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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 3.4.2 documentation</title>

title="Search within Matplotlib 3.4.2 documentation"

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

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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 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_and_spines/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, 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

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',

'epoch2num', 'num2epoch', 'set_epoch', 'get_epoch', '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',

'DateConverter', 'ConciseDateConverter')

_log = logging.getLogger(__name__)

UTC = datetime.timezone.utc

def _get_rc_timezone():

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

s = mpl.rcParams['timezone']

if s == 'UTC':

return UTC

return dateutil.tz.gettz(s)

"""

Time-related constants.

"""

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

# EPOCH_OFFSET is not used by matplotlib

JULIAN_OFFSET = 1721424.5 # Julian date at 0000-12-31

# note that the Julian day epoch is achievable w/

# np.datetime64('-4713-11-24T12:00:00'); datetime64 is proleptic

# Gregorian and BC has a one-year offset. So

# np.datetime64('0000-12-31') - np.datetime64('-4713-11-24T12:00') = 1721424.5

# Ref: https://en.wikipedia.org/wiki/Julian_day

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

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

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

"""

Get the epoch used by `.dates`.

Returns

-------

epoch : str

String for the epoch (parsable by `numpy.datetime64`).

"""

global _epoch

if _epoch is None:

_epoch = mpl.rcParams['date.epoch']

return _epoch</div>

def _dt64_to_ordinalf(d):

"""

Convert `numpy.datetime64` or an 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)

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()

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 errors

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:

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)

<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 `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 = [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 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.

"""

if hasattr(d, "values"):

# this unpacks pandas series or dataframes...

d = d.values

# make an iterable, but save state to unpack later:

iterable = np.iterable(d)

if not iterable:

d = [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 = d.astype('datetime64[us]')

d = _dt64_to_ordinalf(d)

return d if iterable else d[0]</div>

def julian2num(j):

"""

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

Parameters

----------

j : float or sequence of floats

Julian dates (days relative to 4713 BC Jan 1, 12:00:00 Julian

calendar or 4714 BC Nov 24, 12:00:00, proleptic Gregorian calendar).

Returns

-------

float or sequence of floats

Matplotlib dates (days relative to `.get_epoch`).

"""

ep = np.datetime64(get_epoch(), 'h').astype(float) / 24.

ep0 = np.datetime64('0000-12-31T00:00:00', 'h').astype(float) / 24.

# Julian offset defined above is relative to 0000-12-31, but we need

# relative to our current epoch:

dt = JULIAN_OFFSET - ep0 + ep

return np.subtract(j, dt) # 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 dates (days relative to `.get_epoch`).

Returns

-------

float or sequence of floats

Julian dates (days relative to 4713 BC Jan 1, 12:00:00).

"""

# Julian offset defined above is relative to 0000-12-31, but we need

# relative to our current epoch:

dt = JULIAN_OFFSET - ep0 + ep

return np.add(n, dt) # 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 the epoch. See `.get_epoch` for the

epoch, which can be changed by :rc:`date.epoch` or `.set_epoch`.

tz : str, default: :rc:`timezone`

Timezone of *x*.

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 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_np_vectorized(x, tz).tolist()</div>

_ordinalf_to_timedelta_np_vectorized = np.vectorize(

lambda x: datetime.timedelta(days=x), otypes="O")

<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_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

-------

`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>

def _wrap_in_tex(text):

# Braces ensure dashes are not spaced like binary operators.

return '$\\mathdefault{' + text.replace('-', '{-}') + '}$'

## 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.

"""

@_api.deprecated("3.3")

@property

def illegal_s(self):

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

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

"""

Parameters

----------

fmt : str

`~datetime.datetime.strftime` format string

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

Ticks timezone.

usetex : bool, default: :rc:`text.usetex`

To enable/disable the use of TeX's math mode for rendering the

results of the formatter.

"""

if tz is None:

tz = _get_rc_timezone()

self.fmt = fmt

self.tz = tz

self._usetex = (usetex if usetex is not None else

mpl.rcParams['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

<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>@_api.deprecated("3.3")

class IndexDateFormatter(ticker.Formatter):

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

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

"""

Parameters

----------

t : list of float

A sequence of dates (floating point days).

fmt : str

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):

"""

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, 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 `~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_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, *, 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. 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[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

mpl.rcParams['text.usetex'])

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

usetex=self._usetex)

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:

# level is less than 2 so a year is already present in the axis

if (level < 2):

self.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:

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])

if self._usetex:

self.offset_string = _wrap_in_tex(self.offset_string)

if self._usetex:

return [_wrap_in_tex(l) for l in labels]

else:

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):

"""

A `.Formatter` which 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):

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