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EduardoJFigueroaEduardoJFigueroa
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Merge branch 'main' of github.com:brain-image-library/py-inventory into main
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‎README.md‎

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# Brain Image Library
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The Brain Image Library (BIL) is a national public resource enabling researchers to deposit, analyze, mine, share and interact with large brain image datasets. BIL encompasses the deposition of datasets, the integration of datasets into a searchable web-accessible system, the redistribution of datasets, and a computational enclave to allow researchers to process datasets in-place and share restricted and pre-release datasets.
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BIL serves a geographically distributed customer base including large confocal imaging centers that are generating petabytes of confocal imaging datasets per year. For these customers, the library serves as an archive facility for whole brain volumetric datasets from mammals. BIL also serves geographically distributed researchers who want to interact with and download library datasets.
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The Brain Image Library is operated as a partnership between the Biomedical Applications Group at the [Pittsburgh Supercomputing Center](https://www.psc.edu/) and the Center for Biological Imaging at the University of Pittsburgh.
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---
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Copyright © 2023 Pittsburgh Supercomputing Center. All Rights Reserved.
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The [Biomedical Applications Group](https://www.psc.edu/biomedical-applications/) at the [Pittsburgh Supercomputing
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Center](http://www.psc.edu) in the [Mellon College of Science](https://www.cmu.edu/mcs/) at [Carnegie Mellon University](http://www.cmu.edu).

‎braininventory/get.py‎

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def __get_completeness_score(df):
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return df['score'].sum()/len(df)
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def __is_reachable(url):
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response = requests.get(url)
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if response.status_code == 200:
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return True
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else:
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return False
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def __get_metadata_version(df):
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return df['metadata_version'] .value_counts().to_dict()
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def __get_species(df):
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return df['species'].value_counts().to_dict()
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def __ get_cnbtaxonomy(df):
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def __get_cnbtaxonomy(df):
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return df['cnbtaxonomy'].value_counts().to_dict()
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def __get_samplelocalid(df):
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report['number_of_datasets'] = __get_number_of_datasets(df)
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report['completeness_score'] = __get_completeness_score(df)
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report['metadata_version'] = __get_metadata_version(df)
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report['contributor'] = __get_contributor(df)
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report['affiliation'] = __get_affilation(df)
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report['award_number'] = __get_award_number(df)
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report['species'] = __get_species(df)
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report['cnbtaxonomy'] = __ get_cnbtaxonomy(df)
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report['samplelocalid'] = __get_samplelocalid(df)
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report['genotype'] = __get_genotype(df)
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report['generalmodality'] = __get_generalmodality(df)
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report['technique'] = __get_technique(df)
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report['locations'] = __get_locations(df)
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report['contributor'] = __get_contributor(df)
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report['affiliation'] = __get_affilation(df)
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report['award_number'] = __get_award_number(df)
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report['species'] = __get_species(df)
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report['cnbtaxonomy'] = __get_cnbtaxonomy(df)
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report['samplelocalid'] = __get_samplelocalid(df)
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report['genotype'] = __get_genotype(df)
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report['generalmodality'] = __get_generalmodality(df)
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report['technique'] = __get_technique(df)
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report['locations'] = __get_locations(df)
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report['is_reachable'] = df['URL'].apply(__is_reachable)
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return report
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‎notebooks/Untitled.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "ec311d94-3350-4a60-9418-e934fb65487a",
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"metadata": {},
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"outputs": [],
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"source": [
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"import braininventory"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "cd60e1b5-e898-4c20-81fd-61bda774e59c",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'date': '2023-07-07',\n",
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" 'number_of_datasets': 7198,\n",
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" 'completeness_score': 0.997031232284329,\n",
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" 'metadata_version': {1.0: 5755, 2.0: 1443}}"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"data = braininventory.get.today()\n",
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"report = braininventory.get.report()\n",
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"report"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "078104fd-1d0d-4fb9-b170-8fea75802669",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Index(['score', 'metadata_version', 'dataset_uuid', 'bildirectory', 'exists',\n",
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" 'project', 'is_biccn', 'bil_uuid', 'contributorname', 'affiliation',\n",
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" 'award_number', 'species', 'ncbitaxonomy', 'samplelocalid', 'genotype',\n",
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" 'generalmodality', 'technique', 'locations', 'URL', 'temp_file',\n",
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" 'json_file', 'md5_coverage', 'sha256_coverage'],\n",
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" dtype='object')"
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]
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"data.keys()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "e0b560fa-fdeb-44af-9653-ecd28f29cef1",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0 https://download.brainimagelibrary.org/f1/60/f...\n",
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"1 https://download.brainimagelibrary.org/90/a9/9...\n",
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"2 https://download.brainimagelibrary.org/dd/90/d...\n",
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"3 https://download.brainimagelibrary.org/9a/ae/9...\n",
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"4 https://download.brainimagelibrary.org/9a/ae/9...\n",
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" ... \n",
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"7193 https://download.brainimagelibrary.org/84/d8/8...\n",
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"7194 https://download.brainimagelibrary.org/77/72/7...\n",
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"7195 https://download.brainimagelibrary.org/dd/c8/d...\n",
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"7196 https://download.brainimagelibrary.org/6e/38/6...\n",
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"7197 https://download.brainimagelibrary.org/01/fc/0...\n",
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"Name: URL, Length: 7198, dtype: object"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"data['URL']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "596642ad-c204-41fd-b6c8-93e3dc53fd04",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.8"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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