from __future__ import annotations import contextlib import datetime as pydt from datetime import ( datetime, timedelta, tzinfo, ) import functools from typing import ( TYPE_CHECKING, Any, Final, cast, ) import matplotlib.dates as mdates from matplotlib.ticker import ( AutoLocator, Formatter, Locator, ) from matplotlib.transforms import nonsingular import matplotlib.units as munits import numpy as np from pandas._libs import lib from pandas._libs.tslibs import ( Timestamp, to_offset, ) from pandas._libs.tslibs.dtypes import FreqGroup from pandas._typing import F from pandas.core.dtypes.common import ( is_float, is_float_dtype, is_integer, is_integer_dtype, is_nested_list_like, ) from pandas import ( Index, Series, get_option, ) import pandas.core.common as com from pandas.core.indexes.datetimes import date_range from pandas.core.indexes.period import ( Period, PeriodIndex, period_range, ) import pandas.core.tools.datetimes as tools if TYPE_CHECKING: from collections.abc import Generator from pandas._libs.tslibs.offsets import BaseOffset # constants HOURS_PER_DAY: Final = 24.0 MIN_PER_HOUR: Final = 60.0 SEC_PER_MIN: Final = 60.0 SEC_PER_HOUR: Final = SEC_PER_MIN * MIN_PER_HOUR SEC_PER_DAY: Final = SEC_PER_HOUR * HOURS_PER_DAY MUSEC_PER_DAY: Final = 10**6 * SEC_PER_DAY _mpl_units = {} # Cache for units overwritten by us def get_pairs(): pairs = [ (Timestamp, DatetimeConverter), (Period, PeriodConverter), (pydt.datetime, DatetimeConverter), (pydt.date, DatetimeConverter), (pydt.time, TimeConverter), (np.datetime64, DatetimeConverter), ] return pairs def register_pandas_matplotlib_converters(func: F) -> F: """ Decorator applying pandas_converters. """ @functools.wraps(func) def wrapper(*args, **kwargs): with pandas_converters(): return func(*args, **kwargs) return cast(F, wrapper) @contextlib.contextmanager def pandas_converters() -> Generator[None, None, None]: """ Context manager registering pandas' converters for a plot. See Also -------- register_pandas_matplotlib_converters : Decorator that applies this. """ value = get_option("plotting.matplotlib.register_converters") if value: # register for True or "auto" register() try: yield finally: if value == "auto": # only deregister for "auto" deregister() def register() -> None: pairs = get_pairs() for type_, cls in pairs: # Cache previous converter if present if type_ in munits.registry and not isinstance(munits.registry[type_], cls): previous = munits.registry[type_] _mpl_units[type_] = previous # Replace with pandas converter munits.registry[type_] = cls() def deregister() -> None: # Renamed in pandas.plotting.__init__ for type_, cls in get_pairs(): # We use type to catch our classes directly, no inheritance if type(munits.registry.get(type_)) is cls: munits.registry.pop(type_) # restore the old keys for unit, formatter in _mpl_units.items(): if type(formatter) not in {DatetimeConverter, PeriodConverter, TimeConverter}: # make it idempotent by excluding ours. munits.registry[unit] = formatter def _to_ordinalf(tm: pydt.time) -> float: tot_sec = tm.hour * 3600 + tm.minute * 60 + tm.second + tm.microsecond / 10**6 return tot_sec def time2num(d): if isinstance(d, str): parsed = Timestamp(d) return _to_ordinalf(parsed.time()) if isinstance(d, pydt.time): return _to_ordinalf(d) return d class TimeConverter(munits.ConversionInterface): @staticmethod def convert(value, unit, axis): valid_types = (str, pydt.time) if isinstance(value, valid_types) or is_integer(value) or is_float(value): return time2num(value) if isinstance(value, Index): return value.map(time2num) if isinstance(value, (list, tuple, np.ndarray, Index)): return [time2num(x) for x in value] return value @staticmethod def axisinfo(unit, axis) -> munits.AxisInfo | None: if unit != "time": return None majloc = AutoLocator() majfmt = TimeFormatter(majloc) return munits.AxisInfo(majloc=majloc, majfmt=majfmt, label="time") @staticmethod def default_units(x, axis) -> str: return "time" # time formatter class TimeFormatter(Formatter): def __init__(self, locs) -> None: self.locs = locs def __call__(self, x, pos: int = 0) -> str: """ Return the time of day as a formatted string. Parameters ---------- x : float The time of day specified as seconds since 00:00 (midnight), with up to microsecond precision. pos Unused Returns ------- str A string in HH:MM:SS.mmmuuu format. Microseconds, milliseconds and seconds are only displayed if non-zero. """ fmt = "%H:%M:%S.%f" s = int(x) msus = round((x - s) * 10**6) ms = msus // 1000 us = msus % 1000 m, s = divmod(s, 60) h, m = divmod(m, 60) _, h = divmod(h, 24) if us != 0: return pydt.time(h, m, s, msus).strftime(fmt) elif ms != 0: return pydt.time(h, m, s, msus).strftime(fmt)[:-3] elif s != 0: return pydt.time(h, m, s).strftime("%H:%M:%S") return pydt.time(h, m).strftime("%H:%M") # Period Conversion class PeriodConverter(mdates.DateConverter): @staticmethod def convert(values, units, axis): if is_nested_list_like(values): values = [PeriodConverter._convert_1d(v, units, axis) for v in values] else: values = PeriodConverter._convert_1d(values, units, axis) return values @staticmethod def _convert_1d(values, units, axis): if not hasattr(axis, "freq"): raise TypeError("Axis must have `freq` set to convert to Periods") valid_types = (str, datetime, Period, pydt.date, pydt.time, np.datetime64) if isinstance(values, valid_types) or is_integer(values) or is_float(values): return get_datevalue(values, axis.freq) elif isinstance(values, PeriodIndex): return values.asfreq(axis.freq).asi8 elif isinstance(values, Index): return values.map(lambda x: get_datevalue(x, axis.freq)) elif lib.infer_dtype(values, skipna=False) == "period": # https://github.com/pandas-dev/pandas/issues/24304 # convert ndarray[period] -> PeriodIndex return PeriodIndex(values, freq=axis.freq).asi8 elif isinstance(values, (list, tuple, np.ndarray, Index)): return [get_datevalue(x, axis.freq) for x in values] return values def get_datevalue(date, freq): if isinstance(date, Period): return date.asfreq(freq).ordinal elif isinstance(date, (str, datetime, pydt.date, pydt.time, np.datetime64)): return Period(date, freq).ordinal elif ( is_integer(date) or is_float(date) or (isinstance(date, (np.ndarray, Index)) and (date.size == 1)) ): return date elif date is None: return None raise ValueError(f"Unrecognizable date '{date}'") # Datetime Conversion class DatetimeConverter(mdates.DateConverter): @staticmethod def convert(values, unit, axis): # values might be a 1-d array, or a list-like of arrays. if is_nested_list_like(values): values = [DatetimeConverter._convert_1d(v, unit, axis) for v in values] else: values = DatetimeConverter._convert_1d(values, unit, axis) return values @staticmethod def _convert_1d(values, unit, axis): def try_parse(values): try: return mdates.date2num(tools.to_datetime(values)) except Exception: return values if isinstance(values, (datetime, pydt.date, np.datetime64, pydt.time)): return mdates.date2num(values) elif is_integer(values) or is_float(values): return values elif isinstance(values, str): return try_parse(values) elif isinstance(values, (list, tuple, np.ndarray, Index, Series)): if isinstance(values, Series): # https://github.com/matplotlib/matplotlib/issues/11391 # Series was skipped. Convert to DatetimeIndex to get asi8 values = Index(values) if isinstance(values, Index): values = values.values if not isinstance(values, np.ndarray): values = com.asarray_tuplesafe(values) if is_integer_dtype(values) or is_float_dtype(values): return values try: values = tools.to_datetime(values) except Exception: pass values = mdates.date2num(values) return values @staticmethod def axisinfo(unit: tzinfo | None, axis) -> munits.AxisInfo: """ Return the :class:`~matplotlib.units.AxisInfo` for *unit*. *unit* is a tzinfo instance or None. The *axis* argument is required but not used. """ tz = unit majloc = PandasAutoDateLocator(tz=tz) majfmt = PandasAutoDateFormatter(majloc, tz=tz) datemin = pydt.date(2000, 1, 1) datemax = pydt.date(2010, 1, 1) return munits.AxisInfo( majloc=majloc, majfmt=majfmt, label="", default_limits=(datemin, datemax) ) class PandasAutoDateFormatter(mdates.AutoDateFormatter): def __init__(self, locator, tz=None, defaultfmt: str = "%Y-%m-%d") -> None: mdates.AutoDateFormatter.__init__(self, locator, tz, defaultfmt) class PandasAutoDateLocator(mdates.AutoDateLocator): def get_locator(self, dmin, dmax): """Pick the best locator based on a distance.""" tot_sec = (dmax - dmin).total_seconds() if abs(tot_sec) < self.minticks: self._freq = -1 locator = MilliSecondLocator(self.tz) locator.set_axis(self.axis) locator.axis.set_view_interval(*self.axis.get_view_interval()) locator.axis.set_data_interval(*self.axis.get_data_interval()) return locator return mdates.AutoDateLocator.get_locator(self, dmin, dmax) def _get_unit(self): return MilliSecondLocator.get_unit_generic(self._freq) class MilliSecondLocator(mdates.DateLocator): UNIT = 1.0 / (24 * 3600 * 1000) def __init__(self, tz) -> None: mdates.DateLocator.__init__(self, tz) self._interval = 1.0 def _get_unit(self): return self.get_unit_generic(-1) @staticmethod def get_unit_generic(freq): unit = mdates.RRuleLocator.get_unit_generic(freq) if unit < 0: return MilliSecondLocator.UNIT return unit def __call__(self): # if no data have been set, this will tank with a ValueError try: dmin, dmax = self.viewlim_to_dt() except ValueError: return [] # We need to cap at the endpoints of valid datetime nmax, nmin = mdates.date2num((dmax, dmin)) num = (nmax - nmin) * 86400 * 1000 max_millis_ticks = 6 for interval in [1, 10, 50, 100, 200, 500]: if num <= interval * (max_millis_ticks - 1): self._interval = interval break # We went through the whole loop without breaking, default to 1 self._interval = 1000.0 estimate = (nmax - nmin) / (self._get_unit() * self._get_interval()) if estimate > self.MAXTICKS * 2: raise RuntimeError( "MillisecondLocator estimated to generate " f"{estimate:d} ticks from {dmin} to {dmax}: exceeds Locator.MAXTICKS" f"* 2 ({self.MAXTICKS * 2:d}) " ) interval = self._get_interval() freq = f"{interval}L" tz = self.tz.tzname(None) st = dmin.replace(tzinfo=None) ed = dmin.replace(tzinfo=None) all_dates = date_range(start=st, end=ed, freq=freq, tz=tz).astype(object) try: if len(all_dates) > 0: locs = self.raise_if_exceeds(mdates.date2num(all_dates)) return locs except Exception: # pragma: no cover pass lims = mdates.date2num([dmin, dmax]) return lims def _get_interval(self): return self._interval def autoscale(self): """ Set the view limits to include the data range. """ # We need to cap at the endpoints of valid datetime dmin, dmax = self.datalim_to_dt() vmin = mdates.date2num(dmin) vmax = mdates.date2num(dmax) return self.nonsingular(vmin, vmax) def _from_ordinal(x, tz: tzinfo | None = None) -> datetime: ix = int(x) dt = datetime.fromordinal(ix) remainder = float(x) - ix hour, remainder = divmod(24 * remainder, 1) minute, remainder = divmod(60 * remainder, 1) second, remainder = divmod(60 * remainder, 1) microsecond = int(1_000_000 * remainder) if microsecond < 10: microsecond = 0 # compensate for rounding errors dt = datetime( dt.year, dt.month, dt.day, int(hour), int(minute), int(second), microsecond ) if tz is not None: dt = dt.astimezone(tz) if microsecond > 999990: # compensate for rounding errors dt += timedelta(microseconds=1_000_000 - microsecond) return dt # Fixed frequency dynamic tick locators and formatters # ------------------------------------------------------------------------- # --- Locators --- # ------------------------------------------------------------------------- def _get_default_annual_spacing(nyears) -> tuple[int, int]: """ Returns a default spacing between consecutive ticks for annual data. """ if nyears < 11: (min_spacing, maj_spacing) = (1, 1) elif nyears < 20: (min_spacing, maj_spacing) = (1, 2) elif nyears < 50: (min_spacing, maj_spacing) = (1, 5) elif nyears < 100: (min_spacing, maj_spacing) = (5, 10) elif nyears < 200: (min_spacing, maj_spacing) = (5, 25) elif nyears < 600: (min_spacing, maj_spacing) = (10, 50) else: factor = nyears // 1000 + 1 (min_spacing, maj_spacing) = (factor * 20, factor * 100) return (min_spacing, maj_spacing) def period_break(dates: PeriodIndex, period: str) -> np.ndarray: """ Returns the indices where the given period changes. Parameters ---------- dates : PeriodIndex Array of intervals to monitor. period : str Name of the period to monitor. """ current = getattr(dates, period) previous = getattr(dates - 1 * dates.freq, period) return np.nonzero(current - previous)[0] def has_level_label(label_flags: np.ndarray, vmin: float) -> bool: """ Returns true if the ``label_flags`` indicate there is at least one label for this level. if the minimum view limit is not an exact integer, then the first tick label won't be shown, so we must adjust for that. """ if label_flags.size == 0 or ( label_flags.size == 1 and label_flags[0] == 0 and vmin % 1 > 0.0 ): return False else: return True def _daily_finder(vmin, vmax, freq: BaseOffset): # error: "BaseOffset" has no attribute "_period_dtype_code" dtype_code = freq._period_dtype_code # type: ignore[attr-defined] freq_group = FreqGroup.from_period_dtype_code(dtype_code) periodsperday = -1 if dtype_code >= FreqGroup.FR_HR.value: if freq_group == FreqGroup.FR_NS: periodsperday = 24 * 60 * 60 * 1000000000 elif freq_group == FreqGroup.FR_US: periodsperday = 24 * 60 * 60 * 1000000 elif freq_group == FreqGroup.FR_MS: periodsperday = 24 * 60 * 60 * 1000 elif freq_group == FreqGroup.FR_SEC: periodsperday = 24 * 60 * 60 elif freq_group == FreqGroup.FR_MIN: periodsperday = 24 * 60 elif freq_group == FreqGroup.FR_HR: periodsperday = 24 else: # pragma: no cover raise ValueError(f"unexpected frequency: {dtype_code}") periodsperyear = 365 * periodsperday periodspermonth = 28 * periodsperday elif freq_group == FreqGroup.FR_BUS: periodsperyear = 261 periodspermonth = 19 elif freq_group == FreqGroup.FR_DAY: periodsperyear = 365 periodspermonth = 28 elif freq_group == FreqGroup.FR_WK: periodsperyear = 52 periodspermonth = 3 else: # pragma: no cover raise ValueError("unexpected frequency") # save this for later usage vmin_orig = vmin (vmin, vmax) = ( Period(ordinal=int(vmin), freq=freq), Period(ordinal=int(vmax), freq=freq), ) assert isinstance(vmin, Period) assert isinstance(vmax, Period) span = vmax.ordinal - vmin.ordinal + 1 dates_ = period_range(start=vmin, end=vmax, freq=freq) # Initialize the output info = np.zeros( span, dtype=[("val", np.int64), ("maj", bool), ("min", bool), ("fmt", "|S20")] ) info["val"][:] = dates_.asi8 info["fmt"][:] = "" info["maj"][[0, -1]] = True # .. and set some shortcuts info_maj = info["maj"] info_min = info["min"] info_fmt = info["fmt"] def first_label(label_flags): if (label_flags[0] == 0) and (label_flags.size > 1) and ((vmin_orig % 1) > 0.0): return label_flags[1] else: return label_flags[0] # Case 1. Less than a month if span <= periodspermonth: day_start = period_break(dates_, "day") month_start = period_break(dates_, "month") def _hour_finder(label_interval, force_year_start) -> None: _hour = dates_.hour _prev_hour = (dates_ - 1 * dates_.freq).hour hour_start = (_hour - _prev_hour) != 0 info_maj[day_start] = True info_min[hour_start & (_hour % label_interval == 0)] = True year_start = period_break(dates_, "year") info_fmt[hour_start & (_hour % label_interval == 0)] = "%H:%M" info_fmt[day_start] = "%H:%M\n%d-%b" info_fmt[year_start] = "%H:%M\n%d-%b\n%Y" if force_year_start and not has_level_label(year_start, vmin_orig): info_fmt[first_label(day_start)] = "%H:%M\n%d-%b\n%Y" def _minute_finder(label_interval) -> None: hour_start = period_break(dates_, "hour") _minute = dates_.minute _prev_minute = (dates_ - 1 * dates_.freq).minute minute_start = (_minute - _prev_minute) != 0 info_maj[hour_start] = True info_min[minute_start & (_minute % label_interval == 0)] = True year_start = period_break(dates_, "year") info_fmt = info["fmt"] info_fmt[minute_start & (_minute % label_interval == 0)] = "%H:%M" info_fmt[day_start] = "%H:%M\n%d-%b" info_fmt[year_start] = "%H:%M\n%d-%b\n%Y" def _second_finder(label_interval) -> None: minute_start = period_break(dates_, "minute") _second = dates_.second _prev_second = (dates_ - 1 * dates_.freq).second second_start = (_second - _prev_second) != 0 info["maj"][minute_start] = True info["min"][second_start & (_second % label_interval == 0)] = True year_start = period_break(dates_, "year") info_fmt = info["fmt"] info_fmt[second_start & (_second % label_interval == 0)] = "%H:%M:%S" info_fmt[day_start] = "%H:%M:%S\n%d-%b" info_fmt[year_start] = "%H:%M:%S\n%d-%b\n%Y" if span < periodsperday / 12000: _second_finder(1) elif span < periodsperday / 6000: _second_finder(2) elif span < periodsperday / 2400: _second_finder(5) elif span < periodsperday / 1200: _second_finder(10) elif span < periodsperday / 800: _second_finder(15) elif span < periodsperday / 400: _second_finder(30) elif span < periodsperday / 150: _minute_finder(1) elif span < periodsperday / 70: _minute_finder(2) elif span < periodsperday / 24: _minute_finder(5) elif span < periodsperday / 12: _minute_finder(15) elif span < periodsperday / 6: _minute_finder(30) elif span < periodsperday / 2.5: _hour_finder(1, False) elif span < periodsperday / 1.5: _hour_finder(2, False) elif span < periodsperday * 1.25: _hour_finder(3, False) elif span < periodsperday * 2.5: _hour_finder(6, True) elif span < periodsperday * 4: _hour_finder(12, True) else: info_maj[month_start] = True info_min[day_start] = True year_start = period_break(dates_, "year") info_fmt = info["fmt"] info_fmt[day_start] = "%d" info_fmt[month_start] = "%d\n%b" info_fmt[year_start] = "%d\n%b\n%Y" if not has_level_label(year_start, vmin_orig): if not has_level_label(month_start, vmin_orig): info_fmt[first_label(day_start)] = "%d\n%b\n%Y" else: info_fmt[first_label(month_start)] = "%d\n%b\n%Y" # Case 2. Less than three months elif span <= periodsperyear // 4: month_start = period_break(dates_, "month") info_maj[month_start] = True if dtype_code < FreqGroup.FR_HR.value: info["min"] = True else: day_start = period_break(dates_, "day") info["min"][day_start] = True week_start = period_break(dates_, "week") year_start = period_break(dates_, "year") info_fmt[week_start] = "%d" info_fmt[month_start] = "\n\n%b" info_fmt[year_start] = "\n\n%b\n%Y" if not has_level_label(year_start, vmin_orig): if not has_level_label(month_start, vmin_orig): info_fmt[first_label(week_start)] = "\n\n%b\n%Y" else: info_fmt[first_label(month_start)] = "\n\n%b\n%Y" # Case 3. Less than 14 months ............... elif span <= 1.15 * periodsperyear: year_start = period_break(dates_, "year") month_start = period_break(dates_, "month") week_start = period_break(dates_, "week") info_maj[month_start] = True info_min[week_start] = True info_min[year_start] = False info_min[month_start] = False info_fmt[month_start] = "%b" info_fmt[year_start] = "%b\n%Y" if not has_level_label(year_start, vmin_orig): info_fmt[first_label(month_start)] = "%b\n%Y" # Case 4. Less than 2.5 years ............... elif span <= 2.5 * periodsperyear: year_start = period_break(dates_, "year") quarter_start = period_break(dates_, "quarter") month_start = period_break(dates_, "month") info_maj[quarter_start] = True info_min[month_start] = True info_fmt[quarter_start] = "%b" info_fmt[year_start] = "%b\n%Y" # Case 4. Less than 4 years ................. elif span <= 4 * periodsperyear: year_start = period_break(dates_, "year") month_start = period_break(dates_, "month") info_maj[year_start] = True info_min[month_start] = True info_min[year_start] = False month_break = dates_[month_start].month jan_or_jul = month_start[(month_break == 1) | (month_break == 7)] info_fmt[jan_or_jul] = "%b" info_fmt[year_start] = "%b\n%Y" # Case 5. Less than 11 years ................ elif span <= 11 * periodsperyear: year_start = period_break(dates_, "year") quarter_start = period_break(dates_, "quarter") info_maj[year_start] = True info_min[quarter_start] = True info_min[year_start] = False info_fmt[year_start] = "%Y" # Case 6. More than 12 years ................ else: year_start = period_break(dates_, "year") year_break = dates_[year_start].year nyears = span / periodsperyear (min_anndef, maj_anndef) = _get_default_annual_spacing(nyears) major_idx = year_start[(year_break % maj_anndef == 0)] info_maj[major_idx] = True minor_idx = year_start[(year_break % min_anndef == 0)] info_min[minor_idx] = True info_fmt[major_idx] = "%Y" return info def _monthly_finder(vmin, vmax, freq): periodsperyear = 12 vmin_orig = vmin (vmin, vmax) = (int(vmin), int(vmax)) span = vmax - vmin + 1 # Initialize the output info = np.zeros( span, dtype=[("val", int), ("maj", bool), ("min", bool), ("fmt", "|S8")] ) info["val"] = np.arange(vmin, vmax + 1) dates_ = info["val"] info["fmt"] = "" year_start = (dates_ % 12 == 0).nonzero()[0] info_maj = info["maj"] info_fmt = info["fmt"] if span <= 1.15 * periodsperyear: info_maj[year_start] = True info["min"] = True info_fmt[:] = "%b" info_fmt[year_start] = "%b\n%Y" if not has_level_label(year_start, vmin_orig): if dates_.size > 1: idx = 1 else: idx = 0 info_fmt[idx] = "%b\n%Y" elif span <= 2.5 * periodsperyear: quarter_start = (dates_ % 3 == 0).nonzero() info_maj[year_start] = True # TODO: Check the following : is it really info['fmt'] ? info["fmt"][quarter_start] = True info["min"] = True info_fmt[quarter_start] = "%b" info_fmt[year_start] = "%b\n%Y" elif span <= 4 * periodsperyear: info_maj[year_start] = True info["min"] = True jan_or_jul = (dates_ % 12 == 0) | (dates_ % 12 == 6) info_fmt[jan_or_jul] = "%b" info_fmt[year_start] = "%b\n%Y" elif span <= 11 * periodsperyear: quarter_start = (dates_ % 3 == 0).nonzero() info_maj[year_start] = True info["min"][quarter_start] = True info_fmt[year_start] = "%Y" else: nyears = span / periodsperyear (min_anndef, maj_anndef) = _get_default_annual_spacing(nyears) years = dates_[year_start] // 12 + 1 major_idx = year_start[(years % maj_anndef == 0)] info_maj[major_idx] = True info["min"][year_start[(years % min_anndef == 0)]] = True info_fmt[major_idx] = "%Y" return info def _quarterly_finder(vmin, vmax, freq): periodsperyear = 4 vmin_orig = vmin (vmin, vmax) = (int(vmin), int(vmax)) span = vmax - vmin + 1 info = np.zeros( span, dtype=[("val", int), ("maj", bool), ("min", bool), ("fmt", "|S8")] ) info["val"] = np.arange(vmin, vmax + 1) info["fmt"] = "" dates_ = info["val"] info_maj = info["maj"] info_fmt = info["fmt"] year_start = (dates_ % 4 == 0).nonzero()[0] if span <= 3.5 * periodsperyear: info_maj[year_start] = True info["min"] = True info_fmt[:] = "Q%q" info_fmt[year_start] = "Q%q\n%F" if not has_level_label(year_start, vmin_orig): if dates_.size > 1: idx = 1 else: idx = 0 info_fmt[idx] = "Q%q\n%F" elif span <= 11 * periodsperyear: info_maj[year_start] = True info["min"] = True info_fmt[year_start] = "%F" else: # https://github.com/pandas-dev/pandas/pull/47602 years = dates_[year_start] // 4 + 1970 nyears = span / periodsperyear (min_anndef, maj_anndef) = _get_default_annual_spacing(nyears) major_idx = year_start[(years % maj_anndef == 0)] info_maj[major_idx] = True info["min"][year_start[(years % min_anndef == 0)]] = True info_fmt[major_idx] = "%F" return info def _annual_finder(vmin, vmax, freq): (vmin, vmax) = (int(vmin), int(vmax + 1)) span = vmax - vmin + 1 info = np.zeros( span, dtype=[("val", int), ("maj", bool), ("min", bool), ("fmt", "|S8")] ) info["val"] = np.arange(vmin, vmax + 1) info["fmt"] = "" dates_ = info["val"] (min_anndef, maj_anndef) = _get_default_annual_spacing(span) major_idx = dates_ % maj_anndef == 0 info["maj"][major_idx] = True info["min"][(dates_ % min_anndef == 0)] = True info["fmt"][major_idx] = "%Y" return info def get_finder(freq: BaseOffset): # error: "BaseOffset" has no attribute "_period_dtype_code" dtype_code = freq._period_dtype_code # type: ignore[attr-defined] fgroup = FreqGroup.from_period_dtype_code(dtype_code) if fgroup == FreqGroup.FR_ANN: return _annual_finder elif fgroup == FreqGroup.FR_QTR: return _quarterly_finder elif fgroup == FreqGroup.FR_MTH: return _monthly_finder elif (dtype_code >= FreqGroup.FR_BUS.value) or fgroup == FreqGroup.FR_WK: return _daily_finder else: # pragma: no cover raise NotImplementedError(f"Unsupported frequency: {dtype_code}") class TimeSeries_DateLocator(Locator): """ Locates the ticks along an axis controlled by a :class:`Series`. Parameters ---------- freq : BaseOffset Valid frequency specifier. minor_locator : {False, True}, optional Whether the locator is for minor ticks (True) or not. dynamic_mode : {True, False}, optional Whether the locator should work in dynamic mode. base : {int}, optional quarter : {int}, optional month : {int}, optional day : {int}, optional """ def __init__( self, freq: BaseOffset, minor_locator: bool = False, dynamic_mode: bool = True, base: int = 1, quarter: int = 1, month: int = 1, day: int = 1, plot_obj=None, ) -> None: freq = to_offset(freq) self.freq = freq self.base = base (self.quarter, self.month, self.day) = (quarter, month, day) self.isminor = minor_locator self.isdynamic = dynamic_mode self.offset = 0 self.plot_obj = plot_obj self.finder = get_finder(freq) def _get_default_locs(self, vmin, vmax): """Returns the default locations of ticks.""" if self.plot_obj.date_axis_info is None: self.plot_obj.date_axis_info = self.finder(vmin, vmax, self.freq) locator = self.plot_obj.date_axis_info if self.isminor: return np.compress(locator["min"], locator["val"]) return np.compress(locator["maj"], locator["val"]) def __call__(self): """Return the locations of the ticks.""" # axis calls Locator.set_axis inside set_m_formatter vi = tuple(self.axis.get_view_interval()) if vi != self.plot_obj.view_interval: self.plot_obj.date_axis_info = None self.plot_obj.view_interval = vi vmin, vmax = vi if vmax < vmin: vmin, vmax = vmax, vmin if self.isdynamic: locs = self._get_default_locs(vmin, vmax) else: # pragma: no cover base = self.base (d, m) = divmod(vmin, base) vmin = (d + 1) * base locs = list(range(vmin, vmax + 1, base)) return locs def autoscale(self): """ Sets the view limits to the nearest multiples of base that contain the data. """ # requires matplotlib >= 0.98.0 (vmin, vmax) = self.axis.get_data_interval() locs = self._get_default_locs(vmin, vmax) (vmin, vmax) = locs[[0, -1]] if vmin == vmax: vmin -= 1 vmax += 1 return nonsingular(vmin, vmax) # ------------------------------------------------------------------------- # --- Formatter --- # ------------------------------------------------------------------------- class TimeSeries_DateFormatter(Formatter): """ Formats the ticks along an axis controlled by a :class:`PeriodIndex`. Parameters ---------- freq : BaseOffset Valid frequency specifier. minor_locator : bool, default False Whether the current formatter should apply to minor ticks (True) or major ticks (False). dynamic_mode : bool, default True Whether the formatter works in dynamic mode or not. """ def __init__( self, freq: BaseOffset, minor_locator: bool = False, dynamic_mode: bool = True, plot_obj=None, ) -> None: freq = to_offset(freq) self.format = None self.freq = freq self.locs: list[Any] = [] # unused, for matplotlib compat self.formatdict: dict[Any, Any] | None = None self.isminor = minor_locator self.isdynamic = dynamic_mode self.offset = 0 self.plot_obj = plot_obj self.finder = get_finder(freq) def _set_default_format(self, vmin, vmax): """Returns the default ticks spacing.""" if self.plot_obj.date_axis_info is None: self.plot_obj.date_axis_info = self.finder(vmin, vmax, self.freq) info = self.plot_obj.date_axis_info if self.isminor: format = np.compress(info["min"] & np.logical_not(info["maj"]), info) else: format = np.compress(info["maj"], info) self.formatdict = {x: f for (x, _, _, f) in format} return self.formatdict def set_locs(self, locs) -> None: """Sets the locations of the ticks""" # don't actually use the locs. This is just needed to work with # matplotlib. Force to use vmin, vmax self.locs = locs (vmin, vmax) = vi = tuple(self.axis.get_view_interval()) if vi != self.plot_obj.view_interval: self.plot_obj.date_axis_info = None self.plot_obj.view_interval = vi if vmax < vmin: (vmin, vmax) = (vmax, vmin) self._set_default_format(vmin, vmax) def __call__(self, x, pos: int = 0) -> str: if self.formatdict is None: return "" else: fmt = self.formatdict.pop(x, "") if isinstance(fmt, np.bytes_): fmt = fmt.decode("utf-8") period = Period(ordinal=int(x), freq=self.freq) assert isinstance(period, Period) return period.strftime(fmt) class TimeSeries_TimedeltaFormatter(Formatter): """ Formats the ticks along an axis controlled by a :class:`TimedeltaIndex`. """ @staticmethod def format_timedelta_ticks(x, pos, n_decimals: int) -> str: """ Convert seconds to 'D days HH:MM:SS.F' """ s, ns = divmod(x, 10**9) m, s = divmod(s, 60) h, m = divmod(m, 60) d, h = divmod(h, 24) decimals = int(ns * 10 ** (n_decimals - 9)) s = f"{int(h):02d}:{int(m):02d}:{int(s):02d}" if n_decimals > 0: s += f".{decimals:0{n_decimals}d}" if d != 0: s = f"{int(d):d} days {s}" return s def __call__(self, x, pos: int = 0) -> str: (vmin, vmax) = tuple(self.axis.get_view_interval()) n_decimals = min(int(np.ceil(np.log10(100 * 10**9 / abs(vmax - vmin)))), 9) return self.format_timedelta_ticks(x, pos, n_decimals)