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

我在一个用js解决的问题中使用了这个解决方案,所以我想我将分享翻译。谢谢你的解释和解决方案。

function remap( x, oMin, oMax, nMin, nMax ){
//range check
if (oMin == oMax){
    console.log("Warning: Zero input range");
    return None;
};

if (nMin == nMax){
    console.log("Warning: Zero output range");
    return None
}

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

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

var portion = (x-oldMin)*(newMax-newMin)/(oldMax-oldMin)
if (reverseInput){
    portion = (oldMax-x)*(newMax-newMin)/(oldMax-oldMin);
};

var result = portion + newMin
if (reverseOutput){
    result = newMax - portion;
}

return result;
}

其他回答

增加了KOTLIN版本的数学解释

假设我们有一个介于(OMin, Omax)之间的刻度,我们在这个范围内有一个值X

我们要把它转换成比例(NMin, NMax)

我们知道X,我们需要找到Y,比值必须相等:

 => (Y-NMin)/(NMax-NMin) = (X-OMin)/(OMax-OMin)  
      
 =>  (Y-NMin)/NewRange = (X-OMin)/OldRange 

 =>   Y = ((X-OMin)*NewRange)/oldRange)+NMin  Answer
   

从实用主义的角度来看,我们可以这样写这个问句:

 private fun  convertScale(oldValueToConvert:Int): Float {
       // Old Scale 50-100
       val oldScaleMin = 50
       val oldScaleMax = 100
       val oldScaleRange= (oldScaleMax - oldScaleMin)

       //new Scale 0-1
       val newScaleMin = 0.0f
       val newScaleMax = 1.0f
       val newScaleRange=  (newScaleMax - newScaleMin)
     
       return ((oldValueToConvert - oldScaleMin)* newScaleRange/ oldScaleRange) + newScaleMin
    }

JAVA

/**
     * 
     * @param x
     * @param inMin
     * @param inMax
     * @param outMin
     * @param outMax
     * @return
     */
        private long normalize(long x, long inMin, long inMax, long outMin, long outMax) {
          long outRange = outMax - outMin;
          long inRange  = inMax - inMin;
          return (x - inMin) *outRange / inRange + outMin;
        }

用法:

float brightness = normalize(progress, 0, 10, 0,255);

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]

有一种情况,当您检查的所有值都相同时,@jerryjvl的代码将返回NaN。

if (OldMin != OldMax && NewMin != NewMax):
    return (((OldValue - OldMin) * (NewMax - NewMin)) / (OldMax - OldMin)) + NewMin
else:
    return (NewMax + NewMin) / 2

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])
NewValue = (((OldValue - OldMin) * (NewMax - NewMin)) / (OldMax - OldMin)) + NewMin

或者更容易读懂:

OldRange = (OldMax - OldMin)  
NewRange = (NewMax - NewMin)  
NewValue = (((OldValue - OldMin) * NewRange) / OldRange) + NewMin

或者如果你想保护旧范围为0的情况(OldMin = OldMax):

OldRange = (OldMax - OldMin)
if (OldRange == 0)
    NewValue = NewMin
else
{
    NewRange = (NewMax - NewMin)  
    NewValue = (((OldValue - OldMin) * NewRange) / OldRange) + NewMin
}

注意,在这种情况下,我们被迫任意选择一个可能的新范围值。根据上下文,明智的选择可能是:NewMin(见示例),NewMax或(NewMin + NewMax) / 2