Source code for salishsea_tools.timeseries_tools

# Copyright 2013-2017 The Salish Sea MEOPAR contributors
# and The University of British Columbia

# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at


# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

"""A library of Python functions for loading SalishSeaCast timeseries while
conserving memory.

from salishsea_tools import viz_tools, utilities
from dateutil.parser import parse
from datetime import timedelta
import xarray as xr
import numpy as np
import os

[docs]def load_NEMO_timeseries( filenames, mask, field, dim, index=0, spacing=1, shape='grid', unstagger_dim=None ): """ """ # Reshape mask, grid, and depth tmask, coords, ngrid, ngrid_water = reshape_coords( mask, dim, index=index, spacing=spacing) # Initialize output array date = np.empty(0, dtype='datetime64[ns]') data = np.empty((0, ngrid_water)) # Loop through filenames bar = utilities.statusbar( 'Loading {}, {}={}'.format(field, dim, index), width=90 ) for findex, filename in enumerate(bar(filenames)): # Open NEMO results and flatten (depth averages added here) data_grid = xr.open_dataset(filename)[field].isel(**{dim: index}) # Unstagger if velocity field if unstagger_dim is not None: data_grid = viz_tools.unstagger_xarray(data_grid, unstagger_dim) # Reshape field data_trim = reshape_to_ts( data_grid.values, tmask, ngrid, ngrid_water, spacing=spacing) # Store trimmed arrays date = np.concatenate([date, data_grid.time_counter.values]) data = np.concatenate([data, data_trim], axis=0) # Reshape to grid if shape is 'grid': # Correct for depth dimension name if dim.find('depth') is not -1: dim1, dim2, dimslice = 'gridY', 'gridX', 'z' elif dim.find('y') is not -1: dim1, dim2, dimslice = 'gridZ', 'gridX', 'y' elif dim.find('x') is not -1: dim1, dim2, dimslice = 'gridZ', 'gridY', 'x' # Reshape data to grid data = reshape_to_grid( data, [coords[dim1], coords[dim2]], mask.gdept_0.isel(**{'t': 0, dimslice: 0}).shape ) # Redefine coords for grid coords = { 'depth': mask.gdept_1d.isel(t=0).values, 'gridZ': mask.z.values, 'gridY': mask.y.values, 'gridX': mask.x.values } # Coords dict coords['date'] = date return data, coords
[docs]def make_filename_list( timerange, qty, model='nowcast', resolution='h', path='/results/SalishSea' ): """Return a sequential list of Nowcast results filenames to be passed into `xarray.open_mfdataset` or `timeseries_tools.load_NEMO_timeseries`. :arg timerange: list or tuple of date strings (e.g., ['2017 Jan 1 00:00', '2017 Jan 31 23:00']) :type timerange: list or tuple of str :arg qty: quantity type ('U' for zonal velocity, 'V' for meridional velocity, 'W' for vertical velocity, 'T' for tracers) :type qty: str :arg model: forecast type (e.g., 'nowcast', 'nowcast-green', 'forecast') :type model: str :arg resolution: time resolution ('h' for hourly, 'd', for daily) :type resolution: str :arg path: path to results archive :type path: str :returns: Sequential list of Nowcast results filenames :rtype: list of str """ date, enddate = map(parse, timerange) filenames = [] while date < enddate: datestr1 = date.strftime('%d%b%y').lower() datestr2 = date.strftime('%Y%m%d') filename = 'SalishSea_1{}_{}_{}_grid_{}.nc'.format( resolution, datestr2, datestr2, qty) filenames.append(os.path.join(path, model, datestr1, filename)) date = date + timedelta(days=1) return filenames
[docs]def reshape_coords(mask_in, dim_in, index=0, spacing=1): """Prepare the mask and grid for the selected timeseries slice, and reshape into 1 spatial dimension """ # Correct for depth dimension name if dim_in.find('depth') is not -1: dim = 'deptht' else: dim = dim_in # Create full gridded mask, grid and depth Numpy ndarrays gridZ, gridY, gridX = np.meshgrid( mask_in.z, mask_in.y, mask_in.x, indexing='ij') gridmask = xr.Dataset({ 'tmask': ( ['deptht', 'y', 'x'], mask_in.tmask.isel(t=0).values.astype(bool), ), 'depth': (['deptht', 'y', 'x'], mask_in.gdept_0.isel(t=0).values), 'gridZ': (['deptht', 'y', 'x'], gridZ), 'gridY': (['deptht', 'y', 'x'], gridY), 'gridX': (['deptht', 'y', 'x'], gridX)}, coords={'deptht': mask_in.gdept_1d.isel(t=0).values, 'y': mask_in.y, 'x': mask_in.x}) # Slice and subsample mask mask = gridmask.tmask.isel(**{dim: index}).values[::spacing, ::spacing] # Slice and subsample grid and depth into dict coords = { 'depth': gridmask.depth.isel(**{dim: index}).values[::spacing, ::spacing], 'gridZ': gridmask.gridZ.isel(**{dim: index}).values[::spacing, ::spacing], 'gridY': gridmask.gridY.isel(**{dim: index}).values[::spacing, ::spacing], 'gridX': gridmask.gridX.isel(**{dim: index}).values[::spacing, ::spacing], } # Number of grid points ngrid = mask.shape[0] * mask.shape[1] ngrid_water = mask.sum() # Reshape mask, grid, and depth mask = mask.reshape(ngrid) coords['depth'] = coords['depth'].reshape(ngrid)[mask] coords['gridZ'] = coords['gridZ'].reshape(ngrid)[mask] coords['gridY'] = coords['gridY'].reshape(ngrid)[mask] coords['gridX'] = coords['gridX'].reshape(ngrid)[mask] return mask, coords, ngrid, ngrid_water
[docs]def reshape_coords_GEM(grid, mask_in): """ """ coords = {} # Create full gridded mask, grid and depth Numpy ndarrays coords['gridY'], coords['gridX'] = np.meshgrid( grid.y, grid.x, indexing='ij') # Number of grid points ngrid = mask_in.shape[0] * mask_in.shape[1] ngrid_water = mask_in.sum() # Reshape mask, grid, and depth mask = mask_in.reshape(ngrid) coords['gridY'] = coords['gridY'].reshape(ngrid)[mask.astype(bool)] coords['gridX'] = coords['gridX'].reshape(ngrid)[mask.astype(bool)] return mask, coords, ngrid, ngrid_water
[docs]def reshape_to_ts(data_grid, mask, ngrid, ngrid_water, spacing=1): """ """ # Convert to Numpy ndarray, subsample, and reshape data_flat = data_grid[:, ::spacing, ::spacing].reshape((-1, ngrid)) # Preallocate trimmed array data_trim = np.zeros((data_flat.shape[0], ngrid_water)) # Trim land points for tindex, data_t in enumerate(data_flat): data_trim[tindex, :] = data_t[mask] return data_trim
[docs]def reshape_to_grid(data_flat, coords, shape): """Given a flattened array of data with the corresponding Y and X coordinates and the desired grid shape, return the grid of desired shape with the data given. Assumes flattened array has a time dimension as first dimension. :arg data_flat: 2d array of data. First dimension is assumed to be time. :arg coords: List of form [Ycoords, Xcoords] for each data point given. :arg shape: 2d tuple corresponding to desired grid shape. For Salish Sea model, shape would be (898,398). :returns: Array of with dimensions corresponding to shape given with data in coordinates given. """ # Preallocate gridded array data_grid = np.zeros((data_flat.shape[0],) + shape) # Reshape flattened data to grid for coord1, coord2, data_xyz in zip(*(coords + [data_flat.T])): data_grid[:, coord1, coord2] = data_xyz return data_grid