def test_ApplyMS(self):
logging.debug('test_ApplyMS')
# load zone with count
zone_data = self.test_LoadZone2(True, 2)
zone_stats = layer_field_stats(zone_data[0].value, self.zone2_field)
# load ms
ms_opdata = self.test_LoadMS(True)
ms = ms_opdata[0].value
stats = ms.get_assignment_by_name('ALL')
for zone in zone_stats.keys():
newZone = MappingSchemeZone(zone)
ms.assign(newZone, stats)
# apply mapping scheme
ms_applier = ZoneMSApplier(self.operator_options)
ms_applier.inputs = [
zone_data[0],
OperatorData(OperatorDataTypes.StringAttribute, self.zone2_field),
OperatorData(OperatorDataTypes.StringAttribute,
self.zone2_bldgcount_field),
ms_opdata[0],
]
ms_applier.outputs = [
OperatorData(OperatorDataTypes.Exposure),
OperatorData(OperatorDataTypes.Shapefile),
]
ms_applier.do_operation()
self.assertTrue(os.path.exists(ms_applier.outputs[1].value))
# cleanup
self._clean_layer(zone_data)
self._clean_layer(ms_applier.outputs)
del ms_applier
# testing apply MS on grid
############################
ms_applier = GridMSApplier(self.operator_options)
ms_applier.inputs = [
OperatorData(OperatorDataTypes.Grid,
load_shapefile(self.grid2_path, 'test_input_grid')),
OperatorData(OperatorDataTypes.StringAttribute, self.zone2_field),
OperatorData(OperatorDataTypes.StringAttribute,
self.zone2_bldgcount_field),
ms_opdata[0],
]
ms_applier.outputs = [
OperatorData(OperatorDataTypes.Exposure),
OperatorData(OperatorDataTypes.Shapefile),
]
ms_applier.do_operation()
self.assertTrue(os.path.exists(ms_applier.outputs[1].value))
# cleanup
self._clean_layer(ms_applier.outputs)
del ms_applier
def test_ApplyMS(self):
logging.debug("test_ApplyMS")
# load zone with count
zone_data = self.test_LoadZone2(True, 2)
zone_stats = layer_field_stats(zone_data[0].value, self.zone2_field)
# load ms
ms_opdata = self.test_LoadMS(True)
ms = ms_opdata[0].value
stats = ms.get_assignment_by_name("ALL")
for zone in zone_stats.keys():
newZone = MappingSchemeZone(zone)
ms.assign(newZone, stats)
# apply mapping scheme
ms_applier = ZoneMSApplier(self.operator_options)
ms_applier.inputs = [
zone_data[0],
OperatorData(OperatorDataTypes.StringAttribute, self.zone2_field),
OperatorData(OperatorDataTypes.StringAttribute, self.zone2_bldgcount_field),
ms_opdata[0],
]
ms_applier.outputs = [OperatorData(OperatorDataTypes.Exposure), OperatorData(OperatorDataTypes.Shapefile)]
ms_applier.do_operation()
self.assertTrue(os.path.exists(ms_applier.outputs[1].value))
# cleanup
self._clean_layer(zone_data)
self._clean_layer(ms_applier.outputs)
del ms_applier
# testing apply MS on grid
############################
ms_applier = GridMSApplier(self.operator_options)
ms_applier.inputs = [
OperatorData(OperatorDataTypes.Grid, load_shapefile(self.grid2_path, "test_input_grid")),
OperatorData(OperatorDataTypes.StringAttribute, self.zone2_field),
OperatorData(OperatorDataTypes.StringAttribute, self.zone2_bldgcount_field),
ms_opdata[0],
]
ms_applier.outputs = [OperatorData(OperatorDataTypes.Exposure), OperatorData(OperatorDataTypes.Shapefile)]
ms_applier.do_operation()
self.assertTrue(os.path.exists(ms_applier.outputs[1].value))
# cleanup
self._clean_layer(ms_applier.outputs)
del ms_applier
def do_operation(self):
""" perform create mapping scheme operation """
# input/output verification already performed during set input/ouput
zone_layer = self.inputs[0].value
zone_field = self.inputs[1].value
# load zone
try:
zone_classes = layer_field_stats(zone_layer, zone_field)
except AssertionError as err:
raise OperatorError(str(err), self.__class__)
# merge to create stats
ms = MappingScheme(self._taxonomy)
for _zone, _count in zone_classes.iteritems():
stats = Statistics(self._taxonomy)
stats.finalize()
stats.get_tree().value = _zone
ms.assign(MappingSchemeZone(_zone), stats)
self.outputs[0].value = ms
def do_operation(self):
""" perform create mapping scheme operation """
# input/output verification already performed during set input/ouput
zone_layer = self.inputs[0].value
zone_field = self.inputs[1].value
# load zone
try:
zone_classes = layer_field_stats(zone_layer, zone_field)
except AssertionError as err:
raise OperatorError(str(err), self.__class__)
# merge to create stats
ms = MappingScheme(self._taxonomy)
for _zone, _count in zone_classes.iteritems():
stats = Statistics(self._taxonomy)
stats.finalize()
stats.get_tree().value = _zone
ms.assign(MappingSchemeZone(_zone), stats)
self.outputs[0].value = ms
def do_operation(self):
# input/output verification not performed yet
fp_layer = self.inputs[0].value
area_field = self.inputs[1].value
ht_field = self.inputs[2].value
zone_layer = self.inputs[3].value
zone_field = self.inputs[4].value
svy_layer = self.inputs[5].value
# make sure required data fields are populated
area_idx = layer_field_index(fp_layer, area_field)
if area_idx == -1:
raise OperatorError(
"Field %s does not exist in %s" %
(area_field, fp_layer.name()), self.__class__)
ht_idx = layer_field_index(fp_layer, ht_field)
if ht_idx == -1:
raise OperatorError(
"Field %s does not exist in %s" % (ht_field, fp_layer.name()),
self.__class__)
zone_idx = layer_field_index(zone_layer, zone_field)
if zone_idx == -1:
raise OperatorError(
"Field %s does not exist in %s" %
(zone_field, zone_layer.name()), self.__class__)
svy_samp_idx = layer_field_index(svy_layer, GRP_FIELD_NAME)
if svy_samp_idx == -1:
raise OperatorError(
"Field %s does not exist in %s" %
(GRP_FIELD_NAME, svy_layer.name()), self.__class__)
svy_ht_idx = layer_field_index(svy_layer, HT_FIELD_NAME)
if svy_ht_idx == -1:
raise OperatorError(
"Field %s does not exist in %s" %
(HT_FIELD_NAME, svy_layer.name()), self.__class__)
svy_size_idx = layer_field_index(svy_layer, AREA_FIELD_NAME)
if svy_size_idx == -1:
raise OperatorError("Field %s does not exist in %s" %
(AREA_FIELD_NAME, svy_layer.name()))
tax_idx = layer_field_index(svy_layer, TAX_FIELD_NAME)
if tax_idx == -1:
raise OperatorError("Field %s does not exist in %s" %
(TAX_FIELD_NAME, svy_layer.name()))
# load zone classes
# the operations below must be performed for each zone
try:
zone_classes = layer_field_stats(zone_layer, zone_field)
except AssertionError as err:
raise OperatorError(str(err), self.__class__)
# join survey with zones
logAPICall.log('merge survey & zone', logAPICall.DEBUG)
tmp_join_layername = 'join_%s' % get_unique_filename()
tmp_join_file = self._tmp_dir + tmp_join_layername + '.shp'
analyzer = QgsOverlayAnalyzer()
analyzer.intersection(svy_layer, zone_layer, tmp_join_file)
tmp_join_layer = load_shapefile(tmp_join_file, tmp_join_layername)
logAPICall.log('compile zone statistics', logAPICall.DEBUG)
zone_idx = layer_field_index(tmp_join_layer, zone_field)
svy_samp_idx = layer_field_index(tmp_join_layer, GRP_FIELD_NAME)
svy_ht_idx = layer_field_index(tmp_join_layer, HT_FIELD_NAME)
svy_size_idx = layer_field_index(tmp_join_layer, AREA_FIELD_NAME)
if svy_size_idx == -1:
raise OperatorError("Field %s does not exist in %s" %
(AREA_FIELD_NAME, svy_layer.name()))
tax_idx = layer_field_index(tmp_join_layer, TAX_FIELD_NAME)
if tax_idx == -1:
raise OperatorError("Field %s does not exist in %s" %
(TAX_FIELD_NAME, svy_layer.name()))
# empty fields for holding the stats
_zone_n_exp, _zone_p_exp, _zone_a_exp, _zone_e_exp = {}, {}, {}, {}
_zone_group_counts, _zone_group_stories, _zone_group_weight = {}, {}, {}
_zone_total_area, _zone_total_count, _zone_total_ht = {}, {}, {}
for _zone in zone_classes.iterkeys():
_zone_n_exp[_zone] = {}
_zone_p_exp[_zone] = {}
_zone_a_exp[_zone] = {}
_zone_e_exp[_zone] = {}
_zone_group_counts[_zone] = {}
_zone_group_stories[_zone] = {}
_zone_group_weight[_zone] = {}
_zone_total_area[_zone] = 0
_zone_total_count[_zone] = 0
_zone_total_ht[_zone] = 0
# associate group to ratio value
for _rec in layer_features(tmp_join_layer):
_ht = _rec.attributeMap()[svy_ht_idx].toInt()[0]
_samp_grp = str(_rec.attributeMap()[svy_samp_idx].toString())
_tax_str = str(_rec.attributeMap()[tax_idx].toString())
try:
self._taxonomy.parse(_tax_str)
self.increment_dict(_zone_group_counts[_zone], _samp_grp, 1)
self.increment_dict(_zone_group_stories[_zone], _samp_grp, _ht)
except Exception as err:
logAPICall.log("Error processing record %s" % err,
logAPICall.WARNING)
for _zone in zone_classes.iterkeys():
if len(_zone_group_counts[_zone]) != 3:
raise OperatorError("Survey must have 3 sampling groups",
self.__class__)
cmp_value = -1
for _grp, _count in _zone_group_counts[_zone].iteritems():
if cmp_value == -1:
cmp_value = _count
if cmp_value != _count:
raise OperatorError(
"Survey groups must have same number of samples",
self.__class__)
# sort by stories
group_stories_for_sort = {}
for _grp, _ht in _zone_group_stories[_zone].iteritems():
group_stories_for_sort[_ht] = _grp
sorted_keys = group_stories_for_sort.keys()
sorted_keys.sort()
# assign group to weight
for idx, key in enumerate(sorted_keys):
_zone_group_weight[_zone][
group_stories_for_sort[key]] = self.weights[idx]
# aggregate values from survey for each building type
# - count (n)
# - floor area (p)
# - total area (a)
for _f in layer_features(tmp_join_layer):
_zone_str = str(_f.attributeMap()[zone_idx].toString())
_tax_str = str(_f.attributeMap()[tax_idx].toString())
_sample_grp = str(_f.attributeMap()[svy_samp_idx].toString())
_sample_size = _f.attributeMap()[svy_size_idx].toDouble()[0]
_sample_ht = _f.attributeMap()[svy_size_idx].toDouble()[0]
group_weight = _zone_group_weight[_zone]
try:
self._taxonomy.parse(_tax_str)
self.increment_dict(_zone_n_exp[_zone_str], _tax_str,
group_weight[_sample_grp])
self.increment_dict(_zone_p_exp[_zone_str], _tax_str,
_sample_size * group_weight[_sample_grp])
self.increment_dict(
_zone_a_exp[_zone_str], _tax_str,
_sample_size * _ht * group_weight[_sample_grp])
self.increment_dict(_zone_e_exp[_zone_str], _tax_str, 0)
except Exception as err:
logAPICall.log(
"error processing sample with building type: %s" %
_tax_str, logAPICall.WARNING)
pass
# adjust ratio using footprint ht/area
tmp_join_layername2 = 'join_%s' % get_unique_filename()
tmp_join_file2 = self._tmp_dir + tmp_join_layername2 + '.shp'
analyzer = QgsOverlayAnalyzer()
analyzer.intersection(fp_layer, zone_layer, tmp_join_file2)
tmp_join_layer2 = load_shapefile(tmp_join_file2, tmp_join_layername)
zone_idx = layer_field_index(tmp_join_layer2, zone_field)
area_idx = layer_field_index(tmp_join_layer2, area_field)
ht_idx = layer_field_index(tmp_join_layer2, ht_field)
for _f in layer_features(tmp_join_layer2):
_zone_str = str(_f.attributeMap()[zone_idx].toString())
_area = _f.attributeMap()[area_idx].toDouble()[0]
_ht = _f.attributeMap()[ht_idx].toDouble()[0]
_zone_total_area[_zone_str] += _area
_zone_total_count[_zone_str] += 1
_zone_total_ht[_zone_str] += _ht
# calculate building ratios for each zone
for _zone in zone_classes.iterkeys():
# for total count (n) and area (a)
e_nt_cluster_total = sum(_zone_n_exp[_zone].itervalues())
e_at_cluster_total = sum(_zone_a_exp[_zone].itervalues())
# E[A] estimated total building area for zone
e_at_total = _zone_total_area[_zone] * _zone_total_ht[
_zone] / _zone_total_count[_zone]
# calculate expected values
for t, e_at_cluster in _zone_a_exp[_zone].iteritems():
e_nt_cluster = _zone_n_exp[_zone][t]
if e_at_cluster == 0 or e_at_total == 0:
# area is missing, use count instead
_zone_e_exp[_zone][t] = e_nt_cluster / e_nt_cluster_total
_zone_a_exp[_zone][t] = 0
else:
# use ratio of area over total area
# E[f(t)] building fraction based on sampled area
e_ft_cluster = e_at_cluster / e_at_cluster_total
# E[G(t)] average area per building
e_gt_cluster = e_at_cluster / e_nt_cluster
# E[A(t)] estimated total building area for zone for building type
e_at = e_at_total * e_ft_cluster
# E[N(t)] estimated total number of buildings zone-wide by type
e_nt = e_at / e_gt_cluster
_zone_e_exp[_zone][t] = e_nt
_zone_a_exp[_zone][t] = e_ft_cluster
# convert the building ratios
logAPICall.log('create mapping scheme for zones', logAPICall.DEBUG)
ms = MappingScheme(self._taxonomy)
for _zone in zone_classes.iterkeys():
# create mapping scheme for zone
stats = Statistics(self._taxonomy)
# use building ratio to create statistic
for _tax_str, _e_exp in _zone_e_exp[_zone].iteritems():
stats.add_case(_tax_str,
self._parse_order,
self._parse_modifiers,
add_times=int(_e_exp * 1000))
# finalize call is required
stats.finalize()
ms.assign(MappingSchemeZone(_zone), stats)
# clean up
del tmp_join_layer, analyzer
remove_shapefile(tmp_join_file)
# assign output
self.outputs[0].value = ms
self.outputs[1].value = _zone_a_exp
def do_operation(self):
""" perform create mapping scheme operation """
# input/output verification already performed during set input/ouput
survey_layer = self.inputs[0].value
zone_layer = self.inputs[1].value
zone_field = self.inputs[2].value
tax_field = self._tax_field
logAPICall.log(
'survey %s, taxfield %s, zone %s, zone_field, %s' %
(survey_layer.name(), tax_field, zone_layer.name(), zone_field),
logAPICall.DEBUG)
tmp_join_layername = 'join_%s' % get_unique_filename()
tmp_join_file = self._tmp_dir + tmp_join_layername + '.shp'
# load zone classes
try:
zone_classes = layer_field_stats(zone_layer, zone_field)
except AssertionError as err:
raise OperatorError(str(err), self.__class__)
# merge to create stats
logAPICall.log('merge survey & zone', logAPICall.DEBUG)
analyzer = QgsOverlayAnalyzer()
analyzer.intersection(survey_layer, zone_layer, tmp_join_file)
tmp_join_layer = load_shapefile(tmp_join_file, tmp_join_layername)
logAPICall.log('create mapping schemes', logAPICall.DEBUG)
ms = MappingScheme(self._taxonomy)
for _zone, _count in zone_classes.iteritems():
stats = Statistics(self._taxonomy)
ms.assign(MappingSchemeZone(_zone), stats)
# loop through all input features
zone_idx = layer_field_index(tmp_join_layer, zone_field)
tax_idx = layer_field_index(tmp_join_layer, tax_field)
area_idx = layer_field_index(tmp_join_layer, AREA_FIELD_NAME)
cost_idx = layer_field_index(tmp_join_layer, COST_FIELD_NAME)
for _f in layer_features(tmp_join_layer):
_zone_str = str(_f.attributeMap()[zone_idx].toString())
_tax_str = str(_f.attributeMap()[tax_idx].toString())
additional = {}
_area = _f.attributeMap()[area_idx].toDouble()[0]
if _area > 0:
additional = {StatisticNode.AverageSize: _area}
_cost = _f.attributeMap()[cost_idx].toDouble()[0]
if _cost > 0:
additional = {StatisticNode.UnitCost: _cost}
logAPICall.log('zone %s => %s' % (_zone_str, _tax_str),
logAPICall.DEBUG_L2)
try:
ms.get_assignment_by_name(_zone_str).add_case(
_tax_str, self._parse_order, self._parse_modifiers,
additional)
except TaxonomyParseError as perr:
logAPICall.log(
"error parsing case %s, %s" % (str(_tax_str), str(perr)),
logAPICall.WARNING)
# store data in output
for _zone, _stats in ms.assignments():
_stats.finalize()
_stats.get_tree().value = _zone.name
# clean up
del tmp_join_layer, analyzer
remove_shapefile(tmp_join_file)
self.outputs[0].value = ms
def do_operation(self):
# input/output verification not performed yet
fp_layer = self.inputs[0].value
area_field = self.inputs[1].value
ht_field = self.inputs[2].value
zone_layer = self.inputs[3].value
zone_field = self.inputs[4].value
svy_layer = self.inputs[5].value
# make sure required data fields are populated
area_idx = layer_field_index(fp_layer, area_field)
if area_idx == -1:
raise OperatorError("Field %s does not exist in %s" %(area_field, fp_layer.name()), self.__class__)
ht_idx = layer_field_index(fp_layer, ht_field)
if ht_idx == -1:
raise OperatorError("Field %s does not exist in %s" %(ht_field, fp_layer.name()), self.__class__)
zone_idx = layer_field_index(zone_layer, zone_field)
if zone_idx == -1:
raise OperatorError("Field %s does not exist in %s" %(zone_field, zone_layer.name()), self.__class__)
svy_samp_idx = layer_field_index(svy_layer, GRP_FIELD_NAME)
if svy_samp_idx == -1:
raise OperatorError("Field %s does not exist in %s" %(GRP_FIELD_NAME, svy_layer.name()), self.__class__)
svy_ht_idx = layer_field_index(svy_layer, HT_FIELD_NAME)
if svy_ht_idx == -1:
raise OperatorError("Field %s does not exist in %s" %(HT_FIELD_NAME, svy_layer.name()), self.__class__)
svy_size_idx = layer_field_index(svy_layer, AREA_FIELD_NAME)
if svy_size_idx == -1:
raise OperatorError("Field %s does not exist in %s" %(AREA_FIELD_NAME, svy_layer.name()))
tax_idx = layer_field_index(svy_layer, TAX_FIELD_NAME)
if tax_idx == -1:
raise OperatorError("Field %s does not exist in %s" %(TAX_FIELD_NAME, svy_layer.name()))
# load zone classes
# the operations below must be performed for each zone
try:
zone_classes = layer_field_stats(zone_layer, zone_field)
except AssertionError as err:
raise OperatorError(str(err), self.__class__)
# join survey with zones
logAPICall.log('merge survey & zone', logAPICall.DEBUG)
tmp_join_layername = 'join_%s' % get_unique_filename()
tmp_join_file = self._tmp_dir + tmp_join_layername + '.shp'
analyzer = QgsOverlayAnalyzer()
analyzer.intersection(svy_layer, zone_layer, tmp_join_file)
tmp_join_layer = load_shapefile(tmp_join_file, tmp_join_layername)
logAPICall.log('compile zone statistics', logAPICall.DEBUG)
zone_idx = layer_field_index(tmp_join_layer, zone_field)
svy_samp_idx = layer_field_index(tmp_join_layer, GRP_FIELD_NAME)
svy_ht_idx = layer_field_index(tmp_join_layer, HT_FIELD_NAME)
svy_size_idx = layer_field_index(tmp_join_layer, AREA_FIELD_NAME)
if svy_size_idx == -1:
raise OperatorError("Field %s does not exist in %s" %(AREA_FIELD_NAME, svy_layer.name()))
tax_idx = layer_field_index(tmp_join_layer, TAX_FIELD_NAME)
if tax_idx == -1:
raise OperatorError("Field %s does not exist in %s" %(TAX_FIELD_NAME, svy_layer.name()))
# empty fields for holding the stats
_zone_n_exp, _zone_p_exp, _zone_a_exp, _zone_e_exp = {}, {}, {}, {}
_zone_group_counts, _zone_group_stories, _zone_group_weight = {}, {}, {}
_zone_total_area, _zone_total_count, _zone_total_ht = {}, {}, {}
for _zone in zone_classes.iterkeys():
_zone_n_exp[_zone] = {}
_zone_p_exp[_zone] = {}
_zone_a_exp[_zone] = {}
_zone_e_exp[_zone] = {}
_zone_group_counts[_zone] = {}
_zone_group_stories[_zone] = {}
_zone_group_weight[_zone] = {}
_zone_total_area[_zone] = 0
_zone_total_count[_zone] = 0
_zone_total_ht[_zone] = 0
# associate group to ratio value
for _rec in layer_features(tmp_join_layer):
_ht = _rec.attributeMap()[svy_ht_idx].toInt()[0]
_samp_grp = str(_rec.attributeMap()[svy_samp_idx].toString())
_tax_str = str(_rec.attributeMap()[tax_idx].toString())
try:
self._taxonomy.parse(_tax_str)
self.increment_dict(_zone_group_counts[_zone], _samp_grp, 1)
self.increment_dict(_zone_group_stories[_zone], _samp_grp, _ht)
except Exception as err:
logAPICall.log("Error processing record %s" % err, logAPICall.WARNING)
for _zone in zone_classes.iterkeys():
if len(_zone_group_counts[_zone]) != 3:
raise OperatorError("Survey must have 3 sampling groups", self.__class__)
cmp_value = -1
for _grp, _count in _zone_group_counts[_zone].iteritems():
if cmp_value==-1:
cmp_value = _count
if cmp_value != _count:
raise OperatorError("Survey groups must have same number of samples", self.__class__)
# sort by stories
group_stories_for_sort = {}
for _grp, _ht in _zone_group_stories[_zone].iteritems():
group_stories_for_sort[_ht] = _grp
sorted_keys = group_stories_for_sort.keys()
sorted_keys.sort()
# assign group to weight
for idx, key in enumerate(sorted_keys):
_zone_group_weight[_zone][group_stories_for_sort[key]] = self.weights[idx]
# aggregate values from survey for each building type
# - count (n)
# - floor area (p)
# - total area (a)
for _f in layer_features(tmp_join_layer):
_zone_str = str(_f.attributeMap()[zone_idx].toString())
_tax_str = str(_f.attributeMap()[tax_idx].toString())
_sample_grp = str(_f.attributeMap()[svy_samp_idx].toString())
_sample_size = _f.attributeMap()[svy_size_idx].toDouble()[0]
_sample_ht = _f.attributeMap()[svy_size_idx].toDouble()[0]
group_weight = _zone_group_weight[_zone]
try:
self._taxonomy.parse(_tax_str)
self.increment_dict(_zone_n_exp[_zone_str], _tax_str, group_weight[_sample_grp])
self.increment_dict(_zone_p_exp[_zone_str], _tax_str, _sample_size*group_weight[_sample_grp])
self.increment_dict(_zone_a_exp[_zone_str], _tax_str, _sample_size*_ht*group_weight[_sample_grp])
self.increment_dict(_zone_e_exp[_zone_str], _tax_str, 0)
except Exception as err:
logAPICall.log("error processing sample with building type: %s" % _tax_str, logAPICall.WARNING)
pass
# adjust ratio using footprint ht/area
tmp_join_layername2 = 'join_%s' % get_unique_filename()
tmp_join_file2 = self._tmp_dir + tmp_join_layername2 + '.shp'
analyzer = QgsOverlayAnalyzer()
analyzer.intersection(fp_layer, zone_layer, tmp_join_file2)
tmp_join_layer2 = load_shapefile(tmp_join_file2, tmp_join_layername)
zone_idx = layer_field_index(tmp_join_layer2, zone_field)
area_idx = layer_field_index(tmp_join_layer2, area_field)
ht_idx = layer_field_index(tmp_join_layer2, ht_field)
for _f in layer_features(tmp_join_layer2):
_zone_str = str(_f.attributeMap()[zone_idx].toString())
_area = _f.attributeMap()[area_idx].toDouble()[0]
_ht = _f.attributeMap()[ht_idx].toDouble()[0]
_zone_total_area[_zone_str] += _area
_zone_total_count[_zone_str] += 1
_zone_total_ht[_zone_str] += _ht
# calculate building ratios for each zone
for _zone in zone_classes.iterkeys():
# for total count (n) and area (a)
e_nt_cluster_total = sum(_zone_n_exp[_zone].itervalues())
e_at_cluster_total = sum(_zone_a_exp[_zone].itervalues())
# E[A] estimated total building area for zone
e_at_total = _zone_total_area[_zone] * _zone_total_ht[_zone]/_zone_total_count[_zone]
# calculate expected values
for t, e_at_cluster in _zone_a_exp[_zone].iteritems():
e_nt_cluster = _zone_n_exp[_zone][t]
if e_at_cluster == 0 or e_at_total == 0:
# area is missing, use count instead
_zone_e_exp[_zone][t] = e_nt_cluster / e_nt_cluster_total
_zone_a_exp[_zone][t] = 0
else:
# use ratio of area over total area
# E[f(t)] building fraction based on sampled area
e_ft_cluster = e_at_cluster / e_at_cluster_total
# E[G(t)] average area per building
e_gt_cluster = e_at_cluster / e_nt_cluster
# E[A(t)] estimated total building area for zone for building type
e_at = e_at_total * e_ft_cluster
# E[N(t)] estimated total number of buildings zone-wide by type
e_nt = e_at / e_gt_cluster
_zone_e_exp[_zone][t] = e_nt
_zone_a_exp[_zone][t] = e_ft_cluster
# convert the building ratios
logAPICall.log('create mapping scheme for zones', logAPICall.DEBUG)
ms = MappingScheme(self._taxonomy)
for _zone in zone_classes.iterkeys():
# create mapping scheme for zone
stats = Statistics(self._taxonomy)
# use building ratio to create statistic
for _tax_str, _e_exp in _zone_e_exp[_zone].iteritems():
stats.add_case(_tax_str, self._parse_order, self._parse_modifiers, add_times=int(_e_exp*1000))
# finalize call is required
stats.finalize()
ms.assign(MappingSchemeZone(_zone), stats)
# clean up
del tmp_join_layer, analyzer
remove_shapefile(tmp_join_file)
# assign output
self.outputs[0].value = ms
self.outputs[1].value = _zone_a_exp
def do_operation(self):
""" perform create mapping scheme operation """
# input/output verification already performed during set input/ouput
survey_layer = self.inputs[0].value
zone_layer = self.inputs[1].value
zone_field = self.inputs[2].value
tax_field = self._tax_field
logAPICall.log('survey %s, taxfield %s, zone %s, zone_field, %s' % (survey_layer.name(), tax_field, zone_layer.name(), zone_field),
logAPICall.DEBUG)
tmp_join_layername = 'join_%s' % get_unique_filename()
tmp_join_file = self._tmp_dir + tmp_join_layername + '.shp'
# load zone classes
try:
zone_classes = layer_field_stats(zone_layer, zone_field)
except AssertionError as err:
raise OperatorError(str(err), self.__class__)
# merge to create stats
logAPICall.log('merge survey & zone', logAPICall.DEBUG)
analyzer = QgsOverlayAnalyzer()
analyzer.intersection(survey_layer, zone_layer, tmp_join_file)
tmp_join_layer = load_shapefile(tmp_join_file, tmp_join_layername)
logAPICall.log('create mapping schemes', logAPICall.DEBUG)
ms = MappingScheme(self._taxonomy)
for _zone, _count in zone_classes.iteritems():
stats = Statistics(self._taxonomy)
ms.assign(MappingSchemeZone(_zone), stats)
# loop through all input features
zone_idx = layer_field_index(tmp_join_layer, zone_field)
tax_idx = layer_field_index(tmp_join_layer, tax_field)
area_idx = layer_field_index(tmp_join_layer, AREA_FIELD_NAME)
cost_idx = layer_field_index(tmp_join_layer, COST_FIELD_NAME)
for _f in layer_features(tmp_join_layer):
_zone_str = str(_f.attributeMap()[zone_idx].toString())
_tax_str = str(_f.attributeMap()[tax_idx].toString())
additional = {}
_area = _f.attributeMap()[area_idx].toDouble()[0]
if _area > 0:
additional = {StatisticNode.AverageSize: _area}
_cost = _f.attributeMap()[cost_idx].toDouble()[0]
if _cost > 0:
additional = {StatisticNode.UnitCost: _cost}
logAPICall.log('zone %s => %s' % (_zone_str, _tax_str) , logAPICall.DEBUG_L2)
try:
ms.get_assignment_by_name(_zone_str).add_case(_tax_str, self._parse_order, self._parse_modifiers, additional)
except TaxonomyParseError as perr:
logAPICall.log("error parsing case %s, %s" % (str(_tax_str), str(perr)), logAPICall.WARNING)
# store data in output
for _zone, _stats in ms.assignments():
_stats.finalize()
_stats.get_tree().value = _zone.name
# clean up
del tmp_join_layer, analyzer
remove_shapefile(tmp_join_file)
self.outputs[0].value = ms
