def test_full_args(self):
''' Verifica se os parâmetros são criados corretamente '''
r_args = {
"categorias": 'a,b',
"valor": 'c,d',
"agregacao": 'e,f',
"ordenacao": 'g,h',
"filtros": r'eq-o-comma\,separated,and,eq-p-q',
"pivot": 'i,j',
"limit": '10',
"offset": '11',
"calcs": 'k,l',
"partition": 'm,n'
}
opts = QueryOptionsBuilder.build_options(r_args)
self.assertEqual(
opts, {
"categorias": ['a', 'b'],
"valor": ['c', 'd'],
"agregacao": ['e', 'f'],
"ordenacao": ['g', 'h'],
"where": ['eq-o-comma,separated', 'and', 'eq-p-q'],
"pivot": ['i', 'j'],
"limit": '10',
"offset": '11',
"calcs": ['k', 'l'],
"partition": ['m', 'n']
})
def test_full_args_estabelecimento(self):
''' Verifica se os parâmetros são criados corretamente para empresa '''
r_args = {
"dados": 'a',
"competencia": 'b',
"id_pf": '12345678900',
"perspective": 'c',
"only_meta": 'S',
"reduzido": 'S',
"id_inv": '00000000000000'
}
opts = QueryOptionsBuilder.build_person_options(r_args,
mod='estabelecimento')
self.assertEqual(
opts, {
"column_family": 'a',
"column": 'b',
"id_pf": '12345678900',
"perspective": 'c',
"only_meta": True,
"reduzido": True,
"cnpj": '00000000000000',
"cnpj_raiz": '00000000'
})
def test_main_theme(self):
''' Verifica se os parâmetros são criados corretamente '''
r_args = {
"categorias": 'a,b',
"valor": 'c,d',
"agregacao": 'e,f',
"ordenacao": 'g,h',
"where": r'eq-o-comma\,separated,and,eq-p-q',
"pivot": 'i,j',
"limit": '10',
"offset": '11',
"calcs": 'k,l',
"partition": 'm,n'
}
opts = QueryOptionsBuilder.build_options(r_args)
self.assertEqual(opts.get('theme'), None)
def api_to_options(cls, api_call_obj, custom_args=None):
''' Transforms API string into datahub options '''
url = cls.get_api_url(api_call_obj, custom_args)
url_parts = urllib.parse.urlparse(url)
args_dict = {arg.split('=')[0]: arg.split('=')[1] for arg in url_parts.query.split('&')}
options = QueryOptionsBuilder.build_options(args_dict)
path_parts = url_parts.path.split('/')
if path_parts[0] == '':
path_parts.pop(0)
if path_parts[0] == 'thematic':
options['theme'] = path_parts[1]
elif '-' in path_parts[-1]:
options['theme'] = ''.join(path_parts[:-1])
options['operation'] = path_parts[-1]
else:
options['theme'] = ''.join(path_parts)
return options
def get_template_data_collection(self, structure, options):
''' Fetches data from impala '''
data_collection = {}
any_nodata = False
for each_obj_struct in structure:
each_options = self.remove_templates(each_obj_struct['endpoint'],
options)
if each_options.get('theme') is None:
each_options["theme"] = options.get("theme")
# Adds complimentary options
each_options = QueryOptionsBuilder.build_options(each_options)
each_options["as_pandas"] = True
# Builds the options to query impala
each_obj = self.find_dataset(each_options)
# Transforms the dataset with coefficient
if 'coefficient' in options:
each_obj = TemplateHelper.apply_coefficient(
options['coefficient'], each_obj)
# Runs formatters from config
if 'formatters' in each_obj_struct:
each_obj = TemplateHelper.run_formatters(
each_obj_struct['formatters'], each_obj)
# Gets derived attributes
any_blank = False
if 'instances' in each_obj_struct:
(data_collection,
any_blank) = self.build_derivatives(each_obj_struct, options,
each_obj, data_collection)
if any_blank:
any_nodata = True
# Converts pandas dataframe to conventional dict, to add to response
each_obj['dataset'] = each_obj['dataset'].to_dict('records')
data_collection[each_obj_struct['name']] = each_obj
return (data_collection, any_nodata)
def build_person_options(r_args, mod='empresa'):
''' Constrói as opções da pesquisa '''
return QueryOptionsBuilder.build_person_options(r_args, mod)
def build_options(r_args, rules='query'):
''' Constrói as opções da pesquisa '''
return QueryOptionsBuilder.build_options(r_args, rules)