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)
  }
}

其他回答

这是一个简单的线性变换。

new_value = ( (old_value - old_min) / (old_max - old_min) ) * (new_max - new_min) + new_min

因此,将10000在-16000到16000的范围内转换为0到100的新范围会得到:

old_value = 10000
old_min = -16000
old_max = 16000
new_min = 0
new_max = 100

new_value = ( ( 10000 - -16000 ) / (16000 - -16000) ) * (100 - 0) + 0
          = 81.25

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

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;
}

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