Merge pull request #43 from RichieHakim/dev

Dev

.github/workflows/build.yml

@@ -37,14 +37,14 @@ jobs:
           windows-2019,
           # # macos-latest,
           macos-12,
-          macos-11.0,
+          # macos-11.0,
           # macos-10.15,
         ]
         python-version: [ 
           # "3.9",
           # "3.10",
           "3.11",
-          "3.12",
+          # "3.12",
         ]
         extra: [
           all,

.gitignore

@@ -16,4 +16,12 @@ build
 .pytest_cache
 
 face_rhythm.egg-info
-dist
+dist
+
+# RH added
+.DS_Store
+data_test/
+.vscode/
+.fuse_hidden*
+__pycache__/
+.fr-*

LICENSE

@@ -1,3 +1,3 @@
 
 All rights reserved.
-Copyright (c) 2020, Rich Hakim
+Copyright (c) 2020-2024, Rich Hakim

README.md

@@ -10,9 +10,13 @@ Learn more at https://face-rhythm.readthedocs.io/
 # Installation
 
 #### 0. Requirements <br>
+- Operating system:
+  - Ubuntu >= 18.04 (other linux versions usually okay but not actively maintained)
+  - Windows >= 10
+  - Mac >= 12
 - [Anaconda](https://www.anaconda.com/distribution/) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html).
 - If using linux/unix: GCC >= 5.4.0, ideally == 9.2.0. Google how to do this on your operating system. Check with: `gcc --version`.
-- **Optional:** [CUDA compatible NVIDIA GPU](https://developer.nvidia.com/cuda-gpus) and [drivers](https://developer.nvidia.com/cuda-toolkit-archive). Using a GPU can increase ROICaT speeds ~5-50x, though without it, ROICaT will still run reasonably quick. GPU support is not available for Macs.
+- **Optional:** [CUDA compatible NVIDIA GPU](https://developer.nvidia.com/cuda-gpus) and [drivers](https://developer.nvidia.com/cuda-toolkit-archive). Using a GPU can increase the speeds for the TCA step, but is not necessary.
 - The below commands should be run in the terminal (Mac/Linux) or Anaconda Prompt (Windows).
 <br>
 
@@ -42,9 +46,9 @@ you analyze a new dataset. You may want to save a copy of the .ipynb file you us
 
 #### 2. Copy the interactive notebook to your project directory 
 We recommend copying the interactive notebook from your face-rhythm repository to your project folder each time you make a new project. This will allow you to have one notebook per project, which will keep your analyses from potentially conflicting if you run different datasets through the same notebooks. 
-**`cp /path to face-rhythm repo/face-rhythm/notebooks/interactive_pipeline_basic.ipynb.ipynb /path to project/face_rhythm_run/`**<br>
+**`cp /path to face-rhythm repo/face-rhythm/notebooks/interactive_pipeline_basic.ipynb /path to project/face_rhythm_run/`**<br>
 
-`interactive_pipeline_basic.ipynb.ipynb` is a basic demo notebook that runs through the entire pipeline.
+`interactive_pipeline_basic.ipynb` is a basic demo notebook that runs through the entire pipeline.
 See the `notebooks/other` folder for some notebooks demonstrating other kinds of analyses. These are more experimental and are subject to change as we develop new analyses. 
 
 #### 3. Open up jupyter notebook! The plots display better using Jupyter Notebook than Jupyter Lab or VSCode. <br>

batch_run/dispatcher.py

@@ -13,7 +13,7 @@
 
 # date = '20221011'
 
-# path_script = f'/n/data1/hms/neurobio/sabatini/rich/github_repos/face-rhythm/scripts/pipeline_basic.py'
+# path_script = f'/n/data1/hms/neurobio/sabatini/rich/github_repos/face-rhythm/scripts/run_pipeline_basic.py'
 # dir_save = f'/n/data1/hms/neurobio/sabatini/rich/analysis/faceRhythm/{mouse}/run_20230701/'
 # name_job = f'faceRhythm_{date}_'
 # name_slurm = f'rh_{date}'
@@ -238,7 +238,7 @@
 #SBATCH --output={path}
 #SBATCH --constraint=intel
 #SBATCH --partition=short
-#SBATCH -c 4
+#SBATCH -c 8
 #SBATCH -n 1
 #SBATCH --mem=48GB
 #SBATCH --time=0-00:30:00