popt, pcov = curve_fit(func, linear_dark, deviation_mean, p0=[1.2, 0.0032])
ax2.plot(linear_dark, deviation_mean, c="k")
ax2.plot(linear_dark, func(linear_dark, *popt), "k--")
ax1.set_ylabel("Detector counts (per pixel per accumulation)")
ax2.set_ylabel("Deviation from linear fit")
ax2.set_xlabel("Detector counts linear fit")
fig.suptitle("SO Non Linearity")
print("fit: a=%0.6f, b=%0.6f" % tuple(popt))
return linear_dark, deviation_mean
if title == "SO MCO Dark":
hdf5Files, hdf5Filenames, titles = makeFileList(obspaths, fileLevel)
writeSteppingData(hdf5Files, hdf5Filenames, "so")
if title == "LNO MCO Dark":
hdf5Files, hdf5Filenames, titles = makeFileList(obspaths, fileLevel)
writeSteppingData(hdf5Files, hdf5Filenames, "lno")
if title == "SO Bad Pixel Map":
writeBadPixelMap(obspaths, "so")
if title == "LNO Bad Pixel Map":
writeBadPixelMap(obspaths, "lno")
if title == "SO Non Linearity Correction":
linear_dark, deviation_mean = writeNonLinearity2(obspaths, "so")
plt.tight_layout()
if save_figs:
plt.savefig("UVIS_frame_%i_%s_raw_data.png" %
(frame_to_plot, obspaths[file_index]))
plt.figure(figsize=(10, 8))
plt.xlabel("Time")
plt.ylabel("Signal sum")
plt.plot(detector_data, "o", linewidth=0)
plt.legend()
plt.title(title + ": Detector column %i versus time" % pixel_column)
if save_figs: plt.savefig(BASE_DIRECTORY + os.sep + title + ".png")
import os
import numpy as np
from hdf5_functions_v03 import makeFileList
DATA_DIRECTORY = os.path.normcase(
r"W:\data\SATELLITE\TRACE-GAS-ORBITER\NOMAD\hdf5\test\iant\hdf5")
#DATA_DIRECTORY = os.path.normcase(r"/bira-iasb/data/SATELLITE/TRACE-GAS-ORBITER/NOMAD/hdf5/")
"""list UVIS acquisiton modes"""
#obspaths = ["*20180601*UVIS*_E"]
fileLevel = "hdf5_level_0p2c"
#hdf5Files, hdf5Filenames, _ = makeFileList(["*201806*UVIS*_I"], fileLevel)
#for hdf5File, hdf5Filename in zip(hdf5Files, hdf5Filenames):
# print(hdf5Filename + ": "+str(hdf5File["Channel/AcquisitionMode"][0]))
hdf5Files, hdf5Filenames, _ = makeFileList(["*201806*UVIS*_E"], fileLevel)
for hdf5File, hdf5Filename in zip(hdf5Files, hdf5Filenames):
print(hdf5Filename + ": " + str(hdf5File["Channel/AcquisitionMode"][0]))
yfit = dataFitInterp[minIndex:(nPoints)]
y = dataInterp[0:(nPoints - minIndex)] / yfit
if plot:
plt.subplot(2, 1, 2)
plt.plot(x, y, label="residual")
plt.ylim([0.95, 1.02])
plt.legend()
plt.tight_layout()
plt.show()
return x, yfit, y
hdf5Files, hdf5Filenames, _ = makeFileList(obspaths, fileLevel, silent=True)
#from plot_animations_v01 import animateLines
plt.figure()
#for fileIndex, (hdf5_file, hdf5_filename) in enumerate(zip(hdf5Files, hdf5Filenames)):
for fileIndex, (hdf5_file, hdf5_filename) in enumerate(
zip(hdf5Files[3:4], hdf5Filenames[3:4])):
detectorData = hdf5_file["Science/Y"][...]
aotfFrequency = hdf5_file["Channel/AOTFFrequency"][...]
exponent = hdf5_file["Channel/Exponent"][...]
detectorDataMean = np.mean(detectorData[:, 12:13], axis=1)
# a = animateLines([detectorDataMean])
# plotIndices = [0,1,2,3,256,257]
@author: iant
"""
from plot_occultations_v02 import joinTransmittanceFiles
from hdf5_functions_v03 import makeFileList
import numpy as np
from scipy.signal import butter, lfilter
import matplotlib.pyplot as plt
#
#hdf5Files, hdf5Filenames, _ = makeFileList(["20180421_202111_0p3a_SO_1_E_134"], "hdf5_level_0p3a", silent=True)
#hdf5Files, hdf5Filenames, _ = makeFileList(["20180618_001805_0p3a_SO_1_E_134"], "hdf5_level_0p3a", silent=True)
hdf5Files, hdf5Filenames, _ = makeFileList(["20180613_073640_0p3a_SO_2_I_134"],
"hdf5_level_0p3a",
silent=True)
#
#
obsDict = joinTransmittanceFiles(hdf5Files[0],
hdf5Filenames[0],
3,
silent=False,
top_of_atmosphere=60.0)
def butter_lowpass(cutoff, fs, order=5):
nyq = 0.5 * fs
normal_cutoff = cutoff / nyq
b, a = butter(order, normal_cutoff, btype='low', analog=False)
return b, a