# HG changeset patch # User ecology # Date 1622969383 0 # Node ID 663268794710867f2a2e930e6f95d48f1e7cfcbf # Parent e8650cdf092fc23609dd2d5fb0cbbc55b78a86d4 "planemo upload for repository https://github.com/galaxyecology/tools-ecology/tree/master/tools/data_manipulation/xarray/ commit 57b6d23e3734d883e71081c78e77964d61be82ba" diff -r e8650cdf092f -r 663268794710 macros.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/macros.xml Sun Jun 06 08:49:43 2021 +0000 @@ -0,0 +1,185 @@ + + 0.18.2 + 0 + + + topic_0610 + topic_3050 + + + + + + @article{hoyer2017xarray, + title = {xarray: {N-D} labeled arrays and datasets in {Python}}, + author = {Hoyer, S. and J. Hamman}, + journal = {Journal of Open Research Software}, + volume = {5}, + number = {1}, + year = {2017}, + publisher = {Ubiquity Press}, + doi = {10.5334/jors.148}, + url = {http://doi.org/10.5334/jors.148} + } + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff -r e8650cdf092f -r 663268794710 test-data/all.netcdf Binary file test-data/all.netcdf has changed diff -r e8650cdf092f -r 663268794710 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 e8650cdf092f -r 663268794710 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 e8650cdf092f -r 663268794710 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 e8650cdf092f -r 663268794710 test-data/depth.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/depth.tabular Sun Jun 06 08:49:43 2021 +0000 @@ -0,0 +1,1 @@ +0 0.50576 diff -r e8650cdf092f -r 663268794710 test-data/latitude.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/latitude.tabular Sun Jun 06 08:49:43 2021 +0000 @@ -0,0 +1,97 @@ +0 43.0 +1 43.083332 +2 43.166664 +3 43.25 +4 43.333332 +5 43.416664 +6 43.5 +7 43.583332 +8 43.666664 +9 43.75 +10 43.833332 +11 43.916664 +12 44.0 +13 44.083332 +14 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50.833332 +95 50.916664 +96 51.0 diff -r e8650cdf092f -r 663268794710 test-data/longitude.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/longitude.tabular Sun Jun 06 08:49:43 2021 +0000 @@ -0,0 +1,103 @@ +0 -6.0000005 +1 -5.916667 +2 -5.833334 +3 -5.7500005 +4 -5.666667 +5 -5.583334 +6 -5.5000005 +7 -5.416667 +8 -5.333334 +9 -5.2500005 +10 -5.166667 +11 -5.083334 +12 -5.0000005 +13 -4.9166675 +14 -4.833334 +15 -4.7500005 +16 -4.6666675 +17 -4.583334 +18 -4.5000005 +19 -4.4166675 +20 -4.333334 +21 -4.2500005 +22 -4.1666675 +23 -4.083334 +24 -4.0000005 +25 -3.9166672 +26 -3.833334 +27 -3.7500007 +28 -3.6666672 +29 -3.583334 +30 -3.5000007 +31 -3.4166672 +32 -3.333334 +33 -3.2500007 +34 -3.1666672 +35 -3.083334 +36 -3.0000007 +37 -2.9166672 +38 -2.833334 +39 -2.7500007 +40 -2.6666672 +41 -2.583334 +42 -2.5000007 +43 -2.4166672 +44 -2.333334 +45 -2.2500007 +46 -2.1666672 +47 -2.083334 +48 -2.0000007 +49 -1.9166673 +50 -1.833334 +51 -1.7500007 +52 -1.6666673 +53 -1.5833341 +54 -1.5000007 +55 -1.4166673 +56 -1.3333341 +57 -1.2500007 +58 -1.1666673 +59 -1.0833341 +60 -1.0000007 +61 -0.9166674 +62 -0.8333341 +63 -0.7500007 +64 -0.6666674 +65 -0.5833341 +66 -0.5000007 +67 -0.4166674 +68 -0.3333341 +69 -0.25000075 +70 -0.16666742 +71 -0.08333409 +72 -7.6e-07 +73 0.08333257 +74 0.1666659 +75 0.24999923 +76 0.33333257 +77 0.41666588 +78 0.49999923 +79 0.58333254 +80 0.66666585 +81 0.7499992 +82 0.83333254 +83 0.91666585 +84 0.9999992 +85 1.0833325 +86 1.1666659 +87 1.2499992 +88 1.3333325 +89 1.4166658 +90 1.4999992 +91 1.5833325 +92 1.6666658 +93 1.7499992 +94 1.8333325 +95 1.9166658 +96 1.9999992 +97 2.0833325 +98 2.1666658 +99 2.249999 +100 2.3333325 +101 2.4166658 +102 2.499999 diff -r e8650cdf092f -r 663268794710 test-data/small.netcdf Binary file test-data/small.netcdf has changed diff -r e8650cdf092f -r 663268794710 test-data/time.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/time.tabular Sun Jun 06 08:49:43 2021 +0000 @@ -0,0 +1,145 @@ +0 2002-12-15 +1 2003-01-15 +2 2003-02-15 +3 2003-03-15 +4 2003-04-15 +5 2003-05-15 +6 2003-06-15 +7 2003-07-15 +8 2003-08-15 +9 2003-09-15 +10 2003-10-15 +11 2003-11-15 +12 2003-12-15 +13 2004-01-15 +14 2004-02-15 +15 2004-03-15 +16 2004-04-15 +17 2004-05-15 +18 2004-06-15 +19 2004-07-15 +20 2004-08-15 +21 2004-09-15 +22 2004-10-15 +23 2004-11-15 +24 2004-12-15 +25 2005-01-15 +26 2005-02-15 +27 2005-03-15 +28 2005-04-15 +29 2005-05-15 +30 2005-06-15 +31 2005-07-15 +32 2005-08-15 +33 2005-09-15 +34 2005-10-15 +35 2005-11-15 +36 2005-12-15 +37 2006-01-15 +38 2006-02-15 +39 2006-03-15 +40 2006-04-15 +41 2006-05-15 +42 2006-06-15 +43 2006-07-15 +44 2006-08-15 +45 2006-09-15 +46 2006-10-15 +47 2006-11-15 +48 2006-12-15 +49 2007-01-15 +50 2007-02-15 +51 2007-03-15 +52 2007-04-15 +53 2007-05-15 +54 2007-06-15 +55 2007-07-15 +56 2007-08-15 +57 2007-09-15 +58 2007-10-15 +59 2007-11-15 +60 2007-12-15 +61 2008-01-15 +62 2008-02-15 +63 2008-03-15 +64 2008-04-15 +65 2008-05-15 +66 2008-06-15 +67 2008-07-15 +68 2008-08-15 +69 2008-09-15 +70 2008-10-15 +71 2008-11-15 +72 2008-12-15 +73 2009-01-15 +74 2009-02-15 +75 2009-03-15 +76 2009-04-15 +77 2009-05-15 +78 2009-06-15 +79 2009-07-15 +80 2009-08-15 +81 2009-09-15 +82 2009-10-15 +83 2009-11-15 +84 2009-12-15 +85 2010-01-15 +86 2010-02-15 +87 2010-03-15 +88 2010-04-15 +89 2010-05-15 +90 2010-06-15 +91 2010-07-15 +92 2010-08-15 +93 2010-09-15 +94 2010-10-15 +95 2010-11-15 +96 2010-12-15 +97 2011-01-15 +98 2011-02-15 +99 2011-03-15 +100 2011-04-15 +101 2011-05-15 +102 2011-06-15 +103 2011-07-15 +104 2011-08-15 +105 2011-09-15 +106 2011-10-15 +107 2011-11-15 +108 2011-12-15 +109 2012-01-15 +110 2012-02-15 +111 2012-03-15 +112 2012-04-15 +113 2012-05-15 +114 2012-06-15 +115 2012-07-15 +116 2012-08-15 +117 2012-09-15 +118 2012-10-15 +119 2012-11-15 +120 2012-12-15 +121 2013-01-15 +122 2013-02-15 +123 2013-03-15 +124 2013-04-15 +125 2013-05-15 +126 2013-06-15 +127 2013-07-15 +128 2013-08-15 +129 2013-09-15 +130 2013-10-15 +131 2013-11-15 +132 2013-12-15 +133 2014-01-15 +134 2014-02-15 +135 2014-03-15 +136 2014-04-15 +137 2014-05-15 +138 2014-06-15 +139 2014-07-15 +140 2014-08-15 +141 2014-09-15 +142 2014-10-15 +143 2014-11-15 +144 2014-12-15 diff -r e8650cdf092f -r 663268794710 test-data/version.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/version.tabular Sun Jun 06 08:49:43 2021 +0000 @@ -0,0 +1,1 @@ +Galaxy xarray version 0.18.2 diff -r e8650cdf092f -r 663268794710 xarray_mapplot.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/xarray_mapplot.py Sun Jun 06 08:49:43 2021 +0000 @@ -0,0 +1,457 @@ +#!/usr/bin/env python3 +# +# +# usage: xarray_mapplot.py [-h] [--proj PROJ] +# [--cmap CMAP] +# [--output OUTPUT] +# [--time TIMES] +# [--nrow NROW] +# [--ncol NCOL] +# [--title title] +# [--latitude LATITUDE] +# [--longitude LONGITUDE] +# [--land ALPHA-LAND] +# [--ocean ALPHA-OCEAN] +# [--coastline ALPHA-COASTLINE] +# [--borders ALPHA-BORDERS] +# [--xlim "x1,x2"] +# [--ylim "y1,y2"] +# [--range "valmin,valmax"] +# [--threshold VAL] +# [--label label-colorbar] +# [--shift] +# [-v] +# input varname +# +# positional arguments: +# input input filename with geographical coordinates (netCDF +# format) +# varname Specify which variable to plot (case sensitive) +# +# optional arguments: +# -h, --help show this help message and exit +# --proj PROJ Specify the projection on which we draw +# --cmap CMAP Specify which colormap to use for plotting +# --output OUTPUT output filename to store resulting image (png format) +# --time TIMES time index from the file for multiple plots ("0 1 2 3") +# --title plot or subplot title +# --latitude variable name for latitude +# --longitude variable name for longitude +# --land add land on plot with alpha value [0-1] +# --ocean add oceans on plot with alpha value [0-1] +# --coastline add coastline with alpha value [0-1] +# --borders add country borders with alpha value [0-1] +# --xlim limited geographical area longitudes "x1,x2" +# --ylim limited geographical area latitudes "y1,y2" +# --range "valmin,valmax" for plotting +# --threshold do not plot values below threshold +# --label set a label for colormap +# --shift shift longitudes if specified +# -v, --verbose switch on verbose mode +# + +import argparse +import ast +import warnings +from pathlib import Path + +import cartopy.crs as ccrs +import cartopy.feature as feature + +from cmcrameri import cm + +import matplotlib as mpl +mpl.use('Agg') +from matplotlib import pyplot # noqa: I202,E402 + +import xarray as xr # noqa: E402 + + +class MapPlotXr (): + def __init__(self, input, proj, varname, cmap, output, verbose=False, + time=[], title="", latitude="latitude", + longitude="longitude", land=0, ocean=0, + coastline=0, borders=0, xlim=[], ylim=[], + threshold="", label="", shift=False, + range_values=[]): + self.input = input + print("PROJ", proj) + if proj != "" and proj is not None: + self.proj = proj.replace('X', ':') + else: + self.proj = proj + self.varname = varname + self.get_cmap(cmap) + self.time = time + self.latitude = latitude + self.longitude = longitude + self.land = land + self.ocean = ocean + self.coastline = coastline + self.borders = borders + self.xlim = xlim + self.ylim = ylim + self.range = range_values + self.threshold = threshold + self.shift = shift + self.xylim_supported = False + self.colorbar = True + self.title = title + if output is None: + self.output = Path(input).stem + '.png' + else: + self.output = output + self.verbose = verbose + self.dset = xr.open_dataset(self.input, use_cftime=True) + + self.label = {} + if label != "" and label is not None: + self.label['label'] = label + if verbose: + print("input: ", self.input) + print("proj: ", self.proj) + print("varname: ", self.varname) + print("time: ", self.time) + print("minval, maxval: ", self.range) + print("title: ", self.title) + print("output: ", self.output) + print("label: ", self.label) + print("shift: ", self.shift) + print("ocean: ", self.ocean) + print("land: ", self.land) + print("coastline: ", self.coastline) + print("borders: ", self.borders) + print("latitude: ", self.latitude) + print("longitude: ", self.longitude) + print("xlim: ", self.xlim) + print("ylim: ", self.ylim) + + def get_cmap(self, cmap): + if cmap[0:3] == 'cm.': + self.cmap = cm.__dict__[cmap[3:]] + else: + self.cmap = cmap + + def projection(self): + if self.proj is None: + return ccrs.PlateCarree() + + proj_dict = ast.literal_eval(self.proj) + + user_proj = proj_dict.pop("proj") + if user_proj == 'PlateCarree': + self.xylim_supported = True + return ccrs.PlateCarree(**proj_dict) + elif user_proj == 'AlbersEqualArea': + return ccrs.AlbersEqualArea(**proj_dict) + elif user_proj == 'AzimuthalEquidistant': + return ccrs.AzimuthalEquidistant(**proj_dict) + elif user_proj == 'EquidistantConic': + return ccrs.EquidistantConic(**proj_dict) + elif user_proj == 'LambertConformal': + return ccrs.LambertConformal(**proj_dict) + elif user_proj == 'LambertCylindrical': + return ccrs.LambertCylindrical(**proj_dict) + elif user_proj == 'Mercator': + return ccrs.Mercator(**proj_dict) + elif user_proj == 'Miller': + return ccrs.Miller(**proj_dict) + elif user_proj == 'Mollweide': + return ccrs.Mollweide(**proj_dict) + elif user_proj == 'Orthographic': + return ccrs.Orthographic(**proj_dict) + elif user_proj == 'Robinson': + return ccrs.Robinson(**proj_dict) + elif user_proj == 'Sinusoidal': + return ccrs.Sinusoidal(**proj_dict) + elif user_proj == 'Stereographic': + return ccrs.Stereographic(**proj_dict) + elif user_proj == 'TransverseMercator': + return ccrs.TransverseMercator(**proj_dict) + elif user_proj == 'UTM': + return ccrs.UTM(**proj_dict) + elif user_proj == 'InterruptedGoodeHomolosine': + return ccrs.InterruptedGoodeHomolosine(**proj_dict) + elif user_proj == 'RotatedPole': + return ccrs.RotatedPole(**proj_dict) + elif user_proj == 'OSGB': + self.xylim_supported = False + return ccrs.OSGB(**proj_dict) + elif user_proj == 'EuroPP': + self.xylim_supported = False + return ccrs.EuroPP(**proj_dict) + elif user_proj == 'Geostationary': + return ccrs.Geostationary(**proj_dict) + elif user_proj == 'NearsidePerspective': + return ccrs.NearsidePerspective(**proj_dict) + elif user_proj == 'EckertI': + return ccrs.EckertI(**proj_dict) + elif user_proj == 'EckertII': + return ccrs.EckertII(**proj_dict) + elif user_proj == 'EckertIII': + return ccrs.EckertIII(**proj_dict) + elif user_proj == 'EckertIV': + return ccrs.EckertIV(**proj_dict) + elif user_proj == 'EckertV': + return ccrs.EckertV(**proj_dict) + elif user_proj == 'EckertVI': + return ccrs.EckertVI(**proj_dict) + elif user_proj == 'EqualEarth': + return ccrs.EqualEarth(**proj_dict) + elif user_proj == 'Gnomonic': + return ccrs.Gnomonic(**proj_dict) + elif user_proj == 'LambertAzimuthalEqualArea': + return ccrs.LambertAzimuthalEqualArea(**proj_dict) + elif user_proj == 'NorthPolarStereo': + return ccrs.NorthPolarStereo(**proj_dict) + elif user_proj == 'OSNI': + return ccrs.OSNI(**proj_dict) + elif user_proj == 'SouthPolarStereo': + return ccrs.SouthPolarStereo(**proj_dict) + + def plot(self, ts=None): + if self.shift: + if self.longitude == 'longitude': + self.dset = self.dset.assign_coords( + longitude=((( + self.dset[self.longitude] + + 180) % 360) - 180)) + elif self.longitude == 'lon': + self.dset = self.dset.assign_coords( + lon=(((self.dset[self.longitude] + + 180) % 360) - 180)) + + pyplot.figure(1, figsize=[20, 10]) + + # Set the projection to use for plotting + ax = pyplot.subplot(1, 1, 1, projection=self.projection()) + if self.land: + ax.add_feature(feature.LAND, alpha=self.land) + + if self.ocean: + ax.add_feature(feature.OCEAN, alpha=self.ocean) + if self.coastline: + ax.coastlines(resolution='10m', alpha=self.coastline) + if self.borders: + ax.add_feature(feature.BORDERS, linestyle=':', alpha=self.borders) + + if self.xlim: + min_lon = min(self.xlim[0], self.xlim[1]) + max_lon = max(self.xlim[0], self.xlim[1]) + else: + min_lon = self.dset[self.longitude].min() + max_lon = self.dset[self.longitude].max() + + if self.ylim: + min_lat = min(self.ylim[0], self.ylim[1]) + max_lat = max(self.ylim[0], self.ylim[1]) + else: + min_lat = self.dset[self.latitude].min() + max_lat = self.dset[self.latitude].max() + + if self.xylim_supported: + pyplot.xlim(min_lon, max_lon) + pyplot.ylim(min_lat, max_lat) + + # Fix extent + if self.threshold == "" or self.threshold is None: + threshold = self.dset[self.varname].min() + else: + threshold = float(self.threshold) + + if self.range == []: + minval = self.dset[self.varname].min() + maxval = self.dset[self.varname].max() + else: + minval = self.range[0] + maxval = self.range[1] + + if self.verbose: + print("minval: ", minval) + print("maxval: ", maxval) + + # pass extent with vmin and vmax parameters + proj_t = ccrs.PlateCarree() + if ts is None: + self.dset.where( + self.dset[self.varname] > threshold + )[self.varname].plot(ax=ax, + vmin=minval, + vmax=maxval, + transform=proj_t, + cmap=self.cmap, + cbar_kwargs=self.label + ) + if self.title != "" and self.title is not None: + pyplot.title(self.title) + pyplot.savefig(self.output) + else: + if self.colorbar: + self.dset.where( + self.dset[self.varname] > threshold + )[self.varname].isel(time=ts).plot(ax=ax, + vmin=minval, + vmax=maxval, + transform=proj_t, + cmap=self.cmap, + cbar_kwargs=self.label + ) + else: + self.dset.where( + self.dset[self.varname] > minval + )[self.varname].isel(time=ts).plot(ax=ax, + vmin=minval, + vmax=maxval, + transform=proj_t, + cmap=self.cmap, + add_colorbar=False) + if self.title != "" and self.title is not None: + pyplot.title(self.title + "(time = " + str(ts) + ')') + pyplot.savefig(self.output[:-4] + "_time" + str(ts) + + self.output[-4:]) # assume png format + + +if __name__ == '__main__': + warnings.filterwarnings("ignore") + parser = argparse.ArgumentParser() + parser.add_argument( + 'input', + help='input filename with geographical coordinates (netCDF format)' + ) + + parser.add_argument( + '--proj', + help='Specify the projection on which we draw' + ) + parser.add_argument( + 'varname', + help='Specify which variable to plot (case sensitive)' + ) + parser.add_argument( + '--cmap', + help='Specify which colormap to use for plotting' + ) + parser.add_argument( + '--output', + help='output filename to store resulting image (png format)' + ) + parser.add_argument( + '--time', + help='list of times to plot for multiple plots' + ) + parser.add_argument( + '--title', + help='plot title' + ) + parser.add_argument( + '--latitude', + help='variable name for latitude' + ) + parser.add_argument( + '--longitude', + help='variable name for longitude' + ) + parser.add_argument( + '--land', + help='add land on plot with alpha value [0-1]' + ) + parser.add_argument( + '--ocean', + help='add oceans on plot with alpha value [0-1]' + ) + parser.add_argument( + '--coastline', + help='add coastline with alpha value [0-1]' + ) + parser.add_argument( + '--borders', + help='add country borders with alpha value [0-1]' + ) + parser.add_argument( + '--xlim', + help='limited geographical area longitudes "x1,x2"' + ) + parser.add_argument( + '--ylim', + help='limited geographical area latitudes "y1,y2"' + ) + parser.add_argument( + '--range', + help='min and max values for plotting "minval,maxval"' + ) + parser.add_argument( + '--threshold', + help='do not plot values below threshold' + ) + parser.add_argument( + '--label', + help='set a label for colorbar' + ) + parser.add_argument( + '--shift', + help='shift longitudes if specified', + action="store_true" + ) + parser.add_argument( + "-v", "--verbose", + help="switch on verbose mode", + action="store_true") + args = parser.parse_args() + + if args.time is None: + time = [] + else: + time = list(map(int, args.time.split(","))) + if args.xlim is None: + xlim = [] + else: + xlim = list(map(float, args.xlim.split(","))) + if args.ylim is None: + ylim = [] + else: + ylim = list(map(float, args.ylim.split(","))) + if args.range is None: + range_values = [] + else: + range_values = list(map(float, args.range.split(","))) + if args.latitude is None: + latitude = "latitude" + else: + latitude = args.latitude + if args.longitude is None: + longitude = "longitude" + else: + longitude = args.longitude + if args.land is None: + land = 0 + else: + land = float(args.land) + if args.ocean is None: + ocean = 0 + else: + ocean = float(args.ocean) + if args.coastline is None: + coastline = 0 + else: + coastline = float(args.coastline) + if args.borders is None: + borders = 0 + else: + borders = float(args.borders) + + dset = MapPlotXr(input=args.input, proj=args.proj, varname=args.varname, + cmap=args.cmap, output=args.output, verbose=args.verbose, + time=time, title=args.title, + latitude=latitude, longitude=longitude, land=land, + ocean=ocean, coastline=coastline, borders=borders, + xlim=xlim, ylim=ylim, threshold=args.threshold, + label=args.label, shift=args.shift, + range_values=range_values) + + if dset.time == []: + dset.plot() + else: + for t in dset.time: + dset.plot(t) + dset.shift = False # only shift once + dset.colorbar = True diff -r e8650cdf092f -r 663268794710 xarray_metadata_info.xml --- a/xarray_metadata_info.xml Sat Oct 31 11:00:53 2020 +0000 +++ b/xarray_metadata_info.xml Sun Jun 06 08:49:43 2021 +0000 @@ -1,11 +1,15 @@ - + summarize content of a Netcdf file + + macros.xml + + python - netcdf4 - xarray - geopandas - shapely + netcdf4 + xarray + geopandas + shapely - - topic_0610 - topic_3050 - - + diff -r e8650cdf092f -r 663268794710 xarray_netcdf2netcdf.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/xarray_netcdf2netcdf.py Sun Jun 06 08:49:43 2021 +0000 @@ -0,0 +1,133 @@ +#!/usr/bin/env python3 +# +# Apply operations on selected variables +# - scale +# one can also select the range of time (for timeseries) +# to apply these operations over the range only +# when a range of time is selected and when scaling, one +# can choose to save the entire timeseries or +# the selected range only. +# when scaling, one can add additional filters on dimensions +# (typically used to filter over latitudes and longitudes) + + +import argparse +import warnings + +import xarray as xr # noqa: E402 + + +class netCDF2netCDF (): + def __init__(self, infile, varname, scale="", + output="output.netcdf", + write_all=False, + filter_list="", + verbose=False): + self.infile = infile + self.verbose = verbose + self.varname = varname + self.write_all = write_all + self.filter = filter_list + self.selection = {} + if scale == "" or scale is None: + self.scale = 1 + else: + self.scale = float(scale) + if output is None: + self.output = "output.netcdf" + else: + self.output = output + # initialization + self.dset = None + self.subset = None + if self.verbose: + print("infile: ", self.infile) + print("varname: ", self.varname) + print("filter_list: ", self.filter) + print("scale: ", self.scale) + print("write_all: ", self.write_all) + print("output: ", self.output) + + def dimension_selection(self, single_filter): + split_filter = single_filter.split('#') + dimension_varname = split_filter[0] + op = split_filter[1] + ll = int(split_filter[2]) + if (op == 'sl'): + rl = int(split_filter[3]) + self.selection[dimension_varname] = slice(ll, rl) + elif (op == 'to'): + self.selection[dimension_varname] = slice(None, ll) + elif (op == 'from'): + self.selection[dimension_varname] = slice(ll, None) + elif (op == 'is'): + self.selection[dimension_varname] = ll + + def filter_selection(self): + for single_filter in self.filter: + self.dimension_selection(single_filter) + if self.write_all: + self.ds[self.varname] = \ + self.ds[self.varname].isel(self.selection)*self.scale + else: + self.dset = \ + self.ds[self.varname].isel(self.selection)*self.scale + + def compute(self): + if self.dset is None: + self.ds = xr.open_dataset(self.infile) + if self.filter: + self.filter_selection() + if self.verbose: + print(self.selection) + elif self.write_all is not None: + self.dset = self.ds[self.varname] + + def save(self): + if self.write_all: + self.ds.to_netcdf(self.output) + else: + self.dset.to_netcdf(self.output) + + +if __name__ == '__main__': + warnings.filterwarnings("ignore") + parser = argparse.ArgumentParser() + parser.add_argument( + 'input', + help='input filename in netCDF format' + ) + parser.add_argument( + 'varname', + help='Specify which variable to plot (case sensitive)' + ) + parser.add_argument( + '--filter', + nargs="*", + help='Filter list variable#operator#value_s#value_e' + ) + parser.add_argument( + '--output', + help='Output filename to store the resulting netCDF file' + ) + parser.add_argument( + '--scale', + help='scale factor to apply to selection (float)' + ) + parser.add_argument( + "--write_all", + help="write all data to netCDF", + action="store_true") + parser.add_argument( + "-v", "--verbose", + help="switch on verbose mode", + action="store_true") + args = parser.parse_args() + + dset = netCDF2netCDF(infile=args.input, varname=args.varname, + scale=args.scale, output=args.output, + filter_list=args.filter, + write_all=args.write_all, + verbose=args.verbose) + dset.compute() + dset.save() diff -r e8650cdf092f -r 663268794710 xarray_tool.py --- a/xarray_tool.py Sat Oct 31 11:00:53 2020 +0000 +++ b/xarray_tool.py Sun Jun 06 08:49:43 2021 +0000 @@ -4,6 +4,7 @@ import argparse import csv +import os import warnings import geopandas as gdp @@ -21,8 +22,8 @@ select="", outfile="", outputdir="", latname="", latvalN="", latvalS="", lonname="", lonvalE="", lonvalW="", filter_list="", coords="", time="", - verbose=False - ): + verbose=False, no_missing=False, coords_info=None, + tolerance=None): self.infile = infile self.outfile_info = outfile_info self.outfile_summary = outfile_summary @@ -30,6 +31,10 @@ 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: @@ -51,9 +56,11 @@ 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) @@ -71,6 +78,7 @@ 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') @@ -113,7 +121,9 @@ 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)) + 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': @@ -141,9 +151,21 @@ self.filter_selection() self.area_selection() - # convert to dataframe - self.gset = self.gset.to_dataframe().dropna(how='all').reset_index() - self.gset.to_csv(self.outfile, header=True, sep='\t') + 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('#') @@ -165,6 +187,7 @@ self.rowfilter(single_filter) def area_selection(self): + if self.latvalS != "" and self.lonvalW != "": # Select geographical area self.gset = self.dset.sel({self.latname: @@ -173,10 +196,21 @@ slice(self.lonvalW, self.lonvalE)}) elif self.latvalN != "" and self.lonvalE != "": # select nearest location - self.nearest_location() # find nearest location without NaN values - self.gset = self.dset.sel({self.latname: self.nearest_latvalN, - self.lonname: self.nearest_lonvalE}, - method='nearest') + 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 @@ -206,9 +240,21 @@ for row in fcoords.itertuples(): self.latvalN = row[0] self.lonvalE = row[1] - self.outfile = (self.outputdir + '/' + self.select + '_' + str(row.Index) + '.tabular') + 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") @@ -255,11 +301,21 @@ 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' @@ -283,13 +339,20 @@ 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.coords, args.time, args.verbose, + args.no_missing, args.coords_info, args.tolerance) if args.info: p.info() if args.summary: @@ -298,3 +361,5 @@ p.selection_from_coords() elif args.select: p.selection() + elif args.coords_info: + p.get_coords_info()