8000 DOC: for datetime64 support by jklymak · Pull Request #9794 · matplotlib/matplotlib · GitHub
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DOC: for datetime64 support #9794

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Nov 16, 2017
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DOC: for datetime64 support #9794

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10 changes: 10 additions & 0 deletions doc/users/next_whats_new/2017_11_15_datetime64.rst
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@@ -0,0 +1,10 @@
Support for numpy.datetime64
----------------------------

Matplotlib has supported `datetime.datetime` dates for a long time in
`matplotlib.dates`. We
now support `numpy.datetime64` dates as well. Anywhere that
`dateime.datetime` could be used, `numpy.datetime64` can be used. eg::

time = np.arange('2005-02-01', '2005-02-02', dtype='datetime64[h]')
plt.plot(time)
25 changes: 12 additions & 13 deletions examples/api/date.py
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Expand Up @@ -7,13 +7,15 @@
formatters. See major_minor_demo1.py for more information on
controlling major and minor ticks

All matplotlib date plotting is done by converting date instances into
days since the 0001-01-01 UTC. The conversion, tick locating and
formatting is done behind the scenes so this is most transparent to
you. The dates module provides several converter functions date2num
and num2date

All matplotlib date plotting is done by converting date instances into days
since 0001-01-01 00:00:00 UTC plus one day (for historical reasons). The
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What historical reasons? Is there a link to the discussion anywhere?

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http://matplotlib.org/api/dates_api.html

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Awesome. can you link to that discussion in this text to provide context.

conversion, tick locating and formatting is done behind the scenes so this
is most transparent to you. The dates module provides several converter
functions `matplotlib.dates.date2num` and `matplotlib.dates.num2date`.
These can convert between `datetime.datetime` objects and
:class:`numpy.datetime64` objects.
"""

import datetime
import numpy as np
import matplotlib.pyplot as plt
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# stores the date as an np.datetime64 with a day unit ('D') in the date column.
with cbook.get_sample_data('goog.npz') as datafile:
r = np.load(datafile)['price_data'].view(np.recarray)
# Matplotlib works better with datetime.datetime than np.datetime64, but the
# latter is more portable.
date = r.date.astype('O')

fig, ax = plt.subplots()
ax.plot(date, r.adj_close)

ax.plot(r.date, r.adj_close)

# format the ticks
ax.xaxis.set_major_locator(years)
ax.xaxis.set_major_formatter(yearsFmt)
ax.xaxis.set_minor_locator(months)

datemin = datetime.date(date.min().year, 1, 1)
datemax = datetime.date(date.max().year + 1, 1, 1)
# round to nearest years...
datemin = np.datetime64(r.date[0], 'Y')
datemax = np.datetime64(r.date[-1], 'Y') + np.timedelta64(1, 'Y')
ax.set_xlim(datemin, datemax)


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