def Precip(watershed_poly):
# Local variables
precipchago = r'D:\Jackson\StorageCapacity\ToolData\precipchago'
precip_raster = Raster(precipchago)
prec_table = 'prec_table'
prec_table_avg = 'prec_table_avg'
if precipchago == '#' or not precipchago:
precipchago = "precipchago" # provide a default value if unspecified
# Process
arcpy.AddMessage("Extract precipitation values from raster to table")
arcpy.ExtractValuesToTable_ga(watershed_poly, precip_raster, prec_table,
"", "true")
arcpy.AddField_management(watershed_poly, "Prec_ID", "SHORT", "", "", "",
"", "NULLABLE", "NON_REQUIRED")
arcpy.CalculateField_management(watershed_poly, "Prec_ID", 1, "PYTHON", "")
arcpy.AddMessage("Determine Average precipitation across watershed")
arcpy.Statistics_analysis(prec_table, prec_table_avg, "Value MEAN", "")
arcpy.AddField_management(prec_table_avg, "Av_Prec", "DOUBLE", "", "", "",
"", "NULLABLE", "NON_REQUIRED")
arcpy.AddField_management(prec_table_avg, "ID")
arcpy.CalculateField_management(prec_table_avg, "ID", 1, "PYTHON")
arcpy.CalculateField_management(prec_table_avg, "Av_Prec", "!MEAN_Value!",
"PYTHON", "")
arcpy.JoinField_management(watershed_poly, "Prec_ID", prec_table_avg, "ID",
"Av_Prec")
def extractValues():
'''Extract values of daily rainfall data to csv table based on household
point shapefile. Add option to output table with names of input rasters
'''
env.workspace = pathlist[0] # Set workplace for CHIRPS daily rasters
raslist = arcpy.ListRasters() # create raster list of all rasters
outname = "CHIRPS_tanz.dbf"
arcpy.ExtractValuesToTable_ga(pathlist[2] + "BalancedGeoVars.shp", raslist,
pathlist[1] + outname,
pathlist[1] + "rasnames.dbf", "FALSE")
print "Completed extracting values on", outname
def calcstats(mydir):
lTIFs = [
] # Create a blank list that would be populated by input geotiff files later
for path, subdirs, files in os.walk(mydir):
arcpy.AddMessage("\n" + 'Processing Folder ' + mydir + "\n")
for name in files:
if fnmatch(name, pattern):
TIF = os.path.join(path, name)
lTIFs.append(TIF)
# Loop through each raster file and calculate statistics
for tif in lTIFs:
tifpath, tifname = os.path.split(tif) # Split filenames and paths
tbloutfield = tifname.split('.')[
0] # Get output csv filname without extension
tbloutfield = tbloutfield.replace('prate_',
'') # Strip _pt from name
outcsv = tbloutfield + '.csv'
tbloutfield = "d" + tbloutfield[
1:] # Replace first char of date(year) by "d" to overrule a restriction
###################################################################
## Definition of variables related to Point Shapefile Processing ##
###################################################################
ptout = tif.replace(
'.tif', '.dbf') # Full name & Path of temp output point shp
##############################################################
## Start process to calulate statistics for point shapefile ##
##############################################################
# Skip if output table already exists
if not os.path.exists(ptout):
arcpy.AddMessage('Processing ' + tifname)
try:
arcpy.ExtractValuesToTable_ga(ptshp, tif, ptout, "", "")
fmap = '{} \\\"'.format(
tbloutfield
) + tbloutfield + "\\\" " + "true true false 19 Double 0 0 ,First,#," + ptout + ",Value,-1,-1;SrcID_Feat \\\"SrcID_Feat\\\" true true false 10 Long 0 10 ,First,#," + ptout + ",SrcID_Feat,-1,-1"
arcpy.TableToTable_conversion(ptout, tifpath, outcsv, "",
fmap, "")
# Define local variables for calculating statistics
if ptinter == True:
arcpy.Delete_management(ptout)
except:
arcpy.AddMessage('Error in processing ' + tifname)
else:
arcpy.AddMessage('Skipping ' + tifname + " (Already Exists)")
del ptout, tif, tbloutfield, fmap, outcsv
lTIFs = []
def Find_Slope(watershed_poly):
# Local variables
watershed_dem = 'watershed_dem'
slope1 = 'slope1'
slope_table = 'slope_table'
slope_stats = 'slope_stats'
# Extract slope raster by watershed polygon
arcpy.AddMessage("clip DEM to watershed polygon")
arcpy.gp.ExtractByMask_sa(filled_dem, watershed_poly, watershed_dem)
arcpy.AddMessage("Calculate slope for watershed")
arcpy.gp.Slope_sa(watershed_dem, slope1, "PERCENT_RISE", "1")
arcpy.ExtractValuesToTable_ga(watershed_poly, slope1, slope_table)
arcpy.AddMessage("Find average slope across the watershed")
arcpy.Statistics_analysis(slope_table, slope_stats, "Value MEAN")
arcpy.AddField_management(slope_stats, "Slope_Avg", "LONG", "", "", "", "", "NULLABLE", "NON_REQUIRED", "")
arcpy.CalculateField_management(slope_stats, "Slope_Avg", "!MEAN_Value!", "PYTHON", "")
arcpy.AddMessage("Join field to polygon")
arcpy.AddField_management(slope_stats, "Prec_ID")
arcpy.CalculateField_management(slope_stats, "Prec_ID", 1, "PYTHON")
arcpy.JoinField_management(watershed_poly, "Prec_ID", slope_stats, "OBJECTID", "Slope_Avg")
# Mask Hillshade with Cloudless LST pixels
masked_hillshade_raster = Con(
Raster(
"C:/Users/eric-/Desktop/MGST_Final/ECOSTRESS_processing/GeoRef/output/merged_"
+ the_date + ".tif"),
Raster(
"C:/Users/eric-/Desktop/MGST_Final/ECOSTRESS_processing/GeoRef/output/hillshade_"
+ the_date + ".tif"))
masked_hillshade_raster.save(
"C:/Users/eric-/Desktop/MGST_Final/ECOSTRESS_processing/GeoRef/output/masked_hillshade_"
+ the_date + ".tif")
# Extract Values to Table (surface temp)
arcpy.ExtractValuesToTable_ga(
"C:/Users/eric-/Desktop/MGST_Final/MGST_Final/all_stations_online_XYTableT2/all_stations_online_XYTableT2.shp",
"C:/Users/eric-/Desktop/MGST_Final/ECOSTRESS_processing/GeoRef/output/merged_"
+ the_date + ".tif",
"C:/Users/eric-/Desktop/MGST_Final/ECOSTRESS_processing/GeoRef/output/"
+ the_date + ".txt", "", "")
# Extract Values to Table (masked hillshade status)
arcpy.ExtractValuesToTable_ga(
"C:/Users/eric-/Desktop/MGST_Final/MGST_Final/all_stations_online_XYTableT2/all_stations_online_XYTableT2.shp",
"C:/Users/eric-/Desktop/MGST_Final/ECOSTRESS_processing/GeoRef/output/masked_hillshade_"
+ the_date + ".tif",
"C:/Users/eric-/Desktop/MGST_Final/ECOSTRESS_processing/GeoRef/output/"
+ the_date + "_shadestatus.txt", "", "")
except:
pass
##################
# Step THREE: Establish the "hot" and "cold" spots for eight groups (see 'ex' variable in code block)