def test_taylor_diagrams(self):
values = np.array([0, 15, 2, 3, 15, 8, 5, 3, 9, 11, 12, 1, 7, 7, 6])
reference_values = np.array(
[9, 10, 1, 2, 11, 3, 7, 5, 4, 12, 7, 8, 5, 1, 14])
stats = processor.calculate_statistics(
model_values=values,
reference_values=reference_values,
model_name='Kate',
unit='megazork')
values1 = np.array([2, 14, 8, 6, 10, 9, 6, 7, 2, 15, 10, 0, 2, 2, 8])
reference_values1 = np.array(
[9, 10, 1, 2, 11, 3, 7, 5, 4, 12, 7, 8, 5, 1, 14])
stats1 = processor.calculate_statistics(
model_values=values1,
reference_values=reference_values1,
model_name='Linda',
unit='megazork')
values2 = np.array(
[-2, -14, -8, -6, -10, -9, -6, 7, 2, 15, 10, 0, 2, 2, 8])
reference_values2 = np.array(
[9, 10, 1, 2, 11, 3, 7, 5, 4, 12, 7, 8, 5, 1, 14])
stats2 = processor.calculate_statistics(
model_values=values2,
reference_values=reference_values2,
model_name='Linda',
unit='gimpel/m^3')
diagrams = plotter.create_taylor_diagrams((stats, stats1, stats2))
self.assertEqual(2, len(diagrams))
for i, d in enumerate(diagrams):
d.write('resources/taylor_test_%s.png' % i)
def test_taylor_diagram(self):
values = np.array([0, 15, 2, 3, 15, 8, 5, 3, 9, 11, 12, 1, 7, 7, 6])
reference_values = np.array([9, 10, 1, 2, 11, 3, 7, 5, 4, 12, 7, 8, 5, 1, 14])
stats = processor.calculate_statistics(model_values=values, reference_values=reference_values, unit='mg')
values1 = np.array([2, 14, 8, 6, 10, 9, 6, 7, 2, 15, 10, 0, 2, 2, 8])
reference_values1 = np.array([9, 10, 1, 2, 11, 3, 7, 5, 4, 12, 7, 8, 5, 1, 14])
stats1 = processor.calculate_statistics(model_values=values1, reference_values=reference_values1, model_name='Kate', unit='mg')
values2 = np.array([-2, -14, -8, -6, -10, -9, -6, 7, 2, 15, 10, 0, 2, 2, 8])
reference_values2 = np.array([9, 10, 1, 2, 11, 3, 7, 5, 4, 12, 7, 8, 5, 1, 14])
stats2 = processor.calculate_statistics(model_values=values2, reference_values=reference_values2, unit='g')
# print('ref_stddev: %s' % stats['ref_stddev'])
# print('stddev: %s' % stats['stddev'])
# print('unbiased rmse: %s' % stats['unbiased_rmse'])
# print('corrcoeff: %s' % stats['corrcoeff'])
# print('ref_stddev: %s' % stats2['ref_stddev'])
# print('stddev: %s' % stats2['stddev'])
# print('unbiased rmse: %s' % stats2['unbiased_rmse'])
# print('corrcoeff: %s' % stats2['corrcoeff'])
diagram = plotter.create_taylor_diagrams((stats, stats1))[0]
diagram.plot_sample(stats2['corrcoeff'], stats2['stddev'], model_name='Linda', unit=stats2['unit'])
diagram.write('resources/taylor_test.png')
def taylor(self, statistics, target_file=None):
diagrams = plotter.create_taylor_diagrams(statistics, config=self.config)
result = []
if target_file is not None:
for i, diagram in enumerate(diagrams):
last_index_of_dot = target_file.rfind('.')
new_target_file = target_file[:last_index_of_dot] + '_' + str(i) + target_file[last_index_of_dot:]
diagram.write(new_target_file)
result.append(new_target_file)
return result, diagrams
def taylor(self, statistics, target_file=None):
diagrams = plotter.create_taylor_diagrams(statistics,
config=self.config)
result = []
if target_file is not None:
for i, diagram in enumerate(diagrams):
last_index_of_dot = target_file.rfind('.')
new_target_file = target_file[:last_index_of_dot] + '_' + str(
i) + target_file[last_index_of_dot:]
diagram.write(new_target_file)
result.append(new_target_file)
return result, diagrams
def test_taylor_diagrams(self):
values = np.array([0, 15, 2, 3, 15, 8, 5, 3, 9, 11, 12, 1, 7, 7, 6])
reference_values = np.array([9, 10, 1, 2, 11, 3, 7, 5, 4, 12, 7, 8, 5, 1, 14])
stats = processor.calculate_statistics(model_values=values, reference_values=reference_values, model_name='Kate', unit='megazork')
values1 = np.array([2, 14, 8, 6, 10, 9, 6, 7, 2, 15, 10, 0, 2, 2, 8])
reference_values1 = np.array([9, 10, 1, 2, 11, 3, 7, 5, 4, 12, 7, 8, 5, 1, 14])
stats1 = processor.calculate_statistics(model_values=values1, reference_values=reference_values1, model_name='Linda', unit='megazork')
values2 = np.array([-2, -14, -8, -6, -10, -9, -6, 7, 2, 15, 10, 0, 2, 2, 8])
reference_values2 = np.array([9, 10, 1, 2, 11, 3, 7, 5, 4, 12, 7, 8, 5, 1, 14])
stats2 = processor.calculate_statistics(model_values=values2, reference_values=reference_values2, model_name='Linda', unit='gimpel/m^3')
diagrams = plotter.create_taylor_diagrams((stats, stats1, stats2))
self.assertEqual(2, len(diagrams))
for i, d in enumerate(diagrams):
d.write('resources/taylor_test_%s.png' % i)
def test_taylor_diagram(self):
values = np.array([0, 15, 2, 3, 15, 8, 5, 3, 9, 11, 12, 1, 7, 7, 6])
reference_values = np.array(
[9, 10, 1, 2, 11, 3, 7, 5, 4, 12, 7, 8, 5, 1, 14])
stats = processor.calculate_statistics(
model_values=values, reference_values=reference_values, unit='mg')
values1 = np.array([2, 14, 8, 6, 10, 9, 6, 7, 2, 15, 10, 0, 2, 2, 8])
reference_values1 = np.array(
[9, 10, 1, 2, 11, 3, 7, 5, 4, 12, 7, 8, 5, 1, 14])
stats1 = processor.calculate_statistics(
model_values=values1,
reference_values=reference_values1,
model_name='Kate',
unit='mg')
values2 = np.array(
[-2, -14, -8, -6, -10, -9, -6, 7, 2, 15, 10, 0, 2, 2, 8])
reference_values2 = np.array(
[9, 10, 1, 2, 11, 3, 7, 5, 4, 12, 7, 8, 5, 1, 14])
stats2 = processor.calculate_statistics(
model_values=values2, reference_values=reference_values2, unit='g')
# print('ref_stddev: %s' % stats['ref_stddev'])
# print('stddev: %s' % stats['stddev'])
# print('unbiased rmse: %s' % stats['unbiased_rmse'])
# print('corrcoeff: %s' % stats['corrcoeff'])
# print('ref_stddev: %s' % stats2['ref_stddev'])
# print('stddev: %s' % stats2['stddev'])
# print('unbiased rmse: %s' % stats2['unbiased_rmse'])
# print('corrcoeff: %s' % stats2['corrcoeff'])
diagram = plotter.create_taylor_diagrams((stats, stats1))[0]
diagram.plot_sample(stats2['corrcoeff'],
stats2['stddev'],
model_name='Linda',
unit=stats2['unit'])
diagram.write('resources/taylor_test.png')
