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43 lines
1.4 KiB
43 lines
1.4 KiB
# -*- 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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