A new class for saving and loading datasets.

1 files changed, 0 insertions, 51 deletions

diff --git a/demo.py b/demo.py
deleted file mode 100644
index 3c4b933..0000000
--- a/demo.py
+++ /dev/null
@@ -1,51 +0,0 @@
-import os
-import matplotlib.pyplot as plt
-
-from colour import SpectralShape, COLOURCHECKER_SDS, sd_to_XYZ
-
-from otsu2018 import load_Otsu2018_spectra, Tree
-
-
-if __name__ == '__main__':
-    print('Loading spectral data...')
-    sds = load_Otsu2018_spectra('CommonData/spectrum_m.csv', every_nth=50)
-    shape = SpectralShape(380, 730, 10)
-
-    print('Initializing the tree...')
-    tree = Tree(sds, shape)
-
-    print('Tree...')
-    before = tree.total_reconstruction_error()
-    tree.optimise()
-    after = tree.total_reconstruction_error()
-
-    print('Error before: %g' % before)
-    print('Error after:  %g' % after)
-
-    print('Saving the dataset...')
-    os.makedirs('datasets', exist_ok=True)
-    tree.write_python_dataset('datasets/otsu2018.py')
-
-    print('Plotting...')
-    tree.visualise()
-
-    plt.figure()
-
-    examples = COLOURCHECKER_SDS['ColorChecker N Ohta'].items()
-    for i, (name, sd) in enumerate(examples):
-        plt.subplot(2, 3, 1 + i)
-        plt.title(name)
-
-        plt.plot(sd.wavelengths, sd.values, label='Original')
-
-        XYZ = sd_to_XYZ(sd) / 100
-        recovered_sd = tree.reconstruct(XYZ)
-        plt.plot(recovered_sd.wavelengths, recovered_sd.values,
-                 label='Recovered')
-
-        plt.legend()
-
-        if i + 1 == 6:
-            break
-
-    plt.show()