BaseCartesianData¶
- class glue.core.data.BaseCartesianData(coords=None)[source]¶
Bases:
BaseData
Base class for any glue data object which indicates which methods should be provided at a minimum.
The underlying data can be any kind of data (structured or unstructured) but it needs to expose an interface that looks like a regular n-dimensional cartesian dataset. This means exposing e.g.
shape
andndim
, and means that get_data can expect ndarray slices. Non-regular datasets should therefore have the concept of ‘virtual’ pixel coordinates and should typically match the highest resolution a user might want to access the data at.Attributes Summary
The coordinates object for the data.
The number of dimensions of the data, as an integer.
Information about other datasets in the same data collection that have matching or a subset of pixel component IDs.
The n-dimensional shape of the dataset, as a tuple.
The size of the data (the product of the shape dimensions), as an integer.
A list of
ComponentID
giving all world coordinate component IDs in the data.Methods Summary
compute_fixed_resolution_buffer
(bounds[, ...])Get a fixed-resolution buffer.
compute_histogram
(cids[, weights, range, ...])Compute an n-dimensional histogram with regularly spaced bins.
compute_statistic
(statistic, cid[, ...])Compute a statistic for the data.
get_data
(cid[, view])Get the data values for a given component
get_mask
(subset_state[, view])Get a boolean mask for a given subset state.
Attributes Documentation
- pixel_aligned_data[source]¶
Information about other datasets in the same data collection that have matching or a subset of pixel component IDs.
This is returned as a dictionary where each key is a dataset with matching pixel component IDs, and the value is the order in which the pixel component IDs of the other dataset can be found in the current one.
- world_component_ids[source]¶
A list of
ComponentID
giving all world coordinate component IDs in the data.
Methods Documentation
- compute_fixed_resolution_buffer(bounds, target_data=None, target_cid=None, subset_state=None, broadcast=True)[source]¶
Get a fixed-resolution buffer.
- Parameters
- boundslist
The list of bounds for the fixed resolution buffer. This list should have as many items as there are dimensions in
target_data
. Each item should either be a scalar value, or a tuple of(min, max, nsteps)
.- target_data
Data
, optional The data in whose frame of reference the bounds are defined. Defaults to
data
.- target_cid
ComponentID
, optional If specified, gives the component ID giving the component to use for the data values. Alternatively, use
subset_state
to get a subset mask.- subset_state
SubsetState
, optional If specified, gives the subset state for which to compute a mask. Alternatively, use
target_cid
if you want to get data values.- broadcastbool, optional
If True, then if a dimension in
target_data
for whichbounds
is not a scalar does not affect any of the dimensions indata
, then the final array will be effectively broadcast along this dimension, otherwise an error will be raised.
- abstract compute_histogram(cids, weights=None, range=None, bins=None, log=None, subset_state=None)[source]¶
Compute an n-dimensional histogram with regularly spaced bins.
- Parameters
- cidslist of str or
ComponentID
Component IDs to compute the histogram over.
- weightsstr or
ComponentID
Component IDs to use for the histogram weights.
- rangelist of tuple
The
(min, max)
of the histogram range.- binslist of int
The number of bins.
- loglist of bool
Whether to compute the histogram in log space.
- subset_state
SubsetState
, optional If specified, the histogram will only take into account values in the subset state.
- cidslist of str or
- abstract compute_statistic(statistic, cid, subset_state=None, axis=None, finite=True, positive=False, percentile=None, view=None, random_subset=None)[source]¶
Compute a statistic for the data.
- Parameters
- statistic{‘minimum’, ‘maximum’, ‘mean’, ‘median’, ‘sum’, ‘percentile’}
The statistic to compute
- cid
ComponentID
or str The component ID to compute the statistic on - if given as a string this will be assumed to be for the component belonging to the dataset (not external links).
- subset_state
SubsetState
, optional If specified, the statistic will only include the values that are in the subset specified by this subset state.
- axisint or tuple of int, optional
If specified, the axis/axes to compute the statistic over.
- finitebool, optional
Whether to include only finite values in the statistic. This should be True to ignore NaN/Inf values
- positivebool, optional
Whether to include only (strictly) positive values in the statistic. This is used for example when computing statistics of data shown in log space.
- percentilefloat, optional
If
statistic
is'percentile'
, thepercentile
argument should be given and specify the percentile to calculate in the range [0:100]- random_subsetint, optional
If specified, this should be an integer giving the number of values to use for the statistic. This can only be used if
axis
is None
- get_data(cid, view=None)[source]¶
Get the data values for a given component
- Parameters
- cid
ComponentID
The component ID to get the data for.
- viewslice
The ‘view’ on the data - anything that is considered a valid Numpy slice/index.
- cid
- abstract get_mask(subset_state, view=None)[source]¶
Get a boolean mask for a given subset state.
- Parameters
- subset_state
SubsetState
The subset state to use to compute the mask
- viewslice
The ‘view’ on the mask - anything that is considered a valid Numpy slice/index.
- subset_state