vamseeachanta
vamseeachanta/workspace-hub
相关职业SOC
基于 SOC 职业分类
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/vamseeachanta/workspace-hub --skill ship-dynamics-6dof-6-seakeeping-analysis命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Write outbound email and external messages in Vamsee Achanta's voice — a subtle offer to help, never bold or rash claims. Load before drafting ANY email, LinkedIn/Collide reply, proposal note, or outreach sent under his name.
Save/publish analysis or computation results from ANY ecosystem repo to Hugging Face as a queryable, viewer-renderable dataset. Use when the user wants to "save results to hugging face", "publish dataset to HF", "hugging face data saving", "save analysis results", "hf dataset", "make results queryable", or "render via datasets-server API". Reshapes nested results into flat parquet tables, writes a dataset card with a viewer `configs:` block and provenance, applies license/public-vs-private routing, enforces a domain data-quality gate (faithful-to-source != correct), publishes to `aceengineer/<repo>-<projection>`, and verifies via the datasets-server API.
Clone, create, fork, configure, and manage GitHub repositories. Manage remotes, secrets, releases, and workflows. Works with gh CLI or falls back to git + GitHub REST API via curl.
正在显示 SKILL.md
基于 SOC 职业分类
| name | ship-dynamics-6dof-6-seakeeping-analysis |
| description | Sub-skill of ship-dynamics-6dof: 6. Seakeeping Analysis. |
| version | 1.0.0 |
| category | engineering |
| type | reference |
| scripts_exempt | true |
Motion Statistics:
def calculate_seakeeping_statistics(
motion_time_series: np.ndarray,
dt: float,
dof_name: str = "Motion"
) -> dict:
"""
Calculate seakeeping statistics from motion time series.
Args:
motion_time_series: Time series of motion
dt: Time step
dof_name: Name of DOF
Returns:
Statistical parameters
"""
# Basic statistics
mean = np.mean(motion_time_series)
std = np.std(motion_time_series)
# Significant amplitude (1/3 highest)
sorted_amplitudes = np.sort(np.abs(motion_time_series))
n_third = len(sorted_amplitudes) // 3
significant_amplitude = np.mean(sorted_amplitudes[-n_third:])
# Maximum
max_amplitude = np.max(np.abs(motion_time_series))
# Zero crossing period
zero_crossings = np.where(np.diff(np.sign(motion_time_series)))[0]
if len(zero_crossings) > 1:
Tz = np.mean(np.diff(zero_crossings)) * dt * 2 # Up and down
else:
Tz = np.nan
# RMS
rms = np.sqrt(np.mean(motion_time_series**2))
return {
'dof': dof_name,
'mean': mean,
'std_dev': std,
'rms': rms,
'significant_amplitude': significant_amplitude,
'max_amplitude': max_amplitude,
'zero_crossing_period': Tz
}
# Example
heave_motion = result['displacement'][:, 2]
heave_stats = calculate_seakeeping_statistics(heave_motion, dt, 'Heave')
print(f"Heave Statistics:")
print(f" Significant amplitude: {heave_stats['significant_amplitude']:.2f} m")
print(f" Max amplitude: {heave_stats['max_amplitude']:.2f} m")
print(f" Zero-crossing period: {heave_stats['zero_crossing_period']:.2f} s")
Motion Sickness Incidence (MSI):
def calculate_motion_sickness_incidence(
acceleration_rms: float,
frequency_hz: float,
duration_hours: float = 2
) -> float:
"""
Calculate Motion Sickness Incidence (MSI) using ISO 2631-1.
MSI = % of people experiencing motion sickness
Args:
acceleration_rms: RMS vertical acceleration (m/s²)
frequency_hz: Dominant frequency (Hz)
duration_hours: Exposure duration (hours)
Returns:
MSI percentage
"""
# Weighting factor (ISO 2631-1)
# Peak sensitivity at 0.16 Hz
if 0.1 <= frequency_hz <= 0.5:
weighting = 1.0
else:
weighting = 0.5
# Weighted acceleration
a_w = acceleration_rms * weighting
# Time factor
time_factor = (duration_hours / 2) ** 0.5
# MSI calculation (simplified O'Hanlon-McCauley)
msdv = a_w * time_factor # Motion Sickness Dose Value
# Convert to percentage (empirical correlation)
MSI = 100 * (1 / (1 + np.exp(-(msdv - 3.5) / 0.7)))
return MSI
# Example: Calculate MSI for heave acceleration
a_heave = np.std(np.diff(result['velocity'][:, 2]) / dt)
freq_heave = 1 / heave_stats['zero_crossing_period']
msi = calculate_motion_sickness_incidence(a_heave, freq_heave, duration_hours=2)
print(f"Motion Sickness Incidence (2 hours): {msi:.1f}%")