def store_results_per_car(
h5db,
specs,
overrides,
pwots,
cycles,
all_cases=None,
props_group_suffix="prop",
overrides_group_suffix="override",
wot_group_suffix="wot",
cycle_group_suffix="cycle",
c_vehnum=c_vehnum,
c_case_id=c_case,
):
"""
Populate h5db with results collected from a folder full of (`V123.xls`, ...) exchel-files.
:param cycles:
if none, also `specs` are not stored!
vehicles/
+--v001/
| +--props (series) scalar inputs & outputs generated by AccDB
| +--wot (df) not all vehicles have wot
| +--cycle (df) cycle-run generated by AccDB
| +--override (series) pyalgo must also override these kv-pairs
+...
"""
base = vehdb.provenance_info(files=[veh_inputs_excel[0], veh_results_excel[0]])
if all_cases is None:
all_cases = set()
if specs is not None:
all_cases.update(specs[c_case_id])
if overrides is not None:
all_cases.update(overrides.keys())
if cycles is not None:
all_cases.update(cycles[c_case_id].unique())
log.info("Will store %s cases: %s", len(all_cases), all_cases)
for case in sorted(all_cases):
vehnum = cycles is not None and int(
cycles.loc[cycles[c_case_id] == case, c_vehnum].iloc[0]
)
log.info(f"+++ Case: %s, Veh: %s (out of %s)...", case, vehnum, len(all_cases))
## Store WOT
#
if pwots is not None:
pwot = pwots.loc[pwots[c_engno] == vehnum, :]
assert pwot.size, (case, vehnum)
pwot, SM = extract_SM_from_pwot(pwot)
assert SM and pwot.size, (case, vehnum, SM, pwot)
pwot_node = vehdb.vehnode(vehnum, wot_group_suffix)
if not skip_h5_write:
h5db.put(pwot_node, pwot)
vehdb.provenir_h5node(
h5db,
pwot_node,
title="Full-load-curve of the test-car, as delivered by Heinz on 13 May 2019",
files=[wots_excel[0]],
base=base,
)
if specs is not None and cycles is not None:
cyc = cycles.loc[cycles[c_case_id] == case].set_index("tim")
try:
cyc, oprops = vehdb.drop_scalar_columns(cyc, scalar_columns)
except Exception:
display(case, vehdb.grid(cyc, fitcols=False))
raise
## Store INP/OUT props
#
props = specs.loc[specs[c_case_id] == vehnum, :].squeeze()
assert isinstance(props, pd.Series), props
props.update(pd.Series(oprops))
props[c_SM] = SM
props_group = vehdb.vehnode(case, props_group_suffix)
if not skip_h5_write:
h5db.put(props_group, pd.Series(props))
vehdb.provenir_h5node(
h5db,
props_group,
title="Input specs & scalar results produced by AccDB",
base=base,
)
## Store CYCLE
#
cycle_group = vehdb.vehnode(case, cycle_group_suffix)
if not skip_h5_write:
h5db.put(cycle_group, cyc)
vehdb.provenir_h5node(
h5db, cycle_group, title="Cycle-run produced by AccDB", base=base
)
## Store OVERRIDES
#
if overrides is not None and case in overrides:
overrides_group = vehdb.vehnode(case, overrides_group_suffix)
if not skip_h5_write:
h5db.put(overrides_group, pd.Series(overrides[case]))
vehdb.provenir_h5node(
h5db,
overrides_group,
title="Values to override, as specified manually by Heinz",
files=[
"VehData/Differences between ACCESS tool versions 16_ and 20_09_2019.xlsx"
],
base=base,
)
def grid(df):
"""Display a dynamic grid if given `df` too long. """
display(vehdb.grid(df, fitcols=len(df.shape) < 2 or df.shape[1] < 12))
# pmr_tm rated_speed ndv_3 ndv_9 v_max_1 v_max_7 f0 Pres_130 n_max3
# IDclass idling_speed ndv_4 ndv_10 v_max_2 v_max_8 f1 Pres_130_Prated n_max_wot
# class v_max_declared ndv_5 v_max_transmission v_max_3 v_max_9 f2 n95_low below_s
#
# Index(['no_engine', 'n', 'Pwot', 'Twot', 'Pwot_norm', 'Twot_norm', 'SM', 'ASM',
# 'Pavai'],
# dtype='object')
# ```
# %%
import qgrid
print(columnize(list(specs.columns), displaywidth=160))
print(pwots.columns)
print(specs[c_vehnum].unique(), specs[c_case].unique())
display(vehdb.grid(specs, fitcols=False), vehdb.grid(pwots))
# %%
def extract_SM_from_pwot(
pwot, c_n=c_n, c_pwot=c_pwot, c_SM=c_SM, c_ASM=c_ASM
) -> "Tuple(pd.DataFrame, float)":
"""
Keep just (n, Pwot, ASM) columns & extract SM column as scalar value.
:param pwot:
the wot-curve dataframe for a single vehicle, with columns::
IX no_engine n Pwot Twot Pwot_norm Twot_norm SM ASM Pavai
:return:
p1, c1, p2, c2 = load_accdb_and_python_datasets()
# %%
## EXPORT RESULTS to upload them for the GS-group when releasing.
#
# p2.drop('v116', axis=0).to_excel('pyalgo-props-124cases-1.0.0.dev12.xlsx')
# c2.drop('v116', axis=0).to_excel('pyalgo-cycles-124cases-1.0.0.dev12.xlsx')
# %%
# Available PROPs
print(columnize(list(p1.columns), displaywidth=160))
print(columnize(list(p2.columns), displaywidth=160))
# %%
display(
vehdb.grid(p1, fitcols=False),
vehdb.grid(p2, fitcols=False),
# vehdb.grid(c1, fitcols=0),
)
# %%
sr_cmpr = vehdb.Comparator(lambda d, c: d[:, c], no_styling=True)
dataset_names = "accdb Python".split() # Must sort with "diff" column.
# %%
## Report PROP differences
#
# ACCDB, PYALGO
equivalent_columns = [
("Description", None),
("test_mass", None),