sensor_value = 256
actuator_value = translate(sensor_value, 1, 512, 5, 10)

def translate(value, leftMin, leftMax, rightMin, rightMax):
    # Figure out how 'wide' each range is
    leftSpan = leftMax - leftMin
    rightSpan = rightMax - rightMin

    # Convert the left range into a 0-1 range (float)
    valueScaled = float(value - leftMin) / float(leftSpan)

    # Convert the 0-1 range into a value in the right range.
    return rightMin + (valueScaled * rightSpan)

>>> from scipy.interpolate import interp1d
>>> m = interp1d([1,512],[5,10])
>>> m(256)
array(7.4951076320939336)

>>> float(m(256))
7.4951076320939336

>>> m([100,200,300])
array([ 5.96868885,  6.94716243,  7.92563601])

>>> m = interp1d([1,128,256,512],[1,10,90,100])
>>> float(m(400))
95.625

>>> from numpy import interp
>>> interp(256,[1,512],[5,10])
7.4951076320939336

def make_interpolater(left_min, left_max, right_min, right_max):
    # Figure out how 'wide' each range is
    leftSpan = left_max - left_min
    rightSpan = right_max - right_min

    # Compute the scale factor between left and right values
    scaleFactor = float(rightSpan) / float(leftSpan)

    # create interpolation function using pre-calculated scaleFactor
    def interp_fn(value):
        return right_min + (value-left_min)*scaleFactor

    return interp_fn

# create function for doing interpolation of the desired
# ranges
scaler = make_interpolater(1, 512, 5, 10)

# receive list of raw values from sensor, assign to data_list

# now convert to scaled values using map
scaled_data = map(scaler, data_list)

# or a list comprehension, if you prefer
scaled_data = [scaler(x) for x in data_list]

def translate(sensor_val, in_from, in_to, out_from, out_to):
    out_range = out_to - out_from
    in_range = in_to - in_from
    in_val = sensor_val - in_from
    val=(float(in_val)/in_range)*out_range
    out_val = out_from+val
    return out_val