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# <examples/doc_model_with_nan_policy.py>
import matplotlib.pyplot as plt
import numpy as np
from lmfit.models import GaussianModel
data = np.loadtxt('model1d_gauss.dat')
x = data[:, 0]
y = data[:, 1]
y[44] = np.nan
y[65] = np.nan
# nan_policy = 'raise'
# nan_policy = 'propagate'
nan_policy = 'omit'
gmodel = GaussianModel()
result = gmodel.fit(y, x=x, amplitude=5, center=6, sigma=1,
nan_policy=nan_policy)
print(result.fit_report())
# make sure nans are removed for plotting:
x_ = x[np.where(np.isfinite(y))]
y_ = y[np.where(np.isfinite(y))]
plt.plot(x_, y_, 'bo')
plt.plot(x_, result.init_fit, 'k--', label='initial fit')
plt.plot(x_, result.best_fit, 'r-', label='best fit')
plt.legend(loc='best')
plt.show()
# <end examples/doc_model_with_nan_policy.py>
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