Perform various data analysis on SEC 13-F and obtain some insights of fund activities such as number of holdings, AUM, and change of holdings between two quarters.
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Estimate heart rate (HR) and breathing rate (BR) in bpm from a cleaned 1-D physiological time series such as radar phase, WiFi CSI amplitude, PPG, or a chest-motion signal. Use when Claude needs to choose HR/BR bandpass edges, pick a peak frequency from a noisy band, reject the HR second harmonic that often dominates the fundamental, handle respiration-harmonic leakage into the HR band on slow breathers, or flag low-confidence estimates. Not for arrhythmia / beat-to-beat analysis, multi-subject source separation, or rapidly non-stationary rates.
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实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
Given a cleaned 1-D periodic signal (radar phase, WiFi CSI, PPG, piezo), estimate heart rate and breathing rate in bpm.
Upstream ingestion of radar captures is the radar-signal-processing skill. This skill picks up after a 1-D signal is extracted.
butter(4, [0.08, 0.5], btype='band', fs=fs) — 4.8–30 bpmbutter(4, [0.7, 3.0], btype='band', fs=fs) — 42–180 bpmnperseg = min(len(x), int(fs * 25))
f, p = welch(x, fs=fs, nperseg=nperseg, noverlap=nperseg//2,
nfft=8*nperseg, detrend='constant')
in_band = (f >= lo) & (f <= hi)
peak_hz = f[in_band][np.argmax(p[in_band])]
f_sub = f_peak / 2.0
if 0.7 <= f_sub <= 3.0:
p_sub = np.interp(f_sub, f, p)
p_top = np.interp(f_peak, f, p)
if p_sub > 0.5 * p_top:
f_peak = f_sub # the peak was the 2nd harmonic
hr_bpm = f_peak * 60
ac = np.correlate(x - x.mean(), x - x.mean(), mode='full')
ac = ac[len(ac)//2:] / ac[len(ac)//2]
lag = int(fs/f_hi) + np.argmax(ac[int(fs/f_hi):int(fs/f_lo)])
bpm_ac = 60 * fs / lag
If abs(bpm_ac - bpm_psd) > 5, flag as low confidence.| If | Then |
|---|---|
f_peak/2 in HR band and p_sub > 0.5 × p_top | Pick sub-harmonic (fundamental) |
| PSD and autocorrelation disagree by > 5 bpm | Flag low confidence; do not commit to one value |
| BR estimate < 10 bpm (slow breather) | Expect HR-band contamination — see references/harmonic-pitfalls.md |
| HR > 150 bpm (tachycardia) | Widen HR band upper to 3.3 Hz, re-estimate |
| Clip < 15 s long | PSD bin spacing > tolerance — prefer autocorrelation or flag inconclusive |
BR < HR. Always true for a live adult at rest — if violated you swapped bands.HR × duration_minutes ≈ peak count in find_peaks(bandpassed_hr). Off by 2× → harmonic error slipped through.See references/band-rationale.md. Short version: textbook bands miss slow breathers (supine clinical subjects breathe 5–10 bpm) and bradycardia (athletes, post-tilt-down).
See references/harmonic-pitfalls.md. These are the two failure modes that cost naïve pipelines the most accuracy on real data.
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Perform various data analysis on SEC 13-F and obtain some insights of fund activities such as number of holdings, AUM, and change of holdings between two quarters.
中文简介正在准备中。当前显示原文说明。
AC branch pi-model power flow equations (P/Q and |S|) with transformer tap ratio and phase shift, matching `acopf-math-model.md` and MATPOWER branch fields. Use when computing branch flows in either direction, aggregating bus injections for nodal balance, checking MVA (rateA) limits, computing branch loading %, or debugging sign/units issues in AC power flow.
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Redact text from PDF documents for blind review anonymization
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Use when checking simplified ADA-derived plan-view bathroom accessibility constraints such as turning space, door clear width, toilet centerline, grab bars, and lavatory knee/toe clearance.
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Analyze failed GitHub Action jobs for a pull request.
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Use when extracting plan-view architectural geometry from DXF files with semantic CAD layers, especially when outputs must normalize rooms, doors, fixtures, clearances, and grab bars into machine-checkable JSON.
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