"""Correlation plot functions."""
import numpy as np
from statsmodels.graphics import utils
from statsmodels.tsa.stattools import acf, pacf
[docs]def plot_acf(x, ax=None, lags=None, alpha=.05, use_vlines=True, unbiased=False,
fft=False, **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 : array_like, optional
Array of lag values, used on horizontal axis.
If not given, ``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.
**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
mpl_examples/pylab_examples/xcorr_demo.py
Notes
-----
Adapted from matplotlib's `xcorr`.
Data are plotted as ``plot(lags, corr, **kwargs)``
"""
fig, ax = utils.create_mpl_ax(ax)
if lags is None:
lags = np.arange(len(x))
nlags = len(lags) - 1
else:
nlags = lags
lags = np.arange(lags + 1) # +1 for zero lag
acf_x, confint = acf(x, nlags=nlags, alpha=alpha, fft=fft,
unbiased=unbiased)
if use_vlines:
ax.vlines(lags, [0], acf_x, **kwargs)
ax.axhline(**kwargs)
# center the confidence interval TODO: do in acf?
confint = confint - confint.mean(1)[:,None]
kwargs.setdefault('marker', 'o')
kwargs.setdefault('markersize', 5)
kwargs.setdefault('linestyle', 'None')
ax.margins(.05)
ax.plot(lags, acf_x, **kwargs)
ax.fill_between(lags, confint[:,0], confint[:,1], alpha=.25)
ax.set_title("Autocorrelation")
return fig
[docs]def plot_pacf(x, ax=None, lags=None, alpha=.05, method='ywm',
use_vlines=True, **kwargs):
"""Plot the partial 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 : array_like, optional
Array of lag values, used on horizontal axis.
If not given, ``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
1/sqrt(len(x))
method : 'ywunbiased' (default) or 'ywmle' or 'ols'
specifies which method for the calculations to use:
- yw or ywunbiased : yule walker with bias correction in denominator
for acovf
- 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.
**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
mpl_examples/pylab_examples/xcorr_demo.py
Notes
-----
Adapted from matplotlib's `xcorr`.
Data are plotted as ``plot(lags, corr, **kwargs)``
"""
fig, ax = utils.create_mpl_ax(ax)
if lags is None:
lags = np.arange(len(x))
nlags = len(lags) - 1
else:
nlags = lags
lags = np.arange(lags + 1) # +1 for zero lag
acf_x, confint = pacf(x, nlags=nlags, alpha=alpha, method=method)
if use_vlines:
ax.vlines(lags, [0], acf_x, **kwargs)
ax.axhline(**kwargs)
# center the confidence interval TODO: do in acf?
confint = confint - confint.mean(1)[:,None]
kwargs.setdefault('marker', 'o')
kwargs.setdefault('markersize', 5)
kwargs.setdefault('linestyle', 'None')
ax.margins(.05)
ax.plot(lags, acf_x, **kwargs)
ax.fill_between(lags, confint[:,0], confint[:,1], alpha=.25)
ax.set_title("Partial Autocorrelation")
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
"""
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()
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='k')
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 = map(lambda x : pd.datetools.parse('1 '+' '.join(x)),
... dta.index.values)
>>> dta.index = pd.DatetimeIndex(dates, freq='M')
>>> fig = sm.graphics.tsa.month_plot(dta)
.. plot:: plots/graphics_month_plot.py
"""
from pandas import DataFrame
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
"""
from pandas import DataFrame
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)
if __name__ == "__main__":
import pandas as pd
#R code to run to load that dataset in this directory
#data(co2)
#library(zoo)
#write.csv(as.data.frame(list(date=as.Date(co2), co2=coredata(co2))), "co2.csv", row.names=FALSE)
co2 = pd.read_csv("co2.csv", index_col=0, parse_dates=True)
month_plot(co2.co2)
#will work when dates are sorted
#co2 = sm.datasets.get_rdataset("co2", cache=True)
x = pd.Series(np.arange(20),
index=pd.PeriodIndex(start='1/1/1990', periods=20, freq='Q'))
quarter_plot(x)
