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Rename the timeslices table's timeslice_id column to timeslice #140

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Rename the timeslices table's timeslice_id column to timeslice #140

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19e22a7
Rename the timeslices table's timeslice_id column to timeslice
nick-gorman Aug 24, 2026
b24a95b
Prefer an explicit copy over assign when localising the date parse
nick-gorman Aug 24, 2026
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10 changes: 5 additions & 5 deletions src/ispypsa/pypsa_build/links.py
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Original file line number Diff line number Diff line change
Expand Up @@ -42,7 +42,7 @@ def _add_links_to_network(
CQ-NQ_existing p_min_pu , -0.714 # fallback only

timeslice_snapshots:
timeslice_id investment_periods snapshots
timeslice investment_periods snapshots
qld_peak_demand 2025 2025-01-13 12:00

network.snapshots:
Expand Down Expand Up @@ -115,7 +115,7 @@ def _build_link_pu_overrides(
CQ-NQ_existing p_min_pu , -0.714 # fallback only

timeslice_snapshots:
timeslice_id investment_periods snapshots
timeslice investment_periods snapshots
qld_peak_demand 2025 2025-01-13 12:00

snapshots:
Expand Down Expand Up @@ -161,7 +161,7 @@ def _expand_limits_to_snapshots(
CQ-NQ_existing p_min_pu qld_peak_demand -0.9 # no fallback

timeslice_snapshots:
timeslice_id investment_periods snapshots
timeslice investment_periods snapshots
qld_peak_demand 2025 2025-01-13 12:00

snapshots:
Expand Down Expand Up @@ -234,14 +234,14 @@ def _place_named_limits_at_snapshots(
CQ-NQ_existing p_max_pu qld_peak_demand 0.857

timeslice_snapshots:
timeslice_id investment_periods snapshots
timeslice investment_periods snapshots
qld_peak_demand 2025 2025-01-13 12:00

returns:
name attribute investment_periods snapshots value
CQ-NQ_existing p_max_pu 2025 2025-01-13 12:00 0.857
"""
active_at = timeslice_snapshots.rename(columns={"timeslice_id": "timeslice"})
active_at = timeslice_snapshots.copy()
active_at["snapshots"] = pd.to_datetime(active_at["snapshots"])
placed = named.merge(active_at, on="timeslice")
return placed.loc[:, _LIMIT_PER_SNAPSHOT_COLUMNS]
Expand Down
52 changes: 26 additions & 26 deletions src/ispypsa/templater/timeslices.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,7 +21,7 @@
from ispypsa.templater.custom_constraints_from_plexos import _tag_to_timeslice

_TIMESLICE_COLUMNS = [
"timeslice_id",
"timeslice",
"reference_year",
"start_month_day",
"end_month_day",
Expand Down Expand Up @@ -90,7 +90,7 @@ def _template_timeslices(
2026 2015

returns:
timeslice_id reference_year start_month_day end_month_day
timeslice reference_year start_month_day end_month_day
nsw_peak_demand 2015 11-18 11-20 # end exclusive
"""
events = _parse_calendar_events(timeslice_calendar)
Expand All @@ -115,29 +115,29 @@ def _parse_calendar_events(calendar: pd.DataFrame) -> pd.DataFrame:

I/O Example:
DATETIME=18/11/2021, NAME="NSW Hot Day", TIMESLICE=-1
-> DATETIME=2021-11-18, timeslice_id="nsw_peak_demand", TIMESLICE=-1
-> DATETIME=2021-11-18, timeslice="nsw_peak_demand", TIMESLICE=-1
"""
events = calendar.copy()
events["DATETIME"] = pd.to_datetime(events["DATETIME"], dayfirst=True)
events["timeslice_id"] = events["NAME"].map(_tag_to_timeslice)
return events.sort_values(["timeslice_id", "DATETIME"])
events["timeslice"] = events["NAME"].map(_tag_to_timeslice)
return events.sort_values(["timeslice", "DATETIME"])


def _add_next_event_columns(events: pd.DataFrame) -> pd.DataFrame:
"""Annotates each event with the date and state of the next event for the
same timeslice (NaN/NaT on each timeslice's last event).

I/O Example:
timeslice_id DATETIME TIMESLICE
timeslice DATETIME TIMESLICE
nsw_peak_demand 2021-11-18 -1
nsw_peak_demand 2021-11-20 0

->
timeslice_id DATETIME TIMESLICE next_date next_state
timeslice DATETIME TIMESLICE next_date next_state
nsw_peak_demand 2021-11-18 -1 2021-11-20 0
nsw_peak_demand 2021-11-20 0 NaT NaN
"""
grouped = events.groupby("timeslice_id")
grouped = events.groupby("timeslice")
events["next_date"] = grouped["DATETIME"].shift(-1)
events["next_state"] = grouped["TIMESLICE"].shift(-1)
return events
Expand All @@ -163,19 +163,19 @@ def _extract_windows(events: pd.DataFrame) -> pd.DataFrame:
"""Turns each on event into a window row ending at the paired off event.

I/O Example:
timeslice_id DATETIME TIMESLICE next_date next_state
timeslice DATETIME TIMESLICE next_date next_state
nsw_peak_demand 2021-11-18 -1 2021-11-20 0
nsw_peak_demand 2021-11-20 0 NaT NaN

returns:
timeslice_id start_date end_date
timeslice start_date end_date
nsw_peak_demand 2021-11-18 2021-11-20
"""
windows = events[events["TIMESLICE"] == -1]
windows = windows.rename(
columns={"DATETIME": "start_date", "next_date": "end_date"}
)
return windows[["timeslice_id", "start_date", "end_date"]].reset_index(drop=True)
return windows[["timeslice", "start_date", "end_date"]].reset_index(drop=True)


def _drop_horizon_truncated_planning_years(windows: pd.DataFrame) -> pd.DataFrame:
Expand All @@ -187,7 +187,7 @@ def _drop_horizon_truncated_planning_years(windows: pd.DataFrame) -> pd.DataFram
nothing is lost by dropping the year entirely.

I/O Example:
timeslice_id start_date end_date planning_year
timeslice start_date end_date planning_year
nsw_peak_demand 2057-11-18 2057-11-20 2058 # dropped: shares
nsw_winter_reference 2058-04-01 NaT 2058 # the truncated year
nsw_peak_demand 2056-11-18 2056-11-20 2057 # kept
Expand Down Expand Up @@ -228,7 +228,7 @@ def _extend_sequence_to_horizon(
2027 2011

windows (only planning_year is read):
timeslice_id start_date end_date planning_year
timeslice start_date end_date planning_year
nsw_peak_demand 2028-11-18 2028-11-20 2029

returns:
Expand Down Expand Up @@ -256,17 +256,17 @@ def _convert_windows_to_month_days(windows: pd.DataFrame) -> pd.DataFrame:
next calendar year, and winter's 04-01 -> 10-01 extends past 30 June).

I/O Example:
timeslice_id start_date end_date planning_year reference_year
timeslice start_date end_date planning_year reference_year
nsw_peak_demand 2025-11-18 2025-11-20 2026 2015

->
timeslice_id reference_year planning_year start_month_day end_month_day
timeslice reference_year planning_year start_month_day end_month_day
nsw_peak_demand 2015 2026 11-18 11-20
"""
windows["start_month_day"] = windows["start_date"].dt.strftime("%m-%d")
windows["end_month_day"] = windows["end_date"].dt.strftime("%m-%d")
return windows[
["timeslice_id", "reference_year", "planning_year"]
["timeslice", "reference_year", "planning_year"]
+ ["start_month_day", "end_month_day"]
]

Expand All @@ -278,7 +278,7 @@ def _raise_on_inconsistent_reference_year_patterns(patterns: pd.DataFrame) -> No
decoding one pattern per reference year would silently lose windows."""
occurrences = patterns.groupby(["reference_year", "planning_year"]).apply(
lambda x: frozenset(
zip(x["timeslice_id"], x["start_month_day"], x["end_month_day"])
zip(x["timeslice"], x["start_month_day"], x["end_month_day"])
),
include_groups=False,
)
Expand All @@ -296,12 +296,12 @@ def _keep_first_occurrence_per_reference_year(patterns: pd.DataFrame) -> pd.Data
occurrences are identical — validated before this is called).

I/O Example:
timeslice_id reference_year planning_year start_month_day end_month_day
timeslice reference_year planning_year start_month_day end_month_day
nsw_peak_demand 2015 2026 11-18 11-20
nsw_peak_demand 2015 2031 11-18 11-20

returns:
timeslice_id reference_year start_month_day end_month_day
timeslice reference_year start_month_day end_month_day
nsw_peak_demand 2015 11-18 11-20
"""
first_occurrence = patterns.groupby("reference_year")["planning_year"].transform(
Expand All @@ -316,12 +316,12 @@ def _raise_unless_windows_tile_the_year(timeslices: pd.DataFrame) -> None:
"""Raise unless, within every region and reference year, the windows tile
the year exactly — no day left uncovered and none covered twice. A gap there
would let a snapshot fall in no timeslice (silently taking a base limit); an
overlap would let it fall in two. The region is the timeslice_id prefix
overlap would let it fall in two. The region is the timeslice prefix
before the first underscore.
"""
# add a region column from the timeslice_id prefix (nsw_peak_demand -> nsw)
# add a region column from the timeslice prefix (nsw_peak_demand -> nsw)
# to group by, so each region's windows are checked for tiling independently
tagged = timeslices.assign(region=timeslices["timeslice_id"].str.split("_").str[0])
tagged = timeslices.assign(region=timeslices["timeslice"].str.split("_").str[0])
not_tiling = sorted(
(region, int(year))
for (region, year), windows in tagged.groupby(["region", "reference_year"])
Expand Down Expand Up @@ -371,8 +371,8 @@ def _raise_unless_only_winter_crosses_financial_year(timeslices: pd.DataFrame) -
),
axis=1,
)
is_winter = timeslices["timeslice_id"].str.endswith("_winter_reference")
crossing = sorted(timeslices.loc[spans_july & ~is_winter, "timeslice_id"].unique())
is_winter = timeslices["timeslice"].str.endswith("_winter_reference")
crossing = sorted(timeslices.loc[spans_july & ~is_winter, "timeslice"].unique())
if crossing:
raise ValueError(
f"Only winter_reference windows may cross the 1 July financial-year "
Expand Down Expand Up @@ -403,8 +403,8 @@ def _raise_unless_winter_is_constant_per_region(timeslices: pd.DataFrame) -> Non
reference years, so which reference year owns its end never changes its
value. This is the assumption documented on _template_timeslices.
"""
winter = timeslices[timeslices["timeslice_id"].str.endswith("_winter_reference")]
winter = winter.assign(region=winter["timeslice_id"].str.split("_").str[0])
winter = timeslices[timeslices["timeslice"].str.endswith("_winter_reference")]
winter = winter.assign(region=winter["timeslice"].str.split("_").str[0])
varying = sorted(
region
for region, group in winter.groupby("region")
Expand Down
2 changes: 1 addition & 1 deletion src/ispypsa/validation/schemas/custom_constraints_rhs.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -22,7 +22,7 @@ columns:
type: string
required: false
allowed_values_from:
- timeslices: timeslice_id
- timeslices: timeslice
description: >
Demand condition the limit applies to: the constraint binds only at
snapshots inside the timeslice's active windows.
Expand Down
6 changes: 3 additions & 3 deletions src/ispypsa/validation/schemas/network_transmission_path_limits.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -33,7 +33,7 @@ custom_validation:
A path that names timeslices must name them all for its region, or add an empty-timeslice
fallback, in both directions; a single all-empty row covers the whole space.
Because the timeslices table's windows partition the year, covering every
region timeslice_id covers every snapshot downstream.
region timeslice covers every snapshot downstream.
columns:
path_id:
type: string
Expand All @@ -60,10 +60,10 @@ columns:
type: string
required: false
allowed_values_from:
- timeslices: timeslice_id
- timeslices: timeslice
description: >
Demand condition the limit applies to: the limit binds only at snapshots
inside the timeslice's active windows. A timeslice_id's region is the prefix
inside the timeslice's active windows. A timeslice's region is the prefix
before its first underscore (e.g. qld in qld_peak_demand).

If absent (or empty):
Expand Down
8 changes: 4 additions & 4 deletions src/ispypsa/validation/schemas/timeslices.yaml
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Original file line number Diff line number Diff line change
@@ -1,19 +1,19 @@
table: timeslices
required: false
unique:
- [timeslice_id, reference_year, start_month_day]
- [timeslice, reference_year, start_month_day]
custom_validation:
- name: no_overlapping_windows_per_region_and_reference_year
description: >
Within a region (the timeslice_id prefix before the first underscore)
Within a region (the timeslice prefix before the first underscore)
and reference_year, no two windows may cover the same day. Windows are
compared as expanded [start, end) day ranges, not raw month-day strings:
an end_month_day at or before its start wraps into the following calendar
year (e.g. 11-20 to 03-20), and winter windows run past 30 June into the
next financial year (04-01 to 10-01).
- name: windows_cover_full_year_per_region_and_reference_year
description: >
Within a region (the timeslice_id prefix before the first underscore)
Within a region (the timeslice prefix before the first underscore)
and reference_year, the windows must cover every day of the year, leaving
no day with no active timeslice. Together with
no_overlapping_windows_per_region_and_reference_year this makes the
Expand Down Expand Up @@ -52,7 +52,7 @@ description: >
No timeslice is ever active: timeslice-tagged transmission path limits and
custom-constraint RHS values never apply to any snapshot.
columns:
timeslice_id:
timeslice:
type: string
required: true
allowed_values: [
Expand Down
20 changes: 10 additions & 10 deletions tests/test_model/test_add_links_with_timeslice_limits.py
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Expand Up @@ -48,7 +48,7 @@ def test_named_timeslices_overlay_the_fallback(csv_str_to_df):
CQ-NQ_existing, p_min_pu, , -0.714
""")
timeslice_snapshots = csv_str_to_df("""
timeslice_id, investment_periods, snapshots
timeslice, investment_periods, snapshots
qld_peak_demand, 2025, 2025-01-01 01:00:00
qld_peak_demand, 2025, 2025-01-01 02:00:00
""")
Expand Down Expand Up @@ -79,7 +79,7 @@ def test_fallback_only_attribute_gets_the_fallback_at_every_snapshot(csv_str_to_
CQ-NQ_existing, p_min_pu, , -0.714
""")
timeslice_snapshots = csv_str_to_df("""
timeslice_id, investment_periods, snapshots
timeslice, investment_periods, snapshots
qld_peak_demand, 2025, 2025-01-01 01:00:00
""")

Expand Down Expand Up @@ -114,7 +114,7 @@ def test_fallback_only_limits_with_no_timeslices_apply_at_every_snapshot(csv_str
CQ-NQ_existing, p_min_pu, , 0.0
""")
timeslice_snapshots = csv_str_to_df("""
timeslice_id, investment_periods, snapshots
timeslice, investment_periods, snapshots
""")

_add_links_to_network(
Expand All @@ -141,7 +141,7 @@ def test_named_timeslices_that_tile_the_snapshots_need_no_fallback(csv_str_to_df
CQ-NQ_existing, p_min_pu, , -0.714
""")
timeslice_snapshots = csv_str_to_df("""
timeslice_id, investment_periods, snapshots
timeslice, investment_periods, snapshots
qld_winter_reference, 2025, 2025-01-01 00:00:00
qld_peak_demand, 2025, 2025-01-01 01:00:00
qld_peak_demand, 2025, 2025-01-01 02:00:00
Expand Down Expand Up @@ -172,7 +172,7 @@ def test_named_timeslice_with_no_snapshots_leaves_the_fallback(csv_str_to_df):
CQ-NQ_existing, p_min_pu, , -0.714
""")
timeslice_snapshots = csv_str_to_df("""
timeslice_id, investment_periods, snapshots
timeslice, investment_periods, snapshots
""")

_add_links_to_network(
Expand All @@ -198,7 +198,7 @@ def test_snapshot_covered_by_neither_named_timeslice_nor_fallback_raises(csv_str
CQ-NQ_existing, p_min_pu, , -0.714
""")
timeslice_snapshots = csv_str_to_df("""
timeslice_id, investment_periods, snapshots
timeslice, investment_periods, snapshots
qld_peak_demand, 2025, 2025-01-01 01:00:00
""")

Expand Down Expand Up @@ -226,7 +226,7 @@ def test_existing_link_with_no_timeslice_limits_raises(csv_str_to_df):
name, attribute, timeslice, value
""")
timeslice_snapshots = csv_str_to_df("""
timeslice_id, investment_periods, snapshots
timeslice, investment_periods, snapshots
""")

with pytest.raises(ValueError) as excinfo:
Expand All @@ -252,7 +252,7 @@ def test_existing_link_missing_one_attribute_raises(csv_str_to_df):
CQ-NQ_existing, p_max_pu, , 1.0
""")
timeslice_snapshots = csv_str_to_df("""
timeslice_id, investment_periods, snapshots
timeslice, investment_periods, snapshots
qld_peak_demand, 2025, 2025-01-01 01:00:00
""")

Expand Down Expand Up @@ -286,7 +286,7 @@ def test_expansion_links_keep_their_static_values(csv_str_to_df):
CQ-NQ_existing, p_min_pu, , -0.714
""")
timeslice_snapshots = csv_str_to_df("""
timeslice_id, investment_periods, snapshots
timeslice, investment_periods, snapshots
qld_peak_demand, 2025, 2025-01-01 01:00:00
""")

Expand Down Expand Up @@ -341,7 +341,7 @@ def test_expand_limits_to_snapshots(csv_str_to_df):
NQ-CQ_other, p_max_pu, , 0.8
""")
timeslice_snapshots = csv_str_to_df("""
timeslice_id, investment_periods, snapshots
timeslice, investment_periods, snapshots
qld_peak_demand, 2025, 2025-01-01 01:00:00
""")
snapshots = pd.MultiIndex.from_arrays(
Expand Down
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