def pp_fig(name, root=level2_root):
file_list = os.listdir(layer_root)
files = [
file for file in file_list if file.find(name) >= 0
and file.find('distribution') < 0 and file.find('npy') > 0
]
data = np.load(layer_root + files[0])
a = Lc.imp(data)
a1 = a.dB(data)
min_d = -30
max_d = 0
img = (a1 - min_d) * 255 / (max_d - min_d)
img[img > 255] = 255
img[img < 0] = 0
rows, cols = a1.shape
dpi = 100
fig = plt.figure(figsize=(cols / dpi, rows / dpi), dpi=dpi)
ax = fig.add_subplot(111)
ax.imshow(img, cmap='gray')
rows, cols = img.shape
cs = np.ones(rows, dtype=np.int)
rs = np.ones(cols, dtype=np.int)
cstd = np.arange(rows, dtype=np.int)
rstd = np.arange(cols, dtype=np.int)
for r in range(0, rows, 150):
plt.plot(rstd, rs * r, 'b')
for c in range(0, cols, 150):
plt.plot(cs * c, cstd, 'b')
plt.savefig(root + 'add_grid-' + name + '.png')
plt.close()
def save_fig(name, r, c, u, v, root=level2_root):
file_list = os.listdir(layer_root)
files = [
file for file in file_list if file.find(name) >= 0
and file.find('distribution') < 0 and file.find('npy') > 0
]
data = np.load(layer_root + files[0])
a = Lc.imp(data)
a1 = a.dB(data)
min_d = -30
max_d = 0
img = (a1 - min_d) * 255 / (max_d - min_d)
img[img > 255] = 255
img[img < 0] = 0
rows, cols = a1.shape
dpi = 100
fig = plt.figure(figsize=(cols / dpi, rows / dpi), dpi=dpi)
ax = fig.add_subplot(111)
ax.imshow(img, cmap='gray')
norm = mcolor.Normalize(vmin=0, vmax=20)
m = [np.sqrt(u[i]**2 + v[i]**2) for i in range(len(r))]
q = ax.quiver(c,
r,
u,
v,
m,
width=0.001,
headwidth=1,
cmap='bwr',
norm=norm)
# q = ax.quiver(c,r,u,v, m,cmap = 'bwr', norm = norm)
fig.colorbar(q, ax=ax)
plt.savefig(root + name + '.png')
plt.close()
def eliminate(sss):
# N_list=list(range(38, 72))
# N_list.remove(39)
# N_list.remove(58)
# N_list.remove(69)
N_list = list(range(36, 67))
temp = rd.Grid_data()
temp.LoadData(ty=sss)
wspeed_cds = np.sqrt(temp.u_cds * temp.u_cds + temp.v_cds * temp.v_cds)
wspeed_s2 = np.sqrt(temp.u_s2 * temp.u_s2 + temp.v_s2 * temp.v_s2)
wd_cds = np.arctan2(-temp.u_cds, -temp.v_cds) * 180 / np.pi
wd_s2 = np.arctan2(-temp.u_s2, -temp.v_s2) * 180 / np.pi
NRCS = temp.NRCS
del temp
tt = np.zeros(15, dtype=np.int8)
ss = np.zeros_like(tt)
ss[:] = 18
for r in range(15):
for c in range(19):
if wspeed_s2[42, r, c] > 0:
tt[r] = max(tt[r], c)
ss[r] = min(ss[r], c)
nums = len(N_list)
ws2 = np.zeros([nums, 15, 19], dtype=np.float_)
wcds = np.zeros_like(ws2)
ds2 = np.zeros_like(ws2)
dcds = np.zeros_like(ws2)
sigma = np.zeros_like(ws2)
for r in range(15):
if ss[r] <= tt[r]:
for c in range(ss[r], tt[r] + 1):
slist = Lc.Extract_NRCS(r, c, NRCS)
sigma[:, r, c] = slist
now = 0
for n in N_list:
ws2[now, r, c] = wspeed_s2[n, r, c]
wcds[now, r, c] = wspeed_cds[n, r, c]
ds2[now, r, c] = wd_s2[n, r, c]
dcds[now, r, c] = wd_cds[n, r, c]
now += 1
# ds2 = np.where(ds2<0, ds2+360, ds2)
for n in range(len(N_list)):
for r in range(15):
for c in range(19):
if np.logical_not(np.isnan(ds2[n, r, c])):
if ds2[n, r, c] < 0:
ds2[n, r, c] += 360
if np.logical_not(np.isnan(dcds[n, r, c])):
if dcds[n, r, c] < 0:
dcds[n, r, c] += 360
# dcds = np.where(dcds<0, dcds+360, dcds)
return sigma, ws2, wcds, ds2, dcds, ss, tt
def ResampleWind():
tt = np.load(coast_root + 'Left coast for each row.npy')
d = rd.SentinelData()
d.Get_List_NetCDF(ocn_root)
for indice in range(len(d.series)):
temp = rd.Data_Level2(d.series[indice], d.FList[indice])
temp.Get_WindData()
temp.CalWindVector()
mask = temp.windX.mask.reshape(-1)
Ind = np.where(mask == 0)[0]
lon = temp.lon.data.reshape(-1)
lat = temp.lat.data.reshape(-1)
U = temp.windX.data.reshape(-1)
V = temp.windY.data.reshape(-1)
lon, lat, U, V = lon[Ind], lat[Ind], U[Ind], V[Ind]
r, c, u, v = md2.GridLL_Wind(lon, lat, U, V)
ind = [xx for xx in range(len(r)) if tt[r[xx]] > c[xx]]
r_new = [r[xx] for xx in ind]
c_new = [c[xx] for xx in ind]
u_new = [u[xx] for xx in ind]
v_new = [v[xx] for xx in ind]
r, c, u, v = r_new, c_new, u_new, v_new
del r_new, c_new, u_new, v_new, ind
# md2.save_fig(d.series[indice], r, c, u, v)
if len(r) >= 4:
u10, v10 = Lc.InterpolationWinds(r, c, u, v)
np.save(level2_root + 'Interp/' + d.series[indice] + '-u10.npy',
u10)
np.save(level2_root + 'Interp/' + d.series[indice] + '-v10.npy',
v10)
u10 = np.zeros([15, 19], dtype=np.float16)
v10 = np.zeros_like(u10)
du = {}
dv = {}
for ii in range(len(r)):
r1, c1 = r[ii] // 150, c[ii] // 150
if (r1, c1) in du:
du[r1, c1].append(u[ii])
dv[r1, c1].append(v[ii])
else:
du[r1, c1] = [u[ii]]
dv[r1, c1] = [v[ii]]
for rr, cc in du.keys():
u10[rr, cc] = np.mean(du[rr, cc])
v10[rr, cc] = np.mean(dv[rr, cc])
np.save(level2_root + 'NotInterp/' + d.series[indice] + '-u10.npy',
u10)
np.save(level2_root + 'NotInterp/' + d.series[indice] + '-v10.npy',
v10)
return None
def MatchCDS():
tt = np.load(coast_root + 'Left coast for each row.npy')
data = rd.layers()
data.LoadData()
for indice in range(len(data.t)):
lon, lat, u, v = md2.ReadCDSData(data.t[indice][:-1])
rows, cols = u.shape
lon_N = [lon[c] for r in range(rows) for c in range(cols)]
lat_N = [lat[r] for r in range(rows) for c in range(cols)]
lon_N = np.array(lon_N)
lat_N = np.array(lat_N)
u, v = u.reshape(-1), v.reshape(-1)
r, c, u, v = md2.GridLL_Wind(lon_N, lat_N, u, v)
# ind = [xx for xx in range(len(r)) if tt[r[xx]] > c[xx]]
# r_new = [r[xx] for xx in ind]
# c_new = [c[xx] for xx in ind]
# u_new = [u[xx] for xx in ind]
# v_new = [v[xx] for xx in ind]
# r, c, u, v = r_new, c_new, u_new, v_new
# del r_new, c_new, u_new, v_new, ind
# md2.save_fig(data.t[indice][:-1], r, c, u, v, cds_root)
# if len(r)>=4:
# u10, v10 = Lc.InterpolationWinds(r, c, u, v)
# np.save(cds_root + 'Interp/' + data.t[indice][:-1] + '-u10.npy', u10)
# np.save(cds_root + 'Interp/' + data.t[indice][:-1] + '-v10.npy', v10)
# u10 = np.zeros([15,19], dtype = np.float16)
# v10 = np.zeros_like(u10)
du = {}
dv = {}
for ii in range(len(r)):
r1, c1 = r[ii] // 150, c[ii] // 150
if (r1, c1) in du:
du[r1, c1].append(u[ii])
dv[r1, c1].append(v[ii])
else:
du[r1, c1] = [u[ii]]
dv[r1, c1] = [v[ii]]
# for rr, cc in du.keys():
# u10[rr, cc] = np.mean(du[rr, cc])
# v10[rr, cc] = np.mean(dv[rr, cc])
u10, v10 = Lc.InterpolationCDSwind(du, dv)
np.save(cds_root + 'NotInterp/' + data.t[indice][:-1] + '-u10.npy',
u10)
np.save(cds_root + 'NotInterp/' + data.t[indice][:-1] + '-v10.npy',
v10)
return None
def OtherLayer(d, n):
for indice in range(n, len(d.series)):
temp = rd.Data_Level1(d.series[indice], d.FList[indice])
temp.OneStep()
GCP = temp.GCPs
SigmaNaught = temp.denoiseNRCS
name = 'Layer' + str(indice) + '-' + d.series[indice]
del temp
y1, x1 = SigmaNaught.shape
gm.AllLonLat(name, x1, y1, GCP, 5)
start = time.time()
SigmaNaught = Lc.Resize_SigmaNaught(SigmaNaught, 5)
print('Time for resize SigmaNaught:', (time.time() - start) / 60,
'mins')
Sigma_N, nums_arr = gm.GridSigmaNaught(SigmaNaught)
print('Time for resampling 1 SAR image:', (time.time() - start) / 60,
'mins')
jo.Matrix_save(Sigma_N, name, layer_root)
return None
def DrawMap(name, layer, alpha, vmin, vmax):
data = np.load('D:/Academic/MPS/Internship/Data/Sentinel/Level1/Layer/Layer40-20180518.npy')
a = Lc.imp(data)
a1 = a.dB(data)
min_d, max_d = -30, 0
img = (a1 - min_d) * 255 / (max_d - min_d)
img[np.isnan(img)] = 255
img[img > 255] = 255
img[img < 0] = 0
del a, a1
rows, cols = img.shape
dpi = 100
fig = plt.figure(figsize=(cols / dpi, rows / dpi), dpi=dpi)
ax = fig.add_subplot(111)
ax.imshow(img, cmap='gray')
cover = ax.imshow(layer.astype(np.float_), cmap = 'seismic', vmin =vmin, vmax = vmax, alpha = alpha)
cbar = plt.colorbar(cover, fraction = 0.025, pad = 0.08)
plt.gca().set_axis_off()
plt.title(name, fontdict={'fontsize':36}, pad = 2.2)
return None
def OneStepCoastline():
lon_grid, lat_grid = MergeGridLL()
coastXYZ = Lc.LoadCoastlineXYZ()
Line2Nodes(coastXYZ, lon_grid, lat_grid)
line = np.load(coast_root + '/Coastline in Grid.npy')
return line