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