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"""
Utilities to provide numerical verification for the ISMIP test cases
"""
import os
import numpy as np
import matplotlib.pyplot as plt
import livvkit
from livvkit.util.LIVVDict import LIVVDict
from livvkit import elements
from livvkit.util import functions
case_color = {'bench': '#d7191c',
'test': '#fc8d59'}
line_style = {'bench': 'o-',
'test': '-'}
setup = None
[docs]def set_up():
global setup
setup = functions.read_json(os.path.join(os.path.dirname(__file__), 'ismip.json'))
for exp, size in [('ismip-hom-a', '005'), ('ismip-hom-c', '005'), ('ismip-hom-f', '000')]:
recreate_file = os.path.join(livvkit.__path__[0], setup[exp]["data_dir"],
setup[exp]['pattern'][0].replace('???', size))
setup[exp]['interp_points'] = \
np.genfromtxt(recreate_file, delimiter=',', missing_values='nan',
usecols=(0,), unpack=True)
if exp == 'ismip-hom-f':
setup[exp]['interp_points'] = setup[exp]['interp_points']*100 - 50
[docs]def get_case_length(case):
return str(int(case.split('-')[-1][1:])).zfill(3)
[docs]def run(config, analysis_data):
case = config['name']
if case in ['ismip-hom-a', 'ismip-hom-c', 'ismip-hom-f']:
coord = 'x_hat'
else:
coord = 'y_hat'
lengths = list(set(
[get_case_length(d) for d in analysis_data]
))
plot_list = []
for p, pattern in enumerate(sorted(setup[case]['pattern'])):
fig_label = pattern.split('_')[1]
description = ''
for l in sorted(lengths):
plt.figure(figsize=(10, 8), dpi=150)
plt.xlabel(setup[case]['xlabel'][p])
plt.ylabel(setup[case]['ylabel'][p])
if case in ['ismip-hom-a', 'ismip-hom-c']:
plt.title(str(int(l))+' km')
title = fig_label[0:-1]+'. '+fig_label[-1]+': '+str(int(l))+' km'
else:
plt.title('No-Slip Bed')
title = fig_label[0:-2]+'. '+fig_label[-2:]+': No-Slip Bed'
plot_file = os.path.join(config["plot_dir"], config['name']+'_'+fig_label+'_'+l+'.png')
recreate_file = os.path.join(
livvkit.__path__[0], setup[case]["data_dir"], pattern
).replace('???', l)
axis, fs_amin, fs_amax, fs_mean, fs_std, ho_amin, ho_amax, ho_mean, ho_std = \
np.genfromtxt(recreate_file, delimiter=',', missing_values='nan', unpack=True)
if case in ['ismip-hom-f']:
axis = axis*100.0 - 50.0
plt.fill_between(axis, ho_amin, ho_amax, facecolor='green', alpha=0.5)
plt.fill_between(axis, fs_amin, fs_amax, facecolor='blue', alpha=0.5)
plt.plot(axis, fs_mean, 'b-', linewidth=2, label='Full stokes')
plt.plot(axis, ho_mean, 'g-', linewidth=2, label='Higher order')
analysis = {}
for a in analysis_data:
if int(l) == int(a.split('-')[-1][1:]):
analysis[a] = analysis_data[a]
for a in analysis:
for model in sorted(analysis[a]):
plt.plot(analysis[a][model][coord],
analysis[a][model][config['plot_vars'][p]],
line_style[model],
color=case_color[model],
linewidth=2,
label=a+'-'+model)
plt.legend(loc='best')
plt.savefig(plot_file)
plt.close()
image = elements.Image(title, description, plot_file)
plot_list.append(image)
return elements.Gallery("Numerics Plots", plot_list)
[docs]def summarize_result(data, config):
case = config['name']
summary = LIVVDict()
lengths = list(set([get_case_length(d) for d in data]))
for p, pattern in enumerate(sorted(setup[case]['pattern'])):
for l in sorted(lengths):
recreate_file = os.path.join(
livvkit.__path__[0], setup[case]["data_dir"], pattern
).replace('???', l)
axis, fs_amin, fs_amax, fs_mean, fs_std, ho_amin, ho_amax, ho_mean, ho_std = \
np.genfromtxt(recreate_file, delimiter=',', missing_values='nan', unpack=True)
analysis = {}
for a in data:
if int(l) == int(a.split('-')[-1][1:]):
analysis[a] = data[a]
for a in analysis:
for model in sorted(analysis[a]):
if setup[case]['ylabel'][p].split(" ")[0].lower() == 'surface':
percent_errors = np.divide(analysis[a][model][config['plot_vars'][p]]
- ho_mean, ho_mean+1000)
coefficient = np.divide(ho_std, ho_mean+1000)
else:
percent_errors = np.divide(analysis[a][model][config['plot_vars'][p]]
- ho_mean, ho_mean)
coefficient = np.divide(ho_std, ho_mean)
label = a+' '+setup[case]['ylabel'][p].split(" ")[0]
if model.lower() == 'bench':
summary[label]['Bench mean % error'] = \
'{:3.2%}'.format(np.nanmean(percent_errors))
else:
summary[label]['Test mean % error'] = \
'{:3.2%}'.format(np.nanmean(percent_errors))
summary[label]['Coefficient of variation'] = \
'{:3.2%}'.format(np.nanmean(coefficient))
return summary
[docs]def print_summary(case, summary):
""" Show some statistics from the run """
for subcase in summary:
message = case + " " + subcase
print(" " + message)
print(" " + "-"*len(message))
for key, val in summary[subcase].items():
print(" "*4 + key.ljust(25) + ":" + val.rjust(7))
print("")
