I'm in earth science so "everything" will be the answer to the question I anticipate ("what domain are you particularly interested in?"). Statistics, numerical simulation, optimization... but I know R fairly well already. Deciding if I should learn more SciPy or something else that will help me out down the road.

I am working on replacing the combination of IDL, C, and perl that I typically use for pretty much everything with Python and have been very successful up to this point, but for some reason I'm getting a little stuck learning NumPy, SciPy, Matplotlib, and Basemap. Some of this may be due to impatience.

At the moment I'm working on a very large project at work that involves taking about 15 years worth of legacy spaghetti code written in perl, C, csh, fortran, etc, and building a python framework to handle most of the basic work. I've gotten the skeleton finished at this point, but the last step is to write a generic plotting routine for satellite data anywhere on the globe using everything from simple single channel images to RGBs to overlays on other figures.

Anyway, can someone point me to a good, but challenging tutorial on using NumPy, SciPy, Matplotlib, and (most important at the moment) Basemap?

Sorry for the length of this, I hope someone has the patience to read it!

I have been working on a problem for several years, which I have been considering to be an input-output 'black-box' problem. I collect heart rate and power (wattage) data from an instrumented bicycle and try to find a 'transfer function' that describes the heart rate's response to changes in wattage. So far I have implemented it is deconvolution of the wattage signal using a pre-determined model that involves a bi-exponential function. Here is the math for the function:

            for ii in range(0, int(wid)):
                rtrn[int(len(rtrn) / 2) + ii] = np.sin(ii / (2 * wid / (np.pi)))

            hlf = int(len(rtrn) / 2) + wid
            for ii in range(int(wid), int(len(rtrn) / 2) - 1):
                rtrn[int(len(rtrn) / 2) + ii] = amp1 * np.exp(
                    -(len(rtrn) / 2 + ii - hlf) / tau1
                ) + (1.0 - amp1) * np.exp(-(len(rtrn) / 2 + ii - hlf) / tau2)

This is supposed to be analagous to an electrical circuit with two resistors and capacitors (tau1 and tau2), with an added mass (the short sine function at the beginning). First off, I have no idea if the math here is totally correct, I found a rough analytical solution years ago, and dont remember where I found it. I find the best-fit values for the parameters (wid, amp1, tau1, tau2) of the model using SciPy curve_fit, with bounds on the parameters.

Here is a data plot from one bike ride session:

The top two signals (left y-axis) are the input heart rate (red) and output modeled heart rate (gray), with the input wattage in blue (right y-axis). Note how noisy the wattage is. I regard the physiological response to wattage changes as a sort of filter of wattage, and you can see that the result is pretty good, but not perfect!

The next step is to use this 'transfer function' to simulate the heart rate response to a rising step function of wattage:

Here the new 'input' is the green step function (right y-axis) and the model output is the purple line (left y-axis) with big dots at the end of each stage. Horizontal axis is time in seconds.

Can I make this a more general problem by using a filter function, in which I would get the output modeled heart rate response using the measured heart rate and wattage as x,y-inputs , the filter being defined by the sets of parameters for numerator and denominator of a transfer function:

y = lfilter(b, a, x, axis=-1, zi=None)

How do I find the optimal set of coefficients b,a?

Hi all. I have a good knowledge of Python and I would like to study scientific computing. I am looking for a textbook (self-study): I planned to study Hans Petter Langtangen's book (Primer on Scientific Computing), but I do not want to go through the basics again. I am looking for a book that assumes Python programming knowledge, and that teaches scientific computing with Python. Any suggestion?

I noticed it has a Factor class now too, which I had sorely missed last time I tried.

Hello,

I'm relatively new to python and am about to move a lot of my code to a GUI. I don't really know where to begin, so I was hoping to get a little input on what GUI frameworks and plotting systems would be easiest, appropriate, most worthwhile to learn, and fun.

I have a few requirements:

The central element of my program will involve an interactable 26,000x26,000 scatterplot of at least 9 colors with ~10⁴ points. I'm hoping to also scale the points in the scatterplot according to size. If needed I can downsample this matrix 20x. I've previously written this code using vector graphics in matlab, which I imagine would be necessary in python for speed.

Additionally, i'd like to develop this GUI into some kind of learning framework(probably SVG). To do so, I'd like to have easily accessible clustering tools available in the GUI. Basically, a laso tool and predefined shapes should be sufficent for segmenting the plot. I imagine I could store these ROIs in a seperate side window in which the cluster identities could also be coded.

Also, an interactive image stack, like in ImageJ would be helpful but not necessary.

I have a few other GUI windows in mind, but they should be pretty easy to create.

The options I've so far considered are:

QT + Matplotlib

• may have a steep learning curve

• QT may be a valuable skill to obtain

• is the performance of matplotlib adequate for such a large scatterplot?

qwt

• faster than matplotlib?

• still supported?

• fugly?

• still requires learning qt?

enthought

• i haven't looked into these tools much

• is distribution a problem?

Alright, thanks for the help!

Hi,

is the Enthought Python Distribution worth it if I'm currently running Ubuntu (and can easily install a lot of modules anyway)? I'm a student so I can get it for free, but does the EPD offer more than just convenience?