def load_power_sweep(filename, description=''):
d = dict(np.load(filename))
uu.copy_string('\n'.join(d.keys()))
o = Bunch(data_dict=d,
keys=d.keys(),
filename=filename,
filetitle=uu.get_file_title(filename),
description=description,
powers=d['power_meter_power'],
N=d['Np'])
if d['power_scan_motorized_collect_apd']:
raise NotImplementedError('APD power scan not implemented')
if d['power_scan_motorized_collect_lifetime']:
o.rep_rate = d['picoharp_count_rate0']
o.bin_size = d['picoharp_Resolution']
o.num_bins = int(np.ceil((1 / o.rep_rate) / (o.bin_size * 1e-12)))
o.time = d['time_array'][0:o.num_bins] # already in ns
o.intensity = d['time_traces'][:, 0:o.num_bins]
if d['power_scan_motorized_collect_lockin']:
raise NotImplementedError('Lockin power scan not implemented')
if d['power_scan_motorized_collect_spectrum']:
o.wavelength = d['wls']
o.intg_time = d['andor_ccd_exposure_time']
o.center_wl = d['acton_spectrometer_center_wl']
o.specs = d['spectra']
o.intg_spectrum_ints = np.sum(o.specs, axis=1)
if d['power_scan_motorized_collect_step_and_glue']:
N = d['Np']
for i in range(N):
wls, ints = merge_step_and_glue_specs(
d['step_and_glue_centers'], d['step_and_glue_wavelengths'][i],
d['step_and_glue_specs'][i])
if i == 0:
o.specs = np.empty([N, wls.shape[0]], dtype=float)
o.wavelength = wls
o.specs[i] = ints
o.intg_time = d['andor_ccd_exposure_time']
o.centers = d['step_and_glue_centers'][0]
o.everything = Bunch()
for key in o.keys:
setattr(o.everything, key, d[key])
return o
def load_spec_and_glue(filename, description=""):
h5_f = h5py.File(filename, 'r')
o = Bunch(
centers=h5_f['andor_ccd_step_and_glue']['center_wl_actual'][()].copy(),
single_specs=h5_f['andor_ccd_step_and_glue']['spectra_data'][(
)].copy(),
single_wavelengths=h5_f['andor_ccd_step_and_glue']['wls'][()].copy(),
filename=filename,
filetitle=uu.get_file_title(filename),
description=description)
# Initialize with the first spectrum
wls = o.single_wavelengths[0][:]
raw_ints = o.single_specs[0][0, :]
# Progressively merge spectra into the lists of wls.
for i in range(1, o.centers.shape[0]):
# Determine the array indices and size of the regions of overlap
i_overlap_start = np.searchsorted(wls, o.single_wavelengths[i][0])
overlap_size_1 = wls.shape[0] - i_overlap_start
overlap_size_2 = np.searchsorted(o.single_wavelengths[i], wls[-1])
#print(i_overlap_start, overlap_size_1, overlap_size_2)
# Generate a uniformly spaced points in the overlap region
wls_overlap = np.linspace(o.single_wavelengths[i][0], wls[-1],
(overlap_size_1 + overlap_size_2) / 2)
# Take the average of the interpolated values from the two data sets in the
# interpolated region.
raw_int_overlap = (
np.interp(wls_overlap, wls[i_overlap_start:],
raw_ints[i_overlap_start:]) +
np.interp(wls_overlap, o.single_wavelengths[i][0:overlap_size_2],
o.single_specs[i][0, 0:overlap_size_2])) / 2.0
# Append the averaged overlapped data to the non-overlapped data at the beginning.
wls = np.append(wls[0:i_overlap_start], wls_overlap)
raw_ints = np.append(raw_ints[0:i_overlap_start], raw_int_overlap)
# Append the remaining non-overlapped data
wls = np.append(wls, o.single_wavelengths[i][overlap_size_2:])
raw_ints = np.append(raw_ints, o.single_specs[i][0, overlap_size_2:])
#print wls
o.wavelength = wls
o.intensity = raw_ints
h5_f.close()
return o