Thanks.
Here is another try inspired from:
http://www.nytimes.com/imagepages/2007/07/29/health/29cancer.graph.web.html
Results
http://www.loria.fr/~rougier/tmp/diseases.png
By the way, the matplotlib gallery (from the website) is incredibly useful when
you need to know how to do something quickly, but the lack of organization in
the different figures make search sometimes difficult. Maybe a rough structure
(1D/2D/3D, plot/bars/imshow ...) in the examples could be even more useful as
well as carefully choosing relevant examples (for example, the
filledmarker_demo produces 12 figures that are more or less the same) ?
Nicolas
Here is the script:
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# -----------------------------------------------------------------------------
# Copyright (C) 2011 Nicolas P. Rougier
#
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# * Neither the name of the glumpy Development Team nor the names of its
# contributors may be used to endorse or promote products derived from this
# software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
#
# -----------------------------------------------------------------------------
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.patches as patches
# ----------
# Data to be represented
diseases = ['Disease 1', 'Disease 2','Disease 3','Disease 4','Disease 5',
'Disease 6', 'Disease 7','Disease 8','Disease 9','Disease 10']
men_deaths = np.random.uniform(50000,100000,len(diseases))
men_cases = men_deaths+np.random.uniform(10000,50000,len(diseases))
women_deaths = np.random.uniform(50000,100000,len(diseases))
women_cases = women_deaths+np.random.uniform(10000,50000,len(diseases))
# ----------
# Choose some nice colors
matplotlib.rc('axes', facecolor = 'white')
matplotlib.rc('figure.subplot', wspace=.65)
matplotlib.rc('grid', color='white')
matplotlib.rc('grid', linewidth=1)
# Make figure background the same colors as axes
fig = plt.figure(figsize=(12,8), facecolor='white')
# --- MEN data ---
axes_left = plt.subplot(121)
# Keep only top and right spines
axes_left.spines['left'].set_color('none')
axes_left.spines['bottom'].set_color('none')
axes_left.xaxis.set_ticks_position('top')
axes_left.yaxis.set_ticks_position('right')
axes_left.spines['top'].set_position(('data',len(diseases)-.75))
axes_left.spines['top'].set_color('w')
# Set axes limits
plt.xlim(150000,0)
plt.ylim(0,len(diseases))
# Set ticks labels
plt.xticks([150000, 100000, 50000, 0],
['150000', '100000', '50000', 'MEN'])
axes_left.get_xticklabels()[-1].set_weight('bold')
axes_left.get_xticklines()[-1].set_markeredgewidth(0)
for label in axes_left.get_xticklabels():
label.set_fontsize(10)
plt.yticks([])
# Plot data
for i in range(len(men_deaths)):
# Death
value = men_cases[i]
p = patches.Rectangle(
(0, i+0.125), value, 0.75, fill=True, transform=axes_left.transData,
lw=0, facecolor='blue', alpha=0.1)
axes_left.add_patch(p)
# New cases
value = men_deaths[i]
p = patches.Rectangle(
(0, i+0.25), value, 0.5, fill=True, transform=axes_left.transData,
lw=0, facecolor='blue', alpha=0.5)
axes_left.add_patch(p)
# Add a grid
axes_left.grid()
# --- WOMEN data ---
axes_right = plt.subplot(122, sharey=axes_left)
# Keep only top and left spines
axes_right.spines['right'].set_color('none')
axes_right.spines['bottom'].set_color('none')
axes_right.xaxis.set_ticks_position('top')
axes_right.yaxis.set_ticks_position('left')
axes_right.spines['top'].set_position(('data',len(diseases)-.75))
axes_right.spines['top'].set_color('w')
# Set axes limits
plt.xlim(0,150000)
plt.ylim(0,len(diseases))
# Set ticks labels
plt.xticks([0, 50000, 100000, 150000],
['WOMEN', '50000', '100000', '150000'])
axes_right.get_xticklabels()[0].set_weight('bold')
for label in axes_right.get_xticklabels():
label.set_fontsize(10)
axes_right.get_xticklines()[1].set_markeredgewidth(0)
plt.yticks([])
# Plot data
for i in range(len(women_deaths)):
# Death
value = women_cases[i]
p = patches.Rectangle(
(0, i+0.125), value, 0.75, fill=True, transform=axes_right.transData,
lw=0, facecolor='red', alpha=0.1)
axes_right.add_patch(p)
# New cases
value = women_deaths[i]
p = patches.Rectangle(
(0, i+0.25), value, 0.5, fill=True, transform=axes_right.transData,
lw=0, facecolor='red', alpha=0.5)
axes_right.add_patch(p)
# Add a grid
axes_right.grid()
# Y axis labels
# We want them to be exactly in the middel of the two y spines
# and it requires some computations
for i in range(len(diseases)):
x1,y1 = axes_left.transData.transform_point( (0,i+.5))
x2,y2 = axes_right.transData.transform_point((0,i+.5))
x,y = fig.transFigure.inverted().transform_point( ((x1+x2)/2,y1) )
plt.text(x, y, diseases[i], transform=fig.transFigure,
horizontalalignment='center', verticalalignment='center')
# Devil hides in the details...
arrowprops = dict(arrowstyle="-", connectionstyle="angle,angleA=0,angleB=90,rad=0")
x = men_cases[0]
axes_left.annotate('NEW CASES', xy=(.9*x, .5), xycoords='data',
horizontalalignment='right', fontsize= 10,
xytext=(-40, -3), textcoords='offset points',
arrowprops=arrowprops)
x = men_deaths[0]
axes_left.annotate('DEATHS', xy=(.9*x, .5), xycoords='data',
horizontalalignment='right', fontsize= 10,
xytext=(-50, -50), textcoords='offset points',
arrowprops=arrowprops)
x = women_cases[0]
axes_right.annotate('NEW CASES', xy=(.9*x, .5), xycoords='data',
horizontalalignment='left', fontsize= 10,
xytext=(+40, -3), textcoords='offset points',
arrowprops=arrowprops)
x = women_deaths[0]
axes_right.annotate('DEATHS', xy=(.9*x, .5), xycoords='data',
horizontalalignment='left', fontsize= 10,
xytext=(+50, -50), textcoords='offset points',
arrowprops=arrowprops)
# Done
plt.savefig('diseases.png', facecolor='white')
plt.show()
On Jul 9, 2011, at 8:45 AM, Chris Petrich wrote:
> I agree, very instructional example.
> As for the width of the tick lines, line 78
> line.set_linewidth(1)
> should probably read
> line.set_markeredgewidth(1)
> though.
>
> Chris
>
>> Date: Fri, 8 Jul 2011 16:18:52 -0600
>> From: G?khan Sever <gokhanse...@gmail.com>
>> Subject: Re: [matplotlib-devel] Contributed example
>> To: Nicolas Rougier <nicolas.roug...@inria.fr>
>> Cc: matplotlib development list
>> <matplotlib-devel@lists.sourceforge.net>
>> Message-ID:
>> <cae5kuyjdbjahkjxecjjk+pcuu0oomihcffj-aihou24gfl-...@mail.gmail.com>
>> Content-Type: text/plain; charset="utf-8"
>>
>> I think this illustration deserves its places amongst the mpl gallery
>> --probably somewhere towards the very beginning.
>>
>> Thanks for the well documented code Nicolas.
>> On Fri, Jul 8, 2011 at 2:09 AM, Nicolas Rougier
>> <nicolas.roug...@inria.fr>wrote:
>>
>>>
>>>
>>> Hi,
>>>
>>>
>>> I've been playing with matplotlib to check if it can produce graphics like:
>>>
>>>
>>> http://www.thebuzzmedia.com/wp-content/uploads/2010/03/anandtech-nvidia-geforce-480-ati-benchmark2.png
>>>
>>>
>>> Here is the result:
>>> http://www.loria.fr/~rougier/tmp/benchmark.png
>>>
>>> and the script (as attachment)
>>>
>>> I do not know if it's worth adding it to examples ?
>>>
>>>
>>>
>>> Nicolas
>>>
>>>
>>>
>>>
>>> ------------------------------------------------------------------------------
>>> All of the data generated in your IT infrastructure is seriously valuable.
>>> Why? It contains a definitive record of application performance, security
>>> threats, fraudulent activity, and more. Splunk takes this data and makes
>>> sense of it. IT sense. And common sense.
>>> http://p.sf.net/sfu/splunk-d2d-c2
>>> _______________________________________________
>>> Matplotlib-devel mailing list
>>> Matplotlib-devel@lists.sourceforge.net
>>> https://lists.sourceforge.net/lists/listinfo/matplotlib-devel
>>>
>>>
>>
>>
>> --
>> G?khan
>> -------------- next part --------------
>> An HTML attachment was scrubbed...
>>
>> ------------------------------
>>
>> ------------------------------------------------------------------------------
>> All of the data generated in your IT infrastructure is seriously valuable.
>> Why? It contains a definitive record of application performance, security
>> threats, fraudulent activity, and more. Splunk takes this data and makes
>> sense of it. IT sense. And common sense.
>> http://p.sf.net/sfu/splunk-d2d-c2
>>
>> ------------------------------
>>
>> _______________________________________________
>> Matplotlib-devel mailing list
>> Matplotlib-devel@lists.sourceforge.net
>> https://lists.sourceforge.net/lists/listinfo/matplotlib-devel
>>
>>
>> End of Matplotlib-devel Digest, Vol 62, Issue 3
>> ***********************************************
------------------------------------------------------------------------------
All of the data generated in your IT infrastructure is seriously valuable.
Why? It contains a definitive record of application performance, security
threats, fraudulent activity, and more. Splunk takes this data and makes
sense of it. IT sense. And common sense.
http://p.sf.net/sfu/splunk-d2d-c2
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