317 lines
9.8 KiB
Python
317 lines
9.8 KiB
Python
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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 typing import TYPE_CHECKING, Optional, cast
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import numpy as np
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from pandas._libs.tslibs import BaseOffset, Period, to_offset
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from pandas._libs.tslibs.dtypes import FreqGroup
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from pandas._typing import FrameOrSeriesUnion
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from pandas.core.dtypes.generic import (
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ABCDatetimeIndex,
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ABCPeriodIndex,
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ABCTimedeltaIndex,
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)
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from pandas.io.formats.printing import pprint_thing
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from pandas.plotting._matplotlib.converter import (
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TimeSeries_DateFormatter,
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TimeSeries_DateLocator,
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TimeSeries_TimedeltaFormatter,
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)
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from pandas.tseries.frequencies import get_period_alias, is_subperiod, is_superperiod
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if TYPE_CHECKING:
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from matplotlib.axes import Axes
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from pandas import DatetimeIndex, Index, Series
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# ---------------------------------------------------------------------
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# Plotting functions and monkey patches
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def maybe_resample(series: "Series", ax: "Axes", 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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# error: "Index" has no attribute "asfreq"
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series.index = series.index.asfreq( # type: ignore[attr-defined]
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ax_freq, how="s"
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)
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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: str, f2: str) -> bool:
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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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)
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def _is_sup(f1: str, f2: str) -> bool:
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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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)
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def _upsample_others(ax: "Axes", 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 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: "Axes", 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, str):
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from pandas.plotting._matplotlib import PLOT_CLASSES
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plotf = PLOT_CLASSES[plotf]._plot
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lines.append(plotf(ax, series.index._mpl_repr(), 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: "Axes", 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: "Axes"):
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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_period_alias(freq) -> Optional[str]:
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freqstr = to_offset(freq).rule_code
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freq = get_period_alias(freqstr)
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return freq
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def _get_freq(ax: "Axes", series: "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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freq = to_offset(freq)
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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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freq = _get_period_alias(freq)
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return freq, ax_freq
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def use_dynamic_x(ax: "Axes", data: FrameOrSeriesUnion) -> bool:
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freq = _get_index_freq(data.index)
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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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freq = _get_period_alias(freq)
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if freq is None:
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return False
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# FIXME: 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 = to_offset(freq)._period_dtype_code
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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_localize(x.tz) == x[0]
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return True
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def _get_index_freq(index: "Index") -> Optional[BaseOffset]:
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freq = getattr(index, "freq", None)
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if freq is None:
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freq = getattr(index, "inferred_freq", None)
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if freq == "B":
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# error: "Index" has no attribute "dayofweek"
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weekdays = np.unique(index.dayofweek) # type: ignore[attr-defined]
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if (5 in weekdays) or (6 in weekdays):
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freq = None
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freq = to_offset(freq)
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return freq
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def maybe_convert_index(ax: "Axes", 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, ABCPeriodIndex)):
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freq = data.index.freq
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if freq is None:
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# We only get here for DatetimeIndex
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data.index = cast("DatetimeIndex", data.index)
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freq = data.index.inferred_freq
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freq = to_offset(freq)
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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_period_alias(freq)
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if isinstance(data.index, ABCDatetimeIndex):
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data = data.tz_localize(None).to_period(freq=freq)
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elif isinstance(data.index, ABCPeriodIndex):
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data.index = data.index.asfreq(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_coord(freq, t, y) -> str:
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time_period = Period(ordinal=int(t), freq=freq)
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return f"t = {time_period} y = {y:8f}"
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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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from matplotlib import pylab
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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(
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freq, dynamic_mode=True, minor_locator=False, plot_obj=subplot
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)
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minlocator = TimeSeries_DateLocator(
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freq, dynamic_mode=True, minor_locator=True, plot_obj=subplot
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)
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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(
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freq, dynamic_mode=True, minor_locator=False, plot_obj=subplot
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)
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minformatter = TimeSeries_DateFormatter(
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freq, dynamic_mode=True, minor_locator=True, plot_obj=subplot
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)
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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(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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