def test_implicit_out():
op = operation(str, "hh", provides=["A", implicit("a")])
pipe = compose(..., op)
got = pipe.compute()
assert got == {"A": ""}
assert "(implicit)" in str(op.plot())
def test_jetsam_n_plot_with_DEBUG():
pipe = compose(
"mix",
operation(
str,
"FUNC",
needs=[
"a",
sfxed("b", "foo", keyword="bb"),
implicit("c"),
sfxed("d", "bar"),
vararg("e"),
varargs("f"),
],
provides=[
"A",
sfxed("b", "FOO", keyword="bb"),
implicit("C"),
sfxed("d", "BAR", optional=True),
sfx("FOOBAR"),
],
aliases={
"A": "aaa",
"b": "bbb",
"d": "ddd"
}, # FIXME: "D" is implicit!
),
)
with debug_enabled(True), pytest.raises(ValueError, match="^Unsolvable"):
pipe.compute()
with debug_enabled(True), pytest.raises(
ValueError, match="^Failed matching inputs <=> needs") as exc:
pipe.compute({
"a": 1,
sfxed("b", "foo"): 2,
"c": 3,
sfxed("d", "bar"): 4,
"e": 5,
"f": [6, 7],
})
exc.value.jetsam.plot_fpath.unlink()
def test_implicit_inp():
op = operation(str, needs=["A", implicit("a")], provides="b")
pipe = compose(..., op)
got = pipe.compute({"A": "val", "a": 1})
assert got == {"A": "val", "a": 1, "b": "val"}
with pytest.raises(ValueError, match="Unsolvable graph"):
pipe.compute({"A": "val"})
assert "(implicit)" in str(op.plot())
def test_cwd_fnop():
op = operation(
str,
None,
needs=[
"a",
"a/b",
"/r/b",
optional("o"),
keyword("k"),
implicit("i"),
vararg("v1"),
varargs("v2"),
sfx("s1"),
sfxed("s2", "s22"),
vcat("vc"),
],
provides=["A/B", "C", "/R"],
aliases=[("A/B", "aa"), ("C", "CC"), ("/R", "RR")],
cwd="root",
)
exp = """
FnOp(name='str',
needs=['root/a'($),
'root/a/b'($),
'/r/b'($),
'root/o'($?'o'),
'root/k'($>'k'),
'root/i'($),
'root/v1'($*),
'root/v2'($+),
sfx('s1'),
sfxed('root/s2'($),
's22'),
'root/vc'($)],
provides=['root/A/B'($),
'root/C'($),
'/R'($),
'root/aa'($),
'root/CC'($),
'root/RR'($)],
aliases=[('root/A/B'($), 'root/aa'($)),
('root/C'($), 'root/CC'($)),
('/R'($), 'root/RR'($))],
fn='str')
"""
assert oneliner(op) == oneliner(exp)
for gnum, n2v in enumerate(n2v_ratios, 1)
}
wot_grid = pd.concat(
wot_grids.values(),
axis=1,
keys=wot_grids.keys(),
names=["gear", "item"],
verify_integrity=True,
).swaplevel(axis=1)
return wot_grid
@autog.autographed(
provides=[sfxed("gwots", "p_avail"), implicit("gwots/p_avail")],
)
def attach_p_avail_in_gwots(gwots: pd.DataFrame, *, f_safety_margin) -> pd.DataFrame:
"""
Attaches both `p_avail` and `p_avail_stable` for all gears.
.. attention:
Must NOT interpolate along with wot on grid, or great INNACCURACIES.
:param gwots:
a df with 2-level multiindex columns, having at least (`g1`, 'p'), and
optionally ('g1', 'ASM')) for each gears
(as returned by :func:`interpolate_wot_on_v_grid()`).
"""
w = wio.pstep_factory.get().wot
(
("a", {
"a": sfx("AA")
}),
ValueError("must not contain `sideffects"),
),
(
(["a", "b"], {
"a": "b"
}),
ValueError(r"clash with existing provides in \['a', 'b'\]"),
),
## Implicits
#
(
([implicit("i")], {
implicit("i"): "II"
}),
ValueError("must not contain `implicits"),
),
(
(["i"], {
"i": implicit("II")
}),
ValueError("must not contain `implicits"),
),
],
)
def test_func_op_validation_aliases_BAD(prov_aliases, ex):
with pytest.raises(type(ex), match=str(ex)):
reparse_operation_data("t", None, *prov_aliases)
ok_n = cycle.loc[:, c.ok_n]
p_remain = cycle.loc[:, c.p_remain]
cycle.loc[idx_miss_gear]
@autog.autographed(
needs=[
vararg("wltc_class_data/V_cycle"),
vararg("V_dsc"),
vararg("V_capped"),
vararg("V_compensated"),
vararg("forced_cycle"),
],
provides=[
sfxed("cycle", "init"),
implicit("cycle/t"),
implicit("cycle/V"),
],
)
def init_cycle_velocity(*velocities: Union[pd.Series, pd.DataFrame]) -> pd.DataFrame:
"""
Concatenate velocities(series)/cycle(dataframe), cloning the last column as `V`.
:param velocities:
one or more velocity (same) time-indexed series & datarames
(properly named), with the last one becoming the `V`,
unless it already exists
:return:
the concatenated cycle with 2-level columns (item, gear)
"""
roots_head[0],
v_max,
wot.loc[v_max - 5 * v_step:v_max + 5 * v_step,
w.p_remain_stable],
)
rec = VMaxRec(v_max, n_v_max, gid, False, wot)
else:
rec = VMaxRec(np.NAN, np.NAN, gid, False, wot)
return rec
@autog.autographed(
needs=[
sfxed("gwots", "p_avail"),
implicit("gwots/p_resist"),
],
provides=[
*VMaxRec._fields[:-2],
keyword("is_n_lim_vmax", "is_n_lim"),
keyword("vmax_gwot", "wot"), # `wot` causes cycle!
],
inp_sideffects=[("gwots", "p_avail")],
returns_dict=True,
)
def calc_v_max(gwots: Union[pd.Series, pd.DataFrame]) -> VMaxRec:
"""
Finds maximum velocity by scanning gears from the top.
TODO: accept `n_lim`