The Tool Desk
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Start with the data and the question
Signal-processing results depend on more than the function name. Before choosing an API, establish what each array axis represents, the sample interval or sampling rate, and whether observations are evenly spaced. Frequency parameters and plotted spectra only make sense in relation to that sampling information.
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- Identify the signal array’s time axis and any other dimensions, such as channels.
- Record the sampling rate or sample spacing; check whether time steps are uniform.
- State the goal: suppress frequencies, smooth data, change the sample rate, locate events, or estimate frequency content.
- Choose the method and parameters that fit the goal, then inspect its response or output.
- Account for boundary behavior, phase, and numerical representation when interpreting filtered data.
The module covers convolution and correlation, filtering and filter design, resampling, detrending, peak finding, windows, and spectral analysis. Its API reference is the SciPy v1.18.0 signal reference.
How do I filter a signal in Python with SciPy?
For a designed digital filter, choose a representation and application function appropriate to the task. lfilter applies an FIR or IIR filter along a selected array axis. For most filtering tasks, however, SciPy recommends second-order sections: design with output='sos' and apply with sosfilt. The lfilter reference says this approach has fewer numerical problems than using the direct form for many filters.
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Choose causal filtering or zero-phase processing
sosfilt and lfilter apply a filter in the forward direction, making them suitable for causal or stateful processing. For offline processing where zero-phase filtering is desired, use a forward-and-backward operation such as sosfiltfilt or filtfilt. These are not interchangeable: forward-and-backward filtering is an offline operation, not a substitute for a causal filter in a live stream.
How do I design a low-pass filter with scipy.signal?
Use the design method that fits the needed response and specify frequencies in relation to the sampling frequency. SciPy offers FIR and IIR design methods. The tutorial’s firwin example uses the window method to design an FIR filter; the cutoff, sampling frequency, and any passband or stopband requirements should reflect the signal and the desired result. There is no universally best design for every application.
FIR filters can provide linear phase, while IIR filters cannot, as the SciPy signal tutorial explains. After designing a filter, inspect its frequency response rather than assuming that the requested design parameters guarantee an acceptable result. For most filtering work, prefer a second-order-section design and filtering path when available.
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How do I change a signal’s sample rate?
Do not treat resampling as simply dropping samples. Decimation includes anti-alias filtering; other functions use different approaches. The API includes decimate, Fourier-method resample, polyphase resample_poly, and upfirdn. Which one fits depends on the input’s sample structure, the conversion ratio, and application constraints. Use detrend when the task is to remove a trend rather than change the sampling rate.
How do I find peaks in a noisy signal?
find_peaks identifies local peaks in a one-dimensional signal and can filter candidates by properties including height, distance, prominence, and width. Related routines calculate peak prominence and width or locate relative extrema. These settings are not universal noise-removal thresholds: choose them for the scale and event definition of your data, and inspect the detected peaks against the original signal.
How do I calculate a power spectrum with SciPy?
For a power spectral density estimate, choose between a single periodogram and Welch’s averaged segment approach. A periodogram summarizes one record; Welch is useful when averaging across segments is desired. SciPy also provides cross-spectral density and coherence for examining relationships between signals. The signal tutorial discusses these spectral methods.
Windows affect spectral estimates by shaping the data used in the analysis. SciPy provides window functions in scipy.signal.windows and the get_window convenience function. Choose and report the window and segmentation settings used; they affect how an estimate should be interpreted. See the window-function reference.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can I analyze frequency changes over time?
A whole-record spectrum provides an overall frequency summary, but it does not show when a component appears or changes. For time-varying frequency content, use short-time Fourier analysis: SciPy documents ShortTimeFFT as well as STFT and spectrogram interfaces. Choose the window and segment settings with the time and frequency detail you need in mind; those settings are part of the analysis, not cosmetic display choices.
Which SciPy function should I use for unevenly sampled data?
The tutorial identifies Lomb–Scargle analysis as an option for observations that are not equally spaced. Do not substitute an ordinary evenly sampled Fourier workflow without accounting for the irregular timing. For uniformly sampled records, periodogram, Welch, or time-frequency methods may fit depending on whether the question concerns overall power, averaged estimates, or change over time.
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