update readme

.circleci/config.yml

@@ -49,6 +49,7 @@ commands:
           command: |
             python3 -m venv venv
             . venv/bin/activate
+            pip install --upgrade pip
             pip install pytest pytest-cov black[jupyter]
             pip install -e .
   fork_test:

docs/notebooks/pipelines/BrainLine/README.md

@@ -1,15 +1,23 @@
-1. Install [Python 3.8](https://www.python.org/downloads/)
-2. Install virtualenv `pip install virtualenv`
-3. Make virtual environment `<python path> -m virtualenv <path to new virtual environment>`
-4. Activate virtual environment e.g. `source <path to new virtual environment>/bin/activate`
-5. Clone the repository `git clone https://github.com/neurodata/brainlit.git`
-5. Install brainlit `pip install brainlit` or `cd brainlit && pip install -e .`
-6. Install [ilastik](https://www.ilastik.org/)
-7. Setup jupyter notebooks either by installing [VSCode](https://code.visualstudio.com/download) or [Jupyter Notebooks](https://jupyter.org/install)
-8. Run the `soma_analysis.ipynb` or `axon_analysis.ipynb`
+TACC Tutorial
+-------------
+
+In Jupyter session on Lonestar 6:
+
+1. In home directory (`cdh`) make python 3.9 virtual environment: `python3 -m venv venv_39`.
+2. Activate virtual environment: `source venv_39/bin/activate`.
+3. Update pip `pip install --upgrade pip`
+4. In scratch directory (`cds`), download ilastik (`wget https://files.ilastik.org/ilastik-1.4.0-Linux.tar.bz2`).
+5. Decompress the ilastik file (`tar –xvf ilastik-1.4.0-Linux.tar.bz2`).
+6. Clone brainlit repository (`git clone https://github.com/neurodata/brainlit.git`).
+7. Install brainlit from source in editable mode: `cd brainlit && pip install -e .`.
+8. Install packages I use for this tutorial: `pip install matplotlib-scalebar jupyter`.
+9. Go back to the home directory (`cdh`) and copy the notebook I made: `cp /home1/09423/tathey1/brainline-tacc-tutorial.ipynb .`.
+10. Create jupyter kernel for this virtual environment: `ipython kernel install --name "venv_39" --user`.
+11. Open the jupyter notebook and select `venv_39` as the kernel.
+12. As you run the notebook, you will need to change a couple variables including `brainlit_path` and `ilastik_path` according to your scratch directory path.
 
 
 Atlas
 -----
 
-If you plan on using BrainLine analysis a lot (in particular, the napari coronal section views), I recommend you download the atlas from [here](https://neurodata.io/data/allen_atlas/).
+If you plan on using BrainLine analysis a lot (in particular, the napari coronal section views), I recommend you download the atlas from [here](https://neurodata.io/data/allen_atlas/).

docs/notebooks/pipelines/BrainLine/soma_analysis.ipynb

@@ -552,7 +552,7 @@
    "metadata": {},
    "source": [
     "```\n",
-    "python -m cloudreg.scripts.registration -input_s3_path precomputed://s3://smartspim-precomputed-volumes/2022_02_02/8604/Ch_561 --output_s3_path precomputed://s3://smartspim-precomputed-volumes/2022_02_02/8604/atlas_to_target --atlas_s3_path https://open-neurodata.s3.amazonaws.com/ara_2016/sagittal_50um/average_50um --parcellation_s3_path https://open-neurodata.s3.amazonaws.com/ara_2016/sagittal_10um/annotation_10um_2017 --atlas_orientation PIR -orientation ARS --rotation 0 0 0 --translation 0 0 0 --fixed_scale .7 -log_s3_path precomputed://s3://smartspim-precomputed-volumes/2022_02_02/8604/atlas_to_target --missing_data_correction True --grid_correction False --bias_correction True --regularization 5000.0 --iterations 3000 --registration_resolution 100\n",
+    "python -m cloudreg.scripts.registration -input_s3_path precomputed://s3://smartspim-precomputed-volumes/2023_04_13/MS36/Ch_561 --output_s3_path precomputed://s3://smartspim-precomputed-volumes/2023_04_13/MS36/atlas_to_target --atlas_s3_path https://open-neurodata.s3.amazonaws.com/ara_2016/sagittal_50um/average_50um --parcellation_s3_path https://open-neurodata.s3.amazonaws.com/ara_2016/sagittal_10um/annotation_10um_2017 --atlas_orientation PIR -orientation RPI --rotation 0 0 0 --translation 0 0 0 --fixed_scale 1 -log_s3_path precomputed://s3://smartspim-precomputed-volumes/2023_04_13/MS36/atlas_to_target --missing_data_correction True --grid_correction False --bias_correction True --regularization 5000.0 --iterations 3000 --registration_resolution 100\n",
     "```"
    ]
   },

netlify.toml

@@ -11,7 +11,7 @@
   publish = "docs/_build/html/"
 
   # Default build command.
-  command = "bash ./.aws.sh; pip install -U pip setuptools; pip install -r docs/requirements.txt; pip install -e .; cd docs; make html; cd .."
+  command = "bash ./.aws.sh; pip install --upgrade pip; pip install -U pip setuptools; pip install -r docs/requirements.txt; pip install -e .; cd docs; make html; cd .."
   # sudo apt-get update; sudo apt-get install python3.8 python3-pip; 
 
   # Directory with the serverless Lambda functions to deploy to AWS.