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

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

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])

列出理解一行的解决方案

color_array_new = [int((((x - min(node_sizes)) * 99) / (max(node_sizes) - min(node_sizes))) + 1) for x in node_sizes]

完整版

def colour_specter(waste_amount):
color_array = []
OldRange = max(waste_amount) - min(waste_amount)
NewRange = 99
for number_value in waste_amount:
    NewValue = int((((number_value - min(waste_amount)) * NewRange) / OldRange) + 1)
    color_array.append(NewValue)
print(color_array)
return color_array

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);

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