exa.core.numerical module¶

Data Objects¶

Data objects are used to store typed data coming from an external source (for example a file on disk). There are three primary data objects provided by this module, Series, DataFrame, and Field. The purpose of these objects is to facilitate conversion of data into “traits” used in visualization and enforce relationships between data objects in a given container. Any of the objects provided by this module may be extended.

class exa.core.numerical.Numerical[source]¶

Bases: object

Base class for Series, DataFrame, and Field objects, providing default trait functionality and clean representations when present as part of containers.

property log¶

slice_naive(key)[source]¶

Slice a data object based on its index, either by value (.loc) or position (.iloc).

Parameters: key – Single index value, slice, tuple, or list of indices/positionals
Returns: data – Slice of self

class exa.core.numerical.BaseSeries(*args, **kwargs)[source]¶

Bases: exa.core.numerical.Numerical

Base class for dense and sparse series objects (labeled arrays).

_sname¶

May have a required name (default None)

Type: str

_iname (str: May have a required index name

_stype¶

May have a required value type

Type: type

_itype¶

May have a required index type

Type: type

class exa.core.numerical.BaseDataFrame(*args, **kwargs)[source]¶

Bases: exa.core.numerical.Numerical

Base class for dense and sparse dataframe objects (labeled matrices).

Note

If the _cardinal attribute is populated, it will automatically be added to the _categories and _columns attributes.

_cardinal¶

Tuple of column name and raw type that acts as foreign key to index of another table

Type: tuple

_index¶

Name of index (may be used as foreign key in another table)

Type: str

_columns¶

Required columns

Type: list

_categories¶

Dict of column names, raw types that if present will be converted to and from categoricals automatically

Type: dict

cardinal_groupby()[source]¶

Group this object on it cardinal dimension (_cardinal).

Returns: grpby – Pandas groupby object (grouped on _cardinal)

slice_cardinal(key)[source]¶: Get the slice of this object by the value or values of the cardinal dimension.

class exa.core.numerical.Series(*args, **kwargs)[source]¶

Bases: exa.core.numerical.BaseSeries, pandas.core.series.Series

A labeled array.

class MySeries(exa.core.numerical.Series):
    _sname = 'data'        # series default name
    _iname = 'data_index'  # series default index name

seri = MySeries(np.random.rand(10**5))

copy(*args, **kwargs)[source]¶: Make a copy of this object.

See also

For arguments and description of behavior see pandas docs.

class exa.core.numerical.DataFrame(*args, **kwargs)[source]¶

Bases: exa.core.numerical.BaseDataFrame, pandas.core.frame.DataFrame

A data table

class MyDF(exa.core.numerical.DataFrame):
    _cardinal = ('cardinal', int)
    _index = 'mydf_index'
    _columns = ['x', 'y', 'z', 'symbol']
    _categories = {'symbol': str}

copy(*args, **kwargs)[source]¶: Make a copy of this object.

See also

For arguments and description of behavior see pandas docs.

class exa.core.numerical.Field(*args, **kwargs)[source]¶

Bases: exa.core.numerical.DataFrame

A field is defined by field data and field values. Field data defines the discretization of the field (i.e. its origin in a given space, number of steps/step spaceing, and endpoint for example). Field values can be scalar (series) and/or vector (dataframe) data defining the magnitude and/or direction at each given point.

Note

The convention for generating the discrete field data and ordering of the field values must be the same (e.g. discrete field points are generated x, y, then z and scalar field values are a series object ordered looping first over x then y, then z).

In addition to the DataFrame attributes, this object has the following:

copy(*args, **kwargs)[source]¶: Make a copy of this object.

Note

Copies both field data and field values.

See also

For arguments and description of behavior see pandas docs.

memory_usage()[source]¶: Get the combined memory usage of the field data and field values.

slice_naive(key)[source]¶

Naively (on index) slice the field data and values.

Parameters: key – Int, slice, or iterable to select data and values
Returns: field – Sliced field object

class exa.core.numerical.Field3D(*args, **kwargs)[source]¶

Bases: exa.core.numerical.Field

Dataframe for storing dimensions of a scalar or vector field of 3D space.

Column	Type	Description
nx	int	number of grid points in x
ny	int	number of grid points in y
nz	int	number of grid points in z
ox	float	field origin point in x
oy	float	field origin point in y
oz	float	field origin point in z
xi	float	First component in x
xj	float	Second component in x
xk	float	Third component in x
yi	float	First component in y
yj	float	Second component in y
yk	float	Third component in y
zi	float	First component in z
zj	float	Second component in z
zk	float	Third component in z

Note

Each field should be flattened into an N x 1 (scalar) or N x 3 (vector) series or dataframe respectively. The orientation of the flattening should have x as the outer loop and z values as the inner loop (for both cases). This is sometimes called C-major or C-style order, and has the last index changing the fastest and the first index changing the slowest.