A Logarithmic Colorbar in Matplotlib Scatter Plot

A logarithmic colorbar in matplotlib scatter plot

There is now a section of the documentation describing how color mapping and normalization works

The way that matplotlib does color mapping is in two steps, first a Normalize function (wrapped up by the sub-classes of matplotlib.colors.Normalize) which maps the data you hand in to [0, 1]. The second step maps values in [0,1] -> RGBA space.

You just need to use the LogNorm normalization class, passed in with the norm kwarg.

plt.scatter(x,y,edgecolors='none',s=marker_size,c=void_fraction,
                norm=matplotlib.colors.LogNorm())

When you want to scale/tweak data for plotting, it is better to let matplotlib do the transformations than to do it your self.

• Normalize doc
• LogNorm doc
• matplotlib.color doc

Logarithmic colorbar?

The answer to the question below would help (this seems to be a duplicate question though);

A logarithmic colorbar in matplotlib scatter plot

Matplotlib also has a dedicated section for colormap normalization;

https://matplotlib.org/users/colormapnorms.html

For your question, you would want to use stored value like following;

pcm = plt.pcolor(self._data, norm = colors.LogNorm())

plt.colorbar(pcm)

Can I plot a colorbar from a list of colors and for loop?

Matplotlib's colorbar needs a ScalarMappable object. By default, it is taken from what's plotted, e.g. a scatter plot that is created in one call. If you need to combine the results of multiple calls, you can create an own ScalarMappable. It needs a colormap and a norm.

from matplotlib import pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
from matplotlib.cm import ScalarMappable
import numpy as np

cycles = 7
N = 100
KL_rest = np.sin(np.linspace(0, 10 * np.pi, cycles * N))
scanpoints = np.arange(N)
cm = LinearSegmentedColormap.from_list('defcol', ["#000000", "#FF0000"])
trace_color = cm(np.linspace(0, 1, cycles))
for k, color in zip(range(cycles), trace_color):
    lis = KL_rest[k::cycles]
    plt.scatter(scanpoints, lis, color=color, marker='^', alpha=0.9)
plt.colorbar(ScalarMappable(cmap=cm, norm=plt.Normalize(0, cycles - 1)), ticks=np.arange(cycles), label='cycles')
plt.show()

Note that in this case, you can create the scatter plot in one go, enabling a default color bar. For this to work, the scanpoints can be repeated for each cycle, and the colors can be indicated by tiling the cycle for each scan point.

If you only want to show the really used colors, you can add N=cycles in the creation of the color map. To put the tick marks for each number in the center of the cells, you can move the default limits by 0.5.

from matplotlib import pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
import numpy as np

cycles = 7
N = 100
KL_rest = np.sin(np.linspace(0, 10 * np.pi, cycles * N))
scanpoints = np.arange(N)
cm = LinearSegmentedColormap.from_list('defcol', ["#000000", "#FF0000"], N=cycles)

plt.scatter(np.repeat(scanpoints, cycles), KL_rest,
            c=np.tile(range(cycles), len(scanpoints)),
            cmap=cm, norm=plt.Normalize(-0.5, cycles - 0.5),
            marker='^', alpha=0.9)
plt.colorbar(ticks=np.arange(cycles), label='cycles')
plt.show()

Python Logarithmic Colormap/palette

a = np.array([[1,2,3],[4,5,6],[7,8,9]])
plt.imshow(a, norm=matplotlib.colors.LogNorm(vmin=a.min(), vmax=a.max()))
plt.colorbar()

If you have values that are not strictly positive, you need to mask them first, here replacing them by nan:

a = np.array([[-1,0,1],[4,5,6],[7,8,9]])
b = np.where(a>0,a,np.nan)

cmap = plt.cm.get_cmap("viridis")
cmap.set_bad("magenta")

plt.imshow(b, norm=matplotlib.colors.LogNorm(vmin=np.nanmin(b), vmax=np.nanmax(b)), cmap=cmap)
plt.colorbar()

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