Ndtyjky

I have some data that I would like to plot as a heatmap, it is essentially a 50x50 numpy array. As a result the heatmap axis labels range from 0 to 50, but actually I want the axis labels to go from -114 to 114 since this is the range of the data. When I set the tick labels however, they end up being bunched up on the axes (see image).

When I put in the lines

ax.set_xticks(ticks)

ax.set_yticks(ticks)

The heatmap ends up getting scaled (see image).

I have put in my code and some sample data, maybe someone can spot what I have done wrong.

import sys

import numpy as np

import pandas as pd

import matplotlib

import matplotlib.pyplot as plt

import os

import cv2 as cv

import seaborn as sns;



filepath = sys.argv[1]

drive, path_and_file = os.path.splitdrive(filepath)

path, file = os.path.split(path_and_file)



line_width = 3



font = {'family' : 'sans',

        'weight' : 'normal',

        'size'   : 18}

matplotlib.rc('font', **font)



bagnames = ["hex_events_only.bag"]

groundtruth = [-92, 0]

noise_levels = ["-1.000000"]

rewards = ["sos"]



gt_angle = np.arctan2(groundtruth[0], groundtruth[1])

gt_mag = np.linalg.norm(groundtruth, axis=0)

print(gt_angle, gt_mag)



for bagname in bagnames:

    print "==========", bagname, "=========="

    for reward in rewards:

        print "      ---", reward, "---       "

        for noise_level in noise_levels:

                filename = filepath + "data_field_" + bagname + "_" + reward + "_" + noise_level

                print filename

                n_samples = (pd.read_csv(filename, delimiter="t", skiprows=1, names=["vx", "vy", "measure"])).values



                x = n_samples[:, 0]

                y = n_samples[:, 1]

                z = n_samples[:, 2]



                yrange = int(np.ptp(x))

                xrange = int(np.ptp(y))



                x_values = np.unique(x).size

                y_values = np.unique(y).size



                num_ticks = 10

                ticks = np.linspace(int(-yrange/2.), int(yrange/2.), num_ticks, dtype=np.int)



                img = np.reshape(z, (x_values, y_values))

                img = img.T

                img = cv.resize(img, (yrange, xrange))



                savename = filepath + "hmap_" + bagname + "_" + reward + "_" + noise_level



                fig, ax = plt.subplots()



                img = cv.GaussianBlur(img, (5, 5), 0)

                ax = sns.heatmap(img, cmap='viridis', yticklabels=ticks, xticklabels=ticks)



                # ax.set_xticks(ticks)

                # ax.set_yticks(ticks)



                # ax.axvline(groundtruth[0], linestyle='--', c='r', linewidth=line_width)

                # ax.axhline(groundtruth[1], linestyle='--', c='r', linewidth=line_width)

                plt.show()

                fig.savefig(savename + ".png", transparent=True, bbox_inches='tight', pad_inches=0)

                plt.close()

https://1drv.ms/u/s!Ap0up1KFhZOughZ3dx9rwq-9yiF9

@ImportanceOfBeingErnest pointed out to me that the approach of using Seaborn was wrong in the first place (see comments). So I changed the approach, which now works exactly as I want it to. In case anyone else runs into this problem, the following code with generate a heatmap from data:

import numpy as np

import pandas as pd

import matplotlib

import matplotlib.pyplot as plt

import cv2 as cv



font = {'family' : 'sans',

        'weight' : 'normal',

        'size'   : 18}

matplotlib.rc('font', **font)



filepath = "path/to/data/"

dataname = "data.txt"

filename = filepath + dataname



n_samples = (pd.read_csv(filename, delimiter="t", skiprows=1, names=["x", "y", "value"])).values



x = n_samples[:, 0]

y = n_samples[:, 1]

z = n_samples[:, 2]



line_width = 2



yrange = int(np.ptp(x))

xrange = int(np.ptp(y))



x_values = np.unique(x).size

y_values = np.unique(y).size



num_ticks = 10

ticks = np.linspace(int(-yrange/2.), int(yrange/2.), num_ticks, dtype=np.int)



img = np.reshape(z, (x_values, y_values))

img = img.T

img = cv.resize(img, (yrange, xrange))



fig, ax = plt.subplots()

im = ax.imshow(img, cmap='viridis', extent=[-xrange/2., xrange/2., -yrange/2., yrange/2.])

ax.axvline(groundtruth[0], linestyle='--', c='r', linewidth=line_width)

ax.axhline(groundtruth[1], linestyle='--', c='r', linewidth=line_width)

ax.set_xlabel("$v_x$")

ax.set_ylabel("$v_y$")

cbar = fig.colorbar(im)

cbar.ax.set_yticklabels([''])

cbar.ax.set_ylabel('Reward')



fig.tight_layout()

savename = filepath + "hmap_" + bagname + "_" + reward + "_" + noise_level

fig.savefig(savename + ".pdf", transparent=True, bbox_inches='tight', pad_inches=0)

plt.close()

# plt.show()

Here's what the output is like: