Skip to main content

A package to facilitate making API requests to the IMPC Solr API

Project description

IMPC_API

impc_api is a Python package.

The functions in this package are intended for use in a Jupyter Notebook.

Installation Instructions

  1. Create a virtual environment (optional but recommended): On Mac:
python3 -m venv .venv
source .venv/bin/activate
  1. Install the package: pip install impc_api
  2. Install Jupyter: pip install jupyter
  3. Run the Jupyter Notebook: jupyter notebook

After executing the command, the Jupyter interface should open in your browser. If it does not, follow the instructions provided in the terminal.

  1. Try it out:

Create a Jupyter Notebook and try some of the examples below:

Available functions

The available functions can be imported as:

from impc_api import solr_request, batch_solr_request

1. Solr request

The most basic request to the IMPC solr API

num_found, df = solr_request(
    core='genotype-phenotype', 
    params={
        'q': '*:*',
        'rows': 10, 
        'fl': 'marker_symbol,allele_symbol,parameter_stable_id'
    }
)

a. Facet request

solr_request allows facet requests

num_found, df = solr_request(
    core="genotype-phenotype",
    params={
         "q": "*:*",
         "rows": 0,
         "facet": "on",
         "facet.field": "zygosity",
         "facet.limit": 15,
         "facet.mincount": 1,
    }
)

b. Solr request validation

A common pitfall when writing a query is the misspelling of core and fields arguments. For this, we have included a validate argument that raises a warning when these values are not as expected. Note this does not prevent you from executing a query; it just alerts you to a potential issue.

Core validation

num_found, df = solr_request(
    core='invalid_core',
    params={
        'q': '*:*',
        'rows': 10
    },
    validate=True
)

> InvalidCoreWarning: Invalid core: "genotype-phenotyp", select from the available cores:
> dict_keys(['experiment', 'genotype-phenotype', 'impc_images', 'phenodigm', 'statistical-result'])

Field list validation

num_found, df = solr_request(
    core='genotype-phenotype',
    params={
        'q': '*:*',
        'rows': 10,
        'fl': 'invalid_field,marker_symbol,allele_symbol'
    },
    validate=True
)
> InvalidFieldWarning: Unexpected field name: "invalid_field". Check the spelling of fields.
> To see expected fields check the documentation at: https://www.ebi.ac.uk/mi/impc/solrdoc/

2. Batch Solr Request

batch_solr_request is available for large queries. This solves issues where a request is too large to fit into memory or where it puts a lot of strain on the API.

Use batch_solr_request for:

  • Large queries (>1,000,000)
  • Querying multiple items in a list
  • Downloading data in json or csv format.

Large queries

For large queries you can choose between seeing them in a DataFrame or downloading them in json or csv format.

a. Large query - see in DataFrame

This will fetch your data using the API responsibly and return a Pandas DataFrame

When your request is larger than recommended and you have not opted for downloading the data, a warning will be presented and you should follow the instructions to proceed.

df = batch_solr_request(
    core='genotype-phenotype',
    params={
        'q':'*:*'
    },
    download=False,
    batch_size=30000
)
print(df.head())

b. Large query - Download

When using the download=True option, a file with the requested information will be saved as filename. The format is selected based on the wt parameter. A DataFrame may be returned, provided it does not exceed the memory available on your laptop. If the DataFrame is too large, an error will be raised. For these cases, we recommend you read the downloaded file in batches/chunks.

df = batch_solr_request(
    core='genotype-phenotype',
    params={
        'q':'*:*',
        'wt':'csv'
    },
    download=True,
    filename='geno_pheno_query',
    batch_size=100000
)
print(df.head())

c. Query by multiple values

batch_solr_request also allows to search multiple items in a list provided they belong to them same field. Pass the list to the field_list param and specify the type of fl in field_type.

# List of gene symbols
genes = ["Zfp580", "Firrm", "Gpld1", "Mbip"]

df = batch_solr_request(
    core='genotype-phenotype',
    params={
        'q':'*:*',
        'fl': 'marker_symbol,mp_term_name,p_value',
        'field_list': genes,
        'field_type': 'marker_symbol'
    },
    download = False
)
print(df.head())

This can be downloaded too:

# List of gene symbols
genes = ["Zfp580", "Firrm", "Gpld1", "Mbip"]

df = batch_solr_request(
    core='genotype-phenotype',
    params={
        'q':'*:*',
        'fl': 'marker_symbol,mp_term_name,p_value',
        'field_list': genes,
        'field_type': 'marker_symbol'
    },
    download = True,
    filename='gene_list_query'
)
print(df.head())

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

impc_api-1.0.4.tar.gz (25.5 kB view details)

Uploaded Source

Built Distribution

impc_api-1.0.4-py3-none-any.whl (17.8 kB view details)

Uploaded Python 3

File details

Details for the file impc_api-1.0.4.tar.gz.

File metadata

  • Download URL: impc_api-1.0.4.tar.gz
  • Upload date:
  • Size: 25.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/5.1.1 CPython/3.12.7

File hashes

Hashes for impc_api-1.0.4.tar.gz
Algorithm Hash digest
SHA256 4305d8e63623dc1666c6440893b086adb13c534d0e6456f2ed3d0473f128a9b3
MD5 488c7fe2bc758913ad880ccb85f94d0d
BLAKE2b-256 5dbe2feaaec6dbbf01f53d872c7191b56d27de2a40e85277f025bf47e3a969f0

See more details on using hashes here.

File details

Details for the file impc_api-1.0.4-py3-none-any.whl.

File metadata

  • Download URL: impc_api-1.0.4-py3-none-any.whl
  • Upload date:
  • Size: 17.8 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/5.1.1 CPython/3.12.7

File hashes

Hashes for impc_api-1.0.4-py3-none-any.whl
Algorithm Hash digest
SHA256 ae8fc455b6d92eed54703361fd718ce1695ee416e004387fb94804575e77528a
MD5 4a2ec29fe48f420153973f27af5d2013
BLAKE2b-256 aab1aa042b3788b93aaf237eab889f61d11c17c0bf3979c4e4326d1104d6484a

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page