Coverage for tests / tests_references / io.py: 59%

1# SPDX-FileCopyrightText: Copyright INRIA 

2# 

3# SPDX-License-Identifier: LGPL-3.0-only 

4# 

5# Copyright INRIA 

6# 

7# This file is part of PhysioBlocks, a library mostly developed by the 

8# [Ananke project-team](https://team.inria.fr/ananke) at INRIA. 

9# 

10# Authors: 

11# - Colin Drieu 

12# - Dominique Chapelle 

13# - François Kimmig 

14# - Philippe Moireau 

15# 

16# PhysioBlocks is free software: you can redistribute it and/or modify it under the 

17# terms of the GNU Lesser General Public License as published by the Free Software 

18# Foundation, version 3 of the License. 

19# 

20# PhysioBlocks is distributed in the hope that it will be useful, but WITHOUT ANY 

21# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A 

22# PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. 

23# 

24# You should have received a copy of the GNU Lesser General Public License along with 

25# PhysioBlocks. If not, see <https://www.gnu.org/licenses/>. 

26 

27import logging 

28 

29import numpy as np 

30import pandas as pd 

31import pytest 

32 

33_logger = logging.getLogger(__name__) 

34 

35 

36def read_reference(reference_file: str) -> pd.DataFrame: 

37 data = pd.read_csv(reference_file, sep=";") 

38 return data 

39 

40 

41def results_close_to_data( 

42 results: pd.DataFrame, 

43 ref: pd.DataFrame, 

44 matching_ids: dict[str, str], 

45 tol: float, 

46 tol_factors: dict[str, float], 

47 interval: tuple[float, float] | None = None, 

48) -> bool: 

49 # if interval is provided, shorten the dataframes to 

50 # the provided interval 

51 results_df = results if interval is None else results[interval[0] : interval[1]] 

52 ref_df = ref if interval is None else ref[interval[0] : interval[1]] 

53 

54 found_differences = False 

55 

56 for var_id, data_id in matching_ids.items(): 

57 result_array = results_df[var_id].to_numpy() 

58 ref_array = ref_df[data_id].to_numpy() 

59 

60 if result_array != pytest.approx(ref_array, abs=tol * tol_factors[var_id]): 

61 found_differences = True 

62 message = str.format( 

63 "Results differ from reference for outputs: {0}", var_id 

64 ) 

65 _logger.info(message) 

66 if result_array.shape != ref_array.shape: 

67 _logger.debug( 

68 str.format( 

69 "Reference and result shape differs: got {0} and {1}", 

70 ref_array.shape, 

71 result_array.shape, 

72 ) 

73 ) 

74 else: 

75 absolute_error = abs(ref_array - result_array) 

76 mean_abs_error = np.mean(absolute_error) 

77 median_abs_error = np.median(absolute_error) 

78 max_abs_error = absolute_error.max() 

79 _logger.debug(str.format("absolute error mean: {0}", mean_abs_error)) 

80 _logger.debug( 

81 str.format("absolute error median: {0}", median_abs_error) 

82 ) 

83 _logger.debug(str.format("absolute error max: {0}", max_abs_error)) 

84 

85 return not found_differences