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Exploring Chaco with IPython
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Chaco has an interactive plotting mode similar to, but currently more limited
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than matplotlib's. This plotting mode is also available as an Envisage plugin,
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and so can be made available within end-user applications that feature an
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Envisage-based Python prompt.
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To get started, you need to run iPython with the ``--gui=wx`` option enabled,
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so that the iPython and wx event loops interact correctly [#guiqt]_ ::
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You could instead start in ``-pylab`` mode if you prefer, which has the advantage of
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pre-loading numpy and some other useful libraries.
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Once you have the iPython prompt, you can accesss the Chaco shell mode commands
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In [1]: from chaco.shell import *
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We'll start by creating some data that we want to plot::
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In [2]: from numpy import *
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In [3]: x = linspace(-2*pi,2*pi, 100)
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.. image:: images/ipython_basic.png
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If you experiment with the plot, you'll see that it has the standard
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Chaco pan and zoom tools enabled. As with Matplotlib, you can specify
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options for the display of the plot as additional arguments and keyword
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arguments to the plot command. The most important of these is is the
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format string argument, which resembles the Matplotlib format strings::
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In [6]: plot(x, y, 'g:')
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This creates a green, dotted line plot of the data. You could instead
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create a red scatter plot of the data with circles for markers using::
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In [7]: plot(x, y, 'ro')
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You'll notice that each of these plot commands replaces the current plot
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with the new plot. If you want to overlay the plots, you need to instruct
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Chaco to ``hold()`` the plots::
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In [9]: plot(x, cos(x), 'b-')
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In [10]: plot(x, sin(2*x), 'y^')
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.. image:: images/ipython_multi.png
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Calling ``hold()`` again will toggle back to the previous
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You can also plot multiple curves with one plot command. The following
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single plot call is equivalent to the above three::
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In [12]: plot(x, y, 'ro', x, cos(x), 'b-', x, sin(2*x), 'y^')
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The Chaco shell interface supports a subset of the standard Chaco plots.
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You can do line, scatter, image, pseudocolor, and contour plots.
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To illustrate some of these different plot types, let's create a couple
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of 2D gaussians and plot them::
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In [13]: x1 = random.normal(-0.5, 1., 100)
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In [14]: y1 = random.normal(-1.25, 0.5, 100)
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In [15]: x2 = random.normal(0 ,0.25, 50)
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In [16]: y2 = random.normal(0, 0.5, 50)
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In [17]: plot(x1, y1, 'ro', x2, y2, 'go')
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We'll now create a kernel density estimator for the combined data set,
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In [18]: x = concatenate((x1, x2))
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In [19]: y = concatenate((y1, y2))
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In [20]: dataset = array([x, y])
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In [21]: import scipy.stats
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In [22]: kde = scipy.stats.gaussian_kde(dataset)
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Now that we have the distribution, we sample it at a bunch of points on a grid::
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In [23]: xs = linspace(-4, 4, 100)
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In [24]: ys = linspace(-4, 4, 100)
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In [25]: xpoints, ypoints = meshgrid(xs, ys)
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In [26]: points = array([xpoints.flatten(), ypoints.flatten()])
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In [27]: z = kde(points)
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In [28]: z.shape = (100, 100)
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Finally, we can plot the contours. For grid-based plots like contours and images,
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we need to supply the x- and y-coordinates of the edges of the pixels, rather than
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In [29]: xedges = linspace(-4.06125, 4.06125, 101)
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In [30]: yedges = linspace(-4.06125, 4.06125, 101)
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In [32]: contour(xedges, yedges, z)
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.. image:: images/ipython_kde.png
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Other related plotting commands which are available include ``imshow()``, ``contourf()``
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and ``pshow()``. For example::
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In [33]: pshow(xedges, yedges, z)
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will plot a pseudo-color image of our sampling of the KDE.
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You can add plot and axis titles to your plot easily::
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In [34]: title('The Kernel Density Estimator')
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and you can toggle whether or not grids are drawn by::
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toggles the display of the legend. These toggling commands can optionally take a
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boolean value which instead of toggling the display will either always show or hide
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the grid or legend. For example::
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In [40]: legend(False)
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will ensure that the legend is hidden. You can toggle the axes completely with::
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but you can additionally gain quite fine-grained control over display of the axes
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by passing keyword arguments to these commands. For example, to display the y-axis
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on the right instead of the left, you would do::
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In [43]: yaxis(orientation='right')
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You can see the complete set of available keyword arguments via ipython's help::
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Base Class: <type 'function'>
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String Form: <function yaxis at 0x1e4e25f0>
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Namespace: Interactive
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File: /Users/cwebster/src/ets/chaco/enthought/chaco/shell/commands.py
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Definition: yaxis(**kwds)
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Configures the y-axis.
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* ``yaxis()``: toggles the vertical axis on or off.
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* ``yaxis(**kwds)``: set parameters of the vertical axis.
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The text of the title
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title_font : KivaFont('modern 12')
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The font in which to render the title
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title_color : color ('color_name' or (red, green, blue, [alpha]) tuple)
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The color in which to render the title
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If you have a plot in a state that you are happy with, you can save the current
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image with the ``save()`` command::
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In [45]: save('my_plot.png')
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Log Plots and Time-Series
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=========================
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The Chaco ipython shell can create plots with logarithmic axes. If you know at the time
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that you create the plot that you want log axes, you can use one of the commands
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``semilogx()``, ``semilogy()`` or ``loglog()`` as you would the usual ``plot()`` command::
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In [46]: x = linspace(0, 10, 101)
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In [47]: y = exp(x**2)
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In [48]: semilogy(x, y)
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If you have already created a plot, and you decide that it would be clearer with a
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logarithmic scale on an axis, you can set this with the `xscale()` and `yscale()`
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In [49]: xscale('log')
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You can set it back to a linear scale in the same way::
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In [50]: xscale('linear')
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Time axes are handled in a similar way. Chaco expects times to be represented as
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floating point numbers giving seconds since the epoch, the same as ``time.time()``.
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Given a plot with a set of index values expressed as times in this fashion, you
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can specify the scale as ``'time'`` and Chaco will display tick marks on the axis
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In [51]: x = linspace(time.time(), time.time()+7*24*60*60, 360)
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In [52]: y = random.uniform(size=360)
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In [54]: xscale('time')
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In addition to the ``hold()`` command discussed earlier, there are several commands
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that you can use to control the creation of new Chaco windows for plotting in to,
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and determining which one is currently active::
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In [55]: figure('fig2', 'My Second Plot')
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creates a new window with the identifier ``'fig2'`` which will have "My Second Plot"
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displayed as the title of the window. Any new plots after this command will appear
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in this window. You can switch to an existing window using the ``activate()`` command,
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referring to the window either by index or name::
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will make the original plot window the current window, while::
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In [57]: activate('fig2')
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will switch back to our second window.
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For advanced users, you can get a reference to the current Chaco plot object using the
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``curplot()`` command. When you have this, you then have full access to the programatic
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Chaco plot API described elsewhere.
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Finally, you can use the ``chaco.shell`` API from Python scripts instead of interactively
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if you prefer. In this case, because you do not have ipython around to set up the GUI
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mainloop with the ``--gui=wx`` option, you need to use the ``show()``
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command to start the GUI mainloop and display the windows that you have created.
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.. rubric:: Footnotes
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.. [#guiqt] Starting from IPython 0.12, it is possible to use the Qt backend
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with ``--gui=qt``. Make sure that the environment variable ``QT_API``
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is set correctly, as described `here
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<http://ipython.org/ipython-doc/dev/interactive/reference.html?highlight=qt_api#pyqt-and-pyside>`_
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docs/source/user_manual/tutorial_ipython.rst
