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runGriddingToolsJD.py
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import arcpy
from arcpy.sa import CreateConstantRaster, Kriging, KrigingModelOrdinary, RadiusFixed, Raster, Con, TimeWithinDay, AreaSolarRadiation
import os
import sys
import csv
import traceback
import numpy
#from scipy import stats
import mysql.connector
from mysql.connector import errorcode
import datetime
import json
import shutil
import subprocess
#Assign input parameters
sFromDate = arcpy.GetParameterAsText(0)
sToDate = arcpy.GetParameterAsText(1)
iTimeStep = int(arcpy.GetParameterAsText(2))
sKrigMethod = arcpy.GetParameterAsText(3)
boolAllTools = arcpy.GetParameter(4)
boolAirTemperature = arcpy.GetParameter(5)
boolConstants = arcpy.GetParameter(6)
dRLConstant = arcpy.GetParameter(7)
dH2OConstant = arcpy.GetParameter(8)
boolDewPoint = arcpy.GetParameter(9)
boolPrecipMass = arcpy.GetParameter(10)
boolSnowDepth = arcpy.GetParameter(11)
boolSnowProperties = arcpy.GetParameter(12)
ll_interp_values = json.loads(arcpy.GetParameter(13).JSON)
ul_interp_values = json.loads(arcpy.GetParameter(14).JSON)
density_interp_values = json.loads(arcpy.GetParameter(15).JSON)
boolSoilTemperature = arcpy.GetParameter(16)
boolSolarRadiation = arcpy.GetParameter(17)
boolThermalRadiation = arcpy.GetParameter(18)
boolVaporPressure = arcpy.GetParameter(19)
boolWindSpeed = arcpy.GetParameter(20)
#tempOutput = arcpy.GetParameterAsText(16)
#Specify workspace
scratchWS = arcpy.env.scratchFolder
arcpy.env.workspace = scratchWS
arcpy.env.scratchWorkspace = scratchWS
arcpy.AddMessage("Scratch Workspace: " + scratchWS)
#scratchWS = r'C:\AA_Thesis_Project\ZZ_MySQL_Work\scratch'
#Make output folder to zip
dateNow = datetime.datetime.now()
sNow = dateNow.strftime("%Y%d%b_%H%M")
os.makedirs(scratchWS + "/Output_" + sNow)
outFolder = scratchWS + "/Output_" + sNow
arcpy.AddMessage("Output Folder: " + outFolder)
scratchGDB = arcpy.env.scratchGDB
arcpy.env.overwriteOutput = True
#Define Functions
def roundTime(dt, roundTo=60):
seconds = (dt - dt.min).seconds
rounding = (seconds+roundTo/2) // roundTo * roundTo
return dt + datetime.timedelta(0,rounding-seconds,-dt.microsecond)
def airTemperature():
arcpy.AddMessage("Calculating Air Temperature")
#Caclulate average air temperatures over the n-hour time step (ignore "no-data" values: -999)
arcpy.MakeTableView_management(scratchGDB + "/climate_table1", "airTemperature_Table", "air_temperature > -500")
arcpy.Statistics_analysis("airTemperature_Table", "in_memory/airTemperature_Table2", "air_temperature MEAN", "site_key")
#Copy air temperature values to station feature class
arcpy.CopyFeatures_management(fcStations_wElevation, scratchGDB +"/tempStations")
lsScratchData_Imd.append(scratchGDB +"/tempStations")
arcpy.JoinField_management(scratchGDB +"/tempStations", "Site_Key", "in_memory/airTemperature_Table2", "site_key", "MEAN_air_temperature")
#Delete rows from station feature class that have negative or null elevations
cursor = arcpy.UpdateCursor(scratchGDB + "/tempStations")
for row in cursor:
if row.getValue("RASTERVALU") < 0 or row.getValue("RASTERVALU") == "None" or row.getValue("RASTERVALU") is None:
arcpy.AddMessage("Elevation: " + row.getValue("MEAN_air_temperature"))
cursor.deleteRow(row)
del cursor
del row
#Delete rows from station feature class that have null values for air temperature
cursor = arcpy.UpdateCursor(scratchGDB + "/tempStations")
for row in cursor:
if row.getValue("MEAN_air_temperature") is None:
arcpy.AddMessage("MEAN air temp: " + str(row.getValue("MEAN_air_temperature")) + "Site_Key:" + str(row.getValue("Site_Key")))
cursor.deleteRow(row)
del cursor
del row
#Check if interpolation method is Empirical Bayesian or Detrended
if sKrigMethod == "Detrended":
#Add unique ID field to tempStations for use in OLS function
arcpy.AddField_management(scratchGDB +"/tempStations", "Unique_ID", "SHORT", "", "", "", "", "NULLABLE", "NON_REQUIRED", "")
arcpy.CalculateField_management(scratchGDB +"/tempStations","Unique_ID","!OBJECTID!","PYTHON_9.3","")
#Run ordinary least squares on tempStations
arcpy.CreateTable_management(scratchGDB, "coef_table")
arcpy.AddMessage(scratchGDB + "/tempStations")
ols = arcpy.OrdinaryLeastSquares_stats(scratchGDB + "/tempStations","Unique_ID","in_memory/fcResid","MEAN_air_temperature","RASTERVALU", scratchGDB + "/coef_table","","")
lsScratchData_Imd.append(scratchGDB + "/coef_table")
# y = m * x + b
# y = Predicted parameter
# m = slope
# x = elevation
# b = intercept
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]
#Calculate residuals and add them to tempStations
arcpy.AddField_management(scratchGDB + "/tempStations", "residual", "DOUBLE", "", "", "", "", "NULLABLE", "NON_REQUIRED", "")
cursor = arcpy.UpdateCursor(scratchGDB + "/tempStations")
for row in cursor:
row.setValue("residual", row.getValue("MEAN_air_temperature") - ((slope * row.getValue("RASTERVALU")) + intercept))
cursor.updateRow(row)
del cursor
del row
#Run ordinary kriging on residuals (March 1, 2008 12:00 hard coded semi-variogram parameters)
arcpy.gp.Kriging_sa(scratchGDB + "/tempStations", "residual", scratchGDB + "/airTemperature_residual", "Linear 37.061494", output_cell_size, "VARIABLE 100", "")
lsScratchData_Imd.append(scratchGDB + "/airTemperature_residual")
#Add back elevation trends and save final raster
output_raster = arcpy.Raster(scratchGDB + "/airTemperature_residual") + (arcpy.Raster(rc_elevation) * slope + intercept)
output_raster.save(outFolder + "/air_temperature_" + sTimeStamp + ".tif")
else:
arcpy.EmpiricalBayesianKriging_ga(in_features=scratchGDB + "/tempStations", z_field="MEAN_air_temperature", out_ga_layer="#", \
out_raster=outFolder + "/air_temperature_" + sTimeStamp + ".tif", 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")
return outFolder + "/air_temperature_" + sTimeStamp + ".tif"
def constants():
arcpy.AddMessage("Calculating Constants")
#Get coordinate system information
desc = arcpy.Describe(rc_elevation)
coordSystem = desc.spatialReference
rlConstant = CreateConstantRaster(dRLConstant, "FLOAT", output_cell_size)
arcpy.DefineProjection_management(rlConstant, coordSystem)
rlConstant.save(outFolder + "/roughness_length_" + sTimeStamp + ".tif")
waterConstant = CreateConstantRaster(dH2OConstant, "FLOAT", output_cell_size)
arcpy.DefineProjection_management(waterConstant, coordSystem)
waterConstant.save(outFolder + "/H2O_saturation_" + sTimeStamp + ".tif")
return outFolder + "/roughness_length_" + sTimeStamp + ".tif", outFolder + "/H2O_saturation_" + sTimeStamp + ".tif"
def dewPoint():
arcpy.AddMessage("Calculating Dew Point")
#Caclulate average dew point temperature values over the n-hour time step (ignore "no-data" values: -999)
arcpy.MakeTableView_management(scratchGDB + "/climate_table1", "dewPoint_Table", "dewpoint_temperature > -500")
arcpy.Statistics_analysis("dewPoint_Table", "in_memory/dewPoint_Table2", "dewpoint_temperature MEAN", "site_key")
#Copy dew point temperature values to station feature class
arcpy.CopyFeatures_management(fcStations_wElevation, scratchGDB +"/tempStations")
lsScratchData_Imd.append(scratchGDB +"/tempStations")
arcpy.JoinField_management(scratchGDB +"/tempStations", "Site_Key", "in_memory/dewPoint_Table2", "site_key", "MEAN_dewpoint_temperature")
#Delete rows from station feature class that have negative or null elevations
cursor = arcpy.UpdateCursor(scratchGDB + "/tempStations")
for row in cursor:
if row.getValue("RASTERVALU") < 0 or row.getValue("RASTERVALU") == "None" or row.getValue("RASTERVALU") is None:
cursor.deleteRow(row)
del cursor
del row
#Delete rows from station feature class that have null values for dew point temperatures
cursor = arcpy.UpdateCursor(scratchGDB + "/tempStations")
for row in cursor:
if row.getValue("MEAN_dewpoint_temperature") is None:
cursor.deleteRow(row)
del cursor
del row
#Check if interpolation method is Empirical Bayesian or Detrended
if sKrigMethod == "Detrended":
#Add unique ID field to tempStations for use in OLS function
arcpy.AddField_management(scratchGDB +"/tempStations", "Unique_ID", "SHORT", "", "", "", "", "NULLABLE", "NON_REQUIRED", "")
arcpy.CalculateField_management(scratchGDB +"/tempStations","Unique_ID","!OBJECTID!","PYTHON_9.3","")
#Run ordinary least squares on tempStations
arcpy.CreateTable_management(scratchGDB, "coef_table")
ols = arcpy.OrdinaryLeastSquares_stats(scratchGDB + "/tempStations","Unique_ID","in_memory/fcResid","MEAN_dewpoint_temperature","RASTERVALU", scratchGDB + "/coef_table","","")
lsScratchData_Imd.append(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]
#Calculate residuals and add them to tempStations
arcpy.AddField_management(scratchGDB + "/tempStations", "residual", "DOUBLE", "", "", "", "", "NULLABLE", "NON_REQUIRED", "")
cursor = arcpy.UpdateCursor(scratchGDB + "/tempStations")
for row in cursor:
row.setValue("residual", row.getValue("MEAN_dewpoint_temperature") - ((slope * row.getValue("RASTERVALU")) + intercept))
cursor.updateRow(row)
del cursor
del row
#Run ordinary kriging on residuals
#arcpy.gp.Kriging_sa(scratchGDB + "/tempStations", "residual", scratchGDB + "/dewPoint_residual", "Linear 37.061494", output_cell_size, "VARIABLE 100", "")
outKrig = Kriging(scratchGDB + "/tempStations", "residual", KrigingModelOrdinary("SPHERICAL", 460, 3686, .1214, .2192), output_cell_size, RadiusFixed(10000, 1))
outKrig.save(scratchGDB + "/dewPoint_residual")
lsScratchData_Imd.append(scratchGDB + "/dewPoint_residual")
#Add back elevation trends and save final raster
output_raster = arcpy.Raster(scratchGDB + "/dewPoint_residual") + (arcpy.Raster(rc_elevation) * slope + intercept)
output_raster.save(outFolder + "/dew_point_temperature_" + sTimeStamp + ".tif")
else:
arcpy.EmpiricalBayesianKriging_ga(in_features=scratchGDB + "/tempStations", z_field="MEAN_dewpoint_temperature", out_ga_layer="#", \
out_raster=outFolder + "/dew_point_temperature_" + sTimeStamp + ".tif", 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")
#Create precipitation properties rasters (% precip as snow, density of snow portion of precip)
#Percent snow conditional:
inRas = Raster(outFolder + "/dew_point_temperature_" + sTimeStamp + ".tif")
outPercentSnowRaster = arcpy.sa.Con(inRas < -5.0, 1.0, Con((inRas >= -5.0) & (inRas < -3.0), 1.0,
Con((inRas >= -3.0) & (inRas < -1.5), 1.0,
Con((inRas >= -1.5) & (inRas < -0.5), 1.0,
Con((inRas >= -0.5) & (inRas < 0.0), 0.75,
Con((inRas >= 0.0) & (inRas < 0.5), 0.25,
Con(inRas >= 0.5,0.0)))))))
arcpy.CopyRaster_management(outPercentSnowRaster, outFolder + "/percent_snow_" + sTimeStamp + ".tif",pixel_type="32_BIT_FLOAT")
#Snow density conditional:
outSnowDensityRaster = arcpy.sa.Con(inRas < -5.0, 75.0, Con((inRas >= -5.0) & (inRas < -3.0), 100.0,
Con((inRas >= -3.0) & (inRas < -1.5), 150.0,
Con((inRas >= -1.5) & (inRas < -0.5), 175.0,
Con((inRas >= -0.5) & (inRas < 0), 200.0,
Con((inRas >= 0.0) & (inRas < 0.5), 250.0,
Con(inRas >= 0.5,0.0)))))))
arcpy.CopyRaster_management(outSnowDensityRaster,outFolder + "/precipitation_snow_density_" + sTimeStamp + ".tif",pixel_type="32_BIT_FLOAT")
return outFolder + "/dew_point_temperature_" + sTimeStamp + ".tif", outFolder + "/percent_snow_" + sTimeStamp + ".tif", outFolder + "/precipitation_snow_density_" + sTimeStamp + ".tif"
def precipMass():
arcpy.AddMessage("Calculating Precipitation Mass")
#Caclulate average precipitation values over the n-hour time step (ignore "no-data" values: -999)
arcpy.MakeTableView_management(scratchGDB + "/precipitation_table1", "precipitation_Table", "ppts > -500")
arcpy.Statistics_analysis("precipitation_Table", "in_memory/precipitation_Table2", "ppts MEAN", "site_key")
#Copy precipitation values to station feature class
arcpy.CopyFeatures_management(fcStations_wElevation, scratchGDB +"/tempStations")
lsScratchData_Imd.append(scratchGDB +"/tempStations")
arcpy.JoinField_management(scratchGDB +"/tempStations", "Site_Key", "in_memory/precipitation_Table2", "site_key", "MEAN_ppts")
#Delete rows from station feature class that have negative or null elevations
cursor = arcpy.UpdateCursor(scratchGDB + "/tempStations")
for row in cursor:
if row.getValue("RASTERVALU") < 0 or row.getValue("RASTERVALU") == "None" or row.getValue("RASTERVALU") is None:
cursor.deleteRow(row)
del cursor
del row
#Delete rows from station feature class that have null values for precipitation
cursor = arcpy.UpdateCursor(scratchGDB + "/tempStations")
for row in cursor:
if row.getValue("MEAN_ppts") is None:
cursor.deleteRow(row)
del cursor
del row
#Check if interpolation method is Empirical Bayesian or Detrended
if sKrigMethod == "Detrended":
#Add unique ID field to tempStations for use in OLS function
arcpy.AddField_management(scratchGDB +"/tempStations", "Unique_ID", "SHORT", "", "", "", "", "NULLABLE", "NON_REQUIRED", "")
arcpy.CalculateField_management(scratchGDB +"/tempStations","Unique_ID","!OBJECTID!","PYTHON_9.3","")
#Run ordinary least squares on tempStations
arcpy.CreateTable_management(scratchGDB, "coef_table")
ols = arcpy.OrdinaryLeastSquares_stats(scratchGDB + "/tempStations","Unique_ID","in_memory/fcResid","MEAN_ppts","RASTERVALU", scratchGDB + "/coef_table","","")
lsScratchData_Imd.append(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]
#Calculate residuals and add them to tempStations
arcpy.AddField_management(scratchGDB + "/tempStations", "residual", "DOUBLE", "", "", "", "", "NULLABLE", "NON_REQUIRED", "")
cursor = arcpy.UpdateCursor(scratchGDB + "/tempStations")
for row in cursor:
row.setValue("residual", row.getValue("MEAN_ppts") - ((slope * row.getValue("RASTERVALU")) + intercept))
cursor.updateRow(row)
del cursor
del row
#Run ordinary kriging on residuals
#arcpy.gp.Kriging_sa(scratchGDB + "/tempStations", "residual", scratchGDB + "/precipitation_residual", "Linear 37.061494", output_cell_size, "VARIABLE 100", "")
outKrig = Kriging(scratchGDB + "/tempStations", "residual", KrigingModelOrdinary("SPHERICAL", 460, 3686, .1214, .2192), output_cell_size, RadiusFixed(10000, 1))
outKrig.save(scratchGDB + "/precipitation_residual")
lsScratchData_Imd.append(scratchGDB + "/precipitation_residual")
#Add back elevation trends and save final raster
output_raster = arcpy.Raster(scratchGDB + "/precipitation_residual") + (arcpy.Raster(rc_elevation) * slope + intercept)
output_raster.save(outFolder + "/precipitation_mass_" + sTimeStamp + ".tif")
else:
arcpy.EmpiricalBayesianKriging_ga(in_features=scratchGDB + "/tempStations", z_field="MEAN_ppts", out_ga_layer="#", \
out_raster=outFolder + "/precipitation_mass_" + sTimeStamp + ".tif", 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")
return outFolder + "/precipitation_mass_" + sTimeStamp + ".tif"
def snowDepth():
arcpy.AddMessage("Calculating Snow Depth")
#Caclulate average snow depth values over the n-hour time step (ignore "no-data" values: -999)
arcpy.MakeTableView_management(scratchGDB + "/snowDepth_table1", "snowDepth_Table", "snow_depth > -500")
arcpy.Statistics_analysis("snowDepth_Table", "in_memory/snowDepth_Table2", "snow_depth MEAN", "site_key")
#Copy snow depth values to station feature class
arcpy.CopyFeatures_management(fcStations_wElevation, scratchGDB +"/tempStations")
lsScratchData_Imd.append(scratchGDB +"/tempStations")
arcpy.JoinField_management(scratchGDB +"/tempStations", "Site_Key", "in_memory/snowDepth_Table2", "site_key", "MEAN_snow_depth")
#Delete rows from station feature class that have negative or null elevations
cursor = arcpy.UpdateCursor(scratchGDB + "/tempStations")
for row in cursor:
if row.getValue("RASTERVALU") < 0 or row.getValue("RASTERVALU") == "None" or row.getValue("RASTERVALU") is None:
cursor.deleteRow(row)
del cursor
del row
#Delete rows from station feature class that have null values for snow depth
cursor = arcpy.UpdateCursor(scratchGDB + "/tempStations")
for row in cursor:
if row.getValue("MEAN_snow_depth") is None:
cursor.deleteRow(row)
del cursor
del row
#Check if interpolation method is Empirical Bayesian or Detrended
if sKrigMethod == "Detrended":
#Add unique ID field to tempStations for use in OLS function
arcpy.AddField_management(scratchGDB +"/tempStations", "Unique_ID", "SHORT", "", "", "", "", "NULLABLE", "NON_REQUIRED", "")
arcpy.CalculateField_management(scratchGDB +"/tempStations","Unique_ID","!OBJECTID!","PYTHON_9.3","")
#Run ordinary least squares on tempStations
arcpy.CreateTable_management(scratchGDB, "coef_table")
ols = arcpy.OrdinaryLeastSquares_stats(scratchGDB + "/tempStations","Unique_ID","in_memory/fcResid","MEAN_snow_depth","RASTERVALU", scratchGDB + "/coef_table","","")
lsScratchData_Imd.append(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]
#Calculate residuals and add them to tempStations
arcpy.AddField_management(scratchGDB + "/tempStations", "residual", "DOUBLE", "", "", "", "", "NULLABLE", "NON_REQUIRED", "")
cursor = arcpy.UpdateCursor(scratchGDB + "/tempStations")
for row in cursor:
row.setValue("residual", row.getValue("MEAN_snow_depth") - ((slope * row.getValue("RASTERVALU")) + intercept))
cursor.updateRow(row)
del cursor
del row
#Run ordinary kriging on residuals
#arcpy.gp.Kriging_sa(scratchGDB + "/tempStations", "residual", scratchGDB + "/snowDepth_residual", "Linear 37.061494", output_cell_size, "VARIABLE 100", "")
outKrig = Kriging(scratchGDB + "/tempStations", "residual", KrigingModelOrdinary("SPHERICAL", 460, 3686, .1214, .2192), output_cell_size, RadiusFixed(10000, 1))
outKrig.save(scratchGDB + "/snowDepth_residual")
lsScratchData_Imd.append(scratchGDB + "/snowDepth_residual")
#Add back elevation trends and save final raster
imdRaster = arcpy.Raster(scratchGDB + "/snowDepth_residual") + (arcpy.Raster(rc_elevation) * slope + intercept)
output_raster = Con(imdRaster > 0, imdRaster, 0)
output_raster.save(outFolder + "/snow_depth_" + sTimeStamp + ".tif")
else:
arcpy.EmpiricalBayesianKriging_ga(in_features=scratchGDB + "/tempStations", z_field="MEAN_snow_depth", out_ga_layer="#", \
out_raster=scratchGDB + "/imdRaster", 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")
output_raster = Con(Raster(scratchGDB + "/imdRaster") > 0, Raster(scratchGDB + "/imdRaster"), 0)
output_raster.save(outFolder + "/snow_depth_" + sTimeStamp + ".tif")
return outFolder + "/snow_depth_" + sTimeStamp + ".tif"
def snowProperties():
arcpy.AddMessage("Calculating Snow Properties")
if len(density_interp_values['features']) <= 1:
#Density Equation: y = -0.0395(elevation) + 405.26
snow_density_raster = -0.0395 * Raster(rc_elevation) + 405.26
snow_density_raster.save(outFolder + "/snow_density_" + sTimeStamp + ".tif")
else:
lsElevation = []
lsDensity = []
for rec in density_interp_values['features']:
lsElevation.append(rec['attributes']['Elevation'])
lsDensity.append(rec['attributes']['Density'])
lr_results = stats.linregress(lsElevation,lsDensity)
slope1 = lr_results[0]
intercept1 = lr_results[1]
snow_density_raster = slope1 * Raster(rc_elevation) + intercept1
snow_density_raster.save(outFolder + "/snow_density_" + sTimeStamp + ".tif")
if len(ul_interp_values['features']) <= 1:
#Upper Layer Temperature Equation: y = -0.0008(elevation) + 0.1053
upper_layer_temperature = -0.0008 * Raster(rc_elevation) + 0.1053
upper_layer_temperature.save(outFolder + "/active_snow_layer_temperature_" + sTimeStamp + ".tif")
else:
lsElevation = []
lsTemperature = []
for rec in ul_interp_values['features']:
lsElevation.append(rec['attributes']['Elevation'])
lsTemperature.append(rec['attributes']['Temperature'])
lr_results = stats.linregress(lsElevation,lsTemperature)
slope2 = lr_results[0]
intercept2 = lr_results[1]
upper_layer_temperature = slope2 * Raster(rc_elevation) + intercept2
upper_layer_temperature.save(outFolder + "/active_snow_layer_temperature_" + sTimeStamp + ".tif")
if len(ll_interp_values['features']) <= 1:
#lower layer temperature equation: y = -0.0008(elevation) + 1.3056
lower_layer_temperature = -0.0008 * Raster(rc_elevation) + 1.3056
else:
lsElevation = []
lsTemperature = []
for rec in ul_interp_values['features']:
lsElevation.append(rec['attributes']['Elevation'])
lsTemperature.append(rec['attributes']['Temperature'])
lr_results = stats.linregress(lsElevation,lsTemperature)
slope3 = lr_results[0]
intercept3 = lr_results[1]
lower_layer_temperature = slope2 * Raster(rc_elevation) + intercept2
#average snowcover temperature is the average of the upper and lower layer temperatures
average_snowcover_temperature = arcpy.sa.CellStatistics([upper_layer_temperature, lower_layer_temperature],"MEAN","NODATA")
average_snowcover_temperature.save(outFolder + "/average_snow_cover_temperature_" + sTimeStamp + ".tif")
return outFolder + "/active_snow_layer_temperature_" + sTimeStamp + ".tif", outFolder + "/average_snow_cover_temperature_" + sTimeStamp + ".tif", outFolder + "/snow_density_" + sTimeStamp + ".tif"
def soilTemperature():
arcpy.AddMessage("Calculating Soil Temperature")
#Set extent to full feature class (all stations)
arcpy.env.extent = extFullFeatures
#Caclulate average soil temperature values over the n-hour time step (ignore "no-data" values: -999)
arcpy.MakeTableView_management(scratchGDB + "/soilTemperature_table1", "soilTemperature_Table", "stm005 > -500")
arcpy.Statistics_analysis("soilTemperature_Table", "in_memory/soilTemperature_Table2", "stm005 MEAN", "site_key")
#Copy soil temperature values to station feature class
fcStations4Soil = r'C:\ReynoldsCreek\Relevant_Data.gdb\station_locations_JD_soil'
arcpy.CopyFeatures_management(fcStations4Soil, scratchGDB +"/tempStations4soil")
lsScratchData_Imd.append(scratchGDB +"/tempStations4soil")
arcpy.JoinField_management(scratchGDB +"/tempStations4soil", "Site_Key", "in_memory/soilTemperature_Table2", "site_key", "MEAN_stm005")
#Delete rows from station feature class that have null values for soil temperature
cursor = arcpy.UpdateCursor(scratchGDB + "/tempStations4soil")
for row in cursor:
if row.getValue("MEAN_stm005") is None:
cursor.deleteRow(row)
del cursor
del row
#Add unique ID field to tempStations for use in OLS function
arcpy.AddField_management(scratchGDB +"/tempStations4soil", "Unique_ID", "SHORT", "", "", "", "", "NULLABLE", "NON_REQUIRED", "")
arcpy.CalculateField_management(scratchGDB +"/tempStations4soil","Unique_ID","!OBJECTID!","PYTHON_9.3","")
#Run ordinary least squares on tempStations
arcpy.CreateTable_management(scratchGDB, "coef_table")
ols = arcpy.OrdinaryLeastSquares_stats(scratchGDB + "/tempStations4soil","Unique_ID","in_memory/fcResid","MEAN_stm005","Elevation",scratchGDB + "/coef_table","","")
lsScratchData_Imd.append(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]
#Create final raster
arcpy.env.extent = extElevation
output_raster = (Raster(rc_elevation) * slope + intercept)
output_raster.save(outFolder + "/soil_temperature_" + sTimeStamp + ".tif")
return outFolder + "/soil_temperature_" + sTimeStamp + ".tif"
def solarRadiation():
arcpy.AddMessage("Calculating Solar Radiation")
#Caclulate average solar radiation values over the n-hour time step (ignore "no-data" values: -999)
arcpy.MakeTableView_management(scratchGDB + "/climate_table1", "solarRadiation_Table", "in_solar_radiation > -500")
arcpy.Statistics_analysis("solarRadiation_Table", "in_memory/solarRadiation_Table2", "in_solar_radiation MEAN", "site_key")
#Copy solar radiation values to station feature class
arcpy.CopyFeatures_management(fcStations_wElevation, scratchGDB +"/tempStations")
lsScratchData_Imd.append(scratchGDB +"/tempStations")
arcpy.JoinField_management(scratchGDB +"/tempStations", "Site_Key", "in_memory/solarRadiation_Table2", "site_key", "MEAN_in_solar_radiation")
#Delete rows from station feature class that have negative or null elevations
cursor = arcpy.UpdateCursor(scratchGDB + "/tempStations")
for row in cursor:
if row.getValue("RASTERVALU") < 0 or row.getValue("RASTERVALU") == "None" or row.getValue("RASTERVALU") is None:
cursor.deleteRow(row)
del cursor
del row
#Delete rows from station feature class that have null values for solar radiation
cursor = arcpy.UpdateCursor(scratchGDB + "/tempStations")
for row in cursor:
if row.getValue("MEAN_in_solar_radiation") is None:
cursor.deleteRow(row)
del cursor
del row
#set up area solar radiation tool parameters and run the tool
#Set up time parameters
day_of_year = dateIncrement.timetuple().tm_yday
iSRStart = int(dateIncrement.strftime("%H"))
iSREnd = iSRStart + iTimeStep
inTWD = TimeWithinDay(day_of_year,iSRStart,iSREnd)
global_radiation_raster = scratchGDB + "/global_radiation_raster"
skySize = 200
outGlobalRadiation = AreaSolarRadiation(rc_elevation, "", skySize, inTWD)
#outGlobalRadiation = AreaSolarRadiation(r'C:\AA_Thesis_Project\ZZ_MySQL_Work\Required_Data.gdb\RC_DEM_100m_South', "", skySize, inTWD)
outGlobalRadiation = outGlobalRadiation / iTimeStep
#Correct global radiation raster for cloud conditions
#Extract simulated global radiation values to station location feature class
arcpy.AlterField_management(scratchGDB + "/tempStations","RASTERVALU","Elevation")
arcpy.gp.ExtractValuesToPoints_sa(scratchGDB + "/tempStations", outGlobalRadiation, scratchGDB + "/simPoints", "NONE", "VALUE_ONLY")
lsScratchData_Imd.append(scratchGDB + "/simPoints")
#add "ratio" field to station feature class
arcpy.AddField_management(scratchGDB + "/simPoints","ratio","FLOAT","#","#","#","#","NULLABLE","NON_REQUIRED","#")
#calculate "ratio" field (observed radiation / simulated radiation)
arcpy.CalculateField_management(scratchGDB + "/simPoints","ratio","!MEAN_in_solar_radiation!/ !RASTERVALU!","PYTHON_9.3","#")
#convert "ratio" field to numpy array
na = arcpy.da.TableToNumPyArray(scratchGDB + "/simPoints", "ratio")
#calculate average ratio
dMeanRatio = numpy.mean(na["ratio"])
dMeanRatio2 = numpy.asscalar(dMeanRatio)
#multiply simulated raster by average ratio
outGlobalRadiation_Corrected = outGlobalRadiation * dMeanRatio2
outGlobalRadiation_Corrected.save(outFolder + "/solar_radiation_" + sTimeStamp + ".tif")
return outFolder + "/solar_radiation_" + sTimeStamp + ".tif"
def thermalRadiation(inAirTemperature, inVaporPressure, inSurfaceTemp):
arcpy.AddMessage("Calculating Thermal Radiation")
#Constants and re-defined variables (See Marks and Dozier (1979), pg. 160)
z = rc_elevation
vf = r'C:\ReynoldsCreek\Relevant_Data.gdb\RC_ViewFactor_10m_South_JD'
T_a = inAirTemperature
vp = inVaporPressure
#Select one station and its data to use as reference for elevation, pressure, and temperature
#Caclulate average air temperature and vapor pressure values over the n-hour time step (ignore "no-data" values: -999)
arcpy.MakeTableView_management(scratchGDB + "/climate_table1", "thermalRadiation_Table", "air_temperature > -500 AND vapor_pressure > -500")
arcpy.Statistics_analysis("thermalRadiation_Table", "in_memory/thermalRadiation_Table2", [["air_temperature", "MEAN"], ["vapor_pressure", "MEAN"]], "site_key")
#Copy vapor pressure values to station feature class
arcpy.CopyFeatures_management(fcStations_wElevation, scratchGDB +"/tempStations")
lsScratchData_Imd.append(scratchGDB +"/tempStations")
arcpy.JoinField_management(scratchGDB +"/tempStations", "Site_Key", "in_memory/thermalRadiation_Table2", "site_key", ["MEAN_air_temperature", "MEAN_vapor_pressure"])
#Delete rows from station feature class that have negative or null elevations
cursor = arcpy.UpdateCursor(scratchGDB + "/tempStations")
for row in cursor:
if row.getValue("RASTERVALU") < 0 or row.getValue("RASTERVALU") == "None" or row.getValue("RASTERVALU") is None:
cursor.deleteRow(row)
del cursor
del row
#Delete rows from station feature class that have null values for vapor pressure or air temperature
cursor = arcpy.UpdateCursor(scratchGDB + "/tempStations")
for row in cursor:
if row.getValue("MEAN_air_temperature") is None or row.getValue("MEAN_vapor_pressure") is None:
cursor.deleteRow(row)
del cursor
del row
#Use update cursor to extract air temperature, vapor pressure, and elevation values for the first station, then delete that station
P_m = 0.0
T_m = 0.0
z_m = 0.0
T_s = inSurfaceTemp
cursor = arcpy.UpdateCursor(scratchGDB + "/tempStations")
for row in cursor:
z_m = row.getValue("RASTERVALU")
P_m = row.getValue("MEAN_vapor_pressure")
T_m = row.getValue("MEAN_air_temperature")
cursor.deleteRow(row)
break
del cursor
del row
arcpy.AddMessage("P_m: " + str(P_m))
arcpy.AddMessage("T_m: " + str(T_m))
arcpy.AddMessage("z_m: " + str(z_m))
arcpy.AddMessage("T_s: " + str(T_s))
g = 9.8
m = 0.0289
R = 8.3143
sigma = 5.6697*10**-8
epsilon_s = 0.95
gamma = -0.006
#convert temperature parameters to Kelvin
T_m = T_m + 274.15
T_s = T_s + 274.15
T_a = arcpy.sa.Float(Raster(T_a) + 274.15)
#convert vapor pressure parameters to mb
P_m = P_m * 0.01
vp = arcpy.sa.Float(Raster(vp) * 0.01)
#Correct air temperature and vapor pressure rasters (Marks and Dozier (1979), pg. 164)
T_prime = T_a + (0.0065 * Raster(rc_elevation)) #(4) corrected air temperature
e_sa = arcpy.sa.Float(6.11 * 10**((7.5*arcpy.sa.Float(T_a))/(237.3 + arcpy.sa.Float(T_a)))) #saturated vapor pressure from original air temperature (T_a)
e_sprime = arcpy.sa.Float(6.11 * 10**((7.5*arcpy.sa.Float(T_a))/(237.3 + arcpy.sa.Float(T_a)))) #saturated vapor pressure from corrected air temperature (T_prime)
rh = arcpy.sa.Float(vp / e_sa) #(5) relative humidity
e_prime = arcpy.sa.Float(rh * e_sprime) #(6) corrected vapor pressure
#Pressure at a given elevation (Marks and Dozier (1979), pg. 168-169)
term1 = ((-g*m)/(R*gamma))
delta_z = Raster(z) - z_m
term2 = ((T_m + gamma * delta_z)) / T_m
lnTerm = arcpy.sa.Ln(term2)
expTerm = arcpy.sa.Exp(term1 * lnTerm)
P_a = P_m * expTerm #(10) air pressure
#effective emissivity (Marks and Dozier (1979), pg. 164)
epsilon_a = arcpy.sa.Float((1.24 * (e_prime / T_prime)**(1/7)) * (P_a / 1013.0)) #(7)
#Incoming longwave radiation (Marks and Dozier (1979), pg. 164)
term3 = arcpy.sa.Float((epsilon_a * sigma * (T_a ** 4)) * vf)
term4 = arcpy.sa.Float(epsilon_s * sigma * (T_s ** 4))
term5 = (1 - Raster(vf))
output_thermal_radiation = arcpy.sa.Float(term3 + (term4 * term5)) #(9)
output_thermal_radiation.save(outFolder + "/thermal_radiation_" + sTimeStamp + ".tif")
return outFolder + "/thermal_radiation_" + sTimeStamp + ".tif"
def vaporPressure():
arcpy.AddMessage("Calculating Vapor Pressure")
#Caclulate average vapor pressure values over the n-hour time step (ignore "no-data" values: -999)
arcpy.MakeTableView_management(scratchGDB + "/climate_table1", "vaporPressure_Table", "vapor_pressure > -500")
arcpy.Statistics_analysis("vaporPressure_Table", "in_memory/vaporPressure_Table2", "vapor_pressure MEAN", "site_key")
#Copy vapor pressure values to station feature class
arcpy.CopyFeatures_management(fcStations_wElevation, scratchGDB +"/tempStations")
lsScratchData_Imd.append(scratchGDB +"/tempStations")
arcpy.JoinField_management(scratchGDB +"/tempStations", "Site_Key", "in_memory/vaporPressure_Table2", "site_key", "MEAN_vapor_pressure")
#Delete rows from station feature class that have negative or null elevations
cursor = arcpy.UpdateCursor(scratchGDB + "/tempStations")
for row in cursor:
if row.getValue("RASTERVALU") < 0 or row.getValue("RASTERVALU") == "None" or row.getValue("RASTERVALU") is None:
cursor.deleteRow(row)
del cursor
del row
#Delete rows from station feature class that have null values for vapor pressure
cursor = arcpy.UpdateCursor(scratchGDB + "/tempStations")
for row in cursor:
if row.getValue("MEAN_vapor_pressure") is None:
cursor.deleteRow(row)
del cursor
del row
#Check if interpolation method is Empirical Bayesian or Detrended
if sKrigMethod == "Detrended":
#Add unique ID field to tempStations for use in OLS function
arcpy.AddField_management(scratchGDB +"/tempStations", "Unique_ID", "SHORT", "", "", "", "", "NULLABLE", "NON_REQUIRED", "")
arcpy.CalculateField_management(scratchGDB +"/tempStations","Unique_ID","!OBJECTID!","PYTHON_9.3","")
#Run ordinary least squares on tempStations
arcpy.CreateTable_management(scratchGDB, "coef_table")
ols = arcpy.OrdinaryLeastSquares_stats(scratchGDB + "/tempStations","Unique_ID","in_memory/fcResid","MEAN_vapor_pressure","RASTERVALU", scratchGDB + "/coef_table","","")
lsScratchData_Imd.append(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]
#Calculate residuals and add them to tempStations
arcpy.AddField_management(scratchGDB + "/tempStations", "residual", "DOUBLE", "", "", "", "", "NULLABLE", "NON_REQUIRED", "")
cursor = arcpy.UpdateCursor(scratchGDB + "/tempStations")
for row in cursor:
row.setValue("residual", row.getValue("MEAN_vapor_pressure") - ((slope * row.getValue("RASTERVALU")) + intercept))
cursor.updateRow(row)
del cursor
del row
#Run ordinary kriging on residuals
#arcpy.gp.Kriging_sa(scratchGDB + "/tempStations", "residual", scratchGDB + "/vaporPressure_residual", "Linear 37.061494", output_cell_size, "VARIABLE 100", "")
outKrig = Kriging(scratchGDB + "/tempStations", "residual", KrigingModelOrdinary("SPHERICAL", 460, 3686, .1214, .2192), output_cell_size, RadiusFixed(10000, 1))
outKrig.save(scratchGDB + "/vaporPressure_residual")
lsScratchData_Imd.append(scratchGDB + "/vaporPressure_residual")
#Add back elevation trends and save final raster
output_raster = arcpy.Raster(scratchGDB + "/vaporPressure_residual") + (arcpy.Raster(rc_elevation) * slope + intercept)
output_raster.save(outFolder + "/vapor_pressure_" + sTimeStamp + ".tif")
else:
arcpy.EmpiricalBayesianKriging_ga(in_features=scratchGDB + "/tempStations", z_field="MEAN_vapor_pressure", out_ga_layer="#", \
out_raster=outFolder + "/vapor_pressure_" + sTimeStamp + ".tif", 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")
return outFolder + "/vapor_pressure_" + sTimeStamp + ".tif"
def windSpeed(inDateTime):
#Caclulate average parameter values (wind speed, wind direction, air temperature) over the n-hour time step (ignore "no-data" values: -999)
arcpy.MakeTableView_management(scratchGDB + "/climate_table1", "wind_Table", "air_temperature > -500 AND wind_speed_average > -500 AND wind_direction > -500")
arcpy.Statistics_analysis("wind_Table", "in_memory/wind_Table2", [["air_temperature", "MEAN"], ["wind_speed_average", "MEAN"], ["wind_direction", "MEAN"]], "site_key")
#Copy parameter values to station feature class
arcpy.CopyFeatures_management(fcStations_wElevation, scratchGDB +"/tempStations")
lsScratchData_Imd.append(scratchGDB +"/tempStations")
arcpy.JoinField_management(scratchGDB +"/tempStations", "Site_Key", "in_memory/wind_Table2", "site_key", ["MEAN_air_temperature", "MEAN_wind_speed_average", "MEAN_wind_direction"])
#Delete rows from station feature class that have null values for air temperature, wind speed or wind direction
cursor = arcpy.UpdateCursor(scratchGDB + "/tempStations")
for row in cursor:
if row.getValue("MEAN_air_temperature") is None or row.getValue("MEAN_wind_speed_average") is None or row.getValue("MEAN_wind_direction") is None or row.getValue("Site_Key") == 'rmsp3':
cursor.deleteRow(row)
del cursor
del row
#Elevation tiff
elev_file = r'C:\ReynoldsCreek\jd_elevation_filled.tif'
#Set up time parameters
ninjaPath = "C:/WindNinja/WindNinja-2.5.1/bin/WindNinja_cli.exe"
sWindDate = inDateTime.split(" ")[0]
sWindTime = inDateTime.split(" ")[1]
lsWindDate = sWindDate.split("-")
lsWindTime = sWindTime.split(":")
sWindYear = lsWindDate[0]
sWindMonth = lsWindDate[1]
sWindDay = lsWindDate[2]
sWindHour = lsWindTime[0]
sWindMinute = lsWindTime[1]
#Build station csv file from SQL data
#open(scratchWS + "/wnStations.csv", 'w').close()
#Add coordinates to station feature class
arcpy.AddGeometryAttributes_management(scratchGDB + "/tempStations", "POINT_X_Y_Z_M")
#Loop through stations in station feature class and write parameter values to a csv file
with open(scratchWS + "/wnStations.csv", 'wb') as csvFile:
a = csv.writer(csvFile)
#write header row
a.writerow(['Station_Name', 'Coord_Sys(PROJCS,GEOGCS)', 'Datum(WGS84,NAD83,NAD27)', 'Lat/YCoord', 'Lon/XCoord', 'Height', 'Height_Units(meters,feet)', 'Speed', 'Speed_Units(mph,kph,mps)', 'Direction(degrees)', 'Temperature', 'Temperature_Units(F,C)', 'Cloud_Cover(%)', 'Radius_of_Influence', 'Radius_of_Influence_Units(miles,feet,meters,km)'])
srchCursor = arcpy.SearchCursor(scratchGDB + "/tempStations")
for row in srchCursor:
a.writerow([row.getValue("Site_Key"), 'PROJCS', 'NAD83', row.getValue("Point_Y"), row.getValue("Point_X"), '3', 'meters', row.getValue("MEAN_wind_speed_average"), 'mps', row.getValue("MEAN_wind_direction"), row.getValue("MEAN_air_temperature"), 'C', '0', '-1', 'miles'])
#List arguments for WindNinja CLI
args = []
args = [ninjaPath,
"--initialization_method", "pointInitialization",
"--elevation_file", elev_file, #elevation raster (cannot contain any "no-data" values)
"--match_points", "false", #match simulations to points (simulation fails if set to true)
"--year", sWindYear,
"--month", sWindMonth,
"--day", sWindDay,
"--hour", sWindHour,
"--minute", sWindMinute,
"--mesh_resolution", output_cell_size, #Resolution of model calculations
"--vegetation", "brush", #Vegetation type (can be 'grass', 'brush', or 'trees')
"--time_zone", "America/Boise", #time zone of target simulation
"--diurnal_winds", "true", #consider diurnal cycles in calculations
"--write_goog_output", "false", #write kml output (boolean: true/false)
"--write_shapefile_output", "false", #write shapefile output (boolean: true/false)
"--write_farsite_atm", "false", #write fire behavior file (boolean: true/false)
"--write_ascii_output", "true", #write ascii file output (this should always be set to true)
"--ascii_out_resolution", "-1", #resolution of output (-1 means same as mesh_resolution)
"--units_ascii_out_resolution", "m",
"--units_mesh_resolution", "m", #units of resolution of model calculations (should be "m" for meters)
"--units_output_wind_height", "m", #units of output wind height
"--output_speed_units", "mps",
"--output_wind_height", "3",
"--wx_station_filename", scratchWS + "/wnStations.csv", #weather station csv file used in point initialization method
"--output_path", scratchWS] #path to output
#run the WindNinja_cli.exe (output is written to same location as elevation raster)
arcpy.AddMessage("Calling WindNinja command line interface")
runfile = subprocess.Popen(args, stdout = subprocess.PIPE, bufsize = -1)
runfile.wait()
output = runfile.stdout.read()
if output is None:
arcpy.AddMessage("Results: None returned\n")
else:
arcpy.AddMessage("Results:\n" + output)
#convert ascii file to new grid
for file in os.listdir(scratchWS):
if file.endswith("_vel.asc"):
path2ASCII = scratchWS + "/" + file
arcpy.ASCIIToRaster_conversion(in_ascii_file=path2ASCII, out_raster=outFolder + "/wind_speed_" + sTimeStamp + ".tif", data_type="FLOAT")
#Get coordinate system information
desc = arcpy.Describe(rc_elevation)
coord_system = desc.spatialReference
arcpy.DefineProjection_management(outFolder + "/wind_speed_" + sTimeStamp + ".tif", coord_system)
return outFolder + "/wind_speed_" + sTimeStamp + ".tif"
def deleteScratch(inList):
for path in inList:
arcpy.Delete_management(path)
#START MAIN SCRIPT
#Calculate time range and number of time steps
dateFrom = roundTime(datetime.datetime.strptime(sFromDate, "%Y-%m-%d %H:%M:%S"), 60*60)
dateTo = roundTime(datetime.datetime.strptime(sToDate, "%Y-%m-%d %H:%M:%S"))
timeDelta = dateTo - dateFrom
iDays = timeDelta.days
iSeconds = timeDelta.seconds + (iDays * 86400)
iMinutes = iSeconds / 60
iHours = iMinutes / 60
iNumSteps = iHours / iTimeStep
#connect to MySQL database
try:
cnx = mysql.connector.connect(user='root', password='',
host='localhost',
database='jd_data',
buffered=True)
except mysql.connector.Error as err:
if err.errno == errorcode.ER_ACCESS_DENIED_ERROR:
arcpy.AddMessage("Something is wrong with your user name or password")
elif err.errno == errorcode.ER_BAD_DB_ERROR:
arcpy.AddMessage("Database does not exist")
else:
arcpy.AddMessage(err)
else:
arcpy.AddMessage("Connection successful")
#Initiate list of scratch data to delete at end of script
lsScratchData = []
#Initiate list of output .tif files
lsOutput = []
#Create master station feature class that will be copied for each gridding function
fcStations_wElevation = scratchGDB + "/stations_wElevation"
lsScratchData.append(fcStations_wElevation)
#arcpy.WFSToFeatureClass_conversion(input_WFS_server="https://miles-gis-sandbox.northwestknowledge.net/arcgis/services/WCWAVE/rc_data_mapService/MapServer/WFSServer", \
# WFS_feature_type="station_locations", out_path=scratchGDB, out_name="station_locations")
#station_locations = scratchGDB + "/station_locations"
#lsScratchData.append(station_locations)
#extFullFeatures = arcpy.Describe(scratchGDB + "/station_locations").extent
station_locations = r'C:\ReynoldsCreek\Relevant_Data.gdb\station_locations_JD'
extFullFeatures = arcpy.Describe(station_locations).extent
rc_elevation = r'C:\ReynoldsCreek\Relevant_Data.gdb\RC_DEM_10m_JD'
arcpy.env.cellSize = rc_elevation
output_cell_size = arcpy.env.cellSize
extElevation = arcpy.Describe(rc_elevation).extent
arcpy.env.extent = extElevation
arcpy.gp.ExtractValuesToPoints_sa(station_locations, rc_elevation, fcStations_wElevation, "NONE", "VALUE_ONLY")
#Run separate gridding functions
delta = datetime.timedelta(hours=iTimeStep)
dateIncrement = dateFrom
while dateIncrement < dateTo:
#Check which tables in the SQL database will have to be queried
if any([boolAllTools, boolAirTemperature, boolDewPoint, boolVaporPressure, boolWindSpeed, boolSolarRadiation, boolThermalRadiation]):
lsScratchData_Imd = []
#Initiate parameter lists
lsSiteKey = []
lsDatetime = []
lsAirTemperature = []
lsVaporPressure = []
lsDewpoint = []
lsSolarRadiation = []
lsWindSpeed = []
lsWindDirection = []
#Query climate table
sFrom = dateIncrement.strftime("%Y-%m-%d %H:%M:%S")
sTimeStamp = dateIncrement.strftime("%Y%m%d_%H")
dateToTemp = dateIncrement + delta
sTo = dateToTemp.strftime("%Y-%m-%d %H:%M:%S")
sQuery = "SELECT Site_Key, date_time, ta, ea, td, si, ws, wd FROM weather WHERE date_time >= '" + sFrom + "' AND date_time < '" + sTo + "'"
cur = cnx.cursor()
cur.execute(sQuery)
iNumReturn = cur.rowcount
arcpy.AddMessage("sQuery: " + sQuery)
arcpy.AddMessage("iNumReturn: " + str(iNumReturn))
#Append query results to parameter lists
for i in range(0,iNumReturn):
row = cur.fetchone()
lsSiteKey.append(row[0])
lsDatetime.append(row[1])
lsAirTemperature.append(row[2])
lsVaporPressure.append(row[3])
lsDewpoint.append(row[4])
lsSolarRadiation.append(row[5])
lsWindSpeed.append(row[6])
lsWindDirection.append(row[7])
cur.close()
#Build climate table to pass to gridding tools
arcpy.CreateTable_management(scratchGDB,"climate_table1")
arcpy.AddField_management(scratchGDB + "/climate_table1", "site_key", "TEXT")
arcpy.AddField_management(scratchGDB + "/climate_table1", "date_time", "DATE")
arcpy.AddField_management(scratchGDB + "/climate_table1", "air_temperature", "FLOAT")
arcpy.AddField_management(scratchGDB + "/climate_table1", "vapor_pressure", "FLOAT")
arcpy.AddField_management(scratchGDB + "/climate_table1", "dewpoint_temperature", "FLOAT")
arcpy.AddField_management(scratchGDB + "/climate_table1", "in_solar_radiation", "FLOAT")
arcpy.AddField_management(scratchGDB + "/climate_table1", "wind_speed_average", "FLOAT")
arcpy.AddField_management(scratchGDB + "/climate_table1", "wind_direction", "FLOAT")
lsScratchData_Imd.append(scratchGDB + "/climate_table1")
#Add values from parameter lists to cliemate table
inCursor = arcpy.InsertCursor(scratchGDB + "/climate_table1")
for j in range(0, iNumReturn):
row = inCursor.newRow()
row.setValue("site_key", lsSiteKey[j])
row.setValue("date_time", lsDatetime[j])
row.setValue("air_temperature", lsAirTemperature[j])
row.setValue("vapor_pressure", lsVaporPressure[j])
row.setValue("dewpoint_temperature", lsDewpoint[j])
row.setValue("in_solar_radiation", lsSolarRadiation[j])
row.setValue("wind_speed_average", lsWindSpeed[j])
row.setValue("wind_direction", lsWindDirection[j])
inCursor.insertRow(row)
del inCursor
del row
#Run gridding functions
if boolAirTemperature:
pathAirTemperature = airTemperature()
lsOutput.append(pathAirTemperature)
if boolDewPoint:
pathDewPointTemperature, pathPercentSnow, pathPrecipSnowDensity = dewPoint()
lsOutput.append(pathDewPointTemperature)
lsOutput.append(pathPercentSnow)
lsOutput.append(pathPrecipSnowDensity)
if boolVaporPressure:
pathVaporPressure = vaporPressure()
lsOutput.append(pathVaporPressure)
if boolWindSpeed:
pathWindSpeed = windSpeed(sFrom)
lsOutput.append(pathWindSpeed)
if boolSolarRadiation:
try:
pathSolarRadiation = solarRadiation()
lsOutput.append(pathSolarRadiation)
except Exception as e:
if "010003" in e.message:
arcpy.AddMessage("Skip Solar Radiation at night")
traceback.print_exc(file=sys.stdout)
else:
print("Traceback")
traceback.print_exc(file=sys.stdout)
if boolThermalRadiation:
#Query database for average air temperature for current day
sFromTR = dateIncrement.strftime("%Y-%m-%d")
sQuery2 = "SELECT AVG(NULLIF(ta, -999)) FROM weather WHERE date_time >= '" + sFromTR + " 00:00:00" + "' AND date_time <= '" + sFromTR + " 23:00:00'"
cur2 = cnx.cursor()
cur2.execute(sQuery2)
dRefTemp = cur2.fetchone()[0]
cur2.close()
pathThermalRadiation = thermalRadiation(pathAirTemperature, pathVaporPressure, dRefTemp)
lsOutput.append(pathThermalRadiation)
#Delete intermediate scratch
deleteScratch(lsScratchData_Imd)
if any([boolAllTools, boolPrecipMass]):
lsScratchData_Imd = []
#Initiate parameter lists
lsSiteKey = []
lsPPTS = []
lsPPTU = []
lsPPTA = []
#Query precipitation table
sFrom = dateIncrement.strftime("%Y-%m-%d %H:%M:%S")
sTimeStamp = dateIncrement.strftime("%Y%m%d_%H")
dateToTemp = dateIncrement + delta
sTo = dateToTemp.strftime("%Y-%m-%d %H:%M:%S")
sQuery = "SELECT * FROM precipitation WHERE date_time >= '" + sFrom + "' AND date_time < '" + sTo + "'"
cur = cnx.cursor()
cur.execute(sQuery)
iNumReturn = cur.rowcount
#Append query results to parameter lists
for i in range(0,iNumReturn):
row = cur.fetchone()
lsSiteKey.append(row[0])
lsPPTS.append(row[2])
lsPPTU.append(row[3])
lsPPTA.append(row[4])
cur.close()
#Build precipitation table to pass to gridding tools
arcpy.CreateTable_management(scratchGDB,"precipitation_table1")
arcpy.AddField_management(scratchGDB + "/precipitation_table1", "site_key", "TEXT")
arcpy.AddField_management(scratchGDB + "/precipitation_table1", "ppts", "FLOAT")
arcpy.AddField_management(scratchGDB + "/precipitation_table1", "pptu", "FLOAT")
arcpy.AddField_management(scratchGDB + "/precipitation_table1", "ppta", "FLOAT")
lsScratchData_Imd.append(scratchGDB + "/precipitation_table1")
#Add values from parameter lists to precipitation table
inCursor = arcpy.InsertCursor(scratchGDB + "/precipitation_table1")
for j in range(0, iNumReturn):
row = inCursor.newRow()
row.setValue("site_key", lsSiteKey[j])
row.setValue("ppts", lsPPTS[j])
row.setValue("pptu", lsPPTU[j])
row.setValue("ppta", lsPPTA[j])
inCursor.insertRow(row)
del inCursor
del row
#Run gridding functions
if boolPrecipMass:
pathPrecipMass = precipMass()
lsOutput.append(pathPrecipMass)
#Delete intermediate scratch
deleteScratch(lsScratchData_Imd)
if any([boolAllTools, boolSoilTemperature]):
lsScratchData_Imd = []
#Initiate parameter lists
lsSiteKey = []
lsSoilTemp = []
#Query soil temperature table
sFrom = dateIncrement.strftime("%Y-%m-%d %H:%M:%S")
sTimeStamp = dateIncrement.strftime("%Y%m%d_%H")
dateToTemp = dateIncrement + delta
sTo = dateToTemp.strftime("%Y-%m-%d %H:%M:%S")
sQuery = "SELECT * FROM soil_temperature WHERE date_time >= '" + sFrom + "' AND date_time < '" + sTo + "'"
cur = cnx.cursor()
cur.execute(sQuery)
iNumReturn = cur.rowcount
#Append query results to parameter lists
for i in range(0,iNumReturn):
row = cur.fetchone()
lsSiteKey.append(row[0])
lsSoilTemp.append(row[3]) #Column 4 is temperature at 10 cm depth
cur.close()