Artificial tick labels for seaborn heatmaps

Question

I have a seaborn heatmap that looks like this:

...generated from a pandas dataframe of randomly generated values a piece of which looks like this:

The values along the y axis are all in the range [0,1], and the ones on the x axis in the range [0,2*pi], and I just want some short floats at regular intervals for my tick labels, but I can only seem to get values that are in my dataframe. When I try specifying the values I want, it doesn't put them in the right place, as seen in the plot above. He's my code right now. How can I get the axis labels that I tried specifying with xticks and yticks in this code in the correct places (which would be evenly spaced along the axes)?

import pandas as pd
import numpy as np
import matplotlib as plt
from matplotlib.mlab import griddata

sns.set_style("darkgrid")
PHI, COSTH = np.meshgrid(phis, cos_thetas)
THICK = griddata(phis, cos_thetas, thicknesses, PHI, COSTH, interp='linear')

thick_df = pd.DataFrame(THICK, columns=phis, index=cos_thetas)
thick_df = thick_df.sort_index(axis=0, ascending=False)
thick_df = thick_df.sort_index(axis=1)

cmap = sns.cubehelix_palette(start=1.6, light=0.8, as_cmap=True, reverse=True)

yticks = np.array([0,0.2,0.4,0.6,0.8,1.0])

xticks = np.array([0,1,2,3,4,5,6])

g = sns.heatmap(thick_df, linewidth=0, xticklabels=xticks, yticklabels=yticks, square=True, cmap=cmap)

plt.show(g)

Answer 1

Here's something that should do what you want:

cmap = sns.cubehelix_palette(start=1.6, light=0.8, as_cmap=True, reverse=True)

yticks = np.linspace(0,1,6)

x_end = 6
xticks = np.arange(x_end+1)

ax = sns.heatmap(thick_df, linewidth=0, xticklabels=xticks, yticklabels=yticks[::-1], square=True, cmap=cmap)

ax.set_xticks(xticks*ax.get_xlim()[1]/(2*math.pi))
ax.set_yticks(yticks*ax.get_ylim()[1])

plt.show()

You could pass ['{:,.2f}'.format(x) for x in xticks] instead of xticks to get a float with 2 decimals.

Note that I'm reversing the yticklabels because that's what seaborn does: see matrix.py#L138.

Seaborn calculates the tick positions around the same place (e.g.: #L148), for you that amounts to:

# thick_df.T.shape[0] = thick_df.shape[1]
xticks: np.arange(0, thick_df.T.shape[0], 1) + .5
yticks: np.arange(0, thick_df.T.shape[1], 1) + .5