Source code for salishsea_tools.diagnosis_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

#    https://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""
"""

import numpy as np
import numpy.ma as ma
import netCDF4 as nc
from salishsea_tools import nc_tools


__all__ = [
    'pcourantu', 'pcourantv','pcourantw'
]

[docs]def pcourantu(files,meshmask): """Given a list of U filenames and a mesh mask, returns an array with the unscaled Courant numbers. :arg files: list of U filenames :arg meshmask: mesh mask :type meshmask: :py:class:`netCDF4.Dataset` :returns: Numpy MaskedArray with unscaled Courant numbers. :rtype: :py:class: `numpy.ma.core.MaskedArray` """ delta_x = meshmask['e1u'][:] with nc_tools.scDataset(files) as f: #merging files nt,nz,ny,nx = f.variables['vozocrtx'].shape umax = np.zeros((nz,ny,nx)) for n in range(nt): utmp = np.abs(f.variables['vozocrtx'][n,:,:,:]) umax = np.maximum(utmp,umax) #taking maximum over time ubdxmax = np.zeros((ny,nx)) for m in range(nz): ubdxtmp = umax[m,...] / delta_x[0,...] ubdxmax = np.maximum(ubdxtmp,ubdxmax) #taking maximum over depth umask = meshmask['umask'][0,0,...] return ma.masked_array(ubdxmax, mask = 1-umask)
[docs]def pcourantv(files,meshmask): """Given a list of V filenames and a mesh mask, returns an array with the unscaled Courant numbers. :arg files: list of V filenames :arg meshmask: mesh mask :type meshmask: :py:class:`netCDF4.Dataset` :returns: Numpy MaskedArray with unscaled Courant numbers. :rtype: :py:class: `numpy.ma.core.MaskedArray` """ delta_y = meshmask['e2v'][:] with nc_tools.scDataset(files) as f: #merging files nt,nz,ny,nx = f.variables['vomecrty'].shape vmax = np.zeros((nz,ny,nx)) for n in range(nt): vtmp = np.abs(f.variables['vomecrty'][n,:,:,:]) vmax = np.maximum(vtmp,vmax) #taking maximum over time vbdymax = np.zeros((ny,nx)) for m in range(nz): vbdytmp = vmax[m,...] / delta_y[0,...] vbdymax = np.maximum(vbdytmp,vbdymax) #taking maximum over depth vmask = meshmask['vmask'][0,0,...] return ma.masked_array(vbdymax, mask = 1-vmask)
[docs]def pcourantw(files,meshmask): """Given a list of W filenames and a mesh mask, returns an array with the unscaled Courant numbers. :arg files: list of W filenames :arg meshmask: mesh mask :type meshmask: :py:class:`netCDF4.Dataset` :returns: Numpy MaskedArray with unscaled Courant numbers. :rtype: :py:class: `numpy.ma.core.MaskedArray` """ with nc_tools.scDataset(files) as f: #merging files nt,nz,ny,nx = f.variables['vovecrtz'].shape delta_z = meshmask['e3w_1d'][0,...] delta_z = delta_z[:,np.newaxis,np.newaxis] wmax = np.zeros((nz,ny,nx)) for n in range(nt): wtmp = np.abs(f.variables['vovecrtz'][n,:,:,:]) wmax = np.maximum(wtmp,wmax) #taking maximum over time wbdz = wmax / delta_z wbdzmax = np.zeros((ny,nx)) for m in range(nz): wbdztmp = wbdz[m,...] wbdzmax = np.maximum(wbdztmp,wbdzmax) #taking maximum over depth tmask = meshmask['tmask'][0,0,...] return ma.masked_array(wbdzmax, mask = 1-tmask)