Question

我有两个numpy数组1D，一个是datetime64格式的测量时间，例如：

array([2011-11-15 01:08:11, 2011-11-16 02:08:04, ..., 2012-07-07 11:08:00], dtype=datetime64[us])

和其他具有整数数据的长度和尺寸相同的数组我想在matplotlib时间与数据中制作一个图。如果我直接输入数据，这就是我得到的：

plot(timeSeries, data)

enter image description here

有没有办法让时间更自然？例如，在这种情况下，月/年可以没事。

编辑：
我试过Gustav Larsson的建议，但是我收到了一个错误：

Out[128]:
[<matplotlib.lines.Line2D at 0x419aad0>]
---------------------------------------------------------------------------
OverflowError                             Traceback (most recent call last)
/usr/lib/python2.7/dist-packages/IPython/zmq/pylab/backend_inline.pyc in show(close)
    100     try:
    101         for figure_manager in Gcf.get_all_fig_managers():
--> 102             send_figure(figure_manager.canvas.figure)
    103     finally:
    104         show._to_draw = []

/usr/lib/python2.7/dist-packages/IPython/zmq/pylab/backend_inline.pyc in send_figure(fig)
    209     """
    210     fmt = InlineBackend.instance().figure_format
--> 211     data = print_figure(fig, fmt)
    212     # print_figure will return None if there's nothing to draw:
    213     if data is None:

/usr/lib/python2.7/dist-packages/IPython/core/pylabtools.pyc in print_figure(fig, fmt)
    102     try:
    103         bytes_io = BytesIO()
--> 104         fig.canvas.print_figure(bytes_io, format=fmt, bbox_inches='tight')
    105         data = bytes_io.getvalue()
    106     finally:

/usr/lib/pymodules/python2.7/matplotlib/backend_bases.pyc in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)
   1981                     orientation=orientation,
   1982                     dryrun=True,
-> 1983                     **kwargs)
   1984                 renderer = self.figure._cachedRenderer
   1985                 bbox_inches = self.figure.get_tightbbox(renderer)

/usr/lib/pymodules/python2.7/matplotlib/backends/backend_agg.pyc in print_png(self, filename_or_obj, *args, **kwargs)
    467 
    468     def print_png(self, filename_or_obj, *args, **kwargs):
--> 469         FigureCanvasAgg.draw(self)
    470         renderer = self.get_renderer()
    471         original_dpi = renderer.dpi

/usr/lib/pymodules/python2.7/matplotlib/backends/backend_agg.pyc in draw(self)
    419 
    420         try:
--> 421             self.figure.draw(self.renderer)
    422         finally:
    423             RendererAgg.lock.release()

/usr/lib/pymodules/python2.7/matplotlib/artist.pyc in draw_wrapper(artist, renderer, *args, **kwargs)
     53     def draw_wrapper(artist, renderer, *args, **kwargs):
     54         before(artist, renderer)
---> 55         draw(artist, renderer, *args, **kwargs)
     56         after(artist, renderer)
     57 

/usr/lib/pymodules/python2.7/matplotlib/figure.pyc in draw(self, renderer)
    896         dsu.sort(key=itemgetter(0))
    897         for zorder, a, func, args in dsu:
--> 898             func(*args)
    899 
    900         renderer.close_group('figure')

/usr/lib/pymodules/python2.7/matplotlib/artist.pyc in draw_wrapper(artist, renderer, *args, **kwargs)
     53     def draw_wrapper(artist, renderer, *args, **kwargs):
     54         before(artist, renderer)
---> 55         draw(artist, renderer, *args, **kwargs)
     56         after(artist, renderer)
     57 

/usr/lib/pymodules/python2.7/matplotlib/axes.pyc in draw(self, renderer, inframe)
   1995 
   1996         for zorder, a in dsu:
-> 1997             a.draw(renderer)
   1998 
   1999         renderer.close_group('axes')

/usr/lib/pymodules/python2.7/matplotlib/artist.pyc in draw_wrapper(artist, renderer, *args, **kwargs)
     53     def draw_wrapper(artist, renderer, *args, **kwargs):
     54         before(artist, renderer)
---> 55         draw(artist, renderer, *args, **kwargs)
     56         after(artist, renderer)
     57 

/usr/lib/pymodules/python2.7/matplotlib/axis.pyc in draw(self, renderer, *args, **kwargs)
   1039         renderer.open_group(__name__)
   1040 
-> 1041         ticks_to_draw = self._update_ticks(renderer)
   1042         ticklabelBoxes, ticklabelBoxes2 = self._get_tick_bboxes(ticks_to_draw, renderer)
   1043 

/usr/lib/pymodules/python2.7/matplotlib/axis.pyc in _update_ticks(self, renderer)
    929 
    930         interval = self.get_view_interval()
--> 931         tick_tups = [ t for t in self.iter_ticks()]
    932         if self._smart_bounds:
    933             # handle inverted limits

/usr/lib/pymodules/python2.7/matplotlib/axis.pyc in iter_ticks(self)
    876         Iterate through all of the major and minor ticks.
    877         """
--> 878         majorLocs = self.major.locator()
    879         majorTicks = self.get_major_ticks(len(majorLocs))
    880         self.major.formatter.set_locs(majorLocs)

/usr/lib/pymodules/python2.7/matplotlib/dates.pyc in __call__(self)
    747     def __call__(self):
    748         'Return the locations of the ticks'
--> 749         self.refresh()
    750         return self._locator()
    751 

/usr/lib/pymodules/python2.7/matplotlib/dates.pyc in refresh(self)
    756     def refresh(self):
    757         'Refresh internal information based on current limits.'
--> 758         dmin, dmax = self.viewlim_to_dt()
    759         self._locator = self.get_locator(dmin, dmax)
    760 

/usr/lib/pymodules/python2.7/matplotlib/dates.pyc in viewlim_to_dt(self)
    528     def viewlim_to_dt(self):
    529         vmin, vmax = self.axis.get_view_interval()
--> 530         return num2date(vmin, self.tz), num2date(vmax, self.tz)
    531 
    532     def _get_unit(self):

/usr/lib/pymodules/python2.7/matplotlib/dates.pyc in num2date(x, tz)
    287     """
    288     if tz is None: tz = _get_rc_timezone()
--> 289     if not cbook.iterable(x): return _from_ordinalf(x, tz)
    290     else: return [_from_ordinalf(val, tz) for val in x]
    291 

/usr/lib/pymodules/python2.7/matplotlib/dates.pyc in _from_ordinalf(x, tz)
    201     if tz is None: tz = _get_rc_timezone()
    202     ix = int(x)
--> 203     dt = datetime.datetime.fromordinal(ix)
    204     remainder = float(x) - ix
    205     hour, remainder = divmod(24*remainder, 1)

OverflowError: signed integer is greater than maximum

这可能是个错误吗？或者我错过了什么。我也尝试过简单的事情：

import matplotlib.pyplot as plt
import numpy as np
dates=np.array(["2011-11-13", "2011-11-14", "2011-11-15", "2011-11-16", "2011-11-19"], dtype='datetime64[us]')
data=np.array([1, 2, 3, 4, 5])
plt.plot_date(dates, data)
plt.show()

我仍然收到此错误：

OverflowError: signed integer is greater than maximum

我不明白我做错了什么。 ipython 0.13，matplotlib 1.1，Ubuntu 12.04 x64。

最终编辑：
似乎matplotlib不支持dtype=datetime64，因此我需要将timeSeries转换为来自datetime.datetime的普通datetime。

Answer 1

from datetime import datetime
a=np.datetime64('2002-06-28').astype(datetime)
plot_date(a,2)

Answer 2

您可能想尝试一下：

plot_date(timeSeries, data)

默认情况下，x轴将被视为日期轴，y将被视为常规轴。这可以定制。

Answer 3

Matplotlib> = 2.2本机支持绘制datetime64数组。参见https://github.com/matplotlib/matplotlib/blob/master/doc/users/prev_whats_new/whats_new_2.2.rst#support-for-numpydatetime64：

Matplotlib在很长一段时间内一直支持datetime.datetime日期 matplotlib.dates。现在，我们也支持numpy.datetime64日期。可以使用dateime.datetime的任何地方，可以使用numpy.datetime64 用过的。例如：
time = np.arange('2005-02-01', '2005-02-02', dtype='datetime64[h]')
plt.plot(time)

Answer 4

我遇到了类似的问题。有时，日期轴正确地绘制了我的 np.datetim64 数组，而有时它没有使用相同的时间数组，而是在日期轴上给出一些无法识别的整数值。

原因是我在第一次使用对数刻度后设置了 ax.xscale('linear')。删除 ax.xscale('linear') 解决了这个问题。我了解到，线性轴不是日期时间轴。

使用matplotlib绘制numpy datetime64

4 个答案: