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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
parse_editable | (editable_req) | Parses an editable requirement into:
- a requirement name
- an URL
- extras
- editable options
Accepted requirements:
svn+http://blahblah@rev#egg=Foobar[baz]&subdirectory=version_subdir
.[some_extra]
| Parses an editable requirement into:
- a requirement name
- an URL
- extras
- editable options
Accepted requirements:
svn+http://blahblah | def parse_editable(editable_req):
# type: (str) -> Tuple[Optional[str], str, Set[str]]
"""Parses an editable requirement into:
- a requirement name
- an URL
- extras
- editable options
Accepted requirements:
svn+http://blahblah@rev#egg=Foobar[baz]&subdirectory=version... | [
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deduce_helpful_msg | (req) | Returns helpful msg in case requirements file does not exist,
or cannot be parsed.
:params req: Requirements file path
| Returns helpful msg in case requirements file does not exist,
or cannot be parsed. | def deduce_helpful_msg(req):
# type: (str) -> str
"""Returns helpful msg in case requirements file does not exist,
or cannot be parsed.
:params req: Requirements file path
"""
msg = ""
if os.path.exists(req):
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_looks_like_path | (name) | Checks whether the string "looks like" a path on the filesystem.
This does not check whether the target actually exists, only judge from the
appearance.
Returns true if any of the following conditions is true:
* a path separator is found (either os.path.sep or os.path.altsep);
* a dot is found (wh... | Checks whether the string "looks like" a path on the filesystem. | def _looks_like_path(name):
# type: (str) -> bool
"""Checks whether the string "looks like" a path on the filesystem.
This does not check whether the target actually exists, only judge from the
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Returns true if any of the following conditions is true:
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_get_url_from_path | (path, name) |
First, it checks whether a provided path is an installable directory
(e.g. it has a setup.py). If it is, returns the path.
If false, check if the path is an archive file (such as a .whl).
The function checks if the path is a file. If false, if the path has
an @, it will treat it as a PEP 440 URL r... |
First, it checks whether a provided path is an installable directory
(e.g. it has a setup.py). If it is, returns the path. | def _get_url_from_path(path, name):
# type: (str, str) -> Optional[str]
"""
First, it checks whether a provided path is an installable directory
(e.g. it has a setup.py). If it is, returns the path.
If false, check if the path is an archive file (such as a .whl).
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install_req_from_line | (
name, # type: str
comes_from=None, # type: Optional[Union[str, InstallRequirement]]
use_pep517=None, # type: Optional[bool]
isolated=False, # type: bool
options=None, # type: Optional[Dict[str, Any]]
constraint=False, # type: bool
line_source=None, # type: Optional[str]
user_sup... | Creates an InstallRequirement from a name, which might be a
requirement, directory containing 'setup.py', filename, or URL.
:param line_source: An optional string describing where the line is from,
for logging purposes in case of an error.
| Creates an InstallRequirement from a name, which might be a
requirement, directory containing 'setup.py', filename, or URL. | def install_req_from_line(
name, # type: str
comes_from=None, # type: Optional[Union[str, InstallRequirement]]
use_pep517=None, # type: Optional[bool]
isolated=False, # type: bool
options=None, # type: Optional[Dict[str, Any]]
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line_source=None, # type: O... | [
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MultipleObjectMixin.get_queryset | (self) |
Return the list of items for this view.
The return value must be an iterable and may be an instance of
`QuerySet` in which case `QuerySet` specific behavior will be enabled.
|
Return the list of items for this view. | def get_queryset(self):
"""
Return the list of items for this view.
The return value must be an iterable and may be an instance of
`QuerySet` in which case `QuerySet` specific behavior will be enabled.
"""
if self.queryset is not None:
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MultipleObjectMixin.get_ordering | (self) |
Return the field or fields to use for ordering the queryset.
|
Return the field or fields to use for ordering the queryset.
| def get_ordering(self):
"""
Return the field or fields to use for ordering the queryset.
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MultipleObjectMixin.paginate_queryset | (self, queryset, page_size) |
Paginate the queryset, if needed.
|
Paginate the queryset, if needed.
| def paginate_queryset(self, queryset, page_size):
"""
Paginate the queryset, if needed.
"""
paginator = self.get_paginator(
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MultipleObjectMixin.get_paginate_by | (self, queryset) |
Get the number of items to paginate by, or ``None`` for no pagination.
|
Get the number of items to paginate by, or ``None`` for no pagination.
| def get_paginate_by(self, queryset):
"""
Get the number of items to paginate by, or ``None`` for no pagination.
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MultipleObjectMixin.get_paginator | (self, queryset, per_page, orphans=0,
allow_empty_first_page=True, **kwargs) |
Return an instance of the paginator for this view.
|
Return an instance of the paginator for this view.
| def get_paginator(self, queryset, per_page, orphans=0,
allow_empty_first_page=True, **kwargs):
"""
Return an instance of the paginator for this view.
"""
return self.paginator_class(
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MultipleObjectMixin.get_paginate_orphans | (self) |
Returns the maximum number of orphans extend the last page by when
paginating.
|
Returns the maximum number of orphans extend the last page by when
paginating.
| def get_paginate_orphans(self):
"""
Returns the maximum number of orphans extend the last page by when
paginating.
"""
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MultipleObjectMixin.get_allow_empty | (self) |
Returns ``True`` if the view should display empty lists, and ``False``
if a 404 should be raised instead.
|
Returns ``True`` if the view should display empty lists, and ``False``
if a 404 should be raised instead.
| def get_allow_empty(self):
"""
Returns ``True`` if the view should display empty lists, and ``False``
if a 404 should be raised instead.
"""
return self.allow_empty | [
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MultipleObjectMixin.get_context_object_name | (self, object_list) |
Get the name of the item to be used in the context.
|
Get the name of the item to be used in the context.
| def get_context_object_name(self, object_list):
"""
Get the name of the item to be used in the context.
"""
if self.context_object_name:
return self.context_object_name
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MultipleObjectMixin.get_context_data | (self, **kwargs) |
Get the context for this view.
|
Get the context for this view.
| def get_context_data(self, **kwargs):
"""
Get the context for this view.
"""
queryset = kwargs.pop('object_list', self.object_list)
page_size = self.get_paginate_by(queryset)
context_object_name = self.get_context_object_name(queryset)
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MultipleObjectTemplateResponseMixin.get_template_names | (self) |
Return a list of template names to be used for the request. Must return
a list. May not be called if render_to_response is overridden.
|
Return a list of template names to be used for the request. Must return
a list. May not be called if render_to_response is overridden.
| def get_template_names(self):
"""
Return a list of template names to be used for the request. Must return
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"""
try:
names = super(MultipleObjectTemplateResponseMixin, self).get_template_names()
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noise_level | (freq_eff, bandwidth, tau_time, antenna_set, Ncore, Nremote,
Nintl) |
Returns the theoretical noise level (in Jy) given the supplied array
antenna_set.
:param bandwidth: in Hz
:param tau_time: in seconds
:param inner: in case of LBA, inner or outer
:param antenna_set: LBA_INNER, LBA_OUTER, LBA_SPARSE, LBA or HBA
|
Returns the theoretical noise level (in Jy) given the supplied array
antenna_set. | def noise_level(freq_eff, bandwidth, tau_time, antenna_set, Ncore, Nremote,
Nintl):
"""
Returns the theoretical noise level (in Jy) given the supplied array
antenna_set.
:param bandwidth: in Hz
:param tau_time: in seconds
:param inner: in case of LBA, inner or outer
:param a... | [
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Aeff_dipole | (freq_eff, distance=None) |
The effective area of each dipole in the array is determined by its
distance to the nearest dipole (d) within the full array.
:param freq_eff: Frequency
:param distance: Distance to nearest dipole, only required for LBA.
|
The effective area of each dipole in the array is determined by its
distance to the nearest dipole (d) within the full array. | def Aeff_dipole(freq_eff, distance=None):
"""
The effective area of each dipole in the array is determined by its
distance to the nearest dipole (d) within the full array.
:param freq_eff: Frequency
:param distance: Distance to nearest dipole, only required for LBA.
"""
wavelength = scipy.c... | [
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system_sensitivity | (freq_eff, Aeff) |
Returns the SEFD of a system, given the freq_eff and effective
collecting area. Returns SEFD in Jansky's.
|
Returns the SEFD of a system, given the freq_eff and effective
collecting area. Returns SEFD in Jansky's.
| def system_sensitivity(freq_eff, Aeff):
"""
Returns the SEFD of a system, given the freq_eff and effective
collecting area. Returns SEFD in Jansky's.
"""
wavelength = scipy.constants.c / freq_eff
# Ts0 = 60 +/- 20 K for Galactic latitudes between 10 and 90 degrees.
Ts0 = 60
# system ef... | [
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Configuration.make_static_lib_name | (self, name) | Return the full filename for the specified library name | Return the full filename for the specified library name | def make_static_lib_name(self, name):
"""Return the full filename for the specified library name"""
if self.is_windows:
assert name == 'c++' # Only allow libc++ to use this function for now.
return 'lib' + name + '.lib'
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Configuration.configure_use_clang_verify | (self) | If set, run clang with -verify on failing tests. | If set, run clang with -verify on failing tests. | def configure_use_clang_verify(self):
'''If set, run clang with -verify on failing tests.'''
self.use_clang_verify = self.get_lit_bool('use_clang_verify')
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Configuration.configure_use_thread_safety | (self) | If set, run clang with -verify on failing tests. | If set, run clang with -verify on failing tests. | def configure_use_thread_safety(self):
'''If set, run clang with -verify on failing tests.'''
has_thread_safety = self.cxx.hasCompileFlag('-Werror=thread-safety')
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Configuration.parse_config_site_and_add_features | (self, header) | parse_config_site_and_add_features - Deduce and add the test
features that that are implied by the #define's in the __config_site
header. Return a dictionary containing the macros found in the
'__config_site' header.
| parse_config_site_and_add_features - Deduce and add the test
features that that are implied by the #define's in the __config_site
header. Return a dictionary containing the macros found in the
'__config_site' header.
| def parse_config_site_and_add_features(self, header):
""" parse_config_site_and_add_features - Deduce and add the test
features that that are implied by the #define's in the __config_site
header. Return a dictionary containing the macros found in the
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dumps | (obj, key=None, salt='django.core.signing', serializer=JSONSerializer, compress=False) |
Returns URL-safe, sha1 signed base64 compressed JSON string. If key is
None, settings.SECRET_KEY is used instead.
If compress is True (not the default) checks if compressing using zlib can
save some space. Prepends a '.' to signify compression. This is included
in the signature, to protect against... |
Returns URL-safe, sha1 signed base64 compressed JSON string. If key is
None, settings.SECRET_KEY is used instead. | def dumps(obj, key=None, salt='django.core.signing', serializer=JSONSerializer, compress=False):
"""
Returns URL-safe, sha1 signed base64 compressed JSON string. If key is
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loads | (s, key=None, salt='django.core.signing', serializer=JSONSerializer, max_age=None) |
Reverse of dumps(), raises BadSignature if signature fails.
The serializer is expected to accept a bytestring.
|
Reverse of dumps(), raises BadSignature if signature fails. | def loads(s, key=None, salt='django.core.signing', serializer=JSONSerializer, max_age=None):
"""
Reverse of dumps(), raises BadSignature if signature fails.
The serializer is expected to accept a bytestring.
"""
# TimestampSigner.unsign always returns unicode but base64 and zlib
# compression o... | [
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TimestampSigner.unsign | (self, value, max_age=None) |
Retrieve original value and check it wasn't signed more
than max_age seconds ago.
|
Retrieve original value and check it wasn't signed more
than max_age seconds ago.
| def unsign(self, value, max_age=None):
"""
Retrieve original value and check it wasn't signed more
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LayerMapping.__init__ | (self, model, data, mapping, layer=0,
source_srs=None, encoding='utf-8',
transaction_mode='commit_on_success',
transform=True, unique=None, using=None) |
A LayerMapping object is initialized using the given Model (not an instance),
a DataSource (or string path to an OGR-supported data file), and a mapping
dictionary. See the module level docstring for more details and keyword
argument usage.
|
A LayerMapping object is initialized using the given Model (not an instance),
a DataSource (or string path to an OGR-supported data file), and a mapping
dictionary. See the module level docstring for more details and keyword
argument usage.
| def __init__(self, model, data, mapping, layer=0,
source_srs=None, encoding='utf-8',
transaction_mode='commit_on_success',
transform=True, unique=None, using=None):
"""
A LayerMapping object is initialized using the given Model (not an instance),
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LayerMapping.check_fid_range | (self, fid_range) | This checks the `fid_range` keyword. | This checks the `fid_range` keyword. | def check_fid_range(self, fid_range):
"This checks the `fid_range` keyword."
if fid_range:
if isinstance(fid_range, (tuple, list)):
return slice(*fid_range)
elif isinstance(fid_range, slice):
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LayerMapping.check_layer | (self) |
This checks the Layer metadata, and ensures that it is compatible
with the mapping information and model. Unlike previous revisions,
there is no need to increment through each feature in the Layer.
|
This checks the Layer metadata, and ensures that it is compatible
with the mapping information and model. Unlike previous revisions,
there is no need to increment through each feature in the Layer.
| def check_layer(self):
"""
This checks the Layer metadata, and ensures that it is compatible
with the mapping information and model. Unlike previous revisions,
there is no need to increment through each feature in the Layer.
"""
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LayerMapping.check_srs | (self, source_srs) | Checks the compatibility of the given spatial reference object. | Checks the compatibility of the given spatial reference object. | def check_srs(self, source_srs):
"Checks the compatibility of the given spatial reference object."
if isinstance(source_srs, SpatialReference):
sr = source_srs
elif isinstance(source_srs, self.spatial_backend.spatial_ref_sys()):
sr = source_srs.srs
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LayerMapping.check_unique | (self, unique) | Checks the `unique` keyword parameter -- may be a sequence or string. | Checks the `unique` keyword parameter -- may be a sequence or string. | def check_unique(self, unique):
"Checks the `unique` keyword parameter -- may be a sequence or string."
if isinstance(unique, (list, tuple)):
# List of fields to determine uniqueness with
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LayerMapping.feature_kwargs | (self, feat) |
Given an OGR Feature, this will return a dictionary of keyword arguments
for constructing the mapped model.
|
Given an OGR Feature, this will return a dictionary of keyword arguments
for constructing the mapped model.
| def feature_kwargs(self, feat):
"""
Given an OGR Feature, this will return a dictionary of keyword arguments
for constructing the mapped model.
"""
# The keyword arguments for model construction.
kwargs = {}
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LayerMapping.unique_kwargs | (self, kwargs) |
Given the feature keyword arguments (from `feature_kwargs`) this routine
will construct and return the uniqueness keyword arguments -- a subset
of the feature kwargs.
|
Given the feature keyword arguments (from `feature_kwargs`) this routine
will construct and return the uniqueness keyword arguments -- a subset
of the feature kwargs.
| def unique_kwargs(self, kwargs):
"""
Given the feature keyword arguments (from `feature_kwargs`) this routine
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LayerMapping.verify_ogr_field | (self, ogr_field, model_field) |
Verifies if the OGR Field contents are acceptable to the Django
model field. If they are, the verified value is returned,
otherwise the proper exception is raised.
|
Verifies if the OGR Field contents are acceptable to the Django
model field. If they are, the verified value is returned,
otherwise the proper exception is raised.
| def verify_ogr_field(self, ogr_field, model_field):
"""
Verifies if the OGR Field contents are acceptable to the Django
model field. If they are, the verified value is returned,
otherwise the proper exception is raised.
"""
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LayerMapping.verify_fk | (self, feat, rel_model, rel_mapping) |
Given an OGR Feature, the related model and its dictionary mapping,
this routine will retrieve the related model for the ForeignKey
mapping.
|
Given an OGR Feature, the related model and its dictionary mapping,
this routine will retrieve the related model for the ForeignKey
mapping.
| def verify_fk(self, feat, rel_model, rel_mapping):
"""
Given an OGR Feature, the related model and its dictionary mapping,
this routine will retrieve the related model for the ForeignKey
mapping.
"""
# TODO: It is expensive to retrieve a model for every record --
... | [
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LayerMapping.verify_geom | (self, geom, model_field) |
Verifies the geometry -- will construct and return a GeometryCollection
if necessary (for example if the model field is MultiPolygonField while
the mapped shapefile only contains Polygons).
|
Verifies the geometry -- will construct and return a GeometryCollection
if necessary (for example if the model field is MultiPolygonField while
the mapped shapefile only contains Polygons).
| def verify_geom(self, geom, model_field):
"""
Verifies the geometry -- will construct and return a GeometryCollection
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LayerMapping.coord_transform | (self) | Returns the coordinate transformation object. | Returns the coordinate transformation object. | def coord_transform(self):
"Returns the coordinate transformation object."
SpatialRefSys = self.spatial_backend.spatial_ref_sys()
try:
# Getting the target spatial reference system
target_srs = SpatialRefSys.objects.using(self.using).get(srid=self.geo_field.srid).srs
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LayerMapping.geometry_field | (self) | Returns the GeometryField instance associated with the geographic column. | Returns the GeometryField instance associated with the geographic column. | def geometry_field(self):
"Returns the GeometryField instance associated with the geographic column."
# Use `get_field()` on the model's options so that we
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LayerMapping.make_multi | (self, geom_type, model_field) |
Given the OGRGeomType for a geometry and its associated GeometryField,
determine whether the geometry should be turned into a GeometryCollection.
|
Given the OGRGeomType for a geometry and its associated GeometryField,
determine whether the geometry should be turned into a GeometryCollection.
| def make_multi(self, geom_type, model_field):
"""
Given the OGRGeomType for a geometry and its associated GeometryField,
determine whether the geometry should be turned into a GeometryCollection.
"""
return (geom_type.num in self.MULTI_TYPES and
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LayerMapping.save | (self, verbose=False, fid_range=False, step=False,
progress=False, silent=False, stream=sys.stdout, strict=False) |
Saves the contents from the OGR DataSource Layer into the database
according to the mapping dictionary given at initialization.
Keyword Parameters:
verbose:
If set, information will be printed subsequent to each model save
executed on the database.
fid_... |
Saves the contents from the OGR DataSource Layer into the database
according to the mapping dictionary given at initialization. | def save(self, verbose=False, fid_range=False, step=False,
progress=False, silent=False, stream=sys.stdout, strict=False):
"""
Saves the contents from the OGR DataSource Layer into the database
according to the mapping dictionary given at initialization.
Keyword Parameters:... | [
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Feature.__init__ | (self, feat, layer) |
Initializes Feature from a pointer and its Layer object.
|
Initializes Feature from a pointer and its Layer object.
| def __init__(self, feat, layer):
"""
Initializes Feature from a pointer and its Layer object.
"""
if not feat:
raise GDALException('Cannot create OGR Feature, invalid pointer given.')
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Feature.__getitem__ | (self, index) |
Gets the Field object at the specified index, which may be either
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is not the field's _value_ -- use the `get` method instead to
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Gets the Field object at the specified index, which may be either
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Feature.__iter__ | (self) | Iterates over each field in the Feature. | Iterates over each field in the Feature. | def __iter__(self):
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Feature.__len__ | (self) | Returns the count of fields in this feature. | Returns the count of fields in this feature. | def __len__(self):
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Feature.__str__ | (self) | The string name of the feature. | The string name of the feature. | def __str__(self):
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Feature.__eq__ | (self, other) | Does equivalence testing on the features. | Does equivalence testing on the features. | def __eq__(self, other):
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Feature.fid | (self) | Returns the feature identifier. | Returns the feature identifier. | def fid(self):
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Feature.layer_name | (self) | Returns the name of the layer for the feature. | Returns the name of the layer for the feature. | def layer_name(self):
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Feature.num_fields | (self) | Returns the number of fields in the Feature. | Returns the number of fields in the Feature. | def num_fields(self):
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Feature.fields | (self) | Returns a list of fields in the Feature. | Returns a list of fields in the Feature. | def fields(self):
"Returns a list of fields in the Feature."
return [capi.get_field_name(capi.get_field_defn(self._layer._ldefn, i))
for i in range(self.num_fields)] | [
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Feature.geom | (self) | Returns the OGR Geometry for this Feature. | Returns the OGR Geometry for this Feature. | def geom(self):
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Feature.geom_type | (self) | Returns the OGR Geometry Type for this Feture. | Returns the OGR Geometry Type for this Feture. | def geom_type(self):
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Feature.get | (self, field) |
Returns the value of the field, instead of an instance of the Field
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|
Returns the value of the field, instead of an instance of the Field
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Feature.index | (self, field_name) | Returns the index of the given field name. | Returns the index of the given field name. | def index(self, field_name):
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BO.__init__ | (self,
results_path=None, results=pd.DataFrame(),
domain_path=None, domain=pd.DataFrame(),
exindex_path=None, exindex=pd.DataFrame(),
model=GP_Model, acquisition_function='EI', init_method='rand',
target=-1, batch_siz... |
Experimental results, experimental domain, and experiment index of
known results can be passed as paths to .csv or .xlsx files or as
DataFrames.
Parameters
----------
results_path : str, optional
Path to experimental results.
results : pand... |
Experimental results, experimental domain, and experiment index of
known results can be passed as paths to .csv or .xlsx files or as
DataFrames.
Parameters
----------
results_path : str, optional
Path to experimental results.
results : pand... | def __init__(self,
results_path=None, results=pd.DataFrame(),
domain_path=None, domain=pd.DataFrame(),
exindex_path=None, exindex=pd.DataFrame(),
model=GP_Model, acquisition_function='EI', init_method='rand',
target=-... | [
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BO.init_sample | (self, seed=None, append=False, export_path=None,
visualize=False) | Generate initial samples via an initialization method.
Parameters
----------
seed : None, int
Random seed used for selecting initial points.
append : bool
Append points to results if computational objective or experiment
index are available.
... | Generate initial samples via an initialization method.
Parameters
----------
seed : None, int
Random seed used for selecting initial points.
append : bool
Append points to results if computational objective or experiment
index are available.
... | def init_sample(self, seed=None, append=False, export_path=None,
visualize=False):
"""Generate initial samples via an initialization method.
Parameters
----------
seed : None, int
Random seed used for selecting initial points.
append : boo... | [
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BO.fit | (self, n_restarts=0, learning_rate=0.1, training_iters=100) | Fit surrogate model.
Parameters
----------
n_restarts : int
Number of restarts used when optimizing GPyTorch model parameters.
learning_rate : float
ADAM learning rate used when optimizing GPyTorch model parameters.
training_iters : int
... | Fit surrogate model.
Parameters
----------
n_restarts : int
Number of restarts used when optimizing GPyTorch model parameters.
learning_rate : float
ADAM learning rate used when optimizing GPyTorch model parameters.
training_iters : int
... | def fit(self, n_restarts=0, learning_rate=0.1, training_iters=100):
"""Fit surrogate model.
Parameters
----------
n_restarts : int
Number of restarts used when optimizing GPyTorch model parameters.
learning_rate : float
ADAM learning rate used whe... | [
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BO.run | (self, append=False, n_restarts=0, learning_rate=0.1,
training_iters=100) | Run a single iteration of optimization with known results.
Note
----
Use run for human-in-the-loop optimization.
Parameters
----------
append : bool
Append points to results if computational objective or experiment
index are avail... | Run a single iteration of optimization with known results.
Note
----
Use run for human-in-the-loop optimization.
Parameters
----------
append : bool
Append points to results if computational objective or experiment
index are avail... | def run(self, append=False, n_restarts=0, learning_rate=0.1,
training_iters=100):
"""Run a single iteration of optimization with known results.
Note
----
Use run for human-in-the-loop optimization.
Parameters
----------
append : bool
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BO.simulate | (self, iterations=1, seed=None, update_priors=False,
n_restarts=0, learning_rate=0.1, training_iters=100) | Run autonomous BO loop.
Run N iterations of optimization with initial results obtained
via initialization method and experiments selected from
experiment index via the acquisition function. Simulations
require know objectives via an index of results or function.
... | Run autonomous BO loop.
Run N iterations of optimization with initial results obtained
via initialization method and experiments selected from
experiment index via the acquisition function. Simulations
require know objectives via an index of results or function.
... | def simulate(self, iterations=1, seed=None, update_priors=False,
n_restarts=0, learning_rate=0.1, training_iters=100):
"""Run autonomous BO loop.
Run N iterations of optimization with initial results obtained
via initialization method and experiments selected from
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BO.clear_results | (self) | Clear results manually.
Note
----
'rand' and 'pam' initialization methods clear results automatically.
| Clear results manually.
Note
----
'rand' and 'pam' initialization methods clear results automatically.
| def clear_results(self):
"""Clear results manually.
Note
----
'rand' and 'pam' initialization methods clear results automatically.
"""
self.obj.clear_results() | [
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BO.plot_convergence | (self, export_path=None) | Plot optimizer convergence.
Parameters
----------
export_path : None, str
Path to export SVG of optimizer optimizer convergence plot.
Returns
----------
matplotlib.pyplot
Plot of optimizer convergence.
| Plot optimizer convergence.
Parameters
----------
export_path : None, str
Path to export SVG of optimizer optimizer convergence plot.
Returns
----------
matplotlib.pyplot
Plot of optimizer convergence.
| def plot_convergence(self, export_path=None):
"""Plot optimizer convergence.
Parameters
----------
export_path : None, str
Path to export SVG of optimizer optimizer convergence plot.
Returns
----------
matplotlib.pyplot
... | [
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BO.acquisition_summary | (self) | Summarize predicted mean and variance for porposed points.
Returns
----------
pandas.DataFrame
Summary table.
| Summarize predicted mean and variance for porposed points.
Returns
----------
pandas.DataFrame
Summary table.
| def acquisition_summary(self):
"""Summarize predicted mean and variance for porposed points.
Returns
----------
pandas.DataFrame
Summary table.
"""
proposed_experiments = self.proposed_experiments.copy()
X = to_torch(proposed_experime... | [
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BO.best | (self) | Best observed objective values and corresponding domain point. | Best observed objective values and corresponding domain point. | def best(self):
"""Best observed objective values and corresponding domain point."""
sort = self.obj.results_input().sort_values(self.obj.target, ascending=False)
return sort.head() | [
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BO.save | (self, path='BO.pkl') | Save BO state.
Parameters
----------
path : str
Path to export <BO state dict>.pkl.
Returns
----------
None
| Save BO state.
Parameters
----------
path : str
Path to export <BO state dict>.pkl.
Returns
----------
None
| def save(self, path='BO.pkl'):
"""Save BO state.
Parameters
----------
path : str
Path to export <BO state dict>.pkl.
Returns
----------
None
"""
file = open(path, 'wb')
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BO.load | (self, path='BO.pkl') | Load BO state.
Parameters
----------
path : str
Path to <BO state dict>.pkl.
Returns
----------
None
| Load BO state.
Parameters
----------
path : str
Path to <BO state dict>.pkl.
Returns
----------
None
| def load(self, path='BO.pkl'):
"""Load BO state.
Parameters
----------
path : str
Path to <BO state dict>.pkl.
Returns
----------
None
"""
file = open(path, 'rb')
tmp_dict = dill.load(file)
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BO_express.__init__ | (self,
reaction_components={}, encoding={}, descriptor_matrices={},
model=GP_Model, acquisition_function='EI', init_method='rand',
target=-1, batch_size=5, computational_objective=None) |
Parameters
----------
reaction_components : dict
Dictionary of reaction components of the form:
Example
-------
Defining reaction components ::
{'A': [a1, a2, a3, ...],
'B... |
Parameters
----------
reaction_components : dict
Dictionary of reaction components of the form:
Example
-------
Defining reaction components ::
{'A': [a1, a2, a3, ...],
'B... | def __init__(self,
reaction_components={}, encoding={}, descriptor_matrices={},
model=GP_Model, acquisition_function='EI', init_method='rand',
target=-1, batch_size=5, computational_objective=None):
"""
Parameters
----------
rea... | [
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BO_express.get_experiments | (self, structures=False) | Return indexed experiments proposed by Bayesian optimization algorithm.
edbo.BO works directly with a standardized encoded reaction space. This
method returns proposed experiments as the origional smiles strings,
categories, or numerical values.
Parameters
---... | Return indexed experiments proposed by Bayesian optimization algorithm.
edbo.BO works directly with a standardized encoded reaction space. This
method returns proposed experiments as the origional smiles strings,
categories, or numerical values.
Parameters
---... | def get_experiments(self, structures=False):
"""Return indexed experiments proposed by Bayesian optimization algorithm.
edbo.BO works directly with a standardized encoded reaction space. This
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BO_express.add_results | (self, results_path=None) | Add experimental results.
Experimental results should be added with the same column headings as
those returned by BO_express.get_experiments. If a path to the results
is not specified, an edbo bot is spawned to help load results. It does
so by exporting the entire reaction space... | Add experimental results.
Experimental results should be added with the same column headings as
those returned by BO_express.get_experiments. If a path to the results
is not specified, an edbo bot is spawned to help load results. It does
so by exporting the entire reaction space... | def add_results(self, results_path=None):
"""Add experimental results.
Experimental results should be added with the same column headings as
those returned by BO_express.get_experiments. If a path to the results
is not specified, an edbo bot is spawned to help load results. It d... | [
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BO_express.export_proposed | (self, path=None) | Export proposed experiments.
edbo.BO works directly with a standardized encoded reaction space. This
method exports proposed experiments as the origional smiles strings,
categories, or numerical values. If a path to the results is not
specified, a CSV file entitled 'experimen... | Export proposed experiments.
edbo.BO works directly with a standardized encoded reaction space. This
method exports proposed experiments as the origional smiles strings,
categories, or numerical values. If a path to the results is not
specified, a CSV file entitled 'experimen... | def export_proposed(self, path=None):
"""Export proposed experiments.
edbo.BO works directly with a standardized encoded reaction space. This
method exports proposed experiments as the origional smiles strings,
categories, or numerical values. If a path to the results is not
... | [
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BO_express.help | (self) | Spawn an edbo bot to help with tasks.
If you are not familiar with edbo commands BO_express.help() will spawn
an edbo bot to help with tasks. Natural language can be used to interact
with edbo bot in the terminal to accomplish tasks such as: initializing
(selecting initial exp... | Spawn an edbo bot to help with tasks.
If you are not familiar with edbo commands BO_express.help() will spawn
an edbo bot to help with tasks. Natural language can be used to interact
with edbo bot in the terminal to accomplish tasks such as: initializing
(selecting initial exp... | def help(self):
"""Spawn an edbo bot to help with tasks.
If you are not familiar with edbo commands BO_express.help() will spawn
an edbo bot to help with tasks. Natural language can be used to interact
with edbo bot in the terminal to accomplish tasks such as: initializing
... | [
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_normalize_name | (name) | Make a name consistent regardless of source (environment or file)
| Make a name consistent regardless of source (environment or file)
| def _normalize_name(name):
# type: (str) -> str
"""Make a name consistent regardless of source (environment or file)
"""
name = name.lower().replace('_', '-')
if name.startswith('--'):
name = name[2:] # only prefer long opts
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53,
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60,
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Configuration.load | (self) | Loads configuration from configuration files and environment
| Loads configuration from configuration files and environment
| def load(self):
# type: () -> None
"""Loads configuration from configuration files and environment
"""
self._load_config_files()
if not self.isolated:
self._load_environment_vars() | [
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Configuration.get_file_to_edit | (self) | Returns the file with highest priority in configuration
| Returns the file with highest priority in configuration
| def get_file_to_edit(self):
# type: () -> Optional[str]
"""Returns the file with highest priority in configuration
"""
assert self.load_only is not None, \
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Configuration.items | (self) | Returns key-value pairs like dict.items() representing the loaded
configuration
| Returns key-value pairs like dict.items() representing the loaded
configuration
| def items(self):
# type: () -> Iterable[Tuple[str, Any]]
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Configuration.get_value | (self, key) | Get a value from the configuration.
| Get a value from the configuration.
| def get_value(self, key):
# type: (str) -> Any
"""Get a value from the configuration.
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Configuration.set_value | (self, key, value) | Modify a value in the configuration.
| Modify a value in the configuration.
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Configuration.unset_value | (self, key) | Unset a value in the configuration. | Unset a value in the configuration. | def unset_value(self, key):
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Configuration.save | (self) | Save the current in-memory state.
| Save the current in-memory state.
| def save(self):
# type: () -> None
"""Save the current in-memory state.
"""
self._ensure_have_load_only()
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Configuration._dictionary | (self) | A dictionary representing the loaded configuration.
| A dictionary representing the loaded configuration.
| def _dictionary(self):
# type: () -> Dict[str, Any]
"""A dictionary representing the loaded configuration.
"""
# NOTE: Dictionaries are not populated if not loaded. So, conditionals
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Configuration._load_config_files | (self) | Loads configuration from configuration files
| Loads configuration from configuration files
| def _load_config_files(self):
# type: () -> None
"""Loads configuration from configuration files
"""
config_files = dict(self.iter_config_files())
if config_files[kinds.ENV][0:1] == [os.devnull]:
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Configuration._load_environment_vars | (self) | Loads configuration from environment variables
| Loads configuration from environment variables
| def _load_environment_vars(self):
# type: () -> None
"""Loads configuration from environment variables
"""
self._config[kinds.ENV_VAR].update(
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Configuration._normalized_keys | (self, section, items) | Normalizes items to construct a dictionary with normalized keys.
This routine is where the names become keys and are made the same
regardless of source - configuration files or environment.
| Normalizes items to construct a dictionary with normalized keys. | def _normalized_keys(self, section, items):
# type: (str, Iterable[Tuple[str, Any]]) -> Dict[str, Any]
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Configuration.get_environ_vars | (self) | Returns a generator with all environmental vars with prefix PIP_ | Returns a generator with all environmental vars with prefix PIP_ | def get_environ_vars(self):
# type: () -> Iterable[Tuple[str, str]]
"""Returns a generator with all environmental vars with prefix PIP_"""
for key, val in os.environ.items():
if key.startswith("PIP_"):
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Configuration.iter_config_files | (self) | Yields variant and configuration files associated with it.
This should be treated like items of a dictionary.
| Yields variant and configuration files associated with it. | def iter_config_files(self):
# type: () -> Iterable[Tuple[Kind, List[str]]]
"""Yields variant and configuration files associated with it.
This should be treated like items of a dictionary.
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# SMELL: Move the conditions out of this function
# environment variables hav... | [
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Configuration.get_values_in_config | (self, variant) | Get values present in a config file | Get values present in a config file | def get_values_in_config(self, variant):
# type: (Kind) -> Dict[str, Any]
"""Get values present in a config file"""
return self._config[variant] | [
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assertDsnEqual | (self, dsn1, dsn2, msg=None) | Check that two conninfo string have the same content | Check that two conninfo string have the same content | def assertDsnEqual(self, dsn1, dsn2, msg=None):
"""Check that two conninfo string have the same content"""
self.assertEqual(set(dsn1.split()), set(dsn2.split()), msg) | [
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decorate_all_tests | (cls, *decorators) |
Apply all the *decorators* to all the tests defined in the TestCase *cls*.
|
Apply all the *decorators* to all the tests defined in the TestCase *cls*.
| def decorate_all_tests(cls, *decorators):
"""
Apply all the *decorators* to all the tests defined in the TestCase *cls*.
"""
for n in dir(cls):
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skip_if_no_uuid | (f) | Decorator to skip a test if uuid is not supported by Py/PG. | Decorator to skip a test if uuid is not supported by Py/PG. | def skip_if_no_uuid(f):
"""Decorator to skip a test if uuid is not supported by Py/PG."""
@wraps(f)
def skip_if_no_uuid_(self):
try:
import uuid # noqa
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try:
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skip_if_tpc_disabled | (f) | Skip a test if the server has tpc support disabled. | Skip a test if the server has tpc support disabled. | def skip_if_tpc_disabled(f):
"""Skip a test if the server has tpc support disabled."""
@wraps(f)
def skip_if_tpc_disabled_(self):
from psycopg2 import ProgrammingError
cnn = self.connect()
cur = cnn.cursor()
try:
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skip_if_no_iobase | (f) | Skip a test if io.TextIOBase is not available. | Skip a test if io.TextIOBase is not available. | def skip_if_no_iobase(f):
"""Skip a test if io.TextIOBase is not available."""
@wraps(f)
def skip_if_no_iobase_(self):
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skip_before_postgres | (*ver) | Skip a test on PostgreSQL before a certain version. | Skip a test on PostgreSQL before a certain version. | def skip_before_postgres(*ver):
"""Skip a test on PostgreSQL before a certain version."""
ver = ver + (0,) * (3 - len(ver))
def skip_before_postgres_(f):
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skip_after_postgres | (*ver) | Skip a test on PostgreSQL after (including) a certain version. | Skip a test on PostgreSQL after (including) a certain version. | def skip_after_postgres(*ver):
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ver = ver + (0,) * (3 - len(ver))
def skip_after_postgres_(f):
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skip_before_libpq | (*ver) | Skip a test if libpq we're linked to is older than a certain version. | Skip a test if libpq we're linked to is older than a certain version. | def skip_before_libpq(*ver):
"""Skip a test if libpq we're linked to is older than a certain version."""
ver = ver + (0,) * (3 - len(ver))
def skip_before_libpq_(f):
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skip_after_libpq | (*ver) | Skip a test if libpq we're linked to is newer than a certain version. | Skip a test if libpq we're linked to is newer than a certain version. | def skip_after_libpq(*ver):
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ver = ver + (0,) * (3 - len(ver))
def skip_after_libpq_(f):
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skip_before_python | (*ver) | Skip a test on Python before a certain version. | Skip a test on Python before a certain version. | def skip_before_python(*ver):
"""Skip a test on Python before a certain version."""
def skip_before_python_(f):
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skip_from_python | (*ver) | Skip a test on Python after (including) a certain version. | Skip a test on Python after (including) a certain version. | def skip_from_python(*ver):
"""Skip a test on Python after (including) a certain version."""
def skip_from_python_(f):
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def skip_from_python__(self):
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368,
0
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380,
28
] | python | en | ['en', 'en', 'en'] | True |
skip_if_no_superuser | (f) | Skip a test if the database user running the test is not a superuser | Skip a test if the database user running the test is not a superuser | def skip_if_no_superuser(f):
"""Skip a test if the database user running the test is not a superuser"""
@wraps(f)
def skip_if_no_superuser_(self):
from psycopg2 import ProgrammingError
try:
return f(self)
except ProgrammingError as e:
import psycopg2.errorcode... | [
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skip_if_windows | (f) | Skip a test if run on windows | Skip a test if run on windows | def skip_if_windows(f):
"""Skip a test if run on windows"""
@wraps(f)
def skip_if_windows_(self):
if platform.system() == 'Windows':
return self.skipTest("Not supported on Windows")
else:
return f(self)
return skip_if_windows_ | [
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script_to_py3 | (script) | Convert a script to Python3 syntax if required. | Convert a script to Python3 syntax if required. | def script_to_py3(script):
"""Convert a script to Python3 syntax if required."""
if sys.version_info[0] < 3:
return script
import tempfile
f = tempfile.NamedTemporaryFile(suffix=".py", delete=False)
f.write(script.encode())
f.flush()
filename = f.name
f.close()
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slow | (f) | Decorator to mark slow tests we may want to skip
Note: in order to find slow tests you can run:
make check 2>&1 | ts -i "%.s" | sort -n
| Decorator to mark slow tests we may want to skip | def slow(f):
"""Decorator to mark slow tests we may want to skip
Note: in order to find slow tests you can run:
make check 2>&1 | ts -i "%.s" | sort -n
"""
@wraps(f)
def slow_(self):
if os.environ.get('PSYCOPG2_TEST_FAST'):
return self.skipTest("slow test")
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ConnectingTestCase.assertQuotedEqual | (self, first, second, msg=None) | Compare two quoted strings disregarding eventual E'' quotes | Compare two quoted strings disregarding eventual E'' quotes | def assertQuotedEqual(self, first, second, msg=None):
"""Compare two quoted strings disregarding eventual E'' quotes"""
def f(s):
if isinstance(s, str):
return re.sub(r"\bE'", "'", s)
elif isinstance(first, bytes):
return re.sub(br"\bE'", b"'", s)
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"\"'\... | [
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