Python GaussianModel.guess_starting_values示例

示例#1

文件： detect_ion_spacing.py 项目： 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

示例#2

文件： example_peakmodel.py 项目： vidyar/lmfit-py

"""
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))
plt.show()

示例#3

文件： model1d_doc1.py 项目： 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()

示例#4

文件： model1d_doc1.py 项目： 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()

示例#5

文件： use_models1d.py 项目： jbzdak/lmfit-py

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)
plt.plot(x, fit)
plt.show()

示例#6

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)
plt.show()