Piecewise function in numpy with multiple arguments












2















I tried to define a function (tent map) as following:



def f(r, x):

    return np.piecewise([r, x], [x < 0.5, x >= 0.5], [lambda r, x: 2*r*x, lambda r, x: 2*r*(1-x)])


And r, x will be numpy arrays:



no_r = 10001

r = np.linspace(0, 4, no_r)

x = np.random.rand(no_r)


I would like the result to be a numpy array matching the shapes of r and x, calculated using each pairs of elements of arrays r and x with the same indicies. For example if r = [0, 1, 2, 3] and x = [0.1, 0.7, 0.3, 1], the result should be [0, 0.6, 1.2, 0].
An error occured: "boolean index did not match indexed array along dimension 0; dimension is 2 but corresponding boolean dimension is 10001"
So what should I do to achieve the intended purpose?






python numpy







share|improve this question



























    2















    I tried to define a function (tent map) as following:



    def f(r, x):
    
    return np.piecewise([r, x], [x < 0.5, x >= 0.5], [lambda r, x: 2*r*x, lambda r, x: 2*r*(1-x)])


    And r, x will be numpy arrays:



    no_r = 10001
    
r = np.linspace(0, 4, no_r)
    
x = np.random.rand(no_r)


    I would like the result to be a numpy array matching the shapes of r and x, calculated using each pairs of elements of arrays r and x with the same indicies. For example if r = [0, 1, 2, 3] and x = [0.1, 0.7, 0.3, 1], the result should be [0, 0.6, 1.2, 0].
    An error occured: "boolean index did not match indexed array along dimension 0; dimension is 2 but corresponding boolean dimension is 10001"
    So what should I do to achieve the intended purpose?






    python numpy







    share|improve this question

























      2












      2








      2








      I tried to define a function (tent map) as following:



      def f(r, x):
      
    return np.piecewise([r, x], [x < 0.5, x >= 0.5], [lambda r, x: 2*r*x, lambda r, x: 2*r*(1-x)])


      And r, x will be numpy arrays:



      no_r = 10001
      
r = np.linspace(0, 4, no_r)
      
x = np.random.rand(no_r)


      I would like the result to be a numpy array matching the shapes of r and x, calculated using each pairs of elements of arrays r and x with the same indicies. For example if r = [0, 1, 2, 3] and x = [0.1, 0.7, 0.3, 1], the result should be [0, 0.6, 1.2, 0].
      An error occured: "boolean index did not match indexed array along dimension 0; dimension is 2 but corresponding boolean dimension is 10001"
      So what should I do to achieve the intended purpose?






      python numpy







      share|improve this question














      I tried to define a function (tent map) as following:



      def f(r, x):
      
    return np.piecewise([r, x], [x < 0.5, x >= 0.5], [lambda r, x: 2*r*x, lambda r, x: 2*r*(1-x)])


      And r, x will be numpy arrays:



      no_r = 10001
      
r = np.linspace(0, 4, no_r)
      
x = np.random.rand(no_r)


      I would like the result to be a numpy array matching the shapes of r and x, calculated using each pairs of elements of arrays r and x with the same indicies. For example if r = [0, 1, 2, 3] and x = [0.1, 0.7, 0.3, 1], the result should be [0, 0.6, 1.2, 0].
      An error occured: "boolean index did not match indexed array along dimension 0; dimension is 2 but corresponding boolean dimension is 10001"
      So what should I do to achieve the intended purpose?






      python numpy




      python numpy






      share|improve this question













      share|improve this question











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      share|improve this question










      asked Nov 24 '18 at 1:22









      SatoSato

      336




      336
























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          what you want to get as result can be done with np.select such as:



          def f(r, x):
          
    return np.select([x < 0.5,x >= 0.5], [2*r*x, 2*r*(1-x)])


          Then with



          r = np.array([0, 1, 2, 3])
          
x = np.array([0.1, 0.7, 0.3, 1])
          

          
print (f(r,x))
          
[0.  0.6 1.2 0. ]


          EDIT: in this case, with only 2 conditions that are exclusive, you can also use np.where:



          def f(r,x):
          
    return np.where(x<0.5,2*r*x, 2*r*(1-x))


          will give the same result.






          share|improve this answer

























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            1 Answer
            1






            active

            oldest

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            1 Answer
            1






            active

            oldest

            votes









            active

            oldest

            votes






            active

            oldest

            votes









            4














            what you want to get as result can be done with np.select such as:



            def f(r, x):
            
    return np.select([x < 0.5,x >= 0.5], [2*r*x, 2*r*(1-x)])


            Then with



            r = np.array([0, 1, 2, 3])
            
x = np.array([0.1, 0.7, 0.3, 1])
            

            
print (f(r,x))
            
[0.  0.6 1.2 0. ]


            EDIT: in this case, with only 2 conditions that are exclusive, you can also use np.where:



            def f(r,x):
            
    return np.where(x<0.5,2*r*x, 2*r*(1-x))


            will give the same result.






            share|improve this answer






























              4














              what you want to get as result can be done with np.select such as:



              def f(r, x):
              
    return np.select([x < 0.5,x >= 0.5], [2*r*x, 2*r*(1-x)])


              Then with



              r = np.array([0, 1, 2, 3])
              
x = np.array([0.1, 0.7, 0.3, 1])
              

              
print (f(r,x))
              
[0.  0.6 1.2 0. ]


              EDIT: in this case, with only 2 conditions that are exclusive, you can also use np.where:



              def f(r,x):
              
    return np.where(x<0.5,2*r*x, 2*r*(1-x))


              will give the same result.






              share|improve this answer




























                4












                4








                4







                what you want to get as result can be done with np.select such as:



                def f(r, x):
                
    return np.select([x < 0.5,x >= 0.5], [2*r*x, 2*r*(1-x)])


                Then with



                r = np.array([0, 1, 2, 3])
                
x = np.array([0.1, 0.7, 0.3, 1])
                

                
print (f(r,x))
                
[0.  0.6 1.2 0. ]


                EDIT: in this case, with only 2 conditions that are exclusive, you can also use np.where:



                def f(r,x):
                
    return np.where(x<0.5,2*r*x, 2*r*(1-x))


                will give the same result.






                share|improve this answer















                what you want to get as result can be done with np.select such as:



                def f(r, x):
                
    return np.select([x < 0.5,x >= 0.5], [2*r*x, 2*r*(1-x)])


                Then with



                r = np.array([0, 1, 2, 3])
                
x = np.array([0.1, 0.7, 0.3, 1])
                

                
print (f(r,x))
                
[0.  0.6 1.2 0. ]


                EDIT: in this case, with only 2 conditions that are exclusive, you can also use np.where:



                def f(r,x):
                
    return np.where(x<0.5,2*r*x, 2*r*(1-x))


                will give the same result.







                share|improve this answer














                share|improve this answer



                share|improve this answer








                edited Nov 24 '18 at 2:11

























                answered Nov 24 '18 at 1:40









                Ben.TBen.T

                5,9772523




                5,9772523






























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