colorview2d 0.6.post4

2d color plotting tool

Use colorview2d to visualize and analize 2d data with (linear) axes.

Features:

• Wide range of adjustable filters (mods) that can be extended easily. - interpolation, - Gaussian and median filters, - scale, rotate, flip, crop - thresholding to extract features, - absolute value, natural logarithm, derivation - something missing? Add a mod easily.

• Plot to pdf or just use the matplotlib figure.

• Annoyed of matplotlib.pyplots 2d colorplot interface? Simple and convenient plot configuration. - Adjust axis labels, their size and font as well as the plot size. - Easily adapt the colorbar to your needs.

• Mass extract linetraces (to depict feature evolution).

• Save cv2d config files and restore any modifications easily

• Save and load data to and from ASCII files (gnplot format)

Installation

You can use the python package index via pip

sudo pip2.7 install –upgrade colorview2d

or easy_install

sudo easy_install –upgrade colorview2d

Note that numpy can not be installed via the python package index. Please install these packages via the package manager that is shipped with your linux distribution.

If you are considering writing your own mods then installation into the userspace is preferable (access to colorview2d/mods to place the mod file).

pip2.7 install –user <username> –upgrade colorview2

Usage

I stronlgy recommend to use ipython interactive shell for this tutorial. We initialize some random data with x and y ranges:

data = np.random.random((100, 100)) xrange = (0., np.random.random()) yrange = (0., np.random.random())

Obtain a colorview2d.Datafile to initialize the colorview2d.CvFig object:

datafile = colorview2d.Datafile(data, (yrange, xrange)) cvfig = colorview2d.CvFig(datafile)

Note that the order of the ranges (y range first) is not a typo. It is reminiscent of the rows-first order of the 2d array.

What is the data about? We add some labels:

cvfig.config[‘Xlabel’] = ‘foo (f)’ cvfig.config[‘Ylabel’] = ‘bar (b)’ cvfig.config[‘Cblabel’] = ‘nicyness (n)’

Let us have a look.

cvfig.show_plt_fig()

We do not like the font and the ticks labels are too small

cvfig.config.update({‘Font’: ‘Ubuntu’, ‘Fontsize’: 16})

Also, the colormap, being default matplotlib’s jet, is not greyscale-compatible, so we change to ‘Blues’ (have a look at the matplotlib documentation to get a list of colormaps).

cvfig.config[‘Colormap’] = ‘Blues’

Its time to plot a pdf and save the config

cvfig.plot_pdf(‘Nice_unmodified.pdf’) cvfig.save_config(‘Nice_unmodified.cv2d’)

Note: Have a look at the plain text Nice_unmodified.cv2d. The config is just read as a dict. If you modify this file, changes get applied accordingly upon calling load_config if you do not misspell parameter names or options.

If you want to reuse the config next time, just use it upon initialization of the cvfig:

cvfig = cv2d.CvFig(original_datafile, cfgfile=’Nice_unmodified.cv2d’)

We realize that there is some (unphysical :) noise in the data. Nicyness does not fluctuate so much along foo or bar and our cheap nice-intstrument produced some additional fluctuations.

cvfig.add_mod(‘Smooth’, (1, 1))

also we are interested more in the change of our nice landscape and not in its absolute values so we derive along the bar axis

cvfig.add_mod(‘Derive’)

Have a look at the mods/ folder for other mods and documentation on the arguments. It is also straightforward to create your own mod there. Just have a look at the other mods in the folder.

We are interested especially in the Nicyness between 0.0 and 0.1.

cvfig.config.update({‘Cbmin’:0.0, ‘Cbmax’:0.1})

To re-use this data later (without having to invoke colorview2d again), we can store the data to a gnuplot-style plain text file.

colorview2d.fileloaders.save_gpfile(‘Nice_smooth_and_derived.dat’, cvfig.datafile)

This tutorial only covers a part of the features. More documentation on colorview2d will be added soon.

26.9.2015, Alois Dirnaichner