Digitizing a numpy array
I have two vectors:
time_vec = np.array([0.2,0.23,0.3,0.4,0.5,...., 28....])
values_vec = np.array([500,200,220,250,200,...., 218....])
time_vec.shape == values_vec.shape
Now I want to take bin for values every 0.5 seconds and take the average. For example,
value_vec = np.array(mean_of(500,200,220,250,200), mean_of(next values in next 0.5 second interval))
Is there any numpy method I have lost in which bin and do you have a value for bins?
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You can use np.ufunc.reduceat
. You just need to fill in where the breakpoints are, i.e. When floor(t / .5)
changes:
say:
>>> t
array([ 0. , 0.025 , 0.2125, 0.2375, 0.2625, 0.3375, 0.475 , 0.6875, 0.7 , 0.7375, 0.8 , 0.9 ,
0.925 , 1.05 , 1.1375, 1.15 , 1.1625, 1.1875, 1.1875, 1.225 ])
>>> b
array([ 0.8144, 0.3734, 1.4734, 0.6307, -0.611 , -0.8762, 1.6064, 0.3863, -0.0103, -1.6889, -0.4328, -0.7373,
1.7856, 0.8938, -1.1574, -0.4029, -0.4352, -0.4412, -1.7819, -0.3298])
breakpoints:
>>> i = np.r_[0, 1 + np.nonzero(np.diff(np.floor(t / .5)))[0]]
>>> i
array([ 0, 7, 13])
and the sum for each interval:
>>> np.add.reduceat(b, i)
array([ 3.411 , -0.6975, -3.6545])
and the average will be the sum over the length of the interval:
>>> np.add.reduceat(b, i) / np.diff(np.r_[i, len(b)])
array([ 0.4873, -0.1162, -0.5221])
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You can pass a parameter weights=
in np.histogram
to calculate the summed values in each time buffer and then normalize by bin count
# 0.5 second time bins to average within
tmin = time_vec.min()
tmax = time_vec.max()
bins = np.arange(tmin - (tmin % 0.5), tmax - (tmax % 0.5) + 0.5, 0.5)
# summed values within each bin
bin_sums, edges = np.histogram(time_vec,bins=bins, weights=values_vec)
# number of values within each bin
bin_counts, edges = np.histogram(time_vec,bins=bins)
# average value within each bin
bin_means = bin_sums / bin_counts
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You can use np.bincount
which is presumably quite efficient for such binning operations. Here's an implementation based on her solution to our case -
# Find indices where 0.5 intervals shifts onto next ones
A = time_vec*2
idx = np.searchsorted(A,np.arange(1,int(np.ceil(A.max()))),'right')
# Setup ID array such that all 0.5 intervals are ID-ed same
out = np.zeros((A.size),dtype=int)
out[idx[idx < A.size]] = 1
ID = out.cumsum()
# Finally use bincount to sum and count elements of same IDs
# and thus get mean values per ID
mean_vec = np.bincount(ID,values_vec)/np.bincount(ID)
Example run -
In [189]: time_vec
Out[189]:
array([ 0.2 , 0.23, 0.3 , 0.4 , 0.5 , 0.7 , 0.8 , 0.92, 0.95,
1. , 1.11, 1.5 , 2. , 2.3 , 2.5 , 4.5 ])
In [190]: values_vec
Out[190]: array([36, 11, 93, 32, 72, 75, 26, 28, 77, 31, 60, 77, 76, 32, 6, 85])
In [191]: ID
Out[191]: array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 3, 4, 4, 5], dtype=int32)
In [192]: mean_vec
Out[192]: array([ 48.8, 47.4, 68.5, 76. , 19. , 85. ])
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