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tamasgal/km3pipe | km3pipe/io/aanet.py | AanetPump.blob_generator | def blob_generator(self):
"""Create a blob generator."""
# pylint: disable:F0401,W0612
import aa # pylint: disablF0401 # noqa
from ROOT import EventFile # pylint: disable F0401
filename = self.filename
log.info("Reading from file: {0}".format(filename))
... | python | def blob_generator(self):
"""Create a blob generator."""
# pylint: disable:F0401,W0612
import aa # pylint: disablF0401 # noqa
from ROOT import EventFile # pylint: disable F0401
filename = self.filename
log.info("Reading from file: {0}".format(filename))
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tamasgal/km3pipe | km3pipe/io/aanet.py | MetaParser.parse_string | def parse_string(self, string):
"""Parse ASCII output of JPrintMeta"""
self.log.info("Parsing ASCII data")
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return
lines = string.splitlines()
application_data = []
application = lines[0].split()[... | python | def parse_string(self, string):
"""Parse ASCII output of JPrintMeta"""
self.log.info("Parsing ASCII data")
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return
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tamasgal/km3pipe | km3pipe/io/aanet.py | MetaParser._record_app_data | def _record_app_data(self, data):
"""Parse raw metadata output for a single application
The usual output is:
ApplicationName RevisionNumber
ApplicationName ROOT_Version
ApplicationName KM3NET
ApplicationName ./command/line --arguments --which --can
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"""Parse raw metadata output for a single application
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ApplicationName RevisionNumber
ApplicationName ROOT_Version
ApplicationName KM3NET
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tamasgal/km3pipe | km3pipe/io/aanet.py | MetaParser.get_table | def get_table(self, name='Meta', h5loc='/meta'):
"""Convert metadata to a KM3Pipe Table.
Returns `None` if there is no data.
Each column's dtype will be set to a fixed size string (numpy.string_)
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strings does n... | python | def get_table(self, name='Meta', h5loc='/meta'):
"""Convert metadata to a KM3Pipe Table.
Returns `None` if there is no data.
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NaPs/Kolekto | kolekto/db.py | MoviesMetadata.itermovieshash | def itermovieshash(self):
""" Iterate over movies hash stored in the database.
"""
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while cur is not None:
yield cur
cur = self._db.nextkey(cur) | python | def itermovieshash(self):
""" Iterate over movies hash stored in the database.
"""
cur = self._db.firstkey()
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LeadPages/gcloud_requests | gcloud_requests/datastore.py | DatastoreRequestsProxy._max_retries_for_error | def _max_retries_for_error(self, error):
"""Handles Datastore response errors according to their documentation.
Parameters:
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Returns:
int or None: The max number of times this error should be
retried or None if it shouldn't.
See also:
... | python | def _max_retries_for_error(self, error):
"""Handles Datastore response errors according to their documentation.
Parameters:
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Returns:
int or None: The max number of times this error should be
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materials-data-facility/toolbox | mdf_toolbox/toolbox.py | anonymous_login | def anonymous_login(services):
"""Initialize services without authenticating to Globus Auth.
Note:
Clients may have reduced functionality without authentication.
Arguments:
services (str or list of str): The services to initialize clients for.
Returns:
dict: The clients reques... | python | def anonymous_login(services):
"""Initialize services without authenticating to Globus Auth.
Note:
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materials-data-facility/toolbox | mdf_toolbox/toolbox.py | logout | def logout(token_dir=DEFAULT_CRED_PATH):
"""Remove ALL tokens in the token directory.
This will force re-authentication to all services.
Arguments:
token_dir (str): The path to the directory to save tokens in and look for
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token_dir (str): The path to the directory to save tokens in and look for
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materials-data-facility/toolbox | mdf_toolbox/toolbox.py | format_gmeta | def format_gmeta(data, acl=None, identifier=None):
"""Format input into GMeta format, suitable for ingesting into Globus Search.
Formats a dictionary into a GMetaEntry.
Formats a list of GMetaEntry into a GMetaList inside a GMetaIngest.
**Example usage**::
glist = []
for document in al... | python | def format_gmeta(data, acl=None, identifier=None):
"""Format input into GMeta format, suitable for ingesting into Globus Search.
Formats a dictionary into a GMetaEntry.
Formats a list of GMetaEntry into a GMetaList inside a GMetaIngest.
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materials-data-facility/toolbox | mdf_toolbox/toolbox.py | gmeta_pop | def gmeta_pop(gmeta, info=False):
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This function can be called on the raw GlobusHTTPResponse that Search returns,
or a string or dictionary representation of it.
Arguments:
gmeta (dict, str, or GlobusHTTPResponse): The Globus Search result to... | python | def gmeta_pop(gmeta, info=False):
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materials-data-facility/toolbox | mdf_toolbox/toolbox.py | translate_index | def translate_index(index_name):
"""Translate a known Globus Search index into the index UUID.
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materials-data-facility/toolbox | mdf_toolbox/toolbox.py | quick_transfer | def quick_transfer(transfer_client, source_ep, dest_ep, path_list, interval=None, retries=10,
notify=True):
"""Perform a Globus Transfer and monitor for success.
Arguments:
transfer_client (TransferClient): An authenticated Transfer client.
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transfer_client (TransferClient): An authenticated Transfer client.
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materials-data-facility/toolbox | mdf_toolbox/toolbox.py | insensitive_comparison | def insensitive_comparison(item1, item2, type_insensitive=False, string_insensitive=False):
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tapilab/brandelion | brandelion/cli/analyze.py | parse_json | def parse_json(json_file, include_date=False):
""" Yield screen_name, text tuples from a json file. """
if json_file[-2:] == 'gz':
fh = gzip.open(json_file, 'rt')
else:
fh = io.open(json_file, mode='rt', encoding='utf8')
for line in fh:
try:
jj = json.loads(line)
... | python | def parse_json(json_file, include_date=False):
""" Yield screen_name, text tuples from a json file. """
if json_file[-2:] == 'gz':
fh = gzip.open(json_file, 'rt')
else:
fh = io.open(json_file, mode='rt', encoding='utf8')
for line in fh:
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tapilab/brandelion | brandelion/cli/analyze.py | extract_tweets | def extract_tweets(json_file):
""" Yield screen_name, string tuples, where the string is the
concatenation of all tweets of this user. """
for screen_name, tweet_iter in groupby(parse_json(json_file), lambda x: x[0]):
tweets = [t[1] for t in tweet_iter]
yield screen_name, ' '.join(tweets) | python | def extract_tweets(json_file):
""" Yield screen_name, string tuples, where the string is the
concatenation of all tweets of this user. """
for screen_name, tweet_iter in groupby(parse_json(json_file), lambda x: x[0]):
tweets = [t[1] for t in tweet_iter]
yield screen_name, ' '.join(tweets) | [
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tapilab/brandelion | brandelion/cli/analyze.py | vectorize | def vectorize(json_file, vec, dofit=True):
""" Return a matrix where each row corresponds to a Twitter account, and
each column corresponds to the number of times a term is used by that
account. """
## CountVectorizer, efficiently.
screen_names = [x[0] for x in extract_tweets(json_file)]
if dofi... | python | def vectorize(json_file, vec, dofit=True):
""" Return a matrix where each row corresponds to a Twitter account, and
each column corresponds to the number of times a term is used by that
account. """
## CountVectorizer, efficiently.
screen_names = [x[0] for x in extract_tweets(json_file)]
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tapilab/brandelion | brandelion/cli/analyze.py | read_follower_file | def read_follower_file(fname, min_followers=0, max_followers=1e10, blacklist=set()):
""" Read a file of follower information and return a dictionary mapping screen_name to a set of follower ids. """
result = {}
with open(fname, 'rt') as f:
for line in f:
parts = line.split()
... | python | def read_follower_file(fname, min_followers=0, max_followers=1e10, blacklist=set()):
""" Read a file of follower information and return a dictionary mapping screen_name to a set of follower ids. """
result = {}
with open(fname, 'rt') as f:
for line in f:
parts = line.split()
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tapilab/brandelion | brandelion/cli/analyze.py | jaccard_merge | def jaccard_merge(brands, exemplars):
""" Return the average Jaccard similarity between a brand's followers and
the followers of each exemplar. We merge all exemplar followers into one
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scores = {}
exemplar_followers = set()
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exem... | python | def jaccard_merge(brands, exemplars):
""" Return the average Jaccard similarity between a brand's followers and
the followers of each exemplar. We merge all exemplar followers into one
big pseudo-account."""
scores = {}
exemplar_followers = set()
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tapilab/brandelion | brandelion/cli/analyze.py | cosine | def cosine(brands, exemplars, weighted_avg=False, sqrt=False):
"""
Return the cosine similarity betwee a brand's followers and the exemplars.
"""
scores = {}
for brand, followers in brands:
if weighted_avg:
scores[brand] = np.average([_cosine(followers, others) for others in exem... | python | def cosine(brands, exemplars, weighted_avg=False, sqrt=False):
"""
Return the cosine similarity betwee a brand's followers and the exemplars.
"""
scores = {}
for brand, followers in brands:
if weighted_avg:
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thebigmunch/google-music-utils | src/google_music_utils/misc.py | suggest_filename | def suggest_filename(metadata):
"""Generate a filename like Google for a song based on metadata.
Parameters:
metadata (~collections.abc.Mapping): A metadata dict.
Returns:
str: A filename string without an extension.
"""
if 'title' in metadata and 'track_number' in metadata: # Music Manager.
suggested_fi... | python | def suggest_filename(metadata):
"""Generate a filename like Google for a song based on metadata.
Parameters:
metadata (~collections.abc.Mapping): A metadata dict.
Returns:
str: A filename string without an extension.
"""
if 'title' in metadata and 'track_number' in metadata: # Music Manager.
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thebigmunch/google-music-utils | src/google_music_utils/misc.py | template_to_filepath | def template_to_filepath(template, metadata, template_patterns=None):
"""Create directory structure and file name based on metadata template.
Note:
A template meant to be a base directory for suggested
names should have a trailing slash or backslash.
Parameters:
template (str or ~os.PathLike): A filepath whic... | python | def template_to_filepath(template, metadata, template_patterns=None):
"""Create directory structure and file name based on metadata template.
Note:
A template meant to be a base directory for suggested
names should have a trailing slash or backslash.
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thebigmunch/google-music-utils | src/google_music_utils/filter.py | _match_field | def _match_field(field_value, pattern, ignore_case=False, normalize_values=False):
"""Match an item metadata field value by pattern.
Note:
Metadata values are lowercased when ``normalized_values`` is ``True``,
so ``ignore_case`` is automatically set to ``True``.
Parameters:
field_value (list or str): A metad... | python | def _match_field(field_value, pattern, ignore_case=False, normalize_values=False):
"""Match an item metadata field value by pattern.
Note:
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thebigmunch/google-music-utils | src/google_music_utils/filter.py | _match_item | def _match_item(item, any_all=any, ignore_case=False, normalize_values=False, **kwargs):
"""Match items by metadata.
Note:
Metadata values are lowercased when ``normalized_values`` is ``True``,
so ``ignore_case`` is automatically set to ``True``.
Parameters:
item (~collections.abc.Mapping, str, os.PathLike):... | python | def _match_item(item, any_all=any, ignore_case=False, normalize_values=False, **kwargs):
"""Match items by metadata.
Note:
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thebigmunch/google-music-utils | src/google_music_utils/filter.py | exclude_items | def exclude_items(items, any_all=any, ignore_case=False, normalize_values=False, **kwargs):
"""Exclude items by matching metadata.
Note:
Metadata values are lowercased when ``normalized_values`` is ``True``,
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Parameters:
items (list): A list of item dicts o... | python | def exclude_items(items, any_all=any, ignore_case=False, normalize_values=False, **kwargs):
"""Exclude items by matching metadata.
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thebigmunch/google-music-utils | src/google_music_utils/filter.py | include_items | def include_items(items, any_all=any, ignore_case=False, normalize_values=False, **kwargs):
"""Include items by matching metadata.
Note:
Metadata values are lowercased when ``normalized_values`` is ``True``,
so ``ignore_case`` is automatically set to ``True``.
Parameters:
items (list): A list of item dicts o... | python | def include_items(items, any_all=any, ignore_case=False, normalize_values=False, **kwargs):
"""Include items by matching metadata.
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IRC-SPHERE/HyperStream | hyperstream/utils/statistics/percentile.py | percentile | def percentile(a, q):
"""
Compute the qth percentile of the data along the specified axis.
Simpler version than the numpy version that always flattens input arrays.
Examples
--------
>>> a = [[10, 7, 4], [3, 2, 1]]
>>> percentile(a, 20)
2.0
>>> percentile(a, 50)
3.5
>>> perc... | python | def percentile(a, q):
"""
Compute the qth percentile of the data along the specified axis.
Simpler version than the numpy version that always flattens input arrays.
Examples
--------
>>> a = [[10, 7, 4], [3, 2, 1]]
>>> percentile(a, 20)
2.0
>>> percentile(a, 50)
3.5
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dgomes/pyipma | pyipma/station.py | Station._filter_closest | def _filter_closest(self, lat, lon, stations):
"""Helper to filter the closest station to a given location."""
current_location = (lat, lon)
closest = None
closest_distance = None
for station in stations:
station_loc = (station.latitude, station.longitude)
... | python | def _filter_closest(self, lat, lon, stations):
"""Helper to filter the closest station to a given location."""
current_location = (lat, lon)
closest = None
closest_distance = None
for station in stations:
station_loc = (station.latitude, station.longitude)
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dgomes/pyipma | pyipma/station.py | Station.get | async def get(cls, websession, lat, lon):
"""Retrieve the nearest station."""
self = Station(websession)
stations = await self.api.stations()
self.station = self._filter_closest(lat, lon, stations)
logger.info("Using %s as weather station", self.station.local)
... | python | async def get(cls, websession, lat, lon):
"""Retrieve the nearest station."""
self = Station(websession)
stations = await self.api.stations()
self.station = self._filter_closest(lat, lon, stations)
logger.info("Using %s as weather station", self.station.local)
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IRC-SPHERE/HyperStream | hyperstream/plugin_manager.py | Plugin.load_channels | def load_channels(self):
"""
Loads the channels and tools given the plugin path specified
:return: The loaded channels, including a tool channel, for the tools found.
"""
channels = []
# Try to get channels
for channel_name in self.channel_names:
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"""
Loads the channels and tools given the plugin path specified
:return: The loaded channels, including a tool channel, for the tools found.
"""
channels = []
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nsfmc/swatch | swatch/writer.py | chunk_count | def chunk_count(swatch):
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"""
if type(swatch) is dict:
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return 1
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"""return the number of byte-chunks in a swatch object
this recursively walks the swatch list, returning 1 for a single color &
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"""
if type(swatch) is dict:
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Big Endian Unsigned Int == len(Bytes in the Header Block)
note _only_ the header, this doesn't include the length of color data
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tapilab/brandelion | brandelion/cli/collect.py | iter_lines | def iter_lines(filename):
""" Iterate over screen names in a file, one per line."""
with open(filename, 'rt') as idfile:
for line in idfile:
screen_name = line.strip()
if len(screen_name) > 0:
yield screen_name.split()[0] | python | def iter_lines(filename):
""" Iterate over screen names in a file, one per line."""
with open(filename, 'rt') as idfile:
for line in idfile:
screen_name = line.strip()
if len(screen_name) > 0:
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tapilab/brandelion | brandelion/cli/collect.py | fetch_tweets | def fetch_tweets(account_file, outfile, limit):
""" Fetch up to limit tweets for each account in account_file and write to
outfile. """
print('fetching tweets for accounts in', account_file)
outf = io.open(outfile, 'wt')
for screen_name in iter_lines(account_file):
print('\nFetching tweets f... | python | def fetch_tweets(account_file, outfile, limit):
""" Fetch up to limit tweets for each account in account_file and write to
outfile. """
print('fetching tweets for accounts in', account_file)
outf = io.open(outfile, 'wt')
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tapilab/brandelion | brandelion/cli/collect.py | fetch_exemplars | def fetch_exemplars(keyword, outfile, n=50):
""" Fetch top lists matching this keyword, then return Twitter screen
names along with the number of different lists on which each appers.. """
list_urls = fetch_lists(keyword, n)
print('found %d lists for %s' % (len(list_urls), keyword))
counts = Counter... | python | def fetch_exemplars(keyword, outfile, n=50):
""" Fetch top lists matching this keyword, then return Twitter screen
names along with the number of different lists on which each appers.. """
list_urls = fetch_lists(keyword, n)
print('found %d lists for %s' % (len(list_urls), keyword))
counts = Counter... | [
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tamasgal/km3pipe | km3pipe/io/ch.py | CHPump._init_controlhost | def _init_controlhost(self):
"""Set up the controlhost connection"""
log.debug("Connecting to JLigier")
self.client = Client(self.host, self.port)
self.client._connect()
log.debug("Subscribing to tags: {0}".format(self.tags))
for tag in self.tags.split(','):
s... | python | def _init_controlhost(self):
"""Set up the controlhost connection"""
log.debug("Connecting to JLigier")
self.client = Client(self.host, self.port)
self.client._connect()
log.debug("Subscribing to tags: {0}".format(self.tags))
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tamasgal/km3pipe | km3pipe/io/ch.py | CHPump.process | def process(self, blob):
"""Wait for the next packet and put it in the blob"""
# self._add_process_dt()
try:
log.debug("Waiting for queue items.")
prefix, data = self.queue.get(timeout=self.timeout)
log.debug("Got {0} bytes from queue.".format(len(data)))
... | python | def process(self, blob):
"""Wait for the next packet and put it in the blob"""
# self._add_process_dt()
try:
log.debug("Waiting for queue items.")
prefix, data = self.queue.get(timeout=self.timeout)
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tamasgal/km3pipe | km3pipe/io/ch.py | CHPump.finish | def finish(self):
"""Clean up the JLigier controlhost connection"""
log.debug("Disconnecting from JLigier.")
self.client.socket.shutdown(socket.SHUT_RDWR)
self.client._disconnect() | python | def finish(self):
"""Clean up the JLigier controlhost connection"""
log.debug("Disconnecting from JLigier.")
self.client.socket.shutdown(socket.SHUT_RDWR)
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ioos/pyoos | pyoos/collectors/hads/hads.py | Hads.list_variables | def list_variables(self):
"""
List available variables and applies any filters.
"""
station_codes = self._get_station_codes()
station_codes = self._apply_features_filter(station_codes)
variables = self._list_variables(station_codes)
if hasattr(self, "_variables")... | python | def list_variables(self):
"""
List available variables and applies any filters.
"""
station_codes = self._get_station_codes()
station_codes = self._apply_features_filter(station_codes)
variables = self._list_variables(station_codes)
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ioos/pyoos | pyoos/collectors/hads/hads.py | Hads._list_variables | def _list_variables(self, station_codes):
"""
Internal helper to list the variables for the given station codes.
"""
# sample output from obs retrieval:
#
# DD9452D0
# HP(SRBM5)
# 2013-07-22 19:30 45.97
# HT(SRBM5)
# ... | python | def _list_variables(self, station_codes):
"""
Internal helper to list the variables for the given station codes.
"""
# sample output from obs retrieval:
#
# DD9452D0
# HP(SRBM5)
# 2013-07-22 19:30 45.97
# HT(SRBM5)
# ... | [
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ioos/pyoos | pyoos/collectors/hads/hads.py | Hads._apply_features_filter | def _apply_features_filter(self, station_codes):
"""
If the features filter is set, this will return the intersection of
those filter items and the given station codes.
"""
# apply features filter
if hasattr(self, "features") and self.features is not None:
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"""
If the features filter is set, this will return the intersection of
those filter items and the given station codes.
"""
# apply features filter
if hasattr(self, "features") and self.features is not None:
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ioos/pyoos | pyoos/collectors/hads/hads.py | Hads._get_station_codes | def _get_station_codes(self, force=False):
"""
Gets and caches a list of station codes optionally within a bbox.
Will return the cached version if it exists unless force is True.
"""
if not force and self.station_codes is not None:
return self.station_codes
... | python | def _get_station_codes(self, force=False):
"""
Gets and caches a list of station codes optionally within a bbox.
Will return the cached version if it exists unless force is True.
"""
if not force and self.station_codes is not None:
return self.station_codes
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SentimensRG/txCelery | txcelery/defer.py | DeferredTask._monitor_task | def _monitor_task(self):
"""Wrapper that handles the actual asynchronous monitoring of the task
state.
"""
if self.task.state in states.UNREADY_STATES:
reactor.callLater(self.POLL_PERIOD, self._monitor_task)
return
if self.task.state == 'SUCCESS':
... | python | def _monitor_task(self):
"""Wrapper that handles the actual asynchronous monitoring of the task
state.
"""
if self.task.state in states.UNREADY_STATES:
reactor.callLater(self.POLL_PERIOD, self._monitor_task)
return
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materials-data-facility/toolbox | mdf_toolbox/search_helper.py | _clean_query_string | def _clean_query_string(q):
"""Clean up a query string for searching.
Removes unmatched parentheses and joining operators.
Arguments:
q (str): Query string to be cleaned
Returns:
str: The clean query string.
"""
q = q.replace("()", "").strip()
if q.endswith("("):
q... | python | def _clean_query_string(q):
"""Clean up a query string for searching.
Removes unmatched parentheses and joining operators.
Arguments:
q (str): Query string to be cleaned
Returns:
str: The clean query string.
"""
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materials-data-facility/toolbox | mdf_toolbox/search_helper.py | _validate_query | def _validate_query(query):
"""Validate and clean up a query to be sent to Search.
Cleans the query string, removes unneeded parameters, and validates for correctness.
Does not modify the original argument.
Raises an Exception on invalid input.
Arguments:
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Cleans the query string, removes unneeded parameters, and validates for correctness.
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materials-data-facility/toolbox | mdf_toolbox/search_helper.py | SearchHelper._term | def _term(self, term):
"""Add a term to the query.
Arguments:
term (str): The term to add.
Returns:
SearchHelper: Self
"""
# All terms must be strings for Elasticsearch
term = str(term)
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... | python | def _term(self, term):
"""Add a term to the query.
Arguments:
term (str): The term to add.
Returns:
SearchHelper: Self
"""
# All terms must be strings for Elasticsearch
term = str(term)
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materials-data-facility/toolbox | mdf_toolbox/search_helper.py | SearchHelper._operator | def _operator(self, op, close_group=False):
"""Add an operator between terms.
There must be a term added before using this method.
All operators have helpers, so this method is usually not necessary to directly invoke.
Arguments:
op (str): The operator to add. Must be in the... | python | def _operator(self, op, close_group=False):
"""Add an operator between terms.
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materials-data-facility/toolbox | mdf_toolbox/search_helper.py | SearchHelper._and_join | def _and_join(self, close_group=False):
"""Combine terms with AND.
There must be a term added before using this method.
Arguments:
close_group (bool): If ``True``, will end the current group and start a new one.
If ``False``, will continue current group.
... | python | def _and_join(self, close_group=False):
"""Combine terms with AND.
There must be a term added before using this method.
Arguments:
close_group (bool): If ``True``, will end the current group and start a new one.
If ``False``, will continue current group.
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materials-data-facility/toolbox | mdf_toolbox/search_helper.py | SearchHelper._or_join | def _or_join(self, close_group=False):
"""Combine terms with OR.
There must be a term added before using this method.
Arguments:
close_group (bool): If ``True``, will end the current group and start a new one.
If ``False``, will continue current group.
... | python | def _or_join(self, close_group=False):
"""Combine terms with OR.
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Arguments:
close_group (bool): If ``True``, will end the current group and start a new one.
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materials-data-facility/toolbox | mdf_toolbox/search_helper.py | SearchHelper._mapping | def _mapping(self):
"""Fetch the entire mapping for the specified index.
Returns:
dict: The full mapping for the index.
"""
return (self.__search_client.get(
"/unstable/index/{}/mapping".format(mdf_toolbox.translate_index(self.index)))
["m... | python | def _mapping(self):
"""Fetch the entire mapping for the specified index.
Returns:
dict: The full mapping for the index.
"""
return (self.__search_client.get(
"/unstable/index/{}/mapping".format(mdf_toolbox.translate_index(self.index)))
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materials-data-facility/toolbox | mdf_toolbox/search_helper.py | SearchHelper.match_term | def match_term(self, value, required=True, new_group=False):
"""Add a fulltext search term to the query.
Warning:
Do not use this method with any other query-building helpers. This method
is only for building fulltext queries (in non-advanced mode). Using other
helpe... | python | def match_term(self, value, required=True, new_group=False):
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Warning:
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materials-data-facility/toolbox | mdf_toolbox/search_helper.py | SearchHelper.match_exists | def match_exists(self, field, required=True, new_group=False):
"""Require a field to exist in the results.
Matches will have some value in ``field``.
Arguments:
field (str): The field to check.
The field must be namespaced according to Elasticsearch rules
... | python | def match_exists(self, field, required=True, new_group=False):
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Matches will have some value in ``field``.
Arguments:
field (str): The field to check.
The field must be namespaced according to Elasticsearch rules
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materials-data-facility/toolbox | mdf_toolbox/search_helper.py | SearchHelper.match_not_exists | def match_not_exists(self, field, new_group=False):
"""Require a field to not exist in the results.
Matches will not have ``field`` present.
Arguments:
field (str): The field to check.
The field must be namespaced according to Elasticsearch rules
... | python | def match_not_exists(self, field, new_group=False):
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Matches will not have ``field`` present.
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field (str): The field to check.
The field must be namespaced according to Elasticsearch rules
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materials-data-facility/toolbox | mdf_toolbox/search_helper.py | SearchHelper.show_fields | def show_fields(self, block=None):
"""Retrieve and return the mapping for the given metadata block.
Arguments:
block (str): The top-level field to fetch the mapping for (for example, ``"mdf"``),
or the special values ``None`` for everything or ``"top"`` for just the
... | python | def show_fields(self, block=None):
"""Retrieve and return the mapping for the given metadata block.
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block (str): The top-level field to fetch the mapping for (for example, ``"mdf"``),
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tamasgal/km3pipe | km3pipe/dataclasses.py | inflate_dtype | def inflate_dtype(arr, names):
"""Create structured dtype from a 2d ndarray with unstructured dtype."""
arr = np.asanyarray(arr)
if has_structured_dt(arr):
return arr.dtype
s_dt = arr.dtype
dt = [(n, s_dt) for n in names]
dt = np.dtype(dt)
return dt | python | def inflate_dtype(arr, names):
"""Create structured dtype from a 2d ndarray with unstructured dtype."""
arr = np.asanyarray(arr)
if has_structured_dt(arr):
return arr.dtype
s_dt = arr.dtype
dt = [(n, s_dt) for n in names]
dt = np.dtype(dt)
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tamasgal/km3pipe | km3pipe/dataclasses.py | Table.from_dict | def from_dict(cls, arr_dict, dtype=None, fillna=False, **kwargs):
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# i hope order of keys == order or values
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tamasgal/km3pipe | km3pipe/dataclasses.py | Table.from_template | def from_template(cls, data, template):
"""Create a table from a predefined datatype.
See the ``templates_avail`` property for available names.
Parameters
----------
data
Data in a format that the ``__init__`` understands.
template: str or dict
N... | python | def from_template(cls, data, template):
"""Create a table from a predefined datatype.
See the ``templates_avail`` property for available names.
Parameters
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data
Data in a format that the ``__init__`` understands.
template: str or dict
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tamasgal/km3pipe | km3pipe/dataclasses.py | Table.append_columns | def append_columns(self, colnames, values, **kwargs):
"""Append new columns to the table.
When appending a single column, ``values`` can be a scalar or an
array of either length 1 or the same length as this array (the one
it's appended to). In case of multiple columns, values must have
... | python | def append_columns(self, colnames, values, **kwargs):
"""Append new columns to the table.
When appending a single column, ``values`` can be a scalar or an
array of either length 1 or the same length as this array (the one
it's appended to). In case of multiple columns, values must have
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tamasgal/km3pipe | km3pipe/dataclasses.py | Table.drop_columns | def drop_columns(self, colnames, **kwargs):
"""Drop columns from the table.
See the docs for ``numpy.lib.recfunctions.drop_fields`` for an
explanation of the remaining options.
"""
new_arr = rfn.drop_fields(
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... | python | def drop_columns(self, colnames, **kwargs):
"""Drop columns from the table.
See the docs for ``numpy.lib.recfunctions.drop_fields`` for an
explanation of the remaining options.
"""
new_arr = rfn.drop_fields(
self, colnames, usemask=False, asrecarray=True, **kwargs
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tamasgal/km3pipe | km3pipe/dataclasses.py | Table.sorted | def sorted(self, by, **kwargs):
"""Sort array by a column.
Parameters
==========
by: str
Name of the columns to sort by(e.g. 'time').
"""
sort_idc = np.argsort(self[by], **kwargs)
return self.__class__(
self[sort_idc],
h5loc=se... | python | def sorted(self, by, **kwargs):
"""Sort array by a column.
Parameters
==========
by: str
Name of the columns to sort by(e.g. 'time').
"""
sort_idc = np.argsort(self[by], **kwargs)
return self.__class__(
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tamasgal/km3pipe | km3pipe/dataclasses.py | Table.merge | def merge(cls, tables, fillna=False):
"""Merge a list of tables"""
cols = set(itertools.chain(*[table.dtype.descr for table in tables]))
tables_to_merge = []
for table in tables:
missing_cols = cols - set(table.dtype.descr)
if missing_cols:
if fi... | python | def merge(cls, tables, fillna=False):
"""Merge a list of tables"""
cols = set(itertools.chain(*[table.dtype.descr for table in tables]))
tables_to_merge = []
for table in tables:
missing_cols = cols - set(table.dtype.descr)
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tamasgal/km3pipe | km3pipe/io/hdf5.py | create_index_tuple | def create_index_tuple(group_ids):
"""An helper function to create index tuples for fast lookup in HDF5Pump"""
max_group_id = np.max(group_ids)
start_idx_arr = np.full(max_group_id + 1, 0)
n_items_arr = np.full(max_group_id + 1, 0)
current_group_id = group_ids[0]
current_idx = 0
item_count... | python | def create_index_tuple(group_ids):
"""An helper function to create index tuples for fast lookup in HDF5Pump"""
max_group_id = np.max(group_ids)
start_idx_arr = np.full(max_group_id + 1, 0)
n_items_arr = np.full(max_group_id + 1, 0)
current_group_id = group_ids[0]
current_idx = 0
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tamasgal/km3pipe | km3pipe/io/hdf5.py | HDF5Header._set_attributes | def _set_attributes(self):
"""Traverse the internal dictionary and set the getters"""
for parameter, data in self._data.items():
if isinstance(data, dict) or isinstance(data, OrderedDict):
field_names, field_values = zip(*data.items())
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"""Traverse the internal dictionary and set the getters"""
for parameter, data in self._data.items():
if isinstance(data, dict) or isinstance(data, OrderedDict):
field_names, field_values = zip(*data.items())
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tamasgal/km3pipe | km3pipe/io/hdf5.py | HDF5Sink._write_ndarrays_cache_to_disk | def _write_ndarrays_cache_to_disk(self):
"""Writes all the cached NDArrays to disk and empties the cache"""
for h5loc, arrs in self._ndarrays_cache.items():
title = arrs[0].title
chunkshape = (self.chunksize,) + arrs[0].shape[1:] if self.chunksize is not\
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"""Writes all the cached NDArrays to disk and empties the cache"""
for h5loc, arrs in self._ndarrays_cache.items():
title = arrs[0].title
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tamasgal/km3pipe | km3pipe/io/hdf5.py | HDF5Sink.flush | def flush(self):
"""Flush tables and arrays to disk"""
self.log.info('Flushing tables and arrays to disk...')
for tab in self._tables.values():
tab.flush()
self._write_ndarrays_cache_to_disk() | python | def flush(self):
"""Flush tables and arrays to disk"""
self.log.info('Flushing tables and arrays to disk...')
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tab.flush()
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cprogrammer1994/GLWindow | GLWindow/__main__.py | main | def main():
'''
Sample program to test GLWindow.
'''
print('GLWindow:', GLWindow.__version__)
print('Python:', sys.version)
print('Platform:', sys.platform)
wnd = GLWindow.create_window((480, 480), title='GLWindow Sample')
wnd.vsync = False
ctx = ModernGL.create_context()
... | python | def main():
'''
Sample program to test GLWindow.
'''
print('GLWindow:', GLWindow.__version__)
print('Python:', sys.version)
print('Platform:', sys.platform)
wnd = GLWindow.create_window((480, 480), title='GLWindow Sample')
wnd.vsync = False
ctx = ModernGL.create_context()
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tamasgal/km3pipe | examples/offline_analysis/k40summary.py | write_header | def write_header(fobj):
"""Add the header to the CSV file"""
fobj.write("# K40 calibration results\n")
fobj.write("det_id\trun_id\tdom_id")
for param in ['t0', 'qe']:
for i in range(31):
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"""Add the header to the CSV file"""
fobj.write("# K40 calibration results\n")
fobj.write("det_id\trun_id\tdom_id")
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tamasgal/km3pipe | km3pipe/math.py | azimuth | def azimuth(v):
"""Return the azimuth angle in radians.
``phi``, ``theta`` is the opposite of ``zenith``, ``azimuth``.
This is the 'normal' azimuth definition -- beware of how you
define your coordinates. KM3NeT defines azimuth
differently than e.g. SLALIB, astropy, the AAS.org
"""
v = np.... | python | def azimuth(v):
"""Return the azimuth angle in radians.
``phi``, ``theta`` is the opposite of ``zenith``, ``azimuth``.
This is the 'normal' azimuth definition -- beware of how you
define your coordinates. KM3NeT defines azimuth
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tamasgal/km3pipe | km3pipe/math.py | unit_vector | def unit_vector(vector, **kwargs):
"""Returns the unit vector of the vector."""
# This also works for a dataframe with columns ['x', 'y', 'z']
# However, the division operation is picky about the shapes
# So, remember input vector shape, cast all up to 2d,
# do the (ugly) conversion, then return uni... | python | def unit_vector(vector, **kwargs):
"""Returns the unit vector of the vector."""
# This also works for a dataframe with columns ['x', 'y', 'z']
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tamasgal/km3pipe | km3pipe/math.py | pld3 | def pld3(pos, line_vertex, line_dir):
"""Calculate the point-line-distance for given point and line."""
pos = np.atleast_2d(pos)
line_vertex = np.atleast_1d(line_vertex)
line_dir = np.atleast_1d(line_dir)
c = np.cross(line_dir, line_vertex - pos)
n1 = np.linalg.norm(c, axis=1)
n2 = np.linalg... | python | def pld3(pos, line_vertex, line_dir):
"""Calculate the point-line-distance for given point and line."""
pos = np.atleast_2d(pos)
line_vertex = np.atleast_1d(line_vertex)
line_dir = np.atleast_1d(line_dir)
c = np.cross(line_dir, line_vertex - pos)
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tamasgal/km3pipe | km3pipe/math.py | dist | def dist(x1, x2, axis=0):
"""Return the distance between two points.
Set axis=1 if x1 is a vector and x2 a matrix to get a vector of distances.
"""
return np.linalg.norm(x2 - x1, axis=axis) | python | def dist(x1, x2, axis=0):
"""Return the distance between two points.
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tamasgal/km3pipe | km3pipe/math.py | com | def com(points, masses=None):
"""Calculate center of mass for given points.
If masses is not set, assume equal masses."""
if masses is None:
return np.average(points, axis=0)
else:
return np.average(points, axis=0, weights=masses) | python | def com(points, masses=None):
"""Calculate center of mass for given points.
If masses is not set, assume equal masses."""
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return np.average(points, axis=0)
else:
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tamasgal/km3pipe | km3pipe/math.py | circ_permutation | def circ_permutation(items):
"""Calculate the circular permutation for a given list of items."""
permutations = []
for i in range(len(items)):
permutations.append(items[i:] + items[:i])
return permutations | python | def circ_permutation(items):
"""Calculate the circular permutation for a given list of items."""
permutations = []
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tamasgal/km3pipe | km3pipe/math.py | inertia | def inertia(x, y, z, weight=None):
"""Inertia tensor, stolen of thomas"""
if weight is None:
weight = 1
tensor_of_inertia = np.zeros((3, 3), dtype=float)
tensor_of_inertia[0][0] = (y * y + z * z) * weight
tensor_of_inertia[0][1] = (-1) * x * y * weight
tensor_of_inertia[0][2] = (-1) * x ... | python | def inertia(x, y, z, weight=None):
"""Inertia tensor, stolen of thomas"""
if weight is None:
weight = 1
tensor_of_inertia = np.zeros((3, 3), dtype=float)
tensor_of_inertia[0][0] = (y * y + z * z) * weight
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tamasgal/km3pipe | km3pipe/math.py | qrot | def qrot(vector, quaternion):
"""Rotate a 3D vector using quaternion algebra.
Implemented by Vladimir Kulikovskiy.
Parameters
----------
vector: np.array
quaternion: np.array
Returns
-------
np.array
"""
t = 2 * np.cross(quaternion[1:], vector)
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"""Rotate a 3D vector using quaternion algebra.
Implemented by Vladimir Kulikovskiy.
Parameters
----------
vector: np.array
quaternion: np.array
Returns
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np.array
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tamasgal/km3pipe | km3pipe/math.py | qeuler | def qeuler(yaw, pitch, roll):
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yaw: number
pitch: number
roll: number
Returns
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np.array
"""
yaw = np.radians(yaw)
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cy = np.cos(yaw * 0.5)
sy ... | python | def qeuler(yaw, pitch, roll):
"""Convert Euler angle to quaternion.
Parameters
----------
yaw: number
pitch: number
roll: number
Returns
-------
np.array
"""
yaw = np.radians(yaw)
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tamasgal/km3pipe | km3pipe/math.py | intersect_3d | def intersect_3d(p1, p2):
"""Find the closes point for a given set of lines in 3D.
Parameters
----------
p1 : (M, N) array_like
Starting points
p2 : (M, N) array_like
End points.
Returns
-------
x : (N,) ndarray
Least-squares solution - the closest point of the ... | python | def intersect_3d(p1, p2):
"""Find the closes point for a given set of lines in 3D.
Parameters
----------
p1 : (M, N) array_like
Starting points
p2 : (M, N) array_like
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astooke/gtimer | gtimer/util.py | compat_py2_py3 | def compat_py2_py3():
""" For Python 2, 3 compatibility. """
if (sys.version_info > (3, 0)):
def iteritems(dictionary):
return dictionary.items()
def itervalues(dictionary):
return dictionary.values()
else:
def iteritems(dictionary):
return dicti... | python | def compat_py2_py3():
""" For Python 2, 3 compatibility. """
if (sys.version_info > (3, 0)):
def iteritems(dictionary):
return dictionary.items()
def itervalues(dictionary):
return dictionary.values()
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tamasgal/km3pipe | km3pipe/io/jpp.py | TimeslicePump.timeslice_generator | def timeslice_generator(self):
"""Uses slice ID as iterator"""
slice_id = 0
while slice_id < self.n_timeslices:
blob = self.get_blob(slice_id)
yield blob
slice_id += 1 | python | def timeslice_generator(self):
"""Uses slice ID as iterator"""
slice_id = 0
while slice_id < self.n_timeslices:
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tamasgal/km3pipe | km3pipe/io/jpp.py | TimeslicePump.get_blob | def get_blob(self, index):
"""Index is slice ID"""
blob = self._current_blob
self.r.retrieve_timeslice(index)
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'timestamp': self.r.utc_seconds,
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blob = self._current_blob
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tamasgal/km3pipe | km3pipe/io/jpp.py | TimeslicePump._slice_generator | def _slice_generator(self, index):
"""A simple slice generator for iterations"""
start, stop, step = index.indices(len(self))
for i in range(start, stop, step):
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tapilab/brandelion | brandelion/cli/diagnose.py | correlation_by_exemplar | def correlation_by_exemplar(brands, exemplars, validation_scores, analyze_fn_str, outf):
""" Report the overall correlation with the validation scores using each exemplar in isolation. """
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keys = sorted(k for k in validation_scores.keys() if k in set(x[0] for x ... | python | def correlation_by_exemplar(brands, exemplars, validation_scores, analyze_fn_str, outf):
""" Report the overall correlation with the validation scores using each exemplar in isolation. """
analyze_fn = getattr(analyze, analyze_fn_str)
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IRC-SPHERE/HyperStream | hyperstream/node/node.py | Node.difference | def difference(self, other):
"""
Summarise the differences between this node and the other node.
:param other: The other node
:return: A tuple containing the diff, the counts of the diff, and whether this plate is a sub-plate of the other
:type other: Node
"""
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"""
Summarise the differences between this node and the other node.
:param other: The other node
:return: A tuple containing the diff, the counts of the diff, and whether this plate is a sub-plate of the other
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astooke/gtimer | gtimer/public/report.py | report | def report(times=None,
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include_stats=True,
delim_mode=False,
format_options=None):
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Produce a formatted report of the current timing data.
Notes:
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include_itrs=True,
include_stats=True,
delim_mode=False,
format_options=None):
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Produce a formatted report of the current timing data.
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astooke/gtimer | gtimer/public/report.py | compare | def compare(times_list=None,
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name=None,
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delim_mode=False,
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astooke/gtimer | gtimer/public/report.py | write_structure | def write_structure(times=None):
"""
Produce a formatted record of a times data structure.
Args:
times (Times, optional): If not provided, uses the current root timer.
Returns:
str: Timer tree hierarchy in a formatted string.
Raises:
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"""
Produce a formatted record of a times data structure.
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times (Times, optional): If not provided, uses the current root timer.
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str: Timer tree hierarchy in a formatted string.
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tamasgal/km3pipe | examples/plot_dom_hits.py | filter_muons | def filter_muons(blob):
"""Write all muons from McTracks to Muons."""
tracks = blob['McTracks']
muons = tracks[tracks.type == -13] # PDG particle code
blob["Muons"] = Table(muons)
return blob | python | def filter_muons(blob):
"""Write all muons from McTracks to Muons."""
tracks = blob['McTracks']
muons = tracks[tracks.type == -13] # PDG particle code
blob["Muons"] = Table(muons)
return blob | [
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aouyar/healthgraph-api | samples/bottle/runkeeper_demo.py | parse_conf_files | def parse_conf_files(conf_paths):
"""Parse the configuration file and return dictionary of configuration
options.
@param conf_paths: List of configuration file paths to parse.
@return: Dictionary of configuration options.
"""
conf_file = ConfigParser.RawConfigParser()
co... | python | def parse_conf_files(conf_paths):
"""Parse the configuration file and return dictionary of configuration
options.
@param conf_paths: List of configuration file paths to parse.
@return: Dictionary of configuration options.
"""
conf_file = ConfigParser.RawConfigParser()
co... | [
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aouyar/healthgraph-api | samples/bottle/runkeeper_demo.py | main | def main(argv=None):
"""Main Block - Configure and run the Bottle Web Server."""
cmd_opts = parse_cmdline(argv)[0]
if cmd_opts.confpath is not None:
if os.path.exists(cmd_opts.confpath):
conf_paths = [cmd_opts.confpath,]
else:
return "Configuration file not found: %s"... | python | def main(argv=None):
"""Main Block - Configure and run the Bottle Web Server."""
cmd_opts = parse_cmdline(argv)[0]
if cmd_opts.confpath is not None:
if os.path.exists(cmd_opts.confpath):
conf_paths = [cmd_opts.confpath,]
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NaPs/Kolekto | kolekto/helpers.py | get_hash | def get_hash(input_string):
""" Return the hash of the movie depending on the input string.
If the input string looks like a symbolic link to a movie in a Kolekto
tree, return its movies hash, else, return the input directly in lowercase.
"""
# Check if the input looks like a link to a movie:
... | python | def get_hash(input_string):
""" Return the hash of the movie depending on the input string.
If the input string looks like a symbolic link to a movie in a Kolekto
tree, return its movies hash, else, return the input directly in lowercase.
"""
# Check if the input looks like a link to a movie:
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NaPs/Kolekto | kolekto/helpers.py | JsonDbm.get | def get(self, key):
""" Get data associated with provided key.
"""
return self._object_class(json.loads(self._db[key])) | python | def get(self, key):
""" Get data associated with provided key.
"""
return self._object_class(json.loads(self._db[key])) | [
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NaPs/Kolekto | kolekto/helpers.py | JsonDbm.save | def save(self, key, data):
""" Save data associated with key.
"""
self._db[key] = json.dumps(data)
self._db.sync() | python | def save(self, key, data):
""" Save data associated with key.
"""
self._db[key] = json.dumps(data)
self._db.sync() | [
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IRC-SPHERE/HyperStream | hyperstream/meta_data/meta_data_manager.py | MetaDataManager.global_meta_data | def global_meta_data(self):
"""
Get the global meta data, which will be stored in a tree structure
:return: The global meta data
"""
with switch_db(MetaDataModel, 'hyperstream'):
return sorted(map(lambda x: x.to_dict(), MetaDataModel.objects),
... | python | def global_meta_data(self):
"""
Get the global meta data, which will be stored in a tree structure
:return: The global meta data
"""
with switch_db(MetaDataModel, 'hyperstream'):
return sorted(map(lambda x: x.to_dict(), MetaDataModel.objects),
... | [
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IRC-SPHERE/HyperStream | hyperstream/meta_data/meta_data_manager.py | MetaDataManager.insert | def insert(self, tag, identifier, parent, data):
"""
Insert the given meta data into the database
:param tag: The tag (equates to meta_data_id)
:param identifier: The identifier (a combination of the meta_data_id and the plate value)
:param parent: The parent plate identifier
... | python | def insert(self, tag, identifier, parent, data):
"""
Insert the given meta data into the database
:param tag: The tag (equates to meta_data_id)
:param identifier: The identifier (a combination of the meta_data_id and the plate value)
:param parent: The parent plate identifier
... | [
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:param data: The data (plate value)
:return: None | [
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IRC-SPHERE/HyperStream | hyperstream/meta_data/meta_data_manager.py | MetaDataManager.delete | def delete(self, identifier):
"""
Delete the meta data with the given identifier from the database
:param identifier: The identifier
:return: None
"""
try:
node = self.global_plate_definitions[identifier]
except NodeIDAbsentError:
logging... | python | def delete(self, identifier):
"""
Delete the meta data with the given identifier from the database
:param identifier: The identifier
:return: None
"""
try:
node = self.global_plate_definitions[identifier]
except NodeIDAbsentError:
logging... | [
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htm-community/menorah | menorah/riverstream.py | RiverStream.load | def load(self):
"""
Loads this stream by calling River View for data.
"""
print "Loading data for %s..." % self.getName()
self._dataHandle = self._stream.data(
since=self._since, until=self._until,
limit=self._limit, aggregate=self._aggregate
)
self._data = self._dataHandle.data... | python | def load(self):
"""
Loads this stream by calling River View for data.
"""
print "Loading data for %s..." % self.getName()
self._dataHandle = self._stream.data(
since=self._since, until=self._until,
limit=self._limit, aggregate=self._aggregate
)
self._data = self._dataHandle.data... | [
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tamasgal/km3pipe | km3pipe/plot.py | hexbin | def hexbin(x, y, color="purple", **kwargs):
"""Seaborn-compatible hexbin plot.
See also: http://seaborn.pydata.org/tutorial/axis_grids.html#mapping-custom-functions-onto-the-grid
"""
if HAS_SEABORN:
cmap = sns.light_palette(color, as_cmap=True)
else:
cmap = "Purples"
plt.hexbin(... | python | def hexbin(x, y, color="purple", **kwargs):
"""Seaborn-compatible hexbin plot.
See also: http://seaborn.pydata.org/tutorial/axis_grids.html#mapping-custom-functions-onto-the-grid
"""
if HAS_SEABORN:
cmap = sns.light_palette(color, as_cmap=True)
else:
cmap = "Purples"
plt.hexbin(... | [
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tamasgal/km3pipe | km3pipe/plot.py | diag | def diag(ax=None, linecolor='0.0', linestyle='--', **kwargs):
"""Plot the diagonal."""
ax = get_ax(ax)
xy_min = np.min((ax.get_xlim(), ax.get_ylim()))
xy_max = np.max((ax.get_ylim(), ax.get_xlim()))
return ax.plot([xy_min, xy_max], [xy_min, xy_max],
ls=linestyle,
... | python | def diag(ax=None, linecolor='0.0', linestyle='--', **kwargs):
"""Plot the diagonal."""
ax = get_ax(ax)
xy_min = np.min((ax.get_xlim(), ax.get_ylim()))
xy_max = np.max((ax.get_ylim(), ax.get_xlim()))
return ax.plot([xy_min, xy_max], [xy_min, xy_max],
ls=linestyle,
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tamasgal/km3pipe | km3pipe/plot.py | automeshgrid | def automeshgrid(
x, y, step=0.02, xstep=None, ystep=None, pad=0.5, xpad=None, ypad=None
):
"""Make a meshgrid, inferred from data."""
if xpad is None:
xpad = pad
if xstep is None:
xstep = step
if ypad is None:
ypad = pad
if ystep is None:
ystep = step
xmi... | python | def automeshgrid(
x, y, step=0.02, xstep=None, ystep=None, pad=0.5, xpad=None, ypad=None
):
"""Make a meshgrid, inferred from data."""
if xpad is None:
xpad = pad
if xstep is None:
xstep = step
if ypad is None:
ypad = pad
if ystep is None:
ystep = step
xmi... | [
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tamasgal/km3pipe | km3pipe/plot.py | prebinned_hist | def prebinned_hist(counts, binlims, ax=None, *args, **kwargs):
"""Plot a histogram with counts, binlims already given.
Example
=======
>>> gaus = np.random.normal(size=100)
>>> counts, binlims = np.histogram(gaus, bins='auto')
>>> prebinned_hist(countsl binlims)
"""
ax = get_ax(ax)
... | python | def prebinned_hist(counts, binlims, ax=None, *args, **kwargs):
"""Plot a histogram with counts, binlims already given.
Example
=======
>>> gaus = np.random.normal(size=100)
>>> counts, binlims = np.histogram(gaus, bins='auto')
>>> prebinned_hist(countsl binlims)
"""
ax = get_ax(ax)
... | [
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>>> gaus = np.random.normal(size=100)
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