def save_constant_absorbed_specs():
# Fluence bins chosen to have similar statistics between data sets
bin_edges_lowest = [(0, 0.0329)]
bin_edges_low = [(0, 2.743), (2.743, 11.830)]
bin_edges_high = [(15, 25), (25, 95)]
data_sets = []
data_lowest_fluence = abs_ana.get_data(LOWEST_FLUENCE_RUNS, True,
(CUTOFF_BOTTOM, LOWEST_CUTOFF))
data_sets.extend(
get_fluence_binned_data(data_lowest_fluence,
bin_edges_lowest,
absorbed=True))
# use higher fluences of set by having low cutoff at 50 percent rather than 30 percent:
# (when run with 30th percentile lower bound instead, produces same result
# [no changes from ALS] within experimental error)
data_low_fluence = abs_ana.get_data(LOW_FLUENCE_RUNS, True,
(CUTOFF_BOTTOM, LOW_CUTOFF))
data_sets.extend(
get_fluence_binned_data(data_low_fluence, bin_edges_low,
absorbed=True))
data_high_fluence = abs_ana.get_data(HIGH_FLUENCE_RUNS, True,
(CUTOFF_BOTTOM, HIGH_CUTOFF))
data_sets.extend(
get_fluence_binned_data(data_high_fluence,
bin_edges_high,
absorbed=True))
spectra = []
for data_set in data_sets:
spec = abs_ana.get_spectra(data_set, bins=PHOT_BIN_EDGES)
spectra.append(spec)
pickle_on = open(CONSTANT_ABSORPTION_FILE, 'wb')
pickle.dump(spectra, pickle_on)
pickle_on.close()
def save_incident_specs():
# Fluence bins chosen to have similar statistics between data sets
bin_edges_lowest = [(-0.399, 1)]
bin_edges_low = [(-1, 100)]
bin_edges_high = [(25, 65), (65, 160)]
data_sets = []
data_lowest_fluence = abs_ana.get_data(LOWEST_FLUENCE_RUNS, True,
(CUTOFF_BOTTOM, LOWEST_CUTOFF))
data_sets.extend(
get_fluence_binned_data(data_lowest_fluence, bin_edges_lowest))
# use higher fluences of set by having low cutoff at 50 percent rather than 30 percent:
data_low_fluence = abs_ana.get_data(LOW_FLUENCE_RUNS, None,
(CUTOFF_BOTTOM, LOW_CUTOFF))
data_sets.extend(get_fluence_binned_data(data_low_fluence, bin_edges_low))
data_high_fluence = abs_ana.get_data(HIGH_FLUENCE_RUNS, True,
(CUTOFF_BOTTOM, HIGH_CUTOFF))
data_sets.extend(get_fluence_binned_data(data_high_fluence,
bin_edges_high))
spectra = []
for ind, data_set in enumerate(data_sets):
spec = abs_ana.get_spectra(data_set, bins=PHOT_BIN_EDGES)
spectra.append(spec)
pickle_on = open(INCIDENT_SPEC_FILE, 'wb')
pickle.dump(spectra, pickle_on)
pickle_on.close()
def plot_profile_lk30(run, coord='sam_x'):
data = abs_ana.get_data(([run], None, (0, 100)))
plot_profile(data[coord], data['andor'], data['mcp'])
plt.title('LK30 Spot size scan, run ' + str(run))
plt.xlabel(coord + ' (mm)')
plt.ylabel('Normalized Value')
save_file_start = '../plots/2016_04_07_spot_size_scan_'
save_file = ''.join([save_file_start, coord[-1], '_run', str(run), '.png'])
plt.savefig(save_file)
def calculate_damage_specs(fluence_set):
max_prev_fluence_key = 'max_prev_fluence'
data = abs_ana.get_data(HIGH_FLUENCE_RUNS,
sample_filter=None,
mcp_filter=(CUTOFF_BOTTOM, HIGH_CUTOFF))
spectra = []
for fluences in fluence_set:
data_set = data[data[max_prev_fluence_key] >= fluences[0]]
data_set = data_set[data_set[max_prev_fluence_key] < fluences[1]]
spec = abs_ana.get_spectra(data_set, bins=PHOT_BIN_EDGES)
spectra.append(spec)
return spectra
def get_no_sample_data():
data = abs_ana.get_data([108], mcp_filter=(0, 100))
num_points = 100
# Normalize data for plotting so that scaling is nice:
mcp_norm = np.amax(data['mcp'][:num_points])
andor_norm = np.amax(data['andor'][:num_points])
mcp_raw = data['mcp_uncorrected'][:num_points]/mcp_norm
mcp = data['mcp'][:num_points]/mcp_norm
andor_raw = data['andor'][:num_points]/andor_norm
andor = andor_raw # Andor values are not corrected
return {'Raw I0': mcp_raw,
'Corrected I0': mcp,
'Raw I1': andor_raw,
'Corrected I1': andor}
def get_lcls_xas(runs,
phot_max=10000,
mcp_filter=(CUTOFF_BOTTOM, 95),
sample_filter=None,
fluence_filter=None):
lcls_data = abs_ana.get_data(runs, sample_filter, mcp_filter=mcp_filter)
if fluence_filter is not None:
lcls_data = lcls_data[lcls_data['fluence_in'] > fluence_filter[0]]
lcls_data = lcls_data[lcls_data['fluence_in'] < fluence_filter[1]]
lcls_spec = abs_ana.get_spectra(lcls_data, bins=50)
lcls_xas = lcls_spec['xas']
# Take out only LCLS photon energies where we know there
# are no artifacts
good = (lcls_xas['phot'] < phot_max) & (~np.isnan(lcls_xas['spec']))
lcls_xas['spec'] = lcls_xas['spec'][good]
lcls_xas['phot'] = lcls_xas['phot'][good]
return lcls_xas
def get_no_sample_data():
no_sample_data = abs_ana.get_data([108], mcp_filter=(0, 100))
return no_sample_data