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train | verify_callable_argspec | Checks the callable_ to make sure that it satisfies the given
expectations.
expected_args should be an iterable of Arguments in the order you expect to
receive them.
expect_starargs means that the function should or should not take a *args
param. expect_kwargs says the callable should or should not ... | zipline/utils/argcheck.py | def verify_callable_argspec(callable_,
expected_args=Argument.ignore,
expect_starargs=Argument.ignore,
expect_kwargs=Argument.ignore):
"""
Checks the callable_ to make sure that it satisfies the given
expectations.
expec... | def verify_callable_argspec(callable_,
expected_args=Argument.ignore,
expect_starargs=Argument.ignore,
expect_kwargs=Argument.ignore):
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Checks the callable_ to make sure that it satisfies the given
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train | Argument.parse_argspec | Takes a callable and returns a tuple with the list of Argument objects,
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train | StaticRestrictions.is_restricted | An asset is restricted for all dts if it is in the static list. | zipline/finance/asset_restrictions.py | def is_restricted(self, assets, dt):
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An asset is restricted for all dts if it is in the static list.
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train | HistoricalRestrictions.is_restricted | Returns whether or not an asset or iterable of assets is restricted
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"""
Returns whether or not an asset or iterable of assets is restricted
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"""
if isinstance(assets, Asset):
return self._is_restricted_for_asset(assets, dt)
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Returns whether or not an asset or iterable of assets is restricted
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train | PositionTracker.handle_splits | Processes a list of splits by modifying any positions as needed.
Parameters
----------
splits: list
A list of splits. Each split is a tuple of (asset, ratio).
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-------
int: The leftover cash from fractional shares after modifying each
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splits: list
A list of splits. Each split is a tuple of (asset, ratio).
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-------
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train | PositionTracker.earn_dividends | Given a list of dividends whose ex_dates are all the next trading
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each dividend's pay date.
Parameters
----------
cash_dividends : iterable of (asset, amount, pay_date) namedtuples
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----------
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train | PositionTracker.pay_dividends | Returns a cash payment based on the dividends that should be paid out
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Returns a cash payment based on the dividends that should be paid out
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"""
net_cash_payment = 0.0
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payments = self._unpa... | def pay_dividends(self, next_trading_day):
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Returns a cash payment based on the dividends that should be paid out
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train | PositionTracker.stats | The current status of the positions.
Returns
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stats : PositionStats
The current stats position stats.
Notes
-----
This is cached, repeated access will not recompute the stats until
the stats may have changed. | zipline/finance/ledger.py | def stats(self):
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Returns
-------
stats : PositionStats
The current stats position stats.
Notes
-----
This is cached, repeated access will not recompute the stats until
the stats may have changed.
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Returns
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stats : PositionStats
The current stats position stats.
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train | Ledger.process_transaction | Add a transaction to ledger, updating the current state as needed.
Parameters
----------
transaction : zp.Transaction
The transaction to execute. | zipline/finance/ledger.py | def process_transaction(self, transaction):
"""Add a transaction to ledger, updating the current state as needed.
Parameters
----------
transaction : zp.Transaction
The transaction to execute.
"""
asset = transaction.asset
if isinstance(asset, Future)... | def process_transaction(self, transaction):
"""Add a transaction to ledger, updating the current state as needed.
Parameters
----------
transaction : zp.Transaction
The transaction to execute.
"""
asset = transaction.asset
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train | Ledger.process_splits | Processes a list of splits by modifying any positions as needed.
Parameters
----------
splits: list[(Asset, float)]
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----------
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train | Ledger.process_order | Keep track of an order that was placed.
Parameters
----------
order : zp.Order
The order to record. | zipline/finance/ledger.py | def process_order(self, order):
"""Keep track of an order that was placed.
Parameters
----------
order : zp.Order
The order to record.
"""
try:
dt_orders = self._orders_by_modified[order.dt]
except KeyError:
self._orders_by_mod... | def process_order(self, order):
"""Keep track of an order that was placed.
Parameters
----------
order : zp.Order
The order to record.
"""
try:
dt_orders = self._orders_by_modified[order.dt]
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train | Ledger.process_commission | Process the commission.
Parameters
----------
commission : zp.Event
The commission being paid. | zipline/finance/ledger.py | def process_commission(self, commission):
"""Process the commission.
Parameters
----------
commission : zp.Event
The commission being paid.
"""
asset = commission['asset']
cost = commission['cost']
self.position_tracker.handle_commission(asse... | def process_commission(self, commission):
"""Process the commission.
Parameters
----------
commission : zp.Event
The commission being paid.
"""
asset = commission['asset']
cost = commission['cost']
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train | Ledger.process_dividends | Process dividends for the next session.
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well as paying out any dividends whose pay-date is the next session | zipline/finance/ledger.py | def process_dividends(self, next_session, asset_finder, adjustment_reader):
"""Process dividends for the next session.
This will earn us any dividends whose ex-date is the next session as
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position_tracker = ... | def process_dividends(self, next_session, asset_finder, adjustment_reader):
"""Process dividends for the next session.
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train | Ledger.transactions | Retrieve the dict-form of all of the transactions in a given bar or
for the whole simulation.
Parameters
----------
dt : pd.Timestamp or None, optional
The particular datetime to look up transactions for. If not passed,
or None is explicitly passed, all of the tr... | zipline/finance/ledger.py | def transactions(self, dt=None):
"""Retrieve the dict-form of all of the transactions in a given bar or
for the whole simulation.
Parameters
----------
dt : pd.Timestamp or None, optional
The particular datetime to look up transactions for. If not passed,
... | def transactions(self, dt=None):
"""Retrieve the dict-form of all of the transactions in a given bar or
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Parameters
----------
dt : pd.Timestamp or None, optional
The particular datetime to look up transactions for. If not passed,
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train | Ledger.orders | Retrieve the dict-form of all of the orders in a given bar or for
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----------
dt : pd.Timestamp or None, optional
The particular datetime to look up order for. If not passed, or
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"""Retrieve the dict-form of all of the orders in a given bar or for
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Parameters
----------
dt : pd.Timestamp or None, optional
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train | Ledger.update_portfolio | Force a computation of the current portfolio state. | zipline/finance/ledger.py | def update_portfolio(self):
"""Force a computation of the current portfolio state.
"""
if not self._dirty_portfolio:
return
portfolio = self._portfolio
pt = self.position_tracker
portfolio.positions = pt.get_positions()
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"""Force a computation of the current portfolio state.
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train | Ledger.override_account_fields | Override fields on ``self.account``. | zipline/finance/ledger.py | def override_account_fields(self,
settled_cash=not_overridden,
accrued_interest=not_overridden,
buying_power=not_overridden,
equity_with_loan=not_overridden,
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train | datashape_type_to_numpy | Given a datashape type, return the associated numpy type. Maps
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Parameters
----------
type_: datashape.coretypes.Type
The datashape type.
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... | zipline/pipeline/loaders/blaze/core.py | def datashape_type_to_numpy(type_):
"""
Given a datashape type, return the associated numpy type. Maps
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numpy datetime returned by datashape isn't supported by pipeline.
Parameters
----------
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"""
Given a datashape type, return the associated numpy type. Maps
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train | new_dataset | Creates or returns a dataset from a blaze expression.
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expr : Expr
The blaze expression representing the values.
missing_values : frozenset((name, value) pairs
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This needs to be a frozenset rat... | zipline/pipeline/loaders/blaze/core.py | def new_dataset(expr, missing_values, domain):
"""
Creates or returns a dataset from a blaze expression.
Parameters
----------
expr : Expr
The blaze expression representing the values.
missing_values : frozenset((name, value) pairs
Association pairs column name and missing_value... | def new_dataset(expr, missing_values, domain):
"""
Creates or returns a dataset from a blaze expression.
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expr : Expr
The blaze expression representing the values.
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train | _check_resources | Validate that the expression and resources passed match up.
Parameters
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name : str
The name of the argument we are checking.
expr : Expr
The potentially bound expr.
resources
The explicitly passed resources to compute expr.
Raises
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"""Validate that the expression and resources passed match up.
Parameters
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name : str
The name of the argument we are checking.
expr : Expr
The potentially bound expr.
resources
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The name of the argument we are checking.
expr : Expr
The potentially bound expr.
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train | _check_datetime_field | Check that a field is a datetime inside some measure.
Parameters
----------
name : str
The name of the field to check.
measure : Record
The record to check the field of.
Raises
------
TypeError
If the field is not a datetime inside ``measure``. | zipline/pipeline/loaders/blaze/core.py | def _check_datetime_field(name, measure):
"""Check that a field is a datetime inside some measure.
Parameters
----------
name : str
The name of the field to check.
measure : Record
The record to check the field of.
Raises
------
TypeError
If the field is not a d... | def _check_datetime_field(name, measure):
"""Check that a field is a datetime inside some measure.
Parameters
----------
name : str
The name of the field to check.
measure : Record
The record to check the field of.
Raises
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train | _get_metadata | Find the correct metadata expression for the expression.
Parameters
----------
field : {'deltas', 'checkpoints'}
The kind of metadata expr to lookup.
expr : Expr
The baseline expression.
metadata_expr : Expr, 'auto', or None
The metadata argument. If this is 'auto', then the... | zipline/pipeline/loaders/blaze/core.py | def _get_metadata(field, expr, metadata_expr, no_metadata_rule):
"""Find the correct metadata expression for the expression.
Parameters
----------
field : {'deltas', 'checkpoints'}
The kind of metadata expr to lookup.
expr : Expr
The baseline expression.
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----------
field : {'deltas', 'checkpoints'}
The kind of metadata expr to lookup.
expr : Expr
The baseline expression.
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train | _ensure_timestamp_field | Verify that the baseline and deltas expressions have a timestamp field.
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----------
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If there is not a ``TS_FIELD_NAME`` on either of the expressions, it will
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train | from_blaze | Create a Pipeline API object from a blaze expression.
Parameters
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expr : Expr
The blaze expression to use.
deltas : Expr, 'auto' or None, optional
The expression to use for the point in time adjustments.
If the string 'auto' is passed, a deltas expr will be looked up
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checkpoints='auto',
loader=None,
resources=None,
odo_kwargs=None,
missing_values=None,
domain=GENERIC,
no_deltas_rule='warn',
no_checkpoints_rule='wa... | def from_blaze(expr,
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checkpoints='auto',
loader=None,
resources=None,
odo_kwargs=None,
missing_values=None,
domain=GENERIC,
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train | bind_expression_to_resources | Bind a Blaze expression to resources.
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expr : bz.Expr
The expression to which we want to bind resources.
resources : dict[bz.Symbol -> any]
Mapping from the loadable terms of ``expr`` to actual data resources.
Returns
-------
bound_expr : bz.Expr
... | zipline/pipeline/loaders/blaze/core.py | def bind_expression_to_resources(expr, resources):
"""
Bind a Blaze expression to resources.
Parameters
----------
expr : bz.Expr
The expression to which we want to bind resources.
resources : dict[bz.Symbol -> any]
Mapping from the loadable terms of ``expr`` to actual data reso... | def bind_expression_to_resources(expr, resources):
"""
Bind a Blaze expression to resources.
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----------
expr : bz.Expr
The expression to which we want to bind resources.
resources : dict[bz.Symbol -> any]
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train | get_materialized_checkpoints | Computes a lower bound and a DataFrame checkpoints.
Parameters
----------
checkpoints : Expr
Bound blaze expression for a checkpoints table from which to get a
computed lower bound.
colnames : iterable of str
The names of the columns for which checkpoints should be computed.
... | zipline/pipeline/loaders/blaze/core.py | def get_materialized_checkpoints(checkpoints, colnames, lower_dt, odo_kwargs):
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Computes a lower bound and a DataFrame checkpoints.
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----------
checkpoints : Expr
Bound blaze expression for a checkpoints table from which to get a
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Computes a lower bound and a DataFrame checkpoints.
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----------
checkpoints : Expr
Bound blaze expression for a checkpoints table from which to get a
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train | ffill_query_in_range | Query a blaze expression in a given time range properly forward filling
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Parameters
----------
expr : Expr
Bound blaze expression.
lower : datetime
The lower date to query for.
upper : datetime
The upper date to query for.
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odo_kwargs=None,
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dataset : DataSet
The pipeline dataset to map to the given expressions.
expr : Expr
The baseline values.
deltas : Expr, optional
The deltas for the data.
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train | BlazeLoader.register_column | Explicitly map a single bound column to a collection of blaze
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columns.
Parameters
----------
column : BoundColumn
The pipeline dataset to map to the given expressions.
expr : Expr
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train | merge_ownership_periods | Given a dict of mappings where the values are lists of
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Orders the periods chronologically, and pushes forward the end date
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"""
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Orders the periods chronologically, and pushes forward th... | def merge_ownership_periods(mappings):
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train | build_ownership_map | Builds a dict mapping to lists of OwnershipPeriods, from a db table. | zipline/assets/assets.py | def build_ownership_map(table, key_from_row, value_from_row):
"""
Builds a dict mapping to lists of OwnershipPeriods, from a db table.
"""
return _build_ownership_map_from_rows(
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"""
return _build_ownership_map_from_rows(
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train | build_grouped_ownership_map | Builds a dict mapping group keys to maps of keys to to lists of
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key_from_row,
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group_key):
"""
Builds a dict mapping group keys to maps of keys to to lists of
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train | _filter_kwargs | Filter out kwargs from a dictionary.
Parameters
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names : set[str]
The names to select from ``dict_``.
dict_ : dict[str, any]
The dictionary to select from.
Returns
-------
kwargs : dict[str, any]
``dict_`` where the keys intersect with ``names`` and the va... | zipline/assets/assets.py | def _filter_kwargs(names, dict_):
"""Filter out kwargs from a dictionary.
Parameters
----------
names : set[str]
The names to select from ``dict_``.
dict_ : dict[str, any]
The dictionary to select from.
Returns
-------
kwargs : dict[str, any]
``dict_`` where the... | def _filter_kwargs(names, dict_):
"""Filter out kwargs from a dictionary.
Parameters
----------
names : set[str]
The names to select from ``dict_``.
dict_ : dict[str, any]
The dictionary to select from.
Returns
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kwargs : dict[str, any]
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train | _convert_asset_timestamp_fields | Takes in a dict of Asset init args and converts dates to pd.Timestamps | zipline/assets/assets.py | def _convert_asset_timestamp_fields(dict_):
"""
Takes in a dict of Asset init args and converts dates to pd.Timestamps
"""
for key in _asset_timestamp_fields & viewkeys(dict_):
value = pd.Timestamp(dict_[key], tz='UTC')
dict_[key] = None if isnull(value) else value
return dict_ | def _convert_asset_timestamp_fields(dict_):
"""
Takes in a dict of Asset init args and converts dates to pd.Timestamps
"""
for key in _asset_timestamp_fields & viewkeys(dict_):
value = pd.Timestamp(dict_[key], tz='UTC')
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return dict_ | [
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train | was_active | Whether or not `asset` was active at the time corresponding to
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Parameters
----------
reference_date_value : int
Date, represented as nanoseconds since EPOCH, for which we want to know
if `asset` was alive. This is generally the result of accessing the
`v... | zipline/assets/assets.py | def was_active(reference_date_value, asset):
"""
Whether or not `asset` was active at the time corresponding to
`reference_date_value`.
Parameters
----------
reference_date_value : int
Date, represented as nanoseconds since EPOCH, for which we want to know
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"""
Whether or not `asset` was active at the time corresponding to
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----------
reference_date_value : int
Date, represented as nanoseconds since EPOCH, for which we want to know
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train | AssetFinder.lookup_asset_types | Retrieve asset types for a list of sids.
Parameters
----------
sids : list[int]
Returns
-------
types : dict[sid -> str or None]
Asset types for the provided sids. | zipline/assets/assets.py | def lookup_asset_types(self, sids):
"""
Retrieve asset types for a list of sids.
Parameters
----------
sids : list[int]
Returns
-------
types : dict[sid -> str or None]
Asset types for the provided sids.
"""
found = {}
... | def lookup_asset_types(self, sids):
"""
Retrieve asset types for a list of sids.
Parameters
----------
sids : list[int]
Returns
-------
types : dict[sid -> str or None]
Asset types for the provided sids.
"""
found = {}
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train | AssetFinder.retrieve_all | Retrieve all assets in `sids`.
Parameters
----------
sids : iterable of int
Assets to retrieve.
default_none : bool
If True, return None for failed lookups.
If False, raise `SidsNotFound`.
Returns
-------
assets : list[Asset o... | zipline/assets/assets.py | def retrieve_all(self, sids, default_none=False):
"""
Retrieve all assets in `sids`.
Parameters
----------
sids : iterable of int
Assets to retrieve.
default_none : bool
If True, return None for failed lookups.
If False, raise `SidsNot... | def retrieve_all(self, sids, default_none=False):
"""
Retrieve all assets in `sids`.
Parameters
----------
sids : iterable of int
Assets to retrieve.
default_none : bool
If True, return None for failed lookups.
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train | AssetFinder._select_most_recent_symbols_chunk | Retrieve the most recent symbol for a set of sids.
Parameters
----------
sid_group : iterable[int]
The sids to lookup. The length of this sequence must be less than
or equal to SQLITE_MAX_VARIABLE_NUMBER because the sids will be
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... | zipline/assets/assets.py | def _select_most_recent_symbols_chunk(self, sid_group):
"""Retrieve the most recent symbol for a set of sids.
Parameters
----------
sid_group : iterable[int]
The sids to lookup. The length of this sequence must be less than
or equal to SQLITE_MAX_VARIABLE_NUMBER ... | def _select_most_recent_symbols_chunk(self, sid_group):
"""Retrieve the most recent symbol for a set of sids.
Parameters
----------
sid_group : iterable[int]
The sids to lookup. The length of this sequence must be less than
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train | AssetFinder._retrieve_assets | Internal function for loading assets from a table.
This should be the only method of `AssetFinder` that writes Assets into
self._asset_cache.
Parameters
---------
sids : iterable of int
Asset ids to look up.
asset_tbl : sqlalchemy.Table
Table fro... | zipline/assets/assets.py | def _retrieve_assets(self, sids, asset_tbl, asset_type):
"""
Internal function for loading assets from a table.
This should be the only method of `AssetFinder` that writes Assets into
self._asset_cache.
Parameters
---------
sids : iterable of int
Ass... | def _retrieve_assets(self, sids, asset_tbl, asset_type):
"""
Internal function for loading assets from a table.
This should be the only method of `AssetFinder` that writes Assets into
self._asset_cache.
Parameters
---------
sids : iterable of int
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train | AssetFinder._lookup_symbol_strict | Resolve a symbol to an asset object without fuzzy matching.
Parameters
----------
ownership_map : dict[(str, str), list[OwnershipPeriod]]
The mapping from split symbols to ownership periods.
multi_country : bool
Does this mapping span multiple countries?
... | zipline/assets/assets.py | def _lookup_symbol_strict(self,
ownership_map,
multi_country,
symbol,
as_of_date):
"""
Resolve a symbol to an asset object without fuzzy matching.
Parameters
---------... | def _lookup_symbol_strict(self,
ownership_map,
multi_country,
symbol,
as_of_date):
"""
Resolve a symbol to an asset object without fuzzy matching.
Parameters
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train | AssetFinder.lookup_symbol | Lookup an equity by symbol.
Parameters
----------
symbol : str
The ticker symbol to resolve.
as_of_date : datetime or None
Look up the last owner of this symbol as of this datetime.
If ``as_of_date`` is None, then this can only resolve the equity
... | zipline/assets/assets.py | def lookup_symbol(self,
symbol,
as_of_date,
fuzzy=False,
country_code=None):
"""Lookup an equity by symbol.
Parameters
----------
symbol : str
The ticker symbol to resolve.
as_of_... | def lookup_symbol(self,
symbol,
as_of_date,
fuzzy=False,
country_code=None):
"""Lookup an equity by symbol.
Parameters
----------
symbol : str
The ticker symbol to resolve.
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train | AssetFinder.lookup_symbols | Lookup a list of equities by symbol.
Equivalent to::
[finder.lookup_symbol(s, as_of, fuzzy) for s in symbols]
but potentially faster because repeated lookups are memoized.
Parameters
----------
symbols : sequence[str]
Sequence of ticker symbols to reso... | zipline/assets/assets.py | def lookup_symbols(self,
symbols,
as_of_date,
fuzzy=False,
country_code=None):
"""
Lookup a list of equities by symbol.
Equivalent to::
[finder.lookup_symbol(s, as_of, fuzzy) for s in symbol... | def lookup_symbols(self,
symbols,
as_of_date,
fuzzy=False,
country_code=None):
"""
Lookup a list of equities by symbol.
Equivalent to::
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train | AssetFinder.lookup_future_symbol | Lookup a future contract by symbol.
Parameters
----------
symbol : str
The symbol of the desired contract.
Returns
-------
future : Future
The future contract referenced by ``symbol``.
Raises
------
SymbolNotFound
... | zipline/assets/assets.py | def lookup_future_symbol(self, symbol):
"""Lookup a future contract by symbol.
Parameters
----------
symbol : str
The symbol of the desired contract.
Returns
-------
future : Future
The future contract referenced by ``symbol``.
R... | def lookup_future_symbol(self, symbol):
"""Lookup a future contract by symbol.
Parameters
----------
symbol : str
The symbol of the desired contract.
Returns
-------
future : Future
The future contract referenced by ``symbol``.
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train | AssetFinder.get_supplementary_field | Get the value of a supplementary field for an asset.
Parameters
----------
sid : int
The sid of the asset to query.
field_name : str
Name of the supplementary field.
as_of_date : pd.Timestamp, None
The last known value on this date is returned... | zipline/assets/assets.py | def get_supplementary_field(self, sid, field_name, as_of_date):
"""Get the value of a supplementary field for an asset.
Parameters
----------
sid : int
The sid of the asset to query.
field_name : str
Name of the supplementary field.
as_of_date : p... | def get_supplementary_field(self, sid, field_name, as_of_date):
"""Get the value of a supplementary field for an asset.
Parameters
----------
sid : int
The sid of the asset to query.
field_name : str
Name of the supplementary field.
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train | AssetFinder._lookup_generic_scalar | Convert asset_convertible to an asset.
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Convert asset_convertible to an asset.
On success, ap... | def _lookup_generic_scalar(self,
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train | AssetFinder.lookup_generic | Convert an object into an Asset or sequence of Assets.
This method exists primarily as a convenience for implementing
user-facing APIs that can handle multiple kinds of input. It should
not be used for internal code where we already know the expected types
of our inputs.
Param... | zipline/assets/assets.py | def lookup_generic(self, obj, as_of_date, country_code):
"""
Convert an object into an Asset or sequence of Assets.
This method exists primarily as a convenience for implementing
user-facing APIs that can handle multiple kinds of input. It should
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"""
Convert an object into an Asset or sequence of Assets.
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train | AssetFinder._compute_asset_lifetimes | Compute and cache a recarray of asset lifetimes. | zipline/assets/assets.py | def _compute_asset_lifetimes(self, country_codes):
"""
Compute and cache a recarray of asset lifetimes.
"""
equities_cols = self.equities.c
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buf = np.array(
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Compute and cache a recarray of asset lifetimes.
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train | AssetFinder.lifetimes | Compute a DataFrame representing asset lifetimes for the specified date
range.
Parameters
----------
dates : pd.DatetimeIndex
The dates for which to compute lifetimes.
include_start_date : bool
Whether or not to count the asset as alive on its start_date.... | zipline/assets/assets.py | def lifetimes(self, dates, include_start_date, country_codes):
"""
Compute a DataFrame representing asset lifetimes for the specified date
range.
Parameters
----------
dates : pd.DatetimeIndex
The dates for which to compute lifetimes.
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"""
Compute a DataFrame representing asset lifetimes for the specified date
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Parameters
----------
dates : pd.DatetimeIndex
The dates for which to compute lifetimes.
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train | AssetFinder.equities_sids_for_country_code | Return all of the sids for a given country.
Parameters
----------
country_code : str
An ISO 3166 alpha-2 country code.
Returns
-------
tuple[int]
The sids whose exchanges are in this country. | zipline/assets/assets.py | def equities_sids_for_country_code(self, country_code):
"""Return all of the sids for a given country.
Parameters
----------
country_code : str
An ISO 3166 alpha-2 country code.
Returns
-------
tuple[int]
The sids whose exchanges are in t... | def equities_sids_for_country_code(self, country_code):
"""Return all of the sids for a given country.
Parameters
----------
country_code : str
An ISO 3166 alpha-2 country code.
Returns
-------
tuple[int]
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train | ContinuousFutureSessionBarReader.load_raw_arrays | Parameters
----------
fields : list of str
'sid'
start_dt: Timestamp
Beginning of the window range.
end_dt: Timestamp
End of the window range.
sids : list of int
The asset identifiers in the window.
Returns
-------
... | zipline/data/continuous_future_reader.py | def load_raw_arrays(self, columns, start_date, end_date, assets):
"""
Parameters
----------
fields : list of str
'sid'
start_dt: Timestamp
Beginning of the window range.
end_dt: Timestamp
End of the window range.
sids : list of in... | def load_raw_arrays(self, columns, start_date, end_date, assets):
"""
Parameters
----------
fields : list of str
'sid'
start_dt: Timestamp
Beginning of the window range.
end_dt: Timestamp
End of the window range.
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train | ContinuousFutureSessionBarReader.get_value | Retrieve the value at the given coordinates.
Parameters
----------
sid : int
The asset identifier.
dt : pd.Timestamp
The timestamp for the desired data point.
field : string
The OHLVC name for the desired data point.
Returns
-... | zipline/data/continuous_future_reader.py | def get_value(self, continuous_future, dt, field):
"""
Retrieve the value at the given coordinates.
Parameters
----------
sid : int
The asset identifier.
dt : pd.Timestamp
The timestamp for the desired data point.
field : string
... | def get_value(self, continuous_future, dt, field):
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Retrieve the value at the given coordinates.
Parameters
----------
sid : int
The asset identifier.
dt : pd.Timestamp
The timestamp for the desired data point.
field : string
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train | ContinuousFutureSessionBarReader.get_last_traded_dt | Get the latest minute on or before ``dt`` in which ``asset`` traded.
If there are no trades on or before ``dt``, returns ``pd.NaT``.
Parameters
----------
asset : zipline.asset.Asset
The asset for which to get the last traded minute.
dt : pd.Timestamp
Th... | zipline/data/continuous_future_reader.py | def get_last_traded_dt(self, asset, dt):
"""
Get the latest minute on or before ``dt`` in which ``asset`` traded.
If there are no trades on or before ``dt``, returns ``pd.NaT``.
Parameters
----------
asset : zipline.asset.Asset
The asset for which to get the... | def get_last_traded_dt(self, asset, dt):
"""
Get the latest minute on or before ``dt`` in which ``asset`` traded.
If there are no trades on or before ``dt``, returns ``pd.NaT``.
Parameters
----------
asset : zipline.asset.Asset
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train | ContinuousFutureMinuteBarReader.load_raw_arrays | Parameters
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fields : list of str
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start_dt: Timestamp
Beginning of the window range.
end_dt: Timestamp
End of the window range.
sids : list of int
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Parameters
----------
fields : list of str
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start_dt: Timestamp
Beginning of the window range.
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Parameters
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start_dt: Timestamp
Beginning of the window range.
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train | Portfolio.current_portfolio_weights | Compute each asset's weight in the portfolio by calculating its held
value divided by the total value of all positions.
Each equity's value is its price times the number of shares held. Each
futures contract's value is its unit price times number of shares held
times the multiplier. | zipline/protocol.py | def current_portfolio_weights(self):
"""
Compute each asset's weight in the portfolio by calculating its held
value divided by the total value of all positions.
Each equity's value is its price times the number of shares held. Each
futures contract's value is its unit price time... | def current_portfolio_weights(self):
"""
Compute each asset's weight in the portfolio by calculating its held
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train | WechatSogouAPI.__hosting_wechat_img | 将微信明细中图片托管到云端，同时将html页面中的对应图片替换
Parameters
----------
content_info : dict 微信文章明细字典
{
'content_img_list': [], # 从微信文章解析出的原始图片列表
'content_html': '', # 从微信文章解析出文章的内容
}
hosting_callback : callable
托管回调函数，传入单个图片链接，返回托管后的图片链接... | wechatsogou/api.py | def __hosting_wechat_img(self, content_info, hosting_callback):
"""将微信明细中图片托管到云端，同时将html页面中的对应图片替换
Parameters
----------
content_info : dict 微信文章明细字典
{
'content_img_list': [], # 从微信文章解析出的原始图片列表
'content_html': '', # 从微信文章解析出文章的内容
}... | def __hosting_wechat_img(self, content_info, hosting_callback):
"""将微信明细中图片托管到云端，同时将html页面中的对应图片替换
Parameters
----------
content_info : dict 微信文章明细字典
{
'content_img_list': [], # 从微信文章解析出的原始图片列表
'content_html': '', # 从微信文章解析出文章的内容
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train | WechatSogouAPI.get_gzh_info | 获取公众号微信号 wechatid 的信息
因为wechatid唯一确定，所以第一个就是要搜索的公众号
Parameters
----------
wecgat_id_or_name : str or unicode
wechat_id or wechat_name
unlock_callback : callable
处理出现验证码页面的函数，参见 unlock_callback_example
identify_image_callback : callable
... | wechatsogou/api.py | def get_gzh_info(self, wecgat_id_or_name, unlock_callback=None, identify_image_callback=None, decode_url=True):
"""获取公众号微信号 wechatid 的信息
因为wechatid唯一确定，所以第一个就是要搜索的公众号
Parameters
----------
wecgat_id_or_name : str or unicode
wechat_id or wechat_name
unlock_ca... | def get_gzh_info(self, wecgat_id_or_name, unlock_callback=None, identify_image_callback=None, decode_url=True):
"""获取公众号微信号 wechatid 的信息
因为wechatid唯一确定，所以第一个就是要搜索的公众号
Parameters
----------
wecgat_id_or_name : str or unicode
wechat_id or wechat_name
unlock_ca... | [
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train | WechatSogouAPI.search_gzh | 搜索 公众号
对于出现验证码的情况，可以由使用者自己提供：
1、函数 unlock_callback ，这个函数 handle 出现验证码到解决的整个流程
2、也可以 只提供函数 identify_image_callback，这个函数输入验证码二进制数据，输出验证码文字，剩下的由 wechatsogou 包来解决
注意：
函数 unlock_callback 和 identify_image_callback 只需要提供一个，如果都提供了，那么 identify_image_callback 不起作用
Par... | wechatsogou/api.py | def search_gzh(self, keyword, page=1, unlock_callback=None, identify_image_callback=None, decode_url=True):
"""搜索 公众号
对于出现验证码的情况，可以由使用者自己提供：
1、函数 unlock_callback ，这个函数 handle 出现验证码到解决的整个流程
2、也可以 只提供函数 identify_image_callback，这个函数输入验证码二进制数据，输出验证码文字，剩下的由 wechatsogou 包来解决
注... | def search_gzh(self, keyword, page=1, unlock_callback=None, identify_image_callback=None, decode_url=True):
"""搜索 公众号
对于出现验证码的情况，可以由使用者自己提供：
1、函数 unlock_callback ，这个函数 handle 出现验证码到解决的整个流程
2、也可以 只提供函数 identify_image_callback，这个函数输入验证码二进制数据，输出验证码文字，剩下的由 wechatsogou 包来解决
注... | [
"搜索",
"公众号"
] | Chyroc/WechatSogou | python | https://github.com/Chyroc/WechatSogou/blob/2e0e9886f555fd8bcfc7ae9718ced6ce955cd24a/wechatsogou/api.py#L243-L296 | [
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train | WechatSogouAPI.search_article | 搜索 文章
对于出现验证码的情况，可以由使用者自己提供：
1、函数 unlock_callback ，这个函数 handle 出现验证码到解决的整个流程
2、也可以 只提供函数 identify_image_callback，这个函数输入验证码二进制数据，输出验证码文字，剩下的由 wechatsogou 包来解决
注意：
函数 unlock_callback 和 identify_image_callback 只需要提供一个，如果都提供了，那么 identify_image_callback 不起作用
Para... | wechatsogou/api.py | def search_article(self, keyword, page=1, timesn=WechatSogouConst.search_article_time.anytime,
article_type=WechatSogouConst.search_article_type.all, ft=None, et=None,
unlock_callback=None,
identify_image_callback=None,
decode_u... | def search_article(self, keyword, page=1, timesn=WechatSogouConst.search_article_time.anytime,
article_type=WechatSogouConst.search_article_type.all, ft=None, et=None,
unlock_callback=None,
identify_image_callback=None,
decode_u... | [
"搜索",
"文章"
] | Chyroc/WechatSogou | python | https://github.com/Chyroc/WechatSogou/blob/2e0e9886f555fd8bcfc7ae9718ced6ce955cd24a/wechatsogou/api.py#L298-L369 | [
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train | WechatSogouAPI.get_gzh_article_by_history | 从 公众号的最近10条群发页面 提取公众号信息 和 文章列表信息
对于出现验证码的情况，可以由使用者自己提供：
1、函数 unlock_callback ，这个函数 handle 出现验证码到解决的整个流程
2、也可以 只提供函数 identify_image_callback，这个函数输入验证码二进制数据，输出验证码文字，剩下的由 wechatsogou 包来解决
注意：
函数 unlock_callback 和 identify_image_callback 只需要提供一个，如果都提供了，那么 identify_image_... | wechatsogou/api.py | def get_gzh_article_by_history(self, keyword=None, url=None,
unlock_callback_sogou=None,
identify_image_callback_sogou=None,
unlock_callback_weixin=None,
identify_image_callback_we... | def get_gzh_article_by_history(self, keyword=None, url=None,
unlock_callback_sogou=None,
identify_image_callback_sogou=None,
unlock_callback_weixin=None,
identify_image_callback_we... | [
"从",
"公众号的最近10条群发页面",
"提取公众号信息",
"和",
"文章列表信息"
] | Chyroc/WechatSogou | python | https://github.com/Chyroc/WechatSogou/blob/2e0e9886f555fd8bcfc7ae9718ced6ce955cd24a/wechatsogou/api.py#L371-L448 | [
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train | WechatSogouAPI.get_gzh_article_by_hot | 获取 首页热门文章
Parameters
----------
hot_index : WechatSogouConst.hot_index
首页热门文章的分类（常量）：WechatSogouConst.hot_index.xxx
page : int
页数
Returns
-------
list[dict]
{
'gzh': {
'headimage': str, # 公... | wechatsogou/api.py | def get_gzh_article_by_hot(self, hot_index, page=1, unlock_callback=None, identify_image_callback=None):
"""获取 首页热门文章
Parameters
----------
hot_index : WechatSogouConst.hot_index
首页热门文章的分类（常量）：WechatSogouConst.hot_index.xxx
page : int
页数
Returns
... | def get_gzh_article_by_hot(self, hot_index, page=1, unlock_callback=None, identify_image_callback=None):
"""获取 首页热门文章
Parameters
----------
hot_index : WechatSogouConst.hot_index
首页热门文章的分类（常量）：WechatSogouConst.hot_index.xxx
page : int
页数
Returns
... | [
"获取",
"首页热门文章"
] | Chyroc/WechatSogou | python | https://github.com/Chyroc/WechatSogou/blob/2e0e9886f555fd8bcfc7ae9718ced6ce955cd24a/wechatsogou/api.py#L450-L489 | [
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train | WechatSogouAPI.get_article_content | 获取文章原文，避免临时链接失效
Parameters
----------
url : str or unicode
原文链接，临时链接
raw : bool
True: 返回原始html
False: 返回处理后的html
del_qqmusic: bool
True:微信原文中有插入的qq音乐，则删除
False:微信源文中有插入的qq音乐，则保留
del_mpvoice: bool
Tru... | wechatsogou/api.py | def get_article_content(self, url, del_qqmusic=True, del_mpvoice=True, unlock_callback=None,
identify_image_callback=None, hosting_callback=None, raw=False):
"""获取文章原文，避免临时链接失效
Parameters
----------
url : str or unicode
原文链接，临时链接
raw : boo... | def get_article_content(self, url, del_qqmusic=True, del_mpvoice=True, unlock_callback=None,
identify_image_callback=None, hosting_callback=None, raw=False):
"""获取文章原文，避免临时链接失效
Parameters
----------
url : str or unicode
原文链接，临时链接
raw : boo... | [
"获取文章原文，避免临时链接失效"
] | Chyroc/WechatSogou | python | https://github.com/Chyroc/WechatSogou/blob/2e0e9886f555fd8bcfc7ae9718ced6ce955cd24a/wechatsogou/api.py#L491-L541 | [
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train | WechatSogouAPI.get_sugg | 获取微信搜狗搜索关键词联想
Parameters
----------
keyword : str or unicode
关键词
Returns
-------
list[str]
联想关键词列表
Raises
------
WechatSogouRequestsException | wechatsogou/api.py | def get_sugg(self, keyword):
"""获取微信搜狗搜索关键词联想
Parameters
----------
keyword : str or unicode
关键词
Returns
-------
list[str]
联想关键词列表
Raises
------
WechatSogouRequestsException
"""
url = 'http://w.sug... | def get_sugg(self, keyword):
"""获取微信搜狗搜索关键词联想
Parameters
----------
keyword : str or unicode
关键词
Returns
-------
list[str]
联想关键词列表
Raises
------
WechatSogouRequestsException
"""
url = 'http://w.sug... | [
"获取微信搜狗搜索关键词联想"
] | Chyroc/WechatSogou | python | https://github.com/Chyroc/WechatSogou/blob/2e0e9886f555fd8bcfc7ae9718ced6ce955cd24a/wechatsogou/api.py#L543-L567 | [
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... | 2e0e9886f555fd8bcfc7ae9718ced6ce955cd24a |
train | unlock_sogou_callback_example | 手动打码解锁
Parameters
----------
url : str or unicode
验证码页面 之前的 url
req : requests.sessions.Session
requests.Session() 供调用解锁
resp : requests.models.Response
requests 访问页面返回的，已经跳转了
img : bytes
验证码图片二进制数据
identify_image_callback : callable
处理验证码函数，输入验证码二进制数... | wechatsogou/identify_image.py | def unlock_sogou_callback_example(url, req, resp, img, identify_image_callback):
"""手动打码解锁
Parameters
----------
url : str or unicode
验证码页面 之前的 url
req : requests.sessions.Session
requests.Session() 供调用解锁
resp : requests.models.Response
requests 访问页面返回的，已经跳转了
img : b... | def unlock_sogou_callback_example(url, req, resp, img, identify_image_callback):
"""手动打码解锁
Parameters
----------
url : str or unicode
验证码页面 之前的 url
req : requests.sessions.Session
requests.Session() 供调用解锁
resp : requests.models.Response
requests 访问页面返回的，已经跳转了
img : b... | [
"手动打码解锁"
] | Chyroc/WechatSogou | python | https://github.com/Chyroc/WechatSogou/blob/2e0e9886f555fd8bcfc7ae9718ced6ce955cd24a/wechatsogou/identify_image.py#L34-L76 | [
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train | unlock_weixin_callback_example | 手动打码解锁
Parameters
----------
url : str or unicode
验证码页面 之前的 url
req : requests.sessions.Session
requests.Session() 供调用解锁
resp : requests.models.Response
requests 访问页面返回的，已经跳转了
img : bytes
验证码图片二进制数据
identify_image_callback : callable
处理验证码函数，输入验证码二进制数... | wechatsogou/identify_image.py | def unlock_weixin_callback_example(url, req, resp, img, identify_image_callback):
"""手动打码解锁
Parameters
----------
url : str or unicode
验证码页面 之前的 url
req : requests.sessions.Session
requests.Session() 供调用解锁
resp : requests.models.Response
requests 访问页面返回的，已经跳转了
img : ... | def unlock_weixin_callback_example(url, req, resp, img, identify_image_callback):
"""手动打码解锁
Parameters
----------
url : str or unicode
验证码页面 之前的 url
req : requests.sessions.Session
requests.Session() 供调用解锁
resp : requests.models.Response
requests 访问页面返回的，已经跳转了
img : ... | [
"手动打码解锁"
] | Chyroc/WechatSogou | python | https://github.com/Chyroc/WechatSogou/blob/2e0e9886f555fd8bcfc7ae9718ced6ce955cd24a/wechatsogou/identify_image.py#L79-L121 | [
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train | WechatSogouRequest.gen_search_article_url | 拼接搜索 文章 URL
Parameters
----------
keyword : str or unicode
搜索文字
page : int, optional
页数 the default is 1
timesn : WechatSogouConst.search_article_time
时间 anytime 没有限制 / day 一天 / week 一周 / month 一月 / year 一年 / specific 自定
默认是 anytim... | wechatsogou/request.py | def gen_search_article_url(keyword, page=1, timesn=WechatSogouConst.search_article_time.anytime,
article_type=WechatSogouConst.search_article_type.all, ft=None, et=None):
"""拼接搜索 文章 URL
Parameters
----------
keyword : str or unicode
搜索文字
... | def gen_search_article_url(keyword, page=1, timesn=WechatSogouConst.search_article_time.anytime,
article_type=WechatSogouConst.search_article_type.all, ft=None, et=None):
"""拼接搜索 文章 URL
Parameters
----------
keyword : str or unicode
搜索文字
... | [
"拼接搜索",
"文章",
"URL"
] | Chyroc/WechatSogou | python | https://github.com/Chyroc/WechatSogou/blob/2e0e9886f555fd8bcfc7ae9718ced6ce955cd24a/wechatsogou/request.py#L17-L86 | [
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train | WechatSogouRequest.gen_search_gzh_url | 拼接搜索 公众号 URL
Parameters
----------
keyword : str or unicode
搜索文字
page : int, optional
页数 the default is 1
Returns
-------
str
search_gzh_url | wechatsogou/request.py | def gen_search_gzh_url(keyword, page=1):
"""拼接搜索 公众号 URL
Parameters
----------
keyword : str or unicode
搜索文字
page : int, optional
页数 the default is 1
Returns
-------
str
search_gzh_url
"""
assert isinst... | def gen_search_gzh_url(keyword, page=1):
"""拼接搜索 公众号 URL
Parameters
----------
keyword : str or unicode
搜索文字
page : int, optional
页数 the default is 1
Returns
-------
str
search_gzh_url
"""
assert isinst... | [
"拼接搜索",
"公众号",
"URL"
] | Chyroc/WechatSogou | python | https://github.com/Chyroc/WechatSogou/blob/2e0e9886f555fd8bcfc7ae9718ced6ce955cd24a/wechatsogou/request.py#L89-L112 | [
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train | WechatSogouRequest.gen_hot_url | 拼接 首页热门文章 URL
Parameters
----------
hot_index : WechatSogouConst.hot_index
首页热门文章的分类（常量）：WechatSogouConst.hot_index.xxx
page : int
页数
Returns
-------
str
热门文章分类的url | wechatsogou/request.py | def gen_hot_url(hot_index, page=1):
"""拼接 首页热门文章 URL
Parameters
----------
hot_index : WechatSogouConst.hot_index
首页热门文章的分类（常量）：WechatSogouConst.hot_index.xxx
page : int
页数
Returns
-------
str
热门文章分类的url
"""
... | def gen_hot_url(hot_index, page=1):
"""拼接 首页热门文章 URL
Parameters
----------
hot_index : WechatSogouConst.hot_index
首页热门文章的分类（常量）：WechatSogouConst.hot_index.xxx
page : int
页数
Returns
-------
str
热门文章分类的url
"""
... | [
"拼接",
"首页热门文章",
"URL"
] | Chyroc/WechatSogou | python | https://github.com/Chyroc/WechatSogou/blob/2e0e9886f555fd8bcfc7ae9718ced6ce955cd24a/wechatsogou/request.py#L115-L158 | [
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train | get_first_of_element | 抽取lxml.etree库中elem对象中文字
Args:
element: lxml.etree.Element
sub: str
Returns:
elem中文字 | wechatsogou/tools.py | def get_first_of_element(element, sub, contype=None):
"""抽取lxml.etree库中elem对象中文字
Args:
element: lxml.etree.Element
sub: str
Returns:
elem中文字
"""
content = element.xpath(sub)
return list_or_empty(content, contype) | def get_first_of_element(element, sub, contype=None):
"""抽取lxml.etree库中elem对象中文字
Args:
element: lxml.etree.Element
sub: str
Returns:
elem中文字
"""
content = element.xpath(sub)
return list_or_empty(content, contype) | [
"抽取lxml",
".",
"etree库中elem对象中文字"
] | Chyroc/WechatSogou | python | https://github.com/Chyroc/WechatSogou/blob/2e0e9886f555fd8bcfc7ae9718ced6ce955cd24a/wechatsogou/tools.py#L46-L57 | [
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train | get_encoding_from_reponse | 获取requests库get或post返回的对象编码
Args:
r: requests库get或post返回的对象
Returns:
对象编码 | wechatsogou/tools.py | def get_encoding_from_reponse(r):
"""获取requests库get或post返回的对象编码
Args:
r: requests库get或post返回的对象
Returns:
对象编码
"""
encoding = requests.utils.get_encodings_from_content(r.text)
return encoding[0] if encoding else requests.utils.get_encoding_from_headers(r.headers) | def get_encoding_from_reponse(r):
"""获取requests库get或post返回的对象编码
Args:
r: requests库get或post返回的对象
Returns:
对象编码
"""
encoding = requests.utils.get_encodings_from_content(r.text)
return encoding[0] if encoding else requests.utils.get_encoding_from_headers(r.headers) | [
"获取requests库get或post返回的对象编码"
] | Chyroc/WechatSogou | python | https://github.com/Chyroc/WechatSogou/blob/2e0e9886f555fd8bcfc7ae9718ced6ce955cd24a/wechatsogou/tools.py#L60-L70 | [
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train | _replace_str_html | 替换html‘"’等转义内容为正常内容
Args:
s: 文字内容
Returns:
s: 处理反转义后的文字 | wechatsogou/tools.py | def _replace_str_html(s):
"""替换html‘"’等转义内容为正常内容
Args:
s: 文字内容
Returns:
s: 处理反转义后的文字
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html_str_list = [
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('"', '"'),
('&', '&'),
('¥', '¥'),
('amp;', ''),
('<', '<'),
('>', '>'),
... | def _replace_str_html(s):
"""替换html‘"’等转义内容为正常内容
Args:
s: 文字内容
Returns:
s: 处理反转义后的文字
"""
html_str_list = [
(''', '\''),
('"', '"'),
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('¥', '¥'),
('amp;', ''),
('<', '<'),
('>', '>'),
... | [
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train | WechatSogouStructuring.get_gzh_by_search | 从搜索公众号获得的文本 提取公众号信息
Parameters
----------
text : str or unicode
搜索公众号获得的文本
Returns
-------
list[dict]
{
'open_id': '', # 微信号唯一ID
'profile_url': '', # 最近10条群发页链接
'headimage': '', # 头像
... | wechatsogou/structuring.py | def get_gzh_by_search(text):
"""从搜索公众号获得的文本 提取公众号信息
Parameters
----------
text : str or unicode
搜索公众号获得的文本
Returns
-------
list[dict]
{
'open_id': '', # 微信号唯一ID
'profile_url': '', # 最近10条群发页链接
... | def get_gzh_by_search(text):
"""从搜索公众号获得的文本 提取公众号信息
Parameters
----------
text : str or unicode
搜索公众号获得的文本
Returns
-------
list[dict]
{
'open_id': '', # 微信号唯一ID
'profile_url': '', # 最近10条群发页链接
... | [
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] | Chyroc/WechatSogou | python | https://github.com/Chyroc/WechatSogou/blob/2e0e9886f555fd8bcfc7ae9718ced6ce955cd24a/wechatsogou/structuring.py#L46-L104 | [
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train | WechatSogouStructuring.get_article_by_search | 从搜索文章获得的文本 提取章列表信息
Parameters
----------
text : str or unicode
搜索文章获得的文本
Returns
-------
list[dict]
{
'article': {
'title': '', # 文章标题
'url': '', # 文章链接
'imgs': '', # ... | wechatsogou/structuring.py | def get_article_by_search(text):
"""从搜索文章获得的文本 提取章列表信息
Parameters
----------
text : str or unicode
搜索文章获得的文本
Returns
-------
list[dict]
{
'article': {
'title': '', # 文章标题
'url': '',... | def get_article_by_search(text):
"""从搜索文章获得的文本 提取章列表信息
Parameters
----------
text : str or unicode
搜索文章获得的文本
Returns
-------
list[dict]
{
'article': {
'title': '', # 文章标题
'url': '',... | [
"从搜索文章获得的文本",
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] | Chyroc/WechatSogou | python | https://github.com/Chyroc/WechatSogou/blob/2e0e9886f555fd8bcfc7ae9718ced6ce955cd24a/wechatsogou/structuring.py#L136-L215 | [
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train | WechatSogouStructuring.get_gzh_info_by_history | 从 历史消息页的文本 提取公众号信息
Parameters
----------
text : str or unicode
历史消息页的文本
Returns
-------
dict
{
'wechat_name': '', # 名称
'wechat_id': '', # 微信id
'introduction': '', # 描述
'authentica... | wechatsogou/structuring.py | def get_gzh_info_by_history(text):
"""从 历史消息页的文本 提取公众号信息
Parameters
----------
text : str or unicode
历史消息页的文本
Returns
-------
dict
{
'wechat_name': '', # 名称
'wechat_id': '', # 微信id
'introd... | def get_gzh_info_by_history(text):
"""从 历史消息页的文本 提取公众号信息
Parameters
----------
text : str or unicode
历史消息页的文本
Returns
-------
dict
{
'wechat_name': '', # 名称
'wechat_id': '', # 微信id
'introd... | [
"从",
"历史消息页的文本",
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] | Chyroc/WechatSogou | python | https://github.com/Chyroc/WechatSogou/blob/2e0e9886f555fd8bcfc7ae9718ced6ce955cd24a/wechatsogou/structuring.py#L218-L253 | [
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train | WechatSogouStructuring.get_article_by_history_json | 从 历史消息页的文本 提取文章列表信息
Parameters
----------
text : str or unicode
历史消息页的文本
article_json : dict
历史消息页的文本 提取出来的文章json dict
Returns
-------
list[dict]
{
'send_id': '', # 群发id，注意不唯一，因为同一次群发多个消息，而群发id一致
... | wechatsogou/structuring.py | def get_article_by_history_json(text, article_json=None):
"""从 历史消息页的文本 提取文章列表信息
Parameters
----------
text : str or unicode
历史消息页的文本
article_json : dict
历史消息页的文本 提取出来的文章json dict
Returns
-------
list[dict]
{
... | def get_article_by_history_json(text, article_json=None):
"""从 历史消息页的文本 提取文章列表信息
Parameters
----------
text : str or unicode
历史消息页的文本
article_json : dict
历史消息页的文本 提取出来的文章json dict
Returns
-------
list[dict]
{
... | [
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train | WechatSogouStructuring.get_gzh_article_by_hot | 从 首页热门搜索 提取公众号信息 和 文章列表信息
Parameters
----------
text : str or unicode
首页热门搜索 页 中 某一页 的文本
Returns
-------
list[dict]
{
'gzh': {
'headimage': str, # 公众号头像
'wechat_name': str, # 公众号名称
... | wechatsogou/structuring.py | def get_gzh_article_by_hot(text):
"""从 首页热门搜索 提取公众号信息 和 文章列表信息
Parameters
----------
text : str or unicode
首页热门搜索 页 中 某一页 的文本
Returns
-------
list[dict]
{
'gzh': {
'headimage': str, # 公众号头像
... | def get_gzh_article_by_hot(text):
"""从 首页热门搜索 提取公众号信息 和 文章列表信息
Parameters
----------
text : str or unicode
首页热门搜索 页 中 某一页 的文本
Returns
-------
list[dict]
{
'gzh': {
'headimage': str, # 公众号头像
... | [
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"首页热门搜索",
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] | Chyroc/WechatSogou | python | https://github.com/Chyroc/WechatSogou/blob/2e0e9886f555fd8bcfc7ae9718ced6ce955cd24a/wechatsogou/structuring.py#L381-L441 | [
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train | WechatSogouStructuring.get_article_detail | 根据微信文章的临时链接获取明细
1. 获取文本中所有的图片链接列表
2. 获取微信文章的html内容页面(去除标题等信息)
Parameters
----------
text : str or unicode
一篇微信文章的文本
del_qqmusic: bool
删除文章中的qq音乐
del_voice: bool
删除文章中的语音内容
Returns
-------
dict
... | wechatsogou/structuring.py | def get_article_detail(text, del_qqmusic=True, del_voice=True):
"""根据微信文章的临时链接获取明细
1. 获取文本中所有的图片链接列表
2. 获取微信文章的html内容页面(去除标题等信息)
Parameters
----------
text : str or unicode
一篇微信文章的文本
del_qqmusic: bool
删除文章中的qq音乐
del_voice: bool
... | def get_article_detail(text, del_qqmusic=True, del_voice=True):
"""根据微信文章的临时链接获取明细
1. 获取文本中所有的图片链接列表
2. 获取微信文章的html内容页面(去除标题等信息)
Parameters
----------
text : str or unicode
一篇微信文章的文本
del_qqmusic: bool
删除文章中的qq音乐
del_voice: bool
... | [
"根据微信文章的临时链接获取明细"
] | Chyroc/WechatSogou | python | https://github.com/Chyroc/WechatSogou/blob/2e0e9886f555fd8bcfc7ae9718ced6ce955cd24a/wechatsogou/structuring.py#L444-L527 | [
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train | _decode_image | Reads and decodes an image from a file object as a Numpy array.
The SUN dataset contains images in several formats (despite the fact that
all of them have .jpg extension). Some of them are:
- BMP (RGB)
- PNG (grayscale, RGBA, RGB interlaced)
- JPEG (RGB)
- GIF (1-frame RGB)
Since TFDS assumes tha... | tensorflow_datasets/image/sun.py | def _decode_image(fobj, session, filename):
"""Reads and decodes an image from a file object as a Numpy array.
The SUN dataset contains images in several formats (despite the fact that
all of them have .jpg extension). Some of them are:
- BMP (RGB)
- PNG (grayscale, RGBA, RGB interlaced)
- JPEG (RGB)... | def _decode_image(fobj, session, filename):
"""Reads and decodes an image from a file object as a Numpy array.
The SUN dataset contains images in several formats (despite the fact that
all of them have .jpg extension). Some of them are:
- BMP (RGB)
- PNG (grayscale, RGBA, RGB interlaced)
- JPEG (RGB)... | [
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train | _process_image_file | Process image files from the dataset. | tensorflow_datasets/image/sun.py | def _process_image_file(fobj, session, filename):
"""Process image files from the dataset."""
# We need to read the image files and convert them to JPEG, since some files
# actually contain GIF, PNG or BMP data (despite having a .jpg extension) and
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i... | def _process_image_file(fobj, session, filename):
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# We need to read the image files and convert them to JPEG, since some files
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train | Sun397._generate_examples | Yields examples. | tensorflow_datasets/image/sun.py | def _generate_examples(self, archive):
"""Yields examples."""
prefix_len = len("SUN397")
with tf.Graph().as_default():
with utils.nogpu_session() as sess:
for filepath, fobj in archive:
if (filepath.endswith(".jpg") and
filepath not in _SUN397_IGNORE_IMAGES):
... | def _generate_examples(self, archive):
"""Yields examples."""
prefix_len = len("SUN397")
with tf.Graph().as_default():
with utils.nogpu_session() as sess:
for filepath, fobj in archive:
if (filepath.endswith(".jpg") and
filepath not in _SUN397_IGNORE_IMAGES):
... | [
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train | _parse_parallel_sentences | Returns examples from parallel SGML or text files, which may be gzipped. | tensorflow_datasets/translate/wmt.py | def _parse_parallel_sentences(f1, f2):
"""Returns examples from parallel SGML or text files, which may be gzipped."""
def _parse_text(path):
"""Returns the sentences from a single text file, which may be gzipped."""
split_path = path.split(".")
if split_path[-1] == "gz":
lang = split_path[-2]
... | def _parse_parallel_sentences(f1, f2):
"""Returns examples from parallel SGML or text files, which may be gzipped."""
def _parse_text(path):
"""Returns the sentences from a single text file, which may be gzipped."""
split_path = path.split(".")
if split_path[-1] == "gz":
lang = split_path[-2]
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train | _parse_tmx | Generates examples from TMX file. | tensorflow_datasets/translate/wmt.py | def _parse_tmx(path):
"""Generates examples from TMX file."""
def _get_tuv_lang(tuv):
for k, v in tuv.items():
if k.endswith("}lang"):
return v
raise AssertionError("Language not found in `tuv` attributes.")
def _get_tuv_seg(tuv):
segs = tuv.findall("seg")
assert len(segs) == 1, "In... | def _parse_tmx(path):
"""Generates examples from TMX file."""
def _get_tuv_lang(tuv):
for k, v in tuv.items():
if k.endswith("}lang"):
return v
raise AssertionError("Language not found in `tuv` attributes.")
def _get_tuv_seg(tuv):
segs = tuv.findall("seg")
assert len(segs) == 1, "In... | [
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train | _parse_tsv | Generates examples from TSV file. | tensorflow_datasets/translate/wmt.py | def _parse_tsv(path, language_pair=None):
"""Generates examples from TSV file."""
if language_pair is None:
lang_match = re.match(r".*\.([a-z][a-z])-([a-z][a-z])\.tsv", path)
assert lang_match is not None, "Invalid TSV filename: %s" % path
l1, l2 = lang_match.groups()
else:
l1, l2 = language_pair
... | def _parse_tsv(path, language_pair=None):
"""Generates examples from TSV file."""
if language_pair is None:
lang_match = re.match(r".*\.([a-z][a-z])-([a-z][a-z])\.tsv", path)
assert lang_match is not None, "Invalid TSV filename: %s" % path
l1, l2 = lang_match.groups()
else:
l1, l2 = language_pair
... | [
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train | _parse_wikiheadlines | Generates examples from Wikiheadlines dataset file. | tensorflow_datasets/translate/wmt.py | def _parse_wikiheadlines(path):
"""Generates examples from Wikiheadlines dataset file."""
lang_match = re.match(r".*\.([a-z][a-z])-([a-z][a-z])$", path)
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l1, l2 = lang_match.groups()
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"""Generates examples from Wikiheadlines dataset file."""
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train | _parse_czeng | Generates examples from CzEng v1.6, with optional filtering for v1.7. | tensorflow_datasets/translate/wmt.py | def _parse_czeng(*paths, **kwargs):
"""Generates examples from CzEng v1.6, with optional filtering for v1.7."""
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train | SubDataset._inject_language | Injects languages into (potentially) template strings. | tensorflow_datasets/translate/wmt.py | def _inject_language(self, src, strings):
"""Injects languages into (potentially) template strings."""
if src not in self.sources:
raise ValueError("Invalid source for '{0}': {1}".format(self.name, src))
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train | WmtTranslate.subsets | Subsets that make up each split of the dataset for the language pair. | tensorflow_datasets/translate/wmt.py | def subsets(self):
"""Subsets that make up each split of the dataset for the language pair."""
source, target = self.builder_config.language_pair
filtered_subsets = {}
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train | WmtTranslate._generate_examples | Returns the examples in the raw (text) form. | tensorflow_datasets/translate/wmt.py | def _generate_examples(self, split_subsets, extraction_map):
"""Returns the examples in the raw (text) form."""
source, _ = self.builder_config.language_pair
def _get_local_paths(ds, extract_dirs):
rel_paths = ds.get_path(source)
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extract_dirs = extract_dirs * ... | def _generate_examples(self, split_subsets, extraction_map):
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train | builder | Fetches a `tfds.core.DatasetBuilder` by string name.
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name: `str`, the registered name of the `DatasetBuilder` (the snake case
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name: `str`, the registered name of the `DatasetBuilder` (the snake case
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Args:
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train | load | Loads the named dataset into a `tf.data.Dataset`.
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`load` is a convenience method that fetches the `tfds.core.DatasetBuilder` by
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as_supervised=False,
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builder_kwargs=None,
download_and_prepare_kwargs=None,
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train | _dataset_name_and_kwargs_from_name_str | Extract kwargs from name str. | tensorflow_datasets/core/registered.py | def _dataset_name_and_kwargs_from_name_str(name_str):
"""Extract kwargs from name str."""
res = _NAME_REG.match(name_str)
if not res:
raise ValueError(_NAME_STR_ERR.format(name_str))
name = res.group("dataset_name")
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try:
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"""Extract kwargs from name str."""
res = _NAME_REG.match(name_str)
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raise ValueError(_NAME_STR_ERR.format(name_str))
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train | _cast_to_pod | Try cast to int, float, bool, str, in that order. | tensorflow_datasets/core/registered.py | def _cast_to_pod(val):
"""Try cast to int, float, bool, str, in that order."""
bools = {"True": True, "False": False}
if val in bools:
return bools[val]
try:
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bools = {"True": True, "False": False}
if val in bools:
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try:
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try:
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train | _try_import | Try importing a module, with an informative error message on failure. | tensorflow_datasets/core/lazy_imports.py | def _try_import(module_name):
"""Try importing a module, with an informative error message on failure."""
try:
mod = importlib.import_module(module_name)
return mod
except ImportError:
err_msg = ("Tried importing %s but failed. See setup.py extras_require. "
"The dataset you are trying ... | def _try_import(module_name):
"""Try importing a module, with an informative error message on failure."""
try:
mod = importlib.import_module(module_name)
return mod
except ImportError:
err_msg = ("Tried importing %s but failed. See setup.py extras_require. "
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train | np_to_list | Returns list from list, tuple or ndarray. | tensorflow_datasets/core/features/sequence_feature.py | def np_to_list(elem):
"""Returns list from list, tuple or ndarray."""
if isinstance(elem, list):
return elem
elif isinstance(elem, tuple):
return list(elem)
elif isinstance(elem, np.ndarray):
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train | _transpose_dict_list | Transpose a nested dict[list] into a list[nested dict]. | tensorflow_datasets/core/features/sequence_feature.py | def _transpose_dict_list(dict_list):
"""Transpose a nested dict[list] into a list[nested dict]."""
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length = {'value... | def _transpose_dict_list(dict_list):
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dict_list = utils.map_nested(np_to_list, dict_list, dict_only=True)
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train | SequenceDict.get_tensor_info | See base class for details. | tensorflow_datasets/core/features/sequence_feature.py | def get_tensor_info(self):
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def add_length_dim(tensor_info):
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train | SequenceDict.get_serialized_info | See base class for details. | tensorflow_datasets/core/features/sequence_feature.py | def get_serialized_info(self):
"""See base class for details."""
# Add the additional length dimension to every serialized features
def add_length_dim(serialized_info):
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Args:
serialized_info: One of tf.io.FixedLenFeature, tf.io.VarLen... | def get_serialized_info(self):
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"""Add the length dimension to the serialized_info.
Args:
serialized_info: One of tf.io.FixedLenFeature, tf.io.VarLen... | [
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] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/features/sequence_feature.py#L103-L137 | [
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train | MNIST._split_generators | Returns SplitGenerators. | tensorflow_datasets/image/mnist.py | def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# Download the full MNIST Database
filenames = {
"train_data": _MNIST_TRAIN_DATA_FILENAME,
"train_labels": _MNIST_TRAIN_LABELS_FILENAME,
"test_data": _MNIST_TEST_DATA_FILENAME,
"test_labels": _MNIST_TEST_... | def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# Download the full MNIST Database
filenames = {
"train_data": _MNIST_TRAIN_DATA_FILENAME,
"train_labels": _MNIST_TRAIN_LABELS_FILENAME,
"test_data": _MNIST_TEST_DATA_FILENAME,
"test_labels": _MNIST_TEST_... | [
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] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/image/mnist.py#L113-L144 | [
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train | MNIST._generate_examples | Generate MNIST examples as dicts.
Args:
num_examples (int): The number of example.
data_path (str): Path to the data files
label_path (str): Path to the labels
Yields:
Generator yielding the next examples | tensorflow_datasets/image/mnist.py | def _generate_examples(self, num_examples, data_path, label_path):
"""Generate MNIST examples as dicts.
Args:
num_examples (int): The number of example.
data_path (str): Path to the data files
label_path (str): Path to the labels
Yields:
Generator yielding the next examples
"""... | def _generate_examples(self, num_examples, data_path, label_path):
"""Generate MNIST examples as dicts.
Args:
num_examples (int): The number of example.
data_path (str): Path to the data files
label_path (str): Path to the labels
Yields:
Generator yielding the next examples
"""... | [
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train | OxfordFlowers102._split_generators | Returns SplitGenerators. | tensorflow_datasets/image/oxford_flowers102.py | def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# Download images and annotations that come in separate archives.
# Note, that the extension of archives is .tar.gz even though the actual
# archives format is uncompressed tar.
dl_paths = dl_manager.download_and_extract({
... | def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# Download images and annotations that come in separate archives.
# Note, that the extension of archives is .tar.gz even though the actual
# archives format is uncompressed tar.
dl_paths = dl_manager.download_and_extract({
... | [
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] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/image/oxford_flowers102.py#L70-L102 | [
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... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | OxfordFlowers102._generate_examples | Yields examples. | tensorflow_datasets/image/oxford_flowers102.py | def _generate_examples(self, images_dir_path, labels_path, setid_path,
split_name):
"""Yields examples."""
with tf.io.gfile.GFile(labels_path, "rb") as f:
labels = tfds.core.lazy_imports.scipy.io.loadmat(f)["labels"][0]
with tf.io.gfile.GFile(setid_path, "rb") as f:
exam... | def _generate_examples(self, images_dir_path, labels_path, setid_path,
split_name):
"""Yields examples."""
with tf.io.gfile.GFile(labels_path, "rb") as f:
labels = tfds.core.lazy_imports.scipy.io.loadmat(f)["labels"][0]
with tf.io.gfile.GFile(setid_path, "rb") as f:
exam... | [
"Yields",
"examples",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/image/oxford_flowers102.py#L104-L118 | [
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"... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
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