I have basic 2-D numpy arrays and I'd like to "downsample" them to a more coarse resolution. Is there a simple numpy or scipy module that can easily do this? I should also note that this array is being displayed geographically via Basemap modules.
SAMPLE:
scikit-image has implemented a working version of downsampling here, although they shy away from calling it downsampling for it not being a downsampling in terms of DSP, if I understand correctly:
http://scikit-image.org/docs/dev/api/skimage.measure.html#skimage.measure.block_reduce
but it works very well, and it is the only downsampler that I found in Python that can deal with np.nan in the image. I have downsampled gigantic images with this very quickly.
When downsampling, interpolation is the wrong thing to do. Always use an aggregated approach.
I use block means to do this, using a "factor" to reduce the resolution.
import numpy as np
from scipy import ndimage
def block_mean(ar, fact):
assert isinstance(fact, int), type(fact)
sx, sy = ar.shape
X, Y = np.ogrid[0:sx, 0:sy]
regions = sy//fact * (X//fact) + Y//fact
res = ndimage.mean(ar, labels=regions, index=np.arange(regions.max() + 1))
res.shape = (sx//fact, sy//fact)
return res
E.g., a (100, 200) shape array using a factor of 5 (5x5 blocks) results in a (20, 40) array result:
ar = np.random.rand(20000).reshape((100, 200))
block_mean(ar, 5).shape # (20, 40)
File "diffplot.py", line 38, in block_mean res.shape = (sx/fact, sy/fact) ValueError: total size of new array must be unchanged
imresize and ndimage.interpolation.zoom look like they do what you want
I haven't tried imresize before but here is how I have used ndimage.interpolation.zoom
a = np.array(64).reshape(8,8)
a = ndimage.interpolation.zoom(a,.5) #decimate resolution
a is then a 4x4 matrix with interpolated values in it
findiff = scipy.misc.imresize(diff, 30., interp='bilinear', mode=None) frefcobj = m.pcolormesh(x,y,findiff,shading='flat',vmin=-15,vmax=15,cmap=cmap,zorder=1) colbar = m.colorbar(frefcobj,"bottom",size="4%",pad="5%",extend='both',ticks=intervals) diff is a 699x699 array. Does not seem to be achieving the task.
Easiest way:
You can use the array[0::2] notation, which only considers every second index.
E.g.
array= np.array([[i+j for i in range(0,10)] for j in range(0,10)])
down_sampled=array[0::2,0::2]
print("array \n", array)
print("array2 \n",down_sampled)
has the output:
array
[[ 0 1 2 3 4 5 6 7 8 9]
[ 1 2 3 4 5 6 7 8 9 10]
[ 2 3 4 5 6 7 8 9 10 11]
[ 3 4 5 6 7 8 9 10 11 12]
[ 4 5 6 7 8 9 10 11 12 13]
[ 5 6 7 8 9 10 11 12 13 14]
[ 6 7 8 9 10 11 12 13 14 15]
[ 7 8 9 10 11 12 13 14 15 16]
[ 8 9 10 11 12 13 14 15 16 17]
[ 9 10 11 12 13 14 15 16 17 18]]
array2
[[ 0 2 4 6 8]
[ 2 4 6 8 10]
[ 4 6 8 10 12]
[ 6 8 10 12 14]
[ 8 10 12 14 16]]
[::2]xarray's "coarsen" method can downsample a xarray.Dataset or xarray.DataArray
For example:
import xarray as xr
import numpy as np
import matplotlib.pyplot as plt
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(15,5))
# Create a 10x10 array of random numbers
a = xr.DataArray(np.random.rand(10,10)*100, dims=['x', 'y'])
# "Downscale" the array, mean of blocks of size (2x2)
b = a.coarsen(x=2, y=2).mean()
# "Downscale" the array, mean of blocks of size (5x5)
c = a.coarsen(x=5, y=5).mean()
# Plot and cosmetics
a.plot(ax=ax1)
ax1.set_title("Full Data")
b.plot(ax=ax2)
ax2.set_title("mean of (2x2) boxes")
c.plot(ax=ax3)
ax3.set_title("mean of (5x5) boxes")
Because the OP just wants a courser resolution, I thought I would share my way for reducing number of pixels by half in each dimension. I takes the mean of 2x2 blocks. This can be applied multiple times to reduce by factors of 2.
from scipy.ndimage import convolve
array_downsampled = convolve(array,
np.array([[0.25,0.25],[0.25,0.25]]))[:array.shape[0]:2,:array.shape[1]:2]
This might not be what you're looking for, but I thought I'd mention it for completeness.
You could try installing scikits.samplerate (docs), which is a Python wrapper for libsamplerate. It provides nice, high-quality resampling algorithms -- BUT as far as I can tell, it only works in 1D. You might be able to resample your 2D signal first along one axis and then along another, but I'd think that might counteract the benefits of high-quality resampling to begin with.
This will take an image of any resolution and return only a quarter of its size by taking the 4th index of the image array.
import cv2
import numpy as np
def quarter_res_drop(im):
resized_image = im[0::4, 0::4]
cv2.imwrite('resize_result_image.png', resized_image)
return resized_image
im = cv2.imread('Your_test_image.png', 1)
quarter_res_drop(im)
