# bibcodex
Library to access, analyze, and display bibliographic information.
[](https://badge.fury.io/py/bibcodex)
## Installation
pip install bibcodex
## Examples
Import the `pandas` and `bibcodex` together and load a dataframe:
```python
import bibcodex
import pandas as pd
# You should always cast your search variables (pmid, doi) to str.
df = pd.read_csv("data/sample_data.csv", dtype={'pmid':str})
```
Valid download methods are: `icite`, `doi2pmid`, `semanticScholar`, or `pubmed`:
```python
# Set the index to search query
df = df.set_index("doi")
# Download the information, and combine it with the original dataframe:
info = df.bibcodex.download('semanticScholar')
print(df.combine_first(info[["title"]]))
"""
doi title
10.1001/jama.2017.18444 Progressive Massive Fibrosis in Coal Miners Fr...
10.1001/jama.2018.0126 Birth Defects Potentially Related to Zika Viru...
10.1001/jama.2018.0708 Association Between Estimated Cumulative Vacci...
10.1001/jama.2018.10488 Electronic Cigarette Sales in the United State...
"""
```
All search queries are cached locally in `./cache`. To clear the cache use:
```python
df.codex.clear()
```
| API | Returned fields |
| ------------- | ------------- |
| [`pubmed`](https://www.ncbi.nlm.nih.gov/home/develop/api/) | title, issue, pages, abstract, journal, authors, pubdate, mesh_terms, publication_types, chemical_list, keywords, doi, references, delete, languages, vernacular_title, affiliations, pmc, other_id, medline_ta, nlm_unique_id, issn_linking, country |
| [`semanticScholar`](https://www.semanticscholar.org/product/api#Fetch-Paper) | abstract, arxivId, authors, citationVelocity, citations, corpusId, fieldsOfStudy, influentialCitationCount, isOpenAccess, isPublisherLicensed, is_open_access, is_publisher_licensed, numCitedBy, numCiting, paperId, references, s2FieldsOfStudy, title, topics, url, venue, year |
| [`icite`](https://icite.od.nih.gov/api) | year, title, authors, journal, is_research_article, relative_citation_ratio, nih_percentile, human, animal, molecular_cellular, apt, is_clinical, citation_count, citations_per_year, expected_citations_per_year, field_citation_rate, provisional, x_coord, y_coord, cited_by_clin, cited_by, references, doi |
| [`doi2pmid`](https://www.ncbi.nlm.nih.gov/pmc/utils/idconv/v1.0) | live, status, errmsg, pmcid, pmid, versions |
## Roadmap
- [x] API access: Pubmed (Parsed MEDLINE data)
- [x] API access: Semantic Scholar (PMID)
- [x] API access: iCite
- [x] API access: Semantic Scholar (DOI)
- [x] API access: DOI to PMID NLM www.ncbi.nlm.nih.gov/pmc/tools/idconv/
- [ ] API access: Pubmed (XML)
- [ ] API access: arXiv
- [ ] API access: CoLIL
- [x] API access, validation of input
- [x] API access, multi item requests
- [x] API access, chunking
- [ ] API access, include status_code in download results
- [ ] API access, better error handling
- [x] API caching, clearing
- [x] Codex, validate PMID
- [x] Codex, validate DOI
- [x] Codex, build dataframe from items
- [x] Testing harness
- [ ] Full testing coverage
- [x] Code linting
- [x] pypi library
- [x] README with examples
- [ ] Status bar for long downloads
- [ ] Embedding functions (SPECTER)
- [ ] Clustering
- [ ] Visualization (streamlit)
## Development
Built with ❤ ️by [@metasemantic](https://twitter.com/metasemantic). Package is linted by [black](https://github.com/psf/black) and conforms to standards by [flake8](https://github.com/PyCQA/flake8). Pull requests accepted, but please provide tests with full coverage for new code.
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