repo stringclasses 56
values | func_sig stringlengths 3 801 | func_type stringclasses 2
values | context stringlengths 113 343k | docstring stringlengths 4 16.7k | func stringlengths 80 318k | metadata dict |
|---|---|---|---|---|---|---|
bbfamily/abu | do_symbols_with_same_factors(target_symbols, benchmark, buy_factors, sell_factors, capital, apply_capital, kl_pd_manager, show, back_target_symbols, func_factors, show_progress) | function | Code snippets that use function:
snippet 1: buy_factors = sub_dict['buy_factors']
sell_factors = sub_dict['sell_factors']
return buy_factors, sell_factors
# 通过funcFactors在内层解开factors dict
return do_symbols_with_same_factors(target_symbols, benchmark, None, None, capital, apply_capital=apply_cap... | 输入为多个择时交易对象，以及相同的择时买入，卖出因子序列，对多个交易对象上实施相同的因子
:param target_symbols: 多个择时交易对象序列
:param benchmark: 交易基准对象，AbuBenchmark实例对象
:param buy_factors: 买入因子序列
:param sell_factors: 卖出因子序列
:param capital: AbuCapital实例对象
:param apply_capital: 是否进行资金对象的融合，多进程环境下将是False
:param kl_pd_manager: 金融时间序列管理对象，AbuK... | from ..TradeBu import ABuTradeProxy
from .ABuPickTimeWorker import AbuPickTimeWorker
import pandas as pd
import logging
from ..UtilBu.ABuProgress import AbuMulPidProgress
from ..TradeBu import ABuTradeExecute
import numpy as np
from ..CoreBu.ABuEnvProcess import add_process_env_sig
from ..TradeBu.ABuKLManager i... | {
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bbfamily/abu | do_symbols_with_same_factors_process(cls, target_symbols, benchmark, buy_factors, sell_factors, capital, kl_pd_manager, n_process_kl, n_process_pick_time, show_progress) | class_method | Code snippets that use function:
snippet 1: # 在择时之前清理一下输出, 不能wait, windows上一些浏览器会卡死
ABuProgress.do_clear_output(wait=False)
# 择时策略运行，多进程方式
orders_pd, action_pd, all_fit_symbols_cnt = AbuPickTimeMaster.do_symbols_with_same_factors_process(
choice_symbols, benchmark,
buy_factors, sell_factors... | 将多个交易对象拆解为多份交易对象序列，多任务并行完成择时工作
:param target_symbols: 多个择时交易对象序列
:param benchmark: 交易基准对象，AbuBenchmark实例对象
:param buy_factors: 买入因子序列
:param sell_factors: 卖出因子序列
:param capital: AbuCapital实例对象
:param kl_pd_manager: 金融时间序列管理对象，AbuKLManager实例
:param n_process_kl: 控制... | from ..CoreBu import ABuEnv
import pandas as pd
from .ABuPickTimeExecute import do_symbols_with_same_factors
import numpy as np
from ..TradeBu import ABuTradeExecute
from ..CoreBu.ABuParallel import delayed, Parallel
from ..MarketBu.ABuMarket import split_k_market
from ..CoreBu.ABuEnvProcess import AbuEnvProcess... | {
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bbfamily/abu | store_abu_result_tuple(abu_result_tuple, n_folds, store_type, custom_name) | function | Code snippets that use function:
snippet 1: :param abu_result_tuple: AbuResultTuple对象类型
:param n_folds: 回测执行了几年，只影响存贮文件名
:param store_type: 回测保存类型EStoreAbu类型，只影响存贮文件名
:param custom_name: 如果store_type=EStoreAbu.E_STORE_CUSTOM_NAME时需要的自定义文件名称
"""
ABuStore.store_abu_result_tuple(abu_result_tuple, n_fol... | 保存abu.run_loop_back的回测结果AbuResultTuple对象，根据n_folds，store_type参数
来定义存储的文件名称，透传参数使用ABuStore.store_abu_result_tuple执行操作
:param abu_result_tuple: AbuResultTuple对象类型
:param n_folds: 回测执行了几年，只影响存贮文件名
:param store_type: 回测保存类型EStoreAbu类型，只影响存贮文件名
:param custom_name: 如果store_type=EStoreAbu.E_STORE_CUSTOM_N... | from ..CoreBu import ABuStore
from ..CoreBu.ABuStore import EStoreAbu
def store_abu_result_tuple(abu_result_tuple, n_folds=None, store_type=EStoreAbu.E_STORE_NORMAL, custom_name=None):
"""
保存abu.run_loop_back的回测结果AbuResultTuple对象，根据n_folds，store_type参数
来定义存储的文件名称，透传参数使用ABuStore.store_abu_result_tuple执行操作
... | {
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bbfamily/abu | load_abu_result_tuple(n_folds, store_type, custom_name) | function | Code snippets that use function:
snippet 1: :param n_folds: 回测执行了几年，只影响读取的文件名
:param store_type: 回测保存类型EStoreAbu类型，只影响读取的文件名
:param custom_name: 如果store_type=EStoreAbu.E_STORE_CUSTOM_NAME时需要的自定义文件名称
:return: AbuResultTuple对象
"""
return ABuStore.load_abu_result_tuple(n_folds, store_type, custom_name=... | 读取使用store_abu_result_tuple保存的回测结果，根据n_folds，store_type参数
来定义读取的文件名称，依次读取orders_pd，action_pd，capital，benchmark后构造
AbuResultTuple对象返回，透传参数使用ABuStore.load_abu_result_tuple执行操作
:param n_folds: 回测执行了几年，只影响读取的文件名
:param store_type: 回测保存类型EStoreAbu类型，只影响读取的文件名
:param custom_name: 如果store_type=EStoreAbu.E_... | from ..CoreBu import ABuStore
from ..CoreBu.ABuStore import EStoreAbu
def load_abu_result_tuple(n_folds=None, store_type=EStoreAbu.E_STORE_NORMAL, custom_name=None):
"""
读取使用store_abu_result_tuple保存的回测结果，根据n_folds，store_type参数
来定义读取的文件名称，依次读取orders_pd，action_pd，capital，benchmark后构造
AbuResultTuple对象返回，... | {
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bbfamily/abu | init_plot_set() | function | Code snippets that use function:
snippet 1: import matplotlib
# conda 5.0后需要添加单独matplotlib的figure设置否则pandas的plot size不生效
matplotlib.rcParams['figure.figsize'] = g_plt_figsize
init_plot_set() | 全局plot设置 | import seaborn as sns
import matplotlib
g_plt_figsize = (14, 7)
def init_plot_set():
"""全局plot设置"""
import seaborn as sns
sns.set_context('notebook', rc={'figure.figsize': g_plt_figsize})
sns.set_style('darkgrid')
import matplotlib
matplotlib.rcParams['figure.figsize'] = g_plt_figsize | {
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bbfamily/abu | check_random_state(seed) | function | Code snippets that use function:
snippet 1: self.shuffle = shuffle
self.random_state = random_state
self.idxs = np.arange(n)
if shuffle:
rng = check_random_state(self.random_state)
rng.shuffle(self.idxs)
def __iter__(s... | No docstring found | import numbers
import numpy as np
def check_random_state(seed):
if seed is None or seed is np.random:
return np.random.mtrand._rand
if isinstance(seed, (numbers.Integral, np.integer)):
return np.random.RandomState(seed)
if isinstance(seed, np.random.RandomState):
return seed
ra... | {
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bbfamily/abu | __pd_object_covert_start(iter_obj) | function | Code snippets that use function:
snippet 1: @functools.wraps(func)
def wrapper(pd_object, pd_object_cm, how, *args, **kwargs):
"""事前装饰工作__pd_object_covert_start，事后根据是否需要转换为np.array工作"""
# 事前装饰工作__pd_object_covert_start
pd_object, ret_covert = __pd_object_covert_start(pd_object)
ret =... | _pd_object_covert中进行参数检测及转换
:param iter_obj: 将要进行操作的可迭代序列
:return: 操作之后的返回值是否需要转换为np.array | from collections import Iterable
import pandas as pd
from ..CoreBu.ABuFixes import six
def __pd_object_covert_start(iter_obj):
"""
_pd_object_covert中进行参数检测及转换
:param iter_obj: 将要进行操作的可迭代序列
:return: 操作之后的返回值是否需要转换为np.array
"""
if isinstance(iter_obj, (pd.Series, pd.DataFrame)):
return ... | {
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bbfamily/abu | _parse_stock_info() | class_method | Code snippets that use function:
snippet 1: def _crawl_imp(self, *args, **kwargs):
for index, symbol in enumerate(self._symbols):
try:
if not ABuXqFile.exist_stock_info(self._market, symbol) or ('replace' in kwargs and kwargs['replace']):
self.get(self._base_url +... | No docstring found | from lxml import etree
company_industry = selector.xpath('//*[@id="relatedIndustry"]/h2/a/text()')
quate_items = selector.xpath('//*[@id="center"]/div[2]/div[2]/div[2]/table/tbody/tr/td')
stock_name = selector.xpath('//*[@id="center"]/div[2]/div[2]/div[1]/div[1]/span[1]/strong/text()')
company_info_p = selector.xpa... | {
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bbfamily/abu | fix_xq_columns_name() | function | Code snippets that use function:
snippet 1: def update_all(markets=('US', 'CN', 'HK')):
crawl_stock_code(markets)
crawl_stock_info(markets)
ABuXqFile.merge_stock_info_to_stock_list(markets)
ABuXqFile.fix_xq_columns_name()
def query_symbol_info(symbol):
m, symbol = ensure_symbol(symbol)
return ... | 雪球获取的数据的key都是中文，dataframe的columns不变与用中文 | from .ABuXqConsts import columns_map
import pandas as pd
from os import path
from ..CoreBu.ABuEnv import g_project_rom_data_dir
stock_df = pd.read_csv(map_stock_list_rom(m), dtype=str)
columns_intersection = stock_df.columns & columns_map.keys()
old_c = df.columns.tolist()
df = pd.read_csv(map_stock_list(market)... | {
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bbfamily/abu | _adjust_returns(returns, adjustment_factor) | function | Code snippets that use function:
snippet 1: if len(returns) < 2:
return np.nan
ann_factor = annualization_factor(period, annualization)
adj_returns = _adjust_returns(returns, required_return)
mu = nanmean(adj_returns, axis=0)
dsr = (_downside_risk if _downside_risk is not None
else ... | Returns the returns series adjusted by adjustment_factor. Optimizes for the
case of adjustment_factor being 0 by returning returns itself, not a copy!
Parameters
----------
returns : pd.Series or np.ndarray
adjustment_factor : pd.Series or np.ndarray or float or int
Returns
-------
pd.... | returns = returns[~np.isnan(returns)]
def _adjust_returns(returns, adjustment_factor):
"""
Returns the returns series adjusted by adjustment_factor. Optimizes for the
case of adjustment_factor being 0 by returning returns itself, not a copy!
Parameters
----------
returns : pd.Series or np.ndar... | {
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bbfamily/abu | cum_returns(returns, starting_value) | function | Code snippets that use function:
snippet 1: """
if len(returns) < 1:
return np.nan
cumulative = cum_returns(returns, starting_value=100)
max_return = np.fmax.accumulate(cumulative)
return nanmin((cumulative - max_return) / max_return)
def annual_return(returns, period=DAILY, annualization=No... | Compute cumulative returns from simple returns.
Parameters
----------
returns : pd.Series or np.ndarray
Returns of the strategy as a percentage, noncumulative.
- Time series with decimal returns.
- Example:
2015-07-16 -0.012143
2015-07-17 0.045350
... | import numpy as np
from functools import wraps
mask = downside_diff > 0
result = np.cumsum(y, axis=axis, dtype=dtype)
y = np.array(a, subok=True)
obj = args[0]
wrap = None
a = alpha_aligned(returns, factor_returns, risk_free, period, annualization, _beta=b)
df_cum = np.exp(nancumsum(np.log1p(returns)))
returns... | {
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bbfamily/abu | nancumsum(a, axis, dtype) | function | Code snippets that use function:
snippet 1: if np.isnan(np.asanyarray(returns)[0]):
returns = returns.copy()
returns[0] = 0.
df_cum = np.exp(nancumsum(np.log1p(returns)))
if starting_value == 0:
return df_cum - 1
else:
return df_cum * starting_value | Return the cumulative sum of array elements over a given axis treating Not
a Numbers (NaNs) as zero. The cumulative sum does not change when NaNs are
encountered and leading NaNs are replaced by zeros.
Handles a subset of the edge cases handled by the nancumsum added in numpy
1.12.0.
Parameters
... | import numpy as np
from functools import wraps
mask = downside_diff > 0
result = np.cumsum(y, axis=axis, dtype=dtype)
y = np.array(a, subok=True)
obj = args[0]
wrap = None
a = alpha_aligned(returns, factor_returns, risk_free, period, annualization, _beta=b)
def array_wrap(arg_name, _not_specified=object()):
... | {
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bbfamily/abu | max_drawdown(returns) | function | Code snippets that use function:
snippet 1: Note
-----
See https://en.wikipedia.org/wiki/Calmar_ratio for more details.
"""
max_dd = max_drawdown(returns=returns)
if max_dd < 0:
temp = annual_return(
returns=returns,
period=period,
annualization=annualiza... | Determines the maximum drawdown of a strategy.
Parameters
----------
returns : pd.Series or np.ndarray
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~empyrical.stats.cum_returns`.
Returns
-------
float
Maximum drawdown.
Note
---... | import numpy as np
from functools import wraps
from .utils import nanmean, nanstd, nanmin
max_return = np.fmax.accumulate(cumulative)
cumulative = cum_returns(returns, starting_value=100)
mask = downside_diff > 0
result = np.cumsum(y, axis=axis, dtype=dtype)
y = np.array(a, subok=True)
obj = args[0]
wrap = Non... | {
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bbfamily/abu | annual_return(returns, period, annualization) | function | Code snippets that use function:
snippet 1: See https://en.wikipedia.org/wiki/Calmar_ratio for more details.
"""
max_dd = max_drawdown(returns=returns)
if max_dd < 0:
temp = annual_return(
returns=returns,
period=period,
annualization=annualization
) / ab... | Determines the mean annual growth rate of returns.
Parameters
----------
returns : pd.Series or np.ndarray
Periodic returns of the strategy, noncumulative.
- See full explanation in :func:`~empyrical.stats.cum_returns`.
period : str, optional
Defines the periodicity of the 'retu... | import numpy as np
from functools import wraps
DAILY = 'daily'
ANNUALIZATION_FACTORS = {DAILY: APPROX_BDAYS_PER_YEAR, WEEKLY: WEEKS_PER_YEAR, MONTHLY: MONTHS_PER_YEAR, YEARLY: 1}
start_value = 100
mask = downside_diff > 0
result = np.cumsum(y, axis=axis, dtype=dtype)
y = np.array(a, subok=True)
obj = args[0]
w... | {
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bbfamily/abu | annual_volatility(returns, period, alpha, annualization) | function | Code snippets that use function:
snippet 1: # 策略平均收益
# noinspection PyUnresolvedReferences
self.mean_algorithm_returns = self.algorithm_returns.cumsum() / np.arange(1, self.num_trading_days + 1,
dtype=np.float64)
#... | Determines the annual volatility of a strategy.
Parameters
----------
returns : pd.Series or np.ndarray
Periodic returns of the strategy, noncumulative.
- See full explanation in :func:`~empyrical.stats.cum_returns`.
period : str, optional
Defines the periodicity of the 'returns... | import numpy as np
from .utils import nanmean, nanstd, nanmin
from six import iteritems
import pandas as pd
from .utils import nanmean, nanstd, nanmin
DAILY = 'daily'
ANNUALIZATION_FACTORS = {DAILY: APPROX_BDAYS_PER_YEAR, WEEKLY: WEEKS_PER_YEAR, MONTHLY: MONTHS_PER_YEAR, YEARLY: 1}
_beta = beta_aligned(returns, ... | {
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bbfamily/abu | sharpe_ratio(returns, risk_free, period, annualization) | function | Code snippets that use function:
snippet 1: self.benchmark_volatility = stats.annual_volatility(self.benchmark_returns)
# noinspection PyTypeChecker
self.algorithm_volatility = stats.annual_volatility(self.algorithm_returns)
# 夏普比率
self.benchmark_sharpe = stats.sharpe_ratio(self.benchma... | Determines the Sharpe ratio of a strategy.
Parameters
----------
returns : pd.Series or np.ndarray
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~empyrical.stats.cum_returns`.
risk_free : int, float
Constant risk-free return throughout the period... | import numpy as np
DAILY = 'daily'
ANNUALIZATION_FACTORS = {DAILY: APPROX_BDAYS_PER_YEAR, WEEKLY: WEEKS_PER_YEAR, MONTHLY: MONTHS_PER_YEAR, YEARLY: 1}
ann_factor = annualization_factor(period, annualization)
factor = annualization
returns = returns[~np.isnan(returns)]
returns_risk_adj = returns_risk_adj[~np.isnan... | {
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bbfamily/abu | downside_risk(returns, required_return, period, annualization) | function | Code snippets that use function:
snippet 1: ann_factor = annualization_factor(period, annualization)
adj_returns = _adjust_returns(returns, required_return)
mu = nanmean(adj_returns, axis=0)
dsr = (_downside_risk if _downside_risk is not None
else downside_risk(returns, required_return))
sor... | Determines the downside deviation below a threshold
Parameters
----------
returns : pd.Series or np.ndarray or pd.DataFrame
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~empyrical.stats.cum_returns`.
required_return: float / series
minimum accep... | import pandas as pd
import numpy as np
from .utils import nanmean, nanstd, nanmin
DAILY = 'daily'
ANNUALIZATION_FACTORS = {DAILY: APPROX_BDAYS_PER_YEAR, WEEKLY: WEEKS_PER_YEAR, MONTHLY: MONTHS_PER_YEAR, YEARLY: 1}
mask = downside_diff > 0
squares = np.square(downside_diff)
returns = returns[~np.isnan(returns)]
... | {
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bbfamily/abu | information_ratio(returns, factor_returns) | function | Code snippets that use function:
snippet 1: # noinspection PyTypeChecker
self.algorithm_sharpe = stats.sharpe_ratio(self.algorithm_returns)
# 信息比率
# noinspection PyUnresolvedReferences
self.information = stats.information_ratio(self.algorithm_returns.values, self.benchmark_returns.value... | Determines the Information ratio of a strategy.
Parameters
----------
returns : pd.Series or np.ndarray
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~empyrical.stats.cum_returns`.
factor_returns: float / series
Benchmark return to compare return... | from .utils import nanmean, nanstd, nanmin
import numpy as np
from .utils import nanmean, nanstd, nanmin
active_return = _adjust_returns(returns, factor_returns)
returns = returns[~np.isnan(returns)]
tracking_error = nanstd(active_return, ddof=1)
def _adjust_returns(returns, adjustment_factor):
"""
Return... | {
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bbfamily/abu | _aligned_series(*many_series) | function | Code snippets that use function:
snippet 1: """
if len(returns) < 2 or len(factor_returns) < 2:
return np.nan, np.nan
return alpha_beta_aligned(*_aligned_series(returns, factor_returns),
risk_free=risk_free, period=period,
annualization=annual... | Return a new list of series containing the data in the input series, but
with their indices aligned. NaNs will be filled in for missing values.
Parameters
----------
many_series : list[pd.Series]
Returns
-------
aligned_series : list[pd.Series]
A new list of series containing the ... | from six import iteritems
import pandas as pd
def _aligned_series(*many_series):
"""
Return a new list of series containing the data in the input series, but
with their indices aligned. NaNs will be filled in for missing values.
Parameters
----------
many_series : list[pd.Series]
Returns... | {
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bbfamily/abu | alpha_beta_aligned(returns, factor_returns, risk_free, period, annualization) | function | Code snippets that use function:
snippet 1: """
if len(returns) < 2 or len(factor_returns) < 2:
return np.nan, np.nan
return alpha_beta_aligned(*_aligned_series(returns, factor_returns),
risk_free=risk_free, period=period,
annualization=annual... | Calculates annualized alpha and beta.
If they are pd.Series, expects returns and factor_returns have already
been aligned on their labels. If np.ndarray, these arguments should have
the same shape.
Parameters
----------
returns : pd.Series or np.ndarray
Daily returns of the strategy, ... | import numpy as np
from .utils import nanmean, nanstd, nanmin
DAILY = 'daily'
ANNUALIZATION_FACTORS = {DAILY: APPROX_BDAYS_PER_YEAR, WEEKLY: WEEKS_PER_YEAR, MONTHLY: MONTHS_PER_YEAR, YEARLY: 1}
_beta = beta_aligned(returns, factor_returns, risk_free)
adj_returns = _adjust_returns(returns, risk_free)
alpha_series ... | {
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bbfamily/abu | alpha_aligned(returns, factor_returns, risk_free, period, annualization, _beta) | function | Code snippets that use function:
snippet 1: float
Beta.
"""
b = beta_aligned(returns, factor_returns, risk_free)
a = alpha_aligned(returns, factor_returns, risk_free, period,
annualization, _beta=b)
return a, b
def alpha(returns, factor_returns, risk_free=0.0, period=DAI... | Calculates annualized alpha.
If they are pd.Series, expects returns and factor_returns have already
been aligned on their labels. If np.ndarray, these arguments should have
the same shape.
Parameters
----------
returns : pd.Series or np.ndarray
Daily returns of the strategy, noncumula... | import numpy as np
from .utils import nanmean, nanstd, nanmin
DAILY = 'daily'
ANNUALIZATION_FACTORS = {DAILY: APPROX_BDAYS_PER_YEAR, WEEKLY: WEEKS_PER_YEAR, MONTHLY: MONTHS_PER_YEAR, YEARLY: 1}
_beta = beta_aligned(returns, factor_returns, risk_free)
adj_returns = _adjust_returns(returns, risk_free)
alpha_series ... | {
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bbfamily/abu | beta_aligned(returns, factor_returns, risk_free) | function | Code snippets that use function:
snippet 1: Alpha.
float
Beta.
"""
b = beta_aligned(returns, factor_returns, risk_free)
a = alpha_aligned(returns, factor_returns, risk_free, period,
annualization, _beta=b)
return a, b
snippet 2: Beta.
"""
if len(returns) < 2 or... | Calculates beta.
If they are pd.Series, expects returns and factor_returns have already
been aligned on their labels. If np.ndarray, these arguments should have
the same shape.
Parameters
----------
returns : pd.Series or np.ndarray
Daily returns of the strategy, noncumulative.
... | import numpy as np
cov = np.cov(joint, ddof=0)
returns = returns[~np.isnan(returns)]
joint = joint[:, ~np.isnan(joint).any(axis=0)]
def _adjust_returns(returns, adjustment_factor):
"""
Returns the returns series adjusted by adjustment_factor. Optimizes for the
case of adjustment_factor being 0 by return... | {
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bbfamily/abu | hash(obj, hash_name, coerce_mmap) | function | Code snippets that use function:
snippet 1: """
if 'numpy' in sys.modules:
hasher = NumpyHasher(hash_name=hash_name, coerce_mmap=coerce_mmap)
else:
hasher = Hasher(hash_name=hash_name)
return hasher.hash(obj)
snippet 2: # but we keep it in a try as it's faster.
self._sequence = s... | Quick calculation of a hash to identify uniquely Python objects
containing numpy arrays.
Parameters
-----------
hash_name: 'md5' or 'sha1'
Hashing algorithm used. sha1 is supposedly safer, but md5 is
faster.
coerce_mmap: boolean
Make no diffe... | import sys
obj = (klass, ('HASHED', obj.descr))
hasher = Hasher(hash_name=hash_name)
class NumpyHasher(Hasher):
""" Special case the hasher for when numpy is loaded.
"""
def __init__(self, hash_name='md5', coerce_mmap=False):
"""
Parameters
----------
hash_nam... | {
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bbfamily/abu | __init__(hash_name, coerce_mmap) | class_method | Code snippets that use function:
snippet 1: class BaseHQCrawlBrower(BaseXQCrawlBrower):
def _crawl_imp(self, *args, **kwargs):
pass
def __init__(self, url):
super(BaseHQCrawlBrower, self).__init__(BASE_XQ_HQ_URL)
self._base_url = self._base_url + url
class NavHQCrawlBrower(BaseHQCrawl... | Parameters
----------
hash_name: string
The hash algorithm to be used
coerce_mmap: boolean
Make no difference between np.memmap and np.ndarray
objects. | import numpy as np
class Hasher(Pickler):
""" A subclass of pickler, to do cryptographic hashing, rather than
pickling.
"""
def __init__(self, hash_name='md5'):
self.stream = io.BytesIO()
protocol = pickle.DEFAULT_PROTOCOL if PY3_OR_LATER else pickle.HIGHEST_PROTOCOL
Pickle... | {
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bbfamily/abu | save(obj) | class_method | Code snippets that use function:
snippet 1: # type(None) or type(module) do not pickle
obj = _MyHash(func_name, inst)
else:
cls = obj.__self__.__class__
obj = _MyHash(func_name, inst, cls)
Pickler.save(self, obj)
def memoize(self, obj):
# ... | Subclass the save method, to hash ndarray subclass, rather
than pickling them. Off course, this is a total abuse of
the Pickler class. | obj = (klass, ('HASHED', obj.descr))
klass = obj.__class__
obj_c_contiguous = obj.flatten()
class Hasher(Pickler):
""" A subclass of pickler, to do cryptographic hashing, rather than
pickling.
"""
def __init__(self, hash_name='md5'):
self.stream = io.BytesIO()
protocol = pickle.D... | {
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bbfamily/abu | pformat(obj, indent, depth) | function | Code snippets that use function:
snippet 1: import numpy as np
print_options = np.get_printoptions()
np.set_printoptions(precision=6, threshold=64, edgeitems=1)
else:
print_options = None
out = pprint.pformat(obj, depth=depth, indent=indent)
if print_options:
np.set_printopti... | No docstring found | import pprint
import numpy as np
import sys
out = pprint.pformat(obj, depth=depth, indent=indent)
print_options = None
def pformat(obj, indent=0, depth=3):
if 'numpy' in sys.modules:
import numpy as np
print_options = np.get_printoptions()
np.set_printoptions(precision=6, threshold=64, ... | {
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bbfamily/abu | __init__(func, cachedir, ignore, mmap_mode, compress, verbose, timestamp) | class_method | Code snippets that use function:
snippet 1: class BaseHQCrawlBrower(BaseXQCrawlBrower):
def _crawl_imp(self, *args, **kwargs):
pass
def __init__(self, url):
super(BaseHQCrawlBrower, self).__init__(BASE_XQ_HQ_URL)
self._base_url = self._base_url + url
class NavHQCrawlBrower(BaseHQCrawl... | Parameters
----------
func: callable
The function to decorate
cachedir: string
The path of the base directory to use as a data store
ignore: list or None
List of variable names to ignore.
mmap_mode: {None, 'r+', ... | import inspect
import time
import functools
import warnings
import re
import pydoc
from .disk import mkdirp, rm_subdirs
from .logger import Logger, format_time, pformat
verbose = self._verbose
timestamp = time.time()
doc = func.__doc__
func = func.func
ignore = []
mmap_mode = self.mmap_mode
cachedir = sel... | {
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"num... |
bbfamily/abu | __init__(cachedir, mmap_mode, compress, verbose) | class_method | Code snippets that use function:
snippet 1: class BaseHQCrawlBrower(BaseXQCrawlBrower):
def _crawl_imp(self, *args, **kwargs):
pass
def __init__(self, url):
super(BaseHQCrawlBrower, self).__init__(BASE_XQ_HQ_URL)
self._base_url = self._base_url + url
class NavHQCrawlBrower(BaseHQCrawl... | Parameters
----------
cachedir: string or None
The path of the base directory to use as a data store
or None. If None is given, no caching is done and
the Memory object is completely transparent.
mmap_mode: {None, 'r+', 'r', 'w+', 'c'},... | import time
import warnings
from .disk import mkdirp, rm_subdirs
from .logger import Logger, format_time, pformat
import os
verbose = self._verbose
mmap_mode = self.mmap_mode
cachedir = self.cachedir[:-7] if self.cachedir is not None else None
def __init__(self, cachedir, mmap_mode=None, compress=False, verbose... | {
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bbfamily/abu | dump(value, filename, compress, protocol, cache_size) | function | Code snippets that use function:
snippet 1: # Initialise the hash obj
self._hash = hashlib.new(hash_name)
def hash(self, obj, return_digest=True):
try:
self.dump(obj)
except pickle.PicklingError as e:
e.args += ('PicklingError while hashing %r: %r' % (obj, e),)
... | Persist an arbitrary Python object into one file.
Parameters
-----------
value: any Python object
The object to store to disk.
filename: str or pathlib.Path
The path of the file in which it is to be stored. The compression
method corresponding to one of the supported filename ex... | from .numpy_pickle_utils import _COMPRESSORS
from ._compat import _basestring, PY3_OR_LATER
from ._compat import _basestring, PY3_OR_LATER
import warnings
import sys
from pathlib import Path
from .numpy_pickle_utils import _read_fileobject, _write_fileobject
compress_level = 3
filename = str(filename)
compress... | {
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"_... |
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