Fix examples claiming matplotlib can't plot np.datetime64.

anntzer · anntzer · commit d45dc9680989 · 2019-11-09T22:27:15.000+01:00
This is simply not true anymore, they are natively handled.
diff --git a/examples/recipes/common_date_problems.py b/examples/recipes/common_date_problems.py
@@ -24,23 +24,11 @@
          '2008-10-13', '2008-10-14'], dtype='datetime64[D]')
 
 The dtype of the NumPy record array for the field ``date`` is ``datetime64[D]``
-which means it is a 64-bit `numpy.datetime64` in 'day' units. While this format
-is more portable, Matplotlib cannot plot this format natively yet. We can plot
-this data by changing the dates to `datetime.date` instances instead, which can
-be achieved by converting to an object array::
-
-  In [67]: r.date.astype('O')
-  array([datetime.date(2004, 8, 19), datetime.date(2004, 8, 20),
-         datetime.date(2004, 8, 23), ..., datetime.date(2008, 10, 10),
-         datetime.date(2008, 10, 13), datetime.date(2008, 10, 14)],
-        dtype=object)
-
-The dtype of this converted array is now ``object`` and it is filled with
-datetime.date instances instead.
+which means it is a 64-bit `numpy.datetime64` in 'day' units.
 
 If you plot the data, ::
 
-  In [67]: plot(r.date.astype('O'), r.close)
+  In [67]: plot(r.date, r.close)
   Out[67]: [<matplotlib.lines.Line2D object at 0x92a6b6c>]
 
 you will see that the x tick labels are all squashed together.
@@ -53,35 +41,28 @@
 with cbook.get_sample_data('goog.npz') as datafile:
     r = np.load(datafile)['price_data'].view(np.recarray)
 
-# Matplotlib prefers datetime instead of np.datetime64.
-date = r.date.astype('O')
 fig, ax = plt.subplots()
-ax.plot(date, r.close)
+ax.plot(r.date, r.close)
 ax.set_title('Default date handling can cause overlapping labels')
 
 ###############################################################################
-# Another annoyance is that if you hover the mouse over the window and
-# look in the lower right corner of the matplotlib toolbar
-# (:ref:`navigation-toolbar`) at the x and y coordinates, you see that
-# the x locations are formatted the same way the tick labels are, e.g.,
-# "Dec 2004".
+# Another annoyance is that if you hover the mouse over the window and look in
+# the lower right corner of the Matplotlib toolbar (:ref:`navigation-toolbar`)
+# at the x and y coordinates, you see that the x locations are formatted the
+# same way the tick labels are, e.g.,  "Dec 2004".
 #
-# What we'd like is for the location in the toolbar to have
-# a higher degree of precision, e.g., giving us the exact date out mouse is
-# hovering over.  To fix the first problem, we can use
-# :func:`matplotlib.figure.Figure.autofmt_xdate` and to fix the second
-# problem we can use the ``ax.fmt_xdata`` attribute which can be set to
-# any function that takes a scalar and returns a string.  matplotlib has
-# a number of date formatters built in, so we'll use one of those.
+# What we'd like is for the location in the toolbar to have a higher degree of
+# precision, e.g., giving us the exact date out mouse is hovering over.  To fix
+# the first problem, we can use `.Figure.autofmt_xdate` and to fix the second
+# problem we can use the ``ax.fmt_xdata`` attribute which can be set to any
+# function that takes a scalar and returns a string.  Matplotlib has a number
+# of date formatters built in, so we'll use one of those.
 
 fig, ax = plt.subplots()
-ax.plot(date, r.close)
-
-# rotate and align the tick labels so they look better
+ax.plot(r.date, r.close)
+# Rotate and align the tick labels so they look better.
 fig.autofmt_xdate()
-
-# use a more precise date string for the x axis locations in the
-# toolbar
+# Use a more precise date string for the x axis locations in the toolbar.
 ax.fmt_xdata = mdates.DateFormatter('%Y-%m-%d')
 ax.set_title('fig.autofmt_xdate fixes the labels')
 
diff --git a/examples/recipes/fill_between_alpha.py b/examples/recipes/fill_between_alpha.py
@@ -2,16 +2,14 @@
 Fill Between and Alpha
 ======================
 
-The :meth:`~matplotlib.axes.Axes.fill_between` function generates a
-shaded region between a min and max boundary that is useful for
-illustrating ranges.  It has a very handy ``where`` argument to
-combine filling with logical ranges, e.g., to just fill in a curve over
-some threshold value.
-
-At its most basic level, ``fill_between`` can be use to enhance a
-graphs visual appearance. Let's compare two graphs of a financial
-times with a simple line plot on the left and a filled line on the
-right.
+The :meth:`~matplotlib.axes.Axes.fill_between` function generates a shaded
+region between a min and max boundary that is useful for illustrating ranges.
+It has a very handy ``where`` argument to combine filling with logical ranges,
+e.g., to just fill in a curve over some threshold value.
+
+At its most basic level, ``fill_between`` can be use to enhance a graphs visual
+appearance. Let's compare two graphs of a financial times with a simple line
+plot on the left and a filled line on the right.
 """
 
 import matplotlib.pyplot as plt
@@ -21,15 +19,13 @@
 # load up some sample financial data
 with cbook.get_sample_data('goog.npz') as datafile:
     r = np.load(datafile)['price_data'].view(np.recarray)
-# Matplotlib prefers datetime instead of np.datetime64.
-date = r.date.astype('O')
 # create two subplots with the shared x and y axes
 fig, (ax1, ax2) = plt.subplots(1, 2, sharex=True, sharey=True)
 
 pricemin = r.close.min()
 
-ax1.plot(date, r.close, lw=2)
-ax2.fill_between(date, pricemin, r.close, facecolor='blue', alpha=0.5)
+ax1.plot(r.date, r.close, lw=2)
+ax2.fill_between(r.date, pricemin, r.close, facecolor='blue', alpha=0.5)
 
 for ax in ax1, ax2:
     ax.grid(True)
diff --git a/examples/text_labels_and_annotations/date_index_formatter.py b/examples/text_labels_and_annotations/date_index_formatter.py
@@ -18,13 +18,10 @@
 with cbook.get_sample_data('goog.npz') as datafile:
     r = np.load(datafile)['price_data'].view(np.recarray)
 r = r[-30:]  # get the last 30 days
-# Matplotlib works better with datetime.datetime than np.datetime64, but the
-# latter is more portable.
-date = r.date.astype('O')
 
 # first we'll do it the default way, with gaps on weekends
 fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(8, 4))
-ax1.plot(date, r.adj_close, 'o-')
+ax1.plot(r.date, r.adj_close, 'o-')
 ax1.set_title("Default")
 fig.autofmt_xdate()
 
@@ -35,7 +32,7 @@
 
 def format_date(x, pos=None):
     thisind = np.clip(int(x + 0.5), 0, N - 1)
-    return date[thisind].strftime('%Y-%m-%d')
+    return r.date[thisind].item().strftime('%Y-%m-%d')
 
 
 ax2.plot(ind, r.adj_close, 'o-')
diff --git a/examples/ticks_and_spines/centered_ticklabels.py b/examples/ticks_and_spines/centered_ticklabels.py
@@ -27,12 +27,9 @@
 with cbook.get_sample_data('aapl.npz') as fh:
     r = np.load(fh)['price_data'].view(np.recarray)
 r = r[-250:]  # get the last 250 days
-# 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)
 
 ax.xaxis.set_major_locator(dates.MonthLocator())
 # 16 is a slight approximation since months differ in number of days.
@@ -47,5 +44,5 @@
     tick.label1.set_horizontalalignment('center')
 
 imid = len(r) // 2
-ax.set_xlabel(str(date[imid].year))
+ax.set_xlabel(str(r.date[imid].item().year))
 plt.show()
