"""Correlation plot functions."""
import numpy as np
from statsmodels.graphics import utils
from statsmodels.tsa.stattools import acf, pacf
def _prepare_data_corr_plot(x, lags, zero):
zero = bool(zero)
irregular = False if zero else True
if lags is None:
# GH 4663 - use a sensible default value
nobs = x.shape[0]
lim = min(int(np.ceil(10 * np.log10(nobs))), nobs - 1)
lags = np.arange(not zero, lim + 1)
elif np.isscalar(lags):
lags = np.arange(not zero, int(lags) + 1) # +1 for zero lag
else:
irregular = True
lags = np.asanyarray(lags).astype(np.int)
nlags = lags.max(0)
return lags, nlags, irregular
def _plot_corr(ax, title, acf_x, confint, lags, irregular, use_vlines,
vlines_kwargs, **kwargs):
if irregular:
acf_x = acf_x[lags]
if confint is not None:
confint = confint[lags]
if use_vlines:
ax.vlines(lags, [0], acf_x, **vlines_kwargs)
ax.axhline(**kwargs)
kwargs.setdefault('marker', 'o')
kwargs.setdefault('markersize', 5)
if 'ls' not in kwargs:
# gh-2369
kwargs.setdefault('linestyle', 'None')
ax.margins(.05)
ax.plot(lags, acf_x, **kwargs)
ax.set_title(title)
if confint is not None:
if lags[0] == 0:
lags = lags[1:]
confint = confint[1:]
acf_x = acf_x[1:]
lags = lags.astype(np.float)
lags[0] -= 0.5
lags[-1] += 0.5
ax.fill_between(lags, confint[:, 0] - acf_x,
confint[:, 1] - acf_x, alpha=.25)
[docs]def plot_acf(x, ax=None, lags=None, alpha=.05, use_vlines=True, unbiased=False,
fft=False, title='Autocorrelation', zero=True,
vlines_kwargs=None, **kwargs):
"""Plot the autocorrelation function
Plots lags on the horizontal and the correlations on vertical axis.
Parameters
----------
x : array_like
Array of time-series values
ax : Matplotlib AxesSubplot instance, optional
If given, this subplot is used to plot in instead of a new figure being
created.
lags : int or array_like, optional
int or Array of lag values, used on horizontal axis. Uses
np.arange(lags) when lags is an int. If not provided,
``lags=np.arange(len(corr))`` is used.
alpha : scalar, optional
If a number is given, the confidence intervals for the given level are
returned. For instance if alpha=.05, 95 % confidence intervals are
returned where the standard deviation is computed according to
Bartlett's formula. If None, no confidence intervals are plotted.
use_vlines : bool, optional
If True, vertical lines and markers are plotted.
If False, only markers are plotted. The default marker is 'o'; it can
be overridden with a ``marker`` kwarg.
unbiased : bool
If True, then denominators for autocovariance are n-k, otherwise n
fft : bool, optional
If True, computes the ACF via FFT.
title : str, optional
Title to place on plot. Default is 'Autocorrelation'
zero : bool, optional
Flag indicating whether to include the 0-lag autocorrelation.
Default is True.
vlines_kwargs : dict, optional
Optional dictionary of keyword arguments that are passed to vlines.
**kwargs : kwargs, optional
Optional keyword arguments that are directly passed on to the
Matplotlib ``plot`` and ``axhline`` functions.
Returns
-------
fig : Matplotlib figure instance
If `ax` is None, the created figure. Otherwise the figure to which
`ax` is connected.
See Also
--------
matplotlib.pyplot.xcorr
matplotlib.pyplot.acorr
Notes
-----
Adapted from matplotlib's `xcorr`.
Data are plotted as ``plot(lags, corr, **kwargs)``
kwargs is used to pass matplotlib optional arguments to both the line
tracing the autocorrelations and for the horizontal line at 0. These
options must be valid for a Line2D object.
vlines_kwargs is used to pass additional optional arguments to the
vertical lines connecting each autocorrelation to the axis. These options
must be valid for a LineCollection object.
Examples
--------
>>> import pandas as pd
>>> import matplotlib.pyplot as plt
>>> import statsmodels.api as sm
>>> dta = sm.datasets.sunspots.load_pandas().data
>>> dta.index = pd.Index(sm.tsa.datetools.dates_from_range('1700', '2008'))
>>> del dta["YEAR"]
>>> sm.graphics.tsa.plot_acf(dta.values.squeeze(), lags=40)
>>> plt.show()
.. plot:: plots/graphics_tsa_plot_acf.py
"""
fig, ax = utils.create_mpl_ax(ax)
lags, nlags, irregular = _prepare_data_corr_plot(x, lags, zero)
vlines_kwargs = {} if vlines_kwargs is None else vlines_kwargs
confint = None
# acf has different return type based on alpha
if alpha is None:
acf_x = acf(x, nlags=nlags, alpha=alpha, fft=fft,
unbiased=unbiased)
else:
acf_x, confint = acf(x, nlags=nlags, alpha=alpha, fft=fft,
unbiased=unbiased)
_plot_corr(ax, title, acf_x, confint, lags, irregular, use_vlines,
vlines_kwargs, **kwargs)
return fig
[docs]def plot_pacf(x, ax=None, lags=None, alpha=.05, method='ywunbiased',
use_vlines=True, title='Partial Autocorrelation', zero=True,
vlines_kwargs=None, **kwargs):
"""
Plot the partial autocorrelation function
Parameters
----------
x : array_like
Array of time-series values
ax : Matplotlib AxesSubplot instance, optional
If given, this subplot is used to plot in instead of a new figure being
created.
lags : int or array_like, optional
int or Array of lag values, used on horizontal axis. Uses
np.arange(lags) when lags is an int. If not provided,
``lags=np.arange(len(corr))`` is used.
alpha : float, optional
If a number is given, the confidence intervals for the given level are
returned. For instance if alpha=.05, 95 % confidence intervals are
returned where the standard deviation is computed according to
1/sqrt(len(x))
method : {'ywunbiased', 'ywmle', 'ols'}
Specifies which method for the calculations to use:
- yw or ywunbiased : yule walker with bias correction in denominator
for acovf. Default.
- ywm or ywmle : yule walker without bias correction
- ols - regression of time series on lags of it and on constant
- ld or ldunbiased : Levinson-Durbin recursion with bias correction
- ldb or ldbiased : Levinson-Durbin recursion without bias correction
use_vlines : bool, optional
If True, vertical lines and markers are plotted.
If False, only markers are plotted. The default marker is 'o'; it can
be overridden with a ``marker`` kwarg.
title : str, optional
Title to place on plot. Default is 'Partial Autocorrelation'
zero : bool, optional
Flag indicating whether to include the 0-lag autocorrelation.
Default is True.
vlines_kwargs : dict, optional
Optional dictionary of keyword arguments that are passed to vlines.
**kwargs : kwargs, optional
Optional keyword arguments that are directly passed on to the
Matplotlib ``plot`` and ``axhline`` functions.
Returns
-------
fig : Matplotlib figure instance
If `ax` is None, the created figure. Otherwise the figure to which
`ax` is connected.
See Also
--------
matplotlib.pyplot.xcorr
matplotlib.pyplot.acorr
Notes
-----
Plots lags on the horizontal and the correlations on vertical axis.
Adapted from matplotlib's `xcorr`.
Data are plotted as ``plot(lags, corr, **kwargs)``
kwargs is used to pass matplotlib optional arguments to both the line
tracing the autocorrelations and for the horizontal line at 0. These
options must be valid for a Line2D object.
vlines_kwargs is used to pass additional optional arguments to the
vertical lines connecting each autocorrelation to the axis. These options
must be valid for a LineCollection object.
Examples
--------
>>> import pandas as pd
>>> import matplotlib.pyplot as plt
>>> import statsmodels.api as sm
>>> dta = sm.datasets.sunspots.load_pandas().data
>>> dta.index = pd.Index(sm.tsa.datetools.dates_from_range('1700', '2008'))
>>> del dta["YEAR"]
>>> sm.graphics.tsa.plot_acf(dta.values.squeeze(), lags=40)
>>> plt.show()
.. plot:: plots/graphics_tsa_plot_pacf.py
"""
fig, ax = utils.create_mpl_ax(ax)
vlines_kwargs = {} if vlines_kwargs is None else vlines_kwargs
lags, nlags, irregular = _prepare_data_corr_plot(x, lags, zero)
confint = None
if alpha is None:
acf_x = pacf(x, nlags=nlags, alpha=alpha, method=method)
else:
acf_x, confint = pacf(x, nlags=nlags, alpha=alpha, method=method)
_plot_corr(ax, title, acf_x, confint, lags, irregular, use_vlines,
vlines_kwargs, **kwargs)
return fig
def seasonal_plot(grouped_x, xticklabels, ylabel=None, ax=None):
"""
Consider using one of month_plot or quarter_plot unless you need
irregular plotting.
Parameters
----------
grouped_x : iterable of DataFrames
Should be a GroupBy object (or similar pair of group_names and groups
as DataFrames) with a DatetimeIndex or PeriodIndex
xticklabels : list of str
List of season labels, one for each group.
ylabel : str
Lable for y axis
ax : Matplotlib AxesSubplot instance, optional
If given, this subplot is used to plot in instead of a new figure being
created.
"""
fig, ax = utils.create_mpl_ax(ax)
start = 0
ticks = []
for season, df in grouped_x:
df = df.copy() # or sort balks for series. may be better way
df.sort_index()
nobs = len(df)
x_plot = np.arange(start, start + nobs)
ticks.append(x_plot.mean())
ax.plot(x_plot, df.values, 'k')
ax.hlines(df.values.mean(), x_plot[0], x_plot[-1], colors='r',
linewidth=3)
start += nobs
ax.set_xticks(ticks)
ax.set_xticklabels(xticklabels)
ax.set_ylabel(ylabel)
ax.margins(.1, .05)
return fig
[docs]def month_plot(x, dates=None, ylabel=None, ax=None):
"""
Seasonal plot of monthly data
Parameters
----------
x : array-like
Seasonal data to plot. If dates is None, x must be a pandas object
with a PeriodIndex or DatetimeIndex with a monthly frequency.
dates : array-like, optional
If `x` is not a pandas object, then dates must be supplied.
ylabel : str, optional
The label for the y-axis. Will attempt to use the `name` attribute
of the Series.
ax : matplotlib.axes, optional
Existing axes instance.
Returns
-------
matplotlib.Figure
Examples
--------
>>> import statsmodels.api as sm
>>> import pandas as pd
>>> dta = sm.datasets.elnino.load_pandas().data
>>> dta['YEAR'] = dta.YEAR.astype(int).astype(str)
>>> dta = dta.set_index('YEAR').T.unstack()
>>> dates = pd.to_datetime(list(map(lambda x: '-'.join(x) + '-1',
... dta.index.values)))
>>> dta.index = pd.DatetimeIndex(dates, freq='MS')
>>> fig = sm.graphics.tsa.month_plot(dta)
.. plot:: plots/graphics_tsa_month_plot.py
"""
if dates is None:
from statsmodels.tools.data import _check_period_index
_check_period_index(x, freq="M")
else:
from pandas import Series, PeriodIndex
x = Series(x, index=PeriodIndex(dates, freq="M"))
xticklabels = ['j', 'f', 'm', 'a', 'm', 'j', 'j', 'a', 's', 'o', 'n', 'd']
return seasonal_plot(x.groupby(lambda y: y.month), xticklabels,
ylabel=ylabel, ax=ax)
[docs]def quarter_plot(x, dates=None, ylabel=None, ax=None):
"""
Seasonal plot of quarterly data
Parameters
----------
x : array-like
Seasonal data to plot. If dates is None, x must be a pandas object
with a PeriodIndex or DatetimeIndex with a monthly frequency.
dates : array-like, optional
If `x` is not a pandas object, then dates must be supplied.
ylabel : str, optional
The label for the y-axis. Will attempt to use the `name` attribute
of the Series.
ax : matplotlib.axes, optional
Existing axes instance.
Returns
-------
matplotlib.Figure
Examples
--------
>>> import statsmodels.api as sm
>>> import pandas as pd
>>> dta = sm.datasets.elnino.load_pandas().data
>>> dta['YEAR'] = dta.YEAR.astype(int).astype(str)
>>> dta = dta.set_index('YEAR').T.unstack()
>>> dates = pd.to_datetime(list(map(lambda x: '-'.join(x) + '-1',
... dta.index.values)))
>>> dta.index = dates.to_period('Q')
>>> fig = sm.graphics.tsa.quarter_plot(dta)
.. plot:: plots/graphics_tsa_quarter_plot.py
"""
if dates is None:
from statsmodels.tools.data import _check_period_index
_check_period_index(x, freq="Q")
else:
from pandas import Series, PeriodIndex
x = Series(x, index=PeriodIndex(dates, freq="Q"))
xticklabels = ['q1', 'q2', 'q3', 'q4']
return seasonal_plot(x.groupby(lambda y: y.quarter), xticklabels,
ylabel=ylabel, ax=ax)
