def setUp(self):
self.figs = []
self.logger = dcs.Logger(10)
self.bpe_results = dcs.BpeResults()
self.opts = dcs.Opts()
self.plots = {'innovs': True, 'convergence': True, 'correlation': True, 'info_svd': True, \
'covariance': True}
def test_pprint(self):
opts = dcs.Opts()
with dcs.capture_output() as out:
opts.pprint(indent=2)
lines = out.getvalue().strip().split('\n')
out.close()
self.assertEqual(lines[0], 'Opts')
self.assertEqual(lines[1], ' case_name = ')
self.assertEqual(lines[3], ' save_plot = False')
self.assertEqual(lines[-1], ' names = []')
def setUp(self):
self.time = np.arange(0, 10, 0.1)
self.data = np.sin(self.time)
self.label = 'Sin'
self.type_ = 'population'
self.opts = dcs.Opts()
self.opts.names = ['Name 1']
self.truth = dcs.TruthPlotter(self.time, np.cos(self.time))
self.data_matrix = np.column_stack((self.data, self.truth.data))
self.second_y_scale = 1000000
self.fig = None
def setUp(self):
self.time = np.arange(0, 5, 1. / 12) + 2000
num_bins = 5
self.data = np.random.rand(len(self.time), num_bins)
mag = self.data.cumsum(axis=1)[:, -1]
self.data = self.data / np.expand_dims(mag, axis=1)
self.label = 'Plot bar testing'
self.legend = ['Value 1', 'Value 2', 'Value 3', 'Value 4', 'Value 5']
self.opts = dcs.Opts()
self.opts.show_plot = False
self.colormap = 'seismic'
self.figs = []
def setUp(self):
self.time = np.arange(0, 10, 0.1) + 2000
num_channels = 5
self.data = np.random.rand(len(self.time), num_channels)
mag = self.data.cumsum(axis=1)[:, -1]
self.data = 10 * self.data / np.expand_dims(mag, axis=1)
self.label = 'Plot description'
self.type_ = 'percentage'
self.opts = dcs.Opts()
self.opts.show_plot = False
self.legend = ['Value 1', 'Value 2', 'Value 3', 'Value 4', 'Value 5']
self.colormap = 'seismic'
self.figs = []
self.second_y_scale = 1000000
def setUp(self):
self.fig = plt.figure()
self.fig.canvas.set_window_title('Figure Title')
ax = self.fig.add_subplot(111)
x = np.arange(0, 10, 0.1)
y = np.sin(x)
ax.plot(x, y)
ax.set_title('X vs Y')
ax.set_xlabel('time [years]')
ax.set_ylabel('value [radians]')
self.opts = dcs.Opts()
self.opts.case_name = 'Testing'
self.opts.show_plot = True
self.opts.save_plot = False
self.opts.save_path = dcs.get_tests_dir()
def setUp(self):
num = 10
self.figs = []
self.data = dcs.unit(np.random.rand(num, num), axis=0)
self.labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']
self.type_ = 'percentage'
self.opts = dcs.Opts()
self.opts.case_name = 'Testing Correlation'
self.matrix_name = 'Not a Correlation Matrix'
self.sym = self.data.copy()
for j in range(num):
for i in range(num):
if i == j:
self.sym[i, j] = 1
elif i > j:
self.sym[i, j] = self.data[j, i]
def test_plot2(self):
opts = dcs.Opts()
(inc_cost, inc_qaly, icer_out, order, icer_data, self.fig) = dcs.icer(self.cost, self.qaly, \
make_plot=True, opts=opts, baseline=0)
temp = self.inc_cost
temp[0] = 0
np.testing.assert_array_equal(inc_cost, temp,
'Incremental cost mismatch.')
temp = self.inc_qaly
temp[0] = 0
np.testing.assert_array_equal(inc_qaly, temp,
'Incremental QALY mismatch.')
temp = self.icer_out
temp[0] = np.nan
np.testing.assert_array_equal(icer_out, temp, 'ICER mismatch.')
np.testing.assert_array_equal(order, self.order, 'Order mismatch.')
self.assertTrue(isinstance(self.fig, plt.Figure))
def setUp(self):
self.costs = np.array([1, 0.1, 0.05, 0.01])
self.opts = dcs.Opts()
self.opts.show_plot = False
self.figs = []
def test_get_names_unsuccessful(self):
opts = dcs.Opts()
opts.names = ['Name 1', 'Name 2']
name = opts.get_names(2)
self.assertEqual(name, '')
def test_new_attr(self):
opts = dcs.Opts()
with self.assertRaises(AttributeError):
opts.new_field_that_does_not_exist = 1
def test_calling(self):
opts = dcs.Opts()
for field in self.opts_fields:
self.assertTrue(hasattr(opts, field))
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)