from __future__ import annotations
import contextlib
import datetime as pydt
from datetime import (
datetime,
timedelta,
tzinfo,
)
import functools
from typing import (
TYPE_CHECKING,
Any,
cast,
)
import warnings
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,
periods_per_day,
)
from pandas._typing import (
F,
npt,
)
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 matplotlib.axis import Axis
from pandas._libs.tslibs.offsets import BaseOffset
_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 | None = 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)
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore", "Period with BDay freq is deprecated", category=FutureWarning
)
warnings.filterwarnings(
"ignore", r"PeriodDtype\[B\] is deprecated", category=FutureWarning
)
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)
# error: Item "None" of "Axis | _DummyAxis | _AxisWrapper | None"
# has no attribute "get_data_interval"
locator.axis.set_view_interval( # type: ignore[union-attr]
*self.axis.get_view_interval() # type: ignore[union-attr]
)
locator.axis.set_data_interval( # type: ignore[union-attr]
*self.axis.get_data_interval() # type: ignore[union-attr]
)
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}ms"
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) -> npt.NDArray[np.intp]:
"""
Returns the indices where the given period changes.
Parameters
----------
dates : PeriodIndex
Array of intervals to monitor.
period : str
Name of the period to monitor.
"""
mask = _period_break_mask(dates, period)
return np.nonzero(mask)[0]
def _period_break_mask(dates: PeriodIndex, period: str) -> npt.NDArray[np.bool_]:
current = getattr(dates, period)
previous = getattr(dates - 1 * dates.freq, period)
return current != previous
def has_level_label(label_flags: npt.NDArray[np.intp], 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 _get_periods_per_ymd(freq: BaseOffset) -> tuple[int, int, int]:
# 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)
ppd = -1 # placeholder for above-day freqs
if dtype_code >= FreqGroup.FR_HR.value:
# error: "BaseOffset" has no attribute "_creso"
ppd = periods_per_day(freq._creso) # type: ignore[attr-defined]
ppm = 28 * ppd
ppy = 365 * ppd
elif freq_group == FreqGroup.FR_BUS:
ppm = 19
ppy = 261
elif freq_group == FreqGroup.FR_DAY:
ppm = 28
ppy = 365
elif freq_group == FreqGroup.FR_WK:
ppm = 3
ppy = 52
elif freq_group == FreqGroup.FR_MTH:
ppm = 1
ppy = 12
elif freq_group == FreqGroup.FR_QTR:
ppm = -1 # placerholder
ppy = 4
elif freq_group == FreqGroup.FR_ANN:
ppm = -1 # placeholder
ppy = 1
else:
raise NotImplementedError(f"Unsupported frequency: {dtype_code}")
return ppd, ppm, ppy
def _daily_finder(vmin, vmax, freq: BaseOffset) -> np.ndarray:
# error: "BaseOffset" has no attribute "_period_dtype_code"
dtype_code = freq._period_dtype_code # type: ignore[attr-defined]
periodsperday, periodspermonth, periodsperyear = _get_periods_per_ymd(freq)
# save this for later usage
vmin_orig = vmin
(vmin, vmax) = (int(vmin), int(vmax))
span = vmax - vmin + 1
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore", "Period with BDay freq is deprecated", category=FutureWarning
)
warnings.filterwarnings(
"ignore", r"PeriodDtype\[B\] is deprecated", category=FutureWarning
)
dates_ = period_range(
start=Period(ordinal=vmin, freq=freq),
end=Period(ordinal=vmax, freq=freq),
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")
year_start = _period_break(dates_, "year")
def _hour_finder(label_interval: int, force_year_start: bool) -> None:
target = dates_.hour
mask = _period_break_mask(dates_, "hour")
info_maj[day_start] = True
info_min[mask & (target % label_interval == 0)] = True
info_fmt[mask & (target % 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: int) -> None:
target = dates_.minute
hour_start = _period_break(dates_, "hour")
mask = _period_break_mask(dates_, "minute")
info_maj[hour_start] = True
info_min[mask & (target % label_interval == 0)] = True
info_fmt[mask & (target % 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: int) -> None:
target = dates_.second
minute_start = _period_break(dates_, "minute")
mask = _period_break_mask(dates_, "second")
info_maj[minute_start] = True
info_min[mask & (target % label_interval == 0)] = True
info_fmt[mask & (target % 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
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: BaseOffset) -> np.ndarray:
_, _, periodsperyear = _get_periods_per_ymd(freq)
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'] ?
# 2023-09-15 this is reached in test_finder_monthly
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: BaseOffset) -> np.ndarray:
_, _, periodsperyear = _get_periods_per_ymd(freq)
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: BaseOffset) -> np.ndarray:
# Note: small difference here vs other finders in adding 1 to vmax
(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
minor_idx = dates_ % min_anndef == 0
info["maj"][major_idx] = True
info["min"][minor_idx] = 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
"""
axis: Axis
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, is_period=True)
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."""
locator = self.finder(vmin, vmax, self.freq)
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<xxxx>_formatter
vi = tuple(self.axis.get_view_interval())
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
# error: No overload variant of "range" matches argument types "float",
# "float", "int"
locs = list(range(vmin, vmax + 1, base)) # type: ignore[call-overload]
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.
"""
axis: Axis
def __init__(
self,
freq: BaseOffset,
minor_locator: bool = False,
dynamic_mode: bool = True,
plot_obj=None,
) -> None:
freq = to_offset(freq, is_period=True)
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."""
info = self.finder(vmin, vmax, self.freq)
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) = tuple(self.axis.get_view_interval())
if vmax < vmin:
(vmin, vmax) = (vmax, vmin)
self._set_default_format(vmin, vmax)
def __call__(self, x, pos: int | None = 0) -> str:
if self.formatdict is None:
return ""
else:
fmt = self.formatdict.pop(x, "")
if isinstance(fmt, np.bytes_):
fmt = fmt.decode("utf-8")
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore",
"Period with BDay freq is deprecated",
category=FutureWarning,
)
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`.
"""
axis: Axis
@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) # TODO(non-nano): this looks like it assumes ns
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 | None = 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)