def form_ed_tuple(ed_name, methods_names_tuple): ed_tuple = [] for method_name in methods_names_tuple: file_path = str(get_project_root().parent) + '/results/best/' + ed_name + method_name ed = read(file_path) ed_tuple.append(ed) return ed_tuple
new_slice_sec[i][j] = [1/255, 215/255, 36/255] #print('FP') else: new_slice_sec[i][j] = [1.0, 1.0, 1.0] axes[k // 2, k % 2].set_title(name_tuple[k]) axes[k // 2, k % 2].imshow(new_slice_sec) for ax in fig.get_axes(): ax.label_outer() # pyplot.subplot(2, 2, k + 1) # pyplot.imshow(new_slice_sec) pyplot.show() #pyplot.savefig(''.join(['../results/', name, '/', optName ,'.png'])) if __name__ == '__main__': file = EMD_2984 ed_name = 'EMD-2984' true_buffer = np.fromfile(''.join([str(get_project_root().parent), '/results/golden/', ed_name + '_c']), dtype=bool) origin = ed_parser.read(str(get_project_root().parent) + file) origin.re_normalize() mf3d = ed_parser.read(str(get_project_root().parent) + '/results/best/' + ed_name +'_mf3d.ccp4') nlm3d = ed_parser.read(str(get_project_root().parent) + '/results/best/' + ed_name + '_nlm3d.ccp4') bm4d = ed_parser.read(str(get_project_root().parent) + '/results/best/' + ed_name + '_bm4d.ccp4') edplot2d(true_buffer, [origin, mf3d, nlm3d, bm4d])
from scripts.ed_parser import read from scripts import get_project_root from scripts.ccp4_parser import to_ccp4_file from scripts import edplot import denoise_methods.BM3D as BMND import numpy as np from scenarios import EMD_2984, _4NRE, EMD_3061, EMD_6479 if __name__ == '__main__': file_names = [EMD_6479] #, file_name2] for name in file_names: file_path = str(get_project_root().parent) + name ed = read(file_path) ed.re_normalize() denoiser = BMND.BM3D(ed.values) denoise_data = denoiser.execute_2d() ed.update_from_values(denoise_data) ed.header.mean = np.mean(ed.buffer) ed.header.stddev = np.std(ed.buffer) ed.header.min = np.min(ed.buffer) ed.header.max = np.max(ed.buffer) ed.header.fields["amean"] = ed.header.mean ed.header.fields["amax"] = ed.header.max ed.header.fields["amin"] = ed.header.min ed.header.fields["sd"] = ed.header.stddev print(ed.header.min, ed.header.max, ed.header.mean, ed.header.stddev)
crop_min = edfile.header.mean - factor * edfile.header.stddev crop_max = edfile.header.mean + factor * edfile.header.stddev def crop(x): return crop_val if x < crop_max else x values = edfile.values[sec] values_cropped = np.vectorize(crop)(values) norm = matplotlib.colors.Normalize(edfile.header.min, edfile.header.max) pyplot.figure() pyplot.suptitle(name) pyplot.subplot(2, 2, 1) pyplot.imshow(values, cmap='gray') pyplot.subplot(2, 2, 2) pyplot.imshow(values, cmap='bwr', norm=norm) pyplot.subplot(2, 2, 3) pyplot.imshow(values_cropped, cmap='gray') pyplot.subplot(2, 2, 4) pyplot.imshow(values_cropped, cmap='bwr', norm=norm) #pyplot.show() pyplot.savefig(''.join(['../results/', name, '/', optName, '.png'])) if __name__ == '__main__': file = '../results/EMD-6479/EMD-6479_mf2_2d.ccp4' data = ed_parser.read(file) data.re_normalize() edplot2d(data)
# # thr_main = ed.header.mean + 1.2 * ed.header.stddev # thr_help = ed_help.header.mean + ed_help.header.stddev # # true_signal = np.zeros((ed.header.fields["NS"] * ed.header.fields["NR"] * ed.header.fields["NC"]), dtype=bool) # # n = len(ed.buffer) # # for i in range(n): # if ed.buffer[i] >= thr_main and ed_help.buffer[i] >= thr_help: # true_signal[i] = True # # true_signal.tofile('EMD-2984') if __name__ == '__main__': name ='EMD-2984' ed = read(str(get_project_root().parent) + '/mol_data/ccp4/vv2.ccp4') ed.re_normalize() true_signal = np.zeros((ed.header.fields["NS"] * ed.header.fields["NR"] * ed.header.fields["NC"]), dtype=bool) n = len(ed.buffer) for i in range(n): if ed.buffer[i] > 0.0: true_signal[i] = True true_signal.tofile(str(get_project_root().parent) + '/results/golden/' + name)
# Step 1. Read data from scripts.ed_parser import read from scripts import get_project_root from scenarios import EMD_2984, _4NRE, EMD_3061, EMD_6479 ed = read(str(get_project_root().parent) + _4NRE) ed.re_normalize() #Step 3. Plot slice from scripts.edplot import edplot2d edplot2d(ed, optName='slice_color')