Source code for compass.ocean.tests.sphere_transport.divergent_2d.analysis

import numpy as np
from compass.step import Step
from ..process_output import *
from netCDF4 import Dataset

[docs]class Analysis(Step): """ A step for visualizing the output from the divergent2D test case Attributes ---------- resolutions : list of int The resolutions of the meshes that have been run """
[docs] def __init__(self, test_case, resolutions): """ Create the step Parameters ---------- test_case : compass.ocean.tests.sphere_transport.divergent_2d.divergent2D The test case this step belongs to resolutions : list of int The resolutions of the meshes that have been run """ super().__init__(test_case=test_case, name='analysis') self.resolutions = resolutions self.tcdata = dict() for resolution in resolutions: self.add_input_file( filename='QU{}_namelist.ocean'.format(resolution), target='../QU{}/init/namelist.ocean'.format(resolution)) self.add_input_file( filename='QU{}'.format(resolution), target='../QU{}/init/'.format(resolution)) self.add_input_file( filename='QU{}'.format(resolution), target='../QU{}/forward/'.format(resolution)) self.add_output_file('divergent2D_QU{}_sol.pdf'.format(resolution)) self.add_output_file('divergent2D_convergence.pdf')
[docs] def run(self): """ Run this step of the test case """ ### # Collect data ### for resolution in self.resolutions: ncd = Dataset('../QU{}/forward/'.format(resolution)) self.tcdata[resolution] = {'dataset': ncd} self.tcdata[resolution]['appx_mesh_size'] = appx_mesh_size(ncd) self.tcdata[resolution]['err'] = compute_error_from_output_ncfile( ncd) print_data_as_csv('divergent2D', self.tcdata) ### # Plot solutions ### # plt.rc('text', usetex=True) # .tex fails on Anvil plt.rc('font', family='sans-serif') plt.rc('ps', useafm=True) plt.rc('pdf', use14corefonts=True) for r in self.tcdata.keys(): tcstr = 'divergent2D_QU{}'.format(r) fig = plt.figure(constrained_layout=True) plot_sol(fig, tcstr, self.tcdata[r]['dataset']) fig.savefig(tcstr + "_sol.pdf", bbox_inches='tight') plt.close(fig) ### # convergence analysis ### dlambda, linf1, linf2, linf3, l21, l22, l23, _, u1, o1, u2, o2, \ u3, o3, mass1, mass2, mass3 = make_convergence_arrays(self.tcdata) linfrate, l2rate = compute_convergence_rates(dlambda, linf1, l21) rvals = sorted(self.tcdata.keys()) rvals.reverse() print_error_conv_table( 'divergent2D', rvals, dlambda, l21, l2rate, linf1, linfrate) fig, ax = plt.subplots() plot_convergence( ax, 'divergent2D', dlambda, rvals, linf1, l21, linf2, l22, linf3, l23) fig.savefig('divergent2D_convergence.pdf', bbox_inches='tight') plt.close(fig) section = self.config['divergent_2d'] all_above_thres = True error_message = '' for tracer in ['tracer1', 'tracer2', 'tracer3']: conv_thresh = section.getfloat(f'{tracer}_conv_thresh') l2_err = list() ncells = list() for resolution in self.resolutions: data = self.tcdata[resolution] l2_err.append(data['err'][tracer]['l2']) ncells.append(len(data['dataset'].dimensions["nCells"])) l2_err = np.array(l2_err) ncells = np.array(ncells) p = np.polyfit(np.log10(ncells), np.log10(l2_err), 1) # factor of 2 because nCells is like an inverse area, and we # want the convergence rate vs. cell size conv = abs(p[0]) * 2.0 if conv < conv_thresh: all_above_thres = False error_message = \ f'{error_message}\n' \ f' {tracer}: {conv:.2f} < {conv_thresh}' if not all_above_thres: raise ValueError('The following tracers have order of convergence ' '< min tolerance:' + error_message)