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#!/usr/bin/env python
import unittest
from cogent3 import make_tree
from cogent3.evolve import substitution_model
from cogent3.evolve.predicate import MotifChange, replacement
def a_c(x, y):
return (x == "A" and y == "C") or (x == "C" and y == "A")
__author__ = "Peter Maxwell and Gavin Huttley"
__copyright__ = "Copyright 2007-2022, The Cogent Project"
__credits__ = ["Peter Maxwell", "Gavin Huttley"]
__license__ = "BSD-3"
__version__ = "2023.2.12a1"
__maintainer__ = "Gavin Huttley"
__email__ = "gavin.huttley@anu.edu.au"
__status__ = "Production"
a_c = MotifChange("A", "C")
trans = MotifChange("A", "G") | MotifChange("T", "C")
TREE = make_tree(tip_names="ab")
class ScaleRuleTests(unittest.TestCase):
def _makeModel(self, predicates, scale_rules=None):
scale_rules = scale_rules or []
return substitution_model.TimeReversibleNucleotide(
equal_motif_probs=True,
model_gaps=False,
predicates=predicates,
scales=scale_rules,
)
def _get_scaled_lengths(self, model, params):
LF = model.make_likelihood_function(TREE)
for param in params:
LF.set_param_rule(param, value=params[param], is_constant=True)
result = {}
for predicate in model.scale_masks:
result[predicate] = LF.get_scaled_lengths(predicate)["a"]
return result
def test_scaled(self):
"""Scale rule requiring matrix entries to have all pars specified"""
model = self._makeModel({"k": trans}, {"ts": trans, "tv": ~trans})
self.assertEqual(
self._get_scaled_lengths(model, {"k": 6.0, "length": 4.0}),
{"ts": 3.0, "tv": 1.0},
)
def test_binned(self):
model = self._makeModel({"k": trans}, {"ts": trans, "tv": ~trans})
LF = model.make_likelihood_function(TREE, bins=2)
LF.set_param_rule("length", value=4.0, is_constant=True)
LF.set_param_rule("k", value=6.0, bin="bin0", is_constant=True)
LF.set_param_rule("k", value=1.0, bin="bin1", is_constant=True)
for (bin, expected) in [("bin0", 3.0), ("bin1", 4.0 / 3), (None, 13.0 / 6)]:
self.assertEqual(LF.get_scaled_lengths("ts", bin=bin)["a"], expected)
def test_scaled_or(self):
"""Scale rule where matrix entries can have any of the pars specified"""
model = self._makeModel(
{"k": trans, "ac": a_c}, {"or": (trans | a_c), "not": ~(trans | a_c)}
)
self.assertEqual(
self._get_scaled_lengths(model, {"k": 6.0, "length": 6.0, "ac": 3.0}),
{"or": 5.0, "not": 1.0},
)
def test_scaling(self):
"""Testing scaling calculations using Dn and Ds as an example."""
model = substitution_model.TimeReversibleCodon(
model_gaps=False,
recode_gaps=True,
predicates={"k": trans, "r": replacement},
motif_probs={
"TAT": 0.0088813702685557206,
"TGT": 0.020511736096426307,
"TCT": 0.024529498836963416,
"TTT": 0.019454430112074435,
"TGC": 0.0010573059843518714,
"TGG": 0.0042292239374074857,
"TAC": 0.002326073165574117,
"TTC": 0.0086699090716853451,
"TCG": 0.0010573059843518714,
"TTA": 0.020723197293296681,
"TTG": 0.01036159864664834,
"TCC": 0.0082469866779445976,
"TCA": 0.022414886868259674,
"GCA": 0.015648128568407697,
"GTA": 0.014590822584055826,
"GCC": 0.0095157538591668436,
"GTC": 0.0063438359061112285,
"GCG": 0.0016916895749629942,
"GTG": 0.0067667582998519769,
"CAA": 0.018185662930852189,
"GTT": 0.021569042080778176,
"GCT": 0.014167900190315077,
"ACC": 0.0042292239374074857,
"GGT": 0.014167900190315077,
"CGA": 0.0012687671812222456,
"CGC": 0.0010573059843518714,
"GAT": 0.030238951152463524,
"AAG": 0.034891097483611758,
"CGG": 0.002326073165574117,
"ACT": 0.028758722774370905,
"GGG": 0.0071896806935927262,
"GGA": 0.016282512159018821,
"GGC": 0.0090928314654260944,
"GAG": 0.031296257136815393,
"AAA": 0.05476844998942694,
"GAC": 0.011207443434129837,
"CGT": 0.0033833791499259885,
"GAA": 0.076337492070205112,
"CTT": 0.010573059843518714,
"ATG": 0.012687671812222457,
"ACA": 0.021991964474518927,
"ACG": 0.00084584478748149711,
"ATC": 0.0076126030873334746,
"AAC": 0.022837809262000422,
"ATA": 0.017762740537111441,
"AGG": 0.013533516599703954,
"CCT": 0.025586804821315288,
"AGC": 0.029393106364982026,
"AGA": 0.021991964474518927,
"CAT": 0.021357580883907802,
"AAT": 0.05772890674561218,
"ATT": 0.019031507718333687,
"CTG": 0.012899133009092831,
"CTA": 0.013744977796574329,
"CTC": 0.0078240642842038483,
"CAC": 0.0050750687248889825,
"CCG": 0.00021146119687037428,
"AGT": 0.03742863184605625,
"CAG": 0.024106576443222668,
"CCA": 0.021357580883907802,
"CCC": 0.0069782194967223515,
},
scales={"dN": replacement, "dS": ~replacement},
mprob_model="tuple",
)
length = 0.1115
a = self._get_scaled_lengths(
model, {"k": 3.6491, "r": 0.6317, "length": length}
)
b = self._get_scaled_lengths(model, {"k": 3.6491, "r": 1.0, "length": length})
dN = length * a["dN"] / (3.0 * b["dN"])
dS = length * a["dS"] / (3.0 * b["dS"])
# following are results from PAML
self.assertEqual(f"{dN:.4f}", "0.0325")
self.assertEqual(f"{dS:.4f}", "0.0514")
if __name__ == "__main__":
unittest.main()
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