NASA Ocean Color

ocssw V2022

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("/")

Namespaces

Functions

Variables