delim_whitespace=True,
skiprows=1)
for file in static_files:
name = file.name
orig = parse(name)
### correct the curve for packing effects
spf_corrected_int = orig.SA / spf.SA
spf_corrected_error = spf_corrected_int * np.sqrt(
(orig.sigSA / orig.SA)**2 + (spf.sigSA / spf.SA)**
2) ### https://terpconnect.umd.edu/~toh/models/ErrorPropagation.pdf
static_spf_corrected = Trace(orig.q, np.empty_like(orig.q),
np.empty_like(orig.q), spf_corrected_error,
spf_corrected_int, np.empty_like(orig.q))
# ### scale back up to proper intensities
static_spf_corrected.scale(orig,
qmin=0.03,
qmax=0.1,
approach="projection")
static_spf_corrected_scaled = Trace(orig.q, np.empty_like(orig.q),
np.empty_like(orig.q),
static_spf_corrected.scaled_sigSA,
static_spf_corrected.scaled_SA,
np.empty_like(orig.q))
static_spf_corrected_scaled.write_dat(
name.replace('.dat', 'spf-corrected.dat'))
# plt.xscale('log')
# plt.legend()
# plt.show()
2) ### https://terpconnect.umd.edu/~toh/models/ErrorPropagation.pdf
avg_off_spf_corrected = Trace(avg_off.q, np.empty_like(avg_off.q),
np.empty_like(avg_off.q), spf_corrected_error,
spf_corrected_int, np.empty_like(avg_off.q))
### scale back up to proper intensities
avg_off_spf_corrected.scale(avg_off,
qmin=0.03,
qmax=0.1,
approach="projection")
avg_off_spf_corrected_scaled = Trace(avg_off.q, np.empty_like(avg_off.q),
np.empty_like(avg_off.q),
avg_off_spf_corrected.scaled_sigSA,
avg_off_spf_corrected.scaled_SA,
np.empty_like(avg_off.q))
avg_off_spf_corrected_scaled.write_dat(
avg_off_protein_only_file.replace('.dat', 'spf-corrected.dat'))
# plt.plot(avg_off.q,avg_off.SA, label='avg_off')
# plt.plot(avg_off_spf_corrected.q,avg_off_spf_corrected.SA, label='avg_off_spf_corr')
# plt.plot(avg_off_spf_corrected_scaled.q,avg_off_spf_corrected_scaled.SA, label='avg_off_spf_corr_scaled')
### add corrected protein-only signal to each TR difference signal
for file in tr_diff_files:
name = file.name
tr_diff = parse(name)
tr_sum = tr_diff.add(avg_off_spf_corrected_scaled)
tr_sum.write_dat(name.replace('.dat', '_spf-corrected.dat'))
# plt.xscale('log')
# plt.legend()
# plt.show()
I_0s_errors.append(I_0_error)
iz = pd.DataFrame(I_0s)
iz.index = iz.index
az = pd.DataFrame(A_s)
az.index = az.index
ierrorz = pd.DataFrame(I_0s_errors)
ierrorz.index = ierrorz.index
new["I_0"] = iz
new["A"] = az
new["I_0s_errors"] = ierrorz
spf = new.I_pc0/new.I_0
spf_error = spf*np.sqrt((new.sigSA_pc0/new.I_pc0)**2 + (new.I_0s_errors/new.I_0)**2) ### https://terpconnect.umd.edu/~toh/models/ErrorPropagation.pdf
structure_packing_factor = Trace(new.q, np.empty_like(new.q), np.empty_like(new.q), spf_error, spf, np.empty_like(new.q))
structure_packing_factor.write_dat(samp+"_"+temp+"_spf"+".dat")
# print(new)
# plt.figure(num=1, figsize=(9,6))
# plt.scatter(new.q,new.A)
# plt.xlabel("q")
# plt.ylabel("A")
# plt.ylim(-0.001,.001)
# plt.xlim(0,1)
# plt.figure(num=2, figsize=(9,6))
# plt.scatter(new.q,new.I_0)
# # plt.scatter(new.q,new.I_pc0)
# # plt.scatter(new.q,new.I_pc1)
# # plt.scatter(new.q,new.I_pc2)