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@@ -161,7 +161,7 @@ This project is licensed under the [Creative Commons Attribution-ShareAlike 4.0 | |
| For collaborations, press inquiries, or questions: | ||
| - Email: [[email protected]](mailto:[email protected]) or [[email protected]](mailto:[email protected]) | ||
| - Discord: soul_syrup | ||
| - TikTok: [@soul.syrup](https://www.tiktok.com/@soul.syrup) | ||
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| ## 📚 Library Testing Invitation | ||
| We invite you to test our PyPI library for human brain cortical organoid/spheroid, EEG, ECoG, and other signal analyses: | ||
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@@ -161,7 +161,7 @@ This project is licensed under the [Creative Commons Attribution-ShareAlike 4.0 | |
| For collaborations, press inquiries, or questions: | ||
| - Email: [[email protected]](mailto:[email protected]) or [[email protected]](mailto:[email protected]) | ||
| - Discord: soul_syrup | ||
| - TikTok: [@soul.syrup](https://www.tiktok.com/@soul.syrup) | ||
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| ## 📚 Library Testing Invitation | ||
| We invite you to test our PyPI library for human brain cortical organoid/spheroid, EEG, ECoG, and other signal analyses: | ||
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45 changes: 45 additions & 0 deletions
45
...Backend/desktop_browser_app/system/nightly/.ipynb_checkpoints/betti_numbers-checkpoint.py
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| import numpy as np | ||
| import gudhi as gd | ||
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| # Assuming `reduced_data_umap` is your dimensionality-reduced data from the previous steps | ||
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| def compute_persistence_diagrams(data): | ||
| """ | ||
| Compute the persistence diagrams for a given dataset using Vietoris-Rips complex. | ||
| :param data: Input dataset, assumed to be the output of a dimensionality reduction method. | ||
| :return: Persistence diagrams for the dataset. | ||
| """ | ||
| rips_complex = gd.RipsComplex(points=data, max_edge_length=2) | ||
| simplex_tree = rips_complex.create_simplex_tree(max_dimension=2) | ||
| persistence = simplex_tree.persistence() | ||
| return persistence | ||
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| def plot_persistence_diagrams(persistence): | ||
| """ | ||
| Plot the persistence diagrams. | ||
| :param persistence: Persistence diagrams. | ||
| """ | ||
| gd.plot_persistence_diagram(persistence) | ||
| plt.show() | ||
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| def calculate_betti_numbers(persistence): | ||
| """ | ||
| Calculate Betti numbers from the persistence diagrams. | ||
| :param persistence: Persistence diagrams. | ||
| :return: Betti numbers (b0, b1, b2) counting the number of connected components, loops, and voids respectively. | ||
| """ | ||
| betti_numbers = {i: 0 for i in range(3)} # Assuming we're only interested in dimensions 0, 1, and 2 | ||
| for interval in persistence: | ||
| if interval[0] < 3: # Filter out infinite persistence intervals | ||
| betti_numbers[interval[0]] += 1 | ||
| return betti_numbers['b0'], betti_numbers['b1'], betti_numbers['b2'] | ||
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| # Compute Persistence Diagrams | ||
| persistence = compute_persistence_diagrams(reduced_data_umap) | ||
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| # Plot Persistence Diagrams | ||
| plot_persistence_diagrams(persistence) | ||
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| # Calculate Betti Numbers | ||
| betti_numbers = calculate_betti_numbers(persistence) | ||
| print("Betti Numbers:", betti_numbers) |
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..._checkpoints/dimensionality_reduction_manifold_operations_symbolic dynamics-checkpoint.py
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| import numpy as np | ||
| import matplotlib.pyplot as plt | ||
| from sklearn.manifold import TSNE | ||
| import umap.umap_ as umap | ||
| from geomstats.geometry.special_orthogonal import SpecialOrthogonal | ||
| import geomstats.learning.frechet_mean as frechet_mean | ||
| import geomstats.geometry.hypersphere as hypersphere | ||
| from geomstats.learning.frechet_mean import FrechetMean | ||
| from geomstats.geometry.euclidean import Euclidean | ||
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| # Assuming the file contains continuous data recorded at 500 Hz from 32 channels | ||
| file_path = 'ecog_data_last_24h.npy' | ||
| ECoG_data = np.load(file_path) | ||
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| # Preprocessing steps (filtering, detrending, artifact removal) are assumed to be done prior | ||
| # This example focuses on the analysis part | ||
| # Apply UMAP for dimensionality reduction to 3D for better visualization and further analysis | ||
| reduced_data_umap = umap.UMAP(n_components=3).fit_transform(ECoG_data) | ||
| # Apply t-SNE for comparison | ||
| reduced_data_tsne = TSNE(n_components=3).fit_transform(ECoG_data) | ||
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| plt.figure(figsize=(12, 6)) | ||
| plt.subplot(1, 2, 1) | ||
| plt.scatter(reduced_data_umap[:, 0], reduced_data_umap[:, 1], s=1) | ||
| plt.title('UMAP Reduction') | ||
| plt.subplot(1, 2, 2) | ||
| plt.scatter(reduced_data_tsne[:, 0], reduced_data_tsne[:, 1], s=1) | ||
| plt.title('t-SNE Reduction') | ||
| plt.show() | ||
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| sphere = hypersphere.Hypersphere(dim=2) | ||
| point_a, point_b = sphere.random_point(), sphere.random_point() | ||
| distance = sphere.metric.dist(point_a, point_b) | ||
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| # Frechet Mean on Hypersphere | ||
| points = sphere.random_point(n_samples=10) | ||
| frechet_mean_sphere = FrechetMean(metric=sphere.metric) | ||
| frechet_mean_sphere.fit(points) | ||
| mean_sphere = frechet_mean_sphere.estimate_ | ||
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| so3 = SpecialOrthogonal(n=3, point_type='vector') | ||
| points_so3 = so3.random_point(n_samples=10) | ||
| frechet_mean_so3 = FrechetMean(metric=so3.metric) | ||
| frechet_mean_so3.fit(points_so3) | ||
| mean_so3 = frechet_mean_so3.estimate_ | ||
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| # Partitioning the reduced space into 4 regions as an example | ||
| quantiles = np.quantile(reduced_data_umap, [0.25, 0.5, 0.75], axis=0) | ||
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| def partition_phase_space(data, quantiles): | ||
| symbols = np.zeros(data.shape[0], dtype=int) | ||
| for i, point in enumerate(data): | ||
| if point[0] < quantiles[0][0]: | ||
| symbols[i] = 0 | ||
| elif point[0] < quantiles[1][0]: | ||
| symbols[i] = 1 | ||
| elif point[0] < quantiles[2][0]: | ||
| symbols[i] = 2 | ||
| else: | ||
| symbols[i] = 3 | ||
| return symbols | ||
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| symbols = partition_phase_space(reduced_data_umap, quantiles) | ||
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| plt.figure(figsize=(6, 6)) | ||
| for i in range(4): | ||
| plt.scatter(reduced_data_umap[symbols == i, 0], reduced_data_umap[symbols == i, 1], s=1, label=f'Partition {i}') | ||
| plt.title('Symbolic Dynamics Partitioning (UMAP)') | ||
| plt.legend() | ||
| plt.show() |
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Software/PC/Backend/desktop_browser_app/system/nightly/betti_numbers.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,45 @@ | ||
| import numpy as np | ||
| import gudhi as gd | ||
|
|
||
| # Assuming `reduced_data_umap` is your dimensionality-reduced data from the previous steps | ||
|
|
||
| def compute_persistence_diagrams(data): | ||
| """ | ||
| Compute the persistence diagrams for a given dataset using Vietoris-Rips complex. | ||
| :param data: Input dataset, assumed to be the output of a dimensionality reduction method. | ||
| :return: Persistence diagrams for the dataset. | ||
| """ | ||
| rips_complex = gd.RipsComplex(points=data, max_edge_length=2) | ||
| simplex_tree = rips_complex.create_simplex_tree(max_dimension=2) | ||
| persistence = simplex_tree.persistence() | ||
| return persistence | ||
|
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||
| def plot_persistence_diagrams(persistence): | ||
| """ | ||
| Plot the persistence diagrams. | ||
| :param persistence: Persistence diagrams. | ||
| """ | ||
| gd.plot_persistence_diagram(persistence) | ||
| plt.show() | ||
|
|
||
| def calculate_betti_numbers(persistence): | ||
| """ | ||
| Calculate Betti numbers from the persistence diagrams. | ||
| :param persistence: Persistence diagrams. | ||
| :return: Betti numbers (b0, b1, b2) counting the number of connected components, loops, and voids respectively. | ||
| """ | ||
| betti_numbers = {i: 0 for i in range(3)} # Assuming we're only interested in dimensions 0, 1, and 2 | ||
| for interval in persistence: | ||
| if interval[0] < 3: # Filter out infinite persistence intervals | ||
| betti_numbers[interval[0]] += 1 | ||
| return betti_numbers['b0'], betti_numbers['b1'], betti_numbers['b2'] | ||
|
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| # Compute Persistence Diagrams | ||
| persistence = compute_persistence_diagrams(reduced_data_umap) | ||
|
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| # Plot Persistence Diagrams | ||
| plot_persistence_diagrams(persistence) | ||
|
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| # Calculate Betti Numbers | ||
| betti_numbers = calculate_betti_numbers(persistence) | ||
| print("Betti Numbers:", betti_numbers) |
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70
...wser_app/system/nightly/dimensionality_reduction_manifold_operations_symbolic dynamics.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,70 @@ | ||
| import numpy as np | ||
| import matplotlib.pyplot as plt | ||
| from sklearn.manifold import TSNE | ||
| import umap.umap_ as umap | ||
| from geomstats.geometry.special_orthogonal import SpecialOrthogonal | ||
| import geomstats.learning.frechet_mean as frechet_mean | ||
| import geomstats.geometry.hypersphere as hypersphere | ||
| from geomstats.learning.frechet_mean import FrechetMean | ||
| from geomstats.geometry.euclidean import Euclidean | ||
|
|
||
| # Assuming the file contains continuous data recorded at 500 Hz from 32 channels | ||
| file_path = 'ecog_data_last_24h.npy' | ||
| ECoG_data = np.load(file_path) | ||
|
|
||
| # Preprocessing steps (filtering, detrending, artifact removal) are assumed to be done prior | ||
| # This example focuses on the analysis part | ||
| # Apply UMAP for dimensionality reduction to 3D for better visualization and further analysis | ||
| reduced_data_umap = umap.UMAP(n_components=3).fit_transform(ECoG_data) | ||
| # Apply t-SNE for comparison | ||
| reduced_data_tsne = TSNE(n_components=3).fit_transform(ECoG_data) | ||
|
|
||
| plt.figure(figsize=(12, 6)) | ||
| plt.subplot(1, 2, 1) | ||
| plt.scatter(reduced_data_umap[:, 0], reduced_data_umap[:, 1], s=1) | ||
| plt.title('UMAP Reduction') | ||
| plt.subplot(1, 2, 2) | ||
| plt.scatter(reduced_data_tsne[:, 0], reduced_data_tsne[:, 1], s=1) | ||
| plt.title('t-SNE Reduction') | ||
| plt.show() | ||
|
|
||
| sphere = hypersphere.Hypersphere(dim=2) | ||
| point_a, point_b = sphere.random_point(), sphere.random_point() | ||
| distance = sphere.metric.dist(point_a, point_b) | ||
|
|
||
| # Frechet Mean on Hypersphere | ||
| points = sphere.random_point(n_samples=10) | ||
| frechet_mean_sphere = FrechetMean(metric=sphere.metric) | ||
| frechet_mean_sphere.fit(points) | ||
| mean_sphere = frechet_mean_sphere.estimate_ | ||
|
|
||
| so3 = SpecialOrthogonal(n=3, point_type='vector') | ||
| points_so3 = so3.random_point(n_samples=10) | ||
| frechet_mean_so3 = FrechetMean(metric=so3.metric) | ||
| frechet_mean_so3.fit(points_so3) | ||
| mean_so3 = frechet_mean_so3.estimate_ | ||
|
|
||
| # Partitioning the reduced space into 4 regions as an example | ||
| quantiles = np.quantile(reduced_data_umap, [0.25, 0.5, 0.75], axis=0) | ||
|
|
||
| def partition_phase_space(data, quantiles): | ||
| symbols = np.zeros(data.shape[0], dtype=int) | ||
| for i, point in enumerate(data): | ||
| if point[0] < quantiles[0][0]: | ||
| symbols[i] = 0 | ||
| elif point[0] < quantiles[1][0]: | ||
| symbols[i] = 1 | ||
| elif point[0] < quantiles[2][0]: | ||
| symbols[i] = 2 | ||
| else: | ||
| symbols[i] = 3 | ||
| return symbols | ||
|
|
||
| symbols = partition_phase_space(reduced_data_umap, quantiles) | ||
|
|
||
| plt.figure(figsize=(6, 6)) | ||
| for i in range(4): | ||
| plt.scatter(reduced_data_umap[symbols == i, 0], reduced_data_umap[symbols == i, 1], s=1, label=f'Partition {i}') | ||
| plt.title('Symbolic Dynamics Partitioning (UMAP)') | ||
| plt.legend() | ||
| plt.show() |
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