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https://github.com/alleninstitute/dynamicrouting-summary

Project summary plots & tables, started for onsite retreat Jan 2024.

https://github.com/alleninstitute/dynamicrouting-summary

Science Score: 13.0%

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    CITATION.cff file
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    .zenodo.json file
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    Scientific vocabulary similarity
    Low similarity (8.4%) to scientific vocabulary
Last synced: 9 months ago · JSON representation

Repository

Project summary plots & tables, started for onsite retreat Jan 2024.

Basic Info
  • Host: GitHub
  • Owner: AllenInstitute
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 4.28 MB
Statistics
  • Stars: 0
  • Watchers: 2
  • Forks: 0
  • Open Issues: 3
  • Releases: 0
Created over 2 years ago · Last pushed about 2 years ago
Metadata Files
Readme

README.md

dynamic-routing-retreat-2024-01

Installation

bash git clone https://github.com/AllenInstitute/dynamicrouting-summary cd dynamicrouting-summary

install with PDM (requires a system Python>=3.9)

bash python -m install pipx pipx run pdm install

install with Conda

bash conda create -n DR-summary python>=3.9 conda activate DR-summary python -m install pdm pdm install

Manage dependencies

  • add dependency: pdm add numpy
    • add dev dependency: pdm add -G dev numpy
  • remove dependency correctly: pdm remove numpy
  • always commit pdm.lock for a reproducible dev environment

Get dataframes

  • first, get .env file with necessary credentials here and copy into the root of the repo

  • get available dataframes (except units):

```python import pandas as pd

import dynamicrouting_summary as dr

dfs: dict[str, pd.DataFrame] = dr.get_dfs() ```

Get units

  • scan virtual dataset of multiple parquet files (one per session), using pyarrow or dask (note: dask cannot handle files with different schemas)

```python import pyarrow.dataset as ds import pyarrow.compute as pc import npc_lims

import dynamicrouting_summary as dr

units = ds.Dataset(npclims.getcache_path('units'))

pc.valuecounts(ds.dataset(d).totable(['structure'])['structure']).topandas() df = dr.addbool_columns(df)

example of push-down filtering

expr = (ds.field('structure') == 'DG') & (ds.field('numspikes') > 100000) df = ds.dataset(d).filter(expr).totable(columns=['spiketimes']).to_pandas()

```

Owner

  • Name: Allen Institute
  • Login: AllenInstitute
  • Kind: organization
  • Location: Seattle, WA

Please visit http://alleninstitute.github.io/ for more information.

GitHub Events

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Last synced: about 1 year ago

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  • Total issues: 9
  • Total pull requests: 0
  • Average time to close issues: 5 days
  • Average time to close pull requests: N/A
  • Total issue authors: 2
  • Total pull request authors: 0
  • Average comments per issue: 1.0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
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  • Bot pull requests: 0
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Top Authors
Issue Authors
  • bjhardcastle (7)
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Dependencies

pyproject.toml pypi
  • npc-lims >=0.1.109
  • npc-session >=0.1.33
  • npc-sessions >=0.0.176
  • seaborn >=0.13.1