def get_documents_by_method(solr_api_url, auth_object, q="*:*", method="BET"):
docs_query = client_solr.StudyDocuments()
docs_query.settings['endpointfilter'] = None
materialfilter = None
docs_query.settings['query_guidance'] = None
docs_query.settings['query_organism'] = None
docs_query.setStudyFilter({
"topcategory_s": "*",
"endpointcategory_s": "*",
"E.method_s": "({})".format(method)
})
docs_query.settings['fields'] = "*"
query = docs_query.getQuery(textfilter=q,
facets=None,
fq=None,
rows=10000,
_params=True,
_conditions=True,
_composition=True)
#print(query)
r = client_solr.post(solr_api_url, query=query, auth=auth_object)
results = None
if r.status_code == 200:
try:
rows = docs_query.parse(r.json()['response']['docs'], process=None)
results = pd.DataFrame(rows)
except:
print(r.text)
else:
logger.info(r.status_code)
return results
def get_docs(query):
docs_query = client_solr.StudyDocuments()
docs_query.settings['endpointfilter'] = None
materialfilter = None
docs_query.settings['query_guidance'] = None
docs_query.settings['query_organism'] = None
#docs_query.setStudyFilter({"topcategory_s" : "*", "endpointcategory_s" : row["endpoint"], "E.method_s" : "({})".format(row["method"])})
#docs_query.setStudyFilter("*")
docs_query.settings['fields'] = "*"
_query = docs_query.getQuery(textfilter=query,
facets=None,
fq=None,
rows=10000,
_params=True,
_conditions=True,
_composition=False)
print(_query)
r = client_solr.post(solr_api_url, query=_query, auth=auth_object)
results = None
if r.status_code == 200:
rows = docs_query.parse(r.json()['response']['docs'], process=None)
results = pd.DataFrame(rows)
return results
else:
print(r.status_code)
return None
def dose_response_overview(query):
_query = "*:*" if query=="" else query
facets = client_solr.Facets()
facets.set_annotation_folder(annotation_folder)
_tag_method = "E.method_s"
_tag_endpoint= "effectendpoint_s"
_tag_endpoint_type= "effectendpoint_type_s"
_tag_conc_unit = '_CONDITION_concentration_UNIT_s'
_tag_conc = '_CONDITION_concentration_d'
_tag_time_unit = '_CONDITION_exposure_time_UNIT_s'
_tag_time = '_CONDITION_exposure_time_d'
df = facets.summary(solr_api_url,auth_object, query=_query,fields=[
"topcategory_s","endpointcategory_s",_tag_method,_tag_endpoint,"effectendpoint_type_s",
_tag_conc,_tag_conc_unit,
_tag_time,_tag_time_unit
],log_query=log_query,log_result=beautify)
df.head()
columns = ['id', 'name_s', 'publicname_s', 'owner_name_s', 'substanceType_s',
's_uuid_s', 'type_s', 'document_uuid_s', 'investigation_uuid_s',
'assay_uuid_s', 'topcategory_s', 'endpointcategory_s', 'guidance_s',
'endpoint_s', 'effectendpoint_s', 'effectendpoint_type_s',
'reference_owner_s', 'reference_year_s', 'reference_s', 'loValue_d',
'unit_s','E.method_s', 'E.cell_type_s',
_tag_conc_unit, _tag_conc,
_tag_time_unit, _tag_time,
'_CONDITION_replicate_s', '_CONDITION_material_s', '_version_', 'err_d',
'errQualifier_s', 'guidance_synonym_ss', 'effectendpoint_synonym_ss',
'E.method_synonym_ss']
for index,row in df.iterrows():
method = row[_tag_method]
endpoint= row[_tag_endpoint]
query = "{}:{} {}:{} {}:[* TO *] ".format(_tag_method,method,_tag_endpoint,endpoint,_tag_conc)
_query = {'q': query, 'fq': 'type_s:study', 'wt': 'json', 'rows': 10000}
r = client_solr.post(solr_api_url,query=_query,auth=auth_object)
tmp = pd.DataFrame(r.json()['response']['docs'])
tmp["time"] = ""
if not _tag_conc in tmp.columns:
continue
time = ""
if _tag_time in tmp.columns:
tmp["time"] = tmp[_tag_time]
if _tag_time_unit in tmp.columns:
tmp["time"] = tmp["time"] + " " + row[_tag_time_unit]
r.close()
cells = tmp["E.cell_type_s"].unique()
for cell in cells:
tmp_cell = tmp.loc[tmp["E.cell_type_s"]==cell]
units = tmp_cell["unit_s"].unique()
for unit in units:
tmp_endpoint_unit = tmp.loc[tmp["unit_s"]==unit]
conc_units = tmp_endpoint_unit[_tag_conc_unit].unique()
for cu in conc_units:
tmp_endpoint_unit_cu = tmp_endpoint_unit.loc[tmp_endpoint_unit[_tag_conc_unit]==cu]
if not tmp_endpoint_unit_cu.empty:
print(method,endpoint,cell,unit,cu)
#print(tmp.columns)
#display(tmp_endpoint_unit_cu[["publicname_s",_tag_conc,_tag_conc_unit,"time","reference_owner_s"]].head())
imgfile = os.path.join(folder_output,"data","{}_{}_{}_{}_{}.png").format(method,endpoint,unit.replace("/","_"),cell,cu.replace("/","_"))
plot_dose_response(tmp_endpoint_unit_cu,method, endpoint,unit, cu,facet_row="time",facet_col="publicname_s",color="reference_owner_s",imgfile=imgfile)
facets = client_solr.Facets()
facets.set_annotation_folder(annotation_folder)
# FAIR - Findable
## Data model
import pandas as pd
import pandas as pd
docs_query = "{} +publicname_s:({})".format(query, "NRCWE-006")
print(docs_query)
_query = {'q': docs_query, 'fq': 'type_s:study', 'wt': 'json', 'rows': 10000}
r = client_solr.post(solr_api_url, query=_query, auth=auth_object)
tmp = pd.DataFrame(r.json()['response']['docs'])
print(tmp.shape)
print(tmp.columns)
tmp.head()
print(tmp["owner_name_s"].unique())
doc_uuids = tmp["document_uuid_s"].unique()
print(tmp["E.method_s"].unique())
#for doc_uuid in doc_uuids:
# doc = tmp.loc[tmp["document_uuid_s"]==doc_uuid]
cols = [
"publicname_s", "document_uuid_s", "effectendpoint_s", "loValue_d",
"E.method_s", "unit_s"
]
fig = px.treemap(