| Previous changeset 3:bf595d613af4 (2022-01-20) |
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Commit message:
planemo upload for repository https://github.com/galaxyecology/tools-ecology/tree/master/tools/data_manipulation/xarray/ commit fd8ad4d97db7b1fd3876ff63e14280474e06fdf7 |
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modified:
macros.xml test-data/dataset-ibi-reanalysis-bio-005-003-monthly-regulargrid_1510914389133_time0.png test-data/dataset-ibi-reanalysis-bio-005-003-monthly-regulargrid_1510914389133_time0_title.png test-data/dataset-ibi-reanalysis-bio-005-003-monthly-regulargrid_1510914389133_time1.png test-data/dataset-ibi-reanalysis-bio-005-003-monthly-regulargrid_1510914389133_time50.png test-data/version.tabular xarray_select.xml |
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added:
macros_timeseries.xml test-data/time_series.png test-data/time_series.tabular test-data/time_series_customized.png test-data/time_series_customized.tabular timeseries.py xarray_info.py xarray_select.py |
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removed:
xarray_tool.py |
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| diff -r bf595d613af4 -r b393815e4cb7 macros.xml --- a/macros.xml Thu Jan 20 17:07:19 2022 +0000 +++ b/macros.xml Sun Jul 31 21:20:41 2022 +0000 |
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| @@ -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"> |
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| diff -r bf595d613af4 -r b393815e4cb7 macros_timeseries.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/macros_timeseries.xml Sun Jul 31 21:20:41 2022 +0000 |
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| @@ -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> |
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| diff -r bf595d613af4 -r b393815e4cb7 test-data/time_series.png |
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| diff -r bf595d613af4 -r b393815e4cb7 test-data/time_series.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/time_series.tabular Sun Jul 31 21:20:41 2022 +0000 |
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| @@ -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 +2011-06-15 00:00:00 -6.0000005 44.75 0.50576 0.03 +2011-07-15 00:00:00 -6.0000005 44.75 0.50576 0.03 +2011-08-15 00:00:00 -6.0000005 44.75 0.50576 0.01 +2011-09-15 00:00:00 -6.0000005 44.75 0.50576 0.01 +2011-10-15 00:00:00 -6.0000005 44.75 0.50576 0.02 +2011-11-15 00:00:00 -6.0000005 44.75 0.50576 0.07 +2011-12-15 00:00:00 -6.0000005 44.75 0.50576 0.34 +2012-01-15 00:00:00 -6.0000005 44.75 0.50576 0.35 +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 +2012-04-15 00:00:00 -6.0000005 44.75 0.50576 1.12 +2012-05-15 00:00:00 -6.0000005 44.75 0.50576 1.16 +2012-06-15 00:00:00 -6.0000005 44.75 0.50576 0.07 +2012-07-15 00:00:00 -6.0000005 44.75 0.50576 0.01 +2012-08-15 00:00:00 -6.0000005 44.75 0.50576 0.02 +2012-09-15 00:00:00 -6.0000005 44.75 0.50576 0.03 +2012-10-15 00:00:00 -6.0000005 44.75 0.50576 0.22 +2012-11-15 00:00:00 -6.0000005 44.75 0.50576 0.34 +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 |
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| diff -r bf595d613af4 -r b393815e4cb7 test-data/time_series_customized.png |
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| diff -r bf595d613af4 -r b393815e4cb7 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 21:20:41 2022 +0000 |
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| @@ -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 +2003-02-15 00:00:00 -5.0000005 43.5 0.50576 0.55 +2003-03-15 00:00:00 -5.0000005 43.5 0.50576 1.0699999 +2003-04-15 00:00:00 -5.0000005 43.5 0.50576 0.89 +2003-05-15 00:00:00 -5.0000005 43.5 0.50576 0.14 +2003-06-15 00:00:00 -5.0000005 43.5 0.50576 0.02 +2003-07-15 00:00:00 -5.0000005 43.5 0.50576 0.02 +2003-08-15 00:00:00 -5.0000005 43.5 0.50576 0.02 +2003-09-15 00:00:00 -5.0000005 43.5 0.50576 0.04 +2003-10-15 00:00:00 -5.0000005 43.5 0.50576 0.08 +2003-11-15 00:00:00 -5.0000005 43.5 0.50576 0.39 +2003-12-15 00:00:00 -5.0000005 43.5 0.50576 0.31 +2004-01-15 00:00:00 -5.0000005 43.5 0.50576 0.38 +2004-02-15 00:00:00 -5.0000005 43.5 0.50576 0.57 +2004-03-15 00:00:00 -5.0000005 43.5 0.50576 1.05 +2004-04-15 00:00:00 -5.0000005 43.5 0.50576 1.43 +2004-05-15 00:00:00 -5.0000005 43.5 0.50576 1.27 +2004-06-15 00:00:00 -5.0000005 43.5 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-5.0000005 43.5 0.50576 1.09 +2014-05-15 00:00:00 -5.0000005 43.5 0.50576 0.37 +2014-06-15 00:00:00 -5.0000005 43.5 0.50576 0.11 +2014-07-15 00:00:00 -5.0000005 43.5 0.50576 0.02 +2014-08-15 00:00:00 -5.0000005 43.5 0.50576 0.02 +2014-09-15 00:00:00 -5.0000005 43.5 0.50576 0.11 +2014-10-15 00:00:00 -5.0000005 43.5 0.50576 0.02 +2014-11-15 00:00:00 -5.0000005 43.5 0.50576 0.07 +2014-12-15 00:00:00 -5.0000005 43.5 0.50576 0.17 |
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| diff -r bf595d613af4 -r b393815e4cb7 test-data/version.tabular --- a/test-data/version.tabular Thu Jan 20 17:07:19 2022 +0000 +++ b/test-data/version.tabular Sun Jul 31 21:20:41 2022 +0000 |
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| @@ -1,1 +1,1 @@ -Galaxy xarray version 0.20.2 +Galaxy xarray version 2022.3.0 |
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| diff -r bf595d613af4 -r b393815e4cb7 timeseries.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/timeseries.py Sun Jul 31 21:20:41 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() |
| b |
| diff -r bf595d613af4 -r b393815e4cb7 xarray_info.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/xarray_info.py Sun Jul 31 21:20:41 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() |
| b |
| diff -r bf595d613af4 -r b393815e4cb7 xarray_select.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/xarray_select.py Sun Jul 31 21:20:41 2022 +0000 |
| [ |
| b'@@ -0,0 +1,294 @@\n+# xarray tool for:\r\n+# - getting metadata information\r\n+# - select data and save results in csv file for further post-processing\r\n+\r\n+import argparse\r\n+import os\r\n+import warnings\r\n+\r\n+import geopandas as gdp\r\n+\r\n+import pandas as pd\r\n+\r\n+from shapely.geometry import Point\r\n+from shapely.ops import nearest_points\r\n+\r\n+import xarray as xr\r\n+\r\n+\r\n+class XarraySelect ():\r\n+ def __init__(self, infile, select="", outfile="", outputdir="",\r\n+ latname="", latvalN="", latvalS="", lonname="",\r\n+ lonvalE="", lonvalW="", filter_list="", coords="",\r\n+ time="", verbose=False, no_missing=False,\r\n+ tolerance=None):\r\n+ self.infile = infile\r\n+ self.select = select\r\n+ self.outfile = outfile\r\n+ self.outputdir = outputdir\r\n+ self.latname = latname\r\n+ if tolerance != "" and tolerance is not None:\r\n+ self.tolerance = float(tolerance)\r\n+ else:\r\n+ self.tolerance = -1\r\n+ if latvalN != "" and latvalN is not None:\r\n+ self.latvalN = float(latvalN)\r\n+ else:\r\n+ self.latvalN = ""\r\n+ if latvalS != "" and latvalS is not None:\r\n+ self.latvalS = float(latvalS)\r\n+ else:\r\n+ self.latvalS = ""\r\n+ self.lonname = lonname\r\n+ if lonvalE != "" and lonvalE is not None:\r\n+ self.lonvalE = float(lonvalE)\r\n+ else:\r\n+ self.lonvalE = ""\r\n+ if lonvalW != "" and lonvalW is not None:\r\n+ self.lonvalW = float(lonvalW)\r\n+ else:\r\n+ self.lonvalW = ""\r\n+ self.filter = filter_list\r\n+ self.time = time\r\n+ self.coords = coords\r\n+ self.verbose = verbose\r\n+ self.no_missing = no_missing\r\n+ # initialization\r\n+ self.dset = None\r\n+ self.gset = None\r\n+ if self.verbose:\r\n+ print("infile: ", self.infile)\r\n+ print("outfile: ", self.outfile)\r\n+ print("select: ", self.select)\r\n+ print("outfile: ", self.outfile)\r\n+ print("outputdir: ", self.outputdir)\r\n+ print("latname: ", self.latname)\r\n+ print("latvalN: ", self.latvalN)\r\n+ print("latvalS: ", self.latvalS)\r\n+ print("lonname: ", self.lonname)\r\n+ print("lonvalE: ", self.lonvalE)\r\n+ print("lonvalW: ", self.lonvalW)\r\n+ print("filter: ", self.filter)\r\n+ print("time: ", self.time)\r\n+ print("coords: ", self.coords)\r\n+\r\n+ def rowfilter(self, single_filter):\r\n+ split_filter = single_filter.split(\'#\')\r\n+ filter_varname = split_filter[0]\r\n+ op = split_filter[1]\r\n+ ll = float(split_filter[2])\r\n+ if (op == \'bi\'):\r\n+ rl = float(split_filter[3])\r\n+ if filter_varname == self.select:\r\n+ # filter on values of the selected variable\r\n+ if op == \'bi\':\r\n+ self.dset = self.dset.where(\r\n+ (self.dset <= rl) & (self.dset >= ll)\r\n+ )\r\n+ elif op == \'le\':\r\n+ self.dset = self.dset.where(self.dset <= ll)\r\n+ elif op == \'ge\':\r\n+ self.dset = self.dset.where(self.dset >= ll)\r\n+ elif op == \'e\':\r\n+ self.dset = self.dset.where(self.dset == ll)\r\n+ else: # filter on other dimensions of the selected variable\r\n+ if op == \'bi\':\r\n+ self.dset = self.dset.sel({filter_varname: slice(ll, rl)})\r\n+ elif op == \'le\':\r\n+ self.dset = self.dset.sel({filter_varname: slice(None, ll)})\r\n+ elif op == \'ge\':\r\n+ self.dset = self.dset.sel({filter_varname: slice(ll, None)})\r\n+ elif op == \'e\':\r\n+ self.dset = self.dset.sel({filter_varname: ll},\r\n+ method=\'nearest\')\r\n+\r\n+ def selection(self):\r\n+ if self.dset is None:\r\n+ sel'..b'to a mask that is the same for\r\n+ # all dimensions in the dataset.\r\n+ dsel_frame = self.dset\r\n+ for dim in self.dset.dims:\r\n+ if dim != self.latname and dim != self.lonname:\r\n+ dsel_frame = dsel_frame.isel({dim: 0})\r\n+ # transform to pandas dataframe\r\n+ dff = dsel_frame.to_dataframe().dropna().reset_index()\r\n+ # transform to geopandas to collocate\r\n+ gdf = gdp.GeoDataFrame(dff,\r\n+ geometry=gdp.points_from_xy(dff[self.lonname],\r\n+ dff[self.latname]))\r\n+ # Find nearest location where values are not null\r\n+ point = Point(self.lonvalE, self.latvalN)\r\n+ multipoint = gdf.geometry.unary_union\r\n+ queried_geom, nearest_geom = nearest_points(point, multipoint)\r\n+ self.nearest_latvalN = nearest_geom.y\r\n+ self.nearest_lonvalE = nearest_geom.x\r\n+\r\n+ def selection_from_coords(self):\r\n+ fcoords = pd.read_csv(self.coords, sep=\'\\t\')\r\n+ for row in fcoords.itertuples():\r\n+ self.latvalN = row[0]\r\n+ self.lonvalE = row[1]\r\n+ self.outfile = (os.path.join(self.outputdir,\r\n+ self.select + \'_\' +\r\n+ str(row.Index) + \'.tabular\'))\r\n+ self.selection()\r\n+\r\n+\r\n+if __name__ == \'__main__\':\r\n+ warnings.filterwarnings("ignore")\r\n+ parser = argparse.ArgumentParser()\r\n+\r\n+ parser.add_argument(\r\n+ \'infile\',\r\n+ help=\'netCDF input filename\'\r\n+ )\r\n+ parser.add_argument(\r\n+ \'--select\',\r\n+ help=\'Variable name to select\'\r\n+ )\r\n+ parser.add_argument(\r\n+ \'--latname\',\r\n+ help=\'Latitude name\'\r\n+ )\r\n+ parser.add_argument(\r\n+ \'--latvalN\',\r\n+ help=\'North latitude value\'\r\n+ )\r\n+ parser.add_argument(\r\n+ \'--latvalS\',\r\n+ help=\'South latitude value\'\r\n+ )\r\n+ parser.add_argument(\r\n+ \'--lonname\',\r\n+ help=\'Longitude name\'\r\n+ )\r\n+ parser.add_argument(\r\n+ \'--lonvalE\',\r\n+ help=\'East longitude value\'\r\n+ )\r\n+ parser.add_argument(\r\n+ \'--lonvalW\',\r\n+ help=\'West longitude value\'\r\n+ )\r\n+ parser.add_argument(\r\n+ \'--tolerance\',\r\n+ help=\'Maximum distance between original and selected value for \'\r\n+ \' inexact matches e.g. abs(index[indexer] - target) <= tolerance\'\r\n+ )\r\n+ parser.add_argument(\r\n+ \'--coords\',\r\n+ help=\'Input file containing Latitude and Longitude\'\r\n+ \'for geographical selection\'\r\n+ )\r\n+ parser.add_argument(\r\n+ \'--filter\',\r\n+ nargs="*",\r\n+ help=\'Filter list variable#operator#value_s#value_e\'\r\n+ )\r\n+ parser.add_argument(\r\n+ \'--time\',\r\n+ help=\'select timeseries variable#operator#value_s[#value_e]\'\r\n+ )\r\n+ parser.add_argument(\r\n+ \'--outfile\',\r\n+ help=\'csv outfile for storing results of the selection\'\r\n+ \'(valid only when --select)\'\r\n+ )\r\n+ parser.add_argument(\r\n+ \'--outputdir\',\r\n+ help=\'folder name for storing results with multiple selections\'\r\n+ \'(valid only when --select)\'\r\n+ )\r\n+ parser.add_argument(\r\n+ "-v", "--verbose",\r\n+ help="switch on verbose mode",\r\n+ action="store_true"\r\n+ )\r\n+ parser.add_argument(\r\n+ "--no_missing",\r\n+ help="""Do not take into account possible null/missing values\r\n+ (only valid for single location)""",\r\n+ action="store_true"\r\n+ )\r\n+ args = parser.parse_args()\r\n+\r\n+ p = XarraySelect(args.infile, args.select, args.outfile, args.outputdir,\r\n+ args.latname, args.latvalN, args.latvalS, args.lonname,\r\n+ args.lonvalE, args.lonvalW, args.filter,\r\n+ args.coords, args.time, args.verbose,\r\n+ args.no_missing, args.tolerance)\r\n+ if args.select:\r\n+ p.selection()\r\n' |
| b |
| diff -r bf595d613af4 -r b393815e4cb7 xarray_select.xml --- a/xarray_select.xml Thu Jan 20 17:07:19 2022 +0000 +++ b/xarray_select.xml Sun Jul 31 21:20:41 2022 +0000 |
| [ |
| @@ -6,17 +6,18 @@ <expand macro="edam_ontology"/> <requirements> <requirement type="package" version="@TOOL_VERSION@">xarray</requirement> - <requirement type="package" version="3">python</requirement> - <requirement type="package" version="1.5.6">netcdf4</requirement> - <requirement type="package" version="0.9.0">geopandas</requirement> - <requirement type="package" version="1.7.1">shapely</requirement> + <requirement type="package" version="3.10">python</requirement> + <requirement type="package" version="1.8.2">shapely</requirement> + <requirement type="package" version="1.6.0">netcdf4</requirement> + <requirement type="package" version="1.4.3">pandas</requirement> + <requirement type="package" version="0.7.0">geopandas</requirement> </requirements> <command detect_errors="exit_code"><![CDATA[ mkdir output_dir && #if $condi_source_coord.coord_source=="coord_from_file" echo "Galaxy xarray version @TOOL_VERSION@"> output_dir/version.tabular && #end if - python '$__tool_directory__/xarray_tool.py' '$input' --select '$var' + python '$__tool_directory__/xarray_select.py' '$input' --select '$var' --verbose --filter #for $i,$uc in enumerate($user_choice) |
| b |
| diff -r bf595d613af4 -r b393815e4cb7 xarray_tool.py --- a/xarray_tool.py Thu Jan 20 17:07:19 2022 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| [ |
| b'@@ -1,365 +0,0 @@\n-# xarray tool for:\r\n-# - getting metadata information\r\n-# - select data and save results in csv file for further post-processing\r\n-\r\n-import argparse\r\n-import csv\r\n-import os\r\n-import warnings\r\n-\r\n-import geopandas as gdp\r\n-\r\n-import pandas as pd\r\n-\r\n-from shapely.geometry import Point\r\n-from shapely.ops import nearest_points\r\n-\r\n-import xarray as xr\r\n-\r\n-\r\n-class XarrayTool ():\r\n- def __init__(self, infile, outfile_info="", outfile_summary="",\r\n- select="", outfile="", outputdir="", latname="",\r\n- latvalN="", latvalS="", lonname="", lonvalE="",\r\n- lonvalW="", filter_list="", coords="", time="",\r\n- verbose=False, no_missing=False, coords_info=None,\r\n- tolerance=None):\r\n- self.infile = infile\r\n- self.outfile_info = outfile_info\r\n- self.outfile_summary = outfile_summary\r\n- self.select = select\r\n- self.outfile = outfile\r\n- self.outputdir = outputdir\r\n- self.latname = latname\r\n- if tolerance != "" and tolerance is not None:\r\n- self.tolerance = float(tolerance)\r\n- else:\r\n- self.tolerance = -1\r\n- if latvalN != "" and latvalN is not None:\r\n- self.latvalN = float(latvalN)\r\n- else:\r\n- self.latvalN = ""\r\n- if latvalS != "" and latvalS is not None:\r\n- self.latvalS = float(latvalS)\r\n- else:\r\n- self.latvalS = ""\r\n- self.lonname = lonname\r\n- if lonvalE != "" and lonvalE is not None:\r\n- self.lonvalE = float(lonvalE)\r\n- else:\r\n- self.lonvalE = ""\r\n- if lonvalW != "" and lonvalW is not None:\r\n- self.lonvalW = float(lonvalW)\r\n- else:\r\n- self.lonvalW = ""\r\n- self.filter = filter_list\r\n- self.time = time\r\n- self.coords = coords\r\n- self.verbose = verbose\r\n- self.no_missing = no_missing\r\n- # initialization\r\n- self.dset = None\r\n- self.gset = None\r\n- self.coords_info = coords_info\r\n- if self.verbose:\r\n- print("infile: ", self.infile)\r\n- print("outfile_info: ", self.outfile_info)\r\n- print("outfile_summary: ", self.outfile_summary)\r\n- print("outfile: ", self.outfile)\r\n- print("select: ", self.select)\r\n- print("outfile: ", self.outfile)\r\n- print("outputdir: ", self.outputdir)\r\n- print("latname: ", self.latname)\r\n- print("latvalN: ", self.latvalN)\r\n- print("latvalS: ", self.latvalS)\r\n- print("lonname: ", self.lonname)\r\n- print("lonvalE: ", self.lonvalE)\r\n- print("lonvalW: ", self.lonvalW)\r\n- print("filter: ", self.filter)\r\n- print("time: ", self.time)\r\n- print("coords: ", self.coords)\r\n- print("coords_info: ", self.coords_info)\r\n-\r\n- def info(self):\r\n- f = open(self.outfile_info, \'w\')\r\n- ds = xr.open_dataset(self.infile)\r\n- ds.info(f)\r\n- f.close()\r\n-\r\n- def summary(self):\r\n- f = open(self.outfile_summary, \'w\')\r\n- ds = xr.open_dataset(self.infile)\r\n- writer = csv.writer(f, delimiter=\'\\t\')\r\n- header = [\'VariableName\', \'NumberOfDimensions\']\r\n- for idx, val in enumerate(ds.dims.items()):\r\n- header.append(\'Dim\' + str(idx) + \'Name\')\r\n- header.append(\'Dim\' + str(idx) + \'Size\')\r\n- writer.writerow(header)\r\n- for name, da in ds.data_vars.items():\r\n- line = [name]\r\n- line.append(len(ds[name].shape))\r\n- for d, s in zip(da.shape, da.sizes):\r\n- line.append(s)\r\n- line.append(d)\r\n- writer.writerow(line)\r\n- for name, da in ds.coords.items():\r\n- line = [name]\r\n- line.append(len(ds[name].shape))\r\n- for d, s in zip(da.shape, da.sizes):\r\n- line.append(s)\r\n- '..b' self.lonvalE = row[1]\r\n- self.outfile = (os.path.join(self.outputdir,\r\n- self.select + \'_\' +\r\n- str(row.Index) + \'.tabular\'))\r\n- self.selection()\r\n-\r\n- def get_coords_info(self):\r\n- ds = xr.open_dataset(self.infile)\r\n- for c in ds.coords:\r\n- filename = os.path.join(self.coords_info,\r\n- c.strip() +\r\n- \'.tabular\')\r\n- pd = ds.coords[c].to_pandas()\r\n- pd.index = range(len(pd))\r\n- pd.to_csv(filename, header=False, sep=\'\\t\')\r\n-\r\n-\r\n-if __name__ == \'__main__\':\r\n- warnings.filterwarnings("ignore")\r\n- parser = argparse.ArgumentParser()\r\n-\r\n- parser.add_argument(\r\n- \'infile\',\r\n- help=\'netCDF input filename\'\r\n- )\r\n- parser.add_argument(\r\n- \'--info\',\r\n- help=\'Output filename where metadata information is stored\'\r\n- )\r\n- parser.add_argument(\r\n- \'--summary\',\r\n- help=\'Output filename where data summary information is stored\'\r\n- )\r\n- parser.add_argument(\r\n- \'--select\',\r\n- help=\'Variable name to select\'\r\n- )\r\n- parser.add_argument(\r\n- \'--latname\',\r\n- help=\'Latitude name\'\r\n- )\r\n- parser.add_argument(\r\n- \'--latvalN\',\r\n- help=\'North latitude value\'\r\n- )\r\n- parser.add_argument(\r\n- \'--latvalS\',\r\n- help=\'South latitude value\'\r\n- )\r\n- parser.add_argument(\r\n- \'--lonname\',\r\n- help=\'Longitude name\'\r\n- )\r\n- parser.add_argument(\r\n- \'--lonvalE\',\r\n- help=\'East longitude value\'\r\n- )\r\n- parser.add_argument(\r\n- \'--lonvalW\',\r\n- help=\'West longitude value\'\r\n- )\r\n- parser.add_argument(\r\n- \'--tolerance\',\r\n- help=\'Maximum distance between original and selected value for \'\r\n- \' inexact matches e.g. abs(index[indexer] - target) <= tolerance\'\r\n- )\r\n- parser.add_argument(\r\n- \'--coords\',\r\n- help=\'Input file containing Latitude and Longitude\'\r\n- \'for geographical selection\'\r\n- )\r\n- parser.add_argument(\r\n- \'--coords_info\',\r\n- help=\'output-folder where for each coordinate, coordinate values \'\r\n- \' are being printed in the corresponding outputfile\'\r\n- )\r\n- parser.add_argument(\r\n- \'--filter\',\r\n- nargs="*",\r\n- help=\'Filter list variable#operator#value_s#value_e\'\r\n- )\r\n- parser.add_argument(\r\n- \'--time\',\r\n- help=\'select timeseries variable#operator#value_s[#value_e]\'\r\n- )\r\n- parser.add_argument(\r\n- \'--outfile\',\r\n- help=\'csv outfile for storing results of the selection\'\r\n- \'(valid only when --select)\'\r\n- )\r\n- parser.add_argument(\r\n- \'--outputdir\',\r\n- help=\'folder name for storing results with multiple selections\'\r\n- \'(valid only when --select)\'\r\n- )\r\n- parser.add_argument(\r\n- "-v", "--verbose",\r\n- help="switch on verbose mode",\r\n- action="store_true"\r\n- )\r\n- parser.add_argument(\r\n- "--no_missing",\r\n- help="""Do not take into account possible null/missing values\r\n- (only valid for single location)""",\r\n- action="store_true"\r\n- )\r\n- args = parser.parse_args()\r\n-\r\n- p = XarrayTool(args.infile, args.info, args.summary, args.select,\r\n- args.outfile, args.outputdir, args.latname,\r\n- args.latvalN, args.latvalS, args.lonname,\r\n- args.lonvalE, args.lonvalW, args.filter,\r\n- args.coords, args.time, args.verbose,\r\n- args.no_missing, args.coords_info, args.tolerance)\r\n- if args.info:\r\n- p.info()\r\n- if args.summary:\r\n- p.summary()\r\n- if args.coords:\r\n- p.selection_from_coords()\r\n- elif args.select:\r\n- p.selection()\r\n- elif args.coords_info:\r\n- p.get_coords_info()\r\n' |
