Python GaussianModel 예제들

예제 #1

파일: detect_ion_spacing.py 프로젝트: HaeffnerLab/cct

 def fit_individual_ions(self, img_1d, ion_edges):
     from lmfit.models1d import  GaussianModel
     centers = []
     for i, (xmin, xmax) in enumerate(ion_edges):
         model = GaussianModel()
         x = np.arange(xmin, xmax, 1)
         y = img_1d[xmin:xmax]
         model.guess_starting_values(y, x=x)
         init_fit = model.model(x=x)
         model.fit(y, x=x)
         final_fit = model.model(x=x)
         centers.append(model.params['center'].value)
     return centers

예제 #2

파일: example_peakmodel.py 프로젝트: vidyar/lmfit-py

"""
Example using the built-in Peak-like models
"""
import numpy as np
from lmfit.models1d import GaussianModel, LorentzianModel, VoigtModel
import matplotlib.pyplot as plt

x  = np.linspace(0, 10, 101)

sca = 1./(2.0*np.sqrt(2*np.pi))
noise =  5e-2*np.random.randn(len(x))
dat = 2.60 -0.04*x + 7.5 * np.exp(-(x-4.0)**2 / (2*0.35)**2) + noise

mod = GaussianModel(background='linear')
# mod = VoigtModel(background='linear')
# mod = LorentzianModel(background='linear')

mod.guess_starting_values(dat, x)


plt.plot(x, dat)

# initial guess
plt.plot(x, mod.model(x=x) + mod.calc_background(x), 'r+')

mod.fit(dat, x=x)

print mod.fit_report()

# best fit
plt.plot(x, mod.model(x=x) + mod.calc_background(x))

예제 #3

파일: model1d_doc2.py 프로젝트: charlesll/lmfit-py

model = VoigtModel(background='linear')

# get default starting values, but then alter them
model.guess_starting_values(y, x=x)
model.params['amplitude'].value = 2.0

init_fit = model.model(x=x)

# the actual fit
model.fit(y, x=x)

print model.fit_report(min_correl=0.25)

vfit = model.model(x=x)


mod2 = GaussianModel(background='linear')

mod2.fit(y, x=x)
gfit = mod2.model(x=x)

print mod2.fit_report(min_correl=0.25)

print 'Voigt    Sum of Squares: ', ((vfit - y)**2).sum()
print 'Gaussian Sum of Squares: ', ((gfit - y)**2).sum()

plt.plot(x, vfit, 'r-')
plt.plot(x, gfit, 'b-')
plt.plot(x, y,    'bo')
plt.show()

예제 #4

파일: model1d_doc1.py 프로젝트: charlesll/lmfit-py

import numpy as np
from lmfit.models1d import  GaussianModel
import matplotlib.pyplot as plt

data = np.loadtxt('model1d_gauss.dat')
x = data[:, 0]
y = data[:, 1]

model = GaussianModel()
model.guess_starting_values(y, x=x)
# model.params['amplitude'].value=6.0

init_fit = model.model(x=x)
model.fit(y, x=x)

print model.fit_report(min_correl=0.25)

final_fit = model.model(x=x)

plt.plot(x, final_fit, 'r-')
plt.plot(x, init_fit, 'k--')
plt.plot(x, y,         'bo')
plt.show()

예제 #5

파일: model1d_doc1.py 프로젝트: vidyar/lmfit-py

import numpy as np
from lmfit.models1d import GaussianModel
import matplotlib.pyplot as plt

data = np.loadtxt('model1d_gauss.dat')
x = data[:, 0]
y = data[:, 1]

model = GaussianModel()
model.guess_starting_values(y, x=x)
# model.params['amplitude'].value=6.0

init_fit = model.model(x=x)
model.fit(y, x=x)

print model.fit_report(min_correl=0.25)

final_fit = model.model(x=x)

plt.plot(x, final_fit, 'r-')
plt.plot(x, init_fit, 'k--')
plt.plot(x, y, 'bo')
plt.show()

예제 #6

파일: use_models1d.py 프로젝트: jbzdak/lmfit-py

sig = 0.47
amp = 12.00
cen = 5.66
eps = 0.15
off = 9
slo = 0.2
sca = 1.0 / (2.0 * np.sqrt(2 * np.pi)) / sig

noise = eps * np.random.randn(len(x))

dat = off + slo * x + amp * sca * np.exp(-(x - cen) ** 2 / (2 * sig) ** 2) + noise

# mod = ExponentialModel(background='linear')
# mod = LinearModel()

mod = GaussianModel(background="quad")
mod = VoigtModel(background="quad")
mod = LorenztianModel(background="quad")
mod.guess_starting_values(dat, x, negative=False)
mod.params["bkg_offset"].value = min(dat)

init = mod.model(x=x) + mod.calc_background(x)
mod.fit(dat, x=x)


print mod.fit_report()

fit = mod.model(x=x) + mod.calc_background(x)

plt.plot(x, dat)
plt.plot(x, init)

예제 #7

sig = 0.47
amp = 12.00
cen = 5.66
eps = 0.15
off = 9
slo = 0.0012
sca = 1. / (2.0 * np.sqrt(2 * np.pi)) / sig

noise = eps * np.random.randn(len(x))

dat = off + slo * x + amp * sca * np.exp(-(x - cen)**2 / (2 * sig)**2) + noise

# mod = ExponentialModel(background='linear')
# mod = LinearModel()

mod = GaussianModel(background='quad')
mod = VoigtModel(background='quad')
mod = LorenztianModel(background='quad')
mod.guess_starting_values(dat, x, negative=False)
mod.params['bkg_offset'].value = min(dat)

init = mod.model(x=x) + mod.calc_background(x)
mod.fit(dat, x=x)

print mod.fit_report()

fit = mod.model(x=x) + mod.calc_background(x)

plt.plot(x, dat)
plt.plot(x, init)
plt.plot(x, fit)

예제 #8

import numpy as np
from lmfit.models1d import GaussianModel
import matplotlib.pyplot as plt

data = np.loadtxt('model1d_gauss.dat')
x = data[:, 0]
y = data[:, 1]

model = GaussianModel()  # background='linear'

# model.guess_starting_values(y, x, negative=False)
# model.params['bkg_offset'].value=min(y)

init_fit = model.model(x=x) + model.calc_background(x)
model.fit(y, x=x)

print model.fit_report()

final_fit = model.model(x=x)

plt.plot(x, y)
plt.plot(x, init_fit)
plt.plot(x, final_fit)
plt.show()

예제 #9

파일: model1d_doc2.py 프로젝트: vidyar/lmfit-py

model = VoigtModel(background='linear')

# get default starting values, but then alter them
model.guess_starting_values(y, x=x)
model.params['amplitude'].value = 2.0

init_fit = model.model(x=x)

# the actual fit
model.fit(y, x=x)

print model.fit_report(min_correl=0.25)

vfit = model.model(x=x)

mod2 = GaussianModel(background='linear')

mod2.fit(y, x=x)
gfit = mod2.model(x=x)

print mod2.fit_report(min_correl=0.25)

print 'Voigt    Sum of Squares: ', ((vfit - y)**2).sum()
print 'Gaussian Sum of Squares: ', ((gfit - y)**2).sum()

plt.plot(x, vfit, 'r-')
plt.plot(x, gfit, 'b-')
plt.plot(x, y, 'bo')
plt.show()

예제 #10

파일: m1.py 프로젝트: charlesll/lmfit-py

import numpy as np
from lmfit.models1d import  GaussianModel
import matplotlib.pyplot as plt

data = np.loadtxt('model1d_gauss.dat')
x = data[:, 0]
y = data[:, 1]

model = GaussianModel()  # background='linear'

# model.guess_starting_values(y, x, negative=False)
# model.params['bkg_offset'].value=min(y)

init_fit = model.model(x=x) + model.calc_background(x)
model.fit(y, x=x)

print model.fit_report()

final_fit = model.model(x=x)

plt.plot(x, y)
plt.plot(x, init_fit)
plt.plot(x, final_fit)
plt.show()