Plotting

dhybridrpy provides built-in plotting capabilities for quick visualization of simulation data.

Basic Plotting

All data objects (Field, Phase) have a plot() method:

import matplotlib.pyplot as plt

Bx = dpy.timestep(1).fields.Bx()
Bx.plot()
plt.show()

The plot method automatically detects the dimensionality of your data and creates the appropriate plot type.

1D Data

For 1D data, a line plot is created:

# 1D field data
field_1d = dpy.timestep(1).fields.Bx()
ax, line = field_1d.plot()
plt.show()

2D Data

For 2D data, a pseudocolor plot (pcolormesh) is created:

# 2D field data
Bx = dpy.timestep(1).fields.Bx()
ax, mesh = Bx.plot(
    colormap="viridis",
    show_colorbar=True
)
plt.show()

3D Data

For 3D data, an interactive slice viewer is created with a slider:

# 3D field data - interactive slice viewer
Bx = dpy.timestep(1).fields.Bx()
ax, mesh = Bx.plot(
    slice_axis="x"  # Slice along x-axis
)
plt.show()

Use the slider to navigate through slices along the chosen axis.

Slice Axis Options

• slice_axis="x": View y-z planes at different x positions
• slice_axis="y": View x-z planes at different y positions
• slice_axis="z": View x-y planes at different z positions

Plot Customization

All Plot Options

Bx.plot(
    # Matplotlib Axes to use (creates new figure if None)
    ax=None,

    # Figure resolution
    dpi=100,

    # Labels and title
    title="Custom Title",
    xlabel="X Label",
    ylabel="Y Label",
    zlabel="Z Label",  # For 3D data

    # Axis limits
    xlim=(0, 10),
    ylim=(0, 5),
    zlim=(0, 5),  # For 3D data

    # Colormap (2D/3D only)
    colormap="viridis",

    # Colorbar options (2D/3D only)
    show_colorbar=True,
    colorbar_label="Field Value",

    # 3D slice options
    slice_axis="x",  # "x", "y", or "z"
)

Available Colormaps

Any matplotlib colormap can be used:

# Sequential
Bx.plot(colormap="viridis")
Bx.plot(colormap="plasma")
Bx.plot(colormap="inferno")

# Diverging (good for fields with positive/negative values)
Bx.plot(colormap="RdBu")
Bx.plot(colormap="coolwarm")
Bx.plot(colormap="seismic")

Subplots

Combine multiple plots in a figure:

import matplotlib.pyplot as plt

fig, axes = plt.subplots(1, 3, figsize=(15, 4))

Bx = dpy.timestep(1).fields.Bx()
By = dpy.timestep(1).fields.By()
Bz = dpy.timestep(1).fields.Bz()

Bx.plot(ax=axes[0], title="Bx")
By.plot(ax=axes[1], title="By")
Bz.plot(ax=axes[2], title="Bz")

plt.tight_layout()
plt.show()

Plotting Derived Quantities

Plot results of arithmetic operations:

import numpy as np

Bx = dpy.timestep(1).fields.Bx()
By = dpy.timestep(1).fields.By()
Bz = dpy.timestep(1).fields.Bz()

# Calculate and plot magnetic field magnitude
B_mag = np.sqrt(Bx**2 + By**2 + Bz**2)
B_mag.plot(title="|B|")
plt.show()

Time Series Animation

Create animations across timesteps:

import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation

fig, ax = plt.subplots()

def update(ts):
    ax.clear()
    Bx = dpy.timestep(ts).fields.Bx()
    Bx.plot(ax=ax)
    return ax,

ani = FuncAnimation(
    fig, update, 
    frames=dpy.timesteps(), 
    interval=200
)
plt.show()

Saving Plots

Bx = dpy.timestep(1).fields.Bx()
Bx.plot()
plt.savefig("Bx_plot.png", dpi=300, bbox_inches="tight")

Plot Return Values

The plot() method returns the axes and plot object for further customization:

ax, mesh = Bx.plot()

# Further customize
mesh.set_clim(-1, 1)  # Set color limits
ax.set_aspect('equal')  # Set aspect ratio