def setUp(self):
self.standardcode = substitution_model.TimeReversibleCodon(
model_gaps=True, gc=1, mprob_model="tuple"
)
self.mitocode = substitution_model.TimeReversibleCodon(
model_gaps=False, gc=2, mprob_model="tuple"
)
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")
