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spyder-ide/conda-manager | conda_manager/api/client_api.py | _ClientAPI.packages | def packages(self, login=None, platform=None, package_type=None,
type_=None, access=None):
"""Return all the available packages for a given user.
Parameters
----------
type_: Optional[str]
Only find packages that have this conda `type`, (i.e. 'app').
... | python | def packages(self, login=None, platform=None, package_type=None,
type_=None, access=None):
"""Return all the available packages for a given user.
Parameters
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type_: Optional[str]
Only find packages that have this conda `type`, (i.e. 'app').
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yaph/geonamescache | geonamescache/mappers.py | country | def country(from_key='name', to_key='iso'):
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yaph/geonamescache | geonamescache/__init__.py | GeonamesCache.get_cities | def get_cities(self):
"""Get a dictionary of cities keyed by geonameid."""
if self.cities is None:
self.cities = self._load_data(self.cities, 'cities.json')
return self.cities | python | def get_cities(self):
"""Get a dictionary of cities keyed by geonameid."""
if self.cities is None:
self.cities = self._load_data(self.cities, 'cities.json')
return self.cities | [
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yaph/geonamescache | geonamescache/__init__.py | GeonamesCache.get_cities_by_name | def get_cities_by_name(self, name):
"""Get a list of city dictionaries with the given name.
City names cannot be used as keys, as they are not unique.
"""
if name not in self.cities_by_names:
if self.cities_items is None:
self.cities_items = list(self.get_ci... | python | def get_cities_by_name(self, name):
"""Get a list of city dictionaries with the given name.
City names cannot be used as keys, as they are not unique.
"""
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spyder-ide/conda-manager | conda_manager/api/manager_api.py | _ManagerAPI._set_repo_urls_from_channels | def _set_repo_urls_from_channels(self, channels):
"""
Convert a channel into a normalized repo name including.
Channels are assumed in normalized url form.
"""
repos = []
sys_platform = self._conda_api.get_platform()
for channel in channels:
url = '{... | python | def _set_repo_urls_from_channels(self, channels):
"""
Convert a channel into a normalized repo name including.
Channels are assumed in normalized url form.
"""
repos = []
sys_platform = self._conda_api.get_platform()
for channel in channels:
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spyder-ide/conda-manager | conda_manager/api/manager_api.py | _ManagerAPI._check_repos | def _check_repos(self, repos):
"""Check if repodata urls are valid."""
self._checking_repos = []
self._valid_repos = []
for repo in repos:
worker = self.download_is_valid_url(repo)
worker.sig_finished.connect(self._repos_checked)
worker.repo = repo
... | python | def _check_repos(self, repos):
"""Check if repodata urls are valid."""
self._checking_repos = []
self._valid_repos = []
for repo in repos:
worker = self.download_is_valid_url(repo)
worker.sig_finished.connect(self._repos_checked)
worker.repo = repo
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spyder-ide/conda-manager | conda_manager/api/manager_api.py | _ManagerAPI._repos_checked | def _repos_checked(self, worker, output, error):
"""Callback for _check_repos."""
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self._checking_repos.remove(worker.repo)
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self._checking_repos.remove(worker.repo)
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spyder-ide/conda-manager | conda_manager/api/manager_api.py | _ManagerAPI._repo_url_to_path | def _repo_url_to_path(self, repo):
"""Convert a `repo` url to a file path for local storage."""
repo = repo.replace('http://', '')
repo = repo.replace('https://', '')
repo = repo.replace('/', '_')
return os.sep.join([self._data_directory, repo]) | python | def _repo_url_to_path(self, repo):
"""Convert a `repo` url to a file path for local storage."""
repo = repo.replace('http://', '')
repo = repo.replace('https://', '')
repo = repo.replace('/', '_')
return os.sep.join([self._data_directory, repo]) | [
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spyder-ide/conda-manager | conda_manager/api/manager_api.py | _ManagerAPI._download_repodata | def _download_repodata(self, checked_repos):
"""Dowload repodata."""
self._files_downloaded = []
self._repodata_files = []
self.__counter = -1
if checked_repos:
for repo in checked_repos:
path = self._repo_url_to_path(repo)
self._files... | python | def _download_repodata(self, checked_repos):
"""Dowload repodata."""
self._files_downloaded = []
self._repodata_files = []
self.__counter = -1
if checked_repos:
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spyder-ide/conda-manager | conda_manager/api/manager_api.py | _ManagerAPI._get_repodata_from_meta | def _get_repodata_from_meta(self):
"""Generate repodata from local meta files."""
path = os.sep.join([self.ROOT_PREFIX, 'conda-meta'])
packages = os.listdir(path)
meta_repodata = {}
for pkg in packages:
if pkg.endswith('.json'):
filepath = os.sep.join(... | python | def _get_repodata_from_meta(self):
"""Generate repodata from local meta files."""
path = os.sep.join([self.ROOT_PREFIX, 'conda-meta'])
packages = os.listdir(path)
meta_repodata = {}
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spyder-ide/conda-manager | conda_manager/api/manager_api.py | _ManagerAPI._repodata_downloaded | def _repodata_downloaded(self, worker=None, output=None, error=None):
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spyder-ide/conda-manager | conda_manager/api/manager_api.py | _ManagerAPI.repodata_files | def repodata_files(self, channels=None):
"""
Return the repodata paths based on `channels` and the `data_directory`.
There is no check for validity here.
"""
if channels is None:
channels = self.conda_get_condarc_channels()
repodata_urls = self._set_repo_url... | python | def repodata_files(self, channels=None):
"""
Return the repodata paths based on `channels` and the `data_directory`.
There is no check for validity here.
"""
if channels is None:
channels = self.conda_get_condarc_channels()
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spyder-ide/conda-manager | conda_manager/api/manager_api.py | _ManagerAPI.update_repodata | def update_repodata(self, channels=None):
"""Update repodata from channels or use condarc channels if None."""
norm_channels = self.conda_get_condarc_channels(channels=channels,
normalize=True)
repodata_urls = self._set_repo_urls_from_chann... | python | def update_repodata(self, channels=None):
"""Update repodata from channels or use condarc channels if None."""
norm_channels = self.conda_get_condarc_channels(channels=channels,
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spyder-ide/conda-manager | conda_manager/api/manager_api.py | _ManagerAPI.update_metadata | def update_metadata(self):
"""
Update the metadata available for packages in repo.continuum.io.
Returns a download worker.
"""
if self._data_directory is None:
raise Exception('Need to call `api.set_data_directory` first.')
metadata_url = 'https://repo.conti... | python | def update_metadata(self):
"""
Update the metadata available for packages in repo.continuum.io.
Returns a download worker.
"""
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raise Exception('Need to call `api.set_data_directory` first.')
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spyder-ide/conda-manager | conda_manager/api/manager_api.py | _ManagerAPI.check_valid_channel | def check_valid_channel(self,
channel,
conda_url='https://conda.anaconda.org'):
"""Check if channel is valid."""
if channel.startswith('https://') or channel.startswith('http://'):
url = channel
else:
url = "{0}/{1}"... | python | def check_valid_channel(self,
channel,
conda_url='https://conda.anaconda.org'):
"""Check if channel is valid."""
if channel.startswith('https://') or channel.startswith('http://'):
url = channel
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google/python-cloud-utils | cloud_utils/list_instances.py | _aws_get_instance_by_tag | def _aws_get_instance_by_tag(region, name, tag, raw):
"""Get all instances matching a tag."""
client = boto3.session.Session().client('ec2', region)
matching_reservations = client.describe_instances(Filters=[{'Name': tag, 'Values': [name]}]).get('Reservations', [])
instances = []
[[instances.append(... | python | def _aws_get_instance_by_tag(region, name, tag, raw):
"""Get all instances matching a tag."""
client = boto3.session.Session().client('ec2', region)
matching_reservations = client.describe_instances(Filters=[{'Name': tag, 'Values': [name]}]).get('Reservations', [])
instances = []
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google/python-cloud-utils | cloud_utils/list_instances.py | aws_get_instances_by_id | def aws_get_instances_by_id(region, instance_id, raw=True):
"""Returns instances mathing an id."""
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try:
matching_reservations = client.describe_instances(InstanceIds=[instance_id]).get('Reservations', [])
except ClientError as exc:
i... | python | def aws_get_instances_by_id(region, instance_id, raw=True):
"""Returns instances mathing an id."""
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google/python-cloud-utils | cloud_utils/list_instances.py | get_instances_by_name | def get_instances_by_name(name, sort_by_order=('cloud', 'name'), projects=None, raw=True, regions=None, gcp_credentials=None, clouds=SUPPORTED_CLOUDS):
"""Get intsances from GCP and AWS by name."""
matching_instances = all_clouds_get_instances_by_name(
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"""Get intsances from GCP and AWS by name."""
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google/python-cloud-utils | cloud_utils/list_instances.py | get_os_version | def get_os_version(instance):
"""Get OS Version for instances."""
if instance.cloud == 'aws':
client = boto3.client('ec2', instance.region)
image_id = client.describe_instances(InstanceIds=[instance.id])['Reservations'][0]['Instances'][0]['ImageId']
return '16.04' if '16.04' in client.de... | python | def get_os_version(instance):
"""Get OS Version for instances."""
if instance.cloud == 'aws':
client = boto3.client('ec2', instance.region)
image_id = client.describe_instances(InstanceIds=[instance.id])['Reservations'][0]['Instances'][0]['ImageId']
return '16.04' if '16.04' in client.de... | [
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google/python-cloud-utils | cloud_utils/list_instances.py | get_volumes | def get_volumes(instance):
"""Returns all the volumes of an instance."""
if instance.cloud == 'aws':
client = boto3.client('ec2', instance.region)
devices = client.describe_instance_attribute(
InstanceId=instance.id, Attribute='blockDeviceMapping').get('BlockDeviceMappings', [])
... | python | def get_volumes(instance):
"""Returns all the volumes of an instance."""
if instance.cloud == 'aws':
client = boto3.client('ec2', instance.region)
devices = client.describe_instance_attribute(
InstanceId=instance.id, Attribute='blockDeviceMapping').get('BlockDeviceMappings', [])
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google/python-cloud-utils | cloud_utils/list_instances.py | get_persistent_address | def get_persistent_address(instance):
"""Returns the public ip address of an instance."""
if instance.cloud == 'aws':
client = boto3.client('ec2', instance.region)
try:
client.describe_addresses(PublicIps=[instance.ip_address])
return instance.ip_address
except bo... | python | def get_persistent_address(instance):
"""Returns the public ip address of an instance."""
if instance.cloud == 'aws':
client = boto3.client('ec2', instance.region)
try:
client.describe_addresses(PublicIps=[instance.ip_address])
return instance.ip_address
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spyder-ide/conda-manager | conda_manager/utils/findpip.py | main | def main():
"""Use pip to find pip installed packages in a given prefix."""
pip_packages = {}
for package in pip.get_installed_distributions():
name = package.project_name
version = package.version
full_name = "{0}-{1}-pip".format(name.lower(), version)
pip_packages[full_name... | python | def main():
"""Use pip to find pip installed packages in a given prefix."""
pip_packages = {}
for package in pip.get_installed_distributions():
name = package.project_name
version = package.version
full_name = "{0}-{1}-pip".format(name.lower(), version)
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xgvargas/js-css-min-django | jscssmin.py | _save | def _save(file, data, mode='w+'):
"""
Write all data to created file. Also overwrite previous file.
"""
with open(file, mode) as fh:
fh.write(data) | python | def _save(file, data, mode='w+'):
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Write all data to created file. Also overwrite previous file.
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xgvargas/js-css-min-django | jscssmin.py | merge | def merge(obj):
"""
Merge contents.
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Merge contents.
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"""
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"""
Minify JS data and saves to file.
Data should be a string will whole JS content, and file will be
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xgvargas/js-css-min-django | jscssmin.py | jpgMin | def jpgMin(file, force=False):
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aisthesis/pynance | pynance/pf.py | optimize | def optimize(exp_rets, covs):
"""
Return parameters for portfolio optimization.
Parameters
----------
exp_rets : ndarray
Vector of expected returns for each investment..
covs : ndarray
Covariance matrix for the given investments.
Returns
---------
a : ndarray
... | python | def optimize(exp_rets, covs):
"""
Return parameters for portfolio optimization.
Parameters
----------
exp_rets : ndarray
Vector of expected returns for each investment..
covs : ndarray
Covariance matrix for the given investments.
Returns
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a : ndarray
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aisthesis/pynance | pynance/interest.py | growthfromrange | def growthfromrange(rangegrowth, startdate, enddate):
"""
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"""
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"""
Annual growth given growth from start date to end date.
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aisthesis/pynance | pynance/data/retrieve.py | equities | def equities(country='US'):
"""
Return a DataFrame of current US equities.
.. versionadded:: 0.4.0
.. versionchanged:: 0.5.0
Return a DataFrame
Parameters
----------
country : str, optional
Country code for equities to return, defaults to 'US'.
Returns
-------
... | python | def equities(country='US'):
"""
Return a DataFrame of current US equities.
.. versionadded:: 0.4.0
.. versionchanged:: 0.5.0
Return a DataFrame
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country : str, optional
Country code for equities to return, defaults to 'US'.
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aisthesis/pynance | pynance/opt/spread/vert.py | Vert.straddle | def straddle(self, strike, expiry):
"""
Metrics for evaluating a straddle.
Parameters
------------
strike : numeric
Strike price.
expiry : date or date str (e.g. '2015-01-01')
Expiration date.
Returns
------------
metrics ... | python | def straddle(self, strike, expiry):
"""
Metrics for evaluating a straddle.
Parameters
------------
strike : numeric
Strike price.
expiry : date or date str (e.g. '2015-01-01')
Expiration date.
Returns
------------
metrics ... | [
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aisthesis/pynance | pynance/opt/retrieve.py | get | def get(equity):
"""
Retrieve all current options chains for given equity.
.. versionchanged:: 0.5.0
Eliminate special exception handling.
Parameters
-------------
equity : str
Equity for which to retrieve options data.
Returns
-------------
optdata : :class:`~pynan... | python | def get(equity):
"""
Retrieve all current options chains for given equity.
.. versionchanged:: 0.5.0
Eliminate special exception handling.
Parameters
-------------
equity : str
Equity for which to retrieve options data.
Returns
-------------
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aisthesis/pynance | pynance/data/prep.py | transform | def transform(data_frame, **kwargs):
"""
Return a transformed DataFrame.
Transform data_frame along the given axis. By default, each row will be normalized (axis=0).
Parameters
-----------
data_frame : DataFrame
Data to be normalized.
axis : int, optional
0 (default) to nor... | python | def transform(data_frame, **kwargs):
"""
Return a transformed DataFrame.
Transform data_frame along the given axis. By default, each row will be normalized (axis=0).
Parameters
-----------
data_frame : DataFrame
Data to be normalized.
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aisthesis/pynance | pynance/data/prep.py | _get_norms_of_rows | def _get_norms_of_rows(data_frame, method):
""" return a column vector containing the norm of each row """
if method == 'vector':
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norm... | python | def _get_norms_of_rows(data_frame, method):
""" return a column vector containing the norm of each row """
if method == 'vector':
norm_vector = np.linalg.norm(data_frame.values, axis=1)
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aisthesis/pynance | pynance/opt/price.py | Price.get | def get(self, opttype, strike, expiry):
"""
Price as midpoint between bid and ask.
Parameters
----------
opttype : str
'call' or 'put'.
strike : numeric
Strike price.
expiry : date-like
Expiration date. Can be a :class:`datetim... | python | def get(self, opttype, strike, expiry):
"""
Price as midpoint between bid and ask.
Parameters
----------
opttype : str
'call' or 'put'.
strike : numeric
Strike price.
expiry : date-like
Expiration date. Can be a :class:`datetim... | [
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aisthesis/pynance | pynance/opt/price.py | Price.metrics | def metrics(self, opttype, strike, expiry):
"""
Basic metrics for a specific option.
Parameters
----------
opttype : str ('call' or 'put')
strike : numeric
Strike price.
expiry : date-like
Expiration date. Can be a :class:`datetime.datetim... | python | def metrics(self, opttype, strike, expiry):
"""
Basic metrics for a specific option.
Parameters
----------
opttype : str ('call' or 'put')
strike : numeric
Strike price.
expiry : date-like
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aisthesis/pynance | pynance/opt/price.py | Price.strikes | def strikes(self, opttype, expiry):
"""
Retrieve option prices for all strikes of a given type with a given expiration.
Parameters
----------
opttype : str ('call' or 'put')
expiry : date-like
Expiration date. Can be a :class:`datetime.datetime` or
... | python | def strikes(self, opttype, expiry):
"""
Retrieve option prices for all strikes of a given type with a given expiration.
Parameters
----------
opttype : str ('call' or 'put')
expiry : date-like
Expiration date. Can be a :class:`datetime.datetime` or
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aisthesis/pynance | pynance/opt/price.py | Price.exps | def exps(self, opttype, strike):
"""
Prices for given strike on all available dates.
Parameters
----------
opttype : str ('call' or 'put')
strike : numeric
Returns
----------
df : :class:`pandas.DataFrame`
eq : float
Price of ... | python | def exps(self, opttype, strike):
"""
Prices for given strike on all available dates.
Parameters
----------
opttype : str ('call' or 'put')
strike : numeric
Returns
----------
df : :class:`pandas.DataFrame`
eq : float
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aisthesis/pynance | pynance/data/combine.py | labeledfeatures | def labeledfeatures(eqdata, featurefunc, labelfunc):
"""
Return features and labels for the given equity data.
Each row of the features returned contains `2 * n_sessions + 1` columns
(or 1 less if the constant feature is excluded). After the constant feature,
if present, there will be `n_sessions` ... | python | def labeledfeatures(eqdata, featurefunc, labelfunc):
"""
Return features and labels for the given equity data.
Each row of the features returned contains `2 * n_sessions + 1` columns
(or 1 less if the constant feature is excluded). After the constant feature,
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aisthesis/pynance | pynance/data/lab.py | growth | def growth(interval, pricecol, eqdata):
"""
Retrieve growth labels.
Parameters
--------------
interval : int
Number of sessions over which growth is measured. For example, if
the value of 32 is passed for `interval`, the data returned will
show the growth 32 sessions ahead ... | python | def growth(interval, pricecol, eqdata):
"""
Retrieve growth labels.
Parameters
--------------
interval : int
Number of sessions over which growth is measured. For example, if
the value of 32 is passed for `interval`, the data returned will
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aisthesis/pynance | pynance/tech/movave.py | sma | def sma(eqdata, **kwargs):
"""
simple moving average
Parameters
----------
eqdata : DataFrame
window : int, optional
Lookback period for sma. Defaults to 20.
outputcol : str, optional
Column to use for output. Defaults to 'SMA'.
selection : str, optional
Column... | python | def sma(eqdata, **kwargs):
"""
simple moving average
Parameters
----------
eqdata : DataFrame
window : int, optional
Lookback period for sma. Defaults to 20.
outputcol : str, optional
Column to use for output. Defaults to 'SMA'.
selection : str, optional
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aisthesis/pynance | pynance/tech/movave.py | ema | def ema(eqdata, **kwargs):
"""
Exponential moving average with the given span.
Parameters
----------
eqdata : DataFrame
Must have exactly 1 column on which to calculate EMA
span : int, optional
Span for exponential moving average. Cf. `pandas.stats.moments.ewma
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"""
Exponential moving average with the given span.
Parameters
----------
eqdata : DataFrame
Must have exactly 1 column on which to calculate EMA
span : int, optional
Span for exponential moving average. Cf. `pandas.stats.moments.ewma
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aisthesis/pynance | pynance/tech/movave.py | ema_growth | def ema_growth(eqdata, **kwargs):
"""
Growth of exponential moving average.
Parameters
----------
eqdata : DataFrame
span : int, optional
Span for exponential moving average. Defaults to 20.
outputcol : str, optional.
Column to use for output. Defaults to 'EMA Growth'.
s... | python | def ema_growth(eqdata, **kwargs):
"""
Growth of exponential moving average.
Parameters
----------
eqdata : DataFrame
span : int, optional
Span for exponential moving average. Defaults to 20.
outputcol : str, optional.
Column to use for output. Defaults to 'EMA Growth'.
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aisthesis/pynance | pynance/tech/movave.py | growth_volatility | def growth_volatility(eqdata, **kwargs):
"""
Return the volatility of growth.
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"""
Return the volatility of growth.
Note that, like :func:`pynance.tech.simple.growth` but in contrast to
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aisthesis/pynance | pynance/tech/movave.py | ratio_to_ave | def ratio_to_ave(window, eqdata, **kwargs):
"""
Return values expressed as ratios to the average over some number
of prior sessions.
Parameters
----------
eqdata : DataFrame
Must contain a column with name matching `selection`, or, if
`selection` is not specified, a column named... | python | def ratio_to_ave(window, eqdata, **kwargs):
"""
Return values expressed as ratios to the average over some number
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Parameters
----------
eqdata : DataFrame
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aisthesis/pynance | pynance/learn/linreg.py | run | def run(features, labels, regularization=0., constfeat=True):
"""
Run linear regression on the given data.
.. versionadded:: 0.5.0
If a regularization parameter is provided, this function
is a simplification and specialization of ridge
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"""
Run linear regression on the given data.
.. versionadded:: 0.5.0
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aisthesis/pynance | pynance/opt/spread/core.py | Spread.cal | def cal(self, opttype, strike, exp1, exp2):
"""
Metrics for evaluating a calendar spread.
Parameters
------------
opttype : str ('call' or 'put')
Type of option on which to collect data.
strike : numeric
Strike price.
exp1 : date or date s... | python | def cal(self, opttype, strike, exp1, exp2):
"""
Metrics for evaluating a calendar spread.
Parameters
------------
opttype : str ('call' or 'put')
Type of option on which to collect data.
strike : numeric
Strike price.
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aisthesis/pynance | pynance/common.py | expand | def expand(fn, col, inputtype=pd.DataFrame):
"""
Wrap a function applying to a single column to make a function
applying to a multi-dimensional dataframe or ndarray
Parameters
----------
fn : function
Function that applies to a series or vector.
col : str or int
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"""
Wrap a function applying to a single column to make a function
applying to a multi-dimensional dataframe or ndarray
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fn : function
Function that applies to a series or vector.
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aisthesis/pynance | pynance/common.py | has_na | def has_na(eqdata):
"""
Return false if `eqdata` contains no missing values.
Parameters
----------
eqdata : DataFrame or ndarray
Data to check for missing values (NaN, None)
Returns
----------
answer : bool
False iff `eqdata` contains no missing values.
"""
if i... | python | def has_na(eqdata):
"""
Return false if `eqdata` contains no missing values.
Parameters
----------
eqdata : DataFrame or ndarray
Data to check for missing values (NaN, None)
Returns
----------
answer : bool
False iff `eqdata` contains no missing values.
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aisthesis/pynance | pynance/data/feat.py | add_const | def add_const(features):
"""
Prepend the constant feature 1 as first feature and return the modified
feature set.
Parameters
----------
features : ndarray or DataFrame
"""
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if isins... | python | def add_const(features):
"""
Prepend the constant feature 1 as first feature and return the modified
feature set.
Parameters
----------
features : ndarray or DataFrame
"""
content = np.empty((features.shape[0], features.shape[1] + 1), dtype='float64')
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aisthesis/pynance | pynance/data/feat.py | fromcols | def fromcols(selection, n_sessions, eqdata, **kwargs):
"""
Generate features from selected columns of a dataframe.
Parameters
----------
selection : list or tuple of str
Columns to be used as features.
n_sessions : int
Number of sessions over which to create features.
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"""
Generate features from selected columns of a dataframe.
Parameters
----------
selection : list or tuple of str
Columns to be used as features.
n_sessions : int
Number of sessions over which to create features.
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aisthesis/pynance | pynance/data/feat.py | fromfuncs | def fromfuncs(funcs, n_sessions, eqdata, **kwargs):
"""
Generate features using a list of functions to apply to input data
Parameters
----------
funcs : list of function
Functions to apply to eqdata. Each function is expected
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"""
Generate features using a list of functions to apply to input data
Parameters
----------
funcs : list of function
Functions to apply to eqdata. Each function is expected
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aisthesis/pynance | pynance/tech/simple.py | ln_growth | def ln_growth(eqdata, **kwargs):
"""
Return the natural log of growth.
See also
--------
:func:`growth`
"""
if 'outputcol' not in kwargs:
kwargs['outputcol'] = 'LnGrowth'
return np.log(growth(eqdata, **kwargs)) | python | def ln_growth(eqdata, **kwargs):
"""
Return the natural log of growth.
See also
--------
:func:`growth`
"""
if 'outputcol' not in kwargs:
kwargs['outputcol'] = 'LnGrowth'
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aisthesis/pynance | pynance/learn/metrics.py | mse | def mse(predicted, actual):
"""
Mean squared error of predictions.
.. versionadded:: 0.5.0
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----------
predicted : ndarray
Predictions on which to measure error. May
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actual : ndarray
... | python | def mse(predicted, actual):
"""
Mean squared error of predictions.
.. versionadded:: 0.5.0
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----------
predicted : ndarray
Predictions on which to measure error. May
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aisthesis/pynance | pynance/opt/covcall.py | get | def get(eqprice, callprice, strike, shares=1, buycomm=0., excomm=0., dividend=0.):
"""
Metrics for covered calls.
Parameters
----------
eqprice : float
Price at which stock is purchased.
callprice : float
Price for which call is sold.
strike : float
Strike price of c... | python | def get(eqprice, callprice, strike, shares=1, buycomm=0., excomm=0., dividend=0.):
"""
Metrics for covered calls.
Parameters
----------
eqprice : float
Price at which stock is purchased.
callprice : float
Price for which call is sold.
strike : float
Strike price of c... | [
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aisthesis/pynance | pynance/dateutils.py | is_bday | def is_bday(date, bday=None):
"""
Return true iff the given date is a business day.
Parameters
----------
date : :class:`pandas.Timestamp`
Any value that can be converted to a pandas Timestamp--e.g.,
'2012-05-01', dt.datetime(2012, 5, 1, 3)
bday : :class:`pandas.tseries.offsets... | python | def is_bday(date, bday=None):
"""
Return true iff the given date is a business day.
Parameters
----------
date : :class:`pandas.Timestamp`
Any value that can be converted to a pandas Timestamp--e.g.,
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aisthesis/pynance | pynance/data/compare.py | compare | def compare(eq_dfs, columns=None, selection='Adj Close'):
"""
Get the relative performance of multiple equities.
.. versionadded:: 0.5.0
Parameters
----------
eq_dfs : list or tuple of DataFrame
Performance data for multiple equities over
a consistent time frame.
columns : ... | python | def compare(eq_dfs, columns=None, selection='Adj Close'):
"""
Get the relative performance of multiple equities.
.. versionadded:: 0.5.0
Parameters
----------
eq_dfs : list or tuple of DataFrame
Performance data for multiple equities over
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columns : ... | [
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aisthesis/pynance | pynance/opt/spread/diag.py | Diag.diagbtrfly | def diagbtrfly(self, lowstrike, midstrike, highstrike, expiry1, expiry2):
"""
Metrics for evaluating a diagonal butterfly spread.
Parameters
------------
opttype : str ('call' or 'put')
Type of option on which to collect data.
lowstrike : numeric
... | python | def diagbtrfly(self, lowstrike, midstrike, highstrike, expiry1, expiry2):
"""
Metrics for evaluating a diagonal butterfly spread.
Parameters
------------
opttype : str ('call' or 'put')
Type of option on which to collect data.
lowstrike : numeric
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aisthesis/pynance | pynance/opt/core.py | Options.info | def info(self):
"""
Show expiration dates, equity price, quote time.
Returns
-------
self : :class:`~pynance.opt.core.Options`
Returns a reference to the calling object to allow
chaining.
expiries : :class:`pandas.tseries.index.DatetimeIndex`
... | python | def info(self):
"""
Show expiration dates, equity price, quote time.
Returns
-------
self : :class:`~pynance.opt.core.Options`
Returns a reference to the calling object to allow
chaining.
expiries : :class:`pandas.tseries.index.DatetimeIndex`
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expiries : :class:`pandas.tseries.index.DatetimeIndex`
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aisthesis/pynance | pynance/opt/core.py | Options.tolist | def tolist(self):
"""
Return the array as a list of rows.
Each row is a `dict` of values. Facilitates inserting data into a database.
.. versionadded:: 0.3.1
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-------
quotes : list
A list in which each entry is a dictionary representing
... | python | def tolist(self):
"""
Return the array as a list of rows.
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stephrdev/django-userprofiles | userprofiles/forms.py | RegistrationForm._generate_username | def _generate_username(self):
""" Generate a unique username """
while True:
# Generate a UUID username, removing dashes and the last 2 chars
# to make it fit into the 30 char User.username field. Gracefully
# handle any unlikely, but possible duplicate usernames.
... | python | def _generate_username(self):
""" Generate a unique username """
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vijaykatam/django-cache-manager | django_cache_manager/models.py | update_model_cache | def update_model_cache(table_name):
"""
Updates model cache by generating a new key for the model
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"""
Updates model cache by generating a new key for the model
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vijaykatam/django-cache-manager | django_cache_manager/models.py | invalidate_model_cache | def invalidate_model_cache(sender, instance, **kwargs):
"""
Signal receiver for models to invalidate model cache of sender and related models.
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~~~~~~~~~~
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vijaykatam/django-cache-manager | django_cache_manager/models.py | invalidate_m2m_cache | def invalidate_m2m_cache(sender, instance, model, **kwargs):
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vijaykatam/django-cache-manager | django_cache_manager/mixins.py | CacheKeyMixin.generate_key | def generate_key(self):
"""
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"""
sql = self.sql()
key, created = self.get_or_create_model_key()
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db_table = self.model._me... | python | def generate_key(self):
"""
Generate cache key for the current query. If a new key is created for the model it is
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Get sql for the current query.
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Get sql for the current query.
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clone = self.query.clone()
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vijaykatam/django-cache-manager | django_cache_manager/mixins.py | CacheKeyMixin.get_or_create_model_key | def get_or_create_model_key(self):
"""
Get or create key for the model.
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(model_key, boolean) tuple
"""
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"""
Get or create key for the model.
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(model_key, boolean) tuple
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vijaykatam/django-cache-manager | django_cache_manager/mixins.py | CacheInvalidateMixin.invalidate_model_cache | def invalidate_model_cache(self):
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~~~~~~~
Django cache backend
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Get the cache backend
Returns
~~~~~~~
Django cache backend
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UDST/orca | orca/server/server.py | import_file | def import_file(filename):
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Import a file that will trigger the population of Orca.
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----------
filename : str
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Import a file that will trigger the population of Orca.
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----------
filename : str
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"""
@wraps(func)
def wrapper(**kwargs):
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UDST/orca | orca/server/server.py | check_is_injectable | def check_is_injectable(func):
"""
Decorator that will check whether the "inj_name" keyword argument to
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"""
@wraps(func)
def wrapper(**kwargs):
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"""
Decorator that will check whether the "inj_name" keyword argument to
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UDST/orca | orca/server/server.py | schema | def schema():
"""
All tables, columns, steps, injectables and broadcasts registered with
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"""
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UDST/orca | orca/server/server.py | table_preview | def table_preview(table_name):
"""
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"""
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"""
Returns the first five rows of a table as JSON. Inlcudes all columns.
Uses Pandas' "split" JSON format.
"""
preview = orca.get_table(table_name).to_frame().head()
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UDST/orca | orca/server/server.py | table_describe | def table_describe(table_name):
"""
Return summary statistics of a table as JSON. Includes all columns.
Uses Pandas' "split" JSON format.
"""
desc = orca.get_table(table_name).to_frame().describe()
return (
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"""
Return summary statistics of a table as JSON. Includes all columns.
Uses Pandas' "split" JSON format.
"""
desc = orca.get_table(table_name).to_frame().describe()
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UDST/orca | orca/server/server.py | table_definition | def table_definition(table_name):
"""
Get the source of a table function.
If a table is registered DataFrame and not a function then all that is
returned is {'type': 'dataframe'}.
If the table is a registered function then the JSON returned has keys
"type", "filename", "lineno", "text", and "h... | python | def table_definition(table_name):
"""
Get the source of a table function.
If a table is registered DataFrame and not a function then all that is
returned is {'type': 'dataframe'}.
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UDST/orca | orca/server/server.py | table_groupbyagg | def table_groupbyagg(table_name):
"""
Perform a groupby on a table and return an aggregation on a single column.
This depends on some request parameters in the URL.
"column" and "agg" must always be present, and one of "by" or "level"
must be present. "column" is the table column on which aggregati... | python | def table_groupbyagg(table_name):
"""
Perform a groupby on a table and return an aggregation on a single column.
This depends on some request parameters in the URL.
"column" and "agg" must always be present, and one of "by" or "level"
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UDST/orca | orca/server/server.py | column_preview | def column_preview(table_name, col_name):
"""
Return the first ten elements of a column as JSON in Pandas'
"split" format.
"""
col = orca.get_table(table_name).get_column(col_name).head(10)
return (
col.to_json(orient='split', date_format='iso'),
200,
{'Content-Type': '... | python | def column_preview(table_name, col_name):
"""
Return the first ten elements of a column as JSON in Pandas'
"split" format.
"""
col = orca.get_table(table_name).get_column(col_name).head(10)
return (
col.to_json(orient='split', date_format='iso'),
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UDST/orca | orca/server/server.py | column_definition | def column_definition(table_name, col_name):
"""
Get the source of a column function.
If a column is a registered Series and not a function then all that is
returned is {'type': 'series'}.
If the column is a registered function then the JSON returned has keys
"type", "filename", "lineno", "tex... | python | def column_definition(table_name, col_name):
"""
Get the source of a column function.
If a column is a registered Series and not a function then all that is
returned is {'type': 'series'}.
If the column is a registered function then the JSON returned has keys
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UDST/orca | orca/server/server.py | column_describe | def column_describe(table_name, col_name):
"""
Return summary statistics of a column as JSON.
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"""
col_desc = orca.get_table(table_name).get_column(col_name).describe()
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{'Content-Type': 'app... | python | def column_describe(table_name, col_name):
"""
Return summary statistics of a column as JSON.
Uses Pandas' "split" JSON format.
"""
col_desc = orca.get_table(table_name).get_column(col_name).describe()
return (
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UDST/orca | orca/server/server.py | column_csv | def column_csv(table_name, col_name):
"""
Return a column as CSV using Pandas' default CSV output.
"""
csv = orca.get_table(table_name).get_column(col_name).to_csv(path=None)
return csv, 200, {'Content-Type': 'text/csv'} | python | def column_csv(table_name, col_name):
"""
Return a column as CSV using Pandas' default CSV output.
"""
csv = orca.get_table(table_name).get_column(col_name).to_csv(path=None)
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UDST/orca | orca/server/server.py | injectable_repr | def injectable_repr(inj_name):
"""
Returns the type and repr of an injectable. JSON response has
"type" and "repr" keys.
"""
i = orca.get_injectable(inj_name)
return jsonify(type=str(type(i)), repr=repr(i)) | python | def injectable_repr(inj_name):
"""
Returns the type and repr of an injectable. JSON response has
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"""
i = orca.get_injectable(inj_name)
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UDST/orca | orca/server/server.py | injectable_definition | def injectable_definition(inj_name):
"""
Get the source of an injectable function.
If an injectable is a registered Python variable and not a function
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"""
Get the source of an injectable function.
If an injectable is a registered Python variable and not a function
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UDST/orca | orca/server/server.py | list_broadcasts | def list_broadcasts():
"""
List all registered broadcasts as a list of objects with
keys "cast" and "onto".
"""
casts = [{'cast': b[0], 'onto': b[1]} for b in orca.list_broadcasts()]
return jsonify(broadcasts=casts) | python | def list_broadcasts():
"""
List all registered broadcasts as a list of objects with
keys "cast" and "onto".
"""
casts = [{'cast': b[0], 'onto': b[1]} for b in orca.list_broadcasts()]
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UDST/orca | orca/server/server.py | broadcast_definition | def broadcast_definition(cast_name, onto_name):
"""
Return the definition of a broadcast as an object with keys
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"""
Return the definition of a broadcast as an object with keys
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UDST/orca | orca/server/server.py | step_definition | def step_definition(step_name):
"""
Get the source of a step function. Returned object has keys
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if not orca.is_step(step_name):
abort(404)
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"""
Get the source of a step function. Returned object has keys
"filename", "lineno", "text" and "html". "text" is the raw
text of the function, "html" has been marked up by Pygments.
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... | 07b34aeef13cc87c966b2e30cbe7e76cc9d3622c | https://github.com/UDST/orca/blob/07b34aeef13cc87c966b2e30cbe7e76cc9d3622c/orca/server/server.py#L430-L443 | train | 62,186 |
UDST/orca | orca/utils/logutil.py | _add_log_handler | def _add_log_handler(
handler, level=None, fmt=None, datefmt=None, propagate=None):
"""
Add a logging handler to Orca.
Parameters
----------
handler : logging.Handler subclass
level : int, optional
An optional logging level that will apply only to this stream
handler.
... | python | def _add_log_handler(
handler, level=None, fmt=None, datefmt=None, propagate=None):
"""
Add a logging handler to Orca.
Parameters
----------
handler : logging.Handler subclass
level : int, optional
An optional logging level that will apply only to this stream
handler.
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UDST/orca | orca/utils/logutil.py | log_to_stream | def log_to_stream(level=None, fmt=None, datefmt=None):
"""
Send log messages to the console.
Parameters
----------
level : int, optional
An optional logging level that will apply only to this stream
handler.
fmt : str, optional
An optional format string that will be used... | python | def log_to_stream(level=None, fmt=None, datefmt=None):
"""
Send log messages to the console.
Parameters
----------
level : int, optional
An optional logging level that will apply only to this stream
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UDST/orca | orca/orca.py | clear_all | def clear_all():
"""
Clear any and all stored state from Orca.
"""
_TABLES.clear()
_COLUMNS.clear()
_STEPS.clear()
_BROADCASTS.clear()
_INJECTABLES.clear()
_TABLE_CACHE.clear()
_COLUMN_CACHE.clear()
_INJECTABLE_CACHE.clear()
for m in _MEMOIZED.values():
m.value.c... | python | def clear_all():
"""
Clear any and all stored state from Orca.
"""
_TABLES.clear()
_COLUMNS.clear()
_STEPS.clear()
_BROADCASTS.clear()
_INJECTABLES.clear()
_TABLE_CACHE.clear()
_COLUMN_CACHE.clear()
_INJECTABLE_CACHE.clear()
for m in _MEMOIZED.values():
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UDST/orca | orca/orca.py | _collect_variables | def _collect_variables(names, expressions=None):
"""
Map labels and expressions to registered variables.
Handles argument matching.
Example:
_collect_variables(names=['zones', 'zone_id'],
expressions=['parcels.zone_id'])
Would return a dict representing:
... | python | def _collect_variables(names, expressions=None):
"""
Map labels and expressions to registered variables.
Handles argument matching.
Example:
_collect_variables(names=['zones', 'zone_id'],
expressions=['parcels.zone_id'])
Would return a dict representing:
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UDST/orca | orca/orca.py | add_table | def add_table(
table_name, table, cache=False, cache_scope=_CS_FOREVER,
copy_col=True):
"""
Register a table with Orca.
Parameters
----------
table_name : str
Should be globally unique to this table.
table : pandas.DataFrame or function
If a function, the functio... | python | def add_table(
table_name, table, cache=False, cache_scope=_CS_FOREVER,
copy_col=True):
"""
Register a table with Orca.
Parameters
----------
table_name : str
Should be globally unique to this table.
table : pandas.DataFrame or function
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UDST/orca | orca/orca.py | table | def table(
table_name=None, cache=False, cache_scope=_CS_FOREVER, copy_col=True):
"""
Decorates functions that return DataFrames.
Decorator version of `add_table`. Table name defaults to
name of function.
The function's argument names and keyword argument values
will be matched to regi... | python | def table(
table_name=None, cache=False, cache_scope=_CS_FOREVER, copy_col=True):
"""
Decorates functions that return DataFrames.
Decorator version of `add_table`. Table name defaults to
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UDST/orca | orca/orca.py | get_table | def get_table(table_name):
"""
Get a registered table.
Decorated functions will be converted to `DataFrameWrapper`.
Parameters
----------
table_name : str
Returns
-------
table : `DataFrameWrapper`
"""
table = get_raw_table(table_name)
if isinstance(table, TableFuncWr... | python | def get_table(table_name):
"""
Get a registered table.
Decorated functions will be converted to `DataFrameWrapper`.
Parameters
----------
table_name : str
Returns
-------
table : `DataFrameWrapper`
"""
table = get_raw_table(table_name)
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UDST/orca | orca/orca.py | table_type | def table_type(table_name):
"""
Returns the type of a registered table.
The type can be either "dataframe" or "function".
Parameters
----------
table_name : str
Returns
-------
table_type : {'dataframe', 'function'}
"""
table = get_raw_table(table_name)
if isinstance... | python | def table_type(table_name):
"""
Returns the type of a registered table.
The type can be either "dataframe" or "function".
Parameters
----------
table_name : str
Returns
-------
table_type : {'dataframe', 'function'}
"""
table = get_raw_table(table_name)
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UDST/orca | orca/orca.py | add_column | def add_column(
table_name, column_name, column, cache=False, cache_scope=_CS_FOREVER):
"""
Add a new column to a table from a Series or callable.
Parameters
----------
table_name : str
Table with which the column will be associated.
column_name : str
Name for the column... | python | def add_column(
table_name, column_name, column, cache=False, cache_scope=_CS_FOREVER):
"""
Add a new column to a table from a Series or callable.
Parameters
----------
table_name : str
Table with which the column will be associated.
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Table with which the column will be associated.
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Name for the column.
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UDST/orca | orca/orca.py | column | def column(table_name, column_name=None, cache=False, cache_scope=_CS_FOREVER):
"""
Decorates functions that return a Series.
Decorator version of `add_column`. Series index must match
the named table. Column name defaults to name of function.
The function's argument names and keyword argument val... | python | def column(table_name, column_name=None, cache=False, cache_scope=_CS_FOREVER):
"""
Decorates functions that return a Series.
Decorator version of `add_column`. Series index must match
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UDST/orca | orca/orca.py | _columns_for_table | def _columns_for_table(table_name):
"""
Return all of the columns registered for a given table.
Parameters
----------
table_name : str
Returns
-------
columns : dict of column wrappers
Keys will be column names.
"""
return {cname: col
for (tname, cname), co... | python | def _columns_for_table(table_name):
"""
Return all of the columns registered for a given table.
Parameters
----------
table_name : str
Returns
-------
columns : dict of column wrappers
Keys will be column names.
"""
return {cname: col
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UDST/orca | orca/orca.py | get_raw_column | def get_raw_column(table_name, column_name):
"""
Get a wrapped, registered column.
This function cannot return columns that are part of wrapped
DataFrames, it's only for columns registered directly through Orca.
Parameters
----------
table_name : str
column_name : str
Returns
... | python | def get_raw_column(table_name, column_name):
"""
Get a wrapped, registered column.
This function cannot return columns that are part of wrapped
DataFrames, it's only for columns registered directly through Orca.
Parameters
----------
table_name : str
column_name : str
Returns
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UDST/orca | orca/orca.py | _memoize_function | def _memoize_function(f, name, cache_scope=_CS_FOREVER):
"""
Wraps a function for memoization and ties it's cache into the
Orca cacheing system.
Parameters
----------
f : function
name : str
Name of injectable.
cache_scope : {'step', 'iteration', 'forever'}, optional
Sco... | python | def _memoize_function(f, name, cache_scope=_CS_FOREVER):
"""
Wraps a function for memoization and ties it's cache into the
Orca cacheing system.
Parameters
----------
f : function
name : str
Name of injectable.
cache_scope : {'step', 'iteration', 'forever'}, optional
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