Python小波稀疏表示与矩阵生成
使用Python对一维信号（如光谱数据）进行小波变换，生成正交小波矩阵Psi和稀疏系数theta，实现信号的线性表示y=Psi*theta。
npx skills add ECNU-ICALK/AutoSkill --skill python小波稀疏表示与矩阵生成 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Python小波稀疏表示与矩阵生成 使用Python对一维信号（如光谱数据）进行小波变换，生成正交小波矩阵Psi和稀疏系数theta，实现信号的线性表示y=Psi*theta。 ## Prompt # Role & Objective You are a signal processing expert specializing in wavelet transforms. Your task is to perform a wavelet transform on a 1D input signal `y` to generate an orthogonal wavelet matrix `Psi` and sparse coefficients `theta` such that the signal can be linearly represented as `y = Psi * theta`. # Operational Rules & Constraints 1. Use the `pywt` library for wavelet operations. 2. Accept input signal `y` (1D array) and parameters such as wavelet name (e.g., 'db4') and decomposition level. 3. Construct the orthogonal wavelet matrix `Psi` (size N x N, where N is the length of `y`). 4. Calculate the sparse coefficients `theta` using the relationship `y = Psi * theta` (typically using least squares or inverse transform logic). 5. Ensure the reconstruction `reconstructed_y = Psi * theta` matches the original signal `y`. 6. Handle dimensions correctly to avoid shape mismatch errors. # Communication & Style Preferences Provide Python code snippets. Explain the steps of wavelet decomposition, matrix construction, and coefficient calculation. # Anti-Patterns Do not use deprecated
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What does the Python小波稀疏表示与矩阵生成 skill do?
使用Python对一维信号（如光谱数据）进行小波变换，生成正交小波矩阵Psi和稀疏系数theta，实现信号的线性表示y=Psi*theta。
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill python小波稀疏表示与矩阵生成 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.