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added new examples
svn path=/trunk/matplotlib/; revision=293
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‎examples/custom_ticker1.py‎

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"""
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The new ticker code was designed to explicity support user customized
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ticking. The documentation
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http://matplotlib.sourceforge.net/matplotlib.ticker.html details this
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process. That code defines a lot of preset tickers but was primarily
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designed to be user extensible.
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In this example a user defined function is used to format the ticks in
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millions of dollars on the y axis
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"""
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from matplotlib.ticker import FuncFormatter
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from matplotlib.matlab import *
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x = arange(4)
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money = [1.5e5, 2.5e6, 5.5e6, 2.0e7]
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def millions(x, pos):
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'The two args are the value and tick position'
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return '$%1.1fM' % (x*1e-6)
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formatter = FuncFormatter(millions)
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ax = subplot(111)
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ax.yaxis.set_major_formatter(formatter)
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bar(x, money)
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ax.set_xticks( x + 0.5)
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ax.set_xticklabels( ('Bill', 'Fred', 'Mary', 'Sue') )
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show()

‎examples/date_demo3.py‎

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#!/usr/bin/env python
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"""
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intdate is a class derived from int that is seconds since the epoch
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and also provides some helper functions for getting day, month, year,
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etc... You can use intdate with any matplotlib plotting function, not
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just plot_date
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"""
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from datetime import datetime
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import time
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from matplotlib.dates import intdate
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from matplotlib.ticker import MinuteLocator, DateFormatter
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from matplotlib.matlab import *
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# simulate collecting data every minute starting at midnight
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t0 = time.mktime(datetime(2004,04,27).timetuple())
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t = t0+arange(0, 2*3600, 60) # 2 hours sampled every 2 minute
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s = rand(len(t))
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ax = subplot(111)
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ax.xaxis.set_major_locator( MinuteLocator(20) )
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ax.xaxis.set_major_formatter( DateFormatter('%H:%M') )
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bar(t, s, width=60)
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show()
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‎examples/histogram_demo_canvasagg.py‎

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"""
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This is an example that shows you how to work directly with the agg
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figure canvas to create a figure using the pythonic API.
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In this example, the contents of the agg canvas are extracted to a
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string, which can in turn be passed off to PIL or put in a numeric
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array
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"""
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#!/usr/bin/env python
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from matplotlib.backends.backend_agg import FigureCanvasAgg
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from matplotlib.figure import Figure
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from matplotlib.axes import Subplot
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from matplotlib.mlab import normpdf
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from matplotlib.numerix import randn
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fig = Figure(figsize=(5,4), dpi=100)
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ax = fig.add_subplot(111)
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canvas = FigureCanvasAgg(fig)
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mu, sigma = 100, 15
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x = mu + sigma*randn(10000)
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# the histogram of the data
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n, bins, patches = ax.hist(x, 50, normed=1)
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# add a 'best fit' line
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y = normpdf( bins, mu, sigma)
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line, = ax.plot(bins, y, 'r--')
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line.set_linewidth(1)
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ax.set_xlabel('Smarts')
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ax.set_ylabel('Probability')
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ax.set_title(r'$\rm{Histogram of IQ: }\mu=100, \sigma=15$')
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ax.set_xlim( (40, 160))
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ax.set_ylim( (0, 0.03))
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canvas.draw()
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s = canvas.tostring_rgb() # save this and convert to bitmap as needed
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# get the figure dimensions for creating bitmaps or numeric arrays,
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# etc.
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l,b,w,h = fig.bbox.get_bounds()
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w, h = int(w), int(h)
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if 0:
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# convert to a Numeric array
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X = fromstring(s, UInt8)
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X.shape = h, w, 3
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if 0:
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# pass off to PIL
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import Image
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im = Image.fromstring( "RGB", (w,h), s)
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im.show()
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‎examples/stem_plot.py‎

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#!/usr/bin/env python
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from matplotlib.matlab import *
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x = linspace(0.1, 2*pi, 10)
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markerline, stemlines, baseline = stem(x, cos(x), '-.')
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set(markerline, 'markerfacecolor', 'b')
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set(baseline, 'color','r', 'linewidth', 2)
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show()

‎examples/to_numeric.py‎

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#!/usr/bin/env python
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"""
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Use backend agg to access the figure canvas as an RGB string and then
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convert it to a Numeric array and pass the string it to PIL for
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rendering
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"""
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from matplotlib.matlab import *
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from matplotlib.backends.backend_agg import FigureCanvasAgg
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plot([1,2,3])
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canvas = get_current_fig_manager().canvas
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agg = canvas.switch_backends(FigureCanvasAgg)
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agg.draw()
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s = agg.tostring_rgb()
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# get the width and the height to resize the matrix
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l,b,w,h = agg.figure.bbox.get_bounds()
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w, h = int(w), int(h)
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X = fromstring(s, UInt8)
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X.shape = h, w, 3
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import Image
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im = Image.fromstring( "RGB", (w,h), s)
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im.show()
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