Fix #635: Time_period cast to String does not follow the time_period_output_format

src/vtlengine/DataTypes/TimeHandling.py

@@ -17,6 +17,18 @@
 PERIOD_INDICATORS = ["A", "S", "Q", "M", "W", "D"]
 
 
+class TimePeriodConfig:
+    _representation = "vtl"  # Default representation
+
+    @classmethod
+    def set_representation(cls, representation: str) -> None:
+        cls._representation = representation
+
+    @classmethod
+    def get_representation(cls) -> str:
+        return cls._representation
+
+
 def date_to_period(date_value: date, period_indicator: str) -> Any:
     if period_indicator == "A":
         return TimePeriodHandler(f"{date_value.year}A")
@@ -387,6 +399,18 @@ def natural_representation(self) -> str:
             period_number_str = str(self.period_number)
         return f"{self.year}-{self.period_indicator}{period_number_str}"
 
+    def external_representation(self) -> str:
+        """Return the representation based on the configured TimePeriodConfig."""
+        representation = TimePeriodConfig.get_representation()
+        if representation == "vtl":
+            return self.vtl_representation()
+        elif representation == "sdmx_gregorian":
+            return self.sdmx_gregorian_representation()
+        elif representation == "sdmx_reporting":
+            return self.sdmx_reporting_representation()
+        else:
+            return self.natural_representation()
+
 
 class TimeIntervalHandler:
     _date1: str = "0"

src/vtlengine/DataTypes/__init__.py

@@ -144,12 +144,13 @@ def explicit_cast(cls, value: Any, from_type: Any) -> str:
             Boolean,
             String,
             Date,
-            TimePeriod,
             TimeInterval,
         }:
             return str(value)
         if from_type == Duration:
             return _SHORTCODE_TO_ISO.get(str(value), str(value))
+        elif from_type == TimePeriod:
+            return TimePeriodHandler(str(value)).external_representation()
 
         raise RunTimeError(
             "2-1-5-1",

src/vtlengine/files/output/_time_period_representation.py

@@ -2,7 +2,7 @@
 from typing import Union
 
 from vtlengine.DataTypes import TimePeriod
-from vtlengine.DataTypes.TimeHandling import TimePeriodHandler
+from vtlengine.DataTypes.TimeHandling import TimePeriodConfig, TimePeriodHandler
 from vtlengine.Exceptions import InputValidationException
 from vtlengine.Model import Dataset, Scalar
 
@@ -21,6 +21,9 @@ def check_value(cls, value: str) -> "TimePeriodRepresentation":
             raise InputValidationException(
                 code="0-1-1-15", value=value, valid_options=valid_options
             )
+
+        TimePeriodConfig.set_representation(value)
+
         return cls(value)
 
 

tests/Cast/data/Dataset/output/GL_563_1-1.csv

@@ -1,15 +1,15 @@
 FREQ,MEASURE,REF_AREA,TIME_PERIOD,UNIT_MEASURE,year_id,OBS_VALUE,ADJUSTMENT,BASE_PER,DECIMALS,OBS_STATUS,UNIT_MULT
-Q,HPI_YDH,HUN,2007-Q1,IX,2007A,128.791663747366,S,2015,1,A,0
-Q,HPI_YDH,HUN,2007-Q2,IX,2007A,135.068262557786,S,2015,1,A,0
-Q,HPI_YDH,HUN,2007-Q3,IX,2007A,137.007469129715,S,2015,1,A,0
-Q,HPI_YDH,HUN,2007-Q4,IX,2007A,133.095968661608,S,2015,1,A,0
-Q,HPI_YDH,HUN,2008-Q1,IX,2008A,133.03554880574,S,2015,1,A,0
-Q,HPI_YDH,HUN,2008-Q2,IX,2008A,134.223860470646,S,2015,1,A,0
-Q,HPI_YDH,HUN,2008-Q3,IX,2008A,133.560759222016,S,2015,1,A,0
-Q,HPI_YDH,HUN,2008-Q4,IX,2008A,126.58631498202,S,2015,1,A,0
-Q,HPI_YDH,HUN,2009-Q1,IX,2009A,129.175020241349,S,2015,1,A,0
-Q,HPI_YDH,HUN,2009-Q2,IX,2009A,128.069036228621,S,2015,1,A,0
-Q,HPI_YDH,HUN,2009-Q3,IX,2009A,120.538647006447,S,2015,1,A,0
-Q,HPI_YDH,HUN,2009-Q4,IX,2009A,122.981069897426,S,2015,1,A,0
-Q,HPI_YDH,HUN,2010-Q1,IX,2010A,120.008425982046,S,2015,1,A,0
-Q,HPI_YDH,HUN,2010-Q2,IX,2010A,117.718433849041,S,2015,1,A,0
+Q,HPI_YDH,HUN,2007-Q1,IX,2007,128.791663747366,S,2015,1,A,0
+Q,HPI_YDH,HUN,2007-Q2,IX,2007,135.068262557786,S,2015,1,A,0
+Q,HPI_YDH,HUN,2007-Q3,IX,2007,137.007469129715,S,2015,1,A,0
+Q,HPI_YDH,HUN,2007-Q4,IX,2007,133.095968661608,S,2015,1,A,0
+Q,HPI_YDH,HUN,2008-Q1,IX,2008,133.03554880574,S,2015,1,A,0
+Q,HPI_YDH,HUN,2008-Q2,IX,2008,134.223860470646,S,2015,1,A,0
+Q,HPI_YDH,HUN,2008-Q3,IX,2008,133.560759222016,S,2015,1,A,0
+Q,HPI_YDH,HUN,2008-Q4,IX,2008,126.58631498202,S,2015,1,A,0
+Q,HPI_YDH,HUN,2009-Q1,IX,2009,129.175020241349,S,2015,1,A,0
+Q,HPI_YDH,HUN,2009-Q2,IX,2009,128.069036228621,S,2015,1,A,0
+Q,HPI_YDH,HUN,2009-Q3,IX,2009,120.538647006447,S,2015,1,A,0
+Q,HPI_YDH,HUN,2009-Q4,IX,2009,122.981069897426,S,2015,1,A,0
+Q,HPI_YDH,HUN,2010-Q1,IX,2010,120.008425982046,S,2015,1,A,0
+Q,HPI_YDH,HUN,2010-Q2,IX,2010,117.718433849041,S,2015,1,A,0

tests/Cast/data/Dataset/output/GL_563_1-2.csv

@@ -1,15 +1,15 @@
 FREQ,MEASURE,REF_AREA,TIME_PERIOD,UNIT_MEASURE,year_id,year_id2,OBS_VALUE,ADJUSTMENT,BASE_PER,DECIMALS,OBS_STATUS,UNIT_MULT
-Q,HPI_YDH,HUN,2007-Q1,IX,2007A,2007A,128.791663747366,S,2015,1,A,0
-Q,HPI_YDH,HUN,2007-Q2,IX,2007A,2007A,135.068262557786,S,2015,1,A,0
-Q,HPI_YDH,HUN,2007-Q3,IX,2007A,2007A,137.007469129715,S,2015,1,A,0
-Q,HPI_YDH,HUN,2007-Q4,IX,2007A,2007A,133.095968661608,S,2015,1,A,0
-Q,HPI_YDH,HUN,2008-Q1,IX,2008A,2008A,133.03554880574,S,2015,1,A,0
-Q,HPI_YDH,HUN,2008-Q2,IX,2008A,2008A,134.223860470646,S,2015,1,A,0
-Q,HPI_YDH,HUN,2008-Q3,IX,2008A,2008A,133.560759222016,S,2015,1,A,0
-Q,HPI_YDH,HUN,2008-Q4,IX,2008A,2008A,126.58631498202,S,2015,1,A,0
-Q,HPI_YDH,HUN,2009-Q1,IX,2009A,2009A,129.175020241349,S,2015,1,A,0
-Q,HPI_YDH,HUN,2009-Q2,IX,2009A,2009A,128.069036228621,S,2015,1,A,0
-Q,HPI_YDH,HUN,2009-Q3,IX,2009A,2009A,120.538647006447,S,2015,1,A,0
-Q,HPI_YDH,HUN,2009-Q4,IX,2009A,2009A,122.981069897426,S,2015,1,A,0
-Q,HPI_YDH,HUN,2010-Q1,IX,2010A,2010A,120.008425982046,S,2015,1,A,0
-Q,HPI_YDH,HUN,2010-Q2,IX,2010A,2010A,117.718433849041,S,2015,1,A,0
+Q,HPI_YDH,HUN,2007-Q1,IX,2007,2007,128.791663747366,S,2015,1,A,0
+Q,HPI_YDH,HUN,2007-Q2,IX,2007,2007,135.068262557786,S,2015,1,A,0
+Q,HPI_YDH,HUN,2007-Q3,IX,2007,2007,137.007469129715,S,2015,1,A,0
+Q,HPI_YDH,HUN,2007-Q4,IX,2007,2007,133.095968661608,S,2015,1,A,0
+Q,HPI_YDH,HUN,2008-Q1,IX,2008,2008,133.03554880574,S,2015,1,A,0
+Q,HPI_YDH,HUN,2008-Q2,IX,2008,2008,134.223860470646,S,2015,1,A,0
+Q,HPI_YDH,HUN,2008-Q3,IX,2008,2008,133.560759222016,S,2015,1,A,0
+Q,HPI_YDH,HUN,2008-Q4,IX,2008,2008,126.58631498202,S,2015,1,A,0
+Q,HPI_YDH,HUN,2009-Q1,IX,2009,2009,129.175020241349,S,2015,1,A,0
+Q,HPI_YDH,HUN,2009-Q2,IX,2009,2009,128.069036228621,S,2015,1,A,0
+Q,HPI_YDH,HUN,2009-Q3,IX,2009,2009,120.538647006447,S,2015,1,A,0
+Q,HPI_YDH,HUN,2009-Q4,IX,2009,2009,122.981069897426,S,2015,1,A,0
+Q,HPI_YDH,HUN,2010-Q1,IX,2010,2010,120.008425982046,S,2015,1,A,0
+Q,HPI_YDH,HUN,2010-Q2,IX,2010,2010,117.718433849041,S,2015,1,A,0

tests/Cast/test_cast.py

@@ -383,9 +383,9 @@ class TestCastTimePeriodToString:
     @pytest.mark.parametrize(
         "input_val, expected",
         [
-            ("2024A", "2024A"),
-            ("2024-Q1", "2024-Q1"),
-            ("2020A", "2020A"),
+            ("2024A", "2024"),
+            ("2024-Q1", "2024Q1"),
+            ("2020A", "2020"),
             ("2020Q1", "2020Q1"),
             (None, None),
         ],

tests/TimePeriod/data/DataSet/output/GL_563_1-1.csv

@@ -1,15 +1,15 @@
 FREQ,MEASURE,REF_AREA,TIME_PERIOD,UNIT_MEASURE,year_id,OBS_VALUE,ADJUSTMENT,BASE_PER,DECIMALS,OBS_STATUS,UNIT_MULT
-Q,HPI_YDH,HUN,2007-Q1,IX,2007A,128.791663747366,S,2015,1,A,0
-Q,HPI_YDH,HUN,2007-Q2,IX,2007A,135.068262557786,S,2015,1,A,0
-Q,HPI_YDH,HUN,2007-Q3,IX,2007A,137.007469129715,S,2015,1,A,0
-Q,HPI_YDH,HUN,2007-Q4,IX,2007A,133.095968661608,S,2015,1,A,0
-Q,HPI_YDH,HUN,2008-Q1,IX,2008A,133.03554880574,S,2015,1,A,0
-Q,HPI_YDH,HUN,2008-Q2,IX,2008A,134.223860470646,S,2015,1,A,0
-Q,HPI_YDH,HUN,2008-Q3,IX,2008A,133.560759222016,S,2015,1,A,0
-Q,HPI_YDH,HUN,2008-Q4,IX,2008A,126.58631498202,S,2015,1,A,0
-Q,HPI_YDH,HUN,2009-Q1,IX,2009A,129.175020241349,S,2015,1,A,0
-Q,HPI_YDH,HUN,2009-Q2,IX,2009A,128.069036228621,S,2015,1,A,0
-Q,HPI_YDH,HUN,2009-Q3,IX,2009A,120.538647006447,S,2015,1,A,0
-Q,HPI_YDH,HUN,2009-Q4,IX,2009A,122.981069897426,S,2015,1,A,0
-Q,HPI_YDH,HUN,2010-Q1,IX,2010A,120.008425982046,S,2015,1,A,0
-Q,HPI_YDH,HUN,2010-Q2,IX,2010A,117.718433849041,S,2015,1,A,0
+Q,HPI_YDH,HUN,2007-Q1,IX,2007,128.791663747366,S,2015,1,A,0
+Q,HPI_YDH,HUN,2007-Q2,IX,2007,135.068262557786,S,2015,1,A,0
+Q,HPI_YDH,HUN,2007-Q3,IX,2007,137.007469129715,S,2015,1,A,0
+Q,HPI_YDH,HUN,2007-Q4,IX,2007,133.095968661608,S,2015,1,A,0
+Q,HPI_YDH,HUN,2008-Q1,IX,2008,133.03554880574,S,2015,1,A,0
+Q,HPI_YDH,HUN,2008-Q2,IX,2008,134.223860470646,S,2015,1,A,0
+Q,HPI_YDH,HUN,2008-Q3,IX,2008,133.560759222016,S,2015,1,A,0
+Q,HPI_YDH,HUN,2008-Q4,IX,2008,126.58631498202,S,2015,1,A,0
+Q,HPI_YDH,HUN,2009-Q1,IX,2009,129.175020241349,S,2015,1,A,0
+Q,HPI_YDH,HUN,2009-Q2,IX,2009,128.069036228621,S,2015,1,A,0
+Q,HPI_YDH,HUN,2009-Q3,IX,2009,120.538647006447,S,2015,1,A,0
+Q,HPI_YDH,HUN,2009-Q4,IX,2009,122.981069897426,S,2015,1,A,0
+Q,HPI_YDH,HUN,2010-Q1,IX,2010,120.008425982046,S,2015,1,A,0
+Q,HPI_YDH,HUN,2010-Q2,IX,2010,117.718433849041,S,2015,1,A,0

tests/TimePeriod/data/DataSet/output/GL_563_1-2.csv

@@ -1,15 +1,15 @@
 FREQ,MEASURE,REF_AREA,TIME_PERIOD,UNIT_MEASURE,year_id,year_id2,OBS_VALUE,ADJUSTMENT,BASE_PER,DECIMALS,OBS_STATUS,UNIT_MULT
-Q,HPI_YDH,HUN,2007-Q1,IX,2007A,2007A,128.791663747366,S,2015,1,A,0
-Q,HPI_YDH,HUN,2007-Q2,IX,2007A,2007A,135.068262557786,S,2015,1,A,0
-Q,HPI_YDH,HUN,2007-Q3,IX,2007A,2007A,137.007469129715,S,2015,1,A,0
-Q,HPI_YDH,HUN,2007-Q4,IX,2007A,2007A,133.095968661608,S,2015,1,A,0
-Q,HPI_YDH,HUN,2008-Q1,IX,2008A,2008A,133.03554880574,S,2015,1,A,0
-Q,HPI_YDH,HUN,2008-Q2,IX,2008A,2008A,134.223860470646,S,2015,1,A,0
-Q,HPI_YDH,HUN,2008-Q3,IX,2008A,2008A,133.560759222016,S,2015,1,A,0
-Q,HPI_YDH,HUN,2008-Q4,IX,2008A,2008A,126.58631498202,S,2015,1,A,0
-Q,HPI_YDH,HUN,2009-Q1,IX,2009A,2009A,129.175020241349,S,2015,1,A,0
-Q,HPI_YDH,HUN,2009-Q2,IX,2009A,2009A,128.069036228621,S,2015,1,A,0
-Q,HPI_YDH,HUN,2009-Q3,IX,2009A,2009A,120.538647006447,S,2015,1,A,0
-Q,HPI_YDH,HUN,2009-Q4,IX,2009A,2009A,122.981069897426,S,2015,1,A,0
-Q,HPI_YDH,HUN,2010-Q1,IX,2010A,2010A,120.008425982046,S,2015,1,A,0
-Q,HPI_YDH,HUN,2010-Q2,IX,2010A,2010A,117.718433849041,S,2015,1,A,0
+Q,HPI_YDH,HUN,2007-Q1,IX,2007,2007,128.791663747366,S,2015,1,A,0
+Q,HPI_YDH,HUN,2007-Q2,IX,2007,2007,135.068262557786,S,2015,1,A,0
+Q,HPI_YDH,HUN,2007-Q3,IX,2007,2007,137.007469129715,S,2015,1,A,0
+Q,HPI_YDH,HUN,2007-Q4,IX,2007,2007,133.095968661608,S,2015,1,A,0
+Q,HPI_YDH,HUN,2008-Q1,IX,2008,2008,133.03554880574,S,2015,1,A,0
+Q,HPI_YDH,HUN,2008-Q2,IX,2008,2008,134.223860470646,S,2015,1,A,0
+Q,HPI_YDH,HUN,2008-Q3,IX,2008,2008,133.560759222016,S,2015,1,A,0
+Q,HPI_YDH,HUN,2008-Q4,IX,2008,2008,126.58631498202,S,2015,1,A,0
+Q,HPI_YDH,HUN,2009-Q1,IX,2009,2009,129.175020241349,S,2015,1,A,0
+Q,HPI_YDH,HUN,2009-Q2,IX,2009,2009,128.069036228621,S,2015,1,A,0
+Q,HPI_YDH,HUN,2009-Q3,IX,2009,2009,120.538647006447,S,2015,1,A,0
+Q,HPI_YDH,HUN,2009-Q4,IX,2009,2009,122.981069897426,S,2015,1,A,0
+Q,HPI_YDH,HUN,2010-Q1,IX,2010,2010,120.008425982046,S,2015,1,A,0
+Q,HPI_YDH,HUN,2010-Q2,IX,2010,2010,117.718433849041,S,2015,1,A,0

tests/TimePeriod/test_time_period_formats.py

@@ -14,6 +14,7 @@
 import pandas as pd
 import pytest
 
+from vtlengine import run
 from vtlengine.DataTypes import TimePeriod
 from vtlengine.DataTypes._time_checking import check_time_period
 from vtlengine.DataTypes.TimeHandling import TimePeriodHandler
@@ -412,3 +413,87 @@ def test_format_external_multiple_values() -> None:
     assert ds.data["Id_1"].equals(
         pd.Series(["2020", "2020M6", "2020Q2"], name="Id_1", dtype="string[pyarrow]")
     )
+
+
+# GH-635: cast(TimePeriod, String) uses the configured external representation
+
+
+def _run_cast_tp_to_string(tp_values: List[str], time_period_output_format: str) -> Dataset:
+    from vtlengine.DataTypes.TimeHandling import TimePeriodConfig
+
+    original = TimePeriodConfig.get_representation()
+    script = "DS_r <- DS_1[calc Me_2 := cast(Me_1, string)];"
+    data_structures = {
+        "datasets": [
+            {
+                "name": "DS_1",
+                "DataStructure": [
+                    {"name": "Id_1", "type": "Integer", "role": "Identifier", "nullable": False},
+                    {"name": "Me_1", "type": "Time_Period", "role": "Measure", "nullable": True},
+                ],
+            }
+        ]
+    }
+    data_df = pd.DataFrame({"Id_1": list(range(1, len(tp_values) + 1)), "Me_1": tp_values})
+    try:
+        result = run(
+            script=script,
+            data_structures=data_structures,
+            datapoints={"DS_1": data_df},
+            time_period_output_format=time_period_output_format,
+        )
+        return result["DS_r"]
+    finally:
+        TimePeriodConfig.set_representation(original)
+
+
+gh_635_params = [
+    pytest.param(
+        ["2020", "2021", "2022"],
+        "natural",
+        ["2020", "2021", "2022"],
+        id="GH_635_1-annual_natural",
+    ),
+    pytest.param(
+        ["2020", "2021", "2022"],
+        "vtl",
+        ["2020", "2021", "2022"],
+        id="GH_635_2-annual_vtl",
+    ),
+    pytest.param(
+        ["2020M1", "2020M6", "2020M12"],
+        "natural",
+        ["2020-01", "2020-06", "2020-12"],
+        id="GH_635_3-monthly_natural",
+    ),
+    pytest.param(
+        ["2020M1", "2020M6", "2020M12"],
+        "vtl",
+        ["2020M1", "2020M6", "2020M12"],
+        id="GH_635_4-monthly_vtl",
+    ),
+    pytest.param(
+        ["2020Q1", "2020Q4"],
+        "sdmx_reporting",
+        ["2020-Q1", "2020-Q4"],
+        id="GH_635_5-quarterly_sdmx_reporting",
+    ),
+    pytest.param(
+        ["2020", "2020M6", "2020Q3"],
+        "natural",
+        ["2020", "2020-06", "2020-Q3"],
+        id="GH_635_6-mixed_natural",
+    ),
+    pytest.param(
+        ["2020", "2021"],
+        "sdmx_reporting",
+        ["2020-A1", "2021-A1"],
+        id="GH_635_7-annual_sdmx_reporting",
+    ),
+]
+
+
+@pytest.mark.parametrize("tp_values, output_format, expected", gh_635_params)
+def test_GH_635(tp_values: List[str], output_format: str, expected: List[str]) -> None:
+    ds = _run_cast_tp_to_string(tp_values, output_format)
+    assert list(ds.data["Me_2"]) == expected