In [66]:

    

from showit import image
import matplotlib.pyplot as plt


    

In [67]:

    

%matplotlib inline


    

In [68]:

    

from extraction.utils import make_gaussian
data, series, truth = make_gaussian(n=5, seed=42, noise=0.5, withparams=True)


    

In [72]:

    

base = data.mean().toarray()
image(base);


    

In [73]:

    

from extraction import NMF
algorithm = NMF(k=5, percentile=95, overlap=0.1)


    

In [74]:

    

model = algorithm.fit(data, chunk_size=(100,200))


    

In [75]:

    

image(model.regions.mask(dims=data.shape[1:], cmap_stroke='rainbow', fill=None, base=base));


    

In [78]:

    

image(truth.regions.mask(dims=data.shape[1:], cmap='rainbow', stroke='black', base=base));


    

In [79]:

    

plt.plot(model.transform(data).toarray().T);
plt.axis('off');


    

In [80]:

    

plt.plot(series.T);
plt.axis('off');