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Merge pull request #104 from catalystneuro/leifer
Add 001075 example figure
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‎001075/001075_notebook_environment.yml

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name: leifer_notebooks_created_8_29_2024
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channels:
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- defaults
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dependencies:
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- bzip2=1.0.8=h2bbff1b_6
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- ca-certificates=2024.7.2=haa95532_0
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- expat=2.6.2=hd77b12b_0
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- libffi=3.4.4=hd77b12b_1
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- openssl=3.0.14=h827c3e9_0
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- pip=24.2=py312haa95532_0
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- python=3.12.4=h14ffc60_1
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- setuptools=72.1.0=py312haa95532_0
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- sqlite=3.45.3=h2bbff1b_0
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- tk=8.6.14=h0416ee5_0
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- vc=14.40=h2eaa2aa_0
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- vs2015_runtime=14.40.33807=h98bb1dd_0
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- wheel=0.43.0=py312haa95532_0
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- xz=5.4.6=h8cc25b3_1
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- zlib=1.2.13=h8cc25b3_1
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- pip:
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- aiobotocore==2.14.0
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- aiohappyeyeballs==2.4.0
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- aiohttp==3.10.5
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- aioitertools==0.11.0
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- aiosignal==1.3.1
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- annotated-types==0.7.0
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- anyio==4.4.0
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- argon2-cffi==23.1.0
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- argon2-cffi-bindings==21.2.0
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- arrow==1.3.0
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- asciitree==0.3.3
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- asttokens==2.4.1
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- async-lru==2.0.4
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- attrs==24.2.0
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- babel==2.16.0
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- beautifulsoup4==4.12.3
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- bidsschematools==0.7.2
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- bleach==6.1.0
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- botocore==1.35.7
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- certifi==2024.8.30
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- cffi==1.17.0
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- charset-normalizer==3.3.2
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- ci-info==0.3.0
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- click==8.1.7
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- click-didyoumean==0.3.1
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- colorama==0.4.6
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- comm==0.2.2
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- contourpy==1.3.0
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- cycler==0.12.1
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- dandi==0.63.1
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- dandischema==0.10.3
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- debugpy==1.8.5
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- decorator==5.1.1
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- defusedxml==0.7.1
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- dnspython==2.6.1
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- email-validator==2.2.0
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- etelemetry==0.3.1
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- executing==2.0.1
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- fasteners==0.19
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- fastjsonschema==2.20.0
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- fonttools==4.53.1
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- fqdn==1.5.1
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- frozenlist==1.4.1
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- fscacher==0.4.1
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- fsspec==2024.6.1
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- h11==0.14.0
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- h5py==3.11.0
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- hdmf==3.14.3
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- hdmf-zarr==0.8.0
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- httpcore==1.0.5
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- httpx==0.27.2
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- humanize==4.10.0
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- idna==3.8
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- interleave==0.2.1
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- ipykernel==6.29.5
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- ipython==8.26.0
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- ipywidgets==8.1.5
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- isodate==0.6.1
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- isoduration==20.11.0
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- jaraco-classes==3.4.0
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- jaraco-context==6.0.1
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- jaraco-functools==4.0.2
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- jedi==0.19.1
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- jinja2==3.1.4
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- jmespath==1.0.1
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- joblib==1.4.2
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- json5==0.9.25
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- jsonpointer==3.0.0
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- jsonschema==4.23.0
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- jsonschema-specifications==2023.12.1
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- jupyter==1.1.0
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- jupyter-client==8.6.2
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- jupyter-console==6.6.3
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- jupyter-core==5.7.2
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- jupyter-events==0.10.0
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- jupyter-lsp==2.2.5
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- jupyter-server==2.14.2
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- jupyter-server-terminals==0.5.3
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- jupyterlab==4.2.5
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- jupyterlab-pygments==0.3.0
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- jupyterlab-server==2.27.3
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- jupyterlab-widgets==3.0.13
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- keyring==25.3.0
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- keyrings-alt==5.0.2
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- kiwisolver==1.4.5
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- markupsafe==2.1.5
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- matplotlib==3.9.2
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- matplotlib-inline==0.1.7
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- mistune==3.0.2
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- more-itertools==10.4.0
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- multidict==6.0.5
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- natsort==8.4.0
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- nbclient==0.10.0
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- nbconvert==7.16.4
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- nbformat==5.10.4
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- nest-asyncio==1.6.0
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- notebook==7.2.2
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- notebook-shim==0.2.4
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- numcodecs==0.13.0
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- numpy==1.26.4
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- nwbinspector==0.5.1
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- overrides==7.7.0
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- packaging==24.1
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- pandas==2.2.2
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- pandocfilters==1.5.1
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- parso==0.8.4
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- pillow==10.4.0
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- platformdirs==4.2.2
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- prometheus-client==0.20.0
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- prompt-toolkit==3.0.47
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- psutil==6.0.0
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- pure-eval==0.2.3
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- pycparser==2.22
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- pycryptodomex==3.20.0
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- pydantic==2.8.2
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- pydantic-core==2.20.1
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- pygments==2.18.0
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- pynwb==2.8.1
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- pyout==0.7.3
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- pyparsing==3.1.4
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- python-dateutil==2.9.0.post0
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- python-json-logger==2.0.7
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- pytz==2024.1
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- pywin32==306
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- pywin32-ctypes==0.2.3
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- pywinpty==2.0.13
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- pyyaml==6.0.2
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- pyzmq==26.2.0
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- referencing==0.35.1
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- remfile==0.1.13
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- requests==2.32.3
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- rfc3339-validator==0.1.4
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- rfc3986-validator==0.1.1
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- rfc3987==1.3.8
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- rpds-py==0.20.0
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- ruamel-yaml==0.18.6
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- ruamel-yaml-clib==0.2.8
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- s3fs==2024.6.1
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- scipy==1.14.1
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- semantic-version==2.10.0
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- send2trash==1.8.3
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- six==1.16.0
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- sniffio==1.3.1
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- soupsieve==2.6
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- stack-data==0.6.3
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- tenacity==9.0.0
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- terminado==0.18.1
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- threadpoolctl==3.5.0
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- tinycss2==1.3.0
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- tornado==6.4.1
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- tqdm==4.66.5
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- traitlets==5.14.3
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- types-python-dateutil==2.9.0.20240821
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- typing-extensions==4.12.2
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- tzdata==2024.1
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- uri-template==1.3.0
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- urllib3==2.2.2
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- wcwidth==0.2.13
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- webcolors==24.8.0
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- webencodings==0.5.1
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- websocket-client==1.8.0
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- widgetsnbextension==4.0.13
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- wrapt==1.16.0
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- yarl==1.9.4
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- zarr==2.18.2
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- zarr-checksum==0.4.2
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prefix: C:\Users\theac\anaconda3\envs\leifer_notebooks_created_8_29_2024

‎001075/001075_paper_figure_1d.ipynb

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‎001075/README.md

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# 001075 - Neural signal propagation atlas of Caenorhabditis elegans
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## How to cite
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Randi, Francesco; Sharma, Anuj; Dvali, Sophie; Leifer, Andrew M. (2024) Neural signal propagation atlas of Caenorhabditis elegans (Version 0.240930.1859) [Data set]. DANDI archive. https://doi.org/10.48324/dandi.001075/0.240930.1859
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## Setup
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Install [Conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html) on your machine.
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Install `git` and clone the example notebook repository:
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```bash
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conda install git
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git clone https://github.com/dandi/example-notebooks.git
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```
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Then create a specific environment for this notebook using:
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```bash
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conda env create --file example-notebooks/001075/001075_notebook_environment.yml
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```
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Then activate the environment:
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```bash
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conda activate leifer_notebooks_created_8_29_2024
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```
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Finally, start Jupyter:
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```bash
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jupyter notebook
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```
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And select the notebook `001075_paper_figure_1d.ipynb` from the file explorer in the browser.
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If interested in creating your own plots, refer to the code in `utils_001075` for how to handle the data streams.
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Note that all timing information is aligned to the NeuroPAL imaging system.
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## Help
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- Dataset: https://dandiarchive.org/dandiset/001075/0.240930.1859
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- Original publication: [Neural signal propagation atlas of Caenorhabditis elegans](https://www.nature.com/articles/s41586-023-06683-4)
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- [Visualize (Neurosift)](https://neurosift.app/?p=/dandiset&dandisetId=001075&dandisetVersion=0.240930.1859)
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- Note: the files are split by 'imaging' (raw) and 'segmentation' (processed). To view both, select a combined
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view for the same session: [example](https://neurosift.app/?p=/nwb&url=https://api.dandiarchive.org/api/assets/5feda038-0c84-494a-a0e0-c3ef8ec194d1/download/&url=https://api.dandiarchive.org/api/assets/40a6799b-4835-4170-89bb-9a866082e503/download/&dandisetId=001075&dandisetVersion=draft).
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- [NWB file basics](https://pynwb.readthedocs.io/en/stable/tutorials/general/plot_file.html#sphx-glr-tutorials-general-plot-file-py)
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- [How to read NWB files](https://pynwb.readthedocs.io/en/stable/tutorials/general/scratch.html#sphx-glr-tutorials-general-scratch-py)
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- [Data analysis with NWB files](https://pynwb.readthedocs.io/en/stable/tutorials/general/scratch.html#sphx-glr-tutorials-general-scratch-py)

‎001075/utils_001075/__init__.py

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from ._waterfall import plot_waterfall
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from ._stream_nwbfile import stream_nwbfile
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__all__ = ["plot_waterfall", "stream_nwbfile"]

‎001075/utils_001075/_stream_nwbfile.py

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from typing import Literal
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import dandi.dandiapi
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import h5py
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import pynwb
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import remfile
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def stream_nwbfile(subject_id: str, session_id: str, session_type: Literal["imaging", "segmentation"]) -> pynwb.NWBFile:
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dandiset_id = "001075"
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dandifile_path = f"sub-{subject_id}/sub-{subject_id}_ses-{session_id}_desc-{session_type}_ophys+ogen.nwb"
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dandi_client = dandi.dandiapi.DandiAPIClient()
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dandiset = dandi_client.get_dandiset(dandiset_id=dandiset_id)
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dandifile = dandiset.get_asset_by_path(path=dandifile_path)
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s3_url = dandifile.get_content_url()
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byte_stream = remfile.File(url=s3_url)
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file = h5py.File(name=byte_stream)
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io = pynwb.NWBHDF5IO(file=file)
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nwbfile = io.read()
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return nwbfile

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