def plot_wide(datasets, axs, n_highlighted=0):
axs[0].spines['left'].set_position(('outward', 5))
axs[0].set_ylabel("Table size")
axs[1].spines['left'].set_color('none')
y_min = 2000
y_max = 0
for model, ax, title in zip(nets, axs, net_titles):
ax.set_title(title)
ax.grid(False)
ax.hlines(1024,
-.5,
len(datasets) * 2 + 1.5,
linewidth=.5,
colors=[(.5, ) * 3],
zorder=0)
ax.spines['top'].set_color('none')
ax.spines['right'].set_color('none')
ax.spines['bottom'].set_color('none')
data = np.zeros((len(datasets), 144))
# Read all the data
for i, (_, fn) in enumerate(datasets):
fn = "{}{}_12_12_xyp.bin".format(fn, model)
with open(fn, "rb") as f:
data[i, :] = np.array(list(read_table_lengths(f).values()))
xs = np.arange(len(datasets)) * 2
elems = ax.violinplot(data.T, xs, showextrema=False)
for b in elems["bodies"][:-n_highlighted]:
b.set_alpha(.3)
b.set_facecolor((.85, .85, .85))
b.set_edgecolor((0, 0, 0))
b.set_linewidth(1)
# The last 2 elements should be slightly highlighted
for b in elems["bodies"][-n_highlighted:]:
b.set_alpha(.8)
b.set_facecolor((.85, .85, .85))
b.set_edgecolor((0, 0, 0))
b.set_linewidth(1)
y_min = min(y_min, np.min(data))
y_max = max(y_max, np.max(data))
ax.set_xlim(-.5, max(xs) + .5)
titles = (t for t, _ in datasets)
ax.set_xticks(xs)
ax.set_xticklabels(titles)
axs[0].set_ylim(y_min, y_max)
axs[0].set_yticks([y_min, 1024, y_max])
def plot_wide(datasets, axs, n_highlighted=0):
axs[0].spines['left'].set_position(('outward', 5))
axs[0].set_ylabel("Table size")
axs[1].spines['left'].set_color('none')
y_min = 2000
y_max = 0
for model, ax, title in zip(nets, axs, net_titles):
ax.set_title(title)
ax.grid(False)
ax.hlines(1024, -.5, len(datasets) * 2 + 1.5, linewidth=.5, colors=[(.5, )*3], zorder=0)
ax.spines['top'].set_color('none')
ax.spines['right'].set_color('none')
ax.spines['bottom'].set_color('none')
data = np.zeros((len(datasets), 144))
# Read all the data
for i, (_, fn) in enumerate(datasets):
fn = "{}{}_12_12_xyp.bin".format(fn, model)
with open(fn, "rb") as f:
data[i, :] = np.array(list(read_table_lengths(f).values()))
xs = np.arange(len(datasets))*2
elems = ax.violinplot(data.T, xs, showextrema=False)
for b in elems["bodies"][:-n_highlighted]:
b.set_alpha(.3)
b.set_facecolor((.85, .85, .85))
b.set_edgecolor((0, 0, 0))
b.set_linewidth(1)
# The last 2 elements should be slightly highlighted
for b in elems["bodies"][-n_highlighted:]:
b.set_alpha(.8)
b.set_facecolor((.85, .85, .85))
b.set_edgecolor((0, 0, 0))
b.set_linewidth(1)
y_min = min(y_min, np.min(data))
y_max = max(y_max, np.max(data))
ax.set_xlim(-.5, max(xs) + .5)
titles = (t for t, _ in datasets)
ax.set_xticks(xs)
ax.set_xticklabels(titles)
axs[0].set_ylim(y_min, y_max)
axs[0].set_yticks([y_min, 1024, y_max])
y_max = 0
for model, ax, title in zip(nets, axs, net_titles):
ax.set_title(title)
ax.grid(False)
ax.hlines(1024, -.5, 8.5, linewidth=.5, colors=[(.5, ) * 3], zorder=0)
ax.spines['top'].set_color('none')
ax.spines['right'].set_color('none')
ax.spines['bottom'].set_color('none')
data = np.zeros((len(methods) + 2, 144))
# Read all the data
fn = "uncompressed/{}_12_12_xyp.bin".format(model)
with open(fn, "rb") as f:
data[0, :] = np.array(list(read_table_lengths(f).values()))
fn = "compressed/remove_default_{}_12_12_xyp.bin".format(model)
with open(fn, "rb") as f:
data[1, :] = np.array(list(read_table_lengths(f).values()))
for j, method in enumerate(methods):
fn = "compressed/{}_{}_12_12_xyp.bin".format(method, model)
with open(fn, "rb") as f:
data[j + 2, :] = np.array(list(read_table_lengths(f).values()))
elems = ax.violinplot(data.T,
np.arange(len(methods) + 2) * 2,
showextrema=False)
for b in elems["bodies"]:
b.set_alpha(.75)
import argparse
from common import read_table_lengths
from matplotlib import pyplot as plt
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("filename", type=str)
args = parser.parse_args()
with open(args.filename, 'rb') as fp:
lens = read_table_lengths(fp)
plt.hist(list(lens.values()))
plt.show()
y_max = 0
for model, ax, title in zip(nets, axs, net_titles):
ax.set_title(title)
ax.grid(False)
ax.hlines(1024, -.5, 8.5, linewidth=.5, colors=[(.5, )*3], zorder=0)
ax.spines['top'].set_color('none')
ax.spines['right'].set_color('none')
ax.spines['bottom'].set_color('none')
data = np.zeros((len(methods) + 2, 144))
# Read all the data
fn = "uncompressed/{}_12_12_xyp.bin".format(model)
with open(fn, "rb") as f:
data[0, :] = np.array(list(read_table_lengths(f).values()))
fn = "compressed/remove_default_{}_12_12_xyp.bin".format(model)
with open(fn, "rb") as f:
data[1, :] = np.array(list(read_table_lengths(f).values()))
for j, method in enumerate(methods):
fn = "compressed/{}_{}_12_12_xyp.bin".format(method, model)
with open(fn, "rb") as f:
data[j+2, :] = np.array(list(read_table_lengths(f).values()))
elems = ax.violinplot(data.T, np.arange(len(methods) + 2)*2, showextrema=False)
for b in elems["bodies"]:
b.set_alpha(.75)
b.set_facecolor((.85, .85, .85))
b.set_edgecolor((0, 0, 0))
import argparse
from common import read_table_lengths
from matplotlib import pyplot as plt
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("filename", type=str)
args = parser.parse_args()
with open(args.filename, 'rb') as fp:
lens = read_table_lengths(fp)
plt.hist(list(lens.values()))
plt.show()
("centroid", "Centroid")]
methods_titles = [("remove_default", "Default\nRouting"),
("esp_tables_no_offset", "Espresso"),
("esp_subtables_full", "Order-\nexploiting\nEspresso"),
("mtrie", "m-Trie"),
("oc_spinnaker", "Ordered\nCovering")]
# Construct the data to go with the titles
max_ys = dict()
min_ys = dict()
datasets = collections.defaultdict(dict)
for model, _ in nets_titles:
# Load the original
data = np.zeros(144, dtype=np.int)
with open("uncompressed/{}_12_12_xyp.bin".format(model), "rb") as fp:
data[:] = list(read_table_lengths(fp).values())
# Update the sizes
max_ys[model] = np.max(data)
min_ys[model] = np.min(data)
# Store the result
datasets[model]["original"] = data
# Load the results
for method, _ in methods_titles:
# Load the routing table lengths
data = np.zeros(144, dtype=np.int)
with open("compressed/{}_{}_12_12_xyp.bin".format(method, model),
"rb") as fp:
data[:] = list(read_table_lengths(fp).values())
