def nansatFigure(nparr, mask, min, max, dir, fn, cmapName='gray'):
f = Figure(nparr)
f.process(cmin=min,
cmax=max,
cmapName=cmapName,
mask_array=mask,
mask_lut={0: [255, 0, 0]})
f.save(os.path.join(dir, fn), transparency=[255, 0, 0])
def nansatFigure(nparr, mask, min, max, dir, fn, cmapName='gray'):
f = Figure(nparr)
f.process(
cmin = min,
cmax = max,
cmapName = cmapName,
mask_array=mask,
mask_lut={0:[255,0,0]}
)
f.save(os.path.join(dir,fn), transparency=[255,0,0])
def test_add_latlon_grids_auto(self):
''' Should create figure with lon/lat gridlines spaced automatically '''
tmpfilename = os.path.join(ntd.tmp_data_path, 'figure_latlon_grids_auto.png')
n = Nansat(self.test_file_gcps)
b = n[1]
lon, lat = n.get_geolocation_grids()
f = Figure(b)
f.process(clim='hist', lonGrid=lon, latGrid=lat)
f.save(tmpfilename)
self.assertEqual(type(f), Figure)
self.assertTrue(os.path.exists(tmpfilename))
def test_add_latlon_grids_number(self):
''' Should create figure with lon/lat gridlines given manually '''
tmpfilename = os.path.join(self.tmp_data_path,
'figure_latlon_grids_number.png')
n = Nansat(self.test_file_gcps, mapper=self.default_mapper)
n.resize(3)
b = n[1]
lon, lat = n.get_geolocation_grids()
f = Figure(b)
f.process(cmax=100, lonGrid=lon, latGrid=lat, lonTicks=7, latTicks=7)
f.save(tmpfilename)
self.assertEqual(type(f), Figure)
self.assertTrue(os.path.exists(tmpfilename))
def test_add_latlon_grids_list(self):
''' Should create figure with lon/lat gridlines given manually '''
tmpfilename = os.path.join(self.tmp_data_path, 'figure_latlon_grids_list.png')
n = Nansat(self.test_file_gcps, mapper=self.default_mapper)
b = n[1]
lon, lat = n.get_geolocation_grids()
f = Figure(b)
f.process(clim='hist', lonGrid=lon,
latGrid=lat,
lonTicks=[28, 29, 30],
latTicks=[70.5, 71, 71.5, 73])
f.save(tmpfilename)
self.assertEqual(type(f), Figure)
self.assertTrue(os.path.exists(tmpfilename))
def test_add_latlon_grids_list(self):
''' Should create figure with lon/lat gridlines given manually '''
tmpfilename = os.path.join(self.tmp_data_path, 'figure_latlon_grids_list.png')
n = Nansat(self.test_file_gcps, mapper=self.default_mapper)
b = n[1]
lon, lat = n.get_geolocation_grids()
f = Figure(b)
f.process(clim='hist', lonGrid=lon,
latGrid=lat,
lonTicks=[28, 29, 30],
latTicks=[70.5, 71, 71.5, 73])
f.save(tmpfilename)
self.assertEqual(type(f), Figure)
self.assertTrue(os.path.exists(tmpfilename))
def test_add_latlon_grids_number(self):
''' Should create figure with lon/lat gridlines given manually '''
tmpfilename = os.path.join(self.tmp_data_path, 'figure_latlon_grids_number.png')
n = Nansat(self.test_file_gcps, mapper=self.default_mapper)
n.resize(3)
b = n[1]
lon, lat = n.get_geolocation_grids()
f = Figure(b)
f.process(cmax=100, lonGrid=lon,
latGrid=lat,
lonTicks=7,
latTicks=7)
f.save(tmpfilename)
self.assertEqual(type(f), Figure)
self.assertTrue(os.path.exists(tmpfilename))
This class has fllolowing methods:
estimate min/max, apply logarithmic scaling, convert to uint8,
append legend, save to a file
'''
# Create a Nansat object (n)
n = Nansat(iFileName)
# get numpy array from the Nansat object
array = n[1]
# Create a Figure object (fig)
fig = Figure(array)
# Set minimum and maximum values
fig.process(cmin=10, cmax=60)
# Save the figure
fig.save(oFileName + '01_clim.png')
# Create a Figure object (fig)
fig = Figure(array)
# Compute min and max valuse from ratio
clim = fig.clim_from_histogram(ratio=1.0)
# Set cmin and cmax values
fig.process(cmin=clim[0], cmax=clim[1])
# Save the figure
fig.save(oFileName + '02_clim.png')
# Create a Figure object (fig)
fig = Figure(array)
# Make indexed image with legend
