Mercurial > repos > ecology > xarray_metadata_info
diff timeseries.py @ 5:00de53d18b99 draft default tip
planemo upload for repository https://github.com/galaxyecology/tools-ecology/tree/master/tools/data_manipulation/xarray/ commit fd8ad4d97db7b1fd3876ff63e14280474e06fdf7
author | ecology |
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date | Sun, 31 Jul 2022 21:22:03 +0000 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/timeseries.py Sun Jul 31 21:22:03 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()