matrix-vis

This program visualizes multidimensional NumPy matrices in the following way:

One (for 2D output) or two (for 3D output) dimensions are chosen as the input axes for the output plot. In the example config (in visualize.py), the third to last dimension is set as the x axis, the penultimate dimension is set as the y axis (and the last dimension is set as data series list, see the sequel), see the lines

# we can filter row indices using e.g. [1, 3] instead of SLICE_ALL
SLICE_ALL = slice(None, None, None)
config[-3] = 'x', SLICE_ALL
config[-2] = 'y', SLICE_ALL

One dimension is chosen as the list of data series. The data series can be filtered and scaled (see the configuration line of code config[-1] = [(0, 1)]: the first data series is displayed scaled with the ratio of 1, i.e. no scaling).

From the rest of dimensions, constant indexes of the displayed rows are chosen. In our example config, the indexes are set to zeroes:

config = [0 for _ in dimensions]

This configuration can be fixed in the code (see the config variable), but you can also dynamically control the graph via Qt GUI (configurator.py).

The input data format is shown in the comprehensive create_example_data.py

Note: The row labels in the data file (dimension definition) are used only in the Qt GUI and in the legend. The input axes in the plot are labelled with numbers 0, 1, ...

Run the example:

Python 3.7.4 was used for the development. Install the requirements (ideally in a virtual environment):

pip install -r requirements.txt

Open two terminals and run each of the following commands in one of them:

python3 create_example_data.py | python3 visualize.py

python3 create_example_data.py | python3 configurator.py

Second format

If you don't like the create_example_data.py approach because of the binary b64-encoded numpy data inside, feel free to express your data using a more JSONic way, as in example.json:

cat example.json | python3 visualize.py

cat example.json | python3 configurator.py