Plotting GDM Models

There are two options for plotting GDM models:

1. Using the plot Function
   This returns an interactive Plotly plot with detailed information displayed on hover.

2. Plotting from a GeoDataFrame
   Export the model to a GeoDataFrame and use standard geospatial plotting libraries (e.g., GeoPandas or Matplotlib) for visualization.

We start by loading a sample distribution system.

from gdm.distribution import DistributionSystem
from gdmloader.constants import GCS_CASE_SOURCE
from gdmloader.source import SystemLoader
from IPython.display import display, HTML
import plotly.io as pio

pio.renderers.default = "notebook_connected"

gdm_loader = SystemLoader()
gdm_loader.add_source(GCS_CASE_SOURCE)

distribution_system: DistributionSystem = gdm_loader.load_dataset(
    source_name=GCS_CASE_SOURCE.name,
    system_type=DistributionSystem,
    dataset_name="p1rhs7_1247",
)
distribution_system.name = "p1rhs7_1247"
distribution_system.info()

System                                
┏━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓
┃ Property             ┃       Value ┃
┡━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩
│ System name          │ p1rhs7_1247 │
│ Data format version  │       2.1.4 │
│ Components attached  │       13370 │
│ Time Series attached │           0 │
│ Description          │             │
└──────────────────────┴─────────────┘

Component Information                       
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┓
┃ Type                             ┃ Count ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━┩
│ DistributionBus                  │  2510 │
│ DistributionLoad                 │  1896 │
│ DistributionTransformer          │   503 │
│ DistributionTransformerEquipment │    10 │
│ DistributionVoltageSource        │     1 │
│ LoadEquipment                    │  1896 │
│ Location                         │  2510 │
│ MatrixImpedanceBranch            │  1841 │
│ MatrixImpedanceBranchEquipment   │    38 │
│ MatrixImpedanceFuse              │    81 │
│ MatrixImpedanceFuseEquipment     │     6 │
│ MatrixImpedanceSwitch            │    84 │
│ MatrixImpedanceSwitchEquipment   │    15 │
│ PhaseLoadEquipment               │  1948 │
│ PhaseVoltageSourceEquipment      │     3 │
│ TimeCurrentCurve                 │     1 │
│ VoltageLimitSet                  │     8 │
│ VoltageSourceEquipment           │     1 │
│ WindingEquipment                 │    18 │
└──────────────────────────────────┴───────┘

Plotting using Plotly

fig = distribution_system.plot(
    flip_coordinates=True,
    show=False, 
)
display(HTML(pio.to_html(fig, include_plotlyjs="cdn", full_html=False)))

from gdm.distribution.enums import MapType, PlotingStyle

fig = distribution_system.plot(
    map_type=MapType.SCATTER_MAP, 
    style=PlotingStyle.OPEN_STREET_MAP, 
    flip_coordinates=True,
    show=False
)
display(HTML(pio.to_html(fig, include_plotlyjs="cdn", full_html=False)))

distribution_system.plot(
    map_type=MapType.SCATTER_MAP, 
    style=PlotingStyle.CARTO_DARKMATTER,  
    flip_coordinates=True,
    show=False
)
display(HTML(pio.to_html(fig, include_plotlyjs="cdn", full_html=False)))

fig = distribution_system.plot(
    map_type=MapType.SCATTER_MAP, 
    style=PlotingStyle.CARTO_POSITRON, 
    flip_coordinates=True,
    show=False
)
display(HTML(pio.to_html(fig, include_plotlyjs="cdn", full_html=False)))

Distribution system plots can be styled by passing plotting options to the function to customize their appearance.

from gdm.distribution.enums import ColorNodeBy, ColorLineBy

distribution_system.plot(
    color_node_by=ColorNodeBy.VOLTAGE_LEVEL, 
    color_line_by=ColorLineBy.PHASE,
    flip_coordinates=True,
)
display(HTML(pio.to_html(fig, include_plotlyjs="cdn", full_html=False)))

## Plotting using Matplotlib

df = distribution_system.to_gdf()
df = df[df["Type"] != "DistributionBus"]
df.plot()

<Axes: >