def __init__(self, filename, mode='r'):
"""Open a tiff file.
see: libtiff.TIFF.open()
"""
self.tiff = TIFF.open(filename, mode)
self.tiff.ninjo_tags_dict = ninjo_tags_dict
self.tiff.ninjo_tags = ninjo_tags
def __init__(self, filename, mode='r'):
"""Open a tiff file.
see: libtiff.TIFF.open()
"""
self.tiff = TIFF.open(filename, mode)
self.tiff.ninjo_tags_dict = ninjo_tags_dict
self.tiff.ninjo_tags = ninjo_tags
def _write(image_data, output_fn, write_rgb=False, **kwargs):
"""Proudly Found Elsewhere (PFE) https://github.com/davidh-ssec/polar2grid
by David Hoese.
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 or 3D numpy array
Satellite image data to be put into the NinJo compatible tiff
An 3D array (HxWx3) is expected for a RGB image.
filename : str
The name of the TIFF file to be created
:Keywords:
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'
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'
physic_unit : str
Physical value units. Examples:
- 'CELSIUS'
- '%'
min_gray_val : int
Minimum gray value (default 0)
max_gray_val : int
Maximum gray value (default 255)
gradient : float
Gradient/Slope
axis_intercept : float
Axis Intercept
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)
transparent_pix : int
Transparent pixel value (default -1)
:Raises:
KeyError :
if required keyword is not provided
"""
def _raise_value_error(text):
log.error(text)
raise ValueError(text)
def _default_colormap(reverse=False):
# Basic B&W colormap
if reverse:
return [[ x*256 for x in range(255, -1, -1) ]]*3
return [[ x*256 for x in range(256) ]]*3
def _eval_or_none(key, eval_func):
try:
return eval_func(kwargs[key])
except KeyError:
return None
log.info("Creating output file '%s'" % (output_fn,))
tiff = TIFF.open(output_fn, "w")
# Extract keyword arguments
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"))
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 = str(kwargs.pop("projection"))
meridian_west = float(kwargs.pop("meridian_west", 0.0))
meridian_east = float(kwargs.pop("meridian_east", 0.0))
radius_a = _eval_or_none("radius_a", float)
radius_b = _eval_or_none("radius_b", float)
ref_lat1 = _eval_or_none("ref_lat1", float)
ref_lat2 = _eval_or_none("ref_lat2", float)
central_meridian = _eval_or_none("central_meridian", float)
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_blac_corrected = int(bool(kwargs.pop("is_blac_corrected", 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 = _eval_or_none("description", str)
physic_value = str(kwargs.pop("physic_value", 'None'))
physic_unit = str(kwargs.pop("physic_unit", 'None'))
gradient = float(kwargs.pop("gradient", 1.0))
axis_intercept = float(kwargs.pop("axis_intercept", 0.0))
transparent_pix = int(kwargs.pop("transparent_pix", -1))
# Keyword checks / verification
if not cmap:
if physic_value == 'T':
reverse = True
else:
reverse = False
cmap = _default_colormap(reverse)
if len(cmap) != 3:
_raise_value_error("Colormap (cmap) must be a list of 3 lists (RGB), not %d" %
len(cmap))
if len(data_cat) != 4:
_raise_value_error("NinJo data type must be 4 characters")
if data_cat[0] not in ["P", "G"]:
_raise_value_error("NinJo data type's first character must be 'P' or 'G' not '%s'" %
data_cat[0])
if data_cat[1] not in ["O", "P"]:
_raise_value_error("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"]:
_raise_value_error("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):
log.info("Writing tags and data for a resolution %dx%d" % image_data.shape[:2])
# Write Tag Data
# Built ins
tiff.SetField("ImageWidth", image_data.shape[1])
tiff.SetField("ImageLength", image_data.shape[0])
tiff.SetField("BitsPerSample", 8)
tiff.SetField("Compression", libtiff.COMPRESSION_DEFLATE)
if write_rgb:
tiff.SetField("Photometric", libtiff.PHOTOMETRIC_RGB)
tiff.SetField("SamplesPerPixel", 3)
else:
tiff.SetField("Photometric", libtiff.PHOTOMETRIC_PALETTE)
tiff.SetField("SamplesPerPixel", 1)
tiff.SetField("ColorMap", cmap)
tiff.SetField("Orientation", libtiff.ORIENTATION_TOPLEFT)
tiff.SetField("SMinSampleValue", 0)
tiff.SetField("SMaxsampleValue", 255)
tiff.SetField("PlanarConfig", libtiff.PLANARCONFIG_CONTIG)
tiff.SetField("TileWidth", tile_width)
tiff.SetField("TileLength", tile_length)
tiff.SetField("SampleFormat", libtiff.SAMPLEFORMAT_UINT)
# NinJo specific tags
if description is not None:
tiff.SetField("Description", description)
if MODEL_PIXEL_SCALE_COUNT == 3:
tiff.SetField("ModelPixelScale", [pixel_xres, pixel_yres, 0.0])
else:
tiff.SetField("ModelPixelScale", [pixel_xres, pixel_yres])
tiff.SetField("ModelTiePoint", [0.0, 0.0, 0.0, origin_lon, origin_lat, 0.0])
tiff.SetField("Magic", "NINJO")
tiff.SetField("SatelliteNameID", sat_id)
tiff.SetField("DateID", image_epoch)
tiff.SetField("CreationDateID", file_epoch)
tiff.SetField("ChannelID", chan_id)
tiff.SetField("HeaderVersion", 2)
tiff.SetField("FileName", output_fn)
tiff.SetField("DataType", data_cat)
tiff.SetField("SatelliteNumber", "\x00") # Hardcoded to 0
if write_rgb:
tiff.SetField("ColorDepth", 24)
elif cmap:
tiff.SetField("ColorDepth", 16)
else:
tiff.SetField("ColorDepth", 8)
tiff.SetField("DataSource", data_source)
tiff.SetField("XMinimum", 1)
tiff.SetField("XMaximum", image_data.shape[1])
tiff.SetField("YMinimum", 1)
tiff.SetField("YMaximum", image_data.shape[0])
tiff.SetField("Projection", projection)
tiff.SetField("MeridianWest", meridian_west)
tiff.SetField("MeridianEast", meridian_east)
if radius_a is not None:
tiff.SetField("EarthRadiusLarge", float(radius_a))
if radius_b is not None:
tiff.SetField("EarthRadiusSmall", float(radius_b))
#tiff.SetField("GeodeticDate", "\x00") # ---?
if ref_lat1 is not None:
tiff.SetField("ReferenceLatitude1", ref_lat1)
if ref_lat2 is not None:
tiff.SetField("ReferenceLatitude2", ref_lat2)
if central_meridian is not None:
tiff.SetField("CentralMeridian", central_meridian)
tiff.SetField("PhysicValue", physic_value)
tiff.SetField("PhysicUnit", physic_unit)
tiff.SetField("MinGrayValue", min_gray_val)
tiff.SetField("MaxGrayValue", max_gray_val)
tiff.SetField("Gradient", gradient)
tiff.SetField("AxisIntercept", axis_intercept)
tiff.SetField("Altitude", altitude)
tiff.SetField("IsBlackLineCorrection", is_blac_corrected)
tiff.SetField("IsAtmosphereCorrected", is_atmo_corrected)
tiff.SetField("IsCalibrated", is_calibrated)
tiff.SetField("IsNormalized", is_normalized)
tiff.SetField("TransparentPixel", transparent_pix)
# Write Base Data Image
tiff.write_tiles(image_data)
tiff.WriteDirectory()
# Write multi-resolution overviews (or not)
tiff.SetDirectory(0)
_write_oneres(image_data, pixel_xres, pixel_yres)
for index, scale in enumerate((2, 4, 8, 16)):
shape = (image_data.shape[0]/scale,
image_data.shape[1]/scale)
if shape[0] > tile_width and shape[1] > tile_length:
tiff.SetDirectory(index + 1)
_write_oneres(image_data[::scale,::scale], pixel_xres*scale, pixel_yres*scale)
tiff.close()
log.info("Successfully created a NinJo tiff file: '%s'" % (output_fn,))
def _write(image_data, output_fn, write_rgb=False, **kwargs):
"""Proudly Found Elsewhere (PFE) https://github.com/davidh-ssec/polar2grid
by David Hoese.
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 or 3D numpy array
Satellite image data to be put into the NinJo compatible tiff
An 3D array (HxWx3) is expected for a RGB image.
filename : str
The name of the TIFF file to be created
:Keywords:
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'
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'
physic_unit : str
Physical value units. Examples:
- 'CELSIUS'
- '%'
min_gray_val : int
Minimum gray value (default 0)
max_gray_val : int
Maximum gray value (default 255)
gradient : float
Gradient/Slope
axis_intercept : float
Axis Intercept
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)
transparent_pix : int
Transparent pixel value (default -1)
:Raises:
KeyError :
if required keyword is not provided
"""
def _raise_value_error(text):
log.error(text)
raise ValueError(text)
def _default_colormap(reverse=False):
# Basic B&W colormap
if reverse:
return [[x * 256 for x in range(255, -1, -1)]] * 3
return [[x * 256 for x in range(256)]] * 3
def _eval_or_none(key, eval_func):
try:
return eval_func(kwargs[key])
except KeyError:
return None
log.info("Creating output file '%s'" % (output_fn, ))
tiff = TIFF.open(output_fn, "w")
# Extract keyword arguments
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"))
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 = str(kwargs.pop("projection"))
meridian_west = float(kwargs.pop("meridian_west", 0.0))
meridian_east = float(kwargs.pop("meridian_east", 0.0))
radius_a = _eval_or_none("radius_a", float)
radius_b = _eval_or_none("radius_b", float)
ref_lat1 = _eval_or_none("ref_lat1", float)
ref_lat2 = _eval_or_none("ref_lat2", float)
central_meridian = _eval_or_none("central_meridian", float)
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_blac_corrected = int(bool(kwargs.pop("is_blac_corrected", 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 = _eval_or_none("description", str)
physic_value = str(kwargs.pop("physic_value", 'None'))
physic_unit = str(kwargs.pop("physic_unit", 'None'))
gradient = float(kwargs.pop("gradient", 1.0))
axis_intercept = float(kwargs.pop("axis_intercept", 0.0))
transparent_pix = int(kwargs.pop("transparent_pix", -1))
# Keyword checks / verification
if not cmap:
if physic_value == 'T':
reverse = True
else:
reverse = False
cmap = _default_colormap(reverse)
if len(cmap) != 3:
_raise_value_error(
"Colormap (cmap) must be a list of 3 lists (RGB), not %d" %
len(cmap))
if len(data_cat) != 4:
_raise_value_error("NinJo data type must be 4 characters")
if data_cat[0] not in ["P", "G"]:
_raise_value_error(
"NinJo data type's first character must be 'P' or 'G' not '%s'" %
data_cat[0])
if data_cat[1] not in ["O", "P"]:
_raise_value_error(
"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"]:
_raise_value_error(
"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):
log.info("Writing tags and data for a resolution %dx%d" %
image_data.shape[:2])
# Write Tag Data
# Built ins
tiff.SetField("ImageWidth", image_data.shape[1])
tiff.SetField("ImageLength", image_data.shape[0])
tiff.SetField("BitsPerSample", 8)
tiff.SetField("Compression", libtiff.COMPRESSION_DEFLATE)
if write_rgb:
tiff.SetField("Photometric", libtiff.PHOTOMETRIC_RGB)
tiff.SetField("SamplesPerPixel", 3)
else:
tiff.SetField("Photometric", libtiff.PHOTOMETRIC_PALETTE)
tiff.SetField("SamplesPerPixel", 1)
tiff.SetField("ColorMap", cmap)
tiff.SetField("Orientation", libtiff.ORIENTATION_TOPLEFT)
tiff.SetField("SMinSampleValue", 0)
tiff.SetField("SMaxsampleValue", 255)
tiff.SetField("PlanarConfig", libtiff.PLANARCONFIG_CONTIG)
tiff.SetField("TileWidth", tile_width)
tiff.SetField("TileLength", tile_length)
tiff.SetField("SampleFormat", libtiff.SAMPLEFORMAT_UINT)
# NinJo specific tags
if description is not None:
tiff.SetField("Description", description)
if MODEL_PIXEL_SCALE_COUNT == 3:
tiff.SetField("ModelPixelScale", [pixel_xres, pixel_yres, 0.0])
else:
tiff.SetField("ModelPixelScale", [pixel_xres, pixel_yres])
tiff.SetField("ModelTiePoint",
[0.0, 0.0, 0.0, origin_lon, origin_lat, 0.0])
tiff.SetField("Magic", "NINJO")
tiff.SetField("SatelliteNameID", sat_id)
tiff.SetField("DateID", image_epoch)
tiff.SetField("CreationDateID", file_epoch)
tiff.SetField("ChannelID", chan_id)
tiff.SetField("HeaderVersion", 2)
tiff.SetField("FileName", output_fn)
tiff.SetField("DataType", data_cat)
tiff.SetField("SatelliteNumber", "\x00") # Hardcoded to 0
if write_rgb:
tiff.SetField("ColorDepth", 24)
elif cmap:
tiff.SetField("ColorDepth", 16)
else:
tiff.SetField("ColorDepth", 8)
tiff.SetField("DataSource", data_source)
tiff.SetField("XMinimum", 1)
tiff.SetField("XMaximum", image_data.shape[1])
tiff.SetField("YMinimum", 1)
tiff.SetField("YMaximum", image_data.shape[0])
tiff.SetField("Projection", projection)
tiff.SetField("MeridianWest", meridian_west)
tiff.SetField("MeridianEast", meridian_east)
if radius_a is not None:
tiff.SetField("EarthRadiusLarge", float(radius_a))
if radius_b is not None:
tiff.SetField("EarthRadiusSmall", float(radius_b))
#tiff.SetField("GeodeticDate", "\x00") # ---?
if ref_lat1 is not None:
tiff.SetField("ReferenceLatitude1", ref_lat1)
if ref_lat2 is not None:
tiff.SetField("ReferenceLatitude2", ref_lat2)
if central_meridian is not None:
tiff.SetField("CentralMeridian", central_meridian)
tiff.SetField("PhysicValue", physic_value)
tiff.SetField("PhysicUnit", physic_unit)
tiff.SetField("MinGrayValue", min_gray_val)
tiff.SetField("MaxGrayValue", max_gray_val)
tiff.SetField("Gradient", gradient)
tiff.SetField("AxisIntercept", axis_intercept)
tiff.SetField("Altitude", altitude)
tiff.SetField("IsBlackLineCorrection", is_blac_corrected)
tiff.SetField("IsAtmosphereCorrected", is_atmo_corrected)
tiff.SetField("IsCalibrated", is_calibrated)
tiff.SetField("IsNormalized", is_normalized)
tiff.SetField("TransparentPixel", transparent_pix)
# Write Base Data Image
tiff.write_tiles(image_data)
tiff.WriteDirectory()
# Write multi-resolution overviews (or not)
tiff.SetDirectory(0)
_write_oneres(image_data, pixel_xres, pixel_yres)
for index, scale in enumerate((2, 4, 8, 16)):
shape = (image_data.shape[0] / scale, image_data.shape[1] / scale)
if shape[0] > tile_width and shape[1] > tile_length:
tiff.SetDirectory(index + 1)
_write_oneres(image_data[::scale, ::scale], pixel_xres * scale,
pixel_yres * scale)
tiff.close()
log.info("Successfully created a NinJo tiff file: '%s'" % (output_fn, ))