benda/nonlinearbaseline2025
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finished new figure captions

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Jan Benda
2025-05-23 11:25:53 +02:00
parent 3e07684093
commit 585e5ec27b
6 changed files with 116 additions and 156 deletions
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17
noisesplit.py
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@@ -5,9 +5,8 @@ from spectral import whitenoise, diag_projection, peakedness
from plotstyle import plot_style
#example_cell = ['2012-07-03-ak-invivo-1', 0]
example_cell = ['2017-07-18-ai-invivo-1', 1] # Take this! at 3% model, 5% data
model_cell = example_cell
example_cell = ['2017-07-18-ai-invivo-1', 1]
model_cell = example_cell[0]
base_path = Path('data')
data_path = base_path / 'cells'
@@ -275,8 +274,8 @@ if __name__ == '__main__':
# model 5%:
axss = axs[1]
data_files = sims_path.glob(f'chi2-noisen-{example_cell[0]}-{1000*data_contrast:03.0f}-*.npz')
files, nums = sort_files(example_cell[0], data_files, 2)
data_files = sims_path.glob(f'chi2-noisen-{model_cell}-{1000*data_contrast:03.0f}-*.npz')
files, nums = sort_files(model_cell, data_files, 2)
axss[1].text(xt, yt, 'P-unit model', fontsize='large',
transform=axs[1, 1].transAxes, color=s.model_color1)
plot_chi2_contrast(axss[1], axss[2], s, files, nums, nsmall, nlarge, ratebase)
@@ -287,16 +286,16 @@ if __name__ == '__main__':
# model 1%:
axss = axs[2]
data_files = sims_path.glob(f'chi2-noisen-{example_cell[0]}-{1000*contrast:03.0f}-*.npz')
files, nums = sort_files(example_cell[0], data_files, 2)
data_files = sims_path.glob(f'chi2-noisen-{model_cell}-{1000*contrast:03.0f}-*.npz')
files, nums = sort_files(model_cell, data_files, 2)
plot_chi2_contrast(axss[1], axss[2], s, files, nums, nsmall, nlarge, ratebase)
axr2 = plot_noise_split(axss[0], contrast, 0, 1, wtime, wnoise)
plot_overn(axss[3], s, files, nmax=1e6)
# model noise split:
axss = axs[3]
data_files = sims_path.glob(f'chi2-split-{example_cell[0]}-*.npz')
files, nums = sort_files(example_cell[0], data_files, 1)
data_files = sims_path.glob(f'chi2-split-{model_cell}-*.npz')
files, nums = sort_files(model_cell, data_files, 1)
axss[1].text(xt, yt, 'P-unit model', fontsize='large',
transform=axss[1].transAxes, color=s.model_color1)
axss[1].text(xt + 0.9, yt, f'(noise split)', fontsize='large',