Schlagwort-Archive: Mac M1

EMT, Python, Numpy, Sympy on Macs with M1

I am currently using an 11′ Mac Air Notebook with the M1 processor as my laptop. We have to admit that Apple has made a big step forward again with this architecture. I never used Macs before because they were expensive and did not offer enough benefit to justify leaving Windows. Admittedly, they always had a much better user experience than Windows, let alone Linux. What works, does work well! But I have been judging the systems by more technical features, like processor power, ease of administrator work, and support for programmers. And in that respect, Macs were a step back. So, I could never justify using a system that serves me worse at a higher cost.

Now, things have changed with the M1 architecture. The laptop I am using is a bargain for what it offers. The processor is very fast, the display excellent, and the battery life extraordinary. The build quality is far better than any notebook I had before, with a rugged, indestructible case that still feels light and slim. For my retirement, that is precisely what I need.

The purchase of this laptop also gives me the chance to test my math programs on that operating system. There is a Windows emulation for Macs, but it does not work well with the M1 processor. So, a program like Euler Math Toolbox will have to be ported which is a lot of work, most likely not worth the effort in the end. I am going to produce a simplified version nevertheless.

Java is working well, and that gave me the chance to fix my program C.a.R. (Compass and Ruler) for Macs. Indeed, there were some annoying quirks that needed to be fixed. It is now running very well. (By the way, the same applies to my editor, JE, which I still use.)

Until I have a working version, Euler Math Toolbox needs to replaced by other, free tools on Macs. For the numerical side, there is Scilab. It has a working version for Macs, and I found no obvious flaw. For the numerical side, there is no free solution. Maxima can only be installed in a very complicated way using a sort of emulator, and I still have to explore this method. More on this on another day.

But, there is Python. We have a numerical matrix library „numpy“ and a symbolic library „sympy“. There is also a very nice plot library „matplotlib“. All those are not as easy to use as Euler Math Toolbox. I’d say, they are clumsy in comparison. Nevertheless, I am going to explore these libraries and compare to EMT in a series of postings on this blog.

You can follow me without a Mac. So let us start with an example which requires symbolic computations. We want to compute the polynomial of degree n with the property

\(p_{n+1}(x)-p_{n+1}(x-1)=x^n, \quad p_n(0)=0\)

for all real x, and natural n (investigated by Bernoulli and Euler). By this equation and induction, the polynomial is defined for all natural numbers x. Thus, the polynomial is uniquely determined if it exists. And it does indeed exist. You just need to interpolate in the first n+2 natural numbers, and since the equation holds there, it holds everywhere.

We now have

\(p_{n+1}(N) = \sum\limits_{k=1}^N k^n.\)

We could use interpolation to determine the polynomials. It is an exercise to do this in any symbolic program. For a start, we use Maxima in Euler Math Toolbox to determine the case n=3.

>p &= sum(a[k]*x^k,k,0,4)
                       4       3       2
                   a  x  + a  x  + a  x  + a  x + a
                    4       3       2       1      0
>equ &= makelist((p with [x=k]) = sum(j^3,j,1,k),k,1,5)
        [a  + a  + a  + a  + a  = 1, 
          4    3    2    1    0
 16 a  + 8 a  + 4 a  + 2 a  + a  = 9, 
     4      3      2      1    0
 81 a  + 27 a  + 9 a  + 3 a  + a  = 36, 
     4       3      2      1    0
 256 a  + 64 a  + 16 a  + 4 a  + a  = 100, 
      4       3       2      1    0
 625 a  + 125 a  + 25 a  + 5 a  + a  = 225]
      4        3       2      1    0
>&factor(p with solve(equ)[1])

                               2        2
                              x  (x + 1)

We have created a polynomial with indexed parameters using the sum() function of Maxima. The command makelist() then creates a list of equations which our polynomial has to satisfy (due to the sum requirement above). This can be solved with solve(). We use the first solution to substitute the values for a[k] back into the polynomial and factor the result. We get the well-known formula for the sum of the cubes of the first x integers.

Let us switch to Python. In Windows, you need to install „Anaconda“ for the following. Then start the „Anaconda Navigator“ and „jupyter notebook“. There you enter the following.

from sympy import *
x, k = symbols('x k')
a = IndexedBase('a')
p = sum([a[k]*x**k for k in range(5)])
equ = [Eq(p.subs(x,k),sum([l**3 for l in range(1,k+1)])) for k in range(1,6)]
sol = solve(equ)
factor(p.subs([(a[k],sol[a[k]]) for k in range(5)]))

This is more difficult to understand than the EMT code above, but it follows the same ideas.

After importing everything from sympy, we need to define the symbols x and k, and the indexed symbol a[]. Then we use the sum function of Python on a list. The result is

x**4*a[4] + x**3*a[3] + x**2*a[2] + x*a[1] + a[0]

After that, we create a list of equations, just as in the Maxima code above. The problem is that we cannot use „==“. This would test the two sides for equality and evaluate to true or false. We need to use „Eq(a,b)“ instead.

[Eq(a[0] + a[1] + a[2] + a[3] + a[4], 1), Eq(a[0] + 2*a[1] + 4*a[2] + 8*a[3] + 16*a[4], 9), Eq(a[0] + 3*a[1] + 9*a[2] + 27*a[3] + 81*a[4], 36), Eq(a[0] + 4*a[1] + 16*a[2] + 64*a[3] + 256*a[4], 100), Eq(a[0] + 5*a[1] + 25*a[2] + 125*a[3] + 625*a[4], 225)]

Then we solve this equation, which yields a dictionary with solutions. Dictionaries are Python structures that contain entries and values. They print as follows.

{a[0]: 0, a[1]: 0, a[2]: 1/4, a[3]: 1/2, a[4]: 1/4}

We now use the subs() function to substitute the values of the parameters into the polynomial p and factor the result. In a jupyter notebook, it appears in pretty format automatically. Here is a screenshot.

So much for a start of the sequence of postings. Clearly, Euler Math Toolbox and Maxima re easier to use. But Python is available for Macs. On Linux, you can use EMT quite nicely with the modern Wine emulation. But you could also use Python.

Finally, as a mathematical side remark, you could differentiate the definition equation for our polynomials and get.

\(p_{n+1}'(x)-p_{n+1}'(x-1) = n x^{n-1}\)

Since our polynomials are uniquely defined by the equation, we get

\(p_n(x) = \dfrac{1}{n} (p_{n+1}'(x) -p_{n+1}'(0))\)

On the right hand side, we have a polynomial which satisfies the equation and has the property p(0)=0. We can use this to compute the next polynomial.

\(p_{n+1}(x) = n \int\limits_0^x p_n(t) \,dt + c x\)

To determine the constant c, we use p(1)=1 for our polynomials. Thus

\( p_{n+1}(x) = n \int\limits_0^x p_n(t) \,dt + (1- n\int\limits_0^1 p_n(t) \,dt) x\)

Let us make a function in Euler Math Toolbox from this formula. The function will call Maxima several times, and thus not be fast.

>function p(n) ...
$pn &= "x";
$if n>1 then
$  for k=1 to n-1;
$    q &= "subst(t,x,pn)";
$    pn &= "@k*integrate(q,t,0,x)+(1-@k*integrate(q,t,0,1))*x";
$  end;
$return &pn;
>p(4); &factor(%)
                               2        2
                              x  (x + 1)

In Euler functions, you need to use „…“ for symbolic expressions. Moreover, you can use numerical values via @… in symbolic expressions.

Of course, loops like this can also be programmed in Maxima direct. You will have to learn the syntax of Maxima for this.

Here is a solution in Python.

def p(n):
    t,x = symbols('t x')
    p = x
    if n<=1:
        return p
    for i in range(1,n):
        q = p.subs(x,t)
        p = i*integrate(q,(t,0,x)) + (1-i*integrate(q,(t,0,1)))*x
    return p

This is a good start.

But it turns out that the Anaconda Navigator does not run on Macs. It looks like it needs Rosetta2. Usually, a program asks if Rosetta should be installed. But Anaconda didn’t on my system.

After installing Anaconda nevertheless, you have a Python 3.9 environment. So you can run „python“ to start the Python interpreter, and „ipython“ to start the advanced Python terminal. For this test, I saved the following to a file „“ using an editor (I am using my JE, of course).

from sympy import *

def p(n):
    t,x = symbols('t x')
    p = x
    if n<=1:
        return p
    for i in range(1,n):
        q = p.subs(x,t)
        p = i*integrate(q,(t,0,x)) + (1-i*integrate(q,(t,0,1)))*x
    return p

Then running „python“ will run this script and output the result in pretty format.

 2        2 ⎛ 2        ⎞ ⎛   4      3    2          ⎞
x ⋅(x + 1) ⋅⎝x  + x - 1⎠⋅⎝2⋅x  + 4⋅x  - x  - 3⋅x + 3⎠

You can also try to run „jupyter notebook“ from the terminal. If things go well, your browser will open and you can start an ipython terminal, clicking on „New“. Use Option+Return to run any command.