Search is not available for this dataset
identifier stringlengths 1 155 | parameters stringlengths 2 6.09k | docstring stringlengths 11 63.4k | docstring_summary stringlengths 0 63.4k | function stringlengths 29 99.8k | function_tokens list | start_point list | end_point list | language stringclasses 1
value | docstring_language stringlengths 2 7 | docstring_language_predictions stringlengths 18 23 | is_langid_reliable stringclasses 2
values |
|---|---|---|---|---|---|---|---|---|---|---|---|
DependencyInfo.get_metadata | (self, dependency) | Obtain repository metadata for a dependency.
Args:
dependency: string providing dependency identifier.
Returns:
A dictionary with the repository metadata as defined in
bazel/repository_locations.bzl.
| Obtain repository metadata for a dependency. | def get_metadata(self, dependency):
"""Obtain repository metadata for a dependency.
Args:
dependency: string providing dependency identifier.
Returns:
A dictionary with the repository metadata as defined in
bazel/repository_locations.bzl.
"""
return REPOSITORY_LOCATIONS_S... | [
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88,
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98,
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] | python | en | ['en', 'en', 'en'] | True |
BuildGraph.query_external_deps | (self, *targets) | Query the build graph for transitive external dependencies.
Args:
targets: Bazel targets.
Returns:
A set of dependency identifiers that are reachable from targets.
| Query the build graph for transitive external dependencies. | def query_external_deps(self, *targets):
"""Query the build graph for transitive external dependencies.
Args:
targets: Bazel targets.
Returns:
A set of dependency identifiers that are reachable from targets.
"""
deps_query = ' union '.join(f'deps({l})' for l in targets)
... | [
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115,
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146,
28
] | python | en | ['en', 'en', 'en'] | True |
BuildGraph.list_extensions | (self) | List all extensions.
Returns:
Dictionary items from source/extensions/extensions_build_config.bzl.
| List all extensions. | def list_extensions(self):
"""List all extensions.
Returns:
Dictionary items from source/extensions/extensions_build_config.bzl.
"""
return extensions_build_config.EXTENSIONS.items() | [
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148,
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154,
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Validator.validate_build_graph_structure | (self) | Validate basic assumptions about dependency relationship in the build graph.
Raises:
DependencyError: on a dependency validation error.
| Validate basic assumptions about dependency relationship in the build graph. | def validate_build_graph_structure(self):
"""Validate basic assumptions about dependency relationship in the build graph.
Raises:
DependencyError: on a dependency validation error.
"""
print('Validating build dependency structure...')
queried_core_ext_deps = self._build_graph.quer... | [
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Validator.validate_test_only_deps | (self) | Validate that test-only dependencies aren't included in //source/...
Raises:
DependencyError: on a dependency validation error.
| Validate that test-only dependencies aren't included in //source/... | def validate_test_only_deps(self):
"""Validate that test-only dependencies aren't included in //source/...
Raises:
DependencyError: on a dependency validation error.
"""
print('Validating test-only dependencies...')
# Validate that //source doesn't depend on test_only
quer... | [
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Validator.validate_data_plane_core_deps | (self) | Validate dataplane_core dependencies.
Check that we at least tag as dataplane_core dependencies that match some
well-known targets for the data-plane.
Raises:
DependencyError: on a dependency validation error.
| Validate dataplane_core dependencies. | def validate_data_plane_core_deps(self):
"""Validate dataplane_core dependencies.
Check that we at least tag as dataplane_core dependencies that match some
well-known targets for the data-plane.
Raises:
DependencyError: on a dependency validation error.
"""
print('Validating data... | [
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"# thes... | [
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235,
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Validator.validate_control_plane_deps | (self) | Validate controlplane dependencies.
Check that we at least tag as controlplane dependencies that match some
well-known targets for
the control-plane.
Raises:
DependencyError: on a dependency validation error.
| Validate controlplane dependencies. | def validate_control_plane_deps(self):
"""Validate controlplane dependencies.
Check that we at least tag as controlplane dependencies that match some
well-known targets for
the control-plane.
Raises:
DependencyError: on a dependency validation error.
"""
print('Validating con... | [
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"# ... | [
237,
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] | [
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59
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Validator.validate_extension_deps | (self, name, target) | Validate that extensions are correctly declared for dataplane_ext and observability_ext.
Args:
name: extension name.
target: extension Bazel target.
Raises:
DependencyError: on a dependency validation error.
| Validate that extensions are correctly declared for dataplane_ext and observability_ext. | def validate_extension_deps(self, name, target):
"""Validate that extensions are correctly declared for dataplane_ext and observability_ext.
Args:
name: extension name.
target: extension Bazel target.
Raises:
DependencyError: on a dependency validation error.
"""
print(f'... | [
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294,
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] | python | en | ['en', 'en', 'en'] | True |
Validator.validate_all | (self) | Collection of all validations.
Raises:
DependencyError: on a dependency validation error.
| Collection of all validations. | def validate_all(self):
"""Collection of all validations.
Raises:
DependencyError: on a dependency validation error.
"""
self.validate_build_graph_structure()
self.validate_test_only_deps()
self.validate_data_plane_core_deps()
self.validate_control_plane_deps()
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] | python | en | ['en', 'en', 'en'] | True |
pure_ds | (x, amplitude=1.0, center=0.0, fwhm=1.0, asym=0.5) | Roughly taken from
https://rdrr.io/github/GSperanza/RxpsG/src/R/XPSFitAlgorithms.r
| Roughly taken from
https://rdrr.io/github/GSperanza/RxpsG/src/R/XPSFitAlgorithms.r
| def pure_ds(x, amplitude=1.0, center=0.0, fwhm=1.0, asym=0.5):
"""Roughly taken from
https://rdrr.io/github/GSperanza/RxpsG/src/R/XPSFitAlgorithms.r
"""
sigma = max(fwhm / 2, tiny)
arg = center - x
am1 = 1 - asym
y = (
amplitude / np.pi * ss.gamma(am1)
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gaussian | (x, amplitude=1.0, center=0.0, fwhm=1.0) | Standard gaussian with amplitude = area. | Standard gaussian with amplitude = area. | def gaussian(x, amplitude=1.0, center=0.0, fwhm=1.0):
"""Standard gaussian with amplitude = area."""
sigma = max(tiny, fwhm / (2 * s2ln2))
arg = center - x
y = amplitude / (s2pi * sigma) * np.exp(-arg**2 / (2 * sigma**2))
return y | [
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lorentzian | (x, amplitude=1.0, center=0.0, fwhm=1.0) | Standard lorentzian with amplitude = area. | Standard lorentzian with amplitude = area. | def lorentzian(x, amplitude=1.0, center=0.0, fwhm=1.0):
"""Standard lorentzian with amplitude = area."""
gamma = max(tiny, fwhm / 2)
arg = center - x
y = amplitude / (gamma * np.pi) * gamma**2 / (arg**2 + gamma**2)
return y | [
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voigt | (x, amplitude=1.0, center=0.0, fwhm=1.0, fwhm_l=None) | Voigt function using Faddeeva function wofz.
https://en.wikipedia.org/wiki/Voigt_profile
Taken from lmfit module, modified to take fwhms:
Gaussian fwhm and Lorentzian fwhm_l
| Voigt function using Faddeeva function wofz.
https://en.wikipedia.org/wiki/Voigt_profile
Taken from lmfit module, modified to take fwhms:
Gaussian fwhm and Lorentzian fwhm_l
| def voigt(x, amplitude=1.0, center=0.0, fwhm=1.0, fwhm_l=None):
"""Voigt function using Faddeeva function wofz.
https://en.wikipedia.org/wiki/Voigt_profile
Taken from lmfit module, modified to take fwhms:
Gaussian fwhm and Lorentzian fwhm_l
"""
if fwhm_l is None:
fwhm_l = fwhm
sigma ... | [
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voigt_defined_fwhm | (x, amplitude=1.0, center=0.0, fwhm=1.0, fwhm_g=None) | Voigt function using Faddeeva function wofz.
https://en.wikipedia.org/wiki/Voigt_profile
Taken from lmfit module, modified to take fwhms:
Full fwhm and Gaussian fwhm (Lorentzian fwhm is inferred, see bottom
of wikipedia link)
| Voigt function using Faddeeva function wofz.
https://en.wikipedia.org/wiki/Voigt_profile
Taken from lmfit module, modified to take fwhms:
Full fwhm and Gaussian fwhm (Lorentzian fwhm is inferred, see bottom
of wikipedia link)
| def voigt_defined_fwhm(x, amplitude=1.0, center=0.0, fwhm=1.0, fwhm_g=None):
"""Voigt function using Faddeeva function wofz.
https://en.wikipedia.org/wiki/Voigt_profile
Taken from lmfit module, modified to take fwhms:
Full fwhm and Gaussian fwhm (Lorentzian fwhm is inferred, see bottom
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gl_sum | (x, amplitude=1.0, center=0.0, fwhm=1.0, fraction=0.5) | Sum of a gaussian and a lorentzian component. | Sum of a gaussian and a lorentzian component. | def gl_sum(x, amplitude=1.0, center=0.0, fwhm=1.0, fraction=0.5):
"""Sum of a gaussian and a lorentzian component."""
G = gaussian(x, amplitude=amplitude, center=center, fwhm=fwhm)
L = lorentzian(x, amplitude=amplitude, center=center, fwhm=fwhm)
return (1 - fraction) * G + fraction * L | [
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gl_prod | (x, amplitude=1.0, center=0.0, fwhm=1.0, fraction=0.5) | Product form of a gaussian and a lorentzian component. | Product form of a gaussian and a lorentzian component. | def gl_prod(x, amplitude=1.0, center=0.0, fwhm=1.0, fraction=0.5):
"""Product form of a gaussian and a lorentzian component."""
# area and fwhm are not determined - don't use!
fwhm_g = fwhm / (1 - fraction)
fwhm_l = fwhm / fraction
sigma = max(tiny, fwhm_g / (2 * s2ln2))
gamma = max(tiny, fwhm_l... | [
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gelius | (x, amplitude=1.0, center=0.0, fwhm=1.0, a=0.5, b=0.5, fwhm_l=0.5) | See http://www.casaxps.com/help_manual/line_shapes.htm
Modified to use Voigt profile instead of GL product | See http://www.casaxps.com/help_manual/line_shapes.htm
Modified to use Voigt profile instead of GL product | def gelius(x, amplitude=1.0, center=0.0, fwhm=1.0, a=0.5, b=0.5, fwhm_l=0.5):
"""See http://www.casaxps.com/help_manual/line_shapes.htm
Modified to use Voigt profile instead of GL product"""
if fwhm_l is None:
fwhm_l = fwhm
arg = center - x
below_c = (x <= center).astype(int)
AW = np.exp... | [
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asymm_tail | (x, center=0, fwhm=1.0, tail=1.0) | Tail for dampening asymmetric lines below x = center. | Tail for dampening asymmetric lines below x = center. | def asymm_tail(x, center=0, fwhm=1.0, tail=1.0):
"""Tail for dampening asymmetric lines below x = center."""
arg = (center - x) / fwhm
try:
zeros = np.zeros(len(x))
except TypeError:
zeros = 0
y = np.exp(-np.maximum(arg, zeros) * tail)
return y | [
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centered_ds | (x, amplitude=1.0, center=0.0, fwhm=1.0, asym=0.5) | DS lineshape with maximum at center. | DS lineshape with maximum at center. | def centered_ds(x, amplitude=1.0, center=0.0, fwhm=1.0, asym=0.5):
"""DS lineshape with maximum at center."""
emax = fwhm / (2 * np.tan(np.pi / (2 - asym)))
center += emax
y = pure_ds(x, amplitude=amplitude, center=center, fwhm=fwhm, asym=asym)
return y | [
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tailed_ds | (x, amplitude=1.0, center=0.0, fwhm=1.0, asym=0.5, tail=1.0) | Centered DS with exponential tail at lower x. | Centered DS with exponential tail at lower x. | def tailed_ds(x, amplitude=1.0, center=0.0, fwhm=1.0, asym=0.5, tail=1.0):
"""Centered DS with exponential tail at lower x."""
emax = fwhm / (2 * np.tan(np.pi / (2 - asym)))
center += emax
ds = pure_ds(x, amplitude=amplitude, center=center, fwhm=fwhm, asym=asym)
as_tail = asymm_tail(x, center=center... | [
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pah2fwhm | (_position, angle, height, shape) | Calculates fwhm from position, angle, height depending on shape. | Calculates fwhm from position, angle, height depending on shape. | def pah2fwhm(_position, angle, height, shape):
"""Calculates fwhm from position, angle, height depending on shape."""
if shape == "PseudoVoigt":
return np.tan(angle) * height
elif shape == "DoniachSunjic":
return np.tan(angle) * height
elif shape == "Voigt":
return np.tan(angle) ... | [
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251,
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] | python | en | ['en', 'en', 'en'] | True |
pah2area | (_position, angle, height, shape) | Calculates area from position, angle, height depending on shape. | Calculates area from position, angle, height depending on shape. | def pah2area(_position, angle, height, shape):
"""Calculates area from position, angle, height depending on shape."""
if shape == "PseudoVoigt":
fwhm = np.tan(angle) * height
area = (height * (fwhm * np.sqrt(np.pi / ln2))
/ (1 + np.sqrt(1 / (np.pi * ln2))))
return area
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PeakModel.guess | (self, data, **kwargs) | Guess the pars. | Guess the pars. | def guess(self, data, **kwargs):
"""Guess the pars."""
x = kwargs.get("x", None)
negative = kwargs.get("negative", False)
pars = guess_from_peak(self, data, x, negative, ampscale=0.5)
return update_param_vals(pars, self.prefix, **kwargs) | [
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PeakModel.get_fwhm | (self, params, x=None) | Generic FWHM calculator:
Searches from center in both directions for values below maximum / 2
| Generic FWHM calculator:
Searches from center in both directions for values below maximum / 2
| def get_fwhm(self, params, x=None):
"""Generic FWHM calculator:
Searches from center in both directions for values below maximum / 2
"""
if x:
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PeakModel.get_area | (self, params, x=None) | Generic area calculator: Integrates interval
(center - self.area_range/2, center + self.area_range/2)
with resolution self.area_res.
| Generic area calculator: Integrates interval
(center - self.area_range/2, center + self.area_range/2)
with resolution self.area_res.
| def get_area(self, params, x=None):
"""Generic area calculator: Integrates interval
(center - self.area_range/2, center + self.area_range/2)
with resolution self.area_res.
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funcargs = self.make_funcargs(params)
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Anomalies.__init__ | (self, X, t, train_size, anomalies_size) | Contructor method which customized dataset to apply anomalies detection techniques.
:param X: Dataset samples
:type X: pd.DataFrame
:param t: Dataset labels
:type t: pd.Series
:param train_size: percentage of samples used for training
:type train_size: float
:par... | Contructor method which customized dataset to apply anomalies detection techniques. | def __init__(self, X, t, train_size, anomalies_size):
"""Contructor method which customized dataset to apply anomalies detection techniques.
:param X: Dataset samples
:type X: pd.DataFrame
:param t: Dataset labels
:type t: pd.Series
:param train_size: percentage of sampl... | [
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Anomalies.perform_IsolationForest | (
self, n_estimators=100, target_names=None, contamination=0, **kwargs
) | Isolation Forest algorithm.
:param n_estimators: number of estimators used, defaults to 100
:type n_estimators: int, optional
:param contamination: percentage os samples included as anomalies in trining set, defaults to 0
:type contamination: int, optional
:return: classificatio... | Isolation Forest algorithm. | def perform_IsolationForest(
self, n_estimators=100, target_names=None, contamination=0, **kwargs
):
"""Isolation Forest algorithm.
:param n_estimators: number of estimators used, defaults to 100
:type n_estimators: int, optional
:param contamination: percentage os samples i... | [
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Anomalies.perform_LOF | (self, n_neighbors=10, target_names=None, novelty=True) | LOF algorithm.
:param n_neighbors: number of data neighbours used, defaults to 10
:type n_neighbors: int, optional
:param novelty: param necessary to detect anomalies, defaults to True
:type novelty: bool, optional
:return: classification report with results
:rtype: str
... | LOF algorithm. | def perform_LOF(self, n_neighbors=10, target_names=None, novelty=True):
"""LOF algorithm.
:param n_neighbors: number of data neighbours used, defaults to 10
:type n_neighbors: int, optional
:param novelty: param necessary to detect anomalies, defaults to True
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Anomalies.perform_autoencoding | (self) | Create an autoencoder neural network. | Create an autoencoder neural network. | def perform_autoencoding(self):
"""Create an autoencoder neural network."""
_, variables = self.X_train.shape
autoencoder = models.Sequential()
autoencoder.add(layers.Dense(1, input_dim=variables, activation="relu"))
autoencoder.add(layers.Dense(variables, activation="relu"))
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Anomalies.train_autoencoding | (self, epochs=200, batch_size=100, **kwargs) | Fits autoencoder neural network.
:param epochs: number of times all data is passed to network, defaults to 200
:type epochs: int, optional
:param batch_size: number of splits of data in batchs, defaults to 100
:type batch_size: int, optional
| Fits autoencoder neural network. | def train_autoencoding(self, epochs=200, batch_size=100, **kwargs):
"""Fits autoencoder neural network.
:param epochs: number of times all data is passed to network, defaults to 200
:type epochs: int, optional
:param batch_size: number of splits of data in batchs, defaults to 100
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Anomalies.plot_autoencoder_validation | (
self,
xlabel: str = "Mean Square Error (MSE)",
ylabel: str = "Iteration (epoch)",
legend: tuple = ("Entrenamiento", "Test"),
figsize: tuple = (12, 4),
) | Plot autoencoder validation curve.
:param xlabel: plot xlabel, defaults to "Mean Square Error (MSE)"
:type xlabel: str, optional
:param ylabel: plot ylabel, defaults to "Iteration (epoch)"
:type ylabel: str, optional
:param legend: plot legend, defaults to ("Entrenamiento", "Tes... | Plot autoencoder validation curve. | def plot_autoencoder_validation(
self,
xlabel: str = "Mean Square Error (MSE)",
ylabel: str = "Iteration (epoch)",
legend: tuple = ("Entrenamiento", "Test"),
figsize: tuple = (12, 4),
):
"""Plot autoencoder validation curve.
:param xlabel: plot xlabel, defaul... | [
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Anomalies.plot_autoencoder_threshold | (
self,
xlabel: str = "Reconstruction error (training)",
ylabel: str = "Number of data",
legend: tuple = ("Threshold"),
figsize: tuple = (12, 4),
) | Predicts anomalies by recontructing samples passed to encoder.
:param xlabel: xlabel plot, defaults to "Reconstruction error (training)"
:type xlabel: str, optional
:param ylabel: ylabel plot, defaults to "Number of data"
:type ylabel: str, optional
:param legend: legend plot, d... | Predicts anomalies by recontructing samples passed to encoder. | def plot_autoencoder_threshold(
self,
xlabel: str = "Reconstruction error (training)",
ylabel: str = "Number of data",
legend: tuple = ("Threshold"),
figsize: tuple = (12, 4),
):
"""Predicts anomalies by recontructing samples passed to encoder.
:param xlabel:... | [
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Anomalies.plot_autoencoder_error | (
self,
xlabel: str = "Data Index",
ylabel: str = "Reconstruction Error",
legend: tuple = ("Training", "Test", "Threshold"),
figsize: tuple = (12, 4),
) | Plot autoencoder error.
:param xlabel: xlabel plot, defaults to "Data Index"
:type xlabel: str, optional
:param ylabel: ylabel plot, defaults to "Reconstruction Error"
:type ylabel: str, optional
:param legend: legend plot, defaults to ("Training", "Test", "Threshold")
:... | Plot autoencoder error. | def plot_autoencoder_error(
self,
xlabel: str = "Data Index",
ylabel: str = "Reconstruction Error",
legend: tuple = ("Training", "Test", "Threshold"),
figsize: tuple = (12, 4),
):
"""Plot autoencoder error.
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Anomalies.get_autoencoder_clreport | (self, target_names=None) | Generate classification report with autoencoders results.
:return: classification report with results
:rtype: str
| Generate classification report with autoencoders results. | def get_autoencoder_clreport(self, target_names=None):
"""Generate classification report with autoencoders results.
:return: classification report with results
:rtype: str
"""
y_test = np.ones((self.t_test.shape))
threshold = np.max(self.mse_train)
y_test[self.ms... | [
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validate_rages | (ranges) |
Validates if weight ranges are feasible
:param ranges: dict
{
"assignment_name": [minimum_percentage, maximum_percentage]
}
:return: True if its feasible else False
|
Validates if weight ranges are feasible | def validate_rages(ranges):
"""
Validates if weight ranges are feasible
:param ranges: dict
{
"assignment_name": [minimum_percentage, maximum_percentage]
}
:return: True if its feasible else False
"""
min_possible = 0
max_possible = 0
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validate_grades_and_ranges | (grades, ranges) |
This function validates grades and ranges to check if they are feasible
and the assignments are identical in grades and ranges
:param grades: dict
{
"assignment_name": [grade1, grade2, ...]
}
:param ranges: dict
{
"assignment_name": [minimum_percentage, maximum_percentage]
... |
This function validates grades and ranges to check if they are feasible
and the assignments are identical in grades and ranges | def validate_grades_and_ranges(grades, ranges):
"""
This function validates grades and ranges to check if they are feasible
and the assignments are identical in grades and ranges
:param grades: dict
{
"assignment_name": [grade1, grade2, ...]
}
:param ranges: dict
{
"assi... | [
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calculate_weights | (grades, ranges) |
This function calculates best possible choice of weights for a specific students' grades
:param grades: dict
{
"assignment_name": [grade1, grade2, ...]
}
:param ranges: dict
{
"assignment_name": [minimum_percentage, maximum_percentage]
}
:return: dict
{
"ass... |
This function calculates best possible choice of weights for a specific students' grades | def calculate_weights(grades, ranges):
"""
This function calculates best possible choice of weights for a specific students' grades
:param grades: dict
{
"assignment_name": [grade1, grade2, ...]
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:param ranges: dict
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_ptp | (x) | Peak-to-peak value of x.
This implementation avoids the problem of signed integer arrays having a
peak-to-peak value that cannot be represented with the array's data type.
This function returns an unsigned value for signed integer arrays.
| Peak-to-peak value of x. | def _ptp(x):
"""Peak-to-peak value of x.
This implementation avoids the problem of signed integer arrays having a
peak-to-peak value that cannot be represented with the array's data type.
This function returns an unsigned value for signed integer arrays.
"""
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_hist_bin_sqrt | (x, range) |
Square root histogram bin estimator.
Bin width is inversely proportional to the data size. Used by many
programs for its simplicity.
Parameters
----------
x : array_like
Input data that is to be histogrammed, trimmed to range. May not
be empty.
Returns
-------
h :... |
Square root histogram bin estimator. | def _hist_bin_sqrt(x, range):
"""
Square root histogram bin estimator.
Bin width is inversely proportional to the data size. Used by many
programs for its simplicity.
Parameters
----------
x : array_like
Input data that is to be histogrammed, trimmed to range. May not
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_hist_bin_sturges | (x, range) |
Sturges histogram bin estimator.
A very simplistic estimator based on the assumption of normality of
the data. This estimator has poor performance for non-normal data,
which becomes especially obvious for large data sets. The estimate
depends only on size of the data.
Parameters
---------... |
Sturges histogram bin estimator. | def _hist_bin_sturges(x, range):
"""
Sturges histogram bin estimator.
A very simplistic estimator based on the assumption of normality of
the data. This estimator has poor performance for non-normal data,
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_hist_bin_rice | (x, range) |
Rice histogram bin estimator.
Another simple estimator with no normality assumption. It has better
performance for large data than Sturges, but tends to overestimate
the number of bins. The number of bins is proportional to the cube
root of data size (asymptotically optimal). The estimate depends
... |
Rice histogram bin estimator. | def _hist_bin_rice(x, range):
"""
Rice histogram bin estimator.
Another simple estimator with no normality assumption. It has better
performance for large data than Sturges, but tends to overestimate
the number of bins. The number of bins is proportional to the cube
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_hist_bin_scott | (x, range) |
Scott histogram bin estimator.
The binwidth is proportional to the standard deviation of the data
and inversely proportional to the cube root of data size
(asymptotically optimal).
Parameters
----------
x : array_like
Input data that is to be histogrammed, trimmed to range. May no... |
Scott histogram bin estimator. | def _hist_bin_scott(x, range):
"""
Scott histogram bin estimator.
The binwidth is proportional to the standard deviation of the data
and inversely proportional to the cube root of data size
(asymptotically optimal).
Parameters
----------
x : array_like
Input data that is to be ... | [
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102,
0
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121,
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_hist_bin_stone | (x, range) |
Histogram bin estimator based on minimizing the estimated integrated squared error (ISE).
The number of bins is chosen by minimizing the estimated ISE against the unknown true distribution.
The ISE is estimated using cross-validation and can be regarded as a generalization of Scott's rule.
https://en.... |
Histogram bin estimator based on minimizing the estimated integrated squared error (ISE). | def _hist_bin_stone(x, range):
"""
Histogram bin estimator based on minimizing the estimated integrated squared error (ISE).
The number of bins is chosen by minimizing the estimated ISE against the unknown true distribution.
The ISE is estimated using cross-validation and can be regarded as a generaliz... | [
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_hist_bin_doane | (x, range) |
Doane's histogram bin estimator.
Improved version of Sturges' formula which works better for
non-normal data. See
stats.stackexchange.com/questions/55134/doanes-formula-for-histogram-binning
Parameters
----------
x : array_like
Input data that is to be histogrammed, trimmed to ran... |
Doane's histogram bin estimator. | def _hist_bin_doane(x, range):
"""
Doane's histogram bin estimator.
Improved version of Sturges' formula which works better for
non-normal data. See
stats.stackexchange.com/questions/55134/doanes-formula-for-histogram-binning
Parameters
----------
x : array_like
Input data that... | [
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_hist_bin_fd | (x, range) |
The Freedman-Diaconis histogram bin estimator.
The Freedman-Diaconis rule uses interquartile range (IQR) to
estimate binwidth. It is considered a variation of the Scott rule
with more robustness as the IQR is less affected by outliers than
the standard deviation. However, the IQR depends on fewer ... |
The Freedman-Diaconis histogram bin estimator. | def _hist_bin_fd(x, range):
"""
The Freedman-Diaconis histogram bin estimator.
The Freedman-Diaconis rule uses interquartile range (IQR) to
estimate binwidth. It is considered a variation of the Scott rule
with more robustness as the IQR is less affected by outliers than
the standard deviation.... | [
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_hist_bin_auto | (x, range) |
Histogram bin estimator that uses the minimum width of the
Freedman-Diaconis and Sturges estimators if the FD bandwidth is non zero
and the Sturges estimator if the FD bandwidth is 0.
The FD estimator is usually the most robust method, but its width
estimate tends to be too large for small `x` and... |
Histogram bin estimator that uses the minimum width of the
Freedman-Diaconis and Sturges estimators if the FD bandwidth is non zero
and the Sturges estimator if the FD bandwidth is 0. | def _hist_bin_auto(x, range):
"""
Histogram bin estimator that uses the minimum width of the
Freedman-Diaconis and Sturges estimators if the FD bandwidth is non zero
and the Sturges estimator if the FD bandwidth is 0.
The FD estimator is usually the most robust method, but its width
estimate te... | [
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_ravel_and_check_weights | (a, weights) | Check a and weights have matching shapes, and ravel both | Check a and weights have matching shapes, and ravel both | def _ravel_and_check_weights(a, weights):
""" Check a and weights have matching shapes, and ravel both """
a = np.asarray(a)
# Ensure that the array is a "subtractable" dtype
if a.dtype == np.bool_:
warnings.warn("Converting input from {} to {} for compatibility."
.format(... | [
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_get_outer_edges | (a, range) |
Determine the outer bin edges to use, from either the data or the range
argument
|
Determine the outer bin edges to use, from either the data or the range
argument
| def _get_outer_edges(a, range):
"""
Determine the outer bin edges to use, from either the data or the range
argument
"""
if range is not None:
first_edge, last_edge = range
if first_edge > last_edge:
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_unsigned_subtract | (a, b) |
Subtract two values where a >= b, and produce an unsigned result
This is needed when finding the difference between the upper and lower
bound of an int16 histogram
|
Subtract two values where a >= b, and produce an unsigned result | def _unsigned_subtract(a, b):
"""
Subtract two values where a >= b, and produce an unsigned result
This is needed when finding the difference between the upper and lower
bound of an int16 histogram
"""
# coerce to a single type
signed_to_unsigned = {
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_get_bin_edges | (a, bins, range, weights) |
Computes the bins used internally by `histogram`.
Parameters
==========
a : ndarray
Ravelled data array
bins, range
Forwarded arguments from `histogram`.
weights : ndarray, optional
Ravelled weights array, or None
Returns
=======
bin_edges : ndarray
... |
Computes the bins used internally by `histogram`. | def _get_bin_edges(a, bins, range, weights):
"""
Computes the bins used internally by `histogram`.
Parameters
==========
a : ndarray
Ravelled data array
bins, range
Forwarded arguments from `histogram`.
weights : ndarray, optional
Ravelled weights array, or None
... | [
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_search_sorted_inclusive | (a, v) |
Like `searchsorted`, but where the last item in `v` is placed on the right.
In the context of a histogram, this makes the last bin edge inclusive
|
Like `searchsorted`, but where the last item in `v` is placed on the right. | def _search_sorted_inclusive(a, v):
"""
Like `searchsorted`, but where the last item in `v` is placed on the right.
In the context of a histogram, this makes the last bin edge inclusive
"""
return np.concatenate((
a.searchsorted(v[:-1], 'left'),
a.searchsorted(v[-1:], 'right')
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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 num... | r"""
Function to calculate only the edges of the bins used by the `histogram`
function. | 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 scala... | [
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histogram | (a, bins=10, range=None, normed=None, weights=None,
density=None) | r"""
Compute the histogram of a set of data.
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 ra... | r"""
Compute the histogram of a set of data. | def histogram(a, bins=10, range=None, normed=None, weights=None,
density=None):
r"""
Compute the histogram of a set of data.
Parameters
----------
a : array_like
Input data. The histogram is computed over the flattened array.
bins : int or sequence of scalars or str, optio... | [
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histogramdd | (sample, bins=10, range=None, normed=None, weights=None,
density=None) |
Compute the multidimensional histogram of some data.
Parameters
----------
sample : (N, D) array, or (D, N) array_like
The data to be histogrammed.
Note the unusual interpretation of sample when an array_like:
* When an array, each row is a coordinate in a D-dimensional space... |
Compute the multidimensional histogram of some data. | def histogramdd(sample, bins=10, range=None, normed=None, weights=None,
density=None):
"""
Compute the multidimensional histogram of some data.
Parameters
----------
sample : (N, D) array, or (D, N) array_like
The data to be histogrammed.
Note the unusual interpreta... | [
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test_resume_training_on_cpu | (tmpdir) | Checks if training can be resumed from a saved checkpoint on CPU | Checks if training can be resumed from a saved checkpoint on CPU | def test_resume_training_on_cpu(tmpdir):
""" Checks if training can be resumed from a saved checkpoint on CPU"""
# Train a model on TPU
model = BoringModel()
trainer = Trainer(
checkpoint_callback=True,
max_epochs=1,
tpu_cores=8,
)
trainer.fit(model)
model_path = tra... | [
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test_if_test_works_after_train | (tmpdir) | Ensure that .test() works after .fit() | Ensure that .test() works after .fit() | def test_if_test_works_after_train(tmpdir):
""" Ensure that .test() works after .fit() """
# Train a model on TPU
model = BoringModel()
trainer = Trainer(max_epochs=1, tpu_cores=8, default_root_dir=tmpdir, fast_dev_run=True)
trainer.fit(model)
assert len(trainer.test(model)) == 1 | [
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test_weight_tying_warning | (tmpdir, capsys=None) |
Ensure a warning is thrown if model parameter lengths do not match
post moving to device.
|
Ensure a warning is thrown if model parameter lengths do not match
post moving to device.
| def test_weight_tying_warning(tmpdir, capsys=None):
"""
Ensure a warning is thrown if model parameter lengths do not match
post moving to device.
"""
model = WeightSharingModule()
trainer = Trainer(checkpoint_callback=True, max_epochs=1, tpu_cores=1)
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test_if_weights_tied | (tmpdir, capsys=None) |
Test if weights are properly tied on `on_post_move_to_device`.
Ensure no warning for parameter mismatch is thrown.
|
Test if weights are properly tied on `on_post_move_to_device`.
Ensure no warning for parameter mismatch is thrown.
| def test_if_weights_tied(tmpdir, capsys=None):
"""
Test if weights are properly tied on `on_post_move_to_device`.
Ensure no warning for parameter mismatch is thrown.
"""
class Model(WeightSharingModule):
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ix_ | (*args) |
Construct an open mesh from multiple sequences.
This function takes N 1-D sequences and returns N outputs with N
dimensions each, such that the shape is 1 in all but one dimension
and the dimension with the non-unit shape value cycles through all
N dimensions.
Using `ix_` one can quickly cons... |
Construct an open mesh from multiple sequences. | def ix_(*args):
"""
Construct an open mesh from multiple sequences.
This function takes N 1-D sequences and returns N outputs with N
dimensions each, such that the shape is 1 in all but one dimension
and the dimension with the non-unit shape value cycles through all
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fill_diagonal | (a, val, wrap=False) | Fill the main diagonal of the given array of any dimensionality.
For an array `a` with ``a.ndim >= 2``, the diagonal is the list of
locations with indices ``a[i, ..., i]`` all identical. This function
modifies the input array in-place, it does not return a value.
Parameters
----------
a : arra... | Fill the main diagonal of the given array of any dimensionality. | def fill_diagonal(a, val, wrap=False):
"""Fill the main diagonal of the given array of any dimensionality.
For an array `a` with ``a.ndim >= 2``, the diagonal is the list of
locations with indices ``a[i, ..., i]`` all identical. This function
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diag_indices | (n, ndim=2) |
Return the indices to access the main diagonal of an array.
This returns a tuple of indices that can be used to access the main
diagonal of an array `a` with ``a.ndim >= 2`` dimensions and shape
(n, n, ..., n). For ``a.ndim = 2`` this is the usual diagonal, for
``a.ndim > 2`` this is the set of in... |
Return the indices to access the main diagonal of an array. | def diag_indices(n, ndim=2):
"""
Return the indices to access the main diagonal of an array.
This returns a tuple of indices that can be used to access the main
diagonal of an array `a` with ``a.ndim >= 2`` dimensions and shape
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diag_indices_from | (arr) |
Return the indices to access the main diagonal of an n-dimensional array.
See `diag_indices` for full details.
Parameters
----------
arr : array, at least 2-D
See Also
--------
diag_indices
Notes
-----
.. versionadded:: 1.4.0
|
Return the indices to access the main diagonal of an n-dimensional array. | def diag_indices_from(arr):
"""
Return the indices to access the main diagonal of an n-dimensional array.
See `diag_indices` for full details.
Parameters
----------
arr : array, at least 2-D
See Also
--------
diag_indices
Notes
-----
.. versionadded:: 1.4.0
"""
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loop_pos.first_group | (self, getter=None) |
Returns true if this item is the start of a new group,
where groups mean that some attribute has changed. The getter
can be None (the item itself changes), an attribute name like
``'.attr'``, a function, or a dict key or list index.
|
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can be None (the item itself changes), an attribute name like
``'.attr'``, a function, or a dict key or list index.
| def first_group(self, getter=None):
"""
Returns true if this item is the start of a new group,
where groups mean that some attribute has changed. The getter
can be None (the item itself changes), an attribute name like
``'.attr'``, a function, or a dict key or list index.
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] | [
136,
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] | python | en | ['en', 'error', 'th'] | False |
loop_pos.last_group | (self, getter=None) |
Returns true if this item is the end of a new group,
where groups mean that some attribute has changed. The getter
can be None (the item itself changes), an attribute name like
``'.attr'``, a function, or a dict key or list index.
|
Returns true if this item is the end of a new group,
where groups mean that some attribute has changed. The getter
can be None (the item itself changes), an attribute name like
``'.attr'``, a function, or a dict key or list index.
| def last_group(self, getter=None):
"""
Returns true if this item is the end of a new group,
where groups mean that some attribute has changed. The getter
can be None (the item itself changes), an attribute name like
``'.attr'``, a function, or a dict key or list index.
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TensorBoardLogger.root_dir | (self) |
Parent directory for all tensorboard checkpoint subdirectories.
If the experiment name parameter is ``None`` or the empty string, no experiment subdirectory is used
and the checkpoint will be saved in "save_dir/version_dir"
|
Parent directory for all tensorboard checkpoint subdirectories.
If the experiment name parameter is ``None`` or the empty string, no experiment subdirectory is used
and the checkpoint will be saved in "save_dir/version_dir"
| def root_dir(self) -> str:
"""
Parent directory for all tensorboard checkpoint subdirectories.
If the experiment name parameter is ``None`` or the empty string, no experiment subdirectory is used
and the checkpoint will be saved in "save_dir/version_dir"
"""
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TensorBoardLogger.log_dir | (self) |
The directory for this run's tensorboard checkpoint. By default, it is named
``'version_${self.version}'`` but it can be overridden by passing a string value
for the constructor's version parameter instead of ``None`` or an int.
|
The directory for this run's tensorboard checkpoint. By default, it is named
``'version_${self.version}'`` but it can be overridden by passing a string value
for the constructor's version parameter instead of ``None`` or an int.
| def log_dir(self) -> str:
"""
The directory for this run's tensorboard checkpoint. By default, it is named
``'version_${self.version}'`` but it can be overridden by passing a string value
for the constructor's version parameter instead of ``None`` or an int.
"""
# create ... | [
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TensorBoardLogger.experiment | (self) | r"""
Actual tensorboard object. To use TensorBoard features in your
:class:`~pytorch_lightning.core.lightning.LightningModule` do the following.
Example::
self.logger.experiment.some_tensorboard_function()
| r"""
Actual tensorboard object. To use TensorBoard features in your
:class:`~pytorch_lightning.core.lightning.LightningModule` do the following. | def experiment(self) -> SummaryWriter:
r"""
Actual tensorboard object. To use TensorBoard features in your
:class:`~pytorch_lightning.core.lightning.LightningModule` do the following.
Example::
self.logger.experiment.some_tensorboard_function()
"""
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TensorBoardLogger.log_hyperparams | (
self,
params: Union[Dict[str, Any], Namespace],
metrics: Optional[Dict[str, Any]] = None,
) |
Record hyperparameters. TensorBoard logs with and without saved hyperparameters
are incompatible, the hyperparameters are then not displayed in the TensorBoard.
Please delete or move the previously saved logs to display the new ones with hyperparameters.
Args:
params: a dic... |
Record hyperparameters. TensorBoard logs with and without saved hyperparameters
are incompatible, the hyperparameters are then not displayed in the TensorBoard.
Please delete or move the previously saved logs to display the new ones with hyperparameters. | def log_hyperparams(
self,
params: Union[Dict[str, Any], Namespace],
metrics: Optional[Dict[str, Any]] = None,
) -> None:
"""
Record hyperparameters. TensorBoard logs with and without saved hyperparameters
are incompatible, the hyperparameters are then not displayed i... | [
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verify_matching_signatures | (implementation, dispatcher) | Verify that a dispatcher function has the right signature. | Verify that a dispatcher function has the right signature. | def verify_matching_signatures(implementation, dispatcher):
"""Verify that a dispatcher function has the right signature."""
implementation_spec = ArgSpec(*getargspec(implementation))
dispatcher_spec = ArgSpec(*getargspec(dispatcher))
if (implementation_spec.args != dispatcher_spec.args or
... | [
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71,
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90,
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] | python | en | ['en', 'en', 'en'] | True |
set_module | (module) | Decorator for overriding __module__ on a function or class.
Example usage::
@set_module('numpy')
def example():
pass
assert example.__module__ == 'numpy'
| Decorator for overriding __module__ on a function or class. | def set_module(module):
"""Decorator for overriding __module__ on a function or class.
Example usage::
@set_module('numpy')
def example():
pass
assert example.__module__ == 'numpy'
"""
def decorator(func):
if module is not None:
func.__module__ ... | [
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array_function_dispatch | (dispatcher, module=None, verify=True,
docs_from_dispatcher=False) | Decorator for adding dispatch with the __array_function__ protocol.
See NEP-18 for example usage.
Parameters
----------
dispatcher : callable
Function that when called like ``dispatcher(*args, **kwargs)`` with
arguments from the NumPy function call returns an iterable of
array-... | Decorator for adding dispatch with the __array_function__ protocol. | def array_function_dispatch(dispatcher, module=None, verify=True,
docs_from_dispatcher=False):
"""Decorator for adding dispatch with the __array_function__ protocol.
See NEP-18 for example usage.
Parameters
----------
dispatcher : callable
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array_function_from_dispatcher | (
implementation, module=None, verify=True, docs_from_dispatcher=True) | Like array_function_dispatcher, but with function arguments flipped. | Like array_function_dispatcher, but with function arguments flipped. | def array_function_from_dispatcher(
implementation, module=None, verify=True, docs_from_dispatcher=True):
"""Like array_function_dispatcher, but with function arguments flipped."""
def decorator(dispatcher):
return array_function_dispatch(
dispatcher, module, verify=verify,
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203,
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test_state_dict | (tmpdir) | test that metric states can be removed and added to state dict | test that metric states can be removed and added to state dict | def test_state_dict(tmpdir):
""" test that metric states can be removed and added to state dict """
metric = Dummy()
assert metric.state_dict() == OrderedDict()
metric.persistent(True)
assert metric.state_dict() == OrderedDict(x=0)
metric.persistent(False)
assert metric.state_dict() == Order... | [
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test_child_metric_state_dict | () | test that child metric states will be added to parent state dict | test that child metric states will be added to parent state dict | def test_child_metric_state_dict():
""" test that child metric states will be added to parent state dict """
class TestModule(nn.Module):
def __init__(self):
super().__init__()
self.metric = Dummy()
self.metric.add_state('a', torch.tensor(0), persistent=True)
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test_metric_collection_wrong_input | (tmpdir) | Check that errors are raised on wrong input | Check that errors are raised on wrong input | def test_metric_collection_wrong_input(tmpdir):
""" Check that errors are raised on wrong input """
m1 = DummyMetric1()
# Not all input are metrics (list)
with pytest.raises(ValueError):
_ = MetricCollection([m1, 5])
# Not all input are metrics (dict)
with pytest.raises(ValueError):
... | [
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test_metric_collection_args_kwargs | (tmpdir) | Check that args and kwargs gets passed correctly in metric collection,
Checks both update and forward method
| Check that args and kwargs gets passed correctly in metric collection,
Checks both update and forward method
| def test_metric_collection_args_kwargs(tmpdir):
""" Check that args and kwargs gets passed correctly in metric collection,
Checks both update and forward method
"""
m1 = DummyMetric1()
m2 = DummyMetric2()
metric_collection = MetricCollection([m1, m2])
# args gets passed to all metrics
... | [
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find_Triplets_with_zero_sum | (arr, num) | find triplets in a given array whose sum is zero
Parameteres :
arr : input array
num = size of input array
Output :
if triplets found return their values
else return "No Triplet Found"
| find triplets in a given array whose sum is zero
Parameteres :
arr : input array
num = size of input array
Output :
if triplets found return their values
else return "No Triplet Found"
| def find_Triplets_with_zero_sum(arr, num):
''' find triplets in a given array whose sum is zero
Parameteres :
arr : input array
num = size of input array
Output :
if triplets found return their values
else return "No Triplet Found"
... | [
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BottomUpBaseDataset.__len__ | (self) | Get dataset length. | Get dataset length. | def __len__(self):
"""Get dataset length."""
return len(self.img_ids) | [
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BottomUpBaseDataset._get_single | (self, idx) | Get anno for a single image. | Get anno for a single image. | def _get_single(self, idx):
"""Get anno for a single image."""
raise NotImplementedError | [
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BottomUpBaseDataset.prepare_train_img | (self, idx) | Prepare image for training given the index. | Prepare image for training given the index. | def prepare_train_img(self, idx):
"""Prepare image for training given the index."""
results = copy.deepcopy(self._get_single(idx))
results['ann_info'] = self.ann_info
return self.pipeline(results) | [
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BottomUpBaseDataset.prepare_test_img | (self, idx) | Prepare image for testing given the index. | Prepare image for testing given the index. | def prepare_test_img(self, idx):
"""Prepare image for testing given the index."""
results = copy.deepcopy(self._get_single(idx))
results['ann_info'] = self.ann_info
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BottomUpBaseDataset.__getitem__ | (self, idx) | Get the sample for either training or testing given index. | Get the sample for either training or testing given index. | def __getitem__(self, idx):
"""Get the sample for either training or testing given index."""
if self.test_mode:
return self.prepare_test_img(idx)
return self.prepare_train_img(idx) | [
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Tuner.scale_batch_size | (
self,
model,
mode: str = 'power',
steps_per_trial: int = 3,
init_val: int = 2,
max_trials: int = 25,
batch_arg_name: str = 'batch_size',
**fit_kwargs
) | r"""
Will iteratively try to find the largest batch size for a given model
that does not give an out of memory (OOM) error.
Args:
model: Model to fit.
mode: string setting the search mode. Either `power` or `binsearch`.
If mode is `power` we keep multipl... | r"""
Will iteratively try to find the largest batch size for a given model
that does not give an out of memory (OOM) error. | def scale_batch_size(
self,
model,
mode: str = 'power',
steps_per_trial: int = 3,
init_val: int = 2,
max_trials: int = 25,
batch_arg_name: str = 'batch_size',
**fit_kwargs
):
r"""
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57,
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112,
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MatrixTestCase.testDet | (self) | Test det function | Test det function | def testDet(self):
"Test det function"
print(self.typeStr, "... ", end=' ', file=sys.stderr)
det = Matrix.__dict__[self.typeStr + "Det"]
matrix = [[8, 7], [6, 9]]
self.assertEqual(det(matrix), 30) | [
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] | python | da | ['da', 'no', 'en'] | False |
MatrixTestCase.testDetBadList | (self) | Test det function with bad list | Test det function with bad list | def testDetBadList(self):
"Test det function with bad list"
print(self.typeStr, "... ", end=' ', file=sys.stderr)
det = Matrix.__dict__[self.typeStr + "Det"]
matrix = [[8, 7], ["e", "pi"]]
self.assertRaises(BadListError, det, matrix) | [
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MatrixTestCase.testDetWrongDim | (self) | Test det function with wrong dimensions | Test det function with wrong dimensions | def testDetWrongDim(self):
"Test det function with wrong dimensions"
print(self.typeStr, "... ", end=' ', file=sys.stderr)
det = Matrix.__dict__[self.typeStr + "Det"]
matrix = [8, 7]
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"self",
".",
"typeStr",
"+",
"\... | [
43,
4
] | [
48,
49
] | python | en | ['en', 'en', 'en'] | True |
MatrixTestCase.testDetWrongSize | (self) | Test det function with wrong size | Test det function with wrong size | def testDetWrongSize(self):
"Test det function with wrong size"
print(self.typeStr, "... ", end=' ', file=sys.stderr)
det = Matrix.__dict__[self.typeStr + "Det"]
matrix = [[8, 7, 6], [5, 4, 3], [2, 1, 0]]
self.assertRaises(TypeError, det, matrix) | [
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"... | [
51,
4
] | [
56,
49
] | python | en | ['en', 'en', 'en'] | True |
MatrixTestCase.testDetNonContainer | (self) | Test det function with non-container | Test det function with non-container | def testDetNonContainer(self):
"Test det function with non-container"
print(self.typeStr, "... ", end=' ', file=sys.stderr)
det = Matrix.__dict__[self.typeStr + "Det"]
self.assertRaises(TypeError, det, None) | [
"def",
"testDetNonContainer",
"(",
"self",
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".",
"typeStr",
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... | [
59,
4
] | [
63,
47
] | python | en | ['en', 'en', 'en'] | True |
MatrixTestCase.testMax | (self) | Test max function | Test max function | def testMax(self):
"Test max function"
print(self.typeStr, "... ", end=' ', file=sys.stderr)
max = Matrix.__dict__[self.typeStr + "Max"]
matrix = [[6, 5, 4], [3, 2, 1]]
self.assertEqual(max(matrix), 6) | [
"def",
"testMax",
"(",
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"typeStr",
"+",
"\"Max\"",... | [
66,
4
] | [
71,
40
] | python | en | ['en', 'en', 'en'] | True |
MatrixTestCase.testMaxBadList | (self) | Test max function with bad list | Test max function with bad list | def testMaxBadList(self):
"Test max function with bad list"
print(self.typeStr, "... ", end=' ', file=sys.stderr)
max = Matrix.__dict__[self.typeStr + "Max"]
matrix = [[6, "five", 4], ["three", 2, "one"]]
self.assertRaises(BadListError, max, matrix) | [
"def",
"testMaxBadList",
"(",
"self",
")",
":",
"print",
"(",
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".",
"typeStr",
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".",
"__dict__",
"[",
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".",
"typeStr",
"+",
"\"... | [
74,
4
] | [
79,
52
] | python | en | ['en', 'en', 'en'] | True |
MatrixTestCase.testMaxNonContainer | (self) | Test max function with non-container | Test max function with non-container | def testMaxNonContainer(self):
"Test max function with non-container"
print(self.typeStr, "... ", end=' ', file=sys.stderr)
max = Matrix.__dict__[self.typeStr + "Max"]
self.assertRaises(TypeError, max, None) | [
"def",
"testMaxNonContainer",
"(",
"self",
")",
":",
"print",
"(",
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"typeStr",
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"max",
"=",
"Matrix",
".",
"__dict__",
"[",
"self",
".",
"typeStr",
"+",
... | [
82,
4
] | [
86,
47
] | python | en | ['en', 'en', 'en'] | True |
MatrixTestCase.testMaxWrongDim | (self) | Test max function with wrong dimensions | Test max function with wrong dimensions | def testMaxWrongDim(self):
"Test max function with wrong dimensions"
print(self.typeStr, "... ", end=' ', file=sys.stderr)
max = Matrix.__dict__[self.typeStr + "Max"]
self.assertRaises(TypeError, max, [0, 1, 2, 3]) | [
"def",
"testMaxWrongDim",
"(",
"self",
")",
":",
"print",
"(",
"self",
".",
"typeStr",
",",
"\"... \"",
",",
"end",
"=",
"' '",
",",
"file",
"=",
"sys",
".",
"stderr",
")",
"max",
"=",
"Matrix",
".",
"__dict__",
"[",
"self",
".",
"typeStr",
"+",
"\... | [
89,
4
] | [
93,
55
] | python | en | ['en', 'en', 'en'] | True |
MatrixTestCase.testMin | (self) | Test min function | Test min function | def testMin(self):
"Test min function"
print(self.typeStr, "... ", end=' ', file=sys.stderr)
min = Matrix.__dict__[self.typeStr + "Min"]
matrix = [[9, 8], [7, 6], [5, 4]]
self.assertEqual(min(matrix), 4) | [
"def",
"testMin",
"(",
"self",
")",
":",
"print",
"(",
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"__dict__",
"[",
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"typeStr",
"+",
"\"Min\"",... | [
96,
4
] | [
101,
40
] | python | en | ['de', 'en', 'en'] | True |
MatrixTestCase.testMinBadList | (self) | Test min function with bad list | Test min function with bad list | def testMinBadList(self):
"Test min function with bad list"
print(self.typeStr, "... ", end=' ', file=sys.stderr)
min = Matrix.__dict__[self.typeStr + "Min"]
matrix = [["nine", "eight"], ["seven", "six"]]
self.assertRaises(BadListError, min, matrix) | [
"def",
"testMinBadList",
"(",
"self",
")",
":",
"print",
"(",
"self",
".",
"typeStr",
",",
"\"... \"",
",",
"end",
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"' '",
",",
"file",
"=",
"sys",
".",
"stderr",
")",
"min",
"=",
"Matrix",
".",
"__dict__",
"[",
"self",
".",
"typeStr",
"+",
"\"... | [
104,
4
] | [
109,
52
] | python | en | ['en', 'en', 'en'] | True |
MatrixTestCase.testMinWrongDim | (self) | Test min function with wrong dimensions | Test min function with wrong dimensions | def testMinWrongDim(self):
"Test min function with wrong dimensions"
print(self.typeStr, "... ", end=' ', file=sys.stderr)
min = Matrix.__dict__[self.typeStr + "Min"]
self.assertRaises(TypeError, min, [1, 3, 5, 7, 9]) | [
"def",
"testMinWrongDim",
"(",
"self",
")",
":",
"print",
"(",
"self",
".",
"typeStr",
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"\"... \"",
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"end",
"=",
"' '",
",",
"file",
"=",
"sys",
".",
"stderr",
")",
"min",
"=",
"Matrix",
".",
"__dict__",
"[",
"self",
".",
"typeStr",
"+",
"\... | [
112,
4
] | [
116,
58
] | python | en | ['en', 'en', 'en'] | True |
MatrixTestCase.testMinNonContainer | (self) | Test min function with non-container | Test min function with non-container | def testMinNonContainer(self):
"Test min function with non-container"
print(self.typeStr, "... ", end=' ', file=sys.stderr)
min = Matrix.__dict__[self.typeStr + "Min"]
self.assertRaises(TypeError, min, False) | [
"def",
"testMinNonContainer",
"(",
"self",
")",
":",
"print",
"(",
"self",
".",
"typeStr",
",",
"\"... \"",
",",
"end",
"=",
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",",
"file",
"=",
"sys",
".",
"stderr",
")",
"min",
"=",
"Matrix",
".",
"__dict__",
"[",
"self",
".",
"typeStr",
"+",
... | [
119,
4
] | [
123,
48
] | python | en | ['en', 'en', 'en'] | True |
MatrixTestCase.testScale | (self) | Test scale function | Test scale function | def testScale(self):
"Test scale function"
print(self.typeStr, "... ", end=' ', file=sys.stderr)
scale = Matrix.__dict__[self.typeStr + "Scale"]
matrix = np.array([[1, 2, 3], [2, 1, 2], [3, 2, 1]], self.typeCode)
scale(matrix, 4)
self.assertEqual((matrix == [[4, 8, 12], [... | [
"def",
"testScale",
"(",
"self",
")",
":",
"print",
"(",
"self",
".",
"typeStr",
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"\"... \"",
",",
"end",
"=",
"' '",
",",
"file",
"=",
"sys",
".",
"stderr",
")",
"scale",
"=",
"Matrix",
".",
"__dict__",
"[",
"self",
".",
"typeStr",
"+",
"\"Sca... | [
126,
4
] | [
132,
85
] | python | en | ['it', 'en', 'en'] | True |
MatrixTestCase.testScaleWrongDim | (self) | Test scale function with wrong dimensions | Test scale function with wrong dimensions | def testScaleWrongDim(self):
"Test scale function with wrong dimensions"
print(self.typeStr, "... ", end=' ', file=sys.stderr)
scale = Matrix.__dict__[self.typeStr + "Scale"]
matrix = np.array([1, 2, 2, 1], self.typeCode)
self.assertRaises(TypeError, scale, matrix) | [
"def",
"testScaleWrongDim",
"(",
"self",
")",
":",
"print",
"(",
"self",
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"typeStr",
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".",
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"[",
"self",
".",
"typeStr",
"+",
... | [
135,
4
] | [
140,
51
] | python | en | ['en', 'en', 'en'] | True |
MatrixTestCase.testScaleWrongSize | (self) | Test scale function with wrong size | Test scale function with wrong size | def testScaleWrongSize(self):
"Test scale function with wrong size"
print(self.typeStr, "... ", end=' ', file=sys.stderr)
scale = Matrix.__dict__[self.typeStr + "Scale"]
matrix = np.array([[1, 2], [2, 1]], self.typeCode)
self.assertRaises(TypeError, scale, matrix) | [
"def",
"testScaleWrongSize",
"(",
"self",
")",
":",
"print",
"(",
"self",
".",
"typeStr",
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".",
"stderr",
")",
"scale",
"=",
"Matrix",
".",
"__dict__",
"[",
"self",
".",
"typeStr",
"+",... | [
143,
4
] | [
148,
51
] | python | en | ['en', 'en', 'en'] | True |
MatrixTestCase.testScaleWrongType | (self) | Test scale function with wrong type | Test scale function with wrong type | def testScaleWrongType(self):
"Test scale function with wrong type"
print(self.typeStr, "... ", end=' ', file=sys.stderr)
scale = Matrix.__dict__[self.typeStr + "Scale"]
matrix = np.array([[1, 2, 3], [2, 1, 2], [3, 2, 1]], 'c')
self.assertRaises(TypeError, scale, matrix) | [
"def",
"testScaleWrongType",
"(",
"self",
")",
":",
"print",
"(",
"self",
".",
"typeStr",
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"stderr",
")",
"scale",
"=",
"Matrix",
".",
"__dict__",
"[",
"self",
".",
"typeStr",
"+",... | [
151,
4
] | [
156,
51
] | python | en | ['en', 'en', 'en'] | True |
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