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    description: "Choose the right direct file or network I/O path in GWexpy, including supported formats, read/write limits, fetch routes, and when to set format= explicitly."
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# File I/O Supported Formats Guide

This is the end-user I/O guide for `gwexpy`.
This page only covers the public `.read()` / `.write()` / `fetch()` style APIs that users call directly to read, write, or fetch data.

It does **not** cover `to_*()` / `from_*()` conversions or object bridges to xarray, ROOT objects, or Zarr arrays. If the question is "how do I convert this object into another library or container?", that belongs to interop instead. For those topics, see the [interop tutorial](../how-to/interop/intro_interop.ipynb) and the [interop API reference](../reference/api/interop).

:::{warning}
**Security Warning: Pickle Files**

**Pickle** (:term:`Pickle`) is convenient, but reading Pickle files from untrusted sources is dangerous. A malicious Pickle file can execute arbitrary code on your system.

For data sharing and long-term storage, prefer structured formats such as **HDF5**, **GWF**, or **Zarr**.
:::

## First: Decision Rules

- If you need a **default GW storage format**, start with **HDF5**. For existing seismic or geophysical assets, start with **MiniSEED / SAC / WIN / ATS**. For general interchange, start with **CSV / NetCDF4 / Zarr**. For logger- or device-specific data, start with **GBD / TDMS / SDB / WAV / Audio**. For **MTH5**, the current public direct-I/O story is only the single **`ats.mth5`** path. A generic standalone **`format="mth5"`** route is not published yet.
- **Auto-detect is fine** when the extension uniquely selects one reader.
- GWpy-backed **`TimeSeries`** and **`TimeSeriesDict`** auto-identify native HDF5 for `.h5` / `.hdf5`; structurally identified NDScope files take precedence. Set **`format="hdf5"` explicitly** for other ambiguous or GWexpy-only HDF5 class families.
- **Set `format=` explicitly** for ambiguous extensions such as `.xml`, for custom lab extensions, or whenever auto-detection is unclear.
- **Pass `timezone` explicitly** when the file stores local wall-clock time without embedded UTC/GPS. In the current user-facing guide, **GBD** is the main required case.
- **Read-only / write-only matters**: `○ / ×` means a format can be read but not written.
- For richer direct-I/O objects beyond plain Series, start with **HDF5** for `Spectrogram`, `Histogram`, and `EventTable`. Field-class direct `.read()` / `.write()` is still under audit and is not published as a stable contract on this page.

## Jump Links

- <a href="#io-formats-en-quick">Quick Selection Table</a>
- <a href="#io-formats-en-basic">Basic `.read()` / `.write()` / `fetch()` Usage</a>
- <a href="#io-formats-en-a">A. GW Standards</a>
- <a href="#io-formats-en-b">B. Seismic and Geophysical Observation</a>
- <a href="#io-formats-en-c">C. General Analysis and Exchange</a>
- <a href="#io-formats-en-d">D. Loggers and Instrument Formats</a>
- <a href="#io-formats-en-dev">Developer Notes</a>

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## Quick Selection Table

| Group | Start here when... | First format | Formats covered here |
|---|---|---|---|
| **A. GW Standards** | You want standard GW storage, exchange, or acquisition paths | **HDF5** | GWF, HDF5, hdf.ndscope, xml.diaggui, NDS2, GWOSC |
| **B. Seismic and Geophysical Observation** | You need to read existing seismic or EM observation data | **mseed** | mseed, SAC, GSE2, K-NET, WIN / WIN32, ATS, ATS.MTH5 (MTH5 standalone is status-only here) |
| **C. General Analysis and Exchange** | You need general-purpose storage or external analysis exchange | **CSV / TXT** or **Zarr** | CSV / TXT, NetCDF4, Zarr, ROOT |
| **D. Loggers and Instrument Formats** | You are working with device- or logger-specific time series | **GBD** or **TDMS** | GBD, TDMS, SDB, WAV, MP3, FLAC, OGG, M4A |

> **Note**: `NDS2` and `GWOSC` are not file formats. They are included in **A. GW Standards** because they are common GW data entry points. In the tables below, they are labeled as `network path`.

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## Basic `.read()` / `.write()` / `fetch()` Usage

- Purpose: show the baseline direct-I/O entry points before format-specific details
- Input: file paths, an explicit `format=` when needed, or a detector/network query
- Output: `TimeSeries`, `TimeSeriesDict`, or other direct-I/O return objects

```python
from gwexpy.timeseries.collections import TimeSeriesDict
from gwexpy.timeseries import TimeSeries

# Auto-detect from extension
tsd = TimeSeriesDict.read("path/to/data.mseed")

# Explicit format
tsd = TimeSeriesDict.read("path/to/data.dat", format="mseed")

# Write out
tsd.write("output.h5", format="hdf5")

# Network path
ts = TimeSeries.fetch_open_data("H1", 1126259446, 1126259478)
```

- `.read()` / `.write()` uses the gwpy-style I/O registry.
- `.xml` is ambiguous, so **use `format="xml.diaggui"` explicitly** for DiagGUI XML data.
- `NDS2` and `GWOSC` are not file readers, so they use `fetch()` / `fetch_open_data()` instead of `.read()`.

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## Supported Classes at a Glance

If the main question is whether a format is for a single channel or multiple channels, use this table first.

| Format / Family | Single | Multi | Other classes |
|---|---|---|---|
| **GWF / mseed / SAC / GSE2 / K-NET / WIN / WIN32 / ATS / SDB / WAV / Audio** | `TimeSeries` | `TimeSeriesDict` | Baseline end-user direct I/O pattern |
| **CSV** | `TimeSeries` | `TimeSeriesDict` | `TimeSeriesDict` also supports manifest-backed collection directories |
| **TXT** | `TimeSeries` | `TimeSeriesDict` | Multi-channel direct I/O uses collection directories |
| **nc / Zarr / GBD / TDMS** | `TimeSeries` | `TimeSeriesDict`, `TimeSeriesMatrix` | Includes matrix-style direct I/O |
| **HDF5** | `TimeSeries`, `FrequencySeries`, and related classes | `TimeSeriesDict` and related collections | Also covers `Spectrogram`, `Histogram`, and `EventTable` |
| **hdf.ndscope** | - | `TimeSeriesDict` | ndscope-compatible schema; aliases: `ndscope-hdf5`, `ndscope_hdf5`, `ndscopehdf5` |
| **xml.diaggui** | - | `TimeSeriesDict` | Requires `products`; legacy alias: `dttxml` |
| **NDS2 / GWOSC** | `TimeSeries` | - | Use `fetch()` / `fetch_open_data()` |
| **ATS.MTH5** | `TimeSeries` | - | Partial single-path support |
| **ROOT** | `EventTable` | - | Direct I/O is limited to EventTable |

- If you are unsure, start by thinking in terms of `TimeSeries` and `TimeSeriesDict`.
- `TimeSeriesMatrix` mainly matters for `NetCDF4`, `Zarr`, `GBD`, and `TDMS`.
- If you need to preserve richer objects beyond Series classes, start with **HDF5**.

## Optional Dependency Matrix

Most direct-I/O routes work in a base GWexpy install. The formats below depend
on optional packages or optional metadata helpers.

| Format / family | Optional dependency | GWexpy extra | Missing-dependency behavior |
|---|---|---|---|
| **WAV metadata** | `tinytag` | `audio` | `.read(..., extract_metadata=True)` warns and skips metadata when `tinytag` is missing; install with the `audio` or `all` extra from the [Installation Guide](../tutorials/installation.md). Basic WAV read/write remains available. |
| **MP3 / FLAC / OGG / M4A** | `pydub`, `tinytag` | `audio` | Audio read/write raises `ImportError`; some codecs also need an external `ffmpeg`/`libav` binary. |
| **TDMS** | `nptdms` | `io` | Reader raises `ImportError` with the required `io` extra in its install guidance. |
| **mseed / SAC / GSE2 / K-NET** | `obspy` | `seismic` | Registered reader/writer raises `ImportError` with the required `seismic` extra in its install guidance. |
| **WIN / WIN32** | `obspy` | `seismic` | Uses conditional registration: when ObsPy is unavailable, the `win` / `win32` registry entries may be absent. |
| **ATS.MTH5** | `mth5` | `seismic` | Reader raises `ImportError` with the required `seismic` extra in its install guidance. |
| **nc / NetCDF4** | `xarray`, `netCDF4` | `netcdf4` | Reader/writer raises `ImportError` with the required `netcdf4` extra in its install guidance. |
| **Zarr** | `zarr` | `zarr` | Reader/writer raises `ImportError` with the required `zarr` extra in its install guidance. |

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## A. GW Standards

These are the standard GW storage, exchange, and acquisition paths.
If you are unsure, start with **HDF5**. Use **GWF** when you need external standard compatibility, and **DTTXML** for diagnostic tool output.

| Format / Path | R / W | Main entry | Best for | Notes |
|---|:---:|---|---|---|
| **GWF** (`.gwf`) | ○ / ○ | `TimeSeries.read()`, `TimeSeriesDict.read()`, `.write()` | Standard LIGO/KAGRA frame exchange | Standard format, via gwpy |
| **HDF5** (`.h5`, `.hdf5`) | ○ / ○ | `TimeSeries.read()` / `.write()`, `TimeSeriesDict.read()` / `.write()`; other classes use `format="hdf5"` | Long-term storage with metadata | Native time-series HDF5 auto-identifies; prefer explicit format for other families |
| **hdf.ndscope** (`.h5`, `.hdf5`) | ○ / ○ | `TimeSeriesDict.read(..., format="hdf.ndscope")`, `.write(..., format="hdf.ndscope")` | ndscope-compatible HDF5 | `TimeSeriesDict` only. Legacy aliases: `ndscope-hdf5`, `ndscope_hdf5`, `ndscopehdf5` |
| **xml.diaggui** (`.xml`, `.xml.gz`) | ○ / × | `TimeSeriesDict.read(..., format="xml.diaggui", products="...")` | DiagGUI / DTT outputs | `products` is required; legacy alias: `dttxml` |
| **NDS2** | ○ / × | `TimeSeries.fetch()` | Detector data server access | Network path |
| **GWOSC** | ○ / × | `TimeSeries.fetch_open_data()` | Open data access | Network path |

- Purpose: compare the main GW-oriented direct-I/O and network entry points
- Input: HDF5, GWF, DTTXML, or detector/open-data access parameters
- Output: `TimeSeries`, `TimeSeriesDict`, or fetched open data

```python
from gwexpy.timeseries.collections import TimeSeriesDict
from gwexpy.timeseries import TimeSeries

tsd = TimeSeriesDict.read("data.h5")  # native HDF5 auto-identification
frame = TimeSeriesDict.read("data.gwf", format="gwf")
merged = TimeSeriesDict.read(["part0.gwf", "part1.gwf"], "H1:STRAIN", pad=float("nan"))
dtt = TimeSeriesDict.read("diag.xml", format="xml.diaggui", products="TS")
open_data = TimeSeries.fetch_open_data("H1", 1126259446, 1126259478)
```

- **HDF5** is the safest general recommendation for structured GW data.
- **GWF** reads accept a list or tuple of `.gwf` files for `TimeSeries` and `TimeSeriesDict`. Files are merged in time-span order; contiguous spans join normally, gaps raise by default, `pad=<value>` or `gap="pad"` fills gaps, and `gap="ignore"` concatenates without filling. Overlapping spans are rejected by default or with `gap="raise"`, while `gap="ignore"` concatenates files in span order and permits overlap concatenation. If `start` or `end` extends beyond available data, the default `gap="raise"` behavior rejects the request; use `pad=<value>` or `gap="pad"` to fill the outer interval. `gap="ignore"` never pads missing samples, including outer `start`/`end` ranges. When channel names are not supplied for multi-file reads, auto-discovery uses the first file and assumes the remaining files expose compatible channels.
- **DTTXML** changes behavior depending on `products`. Keep public direct reads on `TimeSeriesDict.read(..., format="xml.diaggui", products=...)`.
- Frequency-domain DTTXML direct shims and registry adapters are implementation-only, not part of the public direct-I/O contract. Advanced internal users handling complex transfer functions can prefer `native=True` there.
- **NDS2 / GWOSC** are shown inside group A, but explicitly marked as `network path` rather than file formats.

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## B. Seismic and Geophysical Observation

This group is for existing seismic and electromagnetic observation formats.
In practice, **MiniSEED** is the easiest starting point when you need to place a format in context.

| Format | R / W | Main entry | Best for | Notes |
|---|:---:|---|---|---|
| **mseed** (`.mseed`) | ○ / ○ | `TimeSeriesDict.read(..., format="mseed")`, `.write(..., format="mseed")` | Standard seismic waveform exchange | `gap` controls gap handling; legacy alias: `miniseed` |
| **SAC** (`.sac`) | ○ / ○ | `TimeSeriesDict.read(..., format="sac")`, `.write(..., format="sac")` | Seismic waveform analysis | Via ObsPy |
| **GSE2** (`.gse2`) | ○ / ○ | `TimeSeriesDict.read(..., format="gse2")`, `.write(..., format="gse2")` | Seismic waveform exchange | Via ObsPy |
| **K-NET** (`.knet`) | ○ / × | `TimeSeriesDict.read(..., format="knet")` | Strong-motion records | Read-only |
| **WIN / WIN32** (`.win`, `.cnt`) | ○ / × | `TimeSeriesDict.read(..., format="win")`, `TimeSeriesDict.read(..., format="win32")` | Japanese WIN datasets | Improved parser, read-only |
| **ATS** (`.ats`) | ○ / × | `TimeSeries.read(..., format="ats")`, `TimeSeriesDict.read(..., format="ats")` | Metronix observation data | Native binary reader |
| **ATS.MTH5** (`format="ats.mth5"`) | ○ / × | `TimeSeries.read(..., format="ats.mth5")` | Single MTH5-backed path | Partial support |
| **MTH5 standalone** (`.h5`) | In progress | Dedicated `format="mth5"` not yet exposed | Future general MTH5 direct I/O | **Not currently a public direct-I/O format**. The only direct path today is `ats.mth5` |

- Purpose: compare common seismic and geophysical readers without overstating MTH5 support
- Input: existing waveform files such as MiniSEED, WIN/WIN32, or the limited `ats.mth5` path
- Output: `TimeSeries` or `TimeSeriesDict` objects depending on the reader

```python
from gwexpy.timeseries.collections import TimeSeriesDict
from gwexpy.timeseries import TimeSeries

tsd = TimeSeriesDict.read("data.mseed", format="mseed", gap="pad")
win = TimeSeriesDict.read("data.cnt", format="win32")
ats = TimeSeries.read("data.atss", format="ats.mth5")
```

- **MiniSEED** pads gaps with `NaN` by default. Use `gap="raise"` if you want failures instead.
- **K-NET** and **WIN / WIN32** are intentionally read-only.
- **ATS.MTH5** is the limited current direct path.
- **MTH5 standalone** is still in design/publication cleanup. Read this as **"`ats.mth5` has partial support"**, not as **"MTH5 direct I/O is generally complete."**

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## C. General Analysis and Exchange

These formats are useful for analysis notebooks, interchange, and general storage.
The key rule here is not to mix up “format choice” with “library conversion.”

| Format | R / W | Main entry | Best for | Notes |
|---|:---:|---|---|---|
| **CSV** (`.csv`) | ○ / ○ | `TimeSeries.read("data.csv")`, `TimeSeriesDict.read("data.csv")`, `TimeSeriesDict.write(..., format="csv")` | Lightweight exchange and inspection | Auto-identifies `.csv`; simple CSV exchange is metadata-light |
| **TXT** (`.txt`) | ○ / ○ | `TimeSeries.read(..., format="txt")`, `TimeSeriesDict.read(dir, format="txt")`, `TimeSeriesDict.write(dir, format="txt")` | Plain-text exchange | Multi-channel direct I/O uses collection directories |
| **nc** (`.nc`) | ○ / ○ | `TimeSeries.read(..., format="nc")`, `TimeSeriesDict.read(..., format="nc")`, `TimeSeriesMatrix.read(..., format="nc")`, `.write(..., format="nc")` | Scientific storage for time-series-oriented data | Direct I/O here is centered on TimeSeries classes; legacy format alias: `netcdf4` |
| **Zarr** (`.zarr`) | ○ / ○ | `TimeSeries.read(..., format="zarr")`, `TimeSeriesDict.read(..., format="zarr")`, `TimeSeriesMatrix.read(..., format="zarr")`, `.write(..., format="zarr")` | Chunked storage and parallel workflows | Direct I/O here is centered on TimeSeries classes |
| **ROOT** (`.root`) | ○ / ○ | `EventTable.read("events.root")`, `EventTable.write(..., format="root")` | EventTable I/O | Auto-identifies `.root`; direct I/O here is EventTable only and requires `uproot` |

- Purpose: show the general-purpose direct-I/O routes without mixing them with interop-only bridges
- Input: CSV, Zarr, ROOT, or other general exchange formats
- Output: `TimeSeriesDict`, `TimeSeriesMatrix`, or `EventTable`

```python
from gwexpy.timeseries.collections import TimeSeriesDict
from gwexpy.table import EventTable

ascii_data = TimeSeriesDict.read("data.csv")
chunked = TimeSeriesDict.read("data.zarr", format="zarr")
events = EventTable.read("events.root")
```

- **CSV** remains useful for lightweight exchange and inspection. Treat simple CSV files as metadata-light: use HDF5, GWF, Zarr, NetCDF, or a manifest-backed collection directory when name, channel, and unit metadata must be preserved.
- CSV component columns are naive civil time and use the configured
  `timezone`. Ambiguous daylight-saving folds and nonexistent gaps raise a
  line-numbered `ValueError`; the reader never chooses an offset silently.
  Numeric timestamps are absolute and the generated sample-index route remains
  relative; both ignore `timezone` with one warning per top-level read.
- CSV validates numeric and configured component-column cadence before
  resampling, using continuous GPS instants for UTC components. It rejects
  malformed or irregular input and any absolute float64 axis whose rounding
  error or spacing is not strictly below half the cadence. `resample=` applies
  only to requested channels; the 10,000,000-value cap covers all resampled
  values across a top-level single- or multi-file read before allocation.
- Malformed source rows include their physical line number. A finite, positive
  `sample_rate` declares source cadence and is used for single-row input;
  without it, the legacy one-second fallback remains. Component streams that
  cross a leap second without a representable `second=60` row fail closed as a
  missing sample.
- Numeric CSV timestamps retain the legacy GPS-second interpretation.
  v0.1.14 does not add `time_scale=` or `time_unit=`; convert other scales
  before reading.
- **TXT** direct I/O is more limited: single-series paths are explicit `format="txt"`, and multi-channel paths use collection directories.
- **Pickle** portability notes still exist in class references, but Pickle is not a published direct `.read()` / `.write()` format on this page.
- **NetCDF4 / Zarr** are treated here only as **direct TimeSeries-style I/O**. Field/xarray bridges belong to interop. For NetCDF, `netcdf4` is a legacy format token alias for `nc`; `.netcdf4` is not a documented auto-identified extension alias.
- **Zarr** direct I/O now expects per-array timing metadata explicitly. `sample_rate` is the primary key, `dt` is accepted as a fallback, and reads raise `ValueError` if neither is present unless you intentionally recover a legacy store with `sample_rate_override=...` or `dt_override=...`.
- **ROOT** object-level export/import belongs to interop. This page only covers EventTable direct I/O, which requires `uproot`.

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## D. Loggers and Instrument Formats

This group is for logger and instrument-specific time-series formats.
Time handling, units, and audio `t0` semantics are the main points to watch.

| Format | R / W | Main entry | Best for | Notes |
|---|:---:|---|---|---|
| **GBD** (`.gbd`) | ○ / × | `TimeSeries.read(..., format="gbd", timezone=...)`, `TimeSeriesDict.read(..., format="gbd", timezone=...)`, `TimeSeriesMatrix.read(..., format="gbd", timezone=...)` | GRAPHTEC loggers | `timezone` is required for published reads |
| **TDMS** (`.tdms`) | ○ / × | `TimeSeries.read(..., format="tdms")`, `TimeSeriesDict.read(..., format="tdms")`, `TimeSeriesMatrix.read(..., format="tdms")` | National Instruments data | Read-only; requires `nptdms` |
| **SDB** (`.sdb`) | ○ / × | `TimeSeries.read(..., format="sdb")`, `TimeSeriesDict.read(..., format="sdb")` | WeeWX and similar archives | Read-only; if present, every `usUnits` value must be integer `1` |
| **WAV** (`.wav`) | ○ / ○ | `TimeSeries.read(..., format="wav")`, `TimeSeriesDict.read(..., format="wav")`, `TimeSeries.write(..., format="wav")` | Uncompressed audio | Public write is single-series only; does not preserve absolute time |
| **MP3 / FLAC / OGG / M4A** | ○ / ○ | `TimeSeries.read(..., format="mp3" / "flac" / "ogg" / "m4a")`, `TimeSeriesDict.read(..., format=...)`, `.write(...)` | Compressed audio | Uses `pydub`; some formats also need `ffmpeg` |

- Purpose: highlight logger-specific and audio-specific direct-I/O requirements
- Input: logger data, SDB archives, or audio files
- Output: `TimeSeries`, `TimeSeriesDict`, or `TimeSeriesMatrix`

```python
from gwexpy.timeseries.collections import TimeSeriesDict

logger = TimeSeriesDict.read("data.gbd", timezone="Asia/Tokyo")
weather = TimeSeriesDict.read("archive.sdb", format="sdb")
audio = TimeSeriesDict.read("sound.flac", format="flac")
```

- **GBD** requires `timezone`.
- **TDMS** requires the optional `nptdms` dependency.
- **MP3 / FLAC / OGG / M4A** require the optional `pydub` dependency. MP3/M4A commonly also need `ffmpeg`.
- **SDB** uses the canonical `.sdb` extension and `format="sdb"` name.
- **WAV / compressed-audio formats** do not preserve absolute timestamps. Reading with `t0=0.0` is a convenience convention, not a claim that the source had an absolute epoch.

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## Developer Notes

Most users can skip this section.
It tracks implementation status for contributors: formats that work but are not yet prominent in the public tables, and format tokens reserved for future work.

### Managed in design, but not prominent in the public page

| Format | Status | Notes |
|---|---|---|
| `hdf.ndscope` | Implemented, not yet prominent | `TimeSeriesDict`-only HDF5 schema. Legacy aliases: `ndscope-hdf5`, `ndscope_hdf5`, `ndscopehdf5` |
| `ATS.MTH5` | Implemented with partial scope | Current public direct path backed by MTH5 |
| `MTH5 standalone` | In progress | Dedicated `format="mth5"` is not exposed yet; not published as public direct I/O |

### Planned Format Tokens

The entries below are FrequencySeries-specific contributor tracking only. They
do not describe the separately supported TimeSeries and TimeSeriesDict routes
for WIN, WIN32, or SDB shown above.

#### TimeSeries stubs

| Format | Status |
|---|---|
| `orf` | Planned |
| `mem` | Planned |
| `wvf` | Planned |
| `wdf` | Planned |
| `taffmat` | Planned |
| `lsf` | Planned |
| `li` | Planned |

#### FrequencySeries stubs

| Format | Status |
|---|---|
| `win` | Planned |
| `win32` | Planned |
| `sdb` | Planned |
| `orf` | Planned |
| `mem` | Planned |
| `wvf` | Planned |
| `wdf` | Planned |
| `taffmat` | Planned |
| `lsf` | Planned |
| `li` | Planned |

## Related Pages

- [Interop tutorial](../how-to/interop/intro_interop.ipynb)
- [Interop API reference](../reference/api/interop)
- [Verification and Quality Signals](../explanation/verification_and_quality.md)
- [Installation guide](../tutorials/installation.md)

## Next to Read

- [Interop / Conversion Guide](interop_capabilities.md) for `to_*()` / `from_*()` bridges and object-level conversion
- [GPS Time Utility Functions](../how-to/time_utilities.md) if your I/O workflow needs timezone or GPS-time handling
- [Installation guide](../tutorials/installation.md) if you need optional dependencies before using a format backend

## Page-End Navigation

- <a href="#io-formats-en-quick">Back to Quick Selection Table</a>
- <a href="#io-formats-en-basic">Back to Basic Usage</a>
- <a href="#io-formats-en-top">Back to Top</a>