https://github.com/jni/i2k-skimage-napari

Repository for the scikit-image, napari, & friends tutorial at I2K 2020
https://github.com/jni/i2k-skimage-napari

Last synced: about 1 month ago
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Repository for the scikit-image, napari, & friends tutorial at I2K 2020

• Host: GitHub
• URL: https://github.com/jni/i2k-skimage-napari
• Owner: jni
• License: bsd-3-clause
• Created: 2020-11-27T08:55:01.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2021-08-09T06:28:42.000Z (over 3 years ago)
• Last Synced: 2024-10-05T00:31:22.331Z (about 2 months ago)
• Language: Jupyter Notebook
• Size: 65.3 MB
• Stars: 5
• Watchers: 4
• Forks: 5
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# i2k-skimage-napari

Repository for the scikit-image, napari, & friends tutorial at I2K 2020

## Installation

### with conda

Use:

```

conda env create -f environment.yml

```

then

```

conda activate i2k

```

### with pip

In an environment including pip, use:

```

pip install -U -r requirements.txt

```

We recommend that you use Python 3.8 for this tutorial. Both 3.7 and 3.9 should

also work but have not been tested.

## Datasets

For the dask tutorial, we are going to be using some 3D + t datasets from the

[Cell Tracking Challenge](http://celltrackingchallenge.net/3d-datasets/),

specifically:

- the [C. elegans developing embryo training

  dataset](http://data.celltrackingchallenge.net/training-datasets/Fluo-N3DH-CE.zip)

  (3GB), **OR**, if that is too large for you to comfortably download,

- the [Chinese Hamster Ovarian (CHO) nuclei overexpressing GFP-PCNA training

  dataset](http://data.celltrackingchallenge.net/training-datasets/Fluo-N3DH-CHO.zip)

  (98MB)