def params_Lorentzian(x, y):
mod = LorentzianModel()
params = mod.guess(y, x)
print(params)
out = mod.fit(y, params, x=x)
print(out.fit_report(min_correl=0.3))
init = mod.eval(params, x=x)
plt.figure(2)
plt.plot(x, y, 'b')
plt.plot(x, init, 'k--')
plt.plot(x, out.best_fit, 'r-')
T = nm_per_px y = projection yf = scipy.fftpack.fft(y) xf = np.linspace(0.0, 1.0/(2.0*T), N/2) fig1, ax = plt.subplots(1) thegraphy = 2.0/N * np.abs(yf[:N//2]) fromj = 9 toj = 17 ax.plot(xf[:50], thegraphy[:50], '-o') y = thegraphy[fromj:toj] x = xf[fromj:toj] pars = mod.guess(y, x=x) out = mod.fit(y, pars, x=x) x2 = np.linspace(np.min(x), np.max(x), 300) y2 = mod.eval(out.params, x=x2) plt.plot(x2, y2, '--', alpha=1) print(out.fit_report(min_correl=0.25)) fromj = 18 toj = 25 y = thegraphy[fromj:toj] x = xf[fromj:toj] pars = mod.guess(y, x=x) out = mod.fit(y, pars, x=x) x2 = np.linspace(np.min(x), np.max(x), 300) params2 = out.params # params2['center'] = 0.15390835*1.3 y2 = mod.eval(params2, x=x2) plt.plot(x2, y2, '--', alpha=1) print(out.fit_report(min_correl=0.25))
xls = pd.ExcelFile(Matrix_results_name)
sheets = xls.sheet_names
##
for sh in sheets:
Matrix_results = pd.read_excel(Matrix_results_name, sheet_name=sh)
df = Matrix_results.iloc[:180, :] ### just the quadrant
df=pd.DataFrame(df)
for TR in df.columns:
data=df[TR]
X=data.index.values
Y=data.values
mod = LorentzianModel()
#mod =GaussianModel()
pars = mod.guess(Y, x=X)
out = mod.fit(Y, pars, x=X)
Y_lorenz = mod.eval(pars, x=X)
#print(out.fit_report(min_correl=0.25))
#plt.plot(X, Y_lorenz, 'k--', label='Lorentzian')
#dec_angle_lorenz = (90-np.where(Y_lorenz==max(Y_lorenz))[0][0])/2 #out.params['center'].value / 2
#error = abs( (90-np.where(Y_lorenz==max(Y_lorenz))[0][0])/2 ) #round(ref_angle - dec_angle_lorenz, 3)
error = (90-np.where(Y_lorenz==max(Y_lorenz))[0][0])/2 #round(ref_angle - dec_angle_lorenz, 3)
results.append( [error, TR, CONDITION, SUBJECT_USE_ANALYSIS, sh[-1], brain_region])
df = pd.DataFrame(np.array(results))
df.columns = ['error', 'TR', 'CONDITION', 'subject', 'session', 'ROI']
df['TR'] = df.TR.astype(float)
df['TR'] = df['TR'] * 2
def residuals(force_pickrange=False):
datafolder = filedialog.askopenfilenames(
initialdir="C:\\Users\Josh\IdeaProjects\OpticalPumping",
title="Select data for bulk plotting")
for filename in datafolder:
if 'data' in filename:
global dat3
name_ext = filename.split('/')[-1]
name_no_ending = name_ext.split('.csv')[0]
parts = name_no_ending.split('_')
print(parts)
dat1 = read_csv(
"C:\\Users\\Josh\\IdeaProjects\\OpticalPumping\\Sweep_dat\\{}".
format(name_ext),
names=['xdat', 'ydat'])
dat1 = dat1[np.abs(dat1['xdat'] - dat1['xdat'].mean()) <= (
3 * dat1['xdat'].std())]
xdat = np.array(dat1['xdat'])
ydat = np.array(dat1['ydat'])
if force_pickrange:
fig1, ax1 = plt.subplots()
plt.title('Pick Ranges for Exponential decay fit')
Figure1, = ax1.plot(xdat, ydat, '.')
print(xdat[5], xdat[-5])
plt.xlim([xdat[5], xdat[-5]])
Sel3 = RangeTool(xdat, ydat, Figure1, ax1, name_no_ending)
fullscreen()
plt.show()
dat3 = Sel3.return_range()
if not force_pickrange:
try:
dat3 = read_csv(
"C:\\Users\\Josh\\IdeaProjects\\OpticalPumping\\Sweep_ranges\\{}"
.format(name_ext),
names=[
'Lower Bound', 'LowerIndex', 'Upper Bound',
'UpperIndex'
])
except FileNotFoundError or force_pickrange:
fig1, ax1 = plt.subplots()
plt.title('Pick Ranges for Exponential decay fit')
Figure1, = ax1.plot(xdat, ydat, '.')
print(xdat[5], xdat[-5])
plt.xlim([xdat[5], xdat[-5]])
Sel3 = RangeTool(xdat, ydat, Figure1, ax1, name_no_ending)
fullscreen()
plt.show()
dat3 = Sel3.return_range()
mdl = LorentzianModel()
try:
lowerindex = int(dat3.at[0, 'LowerIndex'])
upperindex = int(dat3.at[0, 'UpperIndex'])
except ValueError:
pass
try:
params = mdl.guess(data=ydat[lowerindex:upperindex],
x=xdat[lowerindex:upperindex])
result = mdl.fit(ydat[lowerindex:upperindex],
params,
x=xdat[lowerindex:upperindex])
resultdata = mdl.eval(x=xdat[lowerindex:upperindex],
params=result.params)
# print(result.fit_report())
MultiPlot(xdat[lowerindex:upperindex],
ydat[lowerindex:upperindex], resultdata, xdat, ydat,
name_no_ending, parts[1])
except UnboundLocalError:
pass
