plt.subplot(211) plt.imshow(ratio) plt.colorbar() plt.title(ratio_name) plt.subplot(212) data = ratio.ravel() plt.hist(data[np.isfinite(data)], bins=40) plt.show() if want_plot: element = 'Ca' ax = plt.subplot(221) raw, raw_stats = project.get_raw(element, want_stats=True) plt.imshow(raw) plt.colorbar() plt.title('{} raw'.format(element)) plt.subplot(222, sharex=ax, sharey=ax) if want_normalised: filtered, filtered_stats = project.get_normalised(element, want_stats=True) plt.imshow(filtered) plt.colorbar() plt.title('{} normalised'.format(element)) else: filtered, filtered_stats = project.get_filtered(element, want_stats=True) print('filtered_stats', filtered_stats)
def consistency_impl(directory, pixel_totals, median): # Test that project is consistent, e.g. size of arrays, common masks. # This test works on all models regardless of elements, etc. p = QACDProject() p.set_filename(temp_filename) p.import_raw_csv_files( directory) #, [el+' K series.csv' for el in ['Mg', 'Ca', 'Na']]) p.filter(pixel_totals=pixel_totals, median=median) p.normalise() p.create_h_factor() elements = p.elements files = fnmatch.filter(os.listdir(directory), '* K series.csv') assert elements == sorted([file_.split()[0] for file_ in files]) for i, element in enumerate(elements): raw, raw_stats = p.get_raw(element, want_stats=True) filtered, filtered_stats = p.get_filtered(element, want_stats=True) normalised, normalised_stats = p.get_normalised(element, want_stats=True) if i == 0: ny, nx = raw.shape raw_cumulative = raw.copy() raw_mask = raw.mask filtered_cumulative = filtered.copy() filtered_mask = filtered.mask normalised_cumulative = normalised.copy() normalised_mask = normalised.mask else: raw_cumulative += raw filtered_cumulative += filtered normalised_cumulative += normalised check_masked_array(raw, (ny, nx), np.int32, raw_mask, -1) check_stats(raw, raw_stats, 'min max mean median std invalid valid') check_masked_array(filtered, (ny, nx), np.float64, filtered_mask, np.nan) check_stats(filtered, filtered_stats, 'min max mean median std invalid valid') check_masked_array(normalised, (ny, nx), np.float64, normalised_mask, np.nan) check_stats(normalised, normalised_stats, 'min max mean median std invalid valid') # Check raw masked carry across to filtered and normalised. assert np.all(filtered.data[raw.mask] == np.nan) assert np.all(normalised.data[raw.mask] == np.nan) raw_total, raw_total_stats = p.get_raw_total(want_stats=True) assert np.allclose(raw_total, raw_cumulative) check_masked_array(raw_total, (ny, nx), np.int32, raw_mask, -1) check_stats(raw_total, raw_total_stats, 'min max mean median std invalid valid') filtered_total, filtered_total_stats = p.get_filtered_total( want_stats=True) assert np.allclose(filtered_total, filtered_cumulative) check_masked_array(filtered_total, (ny, nx), np.float64, filtered_mask, np.nan) check_stats(filtered_total, filtered_total_stats, 'min max mean median std invalid valid') assert np.allclose(normalised_cumulative, 1.0) np.testing.assert_array_equal(normalised.mask, filtered.mask) # h factor. h_factor, h_factor_stats = p.get_h_factor(want_stats=True) np.testing.assert_array_equal(h_factor.mask, filtered.mask) check_masked_array(h_factor, (ny, nx), np.float64, h_factor.mask, np.nan) check_stats(h_factor, h_factor_stats, 'min max mean median std invalid valid') # Ratio. ratio_elements = ['Ca', 'Mg'] formula = '{0}/({0}+{1})'.format(ratio_elements[0], ratio_elements[1]) for name, correction_model in zip(['ratioA', 'ratioB'], [None, 'garnet']): p.create_ratio_map(name, elements=ratio_elements, correction_model=correction_model) ratio_entry = p.ratios[name] assert formula, name == ratio_entry ratio, ratio_stats = p.get_ratio(name, want_stats=True) ratio_stats.pop('formula') ratio_stats.pop('correction_model') ratio_stats.pop('preset') check_masked_array(ratio, (ny, nx), np.float64, ratio.mask, np.nan) check_stats(ratio, ratio_stats, 'min max mean median std invalid valid') # k-means clustering k_min = 5 k_max = 10 p.k_means_clustering(k_min, k_max, want_all_elements=True) for k in range(k_min, k_max + 1): cluster, cluster_stats = p.get_cluster_indices(k, want_stats=True) np.testing.assert_array_equal(cluster.mask, filtered.mask) check_masked_array(cluster, (ny, nx), np.int8, cluster.mask, -1) check_stats(cluster, cluster_stats, 'min max invalid valid') # Phase map by thresholding. elements_and_thresholds = [['Ca', 100, 1000], ['Mg', 0, 10000]] name = 'two' p.create_phase_map_by_thresholding(name, elements_and_thresholds) phase, phase_stats = p.get_phase(name, want_stats=True) source = phase_stats.pop('source') assert source == 'thresholding' phase_stats.pop('elements_and_thresholds') assert p.phases[name] == ('thresholding', elements_and_thresholds) mask = phase == False check_masked_array(phase, (ny, nx), np.bool, mask, False) check_stats(phase, phase_stats, 'invalid valid') # Phase map from cluster. k = k_min original_values = [0] name = 'three' phase_map = cluster == 0 p.create_phase_map_from_cluster(name, phase_map, k, original_values) phase, phase_stats = p.get_phase(name, want_stats=True) source = phase_stats.pop('source') assert source == 'cluster' phase_stats.pop('k') phase_stats.pop('original_values') assert p.phases[name] == ('cluster', k, original_values) mask = phase == False check_masked_array(phase, (ny, nx), np.bool, mask, False) check_stats(phase, phase_stats, 'invalid valid') # Region. region = p.calculate_region_ellipse((100, 60), (40, 20)) name = 'ellipse1' shape_string = 'ellipse' p.create_region(name, shape_string, region) region, region_stats = p.get_region(name, want_stats=True) shape = region_stats.pop('shape') assert shape == shape_string mask = region == False check_masked_array(region, (ny, nx), np.bool, mask, False) check_stats(region, region_stats, 'invalid valid') # Display options. options = p.display_options # Check defaults. assert options.colourmap_name == 'rainbow' assert options.show_ticks_and_labels == True assert options.overall_title == '' assert options.show_project_filename == False assert options.show_date == False assert options.use_scale == False assert options.pixel_size == 1.0 assert options.units == '\u03BCm' assert options.show_scale_bar == True assert options.scale_bar_location == 'lower left' assert options.scale_bar_colour == 'black' assert options.histogram_bin_count == 100 # Set options and check can read them back OK. options.set_colourmap_name('viridis') assert options.colourmap_name == 'viridis' options.set_labels_and_scale(False, 'Title', True, True, True, 23.2, 'mm', False, 'upper right', 'white') assert options.show_ticks_and_labels == False assert options.overall_title == 'Title' assert options.show_project_filename == True assert options.show_date == True assert options.use_scale == True assert options.pixel_size == 23.2 assert options.units == 'mm' assert options.show_scale_bar == False assert options.scale_bar_location == 'upper right' assert options.scale_bar_colour == 'white' options.set_labels_and_scale(False, 'Title', True, True, True, 23.2, 'nm', False, 'upper left', 'white') assert options.units == 'nm' assert options.scale_bar_location == 'upper left' options.set_labels_and_scale(False, 'Title', True, True, True, 23.2, 'nm', False, 'lower right', 'white') assert options.scale_bar_location == 'lower right' with pytest.raises(RuntimeError): # Invalid colourmap name. options.set_colourmap_name('unknown colourmap') with pytest.raises(RuntimeError): # Zero pixel size. options.set_labels_and_scale(False, 'Title', True, True, True, 0.0, 'mm', False, 'lower left', 'white') with pytest.raises(RuntimeError): # Negative pixel size. options.set_labels_and_scale(False, 'Title', True, True, True, -0.1, 'mm', False, 'lower left', 'white') with pytest.raises(RuntimeError): # Invalid units. options.set_labels_and_scale(False, 'Title', True, True, True, 23.2, 'pm', False, 'lower left', 'white') with pytest.raises(RuntimeError): # Invalid scale bar location. options.set_labels_and_scale(False, 'Title', True, True, True, 23.2, 'mm', False, 'centre', 'white') with pytest.raises(RuntimeError): # Invalid scale bar colour. options.set_labels_and_scale(False, 'Title', True, True, True, 23.2, 'mm', False, 'lower left', 'purple') options.set_histogram(True, 20, 0.1, 1000, False) assert options.histogram_bin_count == 20 # Cleanup. if os.path.isfile(temp_filename): os.remove(temp_filename)
element = 'Ca' p = QACDProject() p.set_filename('out.quack') p.import_raw_csv_files('test_data') p.filter(pixel_totals=True, median=True) p.normalise() p.create_h_factor() print(p.elements) raw = p.get_raw(element) print(type(raw), raw.dtype, np.ma.is_masked(raw)) filtered = p.get_filtered(element) print(type(filtered), filtered.dtype, np.ma.is_masked(filtered)) normalised = p.get_normalised(element) print(type(normalised), normalised.dtype, np.ma.is_masked(normalised)) if 1: plt.subplot(2, 3, 1) plt.imshow(raw) plt.title(element + ' raw') plt.colorbar() plt.subplot(2, 3, 2)
def test_sequence(): # Test that sequence of operations is correct, which is mostly controlled # by project state. p = QACDProject() assert p.state == State.INVALID # INVALID -> EMPTY. p.set_filename(temp_filename) assert p.state == State.EMPTY assert p.elements is None with pytest.raises(RuntimeError): p.get_raw('Mg') with pytest.raises(RuntimeError): p.get_raw_total() # EMPTY -> RAW. p.import_raw_csv_files(test_dir, [el + ' K series.csv' for el in ['Na', 'Mg', 'Ca']]) assert p.state == State.RAW with pytest.raises(RuntimeError): p.set_filename('') assert sorted(p.elements) == p.elements assert p.elements == ['Ca', 'Mg', 'Na'] raw = p.get_raw('Mg') raw_total = p.get_raw_total() with pytest.raises(RuntimeError): p.get_raw('Aa') with pytest.raises(RuntimeError): p.get_filtered('Mg') with pytest.raises(RuntimeError): p.create_phase_map_by_thresholding('name', [['Ca', 100, 1000]]) # RAW -> FILTERED. p.filter(pixel_totals=True, median=True) assert p.state == State.FILTERED with pytest.raises(RuntimeError): p.import_raw_csv_files(test_dir) p.get_filtered('Mg') p.get_filtered_total() with pytest.raises(RuntimeError): p.get_filtered('Aa') with pytest.raises(RuntimeError): p.get_normalised('Mg') # FILTERED -> NORMALISED. p.normalise() assert p.state == State.NORMALISED with pytest.raises(RuntimeError): p.filter(pixel_totals=True, median=True) p.get_normalised('Mg') with pytest.raises(RuntimeError): p.get_normalised('Aa') with pytest.raises(RuntimeError): p.get_h_factor() k_min = 5 k_max = 8 with pytest.raises(RuntimeError): p.k_means_clustering(k_min, k_max, want_all_elements=True) with pytest.raises(RuntimeError): p.create_ratio_map('some_ratio', ['Mg', 'Ca']) # NORMALISED -> H_FACTOR. p.create_h_factor() assert p.state == State.H_FACTOR with pytest.raises(RuntimeError): p.normalise() p.get_h_factor() # H_FACTOR -> CLUSTERING. assert not p.has_cluster() p.k_means_clustering(k_min, k_max, want_all_elements=True) assert p.state == State.H_FACTOR assert p.has_cluster() with pytest.raises(RuntimeError): p.create_h_factor() p.get_cluster_indices(k_min) p.get_cluster_indices(k_max) with pytest.raises(RuntimeError): p.get_cluster_indices(k_min - 1) with pytest.raises(RuntimeError): p.get_cluster_indices(k_max + 1) # Ratio maps - custom. p.create_ratio_map('some_ratio', elements=['Mg', 'Ca']) p.get_ratio('some_ratio') assert len(p.ratios) == 1 assert 'some_ratio' in p.ratios assert p.ratios['some_ratio'][0] == 'Mg / (Mg+Ca)' with pytest.raises(RuntimeError): p.create_ratio_map('some_ratio', elements=['Mg', 'Ca']) # Already exists. with pytest.raises(RuntimeError): p.create_ratio_map('ratio_no_such_element', elements=['Mg', 'Aa']) p.create_ratio_map('corrected_ratio', elements=['Ca', 'Na'], correction_model='feldspar') assert len(p.ratios) == 2 assert 'corrected_ratio' in p.ratios assert p.ratios['corrected_ratio'][0] == 'Ca / (Ca+Na)' with pytest.raises(RuntimeError): p.create_ratio_map('invalid_correction', elements=['Mg', 'Ca'], correction_model='no_such_correction_name') # Ratio maps - presets. p.create_ratio_map('anorthite blah', preset='anorthite') p.get_ratio('some_ratio') assert len(p.ratios) == 3 assert p.ratios['anorthite blah'][0] == 'Ca / (Ca+Na)' # Phase maps - by thresholding. p.create_phase_map_by_thresholding('one', [['Ca', 100, 1000]]) assert len(p.phases) == 1 assert 'one' in p.phases p.create_phase_map_by_thresholding('two', [['Ca', 100, 1000], ['Mg', 0, 20000]]) assert len(p.phases) == 2 assert 'two' in p.phases with pytest.raises(RuntimeError): p.create_phase_map_by_thresholding( 'one', [['Ca', 100, 1000]]) # Already exists. with pytest.raises(RuntimeError): p.create_phase_map_by_thresholding('no elements', [[]]) with pytest.raises(RuntimeError): p.create_phase_map_by_thresholding('no such element', [['Aa', 0, 1]]) # Phase maps - from cluster. cluster = p.get_cluster_indices(k_min) phase_map = cluster == 0 p.create_phase_map_from_cluster('from_cluster', phase_map, k_min, [0]) assert len(p.phases) == 3 assert 'from_cluster' in p.phases with pytest.raises(RuntimeError): p.create_phase_map_from_cluster('from_cluster', phase_map, k_min, [0]) # Already exists. with pytest.raises(RuntimeError): p.create_phase_map_from_cluster('no original values', phase_map, k_min, []) # Regions. region = p.calculate_region_ellipse((100, 60), (40, 20)) p.create_region('an_ellipse', 'ellipse', region) assert len(p.regions) == 1 assert 'an_ellipse' in p.regions with pytest.raises(RuntimeError): p.create_region('an_ellipse', 'ellipse', region) # Same name # Cleanup. if os.path.isfile(temp_filename): os.remove(temp_filename)