From: <jd...@us...> - 2010-11-04 21:11:58
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Revision: 8771 http://matplotlib.svn.sourceforge.net/matplotlib/?rev=8771&view=rev Author: jdh2358 Date: 2010-11-04 21:11:50 +0000 (Thu, 04 Nov 2010) Log Message: ----------- Merged revisions 8769-8770 via svnmerge from https://matplotlib.svn.sourceforge.net/svnroot/matplotlib/branches/v1_0_maint ........ r8769 | jdh2358 | 2010-11-04 11:28:03 -0700 (Thu, 04 Nov 2010) | 1 line added recipes ........ r8770 | jdh2358 | 2010-11-04 13:50:49 -0700 (Thu, 04 Nov 2010) | 1 line some updates to recipes ........ Modified Paths: -------------- trunk/matplotlib/doc/users/index.rst Added Paths: ----------- trunk/matplotlib/doc/users/recipes.rst Property Changed: ---------------- trunk/matplotlib/ trunk/matplotlib/doc/pyplots/README trunk/matplotlib/doc/sphinxext/gen_gallery.py trunk/matplotlib/doc/sphinxext/gen_rst.py trunk/matplotlib/examples/misc/multiprocess.py trunk/matplotlib/examples/mplot3d/contour3d_demo.py trunk/matplotlib/examples/mplot3d/contourf3d_demo.py trunk/matplotlib/examples/mplot3d/polys3d_demo.py trunk/matplotlib/examples/mplot3d/scatter3d_demo.py trunk/matplotlib/examples/mplot3d/surface3d_demo.py trunk/matplotlib/examples/mplot3d/wire3d_demo.py trunk/matplotlib/lib/matplotlib/sphinxext/mathmpl.py trunk/matplotlib/lib/matplotlib/sphinxext/only_directives.py trunk/matplotlib/lib/matplotlib/sphinxext/plot_directive.py trunk/matplotlib/lib/matplotlib/tests/baseline_images/test_spines/spines_axes_positions.png Property changes on: trunk/matplotlib ___________________________________________________________________ Modified: svnmerge-integrated - 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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 +============================= + +matplotlib allows you to natively plots python datetime instances, and +for the most part does a good job picking tick locations and string +formats. There are a couple of things it does not handle so +gracefully, and here are some tricks to help you work around them. +We'll load up some sample date data which contains datetime.date +objects in a numpy record array:: + + In [63]: datafile = cbook.get_sample_data('goog.npy') + + 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')]) + + In [66]: r.date + Out[66]: + array([2004-08-19, 2004-08-20, 2004-08-23, ..., 2008-10-10, 2008-10-13, + 2008-10-14], dtype=object) + +The dtype of the numpy record array for the field 'date' is '|O4' +which means it is a 4-byte python object pointer; in this case the +objects are datetime.date instances, which we can see when we print +some samples in the ipython terminal window. + +If you plot the data, you will see that the x tick labels are all +squashed together:: + + In [67]: plot(r.date, r.close) + Out[67]: [<matplotlib.lines.Line2D object at 0x92a6b6c>] + +.. plot:: + + import matplotlib.cbook as cbook + datafile = cbook.get_sample_data('goog.npy') + r = np.load(datafile).view(np.recarray) + plt.figure() + plt.plot(r.date, r.close) + 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 +y coordinates, you see that the x locations are formatted the same way +the tick labels are, eg "Dec 2004". What we'd like is for the +location in the toolbar to have a higher degree of precision, eg +giving us the exact date out mouse is hovering over. To fix the first +problem, we can use method:`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 position and +returns a string. matplotlib has a number of date formatters built +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) + fig, ax = plt.subplots(1) + 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 + 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 2004-12-01. + +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, eg to just fill in a curve over +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 +times with a simple line plot on the left and a filled line on the +right. + +.. plot:: + :include-source: + + import matplotlib.cbook as cbook + + # load up some sample financial data + datafile = cbook.get_sample_data('goog.npy') + r = np.load(datafile).view(np.recarray) + + # 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(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) + + ax1.set_ylabel('price') + fig.suptitle('Google (GOOG) daily closing price') + fig.autofmt_xdate() + +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 +see below, the alpha channel is functionally useful as the shaded +regions can overlap and alpha allows you to see both. Note that the +postscript format does not support alpha (this is a postscript +limitation, not a matplotlib limitation), so when using alpha save +your figures in PNG, PDF or SVG. + +Our next example computes two populations of random walkers with a +different mean and standard deviation of the normal distributions from +which there steps are drawn. We use shared regions to plot +/- one +standard deviation of the mean position of the population. Here the +alpha channel is useful, not just aesthetic. + +.. plot:: + :include-source: + + Nsteps, Nwalkers = 100, 250 + t = np.arange(Nsteps) + + # an Nsteps x Nwalkers array of random walk steps + S1 = 0.002 + 0.01*np.random.randn(Nsteps, Nwalkers) + S2 = 0.004 + 0.02*np.random.randn(Nsteps, Nwalkers) + + # an Nsteps x Nwalkers array of random walker positions + X1 = S1.cumsum(axis=0) + X2 = S2.cumsum(axis=0) + + + # Nsteps length arrays empirical means and standard deviations of both + # populations over time + mu1 = X1.mean(axis=1) + sigma1 = X1.std(axis=1) + mu2 = X2.mean(axis=1) + sigma2 = X2.std(axis=1) + + # plot it! + fig, ax = plt.subplots(1) + ax.plot(t, mu1, lw=2, label='mean population 1', color='blue') + ax.plot(t, mu1, lw=2, label='mean population 2', color='yellow') + ax.fill_between(t, mu1+sigma1, mu1-sigma1, facecolor='blue', alpha=0.5) + ax.fill_between(t, mu2+sigma2, mu2-sigma2, facecolor='yellow', alpha=0.5) + ax.set_title('random walkers empirical $\mu$ and $\pm \sigma$ interval') + ax.legend(loc='upper left') + ax.set_xlabel('num steps') + ax.set_ylabel('position') + ax.grid() + + +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 +boolean mask is True. In the example below, we take a a single random +walker and compute the analytic mean and standard deviation of the +population positions. The population mean is shown as the black +dashed line, and the plus/minus one sigma deviation from the mean is +showsn as the yellow filled region. We use the where mask +``X>upper_bound`` to find the region where the walker is above the +one sigma boundary, and shade that region blue. + +.. plot:: + :include-source: + + np.random.seed(1234) + + Nsteps = 500 + t = np.arange(Nsteps) + + mu = 0.002 + sigma = 0.01 + + # the steps and position + S = mu + sigma*np.random.randn(Nsteps) + X = S.cumsum() + + # the 1 sigma upper and lower population bounds + lower_bound = mu*t - sigma*np.sqrt(t) + upper_bound = mu*t + sigma*np.sqrt(t) + + fig, ax = plt.subplots(1) + ax.plot(t, X, lw=2, label='walker position', color='blue') + ax.plot(t, mu*t, lw=1, label='population mean', color='black', ls='--') + ax.fill_between(t, lower_bound, upper_bound, facecolor='yellow', alpha=0.5, + label='1 sigma range') + ax.legend(loc='upper left') + + # here we use the where argument to only fill the region where the + # walker is above the population 1 sigma boundary + ax.fill_between(t, upper_bound, X, where=X>upper_bound, facecolor='blue', alpha=0.5) + ax.set_xlabel('num steps') + ax.set_ylabel('position') + ax.grid() + + +Another handy use of filled regions is to highlight horizontal or +vertical spans of an axes -- for that matplotlib has some helper +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) + Property changes on: trunk/matplotlib/examples/misc/multiprocess.py ___________________________________________________________________ Modified: svn:mergeinfo - /branches/v0_91_maint/examples/misc/log.py:5753-5771 /branches/v0_98_5_maint/examples/misc/log.py:6581,6585,6587,6589-6609,6614,6616,6625,6652,6660-6662,6672-6673,6714-6715,6717-6732,6752-6754,6761-6773,6781,6792,6800,6802,6805,6809,6811,6822,6827,6850,6854,6856,6859,6861-6873,6883-6884,6886,6890-6891,6906-6909,6911-6912,6915-6916,6918,6920-6925,6927-6928,6934,6941,6946,6948,6950,6952,6960,6972,6984-6985,6990,6995,6997-7001,7014,7016,7018,7024-7025,7033,7035,7042,7072,7080 /branches/v0_99_maint/examples/misc/multiprocess.py:7323-7338,7393,7395-7404,7407-7424,7428-7433,7442-7444,7446,7475-7482,7484,7486,7489-7523,7567,7569,7582-7584,7616-7618,7633,7638,7703,7727-7734,7740-7741,7745,7751,7756,7762,7770,7772,7774,7776-7778,7780,7784,7788,7790,7792,7794,7796,7800,7803,7808,7822,7827,7834,7837,7844,7846-7847,7849,7858,7864,7866-7867,7874,7884,7896,7901-7903,7916,7919,7924,7928,7944,7952,7970,7972,7981,7983,7989-7991,7998,8001,8003,8016-8057,8068,8070,8092-8116,8121-8135 /branches/v1_0_maint/examples/misc/multiprocess.py:8521,8524-8541,8543,8549,8554,8557,8559-8564,8566,8573,8575,8577,8579,8581,8583,8585,8588,8590,8593,8595,8601,8603,8610,8614,8627-8628,8630,8632,8643,8645,8647-8667,8673,8675,8677,8679,8690-8697,8704,8706,8708,8714-8715,8717-8720,8722-8725,8732-8734,8747,8764 + /branches/v0_91_maint/examples/misc/log.py:5753-5771 /branches/v0_98_5_maint/examples/misc/log.py:6581,6585,6587,6589-6609,6614,6616,6625,6652,6660-6662,6672-6673,6714-6715,6717-6732,6752-6754,6761-6773,6781,6792,6800,6802,6805,6809,6811,6822,6827,6850,6854,6856,6859,6861-6873,6883-6884,6886,6890-6891,6906-6909,6911-6912,6915-6916,6918,6920-6925,6927-6928,6934,6941,6946,6948,6950,6952,6960,6972,6984-6985,6990,6995,6997-7001,7014,7016,7018,7024-7025,7033,7035,7042,7072,7080 /branches/v0_99_maint/examples/misc/multiprocess.py:7323-7338,7393,7395-7404,7407-7424,7428-7433,7442-7444,7446,7475-7482,7484,7486,7489-7523,7567,7569,7582-7584,7616-7618,7633,7638,7703,7727-7734,7740-7741,7745,7751,7756,7762,7770,7772,7774,7776-7778,7780,7784,7788,7790,7792,7794,7796,7800,7803,7808,7822,7827,7834,7837,7844,7846-7847,7849,7858,7864,7866-7867,7874,7884,7896,7901-7903,7916,7919,7924,7928,7944,7952,7970,7972,7981,7983,7989-7991,7998,8001,8003,8016-8057,8068,8070,8092-8116,8121-8135 /branches/v1_0_maint/examples/misc/multiprocess.py:8521,8524-8541,8543,8549,8554,8557,8559-8564,8566,8573,8575,8577,8579,8581,8583,8585,8588,8590,8593,8595,8601,8603,8610,8614,8627-8628,8630,8632,8643,8645,8647-8667,8673,8675,8677,8679,8690-8697,8704,8706,8708,8714-8715,8717-8720,8722-8725,8732-8734,8747,8764,8769-8770 Property changes on: trunk/matplotlib/examples/mplot3d/contour3d_demo.py ___________________________________________________________________ Modified: svn:mergeinfo - 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