Sympy Latex

Sympy Latex

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Say I'm building a class and inside it I have a __repr__ method for printing it. Is there a way of using sympy's latex printing for the __repr__ output to be rendered?
In this example, if I make an instance like this: v = vector([1, 0, 0], [e1, e2, e3]) I would love for print(v) to look like $1e_1 + 0e_2 + 0e_3$. Is there a way to achieve this?
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The LaTeX display of Sympy object in IPython notebook is handled by IPython's display module. If an object has a _repr_latex_() function defined, when Sympy init_printing() is called, IPython will render the LaTeX representation of the object. The below code should be used with IPython. In the latest version of IPython, the display() function does not need to be imported. For older versions, we may have to include from IPython.display import display .
I'm not sure I understand the restriction against sympy.add . Would this suffice?
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>>> from sympy.parsing.sympy_parser import parse_expr
>>> parse_expr ( "1/2" )
1/2
>>> type ( _ )

>>> from sympy.parsing.sympy_parser import standard_transformations , \
... implicit_multiplication_application
>>> transformations = ( standard_transformations +
... ( implicit_multiplication_application ,))
>>> parse_expr ( "2x" , transformations = transformations )
2*x

>>> parse_expr ( "2**3" ), parse_expr ( "2**3" , evaluate = False )
(8, 2**3)

>>> a = parse_expr ( '1 + x' , evaluate = False )
>>> b = parse_expr ( 'x + 1' , evaluate = 0 )
>>> a == b
False
>>> a . args
(1, x)
>>> b . args
(x, 1)

>>> assert str ( a ) == str ( b )

>>> from sympy.parsing.sympy_parser import transformations

>>> print ( transformations )
0: lambda_notation
1: auto_symbol
2: repeated_decimals
3: auto_number
4: factorial_notation
5: implicit_multiplication_application
6: convert_xor
7: implicit_application
8: implicit_multiplication
9: convert_equals_signs
10: function_exponentiation
11: rationalize

>>> from sympy.parsing.sympy_parser import T

>>> str ( T ) == str ( transformations )
True

>>> T [: 5 ] == standard_transformations
True

>>> parse_expr ( "2x" , transformations = T [: 5 ])
Traceback (most recent call last):
...
SyntaxError : invalid syntax
>>> parse_expr ( "2x" , transformations = T [: 6 ])
2*x
>>> parse_expr ( '.3' , transformations = T [ 3 , 11 ])
3/10
>>> parse_expr ( '.3x' , transformations = T [:])
3*x/10

>>> parse_expr ( '.3x' , transformations = 'all' )
3*x/10

>>> from sympy.parsing.mathematica import parse_mathematica
>>> parse_mathematica ( "Sin[x]^2 Tan[y]" )
sin(x)**2*tan(y)
>>> e = parse_mathematica ( "F[7,5,3]" )
>>> e
F(7, 5, 3)
>>> from sympy import Function , Max , Min
>>> e . replace ( Function ( "F" ), lambda * x : Max ( * x ) * Min ( * x ))
21

>>> parse_mathematica ( "x*(a + b)" )
x*(a + b)
>>> parse_mathematica ( "Times[x, Plus[a, b]]" )
x*(a + b)

>>> m = parse_mathematica ( "{{a, b}, {c, d}}" )
>>> m
((a, b), (c, d))
>>> from sympy import Matrix
>>> Matrix ( m )
Matrix([
[a, b],
[c, d]])

>>> parse_mathematica ( "x_." )
Optional(Pattern(x, Blank()))
>>> parse_mathematica ( "Plus @@ {x, y, z}" )
Apply(Plus, (x, y, z))
>>> parse_mathematica ( "f[x_, 3] := x^3 /; x > 0" )
SetDelayed(f(Pattern(x, Blank()), 3), Condition(x**3, x > 0))

>>> from sympy.parsing.sympy_parser import ( parse_expr , _token_splittable ,
... standard_transformations , implicit_multiplication ,
... split_symbols_custom )
>>> def can_split ( symbol ):
... if symbol not in ( 'list' , 'of' , 'unsplittable' , 'names' ):
... return _token_splittable ( symbol )
... return False
...
>>> transformation = split_symbols_custom ( can_split )
>>> parse_expr ( 'unsplittable' , transformations = standard_transformations +
... ( transformation , implicit_multiplication ))
unsplittable

>>> from sympy.parsing.sympy_parser import ( parse_expr ,
... standard_transformations , implicit_multiplication )
>>> transformations = standard_transformations + ( implicit_multiplication ,)
>>> parse_expr ( '3 x y' , transformations = transformations )
3*x*y

>>> from sympy.parsing.sympy_parser import ( parse_expr ,
... standard_transformations , implicit_application )
>>> transformations = standard_transformations + ( implicit_application ,)
>>> parse_expr ( 'cot z + csc z' , transformations = transformations )
cot(z) + csc(z)

>>> from sympy.parsing.sympy_parser import ( parse_expr ,
... standard_transformations , function_exponentiation )
>>> transformations = standard_transformations + ( function_exponentiation ,)
>>> parse_expr ( 'sin**4(x)' , transformations = transformations )
sin(x)**4

>>> from sympy.parsing.sympy_parser import ( parse_expr ,
... standard_transformations , implicit_multiplication_application )
>>> parse_expr ( "10sin**2 x**2 + 3xyz + tan theta" ,
... transformations = ( standard_transformations +
... ( implicit_multiplication_application ,)))
3*x*y*z + 10*sin(x**2)**2 + tan(theta)

>>> from sympy.parsing.latex import parse_latex
>>> expr = parse_latex ( r "\frac {1 + \sqrt {\a}} {\b}" )
>>> expr
(sqrt(a) + 1)/b
>>> expr . evalf ( 4 , subs = dict ( a = 5 , b = 2 ))
1.618

>>> from sympy.parsing.sym_expr import SymPyExpression
>>> src = '''
... int a,b;
... float c = 2, d =4;
... '''
>>> a = SymPyExpression ( src , 'c' )
>>> a . return_expr ()
[Declaration(Variable(a, type=intc)),
Declaration(Variable(b, type=intc)),
Declaration(Variable(c, type=float32, value=2.0)),
Declaration(Variable(d, type=float32, value=4.0))]

>>> from sympy.parsing.sym_expr import SymPyExpression
>>> src2 = '''
... integer :: a, b, c, d
... real :: p, q, r, s
... '''
>>> p = SymPyExpression ()
>>> p . convert_to_expr ( src2 , 'f' )
>>> p . convert_to_c ()
['int a = 0', 'int b = 0', 'int c = 0', 'int d = 0', 'double p = 0.0', 'double q = 0.0', 'double r = 0.0', 'double s = 0.0']

>>> from sympy.parsing.sym_expr import SymPyExpression
>>> src3 = '''
... integer :: a, b, c, d, e
... d = a + b - c
... e = b * d + c * e / a
... '''
>>> p = SymPyExpression ( src3 , 'f' )
>>> p . convert_to_python ()
['a = 0', 'b = 0', 'c = 0', 'd = 0', 'e = 0', 'd = a + b - c', 'e = b*d + c*e/a']

>>> from sympy.parsing.sym_expr import SymPyExpression
>>> src = '''
... integer function f(a,b)
... integer, intent(in) :: a, b
... integer :: r
... end function
... '''
>>> a = SymPyExpression ( src , 'f' )
>>> a . convert_to_python ()
['def f(a, b):\n f = 0\n r = 0\n return f']

>>> from sympy.parsing.sym_expr import SymPyExpression
>>> src2 = '''
... integer :: a, b, c, d
... real :: p, q, r, s
... c = a/b
... d = c/a
... s = p/q
... r = q/p
... '''
>>> p = SymPyExpression ()
>>> p . convert_to_expr ( src2 , 'f' )
>>> p . convert_to_c ()
['int a = 0', 'int b = 0', 'int c = 0', 'int d = 0', 'double p = 0.0', 'double q = 0.0', 'double r = 0.0', 'double s = 0.0', 'c = a/b;', 'd = c/a;', 's = p/q;', 'r = q/p;']

>>> from sympy.parsing.sym_expr import SymPyExpression
>>> src3 = '''
... integer function f(a,b) result(r)
... integer, intent(in) :: a, b
... integer :: x
... r = a + b -x
... end function
... '''
>>> p = SymPyExpression ()
>>> p . convert_to_expr ( src3 , 'f' )
>>> p . return_expr ()
[FunctionDefinition(integer, name=f, parameters=(Variable(a), Variable(b)), body=CodeBlock(
Declaration(Variable(r, type=integer, value=0)),
Declaration(Variable(x, type=integer, value=0)),
Assignment(Variable(r), a + b - x),
Return(Variable(r))
))]

>>> from sympy.parsing.sym_expr import SymPyExpression
>>> src2 = '''
... integer :: a, b, c, d
... real :: p, q, r, s
... c = a/b
... d = c/a
... s = p/q
... r = q/p
... '''
>>> p = SymPyExpression ( src2 , 'f' )
>>> p . convert_to_fortran ()
[' integer*4 a', ' integer*4 b', ' integer*4 c', ' integer*4 d', ' real*8 p', ' real*8 q', ' real*8 r', ' real*8 s', ' c = a/b', ' d = c/a', ' s = p/q', ' r = q/p']

>>> from sympy.parsing.sym_expr import SymPyExpression
>>> src2 = '''
... integer :: a, b, c, d
... real :: p, q, r, s
... c = a/b
... d = c/a
... s = p/q
... r = q/p
... '''
>>> p = SymPyExpression ( src2 , 'f' )
>>> p . convert_to_python ()
['a = 0', 'b = 0', 'c = 0', 'd = 0', 'p = 0.0', 'q = 0.0', 'r = 0.0', 's = 0.0', 'c = a/b', 'd = c/a', 's = p/q', 'r = q/p']

>>> from sympy.parsing.sym_expr import SymPyExpression
>>> src3 = '''
... integer function f(a,b)
... integer, intent(in) :: a, b
... integer :: r
... r = a+b
... f = r
... end function
... '''
>>> p = SymPyExpression ()
>>> p . convert_to_expr ( src3 , 'f' )
>>> p . return_expr ()
[FunctionDefinition(integer, name=f, parameters=(Variable(a), Variable(b)), body=CodeBlock(
Declaration(Variable(f, type=integer, value=0)),
Declaration(Variable(r, type=integer, value=0)),
Assignment(Variable(f), Variable(r)),
Return(Variable(f))
))]

$ pip install antlr4-python3-runtime==4.10

$ conda install -c conda-forge antlr-python-runtime==4.10

$ conda install -c conda-forge lfortran clang

Copyright © 2022 SymPy Development Team

Converts the string s to a SymPy expression, in local_dict
A dictionary of local variables to use when parsing.
A dictionary of global variables. By default, this is initialized
with from sympy import * ; provide this parameter to override
this behavior (for instance, to parse "Q & S" ).
A tuple of transformation functions used to modify the tokens of the
parsed expression before evaluation. The default transformations
convert numeric literals into their SymPy equivalents, convert
undefined variables into SymPy symbols, and allow the use of standard
mathematical factorial notation (e.g. x! ). Selection via
string is available (see below).
When False, the order of the arguments will remain as they were in the
string and automatic simplification that would normally occur is
suppressed. (see examples)
When evaluate=False, some automatic simplifications will not occur:
In addition the order of the arguments will not be made canonical.
This feature allows one to tell exactly how the expression was entered:
Note, however, that when these expressions are printed they will
appear the same:
As a convenience, transformations can be seen by printing transformations :
The T object provides a way to select these transformations:
If you print it, you will see the same list as shown above.
Standard slicing will return a tuple of transformations:
So T can be used to specify the parsing transformations:
As a further convenience, strings ‘implicit’ and ‘all’ can be used
to select 0-5 and all the transformations, respectively.
Converts the string s to Python code, in local_dict
Generally, parse_expr should be used.
Evaluate Python code generated by stringify_expr .
Generally, parse_expr should be used.
Translate a string containing a Wolfram Mathematica expression to a SymPy
expression.
If the translator is unable to find a suitable SymPy expression, the
FullForm of the Mathematica expression will be output, using SymPy
Function objects as nodes of the syntax tree.
Both standard input form and Mathematica full form are supported:
To get a matrix from Wolfram’s code:
If the translation into equivalent SymPy expressions fails, an SymPy
expression equivalent to Wolfram Mathematica’s “FullForm” will be created:
A transformation is a function that accepts the arguments tokens,
local_dict, global_dict and returns a list of transformed tokens. They can
be used by passing a list of functions to parse_expr() and are
applied in the order given.
Standard transformations for parse_expr() .
Inserts calls to Symbol , Integer , and other SymPy
datatypes and allows the use of standard factorial notation (e.g. x! ).
Splits symbol names for implicit multiplication.
Intended to let expressions like xyz be parsed as x*y*z . Does not
split Greek character names, so theta will not become
t*h*e*t*a . Generally this should be used with
implicit_multiplication .
Creates a transformation that splits symbol names.
predicate should return True if the symbol name is to be split.
For instance, to retain the default behavior but avoid splitting certain
symbol names, a predicate like this would work:
Makes the multiplication operator optional in most cases.
Use this before implicit_application() , otherwise expressions like
sin 2x will be parsed as x * sin(2) rather than sin(2*x) .
Makes parentheses optional in some cases for function calls.
Use this after implicit_multiplication() , otherwise expressions
like sin 2x will be parsed as x * sin(2) rather than
sin(2*x) .
Allows functions to be exponentiated, e.g. cos**2(x) .
Parentheses for single-argument method calls are optional.
Symbol names can be split (i.e. spaces are not needed between
symbols).
Converts floats into Rational . Run AFTER auto_number .
Treats XOR, ^ , as exponentiation, ** .
These are included in
sympy.parsing.sympy_parser.standard_transformations and generally
don’t need to be manually added by the user.
Substitutes “lambda” with its SymPy equivalent Lambda().
However, the conversion does not take place if only “lambda”
is passed because that is a syntax error.
Inserts calls to Symbol / Function for undefined variables.
Allows 0.2[1] notation to represent the repeated decimal 0.2111… (19/90)
Converts numeric literals to use SymPy equivalents.
Complex numbers use I , integer literals use Integer , and float
literals use Float .
Allows standard notation for factorial.
$\mathrm{\LaTeX}$ parsing was ported from
latex2sympy . While functional
and its API should remain stable, the parsing behavior or backend m
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