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45,200 | 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
Price of ... | [
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45,201 | 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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45,202 | 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
show the growth 32 sessions ahead ... | [
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45,203 | 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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45,204 | 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
<http://... | python | 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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45,205 | 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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45,206 | aisthesis/pynance | pynance/tech/movave.py | growth_volatility | def growth_volatility(eqdata, **kwargs):
"""
Return the volatility of growth.
Note that, like :func:`pynance.tech.simple.growth` but in contrast to
:func:`volatility`, :func:`growth_volatility`
applies directly to a dataframe like that returned by
:func:`pynance.data.retrieve.get`, not necess... | python | def growth_volatility(eqdata, **kwargs):
"""
Return the volatility of growth.
Note that, like :func:`pynance.tech.simple.growth` but in contrast to
:func:`volatility`, :func:`growth_volatility`
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45,207 | 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
of prior sessions.
Parameters
----------
eqdata : DataFrame
Must contain a column with name matching `selection`, or, if
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45,208 | 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
regression, as implemented in `scikit-learn
<http://sc... | python | 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
regression, as implemented in `scikit-learn
<http://sc... | [
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45,209 | 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.
exp1 : date or date s... | [
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Type of option on which to collect data.
strike : numeric
Strike price.
exp1 : date or date str (e.g. '2015-01-01')
Earlier expiration date.
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45,210 | 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
Index of co... | python | 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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45,211 | 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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45,212 | 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
"""
content = np.empty((features.shape[0], features.shape[1] + 1), dtype='float64')
content[:, 0] = 1.
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')
content[:, 0] = 1.
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45,213 | 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.
eqda... | python | 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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45,214 | 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
to output a dataframe with index identical to a slice of ... | python | 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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45,215 | 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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45,216 | aisthesis/pynance | pynance/learn/metrics.py | mse | def mse(predicted, actual):
"""
Mean squared error of predictions.
.. versionadded:: 0.5.0
Parameters
----------
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
Parameters
----------
predicted : ndarray
Predictions on which to measure error. May
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45,217 | 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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45,218 | 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.,
'2012-05-01', dt.datetime(2012, 5, 1, 3)
bday : :class:`pandas.tseries.offsets... | [
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45,219 | 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
a consistent time frame.
columns : ... | [
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45,220 | 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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45,221 | 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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45,222 | aisthesis/pynance | pynance/opt/core.py | Options.tolist | def tolist(self):
"""
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Each row is a `dict` of values. Facilitates inserting data into a database.
.. versionadded:: 0.3.1
Returns
-------
quotes : list
A list in which each entry is a dictionary representing
... | python | 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
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45,223 | 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 """
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.
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45,224 | 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
"""
model_cache_info = ModelCacheInfo(table_name, uuid.uuid4().hex)
model_cache_backend.share_model_cache_info(model_cache_info) | python | def update_model_cache(table_name):
"""
Updates model cache by generating a new key for the model
"""
model_cache_info = ModelCacheInfo(table_name, uuid.uuid4().hex)
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45,225 | 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.
Model cache is invalidated by generating new key for each model.
Parameters
~~~~~~~~~~
sender
The model class
instance
The actual in... | python | def invalidate_model_cache(sender, instance, **kwargs):
"""
Signal receiver for models to invalidate model cache of sender and related models.
Model cache is invalidated by generating new key for each model.
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45,226 | vijaykatam/django-cache-manager | django_cache_manager/models.py | invalidate_m2m_cache | def invalidate_m2m_cache(sender, instance, model, **kwargs):
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Parameters
~~~~~~~~~~
sender
The model class
instance
The instance whose many-to-many relation is updated.
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The model class
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The instance whose many-to-many relation is updated.
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45,227 | vijaykatam/django-cache-manager | django_cache_manager/mixins.py | CacheKeyMixin.generate_key | def generate_key(self):
"""
Generate cache key for the current query. If a new key is created for the model it is
then shared with other consumers.
"""
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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45,228 | vijaykatam/django-cache-manager | django_cache_manager/mixins.py | CacheKeyMixin.sql | def sql(self):
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Get sql for the current query.
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45,229 | 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.
Returns
~~~~~~~
(model_key, boolean) tuple
"""
model_cache_info = model_cache_backend.retrieve_model_cache_info(self.model._meta.db_table)
if not model_cache_info:
return... | python | def get_or_create_model_key(self):
"""
Get or create key for the model.
Returns
~~~~~~~
(model_key, boolean) tuple
"""
model_cache_info = model_cache_backend.retrieve_model_cache_info(self.model._meta.db_table)
if not model_cache_info:
return... | [
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45,230 | vijaykatam/django-cache-manager | django_cache_manager/mixins.py | CacheInvalidateMixin.invalidate_model_cache | def invalidate_model_cache(self):
"""
Invalidate model cache by generating new key for the model.
"""
logger.info('Invalidating cache for table {0}'.format(self.model._meta.db_table))
if django.VERSION >= (1, 8):
related_tables = set(
[f.related_model.... | python | def invalidate_model_cache(self):
"""
Invalidate model cache by generating new key for the model.
"""
logger.info('Invalidating cache for table {0}'.format(self.model._meta.db_table))
if django.VERSION >= (1, 8):
related_tables = set(
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45,231 | vijaykatam/django-cache-manager | django_cache_manager/mixins.py | CacheBackendMixin.cache_backend | def cache_backend(self):
"""
Get the cache backend
Returns
~~~~~~~
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"""
if not hasattr(self, '_cache_backend'):
if hasattr(django.core.cache, 'caches'):
self._cache_backend = django.core.cache.caches[_cache_name]
... | python | def cache_backend(self):
"""
Get the cache backend
Returns
~~~~~~~
Django cache backend
"""
if not hasattr(self, '_cache_backend'):
if hasattr(django.core.cache, 'caches'):
self._cache_backend = django.core.cache.caches[_cache_name]
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45,232 | UDST/orca | orca/server/server.py | import_file | def import_file(filename):
"""
Import a file that will trigger the population of Orca.
Parameters
----------
filename : str
"""
pathname, filename = os.path.split(filename)
modname = re.match(
r'(?P<modname>\w+)\.py', filename).group('modname')
file, path, desc = imp.find_m... | python | def import_file(filename):
"""
Import a file that will trigger the population of Orca.
Parameters
----------
filename : str
"""
pathname, filename = os.path.split(filename)
modname = re.match(
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45,233 | UDST/orca | orca/server/server.py | check_is_table | def check_is_table(func):
"""
Decorator that will check whether the "table_name" keyword argument
to the wrapped function matches a registered Orca table.
"""
@wraps(func)
def wrapper(**kwargs):
if not orca.is_table(kwargs['table_name']):
abort(404)
return func(**kwa... | python | def check_is_table(func):
"""
Decorator that will check whether the "table_name" keyword argument
to the wrapped function matches a registered Orca table.
"""
@wraps(func)
def wrapper(**kwargs):
if not orca.is_table(kwargs['table_name']):
abort(404)
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45,234 | UDST/orca | orca/server/server.py | check_is_column | def check_is_column(func):
"""
Decorator that will check whether the "table_name" and "col_name"
keyword arguments to the wrapped function match a registered Orca
table and column.
"""
@wraps(func)
def wrapper(**kwargs):
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col_name = kwargs['c... | python | def check_is_column(func):
"""
Decorator that will check whether the "table_name" and "col_name"
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"""
@wraps(func)
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45,235 | 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
the wrapped function matches a registered Orca injectable.
"""
@wraps(func)
def wrapper(**kwargs):
name = kwargs['inj_name']
if not orca.is_injectable(name):
abort... | python | def check_is_injectable(func):
"""
Decorator that will check whether the "inj_name" keyword argument to
the wrapped function matches a registered Orca injectable.
"""
@wraps(func)
def wrapper(**kwargs):
name = kwargs['inj_name']
if not orca.is_injectable(name):
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45,236 | UDST/orca | orca/server/server.py | schema | def schema():
"""
All tables, columns, steps, injectables and broadcasts registered with
Orca. Includes local columns on tables.
"""
tables = orca.list_tables()
cols = {t: orca.get_table(t).columns for t in tables}
steps = orca.list_steps()
injectables = orca.list_injectables()
broa... | python | def schema():
"""
All tables, columns, steps, injectables and broadcasts registered with
Orca. Includes local columns on tables.
"""
tables = orca.list_tables()
cols = {t: orca.get_table(t).columns for t in tables}
steps = orca.list_steps()
injectables = orca.list_injectables()
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45,237 | UDST/orca | orca/server/server.py | table_preview | def table_preview(table_name):
"""
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()
return (
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200,
{'Conte... | python | def table_preview(table_name):
"""
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()
return (
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45,238 | 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 (
desc.to_json(orient='split', date_format='iso'),
200,
{'Content-... | python | 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 (
desc.to_json(orient='split', date_format='iso'),
200,
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45,239 | 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'}.
If the table is a registered function then the JSON returned has keys
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45,240 | 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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45,241 | 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)
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45,242 | 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
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"""
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45,243 | UDST/orca | orca/server/server.py | column_describe | 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 (
col_desc.to_json(orient='split'),
200,
{'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 (
col_desc.to_json(orient='split'),
200,
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45,244 | 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)
return csv, 200, {'Content-Type': 'text/csv'} | [
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45,245 | 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
"type" and "repr" keys.
"""
i = orca.get_injectable(inj_name)
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45,246 | 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
then all that is returned is {'type': 'variable'}.
If the column is a registered function then the JSON returned has keys
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"""
Get the source of an injectable function.
If an injectable is a registered Python variable and not a function
then all that is returned is {'type': 'variable'}.
If the column is a registered function then the JSON returned has keys
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45,247 | 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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45,248 | 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
"cast", "onto", "cast_on", "onto_on", "cast_index", and "onto_index".
These are the same as the arguments to the ``broadcast`` function.
"""
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"""
Return the definition of a broadcast as an object with keys
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These are the same as the arguments to the ``broadcast`` function.
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45,249 | UDST/orca | orca/server/server.py | step_definition | def step_definition(step_name):
"""
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.
"""
if not orca.is_step(step_name):
abort(404)
filename, lineno, s... | python | def step_definition(step_name):
"""
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.
"""
if not orca.is_step(step_name):
abort(404)
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45,250 | 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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45,251 | 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
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"""
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----------
level : int, optional
An optional logging level that will apply only to this stream
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45,252 | 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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45,253 | 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.
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45,254 | 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.
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45,255 | 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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45,256 | 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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45,257 | 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'}
"""
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45,258 | 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
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table_name : str
Table with which the column will be associated.
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45,259 | 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.
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"""
Decorates functions that return a Series.
Decorator version of `add_column`. Series index must match
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45,260 | 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.
"""
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45,261 | 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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45,262 | 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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45,263 | UDST/orca | orca/orca.py | add_injectable | def add_injectable(
name, value, autocall=True, cache=False, cache_scope=_CS_FOREVER,
memoize=False):
"""
Add a value that will be injected into other functions.
Parameters
----------
name : str
value
If a callable and `autocall` is True then the function's
argum... | python | def add_injectable(
name, value, autocall=True, cache=False, cache_scope=_CS_FOREVER,
memoize=False):
"""
Add a value that will be injected into other functions.
Parameters
----------
name : str
value
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45,264 | UDST/orca | orca/orca.py | injectable | def injectable(
name=None, autocall=True, cache=False, cache_scope=_CS_FOREVER,
memoize=False):
"""
Decorates functions that will be injected into other functions.
Decorator version of `add_injectable`. Name defaults to
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name=None, autocall=True, cache=False, cache_scope=_CS_FOREVER,
memoize=False):
"""
Decorates functions that will be injected into other functions.
Decorator version of `add_injectable`. Name defaults to
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45,265 | UDST/orca | orca/orca.py | get_injectable_func_source_data | def get_injectable_func_source_data(name):
"""
Return data about an injectable function's source, including file name,
line number, and source code.
Parameters
----------
name : str
Returns
-------
filename : str
lineno : int
The line number on which the function starts... | python | def get_injectable_func_source_data(name):
"""
Return data about an injectable function's source, including file name,
line number, and source code.
Parameters
----------
name : str
Returns
-------
filename : str
lineno : int
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45,266 | UDST/orca | orca/orca.py | add_step | def add_step(step_name, func):
"""
Add a step function to Orca.
The function's argument names and keyword argument values
will be matched to registered variables when the function
needs to be evaluated by Orca.
The argument name "iter_var" may be used to have the current
iteration variable ... | python | def add_step(step_name, func):
"""
Add a step function to Orca.
The function's argument names and keyword argument values
will be matched to registered variables when the function
needs to be evaluated by Orca.
The argument name "iter_var" may be used to have the current
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45,267 | UDST/orca | orca/orca.py | step | def step(step_name=None):
"""
Decorates functions that will be called by the `run` function.
Decorator version of `add_step`. step name defaults to
name of function.
The function's argument names and keyword argument values
will be matched to registered variables when the function
needs to... | python | def step(step_name=None):
"""
Decorates functions that will be called by the `run` function.
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45,268 | UDST/orca | orca/orca.py | broadcast | def broadcast(cast, onto, cast_on=None, onto_on=None,
cast_index=False, onto_index=False):
"""
Register a rule for merging two tables by broadcasting one onto
the other.
Parameters
----------
cast, onto : str
Names of registered tables.
cast_on, onto_on : str, optional... | python | def broadcast(cast, onto, cast_on=None, onto_on=None,
cast_index=False, onto_index=False):
"""
Register a rule for merging two tables by broadcasting one onto
the other.
Parameters
----------
cast, onto : str
Names of registered tables.
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45,269 | UDST/orca | orca/orca.py | _get_broadcasts | def _get_broadcasts(tables):
"""
Get the broadcasts associated with a set of tables.
Parameters
----------
tables : sequence of str
Table names for which broadcasts have been registered.
Returns
-------
casts : dict of `Broadcast`
Keys are tuples of strings like (cast_n... | python | def _get_broadcasts(tables):
"""
Get the broadcasts associated with a set of tables.
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----------
tables : sequence of str
Table names for which broadcasts have been registered.
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45,270 | UDST/orca | orca/orca.py | get_broadcast | def get_broadcast(cast_name, onto_name):
"""
Get a single broadcast.
Broadcasts are stored data about how to do a Pandas join.
A Broadcast object is a namedtuple with these attributes:
- cast: the name of the table being broadcast
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"""
Get a single broadcast.
Broadcasts are stored data about how to do a Pandas join.
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45,271 | UDST/orca | orca/orca.py | _all_reachable_tables | def _all_reachable_tables(t):
"""
A generator that provides all the names of tables that can be
reached via merges starting at the given target table.
"""
for k, v in t.items():
for tname in _all_reachable_tables(v):
yield tname
yield k | python | def _all_reachable_tables(t):
"""
A generator that provides all the names of tables that can be
reached via merges starting at the given target table.
"""
for k, v in t.items():
for tname in _all_reachable_tables(v):
yield tname
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45,272 | UDST/orca | orca/orca.py | _recursive_getitem | def _recursive_getitem(d, key):
"""
Descend into a dict of dicts to return the one that contains
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"""
if key in d:
return d
else:
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... | python | def _recursive_getitem(d, key):
"""
Descend into a dict of dicts to return the one that contains
a given key. Every value in the dict must be another dict.
"""
if key in d:
return d
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45,273 | UDST/orca | orca/orca.py | _next_merge | def _next_merge(merge_node):
"""
Gets a node that has only leaf nodes below it. This table and
the ones below are ready to be merged to make a new leaf node.
"""
if all(_is_leaf_node(d) for d in _dict_value_to_pairs(merge_node)):
return merge_node
else:
for d in tz.remove(_is_le... | python | def _next_merge(merge_node):
"""
Gets a node that has only leaf nodes below it. This table and
the ones below are ready to be merged to make a new leaf node.
"""
if all(_is_leaf_node(d) for d in _dict_value_to_pairs(merge_node)):
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45,274 | UDST/orca | orca/orca.py | get_step_table_names | def get_step_table_names(steps):
"""
Returns a list of table names injected into the provided steps.
Parameters
----------
steps: list of str
Steps to gather table inputs from.
Returns
-------
list of str
"""
table_names = set()
for s in steps:
table_names ... | python | def get_step_table_names(steps):
"""
Returns a list of table names injected into the provided steps.
Parameters
----------
steps: list of str
Steps to gather table inputs from.
Returns
-------
list of str
"""
table_names = set()
for s in steps:
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45,275 | UDST/orca | orca/orca.py | write_tables | def write_tables(fname, table_names=None, prefix=None, compress=False, local=False):
"""
Writes tables to a pandas.HDFStore file.
Parameters
----------
fname : str
File name for HDFStore. Will be opened in append mode and closed
at the end of this function.
table_names: list of ... | python | def write_tables(fname, table_names=None, prefix=None, compress=False, local=False):
"""
Writes tables to a pandas.HDFStore file.
Parameters
----------
fname : str
File name for HDFStore. Will be opened in append mode and closed
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45,276 | UDST/orca | orca/orca.py | run | def run(steps, iter_vars=None, data_out=None, out_interval=1,
out_base_tables=None, out_run_tables=None, compress=False,
out_base_local=True, out_run_local=True):
"""
Run steps in series, optionally repeatedly over some sequence.
The current iteration variable is set as a global injectable
... | python | def run(steps, iter_vars=None, data_out=None, out_interval=1,
out_base_tables=None, out_run_tables=None, compress=False,
out_base_local=True, out_run_local=True):
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Run steps in series, optionally repeatedly over some sequence.
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45,277 | UDST/orca | orca/orca.py | injectables | def injectables(**kwargs):
"""
Temporarily add injectables to the pipeline environment.
Takes only keyword arguments.
Injectables will be returned to their original state when the context
manager exits.
"""
global _INJECTABLES
original = _INJECTABLES.copy()
_INJECTABLES.update(kwa... | python | def injectables(**kwargs):
"""
Temporarily add injectables to the pipeline environment.
Takes only keyword arguments.
Injectables will be returned to their original state when the context
manager exits.
"""
global _INJECTABLES
original = _INJECTABLES.copy()
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45,278 | UDST/orca | orca/orca.py | temporary_tables | def temporary_tables(**kwargs):
"""
Temporarily set DataFrames as registered tables.
Tables will be returned to their original state when the context
manager exits. Caching is not enabled for tables registered via
this function.
"""
global _TABLES
original = _TABLES.copy()
for k,... | python | def temporary_tables(**kwargs):
"""
Temporarily set DataFrames as registered tables.
Tables will be returned to their original state when the context
manager exits. Caching is not enabled for tables registered via
this function.
"""
global _TABLES
original = _TABLES.copy()
for k,... | [
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45,279 | UDST/orca | orca/orca.py | eval_variable | def eval_variable(name, **kwargs):
"""
Execute a single variable function registered with Orca
and return the result. Any keyword arguments are temporarily set
as injectables. This gives the value as would be injected into a function.
Parameters
----------
name : str
Name of variabl... | python | def eval_variable(name, **kwargs):
"""
Execute a single variable function registered with Orca
and return the result. Any keyword arguments are temporarily set
as injectables. This gives the value as would be injected into a function.
Parameters
----------
name : str
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45,280 | UDST/orca | orca/orca.py | DataFrameWrapper.to_frame | def to_frame(self, columns=None):
"""
Make a DataFrame with the given columns.
Will always return a copy of the underlying table.
Parameters
----------
columns : sequence or string, optional
Sequence of the column names desired in the DataFrame. A string
... | python | def to_frame(self, columns=None):
"""
Make a DataFrame with the given columns.
Will always return a copy of the underlying table.
Parameters
----------
columns : sequence or string, optional
Sequence of the column names desired in the DataFrame. A string
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45,281 | UDST/orca | orca/orca.py | DataFrameWrapper.update_col | def update_col(self, column_name, series):
"""
Add or replace a column in the underlying DataFrame.
Parameters
----------
column_name : str
Column to add or replace.
series : pandas.Series or sequence
Column data.
"""
logger.debug... | python | def update_col(self, column_name, series):
"""
Add or replace a column in the underlying DataFrame.
Parameters
----------
column_name : str
Column to add or replace.
series : pandas.Series or sequence
Column data.
"""
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45,282 | UDST/orca | orca/orca.py | DataFrameWrapper.column_type | def column_type(self, column_name):
"""
Report column type as one of 'local', 'series', or 'function'.
Parameters
----------
column_name : str
Returns
-------
col_type : {'local', 'series', 'function'}
'local' means that the column is part of... | python | def column_type(self, column_name):
"""
Report column type as one of 'local', 'series', or 'function'.
Parameters
----------
column_name : str
Returns
-------
col_type : {'local', 'series', 'function'}
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45,283 | UDST/orca | orca/orca.py | DataFrameWrapper.update_col_from_series | def update_col_from_series(self, column_name, series, cast=False):
"""
Update existing values in a column from another series.
Index values must match in both column and series. Optionally
casts data type to match the existing column.
Parameters
---------------
c... | python | def update_col_from_series(self, column_name, series, cast=False):
"""
Update existing values in a column from another series.
Index values must match in both column and series. Optionally
casts data type to match the existing column.
Parameters
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45,284 | UDST/orca | orca/orca.py | DataFrameWrapper.clear_cached | def clear_cached(self):
"""
Remove cached results from this table's computed columns.
"""
_TABLE_CACHE.pop(self.name, None)
for col in _columns_for_table(self.name).values():
col.clear_cached()
logger.debug('cleared cached columns for table {!r}'.format(self.... | python | def clear_cached(self):
"""
Remove cached results from this table's computed columns.
"""
_TABLE_CACHE.pop(self.name, None)
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col.clear_cached()
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45,285 | UDST/orca | orca/orca.py | TableFuncWrapper._call_func | def _call_func(self):
"""
Call the wrapped function and return the result wrapped by
DataFrameWrapper.
Also updates attributes like columns, index, and length.
"""
if _CACHING and self.cache and self.name in _TABLE_CACHE:
logger.debug('returning table {!r} fr... | python | def _call_func(self):
"""
Call the wrapped function and return the result wrapped by
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Also updates attributes like columns, index, and length.
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45,286 | UDST/orca | orca/orca.py | _ColumnFuncWrapper.clear_cached | def clear_cached(self):
"""
Remove any cached result of this column.
"""
x = _COLUMN_CACHE.pop((self.table_name, self.name), None)
if x is not None:
logger.debug(
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self.na... | python | def clear_cached(self):
"""
Remove any cached result of this column.
"""
x = _COLUMN_CACHE.pop((self.table_name, self.name), None)
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45,287 | UDST/orca | orca/orca.py | _InjectableFuncWrapper.clear_cached | def clear_cached(self):
"""
Clear a cached result for this injectable.
"""
x = _INJECTABLE_CACHE.pop(self.name, None)
if x:
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"""
Clear a cached result for this injectable.
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45,288 | UDST/orca | orca/orca.py | _StepFuncWrapper._tables_used | def _tables_used(self):
"""
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Returns
-------
tables : set of str
"""
args = list(self._argspec.args)
if self._argspec.defaults:
default_args = list(self._argspec.defaults)
else:
default_args =... | python | def _tables_used(self):
"""
Tables injected into the step.
Returns
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tables : set of str
"""
args = list(self._argspec.args)
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45,289 | versae/qbe | django_qbe/utils.py | qbe_tree | def qbe_tree(graph, nodes, root=None):
"""
Given a graph, nodes to explore and an optinal root, do a breadth-first
search in order to return the tree.
"""
if root:
start = root
else:
index = random.randint(0, len(nodes) - 1)
start = nodes[index]
# A queue to BFS inste... | python | def qbe_tree(graph, nodes, root=None):
"""
Given a graph, nodes to explore and an optinal root, do a breadth-first
search in order to return the tree.
"""
if root:
start = root
else:
index = random.randint(0, len(nodes) - 1)
start = nodes[index]
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45,290 | versae/qbe | django_qbe/utils.py | combine | def combine(items, k=None):
"""
Create a matrix in wich each row is a tuple containing one of solutions or
solution k-esima.
"""
length_items = len(items)
lengths = [len(i) for i in items]
length = reduce(lambda x, y: x * y, lengths)
repeats = [reduce(lambda x, y: x * y, lengths[i:])
... | python | def combine(items, k=None):
"""
Create a matrix in wich each row is a tuple containing one of solutions or
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"""
length_items = len(items)
lengths = [len(i) for i in items]
length = reduce(lambda x, y: x * y, lengths)
repeats = [reduce(lambda x, y: x * y, lengths[i:])
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45,291 | versae/qbe | django_qbe/utils.py | pickle_encode | def pickle_encode(session_dict):
"Returns the given session dictionary pickled and encoded as a string."
pickled = pickle.dumps(session_dict, pickle.HIGHEST_PROTOCOL)
return base64.encodestring(pickled + get_query_hash(pickled).encode()) | python | def pickle_encode(session_dict):
"Returns the given session dictionary pickled and encoded as a string."
pickled = pickle.dumps(session_dict, pickle.HIGHEST_PROTOCOL)
return base64.encodestring(pickled + get_query_hash(pickled).encode()) | [
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45,292 | UDST/orca | orca/utils/utils.py | func_source_data | def func_source_data(func):
"""
Return data about a function source, including file name,
line number, and source code.
Parameters
----------
func : object
May be anything support by the inspect module, such as a function,
method, or class.
Returns
-------
filename ... | python | def func_source_data(func):
"""
Return data about a function source, including file name,
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Parameters
----------
func : object
May be anything support by the inspect module, such as a function,
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-------
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45,293 | versae/qbe | django_qbe/forms.py | BaseQueryByExampleFormSet.clean | def clean(self):
"""
Checks that there is almost one field to select
"""
if any(self.errors):
# Don't bother validating the formset unless each form is valid on
# its own
return
(selects, aliases, froms, wheres, sorts, groups_by,
param... | python | def clean(self):
"""
Checks that there is almost one field to select
"""
if any(self.errors):
# Don't bother validating the formset unless each form is valid on
# its own
return
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45,294 | versae/qbe | django_qbe/forms.py | BaseQueryByExampleFormSet.get_results | def get_results(self, limit=None, offset=None, query=None, admin_name=None,
row_number=False):
"""
Fetch all results after perform SQL query and
"""
add_extra_ids = (admin_name is not None)
if not query:
sql = self.get_raw_query(limit=limit, offset... | python | def get_results(self, limit=None, offset=None, query=None, admin_name=None,
row_number=False):
"""
Fetch all results after perform SQL query and
"""
add_extra_ids = (admin_name is not None)
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45,295 | jgorset/django-respite | respite/utils/parsers.py | parse_content_type | def parse_content_type(content_type):
"""
Return a tuple of content type and charset.
:param content_type: A string describing a content type.
"""
if '; charset=' in content_type:
return tuple(content_type.split('; charset='))
else:
if 'text' in content_type:
encodin... | python | def parse_content_type(content_type):
"""
Return a tuple of content type and charset.
:param content_type: A string describing a content type.
"""
if '; charset=' in content_type:
return tuple(content_type.split('; charset='))
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45,296 | jgorset/django-respite | respite/utils/parsers.py | parse_http_accept_header | def parse_http_accept_header(header):
"""
Return a list of content types listed in the HTTP Accept header
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:param header: A string describing the contents of the HTTP Accept header.
"""
components = [item.strip() for item in header.split(',')]
l = []
for component in... | python | def parse_http_accept_header(header):
"""
Return a list of content types listed in the HTTP Accept header
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:param header: A string describing the contents of the HTTP Accept header.
"""
components = [item.strip() for item in header.split(',')]
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45,297 | jgorset/django-respite | respite/utils/parsers.py | parse_multipart_data | def parse_multipart_data(request):
"""
Parse a request with multipart data.
:param request: A HttpRequest instance.
"""
return MultiPartParser(
META=request.META,
input_data=StringIO(request.body),
upload_handlers=request.upload_handlers,
encoding=request.encoding
... | python | def parse_multipart_data(request):
"""
Parse a request with multipart data.
:param request: A HttpRequest instance.
"""
return MultiPartParser(
META=request.META,
input_data=StringIO(request.body),
upload_handlers=request.upload_handlers,
encoding=request.encoding
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45,298 | jgorset/django-respite | respite/decorators.py | override_supported_formats | def override_supported_formats(formats):
"""
Override the views class' supported formats for the decorated function.
Arguments:
formats -- A list of strings describing formats, e.g. ``['html', 'json']``.
"""
def decorator(function):
@wraps(function)
def wrapper(self, *args, **kw... | python | def override_supported_formats(formats):
"""
Override the views class' supported formats for the decorated function.
Arguments:
formats -- A list of strings describing formats, e.g. ``['html', 'json']``.
"""
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45,299 | jgorset/django-respite | respite/decorators.py | route | def route(regex, method, name):
"""
Route the decorated view.
:param regex: A string describing a regular expression to which the request path will be matched.
:param method: A string describing the HTTP method that this view accepts.
:param name: A string describing the name of the URL pattern.
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"""
Route the decorated view.
:param regex: A string describing a regular expression to which the request path will be matched.
:param method: A string describing the HTTP method that this view accepts.
:param name: A string describing the name of the URL pattern.
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"view",
"."
] | 719469d11baf91d05917bab1623bd82adc543546 | https://github.com/jgorset/django-respite/blob/719469d11baf91d05917bab1623bd82adc543546/respite/decorators.py#L24-L52 |
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