[英]Define model function parameters as a function of independent variable using lmfit python
[英]With using lmfit module in python, how to call the parameters in the model?
import numpy as np
import matplotlib.pyplot as plt
from lmfit import minimize, Parameters, Parameter, report_fit
# create data to be fitted
x = np.linspace(0, 15, 301)
data = (5. * np.sin(2 * x - 0.1) * np.exp(-x*x*0.025) +
np.random.normal(size=len(x), scale=0.2) )
# define objective function: returns the array to be minimized
def fcn2min(params, x, data):
""" model decaying sine wave, subtract data"""
amp = params['amp'].value
shift = params['shift'].value
omega = params['omega'].value
decay = params['decay'].value
model = amp * np.sin(x * omega + shift) * np.exp(-x*x*decay)
return model - data
# create a set of Parameters
params = Parameters()
params.add('amp', value= 10, min=0)
params.add('decay', value= 0.1)
params.add('shift', value= 0.0, min=-np.pi/2., max=np.pi/2)
params.add('omega', value= 5.0)
# do fit, here with leastsq model
result = minimize(fcn2min, params, args=(x, data))
# calculate final result
final = data + result.residual
# try to plot results
plt.plot(x,data,'k+')
plt.plot(x,final,'r')
plt.show()
在這段代碼中,我想在python中調用“ amp”,“ shift”之類的參數。 Print(amp)..各種各樣的東西擬合后如何在python中調用這些參數? 當我使用print(amp)時,將顯示錯誤消息。 未定義名稱“ amp”。 如何使用打印功能打印這些適合的參數? (等等。print(amp))
您可能試圖在功能之外打印該數據。 amp
, shift
, omega
和decay
變量位於fc2min
的本地范圍內,因此只能在函數內部訪問。 您的數據分析技能似乎遠遠超出了您的Python知識,因此我在這段代碼中添加了一些有用的提示:
import numpy as np
import matplotlib.pyplot as plt
from lmfit import minimize, Parameters, Parameter, report_fit
# create data to be fitted
x = np.linspace(0, 15, 301)
data = (5. * np.sin(2 * x - 0.1) * np.exp(-x*x*0.025) +
np.random.normal(size=len(x), scale=0.2) )
# define objective function: returns the array to be minimized
def fcn2min(params, x, data):
""" model decaying sine wave, subtract data"""
amp = params['amp'].value
shift = params['shift'].value
omega = params['omega'].value
decay = params['decay'].value
model = amp * np.sin(x * omega + shift) * np.exp(-x*x*decay)
# tell Python we're modifying the model_data list
# that was declared outside of this function
global model_data
# store the model data produced by this function call
# add any data you want to display later to this "dictionary"
model_data += [{
"amp": amp,
"shift": shift,
"omega": omega,
"decay": decay
}]
return model - data
# create a set of Parameters
params = Parameters()
params.add('amp', value= 10, min=0)
params.add('decay', value= 0.1)
params.add('shift', value= 0.0, min=-np.pi/2., max=np.pi/2)
params.add('omega', value= 5.0)
# declare an empty list to hold the model data
model_data = []
# do fit, here with leastsq model
result = minimize(fcn2min, params, args=(x, data))
# print each item in the model data list
for datum in model_data:
for key in datum:
#the 5 in %.5f controls the precision of the floating point value
print("%s: %.5f ") % (key, datum[key]),
print
# calculate final result
final = data + result.residual
# try to plot results
plt.plot(x,data,'k+')
plt.plot(x,final,'r')
plt.show()
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