Vibe Math MCP

"""Tests for statistical analysis tools.""" import json import pytest @pytest.mark.asyncio async def test_statistics_describe(mcp_client, sample_data_list): """Test descriptive statistics.""" result = await mcp_client.call_tool( "statistics", {"data": sample_data_list, "analyses": ["describe"]} ) data = json.loads(result.content[0].text) assert data["result"]["describe"]["count"] == 10 assert data["result"]["describe"]["mean"] == 5.5 assert data["result"]["describe"]["min"] == 1.0 assert data["result"]["describe"]["max"] == 10.0 @pytest.mark.asyncio async def test_statistics_quartiles(mcp_client, sample_data_list): """Test quartile calculation.""" result = await mcp_client.call_tool( "statistics", {"data": sample_data_list, "analyses": ["quartiles"]} ) data = json.loads(result.content[0].text) assert "Q1" in data["result"]["quartiles"] assert "Q2" in data["result"]["quartiles"] assert "Q3" in data["result"]["quartiles"] assert "IQR" in data["result"]["quartiles"] @pytest.mark.asyncio async def test_statistics_outliers(mcp_client): """Test outlier detection.""" data_with_outliers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 100] result = await mcp_client.call_tool( "statistics", {"data": data_with_outliers, "analyses": ["outliers"]} ) data = json.loads(result.content[0].text) assert len(data["result"]["outliers"]["outlier_values"]) > 0 @pytest.mark.asyncio async def test_pivot_table(mcp_client): """Test pivot table creation.""" data = [ {"region": "North", "product": "A", "sales": 100}, {"region": "North", "product": "B", "sales": 150}, {"region": "South", "product": "A", "sales": 200}, {"region": "South", "product": "B", "sales": 250}, ] result = await mcp_client.call_tool( "pivot_table", { "data": data, "index": "region", "columns": "product", "values": "sales", "aggfunc": "sum", }, ) result_data = json.loads(result.content[0].text) assert "result" in result_data @pytest.mark.asyncio async def test_correlation_pearson(mcp_client): """Test Pearson correlation.""" data = { "x": [1.0, 2.0, 3.0, 4.0, 5.0], "y": [2.0, 4.0, 6.0, 8.0, 10.0], "z": [1.0, 1.0, 1.0, 1.0, 1.0], } result = await mcp_client.call_tool( "correlation", {"data": data, "method": "pearson", "output_format": "matrix"} ) result_data = json.loads(result.content[0].text) assert "result" in result_data # x and y should be perfectly correlated assert ( result_data["result"]["x"]["y"] == 1.0 or abs(result_data["result"]["x"]["y"] - 1.0) < 1e-10 ) @pytest.mark.asyncio async def test_correlation_spearman(mcp_client): """Test Spearman rank correlation.""" data = { "x": [1.0, 2.0, 3.0, 4.0, 5.0], "y": [1.0, 4.0, 9.0, 16.0, 25.0], # Non-linear but monotonic } result = await mcp_client.call_tool( "correlation", {"data": data, "method": "spearman", "output_format": "matrix"} ) result_data = json.loads(result.content[0].text) assert "result" in result_data # Spearman correlation should be perfect for monotonic relationship assert abs(result_data["result"]["x"]["y"] - 1.0) < 1e-10 @pytest.mark.asyncio async def test_correlation_pairs_format(mcp_client): """Test correlation with pairs output format.""" data = { "a": [1.0, 2.0, 3.0], "b": [2.0, 4.0, 6.0], "c": [3.0, 6.0, 9.0], } result = await mcp_client.call_tool( "correlation", {"data": data, "method": "pearson", "output_format": "pairs"} ) result_data = json.loads(result.content[0].text) assert "result" in result_data # Should return pairwise correlations assert isinstance(result_data["result"], list) # Should have 3 pairs: (a,b), (a,c), (b,c) assert len(result_data["result"]) == 3 assert all("var1" in pair and "var2" in pair for pair in result_data["result"]) @pytest.mark.asyncio async def test_correlation_unequal_lengths(mcp_client): """Test error when variables have unequal lengths.""" data = { "x": [1.0, 2.0, 3.0], "y": [1.0, 2.0], # Different length } with pytest.raises(Exception) as exc_info: await mcp_client.call_tool("correlation", {"data": data, "method": "pearson"}) # Error should mention height/shape mismatch or same number of observations error_msg = str(exc_info.value).lower() assert ("same number" in error_msg or "height" in error_msg or "shape" in error_msg) @pytest.mark.asyncio async def test_pivot_table_mean_aggfunc(mcp_client): """Test pivot table with mean aggregation.""" data = [ {"region": "North", "product": "A", "sales": 100}, {"region": "North", "product": "A", "sales": 150}, {"region": "South", "product": "A", "sales": 200}, ] result = await mcp_client.call_tool( "pivot_table", { "data": data, "index": "region", "columns": "product", "values": "sales", "aggfunc": "mean", }, ) result_data = json.loads(result.content[0].text) assert "result" in result_data @pytest.mark.asyncio async def test_pivot_table_count_aggfunc(mcp_client): """Test pivot table with count aggregation.""" data = [ {"region": "North", "product": "A", "sales": 100}, {"region": "North", "product": "A", "sales": 150}, {"region": "North", "product": "B", "sales": 200}, ] result = await mcp_client.call_tool( "pivot_table", { "data": data, "index": "region", "columns": "product", "values": "sales", "aggfunc": "count", }, ) result_data = json.loads(result.content[0].text) assert "result" in result_data @pytest.mark.asyncio async def test_pivot_table_missing_column(mcp_client): """Test error when pivot table references missing column.""" data = [ {"region": "North", "product": "A", "sales": 100}, ] with pytest.raises(Exception) as exc_info: await mcp_client.call_tool( "pivot_table", { "data": data, "index": "region", "columns": "category", # Doesn't exist "values": "sales", "aggfunc": "sum", }, ) # Should mention the missing column in error assert "column" in str(exc_info.value).lower() or "category" in str(exc_info.value) @pytest.mark.asyncio async def test_statistics_combined_analyses(mcp_client): """Test multiple analyses at once.""" data = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 100.0] result = await mcp_client.call_tool( "statistics", {"data": data, "analyses": ["describe", "quartiles", "outliers"]} ) result_data = json.loads(result.content[0].text) # All three analyses should be present assert "describe" in result_data["result"] assert "quartiles" in result_data["result"] assert "outliers" in result_data["result"] # Outliers should detect the 100 assert 100.0 in result_data["result"]["outliers"]["outlier_values"]