Coverage for mlair/reference_models/reference_model_intellio3_v1.py: 38%

1""" 

2Extract forecasts from intelliO3 and store them for MLAir 

3 

4""" 

5 

6__author__ = "Felix Kleinert" 

7__date__ = "2021-01-29" 

8 

9import os 

10import xarray as xr 

11import shutil 

12 

13from mlair.configuration.path_config import check_path_and_create 

14from mlair.reference_models.abstract_reference_model import AbstractReferenceB2share 

15 

16 

17class IntelliO3_ts_v1(AbstractReferenceB2share): 

18 """ 

19 Reference handler that extracts IntelliO3-ts v1.0 forecasts (Kleinert, 2021). 

20 

21 IntelliO3 forecasts can be used as a competitive model within MLAir. Downloads the IntelliO3 tar-ball and extracts 

22 the forecasts. 

23 

24 Kleinert, F., Leufen, L. H., and Schultz, M. G.: IntelliO3-ts v1.0: a neural network approach to predict 

25 near-surface ozone concentrations in Germany, Geosci. Model Dev., 14, 1–25, 

26 https://doi.org/10.5194/gmd-14-1-2021, 2021. 

27 """ 

28 

29 def __init__(self, ref_name: str, ref_store_path: str = None): 

30 """ 

31 :param ref_name: Desired Name of reference forecast 

32 :type ref_name: str 

33 :param ref_store_path: Path to store reference forecasts 

34 :type ref_store_path: str 

35 """ 

36 super().__init__(b2share_hosturl="https://b2share.eudat.eu", 

37 b2share_bucket="0cae9db2-f388-4136-8d28-9d9c5665d641", 

38 b2share_key="IntelliO3-ts.tar.gz", 

39 ) 

40 self.ref_name = ref_name 

41 if ref_store_path is None: 

42 ref_store_path = f"{self.ref_name}/" 

43 self.ref_store_path = ref_store_path 

44 self.tmp_extract_path = "tmp_downloads/" 

45 self.orig_forecast_path = "IntelliO3-ts/IntelliO3-ts_network/forecasts/" 

46 self.file_pattern = "forecasts_DE?????_test.nc" 

47 

48 def untar_forecasts(self): 

49 """ 

50 Extracts IntelliO3 forecasts from tar-ball. 

51 """ 

52 cmd = f"tar -xf {self.tmp_extract_path}{self.b2share_key} --directory {self.tmp_extract_path} --wildcards --no-anchored '{self.orig_forecast_path}{self.file_pattern}'" 

53 os.system(cmd) 

54 

55 def file_list(self): 

56 """ 

57 :return: base dir of tmp path and list of forecast files 

58 :rtype: tuple(str, list(str)) 

59 """ 

60 for base_dir, dirs, file_names in os.walk(self.tmp_extract_path + self.orig_forecast_path): 

61 pass 

62 return base_dir, file_names 

63 

64 def read_and_drop(self, sel_coords: dict = None): 

65 """ 

66 Reads original forecast files, renames coord type and store forecasts as NetCdf4 files 

67 :param sel_coords: 

68 """ 

69 if sel_coords is None: 

70 sel_coords = {'type': ['CNN']} 

71 in_path, files = self.file_list() 

72 check_path_and_create(self.ref_store_path) 

73 for infile in files: 

74 data = xr.open_dataarray(os.path.join(in_path, infile)) 

75 data = data.sel(**sel_coords) 

76 data.coords['type'] = ['nn'] 

77 data.to_netcdf(os.path.join(self.ref_store_path, infile)) 

78 

79 def make_reference_available_locally(self, remove_tmp_dir: bool = True): 

80 """ 

81 

82 :return: 

83 :rtype: 

84 """ 

85 if not self.is_reference_available_locally(self.ref_store_path): 

86 if not os.path.exists(self.tmp_extract_path+self.b2share_key): 

87 self.download_from_b2share(tmp_download_path=self.tmp_extract_path) 

88 self.untar_forecasts() 

89 self.read_and_drop() 

90 if remove_tmp_dir: 

91 shutil.rmtree(self.tmp_extract_path) 

92 

93 

94if __name__ == '__main__': 

95 io3 = IntelliO3_ts_v1('IntelliO3-ts') 

96 io3.make_reference_available_locally() 

97