Merge pull request #358 from ie3-institute/df/#357-fix-filter-for-interval

Fix error in filter_data_for_time_interval()

Author: staudtMarius

CHANGELOG.md

@@ -19,6 +19,7 @@ All notable changes to this project will be documented in this file.
 - Removed `q_characteristics` from `create_energy_management_systems_data` [#326](https://github.com/ie3-institute/pypsdm/issues/326)
 - Fix `emInput` from `parent_em` to `controlling_em` [#333](https://github.com/ie3-institute/pypsdm/issues/333)
 - Fix also from `em` to `controlling_em` for all `SystemParticipants` [#337](https://github.com/ie3-institute/pypsdm/issues/337)
+- Fix error in `filter_data_for_time_interval()` [#357](https://github.com/ie3-institute/pypsdm/issues/357)
 
 
 ## 0.0.6

pypsdm/processing/dataframe.py

@@ -69,7 +69,8 @@ def filter_data_for_time_interval(
     if all_after_start and all_after_end:
         return data.drop(data.index)
     elif all_after_start:
-        start_row = data.index[1]
+        start_row = data.index[0]
+        start = start_row
     else:
         before_start = data.index[data.index <= start]
         start_row = before_start[-1]

tests/processing/test_dataframe.py

@@ -1,7 +1,11 @@
 import pandas as pd
 from numpy import float64
 
-from pypsdm.processing.dataframe import add_df, divide_positive_negative
+from pypsdm.processing.dataframe import (
+    add_df,
+    divide_positive_negative,
+    filter_data_for_time_interval,
+)
 
 index = pd.date_range("2012-01-01 10:00:00", "2012-01-01 13:00:00", freq="h")
 data = [1, 2, -2, 3]
@@ -37,3 +41,139 @@ def test_add_df():
     b = b.reindex(index=[3, 2])
     res = add_df(a, b)
     pd.testing.assert_frame_equal(res, expected)
+
+
+def test_filter_data_for_time_interval():
+    input_data = {
+        "time": [
+            "2016-04-01 17:30:00",
+            "2016-04-01 18:15:00",
+            "2016-04-01 19:30:00",
+            "2016-04-01 20:45:00",
+        ],
+        "p": [0.022, 0.0, 0.022, 0.0],
+        "q": [0.0, 0.0, 0.0, 0.0],
+    }
+
+    input_df = pd.DataFrame(input_data)
+
+    input_df["time"] = pd.to_datetime(input_df["time"])
+    input_df.set_index("time", inplace=True)
+
+    start_1 = pd.to_datetime("2016-04-01 17:00:00")
+    end_1 = pd.to_datetime("2016-04-01 21:00:00")
+    res_1 = filter_data_for_time_interval(input_df, start_1, end_1)
+
+    expected_data_1 = {
+        "p": [0.022, 0.000, 0.022, 0.000],
+        "q": [0.000, 0.000, 0.000, 0.000],
+    }
+
+    expected_index_1 = [
+        pd.Timestamp("2016-04-01 17:30:00"),
+        pd.Timestamp("2016-04-01 18:15:00"),
+        pd.Timestamp("2016-04-01 19:30:00"),
+        pd.Timestamp("2016-04-01 20:45:00"),
+    ]
+
+    expected_1 = pd.DataFrame(expected_data_1)
+    expected_1.index = expected_index_1
+    expected_1.index.name = "time"
+
+    pd.testing.assert_frame_equal(res_1, expected_1)
+
+    start_2 = pd.to_datetime("2016-04-01 17:30:00")
+    end_2 = pd.to_datetime("2016-04-01 21:00:00")
+    res_2 = filter_data_for_time_interval(input_df, start_2, end_2)
+
+    expected_data_2 = {
+        "p": [0.022, 0.000, 0.022, 0.000],
+        "q": [0.000, 0.000, 0.000, 0.000],
+    }
+
+    expected_index_2 = [
+        pd.Timestamp("2016-04-01 17:30:00"),
+        pd.Timestamp("2016-04-01 18:15:00"),
+        pd.Timestamp("2016-04-01 19:30:00"),
+        pd.Timestamp("2016-04-01 20:45:00"),
+    ]
+
+    expected_2 = pd.DataFrame(expected_data_2)
+    expected_2.index = expected_index_2
+    expected_2.index.name = "time"
+
+    pd.testing.assert_frame_equal(res_2, expected_2)
+
+    start_2 = pd.to_datetime("2016-04-01 18:00:00")
+    end_2 = pd.to_datetime("2016-04-01 21:00:00")
+    res_2 = filter_data_for_time_interval(input_df, start_2, end_2)
+
+    expected_data_2 = {
+        "p": [0.022, 0.000, 0.022, 0.000],
+        "q": [0.000, 0.000, 0.000, 0.000],
+    }
+
+    expected_index_2 = [
+        pd.Timestamp("2016-04-01 18:00:00"),
+        pd.Timestamp("2016-04-01 18:15:00"),
+        pd.Timestamp("2016-04-01 19:30:00"),
+        pd.Timestamp("2016-04-01 20:45:00"),
+    ]
+
+    expected_2 = pd.DataFrame(expected_data_2)
+    expected_2.index = expected_index_2
+    expected_2.index.name = "time"
+
+    pd.testing.assert_frame_equal(res_2, expected_2)
+
+    start_3 = pd.to_datetime("2016-04-01 18:15:00")
+    end_3 = pd.to_datetime("2016-04-01 21:00:00")
+    res_3 = filter_data_for_time_interval(input_df, start_3, end_3)
+
+    expected_data_3 = {"p": [0.000, 0.022, 0.000], "q": [0.000, 0.000, 0.000]}
+
+    expected_index_3 = [
+        pd.Timestamp("2016-04-01 18:15:00"),
+        pd.Timestamp("2016-04-01 19:30:00"),
+        pd.Timestamp("2016-04-01 20:45:00"),
+    ]
+
+    expected_3 = pd.DataFrame(expected_data_3)
+    expected_3.index = expected_index_3
+    expected_3.index.name = "time"
+
+    pd.testing.assert_frame_equal(res_3, expected_3)
+
+    start_4 = pd.to_datetime("2016-04-01 17:00:00")
+    end_4 = pd.to_datetime("2016-04-01 18:30:00")
+    res_4 = filter_data_for_time_interval(input_df, start_4, end_4)
+
+    expected_data_4 = {"p": [0.022, 0.000], "q": [0.000, 0.000]}
+
+    expected_index_4 = [
+        pd.Timestamp("2016-04-01 17:30:00"),
+        pd.Timestamp("2016-04-01 18:15:00"),
+    ]
+
+    expected_4 = pd.DataFrame(expected_data_4)
+    expected_4.index = expected_index_4
+    expected_4.index.name = "time"
+
+    pd.testing.assert_frame_equal(res_4, expected_4)
+
+    start_5 = pd.to_datetime("2016-04-01 17:00:00")
+    end_5 = pd.to_datetime("2016-04-01 17:15:00")
+    res_5 = filter_data_for_time_interval(input_df, start_5, end_5)
+
+    expected_data_5 = {
+        "p": [],
+        "q": [],
+    }
+
+    expected_index_5 = pd.DatetimeIndex([])
+
+    expected_5 = pd.DataFrame(expected_data_5)
+    expected_5.index = expected_index_5
+    expected_5.index.name = "time"
+
+    pd.testing.assert_frame_equal(res_5, expected_5)