Plotting Variables and Blocks¶

This notebook demonstrates the built-in plotting features of pySMSpp for inspecting model data. Both Variable and Block objects expose a plot() method that automatically selects an appropriate chart type based on the data dimensionality:

Variable shape	Default plot
scalar (0-D)	bar chart
1-D array	line plot
2-D array	heatmap (kind="heatmap") or line plot with legend (kind="line")

In [1]:

import numpy as np
from pysmspp import Block, Variable

import numpy as np from pysmspp import Block, Variable

Plotting a Single Variable¶

Scalar Variable¶

A scalar variable (0-D data) is displayed as a bar chart.

In [2]:

scalar_var = Variable("MaxPower", "float", (), 100.0)
ax = scalar_var.plot()
ax.set_ylabel("kW")

scalar_var = Variable("MaxPower", "float", (), 100.0) ax = scalar_var.plot() ax.set_ylabel("kW")

Matplotlib is building the font cache; this may take a moment.

Out[2]:

Text(0, 0.5, 'kW')

1-D Variable¶

A 1-D variable is displayed as a line plot. The first (and only) dimension name is used as the x-axis label.

In [3]:

time_steps = 24
demand_1d = np.abs(np.sin(np.linspace(0, 2 * np.pi, time_steps))) * 80 + 20

var_1d = Variable("ActivePowerDemand", "float", ("TimeHorizon",), demand_1d)
ax = var_1d.plot()
ax.set_ylabel("kW")

time_steps = 24 demand_1d = np.abs(np.sin(np.linspace(0, 2 * np.pi, time_steps))) * 80 + 20 var_1d = Variable("ActivePowerDemand", "float", ("TimeHorizon",), demand_1d) ax = var_1d.plot() ax.set_ylabel("kW")

Out[3]:

Text(0, 0.5, 'kW')

2-D Variable – Heatmap (default)¶

A 2-D variable is displayed as a heatmap by default (kind="heatmap"). The first dimension labels the y-axis and the second dimension labels the x-axis.

In [4]:

n_nodes = 3
demand_2d = np.outer(np.array([1.0, 0.8, 0.6]), demand_1d)

var_2d = Variable(
    "ActivePowerDemand",
    "float",
    ("NumberNodes", "TimeHorizon"),
    demand_2d,
)
ax = var_2d.plot()  # kind="heatmap" is the default

n_nodes = 3 demand_2d = np.outer(np.array([1.0, 0.8, 0.6]), demand_1d) var_2d = Variable( "ActivePowerDemand", "float", ("NumberNodes", "TimeHorizon"), demand_2d, ) ax = var_2d.plot() # kind="heatmap" is the default

2-D Variable – Line Plot with Legend¶

Pass kind="line" to get a line plot where each row becomes a separate labelled line.

In [5]:

ax = var_2d.plot(kind="line")
ax.set_ylabel("kW")

ax = var_2d.plot(kind="line") ax.set_ylabel("kW")

Out[5]:

Text(0, 0.5, 'kW')

Plotting All Variables in a Block¶

Block.plot() creates one subplot per variable and arranges them vertically in a single figure. Scalar variables are skipped by default (they contain no time-series information).

In [6]:

block = Block()
block.block_type = "UCBlock"

# 1-D variables
block.add_variable(
    "ActivePowerDemand",
    "float",
    ("TimeHorizon",),
    demand_1d,
)
block.add_variable(
    "MaxPowerFlow",
    "float",
    ("NumberLines",),
    np.array([100.0, 80.0, 60.0]),
)

# Scalar – skipped unless explicitly requested
block.add_variable("NumberUnits", "int", (), 3)

fig = block.plot()

block = Block() block.block_type = "UCBlock" # 1-D variables block.add_variable( "ActivePowerDemand", "float", ("TimeHorizon",), demand_1d, ) block.add_variable( "MaxPowerFlow", "float", ("NumberLines",), np.array([100.0, 80.0, 60.0]), ) # Scalar – skipped unless explicitly requested block.add_variable("NumberUnits", "int", (), 3) fig = block.plot()

Selecting Specific Variables¶

Pass a list of variable names to variables to control which variables are plotted.

In [7]:

fig = block.plot(variables=["ActivePowerDemand"])

fig = block.plot(variables=["ActivePowerDemand"])

Plotting a 2-D Block Variable as Lines¶

Extra keyword arguments are forwarded to each Variable.plot() call, so you can use kind="line" to switch all 2-D subplots to line-plot mode.

In [8]:

block2d = Block()
block2d.block_type = "UCBlock"
block2d.add_variable(
    "ActivePowerDemand",
    "float",
    ("NumberNodes", "TimeHorizon"),
    demand_2d,
)

# Heatmap (default)
fig_heatmap = block2d.plot()

block2d = Block() block2d.block_type = "UCBlock" block2d.add_variable( "ActivePowerDemand", "float", ("NumberNodes", "TimeHorizon"), demand_2d, ) # Heatmap (default) fig_heatmap = block2d.plot()

In [9]:

# Line plot with per-node legend
fig_line = block2d.plot(kind="line")

# Line plot with per-node legend fig_line = block2d.plot(kind="line")