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Fixed option overriding from squish command utility

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This commit is contained in:
Kenneth Jao 2021-12-06 19:50:44 -05:00
parent 8948b0cf16
commit 2e9ed2f072
3 changed files with 7 additions and 274 deletions
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267
scripts/width_diagrams.py
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@ -1,267 +0,0 @@
from __future__ import annotations
from typing import List, Tuple, Dict
import argparse, math, numpy as np, os
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
from multiprocessing import Pool, cpu_count
from pathlib import Path
import squish.ordered as order
from squish import Simulation, DomainParams
from squish.common import OUTPUT_DIR
def order_process(domain: DomainParams) -> Tuple[float, float, float]:
energies = []
configs = order.configurations(domain)
for config in configs:
energies.append(
2 * domain.w * domain.h
+ 2
* math.pi
* domain.n
* (domain.r ** 2 - 2 * domain.r * order.avg_radius(domain, config))
)
return domain.w, min(energies), max(energies)
def get_ordered_energies(orig_domain: DomainParams, widths: np.ndarray) -> Dict:
data = {}
domains = [
DomainParams(orig_domain.n, w, orig_domain.h, orig_domain.r) for w in widths
]
with Pool(cpu_count()) as pool:
mins, maxes = {}, {}
for i, res in enumerate(pool.imap_unordered(order_process, domains)):
mins[res[0]] = res[1]
maxes[res[0]] = res[2]
hashes = int(21 * i / len(widths))
print(
f'Generating at width {res[0]:.02f}... |{"#"*hashes}{" "*(20-hashes)}|'
+ f" {i+1}/{len(widths)} completed.",
flush=True,
end="\r",
)
print(flush=True)
data["min"] = list([x[1] for x in sorted(mins.items())])
data["max"] = list([x[1] for x in sorted(maxes.items())])
return data
def eq_file_process(file: Path) -> Tuple[float, List[float], List[float]]:
sim, frames = Simulation.load(file)
alls = []
for frame_info in frames:
alls.append(
[
frame_info["energy"],
np.var(frame_info["stats"]["avg_radius"]) <= 1e-8,
np.count_nonzero(frame_info["stats"]["site_edge_count"] != 6),
]
)
sim, frames = Simulation.load(file)
sim.frames = list(frames)
counts = sim.get_distinct()
distincts = []
for j, frame_info in enumerate(sim.frames):
distincts.append(
[
frame_info["energy"],
np.var(frame_info["stats"]["avg_radius"]) <= 1e-8,
np.count_nonzero(frame_info["stats"]["site_edge_count"] != 6),
counts[j],
]
)
return sim.domain.w, alls, distincts
def get_equilibria_data(filepath: Path) -> Tuple[Dict, numpy.ndarray, DomainParams]:
data = {"all": {}, "distinct": {}}
files = list(Path(filepath).iterdir())
with Pool(cpu_count()) as pool:
for i, res in enumerate(pool.imap_unordered(eq_file_process, files)):
data["all"][res[0]] = res[1]
data["distinct"][res[0]] = res[2]
hashes = int(21 * i / len(files))
print(
f'Loading simulations... |{"#"*hashes}{" "*(20-hashes)}|'
+ f" {i+1}/{len(files)} simulations loaded.",
flush=True,
end="\r",
)
print(flush=True)
sim, frames = Simulation.load(files[0])
widths = np.asarray(sorted(data["all"]))
domain = DomainParams(sim.domain.n, widths[-1], sim.domain.h, sim.domain.r)
return data, widths, domain
def axis_settings(ax, widths):
ax.invert_xaxis()
ax.grid(zorder=0)
ax.set_xticks([round(w, 2) for w in widths[::-2]])
ax.set_xticklabels(ax.get_xticks(), rotation=90)
plt.subplots_adjust(0.07, 0.12, 0.97, 0.9)
def main():
# Loading arguments.
parser = argparse.ArgumentParser("Outputs width search data into diagrams")
parser.add_argument(
"sims_path",
metavar="path/to/data",
help="folder that contains simulation files, or cached data file.",
)
parser.add_argument(
"-q",
"--quiet",
dest="quiet",
action="store_true",
default=False,
help="suppress all normal output",
)
args = parser.parse_args()
data, widths, domain = get_equilibria_data(Path(args.sims_path))
order_data = get_ordered_energies(domain, widths)
fig_folder = OUTPUT_DIR / Path(f"ShrinkEnergyComparison - N{domain.n}")
fig_folder.mkdir(exist_ok=True)
# Torus minimum energies used as reference.
# Probability of disorder diagram.
fig, ax = plt.subplots(figsize=(16, 8))
all_disorder_count = []
for width in widths:
equal_shape = list([c[1] for c in data["all"][width]])
all_disorder_count.append(
100 * equal_shape.count(False) / len(data["all"][width])
)
ax.plot(widths, all_disorder_count)
axis_settings(ax, widths)
ax.yaxis.set_major_formatter(mtick.PercentFormatter())
ax.title.set_text(f"Probability of Disorder - N{domain.n}")
ax.set_xlabel("Width")
ax.set_ylabel("Disordered Equilibria")
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 / "Probability of Disorder.png")
# Density of States diagram.
fig, ax = plt.subplots(figsize=(16, 8))
distinct_ordered, distinct_unordered = [], []
for width in widths:
equal_shape = list([c[1] for c in data["distinct"][width]])
distinct_ordered.append(equal_shape.count(True))
distinct_unordered.append(equal_shape.count(False))
ax2 = ax.twinx()
ax.plot(widths, distinct_unordered, label="Unordered Equilibria", color="C0")
ax2.plot(widths, distinct_ordered, label="Ordered Equilibria", color="C1")
axis_settings(ax, widths)
ax.title.set_text(f"Density of States - N{domain.n}")
ax.set_xlabel("Width")
ax.set_ylabel("Number of States (Disordered)", color="C0")
ax2.set_ylabel("Number of States (Ordered)", color="C1")
dos_y_max_unorder = 1.05 * max(distinct_unordered)
dos_y_max_order = 1.05 * max(distinct_ordered)
ax.set_yticks(np.linspace(0, dos_y_max_unorder, 20).astype(int))
# ax.set_yticks(np.arange(0, dos_y_max_unorder, round(dos_y_max_unorder/200, 1)*10))
ax2.set_yticks(np.arange(0, dos_y_max_order))
fig.savefig(fig_folder / "Density Of States.png")
# Defect density diagram
fig, ax = plt.subplots(figsize=(16, 8))
defects = []
for width in widths:
defects.append(
sum([c[2] for c in data["all"][width] if not c[1]])
/ len(data["all"][width])
)
ax.plot(widths, defects)
axis_settings(ax, widths)
ax.title.set_text(f"Average Defects - N{domain.n}")
ax.set_xlabel("Width")
ax.set_ylabel("Defects")
ax.set_yticks(np.arange(0, 1 + max(defects), 0.5))
fig.savefig(fig_folder / "Defects.png")
# Bifurcation diagram
fig, ax = plt.subplots(figsize=(16, 8))
ordered_energies, unordered_energies = [], []
for width in widths:
ordered_energies.append([c[0] for c in data["distinct"][width] if c[1]])
unordered_energies.append([c[0] for c in data["distinct"][width] if not c[1]])
for i in range(len(order_data["min"])):
ordered_energies[i].append(order_data["min"][i])
ordered_energies[i].append(order_data["max"][i])
null_unorder = []
for i, energies in enumerate(unordered_energies):
if len(energies) == 0:
null_unorder.append(i)
energies.append(order_data["min"][i])
min_order = np.asarray([min(width) for width in ordered_energies])
max_order = np.asarray([max(width) for width in ordered_energies])
min_unorder = np.asarray([min(width) for width in unordered_energies])
max_unorder = np.asarray([max(width) for width in unordered_energies])
offset = np.array(order_data["min"])
# offset = np.array(min_order)
min_unorder_off = min_unorder - offset
max_unorder_off = max_unorder - offset
ax.plot(widths, min_order - offset, color="C1")
# ax.plot(widths, max_order - offset, color='C1', linestyle='dotted')
ax.plot(widths, min_unorder_off, color="C0")
ax.plot(widths, max_unorder_off, color="C0", linestyle="dotted")
axis_settings(ax, widths)
for i in null_unorder:
ax.scatter(widths[i], 0, marker="X", color="blue", s=50, zorder=4)
# ax.scatter(widths[i], max_unorder[i] - offset[i],
# marker='X', edgecolors="blue", facecolors='none', s=100, zorder=4)
ax.title.set_text(f"Reduced Energy vs. Width - N{domain.n}")
ax.set_xlabel("Width")
ax.set_ylabel("Reduced Energy")
bif_y_max = np.max(np.abs(np.concatenate((min_unorder_off, max_unorder_off))))
bif_top = np.arange(
0, bif_y_max, round(bif_y_max / 20, -math.floor(math.log10(bif_y_max / 20)))
)
ax.set_yticks(np.concatenate((-bif_top[1:][::-1], bif_top)))
fig.savefig(fig_folder / "Bifurcation.png")
print(f"Wrote to {fig_folder}.")
if __name__ == "__main__":
os.environ["QT_LOGGING_RULES"] = "*=false"
try:
main()
except KeyboardInterrupt:
print("Program terminated by user.")

2
setup.cfg
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@ -1,6 +1,6 @@
[metadata]
name = squish
version = 0.1.5
version = 0.1.5.post2
author = Kenneth Jao
author_email = ksjdragon@gmail.com
description = squish is Python program which perform simulations for the flow of 'soft' or 'compressible' objects under some energy in a periodic domain.

12
squish/squish.py
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@ -98,13 +98,13 @@ def do_sim(args, file):
dmn_params, sim_params = params["domain"], params["simulation"]
overrides = {
args.n: "n_objects",
args.w: "width",
args.h: "height",
args.r: "natural_radius",
args.energy: "energy",
"n_objects": args.n,
"width": args.w,
"height": args.h,
"natural_radius": args.r,
"energy": args.energy,
}
for arg, arg_name in overrides.items():
for arg_name, arg in overrides.items():
if arg is not None:
dmn_params[arg_name] = arg