如何在numpy数组中找到最近的值?例子:
np.find_nearest(array, value)
如何在numpy数组中找到最近的值?例子:
np.find_nearest(array, value)
当前回答
稍微修改一下,上面的答案适用于任意维度的数组(1d, 2d, 3d,…):
def find_nearest(a, a0):
"Element in nd array `a` closest to the scalar value `a0`"
idx = np.abs(a - a0).argmin()
return a.flat[idx]
或者,写成一行:
a.flat[np.abs(a - a0).argmin()]
其他回答
对于大型数组,@Demitri给出的(优秀)答案比目前标记为最佳的答案快得多。我从以下两个方面调整了他的精确算法:
不管输入数组是否排序,下面的函数都有效。 下面的函数返回与最接近的值对应的输入数组的索引,这有点更一般。
请注意,下面的函数还处理了一个特定的边缘情况,这将导致@Demitri编写的原始函数中的错误。否则,我的算法和他的一样。
def find_idx_nearest_val(array, value):
idx_sorted = np.argsort(array)
sorted_array = np.array(array[idx_sorted])
idx = np.searchsorted(sorted_array, value, side="left")
if idx >= len(array):
idx_nearest = idx_sorted[len(array)-1]
elif idx == 0:
idx_nearest = idx_sorted[0]
else:
if abs(value - sorted_array[idx-1]) < abs(value - sorted_array[idx]):
idx_nearest = idx_sorted[idx-1]
else:
idx_nearest = idx_sorted[idx]
return idx_nearest
稍微修改一下,上面的答案适用于任意维度的数组(1d, 2d, 3d,…):
def find_nearest(a, a0):
"Element in nd array `a` closest to the scalar value `a0`"
idx = np.abs(a - a0).argmin()
return a.flat[idx]
或者,写成一行:
a.flat[np.abs(a - a0).argmin()]
下面是一个使用2D数组的版本,如果用户拥有scipy的cdist函数,则使用它,如果用户没有,则使用更简单的距离计算。
默认情况下,输出是最接近输入值的索引,但您可以使用output关键字将其更改为'index', 'value'或'both'之一,其中'value'输出数组[index], 'both'输出索引,数组[index]。
对于非常大的数组,您可能需要使用kind='euclidean',因为默认的scipy cdist函数可能会耗尽内存。
这可能不是绝对最快的解决方案,但已经很接近了。
def find_nearest_2d(array, value, kind='cdist', output='index'):
# 'array' must be a 2D array
# 'value' must be a 1D array with 2 elements
# 'kind' defines what method to use to calculate the distances. Can choose one
# of 'cdist' (default) or 'euclidean'. Choose 'euclidean' for very large
# arrays. Otherwise, cdist is much faster.
# 'output' defines what the output should be. Can be 'index' (default) to return
# the index of the array that is closest to the value, 'value' to return the
# value that is closest, or 'both' to return index,value
import numpy as np
if kind == 'cdist':
try: from scipy.spatial.distance import cdist
except ImportError:
print("Warning (find_nearest_2d): Could not import cdist. Reverting to simpler distance calculation")
kind = 'euclidean'
index = np.where(array == value)[0] # Make sure the value isn't in the array
if index.size == 0:
if kind == 'cdist': index = np.argmin(cdist([value],array)[0])
elif kind == 'euclidean': index = np.argmin(np.sum((np.array(array)-np.array(value))**2.,axis=1))
else: raise ValueError("Keyword 'kind' must be one of 'cdist' or 'euclidean'")
if output == 'index': return index
elif output == 'value': return array[index]
elif output == 'both': return index,array[index]
else: raise ValueError("Keyword 'output' must be one of 'index', 'value', or 'both'")
下面是一个处理非标量“values”数组的版本:
import numpy as np
def find_nearest(array, values):
indices = np.abs(np.subtract.outer(array, values)).argmin(0)
return array[indices]
如果输入是标量,则返回数字类型(例如int, float)的版本:
def find_nearest(array, values):
values = np.atleast_1d(values)
indices = np.abs(np.subtract.outer(array, values)).argmin(0)
out = array[indices]
return out if len(out) > 1 else out[0]
这个函数使用numpy searchsorted处理任意数量的查询,因此在对输入数组进行排序之后,它的速度也一样快。 它可以在2d, 3d的规则网格上工作…:
#!/usr/bin/env python3
# keywords: nearest-neighbor regular-grid python numpy searchsorted Voronoi
import numpy as np
#...............................................................................
class Near_rgrid( object ):
""" nearest neighbors on a Manhattan aka regular grid
1d:
near = Near_rgrid( x: sorted 1d array )
nearix = near.query( q: 1d ) -> indices of the points x_i nearest each q_i
x[nearix[0]] is the nearest to q[0]
x[nearix[1]] is the nearest to q[1] ...
nearpoints = x[nearix] is near q
If A is an array of e.g. colors at x[0] x[1] ...,
A[nearix] are the values near q[0] q[1] ...
Query points < x[0] snap to x[0], similarly > x[-1].
2d: on a Manhattan aka regular grid,
streets running east-west at y_i, avenues north-south at x_j,
near = Near_rgrid( y, x: sorted 1d arrays, e.g. latitide longitude )
I, J = near.query( q: nq × 2 array, columns qy qx )
-> nq × 2 indices of the gridpoints y_i x_j nearest each query point
gridpoints = np.column_stack(( y[I], x[J] )) # e.g. street corners
diff = gridpoints - querypoints
distances = norm( diff, axis=1, ord= )
Values at an array A definded at the gridpoints y_i x_j nearest q: A[I,J]
3d: Near_rgrid( z, y, x: 1d axis arrays ) .query( q: nq × 3 array )
See Howitworks below, and the plot Voronoi-random-regular-grid.
"""
def __init__( self, *axes: "1d arrays" ):
axarrays = []
for ax in axes:
axarray = np.asarray( ax ).squeeze()
assert axarray.ndim == 1, "each axis should be 1d, not %s " % (
str( axarray.shape ))
axarrays += [axarray]
self.midpoints = [_midpoints( ax ) for ax in axarrays]
self.axes = axarrays
self.ndim = len(axes)
def query( self, queries: "nq × dim points" ) -> "nq × dim indices":
""" -> the indices of the nearest points in the grid """
queries = np.asarray( queries ).squeeze() # or list x y z ?
if self.ndim == 1:
assert queries.ndim <= 1, queries.shape
return np.searchsorted( self.midpoints[0], queries ) # scalar, 0d ?
queries = np.atleast_2d( queries )
assert queries.shape[1] == self.ndim, [
queries.shape, self.ndim]
return [np.searchsorted( mid, q ) # parallel: k axes, k processors
for mid, q in zip( self.midpoints, queries.T )]
def snaptogrid( self, queries: "nq × dim points" ):
""" -> the nearest points in the grid, 2d [[y_j x_i] ...] """
ix = self.query( queries )
if self.ndim == 1:
return self.axes[0][ix]
else:
axix = [ax[j] for ax, j in zip( self.axes, ix )]
return np.array( axix )
def _midpoints( points: "array-like 1d, *must be sorted*" ) -> "1d":
points = np.asarray( points ).squeeze()
assert points.ndim == 1, points.shape
diffs = np.diff( points )
assert np.nanmin( diffs ) > 0, "the input array must be sorted, not %s " % (
points.round( 2 ))
return (points[:-1] + points[1:]) / 2 # floats
#...............................................................................
Howitworks = \
"""
How Near_rgrid works in 1d:
Consider the midpoints halfway between fenceposts | | |
The interval [left midpoint .. | .. right midpoint] is what's nearest each post --
| | | | points
| . | . | . | midpoints
^^^^^^ . nearest points[1]
^^^^^^^^^^^^^^^ nearest points[2] etc.
2d:
I, J = Near_rgrid( y, x ).query( q )
I = nearest in `x`
J = nearest in `y` independently / in parallel.
The points nearest [yi xj] in a regular grid (its Voronoi cell)
form a rectangle [left mid x .. right mid x] × [left mid y .. right mid y]
(in any norm ?)
See the plot Voronoi-random-regular-grid.
Notes
-----
If a query point is exactly halfway between two data points,
e.g. on a grid of ints, the lines (x + 1/2) U (y + 1/2),
which "nearest" you get is implementation-dependent, unpredictable.
"""
Murky = \
""" NaNs in points, in queries ?
"""
__version__ = "2021-10-25 oct denis-bz-py"