for i in range(Ku.shape[0]):
cov[i] = [[Ku["sigmaxx"].iloc[i], Ku["sigmaxy"].iloc[i]],
[Ku["sigmaxy"].iloc[i], Ku["sigmayy"].iloc[i]]]
# i = -1
# a = np.array([[ [dfcov["sigmaxx"].iloc[i], dfcov["sigmaxy"].iloc[i]],
# [dfcov["sigmaxy"].iloc[i], dfcov["sigmayy"].iloc[i]] ]])
# for i in range(int(fetch.size/2)):
# cov = np.vstack((cov, a))
# print(fetch.shape, cov.shape)
# # print(cov)
sigma0 = srf.cross_section(xi, cov)
# sigma = sigma0 + np.random.normal(0, 0.2, sigma0.shape)
# sigma0 = 10*np.log10(srf.cross_section(xi, cov))
sigma = 10 * np.log10(sigma0)
# plt.plot(xi, sigma[:, 2])
# # plt.show()
x, y = np.meshgrid(fetch.T[0], xi)
# plt.figure(figsize=(10, 5))
plt.pcolor(x / 1e3, np.rad2deg(y), (sigma), vmin=sigma.min(), vmax=sigma.max())
# # plt.imshow(x/1e3, np.rad2deg(y), sigma)
# print(sigma.max())
# plt.xlabel("X, км")
# plt.ylabel("$\\theta$, градусы")
# bar = plt.colorbar()
z = df['elevation'].values.reshape(rc.surface.gridSize)
fig, ax = plt.subplots(ncols=2)
ax[1].plot(X[0, :], z[0, :])
Xi = np.linspace(-12, 12, 50)
N = np.zeros_like(Xi, dtype=float)
for i, xi in enumerate(Xi):
theta0 = sat.localIncidence(xi=xi)
ind = sat.sort(theta0, xi=xi)
theta1 = theta0.reshape(rc.surface.gridSize)
# ax[0].plot(X[0,:], np.rad2deg(theta1[0,:]))
# ax[1].plot(X.flatten()[ind], z.flatten()[ind], '.')
N[i] = ind[0].size
ax[0].plot(Xi, N / N.max())
sigma = surface.cross_section(np.deg2rad(Xi), cov)
ax[0].plot(Xi, sigma / sigma.max())
surface.coordinates = [x, y, df1['elevation']]
surface.normal = [df1['slopes x'], df1['slopes y'], np.ones(x.size)]
cov = np.cov(df1['slopes x'], df1['slopes y'])
sat = Experiment.experiment()
z = df1['elevation'].values.reshape(rc.surface.gridSize)
ax[1].plot(X[0, :], z[0, :])
Xi = np.linspace(-12, 12, 50)
N = np.zeros_like(Xi, dtype=float)
for i, xi in enumerate(Xi):
theta0 = sat.localIncidence(xi=xi)
ind = sat.sort(theta0, xi=xi)
for i in range(dfcov.shape[0]):
cov[i] = [[dfcov["sigmaxx"].iloc[i], dfcov["sigmaxy"].iloc[i]],
[dfcov["sigmaxy"].iloc[i], dfcov["sigmayy"].iloc[i]]]
i = -1
a = np.array([[[dfcov["sigmaxx"].iloc[i], dfcov["sigmaxy"].iloc[i]],
[dfcov["sigmaxy"].iloc[i], dfcov["sigmayy"].iloc[i]]]])
for i in range(int(fetch.size / 2)):
cov = np.vstack((cov, a))
print(fetch.shape, cov.shape)
# print(cov)
sigma0 = srf.cross_section(xi, cov)
sigma = sigma0 + np.random.normal(0, 0.2, sigma0.shape)
sigma0 = 10 * np.log10(srf.cross_section(xi, cov))
sigma = 10 * np.log10(np.abs(sigma))
# plt.plot(xi, sigma[:, 2])
# plt.show()
x, y = np.meshgrid(fetch, xi)
plt.figure(figsize=(10, 5))
plt.pcolor(x / 1e3, np.rad2deg(y), (sigma), vmin=sigma.min(), vmax=sigma.max())
# plt.imshow(x/1e3, np.rad2deg(y), sigma)
print(sigma.max())
plt.xlabel("X, км")
plt.ylabel("$\\theta$, градусы")
bar = plt.colorbar()
17,
)
for root, dirs, files in os.walk('./'):
for file in files:
if regex.match(file):
with open(file, "r") as f:
df = pd.read_csv(f, sep="\s+", header=None)
cov = np.zeros((2, 2))
cov[0, 0] = 0.0217
cov[1, 1] = 0.0325
xi_real = df[0].values[np.where(np.abs(df[0]) < 12)]
sigma_real = df[1].values[np.where(np.abs(df[0]) < 12)]
func = lambda xi, x, y: 10 * np.log10(
srf.cross_section(np.deg2rad(xi), np.array([[x, 0], [0, y]]
)).T[0])
from scipy.optimize import curve_fit
popt = curve_fit(func,
xdata=xi_real,
ydata=sigma_real,
p0=[1, 1],
bounds=[0, 1])[0]
A = 0.95
# A = 1
popt[0] *= A
popt[1] *= 1 / A
fig, ax = plt.subplots()
ax.text(
0.5,
0.35,