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import random
from collections import OrderedDict
from copy import deepcopy
import pandas as pd
import pytest
from pyecsca.sca.re.tree import Tree, Map
def test_map():
cfgs = {"a", "b"}
binary_sets = {"a": {1, 2, 3}, "b": {2, 4}}
dmap = Map.from_sets(cfgs, binary_sets)
assert dmap.domain == [1, 2, 3, 4]
assert dmap.codomain == {True, False}
assert dmap.mapping.index.tolist() == [0, 1]
assert set(dmap.cfg_map.index) == cfgs
assert dmap.cfgs == cfgs
assert dmap["a", 1]
assert not dmap["a", 4]
io_map = {"a": {1: 5, 2: 7}, "b": {1: 3}}
dmap = Map.from_io_maps(cfgs, io_map)
assert dmap.domain == [1, 2]
assert dmap.codomain == {5, 3, 7, None}
io_map_full = {"a": {1: 5, 2: 7}, "b": {1: 3, 2: 11}}
dmap = Map.from_io_maps(cfgs, io_map_full)
assert dmap.domain == [1, 2]
assert dmap.codomain == {5, 3, 7, 11}
def test_map_merge():
cfgs = {"a", "b"}
binary_sets = {"a": {1, 2, 3}, "b": {2, 4}}
dmap1 = Map.from_sets(cfgs, binary_sets)
assert len(dmap1.mapping) == 2
cfgs = {"c", "d"}
binary_sets = {"c": {1, 2}, "d": {2, 4, 3}}
dmap2 = Map.from_sets(cfgs, binary_sets)
assert len(dmap2.mapping) == 2
merged = deepcopy(dmap1)
merged.merge(dmap2)
assert len(merged.mapping) == 4
assert len(merged.cfg_map) == 4
assert len(merged.codomain) == 2
for i in [1, 2, 3, 4]:
for cfg in "ab":
assert merged[cfg, i] == dmap1[cfg, i]
for cfg in "cd":
assert merged[cfg, i] == dmap2[cfg, i]
def test_map_deduplicate():
cfgs = {"a", "b", "c", "d"}
binary_sets = {"a": {1, 2, 3}, "b": {2, 3, 4}, "c": {1, 2, 3}, "d": {4, 2}}
dmap = Map.from_sets(cfgs, binary_sets)
original = deepcopy(dmap)
dmap.deduplicate()
for cfg in cfgs:
for i in [1, 2, 3, 4]:
assert dmap[cfg, i] == original[cfg, i]
assert len(dmap.mapping) < len(original.mapping)
assert dmap.cfgs == original.cfgs
dedupped = Map.from_sets(cfgs, binary_sets, deduplicate=True)
for cfg in cfgs:
for i in [1, 2, 3, 4]:
assert dedupped[cfg, i] == original[cfg, i]
assert dedupped.cfgs == original.cfgs
def test_map_with_callable(secp128r1):
add = secp128r1.curve.coordinate_model.formulas["add-2007-bl"]
dbl = secp128r1.curve.coordinate_model.formulas["dbl-2007-bl"]
mdbl = secp128r1.curve.coordinate_model.formulas["mdbl-2007-bl"]
cfgs = [(add, dbl), (add, mdbl)]
binary_sets = {cfgs[0]: {1, 2, 3}, cfgs[1]: {2, 3}}
dmap = Map.from_sets(set(cfgs), binary_sets)
assert dmap[cfgs[0], 1]
@pytest.mark.parametrize("split", ["degree", "largest", "average"])
def test_build_tree(split):
cfgs = ["a", "b", "c"]
cfg_map = pd.DataFrame([0, 1, 2], index=cfgs, columns=["vals"])
inputs1 = [1, 2, 3, 4]
codomain1 = {0, 1, 3, 4, 5}
mapping1 = pd.DataFrame([(0, 4, 5, 0), (0, 3, 0, 0), (1, 4, 0, 0)])
dmap1 = Map(mapping1, cfg_map, inputs1, codomain1)
inputs2 = ["f", "e", "d"]
codomain2 = {0, 1, 2, 3}
mapping2 = pd.DataFrame([(1, 0, 0), (2, 0, 0), (3, 0, 0)])
dmap2 = Map(mapping2, cfg_map, inputs2, codomain2)
tree = Tree.build(set(cfgs), dmap1, dmap2, split=split)
tree.render()
tree.render_basic()
tree.describe()
def test_build_tree_dedup():
"""Make sure that dmap deduplication does not affect the tree."""
cfgs = {"a", "b", "c", "d", "e", "f", "g"}
binary_sets = {
"a": {1, 2, 3},
"b": {2, 3, 4},
"c": {1, 2, 3},
"d": {4, 2},
"e": {4, 2},
"f": {4, 2},
"g": {4, 2},
}
dmap = Map.from_sets(cfgs, binary_sets)
deduplicated = Map.from_sets(cfgs, binary_sets, deduplicate=True)
original = deepcopy(dmap)
dmap.deduplicate()
tree = Tree.build(cfgs, original)
dedup = Tree.build(cfgs, dmap)
dedup_other = Tree.build(cfgs, deduplicated)
assert tree.describe() == dedup.describe()
assert tree.describe() == dedup_other.describe()
def test_build_tree_reorder():
"""Make sure that dmap input order does not affect the tree."""
cfgs = {"a", "b", "c", "d", "e", "f", "g"}
binary_sets = {
"a": {1, 2, 3},
"b": {2, 3, 4},
"c": {1, 2, 3},
"d": {4, 2},
"e": {4, 2},
"f": {4, 2},
"g": {4, 2},
}
trees = set()
for i in range(10):
shuffled = list(binary_sets.items())
random.shuffle(shuffled)
bset = OrderedDict(shuffled)
dmap = Map.from_sets(cfgs, bset)
if i % 2 == 0:
dmap.deduplicate()
trees.add(Tree.build(cfgs, dmap).describe())
assert len(trees) == 1
def test_expand_tree():
cfgs = ["a", "b", "c"]
cfg_map = pd.DataFrame([0, 1, 2], index=cfgs, columns=["vals"])
inputs1 = [1, 2]
codomain1 = {0, 3, 4}
mapping1 = pd.DataFrame([(0, 4), (0, 3), (0, 4)])
dmap1 = Map(mapping1, cfg_map, inputs1, codomain1)
inputs2 = ["f", "e", "d"]
codomain2 = {0, 1, 2, 3}
mapping2 = pd.DataFrame([(1, 0, 0), (2, 0, 0), (3, 0, 0)])
dmap2 = Map(mapping2, cfg_map, inputs2, codomain2)
tree = Tree.build(set(cfgs), dmap1)
res = tree.expand(dmap2)
assert res.height > tree.height
def test_df():
nrows = 12_000_000
ncols = 5
df = pd.DataFrame([random.choices((True, False), k=ncols) for _ in range(nrows)])
cfg_map = pd.DataFrame(
[(i,) for i in range(nrows)],
index=[str(i) for i in range(nrows)],
columns=["vals"],
)
dmap = Map(df, cfg_map, list(range(ncols)), {True, False})
# start = time.perf_counter()
dmap.deduplicate()
# end = time.perf_counter()
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