partition stringclasses 3
values | func_name stringlengths 1 134 | docstring stringlengths 1 46.9k | path stringlengths 4 223 | original_string stringlengths 75 104k | code stringlengths 75 104k | docstring_tokens listlengths 1 1.97k | repo stringlengths 7 55 | language stringclasses 1
value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
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train | Isogeo.dl_hosted | Download hosted resource.
:param str token: API auth token
:param dict resource_link: link dictionary
:param bool encode_clean: option to ensure a clean filename and avoid OS errors
:param str proxy_url: proxy to use to download
:param str prot: https [DEFAULT] or http
... | isogeo_pysdk/isogeo_sdk.py | def dl_hosted(
self,
token: dict = None,
resource_link: dict = None,
encode_clean: bool = 1,
proxy_url: str = None,
prot: str = "https",
) -> tuple:
"""Download hosted resource.
:param str token: API auth token
:param dict resource_link: link ... | def dl_hosted(
self,
token: dict = None,
resource_link: dict = None,
encode_clean: bool = 1,
proxy_url: str = None,
prot: str = "https",
) -> tuple:
"""Download hosted resource.
:param str token: API auth token
:param dict resource_link: link ... | [
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train | Isogeo.xml19139 | Get resource exported into XML ISO 19139.
:param str token: API auth token
:param str id_resource: metadata UUID to export
:param str proxy_url: proxy to use to download
:param str prot: https [DEFAULT] or http
(use it only for dev and tracking needs). | isogeo_pysdk/isogeo_sdk.py | def xml19139(
self,
token: dict = None,
id_resource: str = None,
proxy_url=None,
prot: str = "https",
):
"""Get resource exported into XML ISO 19139.
:param str token: API auth token
:param str id_resource: metadata UUID to export
:param str p... | def xml19139(
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id_resource: str = None,
proxy_url=None,
prot: str = "https",
):
"""Get resource exported into XML ISO 19139.
:param str token: API auth token
:param str id_resource: metadata UUID to export
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train | Isogeo.add_tags_shares | Add shares list to the tags attributes in search results.
:param dict tags: tags dictionary from a search request | isogeo_pysdk/isogeo_sdk.py | def add_tags_shares(self, tags: dict = dict()):
"""Add shares list to the tags attributes in search results.
:param dict tags: tags dictionary from a search request
"""
# check if shares_id have already been retrieved or not
if not hasattr(self, "shares_id"):
shares ... | def add_tags_shares(self, tags: dict = dict()):
"""Add shares list to the tags attributes in search results.
:param dict tags: tags dictionary from a search request
"""
# check if shares_id have already been retrieved or not
if not hasattr(self, "shares_id"):
shares ... | [
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train | Isogeo.get_app_properties | Get information about the application declared on Isogeo.
:param str token: API auth token
:param str prot: https [DEFAULT] or http
(use it only for dev and tracking needs). | isogeo_pysdk/isogeo_sdk.py | def get_app_properties(self, token: dict = None, prot: str = "https"):
"""Get information about the application declared on Isogeo.
:param str token: API auth token
:param str prot: https [DEFAULT] or http
(use it only for dev and tracking needs).
"""
# check if app pro... | def get_app_properties(self, token: dict = None, prot: str = "https"):
"""Get information about the application declared on Isogeo.
:param str token: API auth token
:param str prot: https [DEFAULT] or http
(use it only for dev and tracking needs).
"""
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train | Isogeo.get_directives | Get environment directives which represent INSPIRE limitations.
:param str token: API auth token
:param str prot: https [DEFAULT] or http
(use it only for dev and tracking needs). | isogeo_pysdk/isogeo_sdk.py | def get_directives(self, token: dict = None, prot: str = "https") -> dict:
"""Get environment directives which represent INSPIRE limitations.
:param str token: API auth token
:param str prot: https [DEFAULT] or http
(use it only for dev and tracking needs).
"""
# search... | def get_directives(self, token: dict = None, prot: str = "https") -> dict:
"""Get environment directives which represent INSPIRE limitations.
:param str token: API auth token
:param str prot: https [DEFAULT] or http
(use it only for dev and tracking needs).
"""
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train | Isogeo.get_coordinate_systems | Get available coordinate systems in Isogeo API.
:param str token: API auth token
:param str srs_code: code of a specific coordinate system
:param str prot: https [DEFAULT] or http
(use it only for dev and tracking needs). | isogeo_pysdk/isogeo_sdk.py | def get_coordinate_systems(
self, token: dict = None, srs_code: str = None, prot: str = "https"
) -> dict:
"""Get available coordinate systems in Isogeo API.
:param str token: API auth token
:param str srs_code: code of a specific coordinate system
:param str prot: https [DE... | def get_coordinate_systems(
self, token: dict = None, srs_code: str = None, prot: str = "https"
) -> dict:
"""Get available coordinate systems in Isogeo API.
:param str token: API auth token
:param str srs_code: code of a specific coordinate system
:param str prot: https [DE... | [
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train | Isogeo.get_formats | Get formats.
:param str token: API auth token
:param str format_code: code of a specific format
:param str prot: https [DEFAULT] or http
(use it only for dev and tracking needs). | isogeo_pysdk/isogeo_sdk.py | def get_formats(
self, token: dict = None, format_code: str = None, prot: str = "https"
) -> dict:
"""Get formats.
:param str token: API auth token
:param str format_code: code of a specific format
:param str prot: https [DEFAULT] or http
(use it only for dev and tr... | def get_formats(
self, token: dict = None, format_code: str = None, prot: str = "https"
) -> dict:
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:param str token: API auth token
:param str format_code: code of a specific format
:param str prot: https [DEFAULT] or http
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train | parse_bool | Parses boolean value
:param v: Input string
:param default: Default value if exceptions=False
:param exceptions: Raise exception on error or not
:return: bool | jutil/parse.py | def parse_bool(v, default=None, exceptions: bool=True) -> bool:
"""
Parses boolean value
:param v: Input string
:param default: Default value if exceptions=False
:param exceptions: Raise exception on error or not
:return: bool
"""
if isinstance(v, bool):
return v
s = str(v).l... | def parse_bool(v, default=None, exceptions: bool=True) -> bool:
"""
Parses boolean value
:param v: Input string
:param default: Default value if exceptions=False
:param exceptions: Raise exception on error or not
:return: bool
"""
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train | parse_datetime | Parses datetime
:param v: Input string
:param default: Default value if exceptions=False
:param tz: Default pytz timezone or None if utc
:param exceptions: Raise exception on error or not
:return: datetime | jutil/parse.py | def parse_datetime(v, default=None, tz=None, exceptions: bool=True) -> datetime:
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Parses datetime
:param v: Input string
:param default: Default value if exceptions=False
:param tz: Default pytz timezone or None if utc
:param exceptions: Raise exception on error or not
:return: datetime
... | def parse_datetime(v, default=None, tz=None, exceptions: bool=True) -> datetime:
"""
Parses datetime
:param v: Input string
:param default: Default value if exceptions=False
:param tz: Default pytz timezone or None if utc
:param exceptions: Raise exception on error or not
:return: datetime
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train | convert_boolean | Converts a string to a boolean (see CONVERTERS).
There is a converter function for each column type.
Boolean strings are independent of case. Values interpreted as True
are: "yes", "true", "on", "1". values interpreted as False are
"no", "false", "off", "0". Any other value will result in a ValueError.... | src/python/pipelines_utils/TypedColumnReader.py | def convert_boolean(string_value):
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There is a converter function for each column type.
Boolean strings are independent of case. Values interpreted as True
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There is a converter function for each column type.
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train | TypedColumnReader._handle_hdr | Given the file header line (or one provided when the object
is instantiated) this method populates the ``self._converters`` array,
a list of type converters indexed by the column name.
:param hdr: The header line.
:raises: ContentError for any formatting problems
:raises: Unkno... | src/python/pipelines_utils/TypedColumnReader.py | def _handle_hdr(self, hdr):
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is instantiated) this method populates the ``self._converters`` array,
a list of type converters indexed by the column name.
:param hdr: The header line.
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train | DataGouvFr.search_datasets | Search datasets within uData portal. | isogeo_pysdk/samples/udata_client_datagouvfr.py | def search_datasets(
self,
license=None,
format=None,
query=None,
featured=None,
owner=None,
organization=None,
badge=None,
reuses=None,
page_size=20,
x_fields=None,
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"""Search datasets within uData portal."""
... | def search_datasets(
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format=None,
query=None,
featured=None,
owner=None,
organization=None,
badge=None,
reuses=None,
page_size=20,
x_fields=None,
):
"""Search datasets within uData portal."""
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train | DataGouvFr.get_filters_values | Get different filters values as dicts. | isogeo_pysdk/samples/udata_client_datagouvfr.py | def get_filters_values(self):
"""Get different filters values as dicts."""
# DATASETS --
# badges
self._DST_BADGES = requests.get(self.base_url + "datasets/badges/").json()
# licences
self._DST_LICENSES = {
l.get("id"): l.get("title")
for l in requ... | def get_filters_values(self):
"""Get different filters values as dicts."""
# DATASETS --
# badges
self._DST_BADGES = requests.get(self.base_url + "datasets/badges/").json()
# licences
self._DST_LICENSES = {
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train | deploy | Deploy the app to AppEngine.
Args:
app_id (str):
AppEngine App ID. Overrides config value app_id if given.
version (str):
AppEngine project version. Overrides config values if given.
promote (bool):
If set to **True** promote the current remote app versio... | src/peltak_appengine/logic.py | def deploy(app_id, version, promote, quiet):
# type: (str, str, bool, bool) -> None
""" Deploy the app to AppEngine.
Args:
app_id (str):
AppEngine App ID. Overrides config value app_id if given.
version (str):
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""" Deploy the app to AppEngine.
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AppEngine App ID. Overrides config value app_id if given.
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Port on which the admin interface is served.
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Port on which the admin interface is served.
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train | setup_ci | Setup AppEngine SDK on CircleCI | src/peltak_appengine/logic.py | def setup_ci():
# type: () -> None
""" Setup AppEngine SDK on CircleCI """
gcloud_path = shell.run('which gcloud', capture=True).stdout.strip()
sdk_path = normpath(join(gcloud_path, '../../platform/google_appengine'))
gcloud_cmd = gcloud_path + ' --quiet'
if not exists(sdk_path):
log.in... | def setup_ci():
# type: () -> None
""" Setup AppEngine SDK on CircleCI """
gcloud_path = shell.run('which gcloud', capture=True).stdout.strip()
sdk_path = normpath(join(gcloud_path, '../../platform/google_appengine'))
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train | mark_experimental | Mark function as experimental.
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# type: (FunctionType) -> FunctionType
""" Mark function as experimental.
Args:
fn (FunctionType):
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"""
@wraps(fn)
def wrapper(*args, **kw): # pylint: disable=missing-docstring
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train | in_batches | Split the given iterable into batches.
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batch_size (int):
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The iterable you want to split into batches.
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""" Dump data to a YAML string/file.
Args:
data (YamlData):
The data to serialize as YAML.
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train | cached_result.clear | Clear result cache on the given function.
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If the function has no cached result, this call will do nothing.
Args:
fn (FunctionType):
The function whose cache should be cleared.
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train | start | Start working on a new feature by branching off develop.
This will create a new branch off develop called feature/<name>.
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name (str):
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# type: (str) -> None
""" Start working on a new feature by branching off develop.
This will create a new branch off develop called feature/<name>.
Args:
name (str):
The name of the new feature.
"""
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task_branch ... | def start(name):
# type: (str) -> None
""" Start working on a new feature by branching off develop.
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name (str):
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train | update | Update the feature with updates committed to develop.
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train | finish | Merge current feature branch into develop. | src/peltak/extra/gitflow/logic/task.py | def finish():
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""" Merge current feature branch into develop. """
pretend = context.get('pretend', False)
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train | merged | Cleanup a remotely merged branch. | src/peltak/extra/gitflow/logic/task.py | def merged():
# type: () -> None
""" Cleanup a remotely merged branch. """
base_branch = common.get_base_branch()
branch = git.current_branch(refresh=True)
common.assert_branch_type('task')
# Pull feature branch with the merged task
common.git_checkout(base_branch)
common.git_pull(base... | def merged():
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We require at least Mutagen 1.33, where `IOError` is *never* used,
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train | _pack_asf_image | Pack image data for a WM/Picture tag. | phrydy/mediafile.py | def _pack_asf_image(mime, data, type=3, description=""):
"""Pack image data for a WM/Picture tag.
"""
tag_data = struct.pack('<bi', type, len(data))
tag_data += mime.encode("utf-16-le") + b'\x00\x00'
tag_data += description.encode("utf-16-le") + b'\x00\x00'
tag_data += data
return tag_data | def _pack_asf_image(mime, data, type=3, description=""):
"""Pack image data for a WM/Picture tag.
"""
tag_data = struct.pack('<bi', type, len(data))
tag_data += mime.encode("utf-16-le") + b'\x00\x00'
tag_data += description.encode("utf-16-le") + b'\x00\x00'
tag_data += data
return tag_data | [
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train | _sc_decode | Convert a Sound Check bytestring value to a (gain, peak) tuple as
used by ReplayGain. | phrydy/mediafile.py | def _sc_decode(soundcheck):
"""Convert a Sound Check bytestring value to a (gain, peak) tuple as
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"""
# We decode binary data. If one of the formats gives us a text
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train | _sc_encode | Encode ReplayGain gain/peak values as a Sound Check string. | phrydy/mediafile.py | def _sc_encode(gain, peak):
"""Encode ReplayGain gain/peak values as a Sound Check string.
"""
# SoundCheck stores the peak value as the actual value of the
# sample, rather than the percentage of full scale that RG uses, so
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train | image_mime_type | Return the MIME type of the image data (a bytestring). | phrydy/mediafile.py | def image_mime_type(data):
"""Return the MIME type of the image data (a bytestring).
"""
# This checks for a jpeg file with only the magic bytes (unrecognized by
# imghdr.what). imghdr.what returns none for that type of file, so
# _wider_test_jpeg is run in that case. It still returns None if it did... | def image_mime_type(data):
"""Return the MIME type of the image data (a bytestring).
"""
# This checks for a jpeg file with only the magic bytes (unrecognized by
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train | StorageStyle.deserialize | Given a raw value stored on a Mutagen object, decode and
return the represented value. | phrydy/mediafile.py | def deserialize(self, mutagen_value):
"""Given a raw value stored on a Mutagen object, decode and
return the represented value.
"""
if self.suffix and isinstance(mutagen_value, six.text_type) \
and mutagen_value.endswith(self.suffix):
return mutagen_value[:-len(sel... | def deserialize(self, mutagen_value):
"""Given a raw value stored on a Mutagen object, decode and
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"""
if self.suffix and isinstance(mutagen_value, six.text_type) \
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train | StorageStyle.set | Assign the value for the field using this style. | phrydy/mediafile.py | def set(self, mutagen_file, value):
"""Assign the value for the field using this style.
"""
self.store(mutagen_file, self.serialize(value)) | def set(self, mutagen_file, value):
"""Assign the value for the field using this style.
"""
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train | StorageStyle.serialize | Convert the external Python value to a type that is suitable for
storing in a Mutagen file object. | phrydy/mediafile.py | def serialize(self, value):
"""Convert the external Python value to a type that is suitable for
storing in a Mutagen file object.
"""
if isinstance(value, float) and self.as_type is six.text_type:
value = u'{0:.{1}f}'.format(value, self.float_places)
value = self.... | def serialize(self, value):
"""Convert the external Python value to a type that is suitable for
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"""
if isinstance(value, float) and self.as_type is six.text_type:
value = u'{0:.{1}f}'.format(value, self.float_places)
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train | ListStorageStyle.get_list | Get a list of all values for the field using this style. | phrydy/mediafile.py | def get_list(self, mutagen_file):
"""Get a list of all values for the field using this style.
"""
return [self.deserialize(item) for item in self.fetch(mutagen_file)] | def get_list(self, mutagen_file):
"""Get a list of all values for the field using this style.
"""
return [self.deserialize(item) for item in self.fetch(mutagen_file)] | [
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train | ListStorageStyle.set_list | Set all values for the field using this style. `values`
should be an iterable. | phrydy/mediafile.py | def set_list(self, mutagen_file, values):
"""Set all values for the field using this style. `values`
should be an iterable.
"""
self.store(mutagen_file, [self.serialize(value) for value in values]) | def set_list(self, mutagen_file, values):
"""Set all values for the field using this style. `values`
should be an iterable.
"""
self.store(mutagen_file, [self.serialize(value) for value in values]) | [
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train | MP3ImageStorageStyle.deserialize | Convert APIC frame into Image. | phrydy/mediafile.py | def deserialize(self, apic_frame):
"""Convert APIC frame into Image."""
return Image(data=apic_frame.data, desc=apic_frame.desc,
type=apic_frame.type) | def deserialize(self, apic_frame):
"""Convert APIC frame into Image."""
return Image(data=apic_frame.data, desc=apic_frame.desc,
type=apic_frame.type) | [
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train | MP3ImageStorageStyle.serialize | Return an APIC frame populated with data from ``image``. | phrydy/mediafile.py | def serialize(self, image):
"""Return an APIC frame populated with data from ``image``.
"""
assert isinstance(image, Image)
frame = mutagen.id3.Frames[self.key]()
frame.data = image.data
frame.mime = image.mime_type
frame.desc = image.desc or u''
# For co... | def serialize(self, image):
"""Return an APIC frame populated with data from ``image``.
"""
assert isinstance(image, Image)
frame = mutagen.id3.Frames[self.key]()
frame.data = image.data
frame.mime = image.mime_type
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train | VorbisImageStorageStyle.serialize | Turn a Image into a base64 encoded FLAC picture block. | phrydy/mediafile.py | def serialize(self, image):
"""Turn a Image into a base64 encoded FLAC picture block.
"""
pic = mutagen.flac.Picture()
pic.data = image.data
pic.type = image.type_index
pic.mime = image.mime_type
pic.desc = image.desc or u''
# Encoding with base64 returns... | def serialize(self, image):
"""Turn a Image into a base64 encoded FLAC picture block.
"""
pic = mutagen.flac.Picture()
pic.data = image.data
pic.type = image.type_index
pic.mime = image.mime_type
pic.desc = image.desc or u''
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train | FlacImageStorageStyle.store | ``pictures`` is a list of mutagen.flac.Picture instances. | phrydy/mediafile.py | def store(self, mutagen_file, pictures):
"""``pictures`` is a list of mutagen.flac.Picture instances.
"""
mutagen_file.clear_pictures()
for pic in pictures:
mutagen_file.add_picture(pic) | def store(self, mutagen_file, pictures):
"""``pictures`` is a list of mutagen.flac.Picture instances.
"""
mutagen_file.clear_pictures()
for pic in pictures:
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train | FlacImageStorageStyle.serialize | Turn a Image into a mutagen.flac.Picture. | phrydy/mediafile.py | def serialize(self, image):
"""Turn a Image into a mutagen.flac.Picture.
"""
pic = mutagen.flac.Picture()
pic.data = image.data
pic.type = image.type_index
pic.mime = image.mime_type
pic.desc = image.desc or u''
return pic | def serialize(self, image):
"""Turn a Image into a mutagen.flac.Picture.
"""
pic = mutagen.flac.Picture()
pic.data = image.data
pic.type = image.type_index
pic.mime = image.mime_type
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train | APEv2ImageStorageStyle.delete | Remove all images from the file. | phrydy/mediafile.py | def delete(self, mutagen_file):
"""Remove all images from the file.
"""
for cover_tag in self.TAG_NAMES.values():
try:
del mutagen_file[cover_tag]
except KeyError:
pass | def delete(self, mutagen_file):
"""Remove all images from the file.
"""
for cover_tag in self.TAG_NAMES.values():
try:
del mutagen_file[cover_tag]
except KeyError:
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train | MediaField.styles | Yields the list of storage styles of this field that can
handle the MediaFile's format. | phrydy/mediafile.py | def styles(self, mutagen_file):
"""Yields the list of storage styles of this field that can
handle the MediaFile's format.
"""
for style in self._styles:
if mutagen_file.__class__.__name__ in style.formats:
yield style | def styles(self, mutagen_file):
"""Yields the list of storage styles of this field that can
handle the MediaFile's format.
"""
for style in self._styles:
if mutagen_file.__class__.__name__ in style.formats:
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train | MediaField._none_value | Get an appropriate "null" value for this field's type. This
is used internally when setting the field to None. | phrydy/mediafile.py | def _none_value(self):
"""Get an appropriate "null" value for this field's type. This
is used internally when setting the field to None.
"""
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"""
if self.out_type == int:
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"""Get a 3-item sequence representing the date consisting of a
year, month, and day number. Each number is either an integer or
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"""
# Get the underlying data and split on hyphens and slashes.
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train | DateField._set_date_tuple | Set the value of the field given a year, month, and day
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unset component. | phrydy/mediafile.py | def _set_date_tuple(self, mediafile, year, month=None, day=None):
"""Set the value of the field given a year, month, and day
number. Each number can be an integer or None to indicate an
unset component.
"""
if year is None:
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retur... | def _set_date_tuple(self, mediafile, year, month=None, day=None):
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train | MediaFile.save | Write the object's tags back to the file. May
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"""Write the object's tags back to the file. May
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"""
# Possibly save the tags to ID3v2.3.
kwargs = {}
if self.id3v23:
id3 = self.mgfile
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kwargs = {}
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train | MediaFile.fields | Get the names of all writable properties that reflect
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"""Get the names of all writable properties that reflect
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:class:`MediaField`).
"""
for property, descriptor in cls.__dict__.items():
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train | MediaFile._field_sort_name | Get a sort key for a field name that determines the order
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Fields names are kept unchanged, unless they are instances of
:class:`DateItemField`, in which case `year`, `month`, and `day`
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m... | phrydy/mediafile.py | def _field_sort_name(cls, name):
"""Get a sort key for a field name that determines the order
fields should be written in.
Fields names are kept unchanged, unless they are instances of
:class:`DateItemField`, in which case `year`, `month`, and `day`
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"""Get a sort key for a field name that determines the order
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train | MediaFile.sorted_fields | Get the names of all writable metadata fields, sorted in the
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This is a lexicographic order, except for instances of
:class:`DateItemField`, which are sorted in year-month-day
order. | phrydy/mediafile.py | def sorted_fields(cls):
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train | MediaFile.add_field | Add a field to store custom tags.
:param name: the name of the property the field is accessed
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:param descriptor: an instance of :class:`MediaField`. | phrydy/mediafile.py | def add_field(cls, name, descriptor):
"""Add a field to store custom tags.
:param name: the name of the property the field is accessed
through. It must not already exist on this class.
:param descriptor: an instance of :class:`MediaField`.
"""
if not isinst... | def add_field(cls, name, descriptor):
"""Add a field to store custom tags.
:param name: the name of the property the field is accessed
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:param descriptor: an instance of :class:`MediaField`.
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train | MediaFile.update | Set all field values from a dictionary.
For any key in `dict` that is also a field to store tags the
method retrieves the corresponding value from `dict` and updates
the `MediaFile`. If a key has the value `None`, the
corresponding property is deleted from the `MediaFile`. | phrydy/mediafile.py | def update(self, dict):
"""Set all field values from a dictionary.
For any key in `dict` that is also a field to store tags the
method retrieves the corresponding value from `dict` and updates
the `MediaFile`. If a key has the value `None`, the
corresponding property is deleted ... | def update(self, dict):
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train | MediaFile.samplerate | The audio's sample rate (an int). | phrydy/mediafile.py | def samplerate(self):
"""The audio's sample rate (an int)."""
if hasattr(self.mgfile.info, 'sample_rate'):
return self.mgfile.info.sample_rate
elif self.type == 'opus':
# Opus is always 48kHz internally.
return 48000
return 0 | def samplerate(self):
"""The audio's sample rate (an int)."""
if hasattr(self.mgfile.info, 'sample_rate'):
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train | MediaFile.bitdepth | The number of bits per sample in the audio encoding (an int).
Only available for certain file formats (zero where
unavailable). | phrydy/mediafile.py | def bitdepth(self):
"""The number of bits per sample in the audio encoding (an int).
Only available for certain file formats (zero where
unavailable).
"""
if hasattr(self.mgfile.info, 'bits_per_sample'):
return self.mgfile.info.bits_per_sample
return 0 | def bitdepth(self):
"""The number of bits per sample in the audio encoding (an int).
Only available for certain file formats (zero where
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"""
if hasattr(self.mgfile.info, 'bits_per_sample'):
return self.mgfile.info.bits_per_sample
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train | MediaFile.channels | The number of channels in the audio (an int). | phrydy/mediafile.py | def channels(self):
"""The number of channels in the audio (an int)."""
if hasattr(self.mgfile.info, 'channels'):
return self.mgfile.info.channels
return 0 | def channels(self):
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train | MediaFile.bitrate | The number of bits per seconds used in the audio coding (an
int). If this is provided explicitly by the compressed file
format, this is a precise reflection of the encoding. Otherwise,
it is estimated from the on-disk file size. In this case, some
imprecision is possible because the file... | phrydy/mediafile.py | def bitrate(self):
"""The number of bits per seconds used in the audio coding (an
int). If this is provided explicitly by the compressed file
format, this is a precise reflection of the encoding. Otherwise,
it is estimated from the on-disk file size. In this case, some
imprecisio... | def bitrate(self):
"""The number of bits per seconds used in the audio coding (an
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train | fromfits | Reads a FITS file and returns a 2D numpy array of the data.
Use hdu to specify which HDU you want (default = primary = 0) | bin/lib/Astro_Libraries/cosmics.py | def fromfits(infilename, hdu = 0, verbose = True):
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Reads a FITS file and returns a 2D numpy array of the data.
Use hdu to specify which HDU you want (default = primary = 0)
"""
pixelarray, hdr = pyfits.getdata(infilename, hdu, header=True)
pixelarray = np.asarray(pixelarray).transpose()... | def fromfits(infilename, hdu = 0, verbose = True):
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"""
pixelarray, hdr = pyfits.getdata(infilename, hdu, header=True)
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train | tofits | Takes a 2D numpy array and write it into a FITS file.
If you specify a header (pyfits format, as returned by fromfits()) it will be used for the image.
You can give me boolean numpy arrays, I will convert them into 8 bit integers. | bin/lib/Astro_Libraries/cosmics.py | def tofits(outfilename, pixelarray, hdr = None, verbose = True):
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Takes a 2D numpy array and write it into a FITS file.
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train | subsample | Returns a 2x2-subsampled version of array a (no interpolation, just cutting pixels in 4).
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train | rebin2x2 | Wrapper around rebin that actually rebins 2 by 2 | bin/lib/Astro_Libraries/cosmics.py | def rebin2x2(a):
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train | cosmicsimage.labelmask | Finds and labels the cosmic "islands" and returns a list of dicts containing their positions.
This is made on purpose for visualizations a la f2n.drawstarslist, but could be useful anyway. | bin/lib/Astro_Libraries/cosmics.py | def labelmask(self, verbose = None):
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"""
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train | cosmicsimage.clean | Given the mask, we replace the actual problematic pixels with the masked 5x5 median value.
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This can then be used to avoid these regions in cosmic detection and cleaning procedures.
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"""
Uses the satlevel to find saturated stars (not cosmics !), and puts the result as a mask in self.satstars.
This can then be used to avoid these regions in cosmic detection and cleaning procedures.
Slow ...
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if verbose == None... | def findsatstars(self, verbose = None):
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Uses the satlevel to find saturated stars (not cosmics !), and puts the result as a mask in self.satstars.
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"""
Returns the mask of saturated stars after finding them if not yet done.
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verbose = self.verbose
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train | cosmicsimage.guessbackgroundlevel | Estimates the background level. This could be used to fill pixels in large cosmics. | bin/lib/Astro_Libraries/cosmics.py | def guessbackgroundlevel(self):
"""
Estimates the background level. This could be used to fill pixels in large cosmics.
"""
if self.backgroundlevel == None:
self.backgroundlevel = np.median(self.rawarray.ravel())
return self.backgroundlevel | def guessbackgroundlevel(self):
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Estimates the background level. This could be used to fill pixels in large cosmics.
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train | cosmicsimage.lacosmiciteration | Performs one iteration of the L.A.Cosmic algorithm.
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"""
Performs one iteration of the L.A.Cosmic algorithm.
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... | def lacosmiciteration(self, verbose = None):
"""
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train | cosmicsimage.run | Full artillery :-)
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train | MirageEnvironment.search_project_root | Search your Django project root.
returns:
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"""
Search your Django project root.
returns:
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"""
while True:
current = os.getcwd()
if pathlib.Path("Miragefile.py").is_file() or pathlib.Path("Miragefile").is_file():
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Search your Django project root.
returns:
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while True:
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if pathlib.Path("Miragefile.py").is_file() or pathlib.Path("Miragefile").is_file():
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train | MirageEnvironment.search_app_root | Search your Django application root
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"""
Search your Django application root
returns:
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while True:
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if pathlib.Path("apps.py").is_file():
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Search your Django application root
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train | MirageEnvironment.in_app | Judge where current working directory is in Django application or not.
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"""
Judge where current working directory is in Django application or not.
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if os.path.isfile("apps.py")... | def in_app() -> bool:
"""
Judge where current working directory is in Django application or not.
returns:
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train | start | Start working on a new hotfix.
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develop = conf.get('git.devel_branch', 'develop')
master = conf.get('git.master_branch', 'master')
branch = git.current_branch(refresh=True)
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Run the shell command
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train | ShellCommand.kill | Kill the current non blocking command
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Create an instance of :class:`~ShellCommand` and run it
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train | bces | Bivariate, Correlated Errors and intrinsic Scatter (BCES)
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Linear regression in the presence of heteroscedastic errors on both
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x1 ... | bin/lib/Math_Libraries/lnr_script.py | def bces(x1, x2, x1err=[], x2err=[], cerr=[], logify=True, model='yx', \
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Bivariate, Correlated Errors and intrinsic Scatter (BCES)
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Bivariate, Correlated Errors and intrinsic Scatter (BCES)
translated from the FORTRAN code by Christina Bird and Matthew Bershady
(Akritas & Bershady, 1996)
Lin... | [
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train | scatter | Used mainly to measure scatter for the BCES best-fit | bin/lib/Math_Libraries/lnr_script.py | def scatter(slope, zero, x1, x2, x1err=[], x2err=[]):
"""
Used mainly to measure scatter for the BCES best-fit
"""
n = len(x1)
x2pred = zero + slope * x1
s = sum((x2 - x2pred) ** 2) / (n - 1)
if len(x2err) == n:
s_obs = sum((x2err / x2) ** 2) / n
s0 = s - s_obs
print num... | def scatter(slope, zero, x1, x2, x1err=[], x2err=[]):
"""
Used mainly to measure scatter for the BCES best-fit
"""
n = len(x1)
x2pred = zero + slope * x1
s = sum((x2 - x2pred) ** 2) / (n - 1)
if len(x2err) == n:
s_obs = sum((x2err / x2) ** 2) / n
s0 = s - s_obs
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train | kelly | Python wrapper for the linear regression MCMC of Kelly (2007).
Requires pidly (http://astronomy.sussex.ac.uk/~anthonys/pidly/) and
an IDL license.
Parameters
----------
x1 : array of floats
Independent variable, or observable
x2 : array of floats
... | bin/lib/Math_Libraries/lnr_script.py | def kelly(x1, x2, x1err=[], x2err=[], cerr=[], logify=True,
miniter=5000, maxiter=1e5, metro=True,
silent=True):
"""
Python wrapper for the linear regression MCMC of Kelly (2007).
Requires pidly (http://astronomy.sussex.ac.uk/~anthonys/pidly/) and
an IDL license.
Parameters
... | def kelly(x1, x2, x1err=[], x2err=[], cerr=[], logify=True,
miniter=5000, maxiter=1e5, metro=True,
silent=True):
"""
Python wrapper for the linear regression MCMC of Kelly (2007).
Requires pidly (http://astronomy.sussex.ac.uk/~anthonys/pidly/) and
an IDL license.
Parameters
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train | mcmc | Use emcee to find the best-fit linear relation or power law
accounting for measurement uncertainties and intrinsic scatter
Parameters
----------
x1 : array of floats
Independent variable, or observable
x2 : array of floats
Dependent variable
... | bin/lib/Math_Libraries/lnr_script.py | def mcmc(x1, x2, x1err=[], x2err=[], po=(1,1,0.5), logify=True,
nsteps=5000, nwalkers=100, nburn=500, output='full'):
"""
Use emcee to find the best-fit linear relation or power law
accounting for measurement uncertainties and intrinsic scatter
Parameters
----------
x1 : array... | def mcmc(x1, x2, x1err=[], x2err=[], po=(1,1,0.5), logify=True,
nsteps=5000, nwalkers=100, nburn=500, output='full'):
"""
Use emcee to find the best-fit linear relation or power law
accounting for measurement uncertainties and intrinsic scatter
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----------
x1 : array... | [
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train | mle | Maximum Likelihood Estimation of best-fit parameters
Parameters
----------
x1, x2 : float arrays
the independent and dependent variables.
x1err, x2err : float arrays (optional)
measurement uncertainties on independent and dependent
variables.... | bin/lib/Math_Libraries/lnr_script.py | def mle(x1, x2, x1err=[], x2err=[], cerr=[], s_int=True,
po=(1,0,0.1), verbose=False, logify=True, full_output=False):
"""
Maximum Likelihood Estimation of best-fit parameters
Parameters
----------
x1, x2 : float arrays
the independent and dependent variables.
x... | def mle(x1, x2, x1err=[], x2err=[], cerr=[], s_int=True,
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"""
Maximum Likelihood Estimation of best-fit parameters
Parameters
----------
x1, x2 : float arrays
the independent and dependent variables.
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train | to_log | Take linear measurements and uncertainties and transform to log values. | bin/lib/Math_Libraries/lnr_script.py | def to_log(x1, x2, x1err, x2err):
"""
Take linear measurements and uncertainties and transform to log values.
"""
logx1 = numpy.log10(numpy.array(x1))
logx2 = numpy.log10(numpy.array(x2))
x1err = numpy.log10(numpy.array(x1)+numpy.array(x1err)) - logx1
x2err = numpy.log10(numpy.array(x2)+num... | def to_log(x1, x2, x1err, x2err):
"""
Take linear measurements and uncertainties and transform to log values.
"""
logx1 = numpy.log10(numpy.array(x1))
logx2 = numpy.log10(numpy.array(x2))
x1err = numpy.log10(numpy.array(x1)+numpy.array(x1err)) - logx1
x2err = numpy.log10(numpy.array(x2)+num... | [
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train | wrap_paths | Put quotes around all paths and join them with space in-between. | src/peltak/core/fs.py | def wrap_paths(paths):
# type: (list[str]) -> str
""" Put quotes around all paths and join them with space in-between. """
if isinstance(paths, string_types):
raise ValueError(
"paths cannot be a string. "
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return ' '.join('"... | def wrap_paths(paths):
# type: (list[str]) -> str
""" Put quotes around all paths and join them with space in-between. """
if isinstance(paths, string_types):
raise ValueError(
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train | filtered_walk | Walk recursively starting at *path* excluding files matching *exclude*
Args:
path (str):
A starting path. This has to be an existing directory.
include (list[str]):
A white list of glob patterns. If given, only files that match those
globs will be yielded (filter... | src/peltak/core/fs.py | def filtered_walk(path, include=None, exclude=None):
# type: (str, List[str], List[str]) -> Generator[str]
""" Walk recursively starting at *path* excluding files matching *exclude*
Args:
path (str):
A starting path. This has to be an existing directory.
include (list[str]):
... | def filtered_walk(path, include=None, exclude=None):
# type: (str, List[str], List[str]) -> Generator[str]
""" Walk recursively starting at *path* excluding files matching *exclude*
Args:
path (str):
A starting path. This has to be an existing directory.
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train | match_globs | Test whether the given *path* matches any patterns in *patterns*
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path (str):
A file path to test for matches.
patterns (list[str]):
A list of glob string patterns to test against. If *path* matches
any of those patters, it will return True.
Returns:
... | src/peltak/core/fs.py | def match_globs(path, patterns):
# type: (str, List[str]) -> bool
""" Test whether the given *path* matches any patterns in *patterns*
Args:
path (str):
A file path to test for matches.
patterns (list[str]):
A list of glob string patterns to test against. If *path* m... | def match_globs(path, patterns):
# type: (str, List[str]) -> bool
""" Test whether the given *path* matches any patterns in *patterns*
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path (str):
A file path to test for matches.
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train | search_globs | Test whether the given *path* contains any patterns in *patterns*
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A file path to test for matches.
patterns (list[str]):
A list of glob string patterns to test against. If *path* matches
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""" Test whether the given *path* contains any patterns in *patterns*
Args:
path (str):
A file path to test for matches.
patterns (list[str]):
A list of glob string patterns to test against. If *path*... | def search_globs(path, patterns):
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A file path to test for matches.
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train | write_file | --pretend aware file writing.
You can always write files manually but you should always handle the
--pretend case.
Args:
path (str):
content (str):
mode (str): | src/peltak/core/fs.py | def write_file(path, content, mode='w'):
# type: (Text, Union[Text,bytes], Text) -> None
""" --pretend aware file writing.
You can always write files manually but you should always handle the
--pretend case.
Args:
path (str):
content (str):
mode (str):
"""
from pelt... | def write_file(path, content, mode='w'):
# type: (Text, Union[Text,bytes], Text) -> None
""" --pretend aware file writing.
You can always write files manually but you should always handle the
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Args:
path (str):
content (str):
mode (str):
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from pelt... | [
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train | lint_cli | Run pep8 and pylint on all project files.
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This should be a list of paths that will be linted. If a path to a directory
is given, all files in that directory and it's subdirectories will be
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The pep8 and pylint config pa... | src/peltak/commands/lint.py | def lint_cli(ctx, exclude, skip_untracked, commit_only):
# type: (click.Context, List[str], bool, bool) -> None
""" Run pep8 and pylint on all project files.
You can configure the linting paths using the lint.paths config variable.
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# type: (click.Context, List[str], bool, bool) -> None
""" Run pep8 and pylint on all project files.
You can configure the linting paths using the lint.paths config variable.
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train | run_command | Runs a command using the subprocess module
:param command: List containing the command and all args
:param timeout_sec (float) seconds to wait before killing
the command.
:param output (bool) True collects output, False ignores output
:return: Dict containing the command output and return code
... | pycons3rt/bash.py | def run_command(command, timeout_sec=3600.0, output=True):
"""Runs a command using the subprocess module
:param command: List containing the command and all args
:param timeout_sec (float) seconds to wait before killing
the command.
:param output (bool) True collects output, False ignores outpu... | def run_command(command, timeout_sec=3600.0, output=True):
"""Runs a command using the subprocess module
:param command: List containing the command and all args
:param timeout_sec (float) seconds to wait before killing
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train | get_ip_addresses | Gets the ip addresses from ifconfig
:return: (dict) of devices and aliases with the IPv4 address | pycons3rt/bash.py | def get_ip_addresses():
"""Gets the ip addresses from ifconfig
:return: (dict) of devices and aliases with the IPv4 address
"""
log = logging.getLogger(mod_logger + '.get_ip_addresses')
command = ['/sbin/ifconfig']
try:
result = run_command(command)
except CommandError:
rai... | def get_ip_addresses():
"""Gets the ip addresses from ifconfig
:return: (dict) of devices and aliases with the IPv4 address
"""
log = logging.getLogger(mod_logger + '.get_ip_addresses')
command = ['/sbin/ifconfig']
try:
result = run_command(command)
except CommandError:
rai... | [
"Gets",
"the",
"ip",
"addresses",
"from",
"ifconfig"
] | cons3rt/pycons3rt | python | https://github.com/cons3rt/pycons3rt/blob/f004ab3a35c5bff2f698131fef3b2a8ed5a7596d/pycons3rt/bash.py#L235-L267 | [
"def",
"get_ip_addresses",
"(",
")",
":",
"log",
"=",
"logging",
".",
"getLogger",
"(",
"mod_logger",
"+",
"'.get_ip_addresses'",
")",
"command",
"=",
"[",
"'/sbin/ifconfig'",
"]",
"try",
":",
"result",
"=",
"run_command",
"(",
"command",
")",
"except",
"Com... | f004ab3a35c5bff2f698131fef3b2a8ed5a7596d |
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