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 wave-theory-6-extreme-value-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 | wave-theory-6-extreme-value-analysis |
| description | Sub-skill of wave-theory: 6. Extreme Value Analysis. |
| version | 1.0.0 |
| category | engineering |
| type | reference |
| scripts_exempt | true |
Design Wave from Return Period:
def calculate_extreme_wave_height(
return_period_years: float,
Hs_annual_max: np.ndarray = None,
distribution: str = 'weibull'
) -> dict:
"""
Calculate design wave height for given return period.
Args:
return_period_years: Return period (years)
Hs_annual_max: Array of annual maximum Hs values
distribution: 'weibull' or 'gumbel'
Returns:
Extreme wave height statistics
"""
from scipy.stats import weibull_min, gumbel_r
if Hs_annual_max is None:
# Example data: 25 years of annual maxima
np.random.seed(42)
Hs_annual_max = weibull_min.rvs(c=2, scale=10, size=25)
# Fit distribution
if distribution == 'weibull':
params = weibull_min.fit(Hs_annual_max)
c, loc, scale = params
dist = weibull_min(c, loc, scale)
elif distribution == 'gumbel':
loc, scale = gumbel_r.fit(Hs_annual_max)
dist = gumbel_r(loc, scale)
else:
raise ValueError("Unknown distribution")
# Exceedance probability for return period
exceedance_prob = 1 / return_period_years
# Extreme value
Hs_extreme = dist.ppf(1 - exceedance_prob)
# Confidence intervals (simplified)
Hs_lower = dist.ppf(1 - exceedance_prob - 0.1)
Hs_upper = dist.ppf(1 - exceedance_prob + 0.1)
return {
'return_period_years': return_period_years,
'Hs_extreme': Hs_extreme,
'Hs_lower_bound': Hs_lower,
'Hs_upper_bound': Hs_upper,
'distribution': distribution,
'exceedance_probability': exceedance_prob
}
# Example: 100-year return period
extreme_100yr = calculate_extreme_wave_height(
return_period_years=100,
distribution='weibull'
)
print(f"100-Year Wave:")
print(f" Hs: {extreme_100yr['Hs_extreme']:.2f} m")
print(f" Range: {extreme_100yr['Hs_lower_bound']:.2f} - {extreme_100yr['Hs_upper_bound']:.2f} m")