/
saxs_plots.py
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/
saxs_plots.py
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#Useful tools and functions for SAXS data analysis
import numpy as np
from matplotlib.gridspec import GridSpec
import matplotlib.pyplot as plt
plt.style.use("ggplot")
def kratky_plotter(sample):
plt.figure(num=1, figsize=(4,6), dpi=300)
x = sample.q
y = sample.SA*sample.q**2
data_mask = np.array(x, dtype=bool)
data_mask[x>0.3]=False
x = x[data_mask]
y = y[data_mask]
plt.plot(x,y)
plt.xlabel("$q$")
plt.ylabel("$I*q^2$")
plt.title("Kratky Analysis")
plt.show()
return
def guinier_plotter(sample):
plt.subplots(figsize=(4,6), dpi=300)
plt.subplot(211)
x = sample.q**2
y = np.log(sample.SA)
data_mask = np.array(x, dtype=bool)
data_mask[x>0.008]=False
x = x[data_mask]
y = y[data_mask]
fit = np.polyfit(x,y,1)
fit_fxn = np.poly1d(fit)
plt.scatter(x,y)
plt.plot(x,fit_fxn(x))
plt.xlabel("$q^2$")
plt.ylabel("$\ln(I)$")
plt.xlim(0.0,0.008)
plt.title("Guinier Analysis")
plt.subplot(212)
plt.scatter(x,y-fit_fxn(x))
plt.xlabel("$q^2$")
plt.ylabel("$\ln(I)$")
plt.xlim(0.0,0.008)
plt.title("Guinier Residuals")
plt.tight_layout()
plt.show()
return
def real_space_plotter(samples, name=None, labels=None):
if isinstance(samples,list):
pass
elif isinstance(samples,dict):
samples = [item for item in samples.values()]
else:
samples = [samples]
fig=plt.figure(figsize=(6,6),dpi=100)
fig.suptitle("Real Space Analysis")
gs=GridSpec(2,2) # 2 rows, 2 columns
ax1=fig.add_subplot(gs[0,0]) # First row, first column
ax2=fig.add_subplot(gs[0,1]) # First row, second column
ax3=fig.add_subplot(gs[1,0]) # Second row, first column
ax4=fig.add_subplot(gs[1,1]) # Second row, second column
ii = -1
label_counter = 0
for sample in samples:
if ii < 0:
ii=0
nii = ii
else:
N = plt.cm.inferno.N
ii += int(N/len(samples))
nii = N-ii
x1 = sample.q
y1 = sample.SA
mask1 = np.array(x1, dtype=bool)
mask1[x1>0.5]=False
mask1[x1<0.03]=False
x1 = x1[mask1]
y1 = y1[mask1]
if labels:
ax1.plot(x1,y1, color=plt.cm.inferno(nii),label=labels[label_counter])
else:
ax1.plot(x1,y1, color=plt.cm.inferno(nii))
ax1.set_xscale('log')
ax1.set_xlabel("$\ln(q)$")
ax1.set_ylabel("$I$")
ax1.set_title("Raw Scattering")
x2 = sample.q**2
y2 = np.log(sample.SA)
mask2 = np.array(x2, dtype=bool)
mask2[x2>0.008]=False
mask2[x2<0.00125]=False
x2 = x2[mask2]
y2 = y2[mask2]
fit = np.polyfit(x2,y2,1)
fit_fxn = np.poly1d(fit)
if labels:
ax2.scatter(x2,y2, color=plt.cm.inferno(nii), label=labels[label_counter])
ax2.plot(x2,fit_fxn(x2), color=plt.cm.inferno(nii), label=labels[label_counter])
else:
ax2.scatter(x2,y2, color=plt.cm.inferno(nii))
ax2.plot(x2,fit_fxn(x2), color=plt.cm.inferno(nii))
ax2.set_xlabel("$q^2$")
ax2.set_ylabel("$\ln(I)$")
ax2.set_xlim(0.0,0.008)
ax2.set_title("Guinier Analysis")
if labels:
ax4.scatter(x2,y2-fit_fxn(x2), color=plt.cm.inferno(nii), label=labels[label_counter])
else:
ax4.scatter(x2,y2-fit_fxn(x2), color=plt.cm.inferno(nii))
ax4.set_xlabel("$q^2$")
ax4.set_ylabel("$\ln(I)$")
ax4.set_xlim(0.0,0.008)
ax4.set_title("Guinier Residuals")
x3 = sample.q
y3 = sample.SA*sample.q**2
mask3 = np.array(x3, dtype=bool)
mask3[x3>0.3]=False
x3 = x3[mask3]
y3 = y3[mask3]
if labels:
ax3.plot(x3,y3, color=plt.cm.inferno(nii), label=labels[label_counter])
else:
ax3.plot(x3,y3, color=plt.cm.inferno(nii))
ax3.set_xlabel("$q$")
ax3.set_ylabel("$I*q^2$")
ax3.set_title("Kratky Analysis")
label_counter += 1
plt.legend()
plt.tight_layout()
plt.subplots_adjust(top=0.85)
if name:
plt.savefig(name+"_real-space-plots.png", dpi=300)
else:
plt.savefig("real-space-plots.png", dpi=300)
plt.show()
return
from scipy.stats import linregress
def second_virial_calc(sample, sample_conc):
plt.figure(num=1, figsize=(4,6), dpi=300)
n=0
sv_x = []
sv_y = []
I_0z = []
for item in sample:
x = item.q**2
y = np.log(item.SA)
data_mask = np.array(x, dtype=bool)
data_mask[x>0.008]=False
x = x[data_mask]
y = y[data_mask]
I_0 = np.exp(linregress(x,y)[1])
I_0z.append(I_0)
sv_y.append(1/I_0)
sv_x.append(sample_conc[n])
n+=1
sv_xp = np.array(sv_x)
sv_yp = np.array(sv_y)
fit_I0 = np.polyfit(sv_xp,sv_yp,1)
fit_fxnI0 = np.poly1d(fit_I0)
plt.scatter(sv_xp,sv_yp)
plt.plot(sv_xp,fit_fxnI0(sv_xp))
plt.xlabel("$concentration (g/mL)$")
plt.ylabel("$1/I(c,0)$")
plt.title("Second Virial Plot")
plt.legend()
plt.show()
vir_stats = linregress(sv_xp,sv_yp)
I_0_0 = 1/vir_stats[1]
slope = vir_stats[0]
MW = 18500
A = slope*I_0_0/(2*MW)
print("\nStats for virial fit:\n{}\n".format(vir_stats))
print("I(0,0) = {}".format(I_0_0))
print("A = {}".format(A))
print("I(c,0) for pc0 = {}".format(I_0z[0]))
print("I(c,0) for pc1 = {}".format(I_0z[1]))
print("I(c,0) for pc2 = {}".format(I_0z[2]))
return