diff --git a/src/ispypsa/templater/manually_extracted_template_tables/7.5/reference_year_sequence.csv b/src/ispypsa/templater/manually_extracted_template_tables/7.5/reference_year_sequence.csv
new file mode 100644
index 00000000..84d58130
--- /dev/null
+++ b/src/ispypsa/templater/manually_extracted_template_tables/7.5/reference_year_sequence.csv
@@ -0,0 +1,17 @@
+planning_year,reference_year
+2026,2015
+2027,2011
+2028,2012
+2029,2013
+2030,2014
+2031,2015
+2032,2016
+2033,2017
+2034,2018
+2035,2019
+2036,2020
+2037,2021
+2038,2022
+2039,2023
+2040,2024
+2041,2025
diff --git a/src/ispypsa/templater/plexos/7.5/timeslice_RefYear5000.csv b/src/ispypsa/templater/plexos/7.5/timeslice_RefYear5000.csv
new file mode 100644
index 00000000..8c44038e
--- /dev/null
+++ b/src/ispypsa/templater/plexos/7.5/timeslice_RefYear5000.csv
@@ -0,0 +1,3388 @@
+DATETIME,NAME,TIMESLICE
+30/06/2021,NSW Hot Day,0
+18/12/2021,NSW Hot Day,-1
+20/12/2021,NSW Hot Day,0
+01/02/2022,NSW Hot Day,-1
+02/02/2022,NSW Hot Day,0
+10/02/2022,NSW Hot Day,-1
+11/02/2022,NSW Hot Day,0
+17/02/2022,NSW Hot Day,-1
+18/02/2022,NSW Hot Day,0
+26/01/2023,NSW Hot Day,-1
+27/01/2023,NSW Hot Day,0
+11/02/2023,NSW Hot Day,-1
+12/02/2023,NSW Hot Day,0
+06/03/2023,NSW Hot Day,-1
+07/03/2023,NSW Hot Day,0
+16/03/2023,NSW Hot Day,-1
+17/03/2023,NSW Hot Day,0
+19/03/2023,NSW Hot Day,-1
+20/03/2023,NSW Hot Day,0
+09/12/2023,NSW Hot Day,-1
+10/12/2023,NSW Hot Day,0
+14/12/2023,NSW Hot Day,-1
+15/12/2023,NSW Hot Day,0
+21/01/2024,NSW Hot Day,-1
+22/01/2024,NSW Hot Day,0
+26/01/2024,NSW Hot Day,-1
+27/01/2024,NSW Hot Day,0
+29/02/2024,NSW Hot Day,-1
+01/03/2024,NSW Hot Day,0
+27/11/2024,NSW Hot Day,-1
+28/11/2024,NSW Hot Day,0
+17/12/2024,NSW Hot Day,-1
+18/12/2024,NSW Hot Day,0
+27/12/2024,NSW Hot Day,-1
+28/12/2024,NSW Hot Day,0
+28/01/2025,NSW Hot Day,-1
+29/01/2025,NSW Hot Day,0
+28/02/2025,NSW Hot Day,-1
+01/03/2025,NSW Hot Day,0
+14/11/2025,NSW Hot Day,-1
+15/11/2025,NSW Hot Day,0
+21/11/2025,NSW Hot Day,-1
+22/11/2025,NSW Hot Day,0
+23/11/2025,NSW Hot Day,-1
+24/11/2025,NSW Hot Day,0
+25/01/2026,NSW Hot Day,-1
+26/01/2026,NSW Hot Day,0
+01/03/2026,NSW Hot Day,-1
+02/03/2026,NSW Hot Day,0
+01/01/2027,NSW Hot Day,-1
+02/01/2027,NSW Hot Day,0
+31/01/2027,NSW Hot Day,-1
+02/02/2027,NSW Hot Day,0
+05/02/2027,NSW Hot Day,-1
+06/02/2027,NSW Hot Day,0
+19/02/2027,NSW Hot Day,-1
+20/02/2027,NSW Hot Day,0
+08/11/2027,NSW Hot Day,-1
+10/11/2027,NSW Hot Day,0
+14/11/2027,NSW Hot Day,-1
+15/11/2027,NSW Hot Day,0
+20/11/2027,NSW Hot Day,-1
+21/11/2027,NSW Hot Day,0
+04/01/2028,NSW Hot Day,-1
+05/01/2028,NSW Hot Day,0
+30/11/2028,NSW Hot Day,-1
+02/12/2028,NSW Hot Day,0
+08/01/2029,NSW Hot Day,-1
+09/01/2029,NSW Hot Day,0
+18/01/2029,NSW Hot Day,-1
+19/01/2029,NSW Hot Day,0
+22/01/2029,NSW Hot Day,-1
+23/01/2029,NSW Hot Day,0
+08/11/2029,NSW Hot Day,-1
+09/11/2029,NSW Hot Day,0
+20/12/2029,NSW Hot Day,-1
+21/12/2029,NSW Hot Day,0
+22/12/2029,NSW Hot Day,-1
+24/12/2029,NSW Hot Day,0
+02/01/2030,NSW Hot Day,-1
+03/01/2030,NSW Hot Day,0
+14/11/2030,NSW Hot Day,-1
+15/11/2030,NSW Hot Day,0
+21/11/2030,NSW Hot Day,-1
+22/11/2030,NSW Hot Day,0
+23/11/2030,NSW Hot Day,-1
+24/11/2030,NSW Hot Day,0
+25/01/2031,NSW Hot Day,-1
+26/01/2031,NSW Hot Day,0
+01/03/2031,NSW Hot Day,-1
+02/03/2031,NSW Hot Day,0
+19/11/2031,NSW Hot Day,-1
+21/11/2031,NSW Hot Day,0
+14/01/2032,NSW Hot Day,-1
+15/01/2032,NSW Hot Day,0
+20/01/2032,NSW Hot Day,-1
+21/01/2032,NSW Hot Day,0
+25/02/2032,NSW Hot Day,-1
+26/02/2032,NSW Hot Day,0
+11/01/2033,NSW Hot Day,-1
+12/01/2033,NSW Hot Day,0
+30/01/2033,NSW Hot Day,-1
+01/02/2033,NSW Hot Day,0
+05/02/2033,NSW Hot Day,-1
+06/02/2033,NSW Hot Day,0
+10/02/2033,NSW Hot Day,-1
+12/02/2033,NSW Hot Day,0
+14/12/2033,NSW Hot Day,-1
+15/12/2033,NSW Hot Day,0
+20/12/2033,NSW Hot Day,-1
+21/12/2033,NSW Hot Day,0
+24/12/2033,NSW Hot Day,-1
+25/12/2033,NSW Hot Day,0
+07/01/2034,NSW Hot Day,-1
+08/01/2034,NSW Hot Day,0
+18/03/2034,NSW Hot Day,-1
+19/03/2034,NSW Hot Day,0
+28/12/2034,NSW Hot Day,-1
+30/12/2034,NSW Hot Day,0
+05/01/2035,NSW Hot Day,-1
+06/01/2035,NSW Hot Day,0
+18/01/2035,NSW Hot Day,-1
+19/01/2035,NSW Hot Day,0
+31/01/2035,NSW Hot Day,-1
+01/02/2035,NSW Hot Day,0
+19/12/2035,NSW Hot Day,-1
+20/12/2035,NSW Hot Day,0
+31/12/2035,NSW Hot Day,-1
+01/01/2036,NSW Hot Day,0
+04/01/2036,NSW Hot Day,-1
+05/01/2036,NSW Hot Day,0
+23/01/2036,NSW Hot Day,-1
+24/01/2036,NSW Hot Day,0
+01/02/2036,NSW Hot Day,-1
+02/02/2036,NSW Hot Day,0
+16/11/2036,NSW Hot Day,-1
+17/11/2036,NSW Hot Day,0
+28/11/2036,NSW Hot Day,-1
+30/11/2036,NSW Hot Day,0
+24/01/2037,NSW Hot Day,-1
+25/01/2037,NSW Hot Day,0
+26/01/2037,NSW Hot Day,-1
+27/01/2037,NSW Hot Day,0
+18/12/2037,NSW Hot Day,-1
+20/12/2037,NSW Hot Day,0
+01/02/2038,NSW Hot Day,-1
+02/02/2038,NSW Hot Day,0
+10/02/2038,NSW Hot Day,-1
+11/02/2038,NSW Hot Day,0
+17/02/2038,NSW Hot Day,-1
+18/02/2038,NSW Hot Day,0
+26/01/2039,NSW Hot Day,-1
+27/01/2039,NSW Hot Day,0
+11/02/2039,NSW Hot Day,-1
+12/02/2039,NSW Hot Day,0
+06/03/2039,NSW Hot Day,-1
+07/03/2039,NSW Hot Day,0
+16/03/2039,NSW Hot Day,-1
+17/03/2039,NSW Hot Day,0
+19/03/2039,NSW Hot Day,-1
+20/03/2039,NSW Hot Day,0
+09/12/2039,NSW Hot Day,-1
+10/12/2039,NSW Hot Day,0
+14/12/2039,NSW Hot Day,-1
+15/12/2039,NSW Hot Day,0
+21/01/2040,NSW Hot Day,-1
+22/01/2040,NSW Hot Day,0
+26/01/2040,NSW Hot Day,-1
+27/01/2040,NSW Hot Day,0
+29/02/2040,NSW Hot Day,-1
+01/03/2040,NSW Hot Day,0
+27/11/2040,NSW Hot Day,-1
+28/11/2040,NSW Hot Day,0
+17/12/2040,NSW Hot Day,-1
+18/12/2040,NSW Hot Day,0
+27/12/2040,NSW Hot Day,-1
+28/12/2040,NSW Hot Day,0
+28/01/2041,NSW Hot Day,-1
+29/01/2041,NSW Hot Day,0
+28/02/2041,NSW Hot Day,-1
+01/03/2041,NSW Hot Day,0
+14/11/2041,NSW Hot Day,-1
+15/11/2041,NSW Hot Day,0
+21/11/2041,NSW Hot Day,-1
+22/11/2041,NSW Hot Day,0
+23/11/2041,NSW Hot Day,-1
+24/11/2041,NSW Hot Day,0
+25/01/2042,NSW Hot Day,-1
+26/01/2042,NSW Hot Day,0
+01/03/2042,NSW Hot Day,-1
+02/03/2042,NSW Hot Day,0
+01/01/2043,NSW Hot Day,-1
+02/01/2043,NSW Hot Day,0
+31/01/2043,NSW Hot Day,-1
+02/02/2043,NSW Hot Day,0
+05/02/2043,NSW Hot Day,-1
+06/02/2043,NSW Hot Day,0
+19/02/2043,NSW Hot Day,-1
+20/02/2043,NSW Hot Day,0
+08/11/2043,NSW Hot Day,-1
+10/11/2043,NSW Hot Day,0
+14/11/2043,NSW Hot Day,-1
+15/11/2043,NSW Hot Day,0
+20/11/2043,NSW Hot Day,-1
+21/11/2043,NSW Hot Day,0
+04/01/2044,NSW Hot Day,-1
+05/01/2044,NSW Hot Day,0
+30/11/2044,NSW Hot Day,-1
+02/12/2044,NSW Hot Day,0
+08/01/2045,NSW Hot Day,-1
+09/01/2045,NSW Hot Day,0
+18/01/2045,NSW Hot Day,-1
+19/01/2045,NSW Hot Day,0
+22/01/2045,NSW Hot Day,-1
+23/01/2045,NSW Hot Day,0
+08/11/2045,NSW Hot Day,-1
+09/11/2045,NSW Hot Day,0
+20/12/2045,NSW Hot Day,-1
+21/12/2045,NSW Hot Day,0
+22/12/2045,NSW Hot Day,-1
+24/12/2045,NSW Hot Day,0
+02/01/2046,NSW Hot Day,-1
+03/01/2046,NSW Hot Day,0
+14/11/2046,NSW Hot Day,-1
+15/11/2046,NSW Hot Day,0
+21/11/2046,NSW Hot Day,-1
+22/11/2046,NSW Hot Day,0
+23/11/2046,NSW Hot Day,-1
+24/11/2046,NSW Hot Day,0
+25/01/2047,NSW Hot Day,-1
+26/01/2047,NSW Hot Day,0
+01/03/2047,NSW Hot Day,-1
+02/03/2047,NSW Hot Day,0
+19/11/2047,NSW Hot Day,-1
+21/11/2047,NSW Hot Day,0
+14/01/2048,NSW Hot Day,-1
+15/01/2048,NSW Hot Day,0
+20/01/2048,NSW Hot Day,-1
+21/01/2048,NSW Hot Day,0
+25/02/2048,NSW Hot Day,-1
+26/02/2048,NSW Hot Day,0
+11/01/2049,NSW Hot Day,-1
+12/01/2049,NSW Hot Day,0
+30/01/2049,NSW Hot Day,-1
+01/02/2049,NSW Hot Day,0
+05/02/2049,NSW Hot Day,-1
+06/02/2049,NSW Hot Day,0
+10/02/2049,NSW Hot Day,-1
+12/02/2049,NSW Hot Day,0
+14/12/2049,NSW Hot Day,-1
+15/12/2049,NSW Hot Day,0
+20/12/2049,NSW Hot Day,-1
+21/12/2049,NSW Hot Day,0
+24/12/2049,NSW Hot Day,-1
+25/12/2049,NSW Hot Day,0
+07/01/2050,NSW Hot Day,-1
+08/01/2050,NSW Hot Day,0
+18/03/2050,NSW Hot Day,-1
+19/03/2050,NSW Hot Day,0
+28/12/2050,NSW Hot Day,-1
+30/12/2050,NSW Hot Day,0
+05/01/2051,NSW Hot Day,-1
+06/01/2051,NSW Hot Day,0
+18/01/2051,NSW Hot Day,-1
+19/01/2051,NSW Hot Day,0
+31/01/2051,NSW Hot Day,-1
+01/02/2051,NSW Hot Day,0
+19/12/2051,NSW Hot Day,-1
+20/12/2051,NSW Hot Day,0
+31/12/2051,NSW Hot Day,-1
+01/01/2052,NSW Hot Day,0
+04/01/2052,NSW Hot Day,-1
+05/01/2052,NSW Hot Day,0
+23/01/2052,NSW Hot Day,-1
+24/01/2052,NSW Hot Day,0
+01/02/2052,NSW Hot Day,-1
+02/02/2052,NSW Hot Day,0
+16/11/2052,NSW Hot Day,-1
+17/11/2052,NSW Hot Day,0
+28/11/2052,NSW Hot Day,-1
+30/11/2052,NSW Hot Day,0
+24/01/2053,NSW Hot Day,-1
+25/01/2053,NSW Hot Day,0
+26/01/2053,NSW Hot Day,-1
+27/01/2053,NSW Hot Day,0
+18/12/2053,NSW Hot Day,-1
+20/12/2053,NSW Hot Day,0
+01/02/2054,NSW Hot Day,-1
+02/02/2054,NSW Hot Day,0
+10/02/2054,NSW Hot Day,-1
+11/02/2054,NSW Hot Day,0
+17/02/2054,NSW Hot Day,-1
+18/02/2054,NSW Hot Day,0
+26/01/2055,NSW Hot Day,-1
+27/01/2055,NSW Hot Day,0
+11/02/2055,NSW Hot Day,-1
+12/02/2055,NSW Hot Day,0
+06/03/2055,NSW Hot Day,-1
+07/03/2055,NSW Hot Day,0
+16/03/2055,NSW Hot Day,-1
+17/03/2055,NSW Hot Day,0
+19/03/2055,NSW Hot Day,-1
+20/03/2055,NSW Hot Day,0
+09/12/2055,NSW Hot Day,-1
+10/12/2055,NSW Hot Day,0
+14/12/2055,NSW Hot Day,-1
+15/12/2055,NSW Hot Day,0
+21/01/2056,NSW Hot Day,-1
+22/01/2056,NSW Hot Day,0
+26/01/2056,NSW Hot Day,-1
+27/01/2056,NSW Hot Day,0
+29/02/2056,NSW Hot Day,-1
+01/03/2056,NSW Hot Day,0
+27/11/2056,NSW Hot Day,-1
+28/11/2056,NSW Hot Day,0
+17/12/2056,NSW Hot Day,-1
+18/12/2056,NSW Hot Day,0
+27/12/2056,NSW Hot Day,-1
+28/12/2056,NSW Hot Day,0
+28/01/2057,NSW Hot Day,-1
+29/01/2057,NSW Hot Day,0
+28/02/2057,NSW Hot Day,-1
+01/03/2057,NSW Hot Day,0
+14/11/2057,NSW Hot Day,-1
+15/11/2057,NSW Hot Day,0
+21/11/2057,NSW Hot Day,-1
+22/11/2057,NSW Hot Day,0
+23/11/2057,NSW Hot Day,-1
+24/11/2057,NSW Hot Day,0
+25/01/2058,NSW Hot Day,-1
+26/01/2058,NSW Hot Day,0
+01/03/2058,NSW Hot Day,-1
+02/03/2058,NSW Hot Day,0
+30/06/2021,NSW Typical Summer,0
+01/11/2021,NSW Typical Summer,-1
+18/12/2021,NSW Typical Summer,0
+20/12/2021,NSW Typical Summer,-1
+01/02/2022,NSW Typical Summer,0
+02/02/2022,NSW Typical Summer,-1
+10/02/2022,NSW Typical Summer,0
+11/02/2022,NSW Typical Summer,-1
+17/02/2022,NSW Typical Summer,0
+18/02/2022,NSW Typical Summer,-1
+01/04/2022,NSW Typical Summer,0
+01/11/2022,NSW Typical Summer,-1
+26/01/2023,NSW Typical Summer,0
+27/01/2023,NSW Typical Summer,-1
+11/02/2023,NSW Typical Summer,0
+12/02/2023,NSW Typical Summer,-1
+06/03/2023,NSW Typical Summer,0
+07/03/2023,NSW Typical Summer,-1
+16/03/2023,NSW Typical Summer,0
+17/03/2023,NSW Typical Summer,-1
+19/03/2023,NSW Typical Summer,0
+20/03/2023,NSW Typical Summer,-1
+01/04/2023,NSW Typical Summer,0
+01/11/2023,NSW Typical Summer,-1
+09/12/2023,NSW Typical Summer,0
+10/12/2023,NSW Typical Summer,-1
+14/12/2023,NSW Typical Summer,0
+15/12/2023,NSW Typical Summer,-1
+21/01/2024,NSW Typical Summer,0
+22/01/2024,NSW Typical Summer,-1
+26/01/2024,NSW Typical Summer,0
+27/01/2024,NSW Typical Summer,-1
+29/02/2024,NSW Typical Summer,0
+01/03/2024,NSW Typical Summer,-1
+01/04/2024,NSW Typical Summer,0
+01/11/2024,NSW Typical Summer,-1
+27/11/2024,NSW Typical Summer,0
+28/11/2024,NSW Typical Summer,-1
+17/12/2024,NSW Typical Summer,0
+18/12/2024,NSW Typical Summer,-1
+27/12/2024,NSW Typical Summer,0
+28/12/2024,NSW Typical Summer,-1
+28/01/2025,NSW Typical Summer,0
+29/01/2025,NSW Typical Summer,-1
+28/02/2025,NSW Typical Summer,0
+01/03/2025,NSW Typical Summer,-1
+01/04/2025,NSW Typical Summer,0
+01/11/2025,NSW Typical Summer,-1
+14/11/2025,NSW Typical Summer,0
+15/11/2025,NSW Typical Summer,-1
+21/11/2025,NSW Typical Summer,0
+22/11/2025,NSW Typical Summer,-1
+23/11/2025,NSW Typical Summer,0
+24/11/2025,NSW Typical Summer,-1
+25/01/2026,NSW Typical Summer,0
+26/01/2026,NSW Typical Summer,-1
+01/03/2026,NSW Typical Summer,0
+02/03/2026,NSW Typical Summer,-1
+01/04/2026,NSW Typical Summer,0
+01/11/2026,NSW Typical Summer,-1
+01/01/2027,NSW Typical Summer,0
+02/01/2027,NSW Typical Summer,-1
+31/01/2027,NSW Typical Summer,0
+02/02/2027,NSW Typical Summer,-1
+05/02/2027,NSW Typical Summer,0
+06/02/2027,NSW Typical Summer,-1
+19/02/2027,NSW Typical Summer,0
+20/02/2027,NSW Typical Summer,-1
+01/04/2027,NSW Typical Summer,0
+01/11/2027,NSW Typical Summer,-1
+08/11/2027,NSW Typical Summer,0
+10/11/2027,NSW Typical Summer,-1
+14/11/2027,NSW Typical Summer,0
+15/11/2027,NSW Typical Summer,-1
+20/11/2027,NSW Typical Summer,0
+21/11/2027,NSW Typical Summer,-1
+04/01/2028,NSW Typical Summer,0
+05/01/2028,NSW Typical Summer,-1
+01/04/2028,NSW Typical Summer,0
+01/11/2028,NSW Typical Summer,-1
+30/11/2028,NSW Typical Summer,0
+02/12/2028,NSW Typical Summer,-1
+08/01/2029,NSW Typical Summer,0
+09/01/2029,NSW Typical Summer,-1
+18/01/2029,NSW Typical Summer,0
+19/01/2029,NSW Typical Summer,-1
+22/01/2029,NSW Typical Summer,0
+23/01/2029,NSW Typical Summer,-1
+01/04/2029,NSW Typical Summer,0
+01/11/2029,NSW Typical Summer,-1
+08/11/2029,NSW Typical Summer,0
+09/11/2029,NSW Typical Summer,-1
+20/12/2029,NSW Typical Summer,0
+21/12/2029,NSW Typical Summer,-1
+22/12/2029,NSW Typical Summer,0
+24/12/2029,NSW Typical Summer,-1
+02/01/2030,NSW Typical Summer,0
+03/01/2030,NSW Typical Summer,-1
+01/04/2030,NSW Typical Summer,0
+01/11/2030,NSW Typical Summer,-1
+14/11/2030,NSW Typical Summer,0
+15/11/2030,NSW Typical Summer,-1
+21/11/2030,NSW Typical Summer,0
+22/11/2030,NSW Typical Summer,-1
+23/11/2030,NSW Typical Summer,0
+24/11/2030,NSW Typical Summer,-1
+25/01/2031,NSW Typical Summer,0
+26/01/2031,NSW Typical Summer,-1
+01/03/2031,NSW Typical Summer,0
+02/03/2031,NSW Typical Summer,-1
+01/04/2031,NSW Typical Summer,0
+01/11/2031,NSW Typical Summer,-1
+19/11/2031,NSW Typical Summer,0
+21/11/2031,NSW Typical Summer,-1
+14/01/2032,NSW Typical Summer,0
+15/01/2032,NSW Typical Summer,-1
+20/01/2032,NSW Typical Summer,0
+21/01/2032,NSW Typical Summer,-1
+25/02/2032,NSW Typical Summer,0
+26/02/2032,NSW Typical Summer,-1
+01/04/2032,NSW Typical Summer,0
+01/11/2032,NSW Typical Summer,-1
+11/01/2033,NSW Typical Summer,0
+12/01/2033,NSW Typical Summer,-1
+30/01/2033,NSW Typical Summer,0
+01/02/2033,NSW Typical Summer,-1
+05/02/2033,NSW Typical Summer,0
+06/02/2033,NSW Typical Summer,-1
+10/02/2033,NSW Typical Summer,0
+12/02/2033,NSW Typical Summer,-1
+01/04/2033,NSW Typical Summer,0
+01/11/2033,NSW Typical Summer,-1
+14/12/2033,NSW Typical Summer,0
+15/12/2033,NSW Typical Summer,-1
+20/12/2033,NSW Typical Summer,0
+21/12/2033,NSW Typical Summer,-1
+24/12/2033,NSW Typical Summer,0
+25/12/2033,NSW Typical Summer,-1
+07/01/2034,NSW Typical Summer,0
+08/01/2034,NSW Typical Summer,-1
+18/03/2034,NSW Typical Summer,0
+19/03/2034,NSW Typical Summer,-1
+01/04/2034,NSW Typical Summer,0
+01/11/2034,NSW Typical Summer,-1
+28/12/2034,NSW Typical Summer,0
+30/12/2034,NSW Typical Summer,-1
+05/01/2035,NSW Typical Summer,0
+06/01/2035,NSW Typical Summer,-1
+18/01/2035,NSW Typical Summer,0
+19/01/2035,NSW Typical Summer,-1
+31/01/2035,NSW Typical Summer,0
+01/02/2035,NSW Typical Summer,-1
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diff --git a/src/ispypsa/templater/timeslices.py b/src/ispypsa/templater/timeslices.py
new file mode 100644
index 00000000..b7275568
--- /dev/null
+++ b/src/ispypsa/templater/timeslices.py
@@ -0,0 +1,418 @@
+"""Templates the per-reference-year timeslice window patterns.
+
+AEMO's PLEXOS data package ships a timeslice calendar (e.g.
+``timeslice_RefYear5000.csv``) of on/off events on absolute dates across the
+model horizon. The dates follow AEMO's rolling reference-year sequence: each
+planning financial year's windows are derived from the weather of the
+reference year assigned to it by Table 1 of the Draft 2026 ISP Market Model
+Instructions, with the 16-year sequence repeating until the end of the
+horizon. The templater inverts this: it assigns each calendar window to its
+planning year, joins on the manually extracted sequence table, checks every
+occurrence of a reference year carries an identical month-day pattern, and
+emits one window pattern per reference year. The translator re-sequences the
+patterns to match the user-configured reference_year_cycle, keeping
+timeslices consistent with the demand and VRE traces.
+"""
+
+from importlib.resources import files
+
+import pandas as pd
+
+from ispypsa.templater.custom_constraints_from_plexos import _tag_to_timeslice
+
+_TIMESLICE_COLUMNS = [
+    "timeslice_id",
+    "reference_year",
+    "start_month_day",
+    "end_month_day",
+]
+
+# The PLEXOS timeslice calendar shipped for each supported workbook version.
+# The "RefYearNNNN" suffix is AEMO's identifier for the reference-year
+# sequence the calendar was built with.
+_TIMESLICE_CALENDAR_FILENAMES = {"7.5": "timeslice_RefYear5000.csv"}
+
+
+def load_timeslice_calendar(iasr_workbook_version: str) -> pd.DataFrame:
+    """Loads the PLEXOS timeslice calendar shipped with ISPyPSA for the
+    given IASR workbook version (from ``src/ispypsa/templater/plexos/``)."""
+    extract_dir = files("ispypsa.templater") / "plexos" / iasr_workbook_version
+    return pd.read_csv(
+        extract_dir / _TIMESLICE_CALENDAR_FILENAMES[iasr_workbook_version]
+    )
+
+
+def _template_timeslices(
+    timeslice_calendar: pd.DataFrame, reference_year_sequence: pd.DataFrame
+) -> pd.DataFrame:
+    """Decodes the PLEXOS on/off timeslice calendar into one month-day window
+    pattern per reference year.
+
+    The raw calendar has one row per on/off event: TIMESLICE == -1 turns the
+    named timeslice on at DATETIME, TIMESLICE == 0 turns it off. Each on
+    event is paired with the next event for the same timeslice to form one
+    active window. Windows are grouped into the planning financial year they
+    start in and labelled with that year's reference year from
+    reference_year_sequence (extended cyclically past its last row, matching
+    AEMO's repeating sequence). Every occurrence of a reference year must
+    carry an identical pattern; the first occurrence is kept. The retained
+    patterns must partition each reference year's calendar exactly — no day
+    left uncovered, none covered twice.
+
+    Windows starting before the sequence's first planning year are dropped
+    (the early calendar years precede AEMO's documented sequence), as are
+    windows still open when the calendar ends (a horizon artifact).
+
+    Each window is labelled by the financial year it starts in, so a window
+    crossing the 1 July boundary keeps its start year's reference year even
+    though its end falls in the next. That stays sound only because the
+    weather-varying timeslices (peak, summer) never cross the boundary, so they
+    sit wholly within one reference year, while the one that does — winter — is
+    a fixed seasonal frame identical across reference years, so which reference
+    year "owns" its end never changes the decoded pattern. Both facts are
+    checked by _raise_unless_only_winter_crosses_financial_year and
+    _raise_unless_winter_is_constant_per_region (run on the templated table; see
+    test_shipped_calendar_decodes), not here, so the focused decoder tests can
+    use minimal data.
+
+    I/O Example:
+        timeslice_calendar:
+            DATETIME    NAME         TIMESLICE
+            30/06/2025  NSW Hot Day  0           # off with no preceding on: ignored
+            18/11/2025  NSW Hot Day  -1
+            20/11/2025  NSW Hot Day  0
+            18/11/2041  NSW Hot Day  -1          # 2041-42: sequence repeats from 2026
+            20/11/2041  NSW Hot Day  0
+            01/04/2058  NSW Winter   -1          # never turned off: dropped
+
+        reference_year_sequence:
+            planning_year  reference_year
+            2026           2015
+
+        returns:
+            timeslice_id     reference_year  start_month_day  end_month_day
+            nsw_peak_demand  2015            11-18            11-20  # end exclusive
+    """
+    events = _parse_calendar_events(timeslice_calendar)
+    events = _add_next_event_columns(events)
+    _raise_on_consecutive_on_events(events)
+    windows = _assign_windows_to_planning_years(_extract_windows(events))
+    windows = _drop_horizon_truncated_planning_years(windows)
+    if windows.empty or reference_year_sequence.empty:
+        return pd.DataFrame(columns=_TIMESLICE_COLUMNS)
+    sequence = _extend_sequence_to_horizon(reference_year_sequence, windows)
+    patterns = _convert_windows_to_month_days(
+        windows.merge(sequence, on="planning_year")
+    )
+    _raise_on_inconsistent_reference_year_patterns(patterns)
+    timeslices = _keep_first_occurrence_per_reference_year(patterns)
+    _raise_unless_windows_tile_the_year(timeslices)
+    return timeslices
+
+
+def _parse_calendar_events(calendar: pd.DataFrame) -> pd.DataFrame:
+    """Maps calendar names to canonical timeslice ids and orders the events.
+
+    I/O Example:
+        DATETIME=18/11/2021, NAME="NSW Hot Day", TIMESLICE=-1
+        -> DATETIME=2021-11-18, timeslice_id="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"])
+
+
+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
+        nsw_peak_demand  2021-11-18  -1
+        nsw_peak_demand  2021-11-20  0
+
+        ->
+        timeslice_id     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")
+    events["next_date"] = grouped["DATETIME"].shift(-1)
+    events["next_state"] = grouped["TIMESLICE"].shift(-1)
+    return events
+
+
+def _raise_on_consecutive_on_events(events: pd.DataFrame) -> None:
+    """Raise if any on event is directly followed by another on event for the
+    same timeslice — pairing on events with the next event would silently
+    produce wrong windows otherwise. An on event with no following event is
+    fine: the calendar legitimately ends with windows still open (e.g. winter
+    turning on at the horizon's end)."""
+    on_events = events[events["TIMESLICE"] == -1]
+    doubled = on_events[on_events["next_state"] == -1]
+    if not doubled.empty:
+        dates = sorted(doubled["DATETIME"].dt.strftime("%Y-%m-%d"))
+        raise ValueError(
+            f"Timeslice calendar has on events directly followed by another "
+            f"on event for the same timeslice, starting at: {dates}"
+        )
+
+
+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
+        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
+            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)
+
+
+def _drop_horizon_truncated_planning_years(windows: pd.DataFrame) -> pd.DataFrame:
+    """Drops every window in planning years where the calendar ends with a
+    window still open (e.g. winter turning on just before the horizon's end
+    and never turning off). Keeping the year's other windows would make its
+    pattern incomplete and fail the consistency check against earlier, fully
+    covered occurrences of the same reference year — which is also why
+    nothing is lost by dropping the year entirely.
+
+    I/O Example:
+        timeslice_id          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
+
+        returns the 2057 row only.
+    """
+    truncated_years = set(windows.loc[windows["end_date"].isna(), "planning_year"])
+    keep = ~windows["planning_year"].isin(truncated_years)
+    return windows[keep].reset_index(drop=True)
+
+
+def _assign_windows_to_planning_years(windows: pd.DataFrame) -> pd.DataFrame:
+    """Tags each window with the planning financial year it starts in
+    (year-ending convention: FY2026 spans July 2025 to June 2026).
+
+    I/O Example:
+        start_date 2025-11-16 -> planning_year 2026
+        start_date 2026-04-01 -> planning_year 2026
+    """
+    starts = windows["start_date"]
+    windows["planning_year"] = starts.dt.year + (starts.dt.month >= 7).astype(int)
+    return windows
+
+
+def _extend_sequence_to_horizon(
+    sequence: pd.DataFrame, windows: pd.DataFrame
+) -> pd.DataFrame:
+    """Cyclically repeats the documented reference-year sequence to cover
+    every planning year in the calendar — AEMO: "the 16-year sequence
+    repeating in the same order until the end of the outlook period". Only
+    the windows' latest planning_year is used; if the windows end before the
+    sequence does, the sequence is returned unshortened.
+
+    I/O Example:
+        sequence:
+            planning_year  reference_year
+            2026           2015
+            2027           2011
+
+        windows (only planning_year is read):
+            timeslice_id     start_date  end_date    planning_year
+            nsw_peak_demand  2028-11-18  2028-11-20  2029
+
+        returns:
+            planning_year  reference_year
+            2026           2015
+            2027           2011
+            2028           2015  # cycle repeats
+            2029           2011
+    """
+    sequence = sequence.sort_values("planning_year").reset_index(drop=True)
+    first = sequence["planning_year"].iloc[0]
+    last = max(windows["planning_year"].max(), sequence["planning_year"].max())
+    years = range(first, last + 1)
+    reference_years = [
+        sequence["reference_year"].iloc[(year - first) % len(sequence)]
+        for year in years
+    ]
+    return pd.DataFrame({"planning_year": years, "reference_year": reference_years})
+
+
+def _convert_windows_to_month_days(windows: pd.DataFrame) -> pd.DataFrame:
+    """Expresses each window's dates as month-day strings. The end is
+    exclusive and, when re-expanded into a financial year, is interpreted as
+    its first occurrence after the start (so 11-18 -> 03-20 wraps into the
+    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
+        nsw_peak_demand  2025-11-18  2025-11-20  2026           2015
+
+        ->
+        timeslice_id     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"]
+        + ["start_month_day", "end_month_day"]
+    ]
+
+
+def _raise_on_inconsistent_reference_year_patterns(patterns: pd.DataFrame) -> None:
+    """Raise if two planning years assigned the same reference year carry
+    different window patterns — that would mean the sequence table (or its
+    cyclic extension) doesn't match how the calendar was actually built, and
+    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"])
+        ),
+        include_groups=False,
+    )
+    distinct_patterns = occurrences.groupby("reference_year").nunique()
+    inconsistent = sorted(distinct_patterns[distinct_patterns > 1].index)
+    if inconsistent:
+        raise ValueError(
+            f"Reference years whose timeslice window patterns differ between "
+            f"planning years in the calendar: {inconsistent}"
+        )
+
+
+def _keep_first_occurrence_per_reference_year(patterns: pd.DataFrame) -> pd.DataFrame:
+    """Keeps each reference year's pattern from its first planning year (all
+    occurrences are identical — validated before this is called).
+
+    I/O Example:
+        timeslice_id     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
+            nsw_peak_demand  2015            11-18            11-20
+    """
+    first_occurrence = patterns.groupby("reference_year")["planning_year"].transform(
+        "min"
+    )
+    deduped = patterns[patterns["planning_year"] == first_occurrence]
+    deduped = deduped.drop(columns="planning_year")
+    return deduped.sort_values(_TIMESLICE_COLUMNS).reset_index(drop=True)
+
+
+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
+    before the first underscore.
+    """
+    # add a region column from the timeslice_id 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])
+    not_tiling = sorted(
+        (region, int(year))
+        for (region, year), windows in tagged.groupby(["region", "reference_year"])
+        if not _windows_tile_the_year(windows)
+    )
+    if not_tiling:
+        raise ValueError(
+            f"Timeslice windows must tile each region and reference year's "
+            f"calendar exactly (no gaps, no overlaps). Offending "
+            f"region/reference-year pairs: {not_tiling}"
+        )
+
+
+def _windows_tile_the_year(windows: pd.DataFrame) -> bool:
+    """True if the [start_month_day, end_month_day) windows form a gap-free,
+    overlap-free cycle: sorted by start, each window ends where the next begins,
+    and the last ends where the first begins. No day arithmetic needed — a gap
+    or an overlap shows up as an end that does not meet the following start.
+
+    I/O Example:
+        starts 04-01, 11-01 / ends 11-01, 04-01 -> True   # two halves meet and wrap
+        starts 04-01, 11-01 / ends 10-01, 04-01 -> False  # gap from 10-01 to 11-01
+        starts 04-01, 11-01 / ends 12-01, 04-01 -> False  # overlap from 11-01 to 12-01
+    """
+    ordered = windows.sort_values("start_month_day")
+    starts = list(ordered["start_month_day"])
+    ends = list(ordered["end_month_day"])
+    # rotate starts left by one so each window's end lines up with the next
+    # window's start; the last window's "next" wraps round to the first
+    next_starts = starts[1:] + starts[:1]
+    return ends == next_starts
+
+
+def _raise_unless_only_winter_crosses_financial_year(timeslices: pd.DataFrame) -> None:
+    """Raise if any non-winter window spans 1 July (the financial-year boundary).
+
+    The decode labels each window by the financial year it starts in, so a
+    window crossing the boundary keeps its start year's reference year even
+    though its end falls in the next. That only stays sound if the
+    weather-varying timeslices (peak, summer) never cross — they must sit wholly
+    within one reference year. Winter, the fixed seasonal frame, may cross; this
+    is the assumption documented on _template_timeslices.
+    """
+    spans_july = timeslices.apply(
+        lambda row: _window_spans_july_first(
+            row["start_month_day"], row["end_month_day"]
+        ),
+        axis=1,
+    )
+    is_winter = timeslices["timeslice_id"].str.endswith("_winter_reference")
+    crossing = sorted(timeslices.loc[spans_july & ~is_winter, "timeslice_id"].unique())
+    if crossing:
+        raise ValueError(
+            f"Only winter_reference windows may cross the 1 July financial-year "
+            f"boundary (weather-varying timeslices must stay within one reference "
+            f"year); these cross it: {crossing}"
+        )
+
+
+def _window_spans_july_first(start_month_day: str, end_month_day: str) -> bool:
+    """True if the [start, end) month-day window is active across 1 July. The
+    month-day strings compare chronologically.
+
+    I/O Example:
+        '04-01', '11-01' -> True    # April to November spans 1 July
+        '11-20', '03-20' -> False   # November to March (wraps) does not
+    """
+    if end_month_day > start_month_day:  # window does not wrap past year-end
+        return start_month_day <= "07-01" < end_month_day
+    return "07-01" >= start_month_day or "07-01" < end_month_day  # wraps past year-end
+
+
+def _raise_unless_winter_is_constant_per_region(timeslices: pd.DataFrame) -> None:
+    """Raise if winter_reference is not the same window in every reference year
+    of a region.
+
+    Winter is the seasonal frame that crosses the financial-year boundary; the
+    decode is only blur-free because that crossing window is identical across
+    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])
+    varying = sorted(
+        region
+        for region, group in winter.groupby("region")
+        if group[["start_month_day", "end_month_day"]].drop_duplicates().shape[0] > 1
+    )
+    if varying:
+        raise ValueError(
+            f"winter_reference must be the same window in every reference year "
+            f"(it is the fixed frame the per-reference-year decode relies on); it "
+            f"varies in regions: {varying}"
+        )
diff --git a/src/ispypsa/validation/schemas/timeslices.yaml b/src/ispypsa/validation/schemas/timeslices.yaml
new file mode 100644
index 00000000..e0c1ab96
--- /dev/null
+++ b/src/ispypsa/validation/schemas/timeslices.yaml
@@ -0,0 +1,94 @@
+table: timeslices
+required: false
+unique:
+  - [timeslice_id, 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)
+      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)
+      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
+      windows an exact partition of the year: sorted by start, each window ends
+      where the next begins and the last wraps to the first. A region need not
+      use all three timeslice types (e.g. tas has no peak_demand) as long as the
+      windows it does have tile the year.
+description: >
+  Month-day windows during which each demand-condition timeslice is active,
+  one pattern per reference (weather) year.
+
+  One row per active window. The translator re-sequences the patterns to the
+  configured reference_year_cycle — each model financial year gets the
+  windows of the reference year assigned to it, the same assignment used for
+  demand and VRE traces — to build time-varying transmission path limits and
+  to restrict custom constraints to the snapshots their RHS values apply to.
+
+  Source tables:
+    - `timeslice_RefYear5000` (PLEXOS data package extract)
+    - `reference_year_sequence` (manually extracted)
+
+  Source notes:
+  The calendar is AEMO's Draft 2026 ISP timeslice trace
+  (timeslice_RefYear5000.csv), which marks windows on absolute dates across
+  the model horizon following AEMO's rolling reference-year sequence. The
+  templater decodes it into one pattern per reference year using the
+  sequence documented in Table 1 of the Draft 2026 ISP Market Model
+  Instructions (transcribed as the manually extracted
+  reference_year_sequence table), validating that every occurrence of a
+  reference year carries an identical pattern. A timeslice in the canonical
+  vocabulary may have no windows at all (e.g. tas_peak_demand never
+  activates in the Draft 2026 ISP calendar), in which case limits or RHS
+  values tagged with it are never active.
+
+  If absent:
+  No timeslice is ever active: timeslice-tagged transmission path limits and
+  custom-constraint RHS values never apply to any snapshot.
+columns:
+  timeslice_id:
+    type: string
+    required: true
+    allowed_values: [
+      qld_peak_demand, qld_summer_typical, qld_winter_reference,
+      nsw_peak_demand, nsw_summer_typical, nsw_winter_reference,
+      vic_peak_demand, vic_summer_typical, vic_winter_reference,
+      sa_peak_demand, sa_summer_typical, sa_winter_reference,
+      tas_peak_demand, tas_summer_typical, tas_winter_reference,
+    ]
+    description: >
+      Region-prefixed canonical timeslice the window belongs to. Within a
+      region and reference year, windows of the three timeslices never
+      overlap.
+  reference_year:
+    type: int
+    required: true
+    description: >
+      Reference (weather) year the window pattern belongs to, in financial
+      year-ending convention (2015 means FY2014-15) — the same convention as
+      the reference_year_cycle model configuration and the trace data parsed
+      by isp-trace-parser.
+  start_month_day:
+    type: string
+    required: true
+    format: "%m-%d"
+    description: >
+      First day of the window (inclusive), e.g. "11-18". Windows belong to
+      the financial year they start in: months July-December fall in the
+      financial year's first calendar year, January-June in its second.
+  end_month_day:
+    type: string
+    required: true
+    format: "%m-%d"
+    description: >
+      Day the window ends (exclusive), interpreted as its first occurrence
+      after the start — "11-18" to "03-20" wraps into the next calendar
+      year, and winter's "04-01" to "10-01" extends past 30 June into the
+      next financial year. "02-29" boundaries (from leap reference years)
+      are clamped to "02-28" when expanded into non-leap model years.
diff --git a/tests/test_templater/test_timeslices.py b/tests/test_templater/test_timeslices.py
new file mode 100644
index 00000000..76386757
--- /dev/null
+++ b/tests/test_templater/test_timeslices.py
@@ -0,0 +1,418 @@
+import pandas as pd
+import pytest
+
+from ispypsa.templater.manual_tables import load_manually_extracted_tables
+from ispypsa.templater.timeslices import (
+    _TIMESLICE_COLUMNS,
+    _convert_windows_to_month_days,
+    _raise_unless_only_winter_crosses_financial_year,
+    _raise_unless_windows_tile_the_year,
+    _raise_unless_winter_is_constant_per_region,
+    _template_timeslices,
+    load_timeslice_calendar,
+)
+
+
+def test_template_timeslices_decodes_one_pattern_per_reference_year(csv_str_to_df):
+    # Rows are grouped by region (NSW then QLD), each in date order, so each
+    # region's year reads top to bottom.
+    #
+    # The data is minimal, not realistic: NSW's FY2026 winter ends (18/11/2026)
+    # before its FY2027 peak begins (10/12/2026). That gap is between two
+    # reference years, which the templater decodes and checks independently, so
+    # gaps between reference years are never examined — only each year tiles.
+    timeslice_calendar = csv_str_to_df("""
+        DATETIME,    NAME,                TIMESLICE
+        30/06/2025,  NSW Hot Day,         0
+        18/11/2025,  NSW Hot Day,         -1
+        20/11/2025,  NSW Hot Day,         0
+        20/11/2025,  NSW Typical Summer,  -1
+        20/03/2026,  NSW Typical Summer,  0
+        20/03/2026,  NSW Winter,          -1
+        18/11/2026,  NSW Winter,          0
+        10/12/2026,  NSW Hot Day,         -1
+        12/12/2026,  NSW Hot Day,         0
+        12/12/2026,  NSW Winter,          -1
+        10/12/2027,  NSW Winter,          0
+        01/10/2025,  QLD Typical Summer,  -1
+        01/04/2026,  QLD Typical Summer,  0
+        01/04/2026,  QLD Winter,          -1
+        01/10/2026,  QLD Winter,          0
+    """)
+    reference_year_sequence = csv_str_to_df("""
+        planning_year,  reference_year
+        2026,           2015
+        2027,           2011
+    """)
+
+    result = _template_timeslices(timeslice_calendar, reference_year_sequence)
+
+    # The QLD winter window starts in FY2026 and ends past 30 June; it
+    # belongs to FY2026's reference year. The typical summer end wraps past
+    # the new year.
+    expected = csv_str_to_df("""
+        timeslice_id,          reference_year,  start_month_day,  end_month_day
+        nsw_peak_demand,       2011,            12-10,            12-12
+        nsw_winter_reference,  2011,            12-12,            12-10
+        nsw_peak_demand,       2015,            11-18,            11-20
+        nsw_summer_typical,    2015,            11-20,            03-20
+        nsw_winter_reference,  2015,            03-20,            11-18
+        qld_summer_typical,    2015,            10-01,            04-01
+        qld_winter_reference,  2015,            04-01,            10-01
+    """)
+    # expected rows are grouped by reference year for readability; the templater
+    # returns them in timeslice_id order, so compare on a shared sort key
+    sort_key = ["reference_year", "timeslice_id", "start_month_day"]
+    pd.testing.assert_frame_equal(
+        result.sort_values(sort_key).reset_index(drop=True),
+        expected.sort_values(sort_key).reset_index(drop=True),
+    )
+
+
+def test_template_timeslices_deduplicates_repeated_reference_year(csv_str_to_df):
+    timeslice_calendar = csv_str_to_df("""
+        DATETIME,    NAME,         TIMESLICE
+        18/11/2025,  NSW Hot Day,  -1
+        20/11/2025,  NSW Hot Day,  0
+        20/11/2025,  NSW Winter,   -1
+        18/11/2026,  NSW Winter,   0
+        18/11/2030,  NSW Hot Day,  -1
+        20/11/2030,  NSW Hot Day,  0
+        20/11/2030,  NSW Winter,   -1
+        18/11/2031,  NSW Winter,   0
+    """)
+    reference_year_sequence = csv_str_to_df("""
+        planning_year,  reference_year
+        2026,           2015
+        2027,           2011
+        2028,           2012
+        2029,           2013
+        2030,           2014
+        2031,           2015
+    """)
+
+    result = _template_timeslices(timeslice_calendar, reference_year_sequence)
+
+    expected = csv_str_to_df("""
+        timeslice_id,          reference_year,  start_month_day,  end_month_day
+        nsw_peak_demand,       2015,            11-18,            11-20
+        nsw_winter_reference,  2015,            11-20,            11-18
+    """)
+    pd.testing.assert_frame_equal(result, expected)
+
+
+def test_template_timeslices_extends_sequence_cyclically(csv_str_to_df):
+    # FY2030 is past the sequence's last row, so the cycle repeats: 2030 gets
+    # 2026's reference year (2015) and must carry the same pattern.
+    timeslice_calendar = csv_str_to_df("""
+        DATETIME,    NAME,         TIMESLICE
+        18/11/2025,  NSW Hot Day,  -1
+        20/11/2025,  NSW Hot Day,  0
+        20/11/2025,  NSW Winter,   -1
+        18/11/2026,  NSW Winter,   0
+        10/12/2026,  NSW Hot Day,  -1
+        12/12/2026,  NSW Hot Day,  0
+        12/12/2026,  NSW Winter,   -1
+        10/12/2027,  NSW Winter,   0
+        18/11/2029,  NSW Hot Day,  -1
+        20/11/2029,  NSW Hot Day,  0
+        20/11/2029,  NSW Winter,   -1
+        18/11/2030,  NSW Winter,   0
+    """)
+    reference_year_sequence = csv_str_to_df("""
+        planning_year,  reference_year
+        2026,           2015
+        2027,           2011
+    """)
+
+    result = _template_timeslices(timeslice_calendar, reference_year_sequence)
+
+    expected = csv_str_to_df("""
+        timeslice_id,          reference_year,  start_month_day,  end_month_day
+        nsw_peak_demand,       2011,            12-10,            12-12
+        nsw_peak_demand,       2015,            11-18,            11-20
+        nsw_winter_reference,  2011,            12-12,            12-10
+        nsw_winter_reference,  2015,            11-20,            11-18
+    """)
+    pd.testing.assert_frame_equal(result, expected)
+
+
+def test_template_timeslices_raises_on_inconsistent_reference_year_patterns(
+    csv_str_to_df,
+):
+    # The second window falls in planning year 2028 (its December start rolls to
+    # the next FY), which is past the two-row sequence; cyclic extension maps it
+    # back to 2026's reference year, 2015. So 2015 ends up with two different
+    # patterns, which is what trips the guard.
+    timeslice_calendar = csv_str_to_df("""
+        DATETIME,    NAME,         TIMESLICE
+        18/11/2025,  NSW Hot Day,  -1
+        20/11/2025,  NSW Hot Day,  0
+        10/12/2027,  NSW Hot Day,  -1
+        12/12/2027,  NSW Hot Day,  0
+    """)
+    reference_year_sequence = csv_str_to_df("""
+        planning_year,  reference_year
+        2026,           2015
+        2027,           2011
+    """)
+
+    with pytest.raises(ValueError, match=r"timeslice window patterns differ.*\[2015\]"):
+        _template_timeslices(timeslice_calendar, reference_year_sequence)
+
+
+def test_template_timeslices_raises_on_consecutive_on_events(csv_str_to_df):
+    timeslice_calendar = csv_str_to_df("""
+        DATETIME,    NAME,         TIMESLICE
+        18/11/2025,  NSW Hot Day,  -1
+        20/11/2025,  NSW Hot Day,  -1
+        22/11/2025,  NSW Hot Day,  0
+    """)
+    reference_year_sequence = csv_str_to_df("""
+        planning_year,  reference_year
+        2026,           2015
+    """)
+
+    with pytest.raises(
+        ValueError, match=r"directly followed by another on event.*2025-11-18"
+    ):
+        _template_timeslices(timeslice_calendar, reference_year_sequence)
+
+
+def test_template_timeslices_drops_horizon_truncated_planning_year(csv_str_to_df):
+    # FY2027 ends with winter still on (no off event before the calendar
+    # ends), so all of FY2027's windows are dropped, not just the open one —
+    # its reference year already has a complete pattern from FY2026.
+    timeslice_calendar = csv_str_to_df("""
+        DATETIME,    NAME,                TIMESLICE
+        01/10/2025,  NSW Typical Summer,  -1
+        18/11/2025,  NSW Typical Summer,  0
+        18/11/2025,  NSW Hot Day,         -1
+        20/11/2025,  NSW Hot Day,         0
+        20/11/2025,  NSW Typical Summer,  -1
+        01/04/2026,  NSW Typical Summer,  0
+        01/04/2026,  NSW Winter,          -1
+        01/10/2026,  NSW Winter,          0
+        18/11/2026,  NSW Hot Day,         -1
+        20/11/2026,  NSW Hot Day,         0
+        01/04/2027,  NSW Winter,          -1
+    """)
+    reference_year_sequence = csv_str_to_df("""
+        planning_year,  reference_year
+        2026,           2015
+        2027,           2015
+    """)
+
+    result = _template_timeslices(timeslice_calendar, reference_year_sequence)
+
+    expected = csv_str_to_df("""
+        timeslice_id,          reference_year,  start_month_day,  end_month_day
+        nsw_peak_demand,       2015,            11-18,            11-20
+        nsw_summer_typical,    2015,            10-01,            11-18
+        nsw_summer_typical,    2015,            11-20,            04-01
+        nsw_winter_reference,  2015,            04-01,            10-01
+    """)
+    pd.testing.assert_frame_equal(result, expected)
+
+
+def test_template_timeslices_drops_windows_before_sequence_start(csv_str_to_df):
+    # The real calendar starts several planning years before AEMO's documented
+    # sequence; those years' windows are dropped.
+    timeslice_calendar = csv_str_to_df("""
+        DATETIME,    NAME,         TIMESLICE
+        18/11/2021,  NSW Hot Day,  -1
+        20/11/2021,  NSW Hot Day,  0
+        10/12/2025,  NSW Hot Day,  -1
+        12/12/2025,  NSW Hot Day,  0
+        12/12/2025,  NSW Winter,   -1
+        10/12/2026,  NSW Winter,   0
+    """)
+    reference_year_sequence = csv_str_to_df("""
+        planning_year,  reference_year
+        2026,           2015
+    """)
+
+    result = _template_timeslices(timeslice_calendar, reference_year_sequence)
+
+    expected = csv_str_to_df("""
+        timeslice_id,          reference_year,  start_month_day,  end_month_day
+        nsw_peak_demand,       2015,            12-10,            12-12
+        nsw_winter_reference,  2015,            12-12,            12-10
+    """)
+    pd.testing.assert_frame_equal(result, expected)
+
+
+def test_template_timeslices_empty_calendar(csv_str_to_df):
+    timeslice_calendar = pd.DataFrame(columns=["DATETIME", "NAME", "TIMESLICE"])
+    reference_year_sequence = csv_str_to_df("""
+        planning_year,  reference_year
+        2026,           2015
+    """)
+
+    result = _template_timeslices(timeslice_calendar, reference_year_sequence)
+
+    expected = csv_str_to_df("""
+        timeslice_id,  reference_year,  start_month_day,  end_month_day
+    """)
+    pd.testing.assert_frame_equal(result, expected, check_dtype=False)
+
+
+def test_shipped_calendar_decodes():
+    # The shipped v7.5 calendar must decode without tripping the consecutive-on
+    # or inconsistent-pattern guards: those encode invariants of AEMO's data, so
+    # this also catches a future calendar or sequence refresh that breaks them.
+    # Decoding behaviour itself is covered by the synthetic tests above.
+    calendar = load_timeslice_calendar("7.5")
+    sequence = load_manually_extracted_tables("7.5")["reference_year_sequence"]
+
+    result = _template_timeslices(calendar, sequence)
+
+    assert list(result.columns) == _TIMESLICE_COLUMNS
+    # The calendar spans AEMO's 2011-2025 reference (weather) years.
+    assert sorted(result["reference_year"].unique()) == list(range(2011, 2026))
+    # tas_peak_demand never activates in the Draft 2026 ISP calendar; the other
+    # 14 region-prefixed timeslices all do.
+    assert "tas_peak_demand" not in set(result["timeslice_id"])
+    assert result["timeslice_id"].nunique() == 14
+    # Reaching here means the inline partition guard passed: the shipped
+    # calendar's windows tile each reference year exactly. The reference-year
+    # attribution also relies on only winter crossing 1 July and winter being
+    # constant per region; the shipped calendar must satisfy both.
+    _raise_unless_only_winter_crosses_financial_year(result)
+    _raise_unless_winter_is_constant_per_region(result)
+
+
+def test_template_timeslices_empty_sequence(csv_str_to_df):
+    timeslice_calendar = csv_str_to_df("""
+        DATETIME,    NAME,         TIMESLICE
+        18/11/2025,  NSW Hot Day,  -1
+        20/11/2025,  NSW Hot Day,  0
+    """)
+    reference_year_sequence = pd.DataFrame(columns=["planning_year", "reference_year"])
+
+    result = _template_timeslices(timeslice_calendar, reference_year_sequence)
+
+    expected = csv_str_to_df("""
+        timeslice_id,  reference_year,  start_month_day,  end_month_day
+    """)
+    pd.testing.assert_frame_equal(result, expected, check_dtype=False)
+
+
+def test_coverage_guard_passes_on_full_partition(csv_str_to_df):
+    # peak carved out of summer, winter filling the cool half: tiles the year.
+    timeslices = csv_str_to_df("""
+        timeslice_id,          reference_year,  start_month_day,  end_month_day
+        nsw_peak_demand,       2015,            01-25,            01-26
+        nsw_summer_typical,    2015,            01-26,            04-01
+        nsw_winter_reference,  2015,            04-01,            11-01
+        nsw_summer_typical,    2015,            11-01,            01-25
+    """)
+
+    _raise_unless_windows_tile_the_year(timeslices)  # must not raise
+
+
+def test_coverage_guard_passes_without_peak_timeslice(csv_str_to_df):
+    # tas has no peak_demand; summer + winter alone still tile the year.
+    timeslices = csv_str_to_df("""
+        timeslice_id,          reference_year,  start_month_day,  end_month_day
+        tas_summer_typical,    2015,            01-01,            07-01
+        tas_winter_reference,  2015,            07-01,            01-01
+    """)
+
+    _raise_unless_windows_tile_the_year(timeslices)  # must not raise
+
+
+def test_coverage_guard_raises_on_gap(csv_str_to_df):
+    # June (06-01 to 07-01) is covered by no window.
+    timeslices = csv_str_to_df("""
+        timeslice_id,          reference_year,  start_month_day,  end_month_day
+        nsw_summer_typical,    2015,            01-01,            06-01
+        nsw_winter_reference,  2015,            07-01,            01-01
+    """)
+
+    with pytest.raises(
+        ValueError, match=r"Offending region/reference-year pairs: \[\('nsw', 2015\)\]"
+    ):
+        _raise_unless_windows_tile_the_year(timeslices)
+
+
+def test_coverage_guard_raises_on_overlap(csv_str_to_df):
+    # July (07-01 to 08-01) is covered by both summer and winter.
+    timeslices = csv_str_to_df("""
+        timeslice_id,          reference_year,  start_month_day,  end_month_day
+        nsw_summer_typical,    2015,            01-01,            08-01
+        nsw_winter_reference,  2015,            07-01,            01-01
+    """)
+
+    with pytest.raises(
+        ValueError, match=r"Offending region/reference-year pairs: \[\('nsw', 2015\)\]"
+    ):
+        _raise_unless_windows_tile_the_year(timeslices)
+
+
+def test_only_winter_may_cross_financial_year_boundary_raises(csv_str_to_df):
+    # A summer window spanning 1 July is weather-varying AND boundary-crossing:
+    # exactly the case that could blur reference years.
+    timeslices = csv_str_to_df("""
+        timeslice_id,        reference_year,  start_month_day,  end_month_day
+        nsw_summer_typical,  2015,            06-01,            08-01
+    """)
+
+    with pytest.raises(ValueError, match=r"these cross it: \['nsw_summer_typical'\]"):
+        _raise_unless_only_winter_crosses_financial_year(timeslices)
+
+
+def test_only_winter_may_cross_financial_year_boundary_passes(csv_str_to_df):
+    # Winter spans 1 July (allowed); the wrapping summer covers Nov-Apr, not July.
+    timeslices = csv_str_to_df("""
+        timeslice_id,          reference_year,  start_month_day,  end_month_day
+        nsw_winter_reference,  2015,            04-01,            11-01
+        nsw_summer_typical,    2015,            11-01,            04-01
+    """)
+
+    _raise_unless_only_winter_crosses_financial_year(timeslices)  # must not raise
+
+
+def test_winter_must_be_constant_per_region_raises(csv_str_to_df):
+    # NSW winter differs between its two reference years.
+    timeslices = csv_str_to_df("""
+        timeslice_id,          reference_year,  start_month_day,  end_month_day
+        nsw_winter_reference,  2015,            04-01,            11-01
+        nsw_winter_reference,  2011,            04-01,            10-15
+    """)
+
+    with pytest.raises(ValueError, match=r"varies in regions: \['nsw'\]"):
+        _raise_unless_winter_is_constant_per_region(timeslices)
+
+
+def test_winter_constant_per_region_passes(csv_str_to_df):
+    # NSW winter is identical across reference years; TAS may differ from NSW.
+    timeslices = csv_str_to_df("""
+        timeslice_id,          reference_year,  start_month_day,  end_month_day
+        nsw_winter_reference,  2015,            04-01,            11-01
+        nsw_winter_reference,  2011,            04-01,            11-01
+        tas_winter_reference,  2015,            03-01,            12-01
+    """)
+
+    _raise_unless_winter_is_constant_per_region(timeslices)  # must not raise
+
+
+def test_convert_windows_to_month_days_preserves_leap_day(csv_str_to_df):
+    # Reference year 2024 is a leap year with a 29 February hot day in the
+    # shipped calendar. The templater must keep "02-29" — clamping it to 02-28
+    # in non-leap model years is the translator's job, not the templater's.
+    windows = csv_str_to_df("""
+        timeslice_id,     reference_year,  planning_year,  start_date,  end_date
+        nsw_peak_demand,  2024,            2040,           2040-02-29,  2040-03-01
+    """)
+    # _convert_windows_to_month_days strftimes these, so they must be datetimes
+    windows["start_date"] = pd.to_datetime(windows["start_date"])
+    windows["end_date"] = pd.to_datetime(windows["end_date"])
+
+    result = _convert_windows_to_month_days(windows)
+
+    expected = csv_str_to_df("""
+        timeslice_id,     reference_year,  planning_year,  start_month_day,  end_month_day
+        nsw_peak_demand,  2024,            2040,           02-29,            03-01
+    """)
+    pd.testing.assert_frame_equal(result, expected)