From 52678a68af6407082e48baed3240591bd5906cfc Mon Sep 17 00:00:00 2001 From: Christopher JF Cameron Date: Sat, 30 Sep 2023 15:44:51 -0400 Subject: [PATCH 1/9] squashing colab int commits --- README.md | 26 +- conda-build/meta.yaml | 9 +- docs/conf.py | 4 +- docs/environment.yml | 6 +- repic_colab.ipynb | 726 ++++++++++++++++++++++++++++++++++++++++++ 5 files changed, 760 insertions(+), 11 deletions(-) create mode 100644 repic_colab.ipynb diff --git a/README.md b/README.md index 01d64b2..e93129a 100644 --- a/README.md +++ b/README.md @@ -119,18 +119,33 @@ A REPIC Docker image is published on both [DockerHub](https://hub.docker.com/r/c 2. [Install and set up NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) for building and running GPU-accelerated containers 3. Pull REPIC Docker image and convert to Singularity image format (SIF) (requires >8 Gb of memory and ~40 mins for conversion): ``` -apptainer pull docker://cjfcameron/repic:main +apptainer pull docker://cjfcameron/repic ``` If SIF file creation is taking a long time, increase the ```mksquashfs mem``` parameter in the Apptainer config file (apptainer.conf). See [here](https://apptainer.org/docs/admin/1.0/configfiles.html) for more information. 4. Run container with GPU acceleration (example [iter_pick.py](https://github.com/ccameron/REPIC/blob/main/repic/commands/iter_pick.py) command shown below): ``` -apptainer run --nv --bind /REPIC/examples:/examples repic_main.sif repic iter_pick /examples/10057/iter_config.json 4 100 +apptainer run --nv --bind /REPIC/examples:/examples repic_latest.sif repic iter_pick /examples/10057/iter_config.json 4 100 ```

+**
Run using Google Colab

** + +A [Jupyter Notebook](https://jupyter.org/) for installing and running REPIC on [Google Colab](https://colab.google/) is included in the REPIC GitHub repo: [repic_colab.ipynb](https://github.com/ccameron/REPIC/blob/main/repic_colab.ipynb) + +To open the notebook in Google Colab: + +1. Navigate a browser to: https://colab.google/ +2. Select "Open Colab", then "GitHub" +3. Enter the REPIC GitHub web URL: https://github.com/ccameron/REPIC.git +4. Select the "repic_colab.ipynb" Jupyter Notebook + +

+ **
Run using Scipion plugin

** +:warning: **WARNING**: Scipion plugin currently only contains REPIC one-shot mode + REPIC is available as a [Scipion](https://scipion.i2pc.es/) plugin: [https://github.com/scipion-em/scipion-em-repic](https://github.com/scipion-em/scipion-em-repic) See [here](https://scipion-em.github.io/docs/release-3.0.0/docs/scipion-modes/how-to-install.html#installing-other-plugins) for information about installing plugins for Scipion. @@ -188,7 +203,7 @@ repic iter_config examples/10057/ 176 224 /gmodel_phosnet_202005_N63_ A configuration file ``` iter_config.json ``` will be created in the current working directory. -3. Pick particles by iterative ensemble learning using a Python script wrapper [iter_pick.py](https://github.com/ccameron/REPIC/blob/main/repic/commands/iter_pick.py) of [run.sh](https://github.com/ccameron/REPIC/blob/main/repic/iterative_particle_picking/run.sh) (expected run time: 20-30 min/iteration): +3. Pick particles by iterative ensemble learning using [iter_pick.py](https://github.com/ccameron/REPIC/blob/main/repic/commands/iter_pick.py), a wrapper of [run.sh](https://github.com/ccameron/REPIC/blob/main/repic/iterative_particle_picking/run.sh) (expected run time: 20-30 min/iteration): ``` repic iter_pick ./iter_config.json 4 100 @@ -273,7 +288,7 @@ done ` /relion/CtfFind/job00[0-9]/* ` should list all CTFFIND4 output files in RELION's ` CtfFind/ `. -2. Iteratively pick particles using a Python script wrapper [iter_pick.py](https://github.com/ccameron/REPIC/blob/main/repic/commands/iter_pick.py) of [run.sh](https://github.com/ccameron/REPIC/blob/main/repic/iterative_particle_picking/run.sh): +2. Iteratively pick particles using [iter_pick.py](https://github.com/ccameron/REPIC/blob/main/repic/commands/iter_pick.py), a wrapper of [run.sh](https://github.com/ccameron/REPIC/blob/main/repic/iterative_particle_picking/run.sh): ``` usage: repic iter_pick [-h] [--semi_auto] [--score] [--out_file_path OUT_FILE_PATH] config_file num_iter train_size @@ -308,6 +323,9 @@ For other concerns, please email [Christopher JF Cameron](mailto:christopher.cam ## Releases +### new + - Google Colab integrated + ### v0.2.1 - Scipion plugin created - Docker/Singularity/Apptainer integrated diff --git a/conda-build/meta.yaml b/conda-build/meta.yaml index 3dbbca6..9b539b4 100644 --- a/conda-build/meta.yaml +++ b/conda-build/meta.yaml @@ -6,15 +6,14 @@ package: source: url: https://github.com/ccameron/REPIC/archive/v{{ version }}.zip - sha256: 1c6a1701ecc58e4732d82a3b8ef20b473974e662e40a4c42944482aeae325c0f + sha256: fd77f660bf6e1fe10138c145d2aa67b69fd4ff30f249047ee49460a185407633 requirements: host: - python >=3.8.16 - pip - run: - - python + - python >=3.8.16 - matplotlib-base >=3.2.2 - mrcfile >=1.4.3 - networkx >=2.8.4 @@ -29,6 +28,8 @@ build: entry_points: - repic = repic.main:main script: {{ PYTHON }} -m pip install . --ignore-installed --no-deps -vv + run_exports: + - {{ pin_subpackage('repic', max_pin="x") }} test: commands: @@ -39,7 +40,7 @@ about: license: BSD-3-Clause license_family: BSD license_file: LICENSE - summary: REPIC - an ensemble learning approach for cryo-EM particle picking. + summary: REPIC - an ensemble learning approach to cryo-EM particle picking. description: REliable PIcking by Consensus (REPIC) is an ensemble learning approach to cryogenic-electron microscopy (cryo-EM) particle picking. It identifies particles common to multiple picked particle sets (i.e., consensus particles) using graph theory and integer linear programming (ILP). Picked particle sets may be found by a human specialist (manual), template matching, mathematical function (e.g., RELION's Laplacian-of-Gaussian auto-picking), or machine-learning method. REPIC expects particle sets to be in BOX file format (*.box) where each particle has coordinates, a detection box size (in pixels), and (optional) a score [0-1]. dev_url: https://github.com/ccameron/REPIC doc_url: https://repic.readthedocs.io/en/latest/ diff --git a/docs/conf.py b/docs/conf.py index 788cdcc..6335f6b 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -67,10 +67,12 @@ def get_version(): extensions = [ 'autoapi.extension', - 'sphinx.ext.napoleon' + 'sphinx.ext.napoleon', + 'sphinx_search.extension' ] autoapi_dirs = ['../repic'] autoapi_type = "python" +autoapi_ignore = ['../repic/__init__.py'] templates_path = ['_templates'] exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] diff --git a/docs/environment.yml b/docs/environment.yml index 553526e..4107fd7 100644 --- a/docs/environment.yml +++ b/docs/environment.yml @@ -4,10 +4,12 @@ channels: - conda-forge - defaults dependencies: - - python=3.8 + - python + - pip + - astroid==2.15.8 # https://github.com/readthedocs/sphinx-autoapi/issues/407 - repic - pandoc - - sphinx==6.2.1 + - sphinx - sphinx_rtd_theme - sphinx-autoapi - pip: diff --git a/repic_colab.ipynb b/repic_colab.ipynb new file mode 100644 index 0000000..80dcb4b --- /dev/null +++ b/repic_colab.ipynb @@ -0,0 +1,726 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "XU1nb7Qa-Y5s" + }, + "source": [ + "\"REPIC\n", + "\n", + "## Welcome to the official REPIC Jupyter Notebook for Google Colab!\n", + "\n", + "REliable PIcking by Consensus (REPIC) is an ensemble learning approach to cryogenic-electron microscopy (cryo-EM) particle picking. It identifies particles common to multiple picked particle sets (i.e., consensus particles) using graph theory and integer linear programming.\n", + "\n", + "Development: https://github.com/ccameron/REPIC\n", + "\n", + "Documentation: https://repic.readthedocs.io/en/latest/\n", + "\n", + "\n", + "Below are the steps required to install and run both the one-shot and iterative modes of REPIC. Note — GPUs are recommended for the iterative mode (Runtime > Change runtime type > GPU)\n", + "\n", + "\n", + "If you encounter any issues with this notebook, please raise an issue in the REPIC GitHub repo: https://github.com/ccameron/REPIC/issues\n", + "\n", + "Author: Christopher JF Cameron (2023)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "T9fR5eWuEukn" + }, + "source": [ + "### Step 1 — check availability of ```conda``` command" + ] + }, + { + "cell_type": "markdown", + "source": [ + "1. Check the availability of the ```conda``` command" + ], + "metadata": { + "id": "wGrJRzHOXV9h" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "32bKA92WGzVB" + }, + "outputs": [], + "source": [ + "! conda --version" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pp88FSKJx57U" + }, + "source": [ + "If Conda is available, the output will be (may be a different version): ```conda 23.1.0 ```\n", + "\n", + "\n", + "If the output says ```conda: command not found```, install Conda via [condalab](https://github.com/conda-incubator/condacolab) using pip: https://pypi.org/project/condacolab/" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "OGM8LhdwBzha" + }, + "outputs": [], + "source": [ + "! pip install -q condacolab\n", + "import condacolab\n", + "condacolab.install()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zbn7scSq2J0Z" + }, + "source": [ + "After installation, Colab will raise a warning that the kernel has crashed (e.g., \"Your session crashed for an unknown reason.\"). This crash is expected and a result of the ```Restarting kernel...``` condalab step.\n", + "\n", + "2. Update the Conda base environment" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "eQwg-4rI2Lwa" + }, + "outputs": [], + "source": [ + "! conda update -n base -c conda-forge conda" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4YnZioehsqVm" + }, + "source": [ + "### Step 2 — install particle picking algorithms" + ] + }, + { + "cell_type": "markdown", + "source": [ + "1. Install Mamba package manager: https://github.com/mamba-org/mamba" + ], + "metadata": { + "id": "hhOskLGxU_LT" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "PfSR9nVlsv1x" + }, + "outputs": [], + "source": [ + "! conda install -c conda-forge mamba -y" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "a8GJ5ozA3Fjj" + }, + "source": [ + "2. Get CUDA version of GPU using NVIDIA system manager interface (exp v12.0): https://developer.nvidia.com/nvidia-system-management-interface" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "rYSjQ1XHtOlN" + }, + "outputs": [], + "source": [ + "! nvidia-smi" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iLgIXM3K3pkH" + }, + "source": [ + "3. Get version of installed CUDA toolkit (exp v11.8)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "PLV-cmEX2wIy" + }, + "outputs": [], + "source": [ + "! nvcc --version" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lWo6sm2ytXwr" + }, + "source": [ + "_(Optional)_ Update the following install instructions for particle picking algorithms (Steps 4-11) below to reflect the available CUDA versions\n", + "\n", + "**SPHIRE-crYOLO install**\n", + "\n", + "4. Install SPHIRE-crYOLO: https://cryolo.readthedocs.io/en/stable/" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Kid4JYd8tZoF" + }, + "outputs": [], + "source": [ + "! mamba create -n cryolo -c conda-forge -c anaconda pyqt=5 python=3.7 cudatoolkit=10.0.130 cudnn=7.6.5 numpy=1.18.5 libtiff wxPython=4.1.1 adwaita-icon-theme -y\n", + "! source activate cryolo && pip install 'cryolo[gpu]'" + ] + }, + { + "cell_type": "markdown", + "source": [ + "5. Download pre-trained crYOLO LOWPASS model" + ], + "metadata": { + "id": "PhPnf9dmIrz4" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "vEGY8V6kvL7l" + }, + "outputs": [], + "source": [ + "! wget ftp://ftp.gwdg.de/pub/misc/sphire/crYOLO-GENERAL-MODELS/gmodel_phosnet_202005_N63_c17.h5" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Zz_Go0O4wHLh" + }, + "source": [ + "**Topaz install**\n", + "6. Install Topaz: https://topaz-em.readthedocs.io/en/latest/?badge=latest" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "aKNx4P6ZwLf3" + }, + "outputs": [], + "source": [ + "! mamba create -n topaz -c tbepler -c nvidia -c pytorch pytorch torchvision torchaudio pytorch-cuda=11.8 topaz=0.2.5" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FlcvP5DRyVKc" + }, + "source": [ + "**REPIC install**\n", + "7. Install REPIC" + ] + }, + { + "cell_type": "code", + "source": [ + "! mamba install -c bioconda repic -y" + ], + "metadata": { + "id": "5vliH-nvP8U2" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "8. Install Linux basic calculator (bc) package: https://linux.die.net/man/1/bc" + ], + "metadata": { + "id": "Pde-ahNZdATb" + } + }, + { + "cell_type": "code", + "source": [ + "! apt-get install bc" + ], + "metadata": { + "id": "_z8yIMSMb4nr" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**DeepPicker install**\n", + "9. Clone DeepPicker GitHub repository" + ], + "metadata": { + "id": "SYanXcvhQT93" + } + }, + { + "cell_type": "code", + "source": [ + "! git clone https://github.com/nejyeah/DeepPicker-python.git" + ], + "metadata": { + "id": "p5MIguDOQS03" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "10. Apply REPIC patch to DeepPicker Python scripts" + ], + "metadata": { + "id": "hK6RhotWQ4oN" + } + }, + { + "cell_type": "code", + "source": [ + "! cp $(pip show repic | grep -in \"Location\" | cut -f2 -d ' ')/../../../docs/patches/deeppicker/*.py DeepPicker-python/" + ], + "metadata": { + "id": "nfFH2QR2Q_HG" + }, + "execution_count": 17, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "11. Create DeepPicker Conda environment" + ], + "metadata": { + "id": "gdaQWl0eRMEB" + } + }, + { + "cell_type": "code", + "source": [ + "! mamba create -n deep -c anaconda -c conda-forge -c pytorch python=3.7 tensorflow-gpu=2.4.1 cudatoolkit=10.1.243 cudnn=7.6.5 scipy matplotlib scikit-image mrcfile pytorch torchvision" + ], + "metadata": { + "id": "vRkOYkN4ROr3" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**Cleanup**\n", + "\n", + "12. Check that Conda environments can be found (cryolo, deep, topaz)" + ], + "metadata": { + "id": "8rc0qlcSUqRH" + } + }, + { + "cell_type": "code", + "source": [ + "! conda env list" + ], + "metadata": { + "id": "MaytIvvlUufU" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "13. Clean up installation files" + ], + "metadata": { + "id": "RKR_GupWRfDa" + } + }, + { + "cell_type": "code", + "source": [ + "! conda clean --all -y\n", + "! mamba clean --all -y" + ], + "metadata": { + "id": "KpaBw8y0Sm02" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JawwDI-OFSC_" + }, + "source": [ + "### Step 3 — run one-shot REPIC on example data _(optional)_" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-_b7a6NvFZi3" + }, + "source": [ + "1. Clone the REPIC GitHub repo and obtain example data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "SodLgQG8Fqwv" + }, + "outputs": [], + "source": [ + "! git clone https://github.com/ccameron/REPIC" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EZcATLbkF7Fu" + }, + "source": [ + "2. Calculate particle overlap and enumerate cliques using [get_cliques](https://github.com/ccameron/REPIC/blob/main/repic/commands/get_cliques.py).py (expected run time: <1 min)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "TzZavdHeGRyr" + }, + "outputs": [], + "source": [ + "! repic get_cliques /content/REPIC/examples/10017/ /content/REPIC/examples/10017/clique_files/ 180" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ub3K1h6rG7tM" + }, + "source": [ + "3. Find optimal cliques and create consensus particle files using [run_ilp.py](https://github.com/ccameron/REPIC/blob/main/repic/commands/run_ilp.py) (expected run time: <1 min)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "o0aGn5NwHVi1" + }, + "outputs": [], + "source": [ + "! repic run_ilp /content/REPIC/examples/10017/clique_files/ 180" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "X68pqVI6Hlqw" + }, + "source": [ + "A consensus particle coordinate file (in BOX format) for each micrograph will be produced in ```/content/REPIC/examples/10017/clique_files/```" + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4 — run iterative REPIC on example data _(optional)_" + ], + "metadata": { + "id": "-c66wgxGVysJ" + } + }, + { + "cell_type": "markdown", + "source": [ + "1. Download example data" + ], + "metadata": { + "id": "FHIYnBe-V5kp" + } + }, + { + "cell_type": "code", + "source": [ + "! bash $(pip show repic | grep -in \"Location\" | cut -f2 -d ' ')/repic/iterative_particle_picking/get_examples.sh /content/REPIC/examples/10057/data/ &> aws_download.log" + ], + "metadata": { + "id": "_iv2gzKBW9sO" + }, + "execution_count": 34, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "2. Create a configuration file for iterative ensemble particle picking using [iter_config.py](https://github.com/ccameron/REPIC/blob/main/repic/commands/iter_config.py) (expected run time: <1 min)" + ], + "metadata": { + "id": "STcRVFE-XwwY" + } + }, + { + "cell_type": "code", + "source": [ + "! repic iter_config /content/REPIC/examples/10057/ 176 224 /content/gmodel_phosnet_202005_N63_c17.h5 /content/DeepPicker-python 4 22" + ], + "metadata": { + "id": "YnApxf-LXhiO" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "3. Pick particles by iterative ensemble learning using [iter_pick.py](https://github.com/ccameron/REPIC/blob/main/repic/commands/iter_pick.py), a wrapper of [run.sh](https://github.com/ccameron/REPIC/blob/main/repic/iterative_particle_picking/run.sh) (expected run time: 20-30 min/iteration):" + ], + "metadata": { + "id": "vFZkBWgeX9Tb" + } + }, + { + "cell_type": "code", + "source": [ + "! repic iter_pick /content/iter_config.json 4 100" + ], + "metadata": { + "id": "zvLfFeO5YHTo" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "The final set of consensus particles for the testing set will be found in: ```/content/REPIC/examples/10057/iterative_particle_picking/round_4/train_100/clique_files/test/*.box```" + ], + "metadata": { + "id": "G-eth_OR0wEV" + } + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jiYc217QJgaZ" + }, + "source": [ + "### Step 5 — run REPIC on your data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XLX_XSDYJlov" + }, + "source": [ + "**REPIC overview**\n", + "\n", + "REPIC can be run in one of two modes:\n", + "\n", + "1. One shot — find consensus particles from the output of multiple particle picking algroithms\n", + "2. Iterative — _ab-initio_ train an ensemble of particle picking algorithms using either one-shot REPIC output or manually picked particles\n", + "\n", + "One-shot REPIC expects particle coordinate files (in BOX format) to be stored in separate subfolders for each particle picking algorithm:\n", + "\n", + "```console\n", + "folder/\n", + " | particle_picking_algorithm_1/\n", + " | mrc001.box\n", + " | mrc002.box\n", + " ⋮\n", + " | mrc999.box\n", + " | particle_picking_algorithm_2/\n", + " | mrc001.box\n", + " | mrc002.box\n", + " ⋮\n", + " | mrc999.box\n", + " ⋮\n", + " | particle_picking_algorithm_n/\n", + " | mrc001.box\n", + " | mrc002.box\n", + " ⋮\n", + " | mrc999.box\n", + "```\n", + "\n", + "Iterative REPIC expects micrograph (MRC) and particle coordinate (if using manually picked particles) files to be found in a singular folder:\n", + "```console\n", + "folder/\n", + " | mrc001.mrc\n", + " | mrc001.box\n", + " | mrc002.mrc\n", + " | mrc002.box\n", + " ⋮\n", + " | mrc999.mrc\n", + " | mrc999.box\n", + "```\n", + "\n", + "REPIC will create training and validation subsets from files in ```folder/``` before performing _ab-initio_ model training.\n", + "\n", + "**Uploading data to Google Colab - zipped folder**\n", + "\n", + "1. Upload a zipped folder of picked particles:\n", + " \n", + "      Click folder icon in left panel of Colab (\"Files\") > Upload to session storage\n", + "\n", + "2. Unzip folder:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "thh5FNOIL3_h" + }, + "outputs": [], + "source": [ + "! unzip .zip" + ] + }, + { + "cell_type": "markdown", + "source": [ + "3. Example command — calculate the particle overlap and enumerate cliques using [get_cliques.py](https://github.com/ccameron/REPIC/blob/main/repic/commands/get_cliques.py):" + ], + "metadata": { + "id": "v6f8hCpJAGno" + } + }, + { + "cell_type": "code", + "source": [ + "! repic get_cliques /clique_files/ " + ], + "metadata": { + "id": "NgPPfcNtAXR9" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "n-ILyrpJPFxL" + }, + "source": [ + "Replace `````` with the expected detection box size (in pixels).\n", + "\n", + "**Uploading data to Google Colab - Google Drive**\n", + "\n", + "1. Upload your data to Google Drive and mount it:" + ] + }, + { + "cell_type": "code", + "source": [ + "# Load the Drive helper and mount\n", + "from google.colab import drive\n", + "drive.mount('/content/drive')" + ], + "metadata": { + "id": "uwLV9vHM2Ua2" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "2. Example command — calculate the particle overlap and enumerate cliques using [get_cliques.py](https://github.com/ccameron/REPIC/blob/main/repic/commands/get_cliques.py):" + ], + "metadata": { + "id": "Vr0aZ15L_1cQ" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "V-oqRWnhPMHt" + }, + "outputs": [], + "source": [ + "! repic get_cliques /content/drive/MyDrive/ /content/drive/MyDrive//clique_files/ " + ] + }, + { + "cell_type": "markdown", + "source": [ + "For more information about REPIC commands see Steps 3 and 4 above or https://repic.readthedocs.io/en/latest/\n", + "\n", + "**Downloading REPIC results**\n", + "\n", + "Folders containing REPIC output can be downloaded using the following:" + ], + "metadata": { + "id": "aR-beAn22D1Z" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Ku1pXQpuQKyy" + }, + "outputs": [], + "source": [ + "! zip -r repic_out.zip /\n", + "from google.colab import files\n", + "files.download(\"repic_out.zip\")" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file From 5ef8848c3a83112f126c54223a82ebfe53bc1b9e Mon Sep 17 00:00:00 2001 From: ccameron Date: Wed, 13 Dec 2023 12:58:51 -0500 Subject: [PATCH 2/9] squashing colab int commits --- README.md | 2 +- repic/commands/get_cliques.py | 18 +++++++++++++++--- repic/commands/run_ilp.py | 9 ++++++--- 3 files changed, 22 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index e93129a..f8c9d02 100644 --- a/README.md +++ b/README.md @@ -91,7 +91,7 @@ conda activate repic ``` 6. Install REPIC using [pip](https://en.wikipedia.org/wiki/Pip_(package_manager)): ``` -pip install . +python -m pip install . ``` 7. [Obtain a Gurobi license](https://www.gurobi.com/academia/academic-program-and-licenses/) and set Gurobi key: ``` diff --git a/repic/commands/get_cliques.py b/repic/commands/get_cliques.py index 0dfabbe..5cd348d 100644 --- a/repic/commands/get_cliques.py +++ b/repic/commands/get_cliques.py @@ -175,12 +175,12 @@ def main(args): del i, method except (UnboundLocalError, IndexError) as e: # create empty BOX file if particle bounding boxes are not picked by all methods - print("Skipping micrograph - not all methods have picked particles...") + print("Skipping micrograph - not all methods have picked particles") out_file = os.path.join(args.out_dir, ''.join( [basename[1:-1], ".box"])) with open(out_file, 'wt') as o: pass - return + continue print("Calculating Jaccard indices ... ") # build k-d tree from x, y, and z coordinates with method key values @@ -254,6 +254,7 @@ def main(args): # retain row / col indices for sparse matrix cols.extend([j] * k) rows.extend([v.index(val) for val in clique]) + del j, clique, subgraph assert (len(cliques) == len( w)), "Error - concensus coordinates and ILP weight vector are not equal lengths" assert (len(w) == len(confidence) @@ -263,7 +264,18 @@ def main(args): assert (len(cliques) * k == len(cols) ), "Error - consensus coordinates or ILP sparse matrix indices (rows / cols) missing" A = coo_matrix(([1] * len(cols), (rows, cols)), shape=(len(v), n)) - del n, v, rows, cols, j, clique, subgraph + del n, v, rows, cols + + if len(cliques) == 0: + # no cliques found + print("Skipping micrograph - no cliques found") + # write empty BOX file + out_file = os.path.join(args.out_dir, ''.join( + [basename[1:-1], ".box"])) + with open(out_file, 'w') as o: + pass + del out_file, o + continue # write structures to storage for ILP optimization # add multi-out header diff --git a/repic/commands/run_ilp.py b/repic/commands/run_ilp.py index 50eb78f..b0ebbe2 100644 --- a/repic/commands/run_ilp.py +++ b/repic/commands/run_ilp.py @@ -156,6 +156,7 @@ def main(args): # SciPY only optimizes minimization problems w *= -1 + print(w) # restrict clique selection to integers integrality = np.ones_like(w) @@ -243,9 +244,11 @@ def main(args): weights += confidences # plot consensus particle weights - print("\nPlotting consensus particle weights ... ") - plot_particle_weights(args, weights, num_mrc, os.path.dirname(matrix_file)) - + try: + print("\nPlotting consensus particle weights ... ") + plot_particle_weights(args, weights, num_mrc, os.path.dirname(matrix_file)) + except UnboundLocalError: + print("Warning - no ILP matrix files found") if __name__ == '__main__': parser = argparse.ArgumentParser() From 503f43206d8fc284f0bd00327d238cc689ed6289 Mon Sep 17 00:00:00 2001 From: ccameron <30734249+ccameron@users.noreply.github.com> Date: Tue, 16 Jan 2024 13:38:00 -0500 Subject: [PATCH 3/9] Update environment.yml --- docs/environment.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/environment.yml b/docs/environment.yml index 4107fd7..b64b348 100644 --- a/docs/environment.yml +++ b/docs/environment.yml @@ -13,5 +13,5 @@ dependencies: - sphinx_rtd_theme - sphinx-autoapi - pip: - - readthedocs-sphinx-search + - readthedocs-sphinx-search>=0.3.2 prefix: /Users/ccameron/miniconda3/envs/repic From 8aa35519d7c56b998fa62ef11b52ab99f79be1d3 Mon Sep 17 00:00:00 2001 From: Christopher JF Cameron Date: Wed, 7 Feb 2024 11:45:04 -0500 Subject: [PATCH 4/9] squashing resub commits --- README.md | 11 +- repic/commands/iter_config.py | 19 +- repic/commands/iter_pick.py | 14 + repic/iterative_particle_picking/run.sh | 27 +- supp_data_files/supplemental_data_file_2.ods | Bin 8553 -> 0 bytes supp_data_files/supplemental_data_file_2.txt | 375 +++++++++++++++++++ supp_data_files/supplemental_data_file_3.ods | Bin 8801 -> 0 bytes supp_data_files/supplemental_data_file_3.txt | 220 +++++++++++ supp_data_files/supplemental_data_file_4.ods | Bin 0 -> 9455 bytes supp_data_files/supplemental_data_file_5.ods | Bin 0 -> 9741 bytes 10 files changed, 649 insertions(+), 17 deletions(-) delete mode 100644 supp_data_files/supplemental_data_file_2.ods create mode 100644 supp_data_files/supplemental_data_file_2.txt delete mode 100644 supp_data_files/supplemental_data_file_3.ods create mode 100644 supp_data_files/supplemental_data_file_3.txt create mode 100644 supp_data_files/supplemental_data_file_4.ods create mode 100644 supp_data_files/supplemental_data_file_5.ods diff --git a/README.md b/README.md index f8c9d02..dd05ddc 100644 --- a/README.md +++ b/README.md @@ -250,7 +250,8 @@ optional arguments: 1. Create a configuration file for iterative ensemble particle picking using [iter_config.py](https://github.com/ccameron/REPIC/blob/main/repic/commands/iter_config.py): ``` -usage: repic iter_config [-h] [--cryolo_env CRYOLO_ENV] [--deep_env DEEP_ENV] [--topaz_env TOPAZ_ENV] [--out_file_path OUT_FILE_PATH] +usage: repic iter_config [-h] [--cryolo_env CRYOLO_ENV] [--deep_env DEEP_ENV] [--deep_model DEEP_MODEL] [--topaz_env TOPAZ_ENV] + [--topaz_model TOPAZ_MODEL] [--out_file_path OUT_FILE_PATH] data_dir box_size exp_particles cryolo_model deep_dir topaz_scale topaz_rad positional arguments: @@ -267,8 +268,12 @@ optional arguments: --cryolo_env CRYOLO_ENV Conda environment name or prefix for SPHIRE-crYOLO installation (default:cryolo) --deep_env DEEP_ENV Conda environment name or prefix for DeepPicker installation (default:deep) + --deep_model DEEP_MODEL + path to pre-trained DeepPicker model (default:out-of-the-box model) --topaz_env TOPAZ_ENV Conda environment name or prefix for Topaz installation (default:topaz) + --topaz_model TOPAZ_MODEL + path to pre-trained Topaz model (default:out-of-the-box model) --out_file_path OUT_FILE_PATH path for created config file (default:./iter_config.json) ``` @@ -290,7 +295,7 @@ done 2. Iteratively pick particles using [iter_pick.py](https://github.com/ccameron/REPIC/blob/main/repic/commands/iter_pick.py), a wrapper of [run.sh](https://github.com/ccameron/REPIC/blob/main/repic/iterative_particle_picking/run.sh): ``` -usage: repic iter_pick [-h] [--semi_auto] [--score] [--out_file_path OUT_FILE_PATH] config_file num_iter train_size +usage: repic iter_pick [-h] [--semi_auto] [--sample_prob SAMPLE_PROB] [--score] [--out_file_path OUT_FILE_PATH] config_file num_iter train_size positional arguments: config_file path to REPIC config file @@ -300,6 +305,8 @@ positional arguments: optional arguments: -h, --help show this help message and exit --semi_auto initialize training labels with known particles (semi-automatic) + --sample_prob SAMPLE_PROB + sampling probability of initial training labels for 'semi_auto' (default:1.) --score evaluate picked particle sets --out_file_path OUT_FILE_PATH path for picking log file (default:/iter_pick.log) diff --git a/repic/commands/iter_config.py b/repic/commands/iter_config.py index 1c49a94..0f30be5 100644 --- a/repic/commands/iter_config.py +++ b/repic/commands/iter_config.py @@ -48,14 +48,15 @@ def add_arguments(parser): None """ parser.add_argument( - "data_dir", help="path to directory containing training data") + "data_dir", type=str, help="path to directory containing training data") parser.add_argument("box_size", type=int, help="particle bounding box size (in int[pixels])") parser.add_argument("exp_particles", type=int, help="number of expected particles (int)") parser.add_argument( - "cryolo_model", help="path to LOWPASS SPHIRE-crYOLO model") - parser.add_argument("deep_dir", help="path to DeepPicker scripts") + "cryolo_model", type=str, help="path to LOWPASS SPHIRE-crYOLO model") + parser.add_argument("deep_dir", type=str, + help="path to DeepPicker scripts") parser.add_argument("topaz_scale", type=int, help="Topaz scale value (int)") parser.add_argument("topaz_rad", type=int, @@ -64,8 +65,12 @@ def add_arguments(parser): help=f"Conda environment name or prefix for SPHIRE-crYOLO installation (default:{env_dict['cryolo']})") parser.add_argument("--deep_env", type=str, default=env_dict['deep'], help=f"Conda environment name or prefix for DeepPicker installation (default:{env_dict['deep']})") + parser.add_argument( + "--deep_model", help="path to pre-trained DeepPicker model (default:out-of-the-box model)") parser.add_argument("--topaz_env", type=str, default=env_dict['topaz'], help=f"Conda environment name or prefix for Topaz installation (default:{env_dict['topaz']})") + parser.add_argument( + "--topaz_model", help="path to pre-trained Topaz model (default:out-of-the-box model)") parser.add_argument("--out_file_path", type=str, default="iter_config.json", help="path for created config file (default:./iter_config.json)") @@ -105,6 +110,14 @@ def main(args): 0), f"Error - Conda environment(s) not found: {', '.join(envs)}" del envs, stdout + # check if optional models provided and are valid + if not args.deep_model is None: + assert (os.path.exists(os.path.dirname(args.deep_model)) + ), f"Error - provided DeepPicker model not found: {args.deep_model}" + if not args.topaz_model is None: + assert (os.path.exists(args.topaz_model) + ), f"Error - provided Topaz model not found: {args.topaz_model}" + # write JSON file of iter_pick parameters print(f"Writing config file to {args.out_file_path}") params_dict = vars(copy.deepcopy(args)) diff --git a/repic/commands/iter_pick.py b/repic/commands/iter_pick.py index 1f663bc..a90f771 100644 --- a/repic/commands/iter_pick.py +++ b/repic/commands/iter_pick.py @@ -29,6 +29,8 @@ def add_arguments(parser): help="training subset percentage (int)") parser.add_argument("--semi_auto", action="store_true", help="initialize training labels with known particles (semi-automatic)") + parser.add_argument("--sample_prob", type=float, default=1., + help=f"sampling probability of initial training labels for 'semi_auto' (default:1.)") parser.add_argument("--score", action="store_true", help="evaluate picked particle sets") parser.add_argument("--out_file_path", type=str, @@ -42,6 +44,14 @@ def main(args): Args: args (obj): argparse command line argument object """ + # validate command line arguments + assert (os.path.exists(args.config_file) + ), f"Error - provided config file not found: {args.config_file}" + assert (isinstance(args.num_iter, int) + ), f"Error - num_iter should be an integer. Found {args.num_iter}" + assert (0. <= args.sample_prob <= 1. + ), f"Error - sample_prob should be a float within [0, 1]. Found: {args.sample_prob}" + # load JSON config file with open(args.config_file, 'rt') as f: params_dict = json.load(f) @@ -61,12 +71,16 @@ def main(args): str(params_dict["exp_particles"]), ''.join(["train_", str(args.train_size)]), "semi" if args.semi_auto else "auto", + str(args.sample_prob), '1' if args.score else '0', params_dict["cryolo_env"], params_dict["cryolo_model"], params_dict["deep_env"], params_dict["deep_dir"], + params_dict["deep_model"] if not params_dict["deep_model"] is None else os.path.join( + params_dict["deep_env"], "trained_model", "model_demo_type3"), params_dict["topaz_env"], + str(params_dict["topaz_model"]), str(params_dict["topaz_scale"]), str(params_dict["topaz_rad"]), ] diff --git a/repic/iterative_particle_picking/run.sh b/repic/iterative_particle_picking/run.sh index 6b4b165..5f474e6 100644 --- a/repic/iterative_particle_picking/run.sh +++ b/repic/iterative_particle_picking/run.sh @@ -21,17 +21,20 @@ export REPIC_NUM_PARTICLES=${4} LABEL=${5} export REPIC_TRAIN_MRC=${IN_DIR}/iterative_particle_picking/train/${LABEL} if [ "auto" = ${6} ]; then USE_MANUAL_LABELS=false; else USE_MANUAL_LABELS=true;fi -if [[ "${7}" == 1 ]]; then GET_SCORE=true; else GET_SCORE=false; fi +export SAMPLE_PROB=${7} +if [[ "${8}" == 1 ]]; then GET_SCORE=true; else GET_SCORE=false; fi export REPIC_VAL_MRC=${IN_DIR}/iterative_particle_picking/val/ export REPIC_TEST_MRC=${IN_DIR}/iterative_particle_picking/test REPIC_COORD=${IN_DIR}/iterative_particle_picking -export CRYOLO_ENV=${8} -export CRYOLO_MODEL=${9} -export DEEP_ENV=${10} -export DEEP_DIR=${11} -export TOPAZ_ENV=${12} -export TOPAZ_SCALE=${13} -export TOPAZ_PARTICLE_RAD=${14} +export CRYOLO_ENV=${9} +export CRYOLO_MODEL=${10} +export DEEP_ENV=${11} +export DEEP_DIR=${12} +export DEEP_MODEL=${13} +export TOPAZ_ENV=${14} +if [ "None" = ${15} ]; then :; else export TOPAZ_MODEL=${15}; fi +export TOPAZ_SCALE=${16} +export TOPAZ_PARTICLE_RAD=${17} # CrYOLO filtered micrograph directory export CRYOLO_FILTERED_DIR=${IN_DIR}/iterative_particle_picking/cryolo_filtered_tmp @@ -60,11 +63,11 @@ mkdir -p ${SUB_DIR} if ! ${USE_MANUAL_LABELS}; then ## - # step 2 - apply general models to train and test sets + # step 2 - apply pre-trained models to train and test sets ## echo -e """\n--- Identifying particles in '${LABEL}' --- - Applying general models to train, val, and test micrographs ... + Applying pre-trained models to train, val, and test micrographs ... --- Round: 0 --- \tSPHIRE-crYOLO ... " # SPHIRE-crYOLO @@ -187,7 +190,7 @@ else while [ $(cat ${REPIC_OUT_DIR}/*.box | wc -l) -le 4 ]; do # ensure there are enough examples sampled for file in ${REPIC_TRAIN_MRC}/*.box; do base=$(basename ${file}) - cat ${file} | awk 'BEGIN {srand()} !/^$/ { if (rand() <= 0.01) print $0}' > ${REPIC_OUT_DIR}/${base} + cat ${file} | awk -v prob=${SAMPLE_PROB} 'BEGIN {srand()} !/^$/ { if (rand() <= prob) print $0}' > ${REPIC_OUT_DIR}/${base} perl -i -pe 's/(\d*\.\d*)/int($1+0.5)/ge' ${REPIC_OUT_DIR}/${base} done done @@ -199,7 +202,7 @@ else while [ $(cat ${REPIC_OUT_DIR}/*.box | wc -l) -le 4 ]; do for file in ${REPIC_VAL_MRC}/*.box; do base=$(basename ${file}) - cat ${file} | awk 'BEGIN {srand()} !/^$/ { if (rand() <= 0.01) print $0}' > ${REPIC_OUT_DIR}/${base} + cat ${file} | awk -v prob=${SAMPLE_PROB} 'BEGIN {srand()} !/^$/ { if (rand() <= prob) print $0}' > ${REPIC_OUT_DIR}/${base} perl -i -pe 's/(\d*\.\d*)/int($1+0.5)/ge' ${REPIC_OUT_DIR}/${base} done done diff --git 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z2o(=FODsp<&wmhLf?3?WH%j2n@w|1SXFE`fwj}ykBw4ocGS}bbUEj{ znAinLe-3=|7o@g5x<<||LuV0gyk%zfrrQN&#uK|aClBmPLOT9_zhOZL(ERbRjyN&G zO1ASXEyA(k$rb_G75;6gf@N*!#huuW-L6T0+1eT2+R3r|$8hr4i(4==(EukvKMVm| zUi(;M%&my}3w=KEt6{&E(bPq|B(-YPKAc3ZQf-NcCvOXoQclFQM^c{2XA5QNE+R#% zPGjrBkvrK`r$d@9BUK6bGrvL1sdK}vjgaZM;MvX0Gb*6rft4Boa!A9Ts*eBwq$2_V zXfK=4PS&0l|8}5b61m^^bK!>hFg(fbkPW+mfK7>>rRK71VL>qW>Z&lXVzcaM_gl9_ z7Eh^|Kb@y*t24irJ*o;gYt_k3XvyCK5!qE4uj`~-?rk3;>GxWzTX@JZuCTD3#+I{I zD6v6+iWobGDQXCOrnWc}`RLr%?%K?6iepik zaDOMDAMJx-g(Wzy2>%r!rk02dSb2(X&*0tT#{?e}5+4J=zD)yN{+EIelk#$8EPKbO zH#|7Jl+q!;))p@0F&wgm|H5(c@^ReoO(b$B5D6FE7!4tV0l9bgq!QoIiz~8?`6;qg z*gJPHUcc_OgztTb?TYq(%bFtyL;oFWs&c7Z+g%rC?)m!SGmo(wj&)`cnL1D%?%!r( zFmwW^NrJm-REZ{fQAYJB0B)% zW%hC;rE<8v{y#qgB;j)^A*$0SQL&M*{qI6T0Lp)Xp#%JXsQ&kaf1|;hq+CvN6gb1b G$$tQwMXn_P From 17f383a653d0e1daa9b74372ea788f4b0c780c11 Mon Sep 17 00:00:00 2001 From: Christopher JF Cameron Date: Mon, 24 Jun 2024 10:25:18 -0400 Subject: [PATCH 6/9] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index dd05ddc..f6eec07 100644 --- a/README.md +++ b/README.md @@ -7,7 +7,7 @@ ## Overview -REliable PIcking by Consensus (REPIC) is an ensemble learning approach to cryogenic-electron microscopy (cryo-EM) particle picking. It identifies particles common to multiple picked particle sets (i.e., consensus particles) using graph theory and integer linear programming (ILP). Picked particle sets may be found by a human specialist (manual), template matching, mathematical function (e.g., RELION's Laplacian-of-Gaussian auto-picking), or machine-learning method. A schematic representation of REPIC applied to the output of three CNN-based particle pickers is below: +REliable PIcking by Consensus (REPIC) is a consensus methodology for harnessing multiple cryogenic-electron microscopy (cryo-EM) particle picking algorithms. It identifies particles common to multiple picked particle sets (i.e., consensus particles) using graph theory and integer linear programming (ILP). Picked particle sets may be found by a human specialist (manual), template matching, mathematical function (e.g., RELION's Laplacian-of-Gaussian auto-picking), or machine-learning method. A schematic representation of REPIC applied to the output of three CNN-based particle pickers is below:

From 8eff24f23a0f3f37a31a242e86c68f936999e1a8 Mon Sep 17 00:00:00 2001 From: Christopher JF Cameron Date: Mon, 24 Jun 2024 10:45:06 -0400 Subject: [PATCH 7/9] resub updates --- conda-build/meta.yaml | 4 +- repic_colab.ipynb | 264 +++++++++++++++++++++--------------------- setup.py | 31 +++-- 3 files changed, 153 insertions(+), 146 deletions(-) diff --git a/conda-build/meta.yaml b/conda-build/meta.yaml index 9b539b4..74d02ff 100644 --- a/conda-build/meta.yaml +++ b/conda-build/meta.yaml @@ -40,8 +40,8 @@ about: license: BSD-3-Clause license_family: BSD license_file: LICENSE - summary: REPIC - an ensemble learning approach to cryo-EM particle picking. - description: REliable PIcking by Consensus (REPIC) is an ensemble learning approach to cryogenic-electron microscopy (cryo-EM) particle picking. It identifies particles common to multiple picked particle sets (i.e., consensus particles) using graph theory and integer linear programming (ILP). Picked particle sets may be found by a human specialist (manual), template matching, mathematical function (e.g., RELION's Laplacian-of-Gaussian auto-picking), or machine-learning method. REPIC expects particle sets to be in BOX file format (*.box) where each particle has coordinates, a detection box size (in pixels), and (optional) a score [0-1]. + summary: REPIC - a consensus methodology for harnessing multiple cryo-EM particle pickers. + description: REliable PIcking by Consensus (REPIC) is a consensus methodology for harnessing multiple cryogenic-electron microscopy (cryo-EM) particle picking algorithms. It identifies particles common to multiple picked particle sets (i.e., consensus particles) using graph theory and integer linear programming (ILP). Picked particle sets may be found by a human specialist (manual), template matching, mathematical function (e.g., RELION's Laplacian-of-Gaussian auto-picking), or machine-learning method. REPIC expects particle sets to be in BOX file format (*.box) where each particle has coordinates, a detection box size (in pixels), and (optional) a score [0-1]. dev_url: https://github.com/ccameron/REPIC doc_url: https://repic.readthedocs.io/en/latest/ doc_source_url: https://github.com/ccameron/REPIC/blob/main/README.md diff --git a/repic_colab.ipynb b/repic_colab.ipynb index 80dcb4b..171d6db 100644 --- a/repic_colab.ipynb +++ b/repic_colab.ipynb @@ -10,7 +10,7 @@ "\n", "## Welcome to the official REPIC Jupyter Notebook for Google Colab!\n", "\n", - "REliable PIcking by Consensus (REPIC) is an ensemble learning approach to cryogenic-electron microscopy (cryo-EM) particle picking. It identifies particles common to multiple picked particle sets (i.e., consensus particles) using graph theory and integer linear programming.\n", + "REliable PIcking by Consensus (REPIC) is a consensus methodology for harnessing multiple cryogenic-electron microscopy (cryo-EM) particle picking algorithms. It identifies particles common to multiple picked particle sets (i.e., consensus particles) using graph theory and integer linear programming.\n", "\n", "Development: https://github.com/ccameron/REPIC\n", "\n", @@ -36,12 +36,12 @@ }, { "cell_type": "markdown", - "source": [ - "1. Check the availability of the ```conda``` command" - ], "metadata": { "id": "wGrJRzHOXV9h" - } + }, + "source": [ + "1. Check the availability of the ```conda``` command" + ] }, { "cell_type": "code", @@ -112,12 +112,12 @@ }, { "cell_type": "markdown", - "source": [ - "1. Install Mamba package manager: https://github.com/mamba-org/mamba" - ], "metadata": { "id": "hhOskLGxU_LT" - } + }, + "source": [ + "1. Install Mamba package manager: https://github.com/mamba-org/mamba" + ] }, { "cell_type": "code", @@ -197,12 +197,12 @@ }, { "cell_type": "markdown", - "source": [ - "5. Download pre-trained crYOLO LOWPASS model" - ], "metadata": { "id": "PhPnf9dmIrz4" - } + }, + "source": [ + "5. Download pre-trained crYOLO LOWPASS model" + ] }, { "cell_type": "code", @@ -248,138 +248,138 @@ }, { "cell_type": "code", - "source": [ - "! mamba install -c bioconda repic -y" - ], + "execution_count": null, "metadata": { "id": "5vliH-nvP8U2" }, - "execution_count": null, - "outputs": [] + "outputs": [], + "source": [ + "! mamba install -c bioconda repic -y" + ] }, { "cell_type": "markdown", - "source": [ - "8. Install Linux basic calculator (bc) package: https://linux.die.net/man/1/bc" - ], "metadata": { "id": "Pde-ahNZdATb" - } + }, + "source": [ + "8. Install Linux basic calculator (bc) package: https://linux.die.net/man/1/bc" + ] }, { "cell_type": "code", - "source": [ - "! apt-get install bc" - ], + "execution_count": null, "metadata": { "id": "_z8yIMSMb4nr" }, - "execution_count": null, - "outputs": [] + "outputs": [], + "source": [ + "! apt-get install bc" + ] }, { "cell_type": "markdown", + "metadata": { + "id": "SYanXcvhQT93" + }, "source": [ "**DeepPicker install**\n", "9. Clone DeepPicker GitHub repository" - ], - "metadata": { - "id": "SYanXcvhQT93" - } + ] }, { "cell_type": "code", - "source": [ - "! git clone https://github.com/nejyeah/DeepPicker-python.git" - ], + "execution_count": null, "metadata": { "id": "p5MIguDOQS03" }, - "execution_count": null, - "outputs": [] + "outputs": [], + "source": [ + "! git clone https://github.com/nejyeah/DeepPicker-python.git" + ] }, { "cell_type": "markdown", - "source": [ - "10. Apply REPIC patch to DeepPicker Python scripts" - ], "metadata": { "id": "hK6RhotWQ4oN" - } + }, + "source": [ + "10. Apply REPIC patch to DeepPicker Python scripts" + ] }, { "cell_type": "code", - "source": [ - "! cp $(pip show repic | grep -in \"Location\" | cut -f2 -d ' ')/../../../docs/patches/deeppicker/*.py DeepPicker-python/" - ], + "execution_count": 17, "metadata": { "id": "nfFH2QR2Q_HG" }, - "execution_count": 17, - "outputs": [] + "outputs": [], + "source": [ + "! cp $(pip show repic | grep -in \"Location\" | cut -f2 -d ' ')/../../../docs/patches/deeppicker/*.py DeepPicker-python/" + ] }, { "cell_type": "markdown", - "source": [ - "11. Create DeepPicker Conda environment" - ], "metadata": { "id": "gdaQWl0eRMEB" - } + }, + "source": [ + "11. Create DeepPicker Conda environment" + ] }, { "cell_type": "code", - "source": [ - "! mamba create -n deep -c anaconda -c conda-forge -c pytorch python=3.7 tensorflow-gpu=2.4.1 cudatoolkit=10.1.243 cudnn=7.6.5 scipy matplotlib scikit-image mrcfile pytorch torchvision" - ], + "execution_count": null, "metadata": { "id": "vRkOYkN4ROr3" }, - "execution_count": null, - "outputs": [] + "outputs": [], + "source": [ + "! mamba create -n deep -c anaconda -c conda-forge -c pytorch python=3.7 tensorflow-gpu=2.4.1 cudatoolkit=10.1.243 cudnn=7.6.5 scipy matplotlib scikit-image mrcfile pytorch torchvision" + ] }, { "cell_type": "markdown", + "metadata": { + "id": "8rc0qlcSUqRH" + }, "source": [ "**Cleanup**\n", "\n", "12. Check that Conda environments can be found (cryolo, deep, topaz)" - ], - "metadata": { - "id": "8rc0qlcSUqRH" - } + ] }, { "cell_type": "code", - "source": [ - "! conda env list" - ], + "execution_count": null, "metadata": { "id": "MaytIvvlUufU" }, - "execution_count": null, - "outputs": [] + "outputs": [], + "source": [ + "! conda env list" + ] }, { "cell_type": "markdown", - "source": [ - "13. Clean up installation files" - ], "metadata": { "id": "RKR_GupWRfDa" - } + }, + "source": [ + "13. Clean up installation files" + ] }, { "cell_type": "code", - "source": [ - "! conda clean --all -y\n", - "! mamba clean --all -y" - ], + "execution_count": null, "metadata": { "id": "KpaBw8y0Sm02" }, - "execution_count": null, - "outputs": [] + "outputs": [], + "source": [ + "! conda clean --all -y\n", + "! mamba clean --all -y" + ] }, { "cell_type": "markdown", @@ -461,81 +461,81 @@ }, { "cell_type": "markdown", - "source": [ - "### Step 4 — run iterative REPIC on example data _(optional)_" - ], "metadata": { "id": "-c66wgxGVysJ" - } + }, + "source": [ + "### Step 4 — run iterative REPIC on example data _(optional)_" + ] }, { "cell_type": "markdown", - "source": [ - "1. Download example data" - ], "metadata": { "id": "FHIYnBe-V5kp" - } + }, + "source": [ + "1. Download example data" + ] }, { "cell_type": "code", - "source": [ - "! bash $(pip show repic | grep -in \"Location\" | cut -f2 -d ' ')/repic/iterative_particle_picking/get_examples.sh /content/REPIC/examples/10057/data/ &> aws_download.log" - ], + "execution_count": 34, "metadata": { "id": "_iv2gzKBW9sO" }, - "execution_count": 34, - "outputs": [] + "outputs": [], + "source": [ + "! bash $(pip show repic | grep -in \"Location\" | cut -f2 -d ' ')/repic/iterative_particle_picking/get_examples.sh /content/REPIC/examples/10057/data/ &> aws_download.log" + ] }, { "cell_type": "markdown", - "source": [ - "2. Create a configuration file for iterative ensemble particle picking using [iter_config.py](https://github.com/ccameron/REPIC/blob/main/repic/commands/iter_config.py) (expected run time: <1 min)" - ], "metadata": { "id": "STcRVFE-XwwY" - } + }, + "source": [ + "2. Create a configuration file for iterative ensemble particle picking using [iter_config.py](https://github.com/ccameron/REPIC/blob/main/repic/commands/iter_config.py) (expected run time: <1 min)" + ] }, { "cell_type": "code", - "source": [ - "! repic iter_config /content/REPIC/examples/10057/ 176 224 /content/gmodel_phosnet_202005_N63_c17.h5 /content/DeepPicker-python 4 22" - ], + "execution_count": null, "metadata": { "id": "YnApxf-LXhiO" }, - "execution_count": null, - "outputs": [] + "outputs": [], + "source": [ + "! repic iter_config /content/REPIC/examples/10057/ 176 224 /content/gmodel_phosnet_202005_N63_c17.h5 /content/DeepPicker-python 4 22" + ] }, { "cell_type": "markdown", - "source": [ - "3. Pick particles by iterative ensemble learning using [iter_pick.py](https://github.com/ccameron/REPIC/blob/main/repic/commands/iter_pick.py), a wrapper of [run.sh](https://github.com/ccameron/REPIC/blob/main/repic/iterative_particle_picking/run.sh) (expected run time: 20-30 min/iteration):" - ], "metadata": { "id": "vFZkBWgeX9Tb" - } + }, + "source": [ + "3. Pick particles by iterative ensemble learning using [iter_pick.py](https://github.com/ccameron/REPIC/blob/main/repic/commands/iter_pick.py), a wrapper of [run.sh](https://github.com/ccameron/REPIC/blob/main/repic/iterative_particle_picking/run.sh) (expected run time: 20-30 min/iteration):" + ] }, { "cell_type": "code", - "source": [ - "! repic iter_pick /content/iter_config.json 4 100" - ], + "execution_count": null, "metadata": { "id": "zvLfFeO5YHTo" }, - "execution_count": null, - "outputs": [] + "outputs": [], + "source": [ + "! repic iter_pick /content/iter_config.json 4 100" + ] }, { "cell_type": "markdown", - "source": [ - "The final set of consensus particles for the testing set will be found in: ```/content/REPIC/examples/10057/iterative_particle_picking/round_4/train_100/clique_files/test/*.box```" - ], "metadata": { "id": "G-eth_OR0wEV" - } + }, + "source": [ + "The final set of consensus particles for the testing set will be found in: ```/content/REPIC/examples/10057/iterative_particle_picking/round_4/train_100/clique_files/test/*.box```" + ] }, { "cell_type": "markdown", @@ -617,23 +617,23 @@ }, { "cell_type": "markdown", - "source": [ - "3. Example command — calculate the particle overlap and enumerate cliques using [get_cliques.py](https://github.com/ccameron/REPIC/blob/main/repic/commands/get_cliques.py):" - ], "metadata": { "id": "v6f8hCpJAGno" - } + }, + "source": [ + "3. Example command — calculate the particle overlap and enumerate cliques using [get_cliques.py](https://github.com/ccameron/REPIC/blob/main/repic/commands/get_cliques.py):" + ] }, { "cell_type": "code", - "source": [ - "! repic get_cliques /clique_files/ " - ], + "execution_count": null, "metadata": { "id": "NgPPfcNtAXR9" }, - "execution_count": null, - "outputs": [] + "outputs": [], + "source": [ + "! repic get_cliques /clique_files/ " + ] }, { "cell_type": "markdown", @@ -650,25 +650,25 @@ }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "uwLV9vHM2Ua2" + }, + "outputs": [], "source": [ "# Load the Drive helper and mount\n", "from google.colab import drive\n", "drive.mount('/content/drive')" - ], - "metadata": { - "id": "uwLV9vHM2Ua2" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "markdown", - "source": [ - "2. Example command — calculate the particle overlap and enumerate cliques using [get_cliques.py](https://github.com/ccameron/REPIC/blob/main/repic/commands/get_cliques.py):" - ], "metadata": { "id": "Vr0aZ15L_1cQ" - } + }, + "source": [ + "2. Example command — calculate the particle overlap and enumerate cliques using [get_cliques.py](https://github.com/ccameron/REPIC/blob/main/repic/commands/get_cliques.py):" + ] }, { "cell_type": "code", @@ -683,16 +683,16 @@ }, { "cell_type": "markdown", + "metadata": { + "id": "aR-beAn22D1Z" + }, "source": [ "For more information about REPIC commands see Steps 3 and 4 above or https://repic.readthedocs.io/en/latest/\n", "\n", "**Downloading REPIC results**\n", "\n", "Folders containing REPIC output can be downloaded using the following:" - ], - "metadata": { - "id": "aR-beAn22D1Z" - } + ] }, { "cell_type": "code", @@ -723,4 +723,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} diff --git a/setup.py b/setup.py index 6bac2d7..1cb9944 100644 --- a/setup.py +++ b/setup.py @@ -11,7 +11,9 @@ # package meta-data NAME = "repic" -DESCRIPTION = "REPIC - an ensemble learning approach for cryo-EM particle picking." +DESCRIPTION = ( + "REPIC - a consensus methodology for harnessing multiple cryo-EM particle pickers." +) URL = "https://github.com/ccameron/REPIC" EMAIL = "christopher.cameron@yale.edu" AUTHOR = "Christopher JF Cameron" @@ -25,7 +27,7 @@ "numpy>=1.24.2", "pandas>=2.0.2", "scipy>=1.10.0", - "tqdm>=4.65.0" + "tqdm>=4.65.0", ] work_dir = os.path.abspath(os.path.dirname(__file__)) @@ -33,13 +35,13 @@ # import README and use as the long-description try: with open(os.path.join(work_dir, "README.md"), encoding="utf-8") as f: - long_description = '\n' + f.read() + long_description = "\n" + f.read() except FileNotFoundError: long_description = DESCRIPTION # load the package's __version__.py module as a dictionary about = {} -project_slug = NAME.lower().replace('-', '_').replace(' ', '_') +project_slug = NAME.lower().replace("-", "_").replace(" ", "_") with open(os.path.join(work_dir, project_slug, "__version__.py")) as f: exec(f.read(), about) @@ -57,24 +59,29 @@ include_package_data=True, data_files=[ ("docs", ["docs/cryolo.md", "docs/deeppicker.md", "docs/topaz.md"]), - ("docs/patches/deeppicker", ["docs/patches/deeppicker/autoPick.py", - "docs/patches/deeppicker/autoPicker.py", - "docs/patches/deeppicker/dataLoader.py", - "docs/patches/deeppicker/deepModel.py", - "docs/patches/deeppicker/starReader.py", - "docs/patches/deeppicker/train.py"]) + ( + "docs/patches/deeppicker", + [ + "docs/patches/deeppicker/autoPick.py", + "docs/patches/deeppicker/autoPicker.py", + "docs/patches/deeppicker/dataLoader.py", + "docs/patches/deeppicker/deepModel.py", + "docs/patches/deeppicker/starReader.py", + "docs/patches/deeppicker/train.py", + ], + ), ], entry_points={ "console_scripts": ["repic=repic.main:main"], }, install_requires=REQUIRED, - license='BSD-3-Clause', + license="BSD-3-Clause", classifiers=[ "License :: OSI Approved :: BSD License", "Programming Language :: Python", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.8", "Programming Language :: Python :: Implementation :: CPython", - 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