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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"

"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">

<head>

<title>matplotlib.dates — Matplotlib 3.2.0 documentation</title>

title="Search within Matplotlib 3.2.0 documentation"

href="../../_static/opensearch.xml"/>

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<div id="unreleased-message"> You are reading an old version of the documentation (v3.2.0). For the latest version see <a href="/stable/">https://matplotlib.org/stable/</a> </div>

<div style="float: left; position: absolute; width: 496px; bottom: 0; padding-bottom: 24px">Version 3.2.0</div>

<div id="forkongithub"><a href="https://github.com/matplotlib/matplotlib">Fork me on GitHub</a></div>

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<h1>Source code for matplotlib.dates</h1><div class="highlight"><pre>

"""

Matplotlib provides sophisticated date plotting capabilities, standing on the

shoulders of python :mod:`datetime` and the add-on module :mod:`dateutil`.

.. _date-format:

Matplotlib date format

----------------------

Matplotlib represents dates using floating point numbers specifying the number

of days since 0001-01-01 UTC, plus 1. For example, 0001-01-01, 06:00 is 1.25,

not 0.25. Values < 1, i.e. dates before 0001-01-01 UTC, are not supported.

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

epoch2num

num2epoch

drange

.. note::

Like Python's 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, tickers and formatters are timezone aware.

If no explicit timezone is provided, :rc:`timezone` is assumed. If you want to

use a custom time zone, pass a `datetime.tzinfo` instance with the tz keyword

argument to `num2date`, `~.Axes.plot_date`, and any custom date tickers or

locators you create.

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 tickers

------------

Most of the date tickers can locate single or multiple values. 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 tickers 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 `matplotlib.dates.rrulewrapper`.

`.rrulewrapper` is a simple wrapper around dateutil_'s `dateutil.rrule` which

allow almost arbitrary date tick specifications. See :doc:`rrule example

</gallery/ticks_and_spines/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. E.g. 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

---------------

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.

* `IndexDateFormatter`: date plots with implicit *x* indexing.

"""

import datetime

import functools

import logging

import math

import re

import time

import warnings

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

from matplotlib import rcParams

import matplotlib.units as units

import matplotlib.cbook as cbook

import matplotlib.ticker as ticker

__all__ = ('datestr2num', 'date2num', 'num2date', 'num2timedelta', 'drange',

'epoch2num', 'num2epoch', 'mx2num', 'DateFormatter',

'ConciseDateFormatter', 'IndexDateFormatter', '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',

'seconds', 'minutes', 'hours', 'weeks')

_log = logging.getLogger(__name__)

UTC = datetime.timezone.utc

def _get_rc_timezone():

"""Retrieve the preferred timezone from the rcParams dictionary."""

s = matplotlib.rcParams['timezone']

if s == 'UTC':

return UTC

return dateutil.tz.gettz(s)

"""

Time-related constants.

"""

EPOCH_OFFSET = float(datetime.datetime(1970, 1, 1).toordinal())

JULIAN_OFFSET = 1721424.5 # Julian date at 0001-01-01

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)

def _to_ordinalf(dt):

"""

Convert :mod:`datetime` or :mod:`date` to the Gregorian date as UTC float

days, preserving hours, minutes, seconds and microseconds. Return value

is a :func:`float`.

"""

# Convert to UTC

tzi = getattr(dt, 'tzinfo', None)

if tzi is not None:

dt = dt.astimezone(UTC)

tzi = UTC

base = float(dt.toordinal())

# If it's sufficiently datetime-like, it will have a `date()` method

cdate = getattr(dt, 'date', lambda: None)()

if cdate is not None:

# Get a datetime object at midnight UTC

midnight_time = datetime.time(0, tzinfo=tzi)

rdt = datetime.datetime.combine(cdate, midnight_time)

# Append the seconds as a fraction of a day

base += (dt - rdt).total_seconds() / SEC_PER_DAY

return base

# a version of _to_ordinalf that can operate on numpy arrays

_to_ordinalf_np_vectorized = np.vectorize(_to_ordinalf)

def _dt64_to_ordinalf(d):

"""

Convert `numpy.datetime64` or an ndarray of those types to Gregorian

date as UTC float. Roundoff is via float64 precision. Practically:

microseconds for dates between 290301 BC, 294241 AD, milliseconds for

larger dates (see `numpy.datetime64`). Nanoseconds aren't possible

because we do times compared to ``0001-01-01T00:00:00`` (plus one day).

"""

# the "extra" ensures that we at least allow the dynamic range out to

# seconds. That should get out to +/-2e11 years.

extra = (d - d.astype('datetime64[s]')).astype('timedelta64[ns]')

t0 = np.datetime64('0001-01-01T00:00:00', 's')

dt = (d.astype('datetime64[s]') - t0).astype(np.float64)

dt += extra.astype(np.float64) / 1.0e9

dt = dt / SEC_PER_DAY + 1.0

NaT_int = np.datetime64('NaT').astype(np.int64)

d_int = d.astype(np.int64)

try:

dt[d_int == NaT_int] = np.nan

except TypeError:

if d_int == NaT_int:

dt = 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`.

"""

if tz is None:

tz = _get_rc_timezone()

ix, remainder = divmod(x, 1)

ix = int(ix)

if ix < 1:

raise ValueError('Cannot convert {} to a date. This often happens if '

'non-datetime values are passed to an axis that '

'expects datetime objects.'.format(ix))

dt = datetime.datetime.fromordinal(ix).replace(tzinfo=UTC)

# Since the input date *x* float is unable to preserve microsecond

# precision of time representation in non-antique years, the

# resulting datetime is rounded to the nearest multiple of

# `musec_prec`. A value of 20 is appropriate for current dates.

musec_prec = 20

remainder_musec = int(round(remainder * MUSECONDS_PER_DAY / musec_prec)

* musec_prec)

# For people trying to plot with full microsecond precision, enable

# an early-year workaround

if x < 30 * 365:

# add hours, minutes, seconds, microseconds

dt += datetime.timedelta(microseconds=remainder_musec)

return dt.astimezone(tz)

# a version of _from_ordinalf that can operate on numpy arrays

_from_ordinalf_np_vectorized = np.vectorize(_from_ordinalf)

@cbook.deprecated(

"3.1", alternative="time.strptime or dateutil.parser.parse or datestr2num")

class strpdate2num:

"""

Use this class to parse date strings to matplotlib datenums when

you know the date format string of the date you are parsing.

"""

def __init__(self, fmt):

"""

Parameters

----------

fmt : any valid strptime format

"""

self.fmt = fmt

def __call__(self, s):

"""

Parameters

----------

s : str

Returns

-------

date2num float

"""

return date2num(datetime.datetime(*time.strptime(s, self.fmt)[:6]))

@cbook.deprecated(

class bytespdate2num(strpdate2num):

"""

Use this class to parse date strings to matplotlib datenums when

you know the date format string of the date you are parsing. See

:doc:`/gallery/misc/load_converter.py`.

"""

def __init__(self, fmt, encoding='utf-8'):

"""

Parameters

----------

fmt : any valid strptime format

encoding : str

Encoding to use on byte input.

"""

super().__init__(fmt)

self.encoding = encoding

def __call__(self, b):

"""

Parameters

----------

b : bytes

Returns

-------

date2num float

"""

s = b.decode(self.encoding)

return super().__call__(s)

# a version of dateutil.parser.parse that can operate on numpy arrays

_dateutil_parser_parse_np_vectorized = np.vectorize(dateutil.parser.parse)

<div class="viewcode-block" id="datestr2num"><a class="viewcode-back" href="../../api/dates_api.html#matplotlib.dates.datestr2num">[docs]</a>def datestr2num(d, default=None):

"""

Convert a date string to a datenum using :func:`dateutil.parser.parse`.

Parameters

----------

d : str or sequence of str

The dates to convert.

default : datetime instance, 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 = [dateutil.parser.parse(s, default=default) for s in d]

d = np.asarray(d)

if not d.size:

return d

return date2num(_dateutil_parser_parse_np_vectorized(d))</div>

<div class="viewcode-block" id="date2num"><a class="viewcode-back" href="../../api/dates_api.html#matplotlib.dates.date2num">[docs]</a>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 (fraction part represents hours, minutes, seconds, ms)

since 0001-01-01 00:00:00 UTC, plus one.

Notes

-----

The addition of one here is a historical artifact. Also, note that the

Gregorian calendar is assumed; this is not universal practice.

For details see the module docstring.

"""

if hasattr(d, "values"):

# this unpacks pandas series or dataframes...

d = d.values

if not np.iterable(d):

if (isinstance(d, np.datetime64) or

(isinstance(d, np.ndarray) and

np.issubdtype(d.dtype, np.datetime64))):

return _dt64_to_ordinalf(d)

return _to_ordinalf(d)

else:

if np.issubdtype(d.dtype, np.datetime64):

return _dt64_to_ordinalf(d)

if not d.size:

return d

return _to_ordinalf_np_vectorized(d)</div>

def julian2num(j):

"""

Convert a Julian date (or sequence) to a Matplotlib date (or sequence).

Parameters

----------

j : float or sequence of floats

Julian date(s)

Returns

-------

float or sequence of floats

Matplotlib date(s)

"""

return np.subtract(j, JULIAN_OFFSET) # Handles both scalar & nonscalar j.

def num2julian(n):

"""

Convert a Matplotlib date (or sequence) to a Julian date (or sequence).

Parameters

----------

n : float or sequence of floats

Matplotlib date(s)

Returns

-------

float or sequence of floats

Julian date(s)

"""

return np.add(n, JULIAN_OFFSET) # Handles both scalar & nonscalar j.

<div class="viewcode-block" id="num2date"><a class="viewcode-back" href="../../api/dates_api.html#matplotlib.dates.num2date">[docs]</a>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 0001-01-01 00:00:00 UTC, plus one.

tz : str, optional

Timezone of *x* (defaults to rcparams ``timezone``).

Returns

-------

`~datetime.datetime` or sequence of `~datetime.datetime`

Dates are returned in timezone *tz*.

If *x* is a sequence, a sequence of :class:`datetime` objects will

be returned.

Notes

-----

The addition of one here is a historical artifact. Also, note that the

Gregorian calendar is assumed; this is not universal practice.

For details, see the module docstring.

"""

if tz is None:

tz = _get_rc_timezone()

return _from_ordinalf(x, tz)

else:

x = np.asarray(x)

if not x.size:

return x

return _from_ordinalf_np_vectorized(x, tz).tolist()</div>

def _ordinalf_to_timedelta(x):

return datetime.timedelta(days=x)

_ordinalf_to_timedelta_np_vectorized = np.vectorize(_ordinalf_to_timedelta)

<div class="viewcode-block" id="num2timedelta"><a class="viewcode-back" href="../../api/dates_api.html#matplotlib.dates.num2timedelta">[docs]</a>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(x)

else:

if not x.size:

return x

return _ordinalf_to_timedelta_np_vectorized(x).tolist()</div>

<div class="viewcode-block" id="drange"><a class="viewcode-back" href="../../api/dates_api.html#matplotlib.dates.drange">[docs]</a>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

-------

drange : `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 dend, just subtract one delta

dinterval_end -= delta

num -= 1

f2 = date2num(dinterval_end) # new float-endpoint

return np.linspace(f1, f2, num + 1)</div>

## date tickers and formatters ###

<div class="viewcode-block" id="DateFormatter"><a class="viewcode-back" href="../../api/dates_api.html#matplotlib.dates.DateFormatter">[docs]</a>class DateFormatter(ticker.Formatter):

"""

Format a tick (in days since the epoch) with a

`~datetime.datetime.strftime` format string.

"""

illegal_s = re.compile(r"((^|[^%])(%%)*%s)")

def __init__(self, fmt, tz=None):

"""

Parameters

----------

fmt : str

`~datetime.datetime.strftime` format string

tz : `tzinfo`, default: :rc:`timezone`

Ticks timezone.

"""

if tz is None:

tz = _get_rc_timezone()

self.fmt = fmt

self.tz = tz

def __call__(self, x, pos=0):

if x == 0:

raise ValueError('DateFormatter found a value of x=0, which is '

'an illegal date; this usually occurs because '

'you have not informed the axis that it is '

'plotting dates, e.g., with ax.xaxis_date()')

return num2date(x, self.tz).strftime(self.fmt)

<div class="viewcode-block" id="DateFormatter.set_tzinfo"><a class="viewcode-back" href="../../api/dates_api.html#matplotlib.dates.DateFormatter.set_tzinfo">[docs]</a> def set_tzinfo(self, tz):

self.tz = tz</div></div>

<div class="viewcode-block" id="IndexDateFormatter"><a class="viewcode-back" href="../../api/dates_api.html#matplotlib.dates.IndexDateFormatter">[docs]</a>class IndexDateFormatter(ticker.Formatter):

"""Use with `.IndexLocator` to cycle format strings by index."""

def __init__(self, t, fmt, tz=None):

"""

*t* is a sequence of dates (floating point days). *fmt* is a

`~datetime.datetime.strftime` format string.

"""

if tz is None:

tz = _get_rc_timezone()

self.t = t

self.fmt = fmt

self.tz = tz

'Return the label for time *x* at position *pos*'

ind = int(round(x))

if ind >= len(self.t) or ind <= 0:

return ''

return num2date(self.t[ind], self.tz).strftime(self.fmt)</div>

<div class="viewcode-block" id="ConciseDateFormatter"><a class="viewcode-back" href="../../api/dates_api.html#matplotlib.dates.ConciseDateFormatter">[docs]</a>class ConciseDateFormatter(ticker.Formatter):

"""

This class 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, optional

Passed to `.dates.date2num`.

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 `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

Whether to show the offset or not. Default is ``True``.

Examples

--------

See :doc:`/gallery/ticks_and_spines/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):

"""

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. These are

# ticks that should be labeled w/ info the level above.

# like 1 Jan can just be labled "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[3] = '%b-%d'

if offset_formats:

if len(offset_formats) != 6:

raise ValueError('offsetfmts 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 %H:%M']

self.offset_string = ''

self.show_offset = show_offset

formatter = DateFormatter(self.defaultfmt, self._tz)

return formatter(x, pos=pos)

<div class="viewcode-block" id="ConciseDateFormatter.format_ticks"><a class="viewcode-back" href="../../api/dates_api.html#matplotlib.dates.ConciseDateFormatter.format_ticks">[docs]</a> 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

# 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):

if len(np.unique(tickdate[:, level])) > 1:

break

# 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:

if '.' in labels[nn]:

labels[nn] = labels[nn][:-trailing_zeros].rstrip('.')

if self.show_offset:

# set the offset string:

self.offset_string = tickdatetime[-1].strftime(offsetfmts[level])

return labels</div>

<div class="viewcode-block" id="ConciseDateFormatter.get_offset"><a class="viewcode-back" href="../../api/dates_api.html#matplotlib.dates.ConciseDateFormatter.get_offset">[docs]</a> def get_offset(self):

return self.offset_string</div>

<div class="viewcode-block" id="ConciseDateFormatter.format_data_short"><a class="viewcode-back" href="../../api/dates_api.html#matplotlib.dates.ConciseDateFormatter.format_data_short">[docs]</a> def format_data_short(self, value):

return num2date(value, tz=self._tz).strftime('%Y-%m-%d %H:%M:%S')</div></div>

<div class="viewcode-block" id="AutoDateFormatter"><a class="viewcode-back" href="../../api/dates_api.html#matplotlib.dates.AutoDateFormatter">[docs]</a>class AutoDateFormatter(ticker.Formatter):

"""

This class attempts to figure out the best format to use. This is

most useful when used with the `AutoDateLocator`.

The AutoDateFormatter has a scale dictionary that maps the scale

of the tick (the distance in days between one major tick) and a

format string. The default looks like this::

self.scaled = {

DAYS_PER_YEAR: rcParams['date.autoformat.year'],

DAYS_PER_MONTH: rcParams['date.autoformat.month'],

1.0: rcParams['date.autoformat.day'],

1. / HOURS_PER_DAY: rcParams['date.autoformat.hour'],

1. / (MINUTES_PER_DAY): rcParams['date.autoformat.minute'],

1. / (SEC_PER_DAY): rcParams['date.autoformat.second'],

1. / (MUSECONDS_PER_DAY): rcParams['date.autoformat.microsecond'],

}

The algorithm picks the key in the dictionary that is >= the

current scale and uses that format string. You can customize this

dictionary by doing::

>>> locator = AutoDateLocator()

>>> formatter = AutoDateFormatter(locator)

>>> formatter.scaled[1/(24.*60.)] = '%M:%S' # only show min and sec

A custom `.FuncFormatter` can also be used. 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

>>> from matplotlib.ticker import FuncFormatter

>>> formatter.scaled[1/(24.*60.)] = FuncFormatter(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...

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