empeaks 1.0.1

'high-throughput spectrum peak modeling tools by using Spectrum adapted EM algorithms'

EMPeaks

This package is for high-throughput peak analysis by using Spectrum Adapted EM algorithm. Please refer the following paper when using this package: Sci. Tech. Adv. Mater. 20, 733-735 (2019).

version 1.0.0

In version 1.0.0, Gaussian　Mixture　Model (GMM) is only available.

Brief Explanation

class: GaussianMixture(data2d, K=2):

class for GMM object.
parameters:
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numpy_array data2d: [energy, intensity]
integer          K: number of Gaussians
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class SpectrumAdaptedEM(GaussianMixture)(data2d, K=2, max_iter=500):

Class for Spectrum Adapted EM algorithm opject.
parameters:
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integer max_iter: max iteration for E-step and M-step.
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def fit(iter_log=False, sampling=1):
    fitting GMM to the data2d via EM algorithm.
    parameters:
        boolean iter_log: switch to write the iteration log.
        integer sampling: sampling number for initial parameters. 
                          The largest likelihood　model in samples is selected.

def plot_fitting_summary(self, dpi=100, save=False):

def plot_param_history(self, dpi=100, save=False):
    
def ani_gmm_history(self, dx=0.05, dpi=100, save=False, interval=100, repeat_delay=1500):

Examples

We prepared three examples to test this package.

1. test_em(N: int)

    from EMPeaks import GaussianMixture
    test = GaussianMixture.Tests
    test.test_em(N=10000,sampling=5)

2. test_spectrum_adapted_em(N: int)

from EMPeaks import GaussianMixture
test = GaussianMixture.Tests
test.test_spectrum_adapted_em(N=10000,sampling=5)

3. test_exp_data()

from EMPeaks import GaussianMixture
test = GaussianMixture.Tests
test.test_exp_data('GFET0126_25V_C1s.txt', sampling=5)

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