def EBK_ga(inPointFeatures, outPath):
try:
arcpy.AddField_management(inPointFeatures, "PM102", "FLOAT", 9)
except:
traceback.print_exc()
try:
arcpy.CalculateField_management(inPointFeatures, 'PM102',
"float(!PM10__μg_!)", "PYTHON_9.3")
except:
traceback.print_exc()
name = os.path.split(inPointFeatures)[1][:-4]
outRaster = os.path.join(outPath, name + '.tif')
cellSize = 0.01
transformation = "NONE"
maxLocalPoints = 50
overlapFactor = 0.5
numberSemivariograms = 100
# Set variables for search neighborhood
radius = 1.0
smooth = 0.14
try:
#lyr to shp
searchNeighbourhood = arcpy.SearchNeighborhoodSmoothCircular(
radius, smooth)
except Exception as err:
arcpy.AddMessage("SearchNeighborhoodSmoothCircular: " + " Failed")
arcpy.AddMessage(err.message)
outputType = "PREDICTION"
Output_geostatistical_layer = ''
quantileValue = ""
thresholdType = ""
probabilityThreshold = ""
semivariogram = "POWER"
tempEnvironment0 = arcpy.env.extent
arcpy.env.extent = Extent
# Execute EmpiricalBayesianKriging
start = time.time()
try:
arcpy.EmpiricalBayesianKriging_ga(
inPointFeatures, 'PM102', Output_geostatistical_layer, outRaster,
cellSize, transformation, maxLocalPoints, overlapFactor,
numberSemivariograms, searchNeighbourhood, outputType,
quantileValue, thresholdType, probabilityThreshold)
print('Converting {} to {}'.format(inPointFeatures, outRasNa))
arcpy.AddMessage(normaltime + ":" + "经验贝叶斯克里金插值完成")
except Exception as err:
arcpy.AddMessage("EmpiricalBayesianKriging_ga: " + " Failed")
arcpy.AddMessage(err.message)
end = time.time()
print('Running time: %s Seconds' % (end - start))
arcpy.env.extent = tempEnvironment0
def runInterpolationOnGroupLayer(self, groupKey):
try:
EBK = arcpy.EmpiricalBayesianKriging_ga(
in_features=self.nameOfLayerOut(groupKey),
z_field=self.elevationField,
out_ga_layer="",
out_raster=makeFullPath(tempGDB, self.nameOfEBKOut(groupKey)),
cell_size="",
transformation_type="NONE",
max_local_points="100",
overlap_factor="1",
number_semivariograms="100",
search_neighborhood="",
output_type="PREDICTION",
quantile_value="0.5",
threshold_type="EXCEED",
probability_threshold="",
semivariogram_model_type="POWER")
except Exception:
pass
"MEAN_vapor_pressure", "RASTERVALU",
scratchGDB + "/coef_table", "", "")
#arcpy.TableToTable_conversion(scratchGDB + "/coef_table", "in_memory", "coef_table")
#arcpy.Delete_management(scratchGDB + "/coef_table")
intercept = list(
(row.getValue("Coef")
for row in arcpy.SearchCursor(scratchGDB +
"/coef_table", fields="Coef")))[0]
slope = list(
(row.getValue("Coef")
for row in arcpy.SearchCursor(scratchGDB +
"/coef_table", fields="Coef")))[1]
#Estimate air temperature at all stations using elevation, and slope and intercept from above
arcpy.AddMessage("Estimating vapor pressure at all stations")
cursor = arcpy.UpdateCursor(fcStations_wElevation)
for row in cursor:
if str(row.getValue("MEAN_vapor_pressure")) == "None":
row.setValue("MEAN_vapor_pressure",
(row.getValue("RASTERVALU") * slope) + intercept)
cursor.updateRow(row)
del cursor
del row
#Run empirical bayesian kriging
arcpy.AddMessage("Running Empirical Bayesian Kriging")
arcpy.EmpiricalBayesianKriging_ga(in_features=fcStations_wElevation, z_field="MEAN_vapor_pressure", out_ga_layer="#", \
out_raster=outRaster, cell_size=output_cell_size, transformation_type="EMPIRICAL", max_local_points="100", overlap_factor="1", \
number_semivariograms="100", search_neighborhood="NBRTYPE=SmoothCircular RADIUS=10000.9518700025 SMOOTH_FACTOR=0.2", \
output_type="PREDICTION", quantile_value="0.5", threshold_type="EXCEED", probability_threshold="", semivariogram_model_type="WHITTLE_DETRENDED")
def interpolate_wle(self):
"""
Interpolates water level elevation, used as preliminary step for getting depth to groundwater and disconnected wetted areas.
Args:
self.method: 'Kriging', 'IDW', or 'Nearest Neighbor'. Determines the method used to interpolate WLE.
Saves interpolated WLE raster to self.out_dir (also saves a WLE variance raster if Kriging method is used).
"""
try:
arcpy.CheckOutExtension('Spatial') # check out license
arcpy.gp.overwriteOutput = True
arcpy.env.workspace = self.cache
try:
self.logger.info("Reading input rasters ...")
ras_h = arcpy.Raster(self.path2h_ras)
ras_dem = arcpy.Raster(self.path2dem_ras)
arcpy.env.extent = ras_dem.extent
cell_size = arcpy.GetRasterProperties_management(
ras_dem, 'CELLSIZEX').getOutput(0)
self.logger.info("OK")
except:
self.logger.info(
"ERROR: Could not find / access input rasters.")
return True
try:
self.logger.info("Making WSE raster ...")
ras_wse = Con(ras_h > 0, (ras_dem + ras_h))
temp_dem = Con(((ras_dem > 0) & IsNull(ras_h)), ras_dem)
ras_dem = temp_dem
self.logger.info("OK")
except:
self.logger.info("ERROR: Input rasters contain invalid data.")
return True
try:
self.logger.info("Converting WSE raster to points ...")
pts_wse = arcpy.RasterToPoint_conversion(
ras_wse, os.path.join(self.cache, "pts_wse.shp"))
self.logger.info("OK")
except arcpy.ExecuteError:
self.logger.info(arcpy.AddError(arcpy.GetMessages(2)))
return True
except Exception as e:
self.logger.info(arcpy.GetMessages(2))
self.logger.info(e.args[0])
return True
if self.method == "Kriging":
try:
self.logger.info("Ordinary Kriging interpolation ...")
# Spherical semivariogram using 12 nearest points to interpolate
ras_wle_dem = Kriging(in_point_features=pts_wse,
z_field="grid_code",
kriging_model=KrigingModelOrdinary(
"Spherical", lagSize=cell_size),
cell_size=cell_size,
search_radius="Variable 12")
ras_wle_dem.save(os.path.join(self.cache, "ras_wle_dem"))
# out_variance_prediction_raster=os.path.join(self.cache, "ras_wle_var") ***
ras_wle_dem = arcpy.Raster(
os.path.join(self.cache, "ras_wle_dem"))
# ras_wle_var = arcpy.Raster(os.path.join(self.cache, "ras_wle_var")) ***
self.logger.info("OK")
except arcpy.ExecuteError:
if "010079" in str(arcpy.GetMessages(2)):
self.logger.info(
"Could not fit semivariogram, increasing lag size and retrying..."
)
empty_bins = True
itr = 2
while empty_bins:
try:
ras_wle_dem = Kriging(
in_point_features=pts_wse,
z_field="grid_code",
kriging_model=KrigingModelOrdinary(
"Spherical", lagSize=cell_size * itr),
cell_size=cell_size,
search_radius="Variable 12")
try:
ras_wle_dem.save(
os.path.join(self.cache,
"ras_wle_dem"))
# out_variance_prediction_raster=os.path.join(self.cache, "ras_wle_var") ***
ras_wle_dem = arcpy.Raster(
os.path.join(self.cache,
"ras_wle_dem"))
# ras_wle_var = arcpy.Raster(os.path.join(self.cache, "ras_wle_var")) ***
self.logger.info("OK")
empty_bins = False
except:
self.logger.info(
"ERROR: Failed to produce interpolated raster."
)
return True
except:
self.logger.info(
"Still could not fit semivariogram, increasing lag and retrying..."
)
itr *= 2
if itr > 16:
break
self.logger.info(arcpy.AddError(arcpy.GetMessages(2)))
return True
except Exception as e:
self.logger.info(arcpy.GetMessages(2))
self.logger.info(e.args[0])
return True
elif self.method == "IDW":
try:
self.logger.info("IDW interpolation...")
# using IDW power of 2 with 12 nearest neighbors
arcpy.Idw_3d(in_point_features=pts_wse,
z_field="grid_code",
out_raster=os.path.join(
self.cache, "ras_wle_dem"),
cell_size=cell_size,
search_radius="Variable 12")
ras_wle_dem = arcpy.Raster(
os.path.join(self.cache, "ras_wle_dem"))
self.logger.info("OK")
except arcpy.ExecuteError:
self.logger.info(arcpy.AddError(arcpy.GetMessages(2)))
return True
except Exception as e:
self.logger.info(arcpy.GetMessages(2))
self.logger.info(e.args[0])
return True
elif self.method == "Nearest Neighbor":
try:
self.logger.info("Nearest Neighbor interpolation...")
# using IDW with 1 nearest neighbor
arcpy.Idw_3d(in_point_features=pts_wse,
z_field="grid_code",
out_raster=os.path.join(
self.cache, "ras_wle_dem"),
cell_size=cell_size,
search_radius="Variable 1")
ras_wle_dem = arcpy.Raster(
os.path.join(self.cache, "ras_wle_dem"))
self.logger.info("OK")
except arcpy.ExecuteError:
self.logger.info(arcpy.AddError(arcpy.GetMessages(2)))
return True
except Exception as e:
self.logger.info(arcpy.GetMessages(2))
self.logger.info(e.args[0])
return True
elif self.method == "EBK":
try:
self.logger.info(
"Empirical Bayesian Kriging interpolation...")
search_nbrhood = arcpy.SearchNeighborhoodStandardCircular(
nbrMin=12, nbrMax=12)
arcpy.EmpiricalBayesianKriging_ga(
in_features=pts_wse,
z_field="grid_code",
out_raster=os.path.join(self.cache, "ras_wle_dem"),
cell_size=cell_size,
transformation_type="EMPIRICAL",
number_semivariograms=100,
search_neighborhood=search_nbrhood,
output_type="PREDICTION",
semivariogram_model_type="EXPONENTIAL")
ras_wle_dem = arcpy.Raster(
os.path.join(self.cache, "ras_wle_dem"))
self.logger.info("OK")
except arcpy.ExecuteError:
self.logger.info(arcpy.AddError(arcpy.GetMessages(2)))
return True
except Exception as e:
self.logger.info(arcpy.GetMessages(2))
self.logger.info(e.args[0])
return True
else:
self.logger.info(
"ERROR: invalid method for WSE interpolation: '%s'." %
self.method)
return True
try:
self.logger.info("Saving WLE raster to: %s" %
os.path.join(self.out_dir, self.out_wle))
ras_wle_dem.save(os.path.join(self.out_dir, self.out_wle))
self.logger.info("OK")
self.save_info_file(os.path.join(self.out_dir, self.out_wle))
""" ***
if self.method == "Kriging":
self.logger.info("Saving WLE Kriging variance raster (%s) ..." % os.path.join(self.out_dir, self.out_wle_var))
ras_wle_var.save(os.path.join(self.out_dir, self.out_wle_var))
self.logger.info("OK")
"""
arcpy.CheckInExtension('Spatial')
except arcpy.ExecuteError:
self.logger.info(arcpy.AddError(arcpy.GetMessages(2)))
return True
except Exception as e:
self.logger.info(arcpy.GetMessages(2))
self.logger.info(e.args[0])
return True
except arcpy.ExecuteError:
self.logger.info("ExecuteERROR: (arcpy).")
self.logger.info(arcpy.GetMessages(2))
return True
except Exception as e:
self.logger.info("ExceptionERROR: (arcpy).")
self.logger.info(e.args[0])
return True
self.clean_up()
# return Boolean False if successful.
return False
def EBK_ga(out_file, zField):
normaltime = time.strftime('%Y-%m-%d %H:%M:%S',
time.localtime(time.time()))
arcpy.AddMessage(normaltime + ":" + out_file + "正在进行经验贝叶斯克里金插值...")
outTableName = arcpy.ValidateTableName(
os.path.basename(out_file.strip(".xls")), out_gdb)
print(outTableName)
outTable = os.path.join(out_gdb, outTableName)
print('Converting sheet1 to {}'.format(outTable))
# Perform the conversion
dbfTable = os.path.join(outdBASEPath, outTableName + '.dbf')
try:
arcpy.ExcelToTable_conversion(out_file, outTable,
"Sheet1") # Excel to Table
arcpy.TableToDBASE_conversion(outTable, outdBASEPath) #Table to dbf
except Exception as err:
print("{} is existing".format(dbfTable))
arcpy.AddMessage(err.message)
# dbaseTableName = filename.strip(".xls")
# print (dbaseTableName)
# outTable = os.path.join(outgdb, dbaseTableName)
# print (outTable)
# arcpy.ExcelToTable_conversion(xlsTable, outTable, "Sheet1")
x_coords = 'Long' #list(date[u'Long'].head())
y_coords = 'Lat' #list(date[u'Lat'].values)
outLayerName = outTableName + '.lyr'
outLayer = os.path.join(outLayerPath, outLayerName)
spRef = "Coordinate Systems\Geographic Coordinate Systems\World\WGS 1984.prj"
try:
arcpy.MakeXYEventLayer_management(dbfTable, x_coords, y_coords,
outLayerName, spRef)
except Exception as err:
arcpy.AddMessage("MakeXYEventLayer_management: " + outLayerName +
" created Failed")
arcpy.AddMessage(err.message)
try:
arcpy.SaveToLayerFile_management(outLayerName, outLayer)
except Exception as err:
arcpy.AddMessage("SaveToLayerFile_management: " + outLayer +
" created Failed")
arcpy.AddMessage(err.message)
try:
#lyr to shp
arcpy.FeatureClassToShapefile_conversion(outLayer, outShpPath)
except Exception as err:
arcpy.AddMessage("FeatureClassToShapefile_conversion: " + outShpPath +
" created Failed")
arcpy.AddMessage(err.message)
# Set local variables
inPointFeatures = os.path.join(outShpPath, outTableName + '_lyr.shp')
Output_geostatistical_layer = ""
outRasNa = outTableName + '.tif'
nt = time.strftime('%Y%m%d', time.localtime(time.time()))
dt = time.strftime('%m%d%H', time.localtime(time.time()))
outFilePath = "F:\\xiaju\\" + nt + "\\" + houtime + "\\" + zField + "\\" + "tif"
try:
os.makedirs(outFilePath)
except:
print("")
outRaster = os.path.join(outFilePath, outRasNa)
cellSize = 0.001
transformation = "NONE"
maxLocalPoints = 50
overlapFactor = 0.5
numberSemivariograms = 100
# Set variables for search neighborhood
radius = 0.3
smooth = 0.1
try:
#lyr to shp
searchNeighbourhood = arcpy.SearchNeighborhoodSmoothCircular(
radius, smooth)
except Exception as err:
arcpy.AddMessage("SearchNeighborhoodSmoothCircular: " + " Failed")
arcpy.AddMessage(err.message)
outputType = "PREDICTION"
quantileValue = ""
thresholdType = ""
probabilityThreshold = ""
semivariogram = "POWER"
tempEnvironment0 = arcpy.env.extent
arcpy.env.extent = Extent
# Execute EmpiricalBayesianKriging
try:
arcpy.EmpiricalBayesianKriging_ga(
inPointFeatures, zField, Output_geostatistical_layer, outRaster,
cellSize, transformation, maxLocalPoints, overlapFactor,
numberSemivariograms, searchNeighbourhood, outputType,
quantileValue, thresholdType, probabilityThreshold)
print('Converting {} to {}'.format(inPointFeatures, outRasNa))
arcpy.AddMessage(normaltime + ":" + "经验贝叶斯克里金插值完成")
except Exception as err:
arcpy.AddMessage("EmpiricalBayesianKriging_ga: " + " Failed")
arcpy.AddMessage(err.message)
arcpy.env.extent = tempEnvironment0
# Set local variables
inPointFeatures = "ca_ozone_pts.shp"
zField = "ozone"
outLayer = "outEBK"
outRaster = "C:/gapyexamples/output/ebkout"
cellSize = 10000.0
transformation = "EMPIRICAL"
maxLocalPoints = 50
overlapFactor = 0.5
numberSemivariograms = 100
# Set variables for search neighborhood
radius = 300000
smooth = 0.6
searchNeighbourhood = arcpy.SearchNeighborhoodSmoothCircular(radius, smooth)
outputType = "PREDICTION"
quantileValue = ""
thresholdType = ""
probabilityThreshold = ""
semivariogram = "K_BESSEL"
# Check out the ArcGIS Geostatistical Analyst extension license
arcpy.CheckOutExtension("GeoStats")
# Execute EmpiricalBayesianKriging
arcpy.EmpiricalBayesianKriging_ga(inPointFeatures, zField, outLayer, outRaster,
cellSize, transformation, maxLocalPoints,
overlapFactor, numberSemivariograms,
searchNeighbourhood, outputType,
quantileValue, thresholdType,
probabilityThreshold, semivariogram)
# outputType = Either PREDICTION or PREDICTION_STANDARD_ERROR as set in the EBK function
quantileValue = ""
thresholdType = ""
probabilityThreshold = ""
# Execute Empirical Bayesian Kriging for Prediction Surfaces
print "\n\t 1 of 1: Starting EBK..."
for s in zField:
print "\t\t Creating the prediction surface for %s..." % s
arcpy.EmpiricalBayesianKriging_ga(
inPointFeatures, s, outLayer, outRaster + "EBK" + s, cellSize,
transformation, maxLocalPoints, overlapFactor,
numberSemivariograms, searchNeighborhood, "PREDICTION",
quantileValue, thresholdType, probabilityThreshold)
print "\t\t\t Finished the prediction surface for %s!" % s
# Execute Empirical Bayesian Kriging for Standard Error Surfaces
print "\n\t\t Creating the standard error surface for %s..." % s
arcpy.EmpiricalBayesianKriging_ga(
inPointFeatures, s, outLayer, outRaster + "EBK" + s + "_SE",
cellSize, transformation, maxLocalPoints, overlapFactor,
numberSemivariograms, searchNeighborhood,
"PREDICTION_STANDARD_ERROR", quantileValue, thresholdType,
probabilityThreshold)
# Set contour filling parameters
out_vector = 'kriged_vectors/' + img + '/' + img + '_krig_vector.shp'
class_type = 'QUANTILE'
contour_type = 'FILLED_CONTOUR'
classes_count = 15
# Set cv parameters
out_cv_shp = 'kriged_vectors/' + img + '/' + img + '_krig_cv.shp'
in_cv_dbf = 'kriged_vectors/' + img + '/' + img + '_krig_cv.dbf'
out_cv_csv_name = img + '_krig_cv.csv'
# Empirical Bayesian Kriging
ebayes_krig = arcpy.EmpiricalBayesianKriging_ga(
in_features=in_features,
z_field=field,
out_ga_layer='ga_layer',
cell_size=cellsize,
transformation_type=transformation,
output_type=output_type,
semivariogram_model_type=semivariogram)
arcpy.SaveToLayerFile_management(in_layer=ebayes_krig,
out_layer=out_ga_layer,
is_relative_path='ABSOLUTE')
# Cross-validate kriged raster and export prediction vs. actual as table
cv = arcpy.CrossValidation_ga(in_geostat_layer=out_ga_layer,
out_point_feature_class=out_cv_shp)
arcpy.TableToTable_conversion(in_rows=in_cv_dbf,
out_path=str(raster_path),
out_name=out_cv_csv_name)
arcpy.Delete_management(out_cv_shp)
