python - Error when re-sizing data in numpy array -

i have 2 arrays want re-size, want retain original values. code below re-sizes arrays, problem over-writes original values, can see when @ output

print(x)  print(y) 

commands @ end of script. however, if comment out line

# newx,newy=resize(x,y,xmin=minrr,xmax=maxrr,ymin=minlvet,ymax=maxlvet)  

then original values of x , y print out properly. however, if remove comment , leave code is, x , y apparently over-written becaue

print(x)  print(y) 

commands output values newx , newy, respectively.

my code below. can show me how fix code below x , y retain original values, , newx , newy newly resized values?

import numpy np  def getminrr(age):     maxhr = 208-(0.7*age)     minrr = (60/maxhr)*1000     return minrr  def resize(x,y,xmin=0.0,xmax=1.0,ymin=0.0,ymax=1.0):     # create local variables     newx = x     newy = y     # if mins greater maxs, flip them.     if xmin>xmax: xmin,xmax=xmax,xmin      if ymin>ymax: ymin,ymax=ymax,ymin     #----------------------------------------------------------------------------------------------         # rest of code below re-calculates values in x , in y these steps:     #       1.) subtract actual minimum of input x-vector each value of x     #       2.) multiply each resulting value of x result of dividing difference     #           between new xmin , xmax actual maximum of input x-vector     #       3.) add new minimum each value of x     # note: wrote in x-notation, identical process repeated y     #----------------------------------------------------------------------------------------------         # subtracts right operand left operand , assigns result left operand.     # note: c -= equivalent c = c -     newx -= x.min()      # multiplies right operand left operand , assigns result left operand.     # note: c *= equivalent c = c *     newx *= (xmax-xmin)/newx.max()      # adds right operand left operand , assigns result left operand.     # note: c += equivalent c = c +     newx += xmin      # subtracts right operand left operand , assigns result left operand.     # note: c -= equivalent c = c -     newy -= y.min()      # multiplies right operand left operand , assigns result left operand.     # note: c *= equivalent c = c *     newy *= (ymax-ymin)/newy.max()      # adds right operand left operand , assigns result left operand.     # note: c += equivalent c = c +     newy += ymin      return (newx,newy)  # declare raw data use in creating logistic regression equation x = np.array([821,576,473,377,326],dtype='float')  y = np.array([255,235,208,166,157],dtype='float')   # call resize() function re-calculate coordinates used equation minrr=getminrr(34) maxrr=1200 minlvet=(y[4]/x[4])*minrr maxlvet=(y[0]/x[0])*maxrr newx,newy=resize(x,y,xmin=minrr,xmax=maxrr,ymin=minlvet,ymax=maxlvet)   print 'x is:  ',x  print 'y is:  ',y 

newx = x.copy() newy = y.copy() 

numpy arrays support __copy__ interface, , can copied copy module, work:

newx = copy.copy(x) newy = copy.copy(y) 

if want retain current behaviour of function as-is, you'd need replace occurences of x , y newx , newy. if current behaviour of function wrong, might keep them are.