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From: <jd...@us...> - 2008-06-27 15:42:47
|
Revision: 5693
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=5693&view=rev
Author: jdh2358
Date: 2008-06-27 08:42:44 -0700 (Fri, 27 Jun 2008)
Log Message:
-----------
cleaned up some pyplots examples that got funkily duplicated
Modified Paths:
--------------
trunk/matplotlib/doc/pyplots/boxplot_demo.py
trunk/matplotlib/doc/pyplots/contour_demo.py
Modified: trunk/matplotlib/doc/pyplots/boxplot_demo.py
===================================================================
--- trunk/matplotlib/doc/pyplots/boxplot_demo.py 2008-06-27 15:40:06 UTC (rev 5692)
+++ trunk/matplotlib/doc/pyplots/boxplot_demo.py 2008-06-27 15:42:44 UTC (rev 5693)
@@ -27,119 +27,3 @@
plt.boxplot(data)
plt.show()
-import numpy as np
-import matplotlib.pyplot as plt
-
-spread = np.random.rand(50) * 100
-center = np.ones(25) * 50
-flier_high = np.random.rand(10) * 100 + 100
-flier_low = np.random.rand(10) * -100
-data = np.concatenate((spread, center, flier_high, flier_low), 0)
-
-# fake up some more data
-spread = np.random.rand(50) * 100
-center = np.ones(25) * 40
-flier_high = np.random.rand(10) * 100 + 100
-flier_low = np.random.rand(10) * -100
-d2 = np.concatenate( (spread, center, flier_high, flier_low), 0 )
-data.shape = (-1, 1)
-d2.shape = (-1, 1)
-
-#data = concatenate( (data, d2), 1 )
-# Making a 2-D array only works if all the columns are the
-# same length. If they are not, then use a list instead.
-# This is actually more efficient because boxplot converts
-# a 2-D array into a list of vectors internally anyway.
-data = [data, d2, d2[::2,0]]
-# multiple box plots on one figure
-
-plt.boxplot(data)
-plt.show()
-
-import numpy as np
-import matplotlib.pyplot as plt
-
-spread = np.random.rand(50) * 100
-center = np.ones(25) * 50
-flier_high = np.random.rand(10) * 100 + 100
-flier_low = np.random.rand(10) * -100
-data = np.concatenate((spread, center, flier_high, flier_low), 0)
-
-# fake up some more data
-spread = np.random.rand(50) * 100
-center = np.ones(25) * 40
-flier_high = np.random.rand(10) * 100 + 100
-flier_low = np.random.rand(10) * -100
-d2 = np.concatenate( (spread, center, flier_high, flier_low), 0 )
-data.shape = (-1, 1)
-d2.shape = (-1, 1)
-
-#data = concatenate( (data, d2), 1 )
-# Making a 2-D array only works if all the columns are the
-# same length. If they are not, then use a list instead.
-# This is actually more efficient because boxplot converts
-# a 2-D array into a list of vectors internally anyway.
-data = [data, d2, d2[::2,0]]
-# multiple box plots on one figure
-
-plt.boxplot(data)
-plt.show()
-
-import numpy as np
-import matplotlib.pyplot as plt
-
-spread = np.random.rand(50) * 100
-center = np.ones(25) * 50
-flier_high = np.random.rand(10) * 100 + 100
-flier_low = np.random.rand(10) * -100
-data = np.concatenate((spread, center, flier_high, flier_low), 0)
-
-# fake up some more data
-spread = np.random.rand(50) * 100
-center = np.ones(25) * 40
-flier_high = np.random.rand(10) * 100 + 100
-flier_low = np.random.rand(10) * -100
-d2 = np.concatenate( (spread, center, flier_high, flier_low), 0 )
-data.shape = (-1, 1)
-d2.shape = (-1, 1)
-
-#data = concatenate( (data, d2), 1 )
-# Making a 2-D array only works if all the columns are the
-# same length. If they are not, then use a list instead.
-# This is actually more efficient because boxplot converts
-# a 2-D array into a list of vectors internally anyway.
-data = [data, d2, d2[::2,0]]
-# multiple box plots on one figure
-
-plt.boxplot(data)
-plt.show()
-
-import numpy as np
-import matplotlib.pyplot as plt
-
-spread = np.random.rand(50) * 100
-center = np.ones(25) * 50
-flier_high = np.random.rand(10) * 100 + 100
-flier_low = np.random.rand(10) * -100
-data = np.concatenate((spread, center, flier_high, flier_low), 0)
-
-# fake up some more data
-spread = np.random.rand(50) * 100
-center = np.ones(25) * 40
-flier_high = np.random.rand(10) * 100 + 100
-flier_low = np.random.rand(10) * -100
-d2 = np.concatenate( (spread, center, flier_high, flier_low), 0 )
-data.shape = (-1, 1)
-d2.shape = (-1, 1)
-
-#data = concatenate( (data, d2), 1 )
-# Making a 2-D array only works if all the columns are the
-# same length. If they are not, then use a list instead.
-# This is actually more efficient because boxplot converts
-# a 2-D array into a list of vectors internally anyway.
-data = [data, d2, d2[::2,0]]
-# multiple box plots on one figure
-
-plt.boxplot(data)
-plt.show()
-
Modified: trunk/matplotlib/doc/pyplots/contour_demo.py
===================================================================
--- trunk/matplotlib/doc/pyplots/contour_demo.py 2008-06-27 15:40:06 UTC (rev 5692)
+++ trunk/matplotlib/doc/pyplots/contour_demo.py 2008-06-27 15:42:44 UTC (rev 5693)
@@ -64,267 +64,3 @@
#savefig('contour_demo')
plt.show()
-#!/usr/bin/env python
-"""
-Illustrate simple contour plotting, contours on an image with
-a colorbar for the contours, and labelled contours.
-
-See also contour_image.py.
-"""
-import matplotlib
-import numpy as np
-import matplotlib.cm as cm
-import matplotlib.mlab as mlab
-import matplotlib.pyplot as plt
-
-matplotlib.rcParams['xtick.direction'] = 'out'
-matplotlib.rcParams['ytick.direction'] = 'out'
-
-delta = 0.025
-x = np.arange(-3.0, 3.0, delta)
-y = np.arange(-2.0, 2.0, delta)
-X, Y = np.meshgrid(x, y)
-Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
-Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
-# difference of Gaussians
-Z = 10.0 * (Z2 - Z1)
-
-
-# You can use a colormap to specify the colors; the default
-# colormap will be used for the contour lines
-plt.figure()
-im = plt.imshow(Z, interpolation='bilinear', origin='lower',
- cmap=cm.gray, extent=(-3,3,-2,2))
-levels = np.arange(-1.2, 1.6, 0.2)
-CS = plt.contour(Z, levels,
- origin='lower',
- linewidths=2,
- extent=(-3,3,-2,2))
-
-#Thicken the zero contour.
-zc = CS.collections[6]
-plt.setp(zc, linewidth=4)
-
-plt.clabel(CS, levels[1::2], # label every second level
- inline=1,
- fmt='%1.1f',
- fontsize=14)
-
-# make a colorbar for the contour lines
-CB = plt.colorbar(CS, shrink=0.8, extend='both')
-
-plt.title('Lines with colorbar')
-#plt.hot() # Now change the colormap for the contour lines and colorbar
-plt.flag()
-
-# We can still add a colorbar for the image, too.
-CBI = plt.colorbar(im, orientation='horizontal', shrink=0.8)
-
-# This makes the original colorbar look a bit out of place,
-# so let's improve its position.
-
-l,b,w,h = plt.gca().get_position().bounds
-ll,bb,ww,hh = CB.ax.get_position().bounds
-CB.ax.set_position([ll, b+0.1*h, ww, h*0.8])
-
-
-#savefig('contour_demo')
-plt.show()
-#!/usr/bin/env python
-"""
-Illustrate simple contour plotting, contours on an image with
-a colorbar for the contours, and labelled contours.
-
-See also contour_image.py.
-"""
-import matplotlib
-import numpy as np
-import matplotlib.cm as cm
-import matplotlib.mlab as mlab
-import matplotlib.pyplot as plt
-
-matplotlib.rcParams['xtick.direction'] = 'out'
-matplotlib.rcParams['ytick.direction'] = 'out'
-
-delta = 0.025
-x = np.arange(-3.0, 3.0, delta)
-y = np.arange(-2.0, 2.0, delta)
-X, Y = np.meshgrid(x, y)
-Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
-Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
-# difference of Gaussians
-Z = 10.0 * (Z2 - Z1)
-
-
-# You can use a colormap to specify the colors; the default
-# colormap will be used for the contour lines
-plt.figure()
-im = plt.imshow(Z, interpolation='bilinear', origin='lower',
- cmap=cm.gray, extent=(-3,3,-2,2))
-levels = np.arange(-1.2, 1.6, 0.2)
-CS = plt.contour(Z, levels,
- origin='lower',
- linewidths=2,
- extent=(-3,3,-2,2))
-
-#Thicken the zero contour.
-zc = CS.collections[6]
-plt.setp(zc, linewidth=4)
-
-plt.clabel(CS, levels[1::2], # label every second level
- inline=1,
- fmt='%1.1f',
- fontsize=14)
-
-# make a colorbar for the contour lines
-CB = plt.colorbar(CS, shrink=0.8, extend='both')
-
-plt.title('Lines with colorbar')
-#plt.hot() # Now change the colormap for the contour lines and colorbar
-plt.flag()
-
-# We can still add a colorbar for the image, too.
-CBI = plt.colorbar(im, orientation='horizontal', shrink=0.8)
-
-# This makes the original colorbar look a bit out of place,
-# so let's improve its position.
-
-l,b,w,h = plt.gca().get_position().bounds
-ll,bb,ww,hh = CB.ax.get_position().bounds
-CB.ax.set_position([ll, b+0.1*h, ww, h*0.8])
-
-
-#savefig('contour_demo')
-plt.show()
-#!/usr/bin/env python
-"""
-Illustrate simple contour plotting, contours on an image with
-a colorbar for the contours, and labelled contours.
-
-See also contour_image.py.
-"""
-import matplotlib
-import numpy as np
-import matplotlib.cm as cm
-import matplotlib.mlab as mlab
-import matplotlib.pyplot as plt
-
-matplotlib.rcParams['xtick.direction'] = 'out'
-matplotlib.rcParams['ytick.direction'] = 'out'
-
-delta = 0.025
-x = np.arange(-3.0, 3.0, delta)
-y = np.arange(-2.0, 2.0, delta)
-X, Y = np.meshgrid(x, y)
-Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
-Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
-# difference of Gaussians
-Z = 10.0 * (Z2 - Z1)
-
-
-# You can use a colormap to specify the colors; the default
-# colormap will be used for the contour lines
-plt.figure()
-im = plt.imshow(Z, interpolation='bilinear', origin='lower',
- cmap=cm.gray, extent=(-3,3,-2,2))
-levels = np.arange(-1.2, 1.6, 0.2)
-CS = plt.contour(Z, levels,
- origin='lower',
- linewidths=2,
- extent=(-3,3,-2,2))
-
-#Thicken the zero contour.
-zc = CS.collections[6]
-plt.setp(zc, linewidth=4)
-
-plt.clabel(CS, levels[1::2], # label every second level
- inline=1,
- fmt='%1.1f',
- fontsize=14)
-
-# make a colorbar for the contour lines
-CB = plt.colorbar(CS, shrink=0.8, extend='both')
-
-plt.title('Lines with colorbar')
-#plt.hot() # Now change the colormap for the contour lines and colorbar
-plt.flag()
-
-# We can still add a colorbar for the image, too.
-CBI = plt.colorbar(im, orientation='horizontal', shrink=0.8)
-
-# This makes the original colorbar look a bit out of place,
-# so let's improve its position.
-
-l,b,w,h = plt.gca().get_position().bounds
-ll,bb,ww,hh = CB.ax.get_position().bounds
-CB.ax.set_position([ll, b+0.1*h, ww, h*0.8])
-
-
-#savefig('contour_demo')
-plt.show()
-#!/usr/bin/env python
-"""
-Illustrate simple contour plotting, contours on an image with
-a colorbar for the contours, and labelled contours.
-
-See also contour_image.py.
-"""
-import matplotlib
-import numpy as np
-import matplotlib.cm as cm
-import matplotlib.mlab as mlab
-import matplotlib.pyplot as plt
-
-matplotlib.rcParams['xtick.direction'] = 'out'
-matplotlib.rcParams['ytick.direction'] = 'out'
-
-delta = 0.025
-x = np.arange(-3.0, 3.0, delta)
-y = np.arange(-2.0, 2.0, delta)
-X, Y = np.meshgrid(x, y)
-Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
-Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
-# difference of Gaussians
-Z = 10.0 * (Z2 - Z1)
-
-
-# You can use a colormap to specify the colors; the default
-# colormap will be used for the contour lines
-plt.figure()
-im = plt.imshow(Z, interpolation='bilinear', origin='lower',
- cmap=cm.gray, extent=(-3,3,-2,2))
-levels = np.arange(-1.2, 1.6, 0.2)
-CS = plt.contour(Z, levels,
- origin='lower',
- linewidths=2,
- extent=(-3,3,-2,2))
-
-#Thicken the zero contour.
-zc = CS.collections[6]
-plt.setp(zc, linewidth=4)
-
-plt.clabel(CS, levels[1::2], # label every second level
- inline=1,
- fmt='%1.1f',
- fontsize=14)
-
-# make a colorbar for the contour lines
-CB = plt.colorbar(CS, shrink=0.8, extend='both')
-
-plt.title('Lines with colorbar')
-#plt.hot() # Now change the colormap for the contour lines and colorbar
-plt.flag()
-
-# We can still add a colorbar for the image, too.
-CBI = plt.colorbar(im, orientation='horizontal', shrink=0.8)
-
-# This makes the original colorbar look a bit out of place,
-# so let's improve its position.
-
-l,b,w,h = plt.gca().get_position().bounds
-ll,bb,ww,hh = CB.ax.get_position().bounds
-CB.ax.set_position([ll, b+0.1*h, ww, h*0.8])
-
-
-#savefig('contour_demo')
-plt.show()
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|
|
From: <jd...@us...> - 2008-06-27 15:51:55
|
Revision: 5690
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=5690&view=rev
Author: jdh2358
Date: 2008-06-27 08:34:32 -0700 (Fri, 27 Jun 2008)
Log Message:
-----------
added pyplots examples which I forgot to svn add in prev commit
Added Paths:
-----------
trunk/matplotlib/doc/pyplots/arrow_demo.py
trunk/matplotlib/doc/pyplots/axhspan_demo.py
trunk/matplotlib/doc/pyplots/bar_stacked.py
trunk/matplotlib/doc/pyplots/boxplot_demo.py
trunk/matplotlib/doc/pyplots/broken_barh.py
trunk/matplotlib/doc/pyplots/cohere_demo.py
trunk/matplotlib/doc/pyplots/contour_demo.py
trunk/matplotlib/doc/pyplots/csd_demo.py
trunk/matplotlib/doc/pyplots/errorbar_demo.py
trunk/matplotlib/doc/pyplots/figimage_demo.py
trunk/matplotlib/doc/pyplots/figlegend_demo.py
trunk/matplotlib/doc/pyplots/fill_demo.py
trunk/matplotlib/doc/pyplots/hexbin_demo.py
trunk/matplotlib/doc/pyplots/histogram_demo.py
trunk/matplotlib/doc/pyplots/hline_demo.py
trunk/matplotlib/doc/pyplots/image_demo.py
trunk/matplotlib/doc/pyplots/log_demo.py
trunk/matplotlib/doc/pyplots/xcorr_demo.py
Added: trunk/matplotlib/doc/pyplots/arrow_demo.py
===================================================================
--- trunk/matplotlib/doc/pyplots/arrow_demo.py (rev 0)
+++ trunk/matplotlib/doc/pyplots/arrow_demo.py 2008-06-27 15:34:32 UTC (rev 5690)
@@ -0,0 +1,313 @@
+#!/usr/bin/env python
+"""Arrow drawing example for the new fancy_arrow facilities.
+
+Code contributed by: Rob Knight <ro...@sp...>
+
+usage:
+
+ python arrow_demo.py realistic|full|sample|extreme
+
+
+"""
+from pylab import *
+
+rates_to_bases={'r1':'AT', 'r2':'TA', 'r3':'GA','r4':'AG','r5':'CA','r6':'AC', \
+ 'r7':'GT', 'r8':'TG', 'r9':'CT','r10':'TC','r11':'GC','r12':'CG'}
+numbered_bases_to_rates = dict([(v,k) for k, v in rates_to_bases.items()])
+lettered_bases_to_rates = dict([(v, 'r'+v) for k, v in rates_to_bases.items()])
+def add_dicts(d1, d2):
+ """Adds two dicts and returns the result."""
+ result = d1.copy()
+ result.update(d2)
+ return result
+
+def make_arrow_plot(data, size=4, display='length', shape='right', \
+ max_arrow_width=0.03, arrow_sep = 0.02, alpha=0.5, \
+ normalize_data=False, ec=None, labelcolor=None, \
+ head_starts_at_zero=True, rate_labels=lettered_bases_to_rates,\
+ **kwargs):
+ """Makes an arrow plot.
+
+ Parameters:
+
+ data: dict with probabilities for the bases and pair transitions.
+ size: size of the graph in inches.
+ display: 'length', 'width', or 'alpha' for arrow property to change.
+ shape: 'full', 'left', or 'right' for full or half arrows.
+ max_arrow_width: maximum width of an arrow, data coordinates.
+ arrow_sep: separation between arrows in a pair, data coordinates.
+ alpha: maximum opacity of arrows, default 0.8.
+
+ **kwargs can be anything allowed by a Arrow object, e.g.
+ linewidth and edgecolor.
+ """
+
+ xlim(-0.5,1.5)
+ ylim(-0.5,1.5)
+ gcf().set_size_inches(size,size)
+ xticks([])
+ yticks([])
+ max_text_size = size*12
+ min_text_size = size
+ label_text_size = size*2.5
+ text_params={'ha':'center', 'va':'center', 'family':'sans-serif',\
+ 'fontweight':'bold'}
+ r2 = sqrt(2)
+
+ deltas = {\
+ 'AT':(1,0),
+ 'TA':(-1,0),
+ 'GA':(0,1),
+ 'AG':(0,-1),
+ 'CA':(-1/r2, 1/r2),
+ 'AC':(1/r2, -1/r2),
+ 'GT':(1/r2, 1/r2),
+ 'TG':(-1/r2,-1/r2),
+ 'CT':(0,1),
+ 'TC':(0,-1),
+ 'GC':(1,0),
+ 'CG':(-1,0)
+ }
+
+ colors = {\
+ 'AT':'r',
+ 'TA':'k',
+ 'GA':'g',
+ 'AG':'r',
+ 'CA':'b',
+ 'AC':'r',
+ 'GT':'g',
+ 'TG':'k',
+ 'CT':'b',
+ 'TC':'k',
+ 'GC':'g',
+ 'CG':'b'
+ }
+
+ label_positions = {\
+ 'AT':'center',
+ 'TA':'center',
+ 'GA':'center',
+ 'AG':'center',
+ 'CA':'left',
+ 'AC':'left',
+ 'GT':'left',
+ 'TG':'left',
+ 'CT':'center',
+ 'TC':'center',
+ 'GC':'center',
+ 'CG':'center'
+ }
+
+
+ def do_fontsize(k):
+ return float(clip(max_text_size*sqrt(data[k]),\
+ min_text_size,max_text_size))
+
+ A = text(0,1, '$A_3$', color='r', size=do_fontsize('A'), **text_params)
+ T = text(1,1, '$T_3$', color='k', size=do_fontsize('T'), **text_params)
+ G = text(0,0, '$G_3$', color='g', size=do_fontsize('G'), **text_params)
+ C = text(1,0, '$C_3$', color='b', size=do_fontsize('C'), **text_params)
+
+ arrow_h_offset = 0.25 #data coordinates, empirically determined
+ max_arrow_length = 1 - 2*arrow_h_offset
+
+ max_arrow_width = max_arrow_width
+ max_head_width = 2.5*max_arrow_width
+ max_head_length = 2*max_arrow_width
+ arrow_params={'length_includes_head':True, 'shape':shape, \
+ 'head_starts_at_zero':head_starts_at_zero}
+ ax = gca()
+ sf = 0.6 #max arrow size represents this in data coords
+
+ d = (r2/2 + arrow_h_offset - 0.5)/r2 #distance for diags
+ r2v = arrow_sep/r2 #offset for diags
+
+ #tuple of x, y for start position
+ positions = {\
+ 'AT': (arrow_h_offset, 1+arrow_sep),
+ 'TA': (1-arrow_h_offset, 1-arrow_sep),
+ 'GA': (-arrow_sep, arrow_h_offset),
+ 'AG': (arrow_sep, 1-arrow_h_offset),
+ 'CA': (1-d-r2v, d-r2v),
+ 'AC': (d+r2v, 1-d+r2v),
+ 'GT': (d-r2v, d+r2v),
+ 'TG': (1-d+r2v, 1-d-r2v),
+ 'CT': (1-arrow_sep, arrow_h_offset),
+ 'TC': (1+arrow_sep, 1-arrow_h_offset),
+ 'GC': (arrow_h_offset, arrow_sep),
+ 'CG': (1-arrow_h_offset, -arrow_sep),
+ }
+
+ if normalize_data:
+ #find maximum value for rates, i.e. where keys are 2 chars long
+ max_val = 0
+ for k, v in data.items():
+ if len(k) == 2:
+ max_val = max(max_val, v)
+ #divide rates by max val, multiply by arrow scale factor
+ for k, v in data.items():
+ data[k] = v/max_val*sf
+
+ def draw_arrow(pair, alpha=alpha, ec=ec, labelcolor=labelcolor):
+ #set the length of the arrow
+ if display == 'length':
+ length = max_head_length+(max_arrow_length-max_head_length)*\
+ data[pair]/sf
+ else:
+ length = max_arrow_length
+ #set the transparency of the arrow
+ if display == 'alph':
+ alpha = min(data[pair]/sf, alpha)
+ else:
+ alpha=alpha
+ #set the width of the arrow
+ if display == 'width':
+ scale = data[pair]/sf
+ width = max_arrow_width*scale
+ head_width = max_head_width*scale
+ head_length = max_head_length*scale
+ else:
+ width = max_arrow_width
+ head_width = max_head_width
+ head_length = max_head_length
+
+ fc = colors[pair]
+ ec = ec or fc
+
+ x_scale, y_scale = deltas[pair]
+ x_pos, y_pos = positions[pair]
+ arrow(x_pos, y_pos, x_scale*length, y_scale*length, \
+ fc=fc, ec=ec, alpha=alpha, width=width, head_width=head_width, \
+ head_length=head_length, **arrow_params)
+
+ #figure out coordinates for text
+ #if drawing relative to base: x and y are same as for arrow
+ #dx and dy are one arrow width left and up
+ #need to rotate based on direction of arrow, use x_scale and y_scale
+ #as sin x and cos x?
+ sx, cx = y_scale, x_scale
+
+ where = label_positions[pair]
+ if where == 'left':
+ orig_position = 3*array([[max_arrow_width, max_arrow_width]])
+ elif where == 'absolute':
+ orig_position = array([[max_arrow_length/2.0, 3*max_arrow_width]])
+ elif where == 'right':
+ orig_position = array([[length-3*max_arrow_width,\
+ 3*max_arrow_width]])
+ elif where == 'center':
+ orig_position = array([[length/2.0, 3*max_arrow_width]])
+ else:
+ raise ValueError, "Got unknown position parameter %s" % where
+
+
+
+ M = array([[cx, sx],[-sx,cx]])
+ coords = dot(orig_position, M) + [[x_pos, y_pos]]
+ x, y = ravel(coords)
+ orig_label = rate_labels[pair]
+ label = '$%s_{_{\mathrm{%s}}}$' % (orig_label[0], orig_label[1:])
+
+ text(x, y, label, size=label_text_size, ha='center', va='center', \
+ color=labelcolor or fc)
+
+ for p in positions.keys():
+ draw_arrow(p)
+
+ #test data
+all_on_max = dict([(i, 1) for i in 'TCAG'] + \
+ [(i+j, 0.6) for i in 'TCAG' for j in 'TCAG'])
+
+realistic_data = {
+ 'A':0.4,
+ 'T':0.3,
+ 'G':0.5,
+ 'C':0.2,
+ 'AT':0.4,
+ 'AC':0.3,
+ 'AG':0.2,
+ 'TA':0.2,
+ 'TC':0.3,
+ 'TG':0.4,
+ 'CT':0.2,
+ 'CG':0.3,
+ 'CA':0.2,
+ 'GA':0.1,
+ 'GT':0.4,
+ 'GC':0.1,
+ }
+
+extreme_data = {
+ 'A':0.75,
+ 'T':0.10,
+ 'G':0.10,
+ 'C':0.05,
+ 'AT':0.6,
+ 'AC':0.3,
+ 'AG':0.1,
+ 'TA':0.02,
+ 'TC':0.3,
+ 'TG':0.01,
+ 'CT':0.2,
+ 'CG':0.5,
+ 'CA':0.2,
+ 'GA':0.1,
+ 'GT':0.4,
+ 'GC':0.2,
+ }
+
+sample_data = {
+ 'A':0.2137,
+ 'T':0.3541,
+ 'G':0.1946,
+ 'C':0.2376,
+ 'AT':0.0228,
+ 'AC':0.0684,
+ 'AG':0.2056,
+ 'TA':0.0315,
+ 'TC':0.0629,
+ 'TG':0.0315,
+ 'CT':0.1355,
+ 'CG':0.0401,
+ 'CA':0.0703,
+ 'GA':0.1824,
+ 'GT':0.0387,
+ 'GC':0.1106,
+ }
+
+
+if __name__ == '__main__':
+ from sys import argv
+ d = None
+ if len(argv) > 1:
+ if argv[1] == 'full':
+ d = all_on_max
+ scaled = False
+ elif argv[1] == 'extreme':
+ d = extreme_data
+ scaled = False
+ elif argv[1] == 'realistic':
+ d = realistic_data
+ scaled = False
+ elif argv[1] == 'sample':
+ d = sample_data
+ scaled = True
+ if d is None:
+ d = all_on_max
+ scaled=False
+ if len(argv) > 2:
+ display = argv[2]
+ else:
+ display = 'length'
+
+ size = 4
+ figure(figsize=(size,size))
+
+ make_arrow_plot(d, display=display, linewidth=0.001, edgecolor=None,
+ normalize_data=scaled, head_starts_at_zero=True, size=size)
+
+ draw()
+ #savefig('arrows.png')
+ #print 'Example saved to file "arrows.png"'
+ show()
Added: trunk/matplotlib/doc/pyplots/axhspan_demo.py
===================================================================
--- trunk/matplotlib/doc/pyplots/axhspan_demo.py (rev 0)
+++ trunk/matplotlib/doc/pyplots/axhspan_demo.py 2008-06-27 15:34:32 UTC (rev 5690)
@@ -0,0 +1,32 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+t = np.arange(-1,2, .01)
+s = np.sin(2*np.pi*t)
+
+plt.plot(t,s)
+# draw a thick red hline at y=0 that spans the xrange
+l = plt.axhline(linewidth=4, color='r')
+
+# draw a default hline at y=1 that spans the xrange
+l = plt.axhline(y=1)
+
+# draw a default vline at x=1 that spans the xrange
+l = plt.axvline(x=1)
+
+# draw a thick blue vline at x=0 that spans the the upper quadrant of
+# the yrange
+l = plt.axvline(x=0, ymin=0.75, linewidth=4, color='b')
+
+# draw a default hline at y=.5 that spans the the middle half of
+# the axes
+l = plt.axhline(y=.5, xmin=0.25, xmax=0.75)
+
+p = plt.axhspan(0.25, 0.75, facecolor='0.5', alpha=0.5)
+
+p = plt.axvspan(1.25, 1.55, facecolor='g', alpha=0.5)
+
+plt.axis([-1,2,-1,2])
+
+
+plt.show()
Added: trunk/matplotlib/doc/pyplots/bar_stacked.py
===================================================================
--- trunk/matplotlib/doc/pyplots/bar_stacked.py (rev 0)
+++ trunk/matplotlib/doc/pyplots/bar_stacked.py 2008-06-27 15:34:32 UTC (rev 5690)
@@ -0,0 +1,25 @@
+#!/usr/bin/env python
+# a stacked bar plot with errorbars
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+N = 5
+menMeans = (20, 35, 30, 35, 27)
+womenMeans = (25, 32, 34, 20, 25)
+menStd = (2, 3, 4, 1, 2)
+womenStd = (3, 5, 2, 3, 3)
+ind = np.arange(N) # the x locations for the groups
+width = 0.35 # the width of the bars: can also be len(x) sequence
+
+p1 = plt.bar(ind, menMeans, width, color='r', yerr=womenStd)
+p2 = plt.bar(ind, womenMeans, width, color='y',
+ bottom=menMeans, yerr=menStd)
+
+plt.ylabel('Scores')
+plt.title('Scores by group and gender')
+plt.xticks(ind+width/2., ('G1', 'G2', 'G3', 'G4', 'G5') )
+plt.yticks(np.arange(0,81,10))
+plt.legend( (p1[0], p2[0]), ('Men', 'Women') )
+
+plt.show()
Added: trunk/matplotlib/doc/pyplots/boxplot_demo.py
===================================================================
--- trunk/matplotlib/doc/pyplots/boxplot_demo.py (rev 0)
+++ trunk/matplotlib/doc/pyplots/boxplot_demo.py 2008-06-27 15:34:32 UTC (rev 5690)
@@ -0,0 +1,145 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+spread = np.random.rand(50) * 100
+center = np.ones(25) * 50
+flier_high = np.random.rand(10) * 100 + 100
+flier_low = np.random.rand(10) * -100
+data = np.concatenate((spread, center, flier_high, flier_low), 0)
+
+# fake up some more data
+spread = np.random.rand(50) * 100
+center = np.ones(25) * 40
+flier_high = np.random.rand(10) * 100 + 100
+flier_low = np.random.rand(10) * -100
+d2 = np.concatenate( (spread, center, flier_high, flier_low), 0 )
+data.shape = (-1, 1)
+d2.shape = (-1, 1)
+
+#data = concatenate( (data, d2), 1 )
+# Making a 2-D array only works if all the columns are the
+# same length. If they are not, then use a list instead.
+# This is actually more efficient because boxplot converts
+# a 2-D array into a list of vectors internally anyway.
+data = [data, d2, d2[::2,0]]
+# multiple box plots on one figure
+
+plt.boxplot(data)
+plt.show()
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+spread = np.random.rand(50) * 100
+center = np.ones(25) * 50
+flier_high = np.random.rand(10) * 100 + 100
+flier_low = np.random.rand(10) * -100
+data = np.concatenate((spread, center, flier_high, flier_low), 0)
+
+# fake up some more data
+spread = np.random.rand(50) * 100
+center = np.ones(25) * 40
+flier_high = np.random.rand(10) * 100 + 100
+flier_low = np.random.rand(10) * -100
+d2 = np.concatenate( (spread, center, flier_high, flier_low), 0 )
+data.shape = (-1, 1)
+d2.shape = (-1, 1)
+
+#data = concatenate( (data, d2), 1 )
+# Making a 2-D array only works if all the columns are the
+# same length. If they are not, then use a list instead.
+# This is actually more efficient because boxplot converts
+# a 2-D array into a list of vectors internally anyway.
+data = [data, d2, d2[::2,0]]
+# multiple box plots on one figure
+
+plt.boxplot(data)
+plt.show()
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+spread = np.random.rand(50) * 100
+center = np.ones(25) * 50
+flier_high = np.random.rand(10) * 100 + 100
+flier_low = np.random.rand(10) * -100
+data = np.concatenate((spread, center, flier_high, flier_low), 0)
+
+# fake up some more data
+spread = np.random.rand(50) * 100
+center = np.ones(25) * 40
+flier_high = np.random.rand(10) * 100 + 100
+flier_low = np.random.rand(10) * -100
+d2 = np.concatenate( (spread, center, flier_high, flier_low), 0 )
+data.shape = (-1, 1)
+d2.shape = (-1, 1)
+
+#data = concatenate( (data, d2), 1 )
+# Making a 2-D array only works if all the columns are the
+# same length. If they are not, then use a list instead.
+# This is actually more efficient because boxplot converts
+# a 2-D array into a list of vectors internally anyway.
+data = [data, d2, d2[::2,0]]
+# multiple box plots on one figure
+
+plt.boxplot(data)
+plt.show()
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+spread = np.random.rand(50) * 100
+center = np.ones(25) * 50
+flier_high = np.random.rand(10) * 100 + 100
+flier_low = np.random.rand(10) * -100
+data = np.concatenate((spread, center, flier_high, flier_low), 0)
+
+# fake up some more data
+spread = np.random.rand(50) * 100
+center = np.ones(25) * 40
+flier_high = np.random.rand(10) * 100 + 100
+flier_low = np.random.rand(10) * -100
+d2 = np.concatenate( (spread, center, flier_high, flier_low), 0 )
+data.shape = (-1, 1)
+d2.shape = (-1, 1)
+
+#data = concatenate( (data, d2), 1 )
+# Making a 2-D array only works if all the columns are the
+# same length. If they are not, then use a list instead.
+# This is actually more efficient because boxplot converts
+# a 2-D array into a list of vectors internally anyway.
+data = [data, d2, d2[::2,0]]
+# multiple box plots on one figure
+
+plt.boxplot(data)
+plt.show()
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+spread = np.random.rand(50) * 100
+center = np.ones(25) * 50
+flier_high = np.random.rand(10) * 100 + 100
+flier_low = np.random.rand(10) * -100
+data = np.concatenate((spread, center, flier_high, flier_low), 0)
+
+# fake up some more data
+spread = np.random.rand(50) * 100
+center = np.ones(25) * 40
+flier_high = np.random.rand(10) * 100 + 100
+flier_low = np.random.rand(10) * -100
+d2 = np.concatenate( (spread, center, flier_high, flier_low), 0 )
+data.shape = (-1, 1)
+d2.shape = (-1, 1)
+
+#data = concatenate( (data, d2), 1 )
+# Making a 2-D array only works if all the columns are the
+# same length. If they are not, then use a list instead.
+# This is actually more efficient because boxplot converts
+# a 2-D array into a list of vectors internally anyway.
+data = [data, d2, d2[::2,0]]
+# multiple box plots on one figure
+
+plt.boxplot(data)
+plt.show()
+
Added: trunk/matplotlib/doc/pyplots/broken_barh.py
===================================================================
--- trunk/matplotlib/doc/pyplots/broken_barh.py (rev 0)
+++ trunk/matplotlib/doc/pyplots/broken_barh.py 2008-06-27 15:34:32 UTC (rev 5690)
@@ -0,0 +1,24 @@
+"""
+Make a "broken" horizontal bar plot, ie one with gaps
+"""
+import matplotlib.pyplot as plt
+
+fig = plt.figure()
+ax = fig.add_subplot(111)
+ax.broken_barh([ (110, 30), (150, 10) ] , (10, 9), facecolors='blue')
+ax.broken_barh([ (10, 50), (100, 20), (130, 10)] , (20, 9),
+ facecolors=('red', 'yellow', 'green'))
+ax.set_ylim(5,35)
+ax.set_xlim(0,200)
+ax.set_xlabel('seconds since start')
+ax.set_yticks([15,25])
+ax.set_yticklabels(['Bill', 'Jim'])
+ax.grid(True)
+ax.annotate('race interrupted', (61, 25),
+ xytext=(0.8, 0.9), textcoords='axes fraction',
+ arrowprops=dict(facecolor='black', shrink=0.05),
+ fontsize=16,
+ horizontalalignment='right', verticalalignment='top')
+
+#fig.savefig('broken_barh', dpi=100)
+plt.show()
Added: trunk/matplotlib/doc/pyplots/cohere_demo.py
===================================================================
--- trunk/matplotlib/doc/pyplots/cohere_demo.py (rev 0)
+++ trunk/matplotlib/doc/pyplots/cohere_demo.py 2008-06-27 15:34:32 UTC (rev 5690)
@@ -0,0 +1,36 @@
+#!/usr/bin/env python
+"""
+Compute the coherence of two signals
+"""
+import numpy as np
+import matplotlib.pyplot as plt
+
+# make a little extra space between the subplots
+plt.subplots_adjust(wspace=0.5)
+
+dt = 0.01
+t = np.arange(0, 30, dt)
+nse1 = np.random.randn(len(t)) # white noise 1
+nse2 = np.random.randn(len(t)) # white noise 2
+r = np.exp(-t/0.05)
+
+cnse1 = np.convolve(nse1, r, mode='same')*dt # colored noise 1
+cnse2 = np.convolve(nse2, r, mode='same')*dt # colored noise 2
+
+# two signals with a coherent part and a random part
+s1 = 0.01*np.sin(2*np.pi*10*t) + cnse1
+s2 = 0.01*np.sin(2*np.pi*10*t) + cnse2
+
+plt.subplot(211)
+plt.plot(t, s1, 'b-', t, s2, 'g-')
+plt.xlim(0,5)
+plt.xlabel('time')
+plt.ylabel('s1 and s2')
+plt.grid(True)
+
+plt.subplot(212)
+cxy, f = plt.cohere(s1, s2, 256, 1./dt)
+plt.ylabel('coherence')
+plt.show()
+
+
Added: trunk/matplotlib/doc/pyplots/contour_demo.py
===================================================================
--- trunk/matplotlib/doc/pyplots/contour_demo.py (rev 0)
+++ trunk/matplotlib/doc/pyplots/contour_demo.py 2008-06-27 15:34:32 UTC (rev 5690)
@@ -0,0 +1,330 @@
+#!/usr/bin/env python
+"""
+Illustrate simple contour plotting, contours on an image with
+a colorbar for the contours, and labelled contours.
+
+See also contour_image.py.
+"""
+import matplotlib
+import numpy as np
+import matplotlib.cm as cm
+import matplotlib.mlab as mlab
+import matplotlib.pyplot as plt
+
+matplotlib.rcParams['xtick.direction'] = 'out'
+matplotlib.rcParams['ytick.direction'] = 'out'
+
+delta = 0.025
+x = np.arange(-3.0, 3.0, delta)
+y = np.arange(-2.0, 2.0, delta)
+X, Y = np.meshgrid(x, y)
+Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
+Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
+# difference of Gaussians
+Z = 10.0 * (Z2 - Z1)
+
+
+# You can use a colormap to specify the colors; the default
+# colormap will be used for the contour lines
+plt.figure()
+im = plt.imshow(Z, interpolation='bilinear', origin='lower',
+ cmap=cm.gray, extent=(-3,3,-2,2))
+levels = np.arange(-1.2, 1.6, 0.2)
+CS = plt.contour(Z, levels,
+ origin='lower',
+ linewidths=2,
+ extent=(-3,3,-2,2))
+
+#Thicken the zero contour.
+zc = CS.collections[6]
+plt.setp(zc, linewidth=4)
+
+plt.clabel(CS, levels[1::2], # label every second level
+ inline=1,
+ fmt='%1.1f',
+ fontsize=14)
+
+# make a colorbar for the contour lines
+CB = plt.colorbar(CS, shrink=0.8, extend='both')
+
+plt.title('Lines with colorbar')
+#plt.hot() # Now change the colormap for the contour lines and colorbar
+plt.flag()
+
+# We can still add a colorbar for the image, too.
+CBI = plt.colorbar(im, orientation='horizontal', shrink=0.8)
+
+# This makes the original colorbar look a bit out of place,
+# so let's improve its position.
+
+l,b,w,h = plt.gca().get_position().bounds
+ll,bb,ww,hh = CB.ax.get_position().bounds
+CB.ax.set_position([ll, b+0.1*h, ww, h*0.8])
+
+
+#savefig('contour_demo')
+plt.show()
+#!/usr/bin/env python
+"""
+Illustrate simple contour plotting, contours on an image with
+a colorbar for the contours, and labelled contours.
+
+See also contour_image.py.
+"""
+import matplotlib
+import numpy as np
+import matplotlib.cm as cm
+import matplotlib.mlab as mlab
+import matplotlib.pyplot as plt
+
+matplotlib.rcParams['xtick.direction'] = 'out'
+matplotlib.rcParams['ytick.direction'] = 'out'
+
+delta = 0.025
+x = np.arange(-3.0, 3.0, delta)
+y = np.arange(-2.0, 2.0, delta)
+X, Y = np.meshgrid(x, y)
+Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
+Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
+# difference of Gaussians
+Z = 10.0 * (Z2 - Z1)
+
+
+# You can use a colormap to specify the colors; the default
+# colormap will be used for the contour lines
+plt.figure()
+im = plt.imshow(Z, interpolation='bilinear', origin='lower',
+ cmap=cm.gray, extent=(-3,3,-2,2))
+levels = np.arange(-1.2, 1.6, 0.2)
+CS = plt.contour(Z, levels,
+ origin='lower',
+ linewidths=2,
+ extent=(-3,3,-2,2))
+
+#Thicken the zero contour.
+zc = CS.collections[6]
+plt.setp(zc, linewidth=4)
+
+plt.clabel(CS, levels[1::2], # label every second level
+ inline=1,
+ fmt='%1.1f',
+ fontsize=14)
+
+# make a colorbar for the contour lines
+CB = plt.colorbar(CS, shrink=0.8, extend='both')
+
+plt.title('Lines with colorbar')
+#plt.hot() # Now change the colormap for the contour lines and colorbar
+plt.flag()
+
+# We can still add a colorbar for the image, too.
+CBI = plt.colorbar(im, orientation='horizontal', shrink=0.8)
+
+# This makes the original colorbar look a bit out of place,
+# so let's improve its position.
+
+l,b,w,h = plt.gca().get_position().bounds
+ll,bb,ww,hh = CB.ax.get_position().bounds
+CB.ax.set_position([ll, b+0.1*h, ww, h*0.8])
+
+
+#savefig('contour_demo')
+plt.show()
+#!/usr/bin/env python
+"""
+Illustrate simple contour plotting, contours on an image with
+a colorbar for the contours, and labelled contours.
+
+See also contour_image.py.
+"""
+import matplotlib
+import numpy as np
+import matplotlib.cm as cm
+import matplotlib.mlab as mlab
+import matplotlib.pyplot as plt
+
+matplotlib.rcParams['xtick.direction'] = 'out'
+matplotlib.rcParams['ytick.direction'] = 'out'
+
+delta = 0.025
+x = np.arange(-3.0, 3.0, delta)
+y = np.arange(-2.0, 2.0, delta)
+X, Y = np.meshgrid(x, y)
+Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
+Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
+# difference of Gaussians
+Z = 10.0 * (Z2 - Z1)
+
+
+# You can use a colormap to specify the colors; the default
+# colormap will be used for the contour lines
+plt.figure()
+im = plt.imshow(Z, interpolation='bilinear', origin='lower',
+ cmap=cm.gray, extent=(-3,3,-2,2))
+levels = np.arange(-1.2, 1.6, 0.2)
+CS = plt.contour(Z, levels,
+ origin='lower',
+ linewidths=2,
+ extent=(-3,3,-2,2))
+
+#Thicken the zero contour.
+zc = CS.collections[6]
+plt.setp(zc, linewidth=4)
+
+plt.clabel(CS, levels[1::2], # label every second level
+ inline=1,
+ fmt='%1.1f',
+ fontsize=14)
+
+# make a colorbar for the contour lines
+CB = plt.colorbar(CS, shrink=0.8, extend='both')
+
+plt.title('Lines with colorbar')
+#plt.hot() # Now change the colormap for the contour lines and colorbar
+plt.flag()
+
+# We can still add a colorbar for the image, too.
+CBI = plt.colorbar(im, orientation='horizontal', shrink=0.8)
+
+# This makes the original colorbar look a bit out of place,
+# so let's improve its position.
+
+l,b,w,h = plt.gca().get_position().bounds
+ll,bb,ww,hh = CB.ax.get_position().bounds
+CB.ax.set_position([ll, b+0.1*h, ww, h*0.8])
+
+
+#savefig('contour_demo')
+plt.show()
+#!/usr/bin/env python
+"""
+Illustrate simple contour plotting, contours on an image with
+a colorbar for the contours, and labelled contours.
+
+See also contour_image.py.
+"""
+import matplotlib
+import numpy as np
+import matplotlib.cm as cm
+import matplotlib.mlab as mlab
+import matplotlib.pyplot as plt
+
+matplotlib.rcParams['xtick.direction'] = 'out'
+matplotlib.rcParams['ytick.direction'] = 'out'
+
+delta = 0.025
+x = np.arange(-3.0, 3.0, delta)
+y = np.arange(-2.0, 2.0, delta)
+X, Y = np.meshgrid(x, y)
+Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
+Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
+# difference of Gaussians
+Z = 10.0 * (Z2 - Z1)
+
+
+# You can use a colormap to specify the colors; the default
+# colormap will be used for the contour lines
+plt.figure()
+im = plt.imshow(Z, interpolation='bilinear', origin='lower',
+ cmap=cm.gray, extent=(-3,3,-2,2))
+levels = np.arange(-1.2, 1.6, 0.2)
+CS = plt.contour(Z, levels,
+ origin='lower',
+ linewidths=2,
+ extent=(-3,3,-2,2))
+
+#Thicken the zero contour.
+zc = CS.collections[6]
+plt.setp(zc, linewidth=4)
+
+plt.clabel(CS, levels[1::2], # label every second level
+ inline=1,
+ fmt='%1.1f',
+ fontsize=14)
+
+# make a colorbar for the contour lines
+CB = plt.colorbar(CS, shrink=0.8, extend='both')
+
+plt.title('Lines with colorbar')
+#plt.hot() # Now change the colormap for the contour lines and colorbar
+plt.flag()
+
+# We can still add a colorbar for the image, too.
+CBI = plt.colorbar(im, orientation='horizontal', shrink=0.8)
+
+# This makes the original colorbar look a bit out of place,
+# so let's improve its position.
+
+l,b,w,h = plt.gca().get_position().bounds
+ll,bb,ww,hh = CB.ax.get_position().bounds
+CB.ax.set_position([ll, b+0.1*h, ww, h*0.8])
+
+
+#savefig('contour_demo')
+plt.show()
+#!/usr/bin/env python
+"""
+Illustrate simple contour plotting, contours on an image with
+a colorbar for the contours, and labelled contours.
+
+See also contour_image.py.
+"""
+import matplotlib
+import numpy as np
+import matplotlib.cm as cm
+import matplotlib.mlab as mlab
+import matplotlib.pyplot as plt
+
+matplotlib.rcParams['xtick.direction'] = 'out'
+matplotlib.rcParams['ytick.direction'] = 'out'
+
+delta = 0.025
+x = np.arange(-3.0, 3.0, delta)
+y = np.arange(-2.0, 2.0, delta)
+X, Y = np.meshgrid(x, y)
+Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
+Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
+# difference of Gaussians
+Z = 10.0 * (Z2 - Z1)
+
+
+# You can use a colormap to specify the colors; the default
+# colormap will be used for the contour lines
+plt.figure()
+im = plt.imshow(Z, interpolation='bilinear', origin='lower',
+ cmap=cm.gray, extent=(-3,3,-2,2))
+levels = np.arange(-1.2, 1.6, 0.2)
+CS = plt.contour(Z, levels,
+ origin='lower',
+ linewidths=2,
+ extent=(-3,3,-2,2))
+
+#Thicken the zero contour.
+zc = CS.collections[6]
+plt.setp(zc, linewidth=4)
+
+plt.clabel(CS, levels[1::2], # label every second level
+ inline=1,
+ fmt='%1.1f',
+ fontsize=14)
+
+# make a colorbar for the contour lines
+CB = plt.colorbar(CS, shrink=0.8, extend='both')
+
+plt.title('Lines with colorbar')
+#plt.hot() # Now change the colormap for the contour lines and colorbar
+plt.flag()
+
+# We can still add a colorbar for the image, too.
+CBI = plt.colorbar(im, orientation='horizontal', shrink=0.8)
+
+# This makes the original colorbar look a bit out of place,
+# so let's improve its position.
+
+l,b,w,h = plt.gca().get_position().bounds
+ll,bb,ww,hh = CB.ax.get_position().bounds
+CB.ax.set_position([ll, b+0.1*h, ww, h*0.8])
+
+
+#savefig('contour_demo')
+plt.show()
Added: trunk/matplotlib/doc/pyplots/csd_demo.py
===================================================================
--- trunk/matplotlib/doc/pyplots/csd_demo.py (rev 0)
+++ trunk/matplotlib/doc/pyplots/csd_demo.py 2008-06-27 15:34:32 UTC (rev 5690)
@@ -0,0 +1,36 @@
+#!/usr/bin/env python
+"""
+Compute the cross spectral density of two signals
+"""
+import numpy as np
+import matplotlib.pyplot as plt
+
+# make a little extra space between the subplots
+plt.subplots_adjust(wspace=0.5)
+
+dt = 0.01
+t = np.arange(0, 30, dt)
+nse1 = np.random.randn(len(t)) # white noise 1
+nse2 = np.random.randn(len(t)) # white noise 2
+r = np.exp(-t/0.05)
+
+cnse1 = np.convolve(nse1, r, mode='same')*dt # colored noise 1
+cnse2 = np.convolve(nse2, r, mode='same')*dt # colored noise 2
+
+# two signals with a coherent part and a random part
+s1 = 0.01*np.sin(2*np.pi*10*t) + cnse1
+s2 = 0.01*np.sin(2*np.pi*10*t) + cnse2
+
+plt.subplot(211)
+plt.plot(t, s1, 'b-', t, s2, 'g-')
+plt.xlim(0,5)
+plt.xlabel('time')
+plt.ylabel('s1 and s2')
+plt.grid(True)
+
+plt.subplot(212)
+cxy, f = plt.csd(s1, s2, 256, 1./dt)
+plt.ylabel('CSD (db)')
+plt.show()
+
+
Added: trunk/matplotlib/doc/pyplots/errorbar_demo.py
===================================================================
--- trunk/matplotlib/doc/pyplots/errorbar_demo.py (rev 0)
+++ trunk/matplotlib/doc/pyplots/errorbar_demo.py 2008-06-27 15:34:32 UTC (rev 5690)
@@ -0,0 +1,8 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+t = np.arange(0.1, 4, 0.1)
+s = np.exp(-t)
+e, f = 0.1*np.absolute(np.random.randn(2, len(s)))
+plt.errorbar(t, s, e, fmt='o') # vertical symmetric
+plt.show()
Added: trunk/matplotlib/doc/pyplots/figimage_demo.py
===================================================================
--- trunk/matplotlib/doc/pyplots/figimage_demo.py (rev 0)
+++ trunk/matplotlib/doc/pyplots/figimage_demo.py 2008-06-27 15:34:32 UTC (rev 5690)
@@ -0,0 +1,21 @@
+"""
+See pcolor_demo2 for a much faster way of generating pcolor plots
+"""
+import numpy as np
+import matplotlib
+import matplotlib.cm as cm
+import matplotlib.pyplot as plt
+
+
+fig = plt.figure(frameon=False)
+Z = np.arange(10000.0)
+Z.shape = 100,100
+Z[:,50:] = 1.
+
+im1 = plt.figimage(Z, xo=50, yo=0, cmap=cm.jet)
+im2 = plt.figimage(Z, xo=100, yo=100, alpha=.8, cmap=cm.jet)
+
+plt.show()
+
+
+
Added: trunk/matplotlib/doc/pyplots/figlegend_demo.py
===================================================================
--- trunk/matplotlib/doc/pyplots/figlegend_demo.py (rev 0)
+++ trunk/matplotlib/doc/pyplots/figlegend_demo.py 2008-06-27 15:34:32 UTC (rev 5690)
@@ -0,0 +1,19 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+fig = plt.figure()
+ax1 = fig.add_axes([0.1, 0.1, 0.4, 0.7])
+ax2 = fig.add_axes([0.55, 0.1, 0.4, 0.7])
+
+x = np.arange(0.0, 2.0, 0.02)
+y1 = np.sin(2*np.pi*x)
+y2 = np.exp(-x)
+l1, l2 = ax1.plot(x, y1, 'rs-', x, y2, 'go')
+
+y3 = np.sin(4*np.pi*x)
+y4 = np.exp(-2*x)
+l3, l4 = ax2.plot(x, y3, 'yd-', x, y3, 'k^')
+
+fig.legend((l1, l2), ('Line 1', 'Line 2'), 'upper left')
+fig.legend((l3, l4), ('Line 3', 'Line 4'), 'upper right')
+plt.show()
Added: trunk/matplotlib/doc/pyplots/fill_demo.py
===================================================================
--- trunk/matplotlib/doc/pyplots/fill_demo.py (rev 0)
+++ trunk/matplotlib/doc/pyplots/fill_demo.py 2008-06-27 15:34:32 UTC (rev 5690)
@@ -0,0 +1,10 @@
+#!/usr/bin/env python
+import numpy as np
+import matplotlib.pyplot as plt
+
+t = np.arange(0.0, 1.01, 0.01)
+s = np.sin(2*2*np.pi*t)
+
+plt.fill(t, s*np.exp(-5*t), 'r')
+plt.grid(True)
+plt.show()
Added: trunk/matplotlib/doc/pyplots/hexbin_demo.py
===================================================================
--- trunk/matplotlib/doc/pyplots/hexbin_demo.py (rev 0)
+++ trunk/matplotlib/doc/pyplots/hexbin_demo.py 2008-06-27 15:34:32 UTC (rev 5690)
@@ -0,0 +1,34 @@
+"""
+hexbin is an axes method or pyplot function that is essentially
+a pcolor of a 2-D histogram with hexagonal cells. It can be
+much more informative than a scatter plot; in the first subplot
+below, try substituting 'scatter' for 'hexbin'.
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+n = 100000
+x = np.random.standard_normal(n)
+y = 2.0 + 3.0 * x + 4.0 * np.random.standard_normal(n)
+xmin = x.min()
+xmax = x.max()
+ymin = y.min()
+ymax = y.max()
+
+plt.subplots_adjust(hspace=0.5)
+plt.subplot(121)
+plt.hexbin(x,y)
+plt.axis([xmin, xmax, ymin, ymax])
+plt.title("Hexagon binning")
+cb = plt.colorbar()
+cb.set_label('counts')
+
+plt.subplot(122)
+plt.hexbin(x,y,bins='log')
+plt.axis([xmin, xmax, ymin, ymax])
+plt.title("With a log color scale")
+cb = plt.colorbar()
+cb.set_label('log10(N)')
+
+plt.show()
+
Added: trunk/matplotlib/doc/pyplots/histogram_demo.py
===================================================================
--- trunk/matplotlib/doc/pyplots/histogram_demo.py (rev 0)
+++ trunk/matplotlib/doc/pyplots/histogram_demo.py 2008-06-27 15:34:32 UTC (rev 5690)
@@ -0,0 +1,22 @@
+#!/usr/bin/env python
+import numpy as np
+import matplotlib.mlab as mlab
+import matplotlib.pyplot as plt
+
+mu, sigma = 100, 15
+x = mu + sigma*np.random.randn(10000)
+
+# the histogram of the data
+n, bins, patches = plt.hist(x, 50, normed=1, facecolor='green', alpha=0.75)
+
+# add a 'best fit' line
+y = mlab.normpdf( bins, mu, sigma)
+l = plt.plot(bins, y, 'r--', linewidth=1)
+
+plt.xlabel('Smarts')
+plt.ylabel('Probability')
+plt.title(r'$\mathrm{Histogram\ of\ IQ:}\ \mu=100,\ \sigma=15$')
+plt.axis([40, 160, 0, 0.03])
+plt.grid(True)
+
+plt.show()
Added: trunk/matplotlib/doc/pyplots/hline_demo.py
===================================================================
--- trunk/matplotlib/doc/pyplots/hline_demo.py (rev 0)
+++ trunk/matplotlib/doc/pyplots/hline_demo.py 2008-06-27 15:34:32 UTC (rev 5690)
@@ -0,0 +1,23 @@
+#!/usr/bin/env python
+import numpy as np
+import matplotlib.pyplot as plt
+
+def f(t):
+ s1 = np.sin(2*np.pi*t)
+ e1 = np.exp(-t)
+ return np.absolute((s1*e1))+.05
+
+
+t = np.arange(0.0, 5.0, 0.1)
+s = f(t)
+nse = np.random.normal(0.0, 0.3, t.shape) * s
+
+
+plt.plot(s+nse, t, 'b^')
+plt.hlines(t, [0], s, lw=2)
+plt.xlabel('time (s)')
+plt.title('Comparison of model with data')
+plt.savefig('test')
+plt.xlim(xmin=0)
+plt.show()
+
Added: trunk/matplotlib/doc/pyplots/image_demo.py
===================================================================
--- trunk/matplotlib/doc/pyplots/image_demo.py (rev 0)
+++ trunk/matplotlib/doc/pyplots/image_demo.py 2008-06-27 15:34:32 UTC (rev 5690)
@@ -0,0 +1,18 @@
+#!/usr/bin/env python
+import numpy as np
+import matplotlib.cm as cm
+import matplotlib.mlab as mlab
+import matplotlib.pyplot as plt
+
+delta = 0.025
+x = y = np.arange(-3.0, 3.0, delta)
+X, Y = np.meshgrid(x, y)
+Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
+Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
+Z = Z2-Z1 # difference of Gaussians
+
+im = plt.imshow(Z, interpolation='bilinear', cmap=cm.gray,
+ origin='lower', extent=[-3,3,-3,3])
+
+plt.show()
+
Added: trunk/matplotlib/doc/pyplots/log_demo.py
===================================================================
--- trunk/matplotlib/doc/pyplots/log_demo.py (rev 0)
+++ trunk/matplotlib/doc/pyplots/log_demo.py 2008-06-27 15:34:32 UTC (rev 5690)
@@ -0,0 +1,25 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+plt.subplots_adjust(hspace=0.4)
+t = np.arange(0.01, 20.0, 0.01)
+
+# log y axis
+plt.subplot(311)
+plt.semilogy(t, np.exp(-t/5.0))
+plt.ylabel('semilogy')
+plt.grid(True)
+
+# log x axis
+plt.subplot(312)
+plt.semilogx(t, np.sin(2*np.pi*t))
+plt.ylabel('semilogx')
+plt.grid(True)
+
+# log x and y axis
+plt.subplot(313)
+plt.loglog(t, 20*np.exp(-t/10.0), basex=4)
+plt.grid(True)
+plt.ylabel('loglog base 4 on x')
+
+plt.show()
Added: trunk/matplotlib/doc/pyplots/xcorr_demo.py
===================================================================
--- trunk/matplotlib/doc/pyplots/xcorr_demo.py (rev 0)
+++ trunk/matplotlib/doc/pyplots/xcorr_demo.py 2008-06-27 15:34:32 UTC (rev 5690)
@@ -0,0 +1,17 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+x,y = np.random.randn(2,100)
+fig = plt.figure()
+ax1 = fig.add_subplot(211)
+ax1.xcorr(x, y, usevlines=True, maxlags=50, normed=True, lw=2)
+ax1.grid(True)
+ax1.axhline(0, color='black', lw=2)
+
+ax2 = fig.add_subplot(212, sharex=ax1)
+ax2.acorr(x, usevlines=True, normed=True, maxlags=50, lw=2)
+ax2.grid(True)
+ax2.axhline(0, color='black', lw=2)
+
+plt.show()
+
This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site.
|
|
From: <jd...@us...> - 2008-06-27 15:53:17
|
Revision: 5691
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=5691&view=rev
Author: jdh2358
Date: 2008-06-27 08:39:28 -0700 (Fri, 27 Jun 2008)
Log Message:
-----------
removing the copied files I accidentally added (sigh). It is confusing to put autogenerated files next to legit files in pyplot...
Removed Paths:
-------------
trunk/matplotlib/doc/pyplots/arrow_demo.py
trunk/matplotlib/doc/pyplots/axhspan_demo.py
trunk/matplotlib/doc/pyplots/bar_stacked.py
trunk/matplotlib/doc/pyplots/broken_barh.py
trunk/matplotlib/doc/pyplots/cohere_demo.py
trunk/matplotlib/doc/pyplots/csd_demo.py
trunk/matplotlib/doc/pyplots/figimage_demo.py
trunk/matplotlib/doc/pyplots/figlegend_demo.py
trunk/matplotlib/doc/pyplots/fill_demo.py
trunk/matplotlib/doc/pyplots/hexbin_demo.py
trunk/matplotlib/doc/pyplots/histogram_demo.py
trunk/matplotlib/doc/pyplots/image_demo.py
trunk/matplotlib/doc/pyplots/log_demo.py
trunk/matplotlib/doc/pyplots/xcorr_demo.py
Deleted: trunk/matplotlib/doc/pyplots/arrow_demo.py
===================================================================
--- trunk/matplotlib/doc/pyplots/arrow_demo.py 2008-06-27 15:34:32 UTC (rev 5690)
+++ trunk/matplotlib/doc/pyplots/arrow_demo.py 2008-06-27 15:39:28 UTC (rev 5691)
@@ -1,313 +0,0 @@
-#!/usr/bin/env python
-"""Arrow drawing example for the new fancy_arrow facilities.
-
-Code contributed by: Rob Knight <ro...@sp...>
-
-usage:
-
- python arrow_demo.py realistic|full|sample|extreme
-
-
-"""
-from pylab import *
-
-rates_to_bases={'r1':'AT', 'r2':'TA', 'r3':'GA','r4':'AG','r5':'CA','r6':'AC', \
- 'r7':'GT', 'r8':'TG', 'r9':'CT','r10':'TC','r11':'GC','r12':'CG'}
-numbered_bases_to_rates = dict([(v,k) for k, v in rates_to_bases.items()])
-lettered_bases_to_rates = dict([(v, 'r'+v) for k, v in rates_to_bases.items()])
-def add_dicts(d1, d2):
- """Adds two dicts and returns the result."""
- result = d1.copy()
- result.update(d2)
- return result
-
-def make_arrow_plot(data, size=4, display='length', shape='right', \
- max_arrow_width=0.03, arrow_sep = 0.02, alpha=0.5, \
- normalize_data=False, ec=None, labelcolor=None, \
- head_starts_at_zero=True, rate_labels=lettered_bases_to_rates,\
- **kwargs):
- """Makes an arrow plot.
-
- Parameters:
-
- data: dict with probabilities for the bases and pair transitions.
- size: size of the graph in inches.
- display: 'length', 'width', or 'alpha' for arrow property to change.
- shape: 'full', 'left', or 'right' for full or half arrows.
- max_arrow_width: maximum width of an arrow, data coordinates.
- arrow_sep: separation between arrows in a pair, data coordinates.
- alpha: maximum opacity of arrows, default 0.8.
-
- **kwargs can be anything allowed by a Arrow object, e.g.
- linewidth and edgecolor.
- """
-
- xlim(-0.5,1.5)
- ylim(-0.5,1.5)
- gcf().set_size_inches(size,size)
- xticks([])
- yticks([])
- max_text_size = size*12
- min_text_size = size
- label_text_size = size*2.5
- text_params={'ha':'center', 'va':'center', 'family':'sans-serif',\
- 'fontweight':'bold'}
- r2 = sqrt(2)
-
- deltas = {\
- 'AT':(1,0),
- 'TA':(-1,0),
- 'GA':(0,1),
- 'AG':(0,-1),
- 'CA':(-1/r2, 1/r2),
- 'AC':(1/r2, -1/r2),
- 'GT':(1/r2, 1/r2),
- 'TG':(-1/r2,-1/r2),
- 'CT':(0,1),
- 'TC':(0,-1),
- 'GC':(1,0),
- 'CG':(-1,0)
- }
-
- colors = {\
- 'AT':'r',
- 'TA':'k',
- 'GA':'g',
- 'AG':'r',
- 'CA':'b',
- 'AC':'r',
- 'GT':'g',
- 'TG':'k',
- 'CT':'b',
- 'TC':'k',
- 'GC':'g',
- 'CG':'b'
- }
-
- label_positions = {\
- 'AT':'center',
- 'TA':'center',
- 'GA':'center',
- 'AG':'center',
- 'CA':'left',
- 'AC':'left',
- 'GT':'left',
- 'TG':'left',
- 'CT':'center',
- 'TC':'center',
- 'GC':'center',
- 'CG':'center'
- }
-
-
- def do_fontsize(k):
- return float(clip(max_text_size*sqrt(data[k]),\
- min_text_size,max_text_size))
-
- A = text(0,1, '$A_3$', color='r', size=do_fontsize('A'), **text_params)
- T = text(1,1, '$T_3$', color='k', size=do_fontsize('T'), **text_params)
- G = text(0,0, '$G_3$', color='g', size=do_fontsize('G'), **text_params)
- C = text(1,0, '$C_3$', color='b', size=do_fontsize('C'), **text_params)
-
- arrow_h_offset = 0.25 #data coordinates, empirically determined
- max_arrow_length = 1 - 2*arrow_h_offset
-
- max_arrow_width = max_arrow_width
- max_head_width = 2.5*max_arrow_width
- max_head_length = 2*max_arrow_width
- arrow_params={'length_includes_head':True, 'shape':shape, \
- 'head_starts_at_zero':head_starts_at_zero}
- ax = gca()
- sf = 0.6 #max arrow size represents this in data coords
-
- d = (r2/2 + arrow_h_offset - 0.5)/r2 #distance for diags
- r2v = arrow_sep/r2 #offset for diags
-
- #tuple of x, y for start position
- positions = {\
- 'AT': (arrow_h_offset, 1+arrow_sep),
- 'TA': (1-arrow_h_offset, 1-arrow_sep),
- 'GA': (-arrow_sep, arrow_h_offset),
- 'AG': (arrow_sep, 1-arrow_h_offset),
- 'CA': (1-d-r2v, d-r2v),
- 'AC': (d+r2v, 1-d+r2v),
- 'GT': (d-r2v, d+r2v),
- 'TG': (1-d+r2v, 1-d-r2v),
- 'CT': (1-arrow_sep, arrow_h_offset),
- 'TC': (1+arrow_sep, 1-arrow_h_offset),
- 'GC': (arrow_h_offset, arrow_sep),
- 'CG': (1-arrow_h_offset, -arrow_sep),
- }
-
- if normalize_data:
- #find maximum value for rates, i.e. where keys are 2 chars long
- max_val = 0
- for k, v in data.items():
- if len(k) == 2:
- max_val = max(max_val, v)
- #divide rates by max val, multiply by arrow scale factor
- for k, v in data.items():
- data[k] = v/max_val*sf
-
- def draw_arrow(pair, alpha=alpha, ec=ec, labelcolor=labelcolor):
- #set the length of the arrow
- if display == 'length':
- length = max_head_length+(max_arrow_length-max_head_length)*\
- data[pair]/sf
- else:
- length = max_arrow_length
- #set the transparency of the arrow
- if display == 'alph':
- alpha = min(data[pair]/sf, alpha)
- else:
- alpha=alpha
- #set the width of the arrow
- if display == 'width':
- scale = data[pair]/sf
- width = max_arrow_width*scale
- head_width = max_head_width*scale
- head_length = max_head_length*scale
- else:
- width = max_arrow_width
- head_width = max_head_width
- head_length = max_head_length
-
- fc = colors[pair]
- ec = ec or fc
-
- x_scale, y_scale = deltas[pair]
- x_pos, y_pos = positions[pair]
- arrow(x_pos, y_pos, x_scale*length, y_scale*length, \
- fc=fc, ec=ec, alpha=alpha, width=width, head_width=head_width, \
- head_length=head_length, **arrow_params)
-
- #figure out coordinates for text
- #if drawing relative to base: x and y are same as for arrow
- #dx and dy are one arrow width left and up
- #need to rotate based on direction of arrow, use x_scale and y_scale
- #as sin x and cos x?
- sx, cx = y_scale, x_scale
-
- where = label_positions[pair]
- if where == 'left':
- orig_position = 3*array([[max_arrow_width, max_arrow_width]])
- elif where == 'absolute':
- orig_position = array([[max_arrow_length/2.0, 3*max_arrow_width]])
- elif where == 'right':
- orig_position = array([[length-3*max_arrow_width,\
- 3*max_arrow_width]])
- elif where == 'center':
- orig_position = array([[length/2.0, 3*max_arrow_width]])
- else:
- raise ValueError, "Got unknown position parameter %s" % where
-
-
-
- M = array([[cx, sx],[-sx,cx]])
- coords = dot(orig_position, M) + [[x_pos, y_pos]]
- x, y = ravel(coords)
- orig_label = rate_labels[pair]
- label = '$%s_{_{\mathrm{%s}}}$' % (orig_label[0], orig_label[1:])
-
- text(x, y, label, size=label_text_size, ha='center', va='center', \
- color=labelcolor or fc)
-
- for p in positions.keys():
- draw_arrow(p)
-
- #test data
-all_on_max = dict([(i, 1) for i in 'TCAG'] + \
- [(i+j, 0.6) for i in 'TCAG' for j in 'TCAG'])
-
-realistic_data = {
- 'A':0.4,
- 'T':0.3,
- 'G':0.5,
- 'C':0.2,
- 'AT':0.4,
- 'AC':0.3,
- 'AG':0.2,
- 'TA':0.2,
- 'TC':0.3,
- 'TG':0.4,
- 'CT':0.2,
- 'CG':0.3,
- 'CA':0.2,
- 'GA':0.1,
- 'GT':0.4,
- 'GC':0.1,
- }
-
-extreme_data = {
- 'A':0.75,
- 'T':0.10,
- 'G':0.10,
- 'C':0.05,
- 'AT':0.6,
- 'AC':0.3,
- 'AG':0.1,
- 'TA':0.02,
- 'TC':0.3,
- 'TG':0.01,
- 'CT':0.2,
- 'CG':0.5,
- 'CA':0.2,
- 'GA':0.1,
- 'GT':0.4,
- 'GC':0.2,
- }
-
-sample_data = {
- 'A':0.2137,
- 'T':0.3541,
- 'G':0.1946,
- 'C':0.2376,
- 'AT':0.0228,
- 'AC':0.0684,
- 'AG':0.2056,
- 'TA':0.0315,
- 'TC':0.0629,
- 'TG':0.0315,
- 'CT':0.1355,
- 'CG':0.0401,
- 'CA':0.0703,
- 'GA':0.1824,
- 'GT':0.0387,
- 'GC':0.1106,
- }
-
-
-if __name__ == '__main__':
- from sys import argv
- d = None
- if len(argv) > 1:
- if argv[1] == 'full':
- d = all_on_max
- scaled = False
- elif argv[1] == 'extreme':
- d = extreme_data
- scaled = False
- elif argv[1] == 'realistic':
- d = realistic_data
- scaled = False
- elif argv[1] == 'sample':
- d = sample_data
- scaled = True
- if d is None:
- d = all_on_max
- scaled=False
- if len(argv) > 2:
- display = argv[2]
- else:
- display = 'length'
-
- size = 4
- figure(figsize=(size,size))
-
- make_arrow_plot(d, display=display, linewidth=0.001, edgecolor=None,
- normalize_data=scaled, head_starts_at_zero=True, size=size)
-
- draw()
- #savefig('arrows.png')
- #print 'Example saved to file "arrows.png"'
- show()
Deleted: trunk/matplotlib/doc/pyplots/axhspan_demo.py
===================================================================
--- trunk/matplotlib/doc/pyplots/axhspan_demo.py 2008-06-27 15:34:32 UTC (rev 5690)
+++ trunk/matplotlib/doc/pyplots/axhspan_demo.py 2008-06-27 15:39:28 UTC (rev 5691)
@@ -1,32 +0,0 @@
-import numpy as np
-import matplotlib.pyplot as plt
-
-t = np.arange(-1,2, .01)
-s = np.sin(2*np.pi*t)
-
-plt.plot(t,s)
-# draw a thick red hline at y=0 that spans the xrange
-l = plt.axhline(linewidth=4, color='r')
-
-# draw a default hline at y=1 that spans the xrange
-l = plt.axhline(y=1)
-
-# draw a default vline at x=1 that spans the xrange
-l = plt.axvline(x=1)
-
-# draw a thick blue vline at x=0 that spans the the upper quadrant of
-# the yrange
-l = plt.axvline(x=0, ymin=0.75, linewidth=4, color='b')
-
-# draw a default hline at y=.5 that spans the the middle half of
-# the axes
-l = plt.axhline(y=.5, xmin=0.25, xmax=0.75)
-
-p = plt.axhspan(0.25, 0.75, facecolor='0.5', alpha=0.5)
-
-p = plt.axvspan(1.25, 1.55, facecolor='g', alpha=0.5)
-
-plt.axis([-1,2,-1,2])
-
-
-plt.show()
Deleted: trunk/matplotlib/doc/pyplots/bar_stacked.py
===================================================================
--- trunk/matplotlib/doc/pyplots/bar_stacked.py 2008-06-27 15:34:32 UTC (rev 5690)
+++ trunk/matplotlib/doc/pyplots/bar_stacked.py 2008-06-27 15:39:28 UTC (rev 5691)
@@ -1,25 +0,0 @@
-#!/usr/bin/env python
-# a stacked bar plot with errorbars
-import numpy as np
-import matplotlib.pyplot as plt
-
-
-N = 5
-menMeans = (20, 35, 30, 35, 27)
-womenMeans = (25, 32, 34, 20, 25)
-menStd = (2, 3, 4, 1, 2)
-womenStd = (3, 5, 2, 3, 3)
-ind = np.arange(N) # the x locations for the groups
-width = 0.35 # the width of the bars: can also be len(x) sequence
-
-p1 = plt.bar(ind, menMeans, width, color='r', yerr=womenStd)
-p2 = plt.bar(ind, womenMeans, width, color='y',
- bottom=menMeans, yerr=menStd)
-
-plt.ylabel('Scores')
-plt.title('Scores by group and gender')
-plt.xticks(ind+width/2., ('G1', 'G2', 'G3', 'G4', 'G5') )
-plt.yticks(np.arange(0,81,10))
-plt.legend( (p1[0], p2[0]), ('Men', 'Women') )
-
-plt.show()
Deleted: trunk/matplotlib/doc/pyplots/broken_barh.py
===================================================================
--- trunk/matplotlib/doc/pyplots/broken_barh.py 2008-06-27 15:34:32 UTC (rev 5690)
+++ trunk/matplotlib/doc/pyplots/broken_barh.py 2008-06-27 15:39:28 UTC (rev 5691)
@@ -1,24 +0,0 @@
-"""
-Make a "broken" horizontal bar plot, ie one with gaps
-"""
-import matplotlib.pyplot as plt
-
-fig = plt.figure()
-ax = fig.add_subplot(111)
-ax.broken_barh([ (110, 30), (150, 10) ] , (10, 9), facecolors='blue')
-ax.broken_barh([ (10, 50), (100, 20), (130, 10)] , (20, 9),
- facecolors=('red', 'yellow', 'green'))
-ax.set_ylim(5,35)
-ax.set_xlim(0,200)
-ax.set_xlabel('seconds since start')
-ax.set_yticks([15,25])
-ax.set_yticklabels(['Bill', 'Jim'])
-ax.grid(True)
-ax.annotate('race interrupted', (61, 25),
- xytext=(0.8, 0.9), textcoords='axes fraction',
- arrowprops=dict(facecolor='black', shrink=0.05),
- fontsize=16,
- horizontalalignment='right', verticalalignment='top')
-
-#fig.savefig('broken_barh', dpi=100)
-plt.show()
Deleted: trunk/matplotlib/doc/pyplots/cohere_demo.py
===================================================================
--- trunk/matplotlib/doc/pyplots/cohere_demo.py 2008-06-27 15:34:32 UTC (rev 5690)
+++ trunk/matplotlib/doc/pyplots/cohere_demo.py 2008-06-27 15:39:28 UTC (rev 5691)
@@ -1,36 +0,0 @@
-#!/usr/bin/env python
-"""
-Compute the coherence of two signals
-"""
-import numpy as np
-import matplotlib.pyplot as plt
-
-# make a little extra space between the subplots
-plt.subplots_adjust(wspace=0.5)
-
-dt = 0.01
-t = np.arange(0, 30, dt)
-nse1 = np.random.randn(len(t)) # white noise 1
-nse2 = np.random.randn(len(t)) # white noise 2
-r = np.exp(-t/0.05)
-
-cnse1 = np.convolve(nse1, r, mode='same')*dt # colored noise 1
-cnse2 = np.convolve(nse2, r, mode='same')*dt # colored noise 2
-
-# two signals with a coherent part and a random part
-s1 = 0.01*np.sin(2*np.pi*10*t) + cnse1
-s2 = 0.01*np.sin(2*np.pi*10*t) + cnse2
-
-plt.subplot(211)
-plt.plot(t, s1, 'b-', t, s2, 'g-')
-plt.xlim(0,5)
-plt.xlabel('time')
-plt.ylabel('s1 and s2')
-plt.grid(True)
-
-plt.subplot(212)
-cxy, f = plt.cohere(s1, s2, 256, 1./dt)
-plt.ylabel('coherence')
-plt.show()
-
-
Deleted: trunk/matplotlib/doc/pyplots/csd_demo.py
===================================================================
--- trunk/matplotlib/doc/pyplots/csd_demo.py 2008-06-27 15:34:32 UTC (rev 5690)
+++ trunk/matplotlib/doc/pyplots/csd_demo.py 2008-06-27 15:39:28 UTC (rev 5691)
@@ -1,36 +0,0 @@
-#!/usr/bin/env python
-"""
-Compute the cross spectral density of two signals
-"""
-import numpy as np
-import matplotlib.pyplot as plt
-
-# make a little extra space between the subplots
-plt.subplots_adjust(wspace=0.5)
-
-dt = 0.01
-t = np.arange(0, 30, dt)
-nse1 = np.random.randn(len(t)) # white noise 1
-nse2 = np.random.randn(len(t)) # white noise 2
-r = np.exp(-t/0.05)
-
-cnse1 = np.convolve(nse1, r, mode='same')*dt # colored noise 1
-cnse2 = np.convolve(nse2, r, mode='same')*dt # colored noise 2
-
-# two signals with a coherent part and a random part
-s1 = 0.01*np.sin(2*np.pi*10*t) + cnse1
-s2 = 0.01*np.sin(2*np.pi*10*t) + cnse2
-
-plt.subplot(211)
-plt.plot(t, s1, 'b-', t, s2, 'g-')
-plt.xlim(0,5)
-plt.xlabel('time')
-plt.ylabel('s1 and s2')
-plt.grid(True)
-
-plt.subplot(212)
-cxy, f = plt.csd(s1, s2, 256, 1./dt)
-plt.ylabel('CSD (db)')
-plt.show()
-
-
Deleted: trunk/matplotlib/doc/pyplots/figimage_demo.py
===================================================================
--- trunk/matplotlib/doc/pyplots/figimage_demo.py 2008-06-27 15:34:32 UTC (rev 5690)
+++ trunk/matplotlib/doc/pyplots/figimage_demo.py 2008-06-27 15:39:28 UTC (rev 5691)
@@ -1,21 +0,0 @@
-"""
-See pcolor_demo2 for a much faster way of generating pcolor plots
-"""
-import numpy as np
-import matplotlib
-import matplotlib.cm as cm
-import matplotlib.pyplot as plt
-
-
-fig = plt.figure(frameon=False)
-Z = np.arange(10000.0)
-Z.shape = 100,100
-Z[:,50:] = 1.
-
-im1 = plt.figimage(Z, xo=50, yo=0, cmap=cm.jet)
-im2 = plt.figimage(Z, xo=100, yo=100, alpha=.8, cmap=cm.jet)
-
-plt.show()
-
-
-
Deleted: trunk/matplotlib/doc/pyplots/figlegend_demo.py
===================================================================
--- trunk/matplotlib/doc/pyplots/figlegend_demo.py 2008-06-27 15:34:32 UTC (rev 5690)
+++ trunk/matplotlib/doc/pyplots/figlegend_demo.py 2008-06-27 15:39:28 UTC (rev 5691)
@@ -1,19 +0,0 @@
-import numpy as np
-import matplotlib.pyplot as plt
-
-fig = plt.figure()
-ax1 = fig.add_axes([0.1, 0.1, 0.4, 0.7])
-ax2 = fig.add_axes([0.55, 0.1, 0.4, 0.7])
-
-x = np.arange(0.0, 2.0, 0.02)
-y1 = np.sin(2*np.pi*x)
-y2 = np.exp(-x)
-l1, l2 = ax1.plot(x, y1, 'rs-', x, y2, 'go')
-
-y3 = np.sin(4*np.pi*x)
-y4 = np.exp(-2*x)
-l3, l4 = ax2.plot(x, y3, 'yd-', x, y3, 'k^')
-
-fig.legend((l1, l2), ('Line 1', 'Line 2'), 'upper left')
-fig.legend((l3, l4), ('Line 3', 'Line 4'), 'upper right')
-plt.show()
Deleted: trunk/matplotlib/doc/pyplots/fill_demo.py
===================================================================
--- trunk/matplotlib/doc/pyplots/fill_demo.py 2008-06-27 15:34:32 UTC (rev 5690)
+++ trunk/matplotlib/doc/pyplots/fill_demo.py 2008-06-27 15:39:28 UTC (rev 5691)
@@ -1,10 +0,0 @@
-#!/usr/bin/env python
-import numpy as np
-import matplotlib.pyplot as plt
-
-t = np.arange(0.0, 1.01, 0.01)
-s = np.sin(2*2*np.pi*t)
-
-plt.fill(t, s*np.exp(-5*t), 'r')
-plt.grid(True)
-plt.show()
Deleted: trunk/matplotlib/doc/pyplots/hexbin_demo.py
===================================================================
--- trunk/matplotlib/doc/pyplots/hexbin_demo.py 2008-06-27 15:34:32 UTC (rev 5690)
+++ trunk/matplotlib/doc/pyplots/hexbin_demo.py 2008-06-27 15:39:28 UTC (rev 5691)
@@ -1,34 +0,0 @@
-"""
-hexbin is an axes method or pyplot function that is essentially
-a pcolor of a 2-D histogram with hexagonal cells. It can be
-much more informative than a scatter plot; in the first subplot
-below, try substituting 'scatter' for 'hexbin'.
-"""
-
-import numpy as np
-import matplotlib.pyplot as plt
-n = 100000
-x = np.random.standard_normal(n)
-y = 2.0 + 3.0 * x + 4.0 * np.random.standard_normal(n)
-xmin = x.min()
-xmax = x.max()
-ymin = y.min()
-ymax = y.max()
-
-plt.subplots_adjust(hspace=0.5)
-plt.subplot(121)
-plt.hexbin(x,y)
-plt.axis([xmin, xmax, ymin, ymax])
-plt.title("Hexagon binning")
-cb = plt.colorbar()
-cb.set_label('counts')
-
-plt.subplot(122)
-plt.hexbin(x,y,bins='log')
-plt.axis([xmin, xmax, ymin, ymax])
-plt.title("With a log color scale")
-cb = plt.colorbar()
-cb.set_label('log10(N)')
-
-plt.show()
-
Deleted: trunk/matplotlib/doc/pyplots/histogram_demo.py
===================================================================
--- trunk/matplotlib/doc/pyplots/histogram_demo.py 2008-06-27 15:34:32 UTC (rev 5690)
+++ trunk/matplotlib/doc/pyplots/histogram_demo.py 2008-06-27 15:39:28 UTC (rev 5691)
@@ -1,22 +0,0 @@
-#!/usr/bin/env python
-import numpy as np
-import matplotlib.mlab as mlab
-import matplotlib.pyplot as plt
-
-mu, sigma = 100, 15
-x = mu + sigma*np.random.randn(10000)
-
-# the histogram of the data
-n, bins, patches = plt.hist(x, 50, normed=1, facecolor='green', alpha=0.75)
-
-# add a 'best fit' line
-y = mlab.normpdf( bins, mu, sigma)
-l = plt.plot(bins, y, 'r--', linewidth=1)
-
-plt.xlabel('Smarts')
-plt.ylabel('Probability')
-plt.title(r'$\mathrm{Histogram\ of\ IQ:}\ \mu=100,\ \sigma=15$')
-plt.axis([40, 160, 0, 0.03])
-plt.grid(True)
-
-plt.show()
Deleted: trunk/matplotlib/doc/pyplots/image_demo.py
===================================================================
--- trunk/matplotlib/doc/pyplots/image_demo.py 2008-06-27 15:34:32 UTC (rev 5690)
+++ trunk/matplotlib/doc/pyplots/image_demo.py 2008-06-27 15:39:28 UTC (rev 5691)
@@ -1,18 +0,0 @@
-#!/usr/bin/env python
-import numpy as np
-import matplotlib.cm as cm
-import matplotlib.mlab as mlab
-import matplotlib.pyplot as plt
-
-delta = 0.025
-x = y = np.arange(-3.0, 3.0, delta)
-X, Y = np.meshgrid(x, y)
-Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
-Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
-Z = Z2-Z1 # difference of Gaussians
-
-im = plt.imshow(Z, interpolation='bilinear', cmap=cm.gray,
- origin='lower', extent=[-3,3,-3,3])
-
-plt.show()
-
Deleted: trunk/matplotlib/doc/pyplots/log_demo.py
===================================================================
--- trunk/matplotlib/doc/pyplots/log_demo.py 2008-06-27 15:34:32 UTC (rev 5690)
+++ trunk/matplotlib/doc/pyplots/log_demo.py 2008-06-27 15:39:28 UTC (rev 5691)
@@ -1,25 +0,0 @@
-import numpy as np
-import matplotlib.pyplot as plt
-
-plt.subplots_adjust(hspace=0.4)
-t = np.arange(0.01, 20.0, 0.01)
-
-# log y axis
-plt.subplot(311)
-plt.semilogy(t, np.exp(-t/5.0))
-plt.ylabel('semilogy')
-plt.grid(True)
-
-# log x axis
-plt.subplot(312)
-plt.semilogx(t, np.sin(2*np.pi*t))
-plt.ylabel('semilogx')
-plt.grid(True)
-
-# log x and y axis
-plt.subplot(313)
-plt.loglog(t, 20*np.exp(-t/10.0), basex=4)
-plt.grid(True)
-plt.ylabel('loglog base 4 on x')
-
-plt.show()
Deleted: trunk/matplotlib/doc/pyplots/xcorr_demo.py
===================================================================
--- trunk/matplotlib/doc/pyplots/xcorr_demo.py 2008-06-27 15:34:32 UTC (rev 5690)
+++ trunk/matplotlib/doc/pyplots/xcorr_demo.py 2008-06-27 15:39:28 UTC (rev 5691)
@@ -1,17 +0,0 @@
-import matplotlib.pyplot as plt
-import numpy as np
-
-x,y = np.random.randn(2,100)
-fig = plt.figure()
-ax1 = fig.add_subplot(211)
-ax1.xcorr(x, y, usevlines=True, maxlags=50, normed=True, lw=2)
-ax1.grid(True)
-ax1.axhline(0, color='black', lw=2)
-
-ax2 = fig.add_subplot(212, sharex=ax1)
-ax2.acorr(x, usevlines=True, normed=True, maxlags=50, lw=2)
-ax2.grid(True)
-ax2.axhline(0, color='black', lw=2)
-
-plt.show()
-
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