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Fixed database typo and removed unnecessary class identifier.
2020-10-14 14:10:37 +00:00
"""
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
import matplotlib.units as units
import matplotlib.cbook as cbook
import matplotlib.ticker as ticker
__all__ = ('datestr2num', 'date2num', 'num2date', 'num2timedelta', 'drange',
'epoch2num', 'num2epoch', 'mx2num', '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 = 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())
# 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
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 fo 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
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 = matplotlib.rcParams['date.epoch']
return _epoch
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 `float`.
"""
# Convert to UTC
tzi = getattr(dt, 'tzinfo', None)
if tzi is not None:
dt = dt.astimezone(UTC)
dt = dt.replace(tzinfo=None)
dt64 = np.datetime64(dt)
return _dt64_to_ordinalf(dt64)
# 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 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:
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 = [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))
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
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:
d = np.asarray(d)
if np.issubdtype(d.dtype, np.datetime64):
return _dt64_to_ordinalf(d)
if not d.size:
return d
return _to_ordinalf_np_vectorized(d)
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).
"""
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.add(n, dt) # Handles both scalar & nonscalar j.
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, optional
Timezone of *x* (defaults to :rc:`timezone`).
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()
_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 dend, just subtract one delta
dinterval_end -= delta
num -= 1
f2 = date2num(dinterval_end) # new float-endpoint
return np.linspace(f1, f2, num + 1)
## date tickers and formatters ###
class DateFormatter(ticker.Formatter):
"""
Format a tick (in days since the epoch) with a
`~datetime.datetime.strftime` format string.
"""
@cbook.deprecated("3.3")
@property
def illegal_s(self):
return re.compile(r"((^|[^%])(%%)*%s)")
def __init__(self, fmt, tz=None):
"""
Parameters
----------
fmt : str
`~datetime.datetime.strftime` format string
tz : `datetime.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):
return num2date(x, self.tz).strftime(self.fmt)
def set_tzinfo(self, tz):
self.tz = tz
@cbook.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
def __call__(self, x, pos=0):
"""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)
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.
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 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('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',
'%Y-%b-%d %H:%M']
self.offset_string = ''
self.show_offset = show_offset
def __call__(self, x, pos=None):
formatter = DateFormatter(self.defaultfmt, self._tz)
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
# 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:
for nn in range(len(labels)):
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
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`.
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...
def __init__(self, locator, tz=None, defaultfmt='%Y-%m-%d'):
"""
Autoformat the date labels. The default format is the one to use
if none of the values in ``self.scaled`` are greater than the unit
returned by ``locator._get_unit()``.
"""
self._locator = locator
self._tz = tz
self.defaultfmt = defaultfmt
self._formatter = DateFormatter(self.defaultfmt, tz)
rcParams = matplotlib.rcParams
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)
result = self._formatter(x, pos)
elif callable(fmt):
result = fmt(x, pos)
else:
raise TypeError('Unexpected type passed to {0!r}.'.format(self))
return result
class rrulewrapper:
def __init__(self, freq, tzinfo=None, **kwargs):
kwargs['freq'] = freq
self._base_tzinfo = tzinfo
self._update_rrule(**kwargs)
def set(self, **kwargs):
self._construct.update(kwargs)
self._update_rrule(**self._construct)
def _update_rrule(self, **kwargs):
tzinfo = self._base_tzinfo
# rrule does not play nicely with time zones - 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)
def _aware_return_wrapper(self, f, returns_list=False):
"""Decorator function that allows rrule methods to handle tzinfo."""
# This is only necessary if we're actually attaching a tzinfo
if self._tzinfo is None:
return f
# All datetime arguments must be naive. If they are not naive, they are
# converted to the _tzinfo zone before dropping the zone.
def normalize_arg(arg):
if isinstance(arg, datetime.datetime) and arg.tzinfo is not None:
if arg.tzinfo is not self._tzinfo:
arg = arg.astimezone(self._tzinfo)
return arg.replace(tzinfo=None)
return arg
def normalize_args(args, kwargs):
args = tuple(normalize_arg(arg) for arg in args)
kwargs = {kw: normalize_arg(arg) for kw, arg in kwargs.items()}
return args, kwargs
# There are two kinds of functions we care about - ones that return
# dates and ones that return lists of dates.
if not returns_list:
def inner_func(*args, **kwargs):
args, kwargs = normalize_args(args, kwargs)
dt = f(*args, **kwargs)
return self._attach_tzinfo(dt, self._tzinfo)
else:
def inner_func(*args, **kwargs):
args, kwargs = normalize_args(args, kwargs)
dts = f(*args, **kwargs)
return [self._attach_tzinfo(dt, self._tzinfo) for dt in dts]
return functools.wraps(f)(inner_func)
def __getattr__(self, name):
if name in self.__dict__:
return self.__dict__[name]
f = getattr(self._rrule, name)
if name in {'after', 'before'}:
return self._aware_return_wrapper(f)
elif name in {'xafter', 'xbefore', 'between'}:
return self._aware_return_wrapper(f, returns_list=True)
else:
return f
def __setstate__(self, state):
self.__dict__.update(state)
class DateLocator(ticker.Locator):
"""
Determines the tick locations when plotting dates.
This class is subclassed by other Locators and
is not meant to be used on its own.
"""
hms0d = {'byhour': 0, 'byminute': 0, 'bysecond': 0}
def __init__(self, tz=None):
"""
Parameters
----------
tz : `datetime.tzinfo`
"""
if tz is None:
tz = _get_rc_timezone()
self.tz = tz
def set_tzinfo(self, tz):
"""
Set time zone info.
"""
self.tz = tz
def datalim_to_dt(self):
"""Convert axis data interval to datetime objects."""
dmin, dmax = self.axis.get_data_interval()
if dmin > dmax:
dmin, dmax = dmax, dmin
return num2date(dmin, self.tz), num2date(dmax, self.tz)
def viewlim_to_dt(self):
"""Convert the view interval to datetime objects."""
vmin, vmax = self.axis.get_view_interval()
if vmin > vmax:
vmin, vmax = vmax, vmin
return num2date(vmin, self.tz), num2date(vmax, self.tz)
def _get_unit(self):
"""
Return how many days a unit of the locator is; used for
intelligent autoscaling.
"""
return 1
def _get_interval(self):
"""
Return the number of units for each tick.
"""
return 1
def nonsingular(self, vmin, vmax):
"""
Given the proposed upper and lower extent, adjust the range
if it is too close to being singular (i.e. a range of ~0).
"""
if not np.isfinite(vmin) or not np.isfinite(vmax):
# Except if there is no data, then use 2000-2010 as default.
return (date2num(datetime.date(2000, 1, 1)),
date2num(datetime.date(2010, 1, 1)))
if vmax < vmin:
vmin, vmax = vmax, vmin
unit = self._get_unit()
interval = self._get_interval()
if abs(vmax - vmin) < 1e-6:
vmin -= 2 * unit * interval
vmax += 2 * unit * interval
return vmin, vmax
class RRuleLocator(DateLocator):
# use the dateutil rrule instance
def __init__(self, o, tz=None):
DateLocator.__init__(self, tz)
self.rule = o
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 []
return self.tick_values(dmin, dmax)
def tick_values(self, vmin, vmax):
delta = relativedelta(vmax, vmin)
# We need to cap at the endpoints of valid datetime
try:
start = vmin - delta
except (ValueError, OverflowError):
# cap
start = datetime.datetime(1, 1, 1, 0, 0, 0,
tzinfo=datetime.timezone.utc)
try:
stop = vmax + delta
except (ValueError, OverflowError):
# cap
stop = datetime.datetime(9999, 12, 31, 23, 59, 59,
tzinfo=datetime.timezone.utc)
self.rule.set(dtstart=start, until=stop)
dates = self.rule.between(vmin, vmax, True)
if len(dates) == 0:
return date2num([vmin, vmax])
return self.raise_if_exceeds(date2num(dates))
def _get_unit(self):
# docstring inherited
freq = self.rule._rrule._freq
return self.get_unit_generic(freq)
@staticmethod
def get_unit_generic(freq):
if freq == YEARLY:
return DAYS_PER_YEAR
elif freq == MONTHLY:
return DAYS_PER_MONTH
elif freq == WEEKLY:
return DAYS_PER_WEEK
elif freq == DAILY:
return 1.0
elif freq == HOURLY:
return 1.0 / HOURS_PER_DAY
elif freq == MINUTELY:
return 1.0 / MINUTES_PER_DAY
elif freq == SECONDLY:
return 1.0 / SEC_PER_DAY
else:
# error
return -1 # or should this just return '1'?
def _get_interval(self):
return self.rule._rrule._interval
@cbook.deprecated("3.2")
def autoscale(self):
"""
Set the view limits to include the data range.
"""
dmin, dmax = self.datalim_to_dt()
delta = relativedelta(dmax, dmin)
# We need to cap at the endpoints of valid datetime
try:
start = dmin - delta
except ValueError:
start = _from_ordinalf(1.0)
try:
stop = dmax + delta
except ValueError:
# The magic number!
stop = _from_ordinalf(3652059.9999999)
self.rule.set(dtstart=start, until=stop)
dmin, dmax = self.datalim_to_dt()
vmin = self.rule.before(dmin, True)
if not vmin:
vmin = dmin
vmax = self.rule.after(dmax, True)
if not vmax:
vmax = dmax
vmin = date2num(vmin)
vmax = date2num(vmax)
return self.nonsingular(vmin, vmax)
class AutoDateLocator(DateLocator):
"""
On autoscale, this class picks the best `DateLocator` to set the view
limits and the tick locations.
Attributes
----------
intervald : dict
Mapping of tick frequencies to multiples allowed for that ticking.
The default is ::
self.intervald = {
YEARLY : [1, 2, 4, 5, 10, 20, 40, 50, 100, 200, 400, 500,
1000, 2000, 4000, 5000, 10000],
MONTHLY : [1, 2, 3, 4, 6],
DAILY : [1, 2, 3, 7, 14, 21],
HOURLY : [1, 2, 3, 4, 6, 12],
MINUTELY: [1, 5, 10, 15, 30],
SECONDLY: [1, 5, 10, 15, 30],
MICROSECONDLY: [1, 2, 5, 10, 20, 50, 100, 200, 500,
1000, 2000, 5000, 10000, 20000, 50000,
100000, 200000, 500000, 1000000],
}
where the keys are defined in `dateutil.rrule`.
The interval is used to specify multiples that are appropriate for
the frequency of ticking. For instance, every 7 days is sensible
for daily ticks, but for minutes/seconds, 15 or 30 make sense.
When customizing, you should only modify the values for the existing
keys. You should not add or delete entries.
Example for forcing ticks every 3 hours::
locator = AutoDateLocator()
locator.intervald[HOURLY] = [3] # only show every 3 hours
"""
def __init__(self, tz=None, minticks=5, maxticks=None,
interval_multiples=True):
"""
Parameters
----------
tz : `datetime.tzinfo`
Ticks timezone.
minticks : int
The minimum number of ticks desired; controls whether ticks occur
yearly, monthly, etc.
maxticks : int
The maximum number of ticks desired; controls the interval between
ticks (ticking every other, every 3, etc.). For fine-grained
control, this can be a dictionary mapping individual rrule
frequency constants (YEARLY, MONTHLY, etc.) to their own maximum
number of ticks. This can be used to keep the number of ticks
appropriate to the format chosen in `AutoDateFormatter`. Any
frequency not specified in this dictionary is given a default
value.
interval_multiples : bool, default: True
Whether ticks should be chosen to be multiple of the interval,
locking them to 'nicer' locations. For example, this will force
the ticks to be at hours 0, 6, 12, 18 when hourly ticking is done
at 6 hour intervals.
"""
DateLocator.__init__(self, tz)
self._freq = YEARLY
self._freqs = [YEARLY, MONTHLY, DAILY, HOURLY, MINUTELY,
SECONDLY, MICROSECONDLY]
self.minticks = minticks
self.maxticks = {YEARLY: 11, MONTHLY: 12, DAILY: 11, HOURLY: 12,
MINUTELY: 11, SECONDLY: 11, MICROSECONDLY: 8}
if maxticks is not None:
try:
self.maxticks.update(maxticks)
except TypeError:
# Assume we were given an integer. Use this as the maximum
# number of ticks for every frequency and create a
# dictionary for this
self.maxticks = dict.fromkeys(self._freqs, maxticks)
self.interval_multiples = interval_multiples
self.intervald = {
YEARLY: [1, 2, 4, 5, 10, 20, 40, 50, 100, 200, 400, 500,
1000, 2000, 4000, 5000, 10000],
MONTHLY: [1, 2, 3, 4, 6],
DAILY: [1, 2, 3, 7, 14, 21],
HOURLY: [1, 2, 3, 4, 6, 12],
MINUTELY: [1, 5, 10, 15, 30],
SECONDLY: [1, 5, 10, 15, 30],
MICROSECONDLY: [1, 2, 5, 10, 20, 50, 100, 200, 500, 1000, 2000,
5000, 10000, 20000, 50000, 100000, 200000, 500000,
1000000],
}
if interval_multiples:
# Swap "3" for "4" in the DAILY list; If we use 3 we get bad
# tick loc for months w/ 31 days: 1, 4, ..., 28, 31, 1
# If we use 4 then we get: 1, 5, ... 25, 29, 1
self.intervald[DAILY] = [1, 2, 4, 7, 14, 21]
self._byranges = [None, range(1, 13), range(1, 32),
range(0, 24), range(0, 60), range(0, 60), None]
def __call__(self):
# docstring inherited
dmin, dmax = self.viewlim_to_dt()
locator = self.get_locator(dmin, dmax)
return locator()
def tick_values(self, vmin, vmax):
return self.get_locator(vmin, vmax).tick_values(vmin, vmax)
def nonsingular(self, vmin, vmax):
# whatever is thrown at us, we can scale the unit.
# But default nonsingular date plots at an ~4 year period.
if not np.isfinite(vmin) or not np.isfinite(vmax):
# Except if there is no data, then use 2000-2010 as default.
return (date2num(datetime.date(2000, 1, 1)),
date2num(datetime.date(2010, 1, 1)))
if vmax < vmin:
vmin, vmax = vmax, vmin
if vmin == vmax:
vmin = vmin - DAYS_PER_YEAR * 2
vmax = vmax + DAYS_PER_YEAR * 2
return vmin, vmax
def _get_unit(self):
if self._freq in [MICROSECONDLY]:
return 1. / MUSECONDS_PER_DAY
else:
return RRuleLocator.get_unit_generic(self._freq)
@cbook.deprecated("3.2")
def autoscale(self):
"""Try to choose the view limits intelligently."""
dmin, dmax = self.datalim_to_dt()
return self.get_locator(dmin, dmax).autoscale()
def get_locator(self, dmin, dmax):
"""Pick the best locator based on a distance."""
delta = relativedelta(dmax, dmin)
tdelta = dmax - dmin
# take absolute difference
if dmin > dmax:
delta = -delta
tdelta = -tdelta
# The following uses a mix of calls to relativedelta and timedelta
# methods because there is incomplete overlap in the functionality of
# these similar functions, and it's best to avoid doing our own math
# whenever possible.
numYears = float(delta.years)
numMonths = numYears * MONTHS_PER_YEAR + delta.months
numDays = tdelta.days # Avoids estimates of days/month, days/year
numHours = numDays * HOURS_PER_DAY + delta.hours
numMinutes = numHours * MIN_PER_HOUR + delta.minutes
numSeconds = np.floor(tdelta.total_seconds())
numMicroseconds = np.floor(tdelta.total_seconds() * 1e6)
nums = [numYears, numMonths, numDays, numHours, numMinutes,
numSeconds, numMicroseconds]
use_rrule_locator = [True] * 6 + [False]
# Default setting of bymonth, etc. to pass to rrule
# [unused (for year), bymonth, bymonthday, byhour, byminute,
# bysecond, unused (for microseconds)]
byranges = [None, 1, 1, 0, 0, 0, None]
# Loop over all the frequencies and try to find one that gives at
# least a minticks tick positions. Once this is found, look for
# an interval from an list specific to that frequency that gives no
# more than maxticks tick positions. Also, set up some ranges
# (bymonth, etc.) as appropriate to be passed to rrulewrapper.
for i, (freq, num) in enumerate(zip(self._freqs, nums)):
# If this particular frequency doesn't give enough ticks, continue
if num < self.minticks:
# Since we're not using this particular frequency, set
# the corresponding by_ to None so the rrule can act as
# appropriate
byranges[i] = None
continue
# Find the first available interval that doesn't give too many
# ticks
for interval in self.intervald[freq]:
if num <= interval * (self.maxticks[freq] - 1):
break
else:
# We went through the whole loop without breaking, default to
# the last interval in the list and raise a warning
cbook._warn_external(
f"AutoDateLocator was unable to pick an appropriate "
f"interval for this date range. It may be necessary to "
f"add an interval value to the AutoDateLocator's "
f"intervald dictionary. Defaulting to {interval}.")
# Set some parameters as appropriate
self._freq = freq
if self._byranges[i] and self.interval_multiples:
byranges[i] = self._byranges[i][::interval]
if i in (DAILY, WEEKLY):
if interval == 14:
# just make first and 15th. Avoids 30th.
byranges[i] = [1, 15]
elif interval == 7:
byranges[i] = [1, 8, 15, 22]
interval = 1
else:
byranges[i] = self._byranges[i]
break
else:
interval = 1
if (freq == YEARLY) and self.interval_multiples:
locator = YearLocator(interval, tz=self.tz)
elif use_rrule_locator[i]:
_, bymonth, bymonthday, byhour, byminute, bysecond, _ = byranges
rrule = rrulewrapper(self._freq, interval=interval,
dtstart=dmin, until=dmax,
bymonth=bymonth, bymonthday=bymonthday,
byhour=byhour, byminute=byminute,
bysecond=bysecond)
locator = RRuleLocator(rrule, self.tz)
else:
locator = MicrosecondLocator(interval, tz=self.tz)
if date2num(dmin) > 70 * 365 and interval < 1000:
cbook._warn_external(
'Plotting microsecond time intervals for dates far from '
f'the epoch (time origin: {get_epoch()}) is not well-'
'supported. See matplotlib.dates.set_epoch to change the '
'epoch.')
locator.set_axis(self.axis)
if self.axis is not None:
locator.set_view_interval(*self.axis.get_view_interval())
locator.set_data_interval(*self.axis.get_data_interval())
return locator
class YearLocator(DateLocator):
"""
Make ticks on a given day of each year that is a multiple of base.
Examples::
# Tick every year on Jan 1st
locator = YearLocator()
# Tick every 5 years on July 4th
locator = YearLocator(5, month=7, day=4)
"""
def __init__(self, base=1, month=1, day=1, tz=None):
"""
Mark years that are multiple of base on a given month and day
(default jan 1).
"""
DateLocator.__init__(self, tz)
self.base = ticker._Edge_integer(base, 0)
self.replaced = {'month': month,
'day': day,
'hour': 0,
'minute': 0,
'second': 0,
}
if not hasattr(tz, 'localize'):
# if tz is pytz, we need to do this w/ the localize fcn,
# otherwise datetime.replace works fine...
self.replaced['tzinfo'] = tz
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 []
return self.tick_values(dmin, dmax)
def tick_values(self, vmin, vmax):
ymin = self.base.le(vmin.year) * self.base.step
ymax = self.base.ge(vmax.year) * self.base.step
vmin = vmin.replace(year=ymin, **self.replaced)
if hasattr(self.tz, 'localize'):
# look after pytz
if not vmin.tzinfo:
vmin = self.tz.localize(vmin, is_dst=True)
ticks = [vmin]
while True:
dt = ticks[-1]
if dt.year >= ymax:
return date2num(ticks)
year = dt.year + self.base.step
dt = dt.replace(year=year, **self.replaced)
if hasattr(self.tz, 'localize'):
# look after pytz
if not dt.tzinfo:
dt = self.tz.localize(dt, is_dst=True)
ticks.append(dt)
@cbook.deprecated("3.2")
def autoscale(self):
"""
Set the view limits to include the data range.
"""
dmin, dmax = self.datalim_to_dt()
ymin = self.base.le(dmin.year)
ymax = self.base.ge(dmax.year)
vmin = dmin.replace(year=ymin, **self.replaced)
vmin = vmin.astimezone(self.tz)
vmax = dmax.replace(year=ymax, **self.replaced)
vmax = vmax.astimezone(self.tz)
vmin = date2num(vmin)
vmax = date2num(vmax)
return self.nonsingular(vmin, vmax)
class MonthLocator(RRuleLocator):
"""
Make ticks on occurrences of each month, e.g., 1, 3, 12.
"""
def __init__(self, bymonth=None, bymonthday=1, interval=1, tz=None):
"""
Mark every month in *bymonth*; *bymonth* can be an int or
sequence. Default is ``range(1, 13)``, i.e. every month.
*interval* is the interval between each iteration. For
example, if ``interval=2``, mark every second occurrence.
"""
if bymonth is None:
bymonth = range(1, 13)
elif isinstance(bymonth, np.ndarray):
# This fixes a bug in dateutil <= 2.3 which prevents the use of
# numpy arrays in (among other things) the bymonthday, byweekday
# and bymonth parameters.
bymonth = [x.item() for x in bymonth.astype(int)]
rule = rrulewrapper(MONTHLY, bymonth=bymonth, bymonthday=bymonthday,
interval=interval, **self.hms0d)
RRuleLocator.__init__(self, rule, tz)
class WeekdayLocator(RRuleLocator):
"""
Make ticks on occurrences of each weekday.
"""
def __init__(self, byweekday=1, interval=1, tz=None):
"""
Mark every weekday in *byweekday*; *byweekday* can be a number or
sequence.
Elements of *byweekday* must be one of MO, TU, WE, TH, FR, SA,
SU, the constants from :mod:`dateutil.rrule`, which have been
imported into the :mod:`matplotlib.dates` namespace.
*interval* specifies the number of weeks to skip. For example,
``interval=2`` plots every second week.
"""
if isinstance(byweekday, np.ndarray):
# This fixes a bug in dateutil <= 2.3 which prevents the use of
# numpy arrays in (among other things) the bymonthday, byweekday
# and bymonth parameters.
[x.item() for x in byweekday.astype(int)]
rule = rrulewrapper(DAILY, byweekday=byweekday,
interval=interval, **self.hms0d)
RRuleLocator.__init__(self, rule, tz)
class DayLocator(RRuleLocator):
"""
Make ticks on occurrences of each day of the month. For example,
1, 15, 30.
"""
def __init__(self, bymonthday=None, interval=1, tz=None):
"""
Mark every day in *bymonthday*; *bymonthday* can be an int or sequence.
Default is to tick every day of the month: ``bymonthday=range(1, 32)``.
"""
if interval != int(interval) or interval < 1:
raise ValueError("interval must be an integer greater than 0")
if bymonthday is None:
bymonthday = range(1, 32)
elif isinstance(bymonthday, np.ndarray):
# This fixes a bug in dateutil <= 2.3 which prevents the use of
# numpy arrays in (among other things) the bymonthday, byweekday
# and bymonth parameters.
bymonthday = [x.item() for x in bymonthday.astype(int)]
rule = rrulewrapper(DAILY, bymonthday=bymonthday,
interval=interval, **self.hms0d)
RRuleLocator.__init__(self, rule, tz)
class HourLocator(RRuleLocator):
"""
Make ticks on occurrences of each hour.
"""
def __init__(self, byhour=None, interval=1, tz=None):
"""
Mark every hour in *byhour*; *byhour* can be an int or sequence.
Default is to tick every hour: ``byhour=range(24)``
*interval* is the interval between each iteration. For
example, if ``interval=2``, mark every second occurrence.
"""
if byhour is None:
byhour = range(24)
rule = rrulewrapper(HOURLY, byhour=byhour, interval=interval,
byminute=0, bysecond=0)
RRuleLocator.__init__(self, rule, tz)
class MinuteLocator(RRuleLocator):
"""
Make ticks on occurrences of each minute.
"""
def __init__(self, byminute=None, interval=1, tz=None):
"""
Mark every minute in *byminute*; *byminute* can be an int or
sequence. Default is to tick every minute: ``byminute=range(60)``
*interval* is the interval between each iteration. For
example, if ``interval=2``, mark every second occurrence.
"""
if byminute is None:
byminute = range(60)
rule = rrulewrapper(MINUTELY, byminute=byminute, interval=interval,
bysecond=0)
RRuleLocator.__init__(self, rule, tz)
class SecondLocator(RRuleLocator):
"""
Make ticks on occurrences of each second.
"""
def __init__(self, bysecond=None, interval=1, tz=None):
"""
Mark every second in *bysecond*; *bysecond* can be an int or
sequence. Default is to tick every second: ``bysecond = range(60)``
*interval* is the interval between each iteration. For
example, if ``interval=2``, mark every second occurrence.
"""
if bysecond is None:
bysecond = range(60)
rule = rrulewrapper(SECONDLY, bysecond=bysecond, interval=interval)
RRuleLocator.__init__(self, rule, tz)
class MicrosecondLocator(DateLocator):
"""
Make ticks on regular intervals of one or more microsecond(s).
.. note::
By default, Matplotlib uses a floating point representation of time in
days since the epoch, so plotting data with
microsecond time resolution does not work well for
dates that are far (about 70 years) from the epoch (check with
`~.dates.get_epoch`).
If you want sub-microsecond resolution time plots, it is strongly
recommended to use floating point seconds, not datetime-like
time representation.
If you really must use datetime.datetime() or similar and still
need microsecond precision, change the time origin via
`.dates.set_epoch` to something closer to the dates being plotted.
See :doc:`/gallery/ticks_and_spines/date_precision_and_epochs`.
"""
def __init__(self, interval=1, tz=None):
"""
*interval* is the interval between each iteration. For
example, if ``interval=2``, mark every second microsecond.
"""
self._interval = interval
self._wrapped_locator = ticker.MultipleLocator(interval)
self.tz = tz
def set_axis(self, axis):
self._wrapped_locator.set_axis(axis)
return DateLocator.set_axis(self, axis)
def set_view_interval(self, vmin, vmax):
self._wrapped_locator.set_view_interval(vmin, vmax)
return DateLocator.set_view_interval(self, vmin, vmax)
def set_data_interval(self, vmin, vmax):
self._wrapped_locator.set_data_interval(vmin, vmax)
return DateLocator.set_data_interval(self, vmin, vmax)
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 []
return self.tick_values(dmin, dmax)
def tick_values(self, vmin, vmax):
nmin, nmax = date2num((vmin, vmax))
t0 = np.floor(nmin)
nmax = nmax - t0
nmin = nmin - t0
nmin *= MUSECONDS_PER_DAY
nmax *= MUSECONDS_PER_DAY
ticks = self._wrapped_locator.tick_values(nmin, nmax)
ticks = ticks / MUSECONDS_PER_DAY + t0
return ticks
def _get_unit(self):
# docstring inherited
return 1. / MUSECONDS_PER_DAY
def _get_interval(self):
# docstring inherited
return self._interval
def epoch2num(e):
"""
Convert UNIX time to days since Matplotlib epoch.
Parameters
----------
e : list of floats
Time in seconds since 1970-01-01.
Returns
-------
`numpy.array`
Time in days since Matplotlib epoch (see `~.dates.get_epoch()`).
"""
dt = (np.datetime64('1970-01-01T00:00:00', 's') -
np.datetime64(get_epoch(), 's')).astype(float)
return (dt + np.asarray(e)) / SEC_PER_DAY
def num2epoch(d):
"""
Convert days since Matplotlib epoch to UNIX time.
Parameters
----------
d : list of floats
Time in days since Matplotlib epoch (see `~.dates.get_epoch()`).
Returns
-------
`numpy.array`
Time in seconds since 1970-01-01.
"""
dt = (np.datetime64('1970-01-01T00:00:00', 's') -
np.datetime64(get_epoch(), 's')).astype(float)
return np.asarray(d) * SEC_PER_DAY - dt
@cbook.deprecated("3.2")
def mx2num(mxdates):
"""
Convert mx :class:`datetime` instance (or sequence of mx
instances) to the new date format.
"""
scalar = False
if not np.iterable(mxdates):
scalar = True
mxdates = [mxdates]
ret = epoch2num([m.ticks() for m in mxdates])
if scalar:
return ret[0]
else:
return ret
def date_ticker_factory(span, tz=None, numticks=5):
"""
Create a date locator with *numticks* (approx) and a date formatter
for *span* in days. Return value is (locator, formatter).
"""
if span == 0:
span = 1 / HOURS_PER_DAY
mins = span * MINUTES_PER_DAY
hrs = span * HOURS_PER_DAY
days = span
wks = span / DAYS_PER_WEEK
months = span / DAYS_PER_MONTH # Approx
years = span / DAYS_PER_YEAR # Approx
if years > numticks:
locator = YearLocator(int(years / numticks), tz=tz) # define
fmt = '%Y'
elif months > numticks:
locator = MonthLocator(tz=tz)
fmt = '%b %Y'
elif wks > numticks:
locator = WeekdayLocator(tz=tz)
fmt = '%a, %b %d'
elif days > numticks:
locator = DayLocator(interval=math.ceil(days / numticks), tz=tz)
fmt = '%b %d'
elif hrs > numticks:
locator = HourLocator(interval=math.ceil(hrs / numticks), tz=tz)
fmt = '%H:%M\n%b %d'
elif mins > numticks:
locator = MinuteLocator(interval=math.ceil(mins / numticks), tz=tz)
fmt = '%H:%M:%S'
else:
locator = MinuteLocator(tz=tz)
fmt = '%H:%M:%S'
formatter = DateFormatter(fmt, tz=tz)
return locator, formatter
class DateConverter(units.ConversionInterface):
"""
Converter for `datetime.date` and `datetime.datetime` data, or for
date/time data represented as it would be converted by `date2num`.
The 'unit' tag for such data is None or a tzinfo instance.
"""
@staticmethod
def axisinfo(unit, axis):
"""
Return the `~matplotlib.units.AxisInfo` for *unit*.
*unit* is a tzinfo instance or None.
The *axis* argument is required but not used.
"""
tz = unit
majloc = AutoDateLocator(tz=tz)
majfmt = AutoDateFormatter(majloc, tz=tz)
datemin = datetime.date(2000, 1, 1)
datemax = datetime.date(2010, 1, 1)
return units.AxisInfo(majloc=majloc, majfmt=majfmt, label='',
default_limits=(datemin, datemax))
@staticmethod
def convert(value, unit, axis):
"""
If *value* is not already a number or sequence of numbers, convert it
with `date2num`.
The *unit* and *axis* arguments are not used.
"""
return date2num(value)
@staticmethod
def default_units(x, axis):
"""
Return the tzinfo instance of *x* or of its first element, or None
"""
if isinstance(x, np.ndarray):
x = x.ravel()
try:
x = cbook.safe_first_element(x)
except (TypeError, StopIteration):
pass
try:
return x.tzinfo
except AttributeError:
pass
return None
class ConciseDateConverter(DateConverter):
# docstring inherited
def __init__(self, formats=None, zero_formats=None, offset_formats=None,
show_offset=True):
self._formats = formats
self._zero_formats = zero_formats
self._offset_formats = offset_formats
self._show_offset = show_offset
super().__init__()
def axisinfo(self, unit, axis):
# docstring inherited
tz = unit
majloc = AutoDateLocator(tz=tz)
majfmt = ConciseDateFormatter(majloc, tz=tz, formats=self._formats,
zero_formats=self._zero_formats,
offset_formats=self._offset_formats,
show_offset=self._show_offset)
datemin = datetime.date(2000, 1, 1)
datemax = datetime.date(2010, 1, 1)
return units.AxisInfo(majloc=majloc, majfmt=majfmt, label='',
default_limits=(datemin, datemax))
units.registry[np.datetime64] = DateConverter()
units.registry[datetime.date] = DateConverter()
units.registry[datetime.datetime] = DateConverter()
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