def get_qfac(protein_file, npc, res_num, q_fac_dict):
"""Fit and individual tensor to each model in the bundle and save each Q-factor into an universal dictionary"""
# Load the protein, load the npc
prot = protein.load_pdb(protein_file)
rawData = dataparse.read_pcs(npc)
qfactor_sep = {}
# Initialize metal instance for search and set the initial position
mStart = metal.Metal()
mStart.position = prot[0]['A'][res_num]['CA'].position
# Loop: for every model fit an individual tensor and store Q-factors and tensor components into a dict
for model in prot:
parsedData = prot.parse(rawData, models=model.id)
[mGuess], _ = fit.svd_gridsearch_fit_metal_from_pcs([mStart],
[parsedData],
radius=10,
points=10)
[mFit], [data] = fit.nlr_fit_metal_from_pcs([mGuess], [parsedData])
qfactor_sep[model.id] = fit.qfactor(data)
# Save in universal dictionary
if type(q_fact_dict[model.id]) == list:
q_fac_dict[model.id].append(fit.qfactor(data))
else:
q_fac_dict[model.id] = [fit.qfactor(data)]
minModel, minQfac = sorted(qfactor_sep.items(), key=lambda x: x[1])[0]
for metal, data in zip(mFit, parsedData):
ex = []
ca = []
for atom, exp_pcs, error in data:
ex.append(exp_pcs)
ca.append(metal.atom_pcs(atom))
exp.append(ex)
cal.append(ca)
#### Plot the correlation ####
from matplotlib import pyplot as plt
fig, ax = plt.subplots(figsize=(5, 5))
# Plot the data
for e, c, name, colour in zip(exp, cal, ['Tb', 'Er', 'Yb'], ['r', 'g', 'b']):
qfactor = fit.qfactor(e, c)
ax.plot(e,
c,
marker='o',
lw=0,
ms=1,
c=colour,
label="{0:} - {1:5.3f}".format(name, qfactor))
# Plot a diagonal
l, h = ax.get_xlim()
ax.plot([l, h], [l, h], '-k', zorder=0)
ax.set_xlim(l, h)
ax.set_ylim(l, h)
# Axis labels
mStart = metal.Metal()
# Set the starting position to an atom close to the metal
mStart.position = prot[0]['A'][56]['CA'].position
# Calculate an initial tensor from an SVD gridsearch
[mGuess], [data] = fit.svd_gridsearch_fit_metal_from_pcs([mStart],
[parsedData],
radius=10,
points=10)
# Refine the tensor using non-linear regression
[mFit], [data] = fit.nlr_fit_metal_from_pcs([mGuess], [parsedData])
# Calculate the Q-factor
qfac = fit.qfactor(data)
# Save the fitted tensor to file
mFit.save('calbindin_Er_HN_PCS_tensor.txt')
#### Plot the correlation ####
from matplotlib import pyplot as plt
fig, ax = plt.subplots(figsize=(5, 5))
# Plot the data
ax.plot(data['exp'],
data['cal'],
marker='o',
lw=0,
ms=3,
c='r',
#### Plot the correlation ####
from matplotlib import pyplot as plt
fig = plt.figure(figsize=(5, 10))
ax1 = fig.add_subplot(211)
ax2 = fig.add_subplot(212)
ax1.set_title('A28C-C1-Tb')
ax2.set_title('S57C-C1-Tb')
for sol, ax, data in zip([sol1, sol2], [ax1, ax2], [data1, data2]):
# Calculate ensemble averages
dataEAv = fit.ensemble_average(data)
# Calculate the Q-factor
qfac = fit.qfactor(data, ensembleAverage=True)
# Plot all models
ax.plot(data['exp'],
data['cal'],
marker='o',
lw=0,
ms=2,
c='b',
alpha=0.5,
label="All models: Q = {:5.4f}".format(qfac))
# Plot the ensemble average
ax.plot(dataEAv['exp'],
dataEAv['cal'],
marker='o',
# Refine the tensors using non-linear regression
fitParameters = ['x', 'y', 'z', 'ax', 'rh', 'a', 'b', 'g']
mFit, datas = fit.nlr_fit_metal_from_pcs(mGuess, parsedData, fitParameters)
# Save the fitted tensors to files
for name, metal in zip(['Tb', 'Er', 'Yb'], mFit):
metal.save("tensor_{}.txt".format(name))
#### Plot the correlation ####
from matplotlib import pyplot as plt
fig, ax = plt.subplots(figsize=(5, 5))
# Plot the data
for d, name, colour in zip(datas, ['Tb', 'Er', 'Yb'], ['r', 'g', 'b']):
qfactor = fit.qfactor(d)
ax.plot(d['exp'],
d['cal'],
marker='o',
lw=0,
ms=1,
c=colour,
label="{0:} - {1:5.3f}".format(name, qfactor))
# Plot a diagonal
l, h = ax.get_xlim()
ax.plot([l, h], [l, h], '-k', zorder=0)
ax.set_xlim(l, h)
ax.set_ylim(l, h)
# Axis labels