def test_read_tags(*args):
if len(args) == 0:
tiff_fn = "test_ninjo.tif"
else:
tiff_fn = args[0]
if not os.path.exists(tiff_fn):
LOG.error("TIFF input file %s doesn't exists" % (tiff_fn, ))
return -1
a = TIFF.open(tiff_fn, "r")
image = a.read_image()
print "Image data has shape %r" % (image.shape, )
print "Tag %s: %s" % ("ModelTiePoint", a.GetField("ModelTiePoint"))
def test_read_tags(*args):
if len(args) == 0:
tiff_fn = "test_ninjo.tif"
else:
tiff_fn = args[0]
if not os.path.exists(tiff_fn):
LOG.error("TIFF input file %s doesn't exists" % (tiff_fn,))
return -1
a = TIFF.open(tiff_fn, "r")
image = a.read_image()
print "Image data has shape %r" % (image.shape,)
print "Tag %s: %s" % ("ModelTiePoint", a.GetField("ModelTiePoint"))
def test_write(*args):
"""Recreate a simple NinJo compatible tiff file from an original
GOESE example.
Mainly a test of the tags.
"""
if len(args) == 0:
tiff_fn = "test_ninjo.tif"
else:
tiff_fn = args[0]
# Represents original high resolution data array
#data_array = numpy.zeros((2500,2500), dtype=numpy.uint8)
data_array = numpy.tile(range(500), (2500, 5)).astype(numpy.uint8)
print data_array.shape
# Open file
tiff_file = TIFF.open(tiff_fn, "w")
### Write first directory and tags ###
tiff_file.SetDirectory(0)
tiff_file.SetField("ImageWidth", 2500)
tiff_file.SetField("ImageLength", 2500)
tiff_file.SetField("BITspersample", 8)
tiff_file.SetField("compression", libtiff.COMPRESSION_LZW)
#FIXME: Sample file used colormap
#tiff_file.SetField("PHOTOMETRIC", libtiff.PHOTOMETRIC_MINISBLACK)
tiff_file.SetField("PHOTOMETRIC", libtiff.PHOTOMETRIC_PALETTE)
tiff_file.SetField("ORIENTATION", libtiff.ORIENTATION_TOPLEFT)
tiff_file.SetField("SamplesPerPixel", 1)
tiff_file.SetField("SMinSampleValue", 0)
tiff_file.SetField("SMaxsampleValue", 255)
tiff_file.SetField("Planarconfig", libtiff.PLANARCONFIG_CONTIG)
#tiff_file.SetField("ColorMap", [ [ x*256 for x in range(256) ],[0]*256,[0]*256 ])
tiff_file.SetField("ColorMap", [[x * 256 for x in range(256)]] * 3)
tiff_file.SetField("TILEWIDTH", 512)
tiff_file.SetField("TILELENGTH", 512)
tiff_file.SetField("sampleformat", libtiff.SAMPLEFORMAT_UINT)
### NINJO SPECIFIC ###
tiff_file.SetField("ModelPixelScale", [0.071957, 0.071957])
tiff_file.SetField("ModelTiePoint",
[0.0, 0.0, 0.0, -164.874313, 89.874321, 0.0])
tiff_file.SetField("NinjoName", "NINJO")
tiff_file.SetField("SatelliteNameID", 7300014)
tiff_file.SetField("DateID", 1337169600)
tiff_file.SetField("CreationDateID", 1337171130)
tiff_file.SetField("ChannelID", 900015)
tiff_file.SetField("HeaderVersion", 2)
tiff_file.SetField("FileName",
"GOESE_AMERIKA_IR107_nq075W8km_1205161200.tif")
tiff_file.SetField("DataType", "GORN")
tiff_file.SetField("SatelliteNumber", "\x00") # Hardcoded to 0
tiff_file.SetField("ColorDepth", 16) #Um 8? hardcoded to 8, but 16?
tiff_file.SetField("DataSource", "EUMCAST")
tiff_file.SetField("XMinimum", 1)
tiff_file.SetField("XMaximum", 2500)
tiff_file.SetField("YMinimum", 1)
tiff_file.SetField("YMaximum", 2500)
tiff_file.SetField("Projection", "PLAT")
tiff_file.SetField("MeridianWest", 0.0)
tiff_file.SetField("MeridianEast", 0.0)
tiff_file.SetField("EarthRadiusLarge", 6370000.0)
tiff_file.SetField("EarthRadiusSmall", 6370000.0)
tiff_file.SetField("GeodeticDate", "\x00") # ---?
tiff_file.SetField("ReferenceLatitude1", 0.0)
tiff_file.SetField("ReferenceLatitude2", 0.0)
tiff_file.SetField("CentralMeridian", -75.000)
tiff_file.SetField("PhysicValue", "T")
tiff_file.SetField("PhysicUnit", "CELSIUS")
tiff_file.SetField("MinGrayValue", 0)
tiff_file.SetField("MaxGrayValue", 255)
tiff_file.SetField("Gradient", -0.5)
tiff_file.SetField("AxisIntercept", 40.0)
tiff_file.SetField("Altitude", 42164.0)
tiff_file.SetField("IsCalibrated", 1)
tiff_file.SetField("IsNormalized", 0)
tiff_file.write_tiles(data_array)
tiff_file.WriteDirectory()
### Directory 2 ###
#tiff_file.SetDirectory(1)
#tiff_file.SetField("ModelTiePoint", write_list([0.0, 0.0, 0.0, -164.838348, 89.838341, 0.0], ctypes.c_double))
#tiff_file.write_image(data_array[::2, ::2])
return 0
def test_write_tags(*args):
"""Create a sample NinJo file that writes all ninjo tags and a fake data
array to a new tiff file.
"""
if len(args) == 0:
tiff_fn = "test_ninjo.tif"
else:
tiff_fn = args[0]
# Represents original high resolution data array
#data_array = numpy.zeros((5,5), dtype=numpy.uint8)
data_array = numpy.zeros((2500, 2500), dtype=numpy.uint8)
# Open file
tiff_file = TIFF.open(tiff_fn, "w")
tiff_file.SetDirectory(0)
### Write first set of tags ###
print "ModelTiePoint"
tiff_file.SetField("ModelTiePoint", [1, 2, 3, 4, 5, 6])
print "ModelPixelScale"
tiff_file.SetField("ModelPixelScale", [1, 2])
print "TransparentPixel"
tiff_file.SetField("TransparentPixel", 1)
print "NinjoName"
tiff_file.SetField("NinjoName", "NINJO")
print "SatelliteNameID"
tiff_file.SetField("SatelliteNameID", 1234)
print "DateID"
tiff_file.SetField("DateID", 1234)
print "CreationDateID"
tiff_file.SetField("CreationDateID", 1234)
print "ChannelID"
tiff_file.SetField("ChannelID", 1234)
print "HeaderVersion"
tiff_file.SetField("HeaderVersion", 2)
print "Filename"
tiff_file.SetField("Filename", "a_fake_satellite_file.h5")
print "DataType"
tiff_file.SetField("DataType", "P**N")
print "SatelliteNumber"
tiff_file.SetField("SatelliteNumber", "7") #?
print "ColorDepth"
tiff_file.SetField("ColorDepth", 8)
print "DataSource"
tiff_file.SetField("DataSource", "PDUS")
print "XMinimum"
tiff_file.SetField("XMinimum", 0)
print "XMaximum"
tiff_file.SetField("XMaximum", 2500)
print "YMinimum"
tiff_file.SetField("YMinimum", 0)
print "YMaximum"
tiff_file.SetField("YMaximum", 2500)
print "Projection"
tiff_file.SetField("Projection", "NPOL")
print "MeridianWest"
tiff_file.SetField("MeridianWest", -180.0)
print "MeridianEast"
tiff_file.SetField("MeridianEast", 180.0)
print "EarthRadiusLarge"
tiff_file.SetField("EarthRadiusLarge", 6370000.0)
print "EarthRadiusSmall"
tiff_file.SetField("EarthRadiusSmall", 6370000.0)
### Write second set of tags ###
print "GeodeticDate"
tiff_file.SetField("GeodeticDate", "wgs84")
print "ReferenceLatitude1"
tiff_file.SetField("ReferenceLatitude1", 45.0)
print "ReferenceLatitude2"
tiff_file.SetField("ReferenceLatitude2", 45.0)
print "CentralMeridian"
tiff_file.SetField("CentralMeridian", 0.0)
print "PhysicValue"
tiff_file.SetField("PhysicValue", "T")
print "PhysicUnit"
tiff_file.SetField("PhysicUnit", "CELSIUS")
print "MinGrayValue"
tiff_file.SetField("MinGrayValue", 0)
print "MaxGrayValue"
tiff_file.SetField("MaxGrayValue", 255)
print "Gradient"
tiff_file.SetField("Gradient", 5.0)
print "AxisIntercept"
tiff_file.SetField("AxisIntercept", 0.0)
print "ColorTable"
tiff_file.SetField("ColorTable", "some color table")
print "Description"
tiff_file.SetField("Description",
"this is a fake/test/sample ninjo tiff file")
print "OverflightDirection"
tiff_file.SetField("OverflightDirection", "S")
print "GeoLatitude"
tiff_file.SetField("GeoLatitude", 0.0)
print "GeoLongitude"
tiff_file.SetField("GeoLongitude", 0.0)
print "Altitude"
tiff_file.SetField("Altitude", 10000.0)
print "AOSAzimuth"
tiff_file.SetField("AOSAzimuth", 180.0)
print "LOSAzimuth"
tiff_file.SetField("LOSAzimuth", 180.0)
print "MaxElevation"
tiff_file.SetField("MaxElevation", 45.0)
print "OverFlightTime"
tiff_file.SetField("OverFlightTime", 1000.0)
print "IsBlackLinesCorrection"
tiff_file.SetField("IsBlackLinesCorrection", 0)
print "IsAtmosphereCorrected"
tiff_file.SetField("IsAtmosphereCorrected", 0)
print "IsCalibrated"
tiff_file.SetField("IsCalibrated", 0)
print "IsNormalized"
tiff_file.SetField("IsNormalized", 0)
print "OriginalHeader"
tiff_file.SetField("OriginalHeader", "some header")
#print "IsValueTableAvailable"
#tiff_file.SetField("IsValueTableAvailable", 0)
#print "ValueTableStringField"
#tiff_file.SetField("ValueTableStringField", "Cirrus")
#print "ValueTableFloatField"
#tiff_file.SetField("ValueTableFloatField", 0)
print "Writing image data..."
tiff_file.write_image(data_array)
print "SUCCESS"
def create_ninjo_tiff(image_data, output_fn, **kwargs):
"""Create a NinJo compatible TIFF file with the tags used
by the DWD's version of NinJo. Also stores the image as tiles on disk
and creates a multi-resolution/pyramid/overview set of images
(deresolution: 2,4,8,16).
:Parameters:
image_data : 2D numpy array
Satellite image data to be put into the NinJo compatible tiff
output_fn : str
The name of the TIFF file to be created
:Keywords:
data_kind : int
polar2grid constant describing the sensor type of the
image data, such as DKIND_REFLECTANCE or DKIND_BTEMP. This is
optional,
but if not specified then certain keywords below are required. If
it is specified then a default can be determined for some of the
keywords (such as `physic_value`).
cmap : tuple/list of 3 lists of uint16's
Individual RGB arrays describing the color value for the
corresponding data value. For example, image data with a data
type of unsigned 8-bit integers have 256 possible values (0-255).
So each list in cmap will have 256 values ranging from 0 to
65535 (2**16 - 1). (default linear B&W colormap)
sat_id : int
DWD NinJo Satellite ID number
chan_id : int
DWD NinJo Satellite Channel ID number
data_source : str
String describing where the data came from (SSEC, EUMCAST)
tile_width : int
Width of tiles on disk (default 512)
tile_length : int
Length of tiles on disk (default 512)
data_cat : str
NinJo specific data category
- data_cat[0] = P (polar) or G (geostat)
- data_cat[1] = O (original) or P (product)
- data_cat[2:4] = RN or RB or RA or RN or AN (Raster, Bufr, ASCII, NIL)
Example: 'P**N' or 'GORN' or 'GPRN' or 'PPRN'
(default 'P**N')
pixel_xres : float
Nadir view pixel resolution in degrees longitude
pixel_yres : float
Nadir view pixel resolution in degrees latitude
origin_lat : float
Top left corner latitude
origin_lon : float
Top left corner longitude
image_dt : datetime object
Python datetime object describing the date and time of the image
data provided in UTC
projection : str
NinJo compatible projection name (NPOL,PLAT,etc.)
meridian_west : float
Western image border (default 0.0)
meridian_east : float
Eastern image border (default 0.0)
radius_a : float
Large/equatorial radius of the earth (default <not written>)
radius_b : float
Small/polar radius of the earth (default <not written>)
ref_lat1 : float
Reference latitude 1 (default <not written>)
ref_lat2 : float
Reference latitude 2 (default <not written>)
central_meridian : float
Central Meridian (default <not written>)
physic_value : str
Physical value type. Examples:
- Temperature = 'T'
- Albedo = 'ALBEDO'
Defaults to appropriate value based on `data_kind`, see `itype2physical`
Specifying this overrides the default of `itype2physical`. If `data_kind`
is not specified then this keyword is required.
physic_unit : str
Physical value units. Examples:
- 'CELSIUS'
- '%'
Defaults to appropriate value based on `data_kind`, see `itype2physical`
Specifying this overrides the default of `itype2physical`. If `data_kind`
is not specified then this keyword is required.
min_gray_val : int
Minimum gray value (default 0)
max_gray_val : int
Maximum gray value (default 255)
gradient : float
Gradient/Slope
Defaults to appropriate value based on `data_kind`, see `itype2grad`
Specifying this overrides the default of `itype2grad`. If `data_kind`
is not specified then this keyword is required.
axis_intercept : float
Axis Intercept
Defaults to appropriate value based on `data_kind`, see `itype2grad`
Specifying this overrides the default of `itype2grad`. If `data_kind`
is not specified then this keyword is required.
altitude : float
Altitude of the data provided (default 0.0)
is_atmo_corrected : bool
Is the data atmosphere corrected? (True/1 for yes) (default False/0)
is_calibrated : bool
Is the data calibrated? (True/1 for yes) (default False/0)
is_normalized : bool
Is the data normalized (True/1 for yes) (default False/0)
description : str
Description string to be placed in the output TIFF (optional)
:Raises:
KeyError :
if required keyword is not provided
"""
LOG.debug("Creating NinJo TIFF file '%s'" % (output_fn, ))
out_tiff = TIFF.open(output_fn, "w")
image_data = clip_to_data_type(image_data, DTYPE_UINT8)
# Extract keyword arguments
data_kind = kwargs.pop("data_kind", None) # called as a backend
if data_kind is not None and (data_kind not in dkind2physical
or data_kind not in dkind2grad):
# Must do the check here since it matters when pulling out physic value
LOG.warning(
"'data_kind' is not known to the ninjo tiff creator, it will be ignored"
)
data_kind = None
cmap = kwargs.pop("cmap", None)
sat_id = int(kwargs.pop("sat_id"))
chan_id = int(kwargs.pop("chan_id"))
data_source = str(kwargs.pop("data_source"))
tile_width = int(kwargs.pop("tile_width", 512))
tile_length = int(kwargs.pop("tile_length", 512))
data_cat = str(kwargs.pop("data_cat", "P**N"))
pixel_xres = float(kwargs.pop("pixel_xres"))
pixel_yres = float(kwargs.pop("pixel_yres"))
origin_lat = float(kwargs.pop("origin_lat"))
origin_lon = float(kwargs.pop("origin_lon"))
image_dt = kwargs.pop("image_dt")
projection = kwargs.pop("projection")
meridian_west = float(kwargs.pop("meridian_west", 0.0))
meridian_east = float(kwargs.pop("meridian_east", 0.0))
radius_a = kwargs.pop("radius_a", None)
radius_b = kwargs.pop("radius_b", None)
ref_lat1 = kwargs.pop("ref_lat1", None)
ref_lat2 = kwargs.pop("ref_lat2", None)
central_meridian = kwargs.pop("central_meridian", None)
min_gray_val = int(kwargs.pop("min_gray_val", 0))
max_gray_val = int(kwargs.pop("max_gray_val", 255))
altitude = float(kwargs.pop("altitude", 0.0))
is_atmo_corrected = int(bool(kwargs.pop("is_atmo_corrected", 0)))
is_calibrated = int(bool(kwargs.pop("is_calibrated", 0)))
is_normalized = int(bool(kwargs.pop("is_normalized", 0)))
description = kwargs.pop("description", None)
# Special cases
if data_kind is not None:
physic_value, physic_unit = dkind2physical[data_kind]
gradient, axis_intercept = dkind2grad[data_kind]
physic_value = kwargs.pop("physic_value", physic_value)
physic_unit = kwargs.pop("physic_unit", physic_unit)
gradient = float(kwargs.pop("gradient", gradient))
axis_intercept = float(kwargs.pop("axis_intercept", axis_intercept))
else:
physic_value = kwargs.pop("physic_value")
physic_unit = kwargs.pop("physic_unit")
gradient = float(kwargs.pop("gradient"))
axis_intercept = float(kwargs.pop("axis_intercept"))
# Keyword checks / verification
if cmap is None:
if data_kind == "brightness_temperature":
cmap = get_default_lw_colortable()
else:
cmap = get_default_sw_colortable()
elif len(cmap) != 3:
LOG.error("Colormap (cmap) must be a list of 3 lists (RGB), not %d" %
len(cmap))
if len(data_cat) != 4:
LOG.error("NinJo data type must be 4 characters")
raise ValueError("NinJo data type must be 4 characters")
if data_cat[0] not in ["P", "G"]:
LOG.error(
"NinJo data type's first character must be 'P' or 'G' not '%s'" %
data_cat[0])
raise ValueError(
"NinJo data type's first character must be 'P' or 'G' not '%s'" %
data_cat[0])
if data_cat[1] not in ["O", "P"]:
LOG.error(
"NinJo data type's second character must be 'O' or 'P' not '%s'" %
data_cat[1])
raise ValueError(
"NinJo data type's second character must be 'O' or 'P' not '%s'" %
data_cat[1])
if data_cat[2:4] not in ["RN", "RB", "RA", "BN", "AN"]:
LOG.error(
"NinJo data type's last 2 characters must be one of %s not '%s'" %
("['RN','RB','RA','BN','AN']", data_cat[2:4]))
raise ValueError(
"NinJo data type's last 2 characters must be one of %s not '%s'" %
("['RN','RB','RA','BN','AN']", data_cat[2:4]))
if description is not None and len(description) >= 1000:
LOG.error("NinJo description must be less than 1000 characters")
raise ValueError("NinJo description must be less than 1000 characters")
file_dt = datetime.utcnow()
file_epoch = calendar.timegm(file_dt.timetuple())
image_epoch = calendar.timegm(image_dt.timetuple())
def _write_oneres(image_data, pixel_xres, pixel_yres, subfile=False):
LOG.debug("Writing tag data for a resolution of the output file '%s'" %
(output_fn, ))
### Write Tag Data ###
# Built ins
out_tiff.SetField("ImageWidth", image_data.shape[1])
out_tiff.SetField("ImageLength", image_data.shape[0])
out_tiff.SetField("BitsPerSample", 8)
out_tiff.SetField("Compression", libtiff.COMPRESSION_LZW)
out_tiff.SetField("Photometric", libtiff.PHOTOMETRIC_PALETTE)
out_tiff.SetField("Orientation", libtiff.ORIENTATION_TOPLEFT)
out_tiff.SetField("SamplesPerPixel", 1)
out_tiff.SetField("SMinSampleValue", 0)
out_tiff.SetField("SMaxsampleValue", 255)
out_tiff.SetField("PlanarConfig", libtiff.PLANARCONFIG_CONTIG)
out_tiff.SetField("ColorMap", cmap) # Basic B&W colormap
out_tiff.SetField("TileWidth", tile_width)
out_tiff.SetField("TileLength", tile_length)
out_tiff.SetField("SampleFormat", libtiff.SAMPLEFORMAT_UINT)
# NinJo specific tags
if description is not None:
out_tiff.SetField("Description", description)
out_tiff.SetField("ModelPixelScale", [pixel_xres, pixel_yres])
out_tiff.SetField("ModelTiePoint",
[0.0, 0.0, 0.0, origin_lon, origin_lat, 0.0])
out_tiff.SetField("NinjoName", "NINJO")
out_tiff.SetField("SatelliteNameID", sat_id)
out_tiff.SetField("DateID", image_epoch)
out_tiff.SetField("CreationDateID", file_epoch)
out_tiff.SetField("ChannelID", chan_id)
out_tiff.SetField("HeaderVersion", 2)
out_tiff.SetField("FileName", output_fn)
out_tiff.SetField("DataType", data_cat)
out_tiff.SetField("SatelliteNumber", "\x00") # Hardcoded to 0
out_tiff.SetField("ColorDepth", 8) # Hardcoded to 8
out_tiff.SetField("DataSource", data_source)
out_tiff.SetField("XMinimum", 1)
out_tiff.SetField("XMaximum", image_data.shape[1])
out_tiff.SetField("YMinimum", 1)
out_tiff.SetField("YMaximum", image_data.shape[0])
out_tiff.SetField("Projection", projection)
out_tiff.SetField("MeridianWest", meridian_west)
out_tiff.SetField("MeridianEast", meridian_east)
if radius_a is not None:
out_tiff.SetField("EarthRadiusLarge", float(radius_a))
if radius_b is not None:
out_tiff.SetField("EarthRadiusSmall", float(radius_b))
out_tiff.SetField("GeodeticDate", "\x00") # ---?
if ref_lat1 is not None:
out_tiff.SetField("ReferenceLatitude1", ref_lat1)
if ref_lat2 is not None:
out_tiff.SetField("ReferenceLatitude2", ref_lat2)
if central_meridian is not None:
out_tiff.SetField("CentralMeridian", central_meridian)
out_tiff.SetField("PhysicValue", physic_value)
out_tiff.SetField("PhysicUnit", physic_unit)
out_tiff.SetField("MinGrayValue", min_gray_val)
out_tiff.SetField("MaxGrayValue", max_gray_val)
out_tiff.SetField("Gradient", gradient)
out_tiff.SetField("AxisIntercept", axis_intercept)
out_tiff.SetField("Altitude", altitude)
out_tiff.SetField("IsAtmosphereCorrected", is_atmo_corrected)
out_tiff.SetField("IsCalibrated", is_calibrated)
out_tiff.SetField("IsNormalized", is_normalized)
### Write Base Data Image ###
out_tiff.write_tiles(image_data)
out_tiff.WriteDirectory()
### Write multi-resolution overviews ###
out_tiff.SetDirectory(0)
_write_oneres(image_data, pixel_xres, pixel_yres)
out_tiff.SetDirectory(1)
_write_oneres(image_data[::2, ::2], pixel_xres * 2, pixel_yres * 2)
out_tiff.SetDirectory(2)
_write_oneres(image_data[::4, ::4], pixel_xres * 4, pixel_yres * 4)
out_tiff.SetDirectory(3)
_write_oneres(image_data[::8, ::8], pixel_xres * 8, pixel_yres * 8)
out_tiff.SetDirectory(4)
_write_oneres(image_data[::16, ::16], pixel_xres * 16, pixel_yres * 16)
out_tiff.close()
LOG.info("Successfully created a NinJo tiff file: '%s'" % (output_fn, ))
return
def test_write(*args):
"""Recreate a simple NinJo compatible tiff file from an original
GOESE example.
Mainly a test of the tags.
"""
if len(args) == 0:
tiff_fn = "test_ninjo.tif"
else:
tiff_fn = args[0]
# Represents original high resolution data array
#data_array = numpy.zeros((2500,2500), dtype=numpy.uint8)
data_array = numpy.tile(range(500), (2500,5)).astype(numpy.uint8)
print data_array.shape
# Open file
tiff_file = TIFF.open(tiff_fn, "w")
### Write first directory and tags ###
tiff_file.SetDirectory(0)
tiff_file.SetField("ImageWidth", 2500)
tiff_file.SetField("ImageLength", 2500)
tiff_file.SetField("BITspersample", 8)
tiff_file.SetField("compression", libtiff.COMPRESSION_LZW)
#FIXME: Sample file used colormap
#tiff_file.SetField("PHOTOMETRIC", libtiff.PHOTOMETRIC_MINISBLACK)
tiff_file.SetField("PHOTOMETRIC", libtiff.PHOTOMETRIC_PALETTE)
tiff_file.SetField("ORIENTATION", libtiff.ORIENTATION_TOPLEFT)
tiff_file.SetField("SamplesPerPixel", 1)
tiff_file.SetField("SMinSampleValue", 0)
tiff_file.SetField("SMaxsampleValue", 255)
tiff_file.SetField("Planarconfig", libtiff.PLANARCONFIG_CONTIG)
#tiff_file.SetField("ColorMap", [ [ x*256 for x in range(256) ],[0]*256,[0]*256 ])
tiff_file.SetField("ColorMap", [[ x*256 for x in range(256) ]]*3)
tiff_file.SetField("TILEWIDTH", 512)
tiff_file.SetField("TILELENGTH", 512)
tiff_file.SetField("sampleformat", libtiff.SAMPLEFORMAT_UINT)
### NINJO SPECIFIC ###
tiff_file.SetField("ModelPixelScale", [0.071957,0.071957])
tiff_file.SetField("ModelTiePoint", [0.0, 0.0, 0.0, -164.874313, 89.874321, 0.0])
tiff_file.SetField("NinjoName", "NINJO")
tiff_file.SetField("SatelliteNameID", 7300014)
tiff_file.SetField("DateID", 1337169600)
tiff_file.SetField("CreationDateID", 1337171130)
tiff_file.SetField("ChannelID", 900015)
tiff_file.SetField("HeaderVersion", 2)
tiff_file.SetField("FileName", "GOESE_AMERIKA_IR107_nq075W8km_1205161200.tif")
tiff_file.SetField("DataType", "GORN")
tiff_file.SetField("SatelliteNumber", "\x00") # Hardcoded to 0
tiff_file.SetField("ColorDepth", 16) #Um 8? hardcoded to 8, but 16?
tiff_file.SetField("DataSource", "EUMCAST")
tiff_file.SetField("XMinimum", 1)
tiff_file.SetField("XMaximum", 2500)
tiff_file.SetField("YMinimum", 1)
tiff_file.SetField("YMaximum", 2500)
tiff_file.SetField("Projection", "PLAT")
tiff_file.SetField("MeridianWest", 0.0)
tiff_file.SetField("MeridianEast", 0.0)
tiff_file.SetField("EarthRadiusLarge", 6370000.0)
tiff_file.SetField("EarthRadiusSmall", 6370000.0)
tiff_file.SetField("GeodeticDate", "\x00") # ---?
tiff_file.SetField("ReferenceLatitude1", 0.0)
tiff_file.SetField("ReferenceLatitude2", 0.0)
tiff_file.SetField("CentralMeridian", -75.000)
tiff_file.SetField("PhysicValue", "T")
tiff_file.SetField("PhysicUnit", "CELSIUS")
tiff_file.SetField("MinGrayValue", 0)
tiff_file.SetField("MaxGrayValue", 255)
tiff_file.SetField("Gradient", -0.5)
tiff_file.SetField("AxisIntercept", 40.0)
tiff_file.SetField("Altitude", 42164.0)
tiff_file.SetField("IsCalibrated", 1)
tiff_file.SetField("IsNormalized", 0)
tiff_file.write_tiles(data_array)
tiff_file.WriteDirectory()
### Directory 2 ###
#tiff_file.SetDirectory(1)
#tiff_file.SetField("ModelTiePoint", write_list([0.0, 0.0, 0.0, -164.838348, 89.838341, 0.0], ctypes.c_double))
#tiff_file.write_image(data_array[::2, ::2])
return 0
def test_write_tags(*args):
"""Create a sample NinJo file that writes all ninjo tags and a fake data
array to a new tiff file.
"""
if len(args) == 0:
tiff_fn = "test_ninjo.tif"
else:
tiff_fn = args[0]
# Represents original high resolution data array
#data_array = numpy.zeros((5,5), dtype=numpy.uint8)
data_array = numpy.zeros((2500,2500), dtype=numpy.uint8)
# Open file
tiff_file = TIFF.open(tiff_fn, "w")
tiff_file.SetDirectory(0)
### Write first set of tags ###
print "ModelTiePoint"
tiff_file.SetField("ModelTiePoint", [1,2,3,4,5,6])
print "ModelPixelScale"
tiff_file.SetField("ModelPixelScale", [1,2])
print "TransparentPixel"
tiff_file.SetField("TransparentPixel", 1)
print "NinjoName"
tiff_file.SetField("NinjoName", "NINJO")
print "SatelliteNameID"
tiff_file.SetField("SatelliteNameID", 1234)
print "DateID"
tiff_file.SetField("DateID", 1234)
print "CreationDateID"
tiff_file.SetField("CreationDateID", 1234)
print "ChannelID"
tiff_file.SetField("ChannelID", 1234)
print "HeaderVersion"
tiff_file.SetField("HeaderVersion", 2)
print "Filename"
tiff_file.SetField("Filename", "a_fake_satellite_file.h5")
print "DataType"
tiff_file.SetField("DataType", "P**N")
print "SatelliteNumber"
tiff_file.SetField("SatelliteNumber", "7") #?
print "ColorDepth"
tiff_file.SetField("ColorDepth", 8)
print "DataSource"
tiff_file.SetField("DataSource", "PDUS")
print "XMinimum"
tiff_file.SetField("XMinimum", 0)
print "XMaximum"
tiff_file.SetField("XMaximum", 2500)
print "YMinimum"
tiff_file.SetField("YMinimum", 0)
print "YMaximum"
tiff_file.SetField("YMaximum", 2500)
print "Projection"
tiff_file.SetField("Projection", "NPOL")
print "MeridianWest"
tiff_file.SetField("MeridianWest", -180.0)
print "MeridianEast"
tiff_file.SetField("MeridianEast", 180.0)
print "EarthRadiusLarge"
tiff_file.SetField("EarthRadiusLarge", 6370000.0)
print "EarthRadiusSmall"
tiff_file.SetField("EarthRadiusSmall", 6370000.0)
### Write second set of tags ###
print "GeodeticDate"
tiff_file.SetField("GeodeticDate", "wgs84")
print "ReferenceLatitude1"
tiff_file.SetField("ReferenceLatitude1", 45.0)
print "ReferenceLatitude2"
tiff_file.SetField("ReferenceLatitude2", 45.0)
print "CentralMeridian"
tiff_file.SetField("CentralMeridian", 0.0)
print "PhysicValue"
tiff_file.SetField("PhysicValue", "T")
print "PhysicUnit"
tiff_file.SetField("PhysicUnit", "CELSIUS")
print "MinGrayValue"
tiff_file.SetField("MinGrayValue", 0)
print "MaxGrayValue"
tiff_file.SetField("MaxGrayValue", 255)
print "Gradient"
tiff_file.SetField("Gradient", 5.0)
print "AxisIntercept"
tiff_file.SetField("AxisIntercept", 0.0)
print "ColorTable"
tiff_file.SetField("ColorTable", "some color table")
print "Description"
tiff_file.SetField("Description", "this is a fake/test/sample ninjo tiff file")
print "OverflightDirection"
tiff_file.SetField("OverflightDirection", "S")
print "GeoLatitude"
tiff_file.SetField("GeoLatitude", 0.0)
print "GeoLongitude"
tiff_file.SetField("GeoLongitude", 0.0)
print "Altitude"
tiff_file.SetField("Altitude", 10000.0)
print "AOSAzimuth"
tiff_file.SetField("AOSAzimuth", 180.0)
print "LOSAzimuth"
tiff_file.SetField("LOSAzimuth", 180.0)
print "MaxElevation"
tiff_file.SetField("MaxElevation", 45.0)
print "OverFlightTime"
tiff_file.SetField("OverFlightTime", 1000.0)
print "IsBlackLinesCorrection"
tiff_file.SetField("IsBlackLinesCorrection", 0)
print "IsAtmosphereCorrected"
tiff_file.SetField("IsAtmosphereCorrected", 0)
print "IsCalibrated"
tiff_file.SetField("IsCalibrated", 0)
print "IsNormalized"
tiff_file.SetField("IsNormalized", 0)
print "OriginalHeader"
tiff_file.SetField("OriginalHeader", "some header")
#print "IsValueTableAvailable"
#tiff_file.SetField("IsValueTableAvailable", 0)
#print "ValueTableStringField"
#tiff_file.SetField("ValueTableStringField", "Cirrus")
#print "ValueTableFloatField"
#tiff_file.SetField("ValueTableFloatField", 0)
print "Writing image data..."
tiff_file.write_image(data_array)
print "SUCCESS"
def create_ninjo_tiff(image_data, output_fn, **kwargs):
"""Create a NinJo compatible TIFF file with the tags used
by the DWD's version of NinJo. Also stores the image as tiles on disk
and creates a multi-resolution/pyramid/overview set of images
(deresolution: 2,4,8,16).
:Parameters:
image_data : 2D numpy array
Satellite image data to be put into the NinJo compatible tiff
output_fn : str
The name of the TIFF file to be created
:Keywords:
data_kind : int
polar2grid constant describing the sensor type of the
image data, such as DKIND_REFLECTANCE or DKIND_BTEMP. This is
optional,
but if not specified then certain keywords below are required. If
it is specified then a default can be determined for some of the
keywords (such as `physic_value`).
cmap : tuple/list of 3 lists of uint16's
Individual RGB arrays describing the color value for the
corresponding data value. For example, image data with a data
type of unsigned 8-bit integers have 256 possible values (0-255).
So each list in cmap will have 256 values ranging from 0 to
65535 (2**16 - 1). (default linear B&W colormap)
sat_id : int
DWD NinJo Satellite ID number
chan_id : int
DWD NinJo Satellite Channel ID number
data_source : str
String describing where the data came from (SSEC, EUMCAST)
tile_width : int
Width of tiles on disk (default 512)
tile_length : int
Length of tiles on disk (default 512)
data_cat : str
NinJo specific data category
- data_cat[0] = P (polar) or G (geostat)
- data_cat[1] = O (original) or P (product)
- data_cat[2:4] = RN or RB or RA or RN or AN (Raster, Bufr, ASCII, NIL)
Example: 'P**N' or 'GORN' or 'GPRN' or 'PPRN'
(default 'P**N')
pixel_xres : float
Nadir view pixel resolution in degrees longitude
pixel_yres : float
Nadir view pixel resolution in degrees latitude
origin_lat : float
Top left corner latitude
origin_lon : float
Top left corner longitude
image_dt : datetime object
Python datetime object describing the date and time of the image
data provided in UTC
projection : str
NinJo compatible projection name (NPOL,PLAT,etc.)
meridian_west : float
Western image border (default 0.0)
meridian_east : float
Eastern image border (default 0.0)
radius_a : float
Large/equatorial radius of the earth (default <not written>)
radius_b : float
Small/polar radius of the earth (default <not written>)
ref_lat1 : float
Reference latitude 1 (default <not written>)
ref_lat2 : float
Reference latitude 2 (default <not written>)
central_meridian : float
Central Meridian (default <not written>)
physic_value : str
Physical value type. Examples:
- Temperature = 'T'
- Albedo = 'ALBEDO'
Defaults to appropriate value based on `data_kind`, see `itype2physical`
Specifying this overrides the default of `itype2physical`. If `data_kind`
is not specified then this keyword is required.
physic_unit : str
Physical value units. Examples:
- 'CELSIUS'
- '%'
Defaults to appropriate value based on `data_kind`, see `itype2physical`
Specifying this overrides the default of `itype2physical`. If `data_kind`
is not specified then this keyword is required.
min_gray_val : int
Minimum gray value (default 0)
max_gray_val : int
Maximum gray value (default 255)
gradient : float
Gradient/Slope
Defaults to appropriate value based on `data_kind`, see `itype2grad`
Specifying this overrides the default of `itype2grad`. If `data_kind`
is not specified then this keyword is required.
axis_intercept : float
Axis Intercept
Defaults to appropriate value based on `data_kind`, see `itype2grad`
Specifying this overrides the default of `itype2grad`. If `data_kind`
is not specified then this keyword is required.
altitude : float
Altitude of the data provided (default 0.0)
is_atmo_corrected : bool
Is the data atmosphere corrected? (True/1 for yes) (default False/0)
is_calibrated : bool
Is the data calibrated? (True/1 for yes) (default False/0)
is_normalized : bool
Is the data normalized (True/1 for yes) (default False/0)
description : str
Description string to be placed in the output TIFF (optional)
:Raises:
KeyError :
if required keyword is not provided
"""
LOG.debug("Creating NinJo TIFF file '%s'" % (output_fn,))
out_tiff = TIFF.open(output_fn, "w")
image_data = clip_to_data_type(image_data, DTYPE_UINT8)
# Extract keyword arguments
data_kind = kwargs.pop("data_kind", None) # called as a backend
if data_kind is not None and (data_kind not in dkind2physical or data_kind not in dkind2grad):
# Must do the check here since it matters when pulling out physic value
LOG.warning("'data_kind' is not known to the ninjo tiff creator, it will be ignored")
data_kind = None
cmap = kwargs.pop("cmap", None)
sat_id = int(kwargs.pop("sat_id"))
chan_id = int(kwargs.pop("chan_id"))
data_source = str(kwargs.pop("data_source"))
tile_width = int(kwargs.pop("tile_width", 512))
tile_length = int(kwargs.pop("tile_length", 512))
data_cat = str(kwargs.pop("data_cat", "P**N"))
pixel_xres = float(kwargs.pop("pixel_xres"))
pixel_yres = float(kwargs.pop("pixel_yres"))
origin_lat = float(kwargs.pop("origin_lat"))
origin_lon = float(kwargs.pop("origin_lon"))
image_dt = kwargs.pop("image_dt")
projection = kwargs.pop("projection")
meridian_west = float(kwargs.pop("meridian_west", 0.0))
meridian_east = float(kwargs.pop("meridian_east", 0.0))
radius_a = kwargs.pop("radius_a", None)
radius_b = kwargs.pop("radius_b", None)
ref_lat1 = kwargs.pop("ref_lat1", None)
ref_lat2 = kwargs.pop("ref_lat2", None)
central_meridian = kwargs.pop("central_meridian", None)
min_gray_val = int(kwargs.pop("min_gray_val", 0))
max_gray_val = int(kwargs.pop("max_gray_val", 255))
altitude = float(kwargs.pop("altitude", 0.0))
is_atmo_corrected = int(bool(kwargs.pop("is_atmo_corrected", 0)))
is_calibrated = int(bool(kwargs.pop("is_calibrated", 0)))
is_normalized = int(bool(kwargs.pop("is_normalized", 0)))
description = kwargs.pop("description", None)
# Special cases
if data_kind is not None:
physic_value,physic_unit = dkind2physical[data_kind]
gradient,axis_intercept = dkind2grad[data_kind]
physic_value = kwargs.pop("physic_value", physic_value)
physic_unit = kwargs.pop("physic_unit", physic_unit)
gradient = float(kwargs.pop("gradient", gradient))
axis_intercept = float(kwargs.pop("axis_intercept", axis_intercept))
else:
physic_value = kwargs.pop("physic_value")
physic_unit = kwargs.pop("physic_unit")
gradient = float(kwargs.pop("gradient"))
axis_intercept = float(kwargs.pop("axis_intercept"))
# Keyword checks / verification
if cmap is None:
if data_kind == "brightness_temperature":
cmap = get_default_lw_colortable()
else:
cmap = get_default_sw_colortable()
elif len(cmap) != 3:
LOG.error("Colormap (cmap) must be a list of 3 lists (RGB), not %d" % len(cmap))
if len(data_cat) != 4:
LOG.error("NinJo data type must be 4 characters")
raise ValueError("NinJo data type must be 4 characters")
if data_cat[0] not in ["P", "G"]:
LOG.error("NinJo data type's first character must be 'P' or 'G' not '%s'" % data_cat[0])
raise ValueError("NinJo data type's first character must be 'P' or 'G' not '%s'" % data_cat[0])
if data_cat[1] not in ["O", "P"]:
LOG.error("NinJo data type's second character must be 'O' or 'P' not '%s'" % data_cat[1])
raise ValueError("NinJo data type's second character must be 'O' or 'P' not '%s'" % data_cat[1])
if data_cat[2:4] not in ["RN","RB","RA","BN","AN"]:
LOG.error("NinJo data type's last 2 characters must be one of %s not '%s'" % ("['RN','RB','RA','BN','AN']", data_cat[2:4]))
raise ValueError("NinJo data type's last 2 characters must be one of %s not '%s'" % ("['RN','RB','RA','BN','AN']", data_cat[2:4]))
if description is not None and len(description) >= 1000:
LOG.error("NinJo description must be less than 1000 characters")
raise ValueError("NinJo description must be less than 1000 characters")
file_dt = datetime.utcnow()
file_epoch = calendar.timegm(file_dt.timetuple())
image_epoch = calendar.timegm(image_dt.timetuple())
def _write_oneres(image_data, pixel_xres, pixel_yres, subfile=False):
LOG.debug("Writing tag data for a resolution of the output file '%s'" % (output_fn,))
### Write Tag Data ###
# Built ins
out_tiff.SetField("ImageWidth", image_data.shape[1])
out_tiff.SetField("ImageLength", image_data.shape[0])
out_tiff.SetField("BitsPerSample", 8)
out_tiff.SetField("Compression", libtiff.COMPRESSION_LZW)
out_tiff.SetField("Photometric", libtiff.PHOTOMETRIC_PALETTE)
out_tiff.SetField("Orientation", libtiff.ORIENTATION_TOPLEFT)
out_tiff.SetField("SamplesPerPixel", 1)
out_tiff.SetField("SMinSampleValue", 0)
out_tiff.SetField("SMaxsampleValue", 255)
out_tiff.SetField("PlanarConfig", libtiff.PLANARCONFIG_CONTIG)
out_tiff.SetField("ColorMap", cmap) # Basic B&W colormap
out_tiff.SetField("TileWidth", tile_width)
out_tiff.SetField("TileLength", tile_length)
out_tiff.SetField("SampleFormat", libtiff.SAMPLEFORMAT_UINT)
# NinJo specific tags
if description is not None:
out_tiff.SetField("Description", description)
out_tiff.SetField("ModelPixelScale", [pixel_xres,pixel_yres])
out_tiff.SetField("ModelTiePoint", [0.0, 0.0, 0.0, origin_lon, origin_lat, 0.0])
out_tiff.SetField("NinjoName", "NINJO")
out_tiff.SetField("SatelliteNameID", sat_id)
out_tiff.SetField("DateID", image_epoch)
out_tiff.SetField("CreationDateID", file_epoch)
out_tiff.SetField("ChannelID", chan_id)
out_tiff.SetField("HeaderVersion", 2)
out_tiff.SetField("FileName", output_fn)
out_tiff.SetField("DataType", data_cat)
out_tiff.SetField("SatelliteNumber", "\x00") # Hardcoded to 0
out_tiff.SetField("ColorDepth", 8) # Hardcoded to 8
out_tiff.SetField("DataSource", data_source)
out_tiff.SetField("XMinimum", 1)
out_tiff.SetField("XMaximum", image_data.shape[1])
out_tiff.SetField("YMinimum", 1)
out_tiff.SetField("YMaximum", image_data.shape[0])
out_tiff.SetField("Projection", projection)
out_tiff.SetField("MeridianWest", meridian_west)
out_tiff.SetField("MeridianEast", meridian_east)
if radius_a is not None:
out_tiff.SetField("EarthRadiusLarge", float(radius_a))
if radius_b is not None:
out_tiff.SetField("EarthRadiusSmall", float(radius_b))
out_tiff.SetField("GeodeticDate", "\x00") # ---?
if ref_lat1 is not None:
out_tiff.SetField("ReferenceLatitude1", ref_lat1)
if ref_lat2 is not None:
out_tiff.SetField("ReferenceLatitude2", ref_lat2)
if central_meridian is not None:
out_tiff.SetField("CentralMeridian", central_meridian)
out_tiff.SetField("PhysicValue", physic_value)
out_tiff.SetField("PhysicUnit", physic_unit)
out_tiff.SetField("MinGrayValue", min_gray_val)
out_tiff.SetField("MaxGrayValue", max_gray_val)
out_tiff.SetField("Gradient", gradient)
out_tiff.SetField("AxisIntercept", axis_intercept)
out_tiff.SetField("Altitude", altitude)
out_tiff.SetField("IsAtmosphereCorrected", is_atmo_corrected)
out_tiff.SetField("IsCalibrated", is_calibrated)
out_tiff.SetField("IsNormalized", is_normalized)
### Write Base Data Image ###
out_tiff.write_tiles(image_data)
out_tiff.WriteDirectory()
### Write multi-resolution overviews ###
out_tiff.SetDirectory(0)
_write_oneres(image_data, pixel_xres, pixel_yres)
out_tiff.SetDirectory(1)
_write_oneres(image_data[::2,::2], pixel_xres*2, pixel_yres*2)
out_tiff.SetDirectory(2)
_write_oneres(image_data[::4,::4], pixel_xres*4, pixel_yres*4)
out_tiff.SetDirectory(3)
_write_oneres(image_data[::8,::8], pixel_xres*8, pixel_yres*8)
out_tiff.SetDirectory(4)
_write_oneres(image_data[::16,::16], pixel_xres*16, pixel_yres*16)
out_tiff.close()
LOG.info("Successfully created a NinJo tiff file: '%s'" % (output_fn,))
return
def read_image(self, verbose=False, as3d=True):
"""Read image from TIFF and return it as a numpy array.
if as3d is passed True (default), will attempt to read
multiple directories, and restore as slices in a 3D array.
"""
if not as3d:
return TIFF.read_image(self, verbose)
# Code is initially copy-paste from TIFF:
width = self.GetField("ImageWidth")
height = self.GetField("ImageLength")
bits = self.GetField("BitsPerSample")
sample_format = self.GetField("SampleFormat")
compression = self.GetField("Compression")
typ = self.get_numpy_type(bits, sample_format)
if typ is None:
if bits == 1:
typ = np.uint8
itemsize = 1
elif bits == 4:
typ = np.uint32
itemsize = 4
else:
raise NotImplementedError(` bits `)
else:
itemsize = bits / 8
# in order to allocate the numpy array, we must count the directories:
# code borrowed from TIFF.iter_images():
depth = 0
while True:
depth += 1
if self.LastDirectory():
break
self.ReadDirectory()
self.SetDirectory(0)
# we proceed assuming all directories have the same properties from above.
layer_size = width * height * itemsize
total_size = layer_size * depth
arr = np.zeros((depth, height, width), typ)
if compression == COMPRESSION_NONE:
ReadStrip = self.ReadRawStrip
else:
ReadStrip = self.ReadEncodedStrip
layer = 0
while True:
pos = 0
elem = None
for strip in range(self.NumberOfStrips()):
if elem is None:
elem = ReadStrip(strip, arr.ctypes.data + layer * layer_size + pos, layer_size)
elif elem:
elem = ReadStrip(strip, arr.ctypes.data + layer * layer_size + pos, min(layer_size - pos, elem))
pos += elem
if self.LastDirectory():
break
self.ReadDirectory()
layer += 1
self.SetDirectory(0)
return arr