-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathfilter_selector.py
191 lines (165 loc) · 6.97 KB
/
filter_selector.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from unibe import *
from make_filters import make_filter
from scipy.interpolate import interp1d
from comet import ref_rock
from scipy.integrate import quad
from camera import Camera
import scipy.constants as const
from SNR import snr
def get_mirror():
df_mirror = pd.read_csv("data/mirrors_transmission.txt", delimiter="\s")
M = interp1d(df_mirror.wavelength, df_mirror.transmission, fill_value="extrapolate")
# percent
return M
def get_detector():
df_qe = pd.read_csv("data/qe.txt", delimiter=",")
Q = interp1d(df_qe.Wavelength, df_qe.QE / 100, fill_value="extrapolate")
# electrons per photons
return Q
def get_solar():
df_solar = pd.read_csv("data/solar.csv", delimiter=";", skiprows=1)
S = interp1d(df_solar["Wavelength (nm)"], df_solar["Extraterrestrial W*m-2*nm-1"], fill_value="extrapolate")
# W per meter squared per nanometer
return S
class DraggableScatter():
epsilon = 10
def __init__(self, scatter, filters, fig, v, mode):
df = pd.read_csv(f"data/widths.csv")
self.width = interp1d(df.c, df.widths * 2, kind="linear", fill_value="extrapolate")
self.scatter = scatter
self.filters = filters
self._ind = None
self.ax = scatter.axes
self.canvas = self.ax.figure.canvas
self.filter_data = [
{"center": self.scatter.get_offsets()[0][0],
"width": self.scatter.get_offsets()[0][1]},
{"center": self.scatter.get_offsets()[1][0],
"width": self.scatter.get_offsets()[1][1]},
{"center": self.scatter.get_offsets()[2][0],
"width": self.scatter.get_offsets()[2][1]},
{"center": self.scatter.get_offsets()[3][0],
"width": self.scatter.get_offsets()[3][1]},
]
self.canvas.mpl_connect('button_press_event', self.button_press_callback)
self.canvas.mpl_connect('button_release_event', self.button_release_callback)
self.canvas.mpl_connect('motion_notify_event', self.motion_notify_callback)
plt.tight_layout()
plt.show()
fig.savefig(f"plots/selected_filters_v{v}_mode_{mode}.pdf")
for filter in self.filter_data:
print(filter)
def get_ind_under_point(self, event):
xy = np.asarray(self.scatter.get_offsets())
xyt = self.ax.transData.transform(xy)
xt, yt = xyt[:, 0], xyt[:, 1]
d = np.sqrt((xt - event.x) ** 2 + (yt - event.y) ** 2)
ind = d.argmin()
if d[ind] >= self.epsilon:
ind = None
return ind
def button_press_callback(self, event):
if event.inaxes is None:
return
if event.button != 1:
return
self._ind = self.get_ind_under_point(event)
def button_release_callback(self, event):
if event.button != 1:
return
self._ind = None
def motion_notify_callback(self, event):
if self._ind is None:
return
if event.inaxes is None:
return
if event.button != 1:
return
x, y = event.xdata, event.ydata
if x > 1000: x = 1000
if x < 400: x = 400
wavelengths = np.linspace(300, 1100, 1000)
filter = make_filter(x, y)
self.filter_data[self._ind]["center"] = x
self.filter_data[self._ind]["width"] = y # float(self.width(x))
# y = self.width(x)
self.filters[self._ind].set_ydata(100 * filter(wavelengths))
xy = np.asarray(self.scatter.get_offsets())
xy[self._ind] = np.array([x, y])
self.scatter.set_offsets(xy)
self.canvas.draw_idle()
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)
if __name__ == "__main__":
# main("A", alpha=11)
main("D", alpha=11)