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(WIP) BUG/CLN: Better timeseries plotting / refactoring tsplot #7670

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(WIP) BUG/CLN: Better timeseries plotting / refactoring tsplot #7670

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46 changes: 23 additions & 23 deletions pandas/tools/plotting.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1665,21 +1665,35 @@ def __init__(self, data, **kwargs):

def _is_ts_plot(self):
# this is slightly deceptive
return not self.x_compat and self.use_index and self._use_dynamic_x()

def _use_dynamic_x(self):
from pandas.tseries.plotting import _use_dynamic_x
return _use_dynamic_x(self._get_ax(0), self.data)
from pandas.tseries.plotting import _use_dynamic_x, _get_freq
ax = self._get_ax(0)
freq, ax_freq = _get_freq(ax, self.data)
dynamic_x = _use_dynamic_x(ax, self.data)
return (not self.x_compat and self.use_index and
dynamic_x and freq is not None)

def _make_plot(self):
if self._is_ts_plot():
print('tsplot-path!!')
from pandas.tseries.plotting import _maybe_convert_index
data = _maybe_convert_index(self._get_ax(0), self.data)

from pandas.tseries.plotting import _maybe_resample
for ax in self.axes:
# resample data and replot if required
kwds = self.kwds.copy()
data = _maybe_resample(data, ax, kwds)

x = data.index # dummy, not used
plotf = self._ts_plot
it = self._iter_data(data=data, keep_index=True)
else:
print('xcompat-path!!')
from pandas.tseries.plotting import _replot_x_compat
for ax in self.axes:
# if ax holds _plot_data, replot them on the x_compat scale
_replot_x_compat(ax)

x = self._get_xticks(convert_period=True)
plotf = self._plot
it = self._iter_data()
Expand Down Expand Up @@ -1723,24 +1737,15 @@ def _plot(cls, ax, x, y, style=None, column_num=None,

@classmethod
def _ts_plot(cls, ax, x, data, style=None, **kwds):
from pandas.tseries.plotting import (_maybe_resample,
_decorate_axes,
format_dateaxis)
from pandas.tseries.plotting import _maybe_resample, format_dateaxis
# accept x to be consistent with normal plot func,
# x is not passed to tsplot as it uses data.index as x coordinate
# column_num must be in kwds for stacking purpose
freq, data = _maybe_resample(data, ax, kwds)

# Set ax with freq info
_decorate_axes(ax, freq, kwds)
# digging deeper
if hasattr(ax, 'left_ax'):
_decorate_axes(ax.left_ax, freq, kwds)
if hasattr(ax, 'right_ax'):
_decorate_axes(ax.right_ax, freq, kwds)
data = _maybe_resample(data, ax, kwds)
ax._plot_data.append((data, cls._kind, kwds))

lines = cls._plot(ax, data.index, data.values, style=style, **kwds)

# set date formatter, locators and rescale limits
format_dateaxis(ax, ax.freq)
return lines
Expand Down Expand Up @@ -1790,14 +1795,9 @@ def _update_stacker(cls, ax, stacking_id, values):
ax._stacker_neg_prior[stacking_id] += values

def _post_plot_logic(self, ax, data):
condition = (not self._use_dynamic_x() and
data.index.is_all_dates and
not self.subplots or
(self.subplots and self.sharex))

index_name = self._get_index_name()

if condition:
if not self._is_ts_plot():
# irregular TS rotated 30 deg. by default
# probably a better place to check / set this.
if not self._rot_set:
Expand Down
162 changes: 100 additions & 62 deletions pandas/tseries/plotting.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,6 +9,8 @@

from matplotlib import pylab
from pandas.tseries.period import Period
import numpy as np

from pandas.tseries.offsets import DateOffset
import pandas.tseries.frequencies as frequencies
from pandas.tseries.index import DatetimeIndex
Expand Down Expand Up @@ -41,18 +43,14 @@ def tsplot(series, plotf, ax=None, **kwargs):
import matplotlib.pyplot as plt
ax = plt.gca()

freq, series = _maybe_resample(series, ax, kwargs)

# Set ax with freq info
_decorate_axes(ax, freq, kwargs)
series = _maybe_resample(series, ax, kwargs)
ax._plot_data.append((series, plotf, kwargs))
lines = plotf(ax, series.index._mpl_repr(), series.values, **kwargs)

# set date formatter, locators and rescale limits
format_dateaxis(ax, ax.freq)
return lines


def _maybe_resample(series, ax, kwargs):
# resample against axes freq if necessary
freq, ax_freq = _get_freq(ax, series)
Expand All @@ -75,11 +73,20 @@ def _maybe_resample(series, ax, kwargs):
series = getattr(series.resample(ax_freq), how)().dropna()
freq = ax_freq
elif frequencies.is_subperiod(freq, ax_freq) or _is_sub(freq, ax_freq):
_upsample_others(ax, freq, kwargs)
_upsample_others(ax, freq)
ax_freq = freq
else: # pragma: no cover
raise ValueError('Incompatible frequency conversion')
return freq, series

# Set ax with freq info
_decorate_axes(ax, freq)
# digging deeper
if hasattr(ax, 'left_ax'):
_decorate_axes(ax.left_ax, freq)
elif hasattr(ax, 'right_ax'):
_decorate_axes(ax.right_ax, freq)

return series


def _is_sub(f1, f2):
Expand All @@ -92,81 +99,112 @@ def _is_sup(f1, f2):
(f2.startswith('W') and frequencies.is_superperiod(f1, 'D')))


def _upsample_others(ax, freq, kwargs):
legend = ax.get_legend()
lines, labels = _replot_ax(ax, freq, kwargs)
_replot_ax(ax, freq, kwargs)
def _get_plot_func(plotf):
""" get actual function when plotf is specified with str """
# for tsplot
if isinstance(plotf, compat.string_types):
from pandas.tools.plotting import _plot_klass
plotf = _plot_klass[plotf]._plot
return plotf


def _upsample_others(ax, freq):

other_ax = None
def _replot(ax):
data = getattr(ax, '_plot_data', None)
if data is None:
return

# preserve legend
leg = ax.get_legend()
handles, labels = ax.get_legend_handles_labels()

ax._plot_data = []
ax.clear()
_decorate_axes(ax, freq)

for series, plotf, kwds in data:
series = series.copy()
idx = series.index.asfreq(freq, how='s')
series.index = idx
ax._plot_data.append((series, plotf, kwds))

plotf = _get_plot_func(plotf)
plotf(ax, series.index._mpl_repr(), series.values, **kwds)


if leg is not None:
ax.legend(handles, labels, title=leg.get_title().get_text())

_replot(ax)
if hasattr(ax, 'left_ax'):
other_ax = ax.left_ax
if hasattr(ax, 'right_ax'):
other_ax = ax.right_ax
_replot(ax.left_ax)
elif hasattr(ax, 'right_ax'):
_replot(ax.right_ax)

if other_ax is not None:
rlines, rlabels = _replot_ax(other_ax, freq, kwargs)
lines.extend(rlines)
labels.extend(rlabels)

if (legend is not None and kwargs.get('legend', True) and
len(lines) > 0):
title = legend.get_title().get_text()
if title == 'None':
title = None
ax.legend(lines, labels, loc='best', title=title)
def _replot_x_compat(ax):

def _replot(ax):
data = getattr(ax, '_plot_data', None)
if data is None:
return

def _replot_ax(ax, freq, kwargs):
data = getattr(ax, '_plot_data', None)
# preserve legend
leg = ax.get_legend()
handles, labels = ax.get_legend_handles_labels()

# clear current axes and data
ax._plot_data = []
ax.clear()
ax._plot_data = None
ax.clear()

_decorate_axes(ax, freq, kwargs)
_decorate_axes(ax, None)

lines = []
labels = []
if data is not None:
for series, plotf, kwds in data:
series = series.copy()
idx = series.index.asfreq(freq, how='S')
idx = series.index.to_timestamp(how='s')
series.index = idx
ax._plot_data.append((series, plotf, kwds))

# for tsplot
if isinstance(plotf, compat.string_types):
from pandas.tools.plotting import _plot_klass
plotf = _plot_klass[plotf]._plot
plotf = _get_plot_func(plotf)
plotf(ax, series.index._mpl_repr(), series, **kwds)

lines.append(plotf(ax, series.index._mpl_repr(),
series.values, **kwds)[0])
labels.append(pprint_thing(series.name))
if leg is not None:
ax.legend(handles, labels, title=leg.get_title().get_text())

return lines, labels
_replot(ax)
if hasattr(ax, 'left_ax'):
_replot(ax.left_ax)
elif hasattr(ax, 'right_ax'):
_replot(ax.right_ax)


def _decorate_axes(ax, freq, kwargs):
def _decorate_axes(ax, freq):
"""Initialize axes for time-series plotting"""
if not hasattr(ax, '_plot_data'):
ax._plot_data = []

ax.freq = freq
xaxis = ax.get_xaxis()
xaxis.freq = freq
if not hasattr(ax, 'legendlabels'):
ax.legendlabels = [kwargs.get('label', None)]
else:
ax.legendlabels.append(kwargs.get('label', None))
ax.view_interval = None
ax.date_axis_info = None


def _get_freq(ax, series):
def _get_index_freq(data):
freq = getattr(data.index, 'freq', None)
if freq is None:
freq = getattr(data.index, 'inferred_freq', None)
if freq == 'B':
weekdays = np.unique(data.index.dayofweek)
if (5 in weekdays) or (6 in weekdays):
freq = None
return freq


def _get_freq(ax, data):
# get frequency from data
freq = getattr(series.index, 'freq', None)
freq = getattr(data.index, 'freq', None)

if freq is None:
freq = getattr(series.index, 'inferred_freq', None)
freq = getattr(data.index, 'inferred_freq', None)

ax_freq = getattr(ax, 'freq', None)
if ax_freq is None:
Expand All @@ -175,17 +213,17 @@ def _get_freq(ax, series):
elif hasattr(ax, 'right_ax'):
ax_freq = getattr(ax.right_ax, 'freq', None)

# use axes freq if no data freq
if freq is None:
freq = ax_freq
if freq is not None:
# get the period frequency
if isinstance(freq, DateOffset):
freq = freq.rule_code
else:
freq = frequencies.get_base_alias(freq)

# get the period frequency
if isinstance(freq, DateOffset):
freq = freq.rule_code
else:
freq = frequencies.get_base_alias(freq)
if freq is None:
raise ValueError('Could not get frequency alias for plotting')
freq = frequencies.get_period_alias(freq)

freq = frequencies.get_period_alias(freq)
return freq, ax_freq


Expand Down
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