def create_categorical(dataset, categories, rules, name, alias, description=""):
"""
method for creating a categorical variable
"""
if not ((len(categories) - 1) <= len(rules) <= len(categories)):
raise ValueError("Amount of rules should match categories (or categories -1")
if not hasattr(dataset, "variables"):
dataset.refresh()
args = [
{"column": [c["id"] for c in categories], "type": {"value": {"class": "categorical", "categories": categories}}}
]
more_args = []
for rule in rules:
more_args.append(parse_expr(rule))
more_args = process_expr(more_args, dataset)
expr = dict(function="case", args=args + more_args)
payload = dict(element="shoji:entity", body=dict(alias=alias, name=name, expr=expr, description=description))
return dataset.variables.create(payload)
def create_categorical(self, categories, rules, name, alias, description="", missing=True):
"""
creates a categorical variable deriving from other variables
"""
if not ((len(categories) - 1) <= len(rules) <= len(categories)):
raise ValueError("Amount of rules should match categories (or categories -1)")
if not hasattr(self, "variables"):
self.refresh()
args = [
{
"column": [c["id"] for c in categories],
"type": {"value": {"class": "categorical", "categories": categories}},
}
]
for cat in args[0]["type"]["value"]["categories"]:
cat.setdefault("missing", False)
if missing:
args[0]["column"].append(-1)
args[0]["type"]["value"]["categories"].append(dict(id=-1, name="No Data", numeric_value=None, missing=True))
more_args = []
for rule in rules:
more_args.append(parse_expr(rule))
more_args = process_expr(more_args, self)
expr = dict(function="case", args=args + more_args)
payload = dict(element="shoji:entity", body=dict(alias=alias, name=name, expr=expr, description=description))
return self.variables.create(payload)
def create_categorical(self,
categories,
rules,
name,
alias,
description='',
missing=True):
"""
creates a categorical variable deriving from other variables
"""
if not ((len(categories) - 1) <= len(rules) <= len(categories)):
raise ValueError(
'Amount of rules should match categories (or categories -1)')
if not hasattr(self, 'variables'):
self.refresh()
args = [{
'column': [c['id'] for c in categories],
'type': {
'value': {
'class': 'categorical',
'categories': categories
}
}
}]
for cat in args[0]['type']['value']['categories']:
cat.setdefault('missing', False)
if missing:
args[0]['column'].append(-1)
args[0]['type']['value']['categories'].append(
dict(id=-1, name='No Data', numeric_value=None, missing=True))
more_args = []
for rule in rules:
more_args.append(parse_expr(rule))
more_args = process_expr(more_args, self)
expr = dict(function='case', args=args + more_args)
payload = dict(element='shoji:entity',
body=dict(alias=alias,
name=name,
expr=expr,
description=description))
return self.variables.create(payload)
def exclude(self, expr=None):
"""
Given a dataset object, apply an exclusion filter to it (defined as an
expression string).
If the `expr` parameter is None, an empty expression object is sent
as part of the PATCH request, which effectively removes the exclusion
filter (if any).
"""
if isinstance(expr, six.string_types):
expr_obj = parse_expr(expr)
expr_obj = process_expr(expr_obj, self) # cause we need URLs
elif expr is None:
expr_obj = {}
else:
expr_obj = expr
return self.session.patch(self.fragments.exclusion, data=json.dumps(dict(expression=expr_obj)))
def exclude(self, expr=None):
"""
Given a dataset object, apply an exclusion filter to it (defined as an
expression string).
If the `expr` parameter is None, an empty expression object is sent
as part of the PATCH request, which effectively removes the exclusion
filter (if any).
"""
if isinstance(expr, six.string_types):
expr_obj = parse_expr(expr)
expr_obj = process_expr(expr_obj, self) # cause we need URLs
elif expr is None:
expr_obj = {}
else:
expr_obj = expr
return self.session.patch(self.fragments.exclusion,
data=json.dumps(dict(expression=expr_obj)))