lib.data_vector.binned_statistic.BinnedStatistic¶

class lib.data_vector.binned_statistic.BinnedStatistic(data=None, edges=None, dims=None, attrs=None)¶

Bases: cosmopipe.lib.utils.BaseClass

Class representing a binned statistic, similar to https://github.com/bccp/nbodykit/blob/master/nbodykit/binned_statistic.py.

data¶

Dictionary of data arrays, of same shape.

Type: dict

edges¶

Dictionary of edges.

Type: dict

dims¶

List of dimension names.

Type: list

attrs¶

Dictionary of other attributes.

Type: dict

Initialize BinnedStatistic.

Parameters

data (dict, default=None) – Dictionary of data arrays, of same shape. Defaults to empty dictionary.
edges (dict, list, default=None) – Dictionary of edges, or list of edges corresponding to dims. If None, no edges considered.
dims (list, default=None) – List of dimension names. If None, defaults to edges dictionary keys.
attrs (dict, default=None) – Dictionary of other attributes.

Methods

`average`	Average binned data along dimensions `dims`.
`copy`	Return shallow copy of `self`.
`deepcopy`
`from_state`	Instantiate and initalize class with state dictionary.
`get_header_txt`	Dump header:
`get_title_label`	Return title, as `module.class`.
`has_edges`	Has specified edges?
`is_mpi_broadcast`
`is_mpi_gathered`
`is_mpi_root`
`is_mpi_scattered`
`load`	Load class in numpy binary format from disk.
`load_auto`	If different formats are possible, this method should between them based on file name extension.
`load_txt`	Load `BinnedStatistic` from disk.
`log_critical`
`log_debug`
`log_error`
`log_info`
`log_warning`
`read_header_txt`	Read and decode header.
`read_title_label`	Decode title `line`, splitting `module.class` into (`module`, `class`).
`rebin`	Rebin data by factor `factors`.
`save`	Save class to disk.
`save_auto`	If different formats are possible, this method should between them based on file name extension.
`save_txt`	Dump `BinnedStatistic`.
`set_new_edges`	Interpolate binned data within new edges.
`squeeze`	Squeeze binned data along dimensions `dims`.

Attributes

`columns`	Entries in `data`.
`logger`
`mpiattrs`	MPI attributes
`mpicomm`
`mpiroot`
`mpistate`
`ndim`	Number of dimensions of binned data.
`shape`	Shape of binned data; if `edges`, return tuple of length of edges - 1, else shape of first array in `data`
`size`	Total size of binned data, i.e. product of length over all dimensions.

average(dims=None, weights=None, columns_to_sum=None)¶: Average binned data along dimensions dims. This is equivalent to rebin() with factors corresponding to lengths along dimensions dims, followed by squeeze() along those dimensions.

property columns¶: Entries in data.

copy()¶: Return shallow copy of self.

classmethod from_state(state, mpiroot=0, mpicomm=None)¶: Instantiate and initalize class with state dictionary.

get_header_txt(comments='#', ignore_json_errors=True)¶

Dump header:

items in attrs
shape
dims
edges
columns

Parameters

comments (string, default='#') – String to be prepended to the header lines.
ignore_json_errors (bool, default=True) – When trying to dump attrs using json, ignore errors.

Returns

header – List of strings (lines).

Return type

list

classmethod get_title_label()¶: Return title, as module.class.

has_edges()¶: Has specified edges?

classmethod load(filename, mpiroot=0, mpicomm=None)¶: Load class in numpy binary format from disk. If the loaded state contains __class__ and that exists in cls._registry, return instance of cls._registry[__class__] (instead of cls).

load_auto(*args, **kwargs)¶: If different formats are possible, this method should between them based on file name extension.

classmethod load_txt(filename, comments='#', usecols=None, skip_rows=0, max_rows=None, mapping_header=None, pattern_header=None, attrs=None, **kwargs)¶

Load BinnedStatistic from disk.

Note

If previously saved using save_txt(), loading the BinnedStatistic only requires filename. In this case, the returned instance will be of the class that was used to create it (e.g. BinnedProjection below) - not necessarily BinnedStatistic.

Parameters

filename (string) – File name to read in.
comments (string, default='#') – Characters used to indicate the start of a comment.
usecols (list, default=None) – Which columns to read, with 0 being the first.
skip_rows (int, default=0) – Skip the first skip_rows lines, including comments.
max_rows (int, default=None) – Read max_rows lines of content after skip_rows lines. The default is to read all the lines.
mapping_header (dict, default=None) – Dictionary holding key:regex mapping or (regex, type) to provide the type. The corresponding values, read in the header, will be saved in the attrs dictionary.
pattern_header (string, default=None) – A regex pattern for header with groups corresponding to key, value to add into the attrs dictionary.
attrs (dict, default=None) – Attributes to save in the attrs dictionary.
kwargs (dict) – Arguments for __init__() (other than data and attrs).

Returns

data

Return type

BinnedStatistic

property mpiattrs¶: MPI attributes

property ndim¶: Number of dimensions of binned data.

classmethod read_header_txt(file, comments='#', mapping_header=None, pattern_header=None, ignore_json_errors=True)¶

Read and decode header.

Parameters

file (list, iterator) – List of lines.
comments (string, default='#') – Characters used to indicate the start of a header line.
mapping_header (dict, default=None) – Dictionary holding key:regex mapping or (regex, type) to provide the type. Type can be unspecified (or None), in which case decoded will be tried with json, a string corresponding to __builtins__, or a callable.
pattern_header (string, default=None) – A regex pattern with groups corresponding to key:value.
ignore_json_errors (bool, default=True) – When trying to decode header values using json, ignore errors.

Returns

attrs

Return type

dict

classmethod read_title_label(line)¶: Decode title line, splitting module.class into (module, class). It loads module, then if class is in _registry, return corresponding class. Else return None.

rebin(factors, dims=None, weights=None, columns_to_sum=None)¶

Rebin data by factor factors.

Parameters

factors (dict, list) – dim: rebinning factor mapping. If list, should contain rebinning factor for each dim of dims.
dims (list, default=None) – List of dimension names. Defaults to dims.
weights (array, default=None) – Array of weights (of shape shape). If None, defaults to 1.
columns_to_sum (list) – List of columns to sum, i.e. to not renormalize after rebinning.

save(filename)¶: Save class to disk.

save_auto(*args, **kwargs)¶: If different formats are possible, this method should between them based on file name extension.

save_txt(filename=None, fmt='.18e', comments='#', ignore_json_errors=True)¶

Dump BinnedStatistic.

Parameters

filename (string, default=None) – ASCII file name where to save binned data. If None, do not write on disk.
fmt (string, default='.18e') – Floating point format.
comments (string, default='#') – String that will be prepended to the header lines.
ignore_json_errors (bool, default=True) – When trying to dump attrs using json, ignore errors.

Returns

lines – List of strings (lines).

Return type

list

set_new_edges(edges, dims=None, weights=None, columns_to_sum=None)¶

Interpolate binned data within new edges. Perform linear interpolation if edges are not a simple concatenation of current edges.

Parameters

edges (dict, list) – New dim: edges mapping. If list, should contain edges for each dim of dims.
dims (list, default=None) – List of dimension names. Defaults to dims.
weights (array, default=None) – Array of weights (of shape shape). If None, defaults to 1.
columns_to_sum (list) – List of columns to sum, i.e. to not renormalize after rebinning.

Warning

Requires edges to be set.

property shape¶: Shape of binned data; if edges, return tuple of length of edges - 1, else shape of first array in data

property size¶: Total size of binned data, i.e. product of length over all dimensions.

squeeze(dims=None)¶: Squeeze binned data along dimensions dims. If dims is None, defaults to dimensions with length <= 1. dims and edges are updated to match new shape.