(third, context)
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https://github.com/windystrife/UnrealEngine_NVIDIAGameWorks/blob/b50e6338a7c5b26374d66306ebc7807541ff815e/Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/decimal.py#L1809-L1851
Xilinx/Vitis-AI
fc74d404563d9951b57245443c73bef389f3657f
tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/distribute/distribute_coordinator.py
python
_WorkerContext._is_chief
(self)
return False
Return whether the task is the chief worker.
Return whether the task is the chief worker.
[ "Return", "whether", "the", "task", "is", "the", "chief", "worker", "." ]
def _is_chief(self): """Return whether the task is the chief worker.""" if (not self._cluster_spec or self._task_type in [_TaskType.CHIEF, _TaskType.EVALUATOR, None]): return True # If not local and chief not in the cluster_spec, use the first worker as # chief. if (_TaskType.CHIEF not in self._cluster_spec.jobs and self._task_type == _TaskType.WORKER and self._task_id == 0): return True return False
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https://github.com/Xilinx/Vitis-AI/blob/fc74d404563d9951b57245443c73bef389f3657f/tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/distribute/distribute_coordinator.py#L194-L205
facebook/fboss
60063db1df37c2ec0e7dcd0955c54885ea9bf7f0
fboss/py/fboss/cli/cli.py
python
RouteCli._ip
(cli_opts, ip, vrf)
Show the route to a specific IP
Show the route to a specific IP
[ "Show", "the", "route", "to", "a", "specific", "IP" ]
def _ip(cli_opts, ip, vrf): """Show the route to a specific IP""" route.RouteIpCmd(cli_opts).run(ip, vrf)
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https://github.com/facebook/fboss/blob/60063db1df37c2ec0e7dcd0955c54885ea9bf7f0/fboss/py/fboss/cli/cli.py#L561-L563
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numpy/distutils/misc_util.py
python
generate_config_py
(target)
return target
Generate config.py file containing system_info information used during building the package. Usage: config['py_modules'].append((packagename, '__config__',generate_config_py))
Generate config.py file containing system_info information used during building the package.
[ "Generate", "config", ".", "py", "file", "containing", "system_info", "information", "used", "during", "building", "the", "package", "." ]
def generate_config_py(target): """Generate config.py file containing system_info information used during building the package. Usage: config['py_modules'].append((packagename, '__config__',generate_config_py)) """ from numpy.distutils.system_info import system_info from distutils.dir_util import mkpath mkpath(os.path.dirname(target)) with open(target, 'w') as f: f.write('# This file is generated by numpy\'s %s\n' % (os.path.basename(sys.argv[0]))) f.write('# It contains system_info results at the time of building this package.\n') f.write('__all__ = ["get_info","show"]\n\n') # For gfortran+msvc combination, extra shared libraries may exist f.write(textwrap.dedent(""" import os import sys extra_dll_dir = os.path.join(os.path.dirname(__file__), '.libs') if sys.platform == 'win32' and os.path.isdir(extra_dll_dir): if sys.version_info >= (3, 8): os.add_dll_directory(extra_dll_dir) else: os.environ.setdefault('PATH', '') os.environ['PATH'] += os.pathsep + extra_dll_dir """)) for k, i in system_info.saved_results.items(): f.write('%s=%r\n' % (k, i)) f.write(textwrap.dedent(r''' def get_info(name): g = globals() return g.get(name, g.get(name + "_info", {})) def show(): for name,info_dict in globals().items(): if name[0] == "_" or type(info_dict) is not type({}): continue print(name + ":") if not info_dict: print(" NOT AVAILABLE") for k,v in info_dict.items(): v = str(v) if k == "sources" and len(v) > 200: v = v[:60] + " ...\n... " + v[-60:] print(" %s = %s" % (k,v)) ''')) return target
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numpy/distutils/misc_util.py#L2308-L2359
hanpfei/chromium-net
392cc1fa3a8f92f42e4071ab6e674d8e0482f83f
third_party/catapult/third_party/WebOb/webob/dec.py
python
wsgify.__call__
(self, req, *args, **kw)
Call this as a WSGI application or with a request
Call this as a WSGI application or with a request
[ "Call", "this", "as", "a", "WSGI", "application", "or", "with", "a", "request" ]
def __call__(self, req, *args, **kw): """Call this as a WSGI application or with a request""" func = self.func if func is None: if args or kw: raise TypeError( "Unbound %s can only be called with the function it " "will wrap" % self.__class__.__name__) func = req return self.clone(func) if isinstance(req, dict): if len(args) != 1 or kw: raise TypeError( "Calling %r as a WSGI app with the wrong signature") environ = req start_response = args[0] req = self.RequestClass(environ) req.response = req.ResponseClass() try: args = self.args if self.middleware_wraps: args = (self.middleware_wraps,) + args resp = self.call_func(req, *args, **self.kwargs) except HTTPException as exc: resp = exc if resp is None: ## FIXME: I'm not sure what this should be? resp = req.response if isinstance(resp, text_type): resp = bytes_(resp, req.charset) if isinstance(resp, bytes): body = resp resp = req.response resp.write(body) if resp is not req.response: resp = req.response.merge_cookies(resp) return resp(environ, start_response) else: if self.middleware_wraps: args = (self.middleware_wraps,) + args return self.func(req, *args, **kw)
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https://github.com/hanpfei/chromium-net/blob/392cc1fa3a8f92f42e4071ab6e674d8e0482f83f/third_party/catapult/third_party/WebOb/webob/dec.py#L108-L148
gimli-org/gimli
17aa2160de9b15ababd9ef99e89b1bc3277bbb23
pygimli/physics/traveltime/plotting.py
python
drawFirstPicks
(ax, data, tt=None, plotva=False, **kwargs)
Plot first arrivals as lines.
Plot first arrivals as lines.
[ "Plot", "first", "arrivals", "as", "lines", "." ]
def drawFirstPicks(ax, data, tt=None, plotva=False, **kwargs): """Plot first arrivals as lines.""" px = pg.x(data) gx = np.array([px[int(g)] for g in data("g")]) sx = np.array([px[int(s)] for s in data("s")]) if tt is None: tt = np.array(data("t")) if plotva: tt = np.absolute(gx - sx) / tt uns = np.unique(sx) cols = plt.cm.tab10(np.arange(10)) kwargs.setdefault('marker', 'x') kwargs.setdefault('markersize', 8) kwargs.setdefault('linestyle', '-') for i, si in enumerate(uns): ti = tt[sx == si] gi = gx[sx == si] ii = gi.argsort() ax.plot(gi[ii], ti[ii], color=cols[i % 10], **kwargs) ax.plot(si, 0., 's', color=cols[i % 10]) ax.grid(True) if plotva: ax.set_ylabel("Apparent velocity (m/s)") else: ax.set_ylabel("Traveltime (s)") ax.set_xlabel("x (m)") ax.invert_yaxis()
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https://github.com/gimli-org/gimli/blob/17aa2160de9b15ababd9ef99e89b1bc3277bbb23/pygimli/physics/traveltime/plotting.py#L72-L102
mantidproject/mantid
03deeb89254ec4289edb8771e0188c2090a02f32
tools/DOI/doi.py
python
_http_request
(body, method, url, options, content_type=None)
return result
Issue an HTTP request with the given options. We are forced to use a command line tool for this rather than use the in-built Python libraries since httplib, urllib and urllib2 all seem to have problems using HTTPS through the proxy at RAL. HTTP works fine, but the DOI API is encrypted so that is not an option. We prefer cURL to wget since it exists on many Linux machines and even comes bundled with Git Bash for Windows! Some good info on scripting with cURL can be found at: http://curl.haxx.se/docs/httpscripting.html
Issue an HTTP request with the given options.
[ "Issue", "an", "HTTP", "request", "with", "the", "given", "options", "." ]
def _http_request(body, method, url, options, content_type=None): '''Issue an HTTP request with the given options. We are forced to use a command line tool for this rather than use the in-built Python libraries since httplib, urllib and urllib2 all seem to have problems using HTTPS through the proxy at RAL. HTTP works fine, but the DOI API is encrypted so that is not an option. We prefer cURL to wget since it exists on many Linux machines and even comes bundled with Git Bash for Windows! Some good info on scripting with cURL can be found at: http://curl.haxx.se/docs/httpscripting.html''' args = [ 'curl', '--user', options.username + ':' + options.password, # The bodies of HTTP messages must be encoded: '--data', body, '--request', method, ] if content_type is not None: args.extend(['--header', content_type, ]) if 'http_proxy' in os.environ: args.extend(['--proxy', os.environ['http_proxy']]) # Set how loud cURL should be while running. if options.debug: args.append('--verbose') else: args.append('--silent') args.append(url) proc = subprocess.Popen(args, stdout=subprocess.PIPE) result = proc.stdout.readlines() result = [x.decode() for x in result] print("Server Response: " + str(result)) return result
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https://github.com/mantidproject/mantid/blob/03deeb89254ec4289edb8771e0188c2090a02f32/tools/DOI/doi.py#L201-L241
apple/swift-lldb
d74be846ef3e62de946df343e8c234bde93a8912
scripts/Python/static-binding/lldb.py
python
SBData.GetDouble
(self, error, offset)
return _lldb.SBData_GetDouble(self, error, offset)
GetDouble(SBData self, SBError error, lldb::offset_t offset) -> double
GetDouble(SBData self, SBError error, lldb::offset_t offset) -> double
[ "GetDouble", "(", "SBData", "self", "SBError", "error", "lldb", "::", "offset_t", "offset", ")", "-", ">", "double" ]
def GetDouble(self, error, offset): """GetDouble(SBData self, SBError error, lldb::offset_t offset) -> double""" return _lldb.SBData_GetDouble(self, error, offset)
[ "def", "GetDouble", "(", "self", ",", "error", ",", "offset", ")", ":", "return", "_lldb", ".", "SBData_GetDouble", "(", "self", ",", "error", ",", "offset", ")" ]
https://github.com/apple/swift-lldb/blob/d74be846ef3e62de946df343e8c234bde93a8912/scripts/Python/static-binding/lldb.py#L3347-L3349
fatih/subvim
241b6d170597857105da219c9b7d36059e9f11fb
vim/base/YouCompleteMe/third_party/jedi/jedi/evaluate.py
python
find_assignments
(lhs, results, seek_name)
Check if `seek_name` is in the left hand side `lhs` of assignment. `lhs` can simply be a variable (`pr.Call`) or a tuple/list (`pr.Array`) representing the following cases:: a = 1 # lhs is pr.Call (a, b) = 2 # lhs is pr.Array :type lhs: pr.Call :type results: list :type seek_name: str
Check if `seek_name` is in the left hand side `lhs` of assignment.
[ "Check", "if", "seek_name", "is", "in", "the", "left", "hand", "side", "lhs", "of", "assignment", "." ]
def find_assignments(lhs, results, seek_name): """ Check if `seek_name` is in the left hand side `lhs` of assignment. `lhs` can simply be a variable (`pr.Call`) or a tuple/list (`pr.Array`) representing the following cases:: a = 1 # lhs is pr.Call (a, b) = 2 # lhs is pr.Array :type lhs: pr.Call :type results: list :type seek_name: str """ if isinstance(lhs, pr.Array): return assign_tuples(lhs, results, seek_name) elif lhs.name.names[-1] == seek_name: return results else: return []
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https://github.com/fatih/subvim/blob/241b6d170597857105da219c9b7d36059e9f11fb/vim/base/YouCompleteMe/third_party/jedi/jedi/evaluate.py#L573-L592
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numpy/lib/shape_base.py
python
kron
(a, b)
return result
Kronecker product of two arrays. Computes the Kronecker product, a composite array made of blocks of the second array scaled by the first. Parameters ---------- a, b : array_like Returns ------- out : ndarray See Also -------- outer : The outer product Notes ----- The function assumes that the number of dimensions of `a` and `b` are the same, if necessary prepending the smallest with ones. If `a.shape = (r0,r1,..,rN)` and `b.shape = (s0,s1,...,sN)`, the Kronecker product has shape `(r0*s0, r1*s1, ..., rN*SN)`. The elements are products of elements from `a` and `b`, organized explicitly by:: kron(a,b)[k0,k1,...,kN] = a[i0,i1,...,iN] * b[j0,j1,...,jN] where:: kt = it * st + jt, t = 0,...,N In the common 2-D case (N=1), the block structure can be visualized:: [[ a[0,0]*b, a[0,1]*b, ... , a[0,-1]*b ], [ ... ... ], [ a[-1,0]*b, a[-1,1]*b, ... , a[-1,-1]*b ]] Examples -------- >>> np.kron([1,10,100], [5,6,7]) array([ 5, 6, 7, ..., 500, 600, 700]) >>> np.kron([5,6,7], [1,10,100]) array([ 5, 50, 500, ..., 7, 70, 700]) >>> np.kron(np.eye(2), np.ones((2,2))) array([[1., 1., 0., 0.], [1., 1., 0., 0.], [0., 0., 1., 1.], [0., 0., 1., 1.]]) >>> a = np.arange(100).reshape((2,5,2,5)) >>> b = np.arange(24).reshape((2,3,4)) >>> c = np.kron(a,b) >>> c.shape (2, 10, 6, 20) >>> I = (1,3,0,2) >>> J = (0,2,1) >>> J1 = (0,) + J # extend to ndim=4 >>> S1 = (1,) + b.shape >>> K = tuple(np.array(I) * np.array(S1) + np.array(J1)) >>> c[K] == a[I]*b[J] True
Kronecker product of two arrays.
[ "Kronecker", "product", "of", "two", "arrays", "." ]
def kron(a, b): """ Kronecker product of two arrays. Computes the Kronecker product, a composite array made of blocks of the second array scaled by the first. Parameters ---------- a, b : array_like Returns ------- out : ndarray See Also -------- outer : The outer product Notes ----- The function assumes that the number of dimensions of `a` and `b` are the same, if necessary prepending the smallest with ones. If `a.shape = (r0,r1,..,rN)` and `b.shape = (s0,s1,...,sN)`, the Kronecker product has shape `(r0*s0, r1*s1, ..., rN*SN)`. The elements are products of elements from `a` and `b`, organized explicitly by:: kron(a,b)[k0,k1,...,kN] = a[i0,i1,...,iN] * b[j0,j1,...,jN] where:: kt = it * st + jt, t = 0,...,N In the common 2-D case (N=1), the block structure can be visualized:: [[ a[0,0]*b, a[0,1]*b, ... , a[0,-1]*b ], [ ... ... ], [ a[-1,0]*b, a[-1,1]*b, ... , a[-1,-1]*b ]] Examples -------- >>> np.kron([1,10,100], [5,6,7]) array([ 5, 6, 7, ..., 500, 600, 700]) >>> np.kron([5,6,7], [1,10,100]) array([ 5, 50, 500, ..., 7, 70, 700]) >>> np.kron(np.eye(2), np.ones((2,2))) array([[1., 1., 0., 0.], [1., 1., 0., 0.], [0., 0., 1., 1.], [0., 0., 1., 1.]]) >>> a = np.arange(100).reshape((2,5,2,5)) >>> b = np.arange(24).reshape((2,3,4)) >>> c = np.kron(a,b) >>> c.shape (2, 10, 6, 20) >>> I = (1,3,0,2) >>> J = (0,2,1) >>> J1 = (0,) + J # extend to ndim=4 >>> S1 = (1,) + b.shape >>> K = tuple(np.array(I) * np.array(S1) + np.array(J1)) >>> c[K] == a[I]*b[J] True """ b = asanyarray(b) a = array(a, copy=False, subok=True, ndmin=b.ndim) ndb, nda = b.ndim, a.ndim if (nda == 0 or ndb == 0): return _nx.multiply(a, b) as_ = a.shape bs = b.shape if not a.flags.contiguous: a = reshape(a, as_) if not b.flags.contiguous: b = reshape(b, bs) nd = ndb if (ndb != nda): if (ndb > nda): as_ = (1,)*(ndb-nda) + as_ else: bs = (1,)*(nda-ndb) + bs nd = nda result = outer(a, b).reshape(as_+bs) axis = nd-1 for _ in range(nd): result = concatenate(result, axis=axis) wrapper = get_array_prepare(a, b) if wrapper is not None: result = wrapper(result) wrapper = get_array_wrap(a, b) if wrapper is not None: result = wrapper(result) return result
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numpy/lib/shape_base.py#L1066-L1162
tensorflow/tensorflow
419e3a6b650ea4bd1b0cba23c4348f8a69f3272e
tensorflow/python/tools/api/generator/create_python_api.py
python
get_module
(dir_path, relative_to_dir)
return dir_path.replace('/', '.').strip('.')
Get module that corresponds to path relative to relative_to_dir. Args: dir_path: Path to directory. relative_to_dir: Get module relative to this directory. Returns: Name of module that corresponds to the given directory.
Get module that corresponds to path relative to relative_to_dir.
[ "Get", "module", "that", "corresponds", "to", "path", "relative", "to", "relative_to_dir", "." ]
def get_module(dir_path, relative_to_dir): """Get module that corresponds to path relative to relative_to_dir. Args: dir_path: Path to directory. relative_to_dir: Get module relative to this directory. Returns: Name of module that corresponds to the given directory. """ dir_path = dir_path[len(relative_to_dir):] # Convert path separators to '/' for easier parsing below. dir_path = dir_path.replace(os.sep, '/') return dir_path.replace('/', '.').strip('.')
[ "def", "get_module", "(", "dir_path", ",", "relative_to_dir", ")", ":", "dir_path", "=", "dir_path", "[", "len", "(", "relative_to_dir", ")", ":", "]", "# Convert path separators to '/' for easier parsing below.", "dir_path", "=", "dir_path", ".", "replace", "(", "os", ".", "sep", ",", "'/'", ")", "return", "dir_path", ".", "replace", "(", "'/'", ",", "'.'", ")", ".", "strip", "(", "'.'", ")" ]
https://github.com/tensorflow/tensorflow/blob/419e3a6b650ea4bd1b0cba23c4348f8a69f3272e/tensorflow/python/tools/api/generator/create_python_api.py#L523-L536
benoitsteiner/tensorflow-opencl
cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5
tensorflow/python/estimator/estimator.py
python
Estimator.__init__
(self, model_fn, model_dir=None, config=None, params=None)
Constructs an `Estimator` instance. Args: model_fn: Model function. Follows the signature: * Args: * `features`: This is the first item returned from the `input_fn` passed to `train`, `evaluate`, and `predict`. This should be a single `Tensor` or `dict` of same. * `labels`: This is the second item returned from the `input_fn` passed to `train`, `evaluate`, and `predict`. This should be a single `Tensor` or `dict` of same (for multi-head models). If mode is `ModeKeys.PREDICT`, `labels=None` will be passed. If the `model_fn`'s signature does not accept `mode`, the `model_fn` must still be able to handle `labels=None`. * `mode`: Optional. Specifies if this training, evaluation or prediction. See `ModeKeys`. * `params`: Optional `dict` of hyperparameters. Will receive what is passed to Estimator in `params` parameter. This allows to configure Estimators from hyper parameter tuning. * `config`: Optional configuration object. Will receive what is passed to Estimator in `config` parameter, or the default `config`. Allows updating things in your model_fn based on configuration such as `num_ps_replicas`, or `model_dir`. * Returns: `EstimatorSpec` model_dir: Directory to save model parameters, graph and etc. This can also be used to load checkpoints from the directory into a estimator to continue training a previously saved model. If `None`, the model_dir in `config` will be used if set. If both are set, they must be same. If both are `None`, a temporary directory will be used. config: Configuration object. params: `dict` of hyper parameters that will be passed into `model_fn`. Keys are names of parameters, values are basic python types. Raises: ValueError: parameters of `model_fn` don't match `params`. ValueError: if this is called via a subclass and if that class overrides a member of `Estimator`.
Constructs an `Estimator` instance.
[ "Constructs", "an", "Estimator", "instance", "." ]
def __init__(self, model_fn, model_dir=None, config=None, params=None): """Constructs an `Estimator` instance. Args: model_fn: Model function. Follows the signature: * Args: * `features`: This is the first item returned from the `input_fn` passed to `train`, `evaluate`, and `predict`. This should be a single `Tensor` or `dict` of same. * `labels`: This is the second item returned from the `input_fn` passed to `train`, `evaluate`, and `predict`. This should be a single `Tensor` or `dict` of same (for multi-head models). If mode is `ModeKeys.PREDICT`, `labels=None` will be passed. If the `model_fn`'s signature does not accept `mode`, the `model_fn` must still be able to handle `labels=None`. * `mode`: Optional. Specifies if this training, evaluation or prediction. See `ModeKeys`. * `params`: Optional `dict` of hyperparameters. Will receive what is passed to Estimator in `params` parameter. This allows to configure Estimators from hyper parameter tuning. * `config`: Optional configuration object. Will receive what is passed to Estimator in `config` parameter, or the default `config`. Allows updating things in your model_fn based on configuration such as `num_ps_replicas`, or `model_dir`. * Returns: `EstimatorSpec` model_dir: Directory to save model parameters, graph and etc. This can also be used to load checkpoints from the directory into a estimator to continue training a previously saved model. If `None`, the model_dir in `config` will be used if set. If both are set, they must be same. If both are `None`, a temporary directory will be used. config: Configuration object. params: `dict` of hyper parameters that will be passed into `model_fn`. Keys are names of parameters, values are basic python types. Raises: ValueError: parameters of `model_fn` don't match `params`. ValueError: if this is called via a subclass and if that class overrides a member of `Estimator`. """ Estimator._assert_members_are_not_overridden(self) if config is None: self._config = run_config.RunConfig() logging.info('Using default config.') else: if not isinstance(config, run_config.RunConfig): raise ValueError( 'config must be an instance of RunConfig, but provided %s.' % config) self._config = config # Model directory. if (model_dir is not None) and (self._config.model_dir is not None): if model_dir != self._config.model_dir: # TODO(alanyee): remove this suppression after it is no longer needed # pylint: disable=g-doc-exception raise ValueError( "model_dir are set both in constructor and RunConfig, but with " "different values. In constructor: '{}', in RunConfig: " "'{}' ".format(model_dir, self._config.model_dir)) # pylint: enable=g-doc-exception self._model_dir = model_dir or self._config.model_dir if self._model_dir is None: self._model_dir = tempfile.mkdtemp() logging.warning('Using temporary folder as model directory: %s', self._model_dir) if self._config.model_dir is None: self._config = self._config.replace(model_dir=self._model_dir) logging.info('Using config: %s', str(vars(self._config))) if self._config.session_config is None: self._session_config = config_pb2.ConfigProto(allow_soft_placement=True) else: self._session_config = self._config.session_config self._device_fn = _get_replica_device_setter(self._config) if model_fn is None: raise ValueError('model_fn must be provided to Estimator.') _verify_model_fn_args(model_fn, params) self._model_fn = model_fn self._params = copy.deepcopy(params or {})
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https://github.com/benoitsteiner/tensorflow-opencl/blob/cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5/tensorflow/python/estimator/estimator.py#L93-L180
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/osx_cocoa/stc.py
python
StyledTextCtrl.SetMultiPaste
(*args, **kwargs)
return _stc.StyledTextCtrl_SetMultiPaste(*args, **kwargs)
SetMultiPaste(self, int multiPaste)
SetMultiPaste(self, int multiPaste)
[ "SetMultiPaste", "(", "self", "int", "multiPaste", ")" ]
def SetMultiPaste(*args, **kwargs): """SetMultiPaste(self, int multiPaste)""" return _stc.StyledTextCtrl_SetMultiPaste(*args, **kwargs)
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_cocoa/stc.py#L4277-L4279
wujian16/Cornell-MOE
df299d1be882d2af9796d7a68b3f9505cac7a53e
moe/optimal_learning/python/base_prior.py
python
NormalPrior.sample_from_prior
(self, n_samples)
return p0[:, np.newaxis]
Returns N samples from the prior. Parameters ---------- n_samples : int The number of samples that will be drawn. Returns ------- (N, D) np.array The samples from the prior.
Returns N samples from the prior.
[ "Returns", "N", "samples", "from", "the", "prior", "." ]
def sample_from_prior(self, n_samples): """ Returns N samples from the prior. Parameters ---------- n_samples : int The number of samples that will be drawn. Returns ------- (N, D) np.array The samples from the prior. """ p0 = np.random.normal(loc=self.mean, scale=self.sigma, size=n_samples) return p0[:, np.newaxis]
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https://github.com/wujian16/Cornell-MOE/blob/df299d1be882d2af9796d7a68b3f9505cac7a53e/moe/optimal_learning/python/base_prior.py#L356-L374
tzutalin/dlib-android
989627cb7fe81cd1d41d73434b0e91ce1dd2683f
tools/lint/cpplint.py
python
FindStartOfExpressionInLine
(line, endpos, stack)
return (-1, stack)
Find position at the matching start of current expression. This is almost the reverse of FindEndOfExpressionInLine, but note that the input position and returned position differs by 1. Args: line: a CleansedLines line. endpos: start searching at this position. stack: nesting stack at endpos. Returns: On finding matching start: (index at matching start, None) On finding an unclosed expression: (-1, None) Otherwise: (-1, new stack at beginning of this line)
Find position at the matching start of current expression. This is almost the reverse of FindEndOfExpressionInLine, but note that the input position and returned position differs by 1. Args: line: a CleansedLines line. endpos: start searching at this position. stack: nesting stack at endpos. Returns: On finding matching start: (index at matching start, None) On finding an unclosed expression: (-1, None) Otherwise: (-1, new stack at beginning of this line)
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def FindStartOfExpressionInLine(line, endpos, stack): """Find position at the matching start of current expression. This is almost the reverse of FindEndOfExpressionInLine, but note that the input position and returned position differs by 1. Args: line: a CleansedLines line. endpos: start searching at this position. stack: nesting stack at endpos. Returns: On finding matching start: (index at matching start, None) On finding an unclosed expression: (-1, None) Otherwise: (-1, new stack at beginning of this line) """ i = endpos while i >= 0: char = line[i] if char in ')]}': # Found end of expression, push to expression stack stack.append(char) elif char == '>': # Found potential end of template argument list. # # Ignore it if it's a "->" or ">=" or "operator>" if (i > 0 and (line[i - 1] == '-' or Match(r'\s>=\s', line[i - 1:]) or Search(r'\boperator\s*$', line[0:i]))): i -= 1 else: stack.append('>') elif char == '<': # Found potential start of template argument list if i > 0 and line[i - 1] == '<': # Left shift operator i -= 1 else: # If there is a matching '>', we can pop the expression stack. # Otherwise, ignore this '<' since it must be an operator. if stack and stack[-1] == '>': stack.pop() if not stack: return (i, None) elif char in '([{': # Found start of expression. # # If there are any unmatched '>' on the stack, they must be # operators. Remove those. while stack and stack[-1] == '>': stack.pop() if not stack: return (-1, None) if ((char == '(' and stack[-1] == ')') or (char == '[' and stack[-1] == ']') or (char == '{' and stack[-1] == '}')): stack.pop() if not stack: return (i, None) else: # Mismatched parentheses return (-1, None) elif char == ';': # Found something that look like end of statements. If we are currently # expecting a '<', the matching '>' must have been an operator, since # template argument list should not contain statements. while stack and stack[-1] == '>': stack.pop() if not stack: return (-1, None) i -= 1 return (-1, stack)
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https://github.com/tzutalin/dlib-android/blob/989627cb7fe81cd1d41d73434b0e91ce1dd2683f/tools/lint/cpplint.py#L1524-L1595
yun-liu/RCF
91bfb054ad04187dbbe21e539e165ad9bd3ff00b
scripts/cpp_lint.py
python
FileInfo.RepositoryName
(self)
return fullname
FullName after removing the local path to the repository. If we have a real absolute path name here we can try to do something smart: detecting the root of the checkout and truncating /path/to/checkout from the name so that we get header guards that don't include things like "C:\Documents and Settings\..." or "/home/username/..." in them and thus people on different computers who have checked the source out to different locations won't see bogus errors.
FullName after removing the local path to the repository.
[ "FullName", "after", "removing", "the", "local", "path", "to", "the", "repository", "." ]
def RepositoryName(self): """FullName after removing the local path to the repository. If we have a real absolute path name here we can try to do something smart: detecting the root of the checkout and truncating /path/to/checkout from the name so that we get header guards that don't include things like "C:\Documents and Settings\..." or "/home/username/..." in them and thus people on different computers who have checked the source out to different locations won't see bogus errors. """ fullname = self.FullName() if os.path.exists(fullname): project_dir = os.path.dirname(fullname) if os.path.exists(os.path.join(project_dir, ".svn")): # If there's a .svn file in the current directory, we recursively look # up the directory tree for the top of the SVN checkout root_dir = project_dir one_up_dir = os.path.dirname(root_dir) while os.path.exists(os.path.join(one_up_dir, ".svn")): root_dir = os.path.dirname(root_dir) one_up_dir = os.path.dirname(one_up_dir) prefix = os.path.commonprefix([root_dir, project_dir]) return fullname[len(prefix) + 1:] # Not SVN <= 1.6? Try to find a git, hg, or svn top level directory by # searching up from the current path. root_dir = os.path.dirname(fullname) while (root_dir != os.path.dirname(root_dir) and not os.path.exists(os.path.join(root_dir, ".git")) and not os.path.exists(os.path.join(root_dir, ".hg")) and not os.path.exists(os.path.join(root_dir, ".svn"))): root_dir = os.path.dirname(root_dir) if (os.path.exists(os.path.join(root_dir, ".git")) or os.path.exists(os.path.join(root_dir, ".hg")) or os.path.exists(os.path.join(root_dir, ".svn"))): prefix = os.path.commonprefix([root_dir, project_dir]) return fullname[len(prefix) + 1:] # Don't know what to do; header guard warnings may be wrong... return fullname
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https://github.com/yun-liu/RCF/blob/91bfb054ad04187dbbe21e539e165ad9bd3ff00b/scripts/cpp_lint.py#L885-L928
lammps/lammps
b75c3065430a75b1b5543a10e10f46d9b4c91913
tools/amber2lmp/amber2lammps.py
python
Amber.Read_TOP
(self, Basename)
Read the Amber parameter/topology (.top) file
Read the Amber parameter/topology (.top) file
[ "Read", "the", "Amber", "parameter", "/", "topology", "(", ".", "top", ")", "file" ]
def Read_TOP(self, Basename): 'Read the Amber parameter/topology (.top) file' Item_list = self.Read_data(Basename + '.top') if Item_list == None: return elif len(Item_list) < 31: print '(error: File too short!)' return # Parse the data if self.__dict__.has_key('ITITL'): if Pop(Item_list,0) != self.ITITL: print '(warning: ITITL differs!)' else: self.ITITL = Pop(Item_list,0) print self.ITITL # Printing Self Title if self.__dict__.has_key('NATOM'): if eval(Pop(Item_list,0)) != self.NATOM: print '(error: NATOM differs!)' return else: self.NATOM = eval(Pop(Item_list,0)) print self.NATOM # Printing total number of atoms just to make sure that thing are going right self.NTYPES = eval(Pop(Item_list,0)) self.NBONH = eval(Pop(Item_list,0)) self.MBONA = eval(Pop(Item_list,0)) self.NTHETH = eval(Pop(Item_list,0)) self.MTHETA = eval(Pop(Item_list,0)) self.NPHIH = eval(Pop(Item_list,0)) self.MPHIA = eval(Pop(Item_list,0)) self.NHPARM = eval(Pop(Item_list,0)) self.NPARM = eval(Pop(Item_list,0)) self.NEXT = eval(Pop(Item_list,0)) self.NRES = eval(Pop(Item_list,0)) self.NBONA = eval(Pop(Item_list,0)) self.NTHETA = eval(Pop(Item_list,0)) self.NPHIA = eval(Pop(Item_list,0)) self.NUMBND = eval(Pop(Item_list,0)) self.NUMANG = eval(Pop(Item_list,0)) self.NPTRA = eval(Pop(Item_list,0)) self.NATYP = eval(Pop(Item_list,0)) self.NPHB = eval(Pop(Item_list,0)) self.IFPERT = eval(Pop(Item_list,0)) self.NBPER = eval(Pop(Item_list,0)) self.NGPER = eval(Pop(Item_list,0)) self.NDPER = eval(Pop(Item_list,0)) self.MBPER = eval(Pop(Item_list,0)) self.MGPER = eval(Pop(Item_list,0)) self.MDPER = eval(Pop(Item_list,0)) self.IFBOX = eval(Pop(Item_list,0)) self.NMXRS = eval(Pop(Item_list,0)) self.IFCAP = eval(Pop(Item_list,0)) #.................................................... if len(Item_list) < 5 * self.NATOM + self.NTYPES**2 + \ 2*(self.NRES + self.NUMBND + self.NUMANG) + \ 3*self.NPTRA + self.NATYP: print '(error: File too short!)' return -1 self.IGRAPH = [] Pop(Item_list,0) # A little kludge is needed here, since the IGRAPH strings are # not separated by spaces if 4 characters in length. for i in range(self.NATOM): if len(Item_list[0]) > 4: Item_list.insert(1, Item_list[0][4:]) Item_list.insert(1, Item_list[0][0:4]) del Item_list[0] self.IGRAPH.append(Pop(Item_list,0)) # Vikas' Modification : In the following section, I am printing out each quantity which is currently being read from the topology file. print 'Reading Charges...' self.CHRG = [] for i in range(self.NATOM): self.CHRG.append(eval(Pop(Item_list,0))) print 'Reading Atomic Number...' self.ANUMBER = [] for i in range(self.NATOM): self.ANUMBER.append(eval(Pop(Item_list,0))) print 'Reading Atomic Masses...' self.AMASS = [] for i in range(self.NATOM): self.AMASS.append(eval(Pop(Item_list,0))) print 'Reading Atom Types...' self.IAC = [] for i in range(self.NATOM): self.IAC.append(eval(Pop(Item_list,0))) print 'Reading Excluded Atoms...' self.NUMEX = [] for i in range(self.NATOM): self.NUMEX.append(eval(Pop(Item_list,0))) print 'Reading Non-bonded Parameter Index...' self.ICO = [] for i in range(self.NTYPES**2): self.ICO.append(eval(Pop(Item_list,0))) print 'Reading Residue Labels...' self.LABRES = [] for i in range(self.NRES): self.LABRES.append(Pop(Item_list,0)) print 'Reading Residues Starting Pointers...' self.IPRES = [] for i in range(self.NRES): self.IPRES.append(eval(Pop(Item_list,0))) print 'Reading Bond Force Constants...' self.RK = [] for i in range(self.NUMBND): self.RK.append(eval(Pop(Item_list,0))) print 'Reading Equilibrium Bond Values...' self.REQ = [] for i in range(self.NUMBND): self.REQ.append(eval(Pop(Item_list,0))) print 'Reading Angle Force Constants...' self.TK = [] for i in range(self.NUMANG): self.TK.append(eval(Pop(Item_list,0))) print 'Reading Equilibrium Angle Values...' self.TEQ = [] for i in range(self.NUMANG): self.TEQ.append(eval(Pop(Item_list,0))) print 'Reading Dihedral Force Constants...' self.PK = [] for i in range(self.NPTRA): self.PK.append(eval(Pop(Item_list,0))) print 'Reading Dihedral Periodicity...' self.PN = [] for i in range(self.NPTRA): self.PN.append(eval(Pop(Item_list,0))) print 'Reading Dihedral Phase...' self.PHASE = [] for i in range(self.NPTRA): self.PHASE.append(eval(Pop(Item_list,0))) print 'Reading 1-4 Electrostatic Scaling Factor...' self.SCEEFAC = [] for i in range(self.NPTRA): self.SCEEFAC.append(eval(Pop(Item_list,0))) print 'Reading 1-4 Van der Waals Scaling Factor...' self.SCNBFAC = [] for i in range(self.NPTRA): self.SCNBFAC.append(eval(Pop(Item_list,0))) print 'Reading Solty...' #I think this is currently not used in AMBER. Check it out, though self.SOLTY = [] for i in range(self.NATYP): self.SOLTY.append(eval(Pop(Item_list,0))) #.................................................... if len(Item_list) < 2 * self.NTYPES * (self.NTYPES + 1) / 2: print '(error: File too short!)' return -1 print 'Reading LJ A Coefficient...' self.CN1 = [] for i in range(self.NTYPES * (self.NTYPES + 1) / 2): self.CN1.append(eval(Pop(Item_list,0))) print 'Reading LJ B Coefficient...' self.CN2 = [] for i in range(self.NTYPES * (self.NTYPES + 1) / 2): self.CN2.append(eval(Pop(Item_list,0))) #.................................................... if len(Item_list) < 3 * (self.NBONH + self.NBONA) + \ 4 * (self.NTHETH + self.NTHETA) + 5 * (self.NPHIH + self.NPHIA): print '(error: File too short!)' return -1 print 'Reading Bonds which include hydrogen...' self.IBH = [] self.JBH = [] self.ICBH = [] for i in range(self.NBONH): self.IBH.append(eval(Pop(Item_list,0))) self.JBH.append(eval(Pop(Item_list,0))) self.ICBH.append(eval(Pop(Item_list,0))) print 'Reading Bonds which dont include hydrogen...' self.IB = [] self.JB = [] self.ICB = [] for i in range(self.NBONA): self.IB.append(eval(Pop(Item_list,0))) self.JB.append(eval(Pop(Item_list,0))) self.ICB.append(eval(Pop(Item_list,0))) print 'Reading Angles which include hydrogen...' self.ITH = [] self.JTH = [] self.KTH = [] self.ICTH = [] for i in range(self.NTHETH): self.ITH.append(eval(Pop(Item_list,0))) self.JTH.append(eval(Pop(Item_list,0))) self.KTH.append(eval(Pop(Item_list,0))) self.ICTH.append(eval(Pop(Item_list,0))) print 'Reading Angles which dont include hydrogen...' self.IT = [] self.JT = [] self.KT = [] self.ICT = [] for i in range(self.NTHETA): self.IT.append(eval(Pop(Item_list,0))) self.JT.append(eval(Pop(Item_list,0))) self.KT.append(eval(Pop(Item_list,0))) self.ICT.append(eval(Pop(Item_list,0))) print 'Reading Dihedrals which include hydrogen...' self.IPH = [] self.JPH = [] self.KPH = [] self.LPH = [] self.ICPH = [] for i in range(self.NPHIH): self.IPH.append(eval(Pop(Item_list,0))) self.JPH.append(eval(Pop(Item_list,0))) self.KPH.append(eval(Pop(Item_list,0))) self.LPH.append(eval(Pop(Item_list,0))) self.ICPH.append(eval(Pop(Item_list,0))) print 'Reading Dihedrals which dont include hydrogen...' self.IP = [] self.JP = [] self.KP = [] self.LP = [] self.ICP = [] for i in range(self.NPHIA): self.IP.append(eval(Pop(Item_list,0))) self.JP.append(eval(Pop(Item_list,0))) self.KP.append(eval(Pop(Item_list,0))) self.LP.append(eval(Pop(Item_list,0))) self.ICP.append(eval(Pop(Item_list,0))) #.................................................... if len(Item_list) < self.NEXT + 3 * self.NPHB + 4 * self.NATOM: print '(error: File too short!)' return -1 print 'Reading Excluded Atom List...' self.NATEX = [] for i in range(self.NEXT): self.NATEX.append(eval(Pop(Item_list,0))) print 'Reading H-Bond A Coefficient, corresponding to r**12 term for all possible types...' self.ASOL = [] for i in range(self.NPHB): self.ASOL.append(eval(Pop(Item_list,0))) print 'Reading H-Bond B Coefficient, corresponding to r**10 term for all possible types...' self.BSOL = [] for i in range(self.NPHB): self.BSOL.append(eval(Pop(Item_list,0))) print 'Reading H-Bond Cut...' # I think it is not being used nowadays self.HBCUT = [] for i in range(self.NPHB): self.HBCUT.append(eval(Pop(Item_list,0))) print 'Reading Amber Atom Types for each atom...' self.ISYMBL = [] for i in range(self.NATOM): self.ISYMBL.append(Pop(Item_list,0)) print 'Reading Tree Chain Classification...' self.ITREE = [] for i in range(self.NATOM): self.ITREE.append(Pop(Item_list,0)) print 'Reading Join Array: Tree joining information' # Currently unused in Sander, an AMBER module self.JOIN = [] for i in range(self.NATOM): self.JOIN.append(eval(Pop(Item_list,0))) print 'Reading IRotate...' # Currently unused in Sander and Gibbs self.IROTAT = [] for i in range(self.NATOM): self.IROTAT.append(eval(Pop(Item_list,0))) #.................................................... if self.IFBOX > 0: if len(Item_list) < 3: print '(error: File too short!)' return -1 print 'Reading final residue which is part of solute...' self.IPTRES = eval(Pop(Item_list,0)) print 'Reading total number of molecules...' self.NSPM = eval(Pop(Item_list,0)) print 'Reading first solvent moleule index...' self.NSPSOL = eval(Pop(Item_list,0)) if len(Item_list) < self.NSPM + 4: print '(error: File too short!)' return -1 print 'Reading atom per molecule...' self.NSP = [] for i in range(self.NSPM): self.NSP.append(eval(Pop(Item_list,0))) self.BETA = eval(Pop(Item_list,0)) print 'Reading Box Dimensions...' if self.__dict__.has_key('BOX'): BOX = [] for i in range(3): BOX.append(eval(Pop(Item_list,0))) for i in range(3): if BOX[i] != self.BOX[i]: print '(warning: BOX differs!)', break del BOX else: self.BOX = [] for i in range(3): self.BOX.append(eval(Pop(Item_list,0))) #.................................................... if self.IFCAP > 0: if len(Item_list) < 5: print '(error: File too short!)' return -1 print 'Reading ICAP variables::: For details, refer to online AMBER format manual' self.NATCAP = eval(Pop(Item_list,0)) self.CUTCAP = eval(Pop(Item_list,0)) self.XCAP = eval(Pop(Item_list,0)) self.YCAP = eval(Pop(Item_list,0)) self.ZCAP = eval(Pop(Item_list,0)) #.................................................... if self.IFPERT > 0: if len(Item_list) < 4 * self.NBPER + 5 * self.NGPER + \ 6 * self.NDPER + self.NRES + 6 * self.NATOM: print '(error: File too short!)' return -1 print 'Reading perturb variables, 1. Bond, 2. Angles, 3. Dihedrals, etc etc.::: For details, refer to online AMBER format manual' self.IBPER = [] self.JBPER = [] for i in range(self.NBPER): self.IBPER.append(eval(Pop(Item_list,0))) self.JBPER.append(eval(Pop(Item_list,0))) self.ICBPER = [] for i in range(2 * self.NBPER): self.ICBPER.append(eval(Pop(Item_list,0))) self.ITPER = [] self.JTPER = [] self.KTPER = [] for i in range(self.NGPER): self.ITPER.append(eval(Pop(Item_list,0))) self.JTPER.append(eval(Pop(Item_list,0))) self.KTPER.append(eval(Pop(Item_list,0))) self.ICTPER = [] for i in range(2 * self.NGPER): self.ICTPER.append(eval(Pop(Item_list,0))) self.IPPER = [] self.JPPER = [] self.KPPER = [] self.LPPER = [] for i in range(self.NDPER): self.IPPER.append(eval(Pop(Item_list,0))) self.JPPER.append(eval(Pop(Item_list,0))) self.KPPER.append(eval(Pop(Item_list,0))) self.LPPER.append(eval(Pop(Item_list,0))) self.ICPPER = [] for i in range(2 * self.NDPER): self.ICPPER.append(eval(Pop(Item_list,0))) LABRES = [] for i in range(self.NRES): LABRES.append(Pop(Item_list,0)) for i in range(self.NRES): if LABRES[i] != self.LABRES[i]: print '(warning: BOX differs!)', break self.IGRPER = [] for i in range(self.NATOM): self.IGRPER.append(eval(Pop(Item_list,0))) self.ISMPER = [] for i in range(self.NATOM): self.ISMPER.append(eval(Pop(Item_list,0))) self.ALMPER = [] for i in range(self.NATOM): self.ALMPER.append(eval(Pop(Item_list,0))) self.IAPER = [] for i in range(self.NATOM): self.IAPER.append(eval(Pop(Item_list,0))) self.IACPER = [] for i in range(self.NATOM): self.IACPER.append(eval(Pop(Item_list,0))) self.CGPER = [] for i in range(self.NATOM): self.CGPER.append(eval(Pop(Item_list,0))) #.................................................... self.IPOL = 0 if self.IPOL == 1: if len(Item_list) < self.NATOM: print '(error: File too short!)' return -1 print 'Reading Polarizability Data. For details, refer to online AMBER format manual' self.ATPOL = [] for i in range(self.NATOM): self.ATPOL.append(eval(Pop(Item_list,0))) if self.IFPERT == 1: if len(Item_list) < self.NATOM: print '(error: File too short!)' return -1 self.ATPOL1 = [] for i in range(self.NATOM): self.ATPOL1.append(eval(Pop(Item_list,0))) #.................................................... if len(Item_list): print '(warning: File too large!)', print 'done.' self.TOP_is_read = 1
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"Item_list", ",", "0", ")", ")", "self", ".", "MBPER", "=", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", "self", ".", "MGPER", "=", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", "self", ".", "MDPER", "=", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", "self", ".", "IFBOX", "=", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", "self", ".", "NMXRS", "=", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", "self", ".", "IFCAP", "=", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", "#....................................................", "if", "len", "(", "Item_list", ")", "<", "5", "*", "self", ".", "NATOM", "+", "self", ".", "NTYPES", "**", "2", "+", "2", "*", "(", "self", ".", "NRES", "+", "self", ".", "NUMBND", "+", "self", ".", "NUMANG", ")", "+", "3", "*", "self", ".", "NPTRA", "+", "self", ".", "NATYP", ":", "print", "'(error: File too short!)'", "return", "-", "1", "self", ".", "IGRAPH", "=", "[", "]", "Pop", "(", "Item_list", ",", "0", ")", "# A little kludge is needed here, since the IGRAPH strings are", "# not separated by spaces if 4 characters in length.", "for", "i", "in", "range", "(", "self", ".", "NATOM", ")", ":", "if", "len", "(", "Item_list", "[", "0", "]", ")", ">", "4", ":", "Item_list", ".", "insert", "(", "1", ",", "Item_list", "[", "0", "]", "[", "4", ":", "]", ")", "Item_list", ".", "insert", "(", "1", ",", "Item_list", "[", "0", "]", "[", "0", ":", "4", "]", ")", "del", "Item_list", "[", "0", "]", "self", ".", "IGRAPH", ".", "append", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", "# Vikas' Modification : In the following section, I am printing out each quantity which is currently being read from the topology file.", "print", "'Reading Charges...'", "self", ".", "CHRG", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NATOM", ")", ":", "self", ".", "CHRG", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "print", "'Reading Atomic Number...'", "self", ".", "ANUMBER", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NATOM", ")", ":", "self", ".", "ANUMBER", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "print", "'Reading Atomic Masses...'", "self", ".", "AMASS", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NATOM", ")", ":", "self", ".", "AMASS", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "print", "'Reading Atom Types...'", "self", ".", "IAC", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NATOM", ")", ":", "self", ".", "IAC", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "print", "'Reading Excluded Atoms...'", "self", ".", "NUMEX", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NATOM", ")", ":", "self", ".", "NUMEX", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "print", "'Reading Non-bonded Parameter Index...'", "self", ".", "ICO", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NTYPES", "**", "2", ")", ":", "self", ".", "ICO", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "print", "'Reading Residue Labels...'", "self", ".", "LABRES", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NRES", ")", ":", "self", ".", "LABRES", ".", "append", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", "print", "'Reading Residues Starting Pointers...'", "self", ".", "IPRES", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NRES", ")", ":", "self", ".", "IPRES", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "print", "'Reading Bond Force Constants...'", "self", ".", "RK", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NUMBND", ")", ":", "self", ".", "RK", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "print", "'Reading Equilibrium Bond Values...'", "self", ".", "REQ", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NUMBND", ")", ":", "self", ".", "REQ", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "print", "'Reading Angle Force Constants...'", "self", ".", "TK", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NUMANG", ")", ":", "self", ".", "TK", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "print", "'Reading Equilibrium Angle Values...'", "self", ".", "TEQ", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NUMANG", ")", ":", "self", ".", "TEQ", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "print", "'Reading Dihedral Force Constants...'", "self", ".", "PK", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NPTRA", ")", ":", "self", ".", "PK", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "print", "'Reading Dihedral Periodicity...'", "self", ".", "PN", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NPTRA", ")", ":", "self", ".", "PN", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "print", "'Reading Dihedral Phase...'", "self", ".", "PHASE", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NPTRA", ")", ":", "self", ".", "PHASE", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "print", "'Reading 1-4 Electrostatic Scaling Factor...'", "self", ".", "SCEEFAC", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NPTRA", ")", ":", "self", ".", "SCEEFAC", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "print", "'Reading 1-4 Van der Waals Scaling Factor...'", "self", ".", "SCNBFAC", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NPTRA", ")", ":", "self", ".", "SCNBFAC", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "print", "'Reading Solty...'", "#I think this is currently not used in AMBER. Check it out, though", "self", ".", "SOLTY", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NATYP", ")", ":", "self", ".", "SOLTY", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "#....................................................", "if", "len", "(", "Item_list", ")", "<", "2", "*", "self", ".", "NTYPES", "*", "(", "self", ".", "NTYPES", "+", "1", ")", "/", "2", ":", "print", "'(error: File too short!)'", "return", "-", "1", "print", "'Reading LJ A Coefficient...'", "self", ".", "CN1", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NTYPES", "*", "(", "self", ".", "NTYPES", "+", "1", ")", "/", "2", ")", ":", "self", ".", "CN1", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "print", "'Reading LJ B Coefficient...'", "self", ".", "CN2", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NTYPES", "*", "(", "self", ".", "NTYPES", "+", "1", ")", "/", "2", ")", ":", "self", ".", "CN2", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "#....................................................", "if", "len", "(", "Item_list", ")", "<", "3", "*", "(", "self", ".", "NBONH", "+", "self", ".", "NBONA", ")", "+", "4", "*", "(", "self", ".", "NTHETH", "+", "self", ".", "NTHETA", ")", "+", "5", "*", "(", "self", ".", "NPHIH", "+", "self", ".", "NPHIA", ")", ":", "print", "'(error: File too short!)'", "return", "-", "1", "print", "'Reading Bonds which include hydrogen...'", "self", ".", "IBH", "=", "[", "]", "self", ".", "JBH", "=", "[", "]", "self", ".", "ICBH", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NBONH", ")", ":", "self", ".", "IBH", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "self", ".", "JBH", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "self", ".", "ICBH", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "print", "'Reading Bonds which dont include hydrogen...'", "self", ".", "IB", "=", "[", "]", "self", ".", "JB", "=", "[", "]", "self", ".", "ICB", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NBONA", ")", ":", "self", ".", "IB", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "self", ".", "JB", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "self", ".", "ICB", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "print", "'Reading Angles which include hydrogen...'", "self", ".", "ITH", "=", "[", "]", "self", ".", "JTH", "=", "[", "]", "self", ".", "KTH", "=", "[", "]", "self", ".", "ICTH", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NTHETH", ")", ":", "self", ".", "ITH", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "self", ".", "JTH", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "self", ".", "KTH", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "self", ".", "ICTH", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "print", "'Reading Angles which dont include hydrogen...'", "self", ".", "IT", "=", "[", "]", "self", ".", "JT", "=", "[", "]", "self", ".", "KT", "=", "[", "]", "self", ".", "ICT", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NTHETA", ")", ":", "self", ".", "IT", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "self", ".", "JT", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "self", ".", "KT", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "self", ".", "ICT", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "print", "'Reading Dihedrals which include hydrogen...'", "self", ".", "IPH", "=", "[", "]", "self", ".", "JPH", "=", "[", "]", "self", ".", "KPH", "=", "[", "]", "self", ".", "LPH", "=", "[", "]", "self", ".", "ICPH", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NPHIH", ")", ":", "self", ".", "IPH", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "self", ".", "JPH", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "self", ".", "KPH", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "self", ".", "LPH", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "self", ".", "ICPH", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "print", "'Reading Dihedrals which dont include hydrogen...'", "self", ".", "IP", "=", "[", "]", "self", ".", "JP", "=", "[", "]", "self", ".", "KP", "=", "[", "]", "self", ".", "LP", "=", "[", "]", "self", ".", "ICP", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NPHIA", ")", ":", "self", ".", "IP", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "self", ".", "JP", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "self", ".", "KP", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "self", ".", "LP", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "self", ".", "ICP", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "#....................................................", "if", "len", "(", "Item_list", ")", "<", "self", ".", "NEXT", "+", "3", "*", "self", ".", "NPHB", "+", "4", "*", "self", ".", "NATOM", ":", "print", "'(error: File too short!)'", "return", "-", "1", "print", "'Reading Excluded Atom List...'", "self", ".", "NATEX", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NEXT", ")", ":", "self", ".", "NATEX", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "print", "'Reading H-Bond A Coefficient, corresponding to r**12 term for all possible types...'", "self", ".", "ASOL", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NPHB", ")", ":", "self", ".", "ASOL", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "print", "'Reading H-Bond B Coefficient, corresponding to r**10 term for all possible types...'", "self", ".", "BSOL", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NPHB", ")", ":", "self", ".", "BSOL", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "print", "'Reading H-Bond Cut...'", "# I think it is not being used nowadays", "self", ".", "HBCUT", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NPHB", ")", ":", "self", ".", "HBCUT", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "print", "'Reading Amber Atom Types for each atom...'", "self", ".", "ISYMBL", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NATOM", ")", ":", "self", ".", "ISYMBL", ".", "append", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", "print", "'Reading Tree Chain Classification...'", "self", ".", "ITREE", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NATOM", ")", ":", "self", ".", "ITREE", ".", "append", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", "print", "'Reading Join Array: Tree joining information'", "# Currently unused in Sander, an AMBER module", "self", ".", "JOIN", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NATOM", ")", ":", "self", ".", "JOIN", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "print", "'Reading IRotate...'", "# Currently unused in Sander and Gibbs", "self", ".", "IROTAT", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NATOM", ")", ":", "self", ".", "IROTAT", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "#....................................................", "if", "self", ".", "IFBOX", ">", "0", ":", "if", "len", "(", "Item_list", ")", "<", "3", ":", "print", "'(error: File too short!)'", "return", "-", "1", "print", "'Reading final residue which is part of solute...'", "self", ".", "IPTRES", "=", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", "print", "'Reading total number of molecules...'", "self", ".", "NSPM", "=", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", "print", "'Reading first solvent moleule index...'", "self", ".", "NSPSOL", "=", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", "if", "len", "(", "Item_list", ")", "<", "self", ".", "NSPM", "+", "4", ":", "print", "'(error: File too short!)'", "return", "-", "1", "print", "'Reading atom per molecule...'", "self", ".", "NSP", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NSPM", ")", ":", "self", ".", "NSP", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "self", ".", "BETA", "=", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", "print", "'Reading Box Dimensions...'", "if", "self", ".", "__dict__", ".", "has_key", "(", "'BOX'", ")", ":", "BOX", "=", "[", "]", "for", "i", "in", "range", "(", "3", ")", ":", "BOX", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "for", "i", "in", "range", "(", "3", ")", ":", "if", "BOX", "[", "i", "]", "!=", "self", ".", "BOX", "[", "i", "]", ":", "print", "'(warning: BOX differs!)'", ",", "break", "del", "BOX", "else", ":", "self", ".", "BOX", "=", "[", "]", "for", "i", "in", "range", "(", "3", ")", ":", "self", ".", "BOX", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "#....................................................", "if", "self", ".", "IFCAP", ">", "0", ":", "if", "len", "(", "Item_list", ")", "<", "5", ":", "print", "'(error: File too short!)'", "return", "-", "1", "print", "'Reading ICAP variables::: For details, refer to online AMBER format manual'", "self", ".", "NATCAP", "=", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", "self", ".", "CUTCAP", "=", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", "self", ".", "XCAP", "=", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", "self", ".", "YCAP", "=", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", "self", ".", "ZCAP", "=", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", "#....................................................", "if", "self", ".", "IFPERT", ">", "0", ":", "if", "len", "(", "Item_list", ")", "<", "4", "*", "self", ".", "NBPER", "+", "5", "*", "self", ".", "NGPER", "+", "6", "*", "self", ".", "NDPER", "+", "self", ".", "NRES", "+", "6", "*", "self", ".", "NATOM", ":", "print", "'(error: File too short!)'", "return", "-", "1", "print", "'Reading perturb variables, 1. Bond, 2. Angles, 3. Dihedrals, etc etc.::: For details, refer to online AMBER format manual'", "self", ".", "IBPER", "=", "[", "]", "self", ".", "JBPER", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NBPER", ")", ":", "self", ".", "IBPER", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "self", ".", "JBPER", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "self", ".", "ICBPER", "=", "[", "]", "for", "i", "in", "range", "(", "2", "*", "self", ".", "NBPER", ")", ":", "self", ".", "ICBPER", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "self", ".", "ITPER", "=", "[", "]", "self", ".", "JTPER", "=", "[", "]", "self", ".", "KTPER", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NGPER", ")", ":", "self", ".", "ITPER", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "self", ".", "JTPER", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "self", ".", "KTPER", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "self", ".", "ICTPER", "=", "[", "]", "for", "i", "in", "range", "(", "2", "*", "self", ".", "NGPER", ")", ":", "self", ".", "ICTPER", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "self", ".", "IPPER", "=", "[", "]", "self", ".", "JPPER", "=", "[", "]", "self", ".", "KPPER", "=", "[", "]", "self", ".", "LPPER", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NDPER", ")", ":", "self", ".", "IPPER", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "self", ".", "JPPER", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "self", ".", "KPPER", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "self", ".", "LPPER", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "self", ".", "ICPPER", "=", "[", "]", "for", "i", "in", "range", "(", "2", "*", "self", ".", "NDPER", ")", ":", "self", ".", "ICPPER", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "LABRES", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NRES", ")", ":", "LABRES", ".", "append", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", "for", "i", "in", "range", "(", "self", ".", "NRES", ")", ":", "if", "LABRES", "[", "i", "]", "!=", "self", ".", "LABRES", "[", "i", "]", ":", "print", "'(warning: BOX differs!)'", ",", "break", "self", ".", "IGRPER", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NATOM", ")", ":", "self", ".", "IGRPER", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "self", ".", "ISMPER", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NATOM", ")", ":", "self", ".", "ISMPER", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "self", ".", "ALMPER", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NATOM", ")", ":", "self", ".", "ALMPER", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "self", ".", "IAPER", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NATOM", ")", ":", "self", ".", "IAPER", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "self", ".", "IACPER", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NATOM", ")", ":", "self", ".", "IACPER", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "self", ".", "CGPER", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NATOM", ")", ":", "self", ".", "CGPER", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "#....................................................", "self", ".", "IPOL", "=", "0", "if", "self", ".", "IPOL", "==", "1", ":", "if", "len", "(", "Item_list", ")", "<", "self", ".", "NATOM", ":", "print", "'(error: File too short!)'", "return", "-", "1", "print", "'Reading Polarizability Data. For details, refer to online AMBER format manual'", "self", ".", "ATPOL", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NATOM", ")", ":", "self", ".", "ATPOL", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "if", "self", ".", "IFPERT", "==", "1", ":", "if", "len", "(", "Item_list", ")", "<", "self", ".", "NATOM", ":", "print", "'(error: File too short!)'", "return", "-", "1", "self", ".", "ATPOL1", "=", "[", "]", "for", "i", "in", "range", "(", "self", ".", "NATOM", ")", ":", "self", ".", "ATPOL1", ".", "append", "(", "eval", "(", "Pop", "(", "Item_list", ",", "0", ")", ")", ")", "#....................................................", "if", "len", "(", "Item_list", ")", ":", "print", "'(warning: File too large!)'", ",", "print", "'done.'", "self", ".", "TOP_is_read", "=", "1" ]
https://github.com/lammps/lammps/blob/b75c3065430a75b1b5543a10e10f46d9b4c91913/tools/amber2lmp/amber2lammps.py#L476-L933
ouster-lidar/ouster_example
13ea8e8b8a4951fb630dbc9108666995c8443bf6
python/src/ouster/sdk/examples/pcap.py
python
main
()
Pcap examples runner.
Pcap examples runner.
[ "Pcap", "examples", "runner", "." ]
def main(): """Pcap examples runner.""" examples = { "open3d-one-scan": pcap_3d_one_scan, "plot-xyz-points": pcap_display_xyz_points, "pcap-to-csv": pcap_to_csv, "pcap-to-las": pcap_to_las, "pcap-to-pcd": pcap_to_pcd, "query-scan": pcap_query_scan, "read-packets": pcap_read_packets, } description = "Ouster Python SDK Pcap examples. The EXAMPLE must be one of:\n " + str.join( '\n ', examples.keys()) parser = argparse.ArgumentParser( description=description, formatter_class=argparse.RawTextHelpFormatter) parser.add_argument('pcap_path', metavar='PCAP', help='path to pcap file') parser.add_argument('metadata_path', metavar='METADATA', help='path to metadata json') parser.add_argument('example', metavar='EXAMPLE', choices=examples.keys(), help='name of the example to run') parser.add_argument('--scan-num', type=int, default=1, help='index of scan to use') args = parser.parse_args() try: example = examples[args.example] except KeyError: print(f"No such example: {args.example}") print(description) exit(1) if not args.metadata_path or not os.path.exists(args.metadata_path): print(f"Metadata file does not exist: {args.metadata_path}") exit(1) print(f'example: {args.example}') with open(args.metadata_path, 'r') as f: metadata = client.SensorInfo(f.read()) source = pcap.Pcap(args.pcap_path, metadata) with closing(source): example(source, metadata, args.scan_num)
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https://github.com/ouster-lidar/ouster_example/blob/13ea8e8b8a4951fb630dbc9108666995c8443bf6/python/src/ouster/sdk/examples/pcap.py#L308-L358
toggl-open-source/toggldesktop
91865205885531cc8fd9e8d613dad49d625d56e7
third_party/cpplint/cpplint.py
python
CheckForNonStandardConstructs
(filename, clean_lines, linenum, nesting_state, error)
r"""Logs an error if we see certain non-ANSI constructs ignored by gcc-2. Complain about several constructs which gcc-2 accepts, but which are not standard C++. Warning about these in lint is one way to ease the transition to new compilers. - put storage class first (e.g. "static const" instead of "const static"). - "%lld" instead of %qd" in printf-type functions. - "%1$d" is non-standard in printf-type functions. - "\%" is an undefined character escape sequence. - text after #endif is not allowed. - invalid inner-style forward declaration. - >? and <? operators, and their >?= and <?= cousins. Additionally, check for constructor/destructor style violations and reference members, as it is very convenient to do so while checking for gcc-2 compliance. Args: filename: The name of the current file. clean_lines: A CleansedLines instance containing the file. linenum: The number of the line to check. nesting_state: A NestingState instance which maintains information about the current stack of nested blocks being parsed. error: A callable to which errors are reported, which takes 4 arguments: filename, line number, error level, and message
r"""Logs an error if we see certain non-ANSI constructs ignored by gcc-2.
[ "r", "Logs", "an", "error", "if", "we", "see", "certain", "non", "-", "ANSI", "constructs", "ignored", "by", "gcc", "-", "2", "." ]
def CheckForNonStandardConstructs(filename, clean_lines, linenum, nesting_state, error): r"""Logs an error if we see certain non-ANSI constructs ignored by gcc-2. Complain about several constructs which gcc-2 accepts, but which are not standard C++. Warning about these in lint is one way to ease the transition to new compilers. - put storage class first (e.g. "static const" instead of "const static"). - "%lld" instead of %qd" in printf-type functions. - "%1$d" is non-standard in printf-type functions. - "\%" is an undefined character escape sequence. - text after #endif is not allowed. - invalid inner-style forward declaration. - >? and <? operators, and their >?= and <?= cousins. Additionally, check for constructor/destructor style violations and reference members, as it is very convenient to do so while checking for gcc-2 compliance. Args: filename: The name of the current file. clean_lines: A CleansedLines instance containing the file. linenum: The number of the line to check. nesting_state: A NestingState instance which maintains information about the current stack of nested blocks being parsed. error: A callable to which errors are reported, which takes 4 arguments: filename, line number, error level, and message """ # Remove comments from the line, but leave in strings for now. line = clean_lines.lines[linenum] if Search(r'printf\s*\(.*".*%[-+ ]?\d*q', line): error(filename, linenum, 'runtime/printf_format', 3, '%q in format strings is deprecated. Use %ll instead.') if Search(r'printf\s*\(.*".*%\d+\$', line): error(filename, linenum, 'runtime/printf_format', 2, '%N$ formats are unconventional. Try rewriting to avoid them.') # Remove escaped backslashes before looking for undefined escapes. line = line.replace('\\\\', '') if Search(r'("|\').*\\(%|\[|\(|{)', line): error(filename, linenum, 'build/printf_format', 3, '%, [, (, and { are undefined character escapes. Unescape them.') # For the rest, work with both comments and strings removed. line = clean_lines.elided[linenum] if Search(r'\b(const|volatile|void|char|short|int|long' r'|float|double|signed|unsigned' r'|schar|u?int8|u?int16|u?int32|u?int64)' r'\s+(register|static|extern|typedef)\b', line): error(filename, linenum, 'build/storage_class', 5, 'Storage class (static, extern, typedef, etc) should be first.') if Match(r'\s*#\s*endif\s*[^/\s]+', line): error(filename, linenum, 'build/endif_comment', 5, 'Uncommented text after #endif is non-standard. Use a comment.') if Match(r'\s*class\s+(\w+\s*::\s*)+\w+\s*;', line): error(filename, linenum, 'build/forward_decl', 5, 'Inner-style forward declarations are invalid. Remove this line.') if Search(r'(\w+|[+-]?\d+(\.\d*)?)\s*(<|>)\?=?\s*(\w+|[+-]?\d+)(\.\d*)?', line): error(filename, linenum, 'build/deprecated', 3, '>? and <? (max and min) operators are non-standard and deprecated.') if Search(r'^\s*const\s*string\s*&\s*\w+\s*;', line): # TODO(unknown): Could it be expanded safely to arbitrary references, # without triggering too many false positives? The first # attempt triggered 5 warnings for mostly benign code in the regtest, hence # the restriction. # Here's the original regexp, for the reference: # type_name = r'\w+((\s*::\s*\w+)|(\s*<\s*\w+?\s*>))?' # r'\s*const\s*' + type_name + '\s*&\s*\w+\s*;' error(filename, linenum, 'runtime/member_string_references', 2, 'const string& members are dangerous. It is much better to use ' 'alternatives, such as pointers or simple constants.') # Everything else in this function operates on class declarations. # Return early if the top of the nesting stack is not a class, or if # the class head is not completed yet. classinfo = nesting_state.InnermostClass() if not classinfo or not classinfo.seen_open_brace: return # The class may have been declared with namespace or classname qualifiers. # The constructor and destructor will not have those qualifiers. base_classname = classinfo.name.split('::')[-1] # Look for single-argument constructors that aren't marked explicit. # Technically a valid construct, but against style. Also look for # non-single-argument constructors which are also technically valid, but # strongly suggest something is wrong. explicit_constructor_match = Match( r'\s+(?:inline\s+)?(explicit\s+)?(?:inline\s+)?%s\s*' r'\(((?:[^()]|\([^()]*\))*)\)' % re.escape(base_classname), line) if explicit_constructor_match: is_marked_explicit = explicit_constructor_match.group(1) if not explicit_constructor_match.group(2): constructor_args = [] else: constructor_args = explicit_constructor_match.group(2).split(',') # collapse arguments so that commas in template parameter lists and function # argument parameter lists don't split arguments in two i = 0 while i < len(constructor_args): constructor_arg = constructor_args[i] while (constructor_arg.count('<') > constructor_arg.count('>') or constructor_arg.count('(') > constructor_arg.count(')')): constructor_arg += ',' + constructor_args[i + 1] del constructor_args[i + 1] constructor_args[i] = constructor_arg i += 1 defaulted_args = [arg for arg in constructor_args if '=' in arg] noarg_constructor = (not constructor_args or # empty arg list # 'void' arg specifier (len(constructor_args) == 1 and constructor_args[0].strip() == 'void')) onearg_constructor = ((len(constructor_args) == 1 and # exactly one arg not noarg_constructor) or # all but at most one arg defaulted (len(constructor_args) >= 1 and not noarg_constructor and len(defaulted_args) >= len(constructor_args) - 1)) initializer_list_constructor = bool( onearg_constructor and Search(r'\bstd\s*::\s*initializer_list\b', constructor_args[0])) copy_constructor = bool( onearg_constructor and Match(r'(const\s+)?%s(\s*<[^>]*>)?(\s+const)?\s*(?:<\w+>\s*)?&' % re.escape(base_classname), constructor_args[0].strip())) if (not is_marked_explicit and onearg_constructor and not initializer_list_constructor and not copy_constructor): if defaulted_args: error(filename, linenum, 'runtime/explicit', 5, 'Constructors callable with one argument ' 'should be marked explicit.') else: error(filename, linenum, 'runtime/explicit', 5, 'Single-parameter constructors should be marked explicit.') elif is_marked_explicit and not onearg_constructor: if noarg_constructor: error(filename, linenum, 'runtime/explicit', 5, 'Zero-parameter constructors should not be marked explicit.') else: error(filename, linenum, 'runtime/explicit', 0, 'Constructors that require multiple arguments ' 'should not be marked explicit.')
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Also look for", "# non-single-argument constructors which are also technically valid, but", "# strongly suggest something is wrong.", "explicit_constructor_match", "=", "Match", "(", "r'\\s+(?:inline\\s+)?(explicit\\s+)?(?:inline\\s+)?%s\\s*'", "r'\\(((?:[^()]|\\([^()]*\\))*)\\)'", "%", "re", ".", "escape", "(", "base_classname", ")", ",", "line", ")", "if", "explicit_constructor_match", ":", "is_marked_explicit", "=", "explicit_constructor_match", ".", "group", "(", "1", ")", "if", "not", "explicit_constructor_match", ".", "group", "(", "2", ")", ":", "constructor_args", "=", "[", "]", "else", ":", "constructor_args", "=", "explicit_constructor_match", ".", "group", "(", "2", ")", ".", "split", "(", "','", ")", "# collapse arguments so that commas in template parameter lists and function", "# argument parameter lists don't split arguments in two", "i", "=", "0", "while", "i", "<", "len", "(", "constructor_args", ")", ":", "constructor_arg", "=", "constructor_args", "[", "i", "]", "while", "(", "constructor_arg", ".", "count", "(", "'<'", ")", ">", "constructor_arg", ".", "count", "(", "'>'", ")", "or", "constructor_arg", ".", "count", "(", "'('", ")", ">", "constructor_arg", ".", "count", "(", "')'", ")", ")", ":", "constructor_arg", "+=", "','", "+", "constructor_args", "[", "i", "+", "1", "]", "del", "constructor_args", "[", "i", "+", "1", "]", "constructor_args", "[", "i", "]", "=", "constructor_arg", "i", "+=", "1", "defaulted_args", "=", "[", "arg", "for", "arg", "in", "constructor_args", "if", "'='", "in", "arg", "]", "noarg_constructor", "=", "(", "not", "constructor_args", "or", "# empty arg list", "# 'void' arg specifier", "(", "len", "(", "constructor_args", ")", "==", "1", "and", "constructor_args", "[", "0", "]", ".", "strip", "(", ")", "==", "'void'", ")", ")", "onearg_constructor", "=", "(", "(", "len", "(", "constructor_args", ")", "==", "1", "and", "# exactly one arg", "not", "noarg_constructor", ")", "or", "# all but at most one arg defaulted", "(", "len", "(", "constructor_args", ")", ">=", "1", "and", "not", "noarg_constructor", "and", "len", "(", "defaulted_args", ")", ">=", "len", "(", "constructor_args", ")", "-", "1", ")", ")", "initializer_list_constructor", "=", "bool", "(", "onearg_constructor", "and", "Search", "(", "r'\\bstd\\s*::\\s*initializer_list\\b'", ",", "constructor_args", "[", "0", "]", ")", ")", "copy_constructor", "=", "bool", "(", "onearg_constructor", "and", "Match", "(", "r'(const\\s+)?%s(\\s*<[^>]*>)?(\\s+const)?\\s*(?:<\\w+>\\s*)?&'", "%", "re", ".", "escape", "(", "base_classname", ")", ",", "constructor_args", "[", "0", "]", ".", "strip", "(", ")", ")", ")", "if", "(", "not", "is_marked_explicit", "and", "onearg_constructor", "and", "not", "initializer_list_constructor", "and", "not", "copy_constructor", ")", ":", "if", "defaulted_args", ":", "error", "(", "filename", ",", "linenum", ",", "'runtime/explicit'", ",", "5", ",", "'Constructors callable with one argument '", "'should be marked explicit.'", ")", "else", ":", "error", "(", "filename", ",", "linenum", ",", "'runtime/explicit'", ",", "5", ",", "'Single-parameter constructors should be marked explicit.'", ")", "elif", "is_marked_explicit", "and", "not", "onearg_constructor", ":", "if", "noarg_constructor", ":", "error", "(", "filename", ",", "linenum", ",", "'runtime/explicit'", ",", "5", ",", "'Zero-parameter constructors should not be marked explicit.'", ")", "else", ":", "error", "(", "filename", ",", "linenum", ",", "'runtime/explicit'", ",", "0", ",", "'Constructors that require multiple arguments '", "'should not be marked explicit.'", ")" ]
https://github.com/toggl-open-source/toggldesktop/blob/91865205885531cc8fd9e8d613dad49d625d56e7/third_party/cpplint/cpplint.py#L2573-L2734
catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/python/scipy/py2/scipy/signal/ltisys.py
python
ZerosPolesGain.to_zpk
(self)
return copy.deepcopy(self)
Return a copy of the current 'ZerosPolesGain' system. Returns ------- sys : instance of `ZerosPolesGain` The current system (copy)
Return a copy of the current 'ZerosPolesGain' system.
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def to_zpk(self): """ Return a copy of the current 'ZerosPolesGain' system. Returns ------- sys : instance of `ZerosPolesGain` The current system (copy) """ return copy.deepcopy(self)
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/scipy/py2/scipy/signal/ltisys.py#L1045-L1055
apple/turicreate
cce55aa5311300e3ce6af93cb45ba791fd1bdf49
src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers.py
python
_get_elementwise_name_from_keras_layer
(keras_layer)
Get the keras layer name from the activation name.
Get the keras layer name from the activation name.
[ "Get", "the", "keras", "layer", "name", "from", "the", "activation", "name", "." ]
def _get_elementwise_name_from_keras_layer(keras_layer): """ Get the keras layer name from the activation name. """ mode = keras_layer.mode if mode == "sum": return "ADD" elif mode == "mul": return "MULTIPLY" elif mode == "concat": if len(keras_layer.input_shape[0]) == 3 and ( keras_layer.concat_axis == 1 or keras_layer.concat_axis == -2 ): return "SEQUENCE_CONCAT" elif len(keras_layer.input_shape[0]) == 4 and ( keras_layer.concat_axis == 3 or keras_layer.concat_axis == -1 ): return "CONCAT" elif len(keras_layer.input_shape[0]) == 2 and ( keras_layer.concat_axis == 1 or keras_layer.concat_axis == -1 ): return "CONCAT" else: option = "input_shape = %s concat_axis = %s" % ( str(keras_layer.input_shape[0]), str(keras_layer.concat_axis), ) _utils.raise_error_unsupported_option(option, mode, keras_layer.name) elif mode == "cos": if len(keras_layer.input_shape[0]) == 2: return "COS" else: option = "input_shape = %s" % (str(keras_layer.input_shape[0])) _utils.raise_error_unsupported_option(option, mode, keras_layer.name) elif mode == "dot": if len(keras_layer.input_shape[0]) == 2: return "DOT" else: option = "input_shape = %s" % (str(keras_layer.input_shape[0])) _utils.raise_error_unsupported_option(option, mode, keras_layer.name) elif mode == "max": return "MAX" elif mode == "ave": return "AVE" else: _utils.raise_error_unsupported_categorical_option( "mode", mode, "Merge", keras_layer.name )
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https://github.com/apple/turicreate/blob/cce55aa5311300e3ce6af93cb45ba791fd1bdf49/src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers.py#L73-L120
nyuwireless-unipd/ns3-mmwave
4ff9e87e8079764e04cbeccd8e85bff15ae16fb3
bindings/python/rad_util.py
python
is_rotated
(seq1, seq2)
return False
Return true if the first sequence is a rotation of the second sequence. >>> seq1 = ['A', 'B', 'C', 'D'] >>> seq2 = ['C', 'D', 'A', 'B'] >>> int(is_rotated(seq1, seq2)) 1 >>> seq2 = ['C', 'D', 'B', 'A'] >>> int(is_rotated(seq1, seq2)) 0 >>> seq1 = ['A', 'B', 'C', 'A'] >>> seq2 = ['A', 'A', 'B', 'C'] >>> int(is_rotated(seq1, seq2)) 1 >>> seq2 = ['A', 'B', 'C', 'A'] >>> int(is_rotated(seq1, seq2)) 1 >>> seq2 = ['A', 'A', 'C', 'B'] >>> int(is_rotated(seq1, seq2)) 0
Return true if the first sequence is a rotation of the second sequence.
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def is_rotated(seq1, seq2): """Return true if the first sequence is a rotation of the second sequence. >>> seq1 = ['A', 'B', 'C', 'D'] >>> seq2 = ['C', 'D', 'A', 'B'] >>> int(is_rotated(seq1, seq2)) 1 >>> seq2 = ['C', 'D', 'B', 'A'] >>> int(is_rotated(seq1, seq2)) 0 >>> seq1 = ['A', 'B', 'C', 'A'] >>> seq2 = ['A', 'A', 'B', 'C'] >>> int(is_rotated(seq1, seq2)) 1 >>> seq2 = ['A', 'B', 'C', 'A'] >>> int(is_rotated(seq1, seq2)) 1 >>> seq2 = ['A', 'A', 'C', 'B'] >>> int(is_rotated(seq1, seq2)) 0 """ # Do a sanity check. if len(seq1) != len(seq2): return False # Look for occurrences of second sequence head item in first sequence. start_indexes = [] head_item = seq2[0] for index1 in range(len(seq1)): if seq1[index1] == head_item: start_indexes.append(index1) # Check that wrapped sequence matches. double_seq1 = seq1 + seq1 for index1 in start_indexes: if double_seq1[index1:index1+len(seq1)] == seq2: return True return False
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https://github.com/nyuwireless-unipd/ns3-mmwave/blob/4ff9e87e8079764e04cbeccd8e85bff15ae16fb3/bindings/python/rad_util.py#L735-L775
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Gems/CloudGemDefectReporter/v1/AWS/common-code/Lib/setuptools/command/build_py.py
python
build_py._get_platform_patterns
(spec, package, src_dir)
return ( # Each pattern has to be converted to a platform-specific path os.path.join(src_dir, convert_path(pattern)) for pattern in raw_patterns )
yield platform-specific path patterns (suitable for glob or fn_match) from a glob-based spec (such as self.package_data or self.exclude_package_data) matching package in src_dir.
yield platform-specific path patterns (suitable for glob or fn_match) from a glob-based spec (such as self.package_data or self.exclude_package_data) matching package in src_dir.
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def _get_platform_patterns(spec, package, src_dir): """ yield platform-specific path patterns (suitable for glob or fn_match) from a glob-based spec (such as self.package_data or self.exclude_package_data) matching package in src_dir. """ raw_patterns = itertools.chain( spec.get('', []), spec.get(package, []), ) return ( # Each pattern has to be converted to a platform-specific path os.path.join(src_dir, convert_path(pattern)) for pattern in raw_patterns )
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemDefectReporter/v1/AWS/common-code/Lib/setuptools/command/build_py.py#L220-L235
cvxpy/cvxpy
5165b4fb750dfd237de8659383ef24b4b2e33aaf
cvxpy/atoms/min.py
python
min.is_atom_log_log_convex
(self)
return False
Is the atom log-log convex?
Is the atom log-log convex?
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def is_atom_log_log_convex(self) -> bool: """Is the atom log-log convex? """ return False
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https://github.com/cvxpy/cvxpy/blob/5165b4fb750dfd237de8659383ef24b4b2e33aaf/cvxpy/atoms/min.py#L84-L87
ChromiumWebApps/chromium
c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7
third_party/bintrees/bintrees/rbtree.py
python
RBTree.root
(self)
return self._root
root node of T
root node of T
[ "root", "node", "of", "T" ]
def root(self): """ root node of T """ return self._root
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https://github.com/ChromiumWebApps/chromium/blob/c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7/third_party/bintrees/bintrees/rbtree.py#L142-L144
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/osx_carbon/dataview.py
python
DataViewTreeCtrl.InsertItem
(*args, **kwargs)
return _dataview.DataViewTreeCtrl_InsertItem(*args, **kwargs)
InsertItem(self, DataViewItem parent, DataViewItem previous, String text, int icon=-1, wxClientData data=None) -> DataViewItem
InsertItem(self, DataViewItem parent, DataViewItem previous, String text, int icon=-1, wxClientData data=None) -> DataViewItem
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def InsertItem(*args, **kwargs): """ InsertItem(self, DataViewItem parent, DataViewItem previous, String text, int icon=-1, wxClientData data=None) -> DataViewItem """ return _dataview.DataViewTreeCtrl_InsertItem(*args, **kwargs)
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_carbon/dataview.py#L2497-L2502
libLAS/libLAS
e6a1aaed412d638687b8aec44f7b12df7ca2bbbb
python/liblas/point.py
python
Point.set_raw_z
(self, value)
return core.las.LASPoint_SetRawZ(self.handle, value)
Sets the Z coordinate of the LAS point to an integer value value. ..note:: The point will be scaled according to the obj:`liblas.point.Point.header`'s scale value for the Z dimension when returned as a double obj:`liblas.point.Point.y`.
Sets the Z coordinate of the LAS point to an integer value value.
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def set_raw_z(self, value): """Sets the Z coordinate of the LAS point to an integer value value. ..note:: The point will be scaled according to the obj:`liblas.point.Point.header`'s scale value for the Z dimension when returned as a double obj:`liblas.point.Point.y`. """ return core.las.LASPoint_SetRawZ(self.handle, value)
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https://github.com/libLAS/libLAS/blob/e6a1aaed412d638687b8aec44f7b12df7ca2bbbb/python/liblas/point.py#L200-L208
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numpy/core/fromnumeric.py
python
nonzero
(a)
return _wrapfunc(a, 'nonzero')
Return the indices of the elements that are non-zero. Returns a tuple of arrays, one for each dimension of `a`, containing the indices of the non-zero elements in that dimension. The values in `a` are always tested and returned in row-major, C-style order. To group the indices by element, rather than dimension, use `argwhere`, which returns a row for each non-zero element. .. note:: When called on a zero-d array or scalar, ``nonzero(a)`` is treated as ``nonzero(atleast1d(a))``. .. deprecated:: 1.17.0 Use `atleast1d` explicitly if this behavior is deliberate. Parameters ---------- a : array_like Input array. Returns ------- tuple_of_arrays : tuple Indices of elements that are non-zero. See Also -------- flatnonzero : Return indices that are non-zero in the flattened version of the input array. ndarray.nonzero : Equivalent ndarray method. count_nonzero : Counts the number of non-zero elements in the input array. Notes ----- While the nonzero values can be obtained with ``a[nonzero(a)]``, it is recommended to use ``x[x.astype(bool)]`` or ``x[x != 0]`` instead, which will correctly handle 0-d arrays. Examples -------- >>> x = np.array([[3, 0, 0], [0, 4, 0], [5, 6, 0]]) >>> x array([[3, 0, 0], [0, 4, 0], [5, 6, 0]]) >>> np.nonzero(x) (array([0, 1, 2, 2]), array([0, 1, 0, 1])) >>> x[np.nonzero(x)] array([3, 4, 5, 6]) >>> np.transpose(np.nonzero(x)) array([[0, 0], [1, 1], [2, 0], [2, 1]]) A common use for ``nonzero`` is to find the indices of an array, where a condition is True. Given an array `a`, the condition `a` > 3 is a boolean array and since False is interpreted as 0, np.nonzero(a > 3) yields the indices of the `a` where the condition is true. >>> a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) >>> a > 3 array([[False, False, False], [ True, True, True], [ True, True, True]]) >>> np.nonzero(a > 3) (array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2])) Using this result to index `a` is equivalent to using the mask directly: >>> a[np.nonzero(a > 3)] array([4, 5, 6, 7, 8, 9]) >>> a[a > 3] # prefer this spelling array([4, 5, 6, 7, 8, 9]) ``nonzero`` can also be called as a method of the array. >>> (a > 3).nonzero() (array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))
Return the indices of the elements that are non-zero.
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def nonzero(a): """ Return the indices of the elements that are non-zero. Returns a tuple of arrays, one for each dimension of `a`, containing the indices of the non-zero elements in that dimension. The values in `a` are always tested and returned in row-major, C-style order. To group the indices by element, rather than dimension, use `argwhere`, which returns a row for each non-zero element. .. note:: When called on a zero-d array or scalar, ``nonzero(a)`` is treated as ``nonzero(atleast1d(a))``. .. deprecated:: 1.17.0 Use `atleast1d` explicitly if this behavior is deliberate. Parameters ---------- a : array_like Input array. Returns ------- tuple_of_arrays : tuple Indices of elements that are non-zero. See Also -------- flatnonzero : Return indices that are non-zero in the flattened version of the input array. ndarray.nonzero : Equivalent ndarray method. count_nonzero : Counts the number of non-zero elements in the input array. Notes ----- While the nonzero values can be obtained with ``a[nonzero(a)]``, it is recommended to use ``x[x.astype(bool)]`` or ``x[x != 0]`` instead, which will correctly handle 0-d arrays. Examples -------- >>> x = np.array([[3, 0, 0], [0, 4, 0], [5, 6, 0]]) >>> x array([[3, 0, 0], [0, 4, 0], [5, 6, 0]]) >>> np.nonzero(x) (array([0, 1, 2, 2]), array([0, 1, 0, 1])) >>> x[np.nonzero(x)] array([3, 4, 5, 6]) >>> np.transpose(np.nonzero(x)) array([[0, 0], [1, 1], [2, 0], [2, 1]]) A common use for ``nonzero`` is to find the indices of an array, where a condition is True. Given an array `a`, the condition `a` > 3 is a boolean array and since False is interpreted as 0, np.nonzero(a > 3) yields the indices of the `a` where the condition is true. >>> a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) >>> a > 3 array([[False, False, False], [ True, True, True], [ True, True, True]]) >>> np.nonzero(a > 3) (array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2])) Using this result to index `a` is equivalent to using the mask directly: >>> a[np.nonzero(a > 3)] array([4, 5, 6, 7, 8, 9]) >>> a[a > 3] # prefer this spelling array([4, 5, 6, 7, 8, 9]) ``nonzero`` can also be called as a method of the array. >>> (a > 3).nonzero() (array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2])) """ return _wrapfunc(a, 'nonzero')
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numpy/core/fromnumeric.py#L1805-L1896
NVIDIAGameWorks/kaolin
e5148d05e9c1e2ce92a07881ce3593b1c5c3f166
kaolin/metrics/trianglemesh.py
python
average_edge_length
(vertices, faces)
return edge_length
r"""Returns the average length of each faces in a mesh. Args: vertices (torch.Tensor): Batched vertices, of shape :math:`(\text{batch_size}, \text{num_vertices}, 3)`. faces (torch.LongTensor): Faces, of shape :math:`(\text{num_faces}, 3)`. Returns: (torch.Tensor): average length of each edges in a face, of shape :math:`(\text{batch_size}, \text{num_faces})`. Example: >>> vertices = torch.tensor([[[1, 0, 0], ... [0, 1, 0], ... [0, 0, 1]]], dtype=torch.float) >>> faces = torch.tensor([[0, 1, 2]]) >>> average_edge_length(vertices, faces) tensor([[1.4142]])
r"""Returns the average length of each faces in a mesh.
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def average_edge_length(vertices, faces): r"""Returns the average length of each faces in a mesh. Args: vertices (torch.Tensor): Batched vertices, of shape :math:`(\text{batch_size}, \text{num_vertices}, 3)`. faces (torch.LongTensor): Faces, of shape :math:`(\text{num_faces}, 3)`. Returns: (torch.Tensor): average length of each edges in a face, of shape :math:`(\text{batch_size}, \text{num_faces})`. Example: >>> vertices = torch.tensor([[[1, 0, 0], ... [0, 1, 0], ... [0, 0, 1]]], dtype=torch.float) >>> faces = torch.tensor([[0, 1, 2]]) >>> average_edge_length(vertices, faces) tensor([[1.4142]]) """ batch_size = vertices.shape[0] p1 = torch.index_select(vertices, 1, faces[:, 0]) p2 = torch.index_select(vertices, 1, faces[:, 1]) p3 = torch.index_select(vertices, 1, faces[:, 2]) # get edge lentgh e1 = p2 - p1 e2 = p3 - p1 e3 = p2 - p3 el1 = torch.sqrt((torch.sum(e1**2, dim=2))) el2 = torch.sqrt((torch.sum(e2**2, dim=2))) el3 = torch.sqrt((torch.sum(e3**2, dim=2))) edge_length = (el1 + el2 + el3) / 3. return edge_length
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https://github.com/NVIDIAGameWorks/kaolin/blob/e5148d05e9c1e2ce92a07881ce3593b1c5c3f166/kaolin/metrics/trianglemesh.py#L265-L302
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/gtk/aui.py
python
AuiTabContainer.__init__
(self, *args, **kwargs)
__init__(self) -> AuiTabContainer
__init__(self) -> AuiTabContainer
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def __init__(self, *args, **kwargs): """__init__(self) -> AuiTabContainer""" _aui.AuiTabContainer_swiginit(self,_aui.new_AuiTabContainer(*args, **kwargs))
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/gtk/aui.py#L1124-L1126
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/site-packages/pip/_vendor/pkg_resources/__init__.py
python
Environment.can_add
(self, dist)
return py_compat and compatible_platforms(dist.platform, self.platform)
Is distribution `dist` acceptable for this environment? The distribution must match the platform and python version requirements specified when this environment was created, or False is returned.
Is distribution `dist` acceptable for this environment?
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def can_add(self, dist): """Is distribution `dist` acceptable for this environment? The distribution must match the platform and python version requirements specified when this environment was created, or False is returned. """ py_compat = ( self.python is None or dist.py_version is None or dist.py_version == self.python ) return py_compat and compatible_platforms(dist.platform, self.platform)
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/site-packages/pip/_vendor/pkg_resources/__init__.py#L986-L998
mindspore-ai/mindspore
fb8fd3338605bb34fa5cea054e535a8b1d753fab
mindspore/python/mindspore/ops/_grad_experimental/grad_math_ops.py
python
get_bprop_index_addcmul
(self)
return bprop
Generate bprop for Addcmul
Generate bprop for Addcmul
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def get_bprop_index_addcmul(self): """Generate bprop for Addcmul""" mul_op = P.Mul() def bprop(input_data, x1, x2, value, out, dout): dx1 = mul_op(dout, mul_op(value, x2)) dx2 = mul_op(dout, mul_op(value, x1)) dvalue = mul_op(dout, mul_op(x1, x2)) return dout, dx1, dx2, dvalue return bprop
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https://github.com/mindspore-ai/mindspore/blob/fb8fd3338605bb34fa5cea054e535a8b1d753fab/mindspore/python/mindspore/ops/_grad_experimental/grad_math_ops.py#L103-L113
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/osx_carbon/_misc.py
python
FileTypeInfo.SetIcon
(*args, **kwargs)
return _misc_.FileTypeInfo_SetIcon(*args, **kwargs)
SetIcon(self, String iconFile, int iconIndex=0)
SetIcon(self, String iconFile, int iconIndex=0)
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def SetIcon(*args, **kwargs): """SetIcon(self, String iconFile, int iconIndex=0)""" return _misc_.FileTypeInfo_SetIcon(*args, **kwargs)
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mozilla/DeepSpeech
aa1d28530d531d0d92289bf5f11a49fe516fdc86
bin/import_gram_vaani.py
python
setup_logging
(level)
Setup basic logging Args: level (int): minimum log level for emitting messages
Setup basic logging Args: level (int): minimum log level for emitting messages
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def setup_logging(level): """Setup basic logging Args: level (int): minimum log level for emitting messages """ format = "[%(asctime)s] %(levelname)s:%(name)s:%(message)s" logging.basicConfig( level=level, stream=sys.stdout, format=format, datefmt="%Y-%m-%d %H:%M:%S" )
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https://github.com/mozilla/DeepSpeech/blob/aa1d28530d531d0d92289bf5f11a49fe516fdc86/bin/import_gram_vaani.py#L78-L86
openvinotoolkit/openvino
dedcbeafa8b84cccdc55ca64b8da516682b381c7
tools/pot/openvino/tools/pot/api/samples/face_detection/face_detection_sample.py
python
MTCNNEngine.set_model
(self, model)
Loads NetworkX model into InferenceEngine and stores it in Engine class :param model: CompressedModel instance
Loads NetworkX model into InferenceEngine and stores it in Engine class :param model: CompressedModel instance
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def set_model(self, model): """ Loads NetworkX model into InferenceEngine and stores it in Engine class :param model: CompressedModel instance """ # save graph to IR and use it to initialize IE Network self._model = self._set_model(model) self._output_layers = {} stage_names = ['pnet', 'rnet', 'onet'] for stage, model_dict in enumerate(model.models): self._output_layers[stage_names[stage]] = { 'probabilities': model_dict['name'] + '_' + self.config['outputs']['probabilities'][stage], 'regions': model_dict['name'] + '_' + self.config['outputs']['regions'][stage], }
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https://github.com/openvinotoolkit/openvino/blob/dedcbeafa8b84cccdc55ca64b8da516682b381c7/tools/pot/openvino/tools/pot/api/samples/face_detection/face_detection_sample.py#L93-L105
catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/python/scikit-learn/py2/sklearn/cluster/hierarchical.py
python
FeatureAgglomeration.fit
(self, X, y=None, **params)
return AgglomerativeClustering.fit(self, X.T, **params)
Fit the hierarchical clustering on the data Parameters ---------- X : array-like, shape = [n_samples, n_features] The data Returns ------- self
Fit the hierarchical clustering on the data
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def fit(self, X, y=None, **params): """Fit the hierarchical clustering on the data Parameters ---------- X : array-like, shape = [n_samples, n_features] The data Returns ------- self """ X = check_array(X, accept_sparse=['csr', 'csc', 'coo'], ensure_min_features=2, estimator=self) return AgglomerativeClustering.fit(self, X.T, **params)
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