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"""
Matplotlib provides sophisticated date plotting capabilities, standing on the
shoulders of python :mod:`datetime` and the add-on module dateutil_.
By default, Matplotlib uses the units machinery described in
`~matplotlib.units` to convert `datetime.datetime`, and `numpy.datetime64`
objects when plotted on an x- or y-axis. The user does not
need to do anything for dates to be formatted, but dates often have strict
formatting needs, so this module provides many tick locators and formatters.
A basic example using `numpy.datetime64` is::
import numpy as np
times = np.arange(np.datetime64('2001-01-02'),
np.datetime64('2002-02-03'), np.timedelta64(75, 'm'))
y = np.random.randn(len(times))
fig, ax = plt.subplots()
ax.plot(times, y)
.. seealso::
- :doc:`/gallery/text_labels_and_annotations/date`
- :doc:`/gallery/ticks/date_concise_formatter`
- :doc:`/gallery/ticks/date_demo_convert`
.. _date-format:
Matplotlib date format
----------------------
Matplotlib represents dates using floating point numbers specifying the number
of days since a default epoch of 1970-01-01 UTC; for example,
1970-01-01, 06:00 is the floating point number 0.25. The formatters and
locators require the use of `datetime.datetime` objects, so only dates between
year 0001 and 9999 can be represented. Microsecond precision
is achievable for (approximately) 70 years on either side of the epoch, and
20 microseconds for the rest of the allowable range of dates (year 0001 to
9999). The epoch can be changed at import time via `.dates.set_epoch` or
:rc:`dates.epoch` to other dates if necessary; see
:doc:`/gallery/ticks/date_precision_and_epochs` for a discussion.
.. note::
Before Matplotlib 3.3, the epoch was 0000-12-31 which lost modern
microsecond precision and also made the default axis limit of 0 an invalid
datetime. In 3.3 the epoch was changed as above. To convert old
ordinal floats to the new epoch, users can do::
new_ordinal = old_ordinal + mdates.date2num(np.datetime64('0000-12-31'))
There are a number of helper functions to convert between :mod:`datetime`
objects and Matplotlib dates:
.. currentmodule:: matplotlib.dates
.. autosummary::
:nosignatures:
datestr2num
date2num
num2date
num2timedelta
drange
set_epoch
get_epoch
.. note::
Like Python's `datetime.datetime`, Matplotlib uses the Gregorian calendar
for all conversions between dates and floating point numbers. This practice
is not universal, and calendar differences can cause confusing
differences between what Python and Matplotlib give as the number of days
since 0001-01-01 and what other software and databases yield. For
example, the US Naval Observatory uses a calendar that switches
from Julian to Gregorian in October, 1582. Hence, using their
calculator, the number of days between 0001-01-01 and 2006-04-01 is
732403, whereas using the Gregorian calendar via the datetime
module we find::
In [1]: date(2006, 4, 1).toordinal() - date(1, 1, 1).toordinal()
Out[1]: 732401
All the Matplotlib date converters, locators and formatters are timezone aware.
If no explicit timezone is provided, :rc:`timezone` is assumed, provided as a
string. If you want to use a different timezone, pass the *tz* keyword
argument of `num2date` to any date tick locators or formatters you create. This
can be either a `datetime.tzinfo` instance or a string with the timezone name
that can be parsed by `~dateutil.tz.gettz`.
A wide range of specific and general purpose date tick locators and
formatters are provided in this module. See
:mod:`matplotlib.ticker` for general information on tick locators
and formatters. These are described below.
The dateutil_ module provides additional code to handle date ticking, making it
easy to place ticks on any kinds of dates. See examples below.
.. _dateutil: https://dateutil.readthedocs.io
.. _date-locators:
Date tick locators
------------------
Most of the date tick locators can locate single or multiple ticks. For example::
# import constants for the days of the week
from matplotlib.dates import MO, TU, WE, TH, FR, SA, SU
# tick on Mondays every week
loc = WeekdayLocator(byweekday=MO, tz=tz)
# tick on Mondays and Saturdays
loc = WeekdayLocator(byweekday=(MO, SA))
In addition, most of the constructors take an interval argument::
# tick on Mondays every second week
loc = WeekdayLocator(byweekday=MO, interval=2)
The rrule locator allows completely general date ticking::
# tick every 5th easter
rule = rrulewrapper(YEARLY, byeaster=1, interval=5)
loc = RRuleLocator(rule)
The available date tick locators are:
* `MicrosecondLocator`: Locate microseconds.
* `SecondLocator`: Locate seconds.
* `MinuteLocator`: Locate minutes.
* `HourLocator`: Locate hours.
* `DayLocator`: Locate specified days of the month.
* `WeekdayLocator`: Locate days of the week, e.g., MO, TU.
* `MonthLocator`: Locate months, e.g., 7 for July.
* `YearLocator`: Locate years that are multiples of base.
* `RRuleLocator`: Locate using a `rrulewrapper`.
`rrulewrapper` is a simple wrapper around dateutil_'s `dateutil.rrule`
which allow almost arbitrary date tick specifications.
See :doc:`rrule example </gallery/ticks/date_demo_rrule>`.
* `AutoDateLocator`: On autoscale, this class picks the best `DateLocator`
(e.g., `RRuleLocator`) to set the view limits and the tick locations. If
called with ``interval_multiples=True`` it will make ticks line up with
sensible multiples of the tick intervals. For example, if the interval is
4 hours, it will pick hours 0, 4, 8, etc. as ticks. This behaviour is not
guaranteed by default.
.. _date-formatters:
Date format
C8E2
ters
---------------
The available date formatters are:
* `AutoDateFormatter`: attempts to figure out the best format to use. This is
most useful when used with the `AutoDateLocator`.
* `ConciseDateFormatter`: also attempts to figure out the best format to use,
and to make the format as compact as possible while still having complete
date information. This is most useful when used with the `AutoDateLocator`.
* `DateFormatter`: use `~datetime.datetime.strftime` format strings.
"""
import datetime
import functools
import logging
import re
from dateutil.rrule import (rrule, MO, TU, WE, TH, FR, SA, SU, YEARLY,
MONTHLY, WEEKLY, DAILY, HOURLY, MINUTELY,
SECONDLY)
from dateutil.relativedelta import relativedelta
import dateutil.parser
import dateutil.tz
import numpy as np
import matplotlib as mpl
from matplotlib import _api, cbook, ticker, units
__all__ = ('datestr2num', 'date2num', 'num2date', 'num2timedelta', 'drange',
'set_epoch', 'get_epoch', 'DateFormatter', 'ConciseDateFormatter',
'AutoDateFormatter', 'DateLocator', 'RRuleLocator',
'AutoDateLocator', 'YearLocator', 'MonthLocator', 'WeekdayLocator',
'DayLocator', 'HourLocator', 'MinuteLocator',
'SecondLocator', 'MicrosecondLocator',
'rrule', 'MO', 'TU', 'WE', 'TH', 'FR', 'SA', 'SU',
'YEARLY', 'MONTHLY', 'WEEKLY', 'DAILY',
'HOURLY', 'MINUTELY', 'SECONDLY', 'MICROSECONDLY', 'relativedelta',
'DateConverter', 'ConciseDateConverter', 'rrulewrapper')
_log = logging.getLogger(__name__)
UTC = datetime.timezone.utc
def _get_tzinfo(tz=None):
"""
Generate `~datetime.tzinfo` from a string or return `~datetime.tzinfo`.
If None, retrieve the preferred timezone from the rcParams dictionary.
"""
tz = mpl._val_or_rc(tz, 'timezone')
if tz == 'UTC':
return UTC
if isinstance(tz, str):
tzinfo = dateutil.tz.gettz(tz)
if tzinfo is None:
raise ValueError(f"{tz} is not a valid timezone as parsed by"
" dateutil.tz.gettz.")
return tzinfo
if isinstance(tz, datetime.tzinfo):
return tz
raise TypeError(f"tz must be string or tzinfo subclass, not {tz!r}.")
# Time-related constants.
EPOCH_OFFSET = float(datetime.datetime(1970, 1, 1).toordinal())
# EPOCH_OFFSET is not used by matplotlib
MICROSECONDLY = SECONDLY + 1
HOURS_PER_DAY = 24.
MIN_PER_HOUR = 60.
SEC_PER_MIN = 60.
MONTHS_PER_YEAR = 12.
DAYS_PER_WEEK = 7.
DAYS_PER_MONTH = 30.
DAYS_PER_YEAR = 365.0
MINUTES_PER_DAY = MIN_PER_HOUR * HOURS_PER_DAY
SEC_PER_HOUR = SEC_PER_MIN * MIN_PER_HOUR
SEC_PER_DAY = SEC_PER_HOUR * HOURS_PER_DAY
SEC_PER_WEEK = SEC_PER_DAY * DAYS_PER_WEEK
MUSECONDS_PER_DAY = 1e6 * SEC_PER_DAY
MONDAY, TUESDAY, WEDNESDAY, THURSDAY, FRIDAY, SATURDAY, SUNDAY = (
MO, TU, WE, TH, FR, SA, SU)
WEEKDAYS = (MONDAY, TUESDAY, WEDNESDAY, THURSDAY, FRIDAY, SATURDAY, SUNDAY)
# default epoch: passed to np.datetime64...
_epoch = None
def _reset_epoch_test_example():
"""
Reset the Matplotlib date epoch so it can be set again.
Only for use in tests and examples.
"""
global _epoch
_epoch = None
def set_epoch(epoch):
"""
Set the epoch (origin for dates) for datetime calculations.
The default epoch is :rc:`dates.epoch` (by default 1970-01-01T00:00).
If microsecond accuracy is desired, the date being plotted needs to be
within approximately 70 years of the epoch. Matplotlib internally
represents dates as days since the epoch, so floating point dynamic
range needs to be within a factor of 2^52.
`~.dates.set_epoch` must be called before any dates are converted
(i.e. near the import section) or a RuntimeError will be raised.
See also :doc:`/gallery/ticks/date_precision_and_epochs`.
Parameters
----------
epoch : str
valid UTC date parsable by `numpy.datetime64` (do not include
timezone).
"""
global _epoch
if _epoch is not None:
raise RuntimeError('set_epoch must be called before dates plotted.')
_epoch = epoch
def get_epoch():
"""
Get the epoch used by `.dates`.
Returns
-------
epoch : str
String for the epoch (parsable by `numpy.datetime64`).
"""
global _epoch
_epoch = mpl._val_or_rc(_epoch, 'date.epoch')
return _epoch
def _dt64_to_ordinalf(d):
"""
Convert `numpy.datetime64` or an `numpy.ndarray` of those types to
Gregorian date as UTC float relative to the epoch (see `.get_epoch`).
Roundoff is float64 precision. Practically: microseconds for dates
between 290301 BC, 294241 AD, milliseconds for larger dates
(see `numpy.datetime64`).
"""
# the "extra" ensures that we at least allow the dynamic range out to
# seconds. That should get out to +/-2e11 years.
dseconds = d.astype('datetime64[s]')
extra = (d - dseconds).astype('timedelta64[ns]')
t0 = np.datetime64(get_epoch(), 's')
dt = (dseconds - t0).astype(np.float64)
dt += extra.astype(np.float64) / 1.0e9
dt = dt / SEC_PER_DAY
NaT_int = np.datetime64('NaT').astype(np.int64)
d_int = d.astype(np.int64)
dt[d_int == NaT_int] = np.nan
return dt
def _from_ordinalf(x, tz=None):
"""
Convert Gregorian float of the date, preserving hours, minutes,
seconds and microseconds. Return value is a `.datetime`.
The input date *x* is a float in ordinal days at UTC, and the output will
be the specified `.datetime` object corresponding to that time in
timezone *tz*, or if *tz* is ``None``, in the timezone specified in
:rc:`timezone`.
"""
tz = _get_tzinfo(tz)
dt = (np.datetime64(get_epoch()) +
np.timedelta64(int(np.round(x * MUSECONDS_PER_DAY)), 'us'))
if dt < np.datetime64('0001-01-01') or dt >= np.datetime64('10000-01-01'):
raise ValueError(f'Date ordinal {x} converts to {dt} (using '
f'epoch {get_epoch()}), but Matplotlib dates must be '
'between year 0001 and 9999.')
# convert from datetime64 to datetime:
dt = dt.tolist()
# datetime64 is always UTC:
dt = dt.replace(tzinfo=dateutil.tz.gettz('UTC'))
# but maybe we are working in a different timezone so move.
dt = dt.astimezone(tz)
# fix round off erro
42DE
rs
if np.abs(x) > 70 * 365:
# if x is big, round off to nearest twenty microseconds.
# This avoids floating point roundoff error
ms = round(dt.microsecond / 20) * 20
if ms == 1000000:
dt = dt.replace(microsecond=0) + datetime.timedelta(seconds=1)
else:
dt = dt.replace(microsecond=ms)
return dt
# a version of _from_ordinalf that can operate on numpy arrays
_from_ordinalf_np_vectorized = np.vectorize(_from_ordinalf, otypes="O")
# a version of dateutil.parser.parse that can operate on numpy arrays
_dateutil_parser_parse_np_vectorized = np.vectorize(dateutil.parser.parse)
def datestr2num(d, default=None):
"""
Convert a date string to a datenum using `dateutil.parser.parse`.
Parameters
----------
d : str or sequence of str
The dates to convert.
default : datetime.datetime, optional
The default date to use when fields are missing in *d*.
"""
if isinstance(d, str):
dt = dateutil.parser.parse(d, default=default)
return date2num(dt)
else:
if default is not None:
d = [date2num(dateutil.parser.parse(s, default=default))
for s in d]
return np.asarray(d)
d = np.asarray(d)
if not d.size:
return d
return date2num(_dateutil_parser_parse_np_vectorized(d))
def date2num(d):
"""
Convert datetime objects to Matplotlib dates.
Parameters
----------
d : `datetime.datetime` or `numpy.datetime64` or sequences of these
Returns
-------
float or sequence of floats
Number of days since the epoch. See `.get_epoch` for the
epoch, which can be changed by :rc:`date.epoch` or `.set_epoch`. If
the epoch is "1970-01-01T00:00:00" (default) then noon Jan 1 1970
("1970-01-01T12:00:00") returns 0.5.
Notes
-----
The Gregorian calendar is assumed; this is not universal practice.
For details see the module docstring.
"""
# Unpack in case of e.g. Pandas or xarray object
d = cbook._unpack_to_numpy(d)
# make an iterable, but save state to unpack later:
iterable = np.iterable(d)
if not iterable:
d = [d]
masked = np.ma.is_masked(d)
mask = np.ma.getmask(d)
d = np.asarray(d)
# convert to datetime64 arrays, if not already:
if not np.issubdtype(d.dtype, np.datetime64):
# datetime arrays
if not d.size:
# deals with an empty array...
return d
tzi = getattr(d[0], 'tzinfo', None)
if tzi is not None:
# make datetime naive:
d = [dt.astimezone(UTC).replace(tzinfo=None) for dt in d]
d = np.asarray(d)
d = d.astype('datetime64[us]')
d = np.ma.masked_array(d, mask=mask) if masked else d
d = _dt64_to_ordinalf(d)
return d if iterable else d[0]
def num2date(x, tz=None):
"""
Convert Matplotlib dates to `~datetime.datetime` objects.
Parameters
----------
x : float or sequence of floats
Number of days (fraction part represents hours, minutes, seconds)
since the epoch. See `.get_epoch` for the
epoch, which can be changed by :rc:`date.epoch` or `.set_epoch`.
tz : str or `~datetime.tzinfo`, default: :rc:`timezone`
Timezone of *x*. If a string, *tz* is passed to `dateutil.tz`.
Returns
-------
`~datetime.datetime` or sequence of `~datetime.datetime`
Dates are returned in timezone *tz*.
If *x* is a sequence, a sequence of `~datetime.datetime` objects will
be returned.
Notes
-----
The Gregorian calendar is assumed; this is not universal practice.
For details, see the module docstring.
"""
tz = _get_tzinfo(tz)
return _from_ordinalf_np_vectorized(x, tz).tolist()
_ordinalf_to_timedelta_np_vectorized = np.vectorize(
lambda x: datetime.timedelta(days=x), otypes="O")
def num2timedelta(x):
"""
Convert number of days to a `~datetime.timedelta` object.
If *x* is a sequence, a sequence of `~datetime.timedelta` objects will
be returned.
Parameters
----------
x : float, sequence of floats
Number of days. The fraction part represents hours, minutes, seconds.
Returns
-------
`datetime.timedelta` or list[`datetime.timedelta`]
"""
return _ordinalf_to_timedelta_np_vectorized(x).tolist()
def drange(dstart, dend, delta):
"""
Return a sequence of equally spaced Matplotlib dates.
The dates start at *dstart* and reach up to, but not including *dend*.
They are spaced by *delta*.
Parameters
----------
dstart, dend : `~datetime.datetime`
The date limits.
delta : `datetime.timedelta`
Spacing of the dates.
Returns
-------
`numpy.array`
A list floats representing Matplotlib dates.
"""
f1 = date2num(dstart)
f2 = date2num(dend)
step = delta.total_seconds() / SEC_PER_DAY
# calculate the difference between dend and dstart in times of delta
num = int(np.ceil((f2 - f1) / step))
# calculate end of the interval which will be generated
dinterval_end = dstart + num * delta
# ensure, that an half open interval will be generated [dstart, dend)
if dinterval_end >= dend:
# if the endpoint is greater than or equal to dend,
# just subtract one delta
dinterval_end -= delta
num -= 1
f2 = date2num(dinterval_end) # new float-endpoint
return np.linspace(f1, f2, num + 1)
def _wrap_in_tex(text):
p = r'([a-zA-Z]+)'
ret_text = re.sub(p, r'}$\1$\\mathdefault{', text)
# Braces ensure symbols are not spaced like binary operators.
ret_text = ret_text.replace('-', '{-}').replace(':', '{:}')
# To not concatenate space between numbers.
ret_text = ret_text.replace(' ', r'\;')
ret_text = '$\\mathdefault{' + ret_text + '}$'
ret_text = ret_text.replace('$\\mathdefault{}$', '')
return ret_text
## date tick locators and formatters ###
class DateFormatter(ticker.Formatter):
"""
Format a tick (in days since the epoch) with a
`~datetime.datetime.strftime` format string.
"""
def __init__(self, fmt, tz=None, *, usetex=None):
"""
Parameters
----------
fmt : str
`~datetime.datetime.strftime` format string
tz : str or `~datetime.tzinfo`, default: :rc:`timezone`
Ticks timezone. If a string, *tz* is passed to `dateutil.tz`.
usetex : bool, default: :rc:`text.usetex`
To enable/disable the use of TeX's math mode for rendering the
results of the formatter.
"""
self.tz = _get_tzinfo(tz)
self.fmt = fmt
self._usetex = mpl._val_or_rc(usetex, 'text.usetex')
def __call__(self, x, pos=0):
result = num2date(x, self.tz).strftime(self.fmt)
return _wrap_in_tex(result) if self._usetex else result
def set_tzinfo(self, tz):
self.tz = _get_tzinfo(tz)
class ConciseDateFormatter(ticker.Formatter):
"""
A `.Formatter` which attempts to figure out the best format to use for the
date, and to make it as compact as possible, but still be complete. This is
most useful when used with the `AutoDateLocator`::
>>> locator = AutoDateLocator()
>>> formatter = ConciseDateFormatter(locator)
Parameters
----------
locator : `.ticker.Locator`
Locator that this axis is using.
tz : str or `~datetime.tzinfo`, default: :rc:`timezone`
Ticks timezone, passed to `.dates.num2date`.
formats : list of 6 strings, optional
Format strings for 6 levels of tick labelling: mostly years,
months, days, hours, minutes, and seconds. Strings use
the same format codes as `~datetime.datetime.strftime`. Default is
``['%Y', '%b', '%d', '%H:%M', '%H:%M', '%S.%f']``
zero_formats : list of 6 strings, optional
Format strings for tick labels that are "zeros" for a given tick
level. For instance, if most ticks are months, ticks around 1 Jan 2005
will be labeled "Dec", "2005", "Feb". The default is
``['', '%Y', '%b', '%b-%d', '%H:%M', '%H:%M']``
offset_formats : list of 6 strings, optional
Format strings for the 6 levels that is applied to the "offset"
string found on the right side of an x-axis, or top of a y-axis.
Combined with the tick labels this should completely specify the
date. The default is::
['', '%Y', '%Y-%b', '%Y-%b-%d', '%Y-%b-%d', '%Y-%b-%d %H:%M']
show_offset : bool, default: True
Whether to show the offset or not.
usetex : bool, default: :rc:`text.usetex`
To enable/disable the use of TeX's math mode for rendering the results
of the formatter.
Examples
--------
See :doc:`/gallery/ticks/date_concise_formatter`
.. plot::
import datetime
import matplotlib.dates as mdates
base = datetime.datetime(2005, 2, 1)
dates = np.array([base + datetime.timedelta(hours=(2 * i))
for i in range(732)])
N = len(dates)
np.random.seed(19680801)
y = np.cumsum(np.random.randn(N))
fig, ax = plt.subplots(constrained_layout=True)
locator = mdates.AutoDateLocator()
formatter = mdates.ConciseDateFormatter(locator)
ax.xaxis.set_major_locator(locator)
ax.xaxis.set_major_formatter(formatter)
ax.plot(dates, y)
ax.set_title('Concise Date Formatter')
"""
def __init__(self, locator, tz=None, formats=None, offset_formats=None,
zero_formats=None, show_offset=True, *, usetex=None):
"""
Autoformat the date labels. The default format is used to form an
initial string, and then redundant elements are removed.
"""
self._locator = locator
self._tz = tz
self.defaultfmt = '%Y'
# there are 6 levels with each level getting a specific format
# 0: mostly years, 1: months, 2: days,
# 3: hours, 4: minutes, 5: seconds
if formats:
if len(formats) != 6:
raise ValueError('formats argument must be a list of '
'6 format strings (or None)')
self.formats = formats
else:
self.formats = ['%Y', # ticks are mostly years
'%b', # ticks are mostly months
'%d', # ticks are mostly days
'%H:%M', # hrs
'%H:%M', # min
'%S.%f', # secs
]
# fmt for zeros ticks at this level.
C8E2
These are
# ticks that should be labeled w/ info the level above.
# like 1 Jan can just be labelled "Jan". 02:02:00 can
# just be labeled 02:02.
if zero_formats:
if len(zero_formats) != 6:
raise ValueError('zero_formats argument must be a list of '
'6 format strings (or None)')
self.zero_formats = zero_formats
elif formats:
# use the users formats for the zero tick formats
self.zero_formats = [''] + self.formats[:-1]
else:
# make the defaults a bit nicer:
self.zero_formats = [''] + self.formats[:-1]
self.zero_formats[3] = '%b-%d'
if offset_formats:
if len(offset_formats) != 6:
raise ValueError('offset_formats argument must be a list of '
'6 format strings (or None)')
self.offset_formats = offset_formats
else:
self.offset_formats = ['',
'%Y',
'%Y-%b',
'%Y-%b-%d',
'%Y-%b-%d',
'%Y-%b-%d %H:%M']
self.offset_string = ''
self.show_offset = show_offset
self._usetex = mpl._val_or_rc(usetex, 'text.usetex')
def __call__(self, x, pos=None):
formatter = DateFormatter(self.defaultfmt, self._tz,
usetex=self._usetex)
return formatter(x, pos=pos)
def format_ticks(self, values):
tickdatetime = [num2date(value, tz=self._tz) for value in values]
tickdate = np.array([tdt.timetuple()[:6] for tdt in tickdatetime])
# basic algorithm:
# 1) only display a part of the date if it changes over the ticks.
# 2) don't display the smaller part of the date if:
# it is always the same or if it is the start of the
# year, month, day etc.
# fmt for most ticks at this level
fmts = self.formats
# format beginnings of days, months, years, etc.
zerofmts = self.zero_formats
# offset fmt are for the offset in the upper left of the
# or lower right of the axis.
offsetfmts = self.offset_formats
show_offset = self.show_offset
# determine the level we will label at:
# mostly 0: years, 1: months, 2: days,
# 3: hours, 4: minutes, 5: seconds, 6: microseconds
for level in range(5, -1, -1):
unique = np.unique(tickdate[:, level])
if len(unique) > 1:
# if 1 is included in unique, the year is shown in ticks
if level < 2 and np.any(unique == 1):
show_offset = False
break
elif level == 0:
# all tickdate are the same, so only micros might be different
# set to the most precise (6: microseconds doesn't exist...)
level = 5
# level is the basic level we will label at.
# now loop through and decide the actual ticklabels
zerovals = [0, 1, 1, 0, 0, 0, 0]
labels = [''] * len(tickdate)
for nn in range(len(tickdate)):
if level < 5:
if tickdate[nn][level] == zerovals[level]:
fmt = zerofmts[level]
else:
fmt = fmts[level]
else:
# special handling for seconds + microseconds
if (tickdatetime[nn].second == tickdatetime[nn].microsecond
== 0):
fmt = zerofmts[level]
else:
fmt = fmts[level]
labels[nn] = tickdatetime[nn].strftime(fmt)
# special handling of seconds and microseconds:
# strip extra zeros and decimal if possible.
# this is complicated by two factors. 1) we have some level-4 strings
# here (i.e. 03:00, '0.50000', '1.000') 2) we would like to have the
# same number of decimals for each string (i.e. 0.5 and 1.0).
if level >= 5:
trailing_zeros = min(
(len(s) - len(s.rstrip('0')) for s in labels if '.' in s),
default=None)
if trailing_zeros:
for nn in range(len(labels)):
if '.' in labels[nn]:
labels[nn] = labels[nn][:-trailing_zeros].rstrip('.')
if show_offset:
# set the offset string:
self.offset_string = tickdatetime[-1].strftime(offsetfmts[level])
if self._usetex:
self.offset_string = _wrap_in_tex(self.offset_string)
else:
self.offset_string = ''
if self._usetex:
return [_wrap_in_tex(l) for l in labels]
else:
return labels
def get_offset(self):
return self.offset_string
def format_data_short(self, value):
return num2date(value, tz=self._tz).strftime('%Y-%m-%d %H:%M:%S')
class AutoDateFormatter(ticker.Formatter):
"""
A `.Formatter` which attempts to figure out the best format to use. This
is most useful when used with the `AutoDateLocator`.
`.AutoDateFormatter` has a ``.scale`` dictionary that maps tick scales (the
interval in days between one major tick) to format strings; this dictionary
defaults to ::
self.scaled = {
DAYS_PER_YEAR: rcParams['date.autoformatter.year'],
DAYS_PER_MONTH: rcParams['date.autoformatter.month'],
1: rcParams['date.autoformatter.day'],
1 / HOURS_PER_DAY: rcParams['date.autoformatter.hour'],
1 / MINUTES_PER_DAY: rcParams['date.autoformatter.minute'],
1 / SEC_PER_DAY: rcParams['date.autoformatter.second'],
1 / MUSECONDS_PER_DAY: rcParams['date.autoformatter.microsecond'],
}
The formatter uses the format string corresponding to the lowest key in
the dictionary that is greater or equal to the current scale. Dictionary
entries can be customized::
locator = AutoDateLocator()
formatter = AutoDateFormatter(locator)
formatter.scaled[1/(24*60)] = '%M:%S' # only show min and sec
Custom callables can also be used instead of format strings. The following
example shows how to use a custom format function to strip trailing zeros
from decimal seconds and adds the date to the first ticklabel::
def my_format_function(x, pos=None):
x = matplotlib.dates.num2date(x)
if pos == 0:
fmt = '%D %H:%M:%S.%f'
else:
fmt = '%H:%M:%S.%f'
label = x.strftime(fmt)
label = label.rstrip("0")
label = label.rstrip(".")
return label
formatter.scaled[1/(24*60)] = my_format_function
"""
# This can be improved by providing some user-level direction on
# how to choose the best format (precedence, etc.).
# Perhaps a 'struct' that has a field for each time-type where a
# zero would indicate "don't show" and a number would indicate
# "show" with some sort of priority. Same priorities could mean
# show all with the same priority.
# Or more simply, perhaps just a format string for each
# possibility...
def __init__(self, locator, tz=None, defaultfmt='%Y-%m-%d', *,
usetex=None):
"""
Autoformat the date labels.
Parameters
----------
locator : `.ticker.Locator`
Locator that this axis is using.
tz : str or `~datetime.tzinfo`, default: :rc:`timezone`
Ticks timezone. If a string, *tz* is passed to `dateutil.tz`.
defaultfmt : str
42DE
The default format to use if none of the values in ``self.scaled``
are greater than the unit returned by ``locator._get_unit()``.
usetex : bool, default: :rc:`text.usetex`
To enable/disable the use of TeX's math mode for rendering the
results of the formatter. If any entries in ``self.scaled`` are set
as functions, then it is up to the customized function to enable or
disable TeX's math mode itself.
"""
self._locator = locator
self._tz = tz
self.defaultfmt = defaultfmt
self._formatter = DateFormatter(self.defaultfmt, tz)
rcParams = mpl.rcParams
self._usetex = mpl._val_or_rc(usetex, 'text.usetex')
self.scaled = {
DAYS_PER_YEAR: rcParams['date.autoformatter.year'],
DAYS_PER_MONTH: rcParams['date.autoformatter.month'],
1: rcParams['date.autoformatter.day'],
1 / HOURS_PER_DAY: rcParams['date.autoformatter.hour'],
1 / MINUTES_PER_DAY: rcParams['date.autoformatter.minute'],
1 / SEC_PER_DAY: rcParams['date.autoformatter.second'],
1 / MUSECONDS_PER_DAY: rcParams['date.autoformatter.microsecond']
}
def _set_locator(self, locator):
self._locator = locator
def __call__(self, x, pos=None):
try:
locator_unit_scale = float(self._locator._get_unit())
except AttributeError:
locator_unit_scale = 1
# Pick the first scale which is greater than the locator unit.
fmt = next((fmt for scale, fmt in sorted(self.scaled.items())
if scale >= locator_unit_scale),
self.defaultfmt)
if isinstance(fmt, str):
self._formatter = DateFormatter(fmt, self._tz, usetex=self._usetex)
result = self._formatter(x, pos)
elif callable(fmt):
result = fmt(x, pos)
else:
raise TypeError(f'Unexpected type passed to {self!r}.')
return result
class rrulewrapper:
"""
A simple wrapper around a `dateutil.rrule` allowing flexible
date tick specifications.
"""
def __init__(self, freq, tzinfo=None, **kwargs):
"""
Parameters
----------
freq : {YEARLY, MONTHLY, WEEKLY, DAILY, HOURLY, MINUTELY, SECONDLY}
Tick frequency. These constants are defined in `dateutil.rrule`,
but they are accessible from `matplotlib.dates` as well.
tzinfo : `datetime.tzinfo`, optional
Time zone information. The default is None.
**kwargs
Additional keyword arguments are passed to the `dateutil.rrule`.
"""
kwargs['freq'] = freq
self._base_tzinfo = tzinfo
self._update_rrule(**kwargs)
def set(self, **kwargs):
"""Set parameters for an existing wrapper."""
self._construct.update(kwargs)
self._update_rrule(**self._construct)
def _update_rrule(self, **kwargs):
tzinfo = self._base_tzinfo
# rrule does not play nicely with timezones - especially pytz time
# zones, it's best to use naive zones and attach timezones once the
# datetimes are returned
if 'dtstart' in kwargs:
dtstart = kwargs['dtstart']
if dtstart.tzinfo is not None:
if tzinfo is None:
tzinfo = dtstart.tzinfo
else:
dtstart = dtstart.astimezone(tzinfo)
kwargs['dtstart'] = dtstart.replace(tzinfo=None)
if 'until' in kwargs:
until = kwargs['until']
if until.tzinfo is not None:
if tzinfo is not None:
until = until.astimezone(tzinfo)
else:
raise ValueError('until cannot be aware if dtstart '
'is naive and tzinfo is None')
kwargs['until'] = until.replace(tzinfo=None)
self._construct = kwargs.copy()
self._tzinfo = tzinfo
self._rrule = rrule(**self._construct)
def _attach_tzinfo(self, dt, tzinfo):
# pytz zones are attached by "localizing" the datetime
if hasattr(tzinfo, 'localize'):
return tzinfo.localize(dt, is_dst=True)
return dt.replace(tzinfo=tzinfo)