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')