From f816ee701f3c1e60a9f3813b33bb268d487d5f66 Mon Sep 17 00:00:00 2001
From: schmidtfa <schmidtfa91@gmail.com>
Date: Tue, 3 Dec 2024 12:44:56 +0000
Subject: [PATCH] dropped old

---
 .gitignore                                  |   4 +-
 old/__pycache__/raw_cleaner.cpython-312.pyc | Bin 1489 -> 0 bytes
 old/calc_headmodels.py                      | 135 --------------------
 old/preproc_sbg_raw.py                      |  67 ----------
 old/raw_cleaner.py                          |  60 ---------
 5 files changed, 2 insertions(+), 264 deletions(-)
 delete mode 100644 old/__pycache__/raw_cleaner.cpython-312.pyc
 delete mode 100644 old/calc_headmodels.py
 delete mode 100644 old/preproc_sbg_raw.py
 delete mode 100644 old/raw_cleaner.py

diff --git a/.gitignore b/.gitignore
index e489273..1895afe 100644
--- a/.gitignore
+++ b/.gitignore
@@ -37,9 +37,9 @@ dist
 
 #ignore some random folders
 test_data
-old
+
 data_old
 
 .mypy_cache
 .pytest_cache
-.ruff_cache
+.ruff_cache
\ No newline at end of file
diff --git a/old/__pycache__/raw_cleaner.cpython-312.pyc b/old/__pycache__/raw_cleaner.cpython-312.pyc
deleted file mode 100644
index 222fdaafdf571a140b45d5bf4c625041aab7ef2c..0000000000000000000000000000000000000000
GIT binary patch
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diff --git a/old/calc_headmodels.py b/old/calc_headmodels.py
deleted file mode 100644
index 36a5ff6..0000000
--- a/old/calc_headmodels.py
+++ /dev/null
@@ -1,135 +0,0 @@
-#%%
-from cluster_jobs.meta_job import Job
-#%%
-import numpy as np
-import mne
-from mne.coreg import Coregistration
-from os.path import join
-import os
-import pandas as pd
-import pickle
-from mne_bids import BIDSPath, read_raw_bids
-
-#%%
-class HeadModelJob(Job):
-    
-    job_data_folder = 'headmodels'
-    data_file_suffix = '-trans.fif'
-    
-    def run(self, 
-            subject_id,
-            base_dir,
-            savefig=True):
-      
-        #debug
-        #subject_id = 'CC320621'
-        base_path = '/home/schmidtfa/experiments/brain_age/data/data_cam_can'
-        mri_path = join(base_path, 'freesurfer')
-
-        out_folder = join(base_path, 'headmodels', subject_id)
-        trans = 'fsaverage' 
-        
-
-        bids_path = BIDSPath(root=base_dir, subject=subject_id, session='rest', task='rest',
-                                 extension='.fif')
-        raw = read_raw_bids(bids_path=bids_path)
-        info = mne.pick_info(raw.info, mne.pick_types(raw.info, meg=True))
-
-        #%% do the coregistration
-        coreg = Coregistration(info, trans, mri_path)
-        coreg.set_scale_mode('3-axis')
-        coreg.fit_fiducials(verbose=True)
-        coreg.fit_icp(n_iterations=6, nasion_weight=2, verbose=True)
-        coreg.omit_head_shape_points(distance=5 / 1000)
-        coreg.fit_icp(n_iterations=20, nasion_weight=10, verbose=True)
-
-        dists = coreg.compute_dig_mri_distances() * 1e3  # in mm
-        print(f"Distance between HSP and MRI (mean/min/max):\n{np.mean(dists):.2f} mm "f"/ {np.min(dists):.2f} mm / "
-            f"{np.max(dists):.2f} mm")
-        
-        #%% create and save a scaled copy of mri subject fs average
-        new_source_identifier = subject_id + '_from_template'
-
-        #Dont forget this sdcales bem, atlas and source space automatically too
-        mne.coreg.scale_mri("fsaverage", new_source_identifier, 
-                            scale=coreg.scale, 
-                            subjects_dir=mri_path, 
-                            annot=True, overwrite=True)
-
-        if os.path.isdir(out_folder) == False:
-            os.makedirs(out_folder)
-        file = open(join(out_folder, subject_id) + "info.pickle", 'wb')
-        pickle.dump(info, file)
-        file.close()    
-        
-        mne.write_trans(join(out_folder, subject_id) + '-trans.fif', coreg.trans, overwrite=True)
-        print('Coregistration done!')
-
-        if savefig: # REDO IT LATER
-                
-                # PLACEHOLDER: plot 3d stuff when xvfb is available
-                import matplotlib.pyplot as plt  # Assuming 'info' is your data structure containing sensor and digitization information
-                head_mri_t = mne.transforms._get_trans(coreg.trans, "head", "mri")[0]
-                coord_frame = "head"
-                to_cf_t = mne.transforms._get_transforms_to_coord_frame(info, head_mri_t, coord_frame=coord_frame)
-
-                sensor_locs = np.array([ch['loc'][:3] for ch in info['chs'] if ch['ch_name'].startswith('MEG')])
-                sensor_locs = mne.transforms.apply_trans(to_cf_t['meg'], sensor_locs)
-
-                # Extract Digitization Points (excluding fiducials)
-                head_shape_points = np.array([point['r'] for point in info['dig'] if point['kind'] == 4])
-                head_shape_points = mne.transforms.apply_trans(to_cf_t['head'], head_shape_points)
-
-                # Create a 2x2 subplot layout
-                fig = plt.figure(figsize=(10, 10))
-                fig.suptitle(f'MEG - DIG Coregistration of {subject_id}', fontsize=16)
-
-                # Axial view
-                ax1 = fig.add_subplot(221)
-                ax1.scatter(sensor_locs[:, 0], sensor_locs[:, 1], s=20, c='r', label='Sensors')
-                ax1.scatter(head_shape_points[:, 0], head_shape_points[:, 1], s=10, c='b', label='Head Shape')
-                ax1.set_title('Axial View')
-                ax1.set_xlabel('Distance (m)')
-                ax1.set_ylabel('Distance (m)')
-                ax1.legend()
-
-                # Coronal view
-                ax2 = fig.add_subplot(222)
-                ax2.scatter(sensor_locs[:, 0], sensor_locs[:, 2], s=20, c='r')
-                ax2.scatter(head_shape_points[:, 0], head_shape_points[:, 2], s=10, c='b')
-                ax2.set_title('Coronal View')
-                ax2.set_xlabel('Distance (m)')
-                ax2.set_ylabel('Distance (m)')
-
-                # Sagittal view
-                ax3 = fig.add_subplot(223)
-                ax3.scatter(sensor_locs[:, 1], sensor_locs[:, 2], s=20, c='r')
-                ax3.scatter(head_shape_points[:, 1], head_shape_points[:, 2], s=10, c='b')
-                ax3.set_title('Sagittal View')
-                ax3.set_xlabel('Distance (m)')
-                ax3.set_ylabel('Distance (m)')
-
-                # 3D plot
-                ax4 = fig.add_subplot(224, projection='3d')
-                ax4.scatter(sensor_locs[:, 0], sensor_locs[:, 1], sensor_locs[:, 2], s=20, c='r', label='Sensors')
-                ax4.plot(sensor_locs[:, 0], sensor_locs[:, 1], sensor_locs[:, 2], color='k', linewidth=0.5)  # Connect sensors with lines
-                ax4.scatter(head_shape_points[:, 0], head_shape_points[:, 1], head_shape_points[:, 2], s=10, c='b', label='Head Shape')
-                ax4.set_title('3D View')
-                ax4.grid(False)  # Remove grid
-                ax4.axis('off')  # Remove axis
-
-                plt.tight_layout()
-                # save 
-                plt.savefig(join(out_folder, subject_id) +  '_coreg.png', dpi=300)
-                # not sohw ... plt.show()
-                
-
-# %% UNCOMMENT FOR TESTING
-if __name__ == '__main__':
-
-    subject_id = '19670901igsr'
-
-    job = HeadModelJob(subject_id=subject_id)
-    job.run_private()
-
-# %%
\ No newline at end of file
diff --git a/old/preproc_sbg_raw.py b/old/preproc_sbg_raw.py
deleted file mode 100644
index cc170c7..0000000
--- a/old/preproc_sbg_raw.py
+++ /dev/null
@@ -1,67 +0,0 @@
-
-
-from obob_mne.raw import Raw as RawTemplate
-import numpy as np
-import mne
-from old.raw_cleaner import raw_cleaner
-
-
-class Raw(RawTemplate):
-    sinuhe_root = '/home/schmidtfa/experiments/ncc/data/'
-    study_acronym = 'aw_ncc'
-    file_glob_patterns = ['%s_block%02d.fif',
-                          '%s_block%d.fif']
-
-
-    def run_cleaner(subject_id, 
-                    maxfilter = True,
-                    ica = True,
-                    ica_threshold = 0.5,
-                    notch = False,
-                    downsample_f=None,
-                    l_pass = 40,
-                    h_pass = 0.1):
-
-        
-        n_blocks = Raw.get_number_of_runs(subject_id)
-        
-        #get average head pos
-        block_pos_l = []
-        for block in np.arange(1, n_blocks + 1):
-            raw = Raw(subject_id, block_nr=block, preload=False)
-            block_pos_l.append(raw.info["dev_head_t"]['trans'][:3, 3])
-
-        blocks_pos = np.array(block_pos_l)
-        all_distances = np.sqrt(blocks_pos[:,0]**2 + blocks_pos[:,1]**2 + blocks_pos[:,2]**2)
-        mean_distance = np.median(all_distances)
-        mean_d_idx = (np.abs(all_distances - mean_distance)).argmin()
-        #TODO: maybe do np.median(np.array(block_pos_l), axis=0)
-                        
-        raw_all, first_samples = [], []
-        for block in np.arange(1, n_blocks + 1):
-            raw_tmp = Raw(subject_id, block_nr=block, preload=True)
-            
-
-        #% Salzburg specific: make sure that if channels are set as bio that they get added correctly
-        if 'BIO003' in raw.ch_names:
-            raw.set_channel_types({'BIO001': 'eog',
-                                'BIO002': 'eog',
-                                'BIO003': 'ecg',})
-
-            mne.rename_channels(raw.info, {'BIO001': 'EOG001',
-                                            'BIO002': 'EOG002',
-                                            'BIO003': 'ECG003',})
-                
-        #TODO: Only if trans average or trans default
-        eog_list = ['MEG0121','MEG0311' ,'MEG1211' ,'MEG1411']
-        eog_list.extend([name for name in ['EOG001', 'EOG002'] if name in raw.ch_names])
-
-        # Determine the destination parameter for maxwell_filter
-        if trans_option == 'standard':
-            destination = (0, 0, 0.05)
-        elif trans_option == 'average' and input_files:
-            destination = compute_average_position(input_files)
-        else:
-            destination = None
-
-        raw = raw_cleaner()
\ No newline at end of file
diff --git a/old/raw_cleaner.py b/old/raw_cleaner.py
deleted file mode 100644
index f42f083..0000000
--- a/old/raw_cleaner.py
+++ /dev/null
@@ -1,60 +0,0 @@
-import mne
-import numpy as np
-from datetime import datetime
-from os import listdir
-from os.path import join
-
-#from rm_train import rm_train_ica
-
-def raw_cleaner(raw, 
-                 l_pass=None,
-                 h_pass=None,
-                 #ica part  
-                 ica=False,
-                 ic_eog=True,
-                 ic_ecg=True,
-                 ic_muscle=False,
-                 ic_train=False,
-                 ic_train_freq=16.6,
-                 ic_threshold=0.5,
-                 #maxwell part
-                 mw=True,
-                 mw_coord_frame='head',
-                 mw_destination=None,
-                 mw_calibration_file = '../template_files/maxwell/sss_cal_sbg.dat',
-                 mw_cross_talk_file = '../template_files/maxwell/ct_sparse_sbg.fif',
-                 mw_st_duration=None,):
-    
-
-    #maxfilter
-    if mw:
-        from utils.maxwell_utils import run_maxwell
-        maxwell_kwargs = {'coord_frame': mw_coord_frame,
-                          'destination': mw_destination,
-                          'calibration_file': mw_calibration_file,
-                          'cross_talk_file': mw_cross_talk_file,
-                          'st_duration': mw_st_duration}
-
-        # find bad channels first
-        raw = run_maxwell(raw, **maxwell_kwargs)
-
-    if np.logical_or(l_pass != None, h_pass != None):
-        raw.filter(l_freq=h_pass, h_freq=l_pass)
-
-    #ica
-    if ica:
-        from utils.ica_utils import run_ica
-        ica_kwargs = {'resample_freq': 200,
-                      'eog': ic_eog,
-                      'ecg': ic_ecg,
-                      'muscle':ic_muscle,
-                      'train':ic_train,
-                      'train_freq': ic_train_freq,
-                      'ica_corr_thresh': ic_threshold}
-        
-        raw, ics = run_ica(raw, **ica_kwargs)
-
-        return raw, ics
-    
-    else:
-        return raw
\ No newline at end of file