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tamasgal/km3pipe | km3pipe/io/evt.py | EvtPump.prepare_blobs | def prepare_blobs(self):
"""Populate the blobs"""
self.raw_header = self.extract_header()
if self.cache_enabled:
self._cache_offsets() | python | def prepare_blobs(self):
"""Populate the blobs"""
self.raw_header = self.extract_header()
if self.cache_enabled:
self._cache_offsets() | [
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tamasgal/km3pipe | km3pipe/io/evt.py | EvtPump.extract_header | def extract_header(self):
"""Create a dictionary with the EVT header information"""
self.log.info("Extracting the header")
raw_header = self.raw_header = defaultdict(list)
first_line = self.blob_file.readline()
first_line = try_decode_string(first_line)
self.blob_file.see... | python | def extract_header(self):
"""Create a dictionary with the EVT header information"""
self.log.info("Extracting the header")
raw_header = self.raw_header = defaultdict(list)
first_line = self.blob_file.readline()
first_line = try_decode_string(first_line)
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tamasgal/km3pipe | km3pipe/io/evt.py | EvtPump.get_blob | def get_blob(self, index):
"""Return a blob with the event at the given index"""
self.log.info("Retrieving blob #{}".format(index))
if index > len(self.event_offsets) - 1:
self.log.info("Index not in cache, caching offsets")
self._cache_offsets(index, verbose=False)
... | python | def get_blob(self, index):
"""Return a blob with the event at the given index"""
self.log.info("Retrieving blob #{}".format(index))
if index > len(self.event_offsets) - 1:
self.log.info("Index not in cache, caching offsets")
self._cache_offsets(index, verbose=False)
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tamasgal/km3pipe | km3pipe/io/evt.py | EvtPump.process | def process(self, blob=None):
"""Pump the next blob to the modules"""
try:
blob = self.get_blob(self.index)
except IndexError:
self.log.info("Got an IndexError, trying the next file")
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or self.filenames) and self.file_inde... | python | def process(self, blob=None):
"""Pump the next blob to the modules"""
try:
blob = self.get_blob(self.index)
except IndexError:
self.log.info("Got an IndexError, trying the next file")
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tamasgal/km3pipe | km3pipe/io/evt.py | EvtPump._cache_offsets | def _cache_offsets(self, up_to_index=None, verbose=True):
"""Cache all event offsets."""
if not up_to_index:
if verbose:
self.print("Caching event file offsets, this may take a bit.")
self.blob_file.seek(0, 0)
self.event_offsets = []
if not... | python | def _cache_offsets(self, up_to_index=None, verbose=True):
"""Cache all event offsets."""
if not up_to_index:
if verbose:
self.print("Caching event file offsets, this may take a bit.")
self.blob_file.seek(0, 0)
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tamasgal/km3pipe | km3pipe/io/evt.py | EvtPump._record_offset | def _record_offset(self):
"""Stores the current file pointer position"""
offset = self.blob_file.tell()
self.event_offsets.append(offset) | python | def _record_offset(self):
"""Stores the current file pointer position"""
offset = self.blob_file.tell()
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tamasgal/km3pipe | km3pipe/io/evt.py | EvtPump._create_blob | def _create_blob(self):
"""Parse the next event from the current file position"""
blob = None
for line in self.blob_file:
line = try_decode_string(line)
line = line.strip()
if line == '':
self.log.info("Ignoring empty line...")
... | python | def _create_blob(self):
"""Parse the next event from the current file position"""
blob = None
for line in self.blob_file:
line = try_decode_string(line)
line = line.strip()
if line == '':
self.log.info("Ignoring empty line...")
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abiiranathan/db2 | db2/__main__.py | runserver | def runserver(project_name):
'''
Runs a python cgi server in a subprocess.
'''
DIR = os.listdir(project_name)
if 'settings.py' not in DIR:
raise NotImplementedError('No file called: settings.py found in %s'%project_name)
CGI_BIN_FOLDER = os.path.join(project_name, 'cgi', 'cgi-bin')
CGI_FOLDER = os.path.join(... | python | def runserver(project_name):
'''
Runs a python cgi server in a subprocess.
'''
DIR = os.listdir(project_name)
if 'settings.py' not in DIR:
raise NotImplementedError('No file called: settings.py found in %s'%project_name)
CGI_BIN_FOLDER = os.path.join(project_name, 'cgi', 'cgi-bin')
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PrefPy/prefpy | prefpy/utilityFunction.py | UtilityFunction.getUtility | def getUtility(self, decision, sample, aggregationMode = "avg"):
"""
Get the utility of a given decision given a preference.
:ivar list<int> decision: Contains a list of integer representations of candidates in the
current decision.
:ivar sample: A representation of a pref... | python | def getUtility(self, decision, sample, aggregationMode = "avg"):
"""
Get the utility of a given decision given a preference.
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PrefPy/prefpy | prefpy/utilityFunction.py | UtilityFunctionMallowsPosScoring.getUtilities | def getUtilities(self, decision, orderVector):
"""
Returns a floats that contains the utilities of every candidate in the decision.
:ivar list<int> decision: Contains a list of integer representations of candidates in the
current decision.
:ivar list<int> orderVector: A lis... | python | def getUtilities(self, decision, orderVector):
"""
Returns a floats that contains the utilities of every candidate in the decision.
:ivar list<int> decision: Contains a list of integer representations of candidates in the
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PrefPy/prefpy | prefpy/utilityFunction.py | UtilityFunctionCondorcetTopK.getUtilities | def getUtilities(self, decision, binaryRelations):
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:ivar list<int> decision: Contains a list of integer representations of candidates in the
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tamasgal/km3pipe | km3pipe/config.py | Config.db_credentials | def db_credentials(self):
"""Return username and password for the KM3NeT WebDB."""
try:
username = self.config.get('DB', 'username')
password = self.config.get('DB', 'password')
except Error:
username = input("Please enter your KM3NeT DB username: ")
... | python | def db_credentials(self):
"""Return username and password for the KM3NeT WebDB."""
try:
username = self.config.get('DB', 'username')
password = self.config.get('DB', 'password')
except Error:
username = input("Please enter your KM3NeT DB username: ")
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lexibank/pylexibank | src/pylexibank/__main__.py | get_path | def get_path(src): # pragma: no cover
"""
Prompts the user to input a local path.
:param src: github repository name
:return: Absolute local path
"""
res = None
while not res:
if res is False:
print(colored('You must provide a path to an existing directory!', 'red'))
... | python | def get_path(src): # pragma: no cover
"""
Prompts the user to input a local path.
:param src: github repository name
:return: Absolute local path
"""
res = None
while not res:
if res is False:
print(colored('You must provide a path to an existing directory!', 'red'))
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IRC-SPHERE/HyperStream | hyperstream/workflow/workflow_manager.py | WorkflowManager.execute_all | def execute_all(self):
"""
Execute all workflows
"""
for workflow_id in self.workflows:
if self.workflows[workflow_id].online:
for interval in self.workflows[workflow_id].requested_intervals:
logging.info("Executing workflow {} over interva... | python | def execute_all(self):
"""
Execute all workflows
"""
for workflow_id in self.workflows:
if self.workflows[workflow_id].online:
for interval in self.workflows[workflow_id].requested_intervals:
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IRC-SPHERE/HyperStream | hyperstream/tool/tool.py | Tool.execute | def execute(self, sources, sink, interval, alignment_stream=None):
"""
Execute the tool over the given time interval.
If an alignment stream is given, the output instances will be aligned to this stream
:param sources: The source streams (possibly None)
:param sink: The sink str... | python | def execute(self, sources, sink, interval, alignment_stream=None):
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Execute the tool over the given time interval.
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IRC-SPHERE/HyperStream | hyperstream/channels/memory_channel.py | MemoryChannel.create_stream | def create_stream(self, stream_id, sandbox=None):
"""
Must be overridden by deriving classes, must create the stream according to the tool and return its unique
identifier stream_id
"""
if stream_id in self.streams:
raise StreamAlreadyExistsError("Stream with id '{}' ... | python | def create_stream(self, stream_id, sandbox=None):
"""
Must be overridden by deriving classes, must create the stream according to the tool and return its unique
identifier stream_id
"""
if stream_id in self.streams:
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Unpickle file contents.
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dgomes/pyipma | pyipma/api.py | IPMA_API.retrieve | async def retrieve(self, url, **kwargs):
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try:
async with self.websession.request('GET', url, **kwargs) as res:
if res.status != 200:
raise Exception("Could not retrieve information from API")
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"""Issue API requests."""
try:
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dgomes/pyipma | pyipma/api.py | IPMA_API._to_number | def _to_number(cls, string):
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try:
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return int(string)
return float(string)
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try:
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dgomes/pyipma | pyipma/api.py | IPMA_API.stations | async def stations(self):
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data = await self.retrieve(API_DISTRITS)
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"""Retrieve stations."""
data = await self.retrieve(API_DISTRITS)
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dgomes/pyipma | pyipma/api.py | IPMA_API.weather_type_classe | async def weather_type_classe(self):
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data = await self.retrieve(url=API_WEATHER_TYPE)
self.weather_type = dict()
for _type in data['data']:
self.weather_type[_type['idWeatherType']] = _type['descIdWeatherTypePT']
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data = await self.retrieve(url=API_WEATHER_TYPE)
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dgomes/pyipma | pyipma/api.py | IPMA_API.wind_type_classe | async def wind_type_classe(self):
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self.wind_type = dict()
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jdodds/feather | feather/dispatcher.py | Dispatcher.register | def register(self, plugin):
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for listener in plugin.listeners:
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self.recieve('APP_START')
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tamasgal/km3pipe | km3pipe/style/__init__.py | ColourCycler.choose | def choose(self, palette):
"""Pick a palette"""
try:
self._cycler = cycle(self.colours[palette])
except KeyError:
raise KeyError(
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) | python | def choose(self, palette):
"""Pick a palette"""
try:
self._cycler = cycle(self.colours[palette])
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tamasgal/km3pipe | km3pipe/style/__init__.py | ColourCycler.refresh_styles | def refresh_styles(self):
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dsoprea/PySchedules | pyschedules/retrieve.py | get_file_object | def get_file_object(username, password, utc_start=None, utc_stop=None):
"""Make the connection. Return a file-like object."""
if not utc_start:
utc_start = datetime.now()
if not utc_stop:
utc_stop = utc_start + timedelta(days=1)
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"""Make the connection. Return a file-like object."""
if not utc_start:
utc_start = datetime.now()
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dsoprea/PySchedules | pyschedules/retrieve.py | process_file_object | def process_file_object(file_obj, importer, progress):
"""Parse the data using the connected file-like object."""
logging.info("Processing schedule data.")
try:
handler = XmlCallbacks(importer, progress)
parser = sax.make_parser()
parser.setContentHandler(handler)
parser.se... | python | def process_file_object(file_obj, importer, progress):
"""Parse the data using the connected file-like object."""
logging.info("Processing schedule data.")
try:
handler = XmlCallbacks(importer, progress)
parser = sax.make_parser()
parser.setContentHandler(handler)
parser.se... | [
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dsoprea/PySchedules | pyschedules/retrieve.py | parse_schedules | def parse_schedules(username, password, importer, progress, utc_start=None,
utc_stop=None):
"""A utility function to marry the connecting and reading functions."""
file_obj = get_file_object(username, password, utc_start, utc_stop)
process_file_object(file_obj, importer, progress) | python | def parse_schedules(username, password, importer, progress, utc_start=None,
utc_stop=None):
"""A utility function to marry the connecting and reading functions."""
file_obj = get_file_object(username, password, utc_start, utc_stop)
process_file_object(file_obj, importer, progress) | [
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tamasgal/km3pipe | km3pipe/utils/km3h5concat.py | km3h5concat | def km3h5concat(input_files, output_file, n_events=None, **kwargs):
"""Concatenate KM3HDF5 files via pipeline."""
from km3pipe import Pipeline # noqa
from km3pipe.io import HDF5Pump, HDF5Sink # noqa
pipe = Pipeline()
pipe.attach(HDF5Pump, filenames=input_files, **kwargs)
pipe.attach(Statu... | python | def km3h5concat(input_files, output_file, n_events=None, **kwargs):
"""Concatenate KM3HDF5 files via pipeline."""
from km3pipe import Pipeline # noqa
from km3pipe.io import HDF5Pump, HDF5Sink # noqa
pipe = Pipeline()
pipe.attach(HDF5Pump, filenames=input_files, **kwargs)
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tamasgal/km3pipe | km3pipe/utils/streamds.py | get_data | def get_data(stream, parameters, fmt):
"""Retrieve data for given stream and parameters, or None if not found"""
sds = kp.db.StreamDS()
if stream not in sds.streams:
log.error("Stream '{}' not found in the database.".format(stream))
return
params = {}
if parameters:
for param... | python | def get_data(stream, parameters, fmt):
"""Retrieve data for given stream and parameters, or None if not found"""
sds = kp.db.StreamDS()
if stream not in sds.streams:
log.error("Stream '{}' not found in the database.".format(stream))
return
params = {}
if parameters:
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tamasgal/km3pipe | km3pipe/utils/streamds.py | available_streams | def available_streams():
"""Show a short list of available streams."""
sds = kp.db.StreamDS()
print("Available streams: ")
print(', '.join(sorted(sds.streams))) | python | def available_streams():
"""Show a short list of available streams."""
sds = kp.db.StreamDS()
print("Available streams: ")
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tamasgal/km3pipe | km3pipe/utils/streamds.py | upload_runsummary | def upload_runsummary(csv_filename, dryrun=False):
"""Reads the CSV file and uploads its contents to the runsummary table"""
print("Checking '{}' for consistency.".format(csv_filename))
if not os.path.exists(csv_filename):
log.critical("{} -> file not found.".format(csv_filename))
return
... | python | def upload_runsummary(csv_filename, dryrun=False):
"""Reads the CSV file and uploads its contents to the runsummary table"""
print("Checking '{}' for consistency.".format(csv_filename))
if not os.path.exists(csv_filename):
log.critical("{} -> file not found.".format(csv_filename))
return
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tamasgal/km3pipe | km3pipe/utils/streamds.py | convert_runsummary_to_json | def convert_runsummary_to_json(
df, comment='Uploaded via km3pipe.StreamDS', prefix='TEST_'
):
"""Convert a Pandas DataFrame with runsummary to JSON for DB upload"""
data_field = []
comment += ", by {}".format(getpass.getuser())
for det_id, det_data in df.groupby('det_id'):
runs_field = ... | python | def convert_runsummary_to_json(
df, comment='Uploaded via km3pipe.StreamDS', prefix='TEST_'
):
"""Convert a Pandas DataFrame with runsummary to JSON for DB upload"""
data_field = []
comment += ", by {}".format(getpass.getuser())
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PrefPy/prefpy | prefpy/mechanismMcmcSampleGenerator.py | MechanismMcmcSampleGeneratorMallows.calcAcceptanceRatio | def calcAcceptanceRatio(self, V, W):
"""
Given a order vector V and a proposed order vector W, calculate the acceptance ratio for
changing to W when using MCMC.
ivar: dict<int,<dict,<int,int>>> wmg: A two-dimensional dictionary that associates integer
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"""
Given a order vector V and a proposed order vector W, calculate the acceptance ratio for
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PrefPy/prefpy | prefpy/mechanismMcmcSampleGenerator.py | MechanismMcmcSampleGeneratorMallowsAdjacentPairwiseFlip.getNextSample | def getNextSample(self, V):
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Generate the next sample by randomly flipping two adjacent candidates.
:ivar list<int> V: Contains integer representations of each candidate in order of their
ranking in a vote, from first to last. This is the current sample.
"""
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"""
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PrefPy/prefpy | prefpy/mechanismMcmcSampleGenerator.py | MechanismMcmcSampleGeneratorMallowsRandShuffle.getNextSample | def getNextSample(self, V):
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:ivar list<int> V: Contains integer representations of each candidate in order of their
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positions = rang... | python | def getNextSample(self, V):
"""
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PrefPy/prefpy | prefpy/mechanismMcmcSampleGenerator.py | MechanismMcmcSampleGeneratorMallowsPlakettLuce.getNextSample | def getNextSample(self, V):
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PrefPy/prefpy | prefpy/mechanismMcmcSampleGenerator.py | MechanismMcmcSampleGeneratorMallowsPlakettLuce.calcDrawingProbs | def calcDrawingProbs(self):
"""
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"""
wmg = self.wmg
phi = self.phi
# We say the weight o... | python | def calcDrawingProbs(self):
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PrefPy/prefpy | prefpy/mechanismMcmcSampleGenerator.py | MechanismMcmcSampleGeneratorMallowsPlakettLuce.drawRankingPlakettLuce | def drawRankingPlakettLuce(self, rankList):
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Given an order vector over the candidates, draw candidates to generate a new order vector.
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"""
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PrefPy/prefpy | prefpy/mechanismMcmcSampleGenerator.py | MechanismMcmcSampleGeneratorMallowsPlakettLuce.calcProbOfVFromW | def calcProbOfVFromW(self, V, W):
"""
Given a order vector V and an order vector W, calculate the probability that we generate
V as our next sample if our current sample was W.
:ivar list<int> V: Contains integer representations of each candidate in order of their
ranking in... | python | def calcProbOfVFromW(self, V, W):
"""
Given a order vector V and an order vector W, calculate the probability that we generate
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tamasgal/km3pipe | km3pipe/io/root.py | get_hist | def get_hist(rfile, histname, get_overflow=False):
"""Read a 1D Histogram."""
import root_numpy as rnp
rfile = open_rfile(rfile)
hist = rfile[histname]
xlims = np.array(list(hist.xedges()))
bin_values = rnp.hist2array(hist, include_overflow=get_overflow)
rfile.close()
return bin_values,... | python | def get_hist(rfile, histname, get_overflow=False):
"""Read a 1D Histogram."""
import root_numpy as rnp
rfile = open_rfile(rfile)
hist = rfile[histname]
xlims = np.array(list(hist.xedges()))
bin_values = rnp.hist2array(hist, include_overflow=get_overflow)
rfile.close()
return bin_values,... | [
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tamasgal/km3pipe | km3pipe/io/root.py | interpol_hist2d | def interpol_hist2d(h2d, oversamp_factor=10):
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Root's hist2d has a weird internal interpolation routine,
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"""
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cprogrammer1994/GLWindow | GLWindow/__init__.py | create_window | def create_window(size=None, samples=16, *, fullscreen=False, title=None, threaded=True) -> Window:
'''
Create the main window.
Args:
size (tuple): The width and height of the window.
samples (int): The number of samples.
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'''
Create the main window.
Args:
size (tuple): The width and height of the window.
samples (int): The number of samples.
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cprogrammer1994/GLWindow | GLWindow/__init__.py | Window.clear | def clear(self, red=0.0, green=0.0, blue=0.0, alpha=0.0) -> None:
'''
Clear the window.
'''
self.wnd.clear(red, green, blue, alpha) | python | def clear(self, red=0.0, green=0.0, blue=0.0, alpha=0.0) -> None:
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cprogrammer1994/GLWindow | GLWindow/__init__.py | Window.windowed | def windowed(self, size) -> None:
'''
Set the window to windowed mode.
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width, height = size
self.wnd.windowed(width, height) | python | def windowed(self, size) -> None:
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Set the window to windowed mode.
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width, height = size
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developmentseed/sentinel-s3 | sentinel_s3/main.py | product_metadata | def product_metadata(product, dst_folder, counter=None, writers=[file_writer], geometry_check=None):
""" Extract metadata for a specific product """
if not counter:
counter = {
'products': 0,
'saved_tiles': 0,
'skipped_tiles': 0,
'skipped_tiles_paths': []... | python | def product_metadata(product, dst_folder, counter=None, writers=[file_writer], geometry_check=None):
""" Extract metadata for a specific product """
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developmentseed/sentinel-s3 | sentinel_s3/main.py | daily_metadata | def daily_metadata(year, month, day, dst_folder, writers=[file_writer], geometry_check=None,
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threaded = False
counter = {
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counter = {
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developmentseed/sentinel-s3 | sentinel_s3/main.py | range_metadata | def range_metadata(start, end, dst_folder, num_worker_threads=0, writers=[file_writer], geometry_check=None):
""" Extra metadata for all products in a date range """
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NaPs/Kolekto | kolekto/tmdb_proxy.py | get_on_tmdb | def get_on_tmdb(uri, **kwargs):
""" Get a resource on TMDB.
"""
kwargs['api_key'] = app.config['TMDB_API_KEY']
response = requests_session.get((TMDB_API_URL + uri).encode('utf8'), params=kwargs)
response.raise_for_status()
return json.loads(response.text) | python | def get_on_tmdb(uri, **kwargs):
""" Get a resource on TMDB.
"""
kwargs['api_key'] = app.config['TMDB_API_KEY']
response = requests_session.get((TMDB_API_URL + uri).encode('utf8'), params=kwargs)
response.raise_for_status()
return json.loads(response.text) | [
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NaPs/Kolekto | kolekto/tmdb_proxy.py | search | def search():
""" Search a movie on TMDB.
"""
redis_key = 's_%s' % request.args['query'].lower()
cached = redis_ro_conn.get(redis_key)
if cached:
return Response(cached)
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""" Search a movie on TMDB.
"""
redis_key = 's_%s' % request.args['query'].lower()
cached = redis_ro_conn.get(redis_key)
if cached:
return Response(cached)
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NaPs/Kolekto | kolekto/tmdb_proxy.py | get_movie | def get_movie(tmdb_id):
""" Get informations about a movie using its tmdb id.
"""
redis_key = 'm_%s' % tmdb_id
cached = redis_ro_conn.get(redis_key)
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""" Get informations about a movie using its tmdb id.
"""
redis_key = 'm_%s' % tmdb_id
cached = redis_ro_conn.get(redis_key)
if cached:
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LeadPages/gcloud_requests | gcloud_requests/proxy.py | RequestsProxy._handle_response_error | def _handle_response_error(self, response, retries, **kwargs):
r"""Provides a way for each connection wrapper to handle error
responses.
Parameters:
response(Response): An instance of :class:`.requests.Response`.
retries(int): The number of times :meth:`.request` has been
... | python | def _handle_response_error(self, response, retries, **kwargs):
r"""Provides a way for each connection wrapper to handle error
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response(Response): An instance of :class:`.requests.Response`.
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Parameters:
response(Response): The response object.
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NaPs/Kolekto | kolekto/pattern.py | parse_pattern | def parse_pattern(format_string, env, wrapper=lambda x, y: y):
""" Parse the format_string and return prepared data according to the env.
Pick each field found in the format_string from the env(ironment), apply
the wrapper on each data and return a mapping between field-to-replace and
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... | python | def parse_pattern(format_string, env, wrapper=lambda x, y: y):
""" Parse the format_string and return prepared data according to the env.
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tamasgal/km3pipe | km3pipe/stats.py | perc | def perc(arr, p=95, **kwargs):
"""Create symmetric percentiles, with ``p`` coverage."""
offset = (100 - p) / 2
return np.percentile(arr, (offset, 100 - offset), **kwargs) | python | def perc(arr, p=95, **kwargs):
"""Create symmetric percentiles, with ``p`` coverage."""
offset = (100 - p) / 2
return np.percentile(arr, (offset, 100 - offset), **kwargs) | [
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tamasgal/km3pipe | km3pipe/stats.py | resample_1d | def resample_1d(arr, n_out=None, random_state=None):
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Parameters
==========
arr: np.ndarray
The array is resampled along the first axis.
n_out: int, optional
Number of samples to return. If not specified,
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"""Resample an array, with replacement.
Parameters
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The array is resampled along the first axis.
n_out: int, optional
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tamasgal/km3pipe | km3pipe/stats.py | bootstrap_params | def bootstrap_params(rv_cont, data, n_iter=5, **kwargs):
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==========
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The distribution which to fit.
data: array-like, 1d
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Parameters
==========
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The distribution which to fit.
data: array-like, 1d
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tamasgal/km3pipe | km3pipe/stats.py | param_describe | def param_describe(params, quant=95, axis=0):
"""Get mean + quantile range from bootstrapped params."""
par = np.mean(params, axis=axis)
lo, up = perc(quant)
p_up = np.percentile(params, up, axis=axis)
p_lo = np.percentile(params, lo, axis=axis)
return par, p_lo, p_up | python | def param_describe(params, quant=95, axis=0):
"""Get mean + quantile range from bootstrapped params."""
par = np.mean(params, axis=axis)
lo, up = perc(quant)
p_up = np.percentile(params, up, axis=axis)
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tamasgal/km3pipe | km3pipe/stats.py | bootstrap_fit | def bootstrap_fit(
rv_cont, data, n_iter=10, quant=95, print_params=True, **kwargs
):
"""Bootstrap a distribution fit + get confidence intervals for the params.
Parameters
==========
rv_cont: scipy.stats.rv_continuous instance
The distribution which to fit.
data: array-like, 1d
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Parameters
==========
rv_cont: scipy.stats.rv_continuous instance
The distribution which to fit.
data: array-like, 1d
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tamasgal/km3pipe | km3pipe/stats.py | rv_kde.rvs | def rvs(self, *args, **kwargs):
"""Draw Random Variates.
Parameters
----------
size: int, optional (default=1)
random_state_: optional (default=None)
"""
# TODO REVERSE THIS FUCK PYTHON2
size = kwargs.pop('size', 1)
random_state = kwargs.pop('size... | python | def rvs(self, *args, **kwargs):
"""Draw Random Variates.
Parameters
----------
size: int, optional (default=1)
random_state_: optional (default=None)
"""
# TODO REVERSE THIS FUCK PYTHON2
size = kwargs.pop('size', 1)
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tamasgal/km3pipe | km3pipe/utils/i3shower2hdf5.py | main | def main():
"""Entry point when running as script from commandline."""
from docopt import docopt
args = docopt(__doc__)
infile = args['INFILE']
outfile = args['OUTFILE']
i3extract(infile, outfile) | python | def main():
"""Entry point when running as script from commandline."""
from docopt import docopt
args = docopt(__doc__)
infile = args['INFILE']
outfile = args['OUTFILE']
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IRC-SPHERE/HyperStream | hyperstream/client.py | Client.connect | def connect(self, server_config):
"""Connect using the configuration given
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if 'connection_string' in server_config:
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server_config['connection_string'])
self.... | python | def connect(self, server_config):
"""Connect using the configuration given
:param server_config: The server configuration
"""
if 'connection_string' in server_config:
self.client = pymongo.MongoClient(
server_config['connection_string'])
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tamasgal/km3pipe | km3pipe/utils/ptconcat.py | ptconcat | def ptconcat(output_file, input_files, overwrite=False):
"""Concatenate HDF5 Files"""
filt = tb.Filters(
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)
out_tabs = {}
dt_file = input_files[0]
log.info("Reading data struct '%s'..." % dt_file)
h5struc = tb.open_file(dt_fil... | python | def ptconcat(output_file, input_files, overwrite=False):
"""Concatenate HDF5 Files"""
filt = tb.Filters(
complevel=5, shuffle=True, fletcher32=True, complib='zlib'
)
out_tabs = {}
dt_file = input_files[0]
log.info("Reading data struct '%s'..." % dt_file)
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tamasgal/km3pipe | km3modules/k40.py | calibrate_dom | def calibrate_dom(
dom_id,
data,
detector,
livetime=None,
fit_ang_dist=False,
scale_mc_to_data=True,
ad_fit_shape='pexp',
fit_background=True,
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):
"""Calibrate intra DOM PMT time offsets, efficiencies and sigmas
Parameters
... | python | def calibrate_dom(
dom_id,
data,
detector,
livetime=None,
fit_ang_dist=False,
scale_mc_to_data=True,
ad_fit_shape='pexp',
fit_background=True,
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Parameters
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tamasgal/km3pipe | km3modules/k40.py | load_k40_coincidences_from_hdf5 | def load_k40_coincidences_from_hdf5(filename, dom_id):
"""Load k40 coincidences from hdf5 file
Parameters
----------
filename: filename of hdf5 file
dom_id: DOM ID
Returns
-------
data: numpy array of coincidences
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with h5py.File(filena... | python | def load_k40_coincidences_from_hdf5(filename, dom_id):
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----------
filename: filename of hdf5 file
dom_id: DOM ID
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-------
data: numpy array of coincidences
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tamasgal/km3pipe | km3modules/k40.py | load_k40_coincidences_from_rootfile | def load_k40_coincidences_from_rootfile(filename, dom_id):
"""Load k40 coincidences from JMonitorK40 ROOT file
Parameters
----------
filename: root file produced by JMonitorK40
dom_id: DOM ID
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-------
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"""Load k40 coincidences from JMonitorK40 ROOT file
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----------
filename: root file produced by JMonitorK40
dom_id: DOM ID
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tamasgal/km3pipe | km3modules/k40.py | calculate_angles | def calculate_angles(detector, combs):
"""Calculates angles between PMT combinations according to positions in
detector_file
Parameters
----------
detector_file: file from which to read the PMT positions (.detx)
combs: pmt combinations
Returns
-------
angles: numpy array of angles ... | python | def calculate_angles(detector, combs):
"""Calculates angles between PMT combinations according to positions in
detector_file
Parameters
----------
detector_file: file from which to read the PMT positions (.detx)
combs: pmt combinations
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tamasgal/km3pipe | km3modules/k40.py | fit_angular_distribution | def fit_angular_distribution(angles, rates, rate_errors, shape='pexp'):
"""Fits angular distribution of rates.
Parameters
----------
rates: numpy array
with rates for all PMT combinations
angles: numpy array
with angles for all PMT combinations
shape:
which function to fit; ex... | python | def fit_angular_distribution(angles, rates, rate_errors, shape='pexp'):
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Parameters
----------
rates: numpy array
with rates for all PMT combinations
angles: numpy array
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tamasgal/km3pipe | km3modules/k40.py | minimize_t0s | def minimize_t0s(means, weights, combs):
"""Varies t0s to minimize the deviation of the gaussian means from zero.
Parameters
----------
means: numpy array of means of all PMT combinations
weights: numpy array of weights for the squared sum
combs: pmt combinations to use for minimization
Re... | python | def minimize_t0s(means, weights, combs):
"""Varies t0s to minimize the deviation of the gaussian means from zero.
Parameters
----------
means: numpy array of means of all PMT combinations
weights: numpy array of weights for the squared sum
combs: pmt combinations to use for minimization
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weights: numpy array of weights for the squared sum
combs: pmt combinations to use for minimization
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tamasgal/km3pipe | km3modules/k40.py | minimize_qes | def minimize_qes(fitted_rates, rates, weights, combs):
"""Varies QEs to minimize the deviation of the rates from the fitted_rates.
Parameters
----------
fitted_rates: numpy array of fitted rates from fit_angular_distribution
rates: numpy array of rates of all PMT combinations
weights: numpy arr... | python | def minimize_qes(fitted_rates, rates, weights, combs):
"""Varies QEs to minimize the deviation of the rates from the fitted_rates.
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fitted_rates: numpy array of fitted rates from fit_angular_distribution
rates: numpy array of rates of all PMT combinations
weights: numpy arr... | [
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tamasgal/km3pipe | km3modules/k40.py | correct_means | def correct_means(means, opt_t0s, combs):
"""Applies optimal t0s to gaussians means.
Should be around zero afterwards.
Parameters
----------
means: numpy array of means of gaussians of all PMT combinations
opt_t0s: numpy array of optimal t0 values for all PMTs
combs: pmt combinations used ... | python | def correct_means(means, opt_t0s, combs):
"""Applies optimal t0s to gaussians means.
Should be around zero afterwards.
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means: numpy array of means of gaussians of all PMT combinations
opt_t0s: numpy array of optimal t0 values for all PMTs
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tamasgal/km3pipe | km3modules/k40.py | correct_rates | def correct_rates(rates, opt_qes, combs):
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Should be closer to fitted_rates afterwards.
Parameters
----------
rates: numpy array of rates of all PMT combinations
opt_qes: numpy array of optimal qe values for all PMTs
combs: pmt combinations used to correct
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"""Applies optimal qes to rates.
Should be closer to fitted_rates afterwards.
Parameters
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rates: numpy array of rates of all PMT combinations
opt_qes: numpy array of optimal qe values for all PMTs
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tamasgal/km3pipe | km3modules/k40.py | calculate_rms_means | def calculate_rms_means(means, corrected_means):
"""Calculates RMS of means from zero before and after correction
Parameters
----------
means: numpy array of means of gaussians of all PMT combinations
corrected_means: numpy array of corrected gaussian means for all PMT combs
Returns
------... | python | def calculate_rms_means(means, corrected_means):
"""Calculates RMS of means from zero before and after correction
Parameters
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means: numpy array of means of gaussians of all PMT combinations
corrected_means: numpy array of corrected gaussian means for all PMT combs
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tamasgal/km3pipe | km3modules/k40.py | calculate_rms_rates | def calculate_rms_rates(rates, fitted_rates, corrected_rates):
"""Calculates RMS of rates from fitted_rates before and after correction
Parameters
----------
rates: numpy array of rates of all PMT combinations
corrected_rates: numpy array of corrected rates for all PMT combinations
Returns
... | python | def calculate_rms_rates(rates, fitted_rates, corrected_rates):
"""Calculates RMS of rates from fitted_rates before and after correction
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----------
rates: numpy array of rates of all PMT combinations
corrected_rates: numpy array of corrected rates for all PMT combinations
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tamasgal/km3pipe | km3modules/k40.py | add_to_twofold_matrix | def add_to_twofold_matrix(times, tdcs, mat, tmax=10):
"""Add counts to twofold coincidences for a given `tmax`.
Parameters
----------
times: np.ndarray of hit times (int32)
tdcs: np.ndarray of channel_ids (uint8)
mat: ref to a np.array((465, tmax * 2 + 1))
tmax: int (time window)
Retur... | python | def add_to_twofold_matrix(times, tdcs, mat, tmax=10):
"""Add counts to twofold coincidences for a given `tmax`.
Parameters
----------
times: np.ndarray of hit times (int32)
tdcs: np.ndarray of channel_ids (uint8)
mat: ref to a np.array((465, tmax * 2 + 1))
tmax: int (time window)
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tamasgal/km3pipe | km3modules/k40.py | TwofoldCounter.reset | def reset(self):
"""Reset coincidence counter"""
self.counts = defaultdict(partial(np.zeros, (465, self.tmax * 2 + 1)))
self.n_timeslices = defaultdict(int) | python | def reset(self):
"""Reset coincidence counter"""
self.counts = defaultdict(partial(np.zeros, (465, self.tmax * 2 + 1)))
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tamasgal/km3pipe | km3modules/k40.py | TwofoldCounter.dump | def dump(self):
"""Write coincidence counts into a Python pickle"""
self.print("Dumping data to {}".format(self.dump_filename))
pickle.dump({
'data': self.counts,
'livetime': self.get_livetime()
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"""Write coincidence counts into a Python pickle"""
self.print("Dumping data to {}".format(self.dump_filename))
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'data': self.counts,
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ioos/pyoos | pyoos/parsers/ioos/one/describe_sensor.py | DescribeSensor.get_named_by_definition | def get_named_by_definition(cls, element_list, string_def):
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try:
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try:
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ioos/pyoos | pyoos/parsers/ioos/one/describe_sensor.py | DescribeSensor.get_ioos_def | def get_ioos_def(self, ident, elem_type, ont):
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getter_fn = self.system.get_identifiers_by_name
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"""Gets a definition given an identifier and where to search for it"""
if elem_type == "identifier":
getter_fn = self.system.get_identifiers_by_name
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mouse-reeve/horoscope-generator | horoscope_generator/HoroscopeGenerator.py | get_sentence | def get_sentence(start=None, depth=7):
''' follow the grammatical patterns to generate a random sentence '''
if not GRAMMAR:
return 'Please set a GRAMMAR file'
start = start if start else GRAMMAR.start()
if isinstance(start, Nonterminal):
productions = GRAMMAR.productions(start)
... | python | def get_sentence(start=None, depth=7):
''' follow the grammatical patterns to generate a random sentence '''
if not GRAMMAR:
return 'Please set a GRAMMAR file'
start = start if start else GRAMMAR.start()
if isinstance(start, Nonterminal):
productions = GRAMMAR.productions(start)
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mouse-reeve/horoscope-generator | horoscope_generator/HoroscopeGenerator.py | format_sentence | def format_sentence(sentence):
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''' fix display formatting of a sentence array '''
for index, word in enumerate(sentence):
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dsoprea/PySchedules | pyschedules/examples/read.py | EntityTrigger.new_station | def new_station(self, _id, callSign, name, affiliate, fccChannelNumber):
"""Callback run for each new station"""
if self.__v_station:
# [Station: 11440, WFLX, WFLX, Fox Affiliate, 29]
# [Station: 11836, WSCV, WSCV, TELEMUNDO (HBC) Affiliate, 51]
# [Station: 11867, TB... | python | def new_station(self, _id, callSign, name, affiliate, fccChannelNumber):
"""Callback run for each new station"""
if self.__v_station:
# [Station: 11440, WFLX, WFLX, Fox Affiliate, 29]
# [Station: 11836, WSCV, WSCV, TELEMUNDO (HBC) Affiliate, 51]
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dsoprea/PySchedules | pyschedules/examples/read.py | EntityTrigger.new_lineup | def new_lineup(self, name, location, device, _type, postalCode, _id):
"""Callback run for each new lineup"""
if self.__v_lineup:
# [Lineup: Comcast West Palm Beach /Palm Beach Co., West Palm Beach, Digital, CableDigital, 33436, FL09567:X]
print("[Lineup: %s, %s, %s, %s, %s, %s]"... | python | def new_lineup(self, name, location, device, _type, postalCode, _id):
"""Callback run for each new lineup"""
if self.__v_lineup:
# [Lineup: Comcast West Palm Beach /Palm Beach Co., West Palm Beach, Digital, CableDigital, 33436, FL09567:X]
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dsoprea/PySchedules | pyschedules/examples/read.py | EntityTrigger.new_genre | def new_genre(self, program, genre, relevance):
"""Callback run for each new program genre entry"""
if self.__v_genre:
# [Genre: SP002709210000, Sports event, 0]
# [Genre: SP002709210000, Basketball, 1]
# [Genre: SP002737310000, Sports event, 0]
# [Genre:... | python | def new_genre(self, program, genre, relevance):
"""Callback run for each new program genre entry"""
if self.__v_genre:
# [Genre: SP002709210000, Sports event, 0]
# [Genre: SP002709210000, Basketball, 1]
# [Genre: SP002737310000, Sports event, 0]
# [Genre:... | [
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tamasgal/km3pipe | km3pipe/shell.py | qsub | def qsub(script, job_name, dryrun=False, *args, **kwargs):
"""Submit a job via qsub."""
print("Preparing job script...")
job_string = gen_job(script=script, job_name=job_name, *args, **kwargs)
env = os.environ.copy()
if dryrun:
print(
"This is a dry run! Here is the generated job... | python | def qsub(script, job_name, dryrun=False, *args, **kwargs):
"""Submit a job via qsub."""
print("Preparing job script...")
job_string = gen_job(script=script, job_name=job_name, *args, **kwargs)
env = os.environ.copy()
if dryrun:
print(
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tamasgal/km3pipe | km3pipe/shell.py | gen_job | def gen_job(
script,
job_name,
log_path='qlogs',
group='km3net',
platform='cl7',
walltime='00:10:00',
vmem='8G',
fsize='8G',
shell=None,
email=None,
send_mail='n',
job_array_start=1,
job_array_stop=None,
job_... | python | def gen_job(
script,
job_name,
log_path='qlogs',
group='km3net',
platform='cl7',
walltime='00:10:00',
vmem='8G',
fsize='8G',
shell=None,
email=None,
send_mail='n',
job_array_start=1,
job_array_stop=None,
job_... | [
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tamasgal/km3pipe | km3pipe/shell.py | get_jpp_env | def get_jpp_env(jpp_dir):
"""Return the environment dict of a loaded Jpp env.
The returned env can be passed to `subprocess.Popen("J...", env=env)`
to execute Jpp commands.
"""
env = {
v[0]: ''.join(v[1:])
for v in [
l.split('=') for l in os.popen(
"sour... | python | def get_jpp_env(jpp_dir):
"""Return the environment dict of a loaded Jpp env.
The returned env can be passed to `subprocess.Popen("J...", env=env)`
to execute Jpp commands.
"""
env = {
v[0]: ''.join(v[1:])
for v in [
l.split('=') for l in os.popen(
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tamasgal/km3pipe | km3pipe/shell.py | Script.iget | def iget(self, irods_path, attempts=1, pause=15):
"""Add an iget command to retrieve a file from iRODS.
Parameters
----------
irods_path: str
Filepath which should be fetched using iget
attempts: int (default: 1)
Number of retries, if iROD... | python | def iget(self, irods_path, attempts=1, pause=15):
"""Add an iget command to retrieve a file from iRODS.
Parameters
----------
irods_path: str
Filepath which should be fetched using iget
attempts: int (default: 1)
Number of retries, if iROD... | [
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Parameters
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irods_path: str
Filepath which should be fetched using iget
attempts: int (default: 1)
Number of retries, if iRODS access fails
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tamasgal/km3pipe | km3pipe/shell.py | Script._add_two_argument_command | def _add_two_argument_command(self, command, arg1, arg2):
"""Helper function for two-argument commands"""
self.lines.append("{} {} {}".format(command, arg1, arg2)) | python | def _add_two_argument_command(self, command, arg1, arg2):
"""Helper function for two-argument commands"""
self.lines.append("{} {} {}".format(command, arg1, arg2)) | [
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dlbroadfoot/pygogogate2 | pygogogate2/__init__.py | Gogogate2API.get_devices | def get_devices(self):
"""List all garage door devices."""
devices = self.make_request('["{username}","{password}","info","",""]'.format(
username=self.username,
password=self.password))
if devices != False:
garage_doors = []
try:
... | python | def get_devices(self):
"""List all garage door devices."""
devices = self.make_request('["{username}","{password}","info","",""]'.format(
username=self.username,
password=self.password))
if devices != False:
garage_doors = []
try:
... | [
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dlbroadfoot/pygogogate2 | pygogogate2/__init__.py | Gogogate2API.get_status | def get_status(self, device_id):
"""List only MyQ garage door devices."""
devices = self.get_devices()
if devices != False:
for device in devices:
if device['door'] == device_id:
return device['status']
return False | python | def get_status(self, device_id):
"""List only MyQ garage door devices."""
devices = self.get_devices()
if devices != False:
for device in devices:
if device['door'] == device_id:
return device['status']
return False | [
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lexibank/pylexibank | src/pylexibank/transcription.py | analyze | def analyze(segments, analysis, lookup=dict(bipa={}, dolgo={})):
"""
Test a sequence for compatibility with CLPA and LingPy.
:param analysis: Pass a `TranscriptionAnalysis` instance for cumulative reporting.
"""
# raise a ValueError in case of empty segments/strings
if not segments:
rai... | python | def analyze(segments, analysis, lookup=dict(bipa={}, dolgo={})):
"""
Test a sequence for compatibility with CLPA and LingPy.
:param analysis: Pass a `TranscriptionAnalysis` instance for cumulative reporting.
"""
# raise a ValueError in case of empty segments/strings
if not segments:
rai... | [
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tamasgal/km3pipe | km3pipe/mc.py | most_energetic | def most_energetic(df):
"""Grab most energetic particle from mc_tracks dataframe."""
idx = df.groupby(['event_id'])['energy'].transform(max) == df['energy']
return df[idx].reindex() | python | def most_energetic(df):
"""Grab most energetic particle from mc_tracks dataframe."""
idx = df.groupby(['event_id'])['energy'].transform(max) == df['energy']
return df[idx].reindex() | [
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tamasgal/km3pipe | km3pipe/controlhost.py | Client._connect | def _connect(self):
"""Connect to JLigier"""
log.debug("Connecting to JLigier")
self.socket = socket.socket()
self.socket.connect((self.host, self.port)) | python | def _connect(self):
"""Connect to JLigier"""
log.debug("Connecting to JLigier")
self.socket = socket.socket()
self.socket.connect((self.host, self.port)) | [
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