colorview2d Readme

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.

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