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quantopian/alphalens | alphalens/tears.py | create_information_tear_sheet | def create_information_tear_sheet(factor_data,
group_neutral=False,
by_group=False):
"""
Creates a tear sheet for information analysis of a factor.
Parameters
----------
factor_data : pd.DataFrame - MultiIndex
A MultiIndex ... | python | def create_information_tear_sheet(factor_data,
group_neutral=False,
by_group=False):
"""
Creates a tear sheet for information analysis of a factor.
Parameters
----------
factor_data : pd.DataFrame - MultiIndex
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quantopian/alphalens | alphalens/tears.py | create_turnover_tear_sheet | 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),
contain... | python | 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),
contain... | [
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quantopian/alphalens | alphalens/tears.py | create_full_tear_sheet | def create_full_tear_sheet(factor_data,
long_short=True,
group_neutral=False,
by_group=False):
"""
Creates a full tear sheet for analysis and evaluating single
return predicting (alpha) factor.
Parameters
----------
... | python | def create_full_tear_sheet(factor_data,
long_short=True,
group_neutral=False,
by_group=False):
"""
Creates a full tear sheet for analysis and evaluating single
return predicting (alpha) factor.
Parameters
----------
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quantopian/alphalens | alphalens/tears.py | create_event_returns_tear_sheet | def create_event_returns_tear_sheet(factor_data,
prices,
avgretplot=(5, 15),
long_short=True,
group_neutral=False,
std_bar=True,
... | python | def create_event_returns_tear_sheet(factor_data,
prices,
avgretplot=(5, 15),
long_short=True,
group_neutral=False,
std_bar=True,
... | [
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quantopian/alphalens | alphalens/tears.py | create_event_study_tear_sheet | def create_event_study_tear_sheet(factor_data,
prices=None,
avgretplot=(5, 15),
rate_of_ret=True,
n_bars=50):
"""
Creates an event study tear sheet for analysis of a specific e... | python | def create_event_study_tear_sheet(factor_data,
prices=None,
avgretplot=(5, 15),
rate_of_ret=True,
n_bars=50):
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quantopian/alphalens | alphalens/utils.py | rethrow | def rethrow(exception, additional_message):
"""
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
m = additional_message
if ... | python | def rethrow(exception, additional_message):
"""
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
m = additional_message
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quantopian/alphalens | alphalens/utils.py | non_unique_bin_edges_error | def non_unique_bin_edges_error(func):
"""
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... | python | def non_unique_bin_edges_error(func):
"""
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... | [
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quantopian/alphalens | alphalens/utils.py | quantize_factor | def quantize_factor(factor_data,
quantiles=5,
bins=None,
by_group=False,
no_raise=False,
zero_aware=False):
"""
Computes period wise factor quantiles.
Parameters
----------
factor_data : pd.DataFrame... | python | def quantize_factor(factor_data,
quantiles=5,
bins=None,
by_group=False,
no_raise=False,
zero_aware=False):
"""
Computes period wise factor quantiles.
Parameters
----------
factor_data : pd.DataFrame... | [
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quantopian/alphalens | alphalens/utils.py | infer_trading_calendar | 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 ... | python | 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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quantopian/alphalens | alphalens/utils.py | compute_forward_returns | def compute_forward_returns(factor,
prices,
periods=(1, 5, 10),
filter_zscore=None,
cumulative_returns=True):
"""
Finds the N period forward returns (as percent change) for each asset
provided.
... | python | def compute_forward_returns(factor,
prices,
periods=(1, 5, 10),
filter_zscore=None,
cumulative_returns=True):
"""
Finds the N period forward returns (as percent change) for each asset
provided.
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quantopian/alphalens | alphalens/utils.py | demean_forward_returns | def demean_forward_returns(factor_data, grouper=None):
"""
Convert forward returns to returns relative to mean
period wise all-universe or group returns.
group-wise normalization incorporates the assumption of a
group neutral portfolio constraint and thus allows allows the
factor to be evaluated... | python | def demean_forward_returns(factor_data, grouper=None):
"""
Convert forward returns to returns relative to mean
period wise all-universe or group returns.
group-wise normalization incorporates the assumption of a
group neutral portfolio constraint and thus allows allows the
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quantopian/alphalens | alphalens/utils.py | print_table | def print_table(table, name=None, fmt=None):
"""
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... | python | def print_table(table, name=None, fmt=None):
"""
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
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quantopian/alphalens | alphalens/utils.py | get_clean_factor | def get_clean_factor(factor,
forward_returns,
groupby=None,
binning_by_group=False,
quantiles=5,
bins=None,
groupby_labels=None,
max_loss=0.35,
zero_awa... | python | def get_clean_factor(factor,
forward_returns,
groupby=None,
binning_by_group=False,
quantiles=5,
bins=None,
groupby_labels=None,
max_loss=0.35,
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quantopian/alphalens | alphalens/utils.py | get_clean_factor_and_forward_returns | def get_clean_factor_and_forward_returns(factor,
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bins=No... | [
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quantopian/alphalens | alphalens/utils.py | rate_of_return | 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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rate
Parameters
----------
period_ret: pd.DataFrame
DataFrame containing returns values... | python | def rate_of_return(period_ret, base_period):
"""
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
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period_ret: pd.DataFrame
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quantopian/alphalens | alphalens/utils.py | std_conversion | 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.
... | python | 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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quantopian/alphalens | alphalens/utils.py | get_forward_returns_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))+$", re.IGNORECASE)
valid_columns = [(pattern.match(col) is not None) for col in columns]
return columns[valid_columns] | python | def get_forward_returns_columns(columns):
"""
Utility that detects and returns the columns that are forward returns
"""
pattern = re.compile(r"^(\d+([Dhms]|ms|us|ns))+$", re.IGNORECASE)
valid_columns = [(pattern.match(col) is not None) for col in columns]
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quantopian/alphalens | alphalens/utils.py | timedelta_to_string | 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'
"""
... | python | 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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"""
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is used to deal with custom calendars, such as a trading calendar
Parameters
----------
input : pd.DatetimeIndex or pd.Timestamp
timedelta : pd.Timedelta... | python | def add_custom_calendar_timedelta(input, timedelta, freq):
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Add timedelta to 'input' taking into consideration custom frequency, which
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quantopian/alphalens | alphalens/utils.py | diff_custom_calendar_timedeltas | def diff_custom_calendar_timedeltas(start, end, freq):
"""
Compute the difference between two pd.Timedelta taking into consideration
custom frequency, which is used to deal with custom calendars, such as a
trading calendar
Parameters
----------
start : pd.Timestamp
end : pd.Timestamp
... | python | def diff_custom_calendar_timedeltas(start, end, freq):
"""
Compute the difference between two pd.Timedelta taking into consideration
custom frequency, which is used to deal with custom calendars, such as a
trading calendar
Parameters
----------
start : pd.Timestamp
end : pd.Timestamp
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quantopian/alphalens | alphalens/plotting.py | customize | 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)
if set_context:
color_palette = sns.color_palette('colorblind')
... | python | 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)
if set_context:
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quantopian/alphalens | alphalens/plotting.py | plot_ic_ts | 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.
... | python | 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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quantopian/alphalens | alphalens/plotting.py | plot_ic_hist | def plot_ic_hist(ic, ax=None):
"""
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
... | python | def plot_ic_hist(ic, ax=None):
"""
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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quantopian/alphalens | alphalens/plotting.py | plot_ic_qq | 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... | python | 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.
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quantopian/alphalens | alphalens/plotting.py | plot_quantile_returns_bar | 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
... | python | 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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quantopian/alphalens | alphalens/plotting.py | plot_quantile_returns_violin | 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... | python | 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
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quantopian/alphalens | alphalens/plotting.py | plot_mean_quantile_returns_spread_time_series | 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... | python | def plot_mean_quantile_returns_spread_time_series(mean_returns_spread,
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bandwidth=1,
ax=None):
"""
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quantopian/alphalens | alphalens/plotting.py | plot_ic_by_group | 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... | python | 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
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quantopian/alphalens | alphalens/plotting.py | plot_factor_rank_auto_correlation | 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... | python | 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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factor_autocorrelation : pd.Series
Rolling 1 period (defined by time_rule) autocorrelation
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quantopian/alphalens | alphalens/plotting.py | plot_top_bottom_quantile_turnover | 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... | python | 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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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
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quantopian/alphalens | alphalens/plotting.py | plot_monthly_ic_heatmap | 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... | python | 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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mean_monthly_ic : pd.DataFrame
The mean monthly IC for N periods forward.
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-------
ax : matplotlib.Axes
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quantopian/alphalens | alphalens/plotting.py | plot_cumulative_returns | 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... | python | 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... | [
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Period wise returns of dollar neutral portfolio weighted by factor
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quantopian/alphalens | alphalens/plotting.py | plot_cumulative_returns_by_quantile | def plot_cumulative_returns_by_quantile(quantile_returns,
period,
freq,
ax=None):
"""
Plots the cumulative returns of various factor quantiles.
Parameters
----------
quantile_retu... | python | 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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Length of period for which the returns are computed (e.g. 1 day)
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quantopian/alphalens | alphalens/plotting.py | plot_quantile_average_cumulative_return | def plot_quantile_average_cumulative_return(avg_cumulative_returns,
by_quantile=False,
std_bar=False,
title=None,
ax=None):
"""
Plots se... | python | def plot_quantile_average_cumulative_return(avg_cumulative_returns,
by_quantile=False,
std_bar=False,
title=None,
ax=None):
"""
Plots se... | [
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avg_cumulative_returns: pd.Dataframe
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quantopian/alphalens | alphalens/plotting.py | plot_events_distribution | 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... | python | 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
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"""
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 | python | 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:
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PyMySQL/PyMySQL | pymysql/cursors.py | Cursor._nextset | def _nextset(self, unbuffered=False):
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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
self._re... | python | 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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"""
conn = self._get_db()
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"""
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.
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conn = self._get_db()
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"""Execute a query
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:param args: parameters used with query. (optional)
:type args: tuple, list or dict
:return: Number of affected rows
:rtype: int
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:param str query: Query to execute.
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PyMySQL/PyMySQL | pymysql/cursors.py | Cursor.callproc | def callproc(self, procname, args=()):
"""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 m... | python | 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
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self.rownumber += 1
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"""Fetch the next row"""
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return None
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self.rownumber += 1
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self._check_executed()
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PyMySQL/PyMySQL | pymysql/converters.py | convert_datetime | 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... | python | 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)
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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)
Illegal values are returned as None:
>>> timedelta_or_None('random crap') is... | python | def convert_timedelta(obj):
"""Returns a TIME column as a timedelta object:
>>> timedelta_or_None('25:06:17')
datetime.timedelta(1, 3977)
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datetime.timedelta(-2, 83177)
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PyMySQL/PyMySQL | pymysql/converters.py | convert_time | def convert_time(obj):
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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
True
>>> time_or_None('random crap') is None
True
Note that MySQL always ... | python | def convert_time(obj):
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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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"""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 ... | python | def convert_date(obj):
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>>> 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
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PyMySQL/PyMySQL | pymysql/_socketio.py | SocketIO.write | def write(self, b):
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PyMySQL/PyMySQL | pymysql/protocol.py | MysqlPacket.read | 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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"""Read the first 'size' bytes in packet and advance cursor past them."""
result = self._data[self._position:(self._position+size)]
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PyMySQL/PyMySQL | pymysql/protocol.py | MysqlPacket.read_all | 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 | python | 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()
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PyMySQL/PyMySQL | pymysql/protocol.py | MysqlPacket.advance | def advance(self, length):
"""Advance the cursor in data buffer 'length' bytes."""
new_position = self._position + length
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raise Exception('Invalid advance amount (%s) for cursor. '
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"""Advance the cursor in data buffer 'length' bytes."""
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"""Set the position of the data buffer cursor to 'position'."""
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PyMySQL/PyMySQL | pymysql/protocol.py | MysqlPacket.read_length_encoded_integer | def read_length_encoded_integer(self):
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"""
c = self.read_uint8()
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"""Read a 'Length Coded Binary' number from the data buffer.
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on the value of the first byte.
"""
c = self.read_uint8()
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PyMySQL/PyMySQL | pymysql/protocol.py | FieldDescriptorPacket._parse_field_descriptor | 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... | python | 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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PyMySQL/PyMySQL | pymysql/protocol.py | FieldDescriptorPacket.description | def description(self):
"""Provides a 7-item tuple compatible with the Python PEP249 DB Spec."""
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None, # TODO: display_length; should this be self.length?
self.get_column_length(), # 'internal_size'
self.get... | python | def description(self):
"""Provides a 7-item tuple compatible with the Python PEP249 DB Spec."""
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self.name,
self.type_code,
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PyMySQL/PyMySQL | pymysql/connections.py | Connection.close | 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... | python | 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:
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PyMySQL/PyMySQL | pymysql/connections.py | Connection._force_close | 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 | python | 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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PyMySQL/PyMySQL | pymysql/connections.py | Connection._send_autocommit_mode | 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() | python | 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))
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PyMySQL/PyMySQL | pymysql/connections.py | Connection.escape | 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)):
... | python | 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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PyMySQL/PyMySQL | pymysql/connections.py | Connection.ping | 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()
... | python | 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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PyMySQL/PyMySQL | pymysql/connections.py | Connection._read_packet | 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... | python | def _read_packet(self, packet_type=MysqlPacket):
"""Read an entire "mysql packet" in its entirety from the network
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PyMySQL/PyMySQL | pymysql/connections.py | Connection._execute_command | 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... | python | 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, '')")
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PyMySQL/PyMySQL | pymysql/connections.py | LoadLocalFile.send_data | 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... | python | 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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PyMySQL/PyMySQL | pymysql/_auth.py | scramble_native_password | def scramble_native_password(password, message):
"""Scramble used for mysql_native_password"""
if not password:
return b''
stage1 = sha1_new(password).digest()
stage2 = sha1_new(stage1).digest()
s = sha1_new()
s.update(message[:SCRAMBLE_LENGTH])
s.update(stage2)
result = s.diges... | python | def scramble_native_password(password, message):
"""Scramble used for mysql_native_password"""
if not password:
return b''
stage1 = sha1_new(password).digest()
stage2 = sha1_new(stage1).digest()
s = sha1_new()
s.update(message[:SCRAMBLE_LENGTH])
s.update(stage2)
result = s.diges... | [
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PyMySQL/PyMySQL | pymysql/_auth.py | sha2_rsa_encrypt | def sha2_rsa_encrypt(password, salt, public_key):
"""Encrypt password with salt and public_key.
Used for sha256_password and caching_sha2_password.
"""
if not _have_cryptography:
raise RuntimeError("'cryptography' package is required for sha256_password or caching_sha2_password auth methods")
... | python | def sha2_rsa_encrypt(password, salt, public_key):
"""Encrypt password with salt and public_key.
Used for sha256_password and caching_sha2_password.
"""
if not _have_cryptography:
raise RuntimeError("'cryptography' package is required for sha256_password or caching_sha2_password auth methods")
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PyMySQL/PyMySQL | pymysql/_auth.py | scramble_caching_sha2 | def scramble_caching_sha2(password, nonce):
# (bytes, bytes) -> bytes
"""Scramble algorithm used in cached_sha2_password fast path.
XOR(SHA256(password), SHA256(SHA256(SHA256(password)), nonce))
"""
if not password:
return b''
p1 = hashlib.sha256(password).digest()
p2 = hashlib.sha... | python | def scramble_caching_sha2(password, nonce):
# (bytes, bytes) -> bytes
"""Scramble algorithm used in cached_sha2_password fast path.
XOR(SHA256(password), SHA256(SHA256(SHA256(password)), nonce))
"""
if not password:
return b''
p1 = hashlib.sha256(password).digest()
p2 = hashlib.sha... | [
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coleifer/peewee | playhouse/sqlite_ext.py | ClosureTable | def ClosureTable(model_class, foreign_key=None, referencing_class=None,
referencing_key=None):
"""Model factory for the transitive closure extension."""
if referencing_class is None:
referencing_class = model_class
if foreign_key is None:
for field_obj in model_class._meta.... | python | def ClosureTable(model_class, foreign_key=None, referencing_class=None,
referencing_key=None):
"""Model factory for the transitive closure extension."""
if referencing_class is None:
referencing_class = model_class
if foreign_key is None:
for field_obj in model_class._meta.... | [
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coleifer/peewee | playhouse/sqlite_ext.py | bm25 | def bm25(raw_match_info, *args):
"""
Usage:
# Format string *must* be pcnalx
# Second parameter to bm25 specifies the index of the column, on
# the table being queries.
bm25(matchinfo(document_tbl, 'pcnalx'), 1) AS rank
"""
match_info = _parse_match_info(raw_match_info)
... | python | def bm25(raw_match_info, *args):
"""
Usage:
# Format string *must* be pcnalx
# Second parameter to bm25 specifies the index of the column, on
# the table being queries.
bm25(matchinfo(document_tbl, 'pcnalx'), 1) AS rank
"""
match_info = _parse_match_info(raw_match_info)
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coleifer/peewee | playhouse/sqlite_ext.py | FTSModel.search | def search(cls, term, weights=None, with_score=False, score_alias='score',
explicit_ordering=False):
"""Full-text search using selected `term`."""
return cls._search(
term,
weights,
with_score,
score_alias,
cls.rank,
... | python | def search(cls, term, weights=None, with_score=False, score_alias='score',
explicit_ordering=False):
"""Full-text search using selected `term`."""
return cls._search(
term,
weights,
with_score,
score_alias,
cls.rank,
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coleifer/peewee | playhouse/sqlite_ext.py | FTSModel.search_bm25 | def search_bm25(cls, term, weights=None, with_score=False,
score_alias='score', explicit_ordering=False):
"""Full-text search for selected `term` using BM25 algorithm."""
return cls._search(
term,
weights,
with_score,
score_alias,
... | python | def search_bm25(cls, term, weights=None, with_score=False,
score_alias='score', explicit_ordering=False):
"""Full-text search for selected `term` using BM25 algorithm."""
return cls._search(
term,
weights,
with_score,
score_alias,
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coleifer/peewee | playhouse/sqlite_ext.py | FTSModel.search_bm25f | def search_bm25f(cls, term, weights=None, with_score=False,
score_alias='score', explicit_ordering=False):
"""Full-text search for selected `term` using BM25 algorithm."""
return cls._search(
term,
weights,
with_score,
score_alias,
... | python | def search_bm25f(cls, term, weights=None, with_score=False,
score_alias='score', explicit_ordering=False):
"""Full-text search for selected `term` using BM25 algorithm."""
return cls._search(
term,
weights,
with_score,
score_alias,
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coleifer/peewee | playhouse/sqlite_ext.py | FTSModel.search_lucene | def search_lucene(cls, term, weights=None, with_score=False,
score_alias='score', explicit_ordering=False):
"""Full-text search for selected `term` using BM25 algorithm."""
return cls._search(
term,
weights,
with_score,
score_alias,
... | python | def search_lucene(cls, term, weights=None, with_score=False,
score_alias='score', explicit_ordering=False):
"""Full-text search for selected `term` using BM25 algorithm."""
return cls._search(
term,
weights,
with_score,
score_alias,
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coleifer/peewee | playhouse/sqlite_ext.py | FTS5Model.validate_query | def validate_query(query):
"""
Simple helper function to indicate whether a search query is a
valid FTS5 query. Note: this simply looks at the characters being
used, and is not guaranteed to catch all problematic queries.
"""
tokens = _quote_re.findall(query)
for ... | python | def validate_query(query):
"""
Simple helper function to indicate whether a search query is a
valid FTS5 query. Note: this simply looks at the characters being
used, and is not guaranteed to catch all problematic queries.
"""
tokens = _quote_re.findall(query)
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coleifer/peewee | playhouse/sqlite_ext.py | FTS5Model.clean_query | def clean_query(query, replace=chr(26)):
"""
Clean a query of invalid tokens.
"""
accum = []
any_invalid = False
tokens = _quote_re.findall(query)
for token in tokens:
if token.startswith('"') and token.endswith('"'):
accum.append(token... | python | def clean_query(query, replace=chr(26)):
"""
Clean a query of invalid tokens.
"""
accum = []
any_invalid = False
tokens = _quote_re.findall(query)
for token in tokens:
if token.startswith('"') and token.endswith('"'):
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coleifer/peewee | playhouse/sqlite_ext.py | FTS5Model.search | def search(cls, term, weights=None, with_score=False, score_alias='score',
explicit_ordering=False):
"""Full-text search using selected `term`."""
return cls.search_bm25(
FTS5Model.clean_query(term),
weights,
with_score,
score_alias,
... | python | def search(cls, term, weights=None, with_score=False, score_alias='score',
explicit_ordering=False):
"""Full-text search using selected `term`."""
return cls.search_bm25(
FTS5Model.clean_query(term),
weights,
with_score,
score_alias,
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coleifer/peewee | playhouse/sqlite_ext.py | FTS5Model.search_bm25 | def search_bm25(cls, term, weights=None, with_score=False,
score_alias='score', explicit_ordering=False):
"""Full-text search using selected `term`."""
if not weights:
rank = SQL('rank')
elif isinstance(weights, dict):
weight_args = []
for ... | python | def search_bm25(cls, term, weights=None, with_score=False,
score_alias='score', explicit_ordering=False):
"""Full-text search using selected `term`."""
if not weights:
rank = SQL('rank')
elif isinstance(weights, dict):
weight_args = []
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coleifer/peewee | playhouse/pool.py | PooledDatabase.manual_close | def manual_close(self):
"""
Close the underlying connection without returning it to the pool.
"""
if self.is_closed():
return False
# Obtain reference to the connection in-use by the calling thread.
conn = self.connection()
# A connection will only b... | python | def manual_close(self):
"""
Close the underlying connection without returning it to the pool.
"""
if self.is_closed():
return False
# Obtain reference to the connection in-use by the calling thread.
conn = self.connection()
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coleifer/peewee | examples/analytics/reports.py | Report.top_pages_by_time_period | def top_pages_by_time_period(self, interval='day'):
"""
Get a breakdown of top pages per interval, i.e.
day url count
2014-01-01 /blog/ 11
2014-01-02 /blog/ 14
2014-01-03 /blog/ 9
"""
date_trunc = fn.date_trunc(interval, PageView.timesta... | python | def top_pages_by_time_period(self, interval='day'):
"""
Get a breakdown of top pages per interval, i.e.
day url count
2014-01-01 /blog/ 11
2014-01-02 /blog/ 14
2014-01-03 /blog/ 9
"""
date_trunc = fn.date_trunc(interval, PageView.timesta... | [
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coleifer/peewee | examples/analytics/reports.py | Report.cookies | def cookies(self):
"""
Retrieve the cookies header from all the users who visited.
"""
return (self.get_query()
.select(PageView.ip, PageView.headers['Cookie'])
.where(PageView.headers['Cookie'].is_null(False))
.tuples()) | python | def cookies(self):
"""
Retrieve the cookies header from all the users who visited.
"""
return (self.get_query()
.select(PageView.ip, PageView.headers['Cookie'])
.where(PageView.headers['Cookie'].is_null(False))
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coleifer/peewee | examples/analytics/reports.py | Report.user_agents | def user_agents(self):
"""
Retrieve user-agents, sorted by most common to least common.
"""
return (self.get_query()
.select(
PageView.headers['User-Agent'],
fn.Count(PageView.id))
.group_by(PageView.headers['User-Ag... | python | def user_agents(self):
"""
Retrieve user-agents, sorted by most common to least common.
"""
return (self.get_query()
.select(
PageView.headers['User-Agent'],
fn.Count(PageView.id))
.group_by(PageView.headers['User-Ag... | [
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coleifer/peewee | examples/analytics/reports.py | Report.languages | def languages(self):
"""
Retrieve languages, sorted by most common to least common. The
Accept-Languages header sometimes looks weird, i.e.
"en-US,en;q=0.8,is;q=0.6,da;q=0.4" We will split on the first semi-
colon.
"""
language = PageView.headers['Accept-Language'... | python | def languages(self):
"""
Retrieve languages, sorted by most common to least common. The
Accept-Languages header sometimes looks weird, i.e.
"en-US,en;q=0.8,is;q=0.6,da;q=0.4" We will split on the first semi-
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"""
language = PageView.headers['Accept-Language'... | [
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coleifer/peewee | examples/analytics/reports.py | Report.trail | def trail(self):
"""
Get all visitors by IP and then list the pages they visited in order.
"""
inner = (self.get_query()
.select(PageView.ip, PageView.url)
.order_by(PageView.timestamp))
return (PageView
.select(
... | python | def trail(self):
"""
Get all visitors by IP and then list the pages they visited in order.
"""
inner = (self.get_query()
.select(PageView.ip, PageView.url)
.order_by(PageView.timestamp))
return (PageView
.select(
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coleifer/peewee | examples/analytics/reports.py | Report.top_referrers | def top_referrers(self, domain_only=True):
"""
What domains send us the most traffic?
"""
referrer = self._referrer_clause(domain_only)
return (self.get_query()
.select(referrer, fn.Count(PageView.id))
.group_by(referrer)
.order_by(... | python | def top_referrers(self, domain_only=True):
"""
What domains send us the most traffic?
"""
referrer = self._referrer_clause(domain_only)
return (self.get_query()
.select(referrer, fn.Count(PageView.id))
.group_by(referrer)
.order_by(... | [
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coleifer/peewee | examples/diary.py | add_entry | def add_entry():
"""Add entry"""
print('Enter your entry. Press ctrl+d when finished.')
data = sys.stdin.read().strip()
if data and raw_input('Save entry? [Yn] ') != 'n':
Entry.create(content=data)
print('Saved successfully.') | python | def add_entry():
"""Add entry"""
print('Enter your entry. Press ctrl+d when finished.')
data = sys.stdin.read().strip()
if data and raw_input('Save entry? [Yn] ') != 'n':
Entry.create(content=data)
print('Saved successfully.') | [
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coleifer/peewee | examples/diary.py | view_entries | def view_entries(search_query=None):
"""View previous entries"""
query = Entry.select().order_by(Entry.timestamp.desc())
if search_query:
query = query.where(Entry.content.contains(search_query))
for entry in query:
timestamp = entry.timestamp.strftime('%A %B %d, %Y %I:%M%p')
pr... | python | def view_entries(search_query=None):
"""View previous entries"""
query = Entry.select().order_by(Entry.timestamp.desc())
if search_query:
query = query.where(Entry.content.contains(search_query))
for entry in query:
timestamp = entry.timestamp.strftime('%A %B %d, %Y %I:%M%p')
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coleifer/peewee | examples/blog/app.py | Entry.html_content | def html_content(self):
"""
Generate HTML representation of the markdown-formatted blog entry,
and also convert any media URLs into rich media objects such as video
players or images.
"""
hilite = CodeHiliteExtension(linenums=False, css_class='highlight')
extras =... | python | def html_content(self):
"""
Generate HTML representation of the markdown-formatted blog entry,
and also convert any media URLs into rich media objects such as video
players or images.
"""
hilite = CodeHiliteExtension(linenums=False, css_class='highlight')
extras =... | [
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players or images. | Pu7Z6IJCgH3a,vcEHXBQXuDuh,sHOWSIAKtU58,ZVWAAMjVVHHl,qRin5pdYOdbB,IySsVMyKT3tF,FwEHNICjJCy0,yISIa0MMKKfB,GAtvbI59wr0o,OmNM6rT0Sgul,gu1MSKhYvigU,S2TTo9DhhiSh,aaLV7ZjAfkcR,ker4pIJmdvxf,WaQEaQCVMQ03,xV97BFGi0hY9,YnM1HtHE4j7G,X5FyJb4ToTo6,jLmadlzMdunT,GGFwFLsDF9Fv,prtR0Uw1GMh5,oNamnshN4dFG,QZzQeAYvsoum,VHAt7CcYKC2T,cKsTbNGL... |
coleifer/peewee | playhouse/shortcuts.py | model_to_dict | def model_to_dict(model, recurse=True, backrefs=False, only=None,
exclude=None, seen=None, extra_attrs=None,
fields_from_query=None, max_depth=None, manytomany=False):
"""
Convert a model instance (and any related objects) to a dictionary.
:param bool recurse: Whether fo... | python | def model_to_dict(model, recurse=True, backrefs=False, only=None,
exclude=None, seen=None, extra_attrs=None,
fields_from_query=None, max_depth=None, manytomany=False):
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coleifer/peewee | peewee.py | ColumnBase._e | def _e(op, inv=False):
"""
Lightweight factory which returns a method that builds an Expression
consisting of the left-hand and right-hand operands, using `op`.
"""
def inner(self, rhs):
if inv:
return Expression(rhs, op, self)
return Expre... | python | def _e(op, inv=False):
"""
Lightweight factory which returns a method that builds an Expression
consisting of the left-hand and right-hand operands, using `op`.
"""
def inner(self, rhs):
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RomelTorres/alpha_vantage | alpha_vantage/foreignexchange.py | ForeignExchange.get_currency_exchange_intraday | def get_currency_exchange_intraday(self, from_symbol, to_symbol, interval='15min', outputsize='compact'):
""" Returns the intraday exchange rate for any pair of physical
currency (e.g., EUR) or physical currency (e.g., USD).
Keyword Arguments:
from_symbol: The currency you would lik... | python | def get_currency_exchange_intraday(self, from_symbol, to_symbol, interval='15min', outputsize='compact'):
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RomelTorres/alpha_vantage | alpha_vantage/alphavantage.py | AlphaVantage._call_api_on_func | def _call_api_on_func(cls, func):
""" Decorator for forming the api call with the arguments of the
function, it works by taking the arguments given to the function
and building the url to call the api on it
Keyword Arguments:
func: The function to be decorated
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... | python | def _call_api_on_func(cls, func):
""" Decorator for forming the api call with the arguments of the
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and building the url to call the api on it
Keyword Arguments:
func: The function to be decorated
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RomelTorres/alpha_vantage | alpha_vantage/alphavantage.py | AlphaVantage._output_format | def _output_format(cls, func, override=None):
""" Decorator in charge of giving the output its right format, either
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Keyword Arguments:
func: The function to be decorated
override: Override the internal format of the call, default None
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Keyword Arguments:
func: The function to be decorated
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RomelTorres/alpha_vantage | alpha_vantage/alphavantage.py | AlphaVantage.map_to_matype | def map_to_matype(self, matype):
""" Convert to the alpha vantage math type integer. It returns an
integer correspondent to the type of math to apply to a function. It
raises ValueError if an integer greater than the supported math types
is given.
Keyword Arguments:
... | python | def map_to_matype(self, matype):
""" Convert to the alpha vantage math type integer. It returns an
integer correspondent to the type of math to apply to a function. It
raises ValueError if an integer greater than the supported math types
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RomelTorres/alpha_vantage | alpha_vantage/alphavantage.py | AlphaVantage._handle_api_call | def _handle_api_call(self, url):
""" Handle the return call from the api and return a data and meta_data
object. It raises a ValueError on problems
Keyword Arguments:
url: The url of the service
data_key: The key for getting the data from the jso object
me... | python | def _handle_api_call(self, url):
""" Handle the return call from the api and return a data and meta_data
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Keyword Arguments:
url: The url of the service
data_key: The key for getting the data from the jso object
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RomelTorres/alpha_vantage | alpha_vantage/sectorperformance.py | SectorPerformances._output_format_sector | def _output_format_sector(func, override=None):
""" Decorator in charge of giving the output its right format, either
json or pandas (replacing the % for usable floats, range 0-1.0)
Keyword Arguments:
func: The function to be decorated
override: Override the internal for... | python | def _output_format_sector(func, override=None):
""" Decorator in charge of giving the output its right format, either
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func: The function to be decorated
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RomelTorres/alpha_vantage | alpha_vantage/techindicators.py | TechIndicators.get_macd | def get_macd(self, symbol, interval='daily', series_type='close',
fastperiod=None, slowperiod=None, signalperiod=None):
""" Return the moving average convergence/divergence time series in two
json objects as data and meta_data. It raises ValueError when problems
arise
K... | python | def get_macd(self, symbol, interval='daily', series_type='close',
fastperiod=None, slowperiod=None, signalperiod=None):
""" Return the moving average convergence/divergence time series in two
json objects as data and meta_data. It raises ValueError when problems
arise
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] | 4e0b5057e520e3e3de69cf947301765817290121 | https://github.com/RomelTorres/alpha_vantage/blob/4e0b5057e520e3e3de69cf947301765817290121/alpha_vantage/techindicators.py#L186-L204 | train | This function returns the moving average convergence / divergence time series in two
json objects as data and meta_data. It raises ValueError when problems are raised | Pu7Z6IJCgH3a,vcEHXBQXuDuh,sHOWSIAKtU58,ZVWAAMjVVHHl,qRin5pdYOdbB,IySsVMyKT3tF,FwEHNICjJCy0,yISIa0MMKKfB,GAtvbI59wr0o,OmNM6rT0Sgul,gu1MSKhYvigU,S2TTo9DhhiSh,aaLV7ZjAfkcR,ker4pIJmdvxf,WaQEaQCVMQ03,xV97BFGi0hY9,YnM1HtHE4j7G,X5FyJb4ToTo6,jLmadlzMdunT,GGFwFLsDF9Fv,prtR0Uw1GMh5,oNamnshN4dFG,QZzQeAYvsoum,VHAt7CcYKC2T,cKsTbNGL... |
RomelTorres/alpha_vantage | alpha_vantage/techindicators.py | TechIndicators.get_macdext | def get_macdext(self, symbol, interval='daily', series_type='close',
fastperiod=None, slowperiod=None, signalperiod=None, fastmatype=None,
slowmatype=None, signalmatype=None):
""" Return the moving average convergence/divergence time series in two
json objects as ... | python | def get_macdext(self, symbol, interval='daily', series_type='close',
fastperiod=None, slowperiod=None, signalperiod=None, fastmatype=None,
slowmatype=None, signalmatype=None):
""" Return the moving average convergence/divergence time series in two
json objects as ... | [
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symbol: the symbol for the equity we want to get its data
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RomelTorres/alpha_vantage | alpha_vantage/techindicators.py | TechIndicators.get_stoch | def get_stoch(self, symbol, interval='daily', fastkperiod=None,
slowkperiod=None, slowdperiod=None, slowkmatype=None, slowdmatype=None):
""" Return the stochatic oscillator values in two
json objects as data and meta_data. It raises ValueError when problems
arise
Keywo... | python | def get_stoch(self, symbol, interval='daily', fastkperiod=None,
slowkperiod=None, slowdperiod=None, slowkmatype=None, slowdmatype=None):
""" Return the stochatic oscillator values in two
json objects as data and meta_data. It raises ValueError when problems
arise
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