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From: <as...@us...> - 2007-10-27 06:23:15
|
Revision: 4036
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4036&view=rev
Author: astraw
Date: 2007-10-26 23:23:13 -0700 (Fri, 26 Oct 2007)
Log Message:
-----------
new list
Modified Paths:
--------------
trunk/py4science/doc/linkfest
Modified: trunk/py4science/doc/linkfest
===================================================================
--- trunk/py4science/doc/linkfest 2007-10-27 04:31:54 UTC (rev 4035)
+++ trunk/py4science/doc/linkfest 2007-10-27 06:23:13 UTC (rev 4036)
@@ -76,3 +76,6 @@
svn diff : see the changes since the last commit - useful for
making patches
+= mailing list =
+
+ py4science: http://code.astraw.com/cgi-bin/mailman/listinfo/py4science
This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site.
|
|
From: <jd...@us...> - 2007-10-27 04:32:05
|
Revision: 4035
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4035&view=rev
Author: jdh2358
Date: 2007-10-26 21:31:54 -0700 (Fri, 26 Oct 2007)
Log Message:
-----------
added andy
Modified Paths:
--------------
trunk/py4science/workbook/main.pdf
trunk/py4science/workbook/main.tex
Modified: trunk/py4science/workbook/main.pdf
===================================================================
--- trunk/py4science/workbook/main.pdf 2007-10-27 04:10:21 UTC (rev 4034)
+++ trunk/py4science/workbook/main.pdf 2007-10-27 04:31:54 UTC (rev 4035)
@@ -123,13 +123,14 @@
<< /S /GoTo /D [86 0 R /Fit ] >>
endobj
88 0 obj <<
-/Length 292
+/Length 321
/Filter /FlateDecode
>>
stream
-xڍQ;O\xC30\xDE\xF3+<ڃ\x9F_\x8F\xBC
-\xEAT\x89H \x94\xA1\xA4\xA1\x8DJm\xB5\xFCz\xEC\x86VEb\xA8,\xD9:/\xDF\x99\xCA Y0\xE0\x95Ҍ\x8C\x81`\x89X\xBB\xAD[e\xEC\xA1\xC2_\x8E\xD5(\x9F\x8BP\xE9\xB0B\xCD\xE4\xB9\xC9MS]M\x8Cch\xC1X\xAFY\xF3δR\xA0\x9D\xAF\x99G\xB5Ɋf\xF9\xC2gâ\xDD\xF5\xED\xE2CH\xE3\x89?\xB5}\x85Q|'\x90\xF7B"\x9F+c\xDBz~\x9B\xB6\xE5\xE6s\xE3J\xBC6SF\xD11\xA9P+:8\xF7q\xB4\x9C}f\x91\xACS<\xD0e |
|
From: <jd...@us...> - 2007-10-27 04:10:25
|
Revision: 4034
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4034&view=rev
Author: jdh2358
Date: 2007-10-26 21:10:21 -0700 (Fri, 26 Oct 2007)
Log Message:
-----------
late nite pomona workbook build
Modified Paths:
--------------
trunk/py4science/intro_talk/intro_python_scicomp.lyx
Modified: trunk/py4science/intro_talk/intro_python_scicomp.lyx
===================================================================
--- trunk/py4science/intro_talk/intro_python_scicomp.lyx 2007-10-27 04:09:13 UTC (rev 4033)
+++ trunk/py4science/intro_talk/intro_python_scicomp.lyx 2007-10-27 04:10:21 UTC (rev 4034)
@@ -298,14 +298,14 @@
\end_layout
\begin_layout Date
-University of Michigan at Ann Arbor
+Claremont Colleges
\newline
-April 13, 2007
+Oct 27, 2007
\begin_inset OptArg
status open
\begin_layout Standard
-U Mich, 04/13/06
+Pomona, 10/27/07
\end_layout
\end_inset
This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site.
|
|
From: <jd...@us...> - 2007-10-27 04:09:16
|
Revision: 4033
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4033&view=rev
Author: jdh2358
Date: 2007-10-26 21:09:13 -0700 (Fri, 26 Oct 2007)
Log Message:
-----------
late nite pomona workbook build
Modified Paths:
--------------
trunk/py4science/workbook/convolution.tex
trunk/py4science/workbook/intro_stats.tex
trunk/py4science/workbook/main.pdf
trunk/py4science/workbook/stats_distributions.tex
Modified: trunk/py4science/workbook/convolution.tex
===================================================================
--- trunk/py4science/workbook/convolution.tex 2007-10-27 04:00:59 UTC (rev 4032)
+++ trunk/py4science/workbook/convolution.tex 2007-10-27 04:09:13 UTC (rev 4033)
@@ -4,7 +4,7 @@
The output of a linear system is given by the convolution of its
impulse response function with the input. Mathematically
\[
- y(t) = \int_0^\t x(\tau)r(t-\tau)d\tau
+ y(t) = \int_0^t x(\tau)r(t-\tau)d\tau
\]
This fundamental relationship lies at the heart of linear systems
analysis. It is used to model the dynamics of calcium buffers in
@@ -85,7 +85,7 @@
function $r$ by studying the amplitude and phase spectrum of its
transform $R$. In the example below, however, we simply use the
multiplication property to perform the same convolution in Fourier
-space to confirm the numerical result from \textt{numpy.convolve}.
+space to confirm the numerical result from \texttt{numpy.convolve}.
\lstinputlisting[label=code:convolution_demo,caption={IGNORED}]{skel/convolution_demo_skel.py}
Modified: trunk/py4science/workbook/intro_stats.tex
===================================================================
--- trunk/py4science/workbook/intro_stats.tex 2007-10-27 04:00:59 UTC (rev 4032)
+++ trunk/py4science/workbook/intro_stats.tex 2007-10-27 04:09:13 UTC (rev 4033)
@@ -1,16 +1,16 @@
-\textt{R}, a statistical package based on \textt{S}, is viewd by some
+\texttt{R}, a statistical package based on \texttt{S}, is viewd by some
as the best statistical software on the planet, and in the open source
world it is the clear choice for sophisticated statistical analysis.
-Like python, \textt{R} is an interpreted language written in C with an
+Like python, \texttt{R} is an interpreted language written in C with an
interactive shell. Unlike python, which is a general purpose
-programming language, \textt{R} is a specialized statistical language.
+programming language, \texttt{R} is a specialized statistical language.
Since python is a excellent glue language, with facilities for
providing a transparent interface to FORTRAN, C, C++ and other
languages, it should come as no surprise that you can harness
-\textt{R}'s immense statistical power from python, through the
+\texttt{R}'s immense statistical power from python, through the
\texttt{rpy} third part extension library.
-However, \textt{R} is not without its warts. As a language, it lacks
+However, \texttt{R} is not without its warts. As a language, it lacks
python's elegance and advanced programming constructs and idioms. It
is also GPL, which means you cannot distribute code based upon it
unhindered: the code you distribute must be GPL as well (python, and
@@ -19,10 +19,10 @@
application).
Fortunately, the core tools scientific libraries for python (primarily
-\textt{numpy} and \texttt{scipy.stats}) provide a wide array of
+\texttt{numpy} and \texttt{scipy.stats}) provide a wide array of
statistical tools, from basic descriptive statistics (mean, variance,
skew, kurtosis, correlation, \dots) to hypothesis testing (t-tests,
-$\Chi$-Square, analysis of variance, general linear models, \dots) to
+$\chi$-Square, analysis of variance, general linear models, \dots) to
analytical and numerical tools for working with almost every discrete
and continuous statistical distribution you can think of (normal,
gamma, poisson, weibull, lognormal, levy stable, \dots).
Modified: trunk/py4science/workbook/main.pdf
===================================================================
--- trunk/py4science/workbook/main.pdf 2007-10-27 04:00:59 UTC (rev 4032)
+++ trunk/py4science/workbook/main.pdf 2007-10-27 04:09:13 UTC (rev 4033)
@@ -171,7 +171,7 @@
/ProcSet [ /PDF ]
>> endobj
104 0 obj <<
-/Length 1521
+/Length 1520
/Filter /FlateDecode
>>
stream
@@ -181,8 +181,9 @@
\xBF;\xF8N븫+\xA9K\xE1(\xDC\xC7N\xD67\xA5\x94\x84\xB1N0}k\xCC \xE3V?L\x99\xD9qOB3a\xA1\xA3X\xEEI4\xC1\x94'# WK\x9F\xD8\xCE`\x87CqySKu9\x9C\x9D _\xEB\xAB\֫\xB3\x8B\xA6g\x9D\xE9
УnC\xE5慜\xEF\xD5/\x9B\xAA\x8C\xF2n\xA4}u#j&\x9EV\xDFN<\xFA*A\xA8ۅ,\xD6M0\xA5o\x84\xE1\xC2\xF9\xE0\xF4-\x9D]A\xD1ݿ\xE7b7 |
|
From: <jd...@us...> - 2007-10-27 04:01:05
|
Revision: 4032
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4032&view=rev
Author: jdh2358
Date: 2007-10-26 21:00:59 -0700 (Fri, 26 Oct 2007)
Log Message:
-----------
added a few more workbook examples
Modified Paths:
--------------
trunk/py4science/examples/skel/stats_descriptives_skel.py
trunk/py4science/examples/stats_descriptives.py
trunk/py4science/workbook/convolution.tex
trunk/py4science/workbook/intro_stats.tex
trunk/py4science/workbook/stats_descriptives.tex
trunk/py4science/workbook/stats_distributions.tex
Modified: trunk/py4science/examples/skel/stats_descriptives_skel.py
===================================================================
--- trunk/py4science/examples/skel/stats_descriptives_skel.py 2007-10-26 20:21:23 UTC (rev 4031)
+++ trunk/py4science/examples/skel/stats_descriptives_skel.py 2007-10-27 04:00:59 UTC (rev 4032)
@@ -56,14 +56,27 @@
maxlags : max number of lags for the autocorr
- detrend : a function used to detrend the data for the correlation and spectral functions
+ detrend : a function used to detrend the data for the
+ correlation and spectral functions
fmt : the plot format string
"""
data = self.samples
- class Bunch: pass
- c = Bunch()
+ # Here we use a rather strange idiom: we create an empty do
+ # nothing class C and simply attach attributes to it for
+ # return value (which we carefully describe in the docstring).
+ # The alternative is either to return a tuple a,b,c,d or a
+ # dictionary {'a':someval, 'b':someotherval} but both of these
+ # methods have problems. If you return a tuple, and later
+ # want to return something new, you have to change all the
+ # code that calls this function. Dictionaries work fine, but
+ # I find the client code harder to use d['a'] vesus d.a. The
+ # final alternative, which is most suitable for production
+ # code, is to define a custom class to store (and pretty
+ # print) your return object
+ class C: pass
+ c = C()
N = 5
fig = c.fig = figfunc()
ax = c.ax1 = fig.add_subplot(N,1,1)
Modified: trunk/py4science/examples/stats_descriptives.py
===================================================================
--- trunk/py4science/examples/stats_descriptives.py 2007-10-26 20:21:23 UTC (rev 4031)
+++ trunk/py4science/examples/stats_descriptives.py 2007-10-27 04:00:59 UTC (rev 4032)
@@ -72,6 +72,18 @@
"""
data = self.samples
+ # Here we use a rather strange idiom: we create an empty do
+ # nothing class C and simply attach attributes to it for
+ # return value (which we carefully describe in the docstring).
+ # The alternative is either to return a tuple a,b,c,d or a
+ # dictionary {'a':someval, 'b':someotherval} but both of these
+ # methods have problems. If you return a tuple, and later
+ # want to return something new, you have to change all the
+ # code that calls this function. Dictionaries work fine, but
+ # I find the client code harder to use d['a'] vesus d.a. The
+ # final alternative, which is most suitable for production
+ # code, is to define a custom class to store (and pretty
+ # print) your return object
class C: pass
c = C()
N = 5
Modified: trunk/py4science/workbook/convolution.tex
===================================================================
--- trunk/py4science/workbook/convolution.tex 2007-10-26 20:21:23 UTC (rev 4031)
+++ trunk/py4science/workbook/convolution.tex 2007-10-27 04:00:59 UTC (rev 4032)
@@ -1,14 +1,61 @@
\section{Convolution}
\label{sec:convolution}
+The output of a linear system is given by the convolution of its
+impulse response function with the input. Mathematically
+\[
+ y(t) = \int_0^\t x(\tau)r(t-\tau)d\tau
+\]
+This fundamental relationship lies at the heart of linear systems
+analysis. It is used to model the dynamics of calcium buffers in
+neuronal synapses, where incoming action potentials are represented as
+Dirac $\delta$-functions and the calcium stores are represented with a
+response function with multiple exponential time constants. It is
+used in microscopy, in which the image distortions introduced by the
+lenses are \textit{deconvolved} out using a measured point spread
+function to provide a better picture of the true image input. It is
+essential in structural engineering to determine how materials respond
+to shocks.
+The impulse response function $r$ is the system response to a
+pulsatile input. For example, in Figure~\ref{fig:convolve_explain}
+below, the response function is the sum of two exponentials with
+different time constants and signs. This is a typical function used
+to model synaptic current following a neuronal action potential. The
+figure shows three $\delta$ inputs at different times and with
+different amplitudes. The corresponsing impulse response for each
+input is shown following it, and is color coded with the impulse input
+color. If the system response is linear, by definition, the response
+to a sum of inputs is the sum of the responses to the individual
+inputs, and the lower panel shows the sum of the responses, or
+equivalently, the convolution of the impulse response function with
+the input function.
+
\begin{center}%
\begin{figure}
\begin{centering}\includegraphics[width=4in]{fig/convolve_explain}\par\end{centering}
-\caption{\label{fig:convolve_explain}The output of a linear system to a series of impulse inputs is equal to the sum of the scaled and time shifted impulse response functions.}
+\caption{\label{fig:convolve_explain}The output of a linear system to
+a series of impulse inputs is equal to the sum of the scaled and time
+shifted impulse response functions.}
\end{figure}
\par\end{center}
+In Figure~\ref{fig:convolve_explain}, the summing of the impulse
+response function over the three inputs is conceptually and visually
+easy to understand. Some find the concept of a convolution of an
+impulse response function with a continuos time function, such as a
+sinusoid or a noise process, conceptually more difficult. It
+shouldn't be. By the \textit{sampling theorem}, we can represent any
+finite bandwidth continuous time signal as the sum of Dirac-$\delta$
+functions where the height of the $\delta$ function at each time point
+is simply the amplitude of the signal at that time point. The only
+requirement is that the sampling frequency be at least as high as the
+Nyquist frequency, defined as the highest spectral frequency in the
+signal divided by 2. See Figure~\ref{fig:convolve_deltas} for a
+representation of a delta function sampling of a damped, oscillatory,
+exponential function.
+
+
\begin{center}%
\begin{figure}
\begin{centering}\includegraphics[width=4in]{fig/convolve_deltas}\par\end{centering}
@@ -17,6 +64,29 @@
\par\end{center}
+In the exercise below, we will convolve a sample from the normal
+distribution (white noise) with a double exponential impulse response
+function. Such a function acts as a low pass filter, so the resultant
+output will look considerably smoother than the input. You can use
+\texttt{numpy.convolve} to perform the convolution numerically.
+
+We also explore the important relationship that a convolution in the
+tempoeral (or spatial) domain becomes a multiplication in the spectral
+domain, which is mathematically much easier to work with.
+\[
+Y = R*X
+\]
+
+where $Y$, $X$, and $R$ are the Fourier transforms of the respective
+variable in the temporal convolution equation above. The Fourier
+transform of the impulse response function serves as an amplitude
+weighting and phase shifting operator for each frequency component.
+Thus, we can get deeper insight into the effects of impulse response
+function $r$ by studying the amplitude and phase spectrum of its
+transform $R$. In the example below, however, we simply use the
+multiplication property to perform the same convolution in Fourier
+space to confirm the numerical result from \textt{numpy.convolve}.
+
\lstinputlisting[label=code:convolution_demo,caption={IGNORED}]{skel/convolution_demo_skel.py}
Modified: trunk/py4science/workbook/intro_stats.tex
===================================================================
--- trunk/py4science/workbook/intro_stats.tex 2007-10-26 20:21:23 UTC (rev 4031)
+++ trunk/py4science/workbook/intro_stats.tex 2007-10-27 04:00:59 UTC (rev 4032)
@@ -1 +1,31 @@
-TODO
\ No newline at end of file
+\textt{R}, a statistical package based on \textt{S}, is viewd by some
+as the best statistical software on the planet, and in the open source
+world it is the clear choice for sophisticated statistical analysis.
+Like python, \textt{R} is an interpreted language written in C with an
+interactive shell. Unlike python, which is a general purpose
+programming language, \textt{R} is a specialized statistical language.
+Since python is a excellent glue language, with facilities for
+providing a transparent interface to FORTRAN, C, C++ and other
+languages, it should come as no surprise that you can harness
+\textt{R}'s immense statistical power from python, through the
+\texttt{rpy} third part extension library.
+
+However, \textt{R} is not without its warts. As a language, it lacks
+python's elegance and advanced programming constructs and idioms. It
+is also GPL, which means you cannot distribute code based upon it
+unhindered: the code you distribute must be GPL as well (python, and
+the core scientific extension libraries, carry a more permissive
+license which support distribution in closed source, proprietary
+application).
+
+Fortunately, the core tools scientific libraries for python (primarily
+\textt{numpy} and \texttt{scipy.stats}) provide a wide array of
+statistical tools, from basic descriptive statistics (mean, variance,
+skew, kurtosis, correlation, \dots) to hypothesis testing (t-tests,
+$\Chi$-Square, analysis of variance, general linear models, \dots) to
+analytical and numerical tools for working with almost every discrete
+and continuous statistical distribution you can think of (normal,
+gamma, poisson, weibull, lognormal, levy stable, \dots).
+
+
+
Modified: trunk/py4science/workbook/stats_descriptives.tex
===================================================================
--- trunk/py4science/workbook/stats_descriptives.tex 2007-10-26 20:21:23 UTC (rev 4031)
+++ trunk/py4science/workbook/stats_descriptives.tex 2007-10-27 04:00:59 UTC (rev 4032)
@@ -1,7 +1,49 @@
\section{Descriptive statistics}
\label{sec:stats_descriptives}
+The first step in any statistical analysis should be to describe,
+charaterize and importantly, visualize your data. The normal
+distribution (aka Gaussian or bell curve) lies at the heart of much of
+formal statistical analysis, and normal distributions have the tidy
+property that they are completely characterized by their mean and
+variance. As you may have observed in your interactions with family
+and friends, most of the world is not normal, and many statistical
+analyses are flawed by summarizing data with just the mean and
+standard deviation (square root of variance) and associated
+signficance tests (eg the T-Test) as if it were normally distributed
+data.
+In the exercise below, we write a class to provide descriptive
+statistics of a data set passed into the constructor, with class
+methods to pretty print the results and to create a battery of
+standard plots which may show structure missing in a casual analysis.
+Many new programmers, or even experienced programmers used to a
+proceedural environment, are uncomfortable with the idea of classes,
+having hear their geekier programmer friends talk about them but not
+really sure what to do with them. There are many interesting things
+one can do with classes (aka object oriented programming) but at their
+hear they are a way of bundling data with methods that operate on that
+data. The \texttt{self} variable is special in python and is how the
+class refers to its own data and methods. Here is a toy example
+
+\begin{lstlisting}
+
+In [115]: class MyData:
+ .....: def __init__(self, x):
+ .....: self.x = x
+ .....: def sumsquare(self):
+ .....: return (self.x**2).sum()
+ .....:
+ .....:
+
+In [116]: nse = npy.random.rand(100)
+
+In [117]: mydata.sumsquare()
+Out[117]: 29.6851135284
+
+\end{lstlisting}
+
+
\lstinputlisting[label=code:stats_descriptives_skel,caption={IGNORED}]{skel/stats_descriptives_skel.py}
\begin{figure}
Modified: trunk/py4science/workbook/stats_distributions.tex
===================================================================
--- trunk/py4science/workbook/stats_distributions.tex 2007-10-26 20:21:23 UTC (rev 4031)
+++ trunk/py4science/workbook/stats_distributions.tex 2007-10-27 04:00:59 UTC (rev 4032)
@@ -1,9 +1,83 @@
\section{Statistical distributions}
\label{sec:stats_distributions}
+We explore a handful of the statistical distributions in
+\texttt{scipy.stats} module and the connections between them. The
+organization of the distribution functions in \texttt{scipy.stats} is
+quite elegant, with each distribution providing random variates
+(\texttt{rvs}), analytical moments (mean, variance, skew, kurtosis),
+analytic density (\texttt{pdf}, \texttt{cdf}) and survival functions
+(\texttt{sf}, \textt{isf}) (where available) and tools for fitting
+empirical distributions to the analytic distributions (\textt{fit}).
+in the exercise below, we will simulate a radioactive particle
+emitter, and look at the empirical distribution of waiting times
+compared with the expected analytical distributions. Our radioative
+particle emitter has an equal likelihood of emitting a particle in any
+equal time interval, and emits particles at a rate of 20~Hz. We will
+discretely sample time at a high frequency, and record a 1 of a
+particle is emitted and a 0 otherwise, and then look at the
+distribution of waiting times between emissions. The probability of a
+particle emission in one of our sample intervals (assumed to be very
+small compared to the average interval between emissions) is
+proportional to the rate and the sample interval $\Delta t$, ie
+$p(\Delta t) = \alpha \Delta t$ where $\alpha$ is the emission rate in
+particles per second.
+\begin{lstlisting}
+# a uniform distribution [0..1]
+In [62]: uniform = scipy.stats.uniform()
+
+# our sample interval in seconds
+In [63]: deltat = 0.001
+
+# the emission rate, 20Hz
+In [65]: alpha = 20
+
+# 1000 random numbers
+In [66]: rvs = uniform.rvs(1000)
+
+# a look at the 1st 20 random variates
+In [67]: rvs[:20]
+Out[67]:
+array([ 0.71167172, 0.01723161, 0.25849255, 0.00599207, 0.58656146,
+ 0.12765225, 0.17898621, 0.77724693, 0.18042977, 0.91935639,
+ 0.97659579, 0.59045477, 0.94730366, 0.00764026, 0.12153159,
+ 0.82286929, 0.18990484, 0.34608396, 0.63931108, 0.57199175])
+
+# we simulate an emission when the random number is less than
+# p(Delta t) = alpha * deltat
+In [84]: emit = rvs < (alpha * deltat)
+
+
+# there were 3 emissions in the first 20 observations
+In [85]: emit[:20]
+Out[85]:
+array([False, True, False, True, False, False, False, False, False,
+ False, False, False, False, True, False, False, False, False,
+ False, False], dtype=bool)
+\end{lstlisting}
+
+The waiting times between the emissions should follow an exponential
+distribution (see \texttt{scipy.stats.expon}) with a mean of
+$1/\alpha$. In the exercise below, you will generate a long array of
+emissions, compute the waiting times between emissions, between 2
+emissions, and between 10 emissions. These should approach an 1st
+order gamma (aka exponential) distribution, 2nd order gamma, and 10th
+order gamma (see \texttt{scipy.stats.gamma}). Use the probability
+density functions for these distributions in \texttt{scipy.stats} to
+compare your simulated distributions and moments with the analytic
+versions provided by \texttt{scipy.stats}. With 10 waiting times, we
+should be approaching a normal distribution since we are summing 10
+waiting times and under the central limit theorem the sum of
+independent samples from a finite variance process approaches the
+normal distribution (see \texttt{scipy.stats.norm}). In the final
+part of the exercise below, you will be asked to approximate the 10th
+order gamma distribution with a normal distribution. The results
+should look something like those in
+Figure~\ref{fig:stats_distributions}.
+
\lstinputlisting[label=code:stats_distributions_skel,caption={IGNORED}]{skel/stats_distributions_skel.py}
\begin{figure}
This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site.
|
|
From: <jd...@us...> - 2007-10-26 20:21:36
|
Revision: 4031
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4031&view=rev
Author: jdh2358
Date: 2007-10-26 13:21:23 -0700 (Fri, 26 Oct 2007)
Log Message:
-----------
added stats workbooks skel
Modified Paths:
--------------
trunk/py4science/workbook/main.pdf
trunk/py4science/workbook/main.tex
Added Paths:
-----------
trunk/py4science/workbook/intro_stats.tex
trunk/py4science/workbook/stats_descriptives.tex
trunk/py4science/workbook/stats_distributions.tex
Added: trunk/py4science/workbook/intro_stats.tex
===================================================================
--- trunk/py4science/workbook/intro_stats.tex (rev 0)
+++ trunk/py4science/workbook/intro_stats.tex 2007-10-26 20:21:23 UTC (rev 4031)
@@ -0,0 +1 @@
+TODO
\ No newline at end of file
Modified: trunk/py4science/workbook/main.pdf
===================================================================
--- trunk/py4science/workbook/main.pdf 2007-10-26 20:20:12 UTC (rev 4030)
+++ trunk/py4science/workbook/main.pdf 2007-10-26 20:21:23 UTC (rev 4031)
@@ -102,9 +102,27 @@
(2. FFT Image Denoising)
endobj
73 0 obj
-<< /S /GoTo /D [74 0 R /Fit ] >>
+<< /S /GoTo /D (chapter.7) >>
endobj
-76 0 obj <<
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+(Chapter 7. Statistics)
+endobj
+77 0 obj
+<< /S /GoTo /D (section.7.1) >>
+endobj
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+(1. Descriptive statistics)
+endobj
+81 0 obj
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+endobj
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+(2. Statistical distributions)
+endobj
+85 0 obj
+<< /S /GoTo /D [86 0 R /Fit ] >>
+endobj
+88 0 obj <<
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>>
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|
From: <jd...@us...> - 2007-10-26 20:20:36
|
Revision: 4030
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4030&view=rev
Author: jdh2358
Date: 2007-10-26 13:20:12 -0700 (Fri, 26 Oct 2007)
Log Message:
-----------
added stats distro figs
Added Paths:
-----------
trunk/py4science/workbook/fig/stats_distributions.eps
trunk/py4science/workbook/fig/stats_distributions.png
Added: trunk/py4science/workbook/fig/stats_distributions.eps
===================================================================
--- trunk/py4science/workbook/fig/stats_distributions.eps (rev 0)
+++ trunk/py4science/workbook/fig/stats_distributions.eps 2007-10-26 20:20:12 UTC (rev 4030)
@@ -0,0 +1,6825 @@
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|
From: <jd...@us...> - 2007-10-26 20:16:08
|
Revision: 4029
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4029&view=rev
Author: jdh2358
Date: 2007-10-26 13:15:51 -0700 (Fri, 26 Oct 2007)
Log Message:
-----------
added stats distributions examples
Modified Paths:
--------------
trunk/py4science/examples/stats_descriptives.py
trunk/py4science/examples/stats_distributions.py
Modified: trunk/py4science/examples/stats_descriptives.py
===================================================================
--- trunk/py4science/examples/stats_descriptives.py 2007-10-26 20:15:42 UTC (rev 4028)
+++ trunk/py4science/examples/stats_descriptives.py 2007-10-26 20:15:51 UTC (rev 4029)
@@ -76,21 +76,27 @@
c = C()
N = 5
fig = c.fig = figfunc()
+ fig.subplots_adjust(hspace=0.3)
ax = c.ax1 = fig.add_subplot(N,1,1)
c.plot = ax.plot(data, fmt)
+ ax.set_ylabel('data')
ax = c.ax2 = fig.add_subplot(N,1,2)
c.hist = ax.hist(data, bins)
+ ax.set_ylabel('hist')
-
ax = c.ax3 = fig.add_subplot(N,1,3)
- c.acorr = ax.acorr(data, detrend=detrend, usevlines=True, maxlags=maxlags)
+ c.acorr = ax.acorr(data, detrend=detrend, usevlines=True,
+ maxlags=maxlags, normed=True)
+ ax.set_ylabel('acorr')
ax = c.ax4 = fig.add_subplot(N,1,4)
c.psd = ax.psd(data, Fs=Fs, detrend=detrend)
+ ax.set_ylabel('psd')
ax = c.ax5 = fig.add_subplot(N,1,5)
c.specgtram = ax.specgram(data, Fs=Fs, detrend=detrend)
+ ax.set_ylabel('specgram')
return c
@@ -111,6 +117,9 @@
desc = Descriptives(data)
print desc
- c = desc.plots(pylab.figure, Fs=12, fmt='-o')
+ c = desc.plots(pylab.figure, Fs=12, fmt='-')
c.ax1.set_title(fname)
+
+ c.fig.savefig('stats_descriptives.png', dpi=150)
+ c.fig.savefig('stats_descriptives.eps')
pylab.show()
Modified: trunk/py4science/examples/stats_distributions.py
===================================================================
--- trunk/py4science/examples/stats_distributions.py 2007-10-26 20:15:42 UTC (rev 4028)
+++ trunk/py4science/examples/stats_distributions.py 2007-10-26 20:15:51 UTC (rev 4029)
@@ -35,18 +35,18 @@
# 1/lambda. Plot all three on the same graph and make a legend.
# Decorate your graphs with an xlabel, ylabel and title
fig = figure()
-ax = fig.add_subplot(111)
+ax = fig.add_subplot(311)
p, bins, patches = ax.hist(wait_times, 100, normed=True)
l1, = ax.plot(bins, rate*numpy.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')
+
ax.set_ylabel('PDF')
-ax.set_title('waiting time density of a %dHz Poisson emitter'%rate)
+ax.set_title('waiting time densities of a %dHz Poisson emitter'%rate)
+ax.text(0.05, 0.9, 'one interval', transform=ax.transAxes)
ax.legend((patches[0], l1, l2), ('simulated', 'analytic', 'scipy.stats.expon'))
-
# plot the distribution of waiting times for two events; the
# distribution of waiting times for N events should equal a N-th order
# gamma distribution (the exponential distribution is a 1st order
@@ -54,17 +54,15 @@
# Hint: you can stride your emission times array to get every 2nd
# emission
wait_times2 = numpy.diff(emit_times[::2])
-fig = figure()
-ax = fig.add_subplot(111)
+ax = fig.add_subplot(312)
p, bins, patches = ax.hist(wait_times2, 100, normed=True)
l1, = ax.plot(bins, scipy.stats.gamma.pdf(bins, 2, 0, 1./rate),
lw=2, ls='-', color='red')
-ax.set_xlabel('2 event waiting time 2 events')
+
ax.set_ylabel('PDF')
-ax.set_title('waiting time density of a %dHz Poisson emitter'%rate)
+ax.text(0.05, 0.9, 'two intervals', transform=ax.transAxes)
ax.legend((patches[0], l1), ('simulated', 'scipy.stats.gamma'))
-
# plot the distribution of waiting times for 10 events; again the
# distribution will be a 10th order gamma distribution so plot that
# along with the empirical density. The central limit thm says that
@@ -81,19 +79,20 @@
mu, var = 10*expon_mean, 10*expon_var
sigma = numpy.sqrt(var)
wait_times10 = numpy.diff(emit_times[::10])
-fig = figure()
-ax = fig.add_subplot(111)
+ax = fig.add_subplot(313)
p, bins, patches = ax.hist(wait_times10, 100, normed=True)
l1, = ax.plot(bins, scipy.stats.gamma.pdf(bins, 10, 0, 1./rate),
lw=2, ls='-', color='red')
l2, = ax.plot(bins, scipy.stats.norm.pdf(bins, mu, sigma),
lw=2, ls='--', color='green')
-ax.set_xlabel('waiting time 10 events')
+ax.set_xlabel('waiting times')
ax.set_ylabel('PDF')
-ax.set_title('10 event waiting time density of a %dHz Poisson emitter'%rate)
+ax.text(0.1, 0.9, 'ten intervals', transform=ax.transAxes)
ax.legend((patches[0], l1, l2), ('simulated', 'scipy.stats.gamma', 'normal approx'))
+fig.savefig('stats_distributions.png', dpi=150)
+fig.savefig('stats_distributions.eps')
show()
This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site.
|
|
From: <jd...@us...> - 2007-10-26 20:16:08
|
Revision: 4028
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4028&view=rev
Author: jdh2358
Date: 2007-10-26 13:15:42 -0700 (Fri, 26 Oct 2007)
Log Message:
-----------
added stats distributions examples
Added Paths:
-----------
trunk/py4science/workbook/fig/stats_descriptives.eps
trunk/py4science/workbook/fig/stats_descriptives.png
Added: trunk/py4science/workbook/fig/stats_descriptives.eps
===================================================================
--- trunk/py4science/workbook/fig/stats_descriptives.eps (rev 0)
+++ trunk/py4science/workbook/fig/stats_descriptives.eps 2007-10-26 20:15:42 UTC (rev 4028)
@@ -0,0 +1,6621 @@
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[truncated message content] |
|
From: <jd...@us...> - 2007-10-26 19:57:07
|
Revision: 4027
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4027&view=rev
Author: jdh2358
Date: 2007-10-26 12:57:04 -0700 (Fri, 26 Oct 2007)
Log Message:
-----------
modified stats descriptives
Modified Paths:
--------------
trunk/py4science/examples/stats_descriptives.py
Modified: trunk/py4science/examples/stats_descriptives.py
===================================================================
--- trunk/py4science/examples/stats_descriptives.py 2007-10-26 19:55:21 UTC (rev 4026)
+++ trunk/py4science/examples/stats_descriptives.py 2007-10-26 19:57:04 UTC (rev 4027)
@@ -101,7 +101,7 @@
# will have to do some extra parsing
data = []
fname = 'data/nm560.dat' # tree rings in New Mexico 837-1987
- #fname = 'data/hsales.dat' # home sales
+ fname = 'data/hsales.dat' # home sales
for line in file(fname):
line = line.strip()
if not line: continue
@@ -109,8 +109,8 @@
val = vals[0]
data.append(float(val))
- #desc = Descriptives(data)
- #print desc
- #c = desc.plots(pylab.figure, Fs=12, fmt='-o')
- #c.ax1.set_title(fname)
- #pylab.show()
+ desc = Descriptives(data)
+ print desc
+ c = desc.plots(pylab.figure, Fs=12, fmt='-o')
+ c.ax1.set_title(fname)
+ pylab.show()
This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site.
|
|
From: <jd...@us...> - 2007-10-26 19:55:27
|
Revision: 4026
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4026&view=rev
Author: jdh2358
Date: 2007-10-26 12:55:21 -0700 (Fri, 26 Oct 2007)
Log Message:
-----------
updated workbook
Modified Paths:
--------------
trunk/py4science/workbook/convolution.tex
trunk/py4science/workbook/fft_imdenoise.tex
trunk/py4science/workbook/main.pdf
trunk/py4science/workbook/quad_newton.tex
trunk/py4science/workbook/trapezoid.tex
Modified: trunk/py4science/workbook/convolution.tex
===================================================================
--- trunk/py4science/workbook/convolution.tex 2007-10-26 19:50:21 UTC (rev 4025)
+++ trunk/py4science/workbook/convolution.tex 2007-10-26 19:55:21 UTC (rev 4026)
@@ -1,3 +1,5 @@
+\section{Convolution}
+\label{sec:convolution}
\begin{center}%
Modified: trunk/py4science/workbook/fft_imdenoise.tex
===================================================================
--- trunk/py4science/workbook/fft_imdenoise.tex 2007-10-26 19:50:21 UTC (rev 4025)
+++ trunk/py4science/workbook/fft_imdenoise.tex 2007-10-26 19:55:21 UTC (rev 4026)
@@ -1,3 +1,6 @@
+\section{FFT Image Denoising}
+\label{sec:fft_imdenoise}
+
Convolution of an input with with a linear filter in the termporal or
spatial domain is equivalent to multiplication by the fourier
transforms of the input and the filter in the spectral domain. This
Modified: trunk/py4science/workbook/main.pdf
===================================================================
--- trunk/py4science/workbook/main.pdf 2007-10-26 19:50:21 UTC (rev 4025)
+++ trunk/py4science/workbook/main.pdf 2007-10-26 19:55:21 UTC (rev 4026)
@@ -90,9 +90,21 @@
(Chapter 6. Signal processing)
endobj
65 0 obj
-<< /S /GoTo /D [66 0 R /Fit ] >>
+<< /S /GoTo /D (section.6.1) >>
endobj
-68 0 obj <<
+68 0 obj
+(1. Convolution)
+endobj
+69 0 obj
+<< /S /GoTo /D (section.6.2) >>
+endobj
+72 0 obj
+(2. FFT Image Denoising)
+endobj
+73 0 obj
+<< /S /GoTo /D [74 0 R /Fit ] >>
+endobj
+76 0 obj <<
/Length 292
/Filter /FlateDecode
>>
@@ -101,24 +113,24 @@
\xEAT\x89H \x94\xA1\xA4\xA1\x8DJm\xB5\xFCz\xEC\x86VEb\xA8,\xD9:/\xDF\x99\xCA Y0\xE0\x95Ҍ\x8C\x81`\x89X\xBB\xAD[e\xEC\xA1\xC2_\x8E\xD5(\x9F\x8BP\xE9\xB0B\xCD\xE4\xB9\xC9MS]M\x8Cch\xC1X\xAFY\xF3δR\xA0\x9D\xAF\x99G\xB5Ɋf\xF9\xC2gâ\xDD\xF5\xED\xE2CH\xE3\x89?\xB5}\x85Q|'\x90\xF7B"\x9F+c\xDBz~\x9B\xB6\xE5\xE6s\xE3J\xBC6SF\xD11\xA9P+:8\xF7q\xB4\x9C}f\x91\xACS<\xD0e |
|
From: <jd...@us...> - 2007-10-26 19:50:24
|
Revision: 4025
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4025&view=rev
Author: jdh2358
Date: 2007-10-26 12:50:21 -0700 (Fri, 26 Oct 2007)
Log Message:
-----------
updated workbook
Modified Paths:
--------------
trunk/py4science/workbook/main.pdf
Modified: trunk/py4science/workbook/main.pdf
===================================================================
--- trunk/py4science/workbook/main.pdf 2007-10-26 19:48:03 UTC (rev 4024)
+++ trunk/py4science/workbook/main.pdf 2007-10-26 19:50:21 UTC (rev 4025)
@@ -141,12 +141,20 @@
/ProcSet [ /PDF ]
>> endobj
84 0 obj <<
-/Length 163
+/Length 1246
/Filter /FlateDecode
>>
stream
-x\xDAm\x8F\xBD\xC20 \x84\xF7<\x85\xC7v\xA8\xB1\xE3$\x8EW 1gCL\xFCM\xC0 |
|
From: <jd...@us...> - 2007-10-26 19:48:15
|
Revision: 4024
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4024&view=rev
Author: jdh2358
Date: 2007-10-26 12:48:03 -0700 (Fri, 26 Oct 2007)
Log Message:
-----------
updates to workbook
Modified Paths:
--------------
trunk/py4science/doc/linkfest
trunk/py4science/examples/convolution_demo.py
trunk/py4science/examples/fft_imdenoise.py
trunk/py4science/examples/stock_records.py
trunk/py4science/workbook/main.tex
Added Paths:
-----------
trunk/py4science/workbook/fft_imdenoise.tex
trunk/py4science/workbook/fig/fft_imdenoise.eps
trunk/py4science/workbook/fig/fft_imdenoise.png
Modified: trunk/py4science/doc/linkfest
===================================================================
--- trunk/py4science/doc/linkfest 2007-10-26 19:46:12 UTC (rev 4023)
+++ trunk/py4science/doc/linkfest 2007-10-26 19:48:03 UTC (rev 4024)
@@ -62,9 +62,9 @@
ipython: svn co http://ipython.scipy.org/svn/ipython/ipython/trunk ipython
- matplotlib: svn co https://svn.sourceforge.net/svnroot/matplotlib/trunk/matplotlib matplotlib
+ matplotlib: svn co https://matplotlib.svn.sourceforge.net/svnroot/matplotlib/trunk/matplotlib
- py4science: svn co https://svn.sourceforge.net/svnroot/matplotlib/trunk/py4science py4science
+ py4science: svn co https://matplotlib.svn.sourceforge.net/svnroot/matplotlib/trunk/py4science
Important commands:
Modified: trunk/py4science/examples/convolution_demo.py
===================================================================
--- trunk/py4science/examples/convolution_demo.py 2007-10-26 19:46:12 UTC (rev 4023)
+++ trunk/py4science/examples/convolution_demo.py 2007-10-26 19:48:03 UTC (rev 4024)
@@ -71,4 +71,6 @@
ax2.plot(t, yi, label='fft')
ax2.legend(loc='best')
+fig.savefig('convolution_demo.png', dpi=150)
+fig.savefig('convolution_demo.eps')
show()
Modified: trunk/py4science/examples/fft_imdenoise.py
===================================================================
--- trunk/py4science/examples/fft_imdenoise.py 2007-10-26 19:46:12 UTC (rev 4023)
+++ trunk/py4science/examples/fft_imdenoise.py 2007-10-26 19:48:03 UTC (rev 4024)
@@ -73,4 +73,6 @@
P.title('Reconstructed Image')
P.imshow(im_new, P.cm.gray)
+P.savefig('fft_imdenoise.png', dpi=150)
+P.savefig('fft_imdenoise.eps')
P.show()
Modified: trunk/py4science/examples/stock_records.py
===================================================================
--- trunk/py4science/examples/stock_records.py 2007-10-26 19:46:12 UTC (rev 4023)
+++ trunk/py4science/examples/stock_records.py 2007-10-26 19:48:03 UTC (rev 4024)
@@ -37,7 +37,7 @@
r.sort()
return r
-tickers = 'INTC', 'MSFT', 'YHOO', 'GOOG', 'GE', 'WMT', 'AAPL'
+tickers = 'SPY', 'QQQQ', 'INTC', 'MSFT', 'YHOO', 'GOOG', 'GE', 'WMT', 'AAPL'
# we want to compute returns since 2003, so define the start date
startdate = datetime.datetime(2003,1,1)
Added: trunk/py4science/workbook/fft_imdenoise.tex
===================================================================
--- trunk/py4science/workbook/fft_imdenoise.tex (rev 0)
+++ trunk/py4science/workbook/fft_imdenoise.tex 2007-10-26 19:48:03 UTC (rev 4024)
@@ -0,0 +1,47 @@
+Convolution of an input with with a linear filter in the termporal or
+spatial domain is equivalent to multiplication by the fourier
+transforms of the input and the filter in the spectral domain. This
+provides a conceptually simple way to think about filtering: transform
+your signal into the frequency domain, dampen the frequencies you are
+not interested in by multiplying the frequency spectrum by the desired
+weights, and then inverse transform the multiplies spectrum back into
+the original domain. In the example below, we will simply set the
+weights of the frequencies we are uninterested in (the high frequency
+noise) to zero rather than dampening them with a smoothly varying
+function. Although this is not usually the best thing to do, since
+sharp edges in one domain usually introduce artifacts in another (eg
+high frequency ``ringing''), it is easy to do and sometimes provides
+satisfactory results.
+
+The image in the upper left panel of Figure~\ref{fig:fft_imdenoise} is
+a grayscale photo of the moon landing. There is a banded pattern of
+high frequency noise polluting the image. In the upper right panel we
+see the 2D spatial frequency spectrum. The FFT output in
+\texttt{scipy} is packed with the lower freqeuencies starting in the
+upper left, and proceeding to higher frequencies as one moves to the
+center of the spectrum (this is the most efficient way numerically to
+fill the output of the FFT algorithm). Because the input signal is
+real, the output spectrum is complex and symmetrical: the
+transformation values beyond the midpoint of the frequency spectrum
+(the Nyquist frequency) correspond to the values for negative
+frequencies and are simply the mirror image of the positive
+frequencies below the Nyquist (this is true for the 1D, 2D and ND FFTs
+in \texttt{numpy}).
+
+In this exercise we will compute the 2D spatial frequency spectra of
+the luminance image, zero out the high frequency components, and
+inverse transform back into the time domain. We can plot the input
+and output images with the \texttt{pylab.imshow} function, but the
+images must first be scaled to be withing the 0..1 luminance range.
+For best results, it helps to \textit{amplify} the image by some scale
+factor, and then \textit{clip} it to set all values greater than one
+to one. This serves to enhance contrast among the darker elements of
+the image, so it is not completely dominated by the brighter segments
+
+\lstinputlisting[label=code:fft_imdenoise_skel,caption={IGNORED}]{skel/fft_imdenoise_skel.py}
+
+\begin{figure}
+\begin{centering}\includegraphics[width=3in]{fig/fft_imdenoise}\par\end{centering}
+
+\caption{\label{fig:fft_imdenoise}High freqeuency noise filtering of a 2D image in the Fourier domain. The upper panels show the original image (left) and spectral power (right) and the lower panels show the same data with the high frequency power set to zero. Although the input and output images are grayscale, you can provide colormaps to \texttt{pylab.imshow} to plot them in psudo-color}
+\end{figure}
Added: trunk/py4science/workbook/fig/fft_imdenoise.eps
===================================================================
--- trunk/py4science/workbook/fig/fft_imdenoise.eps (rev 0)
+++ trunk/py4science/workbook/fig/fft_imdenoise.eps 2007-10-26 19:48:03 UTC (rev 4024)
@@ -0,0 +1,9710 @@
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[truncated message content] |
|
From: <jd...@us...> - 2007-10-26 19:46:16
|
Revision: 4023
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4023&view=rev
Author: jdh2358
Date: 2007-10-26 12:46:12 -0700 (Fri, 26 Oct 2007)
Log Message:
-----------
added some data files
Added Paths:
-----------
trunk/py4science/examples/data/ascii_data.dat
trunk/py4science/examples/data/noisy_sine.dat
Added: trunk/py4science/examples/data/ascii_data.dat
===================================================================
--- trunk/py4science/examples/data/ascii_data.dat (rev 0)
+++ trunk/py4science/examples/data/ascii_data.dat 2007-10-26 19:46:12 UTC (rev 4023)
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Added: trunk/py4science/examples/data/noisy_sine.dat
===================================================================
--- trunk/py4science/examples/data/noisy_sine.dat (rev 0)
+++ trunk/py4science/examples/data/noisy_sine.dat 2007-10-26 19:46:12 UTC (rev 4023)
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This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site.
|
|
From: <jd...@us...> - 2007-10-26 19:44:09
|
Revision: 4022
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4022&view=rev
Author: jdh2358
Date: 2007-10-26 12:44:07 -0700 (Fri, 26 Oct 2007)
Log Message:
-----------
added some more workbook files
Added Paths:
-----------
trunk/py4science/workbook/glass_dots.tex
trunk/py4science/workbook/intro_linalg.tex
trunk/py4science/workbook/intro_sigproc.tex
Added: trunk/py4science/workbook/glass_dots.tex
===================================================================
--- trunk/py4science/workbook/glass_dots.tex (rev 0)
+++ trunk/py4science/workbook/glass_dots.tex 2007-10-26 19:44:07 UTC (rev 4022)
@@ -0,0 +1,52 @@
+\section{Glass Moir\'e Patterns}
+\label{sec:glass_patterns}
+
+When a random dot pattern is scaled, rotated, and superimposed over
+the original dots, interesting visual patterns known as Glass Patterns
+emerge\footnote{L. Glass. 'Moir\'e effect from random dots' Nature 223,
+ 578580 (1969).} In this exercise, we generate random dot fields
+using numpy's uniform distribution function, and apply
+transformations to the random dot field using a scale $\mathbf{S}$
+and rotation $\mathbf{R}$ matrix $\mathbf{X_2} = \mathbf{S} \mathbf{R}
+\mathbf{X_1}$.
+
+If the scale and rotation factors are small, the transformation is
+analogous to a single step in the numerical solution of a 2D ODE, and
+the plot of both $\mathbf{X_1}$ and $\mathbf{X_2}$ will reveal the
+structure of the vecotr field flow around the fixed point (the
+invariant under the transformation); see for example the
+\textit{stable focus}, aka \textit{spiral}, in
+Figure~\ref{fig:glass_dots1}.
+
+The eigenvalues of the tranformation matrix $\mathbf{M} =
+\mathbf{S}\mathbf{R}$ determine the type of fix point:
+\textit{center}, \textit{stable focus}, \textit{saddle node},
+etc\dots. For example, if the two eigenvalues are real but differing
+in signs, the fixed point is a \textit{saddle node}. If the real
+parts of both eigenvalues are negative and the eigenvalues are
+complex, the fixed point is a \textit{stable focus}. The complex part
+of the eigenvalue determines whether there is any rotation in the
+matrix transformation, so another way to look at this is to break out
+the scaling and rotation components of the transformation
+$\textbf{M}$. If there is a rotation component, then the fixed point
+will be a \textit{center} or a \textit{focus}. If the scaling
+components are both one, the rotation will be a \textit{center}, if
+they are both less than one (contraction), it will be a \textit{stable
+ focus}. Likewise, if there is no rotation component, the fixed
+point will be a \textit{node}, and the scaling components will
+determine the type of node. If both are less than one, we have a
+\textit{stable node}, if one is greater than one and the other less
+than one, we have a \textit{saddle node}.
+
+\lstinputlisting[label=code:glass_dots1_skel,caption={IGNORED}]{skel/glass_dots1_skel.py}
+
+
+
+\begin{center}%
+\begin{figure}
+\begin{centering}\includegraphics[width=4in]{fig/glass_dots1}\par\end{centering}
+
+
+\caption{\label{fig:glass_dots1}Glass pattern showing a stable focus}
+\end{figure}
+\par\end{center}
Added: trunk/py4science/workbook/intro_linalg.tex
===================================================================
--- trunk/py4science/workbook/intro_linalg.tex (rev 0)
+++ trunk/py4science/workbook/intro_linalg.tex 2007-10-26 19:44:07 UTC (rev 4022)
@@ -0,0 +1,49 @@
+Like matlab, numpy and scipy have support for fast linear algebra
+built upon the highly optimized LAPACK, BLAS and ATLAS fortran linear
+algebra libraries. Unlike Matlab, in which everything is a matrix or
+vector, and the '*' operator always means matrix multiple, the default
+object in numpy is an \texttt{array}, and the '*' operator on arrays means
+element-wise multiplication.
+
+Instead, numpy provides a \texttt{matrix} class if you want to do
+standard matrix-matrix multiplication with the '*' operator, or the
+\texttt{dot} function if you want to do matrix multiplies with plain
+arrays. The basic linear algebra functionality is found in
+\texttt{numpy.linalg}
+
+\begin{lstlisting}
+In [1]: import numpy as npy
+In [2]: import numpy.linalg as linalg
+
+# X and Y are arrays
+In [3]: X = npy.random.rand(3,3)
+In [4]: Y = npy.random.rand(3,3)
+
+# * operator is element wise multiplication, not matrix matrix
+In [5]: print X*Y
+[[ 0.00973215 0.18086148 0.05539387]
+ [ 0.00817516 0.63354021 0.2017993 ]
+ [ 0.34287698 0.25788149 0.15508982]]
+
+# the dot function will use optimized LAPACK to do matrix-matix
+# multiply
+In [6]: print npy.dot(X, Y)
+[[ 0.10670678 0.68340331 0.39236388]
+ [ 0.27840642 1.14561885 0.62192324]
+ [ 0.48192134 1.32314856 0.51188578]]
+
+# the matrix class will create matrix objects that support matrix
+# multiplication with *
+In [7]: Xm = npy.matrix(X)
+In [8]: Ym = npy.matrix(Y)
+In [9]: print Xm*Ym
+[[ 0.10670678 0.68340331 0.39236388]
+ [ 0.27840642 1.14561885 0.62192324]
+ [ 0.48192134 1.32314856 0.51188578]]
+
+# the linalg module provides functions to compute eigenvalues,
+# determinants, etc. See help(linalg) for more info
+In [10]: print linalg.eigvals(X)
+[ 1.46131600+0.j 0.46329211+0.16501143j 0.46329211-0.16501143j]
+
+\end{lstlisting}
Added: trunk/py4science/workbook/intro_sigproc.tex
===================================================================
--- trunk/py4science/workbook/intro_sigproc.tex (rev 0)
+++ trunk/py4science/workbook/intro_sigproc.tex 2007-10-26 19:44:07 UTC (rev 4022)
@@ -0,0 +1,15 @@
+\texttt{numpy} and \texttt{scipy} provide many of the essential tools
+for digital signal processing. \texttt{scipy.signal} provides basic
+tools for digital filter design and filtering (eg Butterworth
+filters), a linear systems toolkit, standard waveforms such as square
+waves, and saw tooth functions, and some basic wavelet functionality.
+\texttt{scipy.fftpack} provides a suite of tools for Fourier domain
+analysis, including 1D, 2D, and ND discrete fourier transform and
+inverse functions, in addition to other tools such as analytic signal
+representations via the Hilbert trasformation (\texttt{numpy.fft} also
+provides basic FFT functions). \texttt{pylab} provides Matlab
+compatible functions for computing and plotting standard time series
+analyses, such as historgrams (\texttt{hist}), auto and cross
+correlations (\texttt{acorr} and \texttt{xcorr}), power spectra and
+coherence spectra (\texttt{psd}, \texttt{csd}, \texttt{cohere} and
+\texttt{specgram}.
This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site.
|
|
From: <jd...@us...> - 2007-10-26 19:43:28
|
Revision: 4021
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4021&view=rev
Author: jdh2358
Date: 2007-10-26 12:43:27 -0700 (Fri, 26 Oct 2007)
Log Message:
-----------
added convolution example
Added Paths:
-----------
trunk/py4science/workbook/convolution.tex
Added: trunk/py4science/workbook/convolution.tex
===================================================================
--- trunk/py4science/workbook/convolution.tex (rev 0)
+++ trunk/py4science/workbook/convolution.tex 2007-10-26 19:43:27 UTC (rev 4021)
@@ -0,0 +1,27 @@
+
+
+\begin{center}%
+\begin{figure}
+\begin{centering}\includegraphics[width=4in]{fig/convolve_explain}\par\end{centering}
+\caption{\label{fig:convolve_explain}The output of a linear system to a series of impulse inputs is equal to the sum of the scaled and time shifted impulse response functions.}
+\end{figure}
+\par\end{center}
+
+\begin{center}%
+\begin{figure}
+\begin{centering}\includegraphics[width=4in]{fig/convolve_deltas}\par\end{centering}
+\caption{\label{fig:convolve_deltas}Representing a continuous time signal sampled as a sum of delta functions.}
+\end{figure}
+\par\end{center}
+
+
+\lstinputlisting[label=code:convolution_demo,caption={IGNORED}]{skel/convolution_demo_skel.py}
+
+
+
+\begin{center}%
+\begin{figure}
+\begin{centering}\includegraphics[width=4in]{fig/convolution_demo}\par\end{centering}
+\caption{\label{fig:convolution_demo}Convolution of a white noise process with a double exponential function computed with \texttt{numpy.fft} and \texttt{numpy.convolve}}
+\end{figure}
+\par\end{center}
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|
|
From: <md...@us...> - 2007-10-26 18:44:20
|
Revision: 4020
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4020&view=rev
Author: mdboom
Date: 2007-10-26 11:44:16 -0700 (Fri, 26 Oct 2007)
Log Message:
-----------
Initialized merge tracking via "svnmerge" with revisions "1-3806" from
https://matplotlib.svn.sf.net/svnroot/matplotlib/branches/transforms
Property Changed:
----------------
trunk/matplotlib/
Property changes on: trunk/matplotlib
___________________________________________________________________
Name: svnmerge-integrated
+ /branches/transforms:1-3806
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|
|
From: <md...@us...> - 2007-10-26 18:32:47
|
Revision: 4019
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4019&view=rev
Author: mdboom
Date: 2007-10-26 11:32:44 -0700 (Fri, 26 Oct 2007)
Log Message:
-----------
Can't drag zoom on a polar plot.
Finessing Agg drawing quality a little bit.
Modified Paths:
--------------
branches/transforms/lib/matplotlib/axes.py
branches/transforms/lib/matplotlib/backend_bases.py
branches/transforms/lib/matplotlib/projections/polar.py
branches/transforms/src/_backend_agg.cpp
Modified: branches/transforms/lib/matplotlib/axes.py
===================================================================
--- branches/transforms/lib/matplotlib/axes.py 2007-10-26 18:04:51 UTC (rev 4018)
+++ branches/transforms/lib/matplotlib/axes.py 2007-10-26 18:32:44 UTC (rev 4019)
@@ -1820,6 +1820,12 @@
#### Interactive manipulation
+ def can_zoom(self):
+ """
+ Return True if this axes support the zoom box
+ """
+ return True
+
def get_navigate(self):
"""
Get whether the axes responds to navigation commands
Modified: branches/transforms/lib/matplotlib/backend_bases.py
===================================================================
--- branches/transforms/lib/matplotlib/backend_bases.py 2007-10-26 18:04:51 UTC (rev 4018)
+++ branches/transforms/lib/matplotlib/backend_bases.py 2007-10-26 18:32:44 UTC (rev 4019)
@@ -1361,7 +1361,8 @@
self._xypress=[]
for i, a in enumerate(self.canvas.figure.get_axes()):
- if x is not None and y is not None and a.in_axes(event) and a.get_navigate():
+ if x is not None and y is not None and a.in_axes(event) \
+ and a.get_navigate() and a.can_zoom():
self._xypress.append(( x, y, a, i, a.viewLim.frozen(), a.transData.frozen()))
self.press(event)
Modified: branches/transforms/lib/matplotlib/projections/polar.py
===================================================================
--- branches/transforms/lib/matplotlib/projections/polar.py 2007-10-26 18:04:51 UTC (rev 4018)
+++ branches/transforms/lib/matplotlib/projections/polar.py 2007-10-26 18:32:44 UTC (rev 4019)
@@ -55,8 +55,11 @@
transform_non_affine.__doc__ = Transform.transform_non_affine.__doc__
def transform_path(self, path):
- path = path.interpolated(self._resolution)
- return Path(self.transform(path.vertices), path.codes)
+ vertices = path.vertices
+ if len(vertices) == 2 and vertices[0, 0] == vertices[1, 0]:
+ return Path(self.transform(vertices), path.codes)
+ ipath = path.interpolated(self._resolution)
+ return Path(self.transform(ipath.vertices), ipath.codes)
transform_path.__doc__ = Transform.transform_path.__doc__
transform_path_non_affine = transform_path
@@ -151,7 +154,7 @@
def refresh(self):
return self.base.refresh()
- RESOLUTION = 50
+ RESOLUTION = 75
def __init__(self, *args, **kwargs):
"""
@@ -377,6 +380,12 @@
return 1.0
### Interactive panning
+
+ def can_zoom(self):
+ """
+ Return True if this axes support the zoom box
+ """
+ return False
def start_pan(self, x, y, button):
angle = self._r_label1_position.to_values()[4] / 180.0 * npy.pi
Modified: branches/transforms/src/_backend_agg.cpp
===================================================================
--- branches/transforms/src/_backend_agg.cpp 2007-10-26 18:04:51 UTC (rev 4018)
+++ branches/transforms/src/_backend_agg.cpp 2007-10-26 18:32:44 UTC (rev 4019)
@@ -95,8 +95,8 @@
unsigned vertex(double* x, double* y) {
unsigned cmd = m_source->vertex(x, y);
if (m_quantize && agg::is_vertex(cmd)) {
- *x = int(*x) + 0.5;
- *y = int(*y) + 0.5;
+ *x = round(*x) + 0.5;
+ *y = round(*y) + 0.5;
}
return cmd;
}
@@ -389,7 +389,7 @@
}
trans.transform(&x1, &y1);
- if (!(fabs(x0 - x1) < 0.001 || fabs(y0 - y1) < 0.001)) {
+ if (!(fabs(x0 - x1) < 1e-4 || fabs(y0 - y1) < 1e-4)) {
path.rewind(0);
return false;
}
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|
|
From: <md...@us...> - 2007-10-26 18:04:53
|
Revision: 4018
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4018&view=rev
Author: mdboom
Date: 2007-10-26 11:04:51 -0700 (Fri, 26 Oct 2007)
Log Message:
-----------
Fix polar plot title so it doesn't clash with 90 degree value.
Modified Paths:
--------------
branches/transforms/lib/matplotlib/projections/polar.py
Modified: branches/transforms/lib/matplotlib/projections/polar.py
===================================================================
--- branches/transforms/lib/matplotlib/projections/polar.py 2007-10-26 18:00:23 UTC (rev 4017)
+++ branches/transforms/lib/matplotlib/projections/polar.py 2007-10-26 18:04:51 UTC (rev 4018)
@@ -175,6 +175,8 @@
self.grid(rcParams['polaraxes.grid'])
self.xaxis.set_ticks_position('none')
self.yaxis.set_ticks_position('none')
+
+ self.title.set_y(1.06)
def _set_lim_and_transforms(self):
self.dataLim = Bbox.unit()
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|
|
From: <md...@us...> - 2007-10-26 18:00:26
|
Revision: 4017
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4017&view=rev
Author: mdboom
Date: 2007-10-26 11:00:23 -0700 (Fri, 26 Oct 2007)
Log Message:
-----------
Removed unused imports.
Modified Paths:
--------------
branches/transforms/lib/matplotlib/_mathtext_data.py
branches/transforms/lib/matplotlib/art3d.py
branches/transforms/lib/matplotlib/artist.py
branches/transforms/lib/matplotlib/axes.py
branches/transforms/lib/matplotlib/axes3d.py
branches/transforms/lib/matplotlib/axis.py
branches/transforms/lib/matplotlib/backend_bases.py
branches/transforms/lib/matplotlib/collections.py
branches/transforms/lib/matplotlib/colorbar.py
branches/transforms/lib/matplotlib/colors.py
branches/transforms/lib/matplotlib/contour.py
branches/transforms/lib/matplotlib/dates.py
branches/transforms/lib/matplotlib/figure.py
branches/transforms/lib/matplotlib/finance.py
branches/transforms/lib/matplotlib/font_manager.py
branches/transforms/lib/matplotlib/fontconfig_pattern.py
branches/transforms/lib/matplotlib/image.py
branches/transforms/lib/matplotlib/legend.py
branches/transforms/lib/matplotlib/lines.py
branches/transforms/lib/matplotlib/mathtext.py
branches/transforms/lib/matplotlib/mlab.py
branches/transforms/lib/matplotlib/patches.py
branches/transforms/lib/matplotlib/proj3d.py
branches/transforms/lib/matplotlib/scale.py
branches/transforms/lib/matplotlib/table.py
branches/transforms/lib/matplotlib/texmanager.py
branches/transforms/lib/matplotlib/text.py
branches/transforms/lib/matplotlib/ticker.py
branches/transforms/lib/matplotlib/units.py
Modified: branches/transforms/lib/matplotlib/_mathtext_data.py
===================================================================
--- branches/transforms/lib/matplotlib/_mathtext_data.py 2007-10-26 17:57:02 UTC (rev 4016)
+++ branches/transforms/lib/matplotlib/_mathtext_data.py 2007-10-26 18:00:23 UTC (rev 4017)
@@ -14,8 +14,6 @@
print charcode, glyphind
"""
-from sets import Set
-
latex_to_bakoma = {
r'\oint' : ('cmex10', 45),
r'\bigodot' : ('cmex10', 50),
Modified: branches/transforms/lib/matplotlib/art3d.py
===================================================================
--- branches/transforms/lib/matplotlib/art3d.py 2007-10-26 17:57:02 UTC (rev 4016)
+++ branches/transforms/lib/matplotlib/art3d.py 2007-10-26 18:00:23 UTC (rev 4017)
@@ -10,7 +10,6 @@
import text
from colors import Normalize
-from cm import jet
import numpy as npy
import proj3d
Modified: branches/transforms/lib/matplotlib/artist.py
===================================================================
--- branches/transforms/lib/matplotlib/artist.py 2007-10-26 17:57:02 UTC (rev 4016)
+++ branches/transforms/lib/matplotlib/artist.py 2007-10-26 18:00:23 UTC (rev 4017)
@@ -1,9 +1,7 @@
from __future__ import division
-import sys, re, warnings
+import re, warnings
from cbook import iterable, flatten
-from transforms import Affine2D, Bbox, IdentityTransform, TransformedBbox, \
- TransformedPath
-import matplotlib.units as units
+from transforms import Bbox, IdentityTransform, TransformedBbox, TransformedPath
## Note, matplotlib artists use the doc strings for set and get
# methods to enable the introspection methods of setp and getp. Every
Modified: branches/transforms/lib/matplotlib/axes.py
===================================================================
--- branches/transforms/lib/matplotlib/axes.py 2007-10-26 17:57:02 UTC (rev 4016)
+++ branches/transforms/lib/matplotlib/axes.py 2007-10-26 18:00:23 UTC (rev 4017)
@@ -1,5 +1,5 @@
from __future__ import division, generators
-import math, sys, warnings, copy, new
+import math, warnings, new
import numpy as npy
@@ -9,7 +9,6 @@
rcParams = matplotlib.rcParams
from matplotlib import artist as martist
-from matplotlib import agg
from matplotlib import axis as maxis
from matplotlib import cbook
from matplotlib import collections as mcoll
@@ -21,9 +20,7 @@
from matplotlib import legend as mlegend
from matplotlib import lines as mlines
from matplotlib import mlab
-from matplotlib import cm
from matplotlib import patches as mpatches
-from matplotlib import path as mpath
from matplotlib import quiver as mquiver
from matplotlib import scale as mscale
from matplotlib import table as mtable
Modified: branches/transforms/lib/matplotlib/axes3d.py
===================================================================
--- branches/transforms/lib/matplotlib/axes3d.py 2007-10-26 17:57:02 UTC (rev 4016)
+++ branches/transforms/lib/matplotlib/axes3d.py 2007-10-26 18:00:23 UTC (rev 4017)
@@ -11,12 +11,10 @@
import random
-import matplotlib
from axes import Axes
import cbook
from transforms import unit_bbox
-import figure
import numpy as npy
from colors import Normalize
Modified: branches/transforms/lib/matplotlib/axis.py
===================================================================
--- branches/transforms/lib/matplotlib/axis.py 2007-10-26 17:57:02 UTC (rev 4016)
+++ branches/transforms/lib/matplotlib/axis.py 2007-10-26 18:00:23 UTC (rev 4017)
@@ -2,23 +2,19 @@
Classes for the ticks and x and y axis
"""
from __future__ import division
-import copy
-import math
-import re
-import sys
from artist import Artist, setp
from cbook import enumerate, silent_list, popall, CallbackRegistry
from lines import Line2D, TICKLEFT, TICKRIGHT, TICKUP, TICKDOWN
from matplotlib import rcParams
from patches import bbox_artist
-from ticker import NullFormatter, FixedFormatter, ScalarFormatter, LogFormatter, LogFormatterMathtext
-from ticker import NullLocator, FixedLocator, LinearLocator, LogLocator, AutoLocator
+from ticker import NullFormatter, FixedFormatter, ScalarFormatter
+from ticker import NullLocator, FixedLocator, AutoLocator
from font_manager import FontProperties
-from text import Text, TextWithDash, _process_text_args
-from transforms import Affine2D, Bbox, blended_transform_factory, interval_contains, \
- interval_contains_open, IdentityTransform
+from text import Text, TextWithDash
+from transforms import Affine2D, Bbox, blended_transform_factory, \
+ interval_contains
from patches import bbox_artist
from scale import scale_factory
Modified: branches/transforms/lib/matplotlib/backend_bases.py
===================================================================
--- branches/transforms/lib/matplotlib/backend_bases.py 2007-10-26 17:57:02 UTC (rev 4016)
+++ branches/transforms/lib/matplotlib/backend_bases.py 2007-10-26 18:00:23 UTC (rev 4017)
@@ -4,10 +4,9 @@
"""
from __future__ import division
-import os, sys, warnings, copy, weakref
+import os
import numpy as npy
-import matplotlib.numerix.npyma as ma
import matplotlib.cbook as cbook
import matplotlib.colors as colors
import matplotlib.transforms as transforms
Modified: branches/transforms/lib/matplotlib/collections.py
===================================================================
--- branches/transforms/lib/matplotlib/collections.py 2007-10-26 17:57:02 UTC (rev 4016)
+++ branches/transforms/lib/matplotlib/collections.py 2007-10-26 18:00:23 UTC (rev 4017)
@@ -16,7 +16,6 @@
import matplotlib.transforms as transforms
import matplotlib.artist as artist
import matplotlib.backend_bases as backend_bases
-import matplotlib.nxutils as nxutils
import matplotlib.path as path
class Collection(artist.Artist, cm.ScalarMappable):
Modified: branches/transforms/lib/matplotlib/colorbar.py
===================================================================
--- branches/transforms/lib/matplotlib/colorbar.py 2007-10-26 17:57:02 UTC (rev 4016)
+++ branches/transforms/lib/matplotlib/colorbar.py 2007-10-26 18:00:23 UTC (rev 4017)
@@ -21,7 +21,6 @@
import matplotlib.cm as cm
import matplotlib.ticker as ticker
import matplotlib.cbook as cbook
-import matplotlib.transforms as transforms
import matplotlib.lines as lines
import matplotlib.patches as patches
import matplotlib.collections as collections
Modified: branches/transforms/lib/matplotlib/colors.py
===================================================================
--- branches/transforms/lib/matplotlib/colors.py 2007-10-26 17:57:02 UTC (rev 4016)
+++ branches/transforms/lib/matplotlib/colors.py 2007-10-26 18:00:23 UTC (rev 4017)
@@ -34,7 +34,6 @@
'chartreuse' are supported.
"""
import re
-import warnings
import numpy as npy
import matplotlib.numerix.npyma as ma
import matplotlib.cbook as cbook
Modified: branches/transforms/lib/matplotlib/contour.py
===================================================================
--- branches/transforms/lib/matplotlib/contour.py 2007-10-26 17:57:02 UTC (rev 4016)
+++ branches/transforms/lib/matplotlib/contour.py 2007-10-26 18:00:23 UTC (rev 4017)
@@ -10,7 +10,6 @@
import matplotlib._cntr as _cntr
import matplotlib.path as path
import matplotlib.ticker as ticker
-import matplotlib.transforms as transforms
import matplotlib.cm as cm
import matplotlib.colors as colors
import matplotlib.collections as collections
Modified: branches/transforms/lib/matplotlib/dates.py
===================================================================
--- branches/transforms/lib/matplotlib/dates.py 2007-10-26 17:57:02 UTC (rev 4016)
+++ branches/transforms/lib/matplotlib/dates.py 2007-10-26 18:00:23 UTC (rev 4017)
@@ -76,8 +76,7 @@
DateIndexFormatter - date plots with implicit x indexing.
"""
-import sys, re, time, math, datetime
-import locale
+import re, time, math, datetime
import pytz
import matplotlib
Modified: branches/transforms/lib/matplotlib/figure.py
===================================================================
--- branches/transforms/lib/matplotlib/figure.py 2007-10-26 17:57:02 UTC (rev 4016)
+++ branches/transforms/lib/matplotlib/figure.py 2007-10-26 18:00:23 UTC (rev 4017)
@@ -1,8 +1,6 @@
"""
Figure class -- add docstring here!
"""
-import sys
-
import numpy as npy
import artist
@@ -11,20 +9,15 @@
from cbook import flatten, allequal, Stack, iterable, dedent
import _image
import colorbar as cbar
-from colors import Normalize, rgb2hex
from image import FigureImage
from matplotlib import rcParams
-from patches import Rectangle, Polygon
+from patches import Rectangle
from text import Text, _process_text_args
from legend import Legend
-from ticker import FormatStrFormatter
from transforms import Affine2D, Bbox, BboxTransformTo, TransformedBbox
-from cm import ScalarMappable
-from contour import ContourSet
from projections import projection_factory, get_projection_names, \
get_projection_class
-import warnings
class SubplotParams:
"""
Modified: branches/transforms/lib/matplotlib/finance.py
===================================================================
--- branches/transforms/lib/matplotlib/finance.py 2007-10-26 17:57:02 UTC (rev 4016)
+++ branches/transforms/lib/matplotlib/finance.py 2007-10-26 18:00:23 UTC (rev 4017)
@@ -15,8 +15,7 @@
import numpy as npy
from matplotlib import verbose, get_configdir
-from artist import Artist
-from dates import date2num, num2date
+from dates import date2num
from matplotlib.cbook import Bunch
from matplotlib.collections import LineCollection, PolyCollection
from matplotlib.colors import colorConverter
Modified: branches/transforms/lib/matplotlib/font_manager.py
===================================================================
--- branches/transforms/lib/matplotlib/font_manager.py 2007-10-26 17:57:02 UTC (rev 4016)
+++ branches/transforms/lib/matplotlib/font_manager.py 2007-10-26 18:00:23 UTC (rev 4017)
@@ -33,12 +33,12 @@
see license/LICENSE_TTFQUERY.
"""
-import os, sys, glob, shutil
+import os, sys, glob
from sets import Set
import matplotlib
from matplotlib import afm
from matplotlib import ft2font
-from matplotlib import rcParams, get_home, get_configdir
+from matplotlib import rcParams, get_configdir
from matplotlib.cbook import is_string_like
from matplotlib.fontconfig_pattern import \
parse_fontconfig_pattern, generate_fontconfig_pattern
Modified: branches/transforms/lib/matplotlib/fontconfig_pattern.py
===================================================================
--- branches/transforms/lib/matplotlib/fontconfig_pattern.py 2007-10-26 17:57:02 UTC (rev 4016)
+++ branches/transforms/lib/matplotlib/fontconfig_pattern.py 2007-10-26 18:00:23 UTC (rev 4017)
@@ -18,7 +18,7 @@
License : matplotlib license (PSF compatible)
"""
import re
-from matplotlib.pyparsing import Literal, OneOrMore, ZeroOrMore, \
+from matplotlib.pyparsing import Literal, ZeroOrMore, \
Optional, Regex, StringEnd, ParseException, Suppress
family_punc = r'\\\-:,'
Modified: branches/transforms/lib/matplotlib/image.py
===================================================================
--- branches/transforms/lib/matplotlib/image.py 2007-10-26 17:57:02 UTC (rev 4016)
+++ branches/transforms/lib/matplotlib/image.py 2007-10-26 18:00:23 UTC (rev 4017)
@@ -4,7 +4,7 @@
"""
from __future__ import division
-import sys, os
+import os
import numpy as npy
import numerix.ma as ma
Modified: branches/transforms/lib/matplotlib/legend.py
===================================================================
--- branches/transforms/lib/matplotlib/legend.py 2007-10-26 17:57:02 UTC (rev 4016)
+++ branches/transforms/lib/matplotlib/legend.py 2007-10-26 18:00:23 UTC (rev 4017)
@@ -21,17 +21,17 @@
up the legend
"""
from __future__ import division
-import copy, sys, warnings
+import warnings
import numpy as npy
-from matplotlib import verbose, rcParams
+from matplotlib import rcParams
from artist import Artist
-from cbook import enumerate, is_string_like, iterable, silent_list
+from cbook import is_string_like, iterable, silent_list
from font_manager import FontProperties
from lines import Line2D
from mlab import segments_intersect
-from patches import Patch, Rectangle, RegularPolygon, Shadow, bbox_artist, draw_bbox
+from patches import Patch, Rectangle, Shadow, bbox_artist
from collections import LineCollection, RegularPolyCollection
from text import Text
from transforms import Affine2D, Bbox, BboxTransformTo
Modified: branches/transforms/lib/matplotlib/lines.py
===================================================================
--- branches/transforms/lib/matplotlib/lines.py 2007-10-26 17:57:02 UTC (rev 4016)
+++ branches/transforms/lib/matplotlib/lines.py 2007-10-26 18:00:23 UTC (rev 4017)
@@ -6,14 +6,12 @@
# TODO: expose cap and join style attrs
from __future__ import division
-import sys, math, warnings
-
import numpy as npy
import numerix.ma as ma
from matplotlib import verbose
import artist
-from artist import Artist, setp
+from artist import Artist
from cbook import iterable, is_string_like, is_numlike
from colors import colorConverter
from path import Path
Modified: branches/transforms/lib/matplotlib/mathtext.py
===================================================================
--- branches/transforms/lib/matplotlib/mathtext.py 2007-10-26 17:57:02 UTC (rev 4016)
+++ branches/transforms/lib/matplotlib/mathtext.py 2007-10-26 18:00:23 UTC (rev 4017)
@@ -124,29 +124,27 @@
"""
from __future__ import division
-import os, sys
+import os
from cStringIO import StringIO
-from math import floor, ceil
+from math import ceil
from sets import Set
import unicodedata
from warnings import warn
from numpy import inf, isinf
-from matplotlib import verbose
-from matplotlib.pyparsing import Literal, Word, OneOrMore, ZeroOrMore, \
- Combine, Group, Optional, Forward, NotAny, alphas, nums, alphanums, \
- StringStart, StringEnd, ParseFatalException, FollowedBy, Regex, \
- operatorPrecedence, opAssoc, ParseResults, Or, Suppress, oneOf, \
- ParseException, MatchFirst, NoMatch, Empty
+from matplotlib.pyparsing import Combine, Group, Optional, Forward, \
+ Literal, OneOrMore, ZeroOrMore, ParseException, Empty, \
+ ParseResults, Suppress, oneOf, StringEnd, ParseFatalException, \
+ FollowedBy, Regex
from matplotlib.afm import AFM
-from matplotlib.cbook import enumerate, iterable, Bunch, get_realpath_and_stat, \
+from matplotlib.cbook import Bunch, get_realpath_and_stat, \
is_string_like
from matplotlib.ft2font import FT2Font, FT2Image, KERNING_DEFAULT, LOAD_DEFAULT, LOAD_NO_HINTING
from matplotlib.font_manager import findfont, FontProperties
from matplotlib._mathtext_data import latex_to_bakoma, \
- latex_to_standard, tex2uni, type12uni, tex2type1, uni2type1, \
+ latex_to_standard, tex2uni, tex2type1, uni2type1, \
latex_to_cmex
from matplotlib import get_data_path, rcParams
Modified: branches/transforms/lib/matplotlib/mlab.py
===================================================================
--- branches/transforms/lib/matplotlib/mlab.py 2007-10-26 17:57:02 UTC (rev 4016)
+++ branches/transforms/lib/matplotlib/mlab.py 2007-10-26 18:00:23 UTC (rev 4017)
@@ -55,7 +55,7 @@
"""
from __future__ import division
-import sys, datetime, csv, warnings
+import csv, warnings
import numpy as npy
Modified: branches/transforms/lib/matplotlib/patches.py
===================================================================
--- branches/transforms/lib/matplotlib/patches.py 2007-10-26 17:57:02 UTC (rev 4016)
+++ branches/transforms/lib/matplotlib/patches.py 2007-10-26 18:00:23 UTC (rev 4017)
@@ -6,10 +6,7 @@
import matplotlib.cbook as cbook
import matplotlib.artist as artist
import matplotlib.colors as colors
-import matplotlib.lines as lines
import matplotlib.transforms as transforms
-import matplotlib.nxutils as nxutils
-import matplotlib.mlab as mlab
import matplotlib.artist as artist
from matplotlib.path import Path
Modified: branches/transforms/lib/matplotlib/proj3d.py
===================================================================
--- branches/transforms/lib/matplotlib/proj3d.py 2007-10-26 17:57:02 UTC (rev 4016)
+++ branches/transforms/lib/matplotlib/proj3d.py 2007-10-26 18:00:23 UTC (rev 4017)
@@ -9,7 +9,6 @@
from patches import Circle
import numpy as npy
import numpy.linalg as linalg
-from math import sqrt
def _hide_cross(a,b):
"""
Modified: branches/transforms/lib/matplotlib/scale.py
===================================================================
--- branches/transforms/lib/matplotlib/scale.py 2007-10-26 17:57:02 UTC (rev 4016)
+++ branches/transforms/lib/matplotlib/scale.py 2007-10-26 18:00:23 UTC (rev 4017)
@@ -1,12 +1,9 @@
import numpy as npy
from numpy import ma
-from numpy.linalg import inv
-from ticker import NullFormatter, FixedFormatter, ScalarFormatter, \
- LogFormatter, LogFormatterMathtext
-from ticker import NullLocator, FixedLocator, LinearLocator, LogLocator, \
- AutoLocator
-from transforms import Transform, composite_transform_factory, IdentityTransform
+from ticker import NullFormatter, ScalarFormatter, LogFormatterMathtext
+from ticker import NullLocator, LogLocator, AutoLocator
+from transforms import Transform, IdentityTransform
class ScaleBase(object):
def set_default_locators_and_formatters(self, axis):
Modified: branches/transforms/lib/matplotlib/table.py
===================================================================
--- branches/transforms/lib/matplotlib/table.py 2007-10-26 17:57:02 UTC (rev 4016)
+++ branches/transforms/lib/matplotlib/table.py 2007-10-26 18:00:23 UTC (rev 4017)
@@ -20,14 +20,12 @@
"""
from __future__ import division
-import sys, warnings
+import warnings
-from matplotlib import verbose
-
import artist
from artist import Artist
from patches import Rectangle
-from cbook import enumerate, is_string_like, flatten
+from cbook import is_string_like
from text import Text
from transforms import Bbox
Modified: branches/transforms/lib/matplotlib/texmanager.py
===================================================================
--- branches/transforms/lib/matplotlib/texmanager.py 2007-10-26 17:57:02 UTC (rev 4016)
+++ branches/transforms/lib/matplotlib/texmanager.py 2007-10-26 18:00:23 UTC (rev 4017)
@@ -33,7 +33,7 @@
"""
-import copy, glob, md5, os, shutil, sys, warnings
+import copy, glob, md5, os, shutil, sys
import numpy as npy
import matplotlib as mpl
from matplotlib import rcParams
Modified: branches/transforms/lib/matplotlib/text.py
===================================================================
--- branches/transforms/lib/matplotlib/text.py 2007-10-26 17:57:02 UTC (rev 4016)
+++ branches/transforms/lib/matplotlib/text.py 2007-10-26 18:00:23 UTC (rev 4017)
@@ -2,17 +2,15 @@
Figure and Axes text
"""
from __future__ import division
-import re, math
+import math
import numpy as npy
-import matplotlib
-from matplotlib import verbose
from matplotlib import cbook
from matplotlib import rcParams
import artist
from artist import Artist
-from cbook import enumerate, is_string_like, maxdict, is_numlike
+from cbook import enumerate, is_string_like, maxdict
from font_manager import FontProperties
from patches import bbox_artist, YAArrow
from transforms import Affine2D, Bbox
Modified: branches/transforms/lib/matplotlib/ticker.py
===================================================================
--- branches/transforms/lib/matplotlib/ticker.py 2007-10-26 17:57:02 UTC (rev 4016)
+++ branches/transforms/lib/matplotlib/ticker.py 2007-10-26 18:00:23 UTC (rev 4017)
@@ -104,10 +104,9 @@
from __future__ import division
-import sys, os, re, time, math, warnings
+import math
import numpy as npy
-import matplotlib as mpl
-from matplotlib import verbose, rcParams
+from matplotlib import rcParams
from matplotlib import cbook
from matplotlib import transforms as mtransforms
Modified: branches/transforms/lib/matplotlib/units.py
===================================================================
--- branches/transforms/lib/matplotlib/units.py 2007-10-26 17:57:02 UTC (rev 4016)
+++ branches/transforms/lib/matplotlib/units.py 2007-10-26 18:00:23 UTC (rev 4017)
@@ -43,7 +43,6 @@
units.registry[datetime.date] = DateConverter()
"""
-import matplotlib
from matplotlib.cbook import iterable, is_numlike
class AxisInfo:
This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site.
|
|
From: <jd...@us...> - 2007-10-26 17:57:07
|
Revision: 4016
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4016&view=rev
Author: jdh2358
Date: 2007-10-26 10:57:02 -0700 (Fri, 26 Oct 2007)
Log Message:
-----------
added convolve workbook
Modified Paths:
--------------
trunk/py4science/workbook/main.pdf
Modified: trunk/py4science/workbook/main.pdf
===================================================================
--- trunk/py4science/workbook/main.pdf 2007-10-26 17:56:06 UTC (rev 4015)
+++ trunk/py4science/workbook/main.pdf 2007-10-26 17:57:02 UTC (rev 4016)
@@ -84,9 +84,15 @@
(1. Glass Moir\351 Patterns)
endobj
61 0 obj
-<< /S /GoTo /D [62 0 R /Fit ] >>
+<< /S /GoTo /D (chapter.6) >>
endobj
-64 0 obj <<
+64 0 obj
+(Chapter 6. Signal processing)
+endobj
+65 0 obj
+<< /S /GoTo /D [66 0 R /Fit ] >>
+endobj
+68 0 obj <<
/Length 292
/Filter /FlateDecode
>>
@@ -95,24 +101,24 @@
\xEAT\x89H \x94\xA1\xA4\xA1\x8DJm\xB5\xFCz\xEC\x86VEb\xA8,\xD9:/\xDF\x99\xCA Y0\xE0\x95Ҍ\x8C\x81`\x89X\xBB\xAD[e\xEC\xA1\xC2_\x8E\xD5(\x9F\x8BP\xE9\xB0B\xCD\xE4\xB9\xC9MS]M\x8Cch\xC1X\xAFY\xF3δR\xA0\x9D\xAF\x99G\xB5Ɋf\xF9\xC2gâ\xDD\xF5\xED\xE2CH\xE3\x89?\xB5}\x85Q|'\x90\xF7B"\x9F+c\xDBz~\x9B\xB6\xE5\xE6s\xE3J\xBC6SF\xD11\xA9P+:8\xF7q\xB4\x9C}f\x91\xACS<\xD0e |
|
From: <jd...@us...> - 2007-10-26 17:56:08
|
Revision: 4015
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4015&view=rev
Author: jdh2358
Date: 2007-10-26 10:56:06 -0700 (Fri, 26 Oct 2007)
Log Message:
-----------
added more convolution figs
Modified Paths:
--------------
trunk/py4science/workbook/scripts/convolve_explain.py
Added Paths:
-----------
trunk/py4science/workbook/fig/convolve_explain.eps
trunk/py4science/workbook/fig/convolve_explain.png
Added: trunk/py4science/workbook/fig/convolve_explain.eps
===================================================================
--- trunk/py4science/workbook/fig/convolve_explain.eps (rev 0)
+++ trunk/py4science/workbook/fig/convolve_explain.eps 2007-10-26 17:56:06 UTC (rev 4015)
@@ -0,0 +1,6355 @@
+%!PS-Adobe-3.0 EPSF-3.0
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[truncated message content] |
|
From: <jd...@us...> - 2007-10-26 17:48:18
|
Revision: 4014
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4014&view=rev
Author: jdh2358
Date: 2007-10-26 10:48:16 -0700 (Fri, 26 Oct 2007)
Log Message:
-----------
added convolution demo figs
Added Paths:
-----------
trunk/py4science/workbook/fig/convolution_demo.eps
trunk/py4science/workbook/fig/convolution_demo.png
Added: trunk/py4science/workbook/fig/convolution_demo.eps
===================================================================
--- trunk/py4science/workbook/fig/convolution_demo.eps (rev 0)
+++ trunk/py4science/workbook/fig/convolution_demo.eps 2007-10-26 17:48:16 UTC (rev 4014)
@@ -0,0 +1,9360 @@
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[truncated message content] |
|
From: <jd...@us...> - 2007-10-26 17:43:54
|
Revision: 4013
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4013&view=rev
Author: jdh2358
Date: 2007-10-26 10:43:50 -0700 (Fri, 26 Oct 2007)
Log Message:
-----------
added some figs for convolution example
Added Paths:
-----------
trunk/py4science/workbook/fig/convolve_deltas.eps
trunk/py4science/workbook/fig/convolve_deltas.png
trunk/py4science/workbook/scripts/
trunk/py4science/workbook/scripts/convolve_deltas.py
trunk/py4science/workbook/scripts/convolve_explain.py
Added: trunk/py4science/workbook/fig/convolve_deltas.eps
===================================================================
--- trunk/py4science/workbook/fig/convolve_deltas.eps (rev 0)
+++ trunk/py4science/workbook/fig/convolve_deltas.eps 2007-10-26 17:43:50 UTC (rev 4013)
@@ -0,0 +1,780 @@
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Added: trunk/py4science/workbook/fig/convolve_deltas.png
===================================================================
(Binary files differ)
Property changes on: trunk/py4science/workbook/fig/convolve_deltas.png
___________________________________________________________________
Name: svn:mime-type
+ application/octet-stream
Added: trunk/py4science/workbook/scripts/convolve_deltas.py
===================================================================
--- trunk/py4science/workbook/scripts/convolve_deltas.py (rev 0)
+++ trunk/py4science/workbook/scripts/convolve_deltas.py 2007-10-26 17:43:50 UTC (rev 4013)
@@ -0,0 +1,18 @@
+import numpy as npy
+from pylab import figure, show
+
+dt = 0.025
+t = npy.arange(0.0, 0.5, dt)
+Nt = len(t)
+
+
+s = npy.exp(-2*t)*npy.cos(2*3*npy.pi*t)+1.0
+fig = figure()
+ax = fig.add_subplot(111)
+ax.plot(t, s, color='blue', lw=2)
+ax.bar(t-dt/8., s, facecolor='blue', width=dt/4.)
+ax.axhline(0, color='black', lw=2)
+ax.set_xlim(xmin=0)
+fig.savefig('convolve_deltas.png', dpi=150)
+fig.savefig('convolve_deltas.eps')
+show()
Added: trunk/py4science/workbook/scripts/convolve_explain.py
===================================================================
--- trunk/py4science/workbook/scripts/convolve_explain.py (rev 0)
+++ trunk/py4science/workbook/scripts/convolve_explain.py 2007-10-26 17:43:50 UTC (rev 4013)
@@ -0,0 +1,48 @@
+import numpy as npy
+from pylab import figure, show
+
+dt = 0.01
+t = npy.arange(0.0, 10.0, dt)
+Nt = len(t)
+
+def impulse_response(t):
+ 'double exponential response function'
+ return (npy.exp(-t) - npy.exp(-5*t))
+
+i1 = npy.zeros(len(t))
+i2 = npy.zeros(len(t))
+i3 = npy.zeros(len(t))
+r = impulse_response(t)
+
+ind1, ind2, ind3 = 100, 300, 900
+i1[ind1] = 1
+i2[ind2] = 1.7
+i3[ind3] = 0.6
+
+y1 = npy.convolve(i1, r, mode='full')[:Nt]
+y2 = npy.convolve(i2, r, mode='full')[:Nt]
+y3 = npy.convolve(i3, r, mode='full')[:Nt]
+
+fig = figure()
+ax1 = ax = fig.add_subplot(311)
+ax.plot(t, r, 'k', lw=2)
+ax.set_ylabel('impulse response')
+
+ax = fig.add_subplot(312, sharex=ax1)
+ax.bar(t[ind1], i1[ind1], facecolor='blue', lw=2, edgecolor='blue', width=3*dt)
+ax.bar(t[ind2], i2[ind2], facecolor='green', lw=2, edgecolor='green', width=3*dt)
+ax.bar(t[ind3], i3[ind3], facecolor='red', lw=2, edgecolor='red', width=3*dt)
+ax.plot(t, y1, color='blue', lw=1, label='input 1')
+ax.plot(t, y2, color='green', lw=1, label='input 2')
+ax.plot(t, y3, color='red', lw=1, label='input d')
+ax.set_ylabel('3 inputs')
+
+
+ax = fig.add_subplot(313, sharex=ax1)
+ax.plot(t, y1+y2+y3, color='black', lw=2, label='sum')
+ax.set_ylabel('output')
+#ax.legend(loc='best')
+
+fig.savefig('../fig/convolve_inputs.png', dpi=150)
+fig.savefig('../fig/convolve_inputs.eps')
+show()
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From: <md...@us...> - 2007-10-26 17:01:30
|
Revision: 4012
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4012&view=rev
Author: mdboom
Date: 2007-10-26 10:01:28 -0700 (Fri, 26 Oct 2007)
Log Message:
-----------
Added BboxTransformFrom/To for more efficient bounding box transforms.
Modified Paths:
--------------
branches/transforms/lib/matplotlib/axes.py
branches/transforms/lib/matplotlib/figure.py
branches/transforms/lib/matplotlib/legend.py
branches/transforms/lib/matplotlib/patches.py
branches/transforms/lib/matplotlib/projections/polar.py
branches/transforms/lib/matplotlib/quiver.py
branches/transforms/lib/matplotlib/transforms.py
Modified: branches/transforms/lib/matplotlib/axes.py
===================================================================
--- branches/transforms/lib/matplotlib/axes.py 2007-10-26 15:58:50 UTC (rev 4011)
+++ branches/transforms/lib/matplotlib/axes.py 2007-10-26 17:01:28 UTC (rev 4012)
@@ -560,8 +560,7 @@
"""
self.dataLim = mtransforms.Bbox.unit()
self.viewLim = mtransforms.Bbox.unit()
- self.transAxes = mtransforms.BboxTransform(
- mtransforms.Bbox.unit(), self.bbox)
+ self.transAxes = mtransforms.BboxTransformTo(self.bbox)
# Transforms the x and y axis separately by a scale factor
# It is assumed that this part will have non-linear components
@@ -569,8 +568,8 @@
# An affine transformation on the data, generally to limit the
# range of the axes
- self.transLimits = mtransforms.BboxTransform(
- mtransforms.TransformedBbox(self.viewLim, self.transScale), mtransforms.Bbox.unit())
+ self.transLimits = mtransforms.BboxTransformFrom(
+ mtransforms.TransformedBbox(self.viewLim, self.transScale))
# The parentheses are important for efficiency here -- they
# group the last two (which are usually affines) separately
Modified: branches/transforms/lib/matplotlib/figure.py
===================================================================
--- branches/transforms/lib/matplotlib/figure.py 2007-10-26 15:58:50 UTC (rev 4011)
+++ branches/transforms/lib/matplotlib/figure.py 2007-10-26 17:01:28 UTC (rev 4012)
@@ -19,7 +19,7 @@
from legend import Legend
from ticker import FormatStrFormatter
-from transforms import Affine2D, Bbox, BboxTransform, TransformedBbox
+from transforms import Affine2D, Bbox, BboxTransformTo, TransformedBbox
from cm import ScalarMappable
from contour import ContourSet
from projections import projection_factory, get_projection_names, \
@@ -136,7 +136,7 @@
self.frameon = frameon
- self.transFigure = BboxTransform(Bbox.unit(), self.bbox)
+ self.transFigure = BboxTransformTo(self.bbox)
self.figurePatch = Rectangle(
xy=(0,0), width=1, height=1,
Modified: branches/transforms/lib/matplotlib/legend.py
===================================================================
--- branches/transforms/lib/matplotlib/legend.py 2007-10-26 15:58:50 UTC (rev 4011)
+++ branches/transforms/lib/matplotlib/legend.py 2007-10-26 17:01:28 UTC (rev 4012)
@@ -34,7 +34,7 @@
from patches import Patch, Rectangle, RegularPolygon, Shadow, bbox_artist, draw_bbox
from collections import LineCollection, RegularPolyCollection
from text import Text
-from transforms import Affine2D, Bbox, BboxTransform
+from transforms import Affine2D, Bbox, BboxTransformTo
def line_cuts_bbox(line, bbox):
""" Return True if and only if line cuts bbox. """
@@ -165,7 +165,7 @@
raise TypeError("Legend needs either Axes or Figure as parent")
self.parent = parent
self._offsetTransform = Affine2D()
- self._parentTransform = BboxTransform(Bbox.unit(), parent.bbox)
+ self._parentTransform = BboxTransformTo(parent.bbox)
Artist.set_transform(self, self._offsetTransform + self._parentTransform)
if loc is None:
Modified: branches/transforms/lib/matplotlib/patches.py
===================================================================
--- branches/transforms/lib/matplotlib/patches.py 2007-10-26 15:58:50 UTC (rev 4011)
+++ branches/transforms/lib/matplotlib/patches.py 2007-10-26 17:01:28 UTC (rev 4012)
@@ -346,8 +346,7 @@
left, right = self.convert_xunits((xy[0], xy[0] + width))
bottom, top = self.convert_yunits((xy[1], xy[1] + height))
self._bbox = transforms.Bbox.from_extents(left, bottom, right, top)
- self._rect_transform = transforms.BboxTransform(
- transforms.Bbox.unit(), self._bbox)
+ self._rect_transform = transforms.BboxTransformTo(self._bbox)
__init__.__doc__ = cbook.dedent(__init__.__doc__) % artist.kwdocd
def get_path(self):
Modified: branches/transforms/lib/matplotlib/projections/polar.py
===================================================================
--- branches/transforms/lib/matplotlib/projections/polar.py 2007-10-26 15:58:50 UTC (rev 4011)
+++ branches/transforms/lib/matplotlib/projections/polar.py 2007-10-26 17:01:28 UTC (rev 4012)
@@ -10,8 +10,8 @@
from matplotlib.patches import Circle
from matplotlib.path import Path
from matplotlib.ticker import Formatter, Locator
-from matplotlib.transforms import Affine2D, Affine2DBase, Bbox, BboxTransform, \
- IdentityTransform, Transform, TransformWrapper
+from matplotlib.transforms import Affine2D, Affine2DBase, Bbox, \
+ BboxTransformTo, IdentityTransform, Transform, TransformWrapper
class PolarAxes(Axes):
"""
@@ -179,7 +179,7 @@
def _set_lim_and_transforms(self):
self.dataLim = Bbox.unit()
self.viewLim = Bbox.unit()
- self.transAxes = BboxTransform(Bbox.unit(), self.bbox)
+ self.transAxes = BboxTransformTo(self.bbox)
# Transforms the x and y axis separately by a scale factor
# It is assumed that this part will have non-linear components
Modified: branches/transforms/lib/matplotlib/quiver.py
===================================================================
--- branches/transforms/lib/matplotlib/quiver.py 2007-10-26 15:58:50 UTC (rev 4011)
+++ branches/transforms/lib/matplotlib/quiver.py 2007-10-26 17:01:28 UTC (rev 4012)
@@ -239,7 +239,7 @@
elif self.coord == 'inches':
dx = ax.figure.dpi
bb = transforms.Bbox.from_extents(0, 0, dx, dy)
- trans = transforms.BboxTransform(Bbox.unit(), bb)
+ trans = transforms.BboxTransformTo(bb)
self.set_transform(trans)
else:
raise ValueError('unrecognized coordinates')
Modified: branches/transforms/lib/matplotlib/transforms.py
===================================================================
--- branches/transforms/lib/matplotlib/transforms.py 2007-10-26 15:58:50 UTC (rev 4011)
+++ branches/transforms/lib/matplotlib/transforms.py 2007-10-26 17:01:28 UTC (rev 4012)
@@ -211,6 +211,9 @@
def __array__(self, *args, **kwargs):
return self.get_points()
+ def is_unit(self):
+ return list(self.get_points().flatten()) == [0., 0., 1., 1.]
+
def _get_x0(self):
return self.get_points()[0, 0]
x0 = property(_get_x0)
@@ -1830,6 +1833,86 @@
self._invalid = 0
return self._mtx
get_matrix.__doc__ = Affine2DBase.get_matrix.__doc__
+
+
+class BboxTransformTo(Affine2DBase):
+ """
+ BboxTransformSimple linearly transforms points from the unit Bbox
+ to another Bbox.
+ """
+ is_separable = True
+
+ def __init__(self, boxout):
+ """
+ Create a new BboxTransform that linearly transforms points
+ from the unit Bbox to boxout.
+ """
+ assert boxout.is_bbox
+
+ Affine2DBase.__init__(self)
+ self._boxout = boxout
+ self.set_children(boxout)
+ self._mtx = None
+ self._inverted = None
+
+ def __repr__(self):
+ return "BboxTransformTo(%s)" % (self._boxout)
+ __str__ = __repr__
+
+ def get_matrix(self):
+ if self._invalid:
+ outl, outb, outw, outh = self._boxout.bounds
+ if DEBUG and (outw == 0 or outh == 0):
+ raise ValueError("Transforming to a singular bounding box.")
+ self._mtx = npy.array([[outw, 0.0, outl],
+ [ 0.0, outh, outb],
+ [ 0.0, 0.0, 1.0]],
+ npy.float_)
+ self._inverted = None
+ self._invalid = 0
+ return self._mtx
+ get_matrix.__doc__ = Affine2DBase.get_matrix.__doc__
+
+
+class BboxTransformFrom(Affine2DBase):
+ """
+ BboxTransform linearly transforms points from one Bbox to the unit
+ Bbox.
+ """
+ is_separable = True
+
+ def __init__(self, boxin):
+ """
+ Create a new BboxTransform that linearly transforms points
+ from boxin to the unit Bbox.
+ """
+ assert boxin.is_bbox
+
+ Affine2DBase.__init__(self)
+ self._boxin = boxin
+ self.set_children(boxin)
+ self._mtx = None
+ self._inverted = None
+
+ def __repr__(self):
+ return "BboxTransformFrom(%s)" % (self._boxin)
+ __str__ = __repr__
+
+ def get_matrix(self):
+ if self._invalid:
+ inl, inb, inw, inh = self._boxin.bounds
+ if DEBUG and (inw == 0 or inh == 0):
+ raise ValueError("Transforming from a singular bounding box.")
+ x_scale = 1.0 / inw
+ y_scale = 1.0 / inh
+ self._mtx = npy.array([[x_scale, 0.0 , (-inl*x_scale)],
+ [0.0 , y_scale, (-inb*y_scale)],
+ [0.0 , 0.0 , 1.0 ]],
+ npy.float_)
+ self._inverted = None
+ self._invalid = 0
+ return self._mtx
+ get_matrix.__doc__ = Affine2DBase.get_matrix.__doc__
class TransformedPath(TransformNode):
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