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craftbeerpi4-pione/venv3/lib/python3.7/site-packages/pandas/plotting/_matplotlib/converter.py

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"fix cli main_dir error"
2021-03-03 23:49:41 +01:00
import contextlib
import datetime as pydt
from datetime import datetime, timedelta
import functools
from dateutil.relativedelta import relativedelta
import matplotlib.dates as dates
from matplotlib.ticker import AutoLocator, Formatter, Locator
from matplotlib.transforms import nonsingular
import matplotlib.units as units
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._libs.tslibs.offsets import BaseOffset
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
# constants
HOURS_PER_DAY = 24.0
MIN_PER_HOUR = 60.0
SEC_PER_MIN = 60.0
SEC_PER_HOUR = SEC_PER_MIN * MIN_PER_HOUR
SEC_PER_DAY = SEC_PER_HOUR * HOURS_PER_DAY
MUSEC_PER_DAY = 1e6 * 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):
"""
Decorator applying pandas_converters.
"""
@functools.wraps(func)
def wrapper(*args, **kwargs):
with pandas_converters():
return func(*args, **kwargs)
return wrapper
@contextlib.contextmanager
def pandas_converters():
"""
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():
pairs = get_pairs()
for type_, cls in pairs:
# Cache previous converter if present
if type_ in units.registry and not isinstance(units.registry[type_], cls):
previous = units.registry[type_]
_mpl_units[type_] = previous
# Replace with pandas converter
units.registry[type_] = cls()
def deregister():
# Renamed in pandas.plotting.__init__
for type_, cls in get_pairs():
# We use type to catch our classes directly, no inheritance
if type(units.registry.get(type_)) is cls:
units.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.
units.registry[unit] = formatter
def _to_ordinalf(tm: pydt.time) -> float:
tot_sec = tm.hour * 3600 + tm.minute * 60 + tm.second + float(tm.microsecond / 1e6)
return tot_sec
def time2num(d):
if isinstance(d, str):
parsed = tools.to_datetime(d)
if not isinstance(parsed, datetime):
raise ValueError(f"Could not parse time {d}")
return _to_ordinalf(parsed.time())
if isinstance(d, pydt.time):
return _to_ordinalf(d)
return d
class TimeConverter(units.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):
if unit != "time":
return None
majloc = AutoLocator()
majfmt = TimeFormatter(majloc)
return units.AxisInfo(majloc=majloc, majfmt=majfmt, label="time")
@staticmethod
def default_units(x, axis):
return "time"
# time formatter
class TimeFormatter(Formatter):
def __init__(self, locs):
self.locs = locs
def __call__(self, x, pos=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 = int(round((x - s) * 1e6))
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(dates.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(dates.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 dates.date2num(tools.to_datetime(values))
except Exception:
return values
if isinstance(values, (datetime, pydt.date, np.datetime64, pydt.time)):
return dates.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 = dates.date2num(values)
return values
@staticmethod
def axisinfo(unit, axis):
"""
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 units.AxisInfo(
majloc=majloc, majfmt=majfmt, label="", default_limits=(datemin, datemax)
)
class PandasAutoDateFormatter(dates.AutoDateFormatter):
def __init__(self, locator, tz=None, defaultfmt="%Y-%m-%d"):
dates.AutoDateFormatter.__init__(self, locator, tz, defaultfmt)
class PandasAutoDateLocator(dates.AutoDateLocator):
def get_locator(self, dmin, dmax):
"""Pick the best locator based on a distance."""
delta = relativedelta(dmax, dmin)
num_days = (delta.years * 12.0 + delta.months) * 31.0 + delta.days
num_sec = (delta.hours * 60.0 + delta.minutes) * 60.0 + delta.seconds
tot_sec = num_days * 86400.0 + num_sec
if abs(tot_sec) < self.minticks:
self._freq = -1
locator = MilliSecondLocator(self.tz)
locator.set_axis(self.axis)
locator.set_view_interval(*self.axis.get_view_interval())
locator.set_data_interval(*self.axis.get_data_interval())
return locator
return dates.AutoDateLocator.get_locator(self, dmin, dmax)
def _get_unit(self):
return MilliSecondLocator.get_unit_generic(self._freq)
class MilliSecondLocator(dates.DateLocator):
UNIT = 1.0 / (24 * 3600 * 1000)
def __init__(self, tz):
dates.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 = dates.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 = dates.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
else:
# 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(dates.date2num(all_dates))
return locs
except Exception: # pragma: no cover
pass
lims = dates.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 = dates.date2num(dmin)
vmax = dates.date2num(dmax)
return self.nonsingular(vmin, vmax)
def _from_ordinal(x, tz=None):
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(1e6 * 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=1e6 - microsecond)
return dt
# Fixed frequency dynamic tick locators and formatters
# -------------------------------------------------------------------------
# --- Locators ---
# -------------------------------------------------------------------------
def _get_default_annual_spacing(nyears):
"""
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, period):
"""
Returns the indices where the given period changes.
Parameters
----------
dates : PeriodIndex
Array of intervals to monitor.
period : string
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, vmin):
"""
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):
dtype_code = freq._period_dtype_code
periodsperday = -1
if dtype_code >= FreqGroup.FR_HR:
if dtype_code == FreqGroup.FR_NS:
periodsperday = 24 * 60 * 60 * 1000000000
elif dtype_code == FreqGroup.FR_US:
periodsperday = 24 * 60 * 60 * 1000000
elif dtype_code == FreqGroup.FR_MS:
periodsperday = 24 * 60 * 60 * 1000
elif dtype_code == FreqGroup.FR_SEC:
periodsperday = 24 * 60 * 60
elif dtype_code == FreqGroup.FR_MIN:
periodsperday = 24 * 60
elif dtype_code == FreqGroup.FR_HR:
periodsperday = 24
else: # pragma: no cover
raise ValueError(f"unexpected frequency: {dtype_code}")
periodsperyear = 365 * periodsperday
periodspermonth = 28 * periodsperday
elif dtype_code == FreqGroup.FR_BUS:
periodsperyear = 261
periodspermonth = 19
elif dtype_code == FreqGroup.FR_DAY:
periodsperyear = 365
periodspermonth = 28
elif FreqGroup.get_freq_group(dtype_code) == 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),
)
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):
_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):
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):
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 / 12000.0:
_second_finder(1)
elif span < periodsperday / 6000.0:
_second_finder(2)
elif span < periodsperday / 2400.0:
_second_finder(5)
elif span < periodsperday / 1200.0:
_second_finder(10)
elif span < periodsperday / 800.0:
_second_finder(15)
elif span < periodsperday / 400.0:
_second_finder(30)
elif span < periodsperday / 150.0:
_minute_finder(1)
elif span < periodsperday / 70.0:
_minute_finder(2)
elif span < periodsperday / 24.0:
_minute_finder(5)
elif span < periodsperday / 12.0:
_minute_finder(15)
elif span < periodsperday / 6.0:
_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:
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:
years = dates_[year_start] // 4 + 1
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):
dtype_code = freq._period_dtype_code
fgroup = (dtype_code // 1000) * 1000
if fgroup == FreqGroup.FR_ANN:
return _annual_finder
elif fgroup == FreqGroup.FR_QTR:
return _quarterly_finder
elif dtype_code == FreqGroup.FR_MTH:
return _monthly_finder
elif (dtype_code >= FreqGroup.FR_BUS) 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 : {var}
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,
minor_locator=False,
dynamic_mode=True,
base=1,
quarter=1,
month=1,
day=1,
plot_obj=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<xxxx>_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 : {int, string}
Valid frequency specifier.
minor_locator : {False, True}
Whether the current formatter should apply to minor ticks (True) or
major ticks (False).
dynamic_mode : {True, False}
Whether the formatter works in dynamic mode or not.
"""
def __init__(self, freq, minor_locator=False, dynamic_mode=True, plot_obj=None):
freq = to_offset(freq)
self.format = None
self.freq = freq
self.locs = []
self.formatdict = 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):
"""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=0) -> str:
if self.formatdict is None:
return ""
else:
fmt = self.formatdict.pop(x, "")
if isinstance(fmt, np.bytes_):
fmt = fmt.decode("utf-8")
return Period(ordinal=int(x), freq=self.freq).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, 1e9)
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=0) -> str:
(vmin, vmax) = tuple(self.axis.get_view_interval())
n_decimals = int(np.ceil(np.log10(100 * 1e9 / (vmax - vmin))))
if n_decimals > 9:
n_decimals = 9
return self.format_timedelta_ticks(x, pos, n_decimals)
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