def main():
"""
FlowFill
"""
# netCDF4
try:
from netCDF4 import Dataset
except:
g.fatal(
_("netCDF4 not detected. Install pip3 and "
"then type at the command prompt: "
'"pip3 install netCDF4".'))
options, flags = gscript.parser()
_input = options["input"]
_np = options["np"]
_threshold = options["threshold"]
_h_runoff = options["h_runoff"]
_h_runoff_raster = options["h_runoff_raster"]
_ties = options["ties"]
_ffpath = options["ffpath"]
_output = options["output"]
_water = options["water"]
"""
import os
import numpy as np
from netCDF4 import Dataset
# GRASS
from grass import script as gscript
from grass.script import array as garray
from grass.pygrass.modules.shortcuts import raster as r
from grass.pygrass.modules.shortcuts import general as g
# FOR TESTING:
_input = 'DEM_MODFLOW'
_np = 4
_threshold = 0.001
#_h_runoff = 1.
_h_runoff = ''
#_h_runoff_raster = ''
_h_runoff_raster = 'DEM_MODFLOW'
_ties = 'PREF'
_ffpath = 'flowfill'
_output = 'tmpout'
_water = 'tmpout_water'
"""
# Check for overwrite -- should be unnecessary thanks to GRASS parser
_rasters = np.array(gscript.parse_command("g.list", type="raster").keys())
if (_rasters == _output).any() or (_water == _output).any():
if gscript.overwrite() is False:
g.fatal(_("output would overwrite " + _output))
# Check for proper number of processors
try:
_np = int(_np)
except:
g.fatal(_("Number of processors must be an integer."))
if _np < 3:
g.fatal(_("FlowFill requires 3 or more processors."))
# Check for proper option set
if _h_runoff is not "": # ????? possible ?????
if _h_runoff_raster is not "":
g.fatal(
_('Only one of "h_runoff" and "h_runoff_raster" may be set'))
elif _h_runoff_raster is "":
g.fatal(_('Either "h_runoff" or "h_runoff_raster" must be set'))
if _output is "" and _water is "":
g.warning(_("No output is set."))
# Set up runoff options
if _h_runoff_raster is not "":
_runoff_bool = "Y"
else:
_h_runoff = float(_h_runoff)
_runoff_bool = "N"
# Get computational region
n_columns = gscript.region()["cols"]
n_rows = gscript.region()["rows"]
# Output DEM as temporary file for FORTRAN
temp_FlowFill_input_file = gscript.tempfile(create=False)
dem = garray.array(_input, null=-999999)
dem_array = np.array(dem[:]).astype(np.float32)
del dem
newnc = Dataset(temp_FlowFill_input_file, "w", format="NETCDF4")
newnc.createDimension("x", n_columns)
newnc.createDimension("y", n_rows)
newnc.createVariable("value", "f4", ("y", "x")) # z
newnc.variables["value"][:] = dem_array
newnc.close()
del newnc
# r.out_gdal(input=_input, output=temp_DEM_input_file, format='netCDF',
# overwrite=True)
# Output runoff raster as temporary file for FORTRAN
if _h_runoff_raster is not "":
temp_FlowFill_runoff_file = gscript.tempfile(create=False)
rr = garray.array(_h_runoff_raster, null=0.0)
rr_array = np.array(rr[:]).astype(np.float32)
del rr
newnc = Dataset(temp_FlowFill_runoff_file, "w", format="NETCDF4")
newnc.createDimension("x", n_columns)
newnc.createDimension("y", n_rows)
newnc.createVariable("value", "f4", ("y", "x")) # z
newnc.variables["value"][:] = rr_array
newnc.close()
# Get the mean value for the floating-point depressions correction
_h_runoff = np.mean(rr_array[dem_array != -999999])
else:
_h_runoff_raster = "NoRaster" # A dummy value for the parser
temp_FlowFill_runoff_file = ""
# Run FlowFill
temp_FlowFill_output_file = gscript.tempfile(create=False)
mpirunstr = ("mpirun -np " + str(_np) + " " + _ffpath + " " +
str(_h_runoff) + " " + temp_FlowFill_input_file + " " +
str(n_columns) + " " + str(n_rows) + " " + str(_threshold) +
" " + temp_FlowFill_output_file + " " + _runoff_bool + " " +
temp_FlowFill_runoff_file + " " + _ties)
print("")
print("Sending command to FlowFill:")
print(mpirunstr)
print("")
_mpirun_error_flag = False
popen = subprocess.Popen(mpirunstr,
stdout=subprocess.PIPE,
shell=True,
universal_newlines=True)
for stdout_line in iter(popen.stdout.readline, ""):
print(stdout_line),
if "mpirun was unable to find the specified executable file" in stdout_line:
_mpirun_error_flag = True
popen.stdout.close()
if _mpirun_error_flag:
print("")
g.fatal(
_("FlowFill executable not found.\n"
"If you have not installed FlowFill, please download it "
"from https://github.com/KCallaghan/FlowFill, "
"and follow the directions in the README to compile and "
"install it on your system.\n"
'This should then work with the default "ffpath". '
"Otherwise, you may simply have typed in an incorrect "
'"ffpath".'))
# _stdout = subprocess.Popen(mpirunstr, shell=True, stdout=subprocess.PIPE)
#
# if 'mpirun was unable to find the specified executable file' in \
# ''.join(_stdout.stdout.readlines()):
# else:
# g.message('FlowFill Executable Found.')
# print('')
# subprocess.Popen(mpirunstr, shell=True).wait()
# os.system(mpirunstr)
# subprocess.Popen(mpirunstr, shell=True)
# Import the output -- padded by two cells (remove these)
outrast = np.fromfile(temp_FlowFill_output_file + ".dat", dtype=np.float32)
outrast_water = np.fromfile(temp_FlowFill_output_file + "_water.dat",
dtype=np.float32)
outrast = outrast.reshape(n_rows + 2, n_columns + 2)[:-2, 1:-1]
outrast_water = outrast_water.reshape(n_rows + 2, n_columns + 2)[:-2, 1:-1]
# Mask to return NAN to NAN in GRASS -- FIX SHIFT ISSUE WITH KERRY
dem_array_mask = dem_array.copy()
dem_array_mask[dem_array_mask == -999999] = np.nan
dem_array_mask = dem_array_mask * 0 + 1
outrast *= dem_array_mask
outrast_water *= dem_array_mask
# Save the output to GRASS GIS
dem = garray.array()
dem[:] = outrast
dem.write(_output, overwrite=gscript.overwrite())
dem[:] = outrast_water
dem.write(_water, overwrite=gscript.overwrite())
del dem
def main():
"""
Main program
"""
# Temporary filenames
# The following three are meant for a test step-by-step cwv estimation, see
# unused functions!
# tmp_ti_mean = tmp_map_name('ti_mean') # for cwv
# tmp_tj_mean = tmp_map_name('tj_mean') # for cwv
# tmp_ratio = tmp_map_name('ratio') # for cwv
tmp_avg_lse = tmp_map_name('avg_lse')
tmp_delta_lse = tmp_map_name('delta_lse')
tmp_cwv = tmp_map_name('cwv')
#tmp_lst = tmp_map_name('lst')
# basic equation for mapcalc
global equation, citation_lst
equation = "{result} = {expression}"
# user input
mtl_file = options['mtl']
if not options['prefix']:
b10 = options['b10']
b11 = options['b11']
t10 = options['t10']
t11 = options['t11']
if not options['clouds']:
qab = options['qab']
cloud_map = False
else:
qab = False
cloud_map = options['clouds']
elif options['prefix']:
prefix = options['prefix']
b10 = prefix + '10'
b11 = prefix + '11'
if not options['clouds']:
qab = prefix + 'QA'
cloud_map = False
else:
cloud_map = options['clouds']
qab = False
qapixel = options['qapixel']
lst_output = options['lst']
# save Brightness Temperature maps?
global brightness_temperature_prefix
if options['prefix_bt']:
brightness_temperature_prefix = options['prefix_bt']
else:
brightness_temperature_prefix = None
global cwv_output
cwv_window_size = int(options['window'])
assertion_for_cwv_window_size_msg = ('A spatial window of size 5^2 or less is not '
'recommended. Please select a larger window. '
'Refer to the manual\'s notes for details.')
assert cwv_window_size >= 7, assertion_for_cwv_window_size_msg
cwv_output = options['cwv']
# optional maps
average_emissivity_map = options['emissivity']
delta_emissivity_map = options['delta_emissivity']
# output for in-between maps?
global emissivity_output, delta_emissivity_output
emissivity_output = options['emissivity_out']
delta_emissivity_output = options['delta_emissivity_out']
global landcover_map, emissivity_class
landcover_map = options['landcover']
emissivity_class = options['emissivity_class']
# flags
global info, null
info = flags['i']
scene_extent = flags['e']
timestamping = flags['t']
null = flags['n']
global rounding
rounding = flags['r']
global celsius
celsius = flags['c']
# ToDo:
# shell = flags['g']
#
# Pre-production actions
#
# Set Region
if scene_extent:
grass.use_temp_region() # safely modify the region
msg = "\n|! Matching region extent to map {name}"
# ToDo: check if extent-B10 == extent-B11? Unnecessary?
# Improve below!
if b10:
run('g.region', rast=b10, align=b10)
msg = msg.format(name=b10)
elif t10:
run('g.region', rast=t10, align=t10)
msg = msg.format(name=t10)
g.message(msg)
elif scene_extent:
grass.warning(_('Operating on current region'))
#
# 1. Mask clouds
#
if cloud_map:
# user-fed cloud map?
msg = '\n|i Using {cmap} as a MASK'.format(cmap=cloud_map)
g.message(msg)
r.mask(raster=cloud_map, flags='i', overwrite=True)
else:
# using the quality assessment band and a "QA" pixel value
mask_clouds(qab, qapixel)
#
# 2. TIRS > Brightness Temperatures
#
if mtl_file:
# if MTL and b10 given, use it to compute at-satellite temperature t10
if b10:
# convert DNs to at-satellite temperatures
t10 = tirs_to_at_satellite_temperature(b10, mtl_file)
# likewise for b11 -> t11
if b11:
# convert DNs to at-satellite temperatures
t11 = tirs_to_at_satellite_temperature(b11, mtl_file)
#
# Initialise a SplitWindowLST object
#
split_window_lst = SplitWindowLST(emissivity_class)
citation_lst = split_window_lst.citation
#
# 3. Land Surface Emissivities
#
# use given fixed class?
if emissivity_class:
if split_window_lst.landcover_class is False:
# replace with meaningful error
g.warning('Unknown land cover class string! Note, this string '
'input option is case sensitive.')
if emissivity_class == 'Random':
msg = "\n|! Random emissivity class selected > " + \
split_window_lst.landcover_class + ' '
else:
msg = '\n|! Retrieving average emissivities *only* for {eclass} '
if info:
msg += '| Average emissivities (channels 10, 11): '
msg += str(split_window_lst.emissivity_t10) + ', ' + \
str(split_window_lst.emissivity_t11)
msg = msg.format(eclass=split_window_lst.landcover_class)
g.message(msg)
# use the FROM-GLC map
elif landcover_map:
if average_emissivity_map:
tmp_avg_lse = average_emissivity_map
if not average_emissivity_map:
determine_average_emissivity(tmp_avg_lse, landcover_map,
split_window_lst.average_lse_mapcalc)
if options['emissivity_out']:
tmp_avg_lse = options['emissivity_out']
if delta_emissivity_map:
tmp_delta_lse = delta_emissivity_map
if not delta_emissivity_map:
determine_delta_emissivity(tmp_delta_lse, landcover_map,
split_window_lst.delta_lse_mapcalc)
if options['delta_emissivity_out']:
tmp_delta_lse = options['delta_emissivity_out']
#
# 4. Modified Split-Window Variance-Covariance Matrix > Column Water Vapor
#
if info:
msg = '\n|i Spatial window of size {n} for Column Water Vapor estimation: '
msg = msg.format(n=cwv_window_size)
g.message(msg)
cwv = Column_Water_Vapor(cwv_window_size, t10, t11)
citation_cwv = cwv.citation
estimate_cwv_big_expression(tmp_cwv, t10, t11, cwv._big_cwv_expression())
if cwv_output:
tmp_cwv = cwv_output
#
# 5. Estimate Land Surface Temperature
#
if info and emissivity_class == 'Random':
msg = '\n|* Will pick a random emissivity class!'
grass.verbose(msg)
estimate_lst(lst_output, t10, t11,
tmp_avg_lse, tmp_delta_lse, tmp_cwv,
split_window_lst.sw_lst_mapcalc)
#
# Post-production actions
#
# remove MASK
r.mask(flags='r', verbose=True)
# time-stamping
if timestamping:
add_timestamp(mtl_file, lst_output)
if cwv_output:
add_timestamp(mtl_file, cwv_output)
# Apply color table
if celsius:
run('r.colors', map=lst_output, color='celsius')
else:
# color table for kelvin
run('r.colors', map=lst_output, color='kelvin')
# ToDo: helper function for r.support
# strings for metadata
history_lst = '\n' + citation_lst
history_lst += '\n\n' + citation_cwv
history_lst += '\n\nSplit-Window model: '
history_lst += split_window_lst._equation # :wsw_lst_mapcalc
description_lst = ('Land Surface Temperature derived from a split-window algorithm. ')
if celsius:
title_lst = 'Land Surface Temperature (C)'
units_lst = 'Celsius'
else:
title_lst = 'Land Surface Temperature (K)'
units_lst = 'Kelvin'
landsat8_metadata = Landsat8_MTL(mtl_file)
source1_lst = landsat8_metadata.scene_id
source2_lst = landsat8_metadata.origin
# history entry
run("r.support", map=lst_output, title=title_lst,
units=units_lst, description=description_lst,
source1=source1_lst, source2=source2_lst,
history=history_lst)
# (re)name the LST product
#run("g.rename", rast=(tmp_lst, lst_output))
# restore region
if scene_extent:
grass.del_temp_region() # restoring previous region settings
g.message("|! Original Region restored")
# print citation
if info:
print '\nSource: ' + citation_lst
def main():
"""
Main program
"""
# Temporary filenames
# The following three are meant for a test step-by-step cwv estimation, see
# unused functions!
# tmp_ti_mean = tmp_map_name('ti_mean') # for cwv
# tmp_tj_mean = tmp_map_name('tj_mean') # for cwv
# tmp_ratio = tmp_map_name('ratio') # for cwv
tmp_avg_lse = tmp_map_name('avg_lse')
tmp_delta_lse = tmp_map_name('delta_lse')
tmp_cwv = tmp_map_name('cwv')
#tmp_lst = tmp_map_name('lst')
# basic equation for mapcalc
global equation, citation_lst
equation = "{result} = {expression}"
# user input
mtl_file = options['mtl']
if not options['prefix']:
b10 = options['b10']
b11 = options['b11']
t10 = options['t10']
t11 = options['t11']
if not options['clouds']:
qab = options['qab']
cloud_map = False
else:
qab = False
cloud_map = options['clouds']
elif options['prefix']:
prefix = options['prefix']
b10 = prefix + '10'
b11 = prefix + '11'
if not options['clouds']:
qab = prefix + 'QA'
cloud_map = False
else:
cloud_map = options['clouds']
qab = False
qapixel = options['qapixel']
lst_output = options['lst']
# save Brightness Temperature maps?
global brightness_temperature_prefix
if options['prefix_bt']:
brightness_temperature_prefix = options['prefix_bt']
else:
brightness_temperature_prefix = None
global cwv_output
cwv_window_size = int(options['window'])
assertion_for_cwv_window_size_msg = (
'A spatial window of size 5^2 or less is not '
'recommended. Please select a larger window. '
'Refer to the manual\'s notes for details.')
assert cwv_window_size >= 7, assertion_for_cwv_window_size_msg
cwv_output = options['cwv']
# optional maps
average_emissivity_map = options['emissivity']
delta_emissivity_map = options['delta_emissivity']
# output for in-between maps?
global emissivity_output, delta_emissivity_output
emissivity_output = options['emissivity_out']
delta_emissivity_output = options['delta_emissivity_out']
global landcover_map, emissivity_class
landcover_map = options['landcover']
emissivity_class = options['emissivity_class']
# flags
global info, null
info = flags['i']
scene_extent = flags['e']
timestamping = flags['t']
null = flags['n']
global rounding
rounding = flags['r']
global celsius
celsius = flags['c']
# ToDo:
# shell = flags['g']
#
# Pre-production actions
#
# Set Region
if scene_extent:
grass.use_temp_region() # safely modify the region
msg = "\n|! Matching region extent to map {name}"
# ToDo: check if extent-B10 == extent-B11? Unnecessary?
# Improve below!
if b10:
run('g.region', rast=b10, align=b10)
msg = msg.format(name=b10)
elif t10:
run('g.region', rast=t10, align=t10)
msg = msg.format(name=t10)
g.message(msg)
elif scene_extent:
grass.warning(_('Operating on current region'))
#
# 1. Mask clouds
#
if cloud_map:
# user-fed cloud map?
msg = '\n|i Using {cmap} as a MASK'.format(cmap=cloud_map)
g.message(msg)
r.mask(raster=cloud_map, flags='i', overwrite=True)
else:
# using the quality assessment band and a "QA" pixel value
mask_clouds(qab, qapixel)
#
# 2. TIRS > Brightness Temperatures
#
if mtl_file:
# if MTL and b10 given, use it to compute at-satellite temperature t10
if b10:
# convert DNs to at-satellite temperatures
t10 = tirs_to_at_satellite_temperature(b10, mtl_file)
# likewise for b11 -> t11
if b11:
# convert DNs to at-satellite temperatures
t11 = tirs_to_at_satellite_temperature(b11, mtl_file)
#
# Initialise a SplitWindowLST object
#
split_window_lst = SplitWindowLST(emissivity_class)
citation_lst = split_window_lst.citation
#
# 3. Land Surface Emissivities
#
# use given fixed class?
if emissivity_class:
if split_window_lst.landcover_class is False:
# replace with meaningful error
g.warning('Unknown land cover class string! Note, this string '
'input option is case sensitive.')
if emissivity_class == 'Random':
msg = "\n|! Random emissivity class selected > " + \
split_window_lst.landcover_class + ' '
else:
msg = '\n|! Retrieving average emissivities *only* for {eclass} '
if info:
msg += '| Average emissivities (channels 10, 11): '
msg += str(split_window_lst.emissivity_t10) + ', ' + \
str(split_window_lst.emissivity_t11)
msg = msg.format(eclass=split_window_lst.landcover_class)
g.message(msg)
# use the FROM-GLC map
elif landcover_map:
if average_emissivity_map:
tmp_avg_lse = average_emissivity_map
if not average_emissivity_map:
determine_average_emissivity(tmp_avg_lse, landcover_map,
split_window_lst.average_lse_mapcalc)
if options['emissivity_out']:
tmp_avg_lse = options['emissivity_out']
if delta_emissivity_map:
tmp_delta_lse = delta_emissivity_map
if not delta_emissivity_map:
determine_delta_emissivity(tmp_delta_lse, landcover_map,
split_window_lst.delta_lse_mapcalc)
if options['delta_emissivity_out']:
tmp_delta_lse = options['delta_emissivity_out']
#
# 4. Modified Split-Window Variance-Covariance Matrix > Column Water Vapor
#
if info:
msg = '\n|i Spatial window of size {n} for Column Water Vapor estimation: '
msg = msg.format(n=cwv_window_size)
g.message(msg)
cwv = Column_Water_Vapor(cwv_window_size, t10, t11)
citation_cwv = cwv.citation
estimate_cwv_big_expression(tmp_cwv, t10, t11, cwv._big_cwv_expression())
if cwv_output:
tmp_cwv = cwv_output
#
# 5. Estimate Land Surface Temperature
#
if info and emissivity_class == 'Random':
msg = '\n|* Will pick a random emissivity class!'
grass.verbose(msg)
estimate_lst(lst_output, t10, t11, tmp_avg_lse, tmp_delta_lse, tmp_cwv,
split_window_lst.sw_lst_mapcalc)
#
# Post-production actions
#
# remove MASK
r.mask(flags='r', verbose=True)
# time-stamping
if timestamping:
add_timestamp(mtl_file, lst_output)
if cwv_output:
add_timestamp(mtl_file, cwv_output)
# Apply color table
if celsius:
run('r.colors', map=lst_output, color='celsius')
else:
# color table for kelvin
run('r.colors', map=lst_output, color='kelvin')
# ToDo: helper function for r.support
# strings for metadata
history_lst = '\n' + citation_lst
history_lst += '\n\n' + citation_cwv
history_lst += '\n\nSplit-Window model: '
history_lst += split_window_lst._equation # :wsw_lst_mapcalc
description_lst = (
'Land Surface Temperature derived from a split-window algorithm. ')
if celsius:
title_lst = 'Land Surface Temperature (C)'
units_lst = 'Celsius'
else:
title_lst = 'Land Surface Temperature (K)'
units_lst = 'Kelvin'
landsat8_metadata = Landsat8_MTL(mtl_file)
source1_lst = landsat8_metadata.scene_id
source2_lst = landsat8_metadata.origin
# history entry
run("r.support",
map=lst_output,
title=title_lst,
units=units_lst,
description=description_lst,
source1=source1_lst,
source2=source2_lst,
history=history_lst)
# (re)name the LST product
#run("g.rename", rast=(tmp_lst, lst_output))
# restore region
if scene_extent:
grass.del_temp_region() # restoring previous region settings
g.message("|! Original Region restored")
# print citation
if info:
print '\nSource: ' + citation_lst