diff xarray_tool.py @ 4:b393815e4cb7 draft default tip

planemo upload for repository https://github.com/galaxyecology/tools-ecology/tree/master/tools/data_manipulation/xarray/ commit fd8ad4d97db7b1fd3876ff63e14280474e06fdf7

--- a/xarray_tool.py	Thu Jan 20 17:07:19 2022 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,365 +0,0 @@
-# xarray tool for:
-# - getting metadata information
-# - select data and save results in csv file for further post-processing
-
-import argparse
-import csv
-import os
-import warnings
-
-import geopandas as gdp
-
-import pandas as pd
-
-from shapely.geometry import Point
-from shapely.ops import nearest_points
-
-import xarray as xr
-
-
-class XarrayTool ():
-    def __init__(self, infile, outfile_info="", outfile_summary="",
-                 select="", outfile="", outputdir="", latname="",
-                 latvalN="", latvalS="", lonname="", lonvalE="",
-                 lonvalW="", filter_list="", coords="", time="",
-                 verbose=False, no_missing=False, coords_info=None,
-                 tolerance=None):
-        self.infile = infile
-        self.outfile_info = outfile_info
-        self.outfile_summary = outfile_summary
-        self.select = select
-        self.outfile = outfile
-        self.outputdir = outputdir
-        self.latname = latname
-        if tolerance != "" and tolerance is not None:
-            self.tolerance = float(tolerance)
-        else:
-            self.tolerance = -1
-        if latvalN != "" and latvalN is not None:
-            self.latvalN = float(latvalN)
-        else:
-            self.latvalN = ""
-        if latvalS != "" and latvalS is not None:
-            self.latvalS = float(latvalS)
-        else:
-            self.latvalS = ""
-        self.lonname = lonname
-        if lonvalE != "" and lonvalE is not None:
-            self.lonvalE = float(lonvalE)
-        else:
-            self.lonvalE = ""
-        if lonvalW != "" and lonvalW is not None:
-            self.lonvalW = float(lonvalW)
-        else:
-            self.lonvalW = ""
-        self.filter = filter_list
-        self.time = time
-        self.coords = coords
-        self.verbose = verbose
-        self.no_missing = no_missing
-        # initialization
-        self.dset = None
-        self.gset = None
-        self.coords_info = coords_info
-        if self.verbose:
-            print("infile: ", self.infile)
-            print("outfile_info: ", self.outfile_info)
-            print("outfile_summary: ", self.outfile_summary)
-            print("outfile: ", self.outfile)
-            print("select: ", self.select)
-            print("outfile: ", self.outfile)
-            print("outputdir: ", self.outputdir)
-            print("latname: ", self.latname)
-            print("latvalN: ", self.latvalN)
-            print("latvalS: ", self.latvalS)
-            print("lonname: ", self.lonname)
-            print("lonvalE: ", self.lonvalE)
-            print("lonvalW: ", self.lonvalW)
-            print("filter: ", self.filter)
-            print("time: ", self.time)
-            print("coords: ", self.coords)
-            print("coords_info: ", self.coords_info)
-
-    def info(self):
-        f = open(self.outfile_info, 'w')
-        ds = xr.open_dataset(self.infile)
-        ds.info(f)
-        f.close()
-
-    def summary(self):
-        f = open(self.outfile_summary, 'w')
-        ds = xr.open_dataset(self.infile)
-        writer = csv.writer(f, delimiter='\t')
-        header = ['VariableName', 'NumberOfDimensions']
-        for idx, val in enumerate(ds.dims.items()):
-            header.append('Dim' + str(idx) + 'Name')
-            header.append('Dim' + str(idx) + 'Size')
-        writer.writerow(header)
-        for name, da in ds.data_vars.items():
-            line = [name]
-            line.append(len(ds[name].shape))
-            for d, s in zip(da.shape, da.sizes):
-                line.append(s)
-                line.append(d)
-            writer.writerow(line)
-        for name, da in ds.coords.items():
-            line = [name]
-            line.append(len(ds[name].shape))
-            for d, s in zip(da.shape, da.sizes):
-                line.append(s)
-                line.append(d)
-            writer.writerow(line)
-        f.close()
-
-    def rowfilter(self, single_filter):
-        split_filter = single_filter.split('#')
-        filter_varname = split_filter[0]
-        op = split_filter[1]
-        ll = float(split_filter[2])
-        if (op == 'bi'):
-            rl = float(split_filter[3])
-        if filter_varname == self.select:
-            # filter on values of the selected variable
-            if op == 'bi':
-                self.dset = self.dset.where(
-                     (self.dset <= rl) & (self.dset >= ll)
-                     )
-            elif op == 'le':
-                self.dset = self.dset.where(self.dset <= ll)
-            elif op == 'ge':
-                self.dset = self.dset.where(self.dset >= ll)
-            elif op == 'e':
-                self.dset = self.dset.where(self.dset == ll)
-        else:  # filter on other dimensions of the selected variable
-            if op == 'bi':
-                self.dset = self.dset.sel({filter_varname: slice(ll, rl)})
-            elif op == 'le':
-                self.dset = self.dset.sel({filter_varname: slice(None, ll)})
-            elif op == 'ge':
-                self.dset = self.dset.sel({filter_varname: slice(ll, None)})
-            elif op == 'e':
-                self.dset = self.dset.sel({filter_varname: ll},
-                                          method='nearest')
-
-    def selection(self):
-        if self.dset is None:
-            self.ds = xr.open_dataset(self.infile)
-            self.dset = self.ds[self.select]  # select variable
-            if self.time:
-                self.datetime_selection()
-            if self.filter:
-                self.filter_selection()
-
-        self.area_selection()
-        if self.gset.count() > 1:
-            # convert to dataframe if several rows and cols
-            self.gset = self.gset.to_dataframe().dropna(how='all'). \
-                        reset_index()
-            self.gset.to_csv(self.outfile, header=True, sep='\t')
-        else:
-            data = {
-                self.latname: [self.gset[self.latname].values],
-                self.lonname: [self.gset[self.lonname].values],
-                self.select: [self.gset.values]
-            }
-
-            df = pd.DataFrame(data, columns=[self.latname, self.lonname,
-                                             self.select])
-            df.to_csv(self.outfile, header=True, sep='\t')
-
-    def datetime_selection(self):
-        split_filter = self.time.split('#')
-        time_varname = split_filter[0]
-        op = split_filter[1]
-        ll = split_filter[2]
-        if (op == 'sl'):
-            rl = split_filter[3]
-            self.dset = self.dset.sel({time_varname: slice(ll, rl)})
-        elif (op == 'to'):
-            self.dset = self.dset.sel({time_varname: slice(None, ll)})
-        elif (op == 'from'):
-            self.dset = self.dset.sel({time_varname: slice(ll, None)})
-        elif (op == 'is'):
-            self.dset = self.dset.sel({time_varname: ll}, method='nearest')
-
-    def filter_selection(self):
-        for single_filter in self.filter:
-            self.rowfilter(single_filter)
-
-    def area_selection(self):
-
-        if self.latvalS != "" and self.lonvalW != "":
-            # Select geographical area
-            self.gset = self.dset.sel({self.latname:
-                                       slice(self.latvalS, self.latvalN),
-                                       self.lonname:
-                                       slice(self.lonvalW, self.lonvalE)})
-        elif self.latvalN != "" and self.lonvalE != "":
-            # select nearest location
-            if self.no_missing:
-                self.nearest_latvalN = self.latvalN
-                self.nearest_lonvalE = self.lonvalE
-            else:
-                # find nearest location without NaN values
-                self.nearest_location()
-            if self.tolerance > 0:
-                self.gset = self.dset.sel({self.latname: self.nearest_latvalN,
-                                           self.lonname: self.nearest_lonvalE},
-                                          method='nearest',
-                                          tolerance=self.tolerance)
-            else:
-                self.gset = self.dset.sel({self.latname: self.nearest_latvalN,
-                                           self.lonname: self.nearest_lonvalE},
-                                          method='nearest')
-        else:
-            self.gset = self.dset
-
-    def nearest_location(self):
-        # Build a geopandas dataframe with all first elements in each dimension
-        # so we assume null values correspond to a mask that is the same for
-        # all dimensions in the dataset.
-        dsel_frame = self.dset
-        for dim in self.dset.dims:
-            if dim != self.latname and dim != self.lonname:
-                dsel_frame = dsel_frame.isel({dim: 0})
-        # transform to pandas dataframe
-        dff = dsel_frame.to_dataframe().dropna().reset_index()
-        # transform to geopandas to collocate
-        gdf = gdp.GeoDataFrame(dff,
-                               geometry=gdp.points_from_xy(dff[self.lonname],
-                                                           dff[self.latname]))
-        # Find nearest location where values are not null
-        point = Point(self.lonvalE, self.latvalN)
-        multipoint = gdf.geometry.unary_union
-        queried_geom, nearest_geom = nearest_points(point, multipoint)
-        self.nearest_latvalN = nearest_geom.y
-        self.nearest_lonvalE = nearest_geom.x
-
-    def selection_from_coords(self):
-        fcoords = pd.read_csv(self.coords, sep='\t')
-        for row in fcoords.itertuples():
-            self.latvalN = row[0]
-            self.lonvalE = row[1]
-            self.outfile = (os.path.join(self.outputdir,
-                            self.select + '_' +
-                            str(row.Index) + '.tabular'))
-            self.selection()
-
-    def get_coords_info(self):
-        ds = xr.open_dataset(self.infile)
-        for c in ds.coords:
-            filename = os.path.join(self.coords_info,
-                                    c.strip() +
-                                    '.tabular')
-            pd = ds.coords[c].to_pandas()
-            pd.index = range(len(pd))
-            pd.to_csv(filename, header=False, sep='\t')
-
-
-if __name__ == '__main__':
-    warnings.filterwarnings("ignore")
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument(
-        'infile',
-        help='netCDF input filename'
-    )
-    parser.add_argument(
-        '--info',
-        help='Output filename where metadata information is stored'
-    )
-    parser.add_argument(
-        '--summary',
-        help='Output filename where data summary information is stored'
-    )
-    parser.add_argument(
-        '--select',
-        help='Variable name to select'
-    )
-    parser.add_argument(
-        '--latname',
-        help='Latitude name'
-    )
-    parser.add_argument(
-        '--latvalN',
-        help='North latitude value'
-    )
-    parser.add_argument(
-        '--latvalS',
-        help='South latitude value'
-    )
-    parser.add_argument(
-        '--lonname',
-        help='Longitude name'
-    )
-    parser.add_argument(
-        '--lonvalE',
-        help='East longitude value'
-    )
-    parser.add_argument(
-        '--lonvalW',
-        help='West longitude value'
-    )
-    parser.add_argument(
-        '--tolerance',
-        help='Maximum distance between original and selected value for '
-             ' inexact matches e.g. abs(index[indexer] - target) <= tolerance'
-    )
-    parser.add_argument(
-        '--coords',
-        help='Input file containing Latitude and Longitude'
-             'for geographical selection'
-    )
-    parser.add_argument(
-        '--coords_info',
-        help='output-folder where for each coordinate, coordinate values '
-             ' are being printed in the corresponding outputfile'
-    )
-    parser.add_argument(
-        '--filter',
-        nargs="*",
-        help='Filter list variable#operator#value_s#value_e'
-    )
-    parser.add_argument(
-        '--time',
-        help='select timeseries variable#operator#value_s[#value_e]'
-    )
-    parser.add_argument(
-        '--outfile',
-        help='csv outfile for storing results of the selection'
-             '(valid only when --select)'
-    )
-    parser.add_argument(
-        '--outputdir',
-        help='folder name for storing results with multiple selections'
-             '(valid only when --select)'
-    )
-    parser.add_argument(
-        "-v", "--verbose",
-        help="switch on verbose mode",
-        action="store_true"
-    )
-    parser.add_argument(
-        "--no_missing",
-        help="""Do not take into account possible null/missing values
-                (only valid for single location)""",
-        action="store_true"
-    )
-    args = parser.parse_args()
-
-    p = XarrayTool(args.infile, args.info, args.summary, args.select,
-                   args.outfile, args.outputdir, args.latname,
-                   args.latvalN, args.latvalS, args.lonname,
-                   args.lonvalE, args.lonvalW, args.filter,
-                   args.coords, args.time, args.verbose,
-                   args.no_missing, args.coords_info, args.tolerance)
-    if args.info:
-        p.info()
-    if args.summary:
-        p.summary()
-    if args.coords:
-        p.selection_from_coords()
-    elif args.select:
-        p.selection()
-    elif args.coords_info:
-        p.get_coords_info()