def do_load_model():
x, y = XDAT, YDAT
model = load_model(SAVE_MODEL)
params = model.make_params()
params['exp_decay'].set(100)
params['exp_amplitude'].set(100)
params['g1_center'].set(105, min=75, max=125)
params['g1_sigma'].set(15, min=3)
params['g1_amplitude'].set(2000, min=10)
params['g2_center'].set(155, min=125, max=175)
params['g2_sigma'].set(15, min=3)
params['g2_amplitude'].set(2000, min=10)
result = model.fit(y, params, x=x)
check_fit_results(result)
def test_save_load_model(dill):
"""Save/load Model with/without dill."""
if dill:
pytest.importorskip("dill")
else:
lmfit.jsonutils.HAS_DILL = False
# create/save Model and perform some tests
model, _pars = create_model_params(x, y)
save_model(model, SAVE_MODEL)
file_exists = wait_for_file(SAVE_MODEL, timeout=10)
assert file_exists
with open(SAVE_MODEL, 'r') as fh:
text = fh.read()
assert 1000 < len(text) < 2500
# load the Model, perform fit and assert results
saved_model = load_model(SAVE_MODEL)
params = saved_model.make_params()
params['exp_decay'].set(100)
params['exp_amplitude'].set(100)
params['g1_center'].set(105, min=75, max=125)
params['g1_sigma'].set(15, min=3)
params['g1_amplitude'].set(2000, min=10)
params['g2_center'].set(155, min=125, max=175)
params['g2_sigma'].set(15, min=3)
params['g2_amplitude'].set(2000, min=10)
result = saved_model.fit(y, params, x=x)
check_fit_results(result)
clear_savefile(SAVE_MODEL)
def test_save_load_model(dill):
"""Save/load Model with/without dill."""
if dill:
pytest.importorskip("dill")
else:
lmfit.jsonutils.HAS_DILL = False
# create/save Model and perform some tests
model, _pars = create_model_params(x, y)
save_model(model, SAVE_MODEL)
file_exists = wait_for_file(SAVE_MODEL, timeout=10)
assert file_exists
with open(SAVE_MODEL, 'r') as fh:
text = fh.read()
assert 1000 < len(text) < 2500
# load the Model, perform fit and assert results
saved_model = load_model(SAVE_MODEL)
params = saved_model.make_params()
params['exp_decay'].set(100)
params['exp_amplitude'].set(100)
params['g1_center'].set(105, min=75, max=125)
params['g1_sigma'].set(15, min=3)
params['g1_amplitude'].set(2000, min=10)
params['g2_center'].set(155, min=125, max=175)
params['g2_sigma'].set(15, min=3)
params['g2_amplitude'].set(2000, min=10)
result = saved_model.fit(y, params, x=x)
check_fit_results(result)
clear_savefile(SAVE_MODEL)
# <examples/doc_model_loadmodel.py>
import matplotlib.pyplot as plt
import numpy as np
from lmfit.model import load_model
def mysine(x, amp, freq, shift):
return amp * np.sin(x * freq + shift)
data = np.loadtxt('sinedata.dat')
x = data[:, 0]
y = data[:, 1]
model = load_model('sinemodel.sav', funcdefs={'mysine': mysine})
params = model.make_params(amp=3, freq=0.52, shift=0)
params['shift'].max = 1
params['shift'].min = -1
params['amp'].min = 0.0
result = model.fit(y, params, x=x)
print(result.fit_report())
plt.plot(x, y, 'bo')
plt.plot(x, result.best_fit, 'r-')
plt.show()
# <end examples/doc_model_loadmodel.py>
#!/usr/bin/env python
# <examples/doc_model_loadmodel.py>
import matplotlib.pyplot as plt
import numpy as np
from lmfit.model import load_model
def mysine(x, amp, freq, shift):
return amp * np.sin(x*freq + shift)
data = np.loadtxt('sinedata.dat')
x = data[:, 0]
y = data[:, 1]
model = load_model('sinemodel.sav', funcdefs={'mysine': mysine})
params = model.make_params(amp=3, freq=0.52, shift=0)
params['shift'].max = 1
params['shift'].min = -1
params['amp'].min = 0.0
result = model.fit(y, params, x=x)
print(result.fit_report())
plt.plot(x, y, 'bo')
plt.plot(x, result.best_fit, 'r-')
plt.show()
# <end examples/doc_model_loadmodel.py>
#Finally, plot the raw data, fit data, and print the chisqr and the separation
from astropy.io import fits
from lmfit.model import ModelResult, save_model, load_model
#import fits file from the local directory
#Then save the specific range of the data into variables
#And normalized data
spec68 = fits.open('../1Ds_LAEs_forYuzheng/1D_spectrum_MUSEID_68.fits')
x68 = spec68[1].data['delta_v_Lya'][1956:1996]
y68 = spec68[1].data['flux'][1956:1996]
e68 = spec68[1].data['flux_err'][1956:1996]
Ne68 = e68 / y68.max()
#Load preset model
FW2 = load_model('FW2SG.sav')
#plot the graph
result = FW2.fit(Ny68, params_FW, x=x68, weights=Ne68)
pyplot.step(x68, Ny68, '-', where='mid')
pyplot.plot(x68, result.best_fit, '--')
pyplot.title('2 Skewed gaussian using FWHM as parameters')
pyplot.show()
#print out the result
sep_result = result.params['x0_2'] - result.params['x0_1']
print('The chi square is {:.10f}'.format(result.chisqr))
print('The separation is {:.2f}'.format(sep_result))
#print(result.fit_report())
result.params
