Revision: 3959
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=3959&view=rev
Author: jdh2358
Date: 2007-10-16 18:15:48 -0700 (Tue, 16 Oct 2007)
Log Message:
-----------
made a classes dir for course specific items
Modified Paths:
--------------
trunk/py4science/examples/distributions.py
trunk/py4science/examples/logistic/maplib.pyc
Added Paths:
-----------
trunk/py4science/classes/
trunk/py4science/classes/course_checklist_umich.py
trunk/py4science/classes/pomona_agenda.doc
trunk/py4science/classes/pomona_agenda.txt
Copied: trunk/py4science/classes/course_checklist_umich.py (from rev 3942,
trunk/py4science/course_checklist_umich.py)
===================================================================
--- trunk/py4science/classes/course_checklist_umich.py
(rev 0)
+++ trunk/py4science/classes/course_checklist_umich.py 2007-10-17 01:15:48 UTC
(rev 3959)
@@ -0,0 +1,15 @@
+#!/usr/bin/env python
+"""Minimal test script to check for modules needed in python course"""
+
+modules = ['numpy','scipy','matplotlib','IPython']
+
+
+for mname in modules:
+ try:
+ exec "import %s" % mname
+ except ImportError:
+ print '*** ERROR: module %s could not be imported.' % mname
+ else:
+ print '%s: OK' % mname
+
+print 'Also remember to check that SPE is installed.'
Added: trunk/py4science/classes/pomona_agenda.doc
===================================================================
(Binary files differ)
Property changes on: trunk/py4science/classes/pomona_agenda.doc
___________________________________________________________________
Name: svn:mime-type
+ application/octet-stream
Added: trunk/py4science/classes/pomona_agenda.txt
===================================================================
--- trunk/py4science/classes/pomona_agenda.txt (rev 0)
+++ trunk/py4science/classes/pomona_agenda.txt 2007-10-17 01:15:48 UTC (rev
3959)
@@ -0,0 +1,45 @@
+DAY 1:
+
+Introduction:
+
+ 35 min : Scientific computing in python (standard overhead talk)
+
+ 45 min: The core tools -- ipython, numpy, matplotlib and scipy. (type along)
+
+Break: 15 min
+
+Exercises session 1:
+
+ 45 min: Working with data files, web based resources, date handling,
+ CSV files, and record arrays. Word counting exercise.
+ (urllib, csv, dateutils, matplotlib.mlab)
+
+ 45 min: Numerical integration, trapz and Newton's quadrature
(scipy.integrate)
+
+Lunch Break: 45 min
+
+Exercises session 2:
+
+ 45 min: Linear algebra: Moire Glass patterns
+
+ 45 min: Statisical distributions, random numbers, central limit theorem
(scipy.stats)
+
+ 45 min: Descriptive statistics and graphs: mean, variance, skew,
+ kurtosis, histograms, autocorrelation, power spectra,
+ spectrogram (scipy.stats, matplotlib.mlab and pylab)
+
+Break: 15 min
+
+Exercises session 3:
+
+ 60 min: Interpolation, data modeling and optimization (scipy.interpolate
and scipy.optimize)
+
+ 45 min: Using code from other languages (FORTRAN, C, C++) --
+ Presentation (pyrex, weave, f2py, ctypes)
+
+
+DAY 2:
+
+Exercise Session 4:
+
+ 45 minutes: screen scraping - extracting data from web pages (BeautifulSoup)
\ No newline at end of file
Modified: trunk/py4science/examples/distributions.py
===================================================================
--- trunk/py4science/examples/distributions.py 2007-10-16 19:39:57 UTC (rev
3958)
+++ trunk/py4science/examples/distributions.py 2007-10-17 01:15:48 UTC (rev
3959)
@@ -4,7 +4,7 @@
source using the random number generator. Verify the numerical
results by plotting the analytical density functions from scipy.stats
"""
-import numpy
+import numpy as npy
import scipy.stats
from pylab import figure, show, close
@@ -18,14 +18,14 @@
# in each time interval, the probability of an emission
rate = 20. # the emission rate in Hz
-dx = 0.001 # the sampling interval in seconds
-t = numpy.arange(N)*dx # the time vector
+dt = 0.001 # the sampling interval in seconds
+t = npy.arange(N)*dt # the time vector
# the probability of an emission is proportionate to the rate and the interval
-emit_times = t[uninse < rate*dx]
+emit_times = t[uninse < rate*dt]
# the difference in the emission times is the wait time
-wait_times = numpy.diff(emit_times)
+wait_times = npy.diff(emit_times)
# plot the distribution of waiting times and the expected exponential
# density function lambda exp( lambda wt) where lambda is the rate
@@ -37,7 +37,7 @@
fig = figure()
ax = fig.add_subplot(111)
p, bins, patches = ax.hist(wait_times, 100, normed=True)
-l1, = ax.plot(bins, rate*numpy.exp(-rate * bins), lw=2, color='red')
+l1, = ax.plot(bins, rate*npy.exp(-rate * bins), lw=2, color='red')
l2, = ax.plot(bins, scipy.stats.expon.pdf(bins, 0, 1./rate),
lw=2, ls='--', color='green')
ax.set_xlabel('waiting time')
@@ -53,7 +53,7 @@
# gamma distribution. Use scipy.stats.gamma to compare the fits.
# Hint: you can stride your emission times array to get every 2nd
# emission
-wait_times2 = numpy.diff(emit_times[::2])
+wait_times2 = npy.diff(emit_times[::2])
fig = figure()
ax = fig.add_subplot(111)
p, bins, patches = ax.hist(wait_times2, 100, normed=True)
@@ -79,8 +79,8 @@
# variance
expon_mean, expon_var = scipy.stats.expon(0, 1./rate).stats()
mu, var = 10*expon_mean, 10*expon_var
-sigma = numpy.sqrt(var)
-wait_times10 = numpy.diff(emit_times[::10])
+sigma = npy.sqrt(var)
+wait_times10 = npy.diff(emit_times[::10])
fig = figure()
ax = fig.add_subplot(111)
p, bins, patches = ax.hist(wait_times10, 100, normed=True)
Modified: trunk/py4science/examples/logistic/maplib.pyc
===================================================================
(Binary files differ)
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