Usage¶

To use DLITE in a project:
```
import DLITE
```
To visualize how you can use DLITE, run the example notebooks that are available with the release:
```
jupyter notebook
```

Here, we provide a brief description of each notebook

Generate_Voronoi_data.ipynb¶

Generate random Voronoi tessellations and save as .txt files in a format required by Surface Evolver. This format is described below:

STRING/
space_dimension 2/
vertices/
  |- ...
edges/
  |- ...
faces/
  |- ...
bodies/
  |- ...
read/
gogo/

Demo_notebook_SurfaceEvolver.ipynb¶

Predict tensions in a time-series of synthetic data generated by Surface Evolver. Here, we generate a time-series by stitching together multiple independent Surface Evolver outputs. Each output is a .txt file. For an example of such outputs, take a look at the data in:

/Data/Synthetic_data

For details on how to run Surface Evolver, refer to the documentation.

Demo_notebook_ZO-1.ipynb¶

Predict tensions in a time-series of ZO-1 data. Here, we take tracing outputs generated by the NeuronJ plugin in ImageJ.

The outputs generated by NeuronJ are in the following format:

Tracing N1:/
  |- ...
Tracing N2:/
  |- ...
...

For an example of such outputs, take a look at the data in:

Data/ZO-1_data

Compare_CELLFIT_DLITE.ipynb¶

DLITE offers you the option to compute tensions using CELLFIT. Compare_CELLFIT_DLITE.ipynb helps visualize the differences between the two methods. Datasets available are those described in our paper.

These datasets are organized as:

Data/
  |- Synthetic_data/
     |- Fig_2/
        |- ...
     |- Fig_3/
        |- ...
     |- Fig_5/
        |- ...
     |- FOV_drift/
        |- ...
     |- SOM_example_1/
        |- ...
     |- SOM_example_2/
        |- ...
  |- ZO-1_data/
     |- Time-series_1/
        |- ...
     |- Time-series_2/
        |- ...
     |- Time-series_3/
        |- ...
     |- Time-series_4/
        |- ...

FOV_drift.ipynb¶

Take a single colony image and split it up into multiple smaller panels so as to simulate field of view (FOV) drift.