def generic_filter(self, filters):
json_acceptable_string = filters.replace("'", "\"")
#print json_acceptable_string
d = json.loads(json_acceptable_string)
#print d
values = jerboa_collection.find(d)
r = []
for v in values:
v = unicode(v)
r.append(v)
return r
def generic_filter(self, filters):
json_acceptable_string = filters.replace("'", "\"")
#print json_acceptable_string
d = json.loads(json_acceptable_string)
#print d
values = jerboa_collection.find(d)
r = []
for v in values:
v = unicode(v)
r.append(v);
return r
def filters(self, var, fingerprint_id):
# Go to the rule matcher and ask for the filter for that particular case
comp = False
if fingerprint_id == "COMPARE":
comp = True
mrules = RuleMatcher(comp=comp)
filters = mrules.get_filter(var)
chart = mrules.get_chart(var)
#_filter = charts_conf.
# Should check if any special operation, for now, let's assume: NO!
for _filter in filters:
# Generate query
dict_query = {
'fingerprint_id': fingerprint_id,
'values.Var': chart.title.var,
}
if comp:
dict_query = {
'values.Var': chart.title.var,
}
if _filter.key != None:
dict_query['values.' + _filter.key] = _filter.name
#print _filter
#print _filter.value
#print dict_query
if comp:
print dict_query
print 'values.' + _filter.value
values = jerboa_aggregation_collection.find(
dict_query).distinct('values.' + _filter.value) #
else:
values = jerboa_collection.find(dict_query).distinct(
'values.' + _filter.value) #
values = sorted(values)
#values = jerboa_collection.find( dict_query ).distinct('values.' + _filter.value )
#print values
_filter.values = values
return filters
def get_var(self):
values = jerboa_collection.distinct('values.Var')
# Go to the rule matcher and ask for the filter for that particular case
comp = False
if fingerprint_id == "COMPARE":
comp = True
mrules = RuleMatcher(comp=comp)
filters = mrules.get_filter(var)
chart = mrules.get_chart(var)
# Generate the filters here.
for _filter in filters:
# Generate query
dict_query = {
'fingerprint_id': fingerprint_id,
'values.Var': chart.title.var,
}
if comp:
dict_query = {
'values.Var': chart.title.var,
}
if _filter.key != None:
dict_query['values.' + _filter.key] = _filter.name
if comp:
values = jerboa_aggregation_collection.find(dict_query).distinct(
'values.' + _filter.value) #
else:
values = jerboa_collection.find(dict_query).distinct(
'values.' + _filter.value) #
values = sorted(values)
_filter.values = values
return filters
return values
def filters(self, var, fingerprint_id):
# Go to the rule matcher and ask for the filter for that particular case
comp = False
if fingerprint_id=="COMPARE":
comp=True
mrules = RuleMatcher(comp=comp)
filters = mrules.get_filter(var)
chart = mrules.get_chart(var)
#_filter = charts_conf.
# Should check if any special operation, for now, let's assume: NO!
for _filter in filters:
# Generate query
dict_query = {'fingerprint_id':fingerprint_id,
'values.Var': chart.title.var,
}
if comp:
dict_query = {'values.Var': chart.title.var,}
if _filter.key != None:
dict_query['values.' + _filter.key] = _filter.name
#print _filter
#print _filter.value
#print dict_query
if comp:
values = jerboa_aggregation_collection.find( dict_query ).distinct('values.' + _filter.value )#
else:
values = jerboa_collection.find( dict_query ).distinct('values.' + _filter.value )#
values = sorted(values)
#values = jerboa_collection.find( dict_query ).distinct('values.' + _filter.value )
#print values
_filter.values = values
return filters
def get_var(self):
values = jerboa_collection.distinct( 'values.Var' )
# Go to the rule matcher and ask for the filter for that particular case
comp = False
if fingerprint_id=="COMPARE":
comp=True
mrules = RuleMatcher(comp=comp)
filters = mrules.get_filter(var)
chart = mrules.get_chart(var)
# Generate the filters here.
for _filter in filters:
# Generate query
dict_query = {'fingerprint_id':fingerprint_id,
'values.Var': chart.title.var,
}
if comp:
dict_query = {'values.Var': chart.title.var,}
if _filter.key != None:
dict_query['values.' + _filter.key] = _filter.name
if comp:
values = jerboa_aggregation_collection.find( dict_query ).distinct('values.' + _filter.value )#
else:
values = jerboa_collection.find( dict_query ).distinct('values.' + _filter.value )#
values = sorted(values)
_filter.values = values
return filters
return values
def get_variables(self,
var,
row,
fingerprint_id='abcd',
revision='-1',
filters=[],
vars_that_should_exists=[]):
#db.jerboa_files.distinct( 'values.Var' )
# Need to filter by Fingerprint, otherwise, we're trapped.
#pdb.set_trace()
vars_that_should_exists = ['Count']
mrules = RuleMatcher(type=Fingerprint.objects.get(
fingerprint_hash=fingerprint_id).questionnaire.id)
__filters = mrules.get_filter(var)
c1 = mrules.get_chart(var)
dict_query = {
'fingerprint_id': fingerprint_id,
'revision': revision,
'values.Var': c1.title.var
}
# Comparable
#comparable = True
#values_compare = ["M", "F"]
for ve in vars_that_should_exists:
dict_query['values.' + ve] = {"$exists": True}
for _f in c1.y_axis.static_filters:
dict_query['values.' + _f.key] = _f.value
#print "filters"
#print filters
# Apply filters in the query
dict_query_general = []
for ve in filters:
#print "ve"
#print ve
if isinstance(filters[ve], list):
#if not "$or" in dict_query:
_or_dict_query = {}
_or_dict_query["$or"] = []
for _aux in filters[ve]:
_or_dict_query2 = {ve: _aux}
_or_dict_query["$or"].append(_or_dict_query2)
dict_query_general.append(_or_dict_query)
else:
dict_query[ve] = filters[ve]
if dict_query_general != []:
dict_query["$and"] = dict_query_general
#print dict_query
values = jerboa_collection.find(dict_query)
results = []
def transform(v, transformation, values):
if not type(v) is list:
y = float(values[v])
new_y = eval(transformation)
values[v] = new_y
else:
for _v in v:
y = float(values[_v])
new_y = eval(transformation)
values[_v] = new_y
#print values[_v]
return values
values_app = None
for v in values:
if c1.y_axis.transformation != None:
try:
#print "transformation"
values_app = transform(c1.y_axis.var,
c1.y_axis.transformation,
v[u'values'])
#y = float(v[u'values'][c1.y_axis.var])
#new_y = eval(c1.y_axis.transformation)
#v[u'values'][c1.y_axis.var] = new_y
v[u'values'] = values_app
#print values_app
except:
#raise
print "bastard x error %s, %s " % (c1.y_axis.var,
str(v[u'values']))
results.append(v[u'values'])
vorder = c1.x_axis.var
if c1.x_axis.sort_func != None:
vorder = c1.x_axis.var
results = sorted(results, key=lambda k: eval(c1.x_axis.sort_func))
return results
def get_variables(self, var, row, fingerprint_id='abcd', revision='-1', filters=[], vars_that_should_exists=[]):
#db.jerboa_files.distinct( 'values.Var' )
# Need to filter by Fingerprint, otherwise, we're trapped.
#pdb.set_trace()
vars_that_should_exists = ['Count']
mrules = RuleMatcher(type=Fingerprint.objects.get(fingerprint_hash=fingerprint_id).questionnaire.id)
__filters = mrules.get_filter(var)
c1 = mrules.get_chart(var)
dict_query = {'fingerprint_id':fingerprint_id, 'revision': revision,
'values.Var': c1.title.var}
# Comparable
#comparable = True
#values_compare = ["M", "F"]
for ve in vars_that_should_exists:
dict_query['values.'+ve] = { "$exists" : True }
for _f in c1.y_axis.static_filters:
dict_query['values.'+_f.key] = _f.value
#print "filters"
#print filters
# Apply filters in the query
dict_query_general=[]
for ve in filters:
#print "ve"
#print ve
if isinstance(filters[ve], list):
#if not "$or" in dict_query:
_or_dict_query = {}
_or_dict_query["$or"] = [ ]
for _aux in filters[ve]:
_or_dict_query2 = {ve: _aux}
_or_dict_query["$or"].append(_or_dict_query2)
dict_query_general.append(_or_dict_query)
else:
dict_query[ve] = filters[ve]
if dict_query_general != []:
dict_query["$and"]= dict_query_general
#print dict_query
values = jerboa_collection.find(dict_query )
results = []
def transform(v, transformation, values):
if not type(v) is list:
y = float(values[v])
new_y = eval(transformation)
values[v] = new_y
else:
for _v in v:
y = float(values[_v])
new_y = eval(transformation)
values[_v] = new_y
#print values[_v]
return values
values_app = None
for v in values:
if c1.y_axis.transformation != None:
try:
#print "transformation"
values_app = transform(c1.y_axis.var, c1.y_axis.transformation,v[u'values'])
#y = float(v[u'values'][c1.y_axis.var])
#new_y = eval(c1.y_axis.transformation)
#v[u'values'][c1.y_axis.var] = new_y
v[u'values'] = values_app
#print values_app
except:
#raise
print "bastard x error %s, %s " % (c1.y_axis.var, str(v[u'values']))
results.append(v[u'values'])
vorder = c1.x_axis.var
if c1.x_axis.sort_func!=None:
vorder = c1.x_axis.var
results = sorted(results, key=lambda k: eval(c1.x_axis.sort_func))
return results
