def test_pyplot(setup):
import scipy.misc
import matplotlib
matplotlib.use('TKAgg')
import matplotlib.pyplot as plt
import numpy as np
face = scipy.misc.face()
logger.log_image(face, "face.png")
fig = plt.figure(figsize=(4, 2))
xs = np.linspace(0, 5, 1000)
plt.plot(xs, np.cos(xs))
logger.savefig("face_02.png", fig=fig)
plt.close()
fig = plt.figure(figsize=(4, 2))
xs = np.linspace(0, 5, 1000)
plt.plot(xs, np.cos(xs))
logger.savefig('sine.pdf')
def plot_trajectory_rope(path,
ds,
all_images,
n_cols=8,
title=None,
key="figures/rope_play.png"):
from math import ceil
from matplotlib.gridspec import GridSpec
from ml_logger import logger
stack = all_images[path][:, 0]
n, h, w, *c = stack.shape
n_rows = ceil(n / n_cols)
# todo: add color background -- need to decide on which library to use.
fig = plt.figure(figsize=np.array(1.4, dtype=int) * [n_cols, n_rows])
if title:
plt.suptitle(title, fontsize=14)
gs = GridSpec(n_rows, 10)
index = 0
for _row in range(n_rows):
for _col in range(10):
if index == len(stack):
break
plt.subplot(gs[_row, _col])
plt.imshow(stack[index], cmap='gray')
plt.text(0, 10, f"#{path[index]}", fontsize=8)
if index > 0:
plt.text(0, 60, f"{ds[index - 1]:0.2f}", fontsize=8)
plt.axis('off')
index += 1
plt.tight_layout()
logger.savefig(key=key)
plt.show()
return
short_name = short_names[key]
path, ds = search(G, start, goal, partial(heuristic, G=G, scale=Args.h_scale))
cache.cost[short_name] = len(queries.keys())
cache.len[short_name] = sum(ds)
print(f"{key:>10} len: {len(path)}", f"cost: {len(queries.keys())}")
plt.subplot(2, 2, i + 1)
plt.title(title, pad=10)
# plot_graph(G)
plot_trajectory_2d(ind2pos(G, path, 100), label=short_name)
plt.scatter(*zip(*ind2pos(G, queries.keys(), 100)), color="gray", s=3, alpha=0.6)
set_fig()
# plt.legend(loc="upper left", bbox_to_anchor=(0.45, 0.8), framealpha=1, frameon=False, fontsize=12)
plt.tight_layout()
logger.savefig("../figures/maze_plans.png", dpi=300)
plt.show()
plt.close()
# colors = ['#49b8ff', '#ff7575', '#66c56c', '#f4b247']
fig = plt.figure(figsize=(3.8, 3), dpi=300)
plt.title('Planning Cost')
plt.bar(cache.cost.keys(), cache.cost.values(), color="gray", width=0.8)
plt.ylim(0, max(cache.cost.values()) * 1.2)
plt.gca().spines['top'].set_visible(False)
plt.gca().spines['right'].set_visible(False)
plt.tight_layout()
logger.savefig("../figures/maze_cost.png", dpi=300)
plt.ylabel('# of distance lookup')
plt.show()
plot_trajectory_2d(path, label="heuristics")
G = nx.grid_graph(dim=[5, 5])
G.add_edge((0, 0), (0, 1), weight=1)
for e in G.edges(data=True):
e[-1]['weight'] = 1
path = dijkstra(G, (0, 0), (4, 4))
print(" dijkstra", *path)
plt.subplot(2, 2, 3)
plt.title('Dijkstra')
plot_trajectory_2d(path, label="dijkstra")
path = a_star(G, (0, 0), (4, 4))
print(" a*", *path)
plt.subplot(2, 2, 4)
plt.title('A*')
plot_trajectory_2d(path, label="A*")
plt.legend(loc="upper left",
bbox_to_anchor=(0.45, 0.8),
framealpha=1,
frameon=False,
fontsize=12)
from ml_logger import logger
plt.tight_layout()
logger.savefig("../figures/comparison.png", dpi=300)
plt.show()
cache.cost[short_name] = len(queries.keys())
cache.len[short_name] = len(ds)
print(f"{key:>10} len: {len(path)}", f"cost: {len(queries.keys())}")
plt.subplot(2, 2, i + 1)
plt.title(title, pad=10)
# plot_graph(G)
plot_trajectory_2d(ind2pos(G, path, 100), label=short_name)
plt.scatter(*zip(*ind2pos(G, queries.keys(), 100)),
color="gray",
s=3,
alpha=0.1)
set_fig(dataset)
# plt.legend(loc="upper left", bbox_to_anchor=(0.45, 0.8), framealpha=1, frameon=False, fontsize=12)
plt.tight_layout()
logger.savefig("../figures/streetlearn_plans.png", dpi=300)
plt.show()
plt.close()
# colors = ['#49b8ff', '#ff7575', '#66c56c', '#f4b247']
# for i, (k, v) in enumerate(cache.items()):
# plt.bar(k, v, color=colors[i])
fig = plt.figure(figsize=(3.8, 3), dpi=300)
plt.title('Planning Cost')
plt.bar(cache.cost.keys(), cache.cost.values(), color="gray", width=0.8)
plt.ylim(0, max(cache.cost.values()) * 1.2)
plt.gca().spines['top'].set_visible(False)
plt.gca().spines['right'].set_visible(False)
plt.tight_layout()
logger.savefig("../figures/streetlearn_cost.png", dpi=300)
print(" dijkstra", *path)
plt.subplot(2, 2, 3)
plt.title('Dijkstra')
plot_trajectory_2d(path, label="dijkstra")
plt.scatter(*zip(*queries.keys()),
color="#23aaff",
linewidths=0,
alpha=0.6)
queries.clear()
path, ds = a_star(G, start, goal, heuristic)
print(" a*", *path)
plt.subplot(2, 2, 4)
plt.title('A*')
plot_trajectory_2d(path, label="A*")
plt.scatter(*zip(*queries.keys()),
color="#23aaff",
linewidths=0,
alpha=0.6)
plt.legend(loc="upper left",
bbox_to_anchor=(0.45, 0.8),
framealpha=1,
frameon=False,
fontsize=12)
from ml_logger import logger
plt.tight_layout()
logger.savefig("../figures/search_range.png", dpi=300)
plt.show()
all_images,
title="A*",
key=f"../figures/rope_plans/bfs.png")
# colors = ['#49b8ff', '#ff7575', '#66c56c', '#f4b247']
# for i, (k, v) in enumerate(cache.items()):
# plt.bar(k, v, color=colors[i])
fig = plt.figure(figsize=(3.8, 3), dpi=300)
plt.title('Planning Cost')
plt.bar(cache.cost.keys(), cache.cost.values(), color="gray", width=0.8)
plt.ylim(0, max(cache.cost.values()) * 1.2)
plt.gca().spines['top'].set_visible(False)
plt.gca().spines['right'].set_visible(False)
plt.tight_layout()
logger.savefig("../figures/rope_cost.png", dpi=300)
plt.ylabel('# of distance lookup')
plt.show()
fig = plt.figure(figsize=(3.8, 3), dpi=300)
plt.title('Plan Length')
plt.bar(cache.len.keys(), cache.len.values(), color="gray", width=0.8)
plt.ylim(0, max(cache.len.values()) * 1.2)
plt.gca().spines['top'].set_visible(False)
plt.gca().spines['right'].set_visible(False)
plt.tight_layout()
logger.savefig("../figures/rope_length.png", dpi=300)
plt.ylabel('# of Steps')
plt.show()
logger.print('done', color="green")
