def IR_039c_CO2(self):
"""
IR_039 channel, but compensated for the CO2 absorption
"""
from trollimage.image import Image as trollimage
self.co2corr_chan()
self.check_channels("_IR39Corr")
img = GeoImage(self["_IR39Corr"].data,
self.area,
self.time_slot,
fill_value=(0, 0, 0),
mode="L")
#min_data = prop.min()
#max_data = prop.max()
min_data = 210 # same as for IR_039 in get_input_msg.py
max_data = 340 # same as for IR_039 in get_input_msg.py
colorize = True
if colorize:
# use trollimage to create a color map
cm = deepcopy(rainbow)
cm.set_range(min_data, max_data)
# colorize the image
img.colorize(cm)
return img
def ndvi(self):
"""Make a normalized vegitation index image
from Seviri channels.
"""
from trollimage.image import Image as trollimage
from trollimage.colormap import rdylgn
self.check_channels('VIS006', 'VIS008')
colorize = True
ndvi = (self['VIS008'] - self['VIS006']) / (self['VIS008'] +
self['VIS006'])
## use trollimage to create black white image
## black and white version
img = GeoImage(ndvi.data,
self.area,
self.time_slot,
fill_value=(0, 0, 0),
mode="L")
if colorize:
# use trollimage to create a color map
rdylgn.set_range(-1., +1.)
# colorize the image
img.colorize(rdylgn)
return img
def sza(self):
#self.check_channels("VIS006", "HRV", "IR_108")
import numpy as np
# calculate longitude/latitude and solar zenith angle
from pyorbital.astronomy import sun_zenith_angle
lonlats = self.area.get_lonlats()
#lonlats = self["IR_108"].area.get_lonlats()
sza = sun_zenith_angle(self.time_slot, lonlats[0], lonlats[1])
### stupid numbers for outside disk!!!
#import numpy.ma as ma
#sza=ma.masked_equal(sza, 8.24905797976)
#sza=ma.masked_equal(sza, 8.25871138053)
img = GeoImage(sza,
self.area,
self.time_slot,
fill_value=(0, 0, 0),
mode="L")
from trollimage.colormap import rainbow
cm = deepcopy(rainbow)
cm.set_range(0, 90)
img.colorize(cm)
return img
def sandwich(self):
"""Make a colored 10.8 RGB image with HRV resolution enhacement
from Seviri channels.
"""
from trollimage.image import Image as trollimage
self.check_channels('IR_108', "HRV")
## use trollimage to create black white image
#img = trollimage(self['IR_108'].data, mode="L")
img = GeoImage(self["IR_108"].data, self.area, self.time_slot,
mode="L") #, fill_value=0
# use trollimage to create a color map
greys.set_range(-30 + 273.15, 30 + 273.15)
cm2 = deepcopy(rainbow)
cm2.set_range(-73 + 273.15, -30.00001 + 273.15)
cm2.reverse()
my_cm = cm2 + greys
#luminance = GeoImage((self["HRV"].data), self.area, self.time_slot,
# crange=(0, 100), mode="L")
#luminance.enhance(gamma=2.0)
#img.replace_luminance(luminance.channels[0])
## colorize the image
img.colorize(my_cm)
return img
def daynight_background(self,
cos_scaled=True,
use_HRV=False,
smooth=False,
stretch=False,
colorscale='greys',
fixed_minmax=False,
white_clouds=True):
import numpy as np
# threshold sza
sza1 = 80.
# calculate longitude/latitude and solar zenith angle
from pyorbital.astronomy import sun_zenith_angle
lonlats = self["IR_108"].area.get_lonlats()
sza = sun_zenith_angle(self.time_slot, lonlats[0], lonlats[1])
if not smooth:
mask = np.array(sza > sza1)
else:
mask = np.zeros(ir108.shape)
mask[np.where(sza > sza1)] = 1
# select, if you want HRV or VIS006
if use_HRV:
vis = self["HRV"].data
# fill remaining area with VIS006
vis[vis.mask == True] = self["VIS006"].data[vis.mask == True]
else:
vis = self["VIS006"].data
# sceen out data at night/day part of the disk
ir108 = self["IR_108"].data * mask
# ... and apply cos scaling to mitigate effect of solar zenith angle
if not cos_scaled:
vis = vis * (1 - mask)
else:
vis = vis * (1 - mask) / (np.cos(np.radians(sza)) + 0.05)
# normilize vis reflectivity and ir brightness temperature
# to comparable range between 0 and 1
if fixed_minmax:
# minimum and maximum for ir108
ir1 = 190.
ir2 = 290.
# minimum and maximum for HRV (or VIS006) scale
vis1 = 0.
if not cos_scaled:
vis2 = 75. #for pure vis/hrv
else:
vis2 = 110. #for scaling with cos(sza)
else:
# linear stretch from p1 percentile to p2 percentile
#import matplotlib.pyplot as plt
#plt.hist(ir108[np.where(ir108 > 100)], bins='auto')
#plt.title("Histogram with 'auto' bins")
#plt.show()
#print " min/max(1) IR:", ir108.min(), ir108.max()
#print " min/max(1) VIS:", vis.min(), vis.max()
# percentile limits
p1 = 5
p2 = 95
#
ind_ir108 = np.where(ir108 > 100)
if len(ind_ir108) > 5:
ir1 = np.percentile(ir108[ind_ir108], p1)
ir2 = np.percentile(ir108[ind_ir108], p2)
else:
ir1 = 190.
ir2 = 290.
ind_vis = np.where(vis > 0)
if len(ind_vis) > 5:
vis1 = np.percentile(vis[np.where(vis > 0)], p1)
vis2 = np.percentile(vis[np.where(vis > 0)], p2)
else:
vis1 = 5.
if not cos_scaled:
vis2 = 75.
else:
vis2 = 110.
#print " min/max(2) IR:", ir1, ir2
#print " min/max(2) VIS:", vis1, vis2
# scale the vis and ir channel to the range [0,1] # multiply with mask to keep the zeros for day/night area
ir108 = (ir108 - ir1) / (ir2 - ir1) * mask
vis = (vis - vis1) / (vis2 - vis1) * (1 - mask)
#print " min/max scaled ir: ", ir108.min(), ir108.max()
#print " min/max scaled vis:", vis.min(), vis.max()
# invert ir108
ir108 = (1 - ir108) * mask
img = GeoImage(vis + ir108,
self.area,
self.time_slot,
fill_value=(0, 0, 0),
mode="L")
print(colorscale, white_clouds)
if colorscale == 'rainbow':
from trollimage.colormap import rainbow
cm = deepcopy(rainbow)
elif colorscale == 'greys':
from trollimage.colormap import greys
cm = deepcopy(greys)
if white_clouds:
#cm.reverse() # UH (my change in trollimage/colormap.py): color table is not any more changed, but changed one is returned.
cm = cm.reverse()
cm.set_range(0, 1)
img.colorize(cm)
if stretch:
print("... use streching ", stretch)
img.enhance(stretch=stretch)
return img
