def integrand(w, alpha=0):
M = get_mirror()
Q = get_detector()
S = get_solar()
return w * M(w) * Q(w) * ref_rock(w, alpha) * S(w)
def integrand(self, w, n=4, alpha=0):
return w * self.M(w)**n * self.Q(w) * ref_rock(w, alpha) * self.S(w)
def integrand(w, alpha=0, ice=False):
if ice:
return w * M(w) * Q(w) * ref_ice(w, alpha).T * S(w)
else:
return w * M(w) * Q(w) * ref_rock(w, alpha).T * S(w)
def func(t_exp):
i = quad(integrand, c2 - w2 / 2, c2 + w2 / 2, args=(N, alpha))[0]
signal = CoCa.A_Omega / CoCa.G * t_exp * i / (const.h * const.c *
CoCa.r_h**2) * 1e-9
return signal - 2**14
sol = fsolve(func, 0.001)
print(alpha, sol)
t.append(sol[0])
wavelengths = np.linspace(300, 1100, 100)
fig, axes = plt.subplots(nrows=2)
axes[0].plot(phase_angle, t, color=BLACK, ls="--")
axes[0].set_xlabel("phase angle [°]")
axes[0].set_ylabel("exposure time [s]")
# phase_angle = 51
axes[1].plot(wavelengths,
ref_rock(wavelengths, phase_angle).T,
color=BLACK,
ls="--")
axes[1].set_xlabel("wavelength [nm]")
axes[1].set_ylabel("I/F")
plt.savefig("plots/exposure.png")
plt.show()
def integrand(w, N=4, alpha=0):
return w * M(w)**N * Q(w) * ref_rock(w, alpha).T * S(w)
def integrand_low(w, N=4, alpha=0):
return w * M(w)**N * Q(w) * ref_rock(w, alpha)[0] * S(w)
def plot_signals():
M = get_mirror()
Q = get_detector()
S = get_solar()
F = get_filter_from_data()
F, F2, F3, F4 = init_filters(400, [200, 200, 200, 200])
F, F2, F3, F4 = init_filters_thomas()
ref_low, ref_up = get_reflectance()
def integrand_low(w, N=4, alpha=0):
return w * M(w)**N * Q(w) * ref_rock(w, alpha)[0] * S(w)
def integrand_up(w, N=4, alpha=0):
return w * M(w)**N * Q(w) * ref_ice(w, alpha)[0] * S(w)
phase_angle = np.arange(0, 100, 5)
signals_low = np.zeros(phase_angle.shape)
signals_up = np.zeros(phase_angle.shape)
signals_std = np.zeros(phase_angle.shape)
N = 4
for i, alpha in enumerate(phase_angle):
print(i, alpha)
integral_low, int_err = quad(integrand_low, 300, 1100, args=(N, alpha))
integral_up, int_err = quad(integrand_up, 300, 1100, args=(N, alpha))
widths_up = []
widths_low = []
centers = range(450, 1000, 50)
for filter_center in centers:
def func(width):
G = 2.5 # electron per DN
A = (7e-6)**2
Omega = 0.64e-10
r_h = 1 # A.U.
t_exp = 0.00005 # seconds
i = quad(integrand_up,
filter_center - width / 2,
filter_center + width / 2,
args=(N, alpha))[0]
signal = G * A * Omega * t_exp * i / (const.h *
const.c) / r_h**2
return signal - 2**14
sol = fsolve(func, 100)
print(filter_center, sol)
widths_up.append(sol[0])
def func(width):
G = 2.5 # electron per DN
A = (7e-6)**2
Omega = 0.64e-10
r_h = 1 # A.U.
t_exp = 0.001 # seconds
i = quad(integrand_low,
filter_center - width / 2,
filter_center + width / 2,
args=(N, alpha))[0]
signal = G * A * Omega * t_exp * i / (const.h *
const.c) / r_h**2
return signal - 2**14
sol = fsolve(func, 100)
print(filter_center, sol)
widths_low.append(sol[0])
widths_up = np.array(widths_up)
widths_low = np.array(widths_low)
plt.plot(centers,
widths_low + (widths_up - widths_low) / 2,
color="#e6002e",
alpha=0.5)
plt.fill_between(centers,
widths_low,
widths_up,
color="#e6002e",
alpha=0.5)
w1 = 150
w2 = 100
w3 = 100
w4 = 150
c1 = 460
c2 = 650
c3 = 750
c4 = 900
plt.scatter(c1, w1, label="BLUE", color="#4767af")
plt.scatter(c2, w2, label="GREEN", color="#466553")
plt.scatter(c3, w3, label="RED", color="#e6002e")
plt.scatter(c4, w4, label="NIR", color="black")
plt.xlabel("filter center [nm]")
plt.ylabel("filter width [nm]")
plt.savefig("filter_widths.png")
plt.show()
wavelengths = np.linspace(300, 1100, 1000)
plt.plot(wavelengths,
integrand_low(wavelengths) +
(integrand_up(wavelengths) - integrand_low(wavelengths)) / 2,
color="#e6002e",
alpha=0.5)
plt.fill_between(wavelengths,
integrand_low(wavelengths),
integrand_up(wavelengths),
color="#e6002e",
alpha=0.5)
plt.show()
exit()
G = 2.5 # electron per DN
A = 0.135**2 * 4 * np.pi
A = (7e-6)**2
Omega = 0.64e-10
r_h = 1 # A.U.
t_exp = 0.00005 # seconds
signal_low = G * A * Omega * t_exp * integral_low / (const.h *
const.c) / r_h**2
signal_up = G * A * Omega * t_exp * integral_up / (const.h *
const.c) / r_h**2
# TODO: calculate with uncertainties of other parameters
signals_low[i] = signal_low
signals_up[i] = signal_up
fig, axes = plt.subplots(nrows=6, sharex=True)
wavelengths = np.linspace(300, 1100, 1000)
phase_angle = 58
axes[0].plot(wavelengths, 100 * Q(wavelengths), color="black")
axes[0].set_ylabel(r"$Q$ [%]")
axes[1].plot(wavelengths, 100 * M(wavelengths), color="black")
axes[1].set_ylabel(r"$M$ [%]")
axes[2].plot(wavelengths, 100 * F(wavelengths), color="#4767af") # blue
axes[2].plot(wavelengths, 100 * F2(wavelengths), color="#466553") # green
axes[2].plot(wavelengths, 100 * F3(wavelengths), color="#e6002e") # red
axes[2].plot(wavelengths, 100 * F4(wavelengths), color="black")
axes[2].set_ylabel(r"$T$ [%]")
axes[3].plot(wavelengths,
100 * (ref_rock(wavelengths, phase_angle) +
(ref_ice(wavelengths, phase_angle) -
ref_rock(wavelengths, phase_angle)) / 2),
color="#e6002e")
axes[3].fill_between(wavelengths,
100 * ref_rock(wavelengths, phase_angle),
100 * ref_ice(wavelengths, phase_angle),
color="#e6002e",
alpha=0.5)
axes[3].set_ylabel(r"$R$ [%]")
axes[3].legend()
axes[4].plot(wavelengths,
S(wavelengths),
color="black",
label=r"$F_\odot$")
axes[4].set_ylabel(r"$F_\odot$ [Wm$^{-2}$nm$^{-1}$]")
axes[5].plot(wavelengths,
M(wavelengths)**4 * ref_rock(wavelengths, phase_angle) *
S(wavelengths),
color="#e6002e",
label=r"$F_\odot(\omega)M(\omega)^NR(\omega)$")
axes[5].set_ylabel(r"$F_\odot'$ [Wm$^{-2}$nm$^{-1}$]")
axes[5].set_xlabel(r"$\lambda$ [nm]")
plt.savefig("filters.png")
plt.show()
plt.plot(phase_angle,
signals_low + (signals_up - signal_low) / 2,
color="#e6002e")
plt.fill_between(phase_angle,
signals_low,
signals_up,
color="#e6002e",
alpha=0.5)
plt.fill_between(phase_angle, 0, 8 * 2.5, color="black", alpha=0.5)
plt.axhline(2**14, color="#e6002e", ls="--")
plt.xlabel(r"$\alpha$ [°]")
plt.ylabel(r"$S$ [DN]")
plt.title(fr"$t$ = {t_exp} s")
plt.savefig("signal.png")
plt.show()
def integrand(w, alpha=0):
return w * M(w) * Q(w) * ref_rock(w, alpha) * S(w)
def main():
fig, ax = plt.subplots(ncols=2, nrows=2, sharey=True, figsize=(10, 6))
phase_angles = np.linspace(0.01, 92, 100)
i = phase_angles
e = np.zeros(phase_angles.shape)
phase11_rock = phase_clement(11, "rock")
phase11_ice = phase_clement(11, "ice")
phase51_rock = phase_clement(51, "rock")
phase51_ice = phase_clement(51, "ice")
s = 15
ax[0][0].scatter([11] * len(phase11_rock),
phase11_rock,
marker="x",
s=s,
color=BLACK,
label=r"rock $\alpha=11$°")
ax[0][0].scatter([51] * len(phase51_rock),
phase51_rock,
marker="x",
s=s,
color=BLACK,
label=r"rock $\alpha=51$°")
ax[0][0].scatter([11] * len(phase11_ice),
phase11_ice,
marker="x",
s=s,
color=RED,
label=r"ice $\alpha=11$°")
ax[0][0].scatter([51] * len(phase51_ice),
phase51_ice,
marker="x",
s=s,
color=RED,
label=r"ice $\alpha=51$°")
r = ref_rock(649, phase_angles)
r_ice = ref_ice(649, phase_angles)
ax[0][0].plot(phase_angles, r, color=BLACK, ls="--", label="hapke rock")
ax[1][0].plot(phase_angles, r, color=BLACK, ls="--", label="hapke rock")
ax[0][0].plot(phase_angles, r_ice, color=RED, ls="--", label="hapke ice")
ax[1][0].plot(phase_angles, r_ice, color=RED, ls="--", label="hapke ice")
for material in ["ice", "rock"]:
for phase_angle in [51, 58, 89, 92, "92b"]:
filename = f"data/deshapriya/67p_{material}_alpha_{phase_angle}.csv"
df = pd.read_csv(filename, names=["wavelength", "r"])
print(df.wavelength)
df = df[(df.wavelength < 650) & (df.wavelength > 648)]
if phase_angle == "92b": phase_angle = 92
if material == "ice":
c = RED
ax[0][0].scatter([phase_angle] * len(df.r),
df.r,
marker="o",
s=s,
color=c)
else:
c = BLACK
ax[0][0].scatter([phase_angle] * len(df.r),
df.r,
marker="o",
s=s,
color=c)
for material in ["ice", "rock"]:
for phase_angle in [51, 58, 89, 92, "92b"]:
filename = f"data/deshapriya/67p_{material}_alpha_{phase_angle}.csv"
df = pd.read_csv(filename, names=["wavelength", "r"])
print(df.wavelength)
df = df[(df.wavelength < 950) & (df.wavelength > 930)]
if phase_angle == "92b": phase_angle = 92
if material == "ice":
c = RED
ax[1][0].scatter([phase_angle] * len(df.r),
df.r,
marker="o",
s=s,
color=c)
else:
c = BLACK
ax[1][0].scatter([phase_angle] * len(df.r),
df.r,
marker="o",
s=s,
color=c)
phase11_rock = phase_clement(11, "rock", 932)
phase11_ice = phase_clement(11, "ice", 932)
phase51_rock = phase_clement(51, "rock", 932)
phase51_ice = phase_clement(51, "ice", 932)
ax[1][0].scatter([11] * len(phase11_rock),
phase11_rock,
marker="x",
s=s,
color=BLACK,
label=r"rock $\alpha=11$°")
ax[1][0].scatter([51] * len(phase51_rock),
phase51_rock,
marker="x",
s=s,
color=BLACK,
label=r"rock $\alpha=51$°")
ax[1][0].scatter([11] * len(phase11_ice),
phase11_ice,
marker="x",
s=s,
color=RED,
label=r"ice $\alpha=11$°")
ax[1][0].scatter([51] * len(phase51_ice),
phase51_ice,
marker="x",
s=s,
color=RED,
label=r"ice $\alpha=51$°")
a = mlines.Line2D([], [],
color=BLACK,
marker='o',
ls='',
label='deshapryia rock')
b = mlines.Line2D([], [],
color=RED,
marker='o',
ls='',
label='deshapryia ice')
c = mlines.Line2D([], [],
color=BLACK,
marker='x',
ls='',
label='fornasier rock')
d = mlines.Line2D([], [],
color=RED,
marker='x',
ls='',
label='fornasier ice')
e = mlines.Line2D([], [], color=BLACK, ls='--', label='rock')
f = mlines.Line2D([], [], color=RED, ls='--', label='ice')
ax[0][0].legend(handles=[a, b, c, d, e, f])
ax[0][0].set_xlabel("phase angle [°]")
ax[1][0].set_xlabel("phase angle [°]")
ax[0][0].set_ylabel("I/F")
ax[1][0].set_ylabel("I/F")
ax[0][0].set_title(r"$\lambda$ = 649 nm")
ax[1][0].set_title(r"$\lambda$ = 932 nm")
wave_clement(11, ax[0][1])
wave_clement(51, ax[1][1])
wavelengths = np.linspace(200, 1100)
phase_angles = np.array([11])
rock = ref_rock(wavelengths, phase_angles).T
ice = ref_ice(wavelengths, phase_angles).T
ax[0][1].plot(wavelengths, rock, ls="--", color=BLACK, label="hapke")
ax[0][1].plot(wavelengths, ice, ls="--", color=RED, label="hapke ice")
phase_angles = np.array([51])
rock = ref_rock(wavelengths, phase_angles).T
ice = ref_ice(wavelengths, phase_angles).T
ax[1][1].plot(wavelengths, rock, ls="--", color=BLACK, label="hapke")
ax[1][1].plot(wavelengths, ice, ls="--", color=RED, label="hapke ice")
for material in ["ice", "rock"]:
for phase_angle in [51]:
filename = f"data/deshapriya/67p_{material}_alpha_{phase_angle}.csv"
df = pd.read_csv(filename, names=["wavelength", "r"])
if phase_angle == "92b": phase_angle = 92
if material == "ice":
c = RED
ax[1][1].scatter(df.wavelength, df.r, marker="o", s=s, color=c)
else:
c = BLACK
ax[1][1].scatter(df.wavelength, df.r, marker="o", s=s, color=c)
ax[0][1].set_xlabel("wavelength [nm]")
ax[0][1].set_ylabel("I/F")
ax[0][1].set_title(r"$\alpha$ = 11°")
ax[1][1].set_xlabel("wavelength [nm]")
ax[1][1].set_ylabel("I/F")
ax[1][1].set_title(r"$\alpha$ = 51°")
plt.tight_layout()
plt.savefig("plots/ref.pdf")
plt.show()
plt.plot(wavelengths, ref_rock(wavelengths, 1.3).T)
plt.show()
def main(mode, v=30, alpha=11):
fig, axes = plt.subplots(nrows=2, sharex=True)
if mode != "C":
wavelengths = np.linspace(300, 1100, 1000)
else:
wavelengths = np.linspace(200, 1100, 1000)
df = pd.read_csv(f"data/widths_snr.csv")
widths100_avg = df.widths_100
widths80_avg = df.widths_80
widths60_avg = df.widths_60
centers = df.c
width100 = interp1d(centers,
widths100_avg,
kind="quadratic",
fill_value="extrapolate")
width80 = interp1d(centers,
widths80_avg,
kind="quadratic",
fill_value="extrapolate")
width60 = interp1d(centers,
widths60_avg,
kind="quadratic",
fill_value="extrapolate")
axes[0].plot(np.linspace(380, 1000, 100),
width100(np.linspace(380, 1000, 100)),
color=BLACK,
zorder=-1,
label="SNR 100")
axes[0].plot(np.linspace(380, 1000, 100),
width80(np.linspace(380, 1000, 100)),
ls="-.",
color=BLACK,
zorder=-1,
label="SNR 80")
axes[0].plot(np.linspace(380, 1000, 100),
width60(np.linspace(380, 1000, 100)),
ls="--",
color=BLACK,
zorder=-1,
label="SNR 60")
axes[0].legend()
if mode != "C":
axes[0].set_ylim(0.8 * min(df.widths_60), max(df.widths_80))
else:
axes[0].set_xticks(np.arange(200, 1101, 100))
axes[0].set_xticklabels(np.arange(200, 1101, 100))
axes[0].set_ylabel("width [nm]")
axes[0].set_xlabel("center [nm]")
axes[1].set_xlabel("wavelength [nm]")
axes[1].set_ylabel("transmission [%]")
c1 = 460
c2 = 650
c3 = 764
c4 = 900
df = pd.read_csv(f"data/filters_{mode}.csv")
c1, c2, c3, c4 = df.centers
w1, w2, w3, w4 = df.widths
F0 = make_filter(c1, w1)
F1 = make_filter(c2, w2)
F2 = make_filter(c3, w3)
F3 = make_filter(c4, w4)
if mode == "C":
f0, = axes[1].plot(wavelengths, 100 * F0(wavelengths), color=BLUE)
f1, = axes[1].plot(wavelengths, 100 * F1(wavelengths), color=GREEN)
f2, = axes[1].plot(wavelengths, 100 * F2(wavelengths), color=ORANGE)
f3, = axes[1].plot(wavelengths, 100 * F3(wavelengths), color=RED)
else:
f0, = axes[1].plot(wavelengths, 100 * F0(wavelengths), color=BLUE)
f1, = axes[1].plot(wavelengths, 100 * F1(wavelengths), color=ORANGE)
f2, = axes[1].plot(wavelengths, 100 * F2(wavelengths), color=RED)
f3, = axes[1].plot(wavelengths, 100 * F3(wavelengths), color=BLACK)
axes[1].plot(wavelengths, np.zeros(wavelengths.shape), color=BLACK)
M = get_mirror()
Q = get_detector()
S = get_solar()
signal = M(wavelengths) * Q(wavelengths) * ref_rock(wavelengths,
alpha) * S(wavelengths)
signal = signal / np.max(signal) * 100
# axes[1].plot(wavelengths, signal.T, color=BLACK)
df = pd.read_csv("data/texp.csv")
t = interp1d(df.alpha, df["texp10"], fill_value="extrapolate")
t_exp = t(alpha) / (v / 10) / 1000
# axes[0].set_title(f"phase angle = {alpha}°")
if mode == "C":
scatter = axes[0].scatter([c1, c2, c3, c4], [w1, w2, w3, w4],
color=[BLUE, GREEN, ORANGE, RED],
edgecolor=BLACK)
else:
scatter = axes[0].scatter([c1, c2, c3, c4], [w1, w2, w3, w4],
color=[BLUE, ORANGE, RED, BLACK],
edgecolor=BLACK)
DraggableScatter(scatter, [f0, f1, f2, f3], fig, v, mode)