Mercurial > repos > ecology > xarray_metadata_info
changeset 1:7edbe5ae8b72 draft
"planemo upload for repository https://github.com/galaxyecology/tools-ecology/tree/master/tools/data_manipulation/xarray/ commit f1455c158011dc4aab0fd469cf794be6f4142992"
| author | ecology |
|---|---|
| date | Fri, 22 May 2020 05:18:55 -0400 |
| parents | 965fcab0cd9f |
| children | e8650cdf092f |
| files | README.md test-data/Test1.tabular test-data/Test2.tabular test-data/var_tab_dataset-ibi xarray_tool.py |
| diffstat | 5 files changed, 488 insertions(+), 0 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/README.md Fri May 22 05:18:55 2020 -0400 @@ -0,0 +1,8 @@ +# Xarray tools for netCDF +## netCDF metadata information + +The first tool `xarray_metadata_info ` uses xarray to provide users with general information about variable names, dimensions +and attributes. +Variables that can be extracted and dimensions available are printed in a tabular file. + +The tool also print a general information file. It's the result of the xarray method info().
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/Test1.tabular Fri May 22 05:18:55 2020 -0400 @@ -0,0 +1,146 @@ + time depth longitude latitude phy +0 2002-12-15 0.5057600140571594 -2.0000007 44.0 1.0500183 +1 2003-01-15 0.5057600140571594 -2.0000007 44.0 1.25 +2 2003-02-15 0.5057600140571594 -2.0000007 44.0 1.3000183 +3 2003-03-15 0.5057600140571594 -2.0000007 44.0 6.0599976 +4 2003-04-15 0.5057600140571594 -2.0000007 44.0 2.25 +5 2003-05-15 0.5057600140571594 -2.0000007 44.0 0.6499939 +6 2003-06-15 0.5057600140571594 -2.0000007 44.0 0.42999268 +7 2003-07-15 0.5057600140571594 -2.0000007 44.0 0.42999268 +8 2003-08-15 0.5057600140571594 -2.0000007 44.0 0.480011 +9 2003-09-15 0.5057600140571594 -2.0000007 44.0 0.45999146 +10 2003-10-15 0.5057600140571594 -2.0000007 44.0 0.5 +11 2003-11-15 0.5057600140571594 -2.0000007 44.0 0.9299927 +12 2003-12-15 0.5057600140571594 -2.0000007 44.0 1.3900146 +13 2004-01-15 0.5057600140571594 -2.0000007 44.0 1.7400208 +14 2004-02-15 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/Test2.tabular Fri May 22 05:18:55 2020 -0400 @@ -0,0 +1,25 @@ + time depth latitude longitude nh4 +0 2003-12-15 0.5057600140571594 45.166664123535156 -0.6666674017906189 81.27 +1 2003-12-15 0.5057600140571594 45.416664123535156 -0.8333340883255005 78.08 +2 2003-12-15 0.5057600140571594 45.5 -0.9166674017906189 55.149998 +3 2004-01-15 0.5057600140571594 45.166664123535156 -0.6666674017906189 65.2 +4 2004-01-15 0.5057600140571594 45.416664123535156 -0.8333340883255005 64.11 +5 2004-02-15 0.5057600140571594 45.166664123535156 -0.6666674017906189 51.0 +6 2004-02-15 0.5057600140571594 45.416664123535156 -0.8333340883255005 51.32 +7 2004-05-15 0.5057600140571594 45.166664123535156 -0.6666674017906189 54.53 +8 2004-06-15 0.5057600140571594 45.166664123535156 -0.6666674017906189 79.79 +9 2004-06-15 0.5057600140571594 45.416664123535156 -0.8333340883255005 61.52 +10 2004-07-15 0.5057600140571594 45.166664123535156 -0.6666674017906189 99.159996 +11 2004-07-15 0.5057600140571594 45.416664123535156 -0.8333340883255005 77.93 +12 2004-08-15 0.5057600140571594 45.166664123535156 -0.6666674017906189 110.149994 +13 2004-08-15 0.5057600140571594 45.416664123535156 -0.8333340883255005 86.759995 +14 2004-09-15 0.5057600140571594 45.166664123535156 -0.6666674017906189 112.369995 +15 2004-09-15 0.5057600140571594 45.416664123535156 -0.8333340883255005 91.979996 +16 2004-10-15 0.5057600140571594 45.166664123535156 -0.6666674017906189 109.63 +17 2004-10-15 0.5057600140571594 45.416664123535156 -0.8333340883255005 95.509995 +18 2004-11-15 0.5057600140571594 45.166664123535156 -0.6666674017906189 98.45 +19 2004-11-15 0.5057600140571594 45.416664123535156 -0.8333340883255005 93.11 +20 2004-11-15 0.5057600140571594 45.5 -0.9166674017906189 56.78 +21 2004-12-15 0.5057600140571594 45.166664123535156 -0.6666674017906189 84.25 +22 2004-12-15 0.5057600140571594 45.416664123535156 -0.8333340883255005 81.83 +23 2004-12-15 0.5057600140571594 45.5 -0.9166674017906189 57.07
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/var_tab_dataset-ibi Fri May 22 05:18:55 2020 -0400 @@ -0,0 +1,7 @@ +time 1 time 145 +chl 4 time 145 depth 1 latitude 97 longitude 103 +nh4 4 time 145 depth 1 latitude 97 longitude 103 +longitude 1 longitude 103 +latitude 1 latitude 97 +depth 1 depth 1 +phy 4 time 145 depth 1 latitude 97 longitude 103
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/xarray_tool.py Fri May 22 05:18:55 2020 -0400 @@ -0,0 +1,302 @@ +# xarray tool for: +# - getting metadata information +# - select data and save results in csv file for further post-processing + +import argparse +import csv +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 + ): + 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 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 + # 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("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 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() + # convert to dataframe + self.gset = self.gset.to_dataframe().dropna(how='all').reset_index() + self.gset.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 + 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') + 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 = (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( + '--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( + '--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" + ) + 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) + if args.info: + p.info() + if args.summary: + p.summary() + if args.coords: + p.selection_from_coords() + elif args.select: + p.selection()