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const_density_dtilt_ref_calc_tomo_FSCs.py
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const_density_dtilt_ref_calc_tomo_FSCs.py
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from FSC import *
import mrcfile
import time
import pandas as pd
import os
from resolution_measure_mrc import *
from glob import glob
####### Edit these params
num_cores = 16
cube_size = 50
sub_sampling_zxy = [1,8,8]
sub_region = [50, -1,-1]
#num_angs = [121, 33, 21, 17, 11, 5]
#max_angs = [60,50,40,30,20,10]
num_angs = [11,21,31,41,51,61,71,81]
max_angs = [7.5,15,22.5,30,37.5,45,52.5,60]
output_dir = '240423_baRef_FSC3D_3.3k_1.5deg'
#fake = True
fake = False
#overwrite = False
overwrite = True
###########
# Working with file structure to analyze multiple datasets
home_dir = '/home/atk13/repos/tem-tomo'
#data_path = '/Users/atk42/OneDrive - Yale University/Lab/Projects/TEM_tomo/tomo_data'
#data_path = '/home/atk13/new_project_20471'
#data_path = '/ccdbprod/ccdbprod29/home/CCDB_DATA_USER.portal/CCDB_DATA_USER/acquisition/project_20471/'
#tomo_lst = 'tomo_lists/tomograms_lst - Local Tomograms for FSC.csv'
tomo_lst = 'tomo_lists/tomograms_lst - double_tilt_tomos_3.3k.csv'
#tomo_lst = 'tomo_lists/tomograms_lst - double_tilt_tomos_11k.csv'
df = pd.read_csv(tomo_lst)
for index,row in df.iterrows():
proj = 'microscopy_%i' % int(row['MPID'])
tomo = row['Tomogram']
thickness = row['Thickness']
pixel_size = row['Pixel Size bin 4 (nm)']
ccdbprod = row['ccdbprod']
pid = row['PID']
mag = row['Magnification']
data_path = '/ccdbprod/%s/home/CCDB_DATA_USER.portal/CCDB_DATA_USER/acquisition/project_%s/' % (ccdbprod, pid)
tomo_path = os.sep.join([data_path, proj, 'processed_data',tomo,'txbr-backprojection','limited-bin4'])
for idx,num_ang in enumerate(num_angs): # Num angs matched 1-to-1 to max angs
max_ang = max_angs[idx]
if num_ang == 121 and max_ang == 10:
continue
recon_dir = os.sep.join([tomo_path,'%i-limited[%.1f_-%.1f]' % (num_ang,max_ang,max_ang)])
os.chdir(recon_dir)
a_paths = glob(tomo+'b_z_-*0.out')
if len(a_paths) != 1:
print('Problem dir for a recon: %s' % recon_dir)
print(a_path)
fake = True
else:
a_path = os.sep.join([recon_dir, a_paths[0]])
ref_path = os.sep.join([data_path, proj, 'processed_data',tomo,'txbr-backprojection','bin4-0'])
os.chdir(ref_path)
b_paths = glob(tomo+'a_z_-*0.out')
if len(b_paths) != 1:
print('Problem dir for b recon : %s' % recon_dir)
print(b_path)
fake = True
else:
b_path = os.sep.join([ref_path, b_paths[0]])
ofn = os.sep.join([output_dir, 'FSC3D_%s_%s_%i-limited[%.1f_-%.1f].csv' % (thickness, tomo,num_ang,max_ang,max_ang)])
os.chdir(home_dir)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
print('Calculating FSC for %s' % ofn)
if not fake:
if overwrite or not os.path.isfile(ofn):
resolution_measure(a_path, b_path, num_cores, cube_size, snrt=0.5, pixel_size = pixel_size, sub_region = sub_region, sub_sampling_zxy = sub_sampling_zxy, ofn=ofn)