updated workflow and a bit of docs

.github/workflows/pages.yml

@@ -20,6 +20,9 @@ concurrency:
  group: "pages"
  cancel-in-progress: true
 
+env:
+ TQDM_DISABLE: 1
+
 jobs:
  # Single deploy job since we're just deploying
  deploy:
@@ -43,7 +46,7 @@ jobs:
  with:
  run: |
  pip install -r requirements.txt
- export TQDM_DISABLE=1; jupyter book build tutorial
+ jupyter book build tutorial
  - name: Upload artifact
  uses: actions/upload-pages-artifact@v3
  with:

environment.yml

@@ -23,4 +23,5 @@ dependencies:
  - pip:
  - py-ctcmetrics
  - napari
- - ultrack
+ - git+https:/royerlab/ultrack
+ - napari-plot-profile

requirements.txt

@@ -6,4 +6,4 @@ napari-animation
 matplotlib
 py-ctcmetrics
 cellpose
-ultrack
+git+https:/royerlab/ultrack

tutorial/installation.md

@@ -10,8 +10,10 @@ we will try to help you get unstuck.
 In this tutorial, we will install Python via miniforge, a distribution of
 Python based in the [conda package manager](https://docs.conda.io/en/latest/).
 
+```{note}
 If you already have anaconda, miniconda, or miniforge installed, those will work
 as well and you can skip to the bottom of this page to set up the conda environment.
+```
 
 1. In your web browser, navigate to the
  [miniforge page](https:/conda-forge/miniforge). 

tutorial/tracking.md

@@ -4,7 +4,7 @@ jupytext:
  text_representation:
  extension: .md
  format_name: myst
- format_version: 1.0.4
+ format_version: 0.13
  jupytext_version: 1.16.4
 kernelspec:
  display_name: Python 3 (ipykernel)
@@ -14,18 +14,19 @@ kernelspec:
 
 # 2D cell tracking with multiple hypotheses
 
++++
+
 This tutorial shows Ultrack's multiple hypotheses tracking capabilities. 
 
-Here, rather than searching for an optimal segmentation parameter, we sampled multiple segmentations with different parametrizations and used Ultrack to find the best segments, obtaining more accurate cell tracking.
+We will start with a simple classical image processing solution with ultrack and tracking cells for image intensities directly. Then, we will progress to using Cellpose and finally use multiple Cellpose segmentations to improve tracking performance without training our own segmentation model showcasing the improvement when a single segmentation isn't good enough.
 
 ## Download data
 
 Download the Fluo-C2DL-Huh7 dataset from the [Cell Tracking Challenge](celltrackingchallenge.net), which contains fluorescence microscopy images for cell tracking.
 
 The dataset will be used for demonstrating the segmentation and tracking workflow.
 
-
-```{code-cell} python
+```{code-cell} ipython3
 :tags: [remove-output]
 
 !wget -nc http://data.celltrackingchallenge.net/training-datasets/Fluo-C2DL-Huh7.zip