Plotting Module¶

This page has been pre-filled with the functions that are available in the Plotting module by AI, please check the code for more details.

This module provides functions for visualizing grid components and results.

functions are found in pyflow_acdc.Graph_and_plot

Time Series¶

Time series results¶

This function is used to plot the time series results of the grid.

plot_TS_res(grid, start, end, plotting_choices=None, show=True, path=None, save_format=None, skip_failed=False)[source]¶

Creates plots for time series results. The possible plotting choices are:

‘Power Generation by price zone’
‘Power Generation by generator’
‘Curtailment’
‘Market Prices’
‘AC line loading’
‘DC line loading’
‘AC/DC Converters’
‘Power Generation by generator area chart’
‘Power Generation by price zone area chart’

Parameter	Type	Description	Default
`grid`	Grid	Grid with results	Required
`start`	int	Start timeframe	Required
`end`	int	End timeframe	Required
`plotting_choices`	list	Results types to plot	All
`show`	bool	Whether to show the plot in browser	True
`path`	str	Path to save the plot	Current working directory
`save_format`	str	Format to save the plot, if None, the plot are not saved	None

Example

import pyflow_acdc as pyf
import pandas as pd
from pyflow_tests.test_constants import NS_MTDC_MARKET_PRICES_URL, NS_MTDC_WIND_LOAD_URL
[grid,results] = pyf.cases['NS_MTDC']()
start = 5750
end = 6000
obj = {'Energy_cost': 1}
TS_MK = pd.read_csv(NS_MTDC_MARKET_PRICES_URL)
pyf.add_TimeSeries(grid,TS_MK)
TS_wl = pd.read_csv(NS_MTDC_WIND_LOAD_URL)
pyf.add_TimeSeries(grid,TS_wl)
build_only = not pyf.is_pyomo_solver_available("ipopt")
times=pyf.ts_acdc_opf(grid,start,end,ObjRule=obj,build_only=build_only)
#show is set to False for testing suit, change it to True to see the plot
pyf.plot_TS_res(grid, start, end, save_format='svg', show=False)

Plot shown in browser

Time Series Plot: Power Generation by price zone

Plot saved in current working directory

Time series probability¶

This function is used to plot the probability of the time series parameters or results of the grid. Results currently available are:

‘Power Generation by generator’
‘Prices by price zone’
‘AC line loading’
‘DC line loading’
‘AC/DC Converters loading’

time_series_prob(grid, element_name, save_format=None, path=None, show=True)[source]¶

Plot the PDF/CDF of a time-series input or OPF result column.

Parameters:

grid (Grid) – Grid with Time_series and/or time_series_results populated.
element_name (str) – Time_series name or column in merged OPF result tables.
save_format (str, optional) – File format extension (e.g. 'svg'). If omitted, nothing is saved.
path (str, optional) – Directory for saved figures; defaults to the current working directory.
show (bool, optional) – Open an interactive matplotlib window after plotting.

Examples

>>> import pyflow_acdc as pyf
>>> pyf.time_series_prob(grid, 'OWPP_BE', save_format='svg', show=False)

Parameter	Type	Description	Default
`grid`	Grid Class	Grid to analyze	Required
`element_name`	str	Name of element to analyze	Required
`save_format`	str	Format to save the plot, if None, the plot are not saved	None
`path`	str	Path to save the plot	Current working directory

Example

import pyflow_acdc as pyf
import pandas as pd
from pyflow_tests.test_constants import NS_MTDC_MARKET_PRICES_URL, NS_MTDC_WIND_LOAD_URL
[grid,results] = pyf.cases['NS_MTDC']()
start = 5750
end = 6000
obj = {'Energy_cost': 1}
TS_MK = pd.read_csv(NS_MTDC_MARKET_PRICES_URL)
pyf.add_TimeSeries(grid,TS_MK)
TS_wl = pd.read_csv(NS_MTDC_WIND_LOAD_URL)
pyf.add_TimeSeries(grid,TS_wl)
#show is set to False for testing suit, change it to True to see the plot
pyf.time_series_prob(grid, 'OWPP_BE', show=False)
pyf.time_series_prob(grid, 'BE_price', show=False)
pyf.time_series_prob(grid, 'L_BE', show=False)

Network Graph Visualization¶

Full grid visualization as a network graph¶

plot_graph(Grid, text='inPu', base_node_size=10, G=None, show=True)[source]¶

Creates an interactive network graph visualization using Plotly.

Parameter	Type	Description	Default
`Grid`	Grid	Grid to visualize	Required
`text`	str	Hover text format (‘data’ or ‘inPu’ or ‘abs’)	‘inPu’
`base_node_size`	int	Base size for nodes	10
`G`	Graph	Graph to visualize	Full grid

Example

import pyflow_acdc as pyf
grid,res = pyf.cases['case24_3zones_acdc']()
#show is set to False for testing suit, change it to True to see the plot
pyf.plot_graph(grid,show=False)

Neighbor Graph¶

This function is used to plot the neighbor graph of a node. You can either provide a node or a node name, one or the other must be provided.

plot_neighbour_graph(grid, node=None, base_node_size=10, proximity=1, show=True)[source]¶

Plot the ego graph of a node and its neighbours.

Builds a subgraph within proximity hops of node on grid.Graph_toPlot and opens an interactive Plotly view via plot_graph().

Parameters:

grid (Grid) – Grid whose topology is plotted.
node (Node_AC, Node_DC, or str, optional) – Centre node object, or its name string (searched on AC then DC nodes).
base_node_size (int, optional) – Base marker size passed to plot_graph().
proximity (int, optional) – Hop radius for networkx.ego_graph().

Raises:

ValueError – If node is omitted, not found, or absent from grid.Graph_toPlot.

Examples

>>> import pyflow_acdc as pyf
>>> grid, _ = pyf.cases['case24_3zones_acdc']()
>>> pyf.plot_neighbour_graph(grid, node='111')

Example

import pyflow_acdc as pyf
grid,res = pyf.cases['case24_3zones_acdc']()

pyf.plot_neighbour_graph(grid, node='111',show=False)

case24_3zones_acdc neighbour graph of node 111

Saving the Network Graph¶

For this function, you need to have the svgwrite library installed. You can install it using pip install svgwrite. geometry of objects is required.

save_network_svg(grid, name='grid_network', width=1000, height=800, journal=True, legend=True, square_ratio=False, poly=None, linestrings=None, coloring=None, poly_size=None, tee=False, line_size_factor=1.0, draw_converters=True, scale_ac_nodes_with_rs=False, node_size_factor=1.0, scale_dc_nodes_with_conv=False, dc_node_size_factor=1.0)[source]¶

Save the network as SVG file

Parameters:¶

square_ratiobool: If True, expands the data bounds so both axes span the same range, then scales uniformly. If False, uses the true data extent and one scale factor for both x and y (one data unit in x equals one data unit in y on the figure); the SVG size is then fitted to that extent.
poly_sizetuple or None: If provided and poly is not None, specifies the target size (width, height) in pixels for the polygon. Everything else will be scaled to fit this polygon size. Format: (target_width, target_height) in pixels.
draw_convertersbool: If False, converter line segments are not drawn.
scale_ac_nodes_with_rsbool: If True, AC node marker radius is increased when connected renewable sources exist. The increase scales with total connected np_rsgen and node_size_factor.
node_size_factorfloat: Multiplier used for AC node marker scaling with renewable source multiplicity. Must be >= 0.
scale_dc_nodes_with_convbool: If True, DC node marker radius is increased based on total connected converter np_conv and dc_node_size_factor.
dc_node_size_factorfloat: Multiplier used for DC node marker scaling with connected converter multiplicity. Must be >= 0.

Saves the network graph as an SVG file.

Parameter	Type	Description	Default
`grid`	Grid	Grid to save	Required
`name`	str	Name of the file	‘grid_network’
`width`	int	Width of the file	1000
`height`	int	Height of the file	800

Example

import pyflow_acdc as pyf
grid,res = pyf.cases['NS_MTDC']()
pyf.save_network_svg(grid)

Solver Diagnostics¶

plot_model_feasibility(solver_stats, sol='all', x_axis='time', y_axis='objective', normalize=False, show=True, save_path=None, width_mm=None)[source]¶: Plots solver feasible-solution progress and objective evolution.

3D Grid Plot¶

plot_3D(grid, show=True, save_path=None, coloring='cable_type', line_width=6, node_size=6, title=None, show_unused=False, poly=None, coords_lonlat=False, elevation_grid=None, show_elevation_surface=True, show_elevation_points=False, elevation_opacity=0.35, elevation_colorscale='Viridis', show_verticals=1.0, dev_area=None)[source]¶

Plot the grid network in 3D using plotly.

When coords_lonlat=True (default), node positions and LineString geometries are assumed to be in lon/lat and are converted to local meters so that X, Y and Z (elevation) share the same unit. Set coords_lonlat=True when coordinates are in lon/lat.

Parameters:

grid (Grid object) – Must contain lines_AC_ct with 3D geometry (has_z=True).
show (bool) – Whether to display the figure interactively in the browser.
save_path (str or None) – If given, save the figure as an HTML file.
coloring (str) – ‘cable_type’ colours cables by active_config; ‘loading’ colours by cable loading percentage.
line_width (float) – Width of cable traces.
node_size (float) – Marker size for nodes.
title (str or None) – Figure title.
show_unused (bool) – If True, draw unused cables (active_config < 0) as thin grey lines.
poly (shapely Polygon/MultiPolygon or list, optional) – Development area polygon(s) to draw on the z=0 plane.
coords_lonlat (bool) – If True, convert lon/lat to local meters via equirectangular projection. If False, assume X/Y are already in meters.
elevation_grid (pandas.DataFrame or dict, optional) – Optional set of elevation points to plot as a surface/plane. Expected columns/keys: x, y, elevation. Coordinates are assumed to be in the same system as the plot (if coords_lonlat=True, pass lon/lat so they are converted too).
show_elevation_surface (bool) – If True (default) and elevation_grid is provided, draw a triangulated surface (Mesh3d) colored by elevation.
show_elevation_points (bool) – If True, also plot the elevation points as markers.
elevation_opacity (float) – Opacity for the elevation surface.
elevation_colorscale (str or list) – Plotly colorscale for the elevation surface/points.
show_verticals (float) – Opacity of vertical cable segments from 0 (hidden) to 1 (fully visible).

Generates a 3D network visualization of the grid and selected element attributes.

Example

import pyflow_acdc as pyf
from pyflow_tests.test_constants import CABLE_TYPES_OFF66, MORAY_EAST_CABLE_DECISIONS

grid, res = pyf.cases["moray_east"]()
grid.cab_types_allowed = len(CABLE_TYPES_OFF66)
cable_index = {name: idx for idx, name in enumerate(CABLE_TYPES_OFF66)}
for line in grid.lines_AC_ct:
    line.cable_types = list(CABLE_TYPES_OFF66)
    line.active_config = cable_index.get(MORAY_EAST_CABLE_DECISIONS.get(str(line.name), ""), -1)
#show is set to False for testing suit, change it to True to see the plot
pyf.plot_3D(grid, show=False)