def summary():
long_data = LB51_get_cal_data.get_long_pulse_data()
short_data = LB51_get_cal_data.get_short_pulse_data()
binned_603 = LB51_get_cal_data.bin_burst_data(long_data["603"]["total"],
603)
binned_99 = LB51_get_cal_data.bin_burst_data(short_data["99"]["total"], 99)
short_summary_data = [short_data["359"]]
short_summary_data.extend(binned_99)
long_summary_data = [long_data["641"]]
long_summary_data.extend(binned_603)
f, axs = plt.subplots(
2,
2,
sharex=True,
sharey="row",
figsize=(3.37, 5),
gridspec_kw={"height_ratios": [2, 6]},
)
spectra_series_plot(axs[1, 0], short_summary_data)
spectra_series_plot(axs[1, 1], long_summary_data)
emission_axs = linear_plots([axs[0, 0], axs[0, 1]],
short_data["359"]["sum_intact"])
format_summary_plot(f, axs, emission_axs)
plt.savefig("plots/2021_06_13_summary.eps", dpi=600)
plt.savefig("plots/2021_06_13_summary.png", dpi=600)
def run_quant_ana():
"""Calculate stim. strength of expt. data"""
short_data = LB51_get_cal_data.get_short_pulse_data()
long_data = LB51_get_cal_data.get_long_pulse_data()
short_run_sets_list = ["99", "290", "359", "388"]
short_fluences = []
short_stim_strengths = []
for run_set in short_run_sets_list:
fluence = short_data[run_set]["sum_intact"]["fluence"]
# if run_set == "99":
# fluence = 1898
stim_strength = get_stim_efficiency(short_data[run_set])
short_fluences.append(fluence)
short_stim_strengths.append(stim_strength)
long_run_sets_list = ["641", "554", "603"]
long_fluences = []
long_stim_strengths = []
for run_set in long_run_sets_list:
fluence = long_data[run_set]["sum_intact"]["fluence"]
stim_strength = get_stim_efficiency(long_data[run_set])
long_fluences.append(fluence)
long_stim_strengths.append(stim_strength)
quant_data = {
"short_fluences": np.array(short_fluences),
"long_fluences": np.array(long_fluences),
"short_efficiencies": 100 * np.array(short_stim_strengths),
"long_efficiencies": 100 * np.array(long_stim_strengths),
}
_save_quant_data(quant_data)
def load_data():
long_data = LB51_get_cal_data.get_long_pulse_data()
short_data = LB51_get_cal_data.get_short_pulse_data()
binned_603 = LB51_get_cal_data.bin_burst_data(long_data["603"]["total"],
603)
binned_99 = LB51_get_cal_data.bin_burst_data(short_data["99"]["total"], 99)
data_sets = {
"short_pulse_low_fluence": short_data["359"]["sum_intact"],
"short_pulse_high_fluence_low_photon_energy":
binned_99[0]["sum_intact"],
"short_pulse_high_fluence_high_photon_energy":
binned_99[0]["sum_intact"],
"long_pulse_low_fluence": long_data["641"]["sum_intact"],
"long_pulse_high_fluence_low_photon_energy":
binned_603[0]["sum_intact"],
"long_pulse_high_fluence_high_photon_energy":
binned_603[1]["sum_intact"],
}
return data_sets
def save_markus_data():
long_data = LB51_get_cal_data.get_long_pulse_data()
short_data = LB51_get_cal_data.get_short_pulse_data()
binned_603 = LB51_get_cal_data.bin_burst_data(long_data["603"]["total"],
603)
binned_99 = LB51_get_cal_data.bin_burst_data(short_data["99"]["total"], 99)
data_sets_to_save = {
"short_pulse_low_fluence": short_data["359"]["sum_intact"],
"short_pulse_high_fluence_low_photon_energy":
binned_99[0]["sum_intact"],
"short_pulse_high_fluence_high_photon_energy":
binned_99[0]["sum_intact"],
"long_pulse_low_fluence": long_data["641"]["sum_intact"],
"long_pulse_high_fluence_low_photon_energy":
binned_603[0]["sum_intact"],
"long_pulse_high_fluence_high_photon_energy":
binned_603[1]["sum_intact"],
}
with open("markus_data.pickle", "wb") as f:
pickle.dump(data_sets_to_save, f)
make_test_figure(data_sets_to_save)
def overview_plot():
"""Make plots summarizing the obtained data"""
def individual_shots_plot(ax, data, shots_to_plot):
phot = data["sum_intact"]["phot"]
for ind, shot in enumerate(shots_to_plot):
sam_spec = data["blown"]["sam_spec"][shot]
no_sam_spec = data["blown"]["no_sam_spec"][shot]
norm_sam_spec = sam_spec / np.amax(sam_spec) / 1.5
norm_no_sam_spec = no_sam_spec * NO_SAM / np.amax(sam_spec) / 1.5
if ind == 0:
sam_label = "Top"
no_sam_label = "Bot."
else:
sam_label = "_nolegend_"
no_sam_label = "_nolegend_"
ax.plot(phot, norm_sam_spec + ind, "r", label=sam_label)
ax.plot(phot, norm_no_sam_spec + ind, "k--", label=no_sam_label)
if ind == 0:
ax.legend(loc="best")
def summed_spectra_plot(ax, data):
phot = data["sum_intact"]["phot"]
norm = np.amax(data["sum_intact"]["no_sam_spec"])
sam_spec = data["sum_intact"]["sam_spec"] / norm
no_sam_spec = data["sum_intact"]["no_sam_spec"] / norm
exc_spec = data["sum_intact"]["exc_sam_spec"] / norm
ssrl_trans = np.exp(-1 * data["sum_intact"]["ssrl_absorption"])
lin_sam_spec = no_sam_spec * ssrl_trans
ax.plot(phot, no_sam_spec * NO_SAM, "k--", label="Ref.")
ax.plot(phot, sam_spec, "k", label="Sam.")
ax.plot(phot, lin_sam_spec, "k:", label="Linear Sam.")
ax.fill_between(
phot,
lin_sam_spec,
sam_spec,
where=(exc_spec > 0),
facecolor="b",
edgecolor="w",
)
ax.fill_between(
phot,
lin_sam_spec,
sam_spec,
where=(exc_spec < 0),
facecolor="r",
edgecolor="w",
)
def nonlinear_spectra_plot(ax, data):
phot = data["sum_intact"]["phot"]
norm = np.amax(data["sum_intact"]["no_sam_spec"])
no_sam_spec = data["sum_intact"]["no_sam_spec"] / norm
exc_spec = data["sum_intact"]["exc_sam_spec"] / norm
ax.plot(phot, exc_spec * 5, color="k", label="5 X Diff.")
ax.plot(phot, no_sam_spec, "k--", label="Ref.")
ax.fill_between(
phot,
np.zeros_like(phot),
exc_spec * 5,
where=(exc_spec > 0),
facecolor="b",
edgecolor="w",
)
ax.fill_between(
phot,
np.zeros_like(phot),
exc_spec * 5,
where=(exc_spec < 0),
facecolor="r",
edgecolor="w",
)
def xas_plot(ax, low_data, high_data, threshold=0.15):
high_phot = high_data["sum_intact"]["phot"]
high_threshold = threshold * np.amax(high_data["sum_intact"]["no_sam_spec"])
high_in_range = high_data["sum_intact"]["no_sam_spec"] > high_threshold
ax.plot(
high_phot[high_in_range],
high_data["sum_intact"]["abs"][high_in_range],
color="tab:orange",
label="1335\nmJ/cm$^2$",
)
low_phot = low_data["sum_intact"]["phot"]
low_threshold = threshold * np.amax(low_data["sum_intact"]["no_sam_spec"])
low_in_range = low_data["sum_intact"]["no_sam_spec"] > low_threshold
ax.plot(
low_phot[low_in_range],
low_data["sum_intact"]["abs"][low_in_range],
"g",
label="12\nmJ/cm$^2$",
)
ax.plot(
low_phot, low_data["sum_intact"]["ssrl_absorption"], "k--", label="Sync."
)
short_data = LB51_get_cal_data.get_short_pulse_data()
long_data = LB51_get_cal_data.get_long_pulse_data()
f, axs = plt.subplots(2, 2, figsize=(3.37, 4))
shots_to_plot = [
20,
23,
26,
] # last shots on some samples (blown up) after the first few
individual_shots_plot(axs[0, 0], long_data["603"], shots_to_plot)
summed_spectra_plot(axs[1, 0], short_data["99"])
nonlinear_spectra_plot(axs[0, 1], short_data["99"])
xas_plot(axs[1, 1], short_data["359"], short_data["99"])
format_data_overview_plot(f, axs)
plt.savefig("plots/2021_01_04_overview_for_setup.eps", dpi=600)
plt.savefig("plots/2021_01_04_overview_for_setup.png", dpi=600)
def overview_plot():
"""Make plots summarizing the obtained data"""
def individual_shots_plot(ax, data, shots_to_plot):
phot = data["sum_intact"]["phot"]
for ind, shot in enumerate(shots_to_plot):
sam_spec = data["intact"]["sam_spec"][shot]
no_sam_spec = data["intact"]["no_sam_spec"][shot]
norm_sam_spec = sam_spec / np.amax(sam_spec) / 4
norm_no_sam_spec = no_sam_spec * NO_SAM / np.amax(sam_spec) / 4
if ind == 0:
sam_label = "Co/Pd"
no_sam_label = "Ref."
else:
sam_label = "_nolegend_"
no_sam_label = "_nolegend_"
ax.plot(phot, norm_sam_spec + ind, "k", label=sam_label)
ax.plot(phot, norm_no_sam_spec + ind, "k--", label=no_sam_label)
if ind == 0:
ax.legend(loc="best")
def summed_spectra_plot(ax, data):
phot = data["sum_intact"]["phot"]
norm = np.amax(data["sum_intact"]["no_sam_spec"])
sam_spec = data["sum_intact"]["sam_spec"] / norm
no_sam_spec = data["sum_intact"]["no_sam_spec"] / norm
exc_spec = data["sum_intact"]["exc_sam_spec"] / norm
ssrl_trans = np.exp(-1 * data["sum_intact"]["ssrl_absorption"])
lin_sam_spec = no_sam_spec * ssrl_trans
ax.plot(phot, no_sam_spec * NO_SAM, "k--", label="Ref.")
ax.plot(phot, sam_spec, "k", label="Co/Pd")
ax.plot(phot, lin_sam_spec, "k:", label="Linear Co/Pd")
ax.fill_between(
phot,
lin_sam_spec,
sam_spec,
where=(exc_spec > 0),
facecolor="b",
edgecolor="w",
)
ax.fill_between(
phot,
lin_sam_spec,
sam_spec,
where=(exc_spec < 0),
facecolor="r",
edgecolor="w",
)
def nonlinear_spectra_plot(ax, data):
phot = data["sum_intact"]["phot"]
norm = np.amax(data["sum_intact"]["no_sam_spec"])
no_sam_spec = data["sum_intact"]["no_sam_spec"] / norm
exc_spec = data["sum_intact"]["exc_sam_spec"] / norm
ax.plot(phot, exc_spec * 5, color="k", label="5 X Diff.")
ax.plot(phot, no_sam_spec, "k--", label="Ref.")
ax.fill_between(
phot,
np.zeros_like(phot),
exc_spec * 5,
where=(exc_spec > 0),
facecolor="b",
edgecolor="w",
)
ax.fill_between(
phot,
np.zeros_like(phot),
exc_spec * 5,
where=(exc_spec < 0),
facecolor="r",
edgecolor="w",
)
def xas_plot(ax, low_data, high_data, threshold=0.15):
high_phot = high_data["sum_intact"]["phot"]
high_threshold = threshold * np.amax(
high_data["sum_intact"]["no_sam_spec"])
high_in_range = high_data["sum_intact"]["no_sam_spec"] > high_threshold
ax.plot(
high_phot[high_in_range],
high_data["sum_intact"]["abs"][high_in_range],
color="tab:orange",
label="1335\nmJ/cm$^2$",
)
low_phot = low_data["sum_intact"]["phot"]
low_threshold = threshold * np.amax(
low_data["sum_intact"]["no_sam_spec"])
low_in_range = low_data["sum_intact"]["no_sam_spec"] > low_threshold
ax.plot(
low_phot[low_in_range],
low_data["sum_intact"]["abs"][low_in_range],
"g",
label="12\nmJ/cm$^2$",
)
ax.plot(low_phot,
low_data["sum_intact"]["ssrl_absorption"],
"k--",
label="Sync.")
short_data = LB51_get_cal_data.get_short_pulse_data()
f, axs = plt.subplots(2, 2, sharex=True, figsize=(3.37, 4))
shots_to_plot = [3, 4,
5] # Three consecutive pulses, not at beginning or end
individual_shots_plot(axs[0, 0], short_data["99"], shots_to_plot)
summed_spectra_plot(axs[1, 0], short_data["99"])
nonlinear_spectra_plot(axs[0, 1], short_data["99"])
xas_plot(axs[1, 1], short_data["359"], short_data["99"])
format_data_overview_plot(f, axs)
plt.savefig("plots/2021_06_20_overview.eps", dpi=600)
plt.savefig("plots/2021_06_20_overview.png", dpi=600)
"""Quantifying experimentally measured stimulated RIXS strength
"""
import numpy as np
import pickle
import matplotlib.pyplot as plt
from scipy.stats import percentileofscore
from LB51 import LB51_get_cal_data
MEASURED_STIM_FILE = "data/proc/stim_efficiency.pickle"
SHORT_DATA = LB51_get_cal_data.get_short_pulse_data()
LONG_DATA = LB51_get_cal_data.get_long_pulse_data()
SHORT_RUN_SETS_LIST = ["99", "290", "359", "388"]
SHORT_RUN_SETS_LIST = [
"99",
"290",
"359",
] # remove run set 388 since that one has very limited data
LONG_RUN_SETS_LIST = ["641", "603"]
SSRL_ABSORPTION_COPD = SHORT_DATA["99"]["sum_intact"]["ssrl_absorption"]
OFFSET_REFERENCE = {
"99": SHORT_DATA["99"],
"290": SHORT_DATA["99"],
"359": SHORT_DATA["99"],
"388": SHORT_DATA["99"],
"603": LONG_DATA["603"],
"641": LONG_DATA["603"],