Circos Plot¶

Quick Plot¶

Circos plot is a visualization chart used to display connectivity relationships between different brain regions. By connecting brain regions with arcs, you can quickly understand the connections between brain regions.

from plotfig import plot_circos_figure
from plotfig.utils import gen_symmetric_matrix

# Randomly generate symmetric weighted matrix (diagonal is 0)
connectome = gen_symmetric_matrix(30, mode="nonneg", sparsity=0.1)

# Plot
fig = plot_circos_figure(connectome)

# Save image
# fig.savefig("./figures/circos1.png")

png

Parameter Settings¶

For all parameters, see the API documentation for plot_circos_figure.

from plotfig import plot_circos_figure
from plotfig.utils import gen_symmetric_matrix

# Randomly generate a 10x10 symmetric weighted matrix (diagonal is 0)
connectome = gen_symmetric_matrix(10, mode="nonneg", sparsity=0.2)
node_names = ["lh_A", "lh_B", "lh_C", "lh_D", "lh_E", "rh_A", "rh_B", "rh_C", "rh_D", "rh_E"]
node_colors = ["#ff0000", "blue", "green", "yellow", "orange", "red", "blue", "green", "yellow", "orange"]

# Plot
fig = plot_circos_figure(
    connectome,
    symmetric=True,
    node_names=node_names,
    node_colors=node_colors,
    node_space=2,
    node_label_fontsize=15,
    vmin=0.1,
    vmax=0.9,
    edge_color="purple",
    edge_alpha=0.8,
    colorbar=True,
    colorbar_orientation="horizontal",
    colorbar_label="Conncetivity",
)

# Save image
# fig.savefig("./figures/circos.png")

png

Combining with Other Plots¶

By default, the plot_circos_figure function returns a fig, which can be directly used for saving, such as fig.savefig("./figures/circos.png").

In special cases, we can also return ax to combine it with other plots.

import matplotlib.pyplot as plt
from plotfig import plot_circos_figure
from plotfig.utils import gen_symmetric_matrix

fig = plt.figure(figsize=(6, 3))

ax1 = fig.add_subplot(1, 2, 1)
ax1.plot([1, 2, 3, 4], [2, 1, 4, 3])

ax2 = fig.add_subplot(1, 2, 2, projection="polar")
connectome = gen_symmetric_matrix(10, mode="nonneg", sparsity=0.1)
ax2 = plot_circos_figure(connectome, ax=ax2)

# Save image
# fig.savefig("./figures/circos.png")

png

Symmetric and Asymmetric Circos Plots¶

plotfig can plot both symmetric and asymmetric styles of circos plots. Simply set it through the symmetric parameter.

import matplotlib.pyplot as plt
from plotfig import plot_circos_figure
from plotfig.utils import gen_symmetric_matrix

fig = plt.figure(figsize=(7, 3))
ax1 = fig.add_subplot(1, 2, 1, projection="polar")
ax2 = fig.add_subplot(1, 2, 2, projection="polar")

connectome = gen_symmetric_matrix(10, mode="nonneg", sparsity=0.1)

ax1 = plot_circos_figure(connectome, symmetric=True, ax=ax1, colorbar=False)
ax2 = plot_circos_figure(connectome, symmetric=False, ax=ax2)

# Save image
# fig.savefig("./figures/circos.png")

png

Edge Colors¶

The edge_color parameter can be used to set the color of edges, but regardless, the shade of edges will still be automatically adjusted based on connection weights.

import matplotlib.pyplot as plt
from plotfig import plot_circos_figure
from plotfig.utils import gen_symmetric_matrix

fig = plt.figure(figsize=(12, 3), layout="constrained")
ax1 = fig.add_subplot(1, 3, 1, projection="polar")
ax2 = fig.add_subplot(1, 3, 2, projection="polar")
ax3 = fig.add_subplot(1, 3, 3, projection="polar")

connectome = gen_symmetric_matrix(10, mode="nonneg", sparsity=0.1)

ax1 = plot_circos_figure(connectome, ax=ax1, edge_color="red")
ax2 = plot_circos_figure(connectome, ax=ax2, edge_color="green")
ax3 = plot_circos_figure(connectome, ax=ax3, edge_color="blue")

# Save image
# fig.savefig("./figures/circos.png")

png

You can also apply Matplotlib's built-in common color maps (Colormap) through the cmap parameter.

Warning

When using cmap, the edge_color parameter will no longer take effect.

import matplotlib.pyplot as plt
from plotfig import plot_circos_figure
from plotfig.utils import gen_symmetric_matrix

fig = plt.figure(figsize=(12, 3), layout="constrained")
ax1 = fig.add_subplot(1, 3, 1, projection="polar")
ax2 = fig.add_subplot(1, 3, 2, projection="polar")
ax3 = fig.add_subplot(1, 3, 3, projection="polar")

connectome = gen_symmetric_matrix(10, mode="nonneg", sparsity=0.1)

ax1 = plot_circos_figure(connectome, ax=ax1, cmap="Reds")
ax2 = plot_circos_figure(connectome, ax=ax2, cmap="viridis")
ax3 = plot_circos_figure(connectome, ax=ax3, cmap="bwr")

# Save image
# fig.savefig("./figures/circos.png")

png

When negative values exist in the connectome data, edge colors cannot be customized, and the system will default to using Matplotlib's bwr color map.

from plotfig import plot_circos_figure
from plotfig.utils import gen_symmetric_matrix

# Generate symmetric matrix with negative values
connectome = gen_symmetric_matrix(10, mode="all", sparsity=0.1)

fig = plot_circos_figure(connectome)

# Save image
# fig.savefig("./figures/circos.png")

2025-09-05 15:09:37.347 | WARNING  | plotfig.circos:plot_circos_figure:116 - Due to negative values in connectome, connection colors cannot be customized; positive values will be displayed in red and negative values in blue

png