diff options
Diffstat (limited to 'gn3')
| -rw-r--r-- | gn3/app.py | 2 | ||||
| -rw-r--r-- | gn3/computations/datasets.py | 98 |
2 files changed, 99 insertions, 1 deletions
@@ -8,6 +8,7 @@ from gn3.api.gemma import gemma from gn3.api.general import general from gn3.api.correlation import correlation from gn3.api.traits import trait +from gn3.api.datasets import dataset def create_app(config: Union[Dict, str, None] = None) -> Flask: @@ -30,4 +31,5 @@ def create_app(config: Union[Dict, str, None] = None) -> Flask: app.register_blueprint(gemma, url_prefix="/api/gemma") app.register_blueprint(correlation, url_prefix="/api/correlation") app.register_blueprint(trait, url_prefix="/api/trait") + app.register_blueprint(dataset, url_prefix="/api/dataset") return app diff --git a/gn3/computations/datasets.py b/gn3/computations/datasets.py index 6df5777..8e9d743 100644 --- a/gn3/computations/datasets.py +++ b/gn3/computations/datasets.py @@ -1,9 +1,16 @@ """module contains the code all related to datasets""" +import json from unittest import mock from typing import Optional from typing import List +from dataclasses import dataclass +import requests + +from gn3.experimental_db import database_connector +from gn3.settings import GN2_BASE_URL + def retrieve_trait_sample_data(dataset, trait_name: str, @@ -35,6 +42,10 @@ def fetch_from_db_sample_data(formatted_query: str, database_instance) -> List: """this is the function that does the actual fetching of\ results from the database""" cursor = database_instance.cursor() + _conn = database_connector + # conn, cursor = database_connector() + # cursor = conn.cursor() + cursor.execute(formatted_query) results = cursor.fetchall() @@ -87,7 +98,8 @@ def get_query_for_dataset_sample(dataset_type) -> Optional[str]: SELECT Strain.Name, ProbeSetData.value, ProbeSetSE.error, NStrain.count, Strain.Name2 FROM - (ProbeSetData, ProbeSetFreeze, Strain, ProbeSet, ProbeSetXRef) + (ProbeSetData, ProbeSetFreeze, + Strain, ProbeSet, ProbeSetXRef) left join ProbeSetSE on (ProbeSetSE.DataId = ProbeSetData.Id AND ProbeSetSE.StrainId = ProbeSetData.StrainId) left join NStrain on @@ -108,3 +120,87 @@ def get_query_for_dataset_sample(dataset_type) -> Optional[str]: dataset_query["ProbeSet"] = probeset_query return dataset_query.get(dataset_type) + + +@dataclass +class Dataset: + """class for creating datasets""" + name: Optional[str] = None + dataset_type: Optional[str] = None + dataset_id: int = -1 + + +def create_mrna_tissue_dataset(dataset_name, dataset_type): + """an mrna assay is a quantitative assessment(assay) associated\ + with an mrna trait.This used to be called probeset,but that term\ + only referes specifically to the afffymetrix platform and is\ + far too speficified""" + + return Dataset(name=dataset_name, dataset_type=dataset_type) + + +def dataset_type_getter(dataset_name, redis_instance=None) -> Optional[str]: + """given the dataset name fetch the type\ + of the dataset this in turn enables fetching\ + the creation of the correct object could utilize\ + redis for the case""" + + results = redis_instance.get(dataset_name, None) + + if results: + return results + + return fetch_dataset_type_from_gn2_api(dataset_name) + + +def fetch_dataset_type_from_gn2_api(dataset_name): + """this function is only called when the\ + the redis is empty and does have the specificied\ + dataset_type""" + # should only run once + + dataset_structure = {} + + map_dataset_to_new_type = { + "Phenotypes": "Publish", + "Genotypes": "Geno", + "MrnaTypes": "ProbeSet" + } + + data = json.loads(requests.get( + GN2_BASE_URL + "/api/v_pre1/gen_dropdown", timeout=5).content) + _name = dataset_name + for species in data['datasets']: + for group in data['datasets'][species]: + for dataset_type in data['datasets'][species][group]: + for dataset in data['datasets'][species][group][dataset_type]: + # assumes the first is dataset_short_name + short_dataset_name = next( + item for item in dataset if item != "None" and item is not None) + + dataset_structure[short_dataset_name] = map_dataset_to_new_type.get( + dataset_type, "MrnaTypes") + return dataset_structure + + +def dataset_creator_store(dataset_type): + """function contains key value pairs for\ + the function need to be called to create\ + each dataset_type""" + + dataset_obj = { + "ProbeSet": create_mrna_tissue_dataset + } + + return dataset_obj[dataset_type] + + +def create_dataset(dataset_type=None, dataset_name: str = None): + """function for creating new dataset temp not implemented""" + if dataset_type is None: + dataset_type = dataset_type_getter(dataset_name) + + dataset_creator = dataset_creator_store(dataset_type) + results = dataset_creator( + dataset_name=dataset_name, dataset_type=dataset_type) + return results |