def getFormData(self):
"""Returns an ordered list of (choice label, choice value) for display to the user."""
job_data = []
job_data.append( ('Dataset Category', self.dataset_category) )
job_data.append( ('Dataset', self.dataset) )
if hasText(self.variable):
job_data.append( ('Variable', self.variable) )
if hasText(self.geometry):
job_data.append( ('Shape Type', self.geometry) )
if self.geometry_id is not None and len(self.geometry_id)>0:
job_data.append( ('Shape Geometry', self.geometry_id.replace(",",", ")) )
if hasText(self.latmin):
job_data.append( ('Latitude Minimum', self.latmin) )
if hasText(self.latmax):
job_data.append( ('Latitude Maximum', self.latmax) )
if hasText(self.lonmin):
job_data.append( ('Longitude Minimum', self.lonmin) )
if hasText(self.lonmax):
job_data.append( ('Longitude Maximum', self.lonmax) )
if hasText(self.lat):
job_data.append( ('Latitude', self.lat) )
if hasText(self.lon):
job_data.append( ('Longitude', self.lon) )
job_data.append( ('Start Date Time', self.datetime_start) )
job_data.append( ('Stop Date Time', self.datetime_stop) )
if self.timeregion_month is not None and len(self.timeregion_month)>0:
job_data.append( ('Time Region: Months', get_month_string( map(int, self.timeregion_month.split(",")) ) ))
if self.timeregion_year is not None and hasText(self.timeregion_year):
job_data.append( ('Time Region: Years', self.timeregion_year) )
if self.calc is not None and self.calc != '':
job_data.append( ('Calculation', self.calc) )
if self.par1 is not None and self.par1 != '':
job_data.append( ('Calculation Parameter 1', self.par1) )
if self.par2 is not None and self.par2 != '':
job_data.append( ('Calculation Parameter 2', self.par2) )
if self.par3 is not None and self.par3 != '':
job_data.append( ('Calculation Parameter 3', self.par3) )
if self.calc_group is not None and len(self.calc_group)>0:
job_data.append( ('Calculation Group', self.calc_group.replace(",",", ")) )
job_data.append( ('Calculate Raw?', self.calc_raw) )
job_data.append( ('Spatial Operation', ocgisChoices(Config.SPATIAL_OPERATION)[self.spatial_operation]) )
job_data.append( ('Aggregate', self.aggregate) )
job_data.append( ('Output Format', ocgisChoices(Config.OUTPUT_FORMAT)[self.output_format]) )
job_data.append( ('File Output Prefix', self.prefix) )
job_data.append( ('Include Auxiliary Files', self.with_auxiliary_files) )
return job_data
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 clean(self):
# invoke superclass cleaning method
super(OpenClimateGisForm1, self).clean()
# validate data selection
if 'dataset_category' in self.cleaned_data and hasText(self.cleaned_data['dataset_category']):
if 'dataset' in self.cleaned_data and hasText(self.cleaned_data['dataset']):
jsonData = ocgisDatasets.datasets[self.cleaned_data['dataset_category']][self.cleaned_data['dataset']]
if jsonData['type'] == 'datasets':
if not 'variable' in self.cleaned_data or not hasText(self.cleaned_data['variable']):
self._errors["variable"] = self.error_class(["A variable must be selected when the dataset is not a package."])
# validate geometry
ngeometries = 0
geometry = None
if (hasText(self.cleaned_data['geometry']) or len(self.cleaned_data['geometry_id']) ):
ngeometries += 1
geometry = 'shape'
# NOTE: invalid float values in form result in cleaned_data not being populated for that key
if ( ('latmin' in self.cleaned_data and hasText(self.cleaned_data['latmin']))
or ('latmax' in self.cleaned_data and hasText(self.cleaned_data['latmax']))
or ('lonmin' in self.cleaned_data and hasText(self.cleaned_data['lonmin']))
or ('lonmax' in self.cleaned_data and hasText(self.cleaned_data['lonmax'])) ):
ngeometries += 1
geometry = 'box'
if ( ('lat' in self.cleaned_data and hasText(self.cleaned_data['lat']))
or ('lon' in self.cleaned_data and hasText(self.cleaned_data['lon'])) ):
ngeometries += 1
geometry = 'point'
if ngeometries > 1:
self._errors["geometry"] = self.error_class(["Please choose only one geometry: shape, bounding box or point."])
elif ngeometries==1:
if geometry =='shape':
if not hasText(self.cleaned_data['geometry']):
self._errors["geometry_id"] = self.error_class(["Please select a shape type."])
if len(self.cleaned_data['geometry_id'])==0:
self._errors["geometry_id"] = self.error_class(["Please select a shape geometry."])
elif geometry == 'box':
if ( not 'latmin' in self.cleaned_data or not hasText(self.cleaned_data['latmin'])
or not 'latmax' in self.cleaned_data or not hasText(self.cleaned_data['latmax'])
or not 'lonmin' in self.cleaned_data or not hasText(self.cleaned_data['lonmin'])
or not 'lonmax' in self.cleaned_data or not hasText(self.cleaned_data['lonmax']) ):
self._errors["latmin"] = self.error_class(["Invalid bounding box latitude or longitude values."])
elif geometry == 'point':
if not 'lat' in self.cleaned_data or not hasText(self.cleaned_data['lat']):
self._errors["lat"] = self.error_class(["Invalid point latitude."])
if not 'lon' in self.cleaned_data or not hasText(self.cleaned_data['lon']):
self._errors["lon"] = self.error_class(["Invalid point longitude."])
# validate time range
datetime_start = None
datetime_stop = None
if 'datetime_start' in self.cleaned_data and hasText(self.cleaned_data['datetime_start']):
datetime_start = self.cleaned_data['datetime_start']
if 'datetime_stop' in self.cleaned_data and hasText(self.cleaned_data['datetime_stop']):
datetime_stop = self.cleaned_data['datetime_stop']
if datetime_start is not None and datetime_stop is None:
self._errors["datetime_stop"] = self.error_class(["Invalid value for 'Time Range Stop'."])
if datetime_start is None and datetime_stop is not None:
self._errors["datetime_start"] = self.error_class(["Invalid value for 'Time Range Start'."])
if datetime_start is not None and datetime_stop is not None:
if datetime_start > datetime_stop:
self._errors["datetime_start"] = self.error_class(["'Time Range Start' must be less than 'Time Range Stop'."])
time_range = [datetime_start, datetime_stop]
else:
time_range = None
# validate years time region
time_region = {}
if 'timeregion_year' in self.cleaned_data and hasText(self.cleaned_data['timeregion_year']):
years = str(self.cleaned_data['timeregion_year'].replace(" ","")) # remove blanks
if re.match('^\d{4}-\d{4}', years):
year1 = int(years[0:4])
year2 = int(years[5:9])
if year1 >= year2:
self._errors["timeregion_year"] = self.error_class(["Invalid year selection: must be year1 < year2"])
time_region['year'] = range(year1, year2+1)
else:
years = years.split(',')
time_region['year'] = []
for year in years:
if not re.match('^\d{4}$', year):
self._errors["timeregion_year"] = self.error_class(["Invalid year selection"])
break
time_region['year'].append( int(year) )
# validate months time region
if 'timeregion_month' in self.cleaned_data and len(self.cleaned_data['timeregion_month'])>0:
time_region['month'] = map(int, self.cleaned_data['timeregion_month'])
# validate time range + time region
if time_range is not None and len(time_region)>0:
if not validate_time_subset(time_range, time_region):
self._errors["timeregion_year"] = self.error_class(["Time Range must contain Time Region."])
if not self.is_valid():
print 'VALIDATION ERRORS: %s' % self.errors
# return cleaned data
return self.cleaned_data
def _validateFields(self, field_names):
'''Method for bulk validation of a list of field.'''
for field_name in field_names:
if not hasText( self.cleaned_data[field_name] ):
self._errors[field_name] = self.error_class(["This field is required"])
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
def get_context_data(self, form, **kwargs):
context = super(OpenClimateGisWizard, 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)})
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:
if step != self.steps.current:
cleaned_data = self.get_cleaned_data_for_step(step)
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("variable"):
job_data["variable"] = cleaned_data["variable"]
if cleaned_data.has_key("geometry") and hasText(cleaned_data["geometry"]):
job_data["geometry"] = cleaned_data["geometry"]
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("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"]
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
def encodeArgs(self, openClimateGisJob):
"""Method to transform the OpenClimateGisJob instance into a dictionary of arguments passed on to the ocgis library."""
args = {}
# ocgis.RequestDataset(uri=None, variable=None, alias=None, time_range=None, time_region=None,
# level_range=None, s_proj=None, t_units=None, t_calendar=None, did=None, meta=None)
# retrieve dataset URIs, variable from configuration JSON data
data_type = openClimateGisJob.data_type
args['variable'] = []
args['alias'] = []
args['uri'] = []
args['t_calendar'] = []
args['t_units'] = []
if data_type == 'variable':
# Example:
# {'metadata': {'description': {'long_name': u'<tdk>', 'dataset_category': u'<tdk>', 'dataset': u'<tdk>'},
# 'time_range': [datetime.datetime(1971, 1, 1, 0, 0), datetime.datetime(2000, 12, 31, 0, 0)]},
# 'dataset': [{'variable': u'tas', 'alias': u'tas', 't_calendar': u'standard',
# 'uri': [u'/data/maurer/concatenated/Maurer02new_OBS_tas_daily.1971-2000.nc'],
# 't_units': u'days since 1940-01-01 00:00:00'}]}
dictionaries = self.ocgisDatasets.getDatasets(data_type,
long_name=openClimateGisJob.long_name,
time_frequency=openClimateGisJob.time_frequency,
dataset_category=openClimateGisJob.dataset_category,
dataset=openClimateGisJob.dataset )
elif data_type == 'package':
# Example:
# {'metadata': {'description': {'dataset_category': u'<tdk>', 'package': u'<tdk>'},
# 'time_range': [datetime.datetime(1971, 1, 1, 0, 0), datetime.datetime(2000, 12, 31, 0, 0)]},
# 'dataset': [{'variable': u'pr', 'alias': u'pr', 't_calendar': u'365_day',
# 'uri': [u'/data/downscaled/arrm/arrm_gfdl_2.1.20c3m.pr.NAm.1971-2000.nc'],
# 't_units': u'days since 1959-12-31'},
# {'variable': u'tasmax', 'alias': u'tasmax', 't_calendar': u'365_day',
# 'uri': [u'/data/downscaled/arrm/arrm_gfdl_2.1.20c3m.tasmax.NAm.1971-2000.nc'],
# 't_units': u'days since 1959-12-31'},
# {'variable': u'tasmin', 'alias': u'tasmin', 't_calendar': u'365_day',
# 'uri': [u'/data/downscaled/arrm/arrm_gfdl_2.1.20c3m.tasmin.NAm.1971-2000.nc'],
# 't_units': u'days since 1959-12-31'}]}
dictionaries = self.ocgisDatasets.getDatasets(data_type, package_name=openClimateGisJob.package_name)
args['datasets'] = dictionaries['dataset']
# combine all dictionaries into lists
#for dict in dictionaries:
# args['variable'].append( dict['variable'] )
# args['alias'].append( dict['alias'] )
# args['uri'].append( dict['uri'] )
# args['t_calendar'].append( dict['t_calendar'] )
# args['t_units'].append( dict['t_units'] )
# class ocgis.OcgOperations(dataset=None, spatial_operation='intersects', geom=None,
# aggregate=False, calc=None, calc_grouping=None, calc_raw=False,
# abstraction='polygon', snippet=False, backend='ocg', prefix=None,
# output_format='numpy', agg_selection=False, select_ugid=None,
# vector_wrap=True, allow_empty=False, dir_output=None, slice=None,
# file_only=False, headers=None)
args['geom'] = None
args['select_ugid'] = None
if hasText(openClimateGisJob.geometry_category):
args['geom'] = self.ocgisGeometries.getCategoryKey( openClimateGisJob.geometry_category )
args['select_ugid'] = []
# must transform back from string to list of integers
for geom in openClimateGisJob.geometry_id.split(","):
args['select_ugid'].append( self.ocgisGeometries.getGuid(openClimateGisJob.geometry_category, openClimateGisJob.geometry_subcategory, str(geom)))
elif ( hasText(openClimateGisJob.latmin) and hasText(openClimateGisJob.latmax)
and hasText(openClimateGisJob.lonmin) and hasText(openClimateGisJob.lonmax)):
args['geom'] = [openClimateGisJob.lonmin, openClimateGisJob.lonmax, openClimateGisJob.latmin, openClimateGisJob.latmax]
elif hasText(openClimateGisJob.lat) and hasText(openClimateGisJob.lon):
args['geom'] = [openClimateGisJob.lon, openClimateGisJob.lat]
args['agg_selection'] = openClimateGisJob.agg_selection
if openClimateGisJob.datetime_start is not None and openClimateGisJob.datetime_stop is not None:
args['time_range'] = [openClimateGisJob.datetime_start, openClimateGisJob.datetime_stop]
else:
args['time_range'] = None
args['time_region'] = None
if hasText(openClimateGisJob.timeregion_month) or hasText(openClimateGisJob.timeregion_year):
args['time_region'] = { 'month':None, 'year':None }
if hasText(openClimateGisJob.timeregion_month):
args['time_region']['month'] = []
for i in map(int, openClimateGisJob.timeregion_month.split(",")):
args['time_region']['month'].append(i)
if hasText(openClimateGisJob.timeregion_year):
args['time_region']['year'] = []
years = openClimateGisJob.timeregion_year.replace(" ","")
if re.match('^\d{4}-\d{4}', years):
year1 = int(years[0:4])
year2 = int(years[5:9])
args['time_region']['year'] = range(year1, year2+1)
else:
for i in map(int, openClimateGisJob.timeregion_year.split(",")):
args['time_region']['year'].append(i)
args['calc'] = None
if hasText(openClimateGisJob.calc) and openClimateGisJob.calc.lower() != 'none':
calc = self.ocgisCalculations.getCalc(str(openClimateGisJob.calc))
args['calc'] = [ {'func':str(calc["func"]), 'name':str(calc["name"])} ]
args['calc'][0]['kwds'] = {}
# loop over keywords in order
if "keywords" in calc:
for i, keyword in enumerate(calc["keywords"]):
if i==0:
val = openClimateGisJob.par1
elif i==1:
val = openClimateGisJob.par2
elif i==2:
val = openClimateGisJob.par3
if keyword["type"] == "float":
val = float(val)
elif keyword["type"] == "string":
val = str(val).lower()
args['calc'][0]['kwds'][str(keyword[u'name'])] = val
args['headers'] = HEADERS_CALC
else:
args['headers'] = HEADERS_NOCALC
args['calc_raw'] = openClimateGisJob.calc_raw
if hasText(openClimateGisJob.calc_group):
args['calc_grouping'] = map(str, openClimateGisJob.calc_group.split(","))
else:
args['calc_grouping'] = None
args['spatial_operation'] = openClimateGisJob.spatial_operation
args['aggregate'] = openClimateGisJob.aggregate
args['output_format'] = openClimateGisJob.output_format
args['prefix'] = openClimateGisJob.prefix
args['dir_output'] = str( openClimateGisJob.id )
args['with_auxiliary_files'] = openClimateGisJob.with_auxiliary_files
return args
def encodeArgs(self, openClimateGisJob):
"""Method to transform the OpenClimateGisJob instance into a dictionary of arguments passed on to the ocgis library."""
args = {}
# ocgis.RequestDataset(uri=None, variable=None, alias=None, time_range=None, time_region=None,
# level_range=None, s_proj=None, t_units=None, t_calendar=None, did=None, meta=None)
# retrieve dataset URIs, variable from configuration JSON data
jsonObject = self.ocgisDatasets.datasets[openClimateGisJob.dataset_category][openClimateGisJob.dataset]
if jsonObject['type'] == 'datasets':
# must sub-select data structure by variable
jsonData =jsonObject[openClimateGisJob.variable]
elif jsonObject['type'] == 'package':
jsonData = jsonObject
args['uri'] = jsonData['uri']
args['variable'] = jsonData["variable"]
args['t_calendar'] = jsonData["t_calendar"]
args['t_units'] = jsonData["t_units"]
args['alias'] = jsonData["alias"]
# class ocgis.OcgOperations(dataset=None, spatial_operation='intersects', geom=None,
# aggregate=False, calc=None, calc_grouping=None, calc_raw=False,
# abstraction='polygon', snippet=False, backend='ocg', prefix=None,
# output_format='numpy', agg_selection=False, select_ugid=None,
# vector_wrap=True, allow_empty=False, dir_output=None, slice=None,
# file_only=False, headers=None)
args['geom'] = None
args['select_ugid'] = None
if hasText(openClimateGisJob.geometry):
args['geom'] = self.ocgisGeometries.getCategoryKey( openClimateGisJob.geometry )
args['select_ugid'] = []
# must transform back from string to list of integers
for geom in openClimateGisJob.geometry_id.split(","):
args['select_ugid'].append( self.ocgisGeometries.getGuid(openClimateGisJob.geometry, str(geom)))
elif ( hasText(openClimateGisJob.latmin) and hasText(openClimateGisJob.latmax)
and hasText(openClimateGisJob.lonmin) and hasText(openClimateGisJob.lonmax)):
args['geom'] = [openClimateGisJob.lonmin, openClimateGisJob.lonmax, openClimateGisJob.latmin, openClimateGisJob.latmax]
elif hasText(openClimateGisJob.lat) and hasText(openClimateGisJob.lon):
args['geom'] = [openClimateGisJob.lon, openClimateGisJob.lat]
if openClimateGisJob.datetime_start is not None and openClimateGisJob.datetime_stop is not None:
args['time_range'] = [openClimateGisJob.datetime_start, openClimateGisJob.datetime_stop]
else:
args['time_range'] = None
args['time_region'] = None
if hasText(openClimateGisJob.timeregion_month) or hasText(openClimateGisJob.timeregion_year):
args['time_region'] = { 'month':None, 'year':None }
if hasText(openClimateGisJob.timeregion_month):
args['time_region']['month'] = []
for i in map(int, openClimateGisJob.timeregion_month.split(",")):
args['time_region']['month'].append(i)
if hasText(openClimateGisJob.timeregion_year):
args['time_region']['year'] = []
years = openClimateGisJob.timeregion_year.replace(" ","")
if re.match('^\d{4}-\d{4}', years):
year1 = int(years[0:4])
year2 = int(years[5:9])
args['time_region']['year'] = range(year1, year2+1)
else:
for i in map(int, openClimateGisJob.timeregion_year.split(",")):
args['time_region']['year'].append(i)
args['calc'] = None
if hasText(openClimateGisJob.calc) and openClimateGisJob.calc.lower() != 'none':
calc = self.ocgisCalculations.getCalc(str(openClimateGisJob.calc))
args['calc'] = [ {'func':str(calc["func"]), 'name':str(calc["name"])} ]
args['calc'][0]['kwds'] = {}
# loop over keywords in order
if "keywords" in calc:
for i, keyword in enumerate(calc["keywords"]):
if i==0:
val = openClimateGisJob.par1
elif i==1:
val = openClimateGisJob.par2
elif i==2:
val = openClimateGisJob.par3
if keyword["type"] == "float":
val = float(val)
elif keyword["type"] == "string":
val = str(val).lower()
args['calc'][0]['kwds'][str(keyword[u'name'])] = val
args['headers'] = HEADERS_CALC
else:
args['headers'] = HEADERS_NOCALC
args['calc_raw'] = openClimateGisJob.calc_raw
if hasText(openClimateGisJob.calc_group):
args['calc_grouping'] = map(str, openClimateGisJob.calc_group.split(","))
else:
args['calc_grouping'] = None
args['spatial_operation'] = openClimateGisJob.spatial_operation
args['aggregate'] = openClimateGisJob.aggregate
args['output_format'] = openClimateGisJob.output_format
args['prefix'] = openClimateGisJob.prefix
args['dir_output'] = str( openClimateGisJob.id )
args['with_auxiliary_files'] = openClimateGisJob.with_auxiliary_files
return args
