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bcd600f 4dbc2ee bcd600f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 | #!/usr/bin/env python3
"""Generate analytical plots for the dataset card:
- composition.png frames per subset, stacked by markered/markerless
- resolution_distribution.png 320x240 vs 640x480 per subset
- force_distribution.png FEATS f_z histogram + indenter shapes
- threedcal_coverage.png (x,y) probe-position heatmap
- rtm_digit_distribution.png digit-class counts
"""
import glob
import os
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
import pyarrow.parquet as pq
BASE = "/media/yxma/Disk1/yuxiang/mini_data_parquet"
OUT = f"{BASE}/assets"
SUBSETS = ["fota_labeled", "fota_unlabeled", "threedcal", "feats",
"feelanyforce", "gelslam", "tactile_tracking", "real_tactile_mnist",
"sim_tactile_mnist", "sim_starstruck"]
def scan_subset(sub, columns):
paths = sorted(glob.glob(f"{BASE}/{sub}/*.parquet"))
out = {c: [] for c in columns}
for p in paths:
t = pq.read_table(p, columns=columns)
for c in columns:
out[c].extend(t.column(c).to_pylist())
return out
# 1. composition stacked bar
def plot_composition():
counts = {}
for sub in SUBSETS:
d = scan_subset(sub, ["markered"])
n_m = sum(1 for x in d["markered"] if x)
n_u = sum(1 for x in d["markered"] if not x)
counts[sub] = (n_m, n_u)
labels = SUBSETS
m = np.array([counts[s][0] for s in labels])
u = np.array([counts[s][1] for s in labels])
fig, ax = plt.subplots(figsize=(11, 4.5))
x = np.arange(len(labels))
ax.bar(x, u, label="markerless", color="#4c95d6")
ax.bar(x, m, bottom=u, label="markered", color="#d6794c")
for i, s in enumerate(labels):
total = counts[s][0] + counts[s][1]
ax.text(i, total + max(m+u)*0.005, f"{total:,}",
ha="center", va="bottom", fontsize=9)
ax.set_xticks(x)
ax.set_xticklabels(labels, rotation=20, ha="right")
ax.set_ylabel("Frames")
ax.set_title("gelsight-mini-pretrain · frames per subset (stacked by gel variant)")
ax.legend(loc="upper right", frameon=False)
ax.spines[["top","right"]].set_visible(False)
plt.tight_layout()
plt.savefig(f"{OUT}/composition.png", dpi=140)
plt.close()
print("wrote composition.png")
# 2. resolution distribution
def plot_resolution():
cols = {}
for sub in SUBSETS:
d = scan_subset(sub, ["height", "width"])
from collections import Counter
c = Counter(zip(d["width"], d["height"]))
cols[sub] = c
all_dims = sorted({k for sub in cols.values() for k in sub.keys()})
colors = {(640,480): "#4c95d6", (320,240): "#d6794c"}
fig, ax = plt.subplots(figsize=(11, 4.5))
x = np.arange(len(SUBSETS))
bottom = np.zeros(len(SUBSETS))
for d in all_dims:
vals = np.array([cols[s].get(d, 0) for s in SUBSETS])
ax.bar(x, vals, bottom=bottom,
color=colors.get(d, "#999999"),
label=f"{d[0]}×{d[1]} ({vals.sum():,})")
bottom += vals
ax.set_xticks(x)
ax.set_xticklabels(SUBSETS, rotation=20, ha="right")
ax.set_ylabel("Frames")
ax.set_title("Image resolution per subset (GelSight Mini native modes)")
ax.legend(loc="upper right", frameon=False)
ax.spines[["top","right"]].set_visible(False)
plt.tight_layout()
plt.savefig(f"{OUT}/resolution_distribution.png", dpi=140)
plt.close()
print("wrote resolution_distribution.png")
# 3. FEATS force distribution + indenter mix
def plot_feats_force():
d = scan_subset("feats", ["f_z", "f_x", "f_y", "indenter"])
fz = np.array([x for x in d["f_z"] if x is not None])
fig, axes = plt.subplots(1, 2, figsize=(11, 4))
axes[0].hist(fz, bins=60, color="#4c95d6", edgecolor="white")
axes[0].axvline(0, color="#444", linestyle="--", linewidth=1)
axes[0].set_xlabel("normal force f_z (N)")
axes[0].set_ylabel("Frames")
axes[0].set_title(f"FEATS normal force distribution (n={len(fz):,})")
axes[0].spines[["top","right"]].set_visible(False)
# indenter mix
from collections import Counter
c = Counter(x or "unknown" for x in d["indenter"])
items = sorted(c.items(), key=lambda kv: -kv[1])
keys = [k for k,_ in items]; vals = [v for _,v in items]
axes[1].barh(range(len(keys)), vals, color="#d6794c", edgecolor="white")
axes[1].set_yticks(range(len(keys)))
axes[1].set_yticklabels(keys)
axes[1].invert_yaxis()
axes[1].set_xlabel("Frames")
axes[1].set_title("FEATS indenter-shape mix")
axes[1].spines[["top","right"]].set_visible(False)
for i, v in enumerate(vals):
axes[1].text(v + max(vals)*0.005, i, f"{v:,}", va="center", fontsize=9)
plt.tight_layout()
plt.savefig(f"{OUT}/force_distribution.png", dpi=140)
plt.close()
print("wrote force_distribution.png")
# 4. 3DCal probe-position heatmap
def plot_threedcal_coverage():
d = scan_subset("threedcal", ["x_mm", "y_mm"])
x = np.array([v for v in d["x_mm"] if v is not None])
y = np.array([v for v in d["y_mm"] if v is not None])
fig, ax = plt.subplots(figsize=(6.5, 5.5))
H, xe, ye = np.histogram2d(x, y, bins=[40, 30])
im = ax.pcolormesh(xe, ye, H.T, cmap="magma")
ax.set_xlabel("x (mm)")
ax.set_ylabel("y (mm)")
ax.set_title(f"py3DCal calibration grid — probe coverage (n={len(x):,})")
plt.colorbar(im, ax=ax, label="frames per (x,y) cell")
plt.tight_layout()
plt.savefig(f"{OUT}/threedcal_coverage.png", dpi=140)
plt.close()
print("wrote threedcal_coverage.png")
# 5. RTM digit-class distribution
def plot_rtm_digits():
d = scan_subset("real_tactile_mnist", ["digit_class"])
from collections import Counter
c = Counter(x for x in d["digit_class"] if x is not None)
keys = list(range(10))
vals = [c.get(k, 0) for k in keys]
fig, ax = plt.subplots(figsize=(8, 4))
ax.bar(keys, vals, color="#4c95d6", edgecolor="white")
for k, v in zip(keys, vals):
ax.text(k, v + max(vals)*0.005, f"{v:,}",
ha="center", va="bottom", fontsize=9)
ax.set_xticks(keys)
ax.set_xlabel("digit class")
ax.set_ylabel("frames")
ax.set_title(f"Real Tactile MNIST · digit-class balance (total {sum(vals):,})")
ax.spines[["top","right"]].set_visible(False)
plt.tight_layout()
plt.savefig(f"{OUT}/rtm_digit_distribution.png", dpi=140)
plt.close()
print("wrote rtm_digit_distribution.png")
if __name__ == "__main__":
plot_composition()
plot_resolution()
plot_feats_force()
plot_threedcal_coverage()
plot_rtm_digits()
print("done")
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