def plref(X, bands, theme, title="", oxlab=False):
cloud, cloud_ice, snow_fine, snow_granular, snow_med, soil, veg, ice, water, clear_sky = io_refdata.read_refdata(
"/home/kawahara/exomap/sot/data/refdata")
fontsize = 18
matplotlib.rcParams.update({'font.size': fontsize})
fig = plt.figure(figsize=(8, 5.5))
ax = fig.add_subplot(111)
nnl = 1 #len(np.median(bands,axis=1))
if theme == "3c":
u, val, normvveg = norm(veg)
ax.plot(u, val, c="gray", lw=2, label="vegitation (deciduous)")
u, val, normvsoil = norm(soil)
ax.plot(u, val, c="gray", lw=2, ls="dashed", label="soil")
u, val, normvwater = norm(water)
ax.plot(u, val, c="gray", lw=2, ls="-.", label="water")
else:
normvveg = 1.0
normvsoil = 1.0
normvwater = 1.0
fac = 1.0
mband = np.median(bands, axis=1)
dband = mband[1] - mband[0]
if theme == "3c":
plt.xlim(0.4, 0.9)
fac0 = fac / np.sum(X[0, :]) / dband / normvveg
fac1 = fac / np.sum(X[1, :]) / dband / normvsoil
fac2 = fac / np.sum(X[2, :]) / dband / normvwater
else:
plt.xlim(0.3, 0.9)
try:
fac0 = fac / np.sum(X[0, :]) / dband
fac1 = fac / np.sum(X[1, :]) / dband
fac2 = fac / np.sum(X[2, :]) / dband
fac3 = fac / np.sum(X[3, :]) / dband
except:
print("No 4th comp")
if theme == "3c":
plt.plot(np.median(bands, axis=1),
X[0, :] * fac0,
"o",
label="Component 0",
color="C2")
plt.plot(np.median(bands, axis=1),
X[1, :] * fac1,
"s",
label="Component 1",
color="C1")
plt.plot(np.median(bands, axis=1),
X[2, :] * fac2,
"^",
label="Component 2",
color="C0")
try:
plt.plot(np.median(bands, axis=1),
X[3, :] * fac3,
"^",
label="Component 3",
color="C3")
plt.plot(np.median(bands, axis=1), X[3, :] * fac3, color="C3")
except:
print("No 4th comp")
plt.plot(np.median(bands, axis=1), X[0, :] * fac0, color="C2", lw=2)
plt.plot(np.median(bands, axis=1), X[1, :] * fac1, color="C1", lw=2)
plt.plot(np.median(bands, axis=1), X[2, :] * fac2, color="C0", lw=2)
elif theme == "dscovr":
#c,o,v,l
plt.plot(np.median(bands, axis=1),
X[0, :] * fac0,
"o",
label="Component 0",
color="gray")
plt.plot(np.median(bands, axis=1),
X[1, :] * fac1,
"s",
label="Component 1",
color="C0")
plt.plot(np.median(bands, axis=1),
X[2, :] * fac2,
"^",
label="Component 2",
color="C2")
plt.plot(np.median(bands, axis=1),
X[3, :] * fac2,
"*",
label="Component 3",
color="C3")
plt.plot(np.median(bands, axis=1), X[0, :] * fac0, color="gray", lw=2)
plt.plot(np.median(bands, axis=1), X[1, :] * fac1, color="C0", lw=2)
plt.plot(np.median(bands, axis=1), X[2, :] * fac2, color="C2", lw=2)
plt.plot(np.median(bands, axis=1), X[3, :] * fac2, color="C3", lw=2)
if oxlab:
plt.axvline(0.688, color="blue", alpha=0.2, lw=5)
plt.axvline(0.764, color="blue", alpha=0.2, lw=5)
# plt.text("")
plt.tick_params(labelsize=16)
plt.ylabel("Reflection Spectra", fontsize=16)
plt.xlabel("wavelength [micron]", fontsize=16)
plt.legend(fontsize=12)
plt.title(title)
plt.savefig("ref.pdf", bbox_inches="tight", pad_inches=0.0)
plt.show()
#sys.exit()
## load class map
dataclass = np.load("/home/kawahara/exomap/sot/data/cmap3class.npz")
cmap = dataclass["arr_0"]
npix = len(cmap)
nclass = (len(np.unique(cmap)))
nside = hp.npix2nside(npix)
vals = dataclass["arr_1"]
valexp = dataclass["arr_2"]
print("Nclass=", nclass)
### Set reflectivity
cloud,cloud_ice,snow_fine,snow_granular,snow_med,soil,veg,ice,water,clear_sky\
=io_refdata.read_refdata("/home/kawahara/exomap/sot/data/refdata")
#mean albedo between waves and wavee
#bands=[[0.4,0.5],[0.5,0.6],[0.6,0.7],[0.7,0.8],[0.8,0.9]]#,[0.9,1.0]]
refsurfaces = [water, soil, veg]
malbedo = io_surface_type.set_meanalbedo(0.8, 0.9, refsurfaces, clear_sky)
mmap, Ain, Xin = toymap.make_multiband_map(cmap, refsurfaces, clear_sky, vals,
bands)
ave_band = np.mean(np.array(bands), axis=1)
cc = plt.cm.viridis
hp.mollview(A[:, 0], title="Component 0", flip="geo", cmap=cc) #,min=0,max=1)
plt.savefig("C0.pdf", bbox_inches="tight", pad_inches=0.0)
import healpy as hp
import sys
## load class map
dataclass=np.load("../../data/cmap3class.npz")
cmap=dataclass["arr_0"]
npix=len(cmap)
nclass=(len(np.unique(cmap)))
nside=hp.npix2nside(npix)
vals=dataclass["arr_1"]
valexp=dataclass["arr_2"]
print("Nclass=",nclass)
# ## Set reflectivity
cloud,cloud_ice,snow_fine,snow_granular,snow_med,soil,veg,ice,water,clear_sky\
=io_refdata.read_refdata("../../../data/refdata")
#mean albedo between waves and wavee
print(io_refdata.get_meanalbedo(veg,0.8,0.9),io_refdata.get_meanalbedo(veg,0.4,0.5))
bands=[[0.4,0.5],[0.5,0.6],[0.6,0.7],[0.7,0.8]]
#bands=[[0.4,0.5],[0.6,0.7],[0.8,0.9]]
#bands=[[0.4,0.5],[0.6,0.7],[0.8,0.9],[1.0,1.1]]
#bands=[[0.4,0.45],[0.45,0.5],[0.5,0.55],[0.55,0.6],[0.6,0.65],\
# [0.65,0.7],[0.7,0.75],[0.75,0.8],[0.8,0.85],[0.85,0.9]]
if nclass==4:
refsurfaces=[water,soil,snow_med,veg]
elif nclass==3:
refsurfaces=[water,soil,veg]
else:
print("Nclass should be 3 or 4 currently")
def plref_each(X, bands, ls, lab, symbol=False):
cloud, cloud_ice, snow_fine, snow_granular, snow_med, soil, veg, ice, water, clear_sky = io_refdata.read_refdata(
"/home/kawahara/exomap/sot/data/refdata")
nnl = 1 #len(np.median(bands,axis=1))
u, val, normvveg = pm.norm(veg)
u, val, normvsoil = pm.norm(soil)
u, val, normvwater = pm.norm(water)
plt.xlim(0.4, 0.9)
fac = 1.0
mband = np.median(bands, axis=1)
dband = mband[1] - mband[0]
fac0 = fac / np.sum(X[0, :]) / dband / normvveg
fac1 = fac / np.sum(X[1, :]) / dband / normvsoil
fac2 = fac / np.sum(X[2, :]) / dband / normvwater
plt.plot(np.median(bands, axis=1), X[0, :] * fac0, color="C2", lw=2, ls=ls)
plt.plot(np.median(bands, axis=1), X[1, :] * fac1, color="C1", lw=2, ls=ls)
plt.plot(np.median(bands, axis=1),
X[2, :] * fac2,
color="C0",
lw=2,
ls=ls,
label=lab)
if symbol:
plt.plot(np.median(bands, axis=1),
X[0, :] * fac0,
"o",
color="C2",
lw=2,
ls=ls)
plt.plot(np.median(bands, axis=1),
X[1, :] * fac1,
"s",
color="C1",
lw=2,
ls=ls)
plt.plot(np.median(bands, axis=1),
X[2, :] * fac2,
"^",
color="C0",
lw=2,
ls=ls)
plt.tick_params(labelsize=16)
def plotrefdirect(axfiles, lam):
bands = read_data.getband()
fontsize = 18
matplotlib.rcParams.update({'font.size': fontsize})
fig = plt.figure(figsize=(8, 5.5))
ax = fig.add_subplot(111)
cloud, cloud_ice, snow_fine, snow_granular, snow_med, soil, veg, ice, water, clear_sky = io_refdata.read_refdata(
"/home/kawahara/exomap/sot/data/refdata")
nnl = 1 #len(np.median(bands,axis=1))
u, val, normvveg = pm.norm(veg)
# ax.plot(u,val,c="gray",lw=2,label="vegitation (deciduous)")
ax.plot(u, val, c="gray", lw=2)
u, val, normvsoil = pm.norm(soil)
ax.plot(u, val, c="gray", lw=2, ls="dashed")
# ax.plot(u,val,c="gray",lw=2,ls="dashed",label="soil")
u, val, normvwater = pm.norm(water)
ax.plot(u, val, c="gray", lw=2, ls="-.")
# ax.plot(u,val,c="gray",lw=2,ls="-.",label="water")
lss = ["dashed", "solid", "dotted", "dashdot"]
sy = [False, True, False]
for i in range(0, 3):
axfile = axfiles[i]
A, X, resall = read_data.readax(axfile)
mmrsa = mrsa.mrsa_meanX(X)
print(mmrsa)
print(lam[i])
metric.plref_each(X,
bands,
lss[i],
"$\lambda_X=10^{" + str(int(lam[i])) + "}$",
symbol=sy[i])
plt.ylim(0, 0.6)
plt.ylabel("Unmixed Spectra", fontsize=16)
plt.xlabel("wavelength [micron]", fontsize=16)
plt.legend(fontsize=13)
plt.savefig("refdirect.pdf", bbox_inches="tight", pad_inches=0.0)
plt.show()
