Update and rename test_unpack_interval_to_days.py to test_unpack_inte…

…rvals.py

tests/test_loaders/test_unpack_intervals.py

@@ -0,0 +1,84 @@
+"""Tests of unpack_intervals"""
+
+import pandas as pd
+
+from psycop_feature_generation.loaders.raw.utils import (
+    unpack_intervals,
+)
+from psycop_feature_generation.utils_for_testing import (
+    str_to_df,
+)
+
+
+def test_unpack_intervals_to_days():
+    df_str = """dw_ek_borger,timestamp_start,timestamp_end,value
+                1,2021-01-01 12:00:00,2021-01-01 13:00:00,1.0
+                2,2021-03-03 15:00:00,2021-03-05 15:30:00,48.5
+                3,2021-06-06 00:00:00,2021-06-09 00:00:00,72.0
+            """
+
+    expected_df_str = """dw_ek_borger,timestamp,value
+                1,2021-01-01 12:00:00,1
+                1,2021-01-01 13:00:00,1
+                2,2021-03-03 15:00:00,1
+                2,2021-03-04 15:00:00,1
+                2,2021-03-05 15:00:00,1
+                2,2021-03-05 15:30:00,1
+                3,2021-06-06 00:00:00,1
+                3,2021-06-07 00:00:00,1
+                3,2021-06-08 00:00:00,1
+                3,2021-06-09 00:00:00,1
+            """
+
+    # 1: interval < 1 day (= two rows, one with start time and one with end time)
+    # 2: interval > 1 day and times are not 00:00:00 (= one row with start time, one row per day in-between with timestamp same as start time, and one row with end time)
+    # 3: interval > 1 day and both times are 00:00:00 (= one row per day, includeing start and end day, all times 00:00:00)
+
+    df = str_to_df(df_str, convert_str_to_float=False)
+    expected_df = str_to_df(expected_df_str, convert_str_to_float=False)
+
+    df = unpack_intervals(
+        df,
+        starttime_col="timestamp_start",
+        endtime_col="timestamp_end",
+        unpack_freq="D",
+    )
+
+    for col in df.columns:
+        pd.testing.assert_series_equal(df[col], expected_df[col])
+
+
+def test_unpack_intervals_to_5Hfreq():
+    df_str = """dw_ek_borger,timestamp_start,timestamp_end,value
+                1,2021-01-01 12:00:00,2021-01-01 13:00:00,1.0
+                2,2021-02-02 15:00:00,2021-02-02 20:00:00,5.0
+                3,2021-03-04 16:00:00,2021-03-05 4:00:00,12.0
+            """
+
+    expected_df_str = """dw_ek_borger,timestamp,value
+                1,2021-01-01 12:00:00,1
+                1,2021-01-01 13:00:00,1
+                2,2021-02-02 15:00:00,1
+                2,2021-02-02 20:00:00,1
+                3,2021-03-04 16:00:00,1
+                3,2021-03-04 21:00:00,1
+                3,2021-03-05 02:00:00,1
+                3,2021-03-05 4:00:00,1
+            """
+
+    # 1: interval < 5 hours
+    # 2: interval = 5 hours
+    # 3: interval > 5 hours
+
+    df = str_to_df(df_str, convert_str_to_float=False)
+    expected_df = str_to_df(expected_df_str, convert_str_to_float=False)
+
+    df = unpack_intervals(
+        df,
+        starttime_col="timestamp_start",
+        endtime_col="timestamp_end",
+        unpack_freq="5H",
+    )
+
+    for col in df.columns:
+        pd.testing.assert_series_equal(df[col], expected_df[col])