color_dtdb.py File Reference

Go to the source code of this file.

Namespaces
	color_dtdb

Functions
def	rgb2ycbcr (r, g, b)
def	ycbcr2rgb (y, cb, cr)
def	rgb2yuv (r, g, b)
def	yuv2rgb (y, u, v)
def	rgb2ycbcr2 (r, g, b)
def	ycbcr2rgb2 (y, pb, pr)
def	histeq (im, nbr_bins=256)
def	pseudocolor (val, minval, maxval)
def	plot_scalar_array (ds_str, title_str, mina, maxa)
def	plot_rgb ()

Variables
int	W410 = 0
int	W445 = 1
int	W490 = 2
int	W550 = 3
int	W670 = 4
int	W865 = 5
int	W1240 = 6
int	W1610 = 7
int	W2250 = 8
int	NWL = 9
list	wlstr = ["410","445","490","550","670","865","1240","1610","2250"]
float	Kb = 0.114
float	Kr = 0.299
	RGB2YCbCr = np.array([[0.257, 0.504, 0.098],[-0.148, -0.291, 0.439],[0.439, -0.368, -0.071]])
	YCbCr2RGB = np.array([[1.0, 0.0, 1.596],[1.0, -0.392, -0.813],[1.0, 2.017, 0.0]])
	RGB2YUV = np.array([[0.299, 0.587, 0.114],[-0.14713, -0.28886, 0.436],[0.615, -0.51499, -0.10001]])
	YUV2RGB = np.array([[1.0, 0.0, 1.13983],[1.0, -0.39465, -0.58060],[1.0, 2.03211, 0.0]])
	args = sys.argv
	dtdb_dirpath = args[1]
	dataset = args[2]
string	colors = "123"
int	bc = np.int(args[3][0])-1
int	gc = np.int(args[3][1])-1
int	rc = np.int(args[3][2])-1
string	logfile = ""
string	command = "date > " + logfile
	result = os.system(command)
	dtdb_dircontents = os.listdir(dtdb_dirpath)
string	filepath = dtdb_dirpath + "/" + x
string	image_path = dtdb_dirpath + "/IMAGES"
string	outfilename = x + "_" + dname + ".png"
string	path = image_path + "_" + dname
string	outpath = path + "/" + outfilename
string	title_str = ' Angstrom Exponent'
string	ds_str = 'ae'
float	maxa = 2.0
float	mina = 0.0
string	image_path1 = image_path + "/histograms"
string	outfilename1 = x + "_hist_410.png"
string	outpath1 = image_path1 + "/" + outfilename1
string	image_path2 = image_path + "/maps"
string	outfilename2 = x + "_map.png"
string	outpath2 = image_path2 + "/" + outfilename2
list	ct = [3200,3200]
list	st = [0,0]
int	refwl = W865
int	refbs = W410
int	nbins = 100
int	hmin = 0
int	hmax = 10
float	max0 = 0.0
float	min0 = -2.0
float	max1 = 2.5
float	min1 = -0.5
	hist = np.zeros((NWL, nbins, nbins))
	cmap = cm.get_cmap('turbo')
	fig1 = plt.figure(figsize=(16,10))
	fig2 = plt.figure(figsize=(16,8))
def	irgb = plot_rgb()
	ax2 = fig2.add_subplot(2,4,1)
	rfl0 = xr.load_dataset(filepath,group='/observations')['rhot_'+wlstr[refwl]].values[st[0]:st[0]+ct[0],st[1]:st[1]+ct[1]]
	lrfl0 = np.log10(np.clip(rfl0, a_min=0.0001, a_max=1.0))
	rflbs = xr.load_dataset(filepath,group='/observations')['rhot_'+wlstr[refbs]].values[st[0]:st[0]+ct[0],st[1]:st[1]+ct[1]]
	lrflbs = np.log10(np.clip(rflbs, a_min=0.0001, a_max=1.0))
	ml = mpl.cm.ScalarMappable(norm=None, cmap=cmap)
	aec = ml.to_rgba(lrfl0, alpha=None)
int	wl1 = 1
int	wl2 = 1
bool	bwl2 = False
	rfl1 = xr.load_dataset(filepath,group='/observations')['rhot_'+wlstr[wl]].values[st[0]:st[0]+ct[0],st[1]:st[1]+ct[1]]
	lrfl1 = lrflbs - np.log10(np.clip(rfl1, a_min=0.0001, a_max=1.0))
	edges
	range
	bins
	density
	ax1 = fig1.add_subplot(2,4,wl1)
	xpos0
	ypos0
	cp1 = ax1.contourf(xpos0/2, ypos0/2, np.clip(hist[wl].T, a_min=hmin, a_max=hmax), levels=100, cmap=cmap)
bool	bticks = True
	labelleft
	left
	b
	True
	which
	color
	linestyle
	alpha
	facecolor
	loc = plt.LinearLocator(11)
	tvals = loc.tick_values(hmin, hmax)
	bottom
	top
	right
	wspace
	hspace
	cb_ax = fig1.add_axes([0.93, 0.1, 0.02, 0.8])
	cax
	ticks
	bluec = xr.load_dataset(filepath,group='/observations',mask_and_scale=True)['rhot_490'].values
	greenc = xr.load_dataset(filepath,group='/observations',mask_and_scale=True)['rhot_550'].values
	redc = xr.load_dataset(filepath,group='/observations',mask_and_scale=True)['rhot_670'].values
	ocean = xr.load_dataset(filepath,group='/geophysical_data')['AOT_ocean']
	land = xr.load_dataset(filepath,group='/geophysical_data')['AOT_land']
	cldmsk = xr.load_dataset(filepath,group='/meteorology')['cloud_mask']
	cldtst = xr.load_dataset(filepath,group='/quality')['cloud_test']
	nomask = np.zeros_like(cldmsk, dtype=np.int16)
	ro = np.clip(np.nan_to_num(redc),0,10.0)
	go = np.clip(np.nan_to_num(greenc),0,10.0)
	bo = np.clip(np.nan_to_num(bluec),0,10.0)
	y = y/np.max(y)
	u
	v
	yh
	Y_cdf
	mino_all = min(np.min(ro), np.min(go), np.min(bo))
float	maxo_all = max(np.max(ro), np.max(go), np.max(bo)) + 0.001
float	gamma = 1.0
float	ga = np.ones_like(ro)*gamma
	rg = np.power(ro, ga)
	gg = np.power(go, ga)
	bg = np.power(bo, ga)
	yg = np.power(yh, ga)
	ys = np.divide(yg,y)
int	scale = 255
	im_red = im.fromarray(np.uint8(np.clip(rg,0,1.0)*scale))
	im_green = im.fromarray(np.uint8(np.clip(gg,0,1.0)*scale))
	im_blue = im.fromarray(np.uint8(np.clip(bg,0,1.0)*scale))
	im_rgb = im.merge("RGB", (im_red, im_green, im_blue))
	set = dataset.partition("/")