Source code for compass.ocean.tests.baroclinic_channel.rpe_test.analysis

import numpy as np
import xarray
import matplotlib.pyplot as plt
import cmocean

from compass.step import Step
from compass.ocean.rpe import compute_rpe

[docs] class Analysis(Step): """ A step for plotting the results of a series of RPE runs in the baroclinic channel test group Attributes ---------- resolution : str The resolution of the test case nus : list of float A list of viscosities """
[docs] def __init__(self, test_case, resolution, nus): """ Create the step Parameters ---------- test_case : compass.TestCase The test case this step belongs to resolution : str The resolution of the test case nus : list of float A list of viscosities """ super().__init__(test_case=test_case, name='analysis') self.resolution = resolution self.nus = nus self.add_input_file( filename='', target='../initial_state/') for index, nu in enumerate(nus): self.add_input_file( filename=f'output_{index+1}.nc', target=f'../rpe_test_{index+1}_nu_{int(nu)}/') self.add_output_file( filename=f'sections_baroclinic_channel_{resolution}.png') self.add_output_file(filename='rpe_t.png')
[docs] def run(self): """ Run this step of the test case """ section = self.config['baroclinic_channel'] nx = section.getint('nx') ny = section.getint('ny') rpe = compute_rpe() _plot(nx, ny, self.outputs[0], self.nus, rpe)
def _plot(nx, ny, filename, nus, rpe): """ Plot section of the baroclinic channel at different viscosities Parameters ---------- nx : int The number of cells in the x direction ny : int The number of cells in the y direction (before culling) filename : str The output file name nus : list of float The viscosity values rpe : numpy.ndarray The reference potential energy with size len(nu) x len(time) """ plt.switch_backend('Agg') nanosecondsPerDay = 8.64e13 num_files = len(nus) time = 20 ds = xarray.open_dataset('') times = ds.daysSinceStartOfSim.values times = times.tolist() times = np.divide(times, nanosecondsPerDay) fig = plt.figure() for i in range(num_files): rpe_norm = np.divide((rpe[i, :]-rpe[i, 0]), rpe[i, 0]) plt.plot(times, rpe_norm, label=f"$\\nu_h=${nus[i]}") plt.xlabel('Time, days') plt.ylabel('RPE-RPE(0)/RPE(0)') plt.legend() plt.savefig('rpe_t.png') plt.close(fig) fig, axs = plt.subplots(1, num_files, figsize=( 2.1 * num_files, 5.0), constrained_layout=True) for iCol in range(num_files): ds = xarray.open_dataset(f'output_{iCol + 1}.nc') times = ds.daysSinceStartOfSim.values times = np.divide(times.tolist(), nanosecondsPerDay) tidx = np.argmin(np.abs(times-time)) var = ds.temperature.values var1 = np.reshape(var[tidx, :, 0], [ny, nx]) # flip in y-dir var = np.flipud(var1) # Every other row in y needs to average two neighbors in x on # planar hex mesh var_avg = var for j in range(0, ny, 2): for i in range(1, nx - 2): var_avg[j, i] = (var[j, i + 1] + var[j, i]) / 2.0 ax = axs[iCol] dis = ax.imshow( var_avg, extent=[0, 160, 0, 500], cmap='cmo.thermal', vmin=11.8, vmax=13.0) ax.set_title(f'day {times[tidx]}, ' f'$\\nu_h=${nus[iCol]}') ax.set_xticks(np.arange(0, 161, step=40)) ax.set_yticks(np.arange(0, 501, step=50)) ax.set_xlabel('x, km') if iCol == 0: ax.set_ylabel('y, km') if iCol == num_files - 1: fig.colorbar(dis, ax=axs[num_files - 1], aspect=40) plt.savefig(filename)