def testAreaUnitsToFromString(self):
"""Test converting area units to and from translated strings"""
units = [QgsUnitTypes.AreaSquareMeters,
QgsUnitTypes.AreaSquareKilometers,
QgsUnitTypes.AreaSquareFeet,
QgsUnitTypes.AreaSquareYards,
QgsUnitTypes.AreaSquareMiles,
QgsUnitTypes.AreaHectares,
QgsUnitTypes.AreaAcres,
QgsUnitTypes.AreaSquareNauticalMiles,
QgsUnitTypes.AreaSquareDegrees,
QgsUnitTypes.AreaSquareCentimeters,
QgsUnitTypes.AreaSquareMillimeters,
QgsUnitTypes.AreaUnknownUnit]
for u in units:
res, ok = QgsUnitTypes.stringToAreaUnit(QgsUnitTypes.toString(u))
assert ok
self.assertEqual(res, u)
# Test converting bad strings
res, ok = QgsUnitTypes.stringToAreaUnit('bad')
self.assertFalse(ok)
self.assertEqual(res, QgsUnitTypes.AreaUnknownUnit)
# Test that string is cleaned before conversion
res, ok = QgsUnitTypes.stringToAreaUnit(' {} '.format(QgsUnitTypes.toString(QgsUnitTypes.AreaSquareMiles).upper()))
assert ok
self.assertEqual(res, QgsUnitTypes.AreaSquareMiles)
def testAreaUnitsToFromString(self):
"""Test converting area units to and from translated strings"""
units = [QgsUnitTypes.AreaSquareMeters,
QgsUnitTypes.AreaSquareKilometers,
QgsUnitTypes.AreaSquareFeet,
QgsUnitTypes.AreaSquareYards,
QgsUnitTypes.AreaSquareMiles,
QgsUnitTypes.AreaHectares,
QgsUnitTypes.AreaAcres,
QgsUnitTypes.AreaSquareNauticalMiles,
QgsUnitTypes.AreaSquareDegrees,
QgsUnitTypes.AreaSquareCentimeters,
QgsUnitTypes.AreaSquareMillimeters,
QgsUnitTypes.AreaUnknownUnit]
for u in units:
res, ok = QgsUnitTypes.stringToAreaUnit(QgsUnitTypes.toString(u))
assert ok
self.assertEqual(res, u)
# Test converting bad strings
res, ok = QgsUnitTypes.stringToAreaUnit('bad')
self.assertFalse(ok)
self.assertEqual(res, QgsUnitTypes.AreaUnknownUnit)
# Test that string is cleaned before conversion
res, ok = QgsUnitTypes.stringToAreaUnit(' {} '.format(QgsUnitTypes.toString(QgsUnitTypes.AreaSquareMiles).upper()))
assert ok
self.assertEqual(res, QgsUnitTypes.AreaSquareMiles)
def cell_size(self):
"""Returns the size of the raster cell in hectares"""
# Get the cell dimensions and units of the LULC raster
cell_dim_x = self.raster.rasterUnitsPerPixelX()
cell_dim_y = self.raster.rasterUnitsPerPixelY()
raster_units = self.raster.crs().mapUnits()
# Figure out the conversion factor from map units to hectares
raster_units_square = QgsUnitTypes.distanceToAreaUnit(raster_units)
hectare_unit_code = QgsUnitTypes.stringToAreaUnit("Hectares")[0]
conversion_factor = QgsUnitTypes.fromUnitToUnitFactor(raster_units_square, hectare_unit_code)
# Return raster cell size in hectares
# Look out for floating point issues!
return(cell_dim_x * cell_dim_y * conversion_factor)
def load_config(self, yaml_config):
from AcATaMa.gui.acatama_dockwidget import AcATaMaDockWidget as AcATaMa
# restore the thematic raster
if yaml_config["thematic_raster"]["path"]:
# thematic raster
load_and_select_filepath_in(AcATaMa.dockwidget.QCBox_ThematicRaster,
yaml_config["thematic_raster"]["path"])
AcATaMa.dockwidget.select_thematic_raster(AcATaMa.dockwidget.QCBox_ThematicRaster.currentLayer())
# band number
if "band" in yaml_config["thematic_raster"]:
AcATaMa.dockwidget.QCBox_band_ThematicRaster.setCurrentIndex(yaml_config["thematic_raster"]["band"] - 1)
# nodata
AcATaMa.dockwidget.nodata_ThematicRaster.setValue(yaml_config["thematic_raster"]["nodata"])
# restore the classification settings
AcATaMa.dockwidget.grid_columns.setValue(yaml_config["grid_view_widgets"]["columns"])
AcATaMa.dockwidget.grid_rows.setValue(yaml_config["grid_view_widgets"]["rows"])
self.dialog_size = yaml_config["dialog_size"]
self.grid_columns = yaml_config["grid_view_widgets"]["columns"]
self.grid_rows = yaml_config["grid_view_widgets"]["rows"]
self.current_sample_idx = yaml_config["current_sample_idx"]
self.fit_to_sample = yaml_config["fit_to_sample"]
self.is_completed = yaml_config["is_completed"]
# restore the buttons config
self.buttons_config = yaml_config["classification_buttons"]
# restore the view widget config
self.view_widgets_config = yaml_config["view_widgets_config"]
# support load the old format of config file TODO: delete
for x in self.view_widgets_config.values():
if "render_file" in x:
x["render_file_path"] = x["render_file"]
del x["render_file"]
if "name" in x:
x["view_name"] = x["name"]
del x["name"]
if "layer_name" not in x:
x["layer_name"] = None
# restore the samples order
points_ordered = []
for shape_id in yaml_config["points_order"]:
# point saved exist in shape file
if shape_id in [p.shape_id for p in self.points]:
points_ordered.append([p for p in self.points if p.shape_id == shape_id][0])
# added new point inside shape file that not exists in yaml config
for new_point_id in set([p.shape_id for p in self.points]) - set(yaml_config["points_order"]):
points_ordered.append([p for p in self.points if p.shape_id == new_point_id][0])
# reassign points loaded and ordered
self.points = points_ordered
# restore point status classification
for status in yaml_config["points"].values():
if status["shape_id"] in [p.shape_id for p in self.points]:
point_to_restore = [p for p in self.points if p.shape_id == status["shape_id"]][0]
point_to_restore.classif_id = status["classif_id"]
if point_to_restore.classif_id is not None:
point_to_restore.is_classified = True
# update the status and labels plugin with the current sampling classification
self.reload_classification_status()
AcATaMa.dockwidget.update_the_status_of_classification()
# define if this classification was made with thematic classes
if self.buttons_config and yaml_config["thematic_raster"]["path"] and \
True in [bc["thematic_class"] is not None and bc["thematic_class"] != "" for bc in self.buttons_config.values()]:
self.with_thematic_classes = True
# restore accuracy assessment conf
if "accuracy_assessment_sampling_file" in yaml_config and yaml_config["accuracy_assessment_sampling_file"]:
load_and_select_filepath_in(AcATaMa.dockwidget.QCBox_SamplingFile_AA,
yaml_config["accuracy_assessment_sampling_file"])
if "accuracy_assessment_dialog" in yaml_config:
from AcATaMa.core.accuracy_assessment import AccuracyAssessment
accuracy_assessment = AccuracyAssessment(self)
area_unit, success = QgsUnitTypes.stringToAreaUnit(yaml_config["accuracy_assessment_dialog"]["area_unit"])
if success:
accuracy_assessment.area_unit = area_unit
accuracy_assessment.z_score = yaml_config["accuracy_assessment_dialog"]["z_score"]
accuracy_assessment.csv_separator = yaml_config["accuracy_assessment_dialog"]["csv_separator"]
accuracy_assessment.csv_decimal = yaml_config["accuracy_assessment_dialog"]["csv_decimal"]
self.accuracy_assessment = accuracy_assessment
def restore(file_path):
from AcATaMa.gui.acatama_dockwidget import AcATaMaDockWidget as AcATaMa
# load the yaml file
import yaml
with open(file_path, 'r') as yaml_file:
try:
yaml_config = yaml.load(yaml_file, Loader=yaml.FullLoader)
except yaml.YAMLError as err:
iface.messageBar().pushMessage("AcATaMa", "Error while read the AcATaMa configuration file: {}".format(err),
level=Qgis.Critical)
return
# ######### general configuration ######### #
if "general" in yaml_config:
AcATaMa.dockwidget.tabWidget.setCurrentIndex(yaml_config["general"]["tab_activated"])
# restore the thematic raster
if yaml_config["thematic_raster"]["path"]:
# thematic raster
load_and_select_filepath_in(AcATaMa.dockwidget.QCBox_ThematicRaster,
yaml_config["thematic_raster"]["path"])
AcATaMa.dockwidget.select_thematic_raster(AcATaMa.dockwidget.QCBox_ThematicRaster.currentLayer())
# band number
if "band" in yaml_config["thematic_raster"]:
AcATaMa.dockwidget.QCBox_band_ThematicRaster.setCurrentIndex(yaml_config["thematic_raster"]["band"] - 1)
# nodata
AcATaMa.dockwidget.nodata_ThematicRaster.setValue(yaml_config["thematic_raster"]["nodata"])
# ######### classification configuration ######### #
# restore the classification settings
# load the sampling file save in yaml config
sampling_filepath = yaml_config["sampling_layer"]
if os.path.isfile(sampling_filepath):
sampling_layer = load_and_select_filepath_in(AcATaMa.dockwidget.QCBox_SamplingFile, sampling_filepath)
classification = Classification(sampling_layer)
AcATaMa.dockwidget.grid_columns.setValue(yaml_config["grid_view_widgets"]["columns"])
AcATaMa.dockwidget.grid_rows.setValue(yaml_config["grid_view_widgets"]["rows"])
classification.dialog_size = yaml_config["dialog_size"]
classification.grid_columns = yaml_config["grid_view_widgets"]["columns"]
classification.grid_rows = yaml_config["grid_view_widgets"]["rows"]
classification.current_sample_idx = yaml_config["current_sample_idx"]
classification.fit_to_sample = yaml_config["fit_to_sample"]
classification.is_completed = yaml_config["is_completed"]
# restore the buttons config
classification.buttons_config = yaml_config["classification_buttons"]
# restore the view widget config
classification.view_widgets_config = yaml_config["view_widgets_config"]
# support load the old format of config file TODO: delete
for x in classification.view_widgets_config.values():
if "render_file" in x:
x["render_file_path"] = x["render_file"]
del x["render_file"]
if "name" in x:
x["view_name"] = x["name"]
del x["name"]
if "layer_name" not in x:
x["layer_name"] = None
# restore the samples order
points_ordered = []
for shape_id in yaml_config["points_order"]:
# point saved exist in shape file
if shape_id in [p.shape_id for p in classification.points]:
points_ordered.append([p for p in classification.points if p.shape_id == shape_id][0])
# added new point inside shape file that not exists in yaml config
for new_point_id in set([p.shape_id for p in classification.points]) - set(yaml_config["points_order"]):
points_ordered.append([p for p in classification.points if p.shape_id == new_point_id][0])
# reassign points loaded and ordered
classification.points = points_ordered
# restore point status classification
for status in yaml_config["points"].values():
if status["shape_id"] in [p.shape_id for p in classification.points]:
point_to_restore = [p for p in classification.points if p.shape_id == status["shape_id"]][0]
point_to_restore.classif_id = status["classif_id"]
if point_to_restore.classif_id is not None:
point_to_restore.is_classified = True
# update the status and labels plugin with the current sampling classification
classification.reload_classification_status()
AcATaMa.dockwidget.update_the_status_of_classification()
# define if this classification was made with thematic classes
if classification.buttons_config and yaml_config["thematic_raster"]["path"] and \
True in [bc["thematic_class"] is not None and bc["thematic_class"] != "" for bc in classification.buttons_config.values()]:
classification.with_thematic_classes = True
else:
classification = None
# ######### accuracy assessment ######### #
# restore accuracy assessment settings
# support load the old format of config file TODO: delete
if "accuracy_assessment_sampling_file" in yaml_config and yaml_config["accuracy_assessment_sampling_file"]:
load_and_select_filepath_in(AcATaMa.dockwidget.QCBox_SamplingFile_AA,
yaml_config["accuracy_assessment_sampling_file"])
if "accuracy_assessment_sampling_type" in yaml_config:
AcATaMa.dockwidget.QCBox_SamplingType_AA.setCurrentIndex(yaml_config["accuracy_assessment_sampling_type"])
if "accuracy_assessment_dialog" in yaml_config and classification:
from AcATaMa.core.accuracy_assessment import AccuracyAssessment
accuracy_assessment = AccuracyAssessment(classification)
area_unit, success = QgsUnitTypes.stringToAreaUnit(yaml_config["accuracy_assessment_dialog"]["area_unit"])
if success:
accuracy_assessment.area_unit = area_unit
accuracy_assessment.z_score = yaml_config["accuracy_assessment_dialog"]["z_score"]
accuracy_assessment.csv_separator = yaml_config["accuracy_assessment_dialog"]["csv_separator"]
accuracy_assessment.csv_decimal = yaml_config["accuracy_assessment_dialog"]["csv_decimal"]
classification.accuracy_assessment = accuracy_assessment
# support load the old format end here
if "accuracy_assessment" in yaml_config and "sampling_file" in yaml_config["accuracy_assessment"]:
load_and_select_filepath_in(AcATaMa.dockwidget.QCBox_SamplingFile_AA,
yaml_config["accuracy_assessment"]["sampling_file"])
if "accuracy_assessment" in yaml_config and "sampling_type" in yaml_config["accuracy_assessment"]:
AcATaMa.dockwidget.QCBox_SamplingType_AA.setCurrentIndex(yaml_config["accuracy_assessment"]["sampling_type"])
if "accuracy_assessment" in yaml_config and "dialog" in yaml_config["accuracy_assessment"] and classification:
from AcATaMa.core.accuracy_assessment import AccuracyAssessment
accuracy_assessment = AccuracyAssessment(classification)
area_unit, success = QgsUnitTypes.stringToAreaUnit(yaml_config["accuracy_assessment"]["dialog"]["area_unit"])
if success:
accuracy_assessment.area_unit = area_unit
accuracy_assessment.z_score = yaml_config["accuracy_assessment"]["dialog"]["z_score"]
accuracy_assessment.csv_separator = yaml_config["accuracy_assessment"]["dialog"]["csv_separator"]
accuracy_assessment.csv_decimal = yaml_config["accuracy_assessment"]["dialog"]["csv_decimal"]
classification.accuracy_assessment = accuracy_assessment
# reload sampling file status in accuracy assessment
AcATaMa.dockwidget.set_sampling_file_accuracy_assessment()
