I'm especting something like the following:
>>> Sum(x[i,j],(j, i+1, 3), (i, 1, 3)).doit()
x[1,2] + x[1,3] + x[2,3]
But instead I get:
>>> Sum(x[i,j],(j, i+1, 3), (i, 1, 3)).doit()
Sum(x[i,j],(j, i+1, 3), (i, 1, 3))
Anybody knows how to make the first one?
I'm not looking for a solution like the following:
>>> sum(x[i,j] for i in range(1,4) for j in range(i,4))
x[1,2] + x[1,3] + x[2,3]
This is intended for computing drift forces where the number of combinations is way bigger and the inside term is more complex, but I need away to get it without the summation form.
Related
How to use Sympy to check calculations step by step, for instance that the 2 formulas below are equal?
from sympy import IndexedBase, Sum, symbols
i, n = symbols('i n', integer=True)
a = IndexedBase('a')
Sum(a[i], (i, 0, n - 1)) + a[n]
Sum(a[i], (i, 0, n))
The simplify function is used to check whether two expressions are equal.
from sympy import IndexedBase, Sum, symbols, simplify
i, n = symbols('i n', integer=True)
a = IndexedBase('a')
#to check calculations
n=5
x=Sum(a[i], (i, 0, n - 1)).doit() + a[n].doit()
y=Sum(a[i], (i, 0, n)).doit()
print x,y
#To check functions are equal
if simplify(x-y) == 0:
print "The two functions are equal"
I am trying to make a simple example in SymPy to compute some coefficients and then use them in a sum of legendre polynomials. Finally plot it. Very simple but can not make it work. I want to use it for my electromagnetism course. I get errors in both the attempts below:
%matplotlib inline
from sympy import *
x, y, z = symbols('x y z')
k, m, n = symbols('k m n', integer=True)
f, step, potential = symbols('f step potential', cls=Function)
var('n x')
A=SeqFormula(2*(2*m+1)*Integral(legendre(2*m+1,x),(x,0,1)).doit(),(m,0,oo)).doit()
Sum(A.coeff(m).doit()*legendre(2*m+1,x),(m,0,10)).doit()
B=Tuple.fromiter(2*(2*n+1)*Integral(legendre(2*n+1,x),(x,0,1)).doit() for n in range(50))
Sum(B[m]*legendre(2*m+1,x),(m,0,10)).doit()
Here is a part of an script in Mathematica of what I would like to replicate:
Nn = 50;
Array[A, Nn]
For[i = 0, i <= Nn, i++, A[i + 1] = Integrate[LegendreP[2*i + 1, x]*(2*(2*i + 1) + 1), {x, 0, 1}]];
Step = Sum[A[n + 1]*LegendreP[2*n + 1, #], {n, 0, Nn}] & Plot[Step[x], {x, -1, 1}]
I think the structure you were searching for with A is Python's lambda.
A = lambda m: 2*(2*m+1)*Integral(legendre(2*m+1, x), (x, 0, 1))
f = Sum(A(m)*legendre(2*m+1, x), (m, 0, 10)).doit()
plot(f, (x, -1, 1))
The key point is that m has to be explicit in order for integration to happen; SymPy does not know a general formula for integrating legendre(n, x). So, the integration here is attempted only when A is called with a concrete value of m, like A(0), A(1), etc.
I have a list, for example:
[3, 4, 5, 6]
and I want to multiply each of them by it's list index:
[3*0, 4*1, 5*2, 6*3]
I'm a bigener in python and I want to solve this with for loops and without using any built in functions. it will be really helpful if you help me, thank you.
n=0
for z in range (0, len(numbers)):
numbers[n]=numbers[(z * n)]
n+=1
this is what I have wrote already and it doesn't work.
The problem is in this line:
numbers[n]=numbers[(z * n)]
you want to change numbers[n] to the number at the index n by n
so an alternative may be
for n in range(len(numbers)):
numbers[n] = numbers[n] * n
The objective is to implement a Piecewise expression that gives 0 when n is even, and 1 when n is odd. One way to do it is using the floor function like below:
from sympy import *
from sympy.abc import n
f = Lambda((n,), Piecewise((0, Eq(n, floor(n / S(2)))),
(1, Eq(n, floor(n / S(2))+1))))
print(f(0))
print(f(1))
print(f(2))
print(f(3))
However, this returns the wrong output:
0
1
1
Piecewise()
The correct output should be:
0
1
0
1
Another way to achieve the same is to use:
from sympy import *
from sympy.abc import n
f = Lambda((n,), Piecewise((0, Eq((-1)**n, 1)),
(1, Eq((-1)**n, -1))))
print(f(0))
print(f(1))
print(f(2))
print(f(3))
and this returns the correct output. Is there a way to achieve this using the floor function in the original code?
A better way would be to use Mod, like
Piecewise((0, Eq(Mod(n, 2), 0)), (1, Eq(Mod(n, 2), 1)))
However, since your function coincides exactly with the definition of Mod, you can just use it directly
Mod(n, 2)
or equivalently
n % 2
I would like to try out the Mincemeat map/reduce Python application for matrix multiplication. I am using Python 2.7. I found several web pages that describe how to do matrix multiplication using Hadoop in Java, and I have been referring to this one http://importantfish.com/one-step-matrix-multiplication-with-hadoop/ both because it is simple and because the pseudocode that it displays is very close to Python code already.
I noticed in the Java code that is also included that the matrix dimensions are supplied to the map and reduce functions via an additional argument of type Context. Mincemeat doesn't provide such a thing, but I got a suggestion that I could provide these values to my map and reduce functions using closures. The map and reduce functions I wrote look like this:
def make_map_fn(num_rows_result, num_cols_result):
m = num_rows_result
p = num_cols_result
def map_fn(key, value):
# value is ('A', i, j, a_ij) or ('B', j, k, b_jk)
if value[0] == 'A':
i = value[1]
j = value[2]
a_ij = value[3]
for k in xrange(1, p):
yield ((i, k), ('A', j, a_ij))
else:
j = value[1]
k = value[2]
b_jk = value[3]
for i in xrange(1, m):
yield ((i, k), ('B', j, b_jk))
return map_fn
def make_reduce_fn(inner_dim):
n = inner_dim
def reduce_fn(key, values):
# key is (i, k)
# values is a list of ('A', j, a_ij) and ('B', j, b_jk)
hash_A = {j: a_ij for (x, j, a_ij) in values if x == 'A'}
hash_B = {j: b_jk for (x, j, b_jk) in values if x == 'B'}
result = 0
for j in xrange(1, n):
result += hash_A[j] * hash_B[j]
return (key, result)
return reduce_fn
Then I assign them to Mincemeat like this:
s = mincemeat.Server()
s.mapfn = make_map_fn(num_rows_A, num_cols_B)
s.reducefn = make_reduce_fn(num_cols_A)
When I run this in Mincemeat, I get this error message:
error: uncaptured python exception, closing channel <__main__.Client connected at 0x2ada4d0>
(<type 'exceptions.TypeError'>:arg 5 (closure) must be tuple
[/usr/lib/python2.7/asyncore.py|read|83]
[/usr/lib/python2.7/asyncore.py|handle_read_event|444]
[/usr/lib/python2.7/asynchat.py|handle_read|140]
[/usr/local/lib/python2.7/dist-packages/mincemeat.py|found_terminator|96]
[/usr/local/lib/python2.7/dist-packages/mincemeat.py|process_command|194]
[/usr/local/lib/python2.7/dist-packages/mincemeat.py|set_mapfn|159])
I searched around on the net with search terms like |python closure must be tuple| and the things that I found seemed to be dealing with cases where someone is trying to construct a function using lambda or function() and need to make sure they didn't omit certain things when defining them as closures. In my case, the map_fn and reduce_fn values returned by make_map_fn and make_reduce_fn look like valid function objects, their func_closure values are tuples of cells containing the array dimensions that I want to supply, but something is still missing. What form do I need to pass these functions in to be usable by Mincemeat?
I hate to be the bearer of bad news, but this is just the result of a few off-by-one errors in your code, plus two errors in the input file provided by the site you linked. It is unrelated to your usage of a closure, misleading error messages notwithstanding.
Off-by-one errors
Notice that the innermost loops in the pseudocode look like this:
for k = 1 to p:
for i = 1 to m:
for j = 1 to n:
In pseudocode, this typically indicates that the endpoint is included, i.e. for k = 1 to p means k = 1, 2, ..., p-1, p. On the other hand, the corresponding loops in your code look like this:
for k in xrange(1, p):
for i in xrange(1, m):
for j in xrange(1, n):
And of course, xrange(1, p) yields 1, 2, ..., p-2, p-1. Assuming you indexed the matrices from 0 (as they did on the site you linked), all your xranges should start at 0 (e.g. xrange(0, p)), as their equivalents in the Java code do (for (int k = 0; k < p; k++)). This fixes one of your problems.
Input file errors
In case you didn't catch this, the input file for A and B that the site provides is incorrect - they forgot the (0,0) entries of both matrices. In particular, you should add a line to the beginning of the form A,0,0,0.0, and a line between 9 and 10 of the form B,0,0,0.0. (I guess where exactly you put it doesn't matter, but for consistency, you may as well put them where they naturally fit.)
Once I correct these two errors, mincemeat gives me the result we expect (formatted):
{(0, 1): ((0, 1), 100.0),
(1, 2): ((1, 2), 310.0),
(0, 0): ((0, 0), 90.0),
(0, 2): ((0, 2), 110.0),
(1, 0): ((1, 0), 240.0),
(1, 1): ((1, 1), 275.0)}
I haven't figured out exactly what's going on with the error message, but I think it boils down to the fact that the incorrect loop indices in the map function are resulting in garbage data being passed to the reduce nodes, which is why the error mentions the reduce function.
Basically, what happens is that hash_A and hash_B in the reduce function sometimes don't have the same keys, so when you try to multiply hash_A[j] * hash_B[j], you'll get a KeyError because j is not a key of one or the other, and this gets caught somewhere upstream and rethrown as a TypeError instead.