)
# ^ load the EC data (no longer need to use rename_RGA_cols, parse_RGA_header, or timeshift)
if EC_data["empty"]:
print("WARNING: tag " + tag + " is empty!")
continue
dataset = synchronize([EC_data, MS_data], cut_buffer=120, append=False)
# ^ combine the EC data with the MS data! To save memory, we don't keep all of the MS data:
# Instead, we just keep the part that overlaps with the EC data, plus 120 s on either end.
try:
trigger_cal(dataset)
except IndexError:
print("WARNING!!! " + tag + " couldn't trigger_cal.")
name = folder + "_" + tag
with open("./pickles/" + name + ".pkl",
"wb") as pkl: # and save it as a pickle
pickle.dump(dataset, pkl)
# make the plot:
ax = plot_experiment(
dataset,
tspan=
"all", # including this makes the plot include the MS data on the edges
)
ax[0].legend()
ax[1].set_title(name) # give it a title, so we know what's what
# ax[0].legend(loc='lower left')
plt.savefig("./overviews/" + name + ".png") # and save it in overviews.
H2.F_cal = 2 # what it god damn should be. More accurate than that measured in calibration.py because of the tilt.
H2.cal_mat = {
"M2": 1 / H2.F_cal,
"M4": -0.0007 / H2.F_cal,
} # gets rid of the background due to He double-ionization
with open("../pickles/20A31_18O_01.pkl", "rb") as f:
data = pickle.load(f)
V_str, J_str = sync_metadata(data,
RE_vs_RHE=0.715,
A_el=0.196
# *1e-3, J_str='J / [$\mu$A cm$^{-2}$]'
)
plot_experiment(data, mols=mdict)
# a = b
if True: # plot CO stripping experiment
tspan_CO_strip = [9700, 10500]
t_bg_abs = [9900, 9920]
t_bg = [t - tspan_CO_strip[0] for t in t_bg_abs
] # it should be relative to the start of the interval.
data_strip = cut_dataset(data, tspan=tspan_CO_strip, t_zero="start")
# correct_shunt(data_ox, V_DL=[0.4, 0.6]) # not needed because this dataset is f*****g beautiful.
axes1 = plot_experiment(
data_strip,
CO2_M44, CO2_M46, CO2_M48 = mdict["CO2_M44"], mdict["CO2_M46"], mdict["CO2_M48"]
H2, CO, He = mdict["H2"], mdict["CO"], mdict["He"]
H2.F_cal = 2 # what it god damn should be. More accurate than that measured in calibration.py because of the tilt.
H2.cal_mat = {
"M2": 1 / H2.F_cal,
"M4": -0.0007 / H2.F_cal,
} # gets rid of the background due to He double-ionization
with open("./pickles/20A31_18O_01.pkl", "rb") as f:
data = pickle.load(f)
V_str, J_str = sync_metadata(
data, RE_vs_RHE=0.715, A_el=0.196 # *1e-3, J_str='J / [$\mu$A cm$^{-1}$]'
)
t_str = "time/s"
plot_experiment(data)
# get water isotope ratio from O2 signals
getgamma = True
if getgamma:
# t_bg = [14700, 14750]
# tspan_O2 = [15000, 16000]
t_bg = [14650, 14750]
tspan_O2 = [15400, 15500]
Y = {}
for mass in ["M32", "M34", "M36"]:
x, y = mdict["O2_" + mass].get_flux(
data, tspan=tspan_O2, unit="nmol/s", t_bg=t_bg
)
Y[mass] = np.mean(y)
if False: # complex way to do it
He.F_cal = F_cal * ratio
F = data_directory + folder
pkl_file_name = [
f for f in os.listdir(F) if f[0:2] == tag and f[-4:] == '.pkl'
][0]
with open(F + os.sep + pkl_file_name, 'rb') as pkl:
dataset = pickle.load(pkl)
V_str, J_str = sync_metadata(dataset, RE_vs_RHE=RE_vs_RHE, A_el=A_el)
trigger_cal(dataset)
V_str = correct_ohmic_drop(dataset, R_ohm=R)
make_selector(dataset)
datasets[name] = dataset
data = cut_dataset(dataset, tspan=tspan, t_zero='start')
ax = plot_experiment(data, mols=mols, removebackground=False)
#ax[0].set_ylim([1e-1, 3e2])
plt.savefig('fig2_' + name + '.png')
if False: # zoom-in at 90 mV overpotential point
x, y = mols[2].get_flux(datasets['Reshma1Bi'],
tspan=[2940, 3130],
unit='pmol/s')
fig, ax = plt.subplots()
ax.plot(x - 2965, y - np.mean(y[0:5]), 'g')
ax.set_xlabel('time / s')
ax.set_ylabel('flux / [pmol/s]')
fig.set_figwidth(2)
fig.set_figheight(1.5)
fig.savefig('fig2b_inset')