# HG changeset patch
# User ecology
# Date 1659302400 0
# Node ID c91c27b63fb2e3fa7f74088c979c5d97bd50507b
# Parent  e87073edecd64b9e297c387c7d4835f3f8bcdbca
planemo upload for repository https://github.com/galaxyecology/tools-ecology/tree/master/tools/data_manipulation/xarray/ commit fd8ad4d97db7b1fd3876ff63e14280474e06fdf7

diff -r e87073edecd6 -r c91c27b63fb2 macros.xml
--- a/macros.xml	Thu Jan 20 17:08:29 2022 +0000
+++ b/macros.xml	Sun Jul 31 21:20:00 2022 +0000
@@ -1,5 +1,5 @@
 <macros>
-    <token name="@TOOL_VERSION@">0.20.2</token>
+    <token name="@TOOL_VERSION@">2022.3.0</token>
     <token name="@VERSION_SUFFIX@">0</token>
     <token name="@PROFILE@">20.05</token>
     <xml name="edam_ontology">
diff -r e87073edecd6 -r c91c27b63fb2 macros_timeseries.xml
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/macros_timeseries.xml	Sun Jul 31 21:20:00 2022 +0000
@@ -0,0 +1,47 @@
+<macros>
+    <xml name="config_series">
+        <configfiles>
+            <configfile name="series_customization"><![CDATA[
+{
+#if $condi_datetime.datetime=="yes"
+#if str($condi_datetime.time_name).strip()
+"time_name":'$condi_datetime.time_name',
+#end if
+#if str($condi_datetime.time_start_value).strip()
+"time_start_value":"$condi_datetime.time_start_value",
+#end if
+#if str($condi_datetime.time_end_value).strip()
+"time_end_value":"$condi_datetime.time_end_value",
+#end if
+#end if
+#if str($lon_value).strip()
+"lon_value":'$lon_value',
+#end if
+#if str($lat_value).strip()
+"lat_value":'$lat_value',
+#end if
+#if $lon_name
+"lon_name":'$lon_name',
+#end if
+#if $lat_name
+"lat_name":'$lat_name',
+#end if
+#if str($adv.format_date).strip()
+"format_date":'$adv.format_date',
+#end if
+#if str($adv.plot_title).strip()
+"title":'$adv.plot_title',
+#end if
+#if str($adv.xlabel).strip()
+"xlabel":'$adv.xlabel',
+#end if
+#if str($adv.ylabel).strip()
+"ylabel":'$adv.ylabel',
+#end if
+}
+            ]]>
+            </configfile>
+        </configfiles>
+   
+    </xml>
+</macros>
diff -r e87073edecd6 -r c91c27b63fb2 test-data/dataset-ibi-reanalysis-bio-005-003-monthly-regulargrid_1510914389133_time0.png
Binary file test-data/dataset-ibi-reanalysis-bio-005-003-monthly-regulargrid_1510914389133_time0.png has changed
diff -r e87073edecd6 -r c91c27b63fb2 test-data/dataset-ibi-reanalysis-bio-005-003-monthly-regulargrid_1510914389133_time0_title.png
Binary file test-data/dataset-ibi-reanalysis-bio-005-003-monthly-regulargrid_1510914389133_time0_title.png has changed
diff -r e87073edecd6 -r c91c27b63fb2 test-data/dataset-ibi-reanalysis-bio-005-003-monthly-regulargrid_1510914389133_time1.png
Binary file test-data/dataset-ibi-reanalysis-bio-005-003-monthly-regulargrid_1510914389133_time1.png has changed
diff -r e87073edecd6 -r c91c27b63fb2 test-data/dataset-ibi-reanalysis-bio-005-003-monthly-regulargrid_1510914389133_time50.png
Binary file test-data/dataset-ibi-reanalysis-bio-005-003-monthly-regulargrid_1510914389133_time50.png has changed
diff -r e87073edecd6 -r c91c27b63fb2 test-data/time_series.png
Binary file test-data/time_series.png has changed
diff -r e87073edecd6 -r c91c27b63fb2 test-data/time_series.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/time_series.tabular	Sun Jul 31 21:20:00 2022 +0000
@@ -0,0 +1,38 @@
+time	longitude	latitude	depth	chl
+2010-12-15 00:00:00	-6.0000005	44.75	0.50576	0.31
+2011-01-15 00:00:00	-6.0000005	44.75	0.50576	0.37
+2011-02-15 00:00:00	-6.0000005	44.75	0.50576	0.81
+2011-03-15 00:00:00	-6.0000005	44.75	0.50576	1.41
+2011-04-15 00:00:00	-6.0000005	44.75	0.50576	1.8399999
+2011-05-15 00:00:00	-6.0000005	44.75	0.50576	0.099999994
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+2012-02-15 00:00:00	-6.0000005	44.75	0.50576	0.37
+2012-03-15 00:00:00	-6.0000005	44.75	0.50576	1.5799999
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+2012-12-15 00:00:00	-6.0000005	44.75	0.50576	0.29
+2013-01-15 00:00:00	-6.0000005	44.75	0.50576	0.37
+2013-02-15 00:00:00	-6.0000005	44.75	0.50576	0.38
+2013-03-15 00:00:00	-6.0000005	44.75	0.50576	1.15
+2013-04-15 00:00:00	-6.0000005	44.75	0.50576	1.9
+2013-05-15 00:00:00	-6.0000005	44.75	0.50576	0.5
+2013-06-15 00:00:00	-6.0000005	44.75	0.50576	0.12
+2013-07-15 00:00:00	-6.0000005	44.75	0.50576	0.01
+2013-08-15 00:00:00	-6.0000005	44.75	0.50576	0.0
+2013-09-15 00:00:00	-6.0000005	44.75	0.50576	0.01
+2013-10-15 00:00:00	-6.0000005	44.75	0.50576	0.01
+2013-11-15 00:00:00	-6.0000005	44.75	0.50576	0.12
+2013-12-15 00:00:00	-6.0000005	44.75	0.50576	0.34
diff -r e87073edecd6 -r c91c27b63fb2 test-data/time_series_customized.png
Binary file test-data/time_series_customized.png has changed
diff -r e87073edecd6 -r c91c27b63fb2 test-data/time_series_customized.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/time_series_customized.tabular	Sun Jul 31 21:20:00 2022 +0000
@@ -0,0 +1,146 @@
+time	longitude	latitude	depth	chl
+2002-12-15 00:00:00	-5.0000005	43.5	0.50576	0.34
+2003-01-15 00:00:00	-5.0000005	43.5	0.50576	0.41
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diff -r e87073edecd6 -r c91c27b63fb2 test-data/version.tabular
--- a/test-data/version.tabular	Thu Jan 20 17:08:29 2022 +0000
+++ b/test-data/version.tabular	Sun Jul 31 21:20:00 2022 +0000
@@ -1,1 +1,1 @@
-Galaxy xarray version 0.20.2
+Galaxy xarray version 2022.3.0
diff -r e87073edecd6 -r c91c27b63fb2 timeseries.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/timeseries.py	Sun Jul 31 21:20:00 2022 +0000
@@ -0,0 +1,182 @@
+#!/usr/bin/env python3
+#
+#
+# usage:  netCDF_timeseries.py [-h] [--output output.png]
+#                               [--save timeseries.tabular]
+#                               [--config config-file]
+#                               [-v]
+#                               input varname
+# positional arguments:
+#  input            input filename with geographical coordinates (netCDF
+#                   format)
+#  varname          Specify which variable to extract (case sensitive)
+#
+# optional arguments:
+#  -h, --help                 show this help message and exit
+#  --output output.png        filename to store image (png format)
+#  --save timeseries.tabular  filename to store timeseries (tabular format)
+#  --config                   config file extract parameters
+#  -v, --verbose              switch on verbose mode
+#
+import argparse
+import ast
+import warnings
+
+import cftime  # noqa: F401
+
+import matplotlib as mpl
+mpl.use('Agg')
+
+import matplotlib.pyplot as plt   # noqa: I202,E402
+from matplotlib.dates import DateFormatter   # noqa: I202,E402
+
+import xarray as xr  # noqa: I202,E402
+
+
+class TimeSeries ():
+    def __init__(self, input, varname, output, save, verbose=False,
+                 config_file=""):
+
+        li = list(input.split(","))
+        if len(li) > 1:
+            self.input = li
+        else:
+            self.input = input
+
+        self.varname = varname
+        self.xylim_supported = True
+        if output == "" or output is None:
+            self.output = "Timeseries.png"
+        else:
+            self.output = output
+        if save == "" or save is None:
+            self.save = "Timeseries.tabular"
+        else:
+            self.save = save
+        self.verbose = verbose
+        self.time_start_value = ""
+        self.time_end_value = ""
+        self.lon_value = ""
+        self.lat_value = ""
+        self.lat_name = 'lat'
+        self.lon_name = 'lon'
+        self.time_name = 'time'
+        self.title = ''
+        self.xlabel = ''
+        self.ylabel = ''
+        self.format_date = ''
+        if config_file != "" and config_file is not None:
+            with open(config_file) as f:
+                sdict = ''.join(
+                    f.read().replace("\n", "").split('{')[1].split('}')[0]
+                    )
+                tmp = ast.literal_eval('{' + sdict.strip() + '}')
+                for key in tmp:
+                    if key == 'time_start_value':
+                        self.time_start_value = tmp[key]
+                    if key == 'time_end_value':
+                        self.time_end_value = tmp[key]
+                    if key == 'lon_value':
+                        self.lon_value = tmp[key]
+                    if key == 'lat_value':
+                        self.lat_value = tmp[key]
+                    if key == 'lon_name':
+                        self.lon_name = tmp[key]
+                    if key == 'lat_name':
+                        self.lat_name = tmp[key]
+                    if key == 'time_name':
+                        self.time_name = tmp[key]
+                    if key == 'title':
+                        self.title = tmp[key]
+                    if key == 'xlabel':
+                        self.xlabel = tmp[key]
+                    if key == 'ylabel':
+                        self.ylabel = tmp[key]
+                    if key == 'format_date':
+                        self.format_date = tmp[key]
+                        self.format_date = self.format_date.replace('X', '%')
+
+        if type(self.input) is list:
+            self.dset = xr.open_mfdataset(self.input, use_cftime=True)
+        else:
+            self.dset = xr.open_dataset(self.input, use_cftime=True)
+
+        if verbose:
+            print("input: ", self.input)
+            print("varname: ", self.varname)
+            if self.time_start_value:
+                print("time_start_value: ", self.time_start_value)
+            if self.time_end_value:
+                print("time_end_value: ", self.time_end_value)
+            print("output: ", self.output)
+            if self.lon_value:
+                print(self.lon_name, self.lon_value)
+            if self.lat_value:
+                print(self.lat_name, self.lat_value)
+
+    def plot(self):
+        if self.lon_value:
+            lon_c = float(self.lon_value)
+        if self.lat_value:
+            lat_c = float(self.lat_value)
+        if self.lat_value and self.lon_value:
+            self.df = self.dset.sel({self.lat_name: lat_c,
+                                     self.lon_name: lon_c},
+                                    method='nearest')
+        else:
+            self.df = self.dset
+        if self.time_start_value or self.time_end_value:
+            self.df = self.df.sel({self.time_name: slice(self.time_start_value,
+                                                         self.time_end_value)})
+        # Saving the time series into a tabular
+        self.df = self.df[self.varname].squeeze().to_dataframe()
+        self.df.dropna().to_csv(self.save, sep='\t')
+        # Plot the time series into png image
+        fig = plt.figure(figsize=(15, 5))
+        ax = plt.subplot(111)
+        self.df[self.varname].plot(ax=ax)
+        if self.title:
+            plt.title(self.title)
+        if self.xlabel:
+            plt.xlabel(self.xlabel)
+        if self.ylabel:
+            plt.ylabel(self.ylabel)
+        if self.format_date:
+            ax.xaxis.set_major_formatter(DateFormatter(self.format_date))
+        fig.tight_layout()
+        fig.savefig(self.output)
+
+
+if __name__ == '__main__':
+    warnings.filterwarnings("ignore")
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        'input',
+        help='input filename with geographical coordinates (netCDF format)'
+    )
+    parser.add_argument(
+        'varname',
+        help='Specify which variable to plot (case sensitive)'
+    )
+    parser.add_argument(
+        '--output',
+        help='output filename to store resulting image (png format)'
+    )
+    parser.add_argument(
+        '--save',
+        help='save resulting tabular file (tabular format) into filename'
+    )
+    parser.add_argument(
+        '--config',
+        help='pass timeseries parameters via a config file'
+    )
+    parser.add_argument(
+        "-v", "--verbose",
+        help="switch on verbose mode",
+        action="store_true")
+    args = parser.parse_args()
+
+    dset = TimeSeries(input=args.input, varname=args.varname,
+                      output=args.output, save=args.save, verbose=args.verbose,
+                      config_file=args.config)
+    dset.plot()
diff -r e87073edecd6 -r c91c27b63fb2 xarray_info.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/xarray_info.py	Sun Jul 31 21:20:00 2022 +0000
@@ -0,0 +1,107 @@
+# 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 xarray as xr
+
+
+class XarrayInfo ():
+    def __init__(self, infile, outfile_info="", outfile_summary="",
+                 verbose=False, coords_info=None):
+        self.infile = infile
+        self.outfile_info = outfile_info
+        self.outfile_summary = outfile_summary
+        self.coords_info = coords_info
+        self.verbose = verbose
+        # initialization
+        self.dset = None
+        self.gset = None
+        if self.verbose:
+            print("infile: ", self.infile)
+            print("outfile_info: ", self.outfile_info)
+            print("outfile_summary: ", self.outfile_summary)
+            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 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(
+        '--coords_info',
+        help='output-folder where for each coordinate, coordinate values '
+             ' are being printed in the corresponding outputfile'
+    )
+    parser.add_argument(
+        "-v", "--verbose",
+        help="switch on verbose mode",
+        action="store_true"
+    )
+    args = parser.parse_args()
+
+    p = XarrayInfo(args.infile, args.info, args.summary,
+                   args.verbose, args.coords_info)
+    if args.info:
+        p.info()
+    elif args.coords_info:
+        p.get_coords_info()
+    if args.summary:
+        p.summary()
diff -r e87073edecd6 -r c91c27b63fb2 xarray_netcdf2netcdf.xml
--- a/xarray_netcdf2netcdf.xml	Thu Jan 20 17:08:29 2022 +0000
+++ b/xarray_netcdf2netcdf.xml	Sun Jul 31 21:20:00 2022 +0000
@@ -9,8 +9,8 @@
     <requirements>
         <requirement type="package" version="@TOOL_VERSION@">xarray</requirement>
         <requirement type="package" version="3">python</requirement>
-        <requirement type="package" version="1.5.8">netcdf4</requirement>
-        <requirement type="package" version="2021.12.0">dask</requirement>
+        <requirement type="package" version="1.6.0">netcdf4</requirement>
+        <requirement type="package" version="2022.7.0">dask</requirement>
     </requirements>
     <command detect_errors="exit_code"><![CDATA[
     mkdir output_dir &&
diff -r e87073edecd6 -r c91c27b63fb2 xarray_select.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/xarray_select.py	Sun Jul 31 21:20:00 2022 +0000
@@ -0,0 +1,294 @@
+# xarray tool for:
+# - getting metadata information
+# - select data and save results in csv file for further post-processing
+
+import argparse
+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 XarraySelect ():
+    def __init__(self, infile, select="", outfile="", outputdir="",
+                 latname="", latvalN="", latvalS="", lonname="",
+                 lonvalE="", lonvalW="", filter_list="", coords="",
+                 time="", verbose=False, no_missing=False,
+                 tolerance=None):
+        self.infile = infile
+        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
+        if self.verbose:
+            print("infile: ", self.infile)
+            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)
+
+    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()
+
+
+if __name__ == '__main__':
+    warnings.filterwarnings("ignore")
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        'infile',
+        help='netCDF input filename'
+    )
+    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(
+        '--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 = XarraySelect(args.infile, 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.tolerance)
+    if args.select:
+        p.selection()
diff -r e87073edecd6 -r c91c27b63fb2 xarray_tool.py
--- a/xarray_tool.py	Thu Jan 20 17:08:29 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()