netallocation.common module

Created on Wed Dec 18 17:46:45 2019

@author: fabian

class netallocation.common.AllocationAccessor(xarray_obj)[source]

Bases: object

Accessor for netallocation package.

This accessor enables to call several functions to modify, plot and store the allocation dataset.

as_dense()

Convert sparse dataset/dataarray into a dense dataset.

Parameters

ds (xr.Dataset or xr.DataArray) –

Returns

Dataset or DataArray with dense data.

Return type

xr.Dataset or xr.DataArray

as_sparse()

Convert dense dataset/dataarray into a sparse dataset.

Parameters

ds (xr.Dataset or xr.DataArray) –

Returns

Dataset or DataArray with sparse data.

Return type

xr.Dataset or xr.DataArray

by_carriers(n, chunksize=None)

Breakdown allocation into production and demand carriers.

Use this funtion to breakdown the share of single production carriers (carriers of generators, storage units, stores) and demand carriers (carriers of loads, storage units, stores). Note that carrier names of all components have to be unique. The funtion will return the a dataset or dataarray with two additional dimensions, source_carrier and sink_carrier.

Parameters

  • ds (xarray.Dataset) – Allocation Data with dimension sink and source

  • n (pypsa.Network) – Network which the allocation was derived from

  • chunksize (int) – Chunksize of the snapshot chunks passed to dask for computing faster and with less memory usage for large datasets.

Example

>>> ap = ntl.flow_allocation(n, n.snapshots, method='ap')
>>> ntl.breakdown.carriers(ap, n)
filter_null(dim=None)

Drop all coordinates with only null/nan entries on dimensions dim.

lower()

Clip all positive entries of a xr.Dataset/xr.DataArray.

plot_chord_diagram(agg='mean', minimum_quantile=0, groups=None, size=200, pallette='Category20', fig_inches=4)

Build a chord diagram on the base of holoviews [1].

It visualizes allocated peer-to-peer flows for all buses given in the data. As for compatibility with ipython shell the rendering of the image is passed to matplotlib however to the disfavour of interactivity. Note that the plot becomes only meaningful for networks with N > 5, because of sparse flows otherwise.

[1] http://holoviews.org/reference/elements/bokeh/Chord.html

Parameters

  • allocation (xarray.Dataset) – Dataset with ‘peer_to_peer’ variable.

  • lower_bound (int, default is 0) – filter small power flows by a lower bound

  • groups (pd.Series, default is None) – Specify the groups of your buses, which are then used for coloring. The series must contain values for all allocated buses.

  • size (int, default is 300) – Set the size of the holoview figure

  • save_path (str, default is '/tmp/chord_diagram_pypsa') – set the saving path of your figure

upper()

Clip all negative entries of a xr.Dataset/xr.DataArray.

vip_to_p2p(n, direct=False)
virtual_patterns(n, q=0.5)

Convert a peer-on-branch-to-peer into a virtual flow/injection pattern.

If a virtual flow pattern array is already existent, nothing is done.

Parameters

  • ds (xarray.Dataset or xarray.DataArray) –

  • n (pypsa.Network) – Underlying network. This is needed for determining the Incidence matrix.

  • q (float in [0,1]) – Ratio of net source and net sink contributions in the virtual patterns. If q=0 then only net importers are taken into account (if q=1 only net exporters).

Returns

  • A xarray.Dataset with the virtual flow pattern variable appended if a

  • Dataset was passed, passes the converted DataArray if a DataArray was passed.