Alex Galarce
0ad2798d6b
fix quoting/encoding issue in crosswalk writing to CSV (also formatting) * fix encoding issue * black formatting * isort Approved-by: Katon Minhas
144 lines
4.1 KiB
Python
144 lines
4.1 KiB
Python
import json
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import pandas as pd
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import pytest
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from crosswalk_utils import CrosswalkBuilder, apply_crosswalk
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@pytest.fixture
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def sample_excel_file(tmp_path):
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data = {"from_col": ["A", "B", "C"], "to_col": ["one", "two", "three"]}
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df = pd.DataFrame(data)
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file_path = tmp_path / "sample.xlsx"
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df.to_excel(file_path, index=False)
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return file_path
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@pytest.fixture
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def sample_json_file(tmp_path):
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data = {
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"metadata": {"from_col": "from_col", "to_col": "to_col"},
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"mapping": {"A": "1", "B": "2", "C": "3"},
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}
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file_path = tmp_path / "sample.json"
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with open(file_path, "w") as f:
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json.dump(data, f)
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return file_path
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def test_from_excel(sample_excel_file):
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builder = CrosswalkBuilder()
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builder.from_excel(sample_excel_file, "from_col", "to_col")
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assert builder.mapping == {"A": "one", "B": "two", "C": "three"}
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assert builder.metadata == {"from_col": "from_col", "to_col": "to_col"}
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def test_from_excel_missing_column(sample_excel_file):
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builder = CrosswalkBuilder()
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with pytest.raises(ValueError, match="Column 'nonexistent' not found"):
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builder.from_excel(sample_excel_file, "nonexistent", "to_col")
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def test_from_dict():
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mapping = {"A": "1", "B": "2", "C": "3"}
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builder = CrosswalkBuilder()
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builder.from_dict(mapping)
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assert builder.mapping == mapping
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assert builder.metadata == {"from_col": None, "to_col": None}
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def test_from_json(sample_json_file):
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builder = CrosswalkBuilder()
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builder.from_json(sample_json_file)
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assert builder.mapping == {"A": "1", "B": "2", "C": "3"}
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assert builder.metadata == {"from_col": "from_col", "to_col": "to_col"}
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def test_json_roundtrip(tmp_path):
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# Create and save a crosswalk
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original = CrosswalkBuilder()
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original.from_dict({"A": "1"}, "source", "target")
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json_path = tmp_path / "test.json"
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original.save(json_path)
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# Load it back
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loaded = CrosswalkBuilder().from_json(json_path)
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# Check everything matches
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assert loaded.mapping == original.mapping
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assert loaded.metadata == original.metadata
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def test_save(tmp_path):
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mapping = {"A": "1", "B": "2", "C": "3"}
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builder = CrosswalkBuilder()
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builder.from_dict(mapping, "from_col", "to_col")
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output_path = tmp_path / "output.json"
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builder.save(output_path)
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with open(output_path, "r") as f:
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data = json.load(f)
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assert data["mapping"] == mapping
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assert data["metadata"] == {"from_col": "from_col", "to_col": "to_col"}
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def test_to_csv(tmp_path):
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# Create a builder with some data
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builder = CrosswalkBuilder()
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builder.from_dict(
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mapping={"A": "one", "B": "two", "C": "three"},
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from_col="Source",
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to_col="Target",
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)
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# Export to CSV
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csv_path = tmp_path / "test.csv"
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builder.to_csv(csv_path)
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# Read back and verify
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df = pd.read_csv(csv_path)
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# Check column names match metadata
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assert list(df.columns) == ["Source", "Target"]
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# Check data matches original mapping
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result_dict = dict(zip(df["Source"], df["Target"]))
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assert result_dict == {"A": "one", "B": "two", "C": "three"}
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def test_to_csv_no_metadata(tmp_path):
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# Create a builder without metadata
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builder = CrosswalkBuilder()
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builder.from_dict({"A": "one", "B": "two"})
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# Export to CSV
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csv_path = tmp_path / "test.csv"
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builder.to_csv(csv_path)
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# Read back and verify
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df = pd.read_csv(csv_path)
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# Check default column names
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assert list(df.columns) == ["source", "target"]
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# Check data
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result_dict = dict(zip(df["source"], df["target"]))
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assert result_dict == {"A": "one", "B": "two"}
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def test_apply_crosswalk():
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mapping = {"A": "1", "B": "2", "C": "3"}
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assert apply_crosswalk("A", mapping) == "1"
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assert apply_crosswalk("D", mapping) == "D"
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assert apply_crosswalk("D", mapping, default="0") == "0"
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def test_create_reverse_mapping():
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builder = CrosswalkBuilder()
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builder.from_dict({"A": "1", "B": "1", "C": "2", "D": "2", "E": "3"})
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reversed_mapping = builder.create_reverse_mapping()
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assert reversed_mapping == {"1": "A, B", "2": "C, D", "3": "E"}
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