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myst:
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    description: "Check GWexpy prerequisites, GPS-time assumptions, FFT conventions, and GWpy compatibility before moving into tutorials, theory, or API pages."
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# Prerequisites and Conventions

This page is the entry point for the **shared prerequisites** and **conventions** used across the GWexpy guides and tutorials.
Page-specific assumptions and mathematical details remain on their original pages; this guide is meant to show where to check first.

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## 1. Environment Prerequisites

- The basic user-facing environment assumes **Python 3.11+**.
- The minimum background is **basic Python**, **NumPy array handling**, and optionally **Matplotlib**.
- Optional dependencies unlock additional features. See the [Installation Guide](../tutorials/installation.md) for setup details.

If you want the shortest overall learning path first, start with [Getting Started](../tutorials/getting_started.md).

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## 2. Data and Time Assumptions

- GWexpy is designed to stay compatible with `gwpy`-style time-series and frequency-series containers.
- Some APIs assume **GPS time** explicitly. In particular, forecast timestamps such as ARIMA outputs should not be confused with UTC-style time systems that include leap seconds.
- Some file formats do not preserve absolute timestamps. Audio formats are a representative case where `t0=0.0` may be used only as a convenience convention.
- Formats that only store local wall-clock time may require an explicit `timezone`. A common example is GBD in the [File I/O Supported Formats Guide](../how-to/io_formats.md).

For algorithm-specific assumptions, see [Validated Algorithms](validated_algorithms.md).

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## 3. FFT and Spectral Conventions

- GWexpy treats FFT **normalization**, **one-sided vs two-sided spectra**, and **sign conventions** explicitly.
- `fft_time` and `fft_space` follow different assumptions for target axes and normalization.
- `spectral_density` distinguishes PSD-style density from per-bin spectrum values.

For implementation-level details of each spectral method, see the [API Reference](../reference/index.md).

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## 4. GWpy Compatibility and GWexpy Extensions

- GWexpy is built on top of GWpy and preserves the basic data model and workflow expectations where possible.
- At the same time, it adds Matrix containers, the Field API, broader I/O support, and extra analysis utilities that do not exist in GWpy.
- If you want a quick view of what is still "GWpy-like" and what is GWexpy-specific, the migration guide is the fastest entry point.

For migration-oriented guidance, see [GWexpy for GWpy Users](gwexpy_for_gwpy_users.md) and the [GWpy Difference API Index](../reference/gwpy_added_api.md).

(prerequisites-reference-bridge)=
## 5. Where to Go Next

- First-time users: [Getting Started](../tutorials/getting_started.md)
- Hands-on learning: [Tutorial Index](../tutorials/index.md)
- Spectral method implementation details: [API Reference](../reference/index.md)
- Algorithm assumptions and validation basis: [Validated Algorithms](validated_algorithms.md)
- GWpy migration: [GWexpy for GWpy Users](gwexpy_for_gwpy_users.md)

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## Next to Read

- [Installation Guide](../tutorials/installation.md)
- [Getting Started](../tutorials/getting_started.md)
- [File I/O Supported Formats Guide](../how-to/io_formats.md)
- [Validated Algorithms](validated_algorithms.md)
- [API Reference](../reference/index.md)