def test_no_logging(self):
self.logger.set_level(0)
with dcs.capture_output() as out:
self.figs = dcs.plot_bpe_results(self.bpe_results, plots=self.plots)
output = out.getvalue().strip()
out.close()
self.assertEqual(output, '')
def test_nodata(self):
with dcs.capture_output() as out:
self.figs = dcs.plot_bpe_results(self.bpe_results, plots=self.plots)
lines = out.getvalue().strip().split('\n')
out.close()
self.assertEqual(lines[0], "Data isn't available for Innovations plot.")
self.assertEqual(lines[1], "Data isn't available for convergence plot.")
self.assertEqual(lines[2], "Data isn't available for correlation plot.")
self.assertEqual(lines[3], "Data isn't available for information SVD plot.")
self.assertEqual(lines[4], "Data isn't available for covariance plot.")
def test_nominal(self):
# add data
names = ['a', 'b', 'c', 'd']
matrix = np.random.rand(4, 4)
self.bpe_results.param_names = [x.encode('utf-8') for x in names]
self.bpe_results.begin_innovs = np.array([1, 2, 3, 4], dtype=float)
self.bpe_results.final_innovs = np.array([0.5, 0.25, 0.1, 0.05])
self.bpe_results.costs = np.array([1, 0.1, 0.05, 0.01])
self.bpe_results.correlation = matrix.copy()
self.bpe_results.info_svd = matrix.copy()
self.bpe_results.covariance = matrix.copy()
self.figs = dcs.plot_bpe_results(self.bpe_results, plots=self.plots)
def test_only_one_key(self):
dcs.plot_bpe_results(self.bpe_results, plots={'innovs': False})
def test_bad_plot(self):
with self.assertRaises(ValueError):
dcs.plot_bpe_results(self.bpe_results, plots={'bad_key': False})
def test_no_plots(self):
dcs.plot_bpe_results(self.bpe_results, self.opts)
minstep=0.1))
# Run code
if rerun:
(bpe_results, results) = dcs.run_bpe(opti_opts)
else:
bpe_results = dcs.BpeResults.load(
os.path.join(opti_opts.output_folder, opti_opts.output_results))
results = sim_model(
sim_params) # just re-run, nothing is actually saved by this model
# Plot results
if make_plots:
# build opts
opts = dcs.Opts()
opts.case_name = 'Model Results'
opts.save_path = dcs.get_output_dir()
opts.save_plot = True
# make model plots
dcs.plot_time_history(time,
results,
description='Output vs. Time',
opts=opts,
truth=truth)
# make BPE plots
bpe_plots = {'innovs': True, 'convergence': False, 'correlation': True, 'info_svd': True, \
'covariance': False}
dcs.plot_bpe_results(bpe_results, opts, plots=bpe_plots)
opti_opts.shrink_radius = 0.5
opti_opts.trust_radius = 1.0
# Parameters to estimate
opti_opts.params.append(dcs.OptiParam('magnitude', best=2.5, min_=-10, max_=10, typical=5, minstep=0.01))
opti_opts.params.append(dcs.OptiParam('frequency', best=20, min_=1, max_=1000, typical=60, minstep=0.01))
opti_opts.params.append(dcs.OptiParam('phase', best=180, min_=0, max_=360, typical=100, minstep=0.1))
# Run code
if rerun:
(bpe_results, results) = dcs.run_bpe(opti_opts)
else:
bpe_results = dcs.BpeResults.load(os.path.join(opti_opts.output_folder, opti_opts.output_results))
results = sim_model(sim_params) # just re-run, nothing is actually saved by this model
# Plot results
if make_plots:
# build opts
opts = dcs.Opts()
opts.case_name = 'Model Results'
opts.save_path = dcs.get_output_dir()
opts.save_plot = True
# make model plots
dcs.plot_time_history(time, results, description='Output vs. Time', opts=opts, truth=truth)
# make BPE plots
bpe_plots = {'innovs': True, 'convergence': False, 'correlation': True, 'info_svd': True, \
'covariance': False}
dcs.plot_bpe_results(bpe_results, opts, plots=bpe_plots)