def apply(self, items, pattern, ls, evaluation, options):
"Cases[items_, pattern_, ls_:{1}, OptionsPattern[]]"
if items.is_atom():
return Expression(SymbolList)
from mathics.builtin.patterns import Matcher
if ls.has_form("Rule", 2):
if ls.leaves[0].get_name() == "System`Heads":
heads = ls.leaves[1].is_true()
ls = Expression("List", 1)
else:
return evaluation.message("Position", "level", ls)
else:
heads = self.get_option(options, "Heads", evaluation).is_true()
try:
start, stop = python_levelspec(ls)
except InvalidLevelspecError:
return evaluation.message("Position", "level", ls)
results = []
if pattern.has_form("Rule", 2) or pattern.has_form("RuleDelayed", 2):
match = Matcher(pattern.leaves[0]).match
rule = Rule(pattern.leaves[0], pattern.leaves[1])
def callback(level):
if match(level, evaluation):
result = rule.apply(level, evaluation)
result = result.evaluate(evaluation)
results.append(result)
return level
else:
match = Matcher(pattern).match
def callback(level):
if match(level, evaluation):
results.append(level)
return level
walk_levels(items, start, stop, heads=heads, callback=callback)
return Expression(SymbolList, *results)
def apply(self, expr, test, level, evaluation):
"%(name)s[expr_, test_, level_]"
try:
start, stop = python_levelspec(level)
except InvalidLevelspecError:
evaluation.message("Level", "level", level)
return
def callback(node):
self._short_circuit(Expression(test, node).evaluate(evaluation).is_true())
return node
try:
walk_levels(expr, start, stop, callback=callback)
except _ShortCircuit as e:
return e.result
return self._no_short_circuit()
def apply(self, expr, test, level, evaluation):
"%(name)s[expr_, test_, level_]"
try:
start, stop = python_levelspec(level)
except InvalidLevelspecError:
evaluation.message("Level", "level", level)
return
def callback(node):
self._short_circuit(Expression(test, node).evaluate(evaluation).is_true())
return node
try:
walk_levels(expr, start, stop, callback=callback)
except _ShortCircuit as e:
return e.result
return self._no_short_circuit()
def apply_level(self, f, expr, ls, evaluation, options={}):
'Map[f_, expr_, ls_?LevelQ:{1}, OptionsPattern[Map]]'
try:
start, stop = python_levelspec(ls)
except InvalidLevelspecError:
evaluation.message('Map', 'level', ls)
return
def callback(level):
return Expression(f, level)
heads = self.get_option(options, 'Heads', evaluation).is_true()
result, depth = walk_levels(expr, start, stop, heads=heads, callback=callback)
return result
def apply_level(self, f, expr, ls, evaluation, options={}):
'MapIndexed[f_, expr_, ls_?LevelQ:{1}, OptionsPattern[MapIndexed]]'
try:
start, stop = python_levelspec(ls)
except InvalidLevelspecError:
evaluation.message('MapIndexed', 'level', ls)
return
def callback(level, pos):
return Expression(f, level, Expression('List', *(Integer(p) for p in pos)))
heads = self.get_option(options, 'Heads', evaluation).is_true()
result, depth = walk_levels(expr, start, stop, heads=heads, callback=callback, include_pos=True)
return result
def apply_level(self, f, expr, ls, evaluation, options={}):
'Map[f_, expr_, ls_?LevelQ:{1}, OptionsPattern[Map]]'
try:
start, stop = python_levelspec(ls)
except InvalidLevelspecError:
evaluation.message('Map', 'level', ls)
return
def callback(level):
return Expression(f, level)
heads = self.get_option(options, 'Heads', evaluation).is_true()
result, depth = walk_levels(expr, start, stop, heads=heads, callback=callback)
return result
def apply_level(self, f, expr, ls, evaluation, options={}):
'MapIndexed[f_, expr_, ls_?LevelQ:{1}, OptionsPattern[MapIndexed]]'
try:
start, stop = python_levelspec(ls)
except InvalidLevelspecError:
evaluation.message('MapIndexed', 'level', ls)
return
def callback(level, pos):
return Expression(f, level, Expression('List', *(Integer(p) for p in pos)))
heads = self.get_option(options, 'Heads', evaluation).is_true()
result, depth = walk_levels(expr, start, stop, heads=heads, callback=callback, include_pos=True)
return result
def apply_level(self, f, expr, ls, evaluation, options={}):
"""MapIndexed[f_, expr_, Optional[Pattern[ls, _?LevelQ], {1}],
OptionsPattern[MapIndexed]]"""
try:
start, stop = python_levelspec(ls)
except InvalidLevelspecError:
evaluation.message("MapIndexed", "level", ls)
return
def callback(level, pos):
return Expression(f, level, Expression("List", *[Integer(p) for p in pos]))
heads = self.get_option(options, "Heads", evaluation).is_true()
result, depth = walk_levels(expr, start, stop, heads=heads, callback=callback, include_pos=True)
return result
def apply_level(self, f, expr, ls, evaluation, options={}):
"""Map[f_, expr_, Optional[Pattern[ls, _?LevelQ], {1}],
OptionsPattern[Map]]"""
try:
start, stop = python_levelspec(ls)
except InvalidLevelspecError:
evaluation.message("Map", "level", ls)
return
def callback(level):
return Expression(f, level)
heads = self.get_option(options, "Heads", evaluation).is_true()
result, depth = walk_levels(expr, start, stop, heads=heads, callback=callback)
return result
def apply_level(self, f, expr, ls, evaluation, options={}):
"""Map[f_, expr_, Optional[Pattern[ls, _?LevelQ], {1}],
OptionsPattern[Map]]"""
try:
start, stop = python_levelspec(ls)
except InvalidLevelspecError:
evaluation.message("Map", "level", ls)
return
def callback(level):
return Expression(f, level)
heads = self.get_option(options, "Heads", evaluation).is_true()
result, depth = walk_levels(expr, start, stop, heads=heads, callback=callback)
return result
def apply(self, f, expr, ls, evaluation, options={}):
'Apply[f_, expr_, ls_?LevelQ:{0}, OptionsPattern[Apply]]'
try:
start, stop = python_levelspec(ls)
except InvalidLevelspecError:
evaluation.message('Apply', 'level', ls)
return
def callback(level):
if level.is_atom():
return level
else:
return Expression(f, *level.leaves)
heads = self.get_option(options, 'Heads', evaluation).is_true()
result, depth = walk_levels(expr, start, stop, heads=heads, callback=callback)
return result
def apply(self, f, expr, ls, evaluation, options={}):
'Apply[f_, expr_, ls_?LevelQ:{0}, OptionsPattern[Apply]]'
try:
start, stop = python_levelspec(ls)
except InvalidLevelspecError:
evaluation.message('Apply', 'level', ls)
return
def callback(level):
if level.is_atom():
return level
else:
return Expression(f, *level.leaves)
heads = self.get_option(options, 'Heads', evaluation).is_true()
result, depth = walk_levels(expr, start, stop, heads=heads, callback=callback)
return result
def apply_level(self, f, expr, ls, evaluation, options={}):
'''Scan[f_, expr_, Optional[Pattern[ls, _?LevelQ], {1}],
OptionsPattern[Map]]'''
try:
start, stop = python_levelspec(ls)
except InvalidLevelspecError:
evaluation.message('Map', 'level', ls)
return
def callback(level):
Expression(f, level).evaluate(evaluation)
return level
heads = self.get_option(options, 'Heads', evaluation).is_true()
result, depth = walk_levels(
expr, start, stop, heads=heads, callback=callback)
return Symbol('Null')
def apply_level(self, f, expr, ls, evaluation, options={}):
'''Scan[f_, expr_, Optional[Pattern[ls, _?LevelQ], {1}],
OptionsPattern[Map]]'''
try:
start, stop = python_levelspec(ls)
except InvalidLevelspecError:
evaluation.message('Map', 'level', ls)
return
def callback(level):
Expression(f, level).evaluate(evaluation)
return level
heads = self.get_option(options, 'Heads', evaluation).is_true()
result, depth = walk_levels(
expr, start, stop, heads=heads, callback=callback)
return Symbol('Null')
def apply_level(self, f, expr, ls, evaluation, options={}):
"""MapIndexed[f_, expr_, Optional[Pattern[ls, _?LevelQ], {1}],
OptionsPattern[MapIndexed]]"""
try:
start, stop = python_levelspec(ls)
except InvalidLevelspecError:
evaluation.message("MapIndexed", "level", ls)
return
def callback(level, pos):
return Expression(f, level, Expression("List", *[Integer(p) for p in pos]))
heads = self.get_option(options, "Heads", evaluation).is_true()
result, depth = walk_levels(
expr, start, stop, heads=heads, callback=callback, include_pos=True
)
return result
def apply(self, f, expr, ls, evaluation, options={}):
"""Apply[f_, expr_, Optional[Pattern[ls, _?LevelQ], {0}],
OptionsPattern[Apply]]"""
try:
start, stop = python_levelspec(ls)
except InvalidLevelspecError:
evaluation.message("Apply", "level", ls)
return
def callback(level):
if level.is_atom():
return level
else:
return Expression(f, *level.leaves)
heads = self.get_option(options, "Heads", evaluation).is_true()
result, depth = walk_levels(expr, start, stop, heads=heads, callback=callback)
return result
def apply(self, f, expr, ls, evaluation, options={}):
"""Apply[f_, expr_, Optional[Pattern[ls, _?LevelQ], {0}],
OptionsPattern[Apply]]"""
try:
start, stop = python_levelspec(ls)
except InvalidLevelspecError:
evaluation.message("Apply", "level", ls)
return
def callback(level):
if level.is_atom():
return level
else:
return Expression(f, *level.leaves)
heads = self.get_option(options, "Heads", evaluation).is_true()
result, depth = walk_levels(expr, start, stop, heads=heads, callback=callback)
return result
def apply_list(self, expr, n, h, evaluation):
"Flatten[expr_, n_List, h_]"
# prepare levels
# find max depth which matches `h`
expr, max_depth = walk_levels(expr)
max_depth = {
"max_depth": max_depth
} # hack to modify max_depth from callback
def callback(expr, pos):
if len(pos) < max_depth["max_depth"] and (expr.is_atom()
or expr.head != h):
max_depth["max_depth"] = len(pos)
return expr
expr, depth = walk_levels(expr,
callback=callback,
include_pos=True,
start=0)
max_depth = max_depth["max_depth"]
levels = n.to_python()
# mappings
if isinstance(levels, list) and all(
isinstance(level, int) for level in levels):
levels = [levels]
# verify levels is list of lists of positive ints
if not (isinstance(levels, list) and len(levels) > 0):
evaluation.message("Flatten", "flpi", n)
return
seen_levels = []
for level in levels:
if not (isinstance(level, list) and len(level) > 0):
evaluation.message("Flatten", "flpi", n)
return
for l in level:
if not (isinstance(l, int) and l > 0):
evaluation.message("Flatten", "flpi", n)
return
if l in seen_levels:
# level repeated
evaluation.message("Flatten", "flrep", l)
return
seen_levels.append(l)
# complete the level spec e.g. {{2}} -> {{2}, {1}, {3}}
for l in range(1, max_depth + 1):
if l not in seen_levels:
levels.append([l])
# verify specified levels are smaller max depth
for level in levels:
for l in level:
if l > max_depth:
evaluation.message("Flatten", "fldep", l, n, max_depth,
expr)
return
# assign new indices to each leaf
new_indices = {}
def callback(expr, pos):
if len(pos) == max_depth:
new_depth = tuple(
tuple(pos[i - 1] for i in level) for level in levels)
new_indices[new_depth] = expr
return expr
expr, depth = walk_levels(expr, callback=callback, include_pos=True)
# build new tree inserting nodes as needed
result = Expression(h)
leaves = sorted(new_indices.items())
def insert_leaf(expr, leaves):
# gather leaves into groups with the same leading index
# e.g. [((0, 0), a), ((0, 1), b), ((1, 0), c), ((1, 1), d)]
# -> [[(0, a), (1, b)], [(0, c), (1, d)]]
leading_index = None
grouped_leaves = []
for index, leaf in leaves:
if index[0] == leading_index:
grouped_leaves[-1].append((index[1:], leaf))
else:
leading_index = index[0]
grouped_leaves.append([(index[1:], leaf)])
# for each group of leaves we either insert them into the current level
# or make a new level and recurse
for group in grouped_leaves:
if len(group[0][0]) == 0: # bottom level leaf
assert len(group) == 1
expr.leaves.append(group[0][1])
else:
expr.leaves.append(Expression(h))
insert_leaf(expr.leaves[-1], group)
insert_leaf(result, leaves)
return result
def apply(self, expr, evaluation):
"Depth[expr_]"
expr, depth = walk_levels(expr)
return Integer(depth + 1)
def apply(self, expr, evaluation):
'Depth[expr_]'
expr, depth = walk_levels(expr)
return Integer(depth + 1)
def apply_list(self, expr, n, h, evaluation):
'Flatten[expr_, n_List, h_]'
# prepare levels
# find max depth which matches `h`
expr, max_depth = walk_levels(expr)
max_depth = {'max_depth': max_depth} # hack to modify max_depth from callback
def callback(expr, pos):
if len(pos) < max_depth['max_depth'] and (expr.is_atom() or expr.head != h):
max_depth['max_depth'] = len(pos)
return expr
expr, depth = walk_levels(expr, callback=callback, include_pos=True, start=0)
max_depth = max_depth['max_depth']
levels = n.to_python()
# mappings
if isinstance(levels, list) and all(isinstance(level, int) for level in levels):
levels = [levels]
# verify levels is list of lists of positive ints
if not (isinstance(levels, list) and len(levels) > 0):
evaluation.message('Flatten', 'flpi', n)
return
seen_levels = []
for level in levels:
if not (isinstance(level, list) and len(level) > 0):
evaluation.message('Flatten', 'flpi', n)
return
for l in level:
if not (isinstance(l, int) and l > 0):
evaluation.message('Flatten', 'flpi', n)
return
if l in seen_levels:
# level repeated
evaluation.message('Flatten', 'flrep', l)
return
seen_levels.append(l)
# complete the level spec e.g. {{2}} -> {{2}, {1}, {3}}
for l in range(1, max_depth + 1):
if l not in seen_levels:
levels.append([l])
# verify specified levels are smaller max depth
for level in levels:
for l in level:
if l > max_depth:
evaluation.message('Flatten', 'fldep', l, n, max_depth, expr)
return
# assign new indices to each leaf
new_indices = {}
def callback(expr, pos):
if len(pos) == max_depth:
new_depth = tuple(tuple(pos[i - 1] for i in level) for level in levels)
new_indices[new_depth] = expr
return expr
expr, depth = walk_levels(expr, callback=callback, include_pos=True)
# build new tree inserting nodes as needed
result = Expression(h)
leaves = sorted(six.iteritems(new_indices))
def insert_leaf(expr, leaves):
# gather leaves into groups with the same leading index
# e.g. [((0, 0), a), ((0, 1), b), ((1, 0), c), ((1, 1), d)]
# -> [[(0, a), (1, b)], [(0, c), (1, d)]]
leading_index = None
grouped_leaves = []
for index, leaf in leaves:
if index[0] == leading_index:
grouped_leaves[-1].append((index[1:], leaf))
else:
leading_index = index[0]
grouped_leaves.append([(index[1:], leaf)])
# for each group of leaves we either insert them into the current level
# or make a new level and recurse
for group in grouped_leaves:
if len(group[0][0]) == 0: # bottom level leaf
assert len(group) == 1
expr.leaves.append(group[0][1])
else:
expr.leaves.append(Expression(h))
insert_leaf(expr.leaves[-1], group)
insert_leaf(result, leaves)
return result
