Mercurial > repos > ecology > xarray_select

comparison xarray_netcdf2netcdf.py @ 2:123a9a629bef draft

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
"planemo upload for repository https://github.com/galaxyecology/tools-ecology/tree/master/tools/data_manipulation/xarray/ commit 57b6d23e3734d883e71081c78e77964d61be82ba"
author ecology
date Sun, 06 Jun 2021 08:51:41 +0000
parents
children bf595d613af4
comparison
equal deleted inserted replaced
1:6baac361495b 2:123a9a629bef
1 #!/usr/bin/env python3
2 #
3 # Apply operations on selected variables
4 # - scale
5 # one can also select the range of time (for timeseries)
6 # to apply these operations over the range only
7 # when a range of time is selected and when scaling, one
8 # can choose to save the entire timeseries or
9 # the selected range only.
10 # when scaling, one can add additional filters on dimensions
11 # (typically used to filter over latitudes and longitudes)
12
13
14 import argparse
15 import warnings
16
17 import xarray as xr # noqa: E402
18
19
20 class netCDF2netCDF ():
21 def __init__(self, infile, varname, scale="",
22 output="output.netcdf",
23 write_all=False,
24 filter_list="",
25 verbose=False):
26 self.infile = infile
27 self.verbose = verbose
28 self.varname = varname
29 self.write_all = write_all
30 self.filter = filter_list
31 self.selection = {}
32 if scale == "" or scale is None:
33 self.scale = 1
34 else:
35 self.scale = float(scale)
36 if output is None:
37 self.output = "output.netcdf"
38 else:
39 self.output = output
40 # initialization
41 self.dset = None
42 self.subset = None
43 if self.verbose:
44 print("infile: ", self.infile)
45 print("varname: ", self.varname)
46 print("filter_list: ", self.filter)
47 print("scale: ", self.scale)
48 print("write_all: ", self.write_all)
49 print("output: ", self.output)
50
51 def dimension_selection(self, single_filter):
52 split_filter = single_filter.split('#')
53 dimension_varname = split_filter[0]
54 op = split_filter[1]
55 ll = int(split_filter[2])
56 if (op == 'sl'):
57 rl = int(split_filter[3])
58 self.selection[dimension_varname] = slice(ll, rl)
59 elif (op == 'to'):
60 self.selection[dimension_varname] = slice(None, ll)
61 elif (op == 'from'):
62 self.selection[dimension_varname] = slice(ll, None)
63 elif (op == 'is'):
64 self.selection[dimension_varname] = ll
65
66 def filter_selection(self):
67 for single_filter in self.filter:
68 self.dimension_selection(single_filter)
69 if self.write_all:
70 self.ds[self.varname] = \
71 self.ds[self.varname].isel(self.selection)*self.scale
72 else:
73 self.dset = \
74 self.ds[self.varname].isel(self.selection)*self.scale
75
76 def compute(self):
77 if self.dset is None:
78 self.ds = xr.open_dataset(self.infile)
79 if self.filter:
80 self.filter_selection()
81 if self.verbose:
82 print(self.selection)
83 elif self.write_all is not None:
84 self.dset = self.ds[self.varname]
85
86 def save(self):
87 if self.write_all:
88 self.ds.to_netcdf(self.output)
89 else:
90 self.dset.to_netcdf(self.output)
91
92
93 if __name__ == '__main__':
94 warnings.filterwarnings("ignore")
95 parser = argparse.ArgumentParser()
96 parser.add_argument(
97 'input',
98 help='input filename in netCDF format'
99 )
100 parser.add_argument(
101 'varname',
102 help='Specify which variable to plot (case sensitive)'
103 )
104 parser.add_argument(
105 '--filter',
106 nargs="*",
107 help='Filter list variable#operator#value_s#value_e'
108 )
109 parser.add_argument(
110 '--output',
111 help='Output filename to store the resulting netCDF file'
112 )
113 parser.add_argument(
114 '--scale',
115 help='scale factor to apply to selection (float)'
116 )
117 parser.add_argument(
118 "--write_all",
119 help="write all data to netCDF",
120 action="store_true")
121 parser.add_argument(
122 "-v", "--verbose",
123 help="switch on verbose mode",
124 action="store_true")
125 args = parser.parse_args()
126
127 dset = netCDF2netCDF(infile=args.input, varname=args.varname,
128 scale=args.scale, output=args.output,
129 filter_list=args.filter,
130 write_all=args.write_all,
131 verbose=args.verbose)
132 dset.compute()
133 dset.save()