Visualization

Using cyclic colormaps

import matplotlib.pyplot as plt
import numpy as np
import xarray as xr

from quantify_core.visualization.mpl_plotting import set_cyclic_colormap

zvals = xr.DataArray(np.random.rand(6, 10) * 360)
zvals.attrs["units"] = "deg"
zvals.plot()

<matplotlib.collections.QuadMesh at 0x7c587bbd0f40>

fig, ax = plt.subplots(1, 1)
color_plot = zvals.plot(ax=ax)
set_cyclic_colormap(color_plot)

zvals_shifted = zvals - 180

fig, ax = plt.subplots(1, 1)
color_plot = zvals_shifted.plot(ax=ax)
ax.set_title("Shifted cyclic colormap")
set_cyclic_colormap(color_plot, shifted=zvals_shifted.min() < 0)

fig, ax = plt.subplots(1, 1)
color_plot = (zvals / 2).plot(ax=ax)
ax.set_title("Overwrite clim")
set_cyclic_colormap(color_plot, clim=(0, 180), unit="deg")

fig, ax = plt.subplots(1, 1)
zvals_rad = zvals / 180 * np.pi
zvals_rad.attrs["units"] = "rad"
color_plot = zvals_rad.plot(ax=ax)
ax.set_title("Radians")
set_cyclic_colormap(color_plot, unit=zvals_rad.units)

Creating custom colormaps

In this example we use this function to create a custom colormap using several base colors for which we adjust the saturation and transparency (alpha, only visible when exporting the image).

import colorsys

import matplotlib.colors as mplc
import matplotlib.pyplot as plt
import numpy as np

from quantify_core.visualization.color_utilities import set_hlsa

color_cycle = ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728"]
all_colors = []
for col in color_cycle:
    hls = colorsys.rgb_to_hls(*mplc.to_rgb(mplc.to_rgb(col)))
    sat_vals = (np.linspace(0.0, 1.0, 20) ** 2) * hls[2]
    alpha_vals = np.linspace(0.4, 1.0, 20)

    colors = [
        list(set_hlsa(col, s=s)) for s, a in zip(sat_vals, alpha_vals)
    ]
    all_colors += colors

cmap = mplc.ListedColormap(all_colors)

np.random.seed(19680801)
data = np.random.randn(30, 30)

fig, ax = plt.subplots(1, 1, figsize=(6, 3), constrained_layout=True)

psm = ax.pcolormesh(data, cmap=cmap, rasterized=True, vmin=-4, vmax=4)
fig.colorbar(psm, ax=ax)
plt.show()