def convert_netcdf2tif(filename, n):
"""Convert netcdf to tif aggregating first n bands
Args
* filename: NetCDF multiband raster with extension .nc
* n: Positive integer determining how many bands to use
Returns
* Raster file in tif format. Each pixel will be the maximum
of that pixel in the first n bands in the input file.
"""
if not isinstance(filename, basestring):
msg = "Argument filename should be a string. I got %s" % filename
raise RuntimeError(msg)
basename, ext = os.path.splitext(filename)
msg = "Expected NetCDF file with extension .nc - " "Instead I got %s" % filename
if ext != ".nc":
raise RuntimeError(msg)
try:
n = int(n)
except:
msg = "Argument N should be an integer. I got %s" % n
raise RuntimeError(msg)
print filename, n
# Read NetCDF file
fid = NetCDFFile(filename)
dimensions = fid.dimensions.keys()
variables = fid.variables.keys()
title = getattr(fid, "title")
institution = getattr(fid, "institution")
source = getattr(fid, "source")
history = getattr(fid, "history")
references = getattr(fid, "references")
conventions = getattr(fid, "Conventions")
coordinate_system = getattr(fid, "coordinate_system")
print "Read from %s" % filename
print "Title: %s" % title
print "Institution: %s" % institution
print "Source: %s" % source
print "History: %s" % history
print "References: %s" % references
print "Conventions: %s" % conventions
print "Coordinate system: %s" % coordinate_system
print "Dimensions: %s" % dimensions
print "Variables: %s" % variables
# Get data
x = fid.variables["x"][:]
y = fid.variables["y"][:]
t = fid.variables["time"][:]
inundation_depth = fid.variables["Inundation_Depth"][:]
T = inundation_depth.shape[0] # Number of time steps
M = inundation_depth.shape[1] # Steps in the y direction
N = inundation_depth.shape[2] # Steps in the x direction
# Compute the max of the first n timesteps
A = numpy.zeros((M, N), dtype="float")
for i in range(n):
B = inundation_depth[i, :, :]
A = numpy.maximum(A, B)
geotransform = raster_geometry2geotransform(x, y)
print "Geotransform", geotransform
# Write result to tif file
# NOTE: This assumes a default projection (WGS 84, geographic)
date = os.path.split(basename)[-1].split("_")[0]
print "date", date
R = Raster(
data=A,
geotransform=geotransform,
keywords={
"category": "hazard",
"subcategory": "flood",
"unit": "m",
"title": ("%d hour flood forecast grid " "in Jakarta at %s" % (n, date)),
},
)
tif_filename = "%s_%d_hours.tif" % (basename, n)
R.write_to_file(tif_filename)
print "Success: %d hour forecast written to %s" % (n, R.filename)
return tif_filename
def convert_netcdf2tif(filename, n, verbose=False, output_dir=None):
"""Convert netcdf to tif aggregating first n bands
Args
* filename: NetCDF multiband raster with extension .nc
* n: Positive integer determining how many bands to use
* verbose: Boolean flag controlling whether diagnostics
will be printed to screen. This is useful when run from
a command line script.
Returns
* Raster file in tif format. Each pixel will be the maximum
of that pixel in the first n bands in the input file.
"""
if not isinstance(filename, basestring):
msg = "Argument filename should be a string. I got %s" % filename
raise RuntimeError(msg)
basename, ext = os.path.splitext(filename)
msg = "Expected NetCDF file with extension .nc - " "Instead I got %s" % filename
if ext != ".nc":
raise RuntimeError(msg)
try:
n = int(n)
except:
msg = "Argument N should be an integer. I got %s" % n
raise RuntimeError(msg)
if verbose:
print filename, n, "hours"
# Read NetCDF file
fid = NetCDFFile(filename)
dimensions = fid.dimensions.keys()
variables = fid.variables.keys()
title = getattr(fid, "title")
institution = getattr(fid, "institution")
source = getattr(fid, "source")
history = getattr(fid, "history")
references = getattr(fid, "references")
conventions = getattr(fid, "Conventions")
coordinate_system = getattr(fid, "coordinate_system")
if verbose:
print "Read from %s" % filename
print "Title: %s" % title
print "Institution: %s" % institution
print "Source: %s" % source
print "History: %s" % history
print "References: %s" % references
print "Conventions: %s" % conventions
print "Coordinate system: %s" % coordinate_system
print "Dimensions: %s" % dimensions
print "Variables: %s" % variables
# Get data
x = fid.variables["x"][:]
y = fid.variables["y"][:]
# t = fid.variables['time'][:]
inundation_depth = fid.variables["Inundation_Depth"][:]
T = inundation_depth.shape[0] # Number of time steps
M = inundation_depth.shape[1] # Steps in the y direction
N = inundation_depth.shape[2] # Steps in the x direction
if n > T:
msg = "You requested %i hours prediction, but the " "forecast only contains %i hours" % (n, T)
raise RuntimeError(msg)
# Compute the max of the first n timesteps
A = numpy.zeros((M, N), dtype="float")
for i in range(n):
B = inundation_depth[i, :, :]
A = numpy.maximum(A, B)
# Calculate overall maximal value
total_max = numpy.max(A[:])
# print i, numpy.max(B[:]), total_max
geotransform = raster_geometry2geotransform(x, y)
# Write result to tif file
# NOTE: This assumes a default projection (WGS 84, geographic)
date = os.path.split(basename)[-1].split("_")[0]
if verbose:
print "Overall max depth over %i hours: %.2f m" % (n, total_max)
print "Geotransform", geotransform
print "date", date
# Flip array upside down as it comes with rows ordered from south to north
A = numpy.flipud(A)
R = Raster(
data=A,
geotransform=geotransform,
keywords={
"category": "hazard",
"subcategory": "flood",
"unit": "m",
"title": ("%d hour flood forecast grid " "in Jakarta at %s" % (n, date)),
},
)
tif_filename = "%s_%d_hours_max_%.2f.tif" % (basename, n, total_max)
if output_dir is not None:
subdir_name = os.path.splitext(os.path.basename(tif_filename))[0]
shapefile_dir = os.path.join(output_dir, subdir_name)
if not os.path.isdir(shapefile_dir):
os.mkdir(shapefile_dir)
tif_filename = os.path.join(shapefile_dir, subdir_name + ".tif")
R.write_to_file(tif_filename)
if verbose:
print "Success: %d hour forecast written to %s" % (n, R.filename)
return tif_filename
def convert_netcdf2tif(filename, n):
"""Convert netcdf to tif aggregating first n bands
Args
* filename: NetCDF multiband raster with extension .nc
* n: Positive integer determining how many bands to use
Returns
* Raster file in tif format. Each pixel will be the maximum
of that pixel in the first n bands in the input file.
"""
if not isinstance(filename, basestring):
msg = 'Argument filename should be a string. I got %s' % filename
raise RuntimeError(msg)
basename, ext = os.path.splitext(filename)
msg = ('Expected NetCDF file with extension .nc - '
'Instead I got %s' % filename)
if ext != '.nc':
raise RuntimeError(msg)
try:
n = int(n)
except:
msg = 'Argument N should be an integer. I got %s' % n
raise RuntimeError(msg)
print filename, n
# Read NetCDF file
fid = NetCDFFile(filename)
dimensions = fid.dimensions.keys()
variables = fid.variables.keys()
title = getattr(fid, 'title')
institution = getattr(fid, 'institution')
source = getattr(fid, 'source')
history = getattr(fid, 'history')
references = getattr(fid, 'references')
conventions = getattr(fid, 'Conventions')
coordinate_system = getattr(fid, 'coordinate_system')
print 'Read from %s' % filename
print 'Title: %s' % title
print 'Institution: %s' % institution
print 'Source: %s' % source
print 'History: %s' % history
print 'References: %s' % references
print 'Conventions: %s' % conventions
print 'Coordinate system: %s' % coordinate_system
print 'Dimensions: %s' % dimensions
print 'Variables: %s' % variables
# Get data
x = fid.variables['x'][:]
y = fid.variables['y'][:]
t = fid.variables['time'][:]
inundation_depth = fid.variables['Inundation_Depth'][:]
T = inundation_depth.shape[0] # Number of time steps
M = inundation_depth.shape[1] # Steps in the y direction
N = inundation_depth.shape[2] # Steps in the x direction
# Compute the max of the first n timesteps
A = numpy.zeros((M, N), dtype='float')
for i in range(n):
B = inundation_depth[i, :, :]
A = numpy.maximum(A, B)
geotransform = raster_geometry2geotransform(x, y)
print 'Geotransform', geotransform
# Write result to tif file
# NOTE: This assumes a default projection (WGS 84, geographic)
date = os.path.split(basename)[-1].split('_')[0]
print 'date', date
R = Raster(data=A,
geotransform=geotransform,
keywords={'category': 'hazard',
'subcategory': 'flood',
'unit': 'm',
'title': ('%d hour flood forecast '
'in Jakarta at %s' % (n, date))})
tif_filename = '%s_%d_hours.tif' % (basename, n)
R.write_to_file(tif_filename)
print 'Success: %d hour forecast written to %s' % (n, R.filename)
return tif_filename
def convert_netcdf2tif(filename, n, verbose=False, output_dir=None):
"""Convert netcdf to tif aggregating first n bands
Args
* filename: NetCDF multiband raster with extension .nc
* n: Positive integer determining how many bands to use
* verbose: Boolean flag controlling whether diagnostics
will be printed to screen. This is useful when run from
a command line script.
Returns
* Raster file in tif format. Each pixel will be the maximum
of that pixel in the first n bands in the input file.
"""
if not isinstance(filename, basestring):
msg = 'Argument filename should be a string. I got %s' % filename
raise RuntimeError(msg)
basename, ext = os.path.splitext(filename)
msg = ('Expected NetCDF file with extension .nc - '
'Instead I got %s' % filename)
if ext != '.nc':
raise RuntimeError(msg)
try:
n = int(n)
except:
msg = 'Argument N should be an integer. I got %s' % n
raise RuntimeError(msg)
if verbose:
print filename, n, 'hours'
# Read NetCDF file
fid = NetCDFFile(filename)
dimensions = fid.dimensions.keys()
variables = fid.variables.keys()
title = getattr(fid, 'title')
institution = getattr(fid, 'institution')
source = getattr(fid, 'source')
history = getattr(fid, 'history')
references = getattr(fid, 'references')
conventions = getattr(fid, 'Conventions')
coordinate_system = getattr(fid, 'coordinate_system')
if verbose:
print 'Read from %s' % filename
print 'Title: %s' % title
print 'Institution: %s' % institution
print 'Source: %s' % source
print 'History: %s' % history
print 'References: %s' % references
print 'Conventions: %s' % conventions
print 'Coordinate system: %s' % coordinate_system
print 'Dimensions: %s' % dimensions
print 'Variables: %s' % variables
# Get data
x = fid.variables['x'][:]
y = fid.variables['y'][:]
# t = fid.variables['time'][:]
inundation_depth = fid.variables['Inundation_Depth'][:]
T = inundation_depth.shape[0] # Number of time steps
M = inundation_depth.shape[1] # Steps in the y direction
N = inundation_depth.shape[2] # Steps in the x direction
if n > T:
msg = ('You requested %i hours prediction, but the '
'forecast only contains %i hours' % (n, T))
raise RuntimeError(msg)
# Compute the max of the first n timesteps
A = numpy.zeros((M, N), dtype='float')
for i in range(n):
B = inundation_depth[i, :, :]
A = numpy.maximum(A, B)
# Calculate overall maximal value
total_max = numpy.max(A[:])
#print i, numpy.max(B[:]), total_max
geotransform = raster_geometry2geotransform(x, y)
# Write result to tif file
# NOTE: This assumes a default projection (WGS 84, geographic)
date = os.path.split(basename)[-1].split('_')[0]
if verbose:
print 'Overall max depth over %i hours: %.2f m' % (n, total_max)
print 'Geotransform', geotransform
print 'date', date
# Flip array upside down as it comes with rows ordered from south to north
A = numpy.flipud(A)
R = Raster(data=A,
geotransform=geotransform,
keywords={
'category':
'hazard',
'subcategory':
'flood',
'unit':
'm',
'title': ('%d hour flood forecast grid '
'in Jakarta at %s' % (n, date))
})
tif_filename = '%s_%d_hours_max_%.2f.tif' % (basename, n, total_max)
if output_dir is not None:
subdir_name = os.path.splitext(os.path.basename(tif_filename))[0]
shapefile_dir = os.path.join(output_dir, subdir_name)
if not os.path.isdir(shapefile_dir):
os.mkdir(shapefile_dir)
tif_filename = os.path.join(shapefile_dir, subdir_name + '.tif')
R.write_to_file(tif_filename)
if verbose:
print 'Success: %d hour forecast written to %s' % (n, R.filename)
return tif_filename
def convert_netcdf2tif(filename, n):
"""Convert netcdf to tif aggregating first n bands
Args
* filename: NetCDF multiband raster with extension .nc
* n: Positive integer determining how many bands to use
Returns
* Raster file in tif format. Each pixel will be the maximum
of that pixel in the first n bands in the input file.
"""
if not isinstance(filename, basestring):
msg = 'Argument filename should be a string. I got %s' % filename
raise RuntimeError(msg)
basename, ext = os.path.splitext(filename)
msg = ('Expected NetCDF file with extension .nc - '
'Instead I got %s' % filename)
if ext != '.nc':
raise RuntimeError(msg)
try:
n = int(n)
except:
msg = 'Argument N should be an integer. I got %s' % n
raise RuntimeError(msg)
print filename, n
# Read NetCDF file
fid = NetCDFFile(filename)
dimensions = fid.dimensions.keys()
variables = fid.variables.keys()
title = getattr(fid, 'title')
institution = getattr(fid, 'institution')
source = getattr(fid, 'source')
history = getattr(fid, 'history')
references = getattr(fid, 'references')
conventions = getattr(fid, 'Conventions')
coordinate_system = getattr(fid, 'coordinate_system')
print 'Read from %s' % filename
print 'Title: %s' % title
print 'Institution: %s' % institution
print 'Source: %s' % source
print 'History: %s' % history
print 'References: %s' % references
print 'Conventions: %s' % conventions
print 'Coordinate system: %s' % coordinate_system
print 'Dimensions: %s' % dimensions
print 'Variables: %s' % variables
# Get data
x = fid.variables['x'][:]
y = fid.variables['y'][:]
t = fid.variables['time'][:]
inundation_depth = fid.variables['Inundation_Depth'][:]
T = inundation_depth.shape[0] # Number of time steps
M = inundation_depth.shape[1] # Steps in the y direction
N = inundation_depth.shape[2] # Steps in the x direction
# Compute the max of the first n timesteps
A = numpy.zeros((M, N), dtype='float')
for i in range(n):
B = inundation_depth[i, :, :]
A = numpy.maximum(A, B)
geotransform = raster_geometry2geotransform(x, y)
print 'Geotransform', geotransform
# Write result to tif file
# NOTE: This assumes a default projection (WGS 84, geographic)
date = os.path.split(basename)[-1].split('_')[0]
print 'date', date
R = Raster(data=A,
geotransform=geotransform,
keywords={
'category':
'hazard',
'subcategory':
'flood',
'unit':
'm',
'title': ('%d hour flood forecast grid '
'in Jakarta at %s' % (n, date))
})
tif_filename = '%s_%d_hours.tif' % (basename, n)
R.write_to_file(tif_filename)
print 'Success: %d hour forecast written to %s' % (n, R.filename)
return tif_filename
def convert_netcdf2tif(filename, n):
"""Convert netcdf to tif aggregating firsts n bands
"""
if not isinstance(filename, basestring):
msg = 'Argument filename should be a string. I got %s' % filename
raise RuntimeError(msg)
basename, ext = os.path.splitext(filename)
msg = ('Expected NetCDF file with extension .nc - '
'Instead I got %s' % filename)
if ext != '.nc':
raise RuntimeError(msg)
try:
n = int(n)
except:
msg = 'Argument N should be an integer. I got %s' % n
raise RuntimeError(msg)
print filename, n
# Read NetCDF file
fid = NetCDFFile(filename)
dimensions = fid.dimensions.keys()
variables = fid.variables.keys()
title = getattr(fid, 'title')
institution = getattr(fid, 'institution')
source = getattr(fid, 'source')
history = getattr(fid, 'history')
references = getattr(fid, 'references')
conventions = getattr(fid, 'Conventions')
coordinate_system = getattr(fid, 'coordinate_system')
print 'Read from %s' % filename
print 'Title: %s' % title
print 'Institution: %s' % institution
print 'Source: %s' % source
print 'History: %s' % history
print 'References: %s' % references
print 'Conventions: %s' % conventions
print 'Coordinate system: %s' % coordinate_system
print 'Dimensions: %s' % dimensions
print 'Variables: %s' % variables
# Get data
x = fid.variables['x'][:]
y = fid.variables['y'][:]
t = fid.variables['time'][:]
inundation_depth = fid.variables['Inundation_Depth'][:]
T = inundation_depth.shape[0] # Number of time steps
M = inundation_depth.shape[1] # Steps in the y direction
N = inundation_depth.shape[2] # Steps in the x direction
# Compute the max of the first n timesteps
A = numpy.zeros((M, N), dtype='float')
for i in range(n):
B = inundation_depth[i, :, :]
A = numpy.maximum(A, B)
geotransform = raster_geometry2geotransform(x, y)
print 'Geotransform', geotransform
# Write result to tif file
R = Raster(data=A,
projection="""PROJCS["DGN95 / Indonesia TM-3 zone 48.2",
GEOGCS["DGN95",
DATUM["Datum_Geodesi_Nasional_1995",
SPHEROID["WGS 84",6378137,298.257223563,
AUTHORITY["EPSG","7030"]],
TOWGS84[0,0,0,0,0,0,0],
AUTHORITY["EPSG","6755"]],
PRIMEM["Greenwich",0,
AUTHORITY["EPSG","8901"]],
UNIT["degree",0.01745329251994328,
AUTHORITY["EPSG","9122"]],
AUTHORITY["EPSG","4755"]],
UNIT["metre",1,
AUTHORITY["EPSG","9001"]],
PROJECTION["Transverse_Mercator"],
PARAMETER["latitude_of_origin",0],
PARAMETER["central_meridian",106.5],
PARAMETER["scale_factor",0.9999],
PARAMETER["false_easting",200000],
PARAMETER["false_northing",1500000],
AUTHORITY["EPSG","23834"],
AXIS["X",EAST],
AXIS["Y",NORTH]]""",
geotransform=geotransform,
keywords={'category': 'hazard',
'subcategory': 'flood',
'unit': 'm',
'title': ('Hypothetical %d hour flood forecast '
'in Jakarta' % n)})
R.write_to_file('%s_%d_hours.tif' % (basename, n))
print 'Success: %d hour forecast written to %s' % (n, R.filename)