
import numpy as np
import netCDF4
import matplotlib.pyplot as plt
from scipy import signal
import glob

# plot a sample image
nc = netCDF4.Dataset("../data/uvi_20181126_010115_283_l3b_v10.nc", 'r')
rad = nc.variables["radiance"][0, :, :]   # radiance
inang = nc.variables["inangle"][0, :, :]  # incidence angle
lon = nc.variables["longitude"][:]  # longitude
lat = nc.variables["latitude"][:]   # latitude
nc.close()

model = np.cos(np.pi/180.*inang)
model = np.where(model > 0., model, 0.)
corrected_rad = rad/model       # apply photometric correction

plt.figure(1, (12, 6))
plt.title("corrected image")
plt.pcolormesh(lon, lat, corrected_rad, shading='auto', cmap="gray", vmin=0, vmax=8e7)
plt.show()

C:\ProgramData\Anaconda3\lib\site-packages\numpy\ma\core.py:1158: RuntimeWarning: overflow encountered in true_divide
  result = self.f(da, db, *args, **kwargs)


# time series of the radiance averaged in a rectangular area (subregion)
dataf = sorted(glob.glob("../data/*l3b_*.nc"))   # all images
nf = len(dataf)   # number of files
print("number of images = ", nf)

# prepare lists for the time series
time_list = np.array([])   # time
rad_list = np.array([])   # radiance

# ---- [practice 7] ---- #

# latitude range of the subregion (unit: degrees)
lat1 = 30
lat2 = 40

# longitude range of the subregion (unit: degrees)
lon1 = 200
lon2 = 230

# grid number (grid interval is 0.125 degrees)
x1 = int(lon1/0.125)
x2 = int(lon2/0.125)
y1 = int((lat1+90)/0.125)
y2 = int((lat2+90)/0.125)

if x1 > x2:
    x1, x2 = x2, x1
if y1 > y2:
    y1, y2 = y2, y1

# calculate the mean radiance in the subregion for all images
for i in range(0, nf):
    print(i, end=" ")

    nc = netCDF4.Dataset(dataf[i], 'r')       # open file
    rad = nc.variables["radiance"][0, :, :]   # radiance
    inang = nc.variables["inangle"][0, :, :]  # incidence angle
    lon = nc.variables["longitude"][:]        # longitude
    lat = nc.variables["latitude"][:]         # latitude
    time = nc.variables["time"]               # time in hours since 2000-01-01T00:00:00

    time_list = np.append(time_list, time[0])   # append the time to the list

    corrected_rad = rad/np.cos(np.pi/180.*inang)  # photometric correction

    subimage = corrected_rad[y1:y2, x1:x2]   # extract the subregion
    rad_average = np.average(subimage)       # calculate mean radiance in the subregion

    rad_list = np.append(rad_list, rad_average)   # append the mean radiance to the list

time_list = (time_list - time_list[0])/24.   # days from the start

# plot the time series
plt.xlabel("Days")
plt.ylabel("Radiance")
plt.plot(time_list, rad_list, ".")
plt.show()

# Lomb-Scargle periodogram
p = np.polyfit(time_list, rad_list, 3)   # fitting a polynomial function
rad_list_notrend = rad_list - np.polyval(p, time_list)   # subtracting the polynomial function to extract short-period components

freq = np.arange(0.1, 1, 0.001)   # frequency (1/day)
f = freq*2.*np.pi  # angular frequency
pgram = signal.lombscargle(time_list, rad_list_notrend, f)   # Lomb-Scargle periodogram
pgram = pgram**2  # power

# plot the spectrum
plt.xlabel("Period (days)")
plt.ylabel("Power")
plt.plot(1./freq, pgram)
plt.show()

number of images =  245
0 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244