demo + utils venv
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d3m1g0d
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# TODO: Use the fact that axis can have units to simplify the process
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import functools
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from matplotlib import pylab
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import numpy as np
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from pandas._libs.tslibs.frequencies import (
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FreqGroup, get_base_alias, get_freq, is_subperiod, is_superperiod)
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from pandas._libs.tslibs.period import Period
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import pandas.compat as compat
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from pandas.core.dtypes.generic import (
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ABCDatetimeIndex, ABCPeriodIndex, ABCTimedeltaIndex)
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from pandas.io.formats.printing import pprint_thing
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from pandas.plotting._converter import (
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TimeSeries_DateFormatter, TimeSeries_DateLocator,
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TimeSeries_TimedeltaFormatter)
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import pandas.tseries.frequencies as frequencies
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from pandas.tseries.offsets import DateOffset
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# ---------------------------------------------------------------------
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# Plotting functions and monkey patches
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def tsplot(series, plotf, ax=None, **kwargs):
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import warnings
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"""
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Plots a Series on the given Matplotlib axes or the current axes
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Parameters
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----------
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axes : Axes
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series : Series
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Notes
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_____
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Supports same kwargs as Axes.plot
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.. deprecated:: 0.23.0
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Use Series.plot() instead
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"""
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warnings.warn("'tsplot' is deprecated and will be removed in a "
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"future version. Please use Series.plot() instead.",
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FutureWarning, stacklevel=2)
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# Used inferred freq is possible, need a test case for inferred
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if ax is None:
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import matplotlib.pyplot as plt
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ax = plt.gca()
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freq, series = _maybe_resample(series, ax, kwargs)
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# Set ax with freq info
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_decorate_axes(ax, freq, kwargs)
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ax._plot_data.append((series, plotf, kwargs))
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lines = plotf(ax, series.index._mpl_repr(), series.values, **kwargs)
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# set date formatter, locators and rescale limits
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format_dateaxis(ax, ax.freq, series.index)
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return lines
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def _maybe_resample(series, ax, kwargs):
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# resample against axes freq if necessary
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freq, ax_freq = _get_freq(ax, series)
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if freq is None: # pragma: no cover
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raise ValueError('Cannot use dynamic axis without frequency info')
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# Convert DatetimeIndex to PeriodIndex
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if isinstance(series.index, ABCDatetimeIndex):
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series = series.to_period(freq=freq)
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if ax_freq is not None and freq != ax_freq:
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if is_superperiod(freq, ax_freq): # upsample input
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series = series.copy()
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series.index = series.index.asfreq(ax_freq, how='s')
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freq = ax_freq
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elif _is_sup(freq, ax_freq): # one is weekly
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how = kwargs.pop('how', 'last')
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series = getattr(series.resample('D'), how)().dropna()
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series = getattr(series.resample(ax_freq), how)().dropna()
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freq = ax_freq
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elif is_subperiod(freq, ax_freq) or _is_sub(freq, ax_freq):
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_upsample_others(ax, freq, kwargs)
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else: # pragma: no cover
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raise ValueError('Incompatible frequency conversion')
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return freq, series
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def _is_sub(f1, f2):
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return ((f1.startswith('W') and is_subperiod('D', f2)) or
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(f2.startswith('W') and is_subperiod(f1, 'D')))
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def _is_sup(f1, f2):
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return ((f1.startswith('W') and is_superperiod('D', f2)) or
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(f2.startswith('W') and is_superperiod(f1, 'D')))
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def _upsample_others(ax, freq, kwargs):
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legend = ax.get_legend()
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lines, labels = _replot_ax(ax, freq, kwargs)
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_replot_ax(ax, freq, kwargs)
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other_ax = None
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if hasattr(ax, 'left_ax'):
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other_ax = ax.left_ax
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if hasattr(ax, 'right_ax'):
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other_ax = ax.right_ax
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if other_ax is not None:
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rlines, rlabels = _replot_ax(other_ax, freq, kwargs)
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lines.extend(rlines)
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labels.extend(rlabels)
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if (legend is not None and kwargs.get('legend', True) and
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len(lines) > 0):
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title = legend.get_title().get_text()
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if title == 'None':
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title = None
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ax.legend(lines, labels, loc='best', title=title)
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def _replot_ax(ax, freq, kwargs):
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data = getattr(ax, '_plot_data', None)
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# clear current axes and data
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ax._plot_data = []
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ax.clear()
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_decorate_axes(ax, freq, kwargs)
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lines = []
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labels = []
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if data is not None:
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for series, plotf, kwds in data:
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series = series.copy()
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idx = series.index.asfreq(freq, how='S')
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series.index = idx
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ax._plot_data.append((series, plotf, kwds))
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# for tsplot
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if isinstance(plotf, compat.string_types):
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from pandas.plotting._core import _plot_klass
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plotf = _plot_klass[plotf]._plot
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lines.append(plotf(ax, series.index._mpl_repr(),
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series.values, **kwds)[0])
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labels.append(pprint_thing(series.name))
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return lines, labels
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def _decorate_axes(ax, freq, kwargs):
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"""Initialize axes for time-series plotting"""
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if not hasattr(ax, '_plot_data'):
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ax._plot_data = []
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ax.freq = freq
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xaxis = ax.get_xaxis()
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xaxis.freq = freq
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if not hasattr(ax, 'legendlabels'):
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ax.legendlabels = [kwargs.get('label', None)]
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else:
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ax.legendlabels.append(kwargs.get('label', None))
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ax.view_interval = None
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ax.date_axis_info = None
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def _get_ax_freq(ax):
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"""
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Get the freq attribute of the ax object if set.
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Also checks shared axes (eg when using secondary yaxis, sharex=True
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or twinx)
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"""
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ax_freq = getattr(ax, 'freq', None)
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if ax_freq is None:
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# check for left/right ax in case of secondary yaxis
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if hasattr(ax, 'left_ax'):
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ax_freq = getattr(ax.left_ax, 'freq', None)
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elif hasattr(ax, 'right_ax'):
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ax_freq = getattr(ax.right_ax, 'freq', None)
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if ax_freq is None:
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# check if a shared ax (sharex/twinx) has already freq set
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shared_axes = ax.get_shared_x_axes().get_siblings(ax)
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if len(shared_axes) > 1:
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for shared_ax in shared_axes:
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ax_freq = getattr(shared_ax, 'freq', None)
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if ax_freq is not None:
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break
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return ax_freq
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def _get_freq(ax, series):
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# get frequency from data
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freq = getattr(series.index, 'freq', None)
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if freq is None:
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freq = getattr(series.index, 'inferred_freq', None)
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ax_freq = _get_ax_freq(ax)
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# use axes freq if no data freq
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if freq is None:
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freq = ax_freq
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# get the period frequency
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if isinstance(freq, DateOffset):
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freq = freq.rule_code
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else:
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freq = get_base_alias(freq)
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freq = frequencies.get_period_alias(freq)
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return freq, ax_freq
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def _use_dynamic_x(ax, data):
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freq = _get_index_freq(data)
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ax_freq = _get_ax_freq(ax)
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if freq is None: # convert irregular if axes has freq info
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freq = ax_freq
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else: # do not use tsplot if irregular was plotted first
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if (ax_freq is None) and (len(ax.get_lines()) > 0):
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return False
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if freq is None:
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return False
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if isinstance(freq, DateOffset):
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freq = freq.rule_code
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else:
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freq = get_base_alias(freq)
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freq = frequencies.get_period_alias(freq)
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if freq is None:
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return False
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# hack this for 0.10.1, creating more technical debt...sigh
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if isinstance(data.index, ABCDatetimeIndex):
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base = get_freq(freq)
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x = data.index
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if (base <= FreqGroup.FR_DAY):
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return x[:1].is_normalized
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return Period(x[0], freq).to_timestamp(tz=x.tz) == x[0]
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return True
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def _get_index_freq(data):
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freq = getattr(data.index, 'freq', None)
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if freq is None:
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freq = getattr(data.index, 'inferred_freq', None)
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if freq == 'B':
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weekdays = np.unique(data.index.dayofweek)
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if (5 in weekdays) or (6 in weekdays):
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freq = None
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return freq
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def _maybe_convert_index(ax, data):
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# tsplot converts automatically, but don't want to convert index
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# over and over for DataFrames
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if isinstance(data.index, ABCDatetimeIndex):
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freq = getattr(data.index, 'freq', None)
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if freq is None:
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freq = getattr(data.index, 'inferred_freq', None)
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if isinstance(freq, DateOffset):
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freq = freq.rule_code
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if freq is None:
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freq = _get_ax_freq(ax)
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if freq is None:
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raise ValueError('Could not get frequency alias for plotting')
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freq = get_base_alias(freq)
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freq = frequencies.get_period_alias(freq)
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data = data.to_period(freq=freq)
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return data
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# Patch methods for subplot. Only format_dateaxis is currently used.
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# Do we need the rest for convenience?
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def format_timedelta_ticks(x, pos, n_decimals):
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"""
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Convert seconds to 'D days HH:MM:SS.F'
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"""
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s, ns = divmod(x, 1e9)
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m, s = divmod(s, 60)
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h, m = divmod(m, 60)
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d, h = divmod(h, 24)
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decimals = int(ns * 10**(n_decimals - 9))
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s = r'{:02d}:{:02d}:{:02d}'.format(int(h), int(m), int(s))
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if n_decimals > 0:
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s += '.{{:0{:0d}d}}'.format(n_decimals).format(decimals)
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if d != 0:
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s = '{:d} days '.format(int(d)) + s
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return s
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def _format_coord(freq, t, y):
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return "t = {0} y = {1:8f}".format(Period(ordinal=int(t), freq=freq), y)
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def format_dateaxis(subplot, freq, index):
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"""
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Pretty-formats the date axis (x-axis).
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Major and minor ticks are automatically set for the frequency of the
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current underlying series. As the dynamic mode is activated by
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default, changing the limits of the x axis will intelligently change
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the positions of the ticks.
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"""
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# handle index specific formatting
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# Note: DatetimeIndex does not use this
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# interface. DatetimeIndex uses matplotlib.date directly
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if isinstance(index, ABCPeriodIndex):
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majlocator = TimeSeries_DateLocator(freq, dynamic_mode=True,
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minor_locator=False,
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plot_obj=subplot)
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minlocator = TimeSeries_DateLocator(freq, dynamic_mode=True,
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minor_locator=True,
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plot_obj=subplot)
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subplot.xaxis.set_major_locator(majlocator)
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subplot.xaxis.set_minor_locator(minlocator)
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majformatter = TimeSeries_DateFormatter(freq, dynamic_mode=True,
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minor_locator=False,
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plot_obj=subplot)
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minformatter = TimeSeries_DateFormatter(freq, dynamic_mode=True,
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minor_locator=True,
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plot_obj=subplot)
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subplot.xaxis.set_major_formatter(majformatter)
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subplot.xaxis.set_minor_formatter(minformatter)
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# x and y coord info
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subplot.format_coord = functools.partial(_format_coord, freq)
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elif isinstance(index, ABCTimedeltaIndex):
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subplot.xaxis.set_major_formatter(
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TimeSeries_TimedeltaFormatter())
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else:
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raise TypeError('index type not supported')
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pylab.draw_if_interactive()
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