"""Tests for sequence-visible expression risk (audit #24).

Two things are actually being defended here: that no yield is ever predicted,
and that the rare-codon threshold is derived from the tables rather than
chosen — the second is what makes the first honest rather than merely cautious.
"""
import pytest

from dee.core import expression as E

ADAPTED = "ATG" + "AAACTGGCGCGTGAAGATCTGAAAGCGGTTCTGGAAAAC" * 3 + "TAA"
RARE_HEAVY = "ATG" + "AAACTGGCGAGAAGGATAGATCTGAAAGCGGTTCTGGAAAAC" * 3 + "TAA"


# ── the honesty contract ─────────────────────────────────────────────────
def test_no_expression_level_is_ever_predicted():
    r = E.assess(ADAPTED, "e_coli")
    assert r["ok"]
    assert "does NOT predict an expression level" in r["no_yield"]
    for key in ("yield", "expression_level", "mg_per_l", "predicted_yield",
                "expression_score"):
        assert key not in r


def test_the_result_names_what_it_does_not_cover():
    """An assessment that looks complete is worse than one that lists its own
    blind spots — the user assumes the unchecked thing was checked."""
    n = E.assess(ADAPTED, "e_coli")["not_covered"]
    assert "Shine-Dalgarno" in n
    assert "#38" in n and "#39" in n
    assert "assess_solubility" in n


# ── the threshold is derived, and the derivation is checkable ────────────
def test_the_rare_cutoff_selects_the_codons_rosetta_strains_supply():
    """w < 0.15 against the E. coli table selects AGA, AGG, ATA, CTA — exactly
    the set argU/ileY/leuW exist to supply. That is the evidence the cutoff is
    measuring something real rather than being a round number."""
    selected = set(E.assess(ADAPTED, "e_coli")["rare_codons_in_this_host"])
    assert {"AGA", "AGG", "ATA", "CTA"} <= selected


def test_the_same_cutoff_finds_nothing_rare_in_human_and_says_so():
    """Mammalian codon usage is much flatter. Manufacturing a warning to look
    thorough would be worse than the empty result."""
    r = E.assess(ADAPTED, "human")
    assert r["rare_codons_in_this_host"] == []
    assert r["rare_codon_count"] == 0
    assert "nothing" in r["method"]


def test_the_method_line_lists_the_selected_codons():
    m = E.assess(ADAPTED, "e_coli")["method"]
    assert "Sharp & Li" in m
    assert str(E.RARE_BELOW) in m
    assert "AGA" in m


# ── rare codons and the runs that matter ─────────────────────────────────
def test_an_adapted_sequence_is_clean():
    r = E.assess(ADAPTED, "e_coli")
    assert r["rare_codon_count"] == 0
    assert r["flags"] == []
    assert r["codon_adaptation_index"] > 0.9


def test_rare_codons_lower_the_adaptation_index():
    assert (E.assess(RARE_HEAVY, "e_coli")["codon_adaptation_index"]
            < E.assess(ADAPTED, "e_coli")["codon_adaptation_index"])


def test_consecutive_rare_codons_are_reported_as_a_run():
    """The finding that changes what someone does. A scattered rare codon is
    absorbed; three in a row depletes the local charged-tRNA pool."""
    r = E.assess(RARE_HEAVY, "e_coli")
    assert r["rare_codon_runs"]
    run = r["rare_codon_runs"][0]
    assert run["length"] >= E.RUN_LENGTH
    assert run["codons"] == ["AGA", "AGG", "ATA"]
    flag = [f for f in r["flags"] if "consecutive rare" in f["factor"]][0]
    assert "recoding just this run" in flag["why"]


def test_scattered_rare_codons_do_not_form_a_run():
    dna = "ATG" + ("AGA" + "CTGGCGAAAGATCTGGAA") * 6 + "TAA"
    r = E.assess(dna, "e_coli")
    assert r["rare_codon_count"] >= 6
    assert r["rare_codon_runs"] == []


def test_the_five_prime_ramp_is_reported_separately():
    """A rare codon at codon 5 costs more than the same codon at codon 400."""
    r = E.assess(RARE_HEAVY, "e_coli")
    assert r["rare_in_ramp"]
    assert all(x["codon_number"] <= E.RAMP_CODONS for x in r["rare_in_ramp"])


def test_cai_excludes_single_codon_families():
    """Met and Trp have no synonym to choose between. Including them only
    drags every score toward 1 and makes a bad sequence look adapted."""
    only_mw = "ATG" + "ATGTGG" * 20 + "TAA"
    r = E.assess(only_mw, "e_coli")
    assert r["codon_adaptation_index"] is None or r["codon_adaptation_index"] > 0


def test_cai_does_not_underflow_on_a_long_cds():
    """The direct product of hundreds of fractions underflows to 0.0 and
    silently reports a perfectly adapted gene as unadapted."""
    long_cds = "ATG" + "AAACTGGCGCGTGAAGATCTGAAAGCGGTTCTGGAAAAC" * 60 + "TAA"
    cai = E.assess(long_cds, "e_coli")["codon_adaptation_index"]
    assert cai is not None and cai > 0.5


# ── reading frame ────────────────────────────────────────────────────────
def test_an_internal_stop_is_flagged_as_a_truncated_product():
    dna = "ATG" + "AAACTGGCG" * 4 + "TAA" + "AAACTGGCG" * 6 + "TAA"
    r = E.assess(dna, "e_coli")
    assert r["internal_stops"]
    flag = [f for f in r["flags"] if "internal stop" in f["factor"]][0]
    assert "truncated" in flag["why"]


def test_a_missing_terminal_stop_is_flagged():
    r = E.assess(ADAPTED[:-3], "e_coli")
    assert r["has_terminal_stop"] is False
    assert any("no terminal stop" in f["factor"] for f in r["flags"])


def test_an_out_of_frame_length_is_flagged_not_silently_truncated():
    r = E.assess(ADAPTED + "AT", "e_coli")
    assert r["in_frame"] is False
    assert any("whole number of codons" in f["factor"] for f in r["flags"])


# ── hosts ────────────────────────────────────────────────────────────────
def test_an_unknown_host_declines_instead_of_using_another_table():
    """Scoring a plant construct against the E. coli table would produce
    confident nonsense. The refusal must read as missing data, not as the
    organism being unsupported."""
    r = E.assess(ADAPTED, "plant")
    assert r["ok"] is False and r["kind"] == "no_codon_table"
    assert "missing data file, not an unsupported organism" in r["next"]


def test_a_missing_host_is_refused_because_expression_is_host_specific():
    assert E.assess(ADAPTED, "")["ok"] is False


def test_every_known_host_alias_works():
    for host in ("e_coli", "ecoli", "yeast", "s_cerevisiae", "human", "h_sapiens"):
        assert E.assess(ADAPTED, host)["ok"], host


def test_the_same_sequence_scores_differently_per_host():
    """The point of asking for a host. E. coli-optimised DNA is not
    human-optimised."""
    a = E.assess(ADAPTED, "e_coli")["codon_adaptation_index"]
    b = E.assess(ADAPTED, "human")["codon_adaptation_index"]
    assert a != b


def test_too_short_is_refused():
    assert E.assess("ATGAAACTG", "e_coli")["kind"] == "too_short"


# ── the agent tool ───────────────────────────────────────────────────────
def test_the_tool_runs_and_needs_a_host():
    from dee.core import agent_tools as t
    fn = t._TOOLS["assess_expression"]["fn"]
    assert fn({"dna": ADAPTED, "host": "e_coli"})["ok"]
    assert fn({"host": "e_coli"})["ok"] is False


def test_the_tool_explains_that_protein_input_cannot_answer_this():
    from dee.core import agent_tools as t
    err = t._TOOLS["assess_expression"]["fn"]({"host": "e_coli"})["error"]
    assert "reverse-translate" in err


def test_the_spec_forbids_substituting_another_hosts_table():
    from dee.core import agent_tools as t
    desc = next(s for s in t.TOOL_SPECS
                if s["function"]["name"] == "assess_expression")["function"]["description"]
    assert "never substitute another host's table" in desc
    assert "no_yield" in desc
    # No enum on host — a hardcoded organism list is the hardest gate to see.
    props = next(s for s in t.TOOL_SPECS
                 if s["function"]["name"] == "assess_expression")["function"]["parameters"]["properties"]
    assert "enum" not in props["host"]