Source code for act.io.neon

"""
Modules for reading in NOAA PSL data.
"""


import pandas as pd
import xarray as xr

from .text import read_csv


[docs]def read_neon_csv(files, variable_files=None, position_files=None): """ Reads in the NEON formatted csv files from local paths or urls and returns an Xarray dataset. Parameters ---------- filepath : list Files to read in variable_files : list Name of variable files to read with metadata. Optional but the Dataset will not have any metadata position_files : list Name of file to read with sensor positions. Optional, but the Dataset will not have any location information Return ------ ds : xarray.Dataset Standard Xarray dataset """ # Raise error if empty list is passed in if len(files) == 0: raise ValueError('File list is empty') if isinstance(files, str): files = [files] # Read in optional files multi_ds = [] if variable_files is not None: if isinstance(variable_files, str): variable_files = [variable_files] df = pd.read_csv(variable_files[0]) if position_files is not None: if isinstance(position_files, str): position_files = [position_files] loc_df = pd.read_csv(position_files[0], dtype=str) # Run through each file and read into a dataset for i, f in enumerate(files): ds = read_csv(f) # Create standard time variable time = [pd.to_datetime(t).replace(tzinfo=None) for t in ds['startDateTime'].values] ds['time'] = xr.DataArray(data=time, dims=['index']) ds['time'].attrs['units'] = '' ds = ds.swap_dims({'index': 'time'}) ds = ds.drop_vars('index') # Add some metadata site_code = f.split('/')[-1].split('.')[2] resolution = f.split('/')[-1].split('.')[9] hor_loc = f.split('/')[-1].split('.')[6] ver_loc = f.split('/')[-1].split('.')[7] ds.attrs['_sites'] = site_code ds.attrs['averaging_interval'] = resolution.split('_')[-1] ds.attrs['HOR.VER'] = hor_loc + '.' + ver_loc # Add in metadata from the variables file if variable_files is not None: for v in ds: dummy = df.loc[(df['table'] == resolution) & (df['fieldName'] == v)] ds[v].attrs['units'] = str(dummy['units'].values[0]) ds[v].attrs['long_name'] = str(dummy['description'].values[0]) ds[v].attrs['format'] = str(dummy['pubFormat'].values[0]) # Add in sensor position data if position_files is not None: dloc = loc_df.loc[loc_df['HOR.VER'] == hor_loc + '.' + ver_loc] idx = dloc.index.values if len(idx) > 0: ds['lat'] = xr.DataArray(data=float(loc_df['referenceLatitude'].values[idx])) ds['lon'] = xr.DataArray(data=float(loc_df['referenceLongitude'].values[idx])) ds['alt'] = xr.DataArray(data=float(loc_df['referenceElevation'].values[idx])) variables = [ 'xOffset', 'yOffset', 'zOffset', 'eastOffset', 'northOffset', 'pitch', 'roll', 'azimuth', 'xAzimuth', 'yAzimuth', ] for v in variables: ds[v] = xr.DataArray(data=float(loc_df[v].values[idx])) multi_ds.append(ds) ds = xr.merge(multi_ds) return ds