> ## Documentation Index
> Fetch the complete documentation index at: https://docs.dataerai.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Quickstart

> Extract metadata, convert to netCDF, compute checksums, and batch-process instrument files with dataerai.metaextract.

Install the extractor (see [Overview](/metaextract/overview) for the full extras list):

```bash theme={null}
pip install "dataerai-sdk[metaextract-converters]"
```

## Extract metadata

```python theme={null}
from dataerai.metaextract import extract_metadata

# Format is auto-detected from the file extension
metadata = extract_metadata("data.ibw")
print(metadata)
```

## Convert to netCDF

```python theme={null}
from dataerai.metaextract import convert_file

# Convert and save to disk
dataset = convert_file("data.ibw", output_path="output.nc")

# …or just get the xarray.Dataset back without saving
dataset = convert_file("data.ibw")
```

## Check supported formats

```python theme={null}
from dataerai.metaextract import get_supported_formats

for ext, caps in get_supported_formats().items():
    print(f"{ext}: extract={caps['extract']}, convert={caps['convert']}")
```

## Compute a checksum

```python theme={null}
from dataerai.metaextract import (
    calculate_checksum,
    calculate_multiple_checksums,
    verify_checksum,
)

# Single digest (defaults to blake3 if available, else sha256)
print(calculate_checksum("data.ibw"))
print(calculate_checksum("data.ibw", algorithm="sha256"))

# Several algorithms in one read of the file
print(calculate_multiple_checksums("data.ibw", ["sha256", "md5"]))

# Confirm a file matches an expected digest
verify_checksum("data.ibw", "abc123…", algorithm="sha256")   # -> True / False
```

`[metaextract-checksums]` adds a fast [blake3](https://github.com/BLAKE3-team/BLAKE3) backend; without it, the standard library algorithms (sha256, sha512, md5, blake2b, …) are used.

## Extract or convert in one call

```python theme={null}
from dataerai.metaextract import process_file

metadata = process_file("data.ibw", output="metadata")
dataset  = process_file("data.ibw", output="dataset", output_path="output.nc")
```

## Batch a directory

```python theme={null}
from pathlib import Path
from dataerai.metaextract import extract_metadata

results = [
    {"file": str(p), "metadata": extract_metadata(p)}
    for p in Path("data").glob("*.ibw")
]
```

<Tip>
  Pair extraction with DataErai uploads: extract locally, then attach the dict as the asset's metadata through the [Python SDK](/sdks/python) so it becomes [searchable](/discover/search) alongside everything else.
</Tip>