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).


当前回答

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);

}

其他回答

实际上,在某些情况下,上述答案会失效。 如错误的输入值,错误的输入范围,负输入/输出范围。

def remap( x, oMin, oMax, nMin, nMax ):

    #range check
    if oMin == oMax:
        print "Warning: Zero input range"
        return None

    if nMin == nMax:
        print "Warning: Zero output range"
        return None

    #check reversed input range
    reverseInput = False
    oldMin = min( oMin, oMax )
    oldMax = max( oMin, oMax )
    if not oldMin == oMin:
        reverseInput = True

    #check reversed output range
    reverseOutput = False   
    newMin = min( nMin, nMax )
    newMax = max( nMin, nMax )
    if not newMin == nMin :
        reverseOutput = True

    portion = (x-oldMin)*(newMax-newMin)/(oldMax-oldMin)
    if reverseInput:
        portion = (oldMax-x)*(newMax-newMin)/(oldMax-oldMin)

    result = portion + newMin
    if reverseOutput:
        result = newMax - portion

    return result

#test cases
print remap( 25.0, 0.0, 100.0, 1.0, -1.0 ), "==", 0.5
print remap( 25.0, 100.0, -100.0, -1.0, 1.0 ), "==", -0.25
print remap( -125.0, -100.0, -200.0, 1.0, -1.0 ), "==", 0.5
print remap( -125.0, -200.0, -100.0, -1.0, 1.0 ), "==", 0.5
#even when value is out of bound
print remap( -20.0, 0.0, 100.0, 0.0, 1.0 ), "==", -0.2

下面是一个Javascript版本,它返回一个函数,对预定的源和目标范围进行重新缩放,最大限度地减少每次必须执行的计算量。

// This function returns a function bound to the 
// min/max source & target ranges given.
// oMin, oMax = source
// nMin, nMax = dest.
function makeRangeMapper(oMin, oMax, nMin, nMax ){
    //range check
    if (oMin == oMax){
        console.log("Warning: Zero input range");
        return undefined;
    };

    if (nMin == nMax){
        console.log("Warning: Zero output range");
        return undefined
    }

    //check reversed input range
    var reverseInput = false;
    let oldMin = Math.min( oMin, oMax );
    let oldMax = Math.max( oMin, oMax );
    if (oldMin != oMin){
        reverseInput = true;
    }

    //check reversed output range
    var reverseOutput = false;  
    let newMin = Math.min( nMin, nMax )
    let newMax = Math.max( nMin, nMax )
    if (newMin != nMin){
        reverseOutput = true;
    }

    // Hot-rod the most common case.
    if (!reverseInput && !reverseOutput) {
        let dNew = newMax-newMin;
        let dOld = oldMax-oldMin;
        return (x)=>{
            return ((x-oldMin)* dNew / dOld) + newMin;
        }
    }

    return (x)=>{
        let portion;
        if (reverseInput){
            portion = (oldMax-x)*(newMax-newMin)/(oldMax-oldMin);
        } else {
            portion = (x-oldMin)*(newMax-newMin)/(oldMax-oldMin)
        }
        let result;
        if (reverseOutput){
            result = newMax - portion;
        } else {
            result = portion + newMin;
        }

        return result;
    }   
}

下面是一个使用该函数将0-1缩放到-0x80000000, 0x7FFFFFFF的示例

let normTo32Fn = makeRangeMapper(0, 1, -0x80000000, 0x7FFFFFFF);
let fs = normTo32Fn(0.5);
let fs2 = normTo32Fn(0);

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])

C++变体

我发现PenguinTD的解决方案很有用,所以我把它移植到c++,如果有人需要它:

float remap(float x, float oMin, float oMax, float nMin, float nMax ){ //range check if( oMin == oMax) { //std::cout<< "Warning: Zero input range"; return -1; } if( nMin == nMax){ //std::cout<<"Warning: Zero output range"; return -1; } //check reversed input range bool reverseInput = false; float oldMin = min( oMin, oMax ); float oldMax = max( oMin, oMax ); if (oldMin == oMin) reverseInput = true; //check reversed output range bool reverseOutput = false; float newMin = min( nMin, nMax ); float newMax = max( nMin, nMax ); if (newMin == nMin) reverseOutput = true; float portion = (x-oldMin)*(newMax-newMin)/(oldMax-oldMin); if (reverseInput) portion = (oldMax-x)*(newMax-newMin)/(oldMax-oldMin); float result = portion + newMin; if (reverseOutput) result = newMax - portion; return result; }

下面是一些简单的Python函数,便于复制和粘贴,包括一个扩展整个列表的函数。

def scale_number(unscaled, to_min, to_max, from_min, from_max):
    return (to_max-to_min)*(unscaled-from_min)/(from_max-from_min)+to_min

def scale_list(l, to_min, to_max):
    return [scale_number(i, to_min, to_max, min(l), max(l)) for i in l]

可以这样使用:

scale_list([1,3,4,5], 0, 100)

[0.0, 50.0, 75.0, 100.0]

在我的例子中,我想缩放一条对数曲线,像这样:

scale_list([math.log(i+1) for i in range(5)], 0, 50)

[0.0, 21.533827903669653, 34.130309724299266, 43.06765580733931, 50.0]