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Merge pull request #10841 from jklymak/enh-new-date-formatter
ENH: new date formatter
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‎.flake8

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examples/text_labels_and_annotations/tex_demo.py: E402
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examples/text_labels_and_annotations/watermark_text.py: E402
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examples/ticks_and_spines/auto_ticks.py: E501
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examples/ticks_and_spines/date_concise_formatter.py: E402
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examples/user_interfaces/canvasagg.py: E402
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examples/user_interfaces/embedding_in_gtk3_panzoom_sgskip.py: E402
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examples/user_interfaces/embedding_in_gtk3_sgskip.py: E402

‎doc/users/next_whats_new/concise_date_formatter.rst

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:orphan:
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New date formatter: `~.dates.ConciseDateFormatter`
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--------------------------------------------------
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The automatic date formatter used by default can be quite verbose. A new
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formatter can be accessed that tries to make the tick labels appropriately
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concise.
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.. plot::
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import datetime
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import matplotlib.pyplot as plt
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import matplotlib.dates as mdates
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import numpy as np
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# make a timeseries...
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base = datetime.datetime(2005, 2, 1)
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dates = np.array([base + datetime.timedelta(hours= 2 * i)
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for i in range(732)])
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N = len(dates)
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np.random.seed(19680801)
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y = np.cumsum(np.random.randn(N))
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lims = [(np.datetime64('2005-02'), np.datetime64('2005-04')),
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(np.datetime64('2005-02-03'), np.datetime64('2005-02-15')),
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(np.datetime64('2005-02-03 11:00'), np.datetime64('2005-02-04 13:20'))]
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fig, axs = plt.subplots(3, 1, constrained_layout=True)
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for nn, ax in enumerate(axs):
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# activate the formatter here.
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locator = mdates.AutoDateLocator()
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formatter = mdates.ConciseDateFormatter(locator)
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ax.xaxis.set_major_locator(locator)
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ax.xaxis.set_major_formatter(formatter)
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ax.plot(dates, y)
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ax.set_xlim(lims[nn])
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axs[0].set_title('Concise Date Formatter')
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plt.show()

‎examples/ticks_and_spines/date_concise_formatter.py

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"""
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================================================
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Formatting date ticks using ConciseDateFormatter
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================================================
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Finding good tick values and formatting the ticks for an axis that
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has date data is often a challenge. `~.dates.ConciseDateFormatter` is
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meant to improve the strings chosen for the ticklabels, and to minimize
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the strings used in those tick labels as much as possible.
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.. note::
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This formatter is a candidate to become the default date tick formatter
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in future versions of Matplotlib. Please report any issues or
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suggestions for improvement to the github repository or mailing list.
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"""
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import datetime
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import matplotlib.pyplot as plt
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import matplotlib.dates as mdates
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import numpy as np
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#############################################################################
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# First, the default formatter.
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base = datetime.datetime(2005, 2, 1)
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dates = np.array([base + datetime.timedelta(hours=(2 * i))
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for i in range(732)])
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N = len(dates)
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np.random.seed(19680801)
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y = np.cumsum(np.random.randn(N))
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fig, axs = plt.subplots(3, 1, constrained_layout=True, figsize=(6, 6))
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lims = [(np.datetime64('2005-02'), np.datetime64('2005-04')),
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(np.datetime64('2005-02-03'), np.datetime64('2005-02-15')),
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(np.datetime64('2005-02-03 11:00'), np.datetime64('2005-02-04 13:20'))]
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for nn, ax in enumerate(axs):
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ax.plot(dates, y)
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ax.set_xlim(lims[nn])
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# rotate_labels...
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for label in ax.get_xticklabels():
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label.set_rotation(40)
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label.set_horizontalalignment('right')
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axs[0].set_title('Default Date Formatter')
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plt.show()
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#############################################################################
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# The default date formater is quite verbose, so we have the option of
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# using `~.dates.ConciseDateFormatter`, as shown below. Note that
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# for this example the labels do not need to be rotated as they do for the
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# default formatter because the labels are as small as possible.
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fig, axs = plt.subplots(3, 1, constrained_layout=True, figsize=(6, 6))
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for nn, ax in enumerate(axs):
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locator = mdates.AutoDateLocator(minticks=3, maxticks=7)
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formatter = mdates.ConciseDateFormatter(locator)
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ax.xaxis.set_major_locator(locator)
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ax.xaxis.set_major_formatter(formatter)
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ax.plot(dates, y)
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ax.set_xlim(lims[nn])
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axs[0].set_title('Concise Date Formatter')
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plt.show()
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#############################################################################
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# If all calls to axes that have dates are to be made using this converter,
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# it is probably most convenient to use the units registry where you do
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# imports:
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import matplotlib.units as munits
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converter = mdates.ConciseDateConverter()
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munits.registry[np.datetime64] = converter
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munits.registry[datetime.date] = converter
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munits.registry[datetime.datetime] = converter
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fig, axs = plt.subplots(3, 1, figsize=(6, 6), constrained_layout=True)
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for nn, ax in enumerate(axs):
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ax.plot(dates, y)
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ax.set_xlim(lims[nn])
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axs[0].set_title('Concise Date Formatter')
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plt.show()
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#############################################################################
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# Localization of date formats
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# ============================
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#
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# Dates formats can be localized if the default formats are not desirable by
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# manipulating one of three lists of strings.
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#
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# The ``formatter.formats`` list of formats is for the normal tick labels,
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# There are six levels: years, months, days, hours, minutes, seconds.
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# The ``formatter.offset_formats`` is how the "offset" string on the right
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# of the axis is formatted. This is usually much more verbose than the tick
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# labels. Finally, the ``formatter.zero_formats`` are the formats of the
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# ticks that are "zeros". These are tick values that are either the first of
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# the year, month, or day of month, or the zeroth hour, minute, or second.
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# These are usually the same as the format of
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# the ticks a level above. For example if the axis limts mean the ticks are
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# mostly days, then we label 1 Mar 2005 simply with a "Mar". If the axis
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# limits are mostly hours, we label Feb 4 00:00 as simply "Feb-4".
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#
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# Note that these format lists can also be passed to `.ConciseDateFormatter`
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# as optional kwargs.
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#
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# Here we modify the labels to be "day month year", instead of the ISO
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# "year month day":
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fig, axs = plt.subplots(3, 1, constrained_layout=True, figsize=(6, 6))
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for nn, ax in enumerate(axs):
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locator = mdates.AutoDateLocator()
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formatter = mdates.ConciseDateFormatter(locator)
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formatter.formats = ['%y', # ticks are mostly years
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'%b', # ticks are mostly months
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'%d', # ticks are mostly days
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'%H:%M', # hrs
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'%H:%M', # min
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'%S.%f', ] # secs
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# these are mostly just the level above...
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formatter.zero_formats = [''] + formatter.formats[:-1]
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# ...except for ticks that are mostly hours, then it is nice to have
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# month-day:
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formatter.zero_formats[3] = '%d-%b'
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formatter.offset_formats = ['',
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'%Y',
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'%b %Y',
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'%d %b %Y',
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'%d %b %Y',
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'%d %b %Y %H:%M', ]
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ax.xaxis.set_major_locator(locator)
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ax.xaxis.set_major_formatter(formatter)
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ax.plot(dates, y)
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ax.set_xlim(lims[nn])
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axs[0].set_title('Concise Date Formatter')
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plt.show()
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#############################################################################
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# Registering a converter with localization
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# =========================================
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#
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# `.ConciseDateFormatter` doesn't have rcParams entries, but localization
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# can be accomplished by passing kwargs to `~.ConciseDateConverter` and
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# registering the datatypes you will use with the units registry:
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import datetime
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formats = ['%y', # ticks are mostly years
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'%b', # ticks are mostly months
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'%d', # ticks are mostly days
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'%H:%M', # hrs
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'%H:%M', # min
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'%S.%f', ] # secs
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# these can be the same, except offset by one level....
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zero_formats = [''] + formats[:-1]
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# ...except for ticks that are mostly hours, then its nice to have month-day
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zero_formats[3] = '%d-%b'
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offset_formats = ['',
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'%Y',
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'%b %Y',
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'%d %b %Y',
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'%d %b %Y',
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'%d %b %Y %H:%M', ]
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converter = mdates.ConciseDateConverter(formats=formats,
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zero_formats=zero_formats,
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offset_formats=offset_formats)
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munits.registry[np.datetime64] = converter
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munits.registry[datetime.date] = converter
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munits.registry[datetime.datetime] = converter
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fig, axs = plt.subplots(3, 1, constrained_layout=True, figsize=(6, 6))
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for nn, ax in enumerate(axs):
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ax.plot(dates, y)
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ax.set_xlim(lims[nn])
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axs[0].set_title('Concise Date Formatter registered non-default')
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plt.show()

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