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tree.py
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tree.py
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from __future__ import print_function, division
def pprint_nodes(subtrees):
"""
Prettyprints systems of nodes.
Examples
========
>>> from sympy.printing.tree import pprint_nodes
>>> print(pprint_nodes(["a", "b1\\nb2", "c"]))
+-a
+-b1
| b2
+-c
"""
def indent(s, type=1):
x = s.split("\n")
r = "+-%s\n" % x[0]
for a in x[1:]:
if a == "":
continue
if type == 1:
r += "| %s\n" % a
else:
r += " %s\n" % a
return r
if not subtrees:
return ""
f = ""
for a in subtrees[:-1]:
f += indent(a)
f += indent(subtrees[-1], 2)
return f
def print_node(node, assumptions=True):
"""
Returns information about the "node".
This includes class name, string representation and assumptions.
Parameters
==========
assumptions : bool, optional
See the ``assumptions`` keyword in ``tree``
"""
s = "%s: %s\n" % (node.__class__.__name__, str(node))
if assumptions:
d = node._assumptions
else:
d = None
if d:
for a in sorted(d):
v = d[a]
if v is None:
continue
s += "%s: %s\n" % (a, v)
return s
def tree(node, assumptions=True):
"""
Returns a tree representation of "node" as a string.
It uses print_node() together with pprint_nodes() on node.args recursively.
Parameters
==========
asssumptions : bool, optional
The flag to decide whether to print out all the assumption data
(such as ``is_integer`, ``is_real``) associated with the
expression or not.
Enabling the flag makes the result verbose, and the printed
result may not be determinisitic because of the randomness used
in backtracing the assumptions.
See Also
========
print_tree
"""
subtrees = []
for arg in node.args:
subtrees.append(tree(arg, assumptions=assumptions))
s = print_node(node, assumptions=assumptions) + pprint_nodes(subtrees)
return s
def print_tree(node, assumptions=True):
"""
Prints a tree representation of "node".
Parameters
==========
asssumptions : bool, optional
The flag to decide whether to print out all the assumption data
(such as ``is_integer`, ``is_real``) associated with the
expression or not.
Enabling the flag makes the result verbose, and the printed
result may not be determinisitic because of the randomness used
in backtracing the assumptions.
Examples
========
>>> from sympy.printing import print_tree
>>> from sympy import Symbol
>>> x = Symbol('x', odd=True)
>>> y = Symbol('y', even=True)
Printing with full assumptions information:
>>> print_tree(y**x)
Pow: y**x
+-Symbol: y
| algebraic: True
| commutative: True
| complex: True
| even: True
| extended_real: True
| finite: True
| hermitian: True
| imaginary: False
| infinite: False
| integer: True
| irrational: False
| noninteger: False
| odd: False
| rational: True
| real: True
| transcendental: False
+-Symbol: x
algebraic: True
commutative: True
complex: True
even: False
extended_nonzero: True
extended_real: True
finite: True
hermitian: True
imaginary: False
infinite: False
integer: True
irrational: False
noninteger: False
nonzero: True
odd: True
rational: True
real: True
transcendental: False
zero: False
Hiding the assumptions:
>>> print_tree(y**x, assumptions=False)
Pow: y**x
+-Symbol: y
+-Symbol: x
See Also
========
tree
"""
print(tree(node, assumptions=assumptions))