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more screenshot updates
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‎doc/_static/eeg_large.png‎

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‎doc/_static/eeg_small.png‎

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‎doc/_templates/index.html‎

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@@ -22,12 +22,10 @@ <h1>Welcome</h1>
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For the power user, you have full control of line styles, font
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properties, axes properties, etc, via an object oriented interface
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or via a handle graphics interface familiar to matlab users. A
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summary of the goals of matplotlib and the progress so far can be
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found <a href=goals.html>here</a>.<p>
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or via a handle graphics interface familiar to matlab users.
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The plotting functions in the <a href=api/pyplot_api.html>pyplot
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interface</a> have a high degree of Matlab&reg compatibility.<p>
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The plotting functions in the <a href=api/pyplot_api.html>pyplot</a>
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interface have a high degree of Matlab&reg compatibility.<p>
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<br>
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<table border=1 cellpadding=3 cellspacing=2>

‎doc/devel/documenting_mpl.rst‎

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in mpl and let it handle the scaling. ``:include-source:`` will
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present the contents of the file, marked up as source code.
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You can also point to local files with relative path. Use the
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sym-link for mpl_examples in case we do a reorganization of the doc
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directory at some point, eg::
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.. plot:: ../mpl_examples/pylab_examples/simple_plot.py
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If the example file needs to access data, it is easy to get screwed up
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with relative paths since the python example may be run from a diffent
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location in the plot directive build framework. To work around this,
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you can add your example data to mpl-data/example and refer to it in
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the example file like so::
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import matplotlib
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# datafile is a file object
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datafile = matplotlib.get_example_data('goog.npy')
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r = np.load(datafile).view(np.recarray)
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Try to keep the example datafiles relatively few and relatively small
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to control the size of the binaries we ship.
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Static figures
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--------------
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‎doc/pyplots/plotmap.hires.png‎

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‎doc/pyplots/plotmap.pdf‎

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‎doc/pyplots/plotmap.png‎

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‎doc/pyplots/plotmap.py‎

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nx = int((m.xmax-m.xmin)/40000.)+1; ny = int((m.ymax-m.ymin)/40000.)+1
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topodat,x,y = m.transform_scalar(topoin,lons,lats,nx,ny,returnxy=True)
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# create the figure.
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fig=figure(figsize=(8,8))
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fig=figure(figsize=(6,6))
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# add an axes, leaving room for colorbar on the right.
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ax = fig.add_axes([0.1,0.1,0.7,0.7])
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# plot image over map with imshow.
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colorbar(cax=cax) # draw colorbar
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axes(ax) # make the original axes current again
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# plot blue dot on boulder, colorado and label it as such.
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xpt,ypt = m(-104.237,40.125)
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m.plot([xpt],[ypt],'bo')
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xpt,ypt = m(-104.237,40.125)
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m.plot([xpt],[ypt],'bo')
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text(xpt+100000,ypt+100000,'Boulder')
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# draw coastlines and political boundaries.
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m.drawcoastlines()

‎doc/users/screenshots.rst‎

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The :func:`~matplotlib.pyplot.bar`
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command takes error bars as an optional argument. You can also use up
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and down bars, stacked bars, candlestic' bars, etc, ... See
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and down bars, stacked bars, candlestick bars, etc, ... See
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`bar_stacked.py <examples/pylab_examples/bar_stacked.py>`_ for another example.
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You can make horizontal bar charts with the
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:func:`~matplotlib.pyplot.barh` command.
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The :func:`~matplotlib.pyplot.pie` command
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uses a matlab(TM) compatible syntax to produce py charts. Optional
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features include auto-labeling the percentage of area, "exploding" one
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features include auto-labeling the percentage of area, exploding one
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or more wedges out from the center of the pie, and a shadow effect.
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Take a close look at the attached code that produced this figure; nine
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lines of code.
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The :func:`~matplotlib.pyplot.scatter` command makes a scatter plot
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with (optional) size and color arguments. This example plots changes
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in Intel's stock price from one day to the next with the sizes coding
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in Google stock price from one day to the next with the sizes coding
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trading volume and the colors coding price change in day i. Here the
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alpha attribute is used to make semitransparent circle markers with
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the Agg backend (see :ref:`what-is-a-backend`)
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formatters for both the major and minor ticks; see matplotlib.ticker
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and matplotlib.dates for details and usage.
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todoplot:: ../mpl_examples/api/date_demo.py
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plot:: ../mpl_examples/api/date_demo.py
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.. _screenshots_jdh_demo:
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Financial charts
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================
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You can make much more sophisticated financial plots. This example
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emulates one of the `ChartDirector`<http://www.advsofteng.com/gallery_finance.html>
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emulates one of the `ChartDirector <http://www.advsofteng.com/gallery_finance.html>`
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financial plots. Some of the data in the plot, are real financial
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data, some are random traces that I used since the goal was to
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illustrate plotting techniques, not market analysis!
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todoplot:: ../mpl_examples/pylab_examples/finance_work2.py
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plot:: ../mpl_examples/pylab_examples/finance_work2.py
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.. _screenshots_basemap_demo:
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.. plot:: plotmap.py
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.. _screenshots_log_demo:
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Log plots
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=========
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The :func:`~matplotlib.pyplot.semilogx`,
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:func:`~matplotlib.pyplot.semilogy` and
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:func:`~matplotlib.pyplot.loglog` functions generate log scaling on the
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respective axes. The lower subplot uses a base10 log on the xaxis and
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a base 4 log on the yaxis. Thanks to Andrew Straw, Darren Dale and
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Gregory Lielens for contributions to the log scaling
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infrastructure.
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plot:: ../mpl_examples/pylab_examples/log_demo.py
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.. _screenshots_polar_demo:
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Polar plots
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===========
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The :func:`~matplotlib.pyplot.polar` command generates polar plots.
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plot:: ../mpl_examples/pylab_examples/polar_demo.py
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.. _screenshots_legend_demo:
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Legends
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=======
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The :func:`~matplotlib.pyplot.legend` command automatically
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generates figure legends, with Matlab compatible legend placement
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commands. Thanks to Charles Twardy for input on the legend
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command
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plot:: ../mpl_examples/pylab_examples/legend_demo.py
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.. _screenshots_mathtext_examples_demo:
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Mathtext_examples
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=================
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A sampling of the many TeX expressions now supported by matplotlib's
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internal mathtext engine. The mathtext module provides TeX style
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mathematical expressions using freetype2 and the BaKoMa computer
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modern or STIX fonts. See the matplotlib.mathtext module for
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additional. matplotlib mathtext is an independent implementation, and
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does not required TeX or any external packages installed on your
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computer.
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plot:: ../mpl_examples/pylab_examples/mathtext_examples.py
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.. _screenshots_tex_demo:
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Native TeX rendering
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====================
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Although matplotlib's internal math rendering engine is quite
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powerful, sometimes you need TeX, and matplotlib supports external TeX
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rendering of strings with the *usetex* option.
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plot:: tex_demo.py
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.. _screenshots_eeg_demo:
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=========
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You can embed matplotlib into pygtk, wxpython, Tk, FLTK or Qt
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applications. Here is a screenshot of an eeg viewer called pbrain
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which is part of the NeuroImaging in Python suite `NIPY
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<http://neuroimaging.scipy.org>`. Pbrain is written in pygtk using
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matplotlib. The lower axes uses :func:`~matplotlib.pyplot.specgram`
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to plot the spectrogram of one of the EEG channels. The code demo
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linked above is a much simpler example of embedding matplotlib in
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pygtk. For an example of how to use the navigation toolbar in your
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applications, see examples/user_interfaces/embedding_in_gtk2.py. If
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you want to use matplotlib in a wx application, see
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examples/user_interfaces/embedding_in_wx2.py. If you want to work
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with `glade <http://glade.gnome.org>`_, see
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examples/user_interfaces/mpl_with_glade.py
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.. image:: ../_static/eeg_small.png
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‎examples/api/date_demo.py‎

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and num2date
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"""
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import datetime
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import numpy as np
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import matplotlib
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import matplotlib.pyplot as plt
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import matplotlib.dates as mdates
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import matplotlib.mlab as mlab
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months = mdates.MonthLocator() # every month
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yearsFmt = mdates.DateFormatter('%Y')
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r = mlab.csv2rec('../data/goog.csv')
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r.sort()
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# load a numpy record array from yahoo csv data with fields date,
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# open, close, volume, adj_close from the mpl-data/example directory.
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# The record array stores python datetime.date as an object array in
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# the date column
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datafile = matplotlib.get_example_data('goog.npy')
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r = np.load(datafile).view(np.recarray)
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fig = plt.figure()
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ax = fig.add_subplot(111)
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ax.plot(r.date, r.adj_close)
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# format the ticks
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ax.xaxis.set_major_locator(years)
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ax.xaxis.set_major_formatter(yearsFmt)
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ax.autoscale_view()
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datemin = datetime.date(r.date.min().year, 1, 1)
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datemax = datetime.date(r.date.max().year+1, 1, 1)
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ax.set_xlim(datemin, datemax)
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# format the coords message box
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def price(x): return '$%1.2f'%x

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