From 51ddede5c72c4b753ea3b1428d0a77033abf6906 Mon Sep 17 00:00:00 2001 From: Kenneth Jao Date: Sun, 19 Sep 2021 02:58:20 -0400 Subject: [PATCH] Fixed typos --- .gitignore | 4 +++- README.md | 2 +- shrink_energy_comparison.py | 5 +++-- 3 files changed, 7 insertions(+), 4 deletions(-) diff --git a/.gitignore b/.gitignore index 4ab22c3..04ebaf4 100644 --- a/.gitignore +++ b/.gitignore @@ -7,4 +7,6 @@ src/packsim.c figures simulations -old_simulations \ No newline at end of file +old_simulations + +*.json \ No newline at end of file diff --git a/README.md b/README.md index 6e90fed..93b228b 100644 --- a/README.md +++ b/README.md @@ -69,7 +69,7 @@ This mode simulates the relaxing of the objects to its equilibrium. The threshol ``` #### Search -This mode searches for equilibrium until `eq_stop_count` equilibria are found. Additionally, if the nullity of the Hessian at the equilibrium is greater than 2. (This is due to periodicity, as any translation is another equilibrium.) In this case, the `manifold_step_size` parameter is used to traverse along it. +This mode searches for equilibrium until `eq_stop_count` equilibria are found. Additionally, the nullity of the Hessian at the equilibrium may be greater than 2. (2 is guaranteed by periodicity, as any translation is another equilibrium.) In this case, the `manifold_step_size` parameter is used to traverse along it. ```jsonc { diff --git a/shrink_energy_comparison.py b/shrink_energy_comparison.py index abab8f1..bf3c633 100644 --- a/shrink_energy_comparison.py +++ b/shrink_energy_comparison.py @@ -26,7 +26,7 @@ def get_torus_config_energies(n: int, widths: np.ndarray, h: float, r: float, sim = Simulation(n, w, h, r, energy) configs = [] - for j in range(1): + for j in range(2): for c in range(1,n): # Ignore 0, tends to error. config = (1,c) if j == 0 else (c,1) sim.add_frame(torus=config) @@ -132,7 +132,8 @@ def main(): ax.title.set_text('Basin of Attraction') ax.set_xlabel("Width") ax.set_ylabel("Disordered Equilibria") - ax.set_yticks(np.arange(0,105, 5)) + boa_y_min = round(min(all_disorder_count)/20)*20 - 5 + ax.set_yticks(np.arange(boa_y_min, 100.01, 2.5)) fig.savefig(fig_folder / "Basin of Attraction.png")