Explore dataframes, arrays, scipy and matplotlib interactively w/o coding
To install use pip:
$ pip install jupyter_petrova
For a development installation (requires Node.js and Yarn version 1),
$ git clone https://github.com/redhog/jupyter-petrova.git
$ cd jupyter-petrova
$ pip install -e .
$ jupyter nbextension install --py --symlink --overwrite --sys-prefix jupyter_petrova
$ jupyter nbextension enable --py --sys-prefix jupyter_petrova
When actively developing your extension for JupyterLab, run the command:
$ jupyter labextension develop --overwrite jupyter_petrova
Then you need to rebuild the JS when you make a code change:
$ cd js
$ yarn run build
You then need to refresh the JupyterLab page when your javascript changes.
In a notebook cell, enter:
from jupyter_petrova import *
g = Graph()
g
Use the "Add" field to select python functions to add. A good starting
point might be skimage.io.imread and anything under
skimage.filters. Click and drag to move boxes on the board. Click on
a function box to set its input parameters and view its output. To set
a parameter to the output of another box, select the input field for
that parameter, then shift-click the other box.
To pre-populate the graph,
test = Task("skimage.io.imread", fname="test.jpeg")
filtered = Task("skimage.filters.edges.sobel", image=test)
g = Graph(tasks = {"test": test, "filtered": filtered})
g
Individual tasks can be accessed using the dictionary g.tasks. Each
task has a property value that contains the output value of that
task. The function name is available in the task property name, and
the parameters in params.