Additional custom input tests (#33)

* fix: CI configuration 'build'

* feat: improve custom input tests

.github/workflows/build.yml

@@ -92,7 +92,7 @@ jobs:
     if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags')
     needs:
       - test
-      - typecheck
+      - check
     runs-on: ubuntu-latest
 
     steps:
@@ -117,7 +117,7 @@ jobs:
     if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags')
     needs:
       - test
-      - typecheck
+      - check
     runs-on: ubuntu-latest
 
     steps:

tests/custom_input_ts/data_types_timeseries.csv

@@ -0,0 +1,6 @@
+ints,strings,bools,floats,nulls
+1,a,true,0.187,
+2,b,false,4.123,
+3,c,false,-7896.1,
+4,doc,false,325.1,
+-1,ester,true,897.123,

tests/test_base_oscillations/test_custom_input.py

@@ -1,9 +1,9 @@
 import unittest
 from pathlib import Path
+import warnings
 
 import pandas as pd
 import numpy as np
-import os
 from numpy.random import SeedSequence
 from numpy.testing import assert_array_equal
 
@@ -16,6 +16,9 @@ def setUp(self) -> None:
         self.ctx = GenerationContext(SeedSequence(42)).to_bo()
         self.input_path1 = Path("tests/custom_input_ts/dummy_timeseries.csv")
         self.input_path2 = Path("tests/custom_input_ts/dummy_timeseries_2.csv")
+        self.input_path_datatypes = Path(
+            "tests/custom_input_ts/data_types_timeseries.csv"
+        )
         self.column_idx = 1
         self.length = 100
         self.expected_test = (
@@ -140,19 +143,39 @@ def test_input_too_short(self):
             )
         self.assertRegex(str(e.exception).lower(), "less than the desired length")
 
-    def test_integer_conversion(self):
-        df = pd.DataFrame({"data": [1, 2, 3, 4, 5]})
-        df.to_csv("test_data.csv", index=False)
-        custom_input = CustomInput("test_data.csv")
-        # test if warning is raised
-        with self.assertWarns(UserWarning):
-            # Generate the time series data
-            timeseries = custom_input.generate_only_base(
+    def test_read_floats(self):
+        for tpe in ["floats", "nulls"]:
+            # a warning will raise an error!
+            with warnings.catch_warnings():
+                timeseries = CustomInput().generate_only_base(
+                    ctx=self.ctx,
+                    length=5,
+                    input_timeseries_path_test=self.input_path_datatypes,
+                    use_column_test=tpe,
+                )
+            #  data is properly converted
+            self.assertEqual(timeseries.dtype, np.float_)
+
+    def test_convert_to_float_with_warning(self):
+        for tpe in ["ints", "bools"]:
+            with self.assertWarns(UserWarning) as w:
+                timeseries = CustomInput().generate_only_base(
+                    ctx=self.ctx,
+                    length=5,
+                    input_timeseries_path_test=self.input_path_datatypes,
+                    use_column_test=tpe,
+                )
+            # warning is raised
+            self.assertRegex(str(w.warning), "automatically converted to float")
+            #  data is properly converted
+            self.assertEqual(timeseries.dtype, np.float_)
+
+    def test_error_on_string_type(self):
+        with self.assertRaises(ValueError) as e:
+            CustomInput().generate_only_base(
                 ctx=self.ctx,
                 length=5,
-                input_timeseries_path_test="test_data.csv",
-                use_column_test="data",
+                input_timeseries_path_test=self.input_path_datatypes,
+                use_column_test="strings",
             )
-        # test if data is properly converted
-        self.assertEqual(timeseries.dtype, np.float64)
-        os.remove("test_data.csv")
+        self.assertRegex(str(e.exception), "could not convert string to float")