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Merge pull request #4 from microscopic-image-analysis/downloader-script
Data downloader script + adding requirements.txt
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‎.gitignore‎

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# folders
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data
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dist
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*.zip

‎README.md‎

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For a detailed usage, please check [synthetic_mef.py](scripts/synthetic_mef.py) that uses synthetic ptychography data.
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## Publication results
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## Reproducing results
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Ptychography data used for the publication results can be found at [Zenodo](https://doi.org/10.5281/zenodo.10809893). It also includes the code which is in this repository. Therefore, to replicate the publication results, please follow the below steps:
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1. Download and unzip the dataset and the code from [Zenodo](https://doi.org/10.5281/zenodo.10809893).
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2. Create a virtual environment and install the dependencies as
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To reproduce the ptychographic reconstruction results from the paper, please follow the below steps:
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1. Please clone this repository and install the *pinned* dependencies in a conda environment.
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```bash
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git clone https://github.com/microscopic-image-analysis/bayes-mef.git
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cd bayes-mef
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conda create --name bayes-mef-venv python=3.10.13 # or python version satisfying ">=3.9, <3.12"
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conda create --name bayes-mef-venv python=3.11.5
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```
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2. Now activate the environment and install the *pinned* dependencies.
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```bash
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conda activate bayes-mef-venv
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pip install -e .
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pip install -r requirements.txt
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```
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3. An additional dependency of `ptylab` is required for processing ptychography data
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3. Download the data from [Zenodo](https://zenodo.org/doi/10.5281/zenodo.10964222) with the following command:
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```bash
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pip install git+https://github.com/PtyLab/PtyLab.py.git@main
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./download_data.sh
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```
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4. Please run python scripts under [scripts/](scripts) directory to replicate results in the publication.
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5. Optional: For faster ptychographic reconstructions using GPU, please install `cupy` as given under its [installation guide](https://docs.cupy.dev/en/stable/install.html).
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4. Optional: For faster ptychographic reconstructions using GPU, please install `cupy` as given under its [installation guide](https://docs.cupy.dev/en/stable/install.html).
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5. Run files from the [scripts/](scripts) directory for plotting the results.
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## Citation
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If you found this algorithm or the publication useful, please cite us at:

‎download_data.sh‎

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#!/bin/bash
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# Function to check if the OS is Windows
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is_windows() {
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if [[ "$(uname -s)" =~ "MINGW" || "$(uname -s)" =~ "MSYS" || "$(uname -o)" =~ "Msys" ]]; then
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return 0
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else
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return 1
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fi
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}
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# Function to download the zip file
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download_zip() {
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local URL="$1"
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local ZIP_FILE=$(basename "$URL")
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if is_windows; then
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curl -o "$ZIP_FILE" "$URL" || { echo "Error: Failed to download $ZIP_FILE"; exit 1; }
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else
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wget "$URL" || { echo "Error: Failed to download $ZIP_FILE"; exit 1; }
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fi
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}
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# Function to unzip the file
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unzip_file() {
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local ZIP_FILE="$1"
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if is_windows; then
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powershell Expand-Archive -Path "$ZIP_FILE" -DestinationPath . || { echo "Error: Failed to unzip $ZIP_FILE"; exit 1; }
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else
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unzip "$ZIP_FILE" || { echo "Error: Failed to unzip $ZIP_FILE"; exit 1; }
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fi
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}
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# Function to delete the zip file
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delete_zip() {
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local ZIP_FILE="$1"
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if is_windows; then
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del "$ZIP_FILE" || { echo "Error: Failed to delete $ZIP_FILE"; exit 1; }
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else
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rm "$ZIP_FILE" || { echo "Error: Failed to delete $ZIP_FILE"; exit 1; }
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fi
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}
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# URL of the zip file
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URL="https://zenodo.org/records/10964223/files/data.zip?download=1"
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# File name of the zip file
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ZIP_FILE=$(basename "$URL")
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# Download the zip file
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download_zip "$URL"
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# Unzip the file
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unzip_file "$ZIP_FILE"
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# Delete the zip file
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delete_zip "$ZIP_FILE"
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# Optional: Print a message indicating completion
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echo "Downloaded the data successfully"

‎requirements.txt‎

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absl-py==2.1.0
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alabaster==0.7.16
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app-model==0.2.4
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appdirs==1.4.4
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asttokens==2.4.1
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astunparse==1.6.3
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attrs==23.2.0
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Babel==2.14.0
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-e git+https://github.com/microscopic-image-analysis/bayes-mef.git@366e654b2cfa5ab2b426d6d890638c6452bc624a#egg=bayes_mef
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black==23.12.1
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blosc2==2.5.1
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bokeh==3.3.4
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build==1.1.1
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cachetools==5.3.3
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cachey==0.2.1
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certifi==2024.2.2
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charset-normalizer==3.3.2
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click==8.1.7
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cloudpickle==3.0.0
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comm==0.2.1
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contourpy==1.2.0
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cycler==0.12.1
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dask==2024.3.0
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debugpy==1.8.1
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decorator==5.1.1
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docstring-parser==0.15
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docutils==0.20.1
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executing==2.0.1
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flatbuffers==24.3.7
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fonttools==4.49.0
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freetype-py==2.4.0
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fsspec==2024.2.0
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gast==0.4.0
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google-auth==2.28.2
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google-auth-oauthlib==1.0.0
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google-pasta==0.2.0
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grpcio==1.62.1
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h5py==3.10.0
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HeapDict==1.0.1
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hsluv==5.0.4
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idna==3.6
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imageio==2.34.0
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imagesize==1.4.1
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importlib_metadata==7.0.2
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in-n-out==0.1.9
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ipykernel==6.29.3
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ipython==8.18.1
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jedi==0.19.1
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Jinja2==3.1.3
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joblib==1.3.2
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jsonschema==4.21.1
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jsonschema-specifications==2023.12.1
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jupyter_client==8.6.1
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jupyter_core==5.7.2
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keras==2.13.1
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kiwisolver==1.4.5
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lazy_loader==0.3
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libclang==16.0.6
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llvmlite==0.42.0
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locket==1.0.0
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magicgui==0.8.2
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Markdown==3.5.2
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markdown-it-py==3.0.0
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MarkupSafe==2.1.5
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matplotlib==3.8.3
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matplotlib-inline==0.1.6
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mdurl==0.1.2
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msgpack==1.0.8
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mypy-extensions==1.0.0
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napari==0.4.19.post1
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napari-console==0.0.9
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napari-plugin-engine==0.2.0
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napari-plugin-manager==0.1.0a2
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napari-svg==0.1.10
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ndindex==1.8
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nest-asyncio==1.6.0
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networkx==3.2.1
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npe2==0.7.4
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numba==0.59.0
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numexpr==2.9.0
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numpy==1.24.3
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numpydoc==1.6.0
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oauthlib==3.2.2
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opt-einsum==3.3.0
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packaging==24.0
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pandas==2.2.1
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parso==0.8.3
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partd==1.4.1
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pathspec==0.12.1
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pexpect==4.9.0
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pillow==10.2.0
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Pint==0.23
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platformdirs==4.2.0
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pooch==1.8.1
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prompt-toolkit==3.0.43
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protobuf==4.25.3
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psutil==5.9.8
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psygnal==0.10.1
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ptylab @ git+https://github.com/PtyLab/PtyLab.py.git@94663644c36722b2dc16011c33048a183a9f92de
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ptyprocess==0.7.0
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pure-eval==0.2.2
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py-cpuinfo==9.0.0
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pyasn1==0.5.1
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pyasn1-modules==0.3.0
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pyconify==0.1.6
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pydantic==1.10.14
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pydantic-compat==0.1.2
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Pygments==2.17.2
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PyOpenGL==3.1.7
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pyparsing==3.1.2
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pyproject_hooks==1.0.0
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PyQt5==5.15.10
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PyQt5-Qt5==5.15.2
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PyQt5-sip==12.13.0
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pyqtgraph==0.13.4
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python-dateutil==2.9.0.post0
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pytz==2024.1
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PyWavelets==1.5.0
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PyYAML==6.0.1
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pyzmq==25.1.2
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qtconsole==5.5.1
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QtPy==2.4.1
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referencing==0.33.0
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requests==2.31.0
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requests-oauthlib==1.4.0
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rich==13.7.1
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rpds-py==0.18.0
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rsa==4.9
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scikit-image==0.21.0
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scikit-learn==1.4.1.post1
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scipy==1.12.0
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six==1.16.0
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snowballstemmer==2.2.0
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Sphinx==7.2.6
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sphinxcontrib-applehelp==1.0.8
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sphinxcontrib-devhelp==1.0.6
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sphinxcontrib-htmlhelp==2.0.5
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sphinxcontrib-jsmath==1.0.1
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sphinxcontrib-qthelp==1.0.7
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sphinxcontrib-serializinghtml==1.1.10
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stack-data==0.6.3
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superqt==0.6.2
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tables==3.9.2
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tabulate==0.9.0
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tensorboard==2.13.0
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tensorboard-data-server==0.7.2
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tensorflow==2.13.0
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tensorflow-estimator==2.13.0
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tensorflow-io-gcs-filesystem==0.36.0
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termcolor==2.4.0
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threadpoolctl==3.3.0
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tifffile==2024.2.12
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tomli_w==1.0.0
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toolz==0.12.1
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tornado==6.4
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tqdm==4.66.2
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traitlets==5.14.2
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triangle==20230923
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typer==0.9.0
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typing_extensions==4.5.0
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tzdata==2024.1
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urllib3==2.2.1
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vispy==0.14.1
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wcwidth==0.2.13
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Werkzeug==3.0.1
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wrapt==1.16.0
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xyzservices==2023.10.1
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zipp==3.17.0

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