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Fix res_transformers returning an empty dataframe (#163)
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Fixes #158

The property `res_branches` was using similar technique to `res_transformers`. I changed both to avoid future breakage.
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@alihamdan
alihamdan authored Jan 10, 2024
1 parent 9665328 commit 7cd7718
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2 changes: 2 additions & 0 deletions doc/Changelog.md
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Expand Up @@ -2,6 +2,8 @@

## Unreleased

- {gh-pr}`163` {gh-issue}`158` Fix `ElectricalNetwork.res_transformers` returning an empty dataframe
when max_power is not set.
- {gh-pr}`151` Require Python 3.10 or newer.

## Version 0.6.0
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186 changes: 71 additions & 115 deletions roseau/load_flow/network.py
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Expand Up @@ -769,63 +769,44 @@ def res_branches(self) -> pd.DataFrame:
- `potential2`: The complex potential of the second bus (in Volts) for the given phase.
"""
self._warn_invalid_results()
res_list = []
res_dict = {
"branch_id": [],
"phase": [],
"branch_type": [],
"current1": [],
"current2": [],
"power1": [],
"power2": [],
"potential1": [],
"potential2": [],
}
dtypes = {c: _DTYPES[c] for c in res_dict}
for branch_id, branch in self.branches.items():
currents1, currents2 = branch._res_currents_getter(warning=False)
powers1, powers2 = branch._res_powers_getter(warning=False)
potentials1, potentials2 = branch._res_potentials_getter(warning=False)
res_list.extend(
{
"branch_id": branch_id,
"phase": phase,
"branch_type": branch.branch_type,
"current1": i1,
"current2": None,
"power1": s1,
"power2": None,
"potential1": v1,
"potential2": None,
}
for i1, s1, v1, phase in zip(currents1, powers1, potentials1, branch.phases1, strict=True)
)
res_list.extend(
{
"branch_id": branch_id,
"phase": phase,
"branch_type": branch.branch_type,
"current1": None,
"current2": i2,
"power1": None,
"power2": s2,
"potential1": None,
"potential2": v2,
}
for i2, s2, v2, phase in zip(currents2, powers2, potentials2, branch.phases2, strict=True)
)

columns = [
"branch_id",
"phase",
"branch_type",
"current1",
"current2",
"power1",
"power2",
"potential1",
"potential2",
]
dtypes = {c: _DTYPES[c] for c in columns}
return (
pd.DataFrame.from_records(res_list, columns=columns)
.astype(dtypes)
# aggregate x1 and x2 for the same phase for I, V, S, ...
.groupby(["branch_id", "phase", "branch_type"], observed=True)
# there are 2 values of I, V, S, ...; only one is not nan -> keep it
.mean()
# if all values are nan -> drop the row (the phase does not exist)
.dropna(how="all")
.reset_index(level="branch_type")
)
phases = sorted(set(branch.phases1) | set(branch.phases2))
for phase in phases:
if phase in branch.phases1:
idx1 = branch.phases2.index(phase)
i1, s1, v1 = currents1[idx1], powers1[idx1], potentials1[idx1]
else:
i1, s1, v1 = None, None, None
if phase in branch.phases2:
idx2 = branch.phases2.index(phase)
i2, s2, v2 = currents2[idx2], powers2[idx2], potentials2[idx2]
else:
i2, s2, v2 = None, None, None
res_dict["branch_id"].append(branch_id)
res_dict["phase"].append(phase)
res_dict["branch_type"].append(branch.branch_type)
res_dict["current1"].append(i1)
res_dict["current2"].append(i2)
res_dict["power1"].append(s1)
res_dict["power2"].append(s2)
res_dict["potential1"].append(v1)
res_dict["potential2"].append(v2)
return pd.DataFrame(res_dict).astype(dtypes).set_index(["branch_id", "phase"])

@property
def res_transformers(self) -> pd.DataFrame:
Expand All @@ -852,7 +833,19 @@ def res_transformers(self) -> pd.DataFrame:
- `max_power`: The maximum power loading (in VoltAmps) of the transformer.
"""
self._warn_invalid_results()
res_list = []
res_dict = {
"transformer_id": [],
"phase": [],
"current1": [],
"current2": [],
"power1": [],
"power2": [],
"potential1": [],
"potential2": [],
"max_power": [],
"violated": [],
}
dtypes = {c: _DTYPES[c] for c in res_dict}
for branch in self.branches.values():
if not isinstance(branch, Transformer):
continue
Expand All @@ -863,65 +856,29 @@ def res_transformers(self) -> pd.DataFrame:
violated = None
if s_max is not None:
violated = max(abs(sum(powers1)), abs(sum(powers2))) > s_max
res_list.extend(
{
"transformer_id": branch.id,
"phase": phase,
"current1": i1,
"current2": None,
"power1": s1,
"power2": None,
"potential1": v1,
"potential2": None,
"max_power": s_max,
"violated": violated,
}
for i1, s1, v1, phase in zip(currents1, powers1, potentials1, branch.phases1, strict=True)
)
res_list.extend(
{
"transformer_id": branch.id,
"phase": phase,
"current1": None,
"current2": i2,
"power1": None,
"power2": s2,
"potential1": None,
"potential2": v2,
"max_power": s_max,
"violated": violated,
}
for i2, s2, v2, phase in zip(currents2, powers2, potentials2, branch.phases2, strict=True)
)

columns = [
"transformer_id",
"phase",
"current1",
"current2",
"power1",
"power2",
"potential1",
"potential2",
"max_power",
"violated",
]
dtypes = {c: _DTYPES[c] for c in columns}
res = (
pd.DataFrame.from_records(res_list, columns=columns)
.astype(dtypes)
# aggregate x1 and x2 for the same phase for I, V, S, ...
.groupby(["transformer_id", "phase", "max_power", "violated"], observed=True)
# there are 2 values of I, V, S, ...; only one is not nan -> keep it
.mean()
# if all values are nan -> drop the row (the phase does not exist)
.dropna(how="all")
.reset_index(level=["max_power", "violated"])
)
# move the max_power and violated columns to the end
res["max_power"] = res.pop("max_power")
res["violated"] = res.pop("violated")
return res
phases = sorted(set(branch.phases1) | set(branch.phases2))
for phase in phases:
if phase in branch.phases1:
idx1 = branch.phases2.index(phase)
i1, s1, v1 = currents1[idx1], powers1[idx1], potentials1[idx1]
else:
i1, s1, v1 = None, None, None
if phase in branch.phases2:
idx2 = branch.phases2.index(phase)
i2, s2, v2 = currents2[idx2], powers2[idx2], potentials2[idx2]
else:
i2, s2, v2 = None, None, None
res_dict["transformer_id"].append(branch.id)
res_dict["phase"].append(phase)
res_dict["current1"].append(i1)
res_dict["current2"].append(i2)
res_dict["power1"].append(s1)
res_dict["power2"].append(s2)
res_dict["potential1"].append(v1)
res_dict["potential2"].append(v2)
res_dict["max_power"].append(s_max)
res_dict["violated"].append(violated)
return pd.DataFrame(res_dict).astype(dtypes).set_index(["transformer_id", "phase"])

@property
def res_lines(self) -> pd.DataFrame:
Expand Down Expand Up @@ -1003,8 +960,7 @@ def res_lines(self) -> pd.DataFrame:
res_dict["series_current"].append(i_series)
res_dict["max_current"].append(i_max)
res_dict["violated"].append(violated)
res = pd.DataFrame(res_dict).astype(dtypes).set_index(["line_id", "phase"])
return res
return pd.DataFrame(res_dict).astype(dtypes).set_index(["line_id", "phase"])

@property
def res_switches(self) -> pd.DataFrame:
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