body stringlengths 26 98.2k | body_hash int64 -9,222,864,604,528,158,000 9,221,803,474B | docstring stringlengths 1 16.8k | path stringlengths 5 230 | name stringlengths 1 96 | repository_name stringlengths 7 89 | lang stringclasses 1
value | body_without_docstring stringlengths 20 98.2k |
|---|---|---|---|---|---|---|---|
@property
def z(self):
"\n Sets the aggregation data.\n\n The 'z' property is an array that may be specified as a tuple,\n list, numpy array, or pandas Series\n\n Returns\n -------\n numpy.ndarray\n "
return self['z'] | 989,078,158,267,727,900 | Sets the aggregation data.
The 'z' property is an array that may be specified as a tuple,
list, numpy array, or pandas Series
Returns
-------
numpy.ndarray | packages/python/plotly/plotly/graph_objs/_histogram2dcontour.py | z | labaran1/plotly.py | python | @property
def z(self):
"\n Sets the aggregation data.\n\n The 'z' property is an array that may be specified as a tuple,\n list, numpy array, or pandas Series\n\n Returns\n -------\n numpy.ndarray\n "
return self['z'] |
@property
def zauto(self):
"\n Determines whether or not the color domain is computed with\n respect to the input data (here in `z`) or the bounds set in\n `zmin` and `zmax` Defaults to `false` when `zmin` and `zmax`\n are set by the user.\n\n The 'zauto' property must be specifie... | 1,912,562,263,594,213,400 | Determines whether or not the color domain is computed with
respect to the input data (here in `z`) or the bounds set in
`zmin` and `zmax` Defaults to `false` when `zmin` and `zmax`
are set by the user.
The 'zauto' property must be specified as a bool
(either True, or False)
Returns
-------
bool | packages/python/plotly/plotly/graph_objs/_histogram2dcontour.py | zauto | labaran1/plotly.py | python | @property
def zauto(self):
"\n Determines whether or not the color domain is computed with\n respect to the input data (here in `z`) or the bounds set in\n `zmin` and `zmax` Defaults to `false` when `zmin` and `zmax`\n are set by the user.\n\n The 'zauto' property must be specifie... |
@property
def zhoverformat(self):
"\n Sets the hover text formatting rulefor `z` using d3 formatting\n mini-languages which are very similar to those in Python. For\n numbers, see:\n https://github.com/d3/d3-format/tree/v1.4.5#d3-format.By\n default the values are formatted using... | -2,554,998,917,856,491,500 | Sets the hover text formatting rulefor `z` using d3 formatting
mini-languages which are very similar to those in Python. For
numbers, see:
https://github.com/d3/d3-format/tree/v1.4.5#d3-format.By
default the values are formatted using generic number format.
The 'zhoverformat' property is a string and must be specifie... | packages/python/plotly/plotly/graph_objs/_histogram2dcontour.py | zhoverformat | labaran1/plotly.py | python | @property
def zhoverformat(self):
"\n Sets the hover text formatting rulefor `z` using d3 formatting\n mini-languages which are very similar to those in Python. For\n numbers, see:\n https://github.com/d3/d3-format/tree/v1.4.5#d3-format.By\n default the values are formatted using... |
@property
def zmax(self):
"\n Sets the upper bound of the color domain. Value should have the\n same units as in `z` and if set, `zmin` must be set as well.\n\n The 'zmax' property is a number and may be specified as:\n - An int or float\n\n Returns\n -------\n int... | -230,201,867,242,881,380 | Sets the upper bound of the color domain. Value should have the
same units as in `z` and if set, `zmin` must be set as well.
The 'zmax' property is a number and may be specified as:
- An int or float
Returns
-------
int|float | packages/python/plotly/plotly/graph_objs/_histogram2dcontour.py | zmax | labaran1/plotly.py | python | @property
def zmax(self):
"\n Sets the upper bound of the color domain. Value should have the\n same units as in `z` and if set, `zmin` must be set as well.\n\n The 'zmax' property is a number and may be specified as:\n - An int or float\n\n Returns\n -------\n int... |
@property
def zmid(self):
"\n Sets the mid-point of the color domain by scaling `zmin` and/or\n `zmax` to be equidistant to this point. Value should have the\n same units as in `z`. Has no effect when `zauto` is `false`.\n\n The 'zmid' property is a number and may be specified as:\n ... | 8,848,989,567,395,846,000 | Sets the mid-point of the color domain by scaling `zmin` and/or
`zmax` to be equidistant to this point. Value should have the
same units as in `z`. Has no effect when `zauto` is `false`.
The 'zmid' property is a number and may be specified as:
- An int or float
Returns
-------
int|float | packages/python/plotly/plotly/graph_objs/_histogram2dcontour.py | zmid | labaran1/plotly.py | python | @property
def zmid(self):
"\n Sets the mid-point of the color domain by scaling `zmin` and/or\n `zmax` to be equidistant to this point. Value should have the\n same units as in `z`. Has no effect when `zauto` is `false`.\n\n The 'zmid' property is a number and may be specified as:\n ... |
@property
def zmin(self):
"\n Sets the lower bound of the color domain. Value should have the\n same units as in `z` and if set, `zmax` must be set as well.\n\n The 'zmin' property is a number and may be specified as:\n - An int or float\n\n Returns\n -------\n int... | -7,033,344,151,506,177,000 | Sets the lower bound of the color domain. Value should have the
same units as in `z` and if set, `zmax` must be set as well.
The 'zmin' property is a number and may be specified as:
- An int or float
Returns
-------
int|float | packages/python/plotly/plotly/graph_objs/_histogram2dcontour.py | zmin | labaran1/plotly.py | python | @property
def zmin(self):
"\n Sets the lower bound of the color domain. Value should have the\n same units as in `z` and if set, `zmax` must be set as well.\n\n The 'zmin' property is a number and may be specified as:\n - An int or float\n\n Returns\n -------\n int... |
@property
def zsrc(self):
"\n Sets the source reference on Chart Studio Cloud for `z`.\n\n The 'zsrc' property must be specified as a string or\n as a plotly.grid_objs.Column object\n\n Returns\n -------\n str\n "
return self['zsrc'] | 3,882,254,053,371,198,500 | Sets the source reference on Chart Studio Cloud for `z`.
The 'zsrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str | packages/python/plotly/plotly/graph_objs/_histogram2dcontour.py | zsrc | labaran1/plotly.py | python | @property
def zsrc(self):
"\n Sets the source reference on Chart Studio Cloud for `z`.\n\n The 'zsrc' property must be specified as a string or\n as a plotly.grid_objs.Column object\n\n Returns\n -------\n str\n "
return self['zsrc'] |
def __init__(self, arg=None, autobinx=None, autobiny=None, autocolorscale=None, autocontour=None, bingroup=None, coloraxis=None, colorbar=None, colorscale=None, contours=None, customdata=None, customdatasrc=None, histfunc=None, histnorm=None, hoverinfo=None, hoverinfosrc=None, hoverlabel=None, hovertemplate=None, hover... | -2,595,120,491,532,694,500 | Construct a new Histogram2dContour object
The sample data from which statistics are computed is set in
`x` and `y` (where `x` and `y` represent marginal
distributions, binning is set in `xbins` and `ybins` in this
case) or `z` (where `z` represent the 2D distribution and
binning set, binning is set by `x` and `y` in t... | packages/python/plotly/plotly/graph_objs/_histogram2dcontour.py | __init__ | labaran1/plotly.py | python | def __init__(self, arg=None, autobinx=None, autobiny=None, autocolorscale=None, autocontour=None, bingroup=None, coloraxis=None, colorbar=None, colorscale=None, contours=None, customdata=None, customdatasrc=None, histfunc=None, histnorm=None, hoverinfo=None, hoverinfosrc=None, hoverlabel=None, hovertemplate=None, hover... |
def get_db_dir():
'\n Just return the default dir listed above\n :return: the default location for the sqllite database\n '
return defaultdir | -7,457,437,982,406,235,000 | Just return the default dir listed above
:return: the default location for the sqllite database | taxon/config.py | get_db_dir | linsalrob/EdwardsLab | python | def get_db_dir():
'\n Just return the default dir listed above\n :return: the default location for the sqllite database\n '
return defaultdir |
def compute_corr_mse_accel_gyro(self, exclude_col_names: list=[], accel_column_names: list=['accelerometer_x', 'accelerometer_y', 'accelerometer_z'], gyro_column_names: list=['gyroscope_y', 'gyroscope_x', 'gyroscope_z'], windowDuration: int=None, slideDuration: int=None, groupByColumnName: List[str]=[], startTime=None)... | 3,432,307,631,600,157,000 | Compute correlation and mean standard error of accel and gyro sensors
Args:
exclude_col_names list(str): name of the columns on which features should not be computed
accel_column_names list(str): name of accel data column
gyro_column_names list(str): name of gyro data column
windowDuration (int): durat... | cerebralcortex/markers/brushing/features.py | compute_corr_mse_accel_gyro | MD2Korg/CerebralCortex-2.0 | python | def compute_corr_mse_accel_gyro(self, exclude_col_names: list=[], accel_column_names: list=['accelerometer_x', 'accelerometer_y', 'accelerometer_z'], gyro_column_names: list=['gyroscope_y', 'gyroscope_x', 'gyroscope_z'], windowDuration: int=None, slideDuration: int=None, groupByColumnName: List[str]=[], startTime=None)... |
def compute_fourier_features(self, exclude_col_names: list=[], feature_names=['fft_centroid', 'fft_spread', 'spectral_entropy', 'spectral_entropy_old', 'fft_flux', 'spectral_falloff'], windowDuration: int=None, slideDuration: int=None, groupByColumnName: List[str]=[], startTime=None):
'\n Transforms data from ti... | -5,459,656,174,276,873,000 | Transforms data from time domain to frequency domain.
Args:
exclude_col_names list(str): name of the columns on which features should not be computed
feature_names list(str): names of the features. Supported features are fft_centroid, fft_spread, spectral_entropy, spectral_entropy_old, fft_flux, spectral_fallo... | cerebralcortex/markers/brushing/features.py | compute_fourier_features | MD2Korg/CerebralCortex-2.0 | python | def compute_fourier_features(self, exclude_col_names: list=[], feature_names=['fft_centroid', 'fft_spread', 'spectral_entropy', 'spectral_entropy_old', 'fft_flux', 'spectral_falloff'], windowDuration: int=None, slideDuration: int=None, groupByColumnName: List[str]=[], startTime=None):
'\n Transforms data from ti... |
def stSpectralCentroidAndSpread(X, fs):
'Computes spectral centroid of frame (given abs(FFT))'
ind = (np.arange(1, (len(X) + 1)) * (fs / (2.0 * len(X))))
Xt = X.copy()
Xt = (Xt / Xt.max())
NUM = np.sum((ind * Xt))
DEN = (np.sum(Xt) + eps)
C = (NUM / DEN)
S = np.sqrt((np.sum((((ind - C) *... | 917,355,619,372,886,900 | Computes spectral centroid of frame (given abs(FFT)) | cerebralcortex/markers/brushing/features.py | stSpectralCentroidAndSpread | MD2Korg/CerebralCortex-2.0 | python | def stSpectralCentroidAndSpread(X, fs):
ind = (np.arange(1, (len(X) + 1)) * (fs / (2.0 * len(X))))
Xt = X.copy()
Xt = (Xt / Xt.max())
NUM = np.sum((ind * Xt))
DEN = (np.sum(Xt) + eps)
C = (NUM / DEN)
S = np.sqrt((np.sum((((ind - C) ** 2) * Xt)) / DEN))
C = (C / (fs / 2.0))
S = (... |
def stSpectralFlux(X, Xprev):
'\n Computes the spectral flux feature of the current frame\n ARGUMENTS:\n X: the abs(fft) of the current frame\n Xpre: the abs(fft) of the previous frame\n '
sumX = np.sum((X + eps))
sumPrevX = np.sum((Xprev + eps))
... | 401,404,339,568,127,550 | Computes the spectral flux feature of the current frame
ARGUMENTS:
X: the abs(fft) of the current frame
Xpre: the abs(fft) of the previous frame | cerebralcortex/markers/brushing/features.py | stSpectralFlux | MD2Korg/CerebralCortex-2.0 | python | def stSpectralFlux(X, Xprev):
'\n Computes the spectral flux feature of the current frame\n ARGUMENTS:\n X: the abs(fft) of the current frame\n Xpre: the abs(fft) of the previous frame\n '
sumX = np.sum((X + eps))
sumPrevX = np.sum((Xprev + eps))
... |
def stSpectralRollOff(X, c, fs):
'Computes spectral roll-off'
totalEnergy = np.sum((X ** 2))
fftLength = len(X)
Thres = (c * totalEnergy)
CumSum = (np.cumsum((X ** 2)) + eps)
[a] = np.nonzero((CumSum > Thres))
if (len(a) > 0):
mC = (np.float64(a[0]) / float(fftLength))
else:
... | 413,782,549,393,534,600 | Computes spectral roll-off | cerebralcortex/markers/brushing/features.py | stSpectralRollOff | MD2Korg/CerebralCortex-2.0 | python | def stSpectralRollOff(X, c, fs):
totalEnergy = np.sum((X ** 2))
fftLength = len(X)
Thres = (c * totalEnergy)
CumSum = (np.cumsum((X ** 2)) + eps)
[a] = np.nonzero((CumSum > Thres))
if (len(a) > 0):
mC = (np.float64(a[0]) / float(fftLength))
else:
mC = 0.0
return mC |
def stSpectralEntropy(X, numOfShortBlocks=10):
'Computes the spectral entropy'
L = len(X)
Eol = np.sum((X ** 2))
subWinLength = int(np.floor((L / numOfShortBlocks)))
if (L != (subWinLength * numOfShortBlocks)):
X = X[0:(subWinLength * numOfShortBlocks)]
subWindows = X.reshape(subWinLengt... | -8,852,138,835,898,820,000 | Computes the spectral entropy | cerebralcortex/markers/brushing/features.py | stSpectralEntropy | MD2Korg/CerebralCortex-2.0 | python | def stSpectralEntropy(X, numOfShortBlocks=10):
L = len(X)
Eol = np.sum((X ** 2))
subWinLength = int(np.floor((L / numOfShortBlocks)))
if (L != (subWinLength * numOfShortBlocks)):
X = X[0:(subWinLength * numOfShortBlocks)]
subWindows = X.reshape(subWinLength, numOfShortBlocks, order='F')... |
def __init__(self, after=None, link=None, local_vars_configuration=None):
'NextPage - a model defined in OpenAPI'
if (local_vars_configuration is None):
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._after = None
self._link = None
... | -554,981,027,761,478,850 | NextPage - a model defined in OpenAPI | hubspot/files/files/models/next_page.py | __init__ | Catchoom/hubspot-api-python | python | def __init__(self, after=None, link=None, local_vars_configuration=None):
if (local_vars_configuration is None):
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._after = None
self._link = None
self.discriminator = None
self.af... |
@property
def after(self):
'Gets the after of this NextPage. # noqa: E501\n\n\n :return: The after of this NextPage. # noqa: E501\n :rtype: str\n '
return self._after | -8,255,473,615,383,818,000 | Gets the after of this NextPage. # noqa: E501
:return: The after of this NextPage. # noqa: E501
:rtype: str | hubspot/files/files/models/next_page.py | after | Catchoom/hubspot-api-python | python | @property
def after(self):
'Gets the after of this NextPage. # noqa: E501\n\n\n :return: The after of this NextPage. # noqa: E501\n :rtype: str\n '
return self._after |
@after.setter
def after(self, after):
'Sets the after of this NextPage.\n\n\n :param after: The after of this NextPage. # noqa: E501\n :type: str\n '
if (self.local_vars_configuration.client_side_validation and (after is None)):
raise ValueError('Invalid value for `after`, must not... | -7,818,888,564,485,552,000 | Sets the after of this NextPage.
:param after: The after of this NextPage. # noqa: E501
:type: str | hubspot/files/files/models/next_page.py | after | Catchoom/hubspot-api-python | python | @after.setter
def after(self, after):
'Sets the after of this NextPage.\n\n\n :param after: The after of this NextPage. # noqa: E501\n :type: str\n '
if (self.local_vars_configuration.client_side_validation and (after is None)):
raise ValueError('Invalid value for `after`, must not... |
@property
def link(self):
'Gets the link of this NextPage. # noqa: E501\n\n\n :return: The link of this NextPage. # noqa: E501\n :rtype: str\n '
return self._link | 5,843,383,549,101,338,000 | Gets the link of this NextPage. # noqa: E501
:return: The link of this NextPage. # noqa: E501
:rtype: str | hubspot/files/files/models/next_page.py | link | Catchoom/hubspot-api-python | python | @property
def link(self):
'Gets the link of this NextPage. # noqa: E501\n\n\n :return: The link of this NextPage. # noqa: E501\n :rtype: str\n '
return self._link |
@link.setter
def link(self, link):
'Sets the link of this NextPage.\n\n\n :param link: The link of this NextPage. # noqa: E501\n :type: str\n '
self._link = link | 6,429,752,145,295,531,000 | Sets the link of this NextPage.
:param link: The link of this NextPage. # noqa: E501
:type: str | hubspot/files/files/models/next_page.py | link | Catchoom/hubspot-api-python | python | @link.setter
def link(self, link):
'Sets the link of this NextPage.\n\n\n :param link: The link of this NextPage. # noqa: E501\n :type: str\n '
self._link = link |
def to_dict(self):
'Returns the model properties as a dict'
result = {}
for (attr, _) in six.iteritems(self.openapi_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map((lambda x: (x.to_dict() if hasattr(x, 'to_dict') else x)), value))
e... | 8,442,519,487,048,767,000 | Returns the model properties as a dict | hubspot/files/files/models/next_page.py | to_dict | Catchoom/hubspot-api-python | python | def to_dict(self):
result = {}
for (attr, _) in six.iteritems(self.openapi_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map((lambda x: (x.to_dict() if hasattr(x, 'to_dict') else x)), value))
elif hasattr(value, 'to_dict'):
... |
def to_str(self):
'Returns the string representation of the model'
return pprint.pformat(self.to_dict()) | 5,849,158,643,760,736,000 | Returns the string representation of the model | hubspot/files/files/models/next_page.py | to_str | Catchoom/hubspot-api-python | python | def to_str(self):
return pprint.pformat(self.to_dict()) |
def __repr__(self):
'For `print` and `pprint`'
return self.to_str() | -8,960,031,694,814,905,000 | For `print` and `pprint` | hubspot/files/files/models/next_page.py | __repr__ | Catchoom/hubspot-api-python | python | def __repr__(self):
return self.to_str() |
def __eq__(self, other):
'Returns true if both objects are equal'
if (not isinstance(other, NextPage)):
return False
return (self.to_dict() == other.to_dict()) | -7,321,777,463,093,585,000 | Returns true if both objects are equal | hubspot/files/files/models/next_page.py | __eq__ | Catchoom/hubspot-api-python | python | def __eq__(self, other):
if (not isinstance(other, NextPage)):
return False
return (self.to_dict() == other.to_dict()) |
def __ne__(self, other):
'Returns true if both objects are not equal'
if (not isinstance(other, NextPage)):
return True
return (self.to_dict() != other.to_dict()) | -1,624,190,676,302,696,700 | Returns true if both objects are not equal | hubspot/files/files/models/next_page.py | __ne__ | Catchoom/hubspot-api-python | python | def __ne__(self, other):
if (not isinstance(other, NextPage)):
return True
return (self.to_dict() != other.to_dict()) |
def __init__(self, channel):
'Constructor.\n\n Args:\n channel: A grpc.Channel.\n '
self.GetFeedItemTarget = channel.unary_unary('/google.ads.googleads.v2.services.FeedItemTargetService/GetFeedItemTarget', request_serializer=google_dot_ads_dot_googleads__v2_dot_proto_dot_services_dot_feed__item__targ... | 1,639,354,539,681,269,000 | Constructor.
Args:
channel: A grpc.Channel. | google/ads/google_ads/v2/proto/services/feed_item_target_service_pb2_grpc.py | __init__ | BenRKarl/google-ads-python | python | def __init__(self, channel):
'Constructor.\n\n Args:\n channel: A grpc.Channel.\n '
self.GetFeedItemTarget = channel.unary_unary('/google.ads.googleads.v2.services.FeedItemTargetService/GetFeedItemTarget', request_serializer=google_dot_ads_dot_googleads__v2_dot_proto_dot_services_dot_feed__item__targ... |
def GetFeedItemTarget(self, request, context):
'Returns the requested feed item targets in full detail.\n '
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!') | -4,225,013,039,427,472,000 | Returns the requested feed item targets in full detail. | google/ads/google_ads/v2/proto/services/feed_item_target_service_pb2_grpc.py | GetFeedItemTarget | BenRKarl/google-ads-python | python | def GetFeedItemTarget(self, request, context):
'\n '
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!') |
def MutateFeedItemTargets(self, request, context):
'Creates or removes feed item targets. Operation statuses are returned.\n '
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!') | 4,147,450,015,430,002,000 | Creates or removes feed item targets. Operation statuses are returned. | google/ads/google_ads/v2/proto/services/feed_item_target_service_pb2_grpc.py | MutateFeedItemTargets | BenRKarl/google-ads-python | python | def MutateFeedItemTargets(self, request, context):
'\n '
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!') |
def check_dir_exists(dirname='./pickles'):
'Check if given dirname exists This will contain all the pickle files.'
if (not os.path.exists(dirname)):
print('Directory to store pickes does not exist. Creating one now: ./pickles')
os.mkdir(dirname) | 1,241,679,966,958,779,100 | Check if given dirname exists This will contain all the pickle files. | createpickles.py | check_dir_exists | ansrivas/keras-rest-server | python | def check_dir_exists(dirname='./pickles'):
if (not os.path.exists(dirname)):
print('Directory to store pickes does not exist. Creating one now: ./pickles')
os.mkdir(dirname) |
def save_x_y_scalar(X_train, Y_train):
'Use a normalization method on your current dataset and save the coefficients.\n\n Args:\n X_train: Input X_train\n Y_train: Lables Y_train\n Returns:\n Normalized X_train,Y_train ( currently using StandardScaler from scikit-learn)\n '
s... | -4,774,427,860,463,725,000 | Use a normalization method on your current dataset and save the coefficients.
Args:
X_train: Input X_train
Y_train: Lables Y_train
Returns:
Normalized X_train,Y_train ( currently using StandardScaler from scikit-learn) | createpickles.py | save_x_y_scalar | ansrivas/keras-rest-server | python | def save_x_y_scalar(X_train, Y_train):
'Use a normalization method on your current dataset and save the coefficients.\n\n Args:\n X_train: Input X_train\n Y_train: Lables Y_train\n Returns:\n Normalized X_train,Y_train ( currently using StandardScaler from scikit-learn)\n '
s... |
def create_model(X_train, Y_train):
'create_model will create a very simple neural net model and save the weights in a predefined directory.\n\n Args:\n X_train: Input X_train\n Y_train: Lables Y_train\n '
xin = X_train.shape[1]
model = Sequential()
model.add(Dense(units=4, in... | -6,434,802,664,474,911,000 | create_model will create a very simple neural net model and save the weights in a predefined directory.
Args:
X_train: Input X_train
Y_train: Lables Y_train | createpickles.py | create_model | ansrivas/keras-rest-server | python | def create_model(X_train, Y_train):
'create_model will create a very simple neural net model and save the weights in a predefined directory.\n\n Args:\n X_train: Input X_train\n Y_train: Lables Y_train\n '
xin = X_train.shape[1]
model = Sequential()
model.add(Dense(units=4, in... |
def resize_return_buffer(buf_, size_):
' callback function that resizes return buffer when it is too small\n Args:\n size_: size the return buffer needs to be\n '
try:
if (not tls_var.buf):
tls_var.buf = create_string_buffer(size_)
tls_var.bufSize = size_
elif (tls_va... | -1,352,476,546,927,327,500 | callback function that resizes return buffer when it is too small
Args:
size_: size the return buffer needs to be | senzing/g2/sdk/python/G2ConfigMgr.py | resize_return_buffer | GeoJamesJones/ArcGIS-Senzing-Prototype | python | def resize_return_buffer(buf_, size_):
' callback function that resizes return buffer when it is too small\n Args:\n size_: size the return buffer needs to be\n '
try:
if (not tls_var.buf):
tls_var.buf = create_string_buffer(size_)
tls_var.bufSize = size_
elif (tls_va... |
def initV2(self, module_name_, ini_params_, debug_=False):
' Initializes the G2 config manager\n This should only be called once per process.\n Args:\n moduleName: A short name given to this instance of the config module\n iniParams: A json document that contains G2 system param... | -1,780,151,840,131,302,000 | Initializes the G2 config manager
This should only be called once per process.
Args:
moduleName: A short name given to this instance of the config module
iniParams: A json document that contains G2 system parameters.
verboseLogging: Enable diagnostic logging which will arcpy.AddMessage a massive amount of i... | senzing/g2/sdk/python/G2ConfigMgr.py | initV2 | GeoJamesJones/ArcGIS-Senzing-Prototype | python | def initV2(self, module_name_, ini_params_, debug_=False):
' Initializes the G2 config manager\n This should only be called once per process.\n Args:\n moduleName: A short name given to this instance of the config module\n iniParams: A json document that contains G2 system param... |
def __init__(self):
' Class initialization\n '
try:
if (os.name == 'nt'):
self._lib_handle = cdll.LoadLibrary('G2.dll')
else:
self._lib_handle = cdll.LoadLibrary('libG2.so')
except OSError as ex:
arcpy.AddMessage('ERROR: Unable to load G2. Did you reme... | 6,399,571,678,208,304,000 | Class initialization | senzing/g2/sdk/python/G2ConfigMgr.py | __init__ | GeoJamesJones/ArcGIS-Senzing-Prototype | python | def __init__(self):
' \n '
try:
if (os.name == 'nt'):
self._lib_handle = cdll.LoadLibrary('G2.dll')
else:
self._lib_handle = cdll.LoadLibrary('libG2.so')
except OSError as ex:
arcpy.AddMessage('ERROR: Unable to load G2. Did you remember to setup your e... |
def prepareStringArgument(self, stringToPrepare):
' Internal processing function '
if (stringToPrepare == None):
return None
if (type(stringToPrepare) == str):
return stringToPrepare.encode('utf-8')
elif (type(stringToPrepare) == bytearray):
return stringToPrepare.decode().encode... | 8,941,194,383,144,176,000 | Internal processing function | senzing/g2/sdk/python/G2ConfigMgr.py | prepareStringArgument | GeoJamesJones/ArcGIS-Senzing-Prototype | python | def prepareStringArgument(self, stringToPrepare):
' '
if (stringToPrepare == None):
return None
if (type(stringToPrepare) == str):
return stringToPrepare.encode('utf-8')
elif (type(stringToPrepare) == bytearray):
return stringToPrepare.decode().encode('utf-8')
elif (type(str... |
def prepareIntArgument(self, valueToPrepare):
' Internal processing function '
' This converts many types of values to an integer '
if (valueToPrepare == None):
return None
if (type(valueToPrepare) == str):
return int(valueToPrepare.encode('utf-8'))
elif (type(valueToPrepare) == byte... | 8,874,652,037,414,881,000 | Internal processing function | senzing/g2/sdk/python/G2ConfigMgr.py | prepareIntArgument | GeoJamesJones/ArcGIS-Senzing-Prototype | python | def prepareIntArgument(self, valueToPrepare):
' '
' This converts many types of values to an integer '
if (valueToPrepare == None):
return None
if (type(valueToPrepare) == str):
return int(valueToPrepare.encode('utf-8'))
elif (type(valueToPrepare) == bytearray):
return int(v... |
def addConfig(self, configStr, configComments, configID):
' registers a new configuration document in the datastore\n '
_configStr = self.prepareStringArgument(configStr)
_configComments = self.prepareStringArgument(configComments)
configID[:] = b''
cID = c_longlong(0)
self._lib_handle.G2... | 7,767,073,802,886,320,000 | registers a new configuration document in the datastore | senzing/g2/sdk/python/G2ConfigMgr.py | addConfig | GeoJamesJones/ArcGIS-Senzing-Prototype | python | def addConfig(self, configStr, configComments, configID):
' \n '
_configStr = self.prepareStringArgument(configStr)
_configComments = self.prepareStringArgument(configComments)
configID[:] = b
cID = c_longlong(0)
self._lib_handle.G2ConfigMgr_addConfig.argtypes = [c_char_p, c_char_p, POINT... |
def getConfig(self, configID, response):
' retrieves the registered configuration document from the datastore\n '
configID_ = self.prepareIntArgument(configID)
response[:] = b''
responseBuf = c_char_p(addressof(tls_var.buf))
responseSize = c_size_t(tls_var.bufSize)
self._lib_handle.G2Conf... | -3,640,614,544,183,717,400 | retrieves the registered configuration document from the datastore | senzing/g2/sdk/python/G2ConfigMgr.py | getConfig | GeoJamesJones/ArcGIS-Senzing-Prototype | python | def getConfig(self, configID, response):
' \n '
configID_ = self.prepareIntArgument(configID)
response[:] = b
responseBuf = c_char_p(addressof(tls_var.buf))
responseSize = c_size_t(tls_var.bufSize)
self._lib_handle.G2ConfigMgr_getConfig.restype = c_int
self._lib_handle.G2ConfigMgr_get... |
def getConfigList(self, response):
' retrieves a list of known configurations from the datastore\n '
response[:] = b''
responseBuf = c_char_p(addressof(tls_var.buf))
responseSize = c_size_t(tls_var.bufSize)
self._lib_handle.G2ConfigMgr_getConfigList.restype = c_int
self._lib_handle.G2Conf... | 6,758,571,486,106,685,000 | retrieves a list of known configurations from the datastore | senzing/g2/sdk/python/G2ConfigMgr.py | getConfigList | GeoJamesJones/ArcGIS-Senzing-Prototype | python | def getConfigList(self, response):
' \n '
response[:] = b
responseBuf = c_char_p(addressof(tls_var.buf))
responseSize = c_size_t(tls_var.bufSize)
self._lib_handle.G2ConfigMgr_getConfigList.restype = c_int
self._lib_handle.G2ConfigMgr_getConfigList.argtypes = [POINTER(c_char_p), POINTER(c_... |
def setDefaultConfigID(self, configID):
' sets the default config identifier in the datastore\n '
configID_ = self.prepareIntArgument(configID)
self._lib_handle.G2ConfigMgr_setDefaultConfigID.restype = c_int
self._lib_handle.G2ConfigMgr_setDefaultConfigID.argtypes = [c_longlong]
ret_code = se... | -7,938,155,852,795,214,000 | sets the default config identifier in the datastore | senzing/g2/sdk/python/G2ConfigMgr.py | setDefaultConfigID | GeoJamesJones/ArcGIS-Senzing-Prototype | python | def setDefaultConfigID(self, configID):
' \n '
configID_ = self.prepareIntArgument(configID)
self._lib_handle.G2ConfigMgr_setDefaultConfigID.restype = c_int
self._lib_handle.G2ConfigMgr_setDefaultConfigID.argtypes = [c_longlong]
ret_code = self._lib_handle.G2ConfigMgr_setDefaultConfigID(confi... |
def replaceDefaultConfigID(self, oldConfigID, newConfigID):
' sets the default config identifier in the datastore\n '
oldConfigID_ = self.prepareIntArgument(oldConfigID)
newConfigID_ = self.prepareIntArgument(newConfigID)
self._lib_handle.G2ConfigMgr_replaceDefaultConfigID.restype = c_int
sel... | 6,423,434,265,841,057,000 | sets the default config identifier in the datastore | senzing/g2/sdk/python/G2ConfigMgr.py | replaceDefaultConfigID | GeoJamesJones/ArcGIS-Senzing-Prototype | python | def replaceDefaultConfigID(self, oldConfigID, newConfigID):
' \n '
oldConfigID_ = self.prepareIntArgument(oldConfigID)
newConfigID_ = self.prepareIntArgument(newConfigID)
self._lib_handle.G2ConfigMgr_replaceDefaultConfigID.restype = c_int
self._lib_handle.G2ConfigMgr_replaceDefaultConfigID.ar... |
def getDefaultConfigID(self, configID):
' gets the default config identifier from the datastore\n '
configID[:] = b''
cID = c_longlong(0)
self._lib_handle.G2ConfigMgr_getDefaultConfigID.argtypes = [POINTER(c_longlong)]
self._lib_handle.G2ConfigMgr_getDefaultConfigID.restype = c_int
ret_co... | 3,663,798,139,632,863,000 | gets the default config identifier from the datastore | senzing/g2/sdk/python/G2ConfigMgr.py | getDefaultConfigID | GeoJamesJones/ArcGIS-Senzing-Prototype | python | def getDefaultConfigID(self, configID):
' \n '
configID[:] = b
cID = c_longlong(0)
self._lib_handle.G2ConfigMgr_getDefaultConfigID.argtypes = [POINTER(c_longlong)]
self._lib_handle.G2ConfigMgr_getDefaultConfigID.restype = c_int
ret_code = self._lib_handle.G2ConfigMgr_getDefaultConfigID(cI... |
def clearLastException(self):
' Clears the last exception\n '
self._lib_handle.G2ConfigMgr_clearLastException.restype = None
self._lib_handle.G2ConfigMgr_clearLastException.argtypes = []
self._lib_handle.G2ConfigMgr_clearLastException() | 8,328,367,716,224,782,000 | Clears the last exception | senzing/g2/sdk/python/G2ConfigMgr.py | clearLastException | GeoJamesJones/ArcGIS-Senzing-Prototype | python | def clearLastException(self):
' \n '
self._lib_handle.G2ConfigMgr_clearLastException.restype = None
self._lib_handle.G2ConfigMgr_clearLastException.argtypes = []
self._lib_handle.G2ConfigMgr_clearLastException() |
def getLastException(self):
' Gets the last exception\n '
self._lib_handle.G2ConfigMgr_getLastException.restype = c_int
self._lib_handle.G2ConfigMgr_getLastException.argtypes = [c_char_p, c_size_t]
self._lib_handle.G2ConfigMgr_getLastException(tls_var.buf, sizeof(tls_var.buf))
resultString = ... | 6,679,493,333,561,609,000 | Gets the last exception | senzing/g2/sdk/python/G2ConfigMgr.py | getLastException | GeoJamesJones/ArcGIS-Senzing-Prototype | python | def getLastException(self):
' \n '
self._lib_handle.G2ConfigMgr_getLastException.restype = c_int
self._lib_handle.G2ConfigMgr_getLastException.argtypes = [c_char_p, c_size_t]
self._lib_handle.G2ConfigMgr_getLastException(tls_var.buf, sizeof(tls_var.buf))
resultString = tls_var.buf.value.decod... |
def getLastExceptionCode(self):
' Gets the last exception code\n '
self._lib_handle.G2ConfigMgr_getLastExceptionCode.restype = c_int
self._lib_handle.G2ConfigMgr_getLastExceptionCode.argtypes = []
exception_code = self._lib_handle.G2ConfigMgr_getLastExceptionCode()
return exception_code | -2,972,673,154,366,856,700 | Gets the last exception code | senzing/g2/sdk/python/G2ConfigMgr.py | getLastExceptionCode | GeoJamesJones/ArcGIS-Senzing-Prototype | python | def getLastExceptionCode(self):
' \n '
self._lib_handle.G2ConfigMgr_getLastExceptionCode.restype = c_int
self._lib_handle.G2ConfigMgr_getLastExceptionCode.argtypes = []
exception_code = self._lib_handle.G2ConfigMgr_getLastExceptionCode()
return exception_code |
def destroy(self):
' Uninitializes the engine\n This should be done once per process after init(...) is called.\n After it is called the engine will no longer function.\n\n Args:\n\n Return:\n None\n '
self._lib_handle.G2ConfigMgr_destroy() | -8,557,166,857,811,240,000 | Uninitializes the engine
This should be done once per process after init(...) is called.
After it is called the engine will no longer function.
Args:
Return:
None | senzing/g2/sdk/python/G2ConfigMgr.py | destroy | GeoJamesJones/ArcGIS-Senzing-Prototype | python | def destroy(self):
' Uninitializes the engine\n This should be done once per process after init(...) is called.\n After it is called the engine will no longer function.\n\n Args:\n\n Return:\n None\n '
self._lib_handle.G2ConfigMgr_destroy() |
def start(self):
'\n start monitor,\n it will start a monitor thread.\n '
self.running_lock.acquire()
self.running = True
self.running_lock.release()
self.fetch_thread.setDaemon(True)
self.fetch_thread.start() | 6,365,399,168,440,328,000 | start monitor,
it will start a monitor thread. | python/paddle/fluid/trainer_factory.py | start | 0x45f/Paddle | python | def start(self):
'\n start monitor,\n it will start a monitor thread.\n '
self.running_lock.acquire()
self.running = True
self.running_lock.release()
self.fetch_thread.setDaemon(True)
self.fetch_thread.start() |
def update_board(self, position, flag=False):
'Takes position [x,y] as input\n\t\t\treturns a updated board as a string\n\t\t'
x = (position[0] - 1)
y = (position[1] - 1)
if (flag == True):
if (self.board_data[y][x] == ' ◌ '):
self.board_data[y][x] = ' ▶ '
elif (self.board_da... | 4,688,058,642,854,749,000 | Takes position [x,y] as input
returns a updated board as a string | board.py | update_board | Epirius/minesweeper | python | def update_board(self, position, flag=False):
'Takes position [x,y] as input\n\t\t\treturns a updated board as a string\n\t\t'
x = (position[0] - 1)
y = (position[1] - 1)
if (flag == True):
if (self.board_data[y][x] == ' ◌ '):
self.board_data[y][x] = ' ▶ '
elif (self.board_da... |
def clean_extra_package_management_files():
'Removes either requirements files and folder or the Pipfile.'
use_pipenv = '{{cookiecutter.use_pipenv}}'
use_heroku = '{{cookiecutter.use_heroku}}'
to_delete = []
if (use_pipenv == 'yes'):
to_delete = (to_delete + ['requirements.txt', 'requirement... | 1,898,375,103,263,794,700 | Removes either requirements files and folder or the Pipfile. | hooks/post_gen_project.py | clean_extra_package_management_files | HaeckelK/cookiecutter-flask | python | def clean_extra_package_management_files():
use_pipenv = '{{cookiecutter.use_pipenv}}'
use_heroku = '{{cookiecutter.use_heroku}}'
to_delete = []
if (use_pipenv == 'yes'):
to_delete = (to_delete + ['requirements.txt', 'requirements'])
else:
to_delete.append('Pipfile')
if (use... |
def test_online_tokenizer_config(self):
"this just tests that the online tokenizer files get correctly fetched and\n loaded via its tokenizer_config.json and it's not slow so it's run by normal CI\n "
tokenizer = FSMTTokenizer.from_pretrained(FSMT_TINY2)
self.assertListEqual([tokenizer.src_lan... | -580,064,580,574,381,400 | this just tests that the online tokenizer files get correctly fetched and
loaded via its tokenizer_config.json and it's not slow so it's run by normal CI | tests/test_tokenization_fsmt.py | test_online_tokenizer_config | DATEXIS/adapter-transformers | python | def test_online_tokenizer_config(self):
"this just tests that the online tokenizer files get correctly fetched and\n loaded via its tokenizer_config.json and it's not slow so it's run by normal CI\n "
tokenizer = FSMTTokenizer.from_pretrained(FSMT_TINY2)
self.assertListEqual([tokenizer.src_lan... |
def test_full_tokenizer(self):
' Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt '
tokenizer = FSMTTokenizer(self.langs, self.src_vocab_file, self.tgt_vocab_file, self.merges_file)
text = 'lower'
bpe_tokens = ['low', 'er</w>']
tokens = tokenizer.tokenize(text)
self... | 3,234,412,184,335,168,500 | Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt | tests/test_tokenization_fsmt.py | test_full_tokenizer | DATEXIS/adapter-transformers | python | def test_full_tokenizer(self):
' '
tokenizer = FSMTTokenizer(self.langs, self.src_vocab_file, self.tgt_vocab_file, self.merges_file)
text = 'lower'
bpe_tokens = ['low', 'er</w>']
tokens = tokenizer.tokenize(text)
self.assertListEqual(tokens, bpe_tokens)
input_tokens = (tokens + ['<unk>'])
... |
def fit(self, xi_train, xv_train, y_train, xi_valid=None, xv_valid=None, y_valid=None, early_stopping=False, refit=False):
'\n :param xi_train: [[ind1_1, ind1_2, ...], ..., [indi_1, indi_2, ..., indi_j, ...], ...]\n indi_j is the feature index of feature field j of sample i in the tra... | -999,047,470,642,253,700 | :param xi_train: [[ind1_1, ind1_2, ...], ..., [indi_1, indi_2, ..., indi_j, ...], ...]
indi_j is the feature index of feature field j of sample i in the training set
:param xv_train: [[val1_1, val1_2, ...], ..., [vali_1, vali_2, ..., vali_j, ...], ...]
vali_j is the feature value of fe... | tutorials/chapter_05_ProductNN/ProductNN.py | fit | Daniel1586/Initiative_RecSys | python | def fit(self, xi_train, xv_train, y_train, xi_valid=None, xv_valid=None, y_valid=None, early_stopping=False, refit=False):
'\n :param xi_train: [[ind1_1, ind1_2, ...], ..., [indi_1, indi_2, ..., indi_j, ...], ...]\n indi_j is the feature index of feature field j of sample i in the tra... |
def list_buckets(client=s3_client):
'\n Usage: [arg1]:[initialized s3 client object],\n Description: Gets the list of buckets\n Returns: [list of buckets]\n '
response = s3_client.list_buckets()
buckets = []
for bucket in response['Buckets']:
buckets.append(bucket['Name'])
return... | -6,498,878,433,956,038,000 | Usage: [arg1]:[initialized s3 client object],
Description: Gets the list of buckets
Returns: [list of buckets] | ctrl4bi/aws_connect.py | list_buckets | vkreat-tech/ctrl4bi | python | def list_buckets(client=s3_client):
'\n Usage: [arg1]:[initialized s3 client object],\n Description: Gets the list of buckets\n Returns: [list of buckets]\n '
response = s3_client.list_buckets()
buckets = []
for bucket in response['Buckets']:
buckets.append(bucket['Name'])
return... |
def list_objects(bucket, prefix='', client=s3_client):
'\n Usage: [arg1]:[bucket name],[arg2]:[pattern to match keys in s3],[arg3]:[initialized s3 client object],\n Description: Gets the keys in the S3 location\n Returns: [list of keys], [list of directories]\n '
keys = []
dirs = set()
next_... | -8,144,335,608,066,176,000 | Usage: [arg1]:[bucket name],[arg2]:[pattern to match keys in s3],[arg3]:[initialized s3 client object],
Description: Gets the keys in the S3 location
Returns: [list of keys], [list of directories] | ctrl4bi/aws_connect.py | list_objects | vkreat-tech/ctrl4bi | python | def list_objects(bucket, prefix=, client=s3_client):
'\n Usage: [arg1]:[bucket name],[arg2]:[pattern to match keys in s3],[arg3]:[initialized s3 client object],\n Description: Gets the keys in the S3 location\n Returns: [list of keys], [list of directories]\n '
keys = []
dirs = set()
next_to... |
def download_dir(bucket, prefix, local_path, client=s3_client):
'\n Usage: [arg1]:[bucket name],[arg2]:[pattern to match keys in s3],[arg3]:[local path to folder in which to place files],[arg4]:[initialized s3 client object],\n Description: Downloads the contents to the local path\n '
keys = []
dir... | 5,385,448,467,571,741,000 | Usage: [arg1]:[bucket name],[arg2]:[pattern to match keys in s3],[arg3]:[local path to folder in which to place files],[arg4]:[initialized s3 client object],
Description: Downloads the contents to the local path | ctrl4bi/aws_connect.py | download_dir | vkreat-tech/ctrl4bi | python | def download_dir(bucket, prefix, local_path, client=s3_client):
'\n Usage: [arg1]:[bucket name],[arg2]:[pattern to match keys in s3],[arg3]:[local path to folder in which to place files],[arg4]:[initialized s3 client object],\n Description: Downloads the contents to the local path\n '
keys = []
dir... |
def __init__(self, name=None, channels=None, dependencies=None, local_vars_configuration=None):
'KernelSpec - a model defined in OpenAPI'
if (local_vars_configuration is None):
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._name = None
... | 4,647,245,784,887,724,000 | KernelSpec - a model defined in OpenAPI | submarine-sdk/pysubmarine/submarine/experiment/models/kernel_spec.py | __init__ | KUAN-HSUN-LI/submarine | python | def __init__(self, name=None, channels=None, dependencies=None, local_vars_configuration=None):
if (local_vars_configuration is None):
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._name = None
self._channels = None
self._depend... |
@property
def name(self):
'Gets the name of this KernelSpec. # noqa: E501\n\n\n :return: The name of this KernelSpec. # noqa: E501\n :rtype: str\n '
return self._name | 2,942,202,076,586,926,000 | Gets the name of this KernelSpec. # noqa: E501
:return: The name of this KernelSpec. # noqa: E501
:rtype: str | submarine-sdk/pysubmarine/submarine/experiment/models/kernel_spec.py | name | KUAN-HSUN-LI/submarine | python | @property
def name(self):
'Gets the name of this KernelSpec. # noqa: E501\n\n\n :return: The name of this KernelSpec. # noqa: E501\n :rtype: str\n '
return self._name |
@name.setter
def name(self, name):
'Sets the name of this KernelSpec.\n\n\n :param name: The name of this KernelSpec. # noqa: E501\n :type: str\n '
self._name = name | 2,682,577,613,443,766,300 | Sets the name of this KernelSpec.
:param name: The name of this KernelSpec. # noqa: E501
:type: str | submarine-sdk/pysubmarine/submarine/experiment/models/kernel_spec.py | name | KUAN-HSUN-LI/submarine | python | @name.setter
def name(self, name):
'Sets the name of this KernelSpec.\n\n\n :param name: The name of this KernelSpec. # noqa: E501\n :type: str\n '
self._name = name |
@property
def channels(self):
'Gets the channels of this KernelSpec. # noqa: E501\n\n\n :return: The channels of this KernelSpec. # noqa: E501\n :rtype: list[str]\n '
return self._channels | -3,157,689,001,019,074,000 | Gets the channels of this KernelSpec. # noqa: E501
:return: The channels of this KernelSpec. # noqa: E501
:rtype: list[str] | submarine-sdk/pysubmarine/submarine/experiment/models/kernel_spec.py | channels | KUAN-HSUN-LI/submarine | python | @property
def channels(self):
'Gets the channels of this KernelSpec. # noqa: E501\n\n\n :return: The channels of this KernelSpec. # noqa: E501\n :rtype: list[str]\n '
return self._channels |
@channels.setter
def channels(self, channels):
'Sets the channels of this KernelSpec.\n\n\n :param channels: The channels of this KernelSpec. # noqa: E501\n :type: list[str]\n '
self._channels = channels | 2,079,475,509,252,598,300 | Sets the channels of this KernelSpec.
:param channels: The channels of this KernelSpec. # noqa: E501
:type: list[str] | submarine-sdk/pysubmarine/submarine/experiment/models/kernel_spec.py | channels | KUAN-HSUN-LI/submarine | python | @channels.setter
def channels(self, channels):
'Sets the channels of this KernelSpec.\n\n\n :param channels: The channels of this KernelSpec. # noqa: E501\n :type: list[str]\n '
self._channels = channels |
@property
def dependencies(self):
'Gets the dependencies of this KernelSpec. # noqa: E501\n\n\n :return: The dependencies of this KernelSpec. # noqa: E501\n :rtype: list[str]\n '
return self._dependencies | -8,961,836,283,899,798,000 | Gets the dependencies of this KernelSpec. # noqa: E501
:return: The dependencies of this KernelSpec. # noqa: E501
:rtype: list[str] | submarine-sdk/pysubmarine/submarine/experiment/models/kernel_spec.py | dependencies | KUAN-HSUN-LI/submarine | python | @property
def dependencies(self):
'Gets the dependencies of this KernelSpec. # noqa: E501\n\n\n :return: The dependencies of this KernelSpec. # noqa: E501\n :rtype: list[str]\n '
return self._dependencies |
@dependencies.setter
def dependencies(self, dependencies):
'Sets the dependencies of this KernelSpec.\n\n\n :param dependencies: The dependencies of this KernelSpec. # noqa: E501\n :type: list[str]\n '
self._dependencies = dependencies | 2,991,228,109,283,427,300 | Sets the dependencies of this KernelSpec.
:param dependencies: The dependencies of this KernelSpec. # noqa: E501
:type: list[str] | submarine-sdk/pysubmarine/submarine/experiment/models/kernel_spec.py | dependencies | KUAN-HSUN-LI/submarine | python | @dependencies.setter
def dependencies(self, dependencies):
'Sets the dependencies of this KernelSpec.\n\n\n :param dependencies: The dependencies of this KernelSpec. # noqa: E501\n :type: list[str]\n '
self._dependencies = dependencies |
def to_dict(self):
'Returns the model properties as a dict'
result = {}
for (attr, _) in six.iteritems(self.openapi_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map((lambda x: (x.to_dict() if hasattr(x, 'to_dict') else x)), value))
e... | 8,442,519,487,048,767,000 | Returns the model properties as a dict | submarine-sdk/pysubmarine/submarine/experiment/models/kernel_spec.py | to_dict | KUAN-HSUN-LI/submarine | python | def to_dict(self):
result = {}
for (attr, _) in six.iteritems(self.openapi_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map((lambda x: (x.to_dict() if hasattr(x, 'to_dict') else x)), value))
elif hasattr(value, 'to_dict'):
... |
def to_str(self):
'Returns the string representation of the model'
return pprint.pformat(self.to_dict()) | 5,849,158,643,760,736,000 | Returns the string representation of the model | submarine-sdk/pysubmarine/submarine/experiment/models/kernel_spec.py | to_str | KUAN-HSUN-LI/submarine | python | def to_str(self):
return pprint.pformat(self.to_dict()) |
def __repr__(self):
'For `print` and `pprint`'
return self.to_str() | -8,960,031,694,814,905,000 | For `print` and `pprint` | submarine-sdk/pysubmarine/submarine/experiment/models/kernel_spec.py | __repr__ | KUAN-HSUN-LI/submarine | python | def __repr__(self):
return self.to_str() |
def __eq__(self, other):
'Returns true if both objects are equal'
if (not isinstance(other, KernelSpec)):
return False
return (self.to_dict() == other.to_dict()) | -7,715,880,987,173,101,000 | Returns true if both objects are equal | submarine-sdk/pysubmarine/submarine/experiment/models/kernel_spec.py | __eq__ | KUAN-HSUN-LI/submarine | python | def __eq__(self, other):
if (not isinstance(other, KernelSpec)):
return False
return (self.to_dict() == other.to_dict()) |
def __ne__(self, other):
'Returns true if both objects are not equal'
if (not isinstance(other, KernelSpec)):
return True
return (self.to_dict() != other.to_dict()) | -3,783,474,172,759,675,000 | Returns true if both objects are not equal | submarine-sdk/pysubmarine/submarine/experiment/models/kernel_spec.py | __ne__ | KUAN-HSUN-LI/submarine | python | def __ne__(self, other):
if (not isinstance(other, KernelSpec)):
return True
return (self.to_dict() != other.to_dict()) |
def run_bind_test(self, allow_ips, connect_to, addresses, expected):
'\n Start a node with requested rpcallowip and rpcbind parameters,\n then try to connect, and check if the set of bound addresses\n matches the expected set.\n '
self.log.info(('Bind test for %s' % str(addresses)))
... | -6,699,779,156,219,655,000 | Start a node with requested rpcallowip and rpcbind parameters,
then try to connect, and check if the set of bound addresses
matches the expected set. | test/functional/rpcbind_test.py | run_bind_test | CaliforniaCoinCAC/californiacoin | python | def run_bind_test(self, allow_ips, connect_to, addresses, expected):
'\n Start a node with requested rpcallowip and rpcbind parameters,\n then try to connect, and check if the set of bound addresses\n matches the expected set.\n '
self.log.info(('Bind test for %s' % str(addresses)))
... |
def run_allowip_test(self, allow_ips, rpchost, rpcport):
'\n Start a node with rpcallow IP, and request getnetworkinfo\n at a non-localhost IP.\n '
self.log.info(('Allow IP test for %s:%d' % (rpchost, rpcport)))
base_args = (['-disablewallet', '-nolisten'] + [('-rpcallowip=' + x) for x ... | -8,650,732,974,665,215,000 | Start a node with rpcallow IP, and request getnetworkinfo
at a non-localhost IP. | test/functional/rpcbind_test.py | run_allowip_test | CaliforniaCoinCAC/californiacoin | python | def run_allowip_test(self, allow_ips, rpchost, rpcport):
'\n Start a node with rpcallow IP, and request getnetworkinfo\n at a non-localhost IP.\n '
self.log.info(('Allow IP test for %s:%d' % (rpchost, rpcport)))
base_args = (['-disablewallet', '-nolisten'] + [('-rpcallowip=' + x) for x ... |
def __init__(self, model_description: str, path: str=None, history=None, save_it: bool=True, new_style: bool=False):
'\n The class constructor. \n Attention: File history plotting is not yet implemented!\n :param model_description:str: something to name the image unique and is also the file... | 3,218,054,712,274,708,500 | The class constructor.
Attention: File history plotting is not yet implemented!
:param model_description:str: something to name the image unique and is also the file name
:param path:str: path of a file containing a history
:param history: a history
:param save_it:bool: save the plot instead of showing... | Scripts/Plotter/PlotHistory.py | __init__ | ReleasedBrainiac/GraphToSequenceNN | python | def __init__(self, model_description: str, path: str=None, history=None, save_it: bool=True, new_style: bool=False):
'\n The class constructor. \n Attention: File history plotting is not yet implemented!\n :param model_description:str: something to name the image unique and is also the file... |
def PlotHistory(self):
'\n Thise method allow to plot a history from directly a keras history. \n Plotting from log is not yet implemented!\n '
try:
if self._using_history:
if self._new_style:
self.CollectFromHistory()
self.DirectPlotHist... | -9,081,983,840,649,278,000 | Thise method allow to plot a history from directly a keras history.
Plotting from log is not yet implemented! | Scripts/Plotter/PlotHistory.py | PlotHistory | ReleasedBrainiac/GraphToSequenceNN | python | def PlotHistory(self):
'\n Thise method allow to plot a history from directly a keras history. \n Plotting from log is not yet implemented!\n '
try:
if self._using_history:
if self._new_style:
self.CollectFromHistory()
self.DirectPlotHist... |
def CollectAccFromHistory(self, name: str):
'\n This method collect the accuracy data from the history into 2 lists.\n :param name:str: name of the used acc metric\n '
try:
acc_list: list = []
val_acc_list: list = []
name = re.sub('val_', '', name)
if (n... | -6,079,083,855,885,626,000 | This method collect the accuracy data from the history into 2 lists.
:param name:str: name of the used acc metric | Scripts/Plotter/PlotHistory.py | CollectAccFromHistory | ReleasedBrainiac/GraphToSequenceNN | python | def CollectAccFromHistory(self, name: str):
'\n This method collect the accuracy data from the history into 2 lists.\n :param name:str: name of the used acc metric\n '
try:
acc_list: list = []
val_acc_list: list = []
name = re.sub('val_', , name)
if (nam... |
def CollectLossFromHistory(self):
'\n This method collect the loss metric data from the history.\n '
try:
loss_val: str = 'loss'
if (loss_val in self._history_keys):
self._losses = [s for s in self._history_keys if (loss_val == s)]
self._val_losses = [s for ... | -2,332,289,488,565,356,000 | This method collect the loss metric data from the history. | Scripts/Plotter/PlotHistory.py | CollectLossFromHistory | ReleasedBrainiac/GraphToSequenceNN | python | def CollectLossFromHistory(self):
'\n \n '
try:
loss_val: str = 'loss'
if (loss_val in self._history_keys):
self._losses = [s for s in self._history_keys if (loss_val == s)]
self._val_losses = [s for s in self._history_keys if (('val' + loss_val) in s)]
... |
def CollectLearningRatesFromHistory(self):
'\n This method collect the learning rate metric data from the history.\n '
try:
lr_val: str = 'lr'
if (lr_val in self._history_keys):
self._learning_rates = [s for s in self._history_keys if (lr_val == s)]
if isNot... | -4,803,077,714,445,215,000 | This method collect the learning rate metric data from the history. | Scripts/Plotter/PlotHistory.py | CollectLearningRatesFromHistory | ReleasedBrainiac/GraphToSequenceNN | python | def CollectLearningRatesFromHistory(self):
'\n \n '
try:
lr_val: str = 'lr'
if (lr_val in self._history_keys):
self._learning_rates = [s for s in self._history_keys if (lr_val == s)]
if isNotNone(self._learning_rates):
self._history_keys_list... |
def CollectFromHistory(self):
'\n This method collect all necessary train informations from the history.\n '
if self._using_history:
try:
print('Collect losses from history...')
self.CollectLossFromHistory()
print('Collect learning rate from history...')... | -5,342,395,841,408,103,000 | This method collect all necessary train informations from the history. | Scripts/Plotter/PlotHistory.py | CollectFromHistory | ReleasedBrainiac/GraphToSequenceNN | python | def CollectFromHistory(self):
'\n \n '
if self._using_history:
try:
print('Collect losses from history...')
self.CollectLossFromHistory()
print('Collect learning rate from history...')
self.CollectLearningRatesFromHistory()
print(... |
def DirectPlotHistory(self):
'\n This method helps to plot a keras history containing losses, accuracy and possibly least learning rates.\n '
try:
fig_num: int = 1
self.AccOrLossPlot(fig_num=fig_num, title='Model loss', metric='loss', axis_labels=['train', 'validation'], history_la... | 7,061,467,197,599,251,000 | This method helps to plot a keras history containing losses, accuracy and possibly least learning rates. | Scripts/Plotter/PlotHistory.py | DirectPlotHistory | ReleasedBrainiac/GraphToSequenceNN | python | def DirectPlotHistory(self):
'\n \n '
try:
fig_num: int = 1
self.AccOrLossPlot(fig_num=fig_num, title='Model loss', metric='loss', axis_labels=['train', 'validation'], history_labels=['Loss', 'Epoch'], extender='loss_epoch_plot', train_val_lists=[self._losses, self._val_losses])
... |
def OldPlotHistory(self):
'\n This method plot the history in the old way.\n '
try:
fig_num: int = 1
self.AccOrLossPlot(fig_num=fig_num, title='Model loss', metric='loss', axis_labels=['train', 'validation'], history_labels=['Loss', 'Epoch'], extender='loss_epoch_plot')
fig... | 7,242,522,773,362,320,000 | This method plot the history in the old way. | Scripts/Plotter/PlotHistory.py | OldPlotHistory | ReleasedBrainiac/GraphToSequenceNN | python | def OldPlotHistory(self):
'\n \n '
try:
fig_num: int = 1
self.AccOrLossPlot(fig_num=fig_num, title='Model loss', metric='loss', axis_labels=['train', 'validation'], history_labels=['Loss', 'Epoch'], extender='loss_epoch_plot')
fig_num += 1
if ('acc' in self._history... |
def AccOrLossPlot(self, fig_num: int, title: str, metric: str, axis_labels: list=['train', 'validation'], history_labels: list=['Metric', 'Epoch'], extender: str='_epoch_plot', train_val_lists: list=None):
'\n This method wrapp the plot creation for a single metric of the keras train history.\n :p... | -994,659,522,012,894,500 | This method wrapp the plot creation for a single metric of the keras train history.
:param fig_num:int: figure number
:param title:str: figure title
:param metric:str: desired metric
:param axis_labels:list: axis labels
:param history_labels:list: history labels
:param extender:str: plot file n... | Scripts/Plotter/PlotHistory.py | AccOrLossPlot | ReleasedBrainiac/GraphToSequenceNN | python | def AccOrLossPlot(self, fig_num: int, title: str, metric: str, axis_labels: list=['train', 'validation'], history_labels: list=['Metric', 'Epoch'], extender: str='_epoch_plot', train_val_lists: list=None):
'\n This method wrapp the plot creation for a single metric of the keras train history.\n :p... |
def LearningPlot(self, fig_num: int, title: str='Model Learning Rate', metric: str='lr', axis_labels: list=['train', 'validation'], history_labels: list=['Learning Rate', 'Epoch'], extender: str='learning_rate_epoch_plot'):
'\n This method plot a the single learning rate curve.\n :param fig_num:in... | 8,425,297,315,928,478,000 | This method plot a the single learning rate curve.
:param fig_num:int: figure number
:param title:str: figure title
:param metric:str: desired metric
:param axis_labels:list: axis labels
:param history_labels:list: history labels
:param extender:str: plot file name extender | Scripts/Plotter/PlotHistory.py | LearningPlot | ReleasedBrainiac/GraphToSequenceNN | python | def LearningPlot(self, fig_num: int, title: str='Model Learning Rate', metric: str='lr', axis_labels: list=['train', 'validation'], history_labels: list=['Learning Rate', 'Epoch'], extender: str='learning_rate_epoch_plot'):
'\n This method plot a the single learning rate curve.\n :param fig_num:in... |
def CalcResultAccuracy(self, history, metric: str='acc'):
'\n This method show the train acc results.\n :param history: history of the training\n '
try:
return ('Training accuracy: %.2f%% / Validation accuracy: %.2f%%' % ((100 * history.history[metric][(- 1)]), (100 * history.hi... | -3,302,066,514,028,731,000 | This method show the train acc results.
:param history: history of the training | Scripts/Plotter/PlotHistory.py | CalcResultAccuracy | ReleasedBrainiac/GraphToSequenceNN | python | def CalcResultAccuracy(self, history, metric: str='acc'):
'\n This method show the train acc results.\n :param history: history of the training\n '
try:
return ('Training accuracy: %.2f%% / Validation accuracy: %.2f%%' % ((100 * history.history[metric][(- 1)]), (100 * history.hi... |
def CalcResultLoss(self, history):
'\n This method show the train loss results.\n :param history: history of the training\n '
try:
return ((('Training loss: ' + str(history.history['loss'][(- 1)])[:(- 6)]) + ' / Validation loss: ') + str(history.history['val_loss'][(- 1)])[:(- 6... | -9,213,006,050,517,600,000 | This method show the train loss results.
:param history: history of the training | Scripts/Plotter/PlotHistory.py | CalcResultLoss | ReleasedBrainiac/GraphToSequenceNN | python | def CalcResultLoss(self, history):
'\n This method show the train loss results.\n :param history: history of the training\n '
try:
return ((('Training loss: ' + str(history.history['loss'][(- 1)])[:(- 6)]) + ' / Validation loss: ') + str(history.history['val_loss'][(- 1)])[:(- 6... |
def CalcResultLearnRate(self, history):
'\n This method show the train learn rate.\n :param history: history of the training\n '
try:
return ('Training Learn Rate: ' + str(history.history['lr'][(- 1)]))
except Exception as ex:
template = 'An exception of type {0} occ... | 2,398,961,352,070,724,000 | This method show the train learn rate.
:param history: history of the training | Scripts/Plotter/PlotHistory.py | CalcResultLearnRate | ReleasedBrainiac/GraphToSequenceNN | python | def CalcResultLearnRate(self, history):
'\n This method show the train learn rate.\n :param history: history of the training\n '
try:
return ('Training Learn Rate: ' + str(history.history['lr'][(- 1)]))
except Exception as ex:
template = 'An exception of type {0} occ... |
@staticmethod
def add_prefix(label_words, prefix):
"Add prefix to label words. For example, if a label words is in the middle of a template,\n the prefix should be ``' '``.\n\n Args:\n label_words (:obj:`Union[Sequence[str], Mapping[str, str]]`, optional): The label words that are projected... | 3,213,134,003,616,306,700 | Add prefix to label words. For example, if a label words is in the middle of a template,
the prefix should be ``' '``.
Args:
label_words (:obj:`Union[Sequence[str], Mapping[str, str]]`, optional): The label words that are projected by the labels.
prefix (:obj:`str`, optional): The prefix string of the verbaliz... | openprompt/prompts/one2one_verbalizer.py | add_prefix | BIT-ENGD/OpenPrompt | python | @staticmethod
def add_prefix(label_words, prefix):
"Add prefix to label words. For example, if a label words is in the middle of a template,\n the prefix should be ``' '``.\n\n Args:\n label_words (:obj:`Union[Sequence[str], Mapping[str, str]]`, optional): The label words that are projected... |
def generate_parameters(self) -> List:
'In basic manual template, the parameters are generated from label words directly.\n In this implementation, the label_words should not be tokenized into more than one token.\n '
words_ids = []
for word in self.label_words:
word_ids = self.tokeniz... | 1,266,850,082,361,135,000 | In basic manual template, the parameters are generated from label words directly.
In this implementation, the label_words should not be tokenized into more than one token. | openprompt/prompts/one2one_verbalizer.py | generate_parameters | BIT-ENGD/OpenPrompt | python | def generate_parameters(self) -> List:
'In basic manual template, the parameters are generated from label words directly.\n In this implementation, the label_words should not be tokenized into more than one token.\n '
words_ids = []
for word in self.label_words:
word_ids = self.tokeniz... |
def project(self, logits: torch.Tensor, **kwargs) -> torch.Tensor:
'\n Project the labels, the return value is the normalized (sum to 1) probs of label words.\n\n Args:\n logits (:obj:`torch.Tensor`): The orginal logits of label words.\n\n Returns:\n :obj:`torch.Tensor`: T... | 364,629,113,235,906,700 | Project the labels, the return value is the normalized (sum to 1) probs of label words.
Args:
logits (:obj:`torch.Tensor`): The orginal logits of label words.
Returns:
:obj:`torch.Tensor`: The normalized logits of label words | openprompt/prompts/one2one_verbalizer.py | project | BIT-ENGD/OpenPrompt | python | def project(self, logits: torch.Tensor, **kwargs) -> torch.Tensor:
'\n Project the labels, the return value is the normalized (sum to 1) probs of label words.\n\n Args:\n logits (:obj:`torch.Tensor`): The orginal logits of label words.\n\n Returns:\n :obj:`torch.Tensor`: T... |
def process_logits(self, logits: torch.Tensor, **kwargs):
'A whole framework to process the original logits over the vocabulary, which contains four steps:\n\n (1) Project the logits into logits of label words\n\n if self.post_log_softmax is True:\n\n (2) Normalize over all label words\n\n ... | 1,527,449,169,758,929,200 | A whole framework to process the original logits over the vocabulary, which contains four steps:
(1) Project the logits into logits of label words
if self.post_log_softmax is True:
(2) Normalize over all label words
(3) Calibrate (optional)
Args:
logits (:obj:`torch.Tensor`): The orginal logits.
Retur... | openprompt/prompts/one2one_verbalizer.py | process_logits | BIT-ENGD/OpenPrompt | python | def process_logits(self, logits: torch.Tensor, **kwargs):
'A whole framework to process the original logits over the vocabulary, which contains four steps:\n\n (1) Project the logits into logits of label words\n\n if self.post_log_softmax is True:\n\n (2) Normalize over all label words\n\n ... |
def normalize(self, logits: torch.Tensor) -> torch.Tensor:
'\n Given logits regarding the entire vocabulary, return the probs over the label words set.\n\n Args:\n logits (:obj:`Tensor`): The logits over the entire vocabulary.\n\n Returns:\n :obj:`Tensor`: The logits over ... | -2,564,412,923,553,433,000 | Given logits regarding the entire vocabulary, return the probs over the label words set.
Args:
logits (:obj:`Tensor`): The logits over the entire vocabulary.
Returns:
:obj:`Tensor`: The logits over the label words set. | openprompt/prompts/one2one_verbalizer.py | normalize | BIT-ENGD/OpenPrompt | python | def normalize(self, logits: torch.Tensor) -> torch.Tensor:
'\n Given logits regarding the entire vocabulary, return the probs over the label words set.\n\n Args:\n logits (:obj:`Tensor`): The logits over the entire vocabulary.\n\n Returns:\n :obj:`Tensor`: The logits over ... |
def calibrate(self, label_words_probs: torch.Tensor, **kwargs) -> torch.Tensor:
'\n\n Args:\n label_words_probs (:obj:`torch.Tensor`): The probability distribution of the label words with the shape of [``batch_size``, ``num_classes``, ``num_label_words_per_class``]\n\n Returns:\n ... | 5,181,480,780,885,066,000 | Args:
label_words_probs (:obj:`torch.Tensor`): The probability distribution of the label words with the shape of [``batch_size``, ``num_classes``, ``num_label_words_per_class``]
Returns:
:obj:`torch.Tensor`: The calibrated probability of label words. | openprompt/prompts/one2one_verbalizer.py | calibrate | BIT-ENGD/OpenPrompt | python | def calibrate(self, label_words_probs: torch.Tensor, **kwargs) -> torch.Tensor:
'\n\n Args:\n label_words_probs (:obj:`torch.Tensor`): The probability distribution of the label words with the shape of [``batch_size``, ``num_classes``, ``num_label_words_per_class``]\n\n Returns:\n ... |
def timeline_trimmed_to_range(in_timeline, trim_range):
"Returns a new timeline that is a copy of the in_timeline, but with items\n outside the trim_range removed and items on the ends trimmed to the\n trim_range. Note that the timeline is never expanded, only shortened.\n Please note that you could do nea... | -1,156,443,789,120,338,000 | Returns a new timeline that is a copy of the in_timeline, but with items
outside the trim_range removed and items on the ends trimmed to the
trim_range. Note that the timeline is never expanded, only shortened.
Please note that you could do nearly the same thing non-destructively by
just setting the Track's source_rang... | src/py-opentimelineio/opentimelineio/algorithms/timeline_algo.py | timeline_trimmed_to_range | AWhetter/OpenTimelineIO | python | def timeline_trimmed_to_range(in_timeline, trim_range):
"Returns a new timeline that is a copy of the in_timeline, but with items\n outside the trim_range removed and items on the ends trimmed to the\n trim_range. Note that the timeline is never expanded, only shortened.\n Please note that you could do nea... |
@verbose
def plot_cov(cov, info, exclude=(), colorbar=True, proj=False, show_svd=True, show=True, verbose=None):
"Plot Covariance data.\n\n Parameters\n ----------\n cov : instance of Covariance\n The covariance matrix.\n info : dict\n Measurement info.\n exclude : list of str | str\n ... | -5,992,722,798,221,997,000 | Plot Covariance data.
Parameters
----------
cov : instance of Covariance
The covariance matrix.
info : dict
Measurement info.
exclude : list of str | str
List of channels to exclude. If empty do not exclude any channel.
If 'bads', exclude info['bads'].
colorbar : bool
Show colorbar or not.
proj : b... | mne/viz/misc.py | plot_cov | Aniket-Pradhan/mne-python | python | @verbose
def plot_cov(cov, info, exclude=(), colorbar=True, proj=False, show_svd=True, show=True, verbose=None):
"Plot Covariance data.\n\n Parameters\n ----------\n cov : instance of Covariance\n The covariance matrix.\n info : dict\n Measurement info.\n exclude : list of str | str\n ... |
def plot_source_spectrogram(stcs, freq_bins, tmin=None, tmax=None, source_index=None, colorbar=False, show=True):
'Plot source power in time-freqency grid.\n\n Parameters\n ----------\n stcs : list of SourceEstimate\n Source power for consecutive time windows, one SourceEstimate object\n shou... | -2,642,318,892,408,412,700 | Plot source power in time-freqency grid.
Parameters
----------
stcs : list of SourceEstimate
Source power for consecutive time windows, one SourceEstimate object
should be provided for each frequency bin.
freq_bins : list of tuples of float
Start and end points of frequency bins of interest.
tmin : float
... | mne/viz/misc.py | plot_source_spectrogram | Aniket-Pradhan/mne-python | python | def plot_source_spectrogram(stcs, freq_bins, tmin=None, tmax=None, source_index=None, colorbar=False, show=True):
'Plot source power in time-freqency grid.\n\n Parameters\n ----------\n stcs : list of SourceEstimate\n Source power for consecutive time windows, one SourceEstimate object\n shou... |
def _plot_mri_contours(mri_fname, surfaces, src, orientation='coronal', slices=None, show=True, show_indices=False, show_orientation=False, img_output=False):
'Plot BEM contours on anatomical slices.'
import matplotlib.pyplot as plt
from matplotlib import patheffects
_check_option('orientation', orienta... | -5,006,268,207,408,695,000 | Plot BEM contours on anatomical slices. | mne/viz/misc.py | _plot_mri_contours | Aniket-Pradhan/mne-python | python | def _plot_mri_contours(mri_fname, surfaces, src, orientation='coronal', slices=None, show=True, show_indices=False, show_orientation=False, img_output=False):
import matplotlib.pyplot as plt
from matplotlib import patheffects
_check_option('orientation', orientation, ('coronal', 'axial', 'sagittal'))
... |
def plot_bem(subject=None, subjects_dir=None, orientation='coronal', slices=None, brain_surfaces=None, src=None, show=True, show_indices=True, mri='T1.mgz', show_orientation=True):
'Plot BEM contours on anatomical slices.\n\n Parameters\n ----------\n subject : str\n Subject name.\n subjects_dir ... | 8,103,669,124,879,086,000 | Plot BEM contours on anatomical slices.
Parameters
----------
subject : str
Subject name.
subjects_dir : str | None
Path to the SUBJECTS_DIR. If None, the path is obtained by using
the environment variable SUBJECTS_DIR.
orientation : str
'coronal' or 'axial' or 'sagittal'.
slices : list of int
Slic... | mne/viz/misc.py | plot_bem | Aniket-Pradhan/mne-python | python | def plot_bem(subject=None, subjects_dir=None, orientation='coronal', slices=None, brain_surfaces=None, src=None, show=True, show_indices=True, mri='T1.mgz', show_orientation=True):
'Plot BEM contours on anatomical slices.\n\n Parameters\n ----------\n subject : str\n Subject name.\n subjects_dir ... |
@verbose
def plot_events(events, sfreq=None, first_samp=0, color=None, event_id=None, axes=None, equal_spacing=True, show=True, on_missing='raise', verbose=None):
"Plot events to get a visual display of the paradigm.\n\n Parameters\n ----------\n events : array, shape (n_events, 3)\n The events.\n ... | 1,551,174,879,459,412,200 | Plot events to get a visual display of the paradigm.
Parameters
----------
events : array, shape (n_events, 3)
The events.
sfreq : float | None
The sample frequency. If None, data will be displayed in samples (not
seconds).
first_samp : int
The index of the first sample. Recordings made on Neuromag sys... | mne/viz/misc.py | plot_events | Aniket-Pradhan/mne-python | python | @verbose
def plot_events(events, sfreq=None, first_samp=0, color=None, event_id=None, axes=None, equal_spacing=True, show=True, on_missing='raise', verbose=None):
"Plot events to get a visual display of the paradigm.\n\n Parameters\n ----------\n events : array, shape (n_events, 3)\n The events.\n ... |
def _get_presser(fig):
'Get our press callback.'
import matplotlib
callbacks = fig.canvas.callbacks.callbacks['button_press_event']
func = None
for (key, val) in callbacks.items():
if (LooseVersion(matplotlib.__version__) >= '3'):
func = val()
else:
func = val... | -2,291,499,084,552,287,000 | Get our press callback. | mne/viz/misc.py | _get_presser | Aniket-Pradhan/mne-python | python | def _get_presser(fig):
import matplotlib
callbacks = fig.canvas.callbacks.callbacks['button_press_event']
func = None
for (key, val) in callbacks.items():
if (LooseVersion(matplotlib.__version__) >= '3'):
func = val()
else:
func = val.func
if (func.__... |
def plot_dipole_amplitudes(dipoles, colors=None, show=True):
'Plot the amplitude traces of a set of dipoles.\n\n Parameters\n ----------\n dipoles : list of instance of Dipole\n The dipoles whose amplitudes should be shown.\n colors : list of color | None\n Color to plot with each dipole. ... | 4,548,696,912,232,993,000 | Plot the amplitude traces of a set of dipoles.
Parameters
----------
dipoles : list of instance of Dipole
The dipoles whose amplitudes should be shown.
colors : list of color | None
Color to plot with each dipole. If None default colors are used.
show : bool
Show figure if True.
Returns
-------
fig : matp... | mne/viz/misc.py | plot_dipole_amplitudes | Aniket-Pradhan/mne-python | python | def plot_dipole_amplitudes(dipoles, colors=None, show=True):
'Plot the amplitude traces of a set of dipoles.\n\n Parameters\n ----------\n dipoles : list of instance of Dipole\n The dipoles whose amplitudes should be shown.\n colors : list of color | None\n Color to plot with each dipole. ... |
def adjust_axes(axes, remove_spines=('top', 'right'), grid=True):
'Adjust some properties of axes.\n\n Parameters\n ----------\n axes : list\n List of axes to process.\n remove_spines : list of str\n Which axis spines to remove.\n grid : bool\n Turn grid on (True) or off (False).... | 4,676,078,817,384,858,000 | Adjust some properties of axes.
Parameters
----------
axes : list
List of axes to process.
remove_spines : list of str
Which axis spines to remove.
grid : bool
Turn grid on (True) or off (False). | mne/viz/misc.py | adjust_axes | Aniket-Pradhan/mne-python | python | def adjust_axes(axes, remove_spines=('top', 'right'), grid=True):
'Adjust some properties of axes.\n\n Parameters\n ----------\n axes : list\n List of axes to process.\n remove_spines : list of str\n Which axis spines to remove.\n grid : bool\n Turn grid on (True) or off (False).... |
def _filter_ticks(lims, fscale):
'Create approximately spaced ticks between lims.'
if (fscale == 'linear'):
return (None, None)
lims = np.array(lims)
ticks = list()
if (lims[1] > (20 * lims[0])):
base = np.array([1, 2, 4])
else:
base = np.arange(1, 11)
for exp in rang... | 6,549,562,070,031,120,000 | Create approximately spaced ticks between lims. | mne/viz/misc.py | _filter_ticks | Aniket-Pradhan/mne-python | python | def _filter_ticks(lims, fscale):
if (fscale == 'linear'):
return (None, None)
lims = np.array(lims)
ticks = list()
if (lims[1] > (20 * lims[0])):
base = np.array([1, 2, 4])
else:
base = np.arange(1, 11)
for exp in range(int(np.floor(np.log10(lims[0]))), (int(np.floor... |
def _get_flim(flim, fscale, freq, sfreq=None):
'Get reasonable frequency limits.'
if (flim is None):
if (freq is None):
flim = [(0.1 if (fscale == 'log') else 0.0), (sfreq / 2.0)]
else:
if (fscale == 'linear'):
flim = [freq[0]]
else:
... | 2,384,666,132,643,033,000 | Get reasonable frequency limits. | mne/viz/misc.py | _get_flim | Aniket-Pradhan/mne-python | python | def _get_flim(flim, fscale, freq, sfreq=None):
if (flim is None):
if (freq is None):
flim = [(0.1 if (fscale == 'log') else 0.0), (sfreq / 2.0)]
else:
if (fscale == 'linear'):
flim = [freq[0]]
else:
flim = [(freq[0] if (freq[0]... |
def _check_fscale(fscale):
'Check for valid fscale.'
if ((not isinstance(fscale, str)) or (fscale not in ('log', 'linear'))):
raise ValueError(('fscale must be "log" or "linear", got %s' % (fscale,))) | -175,384,852,521,488,900 | Check for valid fscale. | mne/viz/misc.py | _check_fscale | Aniket-Pradhan/mne-python | python | def _check_fscale(fscale):
if ((not isinstance(fscale, str)) or (fscale not in ('log', 'linear'))):
raise ValueError(('fscale must be "log" or "linear", got %s' % (fscale,))) |
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