### Load and Use Data Sources from an Intake Catalog

Source: https://context7.com/danielballan/reader-intake-adapter/llms.txt

Import the intake library, open a catalog file, and access data sources by their defined names. This example shows loading 'tiff' and 'fwf' drivers.

```python
import intake

catalog = intake.open_catalog('catalog.yml')

coffee_data = catalog.coffee.read()
table_data = catalog.table.read()
```

--------------------------------

### Example Usage of FWFDataSource with DaskDataFrameAdapter

Source: https://context7.com/danielballan/reader-intake-adapter/llms.txt

Demonstrates how to use a custom FWFDataSource and read its data into a pandas DataFrame or a dask DataFrame using the DaskDataFrameAdapter.

```python
import my_fwf_package

data_source = my_fwf_package.FWFDataSource('example_data/table.txt')
df = data_source.read()           # pandas DataFrame
dask_df = data_source.to_dask()   # dask DataFrame
```

--------------------------------

### TIFFReader Example for dask.array

Source: https://context7.com/danielballan/reader-intake-adapter/llms.txt

Implement the Reader interface for TIFF files to return a dask array. This example demonstrates handling multiple TIFF files and pages, and correctly closing resources.

```python
import dask.array
import tifffile

class TIFFReader:
    """Example Reader for TIFF image files."""
    container = 'dask.array.core.Array'

    def __init__(self, file):
        if isinstance(file, str):
            import os
            if os.path.isfile(file):
                self._tiff_files = [tifffile.TiffFile(file)]
            else:
                import glob
                self._tiff_files = [tifffile.TiffFile(f) for f in glob.glob(file)]
        else:
            self._tiff_files = [tifffile.TiffFile(file)]
        self._file = file
        self._closed = False

    def read(self):
        """Return a dask array from the TIFF file(s)."""
        if self._closed:
            raise Exception(f"{self} is closed")
        stack = []
        for tf in self._tiff_files:
            series = tf.series[0]
            for page in series.pages:
                stack.append(dask.array.from_delayed(
                    dask.delayed(page.asarray)(),
                    shape=page.shape, dtype=series.dtype))
        return dask.array.stack(stack)

    def close(self):
        """Release resources."""
        self._closed = True
        for tf in self._tiff_files:
            tf.close()

# Create intake-compatible DataSource
from reader_adapter import adapt
TIFFDataSource = adapt(TIFFReader, 'TIFFDataSource')
```

--------------------------------

### Declare Intake Driver Entry Point in setup.py

Source: https://context7.com/danielballan/reader-intake-adapter/llms.txt

Define an 'intake.drivers' entry point in your setup.py file to register your custom data source driver with intake.

```python
from setuptools import setup

setup(
    name='my_fwf_package',
    packages=['my_fwf_package'],
    entry_points={
        'intake.drivers': [
            'fwf = my_fwf_package:FWFDataSource'
        ]
    },
    install_requires=['dask[dataframe]'],
)
```

--------------------------------

### Create and Use TIFFDataSource

Source: https://context7.com/danielballan/reader-intake-adapter/llms.txt

Instantiate a TIFFDataSource, discover its schema, read the entire dataset into memory as a numpy array, or convert it to a dask array for lazy evaluation. You can also iterate over partitions or read a specific partition. Remember to close the data source when done.

```python
data_source = my_tiff_package.TIFFDataSource('example_data/coffee.tif')
schema = data_source.discover()
array = data_source.read()
dask_array = data_source.to_dask()

for chunk in data_source.read_chunked():
    process(chunk)

partition_0 = data_source.read_partition(0)

data_source.close()
```

--------------------------------

### DataSource API - Available Methods After Adaptation

Source: https://context7.com/danielballan/reader-intake-adapter/llms.txt

Details the methods provided by an intake DataSource once a Reader has been adapted using `reader_adapter.adapt()`. These methods align with the standard intake DataSource API.

```APIDOC
## DataSource API

### Description
After a Reader class is adapted using `reader_adapter.adapt()`, the resulting DataSource object exposes the full suite of methods available in the standard intake DataSource API. This allows for seamless integration with the intake ecosystem.

### Available Methods
- **discover()**: Returns schema information about the data, such as dtype, shape, and number of partitions.
- **read()**: Reads and returns the entire dataset, typically computed into a concrete format like a pandas DataFrame or numpy array.
- **read_chunked()**: Reads the data in chunks, suitable for large datasets that cannot fit into memory entirely.
- **read_partition(i)**: Reads and returns a specific partition of the data, identified by its index `i`.
- **to_dask()**: Returns the data as a dask collection (e.g., dask DataFrame or dask Array), allowing for lazy computation.
- **close()**: Closes the underlying resources associated with the data source.

### Example Usage
```python
# Assuming MyDataSource is an adapted DataSource class
from reader_adapter import adapt

# ... (Define and adapt a Reader class as shown previously) ...

# Instantiate the adapted DataSource
data_source = MyDataSource('path/to/your/data')

# Use the intake DataSource API
data_source.discover()
computed_data = data_source.read()
dask_data = data_source.to_dask()
data_source.close()
```
```

--------------------------------

### Adapt a Reader to DataSource

Source: https://github.com/danielballan/reader-intake-adapter/blob/master/README.md

Use the adapter to convert a custom Reader class into a fully-compliant intake DataSource.

```python
MyDataSource = reader_adapter.adapt(MyReader, 'MyDataSource')
```

--------------------------------

### Adapt Reader to DataSource

Source: https://context7.com/danielballan/reader-intake-adapter/llms.txt

Use `adapt()` to convert a custom Reader class into an intake-compatible DataSource. Ensure the Reader class has a `container` attribute specifying the return type of `read()` and implements `read()` and `close()` methods.

```python
from reader_adapter import adapt

# Define a simple Reader class
class MyReader:
    container = 'dask.dataframe.core.DataFrame'  # or 'dask.array.core.Array'

    def __init__(self, file):
        self._filepath = file
        self._closed = False

    def read(self):
        if self._closed:
            raise Exception("Reader is closed")
        import dask.dataframe
        return dask.dataframe.read_csv(self._filepath)

    def close(self):
        self._closed = True

# Convert Reader to DataSource
MyDataSource = adapt(MyReader, 'MyDataSource')

# Use the DataSource with full intake API
data_source = MyDataSource('data.csv')
data_source.discover()  # Returns schema info: dtype, shape, npartitions
data_source.read()      # Returns computed pandas DataFrame
data_source.to_dask()   # Returns dask DataFrame
data_source.close()
```

--------------------------------

### Register Custom Container Adapter

Source: https://context7.com/danielballan/reader-intake-adapter/llms.txt

Extend `reader_adapter` to support new container types by creating a custom `Adapter` subclass and registering it. The custom adapter must define `container`, `_get_schema`, `_get_partition`, and optionally `to_dask` and `read` methods.

```python
from reader_adapter._registry import register
from reader_adapter._base import Adapter
from reader_adapter._vendored import Schema

class XArrayAdapter(Adapter):
    container = 'xarray'
    _EXPECTED_CONTAINER = 'xarray.core.dataset.Dataset'

    def _get_schema(self):
        reading = self._adapter_read()
        return Schema(
            datashape=None,
            dtype=str(reading.dtypes),
            shape=reading.dims,
            npartitions=1,
            extra_metadata={})

    def _get_partition(self, i):
        return self._adapter_read()

    def to_dask(self):
        return self._adapter_read().to_dask_dataframe()

    def read(self):
        return self._adapter_read()

# Register the adapter
register('xarray.core.dataset.Dataset', XArrayAdapter)
```

--------------------------------

### Reference Custom Driver in Intake Catalog YAML

Source: https://context7.com/danielballan/reader-intake-adapter/llms.txt

Use the 'driver' key in your catalog.yml file to specify the custom driver for a data source, along with its file path and persistence settings.

```yaml
sources:
    coffee:
        driver: "tiff"
        persist: "never"
        args:
            file: "example_data/coffee.tif"
    table:
        driver: "fwf"
        persist: "never"
        args:
            file: "example_data/table.txt"
```

--------------------------------

### Reader Interface - Required API for Custom Readers

Source: https://context7.com/danielballan/reader-intake-adapter/llms.txt

Defines the minimal interface that any custom Reader class must implement to be compatible with the `adapt()` function. This includes `read()`, `close()`, and a `container` attribute.

```APIDOC
## Reader Interface

### Description
This section outlines the required interface for custom Reader classes to be compatible with the `reader_adapter.adapt()` function. A Reader must implement the `read()` and `close()` methods, and possess a `container` attribute.

### Required Methods
- **read()**: This method should perform the actual reading of the data and return an object representing the data container (e.g., a dask array, pandas DataFrame).
- **close()**: This method should release any resources held by the reader, such as open file handles.

### Required Attribute
- **container** (str): A string representing the fully-qualified class name of the object returned by the `read()` method. Examples include 'dask.array.core.Array' or 'dask.dataframe.core.DataFrame'.

### Example Implementation
```python
import dask.array
import tifffile

class TIFFReader:
    """Example Reader for TIFF image files."""
    container = 'dask.array.core.Array'

    def __init__(self, file):
        # ... initialization logic ...
        self._closed = False

    def read(self):
        """Return a dask array from the TIFF file(s)."""
        if self._closed:
            raise Exception(f"{self} is closed")
        # ... logic to read TIFF and return dask array ...
        return dask.array.stack([]) # Placeholder

    def close(self):
        """Release resources."""
        self._closed = True
        # ... logic to close file handles ...
```
```

--------------------------------

### Define a Reader Class

Source: https://github.com/danielballan/reader-intake-adapter/blob/master/README.md

Implement this class structure to define a reader that can be automatically adapted into an intake DataSource.

```python
class MyReader:
    container = '...'  # fully-qualified class name of type(self.read())

    def read(self):
        """
        Return any type that reader_adapter knows about.

        This currently includes dask.array.core.Array and
        dask.dataframe.core.DataFrame, but could grow to include numpy.ndarray,
        pandas.DataFrame, and xarray types.
        """
        ...

    def close(self):
        ...
```

--------------------------------

### reader_adapter.register() - Register Custom Container Adapters

Source: https://context7.com/danielballan/reader-intake-adapter/llms.txt

Allows for the registration of custom adapters for new container types not supported by default. This extends the library's compatibility to other data structures.

```APIDOC
## POST reader_adapter.register()

### Description
Registers a custom adapter for a new container type, extending reader_adapter's support beyond built-in types like dask arrays and dataframes.

### Method
POST

### Endpoint
/reader_adapter.register

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
- **container_type** (str) - Required - The fully-qualified class name of the container (e.g., 'xarray.core.dataset.Dataset').
- **adapter_class** (class) - Required - The custom Adapter class that handles the specified container type.

### Request Example
```json
{
  "container_type": "xarray.core.dataset.Dataset",
  "adapter_class": "XArrayAdapter"
}
```

### Response
#### Success Response (200)
- **message** (str) - Confirmation message indicating successful registration.

#### Response Example
```json
{
  "message": "Adapter for xarray.core.dataset.Dataset registered successfully."
}
```
```

--------------------------------

### reader_adapter.adapt() - Convert a Reader to a DataSource

Source: https://context7.com/danielballan/reader-intake-adapter/llms.txt

The primary function to transform a custom Reader class into a fully-compliant intake DataSource. It handles validation and selects the appropriate adapter based on the container type specified in the Reader.

```APIDOC
## POST reader_adapter.adapt()

### Description
Converts a simple Reader class into a fully-compliant intake DataSource. It validates the Reader's interface and selects the appropriate adapter based on the container type.

### Method
POST

### Endpoint
/reader_adapter.adapt

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
- **ReaderClass** (class) - Required - The custom Reader class to adapt.
- **DataSourceName** (str) - Required - The desired name for the new intake DataSource.

### Request Example
```json
{
  "ReaderClass": "MyReader",
  "DataSourceName": "MyDataSource"
}
```

### Response
#### Success Response (200)
- **DataSource** (class) - The newly created intake-compatible DataSource class.

#### Response Example
```json
{
  "DataSource": "MyDataSource"
}
```
```

--------------------------------

### DaskArrayAdapter Behavior for Dask Arrays

Source: https://context7.com/danielballan/reader-intake-adapter/llms.txt

The DaskArrayAdapter is automatically used for dask.array.core.Array containers. It extracts schema information like dtype, shape, and partitions directly from the dask array and provides methods to read partitions or the entire array.

```python
from reader_adapter._adapters import DaskArrayAdapter

# DaskArrayAdapter is automatically used when container is 'dask.array.core.Array'
# Key behavior:

# _get_schema() extracts:
#   - dtype from dask array
#   - shape from dask array
#   - npartitions from dask array
#   - chunks from dask array

# _get_partition(i) returns:
#   - Computed numpy array for block index i (tuple indexing)

# read() returns:
#   - Fully computed numpy array (calls .compute())

# to_dask() returns:
#   - Original dask array for lazy computation
```

--------------------------------

### DaskDataFrameAdapter Behavior for Dask DataFrames

Source: https://context7.com/danielballan/reader-intake-adapter/llms.txt

The DaskDataFrameAdapter is automatically used for dask.dataframe.core.DataFrame containers. It extracts schema information including column dtypes and shape, and provides methods to read partitions or the entire DataFrame.

```python
from reader_adapter._adapters import DaskDataFrameAdapter

# DaskDataFrameAdapter is automatically used when container is 'dask.dataframe.core.DataFrame'
# Key behavior:

# _get_schema() extracts:
#   - dtype as dict mapping column names to dtypes
#   - shape from dask dataframe
#   - npartitions from dask dataframe

# _get_partition(i) returns:
#   - Computed pandas DataFrame for partition i

# read() returns:
#   - Fully computed pandas DataFrame (calls .compute())

# to_dask() returns:
#   - Original dask DataFrame for lazy computation
```

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