def test_get_auto_ticks_vector(self):
n = Nansat(self.test_file_gcps)
lon, lat = n.get_geolocation_grids()
f = Figure(lon)
lonTicks = f._get_auto_ticks([28, 29, 30, 100], lon)
self.assertEqual(len(lonTicks), 3)
def test_get_auto_ticks_number(self):
n = Nansat(self.test_file_gcps)
lon, lat = n.get_geolocation_grids()
f = Figure(lon)
lonTicks = f._get_auto_ticks(5, lon)
latTicks = f._get_auto_ticks(5, lat)
self.assertEqual(len(lonTicks), 5)
n.logger.error(str(lonTicks))
n.logger.error(str(latTicks))
def test_get_tick_index_from_grid(self):
''' Should return indeces of pixel closest to ticks '''
n = Nansat(self.test_file_gcps)
lon, lat = n.get_geolocation_grids()
f = Figure(lon)
lonTicksIdx = f._get_tick_index_from_grid([28.5, 29], lon, 1, lon.shape[1])
latTicksIdx = f._get_tick_index_from_grid([71, 71.5], lat, lat.shape[0], 1)
n.logger.error(str(lonTicksIdx))
n.logger.error(str(latTicksIdx))
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_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))
def test_make_transparent_color(self, mock1):
f = Figure()
img_array = np.array([[1.,2.],[3.,4.]])
f.pilImg = Image.fromarray(img_array)
f.reprojMask = np.zeros((2,2)) == 1
f.transparency = [1,1,1]
f._make_transparent_color()
self.assertTrue((np.array(f.pilImg)[:,:,3] == np.array([[0,255],[255,255]])).all())
def test_make_transparent_color(self, mock1):
f = Figure()
img_array = np.array([[1.,2.],[3.,4.]])
f.pilImg = Image.fromarray(img_array)
f.reprojMask = np.zeros((2,2)) == 1
f.transparency = [1,1,1]
f._make_transparent_color()
self.assertTrue((np.array(f.pilImg)[:,:,3] == np.array([[0,255],[255,255]])).all())
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_init_array(self):
f = Figure(np.zeros((10,10)))
self.assertEqual(type(f), Figure)
def test_apply_logarithm(self, mock1):
f = Figure()
f.array = np.ones((1,2,2))*0.1
f.apply_logarithm()
self.assertTrue(np.allclose(np.ones((1,2,2))*0.31622777, f.array))
This class consequently create a figrue from 2D numpy array (1band) or
an RGB figure from 3D numpy array (3bands)
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)
def test_apply_logarithm(self, mock1):
f = Figure()
f.array = np.ones((1, 2, 2)) * 0.1
f.apply_logarithm()
self.assertTrue(np.allclose(np.ones((1, 2, 2)) * 0.31622777, f.array))
