import matplotlib.pyplot as plt
from src.model.qacd_project import QACDProject
filename = 'test_data/test_data.quack'
project = QACDProject()
project.load_file(filename)
print('State:', project.state)
print('Elements:', project.elements)
print('Ratios:', project.ratios)
print('Presets:', project.get_valid_preset_ratios())
if len(project.ratios) == 0:
project.create_ratio_map('Mg#', preset='Mg#')
project.create_ratio_map('Example',
elements=('Ca', 'Na'),
correction_model='pyroxene')
print('Ratios:', project.ratios)
else:
names = ['Mg#', 'Example']
for i, name in enumerate(names):
ratio, stats = project.get_ratio(name, want_stats=True)
plt.subplot(len(names), 1, i + 1)
plt.imshow(ratio)
plt.colorbar()
plt.title('{} = {}'.format(stats['name'], stats['formula']))
plt.show()
if 0:
project.import_raw_csv_files('test_data')
#project.import_raw_csv_files('/home/iant/Desktop/Johan_Lissenberg_Xmas2015',
# ['Ca K series.csv', 'Mg K series.csv'])
else:
project.import_raw_csv_files(
'test_data', ['Ca K series.csv', 'Na K series.csv', 'Mg K series.csv'])
project.filter(pixel_totals=True, median=True)
project.normalise()
project.create_h_factor()
project.create_ratio_map('some ratio',
elements=['Mg', 'Ca'],
correction_model='pyroxene')
project.create_ratio_map('Anorthite', preset='anorthite')
print(project.get_valid_preset_ratios())
print(project.ratios)
#project.write_debug()
kmin = 5
kmax = 10
project.k_means_clustering(kmin, kmax, want_all_elements=True)
if 1:
# Plot k-means clustering.
for k in range(kmin, kmax + 1):
plt.subplot(2, 3, k + 1 - kmin)
labels, stats = project.get_cluster_indices(k, want_stats=True)
print(k, stats)
if 1:
cmap = cm.get_cmap('rainbow', k) # k discrete levels