def tirs_to_at_satellite_temperature(
tirs_1x,
mtl_file,
brightness_temperature_prefix=None,
null=False,
quiet=True):
"""
Helper function to convert TIRS bands 10 or 11 in to at-satellite
temperatures.
This function uses the pre-defined functions:
- extract_number_from_string()
- digital_numbers_to_radiance()
- radiance_to_brightness_temperature()
The inputs are:
- a name for the input tirs band (10 or 11)
- a Landsat8 MTL file
The output is a temporary at-Satellite Temperature map.
"""
# which band number and MTL file
band_number = extract_number_from_string(tirs_1x)
tmp_radiance = tmp_map_name('radiance') + '.' + band_number
tmp_brightness_temperature = tmp_map_name('brightness_temperature') + '.' + \
band_number
landsat8 = Landsat8_MTL(mtl_file)
# rescale DNs to spectral radiance
radiance_expression = landsat8.toar_radiance(band_number)
digital_numbers_to_radiance(
tmp_radiance,
tirs_1x,
radiance_expression,
null,
quiet,
)
# convert spectral radiance to at-satellite temperature
temperature_expression = landsat8.radiance_to_temperature(band_number)
radiance_to_brightness_temperature(
tmp_brightness_temperature,
tmp_radiance,
temperature_expression,
quiet,
)
# save Brightness Temperature map?
if brightness_temperature_prefix:
bt_output = brightness_temperature_prefix + band_number
run('g.rename', raster=(tmp_brightness_temperature, bt_output))
tmp_brightness_temperature = bt_output
return tmp_brightness_temperature
def add_timestamp(mtl_filename, outname):
"""
Retrieve metadata from MTL file.
"""
import datetime
metadata = Landsat8_MTL(mtl_filename)
# required format is: day=integer month=string year=integer time=hh:mm:ss.dd
acquisition_date = str(metadata.date_acquired) ### FixMe ###
acquisition_date = datetime.datetime.strptime(
acquisition_date, '%Y-%m-%d').strftime('%d %b %Y')
acquisition_time = str(metadata.scene_center_time)[0:8]
date_time_string = acquisition_date + ' ' + acquisition_time
#msg = "Date and time of acquisition: " + date_time_string
#grass.verbose(msg)
run('r.timestamp', map=outname, date=date_time_string)
def test(mtlfile):
"""
Test the class.
"""
print("! No file defined, testing with default MTl file!")
mtl = Landsat8_MTL(MTLFILE)
print()
print("| Test the object's __str__ method:", mtl)
print("| Test method _get_mtl_lines:\n ", mtl._get_mtl_lines())
print()
print("| Basic metadata:")
print(" > id (original field name is 'LANDSAT_SCENE_ID'):", mtl.scene_id)
print(" > WRS path:", mtl.wrs_path)
print(" > WRS row:", mtl.wrs_row)
print(" > Acquisition date:", mtl.date_acquired)
print(" > Scene's center time:", mtl.scene_center_time)
print(" > Upper left corner:", mtl.corner_ul)
print(" > Lower right corner:", mtl.corner_lr)
print(" > Upper left (projected):", mtl.corner_ul_projection)
print(" > Lower right (projected):", mtl.corner_lr_projection)
print(" > Cloud cover:", mtl.cloud_cover)
def main():
# Temporary filenames
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')
# 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?
if options['prefix_bt']:
brightness_temperature_prefix = options['prefix_bt']
else:
brightness_temperature_prefix = None
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?
emissivity_output = options['emissivity_out']
delta_emissivity_output = options['delta_emissivity_out']
landcover_map = options['landcover']
landcover_class = options['landcover_class']
# flags
info = flags['i']
scene_extent = flags['e']
timestamping = flags['t']
null = flags['n']
rounding = flags['r']
celsius = flags['c']
#
# 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 not 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:
t10 = tirs_to_at_satellite_temperature(
b10,
mtl_file,
brightness_temperature_prefix,
null,
quiet=info,
)
# likewise for b11 -> t11
if b11:
t11 = tirs_to_at_satellite_temperature(
b11,
mtl_file,
brightness_temperature_prefix,
null,
quiet=info,
)
#
# Initialise a SplitWindowLST object
#
split_window_lst = SplitWindowLST(landcover_class)
citation_lst = split_window_lst.citation
#
# 3. Land Surface Emissivities
#
# use given fixed class?
if landcover_class:
if split_window_lst.landcover_class is False:
# replace with meaningful error
grass.warning('Unknown land cover class string! Note, this string '
'input option is case sensitive.')
if landcover_class == 'Random':
msg = "\n|! Random emissivity class selected > " + \
split_window_lst.landcover_class + ' '
if landcover_class == 'Barren_Land':
msg = "\n|! For barren land, the last quadratic term of the Split-Window algorithm will be set to 0" + \
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,
emissivity_output,
landcover_map,
split_window_lst.average_lse_mapcalc,
quiet=info,
)
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,
delta_emissivity_output,
landcover_map,
split_window_lst.delta_lse_mapcalc,
quiet=info,
)
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,
cwv_output,
t10,
t11,
cwv._big_cwv_expression(),
)
if cwv_output:
tmp_cwv = cwv_output
#
# 5. Estimate Land Surface Temperature
#
if info and landcover_class == 'Random':
msg = '\n|* Will pick a random emissivity class!'
grass.verbose(msg)
estimate_lst(
lst_output,
t10,
t11,
landcover_map,
landcover_class,
tmp_avg_lse,
tmp_delta_lse,
tmp_cwv,
split_window_lst.sw_lst_mapcalc,
rounding,
celsius,
quiet=info,
)
#
# 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,
)
# restore region
if scene_extent:
grass.del_temp_region() # restoring previous region settings
g.message("|! Original Region restored")
# print citation
if info:
g.message('\nSource: ' + citation_lst)
def main():
# Temporary filenames
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')
# 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?
if options["prefix_bt"]:
brightness_temperature_prefix = options["prefix_bt"]
else:
brightness_temperature_prefix = None
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?
emissivity_output = options["emissivity_out"]
delta_emissivity_output = options["delta_emissivity_out"]
landcover_map = options["landcover"]
landcover_class = options["landcover_class"]
# flags
info = flags["i"]
scene_extent = flags["e"]
timestamping = flags["t"]
null = flags["n"]
rounding = flags["r"]
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 not 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,
brightness_temperature_prefix,
null,
quiet=info,
)
# likewise for b11 -> t11
if b11:
# convert DNs to at-satellite temperatures
t11 = tirs_to_at_satellite_temperature(
b11,
mtl_file,
brightness_temperature_prefix,
null,
quiet=info,
)
#
# Initialise a SplitWindowLST object
#
split_window_lst = SplitWindowLST(landcover_class)
citation_lst = split_window_lst.citation
#
# 3. Land Surface Emissivities
#
# use given fixed class?
if landcover_class:
if split_window_lst.landcover_class is False:
# replace with meaningful error
grass.warning("Unknown land cover class string! Note, this string "
"input option is case sensitive.")
if landcover_class == "Random":
msg = ("\n|! Random emissivity class selected > " +
split_window_lst.landcover_class + " ")
if landcover_class == "Barren_Land":
msg = (
"\n|! For barren land, the last quadratic term of the Split-Window algorithm will be set to 0"
+ 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,
emissivity_output,
landcover_map,
split_window_lst.average_lse_mapcalc,
quiet=info,
)
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,
delta_emissivity_output,
landcover_map,
split_window_lst.delta_lse_mapcalc,
quiet=info,
)
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,
cwv_output,
t10,
t11,
cwv._big_cwv_expression(),
)
if cwv_output:
tmp_cwv = cwv_output
#
# 5. Estimate Land Surface Temperature
#
if info and landcover_class == "Random":
msg = "\n|* Will pick a random emissivity class!"
grass.verbose(msg)
estimate_lst(
lst_output,
t10,
t11,
landcover_map,
landcover_class,
tmp_avg_lse,
tmp_delta_lse,
tmp_cwv,
split_window_lst.sw_lst_mapcalc,
rounding,
celsius,
quiet=info,
)
#
# 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:
g.message("\nSource: " + citation_lst)
def main():
# Temporary filenames
tmp_avg_lse = tmp_map_name('avg_lse')
tmp_delta_lse = tmp_map_name('delta_lse')
#tmp_lst = tmp_map_name('lst')
# 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?
if options['prefix_bt']:
brightness_temperature_prefix = options['prefix_bt']
else:
brightness_temperature_prefix = None
if options['cwv']:
tmp_cwv = options['cwv']
else:
tmp_cwv = tmp_map_name('cwv')
cwv_window_size = int(options['window'])
assertion_for_cwv_window_size_msg = MSG_ASSERTION_WINDOW_SIZE
assert cwv_window_size >= 7, assertion_for_cwv_window_size_msg
cwv_output = options['cwv_out']
# optional maps
average_emissivity_map = options['emissivity']
delta_emissivity_map = options['delta_emissivity']
# output for in-between maps?
emissivity_output = options['emissivity_out']
delta_emissivity_output = options['delta_emissivity_out']
landcover_map = options['landcover']
landcover_class = options['landcover_class']
# flags
info = flags['i']
null = flags['n']
scene_extent = flags['e']
median = flags['m']
accuracy = flags['a']
rounding = flags['r']
celsius = flags['c']
timestamping = flags['t']
#
# Pre-production actions
#
if scene_extent:
grass.use_temp_region() # safely modify the region, restore at end
msg = WARNING_REGION_MATCHING
# TODO: Check if extent-B10 == extent-B11? #
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)
# ---------------------------------------- #
grass.warning(_(msg))
#
# 1. Mask clouds
#
if cloud_map:
msg = f'\n|i Using user defined \'{cloud_map}\' as a MASK'
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:
t10 = tirs_to_at_satellite_temperature(
b10,
mtl_file,
brightness_temperature_prefix,
null,
info=info,
)
# likewise for b11 -> t11
if b11:
t11 = tirs_to_at_satellite_temperature(
b11,
mtl_file,
brightness_temperature_prefix,
null,
info=info,
)
#
# 3. Land Surface Emissivities
#
split_window_lst = SplitWindowLST(landcover_class)
if landcover_class:
if split_window_lst.landcover_class is False:
# replace with meaningful error
grass.warning(MSG_UNKNOWN_LANDCOVER_CLASS)
if landcover_class == 'Random':
msg = MSG_RANDOM_EMISSIVITY_CLASS + \
split_window_lst.landcover_class + ' '
if landcover_class == 'Barren_Land':
msg = MSG_BARREN_LAND + \
split_window_lst.landcover_class + ' '
else:
msg = MSG_SINGLE_CLASS_AVERAGE_EMISSIVITY + f'{eclass} '
if info:
msg += MSG_AVERAGE_EMISSIVITIES
msg += str(split_window_lst.emissivity_t10) + ', ' + \
str(split_window_lst.emissivity_t11)
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,
emissivity_output,
landcover_map,
split_window_lst.average_lse_mapcalc,
info=info,
)
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,
delta_emissivity_output,
landcover_map,
split_window_lst.delta_lse_mapcalc,
info=info,
)
if options['delta_emissivity_out']:
tmp_delta_lse = options['delta_emissivity_out']
#
# 4. Estimate Column Water Vapor
#
if not options['cwv']:
estimate_cwv(
temporary_map=tmp_cwv,
cwv_map=cwv_output,
t10=t10,
t11=t11,
window_size=cwv_window_size,
median=median,
info=info,
)
else:
msg = f'\n|! User defined map \'{tmp_cwv}\' for atmospheric column water vapor'
g.message(msg)
if cwv_output:
tmp_cwv = cwv_output
#
# 5. Estimate Land Surface Temperature
#
if info and landcover_class == 'Random':
msg = MSG_PICK_RANDOM_CLASS
grass.verbose(msg)
estimate_lst(
outname=lst_output,
t10=t10,
t11=t11,
landcover_map=landcover_map,
landcover_class=landcover_class,
avg_lse_map=tmp_avg_lse,
delta_lse_map=tmp_delta_lse,
cwv_map=tmp_cwv,
lst_expression=split_window_lst.sw_lst_mapcalc,
rounding=rounding,
celsius=celsius,
info=info,
)
#
# Post-production actions
#
# remove MASK
r.mask(flags='r', verbose=True)
if timestamping:
add_timestamp(mtl_file, lst_output)
if cwv_output:
add_timestamp(mtl_file, cwv_output)
if celsius:
run('r.colors', map=lst_output, color='celsius')
else:
run('r.colors', map=lst_output, color='kelvin')
# metadata
history_lst = '\n' + CITATION_SPLIT_WINDOW
history_lst += '\n\n' + CITATION_COLUMN_WATER_VAPOR
history_lst += '\n\nSplit-Window model: '
history_lst += split_window_lst._equation # :wsw_lst_mapcalc
description_lst = DESCRIPTION_LST
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
run(
"r.support",
map=lst_output,
title=title_lst,
units=units_lst,
description=description_lst,
source1=source1_lst,
source2=source2_lst,
history=history_lst,
)
if scene_extent:
grass.del_temp_region() # restoring previous region
grass.warning(WARNING_REGION_RESTORING)
if info:
g.message('\nSource: ' + CITATION_SPLIT_WINDOW)
