Revision: 8770 http://matplotlib.svn.sourceforge.net/matplotlib/?rev=8770&view=rev Author: jdh2358 Date: 2010年11月04日 20:50:49 +0000 (2010年11月04日) Log Message: ----------- some updates to recipes Modified Paths: -------------- branches/v1_0_maint/doc/users/recipes.rst Modified: branches/v1_0_maint/doc/users/recipes.rst =================================================================== --- branches/v1_0_maint/doc/users/recipes.rst 2010年11月04日 18:28:03 UTC (rev 8769) +++ branches/v1_0_maint/doc/users/recipes.rst 2010年11月04日 20:50:49 UTC (rev 8770) @@ -8,6 +8,66 @@ that illustrate some of the useful idioms and tricks to make snazzier figures and overcome some matplotlib warts. + +Sharing axis limits and views +============================= + +It's common to make two or more plots which share an axis, eg two +subplots with time as a common axis. When you pan and zoom around on +one, you want the other to move around with you. To facilitate this, +matplotlib Axes support a ``sharex`` and ``sharey`` attribute. When +you create a :func:`~matplotlib.pyplot.subplot` or +:func:`~matplotlib.pyplot.axes` instance, you can pass in a keyword +indicating what axes you want to share with + +.. sourcecode:: ipython + + In [96]: t = np.arange(0, 10, 0.01) + + In [97]: ax1 = plt.subplot(211) + + In [98]: ax1.plot(t, np.sin(2*np.pi*t)) + Out[98]: [<matplotlib.lines.Line2D object at 0x98719ec>] + + In [99]: ax2 = plt.subplot(212, sharex=ax1) + + In [100]: ax2.plot(t, np.sin(4*np.pi*t)) + Out[100]: [<matplotlib.lines.Line2D object at 0xb7d8fec>] + +Easily creating subplots +======================== + +In early versions of matplotlib, if you wanted to use the pythonic API +and create a figure instance and from that create a grid of subplots, +possibly with shared axes, it involved a fair amount of boilerplate +code. Eg + +.. sourcecode:: python + + # old style + fig = plt.figure() + ax1 = fig.add_subplot(221) + ax2 = fig.add_subplot(222, sharex=ax1, sharey=ax1) + ax3 = fig.add_subplot(223, sharex=ax1, sharey=ax1) + ax3 = fig.add_subplot(224, sharex=ax1, sharey=ax1) + +Fernando Perez has provided a nice top level method to create in +:func:`~matplotlib.pyplots.subplots` (note the "s" at the end) +everything at once, and turn off x and y sharing for the whole bunch. +You can either unpack the axes individually:: + + # new style method 1 + fig, (ax1, ax2, ax3, ax4) = plt.subplots(2, 2, sharex=True, sharey=True) + ax1.plot(x) + +or get them back as a numrows x numcolumns object array which supports +numpy indexing:: + + # new style method 2 + fig, axs = plt.subplots(2, 2, sharex=True, sharey=True) + axs[0,0].plot(x) + + Fixing common date annoyances ============================= @@ -23,7 +83,9 @@ In [64]: r = np.load(datafile).view(np.recarray) In [65]: r.dtype - Out[65]: dtype([('date', '|O4'), ('', '|V4'), ('open', '<f8'), ('high', '<f8'), ('low', '<f8'), ('close', '<f8'), ('volume', '<i8'), ('adj_close', '<f8')]) + Out[65]: dtype([('date', '|O4'), ('', '|V4'), ('open', '<f8'), + ('high', '<f8'), ('low', '<f8'), ('close', '<f8'), + ('volume', '<i8'), ('adj_close', '<f8')]) In [66]: r.date Out[66]: @@ -48,7 +110,7 @@ r = np.load(datafile).view(np.recarray) plt.figure() plt.plot(r.date, r.close) - plt.show() + plt.title('Default date handling can cause overlapping labels') Another annoyance is that if you hover the mouse over a the window and look in the lower right corner of the matplotlib toolbar at the x and @@ -63,8 +125,8 @@ im, so we'll use one of those. .. plot:: + :include-source: - import matplotlib.cbook as cbook datafile = cbook.get_sample_data('goog.npy') r = np.load(datafile).view(np.recarray) @@ -78,17 +140,19 @@ # toolbar import matplotlib.dates as mdates ax.fmt_xdata = mdates.DateFormatter('%Y-%m-%d') + plt.title('autfmt_xdate fixes the labels') +Now when you hover your mouse over the plotted data, you'll see date +format strings like 2004年12月01日. - Fill Between and Alpha ====================== -The :method:`~matplotlib.axes.Axes.fill_between` function generates a +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, eg to just fill in a curve over -some threshold value. +some threshold value. At it's most basic level, ``fill_between`` can be use to enhance a graphs visual appearance. Let's compare two graphs of a financial @@ -118,7 +182,6 @@ ax1.set_ylabel('price') fig.suptitle('Google (GOOG) daily closing price') fig.autofmt_xdate() - plt.show() The alpha channel is not necessary here, but it can be used to soften colors for more visually appealing plots. In other examples, as we'll @@ -167,10 +230,8 @@ ax.set_xlabel('num steps') ax.set_ylabel('position') ax.grid() - plt.show() - The where keyword argument is very handy for highlighting certain regions of the graph. Where takes a boolean mask the same length as the x, ymin and ymax arguments, and only fills in the region where the @@ -214,11 +275,74 @@ ax.set_xlabel('num steps') ax.set_ylabel('position') ax.grid() - plt.show() Another handy use of filled regions is to highlight horizontal or vertical spans of an axes -- for that matplotlib has some helper -functions :method:`~matplotlib.axes.Axes.axhspan` and -:method:`~matplotlib.axes.Axes.axvspan` and example +functions :meth:`~matplotlib.axes.Axes.axhspan` and +:meth:`~matplotlib.axes.Axes.axvspan` and example :ref:`pylab_examples-axhspan_demo`. + + +Transparent, fancy legends +========================== + +Sometimes you know what your data looks like before you plot it, and +mak know for instance that there won't be much data in the upper right +hand corner. Then you can safely create a legend that doesn't overlay +your data:: + + ax.legend(loc='upper right') + +Other times you don't know where your data is, and loc='best' will try +and place the legend:: + + ax.legend(loc='upper right') + +but still, your legend may overlap your data, and in these cases it's +nice to make the legend frame transparent. + + +.. plot:: + :include-source: + + np.random.seed(1234) + fig, ax = plt.subplots(1) + ax.plot(np.random.randn(300), 'o-', label='normal distribution') + ax.plot(np.random.rand(300), 's-', label='uniform distribution') + ax.set_ylim(-3, 3) + leg = ax.legend(loc='best', fancybox=True) + leg.get_frame().set_alpha(0.5) + + ax.set_title('fancy, transparent legends') + + +Placing text boxes +================== + +When decorating axes with text boxes, two useful tricks are to place +the text in axes coordinates (see :ref:`transforms_tutorial`), so the +text doesn't move around with changes in x or y limits. You can also +use the bbox property of text to surround the text with a +:class:`~matplotlib.patches.Patch` instance -- the boox keyword argument +takes a dictionary with keys that are Patch properties. + +.. plot:: + :include-source: + + np.random.seed(1234) + fig, ax = plt.subplots(1) + x = 30*np.random.randn(10000) + mu = x.mean() + median = np.median(x) + sigma = x.std() + textstr = '$\mu=%.2f$\n$\mathrm{median}=%.2f$\n$\sigma=%.2f$'%(mu, median, sigma) + + ax.hist(x, 50) + # these are matplotlib.patch.Patch properies + props = dict(boxstyle='round', facecolor='wheat', alpha=0.5) + + # place a text box in upper left in axes coords + ax.text(0.05, 0.95, textstr, transform=ax.transAxes, fontsize=14, + verticalalignment='top', bbox=props) + This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site.