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figure_4.py
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figure_4.py
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import numpy as np
import matplotlib
matplotlib.use("pgf")
matplotlib.rcParams.update(
{
"pgf.texsystem": "pdflatex",
"font.family": "serif",
"text.usetex": True,
"pgf.rcfonts": False,
}
)
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
# See https://jfly.uni-koeln.de/color/
CUD_CMAP = ListedColormap([
(0, 0, 0), # black
(0.9, 0.6, 0), # orange
(0.35, 0.7, 0.9), # sky blue
(0, 0.6, 0.5), # bluish green
(0.95, 0.9, 0.25), # yellow
(0, 0.45, 0.7), # blue
(0.8, 0.4, 0), # vermilion
(0.8, 0.6, 0.7) # reddish purple
], name="Color Universal Design")
val_201617 = "16/17-val", {
"PERSISTENCE": {
0: 100.00,
30: 81.38,
60: 56.11,
90: 38.08,
120: 27.73,
180: 17.10,
},
"VALIDATION": {
0: [99.94],
30: [88.52, 90.41, 88.43, 89.58],
60: [81.19, 78.50, 78.91, 77.65, 76.44],
90: [73.66, 77.13, 74.14, 72.77],
120: [63.57, 62.63, 70.90, 67.75, 68.89, 67.17],
180: [37.49, 37.28, 38.50, 42.30, 35.45],
},
"TESTING": None,
}
test_201617 = "16/17-test", {
"PERSISTENCE": {
0: 100.00,
30: 80.89,
60: 58.42,
90: 40.37,
120: 28.64,
180: 16.11,
},
"VALIDATION": None,
"TESTING": {
0: [99.68],
30: [87.35, 90.62, 87.41, 89.23],
60: [80.80, 77.80, 78.53, 76.83, 75.02],
90: [74.58, 78.79, 76.21, 75.15],
120: [68.10, 65.19, 73.34, 71.82, 71.11, 70.43],
180: [40.62, 43.59, 39.93, 46.92, 37.98],
},
"MARKER": "o",
"COLOR": matplotlib.colors.to_hex(CUD_CMAP.colors[5]),
}
transfer_202108 = "202108-test", {
"PERSISTENCE": {
0: 100.0,
30: 78.66,
60: 48.79,
90: 31.01,
120: 21.36,
180: 12.25,
},
"VALIDATION": None,
"TESTING": {
0: [99.91],
30: [88.62, 86.66, 89.31, 86.95],
60: [78.69, 75.68, 76.13, 73.95, 72.76],
90: [67.78, 74.31, 68.98, 67.82],
120: [55.31, 58.82, 67.18, 61.63, 63.51, 61.47],
180: [33.13, 31.46, 27.62, 36.78, 30.56],
},
"MARKER": "^",
"COLOR": matplotlib.colors.to_hex(CUD_CMAP.colors[3]),
}
transfer_202109 = "202109-test", {
"PERSISTENCE": {
0: 100.0,
30: 80.08,
60: 57.03,
90: 39.5,
120: 28.42,
180: 16.79,
},
"VALIDATION": None,
"TESTING": {
0: [99.06],
30: [87.17, 86.43, 89.17, 85.84],
60: [77.2, 72.89, 74.15, 70.48, 69.76],
90: [63.53, 72.62, 66.24, 65.16],
120: [47.34, 56.38, 65.24, 57.66, 58.22, 56.73],
180: [23.48, 23.74, 20.60, 32.72, 22.99],
},
"MARKER": "*",
"COLOR": matplotlib.colors.to_hex(CUD_CMAP.colors[1]),
}
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--styles", default=["science", "grid"])
parser.add_argument("--dpi", default=350)
parser.add_argument("--aspect-ratio", default=2./3.)
parser.add_argument("--scale", default=1.0)
parser.add_argument("--name", default="overview_performance_leadtime")
parser.add_argument("--format", nargs="+", default=["png"])
args = parser.parse_args()
plt.style.use(args.styles)
text_width = 5.45776
width = text_width * args.scale
height = width * args.aspect_ratio
c_max = 0.0
for tag, d in [test_201617, transfer_202108, transfer_202109]:
ticks = sorted(list(set.intersection(*(set(d[tag].keys()) for tag in ["VALIDATION", "TESTING", "PERSISTENCE"] if d[tag] is not None))))
per = np.array([np.mean(d["PERSISTENCE"][t]) for t in ticks]) if d["PERSISTENCE"] is not None else None
if per is None:
continue
c_max = np.max([
c_max,
np.max(((np.array([np.mean(d["TESTING"][t]) for t in ticks]) / per) if d["TESTING"] is not None else 0)),
np.max(((np.array([np.mean(d["VALIDATION"][t]) for t in ticks]) / per) if d["VALIDATION"] is not None else 0)),
])
fig = plt.figure(figsize=(width, height), dpi=args.dpi)
ax1 = fig.gca()
ax2 = ax1.twinx()
ax2._get_lines.prop_cycler = ax1._get_lines.prop_cycler
ax2.grid(False)
for tag, d in [test_201617, transfer_202108, transfer_202109]:
VALIDATION = None
TESTING = d["TESTING"]
PERSISTENCE = d["PERSISTENCE"]
desc = tag.split("-")[0]
ticks = sorted(list(set.intersection(*(set(d[tag].keys()) for tag in ["VALIDATION", "TESTING", "PERSISTENCE"] if d[tag] is not None))))
baseline = np.array([PERSISTENCE[t] for t in ticks])
plt_baseline = ax1.plot(ticks, baseline, label=f"Persistence {desc}", linestyle=":", marker=d["MARKER"], color=d["COLOR"], markersize=4)
if TESTING is not None:
test_mean = np.array([np.mean(TESTING[t]) for t in ticks])
test_std = np.array([np.std(TESTING[t]) for t in ticks])
plt_ours_mean_test = ax1.plot(ticks, test_mean, label=f"Testing {desc}", color=plt_baseline[0].get_color(), marker=d["MARKER"], markersize=4)
ax1.fill_between(
ticks,
test_mean - test_std,
test_mean + test_std,
alpha=0.15,
edgecolor=plt_ours_mean_test[0].get_color(),
facecolor=plt_ours_mean_test[0].get_color(),
)
fkt_ovr_baseline_test = test_mean / baseline
ax2.plot(ticks, fkt_ovr_baseline_test, linestyle='--', label=f"Testing {desc}", color=plt_ours_mean_test[0].get_color(), marker=d["MARKER"], markersize=4)
if VALIDATION is not None:
val_mean = np.array([np.mean(VALIDATION[t]) for t in ticks])
val_std = np.array([np.std(VALIDATION[t]) for t in ticks])
plt_ours_mean_val = ax1.plot(ticks, val_mean, label=f"Validation {desc}", color=plt_baseline[0].get_color())
ax1.fill_between(
ticks,
val_mean - val_std,
val_mean + val_std,
alpha=0.15,
edgecolor=plt_ours_mean_val[0].get_color(),
facecolor=plt_ours_mean_val[0].get_color(),
)
fkt_ovr_baseline_val = val_mean / baseline
ax2.plot(ticks, fkt_ovr_baseline_val, linestyle='--', label=f"Validation {desc}", color=plt_ours_mean_val[0].get_color())
plt.xticks(ticks)
plt.xlim((ticks[0], ticks[-1]))
ax1.set_ylim((0, 100))
ax2.set_ylim((1.0, c_max * 1.1))
legend1 = ax1.legend(
fancybox=False,
edgecolor="black",
ncol=2,
fontsize=8,
loc=1,
bbox_to_anchor=(1.08, 1.22),
borderpad=.2,
labelspacing=.2,
columnspacing=.5,
)
legend1.get_frame().set_linewidth(0.5)
legend2 = ax2.legend(
fancybox=False,
edgecolor="black",
ncol=1,
fontsize=8,
loc=2,
bbox_to_anchor=(-0.08, 1.22),
borderpad=.2,
labelspacing=.2,
columnspacing=.5,
)
legend2.get_frame().set_linewidth(0.5)
ax1.set_xlabel("Lead time [min]")
ax1.set_ylabel("Critical Success Index (CSI) [%]")
ax2.set_ylabel("Improvement [factor]")
plt.tight_layout()
for frmt in args.format:
plt.savefig(f"{args.name}.{frmt}", dpi=args.dpi)