merge function-check-values into main

UNFCCC_GHG_data/helper/functions_temp.py

@@ -1,7 +1,9 @@
 """Temporary file for new functions to avoid merging issues due to different automatic formatting. Delete after merge."""
 
-import numpy as np
 import pandas as pd
+import warnings
+import numpy as np
+
 
 
 def find_and_replace_values(
@@ -53,3 +55,106 @@ def find_and_replace_values(
         print(f"Set value for {category}, {entity}, {year} to {new_value}.")
 
     return df
+
+
+def assert_values(
+        df: pd.DataFrame,
+        test_case: tuple[str | float | int],
+        category_column: str = "category (IPCC1996_2006_GIN_Inv)",
+        entity_column: str = "entity",
+) -> None:
+    """
+    Check if a value in a dataframe matches the expected value.
+    Input
+    -----
+    df
+        The data frame to check.
+    test_case
+        The combination of parameters and the expected value.
+        Use the format (<category>, <entity>, <year>, <expected_value>).
+    category_column
+        The columns where to look for the category.
+    entity_column
+        The column where to look for the entity.
+    """
+    category = test_case[0]
+    entity = test_case[1]
+    year = test_case[2]
+    expected_value = test_case[3]
+
+    assert isinstance(expected_value, (float, int)), "This function only works for numbers. Use assert_nan_values to check for NaNs and empty values."
+
+    arr = df.loc[
+        (df[category_column] == category) & (df[entity_column] == entity), year
+    ].values
+
+    # Assert the category exists in the data frame
+    assert (
+            category in df[category_column].unique()
+    ), f"{category} is not a valid category. Choose from {df[category_column].unique()}"
+
+    # Assert the entity exists in the data frame
+    assert (
+            entity in df[entity_column].unique()
+    ), f"{entity} is not a valid entity. Choose from {df[entity_column].unique()}"
+
+    assert (
+            arr.size > 0
+    ), f"No value found for category {category}, entity {entity}, year {year}!"
+
+    assert (
+            arr.size <= 1
+    ), f"More than one value found for category {category}, entity {entity}, year {year}!"
+
+    assert (
+            arr[0] == test_case[3]
+    ), f"Expected value {expected_value}, actual value is {arr[0]}"
+
+    print(
+        f"Value for category {category}, entity {entity}, year {year} is as expected."
+    )
+
+def assert_nan_values(
+        df: pd.DataFrame,
+        test_case: tuple[str, ...],
+        category_column: str = "category (IPCC1996_2006_GIN_Inv)",
+        entity_column: str = "entity",
+) -> None:
+    """
+    Check if values that are empty or NE or NE1 in the PDF tables
+    are not present in the dataset.
+
+    Input
+    -----
+    df
+        The data frame to check.
+    test_case
+        The combination of input parameters.
+        Use the format (<category>, <entity>, <year>).
+    category_column
+        The columns where to look for the category.
+    entity_column
+        The column where to look for the entity.
+
+    """
+    category = test_case[0]
+    entity = test_case[1]
+    year = test_case[2]
+
+    if category not in df[category_column].unique():
+        warning_string = f"{category} is not in the data set. Either all values for this category are NaN or the category never existed in the data set."
+        warnings.warn(warning_string)
+        return
+
+    if entity not in df[entity_column].unique():
+        warning_string = f"{entity} is not in the data set. Either all values for this entity are NaN or the category never existed in the data set."
+        warnings.warn(warning_string)
+        return
+
+    arr = df.loc[
+        (df[category_column] == category) & (df[entity_column] == entity), year
+    ].values
+
+    assert np.isnan(arr[0]), f"Value is {arr[0]} and not NaN."
+
+    print(f"Value for category {category}, entity {entity}, year {year} is NaN.")