import numpy as np from scenarios import EMD_2984, _4NRE, EMD_3061, EMD_6479, EMD_21452 import denoise_methods.BM4D as BM4D if __name__ == '__main__': name = EMD_21452 threshold_list = [5.25] for tr in threshold_list: file_path = str(get_project_root().parent) + name ed = read(file_path) ed.re_normalize() denoiser = BM4D.BM4D(ed.values) denoise_data = denoiser.execute_3d(tr) 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) to_ccp4_file(ed, 'bm4d_14p_' + str(tr))
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) to_ccp4_file(ed, 'bm3d_new')
from scripts.ccp4_parser import to_ccp4_file from scripts import edplot import numpy as np from scenarios import EMD_2984, _4NRE, EMD_3061, EMD_6479 file_names = [EMD_2984] for name in file_names: file_path = str(get_project_root().parent) + name ed = read(file_path) import denoise_methods.median_filter as mf denoiser = mf.MedianFilter(ed.values) denoise_data = denoiser.execute_2d() #ed.values = denoise_data 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) to_ccp4_file(ed, 'mf_2d_v2')
if __name__ == '__main__': file_names = [EMD_6479] for name in file_names: file_path = str(get_project_root().parent) + name ed = read(file_path) import denoise_methods.nl_means3d as nlm #edplot.edplot2d(ed, optName='true') ed.re_normalize() ed.update_from_buffer(ed.buffer) denoiser = nlm.NLMeans(ed.values, 40) denoise_data = denoiser.execute_3d() 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) #ed.re_normalize() #edplot.edplot2d(ed, optName='nlm_2d') to_ccp4_file(ed, 'nlm_3d_v2_1.4s')
file_name1 = '/mol_data/ccp4/EMD-3061.ccp4' # dsn6/4nre_2fofc.dsn6 ccp4/4nre.ccp4 EMD-3061 EMD-6479 file_name2 = '/mol_data/ccp4/EMD-6479.ccp4' file_names = [file_name2] #, file_name2] for name in file_names: file_path = str(get_project_root().parent) + name ed = read(file_path) import denoise_methods.BM3D as BMND ed.re_normalize() denoiser = BMND.BMnD(ed.values, 40) 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) #ed.re_normalize() #edplot.edplot2d(ed,optName='bm3d_') #edplot.edplot2d(ed, optName='true1') to_ccp4_file(ed, 'bm3d_2')
file_names = [_4NRE] for name in file_names: file_path = str(get_project_root().parent) + name ed = read(file_path) import denoise_methods.nl_means as nlm ed.re_normalize() ed.update_from_buffer(ed.buffer) denoiser = nlm.NLMeans(ed.values, 40) 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) #ed.re_normalize() #edplot.edplot2d(ed, optName='nlm_2d') to_ccp4_file(ed, 'nlm_2d_v2')
import numpy as np from scenarios import EMD_2984, _4NRE, EMD_3061, EMD_6479 file_names = [EMD_2984] for name in file_names: file_path = str(get_project_root().parent) + name ed = read(file_path) import denoise_methods.median_filter as mf denoiser = mf.MedianFilter(ed.values) denoise_data = denoiser.execute_3d() #ed.values = denoise_data 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) #ed.re_normalize() edplot.edplot2d(ed) to_ccp4_file(ed, 'mf_3d')