unique_indices_all = [[i for i, v in enumerate(aotf_freq) if v == aotf]
for aotf in unique_aotf]
min_elements = min([len(i) for i in unique_indices_all])
unique_indices = [i[0:min_elements] for i in unique_indices_all]
d["x"] = {"spectrum_%i" % i: [] for i in range(min_elements)}
d["y"] = {"spectrum_%i" % i: [] for i in range(min_elements)}
d["x"]["simulation_old"] = []
d["y"]["simulation_old"] = []
d["x"]["simulation_new"] = []
d["y"]["simulation_new"] = []
if channel == "so":
orders = [m_aotf_so(a) for a in aotf_freq]
elif channel == "lno":
orders = [m_aotf_lno(a) for a in aotf_freq]
print("Starting Order=%i" % orders[0])
order_range_file = [min(orders), max(orders)]
order_range = [
order_range_file[0] - SIMULATION_ADJACENT_ORDERS,
order_range_file[1] + SIMULATION_ADJACENT_ORDERS
]
c_order = int(np.mean(order_range))
spec_res = spec_res_order(c_order)
dim = detector_data_all.shape
detector_centre_data = detector_data_all[:, [
hdf5Files, hdf5Filenames, _ = make_filelist(regex, file_level)
for hdf5_file, hdf5_filename in zip(hdf5Files, hdf5Filenames):
channel = hdf5_filename.split("_")[3].lower()
detector_data_all = hdf5_file["Science/Y"][...]
window_top_all = hdf5_file["Channel/WindowTop"][...]
binning = hdf5_file["Channel/Binning"][0] + 1
n_rows = detector_data_all.shape[1]
frame_indices = range(len(window_top_all))
"""print diffraction order"""
aotf_freq = hdf5_file["Channel/AOTFFrequency"][0]
if channel == "so":
order = m_aotf_so(aotf_freq)
elif channel == "lno":
order = m_aotf_lno(aotf_freq)
print("Order=%i" % order)
dim = detector_data_all.shape
nu = len(list(set(window_top_all))) #number of unique window tops
nff = int(np.floor(dim[0] / nu)) #number of full frames
#make list of detector rows in each frame
row_numbers_all = []
for window_top in window_top_all:
row_numbers = np.arange(window_top, window_top + n_rows, binning)
row_numbers_all.append(row_numbers)
detector_data_reshaped = np.zeros(