我试图将一个范围的数字转换为另一个，保持比率。数学不是我的强项。
I have an image file where point values may range from -16000.00 to 16000.00 though the typical range may be much less. What I want to do is compress these values into the integer range 0-100, where 0 is the value of the smallest point, and 100 is the value of the largest. All points in between should keep a relative ratio even though some precision is being lost I'd like to do this in python but even a general algorithm should suffice. I'd prefer an algorithm where the min/max or either range can be adjusted (ie, the second range could be -50 to 800 instead of 0 to 100).
我写了一个函数用R来做这个，方法和上面一样，但是我需要在R中做很多次，所以我想分享一下，以防它对任何人有帮助。
convertRange <- function(
oldValue,
oldRange = c(-16000.00, 16000.00),
newRange = c(0, 100),
returnInt = TRUE # the poster asked for an integer, so this is an option
){
oldMin <- oldRange[1]
oldMax <- oldRange[2]
newMin <- newRange[1]
newMax <- newRange[2]
newValue = (((oldValue - oldMin)* (newMax - newMin)) / (oldMax - oldMin)) + newMin
if(returnInt){
return(round(newValue))
} else {
return(newValue)
}
}
Java版本
不管你喂它什么，它都能工作!
我把所有内容都展开了，这样便于学习。当然，最后舍入是可选的。
private long remap(long p, long Amin, long Amax, long Bmin, long Bmax ) {
double deltaA = Amax - Amin;
double deltaB = Bmax - Bmin;
double scale = deltaB / deltaA;
double negA = -1 * Amin;
double offset = (negA * scale) + Bmin;
double q = (p * scale) + offset;
return Math.round(q);
}
使用Numpy和interp函数，你可以将你的值从旧范围转换为新范围:
>>> import numpy as np
>>> np.interp(0, [-16000,16000], [0,100])
50.0
你也可以尝试映射一个值列表:
>>> np.interp([-16000,0,12000] ,[-16000,16000], [0,100])
array([ 0. , 50. , 87.5])
