CDM¶

metpy.io.cdm

The Common Data Model (CDM) is a data model for representing a wide array of data. The goal is to be a simple, universal interface to different datasets. This API is a Python implementation in the spirit of the original Java interface in netCDF-Java.

class metpy.io.cdm.AttributeContainer¶

A class to handle maintaining a list of netCDF attributes. Implements the attribute handling for other CDM classes.

Initialize an AttributeContainer.

__init__()¶: Initialize an AttributeContainer.

ncattrs()¶

Get a list of the names of the netCDF attributes.

Returns:	List[str]

class metpy.io.cdm.Dataset¶

A Dataset represents a set of data using the Common Data Model (CDM).

This is currently only a wrapper around the root Group.

Initialize a Dataset.

__init__()¶: Initialize a Dataset.

class metpy.io.cdm.Dimension(group, name, size=None)¶

A Dimension is used to represent a shared dimension between different Variables. For instance, variables that are dependent upon a common set of times.

Initialize a Dimension. Instead of constructing a Dimension directly, you should use Group.createDimension.

Parameters:	group (Group) – The parent Group that owns this Variable. name (str) – The name of this Variable. size (int or None, optional) – The size of the Dimension. Defaults to None, which implies an empty dimension.

See also

Group.createDimension

__init__(group, name, size=None)¶

Initialize a Dimension. Instead of constructing a Dimension directly, you should use Group.createDimension.

Parameters:	group (Group) – The parent Group that owns this Variable. name (str) – The name of this Variable. size (int or None, optional) – The size of the Dimension. Defaults to None, which implies an empty dimension.

See also

Group.createDimension()

group()¶

Get the Group that owns this Dimension.

Returns:	Group – The parent Group.

name = None¶: desc – The name of the Dimension :type: str

size = None¶: desc – The size of this Dimension :type: int

class metpy.io.cdm.Group(parent, name)¶

A Group holds dimensions and variables. Every CDM dataset has at least a root group.

Initialize this Group. Instead of constructing a Group directly, you should use createGroup().

Parameters:	parent (Group or None) – The parent Group for this one. Passing in `None` implies that this is the root `Group`. name (str) – The name of this group

See also

Group.createGroup

__init__(parent, name)¶

Initialize this Group. Instead of constructing a Group directly, you should use createGroup().

Parameters:	parent (Group or None) – The parent Group for this one. Passing in `None` implies that this is the root `Group`. name (str) – The name of this group

See also

Group.createGroup()

createDimension(name, size)¶

Create a new Dimension in this Group.

Parameters:	name (str) – The name of the new Dimension. size (int) – The size of the Dimension
Returns:	Dimension – The newly created `Dimension`

createGroup(name)¶

Create a new Group as a descendant of this one.

Parameters:	name (str) – The name of the new Group.
Returns:	Group – The newly created `Group`

createVariable(name, datatype, dimensions=(), fill_value=None, wrap_array=None)¶

Create a new Variable in this Group.

Parameters:

name (str) – The name of the new Variable.
datatype (str or numpy.dtype) – A valid Numpy dtype that describes the layout of the data within the Variable.
dimensions (tuple[str], optional) – The dimensions of this Variable. Defaults to empty, which implies a scalar variable.
fill_value (number, optional) – A scalar value that is used to fill the created storage. Defaults to None, which performs no filling, leaving the storage uninitialized.
wrap_array (numpy.ndarray, optional) – Instead of creating an array, the Variable instance will assume ownership of the passed in array as its data storage. This is a performance optimization to avoid copying large data blocks. Defaults to None, which means a new array will be created.

Returns:

Variable – The newly created Variable

dimensions = None¶: desc – Dimensions contained within this group :type: dict[str, Dimension]

groups = None¶: desc – Any Groups nested within this one :type: dict[str, Group]

name = None¶: desc – The name of the Group :type: str

variables = None¶: desc – Variables contained within this group :type: dict[str, Variable]

class metpy.io.cdm.Variable(group, name, datatype, dimensions, fill_value, wrap_array)¶

A Variable holds typed data (using a numpy.ndarray), as well as any relevant attributes (e.g. units).

In addition to its various attributes, the Variable supports getting and setting data using the [] operator and indices or slices. Getting data returns numpy.ndarray instances.

Initialize a Variable. Instead of constructing a Variable directly, you should use Group.createVariable().

Parameters:

group (Group) – The parent Group that owns this Variable.
name (str) – The name of this Variable.
datatype (str or numpy.dtype) – A valid Numpy dtype that describes the layout of each element of the data
dimensions (tuple[str], optional) – The dimensions of this Variable. Defaults to empty, which implies a scalar variable.
fill_value (scalar, optional) – A scalar value that is used to fill the created storage. Defaults to None, which performs no filling, leaving the storage uninitialized.
wrap_array (numpy.ndarray, optional) – Instead of creating an array, the Variable instance will assume ownership of the passed in array as its data storage. This is a performance optimization to avoid copying large data blocks. Defaults to None, which means a new array will be created.

See also

Group.createVariable

__init__(group, name, datatype, dimensions, fill_value, wrap_array)¶

Initialize a Variable. Instead of constructing a Variable directly, you should use Group.createVariable().

Parameters:

group (Group) – The parent Group that owns this Variable.
name (str) – The name of this Variable.
datatype (str or numpy.dtype) – A valid Numpy dtype that describes the layout of each element of the data
dimensions (tuple[str], optional) – The dimensions of this Variable. Defaults to empty, which implies a scalar variable.
fill_value (scalar, optional) – A scalar value that is used to fill the created storage. Defaults to None, which performs no filling, leaving the storage uninitialized.
wrap_array (numpy.ndarray, optional) – Instead of creating an array, the Variable instance will assume ownership of the passed in array as its data storage. This is a performance optimization to avoid copying large data blocks. Defaults to None, which means a new array will be created.

See also

Group.createVariable()

datatype¶: numpy.dtype – a valid Numpy dtype that describes the layout of each element of the data

dimensions¶: tuple[str] – all the names of Dimension used by this Variable

dtype¶: numpy.dtype – a valid Numpy dtype that describes the layout of each element of the data

group()¶

Get the Group that owns this Variable.

Returns:	Group – The parent Group.

name¶: str – the name of the variable

ndim¶: int – the number of dimensions used by this variable

shape¶: tuple[int] – a tuple of integers describing the size of the Variable along each of its dimensions

size¶: int – the total number of elements

metpy.io.cdm.cf_to_proj(var)¶

Converts a Variable with projection information conforming to the Climate and Forecasting (CF) netCDF conventions to a Proj.4 Projection instance.

Parameters:	var (Variable) – The projection variable with appropriate attributes.