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value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
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train | FlexReport.df | Same as extract but return the result as a pandas DataFrame. | ib_insync/flexreport.py | def df(self, topic: str, parseNumbers=True):
"""
Same as extract but return the result as a pandas DataFrame.
"""
return util.df(self.extract(topic, parseNumbers)) | def df(self, topic: str, parseNumbers=True):
"""
Same as extract but return the result as a pandas DataFrame.
"""
return util.df(self.extract(topic, parseNumbers)) | [
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train | FlexReport.download | Download report for the given ``token`` and ``queryId``. | ib_insync/flexreport.py | def download(self, token, queryId):
"""
Download report for the given ``token`` and ``queryId``.
"""
url = (
'https://gdcdyn.interactivebrokers.com'
f'/Universal/servlet/FlexStatementService.SendRequest?'
f't={token}&q={queryId}&v=3')
... | def download(self, token, queryId):
"""
Download report for the given ``token`` and ``queryId``.
"""
url = (
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train | FlexReport.load | Load report from XML file. | ib_insync/flexreport.py | def load(self, path):
"""
Load report from XML file.
"""
with open(path, 'rb') as f:
self.data = f.read()
self.root = et.fromstring(self.data) | def load(self, path):
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Load report from XML file.
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train | FlexReport.save | Save report to XML file. | ib_insync/flexreport.py | def save(self, path):
"""
Save report to XML file.
"""
with open(path, 'wb') as f:
f.write(self.data) | def save(self, path):
"""
Save report to XML file.
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train | Contract.create | Create and a return a specialized contract based on the given secType,
or a general Contract if secType is not given. | ib_insync/contract.py | def create(**kwargs):
"""
Create and a return a specialized contract based on the given secType,
or a general Contract if secType is not given.
"""
secType = kwargs.get('secType', '')
cls = {
'': Contract,
'STK': Stock,
'OPT': Option,
... | def create(**kwargs):
"""
Create and a return a specialized contract based on the given secType,
or a general Contract if secType is not given.
"""
secType = kwargs.get('secType', '')
cls = {
'': Contract,
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'OPT': Option,
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train | Ticker.hasBidAsk | See if this ticker has a valid bid and ask. | ib_insync/ticker.py | def hasBidAsk(self) -> bool:
"""
See if this ticker has a valid bid and ask.
"""
return (
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"""
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"""
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train | Ticker.midpoint | Return average of bid and ask, or NaN if no valid bid and ask
are available. | ib_insync/ticker.py | def midpoint(self) -> float:
"""
Return average of bid and ask, or NaN if no valid bid and ask
are available.
"""
return (self.bid + self.ask) / 2 if self.hasBidAsk() else nan | def midpoint(self) -> float:
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Return average of bid and ask, or NaN if no valid bid and ask
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"""
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train | Ticker.marketPrice | Return the first available one of
* last price if within current bid/ask;
* average of bid and ask (midpoint);
* close price. | ib_insync/ticker.py | def marketPrice(self) -> float:
"""
Return the first available one of
* last price if within current bid/ask;
* average of bid and ask (midpoint);
* close price.
"""
price = self.last if (
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"""
Return the first available one of
* last price if within current bid/ask;
* average of bid and ask (midpoint);
* close price.
"""
price = self.last if (
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train | KeywordProcessor.add_keyword_from_file | To add keywords from a file
Args:
keyword_file : path to keywords file
encoding : specify the encoding of the file
Examples:
keywords file format can be like:
>>> # Option 1: keywords.txt content
>>> # java_2e=>java
>>> # java pr... | flashtext/keyword.py | def add_keyword_from_file(self, keyword_file, encoding="utf-8"):
"""To add keywords from a file
Args:
keyword_file : path to keywords file
encoding : specify the encoding of the file
Examples:
keywords file format can be like:
>>> # Option 1: ke... | def add_keyword_from_file(self, keyword_file, encoding="utf-8"):
"""To add keywords from a file
Args:
keyword_file : path to keywords file
encoding : specify the encoding of the file
Examples:
keywords file format can be like:
>>> # Option 1: ke... | [
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train | KeywordProcessor.add_keywords_from_dict | To add keywords from a dictionary
Args:
keyword_dict (dict): A dictionary with `str` key and (list `str`) as value
Examples:
>>> keyword_dict = {
"java": ["java_2e", "java programing"],
"product management": ["PM", "product manager"]
... | flashtext/keyword.py | def add_keywords_from_dict(self, keyword_dict):
"""To add keywords from a dictionary
Args:
keyword_dict (dict): A dictionary with `str` key and (list `str`) as value
Examples:
>>> keyword_dict = {
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... | def add_keywords_from_dict(self, keyword_dict):
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Args:
keyword_dict (dict): A dictionary with `str` key and (list `str`) as value
Examples:
>>> keyword_dict = {
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train | KeywordProcessor.remove_keywords_from_dict | To remove keywords from a dictionary
Args:
keyword_dict (dict): A dictionary with `str` key and (list `str`) as value
Examples:
>>> keyword_dict = {
"java": ["java_2e", "java programing"],
"product management": ["PM", "product manager"]
... | flashtext/keyword.py | def remove_keywords_from_dict(self, keyword_dict):
"""To remove keywords from a dictionary
Args:
keyword_dict (dict): A dictionary with `str` key and (list `str`) as value
Examples:
>>> keyword_dict = {
"java": ["java_2e", "java programing"],
... | def remove_keywords_from_dict(self, keyword_dict):
"""To remove keywords from a dictionary
Args:
keyword_dict (dict): A dictionary with `str` key and (list `str`) as value
Examples:
>>> keyword_dict = {
"java": ["java_2e", "java programing"],
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train | KeywordProcessor.add_keywords_from_list | To add keywords from a list
Args:
keyword_list (list(str)): List of keywords to add
Examples:
>>> keyword_processor.add_keywords_from_list(["java", "python"]})
Raises:
AttributeError: If `keyword_list` is not a list. | flashtext/keyword.py | def add_keywords_from_list(self, keyword_list):
"""To add keywords from a list
Args:
keyword_list (list(str)): List of keywords to add
Examples:
>>> keyword_processor.add_keywords_from_list(["java", "python"]})
Raises:
AttributeError: If `keyword_lis... | def add_keywords_from_list(self, keyword_list):
"""To add keywords from a list
Args:
keyword_list (list(str)): List of keywords to add
Examples:
>>> keyword_processor.add_keywords_from_list(["java", "python"]})
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train | KeywordProcessor.remove_keywords_from_list | To remove keywords present in list
Args:
keyword_list (list(str)): List of keywords to remove
Examples:
>>> keyword_processor.remove_keywords_from_list(["java", "python"]})
Raises:
AttributeError: If `keyword_list` is not a list. | flashtext/keyword.py | def remove_keywords_from_list(self, keyword_list):
"""To remove keywords present in list
Args:
keyword_list (list(str)): List of keywords to remove
Examples:
>>> keyword_processor.remove_keywords_from_list(["java", "python"]})
Raises:
AttributeError:... | def remove_keywords_from_list(self, keyword_list):
"""To remove keywords present in list
Args:
keyword_list (list(str)): List of keywords to remove
Examples:
>>> keyword_processor.remove_keywords_from_list(["java", "python"]})
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train | KeywordProcessor.get_all_keywords | Recursively builds a dictionary of keywords present in the dictionary
And the clean name mapped to those keywords.
Args:
term_so_far : string
term built so far by adding all previous characters
current_dict : dict
current recursive position in dic... | flashtext/keyword.py | def get_all_keywords(self, term_so_far='', current_dict=None):
"""Recursively builds a dictionary of keywords present in the dictionary
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Args:
term_so_far : string
term built so far by adding all previous characters
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"""Recursively builds a dictionary of keywords present in the dictionary
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Args:
term_so_far : string
term built so far by adding all previous characters
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train | KeywordProcessor.extract_keywords | Searches in the string for all keywords present in corpus.
Keywords present are added to a list `keywords_extracted` and returned.
Args:
sentence (str): Line of text where we will search for keywords
Returns:
keywords_extracted (list(str)): List of terms/keywords found ... | flashtext/keyword.py | def extract_keywords(self, sentence, span_info=False):
"""Searches in the string for all keywords present in corpus.
Keywords present are added to a list `keywords_extracted` and returned.
Args:
sentence (str): Line of text where we will search for keywords
Returns:
... | def extract_keywords(self, sentence, span_info=False):
"""Searches in the string for all keywords present in corpus.
Keywords present are added to a list `keywords_extracted` and returned.
Args:
sentence (str): Line of text where we will search for keywords
Returns:
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train | KeywordProcessor.replace_keywords | Searches in the string for all keywords present in corpus.
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Args:
sentence (str): Line of text where we will replace keywords
Returns:
new_sentence (str): Line of text with replaced keywords
... | flashtext/keyword.py | def replace_keywords(self, sentence):
"""Searches in the string for all keywords present in corpus.
Keywords present are replaced by the clean name and a new string is returned.
Args:
sentence (str): Line of text where we will replace keywords
Returns:
new_sente... | def replace_keywords(self, sentence):
"""Searches in the string for all keywords present in corpus.
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Args:
sentence (str): Line of text where we will replace keywords
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train | factor_information_coefficient | Computes the Spearman Rank Correlation based Information Coefficient (IC)
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the factor index.
Parameters
----------
factor_data : pd.DataFrame - MultiIndex
A MultiIndex DataFrame indexed by date (level 0) and asset (level 1... | alphalens/performance.py | def factor_information_coefficient(factor_data,
group_adjust=False,
by_group=False):
"""
Computes the Spearman Rank Correlation based Information Coefficient (IC)
between factor values and N period forward returns for each period in
t... | def factor_information_coefficient(factor_data,
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Computes the Spearman Rank Correlation based Information Coefficient (IC)
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train | mean_information_coefficient | Get the mean information coefficient of specified groups.
Answers questions like:
What is the mean IC for each month?
What is the mean IC for each group for our whole timerange?
What is the mean IC for for each group, each week?
Parameters
----------
factor_data : pd.DataFrame - MultiIndex
... | alphalens/performance.py | def mean_information_coefficient(factor_data,
group_adjust=False,
by_group=False,
by_time=None):
"""
Get the mean information coefficient of specified groups.
Answers questions like:
What is the mean IC fo... | def mean_information_coefficient(factor_data,
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Get the mean information coefficient of specified groups.
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train | factor_weights | Computes asset weights by factor values and dividing by the sum of their
absolute value (achieving gross leverage of 1). Positive factor values will
results in positive weights and negative values in negative weights.
Parameters
----------
factor_data : pd.DataFrame - MultiIndex
A MultiInde... | alphalens/performance.py | def factor_weights(factor_data,
demeaned=True,
group_adjust=False,
equal_weight=False):
"""
Computes asset weights by factor values and dividing by the sum of their
absolute value (achieving gross leverage of 1). Positive factor values will
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Computes asset weights by factor values and dividing by the sum of their
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train | factor_returns | Computes period wise returns for portfolio weighted by factor
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Parameters
----------
factor_data : pd.DataFrame - MultiIndex
A MultiIndex DataFrame indexed by date (level 0) and asset (level 1),
containing the values for a single alpha factor, forward returns for
each per... | alphalens/performance.py | def factor_returns(factor_data,
demeaned=True,
group_adjust=False,
equal_weight=False,
by_asset=False):
"""
Computes period wise returns for portfolio weighted by factor
values.
Parameters
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demeaned=True,
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Computes period wise returns for portfolio weighted by factor
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train | factor_alpha_beta | Compute the alpha (excess returns), alpha t-stat (alpha significance),
and beta (market exposure) of a factor. A regression is run with
the period wise factor universe mean return as the independent variable
and mean period wise return from a portfolio weighted by factor values
as the dependent variable... | alphalens/performance.py | def factor_alpha_beta(factor_data,
returns=None,
demeaned=True,
group_adjust=False,
equal_weight=False):
"""
Compute the alpha (excess returns), alpha t-stat (alpha significance),
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train | cumulative_returns | Builds cumulative returns from 'period' returns. This function simulates
the cumulative effect that a series of gains or losses (the 'returns')
have on an original amount of capital over a period of time.
if F is the frequency at which returns are computed (e.g. 1 day if
'returns' contains daily values... | alphalens/performance.py | def cumulative_returns(returns, period, freq=None):
"""
Builds cumulative returns from 'period' returns. This function simulates
the cumulative effect that a series of gains or losses (the 'returns')
have on an original amount of capital over a period of time.
if F is the frequency at which returns... | def cumulative_returns(returns, period, freq=None):
"""
Builds cumulative returns from 'period' returns. This function simulates
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train | positions | Builds net position values time series, the portfolio percentage invested
in each position.
Parameters
----------
weights: pd.Series
pd.Series containing factor weights, the index contains timestamps at
which the trades are computed and the values correspond to assets
weights
... | alphalens/performance.py | def positions(weights, period, freq=None):
"""
Builds net position values time series, the portfolio percentage invested
in each position.
Parameters
----------
weights: pd.Series
pd.Series containing factor weights, the index contains timestamps at
which the trades are computed... | def positions(weights, period, freq=None):
"""
Builds net position values time series, the portfolio percentage invested
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Parameters
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pd.Series containing factor weights, the index contains timestamps at
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train | mean_return_by_quantile | Computes mean returns for factor quantiles across
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Parameters
----------
factor_data : pd.DataFrame - MultiIndex
A MultiIndex DataFrame indexed by date (level 0) and asset (level 1),
containing the values for a single alpha factor, forward returns for
... | alphalens/performance.py | def mean_return_by_quantile(factor_data,
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"""
Computes mean returns for factor quantiles across
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... | def mean_return_by_quantile(factor_data,
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"""
Computes mean returns for factor quantiles across
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train | compute_mean_returns_spread | Computes the difference between the mean returns of
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of this difference.
Parameters
----------
mean_returns : pd.DataFrame
DataFrame of mean period wise returns by quantile.
MultiIndex containing date and quantile.
See me... | alphalens/performance.py | def compute_mean_returns_spread(mean_returns,
upper_quant,
lower_quant,
std_err=None):
"""
Computes the difference between the mean returns of
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train | quantile_turnover | Computes the proportion of names in a factor quantile that were
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Parameters
----------
quantile_factor : pd.Series
DataFrame with date, asset and factor quantile.
quantile : int
Quantile on which to perform turnover analysis.
period: s... | alphalens/performance.py | def quantile_turnover(quantile_factor, quantile, period=1):
"""
Computes the proportion of names in a factor quantile that were
not in that quantile in the previous period.
Parameters
----------
quantile_factor : pd.Series
DataFrame with date, asset and factor quantile.
quantile : i... | def quantile_turnover(quantile_factor, quantile, period=1):
"""
Computes the proportion of names in a factor quantile that were
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----------
quantile_factor : pd.Series
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train | factor_rank_autocorrelation | Computes autocorrelation of mean factor ranks in specified time spans.
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to account for systematic shifts in the factor values of all names or names
within a group. This metric is useful for measuring the turnover of a
factor. If the ... | alphalens/performance.py | def factor_rank_autocorrelation(factor_data, period=1):
"""
Computes autocorrelation of mean factor ranks in specified time spans.
We must compare period to period factor ranks rather than factor values
to account for systematic shifts in the factor values of all names or names
within a group. This ... | def factor_rank_autocorrelation(factor_data, period=1):
"""
Computes autocorrelation of mean factor ranks in specified time spans.
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train | common_start_returns | A date and equity pair is extracted from each index row in the factor
dataframe and for each of these pairs a return series is built starting
from 'before' the date and ending 'after' the date specified in the pair.
All those returns series are then aligned to a common index (-before to
after) and retur... | alphalens/performance.py | def common_start_returns(factor,
prices,
before,
after,
cumulative=False,
mean_by_date=False,
demean_by=None):
"""
A date and equity pair is extracted from each i... | def common_start_returns(factor,
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train | average_cumulative_return_by_quantile | Plots average cumulative returns by factor quantiles in the period range
defined by -periods_before to periods_after
Parameters
----------
factor_data : pd.DataFrame - MultiIndex
A MultiIndex DataFrame indexed by date (level 0) and asset (level 1),
containing the values for a single alp... | alphalens/performance.py | def average_cumulative_return_by_quantile(factor_data,
prices,
periods_before=10,
periods_after=15,
demeaned=True,
... | def average_cumulative_return_by_quantile(factor_data,
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train | factor_cumulative_returns | Simulate a portfolio using the factor in input and returns the cumulative
returns of the simulated portfolio
Parameters
----------
factor_data : pd.DataFrame - MultiIndex
A MultiIndex DataFrame indexed by date (level 0) and asset (level 1),
containing the values for a single alpha facto... | alphalens/performance.py | def factor_cumulative_returns(factor_data,
period,
long_short=True,
group_neutral=False,
equal_weight=False,
quantiles=None,
groups=None):
... | def factor_cumulative_returns(factor_data,
period,
long_short=True,
group_neutral=False,
equal_weight=False,
quantiles=None,
groups=None):
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train | factor_positions | Simulate a portfolio using the factor in input and returns the assets
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----------
factor_data : pd.DataFrame - MultiIndex
A MultiIndex DataFrame indexed by date (level 0) and asset (level 1),
containing the values for a single al... | alphalens/performance.py | def factor_positions(factor_data,
period,
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equal_weight=False,
quantiles=None,
groups=None):
"""
Simulate a portfolio using the factor in input and r... | def factor_positions(factor_data,
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train | create_pyfolio_input | Simulate a portfolio using the input factor and returns the portfolio
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For more details on how this portfolio is built see:
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capital=None,
long_short=True,
group_neutral=False,
equal_weight=False,
quantiles=None,
groups=None... | def create_pyfolio_input(factor_data,
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group_neutral=False,
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factor_data : pd.DataFrame - MultiIndex
A MultiIndex DataFrame indexed by date (level 0) and asset (level 1),
containing the values for a single alpha factor, forward returns for
... | alphalens/tears.py | def create_summary_tear_sheet(factor_data,
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group_neutral=False):
"""
Creates a small summary tear sheet with returns, information, and turnover
analysis.
Parameters
----------
factor_data : pd.DataFrame - MultiIndex
... | def create_summary_tear_sheet(factor_data,
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Creates a small summary tear sheet with returns, information, and turnover
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Parameters
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factor_data : pd.DataFrame - MultiIndex
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train | create_returns_tear_sheet | Creates a tear sheet for returns analysis of a factor.
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A MultiIndex DataFrame indexed by date (level 0) and asset (level 1),
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Creates a tear sheet for returns analysis of a factor.
Parameters
----------
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train | create_information_tear_sheet | Creates a tear sheet for information analysis of a factor.
Parameters
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factor_data : pd.DataFrame - MultiIndex
A MultiIndex DataFrame indexed by date (level 0) and asset (level 1),
containing the values for a single alpha factor, forward returns for
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"""
Creates a tear sheet for information analysis of a factor.
Parameters
----------
factor_data : pd.DataFrame - MultiIndex
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Creates a tear sheet for information analysis of a factor.
Parameters
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factor_data : pd.DataFrame - MultiIndex
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train | create_turnover_tear_sheet | Creates a tear sheet for analyzing the turnover properties of a factor.
Parameters
----------
factor_data : pd.DataFrame - MultiIndex
A MultiIndex DataFrame indexed by date (level 0) and asset (level 1),
containing the values for a single alpha factor, forward returns for
each perio... | alphalens/tears.py | def create_turnover_tear_sheet(factor_data, turnover_periods=None):
"""
Creates a tear sheet for analyzing the turnover properties of a factor.
Parameters
----------
factor_data : pd.DataFrame - MultiIndex
A MultiIndex DataFrame indexed by date (level 0) and asset (level 1),
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Creates a tear sheet for analyzing the turnover properties of a factor.
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factor_data : pd.DataFrame - MultiIndex
A MultiIndex DataFrame indexed by date (level 0) and asset (level 1),
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Parameters
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A MultiIndex DataFrame indexed by date (level 0) and asset (level 1),
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"""
Creates a full tear sheet for analysis and evaluating single
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Parameters
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group_neutral=False,
by_group=False):
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Creates a full tear sheet for analysis and evaluating single
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train | create_event_returns_tear_sheet | Creates a tear sheet to view the average cumulative returns for a
factor within a window (pre and post event).
Parameters
----------
factor_data : pd.DataFrame - MultiIndex
A MultiIndex Series indexed by date (level 0) and asset (level 1),
containing the values for a single alpha factor... | alphalens/tears.py | def create_event_returns_tear_sheet(factor_data,
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group_neutral=False,
std_bar=True,
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train | create_event_study_tear_sheet | Creates an event study tear sheet for analysis of a specific event.
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factor_data : pd.DataFrame - MultiIndex
A MultiIndex DataFrame indexed by date (level 0) and asset (level 1),
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train | rethrow | Re-raise the last exception that was active in the current scope
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"""
Re-raise the last exception that was active in the current scope
without losing the stacktrace but adding an additional message.
This is hacky because it has to be compatible with both python 2/3
"""
e = exception
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Re-raise the last exception that was active in the current scope
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train | non_unique_bin_edges_error | Give user a more informative error in case it is not possible
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"""
Give user a more informative error in case it is not possible
to properly calculate quantiles on the input dataframe (factor)
"""
message = """
An error occurred while computing bins/quantiles on the input provided.
This usually happens when the inp... | def non_unique_bin_edges_error(func):
"""
Give user a more informative error in case it is not possible
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message = """
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train | quantize_factor | Computes period wise factor quantiles.
Parameters
----------
factor_data : pd.DataFrame - MultiIndex
A MultiIndex DataFrame indexed by date (level 0) and asset (level 1),
containing the values for a single alpha factor, forward returns for
each period, the factor quantile/bin that f... | alphalens/utils.py | def quantize_factor(factor_data,
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bins=None,
by_group=False,
no_raise=False,
zero_aware=False):
"""
Computes period wise factor quantiles.
Parameters
----------
factor_data : pd.DataFrame... | def quantize_factor(factor_data,
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bins=None,
by_group=False,
no_raise=False,
zero_aware=False):
"""
Computes period wise factor quantiles.
Parameters
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train | infer_trading_calendar | Infer the trading calendar from factor and price information.
Parameters
----------
factor_idx : pd.DatetimeIndex
The factor datetimes for which we are computing the forward returns
prices_idx : pd.DatetimeIndex
The prices datetimes associated withthe factor data
Returns
------... | alphalens/utils.py | def infer_trading_calendar(factor_idx, prices_idx):
"""
Infer the trading calendar from factor and price information.
Parameters
----------
factor_idx : pd.DatetimeIndex
The factor datetimes for which we are computing the forward returns
prices_idx : pd.DatetimeIndex
The prices ... | def infer_trading_calendar(factor_idx, prices_idx):
"""
Infer the trading calendar from factor and price information.
Parameters
----------
factor_idx : pd.DatetimeIndex
The factor datetimes for which we are computing the forward returns
prices_idx : pd.DatetimeIndex
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train | compute_forward_returns | Finds the N period forward returns (as percent change) for each asset
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Parameters
----------
factor : pd.Series - MultiIndex
A MultiIndex Series indexed by timestamp (level 0) and asset
(level 1), containing the values for a single alpha factor.
- See full explanation ... | alphalens/utils.py | def compute_forward_returns(factor,
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periods=(1, 5, 10),
filter_zscore=None,
cumulative_returns=True):
"""
Finds the N period forward returns (as percent change) for each asset
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... | def compute_forward_returns(factor,
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"""
Finds the N period forward returns (as percent change) for each asset
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group neutral portfolio constraint and thus allows allows the
factor to be evaluated across groups.
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"""
Convert forward returns to returns relative to mean
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group-wise normalization incorporates the assumption of a
group neutral portfolio constraint and thus allows allows the
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"""
Convert forward returns to returns relative to mean
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train | print_table | Pretty print a pandas DataFrame.
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table : pd.Series or pd.DataFrame
Table to pretty-print.
name : str, optional
Table name to display in upper left corner.
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"""
Pretty print a pandas DataFrame.
Uses HTML output if running inside Jupyter Notebook, otherwise
formatted text output.
Parameters
----------
table : pd.Series or pd.DataFrame
Table to pretty-print.
name : str, optional
Ta... | def print_table(table, name=None, fmt=None):
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groupby_labels=None,
max_loss=0.35,
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groupby_labels=None,
max_loss=0.35,
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binning_by_group=False,
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bins=No... | def get_clean_factor_and_forward_returns(factor,
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binning_by_group=False,
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train | rate_of_return | Convert returns to 'one_period_len' rate of returns: that is the value the
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Parameters
----------
period_ret: pd.DataFrame
DataFrame containing returns values with column headings representing
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"""
Convert returns to 'one_period_len' rate of returns: that is the value the
returns would have every 'one_period_len' if they had grown at a steady
rate
Parameters
----------
period_ret: pd.DataFrame
DataFrame containing returns values... | def rate_of_return(period_ret, base_period):
"""
Convert returns to 'one_period_len' rate of returns: that is the value the
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train | std_conversion | one_period_len standard deviation (or standard error) approximation
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period_std: pd.DataFrame
DataFrame containing standard deviation or standard error values
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base_period: string
The base period length use... | alphalens/utils.py | def std_conversion(period_std, base_period):
"""
one_period_len standard deviation (or standard error) approximation
Parameters
----------
period_std: pd.DataFrame
DataFrame containing standard deviation or standard error values
with column headings representing the return period.
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"""
one_period_len standard deviation (or standard error) approximation
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period_std: pd.DataFrame
DataFrame containing standard deviation or standard error values
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train | get_forward_returns_columns | Utility that detects and returns the columns that are forward returns | alphalens/utils.py | def get_forward_returns_columns(columns):
"""
Utility that detects and returns the columns that are forward returns
"""
pattern = re.compile(r"^(\d+([Dhms]|ms|us|ns))+$", re.IGNORECASE)
valid_columns = [(pattern.match(col) is not None) for col in columns]
return columns[valid_columns] | def get_forward_returns_columns(columns):
"""
Utility that detects and returns the columns that are forward returns
"""
pattern = re.compile(r"^(\d+([Dhms]|ms|us|ns))+$", re.IGNORECASE)
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train | timedelta_to_string | Utility that converts a pandas.Timedelta to a string representation
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Parameters
----------
timedelta: pd.Timedelta
Returns
-------
string
string representation of 'timedelta' | alphalens/utils.py | def timedelta_to_string(timedelta):
"""
Utility that converts a pandas.Timedelta to a string representation
compatible with pandas.Timedelta constructor format
Parameters
----------
timedelta: pd.Timedelta
Returns
-------
string
string representation of 'timedelta'
"""
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Utility that converts a pandas.Timedelta to a string representation
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timedelta: pd.Timedelta
Returns
-------
string
string representation of 'timedelta'
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train | add_custom_calendar_timedelta | Add timedelta to 'input' taking into consideration custom frequency, which
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----------
input : pd.DatetimeIndex or pd.Timestamp
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Add timedelta to 'input' taking into consideration custom frequency, which
is used to deal with custom calendars, such as a trading calendar
Parameters
----------
input : pd.DatetimeIndex or pd.Timestamp
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Add timedelta to 'input' taking into consideration custom frequency, which
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start : pd.Timestamp
end : pd.Timestamp
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Compute the difference between two pd.Timedelta taking into consideration
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trading calendar
Parameters
----------
start : pd.Timestamp
end : pd.Timestamp
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Compute the difference between two pd.Timedelta taking into consideration
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train | customize | Decorator to set plotting context and axes style during function call. | alphalens/plotting.py | def customize(func):
"""
Decorator to set plotting context and axes style during function call.
"""
@wraps(func)
def call_w_context(*args, **kwargs):
set_context = kwargs.pop('set_context', True)
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"""
Decorator to set plotting context and axes style during function call.
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@wraps(func)
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train | plot_ic_ts | Plots Spearman Rank Information Coefficient and IC moving
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Parameters
----------
ic : pd.DataFrame
DataFrame indexed by date, with IC for each forward return.
ax : matplotlib.Axes, optional
Axes upon which to plot.
Returns
-------
ax : matplot... | alphalens/plotting.py | def plot_ic_ts(ic, ax=None):
"""
Plots Spearman Rank Information Coefficient and IC moving
average for a given factor.
Parameters
----------
ic : pd.DataFrame
DataFrame indexed by date, with IC for each forward return.
ax : matplotlib.Axes, optional
Axes upon which to plot.
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"""
Plots Spearman Rank Information Coefficient and IC moving
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Parameters
----------
ic : pd.DataFrame
DataFrame indexed by date, with IC for each forward return.
ax : matplotlib.Axes, optional
Axes upon which to plot.
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train | plot_ic_hist | Plots Spearman Rank Information Coefficient histogram for a given factor.
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ic : pd.DataFrame
DataFrame indexed by date, with IC for each forward return.
ax : matplotlib.Axes, optional
Axes upon which to plot.
Returns
-------
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"""
Plots Spearman Rank Information Coefficient histogram for a given factor.
Parameters
----------
ic : pd.DataFrame
DataFrame indexed by date, with IC for each forward return.
ax : matplotlib.Axes, optional
Axes upon which to plot.
Returns
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Plots Spearman Rank Information Coefficient histogram for a given factor.
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ic : pd.DataFrame
DataFrame indexed by date, with IC for each forward return.
ax : matplotlib.Axes, optional
Axes upon which to plot.
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train | plot_ic_qq | Plots Spearman Rank Information Coefficient "Q-Q" plot relative to
a theoretical distribution.
Parameters
----------
ic : pd.DataFrame
DataFrame indexed by date, with IC for each forward return.
theoretical_dist : scipy.stats._continuous_distns
Continuous distribution generator. sci... | alphalens/plotting.py | def plot_ic_qq(ic, theoretical_dist=stats.norm, ax=None):
"""
Plots Spearman Rank Information Coefficient "Q-Q" plot relative to
a theoretical distribution.
Parameters
----------
ic : pd.DataFrame
DataFrame indexed by date, with IC for each forward return.
theoretical_dist : scipy.s... | def plot_ic_qq(ic, theoretical_dist=stats.norm, ax=None):
"""
Plots Spearman Rank Information Coefficient "Q-Q" plot relative to
a theoretical distribution.
Parameters
----------
ic : pd.DataFrame
DataFrame indexed by date, with IC for each forward return.
theoretical_dist : scipy.s... | [
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train | plot_quantile_returns_bar | Plots mean period wise returns for factor quantiles.
Parameters
----------
mean_ret_by_q : pd.DataFrame
DataFrame with quantile, (group) and mean period wise return values.
by_group : bool
Disaggregated figures by group.
ylim_percentiles : tuple of integers
Percentiles of ob... | alphalens/plotting.py | def plot_quantile_returns_bar(mean_ret_by_q,
by_group=False,
ylim_percentiles=None,
ax=None):
"""
Plots mean period wise returns for factor quantiles.
Parameters
----------
mean_ret_by_q : pd.DataFrame
... | def plot_quantile_returns_bar(mean_ret_by_q,
by_group=False,
ylim_percentiles=None,
ax=None):
"""
Plots mean period wise returns for factor quantiles.
Parameters
----------
mean_ret_by_q : pd.DataFrame
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train | plot_quantile_returns_violin | Plots a violin box plot of period wise returns for factor quantiles.
Parameters
----------
return_by_q : pd.DataFrame - MultiIndex
DataFrame with date and quantile as rows MultiIndex,
forward return windows as columns, returns as values.
ylim_percentiles : tuple of integers
Perc... | alphalens/plotting.py | def plot_quantile_returns_violin(return_by_q,
ylim_percentiles=None,
ax=None):
"""
Plots a violin box plot of period wise returns for factor quantiles.
Parameters
----------
return_by_q : pd.DataFrame - MultiIndex
DataFrame w... | def plot_quantile_returns_violin(return_by_q,
ylim_percentiles=None,
ax=None):
"""
Plots a violin box plot of period wise returns for factor quantiles.
Parameters
----------
return_by_q : pd.DataFrame - MultiIndex
DataFrame w... | [
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train | plot_mean_quantile_returns_spread_time_series | Plots mean period wise returns for factor quantiles.
Parameters
----------
mean_returns_spread : pd.Series
Series with difference between quantile mean returns by period.
std_err : pd.Series
Series with standard error of difference between quantile
mean returns each period.
... | alphalens/plotting.py | def plot_mean_quantile_returns_spread_time_series(mean_returns_spread,
std_err=None,
bandwidth=1,
ax=None):
"""
Plots mean period wise returns for factor quantile... | def plot_mean_quantile_returns_spread_time_series(mean_returns_spread,
std_err=None,
bandwidth=1,
ax=None):
"""
Plots mean period wise returns for factor quantile... | [
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train | plot_ic_by_group | Plots Spearman Rank Information Coefficient for a given factor over
provided forward returns. Separates by group.
Parameters
----------
ic_group : pd.DataFrame
group-wise mean period wise returns.
ax : matplotlib.Axes, optional
Axes upon which to plot.
Returns
-------
a... | alphalens/plotting.py | def plot_ic_by_group(ic_group, ax=None):
"""
Plots Spearman Rank Information Coefficient for a given factor over
provided forward returns. Separates by group.
Parameters
----------
ic_group : pd.DataFrame
group-wise mean period wise returns.
ax : matplotlib.Axes, optional
Ax... | def plot_ic_by_group(ic_group, ax=None):
"""
Plots Spearman Rank Information Coefficient for a given factor over
provided forward returns. Separates by group.
Parameters
----------
ic_group : pd.DataFrame
group-wise mean period wise returns.
ax : matplotlib.Axes, optional
Ax... | [
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train | plot_factor_rank_auto_correlation | Plots factor rank autocorrelation over time.
See factor_rank_autocorrelation for more details.
Parameters
----------
factor_autocorrelation : pd.Series
Rolling 1 period (defined by time_rule) autocorrelation
of factor values.
period: int, optional
Period over which the autoc... | alphalens/plotting.py | def plot_factor_rank_auto_correlation(factor_autocorrelation,
period=1,
ax=None):
"""
Plots factor rank autocorrelation over time.
See factor_rank_autocorrelation for more details.
Parameters
----------
factor_autocorre... | def plot_factor_rank_auto_correlation(factor_autocorrelation,
period=1,
ax=None):
"""
Plots factor rank autocorrelation over time.
See factor_rank_autocorrelation for more details.
Parameters
----------
factor_autocorre... | [
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train | plot_top_bottom_quantile_turnover | Plots period wise top and bottom quantile factor turnover.
Parameters
----------
quantile_turnover: pd.Dataframe
Quantile turnover (each DataFrame column a quantile).
period: int, optional
Period over which to calculate the turnover
ax : matplotlib.Axes, optional
Axes upon w... | alphalens/plotting.py | def plot_top_bottom_quantile_turnover(quantile_turnover, period=1, ax=None):
"""
Plots period wise top and bottom quantile factor turnover.
Parameters
----------
quantile_turnover: pd.Dataframe
Quantile turnover (each DataFrame column a quantile).
period: int, optional
Period ov... | def plot_top_bottom_quantile_turnover(quantile_turnover, period=1, ax=None):
"""
Plots period wise top and bottom quantile factor turnover.
Parameters
----------
quantile_turnover: pd.Dataframe
Quantile turnover (each DataFrame column a quantile).
period: int, optional
Period ov... | [
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train | plot_monthly_ic_heatmap | Plots a heatmap of the information coefficient or returns by month.
Parameters
----------
mean_monthly_ic : pd.DataFrame
The mean monthly IC for N periods forward.
Returns
-------
ax : matplotlib.Axes
The axes that were plotted on. | alphalens/plotting.py | def plot_monthly_ic_heatmap(mean_monthly_ic, ax=None):
"""
Plots a heatmap of the information coefficient or returns by month.
Parameters
----------
mean_monthly_ic : pd.DataFrame
The mean monthly IC for N periods forward.
Returns
-------
ax : matplotlib.Axes
The axes t... | def plot_monthly_ic_heatmap(mean_monthly_ic, ax=None):
"""
Plots a heatmap of the information coefficient or returns by month.
Parameters
----------
mean_monthly_ic : pd.DataFrame
The mean monthly IC for N periods forward.
Returns
-------
ax : matplotlib.Axes
The axes t... | [
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train | plot_cumulative_returns | Plots the cumulative returns of the returns series passed in.
Parameters
----------
factor_returns : pd.Series
Period wise returns of dollar neutral portfolio weighted by factor
value.
period: pandas.Timedelta or string
Length of period for which the returns are computed (e.g. 1... | alphalens/plotting.py | def plot_cumulative_returns(factor_returns, period, freq, title=None, ax=None):
"""
Plots the cumulative returns of the returns series passed in.
Parameters
----------
factor_returns : pd.Series
Period wise returns of dollar neutral portfolio weighted by factor
value.
period: pa... | def plot_cumulative_returns(factor_returns, period, freq, title=None, ax=None):
"""
Plots the cumulative returns of the returns series passed in.
Parameters
----------
factor_returns : pd.Series
Period wise returns of dollar neutral portfolio weighted by factor
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period: pa... | [
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train | plot_cumulative_returns_by_quantile | Plots the cumulative returns of various factor quantiles.
Parameters
----------
quantile_returns : pd.DataFrame
Returns by factor quantile
period: pandas.Timedelta or string
Length of period for which the returns are computed (e.g. 1 day)
if 'period' is a string it must follow p... | alphalens/plotting.py | def plot_cumulative_returns_by_quantile(quantile_returns,
period,
freq,
ax=None):
"""
Plots the cumulative returns of various factor quantiles.
Parameters
----------
quantile_retu... | def plot_cumulative_returns_by_quantile(quantile_returns,
period,
freq,
ax=None):
"""
Plots the cumulative returns of various factor quantiles.
Parameters
----------
quantile_retu... | [
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train | plot_quantile_average_cumulative_return | Plots sector-wise mean daily returns for factor quantiles
across provided forward price movement columns.
Parameters
----------
avg_cumulative_returns: pd.Dataframe
The format is the one returned by
performance.average_cumulative_return_by_quantile
by_quantile : boolean, optional
... | alphalens/plotting.py | def plot_quantile_average_cumulative_return(avg_cumulative_returns,
by_quantile=False,
std_bar=False,
title=None,
ax=None):
"""
Plots se... | def plot_quantile_average_cumulative_return(avg_cumulative_returns,
by_quantile=False,
std_bar=False,
title=None,
ax=None):
"""
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train | plot_events_distribution | Plots the distribution of events in time.
Parameters
----------
events : pd.Series
A pd.Series whose index contains at least 'date' level.
num_bars : integer, optional
Number of bars to plot
ax : matplotlib.Axes, optional
Axes upon which to plot.
Returns
-------
... | alphalens/plotting.py | def plot_events_distribution(events, num_bars=50, ax=None):
"""
Plots the distribution of events in time.
Parameters
----------
events : pd.Series
A pd.Series whose index contains at least 'date' level.
num_bars : integer, optional
Number of bars to plot
ax : matplotlib.Axes... | def plot_events_distribution(events, num_bars=50, ax=None):
"""
Plots the distribution of events in time.
Parameters
----------
events : pd.Series
A pd.Series whose index contains at least 'date' level.
num_bars : integer, optional
Number of bars to plot
ax : matplotlib.Axes... | [
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train | Cursor.close | Closing a cursor just exhausts all remaining data. | pymysql/cursors.py | def close(self):
"""
Closing a cursor just exhausts all remaining data.
"""
conn = self.connection
if conn is None:
return
try:
while self.nextset():
pass
finally:
self.connection = None | def close(self):
"""
Closing a cursor just exhausts all remaining data.
"""
conn = self.connection
if conn is None:
return
try:
while self.nextset():
pass
finally:
self.connection = None | [
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train | Cursor._nextset | Get the next query set | pymysql/cursors.py | def _nextset(self, unbuffered=False):
"""Get the next query set"""
conn = self._get_db()
current_result = self._result
if current_result is None or current_result is not conn._result:
return None
if not current_result.has_next:
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self._re... | def _nextset(self, unbuffered=False):
"""Get the next query set"""
conn = self._get_db()
current_result = self._result
if current_result is None or current_result is not conn._result:
return None
if not current_result.has_next:
return None
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train | Cursor.mogrify | Returns the exact string that is sent to the database by calling the
execute() method.
This method follows the extension to the DB API 2.0 followed by Psycopg. | pymysql/cursors.py | def mogrify(self, query, args=None):
"""
Returns the exact string that is sent to the database by calling the
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This method follows the extension to the DB API 2.0 followed by Psycopg.
"""
conn = self._get_db()
if PY2: # Use bytes on Python 2 alw... | def mogrify(self, query, args=None):
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Returns the exact string that is sent to the database by calling the
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:type args: tuple, list or dict
:return: Number of affected rows
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:return: Number of affected rows
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train | Cursor.callproc | Execute stored procedure procname with args
procname -- string, name of procedure to execute on server
args -- Sequence of parameters to use with procedure
Returns the original args.
Compatibility warning: PEP-249 specifies that any modified
parameters must be returned. This ... | pymysql/cursors.py | def callproc(self, procname, args=()):
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procname -- string, name of procedure to execute on server
args -- Sequence of parameters to use with procedure
Returns the original args.
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train | Cursor.fetchone | Fetch the next row | pymysql/cursors.py | def fetchone(self):
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train | Cursor.fetchmany | Fetch several rows | pymysql/cursors.py | def fetchmany(self, size=None):
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self._check_executed()
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end = self.rownumber + (size or self.arraysize)
result = self._rows[self.rownumber:end]
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train | Cursor.fetchall | Fetch all the rows | pymysql/cursors.py | def fetchall(self):
"""Fetch all the rows"""
self._check_executed()
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return ()
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result = self._rows[self.rownumber:]
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train | SSCursor.fetchone | Fetch next row | pymysql/cursors.py | def fetchone(self):
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train | SSCursor.fetchmany | Fetch many | pymysql/cursors.py | def fetchmany(self, size=None):
"""Fetch many"""
self._check_executed()
if size is None:
size = self.arraysize
rows = []
for i in range_type(size):
row = self.read_next()
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train | convert_datetime | Returns a DATETIME or TIMESTAMP column value as a datetime object:
>>> datetime_or_None('2007-02-25 23:06:20')
datetime.datetime(2007, 2, 25, 23, 6, 20)
>>> datetime_or_None('2007-02-25T23:06:20')
datetime.datetime(2007, 2, 25, 23, 6, 20)
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>>> dat... | pymysql/converters.py | def convert_datetime(obj):
"""Returns a DATETIME or TIMESTAMP column value as a datetime object:
>>> datetime_or_None('2007-02-25 23:06:20')
datetime.datetime(2007, 2, 25, 23, 6, 20)
>>> datetime_or_None('2007-02-25T23:06:20')
datetime.datetime(2007, 2, 25, 23, 6, 20)
Illegal values ar... | def convert_datetime(obj):
"""Returns a DATETIME or TIMESTAMP column value as a datetime object:
>>> datetime_or_None('2007-02-25 23:06:20')
datetime.datetime(2007, 2, 25, 23, 6, 20)
>>> datetime_or_None('2007-02-25T23:06:20')
datetime.datetime(2007, 2, 25, 23, 6, 20)
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train | convert_timedelta | Returns a TIME column as a timedelta object:
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datetime.timedelta(1, 3977)
>>> timedelta_or_None('-25:06:17')
datetime.timedelta(-2, 83177)
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"""Returns a TIME column as a timedelta object:
>>> timedelta_or_None('25:06:17')
datetime.timedelta(1, 3977)
>>> timedelta_or_None('-25:06:17')
datetime.timedelta(-2, 83177)
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>>> timedelta_or_None('random crap') is... | def convert_timedelta(obj):
"""Returns a TIME column as a timedelta object:
>>> timedelta_or_None('25:06:17')
datetime.timedelta(1, 3977)
>>> timedelta_or_None('-25:06:17')
datetime.timedelta(-2, 83177)
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train | convert_time | Returns a TIME column as a time object:
>>> time_or_None('15:06:17')
datetime.time(15, 6, 17)
Illegal values are returned as None:
>>> time_or_None('-25:06:17') is None
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>>> time_or_None('random crap') is None
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>>> time_or_None('15:06:17')
datetime.time(15, 6, 17)
Illegal values are returned as None:
>>> time_or_None('-25:06:17') is None
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>>> time_or_None('random crap') is None
True
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>>> time_or_None('15:06:17')
datetime.time(15, 6, 17)
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>>> time_or_None('-25:06:17') is None
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train | convert_date | Returns a DATE column as a date object:
>>> date_or_None('2007-02-26')
datetime.date(2007, 2, 26)
Illegal values are returned as None:
>>> date_or_None('2007-02-31') is None
True
>>> date_or_None('0000-00-00') is None
True | pymysql/converters.py | def convert_date(obj):
"""Returns a DATE column as a date object:
>>> date_or_None('2007-02-26')
datetime.date(2007, 2, 26)
Illegal values are returned as None:
>>> date_or_None('2007-02-31') is None
True
>>> date_or_None('0000-00-00') is None
True
"""
if not PY2 ... | def convert_date(obj):
"""Returns a DATE column as a date object:
>>> date_or_None('2007-02-26')
datetime.date(2007, 2, 26)
Illegal values are returned as None:
>>> date_or_None('2007-02-31') is None
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>>> date_or_None('0000-00-00') is None
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train | SocketIO.write | Write the given bytes or bytearray object *b* to the socket
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non-blocking and no bytes could be written None is returned. | pymysql/_socketio.py | def write(self, b):
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non-blocking and no bytes could be written None is returned.
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"""Write the given bytes or bytearray object *b* to the socket
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train | SocketIO.close | Close the SocketIO object. This doesn't close the underlying
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train | MysqlPacket.read | Read the first 'size' bytes in packet and advance cursor past them. | pymysql/protocol.py | def read(self, size):
"""Read the first 'size' bytes in packet and advance cursor past them."""
result = self._data[self._position:(self._position+size)]
if len(result) != size:
error = ('Result length not requested length:\n'
'Expected=%s. Actual=%s. Position:... | def read(self, size):
"""Read the first 'size' bytes in packet and advance cursor past them."""
result = self._data[self._position:(self._position+size)]
if len(result) != size:
error = ('Result length not requested length:\n'
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train | MysqlPacket.read_all | Read all remaining data in the packet.
(Subsequent read() will return errors.) | pymysql/protocol.py | def read_all(self):
"""Read all remaining data in the packet.
(Subsequent read() will return errors.)
"""
result = self._data[self._position:]
self._position = None # ensure no subsequent read()
return result | def read_all(self):
"""Read all remaining data in the packet.
(Subsequent read() will return errors.)
"""
result = self._data[self._position:]
self._position = None # ensure no subsequent read()
return result | [
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train | MysqlPacket.advance | Advance the cursor in data buffer 'length' bytes. | pymysql/protocol.py | def advance(self, length):
"""Advance the cursor in data buffer 'length' bytes."""
new_position = self._position + length
if new_position < 0 or new_position > len(self._data):
raise Exception('Invalid advance amount (%s) for cursor. '
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"""Advance the cursor in data buffer 'length' bytes."""
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train | MysqlPacket.rewind | Set the position of the data buffer cursor to 'position'. | pymysql/protocol.py | def rewind(self, position=0):
"""Set the position of the data buffer cursor to 'position'."""
if position < 0 or position > len(self._data):
raise Exception("Invalid position to rewind cursor to: %s." % position)
self._position = position | def rewind(self, position=0):
"""Set the position of the data buffer cursor to 'position'."""
if position < 0 or position > len(self._data):
raise Exception("Invalid position to rewind cursor to: %s." % position)
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train | MysqlPacket.read_length_encoded_integer | Read a 'Length Coded Binary' number from the data buffer.
Length coded numbers can be anywhere from 1 to 9 bytes depending
on the value of the first byte. | pymysql/protocol.py | def read_length_encoded_integer(self):
"""Read a 'Length Coded Binary' number from the data buffer.
Length coded numbers can be anywhere from 1 to 9 bytes depending
on the value of the first byte.
"""
c = self.read_uint8()
if c == NULL_COLUMN:
return None
... | def read_length_encoded_integer(self):
"""Read a 'Length Coded Binary' number from the data buffer.
Length coded numbers can be anywhere from 1 to 9 bytes depending
on the value of the first byte.
"""
c = self.read_uint8()
if c == NULL_COLUMN:
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train | FieldDescriptorPacket._parse_field_descriptor | Parse the 'Field Descriptor' (Metadata) packet.
This is compatible with MySQL 4.1+ (not compatible with MySQL 4.0). | pymysql/protocol.py | def _parse_field_descriptor(self, encoding):
"""Parse the 'Field Descriptor' (Metadata) packet.
This is compatible with MySQL 4.1+ (not compatible with MySQL 4.0).
"""
self.catalog = self.read_length_coded_string()
self.db = self.read_length_coded_string()
self.table_nam... | def _parse_field_descriptor(self, encoding):
"""Parse the 'Field Descriptor' (Metadata) packet.
This is compatible with MySQL 4.1+ (not compatible with MySQL 4.0).
"""
self.catalog = self.read_length_coded_string()
self.db = self.read_length_coded_string()
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train | FieldDescriptorPacket.description | Provides a 7-item tuple compatible with the Python PEP249 DB Spec. | pymysql/protocol.py | def description(self):
"""Provides a 7-item tuple compatible with the Python PEP249 DB Spec."""
return (
self.name,
self.type_code,
None, # TODO: display_length; should this be self.length?
self.get_column_length(), # 'internal_size'
self.get... | def description(self):
"""Provides a 7-item tuple compatible with the Python PEP249 DB Spec."""
return (
self.name,
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train | Connection.close | Send the quit message and close the socket.
See `Connection.close() <https://www.python.org/dev/peps/pep-0249/#Connection.close>`_
in the specification.
:raise Error: If the connection is already closed. | pymysql/connections.py | def close(self):
"""
Send the quit message and close the socket.
See `Connection.close() <https://www.python.org/dev/peps/pep-0249/#Connection.close>`_
in the specification.
:raise Error: If the connection is already closed.
"""
if self._closed:
rais... | def close(self):
"""
Send the quit message and close the socket.
See `Connection.close() <https://www.python.org/dev/peps/pep-0249/#Connection.close>`_
in the specification.
:raise Error: If the connection is already closed.
"""
if self._closed:
rais... | [
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train | Connection._force_close | Close connection without QUIT message | pymysql/connections.py | def _force_close(self):
"""Close connection without QUIT message"""
if self._sock:
try:
self._sock.close()
except: # noqa
pass
self._sock = None
self._rfile = None | def _force_close(self):
"""Close connection without QUIT message"""
if self._sock:
try:
self._sock.close()
except: # noqa
pass
self._sock = None
self._rfile = None | [
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train | Connection._send_autocommit_mode | Set whether or not to commit after every execute() | pymysql/connections.py | def _send_autocommit_mode(self):
"""Set whether or not to commit after every execute()"""
self._execute_command(COMMAND.COM_QUERY, "SET AUTOCOMMIT = %s" %
self.escape(self.autocommit_mode))
self._read_ok_packet() | def _send_autocommit_mode(self):
"""Set whether or not to commit after every execute()"""
self._execute_command(COMMAND.COM_QUERY, "SET AUTOCOMMIT = %s" %
self.escape(self.autocommit_mode))
self._read_ok_packet() | [
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train | Connection.escape | Escape whatever value you pass to it.
Non-standard, for internal use; do not use this in your applications. | pymysql/connections.py | def escape(self, obj, mapping=None):
"""Escape whatever value you pass to it.
Non-standard, for internal use; do not use this in your applications.
"""
if isinstance(obj, str_type):
return "'" + self.escape_string(obj) + "'"
if isinstance(obj, (bytes, bytearray)):
... | def escape(self, obj, mapping=None):
"""Escape whatever value you pass to it.
Non-standard, for internal use; do not use this in your applications.
"""
if isinstance(obj, str_type):
return "'" + self.escape_string(obj) + "'"
if isinstance(obj, (bytes, bytearray)):
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train | Connection.ping | Check if the server is alive.
:param reconnect: If the connection is closed, reconnect.
:raise Error: If the connection is closed and reconnect=False. | pymysql/connections.py | def ping(self, reconnect=True):
"""
Check if the server is alive.
:param reconnect: If the connection is closed, reconnect.
:raise Error: If the connection is closed and reconnect=False.
"""
if self._sock is None:
if reconnect:
self.connect()
... | def ping(self, reconnect=True):
"""
Check if the server is alive.
:param reconnect: If the connection is closed, reconnect.
:raise Error: If the connection is closed and reconnect=False.
"""
if self._sock is None:
if reconnect:
self.connect()
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train | Connection._read_packet | Read an entire "mysql packet" in its entirety from the network
and return a MysqlPacket type that represents the results.
:raise OperationalError: If the connection to the MySQL server is lost.
:raise InternalError: If the packet sequence number is wrong. | pymysql/connections.py | def _read_packet(self, packet_type=MysqlPacket):
"""Read an entire "mysql packet" in its entirety from the network
and return a MysqlPacket type that represents the results.
:raise OperationalError: If the connection to the MySQL server is lost.
:raise InternalError: If the packet seque... | def _read_packet(self, packet_type=MysqlPacket):
"""Read an entire "mysql packet" in its entirety from the network
and return a MysqlPacket type that represents the results.
:raise OperationalError: If the connection to the MySQL server is lost.
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train | Connection._execute_command | :raise InterfaceError: If the connection is closed.
:raise ValueError: If no username was specified. | pymysql/connections.py | def _execute_command(self, command, sql):
"""
:raise InterfaceError: If the connection is closed.
:raise ValueError: If no username was specified.
"""
if not self._sock:
raise err.InterfaceError("(0, '')")
# If the last query was unbuffered, make sure it fini... | def _execute_command(self, command, sql):
"""
:raise InterfaceError: If the connection is closed.
:raise ValueError: If no username was specified.
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if not self._sock:
raise err.InterfaceError("(0, '')")
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train | LoadLocalFile.send_data | Send data packets from the local file to the server | pymysql/connections.py | def send_data(self):
"""Send data packets from the local file to the server"""
if not self.connection._sock:
raise err.InterfaceError("(0, '')")
conn = self.connection
try:
with open(self.filename, 'rb') as open_file:
packet_size = min(conn.max_al... | def send_data(self):
"""Send data packets from the local file to the server"""
if not self.connection._sock:
raise err.InterfaceError("(0, '')")
conn = self.connection
try:
with open(self.filename, 'rb') as open_file:
packet_size = min(conn.max_al... | [
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