podpac.data.Dataset

class podpac.data.Dataset(**kwargs)[source]

Bases: podpac.core.data.file_source.FileKeysMixin, podpac.core.data.file_source.LoadFileMixin, podpac.core.data.file_source.BaseFileSource

Create a DataSource node using xarray.open_dataset.

source

Path to the dataset file. In addition to local paths, file://, http://, ftp://, and s3:// transport protocols are supported.

Type

str

dataset

Dataset object.

Type

xarray.Dataset

coordinates

The coordinates of the data source.

Type

podpac.Coordinates

data_key

data key, default ‘data’

Type

str

lat_key

latitude key, default ‘lat’

Type

str

lon_key

longitude key, default ‘lon’

Type

str

time_key

time key, default ‘time’

Type

str

alt_key

altitude key, default ‘alt’

Type

str

crs

Coordinate reference system of the coordinates

Type

str

extra_dim

In cases where the data contain dimensions other than [‘lat’, ‘lon’, ‘time’, ‘alt’], these dimensions need to be selected. For example, if the data contains [‘lat’, ‘lon’, ‘channel’], the second channel can be selected using extra_dim=dict(channel=1)

Type

dict

Alternative Constructors

from_definition(definition)

Create podpac Node from a dictionary definition.

from_json(s)

Create podpac Node from a JSON definition.

Methods

__init__(**kwargs)

Do not overwrite me

close_dataset()

create_output_array(coords[, data])

Initialize an output data array

eval(coordinates[, output])

Evaluates this node using the supplied coordinates.

eval_group(group)

Evaluate the node for each of the coordinates in the group.

find_coordinates()

Get the available coordinates for the Node.

from_url(url)

Create podpac Node from a WMS/WCS request.

get_cache(key[, coordinates])

Get cached data for this node.

get_coordinates()

Returns a Coordinates object that describes the coordinates of the data source.

get_data(coordinates, coordinates_index)

This method must be defined by the data source implementing the DataSource class.

has_cache(key[, coordinates])

Check for cached data for this node.

init()

Overwrite this method if a node needs to do any additional initialization after the standard initialization.

load(path)

Create podpac Node from file.

open_dataset(fp)

put_cache(data, key[, coordinates, overwrite])

Cache data for this node.

rem_cache(key[, coordinates, mode])

Clear cached data for this node.

save(path)

Write node to file.

set_coordinates(coordinates[, force])

Set the coordinates.

trait_is_defined(name)

Attributes

alt_key

A trait for unicode strings.

anon

A boolean (True, False) trait.

attrs

List of node attributes

available_data_keys

aws_access_key_id

A trait for unicode strings.

aws_client_kwargs

An instance of a Python dict.

aws_region_name

A trait for unicode strings.

aws_requester_pays

A boolean (True, False) trait.

aws_secret_access_key

A trait for unicode strings.

base_ref

Default reference/name in node definitions

boundary

An instance of a Python dict.

cache_coordinates

A boolean (True, False) trait.

cache_ctrl

A trait whose value must be an instance of a specified class.

cache_dataset

A boolean (True, False) trait.

cache_output

A boolean (True, False) trait.

cf_calendar

A trait for unicode strings.

cf_time

A boolean (True, False) trait.

cf_units

A trait for unicode strings.

config_kwargs

An instance of a Python dict.

coordinate_index_type

An enum whose value must be in a given sequence.

coordinates

{coordinates}

crs

A trait for unicode strings.

data_key

A trait type representing a Union type.

dataset

definition

dims

dtype

A trait which allows any value.

extra_dim

An instance of a Python dict.

force_eval

A boolean (True, False) trait.

hash

interpolation

interpolation_class

Get the interpolation class currently set for this data source.

interpolators

Return the interpolators selected for the previous node evaluation interpolation.

json

json_pretty

keys

lat_key

A trait for unicode strings.

lon_key

A trait for unicode strings.

nan_vals

An instance of a Python list.

output

A trait for unicode strings.

outputs

An instance of a Python list.

s3

skip_validation

A boolean (True, False) trait.

source

A trait for unicode strings.

style

A trait whose value must be an instance of a specified class.

time_key

A trait for unicode strings.

units

A trait for unicode strings.

Members

__init__(**kwargs)

Do not overwrite me

close_dataset()[source]
property dims
extra_dim

An instance of a Python dict.

get_data(coordinates, coordinates_index)[source]

This method must be defined by the data source implementing the DataSource class. When data source nodes are evaluated, this method is called with request coordinates and coordinate indexes. The implementing method can choose which input provides the most efficient method of getting data (i.e via coordinates or via the index of the coordinates).

Coordinates and coordinate indexes may be strided or subsets of the source data, but all coordinates and coordinate indexes will match 1:1 with the subset data.

This method may return a numpy array, an xarray DaraArray, or a podpac UnitsDataArray. If a numpy array or xarray DataArray is returned, podpac.data.DataSource.evaluate() will cast the data into a UnitsDataArray using the requested source coordinates. If a podpac UnitsDataArray is passed back, the podpac.data.DataSource.evaluate() method will not do any further processing. The inherited Node method create_output_array can be used to generate the template UnitsDataArray in your DataSource. See podpac.Node.create_output_array() for more details.

Parameters
  • coordinates (podpac.Coordinates) – The coordinates that need to be retrieved from the data source using the coordinate system of the data source

  • coordinates_index (List) – A list of slices or a boolean array that give the indices of the data that needs to be retrieved from the data source. The values in the coordinate_index will vary depending on the coordinate_index_type defined for the data source.

Returns

A subset of the returned data. If a numpy array or xarray DataArray is returned, the data will be cast into UnitsDataArray using the returned data to fill values at the requested source coordinates.

Return type

np.ndarray, xr.DataArray, podpac.UnitsDataArray

property keys
open_dataset(fp)[source]