partition stringclasses 3 values | func_name stringlengths 1 134 | docstring stringlengths 1 46.9k | path stringlengths 4 223 | original_string stringlengths 75 104k | code stringlengths 75 104k | docstring_tokens listlengths 1 1.97k | repo stringlengths 7 55 | language stringclasses 1 value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
|---|---|---|---|---|---|---|---|---|---|---|---|
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 take **kwargs
param.
If expected_args, expect_starargs, or expect_kwargs is Argument.ignore,
then the checks related to that argument will not occur.
Example usage:
callable_check(
f,
[Argument('a'), Argument('b', 1)],
expect_starargs=True,
expect_kwargs=Argument.ignore
) | 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.
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 take **kwargs
param.
If expected_args, expect_starargs, or expect_kwargs is Argument.ignore,
then the checks related to that argument will not occur.
Example usage:
callable_check(
f,
[Argument('a'), Argument('b', 1)],
expect_starargs=True,
expect_kwargs=Argument.ignore
)
"""
if not callable(callable_):
raise NotCallable(callable_)
expected_arg_list = list(
expected_args if expected_args is not Argument.ignore else []
)
args, starargs, kwargs = Argument.parse_argspec(callable_)
exc_args = callable_, args, starargs, kwargs
# Check the *args.
_expect_extra(
expect_starargs,
starargs,
UnexpectedStarargs,
NoStarargs,
exc_args,
)
# Check the **kwargs.
_expect_extra(
expect_kwargs,
kwargs,
UnexpectedKwargs,
NoKwargs,
exc_args,
)
if expected_args is Argument.ignore:
# Ignore the argument list checks.
return
if len(args) < len(expected_arg_list):
# One or more argument that we expected was not present.
raise NotEnoughArguments(
callable_,
args,
starargs,
kwargs,
[arg for arg in expected_arg_list if arg not in args],
)
elif len(args) > len(expected_arg_list):
raise TooManyArguments(
callable_, args, starargs, kwargs
)
# Empty argument that will not match with any actual arguments.
missing_arg = Argument(object(), object())
for expected, provided in zip_longest(expected_arg_list,
args,
fillvalue=missing_arg):
if not expected.matches(provided):
raise MismatchedArguments(
callable_, args, starargs, kwargs
) | 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.
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 take **kwargs
param.
If expected_args, expect_starargs, or expect_kwargs is Argument.ignore,
then the checks related to that argument will not occur.
Example usage:
callable_check(
f,
[Argument('a'), Argument('b', 1)],
expect_starargs=True,
expect_kwargs=Argument.ignore
)
"""
if not callable(callable_):
raise NotCallable(callable_)
expected_arg_list = list(
expected_args if expected_args is not Argument.ignore else []
)
args, starargs, kwargs = Argument.parse_argspec(callable_)
exc_args = callable_, args, starargs, kwargs
# Check the *args.
_expect_extra(
expect_starargs,
starargs,
UnexpectedStarargs,
NoStarargs,
exc_args,
)
# Check the **kwargs.
_expect_extra(
expect_kwargs,
kwargs,
UnexpectedKwargs,
NoKwargs,
exc_args,
)
if expected_args is Argument.ignore:
# Ignore the argument list checks.
return
if len(args) < len(expected_arg_list):
# One or more argument that we expected was not present.
raise NotEnoughArguments(
callable_,
args,
starargs,
kwargs,
[arg for arg in expected_arg_list if arg not in args],
)
elif len(args) > len(expected_arg_list):
raise TooManyArguments(
callable_, args, starargs, kwargs
)
# Empty argument that will not match with any actual arguments.
missing_arg = Argument(object(), object())
for expected, provided in zip_longest(expected_arg_list,
args,
fillvalue=missing_arg):
if not expected.matches(provided):
raise MismatchedArguments(
callable_, args, starargs, kwargs
) | [
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train | Argument.parse_argspec | Takes a callable and returns a tuple with the list of Argument objects,
the name of *args, and the name of **kwargs.
If *args or **kwargs is not present, it will be None.
This returns a namedtuple called Argspec that has three fields named:
args, starargs, and kwargs. | zipline/utils/argcheck.py | def parse_argspec(callable_):
"""
Takes a callable and returns a tuple with the list of Argument objects,
the name of *args, and the name of **kwargs.
If *args or **kwargs is not present, it will be None.
This returns a namedtuple called Argspec that has three fields named:
args, starargs, and kwargs.
"""
args, varargs, keywords, defaults = getargspec(callable_)
defaults = list(defaults or [])
if getattr(callable_, '__self__', None) is not None:
# This is a bound method, drop the self param.
args = args[1:]
first_default = len(args) - len(defaults)
return Argspec(
[Argument(arg, Argument.no_default
if n < first_default else defaults[n - first_default])
for n, arg in enumerate(args)],
varargs,
keywords,
) | def parse_argspec(callable_):
"""
Takes a callable and returns a tuple with the list of Argument objects,
the name of *args, and the name of **kwargs.
If *args or **kwargs is not present, it will be None.
This returns a namedtuple called Argspec that has three fields named:
args, starargs, and kwargs.
"""
args, varargs, keywords, defaults = getargspec(callable_)
defaults = list(defaults or [])
if getattr(callable_, '__self__', None) is not None:
# This is a bound method, drop the self param.
args = args[1:]
first_default = len(args) - len(defaults)
return Argspec(
[Argument(arg, Argument.no_default
if n < first_default else defaults[n - first_default])
for n, arg in enumerate(args)],
varargs,
keywords,
) | [
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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):
"""
An asset is restricted for all dts if it is in the static list.
"""
if isinstance(assets, Asset):
return assets in self._restricted_set
return pd.Series(
index=pd.Index(assets),
data=vectorized_is_element(assets, self._restricted_set)
) | def is_restricted(self, assets, dt):
"""
An asset is restricted for all dts if it is in the static list.
"""
if isinstance(assets, Asset):
return assets in self._restricted_set
return pd.Series(
index=pd.Index(assets),
data=vectorized_is_element(assets, self._restricted_set)
) | [
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train | HistoricalRestrictions.is_restricted | Returns whether or not an asset or iterable of assets is restricted
on a dt. | zipline/finance/asset_restrictions.py | def is_restricted(self, assets, dt):
"""
Returns whether or not an asset or iterable of assets is restricted
on a dt.
"""
if isinstance(assets, Asset):
return self._is_restricted_for_asset(assets, dt)
is_restricted = partial(self._is_restricted_for_asset, dt=dt)
return pd.Series(
index=pd.Index(assets),
data=vectorize(is_restricted, otypes=[bool])(assets)
) | def is_restricted(self, assets, dt):
"""
Returns whether or not an asset or iterable of assets is restricted
on a dt.
"""
if isinstance(assets, Asset):
return self._is_restricted_for_asset(assets, dt)
is_restricted = partial(self._is_restricted_for_asset, dt=dt)
return pd.Series(
index=pd.Index(assets),
data=vectorize(is_restricted, otypes=[bool])(assets)
) | [
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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).
Returns
-------
int: The leftover cash from fractional shares after modifying each
position. | zipline/finance/ledger.py | def handle_splits(self, 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).
Returns
-------
int: The leftover cash from fractional shares after modifying each
position.
"""
total_leftover_cash = 0
for asset, ratio in splits:
if asset in self.positions:
self._dirty_stats = True
# Make the position object handle the split. It returns the
# leftover cash from a fractional share, if there is any.
position = self.positions[asset]
leftover_cash = position.handle_split(asset, ratio)
total_leftover_cash += leftover_cash
return total_leftover_cash | def handle_splits(self, 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).
Returns
-------
int: The leftover cash from fractional shares after modifying each
position.
"""
total_leftover_cash = 0
for asset, ratio in splits:
if asset in self.positions:
self._dirty_stats = True
# Make the position object handle the split. It returns the
# leftover cash from a fractional share, if there is any.
position = self.positions[asset]
leftover_cash = position.handle_split(asset, ratio)
total_leftover_cash += leftover_cash
return total_leftover_cash | [
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train | PositionTracker.earn_dividends | Given a list of dividends whose ex_dates are all the next trading
day, calculate and store the cash and/or stock payments to be paid on
each dividend's pay date.
Parameters
----------
cash_dividends : iterable of (asset, amount, pay_date) namedtuples
stock_dividends: iterable of (asset, payment_asset, ratio, pay_date)
namedtuples. | zipline/finance/ledger.py | def earn_dividends(self, cash_dividends, stock_dividends):
"""Given a list of dividends whose ex_dates are all the next trading
day, calculate and store the cash and/or stock payments to be paid on
each dividend's pay date.
Parameters
----------
cash_dividends : iterable of (asset, amount, pay_date) namedtuples
stock_dividends: iterable of (asset, payment_asset, ratio, pay_date)
namedtuples.
"""
for cash_dividend in cash_dividends:
self._dirty_stats = True # only mark dirty if we pay a dividend
# Store the earned dividends so that they can be paid on the
# dividends' pay_dates.
div_owed = self.positions[cash_dividend.asset].earn_dividend(
cash_dividend,
)
try:
self._unpaid_dividends[cash_dividend.pay_date].append(div_owed)
except KeyError:
self._unpaid_dividends[cash_dividend.pay_date] = [div_owed]
for stock_dividend in stock_dividends:
self._dirty_stats = True # only mark dirty if we pay a dividend
div_owed = self.positions[
stock_dividend.asset
].earn_stock_dividend(stock_dividend)
try:
self._unpaid_stock_dividends[stock_dividend.pay_date].append(
div_owed,
)
except KeyError:
self._unpaid_stock_dividends[stock_dividend.pay_date] = [
div_owed,
] | def earn_dividends(self, cash_dividends, stock_dividends):
"""Given a list of dividends whose ex_dates are all the next trading
day, calculate and store the cash and/or stock payments to be paid on
each dividend's pay date.
Parameters
----------
cash_dividends : iterable of (asset, amount, pay_date) namedtuples
stock_dividends: iterable of (asset, payment_asset, ratio, pay_date)
namedtuples.
"""
for cash_dividend in cash_dividends:
self._dirty_stats = True # only mark dirty if we pay a dividend
# Store the earned dividends so that they can be paid on the
# dividends' pay_dates.
div_owed = self.positions[cash_dividend.asset].earn_dividend(
cash_dividend,
)
try:
self._unpaid_dividends[cash_dividend.pay_date].append(div_owed)
except KeyError:
self._unpaid_dividends[cash_dividend.pay_date] = [div_owed]
for stock_dividend in stock_dividends:
self._dirty_stats = True # only mark dirty if we pay a dividend
div_owed = self.positions[
stock_dividend.asset
].earn_stock_dividend(stock_dividend)
try:
self._unpaid_stock_dividends[stock_dividend.pay_date].append(
div_owed,
)
except KeyError:
self._unpaid_stock_dividends[stock_dividend.pay_date] = [
div_owed,
] | [
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train | PositionTracker.pay_dividends | Returns a cash payment based on the dividends that should be paid out
according to the accumulated bookkeeping of earned, unpaid, and stock
dividends. | zipline/finance/ledger.py | def pay_dividends(self, next_trading_day):
"""
Returns a cash payment based on the dividends that should be paid out
according to the accumulated bookkeeping of earned, unpaid, and stock
dividends.
"""
net_cash_payment = 0.0
try:
payments = self._unpaid_dividends[next_trading_day]
# Mark these dividends as paid by dropping them from our unpaid
del self._unpaid_dividends[next_trading_day]
except KeyError:
payments = []
# representing the fact that we're required to reimburse the owner of
# the stock for any dividends paid while borrowing.
for payment in payments:
net_cash_payment += payment['amount']
# Add stock for any stock dividends paid. Again, the values here may
# be negative in the case of short positions.
try:
stock_payments = self._unpaid_stock_dividends[next_trading_day]
except KeyError:
stock_payments = []
for stock_payment in stock_payments:
payment_asset = stock_payment['payment_asset']
share_count = stock_payment['share_count']
# note we create a Position for stock dividend if we don't
# already own the asset
if payment_asset in self.positions:
position = self.positions[payment_asset]
else:
position = self.positions[payment_asset] = Position(
payment_asset,
)
position.amount += share_count
return net_cash_payment | def pay_dividends(self, next_trading_day):
"""
Returns a cash payment based on the dividends that should be paid out
according to the accumulated bookkeeping of earned, unpaid, and stock
dividends.
"""
net_cash_payment = 0.0
try:
payments = self._unpaid_dividends[next_trading_day]
# Mark these dividends as paid by dropping them from our unpaid
del self._unpaid_dividends[next_trading_day]
except KeyError:
payments = []
# representing the fact that we're required to reimburse the owner of
# the stock for any dividends paid while borrowing.
for payment in payments:
net_cash_payment += payment['amount']
# Add stock for any stock dividends paid. Again, the values here may
# be negative in the case of short positions.
try:
stock_payments = self._unpaid_stock_dividends[next_trading_day]
except KeyError:
stock_payments = []
for stock_payment in stock_payments:
payment_asset = stock_payment['payment_asset']
share_count = stock_payment['share_count']
# note we create a Position for stock dividend if we don't
# already own the asset
if payment_asset in self.positions:
position = self.positions[payment_asset]
else:
position = self.positions[payment_asset] = Position(
payment_asset,
)
position.amount += share_count
return net_cash_payment | [
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train | PositionTracker.stats | The current status of the positions.
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. | zipline/finance/ledger.py | def stats(self):
"""The current status of the positions.
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.
"""
if self._dirty_stats:
calculate_position_tracker_stats(self.positions, self._stats)
self._dirty_stats = False
return self._stats | def stats(self):
"""The current status of the positions.
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.
"""
if self._dirty_stats:
calculate_position_tracker_stats(self.positions, self._stats)
self._dirty_stats = False
return self._stats | [
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"_stats"
] | 77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe |
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):
try:
old_price = self._payout_last_sale_prices[asset]
except KeyError:
self._payout_last_sale_prices[asset] = transaction.price
else:
position = self.position_tracker.positions[asset]
amount = position.amount
price = transaction.price
self._cash_flow(
self._calculate_payout(
asset.price_multiplier,
amount,
old_price,
price,
),
)
if amount + transaction.amount == 0:
del self._payout_last_sale_prices[asset]
else:
self._payout_last_sale_prices[asset] = price
else:
self._cash_flow(-(transaction.price * transaction.amount))
self.position_tracker.execute_transaction(transaction)
# we only ever want the dict form from now on
transaction_dict = transaction.to_dict()
try:
self._processed_transactions[transaction.dt].append(
transaction_dict,
)
except KeyError:
self._processed_transactions[transaction.dt] = [transaction_dict] | 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):
try:
old_price = self._payout_last_sale_prices[asset]
except KeyError:
self._payout_last_sale_prices[asset] = transaction.price
else:
position = self.position_tracker.positions[asset]
amount = position.amount
price = transaction.price
self._cash_flow(
self._calculate_payout(
asset.price_multiplier,
amount,
old_price,
price,
),
)
if amount + transaction.amount == 0:
del self._payout_last_sale_prices[asset]
else:
self._payout_last_sale_prices[asset] = price
else:
self._cash_flow(-(transaction.price * transaction.amount))
self.position_tracker.execute_transaction(transaction)
# we only ever want the dict form from now on
transaction_dict = transaction.to_dict()
try:
self._processed_transactions[transaction.dt].append(
transaction_dict,
)
except KeyError:
self._processed_transactions[transaction.dt] = [transaction_dict] | [
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] | quantopian/zipline | python | https://github.com/quantopian/zipline/blob/77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe/zipline/finance/ledger.py#L479-L523 | [
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train | Ledger.process_splits | Processes a list of splits by modifying any positions as needed.
Parameters
----------
splits: list[(Asset, float)]
A list of splits. Each split is a tuple of (asset, ratio). | zipline/finance/ledger.py | def process_splits(self, splits):
"""Processes a list of splits by modifying any positions as needed.
Parameters
----------
splits: list[(Asset, float)]
A list of splits. Each split is a tuple of (asset, ratio).
"""
leftover_cash = self.position_tracker.handle_splits(splits)
if leftover_cash > 0:
self._cash_flow(leftover_cash) | def process_splits(self, splits):
"""Processes a list of splits by modifying any positions as needed.
Parameters
----------
splits: list[(Asset, float)]
A list of splits. Each split is a tuple of (asset, ratio).
"""
leftover_cash = self.position_tracker.handle_splits(splits)
if leftover_cash > 0:
self._cash_flow(leftover_cash) | [
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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_modified[order.dt] = OrderedDict([
(order.id, order),
])
self._orders_by_id[order.id] = order
else:
self._orders_by_id[order.id] = dt_orders[order.id] = order
# to preserve the order of the orders by modified date
move_to_end(dt_orders, order.id, last=True)
move_to_end(self._orders_by_id, order.id, last=True) | 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_modified[order.dt] = OrderedDict([
(order.id, order),
])
self._orders_by_id[order.id] = order
else:
self._orders_by_id[order.id] = dt_orders[order.id] = order
# to preserve the order of the orders by modified date
move_to_end(dt_orders, order.id, last=True)
move_to_end(self._orders_by_id, order.id, last=True) | [
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] | quantopian/zipline | python | https://github.com/quantopian/zipline/blob/77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe/zipline/finance/ledger.py#L537-L557 | [
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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(asset, cost)
self._cash_flow(-cost) | 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(asset, cost)
self._cash_flow(-cost) | [
"Process",
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] | quantopian/zipline | python | https://github.com/quantopian/zipline/blob/77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe/zipline/finance/ledger.py#L559-L571 | [
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train | Ledger.process_dividends | Process dividends for the next session.
This will earn us any dividends whose ex-date is the next session as
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
well as paying out any dividends whose pay-date is the next session
"""
position_tracker = self.position_tracker
# Earn dividends whose ex_date is the next trading day. We need to
# check if we own any of these stocks so we know to pay them out when
# the pay date comes.
held_sids = set(position_tracker.positions)
if held_sids:
cash_dividends = adjustment_reader.get_dividends_with_ex_date(
held_sids,
next_session,
asset_finder
)
stock_dividends = (
adjustment_reader.get_stock_dividends_with_ex_date(
held_sids,
next_session,
asset_finder
)
)
# Earning a dividend just marks that we need to get paid out on
# the dividend's pay-date. This does not affect our cash yet.
position_tracker.earn_dividends(
cash_dividends,
stock_dividends,
)
# Pay out the dividends whose pay-date is the next session. This does
# affect out cash.
self._cash_flow(
position_tracker.pay_dividends(
next_session,
),
) | 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
well as paying out any dividends whose pay-date is the next session
"""
position_tracker = self.position_tracker
# Earn dividends whose ex_date is the next trading day. We need to
# check if we own any of these stocks so we know to pay them out when
# the pay date comes.
held_sids = set(position_tracker.positions)
if held_sids:
cash_dividends = adjustment_reader.get_dividends_with_ex_date(
held_sids,
next_session,
asset_finder
)
stock_dividends = (
adjustment_reader.get_stock_dividends_with_ex_date(
held_sids,
next_session,
asset_finder
)
)
# Earning a dividend just marks that we need to get paid out on
# the dividend's pay-date. This does not affect our cash yet.
position_tracker.earn_dividends(
cash_dividends,
stock_dividends,
)
# Pay out the dividends whose pay-date is the next session. This does
# affect out cash.
self._cash_flow(
position_tracker.pay_dividends(
next_session,
),
) | [
"Process",
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"."
] | quantopian/zipline | python | https://github.com/quantopian/zipline/blob/77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe/zipline/finance/ledger.py#L582-L621 | [
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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 transactions will be
returned.
Returns
-------
transactions : list[dict]
The transaction information. | 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,
or None is explicitly passed, all of the transactions will be
returned.
Returns
-------
transactions : list[dict]
The transaction information.
"""
if dt is None:
# flatten the by-day transactions
return [
txn
for by_day in itervalues(self._processed_transactions)
for txn in by_day
]
return self._processed_transactions.get(dt, []) | 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,
or None is explicitly passed, all of the transactions will be
returned.
Returns
-------
transactions : list[dict]
The transaction information.
"""
if dt is None:
# flatten the by-day transactions
return [
txn
for by_day in itervalues(self._processed_transactions)
for txn in by_day
]
return self._processed_transactions.get(dt, []) | [
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train | Ledger.orders | Retrieve the dict-form of all of the orders in a given bar or for
the whole simulation.
Parameters
----------
dt : pd.Timestamp or None, optional
The particular datetime to look up order for. If not passed, or
None is explicitly passed, all of the orders will be returned.
Returns
-------
orders : list[dict]
The order information. | zipline/finance/ledger.py | def orders(self, dt=None):
"""Retrieve the dict-form of all of the orders in a given bar or for
the whole simulation.
Parameters
----------
dt : pd.Timestamp or None, optional
The particular datetime to look up order for. If not passed, or
None is explicitly passed, all of the orders will be returned.
Returns
-------
orders : list[dict]
The order information.
"""
if dt is None:
# orders by id is already flattened
return [o.to_dict() for o in itervalues(self._orders_by_id)]
return [
o.to_dict()
for o in itervalues(self._orders_by_modified.get(dt, {}))
] | def orders(self, dt=None):
"""Retrieve the dict-form of all of the orders in a given bar or for
the whole simulation.
Parameters
----------
dt : pd.Timestamp or None, optional
The particular datetime to look up order for. If not passed, or
None is explicitly passed, all of the orders will be returned.
Returns
-------
orders : list[dict]
The order information.
"""
if dt is None:
# orders by id is already flattened
return [o.to_dict() for o in itervalues(self._orders_by_id)]
return [
o.to_dict()
for o in itervalues(self._orders_by_modified.get(dt, {}))
] | [
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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()
position_stats = pt.stats
portfolio.positions_value = position_value = (
position_stats.net_value
)
portfolio.positions_exposure = position_stats.net_exposure
self._cash_flow(self._get_payout_total(pt.positions))
start_value = portfolio.portfolio_value
# update the new starting value
portfolio.portfolio_value = end_value = portfolio.cash + position_value
pnl = end_value - start_value
if start_value != 0:
returns = pnl / start_value
else:
returns = 0.0
portfolio.pnl += pnl
portfolio.returns = (
(1 + portfolio.returns) *
(1 + returns) -
1
)
# the portfolio has been fully synced
self._dirty_portfolio = False | 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()
position_stats = pt.stats
portfolio.positions_value = position_value = (
position_stats.net_value
)
portfolio.positions_exposure = position_stats.net_exposure
self._cash_flow(self._get_payout_total(pt.positions))
start_value = portfolio.portfolio_value
# update the new starting value
portfolio.portfolio_value = end_value = portfolio.cash + position_value
pnl = end_value - start_value
if start_value != 0:
returns = pnl / start_value
else:
returns = 0.0
portfolio.pnl += pnl
portfolio.returns = (
(1 + portfolio.returns) *
(1 + returns) -
1
)
# the portfolio has been fully synced
self._dirty_portfolio = False | [
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] | quantopian/zipline | python | https://github.com/quantopian/zipline/blob/77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe/zipline/finance/ledger.py#L703-L740 | [
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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,
total_positions_value=not_overridden,
total_positions_exposure=not_overridden,
regt_equity=not_overridden,
regt_margin=not_overridden,
initial_margin_requirement=not_overridden,
maintenance_margin_requirement=not_overridden,
available_funds=not_overridden,
excess_liquidity=not_overridden,
cushion=not_overridden,
day_trades_remaining=not_overridden,
leverage=not_overridden,
net_leverage=not_overridden,
net_liquidation=not_overridden):
"""Override fields on ``self.account``.
"""
# mark that the portfolio is dirty to override the fields again
self._dirty_account = True
self._account_overrides = kwargs = {
k: v for k, v in locals().items() if v is not not_overridden
}
del kwargs['self'] | def override_account_fields(self,
settled_cash=not_overridden,
accrued_interest=not_overridden,
buying_power=not_overridden,
equity_with_loan=not_overridden,
total_positions_value=not_overridden,
total_positions_exposure=not_overridden,
regt_equity=not_overridden,
regt_margin=not_overridden,
initial_margin_requirement=not_overridden,
maintenance_margin_requirement=not_overridden,
available_funds=not_overridden,
excess_liquidity=not_overridden,
cushion=not_overridden,
day_trades_remaining=not_overridden,
leverage=not_overridden,
net_leverage=not_overridden,
net_liquidation=not_overridden):
"""Override fields on ``self.account``.
"""
# mark that the portfolio is dirty to override the fields again
self._dirty_account = True
self._account_overrides = kwargs = {
k: v for k, v in locals().items() if v is not not_overridden
}
del kwargs['self'] | [
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] | quantopian/zipline | python | https://github.com/quantopian/zipline/blob/77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe/zipline/finance/ledger.py#L766-L791 | [
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train | datashape_type_to_numpy | Given a datashape type, return the associated numpy type. Maps
datashape's DateTime type to numpy's `datetime64[ns]` dtype, since the
numpy datetime returned by datashape isn't supported by pipeline.
Parameters
----------
type_: datashape.coretypes.Type
The datashape type.
Returns
-------
type_ np.dtype
The numpy dtype. | zipline/pipeline/loaders/blaze/core.py | def datashape_type_to_numpy(type_):
"""
Given a datashape type, return the associated numpy type. Maps
datashape's DateTime type to numpy's `datetime64[ns]` dtype, since the
numpy datetime returned by datashape isn't supported by pipeline.
Parameters
----------
type_: datashape.coretypes.Type
The datashape type.
Returns
-------
type_ np.dtype
The numpy dtype.
"""
if isinstance(type_, Option):
type_ = type_.ty
if isinstance(type_, DateTime):
return np.dtype('datetime64[ns]')
if isinstance(type_, String):
return np.dtype(object)
if type_ in integral:
return np.dtype('int64')
else:
return type_.to_numpy_dtype() | def datashape_type_to_numpy(type_):
"""
Given a datashape type, return the associated numpy type. Maps
datashape's DateTime type to numpy's `datetime64[ns]` dtype, since the
numpy datetime returned by datashape isn't supported by pipeline.
Parameters
----------
type_: datashape.coretypes.Type
The datashape type.
Returns
-------
type_ np.dtype
The numpy dtype.
"""
if isinstance(type_, Option):
type_ = type_.ty
if isinstance(type_, DateTime):
return np.dtype('datetime64[ns]')
if isinstance(type_, String):
return np.dtype(object)
if type_ in integral:
return np.dtype('int64')
else:
return type_.to_numpy_dtype() | [
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"datashape... | quantopian/zipline | python | https://github.com/quantopian/zipline/blob/77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe/zipline/pipeline/loaders/blaze/core.py#L246-L272 | [
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"'dat... | 77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe |
train | new_dataset | 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 for that column.
This needs to be a frozenset rather than a dict or tuple of tuples
because we want a collection that's unordered but still hashable.
domain : zipline.pipeline.domain.Domain
Domain of the dataset to be created.
Returns
-------
ds : type
A new dataset type.
Notes
-----
This function is memoized. repeated calls with the same inputs will return
the same type. | 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 for that column.
This needs to be a frozenset rather than a dict or tuple of tuples
because we want a collection that's unordered but still hashable.
domain : zipline.pipeline.domain.Domain
Domain of the dataset to be created.
Returns
-------
ds : type
A new dataset type.
Notes
-----
This function is memoized. repeated calls with the same inputs will return
the same type.
"""
missing_values = dict(missing_values)
class_dict = {'ndim': 2 if SID_FIELD_NAME in expr.fields else 1}
for name, type_ in expr.dshape.measure.fields:
# Don't generate a column for sid or timestamp, since they're
# implicitly the labels if the arrays that will be passed to pipeline
# Terms.
if name in (SID_FIELD_NAME, TS_FIELD_NAME):
continue
type_ = datashape_type_to_numpy(type_)
if can_represent_dtype(type_):
col = Column(
type_,
missing_values.get(name, NotSpecified),
)
else:
col = NonPipelineField(name, type_)
class_dict[name] = col
if 'domain' in class_dict:
raise ValueError("Got a column named 'domain' in new_dataset(). "
"'domain' is reserved.")
class_dict['domain'] = domain
name = expr._name
if name is None:
name = next(_new_names)
# unicode is a name error in py3 but the branch is only hit
# when we are in python 2.
if PY2 and isinstance(name, unicode): # pragma: no cover # noqa
name = name.encode('utf-8')
return type(name, (DataSet,), class_dict) | 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 for that column.
This needs to be a frozenset rather than a dict or tuple of tuples
because we want a collection that's unordered but still hashable.
domain : zipline.pipeline.domain.Domain
Domain of the dataset to be created.
Returns
-------
ds : type
A new dataset type.
Notes
-----
This function is memoized. repeated calls with the same inputs will return
the same type.
"""
missing_values = dict(missing_values)
class_dict = {'ndim': 2 if SID_FIELD_NAME in expr.fields else 1}
for name, type_ in expr.dshape.measure.fields:
# Don't generate a column for sid or timestamp, since they're
# implicitly the labels if the arrays that will be passed to pipeline
# Terms.
if name in (SID_FIELD_NAME, TS_FIELD_NAME):
continue
type_ = datashape_type_to_numpy(type_)
if can_represent_dtype(type_):
col = Column(
type_,
missing_values.get(name, NotSpecified),
)
else:
col = NonPipelineField(name, type_)
class_dict[name] = col
if 'domain' in class_dict:
raise ValueError("Got a column named 'domain' in new_dataset(). "
"'domain' is reserved.")
class_dict['domain'] = domain
name = expr._name
if name is None:
name = next(_new_names)
# unicode is a name error in py3 but the branch is only hit
# when we are in python 2.
if PY2 and isinstance(name, unicode): # pragma: no cover # noqa
name = name.encode('utf-8')
return type(name, (DataSet,), class_dict) | [
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train | _check_resources | Validate that the expression and resources passed match up.
Parameters
----------
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
------
ValueError
If the resources do not match for an expression. | zipline/pipeline/loaders/blaze/core.py | def _check_resources(name, expr, resources):
"""Validate that the expression and resources passed match up.
Parameters
----------
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
------
ValueError
If the resources do not match for an expression.
"""
if expr is None:
return
bound = expr._resources()
if not bound and resources is None:
raise ValueError('no resources provided to compute %s' % name)
if bound and resources:
raise ValueError(
'explicit and implicit resources provided to compute %s' % name,
) | def _check_resources(name, expr, resources):
"""Validate that the expression and resources passed match up.
Parameters
----------
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
------
ValueError
If the resources do not match for an expression.
"""
if expr is None:
return
bound = expr._resources()
if not bound and resources is None:
raise ValueError('no resources provided to compute %s' % name)
if bound and resources:
raise ValueError(
'explicit and implicit resources provided to compute %s' % name,
) | [
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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 datetime inside ``measure``.
"""
if not isinstance(measure[name], (Date, DateTime)):
raise TypeError(
"'{name}' field must be a '{dt}', not: '{dshape}'".format(
name=name,
dt=DateTime(),
dshape=measure[name],
),
) | 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 datetime inside ``measure``.
"""
if not isinstance(measure[name], (Date, DateTime)):
raise TypeError(
"'{name}' field must be a '{dt}', not: '{dshape}'".format(
name=name,
dt=DateTime(),
dshape=measure[name],
),
) | [
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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 metadata table will
be searched for by walking up the expression tree. If this cannot be
reflected, then an action will be taken based on the
``no_metadata_rule``.
no_metadata_rule : {'warn', 'raise', 'ignore'}
How to handle the case where the metadata_expr='auto' but no expr
could be found.
Returns
-------
metadata : Expr or None
The deltas or metadata table to use. | 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.
metadata_expr : Expr, 'auto', or None
The metadata argument. If this is 'auto', then the metadata table will
be searched for by walking up the expression tree. If this cannot be
reflected, then an action will be taken based on the
``no_metadata_rule``.
no_metadata_rule : {'warn', 'raise', 'ignore'}
How to handle the case where the metadata_expr='auto' but no expr
could be found.
Returns
-------
metadata : Expr or None
The deltas or metadata table to use.
"""
if isinstance(metadata_expr, bz.Expr) or metadata_expr is None:
return metadata_expr
try:
return expr._child['_'.join(((expr._name or ''), field))]
except (ValueError, AttributeError):
if no_metadata_rule == 'raise':
raise ValueError(
"no %s table could be reflected for %s" % (field, expr)
)
elif no_metadata_rule == 'warn':
warnings.warn(NoMetaDataWarning(expr, field), stacklevel=4)
return None | 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.
metadata_expr : Expr, 'auto', or None
The metadata argument. If this is 'auto', then the metadata table will
be searched for by walking up the expression tree. If this cannot be
reflected, then an action will be taken based on the
``no_metadata_rule``.
no_metadata_rule : {'warn', 'raise', 'ignore'}
How to handle the case where the metadata_expr='auto' but no expr
could be found.
Returns
-------
metadata : Expr or None
The deltas or metadata table to use.
"""
if isinstance(metadata_expr, bz.Expr) or metadata_expr is None:
return metadata_expr
try:
return expr._child['_'.join(((expr._name or ''), field))]
except (ValueError, AttributeError):
if no_metadata_rule == 'raise':
raise ValueError(
"no %s table could be reflected for %s" % (field, expr)
)
elif no_metadata_rule == 'warn':
warnings.warn(NoMetaDataWarning(expr, field), stacklevel=4)
return None | [
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":"... | 77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe |
train | _ensure_timestamp_field | Verify that the baseline and deltas expressions have a timestamp field.
If there is not a ``TS_FIELD_NAME`` on either of the expressions, it will
be copied from the ``AD_FIELD_NAME``. If one is provided, then we will
verify that it is the correct dshape.
Parameters
----------
dataset_expr : Expr
The baseline expression.
deltas : Expr or None
The deltas expression if any was provided.
checkpoints : Expr or None
The checkpoints expression if any was provided.
Returns
-------
dataset_expr, deltas : Expr
The new baseline and deltas expressions to use. | zipline/pipeline/loaders/blaze/core.py | def _ensure_timestamp_field(dataset_expr, deltas, checkpoints):
"""Verify that the baseline and deltas expressions have a timestamp field.
If there is not a ``TS_FIELD_NAME`` on either of the expressions, it will
be copied from the ``AD_FIELD_NAME``. If one is provided, then we will
verify that it is the correct dshape.
Parameters
----------
dataset_expr : Expr
The baseline expression.
deltas : Expr or None
The deltas expression if any was provided.
checkpoints : Expr or None
The checkpoints expression if any was provided.
Returns
-------
dataset_expr, deltas : Expr
The new baseline and deltas expressions to use.
"""
measure = dataset_expr.dshape.measure
if TS_FIELD_NAME not in measure.names:
dataset_expr = bz.transform(
dataset_expr,
**{TS_FIELD_NAME: dataset_expr[AD_FIELD_NAME]}
)
deltas = _ad_as_ts(deltas)
checkpoints = _ad_as_ts(checkpoints)
else:
_check_datetime_field(TS_FIELD_NAME, measure)
return dataset_expr, deltas, checkpoints | def _ensure_timestamp_field(dataset_expr, deltas, checkpoints):
"""Verify that the baseline and deltas expressions have a timestamp field.
If there is not a ``TS_FIELD_NAME`` on either of the expressions, it will
be copied from the ``AD_FIELD_NAME``. If one is provided, then we will
verify that it is the correct dshape.
Parameters
----------
dataset_expr : Expr
The baseline expression.
deltas : Expr or None
The deltas expression if any was provided.
checkpoints : Expr or None
The checkpoints expression if any was provided.
Returns
-------
dataset_expr, deltas : Expr
The new baseline and deltas expressions to use.
"""
measure = dataset_expr.dshape.measure
if TS_FIELD_NAME not in measure.names:
dataset_expr = bz.transform(
dataset_expr,
**{TS_FIELD_NAME: dataset_expr[AD_FIELD_NAME]}
)
deltas = _ad_as_ts(deltas)
checkpoints = _ad_as_ts(checkpoints)
else:
_check_datetime_field(TS_FIELD_NAME, measure)
return dataset_expr, deltas, checkpoints | [
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train | from_blaze | Create a Pipeline API object from a blaze expression.
Parameters
----------
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
by stepping up the expression tree and looking for another field
with the name of ``expr._name`` + '_deltas'. If None is passed, no
deltas will be used.
checkpoints : Expr, 'auto' or None, optional
The expression to use for the forward fill checkpoints.
If the string 'auto' is passed, a checkpoints expr will be looked up
by stepping up the expression tree and looking for another field
with the name of ``expr._name`` + '_checkpoints'. If None is passed,
no checkpoints will be used.
loader : BlazeLoader, optional
The blaze loader to attach this pipeline dataset to. If None is passed,
the global blaze loader is used.
resources : dict or any, optional
The data to execute the blaze expressions against. This is used as the
scope for ``bz.compute``.
odo_kwargs : dict, optional
The keyword arguments to pass to odo when evaluating the expressions.
domain : zipline.pipeline.domain.Domain
Domain of the dataset to be created.
missing_values : dict[str -> any], optional
A dict mapping column names to missing values for those columns.
Missing values are required for integral columns.
no_deltas_rule : {'warn', 'raise', 'ignore'}, optional
What should happen if ``deltas='auto'`` but no deltas can be found.
'warn' says to raise a warning but continue.
'raise' says to raise an exception if no deltas can be found.
'ignore' says take no action and proceed with no deltas.
no_checkpoints_rule : {'warn', 'raise', 'ignore'}, optional
What should happen if ``checkpoints='auto'`` but no checkpoints can be
found. 'warn' says to raise a warning but continue.
'raise' says to raise an exception if no deltas can be found.
'ignore' says take no action and proceed with no deltas.
Returns
-------
pipeline_api_obj : DataSet or BoundColumn
Either a new dataset or bound column based on the shape of the expr
passed in. If a table shaped expression is passed, this will return
a ``DataSet`` that represents the whole table. If an array-like shape
is passed, a ``BoundColumn`` on the dataset that would be constructed
from passing the parent is returned. | zipline/pipeline/loaders/blaze/core.py | def from_blaze(expr,
deltas='auto',
checkpoints='auto',
loader=None,
resources=None,
odo_kwargs=None,
missing_values=None,
domain=GENERIC,
no_deltas_rule='warn',
no_checkpoints_rule='warn'):
"""Create a Pipeline API object from a blaze expression.
Parameters
----------
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
by stepping up the expression tree and looking for another field
with the name of ``expr._name`` + '_deltas'. If None is passed, no
deltas will be used.
checkpoints : Expr, 'auto' or None, optional
The expression to use for the forward fill checkpoints.
If the string 'auto' is passed, a checkpoints expr will be looked up
by stepping up the expression tree and looking for another field
with the name of ``expr._name`` + '_checkpoints'. If None is passed,
no checkpoints will be used.
loader : BlazeLoader, optional
The blaze loader to attach this pipeline dataset to. If None is passed,
the global blaze loader is used.
resources : dict or any, optional
The data to execute the blaze expressions against. This is used as the
scope for ``bz.compute``.
odo_kwargs : dict, optional
The keyword arguments to pass to odo when evaluating the expressions.
domain : zipline.pipeline.domain.Domain
Domain of the dataset to be created.
missing_values : dict[str -> any], optional
A dict mapping column names to missing values for those columns.
Missing values are required for integral columns.
no_deltas_rule : {'warn', 'raise', 'ignore'}, optional
What should happen if ``deltas='auto'`` but no deltas can be found.
'warn' says to raise a warning but continue.
'raise' says to raise an exception if no deltas can be found.
'ignore' says take no action and proceed with no deltas.
no_checkpoints_rule : {'warn', 'raise', 'ignore'}, optional
What should happen if ``checkpoints='auto'`` but no checkpoints can be
found. 'warn' says to raise a warning but continue.
'raise' says to raise an exception if no deltas can be found.
'ignore' says take no action and proceed with no deltas.
Returns
-------
pipeline_api_obj : DataSet or BoundColumn
Either a new dataset or bound column based on the shape of the expr
passed in. If a table shaped expression is passed, this will return
a ``DataSet`` that represents the whole table. If an array-like shape
is passed, a ``BoundColumn`` on the dataset that would be constructed
from passing the parent is returned.
"""
if 'auto' in {deltas, checkpoints}:
invalid_nodes = tuple(filter(is_invalid_deltas_node, expr._subterms()))
if invalid_nodes:
raise TypeError(
'expression with auto %s may only contain (%s) nodes,'
" found: %s" % (
' or '.join(
['deltas'] if deltas is not None else [] +
['checkpoints'] if checkpoints is not None else [],
),
', '.join(map(get__name__, valid_deltas_node_types)),
', '.join(
set(map(compose(get__name__, type), invalid_nodes)),
),
),
)
deltas = _get_metadata(
'deltas',
expr,
deltas,
no_deltas_rule,
)
checkpoints = _get_metadata(
'checkpoints',
expr,
checkpoints,
no_checkpoints_rule,
)
# Check if this is a single column out of a dataset.
if bz.ndim(expr) != 1:
raise TypeError(
'expression was not tabular or array-like,'
' %s dimensions: %d' % (
'too many' if bz.ndim(expr) > 1 else 'not enough',
bz.ndim(expr),
),
)
single_column = None
if isscalar(expr.dshape.measure):
# This is a single column. Record which column we are to return
# but create the entire dataset.
single_column = rename = expr._name
field_hit = False
if not isinstance(expr, traversable_nodes):
raise TypeError(
"expression '%s' was array-like but not a simple field of"
" some larger table" % str(expr),
)
while isinstance(expr, traversable_nodes):
if isinstance(expr, bz.expr.Field):
if not field_hit:
field_hit = True
else:
break
rename = expr._name
expr = expr._child
dataset_expr = expr.relabel({rename: single_column})
else:
dataset_expr = expr
measure = dataset_expr.dshape.measure
if not isrecord(measure) or AD_FIELD_NAME not in measure.names:
raise TypeError(
"The dataset must be a collection of records with at least an"
" '{ad}' field. Fields provided: '{fields}'\nhint: maybe you need"
" to use `relabel` to change your field names".format(
ad=AD_FIELD_NAME,
fields=measure,
),
)
_check_datetime_field(AD_FIELD_NAME, measure)
dataset_expr, deltas, checkpoints = _ensure_timestamp_field(
dataset_expr,
deltas,
checkpoints,
)
if deltas is not None and (sorted(deltas.dshape.measure.fields) !=
sorted(measure.fields)):
raise TypeError(
'baseline measure != deltas measure:\n%s != %s' % (
measure,
deltas.dshape.measure,
),
)
if (checkpoints is not None and
(sorted(checkpoints.dshape.measure.fields) !=
sorted(measure.fields))):
raise TypeError(
'baseline measure != checkpoints measure:\n%s != %s' % (
measure,
checkpoints.dshape.measure,
),
)
# Ensure that we have a data resource to execute the query against.
_check_resources('expr', dataset_expr, resources)
_check_resources('deltas', deltas, resources)
_check_resources('checkpoints', checkpoints, resources)
# Create or retrieve the Pipeline API dataset.
if missing_values is None:
missing_values = {}
ds = new_dataset(dataset_expr, frozenset(missing_values.items()), domain)
# Register our new dataset with the loader.
(loader if loader is not None else global_loader).register_dataset(
ds,
bind_expression_to_resources(dataset_expr, resources),
bind_expression_to_resources(deltas, resources)
if deltas is not None else
None,
bind_expression_to_resources(checkpoints, resources)
if checkpoints is not None else
None,
odo_kwargs=odo_kwargs,
)
if single_column is not None:
# We were passed a single column, extract and return it.
return getattr(ds, single_column)
return ds | def from_blaze(expr,
deltas='auto',
checkpoints='auto',
loader=None,
resources=None,
odo_kwargs=None,
missing_values=None,
domain=GENERIC,
no_deltas_rule='warn',
no_checkpoints_rule='warn'):
"""Create a Pipeline API object from a blaze expression.
Parameters
----------
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
by stepping up the expression tree and looking for another field
with the name of ``expr._name`` + '_deltas'. If None is passed, no
deltas will be used.
checkpoints : Expr, 'auto' or None, optional
The expression to use for the forward fill checkpoints.
If the string 'auto' is passed, a checkpoints expr will be looked up
by stepping up the expression tree and looking for another field
with the name of ``expr._name`` + '_checkpoints'. If None is passed,
no checkpoints will be used.
loader : BlazeLoader, optional
The blaze loader to attach this pipeline dataset to. If None is passed,
the global blaze loader is used.
resources : dict or any, optional
The data to execute the blaze expressions against. This is used as the
scope for ``bz.compute``.
odo_kwargs : dict, optional
The keyword arguments to pass to odo when evaluating the expressions.
domain : zipline.pipeline.domain.Domain
Domain of the dataset to be created.
missing_values : dict[str -> any], optional
A dict mapping column names to missing values for those columns.
Missing values are required for integral columns.
no_deltas_rule : {'warn', 'raise', 'ignore'}, optional
What should happen if ``deltas='auto'`` but no deltas can be found.
'warn' says to raise a warning but continue.
'raise' says to raise an exception if no deltas can be found.
'ignore' says take no action and proceed with no deltas.
no_checkpoints_rule : {'warn', 'raise', 'ignore'}, optional
What should happen if ``checkpoints='auto'`` but no checkpoints can be
found. 'warn' says to raise a warning but continue.
'raise' says to raise an exception if no deltas can be found.
'ignore' says take no action and proceed with no deltas.
Returns
-------
pipeline_api_obj : DataSet or BoundColumn
Either a new dataset or bound column based on the shape of the expr
passed in. If a table shaped expression is passed, this will return
a ``DataSet`` that represents the whole table. If an array-like shape
is passed, a ``BoundColumn`` on the dataset that would be constructed
from passing the parent is returned.
"""
if 'auto' in {deltas, checkpoints}:
invalid_nodes = tuple(filter(is_invalid_deltas_node, expr._subterms()))
if invalid_nodes:
raise TypeError(
'expression with auto %s may only contain (%s) nodes,'
" found: %s" % (
' or '.join(
['deltas'] if deltas is not None else [] +
['checkpoints'] if checkpoints is not None else [],
),
', '.join(map(get__name__, valid_deltas_node_types)),
', '.join(
set(map(compose(get__name__, type), invalid_nodes)),
),
),
)
deltas = _get_metadata(
'deltas',
expr,
deltas,
no_deltas_rule,
)
checkpoints = _get_metadata(
'checkpoints',
expr,
checkpoints,
no_checkpoints_rule,
)
# Check if this is a single column out of a dataset.
if bz.ndim(expr) != 1:
raise TypeError(
'expression was not tabular or array-like,'
' %s dimensions: %d' % (
'too many' if bz.ndim(expr) > 1 else 'not enough',
bz.ndim(expr),
),
)
single_column = None
if isscalar(expr.dshape.measure):
# This is a single column. Record which column we are to return
# but create the entire dataset.
single_column = rename = expr._name
field_hit = False
if not isinstance(expr, traversable_nodes):
raise TypeError(
"expression '%s' was array-like but not a simple field of"
" some larger table" % str(expr),
)
while isinstance(expr, traversable_nodes):
if isinstance(expr, bz.expr.Field):
if not field_hit:
field_hit = True
else:
break
rename = expr._name
expr = expr._child
dataset_expr = expr.relabel({rename: single_column})
else:
dataset_expr = expr
measure = dataset_expr.dshape.measure
if not isrecord(measure) or AD_FIELD_NAME not in measure.names:
raise TypeError(
"The dataset must be a collection of records with at least an"
" '{ad}' field. Fields provided: '{fields}'\nhint: maybe you need"
" to use `relabel` to change your field names".format(
ad=AD_FIELD_NAME,
fields=measure,
),
)
_check_datetime_field(AD_FIELD_NAME, measure)
dataset_expr, deltas, checkpoints = _ensure_timestamp_field(
dataset_expr,
deltas,
checkpoints,
)
if deltas is not None and (sorted(deltas.dshape.measure.fields) !=
sorted(measure.fields)):
raise TypeError(
'baseline measure != deltas measure:\n%s != %s' % (
measure,
deltas.dshape.measure,
),
)
if (checkpoints is not None and
(sorted(checkpoints.dshape.measure.fields) !=
sorted(measure.fields))):
raise TypeError(
'baseline measure != checkpoints measure:\n%s != %s' % (
measure,
checkpoints.dshape.measure,
),
)
# Ensure that we have a data resource to execute the query against.
_check_resources('expr', dataset_expr, resources)
_check_resources('deltas', deltas, resources)
_check_resources('checkpoints', checkpoints, resources)
# Create or retrieve the Pipeline API dataset.
if missing_values is None:
missing_values = {}
ds = new_dataset(dataset_expr, frozenset(missing_values.items()), domain)
# Register our new dataset with the loader.
(loader if loader is not None else global_loader).register_dataset(
ds,
bind_expression_to_resources(dataset_expr, resources),
bind_expression_to_resources(deltas, resources)
if deltas is not None else
None,
bind_expression_to_resources(checkpoints, resources)
if checkpoints is not None else
None,
odo_kwargs=odo_kwargs,
)
if single_column is not None:
# We were passed a single column, extract and return it.
return getattr(ds, single_column)
return ds | [
"Create",
"a",
"Pipeline",
"API",
"object",
"from",
"a",
"blaze",
"expression",
"."
] | quantopian/zipline | python | https://github.com/quantopian/zipline/blob/77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe/zipline/pipeline/loaders/blaze/core.py#L512-L695 | [
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"GENERIC... | 77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe |
train | bind_expression_to_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 resources.
Returns
-------
bound_expr : bz.Expr
``expr`` with bound resources. | 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 resources.
Returns
-------
bound_expr : bz.Expr
``expr`` with bound resources.
"""
# bind the resources into the expression
if resources is None:
resources = {}
# _subs stands for substitute. It's not actually private, blaze just
# prefixes symbol-manipulation methods with underscores to prevent
# collisions with data column names.
return expr._subs({
k: bz.data(v, dshape=k.dshape) for k, v in iteritems(resources)
}) | 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 resources.
Returns
-------
bound_expr : bz.Expr
``expr`` with bound resources.
"""
# bind the resources into the expression
if resources is None:
resources = {}
# _subs stands for substitute. It's not actually private, blaze just
# prefixes symbol-manipulation methods with underscores to prevent
# collisions with data column names.
return expr._subs({
k: bz.data(v, dshape=k.dshape) for k, v in iteritems(resources)
}) | [
"Bind",
"a",
"Blaze",
"expression",
"to",
"resources",
"."
] | quantopian/zipline | python | https://github.com/quantopian/zipline/blob/77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe/zipline/pipeline/loaders/blaze/core.py#L1038-L1063 | [
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"# prefixes sym... | 77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe |
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.
lower_dt : pd.Timestamp
The lower date being queried for that serves as an upper bound for
checkpoints.
odo_kwargs : dict, optional
The extra keyword arguments to pass to ``odo``. | zipline/pipeline/loaders/blaze/core.py | def get_materialized_checkpoints(checkpoints, colnames, lower_dt, odo_kwargs):
"""
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.
lower_dt : pd.Timestamp
The lower date being queried for that serves as an upper bound for
checkpoints.
odo_kwargs : dict, optional
The extra keyword arguments to pass to ``odo``.
"""
if checkpoints is not None:
ts = checkpoints[TS_FIELD_NAME]
checkpoints_ts = odo(
ts[ts < lower_dt].max(),
pd.Timestamp,
**odo_kwargs
)
if pd.isnull(checkpoints_ts):
# We don't have a checkpoint for before our start date so just
# don't constrain the lower date.
materialized_checkpoints = pd.DataFrame(columns=colnames)
lower = None
else:
materialized_checkpoints = odo(
checkpoints[ts == checkpoints_ts][colnames],
pd.DataFrame,
**odo_kwargs
)
lower = checkpoints_ts
else:
materialized_checkpoints = pd.DataFrame(columns=colnames)
lower = None # we don't have a good lower date constraint
return lower, materialized_checkpoints | def get_materialized_checkpoints(checkpoints, colnames, lower_dt, odo_kwargs):
"""
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.
lower_dt : pd.Timestamp
The lower date being queried for that serves as an upper bound for
checkpoints.
odo_kwargs : dict, optional
The extra keyword arguments to pass to ``odo``.
"""
if checkpoints is not None:
ts = checkpoints[TS_FIELD_NAME]
checkpoints_ts = odo(
ts[ts < lower_dt].max(),
pd.Timestamp,
**odo_kwargs
)
if pd.isnull(checkpoints_ts):
# We don't have a checkpoint for before our start date so just
# don't constrain the lower date.
materialized_checkpoints = pd.DataFrame(columns=colnames)
lower = None
else:
materialized_checkpoints = odo(
checkpoints[ts == checkpoints_ts][colnames],
pd.DataFrame,
**odo_kwargs
)
lower = checkpoints_ts
else:
materialized_checkpoints = pd.DataFrame(columns=colnames)
lower = None # we don't have a good lower date constraint
return lower, materialized_checkpoints | [
"Computes",
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"a",
"DataFrame",
"checkpoints",
"."
] | quantopian/zipline | python | https://github.com/quantopian/zipline/blob/77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe/zipline/pipeline/loaders/blaze/core.py#L1066-L1105 | [
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"ts... | 77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe |
train | ffill_query_in_range | Query a blaze expression in a given time range properly forward filling
from values that fall before the lower date.
Parameters
----------
expr : Expr
Bound blaze expression.
lower : datetime
The lower date to query for.
upper : datetime
The upper date to query for.
checkpoints : Expr, optional
Bound blaze expression for a checkpoints table from which to get a
computed lower bound.
odo_kwargs : dict, optional
The extra keyword arguments to pass to ``odo``.
ts_field : str, optional
The name of the timestamp field in the given blaze expression.
Returns
-------
raw : pd.DataFrame
A strict dataframe for the data in the given date range. This may
start before the requested start date if a value is needed to ffill. | zipline/pipeline/loaders/blaze/core.py | def ffill_query_in_range(expr,
lower,
upper,
checkpoints=None,
odo_kwargs=None,
ts_field=TS_FIELD_NAME):
"""Query a blaze expression in a given time range properly forward filling
from values that fall before the lower date.
Parameters
----------
expr : Expr
Bound blaze expression.
lower : datetime
The lower date to query for.
upper : datetime
The upper date to query for.
checkpoints : Expr, optional
Bound blaze expression for a checkpoints table from which to get a
computed lower bound.
odo_kwargs : dict, optional
The extra keyword arguments to pass to ``odo``.
ts_field : str, optional
The name of the timestamp field in the given blaze expression.
Returns
-------
raw : pd.DataFrame
A strict dataframe for the data in the given date range. This may
start before the requested start date if a value is needed to ffill.
"""
odo_kwargs = odo_kwargs or {}
computed_lower, materialized_checkpoints = get_materialized_checkpoints(
checkpoints,
expr.fields,
lower,
odo_kwargs,
)
pred = expr[ts_field] <= upper
if computed_lower is not None:
# only constrain the lower date if we computed a new lower date
pred &= expr[ts_field] >= computed_lower
raw = pd.concat(
(
materialized_checkpoints,
odo(
expr[pred],
pd.DataFrame,
**odo_kwargs
),
),
ignore_index=True,
)
raw.loc[:, ts_field] = raw.loc[:, ts_field].astype('datetime64[ns]')
return raw | def ffill_query_in_range(expr,
lower,
upper,
checkpoints=None,
odo_kwargs=None,
ts_field=TS_FIELD_NAME):
"""Query a blaze expression in a given time range properly forward filling
from values that fall before the lower date.
Parameters
----------
expr : Expr
Bound blaze expression.
lower : datetime
The lower date to query for.
upper : datetime
The upper date to query for.
checkpoints : Expr, optional
Bound blaze expression for a checkpoints table from which to get a
computed lower bound.
odo_kwargs : dict, optional
The extra keyword arguments to pass to ``odo``.
ts_field : str, optional
The name of the timestamp field in the given blaze expression.
Returns
-------
raw : pd.DataFrame
A strict dataframe for the data in the given date range. This may
start before the requested start date if a value is needed to ffill.
"""
odo_kwargs = odo_kwargs or {}
computed_lower, materialized_checkpoints = get_materialized_checkpoints(
checkpoints,
expr.fields,
lower,
odo_kwargs,
)
pred = expr[ts_field] <= upper
if computed_lower is not None:
# only constrain the lower date if we computed a new lower date
pred &= expr[ts_field] >= computed_lower
raw = pd.concat(
(
materialized_checkpoints,
odo(
expr[pred],
pd.DataFrame,
**odo_kwargs
),
),
ignore_index=True,
)
raw.loc[:, ts_field] = raw.loc[:, ts_field].astype('datetime64[ns]')
return raw | [
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... | 77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe |
train | BlazeLoader.register_dataset | Explicitly map a datset to a collection of blaze expressions.
Parameters
----------
dataset : DataSet
The pipeline dataset to map to the given expressions.
expr : Expr
The baseline values.
deltas : Expr, optional
The deltas for the data.
checkpoints : Expr, optional
The forward fill checkpoints for the data.
odo_kwargs : dict, optional
The keyword arguments to forward to the odo calls internally.
See Also
--------
:func:`zipline.pipeline.loaders.blaze.from_blaze` | zipline/pipeline/loaders/blaze/core.py | def register_dataset(self,
dataset,
expr,
deltas=None,
checkpoints=None,
odo_kwargs=None):
"""Explicitly map a datset to a collection of blaze expressions.
Parameters
----------
dataset : DataSet
The pipeline dataset to map to the given expressions.
expr : Expr
The baseline values.
deltas : Expr, optional
The deltas for the data.
checkpoints : Expr, optional
The forward fill checkpoints for the data.
odo_kwargs : dict, optional
The keyword arguments to forward to the odo calls internally.
See Also
--------
:func:`zipline.pipeline.loaders.blaze.from_blaze`
"""
expr_data = ExprData(
expr,
deltas,
checkpoints,
odo_kwargs,
)
for column in dataset.columns:
self._table_expressions[column] = expr_data | def register_dataset(self,
dataset,
expr,
deltas=None,
checkpoints=None,
odo_kwargs=None):
"""Explicitly map a datset to a collection of blaze expressions.
Parameters
----------
dataset : DataSet
The pipeline dataset to map to the given expressions.
expr : Expr
The baseline values.
deltas : Expr, optional
The deltas for the data.
checkpoints : Expr, optional
The forward fill checkpoints for the data.
odo_kwargs : dict, optional
The keyword arguments to forward to the odo calls internally.
See Also
--------
:func:`zipline.pipeline.loaders.blaze.from_blaze`
"""
expr_data = ExprData(
expr,
deltas,
checkpoints,
odo_kwargs,
)
for column in dataset.columns:
self._table_expressions[column] = expr_data | [
"Explicitly",
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"of",
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train | BlazeLoader.register_column | Explicitly map a single bound column to a collection of blaze
expressions. The expressions need to have ``timestamp`` and ``as_of``
columns.
Parameters
----------
column : BoundColumn
The pipeline dataset to map to the given expressions.
expr : Expr
The baseline values.
deltas : Expr, optional
The deltas for the data.
checkpoints : Expr, optional
The forward fill checkpoints for the data.
odo_kwargs : dict, optional
The keyword arguments to forward to the odo calls internally.
See Also
--------
:func:`zipline.pipeline.loaders.blaze.from_blaze` | zipline/pipeline/loaders/blaze/core.py | def register_column(self,
column,
expr,
deltas=None,
checkpoints=None,
odo_kwargs=None):
"""Explicitly map a single bound column to a collection of blaze
expressions. The expressions need to have ``timestamp`` and ``as_of``
columns.
Parameters
----------
column : BoundColumn
The pipeline dataset to map to the given expressions.
expr : Expr
The baseline values.
deltas : Expr, optional
The deltas for the data.
checkpoints : Expr, optional
The forward fill checkpoints for the data.
odo_kwargs : dict, optional
The keyword arguments to forward to the odo calls internally.
See Also
--------
:func:`zipline.pipeline.loaders.blaze.from_blaze`
"""
self._table_expressions[column] = ExprData(
expr,
deltas,
checkpoints,
odo_kwargs,
) | def register_column(self,
column,
expr,
deltas=None,
checkpoints=None,
odo_kwargs=None):
"""Explicitly map a single bound column to a collection of blaze
expressions. The expressions need to have ``timestamp`` and ``as_of``
columns.
Parameters
----------
column : BoundColumn
The pipeline dataset to map to the given expressions.
expr : Expr
The baseline values.
deltas : Expr, optional
The deltas for the data.
checkpoints : Expr, optional
The forward fill checkpoints for the data.
odo_kwargs : dict, optional
The keyword arguments to forward to the odo calls internally.
See Also
--------
:func:`zipline.pipeline.loaders.blaze.from_blaze`
"""
self._table_expressions[column] = ExprData(
expr,
deltas,
checkpoints,
odo_kwargs,
) | [
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train | merge_ownership_periods | Given a dict of mappings where the values are lists of
OwnershipPeriod objects, returns a dict with the same structure with
new OwnershipPeriod objects adjusted so that the periods have no
gaps.
Orders the periods chronologically, and pushes forward the end date
of each period to match the start date of the following period. The
end date of the last period pushed forward to the max Timestamp. | zipline/assets/assets.py | def merge_ownership_periods(mappings):
"""
Given a dict of mappings where the values are lists of
OwnershipPeriod objects, returns a dict with the same structure with
new OwnershipPeriod objects adjusted so that the periods have no
gaps.
Orders the periods chronologically, and pushes forward the end date
of each period to match the start date of the following period. The
end date of the last period pushed forward to the max Timestamp.
"""
return valmap(
lambda v: tuple(
OwnershipPeriod(
a.start,
b.start,
a.sid,
a.value,
) for a, b in sliding_window(
2,
concatv(
sorted(v),
# concat with a fake ownership object to make the last
# end date be max timestamp
[OwnershipPeriod(
pd.Timestamp.max.tz_localize('utc'),
None,
None,
None,
)],
),
)
),
mappings,
) | def merge_ownership_periods(mappings):
"""
Given a dict of mappings where the values are lists of
OwnershipPeriod objects, returns a dict with the same structure with
new OwnershipPeriod objects adjusted so that the periods have no
gaps.
Orders the periods chronologically, and pushes forward the end date
of each period to match the start date of the following period. The
end date of the last period pushed forward to the max Timestamp.
"""
return valmap(
lambda v: tuple(
OwnershipPeriod(
a.start,
b.start,
a.sid,
a.value,
) for a, b in sliding_window(
2,
concatv(
sorted(v),
# concat with a fake ownership object to make the last
# end date be max timestamp
[OwnershipPeriod(
pd.Timestamp.max.tz_localize('utc'),
None,
None,
None,
)],
),
)
),
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(
sa.select(table.c).execute().fetchall(),
key_from_row,
value_from_row,
) | 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(
sa.select(table.c).execute().fetchall(),
key_from_row,
value_from_row,
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train | build_grouped_ownership_map | Builds a dict mapping group keys to maps of keys to to lists of
OwnershipPeriods, from a db table. | zipline/assets/assets.py | def build_grouped_ownership_map(table,
key_from_row,
value_from_row,
group_key):
"""
Builds a dict mapping group keys to maps of keys to to lists of
OwnershipPeriods, from a db table.
"""
grouped_rows = groupby(
group_key,
sa.select(table.c).execute().fetchall(),
)
return {
key: _build_ownership_map_from_rows(
rows,
key_from_row,
value_from_row,
)
for key, rows in grouped_rows.items()
} | def build_grouped_ownership_map(table,
key_from_row,
value_from_row,
group_key):
"""
Builds a dict mapping group keys to maps of keys to to lists of
OwnershipPeriods, from a db table.
"""
grouped_rows = groupby(
group_key,
sa.select(table.c).execute().fetchall(),
)
return {
key: _build_ownership_map_from_rows(
rows,
key_from_row,
value_from_row,
)
for key, rows in grouped_rows.items()
} | [
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"... | 77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe |
train | _filter_kwargs | 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 keys intersect with ``names`` and the values are
not None. | 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 keys intersect with ``names`` and the values are
not None.
"""
return {k: v for k, v in dict_.items() if k in names and v is not None} | 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 keys intersect with ``names`` and the values are
not None.
"""
return {k: v for k, v in dict_.items() if k in names and v is not None} | [
"Filter",
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] | quantopian/zipline | python | https://github.com/quantopian/zipline/blob/77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe/zipline/assets/assets.py#L193-L209 | [
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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')
dict_[key] = None if isnull(value) else value
return dict_ | [
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] | quantopian/zipline | python | https://github.com/quantopian/zipline/blob/77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe/zipline/assets/assets.py#L216-L223 | [
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"... | 77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe |
train | was_active | 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
if `asset` was alive. This is generally the result of accessing the
`value` attribute of a pandas Timestamp.
asset : Asset
The asset object to check.
Returns
-------
was_active : bool
Whether or not the `asset` existed at the specified time. | 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
if `asset` was alive. This is generally the result of accessing the
`value` attribute of a pandas Timestamp.
asset : Asset
The asset object to check.
Returns
-------
was_active : bool
Whether or not the `asset` existed at the specified time.
"""
return (
asset.start_date.value
<= reference_date_value
<= asset.end_date.value
) | 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
if `asset` was alive. This is generally the result of accessing the
`value` attribute of a pandas Timestamp.
asset : Asset
The asset object to check.
Returns
-------
was_active : bool
Whether or not the `asset` existed at the specified time.
"""
return (
asset.start_date.value
<= reference_date_value
<= asset.end_date.value
) | [
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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 = {}
missing = set()
for sid in sids:
try:
found[sid] = self._asset_type_cache[sid]
except KeyError:
missing.add(sid)
if not missing:
return found
router_cols = self.asset_router.c
for assets in group_into_chunks(missing):
query = sa.select((router_cols.sid, router_cols.asset_type)).where(
self.asset_router.c.sid.in_(map(int, assets))
)
for sid, type_ in query.execute().fetchall():
missing.remove(sid)
found[sid] = self._asset_type_cache[sid] = type_
for sid in missing:
found[sid] = self._asset_type_cache[sid] = None
return 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 = {}
missing = set()
for sid in sids:
try:
found[sid] = self._asset_type_cache[sid]
except KeyError:
missing.add(sid)
if not missing:
return found
router_cols = self.asset_router.c
for assets in group_into_chunks(missing):
query = sa.select((router_cols.sid, router_cols.asset_type)).where(
self.asset_router.c.sid.in_(map(int, assets))
)
for sid, type_ in query.execute().fetchall():
missing.remove(sid)
found[sid] = self._asset_type_cache[sid] = type_
for sid in missing:
found[sid] = self._asset_type_cache[sid] = None
return 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 or None]
A list of the same length as `sids` containing Assets (or Nones)
corresponding to the requested sids.
Raises
------
SidsNotFound
When a requested sid is not found and default_none=False. | 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 `SidsNotFound`.
Returns
-------
assets : list[Asset or None]
A list of the same length as `sids` containing Assets (or Nones)
corresponding to the requested sids.
Raises
------
SidsNotFound
When a requested sid is not found and default_none=False.
"""
sids = list(sids)
hits, missing, failures = {}, set(), []
for sid in sids:
try:
asset = self._asset_cache[sid]
if not default_none and asset is None:
# Bail early if we've already cached that we don't know
# about an asset.
raise SidsNotFound(sids=[sid])
hits[sid] = asset
except KeyError:
missing.add(sid)
# All requests were cache hits. Return requested sids in order.
if not missing:
return [hits[sid] for sid in sids]
update_hits = hits.update
# Look up cache misses by type.
type_to_assets = self.group_by_type(missing)
# Handle failures
failures = {failure: None for failure in type_to_assets.pop(None, ())}
update_hits(failures)
self._asset_cache.update(failures)
if failures and not default_none:
raise SidsNotFound(sids=list(failures))
# We don't update the asset cache here because it should already be
# updated by `self.retrieve_equities`.
update_hits(self.retrieve_equities(type_to_assets.pop('equity', ())))
update_hits(
self.retrieve_futures_contracts(type_to_assets.pop('future', ()))
)
# We shouldn't know about any other asset types.
if type_to_assets:
raise AssertionError(
"Found asset types: %s" % list(type_to_assets.keys())
)
return [hits[sid] for sid in sids] | 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 `SidsNotFound`.
Returns
-------
assets : list[Asset or None]
A list of the same length as `sids` containing Assets (or Nones)
corresponding to the requested sids.
Raises
------
SidsNotFound
When a requested sid is not found and default_none=False.
"""
sids = list(sids)
hits, missing, failures = {}, set(), []
for sid in sids:
try:
asset = self._asset_cache[sid]
if not default_none and asset is None:
# Bail early if we've already cached that we don't know
# about an asset.
raise SidsNotFound(sids=[sid])
hits[sid] = asset
except KeyError:
missing.add(sid)
# All requests were cache hits. Return requested sids in order.
if not missing:
return [hits[sid] for sid in sids]
update_hits = hits.update
# Look up cache misses by type.
type_to_assets = self.group_by_type(missing)
# Handle failures
failures = {failure: None for failure in type_to_assets.pop(None, ())}
update_hits(failures)
self._asset_cache.update(failures)
if failures and not default_none:
raise SidsNotFound(sids=list(failures))
# We don't update the asset cache here because it should already be
# updated by `self.retrieve_equities`.
update_hits(self.retrieve_equities(type_to_assets.pop('equity', ())))
update_hits(
self.retrieve_futures_contracts(type_to_assets.pop('future', ()))
)
# We shouldn't know about any other asset types.
if type_to_assets:
raise AssertionError(
"Found asset types: %s" % list(type_to_assets.keys())
)
return [hits[sid] for sid in sids] | [
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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
passed in as sql bind params.
Returns
-------
sel : Selectable
The sqlalchemy selectable that will query for the most recent
symbol for each sid.
Notes
-----
This is implemented as an inner select of the columns of interest
ordered by the end date of the (sid, symbol) mapping. We then group
that inner select on the sid with no aggregations to select the last
row per group which gives us the most recently active symbol for all
of the sids. | 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 because the sids will be
passed in as sql bind params.
Returns
-------
sel : Selectable
The sqlalchemy selectable that will query for the most recent
symbol for each sid.
Notes
-----
This is implemented as an inner select of the columns of interest
ordered by the end date of the (sid, symbol) mapping. We then group
that inner select on the sid with no aggregations to select the last
row per group which gives us the most recently active symbol for all
of the sids.
"""
cols = self.equity_symbol_mappings.c
# These are the columns we actually want.
data_cols = (cols.sid,) + tuple(cols[name] for name in symbol_columns)
# Also select the max of end_date so that all non-grouped fields take
# on the value associated with the max end_date. The SQLite docs say
# this:
#
# When the min() or max() aggregate functions are used in an aggregate
# query, all bare columns in the result set take values from the input
# row which also contains the minimum or maximum. Only the built-in
# min() and max() functions work this way.
#
# See https://www.sqlite.org/lang_select.html#resultset, for more info.
to_select = data_cols + (sa.func.max(cols.end_date),)
return sa.select(
to_select,
).where(
cols.sid.in_(map(int, sid_group))
).group_by(
cols.sid,
) | 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 because the sids will be
passed in as sql bind params.
Returns
-------
sel : Selectable
The sqlalchemy selectable that will query for the most recent
symbol for each sid.
Notes
-----
This is implemented as an inner select of the columns of interest
ordered by the end date of the (sid, symbol) mapping. We then group
that inner select on the sid with no aggregations to select the last
row per group which gives us the most recently active symbol for all
of the sids.
"""
cols = self.equity_symbol_mappings.c
# These are the columns we actually want.
data_cols = (cols.sid,) + tuple(cols[name] for name in symbol_columns)
# Also select the max of end_date so that all non-grouped fields take
# on the value associated with the max end_date. The SQLite docs say
# this:
#
# When the min() or max() aggregate functions are used in an aggregate
# query, all bare columns in the result set take values from the input
# row which also contains the minimum or maximum. Only the built-in
# min() and max() functions work this way.
#
# See https://www.sqlite.org/lang_select.html#resultset, for more info.
to_select = data_cols + (sa.func.max(cols.end_date),)
return sa.select(
to_select,
).where(
cols.sid.in_(map(int, sid_group))
).group_by(
cols.sid,
) | [
"Retrieve",
"the",
"most",
"recent",
"symbol",
"for",
"a",
"set",
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"sids",
"."
] | quantopian/zipline | python | https://github.com/quantopian/zipline/blob/77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe/zipline/assets/assets.py#L600-L647 | [
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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 from which to query assets.
asset_type : type
Type of asset to be constructed.
Returns
-------
assets : dict[int -> Asset]
Dict mapping requested sids to the retrieved assets. | 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
Asset ids to look up.
asset_tbl : sqlalchemy.Table
Table from which to query assets.
asset_type : type
Type of asset to be constructed.
Returns
-------
assets : dict[int -> Asset]
Dict mapping requested sids to the retrieved assets.
"""
# Fastpath for empty request.
if not sids:
return {}
cache = self._asset_cache
hits = {}
querying_equities = issubclass(asset_type, Equity)
filter_kwargs = (
_filter_equity_kwargs
if querying_equities else
_filter_future_kwargs
)
rows = self._retrieve_asset_dicts(sids, asset_tbl, querying_equities)
for row in rows:
sid = row['sid']
asset = asset_type(**filter_kwargs(row))
hits[sid] = cache[sid] = asset
# If we get here, it means something in our code thought that a
# particular sid was an equity/future and called this function with a
# concrete type, but we couldn't actually resolve the asset. This is
# an error in our code, not a user-input error.
misses = tuple(set(sids) - viewkeys(hits))
if misses:
if querying_equities:
raise EquitiesNotFound(sids=misses)
else:
raise FutureContractsNotFound(sids=misses)
return hits | 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
Asset ids to look up.
asset_tbl : sqlalchemy.Table
Table from which to query assets.
asset_type : type
Type of asset to be constructed.
Returns
-------
assets : dict[int -> Asset]
Dict mapping requested sids to the retrieved assets.
"""
# Fastpath for empty request.
if not sids:
return {}
cache = self._asset_cache
hits = {}
querying_equities = issubclass(asset_type, Equity)
filter_kwargs = (
_filter_equity_kwargs
if querying_equities else
_filter_future_kwargs
)
rows = self._retrieve_asset_dicts(sids, asset_tbl, querying_equities)
for row in rows:
sid = row['sid']
asset = asset_type(**filter_kwargs(row))
hits[sid] = cache[sid] = asset
# If we get here, it means something in our code thought that a
# particular sid was an equity/future and called this function with a
# concrete type, but we couldn't actually resolve the asset. This is
# an error in our code, not a user-input error.
misses = tuple(set(sids) - viewkeys(hits))
if misses:
if querying_equities:
raise EquitiesNotFound(sids=misses)
else:
raise FutureContractsNotFound(sids=misses)
return hits | [
"Internal",
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"for",
"loading",
"assets",
"from",
"a",
"table",
"."
] | quantopian/zipline | python | https://github.com/quantopian/zipline/blob/77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe/zipline/assets/assets.py#L689-L740 | [
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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?
symbol : str
The symbol to look up.
as_of_date : datetime or None
If multiple assets have held this sid, which day should the
resolution be checked against? If this value is None and multiple
sids have held the ticker, then a MultipleSymbolsFound error will
be raised.
Returns
-------
asset : Asset
The asset that held the given symbol.
Raises
------
SymbolNotFound
Raised when the symbol or symbol as_of_date pair do not map to
any assets.
MultipleSymbolsFound
Raised when multiple assets held the symbol. This happens if
multiple assets held the symbol at disjoint times and
``as_of_date`` is None, or if multiple assets held the symbol at
the same time and``multi_country`` is True.
Notes
-----
The resolution algorithm is as follows:
- Split the symbol into the company and share class component.
- Do a dictionary lookup of the
``(company_symbol, share_class_symbol)`` in the provided ownership
map.
- If there is no entry in the dictionary, we don't know about this
symbol so raise a ``SymbolNotFound`` error.
- If ``as_of_date`` is None:
- If more there is more than one owner, raise
``MultipleSymbolsFound``
- Otherwise, because the list mapped to a symbol cannot be empty,
return the single asset.
- Iterate through all of the owners:
- If the ``as_of_date`` is between the start and end of the ownership
period:
- If multi_country is False, return the found asset.
- Otherwise, put the asset in a list.
- At the end of the loop, if there are no candidate assets, raise a
``SymbolNotFound``.
- If there is exactly one candidate, return it.
- Othewise, raise ``MultipleSymbolsFound`` because the ticker is not
unique across 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
----------
ownership_map : dict[(str, str), list[OwnershipPeriod]]
The mapping from split symbols to ownership periods.
multi_country : bool
Does this mapping span multiple countries?
symbol : str
The symbol to look up.
as_of_date : datetime or None
If multiple assets have held this sid, which day should the
resolution be checked against? If this value is None and multiple
sids have held the ticker, then a MultipleSymbolsFound error will
be raised.
Returns
-------
asset : Asset
The asset that held the given symbol.
Raises
------
SymbolNotFound
Raised when the symbol or symbol as_of_date pair do not map to
any assets.
MultipleSymbolsFound
Raised when multiple assets held the symbol. This happens if
multiple assets held the symbol at disjoint times and
``as_of_date`` is None, or if multiple assets held the symbol at
the same time and``multi_country`` is True.
Notes
-----
The resolution algorithm is as follows:
- Split the symbol into the company and share class component.
- Do a dictionary lookup of the
``(company_symbol, share_class_symbol)`` in the provided ownership
map.
- If there is no entry in the dictionary, we don't know about this
symbol so raise a ``SymbolNotFound`` error.
- If ``as_of_date`` is None:
- If more there is more than one owner, raise
``MultipleSymbolsFound``
- Otherwise, because the list mapped to a symbol cannot be empty,
return the single asset.
- Iterate through all of the owners:
- If the ``as_of_date`` is between the start and end of the ownership
period:
- If multi_country is False, return the found asset.
- Otherwise, put the asset in a list.
- At the end of the loop, if there are no candidate assets, raise a
``SymbolNotFound``.
- If there is exactly one candidate, return it.
- Othewise, raise ``MultipleSymbolsFound`` because the ticker is not
unique across countries.
"""
# split the symbol into the components, if there are no
# company/share class parts then share_class_symbol will be empty
company_symbol, share_class_symbol = split_delimited_symbol(symbol)
try:
owners = ownership_map[company_symbol, share_class_symbol]
assert owners, 'empty owners list for %r' % symbol
except KeyError:
# no equity has ever held this symbol
raise SymbolNotFound(symbol=symbol)
if not as_of_date:
# exactly one equity has ever held this symbol, we may resolve
# without the date
if len(owners) == 1:
return self.retrieve_asset(owners[0].sid)
options = {self.retrieve_asset(owner.sid) for owner in owners}
if multi_country:
country_codes = map(attrgetter('country_code'), options)
if len(set(country_codes)) > 1:
raise SameSymbolUsedAcrossCountries(
symbol=symbol,
options=dict(zip(country_codes, options))
)
# more than one equity has held this ticker, this
# is ambiguous without the date
raise MultipleSymbolsFound(symbol=symbol, options=options)
options = []
country_codes = []
for start, end, sid, _ in owners:
if start <= as_of_date < end:
# find the equity that owned it on the given asof date
asset = self.retrieve_asset(sid)
# if this asset owned the symbol on this asof date and we are
# only searching one country, return that asset
if not multi_country:
return asset
else:
options.append(asset)
country_codes.append(asset.country_code)
if not options:
# no equity held the ticker on the given asof date
raise SymbolNotFound(symbol=symbol)
# if there is one valid option given the asof date, return that option
if len(options) == 1:
return options[0]
# if there's more than one option given the asof date, a country code
# must be passed to resolve the symbol to an asset
raise SameSymbolUsedAcrossCountries(
symbol=symbol,
options=dict(zip(country_codes, options))
) | def _lookup_symbol_strict(self,
ownership_map,
multi_country,
symbol,
as_of_date):
"""
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?
symbol : str
The symbol to look up.
as_of_date : datetime or None
If multiple assets have held this sid, which day should the
resolution be checked against? If this value is None and multiple
sids have held the ticker, then a MultipleSymbolsFound error will
be raised.
Returns
-------
asset : Asset
The asset that held the given symbol.
Raises
------
SymbolNotFound
Raised when the symbol or symbol as_of_date pair do not map to
any assets.
MultipleSymbolsFound
Raised when multiple assets held the symbol. This happens if
multiple assets held the symbol at disjoint times and
``as_of_date`` is None, or if multiple assets held the symbol at
the same time and``multi_country`` is True.
Notes
-----
The resolution algorithm is as follows:
- Split the symbol into the company and share class component.
- Do a dictionary lookup of the
``(company_symbol, share_class_symbol)`` in the provided ownership
map.
- If there is no entry in the dictionary, we don't know about this
symbol so raise a ``SymbolNotFound`` error.
- If ``as_of_date`` is None:
- If more there is more than one owner, raise
``MultipleSymbolsFound``
- Otherwise, because the list mapped to a symbol cannot be empty,
return the single asset.
- Iterate through all of the owners:
- If the ``as_of_date`` is between the start and end of the ownership
period:
- If multi_country is False, return the found asset.
- Otherwise, put the asset in a list.
- At the end of the loop, if there are no candidate assets, raise a
``SymbolNotFound``.
- If there is exactly one candidate, return it.
- Othewise, raise ``MultipleSymbolsFound`` because the ticker is not
unique across countries.
"""
# split the symbol into the components, if there are no
# company/share class parts then share_class_symbol will be empty
company_symbol, share_class_symbol = split_delimited_symbol(symbol)
try:
owners = ownership_map[company_symbol, share_class_symbol]
assert owners, 'empty owners list for %r' % symbol
except KeyError:
# no equity has ever held this symbol
raise SymbolNotFound(symbol=symbol)
if not as_of_date:
# exactly one equity has ever held this symbol, we may resolve
# without the date
if len(owners) == 1:
return self.retrieve_asset(owners[0].sid)
options = {self.retrieve_asset(owner.sid) for owner in owners}
if multi_country:
country_codes = map(attrgetter('country_code'), options)
if len(set(country_codes)) > 1:
raise SameSymbolUsedAcrossCountries(
symbol=symbol,
options=dict(zip(country_codes, options))
)
# more than one equity has held this ticker, this
# is ambiguous without the date
raise MultipleSymbolsFound(symbol=symbol, options=options)
options = []
country_codes = []
for start, end, sid, _ in owners:
if start <= as_of_date < end:
# find the equity that owned it on the given asof date
asset = self.retrieve_asset(sid)
# if this asset owned the symbol on this asof date and we are
# only searching one country, return that asset
if not multi_country:
return asset
else:
options.append(asset)
country_codes.append(asset.country_code)
if not options:
# no equity held the ticker on the given asof date
raise SymbolNotFound(symbol=symbol)
# if there is one valid option given the asof date, return that option
if len(options) == 1:
return options[0]
# if there's more than one option given the asof date, a country code
# must be passed to resolve the symbol to an asset
raise SameSymbolUsedAcrossCountries(
symbol=symbol,
options=dict(zip(country_codes, options))
) | [
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"without",
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"."
] | quantopian/zipline | python | https://github.com/quantopian/zipline/blob/77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe/zipline/assets/assets.py#L742-L865 | [
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"company_symbol",
","... | 77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe |
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
if exactly one equity has ever owned the ticker.
fuzzy : bool, optional
Should fuzzy symbol matching be used? Fuzzy symbol matching
attempts to resolve differences in representations for
shareclasses. For example, some people may represent the ``A``
shareclass of ``BRK`` as ``BRK.A``, where others could write
``BRK_A``.
country_code : str or None, optional
The country to limit searches to. If not provided, the search will
span all countries which increases the likelihood of an ambiguous
lookup.
Returns
-------
equity : Equity
The equity that held ``symbol`` on the given ``as_of_date``, or the
only equity to hold ``symbol`` if ``as_of_date`` is None.
Raises
------
SymbolNotFound
Raised when no equity has ever held the given symbol.
MultipleSymbolsFound
Raised when no ``as_of_date`` is given and more than one equity
has held ``symbol``. This is also raised when ``fuzzy=True`` and
there are multiple candidates for the given ``symbol`` on the
``as_of_date``. Also raised when no ``country_code`` is given and
the symbol is ambiguous across multiple countries. | 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_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
if exactly one equity has ever owned the ticker.
fuzzy : bool, optional
Should fuzzy symbol matching be used? Fuzzy symbol matching
attempts to resolve differences in representations for
shareclasses. For example, some people may represent the ``A``
shareclass of ``BRK`` as ``BRK.A``, where others could write
``BRK_A``.
country_code : str or None, optional
The country to limit searches to. If not provided, the search will
span all countries which increases the likelihood of an ambiguous
lookup.
Returns
-------
equity : Equity
The equity that held ``symbol`` on the given ``as_of_date``, or the
only equity to hold ``symbol`` if ``as_of_date`` is None.
Raises
------
SymbolNotFound
Raised when no equity has ever held the given symbol.
MultipleSymbolsFound
Raised when no ``as_of_date`` is given and more than one equity
has held ``symbol``. This is also raised when ``fuzzy=True`` and
there are multiple candidates for the given ``symbol`` on the
``as_of_date``. Also raised when no ``country_code`` is given and
the symbol is ambiguous across multiple countries.
"""
if symbol is None:
raise TypeError("Cannot lookup asset for symbol of None for "
"as of date %s." % as_of_date)
if fuzzy:
f = self._lookup_symbol_fuzzy
mapping = self._choose_fuzzy_symbol_ownership_map(country_code)
else:
f = self._lookup_symbol_strict
mapping = self._choose_symbol_ownership_map(country_code)
if mapping is None:
raise SymbolNotFound(symbol=symbol)
return f(
mapping,
country_code is None,
symbol,
as_of_date,
) | 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_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
if exactly one equity has ever owned the ticker.
fuzzy : bool, optional
Should fuzzy symbol matching be used? Fuzzy symbol matching
attempts to resolve differences in representations for
shareclasses. For example, some people may represent the ``A``
shareclass of ``BRK`` as ``BRK.A``, where others could write
``BRK_A``.
country_code : str or None, optional
The country to limit searches to. If not provided, the search will
span all countries which increases the likelihood of an ambiguous
lookup.
Returns
-------
equity : Equity
The equity that held ``symbol`` on the given ``as_of_date``, or the
only equity to hold ``symbol`` if ``as_of_date`` is None.
Raises
------
SymbolNotFound
Raised when no equity has ever held the given symbol.
MultipleSymbolsFound
Raised when no ``as_of_date`` is given and more than one equity
has held ``symbol``. This is also raised when ``fuzzy=True`` and
there are multiple candidates for the given ``symbol`` on the
``as_of_date``. Also raised when no ``country_code`` is given and
the symbol is ambiguous across multiple countries.
"""
if symbol is None:
raise TypeError("Cannot lookup asset for symbol of None for "
"as of date %s." % as_of_date)
if fuzzy:
f = self._lookup_symbol_fuzzy
mapping = self._choose_fuzzy_symbol_ownership_map(country_code)
else:
f = self._lookup_symbol_strict
mapping = self._choose_symbol_ownership_map(country_code)
if mapping is None:
raise SymbolNotFound(symbol=symbol)
return f(
mapping,
country_code is None,
symbol,
as_of_date,
) | [
"Lookup",
"an",
"equity",
"by",
"symbol",
"."
] | quantopian/zipline | python | https://github.com/quantopian/zipline/blob/77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe/zipline/assets/assets.py#L955-L1016 | [
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"\"as ... | 77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe |
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 resolve.
as_of_date : pd.Timestamp
Forwarded to ``lookup_symbol``.
fuzzy : bool, optional
Forwarded to ``lookup_symbol``.
country_code : str or None, optional
The country to limit searches to. If not provided, the search will
span all countries which increases the likelihood of an ambiguous
lookup.
Returns
-------
equities : list[Equity] | 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 symbols]
but potentially faster because repeated lookups are memoized.
Parameters
----------
symbols : sequence[str]
Sequence of ticker symbols to resolve.
as_of_date : pd.Timestamp
Forwarded to ``lookup_symbol``.
fuzzy : bool, optional
Forwarded to ``lookup_symbol``.
country_code : str or None, optional
The country to limit searches to. If not provided, the search will
span all countries which increases the likelihood of an ambiguous
lookup.
Returns
-------
equities : list[Equity]
"""
if not symbols:
return []
multi_country = country_code is None
if fuzzy:
f = self._lookup_symbol_fuzzy
mapping = self._choose_fuzzy_symbol_ownership_map(country_code)
else:
f = self._lookup_symbol_strict
mapping = self._choose_symbol_ownership_map(country_code)
if mapping is None:
raise SymbolNotFound(symbol=symbols[0])
memo = {}
out = []
append_output = out.append
for sym in symbols:
if sym in memo:
append_output(memo[sym])
else:
equity = memo[sym] = f(
mapping,
multi_country,
sym,
as_of_date,
)
append_output(equity)
return out | 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 symbols]
but potentially faster because repeated lookups are memoized.
Parameters
----------
symbols : sequence[str]
Sequence of ticker symbols to resolve.
as_of_date : pd.Timestamp
Forwarded to ``lookup_symbol``.
fuzzy : bool, optional
Forwarded to ``lookup_symbol``.
country_code : str or None, optional
The country to limit searches to. If not provided, the search will
span all countries which increases the likelihood of an ambiguous
lookup.
Returns
-------
equities : list[Equity]
"""
if not symbols:
return []
multi_country = country_code is None
if fuzzy:
f = self._lookup_symbol_fuzzy
mapping = self._choose_fuzzy_symbol_ownership_map(country_code)
else:
f = self._lookup_symbol_strict
mapping = self._choose_symbol_ownership_map(country_code)
if mapping is None:
raise SymbolNotFound(symbol=symbols[0])
memo = {}
out = []
append_output = out.append
for sym in symbols:
if sym in memo:
append_output(memo[sym])
else:
equity = memo[sym] = f(
mapping,
multi_country,
sym,
as_of_date,
)
append_output(equity)
return out | [
"Lookup",
"a",
"list",
"of",
"equities",
"by",
"symbol",
"."
] | quantopian/zipline | python | https://github.com/quantopian/zipline/blob/77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe/zipline/assets/assets.py#L1018-L1077 | [
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"None",
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"f... | 77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe |
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
Raised when no contract named 'symbol' is found. | 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``.
Raises
------
SymbolNotFound
Raised when no contract named 'symbol' is found.
"""
data = self._select_asset_by_symbol(self.futures_contracts, symbol)\
.execute().fetchone()
# If no data found, raise an exception
if not data:
raise SymbolNotFound(symbol=symbol)
return self.retrieve_asset(data['sid']) | 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``.
Raises
------
SymbolNotFound
Raised when no contract named 'symbol' is found.
"""
data = self._select_asset_by_symbol(self.futures_contracts, symbol)\
.execute().fetchone()
# If no data found, raise an exception
if not data:
raise SymbolNotFound(symbol=symbol)
return self.retrieve_asset(data['sid']) | [
"Lookup",
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"."
] | quantopian/zipline | python | https://github.com/quantopian/zipline/blob/77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe/zipline/assets/assets.py#L1079-L1105 | [
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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. If None, a
value is returned only if we've only ever had one value for
this sid. If None and we've had multiple values,
MultipleValuesFoundForSid is raised.
Raises
------
NoValueForSid
If we have no values for this asset, or no values was known
on this as_of_date.
MultipleValuesFoundForSid
If we have had multiple values for this asset over time, and
None was passed for as_of_date. | 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 : pd.Timestamp, None
The last known value on this date is returned. If None, a
value is returned only if we've only ever had one value for
this sid. If None and we've had multiple values,
MultipleValuesFoundForSid is raised.
Raises
------
NoValueForSid
If we have no values for this asset, or no values was known
on this as_of_date.
MultipleValuesFoundForSid
If we have had multiple values for this asset over time, and
None was passed for as_of_date.
"""
try:
periods = self.equity_supplementary_map_by_sid[
field_name,
sid,
]
assert periods, 'empty periods list for %r' % (field_name, sid)
except KeyError:
raise NoValueForSid(field=field_name, sid=sid)
if not as_of_date:
if len(periods) > 1:
# This equity has held more than one value, this is ambigious
# without the date
raise MultipleValuesFoundForSid(
field=field_name,
sid=sid,
options={p.value for p in periods},
)
# this equity has only ever held this value, we may resolve
# without the date
return periods[0].value
for start, end, _, value in periods:
if start <= as_of_date < end:
return value
# Could not find a value for this sid on the as_of_date.
raise NoValueForSid(field=field_name, sid=sid) | 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 : pd.Timestamp, None
The last known value on this date is returned. If None, a
value is returned only if we've only ever had one value for
this sid. If None and we've had multiple values,
MultipleValuesFoundForSid is raised.
Raises
------
NoValueForSid
If we have no values for this asset, or no values was known
on this as_of_date.
MultipleValuesFoundForSid
If we have had multiple values for this asset over time, and
None was passed for as_of_date.
"""
try:
periods = self.equity_supplementary_map_by_sid[
field_name,
sid,
]
assert periods, 'empty periods list for %r' % (field_name, sid)
except KeyError:
raise NoValueForSid(field=field_name, sid=sid)
if not as_of_date:
if len(periods) > 1:
# This equity has held more than one value, this is ambigious
# without the date
raise MultipleValuesFoundForSid(
field=field_name,
sid=sid,
options={p.value for p in periods},
)
# this equity has only ever held this value, we may resolve
# without the date
return periods[0].value
for start, end, _, value in periods:
if start <= as_of_date < end:
return value
# Could not find a value for this sid on the as_of_date.
raise NoValueForSid(field=field_name, sid=sid) | [
"Get",
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] | quantopian/zipline | python | https://github.com/quantopian/zipline/blob/77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe/zipline/assets/assets.py#L1142-L1193 | [
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"'empty pe... | 77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe |
train | AssetFinder._lookup_generic_scalar | Convert asset_convertible to an asset.
On success, append to matches.
On failure, append to missing. | zipline/assets/assets.py | def _lookup_generic_scalar(self,
obj,
as_of_date,
country_code,
matches,
missing):
"""
Convert asset_convertible to an asset.
On success, append to matches.
On failure, append to missing.
"""
result = self._lookup_generic_scalar_helper(
obj, as_of_date, country_code,
)
if result is not None:
matches.append(result)
else:
missing.append(obj) | def _lookup_generic_scalar(self,
obj,
as_of_date,
country_code,
matches,
missing):
"""
Convert asset_convertible to an asset.
On success, append to matches.
On failure, append to missing.
"""
result = self._lookup_generic_scalar_helper(
obj, as_of_date, country_code,
)
if result is not None:
matches.append(result)
else:
missing.append(obj) | [
"Convert",
"asset_convertible",
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"asset",
"."
] | quantopian/zipline | python | https://github.com/quantopian/zipline/blob/77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe/zipline/assets/assets.py#L1298-L1316 | [
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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.
Parameters
----------
obj : int, str, Asset, ContinuousFuture, or iterable
The object to be converted into one or more Assets.
Integers are interpreted as sids. Strings are interpreted as
tickers. Assets and ContinuousFutures are returned unchanged.
as_of_date : pd.Timestamp or None
Timestamp to use to disambiguate ticker lookups. Has the same
semantics as in `lookup_symbol`.
country_code : str or None
ISO-3166 country code to use to disambiguate ticker lookups. Has
the same semantics as in `lookup_symbol`.
Returns
-------
matches, missing : tuple
``matches`` is the result of the conversion. ``missing`` is a list
containing any values that couldn't be resolved. If ``obj`` is not
an iterable, ``missing`` will be an empty list. | 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
not be used for internal code where we already know the expected types
of our inputs.
Parameters
----------
obj : int, str, Asset, ContinuousFuture, or iterable
The object to be converted into one or more Assets.
Integers are interpreted as sids. Strings are interpreted as
tickers. Assets and ContinuousFutures are returned unchanged.
as_of_date : pd.Timestamp or None
Timestamp to use to disambiguate ticker lookups. Has the same
semantics as in `lookup_symbol`.
country_code : str or None
ISO-3166 country code to use to disambiguate ticker lookups. Has
the same semantics as in `lookup_symbol`.
Returns
-------
matches, missing : tuple
``matches`` is the result of the conversion. ``missing`` is a list
containing any values that couldn't be resolved. If ``obj`` is not
an iterable, ``missing`` will be an empty list.
"""
matches = []
missing = []
# Interpret input as scalar.
if isinstance(obj, (AssetConvertible, ContinuousFuture)):
self._lookup_generic_scalar(
obj=obj,
as_of_date=as_of_date,
country_code=country_code,
matches=matches,
missing=missing,
)
try:
return matches[0], missing
except IndexError:
if hasattr(obj, '__int__'):
raise SidsNotFound(sids=[obj])
else:
raise SymbolNotFound(symbol=obj)
# Interpret input as iterable.
try:
iterator = iter(obj)
except TypeError:
raise NotAssetConvertible(
"Input was not a AssetConvertible "
"or iterable of AssetConvertible."
)
for obj in iterator:
self._lookup_generic_scalar(
obj=obj,
as_of_date=as_of_date,
country_code=country_code,
matches=matches,
missing=missing,
)
return matches, missing | 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
not be used for internal code where we already know the expected types
of our inputs.
Parameters
----------
obj : int, str, Asset, ContinuousFuture, or iterable
The object to be converted into one or more Assets.
Integers are interpreted as sids. Strings are interpreted as
tickers. Assets and ContinuousFutures are returned unchanged.
as_of_date : pd.Timestamp or None
Timestamp to use to disambiguate ticker lookups. Has the same
semantics as in `lookup_symbol`.
country_code : str or None
ISO-3166 country code to use to disambiguate ticker lookups. Has
the same semantics as in `lookup_symbol`.
Returns
-------
matches, missing : tuple
``matches`` is the result of the conversion. ``missing`` is a list
containing any values that couldn't be resolved. If ``obj`` is not
an iterable, ``missing`` will be an empty list.
"""
matches = []
missing = []
# Interpret input as scalar.
if isinstance(obj, (AssetConvertible, ContinuousFuture)):
self._lookup_generic_scalar(
obj=obj,
as_of_date=as_of_date,
country_code=country_code,
matches=matches,
missing=missing,
)
try:
return matches[0], missing
except IndexError:
if hasattr(obj, '__int__'):
raise SidsNotFound(sids=[obj])
else:
raise SymbolNotFound(symbol=obj)
# Interpret input as iterable.
try:
iterator = iter(obj)
except TypeError:
raise NotAssetConvertible(
"Input was not a AssetConvertible "
"or iterable of AssetConvertible."
)
for obj in iterator:
self._lookup_generic_scalar(
obj=obj,
as_of_date=as_of_date,
country_code=country_code,
matches=matches,
missing=missing,
)
return matches, missing | [
"Convert",
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"object",
"into",
"an",
"Asset",
"or",
"sequence",
"of",
"Assets",
"."
] | quantopian/zipline | python | https://github.com/quantopian/zipline/blob/77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe/zipline/assets/assets.py#L1347-L1414 | [
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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
if country_codes:
buf = np.array(
tuple(
sa.select((
equities_cols.sid,
equities_cols.start_date,
equities_cols.end_date,
)).where(
(self.exchanges.c.exchange == equities_cols.exchange) &
(self.exchanges.c.country_code.in_(country_codes))
).execute(),
),
dtype='f8', # use doubles so we get NaNs
)
else:
buf = np.array([], dtype='f8')
lifetimes = np.recarray(
buf=buf,
shape=(len(buf),),
dtype=[
('sid', 'f8'),
('start', 'f8'),
('end', 'f8')
],
)
start = lifetimes.start
end = lifetimes.end
start[np.isnan(start)] = 0 # convert missing starts to 0
end[np.isnan(end)] = np.iinfo(int).max # convert missing end to INTMAX
# Cast the results back down to int.
return lifetimes.astype([
('sid', 'i8'),
('start', 'i8'),
('end', 'i8'),
]) | def _compute_asset_lifetimes(self, country_codes):
"""
Compute and cache a recarray of asset lifetimes.
"""
equities_cols = self.equities.c
if country_codes:
buf = np.array(
tuple(
sa.select((
equities_cols.sid,
equities_cols.start_date,
equities_cols.end_date,
)).where(
(self.exchanges.c.exchange == equities_cols.exchange) &
(self.exchanges.c.country_code.in_(country_codes))
).execute(),
),
dtype='f8', # use doubles so we get NaNs
)
else:
buf = np.array([], dtype='f8')
lifetimes = np.recarray(
buf=buf,
shape=(len(buf),),
dtype=[
('sid', 'f8'),
('start', 'f8'),
('end', 'f8')
],
)
start = lifetimes.start
end = lifetimes.end
start[np.isnan(start)] = 0 # convert missing starts to 0
end[np.isnan(end)] = np.iinfo(int).max # convert missing end to INTMAX
# Cast the results back down to int.
return lifetimes.astype([
('sid', 'i8'),
('start', 'i8'),
('end', 'i8'),
]) | [
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"."
] | quantopian/zipline | python | https://github.com/quantopian/zipline/blob/77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe/zipline/assets/assets.py#L1416-L1456 | [
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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.
This is useful in a backtesting context where `lifetimes` is being
used to signify "do I have data for this asset as of the morning of
this date?" For many financial metrics, (e.g. daily close), data
isn't available for an asset until the end of the asset's first
day.
country_codes : iterable[str]
The country codes to get lifetimes for.
Returns
-------
lifetimes : pd.DataFrame
A frame of dtype bool with `dates` as index and an Int64Index of
assets as columns. The value at `lifetimes.loc[date, asset]` will
be True iff `asset` existed on `date`. If `include_start_date` is
False, then lifetimes.loc[date, asset] will be false when date ==
asset.start_date.
See Also
--------
numpy.putmask
zipline.pipeline.engine.SimplePipelineEngine._compute_root_mask | 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.
include_start_date : bool
Whether or not to count the asset as alive on its start_date.
This is useful in a backtesting context where `lifetimes` is being
used to signify "do I have data for this asset as of the morning of
this date?" For many financial metrics, (e.g. daily close), data
isn't available for an asset until the end of the asset's first
day.
country_codes : iterable[str]
The country codes to get lifetimes for.
Returns
-------
lifetimes : pd.DataFrame
A frame of dtype bool with `dates` as index and an Int64Index of
assets as columns. The value at `lifetimes.loc[date, asset]` will
be True iff `asset` existed on `date`. If `include_start_date` is
False, then lifetimes.loc[date, asset] will be false when date ==
asset.start_date.
See Also
--------
numpy.putmask
zipline.pipeline.engine.SimplePipelineEngine._compute_root_mask
"""
if isinstance(country_codes, string_types):
raise TypeError(
"Got string {!r} instead of an iterable of strings in "
"AssetFinder.lifetimes.".format(country_codes),
)
# normalize to a cache-key so that we can memoize results.
country_codes = frozenset(country_codes)
lifetimes = self._asset_lifetimes.get(country_codes)
if lifetimes is None:
self._asset_lifetimes[country_codes] = lifetimes = (
self._compute_asset_lifetimes(country_codes)
)
raw_dates = as_column(dates.asi8)
if include_start_date:
mask = lifetimes.start <= raw_dates
else:
mask = lifetimes.start < raw_dates
mask &= (raw_dates <= lifetimes.end)
return pd.DataFrame(mask, index=dates, columns=lifetimes.sid) | 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.
include_start_date : bool
Whether or not to count the asset as alive on its start_date.
This is useful in a backtesting context where `lifetimes` is being
used to signify "do I have data for this asset as of the morning of
this date?" For many financial metrics, (e.g. daily close), data
isn't available for an asset until the end of the asset's first
day.
country_codes : iterable[str]
The country codes to get lifetimes for.
Returns
-------
lifetimes : pd.DataFrame
A frame of dtype bool with `dates` as index and an Int64Index of
assets as columns. The value at `lifetimes.loc[date, asset]` will
be True iff `asset` existed on `date`. If `include_start_date` is
False, then lifetimes.loc[date, asset] will be false when date ==
asset.start_date.
See Also
--------
numpy.putmask
zipline.pipeline.engine.SimplePipelineEngine._compute_root_mask
"""
if isinstance(country_codes, string_types):
raise TypeError(
"Got string {!r} instead of an iterable of strings in "
"AssetFinder.lifetimes.".format(country_codes),
)
# normalize to a cache-key so that we can memoize results.
country_codes = frozenset(country_codes)
lifetimes = self._asset_lifetimes.get(country_codes)
if lifetimes is None:
self._asset_lifetimes[country_codes] = lifetimes = (
self._compute_asset_lifetimes(country_codes)
)
raw_dates = as_column(dates.asi8)
if include_start_date:
mask = lifetimes.start <= raw_dates
else:
mask = lifetimes.start < raw_dates
mask &= (raw_dates <= lifetimes.end)
return pd.DataFrame(mask, index=dates, columns=lifetimes.sid) | [
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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 this country.
"""
sids = self._compute_asset_lifetimes([country_code]).sid
return tuple(sids.tolist()) | 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 this country.
"""
sids = self._compute_asset_lifetimes([country_code]).sid
return tuple(sids.tolist()) | [
"Return",
"all",
"of",
"the",
"sids",
"for",
"a",
"given",
"country",
"."
] | quantopian/zipline | python | https://github.com/quantopian/zipline/blob/77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe/zipline/assets/assets.py#L1516-L1530 | [
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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
-------
list of np.ndarray
A list with an entry per field of ndarrays with shape
(minutes in range, sids) with a dtype of float64, containing the
values for the respective field over start and end dt range. | 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 int
The asset identifiers in the window.
Returns
-------
list of np.ndarray
A list with an entry per field of ndarrays with shape
(minutes in range, sids) with a dtype of float64, containing the
values for the respective field over start and end dt range.
"""
rolls_by_asset = {}
for asset in assets:
rf = self._roll_finders[asset.roll_style]
rolls_by_asset[asset] = rf.get_rolls(
asset.root_symbol,
start_date,
end_date,
asset.offset
)
num_sessions = len(
self.trading_calendar.sessions_in_range(start_date, end_date)
)
shape = num_sessions, len(assets)
results = []
tc = self._bar_reader.trading_calendar
sessions = tc.sessions_in_range(start_date, end_date)
# Get partitions
partitions_by_asset = {}
for asset in assets:
partitions = []
partitions_by_asset[asset] = partitions
rolls = rolls_by_asset[asset]
start = start_date
for roll in rolls:
sid, roll_date = roll
start_loc = sessions.get_loc(start)
if roll_date is not None:
end = roll_date - sessions.freq
end_loc = sessions.get_loc(end)
else:
end = end_date
end_loc = len(sessions) - 1
partitions.append((sid, start, end, start_loc, end_loc))
if roll_date is not None:
start = sessions[end_loc + 1]
for column in columns:
if column != 'volume' and column != 'sid':
out = np.full(shape, np.nan)
else:
out = np.zeros(shape, dtype=np.int64)
for i, asset in enumerate(assets):
partitions = partitions_by_asset[asset]
for sid, start, end, start_loc, end_loc in partitions:
if column != 'sid':
result = self._bar_reader.load_raw_arrays(
[column], start, end, [sid])[0][:, 0]
else:
result = int(sid)
out[start_loc:end_loc + 1, i] = result
results.append(out)
return results | 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 int
The asset identifiers in the window.
Returns
-------
list of np.ndarray
A list with an entry per field of ndarrays with shape
(minutes in range, sids) with a dtype of float64, containing the
values for the respective field over start and end dt range.
"""
rolls_by_asset = {}
for asset in assets:
rf = self._roll_finders[asset.roll_style]
rolls_by_asset[asset] = rf.get_rolls(
asset.root_symbol,
start_date,
end_date,
asset.offset
)
num_sessions = len(
self.trading_calendar.sessions_in_range(start_date, end_date)
)
shape = num_sessions, len(assets)
results = []
tc = self._bar_reader.trading_calendar
sessions = tc.sessions_in_range(start_date, end_date)
# Get partitions
partitions_by_asset = {}
for asset in assets:
partitions = []
partitions_by_asset[asset] = partitions
rolls = rolls_by_asset[asset]
start = start_date
for roll in rolls:
sid, roll_date = roll
start_loc = sessions.get_loc(start)
if roll_date is not None:
end = roll_date - sessions.freq
end_loc = sessions.get_loc(end)
else:
end = end_date
end_loc = len(sessions) - 1
partitions.append((sid, start, end, start_loc, end_loc))
if roll_date is not None:
start = sessions[end_loc + 1]
for column in columns:
if column != 'volume' and column != 'sid':
out = np.full(shape, np.nan)
else:
out = np.zeros(shape, dtype=np.int64)
for i, asset in enumerate(assets):
partitions = partitions_by_asset[asset]
for sid, start, end, start_loc, end_loc in partitions:
if column != 'sid':
result = self._bar_reader.load_raw_arrays(
[column], start, end, [sid])[0][:, 0]
else:
result = int(sid)
out[start_loc:end_loc + 1, i] = result
results.append(out)
return results | [
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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
-------
value : float|int
The value at the given coordinates, ``float`` for OHLC, ``int``
for 'volume'.
Raises
------
NoDataOnDate
If the given dt is not a valid market minute (in minute mode) or
session (in daily mode) according to this reader's tradingcalendar. | 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
The OHLVC name for the desired data point.
Returns
-------
value : float|int
The value at the given coordinates, ``float`` for OHLC, ``int``
for 'volume'.
Raises
------
NoDataOnDate
If the given dt is not a valid market minute (in minute mode) or
session (in daily mode) according to this reader's tradingcalendar.
"""
rf = self._roll_finders[continuous_future.roll_style]
sid = (rf.get_contract_center(continuous_future.root_symbol,
dt,
continuous_future.offset))
return self._bar_reader.get_value(sid, dt, field) | 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
The OHLVC name for the desired data point.
Returns
-------
value : float|int
The value at the given coordinates, ``float`` for OHLC, ``int``
for 'volume'.
Raises
------
NoDataOnDate
If the given dt is not a valid market minute (in minute mode) or
session (in daily mode) according to this reader's tradingcalendar.
"""
rf = self._roll_finders[continuous_future.roll_style]
sid = (rf.get_contract_center(continuous_future.root_symbol,
dt,
continuous_future.offset))
return self._bar_reader.get_value(sid, dt, field) | [
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] | quantopian/zipline | python | https://github.com/quantopian/zipline/blob/77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe/zipline/data/continuous_future_reader.py#L127-L156 | [
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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
The minute at which to start searching for the last traded minute.
Returns
-------
last_traded : pd.Timestamp
The dt of the last trade for the given asset, using the input
dt as a vantage point. | 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 last traded minute.
dt : pd.Timestamp
The minute at which to start searching for the last traded minute.
Returns
-------
last_traded : pd.Timestamp
The dt of the last trade for the given asset, using the input
dt as a vantage point.
"""
rf = self._roll_finders[asset.roll_style]
sid = (rf.get_contract_center(asset.root_symbol,
dt,
asset.offset))
if sid is None:
return pd.NaT
contract = rf.asset_finder.retrieve_asset(sid)
return self._bar_reader.get_last_traded_dt(contract, dt) | 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 last traded minute.
dt : pd.Timestamp
The minute at which to start searching for the last traded minute.
Returns
-------
last_traded : pd.Timestamp
The dt of the last trade for the given asset, using the input
dt as a vantage point.
"""
rf = self._roll_finders[asset.roll_style]
sid = (rf.get_contract_center(asset.root_symbol,
dt,
asset.offset))
if sid is None:
return pd.NaT
contract = rf.asset_finder.retrieve_asset(sid)
return self._bar_reader.get_last_traded_dt(contract, dt) | [
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] | quantopian/zipline | python | https://github.com/quantopian/zipline/blob/77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe/zipline/data/continuous_future_reader.py#L158-L184 | [
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... | 77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe |
train | ContinuousFutureMinuteBarReader.load_raw_arrays | Parameters
----------
fields : list of str
'open', 'high', 'low', 'close', or 'volume'
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
-------
list of np.ndarray
A list with an entry per field of ndarrays with shape
(minutes in range, sids) with a dtype of float64, containing the
values for the respective field over start and end dt range. | zipline/data/continuous_future_reader.py | def load_raw_arrays(self, columns, start_date, end_date, assets):
"""
Parameters
----------
fields : list of str
'open', 'high', 'low', 'close', or 'volume'
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
-------
list of np.ndarray
A list with an entry per field of ndarrays with shape
(minutes in range, sids) with a dtype of float64, containing the
values for the respective field over start and end dt range.
"""
rolls_by_asset = {}
tc = self.trading_calendar
start_session = tc.minute_to_session_label(start_date)
end_session = tc.minute_to_session_label(end_date)
for asset in assets:
rf = self._roll_finders[asset.roll_style]
rolls_by_asset[asset] = rf.get_rolls(
asset.root_symbol,
start_session,
end_session, asset.offset)
sessions = tc.sessions_in_range(start_date, end_date)
minutes = tc.minutes_in_range(start_date, end_date)
num_minutes = len(minutes)
shape = num_minutes, len(assets)
results = []
# Get partitions
partitions_by_asset = {}
for asset in assets:
partitions = []
partitions_by_asset[asset] = partitions
rolls = rolls_by_asset[asset]
start = start_date
for roll in rolls:
sid, roll_date = roll
start_loc = minutes.searchsorted(start)
if roll_date is not None:
_, end = tc.open_and_close_for_session(
roll_date - sessions.freq)
end_loc = minutes.searchsorted(end)
else:
end = end_date
end_loc = len(minutes) - 1
partitions.append((sid, start, end, start_loc, end_loc))
if roll[-1] is not None:
start, _ = tc.open_and_close_for_session(
tc.minute_to_session_label(minutes[end_loc + 1]))
for column in columns:
if column != 'volume':
out = np.full(shape, np.nan)
else:
out = np.zeros(shape, dtype=np.uint32)
for i, asset in enumerate(assets):
partitions = partitions_by_asset[asset]
for sid, start, end, start_loc, end_loc in partitions:
if column != 'sid':
result = self._bar_reader.load_raw_arrays(
[column], start, end, [sid])[0][:, 0]
else:
result = int(sid)
out[start_loc:end_loc + 1, i] = result
results.append(out)
return results | def load_raw_arrays(self, columns, start_date, end_date, assets):
"""
Parameters
----------
fields : list of str
'open', 'high', 'low', 'close', or 'volume'
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
-------
list of np.ndarray
A list with an entry per field of ndarrays with shape
(minutes in range, sids) with a dtype of float64, containing the
values for the respective field over start and end dt range.
"""
rolls_by_asset = {}
tc = self.trading_calendar
start_session = tc.minute_to_session_label(start_date)
end_session = tc.minute_to_session_label(end_date)
for asset in assets:
rf = self._roll_finders[asset.roll_style]
rolls_by_asset[asset] = rf.get_rolls(
asset.root_symbol,
start_session,
end_session, asset.offset)
sessions = tc.sessions_in_range(start_date, end_date)
minutes = tc.minutes_in_range(start_date, end_date)
num_minutes = len(minutes)
shape = num_minutes, len(assets)
results = []
# Get partitions
partitions_by_asset = {}
for asset in assets:
partitions = []
partitions_by_asset[asset] = partitions
rolls = rolls_by_asset[asset]
start = start_date
for roll in rolls:
sid, roll_date = roll
start_loc = minutes.searchsorted(start)
if roll_date is not None:
_, end = tc.open_and_close_for_session(
roll_date - sessions.freq)
end_loc = minutes.searchsorted(end)
else:
end = end_date
end_loc = len(minutes) - 1
partitions.append((sid, start, end, start_loc, end_loc))
if roll[-1] is not None:
start, _ = tc.open_and_close_for_session(
tc.minute_to_session_label(minutes[end_loc + 1]))
for column in columns:
if column != 'volume':
out = np.full(shape, np.nan)
else:
out = np.zeros(shape, dtype=np.uint32)
for i, asset in enumerate(assets):
partitions = partitions_by_asset[asset]
for sid, start, end, start_loc, end_loc in partitions:
if column != 'sid':
result = self._bar_reader.load_raw_arrays(
[column], start, end, [sid])[0][:, 0]
else:
result = int(sid)
out[start_loc:end_loc + 1, i] = result
results.append(out)
return results | [
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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 times number of shares held
times the multiplier.
"""
position_values = pd.Series({
asset: (
position.last_sale_price *
position.amount *
asset.price_multiplier
)
for asset, position in self.positions.items()
})
return position_values / self.portfolio_value | 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 times number of shares held
times the multiplier.
"""
position_values = pd.Series({
asset: (
position.last_sale_price *
position.amount *
asset.price_multiplier
)
for asset, position in self.positions.items()
})
return position_values / self.portfolio_value | [
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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
Returns
-------
dict
{
'content_img_list': '', # 托管后的图片列表
'content_html': '', # 图片链接为托管后的图片链接内容
} | 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
}
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
Returns
-------
dict
{
'content_img_list': '', # 托管后的图片列表
'content_html': '', # 图片链接为托管后的图片链接内容
}
"""
assert callable(hosting_callback)
content_img_list = content_info.pop("content_img_list")
content_html = content_info.pop("content_html")
for idx, img_url in enumerate(content_img_list):
hosting_img_url = hosting_callback(img_url)
if not hosting_img_url:
# todo 定义标准异常
raise Exception()
content_img_list[idx] = hosting_img_url
content_html = content_html.replace(img_url, hosting_img_url)
return dict(content_img_list=content_img_list, content_html=content_html) | 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
}
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
Returns
-------
dict
{
'content_img_list': '', # 托管后的图片列表
'content_html': '', # 图片链接为托管后的图片链接内容
}
"""
assert callable(hosting_callback)
content_img_list = content_info.pop("content_img_list")
content_html = content_info.pop("content_html")
for idx, img_url in enumerate(content_img_list):
hosting_img_url = hosting_callback(img_url)
if not hosting_img_url:
# todo 定义标准异常
raise Exception()
content_img_list[idx] = hosting_img_url
content_html = content_html.replace(img_url, hosting_img_url)
return dict(content_img_list=content_img_list, content_html=content_html) | [
"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"
] | Chyroc/WechatSogou | python | https://github.com/Chyroc/WechatSogou/blob/2e0e9886f555fd8bcfc7ae9718ced6ce955cd24a/wechatsogou/api.py#L138-L171 | [
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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
处理验证码函数，输入验证码二进制数据，输出文字，参见 identify_image_callback_example
Returns
-------
dict or None
{
'open_id': '', # 微信号唯一ID
'profile_url': '', # 最近10条群发页链接
'headimage': '', # 头像
'wechat_name': '', # 名称
'wechat_id': '', # 微信id
'post_perm': '', # 最近一月群发数
'qrcode': '', # 二维码
'introduction': '', # 介绍
'authentication': '' # 认证
} | 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_callback : callable
处理出现验证码页面的函数，参见 unlock_callback_example
identify_image_callback : callable
处理验证码函数，输入验证码二进制数据，输出文字，参见 identify_image_callback_example
Returns
-------
dict or None
{
'open_id': '', # 微信号唯一ID
'profile_url': '', # 最近10条群发页链接
'headimage': '', # 头像
'wechat_name': '', # 名称
'wechat_id': '', # 微信id
'post_perm': '', # 最近一月群发数
'qrcode': '', # 二维码
'introduction': '', # 介绍
'authentication': '' # 认证
}
"""
info = self.search_gzh(wecgat_id_or_name, 1, unlock_callback, identify_image_callback, decode_url)
try:
return next(info)
except StopIteration:
return None | 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_callback : callable
处理出现验证码页面的函数，参见 unlock_callback_example
identify_image_callback : callable
处理验证码函数，输入验证码二进制数据，输出文字，参见 identify_image_callback_example
Returns
-------
dict or None
{
'open_id': '', # 微信号唯一ID
'profile_url': '', # 最近10条群发页链接
'headimage': '', # 头像
'wechat_name': '', # 名称
'wechat_id': '', # 微信id
'post_perm': '', # 最近一月群发数
'qrcode': '', # 二维码
'introduction': '', # 介绍
'authentication': '' # 认证
}
"""
info = self.search_gzh(wecgat_id_or_name, 1, unlock_callback, identify_image_callback, decode_url)
try:
return next(info)
except StopIteration:
return None | [
"获取公众号微信号",
"wechatid",
"的信息"
] | Chyroc/WechatSogou | python | https://github.com/Chyroc/WechatSogou/blob/2e0e9886f555fd8bcfc7ae9718ced6ce955cd24a/wechatsogou/api.py#L208-L241 | [
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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
Parameters
----------
keyword : str or unicode
搜索文字
page : int, optional
页数 the default is 1
unlock_callback : callable
处理出现验证码页面的函数，参见 unlock_callback_example
identify_image_callback : callable
处理验证码函数，输入验证码二进制数据，输出文字，参见 identify_image_callback_example
decode_url : bool
是否解析 url
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
'wechat_name': '', # 名称
'wechat_id': '', # 微信id
'post_perm': '', # 最近一月群发数
'qrcode': '', # 二维码
'introduction': '', # 介绍
'authentication': '' # 认证
}
Raises
------
WechatSogouRequestsException
requests error | 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
Parameters
----------
keyword : str or unicode
搜索文字
page : int, optional
页数 the default is 1
unlock_callback : callable
处理出现验证码页面的函数，参见 unlock_callback_example
identify_image_callback : callable
处理验证码函数，输入验证码二进制数据，输出文字，参见 identify_image_callback_example
decode_url : bool
是否解析 url
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
'wechat_name': '', # 名称
'wechat_id': '', # 微信id
'post_perm': '', # 最近一月群发数
'qrcode': '', # 二维码
'introduction': '', # 介绍
'authentication': '' # 认证
}
Raises
------
WechatSogouRequestsException
requests error
"""
url = WechatSogouRequest.gen_search_gzh_url(keyword, page)
session = requests.session()
resp = self.__get_by_unlock(url,
unlock_platform=self.__unlock_sogou,
unlock_callback=unlock_callback,
identify_image_callback=identify_image_callback,
session=session)
gzh_list = WechatSogouStructuring.get_gzh_by_search(resp.text)
for i in gzh_list:
if decode_url:
i['profile_url'] = self.__format_url(i['profile_url'], url, resp.text, unlock_callback=unlock_callback, identify_image_callback=identify_image_callback, session=session)
yield i | 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
Parameters
----------
keyword : str or unicode
搜索文字
page : int, optional
页数 the default is 1
unlock_callback : callable
处理出现验证码页面的函数，参见 unlock_callback_example
identify_image_callback : callable
处理验证码函数，输入验证码二进制数据，输出文字，参见 identify_image_callback_example
decode_url : bool
是否解析 url
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
'wechat_name': '', # 名称
'wechat_id': '', # 微信id
'post_perm': '', # 最近一月群发数
'qrcode': '', # 二维码
'introduction': '', # 介绍
'authentication': '' # 认证
}
Raises
------
WechatSogouRequestsException
requests error
"""
url = WechatSogouRequest.gen_search_gzh_url(keyword, page)
session = requests.session()
resp = self.__get_by_unlock(url,
unlock_platform=self.__unlock_sogou,
unlock_callback=unlock_callback,
identify_image_callback=identify_image_callback,
session=session)
gzh_list = WechatSogouStructuring.get_gzh_by_search(resp.text)
for i in gzh_list:
if decode_url:
i['profile_url'] = self.__format_url(i['profile_url'], url, resp.text, unlock_callback=unlock_callback, identify_image_callback=identify_image_callback, session=session)
yield i | [
"搜索",
"公众号"
] | Chyroc/WechatSogou | python | https://github.com/Chyroc/WechatSogou/blob/2e0e9886f555fd8bcfc7ae9718ced6ce955cd24a/wechatsogou/api.py#L243-L296 | [
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... | 2e0e9886f555fd8bcfc7ae9718ced6ce955cd24a |
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
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
the default is anytime
article_type : WechatSogouConst.search_article_type
含有内容的类型 image 有图 / video 有视频 / rich 有图和视频 / all 啥都有
ft, et : datetime.date or None
当 tsn 是 specific 时，ft 代表开始时间，如： 2017-07-01
当 tsn 是 specific 时，et 代表结束时间，如： 2017-07-15
unlock_callback : callable
处理出现验证码页面的函数，参见 unlock_callback_example
identify_image_callback : callable
处理验证码函数，输入验证码二进制数据，输出文字，参见 identify_image_callback_example
decode_url : bool
是否解析 url
Returns
-------
list[dict]
{
'article': {
'title': '', # 文章标题
'url': '', # 文章链接
'imgs': '', # 文章图片list
'abstract': '', # 文章摘要
'time': '' # 文章推送时间
},
'gzh': {
'profile_url': '', # 公众号最近10条群发页链接
'headimage': '', # 头像
'wechat_name': '', # 名称
'isv': '', # 是否加v
}
}
Raises
------
WechatSogouRequestsException
requests error | 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_url=True):
"""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
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
the default is anytime
article_type : WechatSogouConst.search_article_type
含有内容的类型 image 有图 / video 有视频 / rich 有图和视频 / all 啥都有
ft, et : datetime.date or None
当 tsn 是 specific 时，ft 代表开始时间，如： 2017-07-01
当 tsn 是 specific 时，et 代表结束时间，如： 2017-07-15
unlock_callback : callable
处理出现验证码页面的函数，参见 unlock_callback_example
identify_image_callback : callable
处理验证码函数，输入验证码二进制数据，输出文字，参见 identify_image_callback_example
decode_url : bool
是否解析 url
Returns
-------
list[dict]
{
'article': {
'title': '', # 文章标题
'url': '', # 文章链接
'imgs': '', # 文章图片list
'abstract': '', # 文章摘要
'time': '' # 文章推送时间
},
'gzh': {
'profile_url': '', # 公众号最近10条群发页链接
'headimage': '', # 头像
'wechat_name': '', # 名称
'isv': '', # 是否加v
}
}
Raises
------
WechatSogouRequestsException
requests error
"""
url = WechatSogouRequest.gen_search_article_url(keyword, page, timesn, article_type, ft, et)
session = requests.session()
resp = self.__get_by_unlock(url, WechatSogouRequest.gen_search_article_url(keyword),
unlock_platform=self.__unlock_sogou,
unlock_callback=unlock_callback,
identify_image_callback=identify_image_callback,
session=session)
article_list = WechatSogouStructuring.get_article_by_search(resp.text)
for i in article_list:
if decode_url:
i['article']['url'] = self.__format_url(i['article']['url'], url, resp.text, unlock_callback=unlock_callback, identify_image_callback=identify_image_callback, session=session)
i['gzh']['profile_url'] = self.__format_url(i['gzh']['profile_url'], url, resp.text, unlock_callback=unlock_callback, identify_image_callback=identify_image_callback, session=session)
yield i | 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_url=True):
"""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
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
the default is anytime
article_type : WechatSogouConst.search_article_type
含有内容的类型 image 有图 / video 有视频 / rich 有图和视频 / all 啥都有
ft, et : datetime.date or None
当 tsn 是 specific 时，ft 代表开始时间，如： 2017-07-01
当 tsn 是 specific 时，et 代表结束时间，如： 2017-07-15
unlock_callback : callable
处理出现验证码页面的函数，参见 unlock_callback_example
identify_image_callback : callable
处理验证码函数，输入验证码二进制数据，输出文字，参见 identify_image_callback_example
decode_url : bool
是否解析 url
Returns
-------
list[dict]
{
'article': {
'title': '', # 文章标题
'url': '', # 文章链接
'imgs': '', # 文章图片list
'abstract': '', # 文章摘要
'time': '' # 文章推送时间
},
'gzh': {
'profile_url': '', # 公众号最近10条群发页链接
'headimage': '', # 头像
'wechat_name': '', # 名称
'isv': '', # 是否加v
}
}
Raises
------
WechatSogouRequestsException
requests error
"""
url = WechatSogouRequest.gen_search_article_url(keyword, page, timesn, article_type, ft, et)
session = requests.session()
resp = self.__get_by_unlock(url, WechatSogouRequest.gen_search_article_url(keyword),
unlock_platform=self.__unlock_sogou,
unlock_callback=unlock_callback,
identify_image_callback=identify_image_callback,
session=session)
article_list = WechatSogouStructuring.get_article_by_search(resp.text)
for i in article_list:
if decode_url:
i['article']['url'] = self.__format_url(i['article']['url'], url, resp.text, unlock_callback=unlock_callback, identify_image_callback=identify_image_callback, session=session)
i['gzh']['profile_url'] = self.__format_url(i['gzh']['profile_url'], url, resp.text, unlock_callback=unlock_callback, identify_image_callback=identify_image_callback, session=session)
yield i | [
"搜索",
"文章"
] | Chyroc/WechatSogou | python | https://github.com/Chyroc/WechatSogou/blob/2e0e9886f555fd8bcfc7ae9718ced6ce955cd24a/wechatsogou/api.py#L298-L369 | [
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"=... | 2e0e9886f555fd8bcfc7ae9718ced6ce955cd24a |
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_callback 不起作用
Parameters
----------
keyword : str or unicode
公众号的id 或者name
url : str or unicode
群发页url，如果不提供url，就先去搜索一遍拿到url
unlock_callback_sogou : callable
处理出现 搜索 的时候出现验证码的函数，参见 unlock_callback_example
identify_image_callback_sogou : callable
处理 搜索 的时候处理验证码函数，输入验证码二进制数据，输出文字，参见 identify_image_callback_example
unlock_callback_weixin : callable
处理出现 历史页 的时候出现验证码的函数，参见 unlock_callback_example
identify_image_callback_weixin : callable
处理 历史页 的时候处理验证码函数，输入验证码二进制数据，输出文字，参见 identify_image_callback_example
Returns
-------
dict
{
'gzh': {
'wechat_name': '', # 名称
'wechat_id': '', # 微信id
'introduction': '', # 描述
'authentication': '', # 认证
'headimage': '' # 头像
},
'article': [
{
'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
'datetime': '', # 群发datatime
'type': '', # 消息类型，均是49，表示图文
'main': 0, # 是否是一次群发的第一次消息
'title': '', # 文章标题
'abstract': '', # 摘要
'fileid': '', #
'content_url': '', # 文章链接
'source_url': '', # 阅读原文的链接
'cover': '', # 封面图
'author': '', # 作者
'copyright_stat': '', # 文章类型，例如：原创啊
},
...
]
}
Raises
------
WechatSogouRequestsException
requests error | 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_weixin=None):
"""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_callback 不起作用
Parameters
----------
keyword : str or unicode
公众号的id 或者name
url : str or unicode
群发页url，如果不提供url，就先去搜索一遍拿到url
unlock_callback_sogou : callable
处理出现 搜索 的时候出现验证码的函数，参见 unlock_callback_example
identify_image_callback_sogou : callable
处理 搜索 的时候处理验证码函数，输入验证码二进制数据，输出文字，参见 identify_image_callback_example
unlock_callback_weixin : callable
处理出现 历史页 的时候出现验证码的函数，参见 unlock_callback_example
identify_image_callback_weixin : callable
处理 历史页 的时候处理验证码函数，输入验证码二进制数据，输出文字，参见 identify_image_callback_example
Returns
-------
dict
{
'gzh': {
'wechat_name': '', # 名称
'wechat_id': '', # 微信id
'introduction': '', # 描述
'authentication': '', # 认证
'headimage': '' # 头像
},
'article': [
{
'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
'datetime': '', # 群发datatime
'type': '', # 消息类型，均是49，表示图文
'main': 0, # 是否是一次群发的第一次消息
'title': '', # 文章标题
'abstract': '', # 摘要
'fileid': '', #
'content_url': '', # 文章链接
'source_url': '', # 阅读原文的链接
'cover': '', # 封面图
'author': '', # 作者
'copyright_stat': '', # 文章类型，例如：原创啊
},
...
]
}
Raises
------
WechatSogouRequestsException
requests error
"""
if url is None:
gzh_list = self.get_gzh_info(keyword, unlock_callback_sogou, identify_image_callback_sogou)
if gzh_list is None:
return {}
if 'profile_url' not in gzh_list:
raise Exception() # todo use ws exception
url = gzh_list['profile_url']
resp = self.__get_by_unlock(url, WechatSogouRequest.gen_search_article_url(keyword),
unlock_platform=self.__unlock_wechat,
unlock_callback=unlock_callback_weixin,
identify_image_callback=identify_image_callback_weixin)
return WechatSogouStructuring.get_gzh_info_and_article_by_history(resp.text) | 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_weixin=None):
"""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_callback 不起作用
Parameters
----------
keyword : str or unicode
公众号的id 或者name
url : str or unicode
群发页url，如果不提供url，就先去搜索一遍拿到url
unlock_callback_sogou : callable
处理出现 搜索 的时候出现验证码的函数，参见 unlock_callback_example
identify_image_callback_sogou : callable
处理 搜索 的时候处理验证码函数，输入验证码二进制数据，输出文字，参见 identify_image_callback_example
unlock_callback_weixin : callable
处理出现 历史页 的时候出现验证码的函数，参见 unlock_callback_example
identify_image_callback_weixin : callable
处理 历史页 的时候处理验证码函数，输入验证码二进制数据，输出文字，参见 identify_image_callback_example
Returns
-------
dict
{
'gzh': {
'wechat_name': '', # 名称
'wechat_id': '', # 微信id
'introduction': '', # 描述
'authentication': '', # 认证
'headimage': '' # 头像
},
'article': [
{
'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
'datetime': '', # 群发datatime
'type': '', # 消息类型，均是49，表示图文
'main': 0, # 是否是一次群发的第一次消息
'title': '', # 文章标题
'abstract': '', # 摘要
'fileid': '', #
'content_url': '', # 文章链接
'source_url': '', # 阅读原文的链接
'cover': '', # 封面图
'author': '', # 作者
'copyright_stat': '', # 文章类型，例如：原创啊
},
...
]
}
Raises
------
WechatSogouRequestsException
requests error
"""
if url is None:
gzh_list = self.get_gzh_info(keyword, unlock_callback_sogou, identify_image_callback_sogou)
if gzh_list is None:
return {}
if 'profile_url' not in gzh_list:
raise Exception() # todo use ws exception
url = gzh_list['profile_url']
resp = self.__get_by_unlock(url, WechatSogouRequest.gen_search_article_url(keyword),
unlock_platform=self.__unlock_wechat,
unlock_callback=unlock_callback_weixin,
identify_image_callback=identify_image_callback_weixin)
return WechatSogouStructuring.get_gzh_info_and_article_by_history(resp.text) | [
"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, # 公众号头像
'wechat_name': str, # 公众号名称
},
'article': {
'url': str, # 文章临时链接
'title': str, # 文章标题
'abstract': str, # 文章摘要
'time': int, # 推送时间，10位时间戳
'open_id': str, # open id
'main_img': str # 封面图片
}
} | 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
-------
list[dict]
{
'gzh': {
'headimage': str, # 公众号头像
'wechat_name': str, # 公众号名称
},
'article': {
'url': str, # 文章临时链接
'title': str, # 文章标题
'abstract': str, # 文章摘要
'time': int, # 推送时间，10位时间戳
'open_id': str, # open id
'main_img': str # 封面图片
}
}
"""
assert hasattr(WechatSogouConst.hot_index, hot_index)
assert isinstance(page, int) and page > 0
url = WechatSogouRequest.gen_hot_url(hot_index, page)
resp = self.__get_by_unlock(url,
unlock_platform=self.__unlock_sogou,
unlock_callback=unlock_callback,
identify_image_callback=identify_image_callback)
resp.encoding = 'utf-8'
return WechatSogouStructuring.get_gzh_article_by_hot(resp.text) | 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
-------
list[dict]
{
'gzh': {
'headimage': str, # 公众号头像
'wechat_name': str, # 公众号名称
},
'article': {
'url': str, # 文章临时链接
'title': str, # 文章标题
'abstract': str, # 文章摘要
'time': int, # 推送时间，10位时间戳
'open_id': str, # open id
'main_img': str # 封面图片
}
}
"""
assert hasattr(WechatSogouConst.hot_index, hot_index)
assert isinstance(page, int) and page > 0
url = WechatSogouRequest.gen_hot_url(hot_index, page)
resp = self.__get_by_unlock(url,
unlock_platform=self.__unlock_sogou,
unlock_callback=unlock_callback,
identify_image_callback=identify_image_callback)
resp.encoding = 'utf-8'
return WechatSogouStructuring.get_gzh_article_by_hot(resp.text) | [
"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
True:微信原文中有插入的语音消息，则删除
False:微信源文中有插入的语音消息，则保留
unlock_callback : callable
处理 文章明细 的时候出现验证码的函数，参见 unlock_callback_example
identify_image_callback : callable
处理 文章明细 的时候处理验证码函数，输入验证码二进制数据，输出文字，参见 identify_image_callback_example
hosting_callback: callable
将微信采集的文章托管到7牛或者阿里云回调函数，输入微信图片源地址，返回托管后地址
Returns
-------
content_html
原文内容
content_img_list
文章中图片列表
Raises
------
WechatSogouRequestsException | 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 : 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
True:微信原文中有插入的语音消息，则删除
False:微信源文中有插入的语音消息，则保留
unlock_callback : callable
处理 文章明细 的时候出现验证码的函数，参见 unlock_callback_example
identify_image_callback : callable
处理 文章明细 的时候处理验证码函数，输入验证码二进制数据，输出文字，参见 identify_image_callback_example
hosting_callback: callable
将微信采集的文章托管到7牛或者阿里云回调函数，输入微信图片源地址，返回托管后地址
Returns
-------
content_html
原文内容
content_img_list
文章中图片列表
Raises
------
WechatSogouRequestsException
"""
resp = self.__get_by_unlock(url,
unlock_platform=self.__unlock_wechat,
unlock_callback=unlock_callback,
identify_image_callback=identify_image_callback)
resp.encoding = 'utf-8'
if '链接已过期' in resp.text:
raise WechatSogouException('get_article_content 链接 [{}] 已过期'.format(url))
if raw:
return resp.text
content_info = WechatSogouStructuring.get_article_detail(resp.text, del_qqmusic=del_qqmusic,
del_voice=del_mpvoice)
if hosting_callback:
content_info = self.__hosting_wechat_img(content_info, hosting_callback)
return content_info | 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 : 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
True:微信原文中有插入的语音消息，则删除
False:微信源文中有插入的语音消息，则保留
unlock_callback : callable
处理 文章明细 的时候出现验证码的函数，参见 unlock_callback_example
identify_image_callback : callable
处理 文章明细 的时候处理验证码函数，输入验证码二进制数据，输出文字，参见 identify_image_callback_example
hosting_callback: callable
将微信采集的文章托管到7牛或者阿里云回调函数，输入微信图片源地址，返回托管后地址
Returns
-------
content_html
原文内容
content_img_list
文章中图片列表
Raises
------
WechatSogouRequestsException
"""
resp = self.__get_by_unlock(url,
unlock_platform=self.__unlock_wechat,
unlock_callback=unlock_callback,
identify_image_callback=identify_image_callback)
resp.encoding = 'utf-8'
if '链接已过期' in resp.text:
raise WechatSogouException('get_article_content 链接 [{}] 已过期'.format(url))
if raw:
return resp.text
content_info = WechatSogouStructuring.get_article_detail(resp.text, del_qqmusic=del_qqmusic,
del_voice=del_mpvoice)
if hosting_callback:
content_info = self.__hosting_wechat_img(content_info, hosting_callback)
return content_info | [
"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.sugg.sogou.com/sugg/ajaj_json.jsp?key={}&type=wxpub&pr=web'.format(
quote(keyword.encode('utf-8')))
r = requests.get(url)
if not r.ok:
raise WechatSogouRequestsException('get_sugg', r)
sugg = re.findall(u'\["' + keyword + '",(.*?),\["', r.text)[0]
return json.loads(sugg) | 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.sugg.sogou.com/sugg/ajaj_json.jsp?key={}&type=wxpub&pr=web'.format(
quote(keyword.encode('utf-8')))
r = requests.get(url)
if not r.ok:
raise WechatSogouRequestsException('get_sugg', r)
sugg = re.findall(u'\["' + keyword + '",(.*?),\["', r.text)[0]
return json.loads(sugg) | [
"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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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据，输出文字，参见 identify_image_callback_example
Returns
-------
dict
{
'code': '',
'msg': '',
} | 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 : 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据，输出文字，参见 identify_image_callback_example
Returns
-------
dict
{
'code': '',
'msg': '',
}
"""
# no use resp
url_quote = url.split('weixin.sogou.com/')[-1]
unlock_url = 'http://weixin.sogou.com/antispider/thank.php'
data = {
'c': identify_image_callback(img),
'r': '%2F' + url_quote,
'v': 5
}
headers = {
'Content-Type': 'application/x-www-form-urlencoded; charset=UTF-8',
'Referer': 'http://weixin.sogou.com/antispider/?from=%2f' + url_quote
}
r_unlock = req.post(unlock_url, data, headers=headers)
r_unlock.encoding = 'utf-8'
if not r_unlock.ok:
raise WechatSogouVcodeOcrException(
'unlock[{}] failed: {}'.format(unlock_url, r_unlock.text, r_unlock.status_code))
return r_unlock.json() | 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 : 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据，输出文字，参见 identify_image_callback_example
Returns
-------
dict
{
'code': '',
'msg': '',
}
"""
# no use resp
url_quote = url.split('weixin.sogou.com/')[-1]
unlock_url = 'http://weixin.sogou.com/antispider/thank.php'
data = {
'c': identify_image_callback(img),
'r': '%2F' + url_quote,
'v': 5
}
headers = {
'Content-Type': 'application/x-www-form-urlencoded; charset=UTF-8',
'Referer': 'http://weixin.sogou.com/antispider/?from=%2f' + url_quote
}
r_unlock = req.post(unlock_url, data, headers=headers)
r_unlock.encoding = 'utf-8'
if not r_unlock.ok:
raise WechatSogouVcodeOcrException(
'unlock[{}] failed: {}'.format(unlock_url, r_unlock.text, r_unlock.status_code))
return r_unlock.json() | [
"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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... | 2e0e9886f555fd8bcfc7ae9718ced6ce955cd24a |
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据，输出文字，参见 identify_image_callback_example
Returns
-------
dict
{
'ret': '',
'errmsg': '',
'cookie_count': '',
} | 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 : 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据，输出文字，参见 identify_image_callback_example
Returns
-------
dict
{
'ret': '',
'errmsg': '',
'cookie_count': '',
}
"""
# no use resp
unlock_url = 'https://mp.weixin.qq.com/mp/verifycode'
data = {
'cert': time.time() * 1000,
'input': identify_image_callback(img)
}
headers = {
'Host': 'mp.weixin.qq.com',
'Content-Type': 'application/x-www-form-urlencoded; charset=UTF-8',
'Referer': url
}
r_unlock = req.post(unlock_url, data, headers=headers)
if not r_unlock.ok:
raise WechatSogouVcodeOcrException(
'unlock[{}] failed: {}[{}]'.format(unlock_url, r_unlock.text, r_unlock.status_code))
return r_unlock.json() | 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 : 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据，输出文字，参见 identify_image_callback_example
Returns
-------
dict
{
'ret': '',
'errmsg': '',
'cookie_count': '',
}
"""
# no use resp
unlock_url = 'https://mp.weixin.qq.com/mp/verifycode'
data = {
'cert': time.time() * 1000,
'input': identify_image_callback(img)
}
headers = {
'Host': 'mp.weixin.qq.com',
'Content-Type': 'application/x-www-form-urlencoded; charset=UTF-8',
'Referer': url
}
r_unlock = req.post(unlock_url, data, headers=headers)
if not r_unlock.ok:
raise WechatSogouVcodeOcrException(
'unlock[{}] failed: {}[{}]'.format(unlock_url, r_unlock.text, r_unlock.status_code))
return r_unlock.json() | [
"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 anytime
article_type : WechatSogouConst.search_article_type
含有内容的类型 image 有图 / video 有视频 / rich 有图和视频 / all 啥都有
默认是 all
ft, et : datetime.date
当 tsn 是 specific 时，ft 代表开始时间，如： 2017-07-01
当 tsn 是 specific 时，et 代表结束时间，如： 2017-07-15
Returns
-------
str
search_article_url | 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
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 anytime
article_type : WechatSogouConst.search_article_type
含有内容的类型 image 有图 / video 有视频 / rich 有图和视频 / all 啥都有
默认是 all
ft, et : datetime.date
当 tsn 是 specific 时，ft 代表开始时间，如： 2017-07-01
当 tsn 是 specific 时，et 代表结束时间，如： 2017-07-15
Returns
-------
str
search_article_url
"""
assert isinstance(page, int) and page > 0
assert timesn in [WechatSogouConst.search_article_time.anytime,
WechatSogouConst.search_article_time.day,
WechatSogouConst.search_article_time.week,
WechatSogouConst.search_article_time.month,
WechatSogouConst.search_article_time.year,
WechatSogouConst.search_article_time.specific]
if timesn == WechatSogouConst.search_article_time.specific:
assert isinstance(ft, datetime.date)
assert isinstance(et, datetime.date)
assert ft <= et
else:
ft = ''
et = ''
interation_image = 458754
interation_video = 458756
if article_type == WechatSogouConst.search_article_type.rich:
interation = '{},{}'.format(interation_image, interation_video)
elif article_type == WechatSogouConst.search_article_type.image:
interation = interation_image
elif article_type == WechatSogouConst.search_article_type.video:
interation = interation_video
else:
interation = ''
qs_dict = OrderedDict()
qs_dict['type'] = _search_type_article
qs_dict['page'] = page
qs_dict['ie'] = 'utf8'
qs_dict['query'] = keyword
qs_dict['interation'] = interation
if timesn != 0:
qs_dict['tsn'] = timesn
qs_dict['ft'] = str(ft)
qs_dict['et'] = str(et)
# TODO 账号内搜索
# '账号内 http://weixin.sogou.com/weixin?type=2&ie=utf8&query=%E9%AB%98%E8%80%83&tsn=3&ft=&et=&interation=458754
# &wxid=oIWsFt1tmWoG6vO6BcsS7St61bRE&usip=nanhangqinggong'
# qs['wxid'] = wxid
# qs['usip'] = usip
return 'http://weixin.sogou.com/weixin?{}'.format(urlencode(qs_dict)) | 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
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 anytime
article_type : WechatSogouConst.search_article_type
含有内容的类型 image 有图 / video 有视频 / rich 有图和视频 / all 啥都有
默认是 all
ft, et : datetime.date
当 tsn 是 specific 时，ft 代表开始时间，如： 2017-07-01
当 tsn 是 specific 时，et 代表结束时间，如： 2017-07-15
Returns
-------
str
search_article_url
"""
assert isinstance(page, int) and page > 0
assert timesn in [WechatSogouConst.search_article_time.anytime,
WechatSogouConst.search_article_time.day,
WechatSogouConst.search_article_time.week,
WechatSogouConst.search_article_time.month,
WechatSogouConst.search_article_time.year,
WechatSogouConst.search_article_time.specific]
if timesn == WechatSogouConst.search_article_time.specific:
assert isinstance(ft, datetime.date)
assert isinstance(et, datetime.date)
assert ft <= et
else:
ft = ''
et = ''
interation_image = 458754
interation_video = 458756
if article_type == WechatSogouConst.search_article_type.rich:
interation = '{},{}'.format(interation_image, interation_video)
elif article_type == WechatSogouConst.search_article_type.image:
interation = interation_image
elif article_type == WechatSogouConst.search_article_type.video:
interation = interation_video
else:
interation = ''
qs_dict = OrderedDict()
qs_dict['type'] = _search_type_article
qs_dict['page'] = page
qs_dict['ie'] = 'utf8'
qs_dict['query'] = keyword
qs_dict['interation'] = interation
if timesn != 0:
qs_dict['tsn'] = timesn
qs_dict['ft'] = str(ft)
qs_dict['et'] = str(et)
# TODO 账号内搜索
# '账号内 http://weixin.sogou.com/weixin?type=2&ie=utf8&query=%E9%AB%98%E8%80%83&tsn=3&ft=&et=&interation=458754
# &wxid=oIWsFt1tmWoG6vO6BcsS7St61bRE&usip=nanhangqinggong'
# qs['wxid'] = wxid
# qs['usip'] = usip
return 'http://weixin.sogou.com/weixin?{}'.format(urlencode(qs_dict)) | [
"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 isinstance(page, int) and page > 0
qs_dict = OrderedDict()
qs_dict['type'] = _search_type_gzh
qs_dict['page'] = page
qs_dict['ie'] = 'utf8'
qs_dict['query'] = keyword
return 'http://weixin.sogou.com/weixin?{}'.format(urlencode(qs_dict)) | 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 isinstance(page, int) and page > 0
qs_dict = OrderedDict()
qs_dict['type'] = _search_type_gzh
qs_dict['page'] = page
qs_dict['ie'] = 'utf8'
qs_dict['query'] = keyword
return 'http://weixin.sogou.com/weixin?{}'.format(urlencode(qs_dict)) | [
"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
"""
assert hasattr(WechatSogouConst.hot_index, hot_index)
assert isinstance(page, int) and page > 0
index_urls = {
WechatSogouConst.hot_index.hot: 0, # 热门
WechatSogouConst.hot_index.gaoxiao: 1, # 搞笑
WechatSogouConst.hot_index.health: 2, # 养生
WechatSogouConst.hot_index.sifanghua: 3, # 私房话
WechatSogouConst.hot_index.gossip: 4, # 八卦
WechatSogouConst.hot_index.technology: 5, # 科技
WechatSogouConst.hot_index.finance: 6, # 财经
WechatSogouConst.hot_index.car: 7, # 汽车
WechatSogouConst.hot_index.life: 8, # 生活
WechatSogouConst.hot_index.fashion: 9, # 时尚
WechatSogouConst.hot_index.mummy: 10, # 辣妈 / 育儿
WechatSogouConst.hot_index.travel: 11, # 旅行
WechatSogouConst.hot_index.job: 12, # 职场
WechatSogouConst.hot_index.food: 13, # 美食
WechatSogouConst.hot_index.history: 14, # 历史
WechatSogouConst.hot_index.study: 15, # 学霸 / 教育
WechatSogouConst.hot_index.constellation: 16, # 星座
WechatSogouConst.hot_index.sport: 17, # 体育
WechatSogouConst.hot_index.military: 18, # 军事
WechatSogouConst.hot_index.game: 19, # 游戏
WechatSogouConst.hot_index.pet: 20, # 萌宠
}
return 'http://weixin.sogou.com/wapindex/wap/0612/wap_{}/{}.html'.format(index_urls[hot_index], page - 1) | 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
"""
assert hasattr(WechatSogouConst.hot_index, hot_index)
assert isinstance(page, int) and page > 0
index_urls = {
WechatSogouConst.hot_index.hot: 0, # 热门
WechatSogouConst.hot_index.gaoxiao: 1, # 搞笑
WechatSogouConst.hot_index.health: 2, # 养生
WechatSogouConst.hot_index.sifanghua: 3, # 私房话
WechatSogouConst.hot_index.gossip: 4, # 八卦
WechatSogouConst.hot_index.technology: 5, # 科技
WechatSogouConst.hot_index.finance: 6, # 财经
WechatSogouConst.hot_index.car: 7, # 汽车
WechatSogouConst.hot_index.life: 8, # 生活
WechatSogouConst.hot_index.fashion: 9, # 时尚
WechatSogouConst.hot_index.mummy: 10, # 辣妈 / 育儿
WechatSogouConst.hot_index.travel: 11, # 旅行
WechatSogouConst.hot_index.job: 12, # 职场
WechatSogouConst.hot_index.food: 13, # 美食
WechatSogouConst.hot_index.history: 14, # 历史
WechatSogouConst.hot_index.study: 15, # 学霸 / 教育
WechatSogouConst.hot_index.constellation: 16, # 星座
WechatSogouConst.hot_index.sport: 17, # 体育
WechatSogouConst.hot_index.military: 18, # 军事
WechatSogouConst.hot_index.game: 19, # 游戏
WechatSogouConst.hot_index.pet: 20, # 萌宠
}
return 'http://weixin.sogou.com/wapindex/wap/0612/wap_{}/{}.html'.format(index_urls[hot_index], page - 1) | [
"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
"""
html_str_list = [
(''', '\''),
('"', '"'),
('&', '&'),
('¥', '¥'),
('amp;', ''),
('<', '<'),
('>', '>'),
(' ', ' '),
('\\', '')
]
for i in html_str_list:
s = s.replace(i[0], i[1])
return s | 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 = [
(''', '\''),
('"', '"'),
('&', '&'),
('¥', '¥'),
('amp;', ''),
('<', '<'),
('>', '>'),
(' ', ' '),
('\\', '')
]
for i in html_str_list:
s = s.replace(i[0], i[1])
return s | [
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"’等转义内容为正常内容"
] | Chyroc/WechatSogou | python | https://github.com/Chyroc/WechatSogou/blob/2e0e9886f555fd8bcfc7ae9718ced6ce955cd24a/wechatsogou/tools.py#L73-L95 | [
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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
'wechat_name': '', # 名称
'wechat_id': '', # 微信id
'post_perm': '', # 最近一月群发数
'view_perm': '', # 最近一月阅读量
'qrcode': '', # 二维码
'introduction': '', # 介绍
'authentication': '' # 认证
} | 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
'headimage': '', # 头像
'wechat_name': '', # 名称
'wechat_id': '', # 微信id
'post_perm': '', # 最近一月群发数
'view_perm': '', # 最近一月阅读量
'qrcode': '', # 二维码
'introduction': '', # 介绍
'authentication': '' # 认证
}
"""
post_view_perms = WechatSogouStructuring.__get_post_view_perm(text)
page = etree.HTML(text)
lis = page.xpath('//ul[@class="news-list2"]/li')
relist = []
for li in lis:
url = get_first_of_element(li, 'div/div[1]/a/@href')
headimage = format_image_url(get_first_of_element(li, 'div/div[1]/a/img/@src'))
wechat_name = get_elem_text(get_first_of_element(li, 'div/div[2]/p[1]'))
info = get_elem_text(get_first_of_element(li, 'div/div[2]/p[2]'))
qrcode = get_first_of_element(li, 'div/div[3]/span/img[1]/@src')
introduction = get_elem_text(get_first_of_element(li, 'dl[1]/dd'))
authentication = get_first_of_element(li, 'dl[2]/dd/text()')
relist.append({
'open_id': headimage.split('/')[-1],
'profile_url': url,
'headimage': headimage,
'wechat_name': wechat_name.replace('red_beg', '').replace('red_end', ''),
'wechat_id': info.replace('微信号：', ''),
'qrcode': qrcode,
'introduction': introduction.replace('red_beg', '').replace('red_end', ''),
'authentication': authentication,
'post_perm': -1,
'view_perm': -1,
})
if post_view_perms:
for i in relist:
if i['open_id'] in post_view_perms:
post_view_perm = post_view_perms[i['open_id']].split(',')
if len(post_view_perm) == 2:
i['post_perm'] = int(post_view_perm[0])
i['view_perm'] = int(post_view_perm[1])
return relist | 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
'headimage': '', # 头像
'wechat_name': '', # 名称
'wechat_id': '', # 微信id
'post_perm': '', # 最近一月群发数
'view_perm': '', # 最近一月阅读量
'qrcode': '', # 二维码
'introduction': '', # 介绍
'authentication': '' # 认证
}
"""
post_view_perms = WechatSogouStructuring.__get_post_view_perm(text)
page = etree.HTML(text)
lis = page.xpath('//ul[@class="news-list2"]/li')
relist = []
for li in lis:
url = get_first_of_element(li, 'div/div[1]/a/@href')
headimage = format_image_url(get_first_of_element(li, 'div/div[1]/a/img/@src'))
wechat_name = get_elem_text(get_first_of_element(li, 'div/div[2]/p[1]'))
info = get_elem_text(get_first_of_element(li, 'div/div[2]/p[2]'))
qrcode = get_first_of_element(li, 'div/div[3]/span/img[1]/@src')
introduction = get_elem_text(get_first_of_element(li, 'dl[1]/dd'))
authentication = get_first_of_element(li, 'dl[2]/dd/text()')
relist.append({
'open_id': headimage.split('/')[-1],
'profile_url': url,
'headimage': headimage,
'wechat_name': wechat_name.replace('red_beg', '').replace('red_end', ''),
'wechat_id': info.replace('微信号：', ''),
'qrcode': qrcode,
'introduction': introduction.replace('red_beg', '').replace('red_end', ''),
'authentication': authentication,
'post_perm': -1,
'view_perm': -1,
})
if post_view_perms:
for i in relist:
if i['open_id'] in post_view_perms:
post_view_perm = post_view_perms[i['open_id']].split(',')
if len(post_view_perm) == 2:
i['post_perm'] = int(post_view_perm[0])
i['view_perm'] = int(post_view_perm[1])
return relist | [
"从搜索公众号获得的文本",
"提取公众号信息"
] | Chyroc/WechatSogou | python | https://github.com/Chyroc/WechatSogou/blob/2e0e9886f555fd8bcfc7ae9718ced6ce955cd24a/wechatsogou/structuring.py#L46-L104 | [
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"'//ul[@class=\"news... | 2e0e9886f555fd8bcfc7ae9718ced6ce955cd24a |
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': '', # 文章图片list
'abstract': '', # 文章摘要
'time': '' # 文章推送时间
},
'gzh': {
'profile_url': '', # 公众号最近10条群发页链接
'headimage': '', # 头像
'wechat_name': '', # 名称
'isv': '', # 是否加v
}
} | 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': '', # 文章链接
'imgs': '', # 文章图片list
'abstract': '', # 文章摘要
'time': '' # 文章推送时间
},
'gzh': {
'profile_url': '', # 公众号最近10条群发页链接
'headimage': '', # 头像
'wechat_name': '', # 名称
'isv': '', # 是否加v
}
}
"""
page = etree.HTML(text)
lis = page.xpath('//ul[@class="news-list"]/li')
articles = []
for li in lis:
url = get_first_of_element(li, 'div[1]/a/@href')
if url:
title = get_first_of_element(li, 'div[2]/h3/a')
imgs = li.xpath('div[1]/a/img/@src')
abstract = get_first_of_element(li, 'div[2]/p')
time = get_first_of_element(li, 'div[2]/div/span/script/text()')
gzh_info = li.xpath('div[2]/div/a')[0]
else:
url = get_first_of_element(li, 'div/h3/a/@href')
title = get_first_of_element(li, 'div/h3/a')
imgs = []
spans = li.xpath('div/div[1]/a')
for span in spans:
img = span.xpath('span/img/@src')
if img:
imgs.append(img)
abstract = get_first_of_element(li, 'div/p')
time = get_first_of_element(li, 'div/div[2]/span/script/text()')
gzh_info = li.xpath('div/div[2]/a')[0]
if title is not None:
title = get_elem_text(title).replace("red_beg", "").replace("red_end", "")
if abstract is not None:
abstract = get_elem_text(abstract).replace("red_beg", "").replace("red_end", "")
time = re.findall('timeConvert\(\'(.*?)\'\)', time)
time = list_or_empty(time, int)
profile_url = get_first_of_element(gzh_info, '@href')
headimage = get_first_of_element(gzh_info, '@data-headimage')
wechat_name = get_first_of_element(gzh_info, 'text()')
gzh_isv = get_first_of_element(gzh_info, '@data-isv', int)
articles.append({
'article': {
'title': title,
'url': url,
'imgs': format_image_url(imgs),
'abstract': abstract,
'time': time
},
'gzh': {
'profile_url': profile_url,
'headimage': headimage,
'wechat_name': wechat_name,
'isv': gzh_isv,
}
})
return articles | 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': '', # 文章链接
'imgs': '', # 文章图片list
'abstract': '', # 文章摘要
'time': '' # 文章推送时间
},
'gzh': {
'profile_url': '', # 公众号最近10条群发页链接
'headimage': '', # 头像
'wechat_name': '', # 名称
'isv': '', # 是否加v
}
}
"""
page = etree.HTML(text)
lis = page.xpath('//ul[@class="news-list"]/li')
articles = []
for li in lis:
url = get_first_of_element(li, 'div[1]/a/@href')
if url:
title = get_first_of_element(li, 'div[2]/h3/a')
imgs = li.xpath('div[1]/a/img/@src')
abstract = get_first_of_element(li, 'div[2]/p')
time = get_first_of_element(li, 'div[2]/div/span/script/text()')
gzh_info = li.xpath('div[2]/div/a')[0]
else:
url = get_first_of_element(li, 'div/h3/a/@href')
title = get_first_of_element(li, 'div/h3/a')
imgs = []
spans = li.xpath('div/div[1]/a')
for span in spans:
img = span.xpath('span/img/@src')
if img:
imgs.append(img)
abstract = get_first_of_element(li, 'div/p')
time = get_first_of_element(li, 'div/div[2]/span/script/text()')
gzh_info = li.xpath('div/div[2]/a')[0]
if title is not None:
title = get_elem_text(title).replace("red_beg", "").replace("red_end", "")
if abstract is not None:
abstract = get_elem_text(abstract).replace("red_beg", "").replace("red_end", "")
time = re.findall('timeConvert\(\'(.*?)\'\)', time)
time = list_or_empty(time, int)
profile_url = get_first_of_element(gzh_info, '@href')
headimage = get_first_of_element(gzh_info, '@data-headimage')
wechat_name = get_first_of_element(gzh_info, 'text()')
gzh_isv = get_first_of_element(gzh_info, '@data-isv', int)
articles.append({
'article': {
'title': title,
'url': url,
'imgs': format_image_url(imgs),
'abstract': abstract,
'time': time
},
'gzh': {
'profile_url': profile_url,
'headimage': headimage,
'wechat_name': wechat_name,
'isv': gzh_isv,
}
})
return articles | [
"从搜索文章获得的文本",
"提取章列表信息"
] | 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
'authentication': '', # 认证
'headimage': '' # 头像
} | 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
'introduction': '', # 描述
'authentication': '', # 认证
'headimage': '' # 头像
}
"""
page = etree.HTML(text)
profile_area = get_first_of_element(page, '//div[@class="profile_info_area"]')
profile_img = get_first_of_element(profile_area, 'div[1]/span/img/@src')
profile_name = get_first_of_element(profile_area, 'div[1]/div/strong/text()')
profile_wechat_id = get_first_of_element(profile_area, 'div[1]/div/p/text()')
profile_desc = get_first_of_element(profile_area, 'ul/li[1]/div/text()')
profile_principal = get_first_of_element(profile_area, 'ul/li[2]/div/text()')
return {
'wechat_name': profile_name.strip(),
'wechat_id': profile_wechat_id.replace('微信号: ', '').strip('\n'),
'introduction': profile_desc,
'authentication': profile_principal,
'headimage': profile_img
} | 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
'introduction': '', # 描述
'authentication': '', # 认证
'headimage': '' # 头像
}
"""
page = etree.HTML(text)
profile_area = get_first_of_element(page, '//div[@class="profile_info_area"]')
profile_img = get_first_of_element(profile_area, 'div[1]/span/img/@src')
profile_name = get_first_of_element(profile_area, 'div[1]/div/strong/text()')
profile_wechat_id = get_first_of_element(profile_area, 'div[1]/div/p/text()')
profile_desc = get_first_of_element(profile_area, 'ul/li[1]/div/text()')
profile_principal = get_first_of_element(profile_area, 'ul/li[2]/div/text()')
return {
'wechat_name': profile_name.strip(),
'wechat_id': profile_wechat_id.replace('微信号: ', '').strip('\n'),
'introduction': profile_desc,
'authentication': profile_principal,
'headimage': profile_img
} | [
"从",
"历史消息页的文本",
"提取公众号信息"
] | 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
'datetime': '', # 群发datatime
'type': '', # 消息类型，均是49，表示图文
'main': 0, # 是否是一次群发的第一次消息
'title': '', # 文章标题
'abstract': '', # 摘要
'fileid': '', #
'content_url': '', # 文章链接
'source_url': '', # 阅读原文的链接
'cover': '', # 封面图
'author': '', # 作者
'copyright_stat': '', # 文章类型，例如：原创啊
} | 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]
{
'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
'datetime': '', # 群发datatime
'type': '', # 消息类型，均是49，表示图文
'main': 0, # 是否是一次群发的第一次消息
'title': '', # 文章标题
'abstract': '', # 摘要
'fileid': '', #
'content_url': '', # 文章链接
'source_url': '', # 阅读原文的链接
'cover': '', # 封面图
'author': '', # 作者
'copyright_stat': '', # 文章类型，例如：原创啊
}
"""
if article_json is None:
article_json = find_article_json_re.findall(text)
if not article_json:
return []
article_json = article_json[0] + '}}]}'
article_json = json.loads(article_json)
items = list()
for listdic in article_json['list']:
if str(listdic['comm_msg_info'].get('type', '')) != '49':
continue
comm_msg_info = listdic['comm_msg_info']
app_msg_ext_info = listdic['app_msg_ext_info']
send_id = comm_msg_info.get('id', '')
msg_datetime = comm_msg_info.get('datetime', '')
msg_type = str(comm_msg_info.get('type', ''))
items.append({
'send_id': send_id,
'datetime': msg_datetime,
'type': msg_type,
'main': 1, 'title': app_msg_ext_info.get('title', ''),
'abstract': app_msg_ext_info.get('digest', ''),
'fileid': app_msg_ext_info.get('fileid', ''),
'content_url': WechatSogouStructuring.__handle_content_url(app_msg_ext_info.get('content_url')),
'source_url': app_msg_ext_info.get('source_url', ''),
'cover': app_msg_ext_info.get('cover', ''),
'author': app_msg_ext_info.get('author', ''),
'copyright_stat': app_msg_ext_info.get('copyright_stat', '')
})
if app_msg_ext_info.get('is_multi', 0) == 1:
for multi_dict in app_msg_ext_info['multi_app_msg_item_list']:
items.append({
'send_id': send_id,
'datetime': msg_datetime,
'type': msg_type,
'main': 0, 'title': multi_dict.get('title', ''),
'abstract': multi_dict.get('digest', ''),
'fileid': multi_dict.get('fileid', ''),
'content_url': WechatSogouStructuring.__handle_content_url(multi_dict.get('content_url')),
'source_url': multi_dict.get('source_url', ''),
'cover': multi_dict.get('cover', ''),
'author': multi_dict.get('author', ''),
'copyright_stat': multi_dict.get('copyright_stat', '')
})
return list(filter(lambda x: x['content_url'], items)) | 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]
{
'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
'datetime': '', # 群发datatime
'type': '', # 消息类型，均是49，表示图文
'main': 0, # 是否是一次群发的第一次消息
'title': '', # 文章标题
'abstract': '', # 摘要
'fileid': '', #
'content_url': '', # 文章链接
'source_url': '', # 阅读原文的链接
'cover': '', # 封面图
'author': '', # 作者
'copyright_stat': '', # 文章类型，例如：原创啊
}
"""
if article_json is None:
article_json = find_article_json_re.findall(text)
if not article_json:
return []
article_json = article_json[0] + '}}]}'
article_json = json.loads(article_json)
items = list()
for listdic in article_json['list']:
if str(listdic['comm_msg_info'].get('type', '')) != '49':
continue
comm_msg_info = listdic['comm_msg_info']
app_msg_ext_info = listdic['app_msg_ext_info']
send_id = comm_msg_info.get('id', '')
msg_datetime = comm_msg_info.get('datetime', '')
msg_type = str(comm_msg_info.get('type', ''))
items.append({
'send_id': send_id,
'datetime': msg_datetime,
'type': msg_type,
'main': 1, 'title': app_msg_ext_info.get('title', ''),
'abstract': app_msg_ext_info.get('digest', ''),
'fileid': app_msg_ext_info.get('fileid', ''),
'content_url': WechatSogouStructuring.__handle_content_url(app_msg_ext_info.get('content_url')),
'source_url': app_msg_ext_info.get('source_url', ''),
'cover': app_msg_ext_info.get('cover', ''),
'author': app_msg_ext_info.get('author', ''),
'copyright_stat': app_msg_ext_info.get('copyright_stat', '')
})
if app_msg_ext_info.get('is_multi', 0) == 1:
for multi_dict in app_msg_ext_info['multi_app_msg_item_list']:
items.append({
'send_id': send_id,
'datetime': msg_datetime,
'type': msg_type,
'main': 0, 'title': multi_dict.get('title', ''),
'abstract': multi_dict.get('digest', ''),
'fileid': multi_dict.get('fileid', ''),
'content_url': WechatSogouStructuring.__handle_content_url(multi_dict.get('content_url')),
'source_url': multi_dict.get('source_url', ''),
'cover': multi_dict.get('cover', ''),
'author': multi_dict.get('author', ''),
'copyright_stat': multi_dict.get('copyright_stat', '')
})
return list(filter(lambda x: x['content_url'], items)) | [
"从",
"历史消息页的文本",
"提取文章列表信息"
] | Chyroc/WechatSogou | python | https://github.com/Chyroc/WechatSogou/blob/2e0e9886f555fd8bcfc7ae9718ced6ce955cd24a/wechatsogou/structuring.py#L256-L334 | [
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"... | 2e0e9886f555fd8bcfc7ae9718ced6ce955cd24a |
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
},
'article': {
'url': str, # 文章临时链接
'title': str, # 文章标题
'abstract': str, # 文章摘要
'time': int, # 推送时间，10位时间戳
'open_id': str, # open id
'main_img': str # 封面图片
}
} | 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
'wechat_name': str, # 公众号名称
},
'article': {
'url': str, # 文章临时链接
'title': str, # 文章标题
'abstract': str, # 文章摘要
'time': int, # 推送时间，10位时间戳
'open_id': str, # open id
'main_img': str # 封面图片
}
}
"""
page = etree.HTML(text)
lis = page.xpath('/html/body/li')
gzh_article_list = []
for li in lis:
url = get_first_of_element(li, 'div[1]/h4/a/@href')
title = get_first_of_element(li, 'div[1]/h4/a/div/text()')
abstract = get_first_of_element(li, 'div[1]/p[1]/text()')
xpath_time = get_first_of_element(li, 'div[1]/p[2]')
open_id = get_first_of_element(xpath_time, 'span/@data-openid')
headimage = get_first_of_element(xpath_time, 'span/@data-headimage')
gzh_name = get_first_of_element(xpath_time, 'span/text()')
send_time = xpath_time.xpath('a/span/@data-lastmodified')
main_img = get_first_of_element(li, 'div[2]/a/img/@src')
try:
send_time = int(send_time[0])
except ValueError:
send_time = send_time[0]
gzh_article_list.append({
'gzh': {
'headimage': headimage,
'wechat_name': gzh_name,
},
'article': {
'url': url,
'title': title,
'abstract': abstract,
'time': send_time,
'open_id': open_id,
'main_img': main_img
}
})
return gzh_article_list | 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
'wechat_name': str, # 公众号名称
},
'article': {
'url': str, # 文章临时链接
'title': str, # 文章标题
'abstract': str, # 文章摘要
'time': int, # 推送时间，10位时间戳
'open_id': str, # open id
'main_img': str # 封面图片
}
}
"""
page = etree.HTML(text)
lis = page.xpath('/html/body/li')
gzh_article_list = []
for li in lis:
url = get_first_of_element(li, 'div[1]/h4/a/@href')
title = get_first_of_element(li, 'div[1]/h4/a/div/text()')
abstract = get_first_of_element(li, 'div[1]/p[1]/text()')
xpath_time = get_first_of_element(li, 'div[1]/p[2]')
open_id = get_first_of_element(xpath_time, 'span/@data-openid')
headimage = get_first_of_element(xpath_time, 'span/@data-headimage')
gzh_name = get_first_of_element(xpath_time, 'span/text()')
send_time = xpath_time.xpath('a/span/@data-lastmodified')
main_img = get_first_of_element(li, 'div[2]/a/img/@src')
try:
send_time = int(send_time[0])
except ValueError:
send_time = send_time[0]
gzh_article_list.append({
'gzh': {
'headimage': headimage,
'wechat_name': gzh_name,
},
'article': {
'url': url,
'title': title,
'abstract': abstract,
'time': send_time,
'open_id': open_id,
'main_img': main_img
}
})
return gzh_article_list | [
"从",
"首页热门搜索",
"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/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
{
'content_html': str # 微信文本内容
'content_img_list': list[img_url1, img_url2, ...] # 微信文本中图片列表
} | 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
删除文章中的语音内容
Returns
-------
dict
{
'content_html': str # 微信文本内容
'content_img_list': list[img_url1, img_url2, ...] # 微信文本中图片列表
}
"""
# 1. 获取微信文本content
html_obj = BeautifulSoup(text, "lxml")
content_text = html_obj.find('div', {'class': 'rich_media_content', 'id': 'js_content'})
# 2. 删除部分标签
if del_qqmusic:
qqmusic = content_text.find_all('qqmusic') or []
for music in qqmusic:
music.parent.decompose()
if del_voice:
# voice是一个p标签下的mpvoice标签以及class为'js_audio_frame db'的span构成，所以将父标签删除
voices = content_text.find_all('mpvoice') or []
for voice in voices:
voice.parent.decompose()
# 3. 获取所有的图片 [img标签，和style中的background-image]
all_img_set = set()
all_img_element = content_text.find_all('img') or []
for ele in all_img_element:
# 删除部分属性
img_url = format_image_url(ele.attrs['data-src'])
del ele.attrs['data-src']
ele.attrs['src'] = img_url
if not img_url.startswith('http'):
raise WechatSogouException('img_url [{}] 不合法'.format(img_url))
all_img_set.add(img_url)
backgroud_image = content_text.find_all(style=re.compile("background-image")) or []
for ele in backgroud_image:
# 删除部分属性
if ele.attrs.get('data-src'):
del ele.attrs['data-src']
if ele.attrs.get('data-wxurl'):
del ele.attrs['data-wxurl']
img_url = re.findall(backgroud_image_p, str(ele))
if not img_url:
continue
all_img_set.add(img_url[0])
# 4. 处理iframe
all_img_element = content_text.find_all('iframe') or []
for ele in all_img_element:
# 删除部分属性
img_url = ele.attrs['data-src']
del ele.attrs['data-src']
ele.attrs['src'] = img_url
# 5. 返回数据
all_img_list = list(all_img_set)
content_html = content_text.prettify()
# 去除div[id=js_content]
content_html = re.findall(js_content, content_html)[0][0]
return {
'content_html': content_html,
'content_img_list': all_img_list
} | 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
删除文章中的语音内容
Returns
-------
dict
{
'content_html': str # 微信文本内容
'content_img_list': list[img_url1, img_url2, ...] # 微信文本中图片列表
}
"""
# 1. 获取微信文本content
html_obj = BeautifulSoup(text, "lxml")
content_text = html_obj.find('div', {'class': 'rich_media_content', 'id': 'js_content'})
# 2. 删除部分标签
if del_qqmusic:
qqmusic = content_text.find_all('qqmusic') or []
for music in qqmusic:
music.parent.decompose()
if del_voice:
# voice是一个p标签下的mpvoice标签以及class为'js_audio_frame db'的span构成，所以将父标签删除
voices = content_text.find_all('mpvoice') or []
for voice in voices:
voice.parent.decompose()
# 3. 获取所有的图片 [img标签，和style中的background-image]
all_img_set = set()
all_img_element = content_text.find_all('img') or []
for ele in all_img_element:
# 删除部分属性
img_url = format_image_url(ele.attrs['data-src'])
del ele.attrs['data-src']
ele.attrs['src'] = img_url
if not img_url.startswith('http'):
raise WechatSogouException('img_url [{}] 不合法'.format(img_url))
all_img_set.add(img_url)
backgroud_image = content_text.find_all(style=re.compile("background-image")) or []
for ele in backgroud_image:
# 删除部分属性
if ele.attrs.get('data-src'):
del ele.attrs['data-src']
if ele.attrs.get('data-wxurl'):
del ele.attrs['data-wxurl']
img_url = re.findall(backgroud_image_p, str(ele))
if not img_url:
continue
all_img_set.add(img_url[0])
# 4. 处理iframe
all_img_element = content_text.find_all('iframe') or []
for ele in all_img_element:
# 删除部分属性
img_url = ele.attrs['data-src']
del ele.attrs['data-src']
ele.attrs['src'] = img_url
# 5. 返回数据
all_img_list = list(all_img_set)
content_html = content_text.prettify()
# 去除div[id=js_content]
content_html = re.findall(js_content, content_html)[0][0]
return {
'content_html': content_html,
'content_img_list': all_img_list
} | [
"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 that all images have the same number of channels, we
convert all of them to RGB.
Args:
fobj: File object to read from.
session: TF session used to decode the images.
filename: Filename of the original image in the archive.
Returns:
Numpy array with shape (height, width, channels). | 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)
- GIF (1-frame RGB)
Since TFDS assumes that all images have the same number of channels, we
convert all of them to RGB.
Args:
fobj: File object to read from.
session: TF session used to decode the images.
filename: Filename of the original image in the archive.
Returns:
Numpy array with shape (height, width, channels).
"""
buf = fobj.read()
image = tfds.core.lazy_imports.cv2.imdecode(
np.fromstring(buf, dtype=np.uint8), flags=3) # Note: Converts to RGB.
if image is None:
logging.warning(
"Image %s could not be decoded by OpenCV, falling back to TF", filename)
try:
image = tf.image.decode_image(buf, channels=3)
image = session.run(image)
except tf.errors.InvalidArgumentError:
logging.fatal("Image %s could not be decoded by Tensorflow", filename)
# The GIF images contain a single frame.
if len(image.shape) == 4: # rank=4 -> rank=3
image = image.reshape(image.shape[1:])
return image | 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)
- GIF (1-frame RGB)
Since TFDS assumes that all images have the same number of channels, we
convert all of them to RGB.
Args:
fobj: File object to read from.
session: TF session used to decode the images.
filename: Filename of the original image in the archive.
Returns:
Numpy array with shape (height, width, channels).
"""
buf = fobj.read()
image = tfds.core.lazy_imports.cv2.imdecode(
np.fromstring(buf, dtype=np.uint8), flags=3) # Note: Converts to RGB.
if image is None:
logging.warning(
"Image %s could not be decoded by OpenCV, falling back to TF", filename)
try:
image = tf.image.decode_image(buf, channels=3)
image = session.run(image)
except tf.errors.InvalidArgumentError:
logging.fatal("Image %s could not be decoded by Tensorflow", filename)
# The GIF images contain a single frame.
if len(image.shape) == 4: # rank=4 -> rank=3
image = image.reshape(image.shape[1:])
return image | [
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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
# some encoding options that will make TF crash in general.
image = _decode_image(fobj, session, filename=filename)
return _encode_jpeg(image) | 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
# some encoding options that will make TF crash in general.
image = _decode_image(fobj, session, filename=filename)
return _encode_jpeg(image) | [
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] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/image/sun.py#L113-L119 | [
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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):
# Note: all files in the tar.gz are in SUN397/...
filename = filepath[prefix_len:]
# Example:
# From filename: /c/car_interior/backseat/sun_aenygxwhhmjtisnf.jpg
# To class: /c/car_interior/backseat
label = "/".join(filename.split("/")[:-1])
image = _process_image_file(fobj, sess, filepath)
yield {
"file_name": filename,
"image": image,
"label": label,
} | 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):
# Note: all files in the tar.gz are in SUN397/...
filename = filepath[prefix_len:]
# Example:
# From filename: /c/car_interior/backseat/sun_aenygxwhhmjtisnf.jpg
# To class: /c/car_interior/backseat
label = "/".join(filename.split("/")[:-1])
image = _process_image_file(fobj, sess, filepath)
yield {
"file_name": filename,
"image": image,
"label": label,
} | [
"Yields",
"examples",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/image/sun.py#L157-L176 | [
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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]
with tf.io.gfile.GFile(path) as f, gzip.GzipFile(fileobj=f) as g:
return g.read().split("\n"), lang
if split_path[-1] == "txt":
# CWMT
lang = split_path[-2].split("_")[-1]
lang = "zh" if lang in ("ch", "cn") else lang
else:
lang = split_path[-1]
with tf.io.gfile.GFile(path) as f:
return f.read().split("\n"), lang
def _parse_sgm(path):
"""Returns sentences from a single SGML file."""
lang = path.split(".")[-2]
sentences = []
# Note: We can't use the XML parser since some of the files are badly
# formatted.
seg_re = re.compile(r"<seg id=\"\d+\">(.*)</seg>")
with tf.io.gfile.GFile(path) as f:
for line in f:
seg_match = re.match(seg_re, line)
if seg_match:
assert len(seg_match.groups()) == 1
sentences.append(seg_match.groups()[0])
return sentences, lang
parse_file = _parse_sgm if f1.endswith(".sgm") else _parse_text
# Some datasets (e.g., CWMT) contain multiple parallel files specified with
# a wildcard. We sort both sets to align them and parse them one by one.
f1_files = tf.io.gfile.glob(f1)
f2_files = tf.io.gfile.glob(f2)
assert f1_files and f2_files, "No matching files found: %s, %s." % (f1, f2)
assert len(f1_files) == len(f2_files), (
"Number of files do not match: %d vs %d for %s vs %s." % (
len(f1_files), len(f2_files), f1, f2))
for f1_i, f2_i in zip(sorted(f1_files), sorted(f2_files)):
l1_sentences, l1 = parse_file(f1_i)
l2_sentences, l2 = parse_file(f2_i)
assert len(l1_sentences) == len(l2_sentences), (
"Sizes do not match: %d vs %d for %s vs %s." % (
len(l1_sentences), len(l2_sentences), f1_i, f2_i))
for s1, s2 in zip(l1_sentences, l2_sentences):
yield {
l1: s1,
l2: s2
} | 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]
with tf.io.gfile.GFile(path) as f, gzip.GzipFile(fileobj=f) as g:
return g.read().split("\n"), lang
if split_path[-1] == "txt":
# CWMT
lang = split_path[-2].split("_")[-1]
lang = "zh" if lang in ("ch", "cn") else lang
else:
lang = split_path[-1]
with tf.io.gfile.GFile(path) as f:
return f.read().split("\n"), lang
def _parse_sgm(path):
"""Returns sentences from a single SGML file."""
lang = path.split(".")[-2]
sentences = []
# Note: We can't use the XML parser since some of the files are badly
# formatted.
seg_re = re.compile(r"<seg id=\"\d+\">(.*)</seg>")
with tf.io.gfile.GFile(path) as f:
for line in f:
seg_match = re.match(seg_re, line)
if seg_match:
assert len(seg_match.groups()) == 1
sentences.append(seg_match.groups()[0])
return sentences, lang
parse_file = _parse_sgm if f1.endswith(".sgm") else _parse_text
# Some datasets (e.g., CWMT) contain multiple parallel files specified with
# a wildcard. We sort both sets to align them and parse them one by one.
f1_files = tf.io.gfile.glob(f1)
f2_files = tf.io.gfile.glob(f2)
assert f1_files and f2_files, "No matching files found: %s, %s." % (f1, f2)
assert len(f1_files) == len(f2_files), (
"Number of files do not match: %d vs %d for %s vs %s." % (
len(f1_files), len(f2_files), f1, f2))
for f1_i, f2_i in zip(sorted(f1_files), sorted(f2_files)):
l1_sentences, l1 = parse_file(f1_i)
l2_sentences, l2 = parse_file(f2_i)
assert len(l1_sentences) == len(l2_sentences), (
"Sizes do not match: %d vs %d for %s vs %s." % (
len(l1_sentences), len(l2_sentences), f1_i, f2_i))
for s1, s2 in zip(l1_sentences, l2_sentences):
yield {
l1: s1,
l2: s2
} | [
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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, "Invalid number of segments: %d" % len(segs)
return segs[0].text
with tf.io.gfile.GFile(path) as f:
for _, elem in ElementTree.iterparse(f):
if elem.tag == "tu":
yield {
_get_tuv_lang(tuv):
_get_tuv_seg(tuv) for tuv in elem.iterfind("tuv")
}
elem.clear() | 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, "Invalid number of segments: %d" % len(segs)
return segs[0].text
with tf.io.gfile.GFile(path) as f:
for _, elem in ElementTree.iterparse(f):
if elem.tag == "tu":
yield {
_get_tuv_lang(tuv):
_get_tuv_seg(tuv) for tuv in elem.iterfind("tuv")
}
elem.clear() | [
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] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/translate/wmt.py#L838-L858 | [
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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
with tf.io.gfile.GFile(path) as f:
for j, line in enumerate(f):
cols = line.split("\t")
if len(cols) != 2:
logging.warning(
"Skipping line %d in TSV (%s) with %d != 2 columns.",
j, path, len(cols))
continue
s1, s2 = cols
yield {
l1: s1.strip(),
l2: s2.strip()
} | 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
with tf.io.gfile.GFile(path) as f:
for j, line in enumerate(f):
cols = line.split("\t")
if len(cols) != 2:
logging.warning(
"Skipping line %d in TSV (%s) with %d != 2 columns.",
j, path, len(cols))
continue
s1, s2 = cols
yield {
l1: s1.strip(),
l2: s2.strip()
} | [
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"file",
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] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/translate/wmt.py#L861-L881 | [
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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])$", path)
assert lang_match is not None, "Invalid Wikiheadlines filename: %s" % path
l1, l2 = lang_match.groups()
with tf.io.gfile.GFile(path) as f:
for line in f:
s1, s2 = line.split("|||")
yield {
l1: s1.strip(),
l2: s2.strip()
} | def _parse_wikiheadlines(path):
"""Generates examples from Wikiheadlines dataset file."""
lang_match = re.match(r".*\.([a-z][a-z])-([a-z][a-z])$", path)
assert lang_match is not None, "Invalid Wikiheadlines filename: %s" % path
l1, l2 = lang_match.groups()
with tf.io.gfile.GFile(path) as f:
for line in f:
s1, s2 = line.split("|||")
yield {
l1: s1.strip(),
l2: s2.strip()
} | [
"Generates",
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] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/translate/wmt.py#L884-L895 | [
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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."""
filter_path = kwargs.get("filter_path", None)
if filter_path:
re_block = re.compile(r"^[^-]+-b(\d+)-\d\d[tde]")
with tf.io.gfile.GFile(filter_path) as f:
bad_blocks = {
blk for blk in re.search(
r"qw{([\s\d]*)}", f.read()).groups()[0].split()
}
logging.info(
"Loaded %d bad blocks to filter from CzEng v1.6 to make v1.7.",
len(bad_blocks))
for path in paths:
for gz_path in tf.io.gfile.glob(path):
with tf.io.gfile.GFile(gz_path, "rb") as g, gzip.GzipFile(fileobj=g) as f:
for line in f:
line = line.decode("utf-8") # required for py3
if not line.strip():
continue
id_, unused_score, cs, en = line.split("\t")
if filter_path:
block_match = re.match(re_block, id_)
if block_match and block_match.groups()[0] in bad_blocks:
continue
yield {
"cs": cs.strip(),
"en": en.strip(),
} | def _parse_czeng(*paths, **kwargs):
"""Generates examples from CzEng v1.6, with optional filtering for v1.7."""
filter_path = kwargs.get("filter_path", None)
if filter_path:
re_block = re.compile(r"^[^-]+-b(\d+)-\d\d[tde]")
with tf.io.gfile.GFile(filter_path) as f:
bad_blocks = {
blk for blk in re.search(
r"qw{([\s\d]*)}", f.read()).groups()[0].split()
}
logging.info(
"Loaded %d bad blocks to filter from CzEng v1.6 to make v1.7.",
len(bad_blocks))
for path in paths:
for gz_path in tf.io.gfile.glob(path):
with tf.io.gfile.GFile(gz_path, "rb") as g, gzip.GzipFile(fileobj=g) as f:
for line in f:
line = line.decode("utf-8") # required for py3
if not line.strip():
continue
id_, unused_score, cs, en = line.split("\t")
if filter_path:
block_match = re.match(re_block, id_)
if block_match and block_match.groups()[0] in bad_blocks:
continue
yield {
"cs": cs.strip(),
"en": en.strip(),
} | [
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] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/translate/wmt.py#L898-L927 | [
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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))
def _format_string(s):
if "{0}" in s and "{1}" and "{src}" in s:
return s.format(*sorted([src, self.target]), src=src)
elif "{0}" in s and "{1}" in s:
return s.format(*sorted([src, self.target]))
elif "{src}" in s:
return s.format(src=src)
else:
return s
return [_format_string(s) for s in strings] | 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))
def _format_string(s):
if "{0}" in s and "{1}" and "{src}" in s:
return s.format(*sorted([src, self.target]), src=src)
elif "{0}" in s and "{1}" in s:
return s.format(*sorted([src, self.target]))
elif "{src}" in s:
return s.format(src=src)
else:
return s
return [_format_string(s) for s in strings] | [
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] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/translate/wmt.py#L97-L110 | [
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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 = {}
for split, ss_names in self._subsets.items():
filtered_subsets[split] = []
for ss_name in ss_names:
ds = DATASET_MAP[ss_name]
if ds.target != target or source not in ds.sources:
logging.info(
"Skipping sub-dataset that does not include language pair: %s",
ss_name)
else:
filtered_subsets[split].append(ss_name)
logging.info("Using sub-datasets: %s", filtered_subsets)
return filtered_subsets | 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 = {}
for split, ss_names in self._subsets.items():
filtered_subsets[split] = []
for ss_name in ss_names:
ds = DATASET_MAP[ss_name]
if ds.target != target or source not in ds.sources:
logging.info(
"Skipping sub-dataset that does not include language pair: %s",
ss_name)
else:
filtered_subsets[split].append(ss_name)
logging.info("Using sub-datasets: %s", filtered_subsets)
return filtered_subsets | [
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] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/translate/wmt.py#L615-L630 | [
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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)
if len(extract_dirs) == 1:
extract_dirs = extract_dirs * len(rel_paths)
return [os.path.join(ex_dir, rel_path) if rel_path else ex_dir
for ex_dir, rel_path in zip(extract_dirs, rel_paths)]
for ss_name in split_subsets:
logging.info("Generating examples from: %s", ss_name)
ds = DATASET_MAP[ss_name]
extract_dirs = extraction_map[ss_name]
files = _get_local_paths(ds, extract_dirs)
if ss_name.startswith("czeng"):
if ss_name.endswith("16pre"):
sub_generator = functools.partial(
_parse_tsv, language_pair=("en", "cs"))
elif ss_name.endswith("17"):
filter_path = _get_local_paths(
_CZENG17_FILTER, extraction_map[_CZENG17_FILTER.name])[0]
sub_generator = functools.partial(
_parse_czeng, filter_path=filter_path)
else:
sub_generator = _parse_czeng
elif len(files) == 2:
if ss_name.endswith("_frde"):
sub_generator = _parse_frde_bitext
else:
sub_generator = _parse_parallel_sentences
elif len(files) == 1:
fname = files[0]
# Note: Due to formatting used by `download_manager`, the file
# extension may not be at the end of the file path.
if ".tsv" in fname:
sub_generator = _parse_tsv
elif ss_name.startswith("newscommentary_v14"):
sub_generator = functools.partial(
_parse_tsv, language_pair=self.builder_config.language_pair)
elif "tmx" in fname:
sub_generator = _parse_tmx
elif ss_name.startswith("wikiheadlines"):
sub_generator = _parse_wikiheadlines
else:
raise ValueError("Unsupported file format: %s" % fname)
else:
raise ValueError("Invalid number of files: %d" % len(files))
for ex in sub_generator(*files):
if not all(ex.values()):
continue
# TODO(adarob): Add subset feature.
# ex["subset"] = subset
yield ex | 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)
if len(extract_dirs) == 1:
extract_dirs = extract_dirs * len(rel_paths)
return [os.path.join(ex_dir, rel_path) if rel_path else ex_dir
for ex_dir, rel_path in zip(extract_dirs, rel_paths)]
for ss_name in split_subsets:
logging.info("Generating examples from: %s", ss_name)
ds = DATASET_MAP[ss_name]
extract_dirs = extraction_map[ss_name]
files = _get_local_paths(ds, extract_dirs)
if ss_name.startswith("czeng"):
if ss_name.endswith("16pre"):
sub_generator = functools.partial(
_parse_tsv, language_pair=("en", "cs"))
elif ss_name.endswith("17"):
filter_path = _get_local_paths(
_CZENG17_FILTER, extraction_map[_CZENG17_FILTER.name])[0]
sub_generator = functools.partial(
_parse_czeng, filter_path=filter_path)
else:
sub_generator = _parse_czeng
elif len(files) == 2:
if ss_name.endswith("_frde"):
sub_generator = _parse_frde_bitext
else:
sub_generator = _parse_parallel_sentences
elif len(files) == 1:
fname = files[0]
# Note: Due to formatting used by `download_manager`, the file
# extension may not be at the end of the file path.
if ".tsv" in fname:
sub_generator = _parse_tsv
elif ss_name.startswith("newscommentary_v14"):
sub_generator = functools.partial(
_parse_tsv, language_pair=self.builder_config.language_pair)
elif "tmx" in fname:
sub_generator = _parse_tmx
elif ss_name.startswith("wikiheadlines"):
sub_generator = _parse_wikiheadlines
else:
raise ValueError("Unsupported file format: %s" % fname)
else:
raise ValueError("Invalid number of files: %d" % len(files))
for ex in sub_generator(*files):
if not all(ex.values()):
continue
# TODO(adarob): Add subset feature.
# ex["subset"] = subset
yield ex | [
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"="... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | builder | Fetches a `tfds.core.DatasetBuilder` by string name.
Args:
name: `str`, the registered name of the `DatasetBuilder` (the snake case
version of the class name). This can be either `"dataset_name"` or
`"dataset_name/config_name"` for datasets with `BuilderConfig`s.
As a convenience, this string may contain comma-separated keyword
arguments for the builder. For example `"foo_bar/a=True,b=3"` would use
the `FooBar` dataset passing the keyword arguments `a=True` and `b=3`
(for builders with configs, it would be `"foo_bar/zoo/a=True,b=3"` to
use the `"zoo"` config and pass to the builder keyword arguments `a=True`
and `b=3`).
**builder_init_kwargs: `dict` of keyword arguments passed to the
`DatasetBuilder`. These will override keyword arguments passed in `name`,
if any.
Returns:
A `tfds.core.DatasetBuilder`.
Raises:
DatasetNotFoundError: if `name` is unrecognized. | tensorflow_datasets/core/registered.py | def builder(name, **builder_init_kwargs):
"""Fetches a `tfds.core.DatasetBuilder` by string name.
Args:
name: `str`, the registered name of the `DatasetBuilder` (the snake case
version of the class name). This can be either `"dataset_name"` or
`"dataset_name/config_name"` for datasets with `BuilderConfig`s.
As a convenience, this string may contain comma-separated keyword
arguments for the builder. For example `"foo_bar/a=True,b=3"` would use
the `FooBar` dataset passing the keyword arguments `a=True` and `b=3`
(for builders with configs, it would be `"foo_bar/zoo/a=True,b=3"` to
use the `"zoo"` config and pass to the builder keyword arguments `a=True`
and `b=3`).
**builder_init_kwargs: `dict` of keyword arguments passed to the
`DatasetBuilder`. These will override keyword arguments passed in `name`,
if any.
Returns:
A `tfds.core.DatasetBuilder`.
Raises:
DatasetNotFoundError: if `name` is unrecognized.
"""
name, builder_kwargs = _dataset_name_and_kwargs_from_name_str(name)
builder_kwargs.update(builder_init_kwargs)
if name in _ABSTRACT_DATASET_REGISTRY:
raise DatasetNotFoundError(name, is_abstract=True)
if name in _IN_DEVELOPMENT_REGISTRY:
raise DatasetNotFoundError(name, in_development=True)
if name not in _DATASET_REGISTRY:
raise DatasetNotFoundError(name)
try:
return _DATASET_REGISTRY[name](**builder_kwargs)
except BaseException:
logging.error("Failed to construct dataset %s", name)
raise | def builder(name, **builder_init_kwargs):
"""Fetches a `tfds.core.DatasetBuilder` by string name.
Args:
name: `str`, the registered name of the `DatasetBuilder` (the snake case
version of the class name). This can be either `"dataset_name"` or
`"dataset_name/config_name"` for datasets with `BuilderConfig`s.
As a convenience, this string may contain comma-separated keyword
arguments for the builder. For example `"foo_bar/a=True,b=3"` would use
the `FooBar` dataset passing the keyword arguments `a=True` and `b=3`
(for builders with configs, it would be `"foo_bar/zoo/a=True,b=3"` to
use the `"zoo"` config and pass to the builder keyword arguments `a=True`
and `b=3`).
**builder_init_kwargs: `dict` of keyword arguments passed to the
`DatasetBuilder`. These will override keyword arguments passed in `name`,
if any.
Returns:
A `tfds.core.DatasetBuilder`.
Raises:
DatasetNotFoundError: if `name` is unrecognized.
"""
name, builder_kwargs = _dataset_name_and_kwargs_from_name_str(name)
builder_kwargs.update(builder_init_kwargs)
if name in _ABSTRACT_DATASET_REGISTRY:
raise DatasetNotFoundError(name, is_abstract=True)
if name in _IN_DEVELOPMENT_REGISTRY:
raise DatasetNotFoundError(name, in_development=True)
if name not in _DATASET_REGISTRY:
raise DatasetNotFoundError(name)
try:
return _DATASET_REGISTRY[name](**builder_kwargs)
except BaseException:
logging.error("Failed to construct dataset %s", name)
raise | [
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] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/registered.py#L137-L172 | [
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"... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | load | Loads the named dataset into a `tf.data.Dataset`.
If `split=None` (the default), returns all splits for the dataset. Otherwise,
returns the specified split.
`load` is a convenience method that fetches the `tfds.core.DatasetBuilder` by
string name, optionally calls `DatasetBuilder.download_and_prepare`
(if `download=True`), and then calls `DatasetBuilder.as_dataset`.
This is roughly equivalent to:
```
builder = tfds.builder(name, data_dir=data_dir, **builder_kwargs)
if download:
builder.download_and_prepare(**download_and_prepare_kwargs)
ds = builder.as_dataset(
split=split, as_supervised=as_supervised, **as_dataset_kwargs)
if with_info:
return ds, builder.info
return ds
```
If you'd like NumPy arrays instead of `tf.data.Dataset`s or `tf.Tensor`s,
you can pass the return value to `tfds.as_numpy`.
Callers must pass arguments as keyword arguments.
**Warning**: calling this function might potentially trigger the download
of hundreds of GiB to disk. Refer to the `download` argument.
Args:
name: `str`, the registered name of the `DatasetBuilder` (the snake case
version of the class name). This can be either `"dataset_name"` or
`"dataset_name/config_name"` for datasets with `BuilderConfig`s.
As a convenience, this string may contain comma-separated keyword
arguments for the builder. For example `"foo_bar/a=True,b=3"` would use
the `FooBar` dataset passing the keyword arguments `a=True` and `b=3`
(for builders with configs, it would be `"foo_bar/zoo/a=True,b=3"` to
use the `"zoo"` config and pass to the builder keyword arguments `a=True`
and `b=3`).
split: `tfds.Split` or `str`, which split of the data to load. If None,
will return a `dict` with all splits (typically `tfds.Split.TRAIN` and
`tfds.Split.TEST`).
data_dir: `str` (optional), directory to read/write data.
Defaults to "~/tensorflow_datasets".
batch_size: `int`, set to > 1 to get batches of examples. Note that
variable length features will be 0-padded. If
`batch_size=-1`, will return the full dataset as `tf.Tensor`s.
download: `bool` (optional), whether to call
`tfds.core.DatasetBuilder.download_and_prepare`
before calling `tf.DatasetBuilder.as_dataset`. If `False`, data is
expected to be in `data_dir`. If `True` and the data is already in
`data_dir`, `download_and_prepare` is a no-op.
as_supervised: `bool`, if `True`, the returned `tf.data.Dataset`
will have a 2-tuple structure `(input, label)` according to
`builder.info.supervised_keys`. If `False`, the default,
the returned `tf.data.Dataset` will have a dictionary with all the
features.
with_info: `bool`, if True, tfds.load will return the tuple
(tf.data.Dataset, tfds.core.DatasetInfo) containing the info associated
with the builder.
builder_kwargs: `dict` (optional), keyword arguments to be passed to the
`tfds.core.DatasetBuilder` constructor. `data_dir` will be passed
through by default.
download_and_prepare_kwargs: `dict` (optional) keyword arguments passed to
`tfds.core.DatasetBuilder.download_and_prepare` if `download=True`. Allow
to control where to download and extract the cached data. If not set,
cache_dir and manual_dir will automatically be deduced from data_dir.
as_dataset_kwargs: `dict` (optional), keyword arguments passed to
`tfds.core.DatasetBuilder.as_dataset`. `split` will be passed through by
default. Example: `{'shuffle_files': True}`.
Note that shuffle_files is False by default unless
`split == tfds.Split.TRAIN`.
try_gcs: `bool`, if True, tfds.load will see if the dataset exists on
the public GCS bucket before building it locally.
Returns:
ds: `tf.data.Dataset`, the dataset requested, or if `split` is None, a
`dict<key: tfds.Split, value: tfds.data.Dataset>`. If `batch_size=-1`,
these will be full datasets as `tf.Tensor`s.
ds_info: `tfds.core.DatasetInfo`, if `with_info` is True, then `tfds.load`
will return a tuple `(ds, ds_info)` containing dataset information
(version, features, splits, num_examples,...). Note that the `ds_info`
object documents the entire dataset, regardless of the `split` requested.
Split-specific information is available in `ds_info.splits`. | tensorflow_datasets/core/registered.py | def load(name,
split=None,
data_dir=None,
batch_size=1,
download=True,
as_supervised=False,
with_info=False,
builder_kwargs=None,
download_and_prepare_kwargs=None,
as_dataset_kwargs=None,
try_gcs=False):
"""Loads the named dataset into a `tf.data.Dataset`.
If `split=None` (the default), returns all splits for the dataset. Otherwise,
returns the specified split.
`load` is a convenience method that fetches the `tfds.core.DatasetBuilder` by
string name, optionally calls `DatasetBuilder.download_and_prepare`
(if `download=True`), and then calls `DatasetBuilder.as_dataset`.
This is roughly equivalent to:
```
builder = tfds.builder(name, data_dir=data_dir, **builder_kwargs)
if download:
builder.download_and_prepare(**download_and_prepare_kwargs)
ds = builder.as_dataset(
split=split, as_supervised=as_supervised, **as_dataset_kwargs)
if with_info:
return ds, builder.info
return ds
```
If you'd like NumPy arrays instead of `tf.data.Dataset`s or `tf.Tensor`s,
you can pass the return value to `tfds.as_numpy`.
Callers must pass arguments as keyword arguments.
**Warning**: calling this function might potentially trigger the download
of hundreds of GiB to disk. Refer to the `download` argument.
Args:
name: `str`, the registered name of the `DatasetBuilder` (the snake case
version of the class name). This can be either `"dataset_name"` or
`"dataset_name/config_name"` for datasets with `BuilderConfig`s.
As a convenience, this string may contain comma-separated keyword
arguments for the builder. For example `"foo_bar/a=True,b=3"` would use
the `FooBar` dataset passing the keyword arguments `a=True` and `b=3`
(for builders with configs, it would be `"foo_bar/zoo/a=True,b=3"` to
use the `"zoo"` config and pass to the builder keyword arguments `a=True`
and `b=3`).
split: `tfds.Split` or `str`, which split of the data to load. If None,
will return a `dict` with all splits (typically `tfds.Split.TRAIN` and
`tfds.Split.TEST`).
data_dir: `str` (optional), directory to read/write data.
Defaults to "~/tensorflow_datasets".
batch_size: `int`, set to > 1 to get batches of examples. Note that
variable length features will be 0-padded. If
`batch_size=-1`, will return the full dataset as `tf.Tensor`s.
download: `bool` (optional), whether to call
`tfds.core.DatasetBuilder.download_and_prepare`
before calling `tf.DatasetBuilder.as_dataset`. If `False`, data is
expected to be in `data_dir`. If `True` and the data is already in
`data_dir`, `download_and_prepare` is a no-op.
as_supervised: `bool`, if `True`, the returned `tf.data.Dataset`
will have a 2-tuple structure `(input, label)` according to
`builder.info.supervised_keys`. If `False`, the default,
the returned `tf.data.Dataset` will have a dictionary with all the
features.
with_info: `bool`, if True, tfds.load will return the tuple
(tf.data.Dataset, tfds.core.DatasetInfo) containing the info associated
with the builder.
builder_kwargs: `dict` (optional), keyword arguments to be passed to the
`tfds.core.DatasetBuilder` constructor. `data_dir` will be passed
through by default.
download_and_prepare_kwargs: `dict` (optional) keyword arguments passed to
`tfds.core.DatasetBuilder.download_and_prepare` if `download=True`. Allow
to control where to download and extract the cached data. If not set,
cache_dir and manual_dir will automatically be deduced from data_dir.
as_dataset_kwargs: `dict` (optional), keyword arguments passed to
`tfds.core.DatasetBuilder.as_dataset`. `split` will be passed through by
default. Example: `{'shuffle_files': True}`.
Note that shuffle_files is False by default unless
`split == tfds.Split.TRAIN`.
try_gcs: `bool`, if True, tfds.load will see if the dataset exists on
the public GCS bucket before building it locally.
Returns:
ds: `tf.data.Dataset`, the dataset requested, or if `split` is None, a
`dict<key: tfds.Split, value: tfds.data.Dataset>`. If `batch_size=-1`,
these will be full datasets as `tf.Tensor`s.
ds_info: `tfds.core.DatasetInfo`, if `with_info` is True, then `tfds.load`
will return a tuple `(ds, ds_info)` containing dataset information
(version, features, splits, num_examples,...). Note that the `ds_info`
object documents the entire dataset, regardless of the `split` requested.
Split-specific information is available in `ds_info.splits`.
"""
name, name_builder_kwargs = _dataset_name_and_kwargs_from_name_str(name)
name_builder_kwargs.update(builder_kwargs or {})
builder_kwargs = name_builder_kwargs
# Set data_dir
if try_gcs and gcs_utils.is_dataset_on_gcs(name):
data_dir = constants.GCS_DATA_DIR
elif data_dir is None:
data_dir = constants.DATA_DIR
dbuilder = builder(name, data_dir=data_dir, **builder_kwargs)
if download:
download_and_prepare_kwargs = download_and_prepare_kwargs or {}
dbuilder.download_and_prepare(**download_and_prepare_kwargs)
if as_dataset_kwargs is None:
as_dataset_kwargs = {}
as_dataset_kwargs = dict(as_dataset_kwargs)
as_dataset_kwargs["split"] = split
as_dataset_kwargs["as_supervised"] = as_supervised
as_dataset_kwargs["batch_size"] = batch_size
ds = dbuilder.as_dataset(**as_dataset_kwargs)
if with_info:
return ds, dbuilder.info
return ds | def load(name,
split=None,
data_dir=None,
batch_size=1,
download=True,
as_supervised=False,
with_info=False,
builder_kwargs=None,
download_and_prepare_kwargs=None,
as_dataset_kwargs=None,
try_gcs=False):
"""Loads the named dataset into a `tf.data.Dataset`.
If `split=None` (the default), returns all splits for the dataset. Otherwise,
returns the specified split.
`load` is a convenience method that fetches the `tfds.core.DatasetBuilder` by
string name, optionally calls `DatasetBuilder.download_and_prepare`
(if `download=True`), and then calls `DatasetBuilder.as_dataset`.
This is roughly equivalent to:
```
builder = tfds.builder(name, data_dir=data_dir, **builder_kwargs)
if download:
builder.download_and_prepare(**download_and_prepare_kwargs)
ds = builder.as_dataset(
split=split, as_supervised=as_supervised, **as_dataset_kwargs)
if with_info:
return ds, builder.info
return ds
```
If you'd like NumPy arrays instead of `tf.data.Dataset`s or `tf.Tensor`s,
you can pass the return value to `tfds.as_numpy`.
Callers must pass arguments as keyword arguments.
**Warning**: calling this function might potentially trigger the download
of hundreds of GiB to disk. Refer to the `download` argument.
Args:
name: `str`, the registered name of the `DatasetBuilder` (the snake case
version of the class name). This can be either `"dataset_name"` or
`"dataset_name/config_name"` for datasets with `BuilderConfig`s.
As a convenience, this string may contain comma-separated keyword
arguments for the builder. For example `"foo_bar/a=True,b=3"` would use
the `FooBar` dataset passing the keyword arguments `a=True` and `b=3`
(for builders with configs, it would be `"foo_bar/zoo/a=True,b=3"` to
use the `"zoo"` config and pass to the builder keyword arguments `a=True`
and `b=3`).
split: `tfds.Split` or `str`, which split of the data to load. If None,
will return a `dict` with all splits (typically `tfds.Split.TRAIN` and
`tfds.Split.TEST`).
data_dir: `str` (optional), directory to read/write data.
Defaults to "~/tensorflow_datasets".
batch_size: `int`, set to > 1 to get batches of examples. Note that
variable length features will be 0-padded. If
`batch_size=-1`, will return the full dataset as `tf.Tensor`s.
download: `bool` (optional), whether to call
`tfds.core.DatasetBuilder.download_and_prepare`
before calling `tf.DatasetBuilder.as_dataset`. If `False`, data is
expected to be in `data_dir`. If `True` and the data is already in
`data_dir`, `download_and_prepare` is a no-op.
as_supervised: `bool`, if `True`, the returned `tf.data.Dataset`
will have a 2-tuple structure `(input, label)` according to
`builder.info.supervised_keys`. If `False`, the default,
the returned `tf.data.Dataset` will have a dictionary with all the
features.
with_info: `bool`, if True, tfds.load will return the tuple
(tf.data.Dataset, tfds.core.DatasetInfo) containing the info associated
with the builder.
builder_kwargs: `dict` (optional), keyword arguments to be passed to the
`tfds.core.DatasetBuilder` constructor. `data_dir` will be passed
through by default.
download_and_prepare_kwargs: `dict` (optional) keyword arguments passed to
`tfds.core.DatasetBuilder.download_and_prepare` if `download=True`. Allow
to control where to download and extract the cached data. If not set,
cache_dir and manual_dir will automatically be deduced from data_dir.
as_dataset_kwargs: `dict` (optional), keyword arguments passed to
`tfds.core.DatasetBuilder.as_dataset`. `split` will be passed through by
default. Example: `{'shuffle_files': True}`.
Note that shuffle_files is False by default unless
`split == tfds.Split.TRAIN`.
try_gcs: `bool`, if True, tfds.load will see if the dataset exists on
the public GCS bucket before building it locally.
Returns:
ds: `tf.data.Dataset`, the dataset requested, or if `split` is None, a
`dict<key: tfds.Split, value: tfds.data.Dataset>`. If `batch_size=-1`,
these will be full datasets as `tf.Tensor`s.
ds_info: `tfds.core.DatasetInfo`, if `with_info` is True, then `tfds.load`
will return a tuple `(ds, ds_info)` containing dataset information
(version, features, splits, num_examples,...). Note that the `ds_info`
object documents the entire dataset, regardless of the `split` requested.
Split-specific information is available in `ds_info.splits`.
"""
name, name_builder_kwargs = _dataset_name_and_kwargs_from_name_str(name)
name_builder_kwargs.update(builder_kwargs or {})
builder_kwargs = name_builder_kwargs
# Set data_dir
if try_gcs and gcs_utils.is_dataset_on_gcs(name):
data_dir = constants.GCS_DATA_DIR
elif data_dir is None:
data_dir = constants.DATA_DIR
dbuilder = builder(name, data_dir=data_dir, **builder_kwargs)
if download:
download_and_prepare_kwargs = download_and_prepare_kwargs or {}
dbuilder.download_and_prepare(**download_and_prepare_kwargs)
if as_dataset_kwargs is None:
as_dataset_kwargs = {}
as_dataset_kwargs = dict(as_dataset_kwargs)
as_dataset_kwargs["split"] = split
as_dataset_kwargs["as_supervised"] = as_supervised
as_dataset_kwargs["batch_size"] = batch_size
ds = dbuilder.as_dataset(**as_dataset_kwargs)
if with_info:
return ds, dbuilder.info
return ds | [
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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")
kwargs = _kwargs_str_to_kwargs(res.group("kwargs"))
try:
for attr in ["config", "version"]:
val = res.group(attr)
if val is None:
continue
if attr in kwargs:
raise ValueError("Dataset %s: cannot pass %s twice." % (name, attr))
kwargs[attr] = val
return name, kwargs
except:
logging.error(_NAME_STR_ERR.format(name_str)) # pylint: disable=logging-format-interpolation
raise | 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")
kwargs = _kwargs_str_to_kwargs(res.group("kwargs"))
try:
for attr in ["config", "version"]:
val = res.group(attr)
if val is None:
continue
if attr in kwargs:
raise ValueError("Dataset %s: cannot pass %s twice." % (name, attr))
kwargs[attr] = val
return name, kwargs
except:
logging.error(_NAME_STR_ERR.format(name_str)) # pylint: disable=logging-format-interpolation
raise | [
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"from",
"name",
"str",
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] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/registered.py#L311-L329 | [
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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:
return int(val)
except ValueError:
try:
return float(val)
except ValueError:
return tf.compat.as_text(val) | 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:
return int(val)
except ValueError:
try:
return float(val)
except ValueError:
return tf.compat.as_text(val) | [
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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 to use may have additional "
"dependencies.")
utils.reraise(err_msg) | 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 to use may have additional "
"dependencies.")
utils.reraise(err_msg) | [
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] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/lazy_imports.py#L27-L36 | [
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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):
return list(elem)
else:
raise ValueError(
'Input elements of a sequence should be either a numpy array, a '
'python list or tuple. Got {}'.format(type(elem))) | 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):
return list(elem)
else:
raise ValueError(
'Input elements of a sequence should be either a numpy array, a '
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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]."""
# 1. Unstack numpy arrays into list
dict_list = utils.map_nested(np_to_list, dict_list, dict_only=True)
# 2. Extract the sequence length (and ensure the length is constant for all
# elements)
length = {'value': None} # dict because `nonlocal` is Python3 only
def update_length(elem):
if length['value'] is None:
length['value'] = len(elem)
elif length['value'] != len(elem):
raise ValueError(
'The length of all elements of one sequence should be the same. '
'Got {} != {}'.format(length['value'], len(elem)))
return elem
utils.map_nested(update_length, dict_list, dict_only=True)
# 3. Extract each individual elements
return [
utils.map_nested(lambda elem: elem[i], dict_list, dict_only=True) # pylint: disable=cell-var-from-loop
for i in range(length['value'])
] | def _transpose_dict_list(dict_list):
"""Transpose a nested dict[list] into a list[nested dict]."""
# 1. Unstack numpy arrays into list
dict_list = utils.map_nested(np_to_list, dict_list, dict_only=True)
# 2. Extract the sequence length (and ensure the length is constant for all
# elements)
length = {'value': None} # dict because `nonlocal` is Python3 only
def update_length(elem):
if length['value'] is None:
length['value'] = len(elem)
elif length['value'] != len(elem):
raise ValueError(
'The length of all elements of one sequence should be the same. '
'Got {} != {}'.format(length['value'], len(elem)))
return elem
utils.map_nested(update_length, dict_list, dict_only=True)
# 3. Extract each individual elements
return [
utils.map_nested(lambda elem: elem[i], dict_list, dict_only=True) # pylint: disable=cell-var-from-loop
for i in range(length['value'])
] | [
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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):
"""See base class for details."""
# Add the additional length dimension to every shape
def add_length_dim(tensor_info):
return feature_lib.TensorInfo(
shape=(self._length,) + tensor_info.shape,
dtype=tensor_info.dtype,
)
tensor_info = super(SequenceDict, self).get_tensor_info()
return utils.map_nested(add_length_dim, tensor_info) | def get_tensor_info(self):
"""See base class for details."""
# Add the additional length dimension to every shape
def add_length_dim(tensor_info):
return feature_lib.TensorInfo(
shape=(self._length,) + tensor_info.shape,
dtype=tensor_info.dtype,
)
tensor_info = super(SequenceDict, self).get_tensor_info()
return utils.map_nested(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):
"""Add the length dimension to the serialized_info.
Args:
serialized_info: One of tf.io.FixedLenFeature, tf.io.VarLenFeature,...
Returns:
new_serialized_info: serialized_info with extended first dimension
"""
if isinstance(serialized_info, tf.io.FixedLenFeature):
if self._length is not None:
return tf.io.FixedLenFeature(
shape=(self._length,) + serialized_info.shape,
dtype=serialized_info.dtype,
)
else:
return tf.io.FixedLenSequenceFeature(
shape=serialized_info.shape,
dtype=serialized_info.dtype,
allow_missing=True,
)
elif isinstance(serialized_info, tf.io.VarLenFeature):
return serialized_info
else:
raise ValueError(
'FixedLenSequenceFeature not supported inside SequenceDict'
)
return serialized_info
tensor_info = super(SequenceDict, self).get_serialized_info()
return utils.map_nested(add_length_dim, tensor_info) | 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):
"""Add the length dimension to the serialized_info.
Args:
serialized_info: One of tf.io.FixedLenFeature, tf.io.VarLenFeature,...
Returns:
new_serialized_info: serialized_info with extended first dimension
"""
if isinstance(serialized_info, tf.io.FixedLenFeature):
if self._length is not None:
return tf.io.FixedLenFeature(
shape=(self._length,) + serialized_info.shape,
dtype=serialized_info.dtype,
)
else:
return tf.io.FixedLenSequenceFeature(
shape=serialized_info.shape,
dtype=serialized_info.dtype,
allow_missing=True,
)
elif isinstance(serialized_info, tf.io.VarLenFeature):
return serialized_info
else:
raise ValueError(
'FixedLenSequenceFeature not supported inside SequenceDict'
)
return serialized_info
tensor_info = super(SequenceDict, self).get_serialized_info()
return utils.map_nested(add_length_dim, tensor_info) | [
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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_LABELS_FILENAME,
}
mnist_files = dl_manager.download_and_extract(
{k: urllib.parse.urljoin(self.URL, v) for k, v in filenames.items()})
# MNIST provides TRAIN and TEST splits, not a VALIDATION split, so we only
# write the TRAIN and TEST splits to disk.
return [
tfds.core.SplitGenerator(
name=tfds.Split.TRAIN,
num_shards=10,
gen_kwargs=dict(
num_examples=_TRAIN_EXAMPLES,
data_path=mnist_files["train_data"],
label_path=mnist_files["train_labels"],
)),
tfds.core.SplitGenerator(
name=tfds.Split.TEST,
num_shards=1,
gen_kwargs=dict(
num_examples=_TEST_EXAMPLES,
data_path=mnist_files["test_data"],
label_path=mnist_files["test_labels"],
)),
] | 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_LABELS_FILENAME,
}
mnist_files = dl_manager.download_and_extract(
{k: urllib.parse.urljoin(self.URL, v) for k, v in filenames.items()})
# MNIST provides TRAIN and TEST splits, not a VALIDATION split, so we only
# write the TRAIN and TEST splits to disk.
return [
tfds.core.SplitGenerator(
name=tfds.Split.TRAIN,
num_shards=10,
gen_kwargs=dict(
num_examples=_TRAIN_EXAMPLES,
data_path=mnist_files["train_data"],
label_path=mnist_files["train_labels"],
)),
tfds.core.SplitGenerator(
name=tfds.Split.TEST,
num_shards=1,
gen_kwargs=dict(
num_examples=_TEST_EXAMPLES,
data_path=mnist_files["test_data"],
label_path=mnist_files["test_labels"],
)),
] | [
"Returns",
"SplitGenerators",
"."
] | 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
"""
images = _extract_mnist_images(data_path, num_examples)
labels = _extract_mnist_labels(label_path, num_examples)
data = list(zip(images, labels))
# Data is shuffled automatically to distribute classes uniformly.
for image, label in data:
yield {
"image": image,
"label": label,
} | 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
"""
images = _extract_mnist_images(data_path, num_examples)
labels = _extract_mnist_labels(label_path, num_examples)
data = list(zip(images, labels))
# Data is shuffled automatically to distribute classes uniformly.
for image, label in data:
yield {
"image": image,
"label": label,
} | [
"Generate",
"MNIST",
"examples",
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"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/image/mnist.py#L146-L166 | [
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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({
"images": tfds.download.Resource(
url=os.path.join(_BASE_URL, "102flowers.tgz"),
extract_method=tfds.download.ExtractMethod.TAR),
"labels": os.path.join(_BASE_URL, "imagelabels.mat"),
"setid": os.path.join(_BASE_URL, "setid.mat"),
})
gen_kwargs = dict(
images_dir_path=os.path.join(dl_paths["images"], "jpg"),
labels_path=dl_paths["labels"],
setid_path=dl_paths["setid"],
)
return [
tfds.core.SplitGenerator(
name=tfds.Split.TRAIN,
num_shards=1,
gen_kwargs=dict(split_name="trnid", **gen_kwargs)),
tfds.core.SplitGenerator(
name=tfds.Split.TEST,
num_shards=1,
gen_kwargs=dict(split_name="tstid", **gen_kwargs)),
tfds.core.SplitGenerator(
name=tfds.Split.VALIDATION,
num_shards=1,
gen_kwargs=dict(split_name="valid", **gen_kwargs)),
] | 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({
"images": tfds.download.Resource(
url=os.path.join(_BASE_URL, "102flowers.tgz"),
extract_method=tfds.download.ExtractMethod.TAR),
"labels": os.path.join(_BASE_URL, "imagelabels.mat"),
"setid": os.path.join(_BASE_URL, "setid.mat"),
})
gen_kwargs = dict(
images_dir_path=os.path.join(dl_paths["images"], "jpg"),
labels_path=dl_paths["labels"],
setid_path=dl_paths["setid"],
)
return [
tfds.core.SplitGenerator(
name=tfds.Split.TRAIN,
num_shards=1,
gen_kwargs=dict(split_name="trnid", **gen_kwargs)),
tfds.core.SplitGenerator(
name=tfds.Split.TEST,
num_shards=1,
gen_kwargs=dict(split_name="tstid", **gen_kwargs)),
tfds.core.SplitGenerator(
name=tfds.Split.VALIDATION,
num_shards=1,
gen_kwargs=dict(split_name="valid", **gen_kwargs)),
] | [
"Returns",
"SplitGenerators",
"."
] | 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:
examples = tfds.core.lazy_imports.scipy.io.loadmat(f)[split_name][0]
for image_id in examples:
file_name = "image_%05d.jpg" % image_id
yield {
"image": os.path.join(images_dir_path, file_name),
"label": labels[image_id - 1] - 1,
"file_name": file_name,
} | 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:
examples = tfds.core.lazy_imports.scipy.io.loadmat(f)[split_name][0]
for image_id in examples:
file_name = "image_%05d.jpg" % image_id
yield {
"image": os.path.join(images_dir_path, file_name),
"label": labels[image_id - 1] - 1,
"file_name": file_name,
} | [
"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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