def __init__(self, nparray, **kwargs):
""" Set attributes
See class Figure(object) for information about:
Modifies
--------
Parameters
----------
Advanced parameters
--------------------
"""
# make a copy of nparray (otherwise a new reference to the same data is
# created and the original input data is destroyed at process())
array = np.array(nparray)
self.logger = add_logger('Nansat')
# if 2D array is given, reshape to 3D
if array.ndim == 2:
self.array = array.reshape(1, array.shape[0], array.shape[1])
else:
self.array = array
# note swaping of axis by PIL
self.width = self.array.shape[2]
self.height = self.array.shape[1]
# modify the default values using input values
self._set_defaults(kwargs)
# set fonts for Legend
self.fontFileName = os.path.join(os.path.dirname(
os.path.realpath(__file__)),
'fonts/DejaVuSans.ttf')
def __init__(self, nparray, **kwargs):
""" Set attributes
See class Figure(object) for information about:
Modifies
--------
Parameters
----------
Advanced parameters
--------------------
"""
# make a copy of nparray (otherwise a new reference to the same data is
# created and the original input data is destroyed at process())
array = np.array(nparray)
self.logger = add_logger('Nansat')
# if 2D array is given, reshape to 3D
if array.ndim == 2:
self.array = array.reshape(1, array.shape[0], array.shape[1])
else:
self.array = array
# note swaping of axis by PIL
self.width = self.array.shape[2]
self.height = self.array.shape[1]
# modify the default values using input values
self._set_defaults(kwargs)
# set fonts for Legend
self.fontFileName = os.path.join(
os.path.dirname(os.path.realpath(__file__)),
'fonts/DejaVuSans.ttf')
def __init__(self,
srs=None,
ext=None,
ds=None,
lon=None,
lat=None,
name='',
logLevel=None):
'''Create Domain from GDALDataset or string options or lat/lon grids
d = Domain(srs, ext)
Size, extent and spatial reference is given by strings
d = Domain(ds=GDALDataset):
Size, extent and spatial reference is copied from input
GDAL dataset
d = Domain(srs, ds=GDALDataset):
Spatial reference is given by srs, but size and extent is
determined
from input GDAL dataset
d = Domain(lon=lonGrid, lat=latGrid)
Size, extent and spatial reference is given by two grids
Parameters
----------
srs : PROJ4 or EPSG or WKT or NSR or osr.SpatialReference()
Input parameter for nansat.NSR()
ext : string
some gdalwarp options + additional options
[http://www.gdal.org/gdalwarp.html]
Specifies extent, resolution / size
Available options: (('-te' or '-lle') and ('-tr' or '-ts'))
(e.g. '-lle -10 30 55 60 -ts 1000 1000' or
'-te 100 2000 300 10000 -tr 300 200')
-tr resolutionx resolutiony
-ts sizex sizey
-te xmin ymin xmax ymax
-lle lonmin latmin lonmax latmax
ds : GDAL dataset
lat : Numpy array
Grid with latitudes
lon : Numpy array
Grid with longitudes
name : string, optional
Name to be added to the Domain object
logLevel : int, optional, default=30
level of logging
Raises
-------
ProjectionError : occurs when Projection() is empty
despite it is required for creating extentDic.
OptionError : occures when the arguments are not proper.
Modifies
---------
self.vrt.datasetset : dataset in memory
dataset is created based on the input arguments
See Also
---------
Nansat.reproject()
[http://www.gdal.org/gdalwarp.html]
[http://trac.osgeo.org/proj/]
[http://spatialreference.org/]
[http://www.gdal.org/ogr/osr_tutorial.html]
'''
# set default attributes
self.logger = add_logger('Nansat', logLevel)
self.name = name
self.logger.debug('ds: %s' % str(ds))
self.logger.debug('srs: %s' % srs)
self.logger.debug('ext: %s' % ext)
# If too much information is given raise error
if ds is not None and srs is not None and ext is not None:
raise OptionError('Ambiguous specification of both '
'dataset, srs- and ext-strings.')
# choose between input opitons:
# ds
# ds and srs
# srs and ext
# lon and lat
# if only a dataset is given:
# copy geo-reference from the dataset
if ds is not None and srs is None:
self.vrt = VRT(gdalDataset=ds)
# If dataset and srs are given (but not ext):
# use AutoCreateWarpedVRT to determine bounds and resolution
elif ds is not None and srs is not None:
srs = NSR(srs)
tmpVRT = gdal.AutoCreateWarpedVRT(ds, None, srs.wkt)
if tmpVRT is None:
raise ProjectionError('Could not warp the given dataset'
'to the given SRS.')
else:
self.vrt = VRT(gdalDataset=tmpVRT)
# If SpatialRef and extent string are given (but not dataset)
elif srs is not None and ext is not None:
srs = NSR(srs)
# create full dictionary of parameters
extentDic = self._create_extentDic(ext)
# convert -lle to -te
if 'lle' in extentDic.keys():
extentDic = self._convert_extentDic(srs, extentDic)
# get size/extent from the created extet dictionary
[geoTransform, rasterXSize,
rasterYSize] = self._get_geotransform(extentDic)
# create VRT object with given geo-reference parameters
self.vrt = VRT(srcGeoTransform=geoTransform,
srcProjection=srs.wkt,
srcRasterXSize=rasterXSize,
srcRasterYSize=rasterYSize)
self.extentDic = extentDic
elif lat is not None and lon is not None:
# create self.vrt from given lat/lon
self.vrt = VRT(lat=lat, lon=lon)
else:
raise OptionError('"dataset" or "srsString and extentString" '
'or "dataset and srsString" are required')
self.logger.debug('vrt.dataset: %s' % str(self.vrt.dataset))
def __init__(self,
srs=None,
ext=None,
ds=None,
lon=None,
lat=None,
name='',
logLevel=None):
"""Create Domain from GDALDataset or string options or lat/lon grids"""
# set default attributes
self.logger = add_logger('Nansat', logLevel)
self.name = name
self.logger.debug('ds: %s' % str(ds))
self.logger.debug('srs: %s' % srs)
self.logger.debug('ext: %s' % ext)
# If too much information is given raise error
if ds is not None and srs is not None and ext is not None:
raise ValueError(
'Ambiguous specification of both dataset, srs- and ext-strings.'
)
# choose between input opitons:
# ds
# ds and srs
# srs and ext
# lon and lat
# if only a dataset is given:
# copy geo-reference from the dataset
if ds is not None and srs is None:
self.vrt = VRT.from_gdal_dataset(ds)
# If dataset and srs are given (but not ext):
# use AutoCreateWarpedVRT to determine bounds and resolution
elif ds is not None and srs is not None:
srs = NSR(srs)
tmp_vrt = gdal.AutoCreateWarpedVRT(ds, None, srs.wkt)
if tmp_vrt is None:
raise NansatProjectionError(
'Could not warp the given dataset to the given SRS.')
else:
self.vrt = VRT.from_gdal_dataset(tmp_vrt)
# If SpatialRef and extent string are given (but not dataset)
elif srs is not None and ext is not None:
srs = NSR(srs)
# create full dictionary of parameters
extent_dict = Domain._create_extent_dict(ext)
# convert -lle to -te
if 'lle' in extent_dict.keys():
extent_dict = self._convert_extentDic(srs, extent_dict)
# get size/extent from the created extent dictionary
geo_transform, raster_x_size, raster_y_size = self._get_geotransform(
extent_dict)
# create VRT object with given geo-reference parameters
self.vrt = VRT.from_dataset_params(x_size=raster_x_size,
y_size=raster_y_size,
geo_transform=geo_transform,
projection=srs.wkt,
gcps=[],
gcp_projection='')
elif lat is not None and lon is not None:
# create self.vrt from given lat/lon
self.vrt = VRT.from_lonlat(lon, lat)
else:
raise ValueError('"dataset" or "srsString and extentString" '
'or "dataset and srsString" are required')
self.logger.debug('vrt.dataset: %s' % str(self.vrt.dataset))
def __init__(self, nparray, **kwargs):
''' Set attributes
Parameters
-----------
array : numpy array (2D or 3D)
dataset from Nansat
cmin : number (int ot float) or [number, number, number]
0, minimum value of varibale in the matrix to be shown
cmax : number (int ot float) or [number, number, number]
1, minimum value of varibale in the matrix to be shown
gamma : float, >0
2, coefficient for tone curve udjustment
subsetArraySize : int
100000, size of the subset array which is used to get histogram
numOfColor : int
250, number of colors for use of the palette.
254th is black and 255th is white.
cmapName : string
'jet', name of Matplotlib colormaps
see --> http://www.scipy.org/Cookbook/Matplotlib/Show_colormaps
ratio : float, [0 1]
1.0, ratio of pixels which are used to write the figure
numOfTicks : int
5, number of ticks on a colorbar
titleString : string
'', title of legend (1st line)
caption : string
'', caption of the legend (2nd line, e.g. long name and units)
fontRatio : positive float
1, factor for changing the fontSize.
fontSize : int
12, size of the font of title, caption and ticks.
If not given, fontSize is calculated using fontRatio:
fontSize = height / 45 * fontRatio.
fontSize has priority over fontRatio
logarithm : boolean, defult = False
If True, tone curve is used to convert pixel values.
If False, linear.
legend : boolean, default = False
if True, information as textString, colorbar, longName and
units are added in the figure.
mask_array : 2D numpy array, int, the shape should be equal
array.shape. If given this array is used for masking land,
clouds, etc on the output image. Value of the array are
indeces. LUT from mask_lut is used for coloring upon this
indeces.
mask_lut : dictionary
Look-Up-Table with colors for masking land, clouds etc. Used
tgether with mask_array:
{0, [0,0,0], 1, [100,100,100], 2: [150,150,150], 3: [0,0,255]}
index 0 - will have black color
1 - dark gray
2 - light gray
3 - blue
logoFileName : string
name of the file with logo
logoLocation : list of two int, default = [0,0]
X and Y offset of the image
If positive - offset is from left, upper edge
If Negative - from right, lower edge
Offset is calculated from the entire image legend inclusive
logoSize : list of two int
desired X,Y size of logo. If None - original size is used
latGrid : numpy array
full size array with latitudes. For adding lat/lon grid lines
lonGrid : numpy array
full size array with longitudes. For adding lat/lon grid lines
nGridLines : int
number of lat/lon grid lines to show
latlonLabels : int
number of lat/lon labels to show along each side.
transparency : int
transparency of the image background(mask), set for PIL alpha
mask in Figure.save()
default : None
Advanced parameters
--------------------
LEGEND_HEIGHT : float, [0 1]
0.1, legend height relative to image height
CBAR_HEIGHTMIN : int
5, minimum colorbar height, pixels
CBAR_HEIGHT : float, [0 1]
0.15, colorbar height relative to image height
CBAR_WIDTH : float [0 1]
0.8, colorbar width relative to legend width
CBAR_LOCATION_X : float [0 1]
0.1, colorbar offset X relative to legend width
CBAR_LOCATION_Y : float [0 1]
0.5, colorbar offset Y relative to legend height
CBTICK_LOC_ADJUST_X : int
5, colorbar tick label offset X, pixels
CBTICK_LOC_ADJUST_Y : int
3, colorbar tick label offset Y, pixels
CAPTION_LOCATION_X : float, [0 1]
0.1, caption offset X relative to legend width
CAPTION_LOCATION_Y : float, [0 1]
0.1, caption offset Y relative to legend height
TITLE_LOCATION_X : float, [0 1]
0.1, title offset X relative to legend width
TITLE_LOCATION_Y :
0.3, title offset Y relative to legend height
DEFAULT_EXTENSION : string
'.png'
--------------------------------------------------
Modifies
---------
self.sizeX, self.sizeY : int
width and height of the image
self.pilImg : PIL image
figure
self.pilImgLegend : PIL image
if pilImgLegend is None, legend is not added to the figure
if it is replaced, pilImgLegend includes text string, color-bar,
longName and units.
'''
# make a copy of nparray (otherwise a new reference to the same data is
# created and the original input data is destroyed at process())
array = np.array(nparray)
self.logger = add_logger('Nansat')
# if 2D array is given, reshape to 3D
if array.ndim == 2:
self.array = array.reshape(1, array.shape[0], array.shape[1])
else:
self.array = array
# note swaping of axis by PIL
self.width = self.array.shape[2]
self.height = self.array.shape[1]
# modify the default values using input values
self._set_defaults(kwargs)
# set fonts for Legend
self.fontFileName = os.path.join(
os.path.dirname(os.path.realpath(__file__)),
'fonts/DejaVuSans.ttf')
def __init__(self, srs=None, ext=None, ds=None, lon=None,
lat=None, name='', logLevel=None):
'''Create Domain from GDALDataset or string options or lat/lon grids
d = Domain(srs, ext)
Size, extent and spatial reference is given by strings
d = Domain(ds=GDALDataset):
Size, extent and spatial reference is copied from input
GDAL dataset
d = Domain(srs, ds=GDALDataset):
Spatial reference is given by srs, but size and extent is
determined
from input GDAL dataset
d = Domain(lon=lonGrid, lat=latGrid)
Size, extent and spatial reference is given by two grids
Parameters
----------
srs : PROJ4 or EPSG or WKT or NSR or osr.SpatialReference()
Input parameter for nansat.NSR()
ext : string
some gdalwarp options + additional options
[http://www.gdal.org/gdalwarp.html]
Specifies extent, resolution / size
Available options: (('-te' or '-lle') and ('-tr' or '-ts'))
(e.g. '-lle -10 30 55 60 -ts 1000 1000' or
'-te 100 2000 300 10000 -tr 300 200')
-tr resolutionx resolutiony
-ts sizex sizey
-te xmin ymin xmax ymax
-lle lonmin latmin lonmax latmax
ds : GDAL dataset
lat : Numpy array
Grid with latitudes
lon : Numpy array
Grid with longitudes
name : string, optional
Name to be added to the Domain object
logLevel : int, optional, default=30
level of logging
Raises
-------
ProjectionError : occurs when Projection() is empty
despite it is required for creating extentDic.
OptionError : occures when the arguments are not proper.
Modifies
---------
self.vrt.datasetset : dataset in memory
dataset is created based on the input arguments
See Also
---------
Nansat.reproject()
[http://www.gdal.org/gdalwarp.html]
[http://trac.osgeo.org/proj/]
[http://spatialreference.org/]
[http://www.gdal.org/ogr/osr_tutorial.html]
'''
# set default attributes
self.logger = add_logger('Nansat', logLevel)
self.name = name
self.logger.debug('ds: %s' % str(ds))
self.logger.debug('srs: %s' % srs)
self.logger.debug('ext: %s' % ext)
# If too much information is given raise error
if ds is not None and srs is not None and ext is not None:
raise OptionError('Ambiguous specification of both '
'dataset, srs- and ext-strings.')
# choose between input opitons:
# ds
# ds and srs
# srs and ext
# lon and lat
# if only a dataset is given:
# copy geo-reference from the dataset
if ds is not None and srs is None:
self.vrt = VRT(gdalDataset=ds)
# If dataset and srs are given (but not ext):
# use AutoCreateWarpedVRT to determine bounds and resolution
elif ds is not None and srs is not None:
srs = NSR(srs)
tmpVRT = gdal.AutoCreateWarpedVRT(ds, None, srs.wkt)
if tmpVRT is None:
raise ProjectionError('Could not warp the given dataset'
'to the given SRS.')
else:
self.vrt = VRT(gdalDataset=tmpVRT)
# If SpatialRef and extent string are given (but not dataset)
elif srs is not None and ext is not None:
srs = NSR(srs)
# create full dictionary of parameters
extentDic = self._create_extentDic(ext)
# convert -lle to -te
if 'lle' in extentDic.keys():
extentDic = self._convert_extentDic(srs, extentDic)
# get size/extent from the created extet dictionary
[geoTransform,
rasterXSize, rasterYSize] = self._get_geotransform(extentDic)
# create VRT object with given geo-reference parameters
self.vrt = VRT(srcGeoTransform=geoTransform,
srcProjection=srs.wkt,
srcRasterXSize=rasterXSize,
srcRasterYSize=rasterYSize)
self.extentDic = extentDic
elif lat is not None and lon is not None:
# create self.vrt from given lat/lon
self.vrt = VRT(lat=lat, lon=lon)
else:
raise OptionError('"dataset" or "srsString and extentString" '
'or "dataset and srsString" are required')
self.logger.debug('vrt.dataset: %s' % str(self.vrt.dataset))
def __init__(self, nparray, **kwargs):
''' Set attributes
Parameters
-----------
array : numpy array (2D or 3D)
dataset from Nansat
cmin : number (int ot float) or [number, number, number]
0, minimum value of varibale in the matrix to be shown
cmax : number (int ot float) or [number, number, number]
1, minimum value of varibale in the matrix to be shown
gamma : float, >0
2, coefficient for tone curve udjustment
subsetArraySize : int
100000, size of the subset array which is used to get histogram
numOfColor : int
250, number of colors for use of the palette.
254th is black and 255th is white.
cmapName : string
'jet', name of Matplotlib colormaps
see --> http://www.scipy.org/Cookbook/Matplotlib/Show_colormaps
ratio : float, [0 1]
1.0, ratio of pixels which are used to write the figure
numOfTicks : int
5, number of ticks on a colorbar
titleString : string
'', title of legend (1st line)
caption : string
'', caption of the legend (2nd line, e.g. long name and units)
fontRatio : positive float
1, factor for changing the fontSize.
fontSize : int
12, size of the font of title, caption and ticks.
If not given, fontSize is calculated using fontRatio:
fontSize = height / 45 * fontRatio.
fontSize has priority over fontRatio
logarithm : boolean, defult = False
If True, tone curve is used to convert pixel values.
If False, linear.
legend : boolean, default = False
if True, information as textString, colorbar, longName and
units are added in the figure.
mask_array : 2D numpy array, int, the shape should be equal
array.shape. If given this array is used for masking land,
clouds, etc on the output image. Value of the array are
indeces. LUT from mask_lut is used for coloring upon this
indeces.
mask_lut : dictionary
Look-Up-Table with colors for masking land, clouds etc. Used
tgether with mask_array:
{0, [0,0,0], 1, [100,100,100], 2: [150,150,150], 3: [0,0,255]}
index 0 - will have black color
1 - dark gray
2 - light gray
3 - blue
logoFileName : string
name of the file with logo
logoLocation : list of two int, default = [0,0]
X and Y offset of the image
If positive - offset is from left, upper edge
If Negative - from right, lower edge
Offset is calculated from the entire image legend inclusive
logoSize : list of two int
desired X,Y size of logo. If None - original size is used
latGrid : numpy array
full size array with latitudes. For adding lat/lon grid lines
lonGrid : numpy array
full size array with longitudes. For adding lat/lon grid lines
nGridLines : int
number of lat/lon grid lines to show
latlonLabels : int
number of lat/lon labels to show along each side.
transparency : int
transparency of the image background(mask), set for PIL alpha
mask in Figure.save()
default : None
Advanced parameters
--------------------
LEGEND_HEIGHT : float, [0 1]
0.1, legend height relative to image height
CBAR_HEIGHTMIN : int
5, minimum colorbar height, pixels
CBAR_HEIGHT : float, [0 1]
0.15, colorbar height relative to image height
CBAR_WIDTH : float [0 1]
0.8, colorbar width relative to legend width
CBAR_LOCATION_X : float [0 1]
0.1, colorbar offset X relative to legend width
CBAR_LOCATION_Y : float [0 1]
0.5, colorbar offset Y relative to legend height
CBTICK_LOC_ADJUST_X : int
5, colorbar tick label offset X, pixels
CBTICK_LOC_ADJUST_Y : int
3, colorbar tick label offset Y, pixels
CAPTION_LOCATION_X : float, [0 1]
0.1, caption offset X relative to legend width
CAPTION_LOCATION_Y : float, [0 1]
0.1, caption offset Y relative to legend height
TITLE_LOCATION_X : float, [0 1]
0.1, title offset X relative to legend width
TITLE_LOCATION_Y :
0.3, title offset Y relative to legend height
DEFAULT_EXTENSION : string
'.png'
--------------------------------------------------
Modifies
---------
self.sizeX, self.sizeY : int
width and height of the image
self.pilImg : PIL image
figure
self.pilImgLegend : PIL image
if pilImgLegend is None, legend is not added to the figure
if it is replaced, pilImgLegend includes text string, color-bar,
longName and units.
'''
# make a copy of nparray (otherwise a new reference to the same data is
# created and the original input data is destroyed at process())
array = np.array(nparray)
self.logger = add_logger('Nansat')
# if 2D array is given, reshape to 3D
if array.ndim == 2:
self.array = array.reshape(1, array.shape[0], array.shape[1])
else:
self.array = array
# note swaping of axis by PIL
self.width = self.array.shape[2]
self.height = self.array.shape[1]
# modify the default values using input values
self._set_defaults(kwargs)
# set fonts for Legend
self.fontFileName = os.path.join(os.path.dirname(
os.path.realpath(__file__)),
'fonts/DejaVuSans.ttf')
