def _convert_notebook_results(self, result, dashboard, query):
cols = [
col['name'] if self.source == 'data' else re.sub(
'^t\.', '', col['name']) for col in result['meta']
]
docs = []
for row in result['data']:
docs.append(dict(
(header, cell) for header, cell in zip(cols, row)))
response = json.loads('''{
"highlighting":{
"GBP":{
},
"EUR":{
}
},
"normalized_facets":[
],
"responseHeader":{
"status":0,
"QTime":0,
"params":{
"rows":"5",
"hl.fragsize":"1000",
"hl.snippets":"5",
"doAs":"romain",
"q":"*:*",
"start":"0",
"wt":"json",
"user.name":"hue",
"hl":"true",
"hl.fl":"*",
"fl":"*"
}
},
"response":{
"start":0,
"numFound":32,
"docs":[]
}
}''')
response['response']['docs'] = docs
response['response']['numFound'] = len(docs)
augment_response(dashboard, query, response)
return response
def _convert_notebook_results(self, result, dashboard, query):
cols = [col['name'] for col in result['meta']]
docs = []
for row in result['data']:
docs.append(dict((header, cell) for header, cell in zip(cols, row)))
response = json.loads('''{
"highlighting":{
"GBP":{
},
"EUR":{
}
},
"normalized_facets":[
],
"responseHeader":{
"status":0,
"QTime":0,
"params":{
"rows":"5",
"hl.fragsize":"1000",
"hl.snippets":"5",
"doAs":"romain",
"q":"*:*",
"start":"0",
"wt":"json",
"user.name":"hue",
"hl":"true",
"hl.fl":"*",
"fl":"*"
}
},
"response":{
"start":0,
"numFound":32,
"docs":[]
}
}''')
response['response']['docs'] = docs
response['response']['numFound'] = len(docs)
augment_response(dashboard, query, response)
return response