I have a scatter plot where I am coloring each data point based on an array:
plt.scatter(xs,ys,c=av,cmap=plt.cm.hot,s=50,alpha=0.5)
In [96]: xs.shape
Out[96]: (5594,)
In [97]: ys.shape
Out[97]: (5594,)
In [98]: av.shape
Out[98]: (5594,)
Now, I want to keep the color but smooth the data points to get a smoothed scatter plot, something like this (from this post ) or this image:
Comment: I figured that if I can add more points to my xs, ys, zs
I can make the scatter plot with more data points, hence, it will look more like a heatmap plot, which is what I want. Now, for every point in xs, ys, zs
, I want to add additional points with similar values around original points. Ideally, these additional points should form a normal distribution around actual original points in the xs, ys, zs
. Is there a statistical tools to do this task? Eg How to change [1, 5, 10]
to [0.9,0.98,1,1.02,1.1, 4.9,4.98,5,5.02,5.1, 9.9,9.98,10,10.02,10.1]
?
OP : Is there a statistical tools to do this task? Eg How to change [1, 5, 10] to [0.9,0.98,1,1.02,1.1, 4.9,4.98,5,5.02,5.1, 9.9,9.98,10,10.02,10.1] ?
obs=[1, 5 ,10]
syntheticobs=np.random.normal(0,0.1,(6,3))+obs
synthethicobs
Out[]:array([[ 1.02166209, 5.00716569, 9.96726293],
[ 0.96727493, 4.94823697, 10.03424305],
[ 1.10756036, 5.12464335, 9.86776082],
[ 0.97866246, 5.12743117, 10.06647638],
[ 0.87842188, 5.00994338, 10.1114983 ],
[ 1.10728294, 4.82523615, 10.03642462]])
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