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')