def clean(self):
# invoke superclass cleaning method
super(ClimateTranslatorForm2, self).clean()
# validate calculation
if 'calc' in self.cleaned_data and hasText(self.cleaned_data['calc']) and self.cleaned_data['calc'].lower() != 'none':
# calculation group must be selected
if len( self.cleaned_data['calc_group'] ) == 0:
self._errors["calc_group"] = self.error_class(["Calculation Group(s) not selected."])
# validate keyword values
func = self.cleaned_data['calc']
calc = ocgisCalculations.getCalc(func)
if 'keywords' in calc:
for i, keyword in enumerate(calc["keywords"]):
parN = 'par%s' % (i+1)
value = self.cleaned_data[parN]
if keyword["type"]=="float":
try:
x = float(value)
except ValueError:
self._errors[parN] = self.error_class(["Invalid float value for keyword: "+keyword["name"]])
elif keyword["type"] == "string":
if "values" in keyword:
if not value.lower() in keyword["values"]:
self._errors[parN] = self.error_class(["Invalid string value for keyword: "+keyword["name"]])
# if no calculation is selected, there cannot be any temporal grouping
else:
self.cleaned_data['calc_group'] = None
if 'prefix' in self.cleaned_data and re.search(INVALID_CHARS, self.cleaned_data['prefix']):
self._errors['prefix'] = self.error_class(["The prefix contains invalid characters."])
if not self.is_valid():
print 'VALIDATION ERRORS: %s' % self.errors
# return cleaned data
return self.cleaned_data
def get_context_data(self, form, **kwargs):
context = super(ClimateTranslatorWizard, self).get_context_data(form=form, **kwargs)
# before rendering of first form: send data and geometry choices
if self.steps.current == self.steps.first:
#context.update({'datasets': json.dumps(ocgisDatasets.datasets) }) # FIXME ?
context.update({'geometries': json.dumps(ocgisGeometries.geometries) })
elif self.steps.current == "1": # note: string type
context.update({'calculations': json.dumps(ocgisCalculations.calcs) })
# before very last view: create summary of user choices
elif self.steps.current == self.steps.last:
job_data = {}
# retrieve form data for all previous views
for step in self.steps.all:
cleaned_data = self.get_cleaned_data_for_step(step)
# first form
if step == '0':
job_data['data_type'] = cleaned_data['data_type']
if cleaned_data.has_key('long_name'):
job_data['long_name'] = cleaned_data['long_name']
if cleaned_data.has_key('time_frequency'):
job_data['time_frequency'] = cleaned_data['time_frequency']
if cleaned_data.has_key("dataset_category"):
job_data['dataset_category'] = cleaned_data['dataset_category']
if cleaned_data.has_key("dataset"):
job_data['dataset'] = cleaned_data['dataset']
if cleaned_data.has_key("dataset_category2"):
job_data['dataset_category2'] = cleaned_data['dataset_category2']
if cleaned_data.has_key("package_name"):
job_data['package_name'] = cleaned_data['package_name']
if cleaned_data.has_key('geometry_category') and hasText(cleaned_data['geometry_category']):
job_data['geometry_category'] = cleaned_data['geometry_category']
if cleaned_data.has_key('geometry_subcategory') and hasText(cleaned_data['geometry_subcategory']):
job_data['geometry_subcategory'] = cleaned_data['geometry_subcategory']
if cleaned_data.has_key('geometry_id') and len( cleaned_data['geometry_id'] )>0:
job_data['geometry_id'] = formatListForDisplay(cleaned_data['geometry_id'])
if cleaned_data.has_key('latmin') and cleaned_data['latmin'] is not None:
job_data['latmin'] = float( cleaned_data['latmin'] )
if cleaned_data.has_key('latmax') and cleaned_data['latmax'] is not None:
job_data['latmax'] = float( cleaned_data['latmax'] )
if cleaned_data.has_key('lonmin') and cleaned_data['lonmin'] is not None:
job_data['lonmin'] = float( cleaned_data['lonmin'] )
if cleaned_data.has_key('lonmax') and cleaned_data['lonmax'] is not None:
job_data['lonmax'] = float( cleaned_data['lonmax'] )
if cleaned_data.has_key('lat') and cleaned_data['lat'] is not None:
job_data['lat'] = float( cleaned_data['lat'] )
if cleaned_data.has_key('lon') and cleaned_data['lon'] is not None:
job_data['lon'] = float( cleaned_data['lon'] )
if cleaned_data.has_key('agg_selection'):
job_data['agg_selection'] = bool(cleaned_data['agg_selection'])
if cleaned_data.has_key('datetime_start') and cleaned_data['datetime_start'] is not None:
job_data['datetime_start'] = cleaned_data['datetime_start']
if cleaned_data.has_key('datetime_stop') and cleaned_data['datetime_stop'] is not None:
job_data['datetime_stop'] = cleaned_data['datetime_stop']
if cleaned_data.has_key('timeregion_month') and cleaned_data['timeregion_month'] is not None:
job_data['timeregion_month'] = get_month_string( cleaned_data['timeregion_month'] )
if cleaned_data.has_key('timeregion_year') and cleaned_data['timeregion_year'] is not None:
job_data['timeregion_year'] = cleaned_data['timeregion_year']
# second form
if step == '1':
if cleaned_data.has_key('calc') and cleaned_data['calc'] is not None and cleaned_data['calc'] != '':
job_data['calc'] = ocgisCalculations.getCalc(cleaned_data['calc'])["name"]
if cleaned_data.has_key('par1') and cleaned_data['par1'] is not None:
job_data['par1'] = cleaned_data['par1']
if cleaned_data.has_key('par2') and cleaned_data['par2'] is not None:
job_data['par2'] = cleaned_data['par2']
if cleaned_data.has_key('par3') and cleaned_data['par3'] is not None:
job_data['par3'] = cleaned_data['par3']
if cleaned_data.has_key('calc_group'):
calc_groups =[]
for group in cleaned_data['calc_group']:
calc_groups.append(ocgisChoices(Config.CALCULATION_GROUP)[group])
job_data['calc_group'] = formatListForDisplay(calc_groups)
if cleaned_data.has_key('calc_raw'):
job_data['calc_raw'] = bool(cleaned_data['calc_raw'])
if cleaned_data.has_key('spatial_operation'):
job_data['spatial_operation'] = ocgisChoices(Config.SPATIAL_OPERATION)[cleaned_data['spatial_operation']]
if cleaned_data.has_key('aggregate'):
job_data['aggregate'] = bool(cleaned_data['aggregate'])
if cleaned_data.has_key('output_format'):
job_data['output_format'] = ocgisChoices(Config.OUTPUT_FORMAT)[cleaned_data['output_format']]
if cleaned_data.has_key('prefix'):
job_data['prefix'] = cleaned_data['prefix']
if cleaned_data.has_key('with_auxiliary_files'):
job_data['with_auxiliary_files'] = bool(cleaned_data['with_auxiliary_files'])
context.update({'job_data': job_data})
return context
