From 7f9704a1d19673fe91f6b7842042dc440d557b68 Mon Sep 17 00:00:00 2001 From: "K. Hauke Kraemer" Date: Tue, 19 Jan 2021 09:39:34 +0100 Subject: [PATCH] version increase; documentation example updated --- README.rst | 5 +++-- docs/compute_documentation_examples.py | 13 ++++++++----- docs/source/README.md | 4 ---- docs/source/images/ar_ts.png | Bin 36908 -> 31929 bytes docs/source/images/continuity_univariate.png | Bin 43608 -> 44409 bytes .../source/images/mi_and_timeseries_multi.png | Bin 133318 -> 114748 bytes .../images/mi_and_timeseries_y_comp.png | Bin 66166 -> 65764 bytes docs/source/images/reconstruction_multi.png | Bin 168572 -> 155926 bytes docs/source/images/reconstruction_y_comp.png | Bin 209077 -> 154276 bytes setup.py | 2 +- .../pecuzal_embedding.cpython-37.pyc | Bin 41019 -> 41025 bytes 11 files changed, 12 insertions(+), 12 deletions(-) delete mode 100644 docs/source/README.md diff --git a/README.rst b/README.rst index e2e657e..0346438 100644 --- a/README.rst +++ b/README.rst @@ -37,8 +37,9 @@ it is well documented and embedded in the `DynamicalSystems.jl `_ ecosystem. For instance, the compuations made in the `Univariate example `_ and the `Multivariate example `_ -in this documentation took approximately `1500s` (approx. 25 mins) and `7500s` (approx. 2 hours!), respectively. In the Julia implementation -the exact same computation took `3s` and `20s`, respectively! (running on a 2.8GHz Quad-Core i7, 16GB 1600 MHz DDR3) +in this documentation took approximately `800s` (approx. 13 mins) and `7500s` (approx. 1 hour and 10 mins!), respectively, even when +using the `econ` option in the function call, for a In the Julia implementation +the exact same computation took `4s` and `25s`, respectively! (running on a 2.8GHz Quad-Core i7, 16GB 1600 MHz DDR3) Documentation diff --git a/docs/compute_documentation_examples.py b/docs/compute_documentation_examples.py index 8bb8281..5937d31 100644 --- a/docs/compute_documentation_examples.py +++ b/docs/compute_documentation_examples.py @@ -17,6 +17,7 @@ from scipy.integrate import odeint import matplotlib.pyplot as plt +from mpl_toolkits import mplot3d from pecuzal_embedding import pecuzal_embedding, mi # integrate Roessler system on standard parameters @@ -49,7 +50,7 @@ def roessler(x,t): plt.subplots_adjust(hspace=.3) plt.savefig('mi_and_timeseries_y_comp.png') -Y_reconstruct, tau_vals, ts_vals, Ls, eps = pecuzal_embedding(y, taus = range(100), theiler = 7) +Y_reconstruct, tau_vals, ts_vals, Ls, eps = pecuzal_embedding(y, taus = range(100), theiler = 7, econ = True) fig = plt.figure(figsize=(14., 8.)) ax = plt.subplot(121, projection='3d') @@ -86,6 +87,8 @@ def roessler(x,t): plt.savefig('continuity_univariate.png') print(Ls) +L_total_uni = np.sum(Ls) +print(L_total_uni) ## Multivariate example @@ -119,14 +122,14 @@ def roessler(x,t): plt.subplots_adjust(hspace=.3) plt.savefig('mi_and_timeseries_multi.png') -Y_reconstruct, tau_vals, ts_vals, Ls, eps = pecuzal_embedding(data, taus = range(100), theiler = 7) +Y_reconstruct, tau_vals, ts_vals, Ls, eps = pecuzal_embedding(data, taus = range(100), theiler = 7, econ = True) print(tau_vals) print(ts_vals) print(Ls) -L_total = np.sum(Ls[:-1]) -print(L_total) +L_total_multi = np.sum(Ls) +print(L_total_multi) ts_labels = ['x','y','z'] @@ -173,4 +176,4 @@ def ar_process(u0, alpha, p, N): plt.grid() plt.savefig('ar_ts.png') -Y_reconstruct, tau_vals, ts_vals, Ls, eps = pecuzal_embedding(data) \ No newline at end of file +Y_reconstruct, tau_vals, ts_vals, Ls, eps = pecuzal_embedding(data, econ = True) diff --git a/docs/source/README.md b/docs/source/README.md deleted file mode 100644 index 5ad7243..0000000 --- a/docs/source/README.md +++ /dev/null @@ -1,4 +0,0 @@ -PECUZAL Python 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