diff --git a/README.md b/README.md index a1dcc6f..bf56260 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# 🔥 torch_brain +# torch_brain **torch_brain** is a Python library for various deep learning models designed for neuroscience. @@ -16,12 +16,6 @@ used as follows: pip install -e ".[xformers]" ``` -## Documentation -> [!WARNING] -> The docs are hosted publically for convenience, please do not share the link. - -You can find the documentation for this package [here](https://chic-dragon-bc9a04.netlify.app/). - ## Contributing If you are planning to contribute to the package, you can install the package in development mode by running the following command: diff --git a/configs/dataset/allen_neuropixels.yaml b/configs/dataset/allen_neuropixels.yaml deleted file mode 100644 index 64cce78..0000000 --- a/configs/dataset/allen_neuropixels.yaml +++ /dev/null @@ -1,232 +0,0 @@ -##### BRAIN OBERVATORY 1.1 SESSIONS #### -# these sessions have all -- selection: - - dandiset: "allen_visual_behavior_neuropixels" - sortsets: - - mouse_723627604_20181026 - - mouse_722882755_20181026 - - mouse_719817805_20180925 - - mouse_726170935_20181026 - - mouse_726141251_20180925 - - mouse_726162197_20181026 - - mouse_732548380_20180925 - - mouse_719828690_20180925 - - mouse_726298253_20181026 - - mouse_730760270_20181026 - - mouse_734865738_20181026 - - mouse_733457989_20181026 - - mouse_730756780_20181026 - - mouse_735109609_20181031 - - mouse_740268986_20181026 - - mouse_739783171_20190119 - - mouse_738651054_20181026 - - mouse_742714475_20181026 - - mouse_745276236_20181026 - - mouse_744915204_20181026 - - mouse_742602892_20181206 - - mouse_757329624_20181210 - - mouse_769360779_20190108 - - mouse_776061251_20190108 - - mouse_775876828_20181221 - - mouse_772616823_20190108 - config: - multitask_readout: - - decoder_id: GABOR_POS_2D - normalize_mean: - - 4.00198751 - - 3.99451069 - normalize_std: - - 2.58303208 - - 2.57868835 - metrics: - - metric: r2 - - decoder_id: GABOR_ORIENTATION - metrics: - - metric: accuracy - - decoder_id: DRIFTING_GRATINGS - metrics: - - metric: accuracy - - decoder_id: DRIFTING_GRATINGS_TEMP_FREQ - metrics: - - metric: accuracy - - decoder_id: RUNNING_SPEED - normalize_mean: 10.97099596 - normalize_std: 19.75150106 - metrics: - - metric: r2 - - decoder_id: GAZE_POS_2D - normalize_mean: - - 4.89581651 - - -1.97602398 - normalize_std: - - 1.26124560 - - 1.19413048 - metrics: - - metric: r2 - - decoder_id: PUPIL_SIZE_2D - normalize_mean: - - 43.09496815 - - 46.94002598 - normalize_std: - - 13.91880302 - - 15.23047149 - metrics: - - metric: r2 - - decoder_id: STATIC_GRATINGS - metrics: - - metric: accuracy - - decoder_id: NATURAL_SCENES - metrics: - - metric: accuracy - -# these sessions have only ('drifting_gratings', 'gabors', 'static_gratings', 'velocity') -# i.e, missing gaze and pupil info -- selection: - - dandiset: "allen_visual_behavior_neuropixels" - sortsets: - - mouse_699733581_20190119 - - mouse_703279284_20190108 - - mouse_707296982_20190108 - - mouse_717038288_20190108 - - mouse_718643567_20180925 - - mouse_716813543_20190108 - config: - multitask_readout: - - decoder_id: GABOR_POS_2D - normalize_mean: - - 4.00198751 - - 3.99451069 - normalize_std: - - 2.58303208 - - 2.57868835 - metrics: - - metric: r2 - - decoder_id: GABOR_ORIENTATION - metrics: - - metric: accuracy - - decoder_id: DRIFTING_GRATINGS - metrics: - - metric: accuracy - - decoder_id: DRIFTING_GRATINGS_TEMP_FREQ - metrics: - - metric: accuracy - - decoder_id: RUNNING_SPEED - normalize_mean: 10.97099596 - normalize_std: 19.75150106 - metrics: - - metric: r2 - - decoder_id: STATIC_GRATINGS - metrics: - - metric: accuracy - - decoder_id: NATURAL_SCENES - metrics: - - metric: accuracy - - -##### FUNCTIONAL CONNECTIVITY SESSIONS ###### - -# these sessions have only ('drifting_gratings', 'gabors', 'gaze', 'pupil', 'velocity') -# i.e missing static_gratings info -- selection: - - dandiset: "allen_visual_behavior_neuropixels" - sortsets: - - mouse_744912849_20190102 - - mouse_753795610_20190102 - - mouse_754488979_20181105 - - mouse_756578435_20181105 - - mouse_759711152_20181210 - - mouse_760938797_20181210 - - mouse_759674770_20181210 - - mouse_760960653_20181210 - - mouse_760946813_20181210 - - mouse_763884103_20181210 - - mouse_763236014_20181210 - - mouse_763808604_20190108 - - mouse_769319624_20190108 - - mouse_774672366_20190119 - - mouse_791857608_20190201 - - mouse_800249587_20190208 - - mouse_795770036_20190208 - - mouse_800250057_20190214 - - mouse_811322619_20190305 - - mouse_803390291_20190306 - - mouse_813701562_20190311 - - mouse_817060751_20190319 - - mouse_821469666_20190321 - - mouse_827809884_20190408 - config: - multitask_readout: - - decoder_id: GABOR_POS_2D - normalize_mean: - - 4.00198751 - - 3.99451069 - normalize_std: - - 2.58303208 - - 2.57868835 - metrics: - - metric: r2 - - decoder_id: GABOR_ORIENTATION - metrics: - - metric: accuracy - - decoder_id: DRIFTING_GRATINGS - metrics: - - metric: accuracy - - decoder_id: DRIFTING_GRATINGS_TEMP_FREQ - metrics: - - metric: accuracy - - decoder_id: RUNNING_SPEED - normalize_mean: 10.97099596 - normalize_std: 19.75150106 - metrics: - - metric: r2 - - decoder_id: GAZE_POS_2D - normalize_mean: - - 4.89581651 - - -1.97602398 - normalize_std: - - 1.26124560 - - 1.19413048 - metrics: - - metric: r2 - - decoder_id: PUPIL_SIZE_2D - normalize_mean: - - 43.09496815 - - 46.94002598 - normalize_std: - - 13.91880302 - - 15.23047149 - metrics: - - metric: r2 - - -# only ('drifting_gratings', 'gabors', 'velocity') -- selection: - - dandiset: "allen_visual_behavior_neuropixels" - sortsets: - - mouse_754477358_20181105 - - mouse_820866121_20190321 - config: - multitask_readout: - - decoder_id: GABOR_ORIENTATION - metrics: - - metric: accuracy - - decoder_id: GABOR_POS_2D - normalize_mean: - - 4.00198751 - - 3.99451069 - normalize_std: - - 2.58303208 - - 2.57868835 - metrics: - - metric: r2 - - decoder_id: DRIFTING_GRATINGS - metrics: - - metric: accuracy - - decoder_id: DRIFTING_GRATINGS_TEMP_FREQ - metrics: - - metric: accuracy - - decoder_id: RUNNING_SPEED - normalize_mean: 10.97099596 - normalize_std: 19.75150106 - metrics: - - metric: r2 \ No newline at end of file diff --git a/configs/dataset/allen_neuropixels_dg.yaml b/configs/dataset/allen_neuropixels_dg.yaml deleted file mode 100644 index 13455de..0000000 --- a/configs/dataset/allen_neuropixels_dg.yaml +++ /dev/null @@ -1,13 +0,0 @@ -# these sessions have all -- selection: - - dandiset: "allen_visual_behavior_neuropixels" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - metrics: - - metric: accuracy - - decoder_id: DRIFTING_GRATINGS_TEMP_FREQ - metrics: - - metric: accuracy diff --git a/configs/dataset/allen_neuropixels_ns.yaml b/configs/dataset/allen_neuropixels_ns.yaml deleted file mode 100644 index 66a2d45..0000000 --- a/configs/dataset/allen_neuropixels_ns.yaml +++ /dev/null @@ -1,43 +0,0 @@ -- selection: - - dandiset: "allen_visual_behavior_neuropixels" - sortsets: # brain_observatory_1.1 - - 'mouse_699733581_20190119' - - 'mouse_703279284_20190108' - - 'mouse_707296982_20190108' - - 'mouse_717038288_20190108' - - 'mouse_718643567_20180925' - - 'mouse_716813543_20190108' - - 'mouse_723627604_20181026' - - 'mouse_722882755_20181026' - - 'mouse_719817805_20180925' - - 'mouse_726170935_20181026' - - 'mouse_726141251_20180925' - - 'mouse_726162197_20181026' - - 'mouse_732548380_20180925' - - 'mouse_719828690_20180925' - - 'mouse_726298253_20181026' - - 'mouse_730760270_20181026' - - 'mouse_734865738_20181026' - - 'mouse_733457989_20181026' - - 'mouse_730756780_20181026' - - 'mouse_735109609_20181031' - - 'mouse_740268986_20181026' - - 'mouse_739783171_20190119' - - 'mouse_738651054_20181026' - - 'mouse_742714475_20181026' - - 'mouse_745276236_20181026' - - 'mouse_744915204_20181026' - - 'mouse_742602892_20181206' - - 'mouse_757329624_20181210' - - 'mouse_769360779_20190108' - - 'mouse_776061251_20190108' - - 'mouse_775876828_20181221' - - 'mouse_772616823_20190108' - - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.natural_scenes.coalesce() - multitask_readout: - - decoder_id: NATURAL_SCENES - metrics: - - metric: accuracy \ No newline at end of file diff --git a/configs/dataset/churchland_shenoy_neural_2012.yaml b/configs/dataset/churchland_shenoy_neural_2012.yaml deleted file mode 100644 index 5be9ea6..0000000 --- a/configs/dataset/churchland_shenoy_neural_2012.yaml +++ /dev/null @@ -1,16 +0,0 @@ -- selection: - - dandiset: churchland_shenoy_neural_2012 - config: - multitask_readout: - - decoder_id: CURSORVELOCITY2D - subtask_weights: - REACHING.RANDOM: 1.0 - REACHING.HOLD: 0.1 - REACHING.REACH: 5.0 - REACHING.RETURN: 1.0 - REACHING.INVALID: 0.1 - REACHING.OUTLIER: 0.0 - metrics: - - metric: r2 - task: REACHING - subtask: REACHING.REACH diff --git a/configs/dataset/gillon_richards_responses_2023.yaml b/configs/dataset/gillon_richards_responses_2023.yaml deleted file mode 100644 index 8e23f66..0000000 --- a/configs/dataset/gillon_richards_responses_2023.yaml +++ /dev/null @@ -1,7 +0,0 @@ -- selection: - - dandiset: "gillon_richards_responses_2023" - config: - multitask_readout: - - decoder_id: GABOR_ORIENTATION - metrics: - - metric: accuracy diff --git a/configs/dataset/mc_maze_small.yaml b/configs/dataset/mc_maze_small.yaml index 3ecf6e9..c2d8a81 100644 --- a/configs/dataset/mc_maze_small.yaml +++ b/configs/dataset/mc_maze_small.yaml @@ -1,8 +1,8 @@ - selection: - - dandiset: "mc_maze_small" + - brainset: "mc_maze_small" config: multitask_readout: - - decoder_id: ARMVELOCITY2D + - readout_id: arm_velocity_2d subtask_weights: REACHING.RANDOM: 1.0 REACHING.HOLD: 1.0 @@ -11,5 +11,7 @@ REACHING.INVALID: 1.0 REACHING.OUTLIER: 0.1 metrics: - - metric: r2 + - metric: + _target_: torchmetrics.R2Score + num_outputs: 2 task: REACHING diff --git a/configs/dataset/perich_miller_population_2018.yaml b/configs/dataset/perich_miller_population_2018.yaml index b896d91..1ffccd3 100644 --- a/configs/dataset/perich_miller_population_2018.yaml +++ b/configs/dataset/perich_miller_population_2018.yaml @@ -91,7 +91,9 @@ REACHING.INVALID: 0.1 REACHING.OUTLIER: 0.0 metrics: - - metric: r2 + - metric: + _target_: torchmetrics.R2Score + num_outputs: 2 task: REACHING subtask: REACHING.REACH @@ -131,6 +133,8 @@ REACHING.INVALID: 0.1 REACHING.OUTLIER: 0.0 metrics: - - metric: r2 + - metric: + _target_: torchmetrics.R2Score + num_outputs: 2 task: REACHING subtask: REACHING.RANDOM diff --git a/configs/dataset/single_allen_715093703.yaml b/configs/dataset/single_allen_715093703.yaml deleted file mode 100644 index c2573e2..0000000 --- a/configs/dataset/single_allen_715093703.yaml +++ /dev/null @@ -1,24 +0,0 @@ -- selection: - - dandiset: "allen_visual_behavior_neuropixels" - sortsets: - - mouse_699733581_20190119 - config: - multitask_readout: - - decoder_id: GABOR_POS_2D - metrics: - - metric: r2 - - decoder_id: GABOR_ORIENTATION - metrics: - - metric: accuracy - - decoder_id: DRIFTING_GRATINGS - metrics: - - metric: accuracy - - decoder_id: DRIFTING_GRATINGS_TEMP_FREQ - metrics: - - metric: accuracy - - decoder_id: RUNNING_SPEED - metrics: - - metric: r2 - - decoder_id: STATIC_GRATINGS - metrics: - - metric: accuracy \ No newline at end of file diff --git a/configs/model/poyo_galaxy.yaml b/configs/model/poyo_galaxy.yaml deleted file mode 100644 index a00ac15..0000000 --- a/configs/model/poyo_galaxy.yaml +++ /dev/null @@ -1,11 +0,0 @@ -_target_: torch_brain.models.POYOPlus -dim: 1024 -dim_head: 64 -num_latents: 128 -depth: 24 -output_dim: 2 -cross_heads: 4 -self_heads: 8 -ffn_dropout: 0.2 -lin_dropout: 0.4 -atn_dropout: 0.2 \ No newline at end of file diff --git a/environments/README.md b/environments/README.md deleted file mode 100644 index 5e5876e..0000000 --- a/environments/README.md +++ /dev/null @@ -1,3 +0,0 @@ -# Environments - -This contains environment-specific training shell scripts and documentation. \ No newline at end of file diff --git a/environments/cc/README.md b/environments/cc/README.md deleted file mode 100644 index 6fe4aef..0000000 --- a/environments/cc/README.md +++ /dev/null @@ -1,78 +0,0 @@ -# Running on Narval - -Running on Narval requires one to recreate an appropriate environment. - -## Installing via conda and pip - -A standard route is to install `conda` and `pip`. This requires internet access and has to be done on a login node. You can use `build_env_cc.sh` for that. Typically, `requirements_cc.txt` will be out-of-date, so you will need to recreate from its current state using `requirements.txt` as a template. A typical set of steps might include: - -* Removing torch and pydantic as dependencies (handled by conda) -* Removing the pandas version qualifier (conflicts with compute-canada-built packages) - -Then, you have to *both* load the right Python module and activate the right conda environment, e.g.: - -``` -source ~/.bashrc # To make conda available -load module python/3.9.6 -conda activate poyo -``` - -A symptom of not doing the latter is e.g. getting glibc errors when loading some libraries, e.g. h5py. To run the allen sdk, you will in addition need to `module load postgresql`. - -After that, you can pip install the module via regular means, i.e. `pip install -e .`. - -## Pre-packaged environment - -[Conda pack](https://conda.github.io/conda-pack/) is an interesting alternative that could be used, eventually, to build an environment on the mila cluster and ship it to Narval. We have not confirmed that it works, however. - -## Using offline wandb - -If you're seeing this network error: `Network error (ConnectionError), entering retry loop`, -this might mean that your network is slow or blocking the connection to the wandb servers. - -You can run the training in offline mode by setting the `WANDB_mode` environment variable to `offline`: -```bash -WANDB_mode=offline CUDA_VISIBLE_DEVICES=0 python train.py --config-name train_mc_maze_small log_dir=./logs -``` - -Be sure to specify the `log_dir` argument to save the wandb logs where you can access them -later (i.e. in a directory that is not deleted after the training is done, or a node -is terminated). - -In CC, this directory is scratch. - -The following can be run from the login node or any node that has access to the logs -directory and to a stable internet connection: - -In stdout, you can identify the wandb run ID, which you will use to sync the run. If you -have a log file you can do the following: -```bash -grep "Wandb ID: " log_file.log -``` -which should output something like: -```bash -Wandb ID: 2tfkbhxk -``` - -You can then use this to find the run folder in the logs directory (replace `./logs/` with the path to the logs directory): -```bash -ls ./logs/wandb/ | grep "2tfkbhxk" -``` -which should output something like: -```bash -offline-run-20240426_135211-2tfkbhxk -``` - -Note that if you didn't get the wandb ID, you can use the timestamp to find the run folder. - -If your run is done, you can sync the run with the following command: -```bash -wandb sync ./logs/wandb/offline-run-20240426_135211-2tfkbhxk -``` - -But you probably want to track your training, which you can do by scheduling a sync every 5 minutes: -```bash -watch -n 300 wandb sync ./logs/wandb/offline-run-20240426_135211-2tfkbhxk -``` - -The run should now be visible in the wandb dashboard `https://wandb.ai/neuro-galaxy/poyo/runs/2tfkbhxk` diff --git a/environments/cc/build_env_cc.sh b/environments/cc/build_env_cc.sh deleted file mode 100755 index 0911904..0000000 --- a/environments/cc/build_env_cc.sh +++ /dev/null @@ -1,36 +0,0 @@ -#!/bin/bash -# This should be run on a login node. -# Very important: need to use module load and conda install with the SAME python version. -set -e -ENV_NAME=testenv -PY_VERSION=3.9.6 -module load python/$PY_VERSION - -# Check if miniconda is already installed -if [ ! -d "$HOME/miniconda3" ] || [ ! -x "$HOME/miniconda3/bin/conda" ]; then - mkdir -p ~/miniconda3 - wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh - bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3 - rm -rf ~/miniconda3/miniconda.sh - ~/miniconda3/bin/conda init bash -else - echo "Miniconda already installed." - ~/miniconda3/bin/conda init bash -fi - -~/miniconda3/bin/conda init bash -conda create -y -n $ENV_NAME python=$PY_VERSION -source ~/.bashrc -conda activate $ENV_NAME - -module load postgresql # For psycopg2 and ultimately allensdk -module load cuda/11.7 - -# Very hard to install with pip, has a hidden Rust dependency, needed by lightning -conda install -y -c conda-forge pydantic=1.10 -conda install -y -c anaconda lz4 -# Kill the existing pytorch and replace with the right one. -conda install -y pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia -# Now install regular requirements -pip install appdirs -pip install -r requirements_cc.txt \ No newline at end of file diff --git a/environments/cc/copy_data.sh b/environments/cc/copy_data.sh deleted file mode 100755 index 21fb0e1..0000000 --- a/environments/cc/copy_data.sh +++ /dev/null @@ -1,7 +0,0 @@ -#!/bin/bash -source ~/.bashrc -module load python/3.9.6 -conda activate poyo - -# Not thread-safe! -snakemake --nolock --rerun-triggers=mtime --config tmp_dir="$SLURM_TMPDIR" -c1 "$1" diff --git a/environments/cc/requirements_cc.txt b/environments/cc/requirements_cc.txt deleted file mode 100644 index 894968b..0000000 --- a/environments/cc/requirements_cc.txt +++ /dev/null @@ -1,35 +0,0 @@ -pandas -matplotlib~=3.7.0 -seaborn~=0.13 -scipy~=1.10.1 -pynwb~=2.2.0 -einops~=0.6.0 -pytest~=7.2.1 -h5py~=3.8.0 -six~=1.16.0 -sympy~=1.11.1 -scikit-learn~=1.2.1 -networkx~=3.0 -jsonschema~=4.17.3 -scikit-image~=0.19.3 -tqdm~=4.64.1 -setuptools~=60.2.0 -bokeh~=3.0.3 -PyYAML~=6.0 -rich==13.3.2 -torch-optimizer==0.3.0 -absl-py==1.2.0 -tensorboard~=2.13 -hydra-core~=1.3.2 -lightning==2.0.8 -wandb~=0.15 -tabulate~=0.9 -zenodo-get~=1.4.0 -pympler~=1.0.1 -msgpack~=1.0.5 -snakemake~=7.32.3 -torchtyping~=0.1 -torchtext>=0.15.0,<0.16.0 # required, from here: https://github.com/pytorch/text#installation -mne~=1.5 -allensdk -xformers \ No newline at end of file diff --git a/environments/cc/run_multinode.sh b/environments/cc/run_multinode.sh deleted file mode 100755 index 06a9f56..0000000 --- a/environments/cc/run_multinode.sh +++ /dev/null @@ -1,68 +0,0 @@ -#!/bin/bash -#SBATCH --job-name=multi-run -#SBATCH --output=slurm_output_%j.txt -#SBATCH --error=slurm_error_%j.txt -#SBATCH --nodes=2 -#SBATCH --ntasks-per-node=1 -#SBATCH --cpus-per-task=6 -#SBATCH --gres=gpu:a100:1 -#SBATCH --mem=124GB -#SBATCH --time=1:00:00 -#SBATCH --switch=1 - -set -e -# For training, one can also use the following options: -#E.g. SBATCH --partition=unkillable and SBATCH --gres=gpu:a100 - -dataset=perich_multi_session - -source ~/.bashrc -module load python/3.9.6 -module load cuda/11.2 -module load httpproxy -conda activate poyo - -# wandb credentials -set -a -source .env -set +a - -# Unpack data to $SLURM_TMPDIR. This needs to be done once per node. -# However, there is a slim chance the prepared data is stale, and if multiple nodes -# try to reconstruct the data and to write to the same place to disk at the same time -# we'd have a bad time. Hence we run snakemake on the master node --until the unfreeze -# rule, then run the thread-safe unfreeze rule proper on each node. Note that -# srun fires of one process per node. -rule=perich_miller_unfreeze -snakemake --rerun-triggers=mtime --config tmp_dir="$SLURM_TMPDIR" -c1 --until $rule -srun copy_data.sh $rule - -export CUDA_VISIBLE_DEVICES=0,1,2,3 -export NCCL_DEBUG=INFO -export PYTHONFAULTHANDLER=1 -export MASTER_ADDR=$(hostname) -export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOBID | tail -c 4)) -export NCCL_BLOCKING_WAIT=1 - -echo $MASTER_ADDR:$MASTER_PORT - -nvidia-smi - -# Run experiments -pwd -which python -srun python train.py \ - data_root=$SLURM_TMPDIR/uncompressed/ \ - train_datasets=$dataset \ - val_datasets=$dataset \ - eval_epochs=2 \ - epochs=1 \ - pct_start=0.9 \ - batch_size=96 \ - name=benchmark_two_node_narval \ - base_lr=1e-5 \ - precision=16 \ - num_workers=6 \ - model=poyo_1 \ - nodes=2 \ - gpus=1 \ No newline at end of file diff --git a/environments/mila/README.md b/environments/mila/README.md deleted file mode 100644 index dbf09c6..0000000 --- a/environments/mila/README.md +++ /dev/null @@ -1,113 +0,0 @@ -# Running on the mila cluster - -Mila is a SLURM-based cluster. `run.sh` gives an example one-node pipeline, `run.sh` -`run_parallel.sh` gives an example single-node, multi-GPU pipeline, `run_multi_node.sh`, -a multi-node multi-GPU pipeline. In practice, multi-node, multi-GPU runs are only -feasible during deadtimes during conferences, but still, it works! - -## Datasets - -We keep a canonical version of the raw data in the common location at -`/network/projects/neuro-galaxy/data/raw`. This prevents having to download the data -multiple times, which could easily take a day. Processed, compressed data is stored in -your personal scratch folder, which prevents undefined behaviour when multiple people -are modifying the same `process_data.py` pipeline. - -Because mila is SLURM-based, data is first copied to the local node (SLURM_TMPDIR), -then processed in jobs. Because the file system is distributed and doesn't like to deal -with small files, we use tarballs compressed with lz4, which is a ridiculously fast -compression algorithm. Typically, the data will be processed in four stages: - -* download to `/network/projects/neuro-galaxy/data/raw` -* processed to `$SLURM_TMPDIR/compressed` -* frozen (i.e. compressed) to `~/scratch/data/compressed` -* unfrozen (i.e. decompressed) to `$SLURM_TMPDIR/uncompressed` - -When files are processed by job and subsequently frozen, once the job is done, the files -in $SLURM_TMPDIR are deleted. This is an unavoidable consequence of the data processing -DAG and the constraints of SLURM. Thus, if we call, e.g. `willett_shenoy_unfreeze`, it -will first attempt to re-process the data, complaining that the intermediate files don't -exist, e.g.: - -``` -job count ---------------------------- ------- -willett_shenoy_freeze 1 -willett_shenoy_prepare_data 1 -willett_shenoy_unfreeze 1 -total 3 - -Select jobs to execute... - -[Thu Nov 16 10:06:57 2023] -rule willett_shenoy_prepare_data: - input: /network/projects/neuro-galaxy/data/raw/willett_shenoy/handwritingBCIData/Datasets/t5.2019.11.25/singleLetters.mat, data/scripts/willett_shenoy/prepare_data.py - output: /Tmp/slurm.3839591.0/processed/willett_shenoy/description.mpk - jobid: 2 - reason: Missing output files: /Tmp/slurm.3839591.0/processed/willett_shenoy/description.mpk -``` - -A workaround is to use timestamps, and not the presence of intermediate files as -the trigger for parent rules, e.g.: - -``` -snakemake --rerun-triggers=mtime -c1 willett_shenoy_unfreeze -``` - -This will not trigger the creation of intermediate artifacts provided the timestamps of -the artifacts make sense. - -## Environment - -Set up a conda environment with the right packages, as defined in `requirements.txt`. - -## Setting up a compute environment -When building the dataset on the Mila clusters, it's important to have a sufficient -amount of memory and compute or else errors will occur, resulting in incomplete -datasets. Here is an example allocation request that works: -``` -salloc -c 10 --mem 32G --gres gpu:1 -``` - -## Partitions - -Mila has a number of partitions, `unkillable`, `short-unkillable`, `main` and `long`. -Use 1-GPU `unkillable` jobs for debugging. Run a 4-GPU job on `short-unkillable` to get -very quick results (3 hours max, but equivalent to 12 hours of a 1 GPU job). Use the -main and long partitions for longer jobs. - -[Reference](https://docs.mila.quebec/Userguide.html#partitioning) - - -## wandb credentials - -Store them in `.env` in the root of the project. This file is ignored by git. It should -look like: - -``` -WANDB_PROJECT=poyo -WANDB_ENTITY=neuro-galaxy -WANDB_API_KEY= -``` - -Get the API key from the wandb website. - -## mila cluster $SLURM_TMPDIR issue -(updated 04/25/2024 - Krystal) - -When running an interactive session on the mila cluster (e.g. `mila_code` in VScode or `salloc` jobs), it is possible that $SLURM_TMPDIR is not expanded and cause a Snakemake error: -``` -/bin/bash: line 1: SLURM_TMPDIR: unbound variable -``` - -Appending the following code to the `~/.bashrc` file to specify the temporary folder can solve this issue. -``` -export SLURM_TMPDIR="/tmp" -``` - -After modifying, you only need to run it once: -``` -source ~/.bashrc -``` - -Now $SLURM_TMPDIR will expand and the unbound variable issue is solved. diff --git a/environments/mila/copy_data.sh b/environments/mila/copy_data.sh deleted file mode 100755 index 040ebad..0000000 --- a/environments/mila/copy_data.sh +++ /dev/null @@ -1,6 +0,0 @@ -#!/bin/bash -conda activate poyo -module load python/3.9.6 - -# Not thread-safe! -snakemake --nolock --rerun-triggers=mtime --config tmp_dir="$SLURM_TMPDIR" -c1 "$1" \ No newline at end of file diff --git a/environments/mila/run.sh b/environments/mila/run.sh deleted file mode 100644 index deb36d7..0000000 --- a/environments/mila/run.sh +++ /dev/null @@ -1,48 +0,0 @@ -#!/bin/bash -#SBATCH --job-name=single_gpu_mila -#SBATCH --output=slurm_output_%j.txt -#SBATCH --error=slurm_error_%j.txt -#SBATCH --nodes=1 -#SBATCH --ntasks-per-node=1 -#SBATCH --cpus-per-task=6 -#SBATCH --gres=gpu:1 -#SBATCH --mem=32GB -#SBATCH --partition=unkillable - -dataset=perich_single_session -rule=perich_miller_unfreeze - -module load anaconda/3 -module load cuda/11.2/nccl/2.8 -module load cuda/11.2 - -conda activate poyo - -# wandb credentials -set -a -source .env -set +a - -# Uncompress the data to SLURM_TMPDIR -snakemake --rerun-triggers=mtime --config -c1 $rule - -nvidia-smi - -# Run experiments -pwd -which python -srun python train.py \ - data_root=$SLURM_TMPDIR/uncompressed/ \ - train_datasets=$dataset \ - val_datasets=$dataset \ - eval_epochs=1 \ - epochs=1 \ - pct_start=0.9 \ - batch_size=128 \ - name=single_gpu_mila \ - base_lr=1e-5 \ - precision=16 \ - num_workers=6 \ - model=poyo_single_session \ - nodes=1 \ - gpus=1 \ No newline at end of file diff --git a/environments/mila/run_multinode.sh b/environments/mila/run_multinode.sh deleted file mode 100644 index a76013d..0000000 --- a/environments/mila/run_multinode.sh +++ /dev/null @@ -1,63 +0,0 @@ -#!/bin/bash -#SBATCH --job-name=multi-node-mila -#SBATCH --output=slurm_output_%j.txt -#SBATCH --error=slurm_error_%j.txt -#SBATCH --nodes=2 -#SBATCH --ntasks-per-node=4 -#SBATCH --cpus-per-task=6 -#SBATCH --gres=gpu:4 -#SBATCH --mem=496GB -#SBATCH --switch=1 -#SBATCH --partition=long - -dataset=perich_single_session -rule=perich_miller_unfreeze - -module load anaconda/3 -module load cuda/11.2/nccl/2.8 -module load cuda/11.2 - -conda activate poyo - -# wandb credentials -set -a -source .env -set +a - -# Unpack data to $SLURM_TMPDIR. This needs to be done once per node. -# However, there is a slim chance the prepared data is stale, and if multiple nodes -# try to reconstruct the data and to write to the same place to disk at the same time -# we'd have a bad time. Hence we run snakemake on the master node --until the unfreeze -# rule, then run the thread-safe unfreeze rule proper on each node. Note that -# srun fires of one process per node. -snakemake --rerun-triggers=mtime --config tmp_dir="$SLURM_TMPDIR" -c1 --until $rule -srun copy_data.sh $rule - -export NCCL_DEBUG=INFO -export PYTHONFAULTHANDLER=1 -export MASTER_ADDR=$(hostname) -export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOBID | tail -c 4)) -export NCCL_BLOCKING_WAIT=1 - -echo $MASTER_ADDR:$MASTER_PORT - -nvidia-smi - -# Run experiments -pwd -which python -srun python train.py \ - data_root=$SLURM_TMPDIR/uncompressed/ \ - train_datasets=$dataset \ - val_datasets=$dataset \ - eval_epochs=1 \ - epochs=1 \ - pct_start=0.9 \ - batch_size=128 \ - name=multi_node_mila \ - base_lr=1e-5 \ - precision=16 \ - num_workers=6 \ - model=poyo_single_session \ - nodes=2 \ - gpus=4 \ No newline at end of file diff --git a/environments/mila/run_parallel.sh b/environments/mila/run_parallel.sh deleted file mode 100644 index 5cca002..0000000 --- a/environments/mila/run_parallel.sh +++ /dev/null @@ -1,57 +0,0 @@ -#!/bin/bash -#SBATCH --job-name=multi_gpu_mila -#SBATCH --output=slurm_output_%j.txt -#SBATCH --error=slurm_error_%j.txt -#SBATCH --nodes=1 -#SBATCH --ntasks-per-node=4 -#SBATCH --cpus=24 -#SBATCH --gres=gpu:4 -#SBATCH --mem=64GB -#SBATCH --partition=short-unkillable - -dataset=perich_single_session -rule=perich_miller_unfreeze - -module load anaconda/3 -module load cuda/11.2/nccl/2.8 -module load cuda/11.2 - -conda activate poyo - -# wandb credentials -set -a -source .env -set +a - -# Uncompress the data to SLURM_TMPDIR -snakemake --rerun-triggers=mtime --config tmp_dir=$SLURM_TMPDIR -c1 $rule - -# Important info for parallel GPU processing -export NCCL_DEBUG=INFO -export PYTHONFAULTHANDLER=1 -export MASTER_ADDR=$(hostname) -export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOBID | tail -c 4)) -export NCCL_BLOCKING_WAIT=1 - -echo $MASTER_ADDR:$MASTER_PORT - -nvidia-smi - -# Run experiments -pwd -which python -srun python train.py \ - data_root=$SLURM_TMPDIR/uncompressed/ \ - train_datasets=$dataset \ - val_datasets=$dataset \ - eval_epochs=1 \ - epochs=1 \ - pct_start=0.9 \ - batch_size=128 \ - name=multi_gpu_mila \ - base_lr=1e-5 \ - precision=16 \ - num_workers=6 \ - model=poyo_single_session \ - nodes=1 \ - gpus=4 \ No newline at end of file diff --git a/examples/capoyo/README.md b/examples/capoyo/README.md deleted file mode 100644 index d862d82..0000000 --- a/examples/capoyo/README.md +++ /dev/null @@ -1,22 +0,0 @@ -# [WIP] CaPOYO: POYO applied to Calcium Imaging Data - -### Datasets -To download and prepare the openscope calcium dataset, run the following inside the -`project-kirby` directory: -```bash -snakemake --cores 8 gillon_richards_responses_2023 -``` -This dataset extracts the Allen Brain Observatory calcium traces from 433 sessions, including only the `drifting grating` stimuli. -To download and prepare data, Run: -```bash -snakemake --cores 8 allen_brain_observatory_calcium_unfreeze -``` - -### Training CaPOYO -(remember to overwrite data root to be your processed data root) -```bash -python train.py --config-name train_openscope_calcium.yaml data_root=/kirby/processed -``` -```bash -python train.py --config-name train_allen_bo.yaml data_root=/kirby/processed -``` \ No newline at end of file diff --git a/examples/capoyo/allenBO_download.py b/examples/capoyo/allenBO_download.py deleted file mode 100644 index 11f992f..0000000 --- a/examples/capoyo/allenBO_download.py +++ /dev/null @@ -1,90 +0,0 @@ -from allensdk.core.brain_observatory_cache import BrainObservatoryCache -import pprint -import numpy as np -import allensdk.brain_observatory.stimulus_info as stim_info -import pandas as pd -from allensdk.brain_observatory.brain_observatory_exceptions import ( - EpochSeparationException, -) -import matplotlib.pyplot as plt -from simplejson.errors import JSONDecodeError - - -if __name__ == "__main__": - # boc = BrainObservatoryCache(manifest_file='/home/mila/x/xuejing.pan/scratch/manifest.json') - boc = BrainObservatoryCache( - manifest_file="/network/projects/neuro-galaxy/data/raw/allen_brain_observatory_calcium/manifest.json" - ) - all_dg_exps = boc.get_ophys_experiments(stimuli=[stim_info.DRIFTING_GRATINGS]) - num_exps = len(all_dg_exps) - - ##CREATING NEW CSV FILES - columns = [ - "exp_id", - "subject_id", - "cre_line", - "depth", - "num_seqs", - "num_ROIs", - "num_timepoints", - ] - - collected_data = [] - - for i in range(num_exps): - print("AT FILE: ", i) - - exp = all_dg_exps[i] - session_id = all_dg_exps[i]["id"] - try: - exp = boc.get_ophys_experiment_data(exp["id"]) - except OSError as e: - continue - except JSONDecodeError as e: - continue - exp_cre_line = all_dg_exps[i]["cre_line"] - exp_depth = all_dg_exps[i]["imaging_depth"] - subject_id = all_dg_exps[i]["donor_name"] - - print(session_id) - - traces = exp.get_dff_traces() - num_rois = traces[1].shape[0] - - num_timepoints = 0 - - try: - master_stim_table = exp.get_stimulus_epoch_table() - for i, stim in enumerate(master_stim_table["stimulus"]): - if stim == "drifting_gratings": - curr_time_points = ( - master_stim_table["end"][i] - master_stim_table["start"][i] - ) - num_timepoints += curr_time_points - - except EpochSeparationException as e: - # num_timepoints = 0 - continue - - stim_table = exp.get_stimulus_table("drifting_gratings") - - num_seqs = stim_table.index[-1] + 1 - - new_row = { - "exp_id": session_id, - "subject_id": subject_id, - "cre_line": exp_cre_line, - "depth": exp_depth, - "num_seqs": num_seqs, - "num_ROIs": num_rois, - "num_timepoints": num_timepoints, - } - print(new_row) - - collected_data.append(new_row) - print("TOTAL FILE: ", num_exps) - - df = pd.DataFrame(collected_data) - df.to_csv( - "/home/mila/x/xuejing.pan/POYO/project-kirby/kirby/AllenBOmeta.csv", index=False - ) diff --git a/examples/capoyo/cc_job.sh b/examples/capoyo/cc_job.sh deleted file mode 100644 index dab0db8..0000000 --- a/examples/capoyo/cc_job.sh +++ /dev/null @@ -1,42 +0,0 @@ -#!/bin/bash -#SBATCH --account=rrg-tyrell-ab -#SBATCH --job-name=capoyo_allen.txt -#SBATCH --output=slurm_output-%j.txt -#SBATCH --error=slurm_error-%j.txt -#SBATCH --ntasks-per-node=4 -#SBATCH --mem=256GB -#SBATCH --nodes=1 -#SBATCH --cpus-per-task=8 -#SBATCH --gres=gpu:4 -#SBATCH --time=12:0:0 - -module load StdEnv/2020 python/3.9 #httpproxy -cd /home/$USER -source ENV/bin/activate -#wandb offline - -cd project-kirby -snakemake --rerun-triggers=mtime --cores 4 allen_brain_observatory_calcium_unfreeze - -# Important info for parallel GPU processing -export NCCL_DEBUG=INFO -export PYTHONFAULTHANDLER=1 -export MASTER_ADDR=$(hostname) -export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOBID | tail -c 4)) -export NCCL_BLOCKING_WAIT=1 - -cd examples/capoyo -export WANDB_mode=offline - -# python train.py --config-name train_allen_bo.yaml -srun python train.py --config-name train_model_5.yaml log_dir=/home/mazabou/scratch \ - eval_epochs=1 \ - epochs=2000 \ - data_root=${SLURM_TMPDIR}/uncompressed \ - pct_start=0.5 \ - batch_size=128 \ - base_lr=1.56e-5 \ - num_workers=4 \ - nodes=1 \ - gpus=4 \ - +wandb_log_model=True diff --git a/examples/capoyo/cc_job_cux2.sh b/examples/capoyo/cc_job_cux2.sh deleted file mode 100644 index 0a4aeff..0000000 --- a/examples/capoyo/cc_job_cux2.sh +++ /dev/null @@ -1,43 +0,0 @@ -#!/bin/bash -#SBATCH --account=rrg-tyrell-ab -#SBATCH --job-name=capoyo_allen.txt -#SBATCH --output=slurm_output-%j.txt -#SBATCH --error=slurm_error-%j.txt -#SBATCH --ntasks-per-node=4 -#SBATCH --mem=256GB -#SBATCH --nodes=1 -#SBATCH --cpus-per-task=8 -#SBATCH --gres=gpu:4 -#SBATCH --time=12:0:0 - -module load StdEnv/2020 python/3.9 #httpproxy -cd /home/$USER -source ENV/bin/activate -#wandb offline - -cd project-kirby -snakemake --rerun-triggers=mtime --cores 4 allen_brain_observatory_calcium_unfreeze - -# Important info for parallel GPU processing -export NCCL_DEBUG=INFO -export PYTHONFAULTHANDLER=1 -export MASTER_ADDR=$(hostname) -export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOBID | tail -c 4)) -export NCCL_BLOCKING_WAIT=1 - -cd examples/capoyo -export WANDB_mode=offline - -srun python train.py --config-name train_model_3_visp.yaml log_dir=/home/mazabou/scratch \ - dataset=allen_brain_observatory_calcium_sub_cux2.yaml \ - name=capoyo_allen_cux2 \ - eval_epochs=1 \ - epochs=2000 \ - data_root=${SLURM_TMPDIR}/uncompressed \ - pct_start=0.9 \ - batch_size=128 \ - base_lr=1.56e-5 \ - num_workers=4 \ - nodes=1 \ - gpus=4 \ - +wandb_log_model=True diff --git a/examples/capoyo/cc_job_e.sh b/examples/capoyo/cc_job_e.sh deleted file mode 100644 index 172276b..0000000 --- a/examples/capoyo/cc_job_e.sh +++ /dev/null @@ -1,42 +0,0 @@ -#!/bin/bash -#SBATCH --account=rrg-tyrell-ab -#SBATCH --job-name=capoyo_allen.txt -#SBATCH --output=slurm_output-%j.txt -#SBATCH --error=slurm_error-%j.txt -#SBATCH --ntasks-per-node=4 -#SBATCH --mem=256GB -#SBATCH --nodes=1 -#SBATCH --cpus-per-task=8 -#SBATCH --gres=gpu:4 -#SBATCH --time=24:0:0 - -module load StdEnv/2020 python/3.9 #httpproxy -cd /home/$USER -source ENV/bin/activate -#wandb offline - -cd project-kirby -snakemake --rerun-triggers=mtime --cores 4 allen_brain_observatory_calcium_unfreeze - -# Important info for parallel GPU processing -export NCCL_DEBUG=INFO -export PYTHONFAULTHANDLER=1 -export MASTER_ADDR=$(hostname) -export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOBID | tail -c 4)) -export NCCL_BLOCKING_WAIT=1 - -cd examples/capoyo -export WANDB_mode=offline - -# python train.py --config-name train_allen_bo.yaml -srun python train.py --config-name train_model_3_e.yaml log_dir=/home/mazabou/scratch \ - eval_epochs=1 \ - epochs=2000 \ - data_root=${SLURM_TMPDIR}/uncompressed \ - pct_start=0.9 \ - batch_size=128 \ - base_lr=1.56e-5 \ - num_workers=4 \ - nodes=1 \ - gpus=4 \ - +wandb_log_model=True diff --git a/examples/capoyo/cc_job_emx1.sh b/examples/capoyo/cc_job_emx1.sh deleted file mode 100644 index 3e14434..0000000 --- a/examples/capoyo/cc_job_emx1.sh +++ /dev/null @@ -1,43 +0,0 @@ -#!/bin/bash -#SBATCH --account=rrg-tyrell-ab -#SBATCH --job-name=capoyo_allen.txt -#SBATCH --output=slurm_output-%j.txt -#SBATCH --error=slurm_error-%j.txt -#SBATCH --ntasks-per-node=4 -#SBATCH --mem=256GB -#SBATCH --nodes=1 -#SBATCH --cpus-per-task=8 -#SBATCH --gres=gpu:4 -#SBATCH --time=12:0:0 - -module load StdEnv/2020 python/3.9 #httpproxy -cd /home/$USER -source ENV/bin/activate -#wandb offline - -cd project-kirby -snakemake --rerun-triggers=mtime --cores 4 allen_brain_observatory_calcium_unfreeze - -# Important info for parallel GPU processing -export NCCL_DEBUG=INFO -export PYTHONFAULTHANDLER=1 -export MASTER_ADDR=$(hostname) -export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOBID | tail -c 4)) -export NCCL_BLOCKING_WAIT=1 - -cd examples/capoyo -export WANDB_mode=offline - -srun python train.py --config-name train_model_3_visp.yaml log_dir=/home/mazabou/scratch \ - dataset=allen_brain_observatory_calcium_sub_emx1.yaml \ - name=capoyo_allen_emx1 \ - eval_epochs=1 \ - epochs=2000 \ - data_root=${SLURM_TMPDIR}/uncompressed \ - pct_start=0.9 \ - batch_size=128 \ - base_lr=1.56e-5 \ - num_workers=4 \ - nodes=1 \ - gpus=4 \ - +wandb_log_model=True diff --git a/examples/capoyo/cc_job_fezf2.sh b/examples/capoyo/cc_job_fezf2.sh deleted file mode 100644 index 52b2883..0000000 --- a/examples/capoyo/cc_job_fezf2.sh +++ /dev/null @@ -1,43 +0,0 @@ -#!/bin/bash -#SBATCH --account=rrg-tyrell-ab -#SBATCH --job-name=capoyo_allen.txt -#SBATCH --output=slurm_output-%j.txt -#SBATCH --error=slurm_error-%j.txt -#SBATCH --ntasks-per-node=4 -#SBATCH --mem=256GB -#SBATCH --nodes=1 -#SBATCH --cpus-per-task=8 -#SBATCH --gres=gpu:4 -#SBATCH --time=12:0:0 - -module load StdEnv/2020 python/3.9 #httpproxy -cd /home/$USER -source ENV/bin/activate -#wandb offline - -cd project-kirby -snakemake --rerun-triggers=mtime --cores 4 allen_brain_observatory_calcium_unfreeze - -# Important info for parallel GPU processing -export NCCL_DEBUG=INFO -export PYTHONFAULTHANDLER=1 -export MASTER_ADDR=$(hostname) -export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOBID | tail -c 4)) -export NCCL_BLOCKING_WAIT=1 - -cd examples/capoyo -export WANDB_mode=offline - -srun python train.py --config-name train_model_3_visp.yaml log_dir=/home/mazabou/scratch \ - dataset=allen_brain_observatory_calcium_sub_fezf2.yaml \ - name=capoyo_allen_fezf2 \ - eval_epochs=1 \ - epochs=2000 \ - data_root=${SLURM_TMPDIR}/uncompressed \ - pct_start=0.9 \ - batch_size=128 \ - base_lr=1.56e-5 \ - num_workers=4 \ - nodes=1 \ - gpus=4 \ - +wandb_log_model=True diff --git a/examples/capoyo/cc_job_novisp.sh b/examples/capoyo/cc_job_novisp.sh deleted file mode 100644 index 4dd022a..0000000 --- a/examples/capoyo/cc_job_novisp.sh +++ /dev/null @@ -1,42 +0,0 @@ -#!/bin/bash -#SBATCH --account=rrg-tyrell-ab -#SBATCH --job-name=capoyo_allen.txt -#SBATCH --output=slurm_output-%j.txt -#SBATCH --error=slurm_error-%j.txt -#SBATCH --ntasks-per-node=4 -#SBATCH --mem=256GB -#SBATCH --nodes=1 -#SBATCH --cpus-per-task=8 -#SBATCH --gres=gpu:4 -#SBATCH --time=24:0:0 - -module load StdEnv/2020 python/3.9 #httpproxy -cd /home/$USER -source ENV/bin/activate -#wandb offline - -cd project-kirby -snakemake --rerun-triggers=mtime --cores 4 allen_brain_observatory_calcium_unfreeze - -# Important info for parallel GPU processing -export NCCL_DEBUG=INFO -export PYTHONFAULTHANDLER=1 -export MASTER_ADDR=$(hostname) -export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOBID | tail -c 4)) -export NCCL_BLOCKING_WAIT=1 - -cd examples/capoyo -export WANDB_mode=offline - -# python train.py --config-name train_allen_bo.yaml -srun python train.py --config-name train_model_3_novisp.yaml log_dir=/home/mazabou/scratch \ - eval_epochs=1 \ - epochs=2000 \ - data_root=${SLURM_TMPDIR}/uncompressed \ - pct_start=0.9 \ - batch_size=128 \ - base_lr=1.56e-5 \ - num_workers=4 \ - nodes=1 \ - gpus=4 \ - +wandb_log_model=True diff --git a/examples/capoyo/cc_job_nr5a.sh b/examples/capoyo/cc_job_nr5a.sh deleted file mode 100644 index a9e20b5..0000000 --- a/examples/capoyo/cc_job_nr5a.sh +++ /dev/null @@ -1,43 +0,0 @@ -#!/bin/bash -#SBATCH --account=rrg-tyrell-ab -#SBATCH --job-name=capoyo_allen.txt -#SBATCH --output=slurm_output-%j.txt -#SBATCH --error=slurm_error-%j.txt -#SBATCH --ntasks-per-node=4 -#SBATCH --mem=256GB -#SBATCH --nodes=1 -#SBATCH --cpus-per-task=8 -#SBATCH --gres=gpu:4 -#SBATCH --time=12:0:0 - -module load StdEnv/2020 python/3.9 #httpproxy -cd /home/$USER -source ENV/bin/activate -#wandb offline - -cd project-kirby -snakemake --rerun-triggers=mtime --cores 4 allen_brain_observatory_calcium_unfreeze - -# Important info for parallel GPU processing -export NCCL_DEBUG=INFO -export PYTHONFAULTHANDLER=1 -export MASTER_ADDR=$(hostname) -export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOBID | tail -c 4)) -export NCCL_BLOCKING_WAIT=1 - -cd examples/capoyo -export WANDB_mode=offline - -srun python train.py --config-name train_model_3_visp.yaml log_dir=/home/mazabou/scratch \ - dataset=allen_brain_observatory_calcium_sub_nr5a.yaml \ - name=capoyo_allen_nr5a \ - eval_epochs=1 \ - epochs=2000 \ - data_root=${SLURM_TMPDIR}/uncompressed \ - pct_start=0.9 \ - batch_size=128 \ - base_lr=1.56e-5 \ - num_workers=4 \ - nodes=1 \ - gpus=4 \ - +wandb_log_model=True diff --git a/examples/capoyo/cc_job_nstr1.sh b/examples/capoyo/cc_job_nstr1.sh deleted file mode 100644 index 6277772..0000000 --- a/examples/capoyo/cc_job_nstr1.sh +++ /dev/null @@ -1,43 +0,0 @@ -#!/bin/bash -#SBATCH --account=rrg-tyrell-ab -#SBATCH --job-name=capoyo_allen.txt -#SBATCH --output=slurm_output-%j.txt -#SBATCH --error=slurm_error-%j.txt -#SBATCH --ntasks-per-node=4 -#SBATCH --mem=256GB -#SBATCH --nodes=1 -#SBATCH --cpus-per-task=8 -#SBATCH --gres=gpu:4 -#SBATCH --time=12:0:0 - -module load StdEnv/2020 python/3.9 #httpproxy -cd /home/$USER -source ENV/bin/activate -#wandb offline - -cd project-kirby -snakemake --rerun-triggers=mtime --cores 4 allen_brain_observatory_calcium_unfreeze - -# Important info for parallel GPU processing -export NCCL_DEBUG=INFO -export PYTHONFAULTHANDLER=1 -export MASTER_ADDR=$(hostname) -export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOBID | tail -c 4)) -export NCCL_BLOCKING_WAIT=1 - -cd examples/capoyo -export WANDB_mode=offline - -srun python train.py --config-name train_model_3_visp.yaml log_dir=/home/mazabou/scratch \ - dataset=allen_brain_observatory_calcium_sub_nstr1.yaml \ - name=capoyo_allen_nstr1 \ - eval_epochs=1 \ - epochs=2000 \ - data_root=${SLURM_TMPDIR}/uncompressed \ - pct_start=0.9 \ - batch_size=128 \ - base_lr=1.56e-5 \ - num_workers=4 \ - nodes=1 \ - gpus=4 \ - +wandb_log_model=True diff --git a/examples/capoyo/cc_job_pvalb.sh b/examples/capoyo/cc_job_pvalb.sh deleted file mode 100644 index 74c4d09..0000000 --- a/examples/capoyo/cc_job_pvalb.sh +++ /dev/null @@ -1,43 +0,0 @@ -#!/bin/bash -#SBATCH --account=rrg-tyrell-ab -#SBATCH --job-name=capoyo_allen.txt -#SBATCH --output=slurm_output-%j.txt -#SBATCH --error=slurm_error-%j.txt -#SBATCH --ntasks-per-node=4 -#SBATCH --mem=256GB -#SBATCH --nodes=1 -#SBATCH --cpus-per-task=8 -#SBATCH --gres=gpu:4 -#SBATCH --time=12:0:0 - -module load StdEnv/2020 python/3.9 #httpproxy -cd /home/$USER -source ENV/bin/activate -#wandb offline - -cd project-kirby -snakemake --rerun-triggers=mtime --cores 4 allen_brain_observatory_calcium_unfreeze - -# Important info for parallel GPU processing -export NCCL_DEBUG=INFO -export PYTHONFAULTHANDLER=1 -export MASTER_ADDR=$(hostname) -export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOBID | tail -c 4)) -export NCCL_BLOCKING_WAIT=1 - -cd examples/capoyo -export WANDB_mode=offline - -srun python train.py --config-name train_model_3_visp.yaml log_dir=/home/mazabou/scratch \ - dataset=allen_brain_observatory_calcium_sub_pvalb.yaml \ - name=capoyo_allen_pvalb \ - eval_epochs=1 \ - epochs=2000 \ - data_root=${SLURM_TMPDIR}/uncompressed \ - pct_start=0.9 \ - batch_size=128 \ - base_lr=1.56e-5 \ - num_workers=4 \ - nodes=1 \ - gpus=4 \ - +wandb_log_model=True diff --git a/examples/capoyo/cc_job_rbp4.sh b/examples/capoyo/cc_job_rbp4.sh deleted file mode 100644 index cf30611..0000000 --- a/examples/capoyo/cc_job_rbp4.sh +++ /dev/null @@ -1,43 +0,0 @@ -#!/bin/bash -#SBATCH --account=rrg-tyrell-ab -#SBATCH --job-name=capoyo_allen.txt -#SBATCH --output=slurm_output-%j.txt -#SBATCH --error=slurm_error-%j.txt -#SBATCH --ntasks-per-node=4 -#SBATCH --mem=256GB -#SBATCH --nodes=1 -#SBATCH --cpus-per-task=8 -#SBATCH --gres=gpu:4 -#SBATCH --time=12:0:0 - -module load StdEnv/2020 python/3.9 #httpproxy -cd /home/$USER -source ENV/bin/activate -#wandb offline - -cd project-kirby -snakemake --rerun-triggers=mtime --cores 4 allen_brain_observatory_calcium_unfreeze - -# Important info for parallel GPU processing -export NCCL_DEBUG=INFO -export PYTHONFAULTHANDLER=1 -export MASTER_ADDR=$(hostname) -export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOBID | tail -c 4)) -export NCCL_BLOCKING_WAIT=1 - -cd examples/capoyo -export WANDB_mode=offline - -srun python train.py --config-name train_model_3_visp.yaml log_dir=/home/mazabou/scratch \ - dataset=allen_brain_observatory_calcium_sub_rbp4.yaml \ - name=capoyo_allen_rbp4 \ - eval_epochs=1 \ - epochs=2000 \ - data_root=${SLURM_TMPDIR}/uncompressed \ - pct_start=0.9 \ - batch_size=128 \ - base_lr=1.56e-5 \ - num_workers=4 \ - nodes=1 \ - gpus=4 \ - +wandb_log_model=True diff --git a/examples/capoyo/cc_job_rorb.sh b/examples/capoyo/cc_job_rorb.sh deleted file mode 100644 index 09ba04b..0000000 --- a/examples/capoyo/cc_job_rorb.sh +++ /dev/null @@ -1,43 +0,0 @@ -#!/bin/bash -#SBATCH --account=rrg-tyrell-ab -#SBATCH --job-name=capoyo_allen.txt -#SBATCH --output=slurm_output-%j.txt -#SBATCH --error=slurm_error-%j.txt -#SBATCH --ntasks-per-node=4 -#SBATCH --mem=256GB -#SBATCH --nodes=1 -#SBATCH --cpus-per-task=8 -#SBATCH --gres=gpu:4 -#SBATCH --time=12:0:0 - -module load StdEnv/2020 python/3.9 #httpproxy -cd /home/$USER -source ENV/bin/activate -#wandb offline - -cd project-kirby -snakemake --rerun-triggers=mtime --cores 4 allen_brain_observatory_calcium_unfreeze - -# Important info for parallel GPU processing -export NCCL_DEBUG=INFO -export PYTHONFAULTHANDLER=1 -export MASTER_ADDR=$(hostname) -export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOBID | tail -c 4)) -export NCCL_BLOCKING_WAIT=1 - -cd examples/capoyo -export WANDB_mode=offline - -srun python train.py --config-name train_model_3_visp.yaml log_dir=/home/mazabou/scratch \ - dataset=allen_brain_observatory_calcium_sub_rorb.yaml \ - name=capoyo_allen_rorb \ - eval_epochs=1 \ - epochs=2000 \ - data_root=${SLURM_TMPDIR}/uncompressed \ - pct_start=0.9 \ - batch_size=128 \ - base_lr=1.56e-5 \ - num_workers=4 \ - nodes=1 \ - gpus=4 \ - +wandb_log_model=True diff --git a/examples/capoyo/cc_job_scnn1a.sh b/examples/capoyo/cc_job_scnn1a.sh deleted file mode 100644 index 98ffe07..0000000 --- a/examples/capoyo/cc_job_scnn1a.sh +++ /dev/null @@ -1,43 +0,0 @@ -#!/bin/bash -#SBATCH --account=rrg-tyrell-ab -#SBATCH --job-name=capoyo_allen.txt -#SBATCH --output=slurm_output-%j.txt -#SBATCH --error=slurm_error-%j.txt -#SBATCH --ntasks-per-node=4 -#SBATCH --mem=256GB -#SBATCH --nodes=1 -#SBATCH --cpus-per-task=8 -#SBATCH --gres=gpu:4 -#SBATCH --time=12:0:0 - -module load StdEnv/2020 python/3.9 #httpproxy -cd /home/$USER -source ENV/bin/activate -#wandb offline - -cd project-kirby -snakemake --rerun-triggers=mtime --cores 4 allen_brain_observatory_calcium_unfreeze - -# Important info for parallel GPU processing -export NCCL_DEBUG=INFO -export PYTHONFAULTHANDLER=1 -export MASTER_ADDR=$(hostname) -export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOBID | tail -c 4)) -export NCCL_BLOCKING_WAIT=1 - -cd examples/capoyo -export WANDB_mode=offline - -srun python train.py --config-name train_model_3_visp.yaml log_dir=/home/mazabou/scratch \ - dataset=allen_brain_observatory_calcium_sub_scnn1a.yaml \ - name=capoyo_allen_scnn1a \ - eval_epochs=1 \ - epochs=2000 \ - data_root=${SLURM_TMPDIR}/uncompressed \ - pct_start=0.9 \ - batch_size=128 \ - base_lr=1.56e-5 \ - num_workers=4 \ - nodes=1 \ - gpus=4 \ - +wandb_log_model=True diff --git a/examples/capoyo/cc_job_slc17a7.sh b/examples/capoyo/cc_job_slc17a7.sh deleted file mode 100644 index 25d06b5..0000000 --- a/examples/capoyo/cc_job_slc17a7.sh +++ /dev/null @@ -1,43 +0,0 @@ -#!/bin/bash -#SBATCH --account=rrg-tyrell-ab -#SBATCH --job-name=capoyo_allen.txt -#SBATCH --output=slurm_output-%j.txt -#SBATCH --error=slurm_error-%j.txt -#SBATCH --ntasks-per-node=4 -#SBATCH --mem=256GB -#SBATCH --nodes=1 -#SBATCH --cpus-per-task=8 -#SBATCH --gres=gpu:4 -#SBATCH --time=12:0:0 - -module load StdEnv/2020 python/3.9 #httpproxy -cd /home/$USER -source ENV/bin/activate -#wandb offline - -cd project-kirby -snakemake --rerun-triggers=mtime --cores 4 allen_brain_observatory_calcium_unfreeze - -# Important info for parallel GPU processing -export NCCL_DEBUG=INFO -export PYTHONFAULTHANDLER=1 -export MASTER_ADDR=$(hostname) -export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOBID | tail -c 4)) -export NCCL_BLOCKING_WAIT=1 - -cd examples/capoyo -export WANDB_mode=offline - -srun python train.py --config-name train_model_3_visp.yaml log_dir=/home/mazabou/scratch \ - dataset=allen_brain_observatory_calcium_sub_slc17a7.yaml \ - name=capoyo_allen_slc17a7 \ - eval_epochs=1 \ - epochs=2000 \ - data_root=${SLURM_TMPDIR}/uncompressed \ - pct_start=0.9 \ - batch_size=128 \ - base_lr=1.56e-5 \ - num_workers=4 \ - nodes=1 \ - gpus=4 \ - +wandb_log_model=True diff --git a/examples/capoyo/cc_job_sst.sh b/examples/capoyo/cc_job_sst.sh deleted file mode 100644 index 5104fe8..0000000 --- a/examples/capoyo/cc_job_sst.sh +++ /dev/null @@ -1,43 +0,0 @@ -#!/bin/bash -#SBATCH --account=rrg-tyrell-ab -#SBATCH --job-name=capoyo_allen.txt -#SBATCH --output=slurm_output-%j.txt -#SBATCH --error=slurm_error-%j.txt -#SBATCH --ntasks-per-node=4 -#SBATCH --mem=256GB -#SBATCH --nodes=1 -#SBATCH --cpus-per-task=8 -#SBATCH --gres=gpu:4 -#SBATCH --time=12:0:0 - -module load StdEnv/2020 python/3.9 #httpproxy -cd /home/$USER -source ENV/bin/activate -#wandb offline - -cd project-kirby -snakemake --rerun-triggers=mtime --cores 4 allen_brain_observatory_calcium_unfreeze - -# Important info for parallel GPU processing -export NCCL_DEBUG=INFO -export PYTHONFAULTHANDLER=1 -export MASTER_ADDR=$(hostname) -export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOBID | tail -c 4)) -export NCCL_BLOCKING_WAIT=1 - -cd examples/capoyo -export WANDB_mode=offline - -srun python train.py --config-name train_model_3_visp.yaml log_dir=/home/mazabou/scratch \ - dataset=allen_brain_observatory_calcium_sub_sst.yaml \ - name=capoyo_allen_sst \ - eval_epochs=1 \ - epochs=2000 \ - data_root=${SLURM_TMPDIR}/uncompressed \ - pct_start=0.9 \ - batch_size=128 \ - base_lr=1.56e-5 \ - num_workers=4 \ - nodes=1 \ - gpus=4 \ - +wandb_log_model=True diff --git a/examples/capoyo/cc_job_tlx3.sh b/examples/capoyo/cc_job_tlx3.sh deleted file mode 100644 index 7ffd001..0000000 --- a/examples/capoyo/cc_job_tlx3.sh +++ /dev/null @@ -1,43 +0,0 @@ -#!/bin/bash -#SBATCH --account=rrg-tyrell-ab -#SBATCH --job-name=capoyo_allen.txt -#SBATCH --output=slurm_output-%j.txt -#SBATCH --error=slurm_error-%j.txt -#SBATCH --ntasks-per-node=4 -#SBATCH --mem=256GB -#SBATCH --nodes=1 -#SBATCH --cpus-per-task=8 -#SBATCH --gres=gpu:4 -#SBATCH --time=12:0:0 - -module load StdEnv/2020 python/3.9 #httpproxy -cd /home/$USER -source ENV/bin/activate -#wandb offline - -cd project-kirby -snakemake --rerun-triggers=mtime --cores 4 allen_brain_observatory_calcium_unfreeze - -# Important info for parallel GPU processing -export NCCL_DEBUG=INFO -export PYTHONFAULTHANDLER=1 -export MASTER_ADDR=$(hostname) -export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOBID | tail -c 4)) -export NCCL_BLOCKING_WAIT=1 - -cd examples/capoyo -export WANDB_mode=offline - -srun python train.py --config-name train_model_3_visp.yaml log_dir=/home/mazabou/scratch \ - dataset=allen_brain_observatory_calcium_sub_tlx3.yaml \ - name=capoyo_allen_tlx3 \ - eval_epochs=1 \ - epochs=2000 \ - data_root=${SLURM_TMPDIR}/uncompressed \ - pct_start=0.9 \ - batch_size=128 \ - base_lr=1.56e-5 \ - num_workers=4 \ - nodes=1 \ - gpus=4 \ - +wandb_log_model=True diff --git a/examples/capoyo/cc_job_vip.sh b/examples/capoyo/cc_job_vip.sh deleted file mode 100644 index 3ef74a0..0000000 --- a/examples/capoyo/cc_job_vip.sh +++ /dev/null @@ -1,43 +0,0 @@ -#!/bin/bash -#SBATCH --account=rrg-tyrell-ab -#SBATCH --job-name=capoyo_allen.txt -#SBATCH --output=slurm_output-%j.txt -#SBATCH --error=slurm_error-%j.txt -#SBATCH --ntasks-per-node=4 -#SBATCH --mem=256GB -#SBATCH --nodes=1 -#SBATCH --cpus-per-task=8 -#SBATCH --gres=gpu:4 -#SBATCH --time=12:0:0 - -module load StdEnv/2020 python/3.9 #httpproxy -cd /home/$USER -source ENV/bin/activate -#wandb offline - -cd project-kirby -snakemake --rerun-triggers=mtime --cores 4 allen_brain_observatory_calcium_unfreeze - -# Important info for parallel GPU processing -export NCCL_DEBUG=INFO -export PYTHONFAULTHANDLER=1 -export MASTER_ADDR=$(hostname) -export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOBID | tail -c 4)) -export NCCL_BLOCKING_WAIT=1 - -cd examples/capoyo -export WANDB_mode=offline - -srun python train.py --config-name train_model_3_visp.yaml log_dir=/home/mazabou/scratch \ - dataset=allen_brain_observatory_calcium_sub_vip.yaml \ - name=capoyo_allen_vip \ - eval_epochs=1 \ - epochs=2000 \ - data_root=${SLURM_TMPDIR}/uncompressed \ - pct_start=0.9 \ - batch_size=128 \ - base_lr=1.56e-5 \ - num_workers=4 \ - nodes=1 \ - gpus=4 \ - +wandb_log_model=True diff --git a/examples/capoyo/cc_job_visal.sh b/examples/capoyo/cc_job_visal.sh deleted file mode 100644 index 4dfbfdd..0000000 --- a/examples/capoyo/cc_job_visal.sh +++ /dev/null @@ -1,43 +0,0 @@ -#!/bin/bash -#SBATCH --account=rrg-tyrell-ab -#SBATCH --job-name=capoyo_allen.txt -#SBATCH --output=slurm_output-%j.txt -#SBATCH --error=slurm_error-%j.txt -#SBATCH --ntasks-per-node=4 -#SBATCH --mem=256GB -#SBATCH --nodes=1 -#SBATCH --cpus-per-task=8 -#SBATCH --gres=gpu:4 -#SBATCH --time=12:0:0 - -module load StdEnv/2020 python/3.9 #httpproxy -cd /home/$USER -source ENV/bin/activate -#wandb offline - -cd project-kirby -snakemake --rerun-triggers=mtime --cores 4 allen_brain_observatory_calcium_unfreeze - -# Important info for parallel GPU processing -export NCCL_DEBUG=INFO -export PYTHONFAULTHANDLER=1 -export MASTER_ADDR=$(hostname) -export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOBID | tail -c 4)) -export NCCL_BLOCKING_WAIT=1 - -cd examples/capoyo -export WANDB_mode=offline - -srun python train.py --config-name train_model_3_visp.yaml log_dir=/home/mazabou/scratch \ - dataset=allen_brain_observatory_calcium_sub_visal.yaml \ - name=capoyo_allen_visal \ - eval_epochs=1 \ - epochs=2000 \ - data_root=${SLURM_TMPDIR}/uncompressed \ - pct_start=0.9 \ - batch_size=128 \ - base_lr=1.56e-5 \ - num_workers=4 \ - nodes=1 \ - gpus=4 \ - +wandb_log_model=True diff --git a/examples/capoyo/cc_job_visam.sh b/examples/capoyo/cc_job_visam.sh deleted file mode 100644 index 23ce8a4..0000000 --- a/examples/capoyo/cc_job_visam.sh +++ /dev/null @@ -1,43 +0,0 @@ -#!/bin/bash -#SBATCH --account=rrg-tyrell-ab -#SBATCH --job-name=capoyo_allen.txt -#SBATCH --output=slurm_output-%j.txt -#SBATCH --error=slurm_error-%j.txt -#SBATCH --ntasks-per-node=4 -#SBATCH --mem=256GB -#SBATCH --nodes=1 -#SBATCH --cpus-per-task=8 -#SBATCH --gres=gpu:4 -#SBATCH --time=12:0:0 - -module load StdEnv/2020 python/3.9 #httpproxy -cd /home/$USER -source ENV/bin/activate -#wandb offline - -cd project-kirby -snakemake --rerun-triggers=mtime --cores 4 allen_brain_observatory_calcium_unfreeze - -# Important info for parallel GPU processing -export NCCL_DEBUG=INFO -export PYTHONFAULTHANDLER=1 -export MASTER_ADDR=$(hostname) -export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOBID | tail -c 4)) -export NCCL_BLOCKING_WAIT=1 - -cd examples/capoyo -export WANDB_mode=offline - -srun python train.py --config-name train_model_3_visp.yaml log_dir=/home/mazabou/scratch \ - dataset=allen_brain_observatory_calcium_sub_visam.yaml \ - name=capoyo_allen_visam \ - eval_epochs=1 \ - epochs=2000 \ - data_root=${SLURM_TMPDIR}/uncompressed \ - pct_start=0.9 \ - batch_size=128 \ - base_lr=1.56e-5 \ - num_workers=4 \ - nodes=1 \ - gpus=4 \ - +wandb_log_model=True diff --git a/examples/capoyo/cc_job_visl.sh b/examples/capoyo/cc_job_visl.sh deleted file mode 100644 index 8712997..0000000 --- a/examples/capoyo/cc_job_visl.sh +++ /dev/null @@ -1,43 +0,0 @@ -#!/bin/bash -#SBATCH --account=rrg-tyrell-ab -#SBATCH --job-name=capoyo_allen.txt -#SBATCH --output=slurm_output-%j.txt -#SBATCH --error=slurm_error-%j.txt -#SBATCH --ntasks-per-node=4 -#SBATCH --mem=256GB -#SBATCH --nodes=1 -#SBATCH --cpus-per-task=8 -#SBATCH --gres=gpu:4 -#SBATCH --time=12:0:0 - -module load StdEnv/2020 python/3.9 #httpproxy -cd /home/$USER -source ENV/bin/activate -#wandb offline - -cd project-kirby -snakemake --rerun-triggers=mtime --cores 4 allen_brain_observatory_calcium_unfreeze - -# Important info for parallel GPU processing -export NCCL_DEBUG=INFO -export PYTHONFAULTHANDLER=1 -export MASTER_ADDR=$(hostname) -export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOBID | tail -c 4)) -export NCCL_BLOCKING_WAIT=1 - -cd examples/capoyo -export WANDB_mode=offline - -srun python train.py --config-name train_model_3_visp.yaml log_dir=/home/mazabou/scratch \ - dataset=allen_brain_observatory_calcium_sub_visl.yaml \ - name=capoyo_allen_visl \ - eval_epochs=1 \ - epochs=2000 \ - data_root=${SLURM_TMPDIR}/uncompressed \ - pct_start=0.9 \ - batch_size=128 \ - base_lr=1.56e-5 \ - num_workers=4 \ - nodes=1 \ - gpus=4 \ - +wandb_log_model=True diff --git a/examples/capoyo/cc_job_visp.sh b/examples/capoyo/cc_job_visp.sh deleted file mode 100644 index 052ed72..0000000 --- a/examples/capoyo/cc_job_visp.sh +++ /dev/null @@ -1,42 +0,0 @@ -#!/bin/bash -#SBATCH --account=rrg-tyrell-ab -#SBATCH --job-name=capoyo_allen.txt -#SBATCH --output=slurm_output-%j.txt -#SBATCH --error=slurm_error-%j.txt -#SBATCH --ntasks-per-node=4 -#SBATCH --mem=256GB -#SBATCH --nodes=1 -#SBATCH --cpus-per-task=8 -#SBATCH --gres=gpu:4 -#SBATCH --time=24:0:0 - -module load StdEnv/2020 python/3.9 #httpproxy -cd /home/$USER -source ENV/bin/activate -#wandb offline - -cd project-kirby -snakemake --rerun-triggers=mtime --cores 4 allen_brain_observatory_calcium_unfreeze - -# Important info for parallel GPU processing -export NCCL_DEBUG=INFO -export PYTHONFAULTHANDLER=1 -export MASTER_ADDR=$(hostname) -export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOBID | tail -c 4)) -export NCCL_BLOCKING_WAIT=1 - -cd examples/capoyo -export WANDB_mode=offline - -# python train.py --config-name train_allen_bo.yaml -srun python train.py --config-name train_model_3_visp.yaml log_dir=/home/mazabou/scratch \ - eval_epochs=1 \ - epochs=2000 \ - data_root=${SLURM_TMPDIR}/uncompressed \ - pct_start=0.9 \ - batch_size=128 \ - base_lr=1.56e-5 \ - num_workers=4 \ - nodes=1 \ - gpus=4 \ - +wandb_log_model=True diff --git a/examples/capoyo/cc_job_vispm.sh b/examples/capoyo/cc_job_vispm.sh deleted file mode 100644 index f94cf58..0000000 --- a/examples/capoyo/cc_job_vispm.sh +++ /dev/null @@ -1,43 +0,0 @@ -#!/bin/bash -#SBATCH --account=rrg-tyrell-ab -#SBATCH --job-name=capoyo_allen.txt -#SBATCH --output=slurm_output-%j.txt -#SBATCH --error=slurm_error-%j.txt -#SBATCH --ntasks-per-node=4 -#SBATCH --mem=256GB -#SBATCH --nodes=1 -#SBATCH --cpus-per-task=8 -#SBATCH --gres=gpu:4 -#SBATCH --time=12:0:0 - -module load StdEnv/2020 python/3.9 #httpproxy -cd /home/$USER -source ENV/bin/activate -#wandb offline - -cd project-kirby -snakemake --rerun-triggers=mtime --cores 4 allen_brain_observatory_calcium_unfreeze - -# Important info for parallel GPU processing -export NCCL_DEBUG=INFO -export PYTHONFAULTHANDLER=1 -export MASTER_ADDR=$(hostname) -export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOBID | tail -c 4)) -export NCCL_BLOCKING_WAIT=1 - -cd examples/capoyo -export WANDB_mode=offline - -srun python train.py --config-name train_model_3_visp.yaml log_dir=/home/mazabou/scratch \ - dataset=allen_brain_observatory_calcium_sub_vispm.yaml \ - name=capoyo_allen_vispm \ - eval_epochs=1 \ - epochs=2000 \ - data_root=${SLURM_TMPDIR}/uncompressed \ - pct_start=0.9 \ - batch_size=128 \ - base_lr=1.56e-5 \ - num_workers=4 \ - nodes=1 \ - gpus=4 \ - +wandb_log_model=True diff --git a/examples/capoyo/cc_job_wt.sh b/examples/capoyo/cc_job_wt.sh deleted file mode 100644 index 2423971..0000000 --- a/examples/capoyo/cc_job_wt.sh +++ /dev/null @@ -1,42 +0,0 @@ -#!/bin/bash -#SBATCH --account=rrg-tyrell-ab -#SBATCH --job-name=capoyo_allen.txt -#SBATCH --output=slurm_output-%j.txt -#SBATCH --error=slurm_error-%j.txt -#SBATCH --ntasks-per-node=4 -#SBATCH --mem=256GB -#SBATCH --nodes=1 -#SBATCH --cpus-per-task=8 -#SBATCH --gres=gpu:4 -#SBATCH --time=24:0:0 - -module load StdEnv/2020 python/3.9 #httpproxy -cd /home/$USER -source ENV/bin/activate -#wandb offline - -cd project-kirby -snakemake --rerun-triggers=mtime --cores 4 allen_brain_observatory_calcium_unfreeze - -# Important info for parallel GPU processing -export NCCL_DEBUG=INFO -export PYTHONFAULTHANDLER=1 -export MASTER_ADDR=$(hostname) -export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOBID | tail -c 4)) -export NCCL_BLOCKING_WAIT=1 - -cd examples/capoyo -export WANDB_mode=offline - -# python train.py --config-name train_allen_bo.yaml -srun python train.py --config-name train_model_3_wt.yaml log_dir=/home/mazabou/scratch \ - eval_epochs=1 \ - epochs=2000 \ - data_root=${SLURM_TMPDIR}/uncompressed \ - pct_start=0.9 \ - batch_size=128 \ - base_lr=1.56e-5 \ - num_workers=4 \ - nodes=1 \ - gpus=4 \ - +wandb_log_model=True diff --git a/examples/capoyo/configs/dataset/allen_bo_weight_pow5.yaml b/examples/capoyo/configs/dataset/allen_bo_weight_pow5.yaml deleted file mode 100644 index 89a736e..0000000 --- a/examples/capoyo/configs/dataset/allen_bo_weight_pow5.yaml +++ /dev/null @@ -1,6448 +0,0 @@ - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "501021421" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.16420664062461185 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "501574836" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 5.254612499987579 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "501729039" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 5.973398506062066 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "501876401" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.4476727659576842 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "501929610" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.851333405786526 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "501933264" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 3.6275627605060703 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "501940850" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 4.050355097237812 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "502115959" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.2524339039644827 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "502199136" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.9277802792842125 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "502205092" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 3.6794317406016432 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "502376461" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.8276061136897213 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "502608215" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 10.840173117140003 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "502666254" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.1680436592876497 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "502793808" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 4.024388912857937 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "502962794" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.9265872772720967 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "503109347" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 4.455207001607901 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "503324629" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.274161302364301 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "503412730" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.620949528359348 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "504115289" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.17514953784938686 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "504568756" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.3023644496821535 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "504853580" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.44521963202210185 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "505407318" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 3.888455844045363 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "505845219" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.7742500051212505 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "506540916" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.025369507288883358 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "506773185" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.520948847352663 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "506773892" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.661235240656012 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "506809539" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.4311101212314306 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "506823562" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.594792873727622 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "507129766" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.10195928537852082 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "507691036" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.2187642932832861 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "507990552" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.873956896114504 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "508356957" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.35821891125574484 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "508563988" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.18666890013185147 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "508753256" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.06846557551560964 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "509580400" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.02785510253924671 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "509904120" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.06513861896653071 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "509958730" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 8.118608432208616 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "510093797" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.8118311261253451 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "510214538" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 3.314239443032754 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "510390912" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.4946417504903935 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "510517131" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.4476727659576842 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "510859641" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.8118311261253451 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "510917254" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.3023644496821535 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "511194579" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.46941277333278303 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "511440894" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.08820268448434164 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "511534603" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.17514885618192602 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "511573879" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.778462607274188 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "511595995" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.5769151387664898 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "512164988" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.9277802792842125 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "512270518" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.2524339039644827 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "512311673" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.4311159094917634 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "524691284" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 6.768789917036953 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "526504941" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.39982930963113233 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "527048992" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 6.562423648208439 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "528402271" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 4.333470535604174 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "529688779" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.3499467691679934 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "530645663" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.0857931165350887 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "531134090" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.2249092631979409 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "539290504" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.07038855949889009 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "540684467" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 3.7561975308890396 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "541010698" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.7380800965392033 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "541290571" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.10945239252166916 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "545446482" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.6066492982670031 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "546641574" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.1680436592876497 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "546716391" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.10945239252166916 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "548379748" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.0759727092823437 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "550455111" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.3300417421687927 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "550490398" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.06513861896653071 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "550851591" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.09488105997992317 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "551834174" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.15381644895969043 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "551888519" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.2249092631979409 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "552410386" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.6066492982670031 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "552427971" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 5.973404804219247 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "552760671" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.0222380211839115 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "553568031" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.7179160920137374 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "554037270" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.01249478815690529 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "555040116" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.11738117284028249 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "555749369" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.005473464910660895 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "556321897" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.06020585241475302 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "556344224" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.023065003016850102 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "556353209" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.010592691803655291 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "556665481" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.008918241945654175 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "557225279" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.5095353175605455 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "557227804" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.020359018390669878 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "557304694" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.1987850529824249 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "557615965" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.006212032905700778 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "557848210" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.778462607274188 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "558476282" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.03340991124365688 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "558670888" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.1530930405110196 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "559192380" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.1453637293050803 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "559382012" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.04717262487766527 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "560027980" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.011194340976742026 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "560578599" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.03340991124365688 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "560809202" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.025369507288883358 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "560866155" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.008918241945654175 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "560898462" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 6.361157476863424 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "560920977" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.036501652522466656 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "561312435" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 8.118608432208616 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "561472633" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 3.0362137206911077 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "562052595" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.7078922205943456 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "562536153" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.5246111372810196 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "562711440" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.013913113500690683 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "563176332" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 4.307685144165887 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "563710064" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.7380800965392033 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "564425777" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 4.024388912857937 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "564607188" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.4946417504903935 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "565216523" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.9265872772720967 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "565698388" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.015457699618034141 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "566096665" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.1987850529824249 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "566307038" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.388904975460396 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "566458505" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.5734831485507546 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "567878987" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.023065003016850102 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "569299884" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.25370787265195377 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "569396924" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.645900001855245 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "569457162" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.02536969761848688 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "569645690" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.33875369710602926 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "569718097" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.130937904407918 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "569739027" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.8913632812558947 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "569792817" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.32014728021358524 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "569896493" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.24677444429842996 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "570008444" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.04717262487766527 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "570236381" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.1987850529824249 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "570278597" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.18666870320810927 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "570305847" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.6697891353029696 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "571006300" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.5483584854178851 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "571137446" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.9334048342565753 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "571177441" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.5095353175605455 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "571541565" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.3023644496821535 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "571642389" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.012153400034968356 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "571684733" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.3295011539739878 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "572606382" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.39982930963113233 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "572722662" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.35821891125574484 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "573083539" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.7380800965392033 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "573261515" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 3.0362137206911077 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "573720508" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.16420664062461185 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "573850303" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.07966748695647881 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "574823092" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.5769151387664898 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "575135986" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.7742500051212505 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "575302108" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.46941277333278303 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "575939366" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.13461516075518398 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "575970700" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.35821891125574484 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "576001843" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.5095353175605455 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "576095926" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.3300417421687927 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "576273468" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.008918241945654175 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "576411246" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.013913113500690683 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "577379202" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.18666890013185147 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "577665023" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.013533153728686532 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "578674360" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.3300417421687927 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "580013262" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.3023644496821535 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "580043440" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.10945239252166916 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "580051759" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.09488105997992317 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "580095647" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.44521963202210185 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "580095655" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 3.1735601790694052 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "580163817" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.08820268448434164 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "581026088" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.35821891125574484 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "581150104" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.25370787265195377 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "581153070" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.2201846066487152 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "581597734" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.8118311261253451 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "582838758" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.0031861867025168578 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "582867147" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.25370787265195377 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "582918858" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.8118311261253451 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "583136567" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.8185601216337741 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "583279803" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.004206774936718933 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "584196534" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.44521963202210185 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "584544569" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.02093072579447404 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "584944065" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.35821891125574484 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "584983136" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.3785688126842985 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "585035184" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.176607684127065 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "585900296" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.004675479618293662 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "587339481" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.5846212664111288 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "587344053" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 6.768789917036953 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "588191926" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.030531811214264803 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "588483711" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.0871675139454602 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "588655112" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.009201005898785102 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "589755795" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.013913113500690683 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "590047029" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.33875527185831794 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "590168385" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 8.363147230017738 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "591430494" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.01249478815690529 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "591460070" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.084421150799257 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "591548033" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.010004500187318006 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "592348507" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.06020585241475302 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "592407200" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.018957790570843847 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "592657427" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.7179160920137374 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "593270603" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.2201846066487152 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "593373156" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.01249478815690529 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "593552712" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.023065003016850102 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "594090967" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.004800392120969689 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "594320795" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.06020585241475302 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "595183197" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.0016893422942237078 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "595263154" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.4946417504903935 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "595718342" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.018957790570843847 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "595808594" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.10646240843119553 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "596509886" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.17514885618192602 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "596584192" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 8.363147230017738 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "596779487" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.32014728021358524 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "597028938" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.18666890013185147 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "598137246" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.02467671457928921 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "598564173" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.23896038044363163 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "598635821" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.25370787265195377 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "599320182" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.010888550367246334 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "599909878" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.04717262487766527 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "601273921" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.0555765530974188 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "601368107" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.10945239252166916 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "601423209" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.04717262487766527 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "601705404" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.02093072579447404 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "601805379" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.13461516075518398 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "601841437" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.10195928537852082 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "601887677" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.33875369710602926 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "601904502" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.043370805966706755 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "601910964" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.010004500187318006 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "602866800" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.004806764029990326 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "603187982" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.01249478815690529 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "603188560" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.39982930963113233 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "603224878" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.025369507288883358 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "603425659" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.5769151387664898 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "603452291" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.02785510253924671 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "603576132" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.851333405786526 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "603592541" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.01249478815690529 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "603978471" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.00683630871515263 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "604145810" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 4.922126366710094 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "604328043" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.33875369710602926 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "604529230" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.6840563012948264 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "605606109" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.15381644895969043 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "605688822" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.06513861896653071 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "605800963" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.03981754069888441 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "605859367" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.2932765374576143 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "605883133" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.340416243913461 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "606353987" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.010888550367246334 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "606802468" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.004806764029990326 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "607063420" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.1177095160323098 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "609517556" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.008918241945654175 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "609894681" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.25370787265195377 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "611638995" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.1257629287749802 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "611658482" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.2773059590202895 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "612044635" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.1257629287749802 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "612534310" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.25370787265195377 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "612536911" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.0036681616512588278 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "612543999" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.004806764029990326 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "612549085" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.0070284144749356535 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "613091721" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.22135263120587237 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "613599811" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.22463785776996853 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "613968705" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.16420664062461185 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "614556106" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.01713620266930891 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "614571626" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.004806764029990326 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "616779893" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.0555765530974188 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "617381605" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 3.5024987595698063 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "617388117" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.011194340976742026 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "617395455" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.011194340976742026 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "623587006" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.7202248573483123 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "626027944" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.010004500187318006 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "627823636" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.008918241945654175 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "627823695" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.3300417421687927 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "637115675" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.015457607046073024 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "637126541" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.6697891353029696 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "637154333" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.005136377604989536 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "637669270" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 3.2626971321126663 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "637671554" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.7202248573483123 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "637998955" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.0042686572473087 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "638056634" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.7797405703733798 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "638262558" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.004806764029990326 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "638862121" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.0555765530974188 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "639117196" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.10945239252166916 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "639117826" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.6697891353029696 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "639251932" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.02785510253924671 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "639931541" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.637592192439886 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "639932847" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.1671327927939656 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "640198011" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.01249478815690529 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "642278925" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.06856763422911456 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "642884591" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.03499579649166685 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "643062797" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.008918241945654175 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "643592303" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.0268713324812213 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "643645390" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.7078922205943456 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "644026238" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.0042686572473087 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "644051974" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.05193423070926114 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "644386884" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.03340991124365688 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "644947716" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.18666890013185147 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "645086975" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.35821891125574484 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "645256361" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.6375913277138987 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "645413759" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 9.411350110519612 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "647143225" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.9770099651109624 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "647155122" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 5.325219906239479 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "647595665" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.0042686572473087 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "647595671" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.26917180642766325 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "647598519" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.043370805966706755 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "647603932" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.2932765374576143 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "649324898" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.00792828607396593 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "650079244" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.0070284144749356535 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "651769499" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.011194340976742026 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "651770186" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.0042686572473087 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "651770380" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.020359018390669878 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "651770794" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.039913880050211 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "652091264" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.011194340976742026 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "652094901" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.403272005336389 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "652094917" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.10161498344419856 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "652096183" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.835144266372599 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "652737867" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.200517017107563 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "652842495" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.0012442981468401377 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "652842572" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.8224859034989325 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "652989442" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.004806764029990326 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "653123929" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 5.08619628678848 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "653125130" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 3.3808865806678354 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "653126877" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.0617235583547489 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "653551965" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.46941277333278303 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "653932505" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.7027587323094481 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "657009581" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.010004500187318006 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "657016267" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.13461516075518398 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "657078119" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.006212032905700778 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "657080632" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.9277802792842125 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "657082055" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.8508504390359611 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "657224241" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.010004500187318006 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "657389972" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.0031861867025168578 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "657390171" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.0036681616512588278 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "657391037" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.06513861896653071 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "657391625" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 4.762346453356005 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "657650110" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.007028380394081791 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "657775947" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.010004500187318006 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "657776356" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.018957790570843847 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "657785850" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.07038855949889009 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "657914280" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.8772364540477496 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "657915168" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.009683929462543437 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "658020691" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.0070284144749356535 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "658518486" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.01249478815690529 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "658533763" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.011759541585987151 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "658854537" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.4476727659576842 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "659491419" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.025369507288883358 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "660064796" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.02785510253924671 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "660510593" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.06513861896653071 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "660513003" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.2481494819380992 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "661328410" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.6697891353029696 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "661437140" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.2524339039644827 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "662033243" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.2138380075850978 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "662219852" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.5355278239174325 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "662348804" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.455641206044372 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "662351164" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.2853837422609336 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "662358771" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.37577651293134423 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "662359728" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.15381644895969043 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "662361096" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.006042280763396215 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "662974315" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.1177095160323098 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "662982346" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.004806764029990326 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "663479824" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.005473464910660895 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "663485329" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.9462355656624517 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "663866413" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.01713620266930891 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "663876406" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.34116530807680584 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "664404274" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.2853837422609336 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "664914611" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.08363992660480626 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "665307545" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.0931973610099635 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "665722301" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.005473464910660895 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "665726618" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.005179257199460329 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "667004159" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.1680436592876497 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "669237515" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.3878657909346162 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "669859475" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.06020584725425155 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "669861524" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.020281463201356417 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "670395725" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.2524339039644827 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "670395999" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.00792828607396593 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "670721589" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.35821891125574484 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "670728674" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.006286042355235338 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "671618887" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.008119881163602558 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "672206735" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.00792828607396593 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "672207947" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 4.164122743198264 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "672211004" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.02785510253924671 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "673171528" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.011462173907497843 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "673475020" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.02785510253924671 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "673914981" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.013913142913642065 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "674275260" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.8508504390359611 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "674276329" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.007502151471752113 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "674679019" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 3.0362137206911077 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "675477919" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.006212032905700778 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "676024666" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.01713620266930891 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "679700458" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.04717262487766527 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "679702884" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.8224859034989325 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "680150733" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.01713620266930891 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "680156911" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 5.7867360210555585 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "682049099" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.13461516075518398 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "682051855" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.2524339039644827 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "683253712" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.08190467276122962 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "683257169" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 5.08619628678848 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "685816006" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.14396072214734543 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "686441799" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.15381644895969043 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "686442556" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.6375913277138987 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "686449092" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.02522937595647806 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "686909240" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.010004500187318006 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "688580172" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.3023644496821535 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "688678766" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 6.768789917036953 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "691197571" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.023065003016850102 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "692345003" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.014818171910401934 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "692345336" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.011194340976742026 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "696156783" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.007324408629647009 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "698260532" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.03789471285263759 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "698762886" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 3.888455844045363 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "699155265" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.4946417504903935 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "701046700" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.10195928537852082 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "702934964" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 7.41918965668555 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "703308071" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.18666870320810927 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "704298735" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.2524339039644827 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "707006626" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.2249092631979409 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "707923645" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 3.2626971321126663 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "710502981" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.010004500187318006 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "710778377" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.023065003016850102 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "712178483" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.0070284144749356535 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "712178511" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.5769151387664898 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "712919665" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.0555765530974188 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "715923832" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.009731273684390087 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "716956096" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.03981754069888441 - metrics: - - metric: accuracy diff --git a/examples/capoyo/configs/dataset/allen_bo_weighted.yaml b/examples/capoyo/configs/dataset/allen_bo_weighted.yaml deleted file mode 100644 index 1d35730..0000000 --- a/examples/capoyo/configs/dataset/allen_bo_weighted.yaml +++ /dev/null @@ -1,6448 +0,0 @@ - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "501021421" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.44941972273458985 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "501574836" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 3.595357781876719 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "501729039" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 3.8828498000137595 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "501876401" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.658898980831049 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "501929610" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.922686514861799 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "501933264" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.878635378873114 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "501940850" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 3.0754846059633545 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "502115959" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.162762999961795 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "502199136" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.5312834893529943 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "502205092" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.9032614531562366 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "502376461" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.1860743441385724 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "502608215" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 5.551871791193672 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "502666254" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.4584553684371449 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "502793808" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 3.063639525999618 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "502962794" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.969204912315843 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "503109347" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 3.256405129180655 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "503324629" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.5365699805383712 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "503412730" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.3686063670917115 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "504115289" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.46715725204253855 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "504568756" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.6482404605396255 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "504853580" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.8176402860784563 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "505407318" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 3.0011244198202536 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "505845219" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.1395842225335202 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "506540916" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.1465564434754596 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "506773185" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.8984517243924183 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "506773892" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.801674695641955 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "506809539" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.2641253982827743 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "506823562" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.7580866231788008 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "507129766" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.3376554527102625 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "507691036" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.533830717781411 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "507990552" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.9367495156405343 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "508356957" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.7176402741675815 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "508563988" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.4853565320479496 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "508753256" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.2658894193306755 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "509580400" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.1550102992201976 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "509904120" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.25806007887704174 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "509958730" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 4.667739976187712 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "510093797" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.1724575206892112 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "510214538" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.726766982541863 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "510390912" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.8709478671160154 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "510517131" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.658898980831049 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "510859641" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.1724575206892112 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "510917254" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.6482404605396255 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "511194579" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.8440159221799373 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "511440894" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.30953287365093357 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "511534603" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.4671561611593439 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "511573879" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.8769147217569397 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "511595995" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.9551792049170512 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "512164988" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.5312834893529943 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "512270518" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.162762999961795 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "512311673" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.2641286326918313 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "524691284" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 4.185278078945336 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "526504941" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.7665535053013005 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "527048992" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 4.108244247670406 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "528402271" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 3.2027217306423283 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "529688779" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.5907710690492627 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "530645663" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.3044312033194255 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "531134090" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.5427778337277501 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "539290504" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.2703453749952244 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "540684467" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.9394545629904147 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "541010698" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.1073369558316397 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "541290571" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.35233261994329534 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "545446482" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.9844197482930284 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "546641574" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.4584553684371449 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "546716391" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.35233261994329534 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "548379748" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.28301683257884874 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "550455111" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.5766556823461766 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "550490398" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.25806007887704174 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "550851591" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.3233891273301655 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "551834174" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.4321349239813761 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "551888519" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.5427778337277501 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "552410386" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.9844197482930284 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "552427971" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 3.8828522563835968 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "552760671" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.346321916830166 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "553568031" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.089085336088257 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "554037270" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.09581975058085923 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "555040116" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.36743182037380184 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "555749369" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.05839492441591674 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "556321897" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.24615061403948038 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "556344224" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.13841711947895496 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "556353209" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.08678037043397435 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "556665481" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.07826818229532366 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "557225279" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.701075794082444 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "557227804" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.1284315720455074 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "557304694" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.5040201894093868 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "557615965" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.06300252367617334 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "557848210" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.8769147217569397 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "558476282" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.17287925837732274 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "558670888" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.1050158777860446 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "559192380" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.41772583080188413 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "559382012" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.212633517407085 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "560027980" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.08970503427094768 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "560578599" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.17287925837732274 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "560809202" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.1465564434754596 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "560866155" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.07826818229532366 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "560898462" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 4.032175123849126 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "560920977" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.1823077846155608 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "561312435" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 4.667739976187712 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "561472633" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.587123142149057 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "562052595" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.8318671464718939 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "562536153" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.315977809817887 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "562711440" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.10220503575980704 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "563176332" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 3.1912738180681406 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "563710064" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.1073369558316397 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "564425777" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 3.063639525999618 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "564607188" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.8709478671160154 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "565216523" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.969204912315843 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "565698388" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.10886909577625198 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "566096665" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.5040201894093868 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "566307038" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.7539174530941634 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "566458505" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.7439537186672993 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "567878987" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.13841711947895496 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "569299884" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.5834693724573836 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "569396924" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.382109678502864 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "569457162" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.14655710318053647 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "569645690" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.6939818685569346 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "569718097" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.3923348881987471 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "569739027" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.2400823937615808 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "569792817" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.6708533420603332 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "569896493" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.5738492353153212 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "570008444" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.212633517407085 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "570236381" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.5040201894093868 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "570278597" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.48535622483583873 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "570305847" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.044673448571901 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "571006300" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.9265234990309444 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "571137446" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.2748518695811442 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "571177441" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.701075794082444 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "571541565" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.6482404605396255 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "571642389" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.09424023785717563 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "571684733" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.6825458990729386 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "572606382" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.7665535053013005 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "572722662" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.7176402741675815 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "573083539" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.1073369558316397 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "573261515" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.587123142149057 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "573720508" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.44941972273458985 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "573850303" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.29119664598504635 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "574823092" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.9551792049170512 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "575135986" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.1395842225335202 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "575302108" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.8440159221799373 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "575939366" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.3989092272585176 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "575970700" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.7176402741675815 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "576001843" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.701075794082444 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "576095926" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.5766556823461766 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "576273468" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.07826818229532366 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "576411246" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.10220503575980704 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "577379202" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.4853565320479496 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "577665023" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.10052107092311645 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "578674360" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.5766556823461766 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "580013262" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.6482404605396255 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "580043440" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.35233261994329534 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "580051759" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.3233891273301655 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "580095647" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.8176402860784563 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "580095655" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.656719727015531 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "580163817" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.30953287365093357 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "581026088" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.7176402741675815 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "581150104" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.5834693724573836 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "581153070" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.4971765725957165 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "581597734" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.1724575206892112 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "582838758" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.04220660599338369 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "582867147" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.5834693724573836 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "582918858" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.1724575206892112 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "583136567" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.178278755812982 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "583279803" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.04986401727820136 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "584196534" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.8176402860784563 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "584544569" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.13058348974509681 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "584944065" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.7176402741675815 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "584983136" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.7418303502235651 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "585035184" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.4648619756301335 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "585900296" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.05312676829707637 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "587339481" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.7513501574269634 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "587344053" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 4.185278078945336 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "588191926" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.16378315723213752 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "588483711" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.3969973350821798 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "588655112" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.07974783160968892 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "589755795" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.10220503575980704 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "590047029" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.6939838042084638 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "590168385" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 4.751596383959022 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "591430494" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.09581975058085923 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "591460070" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.064471921511807 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "591548033" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.08385615318370444 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "592348507" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.24615061403948038 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "592407200" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.12305246853662855 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "592657427" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.089085336088257 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "593270603" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.4971765725957165 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "593373156" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.09581975058085923 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "593552712" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.13841711947895496 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "594090967" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.05397389082370323 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "594320795" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.24615061403948038 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "595183197" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.028843522069112223 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "595263154" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.8709478671160154 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "595718342" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.12305246853662855 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "595808594" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.3465257161379496 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "596509886" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.4671561611593439 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "596584192" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 4.751596383959022 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "596779487" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.6708533420603332 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "597028938" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.4853565320479496 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "598137246" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.14414185288132775 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "598564173" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.5628766568122078 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "598635821" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.5834693724573836 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "599320182" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.08822663925536064 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "599909878" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.212633517407085 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "601273921" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.23461331851365466 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "601368107" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.35233261994329534 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "601423209" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.212633517407085 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "601705404" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.13058348974509681 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "601805379" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.3989092272585176 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "601841437" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.3376554527102625 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "601887677" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.6939818685569346 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "601904502" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.20217906946661496 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "601910964" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.08385615318370444 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "602866800" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.05401686549767201 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "603187982" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.09581975058085923 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "603188560" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.7665535053013005 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "603224878" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.1465564434754596 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "603425659" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.9551792049170512 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "603452291" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.1550102992201976 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "603576132" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.922686514861799 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "603592541" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.09581975058085923 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "603978471" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.06672839784884807 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "604145810" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 3.4570793918510088 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "604328043" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.6939818685569346 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "604529230" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.0579686685663945 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "605606109" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.4321349239813761 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "605688822" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.25806007887704174 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "605800963" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.1920712475672964 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "605859367" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.6364790156799608 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "605883133" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.2130618922429264 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "606353987" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.08822663925536064 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "606802468" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.05401686549767201 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "607063420" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.420414400962701 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "609517556" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.07826818229532366 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "609894681" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.5834693724573836 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "611638995" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.38295635716057747 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "611658482" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.1770606457665016 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "612044635" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.38295635716057747 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "612534310" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.5834693724573836 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "612536911" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.04592897754672523 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "612543999" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.05401686549767201 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "612549085" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.06784722921596877 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "613091721" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.537611451546049 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "613599811" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.5423847460754592 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "613968705" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.44941972273458985 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "614556106" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.11581543737886806 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "614571626" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.05401686549767201 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "616779893" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.23461331851365466 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "617381605" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.818671678423563 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "617388117" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.08970503427094768 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "617395455" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.08970503427094768 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "623587006" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.4220359253155874 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "626027944" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.08385615318370444 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "627823636" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.07826818229532366 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "627823695" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.5766556823461766 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "637115675" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.10886870458333112 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "637126541" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.044673448571901 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "637154333" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.05620977720851299 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "637669270" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.7012436216821 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "637671554" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.4220359253155874 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "637998955" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.0164676483933586 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "638056634" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.1444261597894803 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "638262558" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.05401686549767201 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "638862121" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.23461331851365466 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "639117196" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.35233261994329534 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "639117826" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.044673448571901 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "639251932" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.1550102992201976 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "639931541" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.0142464397060256 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "639932847" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.1132409219737394 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "640198011" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.09581975058085923 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "642278925" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.2661271584919584 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "642884591" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.17775721079656182 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "643062797" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.07826818229532366 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "643592303" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.3499799410726379 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "643645390" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.8318671464718939 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "644026238" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.0164676483933586 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "644051974" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.22526302893120498 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "644386884" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.17287925837732274 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "644947716" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.4853565320479496 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "645086975" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.7176402741675815 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "645256361" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.0142456143708014 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "645413759" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 5.100454240780362 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "647143225" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.3102588256657153 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "647155122" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 3.624267342724115 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "647595665" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.0164676483933586 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "647595671" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.6045544734970384 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "647598519" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.20217906946661496 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "647603932" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.6364790156799608 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "649324898" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.07293320269919541 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "650079244" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.06784722921596877 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "651769499" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.08970503427094768 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "651770186" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.0164676483933586 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "651770380" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.1284315720455074 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "651770794" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.360241819179289 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "652091264" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.08970503427094768 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "652094901" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.6281814949647762 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "652094917" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.33697086141692406 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "652096183" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.1925445170463516 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "652737867" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.4826501640717862 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "652842495" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.02400890594591205 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "652842572" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.4762629892074686 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "652989442" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.05401686549767201 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "653123929" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 3.5257666669853274 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "653125130" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.7595359655147904 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "653126877" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.3772873862115205 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "653551965" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.8440159221799373 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "653932505" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.075230185707182 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "657009581" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.08385615318370444 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "657016267" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.3989092272585176 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "657078119" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.06300252367617334 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "657080632" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.5312834893529943 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "657082055" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.205950922476421 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "657224241" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.08385615318370444 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "657389972" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.04220660599338369 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "657390171" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.04592897754672523 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "657391037" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.25806007887704174 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "657391625" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 3.389302169218975 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "657650110" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.0678470318206312 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "657775947" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.08385615318370444 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "657776356" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.12305246853662855 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "657785850" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.2703453749952244 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "657914280" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.2282526226053683 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "657915168" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.08223347976787895 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "658020691" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.06784722921596877 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "658518486" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.09581975058085923 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "658533763" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.09239573680072659 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "658854537" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.658898980831049 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "659491419" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.1465564434754596 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "660064796" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.1550102992201976 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "660510593" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.25806007887704174 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "660513003" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.5176710788492183 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "661328410" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.044673448571901 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "661437140" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.162762999961795 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "662033243" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.526585185964039 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "662219852" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.9134545043284298 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "662348804" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.2778055409302804 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "662351164" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.6261454583625893 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "662358771" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.7385424740102849 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "662359728" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.4321349239813761 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "662361096" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.06196382783727918 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "662974315" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.420414400962701 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "662982346" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.05401686549767201 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "663479824" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.05839492441591674 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "663485329" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.9812302051553052 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "663866413" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.11581543737886806 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "663876406" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.6969419648212273 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "664404274" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.6261454583625893 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "664914611" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.299823647677456 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "665307545" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.4016411604835577 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "665722301" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.05839492441591674 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "665726618" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.05649085978943922 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "667004159" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.4584553684371449 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "669237515" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.6174325557329197 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "669859475" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.24615060138030617 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "669861524" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.12813780138618497 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "670395725" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.162762999961795 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "670395999" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.07293320269919541 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "670721589" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.7176402741675815 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "670728674" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.06345181940240908 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "671618887" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.07398565136513341 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "672206735" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.07293320269919541 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "672207947" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 3.127028296253404 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "672211004" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.1550102992201976 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "673171528" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.09098669578202988 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "673475020" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.1550102992201976 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "673914981" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.10220516539939391 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "674275260" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.205950922476421 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "674276329" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.07055522721427628 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "674679019" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.587123142149057 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "675477919" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.06300252367617334 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "676024666" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.11581543737886806 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "679700458" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.212633517407085 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "679702884" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.4762629892074686 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "680150733" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.11581543737886806 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "680156911" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 3.8095870381700028 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "682049099" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.3989092272585176 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "682051855" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.162762999961795 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "683253712" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.29607579588646393 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "683257169" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 3.5257666669853274 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "685816006" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.4153020813111212 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "686441799" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.4321349239813761 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "686442556" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 1.0142456143708014 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "686449092" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.14607019289144488 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "686909240" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.08385615318370444 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "688580172" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.6482404605396255 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "688678766" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 4.185278078945336 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "691197571" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.13841711947895496 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "692345003" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.10614376174657016 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "692345336" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.08970503427094768 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "696156783" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.06954745418492055 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "698260532" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.18645106570548603 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "698762886" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 3.0011244198202536 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "699155265" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.8709478671160154 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "701046700" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.3376554527102625 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "702934964" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 4.422130772829204 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "703308071" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.48535622483583873 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "704298735" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.162762999961795 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "707006626" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.5427778337277501 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "707923645" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 2.7012436216821 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "710502981" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.08385615318370444 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "710778377" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.13841711947895496 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "712178483" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.06784722921596877 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "712178511" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.9551792049170512 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "712919665" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.23461331851365466 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "715923832" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.0824744655356459 - metrics: - - metric: accuracy - -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortset: "716956096" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 0.1920712475672964 - metrics: - - metric: accuracy diff --git a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium.yaml b/examples/capoyo/configs/dataset/allen_brain_observatory_calcium.yaml deleted file mode 100644 index 80449b1..0000000 --- a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium.yaml +++ /dev/null @@ -1,48 +0,0 @@ -- selection: - - dandiset: "allen_brain_observatory_calcium" - exclude_sortsets: - - "510514474" - - "512326618" - - "560926639" - - "595806300" - - "562122508" - - "623347352" - - "652737678" - - "555042467" - - "539487468" - - "710504563" - - "676503588" - - "671164733" - - "649401936" - - "595273803" - - "654532828" - - "505695962" - - "539497234" - - "547388708" - - "646016204" - - "637669284" - - "653122667" - - "623339221" - - "589441079" - - "603763073" - - "649938038" - - "645689073" - - "652092676" - - "649409874" -<<<<<<< HEAD - - "669233895" - - "689388034" -======= ->>>>>>> main - - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - metrics: - - metric: accuracy \ No newline at end of file diff --git a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_dg_nm.yaml b/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_dg_nm.yaml deleted file mode 100644 index 48d5490..0000000 --- a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_dg_nm.yaml +++ /dev/null @@ -1,52 +0,0 @@ -- selection: - - dandiset: "allen_brain_observatory_calcium" - exclude_sortsets: - - "510514474" - - "512326618" - - "560926639" - - "595806300" - - "562122508" - - "623347352" - - "652737678" - - "555042467" - - "539487468" - - "710504563" - - "676503588" - - "671164733" - - "649401936" - - "595273803" - - "654532828" - - "505695962" - - "539497234" - - "547388708" - - "646016204" - - "637669284" - - "653122667" - - "623339221" - - "589441079" - - "603763073" - - "649938038" - - "645689073" - - "652092676" - - "649409874" - - "669233895" - - "689388034" - config: - sampling_intervals_modifier: | - if split == "train": - sampling_intervals = sampling_intervals & (data.drifting_gratings | data.natural_movie_one_epochs) - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals & data.drifting_gratings - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - weight: 5.0 - metrics: - - metric: accuracy - - decoder_id: DRIFTING_GRATINGS_TEMP_FREQ - metrics: - - metric: accuracy - - decoder_id: NATURAL_MOVIE_FRAME - metrics: - - metric: frame_diff_acc \ No newline at end of file diff --git a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_model1_wildtype.yaml b/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_model1_wildtype.yaml deleted file mode 100644 index 0b1e553..0000000 --- a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_model1_wildtype.yaml +++ /dev/null @@ -1,109 +0,0 @@ -- selection: - - dandiset: "allen_brain_observatory_calcium" - #Creline: Emx1-IRES-Cre/wt - 40 sessions + #Creline: Slc17a7-IRES2-Cre/wt - 53 sessions in total - sortsets: - - "526504941" - - "528402271" - - "540684467" - - "541010698" - - "545446482" - - "546716391" - - "550455111" - - "557848210" - - "560898462" - #- "560926639" - - "561312435" - - "561472633" - - "562052595" - #- "562122508" - - "562536153" - - "563176332" - - "563710064" - - "564425777" - - "564607188" - - "566096665" - - "566458505" - - "569299884" - - "569792817" - - "573261515" - - "574823092" - - "575135986" - - "575970700" - - "577665023" - - "580013262" - - "580095647" - - "581597734" - - "582838758" - - "583136567" - - "585035184" - - "593270603" - - "594090967" - - "594320795" - - "595263154" - #- "595806300" - - "596584192" - - "604328043" - - "605859367" - - "612536911" - - "613091721" - - "613599811" - #- "637669284" - - "639117826" - - "639931541" - - "644947716" - - "645256361" - #- "646016204" - - "647155122" - - "651770186" - - "651770794" - - "652094901" - - "652096183" - - "652737867" - - "652842572" - #- "653122667" - - "653123929" - - "653125130" - - "653126877" - - "653551965" - - "653932505" - - "657080632" - - "657082055" - - "657391037" - - "657785850" - - "661328410" - - "661437140" - - "662033243" - - "662219852" - - "662348804" - - "662358771" - - "662359728" - - "662974315" - - "663485329" - - "663876406" - - "664404274" - - "664914611" - - "665307545" - - "670721589" - - "672207947" - - "674275260" - - "679700458" - - "679702884" - - "680156911" - - "682051855" - - "683257169" - - "688678766" - - "701046700" - - "703308071" - - "707923645" - - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - metrics: - - metric: accuracy \ No newline at end of file diff --git a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_model2_excitatory.yaml b/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_model2_excitatory.yaml deleted file mode 100644 index e30e178..0000000 --- a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_model2_excitatory.yaml +++ /dev/null @@ -1,348 +0,0 @@ -- selection: - - dandiset: "allen_brain_observatory_calcium" -#Creline: Cux2-CreERT2/Cux2-CreERT2 - 67 sessions - sortsets: - - "501574836" - - "501876401" - - "501933264" - - "501940850" - - "502115959" - - "502199136" - - "502205092" - - "502376461" - - "502608215" - - "502666254" - - "502793808" - - "503109347" - - "503324629" - - "503412730" - - "504115289" - - "505407318" - - "505845219" - - "506773185" - - "506773892" - - "506809539" - - "506823562" - - "507691036" - - "507990552" - - "509958730" - #- "510514474" - - "510517131" - - "510859641" - - "512164988" - - "512311673" - #- "512326618" - - "524691284" - - "529688779" - - "552410386" - - "557225279" - - "557304694" - - "558670888" - - "565216523" - - "566307038" - - "569645690" - - "569739027" - - "570008444" - - "570305847" - - "571177441" - - "571541565" - - "591548033" - - "592657427" - - "593552712" - - "637154333" - - "638056634" - - "645413759" - - "647595665" - - "652094917" - - "657914280" - - "658854537" - - "660510593" - - "660513003" - - "662351164" - - "663866413" - - "698260532" - - "699155265" - - "702934964" - - "704298735" - - "707006626" - - "710502981" - - "712178511" - - "712919665" - - "716956096" - #Creline: Emx1-IRES-Cre/wt - 40 sessions - - "526504941" - - "528402271" - - "540684467" - - "541010698" - - "545446482" - - "546716391" - - "550455111" - - "557848210" - - "560898462" - #- "560926639" - - "561312435" - - "561472633" - - "562052595" - #- "562122508" - - "562536153" - - "563176332" - - "563710064" - - "564425777" - - "564607188" - - "566096665" - - "566458505" - - "569299884" - - "569792817" - - "573261515" - - "574823092" - - "575135986" - - "575970700" - - "577665023" - - "580013262" - - "580095647" - - "581597734" - - "582838758" - - "583136567" - - "585035184" - - "593270603" - - "594090967" - - "594320795" - - "595263154" - #- "595806300" - - "596584192" - #Creline: Fezf2-CreER/wt - 7 sessions in total - - "611658482" - #- "623347352" - - "637998955" - - "639932847" - - "643592303" - #- "652737678" - - "674679019" - #Creline: Nr5a1-Cre/wt - 37 sessions in total - - "539290504" - #- "539487468" - - "548379748" - - "550490398" - - "554037270" - #- "555042467" - - "555749369" - - "556321897" - - "556344224" - - "556665481" - - "557227804" - - "557615965" - - "558476282" - - "559382012" - - "560027980" - - "560578599" - - "560809202" - - "560866155" - - "560920977" - - "562711440" - - "565698388" - - "567878987" - - "570278597" - - "571006300" - - "571642389" - - "593373156" - - "595183197" - - "595808594" - - "603224878" - - "603592541" - - "638262558" - - "638862121" - - "658533763" - - "659491419" - - "660064796" - - "682049099" - - "685816006" - #Creline: Ntsr1-Cre_GN220/wt - 18 sessions in total - #sortsets: - - "603576132" - - "604145810" - - "604529230" - - "605883133" - - "627823695" - - "637126541" - - "645086975" - - "647143225" - - "647595671" - - "665722301" - - "665726618" - - "667004159" - #- "669233895" - - "669237515" - - "669859475" - - "670395725" - #- "689388034" - - "698762886" - #Creline: Rbp4-Cre_KL100/wt - 38 sessions in total - #sortsets: - - "502962794" - - "504568756" - - "508563988" - - "510093797" - - "510917254" - - "511194579" - - "511440894" - - "511595995" - - "555040116" - - "556353209" - - "559192380" - - "571137446" - - "571684733" - - "572606382" - - "572722662" - - "573083539" - - "573850303" - - "575302108" - - "576001843" - - "578674360" - - "580051759" - - "584944065" - - "588191926" - - "588483711" - - "591430494" - - "592407200" - #- "595273803" - - "595718342" - - "598564173" - - "601368107" - - "601887677" - - "601904502" - - "616779893" - - "637115675" - - "642884591" - - "644051974" - - "647603932" - # - "649401936" -#Creline: Rorb-IRES2-Cre - 39 sessions in total - #sortsets: - - "501729039" - - "501929610" - - "504853580" - #- "505695962" - - "506540916" - - "507129766" - - "509580400" - - "509904120" - - "510214538" - - "510390912" - - "511573879" - - "512270518" - - "527048992" - - "546641574" - - "550851591" - - "551834174" - - "551888519" - - "552427971" - - "552760671" - - "553568031" - - "569396924" - - "569457162" - - "569718097" - - "569896493" - - "570236381" - - "576411246" - - "587339481" - - "587344053" - - "588655112" - - "590168385" - - "591460070" - - "605606109" - - "605800963" - - "611638995" - - "640198011" - - "644026238" - - "644386884" - #- "654532828" - - "686441799" -#Creline: Scnn1a-Tg3-Cre/wt - 9 sessions in total - #sortsets: - - "501021421" - - "508356957" - - "508753256" - - "511534603" - - "530645663" - - "531134090" - #- "539497234" - - "541290571" - #- "547388708" -#Creline: Slc17a7-IRES2-Cre/wt - 53 sessions in total - #sortsets: - - "604328043" - - "605859367" - - "612536911" - - "613091721" - - "613599811" - #- "637669284" - - "639117826" - - "639931541" - - "644947716" - - "645256361" - #- "646016204" - - "647155122" - - "651770186" - - "651770794" - - "652094901" - - "652096183" - - "652737867" - - "652842572" - #- "653122667" - - "653123929" - - "653125130" - - "653126877" - - "653551965" - - "653932505" - - "657080632" - - "657082055" - - "657391037" - - "657785850" - - "661328410" - - "661437140" - - "662033243" - - "662219852" - - "662348804" - - "662358771" - - "662359728" - - "662974315" - - "663485329" - - "663876406" - - "664404274" - - "664914611" - - "665307545" - - "670721589" - - "672207947" - - "674275260" - - "679700458" - - "679702884" - - "680156911" - - "682051855" - - "683257169" - - "688678766" - - "701046700" - - "703308071" - - "707923645" - #Creline: Tlx3-Cre_PL56/wt - 9 sessions in total - #sortsets: - - "617381605" - - "623587006" - - "637669270" - - "637671554" - - "643645390" - #- "645689073" - #- "649938038" - - "657016267" - - "657391625" - - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - metrics: - - metric: accuracy \ No newline at end of file diff --git a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_model4_no_visp.yaml b/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_model4_no_visp.yaml deleted file mode 100644 index 97933c2..0000000 --- a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_model4_no_visp.yaml +++ /dev/null @@ -1,182 +0,0 @@ -- selection: - - dandiset: "allen_brain_observatory_calcium" - exclude_sortsets: -#VIS_P - 135 sessions - - "501021421" - - "501574836" - - "501729039" - - "502115959" - - "502205092" - - "502608215" - - "502962794" - - "503109347" - - "508356957" - - "508753256" - - "510214538" - - "510514474" - - "510517131" - - "511534603" - - "512270518" - - "524691284" - - "526504941" - - "527048992" - - "528402271" - - "530645663" - - "531134090" - - "539290504" - - "539487468" - - "539497234" - - "540684467" - - "541010698" - - "541290571" - - "545446482" - - "546716391" - - "547388708" - - "559192380" - - "561312435" - - "570278597" - - "571137446" - - "573720508" - - "575939366" - - "576095926" - - "577379202" - - "580043440" - - "580163817" - - "581026088" - - "581150104" - - "582918858" - - "583279803" - - "584196534" - - "585900296" - - "587344053" - - "590047029" - - "590168385" - - "592407200" - - "593373156" - - "595263154" - - "595806300" - - "596584192" - - "596779487" - - "598564173" - - "598635821" - - "601423209" - - "603224878" - - "603576132" - - "604145810" - - "606353987" - - "609894681" - - "612044635" - - "612543999" - - "613968705" - - "617381605" - - "617395455" - - "623347352" - - "627823695" - - "637115675" - - "637669270" - - "637671554" - - "637998955" - - "643592303" - - "643645390" - - "644026238" - - "645086975" - - "645413759" - - "647155122" - - "649401936" - - "649938038" - - "650079244" - - "652091264" - - "652094901" - - "652842495" - - "652842572" - - "653122667" - - "653125130" - - "653932505" - - "657016267" - - "657078119" - - "657080632" - - "657082055" - - "657389972" - - "657390171" - - "657650110" - - "657775947" - - "658518486" - - "659491419" - - "660513003" - - "661328410" - - "661437140" - - "662033243" - - "662361096" - - "662974315" - - "663479824" - - "663485329" - - "664404274" - - "665722301" - - "669861524" - - "670395999" - - "670728674" - - "671164733" - - "671618887" - - "672206735" - - "672211004" - - "673171528" - - "673914981" - - "674679019" - - "675477919" - - "676503588" - - "679702884" - - "680150733" - - "680156911" - - "683257169" - - "688678766" - - "689388034" - - "692345003" - - "702934964" - - "704298735" - - "710504563" - - "710778377" - - "712178483" - - "712178511" - #make sure to exclude all heldout too - #- "510514474" duplicate - - "512326618" - - "560926639" - #- "595806300" duplicate - - "562122508" - #- "623347352" duplicate - - "652737678" - - "555042467" - #- "539487468" duplicate - #- "710504563" duplicate - #- "676503588" duplicate - #- "671164733" duplicate - #- "649401936" duplicate - - "595273803" - - "654532828" - - "505695962" - #- "539497234" duplicate - #- "547388708" duplicate - - "646016204" - - "637669284" - #- "653122667" duplicate - - "623339221" - - "589441079" - - "603763073" - #- "649938038" duplicate - - "645689073" - - "652092676" - - "649409874" - - "669233895" - #- "689388034" duplicate - - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - metrics: - - metric: accuracy \ No newline at end of file diff --git a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_model5_visp_only.yaml b/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_model5_visp_only.yaml deleted file mode 100644 index 5ea9b82..0000000 --- a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_model5_visp_only.yaml +++ /dev/null @@ -1,151 +0,0 @@ -- selection: - - dandiset: "allen_brain_observatory_calcium" -#VIS_P - 135 sessions (remove ones in heldout) - sortsets: - - "501021421" - - "501574836" - - "501729039" - - "502115959" - - "502205092" - - "502608215" - - "502962794" - - "503109347" - - "508356957" - - "508753256" - - "510214538" - #- "510514474" - - "510517131" - - "511534603" - - "512270518" - - "524691284" - - "526504941" - - "527048992" - - "528402271" - - "530645663" - - "531134090" - - "539290504" - #- "539487468" - #- "539497234" - - "540684467" - - "541010698" - - "541290571" - - "545446482" - - "546716391" - #- "547388708" - - "559192380" - - "561312435" - - "570278597" - - "571137446" - - "573720508" - - "575939366" - - "576095926" - - "577379202" - - "580043440" - - "580163817" - - "581026088" - - "581150104" - - "582918858" - - "583279803" - - "584196534" - - "585900296" - - "587344053" - - "590047029" - - "590168385" - - "592407200" - - "593373156" - - "595263154" - #- "595806300" - - "596584192" - - "596779487" - - "598564173" - - "598635821" - - "601423209" - - "603224878" - - "603576132" - - "604145810" - - "606353987" - - "609894681" - - "612044635" - - "612543999" - - "613968705" - - "617381605" - - "617395455" - #- "623347352" - - "627823695" - - "637115675" - - "637669270" - - "637671554" - - "637998955" - - "643592303" - - "643645390" - - "644026238" - - "645086975" - - "645413759" - - "647155122" - #- "649401936" - #- "649938038" - - "650079244" - - "652091264" - - "652094901" - - "652842495" - - "652842572" - #- "653122667" - - "653125130" - - "653932505" - - "657016267" - - "657078119" - - "657080632" - - "657082055" - - "657389972" - - "657390171" - - "657650110" - - "657775947" - - "658518486" - - "659491419" - - "660513003" - - "661328410" - - "661437140" - - "662033243" - - "662361096" - - "662974315" - - "663479824" - - "663485329" - - "664404274" - - "665722301" - - "669861524" - - "670395999" - - "670728674" - #- "671164733" - - "671618887" - - "672206735" - - "672211004" - - "673171528" - - "673914981" - - "674679019" - - "675477919" - #- "676503588" - - "679702884" - - "680150733" - - "680156911" - - "683257169" - - "688678766" - #- "689388034" - - "692345003" - - "702934964" - - "704298735" - #- "710504563" - - "710778377" - - "712178483" - - "712178511" - - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - metrics: - - metric: accuracy \ No newline at end of file diff --git a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_cux2.yaml b/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_cux2.yaml deleted file mode 100644 index 2099573..0000000 --- a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_cux2.yaml +++ /dev/null @@ -1,79 +0,0 @@ -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortsets: - - "501574836" - - "501876401" - - "501933264" - - "501940850" - - "502115959" - - "502199136" - - "502205092" - - "502376461" - - "502608215" - - "502666254" - - "502793808" - - "503109347" - - "503324629" - - "503412730" - - "504115289" - - "505407318" - - "505845219" - - "506773185" - - "506773892" - - "506809539" - - "506823562" - - "507691036" - - "507990552" - - "509958730" - - "510517131" - - "510859641" - - "512164988" - - "512311673" - - "524691284" - - "529688779" - - "552410386" - - "557225279" - - "557304694" - - "558670888" - - "565216523" - - "566307038" - - "569645690" - - "569739027" - - "570008444" - - "570305847" - - "571177441" - - "571541565" - - "591548033" - - "592657427" - - "593552712" - - "637154333" - - "638056634" - - "645413759" - - "647595665" - - "652094917" - - "657914280" - - "658854537" - - "660510593" - - "660513003" - - "662351164" - - "663866413" - - "698260532" - - "699155265" - - "702934964" - - "704298735" - - "707006626" - - "710502981" - - "712178511" - - "712919665" - - "716956096" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - metrics: - - metric: accuracy \ No newline at end of file diff --git a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_emx1.yaml b/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_emx1.yaml deleted file mode 100644 index 97911be..0000000 --- a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_emx1.yaml +++ /dev/null @@ -1,51 +0,0 @@ -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortsets: - - "526504941" - - "528402271" - - "540684467" - - "541010698" - - "545446482" - - "546716391" - - "550455111" - - "557848210" - - "560898462" - - "561312435" - - "561472633" - - "562052595" - - "562536153" - - "563176332" - - "563710064" - - "564425777" - - "564607188" - - "566096665" - - "566458505" - - "569299884" - - "569792817" - - "573261515" - - "574823092" - - "575135986" - - "575970700" - - "577665023" - - "580013262" - - "580095647" - - "581597734" - - "582838758" - - "583136567" - - "585035184" - - "593270603" - - "594090967" - - "594320795" - - "595263154" - - "596584192" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - metrics: - - metric: accuracy \ No newline at end of file diff --git a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_fezf2.yaml b/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_fezf2.yaml deleted file mode 100644 index c8765bf..0000000 --- a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_fezf2.yaml +++ /dev/null @@ -1,19 +0,0 @@ -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortsets: - - "611658482" - - "637998955" - - "639932847" - - "643592303" - - "674679019" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - metrics: - - metric: accuracy \ No newline at end of file diff --git a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_nr5a.yaml b/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_nr5a.yaml deleted file mode 100644 index cc8b161..0000000 --- a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_nr5a.yaml +++ /dev/null @@ -1,49 +0,0 @@ -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortsets: - - "539290504" - - "548379748" - - "550490398" - - "554037270" - - "555749369" - - "556321897" - - "556344224" - - "556665481" - - "557227804" - - "557615965" - - "558476282" - - "559382012" - - "560027980" - - "560578599" - - "560809202" - - "560866155" - - "560920977" - - "562711440" - - "565698388" - - "567878987" - - "570278597" - - "571006300" - - "571642389" - - "593373156" - - "595183197" - - "595808594" - - "603224878" - - "603592541" - - "638262558" - - "638862121" - - "658533763" - - "659491419" - - "660064796" - - "682049099" - - "685816006" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - metrics: - - metric: accuracy \ No newline at end of file diff --git a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_ntsr1.yaml b/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_ntsr1.yaml deleted file mode 100644 index fe60ab9..0000000 --- a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_ntsr1.yaml +++ /dev/null @@ -1,30 +0,0 @@ -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortsets: - - "603576132" - - "604145810" - - "604529230" - - "605883133" - - "627823695" - - "637126541" - - "645086975" - - "647143225" - - "647595671" - - "665722301" - - "665726618" - - "667004159" - - "669237515" - - "669859475" - - "670395725" - - "698762886" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - metrics: - - metric: accuracy \ No newline at end of file diff --git a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_pvalb.yaml b/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_pvalb.yaml deleted file mode 100644 index 07d00a0..0000000 --- a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_pvalb.yaml +++ /dev/null @@ -1,31 +0,0 @@ -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortsets: - - "669861524" - - "670395999" - - "670728674" - - "671618887" - - "672206735" - - "672211004" - - "673171528" - - "673475020" - - "673914981" - - "674276329" - - "675477919" - - "676024666" - - "680150733" - - "692345003" - - "710778377" - - "712178483" - - "715923832" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - metrics: - - metric: accuracy \ No newline at end of file diff --git a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_rbp4.yaml b/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_rbp4.yaml deleted file mode 100644 index 0c96b14..0000000 --- a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_rbp4.yaml +++ /dev/null @@ -1,50 +0,0 @@ -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortsets: - - "502962794" - - "504568756" - - "508563988" - - "510093797" - - "510917254" - - "511194579" - - "511440894" - - "511595995" - - "555040116" - - "556353209" - - "559192380" - - "571137446" - - "571684733" - - "572606382" - - "572722662" - - "573083539" - - "573850303" - - "575302108" - - "576001843" - - "578674360" - - "580051759" - - "584944065" - - "588191926" - - "588483711" - - "591430494" - - "592407200" - - "595718342" - - "598564173" - - "601368107" - - "601887677" - - "601904502" - - "616779893" - - "637115675" - - "642884591" - - "644051974" - - "647603932" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - metrics: - - metric: accuracy \ No newline at end of file diff --git a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_rorb.yaml b/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_rorb.yaml deleted file mode 100644 index c14aa7d..0000000 --- a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_rorb.yaml +++ /dev/null @@ -1,51 +0,0 @@ -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortsets: - - "501729039" - - "501929610" - - "504853580" - - "506540916" - - "507129766" - - "509580400" - - "509904120" - - "510214538" - - "510390912" - - "511573879" - - "512270518" - - "527048992" - - "546641574" - - "550851591" - - "551834174" - - "551888519" - - "552427971" - - "552760671" - - "553568031" - - "569396924" - - "569457162" - - "569718097" - - "569896493" - - "570236381" - - "576411246" - - "587339481" - - "587344053" - - "588655112" - - "590168385" - - "591460070" - - "605606109" - - "605800963" - - "611638995" - - "640198011" - - "644026238" - - "644386884" - - "686441799" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - metrics: - - metric: accuracy \ No newline at end of file diff --git a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_scnn1a.yaml b/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_scnn1a.yaml deleted file mode 100644 index 1786a93..0000000 --- a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_scnn1a.yaml +++ /dev/null @@ -1,21 +0,0 @@ -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortsets: - - "501021421" - - "508356957" - - "508753256" - - "511534603" - - "530645663" - - "531134090" - - "541290571" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - metrics: - - metric: accuracy \ No newline at end of file diff --git a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_slc17a7.yaml b/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_slc17a7.yaml deleted file mode 100644 index 9221269..0000000 --- a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_slc17a7.yaml +++ /dev/null @@ -1,64 +0,0 @@ -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortsets: - - "604328043" - - "605859367" - - "612536911" - - "613091721" - - "613599811" - - "639117826" - - "639931541" - - "644947716" - - "645256361" - - "647155122" - - "651770186" - - "651770794" - - "652094901" - - "652096183" - - "652737867" - - "652842572" - - "653123929" - - "653125130" - - "653126877" - - "653551965" - - "653932505" - - "657080632" - - "657082055" - - "657391037" - - "657785850" - - "661328410" - - "661437140" - - "662033243" - - "662219852" - - "662348804" - - "662358771" - - "662359728" - - "662974315" - - "663485329" - - "663876406" - - "664404274" - - "664914611" - - "665307545" - - "670721589" - - "672207947" - - "674275260" - - "679700458" - - "679702884" - - "680156911" - - "682051855" - - "683257169" - - "688678766" - - "701046700" - - "703308071" - - "707923645" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - metrics: - - metric: accuracy \ No newline at end of file diff --git a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_sst.yaml b/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_sst.yaml deleted file mode 100644 index 2b4edaa..0000000 --- a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_sst.yaml +++ /dev/null @@ -1,59 +0,0 @@ -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortsets: - - "573720508" - - "575939366" - - "576095926" - - "577379202" - - "580043440" - - "580095655" - - "580163817" - - "581026088" - - "581150104" - - "581153070" - - "582867147" - - "582918858" - - "584196534" - - "584544569" - - "584983136" - - "589755795" - - "590047029" - - "592348507" - - "596509886" - - "596779487" - - "597028938" - - "598137246" - - "598635821" - - "599320182" - - "599909878" - - "601273921" - - "601423209" - - "601805379" - - "601841437" - - "603188560" - - "603425659" - - "605688822" - - "607063420" - - "609894681" - - "612044635" - - "612534310" - - "612549085" - - "613968705" - - "639117196" - - "639251932" - - "642278925" - - "643062797" - - "683253712" - - "686442556" - - "688580172" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - metrics: - - metric: accuracy \ No newline at end of file diff --git a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_tlx3.yaml b/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_tlx3.yaml deleted file mode 100644 index 10f24af..0000000 --- a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_tlx3.yaml +++ /dev/null @@ -1,21 +0,0 @@ -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortsets: - - "617381605" - - "623587006" - - "637669270" - - "637671554" - - "643645390" - - "657016267" - - "657391625" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - metrics: - - metric: accuracy \ No newline at end of file diff --git a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_vip.yaml b/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_vip.yaml deleted file mode 100644 index 7f75e6e..0000000 --- a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_vip.yaml +++ /dev/null @@ -1,60 +0,0 @@ -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortsets: - - "576273468" - - "583279803" - - "585900296" - - "601705404" - - "601910964" - - "602866800" - - "603187982" - - "603452291" - - "603978471" - - "606353987" - - "606802468" - - "609517556" - - "612543999" - - "614556106" - - "614571626" - - "617388117" - - "617395455" - - "626027944" - - "627823636" - - "647598519" - - "649324898" - - "650079244" - - "651769499" - - "651770380" - - "652091264" - - "652842495" - - "652989442" - - "657009581" - - "657078119" - - "657224241" - - "657389972" - - "657390171" - - "657650110" - - "657775947" - - "657776356" - - "657915168" - - "658020691" - - "658518486" - - "662361096" - - "662982346" - - "663479824" - - "686449092" - - "686909240" - - "691197571" - - "692345336" - - "696156783" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - metrics: - - metric: accuracy \ No newline at end of file diff --git a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_visal.yaml b/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_visal.yaml deleted file mode 100644 index bd65d5b..0000000 --- a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_visal.yaml +++ /dev/null @@ -1,50 +0,0 @@ -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortsets: - - "501876401" - - "501933264" - - "502199136" - - "503412730" - - "504568756" - - "504853580" - - "505407318" - - "506773892" - - "509904120" - - "510390912" - - "511440894" - - "512164988" - - "548379748" - - "557225279" - - "558670888" - - "559382012" - - "560898462" - - "561472633" - - "562052595" - - "562536153" - - "563176332" - - "563710064" - - "569299884" - - "569396924" - - "572722662" - - "573083539" - - "584944065" - - "588483711" - - "589441079" - - "591460070" - - "595183197" - - "605859367" - - "638056634" - - "638862121" - - "639931541" - - "685816006" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - metrics: - - metric: accuracy \ No newline at end of file diff --git a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_visam.yaml b/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_visam.yaml deleted file mode 100644 index 371c425..0000000 --- a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_visam.yaml +++ /dev/null @@ -1,47 +0,0 @@ -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortsets: - - "550851591" - - "551834174" - - "552760671" - - "556344224" - - "556353209" - - "556665481" - - "557304694" - - "560027980" - - "562711440" - - "565216523" - - "565698388" - - "566307038" - - "566458505" - - "569457162" - - "569718097" - - "569739027" - - "569792817" - - "570305847" - - "571177441" - - "575302108" - - "576411246" - - "578674360" - - "601904502" - - "605606109" - - "611638995" - - "613599811" - - "616779893" - - "638262558" - - "642884591" - - "647603932" - - "652094917" - - "707006626" - - "712919665" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - metrics: - - metric: accuracy \ No newline at end of file diff --git a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_visl.yaml b/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_visl.yaml deleted file mode 100644 index 8bc6388..0000000 --- a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_visl.yaml +++ /dev/null @@ -1,105 +0,0 @@ -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortsets: - - "501929610" - - "501940850" - - "502793808" - - "506809539" - - "506823562" - - "507129766" - - "507990552" - - "509958730" - - "511194579" - - "511573879" - - "511595995" - - "529688779" - - "546641574" - - "550455111" - - "552410386" - - "552427971" - - "556321897" - - "557848210" - - "558476282" - - "560578599" - - "564425777" - - "567878987" - - "572606382" - - "573261515" - - "573850303" - - "576001843" - - "576273468" - - "580095655" - - "581153070" - - "582867147" - - "583136567" - - "584544569" - - "584983136" - - "585035184" - - "595808594" - - "596509886" - - "597028938" - - "601805379" - - "601841437" - - "601887677" - - "601910964" - - "602866800" - - "603187982" - - "603452291" - - "603978471" - - "605883133" - - "606802468" - - "607063420" - - "611658482" - - "612549085" - - "613091721" - - "614556106" - - "614571626" - - "623587006" - - "627823636" - - "639932847" - - "644051974" - - "645256361" - - "647143225" - - "647595665" - - "651769499" - - "651770186" - - "652737867" - - "653123929" - - "653551965" - - "657009581" - - "657224241" - - "657391625" - - "657915168" - - "662219852" - - "662348804" - - "662358771" - - "662982346" - - "664914611" - - "665726618" - - "667004159" - - "669237515" - - "670395725" - - "672207947" - - "673475020" - - "674276329" - - "676024666" - - "682049099" - - "682051855" - - "686442556" - - "686449092" - - "688580172" - - "698762886" - - "699155265" - - "707923645" - - "715923832" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - metrics: - - metric: accuracy \ No newline at end of file diff --git a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_vispm.yaml b/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_vispm.yaml deleted file mode 100644 index 286360e..0000000 --- a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_vispm.yaml +++ /dev/null @@ -1,97 +0,0 @@ -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortsets: - - "502376461" - - "502666254" - - "503324629" - - "504115289" - - "505845219" - - "506540916" - - "506773185" - - "507691036" - - "508563988" - - "509580400" - - "510093797" - - "510859641" - - "510917254" - - "512311673" - - "550490398" - - "551888519" - - "554037270" - - "555040116" - - "555749369" - - "557227804" - - "557615965" - - "560809202" - - "560920977" - - "564607188" - - "566096665" - - "569896493" - - "571684733" - - "575135986" - - "575970700" - - "587339481" - - "592348507" - - "598137246" - - "599320182" - - "599909878" - - "601273921" - - "601368107" - - "601705404" - - "603188560" - - "603425659" - - "604529230" - - "605688822" - - "605800963" - - "609517556" - - "617388117" - - "626027944" - - "637126541" - - "639117196" - - "639117826" - - "639251932" - - "642278925" - - "643062797" - - "644947716" - - "647595671" - - "647598519" - - "649324898" - - "651770380" - - "651770794" - - "652096183" - - "652989442" - - "653126877" - - "657391037" - - "657776356" - - "657785850" - - "657914280" - - "658020691" - - "658854537" - - "662359728" - - "663866413" - - "663876406" - - "665307545" - - "669859475" - - "670721589" - - "674275260" - - "679700458" - - "683253712" - - "686441799" - - "686909240" - - "691197571" - - "692345336" - - "696156783" - - "701046700" - - "703308071" - - "716956096" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - metrics: - - metric: accuracy \ No newline at end of file diff --git a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_visrl.yaml b/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_visrl.yaml deleted file mode 100644 index 2c29fc3..0000000 --- a/examples/capoyo/configs/dataset/allen_brain_observatory_calcium_sub_visrl.yaml +++ /dev/null @@ -1,53 +0,0 @@ -- selection: - - dandiset: "allen_brain_observatory_calcium" - sortsets: - - "553568031" - - "560866155" - - "569645690" - - "570008444" - - "570236381" - - "571006300" - - "571541565" - - "571642389" - - "574823092" - - "577665023" - - "580013262" - - "580051759" - - "580095647" - - "581597734" - - "582838758" - - "588191926" - - "588655112" - - "589755795" - - "591430494" - - "591548033" - - "592657427" - - "593270603" - - "593552712" - - "594090967" - - "594320795" - - "595718342" - - "603592541" - - "604328043" - - "612534310" - - "612536911" - - "637154333" - - "640198011" - - "644386884" - - "658533763" - - "660064796" - - "660510593" - - "662351164" - - "698260532" - - "710502981" - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.drifting_gratings - if split == "train": - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - else: - sampling_intervals = sampling_intervals.dilate(0.5, max_len=2.0) - multitask_readout: - - decoder_id: DRIFTING_GRATINGS - metrics: - - metric: accuracy \ No newline at end of file diff --git a/examples/capoyo/configs/dataset/allen_natural_movie_calcium.yaml b/examples/capoyo/configs/dataset/allen_natural_movie_calcium.yaml deleted file mode 100644 index 013c1f3..0000000 --- a/examples/capoyo/configs/dataset/allen_natural_movie_calcium.yaml +++ /dev/null @@ -1,15 +0,0 @@ -- selection: - - dandiset: "allen_natural_movie_calcium" - - config: - sampling_intervals_modifier: | - sampling_intervals = sampling_intervals & data.natural_movie_one_epochs - multitask_readout: - - decoder_id: NATURAL_MOVIE_FRAME - #normalize_mean: - # - 450.0 - #normalize_std: - # - 450.0 - metrics: - - metric: accuracy - - metric: frame_diff_acc \ No newline at end of file diff --git a/examples/capoyo/configs/dataset/creline_sess_ids.txt b/examples/capoyo/configs/dataset/creline_sess_ids.txt deleted file mode 100644 index 5945f60..0000000 --- a/examples/capoyo/configs/dataset/creline_sess_ids.txt +++ /dev/null @@ -1,492 +0,0 @@ -(WIP)This file is for the convinicence of specifying session IDs for allen Brain Observatory Calcium dataset. -Now filtered by different crelines: - cre_line_map = { - "Cux2-CreERT2/Cux2-CreERT2": Cre_line.CUX2_CREERT2, - "Cux2-CreERT2/wt": Cre_line.CUX2_CREERT2, - "Emx1-IRES-Cre/wt": Cre_line.EXM1_IRES_CRE, - "Fezf2-CreER/wt": Cre_line.FEZF2_CREER, - "Nr5a1-Cre/wt": Cre_line.NR5A1_CRE, - "Ntsr1-Cre_GN220/wt": Cre_line.NTSR1_CRE_GN220, - "Pvalb-IRES-Cre/wt": Cre_line.PVALB_IRES_CRE, - "Rbp4-Cre_KL100/wt": Cre_line.RBP4_CRE_KL100, - "Rorb-IRES2-Cre/wt": Cre_line.RORB_IRES2_CRE, - "Scnn1a-Tg3-Cre/wt": Cre_line.SCNN1A_TG3_CRE, - "Slc17a7-IRES2-Cre/wt": Cre_line.SLC17A7_IRES2_CRE, - "Sst-IRES-Cre/wt": Cre_line.SST_IRES_CRE, - "Tlx3-Cre_PL56/wt": Cre_line.TLX3_CRE_PL56, - "Vip-IRES-Cre/wt": Cre_line.VIP_IRES_CRE, - } - ----------------------DIVIDER----------------------- -#Creline: Cux2-CreERT2/Cux2-CreERT2 - 67 sessions - sortsets: - - "501574836" - - "501876401" - - "501933264" - - "501940850" - - "502115959" - - "502199136" - - "502205092" - - "502376461" - - "502608215" - - "502666254" - - "502793808" - - "503109347" - - "503324629" - - "503412730" - - "504115289" - - "505407318" - - "505845219" - - "506773185" - - "506773892" - - "506809539" - - "506823562" - - "507691036" - - "507990552" - - "509958730" - - "510514474" - - "510517131" - - "510859641" - - "512164988" - - "512311673" - - "512326618" - - "524691284" - - "529688779" - - "552410386" - - "557225279" - - "557304694" - - "558670888" - - "565216523" - - "566307038" - - "569645690" - - "569739027" - - "570008444" - - "570305847" - - "571177441" - - "571541565" - - "591548033" - - "592657427" - - "593552712" - - "637154333" - - "638056634" - - "645413759" - - "647595665" - - "652094917" - - "657914280" - - "658854537" - - "660510593" - - "660513003" - - "662351164" - - "663866413" - - "698260532" - - "699155265" - - "702934964" - - "704298735" - - "707006626" - - "710502981" - - "712178511" - - "712919665" - - "716956096" ----------------------DIVIDER----------------------- -#Creline: Emx1-IRES-Cre/wt - 40 sessions - sortsets: - - "526504941" - - "528402271" - - "540684467" - - "541010698" - - "545446482" - - "546716391" - - "550455111" - - "557848210" - - "560898462" - - "560926639" - - "561312435" - - "561472633" - - "562052595" - - "562122508" - - "562536153" - - "563176332" - - "563710064" - - "564425777" - - "564607188" - - "566096665" - - "566458505" - - "569299884" - - "569792817" - - "573261515" - - "574823092" - - "575135986" - - "575970700" - - "577665023" - - "580013262" - - "580095647" - - "581597734" - - "582838758" - - "583136567" - - "585035184" - - "593270603" - - "594090967" - - "594320795" - - "595263154" - - "595806300" - - "596584192" ----------------------DIVIDER----------------------- -#Creline: Fezf2-CreER/wt - 7 sessions in total - sortsets: - - "611658482" - - "623347352" - - "637998955" - - "639932847" - - "643592303" - - "652737678" - - "674679019" ----------------------DIVIDER----------------------- -#Creline: Nr5a1-Cre/wt - 37 sessions in total - sortsets: - - "539290504" - - "539487468" - - "548379748" - - "550490398" - - "554037270" - - "555042467" - - "555749369" - - "556321897" - - "556344224" - - "556665481" - - "557227804" - - "557615965" - - "558476282" - - "559382012" - - "560027980" - - "560578599" - - "560809202" - - "560866155" - - "560920977" - - "562711440" - - "565698388" - - "567878987" - - "570278597" - - "571006300" - - "571642389" - - "593373156" - - "595183197" - - "595808594" - - "603224878" - - "603592541" - - "638262558" - - "638862121" - - "658533763" - - "659491419" - - "660064796" - - "682049099" - - "685816006" ----------------------DIVIDER----------------------- -#Creline: Ntsr1-Cre_GN220/wt - 18 sessions in total - sortsets: - - "603576132" - - "604145810" - - "604529230" - - "605883133" - - "627823695" - - "637126541" - - "645086975" - - "647143225" - - "647595671" - - "665722301" - - "665726618" - - "667004159" - - "669233895" - - "669237515" - - "669859475" - - "670395725" - - "689388034" - - "698762886" ----------------------DIVIDER----------------------- -#Creline: Pvalb-IRES-Cre/wt (INHIBITORY) - 20 sessions in total - sortsets: - - "669861524" - - "670395999" - - "670728674" - - "671164733" - - "671618887" - - "672206735" - - "672211004" - - "673171528" - - "673475020" - - "673914981" - - "674276329" - - "675477919" - - "676024666" - - "676503588" - - "680150733" - - "692345003" - - "710504563" - - "710778377" - - "712178483" - - "715923832" ----------------------DIVIDER----------------------- -#Creline: Rbp4-Cre_KL100/wt - 38 sessions in total - sortsets: - - "502962794" - - "504568756" - - "508563988" - - "510093797" - - "510917254" - - "511194579" - - "511440894" - - "511595995" - - "555040116" - - "556353209" - - "559192380" - - "571137446" - - "571684733" - - "572606382" - - "572722662" - - "573083539" - - "573850303" - - "575302108" - - "576001843" - - "578674360" - - "580051759" - - "584944065" - - "588191926" - - "588483711" - - "591430494" - - "592407200" - - "595273803" - - "595718342" - - "598564173" - - "601368107" - - "601887677" - - "601904502" - - "616779893" - - "637115675" - - "642884591" - - "644051974" - - "647603932" - - "649401936" ----------------------DIVIDER----------------------- -#Creline: Rorb-IRES2-Cre - 39 sessions in total - sortsets: - - "501729039" - - "501929610" - - "504853580" - - "505695962" - - "506540916" - - "507129766" - - "509580400" - - "509904120" - - "510214538" - - "510390912" - - "511573879" - - "512270518" - - "527048992" - - "546641574" - - "550851591" - - "551834174" - - "551888519" - - "552427971" - - "552760671" - - "553568031" - - "569396924" - - "569457162" - - "569718097" - - "569896493" - - "570236381" - - "576411246" - - "587339481" - - "587344053" - - "588655112" - - "590168385" - - "591460070" - - "605606109" - - "605800963" - - "611638995" - - "640198011" - - "644026238" - - "644386884" - - "654532828" - - "686441799" ----------------------DIVIDER----------------------- -#Creline: Scnn1a-Tg3-Cre/wt - 9 sessions in total - sortsets: - - "501021421" - - "508356957" - - "508753256" - - "511534603" - - "530645663" - - "531134090" - - "539497234" - - "541290571" - - "547388708" ----------------------DIVIDER----------------------- -#Creline: Slc17a7-IRES2-Cre/wt - 53 sessions in total - sortsets: - - "604328043" - - "605859367" - - "612536911" - - "613091721" - - "613599811" - - "637669284" - - "639117826" - - "639931541" - - "644947716" - - "645256361" - - "646016204" - - "647155122" - - "651770186" - - "651770794" - - "652094901" - - "652096183" - - "652737867" - - "652842572" - - "653122667" - - "653123929" - - "653125130" - - "653126877" - - "653551965" - - "653932505" - - "657080632" - - "657082055" - - "657391037" - - "657785850" - - "661328410" - - "661437140" - - "662033243" - - "662219852" - - "662348804" - - "662358771" - - "662359728" - - "662974315" - - "663485329" - - "663876406" - - "664404274" - - "664914611" - - "665307545" - - "670721589" - - "672207947" - - "674275260" - - "679700458" - - "679702884" - - "680156911" - - "682051855" - - "683257169" - - "688678766" - - "701046700" - - "703308071" - - "707923645" ----------------------DIVIDER----------------------- -#Creline: Sst-IRES-Cre/wt (INHIBITORY) - 48 sessions in total - sortsets: - - "573720508" - - "575939366" - - "576095926" - - "577379202" - - "580043440" - - "580095655" - - "580163817" - - "581026088" - - "581150104" - - "581153070" - - "582867147" - - "582918858" - - "584196534" - - "584544569" - - "584983136" - - "589441079" - - "589755795" - - "590047029" - - "592348507" - - "596509886" - - "596779487" - - "597028938" - - "598137246" - - "598635821" - - "599320182" - - "599909878" - - "601273921" - - "601423209" - - "601805379" - - "601841437" - - "603188560" - - "603425659" - - "603763073" - - "605688822" - - "607063420" - - "609894681" - - "612044635" - - "612534310" - - "612549085" - - "613968705" - - "623339221" - - "639117196" - - "639251932" - - "642278925" - - "643062797" - - "683253712" - - "686442556" - - "688580172" ----------------------DIVIDER----------------------- -#Creline: Tlx3-Cre_PL56/wt - 9 sessions in total - sortsets: - - "617381605" - - "623587006" - - "637669270" - - "637671554" - - "643645390" - - "645689073" - - "649938038" - - "657016267" - - "657391625" ----------------------DIVIDER----------------------- -#Creline: Vip-IRES-Cre/wt (INHIBITORY) - 48 sessions in total - sortsets: - - "576273468" - - "583279803" - - "585900296" - - "601705404" - - "601910964" - - "602866800" - - "603187982" - - "603452291" - - "603978471" - - "606353987" - - "606802468" - - "609517556" - - "612543999" - - "614556106" - - "614571626" - - "617388117" - - "617395455" - - "626027944" - - "627823636" - - "647598519" - - "649324898" - - "649409874" - - "650079244" - - "651769499" - - "651770380" - - "652091264" - - "652092676" - - "652842495" - - "652989442" - - "657009581" - - "657078119" - - "657224241" - - "657389972" - - "657390171" - - "657650110" - - "657775947" - - "657776356" - - "657915168" - - "658020691" - - "658518486" - - "662361096" - - "662982346" - - "663479824" - - "686449092" - - "686909240" - - "691197571" - - "692345336" - - "696156783" ----------------------DIVIDER----------------------- \ No newline at end of file diff --git a/examples/capoyo/configs/dataset/gillon_richards_responses_2023.yaml b/examples/capoyo/configs/dataset/gillon_richards_responses_2023.yaml deleted file mode 100644 index 18821df..0000000 --- a/examples/capoyo/configs/dataset/gillon_richards_responses_2023.yaml +++ /dev/null @@ -1,9 +0,0 @@ -- selection: - - dandiset: "gillon_richards_responses_2023" - sortset: "408021_20180926" - - config: - multitask_readout: - - decoder_id: GABOR_ORIENTATION - metrics: - - metric: accuracy \ No newline at end of file diff --git a/examples/capoyo/configs/model/capoyo1.3M.yaml b/examples/capoyo/configs/model/capoyo1.3M.yaml deleted file mode 100644 index 2dbeb5a..0000000 --- a/examples/capoyo/configs/model/capoyo1.3M.yaml +++ /dev/null @@ -1,12 +0,0 @@ -_target_: kirby.models.CaPOYO -dim: 64 -dim_head: 64 -patch_size: 1 -num_latents: 16 -depth: 6 -cross_heads: 2 -self_heads: 8 -ffn_dropout: 0.2 -lin_dropout: 0.4 -atn_dropout: 0. -use_cre_line_embedding: True \ No newline at end of file diff --git a/examples/capoyo/configs/model/capoyo1.3M_0.yaml b/examples/capoyo/configs/model/capoyo1.3M_0.yaml deleted file mode 100644 index 0bc31fa..0000000 --- a/examples/capoyo/configs/model/capoyo1.3M_0.yaml +++ /dev/null @@ -1,17 +0,0 @@ -_target_: kirby.models.CaPOYO -dim: 64 -dim_head: 64 -patch_size: 1 -num_latents: 16 -depth: 6 -cross_heads: 2 -self_heads: 8 -ffn_dropout: 0.2 -lin_dropout: 0.4 -atn_dropout: 0.2 -use_cre_line_embedding: False -use_depth_embedding: False -use_spatial_embedding: False -use_roi_feat_embedding: False -use_session_embedding: True -use_unit_embedding: True \ No newline at end of file diff --git a/examples/capoyo/configs/model/capoyo1.3M_3.yaml b/examples/capoyo/configs/model/capoyo1.3M_3.yaml deleted file mode 100644 index 61683ea..0000000 --- a/examples/capoyo/configs/model/capoyo1.3M_3.yaml +++ /dev/null @@ -1,13 +0,0 @@ -_target_: kirby.models.CaPOYO -dim: 64 -dim_head: 64 -patch_size: 1 -num_latents: 16 -depth: 6 -cross_heads: 2 -self_heads: 8 -ffn_dropout: 0.2 -lin_dropout: 0.4 -atn_dropout: 0.2 -use_cre_line_embedding: True -use_depth_embedding: True \ No newline at end of file diff --git a/examples/capoyo/configs/model/capoyo1.3M_5.yaml b/examples/capoyo/configs/model/capoyo1.3M_5.yaml deleted file mode 100644 index 8281b11..0000000 --- a/examples/capoyo/configs/model/capoyo1.3M_5.yaml +++ /dev/null @@ -1,14 +0,0 @@ -_target_: kirby.models.CaPOYO -dim: 128 -dim_input: 64 -dim_head: 64 -patch_size: 1 -num_latents: 16 -depth: 6 -cross_heads: 2 -self_heads: 8 -ffn_dropout: 0.2 -lin_dropout: 0.4 -atn_dropout: 0.2 -use_cre_line_embedding: True -use_depth_embedding: True \ No newline at end of file diff --git a/examples/capoyo/configs/model/capoyo1.3M_nu.yaml b/examples/capoyo/configs/model/capoyo1.3M_nu.yaml deleted file mode 100644 index 147fb4e..0000000 --- a/examples/capoyo/configs/model/capoyo1.3M_nu.yaml +++ /dev/null @@ -1,15 +0,0 @@ -_target_: kirby.models.CaPOYO -dim: 64 -dim_head: 64 -patch_size: 1 -num_latents: 16 -depth: 6 -cross_heads: 2 -self_heads: 8 -ffn_dropout: 0.2 -lin_dropout: 0.4 -atn_dropout: 0.2 -use_cre_line_embedding: True -use_depth_embedding: True -use_unit_embedding: False -use_session_embedding: False \ No newline at end of file diff --git a/examples/capoyo/configs/model/capoyo6.6M.yaml b/examples/capoyo/configs/model/capoyo6.6M.yaml deleted file mode 100644 index 177e5c1..0000000 --- a/examples/capoyo/configs/model/capoyo6.6M.yaml +++ /dev/null @@ -1,13 +0,0 @@ -_target_: kirby.models.CaPOYO -dim: 128 -dim_head: 64 -patch_size: 1 -num_latents: 16 -depth: 12 -cross_heads: 4 -self_heads: 8 -ffn_dropout: 0.2 -lin_dropout: 0.4 -atn_dropout: 0.2 -use_cre_line_embedding: True -use_depth_embedding: True diff --git a/examples/capoyo/configs/model/capoyo_large.yaml b/examples/capoyo/configs/model/capoyo_large.yaml deleted file mode 100644 index f06082e..0000000 --- a/examples/capoyo/configs/model/capoyo_large.yaml +++ /dev/null @@ -1,12 +0,0 @@ -_target_: kirby.models.CaPOYO -dim: 128 -dim_head: 64 -patch_size: 1 -num_latents: 32 -depth: 12 -cross_heads: 2 -self_heads: 8 -ffn_dropout: 0.2 -lin_dropout: 0.4 -atn_dropout: 0. -use_cre_line_embedding: True \ No newline at end of file diff --git a/examples/capoyo/configs/train_allen_bo_large.yaml b/examples/capoyo/configs/train_allen_bo_large.yaml deleted file mode 100644 index fd11895..0000000 --- a/examples/capoyo/configs/train_allen_bo_large.yaml +++ /dev/null @@ -1,45 +0,0 @@ -# Path: configs/train.yaml -defaults: - - _self_ - - model: capoyo_large.yaml - - dataset: allen_brain_observatory_calcium.yaml - -train_transforms: - - _target_: kirby.transforms.UnitDropout - field: "calcium_traces.df_over_f" - min_units: 10 - mode_units: 50 - max_units: 300 - tail_right: 100 - peak: 60 - M: 60 - -data_root: ${oc.env:SLURM_TMPDIR}/uncompressed -seed: 42 -batch_size: 128 -eval_epochs: 10 -steps: 0 # Note we either specify epochs or steps, not both. -epochs: 1000 -base_lr: 1.5625e-5 -weight_decay: 0.0001 -# Fraction of epochs to warmup for. -pct_start: 0.5 -num_workers: 4 -log_dir: ./logs -name: allen_brain_observatory_calcium_all -precision: 32 -nodes: 1 -gpus: 1 -patch_size: 1 -# Where to resume/finetune from. Could be null (yaml for None, meaning train from -# scratch) or a fully qualified path to the .ckpt file. -ckpt_path: null - -# Finetuning configuration: -finetune: false -# Num of epochs to freeze perceiver network while finetuning -# -1 => Keep frozen, i.e. perform Unit-identification -# 0 => Train everything -# >0 => Only train unit/session embeddings for first few epochs, -# and then train everything -freeze_perceiver_until_epoch: 0 diff --git a/examples/capoyo/configs/train_allen_bo_large_moderate_dropout.yaml b/examples/capoyo/configs/train_allen_bo_large_moderate_dropout.yaml deleted file mode 100644 index 3d00d73..0000000 --- a/examples/capoyo/configs/train_allen_bo_large_moderate_dropout.yaml +++ /dev/null @@ -1,45 +0,0 @@ -# Path: configs/train.yaml -defaults: - - _self_ - - model: capoyo_large.yaml - - dataset: allen_brain_observatory_calcium.yaml - -train_transforms: - - _target_: kirby.transforms.UnitDropout - field: "calcium_traces.df_over_f" - min_units: 10 - mode_units: 50 - max_units: 500 - tail_right: 150 - peak: 60 - M: 60 - -data_root: ${oc.env:SLURM_TMPDIR}/uncompressed -seed: 42 -batch_size: 128 -eval_epochs: 10 -steps: 0 # Note we either specify epochs or steps, not both. -epochs: 1000 -base_lr: 4e-5 -weight_decay: 1e-3 -# Fraction of epochs to warmup for. -pct_start: 0.5 -num_workers: 4 -log_dir: ./logs -name: allen_brain_observatory_calcium_all -precision: 32 -nodes: 1 -gpus: 1 -patch_size: 1 -# Where to resume/finetune from. Could be null (yaml for None, meaning train from -# scratch) or a fully qualified path to the .ckpt file. -ckpt_path: null - -# Finetuning configuration: -finetune: false -# Num of epochs to freeze perceiver network while finetuning -# -1 => Keep frozen, i.e. perform Unit-identification -# 0 => Train everything -# >0 => Only train unit/session embeddings for first few epochs, -# and then train everything -freeze_perceiver_until_epoch: 0 diff --git a/examples/capoyo/configs/train_allen_bo_large_moderate_minimal_dropout.yaml b/examples/capoyo/configs/train_allen_bo_large_moderate_minimal_dropout.yaml deleted file mode 100644 index bf1b738..0000000 --- a/examples/capoyo/configs/train_allen_bo_large_moderate_minimal_dropout.yaml +++ /dev/null @@ -1,45 +0,0 @@ -# Path: configs/train.yaml -defaults: - - _self_ - - model: capoyo_large.yaml - - dataset: allen_brain_observatory_calcium.yaml - -train_transforms: - - _target_: kirby.transforms.UnitDropout - field: "calcium_traces.df_over_f" - min_units: 100 - mode_units: 50 - max_units: 500 - tail_right: 150 - peak: 1 - M: 1 - -data_root: ${oc.env:SLURM_TMPDIR}/uncompressed -seed: 42 -batch_size: 128 -eval_epochs: 10 -steps: 0 # Note we either specify epochs or steps, not both. -epochs: 1000 -base_lr: 4e-5 -weight_decay: 1e-3 -# Fraction of epochs to warmup for. -pct_start: 0.5 -num_workers: 4 -log_dir: ./logs -name: allen_brain_observatory_calcium_all -precision: 32 -nodes: 1 -gpus: 1 -patch_size: 1 -# Where to resume/finetune from. Could be null (yaml for None, meaning train from -# scratch) or a fully qualified path to the .ckpt file. -ckpt_path: null - -# Finetuning configuration: -finetune: false -# Num of epochs to freeze perceiver network while finetuning -# -1 => Keep frozen, i.e. perform Unit-identification -# 0 => Train everything -# >0 => Only train unit/session embeddings for first few epochs, -# and then train everything -freeze_perceiver_until_epoch: 0 diff --git a/examples/capoyo/configs/train_allen_bo_new_dropout.yaml b/examples/capoyo/configs/train_allen_bo_new_dropout.yaml deleted file mode 100644 index 5beb4d7..0000000 --- a/examples/capoyo/configs/train_allen_bo_new_dropout.yaml +++ /dev/null @@ -1,45 +0,0 @@ -# Path: configs/train.yaml -defaults: - - _self_ - - model: capoyo1.3M.yaml - - dataset: allen_brain_observatory_calcium.yaml - -train_transforms: - - _target_: kirby.transforms.UnitDropout - field: "calcium_traces.df_over_f" - min_units: 10 - mode_units: 50 - max_units: 300 - tail_right: 100 - peak: 50 - M: 50 - -data_root: ${oc.env:SLURM_TMPDIR}/uncompressed -seed: 42 -batch_size: 128 -eval_epochs: 10 -steps: 0 # Note we either specify epochs or steps, not both. -epochs: 1000 -base_lr: 1.5625e-5 -weight_decay: 0.0001 -# Fraction of epochs to warmup for. -pct_start: 0.5 -num_workers: 4 -log_dir: ./logs -name: allen_brain_observatory_calcium_all -precision: 32 -nodes: 1 -gpus: 1 -patch_size: 1 -# Where to resume/finetune from. Could be null (yaml for None, meaning train from -# scratch) or a fully qualified path to the .ckpt file. -ckpt_path: null - -# Finetuning configuration: -finetune: false -# Num of epochs to freeze perceiver network while finetuning -# -1 => Keep frozen, i.e. perform Unit-identification -# 0 => Train everything -# >0 => Only train unit/session embeddings for first few epochs, -# and then train everything -freeze_perceiver_until_epoch: 0 diff --git a/examples/capoyo/configs/train_allen_bo_old_dropout.yaml b/examples/capoyo/configs/train_allen_bo_old_dropout.yaml deleted file mode 100644 index d1dfcab..0000000 --- a/examples/capoyo/configs/train_allen_bo_old_dropout.yaml +++ /dev/null @@ -1,43 +0,0 @@ -# Path: configs/train.yaml -defaults: - - _self_ - - model: capoyo1.3M.yaml - - dataset: allen_brain_observatory_calcium.yaml - -train_transforms: - - _target_: kirby.transforms.UnitDropout - field: "calcium_traces.df_over_f" - max_units: 475 - min_units: 10 - mode_units: 50 - peak: 3 - -data_root: ${oc.env:SLURM_TMPDIR}/uncompressed -seed: 42 -batch_size: 128 -eval_epochs: 10 -steps: 0 # Note we either specify epochs or steps, not both. -epochs: 1000 -base_lr: 1.5625e-5 -weight_decay: 0.0001 -# Fraction of epochs to warmup for. -pct_start: 0.5 -num_workers: 4 -log_dir: ./logs -name: allen_brain_observatory_calcium_all -precision: 32 -nodes: 1 -gpus: 1 -patch_size: 1 -# Where to resume/finetune from. Could be null (yaml for None, meaning train from -# scratch) or a fully qualified path to the .ckpt file. -ckpt_path: null - -# Finetuning configuration: -finetune: false -# Num of epochs to freeze perceiver network while finetuning -# -1 => Keep frozen, i.e. perform Unit-identification -# 0 => Train everything -# >0 => Only train unit/session embeddings for first few epochs, -# and then train everything -freeze_perceiver_until_epoch: 0 diff --git a/examples/capoyo/configs/train_allen_nm.yaml b/examples/capoyo/configs/train_allen_nm.yaml deleted file mode 100644 index 1fe79e0..0000000 --- a/examples/capoyo/configs/train_allen_nm.yaml +++ /dev/null @@ -1,45 +0,0 @@ -# Path: configs/train.yaml -defaults: - - _self_ - - model: capoyo1.3M.yaml - - dataset: allen_natural_movie_calcium.yaml - -train_transforms: - - _target_: kirby.transforms.UnitDropout - field: "calcium_traces.df_over_f" - min_units: 10 - mode_units: 50 - max_units: 300 - tail_right: 100 - peak: 50 - M: 50 - -data_root: ${oc.env:SLURM_TMPDIR}/uncompressed -seed: 42 -batch_size: 128 -eval_epochs: 10 -steps: 0 # Note we either specify epochs or steps, not both. -epochs: 1000 -base_lr: 1.5625e-5 -weight_decay: 0.0001 -# Fraction of epochs to warmup for. -pct_start: 0.5 -num_workers: 4 -log_dir: ./logs -name: allen_brain_observatory_calcium_all -precision: 32 -nodes: 1 -gpus: 1 -patch_size: 1 -# Where to resume/finetune from. Could be null (yaml for None, meaning train from -# scratch) or a fully qualified path to the .ckpt file. -ckpt_path: null - -# Finetuning configuration: -finetune: false -# Num of epochs to freeze perceiver network while finetuning -# -1 => Keep frozen, i.e. perform Unit-identification -# 0 => Train everything -# >0 => Only train unit/session embeddings for first few epochs, -# and then train everything -freeze_perceiver_until_epoch: 0 diff --git a/examples/capoyo/configs/train_model_0.yaml b/examples/capoyo/configs/train_model_0.yaml deleted file mode 100644 index 1367a0c..0000000 --- a/examples/capoyo/configs/train_model_0.yaml +++ /dev/null @@ -1,46 +0,0 @@ -# Path: configs/train.yaml -defaults: - - _self_ - - model: capoyo1.3M_0.yaml - - dataset: allen_brain_observatory_calcium.yaml - -train_transforms: - - _target_: kirby.transforms.UnitDropout - field: "calcium_traces.df_over_f" - min_units: 10 - mode_units: 50 - max_units: 300 - tail_right: 100 - peak: 50 - M: 50 - -data_root: ${oc.env:SLURM_TMPDIR}/uncompressed -seed: 42 -batch_size: 128 -eval_epochs: 10 -use_sparse_lamb: False -steps: 0 # Note we either specify epochs or steps, not both. -epochs: 1000 -base_lr: 1.5625e-5 -weight_decay: 0.0001 -# Fraction of epochs to warmup for. -pct_start: 0.5 -num_workers: 4 -log_dir: ./logs -name: allen_brain_observatory_calcium_all -precision: 32 -nodes: 1 -gpus: 1 -patch_size: 1 -# Where to resume/finetune from. Could be null (yaml for None, meaning train from -# scratch) or a fully qualified path to the .ckpt file. -ckpt_path: null - -# Finetuning configuration: -finetune: false -# Num of epochs to freeze perceiver network while finetuning -# -1 => Keep frozen, i.e. perform Unit-identification -# 0 => Train everything -# >0 => Only train unit/session embeddings for first few epochs, -# and then train everything -freeze_perceiver_until_epoch: 0 diff --git a/examples/capoyo/configs/train_model_0w.yaml b/examples/capoyo/configs/train_model_0w.yaml deleted file mode 100644 index fff1064..0000000 --- a/examples/capoyo/configs/train_model_0w.yaml +++ /dev/null @@ -1,46 +0,0 @@ -# Path: configs/train.yaml -defaults: - - _self_ - - model: capoyo1.3M_0.yaml - - dataset: allen_bo_weighted.yaml - -train_transforms: - - _target_: kirby.transforms.UnitDropout - field: "calcium_traces.df_over_f" - min_units: 10 - mode_units: 50 - max_units: 300 - tail_right: 100 - peak: 50 - M: 50 - -data_root: ${oc.env:SLURM_TMPDIR}/uncompressed -seed: 42 -batch_size: 128 -eval_epochs: 10 -use_sparse_lamb: False -steps: 0 # Note we either specify epochs or steps, not both. -epochs: 1000 -base_lr: 1.5625e-5 -weight_decay: 0.0001 -# Fraction of epochs to warmup for. -pct_start: 0.5 -num_workers: 4 -log_dir: ./logs -name: allen_brain_observatory_calcium_all -precision: 32 -nodes: 1 -gpus: 1 -patch_size: 1 -# Where to resume/finetune from. Could be null (yaml for None, meaning train from -# scratch) or a fully qualified path to the .ckpt file. -ckpt_path: null - -# Finetuning configuration: -finetune: false -# Num of epochs to freeze perceiver network while finetuning -# -1 => Keep frozen, i.e. perform Unit-identification -# 0 => Train everything -# >0 => Only train unit/session embeddings for first few epochs, -# and then train everything -freeze_perceiver_until_epoch: 0 diff --git a/examples/capoyo/configs/train_model_3.yaml b/examples/capoyo/configs/train_model_3.yaml deleted file mode 100644 index 876233c..0000000 --- a/examples/capoyo/configs/train_model_3.yaml +++ /dev/null @@ -1,46 +0,0 @@ -# Path: configs/train.yaml -defaults: - - _self_ - - model: capoyo1.3M_3.yaml - - dataset: allen_brain_observatory_calcium.yaml - -train_transforms: - - _target_: kirby.transforms.UnitDropout - field: "calcium_traces.df_over_f" - min_units: 10 - mode_units: 50 - max_units: 300 - tail_right: 100 - peak: 50 - M: 50 - -data_root: ${oc.env:SLURM_TMPDIR}/uncompressed -seed: 42 -batch_size: 128 -eval_epochs: 10 -use_sparse_lamb: True -steps: 0 # Note we either specify epochs or steps, not both. -epochs: 1000 -base_lr: 1.5625e-5 -weight_decay: 0.0001 -# Fraction of epochs to warmup for. -pct_start: 0.5 -num_workers: 4 -log_dir: ./logs -name: allen_brain_observatory_calcium_all -precision: 32 -nodes: 1 -gpus: 1 -patch_size: 1 -# Where to resume/finetune from. Could be null (yaml for None, meaning train from -# scratch) or a fully qualified path to the .ckpt file. -ckpt_path: null - -# Finetuning configuration: -finetune: false -# Num of epochs to freeze perceiver network while finetuning -# -1 => Keep frozen, i.e. perform Unit-identification -# 0 => Train everything -# >0 => Only train unit/session embeddings for first few epochs, -# and then train everything -freeze_perceiver_until_epoch: 0 diff --git a/examples/capoyo/configs/train_model_3_e.yaml b/examples/capoyo/configs/train_model_3_e.yaml deleted file mode 100644 index 5df4635..0000000 --- a/examples/capoyo/configs/train_model_3_e.yaml +++ /dev/null @@ -1,46 +0,0 @@ -# Path: configs/train.yaml -defaults: - - _self_ - - model: capoyo1.3M_3.yaml - - dataset: allen_brain_observatory_calcium_model2_excitatory.yaml - -train_transforms: - - _target_: kirby.transforms.UnitDropout - field: "calcium_traces.df_over_f" - min_units: 10 - mode_units: 50 - max_units: 300 - tail_right: 100 - peak: 50 - M: 50 - -data_root: ${oc.env:SLURM_TMPDIR}/uncompressed -seed: 42 -batch_size: 128 -eval_epochs: 1 -use_sparse_lamb: True -steps: 0 # Note we either specify epochs or steps, not both. -epochs: 1000 -base_lr: 1.5625e-5 -weight_decay: 0.0001 -# Fraction of epochs to warmup for. -pct_start: 0.5 -num_workers: 4 -log_dir: ./logs -name: model3_excitatory -precision: 32 -nodes: 1 -gpus: 1 -patch_size: 1 -# Where to resume/finetune from. Could be null (yaml for None, meaning train from -# scratch) or a fully qualified path to the .ckpt file. -ckpt_path: null - -# Finetuning configuration: -finetune: false -# Num of epochs to freeze perceiver network while finetuning -# -1 => Keep frozen, i.e. perform Unit-identification -# 0 => Train everything -# >0 => Only train unit/session embeddings for first few epochs, -# and then train everything -freeze_perceiver_until_epoch: 0 diff --git a/examples/capoyo/configs/train_model_3_multitask.yaml b/examples/capoyo/configs/train_model_3_multitask.yaml deleted file mode 100644 index 97390c7..0000000 --- a/examples/capoyo/configs/train_model_3_multitask.yaml +++ /dev/null @@ -1,46 +0,0 @@ -# Path: configs/train.yaml -defaults: - - _self_ - - model: capoyo1.3M_3.yaml - - dataset: allen_brain_observatory_calcium_dg_nm.yaml - -train_transforms: - - _target_: kirby.transforms.UnitDropout - field: "calcium_traces.df_over_f" - min_units: 10 - mode_units: 50 - max_units: 300 - tail_right: 100 - peak: 50 - M: 50 - -data_root: ${oc.env:SLURM_TMPDIR}/uncompressed -seed: 42 -batch_size: 128 -eval_epochs: 10 -use_sparse_lamb: True -steps: 0 # Note we either specify epochs or steps, not both. -epochs: 1000 -base_lr: 1.5625e-5 -weight_decay: 0.0001 -# Fraction of epochs to warmup for. -pct_start: 0.5 -num_workers: 4 -log_dir: ./logs -name: allen_brain_observatory_calcium_all -precision: 32 -nodes: 1 -gpus: 1 -patch_size: 1 -# Where to resume/finetune from. Could be null (yaml for None, meaning train from -# scratch) or a fully qualified path to the .ckpt file. -ckpt_path: null - -# Finetuning configuration: -finetune: false -# Num of epochs to freeze perceiver network while finetuning -# -1 => Keep frozen, i.e. perform Unit-identification -# 0 => Train everything -# >0 => Only train unit/session embeddings for first few epochs, -# and then train everything -freeze_perceiver_until_epoch: 0 diff --git a/examples/capoyo/configs/train_model_3_novisp.yaml b/examples/capoyo/configs/train_model_3_novisp.yaml deleted file mode 100644 index 077648e..0000000 --- a/examples/capoyo/configs/train_model_3_novisp.yaml +++ /dev/null @@ -1,46 +0,0 @@ -# Path: configs/train.yaml -defaults: - - _self_ - - model: capoyo1.3M_3.yaml - - dataset: allen_brain_observatory_calcium_model4_no_visp.yaml - -train_transforms: - - _target_: kirby.transforms.UnitDropout - field: "calcium_traces.df_over_f" - min_units: 10 - mode_units: 50 - max_units: 300 - tail_right: 100 - peak: 50 - M: 50 - -data_root: ${oc.env:SLURM_TMPDIR}/uncompressed -seed: 42 -batch_size: 128 -eval_epochs: 1 -use_sparse_lamb: True -steps: 0 # Note we either specify epochs or steps, not both. -epochs: 1000 -base_lr: 1.5625e-5 -weight_decay: 0.0001 -# Fraction of epochs to warmup for. -pct_start: 0.5 -num_workers: 4 -log_dir: ./logs -name: model3_novisp -precision: 32 -nodes: 1 -gpus: 1 -patch_size: 1 -# Where to resume/finetune from. Could be null (yaml for None, meaning train from -# scratch) or a fully qualified path to the .ckpt file. -ckpt_path: null - -# Finetuning configuration: -finetune: false -# Num of epochs to freeze perceiver network while finetuning -# -1 => Keep frozen, i.e. perform Unit-identification -# 0 => Train everything -# >0 => Only train unit/session embeddings for first few epochs, -# and then train everything -freeze_perceiver_until_epoch: 0 diff --git a/examples/capoyo/configs/train_model_3_nu.yaml b/examples/capoyo/configs/train_model_3_nu.yaml deleted file mode 100644 index c75b53c..0000000 --- a/examples/capoyo/configs/train_model_3_nu.yaml +++ /dev/null @@ -1,46 +0,0 @@ -# Path: configs/train.yaml -defaults: - - _self_ - - model: capoyo1.3M_nu.yaml - - dataset: allen_brain_observatory_calcium.yaml - -train_transforms: - - _target_: kirby.transforms.UnitDropout - field: "calcium_traces.df_over_f" - min_units: 10 - mode_units: 50 - max_units: 300 - tail_right: 100 - peak: 50 - M: 50 - -data_root: ${oc.env:SLURM_TMPDIR}/uncompressed -seed: 42 -batch_size: 128 -eval_epochs: 10 -use_sparse_lamb: False -steps: 0 # Note we either specify epochs or steps, not both. -epochs: 1000 -base_lr: 1.5625e-5 -weight_decay: 0.0001 -# Fraction of epochs to warmup for. -pct_start: 0.5 -num_workers: 4 -log_dir: ./logs -name: allen_capoyo_no_unit_or_session_embeddings -precision: 32 -nodes: 1 -gpus: 1 -patch_size: 1 -# Where to resume/finetune from. Could be null (yaml for None, meaning train from -# scratch) or a fully qualified path to the .ckpt file. -ckpt_path: null - -# Finetuning configuration: -finetune: false -# Num of epochs to freeze perceiver network while finetuning -# -1 => Keep frozen, i.e. perform Unit-identification -# 0 => Train everything -# >0 => Only train unit/session embeddings for first few epochs, -# and then train everything -freeze_perceiver_until_epoch: 0 diff --git a/examples/capoyo/configs/train_model_3_visp.yaml b/examples/capoyo/configs/train_model_3_visp.yaml deleted file mode 100644 index b467f01..0000000 --- a/examples/capoyo/configs/train_model_3_visp.yaml +++ /dev/null @@ -1,46 +0,0 @@ -# Path: configs/train.yaml -defaults: - - _self_ - - model: capoyo1.3M_3.yaml - - dataset: allen_brain_observatory_calcium_model5_visp_only.yaml - -train_transforms: - - _target_: kirby.transforms.UnitDropout - field: "calcium_traces.df_over_f" - min_units: 10 - mode_units: 50 - max_units: 300 - tail_right: 100 - peak: 50 - M: 50 - -data_root: ${oc.env:SLURM_TMPDIR}/uncompressed -seed: 42 -batch_size: 128 -eval_epochs: 1 -use_sparse_lamb: True -steps: 0 # Note we either specify epochs or steps, not both. -epochs: 1000 -base_lr: 1.5625e-5 -weight_decay: 0.0001 -# Fraction of epochs to warmup for. -pct_start: 0.5 -num_workers: 4 -log_dir: ./logs -name: model3_visp -precision: 32 -nodes: 1 -gpus: 1 -patch_size: 1 -# Where to resume/finetune from. Could be null (yaml for None, meaning train from -# scratch) or a fully qualified path to the .ckpt file. -ckpt_path: null - -# Finetuning configuration: -finetune: false -# Num of epochs to freeze perceiver network while finetuning -# -1 => Keep frozen, i.e. perform Unit-identification -# 0 => Train everything -# >0 => Only train unit/session embeddings for first few epochs, -# and then train everything -freeze_perceiver_until_epoch: 0 diff --git a/examples/capoyo/configs/train_model_3_wt.yaml b/examples/capoyo/configs/train_model_3_wt.yaml deleted file mode 100644 index 99653eb..0000000 --- a/examples/capoyo/configs/train_model_3_wt.yaml +++ /dev/null @@ -1,46 +0,0 @@ -# Path: configs/train.yaml -defaults: - - _self_ - - model: capoyo1.3M_3.yaml - - dataset: allen_brain_observatory_calcium_model1_wildtype.yaml - -train_transforms: - - _target_: kirby.transforms.UnitDropout - field: "calcium_traces.df_over_f" - min_units: 10 - mode_units: 50 - max_units: 300 - tail_right: 100 - peak: 50 - M: 50 - -data_root: ${oc.env:SLURM_TMPDIR}/uncompressed -seed: 42 -batch_size: 128 -eval_epochs: 1 -use_sparse_lamb: True -steps: 0 # Note we either specify epochs or steps, not both. -epochs: 1000 -base_lr: 1.5625e-5 -weight_decay: 0.0001 -# Fraction of epochs to warmup for. -pct_start: 0.5 -num_workers: 4 -log_dir: ./logs -name: model3_wildtype -precision: 32 -nodes: 1 -gpus: 1 -patch_size: 1 -# Where to resume/finetune from. Could be null (yaml for None, meaning train from -# scratch) or a fully qualified path to the .ckpt file. -ckpt_path: null - -# Finetuning configuration: -finetune: false -# Num of epochs to freeze perceiver network while finetuning -# -1 => Keep frozen, i.e. perform Unit-identification -# 0 => Train everything -# >0 => Only train unit/session embeddings for first few epochs, -# and then train everything -freeze_perceiver_until_epoch: 0 diff --git a/examples/capoyo/configs/train_model_3rescale.yaml b/examples/capoyo/configs/train_model_3rescale.yaml deleted file mode 100644 index af6042d..0000000 --- a/examples/capoyo/configs/train_model_3rescale.yaml +++ /dev/null @@ -1,47 +0,0 @@ -# Path: configs/train.yaml -defaults: - - _self_ - - model: capoyo1.3M_3.yaml - - dataset: allen_bo_weighted.yaml - -train_transforms: - - _target_: kirby.transforms.UnitDropout - field: "calcium_traces.df_over_f" - min_units: 10 - mode_units: 50 - max_units: 300 - tail_right: 100 - peak: 50 - M: 50 - -data_root: ${oc.env:SLURM_TMPDIR}/uncompressed -seed: 42 -batch_size: 128 -eval_epochs: 10 -use_sparse_lamb: True -gradient_rescale: True -steps: 0 # Note we either specify epochs or steps, not both. -epochs: 1000 -base_lr: 1.5625e-5 -weight_decay: 0.0001 -# Fraction of epochs to warmup for. -pct_start: 0.5 -num_workers: 4 -log_dir: ./logs -name: allen_rescale_session_wise_loss -precision: 32 -nodes: 1 -gpus: 1 -patch_size: 1 -# Where to resume/finetune from. Could be null (yaml for None, meaning train from -# scratch) or a fully qualified path to the .ckpt file. -ckpt_path: null - -# Finetuning configuration: -finetune: false -# Num of epochs to freeze perceiver network while finetuning -# -1 => Keep frozen, i.e. perform Unit-identification -# 0 => Train everything -# >0 => Only train unit/session embeddings for first few epochs, -# and then train everything -freeze_perceiver_until_epoch: 0 diff --git a/examples/capoyo/configs/train_model_5.yaml b/examples/capoyo/configs/train_model_5.yaml deleted file mode 100644 index d07a131..0000000 --- a/examples/capoyo/configs/train_model_5.yaml +++ /dev/null @@ -1,46 +0,0 @@ -# Path: configs/train.yaml -defaults: - - _self_ - - model: capoyo1.3M_5.yaml - - dataset: allen_brain_observatory_calcium.yaml - -train_transforms: - - _target_: kirby.transforms.UnitDropout - field: "calcium_traces.df_over_f" - min_units: 10 - mode_units: 50 - max_units: 300 - tail_right: 100 - peak: 50 - M: 50 - -data_root: ${oc.env:SLURM_TMPDIR}/uncompressed -seed: 42 -batch_size: 128 -eval_epochs: 10 -use_sparse_lamb: True -steps: 0 # Note we either specify epochs or steps, not both. -epochs: 1000 -base_lr: 1.5625e-5 -weight_decay: 0.0001 -# Fraction of epochs to warmup for. -pct_start: 0.5 -num_workers: 4 -log_dir: ./logs -name: allen_brain_observatory_calcium_all -precision: 32 -nodes: 1 -gpus: 1 -patch_size: 1 -# Where to resume/finetune from. Could be null (yaml for None, meaning train from -# scratch) or a fully qualified path to the .ckpt file. -ckpt_path: null - -# Finetuning configuration: -finetune: false -# Num of epochs to freeze perceiver network while finetuning -# -1 => Keep frozen, i.e. perform Unit-identification -# 0 => Train everything -# >0 => Only train unit/session embeddings for first few epochs, -# and then train everything -freeze_perceiver_until_epoch: 0 diff --git a/examples/capoyo/configs/train_openscope_calcium.yaml b/examples/capoyo/configs/train_openscope_calcium.yaml deleted file mode 100644 index 593bfe7..0000000 --- a/examples/capoyo/configs/train_openscope_calcium.yaml +++ /dev/null @@ -1,43 +0,0 @@ -# Path: configs/train.yaml -defaults: - - _self_ - - model: capoyo1.3M.yaml - - dataset: gillon_richards_responses_2023.yaml - -train_transforms: - - _target_: kirby.transforms.UnitDropout - field: "calcium_traces.df_over_f" - max_units: 1000 - min_units: 20 - mode_units: 60 - peak: 4 - -data_root: ./data/uncompressed/ -seed: 42 -batch_size: 128 -eval_epochs: 10 -steps: 0 # Note we either specify epochs or steps, not both. -epochs: 500 -base_lr: 1.5625e-5 -weight_decay: 1e-4 -# Fraction of epochs to warmup for. -pct_start: 0.5 -num_workers: 4 -log_dir: ./logs -name: calcium_openscope_single_sess_all -backend_config: gpu_fp32 -precision: 32 -nodes: 1 -gpus: 1 -# Where to resume/finetune from. Could be null (yaml for None, meaning train from -# scratch) or a fully qualified path to the .ckpt file. -ckpt_path: null - -# Finetuning configuration: -finetune: false -# Num of epochs to freeze perceiver network while finetuning -# -1 => Keep frozen, i.e. perform Unit-identification -# 0 => Train everything -# >0 => Only train unit/session embeddings for first few epochs, -# and then train everything -freeze_perceiver_until_epoch: 0 diff --git a/examples/capoyo/dandi_download_util.py b/examples/capoyo/dandi_download_util.py deleted file mode 100644 index ace118b..0000000 --- a/examples/capoyo/dandi_download_util.py +++ /dev/null @@ -1,339 +0,0 @@ -#!/usr/bin/env python - -""" -dandi_download_util.py - -This module contains functions for downloading the dataset from the Dandi -archive. - -Dandiset 000037 is the Credit Assignment Project dandiset. It comprises data -for 50 sessions. The asset (session file) sizes are in the following ranges: -- Basic data (with everything required for most analyses): - 130 MB to 1.7 GB per asset - ~25 GB total -- Basic data + stimulus template images: - 1.5 to 3.1 GB per asset - ~100 GB total -- Basic data + stimulus template images + full imaging stack: - ~45 GB per asset - ~2.3 TB total - ~1.5 TB total (only sess 1-3 that passed QC, i.e., 33 total) - -URL: https://gui.dandiarchive.org/#/dandiset/000037 - -Authors: Colleen Gillon - -Date: Nov, 2022 - -Note: this code uses python 3.7. - -""" - -import argparse -from pathlib import Path -import warnings - -import numpy as np -import pandas as pd -from dandi import dandiapi -from dandi import download as dandi_download - - -# if running from the main directory -DEFAULT_MOUSE_DF_PATH = Path("mouse_df.csv") - -# Published dandiset version -PUBLISHED_VERSION = "0.230426.0054" # number derived from DOI - - -############################################# -def reformat_n(n): - """ - reformat_n(n) - - Returns reformatted n argument, converting ranges to lists. - - Required args: - - n (str): - number or range (e.g., "1-1", "all") - - Returns: - - n (list): - number or range (e.g., [1, 2, 3], "all") - """ - - if isinstance(n, list): - n = [int(i) for i in n] - elif "-" in str(n): - vals = str(n).split("-") - if len(vals) != 2: - raise ValueError("If n is a range, must have format 1-3.") - st = int(vals[0]) - end = int(vals[1]) + 1 - n = list(range(st, end)) - elif n not in ["all", "any"]: - if not str(n).isdigit(): - raise ValueError(f"'n' expected to be a digit, but found {n}.") - n = [int(n)] - - return n - - -############################################# -def get_dandi_session_ids( - mouse_df, mouse_n="all", sess_n="all", pass_fail="P", sort=True -): - """ - get_dandi_session_ids(mouse_df) - - Returns list of Dandi session IDs that fit the specified criteria. - - Required args: - - mouse_df (Path or pd.DataFrame): path to dataframe containing - information on each session or - dataframe itself - - Optional args: - - mouse_n (int, str or list) : mouse number(s) to retain, - default: "all" - - sess_n (int, str or list) : session number(s) to retain - default: "all" - - pass_fail (str or list) : pass/fail values to retain - ("P", "F", "all") - default: "P" - - Returns: - - dandi_session_ids (list): Dandi session IDs that fit criteria - """ - - if isinstance(mouse_df, (str, Path)): - if not Path(mouse_df).is_file(): - raise OSError(f"{mouse_df} does not exist.") - mouse_df = pd.read_csv(mouse_df) - - criteria = ["runtype: production"] - lines = mouse_df.loc[ - (mouse_df["runtype"] == "prod") - & (mouse_df["sessid"] != 838633305) # excluded session - ] - - # retain lines that fit the session and mouse criteria - col_names = ["mouse_n", "sess_n", "pass_fail"] - col_vals = [mouse_n, sess_n, pass_fail] - - for name, vals in zip(col_names, col_vals): - criteria.append(f"{name}: {vals}") - if vals not in ["all", "any"]: - if name in ["mouse_n", "sess_n"]: - vals = reformat_n(vals) - elif not isinstance(vals, list): - vals = [vals] - lines = lines.loc[(lines[name].isin(vals))] - - if len(lines) == 0: - raise ValueError( - f"No sessions fit the combined criteria: {', '.join(criteria)}" - ) - - dandi_session_ids = lines["sessid"].tolist() - if sort: - dandi_session_ids = sorted(dandi_session_ids) - - return dandi_session_ids - - -############################################# -def get_dandiset_asset_urls( - dandiset_id="000037", - version=PUBLISHED_VERSION, - asset_sessids="all", - incl_stim_templates=False, - incl_full_stacks=False, -): - """ - get_dandiset_asset_urls - """ - - client = dandiapi.DandiAPIClient() - dandi = client.get_dandiset(dandiset_id, version) - - if asset_sessids != "all": - if isinstance(asset_sessids, list): - asset_sessids = [str(asset_sessid) for asset_sessid in asset_sessids] - else: - asset_sessids = [str(asset_sessids)] - - if incl_full_stacks and not incl_stim_templates: - warnings.warn( - "The files that include the full stacks also include the " - "stimulus templates." - ) - incl_stim_templates = True - - asset_urls = [] - selected_asset_sessids = [] - for asset in dandi.get_assets(): - asset_path = Path(asset.path) - asset_sessid = asset_path.parts[1].split("_")[1].replace("ses-", "") - if asset_sessids != "all" and asset_sessid not in asset_sessids: - continue - - stim_templates_included = "+image" in asset_path.parts[1] - if stim_templates_included != incl_stim_templates: - continue - - full_stacks_included = "obj-raw" in asset_path.parts[1] - if full_stacks_included != incl_full_stacks: - continue - - asset_urls.append(asset.download_url) - selected_asset_sessids.append(asset_sessid) - - asset_urls = [asset_urls[i] for i in np.argsort(selected_asset_sessids)] - - if len(asset_urls) == 0: - raise RuntimeError("No dandiset assets found that meet the criteria.") - - return asset_urls - - -############################################# -def download_dandiset_assets( - dandiset_id="000037", - version=PUBLISHED_VERSION, - output=".", - incl_stim_templates=False, - incl_full_stacks=False, - sess_ns="all", - mouse_ns="all", - excluded_sess=True, - mouse_df=DEFAULT_MOUSE_DF_PATH, - dry_run=False, - n_jobs=6, -): - - dandiset_id = f"{int(dandiset_id):06}" # ensure correct ID formatting - - asset_sessids = "all" - if not (excluded_sess and sess_ns in ["all", "any"] and mouse_ns in ["all", "any"]): - if dandiset_id != "000037": - raise NotImplementedError( - "Selecting assets based on session and mouse numbers is only " - "implemented for dandiset 000037." - ) - - pass_fail = "all" if excluded_sess else "P" - asset_sessids = get_dandi_session_ids( - mouse_df, mouse_n=mouse_ns, sess_n=sess_ns, pass_fail=pass_fail, sort=True - ) - - print("Identifying the URLs of dandi assets to download...") - dandiset_urls = get_dandiset_asset_urls( - dandiset_id, - version=version, - asset_sessids=asset_sessids, - incl_stim_templates=incl_stim_templates, - incl_full_stacks=incl_full_stacks, - ) - - action_str = "Identified" if dry_run else "Downloading" - end_str = ". Run without '--dry_run' to download." if dry_run else "..." - - print( - f"{action_str} {len(dandiset_urls)} assets from " - f"dandiset {dandiset_id}{end_str}" - ) - - if dry_run: - return - - try: - dandi_download.download(dandiset_urls, output, jobs=n_jobs, existing="refresh") - except NotImplementedError as err: - if "multiple URLs not supported" not in str(err): - raise err - if n_jobs != 1: - warnings.warn( - "Downloading data sequentially. Upgrade Dandi to version " - "0.50 or above to download from multiple URLs in parallel." - ) - for dandiset_url in dandiset_urls: - dandi_download.download( - dandiset_url, output, jobs=n_jobs, existing="refresh" - ) - - -############################################# -if __name__ == "__main__": - - parser = argparse.ArgumentParser() - - parser.add_argument( - "--dandiset_id", - default="000037", - help=("ID of the dandiset from which to download assets"), - ) - parser.add_argument( - "--version", - default=PUBLISHED_VERSION, - help="version of the dandiset from which to download assets", - ) - parser.add_argument( - "--output", default=".", type=Path, help="where to store the dandiset files" - ) - parser.add_argument( - "--dry_run", - action="store_true", - help=( - "does not download assets, just reports how many assets have " - "been identified for download" - ), - ) - parser.add_argument( - "--n_jobs", default=6, type=int, help="number of downloads to do in parallel" - ) - - # arguments applying only to dandiset 000037 - parser.add_argument( - "--sess_ns", - default="1-3", - help="session numbers of assets to download (e.g., 1, 1-3 or all)", - ) - parser.add_argument( - "--mouse_ns", - default="all", - help="mouse numbers of assets to download (e.g., 1, 1-3 or all)", - ) - parser.add_argument( - "--excluded_sess", - action="store_true", - help=( - "if True, all assets (even those excluded from the paper " - "analyses) are downloaded." - ), - ) - parser.add_argument( - "--mouse_df", - default=DEFAULT_MOUSE_DF_PATH, - type=Path, - help="path to mouse_csv.df, if downloading by sess_ns and/or mouse_ns", - ) - - # type of asset to download - parser.add_argument( - "--incl_stim_templates", - action="store_true", - help=( - "if True, assets containing the stimulus templates are " - "downloaded (~1.5 to 3.1 GB per asset for dandiset 000037)" - ), - ) - parser.add_argument( - "--incl_full_stacks", - action="store_true", - help="if True, assets containing the full imaging stack are downloaded", - ) - - args = parser.parse_args() - - download_dandiset_assets(**args.__dict__) diff --git a/examples/capoyo/notebooks/dataset_size.ipynb b/examples/capoyo/notebooks/dataset_size.ipynb deleted file mode 100644 index b643de7..0000000 --- a/examples/capoyo/notebooks/dataset_size.ipynb +++ /dev/null @@ -1,1822 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from allensdk.core.brain_observatory_cache import BrainObservatoryCache\n", - "import pprint\n", - "import numpy as np\n", - "import allensdk.brain_observatory.stimulus_info as stim_info\n", - "import pandas as pd\n", - "import os\n", - "import matplotlib.pyplot as plt\n", - "from tqdm import tqdm" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "boc = BrainObservatoryCache(manifest_file='/home/mila/x/xuejing.pan/scratch/manifest.json')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Getting all Allen BO metadata" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "all_dg_exps = boc.get_ophys_experiments(stimuli=[stim_info.DRIFTING_GRATINGS])" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "All experiments: 456\n", - "\n" - ] - } - ], - "source": [ - "print(\"All experiments: %d\\n\" % len(all_dg_exps))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "exp = all_dg_exps[16]\n", - "exp_id = all_dg_exps[16]['id']\n", - "exp_depth = all_dg_exps[16][\"donor_name\"]\n", - "#exps = boc.get_ophys_experiments(experiment_container_ids=[exp_id])\n", - "exp = boc.get_ophys_experiment_data(exp['id'])" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'228379'" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "exp_depth" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "traces = exp.get_dff_traces()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "num_rois = traces[1].shape[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "stim_table = exp.get_stimulus_table(\"drifting_gratings\")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " temporal_frequency orientation blank_sweep start end\n", - "0 NaN NaN 1.0 737 796\n", - "1 8.0 45.0 0.0 827 887\n", - "2 4.0 270.0 0.0 918 978\n", - "3 NaN NaN 1.0 1008 1068\n", - "4 8.0 45.0 0.0 1099 1159\n", - ".. ... ... ... ... ...\n", - "623 4.0 90.0 0.0 115093 115153\n", - "624 4.0 135.0 0.0 115183 115243\n", - "625 2.0 135.0 0.0 115274 115334\n", - "626 8.0 135.0 0.0 115364 115424\n", - "627 8.0 315.0 0.0 115455 115515\n", - "\n", - "[628 rows x 5 columns]\n" - ] - } - ], - "source": [ - "print(stim_table)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "num_seqs = stim_table.index[-1]+1\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "ename": "EpochSeparationException", - "evalue": "more than 2 epochs cut", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mEpochSeparationException\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[12], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m master_stim_table \u001b[38;5;241m=\u001b[39m \u001b[43mexp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_stimulus_epoch_table\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/.conda/envs/poyo/lib/python3.9/site-packages/allensdk/core/brain_observatory_nwb_data_set.py:194\u001b[0m, in \u001b[0;36mBrainObservatoryNwbDataSet.get_stimulus_epoch_table\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 192\u001b[0m interval_stimulus_dict \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m 193\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m stimulus \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlist_stimuli():\n\u001b[0;32m--> 194\u001b[0m stimulus_interval_list \u001b[38;5;241m=\u001b[39m \u001b[43mget_epoch_mask_list\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstimulus_table_dict\u001b[49m\u001b[43m[\u001b[49m\u001b[43mstimulus\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mthreshold\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mthreshold_dict\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_session_type\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 195\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m stimulus_interval \u001b[38;5;129;01min\u001b[39;00m stimulus_interval_list:\n\u001b[1;32m 196\u001b[0m interval_stimulus_dict[stimulus_interval] \u001b[38;5;241m=\u001b[39m stimulus\n", - "File \u001b[0;32m~/.conda/envs/poyo/lib/python3.9/site-packages/allensdk/core/brain_observatory_nwb_data_set.py:86\u001b[0m, in \u001b[0;36mget_epoch_mask_list\u001b[0;34m(st, threshold, max_cuts)\u001b[0m\n\u001b[1;32m 80\u001b[0m epoch_mask_list \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 82\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(cut_inds) \u001b[38;5;241m>\u001b[39m max_cuts:\n\u001b[1;32m 83\u001b[0m \n\u001b[1;32m 84\u001b[0m \u001b[38;5;66;03m# See: https://gist.github.com/nicain/bce66cd073e422f07cf337b476c63be7\u001b[39;00m\n\u001b[1;32m 85\u001b[0m \u001b[38;5;66;03m# https://github.com/AllenInstitute/AllenSDK/issues/66\u001b[39;00m\n\u001b[0;32m---> 86\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m EpochSeparationException(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmore than 2 epochs cut\u001b[39m\u001b[38;5;124m'\u001b[39m, delta\u001b[38;5;241m=\u001b[39mdelta)\n\u001b[1;32m 88\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ii \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(cut_inds)\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m):\n\u001b[1;32m 90\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ii \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n", - "\u001b[0;31mEpochSeparationException\u001b[0m: more than 2 epochs cut" - ] - } - ], - "source": [ - "master_stim_table = exp.get_stimulus_epoch_table()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "from allensdk.brain_observatory.brain_observatory_exceptions import EpochSeparationException \n", - "import matplotlib.pyplot as plt\n", - "try:\n", - " exp.get_stimulus_epoch_table()\n", - "except EpochSeparationException as e:\n", - " delta = e.delta\n", - " plt.plot(delta)\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " stimulus start end\n", - "0 drifting_gratings 891 19452\n", - "1 natural_movie_three 20413 39004\n", - "2 natural_movie_one 39934 49230\n", - "3 drifting_gratings 50160 68719\n", - "4 spontaneous 68869 78044\n", - "5 natural_movie_three 78045 96635\n", - "6 drifting_gratings 97565 118745\n" - ] - } - ], - "source": [ - "print(master_stim_table)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "58300\n" - ] - } - ], - "source": [ - "time_all = 0\n", - "for i,stim in enumerate(master_stim_table['stimulus']):\n", - " if stim=='drifting_gratings':\n", - " curr_time_points = master_stim_table['end'][i] - master_stim_table['start'][i]\n", - " time_all += curr_time_points\n", - "\n", - "print(time_all)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Allen data analysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "df = pd.read_csv('/home/mila/x/xuejing.pan/POYO/results/AllenBOmeta.csv')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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exp_idsubject_idcre_linedepthnum_seqsnum_ROIsnum_timepoints
0649409874350249Vip-IRES-Cre1756282658281
1604328043325932Slc17a7-IRES2-Cre3756285256624
2556353209271750Rbp4-Cre_KL1003756282156741
3627823695339814Ntsr1-Cre_GN2205506282156618
4662982346357433Vip-IRES-Cre2506281056751
........................
428564425777283147Emx1-IRES-Cre17562827456745
429510390912232623Rorb-IRES2-Cre2756288756756
430653123929355467Slc17a7-IRES2-Cre27562828756612
431657775947355670Vip-IRES-Cre175628856777
432637115675335039Rbp4-Cre_KL1003756282356761
\n", - "

433 rows × 7 columns

\n", - "
" - ], - "text/plain": [ - " exp_id subject_id cre_line depth num_seqs num_ROIs \\\n", - "0 649409874 350249 Vip-IRES-Cre 175 628 26 \n", - "1 604328043 325932 Slc17a7-IRES2-Cre 375 628 52 \n", - "2 556353209 271750 Rbp4-Cre_KL100 375 628 21 \n", - "3 627823695 339814 Ntsr1-Cre_GN220 550 628 21 \n", - "4 662982346 357433 Vip-IRES-Cre 250 628 10 \n", - ".. ... ... ... ... ... ... \n", - "428 564425777 283147 Emx1-IRES-Cre 175 628 274 \n", - "429 510390912 232623 Rorb-IRES2-Cre 275 628 87 \n", - "430 653123929 355467 Slc17a7-IRES2-Cre 275 628 287 \n", - "431 657775947 355670 Vip-IRES-Cre 175 628 8 \n", - "432 637115675 335039 Rbp4-Cre_KL100 375 628 23 \n", - "\n", - " num_timepoints \n", - "0 58281 \n", - "1 56624 \n", - "2 56741 \n", - "3 56618 \n", - "4 56751 \n", - ".. ... \n", - "428 56745 \n", - "429 56756 \n", - "430 56612 \n", - "431 56777 \n", - "432 56761 \n", - "\n", - "[433 rows x 7 columns]" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "subjects = df['subject_id'].values" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "255\n" - ] - } - ], - "source": [ - "\n", - "unique_values= np.unique(subjects, return_counts=False)\n", - "\n", - "print(len(unique_values))" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "40328\n" - ] - } - ], - "source": [ - "ROIs = df['num_ROIs'].values\n", - "sum_ROIs = np.sum(ROIs)\n", - "print(sum_ROIs)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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/vywWi7KyspSbm6uoqCj7OVarVREREUpPT3dhpQAAwJ14uLqA89555x2dOHFCkydPliTl5uZKkgIDAx3OCwwM1JEjR6q8TklJiUpKSuyvCwsL679YAADgNtxmZmblypUaPny4goKCHNotFovDa8MwKrT9XkJCgvz9/e1HcHBwg9QLAADcg1uEmSNHjuiDDz7Qgw8+aG+z2WyS/m+G5ry8vLwKszW/FxMTo4KCAvuRnZ3dMEUDAAC34BZhZtWqVQoICNCIESPsbSEhIbLZbPZfOEnn1tWkpaUpPDy8ymtZrVa1aNHC4QAAAI2Xy9fMlJeXa9WqVZo0aZI8PP6vHIvFoujoaMXHxys0NFShoaGKj4+Xj4+PJkyY4MKKAQCAO3F5mPnggw909OhR3X///RX6Zs+erVOnTmn69OnKz89XWFiYUlJS5Ofn54JKAQCAO3J5mImKipJhGJX2WSwWxcXFKS4u7uIWBQAATMMt1swAAABcKMIMAAAwNcIMAAAwNcIMAAAwNcIMAAAwNcIMAAAwNcIMAAAwNcIMAAAwNcIMAAAwNcIMAAAwNcIMAAAwNcIMAAAwNcIMAAAwNcIMAAAwNcIMAAAwNcIMAAAwNcIMAAAwNcIMAAAwNcIMAAAwNcIMAAAwNcIMAAAwNcIMAAAwNcIMAAAwNcIMAAAwNcIMAAAwNcIMAAAwNcIMAAAwNcIMAAAwNcIMAAAwNcIMAAAwNcIMAAAwNcIMAAAwNcIMAAAwNcIMAAAwNcIMAAAwNcIMAAAwNZeHmR9//FF/+MMf1KZNG/n4+Khv377as2ePvd8wDMXFxSkoKEje3t6KjIzU119/7cKKAQCAO3FpmMnPz9fAgQPl6emp9957T998842ee+45tWzZ0n5OYmKikpKStGTJEmVkZMhms2nYsGEqKipyXeEAAMBteLjy5s8884yCg4O1atUqe1vnzp3t/2wYhhYvXqzY2FiNHTtWkpScnKzAwECtWbNGU6dOvdglAwAAN+PSmZnNmzerf//+uvPOOxUQEKCrr75aK1assPdnZWUpNzdXUVFR9jar1aqIiAilp6dXes2SkhIVFhY6HAAAoPFyaZj5/vvvtWzZMoWGhur999/XtGnT9Oc//1mvv/66JCk3N1eSFBgY6PC+wMBAe9//SkhIkL+/v/0IDg5u2A8BAABcyqVhpry8XNdcc43i4+N19dVXa+rUqfrTn/6kZcuWOZxnsVgcXhuGUaHtvJiYGBUUFNiP7OzsBqsfAAC4nkvDTPv27dWjRw+Htu7du+vo0aOSJJvNJkkVZmHy8vIqzNacZ7Va1aJFC4cDAAA0Xi4NMwMHDtSBAwcc2g4ePKhOnTpJkkJCQmSz2ZSammrvLy0tVVpamsLDwy9qrQAAwD259NdMf/nLXxQeHq74+Hjddddd+uyzz7R8+XItX75c0rmvl6KjoxUfH6/Q0FCFhoYqPj5ePj4+mjBhgitLBwAAbsKlYebaa6/Vpk2bFBMTo6eeekohISFavHixJk6caD9n9uzZOnXqlKZPn678/HyFhYUpJSVFfn5+LqwcAAC4C5eGGUkaOXKkRo4cWWW/xWJRXFyc4uLiLl5RAADANFz+OAMAAIC6IMwAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTI8wAAABTc2mYiYuLk8VicThsNpu93zAMxcXFKSgoSN7e3oqMjNTXX3/twooBAIC7cfnMTM+ePZWTk2M/9u/fb+9LTExUUlKSlixZooyMDNlsNg0bNkxFRUUurBgAALgTl4cZDw8P2Ww2+9GuXTtJ52ZlFi9erNjYWI0dO1a9evVScnKyiouLtWbNGhdXDQAA3IXLw8yhQ4cUFBSkkJAQ3X333fr+++8lSVlZWcrNzVVUVJT9XKvVqoiICKWnp1d5vZKSEhUWFjocAACg8aqXMHPixIkLel9YWJhef/11vf/++1qxYoVyc3MVHh6uY8eOKTc3V5IUGBjo8J7AwEB7X2USEhLk7+9vP4KDgy+oNgAAYA5Oh5lnnnlG69evt7++66671KZNG1122WX68ssvnbrW8OHDNW7cOPXu3VtDhw7Vv//9b0lScnKy/RyLxeLwHsMwKrT9XkxMjAoKCuxHdna2UzUBAABzcTrMvPLKK/bZjtTUVKWmpuq9997T8OHD9dhjj9WpmObNm6t37946dOiQ/VdN/zsLk5eXV2G25vesVqtatGjhcAAAgMbL6TCTk5NjDzNbtmzRXXfdpaioKM2ePVsZGRl1KqakpETffvut2rdvr5CQENlsNqWmptr7S0tLlZaWpvDw8DrdBwAANB5Oh5lWrVrZv7rZunWrhg4dKunc1z9lZWVOXevRRx9VWlqasrKy9Omnn+qOO+5QYWGhJk2aJIvFoujoaMXHx2vTpk366quvNHnyZPn4+GjChAnOlg0AABopD2ffMHbsWE2YMEGhoaE6duyYhg8fLknKzMxU165dnbrWDz/8oHvuuUe//vqr2rVrp+uvv167d+9Wp06dJEmzZ8/WqVOnNH36dOXn5yssLEwpKSny8/NztmwAANBIOR1m/vGPf6hz587Kzs5WYmKifH19JZ37+mn69OlOXWvdunXV9lssFsXFxSkuLs7ZMgEAwCXC6TDj6empRx99tEJ7dHR0fdQDAADgFKfDjCQdPHhQO3bsUF5ensrLyx36nnzyyXopDAAAoDacDjMrVqzQQw89pLZt28pmszns+WKxWAgzAADgonI6zCxYsEALFy7U448/3hD1AAAAOMXpn2bn5+frzjvvbIhaAAAAnOZ0mLnzzjuVkpLSELUAAAA4zemvmbp27aq5c+dq9+7d6t27tzw9PR36//znP9dbcQAAADVxOswsX75cvr6+SktLU1pamkOfxWIhzAAAgIvK6TCTlZXVEHUAAABcEKfXzPyeYRgyDKO+agEAAHDaBYWZ119/Xb1795a3t7e8vb3Vp08fvfHGG/VdGwAAQI2c/popKSlJc+fO1cyZMzVw4EAZhqFPPvlE06ZN06+//qq//OUvDVEnAABApZwOMy+++KKWLVum++67z942evRo9ezZU3FxcYQZAABwUTn9NVNOTo7Cw8MrtIeHhysnJ6deigIAAKgtp8NM165d9c9//rNC+/r16xUaGlovRQEAANSW018zzZ8/X+PHj9fOnTs1cOBAWSwWffzxx/rwww8rDTkAAAANyemZmXHjxunTTz9V27Zt9c4772jjxo1q27atPvvsM91+++0NUSMAAECVnJ6ZkaR+/frpzTffrO9aAAAAnFarMFNYWKgWLVrY/7k6588DAAC4GGoVZlq1aqWcnBwFBASoZcuWslgsFc4xDEMWi0VlZWX1XiQAAEBVahVmtm3bptatW0uStm/f3qAFAQAAOKNWYSYiIqLSfwYAAHA1p3/NtHXrVn388cf210uXLlXfvn01YcIE5efn12txAAAANXE6zDz22GP2RcD79+/XrFmzdOutt+r777/XrFmz6r1AAACA6jj90+ysrCz16NFDkrRhwwaNGjVK8fHx2rt3r2699dZ6LxAAAKA6Ts/MeHl5qbi4WJL0wQcfKCoqSpLUunXrGn+2DQAAUN+cnpm54YYbNGvWLA0cOFCfffaZ1q9fL0k6ePCgOnToUO8FAgAAVMfpmZklS5bIw8NDb7/9tpYtW6bLLrtMkvTee+/plltuqfcCAQAAquP0zEzHjh21ZcuWCu3/+Mc/6qUgAAAAZzg9M7N3717t37/f/vpf//qXxowZoyeeeEKlpaX1WhwAAEBNnA4zU6dO1cGDByVJ33//ve6++275+Pjorbfe0uzZs+u9QAAAgOo4HWYOHjyovn37SpLeeustDRo0SGvWrNHq1au1YcOG+q4PAACgWk6HGcMwVF5eLuncT7PP7y0THBysX3/9tX6rAwAAqIHTYaZ///5asGCB3njjDaWlpWnEiBGSzm2mFxgYWO8FAgAAVMfpMLN48WLt3btXM2fOVGxsrLp27SpJevvttxUeHl7vBQIAAFTH6TDTp08f7d+/XwUFBZo3b569fdGiRUpOTr7gQhISEmSxWBQdHW1vMwxDcXFxCgoKkre3tyIjI/X1119f8D0AAEDj43SYkaQTJ07o1VdfVUxMjI4fPy5J+uabb5SXl3dBRWRkZGj58uXq06ePQ3tiYqKSkpK0ZMkSZWRkyGazadiwYSoqKrqg+wAAgMbH6TCzb98+hYaG6plnntGzzz6rEydOSJI2bdqkmJgYpwv47bffNHHiRK1YsUKtWrWytxuGocWLFys2NlZjx45Vr169lJycrOLiYq1Zs8bp+wAAgMbJ6TAza9Ys/fGPf9ShQ4fUrFkze/vw4cO1c+dOpwuYMWOGRowYoaFDhzq0Z2VlKTc31/4gS0myWq2KiIhQenp6ldcrKSlRYWGhwwEAABovpx9nkJGRoVdeeaVC+2WXXabc3FynrrVu3Trt3btXGRkZFfrOX+t/fyEVGBioI0eOVHnNhIQEzZ8/36k6AACAeTk9M9OsWbNKZzsOHDigdu3a1fo62dnZeuSRR/Tmm286zPD8L4vF4vDaMIwKbb8XExOjgoIC+5GdnV3rmgAAgPk4HWZGjx6tp556SmfOnJF0LmwcPXpUc+bM0bhx42p9nT179igvL0/9+vWTh4eHPDw8lJaWphdeeEEeHh72GZn/ne3Jy8urdj8bq9WqFi1aOBwAAKDxcjrMPPvss/rll18UEBCgU6dOKSIiQl27dpWfn58WLlxY6+sMGTJE+/fvV2Zmpv3o37+/Jk6cqMzMTF1++eWy2WxKTU21v6e0tFRpaWnsZwMAAOycXjPTokULffzxx9q2bZv27t2r8vJyXXPNNRUW8NbEz89PvXr1cmhr3ry52rRpY2+Pjo5WfHy8QkNDFRoaqvj4ePn4+GjChAnOlg0AABopp8PMeYMHD9bgwYPrs5YKZs+erVOnTmn69OnKz89XWFiYUlJS5Ofn16D3BQAA5lHrr5k+/fRTvffeew5tr7/+ukJCQhQQEKApU6aopKSkTsXs2LFDixcvtr+2WCyKi4tTTk6OTp8+rbS0tAqzOQAA4NJW6zATFxenffv22V/v379fDzzwgIYOHao5c+bo3XffVUJCQoMUCQAAUJVah5nMzEwNGTLE/nrdunUKCwvTihUrNGvWLL3wwgv65z//2SBFAgAAVKXWYSY/P9/hJ9FpaWm65ZZb7K+vvfZa9nQBAAAXXa3DTGBgoLKysiSd+4n03r17NWDAAHt/UVGRPD09679CAACAatQ6zNxyyy2aM2eOPvroI8XExMjHx0c33nijvX/fvn3q0qVLgxQJAABQlVr/NHvBggUaO3asIiIi5Ovrq+TkZHl5edn7X3vtNYeHQgIAAFwMtQ4z7dq100cffaSCggL5+vqqadOmDv1vvfWWfH19671AAACA6ji9aZ6/v3+l7a1bt65zMQAAAM5y+tlMAAAA7oQwAwAATI0wAwAATO2CHzSJc/Ly8lRQUFCh/fDhwyorO+uCigAAuLRcUJj58ccf9cknnygvL0/l5eUOfX/+85/rpTAzyMvL03333K6Sk8cr9BWfLtWxX3JUepYnfAMA0JCcDjOrVq3StGnT5OXlpTZt2shisdj7LBbLJRVmCgoKVHLyuGLHeqlTQDOHvk++KdDf3yhTWVl5Fe8GAAD1wekw8+STT+rJJ59UTEyMmjRhyY0kdQpoptAOPg5th3NPu6gaAAAuLU6nkeLiYt19990EGQAA4BacTiQPPPCA3nrrrYaoBQAAwGlOf82UkJCgkSNHauvWrerdu3eFJ2UnJSXVW3EAAAA1cTrMxMfH6/3331e3bt0kqcICYAAAgIvJ6TCTlJSk1157TZMnT26AcgAAAJzj9JoZq9WqgQMHNkQtAAAATnM6zDzyyCN68cUXG6IWAAAApzn9NdNnn32mbdu2acuWLerZs2eFBcAbN26st+IAAABq4nSYadmypcaOHdsQtQAAADjtgh5nAAAA4C7YxhcAAJia0zMzISEh1e4n8/3339epIAAAAGc4HWaio6MdXp85c0ZffPGFtm7dqscee6y+6gIAAKgVp8PMI488Umn70qVL9fnnn9e5IAAAAGfU25qZ4cOHa8OGDfV1OQAAgFqptzDz9ttvq3Xr1vV1OQAAgFpx+mumq6++2mEBsGEYys3N1S+//KKXXnqpXosDAACoidNhZsyYMQ6vmzRponbt2ikyMlJXXnllfdUFAABQK06HmXnz5jVEHQAAABfE6TCD+lN65qwOHz5caZ+/v78CAgIubkEAAJhQrcNMkyZNqt0sT5IsFovOnj1b65svW7ZMy5Yts/8HvWfPnnryySc1fPhwSefW48yfP1/Lly9Xfn6+wsLCtHTpUvXs2bPW93BXxwrP6Ej2D4qbM01enl4V+q3NW+v1tZsINAAA1KDWYWbTpk1V9qWnp+vFF1+UYRhO3bxDhw56+umn1bVrV0lScnKyRo8erS+++EI9e/ZUYmKikpKStHr1al1xxRVasGCBhg0bpgMHDsjPz8+pe7mbouIyWZuWK2aMl7oFt3DoO5J3Wgs3HldBQQFhBgCAGtQ6zIwePbpC23fffaeYmBi9++67mjhxov7+9787dfNRo0Y5vF64cKGWLVum3bt3q0ePHlq8eLFiY2PtT+lOTk5WYGCg1qxZo6lTpzp1L3fVMcCq0A4+lfSUXvRaAAAwowvaZ+ann37Sn/70J/Xp00dnz55VZmamkpOT1bFjxwsupKysTOvWrdPJkyc1YMAAZWVlKTc3V1FRUfZzrFarIiIilJ6efsH3AQAAjYtTC4ALCgoUHx+vF198UX379tWHH36oG2+8sU4F7N+/XwMGDNDp06fl6+urTZs2qUePHvbAEhgY6HB+YGCgjhw5UuX1SkpKVFJSYn9dWFhYp/oAAIB7q/XMTGJioi6//HJt2bJFa9euVXp6ep2DjCR169ZNmZmZ2r17tx566CFNmjRJ33zzjb3/fxcdG4ZR7ULkhIQE+fv724/g4OA61wgAANxXrWdm5syZI29vb3Xt2lXJyclKTk6u9LyNGzc6VYCXl5d9AXD//v2VkZGh559/Xo8//rgkKTc3V+3bt7efn5eXV2G25vdiYmI0a9Ys++vCwkICDQAAjVitw8x9991X40+z64NhGCopKVFISIhsNptSU1N19dVXS5JKS0uVlpamZ555psr3W61WWa3WBq8TAAC4h1qHmdWrV9f7zZ944gkNHz5cwcHBKioq0rp167Rjxw5t3bpVFotF0dHRio+PV2hoqEJDQxUfHy8fHx9NmDCh3msBAADm5NIdgH/++Wfde++9ysnJkb+/v/r06aOtW7dq2LBhkqTZs2fr1KlTmj59un3TvJSUFNPvMQMAAOqPS8PMypUrq+23WCyKi4tTXFzcxSkIAACYzgXtMwMAAOAuCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUCDMAAMDUXBpmEhISdO2118rPz08BAQEaM2aMDhw44HCOYRiKi4tTUFCQvL29FRkZqa+//tpFFQMAAHfj0jCTlpamGTNmaPfu3UpNTdXZs2cVFRWlkydP2s9JTExUUlKSlixZooyMDNlsNg0bNkxFRUUurBwAALgLD1fefOvWrQ6vV61apYCAAO3Zs0eDBg2SYRhavHixYmNjNXbsWElScnKyAgMDtWbNGk2dOtUVZQMAADfiVmtmCgoKJEmtW7eWJGVlZSk3N1dRUVH2c6xWqyIiIpSenl7pNUpKSlRYWOhwAACAxsttwoxhGJo1a5ZuuOEG9erVS5KUm5srSQoMDHQ4NzAw0N73vxISEuTv728/goODG7ZwAADgUm4TZmbOnKl9+/Zp7dq1FfosFovDa8MwKrSdFxMTo4KCAvuRnZ3dIPUCAAD34NI1M+c9/PDD2rx5s3bu3KkOHTrY2202m6RzMzTt27e3t+fl5VWYrTnParXKarU2bMEAAMBtuHRmxjAMzZw5Uxs3btS2bdsUEhLi0B8SEiKbzabU1FR7W2lpqdLS0hQeHn6xywUAAG7IpTMzM2bM0Jo1a/Svf/1Lfn5+9nUw/v7+8vb2lsViUXR0tOLj4xUaGqrQ0FDFx8fLx8dHEyZMcGXpAADATbg0zCxbtkySFBkZ6dC+atUqTZ48WZI0e/ZsnTp1StOnT1d+fr7CwsKUkpIiPz+/i1wtAABwRy4NM4Zh1HiOxWJRXFyc4uLiGr4gAABgOm7zayYAAIAL4Ra/ZkL9ysvLs29AWBl/f38FBARcxIoAAGg4hJlGJi8vT/fdc7tKTh6v8hxr89Z6fe0mAg0AoFEgzDQyBQUFKjl5XLFjvdQpoFmF/iN5p7Vw43EVFBQQZgAAjQJhppHqFNBMoR18qugtvai1AADQkFgADAAATI2ZmUtQ6ZmzOnz4cKV9LA4GAJgNYeYSc6zwjI5k/6C4OdPk5elVoZ/FwQAAsyHMXGKKistkbVqumDFe6hbcwqGPxcEAADMizFyiOgZYq1ggzOJgAIC5sAAYAACYGmEGAACYGmEGAACYGmEGAACYGmEGAACYGr9mMqmqnox9+PBhlZWddUFFNavuad6lpaXy8qq47815bOYHAKgKYcaEqnsydvHpUh37JUelZ/1cUFnVqqu59MxZZf/4szoHB6pp08r/lWQzPwBAVQgzJlTdk7E/+aZAf3+jTGVl5S6qrnI111yi2bd5VNjIT2IzPwBA9QgzJlbZk7EP5552UTW1U13NVW/kJ7GZHwCgKiwABgAApsbMjJuq7snW7rzIFwCAi40w44ZqerK1uy7yBQDAFQgzbqi6J1tL7rvIFwAAVyDMuLGqFsS6+yJfAAAuJhYAAwAAU2NmBg6qW3gssRMvAMD9EGZgV9PCY4mdeAEA7ocwA7uaFh6zEy8AwB0RZlABO/ECAMyEMANTqG4tD+t4AODSRpiB26tpLQ/reADg0kaYgdurbi0P63gAAIQZmEbVa3lYxwMAlzI2zQMAAKbGzAxMry4b/eXl5amgoOCC3usq7lpzdXW54zgCaDxcGmZ27typRYsWac+ePcrJydGmTZs0ZswYe79hGJo/f76WL1+u/Px8hYWFaenSperZs6frioZbqctGf3l5ebrvnttVcvJ4ldd3t8XF7lpzTXW52zgCaFxcGmZOnjypq666Sn/84x81bty4Cv2JiYlKSkrS6tWrdcUVV2jBggUaNmyYDhw4ID8/PxdUDHdTl43+CgoKVHLyuGLHeqlTQDOn3usq7lpzdXW54zgCaFxcGmaGDx+u4cOHV9pnGIYWL16s2NhYjR07VpKUnJyswMBArVmzRlOnTr2YpcLN1WWjv04BzUy3SaC71lx1Xe45jgAaB7ddM5OVlaXc3FxFRUXZ26xWqyIiIpSenl5lmCkpKVFJSYn9dWFhYYPXeilh8zoAgLtx2zCTm5srSQoMDHRoDwwM1JEjR6p8X0JCgubPn9+gtV2q2LwOAOCO3DbMnGexWBxeG4ZRoe33YmJiNGvWLPvrwsJCBQcHN1h9lxI2rwMAuCO3DTM2m03SuRma9u3b29vz8vIqzNb8ntVqldVqbfD6LmVsXgcAcCduu2leSEiIbDabUlNT7W2lpaVKS0tTeHi4CysDAADuxKUzM7/99pv+85//2F9nZWUpMzNTrVu3VseOHRUdHa34+HiFhoYqNDRU8fHx8vHx0YQJE1xYNapS3eLgw4cPq6zs7MUtCABwSXBpmPn8889100032V+fX+syadIkrV69WrNnz9apU6c0ffp0+6Z5KSkp7DHjhmpaHFx8ulTHfslR6Vn+7AAA9culYSYyMlKGYVTZb7FYFBcXp7i4uItXFC5ITZvXffJNgf7+RpnKyspdUB0AoDFz2wXAMKeqFgcfzj3tgmoAAJcCt10ADAAAUBvMzAANhKdIA8DFQZgBGgBPkQaAi4cwAzQAniINABcPYQZoQDxFGgAaHmEGqAZPCQcA90eYAarAU8IBwBwIM0AVeEo4AJgDYQaoAU8JBwD3xqZ5AADA1JiZQaNX1SLeuj7Jm6eE1151YyVJpaWl8vKquC6pNv11WYhd3caGdb22O94XaKwIM2jUqlvEW5cnefOU8NqraaxKz5xV9o8/q3NwoJo2rfhXUk39F7oQu6aNDetybXe8L9CYEWbQqFW3iLcuT/LmKeG1V7uxKtHs2zyc7q/LQuzqNjas67Xd8b5AY0aYwSWhskW89fEkb54SXns1jdWF9td1IXbVGxvW/drueF+gMWIBMAAAMDVmZgDYmfFJ3zUtLnbXugHUH8IMAEnmfNJ3TYuLJfesG0D9IswAkGTOJ33XtLjYXesGUL8IMwAcmPFJ31UvDpbcuW4A9YMwA7hAQ67zqO7a1W0+V9NGf2bdJLChNk0E4D4IM8BF1pDrPKq7dk2bz1W30Z9ZNwlsqE0TAbgXwgxwkTXkOo+aNwmsaXO6yjf6M+smgQ21aSIA90KYAVykIdd5VLdJYF02+jPrJoENtWkiAPfApnkAAMDUmJkB3BCLVt1DdYue2YwPcB+EGcDNsGjVPdS06JnN+AD3QZgB3AyLVt1DdX8ObMYHuBfCDOCmWLTqHhrqad0A6g9hBgAugDtufFjX+1anuoeQNuR9gdogzACAk9x148O63Lc6NT2EtKHuC9QWYQYAnOSuGx821Fqe6h5C2pD3BWqLMAMAF8jdNj6sj/tWp+qHkDbsfYGasGkeAAAwNWZmAKABuGrjw7osHq6qv641m3HxsBlrbkjVjYc7jIUpwsxLL72kRYsWKScnRz179tTixYt14403urosAKiUqzY+rMvi4er661KzGRcPm7HmhlTTeLjDWLh9mFm/fr2io6P10ksvaeDAgXrllVc0fPhwffPNN+rYsaOrywOACly18WHdn5peeX9dajbj4mEz1tyQqhsPdxkLtw8zSUlJeuCBB/Tggw9KkhYvXqz3339fy5YtU0JCgourA4CquWrjw7o8Nb2hajbj4mEz1tyQqh4P14+FWy8ALi0t1Z49exQVFeXQHhUVpfT0dBdVBQAA3Ilbz8z8+uuvKisrU2BgoEN7YGCgcnNzK31PSUmJSkpK7K/PL1gqLCys9/p+++03nT1bpm+OntRvp8oc+v6TU6zyckPfHS1WWXnFYa6un/fyXt7r3nXxXkdHfzmtU6dL9fXXX+u3335z6Dty5IhKSksr/Xuypve6ihlrbkjVjcfRX07r7Nky/fbbb/X+39nz1zMMo+aTDTf2448/GpKM9PR0h/YFCxYY3bp1q/Q98+bNMyRxcHBwcHBwNIIjOzu7xrzg1jMzbdu2VdOmTSvMwuTl5VWYrTkvJiZGs2bNsr8uLy/X8ePH1aZNG1kslnqrrbCwUMHBwcrOzlaLFhUX06HhMPauw9i7DmPvOoy9axiGoaKiIgUFBdV4rluHGS8vL/Xr10+pqam6/fbb7e2pqakaPXp0pe+xWq2yWq0ObS1btmywGlu0aMG/3C7C2LsOY+86jL3rMPYXn7+/f63Oc+swI0mzZs3Svffeq/79+2vAgAFavny5jh49qmnTprm6NAAA4AbcPsyMHz9ex44d01NPPaWcnBz16tVL/+///T916tTJ1aUBAAA34PZhRpKmT5+u6dOnu7oMB1arVfPmzavwlRYaHmPvOoy96zD2rsPYuz+LYdTmN08AAADuya03zQMAAKgJYQYAAJgaYQYAAJgaYQYAAJgaYeYCvPTSSwoJCVGzZs3Ur18/ffTRR64uyfR27typUaNGKSgoSBaLRe+8845Dv2EYiouLU1BQkLy9vRUZGamvv/7a4ZySkhI9/PDDatu2rZo3b67bbrtNP/zww0X8FOaUkJCga6+9Vn5+fgoICNCYMWN04MABh3MY/4axbNky9enTx74Z24ABA/Tee+/Z+xn3iychIUEWi0XR0dH2NsbfPAgzTlq/fr2io6MVGxurL774QjfeeKOGDx+uo0ePuro0Uzt58qSuuuoqLVmypNL+xMREJSUlacmSJcrIyJDNZtOwYcNUVFRkPyc6OlqbNm3SunXr9PHHH+u3337TyJEjVVZW8UFx+D9paWmaMWOGdu/erdTUVJ09e1ZRUVE6efKk/RzGv2F06NBBTz/9tD7//HN9/vnnGjx4sEaPHm3/DybjfnFkZGRo+fLl6tOnj0M7428idXsU5KXnuuuuM6ZNm+bQduWVVxpz5sxxUUWNjyRj06ZN9tfl5eWGzWYznn76aXvb6dOnDX9/f+Pll182DMMwTpw4YXh6ehrr1q2zn/Pjjz8aTZo0MbZu3XrRam8M8vLyDElGWlqaYRiM/8XWqlUr49VXX2XcL5KioiIjNDTUSE1NNSIiIoxHHnnEMAz+vTcbZmacUFpaqj179igqKsqhPSoqSunp6S6qqvHLyspSbm6uw7hbrVZFRETYx33Pnj06c+aMwzlBQUHq1asXfzZOKigokCS1bt1aEuN/sZSVlWndunU6efKkBgwYwLhfJDNmzNCIESM0dOhQh3bG31xMsQOwu/j1119VVlZW4YndgYGBFZ7sjfpzfmwrG/cjR47Yz/Hy8lKrVq0qnMOfTe0ZhqFZs2bphhtuUK9evSQx/g1t//79GjBggE6fPi1fX19t2rRJPXr0sP/HkHFvOOvWrdPevXuVkZFRoY9/782FMHMBLBaLw2vDMCq0of5dyLjzZ+OcmTNnat++ffr4448r9DH+DaNbt27KzMzUiRMntGHDBk2aNElpaWn2fsa9YWRnZ+uRRx5RSkqKmjVrVuV5jL858DWTE9q2baumTZtWSNx5eXkV0jvqj81mk6Rqx91ms6m0tFT5+flVnoPqPfzww9q8ebO2b9+uDh062NsZ/4bl5eWlrl27qn///kpISNBVV12l559/nnFvYHv27FFeXp769esnDw8PeXh4KC0tTS+88II8PDzs48f4mwNhxgleXl7q16+fUlNTHdpTU1MVHh7uoqoav5CQENlsNodxLy0tVVpamn3c+/XrJ09PT4dzcnJy9NVXX/FnUwPDMDRz5kxt3LhR27ZtU0hIiEM/439xGYahkpISxr2BDRkyRPv371dmZqb96N+/vyZOnKjMzExdfvnljL+ZuGbdsXmtW7fO8PT0NFauXGl88803RnR0tNG8eXPj8OHDri7N1IqKiowvvvjC+OKLLwxJRlJSkvHFF18YR44cMQzDMJ5++mnD39/f2Lhxo7F//37jnnvuMdq3b28UFhbarzFt2jSjQ4cOxgcffGDs3bvXGDx4sHHVVVcZZ8+eddXHMoWHHnrI8Pf3N3bs2GHk5OTYj+LiYvs5jH/DiImJMXbu3GlkZWUZ+/btM5544gmjSZMmRkpKimEYjPvF9vtfMxkG428mhJkLsHTpUqNTp06Gl5eXcc0119h/wooLt337dkNShWPSpEmGYZz7meS8efMMm81mWK1WY9CgQcb+/fsdrnHq1Clj5syZRuvWrQ1vb29j5MiRxtGjR13wacylsnGXZKxatcp+DuPfMO6//3773yXt2rUzhgwZYg8yhsG4X2z/G2YYf/OwGIZhuGZOCAAAoO5YMwMAAEyNMAMAAEyNMAMAAEyNMAMAAEyNMAMAAEyNMAMAAEyNMAMAAEyNMAPgkhAZGano6GhXlwGgARBmADhl8uTJslgsslgs8vDwUMeOHfXQQw9VeNieJKWnp+vWW29Vq1at1KxZM/Xu3VvPPfecysrKHM6zWCx65513Kr3fqFGjNHTo0Er7du3aJYvFor1799b5cwEwL8IMAKfdcsstysnJ0eHDh/Xqq6/q3Xff1fTp0x3O2bRpkyIiItShQwdt375d3333nR555BEtXLhQd999t2q7+fgDDzygbdu26ciRIxX6XnvtNfXt21fXXHNNvXwuAOZEmAHgNKvVKpvNpg4dOigqKkrjx49XSkqKvf/kyZP605/+pNtuu03Lly9X37591blzZz344INKTk7W22+/rX/+85+1utfIkSMVEBCg1atXO7QXFxdr/fr1euCBB3Ts2DHdc8896tChg3x8fNS7d2+tXbu22utWNhvUsmVLh/v8+OOPGj9+vFq1aqU2bdpo9OjROnz4sL1/x44duu6669S8eXO1bNlSAwcOrDR0AWhYhBkAdfL9999r69at8vT0tLelpKTo2LFjevTRRyucP2rUKF1xxRU1ho3zPDw8dN9992n16tUOszlvvfWWSktLNXHiRJ0+fVr9+vXTli1b9NVXX2nKlCm699579emnn17w5youLtZNN90kX19f7dy5Ux9//LF8fX11yy23qLS0VGfPntWYMWMUERGhffv2adeuXZoyZYosFssF3xPAhfFwdQEAzGfLli3y9fVVWVmZTp8+LUlKSkqy9x88eFCS1L1790rff+WVV9rPqY37779fixYt0o4dO3TTTTdJOvcV09ixY9WqVSu1atXKITg9/PDD2rp1q9566y2FhYU5/fkkad26dWrSpIleffVVe0BZtWqVWrZsqR07dqh///4qKCjQyJEj1aVLl2o/L4CGxcwMAKfddNNNyszM1KeffqqHH35YN998sx5++OEK51W1LsYwDKdmMK688kqFh4frtddekyT997//1UcffaT7779fklRWVqaFCxeqT58+atOmjXx9fZWSkqKjR49ewKc7Z8+ePfrPf/4jPz8/+fr6ytfXV61bt9bp06f13//+V61bt9bkyZN18803a9SoUXr++eeVk5NzwfcDcOEIMwCc1rx5c3Xt2lV9+vTRCy+8oJKSEs2fP9/ef8UVV0iSvv3220rf/9133yk0NNSpez7wwAPasGGDCgsLtWrVKnXq1ElDhgyRJD333HP6xz/+odmzZ2vbtm3KzMzUzTffrNLS0iqvZ7FYKoStM2fO2P+5vLxc/fr1U2ZmpsNx8OBBTZgwQdK5mZpdu3YpPDxc69ev1xVXXKHdu3c79bkA1B1hBkCdzZs3T88++6x++uknSVJUVJRat26t5557rsK5mzdv1qFDh3TPPfc4dY+77rpLTZs21Zo1a5ScnKw//vGP9tmdjz76SKNHj9Yf/vAHXXXVVbr88st16NChaq/Xrl07h5mUQ4cOqbi42P76mmuu0aFDhxQQEKCuXbs6HP7+/vbzrr76asXExCg9PV29evXSmjVrnPpcAOqOMAOgziIjI9WzZ0/Fx8dLOjdz88orr+hf//qXpkyZon379unw4cNauXKlJk+erDvuuEN33XWXU/fw9fXV+PHj9cQTT+inn37S5MmT7X1du3ZVamqq0tPT9e2332rq1KnKzc2t9nqDBw/WkiVLtHfvXn3++eeaNm2awyLmiRMnqm3btho9erQ++ugjZWVlKS0tTY888oh++OEHZWVlKSYmRrt27dKRI0eUkpKigwcPsm4GcAHCDIB6MWvWLK1YsULZ2dmSpDvuuEPbt29Xdna2Bg0apG7duikpKUmxsbFat27dBf3q54EHHlB+fr6GDh2qjh072tvnzp2ra665RjfffLMiIyNls9k0ZsyYaq/13HPPKTg4WIMGDdKECRP06KOPysfHx97v4+OjnTt3qmPHjho7dqy6d++u+++/X6dOnVKLFi3k4+Oj7777TuPGjdMVV1yhKVOmaObMmZo6darTnwtA3ViM2u5cBQAA4IaYmQEAAKZGmAEAAKZGmAEAAKZGmAEAAKZGmAEAAKZGmAEAAKZGmAEAAKZGmAEAAKZGmAEAAKZGmAEAAKZGmAEAAKZGmAEAAKb2/wFOAzH63wtG0wAAAABJRU5ErkJggg==", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Creating the histogram\n", - "plt.hist(ROIs, bins=60, alpha=0.75, color='orange', edgecolor='black')\n", - "\n", - "# Adding titles and labels\n", - "plt.title('Histogram of ROIs')\n", - "plt.xlabel('ROI Values')\n", - "plt.ylabel('Num Sessions')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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t2tTh+qLGjRtryZIlWrp0qW677TZ5e3vbf3+AP620r5AGStPlO2e2bdt2xe1du3a95t1Yr7/+uhEZGWlUrlzZ8PLyMmrWrGnExcUZBw8edHhdQkKCER4ebpQrV87hrpaCggJjypQpRv369Q1PT0+jcuXKxuOPP25kZmY6vL6wsNCYNGmSUb16dcPLy8to0qSJ8fnnnxtNmzZ1uMPnancy5eXlGaNGjTKqVatmeHt7Gy1atDBWrFhhDBw40OE4L98NNG3aNIfXFzf2td7H31uxYoXRunVrw9vb2/D19TXat29vfPPNN9e1nyu51r5XrFhhtGvXzggICDCsVqtRq1Yto1evXsa6desc+m3evNno0qWLERgYaFitVqNu3bpF7h76+uuvjQceeMDw9fU1KlSoYNxzzz3GqlWrrruewsJCIzk52ahRo4b9HK5atcqIioq67ruxjh07dsX9/f5OvxMnThh/+ctfjIoVKxo+Pj5Gx44djS1bthiSjDfffPNqb6dhGIbxww8/GB07djS8vb2NoKAgIy4uzvjss88cfm4vS01NNbp27WoEBQUZnp6eRrVq1YyuXbs6nLvLtW/fvt2IjY01/Pz8DH9/f6Nv377Gf/7zH4fxDh48aMTExBj+/v4Od0MW9zNxpfcK+COLYVzHg0QAuKWMjAzdfvvtmjBhgsaOHVva5cCNLVq0SP3799c333yjyMjIm7rvxMRETZw4UceOHStybRNwM/AxFlBGfP/991q8eLEiIyMVEBCgvXv3aurUqQoICFBcXFxplwc3snjxYv36669q3LixypUrpy1btmjatGlq27btTQ86gDsg7ABlhK+vr7777ju9//77OnnypAIDAxUdHa3Jkydf923FuDX4+/tryZIlmjRpks6cOaOqVatq0KBBmjRpUmmXBpQKPsYCAACmxq3nAADA1Ag7AADA1Ag7AADA1LhAWZeegvrbb7/J39//mg8GAwAA7sEwDJ06dUrh4eFXfWAmYUeXvtumRo0apV0GAAD4EzIzM6/6Zb6EHV26TVO69GYFBASUcjUAAOB65ObmqkaNGva/48Uh7Oj/vp04ICCAsAMAQBlzrUtQuEAZAACYWqmGnY0bNyo2Nlbh4eGyWCxasWJFkT4//vijunfvrsDAQPn7++uee+7R4cOH7dvz8vI0fPhwVa5cWb6+vurevbuOHDlyE48CAAC4s1INO2fOnFHTpk01a9asK27/5ZdfdN999+n222/Xhg0b9P3332v8+PHy9va294mPj9fy5cu1ZMkSbdq0SadPn1a3bt1UUFBwsw4DAAC4Mbf5ugiLxaLly5froYcesrc99thj8vT01IIFC674GpvNpipVqmjBggXq06ePpP+7s+p///d/1alTp+vad25urgIDA2Wz2bhmBwCAMuJ6/3677TU7hYWF+uKLL1S/fn116tRJISEhat26tcNHXdu3b9eFCxcUExNjbwsPD1ejRo2UlpZWClUDAAB347ZhJzs7W6dPn9Zrr72mzp07a+3atXr44YfVs2dPpaamSpKysrLk5eWlSpUqObw2NDRUWVlZxY6dl5en3NxchwUAAJiT2956XlhYKEnq0aOHXnzxRUlSs2bNlJaWprfffltRUVHFvtYwjKvehpacnKyJEyc6t2AAAOCW3HZmp3LlyvLw8NCdd97p0H7HHXfY78YKCwtTfn6+cnJyHPpkZ2crNDS02LETEhJks9nsS2ZmpvMPAAAAuAW3DTteXl666667tHfvXof2ffv2qVatWpKkli1bytPTUykpKfbtR48e1e7duxUZGVns2Far1f4AQR4kCACAuZXqx1inT5/Wzz//bF/PyMhQenq6goKCVLNmTY0ePVp9+vRR27Zt1a5dO61evVqrVq3Shg0bJEmBgYGKi4vTyJEjFRwcrKCgII0aNUqNGzdWhw4dSumoAACAOynVW883bNigdu3aFWkfOHCg5s2bJ0n64IMPlJycrCNHjqhBgwaaOHGievToYe97/vx5jR49WosWLdK5c+fUvn17zZ49u0Rf7Mmt5wAAlD3X+/fbbZ6zU5oIOwAAlD1l/jk7AAAAzkDYAQAApua2z9kBSio7O1s2m83p4wYGBiokJMTp4wIAbg7CDkwhOztbA/o+rLwzJ5w+ttU3SB8uXk7gAYAyirADU7DZbMo7c0LjenqpVoi308Y9lH1ek5edkM1mI+wAQBlF2IGp1ArxVkR1HyePmu/k8QAANxMXKAMAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMr1bCzceNGxcbGKjw8XBaLRStWrCi27+DBg2WxWDRjxgyH9ry8PA0fPlyVK1eWr6+vunfvriNHjri2cAAAUGaUatg5c+aMmjZtqlmzZl2134oVK7R161aFh4cX2RYfH6/ly5dryZIl2rRpk06fPq1u3bqpoKDAVWUDAIAyxKM0d96lSxd16dLlqn1+/fVXDRs2TGvWrFHXrl0dttlsNr3//vtasGCBOnToIElauHChatSooXXr1qlTp04uqx0AAJQNbn3NTmFhoZ544gmNHj1aDRs2LLJ9+/btunDhgmJiYuxt4eHhatSokdLS0oodNy8vT7m5uQ4LAAAwJ7cOO1OmTJGHh4eef/75K27PysqSl5eXKlWq5NAeGhqqrKysYsdNTk5WYGCgfalRo4ZT6wYAAO7DbcPO9u3b9eabb2revHmyWCwleq1hGFd9TUJCgmw2m33JzMy80XIBAICbctuw8/XXXys7O1s1a9aUh4eHPDw8dOjQIY0cOVK1a9eWJIWFhSk/P185OTkOr83OzlZoaGixY1utVgUEBDgsAADAnNw27DzxxBPauXOn0tPT7Ut4eLhGjx6tNWvWSJJatmwpT09PpaSk2F939OhR7d69W5GRkaVVOgAAcCOlejfW6dOn9fPPP9vXMzIylJ6erqCgINWsWVPBwcEO/T09PRUWFqYGDRpIkgIDAxUXF6eRI0cqODhYQUFBGjVqlBo3bmy/OwsAANzaSjXsfPfdd2rXrp19fcSIEZKkgQMHat68edc1xhtvvCEPDw/17t1b586dU/v27TVv3jyVL1/eFSUDAIAyplTDTnR0tAzDuO7+Bw8eLNLm7e2tmTNnaubMmU6sDAAAmIXbXrMDAADgDIQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaqUadjZu3KjY2FiFh4fLYrFoxYoV9m0XLlzQyy+/rMaNG8vX11fh4eEaMGCAfvvtN4cx8vLyNHz4cFWuXFm+vr7q3r27jhw5cpOPBAAAuKtSDTtnzpxR06ZNNWvWrCLbzp49qx07dmj8+PHasWOHli1bpn379ql79+4O/eLj47V8+XItWbJEmzZt0unTp9WtWzcVFBTcrMMAAABuzKM0d96lSxd16dLlitsCAwOVkpLi0DZz5kzdfffdOnz4sGrWrCmbzab3339fCxYsUIcOHSRJCxcuVI0aNbRu3Tp16tTJ5ccAAADcW5m6Zsdms8lisahixYqSpO3bt+vChQuKiYmx9wkPD1ejRo2UlpZW7Dh5eXnKzc11WAAAgDmVmbBz/vx5jRkzRv369VNAQIAkKSsrS15eXqpUqZJD39DQUGVlZRU7VnJysgIDA+1LjRo1XFo7AAAoPWUi7Fy4cEGPPfaYCgsLNXv27Gv2NwxDFoul2O0JCQmy2Wz2JTMz05nlAgAAN+L2YefChQvq3bu3MjIylJKSYp/VkaSwsDDl5+crJyfH4TXZ2dkKDQ0tdkyr1aqAgACHBQAAmJNbh53LQWf//v1at26dgoODHba3bNlSnp6eDhcyHz16VLt371ZkZOTNLhcAALihUr0b6/Tp0/r555/t6xkZGUpPT1dQUJDCw8PVq1cv7dixQ59//rkKCgrs1+EEBQXJy8tLgYGBiouL08iRIxUcHKygoCCNGjVKjRs3tt+dBQAAbm2lGna+++47tWvXzr4+YsQISdLAgQOVmJiolStXSpKaNWvm8Lr169crOjpakvTGG2/Iw8NDvXv31rlz59S+fXvNmzdP5cuXvynHAAAA3Fuphp3o6GgZhlHs9qttu8zb21szZ87UzJkznVkaAAAwCbe+ZgcAAOBGEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpOSXsnDx50hnDAAAAOF2Jw86UKVO0dOlS+3rv3r0VHBysatWq6fvvv3dqcQAAADeqxGHnH//4h2rUqCFJSklJUUpKir788kt16dJFo0ePLtFYGzduVGxsrMLDw2WxWLRixQqH7YZhKDExUeHh4apQoYKio6O1Z88ehz55eXkaPny4KleuLF9fX3Xv3l1Hjhwp6WEBAACT8ijpC44ePWoPO59//rl69+6tmJgY1a5dW61bty7RWGfOnFHTpk31l7/8RY888kiR7VOnTtX06dM1b9481a9fX5MmTVLHjh21d+9e+fv7S5Li4+O1atUqLVmyRMHBwRo5cqS6deum7du3q3z58iU9PKfLzs6WzWZzydiBgYEKCQlxydgAAJhFicNOpUqVlJmZqRo1amj16tWaNGmSpEuzMAUFBSUaq0uXLurSpcsVtxmGoRkzZmjcuHHq2bOnJGn+/PkKDQ3VokWLNHjwYNlsNr3//vtasGCBOnToIElauHChatSooXXr1qlTp04lPTynys7O1oC+DyvvzAmXjG/1DdKHi5cTeAAAuIoSh52ePXuqX79+ioiI0PHjx+1hJT09XfXq1XNaYRkZGcrKylJMTIy9zWq1KioqSmlpaRo8eLC2b9+uCxcuOPQJDw9Xo0aNlJaWVmzYycvLU15enn09NzfXaXX/ns1mU96ZExrX00u1QrydOvah7POavOyEbDYbYQcAgKsocdh54403VLt2bWVmZmrq1Kny8/OTdOnjraFDhzqtsKysLElSaGioQ3toaKgOHTpk7+Pl5aVKlSoV6XP59VeSnJysiRMnOq3Wa6kV4q2I6j4uGDnfBWMCAGAuJQ47np6eGjVqVJH2+Ph4Z9RThMVicVg3DKNI2x9dq09CQoJGjBhhX8/NzbVfhwQAAMylxGFHkvbt26cNGzYoOztbhYWFDtv++te/OqWwsLAwSZdmb6pWrWpvz87Ots/2hIWFKT8/Xzk5OQ6zO9nZ2YqMjCx2bKvVKqvV6pQ6AQCAeytx2Hn33Xf17LPPqnLlygoLC3OYQbFYLE4LO3Xq1FFYWJhSUlLUvHlzSVJ+fr5SU1M1ZcoUSVLLli3l6emplJQU9e7dW9Klj9N2796tqVOnOqUOAABQtpU47EyaNEmTJ0/Wyy+/fMM7P336tH7++Wf7ekZGhtLT0xUUFKSaNWsqPj5eSUlJioiIUEREhJKSkuTj46N+/fpJunTrdVxcnEaOHKng4GAFBQVp1KhRaty4sf3uLADO5arHKfAoBQCuUuKwk5OTo0cffdQpO//uu+/Url07+/rl62gGDhyoefPm6aWXXtK5c+c0dOhQ5eTkqHXr1lq7dq39GTvSpQumPTw81Lt3b507d07t27fXvHnz3OIZO4DZuPJxCjxKAYCrlDjsPProo1q7dq2GDBlywzuPjo6WYRjFbrdYLEpMTFRiYmKxfby9vTVz5kzNnDnzhusBcHWuepwCj1IA4EolDjv16tXT+PHjtWXLFjVu3Fienp4O259//nmnFQfAPbnmcQo8SgGAa5Q47Lzzzjvy8/NTamqqUlNTHbZZLBbCDgAAcCslDjsZGRmuqAMAAMAlSvyt579nGMZVr7kBAAAobX8q7Hz44Ydq3LixKlSooAoVKqhJkyZasGCBs2sDAAC4YSX+GGv69OkaP368hg0bpjZt2sgwDH3zzTcaMmSI/vvf/+rFF190RZ0AAAB/SonDzsyZMzVnzhwNGDDA3tajRw81bNhQiYmJhB0AAOBWSvwx1tGjR6/4vVORkZE6evSoU4oCAABwlhKHnXr16umjjz4q0r506VJFREQ4pSgAAABnKfHHWBMnTlSfPn20ceNGtWnTRhaLRZs2bdK//vWvK4YgAACA0lTimZ1HHnlEW7duVeXKlbVixQotW7ZMlStX1rfffquHH37YFTUCAAD8aSWe2ZGkli1bauHChc6uBQAAwOmuK+zk5uYqICDA/t9Xc7kfAACAO7iusFOpUiUdPXpUISEhqlixoiwWS5E+hmHIYrGooKDA6UUCAAD8WdcVdr766isFBQVJktavX+/SggAAAJzpusJOVFTUFf8bAADA3ZX4bqzVq1dr06ZN9vW33npLzZo1U79+/ZSTk+PU4gAAAG5UicPO6NGj7Rcp79q1SyNGjNCDDz6oAwcOaMSIEU4vEAAA4EaU+NbzjIwM3XnnnZKkTz/9VLGxsUpKStKOHTv04IMPOr1AAACAG1HimR0vLy+dPXtWkrRu3TrFxMRIkoKCgq55WzoAAMDNVuKZnfvuu08jRoxQmzZt9O2332rp0qWSpH379ql69epOLxAAAOBGlHhmZ9asWfLw8NAnn3yiOXPmqFq1apKkL7/8Up07d3Z6gQAAADeixDM7NWvW1Oeff16k/Y033nBKQQAAAM5U4pmdHTt2aNeuXfb1zz77TA899JDGjh2r/Px8pxYHAABwo0ocdgYPHqx9+/ZJkg4cOKDHHntMPj4++vjjj/XSSy85vUAAAIAbUeKws2/fPjVr1kyS9PHHH6tt27ZatGiR5s2bp08//dTZ9QEAANyQEocdwzBUWFgo6dKt55efrVOjRg3997//dW51AAAAN6jEYadVq1aaNGmSFixYoNTUVHXt2lXSpYcNhoaGOr1AAACAG1HisDNjxgzt2LFDw4YN07hx41SvXj1J0ieffKLIyEinFwgAAHAjSnzreZMmTRzuxrps2rRpKl++vFOKAgAAcJYSz+xI0smTJ/Xee+8pISFBJ06ckCT98MMPys7OdmpxAAAAN6rEMzs7d+5U+/btVbFiRR08eFBPP/20goKCtHz5ch06dEgffvihK+oEAAD4U0o8szNixAj95S9/0f79++Xt7W1v79KlizZu3OjU4gAAAG5UicPOtm3bNHjw4CLt1apVU1ZWllOKAgAAcJYShx1vb2/l5uYWad+7d6+qVKnilKIuu3jxov7nf/5HderUUYUKFXTbbbfplVdesT/nR7r03J/ExESFh4erQoUKio6O1p49e5xaBwAAKLtKHHZ69OihV155RRcuXJAkWSwWHT58WGPGjNEjjzzi1OKmTJmit99+W7NmzdKPP/6oqVOnatq0aZo5c6a9z9SpUzV9+nTNmjVL27ZtU1hYmDp27KhTp045tRYAAFA2lTjs/O1vf9OxY8cUEhKic+fOKSoqSvXq1ZO/v78mT57s1OI2b96sHj16qGvXrqpdu7Z69eqlmJgYfffdd5IuzerMmDFD48aNU8+ePdWoUSPNnz9fZ8+e1aJFi5xaCwAAKJtKfDdWQECANm3apK+++ko7duxQYWGhWrRooQ4dOji9uPvuu09vv/229u3bp/r16+v777/Xpk2bNGPGDEmXntqclZWlmJgY+2usVquioqKUlpZ2xWuLJCkvL095eXn29St9LAcAAMyhxGHnsgceeEAPPPCAM2sp4uWXX5bNZtPtt9+u8uXLq6CgQJMnT1bfvn0lyX5B9B+/piI0NFSHDh0qdtzk5GRNnDjRdYUDAAC3cd0fY23dulVffvmlQ9uHH36oOnXqKCQkRM8884zDbIkzLF26VAsXLtSiRYu0Y8cOzZ8/X3/72980f/58h34Wi8Vh3TCMIm2/l5CQIJvNZl8yMzOdWjcAAHAf1x12EhMTtXPnTvv6rl27FBcXpw4dOmjMmDFatWqVkpOTnVrc6NGjNWbMGD322GNq3LixnnjiCb344ov2/YSFhUlSkVves7Ozr/qlpFarVQEBAQ4LAAAwp+sOO+np6Wrfvr19fcmSJWrdurXeffddjRgxQn//+9/10UcfObW4s2fPqlw5xxLLly9vv/W8Tp06CgsLU0pKin17fn6+UlNT+VJSAAAgqQTX7OTk5DjMlqSmpqpz58729bvuusvpHwfFxsZq8uTJqlmzpho2bKh///vfmj59up588klJlz6+io+PV1JSkiIiIhQREaGkpCT5+PioX79+Tq0FAACUTdcddkJDQ5WRkaEaNWooPz9fO3bscLjI99SpU/L09HRqcTNnztT48eM1dOhQZWdnKzw8XIMHD9Zf//pXe5+XXnpJ586d09ChQ5WTk6PWrVtr7dq18vf3d2otAACgbLrusNO5c2eNGTNGU6ZM0YoVK+Tj46P777/fvn3nzp2qW7euU4vz9/fXjBkz7LeaX4nFYlFiYqISExOdum8AAGAO1x12Jk2apJ49eyoqKkp+fn6aP3++vLy87Ns/+OADh+fdAAAAuIPrDjtVqlTR119/LZvNJj8/P5UvX95h+8cffyw/Pz+nFwgAAHAjSvxQwcDAwCu2BwUF3XAxAAAAzlbi78YCAAAoSwg7AADA1Ag7AADA1Ag7AADA1P7Ut57/+uuv+uabb5SdnW3/6obLnn/+eacUBgAA4AwlDjtz587VkCFD5OXlpeDgYIdvF7dYLIQdAADgVkocdv7617/qr3/9qxISEop8SScAAIC7KXFaOXv2rB577DGCDgAAKBNKnFji4uL08ccfu6IWAAAApyvxx1jJycnq1q2bVq9ercaNGxf5pvPp06c7rTgAAIAbVeKwk5SUpDVr1qhBgwaSVOQCZQAAAHdS4rAzffp0ffDBBxo0aJALygEAAHCuEl+zY7Va1aZNG1fUAgAA4HQlDjsvvPCCZs6c6YpaAAAAnK7EH2N9++23+uqrr/T555+rYcOGRS5QXrZsmdOKAwAAuFElDjsVK1ZUz549XVELAACA0/2pr4sAAAAoK3gMMgAAMLUSz+zUqVPnqs/TOXDgwA0VBAAA4EwlDjvx8fEO6xcuXNC///1vrV69WqNHj3ZWXQAAAE5R4rDzwgsvXLH9rbfe0nfffXfDBQEAADiT067Z6dKliz799FNnDQcAAOAUTgs7n3zyiYKCgpw1HAAAgFOU+GOs5s2bO1ygbBiGsrKydOzYMc2ePdupxQEAANyoEoedhx56yGG9XLlyqlKliqKjo3X77bc7qy4AAACnKHHYmTBhgivqAAAAcAkeKggAAEztumd2ypUrd9WHCUqSxWLRxYsXb7goAAAAZ7nusLN8+fJit6WlpWnmzJkyDMMpRQEAADjLdYedHj16FGn76aeflJCQoFWrVql///569dVXnVocAADAjfpT1+z89ttvevrpp9WkSRNdvHhR6enpmj9/vmrWrOns+gAAAG5IicKOzWbTyy+/rHr16mnPnj3617/+pVWrVqlRo0auqk+//vqrHn/8cQUHB8vHx0fNmjXT9u3b7dsNw1BiYqLCw8NVoUIFRUdHa8+ePS6rBwAAlC3XHXamTp2q2267TZ9//rkWL16stLQ03X///a6sTTk5OWrTpo08PT315Zdf6ocfftDrr7+uihUrOtQ1ffp0zZo1S9u2bVNYWJg6duyoU6dOubQ2AABQNlz3NTtjxoxRhQoVVK9ePc2fP1/z58+/Yr9ly5Y5rbgpU6aoRo0amjt3rr2tdu3a9v82DEMzZszQuHHj1LNnT0nS/PnzFRoaqkWLFmnw4MFOqwUAAJRN1z2zM2DAAPXu3VtBQUEKDAwsdnGmlStXqlWrVnr00UcVEhKi5s2b691337Vvz8jIUFZWlmJiYuxtVqtVUVFRSktLK3bcvLw85ebmOiwAAMCcrntmZ968eS4s48oOHDigOXPmaMSIERo7dqy+/fZbPf/887JarRowYICysrIkSaGhoQ6vCw0N1aFDh4odNzk5WRMnTnRp7QAAwD249ROUCwsL1aJFCyUlJal58+YaPHiwnn76ac2ZM8eh3x8fdmgYxlUfgJiQkCCbzWZfMjMzXVI/AAAofW4ddqpWrao777zToe2OO+7Q4cOHJUlhYWGSZJ/huSw7O7vIbM/vWa1WBQQEOCwAAMCc3DrstGnTRnv37nVo27dvn2rVqiVJqlOnjsLCwpSSkmLfnp+fr9TUVEVGRt7UWgEAgHsq8bee30wvvviiIiMjlZSUpN69e+vbb7/VO++8o3feeUfSpY+v4uPjlZSUpIiICEVERCgpKUk+Pj7q169fKVcPAADcgVuHnbvuukvLly9XQkKCXnnlFdWpU0czZsxQ//797X1eeuklnTt3TkOHDlVOTo5at26ttWvXyt/fvxQrBwAA7sKtw44kdevWTd26dSt2u8ViUWJiohITE29eUQAAoMxw62t2AAAAbhRhBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmFqZCjvJycmyWCyKj4+3txmGocTERIWHh6tChQqKjo7Wnj17Sq9IAADgVjxKu4DrtW3bNr3zzjtq0qSJQ/vUqVM1ffp0zZs3T/Xr19ekSZPUsWNH7d27V/7+/qVULVC6srOzZbPZnD7uwYMHVVBw0enjAoArlYmwc/r0afXv31/vvvuuJk2aZG83DEMzZszQuHHj1LNnT0nS/PnzFRoaqkWLFmnw4MGlVTJQarKzszWg78PKO3PC6WOfPZ+v48eOKv8i/5AAUHaUibDz3HPPqWvXrurQoYND2MnIyFBWVpZiYmLsbVarVVFRUUpLSys27OTl5SkvL8++npub67rigZvMZrMp78wJjevppVoh3k4d+5sfbHp1QYEKCgqdOi4AuJLbh50lS5Zox44d2rZtW5FtWVlZkqTQ0FCH9tDQUB06dKjYMZOTkzVx4kTnFgq4mVoh3oqo7uPUMQ9mnXfqeABwM7j1BcqZmZl64YUXtHDhQnl7F/8vVIvF4rBuGEaRtt9LSEiQzWazL5mZmU6rGQAAuBe3ntnZvn27srOz1bJlS3tbQUGBNm7cqFmzZmnv3r2SLs3wVK1a1d4nOzu7yGzP71mtVlmtVtcVDgAA3IZbz+y0b99eu3btUnp6un1p1aqV+vfvr/T0dN12220KCwtTSkqK/TX5+flKTU1VZGRkKVYOAADchVvP7Pj7+6tRo0YObb6+vgoODra3x8fHKykpSREREYqIiFBSUpJ8fHzUr1+/0igZAAC4GbcOO9fjpZde0rlz5zR06FDl5OSodevWWrt2Lc/YAQAAkspg2NmwYYPDusViUWJiohITE0ulHgAA4N7c+podAACAG0XYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApubWYSc5OVl33XWX/P39FRISooceekh79+516GMYhhITExUeHq4KFSooOjpae/bsKaWKAQCAu3HrsJOamqrnnntOW7ZsUUpKii5evKiYmBidOXPG3mfq1KmaPn26Zs2apW3btiksLEwdO3bUqVOnSrFyAADgLjxKu4CrWb16tcP63LlzFRISou3bt6tt27YyDEMzZszQuHHj1LNnT0nS/PnzFRoaqkWLFmnw4MGlUTYAAHAjbj2z80c2m02SFBQUJEnKyMhQVlaWYmJi7H2sVquioqKUlpZWKjUCAAD34tYzO79nGIZGjBih++67T40aNZIkZWVlSZJCQ0Md+oaGhurQoUPFjpWXl6e8vDz7em5urgsqBgAA7qDMzOwMGzZMO3fu1OLFi4tss1gsDuuGYRRp+73k5GQFBgbalxo1aji9XgAA4B7KRNgZPny4Vq5cqfXr16t69er29rCwMEn/N8NzWXZ2dpHZnt9LSEiQzWazL5mZma4pHAAAlDq3DjuGYWjYsGFatmyZvvrqK9WpU8dhe506dRQWFqaUlBR7W35+vlJTUxUZGVnsuFarVQEBAQ4LAAAwJ7e+Zue5557TokWL9Nlnn8nf398+gxMYGKgKFSrIYrEoPj5eSUlJioiIUEREhJKSkuTj46N+/fqVcvUAAMAduHXYmTNnjiQpOjraoX3u3LkaNGiQJOmll17SuXPnNHToUOXk5Kh169Zau3at/P39b3K1AADAHbl12DEM45p9LBaLEhMTlZiY6PqCAABAmePW1+wAAADcKMIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNbd+qCAAAK6UnZ0tm83mkrEDAwMVEhLikrFRMoQdAMAtKTs7WwP6Pqy8MydcMr7VN0gfLl5O4HEDhB0AwC3JZrMp78wJjevppVoh3k4d+1D2eU1edkI2m42w4wYIOwCAW1qtEG9FVPdxwcj5LhgTfwYXKAMAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFPzKO0CgFtZdna2bDabU8c8ePCgCgouOnVMACjLCDtAKcnOztaAvg8r78wJp4579ny+jh87qvyL/k4dFwDKKsIOUEpsNpvyzpzQuJ5eqhXi7bRxv/nBplcXFKigoNBpYwJAWUbYAUpZrRBvRVT3cdp4B7POO20sADADLlAGAACmxsxOGZZ/4aIOHjzokrEDAwMVEhLi9HFdcUGu5NqLcl31PnMh8c3jqp87V/2eAKXFrL8rhJ0y6njuBR3KPKLEMUPk5enl9PGtvkH6cPFyp/5wuuqCXMl1F+W68n3mQuKbw5U/d674PQFKi5l/Vwg7ZdSpswWyli9UwkNealAjwKljH8o+r8nLTshmszn1B9NVF+RKrrso15XvMxcS3xyu+rlz1e8JzMNVs8KumiUx8++KacLO7NmzNW3aNB09elQNGzbUjBkzdP/995d2WS5XM8Tq1Itb/0++C8a8xNkX5EquvyjXFe8zFxLfXK74uXPl7wnKNlfOCrt6lsSMvyumCDtLly5VfHy8Zs+erTZt2ugf//iHunTpoh9++EE1a9Ys7fLKJFf8i4RrVHA1XBsFM3HVrLA7zJKURaYIO9OnT1dcXJyeeuopSdKMGTO0Zs0azZkzR8nJyaVcXdnjqn+RcI0KisO1UTAr18y+M6NYUmU+7OTn52v79u0aM2aMQ3tMTIzS0tJKqaqyzVX/IuEaFRSHa6MAuFKZDzv//e9/VVBQoNDQUIf20NBQZWVlXfE1eXl5ysvLs69fvs0uNzfXqbWdPn1aFy8W6IfDZ3T6XIFTx/756FkVFhr66fBZFRQ69zReHvvs+QKn1n0ur8DlNTt77JvxPlOz637mJNf93B0+dl7nzudrz549On36tNPGxc1z6NAh5eXnl6n/R7vy585V78fhY+d18WKBTp8+7fS/s5fHMwzj6h2NMu7XX381JBlpaWkO7ZMmTTIaNGhwxddMmDDBkMTCwsLCwsJigiUzM/OqWaHMz+xUrlxZ5cuXLzKLk52dXWS257KEhASNGDHCvl5YWKgTJ04oODhYFovFpfU6W25urmrUqKHMzEwFBDh3+h9/DufEvXA+3Avnw72U9fNhGIZOnTql8PDwq/Yr82HHy8tLLVu2VEpKih5++GF7e0pKinr06HHF11itVlmtVoe2ihUrurJMlwsICCiTP6hmxjlxL5wP98L5cC9l+XwEBgZes0+ZDzuSNGLECD3xxBNq1aqV7r33Xr3zzjs6fPiwhgwZUtqlAQCAUmaKsNOnTx8dP35cr7zyio4ePapGjRrpf//3f1WrVq3SLg0AAJQyU4QdSRo6dKiGDh1a2mXcdFarVRMmTCjysRxKD+fEvXA+3Avnw73cKufDYhjXul8LAACg7CpX2gUAAAC4EmEHAACYGmEHAACYGmEHAACYGmHHDSUnJ+uuu+6Sv7+/QkJC9NBDD2nv3r0OfQzDUGJiosLDw1WhQgVFR0drz549Dn3y8vI0fPhwVa5cWb6+vurevbuOHDlyMw/FFObMmaMmTZrYH7p177336ssvv7Rv51yUruTkZFksFsXHx9vbOCc3V2JioiwWi8MSFhZm3875uPl+/fVXPf744woODpaPj4+aNWum7du327ffcufkhr+cCk7XqVMnY+7cucbu3buN9PR0o2vXrkbNmjWN06dP2/u89tprhr+/v/Hpp58au3btMvr06WNUrVrVyM3NtfcZMmSIUa1aNSMlJcXYsWOH0a5dO6Np06bGxYsXS+OwyqyVK1caX3zxhbF3715j7969xtixYw1PT09j9+7dhmFwLkrTt99+a9SuXdto0qSJ8cILL9jbOSc314QJE4yGDRsaR48etS/Z2dn27ZyPm+vEiRNGrVq1jEGDBhlbt241MjIyjHXr1hk///yzvc+tdk4IO2VAdna2IclITU01DMMwCgsLjbCwMOO1116z9zl//rwRGBhovP3224ZhGMbJkycNT09PY8mSJfY+v/76q1GuXDlj9erVN/cATKhSpUrGe++9x7koRadOnTIiIiKMlJQUIyoqyh52OCc334QJE4ymTZtecRvn4+Z7+eWXjfvuu6/Y7bfiOeFjrDLAZrNJkoKCgiRJGRkZysrKUkxMjL2P1WpVVFSU0tLSJEnbt2/XhQsXHPqEh4erUaNG9j4ouYKCAi1ZskRnzpzRvffey7koRc8995y6du2qDh06OLRzTkrH/v37FR4erjp16uixxx7TgQMHJHE+SsPKlSvVqlUrPfroowoJCVHz5s317rvv2rffiueEsOPmDMPQiBEjdN9996lRo0aSZP+G9z9+q3toaKh9W1ZWlry8vFSpUqVi++D67dq1S35+frJarRoyZIiWL1+uO++8k3NRSpYsWaIdO3YoOTm5yDbOyc3XunVrffjhh1qzZo3effddZWVlKTIyUsePH+d8lIIDBw5ozpw5ioiI0Jo1azRkyBA9//zz+vDDDyXdmr8jpvm6CLMaNmyYdu7cqU2bNhXZZrFYHNYNwyjS9kfX0wdFNWjQQOnp6Tp58qQ+/fRTDRw4UKmpqfbtnIubJzMzUy+88ILWrl0rb2/vYvtxTm6eLl262P+7cePGuvfee1W3bl3Nnz9f99xzjyTOx81UWFioVq1aKSkpSZLUvHlz7dmzR3PmzNGAAQPs/W6lc8LMjhsbPny4Vq5cqfXr16t69er29st3OfwxXWdnZ9uTelhYmPLz85WTk1NsH1w/Ly8v1atXT61atVJycrKaNm2qN998k3NRCrZv367s7Gy1bNlSHh4e8vDwUGpqqv7+97/Lw8PD/p5yTkqPr6+vGjdurP379/M7UgqqVq2qO++806Htjjvu0OHDhyXdmn9DCDtuyDAMDRs2TMuWLdNXX32lOnXqOGyvU6eOwsLClJKSYm/Lz89XamqqIiMjJUktW7aUp6enQ5+jR49q9+7d9j748wzDUF5eHueiFLRv3167du1Senq6fWnVqpX69++v9PR03XbbbZyTUpaXl6cff/xRVatW5XekFLRp06bI40r27dunWrVqSbpF/4aUymXRuKpnn33WCAwMNDZs2OBwK+fZs2ftfV577TUjMDDQWLZsmbFr1y6jb9++V7xtsHr16sa6deuMHTt2GA888ECZvW2wNCUkJBgbN240MjIyjJ07dxpjx441ypUrZ6xdu9YwDM6FO/j93ViGwTm52UaOHGls2LDBOHDggLFlyxajW7duhr+/v3Hw4EHDMDgfN9u3335reHh4GJMnTzb2799v/POf/zR8fHyMhQsX2vvcaueEsOOGJF1xmTt3rr1PYWGhMWHCBCMsLMywWq1G27ZtjV27djmMc+7cOWPYsGFGUFCQUaFCBaNbt27G4cOHb/LRlH1PPvmkUatWLcPLy8uoUqWK0b59e3vQMQzOhTv4Y9jhnNxcl5/R4unpaYSHhxs9e/Y09uzZY9/O+bj5Vq1aZTRq1MiwWq3G7bffbrzzzjsO22+1c2IxDMMozZklAAAAV+KaHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQD4/6KjoxUfH1/aZQBwMsIOAKcbNGiQLBaLLBaLPDw8VLNmTT377LNFvlRQktLS0vTggw+qUqVK8vb2VuPGjfX666+roKDAoZ/FYtGKFSuuuL/Y2Fh16NDhits2b94si8WiHTt23PBxASibCDsAXKJz5846evSoDh48qPfee0+rVq3S0KFDHfosX75cUVFRql69utavX6+ffvpJL7zwgiZPnqzHHntM1/uA97i4OH311Vc6dOhQkW0ffPCBmjVrphYtWjjluACUPYQdAC5htVoVFham6tWrKyYmRn369NHatWvt28+cOaOnn35a3bt31zvvvKNmzZqpdu3aeuqppzR//nx98skn+uijj65rX926dVNISIjmzZvn0H727FktXbpUcXFxOn78uPr27avq1avLx8dHjRs31uLFi6867pVmkypWrOiwn19//VV9+vRRpUqVFBwcrB49eujgwYP27Rs2bNDdd98tX19fVaxYUW3atLliKAPgOoQdAC534MABrV69Wp6enva2tWvX6vjx4xo1alSR/rGxsapfv/41w8hlHh4eGjBggObNm+cwG/Txxx8rPz9f/fv31/nz59WyZUt9/vnn2r17t5555hk98cQT2rp1658+rrNnz6pdu3by8/PTxo0btWnTJvn5+alz587Kz8/XxYsX9dBDDykqKko7d+7U5s2b9cwzz8hisfzpfQIoOY/SLgCAOX3++efy8/NTQUGBzp8/L0maPn26ffu+ffskSXfccccVX3/77bfb+1yPJ598UtOmTdOGDRvUrl07SZc+wurZs6cqVaqkSpUqOQSr4cOHa/Xq1fr444/VunXrEh+fJC1ZskTlypXTe++9Zw8wc+fOVcWKFbVhwwa1atVKNptN3bp1U926da96vABch5kdAC7Rrl07paena+vWrRo+fLg6deqk4cOHF+lX3HU5hmGUaAbk9ttvV2RkpD744ANJ0i+//KKvv/5aTz75pCSpoKBAkydPVpMmTRQcHCw/Pz+tXbtWhw8f/hNHd8n27dv1888/y9/fX35+fvLz81NQUJDOnz+vX375RUFBQRo0aJA6deqk2NhYvfnmmzp69Oif3h+AP4ewA8AlfH19Va9ePTVp0kR///vflZeXp4kTJ9q3169fX5L0448/XvH1P/30kyIiIkq0z7i4OH366afKzc3V3LlzVatWLbVv316S9Prrr+uNN97QSy+9pK+++krp6enq1KmT8vPzix3PYrEUCWMXLlyw/3dhYaFatmyp9PR0h2Xfvn3q16+fpEszPZs3b1ZkZKSWLl2q+vXra8uWLSU6LgA3hrAD4KaYMGGC/va3v+m3336TJMXExCgoKEivv/56kb4rV67U/v371bdv3xLto3fv3ipfvrwWLVqk+fPn6y9/+Yt9dujrr79Wjx499Pjjj6tp06a67bbbtH///quOV6VKFYeZmP379+vs2bP29RYtWmj//v0KCQlRvXr1HJbAwEB7v+bNmyshIUFpaWlq1KiRFi1aVKLjAnBjCDsAboro6Gg1bNhQSUlJki7N/PzjH//QZ599pmeeeUY7d+7UwYMH9f7772vQoEHq1auXevfuXaJ9+Pn5qU+fPho7dqx+++03DRo0yL6tXr16SklJUVpamn788UcNHjxYWVlZVx3vgQce0KxZs7Rjxw599913GjJkiMNF1v3791flypXVo0cPff3118rIyFBqaqpeeOEFHTlyRBkZGUpISNDmzZt16NAhrV27Vvv27eO6HeAmI+wAuGlGjBihd999V5mZmZKkXr16af369crMzFTbtm3VoEEDTZ8+XePGjdOSJUv+1F1LcXFxysnJUYcOHVSzZk17+/jx49WiRQt16tRJ0dHRCgsL00MPPXTVsV5//XXVqFFDbdu2Vb9+/TRq1Cj5+PjYt/v4+Gjjxo2qWbOmevbsqTvuuENPPvmkzp07p4CAAPn4+Oinn37SI488ovr16+uZZ57RsGHDNHjw4BIfF4A/z2Jc71O7AAAAyiBmdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKn9Pyl5LPDd0fU9AAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "depths = df['depth'].values\n", - "# Creating the histogram\n", - "plt.hist(depths, bins=20, alpha=0.75, color='orange', edgecolor='black')\n", - "\n", - "# Adding titles and labels\n", - "plt.title('Histogram of recording depth')\n", - "plt.xlabel('ROI Values')\n", - "plt.ylabel('Num Sessions')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "24633950\n" - ] - } - ], - "source": [ - "print(np.sum(df['num_timepoints'].values))" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "numbers = [\n", - " 175, 175, 175, 20, 20, 20, 20, 20, 20,\n", - " 175, 175, 175, 375, 375, 375, 75, 75, 75,\n", - " 50, 50, 50, 50, 50, 50, 375, 375, 375,\n", - " 20, 20, 20, 75, 75, 75, 20, 20, 20, 20, 20, 20, 20,\n", - " 375, 375, 375, 375, 175, 175, 175, 175, 175, 175\n", - "]\n", - "\n", - "OS_depths = np.array(numbers)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.hist(OS_depths, bins=20, alpha=0.75, color='blue', edgecolor='black')\n", - "\n", - "# Adding titles and labels\n", - "plt.title('Histogram of recording depth')\n", - "plt.xlabel('ROI Values')\n", - "plt.ylabel('Num Sessions')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## prepping for dataloader" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "manifest_path = os.path.join(\"/home/mila/x/xuejing.pan/scratch\", \"manifest.json\")\n", - "boc = BrainObservatoryCache(manifest_file=manifest_path)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "meta_df = pd.read_csv('/home/mila/x/xuejing.pan/POYO/project-kirby/data/scripts/allen_brain_observatory_calcium/AllenBOmeta.csv')\n", - "sess_ids = meta_df[\"exp_id\"].values" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "curr_sess_id = sess_ids[1]" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "nwbfile = boc.get_ophys_experiment_data(curr_sess_id)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "curr_meta_sess = nwbfile.get_metadata()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'sex': 'male',\n", - " 'targeted_structure': 'VISrl',\n", - " 'ophys_experiment_id': 604328043,\n", - " 'experiment_container_id': 604328040,\n", - " 'excitation_lambda': '910 nanometers',\n", - " 'indicator': 'GCaMP6f',\n", - " 'fov': '400x400 microns (512 x 512 pixels)',\n", - " 'genotype': 'Slc17a7-IRES2-Cre/wt;Camk2a-tTA/wt;Ai93(TITL-GCaMP6f)/wt',\n", - " 'session_start_time': datetime.datetime(2017, 7, 26, 8, 41, 51),\n", - " 'session_type': 'three_session_A',\n", - " 'specimen_name': 'Slc17a7-IRES2-Cre;Camk2a-tTA;Ai93-325932',\n", - " 'cre_line': 'Slc17a7-IRES2-Cre/wt',\n", - " 'imaging_depth_um': 375,\n", - " 'age_days': 105,\n", - " 'device': 'Nikon A1R-MP multiphoton microscope',\n", - " 'device_name': 'CAM2P.2',\n", - " 'pipeline_version': '3.0'}" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "curr_meta_sess" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'20170726'" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "curr_meta_sess['session_start_time'].strftime(\"%Y%m%d\")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "curr_meta=boc.get_ophys_experiments(ids=[curr_sess_id])" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'id': 604328043,\n", - " 'imaging_depth': 375,\n", - " 'targeted_structure': 'VISrl',\n", - " 'cre_line': 'Slc17a7-IRES2-Cre',\n", - " 'reporter_line': 'Ai93(TITL-GCaMP6f)',\n", - " 'acquisition_age_days': 104,\n", - " 'experiment_container_id': 604328040,\n", - " 'session_type': 'three_session_A',\n", - " 'donor_name': '325932',\n", - " 'specimen_name': 'Slc17a7-IRES2-Cre;Camk2a-tTA;Ai93-325932',\n", - " 'fail_eye_tracking': False}]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "curr_meta" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "subject_ids = meta_df[\"subject_id\"].values" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "#traces: (num_ROIs, num_timepoints)\n", - "#timestamps: (num_timepoints,)\n", - "timestamps, traces = nwbfile.get_dff_traces()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[b'605077572' b'605077574' b'605077579' b'605077582' b'605077584'\n", - " b'605077588' b'605077595' b'605077624' b'605077626' b'605077629'\n", - " b'605077641' b'605077643' b'605077648' b'605077652' b'605077657'\n", - " b'605077664' b'605077669' b'605077685' b'605077695' b'605077700'\n", - " b'605077702' b'605077704' b'605077706' b'605077712' b'605077736'\n", - " b'605077752' b'605077761' b'605077765' b'605077777' b'605077779'\n", - " b'605077807' b'605077827' b'605077833' b'605077838' b'605077843'\n", - " b'605077845' b'605077848' b'605077852' b'605077857' b'605077876'\n", - " b'605077880' b'605077886' b'605077890' b'605077894' b'605077896'\n", - " b'605077898' b'605077900' b'605077906' b'605077908' b'605077912'\n", - " b'605077916' b'605077933']\n" - ] - } - ], - "source": [ - "ROI_ids = nwbfile.get_roi_ids()\n", - "print(ROI_ids)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "num_ROIs = ROI_ids.shape[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n", - " 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n", - " 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n", - " 51], dtype=int16)" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.arange(0,num_ROIs).astype(np.int16)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "#ROI pos, height and width\n", - "ROI_masks = nwbfile.get_roi_mask()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "master_stim_table = nwbfile.get_stimulus_table('drifting_gratings')" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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628 rows × 5 columns

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" - ], - "text/plain": [ - " temporal_frequency orientation blank_sweep start end\n", - "0 15.0 90.0 0.0 744 804\n", - "1 4.0 0.0 0.0 835 894\n", - "2 2.0 225.0 0.0 925 985\n", - "3 4.0 90.0 0.0 1015 1075\n", - "4 1.0 225.0 0.0 1106 1165\n", - ".. ... ... ... ... ...\n", - "623 2.0 135.0 0.0 114809 114869\n", - "624 1.0 270.0 0.0 114899 114959\n", - "625 4.0 0.0 0.0 114990 115049\n", - "626 2.0 225.0 0.0 115080 115140\n", - "627 1.0 45.0 0.0 115170 115230\n", - "\n", - "[628 rows x 5 columns]" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "master_stim_table" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "start_times = timestamps[master_stim_table['start']]\n", - "end_times = timestamps[master_stim_table['end']]\n", - "temp_freqs = master_stim_table['temporal_frequency']\n", - "orientation = master_stim_table[\"orientation\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "stim_df = pd.DataFrame(master_stim_table)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "start_times = timestamps[stim_df.loc[(stim_df[\"blank_sweep\"]==0.0), 'start']]\n", - "end_times = timestamps[stim_df.loc[(stim_df[\"blank_sweep\"]==0.0), 'end']]\n", - "temp_freqs = stim_df.loc[(stim_df[\"blank_sweep\"]==0.0), 'temporal_frequency']\n", - "orientations = stim_df.loc[(stim_df[\"blank_sweep\"]==0.0), 'orientation']" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "315.0" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.max(orientations.values)" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [], - "source": [ - "stimulus_epochs = nwbfile.get_stimulus_epoch_table()\n", - "session_start, session_end = (\n", - " timestamps[stimulus_epochs.iloc[0][\"start\"]],\n", - " timestamps[stimulus_epochs.iloc[-1][\"end\"]],\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "31.53391" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "session_start" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## getting all ROI sizes" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n", - "#Using a metadata file instead of dealing with filtering every single time\n", - "meta_df = pd.read_csv('/home/mila/x/xuejing.pan/POYO/project-kirby/data/scripts/allen_brain_observatory_calcium/AllenBOmeta.csv')\n", - "sess_ids = meta_df[\"exp_id\"].values\n", - "subject_ids = meta_df[\"subject_id\"].values\n", - "cre_lines = meta_df[\"cre_line\"].values\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "roi_sizes = []\n", - "roi_heights = []\n", - "roi_widths = []\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n", - "1\n", - "2\n", - "3\n", - "4\n", - "5\n", - "6\n", - "7\n", - "8\n", - "9\n", - "10\n", - "11\n", - "12\n", - "13\n", - "14\n", - "15\n", - "16\n", - "17\n", - "18\n", - "19\n", - "20\n", - "21\n", - "22\n", - "23\n", - "24\n", - "25\n", - "26\n", - "27\n", - "28\n", - "29\n", - "30\n", - "31\n", - "32\n", - "33\n", - "34\n", - "35\n", - "36\n", - "37\n", - "38\n", - "39\n", - "40\n", - "41\n", - "42\n", - "43\n", - "44\n", - "45\n", - "46\n", - "47\n", - "48\n", - "49\n", - "50\n", - "51\n", - "52\n", - "53\n", - "54\n", - "55\n", - "56\n", - "57\n", - "58\n", - "59\n", - "60\n", - "61\n", - "62\n", - "63\n", - "64\n", - "65\n", - "66\n", - "67\n", - 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"for count, curr_sess_id in enumerate(sess_ids):\n", - " nwbfile = boc.get_ophys_experiment_data(curr_sess_id)\n", - " ROI_masks = nwbfile.get_roi_mask()\n", - " print(count)\n", - "\n", - " for i, mask_t in enumerate(ROI_masks):\n", - " mask = ROI_masks[i].get_mask_plane()\n", - " curr_area = np.count_nonzero(mask)\n", - "\n", - " rows, cols = np.where(mask)\n", - " heights = np.max(rows) - np.min(rows) + 1\n", - " widths = np.max(cols) - np.min(cols) + 1\n", - "\n", - " roi_sizes.append(curr_area)\n", - " roi_heights.append(heights)\n", - " roi_widths.append(widths)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "roi_sizes = np.array(roi_sizes)\n", - "roi_heights = np.array(roi_heights)\n", - "roi_widths = np.array(roi_widths)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "101\n", - "551\n", - "8\n", - "44\n", - "9\n", - "42\n" - ] - } - ], - "source": [ - "print(np.min(roi_sizes))\n", - "print(np.max(roi_sizes))\n", - "print(np.min(roi_heights))\n", - "print(np.max(roi_heights))\n", - "print(np.min(roi_widths))\n", - "print(np.max(roi_widths))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def min_max_scale(data, data_min, data_max):\n", - " return 2 * ((data - data_min) / (data_max - data_min)) - 1\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.preprocessing import MinMaxScaler\n", - "\n", - "for count, curr_sess_id in enumerate(sess_ids):\n", - " nwbfile = boc.get_ophys_experiment_data(curr_sess_id)\n", - " ROI_masks = nwbfile.get_roi_mask()\n", - "\n", - " _, traces= nwbfile.get_dff_traces()\n", - " num_rois = traces.shape[0]\n", - "\n", - " areas = np.zeros(num_rois)\n", - " heights = np.zeros(num_rois)\n", - " widths = np.zeros(num_rois)\n", - "\n", - " for count, curr in enumerate(ROI_masks):\n", - " mask = ROI_masks[count].get_mask_plane()\n", - " areas[count] = np.count_nonzero(mask)\n", - "\n", - " rows, cols = np.where(mask)\n", - " heights[count] = np.max(rows) - np.min(rows) + 1\n", - " widths[count] = np.max(cols) - np.min(cols) + 1\n", - "\n", - " normalized_areas = min_max_scale(areas, 101.0, 551.0)\n", - " normalized_heights = min_max_scale(heights, 8.0, 44.0)\n", - " normalized_widths = min_max_scale(widths, 9.0, 42.0)\n", - "\n", - " break" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "feats = np.stack((normalized_heights,normalized_areas,normalized_widths))" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(26, 3)" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.swapaxes(feats,0,1).shape" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([-0.55555556, -0.38888889, -0.72222222, -0.55555556, -0.66666667,\n", - " -0.77777778, -0.5 , -0.55555556, -0.38888889, -0.77777778,\n", - " -0.61111111, -0.66666667, -0.66666667, -0.5 , -0.72222222,\n", - " -0.55555556, -0.44444444, -0.61111111, -0.83333333, -0.77777778,\n", - " -0.61111111, -0.72222222, -0.61111111, -0.72222222, -0.72222222,\n", - " -0.5 ])" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "normalized_heights" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([-0.63636364, -0.75757576, -0.27272727, -0.81818182, -0.27272727,\n", - " -0.81818182, -0.57575758, -0.33333333, -0.45454545, -0.6969697 ,\n", - " -0.57575758, -0.63636364, -0.33333333, -0.93939394, -0.6969697 ,\n", - " -0.75757576, -0.33333333, -0.51515152, -0.45454545, -0.45454545,\n", - " -0.63636364, -0.51515152, -0.6969697 , -0.33333333, -0.39393939,\n", - " -0.75757576])" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "normalized_widths" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/capoyo/notebooks/download_wandb_csv.ipynb b/examples/capoyo/notebooks/download_wandb_csv.ipynb deleted file mode 100644 index 38f6f7d..0000000 --- a/examples/capoyo/notebooks/download_wandb_csv.ipynb +++ /dev/null @@ -1,176 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import wandb\n", - "import pandas as pd\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "api = wandb.Api()\n", - "run = api.run(\"neuro-galaxy/allen_bo_calcium/d86bm2vj\") #You can find this in the \"overview\" tab of the runs. Specifically \"Run path\" section" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "#the code snippit I use to get from a meta file soI don't have to format each one.\n", - "#change to your own path\n", - "meta_file_path = \"/home/mila/x/xuejing.pan/POYO/results/allen_BO/AllenBOmeta_updated.csv\"\n", - "meta_df = pd.read_csv(meta_file_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## GETTING SESSION IDS" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "#These are the heldout session ids\n", - "excluded_session_ids = np.array([512326618,\n", - "712178511,\n", - "562536153,\n", - "595263154,\n", - "611658482,\n", - "652737678,\n", - "555042467,\n", - "539487468,\n", - "669233895,\n", - "689388034,\n", - "671164733,\n", - "676503588,\n", - "502962794,\n", - "649401936,\n", - "505695962,\n", - "654532828,\n", - "541290571,\n", - "547388708,\n", - "637669284,\n", - "670721589,\n", - "581153070,\n", - "603763073,\n", - "637671554,\n", - "649938038,\n", - "649409874,\n", - "691197571])" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "all_session_ids = meta_df[\"exp_id\"].values\n", - "# Finding elements in all_session_ids that are not in excluded_session_ids\n", - "training_session_ids= all_session_ids[~np.isin(all_session_ids, excluded_session_ids)]\n", - "training_session_ids = np.sort(training_session_ids, axis=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## GETTING WANDB RESULTS" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "#getting all column names so wandb knows which one to fetch\n", - "def get_table_names(session_ids):\n", - " table_names = []\n", - "\n", - " for sess in session_ids:\n", - " #depends on what table you're getting. It's the names of the performance tables you see on wandb\n", - " curr_name = \"val_allen_brain_observatory_calcium/{}_drifting_gratings_accuracy\".format(str(sess))\n", - " table_names.append(curr_name)\n", - "\n", - " return table_names" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "#Your session ids go here, here I'm using an example of a run with 7 sessions. For all training session runs, simply use the training_session_ids array we defined above/\n", - "column_names = get_table_names(np.array([611658482,623347352,637998955,639932847,643592303, 652737678, 674679019]))" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "#Getting wandb data\n", - "history_df = run.history(keys=column_names, x_axis=\"epoch\", pandas=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Save the DataFrame as a CSV file\n", - "#change to your own file path\n", - "save_file_path=\"/home/mila/x/xuejing.pan/POYO/results/cross_sess/val/roi_embed_vals_cont.csv\"\n", - "history_df.to_csv(save_file_path, index=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " and voila! " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.19" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/capoyo/notebooks/ind_transfer.ipynb b/examples/capoyo/notebooks/ind_transfer.ipynb deleted file mode 100644 index 4409eba..0000000 --- a/examples/capoyo/notebooks/ind_transfer.ipynb +++ /dev/null @@ -1,425 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "from pynwb import NWBHDF5IO, NWBFile, TimeSeries\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import os\n", - "from tqdm import tqdm" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Loading the runs" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Find the MLP performance for corresponding sessions" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "mlp_results = np.array(\n", - " [83.45588235, 76.47058824, 75.73529412, 67.64705882, 63.60294118,\n", - "56.98529412, 73.52941176, 81.61764706, 71.69117647, 94.48529412,\n", - "81.25 , 71.32352941, 78.67647059, 51.10294118, 72.79411765,\n", - "93.75 , 78.30882353, 84.55882353, 77.20588235, 68.75 ,\n", - "71.32352941, 72.42647059, 77.57352941, 77.57352941, 44.48529412,\n", - "52.94117647, 68.38235294, 58.08823529, 51.83823529, 56.25 ,\n", - "83.82352941, 83.08823529, 73.52941176, 88.97058824, 76.10294118,\n", - "75.0 , 63.23529412, 84.55882353, 86.39705882, 92.27941176,\n", - "80.88235294, 65.07352941, 83.08823529, 83.45588235, 88.97058824,\n", - "94.48529412, 94.48529412, 90.80882353, 93.38235294, 31.25 ])" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "sess_ids = np.array([\n", - " \"758519303\",\"759189643\",\"759660390\",\"759666166\",\"759872185\",\n", - " \"760269100\",\"761730740\",\"762415169\",\"763646681\",\"761624763\", \n", - " \"761944562\",\"762250376\",\"760260459\",\"760659782\",\"761269197\", \n", - " \"763949859\",\"764897534\",\"765427689\",\"766755831\",\"767254594\",\n", - " \"768807532\",\"764704289\",\"765193831\",\"766502238\",\"777496949\", \n", - " \"778374308\",\"779152062\",\"777914830\",\"778864809\",\"779650018\",\n", - " \"826187862\",\"826773996\",\"827833392\",\"826338612\",\"826819032\", \n", - " \"828816509\",\"829283315\",\"823453391\",\"824434038\",\"825180479\", \n", - " \"826659257\",\"827300090\",\"828475005\",\"829520904\",\"832883243\", \n", - " \"833704570\",\"834403597\",\"836968429\",\"837360280\",\"838633305\" \n", - " ])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "test_ids = np.array([\"764704289\", \"765193831\", \"766502238\", \"777496949\", \"778374308\", \"779152062\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.72426471 0.77573529 0.77573529 0.44485294 0.52941176 0.68382353]\n" - ] - } - ], - "source": [ - "# Find the indices of test_ids in sess_ids\n", - "indices = np.array([np.where(sess_ids == test_id)[0][0] for test_id in test_ids])\n", - "\n", - "# Get the values from mlp_results corresponding to these indices\n", - "result_values = mlp_results[indices]\n", - "print(result_values/100)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Helper Functions" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def get_all(pd, data_col):\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Getting runs data" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "#dend\n", - "finetuned_765193831 = pd.read_csv(\"/home/mila/x/xuejing.pan/POYO/results/finetuned/765193831_finetuned.csv\",usecols=[\"epoch\", \"(IMPOR)FINETUNE_ind_transfer_dend - val/session_765193831_accuracy_gabor_orientation\"])\n", - "finetuned_764704289 = pd.read_csv(\"/home/mila/x/xuejing.pan/POYO/results/finetuned/764704289_finetuned.csv\",usecols=[\"epoch\", \"(IMPOR)FINETUNE_ind_transfer_dend - val/session_764704289_accuracy_gabor_orientation\"])\n", - "finetuned_766502238 = pd.read_csv(\"/home/mila/x/xuejing.pan/POYO/results/finetuned/766502238_finetuned.csv\",usecols=[\"epoch\", \"(IMPOR)FINETUNE_ind_transfer_dend - val/session_766502238_accuracy_gabor_orientation\"])\n", - "single_765193831 = pd.read_csv(\"/home/mila/x/xuejing.pan/POYO/results/within-sess/val/765193831_with_ROI.csv\",usecols=[\"epoch\", \"765193831 w/ ROI - val/session_765193831_accuracy_gabor_orientation\"])\n", - "single_764704289 = pd.read_csv(\"/home/mila/x/xuejing.pan/POYO/results/within-sess/val/764704289_with_ROI.csv\",usecols=[\"epoch\", \"764704289 w/ ROI - val/session_764704289_accuracy_gabor_orientation\"])\n", - "single_766502238 = pd.read_csv(\"/home/mila/x/xuejing.pan/POYO/results/within-sess/val/766502238_with_ROI.csv\",usecols=[\"epoch\", \"766502238 w/ ROI - val/session_766502238_accuracy_gabor_orientation\"])\n", - "\n", - "#soma\n", - "finetuned_777496949 = pd.read_csv(\"/home/mila/x/xuejing.pan/POYO/results/finetuned/777496949_finetuned.csv\",usecols=[\"epoch\", \"FINETUNE_ind_transfer_soma - val/session_777496949_accuracy_gabor_orientation\"])\n", - "finetuned_778374308 = pd.read_csv(\"/home/mila/x/xuejing.pan/POYO/results/finetuned/778374308_finetuned.csv\",usecols=[\"epoch\", \"FINETUNE_ind_transfer_soma - val/session_778374308_accuracy_gabor_orientation\"])\n", - "finetuned_779152062 = pd.read_csv(\"/home/mila/x/xuejing.pan/POYO/results/finetuned/779152062_finetuned.csv\",usecols=[\"epoch\", \"FINETUNE_ind_transfer_soma - val/session_779152062_accuracy_gabor_orientation\"])\n", - "single_777496949 = pd.read_csv(\"/home/mila/x/xuejing.pan/POYO/results/within-sess/val/777496949_with_ROI.csv\",usecols=[\"epoch\", \"777496949 w/ ROI - val/session_777496949_accuracy_gabor_orientation\"])\n", - "single_778374308 = pd.read_csv(\"/home/mila/x/xuejing.pan/POYO/results/within-sess/val/778374308_with_ROI.csv\",usecols=[\"epoch\", \"778374308 w/ ROI - val/session_778374308_accuracy_gabor_orientation\"])\n", - "single_779152062 = pd.read_csv(\"/home/mila/x/xuejing.pan/POYO/results/within-sess/val/779152062_with_ROI.csv\",usecols=[\"epoch\", \"779152062 w/ ROI - val/session_779152062_accuracy_gabor_orientation\"])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(150,)\n", - "(1000,)\n" - ] - } - ], - "source": [ - "#dend\n", - "single_765193831_accs = single_765193831[\"765193831 w/ ROI - val/session_765193831_accuracy_gabor_orientation\"]\n", - "single_764704289_accs = single_764704289[\"764704289 w/ ROI - val/session_764704289_accuracy_gabor_orientation\"]\n", - "single_766502238_accs = single_766502238[\"766502238 w/ ROI - val/session_766502238_accuracy_gabor_orientation\"]\n", - "dend_single_epoch = single_766502238[\"epoch\"]\n", - "dend_single_all = np.stack((single_765193831_accs,single_764704289_accs,single_766502238_accs))\n", - "dend_single_mean = np.mean(dend_single_all, axis=0)\n", - "dend_single_std = np.std(dend_single_all, axis=0)\n", - "\n", - "finetuned_765193831_accs = finetuned_765193831[\"(IMPOR)FINETUNE_ind_transfer_dend - val/session_765193831_accuracy_gabor_orientation\"]\n", - "finetuned_764704289_accs = finetuned_764704289[\"(IMPOR)FINETUNE_ind_transfer_dend - val/session_764704289_accuracy_gabor_orientation\"]\n", - "finetuned_766502238_accs = finetuned_766502238[\"(IMPOR)FINETUNE_ind_transfer_dend - val/session_766502238_accuracy_gabor_orientation\"]\n", - "dend_finetuned_all = np.stack((finetuned_765193831_accs,finetuned_764704289_accs,finetuned_766502238_accs))\n", - "dend_finetuned_mean = np.mean(dend_finetuned_all, axis=0)\n", - "dend_finetuned_std = np.std(dend_finetuned_all, axis=0)\n", - "dend_finetuned_epoch = finetuned_766502238[\"epoch\"]\n", - "print(dend_single_std.shape)\n", - "print(dend_finetuned_mean.shape)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "#soma\n", - "single_777496949_accs = single_777496949[\"777496949 w/ ROI - val/session_777496949_accuracy_gabor_orientation\"]\n", - "single_778374308_accs = single_778374308[\"778374308 w/ ROI - val/session_778374308_accuracy_gabor_orientation\"]\n", - "single_779152062_accs = single_779152062[\"779152062 w/ ROI - val/session_779152062_accuracy_gabor_orientation\"]\n", - "soma_single_epoch = single_779152062[\"epoch\"]\n", - "soma_single_all = np.stack((single_777496949_accs, single_778374308_accs, single_779152062_accs))\n", - "soma_single_mean = np.mean(soma_single_all, axis=0)\n", - "soma_single_std = np.std(soma_single_all, axis=0)\n", - "\n", - "finetuned_777496949_accs = finetuned_777496949[\"FINETUNE_ind_transfer_soma - val/session_777496949_accuracy_gabor_orientation\"]\n", - "finetuned_778374308_accs = finetuned_778374308[\"FINETUNE_ind_transfer_soma - val/session_778374308_accuracy_gabor_orientation\"]\n", - "finetuned_779152062_accs = finetuned_779152062[\"FINETUNE_ind_transfer_soma - val/session_779152062_accuracy_gabor_orientation\"]\n", - "soma_finetuned_all = np.stack((finetuned_777496949_accs,finetuned_778374308_accs,finetuned_779152062_accs))\n", - "soma_finetuned_mean = np.mean(soma_finetuned_all, axis=0)\n", - "soma_finetuned_std = np.std(soma_finetuned_all, axis=0)\n", - "soma_finetuned_epoch = finetuned_766502238[\"epoch\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plotting" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plotting\n", - "plt.figure(figsize=(10, 6))\n", - "\n", - "# First line with error shading\n", - "plt.plot(dend_single_epoch, dend_single_mean, label='Single Training Mean Accuracy', color='blue')\n", - "plt.fill_between(dend_single_epoch, dend_single_mean - dend_single_std, dend_single_mean + dend_single_std, color='blue', alpha=0.2)\n", - "\n", - "# Second line with error shading\n", - "plt.plot(dend_finetuned_epoch, dend_finetuned_mean, label='Finetuned Mean Accuracy', color='red')\n", - "plt.fill_between(dend_finetuned_epoch, dend_finetuned_mean - dend_finetuned_std, dend_finetuned_mean + dend_finetuned_std, color='red', alpha=0.2)\n", - "\n", - "p = np.mean(np.array([0.7242647059,0.7757352941,0.7757352941]))\n", - "plt.axhline(y=p, color='green', linestyle='--', label=f'MLP avg ({p:.4f})')\n", - "\n", - "plt.xlabel('Epoch')\n", - "plt.ylabel('Accuracy')\n", - "plt.title('Subject transfer result(413663) - dend')\n", - "plt.legend()\n", - "plt.grid(True)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plotting\n", - "plt.figure(figsize=(10, 6))\n", - "\n", - "# First line with error shading\n", - "plt.plot(soma_single_epoch, soma_single_mean, label='Single Training Mean Accuracy', color='blue')\n", - "plt.fill_between(soma_single_epoch, soma_single_mean - soma_single_std, soma_single_mean + soma_single_std, color='blue', alpha=0.2)\n", - "\n", - "# Second line with error shading\n", - "plt.plot(soma_finetuned_epoch, soma_finetuned_mean, label='Finetuned Mean Accuracy', color='red')\n", - "plt.fill_between(soma_finetuned_epoch, soma_finetuned_mean - soma_finetuned_std, soma_finetuned_mean + soma_finetuned_std, color='red', alpha=0.2)\n", - "\n", - "p = np.mean(np.array([0.44485294,0.52941176,0.68382353]))\n", - "plt.axhline(y=p, color='green', linestyle='--', label=f'MLP avg ({p:.4f})')\n", - "\n", - "plt.xlabel('Epoch')\n", - "plt.ylabel('Accuracy')\n", - "plt.title('Subject transfer result(418779) - soma')\n", - "plt.legend()\n", - "plt.grid(True)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "#soma session - plotting\n", - "def plot_soma_sess(accs_single, accs_finetuned, epochs_single, epochs_finetuned, mlp_result, sess_id):\n", - " plt.figure(figsize=(10, 6))\n", - "\n", - " plt.plot(epochs_single, accs_single, label='Single Training Accuracy', color='blue')\n", - " plt.plot(epochs_finetuned, accs_finetuned, label='Finetuned Training Accuracy', color='red')\n", - "\n", - " plt.axhline(y=mlp_result, color='green', linestyle='--', label=f'MLP avg ({mlp_result:.4f})')\n", - "\n", - " plt.xlabel('Epoch')\n", - " plt.ylabel('Accuracy')\n", - " plt.title(f'transfer result {sess_id} - soma')\n", - " plt.legend()\n", - " plt.grid(True)\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_soma_sess(single_777496949_accs, finetuned_777496949_accs,soma_single_epoch, soma_finetuned_epoch, 0.44485294, \"777496949\")" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_soma_sess(single_778374308_accs, finetuned_778374308_accs,soma_single_epoch, soma_finetuned_epoch, 0.52941176, \"778374308\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_soma_sess(single_778374308_accs, finetuned_778374308_accs,soma_single_epoch, soma_finetuned_epoch, 0.52941176, \"778374308\")" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_soma_sess(single_779152062_accs, finetuned_779152062_accs,soma_single_epoch, soma_finetuned_epoch, 0.68382353, \"779152062\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/capoyo/notebooks/multi_sess_analysis.ipynb b/examples/capoyo/notebooks/multi_sess_analysis.ipynb deleted file mode 100644 index 14baf0d..0000000 --- a/examples/capoyo/notebooks/multi_sess_analysis.ipynb +++ /dev/null @@ -1,1413 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import os\n", - "from tqdm import tqdm\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Helper functions" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "#Helper function to get nwbfile names, session ids and corresponding ROI numbers (same order)\n", - "def getNWBinfo(mouse_csv_path='/home/mila/x/xuejing.pan/thesis/mouse_df.csv'):\n", - " lines = []\n", - " sess_ids = []\n", - " planes = []\n", - "\t\n", - " df = pd.read_csv(mouse_csv_path, usecols = ['sessid','line','runtype','plane'])\n", - "\n", - " #Getting all prod data\n", - " for row, curr_type in enumerate(df.runtype):\n", - " if curr_type == 'prod': \n", - " lines.append(df.line[row])\n", - " sess_ids.append(str(df.sessid[row]))\n", - " planes.append(df.plane[row])\n", - " \n", - " assert len(lines)==len(sess_ids)==len(planes)==50, \"Error in getting session info.\" \n", - "\n", - " return sess_ids, planes, lines\n", - "\n", - "#Helper function to get nwbfile names, session ids and corresponding ROI numbers (same order)\n", - "def getNWBinfo_roi(mouse_csv_path='/home/mila/x/xuejing.pan/thesis/mouse_df.csv'):\n", - " lines = []\n", - " sess_ids = []\n", - " planes = []\n", - " n_rois = []\n", - " n_rois_soma = []\n", - " n_rois_dend = []\n", - "\t\n", - " df = pd.read_csv(mouse_csv_path, usecols = ['sessid','line','runtype','plane','nrois'])\n", - "\n", - " #Getting all prod data\n", - " for row, curr_type in enumerate(df.runtype):\n", - " if curr_type == 'prod': \n", - " lines.append(df.line[row])\n", - " sess_ids.append(str(df.sessid[row]))\n", - " planes.append(df.plane[row])\n", - " n_rois.append(df.nrois[row])\n", - "\n", - " if df.plane[row] == 'soma':\n", - " n_rois_soma.append(df.nrois[row])\n", - " else:\n", - " n_rois_dend.append(df.nrois[row])\n", - " \n", - " assert len(lines)==len(sess_ids)==len(planes)==50, \"Error in getting session info.\" \n", - "\n", - " return sess_ids, planes, lines, n_rois, n_rois_soma, n_rois_dend\n", - "\n", - "\n", - "def get_diff_sess_ids():\n", - " sess_ids, planes, lines = getNWBinfo()\n", - " dend_sess_ids = []\n", - " soma_sess_ids = []\n", - " L23_sess_ids = []\n", - " L5_sess_ids = []\n", - "\n", - " for count, curr_sess_id in enumerate(sess_ids):\n", - " if planes[count] == 'soma':\n", - " soma_sess_ids.append(curr_sess_id)\n", - " else:\n", - " dend_sess_ids.append(curr_sess_id)\n", - "\n", - " if lines[count] == 'L23-Cux2':\n", - " L23_sess_ids.append(curr_sess_id)\n", - " else:\n", - " L5_sess_ids.append(curr_sess_id)\n", - " \n", - " return dend_sess_ids, soma_sess_ids, L23_sess_ids, L5_sess_ids\n", - "\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "def getNWBfilenames(mouse_csv_path='/home/mila/x/xuejing.pan/thesis/mouse_df.csv'):\n", - " filenames = []\n", - " sess_ids = []\n", - " num_rois = []\n", - " lines = []\n", - " planes = []\n", - "\n", - " df = pd.read_csv(mouse_csv_path, usecols = ['sessid','mouseid','runtype','nrois','line','plane'])\n", - "\n", - " #Getting all prod data\n", - " for row, curr_type in enumerate(df.runtype):\n", - " if curr_type == 'prod': \n", - " #f_name = source_dir+\"/sub-\"+str(df.mouseid[row])+\"_ses-\"+str(df.sessid[row])+\"_obj-raw_behavior+image+ophys.nwb\"\n", - " f_name = \"sub-\"+str(df.mouseid[row])+\"_ses-\"+str(df.sessid[row])+\"_obj-raw_behavior+image+ophys.nwb\"\n", - " filenames.append(f_name)\n", - " sess_ids.append(df.sessid[row])\n", - " num_rois.append(df.nrois[row])\n", - " lines.append(df.line[row])\n", - " planes.append(df.plane[row])\n", - "\n", - " return filenames,sess_ids, num_rois, lines, planes\n", - "\n", - "def check_nan(array):\n", - " nan_indices = np.isnan(array)\n", - "\n", - " if np.any(nan_indices):\n", - " non_nan_indices = ~nan_indices\n", - " x = np.where(non_nan_indices)[0]\n", - " y = array[non_nan_indices]\n", - " \n", - " # Use interpolation only if there are non-NaN values\n", - " if len(x) > 0:\n", - " f = interpolate.interp1d(x, y, kind='linear', fill_value='extrapolate')\n", - " array[nan_indices] = f(np.where(nan_indices)[0])\n", - "\n", - " return array\n", - "\n", - "def get_cont_labels(nwbfile):\n", - " behavior_module = nwbfile.processing['behavior']\n", - " BehavioralTimeSeries= behavior_module.get_data_interface('BehavioralTimeSeries')\n", - " pupiltracking = behavior_module.get_data_interface('PupilTracking')\n", - " pupil_diameter = pupiltracking.time_series['pupil_diameter']\n", - " pupil_diameter_data = np.copy(pupil_diameter.data)\n", - " pupil_diameter_data = check_nan(pupil_diameter_data)\n", - " behavior_timestamps= pupil_diameter.timestamps # Same timestamps as roi\n", - "\n", - " return pupil_diameter_data\n", - "\n", - "def calculate_accuracy(prediction, valid_discrete_label):\n", - " if len(prediction) != len(valid_discrete_label):\n", - " return \"Error: Arrays have different lengths.\"\n", - "\n", - " matches = sum(p == v for p, v in zip(prediction, valid_discrete_label))\n", - " accuracy = matches / len(prediction)\n", - " return accuracy\n", - "\n", - "def get_diff_sess_ids():\n", - " filenames,sess_ids, num_rois, lines, planes = getNWBfilenames()\n", - " dend_sess_ids = []\n", - " soma_sess_ids = []\n", - " L23_sess_ids = []\n", - " L5_sess_ids = []\n", - "\n", - " for count, curr_sess_id in enumerate(sess_ids):\n", - " if planes[count] == 'soma':\n", - " soma_sess_ids.append(str(curr_sess_id))\n", - " else:\n", - " dend_sess_ids.append(str(curr_sess_id))\n", - "\n", - " if lines[count] == 'L23-Cux2':\n", - " L23_sess_ids.append(str(curr_sess_id))\n", - " else:\n", - " L5_sess_ids.append(str(curr_sess_id))\n", - " \n", - " return dend_sess_ids, soma_sess_ids, L23_sess_ids, L5_sess_ids" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "def get_acc_for_one_sess(combined_df,sess_id):\n", - " condition = (combined_df[\"sess_id\"] == sess_id)\n", - " accs = combined_df.loc[condition,\"val_accs\"]\n", - "\n", - " condition_soma = (combined_df[\"sess_id\"] == sess_id) & (combined_df[\"plane\"] == \"soma\")\n", - " condition_dend = (combined_df[\"sess_id\"] == sess_id) & (combined_df[\"plane\"] == \"dend\")\n", - " condition_L23 = (combined_df[\"sess_id\"] == sess_id) & (combined_df[\"line\"] == \"L23-Cux2\")\n", - " condition_L5 = (combined_df[\"sess_id\"] == sess_id) & (combined_df[\"line\"] == \"L5-Rbp4\")\n", - "\n", - " accs_soma = combined_df.loc[condition_soma,\"val_accs\"]\n", - " accs_dend = combined_df.loc[condition_dend,\"val_accs\"]\n", - " accs_L23 = combined_df.loc[condition_L23,\"val_accs\"]\n", - " accs_L5 = combined_df.loc[condition_L5,\"val_accs\"]\n", - "\n", - " return accs,accs_dend,accs_soma,accs_L5,accs_L23\n", - "\n", - "def get_losses_for_one_sess(combined_df,sess_id):\n", - " sess_id = int(sess_id)\n", - " condition = (combined_df[\"sess_id\"] == sess_id)\n", - " losses = combined_df.loc[condition,\"train_losses\"]\n", - "\n", - " condition_soma = (combined_df[\"sess_id\"] == sess_id) & (combined_df[\"plane\"] == \"soma\")\n", - " condition_dend = (combined_df[\"sess_id\"] == sess_id) & (combined_df[\"plane\"] == \"dend\")\n", - " condition_L23 = (combined_df[\"sess_id\"] == sess_id) & (combined_df[\"line\"] == \"L23-Cux2\")\n", - " condition_L5 = (combined_df[\"sess_id\"] == sess_id) & (combined_df[\"line\"] == \"L5-Rbp4\")\n", - "\n", - " losses_soma = combined_df.loc[condition_soma,\"train_losses\"]\n", - " losses_dend = combined_df.loc[condition_dend,\"train_losses\"]\n", - " losses_L23 = combined_df.loc[condition_L23,\"train_losses\"]\n", - " losses_L5 = combined_df.loc[condition_L5,\"train_losses\"]\n", - "\n", - " return losses,losses_dend,losses_soma, losses_L5, losses_L23" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "sess_ids, planes, lines = getNWBinfo()\n", - "dend_sess_ids, soma_sess_ids, L23_sess_ids, L5_sess_ids = get_diff_sess_ids()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "def get_accs(df, sessions):\n", - " accs_list = []\n", - " for curr_sess_id in sessions:\n", - " col_name = \"val/session_{}_accuracy_gabor_orientation\".format(curr_sess_id)\n", - " curr_accs = np.array(df[col_name].values)\n", - "\n", - " accs_list.append(curr_accs)\n", - "\n", - " return np.array(accs_list)\n", - "\n", - "def get_mean_std(accs_arr):\n", - " accs_std = np.std(accs_arr)\n", - " accs_avg = np.mean(accs_arr)\n", - "\n", - " print(\"all_std: \",accs_std)\n", - " print(\"all_avg: \",accs_avg)\n", - "\n", - "\n", - " dend_accs = []\n", - " soma_accs = []\n", - "\n", - " for count, curr_sess_id in enumerate(sess_ids):\n", - " if planes[count] == \"soma\":\n", - " soma_accs.append(accs_arr[count])\n", - " else:\n", - " dend_accs.append(accs_arr[count])\n", - "\n", - " dend_accs = np.array(dend_accs)\n", - " soma_accs = np.array(soma_accs)\n", - "\n", - " accs_soma_std = np.std(soma_accs)\n", - " accs_soma_avg = np.mean(soma_accs)\n", - " accs_dend_std = np.std(dend_accs)\n", - " accs_dend_avg = np.mean(dend_accs)\n", - "\n", - " print(\"soma std: \",accs_soma_std)\n", - " print(\"soma avg: \",accs_soma_avg)\n", - " print(\"dend std: \",accs_dend_std)\n", - " print(\"soma avg: \",accs_dend_avg)" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [], - "source": [ - "# plot\n", - "def plot_2_sess(sess_1_acc, sess_2_acc, sess_1_label, sess_2_label, n_rois, epoch):\n", - " # Create scatter plot with variable point sizes\n", - " plt.scatter(sess_1_acc, sess_2_acc, \n", - " s=n_rois, # Point sizes are set here\n", - " label='num_rois', alpha=0.5) # alpha for point transparency\n", - "\n", - " max_value = max(max(sess_1_acc), max(sess_2_acc))\n", - " min_value = min(min(sess_1_acc), min(sess_2_acc))\n", - " plt.plot([min_value, max_value], [min_value, max_value], color='black', linestyle='-', linewidth=2)\n", - "\n", - " # Customize the plot\n", - " plt.xlabel(sess_1_label)\n", - " plt.ylabel(sess_2_label)\n", - " plt.title('{} and {} Performance Comparison ({})'.format(sess_1_label,sess_2_label, epoch))\n", - " plt.legend()\n", - " plt.grid(True)\n", - "\n", - " # Show the plot\n", - " plt.show()\n", - "\n", - "\n", - "def moving_average(data, window_size):\n", - " return np.convolve(data, np.ones(window_size)/window_size, mode='valid')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Multi-sess poyo_single_sess_model" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "csv_file_path = '/home/mila/x/xuejing.pan/POYO/results/cross_sess/loss/multi_sess_combined_losses.csv'\n", - "key_columns = [\"epoch\", \"IMPORTANT - multi_sess - train_loss\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Train loss" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "train_df = pd.read_csv(csv_file_path, usecols=[\"epoch\", \"IMPORTANT - multi_sess - train_loss\"])\n", - "roi_embedding_loss_df = pd.read_csv(\"/home/mila/x/xuejing.pan/POYO/results/cross_sess/loss/roi_embed_loss_combined.csv\", usecols=[\"epoch\", \"train_loss\"])\n", - "#within_loss_df = pd.read_csv(\"/home/mila/x/xuejing.pan/POYO/results/within-sess/combined_train_losses.csv\")\n", - "#dend_loss_df = pd.read_csv(\"/home/mila/x/xuejing.pan/POYO/results/cross_sess/loss/dend_combined_loss.csv\")\n", - "#soma_loss_df = pd.read_csv(\"/home/mila/x/xuejing.pan/POYO/results/cross_sess/loss/soma_combined_loss.csv\")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1358\n" - ] - } - ], - "source": [ - "#min_epoch = min(train_df[\"epoch\"].values[-1],dend_loss_df[\"epoch\"].values[-1], soma_loss_df[\"epoch\"].values[-1])\n", - "min_epoch = min(train_df[\"epoch\"].values[-1],roi_embedding_loss_df[\"epoch\"].values[-1])\n", - "print(min_epoch)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/50 [00:00 3802\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3803\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", - "File \u001b[0;32m~/.conda/envs/poyo/lib/python3.9/site-packages/pandas/_libs/index.pyx:138\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m~/.conda/envs/poyo/lib/python3.9/site-packages/pandas/_libs/index.pyx:165\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5745\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5753\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: 'sess_id'", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[14], line 12\u001b[0m\n\u001b[1;32m 8\u001b[0m accs_L5_list \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sess_id \u001b[38;5;129;01min\u001b[39;00m tqdm(sess_ids):\n\u001b[1;32m 11\u001b[0m \u001b[38;5;66;03m#accs,accs_dend,accs_soma,accs_L5,accs_L23 = get_acc_for_one_sess(combined_val_df,sess_id)\u001b[39;00m\n\u001b[0;32m---> 12\u001b[0m accs,accs_dend,accs_soma,accs_L5,accs_L23 \u001b[38;5;241m=\u001b[39m \u001b[43mget_losses_for_one_sess\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_df\u001b[49m\u001b[43m,\u001b[49m\u001b[43msess_id\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 14\u001b[0m accs_list\u001b[38;5;241m.\u001b[39mappend(accs)\n\u001b[1;32m 16\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(accs_soma) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n", - "Cell \u001b[0;32mIn[4], line 19\u001b[0m, in \u001b[0;36mget_losses_for_one_sess\u001b[0;34m(combined_df, sess_id)\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_losses_for_one_sess\u001b[39m(combined_df,sess_id):\n\u001b[1;32m 18\u001b[0m sess_id \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(sess_id)\n\u001b[0;32m---> 19\u001b[0m condition \u001b[38;5;241m=\u001b[39m (\u001b[43mcombined_df\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msess_id\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m \u001b[38;5;241m==\u001b[39m sess_id)\n\u001b[1;32m 20\u001b[0m losses \u001b[38;5;241m=\u001b[39m combined_df\u001b[38;5;241m.\u001b[39mloc[condition,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain_losses\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 22\u001b[0m condition_soma \u001b[38;5;241m=\u001b[39m (combined_df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msess_id\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m==\u001b[39m sess_id) \u001b[38;5;241m&\u001b[39m (combined_df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mplane\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msoma\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m~/.conda/envs/poyo/lib/python3.9/site-packages/pandas/core/frame.py:3807\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3805\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 3806\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 3807\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3808\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m 3809\u001b[0m indexer \u001b[38;5;241m=\u001b[39m [indexer]\n", - "File \u001b[0;32m~/.conda/envs/poyo/lib/python3.9/site-packages/pandas/core/indexes/base.py:3804\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3802\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m 3803\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m-> 3804\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 3805\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m 3806\u001b[0m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m 3807\u001b[0m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m 3808\u001b[0m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n\u001b[1;32m 3809\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n", - "\u001b[0;31mKeyError\u001b[0m: 'sess_id'" - ] - } - ], - "source": [ - "#Get all validation accs and put in a 2d np array (num_sess(50), num_epochs)\n", - "#epochs = combined_val_df[\"epoch\"] #x-axis\n", - "\n", - "accs_list = []\n", - "accs_dend_list = []\n", - "accs_soma_list = []\n", - "accs_L23_list = []\n", - "accs_L5_list = []\n", - "\n", - "for sess_id in tqdm(sess_ids):\n", - " #accs,accs_dend,accs_soma,accs_L5,accs_L23 = get_acc_for_one_sess(combined_val_df,sess_id)\n", - " accs,accs_dend,accs_soma,accs_L5,accs_L23 = get_losses_for_one_sess(train_df,sess_id)\n", - "\n", - " accs_list.append(accs)\n", - "\n", - " if len(accs_soma) != 0:\n", - " accs_soma_list.append(accs_soma)\n", - " else:\n", - " accs_dend_list.append(accs_dend)\n", - " \n", - " if len(accs_L23) != 0:\n", - " accs_L23_list.append(accs_L23)\n", - " else:\n", - " accs_L5_list.append(accs_L5)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "within_sess_losses_all = np.array(accs_list)\n", - "within_sess_losses_dend = np.array(accs_dend_list)\n", - "within_sess_losses_soma = np.array(accs_soma_list)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "# Define your window size for smoothing\n", - "window_size = 20 # You can adjust this value\n", - "\n", - "# Smoothing the data\n", - "smooth_all_sessions = moving_average(train_df[\"IMPORTANT - multi_sess - train_loss\"][:min_epoch], window_size)\n", - "smooth_all_sessions_ROI = moving_average(roi_embedding_loss_df[\"train_loss\"][:min_epoch], window_size)\n", - "\n", - "#smooth_dend_sessions = moving_average(dend_loss_df[\"multi_sess_dend - train_loss\"][:min_epoch], window_size)\n", - "#smooth_soma_sessions = moving_average(soma_loss_df[\"multi_sess_soma - train_loss\"][:min_epoch], window_size)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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Xr16kpKTwzz//8Oabb9K/f38qVapEixYtstWfNXFiVFRUtmNZk7fZ4meoTJkyAHlOQlhUseQmJCSk0N8TEZGipB52ERE7KleuHM899xw9e/bkoYceyvW8rOHGW7dutSr/6aef8nWf/PQaA3h4eNC0aVOrfzmtCR8WFsbAgQPp168fe/bsISkpiZo1a1K9enW2bNmSrY6C1nW1GjVq8PLLL1O/fn02btyYa/wdO3bkwoULLFiwwKr8888/txy3lU6dOnHq1CmmTJnCLbfcYnlvHTt2ZP78+axbty5fw+Hz2zaF9dVXX1ntf/fdd6Snp1tm1+/QoQMAX375pdV569atY9euXdk+szvvvJMKFSowcuRIli5dypAhQ6z+OFLQ+vJSoUIFnnrqKTp37pxnu+eXr68vTZs2ZcGCBaSmplrKL1y4cM3VAQBuv/12Dhw4QEhISI7f76tnrs+NyWSiYcOG/Pe//yUoKMjy3m6//XYMw+DEiRM51l+/fv1813UlT09P2rZty9tvvw2YZ5jPia+vL82bN2fevHlW38nMzEy+/PJLypcvT40aNfL1HvNSo0YNy+NAV/8RsqhjyU3btm3Zvn17tscNvvnmG7vdU0SkMNTDLiJiZ2+99dY1zwkPD6dTp068+eablCpViooVK/LHH38wb968fN0jqzfu22+/pUqVKnh5eeX4P/95ad68ObfffjsNGjSgVKlS7Nq1iy+++IIWLVrg4+MDwEcffUS3bt3o2rUrAwcOpFy5cpw7d45du3axceNGy7PP16pr69atPPXUU9xzzz1Ur14dDw8Pli1bxtatWy29hzkZMGAAU6dO5aGHHuLw4cPUr1+fv/76izfeeIPu3btf9/PkV+rQoQMmk4nFixczbtw4S3mnTp0sf3zJz/1s0TZ5mTdvHm5ubnTu3JkdO3bwyiuv0LBhQ+69917APOrhscce44MPPsDFxYVu3bpx+PBhXnnlFSIjI3nmmWes6nN1deXJJ5/khRdewNfXN9uz6gWt70pxcXG0b9+e/v37U6tWLfz9/Vm3bp1lZndbGD9+PD169KBr164MGzaMjIwM3nnnHfz8/Dh37lye1w4fPpwffviBNm3a8Mwzz9CgQQMyMzM5evQoixcvZuTIkTRv3jzHa3/55RemTZtG7969qVKlCoZhMG/ePGJjYy0jFFq1asVjjz3Gww8/zPr162nTpg2+vr5ERUXx119/Ub9+fZ544ol81TVmzBiOHz9Ox44dKV++PLGxsbz33nu4u7vTtm3bXN/jm2++SefOnWnfvj3PPvssHh4eTJs2je3btzNnzpx8LX2XH1OnTqVnz57ccsstPPPMM1SoUIGjR4+yaNEiyx+ZiiqWnAwfPpxZs2bRrVs3xo8fT1hYGF9//bVlBJCLi/q0RKSYcMxcdyIiN6YrZ4nPS04zh0dFRRl33323ERwcbAQGBhoPPPCAZbboa80Sf/jwYaNLly6Gv7+/AVhm2C7ILPEvvvii0bRpU6NUqVKGp6enUaVKFeOZZ54xzpw5Y3Xeli1bjHvvvdcIDQ013N3djfDwcKNDhw7GjBkz8l3XqVOnjIEDBxq1atUyfH19DT8/P6NBgwbGf//7XyM9Pd1Sz9WzxBuGYZw9e9YYPHiwERERYbi5uRkVK1Y0Ro0aZSQnJ1udBxhPPvlktvd59WzReWncuLEBGKtXr7aUnThxwgCMkJAQyyzWWQrSNlmzxH///fdW5+e3zbLutWHDBqNnz56Gn5+f4e/vb/Tr1884deqU1bkZGRnG22+/bdSoUcNwd3c3SpcubTzwwAPGsWPHcqz78OHDBmAMHjw4x+MFrS9LcnKyMXjwYKNBgwZGQECA4e3tbdSsWdN49dVXjcTERMt5uc0Sn9/2nD9/vlG/fn3Dw8PDqFChgvHWW28ZQ4cONUqVKnXNay9cuGC8/PLLRs2aNQ0PDw8jMDDQqF+/vvHMM88Y0dHRub633bt3G/369TOqVq1qeHt7G4GBgUazZs2M2bNnZzt31qxZRvPmzQ1fX1/D29vbqFq1qjFgwABj/fr1+a7rl19+Mbp162aUK1fO8PDwMEJDQ43u3bsbq1atspyT23dp1apVRocOHSz3v+WWW4yff/7Z6pzcfpfltqJFTtasWWN069bNCAwMNDw9PY2qVatmW9XA1rHkd5Z4wzCM7du3G506dTK8vLyM4OBgY9CgQcZnn31mAMaWLVuu+f5ERIqCyTDyWFRUREREiqWxY8cybtw4Tp8+bfNnfT/44AOGDh3K9u3bqVu3rk3rdoS0tDQaNWpEuXLlWLx4saPDkWLsscceY86cOZw9e9ZqpQEREUfRkHgREREBzM8+Hzp0iPHjx9OrVy+nTdYHDRpE586diYiIIDo6mhkzZrBr1y7ee+89R4cmxcj48eMpW7YsVapUscxz8Mknn/Dyyy8rWReRYkMJu4iIiADmCeeio6Np3bp1rjP1O4OEhASeffZZTp8+jbu7OzfddBMLFy606RwH4vzc3d155513OH78OOnp6VSvXp3JkyczbNgwR4cmImKhIfEiIiIiIiIixZCmwBQREREREREphpSwi4iIiIiIiBRDSthFREREREREiqESN+lcZmYmJ0+exN/fH5PJ5OhwRERERERE5AZnGAYJCQmULVsWF5f895uXuIT95MmTREZGOjoMERERERERKWGOHTtG+fLl831+iUvY/f39AfMHFRAQ4OBo8paWlsbixYvp0qUL7u7ujg5H7EztXfKozUsetXnJovYuedTmJY/avOQpbJvHx8cTGRlpyUfzq8Ql7FnD4AMCApwiYffx8SEgIEC/AEoAtXfJozYvedTmJYvau+RRm5c8avOS53rbvKCPZWvSOREREREREZFiSAm7iIiIiIiISDGkhF1ERERERESkGCpxz7CLiIiIiIj9GYZBeno6GRkZjg7FbtLS0nBzcyM5OfmGfp9yWV5t7u7ujqurq03vp4RdRERERERsKjU1laioKJKSkhwdil0ZhkF4eDjHjh0r8GRi4pzyanOTyUT58uXx8/Oz2f2UsIuIiIiIiM1kZmZy6NAhXF1dKVu2LB4eHjdsMpuZmcmFCxfw8/PDxUVPG5cEubW5YRicPn2a48ePU716dZv1tCthFxERERERm0lNTSUzM5PIyEh8fHwcHY5dZWZmkpqaipeXlxL2EiKvNi9TpgyHDx8mLS3NZgm7vlUiIiIiImJzSmClpLHHSBL9FImIiIiIiIgUQ0rYRURERERERIohJewiIiIiIiKSp8OHD2Mymdi8ebPN627Xrh3Dhw/P85xKlSoxZcoUy77JZGLBggU2j6W4UcIuIiIiIiJiR7/88gvt2rXD398fHx8fbr75ZmbPnm11TlZCnPUvMDCQW265hZ9//tnqvNmzZxMUFFR0wRdTUVFRdOvWzdFh2J0SdhERERERETv54IMP6NWrFy1btmTt2rVs3bqV++67j8GDB/Pss89mO3/p0qVERUWxdu1amjVrRp8+fdi+fbsDIi/ewsPD8fT0dHQYdqeEXURERERE7MowDJJS04v8n2EY+Y6xXbt2DB06lOeff57g4GDCw8MZO3as1TlHjx6lV69e+Pn5ERAQQN++fYmJicm1zmPHjjFy5EiGDx/OG2+8QZ06dahWrRojR47knXfeYdKkSaxdu9bqmpCQEMLDw6lVqxavv/46aWlpLF++vECf94kTJ+jbty+lSpUiJCSEXr16cfjwYcvxgQMH0rt3b9544w3CwsIICgpi3LhxpKen89xzzxEcHEz58uWZNWtWtrp3795Ny5Yt8fLyom7duqxYscLq+M6dO+nevTt+fn6EhYXx4IMPcubMGcvxxMREBgwYgJ+fHxEREUyaNCnbPWJiYujZsyfe3t5UrlyZr776Kts5Vw6JzxqdMG/ePNq3b4+Pjw8NGzZkzZo1Vtf873//syw3eOeddzJ58uRiP1pB67CLiIiIiIhdXUzLoM6YRUV+353ju+Ljkf+U57PPPmPEiBGsXbuWNWvWMHDgQFq1akXnzp0xDIPevXvj6+vLypUrSU9PZ8iQITzyyCP8+eefOdY3d+5c0tLScuxJf/zxxxk9ejRz5syhefPm2Y6npaXxv//9DwB3d/d8v4ekpCTat29P69at+fPPP3Fzc2PChAncdtttbN26FQ8PDwCWLVtG+fLl+fPPP1m9ejWDBg1izZo1tGnThrVr1/Ltt98yePBgOnfuTGRkpKX+5557jilTplCnTh0mT57MHXfcwaFDhwgJCSEqKoq2bdvyn//8h8mTJ3Px4kVeeOEF7r33XpYtW2a5fvny5cyfP5/w8HBGjx7Nhg0baNSokeUeAwcO5NixYyxbtgwPDw+GDh2a5x9Gsrz00ku8++67VK9enZdeeol+/fqxf/9+3NzcWL16NYMHD+btt9/mjjvuYOnSpbzyyiv5/lwdRQm7iIiIiIgI0KBBA1599VUAqlevzocffsgff/xB586dWbp0KVu3buXQoUOWBPazzz6jfv36rFu3Lseke+/evQQGBhIREZHtmIeHB1WqVGHv3r1W5S1btsTFxYWLFy+SmZlJpUqVuPfee/P9Hr755htcXFz45JNPLOuCf/rppwQFBbFixQq6dOkCQHBwMO+//z4uLi7UrFmTiRMnkpSUxOjRowEYNWoUb731FqtXr+a+++6z1P/UU0/Rp08fAKZPn87vv//OzJkzef7555k+fTo33XQTb7zxhuX8WbNmERkZyd69eylbtiwzZ87k888/p3PnzpbPsHz58laf2W+//cY///xj+UxnzpxJ7dq1r/nen332WXr06AHAuHHjqFu3Lvv376dWrVp88MEHdOvWzfLHkxo1avD333/zyy+/5PuzdQQl7MVV/ElMR9cRkrAL6O7oaERERERECs3b3ZWd47s65L4F0aBBA6v9iIgIS8/url27iIyMtOptrlOnDoGBgezatSvHhP1aDMOwJNVZvv32W2rVqsXevXsZPnw4M2bMIDg4ON91btiwgf379+Pv729VnpyczIEDByz7devWxcXl8hPSYWFh1KtXz7Lv6upKSEhItp7tFi1aWLbd3Nxo2rQpu3btstx7+fLl+Pn5ZYvrwIEDXLx4kdTUVKs6goODqVmzpmV/165dlnqz1KpVK19D169sv6w/ksTExFCrVi327NnDnXfeaXV+s2bNlLDn5c8//+Sdd95hw4YNREVFMX/+fHr37p2va1evXk3btm2pV6+eXZYWcLg9v+H26wiqBTQCRjo6GhERERGRQjOZTAUamu4oVw89N5lMZGZmAjkn13mVg7kXNy4ujpMnT1K2bFmrY6mpqRw8eJAOHTpYlUdGRlK9enWqV6+On58fffr0YefOnYSGhubrPWRmZtKkSZMcn/suU6ZMnu81r/efl6z3n5mZSc+ePXn77beznRMREcG+ffuuWVfWvAO5faZ5uTL+K2PKqvfqOgsyx4GjOHTSucTERBo2bMiHH35YoOvi4uIYMGAAHTt2tFNkjhfrEQ6Aa/JZB0ciIiIiIiJ16tTh6NGjHDt2zFK2c+dO4uPjcx2u3adPH9zc3HKcWG3GjBkkJibSr1+/XO+Z1UH5+uuv5zvOm266iX379hEaGkq1atWs/gUGBua7ntz8888/lu309HQ2bNhArVq1LPfesWMHlSpVynZvX19fqlWrhru7u1Ud58+ft3osoHbt2qSnp7N+/XpL2Z49e4iNjb2uuGvVqsW///5rVXblPYorhybs3bp1Y8KECdx1110Fuu7xxx+nf//+VkMpbjSrz/gA4J2qhF1ERERExNE6depEgwYNuP/++9m4cSP//vuvZVK6K4dvX6lChQpMnDiRKVOm8NJLL7F7924OHDjA5MmTef755xk5cuQ1h9KPHDmSjz76iBMnTuQrzvvvv5/SpUvTq1cvVq1axaFDh1i5ciXDhg3j+PHjBX7fV5s6dSrz589n9+7dPPnkk5w/f55HHnkEgCeffJJz587Rr18//v33Xw4ePMjixYt55JFHyMjIwM/Pj0GDBvHcc8/xxx9/sH37dgYOHGg1NL9mzZrcdttt/Oc//2Ht2rVs2LCBRx99FG9v7+uK++mnn2bhwoVMnjyZffv28dFHH/Hbb78Vqie/KBX/cSlX+fTTTzlw4ABffvklEyZMuOb5KSkppKSkWPbj4+MB86yLaWlpdovzerkGmYfM+JFE0oWz4Bfi4IjE3rK+j8X5eym2pTYvedTmJYvau+RRm5ulpaVhGAaZmZn5Gk5dnGTFfeX+lWXz5s1j6NChtGnTBhcXF7p27crrr7+e7borDRs2jMqVKzN58mTee+89MjIyqFu3LlOnTuXhhx+2XHfl65V1de/enUqVKjFhwgSmTp2a7fyreXl5sWLFCl588UXuuusuEhISKFeuHB06dMDPz4/MzMxs7yu3939lWVb5G2+8wdtvv82mTZuoWrUq8+fPJzg4mMzMTMLDw1m1ahUvvvgiXbt2JSUlhYoVK9K1a1dLzG+//TYJCQnccccd+Pv7M2LECOLi4qzuPXPmTP7zn//Qtm1bwsLCGD9+PMeOHcsWX1ZcOX12V5e1aNGCadOm8dprr/Hyyy/TpUsXhg8fbvWZ5kfWMPqcPquszzYtLQ1XV+v5Ewr7e8FkFJOB+yaT6ZrPsO/bt49bb72VVatWUaNGDcaOHcuCBQvyfIZ97NixjBs3Llv5119/jY+Pjw0it4/98fDI/iGUMl1gWa3XSfCOvPZFIiIiIiIO5ubmRnh4OJGRkZYlxESKo2HDhllmpbeF1NRUjh07RnR0NOnp6VbHkpKS6N+/P3FxcQQEBOS7TqfpYc/IyKB///6MGzeOGjVq5Pu6UaNGMWLECMt+fHw8kZGRdOnSpUAfVFHbd+oCJ/aVppTpAq3qlMeltmaKv9GlpaWxZMkSOnfuXKC1NsV5qc1LHrV5yaL2LnnU5mbJyckcO3YMPz8/vLy8HB2OXRmGQUJCAv7+/sV+aLXApEmT6NSpE76+vvz+++/MmTOHDz/8sEB5YV5tnpycjLe3N23atMn23c8a6V1QTpOwJyQksH79ejZt2sRTTz0FXB5y4ObmxuLFi7PNsAjg6emJp6dntnJ3d/di/Yu0dKA3a41w6nEYl3P7i3WsYlvF/bsptqc2L3nU5iWL2rvkKeltnpGRgclkwsXFxerZ5BtR1pDorPcrxdu6det45513SEhIoEqVKrz//vs89thjBaojrzZ3cXGxzLZ/9e+Awv5OcJqEPSAggG3btlmVTZs2jWXLljF37lwqV67soMjsI8jbg12ZFenp+g8ZUdsoub/yRURERERErt93333n6BAKzKEJ+4ULF9i/f79l/9ChQ2zevJng4GAqVKjAqFGjOHHiBJ9//jkuLi7Uq1fP6vrQ0FC8vLyyld8IPNxcOOhm/iOEKWaHg6MRERERERGRoubQhH39+vW0b9/esp/1rPlDDz3E7NmziYqK4ujRo44Kz+FivKpCCnjEHoSMdHB1mgERIiIiIiIicp0cmgG2a9eOvCapnz17dp7Xjx07lrFjx9o2qGIkwzeU1GRXPMiAhCgI0kzxIiIiIiIiJYVmRijGwoJ8iTIurb8ed8yxwYiIiIiIiEiRUsJejDWODOKEUdq8E1tyHw0QEREREREpiZSwF2NNKlxO2E8d3efgaERERERERKQoKWEvxuqXCyDeIxSAjRv/5UTsRb5ee5R7ZvzN2QspDo5ORERERKRkmj17NkFBQdc8z2QysWDBArvHU1xVqlSJKVOm2LzesWPH0qhRozzPGThwIL1797bst2vXjuHDh9s8FntTwl6Mubm6UCnSvLRb/cyddJvyJ6Pnb2Pd4fN8t/64g6MTERERESmZ+vbty969ey3748aNu2YCmV/t2rXDZDJhMpnw8PCgatWqjBo1ipSU7B12v/zyC+3atcPf3x8fHx9uvvnmbBN3Hz58GJPJxObNm20Sn7OaN28er732mqPDKDAl7MVccmA1DJML5U1n8E0+ZSl/+/fdfL7msOMCExEREREpoby9vQkNDbVb/f/5z3+Iiopi//79TJw4kalTp2ZbHeuDDz6gV69etGzZkrVr17J161buu+8+Bg8ezLPPPmu32JxVcHAw/v7+jg6jwJSwF3Pprt4QUg2AKi4nrY6N+XEHG46cc0RYIiIiIiL5ZxiQmlj0//JYQvpKP//8M0FBQWRmZgKwefNmTCYTzz33nOWcxx9/nH79+gHWQ+K//vprxo8fz5YtWyw941f2cp85c4Y777wTHx8fqlevzk8//XTNeHx8fAgPD6dChQr06dOHzp07s3jxYsvxY8eOMXLkSIYPH84bb7xBnTp1qFatGiNHjuSdd95h0qRJrF27Nl/vHSA1NZXnn3+ecuXK4evrS/PmzVmxYoXleNb7/eWXX6hZsyY+Pj7cfffdJCYm8tlnn1GpUiVKlSrF008/TUZGhlXdCQkJ9O/fHz8/P8qWLcsHH3xgdTwuLo7HHnuM0NBQAgIC6NChA1u2bLE656233iIsLAx/f38GDRpEcnKy1fGMjAxGjBhBUFAQISEhPP/889mWD796SHylSpV44403eOSRR/D396dChQp8/PHHVtf8/fffNGrUCC8vL5o2bcqCBQtwdXVl27Zt+f5sr5dD12GX/DH8IzCd2cvbncuwLqghz3x7+Qs8/pdd/PhkKwdGJyIiIiJyDWlJ8EbZor/v6JPg4XvN09q0aUNCQgKbNm2iSZMmrFy5ktKlS7Ny5UrLOStWrOCZZ57Jdu2dd97JgQMHWLRoEUuXLgUgMDDQcnzcuHFMnDiRd955hw8++ID777+fI0eOEBwcnK+3sGXLFlavXk2lSpUsZXPnziUtLS3HnvTHH3+c0aNHM2fOHJo3b56vezz88MMcPnyYb775hrJlyzJ//nxuu+02tm3bRvXq1QFISkri/fff55tvviEhIYG77rqLu+66i6CgIBYuXMjBgwfp06cPt956K3379rXU/c477zB69GjGjh3LokWLeOaZZ6hVqxadO3fGMAx69OhBcHAwCxcuJDAwkI8++oiOHTuyd+9egoOD+e6773j11VeZOnUqrVu35osvvuD999+nSpUqlntMmjSJWbNmMXPmTOrUqcOkSZOYP38+HTp0yPN9T5o0iddee43Ro0czd+5cnnjiCdq0aUOtWrVISEigZ8+edO/ena+//pojR4445Bl49bA7A/8IAMq7xXJn4/KMu6MuL/eoDcCOE3Ekp2XkdbWIiIiIiOQhMDCQRo0aWXqVs5LzLVu2kJCQQHR0NHv37qVdu3bZrvX29sbPzw83NzfCw8MJDw/H29vbcnzgwIH069ePatWq8cYbb5CYmMi///6bZzzTpk3Dz88PT09PGjVqxOnTp616+/fu3UtgYCARERHZrvXw8KBKlSpWz9jn5cCBA8yZM4fvv/+e1q1bU7VqVZ599lluvfVWPv30U8t5aWlpTJ8+ncaNG9OmTRvuvvtu/vrrL0uSfPvtt9O+fXuWL19uVX+rVq148cUXqVGjBk8//TR33303//3vfwFYvnw527Zt4/vvv6dp06ZUr16dd999l6CgIObOnQvAlClTeOSRR3j00UepWbMmEyZMoE6dOlb3mDJlCqNGjaJPnz7Url2bGTNmWP3RJDfdu3dnyJAhVKtWjRdeeIHSpUtbvgNfffUVJpOJ//3vf9SpU4du3bpZtUFRUQ+7EzD8ws0bCdEAPNSyEoZhMGPlAc5cSGXbiThurpS/v9CJiIiIiBQ5dx9zb7cj7ptP7dq1Y8WKFYwYMYJVq1YxYcIEfvjhB/766y9iY2MJCwujVq1aBQ6hQYMGlm1fX1/8/f2JiYnJ85r777+fl156ifj4eN5++20CAgLo06dPvu9pGAYmkylf527cuBHDMKhRo4ZVeUpKCiEhIZZ9Hx8fqlatatkPCwujUqVK+Pn5WZVd/d5atGiRbT9r5vgNGzZw4cIFq/sAXLx4kQMHDgCwa9cuBg8enK2OrD8MxMXFERUVZXUfNzc3mjZtmm1Y/NWubBuTyUR4eLgl/j179tCgQQO8vLws5zRr1izP+uxBCbszuNTDTvzlX3Imk4n65QJZvuc0L8/fzsJhrXF1yd8PpYiIiIhIkTKZ8jU03ZHatWvHzJkz2bJlCy4uLtSpU4e2bduycuVKzp8/T9u2bQtVr7u7u9W+yWSyPCufm8DAQKpVM89j9eWXX1K3bl1mzpzJoEGDAKhRowZxcXGcPHmSsmWtHzVITU3l4MGD1xwOniUzMxNXV1c2bNiAq6ur1bErk/Gc3kdh3lvWeVn3joiIsHpePkt+ls27XnnFn9MfPa71BwB70JB4J3C5hz3KqnxYJ/NfwfacSuDw2cSiDktERERE5IaR9Rz7lClTaNu2LSaTibZt27JixQpWrFiRZ8Lu4eGRbbI1W3F3d2f06NG8/PLLJCUlAdCnTx/c3NyYNGlStvNnzJhBYmKiZYK8a2ncuDEZGRnExMRQrVo1q3/h4eHXHf8///yTbT9rpMJNN91EdHQ0bm5u2e5dunRpAGrXrp1jHVmyHg24siw9PZ0NGzZcV9y1atVi69atVsvprV+//rrqLAwl7M7A79KSERdOWxU3igyibtkAAA6eVsIuIiIiIlJYWc+xf/nll5Zn1du0acPGjRtzfX49S8WKFTl06BCbN2/mzJkzOa6Zfj369++PyWRi2rRpAFSoUIGJEycyZcoUXnrpJXbv3s2BAweYPHkyzz//PCNHjsz3hHM1atTg/vvvZ8CAAcybN49Dhw6xbt063n77bRYuXHjdsa9evZqJEyeyd+9epk6dyvfff8+wYcMA6NSpEy1atKB3794sWrSIw4cP8/fff/Pyyy9bkuNhw4Yxa9YsZs2axd69e3n11VfZsWOH1T2GDRvGW2+9xfz589m9ezdDhgwhNjb2uuLu378/mZmZPPbYY+zatYtFixbx7rvvXledhaGE3QkYPpee6Ug6k+1Y1TLmYSoHTl8oypBERERERG447du3JyMjw5KclypVijp16lCmTBlq166d63V9+vThtttuo3379pQpU4Y5c+bYNC4PDw+eeuopJk6cyIUL5v/vf+aZZ5g/fz6rVq2iadOm1KtXj6+//prp06cXOLH89NNPGTBgACNHjqRmzZrccccdrF27lsjIyOuOfeTIkWzYsIHGjRvz2muvMWnSJLp27QqYh6AvXLiQNm3a8Mgjj1CjRg3uu+8+Dh8+TFhYGAB9+/ZlzJgxvPDCCzRp0oQjR47wxBNPZLvHgAEDGDhwIC1atMDf358777zzuuIOCAjg559/ZvPmzTRq1IiXXnqJMWPGAFg9125vJsMRA/EdKD4+nsDAQOLi4ggICHB0OHlKS0tj4cKFdO9wK+6TLi1bMDoKPC5PnvHe0n38d+le7mlSnnfuaeigSMUWLO3dvXu252nkxqQ2L3nU5iWL2rvkUZubJScnc+jQISpXrlykiY0jZGZmEh8fT0BAAC4u6gu90X311Vc8/PDDHDlyhLCwsGxtntd3v7B5qCadcwae/uDiDplpkHTWKmGvGmqevEM97CIiIiIiIrbz+eefU6VKFcqVK8eWLVt44YUXuOeee6yW7bM3JezOwGQC39LmSeeSzkDQ5aEpl4fEJxZo+QYRERERERHJXXR0NGPGjCE6OpqIiAjuueceXnvtNdLT04ssBiXszsLnUsKeeNaquHJpX0wmiLuYxrnEVEL8PB0UoIiIiIiIyI3j+eef5/nnn7cqy3oMoqjoQQtn4ZvzxHNe7q6UCzIPydgfo2HxIiIiIiIiNwol7M4ia6b4xOwzxVcubX6O/ci5pKKMSEREREQkVyVsbmsRu3znlbA7C+9S5tfkuGyHIoPNk9AdU8IuIiIiIg6WNUN+UpL+31RKltTUVABcXV1tVqeeYXcWXoHm1xwS9gpK2EVERESkmHB1dSUoKIiYmBgAfHx8btiJkTMzM0lNTSU5OVnLupUQubV5ZmYmp0+fxsfHBzc326XZStidhVeQ+TWnHvZSlxL28xeLMCARERERkZyFh4cDWJL2G5VhGFy8eBFvb+8b9o8SYi2vNndxcaFChQo2/S4oYXcW+ehhP3JWS7uJiIiIiOOZTCYiIiIIDQ0lLS3N0eHYTVpaGn/++Sdt2rSxPAogN7a82tzDw8PmIy2UsDuLPBL2aqF+eLm7cOZCKruiEqhTNqCIgxMRERERyc7V1dWmz/MWN66urqSnp+Pl5aWEvYQo6jbXgxbOIo+E3dvDldbVywCwdNepooxKRERERERE7EQJu7PII2EHaFLRPIv8oTOJRRWRiIiIiIiI2JESdmdhSdhjczwcEegFQFScJp4TERERERG5EShhdxZZs8SnXoCM9GyHwwPMCXt0XHIRBiUiIiIiIiL2ooTdWXhdMZFcSny2wxGB3gBExSVjGEZRRSUiIiIiIiJ2ooTdWbi6g6uHeTs1+3PqoQGeAKSkZ+o5dhERERERkRuAEnZn4m5eb520pGyHvNxdKe1nTtqf+XZzEQYlIiIiIiIi9qCE3Zl4+Jpfc+hhB3i4VSXAPFO8hsWLiIiIiIg4NyXsziSPHnaAR1pVBiA+OZ3zSWlFFZWIiIiIiIjYgRJ2Z+KRlbDnvHSbt4erZXk3PccuIiIiIiLi3JSwO5OsHvZchsQDVAwxn3NYCbuIiIiIiIhTU8LuTK4xJB6gWqgfAHtjEooiIhEREREREbETJezOxOPaPex1IgIB2Hky+1rtIiIiIiIi4jyUsDsT90uzxOfyDDtAnbIBgDlh10zxIiIiIiIizksJuzNx9za/5jEkvla4P64uJs4mpnIqPqWIAhMRERERERFbU8LuTK6xDjuAl7srVcuYz9txMq4oohIRERERERE7UMLuTPIx6RxA3bJ6jl1ERERERMTZKWF3JtdYhz1L3UvPsa/Ye1rPsYuIiIiIiDgpJezOJB/rsAP0aBCBp5sLG46cZ+PR80UQmIiIiIiIiNiaEnZnks8h8RGB3jSvEgLAgZi8k3sREREREREpnpSwOxPLOux5J+wA4QGeAJyKT7ZnRCIiIiIiImInStidiWUd9vwk7F4AnEpQwi4iIiIiIuKMlLA7E4/8DYkHCM1K2LUWu4iIiIiIiFNSwu5M3PM/JD7MkrCrh11ERERERMQZKWF3JpZJ5649kVzWkPitx+P4e/8Ze0YlIiIiIiIidqCE3Znkcx12gOphfgT5uAPw7fpj9oxKRERERERE7EAJuzO5ctK5zMw8T/Vyd+W/9zYCYNvxODsHJiIiIiIiIramhN2ZuHtf3k6/di97w8ggAA6eSSTuYpqdghIRERERERF7UMLuTLKeYYd8TTwX7OtB1TLmXvl5G4/bKyoRERERERGxA4cm7H/++Sc9e/akbNmymEwmFixYkOf58+bNo3PnzpQpU4aAgABatGjBokWLiibY4sDFBdwu9bLnY2k3gIGtKgOwYPNJe0UlIiIiIiIiduDQhD0xMZGGDRvy4Ycf5uv8P//8k86dO7Nw4UI2bNhA+/bt6dmzJ5s2bbJzpMVIAdZiB2hUPgiAU3Fa3k1ERERERMSZuDny5t26daNbt275Pn/KlClW+2+88QY//vgjP//8M40bN7ZxdMWUuw9wNl9D4gHK+HsCcOZCCpmZBi4uJjsGJyIiIiIiIrbi0IT9emVmZpKQkEBwcHCu56SkpJCSkmLZj4+PByAtLY20tOI9EVtWfFfG6ebujQlIvxiHkY/4AzzNCXp6psHp+CSCfT3sEqtcv5zaW25savOSR21esqi9Sx61ecmjNi95Ctvmhf2OmAzDMAp1pY2ZTCbmz59P7969833NO++8w1tvvcWuXbsIDQ3N8ZyxY8cybty4bOVff/01Pj4+OVxRvLXZ8yqlkg7xT5URnApslK9rRq9zJTHdxIsN04lwvrcsIiIiIiLi1JKSkujfvz9xcXEEBATk+zqn7WGfM2cOY8eO5ccff8w1WQcYNWoUI0aMsOzHx8cTGRlJly5dCvRBOUJaWhpLliyhc+fOuLu7A+B6/hM4fIim9aph1O+er3o+2L+a/acTqd24OS2rhtgzZLkOObW33NjU5iWP2rxkUXuXPGrzkkdtXvIUts2zRnoXlFMm7N9++y2DBg3i+++/p1OnTnme6+npiaenZ7Zyd3d3p/mhsoo1sDwAbheiIJ/xhwZ4sf90IrHJGU7znksyZ/puim2ozUsetXnJovYuedTmJY/avOQpaJsX9vvhdOuwz5kzh4EDB/L111/To0cPR4dT9C4l7MSfyPcl5YLMS8FtPhZrh4BERERERETEHhyasF+4cIHNmzezefNmAA4dOsTmzZs5evQoYB7OPmDAAMv5c+bMYcCAAUyaNIlbbrmF6OhooqOjiYuLc0T4jpGVsMcdz/clXeuGA/DL1igyM4vFlAUiIiIiIiJyDQ5N2NevX0/jxo0tS7KNGDGCxo0bM2bMGACioqIsyTvARx99RHp6Ok8++SQRERGWf8OGDXNI/A4RWM78WoCEvXWN0phMcDohhbOJqXYKTERERERERGzJoc+wt2vXjrwmqZ89e7bV/ooVK+wbkDMIKPiQeE83V0r5eHAuMZWziSmWtdlFRERERESk+HK6Z9hLPO9S5tfkeCjAinyl/czrr59JUA+7iIiIiIiIM1DC7mw8fC9tGJCWlO/LQnzNvepnE1PsEJSIiIiIiIjYmhJ2Z+PuA5jM26mJ+b6s9KVh8KcTlLCLiIiIiIg4AyXszsbF5XIve0pCvi8L8TUPidekcyIiIiIiIs5BCbszykrYC9DDnjXR3NkL6mEXERERERFxBkrYnZGHn/m1AAl78KUe9nPqYRcREREREXEKStidkaWH/UK+Lwn0dgcg7mKaPSISERERERERG1PC7ow8/c2vSthFRERERERuWErYnZFl0jkl7CIiIiIiIjcqJezOqBCTzilhFxERERERcS5K2J2RZdK5/C/rFnApYU9OyyQ5LcMeUYmIiIiIiIgNKWF3RoWYJd7f0w2Tybwdr152ERERERGRYk8JuzPyvJSwF+AZdhcXEwFeGhYvIiIiIiLiLJSwOyOvQPNrclyBLtNz7CIiIiIiIs5DCbszKmTCHuzrAUBUXLKtIxIREREREREbU8LujAqZsDcsb75uw5Hzto5IREREREREbEwJuzMqZMLerHIIAP8cPGvriERERERERMTGlLA7o0Im7HXKBgBw7FySrSMSERERERERG1PC7oy8gsyvybEFuqyUj3nSucTUDFLTM20bk4iIiIiIiNiUEnZnlNXDnpYE6an5vszfy92yFnvsxfxfJyIiIiIiIkVPCbsz8gy4vJ0Sn+/LXF1Ml5d2S9LSbiIiIiIiIsWZEnZn5OoGHv7m7QI+xx50KWE/r4RdRERERESkWFPC7qy8LvWyF/A59kAf81rssUkaEi8iIiIiIlKcKWF3Vu4+5te0iwW6LGviudiL6mEXEREREREpzpSwOyuPSwl7asGWaAvSM+wiIiIiIiJOQQm7s3L3Nb+mFTBhvzQk/kxiiq0jEhERERERERtSwu6s3L3NrwVM2KuF+gGw40T+Z5cXERERERGRoqeE3VllDYkvYMLepGIpADYdPU96RqatoxIREREREREbUcLurNwL9wx7jTB/fD1cSUzN4PDZgl0rIiIiIiIiRUcJu7NyL1wPu6uLibAALwDOXNBz7CIiIiIiIsWVEnZn5VG4SecAgn3NE8+dT9Ra7CIiIiIiIsWVEnZnlTXpXAGHxAOUupSwn0tSwi4iIiIiIlJcKWF3VpYh8RcLfGlIVsJ+QQm7iIiIiIhIcaWE3VlZEvbEAl+a1cM+acleth2Ps2VUIiIiIiIiYiNK2J2Vx/X3sAN88tdBW0UkIiIiIiIiNqSE3VlZlnUreA97UmqGZTvE19NWEYmIiIiIiIgNKWF3VoVc1g2gTY0yNg5GREREREREbE0Ju7O6jiHxjSKD6FYvHICE5DRbRiUiIiIiIiI2ooTdWV3HkHiAllVDAEhITrdVRCIiIiIiImJDStid1XUs6wbg7+UOQEKKethFRERERESKIyXszsrd2/xaiGfYAfy93AD1sIuIiIiIiBRXStidlYev+TUtCQyjwJdbetiVsIuIiIiIiBRLStidVdaQeCMT0lMKfPnlHnYNiRcRERERESmOlLA7q6yEHQo1LD4rYY+/qB52ERERERGR4kgJu7NydQNXD/N2IRL2AG/zkPjUjEwupmbYMjIRERERERGxASXszixr4rnUQvSwe7rh7e4KQHR8si2jEhERERERERtQwu7M3K+YeK6ATCYTEUFeAETFFm5pOBEREREREbEfJezOzCNrLfbCLe1WNtDcQ38yTj3sIiIiIiIixY0Sdmd2nWuxRwSqh11ERERERKS4UsLuzLKGxBfiGXaAiCD1sIuIiIiIiBRXStid2XX2sJfN6mGPUw+7iIiIiIhIcaOE3Zl5FH7SObjcwx4Vqx52ERERERGR4kYJuzNzvzTpXCGHxGf1sJ9UD7uIiIiIiEixo4TdmVmGxBcu4c7qYU9ITudCSrqtohIREREREREbcGjC/ueff9KzZ0/Kli2LyWRiwYIF17xm5cqVNGnSBC8vL6pUqcKMGTPsH2hxZRkSn1ioy/083fD3cgM0U7yIiIiIiEhx49CEPTExkYYNG/Lhhx/m6/xDhw7RvXt3WrduzaZNmxg9ejRDhw7lhx9+sHOkxVRWD3shh8SD1mIXEREREREprtwcefNu3brRrVu3fJ8/Y8YMKlSowJQpUwCoXbs269ev591336VPnz52irIYy3qGvZCTzgFEBHmx51SCethFRERERESKGYcm7AW1Zs0aunTpYlXWtWtXZs6cSVpaGu7u7tmuSUlJISUlxbIfHx8PQFpaGmlpafYN+DplxZdbnC6unrgCmSkXyCjkewnz9wTg+LnEYv953Oiu1d5y41Gblzxq85JF7V3yqM1LHrV5yVPYNi/sd8SpEvbo6GjCwsKsysLCwkhPT+fMmTNERERku+bNN99k3Lhx2coXL16Mj4+P3WK1pSVLluRYXvHMARoBp04c5t+FCwtVd8IpE+DKuh37WZiyt9Axiu3k1t5y41Kblzxq85JF7V3yqM1LHrV5yVPQNk9KKtyoaKdK2AFMJpPVvmEYOZZnGTVqFCNGjLDsx8fHExkZSZcuXQgICLBfoDaQlpbGkiVL6Ny5c46jB0zbk+DYp4SV8qd79+6FukfyphMsPLYD14DSdO/e9HpDlutwrfaWG4/avORRm5csau+SR21e8qjNS57CtnnWSO+CcqqEPTw8nOjoaKuymJgY3NzcCAkJyfEaT09PPD09s5W7u7s7zQ9VrrF6+QPgkp6MSyHfS2SwHwDR8SlO83nc6Jzpuym2oTYvedTmJYvau+RRm5c8avOSp6BtXtjvh1Otw96iRYtsQw8WL15M06ZNS+YPiMf1TzpXuYx5abgjZ5NI1FrsIiIiIiIixYZDE/YLFy6wefNmNm/eDJiXbdu8eTNHjx4FzMPZBwwYYDl/8ODBHDlyhBEjRrBr1y5mzZrFzJkzefbZZx0RvuNlzRKfWrh12AEiAr0pX8qbjEyDjUfP2ygwERERERERuV4OTdjXr19P48aNady4MQAjRoygcePGjBkzBoCoqChL8g5QuXJlFi5cyIoVK2jUqBGvvfYa77//fslc0g2uWNbt+pZka1YpGID1h5Wwi4iIiIiIFBcOfYa9Xbt2lknjcjJ79uxsZW3btmXjxo12jMqJeJiHs1/PkHiAGuHmZ+GPnC18T72IiIiIiIjYllM9wy5Xcfc2v15nwl6+lLme4+evr6deREREREREbEcJuzPLGhKfmQ7pqYWuJrKUuR4l7CIiIiIiIsWHEnZnlpWww3X1smf1sJ9KSCYlPeN6oxIREREREREbUMLuzNw8wOXSNATXkbAH+3rg7e6KYUBUbLKNghMREREREZHroYTd2blfmngutfAJu8lkItjXA4DYi2m2iEpERERERESukxJ2Z2ejiecCvN0BiE0q/LPwIiIiIiIiYjtK2J2dR9Za7NeXsAddStjj1MMuIiIiIiJSLChhd3butknYA5Wwi4iIiIiIFCtK2J1dVsJ+Hc+wwxUJe5ISdhERERERkeJACbuzszzDfn1rqAf5qIddRERERESkOFHC7uw8Ls0Sn5Z4XdVYJp1Twi4iIiIiIlIsKGF3drYeEq+EXUREREREpFhQwu7sbLSsW9aQ+POJWtZNRERERESkOFDC7uwsQ+KvL2GvGGyu58DpCxiGcb1RiYiIiIiIyHVSwu7sbDTpXPUwP1xdTJxPSiM6PtkGgYmIiIiIiMj1UMLu7Nwv9bCnXt+kc17urlQr4wfAjhPx1xuViIiIiIiIXCcl7M7O49Kkc9fZww5QNdSc/B8/f33D60VEREREROT6KWF3djaadA4g0NsDgLiL6dddl4iIiIiIiFwfJezOzkZD4uHyTPGxFzVTvIiIiIiIiKMpYXd2Npp0DrQWu4iIiIiISHGihN3ZWZ5ht0EPe1bCnqSEXURERERExNGUsDu7rCHxNuxhP5WgZd1EREREREQcTQm7s8saEp9qg0nnLj3Dvv1EPJ+sOnjd9YmIiIiIiEjhKWF3dh5ZPezXn7AHXZolHmDCr7uuuz4REREREREpPCXszs6Wy7pd6mEXERERERERx1PC7uzcL006l5EKGde3fnppv8s97BVDfK6rLhEREREREbk+StidnfsVifV19rJ7urkya2BTAJJSM66rLhEREREREbk+StidnZsnmC41ow2GxdcKDwAgNikVwzCuuz4REREREREpHCXszs5kumJpNxtMPHfpOfa0DEO97CIiIiIiIg6khP1GYMOl3bzdXfFwM38tzielXnd9IiIiIiIiUjhK2G8EHpeeY7dBD7vJZKLUpV722KS0665PRERERERECkcJ+43A3XYJO0ApH/Ns8WcupNikPhERERERESk4Jew3gqyE3QZD4gEqBJvrO3wm0Sb1iYiIiIiISMEpYb8R2HBIPEDlMuZJ7A4qYRcREREREXEYJew3AhsPia9a2g+AQ0rYRUREREREHEYJ+43AkrBftEl1VUPNPeybj8USE59skzpFRERERESkYJSw3wgsz7Dbpke8Yfkg6kQEkJCczudrjtikThERERERESmYAifsx44d4/jx45b9f//9l+HDh/Pxxx/bNDApABs/w+7m6kK/ZpEA7DgZZ5M6RUREREREpGAKnLD379+f5cuXAxAdHU3nzp35999/GT16NOPHj7d5gJIPNh4SD1ArIgCA3dEJNqtTRERERERE8q/ACfv27dtp1qwZAN999x316tXj77//5uuvv2b27Nm2jk/yw8ZD4gFqhvsDEBWXTGxSqs3qFRERERERkfwpcMKelpaGp6cnAEuXLuWOO+4AoFatWkRFRdk2OskfD9v3sAd4uVMuyBtQL7uIiIiIiIgjFDhhr1u3LjNmzGDVqlUsWbKE2267DYCTJ08SEhJi8wAlH9zNiTVptl2GrXbWsPioeJvWKyIiIiIiItdW4IT97bff5qOPPqJdu3b069ePhg0bAvDTTz9ZhspLEXM3L8Nmyx52gNoR5mHxu6LUwy4iIiIiIlLU3Ap6Qbt27Thz5gzx8fGUKlXKUv7YY4/h4+Nj0+Akn7KGxKfaZpb4LDXCzAn7vhgl7CIiIiIiIkWtwD3sFy9eJCUlxZKsHzlyhClTprBnzx5CQ0NtHqDkg2WWeNsOiS9XyjzU/lR8ik3rFRERERERkWsrcMLeq1cvPv/8cwBiY2Np3rw5kyZNonfv3kyfPt3mAUo+2GFZN4DwAC8ATsUnk5lp2LRuERERERERyVuBE/aNGzfSunVrAObOnUtYWBhHjhzh888/5/3337d5gJIPWZPO2XhIfBl/T1xMkJ5pcCZRvewiIiIiIiJFqcAJe1JSEv7+5mebFy9ezF133YWLiwu33HILR44csXmAkg8elyadS4mHzEybVevu6kJpP/MSfqfilLCLiIiIiIgUpQIn7NWqVWPBggUcO3aMRYsW0aVLFwBiYmIICAiweYCSD0EVwSvQnLAf+MOmVYcHmofFR8cn27ReERERERERyVuBE/YxY8bw7LPPUqlSJZo1a0aLFi0Ac29748aNbR6g5IO7F9S/x7y9d5FNq64UYu69/2PXKZvWKyIiIiIiInkrcMJ+9913c/ToUdavX8+iRZeTw44dO/Lf//7XpsFJAZSpZX5NiLJptQ+2qAjAvI0nSM+w3XB7ERERERERyVuB12EHCA8PJzw8nOPHj2MymShXrhzNmjWzdWxSEP7h5tcLtu0JbxwZBEBqRiaxF9Msz7SLiIiIiIiIfRW4hz0zM5Px48cTGBhIxYoVqVChAkFBQbz22mtk2nDCMykgv0sJe0K0Tat1c3Uh0NsdgNikVJvWLSIiIiIiIrkrcA/7Sy+9xMyZM3nrrbdo1aoVhmGwevVqxo4dS3JyMq+//ro94pRr8b8iYTcMMJlsVnWwrwdxF9M4l5hmszpFREREREQkbwXuYf/ss8/45JNPeOKJJ2jQoAENGzZkyJAh/O9//2P27NkFDmDatGlUrlwZLy8vmjRpwqpVq/I8/6uvvqJhw4b4+PgQERHBww8/zNmzZwt83xuOX5j5NTMNks7ZtOpSPuYe9nOJ6mEXEREREREpKgVO2M+dO0etWrWyldeqVYtz5wqWKH777bcMHz6cl156iU2bNtG6dWu6devG0aNHczz/r7/+YsCAAQwaNIgdO3bw/fffs27dOh599NGCvo0bj5sH+ISYty/Ydlh8sK8HAOc1JF5ERERERKTIFDhhb9iwIR9++GG28g8//JCGDRsWqK7JkyczaNAgHn30UWrXrs2UKVOIjIxk+vTpOZ7/zz//UKlSJYYOHUrlypW59dZbefzxx1m/fn1B38aNyfIcu21nig/yMSfs6mEXEREREREpOgV+hn3ixIn06NGDpUuX0qJFC0wmE3///TfHjh1j4cKF+a4nNTWVDRs28OKLL1qVd+nShb///jvHa1q2bMlLL73EwoUL6datGzExMcydO5cePXrkep+UlBRSUlIs+/Hx8QCkpaWRlla8n8nOii+/cbr6heESs4P02JMYNnxvgV6uAOyPSSj2n5kzK2h7i/NTm5c8avOSRe1d8qjNSx61eclT2DYv7HfEZBiGUdCLTp48ydSpU9m9ezeGYVCnTh2GDBlC2bJlC1RHuXLlWL16NS1btrSUv/HGG3z22Wfs2bMnx+vmzp3Lww8/THJyMunp6dxxxx3MnTsXd3f3HM8fO3Ys48aNy1b+9ddf4+Pjk+94nUHjI/+jwrlV7Iy4m33hd9is3mUnTfx4xJy0v9I4ndJeNqtaRERERETkhpeUlET//v2Ji4sjICAg39cVah32smXLZpsN/tixYzzyyCPMmjWrQHWZrprN3DCMbGVZdu7cydChQxkzZgxdu3YlKiqK5557jsGDBzNz5swcrxk1ahQjRoyw7MfHxxMZGUmXLl0K9EE5QlpaGkuWLKFz5865/kHiSi7LN8Hfq6hZLojqXbvbLI7mF1L48e2VAJSr3ZSOtUNtVrdcVtD2FuenNi951OYli9q75FGblzxq85KnsG2eNdK7oAqVsOfk3LlzfPbZZ/lO2EuXLo2rqyvR0dYTpMXExBAWFpbjNW+++SatWrXiueeeA6BBgwb4+vrSunVrJkyYQERERLZrPD098fT0zFbu7u7uND9U+Y410DzCwTXxFK42fG/hpdxpX7MMy/ecJj4l02k+N2flTN9NsQ21ecmjNi9Z1N4lj9q85FGblzwFbfPCfj8KPOmcrXh4eNCkSROWLFliVb5kyRKrIfJXSkpKwsXFOmRXV/NQ7UKM7L/x+F/6Q0fCKZtXXerSTPHnNFO8iIiIiIhIkXBYwg4wYsQIPvnkE2bNmsWuXbt45plnOHr0KIMHDwbMw9kHDBhgOb9nz57MmzeP6dOnc/DgQVavXs3QoUNp1qxZgZ6fv2H5XxphkGDbZd0Agn20tJuIiIiIiEhRstmQ+MLo27cvZ8+eZfz48URFRVGvXj0WLlxIxYoVAYiKirJak33gwIEkJCTw4YcfMnLkSIKCgujQoQNvv/22o95C8eJ3qYf9QjQYBuQyF0BhZPWwn9fSbiIiIiIiIkUi3wn7XXfdlefx2NjYQgUwZMgQhgwZkuOx2bNnZyt7+umnefrppwt1rxue/6V12DNS4eJ58Am2WdXBWUPiE7VkhYiIiIiISFHId8IeGBh4zeNXDl8XB3DzBO9S5mQ9IdqmCXupK4bE5zWTv4iIiIiIiNhGvhP2Tz/91J5xiK34R1xK2KMgrI7Nqs3qYd9w5DwdJq1kwZOtCPTWTJgiIiIiIiL24tBJ58QOLM+x23am+HKlvC3bh84k8vXao3mcLSIiIiIiItdLCfuNxjJTfJRNqy0b6GW1v2JPjE3rFxEREREREWtK2G80dlqL/epn1qPjk21av4iIiIiIiFhTwn6jsVMPO8AdDS+vdR8Tn4JhGDa/h4iIiIiIiJgpYb/R2OkZdoA376rPu/c0BOBiWgYXUtJtfg8RERERERExy/cs8Vfau3cvK1asICYmhszMTKtjY8aMsUlgUkh27GH39XTj7iblGffTDhJS0olJSMHfSzPFi4iIiIiI2EOBE/b//e9/PPHEE5QuXZrw8HCrZ5tNJpMSdke78hl2wwA7rJdeJsCThNPprDt0jqpl/Gxev4iIiIiIiBQiYZ8wYQKvv/46L7zwgj3ikevlF25+zUiB5FjwLmXzW5T28+Tg6URenLeNe5tG4uJi+z8KiIiIiIiIlHQFfob9/Pnz3HPPPfaIRWzB3Qu8gszbCdF2ucWgWytbts8lpdrlHiIiIiIiIiVdgRP2e+65h8WLF9sjFrGVrOfYV7wFmRk2r75r3XBK+3kAcErLu4mIiIiIiNhFgYfEV6tWjVdeeYV//vmH+vXr4+5uPenY0KFDbRacFJJ/GJzeBTsXwI6eUP9um98i1N+LMxdSiYlPoW7Za58vIiIiIiIiBVPghP3jjz/Gz8+PlStXsnLlSqtjJpNJCXtx4BNyefvUDrsk7GEBnuyMgpgE9bCLiIiIiIjYQ4ET9kOHDtkjDrGli7GXtz187HKLsAAvAE7Fp9ilfhERERERkZKuwM+wixMIqnB5O/GMXW4ReilhP3H+ol3qFxERERERKeny1cM+YsQIXnvtNXx9fRkxYkSe506ePNkmgcl1aD8aNnxq3k48bZdb1C8XCMBf+89gGAYmO6z3LiIiIiIiUpLlK2HftGkTaWlplu3cKGkrJvxC4a7/wbz/2C1hv7VaabzcXTgRe5H3/tjHk+2r4e6qARsiIiIiIiK2kq+Effny5TluSzHmW9r8aqch8d4erjzWugrvL9vPlKX7eP+PfewYdxveHq52uZ+IiIiIiEhJoy7RG5VvqPk19hikXLDLLUZ0qUmzSsEAZBqw51SCXe4jIiIiIiJSEhV4lniAdevW8f3333P06FFSU1Otjs2bN88mgcl1KlMLSlWG84dgw2xo+ZRdbvNWn/p0mGRe3u/wmUQaRQbZ5T4iIiIiIiIlTYF72L/55htatWrFzp07mT9/PmlpaezcuZNly5YRGBhojxilMFzdoOF95u3FL0H0drvcpkoZP+5tWh6Aw2cT7XIPERERERGRkqjACfsbb7zBf//7X3755Rc8PDx477332LVrF/feey8VKlS4dgVSdHxCLm/PaGW321Qq7QvAlKX72B+jYfEiIiIiIiK2UOCE/cCBA/To0QMAT09PEhMTMZlMPPPMM3z88cc2D1Cug3epIrlN88qX/zCweOepIrmniIiIiIjIja7ACXtwcDAJCeZe1HLlyrF9u3modWxsLElJSbaNTq6PT3CR3KZJxVL0bRoJwO4o9bCLiIiIiIjYQoET9tatW7NkyRIA7r33XoYNG8Z//vMf+vXrR8eOHW0eoFwH76sS9sxMu93qtnrhAOyOjrfbPUREREREREqSAs8S/+GHH5KcnAzAqFGjcHd356+//uKuu+7ilVdesXmAch2u7mFPvQBeAXa5Vc1wfwAOnk4kJT0DTzetxy4iIiIiInI9CtTDnp6ezs8//4yLi/kyFxcXnn/+eX766ScmT55MqVJF88y05NPVPezJsXa7VUSgFwFebqRnGuyPsc+67yIiIiIiIiVJgRJ2Nzc3nnjiCVJSUuwVj9iSh6/1fnKc3W5lMpmoFW7uve/x/l988Mc+ktMy7HY/ERERERGRG12Bn2Fv3rw5mzZtskcsYmsmk/W+HRN2gDIBnpbtSUv2MnX5frveT0RERERE5EZW4GfYhwwZwsiRIzl+/DhNmjTB19e6F7dBgwY2C05sYMRueL8xpF+Ei7F2vdXAlpX4dWuUZf/37dGM7FLTrvcUERERERG5UeU7YX/kkUeYMmUKffv2BWDo0KGWYyaTCcMwMJlMZGRoGHSxEhABlVrB/qV272G/uVIwswY25ZHZ6wEo7ed5jStEREREREQkN/lO2D/77DPeeustDh06ZM94xB4CyppfY3ba/Vbta4bSq1FZftx8kpNxF+1+PxERERERkRtVvhN2wzAAqFixot2CETup1gk2fg57FkKXCdmfbbchk8nE87fV4sfNJzlyNonktAy83LXEm4iIiIiISEEVaNI5kx0TPbGjqh3A5ArnDkL8CbvfLjzACy9381dr/C87uZiqxyREREREREQKqkAJe40aNQgODs7znxRDnv4QVte8fWKD3W/n6mLi6Q7VAfh67VFqj/mdb/49avf7ioiIiIiI3EgKNEv8uHHjCAwMtFcsYk/lmkD0VnPCXqeX3W/3UMtKvLNoj2X/z32nua9ZBbvfV0RERERE5EZRoIT9vvvuIzQ01F6xiD1FXFpuL2ZXkdzOz9P6q3UiNrlI7isiIiIiInKjyPeQeD2/7uQCyptf46PyPs+G3rqrPi6XvjZbjsVyLjG1yO4tIiIiIiLi7PKdsGfNEi9OKiDC/HpqGxy3/3PsAPc1q8C/L3Wy7Dd/Yymv/2r/peVERERERERuBPlO2DMzMzUc3pkFlLu8/UmHIrttiK+HZTstw+B/qw5xKl7D40VERERERK6lQLPEixPzLmW9nxxXJLc1mUzUKxdgVXY6IaVI7i0iIiIiIuLMlLCXFFfPQXD+cJHd+uMHmzJ/SEtqhfsDcFbPsouIiIiIiFyTEvaSpOmgy9vnDhXZbcsGedO4QilK+3kCMGXpXhbviNa8CCIiIiIiInlQwl6S9JgEdXqbt88XXcKeJfjS8+ybjsby2Bcb+OKfI0Ueg4iIiIiIiLNQwl6SmEwQWtu8fXpvkd8++IoJ6ADG/LiDHzefKPI4REREREREnIES9pImvL75NXpbkd86MSU9W9mwbzYXeRwiIiIiIiLOQAl7SZOVsJ/eDelFO/lbkI97juV6ll1ERERERCQ7N0cHIEUsMNK8xNvF83D8X6h0a5HdenDbqpyMS+aeJuU5eyGVkd9vASA+OZ1A75yTeRERERERkZJKPewljckENXuYt7d+W6S3DvHzZGr/m2hXM5Q+TcpbkvTR84t+eL6IiIiIiEhxp4S9JKp/t/l13xJw4HD09IxMAH7dGkV0XLLD4hARERERESmOlLCXRBVuARd3SIiCzV87LIzE1AzL9ql4JewiIiIiIiJXUsJeErl7Q9nG5u0fh0BqkkPC6HNTect2TEKKQ2IQEREREREprpSwl1QdXr68ff6QQ0IY07OOZVs97CIiIiIiItYcnrBPmzaNypUr4+XlRZMmTVi1alWe56ekpPDSSy9RsWJFPD09qVq1KrNmzSqiaG8gVdpe7mU/55iEPdDbnfubVwDMPewZmQYf/3mADUfOOSQeERERERGR4sShy7p9++23DB8+nGnTptGqVSs++ugjunXrxs6dO6lQoUKO19x7772cOnWKmTNnUq1aNWJiYkhPTy/iyG8QpSrDyU1w7qDDQgj19wJgxZ4Y3v9jn6X87T716Xtzzt8BERERERGRksChPeyTJ09m0KBBPProo9SuXZspU6YQGRnJ9OnTczz/999/Z+XKlSxcuJBOnTpRqVIlmjVrRsuWLYs48htEcBXz66GVDpstPiLQnLBvPR5nVf7CD9s4GXvRESGJiIiIiIgUCw7rYU9NTWXDhg28+OKLVuVdunTh77//zvGan376iaZNmzJx4kS++OILfH19ueOOO3jttdfw9vbO8ZqUlBRSUi5PaBYfHw9AWloaaWlpNno39pEVn73iNFW8FbdV78L+paRvm4dR+w673CcvzSoF5nps2/HzlPF16CCQImXv9pbiR21e8qjNSxa1d8mjNi951OYlT2HbvLDfEYdlQ2fOnCEjI4OwsDCr8rCwMKKjo3O85uDBg/z11194eXkxf/58zpw5w5AhQzh37lyuz7G/+eabjBs3Llv54sWL8fHxuf43UgSWLFlit7prh/WkxqmfSfxtLCsOOebrUMXflYMJJiJ9DY4lmizl81es5/gOg7hUqBrgkNAcwp7tLcWT2rzkUZuXLGrvkkdtXvKozUuegrZ5UlLhVuZyePelyWSy2jcMI1tZlszMTEwmE1999RWBgeae2cmTJ3P33XczderUHHvZR40axYgRIyz78fHxREZG0qVLFwICincWmJaWxpIlS+jcuTPu7u72uUl8Q/jgZwJSTtK9Uxtw94VcPn97uenWZDYeiaV19dLc9PoyS/lF7zBe23QGgF+fakGNMP8ijauoFUl7S7GiNi951OYli9q75FGblzxq85KnsG2eNdK7oByWsJcuXRpXV9dsvekxMTHZet2zREREUK5cOUuyDlC7dm0Mw+D48eNUr1492zWenp54enpmK3d3d3eaHyq7xloqEkyumDLTcX+nEtz8H+jxrn3ulYvIEHciQ7In4/8ePm/Z3nwigbrlg4syLIdxpu+m2IbavORRm5csau+SR21e8qjNS56Ctnlhvx8Om3TOw8ODJk2aZBtKsGTJklwnkWvVqhUnT57kwoULlrK9e/fi4uJC+fLl7RrvDcvFFQLKXt5f9z/HxQK81rse3u6uAFxMy7CUp6RlOiokERERERERh3DoLPEjRozgk08+YdasWezatYtnnnmGo0ePMnjwYMA8nH3AgAGW8/v3709ISAgPP/wwO3fu5M8//+S5557jkUceyXXSOcmHzIy894vQg7dUZPu4rnSvH25VnpCspftERERERKRkcegz7H379uXs2bOMHz+eqKgo6tWrx8KFC6lYsSIAUVFRHD161HK+n58fS5Ys4emnn6Zp06aEhIRw7733MmHCBEe9hRtDwknr/fiTEBTpmFgAVxcTdcsGsnDb5cclouOTHRaPiIiIiIiIIzh80rkhQ4YwZMiQHI/Nnj07W1mtWrU0C6OtdX8XFj57ef+znjB0U5FPPnel+uWsl3ub8+9Rnu5QjbJBGkkhIiIiIiIlg0OHxEsx0fQRGLoZKt5q3j9/CA4ud2hI9cplX5/9szWHiUlQT7uIiIiIiJQMStjFPPFccGVo/czlsm1zHRcPEOzrka3so5UHueWNP9gfk+CAiERERERERIqWEna5rFon6DXVvH0hxrGx5CLTgJHfbSExRZPQiYiIiIjIjU0Ju1jzLmV+TY51aBgAL3WvDcDLPWpTLdTPUr7leBwDZv3rqLBERERERESKhMMnnZNixivI/HrxvEPDAHi0dWW61g0nMtibgS0r8fzcrczbdAKADUfOk5yWgdelNdtFRERERERuNOphF2tZPezFIGE3mUxUCPHBZDLh5upChRAfq+PbT8Q5KDIRERERERH7U8Iu1iwJeywYhkNDudrVE9HtOXV58rlZfx3ihblbycwsXjGLiIiIiIgUlhJ2seYdZH41MiD+hENDuVopH+uEfdZfhzAMA8MwGP/LTr5df4zVB844KDoRERERERHbUsIu1ty9L2//ty6MDYSNnzsunitc/bz6gdOJVB61kMlL9lrKziWmFnVYIiIiIiIidqGEXa7ttxccHQEAvp6XE3ZXF5Nl+4Nl+y3bSthFRERERORGoYRdri0tyfxqGBB71GHPtt9SOYT7bo7k1Z512Dm+K7UjArKdc+L8RQdEJiIiIiIiYntK2CW7gQuhwyvw4rHLZQnRsHIiTKkP239wSFguLibe6tOAh1tVxtPNld+GtebxNlWszjkZp4RdRERERERuDErYJbtKraDNs+AVAD4h5rJJNWHFG+btZa85LrardKsfYbW/dGcMZy6kOCgaERERERER21HCLnnzLZO9LKRa0ceRi4blA2lX83KMqRmZdHh3BekZmQ6MSkRERERE5PopYZe8VW6TvSyt+Aw7N5lMfDKgKd8PbsF79zUCID45nd3RCSSnZTg2OBERERERkeughF3ydttbcM9s67ILMZCaCH9NgbjjjojKipurCzdXCqZXo3JUDPEB4PYP/qLjpJUcOZvo4OhEREREREQKRwm75M3FFSJvsS67EANf94Wlr8KvIx0TVy7Kl7q8jvyJ2IuM+3mnA6MREREREREpPCXscm1XP8eeEgeHV5m39y4q+njyUD7Ix2p/2e4Y9sdccFA0IiIiIiIihaeEXa7N1S33Y+VvLro48qFs0OUedi9389f7j12n2HDkHCnpeqZdREREREScRx6ZmMgVyt4EJzdCUAWIPXq5PLV49V7fVi+cX7ed5M7G5fF2d2Hszzt587fdluMvda/Nf65au11ERERERKQ4Ug+75M/AX2DoJuj4qnV5zE5Y/b5jYspBzXB/Fj/TlifaVaVj7bBsx19fuIsfNjh+ojwREREREZFrUcIu+ePhC8FVIKRq9mNLXilWS71liQz2oYy/p2XfZDK/vrt4D2cupDgoKhERERERkfxRwi4FE1A+5/IjfxdtHPn0Wq96tKwawqLhbdg+tivBvh5ExSUzet42R4cmIiIiIiKSJyXsUjC+pXMuP/pP0caRT7fVC+fr/9xCzXB/fD3dePOu+gD8feAsGZmGg6MTERERERHJnRJ2KZisceVX2/Y9XIwt0lAKo1PtMHw8XLmQks6B0+YJ846dS6LjpBXM/OuQg6MTERERERG5TAm72Mb5Q/B2Rdj5E8RHOTqaXLm6mGhQPhCAJTtPAfDmb7s4cDqR137Z6cjQRERERERErGhZNym4h36BLXMgsDys/QiSYy8f++5B8+vwbWByBf9wcHF1SJi5ubdpJP8cPMfkJXtZte80/xw8ZzkWl5RGoI+7A6MTERERERExUw+7FFzl1tB7GrQfbV7qLSdrpsF/68DCZ4s2tnzo1agc5Ut5k5FpWCXrABuPnXdQVCIiIiIiItaUsMv18S4F4eaJ3KjW+XL52unm1/Wzij6ma3B1MXF/84o5Hnv/j30YhiajExERERERx1PCLtfHZIJHFsOLR+GBudDsMUdHlC+PtanCmNvrEOhtHv4+unstPNxc2HQ0lj2nEhwcnYiIiIiIiJ5hF1vw8Lm87R+R/XhmJrgUr78NubqYeOTWyjzcqhLnk9II9vVg3eHzLNl5iukrDlAh2IczF1J4sn01ypfyuXaFIiIiIiIiNqaEXWzLLyx72T9ToeXTRR9LPphMJoJ9PQDoUT+CJTtP8ePmk5bjx89f5ItBzR0VnoiIiIiIlGDFq9tTnJ9v6exli1+G1MSij6WAOtYOzVa2at8ZGo5bzPxNxy1lMfHJxF1MK8rQRERERESkBFLCLrZVrTO0eQ76fWtdHr3dMfEUgL+XO891rZmtPO5iGs98u8Wy3eyNP7j1rWVFHZ6IiIiIiJQwStjFtlxcoMPLUPM2KNf0cvnSV2HFW7DxC8fFlg9Ptq+W67GouIvsjooHICElnYRk9bKLiIiIiIj9KGEX+7n3c4hoaN4+ugZWvAk/PQWn9zg2rmvoc1P5HMt3RcUTn5xu2T8Vn1xUIYmIiIiISAmkhF3sJ7Ac3P1p9vIDxXs4+Wu96/L5I82IDPa2Kt8TfYFPVh207EfFKWEXERERERH7UcIu9hVSFRrcZ112cKVjYsknHw832tQow+ePNGdC73o83KoSAG//vpu1h85Zztt36oKDIhQRERERkZJACbvY310fwQPzLu+fKv4T0AFULu3LA7dUpGJwzuuwj/9lJ4t2RBdxVCIiIiIiUlIoYZeiUa0jvHDYvB13DJLjHRpOQZQvlXPCDvD4FxtoNH4xX6w5XHQBiYiIiIhIiaCEXYqOdynwL2vejtnl2FgKoFGFIMt2pRAf+jWL5LmuNfF0M//4xCal8d+l+xwUnYiIiIiI3KjcHB2AlDARDSDhJPz4JJSpCWUbQWhd87PuZbKvgV4clPbzZPr9N/H+sv28c3cD6pULBKBeuUAemvUvAOcSU6n04q/0alSWt/s0wMvd1ZEhi4iIiIjIDUA97FK0Krcxv57dB7t/gWUT4Jt+MLUZbP/BfOz8YTh7wGEh5qRb/Qh+G9bakqwDtK1Rhr9f7GB13o+bTzJ3w/GiDk9ERERERG5AStilaFXtkPuxuY/Ap93hvYbwwU2QUvxnYS/j75mtbOLvu2nx5h98uEzD5EVEREREpPCUsEvRCq0Nd34MtwzJ+fiR1Ze3zx8ukpCuh7tr9h+h+OR0ouKSeXfxXrYdj7OU74lOIDktoyjDExERERERJ6aEXYpew75w25vw1Hro8HLu58UdK7qYbKBRZBD33RxpVTZx0W6i45J5/Iv1dJ3yJy8vcI4l7URERERExPE06Zw4Tunq0PpZCKkGoXXMz7Ff6fwRx8RVQNPuv4kfNhxn0r0N8fFwo3/zCvyyNYqP/zzIqn1nuOXNPyznzt1wnBe71aK0X/ah9CIiIiIiIldSD7s4lskEde80zxDf7DHrY7FHHRNTAXWvH8HMgTcT5OOBh5sLDcoH0TgyKNfzl+w8VXTBiYiIiIiI01LCLsWHX6j1/j9T4f2bYN1MSE10TEyFdOVs8lkig70BWHPgbFGHIyIiIiIiTkhD4qX48PDLXnbuAPw6AvYtgf7fFH1MhRQZ7MOXg5oT5ONO3MU0qpTx5cjZJO77+B+W7DzFkbOJVAzxBSA+OY3v1h3jzobhDo5aRERERESKEyXsUnx4BuR+7OAKyEgDV/ciC+d63Vq9tNV+qL8X9coFsP1EPKPmbePjAU3x83Rj+DebWbY7hn8OnKFnqcvnZ2YauLiYijhqEREREREpLjQkXoqPen2g8QNwz2zrMs8ASL8I3/SHUzsdFt71cnUx8eAtFQH4+8BZWr75B4O/2MCy3TEALN192nLuwm1R1Bu7iMU7oh0Sq4iIiIiIOJ4Sdik+3L2g11TzJHTNHgc3b2g3Cso2Nh/ftxg+ag1pFx0b53WoUubysP/45HR+vyohXxtj4sfNJxny1UaSUjN47IsNRR2iiIiIiIgUE0rYpXi67S0Ydcy89Fu5JpfLM9Nh9u1gGI6L7TpUKe2b5/GvD7jy7A9aq11ERERERJSwS3Hl4nL5efVyN1kfO7Eejq4p+phsINjXI19lIiIiIiIiDk/Yp02bRuXKlfHy8qJJkyasWrUqX9etXr0aNzc3GjVqZN8AxfGu7GHPsvW7oo/DBkwmE18Masb4XnVpWrEUjSKD+Hd0R7rUCcv1mvbvruDg6QtFGKWIiIiIiBQHDk3Yv/32W4YPH85LL73Epk2baN26Nd26dePo0aN5XhcXF8eAAQPo2LFjEUUqDhVQFjq8bF22d5HTDotvXb0MA1pUYu4TLVnwZCvcXF2oEeaf6/mHziTy1m+7SU7LKMIoRURERETE0Ry6rNvkyZMZNGgQjz76KABTpkxh0aJFTJ8+nTfffDPX6x5//HH69++Pq6srCxYsyPMeKSkppKSkWPbj4+MBSEtLIy0t7frfhB1lxVfc4ywSLYZjCqqCy44fcNnzKyScJO3kNgit7ejIbOKhWyJZf/gs5TnDuAc68suO07wwb4fl+OKdp2jy2hIWDLmFSiG+pGVk4moyadk3J6ef8ZJHbV6yqL1LHrV5yaM2L3kK2+aF/Y6YDMMx3ZSpqan4+Pjw/fffc+edd1rKhw0bxubNm1m5cmWO13366adMmzaNNWvWMGHCBBYsWMDmzZtzvc/YsWMZN25ctvKvv/4aHx+f634fUvRu3TuBkMS97Irow97wXgDUO/4lfsnR/FtlKJkuN8Yz4V/td+Hf09aDYG4uncm9VTJ5Z6sr7i7wbIMMDANcHf5wi4iIiIiI5CYpKYn+/fsTFxdHQEBAvq9zWA/7mTNnyMjIICzM+tndsLAwoqNzXnt63759vPjii6xatQo3t/yFPmrUKEaMGGHZj4+PJzIyki5duhTog3KEtLQ0lixZQufOnXF3d3d0OMWGi8sK2LCX2lE/UKN8CEat23HbtBiAbtXcMGp1d2yAhXR1e3dMz2TMTzuZt+mk5ZwUzyDSy0US86+59327a1Vmrj7CtH6N6Fwn1FGhSyHpZ7zkUZuXLGrvkkdtXvKozUuewrZ51kjvgnLokHgwT8J1JcMwspUBZGRk0L9/f8aNG0eNGjXyXb+npyeenp7Zyt3d3Z3mh8qZYi0SZWpaNl3XfQzrPrbsux1YCvXvzOkqp5HV3u7u8MZdDawS9q0n4tk6//JQ+ZmrjwAwZM5mDr/Vgw1HzrHpaCyDbq2c48+RFE/6GS951OYli9q75FGblzxq85KnoG1e2O+HwwbSli5dGldX12y96TExMdl63QESEhJYv349Tz31FG5ubri5uTF+/Hi2bNmCm5sby5YtK6rQxdFq3w7kkozu/BFWToRZt0HimSINyx683F15v19jvN1dr3luZqZBn+lrmPDrLn7acvKa54uIiIiISPHmsB52Dw8PmjRpwpIlS6yeYV+yZAm9evXKdn5AQADbtm2zKps2bRrLli1j7ty5VK5c2e4xSzERWB5ePQ/nD8GSV2HXT1CqMphc4NwBWP66+bxlE6DnFIeGagt3NCxL93rhJKZkMHfjcbrVC+f4+Yvc+5H1WvQr9522bM/59ygVQ3xpFBlUxNGKiIiIiIitOHRI/IgRI3jwwQdp2rQpLVq04OOPP+bo0aMMHjwYMD9/fuLECT7//HNcXFyoV6+e1fWhoaF4eXllK5cSwGSC4CrQ9wuI3g4+IbDrZ/jtucvnHFwOv4yAWt2hWifHxWoDbq4uBPq4MOhW8x+mgn2zT6z38KfrLNv/HDxH76mrea5rTZ5sX63I4hQREREREdtx6NzSffv2ZcqUKYwfP55GjRrx559/snDhQipWrAhAVFTUNddkFyG8HgREQKN+1uXnD8P6mfBlH4eEZU9e7q70qB9xzfPeWbSHE7EXiyAiERERERGxNYcvBjVkyBAOHz5MSkoKGzZsoE2bNpZjs2fPZsWKFbleO3bs2DyXdJMSxtPf0REUqQ/6NeaPkW25vUHeifvSnaeKKCIREREREbElhyfsIjbV4eWcy7d8C4tfhvTUoo3HjlxcTFQt40e3enkn7Kv3n2HEd5uZvuIA0XHJABw7l0RGplEUYYqIiIiISCE5fFk3EZu6dSSEN4ByTeD0bpjdw1w+/zHza1g9aHif4+Kzg+71w/n4wSYkpWbw/NytpGZkWh1ffEUP+5Sle3m1Z11Gz9/G87fVJNjHg8TUDMuz8SIiIiIiUnwoYZcbi4sL1Ohq3va9NfvxnT/dcAm7yWSiS91wAHo1KkvlUQtzPTclPZPR882rLUz8fY+lvEf9CEr7eZCakYmPh34tiIiIiIgUBxoSLze2Jg+bXytfmhvhwB+QcgH2LoK/P4SEaMfFZgcmk4l+zSIBGNG5Rr6ve+3Xnfzn8/W0eHMZJy9NUnf8fBJnLqTYJU4REREREbk2daXJja3LBLjpQSh7E7zf2Lx2+3sNIemM+fjq9+DJteAT7Ng4bejVnnW566byNI4MIsTPg5fmb+ep9tVYsvMUe04l5HjNr1ujLNtjftzB9hNxRMcnEx7gxZpRHTCZTEUVvoiIiIiIXKIedrmxefqZn2c3maDuneayrGQdIDHGvF47wNG18G4N2Da36OO0IS93V26uFIybqwv3N6/I0hFtGdapOtXC/Czn1Ar3p2vdMJpULJXt+qW7ThEdb56cLjo+2bItIiIiIiJFSwm7lBzN/pNzecwu8+t3A+DCKfhhUNHFVASqhfrh7upCStrlyeh+H96Gjx5sSmQp72tevysq3p7hiYiIiIhILpSwS8kRUBb6fw+1e0Kt26H1SHP5n+/AgeVw4Yrn2fcudkyMdlQz3C9bWWJqxjWv2xWV8zB6ERERERGxLyXsUrLU6AJ9v4T7voJKrS+Xf9Hb+ryv7ynSsIrCE+2q8VCLinw/uIWlrFzQ5R72Mv6eOV73w4bjHD+fREx8Miv3nrZ7nCIiIiIiYqZJ56TkqtQaGj8AB1ZA/PHsx9OSwd2ryMOyFz9PN8b1qmdVNqxjdc4mpnJ3k/K0rVGGSi/+CkC9cgE81b46g7/cwMEzibR9ZwUZmQYAMx9qSsfaYUUev4iIiIhISaOEXUouVzfoNdW8vWSMecb4K02sAgN+hMw0+OlpqNYZur1V9HHaUSlfDz7o19iy/+1jt/Dh8v283rs+KemXh8tnJesAr/+6i2PnkkhMzeBCSjrNKwfj6ebKidiL9LmpHCnpmXz5zxEqBPtY1ocXEREREZGCU8IuAtD2BfMs8cf+uVyWlggzO13eP7sfbnvTPOP8Dap5lRCaVwkBsErYr3TwTCJjf95p2Z++4oBlu4y/J79uPcl3680jFj4Z0JROddQbLyIiIiJSGHqGXQTAwxcGLbr2eYkl5xluTzdXml5a9u2V2+vk65pPVh1k9f6zlv1le2LsEpuIiIiISEmgHnaRK4VUM/ekAwRXgXMHrY9HbYGEaIg7Bu4+0GrYDd3jPnPgzSQkp1G+lA99b46k06SVea7LvmrfGav96Dit4S4iIiIiUlhK2EWu9Ogf5oQ8tBZkZkJiDOz8EbbMgZOb4Ku7rc8Prw/VOjom1iIQ6O1OoLc7YJ60btUL7UlJz6Teq9lHIzSpWIoNR85blUXFJZOYko5x6XqA4+eT+GNXDP2bV8DdVYN8RERERERyo4Rd5EreQeZ/AC4u4B8OzR8396if3JT9/ITo7GU3MHdXF6sku2ygF13qhlMpxIcQP89sCfuxc0l0e28VJhMsfqYNnm6u3D19DdHxyfy1/wzje9UlItD76tuIiIiIiAhK2EXyJ7I58EH28gunIDkeDvwBoXXh4HIoexNE3lzkIRal4Z2qM2XpPl6/sz7ta4UC1pPUebi6kJqRyYWUdC6kpAOw8Ugsf+w6ZRlSv2TnKZbtjmHWwJuZvHgPXeuFM6RdtaJ/MyIiIiIixZQSdpH8KN/s8nab5+HPiebtP8aZ/13t6Y2wdKz5mfhOrxZJiEVpWMfqPHhLRUL8PC1lnm6ufDmoOcO+2cTwzjV4ZcF2q2ue/X4LJ2IvWpVlZBo8NOtfALYcj6Nh+SBaVStt/zcgIiIiIuIElLCL5Id/GLR4ChLPQLsXIbAc/Dws9/M/uOnydsunwSfY/jEWIZPJZJWsZ7m1emnWv9wJk8nEifMXmbHy8pJvVyfrOflte5RVwp6ZafDX/jNUC/WjbJCGzouIiIhIyaKEXSS/ur5+eTswMv/XLX/D/Ax8+Zvh1A644wPw9LN9fMWE6dKs+S92q2WVsLetUYYQXw/8vNyYv/EEN1cOpmoZX/636pDlnC//Ocr3648zpW8jutWP4Mu1Rxjz4w483FxY8kwbKob4Fvn7ERERERFxFCXsIoVRqTU07A9n9sCpnZB+Re+xyQWMzMv76/5nft37u/k1IQr6fwdeAUUXr4NMv/8mZqw8wIf9byIy2MdSPr5XPQBW7TttlbADpKRn8sRXGzn4Rnd+2RoFQGp6Jn2m/82SZ9ri5e6KyQRe7q5F90ZERERERBxACbtIYbh5wJ3TL+9PrAJJZ83bpSrDuQM5XwdwdA38OhL6XErkMzPg8F8Q0cR+8TpIt/oRdKsfkevxaqG5jzSoMnqh1f6ZC6mM+3kHqw+cxdVk4qv/NKdqmRt3pIKIiIiIiBZBFrEFV4/L200fNr+G14c6vXM+f9t3l7d/eQY+vwOXdR/bLbziKjzAy7K9dESba56/YPNJTiekEB2fzEeXhtsfO5fEd+uPkZlpABAVd5GjZ5PsE7CIiIiISBFSD7uILfSaCnMfgR6ToO5d4BdmHja/4s3cr/nqHuj+Dmz8DACXVe9Cvem5n38DMplM/PRUK04npFAt1J/KpX05dCbR6pwZD9zE4C83Zrv2u/XHKeXrwcJtURw7d5GklHQGtKhEu3dWkJKeyaZXOlPK1yPbdSIiIiIizkI97CK2UK0jvHAY6t8NLi7Q4F4IiIAKt1if53rFzOr7FsN7DS27Rlg9MAyI3gbpKUUTdzHQoHwQHWuHAfDt47ew4MlW1Ar3txyvHRHAT0+1yvHaj1Ye5Ng58/wBP2w8wbfrj5GSbp4/YPPxWPsGLiIiIiJiZ0rYRWzl0uzoVhrcBx3HmLf9I2DUMes13a+8PDmW8LiNuM9sDx80hRmt4di/dgy4+An196JRZBCVrpgNPizAi8hSlyese7VnnRyv3XYijlHztln2d56M5+DpCySlpmMYhtW5K/bEcPf0v9kVFW/jdyAiIiIiYjtK2EXsycUFWo+E0SfhqfXg5glBOS8JZzqzh+aH3jPvxB2F6K3w8/Cii7WY8nJ3JcjHnYoh5qS9Z8Oy+brunUV76DBpJXXGLGLYN5utjo3/eSfrj5yn23ur2BOdkK814kVEREREipoSdpGi4OF7ee31Gt3MryYXeOEIPDg/9+vO7jMPky9hPN2tfzWZTCbmD2nF2tEdKe3nSdOKpQB4rVdd9k7oxuYxnSntl/vz6j9tOcnE33eTnJYBQHxyuuVY1yl/0u/jf+zwLkREREREro8mnRMpavXvhsx0KNsIvIOgSvvcz81IhSN/Q6VLz3DvWADxJ6HFkCII1HGGdazOsl0xDGxVyVIWfMUEcrMfacaZhBQqlTYPnfdw82DeE634acsJvlp7lKi45Gx1TltxgE1HY/mgf2NSLiXuWY6eSyIuKY1AH3cAtp+II9Db3WrteBERERGRoqYedpGiZjJBo34QWvvyfutnrc95Yg00fsC8PacffNoDJoTB9w/BolFwZl/RxlzEqpTxY/OrXRjZpWaOx/083SzJepYKIT481aE6vw+7vDxc6+qlrc5Zc/As7d9ZQUJKOlc7cs48O/2/h87R88O/6PvRGjIyS97oBhEREREpPpSwixQHHV8h7emtXHQPxihVGUrXgC4ToGxjSImDI39B+hW9xglRkJoEM7vC4pcdF7cdubrkMIlfPgR4Xx445OpiotSlXnMAb3dXq2T9ymNHziZxOiGFF+dtxTDgZFwyaw+dLVQMIiIiIiK2oIRdpLgIKMsfdd4m/fHV4OoG3qXg/rk5nxu1BfYvgWP/wN8fmJN3AczPu2dxMZn4YlBz7mxcjn9f6si791xeRu+2uuFsfKUzfW4qD8CGI+fp8t+VHDx9eR34hduiMAwDwzD4cfMJFu2I5uk5m2j2+lLeXLiLCzn01AOMmreNB2euJSU9I8fjIiIiIiL5oWfYRYqRDBdPcL1i8jTf0uAVCMlx1icufhkqtb68/3YluPdzqHmbef+HRyH2qHlW+oDy0HtazsvO3aDubFyO+ZtO8GT7qtQrF8h/+zYCoEeDCBKS67MrKp4RXWpiMpmoHWFe833234ez1fPlP0cBcDWZ+GzNEatjH/15kLWHzmEywWu96lGvXCAAR88mMedf83UbDp+nZTXrYfkiIiIiIvmlhF2kuLtvDnxxJ9w0AJJjYdv35vLDqy6fk5FiTtLr3w0VW10+J8ve36HunXD75CIL25HeubsBL9xWi/BAr2zH7mtWwWq/d+NyvLFwF1c/ru7r4UpiaoYlac/J5mOxANz+wV+8368x3euFs2hHtOX49pNx2RL237ZF4e3hSruaoQV8VyIiIiJS0mhIvEhxV6kVvHwKerwLQRVyPy81ATZ8CvMezX7s4jlYP9N+MRYzbq4uOSbrOSnt50n3+hFWZY+3qcKXjzbP8fzKpX35+8UO2cqHztnEu4v3su7wOUvZGwt389u2KM4lpvLxnwfYfiKOJ77ayMBP1+VruPzphBSMErisn4iIiIiYqYddxBlkDWdPT8l+rPd02PSVeWK6a7kYax5y7+JqHi6fZfsP5mfmq2ZPREuCCb3rYQDtapShZbXShPp74u7qwrKRben83z8BmPlQU37YeIJnOlUnItCLIB93YpPSrOqZsfJAtrqf+Gpjjvc8cjaJysG5/1Hh5y0neXrOJl7sVovBbasW/s2JiIiIiNNSwi7iTG5+FDZ/BU0GQtwJ2LcIqrSDiIaw+j3Y+m3e1+9fCotfMT8b/9hKOH/IPIHd3EfMx+/9HOr0sve7KHaCfDyY2v+mbOVVyvjx1aWe9luqhFgNY68e6se6w+cLfc8DMReYv+EY6/e7sH/Zfo6eT+bxNlWpUzYAgFd/2gHAW7/tZuLvu+lQK5SPH2yKSyFnzxcRERER56OEXcSZBFeG5w6CiwtkZgKGubc8oCzc9TFENoNfR5rPHXMOfnsB1v3v8vU/DDK/JpyEqE3wdV9IPH35+HcD4D/LoVz25LWkuqVKSI7lb95Vnzun/k3lMr68eVd9wgK8eObbzazad4aqZXw5cMVs8zn5+t+jrNp3BnBh3fKDAGw8ep7fLq0jfy4x1XJupgFLd8Vw6GwiJ2Mv8u6iPXSqHcbTHavb5k2KiIiISLGkhF3E2bi4WL9eqdEDcOxfKH+zOZEvXSP3eha9bJ2sZ9n2vXn99xI0q3xhVAv1Z9UL7fH2cMXTzRWALwY15/CZRAK93Wn82hIASvt58HafBqzad4azianUCvfnnUV7LiXr1o6du8grC7aTmMtycbdN+ZO0DPMz7VuOx9G6Rhn+9+dBqoX68UznPNo6F3EX0/h+/THublKeIJ/LqxOkZWSSlpGJj4f+EyEiIiLiSPq/MZEbibuXuac9S6P+5iXgMnJ49v3o3znX8c80SIiGrm9AwBWTsWVmmP8IIBZXJrlZKpX2tdovF+RNx9phdKwdBkBCcho/bj7B3lMXLOdULePLo62r8NL8bczfdMLqem93V5pWKsWqfWcsyXqW3lNXW7afaFcVL/ec2+fHzSdYvOMU79zTwCoJf+CTtWw7EcehM4m8fmd9S3m/j/9hX8wF/nyuPYE+7tf6GERERETETjRLvMiNzNMPhm+DQUtgbBwMXg0vHoMOL+d93Y55MLkWvFMd/nwXZnaF8cHwcXs4+k/RxO7kaoaZ13fv3bicVbm/lztPtq9m2X/r5nR+H9qKfs0qMLp77Wz1rHqhPXUiAiz7QztWp2/TyGzn7T2VAMDy3TEM/mIDH/95gKi4i8z86xDDvtnMr9ui+PKfy2vJJ6dlsO1EHABLdp6ylBuGwfoj54m7mMbindGIiIiIiOOoh13kRucfZv4HEF7P/NrmOajeBeKOw5G/Yd1MeHghzOwMmVcMx06MgWWvXd4/uRFmdYVXzkJmGqydAdHboW5vqN2zyN6SM/ji0WasO3SernXDsh3rUT+C3dEJ1A7zxTh6eRb5B1tUZMKvu6zODfH1oEH5IABcTDDo1sr8vOUk364/ZnXeHR+uttr/fUc0byzcbVV2LjGNjEwDFxPM3XDcUl42yNuyHZ98uf03HDnPPTn8caAgMjMNjp1PokKwDyY9ZiEiIiJSIOphFympIhpCrR7Q9XV48ah5ormnN+Tv2pidsGE2LB0L2+fCvMcg/fIkaSREQ3K8PaJ2GqH+XvRoEIGba/Zfs26uLrxwWy261Qu3Kvd0c2X+kJZULu1L88rBfP1oc0wmE7fVC+e/fRuyZlRHAr3dqVcusFAxrT10lkbjFvP83K18tfaopXzzsVg2HDlPRqZBdFyypfybdcf44ope+SzJaRk8PWcTn/19+Jr3fHvRbtq+s4Lvr/gDgYiIiIjkjxJ2EQG3S89iB1U0T1h3tWaPWe9/1Bp+f/HyflqSufcdIPYYfND0/+3dd3iT5dfA8W/SpHu3dEMpe6+yp7JkiygiG9RXUEFx4hb33gpulJ8oiAKCLNl7SSmz7E13C90j43n/uNukoWXKKPR8rqsXybPyJHdbeu5xDvyv/zW51Vtd0yp+rHz6NmaOaUPbGoEAOOl13NU0gmBvVbe9ToiX7fhfHmhF/TBvnA16vF0N1A724tketcu89vYTZ8kqMDNr2yniEhw7VO6esoFRU7dwx6drHLa/vSCOvEILmmZfPz91/THm74jn1Xl7eGjavxSarfy6+QRv/L0Xk8XqcP43q1UG/Nfn773CT0QIIYQQouKSKfFCCDudDh5cBkfXws991Lb+X0OTwbDl27LPCaoPyXvUVPn2T4DFBIVZcHobZKeAZ6Xrd/8VhKvRibmPtqPQbKVllD8LanYA1Ppzi1Vjxb5k27EDoyN45PYa3P7hqlLXqRfqzd4SgXtZmevzTBaemBnLmoMpfDmkKZ3rBLP2oL26wD97k3ji91gW7EwAoFWUP93rh5CaXYCvmz1hXXaBmRfn7OKl3vVw0uswOunKnCJ/ICmLjYfTGNqqCgYnPfkmCxarhoeL/HclhBBCiIpH/gISQpRWpY1a4+4XpYJ1AJ8qkGGfRo2bP7R5BEIawa/3qm3rPnG8TkIs1OwGSXvg5GaIHi3l4q6SJpV9S23T6XQYnHQOa9Ir+7sTFehB17rBLItLcji+T+NQOtcJ4suVh0pdq1olD1pW9WfG1pMs3qOSz93/078MaBrOhsNpDscWB+sAy+OS+WXzCdYcKF0ycPrmExSYrSzZk0iTyr7cEx3Bwl0JvNS7HpX93QHo/oka4XfS6xjcsgq9Pl9LodnKP090vKIycwkZeRxOzqFdjQBZQy+EEEKIm44E7EKI0pwMMHSW47b7F0P8dsg4qRLN9f5IlZEDqNcf9s4tfZ1Dy8HJGab1U89dvGHtR2oK/ZBZgAZufuAZdA3fTMUT6uNqe1zJywWAp7rXcgjYq/i7M6JNVTxdDDx9R23+3hmPk07Hw9PV0oazuSYGtajM7JjTFJaY5j77nLJz5zo3Gd65ipPdrT2YahvR33QknR2vdnc47t9j6dxWuxJHUnIAeGR6DD+MbIHJYsXFoL/k4Lvzh6vJM1n4Y2wbmlf1v6RzhBBCCCHKCwnYhRCXxidcfZWlxQOOAXvl1nByE2yeor6K/fmA/fGK1yFuPoQ2UdPw885e3vT5/Ew4tg5qdAGDy2W8kVufv4e9PrxzUdK7uqHeTOpbj6OpOTxfVD6uZN32Po3CHK5xJreQplX8mD++PXEJmXy75oht+ryXiwF0kFUio7xOB57OBrIKzFyujDwT6TmFeLjY72dubDxzY+Ntz1ftT+GZWTv4Z28S2QVm6oR4EerjyqeDmuLjbmT65uM46XTc17KK7ZysfBN5JgugEutJwC6EEEKIm40E7EKI/y6qI4z8G/56FNx8YcRcmDceds06/zlx89W/CbHweVPIPA3D/oTqnUHTzj91PjsZlr+uytGlH4YOT0OXl6/yG7q56XQ67qgfTMyJs3StZy8rN6pd1EXP7d0olAU7ExjZpioAtUO8qB3iRe9GoSRnFeBq0ONs0JNbaGHu9tMMalGZwyk5eLg4cTbXxBt/7yXM141lcUmUyFN3Uc3eWErzSL8LHlNydH9fYhb7ErP4atUhhrWK5MU5u2337+VqpMBs4ZdN9iUcxjKy9QshhBBClHcSsAshro6oDjBhpz3Y7v0ROHuCTwTo9LD8tfOfm1E0jXrl2/DXeKjcAgb+ZN//71T4e0LZ5275VgL2Mnw9LBqzVbvsQPX9uxvRt1EYnWo5znYwOukJL7E23svVyJhO1QGIjrSP6C94TCXAq/niQkwWFbG3rOrPkdRsUrNLlP4rw7/Hz5S5fVjrKmw5ms6BpOxS+3adymCOsz2Q33A4jWZV/Hh7YRxzSgT46TmFWK0aVk0rVWrv753xuBic6Faic+NcmqZh1dTaeiGEEEKI60UCdiHE1VU8Mu7qA30/VY93z760c09tVf/uOQUNB0LKPqg/4PzBOoApDxJ3QUjDK73jW5JOpzKxXy4PFwM9zqkPfyXubV6Z6ZtP0DLKn5kPtcZiVYFy1ecWACpDfZMqvni7Gvl69eELXqtaoCf3RFfmrQV72XrMMajfeCSNjUfsSfDG/G8bOh2lRvd3njpL87eWkW+y8PZdDYlLyKR5VX8aR/gw7tftAMx5pC1Nq/hhsWqsOZiCn7uzLbnfu4v28dOGY8wc04bPlx+kbfUAHuxQ7T9+SkIIIYQQFyYBuxDi2vOJsD9udJ9aq65zgvWf2rdHtIRTW+zPZwxR/y5//cLXtprg6/bq8T0/QoO77fs0TQX0zu7/6fbF5Xu+V13qhXnTvV6ILXs9wNfDmrH9xFkm9qiDXq8jK99EiLcL/ZuGs+NUBn9sO8X8HfEO16od4kWTyr7MGtvWFvBfSFlT8Vfut2etnzAzFoCpG47xTHd7zfqHf4lBr4P4jHxArdXf9nI3UrML+GaNqic//rcYTqbnsWJfMttPnuW+FpXpULNS0etqxJw4Q81gL7xdjWw4nMo3q4/w+p31iQzwuPQPTwghhBCiiATsQohrL6IFdH8LKtVWZd6KlQzYWzzgGLCfK7AWRHWCrd+d/5g/7ldf1bvAkJnw+wi11v3+xZAcB5HtwOv8057F1ePpYmBoq8hS23s0CKVHg1Dbcy9Xo21tfadalehUqxIv9a7L1PXHbCPvdUK8Sl2nWRVf7mwSzqvz9pTa5+ykp1aIJ7tPZ5baV1Kh2cpbC+NszxMz8x32ZxWYmbH1BLEnz9q2nUzPsz1esDOBBTsT+PX/WtG2eiDTNh633c/Xw5ox9heVcb/TB6v4fHBTqlfyoFYl6TwSQgghxKWTLDxCiGtPp4O24xyD9XM1GgQ93wdX37L3NxwInSZe2usdXg5vBML+hZB/Fia3hj9GO2ap1zQ48A/kpqvnKQdg8QuwfxFkJYK54NJeS1x1wd6u+HsYbc8DPO1VAIoD33cGNKJllD3re8kchf880ZHZD7ejZpDnBV+nVdTFs8a/8tceZsdcuJTdkO82sz8xy6HzoDhYL/bYb9vp/fk6NE1jbaKOaSUS4gkhhBBCnM8ND9gnT55MVFQUrq6uREdHs3bt2vMeO3v2bLp160alSpXw9vamTZs2LFmy5DrerRDiqmozTv3b8RkVcbUaA2PW2Pf3eNf+2NVHTaUfHwMTdsPTh+z7Ri+GSRlqFP9Cjq2FNR9CYQ7ETINfB9qn3n/VAjZ9Bb/dBx/Vhqm9VNCeVHoEF3Oh+lr8AmyffmXvXVzQvc0rUzfUm6e713LY3q9xGMufuk1lrw+2j7xHlZhyXjXQA2eDnsUTOjJ/XHuH88d0UuvOn+xWi5lj2nD/eTLnt7zMEnB3fLrm4gcBL/21lz+OOvHGgn3sOpVxWa+RkWfil03Hmbm1dLB/LDWHpXuTLut6QgghhCj/buiU+JkzZzJhwgQmT55Mu3bt+Oabb+jZsyd79+6lSpUqpY5fs2YN3bp14+2338bX15epU6fSt29fNm/eTNOmTW/AOxBC/CddXoV6d0JYM/s2n8r2xzW7Q9phOLDEvjY9oLp9f6eJqsxb5VbqedtxULsnfFHieuda8YaaJn94uXp+YiPkpJU+7vS/8HFdyE2De/8H9fqp7dt/gQVPgbnE9GlXbwiPBu+w0tcRV8TX3ZlFj3e44DF6vY7549qTlJlPlQB3RvywhSdLBPhOeh0NI3x4Z0BDNhxOI8LPjWfvqM2D7avZatW/3KcuE3vWpvZLiwFVw75N9QAm9a3P92uPcDQ1h3/2JvFCrzq8vXDff35fv2+zj9Z/s+Ywnw5qwqxtp2haxZc6Id62fafO5LLhcBr9m4STnlPImgMpPPvnTtv+uIQsthxNZ3S7qtzdLILun66h0Gy1Tc8XQgghxK1Bp2mXUyn36mrVqhXNmjVjypQptm1169alf//+vPPOO5d0jfr16zNo0CBeeeWVSzo+MzMTHx8fMjIy8Pb2vvgJN5DJZGLhwoX06tULo9F48RPETU3au4SkPZCfCZFtruz8uPkwc5h6HFBTjc43HAiLL3FK/bk8g9Uaet9IOLYGzpYxnTm4ATy8vuzzi0vdHV6hAv5eH4K7v7R5ORJz4gx7TmcwrHUkuhLz6zVNIzW7kEpeLjw/eye/bTnJw7dV52yuiYy8QsJ83Ph+3VEAAjyc+XBgY/JNFh6eHnO+l8LHqJFhUq8RGeDO8bRcIvzcmDI0moSMPBbvSWT+jnhMFo2Hb6vOj+uOUmC2nvd6gZ7OtpJ5o9tV5c4m4czfEc/jXWvi7WrkZHou+SYLNYNL5wI4l9Wq8fSsHer99qp7SZ+dOD/5Ga94pM0rHmnziudK2/xK49AbNsJeWFjItm3beO655xy2d+/enQ0bNlzSNaxWK1lZWfj7n3/qYkFBAQUF9rWomZkqCZHJZMJkMl3BnV8/xfdX3u9TXB3S3iX4F42SXulnEdQYg06PTrNiGvIHeIeDpqG3mHFa+uJFT7eGNkWfsN2+ITtJfXH+JTsk7YZJPlh6fojmGQKaFV3SLsjPRL93DlqVNujj/lLH7v4T0yNbMXmE45d9EMvxTegKM9GqdgCj2/lfQ1wzDUM9aRjqidlsLrXP11WPyWRiUu869G8cSv1QL1yMTgDkFpoJ83Ghb6NQfN3t/2nHvHg7T/2xC6sGdzcNw9vNyOift6FHx5i6Zk67RjJ9y2mOp+UCcOpMHn2/XFfqtaesunDJO8Chvv22Y+ks3p1IQkY++xIy+XFEMwZ+vYHEzAKmjY6mTbUAABIy8nnprz2E+7ox8Y5aFJitrD6QSq1gT2YX1a8f2CyMyAB7krzcQjMvzNlL2+r+3Ns8AnFx8nu94pE2r3ikzSueK23zK/0euWEj7PHx8YSHh7N+/Xratm1r2/7222/z888/s3///ote44MPPuDdd98lLi6OoKCgMo+ZNGkSr732Wqntv/76K+7ukq1XiFtVQNY+TE5uZLo7ZiqvkbSASll78M85gEXvwrGA26mdNM/hmJV13iQ4I5bItDV4FCaXuvYJ/3Yk+jQj/MwmQs9uQ8/5Rz/PJ8GnKWfdoqibaK9RfzCoN3vDBlI1bRXJXvXJdQnGL+cwNRPnsSdiCCa9G4XG0j2yeqsJvbUQs8GxdJh37gmqpq0gLnQgJoOUFbvRkvOg0AoRHpBvhqkH9MTn6rBqkG3WORzbP9LC3ONOF7yei16jwKrOa+JvJTa9dFqaKC+No1mO17491MrZQtiepo6/u6qFEzk6tqbo8TRotnvpXdlC9wj7nwgbknTMPKLu6dVmZvxdEEIIIcQlys3NZciQITfPCHuxklMPQU0/PHdbWX777TcmTZrEX3/9dd5gHeD555/nySeftD3PzMykcuXKdO/e/aaYEr906VK6desmU2wqAGnvq63XBbdrqKyb1QCT1QxWC05LJoLFRPu+Y2xpx80nNuI0axi6fJUgTPMMJnToFEKL1qtbEnehxW/DadHTl3V3oRnbCc3Y7rCthu4E1QJP4BT7EwDWyq3Rn9ykjt+7HU3nhHn0EghtYj+pIAvDtD5w9hjmsZvAy14yzfBxTXR5Z4gMcMPS7+fLuj9xbRT/nPft2Y0B/dTPudWq8dPG46RmF/LXjgRqVPLg/VHRhC0/zOTVqv77091q8uHSgw7Xer53PeoEe3I218TttSvxwP9iWHfIMR/DucE6wMoEx8D+mDWAbSlnAceOgw1prrw4uC2VvFRkvnzWLiABgJyAugzrqBL27YnPJNzXzTbDICvfzKvz99KlThC9G4aUev2sfBNpOYVULaM2fW6hGXfnG/6nyVUjv9crHmnzikfavOK50jYvnul9uW7Y/4qBgYE4OTmRmJjosD05OZng4AvXSZ45cyYPPPAAs2bNomvXrhc81sXFBReX0sMARqPxpvmhupnuVfx30t43QtHn3f8r4JzyGdU7wiObVB33arej06wYnUr86qzcTH1VbQdTLmHNvUcQ5JQetQfQJe3CyWKf3lwcrNv2axaMS1+E9KNQuwf0/RzmPQvJKpO98fOGMOB7VT7P1Qfyzqjr7F+AvqzvqX0LYO4jcPcPULPod+mpf8GjEvidU0O9MAcsheDmd/H3KC7q3J/zMbfVBGBiz7rodTr0eh3P9qxL3ybhmCxWGkX4MrhVJFuOpuPjZiTmxBmGt4nCSW8PsO9vH+UQsH8xuCnjf1OdQi2j/Gke6cfkMqbYbztxtsx7PJNrosfn62lc2ZemlX2ZvyvBtu9QSg5Go5Ftx9O5e8omQn1cuSc6gmqVPPhwyQFOn81j/s5E+jerXOq6437axobDafw9vj0Nwn1s23/bcoLnZ+9iytBm9GwYWuo8TdM4mZ5HuJ+bw/sulpVvwmTRbAkFyxP5vV7xSJtXPNLmFc/ltvmVfn/csLJuzs7OREdHs3TpUoftS5cudZgif67ffvuNUaNG8euvv9K7d+9rfZtCCKGyv9foAno9OJ2nnzO4nkpsZ/SAB0r8XgtpCHX62J/3+VjVmy9S6OSBefRSe3b81IssBzq5WQX8MdPg3UjYNctx/+wHYdGzkHbIcXtBlvr3+EbIKir/NWOIqlO/8CmwmCB5H3zfBT5rrJ4XMxfAt7fDp41V1n5xzRic9OhLBKN1Q71pFOELqHr0PRuG0rZGIOM61ywVtN5e23G2Wd/GYXSrF2x7/GyPOix7spNtf50QL1wMF/4zIDPfzNqDqXy+4hAlF9Ct2JdModnKb1tOAmpN/BcrDvHEzB2cPptnO+65P3fywE9bmbcjnudn7+K7NUfYcFh1KswoKk+3PzGL4T9s5vnZuwAc6tmXtGBXAh0/WMknSw+U2qdpGl0+Wk3Lt5aRXVA6D4EQQghxs7qh886efPJJhg8fTvPmzWnTpg3ffvstJ06cYOzYsYCazn769GmmTZsGqGB9xIgRfPbZZ7Ru3do2Ou/m5oaPj895X0cIIa6Lu74BUy64eKkgfd/f0PU1qHY7/POiKhFXqwegU0E1YNa7YAxrCv2nwG+DoTDLfr0LjMYDUHCeOt47Z8JBx85Qjq6BBU9DVjy4+UPnl+z7zhyDt0LAWhzoaDC1pyqZN3QWHF9v70iY/ziMnG9bMnBNZZyGPbOhxf+B0fXav95NTqfT8cfYNgz7YTP3tVClUScPbUbM8TNER6qZETWCPG3HZ+WbL5h9viyfDGrMEzN3kJlvptZLizCUMdJd0oytKqBfvq/09/Evm07wy6bSFRe8XA0cTc3hzb/3MqZTdVpGqcSyr/6lAvkvVx7i6TtqA/DPnkR83IzUDvEiOUslmN2fmEl05PmT0QohhBA3kxsasA8aNIi0tDRef/11EhISaNCgAQsXLiQyUk3FTEhI4MQJ+3/m33zzDWazmUcffZRHH33Utn3kyJH89NNP1/v2hRDCkd5JBesAd3+vRrlDGqrnPcouVWm0FI1GRnWA+6bDuk+gfn9VJi48WgXTHpXA4KrK0p05Bq6+sPsP+0VeToUDi+2l7ADy0tVrJ+0BzapG00vuW2DP7QGUCNaLnNqq/v11kOM0+GNrIW4e1LtTlavbv0jVofeNBN8S05+zU2DV29D6EQiooUbpLxZ0x/4KuenQ+mH1WU7rpz7D7CTo/uaFzxUANK/qT8zL3XArymJvdNLTqigzfLHnetbh3UX7eLlPXbafPMs3xevku9fCy9XIb1tO8O3w5pisVlbtT+GNv/cCMKx1Ffo1DueJmTts1zJbNdrXCOSb4dG4GZ2Yvf00oT6uDP1+8xW/h8MpOdz+4SoAdsdn8N7djViyJ5G0HPtykZfm7mLV/hROncnD08XAr//XyrZvyqojvHu3B4GeF86Kt+14Os/P3sVLvevRsValK75fIYQQ4lq64ZldHnnkER555JEy950bhK9atera35AQQlwNRjd7sF6WotHzNM9a2MKpap3UV0n+UfbHvT+yP/atrIJ7v6rgZIS6fdVa+9kPQeIuaPc43P4i/DJABdnFIttDQSYk7ry093HmqPoCNTvgwGLYM1cF7Dt+g7kPF71fDxi7FgKqQ+oh+OsRNX1//yLwi4ITG6D9k9D1VchKhN2zIXokOHuokfSMk2o9PRpkJ0KXV+3T+nfPgZZjVMeA6yXMpjIXgDm/9LFWa9EMCM+yzyuWuFt1SnR4Ggzlbz30xVwsaduYjtUY0DScIG9XOtcJpl6oN53rBOHlqtbWjWxb1XZs9UqeBHu7MC82nqe61cZJr2PLC11o+fZyANyMTvwwqjkuBtVBcE+0Kvf2dPdafLLsIN+NiOb+n/61Xc/L1UBW/qVPWU/KLGDU1K2ltpccmc8uMNPvy/W258vikhgwOYvpD7bij22neLBDlO29lfTMrJ0cSc1hxI9beKVPPUwWK2M6Vb/kexNCCCGuhxsesAshRIU0agGWTV+zI78hna/k/NteAPdAqN3Tvi2oLjy0Wq1Ldy+aEmwsUb4yqB6M+hsyT8OySdBsBPzc99JezzcSOjxVFLDPhuS9KvAuZsqBL5qVPi8rQX0BrPsYdv4OmafU850zQG+A09scz9nwBcTH2p9nnoJPG6j7H70QVrypZhx0f9Nxar6mqec/91NT+MesBZ8INUJ/YLFKsndoGdy/BCq3hJxUMOU5zgwA+Lqd+ldvgMqt1H6dHrzDVefITU6n0xHkrWY7OBv03Nkk/ILH92kURp9GYbbnQd6uBHq6kJpdwAu969qC9ZIevb0Go9tF4eFi/zPD3dmJ+ePaA1DF352EzHzun7qV/k3D+Wz5AfJNlzc9f0DTcObtiMdsLV2d9kR6Lh3eXwnAZ8sPEujpQt1QLzrVqsSP644yvktNUrMLbMe/XjSLIP5sHi5GJxpH+NK7UenEdxeSW2hm3cFUOtaqhKvxwiX5hLgWth0/Q7VAD/zKYeJFIcSVk4BdCCFuhEq1sPZ4j/yFC6/sfIMztB1Xertebw/WwZ5sDmDMGhXQ+kSoKfsAHZ+BNR+ox/f9BpFt1Oj3mWPg7AmpByCsKdTsrqa2V26lRs5T9qlzXH3hjrdhyfOQf5419SUVB+sACTvOf1zJWQHFkvfCe1XtzxsNUqPuO2aqEfqdv4PFHoSx+Dk10n5omeN11n6k3v/XHdTnM2Gn/TP7d6r9uJVvOZ7XdDjc+eUF394FaRpYLVd+fjkyc0xrth0/w8CiEfVz6XQ6W7A+/cFWPPvHTj4Y2IiqgfZSbuG+bix5oiMA3esHsz8xi54NQsg3WXFzdmLM//5lyZ6kUteOe70HhWYrPu5GMvNNLIu7QJ6HIqnZBaw9WMDag6kAtgR35/p543Hb4yr+7WkYUXpGx1+xp1kWl8zbdzVwGLl/ff5eZmw9yYg2kbx+ZwPbdk2DNQdTaRjhR4CnC2/8vZfGlX24q2nZn524NVitGot3J9As0o8gr/MvB5q28RiaZp/Zcr7yxhcre7zmQAojftxCvVBvFj7e4T/fvxCi/JCAXQghbmUlg/eyRoc7v6SC9rMnIbCG2tbigfNfb9Aval17fIxa2z56MQTVgYjmcGAJJMTC7j8dz6ndW73OhcreFU+3v+15WFVivf/wuao8Xdw82DPH8ZwVb6qOg7PHKdO+v8vefmAxfNdFJeADeL9o2UFIowsvFdj+P3vAfjpGdVwcWAyVW8Ptz5//PICTW+DvJzFkJxEQ9iDQS0VymUX3cGyd6oSpf5d6nnEK3APULADNCtt/gWbDVafIhZZaXCfVK3lSvdJFlhYUaVcjkPXPXXgeScnruTmr0ekvhzTj3m82sr1E2bnlT3XCzdnJdsy5Jdw8nJ0Y0bYqU8ooX3e5/j2eTv0wb1vW/sSMfJbGJfHy3N0ARPq70yDch2kbj/FG/wa2BHvTNh63BexxCVn8c1rHwk0xtK0ewIg2kfy04RgAdzYOd6gIIG4tn604zOTVR+jdKJQvBzdl1rZTNAz3oW6ot+2YQ8lZvFKUTLFGkCdWTWPCjFhqBHkysm1Vdp7KYHznGkyat4elcUn8PLoli/ckEubrxrBWVRwC+LmxpwHYm5CJ2WLFomk4O+kvGOSfT0aeiQ2HUulWLxiDk72SxOGUbMwWjdohKleLxaqh16mR/VX7U3i8a02MTheuPCGEuHwSsAshxK2s2+sqKGw7/vzHGFzswfrFeAbBQytVAOoVAr4qGzmVaqsvgJ4fwNFVKshMOwx9PgGvYHhiD6x6F9qMUwnp9v6ljh/6p1q7n7ATwptBnd6w9BXVkRBZVOazZje1BKAgC+r0gt9HwMEll/45dJ2kOhISi0ZWyyqfdynr+pP2wvpPVSb+YkdWQeNBam3/yS2qBGDdfpCTovIMHF6uOh0AHdD+4NtoH3+tptgnnTPSG9xA3cefD6pAvaQ1ReUAw5tDYQ40vg+C66vPpqSds2Dz13Dvz2o2BcCpbWopQ+tHwCdcVQAwF6hOgbj5RZ9v+cqsbnTS8/P9LXlpzm7m7Yinc52gUp0Ew1pH8u/xMzzepaZtHb7VqjEvNt6hvNyF3BMdwZ74TOISMh22vzZ/L+8sVDNJejQIYd6OeIf9X660l04c+p1jkr21B1VCPDWSrzoXNhxOo0eDENsxR1JzqBHkybwd8Ww+ksYLveraZiWsPpDC3vhMxnaqVirgWrU/mXyThR4NLm/Kvrg+0rILOJ4Nk3epZI4LdibQOMKHt4u+l/a8dgfvLd7HqTN5aCVqJZZM1Jh2NJ3NR9MB+Hq1vfPprYVxbCnavvlIGo91qcmP644ypFUVh4oNy+KSeWHOLppH+vHtiOYAHEjK4kBSFr0bhpYZxH+y9ADfrjnCyLZVOZmey4JdCYxuV5VX+9YHwGSx0vvzteSbrGx+oQs5BWbu/Go93eoFMztGdRYYnHSMbhvFvJ3xuBmduCc64oIzAyxWrVR5SiFEaTqt5G+LCiAzMxMfHx8yMjLw9va++Ak3kMlkYuHChfTq1Quj8eZfNykuTNq74qnQbZ6ZoBLWNR+tEthdrqWvwPrP1GP/6qrUnLs//HK3KkMX3ACS1EgoDy5XMwCsFph2Z9nT7QE8Q6DPx2rq/1ctL+9+6t9VegbAlYgeBdt+urxzxseo0frkvSrb/x+j1faG96rlCnHzVAfDyc1qLf5T++GbjlCQDVEdYf8CVYYwsq3qCPAOAxdvqNfv/K+Znwk/3gGV6sDAomUEp/6FQ8uhw5OlZ3McWaVmDTQdVupSJOyA6ffCbc+p91/GH/dxCZlEBrjjvvkzOLQCBv+mlkOcx6HkLLYcPcMLc1SHSHSkHyfTc0nOKuCO+sHsic/k1BkV0G98vjPerkbqv6o6gJwx8bLhf6y0NmGFtYy8DP9Bn0ah/L1T5XR4/55GNAz3oedn6vtxQNNwPh7UhH+PpXPP1xsB+HpYM3Q6HfNi43mzfwPScgro+vEaAP59qetFM+FfqtxCM4kZ+aw/nMbh5Gxe6VNPRv+vQHpOIV0/XkV6jum6vm6YjyuBXi7sPFV6WdK7AxrSq1EovT9fy8l09T3fOMKHn0a3ZG9CJs2r+uFicKLqcwvKvPb+N3vwy6YT7Dx1lr9iVafV63fW599jZ0p1YhmddJgs9rDCy8VAVoGZLwY3pW/jMIdjP112gMmrDjNrTBtqBXuxcn+yLcmlr3vpNfhWq8aP649SO8SLDjXLV1WHCv1/eQV1pW1+pXGoBOzlmPwCqFikvSseafP/ICsJPqqlHtftq6bqA5jyYf9CqN4Zvu+qAscxa8GpxIQyc4Gadm81qwR3oNbD3/mVOl7T4J+XVGK7wmw1Wn2uOyer0XqjqxpFv9GcXBzX718NHZ9VWfyjR6pge9cfailE0+GQm6ZyBAC8mKiqIkwqsd579CI1g+DUVjVTYPb/qe0PLFMBeXB9dQ7Ajz1VFQFQSyOGFM1eyE5RHSxV20Ov9+HMcfiskdoX0UK1WYsHHQN8qxUKs0CnR/t1EB8fCuULywB2vNKdxMx8Tqbn0rVeMPM27OSX+f+wRavL/jd74GJwIvbkWdYeSOERj+U4LZ4IQNX8X0t9LH7uRnzcjBxLywWge71g/tlbeq19SQ11RziqhZCN+wWPmzeuHZ8vP1jmuvzetb3Zsv84Kagyi5X93fh7fAd83Er/7sjMN+FmdLrk6ckDJq8npsTSg5/vb0mnc0rdpWQV8MWKgwxuWcVhWrew+27NEd5aGHfF5z/Xsw5tqwcwYPKGMpMpXgsdagbycp96dP9kzSWfUyfEiyOpORSaLz1R5NxH2xF/No/v1h7B183Iyv0pANzXojJmq8Yf2+z5TZ65ozYPdojCxeDEmZxCsvLN9PtqHWdzVUfIwbd6XvR7O99kwWLVHBJfFjuZnsvkVYd4sEO1S17acyHl4f/y2JNneXHOLp7vWZf2NQNvyD1UJNc7YJcp8UIIIW4+XsEw7M+iKfYlpvsbXaHBAPX4kY2AzjFYB7UEoOsk9bhqB4jfDu2fsI8K63RwR1HCOXMhNBkCW7+HmGkQUFN1DgTVKXFBncqAX8zJWU017/CUmpIeM011IlyOyPaqQ8GvqppOf7Hp/1c7WAf7FPz1n0Feun370TWqakCx1IOQtMfx3Kk9weAG5nOmpf/QVf3beDDcVdQRUrRcAFA5AX4fqYL0A0sgeY/6qtEVfh1oP+7UVvUVWAuOrFQj+CP+ghVvwbap0Pg+dMfX85QR7h79FD7uRnwMhdQ+tRDmbKbXoVX0c0ngO3MvXMztISOVJq4Wmvhthk0/2z+Cpzux61QavRpVZs/ps/h7ueHtZsSqacyJOc2AZuF4uRqZ+fv/SN+5hBhrTfrfM5LQQB+cnfQ8P3snfglrmeb8Hust9VlvbUAXpxgeLpxAclHgXVLJ8nQA1XTxuGCiru44Hx//mjwXZ3oVvsNRLZST6Xm8u2gf7wxoSL7JQnaBmUBPF46l5tDjszX0qB/Cp/c1tV1L0zTyTBaHsn9mi5UJM2MdgnWAw8nZdKpViTUHUgg3ZFB98XDWap2YdrIDf247xZ7Xe5S6d6FKDJbUoWYgx9JysFohPiOPc4fIvF0NPN+rLokZ+fRrEmYLHn8c1QKLVeP2OkEUmq1kF5i5e8oGjqbmAGp2xj97EqleyZPsAjPTN6syhwEezrSuFsCCXQmXfM9rD6ZeVrAOsC8x64L7PZydyCl0TLDZ/6v1ZR5bnPuhpA+W7GfBzgSSMvNJyykstX/yysMcTsnGy9XA0dQcGkX4MrFHbU6fzWP36Qza16zEPVM2cCa3kCUTOnIkNYfxv27H08XA2Nuq8cRMlfD0aGoOMx5qQ0JGHuk5hdQPU52OVqvGin3JNIv0c8iTkZVvYuj3m6ni7847AxqWWS6ymNWqcSQ1m+qVPC+YR2Dl/mQq+7lTI+i/dRw89+dO9iVmMeyHzRx7t/d/upYof2SEvRwrDz124vqR9q54pM1vIlarY7m8c8Vvh2PrVcK+4pHjknb9AX8+iLVqe/TFU/KLE+0BDJquRvtPblLPn9qvcgQU2/sXpB9VHQzzxqvkc02Hwur3Lnzf/tUg/Yj9ecsxagmCuz/8b4A98d6N0GiQurdTpeusl+IZrMrzncsr1F42sMd7UDQy7qD9k2rpxZyxarnEucrqWCj24AqYOVS9hlcYDPgWFj8PHZ9S+7f+oPIBlMyJENYU7p0GniGkL/8E/41vl7psRkATeic8yJ1V8mnUuitt9r3D7rg4XikcxiEtgv61XNBnnOD1rFfwtDoGR9+ae/O2eajteZ0QLwqT9jPRMIPWxoPcmfcKxzS1vv2b4dG0jgrAR5fDR2sS+WrVYb4f2ZzbagYSfzSOT2PM/FG0/rhYG/0emjRrQ9+2jen1+VpeNfzMaIPqMCqecfCfA4LkfXB4BdS6Q322Vdv/t+uVE58sPcBnyw/anse93gM3ZycsVg0dsPN0Bot2J5CWXcjrd9Z36Dy5mBNpuXyy7ACV/dx4rEtNWzK4lfuTGT1V/Qy90qced0dH8P3aI3i4GHh30b4yr+XpYuDR22uw+3SGLbg3Oul4sVddPlt+kDO5JrrVCybUx5VpJSonnKt9jUDWHUp12FbZ340P7mnM4O82MbJNVYK9XXlvcdn3cTXd1TScJXsSyT2no6BVlD9HU3NIziq7U7NFVT+2HT+DVYNZY9sQfzaPx2fElrrGN8OjWbQ70aHCxBNda9GhViAr9iZy7MhBXhrcmWBfD75aeYgP/zkAwFt3NWBoq0jKsmJfEvf/9C8BHs6sm9jZlkwT4EhKNnkmC/XDfMgrVB1ylbxKL4FJyy7AqkGfL9aSlKne49F3el1RssEbJSWrgL0JmbZZPVarVu6X5MiU+GtMAnZRXkl7VzzS5hVMQTamtGMYvy2q8z4+RiXCi98OA39WwfO8xyCsiUoWeCkKc+HLFmptuikXclOhyTDo97nar3eCr1rZy/BNKrHG9cQmWPiMY7K9xkPg1BZVvi/jlOqAWPuxyv4PanbB+s8uLcgGCG184fJ9tzJXX9XJcx6a3oDOaoag+moWAZAU2JaYJq/RY9MwdGV1UACHiaBP/usscZ7ISS2IUaaJ7HcZiV6n/pz7xxLNWmtDYq01cMbEF85fEKZL5wXTA/xq6YKTXscTPqsZl/c1b5iG8YOll+3abfW7+dX5bU4RzPLb5/HqosO8Z/iWQYZVALTJ/4Ia+tP834BenLL4MbhFBIVzH0N/eBnGsavVzJcy7Dx1lsdnxPJS77p0qRvsuHwC4KFV4BfF8Ww97y89xCO3VbeNdt5MSgbsfRqG8OXQ6Gv+mmaLldfm76VqoAf3t6taKlA7m1uIl6uRfYmZeLoYmLn1JGM6VsfH3cjJ9Fx6f76W6Eg/fhzVAp1Oxz97Etl+8iyPda7JsbQcW46F5U91Iikzn8gAD9YcSCG30EL/JmE88fsO1hxIoVGED5U8XXiqe23qhXmTXWDGw9kJnU7HuoOpDPtBJdWbOroFT/++o9TIeYSfmy2vRFnCfd344J5GDCmRnO9m0ayKL493rcXc7adZdyiVng1CyMo3sywuiax8+6yMrnWD6FQ7iH0JmbZZE+G+brgY9Zw+k8dn9zXhy5WHGNS8MsPbVCUj10S3T1Zjtmp4uxpsy3RqB3vx5ZCm1AxW2fyLE//lFVpYtDuBUB832lQPuP4fxHn0+HQN+xKz+GJwU1KyCnh30T6mPdCS1tXKzz2eSwL2a0wCdlFeSXtXPNLmFY+pIJ+Cjxvh4eqC7rGYskvtXS5zgVp3X5itSs81f8AxIdvaj2D56yrL/rNllDv7uD5knlLLAoqXClxIVpIa2Te4wKYpagmAk1G9/rmqtFUZ9Oc/rp63HKM6I2J+to+Mx/6mOikCasDGL8EjCHq8owL9vXPhrPrDFc9glc3eyUWNzBrd4Oe+pV/TxRsKMktv/y/CmqqOlXOVTG54HX1j7sMYw3nKFhbJ1VzQY8VVZ0+Ats9amVNaIF2d7O+lVf6XvGD8FXfyidClUFevpii/a7qPbNx40zi11LUPWMOZZB7JNJcPMWgq8NpcdSwGzcSGJAN3d2lPaOOu/HsyC++TK3n3n0M8aviLT81380a900QdmuZ4wQZ3w+7ZLPcbyAMJ/QE48lZP9Hv+gI1fQVQH6P4moEbfJs3fQ80gT4a3qarOz4yH5L1sNTQjISOf22qrkTrvoinLuYVmjscnUffoNGg4UOVVCKypOp5+vRc6PQetx0LKfrWMxVwA33aCyHb2pRslFeaA0b1UgsSl/3uPWXF5ZARE89Oj3XFzvTpJAa8ls8WKTqc7b7b29YdS8XY10jCi7A4Uk8XK6v0pREf64edROlkcQIHZQvdP1qADljzRkeTMAjLyTPwZc4p8k5Xnetbhpbm7mV+UxO6jgY15apbq6GsY7sOu0xm80qce97eP4sMl+0nIyOeVvvUY+eMW9iVmUj/Mh23H1dKarnWDWRZXurPr3uYRpOeY2HQkjSe61aJlVX/eW7yPLUfTqR7kWapKRHn39/j2zN8Zzzerj5z3mK51g0jJLiQuIZMudYJYtDvRtq94FN5ssTJ51WFmx5ziWFouHWoG4u/hzLbjZ/hjbFs2HUlj5f5kXuhVl9Nn8/h02UEe7lSdVlH+bDicRvUgD/YlZvHJ0gNEBXowsm1VAjycifBzx0mvIzPfxMwtJ+nXJIxgb1c0TePdxfsI8HDmoY7V0RY8Te6WaZzWAjnu0ZDjWTp+NPfEPyyKsQ00Ku/8nEo9nyestqp2QH4GrPkQDK6ktXiKtxftp3/TsOueiFAC9mtMAnZRXkl7VzzS5hWPyWRi8d9/0eOObhjdr9MIotUKMT9B1Y5ll+87sUkl0Gt+vxqRvxzFdeT1BljwlMo0X6c3fFpUK75ya3hgCeSmg1vRmu3zTdXMTVdr/ev2Bdeiz+afl2FD0WyB/l9Dk8H24zUN/hoHsb/YtwXWgttfBL9ISDkAmybbZwcA6JzUcoC8M9DoPpWf4Cf7CDP/twK+K6Nm/MupqgLB0dWO219IgJxkdS9WM3zZ3L6vw1OYmo7G8HlDdFzkT61G96mShfuLMnV7h0Om41R17v4Bds2yL6Mo55JdqpCUp6eh/thlnde14H0OaREs75pI9XVP2rZb7/mZfX9/yolcI3makfmWNnw/uB6WtCMYV6lg/rHCR3HTFTLaaTF19Cex+FUjt/EoPkluTrNdr9HHyT46qzUZhq7E905ahzcIWPsy1O4NDe+xV1to/gA0GwEhjUCvVyUSf+gGNburjoakXdBlkgr+f+wOwGZDS1pEeqPXzKpspaVA/XyENlH5IBrcrUpkuvqe/+fhXFar6mgozslhtapOsgtUSyhP8oqmqpec9l3S5iNpDPp2E+1qBPDLA634bctJqlfyICrQg81H0+ndMLTUNGmzxUpOoYXkzHwGf7cJZyc90/+vNWaLlR/XH2VY60gGfr0RV6MTCx/rQIiPa6nXzTdZiD+bR+eP1M/2V0Oa0athCAO/3si/x8/w46jmNI7wZdHuRArNVu6OjmDNgRTG/6Y6ve5vF0W/RsE88ONG0goufxr3Z/c1oVOtSny75giTV5XRoXoeHWtVYtORtMtK/FdSuxoBuDsbyMo3selI+sVPOA8nvQ5LGQkSq1XyILfAQmJmvsN2V6OefJO6586hhfx4ZlSpc49YQ1htbWxbigNgqdIO0x3v47r+fVtJ2C8iP+ej/QF4kM/vj3W7rrNyJGC/xiRgF+WVtHfFI21e8VSYNt/4FSybBKMXQ8R/mBZckK1G8fVO0G6CCphKKi5nd+Y4VKoNAdUd91stkHdWBe5rP1TBfOP7wGKyHztnLOz4Dfp9Cc2Gw+Zv1RICr1BY+aYKssasVh0KmybDmg/UecP+VMnwillM8EZRduYB30OjgZhMJlbN+ZnO/gk4rfuw9PszuIGlEJ7ap5Yg/NhDXXPgT/Dvj2pNfq8PVVAXPVqN8n/f5ZI/vl3Wqrzg8iKvFbxHM/2hi58AZOm8yLEaCNGdufjB14hV09mm+N8I6fVG4L/3nFkAATVUromt36kklCU1GgT7FqoKBee6/UXVsbWt9EwF+n6mZpSY81ROi7i/oXZPSI5TS1yiOkLaYdV5s+hZSD2gKlQ0GQIr3lQVKrq/oUb769+lvpesFljyPLQdD+HRqtpC0i41W2DNh2q2SJ0SnVSmfDVDpmRn3eEVKv+Fb6S9Q8FiUj/X5gJoP0HNsNkzV80yKasj8FLlnVFlPoPrcTApi3A/t8ta238xJ9JycTXqCfIuHayXtHRvEv4ezkRHqo7F1OwCjqTk0DKqdN4STdP4M+Y0TSr7UiPIE5PJxG9zF9KqXSdenh/HlqMqAJ4/rj2pOQWgwaHkbI6m5dAqyh+zRbPNINj2UlcCisozHk3N4aN/9vNgh2o0qezLjpNnmbH1JMvjkqji786/x2/cz+S1cJ/TCt41fn/xAy8gVfMmUJeJGScMo+Zdt3wYErBfYxKwi/JK2rvikTaveCpMm2vapY8cXg+aBqdjIKSBCjRKMuVB4m6IaO54zxYz7JyhAuiSCQCzkuDMMajSqvTrLH9dTase+BM4GR3bO3kn/PEAnDmqjnX1gVELVfnA4oDHYlKzFYrvw5SvKh+UdHwjTD0nS7vRA4bPUUHX309AsxHs2LuX3ZWHclt0fZb99TMjj06EsKZYH1jBiunv4uMfRIuc1XByCzS/n6nGe/l0yW4+vrsegfocnNd/wFH/9iyxtuSv2HjeN3zDvYbVFBo8cTLl4KTT+MAymNvvfYwmi+7kbE4+k0wj0Ts5cQ/L6ei0i0TNjz8sHWmn38MhaxjtfFJZkRFKsO4s3Zy2XVLTndICidClnnd/kuaLCcMFj7lactzC8cg7ffEDL4sOLjYD42p7MUl1AMRvt+ewaDgQurwKf0+AQ8vUNhdvqNNHdQps+dae5LLxYKjXH34bpJ7X6gEhDVUpyJ0zIbyZKt0IqqOrIFNVvChWkAX5meATDj/1gWProNtramZJw3uu/PeHuRBS4lTnirPH+Y87HQMuXmpZRKFa943zhUsuXkjJn/PUXDMvzdnNQx2r0eoCa7B/3nAMDxcD90RHXNJrmC1WHvj5X87mmXDSQcyJs9QN9WbK0GZ8s+YwS/cmMaRVJPkmC22qBdCmegATZsQS4edG5zpB5137P6BpOB1qBXI0NZfPSyRMLDb7kbZsO3aG95fsw2TR6FIniOX7VNnJuqHeDksJKnm50LZ6AH/FOiYzHdKqCr8Wrckv5kIhS5wnUlXvuHxhmaUpnfWx6HUaJ62V0Om0S/7Ztjp7oR+7FvyjLun4/0IC9mtMAnZRXkl7VzzS5hWPtHnFUmZ7x82HRRPhrm/U2uwrkZ8JaQfBzV8FQpcS3BxZpcoS+oSf9xCLVSu1lvm1+XuYuv4YeqwcecgLwqMpMJlwNhgwGTxxNuhB0/h8+QFSsk0MblmFV/7azTMNspkWZ2XBETUVesNznQnzdeP3f0/y7B87CXK10rJGMC8EbyFs/YsAfBP4PHWMiXRKUKPRR6whPG4ax6fGr/DU5fGOaQhzre3op9/Ih8YpfGAexHeWPgA00x3gYcN8Vlib0LhpS+7bPabU+1tjaUhHp10O294xDSZZ82WUYQmrrI153DDnoh+lWdNj0JU9FXlM4QS+cf7UcaPeoJZMlBc1u8PBf67ta0R1UoHTrj9UR1SnZ4uW3+xUFR90TipXxaJnS5/rXx3u+UHNBgBViWHpqxDZRnUOrH5PjewfXAqmHNWBUL0LTL9HLYHxqQL1+6slJK0fUbMN4mPUEgSrGb5qqUpvPrpFLYGxmGDsOvA4J8A+tEzdc+XWqkPPzbfMt2r7OW/XCKNXJbXE6Ph6OLRcvd7I+aU73kB1TGz8UnVuVC9jKQ7A8Q2q8yG0sXpuMaHlppG9ZTqeydvQ3fND6cokyXHqPYU2KnoZjU+Wqqz1DSN8Gf9bDBN71GF460hbtYHMfBNPzozFoNdzd3QEJouVMF83mlT2tV1Dp9OhaRqr9qfQINyHSl4urNyXzIf/7OftuxrSuOjY+3/ayoqioH7euHY0ivDl960nefZP1Tmk08HvnXNosf7/SNc8GWyaxJLRVRm03INd8ZmEupopyEohXgtkQEQ2b6c/wTFdOCFaCt7WDF42jeIN40+lPqpcnQdOw2fhUq1d2Z/lVSQB+zUmAbsor6S9Kx5p84pH2rxiuRXaOyWrgAd/3so9zSszvHXZ5anOZ/fpDO78aj2j2lbl5T71APWH/6oDKTQK91FTgbOS4KNa6oTxMWqpgrkQ6+kYpp0MZNLfqmTelhe7EODhwqipW1h7MBU9Vqzoubd5BPVCvTmWlstPG47x7oCG3NeyCr9uOs6R+e+RovkQrT/IAd/2zEqN5EPj1/R12kRKSEe6nBhNplXNuHAvKr9W37KP2S6TAFiktaG6dgI9Gl+Z7+QT5ykAZGlufGsYzFOWHx3e75+W9lQa+h1+a9+gbutuGHbOUCPGdfupJQ9fXmR5SHADNfJ7YmPpfS0fUiPgq99VJSCLBdVTS0eyk9Ra+bJUu0112FyugBrg7OmYByKyXdnlEa8FzxC11OXc3BGXS6c6lUrNZDC6q6UHJdXoqpbY+ETAkZWlr1Wjq5p5kH8WwpvDwX+wegRx4oyJqmllHA+qMyG8GTS8FxY8qRJu9v3cMX9Gtdvtsx3aPQ6pB1WHRHZRorgn94F3KPxxv6ouUqzvZxA9SiXp3PyNquzxfTfQLPCUCtLJS4eguupx2mG0BU+iO71ddWS0ecQ+G6GsmQ2apspv+kQ4zk6yWlSnT1THUrMZlu1NYtL8PbSM8ufDexqjtxbacpxojQeTmWfGZ9dUWPQMSyzN4b7p3FE/hNxCM3mFFgx6PZNXH6JvozAahPuo72+DK5jzSD11mPVZlegeYcb5557o9Xo291jAM7+swWKx8sKQbvRpFFZ2O1xFErBfYxKwi/JK2rvikTaveKTNKxZpb8jKN+FmdLKN5JVp0USVa+Curx0CBqtVY/KqQ1T2d+fOJmpmgNliZefpDL5bcwRPFwNv3tUAF4Naf52UmU+Qlws6nY6cAjOjpm5h67EztK7mz5dDmjEn5jTL9yXx6aCmhPi4ciw1B71Ox5qDKdQJ8WLR7kR+WHeUBzzW8XStFHY2e4MhU7fTo0EIC3YmEOvyf/jqcsiq2h2vkb/DwaUU/DaMf8xN+crcn8fu6Ua3RpFlt7nVqgI0cz48sFStG887q4K3rEQ1mtvpWRUM5qSoQPnUVjXFvNOzjlPK47dD7K9qWnrVEqOJGadh7lg4usa+rc8nKv9BdrIK4r7rrF7TM0QFeus+Vmvfi905Wa2Hj49RVR70elX+ccu3av8L8fD3k2q5SDH3QFVhYvV7KoN+t9fU9t1/qhFqs2PiMYIbqPX4egPojVCQgbgEATUg7SK5KAxuKicCqPwXm79RAXetHqozrDiJZ8njRy1QSyHSj0CXV1TejsMrVJnQkvkaqrRR36uNB6sZA0tfUdsj26mEiHe8BSc2q9kN3mGwZ476XnQPsOf+ePWsyvS+5AWInU5e84dx6/PulX0ehbnqdV082XAolS3H0pnQtdaVXesyScB+jUnALsorae+KR9q84pE2r1ikvW8umqZxPC0Xf09nW0m41OwCfNyMvPLXbo7v284PtTbhdsckW835M1m5LNuXzJ1Nq+Bs0JePNrdaVIAdXF+NgJaUn6GmS3sUJUgsrnBQmA3J+9S083OZC1QSych2ULeP/TzNqtana5qqvmAtWiZwbnLI5Dg1Km/0UNPxPQKKRrxRHTT5mSpgLB45HrdN3VP6Ydg9G3b/obbX6qGqJHR8Rs1Y2PFb6Xvt9aGahn9yk8rsXzxqDWqqvV+kuu8jq9RU907PwNqPS88a8K8GPpXto/tOzo4dG2XQnD3R9fscctKg3p0q2F3z/gXPuWU5uZx/xkdJvT9WswJuMtc7YL96aRiFEEIIIYS4Sel0OqoGOk7vDSzK4P3OgEZAI2Ckw34/L3cGtqh6fW7wUumdoPXDZe8rLplYTKdTo/1ufmUH66CmQvd4p/R5Oid7uUYoHagXC6prn5Jd8nzbPXmrWQC7/4SIFvYkjEF1oEa3ovJ1Pqq0Y/Je1RGh00H3t9RSgOB6ajQ3/aiaNdB0OJzaojoY/v0RFj6tkucN+kWdZ7WqEf7AWuqea3RVCet0etVJ4B0OLp7qHr7ppKpGjNuqAviD/8CSF1WSOldf6Pc55tTDZM9+As+BkzFUK5GlPLKt+tejEjy0SmX2r1RbrUePj1XXM7rCzGHquAeWwQ9FlSc6PAUtHlRr2CNawGdqPTq+kWrNesq+0p+zZ4h9Cv3l8q+mRtjLEtkejq9TQbh/lONrhzdXnUK7/4CzJRLLXUqwDtclQdytQAJ2IYQQQgghxI0T1QEeWq1GwEsyOMOQmfbnIQ3sjz0C7Ini6t9l3+5ktM8saP6AKm8X2tjeSaDXq86Ac18HVEBd0vA5qsPAt4p6XusO9VWC5hnO6jpv0KvyOZUjqt0Gw2ar1/YIVEs+ipVMMnfH22rJQkRz6PwSHFmt1pe7+akcCKA6G1a8qa7hX13VIq/dS3UwTO0JbR+DKq3ht/tUEA32TotBv6jyef7V1NKL6UXXHPqnCv6T90KTofBFM7VcoqTwaLh3Gqx6W3WG5GfCT71V7oTRC+wdNrV6wI/d1eOOz6jlHJqm1tif3gbLXoOMooB+9GLVAZCwQ92fuCgJ2IUQQgghhBA3VliTq39NvV4lfLtS7v7q60rodFCjy8WPa/Oo/XHHZ9TXuer2VV/Fmg1X/3oEwPOn1ZR9vR7Gb1Mj+vkZkLATavdU91H8Hmp2UzMVUvape9Pp7HkQ7p0G236COr3BKxTWf6bWtHsEQO+P7K89fptao14yO32VVnDfbyoBXbVOjvfuH6VmMRxZqWY6OBnPP5tDlEkCdiGEEEIIIYS4GZUsGedfTf3r4qUyu5elyeCyt1duqb6K3ftz2ccFVC97e51eZW8HVRKv5CwIcVkukLJTCCGEEEIIIYQQN4oE7EIIIYQQQgghRDkkAbsQQgghhBBCCFEOScAuhBBCCCGEEEKUQxKwCyGEEEIIIYQQ5ZAE7EIIIYQQQgghRDkkAbsQQgghhBBCCFEOScAuhBBCCCGEEEKUQxKwCyGEEEIIIYQQ5ZAE7EIIIYQQQgghRDkkAbsQQgghhBBCCFEOScAuhBBCCCGEEEKUQxKwCyGEEEIIIYQQ5ZAE7EIIIYQQQgghRDkkAbsQQgghhBBCCFEOScAuhBBCCCGEEEKUQxKwCyGEEEIIIYQQ5ZAE7EIIIYQQQgghRDlkuNE3cL1pmgZAZmbmDb6TizOZTOTm5pKZmYnRaLzRtyOuMWnvikfavOKRNq9YpL0rHmnzikfavOK50jYvjj+L49FLVeEC9qysLAAqV658g+9ECCGEEEIIIURFkpWVhY+PzyUfr9MuN8S/yVmtVuLj4/Hy8kKn093o27mgzMxMKleuzMmTJ/H29r7RtyOuMWnvikfavOKRNq9YpL0rHmnzikfavOK50jbXNI2srCzCwsLQ6y99ZXqFG2HX6/VERETc6Nu4LN7e3vILoAKR9q54pM0rHmnzikXau+KRNq94pM0rnitp88sZWS8mSeeEEEIIIYQQQohySAJ2IYQQQgghhBCiHJKAvRxzcXHh1VdfxcXF5UbfirgOpL0rHmnzikfavGKR9q54pM0rHmnziud6t3mFSzonhBBCCCGEEELcDGSEXQghhBBCCCGEKIckYBdCCCGEEEIIIcohCdiFEEIIIYQQQohySAJ2IYQQQgghhBCiHJKAvZyaPHkyUVFRuLq6Eh0dzdq1a2/0LYkr8M4779CiRQu8vLwICgqif//+7N+/3+EYTdOYNGkSYWFhuLm5cdttt7Fnzx6HYwoKChg/fjyBgYF4eHjQr18/Tp06dT3firgC77zzDjqdjgkTJti2SXvfek6fPs2wYcMICAjA3d2dJk2asG3bNtt+afNbi9ls5qWXXiIqKgo3NzeqVavG66+/jtVqtR0jbX5zW7NmDX379iUsLAydTsfcuXMd9l+t9j1z5gzDhw/Hx8cHHx8fhg8fztmzZ6/xuxNluVCbm0wmJk6cSMOGDfHw8CAsLIwRI0YQHx/vcA1p85vHxX7GSxozZgw6nY5PP/3UYfv1bG8J2MuhmTNnMmHCBF588UW2b99Ohw4d6NmzJydOnLjRtyYu0+rVq3n00UfZtGkTS5cuxWw20717d3JycmzHvP/++3z88cd8+eWXbN26lZCQELp160ZWVpbtmAkTJjBnzhxmzJjBunXryM7Opk+fPlgslhvxtsQl2Lp1K99++y2NGjVy2C7tfWs5c+YM7dq1w2g0smjRIvbu3ctHH32Er6+v7Rhp81vLe++9x9dff82XX35JXFwc77//Ph988AFffPGF7Rhp85tbTk4OjRs35ssvvyxz/9Vq3yFDhhAbG8vixYtZvHgxsbGxDB8+/Jq/P1Hahdo8NzeXmJgYXn75ZWJiYpg9ezYHDhygX79+DsdJm988LvYzXmzu3Lls3ryZsLCwUvuua3trotxp2bKlNnbsWIdtderU0Z577rkbdEfiaklOTtYAbfXq1ZqmaZrVatVCQkK0d99913ZMfn6+5uPjo3399deapmna2bNnNaPRqM2YMcN2zOnTpzW9Xq8tXrz4+r4BcUmysrK0mjVrakuXLtU6deqkPf7445qmSXvfiiZOnKi1b9/+vPulzW89vXv31u6//36HbQMGDNCGDRumaZq0+a0G0ObMmWN7frXad+/evRqgbdq0yXbMxo0bNUDbt2/fNX5X4kLObfOybNmyRQO048ePa5ombX4zO197nzp1SgsPD9d2796tRUZGap988olt3/VubxlhL2cKCwvZtm0b3bt3d9jevXt3NmzYcIPuSlwtGRkZAPj7+wNw9OhREhMTHdrbxcWFTp062dp727ZtmEwmh2PCwsJo0KCBfE+UU48++ii9e/ema9euDtulvW898+bNo3nz5gwcOJCgoCCaNm3Kd999Z9svbX7rad++PcuXL+fAgQMA7Nixg3Xr1tGrVy9A2vxWd7Xad+PGjfj4+NCqVSvbMa1bt8bHx0e+B24CGRkZ6HQ622wqafNbi9VqZfjw4TzzzDPUr1+/1P7r3d6GK3wf4hpJTU3FYrEQHBzssD04OJjExMQbdFfiatA0jSeffJL27dvToEEDAFubltXex48ftx3j7OyMn59fqWPke6L8mTFjBjExMWzdurXUPmnvW8+RI0eYMmUKTz75JC+88AJbtmzhsccew8XFhREjRkib34ImTpxIRkYGderUwcnJCYvFwltvvcXgwYMB+Tm/1V2t9k1MTCQoKKjU9YOCguR7oJzLz8/nueeeY8iQIXh7ewPS5rea9957D4PBwGOPPVbm/uvd3hKwl1M6nc7huaZppbaJm8u4cePYuXMn69atK7XvStpbvifKn5MnT/L444/zzz//4Orqet7jpL1vHVarlebNm/P2228D0LRpU/bs2cOUKVMYMWKE7Thp81vHzJkz+eWXX/j111+pX78+sbGxTJgwgbCwMEaOHGk7Ttr81nY12res4+V7oHwzmUzcd999WK1WJk+efNHjpc1vPtu2beOzzz4jJibmstvlWrW3TIkvZwIDA3FycirV85KcnFyqN1fcPMaPH8+8efNYuXIlERERtu0hISEAF2zvkJAQCgsLOXPmzHmPEeXDtm3bSE5OJjo6GoPBgMFgYPXq1Xz++ecYDAZbe0l73zpCQ0OpV6+ew7a6devakoTKz/it55lnnuG5557jvvvuo2HDhgwfPpwnnniCd955B5A2v9VdrfYNCQkhKSmp1PVTUlLke6CcMplM3HvvvRw9epSlS5faRtdB2vxWsnbtWpKTk6lSpYrtb7njx4/z1FNPUbVqVeD6t7cE7OWMs7Mz0dHRLF261GH70qVLadu27Q26K3GlNE1j3LhxzJ49mxUrVhAVFeWwPyoqipCQEIf2LiwsZPXq1bb2jo6Oxmg0OhyTkJDA7t275XuinOnSpQu7du0iNjbW9tW8eXOGDh1KbGws1apVk/a+xbRr165UqcYDBw4QGRkJyM/4rSg3Nxe93vHPJycnJ1tZN2nzW9vVat82bdqQkZHBli1bbMds3ryZjIwM+R4oh4qD9YMHD7Js2TICAgIc9kub3zqGDx/Ozp07Hf6WCwsL45lnnmHJkiXADWjvy0pRJ66LGTNmaEajUfvhhx+0vXv3ahMmTNA8PDy0Y8eO3ehbE5fp4Ycf1nx8fLRVq1ZpCQkJtq/c3FzbMe+++67m4+OjzZ49W9u1a5c2ePBgLTQ0VMvMzLQdM3bsWC0iIkJbtmyZFhMTo3Xu3Flr3LixZjabb8TbEpehZJZ4TZP2vtVs2bJFMxgM2ltvvaUdPHhQmz59uubu7q798ssvtmOkzW8tI0eO1MLDw7W///5bO3r0qDZ79mwtMDBQe/bZZ23HSJvf3LKysrTt27dr27dv1wDt448/1rZv327LCH612rdHjx5ao0aNtI0bN2obN27UGjZsqPXp0+e6v19x4TY3mUxav379tIiICC02Ntbh77mCggLbNaTNbx4X+xk/17lZ4jXt+ra3BOzl1FdffaVFRkZqzs7OWrNmzWxlwMTNBSjza+rUqbZjrFar9uqrr2ohISGai4uL1rFjR23Xrl0O18nLy9PGjRun+fv7a25ublqfPn20EydOXOd3I67EuQG7tPetZ/78+VqDBg00FxcXrU6dOtq3337rsF/a/NaSmZmpPf7441qVKlU0V1dXrVq1atqLL77o8Ie7tPnNbeXKlWX+3z1y5EhN065e+6alpWlDhw7VvLy8NC8vL23o0KHamTNnrtO7FCVdqM2PHj163r/nVq5cabuGtPnN42I/4+cqK2C/nu2t0zRNu7wxeSGEEEIIIYQQQlxrsoZdCCGEEEIIIYQohyRgF0IIIYQQQgghyiEJ2IUQQgghhBBCiHJIAnYhhBBCCCGEEKIckoBdCCGEEEIIIYQohyRgF0IIIYQQQgghyiEJ2IUQQgghhBBCiHJIAnYhhBBCCCGEEKIckoBdCCGEENecTqdj7ty5N/o2hBBCiJuKBOxCCCHELW7UqFHodLpSXz169LjRtyaEEEKICzDc6BsQQgghxLXXo0cPpk6d6rDNxcXlBt2NEEIIIS6FjLALIYQQFYCLiwshISEOX35+foCarj5lyhR69uyJm5sbUVFRzJo1y+H8Xbt20blzZ9zc3AgICOChhx4iOzvb4Zgff/yR+vXr4+LiQmhoKOPGjXPYn5qayl133YW7uzs1a9Zk3rx51/ZNCyGEEDc5CdiFEEIIwcsvv8zdd9/Njh07GDZsGIMHDyYuLg6A3NxcevTogZ+fH1u3bmXWrFksW7bMISCfMmUKjz76KA899BC7du1i3rx51KhRw+E1XnvtNe6991527txJr169GDp0KOnp6df1fQohhBA3E52madqNvgkhhBBCXDujRo3il19+wdXV1WH7xIkTefnll9HpdIwdO5YpU6bY9rVu3ZpmzZoxefJkvvvuOyZOnMjJkyfx8PAAYOHChfTt25f4+HiCg4MJDw9n9OjRvPnmm2Xeg06n46WXXuKNN94AICcnBy8vLxYuXChr6YUQQojzkDXsQgghRAVw++23OwTkAP7+/rbHbdq0cdjXpk0bYmNjAYiLi6Nx48a2YB2gXbt2WK1W9u/fj06nIz4+ni5dulzwHho1amR77OHhgZeXF8nJyVf6loQQQohbngTsQgghRAXg4eFRaor6xeh0OgA0TbM9LusYNze3S7qe0Wgsda7Var2sexJCCCEqElnDLoQQQgg2bdpU6nmdOnUAqFevHrGxseTk5Nj2r1+/Hr1eT61atfDy8qJq1aosX778ut6zEEIIcauTEXYhhBCiAigoKCAxMdFhm8FgIDAwEIBZs2bRvHlz2rdvz/Tp09myZQs//PADAEOHDuXVV19l5MiRTJo0iZSUFMaPH8/w4cMJDg4GYNKkSYwdO5agoCB69uxJVlYW69evZ/z48df3jQohhBC3EAnYhRBCiApg8eLFhIaGOmyrXbs2+/btA1QG9xkzZvDII48QEhLC9OnTqVevHgDu7u4sWbKExx9/nBYtWuDu7s7dd9/Nxx9/bLvWyJEjyc/P55NPPuHpp58mMDCQe+655/q9QSGEEOIWJFnihRBCiApOp9MxZ84c+vfvf6NvRQghhBAlyBp2IYQQQgghhBCiHJKAXQghhBBCCCGEKIdkDbsQQghRwcnqOCGEEKJ8khF2IYQQQgghhBCiHJKAXQghhBBCCCGEKIckYBdCCCGEEEIIIcohCdiFEEIIIYQQQohySAJ2IYQQQgghhBCiHJKAXQghhBBCCCGEKIckYBdCCCGEEEIIIcohCdiFEEIIIYQQQohy6P8BcRrUEz6MS/EAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "# Plotting the specified columns\n", - "plt.figure(figsize=(12, 6))\n", - "plt.plot(train_df[\"epoch\"][:min_epoch], train_df[\"IMPORTANT - multi_sess - train_loss\"][:min_epoch], label='no ORI embedding')\n", - "plt.plot(roi_embedding_loss_df[\"epoch\"][:min_epoch], roi_embedding_loss_df[\"train_loss\"][:min_epoch], label='with ROI embedding')\n", - "#plt.plot(dend_loss_df[\"epoch\"][:min_epoch], dend_loss_df[\"multi_sess_dend - train_loss\"][:min_epoch], label='dend sessions only')\n", - "#plt.plot(soma_loss_df[\"epoch\"][:min_epoch], soma_loss_df[\"multi_sess_soma - train_loss\"][:min_epoch], label = 'soma sessions only')\n", - "#plt.plot(within_loss_df[\"epoch\"][:700], np.mean(within_sess_losses_all,axis=0), label = 'within sessions all')\n", - "#plt.plot(within_loss_df[\"epoch\"][:700], np.mean(within_sess_losses_dend,axis=0), label = 'within sessions dend')\n", - "#plt.plot(within_loss_df[\"epoch\"][:700], np.mean(within_sess_losses_soma,axis=0), label = 'within sessions soma')\n", - "\n", - "\n", - "plt.title(\"Multi-session with poyo single session config\")\n", - "plt.xlabel(\"Epoch\")\n", - "plt.ylabel(\"Train Loss\")\n", - "plt.grid(True)\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(8, 4))\n", - "plt.plot(train_df[\"epoch\"][:min_epoch - window_size + 1], smooth_all_sessions, label='no ROI embed (smoothed)')\n", - "plt.plot(train_df[\"epoch\"][:min_epoch - window_size + 1], smooth_all_sessions_ROI, label='w/ ROI embed (smoothed)')\n", - "#plt.plot(dend_loss_df[\"epoch\"][:min_epoch - window_size + 1], smooth_dend_sessions, label='dend sessions only (smoothed)')\n", - "#plt.plot(soma_loss_df[\"epoch\"][:min_epoch - window_size + 1], smooth_soma_sessions, label='soma sessions only (smoothed)')\n", - "\n", - "#plt.title(\"Multi-session with Poyo Single Session Config - Smoothed\")\n", - "plt.xlabel(\"Epoch\")\n", - "plt.ylabel(\"Train Loss\")\n", - "#plt.grid(True)\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Val" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "val_dend_df = pd.read_csv(\"/home/mila/x/xuejing.pan/POYO/results/cross_sess/val/dend_combined_vals.csv\")\n", - "val_soma_df = pd.read_csv(\"/home/mila/x/xuejing.pan/POYO/results/cross_sess/val/soma_combined_vals.csv\")\n", - "\n", - "val_df = pd.read_csv(\"/home/mila/x/xuejing.pan/POYO/results/cross_sess/val/multi_sess_combined_vals.csv\")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "#Get all accuracies\n", - "all_accs_list = get_accs(val_df,sess_ids)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "soma_accs_list = get_accs(val_soma_df, soma_sess_ids)\n", - "dend_accs_list = get_accs(val_dend_df, dend_sess_ids)" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "210" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dend_accs_list.shape[1]" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "all_soma_accs_list = get_accs(val_df, soma_sess_ids)\n", - "all_dend_accs_list = get_accs(val_df, dend_sess_ids)\n", - "\n", - "all_dend_accs_list_avg = np.mean(all_dend_accs_list, axis = 0)\n", - "all_soma_accs_list_avg = np.mean(all_soma_accs_list, axis = 0)" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1580\n" - ] - } - ], - "source": [ - "min_epoch_num = min(soma_accs_list.shape[1],dend_accs_list.shape[1],all_accs_list.shape[1])\n", - "print(min_epoch_num*10)\n", - "\n", - "acc_final_all_soma = all_soma_accs_list[:, min_epoch_num-1]\n", - "acc_final_all_dend = all_dend_accs_list[:, min_epoch_num-1]\n", - "acc_final_soma = soma_accs_list[:, min_epoch_num-1]\n", - "acc_final_dend = dend_accs_list[:, -1]" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(22,)" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "acc_final_soma.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#def plot_2_sess(sess_1_acc, sess_2_acc, sess_1_label, sess_2_label, n_rois):\n", - "sess_ids, planes, lines, n_rois, n_rois_soma, n_rois_dend = getNWBinfo_roi()\n", - "plot_2_sess(acc_final_all_dend, acc_final_dend, 'poyo_ssm_all_(epoch_1580)', 'poyo_ssm_dend_(epoch_2100)', np.array(n_rois_dend), '')" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#def plot_2_sess(sess_1_acc, sess_2_acc, sess_1_label, sess_2_label, n_rois):\n", - "sess_ids, planes, lines, n_rois, n_rois_soma, n_rois_dend = getNWBinfo_roi()\n", - "plot_2_sess(acc_final_all_dend, acc_final_dend, 'poyo_ssm_all', 'poyo_ssm_dend', np.array(n_rois_dend), min_epoch_num*10)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(158,)" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.shape(val_df[\"epoch\"].values)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "all_accs_list_avg = np.mean(all_accs_list, axis = 0)\n", - "dend_accs_list_avg = np.mean(dend_accs_list, axis = 0)\n", - "soma_accs_list_avg = np.mean(soma_accs_list, axis = 0)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(12, 6))\n", - "\n", - "#plt.plot(val_df[\"epoch\"].values, all_soma_accs_list_avg, label = \"all_soma\")\n", - "#plt.plot(val_df[\"epoch\"].values, all_dend_accs_list_avg, label = \"all_dend\")\n", - "plt.plot(val_dend_df[\"epoch\"].values, dend_accs_list_avg, label = \"dend\")\n", - "plt.plot(val_soma_df[\"epoch\"].values, soma_accs_list_avg, label = \"soma\")\n", - "plt.title(\"dend and soma performance\")\n", - "plt.legend()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.7444117644429207\n", - "0.7303046232887677\n", - "0.7332887717268683\n" - ] - } - ], - "source": [ - "#avg values\n", - "print(all_accs_list_avg[-1])\n", - "print(dend_accs_list_avg[-1])\n", - "print(soma_accs_list_avg[-1])" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.18837255137955705" - ] - }, - "execution_count": 90, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.std(soma_accs_list[:,-1])" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.83823532 0.66911763 0.70588237 0.74264705 0.625 0.57352942\n", - " 0.7647059 0.83823532 0.80882353 0.91911763 0.74264705 0.66911763\n", - " 0.74264705 0.47058824 0.56617647 0.95588237 0.83823532 0.86764705\n", - " 0.69117647 0.68382353 0.75735295 0.8602941 0.78676468 0.78676468\n", - " 0.31617647 0.6102941 0.64705884 0.6102941 0.47794119 0.5\n", - " 0.77941179 0.80882353 0.80147058 0.84558821 0.875 0.80147058\n", - " 0.66176468 0.94117647 0.94117647 0.94117647 0.81617647 0.6397059\n", - " 0.79411763 0.83088237 0.83088237 0.875 0.94117647 0.8897059\n", - " 0.91176468 0.22794117]\n" - ] - } - ], - "source": [ - "all_accs_last = all_accs_list[:,-1]\n", - "print(all_accs_last)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "filenames,sess_ids, num_rois, lines, planes = getNWBfilenames()" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [], - "source": [ - "def get_mean_std(accs_arr):\n", - " accs_std = np.std(accs_arr)\n", - " accs_avg = np.mean(accs_arr)\n", - "\n", - " print(\"all_std: \",accs_std)\n", - " print(\"all_avg: \",accs_avg)\n", - "\n", - "\n", - " dend_accs = []\n", - " soma_accs = []\n", - "\n", - " for count, curr_sess_id in enumerate(sess_ids):\n", - " if planes[count] == \"soma\":\n", - " soma_accs.append(accs_arr[count])\n", - " else:\n", - " dend_accs.append(accs_arr[count])\n", - "\n", - " dend_accs = np.array(dend_accs)\n", - " soma_accs = np.array(soma_accs)\n", - "\n", - " accs_soma_std = np.std(soma_accs)\n", - " accs_soma_avg = np.mean(soma_accs)\n", - " accs_dend_std = np.std(dend_accs)\n", - " accs_dend_avg = np.mean(dend_accs)\n", - "\n", - " print(\"soma std: \",accs_soma_std)\n", - " print(\"soma avg: \",accs_soma_avg)\n", - " print(\"dend std: \",accs_dend_std)\n", - " print(\"soma avg: \",accs_dend_avg)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "all_std: 0.15716426337973288\n", - "all_avg: 0.7444117644429207\n", - "soma std: 0.18478268280310362\n", - "soma avg: 0.7115641717206348\n", - "dend std: 0.12556448572239293\n", - "soma avg: 0.7702205872961453\n" - ] - } - ], - "source": [ - "get_mean_std(all_accs_last)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## poyo 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plotting loss" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "poyo_1_loss_df = pd.read_csv('/home/mila/x/xuejing.pan/POYO/results/cross_sess/loss/poyo_1_train_loss.csv', usecols=[\"epoch\", \"multi_sess_poyo_1 - train_loss\"])\n", - "poyo_ssm_loss_df = pd.read_csv(\"/home/mila/x/xuejing.pan/POYO/results/cross_sess/loss/multi_sess_combined_losses.csv\", usecols=[\"epoch\", \"IMPORTANT - multi_sess - train_loss\"])\n", - "within_loss_df = pd.read_csv(\"/home/mila/x/xuejing.pan/POYO/results/within-sess/combined_train_losses.csv\")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "poyo_1_loss = poyo_1_loss_df[\"multi_sess_poyo_1 - train_loss\"].values\n", - "poyo_ssm_loss = poyo_ssm_loss_df['IMPORTANT - multi_sess - train_loss'].values" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "within_sess_loss_list = []\n", - "for curr_sess in sess_ids:\n", - " within_sess_losses, _, _, _, _ = get_losses_for_one_sess(within_loss_df,curr_sess)\n", - " within_sess_loss_list.append(within_sess_losses)\n", - "\n", - "poyo_within_sess_loss = np.array(within_sess_loss_list)\n", - "poyo_within_sess_loss_avg = np.mean(poyo_within_sess_loss, axis=0)\n", - "within_std = np.std(poyo_within_sess_loss, axis = 0)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(700,)\n" - ] - } - ], - "source": [ - "print(within_std.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "# Create a figure and axis\n", - "plt.figure(figsize=(10, 6))\n", - "\n", - "# Plot each array\n", - "plt.plot(poyo_1_loss, label='poyo_1')\n", - "plt.plot(poyo_ssm_loss, label='poyo_ssm')\n", - "#plt.plot(poyo_within_sess_loss_avg, label='poyo_within_sess (avg)')\n", - "\n", - "plt.errorbar(range(len(poyo_within_sess_loss_avg)), poyo_within_sess_loss_avg, yerr=within_std, label='poyo_within_sess', fmt='-o', alpha = 0.05)\n", - "\n", - "# Adding titles and labels\n", - "plt.title('Poyo training loss comparison')\n", - "plt.xlabel('Epochs')\n", - "plt.ylabel('Loss')\n", - "\n", - "# Show legend\n", - "plt.legend()\n", - "plt.grid()\n", - "\n", - "# Show the plot\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Accs plots (Mehdi's suggested plot)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "#Get all session final accuracies\n", - "#poyo_1_vals_df = pd.read_csv(\"/home/mila/x/xuejing.pan/POYO/results/cross_sess/val/poyo_1_vals.csv\")\n", - "poyo_ssm_vals_df = pd.read_csv(\"/home/mila/x/xuejing.pan/POYO/results/cross_sess/val/multi_sess_combined_vals.csv\")\n", - "#poyo_within_val_df = pd.read_csv(\"/home/mila/x/xuejing.pan/POYO/results/within-sess/combined_vals.csv\")\n", - "poyo_roi_vals_df = pd.read_csv(\"/home/mila/x/xuejing.pan/POYO/results/cross_sess/val/roi_embed_vals_combined.csv\")" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "#read all accuracies\n", - "poyo_roi_vals = get_accs(poyo_roi_vals_df,sess_ids)\n", - "poyo_ssm_vals = get_accs(poyo_ssm_vals_df,sess_ids)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "poyo_roi_vals_final = poyo_roi_vals[:, -1]\n", - "poyo_ssm_vals_final = poyo_ssm_vals[:,-1]" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(50, 135)" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "poyo_roi_vals.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/50 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "min_epoch_num= 135\n", - "plot_2_sess(poyo_ssm_vals_final,poyo_roi_vals_final, \"poyo ssm without ROI embedding\", \"poyo ssm with ROI embedding\", n_rois, min_epoch_num*10)" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [], - "source": [ - "mlp_accs = np.array([83.45588235, 76.47058824, 75.73529412, 67.64705882, 63.60294118,\n", - " 56.98529412, 73.52941176, 81.61764706, 71.69117647, 94.48529412,\n", - " 81.25 , 71.32352941, 78.67647059, 51.10294118, 72.79411765,\n", - " 93.75 , 78.30882353, 84.55882353, 77.20588235, 68.75 ,\n", - " 71.32352941, 72.42647059, 77.57352941, 77.57352941, 44.48529412,\n", - " 52.94117647, 68.38235294, 58.08823529, 51.83823529, 56.25 ,\n", - " 83.82352941, 83.08823529, 73.52941176, 88.97058824, 76.10294118,\n", - " 75. , 63.23529412, 84.55882353, 86.39705882, 92.27941176,\n", - " 80.88235294, 65.07352941, 83.08823529, 83.45588235, 88.97058824,\n", - " 94.48529412, 94.48529412, 90.80882353, 93.38235294, 31.25 ])\n", - "mlp_accs = mlp_accs/100" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'mlp_accs' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[43], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m plot_2_sess(\u001b[43mmlp_accs\u001b[49m,poyo_1_vals_final, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMLP\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpoyo_1\u001b[39m\u001b[38;5;124m\"\u001b[39m, n_rois, min_epoch_num\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m10\u001b[39m)\n", - "\u001b[0;31mNameError\u001b[0m: name 'mlp_accs' is not defined" - ] - } - ], - "source": [ - "plot_2_sess(mlp_accs,poyo_1_vals_final, \"MLP\", \"poyo_1\", n_rois, min_epoch_num*10)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "poyo_roi_vals_avg_all_sess = np.mean(poyo_roi_vals, axis=0)\n", - "poyo_ssm_vals_avg_all_sess = np.mean(poyo_ssm_vals, axis=0)\n", - "\n", - "poyo_roi_std = np.std(poyo_roi_vals, axis=0)\n", - "poyo_ssm_std = np.std(poyo_ssm_vals, axis=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.7623529398441314" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "poyo_roi_vals_avg_all_sess[-1]" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "# Create a figure and axis\n", - "plt.figure(figsize=(10, 6))\n", - "plt.errorbar(poyo_1_vals_df['epoch'], poyo_1_vals_avg_all_sess, yerr=poyo_1_std, label='poyo_1', fmt='-o', alpha = 0.5)\n", - "plt.errorbar(poyo_ssm_vals_df['epoch'], poyo_ssm_vals_avg_all_sess, yerr=poyo_ssm_std, label='poyo_ssm', fmt='-o', alpha = 0.5)\n", - "\n", - "# Adding titles and labels\n", - "plt.title('Poyo training loss comparison (avergaed over all 50 sessions)')\n", - "plt.xlabel('Epochs')\n", - "plt.ylabel('Accuracy')\n", - "\n", - "# Show legend\n", - "plt.legend()\n", - "plt.grid()\n", - "\n", - "# Show the plot\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Create a figure and axis\n", - "plt.figure(figsize=(12, 6))\n", - "\n", - "# Plot the average values with smaller markers\n", - "plt.plot(poyo_ssm_vals_df['epoch'][:135], poyo_ssm_vals_avg_all_sess[:135], label='no ROI embed', marker='o', markersize=1)\n", - "plt.plot(poyo_roi_vals_df['epoch'], poyo_roi_vals_avg_all_sess, label='with ROI embed', marker='o', markersize=1)\n", - "\n", - "# Create shaded error area for poyo_ssm\n", - "plt.fill_between(poyo_ssm_vals_df['epoch'][:135], \n", - " poyo_ssm_vals_avg_all_sess[:135] - poyo_ssm_std[:135], \n", - " poyo_ssm_vals_avg_all_sess[:135] + poyo_ssm_std[:135], \n", - " alpha=0.2)\n", - "\n", - "# Create shaded error area for poyo_1\n", - "plt.fill_between(poyo_roi_vals_df['epoch'], \n", - " poyo_roi_vals_avg_all_sess - poyo_roi_std, \n", - " poyo_roi_vals_avg_all_sess + poyo_roi_std, \n", - " alpha=0.2)\n", - "\n", - "# Adding titles and labels\n", - "plt.title('Poyo training loss comparison (averaged over all 50 sessions)')\n", - "plt.xlabel('Epochs')\n", - "plt.ylabel('Accuracy')\n", - "\n", - "# Show legend\n", - "plt.legend()\n", - "plt.grid()\n", - "\n", - "# Show the plot\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.86764705, 0.75 , 0.75 , 0.7647059 , 0.64705884,\n", - " 0.54411763, 0.79411763, 0.84558821, 0.78676468, 0.93382353,\n", - " 0.7647059 , 0.72794116, 0.75 , 0.47794119, 0.66176468,\n", - " 0.97794116, 0.77205884, 0.875 , 0.69117647, 0.69117647,\n", - " 0.7352941 , 0.77205884, 0.71323532, 0.81617647, 0.38235295,\n", - " 0.52205884, 0.59558821, 0.72058821, 0.56617647, 0.53676468,\n", - " 0.84558821, 0.80147058, 0.85294116, 0.875 , 0.92647058,\n", - " 0.82352942, 0.69117647, 0.8897059 , 0.94117647, 0.91176468,\n", - " 0.83823532, 0.70588237, 0.84558821, 0.82352942, 0.8897059 ,\n", - " 0.95588237, 0.96323532, 0.89705884, 0.92647058, 0.27941176])" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "poyo_roi_vals_final" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.1501184652049713" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.std(poyo_roi_vals_final)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.6" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/capoyo/notebooks/stitch_csv.ipynb b/examples/capoyo/notebooks/stitch_csv.ipynb deleted file mode 100644 index c4e79e5..0000000 --- a/examples/capoyo/notebooks/stitch_csv.ipynb +++ /dev/null @@ -1,211 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import os\n", - "from tqdm import tqdm\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def append_csv_by_key_columns(original_csv, new_csv, key_columns, output_csv):\n", - " \"\"\"\n", - " Reads two CSVs into pandas DataFrames and appends the values from the key columns of the new CSV \n", - " directly after the values of the original CSV.\n", - "\n", - " :param original_csv: Path to the original CSV file.\n", - " :param new_csv: Path to the new CSV file.\n", - " :param key_columns: List of columns that are used for appending.\n", - " :param output_csv: Path where the appended CSV will be saved.\n", - " \"\"\"\n", - " # Read the original and new CSV files\n", - " original_df = pd.read_csv(original_csv)\n", - " new_df = pd.read_csv(new_csv)\n", - "\n", - " # Validate key columns\n", - " if not all(col in original_df.columns for col in key_columns):\n", - " raise ValueError(\"One or more key columns are not present in the original CSV file.\")\n", - " if not all(col in new_df.columns for col in key_columns):\n", - " raise ValueError(\"One or more key columns are not present in the new CSV file.\")\n", - "\n", - " new_df_filtered = new_df[key_columns]\n", - " # Filter the new DataFrame to keep only the key columns\n", - " new_df_filtered = new_df[key_columns]\n", - "\n", - " # Append the new DataFrame to the original DataFrame\n", - " appended_df = original_df.append(new_df_filtered, ignore_index=True)\n", - "\n", - " \n", - " # Find the index where the epoch resets (starts again from 0)\n", - " reset_index = appended_df[appended_df['epoch'] == min(original_df[\"epoch\"].values)].index[1] # Assumes there are at least two runs\n", - "\n", - " # Get the last epoch number of the first run\n", - " last_epoch_first_run = appended_df.loc[reset_index - 1, 'epoch']\n", - "\n", - " # Increment the epochs of the second run\n", - " appended_df.loc[reset_index:, 'epoch'] += last_epoch_first_run\n", - "\n", - " # Save the appended data to the output CSV\n", - " appended_df.to_csv(output_csv, index=False)\n", - "\n", - " print(f\"Data appended and saved to {output_csv}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "all_sessions = [\n", - " \"758519303\",\"759189643\",\"759660390\",\"759666166\",\"759872185\",\n", - " \"760269100\",\"761730740\",\"762415169\",\"763646681\",\"761624763\", \n", - " \"761944562\",\"762250376\",\"760260459\",\"760659782\",\"761269197\", \n", - " \"763949859\",\"764897534\",\"765427689\",\"766755831\",\"767254594\",\n", - " \"768807532\",\"764704289\",\"765193831\",\"766502238\",\"777496949\", \n", - " \"778374308\",\"779152062\",\"777914830\",\"778864809\",\"779650018\",\n", - " \"826187862\",\"826773996\",\"827833392\",\"826338612\",\"826819032\", \n", - " \"828816509\",\"829283315\",\"823453391\",\"824434038\",\"825180479\", \n", - " \"826659257\",\"827300090\",\"828475005\",\"829520904\",\"832883243\", \n", - " \"833704570\",\"834403597\",\"836968429\",\"837360280\",\"838633305\" \n", - " ]\n", - "dend_sessions = ['759666166', '759872185', '760269100', '761730740', \n", - " '762415169', '763646681', '763949859', '764897534', \n", - " '765427689', '766755831', '767254594', '768807532', \n", - " '764704289', '765193831', '766502238', '777914830', \n", - " '778864809', '779650018', '826187862', '826773996', \n", - " '827833392', '826338612', '826819032', '828816509', \n", - " '829283315', '823453391', '824434038', '825180479']\n", - "\n", - "soma_sessions = ['758519303', '759189643', '759660390', '761624763', \n", - " '761944562', '762250376', '760260459', '760659782', \n", - " '761269197', '777496949', '778374308', '779152062', \n", - " '826659257', '827300090', '828475005', '829520904', \n", - " '832883243', '833704570', '834403597', '836968429', \n", - " '837360280', '838633305']\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "#Read two csvs into pandas df where information needs to be stitched\n", - "original_csv = \"/home/mila/x/xuejing.pan/POYO/results/cross_sess/loss/roi_embed_loss.csv\"\n", - "new_csv = \"/home/mila/x/xuejing.pan/POYO/results/cross_sess/loss/roi_embed_loss_cont.csv\"\n", - "output_csv = \"/home/mila/x/xuejing.pan/POYO/results/cross_sess/loss/roi_embed_loss_combined.csv\"\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def get_cols(all_sessions):\n", - " key_columns = [\"epoch\"]\n", - " val_col_names = []\n", - "\n", - " for sess in all_sessions:\n", - " curr_name = \"val/session_{}_accuracy_gabor_orientation\".format(sess)\n", - " key_columns.append(curr_name)\n", - "\n", - " return key_columns" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data appended and saved to /home/mila/x/xuejing.pan/POYO/results/cross_sess/val/roi_embed_vals_combined.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_54193/1229269154.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " appended_df = original_df.append(new_df_filtered, ignore_index=True)\n" - ] - } - ], - "source": [ - "key_columns = get_cols(all_sessions)\n", - "append_csv_by_key_columns(original_csv,new_csv,key_columns,output_csv)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data appended and saved to /home/mila/x/xuejing.pan/POYO/results/cross_sess/loss/roi_embed_loss_combined.csv\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_54193/1229269154.py:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " appended_df = original_df.append(new_df_filtered, ignore_index=True)\n" - ] - } - ], - "source": [ - "#For train\n", - "key_columns = [\"epoch\", \"train_loss\"]\n", - "append_csv_by_key_columns(original_csv,new_csv,key_columns,output_csv)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "test_newenv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.6" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/capoyo/notebooks/tracedata_analysis.ipynb b/examples/capoyo/notebooks/tracedata_analysis.ipynb deleted file mode 100644 index a1d687f..0000000 --- a/examples/capoyo/notebooks/tracedata_analysis.ipynb +++ /dev/null @@ -1,2733 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "from pynwb import NWBHDF5IO, NWBFile, TimeSeries\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import os\n", - "from tqdm import tqdm" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "names = np.array(['openscope_calcium/758519303/758519303_78', 'openscope_calcium/758519303/758519303_83', 'openscope_calcium/758519303/758519303_80', 'openscope_calcium/758519303/758519303_77', 'openscope_calcium/758519303/758519303_87', 'openscope_calcium/758519303/758519303_76', 'openscope_calcium/758519303/758519303_2', 'openscope_calcium/758519303/758519303_36', 'openscope_calcium/758519303/758519303_17', 'openscope_calcium/758519303/758519303_27', 'openscope_calcium/758519303/758519303_3', 'openscope_calcium/758519303/758519303_9', 'openscope_calcium/758519303/758519303_59', 'openscope_calcium/758519303/758519303_21', 'openscope_calcium/758519303/758519303_47', 'openscope_calcium/758519303/758519303_46', 'openscope_calcium/758519303/758519303_93', 'openscope_calcium/758519303/758519303_16', 'openscope_calcium/758519303/758519303_63', 'openscope_calcium/758519303/758519303_41', 'openscope_calcium/758519303/758519303_23', 'openscope_calcium/758519303/758519303_86', 'openscope_calcium/758519303/758519303_90', 'openscope_calcium/758519303/758519303_39', 'openscope_calcium/758519303/758519303_55', 'openscope_calcium/758519303/758519303_72', 'openscope_calcium/758519303/758519303_68', 'openscope_calcium/758519303/758519303_33', 'openscope_calcium/758519303/758519303_51', 'openscope_calcium/758519303/758519303_42', 'openscope_calcium/758519303/758519303_79', 'openscope_calcium/758519303/758519303_18', 'openscope_calcium/758519303/758519303_69', 'openscope_calcium/758519303/758519303_19', 'openscope_calcium/758519303/758519303_56', 'openscope_calcium/758519303/758519303_95', 'openscope_calcium/758519303/758519303_20', 'openscope_calcium/758519303/758519303_48', 'openscope_calcium/758519303/758519303_66', 'openscope_calcium/758519303/758519303_29', 'openscope_calcium/758519303/758519303_92', 'openscope_calcium/758519303/758519303_82', 'openscope_calcium/758519303/758519303_14', 'openscope_calcium/758519303/758519303_53', 'openscope_calcium/758519303/758519303_81', 'openscope_calcium/758519303/758519303_74', 'openscope_calcium/758519303/758519303_73', 'openscope_calcium/758519303/758519303_57', 'openscope_calcium/758519303/758519303_65', 'openscope_calcium/758519303/758519303_88', 'openscope_calcium/758519303/758519303_91', 'openscope_calcium/758519303/758519303_11', 'openscope_calcium/758519303/758519303_64', 'openscope_calcium/758519303/758519303_31', 'openscope_calcium/758519303/758519303_75', 'openscope_calcium/758519303/758519303_44', 'openscope_calcium/758519303/758519303_1', 'openscope_calcium/758519303/758519303_32', 'openscope_calcium/758519303/758519303_38', 'openscope_calcium/758519303/758519303_4', 'openscope_calcium/758519303/758519303_62', 'openscope_calcium/758519303/758519303_25', 'openscope_calcium/758519303/758519303_85', 'openscope_calcium/758519303/758519303_45', 'openscope_calcium/758519303/758519303_60', 'openscope_calcium/758519303/758519303_0', 'openscope_calcium/758519303/758519303_7', 'openscope_calcium/758519303/758519303_13', 'openscope_calcium/758519303/758519303_50', 'openscope_calcium/758519303/758519303_70', 'openscope_calcium/758519303/758519303_71', 'openscope_calcium/758519303/758519303_58', 'openscope_calcium/758519303/758519303_61', 'openscope_calcium/758519303/758519303_40', 'openscope_calcium/758519303/758519303_67', 'openscope_calcium/758519303/758519303_35', 'openscope_calcium/758519303/758519303_30', 'openscope_calcium/758519303/758519303_10', 'openscope_calcium/758519303/758519303_15', 'openscope_calcium/758519303/758519303_8', 'openscope_calcium/758519303/758519303_5', 'openscope_calcium/758519303/758519303_24', 'openscope_calcium/758519303/758519303_28', 'openscope_calcium/758519303/758519303_37', 'openscope_calcium/758519303/758519303_89', 'openscope_calcium/758519303/758519303_6', 'openscope_calcium/758519303/758519303_94', 'openscope_calcium/758519303/758519303_34', 'openscope_calcium/758519303/758519303_84', 'openscope_calcium/758519303/758519303_49', 'openscope_calcium/758519303/758519303_22', 'openscope_calcium/758519303/758519303_12', 'openscope_calcium/758519303/758519303_52', 'openscope_calcium/758519303/758519303_54', 'openscope_calcium/758519303/758519303_26', 'openscope_calcium/758519303/758519303_43'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test: single sess" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([65]),)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.where(names == \"openscope_calcium/758519303/758519303_0\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "source_dir = \"/network/projects/neuro-galaxy/data/raw/openscope_calcium\"\n", - "#file_name = \"sub-433451_ses-824434038_obj-raw_behavior+image+ophys.nwb\"\n", - "file_name = \"sub-433458_ses-826659257_obj-raw_behavior+image+ophys.nwb\"\n", - "io = NWBHDF5IO(os.path.join(source_dir,file_name), mode=\"r\")\n", - "nwbfile = io.read()" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "op = nwbfile.processing[\"ophys\"]\n", - "df_over_f = op.get_data_interface(\"DfOverF\")\n", - "roi = df_over_f.roi_response_series[\"RoiResponseSeries\"]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [], - "source": [ - "roi_data = roi.data" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "avg_roi_data = np.mean(roi_data, axis = 1)\n", - "\n", - "def smooth_data(data, window_size=200):\n", - " window = np.ones(window_size) / window_size\n", - " return np.convolve(data, window, mode='same')" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "\n", - "# Plotting the average across all ROIs\n", - "#average_activity = np.mean(avg_roi_data, axis=0)\n", - "plt.figure(figsize=(25, 5))\n", - "plt.plot(smooth_data(avg_roi_data), color='blue', label='Average Activity')\n", - "plt.title('Average Activity Across 99 ROIs (soma)')\n", - "plt.xlabel('Timesteps')\n", - "plt.ylim(0, 0.10) # Set y-axis limits\n", - "plt.ylabel('Activity')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "is_module = op.get_data_interface(\"ImageSegmentation\")\n", - "ps = is_module.plane_segmentations[\"PlaneSegmentation\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - "

PlaneSegmentation

description: Segmentation for imaging plane (de Vries et al., 2019, Nat Neurosci)
id
colnames: ('image_mask', 'tracking_id')
columns: (, )
imaging_plane
optical_channel
optical_channel pynwb.ophys.OpticalChannel at 0x140143278088784\n", - "Fields:\n", - " description: 2P Optical Channel\n", - " emission_lambda: 520.0\n", - "
description: ImagingPlane
device
description: Allen Institute two-photon pipeline: CAM2P.2
excitation_lambda: 910.0
imaging_rate: 30.0
indicator: GCaMP6f
location: {'area': 'VISp', 'depth': '375'}
conversion: 1.0
unit: meters
reference_frame: Intrinsic imaging home
" - ], - "text/plain": [ - "PlaneSegmentation pynwb.ophys.PlaneSegmentation at 0x140143278089312\n", - "Fields:\n", - " colnames: ['image_mask' 'tracking_id']\n", - " columns: (\n", - " image_mask ,\n", - " tracking_id \n", - " )\n", - " description: Segmentation for imaging plane (de Vries et al., 2019, Nat Neurosci)\n", - " id: id \n", - " imaging_plane: ImagingPlane pynwb.ophys.ImagingPlane at 0x140143278481312\n", - "Fields:\n", - " conversion: 1.0\n", - " description: ImagingPlane\n", - " device: 2p_microscope pynwb.device.Device at 0x140143278088496\n", - "Fields:\n", - " description: Allen Institute two-photon pipeline: CAM2P.2\n", - "\n", - " excitation_lambda: 910.0\n", - " imaging_rate: 30.0\n", - " indicator: GCaMP6f\n", - " location: {'area': 'VISp', 'depth': '375'}\n", - " optical_channel: (\n", - " optical_channel \n", - " )\n", - " reference_frame: Intrinsic imaging home\n", - " unit: meters\n" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ps" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "roi_masks = ps.columns[0].data[:]" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "def calculate_roi_centroids(roi_masks):\n", - " \"\"\"\n", - " Calculate the centroids of ROIs in the given mask array.\n", - "\n", - " Parameters:\n", - " roi_masks (numpy.ndarray): A 3D numpy array of shape (num_rois, height, width)\n", - "\n", - " Returns:\n", - " numpy.ndarray: A 2D array of centroids, shape (num_rois, 2), where each row contains [y, x] coordinates of the centroid.\n", - " \"\"\"\n", - " num_rois = roi_masks.shape[0]\n", - " centroids = np.zeros((num_rois, 2))\n", - "\n", - " for i in range(num_rois):\n", - " roi = roi_masks[i]\n", - " y_coords, x_coords = np.nonzero(roi)\n", - " if len(y_coords) == 0 or len(x_coords) == 0:\n", - " continue # Skip if ROI is empty\n", - "\n", - " centroid_y = np.mean(y_coords)\n", - " centroid_x = np.mean(x_coords)\n", - " #centroids[i] = [normalize(centroid_y), normalize(centroid_x)]\n", - " centroids[i] = [centroid_y, centroid_x]\n", - "\n", - " return centroids\n" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "centroids = calculate_roi_centroids(roi_masks)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "def get_sinusoidal_encoding(x, y, num_dims):\n", - " assert num_dims % 2 == 0, \"Number of dimensions should be even\"\n", - " assert len(x) == len(y), \"x and y arrays must be of the same length\"\n", - "\n", - " # Creating scale factors that decrease exponentially\n", - " scale_factors = 1 / np.power(10000, (2 * (np.arange(num_dims // 2)) / num_dims))\n", - "\n", - " # Initialize an array to hold the encodings for all positions\n", - " all_encodings = np.zeros((len(x), num_dims * 2))\n", - " #all_encodings = np.zeros((len(x), 64))\n", - "\n", - " # Apply sinusoidal encoding to each pair of positions\n", - " for i, (pos_x, pos_y) in enumerate(zip(x, y)):\n", - " encoding_x = np.array([np.sin(pos_x * scale_factors), np.cos(pos_x * scale_factors)]).flatten('F')\n", - " encoding_y = np.array([np.sin(pos_y * scale_factors), np.cos(pos_y * scale_factors)]).flatten('F')\n", - " all_encodings[i] = np.concatenate((encoding_x, encoding_y))\n", - "\n", - " return all_encodings" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(99, 64)\n" - ] - } - ], - "source": [ - "x = centroids[:, 0]\n", - "y = centroids[:, 1]\n", - "sinu_pos = get_sinusoidal_encoding(x,y,32)\n", - "print(sinu_pos.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def plot_encodings(encodings):\n", - " num_positions = encodings.shape[0]\n", - " num_dims = encodings.shape[1]\n", - "\n", - " # Plot each encoding\n", - " for i in range(num_positions):\n", - " plt.plot(encodings[i], label=f'Position {i}')\n", - "\n", - " plt.xlabel('Dimension')\n", - " plt.ylabel('Value')\n", - " plt.title('Sinusoidal Encodings')\n", - " #plt.legend()\n", - " plt.show()\n", - "\n", - "plot_encodings(sinu_pos)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "\n", - "# Extracting x and y from the data\n", - "x = centroids[:, 0]\n", - "y = centroids[:, 1]\n", - "\n", - "# Creating the scatter plot\n", - "plt.scatter(x, y)\n", - "\n", - "# Adding title and labels (optional)\n", - "plt.title(\"Scatter Plot of 2D Array\")\n", - "plt.xlabel(\"X-axis\")\n", - "plt.ylabel(\"Y-axis\")\n", - "\n", - "# Display the plot\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pos = np.sum(ps.columns[0].data[:], axis = 0)\n", - "heatmap = plt.imshow(pos, cmap='hot')" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "def calculate_roi_dimensions(roi_masks):\n", - " num_rois = roi_masks.shape[0]\n", - " areas = np.zeros(num_rois)\n", - " heights = np.zeros(num_rois)\n", - " widths = np.zeros(num_rois)\n", - "\n", - " for i in range(num_rois):\n", - " roi = roi_masks[i]\n", - "\n", - " # Calculate area\n", - " areas[i] = np.count_nonzero(roi)\n", - "\n", - " # Find rows and columns where ROI is present\n", - " rows, cols = np.where(roi)\n", - " if len(rows) == 0 or len(cols) == 0:\n", - " continue # Skip if ROI is empty\n", - "\n", - " # Calculate height and width\n", - " heights[i] = np.max(rows) - np.min(rows) + 1\n", - " widths[i] = np.max(cols) - np.min(cols) + 1\n", - "\n", - " # Normalize the arrays\n", - " areas = normalize(areas)\n", - " heights = normalize(heights)\n", - " widths = normalize(widths)\n", - "\n", - " return areas, heights, widths" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "areas, heights, widths = calculate_roi_dimensions(roi_masks)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.00000000e+000, 5.82747299e-222, 2.10908320e-219, 2.67855444e-217,\n", - " 5.91960564e-215, 9.70815326e-213, 1.81542466e-210, 5.17396028e-208,\n", - " 1.75914649e-205, 1.89987821e-203, 5.16766874e-201, 1.20406682e-198,\n", - " 3.91321716e-196, 5.63503270e-194, 1.82011556e-191, 4.62309353e-189,\n", - " 9.19995613e-187, 3.11878513e-184, 3.74254215e-182, 8.72012322e-180,\n", - " 1.49114107e-177, 1.53587530e-175, 3.13318562e-173, 3.19584933e-171,\n", - " 5.46490235e-169, 6.28463770e-167, 8.98703192e-165, 2.46244675e-162,\n", - " 6.74710408e-160, 1.59231656e-157, 1.62416289e-155, 2.74483529e-153,\n", - " 3.51338917e-151, 4.81334317e-149, 5.96854553e-147, 1.00271565e-144,\n", - " 3.40923321e-142, 6.68209708e-140, 1.90439767e-137, 4.87525803e-135,\n", - " 7.11787673e-133, 2.10689151e-130, 2.29651175e-128, 3.16918621e-126,\n", - " 4.11994207e-124, 1.05058523e-121, 1.10311449e-119, 2.16210440e-117,\n", - " 2.35669380e-115, 8.05989279e-113, 1.53943952e-110, 3.83320441e-108,\n", - " 4.40818507e-106, 1.45910926e-103, 2.10111733e-101, 2.26920672e-099,\n", - " 4.76533411e-097, 9.95954829e-095, 1.72300185e-092, 5.39299580e-090,\n", - " 1.62329174e-087, 3.40891265e-085, 9.06770764e-083, 1.84981236e-080,\n", - " 4.16207781e-078, 8.65712184e-076, 9.26312037e-074, 9.81890759e-072,\n", - " 3.24023951e-069, 4.17990896e-067, 1.08677633e-064, 3.68417176e-062,\n", - " 3.86838035e-060, 1.34619636e-057, 2.35584363e-055, 2.87412923e-053,\n", - " 9.31217871e-051, 1.08952491e-048, 4.63048086e-046, 1.98184581e-043,\n", - " 6.00499280e-041, 7.08589150e-039, 1.96987784e-036, 3.07300943e-034,\n", - " 3.59542103e-032, 1.00312247e-029, 2.93914883e-027, 3.58576157e-025,\n", - " 3.87262250e-023, 6.54473202e-021, 1.04715712e-018, 1.16234441e-016,\n", - " 1.42968362e-014, 1.60124566e-012, 1.68130794e-010, 5.43062464e-008,\n", - " 1.81382863e-005, 2.79329609e-003, 1.00000000e+000])" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "areas" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "def plot_masks(roi_masks, num_to_plot=3):\n", - " # Determine the number of rows and columns for subplots\n", - " num_rows = int(np.ceil(np.sqrt(num_to_plot)))\n", - " num_cols = int(np.ceil(num_to_plot / num_rows))\n", - "\n", - " fig, axes = plt.subplots(num_rows, num_cols, figsize=(num_cols * 4, num_rows * 4))\n", - " axes = axes.flatten() # Flatten the axes array for easy indexing\n", - "\n", - " for i in range(min(num_to_plot, len(roi_masks))):\n", - " ax = axes[i]\n", - " ax.imshow(roi_masks[i]*1000, cmap='gray')\n", - " ax.set_title(f'ROI {i+1}')\n", - " ax.axis('off')\n", - "\n", - " # Hide any unused subplots\n", - " for j in range(i + 1, len(axes)):\n", - " axes[j].axis('off')\n", - "\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "# Example usage\n", - "# Assuming roi_masks is your numpy array with shape (num_rois, 512, 512)\n", - "# plot_masks(roi_masks, num_to_plot=4)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_masks(roi_masks)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Helper functions" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "def calculate_roi_dimensions(roi_masks):\n", - " num_rois = roi_masks.shape[0]\n", - " areas = np.zeros(num_rois)\n", - " heights = np.zeros(num_rois)\n", - " widths = np.zeros(num_rois)\n", - "\n", - " for i in range(num_rois):\n", - " roi = roi_masks[i]\n", - "\n", - " # Calculate area\n", - " areas[i] = np.count_nonzero(roi)\n", - "\n", - " # Find rows and columns where ROI is present\n", - " rows, cols = np.where(roi)\n", - " if len(rows) == 0 or len(cols) == 0:\n", - " continue # Skip if ROI is empty\n", - "\n", - " # Calculate height and width\n", - " heights[i] = np.max(rows) - np.min(rows) + 1\n", - " widths[i] = np.max(cols) - np.min(cols) + 1\n", - "\n", - " return areas, heights, widths\n", - "\n", - "\n", - "def calculate_roi_centroids(roi_masks):\n", - " \"\"\"\n", - " Calculate the centroids of ROIs in the given mask array.\n", - "\n", - " Parameters:\n", - " roi_masks (numpy.ndarray): A 3D numpy array of shape (num_rois, height, width)\n", - "\n", - " Returns:\n", - " numpy.ndarray: A 2D array of centroids, shape (num_rois, 2), where each row contains [y, x] coordinates of the centroid.\n", - " \"\"\"\n", - " num_rois = roi_masks.shape[0]\n", - " centroids = np.zeros((num_rois, 2))\n", - "\n", - " for i in range(num_rois):\n", - " roi = roi_masks[i]\n", - " y_coords, x_coords = np.nonzero(roi)\n", - " if len(y_coords) == 0 or len(x_coords) == 0:\n", - " continue # Skip if ROI is empty\n", - "\n", - " centroid_y = np.mean(y_coords)\n", - " centroid_x = np.mean(x_coords)\n", - " centroids[i] = [centroid_y, centroid_x]\n", - "\n", - " return centroids\n", - "\n", - "def plot_masks(roi_masks, num_to_plot=3):\n", - " # Determine the number of rows and columns for subplots\n", - " num_rows = int(np.ceil(np.sqrt(num_to_plot)))\n", - " num_cols = int(np.ceil(num_to_plot / num_rows))\n", - "\n", - " fig, axes = plt.subplots(num_rows, num_cols, figsize=(num_cols * 4, num_rows * 4))\n", - " axes = axes.flatten() # Flatten the axes array for easy indexing\n", - "\n", - " for i in range(min(num_to_plot, len(roi_masks))):\n", - " ax = axes[i]\n", - " ax.imshow(roi_masks[i], cmap='gray')\n", - " ax.set_title(f'ROI {i+1}')\n", - " ax.axis('off')\n", - "\n", - " # Hide any unused subplots\n", - " for j in range(i + 1, len(axes)):\n", - " axes[j].axis('off')\n", - "\n", - " plt.tight_layout()\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def getNWBfilenames(mouse_csv_path='/home/mila/x/xuejing.pan/thesis/mouse_df.csv'):\n", - " filenames = []\n", - " sess_ids = []\n", - " num_rois = []\n", - " lines = []\n", - " planes = []\n", - "\t\n", - " df = pd.read_csv(mouse_csv_path, usecols = ['sessid','mouseid','runtype','nrois','line','plane'])\n", - "\n", - " #Getting all prod data\n", - " for row, curr_type in enumerate(df.runtype):\n", - " if curr_type == 'prod': \n", - " #f_name = source_dir+\"/sub-\"+str(df.mouseid[row])+\"_ses-\"+str(df.sessid[row])+\"_obj-raw_behavior+image+ophys.nwb\"\n", - " f_name = \"sub-\"+str(df.mouseid[row])+\"_ses-\"+str(df.sessid[row])+\"_obj-raw_behavior+image+ophys.nwb\"\n", - " filenames.append(f_name)\n", - " sess_ids.append(df.sessid[row])\n", - " num_rois.append(df.nrois[row])\n", - " lines.append(df.line[row])\n", - " planes.append(df.plane[row])\n", - "\n", - " return filenames,sess_ids, num_rois, lines, planes" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def calculate_mean_stats(areas, heights, widths):\n", - " mean_areas = [np.mean(session) for session in areas]\n", - " mean_heights = [np.mean(session) for session in heights]\n", - " mean_widths = [np.mean(session) for session in widths]\n", - " return np.array(mean_areas), np.array(mean_heights), np.array(mean_widths)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Iterate through all sessions" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "source_dir = \"/network/projects/neuro-galaxy/data/raw/openscope_calcium\"\n", - "filenames,sess_ids, num_rois, lines, planes = getNWBfilenames()\n", - "file_nums = len(filenames)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "areas = []\n", - "heights = []\n", - "widths = []" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AT FILE: 0\n", - "sub-408021_ses-758519303_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 1\n", - "sub-408021_ses-759189643_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 2\n", - "sub-408021_ses-759660390_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 3\n", - "sub-411400_ses-759666166_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 4\n", - "sub-411400_ses-759872185_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 5\n", - "sub-411400_ses-760269100_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 6\n", - "sub-411400_ses-761730740_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 7\n", - "sub-411400_ses-762415169_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 8\n", - "sub-411400_ses-763646681_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 9\n", - "sub-411424_ses-761624763_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 10\n", - "sub-411424_ses-761944562_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 11\n", - "sub-411424_ses-762250376_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 12\n", - "sub-411771_ses-760260459_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 13\n", - "sub-411771_ses-760659782_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 14\n", - "sub-411771_ses-761269197_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 15\n", - "sub-412933_ses-763949859_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 16\n", - "sub-412933_ses-764897534_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 17\n", - "sub-412933_ses-765427689_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 18\n", - "sub-412933_ses-766755831_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 19\n", - "sub-412933_ses-767254594_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 20\n", - "sub-412933_ses-768807532_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 21\n", - "sub-413663_ses-764704289_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 22\n", - "sub-413663_ses-765193831_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 23\n", - "sub-413663_ses-766502238_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 24\n", - "sub-418779_ses-777496949_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 25\n", - "sub-418779_ses-778374308_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 26\n", - "sub-418779_ses-779152062_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 27\n", - "sub-420011_ses-777914830_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 28\n", - "sub-420011_ses-778864809_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 29\n", - "sub-420011_ses-779650018_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 30\n", - "sub-433414_ses-826187862_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 31\n", - "sub-433414_ses-826773996_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 32\n", - "sub-433414_ses-827833392_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 33\n", - "sub-433448_ses-826338612_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 34\n", - "sub-433448_ses-826819032_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 35\n", - "sub-433448_ses-828816509_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 36\n", - "sub-433448_ses-829283315_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 37\n", - "sub-433451_ses-823453391_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 38\n", - "sub-433451_ses-824434038_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 39\n", - "sub-433451_ses-825180479_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 40\n", - "sub-433458_ses-826659257_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 41\n", - "sub-433458_ses-827300090_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 42\n", - "sub-433458_ses-828475005_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 43\n", - "sub-433458_ses-829520904_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 44\n", - "sub-440889_ses-832883243_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 45\n", - "sub-440889_ses-833704570_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 46\n", - "sub-440889_ses-834403597_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 47\n", - "sub-440889_ses-836968429_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 48\n", - "sub-440889_ses-837360280_obj-raw_behavior+image+ophys.nwb\n", - "AT FILE: 49\n", - "sub-440889_ses-838633305_obj-raw_behavior+image+ophys.nwb\n" - ] - } - ], - "source": [ - "for count, file_name in enumerate(filenames):\n", - " print(\"AT FILE: \", count) \n", - " print(file_name)\n", - "\n", - " io = NWBHDF5IO(os.path.join(source_dir,file_name), mode=\"r\")\n", - " nwbfile = io.read()\n", - "\n", - " op = nwbfile.processing[\"ophys\"]\n", - " df_over_f = op.get_data_interface(\"DfOverF\")\n", - " roi = df_over_f.roi_response_series[\"RoiResponseSeries\"]\n", - "\n", - " is_module = op.get_data_interface(\"ImageSegmentation\")\n", - " ps = is_module.plane_segmentations[\"PlaneSegmentation\"]\n", - "\n", - " roi_masks = ps.columns[0].data[:]\n", - "\n", - " curr_areas, curr_heights, curr_widths = calculate_roi_dimensions(roi_masks)\n", - "\n", - " areas.append(curr_areas)\n", - " heights.append(curr_heights)\n", - " widths.append(curr_widths)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 88., 59., 128., 65., 33., 122., 81., 79., 159., 45., 165.,\n", - " 47., 118., 82., 59., 29., 24., 55., 80., 119., 156., 50.,\n", - " 140., 59., 56., 82., 303., 39., 51., 108., 49., 60., 62.,\n", - " 115., 57., 47., 75., 76., 215., 53., 137., 17., 139., 85.,\n", - " 110., 124., 46., 45., 67., 15., 18., 79., 33., 153., 33.,\n", - " 38., 13., 168., 111., 37., 20., 90., 27., 49., 38., 221.,\n", - " 104., 282., 54., 82., 14., 80., 60., 38., 53., 24., 80.,\n", - " 71., 21., 59., 58., 22., 31., 49., 16., 44., 25., 26.,\n", - " 18., 114., 167., 20., 16., 55., 68., 100., 110., 125., 67.,\n", - " 21., 157., 22., 102., 89., 55., 27., 25., 30., 24., 14.,\n", - " 17., 52., 112., 26., 26., 50., 53., 67., 80., 27., 18.,\n", - " 19., 25., 60., 16., 95., 47., 97., 234., 44., 73., 73.,\n", - " 25., 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14., 34., 90., 27.,\n", - " 39., 80., 92., 55., 61., 16., 23., 114., 158., 171., 58.,\n", - " 233., 95., 119., 18., 14., 48., 23., 80., 14., 17., 13.,\n", - " 262., 23., 76., 157., 44., 20., 180., 31., 90., 31., 103.,\n", - " 15., 34., 15., 24., 26., 57., 116., 81., 72., 121., 61.,\n", - " 17., 18., 52., 33., 42., 13., 33., 46., 74., 80., 122.,\n", - " 32., 17., 72., 71., 84., 16., 27., 20., 17., 298., 15.,\n", - " 209., 19., 98., 22., 21., 44., 22., 58., 88., 26., 53.,\n", - " 85., 88., 39., 112., 143., 36., 135., 24., 15., 100., 35.,\n", - " 220., 105., 42., 27., 56., 54., 31., 36., 74., 29., 31.,\n", - " 50., 34., 18., 106., 19., 15., 92., 16., 18., 16., 31.,\n", - " 80., 43., 20., 46., 33., 38., 54., 30., 22., 23., 65.,\n", - " 133., 19., 29., 34., 63., 74., 26.])" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "areas[3]" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "#calculate the mean of each session\n", - "\n", - "mean_areas, mean_heights, mean_widths = calculate_mean_stats(areas,heights,widths)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Visualizing" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " soma/dend" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.lines as mlines\n", - "# Session types: 'dend' or 'soma'\n", - "# Assuming you have a list that indicates the type of each session\n", - "session_types = planes # e.g., ['dend', 'soma', 'dend', ...]\n", - "\n", - "# Create a 3D scatter plot\n", - "fig = plt.figure()\n", - "ax = fig.add_subplot(111, projection='3d')\n", - "\n", - "# Plot each session\n", - "for i in range(len(mean_areas)):\n", - " x, y, z = mean_widths[i], mean_heights[i], mean_areas[i] # Switch x and z\n", - " color = 'r' if session_types[i] == 'dend' else 'b'\n", - " label = 'Dend' if session_types[i] == 'dend' and i == 0 else 'Soma' if session_types[i] == 'soma' and i == 0 else \"\"\n", - " ax.scatter(x, y, z, color=color, label=label)\n", - "\n", - "# Create custom legends\n", - "dend_legend = mlines.Line2D([], [], color='red', marker='o', linestyle='None', markersize=10, label='Dend')\n", - "soma_legend = mlines.Line2D([], [], color='blue', marker='o', linestyle='None', markersize=10, label='Soma')\n", - "\n", - "# Labeling axes\n", - "ax.set_xlabel('Mean Width')\n", - "ax.set_ylabel('Mean Height')\n", - "ax.set_zlabel('Mean Area')\n", - "\n", - "# Legend\n", - "ax.legend(handles=[dend_legend, soma_legend])\n", - "\n", - "# Show plot\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([177.10416667, 175.83783784, 171.98130841, 64.44373673,\n", - " 78.66782007, 70.92175573, 101.37619048, 102.22135008,\n", - " 102.81742044, 175.26436782, 172.41111111, 167.9 ,\n", - " 207.6 , 215.25714286, 221.24050633, 91.51585014,\n", - " 90.80907173, 83.42105263, 78.88662791, 76.8452381 ,\n", - " 69.952 , 74.06050955, 72.72413793, 68.06445312,\n", - " 179.33333333, 156.88461538, 160.72413793, 70.84390244,\n", - " 60.10062893, 61.48351648, 47.32324622, 55.13666667,\n", - " 51.24624625, 65.75061125, 83.71685393, 79.78830645,\n", - " 74.90825688, 99.97929607, 98.41690962, 87.44726166,\n", - " 211.4040404 , 227.20689655, 222.89690722, 211.68181818,\n", - " 176.20089286, 173.91517857, 173.46190476, 162.91707317,\n", - " 163.26728111, 155.33039648])" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mean_areas" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "# Assuming mean_areas, mean_heights, and mean_widths are your data lists\n", - "# And session_types is a list indicating the type ('dend' or 'soma') for each session\n", - "\n", - "# Plot 1: Mean Area vs. Mean Height\n", - "plt.figure(figsize=(8, 6))\n", - "for i in range(len(mean_areas)):\n", - " color = 'r' if session_types[i] == 'dend' else 'b'\n", - " label = 'Dend' if session_types[i] == 'dend' and i == 0 else 'Soma' if session_types[i] == 'soma' and i == 0 else \"\"\n", - " plt.scatter(mean_areas[i], mean_heights[i], color=color, label=label)\n", - "\n", - "# Create custom legends\n", - "dend_legend = mlines.Line2D([], [], color='red', marker='o', linestyle='None', markersize=10, label='Dend')\n", - "soma_legend = mlines.Line2D([], [], color='blue', marker='o', linestyle='None', markersize=10, label='Soma')\n", - "\n", - "plt.xlabel('Mean Area')\n", - "plt.ylabel('Mean Height')\n", - "plt.title('Mean Area vs. Mean Height')\n", - "plt.legend(handles=[dend_legend, soma_legend])\n", - "plt.show()\n", - "\n", - "# Plot 2: Mean Area vs. Mean Width\n", - "plt.figure(figsize=(8, 6))\n", - "for i in range(len(mean_areas)):\n", - " color = 'r' if session_types[i] == 'dend' else 'b'\n", - " label = 'Dend' if session_types[i] == 'dend' and i == 0 else 'Soma' if session_types[i] == 'soma' and i == 0 else \"\"\n", - " plt.scatter(mean_areas[i], mean_widths[i], color=color, label=label)\n", - "plt.xlabel('Mean Area')\n", - "plt.ylabel('Mean Width')\n", - "plt.title('Mean Area vs. Mean Width')\n", - "plt.legend(handles=[dend_legend, soma_legend])\n", - "plt.show()\n", - "\n", - "# Plot 3: Mean Height vs. Mean Width\n", - "plt.figure(figsize=(8, 6))\n", - "for i in range(len(mean_heights)):\n", - " color = 'r' if session_types[i] == 'dend' else 'b'\n", - " label = 'Dend' if session_types[i] == 'dend' and i == 0 else 'Soma' if session_types[i] == 'soma' and i == 0 else \"\"\n", - " plt.scatter(mean_heights[i], mean_widths[i], color=color, label=label)\n", - "plt.xlabel('Mean Height')\n", - "plt.ylabel('Mean Width')\n", - "plt.title('Mean Height vs. Mean Width')\n", - "plt.legend(handles=[dend_legend, soma_legend])\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Layer" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.lines as mlines\n", - "# Session types: 'dend' or 'soma'\n", - "# Assuming you have a list that indicates the type of each session\n", - "session_types = lines # e.g., ['dend', 'soma', 'dend', ...]\n", - "\n", - "# Create a 3D scatter plot\n", - "fig = plt.figure()\n", - "ax = fig.add_subplot(111, projection='3d')\n", - "\n", - "# Plot each session\n", - "for i in range(len(mean_areas)):\n", - " x, y, z = mean_widths[i], mean_heights[i], mean_areas[i] # Switch x and z\n", - " color = 'g' if session_types[i] == 'L5-Rbp4' else 'orange'\n", - " label = 'L5-Rbp4' if session_types[i] == 'L5-Rbp4' and i == 0 else 'Soma' if session_types[i] == 'L23-Cux2' and i == 0 else \"\"\n", - " ax.scatter(x, y, z, color=color, label=label)\n", - "\n", - "# Create custom legends\n", - "dend_legend = mlines.Line2D([], [], color='g', marker='o', linestyle='None', markersize=10, label='L5-Rbp4')\n", - "soma_legend = mlines.Line2D([], [], color='orange', marker='o', linestyle='None', markersize=10, label='L23-Cux2')\n", - "\n", - "# Labeling axes\n", - "ax.set_xlabel('Mean Width')\n", - "ax.set_ylabel('Mean Height')\n", - "ax.set_zlabel('Mean Area')\n", - "\n", - "# Legend\n", - "ax.legend(handles=[dend_legend, soma_legend])\n", - "\n", - "# Show plot\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ROI visualizaiton" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "source_dir = \"/network/projects/neuro-galaxy/data/raw/openscope_calcium\"\n", - "#file_name = \"sub-411771_ses-761269197_obj-raw_behavior+image+ophys.nwb\"\n", - "file_name = 'sub-411400_ses-761730740_obj-raw_behavior+image+ophys.nwb'\n", - "io = NWBHDF5IO(os.path.join(source_dir,file_name), mode=\"r\")\n", - "nwbfile = io.read()" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [], - "source": [ - "op = nwbfile.processing[\"ophys\"]\n", - "df_over_f = op.get_data_interface(\"DfOverF\")\n", - "roi = df_over_f.roi_response_series[\"RoiResponseSeries\"]\n", - "roi_data = roi.data" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(126747, 630)" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "roi_data.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - "

root (NWBFile)

session_description: Allen Institute OpenScope dataset
identifier: 761730740_with_stim_and_stack
session_start_time: 2018-10-09 17:50:37.303000-07:00
timestamps_reference_time: 2018-10-09 17:50:37.303000-07:00
file_create_date
2022-09-25 06:46:18.315565-07:00
2023-04-05 06:05:59.678585-07:00
acquisition (1)
motion_corrected_stack
resolution: -1.0
comments: no comments
description: Motion corrected imaging stack (frames x height x width).
conversion: 1.0
offset: 0.0
unit: Fluorescence (A.U.)
data
timestamps
resolution: -1.0
comments: no comments
description: Dendritic ROI traces obtained using booleanized masks.
conversion: 1.0
offset: 0.0
unit: Normalized fluorescence (A.U.)
data
timestamps
timestamps_unit: seconds
interval: 1
rois
description: Segmented dendrites (height x width)
table
description: Segmentation for imaging plane optimized for dendritic ROI extraction (Inan et al., 2017, NIPS)
id
colnames: ('image_mask', 'tracking_id')
columns: (, )
imaging_plane
optical_channel
optical_channel pynwb.ophys.OpticalChannel at 0x140416385839744\n", - "Fields:\n", - " description: 2P Optical Channel\n", - " emission_lambda: 520.0\n", - "
description: ImagingPlane
device
description: Allen Institute two-photon pipeline: CAM2P.2
excitation_lambda: 910.0
imaging_rate: 30.0
indicator: GCaMP6f
location: {'area': 'VISp', 'depth': '20'}
conversion: 1.0
unit: meters
reference_frame: Intrinsic imaging home
timestamp_link
motion_corrected_stack pynwb.ophys.TwoPhotonSeries at 0x140416375787088\n", - "Fields:\n", - " comments: no comments\n", - " conversion: 1.0\n", - " data: \n", - " description: Motion corrected imaging stack (frames x height x width).\n", - " format: byte image (uint8)\n", - " imaging_plane: ImagingPlane pynwb.ophys.ImagingPlane at 0x140416385840128\n", - "Fields:\n", - " conversion: 1.0\n", - " description: ImagingPlane\n", - " device: 2p_microscope pynwb.device.Device at 0x140416385841472\n", - "Fields:\n", - " description: Allen Institute two-photon pipeline: CAM2P.2\n", - "\n", - " excitation_lambda: 910.0\n", - " imaging_rate: 30.0\n", - " indicator: GCaMP6f\n", - " location: {'area': 'VISp', 'depth': '20'}\n", - " optical_channel: (\n", - " optical_channel \n", - " )\n", - " reference_frame: Intrinsic imaging home\n", - " unit: meters\n", - "\n", - " interval: 1\n", - " offset: 0.0\n", - " resolution: -1.0\n", - " timestamps: RoiResponseSeries pynwb.ophys.RoiResponseSeries at 0x140416385841184\n", - "Fields:\n", - " comments: no comments\n", - " conversion: 1.0\n", - " data: \n", - " description: Dendritic ROI traces obtained using booleanized masks.\n", - " interval: 1\n", - " offset: 0.0\n", - " resolution: -1.0\n", - " rois: rois \n", - " timestamp_link: (\n", - " motion_corrected_stack ,\n", - " pupil_diameter ,\n", - " pupil_position_x ,\n", - " pupil_position_y \n", - " )\n", - " timestamps: \n", - " timestamps_unit: seconds\n", - " unit: Normalized fluorescence (A.U.)\n", - "\n", - " timestamps_unit: seconds\n", - " unit: Fluorescence (A.U.)\n", - "
pupil_diameter pynwb.base.TimeSeries at 0x140416377390080\n", - "Fields:\n", - " comments: no comments\n", - " conversion: 1.0\n", - " data: \n", - " description: Diameter of the mouse pupil (right) facing the stimulus presentation screen.\n", - " interval: 1\n", - " offset: 0.0\n", - " resolution: -1.0\n", - " timestamps: RoiResponseSeries pynwb.ophys.RoiResponseSeries at 0x140416385841184\n", - "Fields:\n", - " comments: no comments\n", - " conversion: 1.0\n", - " data: \n", - " description: Dendritic ROI traces obtained using booleanized masks.\n", - " interval: 1\n", - " offset: 0.0\n", - " resolution: -1.0\n", - " rois: rois \n", - " timestamp_link: (\n", - " motion_corrected_stack ,\n", - " pupil_diameter ,\n", - " pupil_position_x ,\n", - " pupil_position_y \n", - " )\n", - " timestamps: \n", - " timestamps_unit: seconds\n", - " unit: Normalized fluorescence (A.U.)\n", - "\n", - " timestamps_unit: seconds\n", - " unit: mm\n", - "
pupil_position_x pynwb.behavior.SpatialSeries at 0x140416377391856\n", - "Fields:\n", - " comments: no comments\n", - " conversion: 1.0\n", - " data: \n", - " description: Position in x of the center of the mouse pupil (right) facing the stimulus presentation screen.\n", - " interval: 1\n", - " offset: 0.0\n", - " reference_frame: Left edge of the pupil recording image (6.528mm wide).\n", - " resolution: -1.0\n", - " timestamps: RoiResponseSeries pynwb.ophys.RoiResponseSeries at 0x140416385841184\n", - "Fields:\n", - " comments: no comments\n", - " conversion: 1.0\n", - " data: \n", - " description: Dendritic ROI traces obtained using booleanized masks.\n", - " interval: 1\n", - " offset: 0.0\n", - " resolution: -1.0\n", - " rois: rois \n", - " timestamp_link: (\n", - " motion_corrected_stack ,\n", - " pupil_diameter ,\n", - " pupil_position_x ,\n", - " pupil_position_y \n", - " )\n", - " timestamps: \n", - " timestamps_unit: seconds\n", - " unit: Normalized fluorescence (A.U.)\n", - "\n", - " timestamps_unit: seconds\n", - " unit: mm\n", - "
pupil_position_y pynwb.behavior.SpatialSeries at 0x140416377389120\n", - "Fields:\n", - " comments: no comments\n", - " conversion: 1.0\n", - " data: \n", - " description: Position in y of the center of the mouse pupil (right) facing the stimulus presentation screen.\n", - " interval: 1\n", - " offset: 0.0\n", - " reference_frame: Top edge of the pupil recording image (4.896mm high).\n", - " resolution: -1.0\n", - " timestamps: RoiResponseSeries pynwb.ophys.RoiResponseSeries at 0x140416385841184\n", - "Fields:\n", - " comments: no comments\n", - " conversion: 1.0\n", - " data: \n", - " description: Dendritic ROI traces obtained using booleanized masks.\n", - " interval: 1\n", - " offset: 0.0\n", - " resolution: -1.0\n", - " rois: rois \n", - " timestamp_link: (\n", - " motion_corrected_stack ,\n", - " pupil_diameter ,\n", - " pupil_position_x ,\n", - " pupil_position_y \n", - " )\n", - " timestamps: \n", - " timestamps_unit: seconds\n", - " unit: Normalized fluorescence (A.U.)\n", - "\n", - " timestamps_unit: seconds\n", - " unit: mm\n", - "
timestamps_unit: seconds
interval: 1
format: byte image (uint8)
imaging_plane
optical_channel
optical_channel pynwb.ophys.OpticalChannel at 0x140416385839744\n", - "Fields:\n", - " description: 2P Optical Channel\n", - " emission_lambda: 520.0\n", - "
description: ImagingPlane
device
description: Allen Institute two-photon pipeline: CAM2P.2
excitation_lambda: 910.0
imaging_rate: 30.0
indicator: GCaMP6f
location: {'area': 'VISp', 'depth': '20'}
conversion: 1.0
unit: meters
reference_frame: Intrinsic imaging home
stimulus (4)
gabors
resolution: -1.0
comments: no comments
description: gabors index
conversion: 1.0
offset: 0.0
unit: N/A
data
timestamps
timestamps_unit: seconds
interval: 1
indexed_timeseries
resolution: -1.0
comments: no comments
description: Template for Gabor sequence stimulus (frames x height x width x channels) (unwarped, masked)
conversion: 1.0
offset: 0.0
unit: N/A
data
timestamps
timestamps_unit: seconds
interval: 1
format: byte image (uint8)
grayscreen
resolution: -1.0
comments: no comments
description: grayscreen index
conversion: 1.0
offset: 0.0
unit: N/A
data
timestamps
timestamps_unit: seconds
interval: 1
indexed_timeseries
resolution: -1.0
comments: no comments
description: Template for Grayscreen stimulus (frames x height x width x channels) (unwarped, masked)
conversion: 1.0
offset: 0.0
unit: N/A
data
timestamps
timestamps_unit: seconds
interval: 1
format: byte image (uint8)
visflow_left
resolution: -1.0
comments: no comments
description: visflow_left index
conversion: 1.0
offset: 0.0
unit: N/A
data
timestamps
timestamps_unit: seconds
interval: 1
indexed_timeseries
resolution: -1.0
comments: no comments
description: Template for leftward (nasal) visual flow stimulus (frames x height x width x channels) (unwarped, masked)
conversion: 1.0
offset: 0.0
unit: N/A
data
timestamps
timestamps_unit: seconds
interval: 1
format: byte image (uint8)
visflow_right
resolution: -1.0
comments: no comments
description: visflow_right index
conversion: 1.0
offset: 0.0
unit: N/A
data
timestamps
timestamps_unit: seconds
interval: 1
indexed_timeseries
resolution: -1.0
comments: no comments
description: Template for rightward (temporal) visual flow stimulus (frames x height x width x channels) (unwarped, masked)
conversion: 1.0
offset: 0.0
unit: N/A
data
timestamps
timestamps_unit: seconds
interval: 1
format: byte image (uint8)
stimulus_template (4)
gabors
resolution: -1.0
comments: no comments
description: Template for Gabor sequence stimulus (frames x height x width x channels) (unwarped, masked)
conversion: 1.0
offset: 0.0
unit: N/A
data
timestamps
timestamps_unit: seconds
interval: 1
format: byte image (uint8)
grayscreen
resolution: -1.0
comments: no comments
description: Template for Grayscreen stimulus (frames x height x width x channels) (unwarped, masked)
conversion: 1.0
offset: 0.0
unit: N/A
data
timestamps
timestamps_unit: seconds
interval: 1
format: byte image (uint8)
visflow_left
resolution: -1.0
comments: no comments
description: Template for leftward (nasal) visual flow stimulus (frames x height x width x channels) (unwarped, masked)
conversion: 1.0
offset: 0.0
unit: N/A
data
timestamps
timestamps_unit: seconds
interval: 1
format: byte image (uint8)
visflow_right
resolution: -1.0
comments: no comments
description: Template for rightward (temporal) visual flow stimulus (frames x height x width x channels) (unwarped, masked)
conversion: 1.0
offset: 0.0
unit: N/A
data
timestamps
timestamps_unit: seconds
interval: 1
format: byte image (uint8)
processing (2)
behavior
description: preprocessed behavioral data
data_interfaces (2)
BehavioralTimeSeries
time_series (1)
running_velocity
resolution: -1.0
comments: no comments
description: Velocity of the mouse on a rotating disc.
conversion: 1.0
offset: 0.0
unit: cm/s
data
timestamps
timestamps_unit: seconds
interval: 1
PupilTracking
time_series (3)
pupil_diameter
resolution: -1.0
comments: no comments
description: Diameter of the mouse pupil (right) facing the stimulus presentation screen.
conversion: 1.0
offset: 0.0
unit: mm
data
timestamps
resolution: -1.0
comments: no comments
description: Dendritic ROI traces obtained using booleanized masks.
conversion: 1.0
offset: 0.0
unit: Normalized fluorescence (A.U.)
data
timestamps
timestamps_unit: seconds
interval: 1
rois
description: Segmented dendrites (height x width)
table
description: Segmentation for imaging plane optimized for dendritic ROI extraction (Inan et al., 2017, NIPS)
id
colnames: ('image_mask', 'tracking_id')
columns: (, )
imaging_plane
optical_channel
optical_channel pynwb.ophys.OpticalChannel at 0x140416385839744\n", - "Fields:\n", - " description: 2P Optical Channel\n", - " emission_lambda: 520.0\n", - "
description: ImagingPlane
device
description: Allen Institute two-photon pipeline: CAM2P.2
excitation_lambda: 910.0
imaging_rate: 30.0
indicator: GCaMP6f
location: {'area': 'VISp', 'depth': '20'}
conversion: 1.0
unit: meters
reference_frame: Intrinsic imaging home
timestamp_link
motion_corrected_stack pynwb.ophys.TwoPhotonSeries at 0x140416375787088\n", - "Fields:\n", - " comments: no comments\n", - " conversion: 1.0\n", - " data: \n", - " description: Motion corrected imaging stack (frames x height x width).\n", - " format: byte image (uint8)\n", - " imaging_plane: ImagingPlane pynwb.ophys.ImagingPlane at 0x140416385840128\n", - "Fields:\n", - " conversion: 1.0\n", - " description: ImagingPlane\n", - " device: 2p_microscope pynwb.device.Device at 0x140416385841472\n", - "Fields:\n", - " description: Allen Institute two-photon pipeline: CAM2P.2\n", - "\n", - " excitation_lambda: 910.0\n", - " imaging_rate: 30.0\n", - " indicator: GCaMP6f\n", - " location: {'area': 'VISp', 'depth': '20'}\n", - " optical_channel: (\n", - " optical_channel \n", - " )\n", - " reference_frame: Intrinsic imaging home\n", - " unit: meters\n", - "\n", - " interval: 1\n", - " offset: 0.0\n", - " resolution: -1.0\n", - " timestamps: RoiResponseSeries pynwb.ophys.RoiResponseSeries at 0x140416385841184\n", - "Fields:\n", - " comments: no comments\n", - " conversion: 1.0\n", - " data: \n", - " description: Dendritic ROI traces obtained using booleanized masks.\n", - " interval: 1\n", - " offset: 0.0\n", - " resolution: -1.0\n", - " rois: rois \n", - " timestamp_link: (\n", - " motion_corrected_stack ,\n", - " pupil_diameter ,\n", - " pupil_position_x ,\n", - " pupil_position_y \n", - " )\n", - " timestamps: \n", - " timestamps_unit: seconds\n", - " unit: Normalized fluorescence (A.U.)\n", - "\n", - " timestamps_unit: seconds\n", - " unit: Fluorescence (A.U.)\n", - "
pupil_diameter pynwb.base.TimeSeries at 0x140416377390080\n", - "Fields:\n", - " comments: no comments\n", - " conversion: 1.0\n", - " data: \n", - " description: Diameter of the mouse pupil (right) facing the stimulus presentation screen.\n", - " interval: 1\n", - " offset: 0.0\n", - " resolution: -1.0\n", - " timestamps: RoiResponseSeries pynwb.ophys.RoiResponseSeries at 0x140416385841184\n", - "Fields:\n", - " comments: no comments\n", - " conversion: 1.0\n", - " data: \n", - " description: Dendritic ROI traces obtained using booleanized masks.\n", - " interval: 1\n", - " offset: 0.0\n", - " resolution: -1.0\n", - " rois: rois \n", - " timestamp_link: (\n", - " motion_corrected_stack ,\n", - " pupil_diameter ,\n", - " pupil_position_x ,\n", - " pupil_position_y \n", - " )\n", - " timestamps: \n", - " timestamps_unit: seconds\n", - " unit: Normalized fluorescence (A.U.)\n", - "\n", - " timestamps_unit: seconds\n", - " unit: mm\n", - "
pupil_position_x pynwb.behavior.SpatialSeries at 0x140416377391856\n", - "Fields:\n", - " comments: no comments\n", - " conversion: 1.0\n", - " data: \n", - " description: Position in x of the center of the mouse pupil (right) facing the stimulus presentation screen.\n", - " interval: 1\n", - " offset: 0.0\n", - " reference_frame: Left edge of the pupil recording image (6.528mm wide).\n", - " resolution: -1.0\n", - " timestamps: RoiResponseSeries pynwb.ophys.RoiResponseSeries at 0x140416385841184\n", - "Fields:\n", - " comments: no comments\n", - " conversion: 1.0\n", - " data: \n", - " description: Dendritic ROI traces obtained using booleanized masks.\n", - " interval: 1\n", - " offset: 0.0\n", - " resolution: -1.0\n", - " rois: rois \n", - " timestamp_link: (\n", - " motion_corrected_stack ,\n", - " pupil_diameter ,\n", - " pupil_position_x ,\n", - " pupil_position_y \n", - " )\n", - " timestamps: \n", - " timestamps_unit: seconds\n", - " unit: Normalized fluorescence (A.U.)\n", - "\n", - " timestamps_unit: seconds\n", - " unit: mm\n", - "
pupil_position_y pynwb.behavior.SpatialSeries at 0x140416377389120\n", - "Fields:\n", - " comments: no comments\n", - " conversion: 1.0\n", - " data: \n", - " description: Position in y of the center of the mouse pupil (right) facing the stimulus presentation screen.\n", - " interval: 1\n", - " offset: 0.0\n", - " reference_frame: Top edge of the pupil recording image (4.896mm high).\n", - " resolution: -1.0\n", - " timestamps: RoiResponseSeries pynwb.ophys.RoiResponseSeries at 0x140416385841184\n", - "Fields:\n", - " comments: no comments\n", - " conversion: 1.0\n", - " data: \n", - " description: Dendritic ROI traces obtained using booleanized masks.\n", - " interval: 1\n", - " offset: 0.0\n", - " resolution: -1.0\n", - " rois: rois \n", - " timestamp_link: (\n", - " motion_corrected_stack ,\n", - " pupil_diameter ,\n", - " pupil_position_x ,\n", - " pupil_position_y \n", - " )\n", - " timestamps: \n", - " timestamps_unit: seconds\n", - " unit: Normalized fluorescence (A.U.)\n", - "\n", - " timestamps_unit: seconds\n", - " unit: mm\n", - "
timestamps_unit: seconds
interval: 1
pupil_position_x
resolution: -1.0
comments: no comments
description: Position in x of the center of the mouse pupil (right) facing the stimulus presentation screen.
conversion: 1.0
offset: 0.0
unit: mm
data
timestamps
resolution: -1.0
comments: no comments
description: Dendritic ROI traces obtained using booleanized masks.
conversion: 1.0
offset: 0.0
unit: Normalized fluorescence (A.U.)
data
timestamps
timestamps_unit: seconds
interval: 1
rois
description: Segmented dendrites (height x width)
table
description: Segmentation for imaging plane optimized for dendritic ROI extraction (Inan et al., 2017, NIPS)
id
colnames: ('image_mask', 'tracking_id')
columns: (, )
imaging_plane
optical_channel
optical_channel pynwb.ophys.OpticalChannel at 0x140416385839744\n", - "Fields:\n", - " description: 2P Optical Channel\n", - " emission_lambda: 520.0\n", - "
description: ImagingPlane
device
description: Allen Institute two-photon pipeline: CAM2P.2
excitation_lambda: 910.0
imaging_rate: 30.0
indicator: GCaMP6f
location: {'area': 'VISp', 'depth': '20'}
conversion: 1.0
unit: meters
reference_frame: Intrinsic imaging home
timestamp_link
motion_corrected_stack pynwb.ophys.TwoPhotonSeries at 0x140416375787088\n", - "Fields:\n", - " comments: no comments\n", - " conversion: 1.0\n", - " data: \n", - " description: Motion corrected imaging stack (frames x height x width).\n", - " format: byte image (uint8)\n", - " imaging_plane: ImagingPlane pynwb.ophys.ImagingPlane at 0x140416385840128\n", - "Fields:\n", - " conversion: 1.0\n", - " description: ImagingPlane\n", - " device: 2p_microscope pynwb.device.Device at 0x140416385841472\n", - "Fields:\n", - " description: Allen Institute two-photon pipeline: CAM2P.2\n", - "\n", - " excitation_lambda: 910.0\n", - " imaging_rate: 30.0\n", - " indicator: GCaMP6f\n", - " location: {'area': 'VISp', 'depth': '20'}\n", - " optical_channel: (\n", - " optical_channel \n", - " )\n", - " reference_frame: Intrinsic imaging home\n", - " unit: meters\n", - "\n", - " interval: 1\n", - " offset: 0.0\n", - " resolution: -1.0\n", - " timestamps: RoiResponseSeries pynwb.ophys.RoiResponseSeries at 0x140416385841184\n", - "Fields:\n", - " comments: no comments\n", - " conversion: 1.0\n", - " data: \n", - " description: Dendritic ROI traces obtained using booleanized masks.\n", - " interval: 1\n", - " offset: 0.0\n", - " resolution: -1.0\n", - " rois: rois \n", - " timestamp_link: (\n", - " motion_corrected_stack ,\n", - " pupil_diameter ,\n", - " pupil_position_x ,\n", - " pupil_position_y \n", - " )\n", - " timestamps: \n", - " timestamps_unit: seconds\n", - " unit: Normalized fluorescence (A.U.)\n", - "\n", - " timestamps_unit: seconds\n", - " unit: Fluorescence (A.U.)\n", - "
pupil_diameter pynwb.base.TimeSeries at 0x140416377390080\n", - "Fields:\n", - " comments: no comments\n", - " conversion: 1.0\n", - " data: \n", - " description: Diameter of the mouse pupil (right) facing the stimulus presentation screen.\n", - " interval: 1\n", - " offset: 0.0\n", - " resolution: -1.0\n", - " timestamps: RoiResponseSeries pynwb.ophys.RoiResponseSeries at 0x140416385841184\n", - "Fields:\n", - " comments: no comments\n", - " conversion: 1.0\n", - " data: \n", - " description: Dendritic ROI traces obtained using booleanized masks.\n", - " interval: 1\n", - " offset: 0.0\n", - " resolution: -1.0\n", - " rois: rois \n", - " timestamp_link: (\n", - " motion_corrected_stack ,\n", - " pupil_diameter ,\n", - " pupil_position_x ,\n", - " pupil_position_y \n", - " )\n", - " timestamps: \n", - " timestamps_unit: seconds\n", - " unit: Normalized fluorescence (A.U.)\n", - "\n", - " timestamps_unit: seconds\n", - " unit: mm\n", - "
pupil_position_x pynwb.behavior.SpatialSeries at 0x140416377391856\n", - "Fields:\n", - " comments: no comments\n", - " conversion: 1.0\n", - " data: \n", - " description: Position in x of the center of the mouse pupil (right) facing the stimulus presentation screen.\n", - " interval: 1\n", - " offset: 0.0\n", - " reference_frame: Left edge of the pupil recording image (6.528mm wide).\n", - " resolution: -1.0\n", - " timestamps: RoiResponseSeries pynwb.ophys.RoiResponseSeries at 0x140416385841184\n", - "Fields:\n", - " comments: no comments\n", - " conversion: 1.0\n", - " data: \n", - " description: Dendritic ROI traces obtained using booleanized masks.\n", - " interval: 1\n", - " offset: 0.0\n", - " resolution: -1.0\n", - " rois: rois \n", - " timestamp_link: (\n", - " motion_corrected_stack ,\n", - " pupil_diameter ,\n", - " pupil_position_x ,\n", - " pupil_position_y \n", - " )\n", - " timestamps: \n", - " timestamps_unit: seconds\n", - " unit: Normalized fluorescence (A.U.)\n", - "\n", - " timestamps_unit: seconds\n", - " unit: mm\n", - "
pupil_position_y pynwb.behavior.SpatialSeries at 0x140416377389120\n", - "Fields:\n", - " comments: no comments\n", - " conversion: 1.0\n", - " data: \n", - " description: Position in y of the center of the mouse pupil (right) facing the stimulus presentation screen.\n", - " interval: 1\n", - " offset: 0.0\n", - " reference_frame: Top edge of the pupil recording image (4.896mm high).\n", - " resolution: -1.0\n", - " timestamps: RoiResponseSeries pynwb.ophys.RoiResponseSeries at 0x140416385841184\n", - "Fields:\n", - " comments: no comments\n", - " conversion: 1.0\n", - " data: \n", - " description: Dendritic ROI traces obtained using booleanized masks.\n", - " interval: 1\n", - " offset: 0.0\n", - " resolution: -1.0\n", - " rois: rois \n", - " timestamp_link: (\n", - " motion_corrected_stack ,\n", - " pupil_diameter ,\n", - " pupil_position_x ,\n", - " pupil_position_y \n", - " )\n", - " timestamps: \n", - " timestamps_unit: seconds\n", - " unit: Normalized fluorescence (A.U.)\n", - "\n", - " timestamps_unit: seconds\n", - " unit: mm\n", - "
timestamps_unit: seconds
interval: 1
reference_frame: Left edge of the pupil recording image (6.528mm wide).
pupil_position_y
resolution: -1.0
comments: no comments
description: Position in y of the center of the mouse pupil (right) facing the stimulus presentation screen.
conversion: 1.0
offset: 0.0
unit: mm
data
timestamps
resolution: -1.0
comments: no comments
description: Dendritic ROI traces obtained using booleanized masks.
conversion: 1.0
offset: 0.0
unit: Normalized fluorescence (A.U.)
data
timestamps
timestamps_unit: seconds
interval: 1
rois
description: Segmented dendrites (height x width)
table
description: Segmentation for imaging plane optimized for dendritic ROI extraction (Inan et al., 2017, NIPS)
id
colnames: ('image_mask', 'tracking_id')
columns: (, )
imaging_plane
optical_channel
optical_channel pynwb.ophys.OpticalChannel at 0x140416385839744\n", - "Fields:\n", - " description: 2P Optical Channel\n", - " emission_lambda: 520.0\n", - "
description: ImagingPlane
device
description: Allen Institute two-photon pipeline: CAM2P.2
excitation_lambda: 910.0
imaging_rate: 30.0
indicator: GCaMP6f
location: {'area': 'VISp', 'depth': '20'}
conversion: 1.0
unit: meters
reference_frame: Intrinsic imaging home
timestamp_link
motion_corrected_stack pynwb.ophys.TwoPhotonSeries at 0x140416375787088\n", - "Fields:\n", - " comments: no comments\n", - " conversion: 1.0\n", - " data: \n", - " description: Motion corrected imaging stack (frames x height x width).\n", - " format: byte image (uint8)\n", - " imaging_plane: ImagingPlane pynwb.ophys.ImagingPlane at 0x140416385840128\n", - "Fields:\n", - " conversion: 1.0\n", - " description: ImagingPlane\n", - " device: 2p_microscope pynwb.device.Device at 0x140416385841472\n", - "Fields:\n", - " description: Allen Institute two-photon pipeline: CAM2P.2\n", - "\n", - " excitation_lambda: 910.0\n", - " imaging_rate: 30.0\n", - " indicator: GCaMP6f\n", - " location: {'area': 'VISp', 'depth': '20'}\n", - " optical_channel: (\n", - " optical_channel \n", - " )\n", - " reference_frame: Intrinsic imaging home\n", - " unit: meters\n", - "\n", - " interval: 1\n", - " offset: 0.0\n", - " resolution: -1.0\n", - " timestamps: RoiResponseSeries pynwb.ophys.RoiResponseSeries at 0x140416385841184\n", - "Fields:\n", - " comments: no comments\n", - " conversion: 1.0\n", - " data: \n", - " description: Dendritic ROI traces obtained using booleanized masks.\n", - " interval: 1\n", - " offset: 0.0\n", - " resolution: -1.0\n", - " rois: rois \n", - " timestamp_link: (\n", - " motion_corrected_stack ,\n", - " pupil_diameter ,\n", - " pupil_position_x ,\n", - " pupil_position_y \n", - " )\n", - " timestamps: \n", - " timestamps_unit: seconds\n", - " unit: Normalized fluorescence (A.U.)\n", - "\n", - " timestamps_unit: seconds\n", - " unit: Fluorescence (A.U.)\n", - "
pupil_diameter pynwb.base.TimeSeries at 0x140416377390080\n", - "Fields:\n", - " comments: no comments\n", - " conversion: 1.0\n", - " data: \n", - " description: Diameter of the mouse pupil (right) facing the stimulus presentation screen.\n", - " interval: 1\n", - " offset: 0.0\n", - " resolution: -1.0\n", - " timestamps: RoiResponseSeries pynwb.ophys.RoiResponseSeries at 0x140416385841184\n", - "Fields:\n", - " comments: no comments\n", - " conversion: 1.0\n", - " data: \n", - " description: Dendritic ROI traces obtained using booleanized masks.\n", - " interval: 1\n", - " offset: 0.0\n", - " resolution: -1.0\n", - " rois: rois \n", - " timestamp_link: (\n", - " motion_corrected_stack ,\n", - " pupil_diameter ,\n", - " pupil_position_x ,\n", - " pupil_position_y \n", - " )\n", - " timestamps: \n", - " timestamps_unit: seconds\n", - " unit: Normalized fluorescence (A.U.)\n", - "\n", - " timestamps_unit: seconds\n", - " unit: mm\n", - "
pupil_position_x pynwb.behavior.SpatialSeries at 0x140416377391856\n", - "Fields:\n", - " comments: no comments\n", - " conversion: 1.0\n", - " data: \n", - " description: Position in x of the center of the mouse pupil (right) facing the stimulus presentation screen.\n", - " interval: 1\n", - " offset: 0.0\n", - " reference_frame: Left edge of the pupil recording image (6.528mm wide).\n", - " resolution: -1.0\n", - " timestamps: RoiResponseSeries pynwb.ophys.RoiResponseSeries at 0x140416385841184\n", - "Fields:\n", - " comments: no comments\n", - " conversion: 1.0\n", - " data: \n", - " description: Dendritic ROI traces obtained using booleanized masks.\n", - " interval: 1\n", - " offset: 0.0\n", - " resolution: -1.0\n", - " rois: rois \n", - " timestamp_link: (\n", - " motion_corrected_stack ,\n", - " pupil_diameter ,\n", - " pupil_position_x ,\n", - " pupil_position_y \n", - " )\n", - " timestamps: \n", - " timestamps_unit: seconds\n", - " unit: Normalized fluorescence (A.U.)\n", - "\n", - " timestamps_unit: seconds\n", - " unit: mm\n", - "
pupil_position_y pynwb.behavior.SpatialSeries at 0x140416377389120\n", - "Fields:\n", - " comments: no comments\n", - " conversion: 1.0\n", - " data: \n", - " description: Position in y of the center of the mouse pupil (right) facing the stimulus presentation screen.\n", - " interval: 1\n", - " offset: 0.0\n", - " reference_frame: Top edge of the pupil recording image (4.896mm high).\n", - " resolution: -1.0\n", - " timestamps: RoiResponseSeries pynwb.ophys.RoiResponseSeries at 0x140416385841184\n", - "Fields:\n", - " comments: no comments\n", - " conversion: 1.0\n", - " data: \n", - " description: Dendritic ROI traces obtained using booleanized masks.\n", - " interval: 1\n", - " offset: 0.0\n", - " resolution: -1.0\n", - " rois: rois \n", - " timestamp_link: (\n", - " motion_corrected_stack ,\n", - " pupil_diameter ,\n", - " pupil_position_x ,\n", - " pupil_position_y \n", - " )\n", - " timestamps: \n", - " timestamps_unit: seconds\n", - " unit: Normalized fluorescence (A.U.)\n", - "\n", - " timestamps_unit: seconds\n", - " unit: mm\n", - "
timestamps_unit: seconds
interval: 1
reference_frame: Top edge of the pupil recording image (4.896mm high).
ophys
description: OpenScope processing pipeline
data_interfaces (3)
DfOverF
roi_response_series (1)
RoiResponseSeries
resolution: -1.0
comments: no comments
description: Dendritic ROI traces obtained using booleanized masks.
conversion: 1.0
offset: 0.0
unit: Normalized fluorescence (A.U.)
data
timestamps
timestamps_unit: seconds
interval: 1
rois
description: Segmented dendrites (height x width)
table
description: Segmentation for imaging plane optimized for dendritic ROI extraction (Inan et al., 2017, NIPS)
id
colnames: ('image_mask', 'tracking_id')
columns: (, )
imaging_plane
optical_channel
optical_channel pynwb.ophys.OpticalChannel at 0x140416385839744\n", - "Fields:\n", - " description: 2P Optical Channel\n", - " emission_lambda: 520.0\n", - "
description: ImagingPlane
device
description: Allen Institute two-photon pipeline: CAM2P.2
excitation_lambda: 910.0
imaging_rate: 30.0
indicator: GCaMP6f
location: {'area': 'VISp', 'depth': '20'}
conversion: 1.0
unit: meters
reference_frame: Intrinsic imaging home
timestamp_link
motion_corrected_stack pynwb.ophys.TwoPhotonSeries at 0x140416375787088\n", - "Fields:\n", - " comments: no comments\n", - " conversion: 1.0\n", - " data: \n", - " description: Motion corrected imaging stack (frames x height x width).\n", - " format: byte image (uint8)\n", - " imaging_plane: ImagingPlane pynwb.ophys.ImagingPlane at 0x140416385840128\n", - "Fields:\n", - " conversion: 1.0\n", - " description: ImagingPlane\n", - " device: 2p_microscope pynwb.device.Device at 0x140416385841472\n", - "Fields:\n", - " description: Allen Institute two-photon pipeline: CAM2P.2\n", - "\n", - " excitation_lambda: 910.0\n", - " imaging_rate: 30.0\n", - " indicator: GCaMP6f\n", - " location: {'area': 'VISp', 'depth': '20'}\n", - " optical_channel: (\n", - " optical_channel \n", - " )\n", - " reference_frame: Intrinsic imaging home\n", - " unit: meters\n", - "\n", - " interval: 1\n", - " offset: 0.0\n", - " resolution: -1.0\n", - " timestamps: RoiResponseSeries pynwb.ophys.RoiResponseSeries at 0x140416385841184\n", - "Fields:\n", - " comments: no comments\n", - " conversion: 1.0\n", - " data: \n", - " description: Dendritic ROI traces obtained using booleanized masks.\n", - " interval: 1\n", - " offset: 0.0\n", - " resolution: -1.0\n", - " rois: rois \n", - " timestamp_link: (\n", - " motion_corrected_stack ,\n", - " pupil_diameter ,\n", - " pupil_position_x ,\n", - " pupil_position_y \n", - " )\n", - " timestamps: \n", - " timestamps_unit: seconds\n", - " unit: Normalized fluorescence (A.U.)\n", - "\n", - " timestamps_unit: seconds\n", - " unit: Fluorescence (A.U.)\n", - "
pupil_diameter pynwb.base.TimeSeries at 0x140416377390080\n", - "Fields:\n", - " comments: no comments\n", - " conversion: 1.0\n", - " data: \n", - " description: Diameter of the mouse pupil (right) facing the stimulus presentation screen.\n", - " interval: 1\n", - " offset: 0.0\n", - " resolution: -1.0\n", - " timestamps: RoiResponseSeries pynwb.ophys.RoiResponseSeries at 0x140416385841184\n", - "Fields:\n", - " comments: no comments\n", - " conversion: 1.0\n", - " data: \n", - " description: Dendritic ROI traces obtained using booleanized masks.\n", - " interval: 1\n", - " offset: 0.0\n", - " resolution: -1.0\n", - " rois: rois \n", - " timestamp_link: (\n", - " motion_corrected_stack ,\n", - " pupil_diameter ,\n", - " pupil_position_x ,\n", - " pupil_position_y \n", - " )\n", - " timestamps: \n", - " timestamps_unit: seconds\n", - " unit: Normalized fluorescence (A.U.)\n", - "\n", - " timestamps_unit: seconds\n", - " unit: mm\n", - "
pupil_position_x pynwb.behavior.SpatialSeries at 0x140416377391856\n", - "Fields:\n", - " comments: no comments\n", - " conversion: 1.0\n", - " data: \n", - " description: Position in x of the center of the mouse pupil (right) facing the stimulus presentation screen.\n", - " interval: 1\n", - " offset: 0.0\n", - " reference_frame: Left edge of the pupil recording image (6.528mm wide).\n", - " resolution: -1.0\n", - " timestamps: RoiResponseSeries pynwb.ophys.RoiResponseSeries at 0x140416385841184\n", - "Fields:\n", - " comments: no comments\n", - " conversion: 1.0\n", - " data: \n", - " description: Dendritic ROI traces obtained using booleanized masks.\n", - " interval: 1\n", - " offset: 0.0\n", - " resolution: -1.0\n", - " rois: rois \n", - " timestamp_link: (\n", - " motion_corrected_stack ,\n", - " pupil_diameter ,\n", - " pupil_position_x ,\n", - " pupil_position_y \n", - " )\n", - " timestamps: \n", - " timestamps_unit: seconds\n", - " unit: Normalized fluorescence (A.U.)\n", - "\n", - " timestamps_unit: seconds\n", - " unit: mm\n", - "
pupil_position_y pynwb.behavior.SpatialSeries at 0x140416377389120\n", - "Fields:\n", - " comments: no comments\n", - " conversion: 1.0\n", - " data: \n", - " description: Position in y of the center of the mouse pupil (right) facing the stimulus presentation screen.\n", - " interval: 1\n", - " offset: 0.0\n", - " reference_frame: Top edge of the pupil recording image (4.896mm high).\n", - " resolution: -1.0\n", - " timestamps: RoiResponseSeries pynwb.ophys.RoiResponseSeries at 0x140416385841184\n", - "Fields:\n", - " comments: no comments\n", - " conversion: 1.0\n", - " data: \n", - " description: Dendritic ROI traces obtained using booleanized masks.\n", - " interval: 1\n", - " offset: 0.0\n", - " resolution: -1.0\n", - " rois: rois \n", - " timestamp_link: (\n", - " motion_corrected_stack ,\n", - " pupil_diameter ,\n", - " pupil_position_x ,\n", - " pupil_position_y \n", - " )\n", - " timestamps: \n", - " timestamps_unit: seconds\n", - " unit: Normalized fluorescence (A.U.)\n", - "\n", - " timestamps_unit: seconds\n", - " unit: mm\n", - "
ImageSegmentation
plane_segmentations (1)
PlaneSegmentation
description: Segmentation for imaging plane optimized for dendritic ROI extraction (Inan et al., 2017, NIPS)
id
colnames: ('image_mask', 'tracking_id')
columns: (, )
imaging_plane
optical_channel
optical_channel pynwb.ophys.OpticalChannel at 0x140416385839744\n", - "Fields:\n", - " description: 2P Optical Channel\n", - " emission_lambda: 520.0\n", - "
description: ImagingPlane
device
description: Allen Institute two-photon pipeline: CAM2P.2
excitation_lambda: 910.0
imaging_rate: 30.0
indicator: GCaMP6f
location: {'area': 'VISp', 'depth': '20'}
conversion: 1.0
unit: meters
reference_frame: Intrinsic imaging home
PlaneImages
description: Plane images.
images (1)
max_projection
epoch_tags: set()
devices (1)
2p_microscope
description: Allen Institute two-photon pipeline: CAM2P.2
imaging_planes (1)
ImagingPlane
optical_channel
optical_channel pynwb.ophys.OpticalChannel at 0x140416385839744\n", - "Fields:\n", - " description: 2P Optical Channel\n", - " emission_lambda: 520.0\n", - "
description: ImagingPlane
device
description: Allen Institute two-photon pipeline: CAM2P.2
excitation_lambda: 910.0
imaging_rate: 30.0
indicator: GCaMP6f
location: {'area': 'VISp', 'depth': '20'}
conversion: 1.0
unit: meters
reference_frame: Intrinsic imaging home
intervals (1)
trials
description: experimental trials
id
colnames: ('start_time', 'stop_time', 'stimulus_type', 'stimulus_template_name', 'unexpected', 'gabor_frame', 'gabor_kappa', 'gabor_mean_orientation', 'gabor_number', 'gabor_locations_x', 'gabor_locations_y', 'gabor_sizes', 'gabor_orientations', 'main_flow_direction', 'num_frames_stim', 'num_frames_twop', 'square_size', 'square_number', 'square_proportion_flipped', 'square_locations_x', 'square_locations_y', 'start_frame_stim_template', 'start_frame_stim', 'start_frame_twop', 'stop_frame_stim', 'stop_frame_twop')
columns: (, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , )
subject
age: P90D
genotype: Rbp4-Cre_KL100;Camk2a-tTA;Ai93
sex: F
species: Mus musculus
subject_id: 411400
trials
description: experimental trials
id
colnames: ('start_time', 'stop_time', 'stimulus_type', 'stimulus_template_name', 'unexpected', 'gabor_frame', 'gabor_kappa', 'gabor_mean_orientation', 'gabor_number', 'gabor_locations_x', 'gabor_locations_y', 'gabor_sizes', 'gabor_orientations', 'main_flow_direction', 'num_frames_stim', 'num_frames_twop', 'square_size', 'square_number', 'square_proportion_flipped', 'square_locations_x', 'square_locations_y', 'start_frame_stim_template', 'start_frame_stim', 'start_frame_twop', 'stop_frame_stim', 'stop_frame_twop')
columns: (, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , )
session_id: 761730740
institution: Allen Institute for Brain Science
" - ], - "text/plain": [ - "root pynwb.file.NWBFile at 0x140416411756000\n", - "Fields:\n", - " acquisition: {\n", - " motion_corrected_stack \n", - " }\n", - " devices: {\n", - " 2p_microscope \n", - " }\n", - " file_create_date: [datetime.datetime(2022, 9, 25, 6, 46, 18, 315565, tzinfo=tzoffset(None, -25200))\n", - " datetime.datetime(2023, 4, 5, 6, 5, 59, 678585, tzinfo=tzoffset(None, -25200))]\n", - " identifier: 761730740_with_stim_and_stack\n", - " imaging_planes: {\n", - " ImagingPlane \n", - " }\n", - " institution: Allen Institute for Brain Science\n", - " intervals: {\n", - " trials \n", - " }\n", - " processing: {\n", - " behavior ,\n", - " ophys \n", - " }\n", - " session_description: Allen Institute OpenScope dataset\n", - " session_id: 761730740\n", - " session_start_time: 2018-10-09 17:50:37.303000-07:00\n", - " stimulus: {\n", - " gabors ,\n", - " grayscreen ,\n", - " visflow_left ,\n", - " visflow_right \n", - " }\n", - " stimulus_template: {\n", - " gabors ,\n", - " grayscreen ,\n", - " visflow_left ,\n", - " visflow_right \n", - " }\n", - " subject: subject pynwb.file.Subject at 0x140416412116832\n", - "Fields:\n", - " age: P90D\n", - " genotype: Rbp4-Cre_KL100;Camk2a-tTA;Ai93\n", - " sex: F\n", - " species: Mus musculus\n", - " subject_id: 411400\n", - "\n", - " timestamps_reference_time: 2018-10-09 17:50:37.303000-07:00\n", - " trials: trials " - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "nwbfile" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [], - "source": [ - "frames = nwbfile.acquisition[\"motion_corrected_stack\"].data" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(126747, 512, 512)" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "frames.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [], - "source": [ - "add_frames = np.sum(frames[:5000],axis=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "plt.imshow(add_frames, aspect='auto', cmap='hot')\n", - "#plt.colorbar(label='Aggregated Intensity')\n", - "#plt.xlabel('Width')\n", - "#plt.ylabel('Height')\n", - "plt.title('dend sess frames (aggregated 4000 frames)')\n", - "plt.axis('off')\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[0. 0. 0. ... 0. 0. 0.]\n", - " [0. 0. 0. ... 0. 0. 0.]\n", - " [0. 0. 0. ... 0. 0. 0.]\n", - " ...\n", - " [0. 0. 0. ... 0. 0. 0.]\n", - " [0. 0. 0. ... 0. 0. 0.]\n", - " [0. 0. 0. ... 0. 0. 0.]]\n" - ] - } - ], - "source": [ - "IS = op.get_data_interface(\"ImageSegmentation\")\n", - "PS = IS.plane_segmentations[\"PlaneSegmentation\"]\n", - "pos = np.sum(PS.columns[0].data[:], axis = 0)\n", - "print(pos)" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "# Create the heatmap\n", - "plt.figure(figsize=(8, 6))\n", - "heatmap = plt.imshow(pos, cmap='hot')\n", - "\n", - "# Add color bar\n", - "#plt.colorbar(heatmap)\n", - "\n", - "# Show the plot\n", - "plt.title('ROIs (dend session example)')\n", - "#plt.xlabel('X-axis')\n", - "#plt.ylabel('Y-axis')\n", - "plt.axis('off')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## all session stats" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "source_dir = \"/network/projects/neuro-galaxy/data/raw/openscope_calcium\"\n", - "filenames,sess_ids, num_rois, lines, planes = getNWBfilenames()\n", - "file_nums = len(filenames)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n", - "1\n", - "2\n", - "3\n", - "4\n", - "5\n", - "6\n", - "7\n", - "8\n", - "9\n", - "10\n", - "11\n", - "12\n", - "13\n", - "14\n", - "15\n", - "16\n", - "17\n", - "18\n", - "19\n", - "20\n", - "21\n", - "22\n", - "23\n", - "24\n", - "25\n", - "26\n", - "27\n", - "28\n", - "29\n", - "30\n", - "31\n", - "32\n", - "33\n", - "34\n", - "35\n", - "36\n", - "37\n", - "38\n", - "39\n", - "40\n", - "41\n", - "42\n", - "43\n", - "44\n", - "45\n", - "46\n", - "47\n", - "48\n", - "49\n" - ] - } - ], - "source": [ - "\n", - "all_areas = []\n", - "all_heights = []\n", - "all_widths = []\n", - "\n", - "for count, file_name in enumerate(filenames):\n", - " io = NWBHDF5IO(os.path.join(source_dir,file_name), mode=\"r\")\n", - " nwbfile = io.read()\n", - " print(count)\n", - "\n", - " curr_sess_id = sess_ids[count]\n", - " \n", - " op = nwbfile.processing[\"ophys\"]\n", - " df_over_f = op.get_data_interface(\"DfOverF\")\n", - " roi = df_over_f.roi_response_series[\"RoiResponseSeries\"]\n", - "\n", - " #adding ROI metadata\n", - " is_module = op.get_data_interface(\"ImageSegmentation\")\n", - " ps = is_module.plane_segmentations[\"PlaneSegmentation\"]\n", - "\n", - " roi_masks = ps.columns[0].data[:]\n", - "\n", - " #(num_rois, 2)\n", - " roi_positions = calculate_roi_centroids(roi_masks)\n", - "\n", - " #(num_rois, )\n", - " roi_areas, roi_heights, roi_widths = calculate_roi_dimensions(roi_masks)\n", - "\n", - " all_areas.append(roi_areas)\n", - " all_heights.append(roi_heights)\n", - " all_widths.append(roi_widths)\n", - "\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "# Concatenate all areas, heights, and widths from 50 sessions\n", - "all_areas_arr = np.concatenate(all_areas, axis = 0)\n", - "all_heights_arr = np.concatenate(all_heights, axis = 0)\n", - "all_widths_arr = np.concatenate(all_widths, axis = 0)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(19115,)\n", - "(19115,)\n", - "(19115,)\n" - ] - } - ], - "source": [ - "print(all_areas_arr.shape)\n", - "print(all_heights_arr.shape)\n", - "print(all_widths_arr.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "area: 6.0, 1108.0\n", - "height: 1.0, 119.0\n", - "width: 3.0, 101.0\n" - ] - } - ], - "source": [ - "print(\"area: {}, {}\".format(np.min(all_areas_arr),np.max(all_areas_arr)))\n", - "print(\"height: {}, {}\".format(np.min(all_heights_arr),np.max(all_heights_arr)))\n", - "print(\"width: {}, {}\".format(np.min(all_widths_arr),np.max(all_widths_arr)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plotting timseries" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "source_dir = \"/network/projects/neuro-galaxy/data/raw/openscope_calcium\"\n", - "filenames,sess_ids, num_rois, lines, planes = getNWBfilenames()\n", - "file_nums = len(filenames)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[758519303, 759189643, 759660390, 759666166, 759872185, 760269100, 761730740, 762415169, 763646681, 761624763, 761944562, 762250376, 760260459, 760659782, 761269197, 763949859, 764897534, 765427689, 766755831, 767254594, 768807532, 764704289, 765193831, 766502238, 777496949, 778374308, 779152062, 777914830, 778864809, 779650018, 826187862, 826773996, 827833392, 826338612, 826819032, 828816509, 829283315, 823453391, 824434038, 825180479, 826659257, 827300090, 828475005, 829520904, 832883243, 833704570, 834403597, 836968429, 837360280, 838633305]\n" - ] - } - ], - "source": [ - "print(sess_ids)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n", - "1\n", - "2\n", - "3\n", - "4\n", - "5\n", - "6\n", - "7\n", - "8\n", - "9\n", - "10\n", - "11\n", - "12\n", - "13\n", - "14\n", - "15\n", - "16\n", - "17\n", - "18\n", - "19\n", - "20\n", - "21\n", - "22\n", - "23\n", - "24\n", - "25\n", - "26\n", - "27\n", - "28\n", - "29\n", - "30\n", - "31\n", - "32\n", - "33\n", - "34\n", - "35\n", - "36\n", - "37\n", - "38\n", - "39\n", - "40\n", - "41\n", - "42\n", - "43\n", - "44\n", - "45\n", - "46\n", - "47\n", - "48\n", - "49\n" - ] - } - ], - "source": [ - "all_roi_data_dend = []\n", - "all_roi_data_soma = []\n", - "for count, file_name in enumerate(filenames):\n", - " io = NWBHDF5IO(os.path.join(source_dir,file_name), mode=\"r\")\n", - " nwbfile = io.read()\n", - " curr_sess_id = sess_ids[count]\n", - "\n", - " print(count)\n", - " \n", - " op = nwbfile.processing[\"ophys\"]\n", - " df_over_f = op.get_data_interface(\"DfOverF\")\n", - " roi = df_over_f.roi_response_series[\"RoiResponseSeries\"]\n", - " roi_data = roi.data[2000:25000]\n", - " \n", - " if planes[count] == 'dend':\n", - " all_roi_data_dend.append(np.mean(roi_data, axis = 1))\n", - " else:\n", - " all_roi_data_soma.append(np.mean(roi_data, axis = 1))" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "all_roi_data_dend = np.array(all_roi_data_dend)\n", - "all_roi_data_soma = np.array(all_roi_data_soma)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "all_roi_data_dend_avg = np.mean(all_roi_data_dend, axis = 0)\n", - "all_roi_data_soma_avg = np.mean(all_roi_data_soma, axis = 0)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "def smooth_data(data, window_size=50):\n", - " window = np.ones(window_size) / window_size\n", - " return np.convolve(data, window, mode='same')\n", - "\n", - "# Plotting the average across all ROIs\n", - "#average_activity = np.mean(avg_roi_data, axis=0)\n", - "plt.figure(figsize=(20, 5))\n", - "plt.plot(smooth_data(all_roi_data_dend_avg), color='black', label='Dendritic Average Activity')\n", - "plt.plot(smooth_data(all_roi_data_soma_avg), color='orange', label='Somatic Average Activity')\n", - "plt.title('Average Activity Across all ROIs in all sessions')\n", - "plt.xlabel('Timesteps')\n", - "#plt.ylim(0, 0.10) # Set y-axis limits\n", - "plt.ylabel('Activity (dF/F)')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## test stim" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "source_dir = \"/network/projects/neuro-galaxy/data/raw/openscope_calcium\"\n", - "#file_name = \"sub-433451_ses-824434038_obj-raw_behavior+image+ophys.nwb\"\n", - "file_name = \"sub-433458_ses-826659257_obj-raw_behavior+image+ophys.nwb\"\n", - "io = NWBHDF5IO(os.path.join(source_dir,file_name), mode=\"r\")\n", - "nwbfile = io.read()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "HEAVY_COLUMNS = [\"start_time\",\"stop_time\",\"stimulus_type\", \"stimulus_template_name\", \n", - " \"unexpected\", 'gabor_frame',\"gabor_mean_orientation\", \n", - " \"start_frame_twop\", \"stop_frame_twop\"]\n", - "stim_df = nwbfile.trials.to_dataframe()\n", - "stim_df = stim_df[HEAVY_COLUMNS]" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "#each entry is a sequence for visflow\n", - "vsfl_l_start_times = stim_df.loc[(stim_df['stimulus_template_name'] == 'visflow_left'), 'start_time']\n", - "vsfl_l_end_times = stim_df.loc[(stim_df['stimulus_template_name'] == 'visflow_left'), 'stop_time']\n", - "\n", - "vsfl_r_start_times = stim_df.loc[(stim_df['stimulus_template_name'] == 'visflow_right'), 'start_time']\n", - "vsfl_r_end_times = stim_df.loc[(stim_df['stimulus_template_name'] == 'visflow_right'), 'stop_time']\n", - "\n", - "if np.array(vsfl_l_start_times)[0] < np.array(vsfl_r_start_times)[0]:\n", - " vsfl_start = np.append(vsfl_l_start_times,vsfl_r_start_times)\n", - "else:\n", - " vsfl_start = np.append(vsfl_r_start_times,vsfl_l_start_times)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "44.12086" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "vsfl_start.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.ones()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.6" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/capoyo/notebooks/within_sess_analysis.ipynb b/examples/capoyo/notebooks/within_sess_analysis.ipynb deleted file mode 100644 index ab1a36d..0000000 --- a/examples/capoyo/notebooks/within_sess_analysis.ipynb +++ /dev/null @@ -1,961 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import os\n", - "from tqdm import tqdm\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## POYO within-sess" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Processing all within-session csvs\n", - "within-sess validation results csv for all sessions\n", - "\n", - "Combine them into one csv for further analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def get_filenames_from_path(path):\n", - " if os.path.exists(path):\n", - " if os.path.isdir(path):\n", - " filenames = [f for f in os.listdir(path) if os.path.isfile(os.path.join(path, f))]\n", - " return filenames\n", - " else:\n", - " return [os.path.basename(path)]\n", - " else:\n", - " return []\n", - "\n", - "#Helper function to get nwbfile names, session ids and corresponding ROI numbers (same order)\n", - "def getNWBinfo(mouse_csv_path='/home/mila/x/xuejing.pan/thesis/mouse_df.csv'):\n", - " lines = []\n", - " sess_ids = []\n", - " planes = []\n", - "\t\n", - " df = pd.read_csv(mouse_csv_path, usecols = ['sessid','line','runtype','plane'])\n", - "\n", - " #Getting all prod data\n", - " for row, curr_type in enumerate(df.runtype):\n", - " if curr_type == 'prod': \n", - " lines.append(df.line[row])\n", - " sess_ids.append(df.sessid[row])\n", - " planes.append(df.plane[row])\n", - " \n", - " assert len(lines)==len(sess_ids)==len(planes)==50, \"Error in getting session info.\" \n", - "\n", - " return sess_ids, planes, lines" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "val_csv_path = '/home/mila/x/xuejing.pan/POYO/results/cross_sess/val'\n", - "#train_csv_path = '/home/mila/x/xuejing.pan/POYO/results/within-sess/train'\n", - "mouse_csv = '/home/mila/x/xuejing.pan/POYO/project-kirby/mouse_df.csv'\n", - "#get all sessions\n", - "all_sessions = [\n", - " \"758519303\",\"759189643\" \"759660390\" \"759666166\" \"759872185\",\n", - " \"760269100\",\"761730740\",\"762415169\",\"763646681\",\"761624763\", \n", - " \"761944562\",\"762250376\",\"760260459\",\"760659782\",\"761269197\", \n", - " \"763949859\",\"764897534\",\"765427689\",\"766755831\",\"767254594\",\n", - " \"768807532\",\"764704289\",\"765193831\",\"766502238\",\"777496949\", \n", - " \"778374308\",\"779152062\",\"777914830\",\"778864809\",\"779650018\",\n", - " \"826187862\",\"826773996\",\"827833392\",\"826338612\",\"826819032\", \n", - " \"828816509\",\"829283315\",\"823453391\",\"824434038\",\"825180479\", \n", - " \"826659257\",\"827300090\",\"828475005\",\"829520904\",\"832883243\", \n", - " \"833704570\",\"834403597\",\"836968429\",\"837360280\",\"838633305\" \n", - " ]" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "ename": "FileNotFoundError", - "evalue": "[Errno 2] No such file or directory: '/home/mila/x/xuejing.pan/POYO/results/cross_sess/val/val_758519303.csv'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[4], line 16\u001b[0m\n\u001b[1;32m 14\u001b[0m file_path \u001b[39m=\u001b[39m os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39mjoin(val_csv_path,\u001b[39m'\u001b[39m\u001b[39mval_\u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m.csv\u001b[39m\u001b[39m'\u001b[39m\u001b[39m.\u001b[39mformat(\u001b[39mstr\u001b[39m(curr_sess_id)))\n\u001b[1;32m 15\u001b[0m \u001b[39m#curr_df = pd.read_csv(file_path, usecols=[\"Step\", \"{} - val/session_{}_accuracy_gabor_orientation\".format(str(curr_sess_id),str(curr_sess_id))])\u001b[39;00m\n\u001b[0;32m---> 16\u001b[0m curr_df \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39;49mread_csv(file_path, usecols\u001b[39m=\u001b[39;49m[\u001b[39m\"\u001b[39;49m\u001b[39mepoch\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39m\"\u001b[39;49m\u001b[39mIMPORTANT - multi_sess - val/session_\u001b[39;49m\u001b[39m{}\u001b[39;49;00m\u001b[39m_accuracy_gabor_orientation\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39m.\u001b[39;49mformat(\u001b[39mstr\u001b[39;49m(curr_sess_id))])\n\u001b[1;32m 19\u001b[0m \u001b[39m#epochs = curr_df[\"Step\"]\u001b[39;00m\n\u001b[1;32m 20\u001b[0m \u001b[39m#epochs = epochs/17.2\u001b[39;00m\n\u001b[1;32m 21\u001b[0m epochs \u001b[39m=\u001b[39m curr_df[\u001b[39m\"\u001b[39m\u001b[39mepoch\u001b[39m\u001b[39m\"\u001b[39m]\n", - "File \u001b[0;32m~/.conda/envs/poyo/lib/python3.9/site-packages/pandas/util/_decorators.py:211\u001b[0m, in \u001b[0;36mdeprecate_kwarg.._deprecate_kwarg..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 209\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 210\u001b[0m kwargs[new_arg_name] \u001b[39m=\u001b[39m new_arg_value\n\u001b[0;32m--> 211\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n", - "File \u001b[0;32m~/.conda/envs/poyo/lib/python3.9/site-packages/pandas/util/_decorators.py:331\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments..decorate..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 325\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(args) \u001b[39m>\u001b[39m num_allow_args:\n\u001b[1;32m 326\u001b[0m warnings\u001b[39m.\u001b[39mwarn(\n\u001b[1;32m 327\u001b[0m msg\u001b[39m.\u001b[39mformat(arguments\u001b[39m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[1;32m 328\u001b[0m \u001b[39mFutureWarning\u001b[39;00m,\n\u001b[1;32m 329\u001b[0m stacklevel\u001b[39m=\u001b[39mfind_stack_level(),\n\u001b[1;32m 330\u001b[0m )\n\u001b[0;32m--> 331\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n", - "File \u001b[0;32m~/.conda/envs/poyo/lib/python3.9/site-packages/pandas/io/parsers/readers.py:950\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m 935\u001b[0m kwds_defaults \u001b[39m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m 936\u001b[0m dialect,\n\u001b[1;32m 937\u001b[0m delimiter,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 946\u001b[0m defaults\u001b[39m=\u001b[39m{\u001b[39m\"\u001b[39m\u001b[39mdelimiter\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39m\"\u001b[39m\u001b[39m,\u001b[39m\u001b[39m\"\u001b[39m},\n\u001b[1;32m 947\u001b[0m )\n\u001b[1;32m 948\u001b[0m kwds\u001b[39m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m--> 950\u001b[0m \u001b[39mreturn\u001b[39;00m _read(filepath_or_buffer, kwds)\n", - "File \u001b[0;32m~/.conda/envs/poyo/lib/python3.9/site-packages/pandas/io/parsers/readers.py:605\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 602\u001b[0m _validate_names(kwds\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39mnames\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m))\n\u001b[1;32m 604\u001b[0m \u001b[39m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 605\u001b[0m parser \u001b[39m=\u001b[39m TextFileReader(filepath_or_buffer, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds)\n\u001b[1;32m 607\u001b[0m \u001b[39mif\u001b[39;00m chunksize \u001b[39mor\u001b[39;00m iterator:\n\u001b[1;32m 608\u001b[0m \u001b[39mreturn\u001b[39;00m parser\n", - "File \u001b[0;32m~/.conda/envs/poyo/lib/python3.9/site-packages/pandas/io/parsers/readers.py:1442\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 1439\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39moptions[\u001b[39m\"\u001b[39m\u001b[39mhas_index_names\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m kwds[\u001b[39m\"\u001b[39m\u001b[39mhas_index_names\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[1;32m 1441\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandles: IOHandles \u001b[39m|\u001b[39m \u001b[39mNone\u001b[39;00m \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m-> 1442\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_engine \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_make_engine(f, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mengine)\n", - "File \u001b[0;32m~/.conda/envs/poyo/lib/python3.9/site-packages/pandas/io/parsers/readers.py:1735\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m 1733\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mb\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m mode:\n\u001b[1;32m 1734\u001b[0m mode \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mb\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m-> 1735\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandles \u001b[39m=\u001b[39m get_handle(\n\u001b[1;32m 1736\u001b[0m f,\n\u001b[1;32m 1737\u001b[0m mode,\n\u001b[1;32m 1738\u001b[0m encoding\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mencoding\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mNone\u001b[39;49;00m),\n\u001b[1;32m 1739\u001b[0m compression\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mcompression\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mNone\u001b[39;49;00m),\n\u001b[1;32m 1740\u001b[0m memory_map\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mmemory_map\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mFalse\u001b[39;49;00m),\n\u001b[1;32m 1741\u001b[0m is_text\u001b[39m=\u001b[39;49mis_text,\n\u001b[1;32m 1742\u001b[0m errors\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mencoding_errors\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39m\"\u001b[39;49m\u001b[39mstrict\u001b[39;49m\u001b[39m\"\u001b[39;49m),\n\u001b[1;32m 1743\u001b[0m storage_options\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mstorage_options\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mNone\u001b[39;49;00m),\n\u001b[1;32m 1744\u001b[0m )\n\u001b[1;32m 1745\u001b[0m \u001b[39massert\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandles \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m 1746\u001b[0m f \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandles\u001b[39m.\u001b[39mhandle\n", - "File \u001b[0;32m~/.conda/envs/poyo/lib/python3.9/site-packages/pandas/io/common.py:856\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m 851\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(handle, \u001b[39mstr\u001b[39m):\n\u001b[1;32m 852\u001b[0m \u001b[39m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[1;32m 853\u001b[0m \u001b[39m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[1;32m 854\u001b[0m \u001b[39mif\u001b[39;00m ioargs\u001b[39m.\u001b[39mencoding \u001b[39mand\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mb\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m ioargs\u001b[39m.\u001b[39mmode:\n\u001b[1;32m 855\u001b[0m \u001b[39m# Encoding\u001b[39;00m\n\u001b[0;32m--> 856\u001b[0m handle \u001b[39m=\u001b[39m \u001b[39mopen\u001b[39;49m(\n\u001b[1;32m 857\u001b[0m handle,\n\u001b[1;32m 858\u001b[0m ioargs\u001b[39m.\u001b[39;49mmode,\n\u001b[1;32m 859\u001b[0m encoding\u001b[39m=\u001b[39;49mioargs\u001b[39m.\u001b[39;49mencoding,\n\u001b[1;32m 860\u001b[0m errors\u001b[39m=\u001b[39;49merrors,\n\u001b[1;32m 861\u001b[0m newline\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m 862\u001b[0m )\n\u001b[1;32m 863\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 864\u001b[0m \u001b[39m# Binary mode\u001b[39;00m\n\u001b[1;32m 865\u001b[0m handle \u001b[39m=\u001b[39m \u001b[39mopen\u001b[39m(handle, ioargs\u001b[39m.\u001b[39mmode)\n", - "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/home/mila/x/xuejing.pan/POYO/results/cross_sess/val/val_758519303.csv'" - ] - } - ], - "source": [ - "sess_ids, planes, lines = getNWBinfo(mouse_csv)\n", - "\n", - "#creating a combined csv for validation information\n", - "combined_val_df_columns = [\"sess_id\", \"epoch\", \"val_accs\", \"line\", \"plane\"]\n", - "combined_val_df = pd.DataFrame(columns=combined_val_df_columns)\n", - "\n", - "all_epochs = 0\n", - "\n", - "for idx, curr_sess_id in enumerate(sess_ids):\n", - " #for each session\n", - " curr_plane = planes[idx]\n", - " curr_line = lines[idx]\n", - "\n", - " file_path = os.path.join(val_csv_path,'val_{}.csv'.format(str(curr_sess_id)))\n", - " #curr_df = pd.read_csv(file_path, usecols=[\"Step\", \"{} - val/session_{}_accuracy_gabor_orientation\".format(str(curr_sess_id),str(curr_sess_id))])\n", - " curr_df = pd.read_csv(file_path, usecols=[\"epoch\", \"IMPORTANT - multi_sess - val/session_{}_accuracy_gabor_orientation\".format(str(curr_sess_id))])\n", - " \n", - " \n", - " #epochs = curr_df[\"Step\"]\n", - " #epochs = epochs/17.2\n", - " epochs = curr_df[\"epoch\"]\n", - " all_epochs = epochs\n", - " \n", - " #val_accs = curr_df[\"{} - val/session_{}_accuracy_gabor_orientation\".format(str(curr_sess_id),str(curr_sess_id))]\n", - " val_accs = curr_df[\"IMPORTANT - multi_sess - val/session_{}_accuracy_gabor_orientation\".format(str(curr_sess_id))]\n", - "\n", - " for count, epoch in enumerate(epochs):\n", - " combined_val_df = combined_val_df.append({\n", - " \"sess_id\": int(curr_sess_id),\n", - " \"line\": curr_line,\n", - " \"plane\": curr_plane,\n", - " \"epoch\": int(epoch),\n", - " \"val_accs\": float(val_accs[count]),\n", - " },\n", - " ignore_index=True)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "combined_train_df_columns = [\"sess_id\", \"epoch\", \"train_losses\", \"line\", \"plane\"]\n", - "combined_train_df = pd.DataFrame(columns=combined_train_df_columns)\n", - "\n", - "for idx, curr_sess_id in enumerate(sess_ids):\n", - " #for each session\n", - " curr_plane = planes[idx]\n", - " curr_line = lines[idx]\n", - "\n", - " #file_path = os.path.join(val_csv_path,'train_losses.csv')\n", - " file_path = os.path.join(val_csv_path, 'train_{}.csv'.format(str(curr_sess_id)))\n", - " curr_df = pd.read_csv(file_path)\n", - " #epochs = curr_df[\"epoch\"]\n", - " train_loss = curr_df[\"{} - train_loss\".format(str(curr_sess_id))]\n", - "\n", - " epochs = curr_df[\"epoch\"]\n", - " all_epochs = epochs\n", - "\n", - " for count, epoch in enumerate(epochs):\n", - " combined_train_df = combined_train_df.append({\n", - " \"sess_id\": int(curr_sess_id),\n", - " \"line\": curr_line,\n", - " \"plane\": curr_plane,\n", - " \"epoch\": int(epoch),\n", - " \"train_losses\": float(train_loss[count]),\n", - " },\n", - " ignore_index=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "#save csv \n", - "combined_val_df.to_csv(os.path.join(val_csv_path,'combined_vals.csv'))" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [], - "source": [ - "combined_train_df.to_csv(os.path.join(val_csv_path,'combined_train_losses.csv'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### use combined_val_df and combined_train_df to construct plots and do analysis\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "combined_val_df = pd.read_csv('/home/mila/x/xuejing.pan/POYO/results/within-sess/combined_vals.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def get_acc_for_one_sess(combined_df,sess_id):\n", - " condition = (combined_df[\"sess_id\"] == sess_id)\n", - " accs = combined_df.loc[condition,\"val_accs\"]\n", - "\n", - " condition_soma = (combined_df[\"sess_id\"] == sess_id) & (combined_df[\"plane\"] == \"soma\")\n", - " condition_dend = (combined_df[\"sess_id\"] == sess_id) & (combined_df[\"plane\"] == \"dend\")\n", - " condition_L23 = (combined_df[\"sess_id\"] == sess_id) & (combined_df[\"line\"] == \"L23-Cux2\")\n", - " condition_L5 = (combined_df[\"sess_id\"] == sess_id) & (combined_df[\"line\"] == \"L5-Rbp4\")\n", - "\n", - " accs_soma = combined_df.loc[condition_soma,\"val_accs\"]\n", - " accs_dend = combined_df.loc[condition_dend,\"val_accs\"]\n", - " accs_L23 = combined_df.loc[condition_L23,\"val_accs\"]\n", - " accs_L5 = combined_df.loc[condition_L5,\"val_accs\"]\n", - "\n", - " return accs,accs_dend,accs_soma,accs_L5,accs_L23\n", - "\n", - "def get_losses_for_one_sess(combined_df,sess_id):\n", - " condition = (combined_df[\"sess_id\"] == sess_id)\n", - " losses = combined_df.loc[condition,\"train_losses\"]\n", - "\n", - " condition_soma = (combined_df[\"sess_id\"] == sess_id) & (combined_df[\"plane\"] == \"soma\")\n", - " condition_dend = (combined_df[\"sess_id\"] == sess_id) & (combined_df[\"plane\"] == \"dend\")\n", - " condition_L23 = (combined_df[\"sess_id\"] == sess_id) & (combined_df[\"line\"] == \"L23-Cux2\")\n", - " condition_L5 = (combined_df[\"sess_id\"] == sess_id) & (combined_df[\"line\"] == \"L5-Rbp4\")\n", - "\n", - " losses_soma = combined_df.loc[condition_soma,\"train_losses\"]\n", - " losses_dend = combined_df.loc[condition_dend,\"train_losses\"]\n", - " losses_L23 = combined_df.loc[condition_L23,\"train_losses\"]\n", - " losses_L5 = combined_df.loc[condition_L5,\"train_losses\"]\n", - "\n", - " return losses,losses_dend,losses_soma, losses_L5, losses_L23\n", - "\n", - "def get_acc_for_diff_types(combined_df):\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "def getNWBfilenames(mouse_csv_path='/home/mila/x/xuejing.pan/thesis/mouse_df.csv'):\n", - " filenames = []\n", - " sess_ids = []\n", - " num_rois = []\n", - " lines = []\n", - " planes = []\n", - "\n", - " df = pd.read_csv(mouse_csv_path, usecols = ['sessid','mouseid','runtype','nrois','line','plane'])\n", - "\n", - " #Getting all prod data\n", - " for row, curr_type in enumerate(df.runtype):\n", - " if curr_type == 'prod': \n", - " #f_name = source_dir+\"/sub-\"+str(df.mouseid[row])+\"_ses-\"+str(df.sessid[row])+\"_obj-raw_behavior+image+ophys.nwb\"\n", - " f_name = \"sub-\"+str(df.mouseid[row])+\"_ses-\"+str(df.sessid[row])+\"_obj-raw_behavior+image+ophys.nwb\"\n", - " filenames.append(f_name)\n", - " sess_ids.append(df.sessid[row])\n", - " num_rois.append(df.nrois[row])\n", - " lines.append(df.line[row])\n", - " planes.append(df.plane[row])\n", - "\n", - " return filenames,sess_ids, num_rois, lines, planes\n", - "\n", - "def check_nan(array):\n", - " nan_indices = np.isnan(array)\n", - "\n", - " if np.any(nan_indices):\n", - " non_nan_indices = ~nan_indices\n", - " x = np.where(non_nan_indices)[0]\n", - " y = array[non_nan_indices]\n", - " \n", - " # Use interpolation only if there are non-NaN values\n", - " if len(x) > 0:\n", - " f = interpolate.interp1d(x, y, kind='linear', fill_value='extrapolate')\n", - " array[nan_indices] = f(np.where(nan_indices)[0])\n", - "\n", - " return array\n", - "\n", - "def get_cont_labels(nwbfile):\n", - " behavior_module = nwbfile.processing['behavior']\n", - " BehavioralTimeSeries= behavior_module.get_data_interface('BehavioralTimeSeries')\n", - " pupiltracking = behavior_module.get_data_interface('PupilTracking')\n", - " pupil_diameter = pupiltracking.time_series['pupil_diameter']\n", - " pupil_diameter_data = np.copy(pupil_diameter.data)\n", - " pupil_diameter_data = check_nan(pupil_diameter_data)\n", - " behavior_timestamps= pupil_diameter.timestamps # Same timestamps as roi\n", - "\n", - " return pupil_diameter_data\n", - "\n", - "def calculate_accuracy(prediction, valid_discrete_label):\n", - " if len(prediction) != len(valid_discrete_label):\n", - " return \"Error: Arrays have different lengths.\"\n", - "\n", - " matches = sum(p == v for p, v in zip(prediction, valid_discrete_label))\n", - " accuracy = matches / len(prediction)\n", - " return accuracy\n", - "\n", - "def get_diff_sess_ids():\n", - " filenames,sess_ids, num_rois, lines, planes = getNWBfilenames()\n", - " dend_sess_ids = []\n", - " soma_sess_ids = []\n", - " L23_sess_ids = []\n", - " L5_sess_ids = []\n", - "\n", - " for count, curr_sess_id in enumerate(sess_ids):\n", - " if planes[count] == 'soma':\n", - " soma_sess_ids.append(curr_sess_id)\n", - " else:\n", - " dend_sess_ids.append(curr_sess_id)\n", - "\n", - " if lines[count] == 'L23-Cux2':\n", - " L23_sess_ids.append(curr_sess_id)\n", - " else:\n", - " L5_sess_ids.append(curr_sess_id)\n", - " \n", - " return dend_sess_ids, soma_sess_ids, L23_sess_ids, L5_sess_ids" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'sess_ids' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[7], line 10\u001b[0m\n\u001b[1;32m 7\u001b[0m accs_L23_list \u001b[39m=\u001b[39m []\n\u001b[1;32m 8\u001b[0m accs_L5_list \u001b[39m=\u001b[39m []\n\u001b[0;32m---> 10\u001b[0m \u001b[39mfor\u001b[39;00m sess_id \u001b[39min\u001b[39;00m tqdm(sess_ids):\n\u001b[1;32m 11\u001b[0m accs,accs_dend,accs_soma,accs_L5,accs_L23 \u001b[39m=\u001b[39m get_acc_for_one_sess(combined_val_df,sess_id)\n\u001b[1;32m 12\u001b[0m \u001b[39m#accs,accs_dend,accs_soma,accs_L5,accs_L23 = get_losses_for_one_sess(combined_train_df,sess_id)\u001b[39;00m\n", - "\u001b[0;31mNameError\u001b[0m: name 'sess_ids' is not defined" - ] - } - ], - "source": [ - "#Get all validation accs and put in a 2d np array (num_sess(50), num_epochs)\n", - "#epochs = combined_val_df[\"epoch\"] #x-axis\n", - "\n", - "accs_list = []\n", - "accs_dend_list = []\n", - "accs_soma_list = []\n", - "accs_L23_list = []\n", - "accs_L5_list = []\n", - "\n", - "for sess_id in tqdm(sess_ids):\n", - " accs,accs_dend,accs_soma,accs_L5,accs_L23 = get_acc_for_one_sess(combined_val_df,sess_id)\n", - " #accs,accs_dend,accs_soma,accs_L5,accs_L23 = get_losses_for_one_sess(combined_train_df,sess_id)\n", - "\n", - " accs_list.append(accs)\n", - "\n", - " if len(accs_soma) != 0:\n", - " accs_soma_list.append(accs_soma)\n", - " else:\n", - " accs_dend_list.append(accs_dend)\n", - " \n", - " if len(accs_L23) != 0:\n", - " accs_L23_list.append(accs_L23)\n", - " else:\n", - " accs_L5_list.append(accs_L5)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(28, 70)\n" - ] - } - ], - "source": [ - "accs_arr = np.array(accs_list)\n", - "accs_dend_arr = np.array(accs_dend_list)\n", - "accs_soma_arr = np.array(accs_soma_list)\n", - "accs_L23_arr = np.array(accs_L23_list)\n", - "accs_L5_arr = np.array(accs_L5_list)\n", - "\n", - "#sanity check\n", - "assert accs_arr.shape[0] == 50, \"err\"\n", - "print(accs_dend_arr.shape)\n", - "assert accs_dend_arr.shape[0] == 28, \"err:dend\"\n", - "assert accs_soma_arr.shape[0] == 22, \"err:soma\"\n", - "assert accs_L5_arr.shape[0] == 26, \"err:L5\"\n", - "assert accs_L23_arr.shape[0] == 24, \"err:L23\"\n", - "\n", - "\n", - "assert accs_arr.shape[1]==accs_dend_arr.shape[1]==accs_soma_arr.shape[1]==accs_L5_arr.shape[1], \"err: epoch\"" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.83823532 0.65441179 0.60294116 0.31617647 0.44117647 0.375\n", - " 0.61764705 0.67647058 0.72058821 0.92647058 0.75735295 0.64705884\n", - " 0.72058821 0.5147059 0.67647058 0.89705884 0.68382353 0.85294116\n", - " 0.63235295 0.64705884 0.5 0.42647058 0.58823532 0.58823532\n", - " 0.41911766 0.4852941 0.69117647 0.4852941 0.30882353 0.25\n", - " 0.41176471 0.63235295 0.30882353 0.58088237 0.47794119 0.34558824\n", - " 0.33823529 0.58823532 0.78676468 0.78676468 0.82352942 0.625\n", - " 0.79411763 0.8602941 0.84558821 0.90441179 0.94117647 0.85294116\n", - " 0.8897059 0.27941176]\n" - ] - } - ], - "source": [ - "accs_arr_last = accs_arr[:,-1]\n", - "print(accs_arr_last)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "filenames,sess_ids, num_rois, lines, planes = getNWBfilenames()\n", - "def get_mean_std(accs_arr):\n", - " accs_std = np.std(accs_arr)\n", - " accs_avg = np.mean(accs_arr)\n", - "\n", - " print(\"all_std: \",accs_std)\n", - " print(\"all_avg: \",accs_avg)\n", - "\n", - "\n", - " dend_accs = []\n", - " soma_accs = []\n", - "\n", - " for count, curr_sess_id in enumerate(sess_ids):\n", - " if planes[count] == \"soma\":\n", - " soma_accs.append(accs_arr[count])\n", - " else:\n", - " dend_accs.append(accs_arr[count])\n", - "\n", - " dend_accs = np.array(dend_accs)\n", - " soma_accs = np.array(soma_accs)\n", - "\n", - " accs_soma_std = np.std(soma_accs)\n", - " accs_soma_avg = np.mean(soma_accs)\n", - " accs_dend_std = np.std(dend_accs)\n", - " accs_dend_avg = np.mean(dend_accs)\n", - "\n", - " print(\"soma std: \",accs_soma_std)\n", - " print(\"soma avg: \",accs_soma_avg)\n", - " print(\"dend std: \",accs_dend_std)\n", - " print(\"dend avg: \",accs_dend_avg)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "all_std: 0.19342993024454921\n", - "all_avg: 0.6202941179275513\n", - "soma std: 0.17263468883647312\n", - "soma avg: 0.7159090909090909\n", - "dend std: 0.1748405340124064\n", - "soma avg: 0.5451680677277702\n" - ] - } - ], - "source": [ - "get_mean_std(accs_arr_last)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(100,)\n" - ] - } - ], - "source": [ - "#Average for epochs for all sessions\n", - "accs_arr_avg = np.mean(accs_arr, axis = 0)\n", - "print(accs_arr_avg.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "#avg for each category\n", - "accs_dend_arr_avg= np.mean(accs_dend_arr, axis=0)\n", - "accs_soma_arr_avg= np.mean(accs_soma_arr, axis=0)\n", - "accs_L23_arr_avg= np.mean(accs_L23_arr, axis=0)\n", - "accs_L5_arr_avg= np.mean(accs_L5_arr, axis=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.7498529410362244\n", - "0.7762605058295386\n", - "0.7162433131174608\n", - "0.7882965691387653\n", - "0.7143665150954173\n" - ] - } - ], - "source": [ - "print(accs_arr_avg[-1])\n", - "print(accs_dend_arr_avg[-1])\n", - "print(accs_soma_arr_avg[-1])\n", - "print(accs_L23_arr_avg[-1])\n", - "print(accs_L5_arr_avg[-1])" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "#Getting train data\n", - "train_df = pd.read_csv(\"/home/mila/x/xuejing.pan/POYO/results/cross_sess/multi_sess_train_losses.csv\", usecols=[\"epoch\",\"IMPORTANT - multi_sess - train_loss\"])\n", - "train_losses = train_df[\"IMPORTANT - multi_sess - train_loss\"]\n", - "train_epochs = train_df[\"epoch\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to apply moving average to data\n", - "def moving_average(data, window_size):\n", - " return np.convolve(data, np.ones(window_size) / window_size, mode='valid')\n", - "\n", - "# Smoothing the data using a moving average with window size 5\n", - "smoothed_accs_arr_avg = moving_average(accs_arr_avg, 5)\n", - "smoothed_accs_dend_arr_avg = moving_average(accs_dend_arr_avg, 5)\n", - "smoothed_accs_soma_arr_avg = moving_average(accs_soma_arr_avg, 5)\n", - "smoothed_accs_L23_arr_avg = moving_average(accs_L23_arr_avg, 5)\n", - "smoothed_accs_L5_arr_avg = moving_average(accs_L5_arr_avg, 5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plots" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plotting the original and smoothed lines\n", - "plt.figure(figsize=(8, 6)) # Adjust figure size if needed\n", - "plt.plot(epochs[:len(smoothed_accs_arr_avg)], smoothed_accs_arr_avg*100, label='all sessions', linewidth=3)\n", - "plt.plot(epochs[:len(smoothed_accs_dend_arr_avg)], smoothed_accs_dend_arr_avg*100, label='dend')\n", - "plt.plot(epochs[:len(smoothed_accs_soma_arr_avg)], smoothed_accs_soma_arr_avg*100, label='soma')\n", - "#plt.plot(epochs[:len(smoothed_accs_L23_arr_avg)], smoothed_accs_L23_arr_avg*100, label='L2/3')\n", - "#plt.plot(epochs[:len(smoothed_accs_L5_arr_avg)], smoothed_accs_L5_arr_avg*100, label='L5')\n", - "\n", - "\n", - "# Plotting the original data as scatter plots\n", - "plt.plot(epochs, accs_arr_avg*100,color=\"blue\", alpha=0.2)\n", - "plt.plot(epochs, accs_dend_arr_avg*100,color = \"orange\", alpha = 0.2)\n", - "plt.plot(epochs, accs_soma_arr_avg*100,color=\"green\", alpha=0.2)\n", - "#plt.plot(epochs, accs_L23_arr_avg*100, alpha=0.2)\n", - "#plt.plot(epochs, accs_L5_arr_avg*100, alpha=0.2)\n", - "\n", - "# Annotating final values of smoothed arrays\n", - "#plt.text(len(smoothed_accs_arr_avg) - 1, smoothed_accs_arr_avg[-1], f\"{smoothed_accs_arr_avg[-1]:.2f}\")\n", - "#plt.text(len(smoothed_accs_dend_arr_avg) - 1, smoothed_accs_dend_arr_avg[-1], f\"{smoothed_accs_dend_arr_avg[-1]:.2f}\")\n", - "#plt.text(len(smoothed_accs_soma_arr_avg) - 1, smoothed_accs_soma_arr_avg[-1], f\"{smoothed_accs_soma_arr_avg[-1]:.2f}\")\n", - "#plt.text(len(smoothed_accs_L23_arr_avg) - 1, smoothed_accs_L23_arr_avg[-1], f\"{smoothed_accs_L23_arr_avg[-1]:.2f}\")\n", - "#plt.text(len(smoothed_accs_L5_arr_avg) - 1, smoothed_accs_L5_arr_avg[-1], f\"{smoothed_accs_L5_arr_avg[-1]:.2f}\")\n", - "\n", - "# Adding labels and title\n", - "plt.xlabel('Epochs')\n", - "plt.ylabel('Accuracy')\n", - "plt.title('within-session accuracy')\n", - "\n", - "# Adding grid and setting y-axis ticks\n", - "#plt.grid(True)\n", - "#plt.yticks(np.arange(0.25, 0.9, 0.05))\n", - "\n", - "# Adding legend\n", - "plt.legend()\n", - "\n", - "# Show plot\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plotting the original and smoothed lines\n", - "plt.figure(figsize=(8, 6)) # Adjust figure size if needed\n", - "plt.plot(epochs[:len(smoothed_accs_arr_avg)], smoothed_accs_arr_avg, label='all sessions', linewidth=3)\n", - "#plt.plot(epochs[:len(smoothed_accs_dend_arr_avg)], smoothed_accs_dend_arr_avg, label='dend')\n", - "#plt.plot(epochs[:len(smoothed_accs_soma_arr_avg)], smoothed_accs_soma_arr_avg, label='soma')\n", - "plt.plot(epochs[:len(smoothed_accs_L23_arr_avg)], smoothed_accs_L23_arr_avg, label='L2/3')\n", - "plt.plot(epochs[:len(smoothed_accs_L5_arr_avg)], smoothed_accs_L5_arr_avg, label='L5')\n", - "\n", - "\n", - "# Plotting the original data as scatter plots\n", - "plt.plot(epochs, accs_arr_avg,color=\"blue\", alpha=0.2)\n", - "#plt.plot(epochs, accs_dend_arr_avg,color = \"orange\", alpha = 0.2)\n", - "#plt.plot(epochs, accs_soma_arr_avg,color=\"green\", alpha=0.2)\n", - "plt.plot(epochs, accs_L23_arr_avg, alpha=0.2)\n", - "plt.plot(epochs, accs_L5_arr_avg, alpha=0.2)\n", - "\n", - "# Adding labels and title\n", - "plt.xlabel('Epochs')\n", - "plt.ylabel('Loss')\n", - "plt.title('within-session train losses')\n", - "\n", - "# Adding grid and setting y-axis ticks\n", - "#plt.grid(True)\n", - "#plt.yticks(np.arange(0.25, 0.9, 0.05))\n", - "\n", - "# Adding legend\n", - "plt.legend()\n", - "\n", - "# Show plot\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## MLP results" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "def getNWBfilenames(mouse_csv_path='/home/mila/x/xuejing.pan/thesis/mouse_df.csv'):\n", - " filenames = []\n", - " sess_ids = []\n", - " num_rois = []\n", - " lines = []\n", - " planes = []\n", - "\n", - " df = pd.read_csv(mouse_csv_path, usecols = ['sessid','mouseid','runtype','nrois','line','plane'])\n", - "\n", - " #Getting all prod data\n", - " for row, curr_type in enumerate(df.runtype):\n", - " if curr_type == 'prod': \n", - " #f_name = source_dir+\"/sub-\"+str(df.mouseid[row])+\"_ses-\"+str(df.sessid[row])+\"_obj-raw_behavior+image+ophys.nwb\"\n", - " f_name = \"sub-\"+str(df.mouseid[row])+\"_ses-\"+str(df.sessid[row])+\"_obj-raw_behavior+image+ophys.nwb\"\n", - " filenames.append(f_name)\n", - " sess_ids.append(df.sessid[row])\n", - " num_rois.append(df.nrois[row])\n", - " lines.append(df.line[row])\n", - " planes.append(df.plane[row])\n", - "\n", - " return filenames,sess_ids, num_rois, lines, planes\n", - "\n", - "def check_nan(array):\n", - " nan_indices = np.isnan(array)\n", - "\n", - " if np.any(nan_indices):\n", - " non_nan_indices = ~nan_indices\n", - " x = np.where(non_nan_indices)[0]\n", - " y = array[non_nan_indices]\n", - " \n", - " # Use interpolation only if there are non-NaN values\n", - " if len(x) > 0:\n", - " f = interpolate.interp1d(x, y, kind='linear', fill_value='extrapolate')\n", - " array[nan_indices] = f(np.where(nan_indices)[0])\n", - "\n", - " return array\n", - "\n", - "def get_cont_labels(nwbfile):\n", - " behavior_module = nwbfile.processing['behavior']\n", - " BehavioralTimeSeries= behavior_module.get_data_interface('BehavioralTimeSeries')\n", - " pupiltracking = behavior_module.get_data_interface('PupilTracking')\n", - " pupil_diameter = pupiltracking.time_series['pupil_diameter']\n", - " pupil_diameter_data = np.copy(pupil_diameter.data)\n", - " pupil_diameter_data = check_nan(pupil_diameter_data)\n", - " behavior_timestamps= pupil_diameter.timestamps # Same timestamps as roi\n", - "\n", - " return pupil_diameter_data\n", - "\n", - "def calculate_accuracy(prediction, valid_discrete_label):\n", - " if len(prediction) != len(valid_discrete_label):\n", - " return \"Error: Arrays have different lengths.\"\n", - "\n", - " matches = sum(p == v for p, v in zip(prediction, valid_discrete_label))\n", - " accuracy = matches / len(prediction)\n", - " return accuracy\n", - "\n", - "def get_diff_sess_ids():\n", - " filenames,sess_ids, num_rois, lines, planes = getNWBfilenames()\n", - " dend_sess_ids = []\n", - " soma_sess_ids = []\n", - " L23_sess_ids = []\n", - " L5_sess_ids = []\n", - "\n", - " for count, curr_sess_id in enumerate(sess_ids):\n", - " if planes[count] == 'soma':\n", - " soma_sess_ids.append(curr_sess_id)\n", - " else:\n", - " dend_sess_ids.append(curr_sess_id)\n", - "\n", - " if lines[count] == 'L23-Cux2':\n", - " L23_sess_ids.append(curr_sess_id)\n", - " else:\n", - " L5_sess_ids.append(curr_sess_id)\n", - " \n", - " return dend_sess_ids, soma_sess_ids, L23_sess_ids, L5_sess_ids" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "mlp_df = pd.read_csv('/home/mila/x/xuejing.pan/POYO/results/grid_search_results_w:info.csv')\n", - "filenames,sess_ids, num_rois, lines, planes = getNWBfilenames()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "sess_ids_wrong_orders = mlp_df[\"sess_id\"].values\n", - "accs_wrong_orders = mlp_df[\"best_test_acc\"].values" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "accs_all = []\n", - "\n", - "for count, curr_sess_id in enumerate(sess_ids):\n", - " idx = np.where(sess_ids_wrong_orders == curr_sess_id)\n", - " accs_all.append(accs_wrong_orders[idx])\n", - "\n", - "accs_all = np.array(accs_all).reshape(50,)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([83.45588235, 76.47058824, 75.73529412, 67.64705882, 63.60294118,\n", - " 56.98529412, 73.52941176, 81.61764706, 71.69117647, 94.48529412,\n", - " 81.25 , 71.32352941, 78.67647059, 51.10294118, 72.79411765,\n", - " 93.75 , 78.30882353, 84.55882353, 77.20588235, 68.75 ,\n", - " 71.32352941, 72.42647059, 77.57352941, 77.57352941, 44.48529412,\n", - " 52.94117647, 68.38235294, 58.08823529, 51.83823529, 56.25 ,\n", - " 83.82352941, 83.08823529, 73.52941176, 88.97058824, 76.10294118,\n", - " 75. , 63.23529412, 84.55882353, 86.39705882, 92.27941176,\n", - " 80.88235294, 65.07352941, 83.08823529, 83.45588235, 88.97058824,\n", - " 94.48529412, 94.48529412, 90.80882353, 93.38235294, 31.25 ])" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "accs_all" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "def get_mean_std(accs_arr):\n", - " accs_std = np.std(accs_arr)\n", - " accs_avg = np.mean(accs_arr)\n", - "\n", - " print(\"all_std: \",accs_std)\n", - " print(\"all_avg: \",accs_avg)\n", - "\n", - "\n", - " dend_accs = []\n", - " soma_accs = []\n", - "\n", - " for count, curr_sess_id in enumerate(sess_ids):\n", - " if planes[count] == \"soma\":\n", - " soma_accs.append(accs_arr[count])\n", - " else:\n", - " dend_accs.append(accs_arr[count])\n", - "\n", - " dend_accs = np.array(dend_accs)\n", - " soma_accs = np.array(soma_accs)\n", - "\n", - " accs_soma_std = np.std(soma_accs)\n", - " accs_soma_avg = np.mean(soma_accs)\n", - " accs_dend_std = np.std(dend_accs)\n", - " accs_dend_avg = np.mean(dend_accs)\n", - "\n", - " print(\"soma std: \",accs_soma_std)\n", - " print(\"soma avg: \",accs_soma_avg)\n", - " print(\"dend std: \",accs_dend_std)\n", - " print(\"soma avg: \",accs_dend_avg)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "all_std: 13.823468788978465\n", - "all_avg: 74.93382352941178\n", - "soma std: 16.897697974006867\n", - "soma avg: 75.31751336898397\n", - "dend std: 10.801657574277783\n", - "soma avg: 74.63235294117648\n" - ] - } - ], - "source": [ - "get_mean_std(accs_all)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "test_newenv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/capoyo/poyo_hparam_sweep/README.md b/examples/capoyo/poyo_hparam_sweep/README.md deleted file mode 100644 index 900ce3d..0000000 --- a/examples/capoyo/poyo_hparam_sweep/README.md +++ /dev/null @@ -1,20 +0,0 @@ -# Hyperparameter Sweep with W&B Sweeps and Hydra -A default hyperparameter sweep config file in `wandb_sweep.yaml` that uses [W&B Sweeps](https://wandb.com/sweeps) in combination with Hydra. - -First initialize a sweep with: -```bash -wandb sweep --name wandb_sweep.yaml -``` - -Then run the sweep agent with the `` provided in the above command: -```bash -wandb agent -``` - -The above command will spawn a sweep agent in wandb's server that will generate hyperparamters as well as run the training script command according to the provided `wandb_sweep.yaml` file. - -The included `train.py` uses the same `train.run_training` module. It overrides the `cfg.name` of each sweep run using `utils.get_sweep_run_name()` to dynamically give an appropriate name to each run. - -_Pro-tip_: You can run `CUDA_VISIBLE_DEVICES=X wandb agent ` on parallel terminal sessions to run multiple agents in parallel. - -For more information on how to use W&B sweeps along with Hydra, refer [this useful report](https://wandb.ai/adrishd/hydra-example/reports/Configuring-W-B-Projects-with-Hydra--VmlldzoxNTA2MzQw?galleryTag=posts) and [W&B's official guide](https://docs.wandb.ai/guides/integrations/hydra). \ No newline at end of file diff --git a/examples/capoyo/poyo_hparam_sweep/configs/train_allen_bo.yaml b/examples/capoyo/poyo_hparam_sweep/configs/train_allen_bo.yaml deleted file mode 100644 index e2cfd4f..0000000 --- a/examples/capoyo/poyo_hparam_sweep/configs/train_allen_bo.yaml +++ /dev/null @@ -1,48 +0,0 @@ -# Path: configs/train.yaml -defaults: - - _self_ - - model: capoyo1.3M.yaml - - dataset: allen_brain_observatory_calcium.yaml - -hydra: - searchpath: - - pkg://kirby/configs - -train_transforms: - - _target_: kirby.transforms.UnitDropout - field: "calcium_traces.df_over_f" - max_units: 475 - min_units: 10 - mode_units: 50 - peak: 3 - -data_root: ${oc.env:SLURM_TMPDIR}/uncompressed -seed: 42 -batch_size: 128 -eval_epochs: 10 -steps: 0 # Note we either specify epochs or steps, not both. -epochs: 1000 -base_lr: 1.5625e-5 -weight_decay: 0.0001 -# Fraction of epochs to warmup for. -pct_start: 0.5 -num_workers: 4 -log_dir: ./logs -name: allen_brain_observatory_calcium_all -precision: 32 -nodes: 1 -gpus: 1 -wandb_log_model: False -# Where to resume/finetune from. Could be null (yaml for None, meaning train from -# scratch) or a fully qualified path to the .ckpt file. -ckpt_path: null -sweep: True - -# Finetuning configuration: -finetune: false -# Num of epochs to freeze perceiver network while finetuning -# -1 => Keep frozen, i.e. perform Unit-identification -# 0 => Train everything -# >0 => Only train unit/session embeddings for first few epochs, -# and then train everything -freeze_perceiver_until_epoch: 0 diff --git a/examples/capoyo/poyo_hparam_sweep/configs/train_mc_maze_small.yaml b/examples/capoyo/poyo_hparam_sweep/configs/train_mc_maze_small.yaml deleted file mode 100644 index 9e896dc..0000000 --- a/examples/capoyo/poyo_hparam_sweep/configs/train_mc_maze_small.yaml +++ /dev/null @@ -1,48 +0,0 @@ -# Path: configs/train.yaml -defaults: - - _self_ - - model: poyo_single_session.yaml - - dataset: mc_maze_small.yaml - -hydra: - searchpath: - - pkg://kirby/configs - -train_transforms: - - _target_: kirby.transforms.UnitDropout - max_units: 1000 - min_units: 60 - mode_units: 300 - peak: 4 - - _target_: kirby.transforms.RandomCrop - crop_len: 1.0 - -data_root: /kirby/processed/ -seed: 42 -batch_size: 128 -eval_epochs: 1 -epochs: 100 -steps: 0 # Note we either specify epochs or steps, not both. -base_lr: 1.5625e-5 -weight_decay: 0.0001 -# Fraction of epochs to warmup for. -pct_start: 0.5 -num_workers: 0 -log_dir: ./logs -name: mcms_poyo_single_session -backend_config: gpu_fp32_var -precision: 32 -nodes: 1 -gpus: 1 -# Where to resume/finetune from. Could be null (yaml for None, meaning train from -# scratch) or a fully qualified path to the .ckpt file. -ckpt_path: null - -# Finetuning configuration: -finetune: false -# Num of epochs to freeze perceiver network while finetuning -# -1 => Keep frozen, i.e. perform Unit-identification -# 0 => Train everything -# >0 => Only train unit/session embeddings for first few epochs, -# and then train everything -freeze_perceiver_until_epoch: 0 diff --git a/examples/capoyo/poyo_hparam_sweep/hparam_sweep.sh b/examples/capoyo/poyo_hparam_sweep/hparam_sweep.sh deleted file mode 100644 index eb34271..0000000 --- a/examples/capoyo/poyo_hparam_sweep/hparam_sweep.sh +++ /dev/null @@ -1,54 +0,0 @@ -#!/bin/bash -#SBATCH --job-name=single_gpu_mila -#SBATCH --output=/home/mila/x/xuejing.pan/POYO/project-kirby/examples/capoyo/slurm_outputs/slurm_output_%j.txt -#SBATCH --error=/home/mila/x/xuejing.pan/POYO/project-kirby/examples/capoyo/slurm_outputs/slurm_error_%j.txt -#SBATCH --nodes=1 -#SBATCH --ntasks-per-node=1 -#SBATCH --cpus-per-task=4 -#SBATCH --gres=gpu:1 -#SBATCH --mem=32G -#SBATCH --partition=unkillable - -dataset=allen_brain_observatory_calcium -export WANDB_PROJECT=allen_bo_calcium - -module load cuda/11.2/nccl/2.8 -module load cuda/11.2 -export CUDA_LAUNCH_BLOCKING=1 -export TORCH_USE_CUDA_DSA - -module load miniconda -conda activate poyo_conda - -# wandb credentials -set -a -source .env -set +a - -cd /home/mila/x/xuejing.pan/POYO/project-kirby - -# Uncompress the data to SLURM_TMPDIR single node -snakemake --rerun-triggers=mtime -c1 allen_brain_observatory_calcium_unfreeze -nvidia-smi -tuning_sess_ids=( - "\"644026238\"" - "\"662348804\"" "\"613968705\"" - #"\"578674360\"" "\"667004159\"" "\"565216523\"" -) -#cd /home/mila/x/xuejing.pan/POYO/project-kirby/examples/capoyo -cd /home/mila/x/xuejing.pan/POYO/project-kirby/examples/capoyo/poyo_hparam_sweep -#wandb agent neuro-galaxy/allen_bo_calcium/tws82q04 -# Loop through each session ID -#for sess_id in "${tuning_sess_ids[@]}" -#do - # Create the sweep and capture the sweep ID - #sweep_output=wandb sweep --name "565216523" wandb_sweep.yaml - - # Extract the sweep ID from the output - #sweep_id=$(echo "$sweep_output" | grep -oP '(?<="id": ")[^"]+') - #echo $sweep_id - - # Run the wandb agent with the extracted sweep ID - #wandb agent --entity neuro-galaxy --project allen_bo_calcium --sweep_id $sweep_id -#done -wandb agent neuro-galaxy/allen_bo_calcium/w05wl2fp diff --git a/examples/capoyo/poyo_hparam_sweep/long_2_hparam_sweep.sh b/examples/capoyo/poyo_hparam_sweep/long_2_hparam_sweep.sh deleted file mode 100644 index 36bdae2..0000000 --- a/examples/capoyo/poyo_hparam_sweep/long_2_hparam_sweep.sh +++ /dev/null @@ -1,55 +0,0 @@ -#!/bin/bash -#SBATCH --job-name=long_mila -#SBATCH --output=/home/mila/x/xuejing.pan/POYO/project-kirby/examples/capoyo/slurm_outputs/slurm_output_%j.txt -#SBATCH --error=/home/mila/x/xuejing.pan/POYO/project-kirby/examples/capoyo/slurm_outputs/slurm_error_%j.txt -#SBATCH --nodes=1 -#SBATCH --ntasks-per-node=1 -#SBATCH --cpus-per-task=4 -#SBATCH --gres=gpu:1 -#SBATCH --mem=16G -#SBATCH --time=32:00:00 -#SBATCH --partition=long - -dataset=allen_brain_observatory_calcium -export WANDB_PROJECT=allen_bo_calcium - -module load cuda/11.2/nccl/2.8 -module load cuda/11.2 -export CUDA_LAUNCH_BLOCKING=1 -export TORCH_USE_CUDA_DSA - -module load miniconda -conda activate poyo_conda - -# wandb credentials -set -a -source .env -set +a - -cd /home/mila/x/xuejing.pan/POYO/project-kirby - -# Uncompress the data to SLURM_TMPDIR single node -snakemake --rerun-triggers=mtime -c1 allen_brain_observatory_calcium_unfreeze -nvidia-smi -tuning_sess_ids=( - "\"644026238\"" - "\"662348804\"" "\"613968705\"" - #"\"578674360\"" "\"667004159\"" "\"565216523\"" -) -#cd /home/mila/x/xuejing.pan/POYO/project-kirby/examples/capoyo -cd /home/mila/x/xuejing.pan/POYO/project-kirby/examples/capoyo/poyo_hparam_sweep -#wandb agent neuro-galaxy/allen_bo_calcium/tws82q04 -# Loop through each session ID -#for sess_id in "${tuning_sess_ids[@]}" -#do - # Create the sweep and capture the sweep ID - #sweep_output=wandb sweep --name "565216523" wandb_sweep.yaml - - # Extract the sweep ID from the output - #sweep_id=$(echo "$sweep_output" | grep -oP '(?<="id": ")[^"]+') - #echo $sweep_id - - # Run the wandb agent with the extracted sweep ID - #wandb agent --entity neuro-galaxy --project allen_bo_calcium --sweep_id $sweep_id -#done -wandb agent neuro-galaxy/allen_bo_calcium/m2ossjrh diff --git a/examples/capoyo/poyo_hparam_sweep/long_3_hparam_sweep.sh b/examples/capoyo/poyo_hparam_sweep/long_3_hparam_sweep.sh deleted file mode 100644 index effa483..0000000 --- a/examples/capoyo/poyo_hparam_sweep/long_3_hparam_sweep.sh +++ /dev/null @@ -1,55 +0,0 @@ -#!/bin/bash -#SBATCH --job-name=long_mila -#SBATCH --output=/home/mila/x/xuejing.pan/POYO/project-kirby/examples/capoyo/slurm_outputs/slurm_output_%j.txt -#SBATCH --error=/home/mila/x/xuejing.pan/POYO/project-kirby/examples/capoyo/slurm_outputs/slurm_error_%j.txt -#SBATCH --nodes=1 -#SBATCH --ntasks-per-node=1 -#SBATCH --cpus-per-task=4 -#SBATCH --gres=gpu:1 -#SBATCH --mem=16G -#SBATCH --time=32:00:00 -#SBATCH --partition=long - -dataset=allen_brain_observatory_calcium -export WANDB_PROJECT=allen_bo_calcium - -module load cuda/11.2/nccl/2.8 -module load cuda/11.2 -export CUDA_LAUNCH_BLOCKING=1 -export TORCH_USE_CUDA_DSA - -module load miniconda -conda activate poyo_conda - -# wandb credentials -set -a -source .env -set +a - -cd /home/mila/x/xuejing.pan/POYO/project-kirby - -# Uncompress the data to SLURM_TMPDIR single node -snakemake --rerun-triggers=mtime -c1 allen_brain_observatory_calcium_unfreeze -nvidia-smi -tuning_sess_ids=( - "\"644026238\"" - "\"662348804\"" "\"613968705\"" - #"\"578674360\"" "\"667004159\"" "\"565216523\"" -) -#cd /home/mila/x/xuejing.pan/POYO/project-kirby/examples/capoyo -cd /home/mila/x/xuejing.pan/POYO/project-kirby/examples/capoyo/poyo_hparam_sweep -#wandb agent neuro-galaxy/allen_bo_calcium/tws82q04 -# Loop through each session ID -#for sess_id in "${tuning_sess_ids[@]}" -#do - # Create the sweep and capture the sweep ID - #sweep_output=wandb sweep --name "565216523" wandb_sweep.yaml - - # Extract the sweep ID from the output - #sweep_id=$(echo "$sweep_output" | grep -oP '(?<="id": ")[^"]+') - #echo $sweep_id - - # Run the wandb agent with the extracted sweep ID - #wandb agent --entity neuro-galaxy --project allen_bo_calcium --sweep_id $sweep_id -#done -wandb agent neuro-galaxy/allen_bo_calcium/lgjm295t \ No newline at end of file diff --git a/examples/capoyo/poyo_hparam_sweep/long_hparam_sweep.sh b/examples/capoyo/poyo_hparam_sweep/long_hparam_sweep.sh deleted file mode 100644 index 40efd4e..0000000 --- a/examples/capoyo/poyo_hparam_sweep/long_hparam_sweep.sh +++ /dev/null @@ -1,55 +0,0 @@ -#!/bin/bash -#SBATCH --job-name=long_mila -#SBATCH --output=/home/mila/x/xuejing.pan/POYO/project-kirby/examples/capoyo/slurm_outputs/slurm_output_%j.txt -#SBATCH --error=/home/mila/x/xuejing.pan/POYO/project-kirby/examples/capoyo/slurm_outputs/slurm_error_%j.txt -#SBATCH --nodes=1 -#SBATCH --ntasks-per-node=1 -#SBATCH --cpus-per-task=4 -#SBATCH --gres=gpu:1 -#SBATCH --mem=16G -#SBATCH --time=32:00:00 -#SBATCH --partition=long - -dataset=allen_brain_observatory_calcium -export WANDB_PROJECT=allen_bo_calcium - -module load cuda/11.2/nccl/2.8 -module load cuda/11.2 -export CUDA_LAUNCH_BLOCKING=1 -export TORCH_USE_CUDA_DSA - -module load miniconda -conda activate poyo_conda - -# wandb credentials -set -a -source .env -set +a - -cd /home/mila/x/xuejing.pan/POYO/project-kirby - -# Uncompress the data to SLURM_TMPDIR single node -snakemake --rerun-triggers=mtime -c1 allen_brain_observatory_calcium_unfreeze -nvidia-smi -tuning_sess_ids=( - "\"644026238\"" - "\"662348804\"" "\"613968705\"" - #"\"578674360\"" "\"667004159\"" "\"565216523\"" -) -#cd /home/mila/x/xuejing.pan/POYO/project-kirby/examples/capoyo -cd /home/mila/x/xuejing.pan/POYO/project-kirby/examples/capoyo/poyo_hparam_sweep -#wandb agent neuro-galaxy/allen_bo_calcium/tws82q04 -# Loop through each session ID -#for sess_id in "${tuning_sess_ids[@]}" -#do - # Create the sweep and capture the sweep ID - #sweep_output=wandb sweep --name "565216523" wandb_sweep.yaml - - # Extract the sweep ID from the output - #sweep_id=$(echo "$sweep_output" | grep -oP '(?<="id": ")[^"]+') - #echo $sweep_id - - # Run the wandb agent with the extracted sweep ID - #wandb agent --entity neuro-galaxy --project allen_bo_calcium --sweep_id $sweep_id -#done -wandb agent neuro-galaxy/allen_bo_calcium/8nrbimsj \ No newline at end of file diff --git a/examples/capoyo/poyo_hparam_sweep/main_16_hparam_sweep.sh b/examples/capoyo/poyo_hparam_sweep/main_16_hparam_sweep.sh deleted file mode 100644 index eea527f..0000000 --- a/examples/capoyo/poyo_hparam_sweep/main_16_hparam_sweep.sh +++ /dev/null @@ -1,41 +0,0 @@ -#!/bin/bash -#SBATCH --job-name=single_gpu_mila -#SBATCH --output=slurm_output_%j.txt -#SBATCH --error=slurm_error_%j.txt -#SBATCH --nodes=1 -#SBATCH --ntasks-per-node=1 -#SBATCH --cpus-per-task=4 -#SBATCH --gres=gpu:1 -#SBATCH --mem=16G -#SBATCH --time=32:00:00 -#SBATCH --partition=main - -dataset=allen_brain_observatory_calcium -export WANDB_PROJECT=allen_bo_calcium - -module load cuda/11.2/nccl/2.8 -module load cuda/11.2 -export CUDA_LAUNCH_BLOCKING=1 -export TORCH_USE_CUDA_DSA - -module load miniconda -conda activate poyo_conda - -# wandb credentials -set -a -source .env -set +a - -cd /home/mila/x/xuejing.pan/POYO/project-kirby - -# Uncompress the data to SLURM_TMPDIR single node -snakemake --rerun-triggers=mtime -c1 allen_brain_observatory_calcium_unfreeze -nvidia-smi -tuning_sess_ids=( - "\"644026238\"" - "\"662348804\"" "\"613968705\"" - #"\"578674360\"" "\"667004159\"" "\"565216523\"" -) -cd /home/mila/x/xuejing.pan/POYO/project-kirby/examples/capoyo/poyo_hparam_sweep - -wandb agent neuro-galaxy/allen_bo_calcium/jbtytr7k diff --git a/examples/capoyo/poyo_hparam_sweep/main_hparam_sweep.sh b/examples/capoyo/poyo_hparam_sweep/main_hparam_sweep.sh deleted file mode 100644 index f22d6de..0000000 --- a/examples/capoyo/poyo_hparam_sweep/main_hparam_sweep.sh +++ /dev/null @@ -1,41 +0,0 @@ -#!/bin/bash -#SBATCH --job-name=single_gpu_mila -#SBATCH --output=slurm_output_%j.txt -#SBATCH --error=slurm_error_%j.txt -#SBATCH --nodes=1 -#SBATCH --ntasks-per-node=1 -#SBATCH --cpus-per-task=4 -#SBATCH --gres=gpu:1 -#SBATCH --mem=16G -#SBATCH --time=72:00:00 -#SBATCH --partition=main - -dataset=allen_brain_observatory_calcium -export WANDB_PROJECT=allen_bo_calcium - -module load cuda/11.2/nccl/2.8 -module load cuda/11.2 -export CUDA_LAUNCH_BLOCKING=1 -export TORCH_USE_CUDA_DSA - -module load miniconda -conda activate poyo_conda - -# wandb credentials -set -a -source .env -set +a - -cd /home/mila/x/xuejing.pan/POYO/project-kirby - -# Uncompress the data to SLURM_TMPDIR single node -snakemake --rerun-triggers=mtime -c1 allen_brain_observatory_calcium_unfreeze -nvidia-smi -tuning_sess_ids=( - "\"644026238\"" - "\"662348804\"" "\"613968705\"" - #"\"578674360\"" "\"667004159\"" "\"565216523\"" -) -cd /home/mila/x/xuejing.pan/POYO/project-kirby/examples/capoyo/poyo_hparam_sweep - -wandb agent neuro-galaxy/allen_bo_calcium/m2ossjrh \ No newline at end of file diff --git a/examples/capoyo/poyo_hparam_sweep/parallel_2_sweep.sh b/examples/capoyo/poyo_hparam_sweep/parallel_2_sweep.sh deleted file mode 100644 index 9d6412e..0000000 --- a/examples/capoyo/poyo_hparam_sweep/parallel_2_sweep.sh +++ /dev/null @@ -1,52 +0,0 @@ -#!/bin/bash -#SBATCH --job-name=multi_gpu_mila -#SBATCH --output=/home/mila/x/xuejing.pan/POYO/project-kirby/examples/capoyo/slurm_outputs/slurm_output_%j.txt -#SBATCH --error=/home/mila/x/xuejing.pan/POYO/project-kirby/examples/capoyo/slurm_outputs/slurm_error_%j.txt -#SBATCH --nodes=1 -#SBATCH --ntasks-per-node=2 -#SBATCH --cpus-per-task=2 -#SBATCH --gres=gpu:1 -#SBATCH --mem=24GB -#SBATCH --partition=main - -export WANDB_PROJECT=allen_bo_calcium - -module load miniconda/3 -module load cuda/11.2/nccl/2.8 -module load cuda/11.2 - -#conda activate poyo -conda activate poyo_conda -# wandb credentials -set -a -source .env -set +a - -cd /home/mila/x/xuejing.pan/POYO/project-kirby/ -# Uncompress the data to SLURM_TMPDIR -snakemake --rerun-triggers=mtime --config tmp_dir=$SLURM_TMPDIR -c4 allen_brain_observatory_calcium_unfreeze - -# Important info for parallel GPU processing -#export NCCL_DEBUG=INFO -#export PYTHONFAULTHANDLER=1 -#export MASTER_ADDR=$(hostname) -#export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOB_ID | tail -c 4)) -#export NCCL_BLOCKING_WAIT=1 - -#echo $MASTER_ADDR:$MASTER_PORT - -nvidia-smi - -# Run experiments -pwd -which python - -cd /home/mila/x/xuejing.pan/POYO/project-kirby/examples/capoyo/poyo_hparam_sweep - -#for runs -#srun --export=ALL,WANDB_PROJECT=allen_bo_calcium python train.py \ -# --config-name train_allen_bo.yaml data_root=$SLURM_TMPDIR/uncompressed gpus=2 epochs=1000 - -#for sweeps -#CUDA_VISIBLE_DEVICES=0 wandb agent neuro-galaxy/allen_bo_calcium/hcrbvrqe -CUDA_VISIBLE_DEVICES=0 wandb agent neuro-galaxy/allen_bo_calcium/hcrbvrqe \ No newline at end of file diff --git a/examples/capoyo/poyo_hparam_sweep/parallel_sweep.sh b/examples/capoyo/poyo_hparam_sweep/parallel_sweep.sh deleted file mode 100644 index f05e833..0000000 --- a/examples/capoyo/poyo_hparam_sweep/parallel_sweep.sh +++ /dev/null @@ -1,55 +0,0 @@ -#!/bin/bash -#SBATCH --job-name=multi_gpu_mila -#SBATCH --output=/home/mila/x/xuejing.pan/POYO/project-kirby/examples/capoyo/slurm_outputs/slurm_output_%j.txt -#SBATCH --error=/home/mila/x/xuejing.pan/POYO/project-kirby/examples/capoyo/slurm_outputs/slurm_error_%j.txt -#SBATCH --nodes=1 -#SBATCH --ntasks-per-node=2 -#SBATCH --cpus-per-task=2 -#SBATCH --gres=gpu:1 -#SBATCH --mem=24GB -#SBATCH --time=32:00:00 -#SBATCH --partition=main - -export WANDB_PROJECT=allen_bo_calcium - -module load miniconda/3 -module load cuda/11.2/nccl/2.8 -module load cuda/11.2 - -#conda activate poyo -conda activate poyo_conda -# wandb credentials -set -a -source .env -set +a - -cd /home/mila/x/xuejing.pan/POYO/project-kirby/ -# Uncompress the data to SLURM_TMPDIR -#snakemake --rerun-triggers=mtime --config tmp_dir=$SLURM_TMPDIR -c4 allen_brain_observatory_calcium_unfreeze -snakemake --rerun-triggers=mtime -c1 allen_brain_observatory_calcium_unfreeze -# Important info for parallel GPU processing -#export NCCL_DEBUG=INFO -#export PYTHONFAULTHANDLER=1 -#export MASTER_ADDR=$(hostname) -#export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOB_ID | tail -c 4)) -#export NCCL_BLOCKING_WAIT=1 - -#echo $MASTER_ADDR:$MASTER_PORT - -nvidia-smi - -# Run experiments -pwd -which python - -cd /home/mila/x/xuejing.pan/POYO/project-kirby/examples/capoyo/poyo_hparam_sweep - -#for runs -#srun --export=ALL,WANDB_PROJECT=allen_bo_calcium python train.py \ -# --config-name train_allen_bo.yaml data_root=$SLURM_TMPDIR/uncompressed gpus=2 epochs=1000 - -#for sweeps -wandb agent neuro-galaxy/allen_bo_calcium/d63jo2y5 - -#CUDA_VISIBLE_DEVICES=0 wandb agent neuro-galaxy/allen_bo_calcium/hcrbvrqe -#CUDA_VISIBLE_DEVICES=1 wandb agent neuro-galaxy/allen_bo_calcium/hcrbvrqe \ No newline at end of file diff --git a/examples/capoyo/poyo_hparam_sweep/train.py b/examples/capoyo/poyo_hparam_sweep/train.py deleted file mode 100644 index 7c9e8e6..0000000 --- a/examples/capoyo/poyo_hparam_sweep/train.py +++ /dev/null @@ -1,32 +0,0 @@ -import pickle - -old_unpickler = pickle.Unpickler # Unfortunate hack to fix a bug in Lightning. -# https://github.com/Lightning-AI/lightning/issues/18152 -# Will likely be fixed by 2.1.0. -pickle.Unpickler = old_unpickler -import hydra -from omegaconf import DictConfig -import sys - -sys.path.insert( - 0, "../" -) # so that we pick the `run_training` from the main `train.py` script -from train import run_training - -from utils import get_sweep_run_name - - -# This loads the config file using Hydra, similar to Flags, but composable. -@hydra.main( - version_base="1.3", config_path="../configs", config_name="train_allen_bo.yaml" -) -def main(cfg: DictConfig): - # If sweep is enabled, dynamically name the run using the helper - if cfg.get("sweep", True): - cfg.name = get_sweep_run_name(cfg) - # Rest of the training is exactly identical to the original train.py script. - run_training(cfg) - - -if __name__ == "__main__": - main() diff --git a/examples/capoyo/poyo_hparam_sweep/utils.py b/examples/capoyo/poyo_hparam_sweep/utils.py deleted file mode 100644 index 4318df8..0000000 --- a/examples/capoyo/poyo_hparam_sweep/utils.py +++ /dev/null @@ -1,22 +0,0 @@ -def get_sweep_run_name(cfg): - """ - Returns the name of the sweep's run based on the hyperparameters being monitored. - - Args: - cfg (Config): The configuration object containing the hyperparameters. - - Returns: - str: The name of the sweep's run. - - Notes: - This helper function can be modified as per the user's requirements and the hparams that are being monitored. - name the sweep's run based on the hyperparameters being monitored. - """ - lr = cfg.base_lr - psz = cfg.model.patch_size - l_dim = cfg.model.num_latents - e_dim = cfg.model.dim - model_name = "capoyo" - dataset_name = cfg.dataset[0].selection[0].sortset - canonical_name = f"sweep/lr:{lr:.2e}/psz:{psz}/latent:{l_dim}/dim:{e_dim}" - return canonical_name diff --git a/examples/capoyo/poyo_hparam_sweep/wandb_sweep.yaml b/examples/capoyo/poyo_hparam_sweep/wandb_sweep.yaml deleted file mode 100644 index 1af4a14..0000000 --- a/examples/capoyo/poyo_hparam_sweep/wandb_sweep.yaml +++ /dev/null @@ -1,39 +0,0 @@ -# This YAML file specifies the configuration for a hyperparameter tuning using wandb -program: train.py - -name: trial_sweep - -project: allen_bo_calcium - -metric: - name: val_loss - goal: minimize - -method: bayes # grid, random, bayes -parameters: - base_lr: - min: !!float 1.5625e-6 - max: !!float 1.5625e-5 - weight_decay: - min: !!float 1e-5 - max: !!float 1e-3 - batch_size: - value: 128 - model.patch_size: - values: [2, 5, 10] - model.num_latents: - values: [16, 32, 64] - model.dim: - values: [84, 128, 256] - train_transforms.0.mode_units: - values: [10, 30, 60, 120, 200] - -command: -- ${env} -- ${interpreter} -- ${program} # first line -- --config-name=train_allen_bo.yaml -- ++dataset.0.selection.0.sortset="578674360" # defining the sort set / session id -- +sweep=True # to make the training script "sweep-aware" -- eval_epochs=1 # quick val_loss feedback -- ${args_no_hyphens} # HP tuning overrides \ No newline at end of file diff --git a/examples/capoyo/poyo_hparam_sweep/wandb_sweep_555042467.yaml b/examples/capoyo/poyo_hparam_sweep/wandb_sweep_555042467.yaml deleted file mode 100644 index eb46134..0000000 --- a/examples/capoyo/poyo_hparam_sweep/wandb_sweep_555042467.yaml +++ /dev/null @@ -1,40 +0,0 @@ -# This YAML file specifies the configuration for a hyperparameter tuning using wandb -program: train.py - -name: trial_sweep - -project: allen_bo_calcium - -metric: - name: val_loss - goal: minimize - -method: random # grid, random, bayes -parameters: - base_lr: - min: !!float 1.5625e-6 - max: !!float 1.5625e-5 - weight_decay: - min: !!float 1e-5 - max: !!float 1e-3 - batch_size: - value: 128 - model.patch_size: - values: [2, 5, 10] - model.num_latents: - values: [16, 32, 64] - model.dim: - values: [84, 128, 256] - train_transforms.0.mode_units: - values: [10, 30, 60, 120, 200] - -command: -- ${env} -- ${interpreter} -- ${program} # first line -- --config-name=train_allen_bo.yaml -- ++dataset.0.selection.0.sortset="555042467" # defining the sort set / session id -- epochs=300 -- +sweep=True # to make the training script "sweep-aware" -- eval_epochs=1 # quick val_loss feedback -- ${args_no_hyphens} # HP tuning overrides \ No newline at end of file diff --git a/examples/capoyo/poyo_hparam_sweep/wandb_sweep_565216523.yaml b/examples/capoyo/poyo_hparam_sweep/wandb_sweep_565216523.yaml deleted file mode 100644 index 09ef87c..0000000 --- a/examples/capoyo/poyo_hparam_sweep/wandb_sweep_565216523.yaml +++ /dev/null @@ -1,40 +0,0 @@ -# This YAML file specifies the configuration for a hyperparameter tuning using wandb -program: train.py - -name: trial_sweep - -project: allen_bo_calcium - -metric: - name: val_loss - goal: minimize - -method: random # grid, random, bayes -parameters: - base_lr: - min: !!float 1.5625e-6 - max: !!float 1.5625e-5 - weight_decay: - min: !!float 1e-5 - max: !!float 1e-3 - batch_size: - value: 128 - model.patch_size: - values: [2, 5, 10] - model.num_latents: - values: [16, 32, 64] - model.dim: - values: [84, 128, 256] - train_transforms.0.mode_units: - values: [10, 30, 60, 120, 200] - -command: -- ${env} -- ${interpreter} -- ${program} # first line -- --config-name=train_allen_bo.yaml -- ++dataset.0.selection.0.sortset="565216523" # defining the sort set / session id -- epochs=300 -- +sweep=True # to make the training script "sweep-aware" -- eval_epochs=1 # quick val_loss feedback -- ${args_no_hyphens} # HP tuning overrides \ No newline at end of file diff --git a/examples/capoyo/poyo_hparam_sweep/wandb_sweep_577665023.yaml b/examples/capoyo/poyo_hparam_sweep/wandb_sweep_577665023.yaml deleted file mode 100644 index 50c3a7f..0000000 --- a/examples/capoyo/poyo_hparam_sweep/wandb_sweep_577665023.yaml +++ /dev/null @@ -1,40 +0,0 @@ -# This YAML file specifies the configuration for a hyperparameter tuning using wandb -program: train.py - -name: trial_sweep - -project: allen_bo_calcium - -metric: - name: val_loss - goal: minimize - -method: random # grid, random, bayes -parameters: - base_lr: - min: !!float 1.5625e-6 - max: !!float 1.5625e-5 - weight_decay: - min: !!float 1e-5 - max: !!float 1e-3 - batch_size: - value: 128 - model.patch_size: - values: [2, 5, 10] - model.num_latents: - values: [16, 32, 64] - model.dim: - values: [84, 128, 256] - train_transforms.0.mode_units: - values: [10, 30, 60, 120, 200] - -command: -- ${env} -- ${interpreter} -- ${program} # first line -- --config-name=train_allen_bo.yaml -- ++dataset.0.selection.0.sortset="577665023" # defining the sort set / session id -- epochs=300 -- +sweep=True # to make the training script "sweep-aware" -- eval_epochs=1 # quick val_loss feedback -- ${args_no_hyphens} # HP tuning overrides \ No newline at end of file diff --git a/examples/capoyo/poyo_hparam_sweep/wandb_sweep_577665023yaml b/examples/capoyo/poyo_hparam_sweep/wandb_sweep_577665023yaml deleted file mode 100644 index 50c3a7f..0000000 --- a/examples/capoyo/poyo_hparam_sweep/wandb_sweep_577665023yaml +++ /dev/null @@ -1,40 +0,0 @@ -# This YAML file specifies the configuration for a hyperparameter tuning using wandb -program: train.py - -name: trial_sweep - -project: allen_bo_calcium - -metric: - name: val_loss - goal: minimize - -method: random # grid, random, bayes -parameters: - base_lr: - min: !!float 1.5625e-6 - max: !!float 1.5625e-5 - weight_decay: - min: !!float 1e-5 - max: !!float 1e-3 - batch_size: - value: 128 - model.patch_size: - values: [2, 5, 10] - model.num_latents: - values: [16, 32, 64] - model.dim: - values: [84, 128, 256] - train_transforms.0.mode_units: - values: [10, 30, 60, 120, 200] - -command: -- ${env} -- ${interpreter} -- ${program} # first line -- --config-name=train_allen_bo.yaml -- ++dataset.0.selection.0.sortset="577665023" # defining the sort set / session id -- epochs=300 -- +sweep=True # to make the training script "sweep-aware" -- eval_epochs=1 # quick val_loss feedback -- ${args_no_hyphens} # HP tuning overrides \ No newline at end of file diff --git a/examples/capoyo/poyo_hparam_sweep/wandb_sweep_578674360.yaml b/examples/capoyo/poyo_hparam_sweep/wandb_sweep_578674360.yaml deleted file mode 100644 index 5f75cab..0000000 --- a/examples/capoyo/poyo_hparam_sweep/wandb_sweep_578674360.yaml +++ /dev/null @@ -1,40 +0,0 @@ -# This YAML file specifies the configuration for a hyperparameter tuning using wandb -program: train.py - -name: trial_sweep - -project: allen_bo_calcium - -metric: - name: val_loss - goal: minimize - -method: random # grid, random, bayes -parameters: - base_lr: - min: !!float 1.5625e-6 - max: !!float 1.5625e-5 - weight_decay: - min: !!float 1e-5 - max: !!float 1e-3 - batch_size: - value: 128 - model.patch_size: - values: [2, 5, 10] - model.num_latents: - values: [16, 32, 64] - model.dim: - values: [84, 128, 256] - train_transforms.0.mode_units: - values: [10, 30, 60, 120, 200] - -command: -- ${env} -- ${interpreter} -- ${program} # first line -- --config-name=train_allen_bo.yaml -- ++dataset.0.selection.0.sortset="578674360" # defining the sort set / session id -- epochs=300 -- +sweep=True # to make the training script "sweep-aware" -- eval_epochs=1 # quick val_loss feedback -- ${args_no_hyphens} # HP tuning overrides \ No newline at end of file diff --git a/examples/capoyo/poyo_hparam_sweep/wandb_sweep_613968705.yaml b/examples/capoyo/poyo_hparam_sweep/wandb_sweep_613968705.yaml deleted file mode 100644 index 1f10c94..0000000 --- a/examples/capoyo/poyo_hparam_sweep/wandb_sweep_613968705.yaml +++ /dev/null @@ -1,40 +0,0 @@ -# This YAML file specifies the configuration for a hyperparameter tuning using wandb -program: train.py - -name: trial_sweep - -project: allen_bo_calcium - -metric: - name: val_loss - goal: minimize - -method: random # grid, random, bayes -parameters: - base_lr: - min: !!float 1.5625e-6 - max: !!float 1.5625e-5 - weight_decay: - min: !!float 1e-5 - max: !!float 1e-3 - batch_size: - value: 128 - model.patch_size: - values: [2, 5, 10] - model.num_latents: - values: [16, 32, 64] - model.dim: - values: [84, 128, 256] - train_transforms.0.mode_units: - values: [10, 30, 60, 120, 200] - -command: -- ${env} -- ${interpreter} -- ${program} # first line -- --config-name=train_allen_bo.yaml -- ++dataset.0.selection.0.sortset="613968705" # defining the sort set / session id -- epochs=300 -- +sweep=True # to make the training script "sweep-aware" -- eval_epochs=1 # quick val_loss feedback -- ${args_no_hyphens} # HP tuning overrides \ No newline at end of file diff --git a/examples/capoyo/poyo_hparam_sweep/wandb_sweep_644026238.yaml b/examples/capoyo/poyo_hparam_sweep/wandb_sweep_644026238.yaml deleted file mode 100644 index b891375..0000000 --- a/examples/capoyo/poyo_hparam_sweep/wandb_sweep_644026238.yaml +++ /dev/null @@ -1,40 +0,0 @@ -# This YAML file specifies the configuration for a hyperparameter tuning using wandb -program: train.py - -name: trial_sweep - -project: allen_bo_calcium - -metric: - name: val_loss - goal: minimize - -method: random # grid, random, bayes -parameters: - base_lr: - min: !!float 1.5625e-6 - max: !!float 1.5625e-5 - weight_decay: - min: !!float 1e-5 - max: !!float 1e-3 - batch_size: - value: 128 - model.patch_size: - values: [2, 5, 10] - model.num_latents: - values: [16, 32, 64] - model.dim: - values: [84, 128, 256] - train_transforms.0.mode_units: - values: [10, 30, 60, 120, 200] - -command: -- ${env} -- ${interpreter} -- ${program} # first line -- --config-name=train_allen_bo.yaml -- ++dataset.0.selection.0.sortset="644026238" # defining the sort set / session id -- epochs=300 -- +sweep=True # to make the training script "sweep-aware" -- eval_epochs=1 # quick val_loss feedback -- ${args_no_hyphens} # HP tuning overrides \ No newline at end of file diff --git a/examples/capoyo/poyo_hparam_sweep/wandb_sweep_662348804.yaml b/examples/capoyo/poyo_hparam_sweep/wandb_sweep_662348804.yaml deleted file mode 100644 index cbccae3..0000000 --- a/examples/capoyo/poyo_hparam_sweep/wandb_sweep_662348804.yaml +++ /dev/null @@ -1,40 +0,0 @@ -# This YAML file specifies the configuration for a hyperparameter tuning using wandb -program: train.py - -name: trial_sweep - -project: allen_bo_calcium - -metric: - name: val_loss - goal: minimize - -method: random # grid, random, bayes -parameters: - base_lr: - min: !!float 1.5625e-6 - max: !!float 1.5625e-5 - weight_decay: - min: !!float 1e-5 - max: !!float 1e-3 - batch_size: - value: 128 - model.patch_size: - values: [2, 5, 10] - model.num_latents: - values: [16, 32, 64] - model.dim: - values: [84, 128, 256] - train_transforms.0.mode_units: - values: [10, 30, 60, 120, 200] - -command: -- ${env} -- ${interpreter} -- ${program} # first line -- --config-name=train_allen_bo.yaml -- ++dataset.0.selection.0.sortset="662348804" # defining the sort set / session id -- epochs=300 -- +sweep=True # to make the training script "sweep-aware" -- eval_epochs=1 # quick val_loss feedback -- ${args_no_hyphens} # HP tuning overrides \ No newline at end of file diff --git a/examples/capoyo/poyo_hparam_sweep/wandb_sweep_667004159.yaml b/examples/capoyo/poyo_hparam_sweep/wandb_sweep_667004159.yaml deleted file mode 100644 index 55383a0..0000000 --- a/examples/capoyo/poyo_hparam_sweep/wandb_sweep_667004159.yaml +++ /dev/null @@ -1,40 +0,0 @@ -# This YAML file specifies the configuration for a hyperparameter tuning using wandb -program: train.py - -name: trial_sweep - -project: allen_bo_calcium - -metric: - name: val_loss - goal: minimize - -method: random # grid, random, bayes -parameters: - base_lr: - min: !!float 1.5625e-6 - max: !!float 1.5625e-5 - weight_decay: - min: !!float 1e-5 - max: !!float 1e-3 - batch_size: - value: 128 - model.patch_size: - values: [2, 5, 10] - model.num_latents: - values: [16, 32, 64] - model.dim: - values: [84, 128, 256] - train_transforms.0.mode_units: - values: [10, 30, 60, 120, 200] - -command: -- ${env} -- ${interpreter} -- ${program} # first line -- --config-name=train_allen_bo.yaml -- ++dataset.0.selection.0.sortset="667004159" # defining the sort set / session id -- epochs=300 -- +sweep=True # to make the training script "sweep-aware" -- eval_epochs=1 # quick val_loss feedback -- ${args_no_hyphens} # HP tuning overrides \ No newline at end of file diff --git a/examples/capoyo/scripts/copy_data.sh b/examples/capoyo/scripts/copy_data.sh deleted file mode 100755 index 040ebad..0000000 --- a/examples/capoyo/scripts/copy_data.sh +++ /dev/null @@ -1,6 +0,0 @@ -#!/bin/bash -conda activate poyo -module load python/3.9.6 - -# Not thread-safe! -snakemake --nolock --rerun-triggers=mtime --config tmp_dir="$SLURM_TMPDIR" -c1 "$1" \ No newline at end of file diff --git a/examples/capoyo/scripts/finetune.sh b/examples/capoyo/scripts/finetune.sh deleted file mode 100644 index 9571ca7..0000000 --- a/examples/capoyo/scripts/finetune.sh +++ /dev/null @@ -1,40 +0,0 @@ -#!/bin/bash -#SBATCH --job-name=single_gpu_mila -#SBATCH --output=slurm_output_%j.txt -#SBATCH --error=slurm_error_%j.txt -#SBATCH --nodes=1 -#SBATCH --ntasks-per-node=1 -#SBATCH --cpus-per-task=4 -#SBATCH --gres=gpu:1 -#SBATCH --mem=32G -#SBATCH --partition=long -#SBATCH --time=48:00:00 -#SBATCH --reservation=ubuntu2204 -# -#set -e -dataset=openscope_calcium - -module load anaconda/3 -module load cuda/11.2/nccl/2.8 -module load cuda/11.2 - -conda activate poyo - -# wandb credentials -set -a -source .env -set +a - -# Uncompress the data to SLURM_TMPDIR single ndoe -#snakemake --forceall --rerun-triggers=mtime -c1 openscope_calcium_unfreeze -snakemake --rerun-triggers=mtime -c1 openscope_calcium_unfreeze -nvidia-smi - -#For multi-session -srun python train.py \ - data_root=$SLURM_TMPDIR/uncompressed/ \ - train_datasets=$dataset \ - val_datasets=$dataset \ - name=FINETUNE_ind_transfer_soma \ - epochs=1000 \ - ckpt_path=/home/mila/x/xuejing.pan/POYO/project-kirby/logs/lightning_logs/8u7542ov/last.ckpt diff --git a/examples/capoyo/scripts/run_Allenmeta.sh b/examples/capoyo/scripts/run_Allenmeta.sh deleted file mode 100644 index 9700b7d..0000000 --- a/examples/capoyo/scripts/run_Allenmeta.sh +++ /dev/null @@ -1,20 +0,0 @@ -#!/bin/bash -#SBATCH --job-name=single_gpu_mila -#SBATCH --output=slurm_output_%j.txt -#SBATCH --error=slurm_error_%j.txt -#SBATCH --nodes=1 -#SBATCH --ntasks-per-node=1 -#SBATCH --cpus-per-task=4 -#SBATCH --gres=gpu:1 -#SBATCH --partition=main -# -#set -e - - -module load anaconda/3 -module load cuda/11.2/nccl/2.8 -module load cuda/11.2 - -conda activate poyo - -srun python /home/mila/x/xuejing.pan/POYO/project-kirby/allenBO_download.py \ No newline at end of file diff --git a/examples/capoyo/scripts/run_allenbo.sh b/examples/capoyo/scripts/run_allenbo.sh deleted file mode 100644 index 28d2c1d..0000000 --- a/examples/capoyo/scripts/run_allenbo.sh +++ /dev/null @@ -1,99 +0,0 @@ -#!/bin/bash -#SBATCH --job-name=single_gpu_mila -#SBATCH --output=/home/mila/x/xuejing.pan/POYO/project-kirby/examples/capoyo/slurm_outputs/slurm_output_%j.txt -#SBATCH --error=/home/mila/x/xuejing.pan/POYO/project-kirby/examples/capoyo/slurm_outputs/slurm_error_%j.txt -#SBATCH --nodes=1 -#SBATCH --ntasks-per-node=1 -#SBATCH --cpus-per-task=4 -#SBATCH --gres=gpu:1 -#SBATCH --mem=32G -#SBATCH --partition=main -# -#set -e -dataset=allen_brain_observatory_calcium -export WANDB_PROJECT=allen_bo_calcium - -module load cuda/11.2/nccl/2.8 -module load cuda/11.2 -export CUDA_LAUNCH_BLOCKING=1 -export TORCH_USE_CUDA_DSA - -module load miniconda -conda activate poyo_conda - -# wandb credentials -set -a -source .env -set +a - -cd /home/mila/x/xuejing.pan/POYO/project-kirby - -# Uncompress the data to SLURM_TMPDIR single node -snakemake --rerun-triggers=mtime --cores 4 allen_brain_observatory_calcium_unfreeze -nvidia-smi - -#srun --export=ALL,WANDB_PROJECT=allen_bo_calcium python /home/mila/x/xuejing.pan/POYO/project-kirby/examples/capoyo/train.py \ -# --config-name train_allen_bo.yaml data_root=$SLURM_TMPDIR/uncompressed name=allen_bo_single - -#### AUTO RUN MULTI_SESSIONS - -#vis_l_sess_ids=( - #"\"662982346\"" "\"645689073\"" "\"613091721\"" "\"502793808\"" "\"501940850\"" - #"\"546641574\"" "\"614556106\"" "\"511194579\"" "\"698762886\"" "\"607063420\"" - #"\"688580172\"" "\"651770186\"" "\"652737678\"" "\"603452291\"" "\"715923832\"" - #"\"627823636\"" "\"662358771\"" "\"662348804\"" "\"606802468\"" "\"670395725\"" - #"\"601887677\"" "\"552427971\"" "\"657391625\"" "\"506823562\"" - #"\"611658482\"" - #"\"674276329\"" "\"557848210\"" "\"612549085\"" "\"601910964\"" "\"603187982\"" - #"\"645256361\"" "\"673475020\"" "\"657224241\"" "\"573850303\"" "\"657009581\"" - #"\"576273468\"" "\"647143225\"" "\"602866800\"" "\"601805379\"" "\"707923645\"" - #"\"529688779\"" "\"686442556\"" "\"651769499\"" "\"558476282\"" "\"601841437\"" - #"\"639932847\"" "\"665726618\"" "\"669237515\"" "\"596509886\"" "\"507129766\"" - #"\"550455111\"" "\"686449092\"" "\"585035184\"" "\"560578599\"" "\"614571626\"" - #"\"597028938\"" "\"605883133\"" "\"581153070\"" "\"657915168\"" "\"560926639\"" - #"\"653551965\"" "\"662219852\"" "\"511595995\"" "\"654532828\"" "\"664914611\"" - #"\"644051974\"" "\"652737867\"" "\"652092676\"" "\"552410386\"" "\"623339221\"" - #"\"506809539\"" "\"556321897\"" "\"646016204\"" "\"595808594\"" "\"667004159\"" - #"\"647595665\"" "\"562122508\"" "\"572606382\"" "\"699155265\"" "\"623587006\"" - #"\"582867147\"" "\"584983136\"" "\"682051855\"" "\"580095655\"" "\"509958730\"" - #"\"511573879\"" "\"603978471\"" "\"584544569\"" "\"672207947\"" "\"576001843\"" - #"\"507990552\"" "\"501929610\"" "\"573261515\"" "\"682049099\"" "\"583136567\"" - #"\"567878987\"" "\"676024666\"" "\"564425777\"" - #"\"653123929\"" - #) - -vis_am_sess_ids=( - #"\"556353209\"" "\"595273803\"" "\"556344224\"" "\"570305847\"" "\"566307038\"" - #"\"638262558\"" "\"552760671\"" "\"565698388\"" "\"616779893\"" "\"637669284\"" - #"\"562711440\"" "\"569792817\"" "\"5f69457162\"" "\"707006626\"" "\"647603932\"" - #"\"569718097\"" "\"556665481\"" "\"571177441\"" "\"712919665\"" "\"560027980\"" - #"\"652094917\"" "\"566458505\"" "\"550851591\"" "\"613599811\"" "\"557304694\"" - #"\"578674360\"" "\"642884591\"" "\"576411246\"" "\"611638995\"" "\"551834174\"" - #"\"569739027\"" "\"601904502\"" "\"605606109\"" "\"575302108\"" "\"565216523\"" -) - -tuning_sess_ids=( - #"\"644026238\"" - #"\"662348804\"" "\"613968705\"" - "\"578674360\"" - #"\"667004159\"" "\"565216523\"" -) - -patch_sizes=( - 2 - # 5 10 30 -) -cd /home/mila/x/xuejing.pan/POYO/project-kirby/examples/capoyo - -#for p_sz in "${patch_sizes[@]}" -#do -for sess_id in "${tuning_sess_ids[@]}" -do - sortset=$sess_id -# - echo $sess_id - srun --export=ALL,WANDB_PROJECT=allen_bo_calcium python /home/mila/x/xuejing.pan/POYO/project-kirby/examples/capoyo/train.py \ - --config-name train_allen_bo.yaml name=$sortset ++dataset.0.selection.0.sortset=$sortset log_dir=/home/mila/x/xuejing.pan/scratch/poyo_logs -done -#done -#data_root=$SLURM_TMPDIR/uncompressed \ No newline at end of file diff --git a/examples/capoyo/scripts/run_allenbo_main.sh b/examples/capoyo/scripts/run_allenbo_main.sh deleted file mode 100644 index 060f308..0000000 --- a/examples/capoyo/scripts/run_allenbo_main.sh +++ /dev/null @@ -1,88 +0,0 @@ -#!/bin/bash -#SBATCH --job-name=single_gpu_mila -#SBATCH --output=slurm_output_%j.txt -#SBATCH --error=slurm_error_%j.txt -#SBATCH --nodes=1 -#SBATCH --ntasks-per-node=1 -#SBATCH --cpus-per-task=4 -#SBATCH --gres=gpu:1 -#SBATCH --mem=32G -#SBATCH --partition=main -# -#set -e -#dataset=allen_brain_observatory_calcium -dataset=allen_brain_observatory_calcium -export WANDB_PROJECT=allen_bo_calcium - -module load anaconda/3 -module load cuda/11.2/nccl/2.8 -module load cuda/11.2 -export CUDA_LAUNCH_BLOCKING=1 -export TORCH_USE_CUDA_DSA - -#conda activate poyo -source $HOME/poyo_env/bin/activate -# wandb credentials -set -a -source .env -set +a - -cd /home/mila/x/xuejing.pan/POYO/project-kirby - -# Uncompress the data to SLURM_TMPDIR single node -snakemake --rerun-triggers=mtime -c1 allen_brain_observatory_calcium_unfreeze -nvidia-smi - -#srun --export=ALL,WANDB_PROJECT=allen_bo_calcium python /home/mila/x/xuejing.pan/POYO/project-kirby/examples/capoyo/train.py \ -# --config-name train_allen_bo.yaml data_root=$SLURM_TMPDIR/uncompressed name=allen_bo_single - -#### AUTO RUN MULTI_SESSIONS -vis_p_sess_ids=( - "\"627823695\"" "\"652094901\"" "\"545446482\"" "\"662361096\"" "\"645086975\"" - "\"657078119\"" "\"581150104\"" "\"644026238\"" "\"652091264\"" "\"613968705\"" - "\"647155122\"" "\"657082055\"" "\"652842495\"" "\"692345003\"" "\"645413759\"" - "\"710504563\"" "\"598635821\"" "\"657016267\"" "\"612543999\"" "\"540684467\"" - "\"502205092\"" "\"680156911\"" "\"623347352\"" "\"539487468\"" "\"604145810\"" - "\"661437140\"" "\"612044635\"" "\"710778377\"" "\"688678766\"" "\"510517131\"" - "\"583279803\"" "\"580163817\"" "\"501021421\"" "\"653125130\"" "\"652842572\"" - "\"689388034\"" "\"539497234\"" "\"582918858\"" "\"606353987\"" "\"511534603\"" - "\"672211004\"" "\"539290504\"" "\"702934964\"" "\"530645663\"" "\"526504941\"" - "\"561312435\"" "\"664404274\"" "\"577379202\"" "\"580043440\"" "\"573720508\"" - "\"674679019\"" "\"508753256\"" "\"547388708\"" "\"575939366\"" "\"593373156\"" - "\"502962794\"" "\"657390171\"" "\"512270518\"" "\"587344053\"" "\"671618887\"" - "\"712178483\"" "\"643645390\"" "\"663485329\"" "\"659491419\"" "\"502115959\"" - "\"601423209\"" "\"528402271\"" "\"649401936\"" "\"546716391\"" "\"510214538\"" - "\"662033243\"" "\"502608215\"" "\"704298735\"" "\"663479824\"" "\"527048992\"" - "\"653932505\"" "\"592407200\"" "\"643592303\"" "\"503109347\"" "\"609894681\"" - "\"670728674\"" "\"596584192\"" "\"531134090\"" "\"669861524\"" "\"675477919\"" - "\"590168385\"" "\"571137446\"" "\"637671554\"" "\"595263154\"" "\"657389972\"" - "\"584196534\"" "\"660513003\"" "\"603576132\"" "\"671164733\"" "\"658518486\"" - "\"661328410\"" "\"596779487\"" "\"676503588\"" "\"672206735\"" "\"657650110\"" - "\"501729039\"" "\"590047029\"" "\"570278597\"" "\"650079244\"" "\"585900296\"" - "\"637998955\"" "\"653122667\"" "\"598564173\"" "\"581026088\"" "\"670395999\"" - "\"637669270\"" "\"679702884\"" "\"665722301\"" "\"680150733\"" "\"541290571\"" - "\"541010698\"" "\"603224878\"" "\"683257169\"" "\"595806300\"" "\"617395455\"" - "\"508356957\"" "\"576095926\"" "\"510514474\"" "\"712178511\"" "\"501574836\"" - "\"657080632\"" "\"649938038\"" "\"673914981\"" "\"559192380\"" "\"524691284\"" - "\"617381605\"" "\"673171528\"" "\"662974315\"" "\"657775947\"" "\"637115675\"" -) - - -for sess_id in "${vis_p_sess_ids[@]}" -do - completion_flag="/home/mila/x/xuejing.pan/scratch/completion_flags/completed_${sess_id}.txt" - - # Check if the session has already been completed - if [ -f "$completion_flag" ]; then - echo "Session $sess_id has already been completed." - continue - fi - echo "Running session $sess_id" - - sortset=$sess_id - echo $sess_id - srun --export=ALL,WANDB_PROJECT=allen_bo_calcium python /home/mila/x/xuejing.pan/POYO/project-kirby/examples/capoyo/train.py \ - --config-name train_allen_bo.yaml data_root=$SLURM_TMPDIR/uncompressed name=$sortset ++dataset.0.selection.0.sortset=$sortset - - touch "$completion_flag" -done diff --git a/examples/capoyo/scripts/run_allenbo_multi.sh b/examples/capoyo/scripts/run_allenbo_multi.sh deleted file mode 100644 index 2822de6..0000000 --- a/examples/capoyo/scripts/run_allenbo_multi.sh +++ /dev/null @@ -1,38 +0,0 @@ -#!/bin/bash -#SBATCH --job-name=single_gpu_mila -#SBATCH --output=slurm_output_%j.txt -#SBATCH --error=slurm_error_%j.txt -#SBATCH --nodes=1 -#SBATCH --ntasks-per-node=1 -#SBATCH --cpus-per-task=4 -#SBATCH --gres=gpu:a100l -#SBATCH --mem=48G -#SBATCH --partition=long -# -#set -e -#dataset=allen_brain_observatory_calcium -dataset=allen_brain_observatory_calcium -WANDB_PROJECT=allen_bo_calcium - -module load anaconda/3 -module load cuda/11.2/nccl/2.8 -module load cuda/11.2 -export CUDA_LAUNCH_BLOCKING=1 -export TORCH_USE_CUDA_DSA - -#conda activate poyo -source $HOME/poyo_env/bin/activate - -# wandb credentials -set -a -source .env -set +a - -cd /home/mila/x/xuejing.pan/POYO/project-kirby - -# Uncompress the data to SLURM_TMPDIR single node -snakemake --rerun-triggers=mtime -c1 allen_brain_observatory_calcium_unfreeze -nvidia-smi - -srun --export=ALL,WANDB_PROJECT=allen_bo_calcium,HYDRA_FULL_ERROR=1,CUDA_LAUNCH_BLOCKING=1,TORCH_USE_CUDA_DSA=1 python /home/mila/x/xuejing.pan/POYO/project-kirby/examples/capoyo/train_all.py \ - --config-name train_allen_bo.yaml data_root=$SLURM_TMPDIR/uncompressed epochs=2000 name=allen_brain_observatory_calcium_all_one_gpu \ No newline at end of file diff --git a/examples/capoyo/scripts/run_gillon.sh b/examples/capoyo/scripts/run_gillon.sh deleted file mode 100644 index fad1955..0000000 --- a/examples/capoyo/scripts/run_gillon.sh +++ /dev/null @@ -1,35 +0,0 @@ -#!/bin/bash -#SBATCH --job-name=single_gpu_mila -#SBATCH --output=slurm_output_%j.txt -#SBATCH --error=slurm_error_%j.txt -#SBATCH --nodes=1 -#SBATCH --ntasks-per-node=1 -#SBATCH --cpus-per-task=4 -#SBATCH --gres=gpu:a100l:1 -#SBATCH --mem=48G -#SBATCH --partition=main -# -#set -e -dataset=gillon_richards_responses_2023 -export WANDB_PROJECT=openscope_calcium - -module load anaconda/3 -module load cuda/11.2/nccl/2.8 -module load cuda/11.2 - -#conda activate poyo -source $HOME/poyo_env/bin/activate - -# wandb credentials -set -a -source .env -set +a - -cd /home/mila/x/xuejing.pan/POYO/project-kirby - -# Uncompress the data to SLURM_TMPDIR single node -snakemake --rerun-triggers=mtime -c1 gillon_richards_responses_2023_unfreeze_data -nvidia-smi - -srun python /home/mila/x/xuejing.pan/POYO/project-kirby/examples/capoyo/train.py \ - --config-name train_openscope_calcium.yaml data_root=$SLURM_TMPDIR/uncompressed \ No newline at end of file diff --git a/examples/capoyo/scripts/run_parallel.sh b/examples/capoyo/scripts/run_parallel.sh deleted file mode 100644 index 6a85ca1..0000000 --- a/examples/capoyo/scripts/run_parallel.sh +++ /dev/null @@ -1,49 +0,0 @@ -#!/bin/bash -#SBATCH --job-name=multi_gpu_mila -#SBATCH --output=/home/mila/x/xuejing.pan/POYO/project-kirby/examples/capoyo/slurm_outputs/slurm_output_%j.txt -#SBATCH --error=/home/mila/x/xuejing.pan/POYO/project-kirby/examples/capoyo/slurm_outputs/slurm_error_%j.txt -#SBATCH --nodes=1 -#SBATCH --ntasks-per-node=2 -#SBATCH --cpus-per-task=2 -#SBATCH --gres=gpu:2 -#SBATCH --mem=32GB -#SBATCH --partition=long - -export WANDB_PROJECT=allen_bo_calcium - -module load miniconda/3 -module load cuda/11.2/nccl/2.8 -module load cuda/11.2 - -#conda activate poyo -conda activate poyo_conda -# wandb credentials -set -a -source .env -set +a - -cd /home/mila/x/xuejing.pan/POYO/project-kirby/ -# Uncompress the data to SLURM_TMPDIR -snakemake --rerun-triggers=mtime --config tmp_dir=$SLURM_TMPDIR -c4 allen_brain_observatory_calcium_unfreeze - -# Important info for parallel GPU processing -export NCCL_DEBUG=INFO -export PYTHONFAULTHANDLER=1 -export MASTER_ADDR=$(hostname) -export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOB_ID | tail -c 4)) -export NCCL_BLOCKING_WAIT=1 - -echo $MASTER_ADDR:$MASTER_PORT - -nvidia-smi - -# Run experiments -pwd -which python - -cd /home/mila/x/xuejing.pan/POYO/project-kirby/examples/capoyo - -#for runs -srun --export=ALL,WANDB_PROJECT=allen_bo_calcium python train.py \ - --config-name train_allen_bo.yaml data_root=$SLURM_TMPDIR/uncompressed gpus=2 epochs=1000 - diff --git a/examples/capoyo/train.py b/examples/capoyo/train.py deleted file mode 100644 index 7650675..0000000 --- a/examples/capoyo/train.py +++ /dev/null @@ -1,430 +0,0 @@ -import pickle -import pandas as pd - -old_unpickler = pickle.Unpickler # Unfortunate hack to fix a bug in Lightning. -# https://github.com/Lightning-AI/lightning/issues/18152 -# Will likely be fixed by 2.1.0. -import lightning - -pickle.Unpickler = old_unpickler - -from collections import OrderedDict -import copy -import logging - -import hydra -import torch -from lightning.pytorch.callbacks import ( - LearningRateMonitor, - ModelCheckpoint, - ModelSummary, -) - -# Flags are absorbed by Hydra. -from omegaconf import DictConfig, OmegaConf -from torch.utils.data import DataLoader -from torch_optimizer import Lamb -from torch_brain.optim import SparseLamb - - -from torch_brain.data import Dataset, collate -from torch_brain.data.sampler import ( - RandomFixedWindowSampler, - SequentialFixedWindowSampler, - DistributedSamplerWrapper, -) -from torch_brain.taxonomy import decoder_registry -from torch_brain.transforms import Compose -from torch_brain.utils import seed_everything, train_wrapper -from torch_brain.models.capoyo import CaPOYOTokenizer -from torch_brain.utils.gradient_rescale import UnitEmbeddingGradientRescaling -import os - -wandb_project = os.environ.get("WANDB_PROJECT") - - -def run_training(cfg: DictConfig): - # Fix random seed, skipped if cfg.seed is None - seed_everything(cfg.seed) - - # Higher speed on machines with tensor cores. - torch.set_float32_matmul_precision("medium") - - log = logging.getLogger(__name__) - - # Device setup is managed by PyTorch Lightning. - - # make model - model = hydra.utils.instantiate( - cfg.model, - decoder_specs=decoder_registry, - _convert_="object", - ) - - # prepare tokenizer and transforms - - # The transform list is defined in the config file. - sequence_length = 1 - transforms = hydra.utils.instantiate( - cfg.train_transforms, sequence_length=sequence_length - ) - - # build tokenizer - tokenizer = CaPOYOTokenizer( - model.unit_emb.tokenizer, - model.session_emb.tokenizer, - decoder_registry=decoder_registry, - dim=model.dim_input, - patch_size=model.patch_size, - latent_step=cfg.get("latent_step", 1.0 / 8.0), - num_latents_per_step=cfg.model.num_latents, - batch_type=model.batch_type, - use_cre_line_embedding=model.use_cre_line_embedding, - use_depth_embedding=model.use_depth_embedding, - use_spatial_embedding=model.use_spatial_embedding, - use_roi_feat_embedding=model.use_roi_feat_embedding, - ) - - transform = Compose([*transforms, tokenizer]) - - log.info("Data root: {}".format(cfg.data_root)) - train_dataset = Dataset( - cfg.data_root, - "train", - include=OmegaConf.to_container(cfg.dataset), # converts to native list[dicts] - transform=transform, - ) - train_dataset.disable_data_leakage_check() - # In Lightning, testing only happens once, at the end of training. To get the - # intended behavior, we need to specify a validation set instead. - val_tokenizer = copy.copy(tokenizer) - val_tokenizer.eval = True - val_dataset = Dataset( - cfg.data_root, - "valid", - include=OmegaConf.to_container(cfg.dataset), # converts to native list[dicts] - transform=val_tokenizer, - ) - val_dataset.disable_data_leakage_check() - - # initialize test dataset - test_tokenizer = copy.copy(tokenizer) - test_tokenizer.eval = True - test_dataset = Dataset( - cfg.data_root, - "test", - include=OmegaConf.to_container(cfg.dataset), # converts to native list[dicts] - transform=test_tokenizer, - ) - test_dataset.disable_data_leakage_check() - - # register units and sessions - if not cfg.finetune: - # Register units and sessions - model.unit_emb.initialize_vocab(train_dataset.unit_ids) - model.session_emb.initialize_vocab(train_dataset.session_ids) - else: - assert ( - cfg.ckpt_path is not None - ), "Missing `ckpt_path`. Checkpoint is required finetuning." - - model = load_model_from_ckpt(model, cfg.ckpt_path) - log.info(f"Loaded model state dict from {cfg.ckpt_path}") - - # Optionally freeze parameters for Unit Identification - if cfg.freeze_perceiver_until_epoch != 0: - model.freeze_middle() - log.info(f"Froze perceiver") - # Register new units and sessions, and delete old ones - try: - model.unit_emb.extend_vocab(train_dataset.unit_ids, exist_ok=False) - except ValueError as err: - print(err) - model.unit_emb.subset_vocab(train_dataset.unit_ids) - - try: - model.session_emb.extend_vocab(train_dataset.session_ids, exist_ok=False) - except ValueError as err: - print(err) - model.session_emb.subset_vocab(train_dataset.session_ids) - - # sampler and dataloader - train_sampler = RandomFixedWindowSampler( - interval_dict=train_dataset.get_sampling_intervals(), - window_length=sequence_length, - generator=torch.Generator().manual_seed(cfg.seed + 1), - ) - - train_loader = DataLoader( - train_dataset, - sampler=train_sampler, - collate_fn=collate, - batch_size=cfg.batch_size, - num_workers=cfg.num_workers, - drop_last=True, - pin_memory=True, - # For debugging. we allow the user to set num_workers to 0. - persistent_workers=True if cfg.num_workers > 0 else False, - prefetch_factor=2 if cfg.num_workers > 0 else None, - ) - - log.info(f"Training on {len(train_sampler)} samples") - log.info(f"Training on {len(train_dataset.unit_ids)} units") - log.info(f"Training on {len(train_dataset.session_ids)} sessions") - - val_sampler = DistributedSamplerWrapper( - SequentialFixedWindowSampler( - interval_dict=val_dataset.get_sampling_intervals(), - window_length=sequence_length, - step=sequence_length / 2, - ) - ) - - val_loader = DataLoader( - val_dataset, - sampler=val_sampler, - collate_fn=collate, - batch_size=cfg.batch_size, - num_workers=2, - ) - - log.info(f"Validation on {len(val_sampler)} samples") - - # Test sampler and dataloader - test_sampler = DistributedSamplerWrapper( - SequentialFixedWindowSampler( - interval_dict=test_dataset.get_sampling_intervals(), - window_length=sequence_length, - step=sequence_length / 2, - ) - ) - test_loader = DataLoader( - test_dataset, - sampler=test_sampler, - collate_fn=collate, - batch_size=cfg.get( - "eval_batch_size", cfg.batch_size - ), # Default to training batch size, but allow override in config. - num_workers=2, - ) - log.info(f"Test on {len(test_sampler)} samples") - - # No need to explicitly use DDP with the model, lightning does this for us. - max_lr = cfg.base_lr * cfg.batch_size - - if cfg.epochs > 0 and cfg.steps == 0: - epochs = cfg.epochs - elif cfg.steps > 0 and cfg.epochs == 0: - epochs = cfg.steps // len(train_loader) + 1 - else: - raise ValueError("Must specify either epochs or steps") - - print(f"Epochs: {epochs}") - - if cfg.finetune: - model.load_from_ckpt( - path=cfg.ckpt_path, - strict_vocab=False, # finetuning, generally, is over a new vocabulary - ) - - # Optionally freeze parameters for Unit Identification - if cfg.freeze_perceiver_until_epoch != 0: - model.freeze_perceiver() - - unit_emb_lr_factor = cfg.get("unit_emb_lr_factor", 1.0) - unit_emb_params = model.unit_emb.parameters() - # session_emb_params = model.session_emb.parameters() - special_emb_params = list(unit_emb_params) # + list(session_emb_params) - - remaining_params = [ - p - for n, p in model.named_parameters() - if "unit_emb" not in n # and "session_emb" not in n - ] - - param_groups = [ - { - "params": special_emb_params, - "lr": max_lr * unit_emb_lr_factor, - "weight_decay": cfg.weight_decay, - "sparse": True, - }, - { - "params": remaining_params, - "lr": max_lr, - "weight_decay": cfg.weight_decay, - "sparse": False, - }, - ] - - if cfg.get("use_sparse_lamb", False): - optimizer = SparseLamb( - param_groups, - # model.parameters(), # filter(lambda p: p.requires_grad, model.parameters()), - # lr=max_lr, - # weight_decay=cfg.weight_decay, - # sparse=True, - ) - else: - optimizer = Lamb( - param_groups, - # model.parameters(), # filter(lambda p: p.requires_grad, model.parameters()), - # lr=max_lr, - # weight_decay=cfg.weight_decay, - ) - - # optimizer = SparseLamb( - # model.parameters(), # filter(lambda p: p.requires_grad, model.parameters()), - # lr=max_lr, - # weight_decay=cfg.weight_decay, - # sparse=True, - # ) - - scheduler = torch.optim.lr_scheduler.OneCycleLR( - optimizer, - max_lr=[max_lr * unit_emb_lr_factor, max_lr], - epochs=epochs, - steps_per_epoch=len(train_loader), - pct_start=cfg.pct_start, - anneal_strategy="cos", - div_factor=1, - ) - - # Now we create the model wrapper. It's a simple shim that contains the train and - # test code. - wrapper = train_wrapper.TrainWrapper( - model=model, - optimizer=optimizer, - scheduler=scheduler, - ) - - tb = lightning.pytorch.loggers.tensorboard.TensorBoardLogger( - save_dir=cfg.log_dir, - ) - - wandb = lightning.pytorch.loggers.WandbLogger( - name=cfg.name, - project=wandb_project, - entity=cfg.get("wandb_entity", None), - log_model=cfg.get("wandb_log_model", False), - save_dir=cfg.log_dir, - ) - print(f"Wandb ID: {wandb.version}") - - model_ckpt_callback = ModelCheckpoint( - # dirpath=f"logs/lightning_logs/{wandb.version}", - dirpath=os.path.join(cfg.log_dir, f"lightning_logs/{wandb.version}"), - save_last=True, - verbose=True, - monitor="average_val_metric", - mode="max", - save_on_train_epoch_end=False, - every_n_epochs=cfg.eval_epochs, - ) - - callbacks = [ - ModelSummary(max_depth=2), # Displays the number of parameters in the model. - model_ckpt_callback, - train_wrapper.CustomValidator(val_loader), - train_wrapper.CustomValidator(test_loader, on_test=True), - LearningRateMonitor( - logging_interval="step" - ), # Create a callback to log the learning rate. - ] - - if cfg.finetune: - if cfg.freeze_perceiver_until_epoch > 0: - callbacks.append( - train_wrapper.UnfreezeAtEpoch(cfg.freeze_perceiver_until_epoch) - ) - - if cfg.get("gradient_rescale", False): - callbacks.append(UnitEmbeddingGradientRescaling(train_dataset)) - - trainer = lightning.Trainer( - logger=[tb, wandb], - default_root_dir=cfg.log_dir, - check_val_every_n_epoch=cfg.eval_epochs, - max_epochs=epochs, - log_every_n_steps=1, - strategy=( - "ddp_find_unused_parameters_true" if torch.cuda.is_available() else "auto" - ), - callbacks=callbacks, - num_sanity_val_steps=0, - precision=cfg.precision, - reload_dataloaders_every_n_epochs=2000, - accelerator="gpu", - devices=cfg.gpus, - num_nodes=cfg.nodes, - ) - - log.info( - f"Local rank/node rank/world size/num nodes: {trainer.local_rank}/{trainer.node_rank}/{trainer.world_size}/trainer.num_nodes" - ) - - for logger in trainer.loggers: - # OmegaConf.to_container converts the config object to a dictionary. - logger.log_hyperparams(OmegaConf.to_container(cfg)) - - test_ckpt = cfg.get("test_ckpt", None) - test_only = test_ckpt is not None - if not test_only: - # Training - - # To resume from a checkpoint rather than training from scratch, - # set ckpt_path on the command line. - trainer.fit( - wrapper, - train_loader, - [0], - ckpt_path=cfg.ckpt_path if not cfg.finetune else None, - ) - # [0] is a hack to force the validation callback to be called. - - # Testing - log.info("Beginning Testing") - - # Load the best model's parameters - if test_ckpt is None: - test_ckpt = model_ckpt_callback.best_model_path - assert len(test_ckpt) > 0, ( - "No best model has been checkpointed yet. " - "Probably because the validator has not been run." - ) - - model = load_model_from_ckpt(model, test_ckpt) - log.info(f"Loaded model state dict from {test_ckpt}") - - trainer.test(wrapper, [0]) - - -def load_model_from_ckpt(model, ckpt_path): - ckpt = torch.load(ckpt_path, map_location="cpu") - state_dict = ckpt["state_dict"] - - # Remove 'model.' prefix from the state dict keys - new_state_dict = {} - for key in state_dict.keys(): - new_key = key.removeprefix("model.") - new_state_dict[new_key] = state_dict[key] - - model.load_state_dict(new_state_dict) - return model - - -# This loads the config file using Hydra, similar to Flags, but composable. -@hydra.main( - version_base="1.3", - config_path="./configs", - config_name="train_openscope_calcium.yaml", -) -def main(cfg: DictConfig): - # Train the whole thing. - # This inner function is unnecessary, but I keep it here to maintain - # a parallel to the original code. - run_training(cfg) - - -if __name__ == "__main__": - main() diff --git a/examples/poyo/README.md b/examples/poyo/README.md index 095a0e2..d054b56 100644 --- a/examples/poyo/README.md +++ b/examples/poyo/README.md @@ -1,58 +1,5 @@ -# project-kirby - -# Installation -## Environment setup with `venv` -Clone the project, enter the project's root directory, and then run the following: -```bash -python3.9 -m venv venv # create an empty virtual environment -source venv/bin/activate # activate it -pip install --upgrade pip # update to the latest version of pip -pip install -e . # install project-kirby into your path -``` - -Currently this project requires the following: -- Python 3.9 (also requires python3.9-dev) -- PyTorch 2.0.0 -- CUDA 11.3 - 11.7 -- xformers is optional, but recommended for training with memory efficient attention - -## Documentation -> [!WARNING] -> The docs are hosted publically for convenience, please do not share the link. - -You can find the documentation for this project [here](https://chic-dragon-bc9a04.netlify.app/). - -## Contributing -Make sure you have `black` and `pre-commit` installed. You can run the following once: -```bash -pre-commit install -``` - -## Downloading and preparing the data -Run the following to download and prepare the data: -```bash -snakemake --cores 8 odoherty_sabes -``` - -To prepare all of the datasets from the NeurIPS paper: -```bash -snakemake --cores 8 poyo_neurips -``` - # Training To train POYO you can run: ```bash -python train.py --config-name train.yaml -``` -Everything is logged to wandb. - -# Finetuning -## Unit-Identification -```bash -python python train.py --config-name unit_identification.yaml -``` - -## Full finetuning -```bash -python python train.py --config-name finetune.yaml +python train.py --config-name train_poyo_mp.yaml ``` diff --git a/examples/poyo/configs/finetune.yaml b/examples/poyo/configs/finetune.yaml deleted file mode 100644 index 0c76ef2..0000000 --- a/examples/poyo/configs/finetune.yaml +++ /dev/null @@ -1,44 +0,0 @@ -# Path: configs/train.yaml -defaults: - - _self_ - - model: poyo_single_session.yaml - - train_datasets: perich_single_session.yaml - - val_datasets: perich_single_session.yaml - -train_transforms: - - _target_: kirby.transforms.UnitDropout - max_units: 1000 - min_units: 60 - mode_units: 300 - peak: 4 - - _target_: kirby.transforms.RandomCrop - crop_len: 1.0 - -data_root: ./data/processed/ -seed: 42 -batch_size: 128 -eval_epochs: 5 -epochs: 100 -steps: 0 # Note we either specify epochs or steps, not both. -base_lr: 7.8125e-7 -weight_decay: 0.0001 -# Fraction of epochs to warmup for. -pct_start: 0.5 -num_workers: 4 -log_dir: ./logs -name: perich_finetune -precision: 32 -nodes: 1 -gpus: 1 -# Where to resume/finetune from. Could be null (yaml for None, meaning train from -# scratch) or a fully qualified path to the .ckpt file. -ckpt_path: logs/lightning_logs/f9sj5g0b/last.ckpt - -# Finetuning configuration: -finetune: true -# Num of epochs to freeze perceiver network while finetuning -# -1 => Keep frozen, i.e. perform Unit-identification -# 0 => Train everything -# >0 => Only train unit/session embeddings for first few epochs, -# and then train everything -freeze_perceiver_until_epoch: 10 diff --git a/examples/poyo/configs/train.yaml b/examples/poyo/configs/train.yaml deleted file mode 100644 index 8d93a1a..0000000 --- a/examples/poyo/configs/train.yaml +++ /dev/null @@ -1,45 +0,0 @@ -# Path: configs/train.yaml -defaults: - - _self_ - - model: poyo_single_session.yaml - - train_datasets: allen_multi_session.yaml - - val_datasets: allen_multi_session.yaml - -train_transforms: - - _target_: kirby.transforms.UnitDropout - max_units: 1000 - min_units: 60 - mode_units: 300 - peak: 4 - - _target_: kirby.transforms.RandomCrop - crop_len: 1.0 - -data_root: ./data/processed/ -seed: 42 -batch_size: 128 -eval_epochs: 10 -epochs: 1000 -steps: 0 # Note we either specify epochs or steps, not both. -base_lr: 1.5625e-5 -weight_decay: 0.0001 -# Fraction of epochs to warmup for. -pct_start: 0.5 -num_workers: 4 -log_dir: ./logs -name: allen_poyo_single_session -wandb_project: poyo -precision: 32 -nodes: 1 -gpus: 1 -# Where to resume/finetune from. Could be null (yaml for None, meaning train from -# scratch) or a fully qualified path to the .ckpt file. -ckpt_path: null - -# Finetuning configuration: -finetune: false -# Num of epochs to freeze perceiver network while finetuning -# -1 => Keep frozen, i.e. perform Unit-identification -# 0 => Train everything -# >0 => Only train unit/session embeddings for first few epochs, -# and then train everything -freeze_perceiver_until_epoch: 0 diff --git a/examples/poyo/configs/train_allen_neuropixels.yaml b/examples/poyo/configs/train_allen_neuropixels.yaml deleted file mode 100644 index 926afa4..0000000 --- a/examples/poyo/configs/train_allen_neuropixels.yaml +++ /dev/null @@ -1,33 +0,0 @@ -defaults: - - base.yaml - - model: poyo_single_session.yaml - - dataset: allen_neuropixels.yaml - - _self_ - -hydra: - searchpath: - - pkg://kirby/configs - -train_transforms: - - _target_: kirby.transforms.UnitDropout - max_units: 1000 - min_units: 60 - mode_units: 300 - peak: 4 - - _target_: kirby.transforms.RandomCrop - crop_len: 1.0 - -data_root: /kirby/processed/allen_all/ - -batch_size: 128 -eval_epochs: 10 - -optim: - base_lr: 1.5625e-5 - weight_decay: 0.0001 - -wandb: - run_name: allen_neuropixels - -backend_config: gpu_fp16 -precision: bf16-mixed \ No newline at end of file diff --git a/examples/poyo/configs/train_mc_maze_small.yaml b/examples/poyo/configs/train_mc_maze_small.yaml index 8ef1775..c4a1de5 100644 --- a/examples/poyo/configs/train_mc_maze_small.yaml +++ b/examples/poyo/configs/train_mc_maze_small.yaml @@ -5,7 +5,7 @@ defaults: - _self_ train_transforms: - - _target_: kirby.transforms.UnitDropout + - _target_: torch_brain.transforms.UnitDropout max_units: 1000 min_units: 60 mode_units: 300 diff --git a/examples/poyo/configs/unit_identification.yaml b/examples/poyo/configs/unit_identification.yaml deleted file mode 100644 index 322375a..0000000 --- a/examples/poyo/configs/unit_identification.yaml +++ /dev/null @@ -1,44 +0,0 @@ -# Path: configs/train.yaml -defaults: - - _self_ - - model: poyo_single_session.yaml - - train_datasets: perich_single_session.yaml - - val_datasets: perich_single_session.yaml - -train_transforms: - - _target_: kirby.transforms.UnitDropout - max_units: 1000 - min_units: 60 - mode_units: 300 - peak: 4 - - _target_: kirby.transforms.RandomCrop - crop_len: 1.0 - -data_root: ./data/processed/ -seed: 42 -batch_size: 128 -eval_epochs: 5 -epochs: 100 -steps: 0 # Note we either specify epochs or steps, not both. -base_lr: 7.8125e-7 -weight_decay: 0.0001 -# Fraction of epochs to warmup for. -pct_start: 0.5 -num_workers: 4 -log_dir: ./logs -name: perich_finetune -precision: 32 -nodes: 1 -gpus: 1 -# Where to resume/finetune from. Could be null (yaml for None, meaning train from -# scratch) or a fully qualified path to the .ckpt file. -ckpt_path: logs/lightning_logs/f9sj5g0b/last.ckpt - -# Finetuning configuration: -finetune: true -# Num of epochs to freeze perceiver network while finetuning -# -1 => Keep frozen, i.e. perform Unit-identification -# 0 => Train everything -# >0 => Only train unit/session embeddings for first few epochs, -# and then train everything -freeze_perceiver_until_epoch: -1 diff --git a/examples/poyo/run.sh b/examples/poyo/run.sh deleted file mode 100644 index f6431b5..0000000 --- a/examples/poyo/run.sh +++ /dev/null @@ -1,68 +0,0 @@ -#!/bin/bash -#SBATCH --job-name=multi-run -#SBATCH --output=slurm_output.txt -#SBATCH --error=slurm_error.txt -#SBATCH --ntasks-per-node=1 -#SBATCH --mem=24GB -#SBATCH --time=1-23:59:59 -#SBATCH --nodes=1 -#SBATCH --cpus-per-task=4 -#SBATCH --partition=main -#SBATCH --gres=gpu:1 - -# For training, one can also use the following options: -#E.g. SBATCH --partition=unkillable and SBATCH --gres=gpu:a100 - -dataset=odoherty_single_session_lfp - -module load anaconda/3 -module load cuda/12.0 - -conda activate poyo - -# wandb credentials -set -a -source .env -set +a - -# Unpack data to $SLURM_TMPDIR, which also symlinked via /home/mila/p/patrick.mineault/slurm_tmpdir -snakemake --rerun-triggers=mtime --config tmp_dir=$SLURM_TMPDIR -c1 odoherty_sabes_unfreeze -# snakemake --rerun-triggers=mtime --config tmp_dir=$SLURM_TMPDIR -c1 willett_shenoy_unfreeze -# snakemake --rerun-triggers=mtime --config tmp_dir=$SLURM_TMPDIR -c1 perich_miller_unfreeze - -export NCCL_DEBUG=INFO -export PYTHONFAULTHANDLER=1 -export MASTER_ADDR="127.0.0.1" -export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOBID | tail -c 4)) -export NCCL_SOCKET_IFNAME=^docker0,lo - -echo $MASTER_ADDR:$MASTER_PORT - -if [ ${ckpt} = true ] ; then - name="${dataset}_continue" - unique_args=( - "name=${name}" - "finetune_path=logs/lightning_logs/tvh4iucy/checkpoints/last.ckpt" - "finetune_epochs=40" - ) -else - name="${dataset}_scratch" - unique_args=("name=${name}") -fi - -# Run experiments -pwd -which python -python scripts/train_single_session_lightning.py \ - data_root=$SLURM_TMPDIR/uncompressed/ \ - train_datasets=$dataset \ - val_datasets=$dataset \ - eval_epochs=10 \ - epochs=500 \ - pct_start=0.9 \ - batch_size=64 \ - name=${dataset} \ - base_lr=1e-5 \ - precision=16 \ - num_workers=4 \ - "${unique_args[@]}" diff --git a/examples/poyo_hparam_sweep/README.md b/examples/poyo_hparam_sweep/README.md deleted file mode 100644 index 900ce3d..0000000 --- a/examples/poyo_hparam_sweep/README.md +++ /dev/null @@ -1,20 +0,0 @@ -# Hyperparameter Sweep with W&B Sweeps and Hydra -A default hyperparameter sweep config file in `wandb_sweep.yaml` that uses [W&B Sweeps](https://wandb.com/sweeps) in combination with Hydra. - -First initialize a sweep with: -```bash -wandb sweep --name wandb_sweep.yaml -``` - -Then run the sweep agent with the `` provided in the above command: -```bash -wandb agent -``` - -The above command will spawn a sweep agent in wandb's server that will generate hyperparamters as well as run the training script command according to the provided `wandb_sweep.yaml` file. - -The included `train.py` uses the same `train.run_training` module. It overrides the `cfg.name` of each sweep run using `utils.get_sweep_run_name()` to dynamically give an appropriate name to each run. - -_Pro-tip_: You can run `CUDA_VISIBLE_DEVICES=X wandb agent ` on parallel terminal sessions to run multiple agents in parallel. - -For more information on how to use W&B sweeps along with Hydra, refer [this useful report](https://wandb.ai/adrishd/hydra-example/reports/Configuring-W-B-Projects-with-Hydra--VmlldzoxNTA2MzQw?galleryTag=posts) and [W&B's official guide](https://docs.wandb.ai/guides/integrations/hydra). \ No newline at end of file diff --git a/examples/poyo_hparam_sweep/configs/train_mc_maze_small.yaml b/examples/poyo_hparam_sweep/configs/train_mc_maze_small.yaml deleted file mode 100644 index c33cef5..0000000 --- a/examples/poyo_hparam_sweep/configs/train_mc_maze_small.yaml +++ /dev/null @@ -1,44 +0,0 @@ -# Path: configs/train.yaml -defaults: - - _self_ - - model: poyo_single_session.yaml - - dataset: mc_maze_small.yaml - -train_transforms: - - _target_: kirby.transforms.UnitDropout - max_units: 1000 - min_units: 60 - mode_units: 300 - peak: 4 - - _target_: kirby.transforms.RandomCrop - crop_len: 1.0 - -data_root: /kirby/processed/ -seed: 42 -batch_size: 128 -eval_epochs: 1 -epochs: 100 -steps: 0 # Note we either specify epochs or steps, not both. -base_lr: 1.5625e-5 -weight_decay: 0.0001 -# Fraction of epochs to warmup for. -pct_start: 0.5 -num_workers: 0 -log_dir: ./logs -name: mcms_poyo_single_session -backend_config: gpu_fp32_var -precision: 32 -nodes: 1 -gpus: 1 -# Where to resume/finetune from. Could be null (yaml for None, meaning train from -# scratch) or a fully qualified path to the .ckpt file. -ckpt_path: null - -# Finetuning configuration: -finetune: false -# Num of epochs to freeze perceiver network while finetuning -# -1 => Keep frozen, i.e. perform Unit-identification -# 0 => Train everything -# >0 => Only train unit/session embeddings for first few epochs, -# and then train everything -freeze_perceiver_until_epoch: 0 diff --git a/examples/poyo_hparam_sweep/train.py b/examples/poyo_hparam_sweep/train.py deleted file mode 100644 index d2825de..0000000 --- a/examples/poyo_hparam_sweep/train.py +++ /dev/null @@ -1,30 +0,0 @@ -import pickle - -old_unpickler = pickle.Unpickler # Unfortunate hack to fix a bug in Lightning. -# https://github.com/Lightning-AI/lightning/issues/18152 -# Will likely be fixed by 2.1.0. -pickle.Unpickler = old_unpickler -import hydra -from omegaconf import DictConfig -import sys - -sys.path.insert( - 0, "../../" -) # so that we pick the `run_training` from the main `train.py` script -from examples.poyo.train import run_training - -from utils import get_sweep_run_name - - -# This loads the config file using Hydra, similar to Flags, but composable. -@hydra.main(version_base="1.3", config_path="./configs", config_name="train.yaml") -def main(cfg: DictConfig): - # If sweep is enabled, dynamically name the run using the helper - if cfg.get("sweep", True): - cfg.name = get_sweep_run_name(cfg) - # Rest of the training is exactly identical to the original train.py script. - run_training(cfg) - - -if __name__ == "__main__": - main() diff --git a/examples/poyo_hparam_sweep/utils.py b/examples/poyo_hparam_sweep/utils.py deleted file mode 100644 index 5227afa..0000000 --- a/examples/poyo_hparam_sweep/utils.py +++ /dev/null @@ -1,19 +0,0 @@ -def get_sweep_run_name(cfg): - """ - Returns the name of the sweep's run based on the hyperparameters being monitored. - - Args: - cfg (Config): The configuration object containing the hyperparameters. - - Returns: - str: The name of the sweep's run. - - Notes: - This helper function can be modified as per the user's requirements and the hparams that are being monitored. - name the sweep's run based on the hyperparameters being monitored. - """ - lr = cfg.base_lr - model_name = "poyo-plus" - dataset_name = cfg.dataset[0].selection[0].dandiset - canonical_name = f"sweep/lr:{lr:.2e}/{dataset_name}/{model_name}" - return canonical_name diff --git a/examples/poyo_hparam_sweep/wandb_sweep.yaml b/examples/poyo_hparam_sweep/wandb_sweep.yaml deleted file mode 100644 index cfe2d56..0000000 --- a/examples/poyo_hparam_sweep/wandb_sweep.yaml +++ /dev/null @@ -1,25 +0,0 @@ -# This YAML file specifies the configuration for a hyperparameter tuning using wandb -program: train.py - -name: trial_sweep - -metric: - name: val_loss - goal: minimize - -method: bayes # grid, random, bayes -parameters: - base_lr: - min: !!float 1.5625e-6 - max: !!float 1.5625e-5 - batch_size: - value: 128 - -command: -- ${env} -- ${interpreter} -- ${program} # first line -- --config-name=train_mc_maze_small.yaml # specify the root-level yaml file -- +sweep=True # to make the training script "sweep-aware" -- eval_epochs=1 # quick val_loss feedback -- ${args_no_hyphens} # HP tuning overrides \ No newline at end of file diff --git a/notebooks/Find best sweeps of baselines.ipynb b/notebooks/Find best sweeps of baselines.ipynb deleted file mode 100644 index bd6f457..0000000 --- a/notebooks/Find best sweeps of baselines.ipynb +++ /dev/null @@ -1,8720 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 113, - "metadata": {}, - "outputs": [], - "source": [ - "import wandb\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 115, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 478 sweeps.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - 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"\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - 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"\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Sorting runs by -summary_metrics.val_accuracy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'val': 0, 'test': 0, 'session_id': '37115675', 'epochs': 'Not specified', 'run count': 100}\n", - "{'val': 0.24444444477558136, 'test': 0.1111111119389534, 'session_id': '657775947', 'sweep': \"['657775947']\", 'lr': '2.28e-03', 'b': '128', 'lyr': '[32, 16]', 'unit_drop': '0.31234328794567745', 'wt_drop': '0.12954738404788496', 'epochs': 100, 'run count': 100}\n", - "{'val': 0.8333333730697632, 'test': 0.7472222447395325, 'session_id': '653123929', 'sweep': \"['653123929']\", 'lr': '3.15e-03', 'b': '128', 'lyr': '[128, 128, 64, 32]', 'unit_drop': '0.01758293653312709', 'wt_drop': '0.26413946279529943', 'epochs': 100, 'run count': 100}\n", - 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"{'val': 0.2666666805744171, 'test': 0.11666666716337204, 'session_id': '603452291', 'sweep': \"['603452291']\", 'lr': '1.47e-04', 'b': '128', 'lyr': '[32, 16]', 'unit_drop': '0.3718659577007999', 'wt_drop': '0.4248416227026505', 'epochs': 100, 'run count': 100}\n", - "{'val': 0.23888888955116272, 'test': 0.1527777761220932, 'session_id': '652091264', 'sweep': \"['652091264']\", 'lr': '8.99e-04', 'b': '128', 'lyr': '[32, 16]', 'unit_drop': '0.045998286214827655', 'wt_drop': '0.1574534411938193', 'epochs': 100, 'run count': 100}\n", - "{'val': 0.5277777910232544, 'test': 0.42222222685813904, 'session_id': '639117826', 'sweep': \"['639117826']\", 'lr': '1.24e-03', 'b': '128', 'lyr': '[64, 32]', 'unit_drop': '0.034383587160282', 'wt_drop': '0.40785498406844534', 'epochs': 100, 'run count': 100}\n", - "{'val': 0.4000000059604645, 'test': 0.2666666805744171, 'session_id': '613968705', 'sweep': \"['613968705']\", 'lr': '9.05e-03', 'b': '128', 'lyr': '[128, 64]', 'unit_drop': '0.021123299044531307', 'wt_drop': '0.05098569945692977', 'epochs': 100, 'run count': 100}\n", - "{'val': 0.4833333492279053, 'test': 0.347222238779068, 'session_id': '688580172', 'sweep': \"['688580172']\", 'lr': '4.51e-03', 'b': '64', 'lyr': '[128, 64]', 'unit_drop': '0.0023480555482324195', 'wt_drop': '0.0900935155295558', 'epochs': 100, 'run count': 100}\n", - "{'val': 0.5, 'test': 0.4194444417953491, 'session_id': '652737678', 'sweep': \"['652737678']\", 'lr': '8.36e-03', 'b': '128', 'lyr': '[128, 64, 32]', 'unit_drop': '0.03006784320828476', 'wt_drop': '0.021413546909854145', 'epochs': 100, 'run count': 100}\n", - "{'val': 0.8578431606292725, 'test': 0.8102189898490906, 'session_id': '647155122', 'sweep': \"['647155122']\", 'lr': '5.92e-03', 'b': '64', 'lyr': '[128, 64]', 'unit_drop': '0.44449054526068155', 'wt_drop': '0.4946170019490432', 'epochs': 100, 'run count': 100}\n", - "{'val': 0.6888889074325562, 'test': 0.5583333373069763, 'session_id': '651770186', 'sweep': \"['651770186']\", 'lr': '5.93e-03', 'b': '128', 'lyr': '[64, 32]', 'unit_drop': '0.21024741402114455', 'wt_drop': '0.3209093690815679', 'epochs': 100, 'run count': 100}\n", - "{'val': 0.3777777850627899, 'test': 0.2686980664730072, 'session_id': '662359728', 'sweep': \"['662359728']\", 'lr': '1.85e-03', 'b': '128', 'lyr': '[64, 32]', 'unit_drop': '0.3776745670634972', 'wt_drop': '0.04087664760826054', 'epochs': 100, 'run count': 100}\n", - "{'val': 0.6722222566604614, 'test': 0.5861111283302307, 'session_id': '644026238', 'sweep': \"['644026238']\", 'lr': '1.71e-03', 'b': '128', 'lyr': '[128, 64, 32]', 'unit_drop': '0.01966466274101009', 'wt_drop': '0.16638231831471423', 'epochs': 100, 'run count': 100}\n", - "{'val': 0.7900552749633789, 'test': 0.675000011920929, 'session_id': '501940850', 'sweep': \"['501940850']\", 'lr': '5.89e-03', 'b': '128', 'lyr': '[64, 32]', 'unit_drop': '0.06069533404934746', 'wt_drop': '0.1926499945716561', 'epochs': 100, 'run count': 100}\n", - "{'val': 0.6277778148651123, 'test': 0.5638889074325562, 'session_id': '546641574', 'sweep': \"['546641574']\", 'lr': '3.56e-03', 'b': '128', 'lyr': '[128, 64, 32]', 'unit_drop': '0.14280241834800567', 'wt_drop': '0.40716678420849256', 'epochs': 100, 'run count': 100}\n", - "{'val': 0.5, 'test': 0.41111111640930176, 'session_id': '645086975', 'sweep': \"['645086975']\", 'lr': '2.40e-03', 'b': '128', 'lyr': '[64, 32]', 'unit_drop': '0.10201667092963984', 'wt_drop': '0.3222235870230037', 'epochs': 100, 'run count': 100}\n", - "{'val': 0.32777777314186096, 'test': 0.22714680433273315, 'session_id': '603763073', 'sweep': \"['603763073']\", 'lr': '8.39e-03', 'b': '128', 'lyr': '[64, 32]', 'unit_drop': '0.011296419764929289', 'wt_drop': '0.009136276512673958', 'epochs': 100, 'run count': 100}\n", - "{'val': 0.2222222238779068, 'test': 0.12465373426675797, 'session_id': '657078119', 'sweep': \"['657078119']\", 'lr': '1.78e-03', 'b': '64', 'lyr': '[128, 64, 32]', 'unit_drop': '0.03898000015863462', 'wt_drop': '0.46300067998406186', 'epochs': 100, 'run count': 100}\n", - "{'val': 0.4555555582046509, 'test': 0.375, 'session_id': '581150104', 'sweep': \"['581150104']\", 'lr': '2.80e-03', 'b': '64', 'lyr': '[64, 32]', 'unit_drop': '0.014048691770846111', 'wt_drop': '0.2887443111888344', 'epochs': 100, 'run count': 100}\n", - "{'val': 0.5277777910232544, 'test': 0.3656509518623352, 'session_id': '545446482', 'sweep': \"['545446482']\", 'lr': '5.63e-03', 'b': '64', 'lyr': '[128, 64]', 'unit_drop': '0.34666605419606006', 'wt_drop': '0.19962194586406443', 'epochs': 100, 'run count': 100}\n", - "{'val': 0.2666666805744171, 'test': 0.17499999701976776, 'session_id': '601705404', 'sweep': \"['601705404']\", 'lr': '2.29e-03', 'b': '128', 'lyr': '[64, 32]', 'unit_drop': '0.06605989061229972', 'wt_drop': '0.4573190086584239', 'epochs': 100, 'run count': 100}\n", - "{'val': 0.7944444417953491, 'test': 0.6861111521720886, 'session_id': '698762886', 'sweep': \"['698762886']\", 'lr': '2.02e-03', 'b': '64', 'lyr': '[32, 16]', 'unit_drop': '0.3780751895872101', 'wt_drop': '0.007142261836805774', 'epochs': 100, 'run count': 100}\n", - "{'val': 0.19337017834186554, 'test': 0.15833333134651184, 'session_id': '662361096', 'sweep': \"['662361096']\", 'lr': '9.60e-03', 'b': '64', 'lyr': '[128, 64]', 'unit_drop': '0.2692875856254575', 'wt_drop': '0.09234938269851956', 'epochs': 100, 'run count': 100}\n", - "{'val': 0.5611111521720886, 'test': 0.5166666507720947, 'session_id': '581597734', 'sweep': \"['581597734']\", 'lr': '4.31e-03', 'b': '128', 'lyr': '[32, 16]', 'unit_drop': '0.32048720642031236', 'wt_drop': '0.11605094218716666', 'epochs': 100, 'run count': 100}\n", - "{'val': 0.5111111402511597, 'test': 0.5416666865348816, 'session_id': '504853580', 'sweep': \"['504853580']\", 'lr': '8.89e-04', 'b': '64', 'lyr': '[64, 32]', 'unit_drop': '0.25897440089923873', 'wt_drop': '0.1975441067386579', 'epochs': 100, 'run count': 100}\n", - "{'val': 0.7833333611488342, 'test': 0.7894736528396606, 'session_id': '502793808', 'sweep': \"['502793808']\", 'lr': '9.31e-03', 'b': '64', 'lyr': '[64, 32]', 'unit_drop': '0.18945690470191817', 'wt_drop': '0.1542620397954968', 'epochs': 100, 'run count': 100}\n", - "{'val': 0.27222222089767456, 'test': 0.236111119389534, 'session_id': '614556106', 'sweep': \"['614556106']\", 'lr': '4.05e-03', 'b': '128', 'lyr': '[32, 16]', 'unit_drop': '0.11734794513198038', 'wt_drop': '0.49276888345178177', 'epochs': 100, 'run count': 100}\n", - "{'val': 0.32777777314186096, 'test': 0.24166667461395264, 'session_id': '638862121', 'sweep': \"['638862121']\", 'lr': '4.30e-03', 'b': '128', 'lyr': '[64, 32]', 'unit_drop': '0.03206690607859014', 'wt_drop': '0.2738615525383281', 'epochs': 100, 'run count': 100}\n", - "{'val': 0.47663548588752747, 'test': 0.359413206577301, 'session_id': '595273803', 'sweep': \"['595273803']\", 'lr': '9.06e-04', 'b': '128', 'lyr': '[128, 128, 64, 32]', 'unit_drop': '0.26039516763750287', 'wt_drop': '0.10684043093486768', 'epochs': 100, 'run count': 100}\n", - "{'val': 0.5277777910232544, 'test': 0.3638888895511627, 'session_id': '511194579', 'sweep': \"['511194579']\", 'lr': '2.95e-03', 'b': '64', 'lyr': '[64, 32]', 'unit_drop': '0.024110073682440503', 'wt_drop': '0.007222490529930126', 'epochs': 100, 'run count': 100}\n", - "{'val': 0.31111112236976624, 'test': 0.2638888955116272, 'session_id': '594320795', 'sweep': \"['594320795']\", 'lr': '2.14e-04', 'b': '128', 'lyr': '[64, 32]', 'unit_drop': '0.003157124756312635', 'wt_drop': '0.19446829188679032', 'epochs': 100, 'run count': 100}\n", - "{'val': 0.2611111104488373, 'test': 0.215469628572464, 'session_id': '560809202', 'sweep': \"['560809202']\", 'lr': '2.40e-04', 'b': '128', 'lyr': '[128, 64]', 'unit_drop': '0.011101643991551556', 'wt_drop': '0.3876012994639578', 'epochs': 100, 'run count': 100}\n", - "{'val': 0.6722222566604614, 'test': 0.6371191143989563, 'session_id': '645689073', 'sweep': \"['645689073']\", 'lr': '2.93e-03', 'b': '128', 'lyr': '[64, 32]', 'unit_drop': '0.2878293019441934', 'wt_drop': '0.4303177046976436', 'epochs': 100, 'run count': 100}\n", - "{'val': 0.43478259444236755, 'test': 0.323184996843338, 'session_id': '613091721', 'sweep': \"['613091721']\", 'lr': '9.14e-04', 'b': '128', 'lyr': '[64, 32]', 'unit_drop': '0.24677991704289187', 'wt_drop': '0.4023242921629474', 'epochs': 100, 'run count': 100}\n", - "{'val': 0.6280193328857422, 'test': 0.497560977935791, 'session_id': '652094901', 'sweep': \"['652094901']\", 'lr': '1.29e-03', 'b': '128', 'lyr': '[32, 16]', 'unit_drop': '0.21724178693299223', 'wt_drop': '0.09834572981205504', 'epochs': 100, 'run count': 100}\n", - "{'val': 0.23888888955116272, 'test': 0.16944444179534912, 'session_id': '657776356', 'sweep': \"['657776356']\", 'lr': '3.72e-03', 'b': '128', 'lyr': '[128, 64]', 'unit_drop': '0.23475706882003083', 'wt_drop': '0.4887926610639801', 'epochs': 100, 'run count': 100}\n", - "{'val': 0.6333333253860474, 'test': 0.5472222566604614, 'session_id': '627823695', 'sweep': \"['627823695']\", 'lr': '5.67e-03', 'b': '128', 'lyr': '[128, 64]', 'unit_drop': '0.3380226274022799', 'wt_drop': '0.057921286087003254', 'epochs': 100, 'run count': 100}\n", - "{'val': 0.21111111342906952, 'test': 0.11911357194185257, 'session_id': '662982346', 'sweep': \"['662982346']\", 'lr': '6.85e-03', 'b': '64', 'lyr': '[64, 32]', 'unit_drop': '0.04599349493024257', 'wt_drop': '0.30941116105649374', 'epochs': 100, 'run count': 100}\n", - "{'val': 0.24725274741649628, 'test': 0.18333333730697632, 'session_id': '556353209', 'sweep': \"['556353209']\", 'lr': '8.43e-04', 'b': '64', 'lyr': '[128, 128, 64, 32]', 'unit_drop': '0.08898171129575883', 'wt_drop': '0.42444927403422894', 'epochs': 100, 'run count': 100}\n", - "{'val': 0.472222238779068, 'test': 0.36944445967674255, 'session_id': '604328043', 'sweep': \"['604328043']\", 'lr': '6.44e-03', 'b': '128', 'lyr': '[128, 128, 64, 32]', 'unit_drop': '0.2974812622245008', 'wt_drop': '0.38019336835475326', 'epochs': 100, 'run count': 100}\n", - "{'val': 0.2153109908103943, 'test': 0.11835748702287674, 'session_id': '649409874', 'sweep': \"['649409874']\", 'lr': '7.57e-03', 'b': '64', 'lyr': '[32, 16]', 'unit_drop': '0.3289495204384161', 'wt_drop': '0.46883326121722974', 'epochs': 100, 'run count': 100}\n" - ] - } - ], - "source": [ - "api = wandb.Api()\n", - "import re\n", - "# Replace 'project_name' with your project's name\n", - "project_name = \"project-kirby-examples_mlp_sweep\"\n", - "project = api.project(project_name)\n", - "hp_dict = {}\n", - "\n", - "sweeps = project.sweeps()\n", - "print(f\"Found {len(sweeps)} sweeps.\")\n", - "for sweep in sweeps:\n", - " best = sweep.best_run()\n", - " runs = api.runs(path=project_name, filters={\"sweep\": sweep.id})\n", - " run_count = len(runs)\n", - " if best:\n", - " epochs = best.config.get('epochs', 'Not specified')\n", - " val_acc = [row[\"val_accuracy\"] for row in best.scan_history(keys=[\"val_accuracy\"])]\n", - " test_acc = [row[\"test_accuracy\"] for row in best.scan_history(keys=[\"test_accuracy\"])]\n", - "\n", - " val_acc.append(0)\n", - " test_acc.append(0)\n", - " # test accuracy is the corresponding test accuracy for the epoch with the best validation accuracy\n", - " # so we will get both the max and argmax\n", - " val_acc = max(val_acc)\n", - " test_acc = max(test_acc)\n", - " \n", - " if (not sweep.name in hp_dict.keys()) or (sweep.name in hp_dict.keys() and val_acc > hp_dict[sweep.name][\"val\"]): #catch duplicate runs\n", - " hp_dict[sweep.name] = {\"val\" : val_acc, \"test\" : test_acc, \"session_id\" : sweep.name}\n", - " parts = best.name.split('/')\n", - " for part in parts:\n", - " if \":\" in part:\n", - " hp, val = tuple(part.split(\":\"))\n", - " hp_dict[sweep.name][hp] = val\n", - " hp_dict[sweep.name][\"epochs\"] = epochs\n", - " try:\n", - " hp_dict[sweep.name][\"run count\"] += run_count\n", - " except:\n", - " hp_dict[sweep.name][\"run count\"] = run_count\n", - "\n", - "for each in hp_dict:\n", - " print(hp_dict[each])" - ] - }, - { - "cell_type": "code", - "execution_count": 152, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 479 sweeps.\n" - ] - } - ], - "source": [ - "api = wandb.Api()\n", - "import re\n", - "# Replace 'project_name' with your project's name\n", - "project_name = \"project-kirby-examples_mlp_sweep\"\n", - "project = api.project(project_name)\n", - "sweeps = project.sweeps()\n", - "print(f\"Found {len(sweeps)} sweeps.\")" - ] - }, - { - "cell_type": "code", - "execution_count": 173, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - ">" - ] - }, - "execution_count": 173, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 180, - "metadata": {}, - "outputs": [], - "source": [ - "from tqdm import tqdm\n", - "\n", - "def process_run(run):\n", - " if not 'dataset' in run.config:\n", - " return dict(failed=True)\n", - " session_id = run.config['dataset'][0]['selection'][0]['sortsets'][0]\n", - " val_acc = [row[\"val_accuracy\"] for row in run.scan_history(keys=[\"val_accuracy\"])]\n", - " test_acc = [row[\"test_accuracy\"] for row in run.scan_history(keys=[\"test_accuracy\"])]\n", - " failed = len(test_acc) == 0\n", - " if failed:\n", - " val_acc = None\n", - " test_acc = None\n", - " else:\n", - " val_acc = max(val_acc)\n", - " assert len(test_acc) == 1\n", - " test_acc = test_acc[0]\n", - " return dict(session_id=session_id, val_acc=val_acc, test_acc=test_acc, failed=failed)\n", - "\n", - "def process_sweep(sweep):\n", - " runs = api.runs(path=project_name, filters={\"sweep\": sweep.id})\n", - " \n", - " run_count = 0\n", - " best_val_acc = 0\n", - " best_test_acc = 0\n", - " session_id = None\n", - " for run in tqdm(runs):\n", - " out = process_run(run)\n", - " if out['failed']:\n", - " continue\n", - " run_count += 1\n", - " if out['val_acc'] > best_val_acc:\n", - " best_val_acc = out['val_acc']\n", - " best_test_acc = out['test_acc']\n", - " if session_id is None:\n", - " session_id = out['session_id']\n", - " else:\n", - " assert session_id == out['session_id']\n", - "\n", - " return dict(session_id=session_id, run_count=run_count, best_val_acc=best_val_acc, best_test_acc=best_test_acc)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 181, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 479 sweeps.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 42/42 [00:22<00:00, 1.90it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'session_id': '649409874', 'run_count': 17, 'best_val_acc': 0.23444975912570953, 'best_test_acc': 0.13768115639686584}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:00<00:00, 427.77it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'session_id': None, 'run_count': 0, 'best_val_acc': 0, 'best_test_acc': 0}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [01:08<00:00, 1.46it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'session_id': '657775947', 'run_count': 100, 'best_val_acc': 0.24444444477558136, 'best_test_acc': 0.1111111119389534}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [01:14<00:00, 1.35it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'session_id': '653123929', 'run_count': 100, 'best_val_acc': 0.8500000238418579, 'best_test_acc': 0.7611111402511597}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - 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Create a new API with an integer timeout larger than 19, e.g., `api = wandb.Api(timeout=29)` to increase the graphql timeout.\n", - "100%|██████████| 100/100 [01:29<00:00, 1.11it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'session_id': '565216523', 'run_count': 100, 'best_val_acc': 0.699999988079071, 'best_test_acc': 0.5069251656532288}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [01:07<00:00, 1.49it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'session_id': '676024666', 'run_count': 100, 'best_val_acc': 0.27222222089767456, 'best_test_acc': 0.09972298890352249}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [01:12<00:00, 1.38it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'session_id': '571006300', 'run_count': 100, 'best_val_acc': 0.5444444417953491, 'best_test_acc': 0.38055557012557983}\n" - 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"output_type": "stream", - "text": [ - "100%|██████████| 4/4 [00:04<00:00, 1.25s/it]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'session_id': None, 'run_count': 0, 'best_val_acc': 0, 'best_test_acc': 0}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 2/2 [00:00<00:00, 3135.93it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'session_id': None, 'run_count': 0, 'best_val_acc': 0, 'best_test_acc': 0}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 2/2 [00:00<00:00, 36314.32it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'session_id': None, 'run_count': 0, 'best_val_acc': 0, 'best_test_acc': 0}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 40/40 [00:45<00:00, 1.15s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'session_id': '698762886', 'run_count': 40, 'best_val_acc': 0.8055555820465088, 'best_test_acc': 0.6833333373069763}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "api = wandb.Api()\n", - "\n", - "project_name = \"project-kirby-examples_mlp_sweep\"\n", - "project = api.project(project_name)\n", - "sweeps = project.sweeps()\n", - "print(f\"Found {len(sweeps)} sweeps.\")\n", - "\n", - "out = []\n", - "for sweep in sweeps:\n", - " result = process_sweep(sweep)\n", - " print(result)\n", - " out.append(result)" - ] - }, - { - "cell_type": "code", - "execution_count": 183, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Duplicate session id: 580095655, result, 175\n", - "Duplicate session id: 650079244, result, 167\n", - "Duplicate session id: 555749369, result, 121\n", - "Duplicate session id: 712919665, result, 124\n", - "Duplicate session id: 502115959, result, 166\n", - "Duplicate session id: 528402271, result, 170\n", - "Duplicate session id: 692345336, result, 151\n", - "Duplicate session id: 529688779, result, 122\n", - "Duplicate session id: 664404274, result, 132\n", - "Duplicate session id: 577379202, result, 199\n", - "Duplicate session id: 561312435, result, 170\n", - "Duplicate session id: 603187982, result, 167\n", - "Duplicate session id: 674275260, result, 124\n", - "Duplicate session id: 606353987, result, 156\n", - "Duplicate session id: 558670888, result, 142\n", - "Duplicate session id: 601887677, result, 152\n", - "Duplicate session id: 601910964, result, 125\n", - "Duplicate session id: 674275260, result, 199\n", - "Duplicate session id: 689388034, result, 121\n", - "Duplicate session id: 653125130, result, 125\n", - "Duplicate session id: 652842572, result, 113\n", - "Duplicate session id: 558670888, result, 148\n", - "Duplicate session id: 553568031, result, 134\n", - "Duplicate session id: 657391625, result, 199\n", - "Duplicate session id: 652842495, result, 200\n", - "Duplicate session id: 660510593, result, 200\n", - "Duplicate session id: 649409874, result, 117\n", - "Duplicate session id: 584196534, result, 121\n", - "Duplicate session id: 584196534, result, 132\n", - "Duplicate session id: 588655112, result, 212\n", - "Duplicate session id: 698762886, result, 140\n" - ] - } - ], - "source": [ - "result_table = {}\n", - "\n", - "for result in out:\n", - " session_id = result['session_id']\n", - " if session_id is None:\n", - " continue\n", - " if session_id in result_table:\n", - " print(f\"Duplicate session id: {session_id}, result, {result['run_count']}\")\n", - " # use count to break ties\n", - " result['run_count'] += result_table[session_id]['run_count']\n", - " if result['best_val_acc'] > result_table[session_id]['best_val_acc']:\n", - " result_table[session_id] = result\n", - "\n", - " result_table[session_id] = result" - ] - }, - { - "cell_type": "code", - "execution_count": 202, - "metadata": {}, - "outputs": [], - "source": [ - "df = pd.DataFrame(result_table.values())\n", - "df.session_id = df.session_id.astype(int)" - ] - }, - { - "cell_type": "code", - "execution_count": 118, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'val': 0.24444444477558136, 'test': 0.1111111119389534, 'session_id': '657775947', 'sweep': \"['657775947']\", 'lr': '2.28e-03', 'b': '128', 'lyr': '[32, 16]', 'unit_drop': '0.31234328794567745', 'wt_drop': '0.12954738404788496', 'epochs': 100, 'run count': 100}\n", - "Data has been written to 'sweep_data.csv'\n" - ] - } - ], - "source": [ - "# make a csv from the dictionary\n", - "import csv\n", - "first = list(hp_dict.keys())\n", - "print(hp_dict[first[1]])\n", - "fieldnames = hp_dict[first[1]].keys()\n", - "\n", - "with open('sweep_data.csv', 'w', newline='') as csvfile:\n", - " writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n", - "\n", - " # Write the header\n", - " writer.writeheader()\n", - " # Write the data rows\n", - " for key in hp_dict:\n", - " writer.writerow(hp_dict[key])\n", - "\n", - "print(\"Data has been written to 'sweep_data.csv'\")" - ] - }, - { - "cell_type": "code", - "execution_count": 321, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "# df = pd.read_csv('sweep_data.csv')\n", - "\n", - "df_capoyo = pd.read_csv('wandb_export_2024-05-09T13_22_15.265-04_00.csv')\n", - "# df_capoyo = pd.read_csv('200epochfrozen+200unfrozen-finetune.csv')\n", - "\n", - "df_stats = pd.read_csv(\"CaPOYO-mastersheet (do not filter by creating a filter! do create a view instead) - Orientation.csv\")" - ] - }, - { - "cell_type": "code", - "execution_count": 298, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "a = \"\"\"{\"columns\": [\"metric\", \"value\"], \"data\": [[\"val_allen_brain_observatory_calcium/546641574_drifting_gratings_accuracy\", 0.550000011920929], 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4501929610FalseFalse222431Rorb-IRES2-CreVISl2752005675367.27272766.666667501929610
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" - ], - "text/plain": [ - " Session ID old heldout new heldout Subject ID Creline Creline.1 \\\n", - "0 501021421 False False 221470 Scnn1a-Tg3-Cre VISp \n", - "1 501574836 False False 222420 Cux2-CreERT2 VISp \n", - "2 501729039 False False 222431 Rorb-IRES2-Cre VISp \n", - "3 501876401 False False 222426 Cux2-CreERT2 VISal \n", - "4 501929610 False False 222431 Rorb-IRES2-Cre VISl \n", - "\n", - " depth num_ROIs num_timepoints MLP_val_accuracy MLP_test_accuracy \\\n", - "0 350 150 56606 33.939394 37.500000 \n", - "1 275 240 56748 90.000000 86.666667 \n", - "2 275 227 56746 90.303030 88.333333 \n", - "3 175 181 56630 70.151515 75.000000 \n", - "4 275 200 56753 67.272727 66.666667 \n", - "\n", - " session_id \n", - "0 501021421 \n", - "1 501574836 \n", - "2 501729039 \n", - "3 501876401 \n", - "4 501929610 " - ] - }, - "execution_count": 302, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_stats[\"session_id\"] = df_stats[\"Session ID\"].astype(int)\n", - "df_stats.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 303, - "metadata": {}, - "outputs": [], - "source": [ - "# truncate metric string to 10 characters\n", - "df_capoyo['session_id'] = df_capoyo['metric'].apply(lambda x: x[36:36+9])\n", - "df_capoyo['session_id'] = df_capoyo['session_id'].astype(int)" - ] - }, - { - "cell_type": "code", - "execution_count": 304, - "metadata": {}, - "outputs": [], - "source": [ - "# merge the two dataframes\n", - "merged = pd.merge(df, df_capoyo, on='session_id', how='inner')\n", - "# merge with stats\n", - "merged = pd.merge(merged, df_stats, on='session_id', how='inner')" - ] - }, - { - "cell_type": "code", - "execution_count": 305, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 501021421\n", - "1 501574836\n", - "2 501729039\n", - "3 501876401\n", - "4 501929610\n", - " ... 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.figure(figsize=(4, 4))\n", - "plt.scatter(merged['best_test_acc'], merged['value'], s=merged[\"num_ROIs\"] * 0.2)\n", - "plt.xlim([0., 1])\n", - "plt.ylim([0., 1])\n", - "# plot x = y\n", - "plt.plot([0, 1], [0, 1], color='red')\n", - "plt.xlabel('MLP Accuracy')\n", - "plt.ylabel('Multi-session CaPOYO Accuracy')\n", - "# make legend for smallest and biggest num_ROIs\n", - "plt.scatter([], [], s=5 * 0.2, c=\"tab:blue\", label='5 ROIs')\n", - "plt.scatter([], [], s=100 * 0.2, c=\"tab:blue\", label='100 ROIs')\n", - "# place legend on bottom right\n", - "plt.legend(loc='lower right')\n", - "\n", - "plt.title(f\"Average improvement: {(merged['value'].mean() - merged['best_test_acc'].mean()) * 100:.2f}\", fontsize=10)" - ] - }, - { - "cell_type": "code", - "execution_count": 319, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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session_idrun_countbest_val_accbest_test_accmetricvalueSession IDold heldoutnew heldoutSubject IDCrelineCreline.1depthnum_ROIsnum_timepointsMLP_val_accuracyMLP_test_accuracy
3395109172541000.4833330.254848val_allen_brain_observatory_calcium/510917254_...0.358333510917254FalseFalse234584Rbp4-Cre_KL100VISpm375615661139.6969722.5
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session_idrun_countbest_val_accbest_test_acc
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" - ], - "text/plain": [ - " session_id run_count best_val_acc best_test_acc\n", - "162 550851591 28 0.383333 0.232687\n", - "210 639932847 36 0.716667 0.716667\n", - "316 510517131 39 0.661111 0.627778" - ] - }, - "execution_count": 222, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[df.run_count < 40]" - ] - }, - { - "cell_type": "code", - "execution_count": 216, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'501729039',\n", - " '504115289',\n", - " '512311673',\n", - " '569457162',\n", - " '570278597',\n", - " '590047029',\n", - " '604529230',\n", - " '637115675',\n", - " '639931541',\n", - " '657650110',\n", - " '669859475',\n", - " '673914981',\n", - " '703308071'}" - ] - }, - "execution_count": 216, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "session_ids = [\"501021421\",\"501574836\",\"501729039\",\"501876401\",\"501929610\",\"501933264\",\"501940850\",\"502115959\",\"502199136\",\"502205092\",\"502376461\",\"502608215\",\"502666254\",\"502793808\",\"502962794\",\"503109347\",\"503324629\",\"503412730\",\"504115289\",\"504568756\",\"504853580\",\"505407318\",\"505695962\",\"505845219\",\"506540916\",\"506773185\",\"506773892\",\"506809539\",\"506823562\",\"507129766\",\"507691036\",\"507990552\",\"508356957\",\"508563988\",\"508753256\",\"509580400\",\"509904120\",\"509958730\",\"510093797\",\"510214538\",\"510390912\",\"510514474\",\"510517131\",\"510859641\",\"510917254\",\"511194579\",\"511440894\",\"511534603\",\"511573879\",\"511595995\",\"512164988\",\"512270518\",\"512311673\",\"512326618\",\"524691284\",\"526504941\",\"527048992\",\"528402271\",\"529688779\",\"530645663\",\"531134090\",\"539290504\",\"539487468\",\"539497234\",\"540684467\",\"541010698\",\"541290571\",\"545446482\",\"546641574\",\"546716391\",\"547388708\",\"548379748\",\"550455111\",\"550490398\",\"550851591\",\"551834174\",\"551888519\",\"552410386\",\"552427971\",\"552760671\",\"553568031\",\"554037270\",\"555040116\",\"555042467\",\"555749369\",\"556321897\",\"556344224\",\"556353209\",\"556665481\",\"557225279\",\"557227804\",\"557304694\",\"557615965\",\"557848210\",\"558476282\",\"558670888\",\"559192380\",\"559382012\",\"560027980\",\"560578599\",\"560809202\",\"560866155\",\"560898462\",\"560920977\",\"560926639\",\"561312435\",\"561472633\",\"562052595\",\"562122508\",\"562536153\",\"562711440\",\"563176332\",\"563710064\",\"564425777\",\"564607188\",\"565216523\",\"565698388\",\"566096665\",\"566307038\",\"566458505\",\"567878987\",\"569299884\",\"569396924\",\"569457162\",\"569645690\",\"569718097\",\"569739027\",\"569792817\",\"569896493\",\"570008444\",\"570236381\",\"570278597\",\"570305847\",\"571006300\",\"571137446\",\"571177441\",\"571541565\",\"571642389\",\"571684733\",\"572606382\",\"572722662\",\"573083539\",\"573261515\",\"573720508\",\"573850303\",\"574823092\",\"575135986\",\"575302108\",\"575939366\",\"575970700\",\"576001843\",\"576095926\",\"576273468\",\"576411246\",\"577379202\",\"577665023\",\"578674360\",\"580013262\",\"580043440\",\"580051759\",\"580095647\",\"580095655\",\"580163817\",\"581026088\",\"581150104\",\"581153070\",\"581597734\",\"582838758\",\"582867147\",\"582918858\",\"583136567\",\"583279803\",\"584196534\",\"584544569\",\"584944065\",\"584983136\",\"585035184\",\"585900296\",\"587339481\",\"587344053\",\"588191926\",\"588483711\",\"588655112\",\"589441079\",\"589755795\",\"590047029\",\"590168385\",\"591430494\",\"591460070\",\"591548033\",\"592348507\",\"592407200\",\"592657427\",\"593270603\",\"593373156\",\"593552712\",\"594090967\",\"594320795\",\"595183197\",\"595263154\",\"595273803\",\"595718342\",\"595806300\",\"595808594\",\"596509886\",\"596584192\",\"596779487\",\"597028938\",\"598137246\",\"598564173\",\"598635821\",\"599320182\",\"599909878\",\"601273921\",\"601368107\",\"601423209\",\"601705404\",\"601805379\",\"601841437\",\"601887677\",\"601904502\",\"601910964\",\"602866800\",\"603187982\",\"603188560\",\"603224878\",\"603425659\",\"603452291\",\"603576132\",\"603592541\",\"603763073\",\"603978471\",\"604145810\",\"604328043\",\"604529230\",\"605606109\",\"605688822\",\"605800963\",\"605859367\",\"605883133\",\"606353987\",\"606802468\",\"607063420\",\"609517556\",\"609894681\",\"611638995\",\"611658482\",\"612044635\",\"612534310\",\"612536911\",\"612543999\",\"612549085\",\"613091721\",\"613599811\",\"613968705\",\"614556106\",\"614571626\",\"616779893\",\"617381605\",\"617388117\",\"617395455\",\"623339221\",\"623347352\",\"623587006\",\"626027944\",\"627823636\",\"627823695\",\"637115675\",\"637126541\",\"637154333\",\"637669270\",\"637669284\",\"637671554\",\"637998955\",\"638056634\",\"638262558\",\"638862121\",\"639117196\",\"639117826\",\"639251932\",\"639931541\",\"639932847\",\"640198011\",\"642278925\",\"642884591\",\"643062797\",\"643592303\",\"643645390\",\"644026238\",\"644051974\",\"644386884\",\"644947716\",\"645086975\",\"645256361\",\"645413759\",\"645689073\",\"646016204\",\"647143225\",\"647155122\",\"647595665\",\"647595671\",\"647598519\",\"647603932\",\"649324898\",\"649401936\",\"649409874\",\"649938038\",\"650079244\",\"651769499\",\"651770186\",\"651770380\",\"651770794\",\"652091264\",\"652092676\",\"652094901\",\"652094917\",\"652096183\",\"652737678\",\"652737867\",\"652842495\",\"652842572\",\"652989442\",\"653122667\",\"653123929\",\"653125130\",\"653126877\",\"653551965\",\"653932505\",\"654532828\",\"657009581\",\"657016267\",\"657078119\",\"657080632\",\"657082055\",\"657224241\",\"657389972\",\"657390171\",\"657391037\",\"657391625\",\"657650110\",\"657775947\",\"657776356\",\"657785850\",\"657914280\",\"657915168\",\"658020691\",\"658518486\",\"658533763\",\"658854537\",\"659491419\",\"660064796\",\"660510593\",\"660513003\",\"661328410\",\"661437140\",\"662033243\",\"662219852\",\"662348804\",\"662351164\",\"662358771\",\"662359728\",\"662361096\",\"662974315\",\"662982346\",\"663479824\",\"663485329\",\"663866413\",\"663876406\",\"664404274\",\"664914611\",\"665307545\",\"665722301\",\"665726618\",\"667004159\",\"669233895\",\"669237515\",\"669859475\",\"669861524\",\"670395725\",\"670395999\",\"670721589\",\"670728674\",\"671164733\",\"671618887\",\"672206735\",\"672207947\",\"672211004\",\"673171528\",\"673475020\",\"673914981\",\"674275260\",\"674276329\",\"674679019\",\"675477919\",\"676024666\",\"676503588\",\"679700458\",\"679702884\",\"680150733\",\"680156911\",\"682049099\",\"682051855\",\"683253712\",\"683257169\",\"685816006\",\"686441799\",\"686442556\",\"686449092\",\"686909240\",\"688580172\",\"688678766\",\"689388034\",\"691197571\",\"692345003\",\"692345336\",\"696156783\",\"698260532\",\"698762886\",\"699155265\",\"701046700\",\"702934964\",\"703308071\",\"704298735\",\"707006626\",\"707923645\",\"710502981\",\"710504563\",\"710778377\",\"712178483\",\"712178511\",\"712919665\",\"715923832\",\"716956096\"]\n", - "\n", - "# find session ids that are in the merged dataframe but not in the session_ids list\n", - "missing = set(session_ids) - set(df['session_id'].astype(str)) \n", - "missing" - ] - }, - { - "cell_type": "code", - "execution_count": 274, - "metadata": {}, - "outputs": [], - "source": [ - "df_capoyo_1 = pd.read_csv('wandb_export_2024-05-09T13_22_15.265-04_00.csv')\n", - "df_capoyo_2 = pd.read_csv('wandb_export_2024-05-15T15_41_42.966-04_00.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 275, - "metadata": {}, - "outputs": [], - "source": [ - "# merge and rename value to value_1 and value_2 respectively\n", - "df_capoyo_merged = pd.merge(df_capoyo_1, df_capoyo_2, on='metric', how='inner', suffixes=('_1', '_2'))" - ] - }, - { - "cell_type": "code", - "execution_count": 281, - "metadata": {}, - "outputs": [], - "source": [ - "df_capoyo_merged['session_id'] = df_capoyo_merged['metric'].apply(lambda x: x[36:36+9]).astype(int)\n", - "# merge with stats\n", - "df_capoyo_merged = pd.merge(df_capoyo_merged, df_stats, on='session_id', how='inner')" - ] - }, - { - "cell_type": "code", - "execution_count": 282, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Average improvement: -1.87')" - ] - }, - "execution_count": 282, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.figure(figsize=(4, 4))\n", - "plt.scatter(df_capoyo_merged['value_1'], df_capoyo_merged['value_2'],) # s=merged[\"num_ROIs\"] * 0.2)\n", - "plt.xlim([0., 1])\n", - "plt.ylim([0., 1])\n", - "# plot x = y\n", - "plt.plot([0, 1], [0, 1], color='red')\n", - "plt.xlabel('Old Accuracy')\n", - "plt.ylabel('New Accuracy')\n", - "# make legend for smallest and biggest num_ROIs\n", - "plt.scatter([], [], s=5 * 0.2, c=\"tab:blue\", label='5 ROIs')\n", - "plt.scatter([], [], s=100 * 0.2, c=\"tab:blue\", label='100 ROIs')\n", - "# place legend on bottom right\n", - "plt.legend(loc='lower right')\n", - "\n", - "plt.title(f\"Average improvement: {(df_capoyo_merged['value_2'].mean() - df_capoyo_merged['value_1'].mean()) * 100:.2f}\", fontsize=10)" - ] - }, - { - "cell_type": "code", - "execution_count": 285, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 285, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ], - "text/plain": [ - " session_id run_count best_val_acc best_test_acc\n", - "401 698762886 240 0.805556 0.683333" - ] - }, - "execution_count": 225, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[df.session_id == 698762886]" - ] - }, - { - "cell_type": "code", - "execution_count": 320, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Average accuracy over sessions')" - ] - }, - "execution_count": 320, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "import wandb\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "\n", - "run_id = \"neuro-galaxy/lightning_logs/sp03hpny\"\n", - "#run_id = \"neuro-galaxy/lightning_logs/3s47ycus\"\n", - "# run_id = \"neuro-galaxy/lightning_logs/8xezifn8\"\n", - "\n", - "api = wandb.Api()\n", - "run = api.run(run_id)\n", - "\n", - "all_keys = list(run.summary.keys())\n", - "val_keys = [x for x in all_keys if x.startswith(\"val_allen\")]\n", - "\n", - "metrics = run.history(x_axis=\"epoch\", keys=val_keys)\n", - "epoch_count = metrics[\"epoch\"].to_numpy()\n", - "metrics = metrics.drop(\"epoch\", axis=1)\n", - "\n", - "plt.figure(figsize=(5,3))\n", - "plt.plot(epoch_count, metrics.mean(axis=1))\n", - "plt.xlabel(\"Epoch\")\n", - "plt.ylabel(\"Average accuracy over sessions\")" - ] - }, - { - "cell_type": "code", - "execution_count": 317, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.6057704364907279" - ] - }, - "execution_count": 317, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "0.995 ** 100" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.8" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/NHP to human transfer bench.ipynb b/notebooks/NHP to human transfer bench.ipynb deleted file mode 100644 index 330ff86..0000000 --- a/notebooks/NHP to human transfer bench.ipynb +++ /dev/null @@ -1,720 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import collections\n", - "import pandas as pd\n", - "import wandb\n", - "\n", - "api = wandb.Api(overrides={\"project\":\"poyo\", \"entity\":\"neuro-galaxy\"})\n", - "\n", - "# Select all the data that has the run name either willett_single_session_continue or willett_single_sesssion_scratch\n", - "# Define the run names to search for\n", - "target_run_names = [\"willett_single_session_continue\", \"willett_single_session_scratch\"]\n", - "\n", - "# Retrieve and filter runs\n", - "runs = api.runs()\n", - "filtered_runs = [run for run in runs if run.name in target_run_names]\n", - "\n", - "counts = collections.defaultdict(int)\n", - "scores = []\n", - "\n", - "# Display the filtered runs\n", - "for run in filtered_runs:\n", - " #print(f\"Run {run.name} (id: {run.id})\")\n", - " # Find the hyperparameter configuration for the dataset\n", - " # It should be under the hyperparameter train_datasets.0.selection.sortset=willett_shenoy_t5/t5.2020.01.13\n", - " # The hyperparameter is a string, so we need to split it\n", - " sortset = run.config['train_datasets'][0]['selection']['sortset'].split('/')[-1]\n", - " metric = [x for x in run.summary.keys() if x.startswith(\"val/\")][0]\n", - "\n", - " scores.append({\n", - " \"variant\": run.name,\n", - " \"dataset\": sortset,\n", - " \"score\": run.summary[metric],\n", - " \"nth\": counts[(run.name, sortset)]\n", - " })\n", - "\n", - " counts[(run.name, sortset)] += 1\n", - "\n", - "target_run_names = [\"willett_multi_session_continue\", \"willett_multi_session_scratch\"]\n", - "\n", - "# Retrieve and filter runs\n", - "runs = api.runs()\n", - "filtered_runs = [run for run in runs if run.name in target_run_names]\n", - "\n", - "# Display the filtered runs\n", - "for run in filtered_runs:\n", - " #print(f\"Run {run.name} (id: {run.id})\")\n", - " # Find the hyperparameter configuration for the dataset\n", - " # It should be under the hyperparameter train_datasets.0.selection.sortset=willett_shenoy_t5/t5.2020.01.13\n", - " # The hyperparameter is a string, so we need to split it\n", - " metrics = [x for x in run.summary.keys() if x.startswith(\"val/\")]\n", - "\n", - " for metric in metrics:\n", - " sortset = metric.split('/')[-1].split('_')[0]\n", - " if counts[(run.name, sortset)] > 0:\n", - " continue\n", - " \n", - " scores.append({\n", - " \"variant\": run.name,\n", - " \"dataset\": sortset,\n", - " \"score\": run.summary[metric],\n", - " \"nth\": counts[(run.name, sortset)]\n", - " })\n", - "\n", - " counts[(run.name, sortset)] += 1" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "df = pd.DataFrame(scores)\n", - "df = df[df.nth < 1]\n", - "df = df[df.dataset != 't5.2019.06.03']" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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nth0
variantdataset
willett_multi_session_continuet5.2019.05.080.947917
t5.2019.11.251.000000
t5.2019.12.090.967742
t5.2019.12.110.935484
t5.2019.12.181.000000
t5.2019.12.200.967742
t5.2020.01.060.870968
t5.2020.01.080.935484
t5.2020.01.130.967742
t5.2020.01.150.935484
willett_multi_session_scratcht5.2019.05.080.937500
t5.2019.11.250.935484
t5.2019.12.090.935484
t5.2019.12.110.935484
t5.2019.12.181.000000
t5.2019.12.200.967742
t5.2020.01.060.935484
t5.2020.01.080.870968
t5.2020.01.130.870968
t5.2020.01.150.870968
willett_single_session_continuet5.2019.05.080.906250
t5.2019.11.250.935484
t5.2019.12.090.935484
t5.2019.12.110.935484
t5.2019.12.180.967742
t5.2019.12.200.935484
t5.2020.01.060.838710
t5.2020.01.080.741935
t5.2020.01.130.774194
t5.2020.01.150.870968
willett_single_session_scratcht5.2019.05.080.875000
t5.2019.11.250.838710
t5.2019.12.090.870968
t5.2019.12.110.903226
t5.2019.12.180.903226
t5.2019.12.200.870968
t5.2020.01.060.806452
t5.2020.01.080.774194
t5.2020.01.130.741935
t5.2020.01.150.741935
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" - ], - "text/plain": [ - "nth 0\n", - "variant dataset \n", - "willett_multi_session_continue t5.2019.05.08 0.947917\n", - " t5.2019.11.25 1.000000\n", - " t5.2019.12.09 0.967742\n", - " t5.2019.12.11 0.935484\n", - " t5.2019.12.18 1.000000\n", - " t5.2019.12.20 0.967742\n", - " t5.2020.01.06 0.870968\n", - " t5.2020.01.08 0.935484\n", - " t5.2020.01.13 0.967742\n", - " t5.2020.01.15 0.935484\n", - "willett_multi_session_scratch t5.2019.05.08 0.937500\n", - " t5.2019.11.25 0.935484\n", - " t5.2019.12.09 0.935484\n", - " t5.2019.12.11 0.935484\n", - " t5.2019.12.18 1.000000\n", - " t5.2019.12.20 0.967742\n", - " t5.2020.01.06 0.935484\n", - " t5.2020.01.08 0.870968\n", - " t5.2020.01.13 0.870968\n", - " t5.2020.01.15 0.870968\n", - "willett_single_session_continue t5.2019.05.08 0.906250\n", - " t5.2019.11.25 0.935484\n", - " t5.2019.12.09 0.935484\n", - " t5.2019.12.11 0.935484\n", - " t5.2019.12.18 0.967742\n", - " t5.2019.12.20 0.935484\n", - " t5.2020.01.06 0.838710\n", - " t5.2020.01.08 0.741935\n", - " t5.2020.01.13 0.774194\n", - " t5.2020.01.15 0.870968\n", - "willett_single_session_scratch t5.2019.05.08 0.875000\n", - " t5.2019.11.25 0.838710\n", - " t5.2019.12.09 0.870968\n", - " t5.2019.12.11 0.903226\n", - " t5.2019.12.18 0.903226\n", - " t5.2019.12.20 0.870968\n", - " t5.2020.01.06 0.806452\n", - " t5.2020.01.08 0.774194\n", - " t5.2020.01.13 0.741935\n", - " t5.2020.01.15 0.741935" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.pivot(index=(\"variant\", \"dataset\"), columns=\"nth\", values=\"score\")#.loc[\"willett_single_session_scratch\"].mean()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "df_baselines = pd.read_csv(\"../data/scripts/willett_shenoy/willett_shenoy_baseline_tuned.csv\")\n", - "df_baselines[['name', 'test_acc', 'test_acc_tw']]\n", - "\n", - "df_b = df_baselines[df_baselines.name != \"t5.2019.06.03\"][['name', 'test_acc', 'test_acc_tw']]\n", - "df_b['variant'] = \"knn-time-warping\"\n", - "df_b.rename({'test_acc_tw': 'score', 'name': 'dataset'}, inplace=True, axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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readable_variantbaselinepoyo0\\nscratchpoyo0\\ntransferpoyo1\\nmulti-sess\\nscratchpoyo1\\nmulti-sess\\ntransfer
mean0.1780.1670.1160.0740.047
sem0.0240.0200.0240.0140.012
\n", - "
" - ], - "text/plain": [ - "readable_variant baseline poyo0\\nscratch poyo0\\ntransfer \\\n", - "mean 0.178 0.167 0.116 \n", - "sem 0.024 0.020 0.024 \n", - "\n", - "readable_variant poyo1\\nmulti-sess\\nscratch poyo1\\nmulti-sess\\ntransfer \n", - "mean 0.074 0.047 \n", - "sem 0.014 0.012 " - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy as np\n", - "\n", - "the_map = {'knn-time-warping': 'baseline', \n", - " 'willett_single_session_scratch': 'poyo0\\nscratch',\n", - " 'willett_single_session_continue': 'poyo0\\ntransfer',\n", - " 'willett_multi_session_scratch': 'poyo1\\nmulti-sess\\nscratch',\n", - " 'willett_multi_session_continue': 'poyo1\\nmulti-sess\\ntransfer',\n", - " 'willett_character_session_causal_scratch': 'poyo2\\nmulti-sess causal\\nscratch',\n", - " 'willett_character_session_causal_continue': 'poyo2\\nmulti-sess causal\\ntransfer'}\n", - "\n", - "df_all = pd.concat([df, df_b], axis=0)\n", - "df_all['readable_variant'] = df_all['variant'].map(the_map)\n", - "df_all['error'] = 1 - df_all['score']\n", - "df_all['error_jittered'] = 100 * (1 - (df_all['score'] + np.random.normal(0, 0.005, size=len(df_all))))\n", - "df_all = df_all.sort_values('readable_variant')\n", - "\n", - "df_results = pd.pivot_table(df_all, index=\"dataset\", columns=\"readable_variant\", values=\"error\")\n", - "df_results.agg(['mean', 'sem'], axis=0).round(3)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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readable_variantbaselinepoyo0\\nscratchpoyo0\\ntransferpoyo1\\nmulti-sess\\nscratchpoyo1\\nmulti-sess\\ntransfer
dataset
t5.2019.05.080.1041670.1250000.0937500.0625000.052083
t5.2019.11.250.0645160.1612900.0645160.0645160.000000
t5.2019.12.090.1612900.1290320.0645160.0645160.032258
t5.2019.12.110.1290320.0967740.0645160.0645160.064516
t5.2019.12.180.1935480.0967740.0322580.0000000.000000
t5.2019.12.200.3225810.1290320.0645160.0322580.032258
t5.2020.01.060.1612900.1935480.1612900.0645160.129032
t5.2020.01.080.1612900.2258060.2580650.1290320.064516
t5.2020.01.130.2258060.2580650.2258060.1290320.032258
t5.2020.01.150.2580650.2580650.1290320.1290320.064516
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" - ], - "text/plain": [ - "readable_variant baseline poyo0\\nscratch poyo0\\ntransfer \\\n", - "dataset \n", - "t5.2019.05.08 0.104167 0.125000 0.093750 \n", - "t5.2019.11.25 0.064516 0.161290 0.064516 \n", - "t5.2019.12.09 0.161290 0.129032 0.064516 \n", - "t5.2019.12.11 0.129032 0.096774 0.064516 \n", - "t5.2019.12.18 0.193548 0.096774 0.032258 \n", - "t5.2019.12.20 0.322581 0.129032 0.064516 \n", - "t5.2020.01.06 0.161290 0.193548 0.161290 \n", - "t5.2020.01.08 0.161290 0.225806 0.258065 \n", - "t5.2020.01.13 0.225806 0.258065 0.225806 \n", - "t5.2020.01.15 0.258065 0.258065 0.129032 \n", - "\n", - "readable_variant poyo1\\nmulti-sess\\nscratch poyo1\\nmulti-sess\\ntransfer \n", - "dataset \n", - "t5.2019.05.08 0.062500 0.052083 \n", - "t5.2019.11.25 0.064516 0.000000 \n", - "t5.2019.12.09 0.064516 0.032258 \n", - "t5.2019.12.11 0.064516 0.064516 \n", - "t5.2019.12.18 0.000000 0.000000 \n", - "t5.2019.12.20 0.032258 0.032258 \n", - "t5.2020.01.06 0.064516 0.129032 \n", - "t5.2020.01.08 0.129032 0.064516 \n", - "t5.2020.01.13 0.129032 0.032258 \n", - "t5.2020.01.15 0.129032 0.064516 " - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_results" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'validated classification error (%)')" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "image/png": { - "height": 403, - "width": 454 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "# plot a violin plot with these three columns in seaborn\n", - "# Use retina mode\n", - "%config InlineBackend.figure_format = 'retina'\n", - "import numpy as np\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "sns.set_style(\"darkgrid\")\n", - "\n", - "plt.figure(figsize=(5, 4))\n", - "\n", - "ax = sns.stripplot(x=\"readable_variant\", y=\"error_jittered\", data=df_all, legend=False, color=\"black\", alpha=0.25)\n", - "sns.pointplot(x=\"readable_variant\", y=\"error_jittered\", data=df_all, legend=False, errorbar=\"se\", ax=ax, color=\"black\", linestyle=\"none\")\n", - "plt.xlabel(\"model\")\n", - "plt.ylabel(\"validated classification error (%)\")\n", - "#for i in range(10):\n", - "# plt.text(4.15, df_all.iloc[i+40].error_jittered, df_all.iloc[i]['dataset'])" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "image/png": { - "height": 404, - "width": 548 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "# plot a violin plot with these three columns in seaborn\n", - "# Use retina mode\n", - "%config InlineBackend.figure_format = 'retina'\n", - "import numpy as np\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "sns.set_style(\"darkgrid\")\n", - "\n", - "plt.figure(figsize=(5, 4))\n", - "\n", - "sns.lineplot(x=\"readable_variant\", y=\"error_jittered\", hue=\"dataset\", data=df_all, legend=False)\n", - "plt.ylabel(\"error rate (%)\")\n", - "for i in range(10):\n", - " plt.text(4.15, df_all.iloc[i+40].error_jittered, df_all.iloc[i]['dataset'])" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "poyo", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/Read artifacts.ipynb b/notebooks/Read artifacts.ipynb deleted file mode 100644 index ff9a448..0000000 --- a/notebooks/Read artifacts.ipynb +++ /dev/null @@ -1,509 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Loading models from checkpoints demo\n", - "\n", - "I have a local ckpt file, and I want to load it and run inference on it." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
[14:38:21] INFO     (੭｡╹▿╹｡)੭ Poyo!                                                                                \n",
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[14:38:24] INFO     Created a temporary directory at /tmp/tmpbc1inpkl                                              \n",
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           INFO     Writing /tmp/tmpbc1inpkl/_remote_module_non_scriptable.py                                      \n",
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It is recommended to ignore them using `self.save_hyperparameters(ignore=['model'])`.\n", - " rank_zero_warn(\n" - ] - } - ], - "source": [ - "from kirby.utils.train_wrapper import TrainWrapper\n", - "wrapper = TrainWrapper.load_from_checkpoint(\"../scripts/logs/lightning_logs/version_30/checkpoints/last.ckpt\")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "PerceiverNM(\n", - " (unit_emb): Embedding(226, 64)\n", - " (spike_type_emb): Embedding(4, 64)\n", - " (task_emb): Embedding(64, 64)\n", - " (latent_emb): Embedding(16, 64)\n", - " (rotary_emb): RotaryEmbedding()\n", - " (dropout): Dropout(p=0.4, inplace=False)\n", - " (enc_atn): RotaryCrossAttention(\n", - " (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", - " (norm_context): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", - " (to_q): Linear(in_features=64, out_features=128, bias=False)\n", - " (to_kv): Linear(in_features=64, out_features=256, bias=False)\n", - " (to_out): Linear(in_features=128, out_features=64, bias=True)\n", - " )\n", - " (enc_ffn): Sequential(\n", - " (0): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", - " (1): FeedForward(\n", - " (net): Sequential(\n", - " (0): Linear(in_features=64, out_features=512, bias=True)\n", - " (1): GEGLU()\n", - " (2): Dropout(p=0.2, inplace=False)\n", - " (3): Linear(in_features=256, out_features=64, bias=True)\n", - " )\n", - " )\n", - " )\n", - " (proc_layers): ModuleList(\n", - " (0-11): 12 x ModuleList(\n", - " (0): RotarySelfAttention(\n", - " (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", - " (to_qkv): Linear(in_features=64, out_features=1536, bias=False)\n", - " (to_out): Linear(in_features=512, out_features=64, bias=True)\n", - " )\n", - " (1): Sequential(\n", - " (0): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", - " (1): FeedForward(\n", - " (net): Sequential(\n", - " (0): Linear(in_features=64, out_features=512, bias=True)\n", - " (1): GEGLU()\n", - " (2): Dropout(p=0.2, inplace=False)\n", - " (3): Linear(in_features=256, out_features=64, bias=True)\n", - " )\n", - " )\n", - " )\n", - " )\n", - " )\n", - " (dec_atn): RotaryCrossAttention(\n", - " (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", - " (norm_context): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", - " (to_q): Linear(in_features=64, out_features=128, bias=False)\n", - " (to_kv): Linear(in_features=64, out_features=256, bias=False)\n", - " (to_out): Linear(in_features=128, out_features=64, bias=True)\n", - " )\n", - " (dec_ffn): Sequential(\n", - " (0): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", - " (1): FeedForward(\n", - " (net): Sequential(\n", - " (0): Linear(in_features=64, out_features=512, bias=True)\n", - " (1): GEGLU()\n", - " (2): Dropout(p=0.2, inplace=False)\n", - " (3): Linear(in_features=256, out_features=64, bias=True)\n", - " )\n", - " )\n", - " )\n", - " (decoder_out): Linear(in_features=64, out_features=2, bias=True)\n", - ")" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "wrapper.model" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor(0.9622, device='cuda:0', grad_fn=)" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "wrapper.model.dec_ffn[0].weight.mean()" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor(0.9622, device='cuda:0')" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import torch\n", - "ckpt_raw = torch.load(\"../scripts/logs/lightning_logs/version_30/checkpoints/last.ckpt\")\n", - "ckpt_raw['state_dict'][\"model.dec_ffn.0.weight\"].mean()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Load from wandb\n", - "\n", - "I've uploaded the checkpoint to wandb via:\n", - "\n", - "```wandb artifact put logs/lightning_logs/version_30/checkpoints/last.ckpt -t model -n single_session```\n", - "\n", - "Instantiate this model from the cloud." - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "Finishing last run (ID:1bgi9owu) before initializing another..." - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Waiting for W&B process to finish... (success)." - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - " View run ruby-breeze-18 at: https://wandb.ai/neuro-galaxy/poyo/runs/1bgi9owu
Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Find logs at: ./wandb/run-20230726_145621-1bgi9owu/logs" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Successfully finished last run (ID:1bgi9owu). Initializing new run:
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "wandb version 0.15.7 is available! To upgrade, please run:\n", - " $ pip install wandb --upgrade" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Tracking run with wandb version 0.15.5" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Run data is saved locally in /home/mila/p/patrick.mineault/Documents/project-kirby/notebooks/wandb/run-20230726_145711-o4zok5id" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Syncing run solar-spaceship-19 to Weights & Biases (docs)
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - " View project at https://wandb.ai/neuro-galaxy/poyo" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - " View run at https://wandb.ai/neuro-galaxy/poyo/runs/o4zok5id" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[34m\u001b[1mwandb\u001b[0m: 1 of 1 files downloaded. \n" - ] - } - ], - "source": [ - "import wandb\n", - "\n", - "run = wandb.init(project=\"poyo\", entity=\"neuro-galaxy\", )\n", - "\n", - "artifact = run.use_artifact(\"neuro-galaxy/poyo/single_session:latest\")\n", - "path = artifact.download()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that the model is downloaded, we can easily recover it using the same mechanism." - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/mila/p/patrick.mineault/.conda/envs/poyo/lib/python3.9/site-packages/lightning/pytorch/utilities/parsing.py:196: UserWarning: Attribute 'model' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['model'])`.\n", - " rank_zero_warn(\n" - ] - } - ], - "source": [ - "wrapper_cloud = TrainWrapper.load_from_checkpoint(f\"{path}/last.ckpt\")" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "PerceiverNM(\n", - " (unit_emb): Embedding(226, 64)\n", - " (spike_type_emb): Embedding(4, 64)\n", - " (task_emb): Embedding(64, 64)\n", - " (latent_emb): Embedding(16, 64)\n", - " (rotary_emb): RotaryEmbedding()\n", - " (dropout): Dropout(p=0.4, inplace=False)\n", - " (enc_atn): RotaryCrossAttention(\n", - " (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", - " (norm_context): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", - " (to_q): Linear(in_features=64, out_features=128, bias=False)\n", - " (to_kv): Linear(in_features=64, out_features=256, bias=False)\n", - " (to_out): Linear(in_features=128, out_features=64, bias=True)\n", - " )\n", - " (enc_ffn): Sequential(\n", - " (0): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", - " (1): FeedForward(\n", - " (net): Sequential(\n", - " (0): Linear(in_features=64, out_features=512, bias=True)\n", - " (1): GEGLU()\n", - " (2): Dropout(p=0.2, inplace=False)\n", - " (3): Linear(in_features=256, out_features=64, bias=True)\n", - " )\n", - " )\n", - " )\n", - " (proc_layers): ModuleList(\n", - " (0-11): 12 x ModuleList(\n", - " (0): RotarySelfAttention(\n", - " (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", - " (to_qkv): Linear(in_features=64, out_features=1536, bias=False)\n", - " (to_out): Linear(in_features=512, out_features=64, bias=True)\n", - " )\n", - " (1): Sequential(\n", - " (0): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", - " (1): FeedForward(\n", - " (net): Sequential(\n", - " (0): Linear(in_features=64, out_features=512, bias=True)\n", - " (1): GEGLU()\n", - " (2): Dropout(p=0.2, inplace=False)\n", - " (3): Linear(in_features=256, out_features=64, bias=True)\n", - " )\n", - " )\n", - " )\n", - " )\n", - " )\n", - " (dec_atn): RotaryCrossAttention(\n", - " (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", - " (norm_context): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", - " (to_q): Linear(in_features=64, out_features=128, bias=False)\n", - " (to_kv): Linear(in_features=64, out_features=256, bias=False)\n", - " (to_out): Linear(in_features=128, out_features=64, bias=True)\n", - " )\n", - " (dec_ffn): Sequential(\n", - " (0): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", - " (1): FeedForward(\n", - " (net): Sequential(\n", - " (0): Linear(in_features=64, out_features=512, bias=True)\n", - " (1): GEGLU()\n", - " (2): Dropout(p=0.2, inplace=False)\n", - " (3): Linear(in_features=256, out_features=64, bias=True)\n", - " )\n", - " )\n", - " )\n", - " (decoder_out): Linear(in_features=64, out_features=2, bias=True)\n", - ")" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "wrapper_cloud.model" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "poyo", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/using_poyo.ipynb b/notebooks/using_poyo.ipynb deleted file mode 100644 index 9402c93..0000000 --- a/notebooks/using_poyo.ipynb +++ /dev/null @@ -1,413 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Using POYO\n", - "\n", - "This notebook will walk you through three concepts:\n", - "1. Loading a dataset using our `Dataset` object\n", - "2. Loading the POYO model and applying pretrained weights\n", - "3. Performing inference and measuring accuracy" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
[16:01:59] INFO     (੭｡╹▿╹｡)੭ Poyo!                                                                                \n",
-       "
\n" - ], - "text/plain": [ - "\u001b[2;36m[16:01:59]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m \u001b[1;38;2;223;109;169m(\u001b[0m\u001b[1;38;2;223;109;169m੭｡╹▿╹｡\u001b[0m\u001b[1;38;2;223;109;169m)\u001b[0m\u001b[1;38;2;223;109;169m੭\u001b[0m Poyo! \n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import torch\n", - "import matplotlib.pyplot as plt\n", - "import random\n", - "\n", - "import omegaconf # for loading model configuration from a file\n", - "from torchmetrics import R2Score # for measuring accuracy\n", - "\n", - "from kirby.data import Dataset, Collate, build_vocab\n", - "from kirby.data.stitcher import stitched_prediction\n", - "from kirby.models import PerceiverNM\n", - "import kirby.taxonomy" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Loading the dataset\n", - "Let's first load a single session recording from [Perich-Miller's dataset](https://dandiarchive.org/dandiset/000688). " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = Dataset(\n", - " root=\"/kirby/processed\",\n", - " split=\"test\", # \"train\"/\"valid\"/\"test\"\n", - " include=[{\n", - " \"selection\": {\n", - " \"dandiset\": \"perich_miller\",\n", - " \"sortset\": \"chewie_20131003\",\n", - " },\n", - " }],\n", - ")\n", - "\n", - "# These are needed by the model to build its unit and session embedding tables\n", - "unit_vocab = build_vocab(dataset.unit_names)\n", - "session_names = dataset.session_names" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's pick a random sample and plot its spike train" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sample_idx = random.randrange(len(dataset))\n", - "data_sample = dataset[sample_idx]\n", - "\n", - "num_units = len(data_sample.units.unit_name)\n", - "spike_times = data_sample.spikes.timestamps\n", - "unit_ids = data_sample.spikes.unit_index\n", - "\n", - "# Separate spike times for each unit\n", - "spikes_per_unit = []\n", - "for uid in range(num_units):\n", - " spikes_for_uid = spike_times[unit_ids == uid].numpy()\n", - " spikes_per_unit.append(spikes_for_uid)\n", - "\n", - "plt.eventplot(spikes_per_unit)\n", - "plt.xlabel(\"time (s)\")\n", - "plt.ylabel(\"Unit ID\")\n", - "plt.title(f\"Sample {sample_idx} spike-train\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Loading model and pretrained weights\n", - "\n", - "We'll now load the model, and apply pretrained weights to it.\n", - "As you can see in the printed logs, we were able to find the unit and session embeddings for this given\n", - "dataset in our checkpoint. This means our dataset was part of the pretraining of these weights." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/varora38/kirby-release-wa1/project-kirby/kirby/models/perceiver_rotary.py:332: UserWarning: Could not find vocab in state_dict. Using existing vocab.\n", - " warnings.warn(\"Could not find vocab in state_dict. Using existing vocab.\")\n" - ] - }, - { - "data": { - "text/html": [ - "
[16:02:02] INFO     Found all required unit embeddings in checkpoint.                                              \n",
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           INFO     Found all required session embeddings in checkpoint.                                           \n",
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\n" - ], - "text/plain": [ - "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m Found all required session embeddings in checkpoint. \n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "PerceiverNM(\n", - " (unit_vocab): Vocab()\n", - " (unit_emb): Embedding(74, 64)\n", - " (session_emb): EmbeddingWithVocab(\n", - " (embedding): Embedding(2, 64)\n", - " )\n", - " (spike_type_emb): Embedding(4, 64)\n", - " (latent_emb): Embedding(16, 64)\n", - " (rotary_emb): RotaryEmbedding()\n", - " (lfp_embedding_layer): Linear(in_features=6, out_features=64, bias=False)\n", - " (dropout): Dropout(p=0.4, inplace=False)\n", - " (enc_atn): RotaryCrossAttention(\n", - " (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", - " (norm_context): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", - " (to_q): Linear(in_features=64, out_features=128, bias=False)\n", - " (to_kv): Linear(in_features=64, out_features=256, bias=False)\n", - " (to_out): Linear(in_features=128, out_features=64, bias=True)\n", - " )\n", - " (enc_ffn): Sequential(\n", - " (0): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", - " (1): FeedForward(\n", - " (net): Sequential(\n", - " (0): Linear(in_features=64, out_features=512, bias=True)\n", - " (1): GEGLU()\n", - " (2): Dropout(p=0.2, inplace=False)\n", - " (3): Linear(in_features=256, out_features=64, bias=True)\n", - " )\n", - " )\n", - " )\n", - " (proc_layers): ModuleList(\n", - " (0-5): 6 x ModuleList(\n", - " (0): RotarySelfAttention(\n", - " (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", - " (to_qkv): Linear(in_features=64, out_features=1536, bias=False)\n", - " (to_out): Linear(in_features=512, out_features=64, bias=True)\n", - " )\n", - " (1): Sequential(\n", - " (0): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", - " (1): FeedForward(\n", - " (net): Sequential(\n", - " (0): Linear(in_features=64, out_features=512, bias=True)\n", - " (1): GEGLU()\n", - " (2): Dropout(p=0.2, inplace=False)\n", - " (3): Linear(in_features=256, out_features=64, bias=True)\n", - " )\n", - " )\n", - " )\n", - " )\n", - " )\n", - " (dec_atn): RotaryCrossAttention(\n", - " (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", - " (norm_context): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", - " (to_q): Linear(in_features=64, out_features=128, bias=False)\n", - " (to_kv): Linear(in_features=64, out_features=256, bias=False)\n", - " (to_out): Linear(in_features=128, out_features=64, bias=True)\n", - " )\n", - " (dec_ffn): Sequential(\n", - " (0): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", - " (1): FeedForward(\n", - " (net): Sequential(\n", - " (0): Linear(in_features=64, out_features=512, bias=True)\n", - " (1): GEGLU()\n", - " (2): Dropout(p=0.2, inplace=False)\n", - " (3): Linear(in_features=256, out_features=64, bias=True)\n", - " )\n", - " )\n", - " )\n", - " (readout): MultitaskReadout(\n", - " (projections): ModuleDict(\n", - " (ARMVELOCITY2D): Linear(in_features=64, out_features=2, bias=True)\n", - " (CURSORVELOCITY2D): Linear(in_features=64, out_features=2, bias=True)\n", - " (CURSOR2D): Linear(in_features=64, out_features=2, bias=True)\n", - " (WRITING_CHARACTER): Linear(in_features=64, out_features=32, bias=True)\n", - " (WRITING_LINE): Linear(in_features=64, out_features=48, bias=True)\n", - " (DRIFTING_GRATINGS): Linear(in_features=64, out_features=8, bias=True)\n", - " (SPEAKING_CVSYLLABLE): Linear(in_features=64, out_features=70, bias=True)\n", - " )\n", - " )\n", - ")" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "device = torch.device('cpu')\n", - "\n", - "# Load model hyper-parameters from config\n", - "model_cfg = omegaconf.OmegaConf.load(\"../configs/model/poyo_single_session.yaml\")\n", - "model_cfg.pop('_target_')\n", - "\n", - "model = PerceiverNM(\n", - " **model_cfg,\n", - " unit_vocab=unit_vocab,\n", - " session_names=session_names,\n", - " use_memory_efficient_attn=False, # Since we're doing this on CPU\n", - ")\n", - "\n", - "ckpt_path = \"../logs/lightning_logs/f9sj5g0b/last.ckpt\"\n", - "model.load_from_ckpt(ckpt_path)\n", - "model.to(device)\n", - "model.eval()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Performing inference\n", - "\n", - "Now, using this model, let's perform inference on our loaded data sample. Our model takes in spikes and outputs a 2D timeseries corresponding to the predicted hand-velocity.\n", - "We'll visualize our prediction relative to the ground truth. At the same time, let's also measure the accuracy of our inference in terms of its $R^2$ score." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "R2 score: 0.656\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Creating the collate function\n", - "collate_fn = Collate(\n", - " num_latents_per_step=model_cfg.num_latents,\n", - " step=1.0 / 8,\n", - " sequence_length=1.0, # seconds\n", - " unit_vocab=unit_vocab,\n", - " metrics=[{\"output_key\": \"CURSORVELOCITY2D\"}],\n", - ")\n", - "\n", - "# Test samples, in general, can be arbitrarily long in time.\n", - "# `stitched_prediction` does the job of breaking long samples into smaller chunks\n", - "# and then stitching together the results. \n", - "# This is needed because POYO currently has a fixed context size of 1 second.\n", - "gt, pred = stitched_prediction(\n", - " data=data_sample,\n", - " collate_fn=collate_fn,\n", - " model=model,\n", - " device=device,\n", - ")\n", - "gt, pred = gt['CURSORVELOCITY2D'], pred['CURSORVELOCITY2D']\n", - "\n", - "# Evaluating R2 score for our prediction\n", - "r2score = R2Score(num_outputs=gt.shape[1])\n", - "r2 = r2score(gt, pred).item()\n", - "print(f\"R2 score: {r2:.3f}\")\n", - "\n", - "# Plotting the result\n", - "plt.plot(gt[:, 0], gt[:, 1], label=\"Ground truth\")\n", - "plt.plot(pred[:, 0], pred[:, 1], label=\"Predicted\")\n", - "plt.title(f\"Sample {sample_idx}, $R^2$ = {r2:.3f}\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Evaluating accuracy over entire dataset\n", - "\n", - "Now that we know how to perform inference and measure its accuracy over one sample, let's measure the average hand-velocity " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "R2 score for entire dataset: 0.771\n" - ] - } - ], - "source": [ - "gt_all, pred_all = [], []\n", - "for i, data_sample in enumerate(dataset):\n", - " gt, pred = stitched_prediction(\n", - " data=data_sample,\n", - " collate_fn=collate_fn,\n", - " model=model,\n", - " device=device,\n", - " )\n", - " gt, pred = gt['CURSORVELOCITY2D'], pred['CURSORVELOCITY2D']\n", - "\n", - " gt_all.append(gt)\n", - " pred_all.append(pred)\n", - "\n", - "gt_all, pred_all = torch.cat(gt_all), torch.cat(pred_all)\n", - "\n", - "r2 = r2score(gt_all, pred_all).item()\n", - "print(f\"R2 score for entire dataset: {r2:.3f}\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/setup.py b/setup.py index 490c2b9..dde8440 100644 --- a/setup.py +++ b/setup.py @@ -11,23 +11,16 @@ packages=find_packages() + find_namespace_packages(include=["hydra_plugins.*"]), include_package_data=True, install_requires=[ - "temporaldata @ git+https://github.com/neuro-galaxy/temporaldata@main#egg=temporaldata-0.1.1", - "brainsets @ git+https://github.com/neuro-galaxy/brainsets@main#egg=brainsets-0.1.0", + "temporaldata==0.1.1", "torch==2.2.0", "einops~=0.6.0", - # "setuptools~=60.2.0", - # "jsonschema~=4.21.1", - # "tqdm~=4.64.1", - # "PyYAML~=6.0", "rich==13.3.2", "torch-optimizer==0.3.0", "tensorboard~=2.13", "hydra-core~=1.3.2", "lightning==2.3.3", "wandb~=0.15", - # "tabulate~=0.9", "torchtyping~=0.1", - # "pydantic~=2.0", ], extras_require={ "dev": [ diff --git a/tests/test_dataset_sim.py b/tests/test_dataset_sim.py index f3d5266..e9d8c20 100644 --- a/tests/test_dataset_sim.py +++ b/tests/test_dataset_sim.py @@ -15,14 +15,20 @@ ArrayDict, ) from torch_brain.data import Dataset -from brainsets.taxonomy import ( - BrainsetDescription, - SubjectDescription, - SessionDescription, - DeviceDescription, -) -from brainsets.taxonomy import Task, Species, RecordingTech -from brainsets import serialize_fn_map + +try: + from brainsets.taxonomy import ( + BrainsetDescription, + SubjectDescription, + SessionDescription, + DeviceDescription, + ) + from brainsets.taxonomy import Task, Species, RecordingTech + from brainsets import serialize_fn_map + + BRAINSETS_AVAILABLE = True +except ImportError: + BRAINSETS_AVAILABLE = False GABOR_POS_2D_MEAN = 10.0 GABOR_POS_2D_STD = 1.0 @@ -30,6 +36,7 @@ RUNNING_SPEED_STD = 2.0 +@pytest.mark.skipif(not BRAINSETS_AVAILABLE, reason="brainsets not installed") @pytest.fixture def dummy_data(tmp_path): @@ -125,6 +132,7 @@ def dummy_data(tmp_path): return tmp_path +@pytest.mark.skipif(not BRAINSETS_AVAILABLE, reason="brainsets not installed") def test_dataset_selection(dummy_data): include_config_1 = [{"selection": [{"brainset": "allen_neuropixels_mock"}]}] include_config_2 = [ @@ -174,6 +182,7 @@ def test_dataset_selection(dummy_data): assert len(ds.recording_dict) == 1 +@pytest.mark.skipif(not BRAINSETS_AVAILABLE, reason="brainsets not installed") def test_get_recording_data(dummy_data): ds = Dataset( dummy_data, @@ -187,6 +196,7 @@ def test_get_recording_data(dummy_data): assert len(data.gabors) == 1000 +@pytest.mark.skipif(not BRAINSETS_AVAILABLE, reason="brainsets not installed") def test_get_subject_ids(dummy_data): with tempfile.NamedTemporaryFile(delete=False, suffix=".yaml") as temp_config_file: yaml.dump( diff --git a/torch_brain/models/__init__.py b/torch_brain/models/__init__.py index b6dc67f..51f22e5 100644 --- a/torch_brain/models/__init__.py +++ b/torch_brain/models/__init__.py @@ -1,4 +1 @@ -# from .poyo import POYO, POYOTokenizer from .poyo_plus import POYOPlus, POYOPlusTokenizer - -# from .capoyo import CaPOYO, CaPOYOTokenizer diff --git a/torch_brain/models/capoyo.py b/torch_brain/models/capoyo.py deleted file mode 100644 index 85b2561..0000000 --- a/torch_brain/models/capoyo.py +++ /dev/null @@ -1,534 +0,0 @@ -from typing import Dict, List, Optional, Tuple, Union - -import numpy as np -import torch -import torch.nn as nn -from torchtyping import TensorType -from einops import rearrange, repeat - -from brainsets.taxonomy import DecoderSpec, Decoder -from brainsets.taxonomy.mice import Cre_line -from torch_brain.nn import ( - Embedding, - InfiniteVocabEmbedding, - MultitaskReadout, - PerceiverRotary, - prepare_for_multitask_readout, -) -from torch_brain.data import pad, chain, track_mask, track_batch -from torch_brain.utils import ( - create_start_end_unit_tokens, - create_linspace_latent_tokens, - get_sinusoidal_encoding, -) - -from torch_brain.models.poyo_plus import BACKEND_CONFIGS - - -class CaPOYO(nn.Module): - def __init__( - self, - *, - dim=512, - dim_input=None, - dim_head=64, - num_latents=64, - patch_size=1, - depth=2, - cross_heads=1, - self_heads=8, - ffn_dropout=0.2, - lin_dropout=0.4, - atn_dropout=0.0, - emb_init_scale=0.02, - use_cre_line_embedding=True, - use_depth_embedding=False, - use_spatial_embedding=True, - use_roi_feat_embedding=True, - use_session_embedding=True, - use_unit_embedding=True, - backend_config="gpu_fp32", - decoder_specs: Dict[str, DecoderSpec], - ): - super().__init__() - - self.dim = dim - self.patch_size = patch_size - - self.use_session_embedding = use_session_embedding - self.use_unit_embedding = use_unit_embedding - - dim_input = dim_input or dim - self.dim_input = dim_input - - # input embs - self.unit_emb = InfiniteVocabEmbedding(dim_input, init_scale=emb_init_scale) - self.token_type_emb = Embedding(4, dim_input, init_scale=emb_init_scale) - self.value_embedding_layer = nn.Linear(patch_size, dim_input, bias=False) - - self.use_roi_feat_embedding = use_roi_feat_embedding - self.use_cre_line_embedding = use_cre_line_embedding - self.use_depth_embedding = use_depth_embedding - self.use_spatial_embedding = use_spatial_embedding - - if self.use_cre_line_embedding: - self.cre_line_embedding_layer = Embedding( - Cre_line.max_value() + 1, dim_input, init_scale=emb_init_scale - ) - - if self.use_roi_feat_embedding: - self.unit_feat_embedding_layer = nn.Linear(3, dim_input, bias=True) - - if self.use_depth_embedding: - self.depth_embedding_layer = Embedding( - Depth_classes.max_value() + 1, dim_input, init_scale=emb_init_scale - ) - if self.use_depth_class_embedding: - self.depth_class_embedding_layer = Embedding( - Depth_classes.max_value() + 1, dim, init_scale=emb_init_scale - ) - - # latent embs - self.latent_emb = Embedding(num_latents, dim, init_scale=emb_init_scale) - - # output embs - self.session_emb = InfiniteVocabEmbedding(dim, init_scale=emb_init_scale) - self.task_emb = Embedding( - Decoder.max_value() + 1, dim, init_scale=emb_init_scale - ) - - # determine backend - if backend_config not in BACKEND_CONFIGS.keys(): - raise ValueError( - f"Invalid backend config: {backend_config}, must be one of" - f" {list(BACKEND_CONFIGS.keys())}" - ) - - self.batch_type = BACKEND_CONFIGS[backend_config][0] - - # the input will be a concatenation of the unit embedding, the value embedding, - # and any additional embeddings - context_dim_factor = 2 + sum( - [ - self.use_cre_line_embedding, - self.use_depth_embedding, - self.use_roi_feat_embedding, - self.use_spatial_embedding, - ] - ) - context_dim = context_dim_factor * dim_input - - self.perceiver_io = PerceiverRotary( - dim=dim, - context_dim=context_dim, - dim_head=dim_head, - depth=depth, - cross_heads=cross_heads, - self_heads=self_heads, - ffn_dropout=ffn_dropout, - lin_dropout=lin_dropout, - atn_dropout=atn_dropout, - backend=BACKEND_CONFIGS[backend_config][1], - ) - - # Output projections + loss - self.readout = MultitaskReadout( - latent_dim=dim, - decoder_specs=decoder_specs, - batch_type=self.batch_type[2], - ) - - self.dim = dim - - def freeze_middle(self) -> List[nn.Module]: - # Freeze everything except the readout, unit embedding, and session embedding - # layers. - middle_modules = [] - banned_modules = [ - self.readout, - self.unit_emb, - self.session_emb, - self.enc_atn, - self.enc_ffn, - ] - for module in self.children(): - if module in banned_modules: - continue - for param in module.parameters(): - param.requires_grad = False - middle_modules.append(module) - - return middle_modules - - def unfreeze_middle(self) -> None: - for module in self.children(): - for param in module.parameters(): - param.requires_grad = True - - def forward( - self, - *, - # input sequence - unit_index, # (B, N_in) - timestamps, # (B, N_in) - patches, # (B, N_in, N_feats) - token_type, # (B, N_in) - unit_feats=None, # (B, N_in, N_feats) - unit_spatial_emb=None, # (B, N_in, dim) - unit_cre_line=None, # (B, N_in) - unit_depth=None, # (B, N_in) - input_mask=None, # (B, N_in) - input_seqlen=None, - # latent sequence - latent_index, # (B, N_latent) - latent_timestamps, # (B, N_latent) - latent_seqlen=None, - # output sequence - session_index, # (B,) - output_timestamps, # (B, N_out) - output_decoder_index, # (B, N_out) - output_seqlen=None, - output_batch_index=None, - output_values: Optional[Dict[str, torch.Tensor]] = None, - output_weights: Optional[Dict[str, torch.Tensor]] = None, - ) -> Tuple[ - Dict[str, TensorType["batch", "*nqueries", "*nchannelsout"]], - torch.Tensor, - Dict[str, torch.Tensor], - ]: - - input_feats = [] - if self.use_unit_embedding: - input_feats.append( - self.unit_emb(unit_index) + self.token_type_emb(token_type) - ) - else: - input_feats.append(self.token_type_emb(token_type)) - - input_feats.append(self.value_embedding_layer(patches)) - - if self.use_roi_feat_embedding: - input_feats.append(self.unit_feat_embedding_layer(unit_feats)) - - if self.use_spatial_embedding: - input_feats.append(unit_spatial_emb) - - if self.use_cre_line_embedding: - input_feats.append(self.cre_line_embedding_layer(unit_cre_line)) - - if self.use_depth_embedding: - input_feats.append(self.depth_embedding_layer(unit_depth)) - - inputs = torch.cat( - input_feats, - dim=-1, - ) - - # latents - latents = self.latent_emb(latent_index) - - # outputs - output_queries = self.task_emb(output_decoder_index) - - if self.use_session_embedding: - output_queries = output_queries + self.session_emb(session_index) - - # feed into perceiver - output_latents = self.perceiver_io( - inputs=inputs, - latents=latents, - output_queries=output_queries, - input_timestamps=timestamps, - latent_timestamps=latent_timestamps, - output_query_timestamps=output_timestamps, - input_mask=input_mask, - input_seqlen=input_seqlen, - latent_seqlen=latent_seqlen, - output_query_seqlen=output_seqlen, - ) - - # Readout layer - output, loss, losses_taskwise = self.readout( - output_latents=output_latents, - output_decoder_index=output_decoder_index, - output_batch_index=output_batch_index, - output_values=output_values, - output_weights=output_weights, - ) - - return output, loss, losses_taskwise - - -class CaPOYOTokenizer: - r"""Tokenizer used to tokenize Data for the POYO1 model. - - This tokenizer can be called as a transform. If you are applying multiple - transforms, make sure to apply this one last. - - Args: - unit_tokenizer (Callable): Tokenizer for the units. - session_tokenizer (Callable): Tokenizer for the sessions. - decoder_registry (Dict): Registry of the decoders. - weight_registry (Dict): Registry of the weights. - latent_step (float): Step size for generating latent tokens. - num_latents_per_step (int): Number of latents per step. - """ - - def __init__( - self, - unit_tokenizer, - session_tokenizer, - decoder_registry, - latent_step, - num_latents_per_step, - dim, - patch_size, - batch_type, - eval=False, - use_cre_line_embedding=False, - use_depth_embedding=False, - use_spatial_embedding=False, - use_roi_feat_embedding=False, - ): - self.unit_tokenizer = unit_tokenizer - self.session_tokenizer = session_tokenizer - - self.decoder_registry = decoder_registry - - self.latent_step = latent_step - self.num_latents_per_step = num_latents_per_step - self.dim = dim - self.patch_size = patch_size - - self.batch_type = batch_type - self.eval = eval - - self.use_cre_line_embedding = use_cre_line_embedding - self.use_depth_embedding = use_depth_embedding - self.use_spatial_embedding = use_spatial_embedding - self.use_roi_feat_embedding = use_roi_feat_embedding - - def __call__(self, data): - # context window - start, end = 0.0, 1.0 - - ### prepare input - unit_ids = data.units.id - - calcium_traces = data.calcium_traces.df_over_f.astype( - np.float32 - ) # (time x num_rois) - timestamps = data.calcium_traces.timestamps.astype(np.float32) - num_rois = calcium_traces.shape[1] - - # patch tokenization - # clip the time dimension to accomodate the patch size - # WARNING: it is important to still have a multiple of patch_size - # this is a fix to deal with the arbitrary slicing that might happen - num_frames = calcium_traces.shape[0] // self.patch_size * self.patch_size - if num_frames == 0: - raise ValueError( - f"The patch size ({self.patch_size}) is larger than " - f"sequence length ({calcium_traces.shape[0]})." - ) - calcium_traces = calcium_traces[:num_frames] - timestamps = timestamps[:num_frames] - - calcium_traces = calcium_traces.reshape( - -1, self.patch_size, calcium_traces.shape[1] - ) - timestamps = timestamps.reshape(-1, self.patch_size).mean(axis=1) - - # now flatten - patches = rearrange(calcium_traces, "t d c -> (t c) d") - unit_index = repeat(np.arange(num_rois), "c -> (t c)", t=timestamps.shape[0]) - - if self.use_spatial_embedding: - if not "imaging_plane_xy" in data.units.keys: - raise ValueError( - "ROI coordinates in the imaging plane are required for ROI spatial embeddings." - ) - unit_lvl_spatial_emb = get_sinusoidal_encoding( - data.units.imaging_plane_xy[:, 0], - data.units.imaging_plane_xy[:, 1], - self.dim // 2, - ).astype(np.float32) - unit_spatial_emb = repeat( - unit_lvl_spatial_emb, "c d -> (t c) d", t=timestamps.shape[0] - ) - else: - unit_spatial_emb = None - - if self.use_roi_feat_embedding: - if not all( - [ - "imaging_plane_area" in data.units.keys, - "imaging_plane_width" in data.units.keys, - "imaging_plane_height" in data.units.keys, - ] - ): - raise ValueError( - "ROI area, width, and height are required for ROI feature embeddings." - ) - unit_lvl_feats = np.stack( - [ - data.units.imaging_plane_area, - data.units.imaging_plane_width, - data.units.imaging_plane_height, - ], - axis=1, - ).astype(np.float32) - unit_feats = repeat(unit_lvl_feats, "c f -> (t c) f", t=timestamps.shape[0]) - else: - unit_feats = None - - timestamps = repeat(timestamps, "t -> (t c)", c=num_rois) - - # create start and end tokens for each unit - ( - se_token_type_index, - se_unit_index, - se_timestamps, - ) = create_start_end_unit_tokens(unit_ids, start, end) - - # append start and end tokens to the spike sequence - token_type_index = np.concatenate( - [se_token_type_index, np.zeros_like(unit_index)] - ) - unit_index = np.concatenate([se_unit_index, unit_index]) - timestamps = np.concatenate([se_timestamps, timestamps]) - patches = np.concatenate( - [ - np.zeros((se_unit_index.shape[0], patches.shape[1]), dtype=np.float32), - patches, - ] - ) - if unit_feats is not None: - unit_feats = np.concatenate( - [ - unit_lvl_feats[se_unit_index], - unit_feats, - ] - ) - if unit_spatial_emb is not None: - unit_spatial_emb = np.concatenate( - [ - unit_lvl_spatial_emb[se_unit_index], - unit_spatial_emb, - ] - ) - - # unit_index is relative to the recording, so we want it to map it to - # the global unit index - local_to_global_map = np.array(self.unit_tokenizer(unit_ids)) - unit_index = local_to_global_map[unit_index] - - ### prepare latents - latent_index, latent_timestamps = create_linspace_latent_tokens( - start, - end, - step=self.latent_step, - num_latents_per_step=self.num_latents_per_step, - ) - - ### prepare outputs - session_index = self.session_tokenizer(data.session) - - ( - output_timestamps, - output_task_index, - output_values, - output_weights, - output_subtask_index, - ) = prepare_for_multitask_readout( - data, - self.decoder_registry, - ) - - if self.use_cre_line_embedding: - subject_cre_line = data.subject.cre_line - subject_cre_line_index = Cre_line.from_string(subject_cre_line).value - unit_cre_line = np.full_like(unit_index, subject_cre_line_index) - - if self.use_depth_embedding: - subject_depth = data.subject.depth_class - subject_depth_index = Depth_classes.from_string(subject_depth).value - unit_depth = np.full_like(unit_index, subject_depth_index) - - batch = {} - if self.batch_type[0] == "stacked": - batch = { - **batch, - # input sequence - "unit_index": pad(unit_index), - "timestamps": pad(timestamps), - "patches": pad(patches), - "token_type": pad(token_type_index), - "input_mask": track_mask(unit_index), - # latent sequence - "latent_index": latent_index, - "latent_timestamps": latent_timestamps, - } - if self.use_spatial_embedding: - batch["unit_spatial_emb"] = pad(unit_spatial_emb) - if self.use_roi_feat_embedding: - batch["unit_feats"] = pad(unit_feats) - if self.use_cre_line_embedding: - batch["unit_cre_line"] = pad(unit_cre_line) - if self.use_depth_embedding: - batch["unit_depth"] = pad(unit_depth) - else: - batch = { - **batch, - # input sequence - "unit_index": chain(unit_index), - "timestamps": chain(timestamps), - "patches": chain(patches), - "token_type": chain(token_type_index), - "input_seqlen": len(unit_index), - # latent sequence - "latent_index": chain(latent_index), - "latent_timestamps": chain(latent_timestamps), - "latent_seqlen": len(latent_index), - } - if self.use_spatial_embedding: - batch["unit_spatial_emb"] = chain(unit_spatial_emb) - if self.use_roi_feat_embedding: - batch["unit_roi_feats"] = chain(unit_feats) - if self.use_cre_line_embedding: - batch["unit_cre_line"] = chain(unit_cre_line) - if self.use_depth_embedding: - batch["unit_depth"] = chain(unit_depth) - if self.batch_type[1] == "chained": - batch["latent_seqlen"] = len(latent_index) - - if self.batch_type[2] == "stacked": - batch = { - **batch, - # output sequence - "session_index": pad(np.repeat(session_index, len(output_timestamps))), - "output_timestamps": pad(output_timestamps), - "output_decoder_index": pad(output_task_index), - "output_values": chain(output_values), - "output_weights": chain(output_weights), - } - else: - batch = { - **batch, - # output sequence - "session_index": chain(session_index), - "output_timestamps": chain(output_timestamps), - "output_decoder_index": chain(output_task_index), - "output_seqlen": len(output_timestamps), - "output_batch_index": track_batch(output_timestamps), - "output_values": chain(output_values), - "output_weights": chain(output_weights), - } - - if self.eval: - # we will add a few more fields needed for evaluation - batch["session_id"] = data.session - batch["absolute_start"] = data.absolute_start - batch["output_subtask_index"] = chain(output_subtask_index) - - return batch diff --git a/torch_brain/models/poyo.py b/torch_brain/models/poyo.py deleted file mode 100644 index 020cac4..0000000 --- a/torch_brain/models/poyo.py +++ /dev/null @@ -1,274 +0,0 @@ -from typing import Dict, List, Optional, Tuple, Union - -import numpy as np -import torch -import torch.nn as nn -from torchtyping import TensorType - -from torch_brain.nn import ( - Embedding, - InfiniteVocabEmbedding, - PerceiverRotary, - compute_loss_or_metric, -) -from torch_brain.data import pad, chain, track_mask, track_batch -from torch_brain.utils import ( - create_start_end_unit_tokens, - create_linspace_latent_tokens, -) -from brainsets.taxonomy import Task, OutputType - - -class POYO(nn.Module): - def __init__( - self, - *, - dim=512, - dim_head=64, - num_latents=64, - depth=2, - cross_heads=1, - self_heads=8, - ffn_dropout=0.2, - lin_dropout=0.4, - atn_dropout=0.0, - emb_init_scale=0.02, - use_memory_efficient_attn=True, - ): - super().__init__() - - self.unit_emb = InfiniteVocabEmbedding(dim, init_scale=emb_init_scale) - self.session_emb = InfiniteVocabEmbedding(dim, init_scale=emb_init_scale) - self.spike_type_emb = Embedding(4, dim, init_scale=emb_init_scale) - self.latent_emb = Embedding(num_latents, dim, init_scale=emb_init_scale) - - self.perceiver_io = PerceiverRotary( - dim=dim, - dim_head=dim_head, - depth=depth, - cross_heads=cross_heads, - self_heads=self_heads, - ffn_dropout=ffn_dropout, - lin_dropout=lin_dropout, - atn_dropout=atn_dropout, - use_memory_efficient_attn=use_memory_efficient_attn, - ) - - # Output projections + loss - self.readout = nn.Linear(dim, 2) - - self.dim = dim - self.using_memory_efficient_attn = self.perceiver_io.using_memory_efficient_attn - - def forward( - self, - *, - # input sequence - spike_unit_index, # (B, N_in) - spike_timestamps, # (B, N_in) - spike_type, # (B, N_in) - input_mask=None, # (B, N_in) - input_seqlen=None, - # latent sequence - latent_index, # (B, N_latent) - latent_timestamps, # (B, N_latent) - latent_seqlen=None, - # output sequence - session_index, # (B,) - output_timestamps, # (B, N_out) - output_seqlen=None, - output_batch_index=None, - output_mask=None, - output_values: Optional[Dict[str, torch.Tensor]] = None, - output_weights: Optional[Dict[str, torch.Tensor]] = None, - ) -> Tuple[ - Dict[str, TensorType["batch", "*nqueries", "*nchannelsout"]], - torch.Tensor, - Dict[str, torch.Tensor], - ]: - - # input - inputs = self.unit_emb(spike_unit_index) + self.spike_type_emb(spike_type) - - # latents - latents = self.latent_emb(latent_index) - - # outputs - output_queries = self.session_emb(session_index) - - # feed into perceiver - output_latents = self.perceiver_io( - inputs=inputs, - latents=latents, - output_queries=output_queries, - input_timestamps=spike_timestamps, - latent_timestamps=latent_timestamps, - output_query_timestamps=output_timestamps, - input_mask=input_mask, - input_seqlen=input_seqlen, - latent_seqlen=latent_seqlen, - output_query_seqlen=output_seqlen, - ) - - # readout layer - output_pred = self.readout(output_latents) - - if self.using_memory_efficient_attn: - loss = compute_loss_or_metric( - "mse", OutputType.CONTINUOUS, output_pred, output_values, output_weights - ) - else: - assert output_mask is not None - loss = compute_loss_or_metric( - "mse", - OutputType.CONTINUOUS, - output_pred[output_mask], - output_values, - output_weights, - ) - - output = [] - if self.using_memory_efficient_attn: - batch_size = output_batch_index.max().item() + 1 - for i in range(batch_size): - output.append(output[output_batch_index == i]) - else: - batch_size = output_latents.shape[0] - for i in range(batch_size): - output.append(output[i, output_mask[i]]) - - return output, loss - - -class POYOTokenizer: - r"""Tokenizer used to tokenize Data for the POYO1 model. - - This tokenizer can be called as a transform. If you are applying multiple - transforms, make sure to apply this one last. - - Args: - unit_tokenizer (Callable): Tokenizer for the units. - session_tokenizer (Callable): Tokenizer for the sessions. - weight_registry (Dict): Registry of the weights. - latent_step (float): Step size for generating latent tokens. - num_latents_per_step (int): Number of latents per step. - """ - - def __init__( - self, - unit_tokenizer, - session_tokenizer, - latent_step, - num_latents_per_step, - using_memory_efficient_attn: bool = True, - eval=False, - ): - self.unit_tokenizer = unit_tokenizer - self.session_tokenizer = session_tokenizer - - self.latent_step = latent_step - self.num_latents_per_step = num_latents_per_step - - self.using_memory_efficient_attn = using_memory_efficient_attn - self.eval = eval - - def __call__(self, data): - # context window - start, end = 0, 1.0 # data.domain, data.end - - ### prepare input - unit_ids = data.units.id - spike_unit_index = data.spikes.unit_index - spike_timestamps = data.spikes.timestamps - - # create start and end tokens for each unit - ( - se_token_type_index, - se_unit_index, - se_timestamps, - ) = create_start_end_unit_tokens(unit_ids, start, end) - - # append start and end tokens to the spike sequence - spike_token_type_index = np.concatenate( - [se_token_type_index, np.zeros_like(spike_unit_index)] - ) - spike_unit_index = np.concatenate([se_unit_index, spike_unit_index]) - spike_timestamps = np.concatenate([se_timestamps, spike_timestamps]) - - # unit_index is relative to the recording, so we want it to map it to - # the global unit index - local_to_global_map = np.array(self.unit_tokenizer(unit_ids)) - spike_unit_index = local_to_global_map[spike_unit_index] - - ### prepare latents - latent_index, latent_timestamps = create_linspace_latent_tokens( - start, - end, - step=self.latent_step, - num_latents_per_step=self.num_latents_per_step, - ) - - ### prepare outputs - session_index = self.session_tokenizer(data.session) - - output_timestamps = data.cursor.timestamps - output_values = data.cursor.vel - output_subtask_index = data.cursor.subtask_index - - # compute weights - weight = data.config["reach_decoder"].get("weight", 1.0) - subtask_weights = data.config["reach_decoder"].get("subtask_weights", {}) - num_subtasks = Task.REACHING.max_value() - subtask_weight_map = np.ones(num_subtasks, dtype=np.float32) - for subtask, subtask_weight in subtask_weights.items(): - subtask_weight_map[Task.from_string(subtask).value] = subtask_weight - subtask_weight_map *= weight - output_weights = subtask_weight_map[output_subtask_index] - - if not self.using_memory_efficient_attn: - # Padding - batch = { - # input sequence - "spike_unit_index": pad(spike_unit_index), - "spike_timestamps": pad(spike_timestamps), - "spike_type": pad(spike_token_type_index), - "input_mask": track_mask(spike_unit_index), - # latent sequence - "latent_index": latent_index, - "latent_timestamps": latent_timestamps, - # output sequence - "session_index": pad(np.repeat(session_index, len(output_timestamps))), - "output_timestamps": pad(output_timestamps), - "output_values": chain(output_values), - "output_weights": chain(output_weights), - } - else: - # Chaining - batch = { - # input sequence - "spike_unit_index": chain(spike_unit_index), - "spike_timestamps": chain(spike_timestamps), - "spike_type": chain(spike_token_type_index), - "input_seqlen": len(spike_unit_index), - # latent sequence - "latent_index": chain(latent_index), - "latent_timestamps": chain(latent_timestamps), - "latent_seqlen": len(latent_index), - # output sequence - "session_index": chain( - np.repeat(session_index, len(output_timestamps)) - ), - "output_timestamps": chain(output_timestamps), - "output_seqlen": len(output_timestamps), - "output_batch_index": track_batch(output_timestamps), - "output_values": chain(output_values), - "output_weights": chain(output_weights), - } - - if self.eval: - # we will add a few more fields needed for evaluation - batch["session_id"] = data.session - batch["absolute_start"] = data.absolute_start - batch["output_subtask_index"] = chain(output_subtask_index) - - return batch diff --git a/torch_brain/models/poyo_plus_efficient.py b/torch_brain/models/poyo_plus_efficient.py deleted file mode 100644 index e9164fd..0000000 --- a/torch_brain/models/poyo_plus_efficient.py +++ /dev/null @@ -1,321 +0,0 @@ -from typing import Dict, List, Optional, Tuple, Union - -import numpy as np -import torch -import torch.nn as nn -from torchtyping import TensorType - -try: - import xformers.ops as xops -except ImportError: - xops = None - - -from brainsets.taxonomy import DecoderSpec, Decoder -from torch_brain.nn import ( - Embedding, - InfiniteVocabEmbedding, - RotaryCrossAttention, - RotarySelfAttention, - FeedForward, - MultitaskReadout, - prepare_for_multitask_readout, -) -from torch_brain.data import chain, track_batch -from torch_brain.utils import ( - create_start_end_unit_tokens, - create_linspace_latent_tokens, -) - - -class POYOPlusE(nn.Module): - def __init__( - self, - *, - dim=512, - dim_head=64, - num_latents=64, - depth=2, - cross_heads=1, - self_heads=8, - ffn_dropout=0.2, - lin_dropout=0.4, - atn_dropout=0.0, - emb_init_scale=0.02, - task_specs: Dict[str, DecoderSpec], - ): - super().__init__() - - if xops is None: - raise ImportError( - "xformers not installed, please install `xformers` to use the efficient " - "version of POYO+, otherwise use the default version." - ) - - # embeddings - self.unit_emb = InfiniteVocabEmbedding(dim, init_scale=emb_init_scale) - self.session_emb = InfiniteVocabEmbedding(dim, init_scale=emb_init_scale) - self.token_type_emb = Embedding(4, dim, init_scale=emb_init_scale) - self.task_emb = Embedding( - Decoder.max_value() + 1, dim, init_scale=emb_init_scale - ) - self.latent_emb = Embedding(num_latents, dim, init_scale=emb_init_scale) - - # encoder layer - self.enc_atn = RotaryCrossAttention( - dim=dim, - heads=cross_heads, - dropout=atn_dropout, - dim_head=dim_head, - rotate_value=True, - ) - self.enc_ffn = nn.Sequential( - nn.LayerNorm(dim), FeedForward(dim=dim, dropout=ffn_dropout) - ) - - # process layers - self.proc_layers = nn.ModuleList([]) - for i in range(depth): - self.proc_layers.append( - nn.Sequential( - RotarySelfAttention( - dim=dim, - heads=self_heads, - dropout=atn_dropout, - dim_head=dim_head, - rotate_value=True, - ), - nn.Sequential( - nn.LayerNorm(dim), - FeedForward(dim=dim, dropout=ffn_dropout), - ), - ) - ) - - # decoder layer - self.dec_atn = RotaryCrossAttention( - dim=dim, - heads=cross_heads, - dropout=atn_dropout, - dim_head=dim_head, - rotate_value=False, - ) - self.dec_ffn = nn.Sequential( - nn.LayerNorm(dim), FeedForward(dim=dim, dropout=ffn_dropout) - ) - - # Output projections + loss - self.readout = MultitaskReadout( - latent_dim=dim, - decoder_specs=task_specs, - batch_type=self.batch_type[2], - ) - - self.dim = dim - - def forward( - self, - *, - # input sequence - input_unit_index, # (total_N_in,) - input_timestamps, # (total_N_in,) - input_token_type, # (total_N_in,) - input_seqlen, # (B,) - # latent sequence - latent_index, # (B, N_latent) - latent_timestamps, # (B, N_latent) - latent_seqlen, - # output sequence - session_index, # (B,) - output_timestamps, # (B, N_out) - output_decoder_index, # (B, N_out) - output_seqlen, - output_batch_index, - output_values: Optional[Dict[str, torch.Tensor]] = None, - output_weights: Optional[Dict[str, torch.Tensor]] = None, - ) -> Tuple[ - Dict[str, TensorType["batch", "*nqueries", "*nchannelsout"]], - torch.Tensor, - Dict[str, torch.Tensor], - ]: - - # input - inputs = self.unit_emb(input_unit_index) + self.token_type_emb(input_token_type) - input_timestamp_emb = self.rotary_emb(input_timestamps) - - # latents - latents = self.latent_emb(latent_index) - latent_timestamp_emb = self.rotary_emb(latent_timestamps) - - # outputs - output_queries = self.session_emb(session_index) + self.task_emb( - output_decoder_index - ) - output_timestamp_emb = self.rotary_emb(output_timestamps) - - # encode - latents = latents + self.enc_atn.forward_varlen( - latents, - inputs, - latent_timestamp_emb, - input_timestamp_emb, - query_seqlen=latent_seqlen, - context_seqlen=input_seqlen, - ) - latents = latents + self.enc_ffn(latents) - - # reshape latents and latent timestamp embeddings - latents = latents.view(len(latent_seqlen), latent_seqlen[0], self.dim) - latent_timestamp_emb = latent_timestamp_emb.view( - len(latent_seqlen), latent_seqlen[0], self.dim - ) - - # process - for self_attn, self_ff in self.proc_layers: - latents = latents + self.dropout(self_attn(latents, latent_timestamp_emb)) - latents = latents + self.dropout(self_ff(latents)) - - # reshape latents again - latents = latents.view(-1, self.dim) - latent_timestamp_emb = latent_timestamp_emb.view(-1, self.dim) - - # decode - output_queries = output_queries + self.dec_atn.forward_varlen( - output_queries, - latents, - output_timestamp_emb, - latent_timestamp_emb, - query_seqlen=output_seqlen, - context_seqlen=latent_seqlen, - ) - output_latents = output_queries + self.dec_ffn(output_queries) - - # multitask readout layer, each task has a seperate linear readout layer - output, loss, losses_taskwise = self.readout( - output_latents=output_latents, - output_decoder_index=output_decoder_index, - output_batch_index=output_batch_index, - output_values=output_values, - output_weights=output_weights, - ) - - return output, loss, losses_taskwise - - -class POYOPlusETokenizer: - r"""Tokenizer used to tokenize Data for the POYO1 model. - - This tokenizer can be called as a transform. If you are applying multiple - transforms, make sure to apply this one last. - - Args: - unit_tokenizer (Callable): Tokenizer for the units. - session_tokenizer (Callable): Tokenizer for the sessions. - decoder_registry (Dict): Registry of the decoders. - weight_registry (Dict): Registry of the weights. - latent_step (float): Step size for generating latent tokens. - num_latents_per_step (int): Number of latents per step. - """ - - def __init__( - self, - unit_tokenizer, - session_tokenizer, - decoder_registry, - latent_step, - num_latents_per_step, - batch_type, - eval=False, - ): - self.unit_tokenizer = unit_tokenizer - self.session_tokenizer = session_tokenizer - - self.decoder_registry = decoder_registry - - self.latent_step = latent_step - self.num_latents_per_step = num_latents_per_step - - self.batch_type = batch_type - self.eval = eval - - def __call__(self, data): - # context window - start, end = 0, 1.0 # data.domain, data.end - - ### prepare input - unit_ids = data.units.id - spike_unit_index = data.spikes.unit_index - spike_timestamps = data.spikes.timestamps - - # create start and end tokens for each unit - ( - se_token_type_index, - se_unit_index, - se_timestamps, - ) = create_start_end_unit_tokens(unit_ids, start, end) - - # append start and end tokens to the spike sequence - spike_token_type_index = np.concatenate( - [se_token_type_index, np.zeros_like(spike_unit_index)] - ) - spike_unit_index = np.concatenate([se_unit_index, spike_unit_index]) - spike_timestamps = np.concatenate([se_timestamps, spike_timestamps]) - - # unit_index is relative to the recording, so we want it to map it to - # the global unit index - local_to_global_map = np.array(self.unit_tokenizer(unit_ids)) - spike_unit_index = local_to_global_map[spike_unit_index] - - ### prepare latents - latent_index, latent_timestamps = create_linspace_latent_tokens( - start, - end, - step=self.latent_step, - num_latents_per_step=self.num_latents_per_step, - ) - - ### prepare outputs - session_index = self.session_tokenizer(data.session) - - ( - output_timestamps, - output_task_index, - output_values, - output_weights, - output_subtask_index, - ) = prepare_for_multitask_readout( - data, - self.decoder_registry, - ) - - session_index = np.repeat(session_index, len(output_timestamps)) - - batch = { - # input sequence - "spike_unit_index": chain(spike_unit_index), - "spike_timestamps": chain(spike_timestamps), - "spike_type": chain(spike_token_type_index), - "input_seqlen": len(spike_unit_index), - # latent sequence - "latent_index": chain(latent_index), - "latent_timestamps": chain(latent_timestamps), - "latent_seqlen": len(latent_index), - # output sequence - "session_index": chain(session_index), - "output_timestamps": chain(output_timestamps), - "output_decoder_index": chain(output_task_index), - "output_seqlen": len(output_timestamps), - "output_batch_index": track_batch(output_timestamps), - "output_values": chain(output_values, allow_missing_keys=True), - "output_weights": chain(output_weights, allow_missing_keys=True), - } - - if self.eval: - # we will add a few more fields needed for evaluation - batch["session_id"] = data.session - batch["absolute_start"] = data.absolute_start - batch["output_subtask_index"] = chain( - output_subtask_index, allow_missing_keys=True - ) - - return batch diff --git a/torch_brain/utils/tokenizers.py b/torch_brain/utils/tokenizers.py index a154653..ad41184 100644 --- a/torch_brain/utils/tokenizers.py +++ b/torch_brain/utils/tokenizers.py @@ -1,11 +1,9 @@ import numpy as np from einops import repeat +from enum import Enum -from brainsets.taxonomy.core import StringIntEnum - - -class TokenType(StringIntEnum): +class TokenType(Enum): DEFAULT = 0 START_OF_SEQUENCE = 1 END_OF_SEQUENCE = 2 @@ -21,7 +19,8 @@ def create_start_end_unit_tokens(unit_ids, start, end): end (float): The end time of the sequence. """ token_type_index = np.array( - [TokenType.START_OF_SEQUENCE, TokenType.END_OF_SEQUENCE], dtype=np.int64 + [TokenType.START_OF_SEQUENCE.value, TokenType.END_OF_SEQUENCE.value], + dtype=np.int64, ) token_type_index = repeat(token_type_index, "u -> (t u)", t=len(unit_ids))