How to drawheat map with large data set in python

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I am trying to plot a sine wave, and the color of the curve at each point is represented by its tangential slope value. For example, a 3600 * 1000 data frame should be filled:

x_axis = list(range(0, 3601))
y_axis = list(range(-1000, 1001))
wave = pd.DataFrame(index = y_axis,columns= x_axis )
for i in range(0, 3601, 1):
    y = int(round(np.sin(np.radians(i / 10)), 3) * 1000)
    wave.loc[y, i] = -abs(y)
wave = wave.fillna(0)
wave[wave == 0] =np.nan
seaborn.heatmap(wave)

and by using seaborn.heatmap(wave) the heatmap will be generated like attached image. But what I am looking for is to draw maybe 50-100 sine wave like this in one picture, so the dataframe size will be much larger to 360000*10000. With this size of dataframe I still want to show similar heatmap, or any type or drawing that can represent the value change for each cell. My work station seems to freeze by using seaborn heatmap with this dataset.

Some of my thoughts would be to normalize all the values to 0-255 and use some GLV plotting function, I am still researching it.

image

pandas seaborn matplotlib dataset python
1 Answers

You could create a similar plot using plt.scatter:

import matplotlib.pyplot as plt
import numpy as np

x_axis = np.arange(0, 360, 0.1)
y = np.round(np.sin(np.radians(x_axis)), 3) * 1000
plt.scatter(x_axis, y, c=-np.abs(y), s=1, cmap='gist_heat')
plt.show()

resulting plot

To get a wider curve, just increase s. To get rid of the white part of the colormap, you can move the color limits (called vmin and vmax). Standard they are the minimum and maximum of the given color values. In this case the maximum is 0 and the minimum is -1000. Setting vmax to +100 would leave out 10% of the color range.

plt.scatter(x_axis, y, c=-np.abs(y), vmax=0.1*y.max(), s=10, cmap='gist_heat')

thicker curve

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