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44 lines
1.4 KiB
44 lines
1.4 KiB
2 years ago
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# -*- coding: utf-8 -*-
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import numpy as np
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import pandas as pd
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import sys
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sys.path.insert(0, './Modules')
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from gratings import grating_model
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from plotting import plot_data, plot_mean_against_index, show_feature
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from persistence import persistence
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from decoding import cohomological_parameterization, remove_feature
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from noisereduction import PCA_reduction, z_cutoff
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## Generate data
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data = grating_model(Nn=8, Np=(18,1,18,1), deltaT=55, random_neurons=True)
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## Apply noise reduction
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# data = PCA_reduction(data, 5)
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# data = z_cutoff(data,2)
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## Analyze shape
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persistence(data,homdim=2,coeff=2)
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persistence(data,homdim=2,coeff=3)
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## Decode first parameter
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decoding1 = cohomological_parameterization(data, coeff=23)
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show_feature(decoding1)
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plot_mean_against_index(data,decoding1,"orientation")
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plot_mean_against_index(data,decoding1,"phase")
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# plot_data(data,transformation="PCA", labels=decoding1,
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# colors=["Twilight","Viridis","Twilight","Viridis","Twilight"])
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## Decode second parameter
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# reduced_data = remove_feature(data, decoding1, cut_amplitude=0.5)
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# decoding2 = cohomological_parameterization(reduced_data, coeff=23)
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# show_feature(decoding2)
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# plot_mean_against_index(data,decoding2,"orientation")
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# plot_mean_against_index(data,decoding2,"phase")
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# plot_data(data,transformation="PCA", labels=decoding2,
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# colors=["Twilight","Viridis","Twilight","Viridis","Twilight"])
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