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2d color plotting tool

Project description

colorview2d Readme
==================

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

Features:
---------

- Interactive colorbar adjustment.
- Wide range of adjustable filters (mods) using routines from numpy, scipy and scikit.images:

- 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 plain text files (gnplot format).

Installation
------------

You can use the python package index via pip

::

sudo pip2.7 install --upgrade colorview2d

*Note*: If you receive a 'Could not find a version that satisfies...' error, try to
upgrade pip, ``pip install --upgrade pip``

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:

::

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

Obtain a :class:`colorview2d.Data` instance to initialize the :class:`colorview2d.View`
object:

::

import colorview2d
data = colorview2d.Data(data, (yrange, xrange))
view = colorview2d.View(data)

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:

::

view.config['Xlabel'] = 'foo (f)'
view.config['Ylabel'] = 'bar (b)'
view.config['Cblabel'] = 'nicyness (n)'

Let us have a look.

::

view.show_plt_fig()

You should see two figures opening, one containing the plot, the
other two simple matplotlib slider widgets to control the colorbar
interactively.

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

::

view.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).

::

view.config['Colormap'] = 'Blues'

Its time to plot a pdf and save the config

::

view.plot_pdf('Nice_unmodified.pdf')
view.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 ``view``:

::

view = cv2d.View(original_data, 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.

::

view.add_Smooth(1, 1)

This call is a shortcut to ``view.add_mod('Smooth', (1, 1))``.
Note that all mods found in the ``colorview2d/mods`` folder can be called
by ``add_<Modname>(arg1, arg2, ...)``.
Now we are interested more in the change of our nice landscape and not
in its absolute values so we derive along the bar axis

::

view.add_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.

::

view.config.update({'Cbmin':0.0, 'Cbmax':0.1})

Alternatively, just use the slider in the second matplotlib figure to control the colorbar
limits.

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', view.data)

Extending colorview2d
---------------------

fileloaders
~~~~~~~~~~~

Have a look at the :class:`colorview2d.Data` definition in the :module:`colorview2d.data`
module. To create ``Data`` we have to provide the 2d array and the
bounds of the y and x ranges.

::

data = colorview2d.Data(
array,
((bottom_on_y_axis, top_on_y_axis),
(left_on_x_axis, right_on_x_axis)))

To save data, just use the ``Data`` attributes, e.g.

::

my_array = my_view.data.zdata # 2d numpy.array
my_x_range = my_view.data.x_range # 1d numpy.array (left-to-right)
my_y_range = my_view.data.y_range # 1d numpy.array (bottom-to-top)

mods
~~~~

If you want to apply your own modifications to the ``data``, just put a
module inside the ``colorview2d/mods`` directory (or package, if you
wish). The module should contain a class
(with the class name becoming the name of the mod)
which inherits from
:class:`colorview2d.IMod` and implements the method
``do_apply(self, data, modargs)``.

This method is also the right place to document your mods usage, i.e., the
required arguments. The docstring of ``<Modname>.do_apply``, where ``<Modname>`` is the class's name,
is displayed when you call

::

help(view.add_<Modname>())

In ``do_apply(self, data, modargs)`` you can modifiy the datafile freely,
there is no error-checking done on
the consistency of the data (axes bounds, dimensions). Have a look at
the ``mods/Derive.py`` module for a *minimal* example.

To see if your mod is added successfully, have a look at
``my_view.modlist``.

6.10.2015, A. Dirnaichner

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