def test_bad_truth_size(self):
self.truth.data = self.truth.data[:-1]
with self.assertRaises(ValueError):
dcs.plot_time_history(self.time, self.data, self.label, self.type_, \
truth=self.truth)
# close uncompleted plot window
plt.close(plt.gcf())
def test_bad_truth_size(self):
self.truth.data = self.truth.data[:-1]
with self.assertRaises(ValueError):
dcs.plot_time_history(self.time, self.data, self.label, self.type_, \
truth=self.truth)
# close uncompleted plot window
plt.close(plt.gcf())
def test_array_data2(self):
data = np.column_stack((self.data, self.data))
self.fig = dcs.plot_time_history(self.time,
data,
self.label,
self.type_,
plot_as_diffs=True)
def test_second_y_scale2(self):
self.fig = dcs.plot_time_history(self.time, self.data, self.label, type_='percentage', \
second_y_scale=self.second_y_scale)
def test_group(self):
self.fig = dcs.plot_time_history(self.time, self.data, self.label, self.type_, \
opts=self.opts, plot_indiv=False)
def test_diffs(self):
self.fig = dcs.plot_time_history(self.time, self.data_matrix, self.label, self.type_, \
plot_as_diffs=True)
def test_truth1(self):
self.fig = dcs.plot_time_history(self.time, self.data, self.label, self.type_, \
truth=self.truth)
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)
def test_diffs_and_opts(self):
self.fig = dcs.plot_time_history(self.time, self.data_matrix, self.label, self.type_, \
opts=self.opts, plot_as_diffs=True)
def test_diffs(self):
self.fig = dcs.plot_time_history(self.time, self.data_matrix, self.label, self.type_, \
plot_as_diffs=True)
def test_opts(self):
self.fig = dcs.plot_time_history(self.time, self.data, self.label, self.type_, opts=self.opts)
def test_truth2(self):
self.truth.data_lo = self.truth.data - 0.1
self.truth.data_hi = self.truth.data + 0.1
self.fig = dcs.plot_time_history(self.time, self.data, self.label, self.type_, \
truth=self.truth)
def test_truth1(self):
self.fig = dcs.plot_time_history(self.time, self.data, self.label, self.type_, \
truth=self.truth)
def test_normal(self):
self.fig = dcs.plot_time_history(self.time, self.data, self.label, self.type_)
def test_plot_empty(self):
self.fig = dcs.plot_time_history([], [], '')
def test_group(self):
self.fig = dcs.plot_time_history(self.time, self.data, self.label, self.type_, \
opts=self.opts, plot_indiv=False)
def test_no_rms_in_legend1(self):
self.fig = dcs.plot_time_history(self.time,
self.data,
self.label,
self.type_,
rms_in_legend=False)
def test_colormap(self):
self.fig = dcs.plot_time_history(self.time, self.data, self.label, self.type_, colormap='Dark2')
def test_normal(self):
self.fig = dcs.plot_time_history(self.time, self.data, self.label,
self.type_)
def test_array_data2(self):
data = np.column_stack((self.data, self.data))
self.fig = dcs.plot_time_history(self.time, data, self.label, self.type_, plot_as_diffs=True)
def test_truth2(self):
self.truth.data_lo = self.truth.data - 0.1
self.truth.data_hi = self.truth.data + 0.1
self.fig = dcs.plot_time_history(self.time, self.data, self.label, self.type_, \
truth=self.truth)
def test_second_y_scale1(self):
self.fig = dcs.plot_time_history(self.time, self.data, self.label, self.type_, \
second_y_scale=self.second_y_scale)
def test_opts(self):
self.fig = dcs.plot_time_history(self.time,
self.data,
self.label,
self.type_,
opts=self.opts)
def test_second_y_scale2(self):
self.fig = dcs.plot_time_history(self.time, self.data, self.label, type_='percentage', \
second_y_scale=self.second_y_scale)
def test_diffs_and_opts(self):
self.fig = dcs.plot_time_history(self.time, self.data_matrix, self.label, self.type_, \
opts=self.opts, plot_as_diffs=True)
def test_simple(self):
self.fig = dcs.plot_time_history(0, 0, 'Text')
def test_colormap(self):
self.fig = dcs.plot_time_history(self.time,
self.data,
self.label,
self.type_,
colormap='Dark2')
def test_plot_empty(self):
self.fig = dcs.plot_time_history([], [], '')
def test_second_y_scale1(self):
self.fig = dcs.plot_time_history(self.time, self.data, self.label, self.type_, \
second_y_scale=self.second_y_scale)
def test_plot_all_nans(self):
self.fig = dcs.plot_time_history(np.array([np.nan, np.nan]), np.array([np.nan, np.nan]), self.label)
def test_simple(self):
self.fig = dcs.plot_time_history(0, 0, 'Text')
def test_no_rms_in_legend1(self):
self.fig = dcs.plot_time_history(self.time, self.data, self.label, self.type_, rms_in_legend=False)
def test_plot_all_nans(self):
self.fig = dcs.plot_time_history(np.array([np.nan, np.nan]),
np.array([np.nan, np.nan]),
self.label)
def test_no_rms_in_legend2(self):
self.fig = dcs.plot_time_history(self.time, self.data, self.label, self.type_, \
rms_in_legend=False, plot_as_diffs=True)
def test_no_rms_in_legend2(self):
self.fig = dcs.plot_time_history(self.time, self.data, self.label, self.type_, \
rms_in_legend=False, plot_as_diffs=True)
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)