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2.94 kB
| import json, glob, math, random, statistics as st | |
| random.seed(7) | |
| def load(p): | |
| d=json.load(open(p)) | |
| if d.get("formatVersion")==2: | |
| i={c:k for k,c in enumerate(d["channels"])}; c=d["columns"]; t=c[i["t"]] | |
| mag=[math.sqrt(c[i["rx"]][k]**2+c[i["ry"]][k]**2+c[i["rz"]][k]**2) for k in range(len(t))] | |
| m=[x["t"] for x in d.get("stitchMarks",[])] | |
| else: | |
| ss=d["samples"]; t=[s["elapsedSeconds"] for s in ss] | |
| mag=[math.sqrt(s["rotationRate"]["x"]**2+s["rotationRate"]["y"]**2+s["rotationRate"]["z"]**2) for s in ss] | |
| m=[x["elapsedSeconds"] for x in d.get("stitchMarks",[])] | |
| return t, mag, sorted(m) | |
| HZ=25.0; HALF=int(2.5*HZ); SEARCH=int(1.5*HZ); L=2*HALF | |
| def z(v): | |
| m=sum(v)/len(v); s=math.sqrt(sum((x-m)**2 for x in v)/len(v)) or 1.0 | |
| return [(x-m)/s for x in v] | |
| grids={} | |
| for p in sorted(glob.glob("crochet-motion_*.json")): | |
| t,mag,marks=load(p) | |
| if len(marks)<5: continue | |
| n=int((t[-1]-t[0])*HZ); g=[0.0]*n; j=0 | |
| for k in range(n): | |
| want=t[0]+k/HZ | |
| while j+1<len(t) and t[j+1]<want: j+=1 | |
| g[k]=mag[j] | |
| dd=[marks[0]] | |
| for x in marks[1:]: | |
| if x-dd[-1]>=2.0: dd.append(x) | |
| grids[p]=(g,[int((mk-t[0])*HZ) for mk in dd],n) | |
| def run(centres_by_file): | |
| wins=[] | |
| for p,(g,_,n) in grids.items(): | |
| for c in centres_by_file[p]: | |
| if c-HALF-SEARCH<0 or c+HALF+SEARCH>=n: continue | |
| wins.append(g[c-HALF-SEARCH:c+HALF+SEARCH]) | |
| if len(wins)<30: return None | |
| tmpl=[sum(w[SEARCH+k] for w in wins)/len(wins) for k in range(L)] | |
| shifts=[0]*len(wins) | |
| for _ in range(8): | |
| tz=z(tmpl); new=[] | |
| for wi,w in enumerate(wins): | |
| best,bo=-9e9,0 | |
| for off in range(0,2*SEARCH): | |
| seg=z(w[off:off+L]); r=sum(seg[k]*tz[k] for k in range(L))/L | |
| if r>best: best,bo=r,off | |
| shifts[wi]=bo-SEARCH; new.append(w[bo:bo+L]) | |
| tmpl=[sum(s[k] for s in new)/len(new) for k in range(L)] | |
| # how well do the aligned windows actually agree with the template? | |
| tz=z(tmpl); rs=[] | |
| for w,sh in zip(wins,shifts): | |
| seg=z(w[sh+SEARCH:sh+SEARCH+L]); rs.append(sum(seg[k]*tz[k] for k in range(L))/L) | |
| return st.mean(rs), st.pstdev(shifts)/HZ | |
| real=run({p:c for p,(g,c,n) in grids.items()}) | |
| print(f"REAL marks mean agreement r = {real[0]:.3f} shift sd = {real[1]:.2f}s") | |
| nulls=[] | |
| for trial in range(20): | |
| fake={} | |
| for p,(g,c,n) in grids.items(): | |
| lo,hi=HALF+SEARCH, n-HALF-SEARCH-1 | |
| fake[p]=sorted(random.randint(lo,hi) for _ in c) if hi>lo else [] | |
| r=run(fake) | |
| if r: nulls.append(r[0]) | |
| print(f"RANDOM marks mean agreement r = {st.mean(nulls):.3f} +- {st.pstdev(nulls):.3f} (20 trials)") | |
| zsc=(real[0]-st.mean(nulls))/(st.pstdev(nulls) or 1e-9) | |
| print(f"\nreal marks beat random by {zsc:+.1f} standard deviations") | |
| print("VERDICT:", "structure is real" if zsc>2 else "NOT distinguishable from random") | |