├── HDDM_Example ├── .local │ └── share │ │ └── jupyter │ │ └── runtime │ │ └── notebook_cookie_secret ├── Hddm_test_parallel.ipynb ├── Readme.txt ├── df_example.csv └── hddm.yml ├── MNE_workshop ├── Behavioral │ ├── all_raw_marker.csv │ └── condition_marker │ │ ├── 10_marker_lists.csv │ │ └── 9_marker_lists.csv ├── README.md ├── Scripts │ ├── ERP_plot_statisctics.py │ ├── ERP_preprocessing_single_event.py │ ├── __pycache__ │ │ ├── event_test.cpython-37.pyc │ │ └── settings.cpython-37.pyc │ ├── event_test.py │ ├── list_rearrange.py │ ├── mne_bids_test.py │ └── settings.py └── example_scripts.ipynb ├── Machine_learning_in_neuroimaging_using_python_LiChao ├── .ipynb_checkpoints │ └── workshop-checkpoint.ipynb ├── BNA_subregions.xlsx ├── README.html ├── README.md ├── classification_performances.pdf ├── easylearn交流群.jpg ├── easylearn操作视频.zip ├── fc_triu.npy ├── outputs.pickle ├── requirements.txt ├── szVShc.json ├── targets.xlsx ├── workshop.ipynb ├── workshop.py └── 一般流程.png ├── PsychoPy ├── Python在实验程序编写中的应用——Ryan.pdf ├── ReadMe.md ├── gonogo.psyexp ├── gonogo.py ├── gonogo_test.py ├── stimuli_list.xlsx ├── stroop.psyexp ├── 微信公众号.png ├── 扫码_搜索联合传播样式-微信标准绿版.png └── 群二维码.png ├── PythonPipeline_DataAnalysis_Psychology_HaiyangGeng ├── PythonPipeline_DataAnalysis_Psychology_DemoCode1118.ipynb ├── README.md ├── SE-QPE_40_demo1118.xlsx ├── SE_all_demo1118.xlsx ├── SE_basic_infor_demo1118.xlsx └── SE_scale_demo1118.xlsx ├── README.md └── fMRIPrep ├── README.md ├── ds003030_select ├── CHANGES ├── dataset_description.json ├── participants.tsv ├── sub-01 │ ├── anat │ │ ├── sub-01_T1w.json │ │ └── sub-01_T1w.nii.gz │ └── func │ │ ├── sub-01_task-FlickeringCheckerBoard_run-1_bold.json │ │ ├── sub-01_task-FlickeringCheckerBoard_run-1_bold.nii.gz │ │ └── sub-01_task-FlickeringCheckerBoard_run-1_events.tsv └── sub-02 │ ├── anat │ ├── sub-02_T1w.json │ └── sub-02_T1w.nii.gz │ └── func │ ├── sub-02_task-FlickeringCheckerBoard_run-1_bold.json │ ├── sub-02_task-FlickeringCheckerBoard_run-1_bold.nii.gz │ └── sub-02_task-FlickeringCheckerBoard_run-1_events.tsv └── output └── README.md /HDDM_Example/.local/share/jupyter/runtime/notebook_cookie_secret: -------------------------------------------------------------------------------- 1 | MPSqnBJKX0wU1bho9V4kA2c4pUvtlMIp4vbP6QklcV8= 2 | -------------------------------------------------------------------------------- /HDDM_Example/Readme.txt: -------------------------------------------------------------------------------- 1 | Please visit here for the installation of docker image of HDDM: 2 | https://github.com/hcp4715/hddm_docker 3 | -------------------------------------------------------------------------------- /HDDM_Example/hddm.yml: -------------------------------------------------------------------------------- 1 | name: hddm 2 | channels: 3 | - conda-forge 4 | - defaults 5 | dependencies: 6 | - _libgcc_mutex=0.1=main 7 | - attrs=19.3.0=py_0 8 | - backcall=0.1.0=py37_0 9 | - blas=1.0=mkl 10 | - bleach=3.1.4=py_0 11 | - ca-certificates=2020.1.1=0 12 | - certifi=2020.4.5.1=py37_0 13 | - dbus=1.13.14=hb2f20db_0 14 | - decorator=4.4.2=py_0 15 | - defusedxml=0.6.0=py_0 16 | - entrypoints=0.3=py37_0 17 | - expat=2.2.6=he6710b0_0 18 | - fontconfig=2.13.0=h9420a91_0 19 | - freetype=2.9.1=h8a8886c_1 20 | - glib=2.63.1=h3eb4bd4_1 21 | - gmp=6.1.2=h6c8ec71_1 22 | - gst-plugins-base=1.14.0=hbbd80ab_1 23 | - gstreamer=1.14.0=hb31296c_0 24 | - icu=58.2=he6710b0_3 25 | - importlib-metadata=1.6.0=py37_0 26 | - importlib_metadata=1.6.0=0 27 | - intel-openmp=2020.1=217 28 | - ipykernel=5.1.4=py37h39e3cac_0 29 | - ipyparallel=6.3.0=py37hc8dfbb8_0 30 | - ipython=7.13.0=py37h5ca1d4c_0 31 | - ipython_genutils=0.2.0=py37_0 32 | - ipywidgets=7.5.1=py_0 33 | - jedi=0.17.0=py37_0 34 | - jinja2=2.11.2=py_0 35 | - jpeg=9b=h024ee3a_2 36 | - jsonschema=3.2.0=py37_0 37 | - jupyter=1.0.0=py37_7 38 | - jupyter_client=6.1.3=py_0 39 | - jupyter_console=6.1.0=py_0 40 | - jupyter_core=4.6.3=py37_0 41 | - kiwisolver=1.2.0=py37hfd86e86_0 42 | - ld_impl_linux-64=2.33.1=h53a641e_7 43 | - libedit=3.1.20181209=hc058e9b_0 44 | - libffi=3.3=he6710b0_1 45 | - libgcc-ng=9.1.0=hdf63c60_0 46 | - libgfortran-ng=7.3.0=hdf63c60_0 47 | - libpng=1.6.37=hbc83047_0 48 | - libsodium=1.0.16=h1bed415_0 49 | - libstdcxx-ng=9.1.0=hdf63c60_0 50 | - libuuid=1.0.3=h1bed415_2 51 | - libxcb=1.13=h1bed415_1 52 | - libxml2=2.9.9=hea5a465_1 53 | - markupsafe=1.1.1=py37h7b6447c_0 54 | - matplotlib-base=3.1.3=py37hef1b27d_0 55 | - mistune=0.8.4=py37h7b6447c_0 56 | - mkl=2020.1=217 57 | - mkl-service=2.3.0=py37he904b0f_0 58 | - mkl_fft=1.0.15=py37ha843d7b_0 59 | - mkl_random=1.1.1=py37h0573a6f_0 60 | - nbconvert=5.6.1=py37_0 61 | - nbformat=5.0.6=py_0 62 | - ncurses=6.2=he6710b0_1 63 | - notebook=6.0.3=py37_0 64 | - numpy-base=1.18.1=py37hde5b4d6_1 65 | - openssl=1.1.1g=h7b6447c_0 66 | - pandoc=2.2.3.2=0 67 | - pandocfilters=1.4.2=py37_1 68 | - parso=0.7.0=py_0 69 | - pcre=8.43=he6710b0_0 70 | - pexpect=4.8.0=py37_0 71 | - pickleshare=0.7.5=py37_0 72 | - pip=20.0.2=py37_3 73 | - prometheus_client=0.7.1=py_0 74 | - prompt-toolkit=3.0.5=py_0 75 | - prompt_toolkit=3.0.5=0 76 | - ptyprocess=0.6.0=py37_0 77 | - pymc==2.3.8 78 | - pygments=2.6.1=py_0 79 | - pyparsing=2.4.7=py_0 80 | - pyqt=5.9.2=py37h05f1152_2 81 | - pyrsistent=0.16.0=py37h7b6447c_0 82 | - python=3.7.7=hcff3b4d_5 83 | - python-dateutil=2.8.1=py_0 84 | - python_abi=3.7=1_cp37m 85 | - pytz=2020.1=py_0 86 | - pyzmq=18.1.1=py37he6710b0_0 87 | - qt=5.9.7=h5867ecd_1 88 | - qtconsole=4.7.4=py_0 89 | - qtpy=1.9.0=py_0 90 | - readline=8.0=h7b6447c_0 91 | - scipy=1.4.1=py37h0b6359f_0 92 | - seaborn=0.10.1=py_0 93 | - send2trash=1.5.0=py37_0 94 | - setuptools=46.4.0=py37_0 95 | - sip=4.19.8=py37hf484d3e_0 96 | - sqlite=3.31.1=h62c20be_1 97 | - terminado=0.8.3=py37_0 98 | - testpath=0.4.4=py_0 99 | - tk=8.6.8=hbc83047_0 100 | - tornado=6.0.4=py37h7b6447c_1 101 | - traitlets=4.3.3=py37_0 102 | - wcwidth=0.1.9=py_0 103 | - webencodings=0.5.1=py37_1 104 | - wheel=0.34.2=py37_0 105 | - widgetsnbextension=3.5.1=py37_0 106 | - xz=5.2.5=h7b6447c_0 107 | - zeromq=4.3.1=he6710b0_3 108 | - zipp=3.1.0=py_0 109 | - zlib=1.2.11=h7b6447c_3 110 | - pip: 111 | - cycler==0.10.0 112 | - hddm==0.8.0 113 | - kabuki==0.6.3 114 | - matplotlib==3.2.1 115 | - numpy==1.18.5 116 | - pandas==1.0.4 117 | - patsy==0.5.1 118 | - six==1.15.0 119 | 120 | -------------------------------------------------------------------------------- /MNE_workshop/Behavioral/all_raw_marker.csv: -------------------------------------------------------------------------------- 1 | trigger 2 | 1 3 | 2 4 | 3 5 | 4 6 | 5 7 | 6 8 | 7 9 | 8 10 | 9 11 | 10 12 | 11 13 | 12 14 | 13 15 | 14 16 | 15 17 | 16 18 | 17 19 | 18 20 | 19 21 | 20 22 | 21 23 | 22 24 | 23 25 | 24 26 | 25 27 | 26 28 | 27 29 | 28 30 | 29 31 | 30 32 | 31 33 | 32 34 | 33 35 | 34 36 | 35 37 | 36 38 | -------------------------------------------------------------------------------- /MNE_workshop/Behavioral/condition_marker/10_marker_lists.csv: -------------------------------------------------------------------------------- 1 | Positive,Control,Cue_only 2 | 17,32,9 3 | 19,33,11 4 | 14,28,10 5 | 22,29,8 6 | 21,34,1 7 | 15,27,2 8 | 13,31,7 9 | 16,26,12 10 | 18,30,6 11 | 24,25,5 12 | 20,35,4 13 | 23,36,3 14 | -------------------------------------------------------------------------------- /MNE_workshop/Behavioral/condition_marker/9_marker_lists.csv: -------------------------------------------------------------------------------- 1 | Positive,Control,Cue_only 2 | 35,5,14 3 | 32,4,18 4 | 30,6,21 5 | 27,1,17 6 | 36,10,19 7 | 33,11,20 8 | 31,7,15 9 | 34,2,23 10 | 28,8,24 11 | 29,9,13 12 | 26,3,16 13 | 25,12,22 14 | -------------------------------------------------------------------------------- /MNE_workshop/README.md: -------------------------------------------------------------------------------- 1 | MNE-Python MNE-BIDS ERP工作坊 2 | 3 | MNE-Python的安装 https://mne.tools/stable/install/mne_python.html 4 | 5 | MNE-BIDS的安装 https://mne.tools/mne-bids/stable/index.html 6 | 7 | MNElab的安装 https://github.com/cbrnr/mnelab 8 | 9 | MNE-BIDS demo数据下载链接: https://pan.baidu.com/s/16T6MGoi_ydXe7or5Dnl9Tg 提取码: yvjj 10 | -------------------------------------------------------------------------------- /MNE_workshop/Scripts/ERP_plot_statisctics.py: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | import mne 5 | import pandas as pd 6 | import os 7 | import settings 8 | 9 | 10 | #%% 11 | os.chdir(settings.base_dir_init()) 12 | 13 | epoch_fname=('Raw_data/epochs/') 14 | 15 | epochs_list=os.listdir(epoch_fname) 16 | condition_marker_file=os.listdir('Behavioral/condition_marker/') 17 | condition_marker=pd.read_csv('Behavioral/condition_marker/' + condition_marker_file[0]) 18 | conditions=condition_marker.columns.values 19 | #%% 20 | evoked_list1=[] 21 | evoked_list2=[] 22 | evoked_list3=[] 23 | 24 | condition_list=[evoked_list1,evoked_list2,evoked_list3] 25 | 26 | for i in range(0,len(epochs_list))[0:7]: 27 | epochs=mne.read_epochs(epoch_fname+epochs_list[i],preload=True) 28 | epochs.apply_baseline(baseline=(None,0)) 29 | for j in conditions: 30 | q=j + '_evoked' 31 | globals()[q]=epochs[j].average() 32 | 33 | for k,j in zip(condition_list,conditions): 34 | k.append(globals()[j+'_evoked']) 35 | 36 | 37 | picks=[epochs.ch_names.index(ch)for ch in ['Oz','POz','O1','O2']] 38 | condition_index={'Positive':evoked_list1,'Control':evoked_list2,'Cue_only':evoked_list3} 39 | mne.viz.plot_compare_evokeds(condition_index,picks=picks,ci=None,combine='mean',show_sensors='upper right') 40 | 41 | 42 | 43 | 44 | -------------------------------------------------------------------------------- /MNE_workshop/Scripts/ERP_preprocessing_single_event.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Mon Sep 28 22:51:24 2020 4 | 5 | @author: Xia Tao 6 | """ 7 | #%% import the module that will be used 8 | #%matplotlib qt 9 | import mne 10 | import os 11 | from mne.preprocessing import (ICA, create_eog_epochs) 12 | from mne_bids import read_raw_bids, BIDSPath 13 | from autoreject import Ransac # 14 | from autoreject.utils import interpolate_bads # 15 | from pyprep.find_noisy_channels import NoisyChannels 16 | import pandas as pd 17 | import numpy as np 18 | import settings 19 | import event_test 20 | #%% define the data path## 21 | 22 | 23 | subjetc_file=settings.bids_root_path_init() 24 | marker_file=settings.marker_list_path_init() 25 | 26 | os.chdir(settings.base_dir_init()) 27 | 28 | os.makedirs('Raw_data/raw_ica/',exist_ok=True) 29 | os.makedirs('Raw_data/epochs/',exist_ok=True) 30 | #%% read the subject list from mne-bids path and remove the participants that predefined 31 | raw_files=os.listdir(subjetc_file) 32 | raw_file_list=[] 33 | for raw_file in raw_files: 34 | if raw_file.split('-')[0]=='sub': 35 | raw_file_list.append(raw_file) 36 | 37 | for i in settings.rm_sub_list: 38 | raw_file_list.remove(i) 39 | 40 | #%% 41 | marker_lists=pd.read_csv('Behavioral/all_raw_marker.csv')['trigger'].to_list() 42 | 43 | raw_events_id=event_test.raw_marker_create(marker_list=marker_lists) 44 | 45 | condition_marker_file=os.listdir('Behavioral/condition_marker/') 46 | 47 | #%% 48 | for q in range(0,len(raw_file_list)): 49 | subject_id=raw_file_list[q].split('-')[1] 50 | bids_path = BIDSPath(subject=subject_id, task=settings.task, root=settings.bids_root_path_init(),session=settings.session,datatype='eeg',suffix='eeg') 51 | raw=read_raw_bids(bids_path=bids_path,extra_params=dict(preload=True)) 52 | raw.info['bads']=['FT7','FT8','T7','T8','TP7','TP8','AF7','AF8','M1','M2','BIP1','BIP2','BIP3','BIP4','BIP5','BIP6','BIP7','BIP8','BIP9','BIP10','BIP11','BIP12','BIP13','BIP14', 53 | 'BIP15','BIP16','BIP17','BIP18','BIP19','BIP20','BIP21','BIP22','BIP23','BIP24'] 54 | raw.drop_channels(ch_names=raw.info['bads']) 55 | montage=mne.channels.make_standard_montage('standard_1020') 56 | raw.set_montage(montage) 57 | filt_raw=raw.copy() 58 | filt_raw.drop_channels('EOG') 59 | nd=NoisyChannels(filt_raw) 60 | nd.find_bad_by_ransac(n_samples=50,channel_wise=True) 61 | raw.info['bads']=nd.bad_by_ransac 62 | raw.notch_filter(np.arange(50,250,50)) 63 | filt_raw.notch_filter(np.arange(50,250,50)) 64 | raw.filter(l_freq=settings.high_filter,h_freq=settings.low_filter,fir_design='firwin') 65 | filt_raw.filter(l_freq=1, h_freq=settings.low_filter) 66 | filt_raw.info['bads']=raw.info['bads'] 67 | raw.interpolate_bads(reset_bads=True,mode='accurate') 68 | filt_raw.interpolate_bads(reset_bads=True,mode='accurate') 69 | raw.resample(200,npad='auto') 70 | filt_raw.resample(200,npad='auto') 71 | raw.set_eeg_reference('average') 72 | filt_raw.set_eeg_reference('average') 73 | eog_evoked = create_eog_epochs(raw).average() 74 | raw.plot(block=True,n_channels=31,duration=30) 75 | ica=ICA(n_components=53,method='picard').fit(filt_raw, reject_by_annotation=True) 76 | eog_indices, eog_scores=ica.find_bads_eog(raw,threshold=0.2,measure='correlation') 77 | ica.exclude = eog_indices 78 | ica.plot_scores(eog_scores,exclude=ica.exclude,axhline=[-0.2,0.2],labels='eog') 79 | ica.plot_properties(raw, picks=eog_indices) 80 | ica.plot_sources(raw,block=True) 81 | ica.plot_sources(eog_evoked) 82 | ica.plot_sources(raw,block=True) 83 | ica.plot_overlay(raw,picks='eeg') 84 | ica.apply(raw) 85 | raw.save('Raw_data/raw_ica/'+raw_file_list[q]+'_raw.fif',overwrite=True) 86 | events,events_id=mne.events_from_annotations(raw,event_id=raw_events_id) 87 | condition_marker=pd.read_csv('Behavioral/condition_marker/' + condition_marker_file[q]) 88 | conditions=condition_marker.columns.values 89 | new_events,new_marker_list=event_test.event_operation(events,conditions_name=conditions,condition_marker=condition_marker) 90 | new_marker_list=list(set(new_marker_list)) 91 | new_events_id=dict(zip(conditions.tolist(),new_marker_list)) 92 | epochs=mne.Epochs(raw,events=new_events,event_id=new_events_id,baseline=None,preload=True, 93 | tmin=settings.epochs_tmin,tmax=settings.epochs_tmax,reject=dict(eeg=300e-6),flat=dict(eeg=1e-6)) 94 | epochs.plot(block=True) 95 | epochs.save('Raw_data/epochs/' + raw_file_list[q] +'-epo.fif',overwrite=True) 96 | 97 | 98 | 99 | -------------------------------------------------------------------------------- /MNE_workshop/Scripts/__pycache__/event_test.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/coolspiderghy/Python-Workshop-SuZhou/0b040d4890f576f2519e771080cdb44eb8815be8/MNE_workshop/Scripts/__pycache__/event_test.cpython-37.pyc -------------------------------------------------------------------------------- /MNE_workshop/Scripts/__pycache__/settings.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/coolspiderghy/Python-Workshop-SuZhou/0b040d4890f576f2519e771080cdb44eb8815be8/MNE_workshop/Scripts/__pycache__/settings.cpython-37.pyc -------------------------------------------------------------------------------- /MNE_workshop/Scripts/event_test.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Mon Nov 2 21:40:12 2020 4 | 5 | @author: Tao XIA 6 | """ 7 | 8 | def event_operation(raw_events,conditions_name,condition_marker): 9 | j=0 10 | new_marker_list=[] 11 | new_events=[] 12 | while j < len(conditions_name): 13 | for i in range(0,len(raw_events)): 14 | print(i) 15 | if raw_events[i][2] in condition_marker[conditions_name[j]].to_list(): 16 | raw_events[i][2]=j+200 17 | new_marker_list.append(j+200) 18 | new_events.append(raw_events[i]) 19 | j=j+1 20 | return new_events,new_marker_list 21 | 22 | def raw_marker_create(marker_list): 23 | key_list=[] 24 | for j in marker_list: 25 | b='Stimulus/s'+str(j) 26 | key_list.append(b) 27 | 28 | event_ids=dict(zip(key_list,marker_list)) 29 | return event_ids 30 | -------------------------------------------------------------------------------- /MNE_workshop/Scripts/list_rearrange.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Thu Nov 12 10:58:33 2020 4 | 5 | @author: Psychology 6 | """ 7 | 8 | import os 9 | import pandas as pd 10 | import numpy as np 11 | import settings 12 | import re 13 | 14 | 15 | os.chdir(settings.base_dir_init()) 16 | marker_fname=('behavioral_data/data/lists/') 17 | 18 | marker_files=os.listdir(marker_fname) 19 | marker_file_list=[] 20 | for marker_file in marker_files: 21 | if os.path.splitext(marker_file)[1]=='.csv': 22 | marker_file_list.append(marker_file) 23 | 24 | positive_list=[] 25 | control_list=[] 26 | cue_only_list=[] 27 | for positive in marker_file_list: 28 | if positive.find('positive')==7 or positive.find('positive')==6: 29 | positive_list.append(positive) 30 | for control in marker_file_list: 31 | if control.find('control')==7 or control.find('control')==6: 32 | control_list.append(control) 33 | for cue in marker_file_list: 34 | if cue.find('cue')==7 or cue.find('cue')==6: 35 | cue_only_list.append(cue) 36 | 37 | for i in range(0,len(positive_list)): 38 | all_conditions=pd.DataFrame(columns=['Positive','Control','Cue_only']) 39 | all_conditions['Positive']=pd.read_csv(marker_fname + positive_list[i])['cue_trigger'] 40 | all_conditions['Control']=pd.read_csv(marker_fname + control_list[i])['cue_trigger'] 41 | all_conditions['Cue_only']=pd.read_csv(marker_fname + cue_only_list[i])['trigger_learn'] 42 | subject_id=re.findall('(\d+)',positive_list[i])[0] 43 | all_conditions.to_csv('Behavioral/condition_marker/'+ subject_id +'_marker_lists.csv',index=False) -------------------------------------------------------------------------------- /MNE_workshop/Scripts/mne_bids_test.py: -------------------------------------------------------------------------------- 1 | #%% 2 | #%matplotlib qt 3 | import mne 4 | import os 5 | from mne_bids import write_raw_bids, BIDSPath,make_dataset_description 6 | import re 7 | import settings 8 | import event_test 9 | import pandas as pd 10 | #%% 11 | subjetc_file=settings.subject_path_init() 12 | os.chdir(settings.base_dir_init()) 13 | #%% 14 | 15 | raw_files=os.listdir(subjetc_file) 16 | raw_file_list=[] 17 | marker_lists=pd.read_csv('Behavioral/all_raw_marker.csv')['trigger'].to_list() 18 | event_ids=event_test.raw_marker_create(marker_lists) 19 | 20 | for raw_file in raw_files: 21 | if os.path.splitext(raw_file)[1]=='.vhdr': 22 | raw_file_list.append(raw_file) 23 | #%% 24 | for q in range(0,len(raw_file_list)): 25 | 26 | raw=mne.io.read_raw_brainvision(os.path.join(subjetc_file,raw_file_list[q]),preload=False) 27 | # if len(raw.info['ch_names'])>60: 28 | # raw.info['bads']=['BIP1','BIP2','BIP3','BIP4','BIP5','BIP6','BIP7','BIP8','BIP9','BIP10','BIP11','BIP12','BIP13','BIP14', 29 | # 'BIP15','BIP16','BIP17','BIP18','BIP19','BIP20','BIP21','BIP22','BIP23','BIP24'] 30 | # raw.drop_channels(ch_names=raw.info['bads']) 31 | raw.info['line_freq'] = 50 32 | raw.set_channel_types(mapping={'EOG':'eog'}) 33 | # montage=mne.channels.make_standard_montage('standard_1020') 34 | # raw.set_montage(montage) 35 | subject_id=re.findall('(\d+)',raw_file_list[q])[0] 36 | bids_path = BIDSPath(subject=subject_id, task=settings.task, root=settings.bids_root_path_init(),session=settings.session,datatype='eeg',suffix='eeg') 37 | #events,events_id=mne.events_from_annotations(raw,event_id=event_ids) 38 | write_raw_bids(raw, bids_path,overwrite=True) 39 | 40 | make_dataset_description(path=settings.bids_root_path_init(),name=settings.project_name,authors=settings.authors, 41 | acknowledgements=settings.acknowledgements,funding=settings.funding_support,overwrite=True) 42 | 43 | -------------------------------------------------------------------------------- /MNE_workshop/Scripts/settings.py: -------------------------------------------------------------------------------- 1 | import os 2 | import numpy as np 3 | import pandas as pd 4 | #%% ###### DIRECTORY STRUCTURE ###### 5 | def base_dir_init(): # Base directory for EEG analysis 6 | base_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__))) 7 | return base_dir 8 | 9 | def script_path_init(): 10 | script_path = base_dir_init() + '\Scripts' 11 | return script_path 12 | 13 | def bids_root_path_init(): 14 | bids_path = base_dir_init() + '\mne_bids' 15 | return bids_path 16 | 17 | def subject_path_init(): # Subjects directory, for storing EEG data 18 | subject_path = base_dir_init() + '\Raw_data' 19 | return subject_path 20 | 21 | def marker_list_path_init(): 22 | marker_path = base_dir_init() + "\Behaviorl" 23 | return marker_path 24 | 25 | if __name__ == '__main__': 26 | base_dir = base_dir_init() 27 | print('====== Current directory ======') 28 | print(base_dir) 29 | 30 | 31 | #%% remove the data that did not meet your criteria, you shoudl use the "sub-number" to represent the subjects no matter the filename of your data# 32 | 33 | rm_sub_list=['sub-01','sub-02','sub-04','sub-05','sub-06','sub-12','sub-17','sub-19','sub-21','sub-25','sub-32','sub-37','sub-39'] 34 | 35 | 36 | #%% ###################First we need to convert our raw EEG data into a BIDS-compliant folder structure############## 37 | #######################In this section we need define the following parameters for data description################## 38 | 39 | 40 | task='ForcedChioce' 41 | 42 | session='3' 43 | 44 | 45 | project_name='Sleep_learning_Forced_chioce_task' 46 | 47 | authors='Tao Xia, Xiaoqing Hu' 48 | 49 | acknowledgements='The authors would like to thank the subjects for their participantion' 50 | 51 | funding_support='GRF1004' 52 | 53 | #################let's define the dict object of events_ids for MNE analysis############ 54 | #marker_lists=pd.read_csv('Behavioral/all_raw_marker.csv')['trigger'].to_list() # to do: events operation 55 | 56 | low_filter=30 57 | high_filter=0.1 58 | 59 | epochs_tmin=-0.2 60 | epochs_tmax=1 61 | 62 | 63 | 64 | # %% 65 | -------------------------------------------------------------------------------- /Machine_learning_in_neuroimaging_using_python_LiChao/BNA_subregions.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/coolspiderghy/Python-Workshop-SuZhou/0b040d4890f576f2519e771080cdb44eb8815be8/Machine_learning_in_neuroimaging_using_python_LiChao/BNA_subregions.xlsx -------------------------------------------------------------------------------- /Machine_learning_in_neuroimaging_using_python_LiChao/README.html: -------------------------------------------------------------------------------- 1 | README

Python在神经影像机器学习中的应用

1059 |
1060 |

作者：黎超
1061 | Email: lichao19870617@163.com
1062 | easylearn为一站式机器学习图形界面软件，网址：https://github.com/lichao312214129/easylearn
1063 | easylearn微信交流群:
1064 |

1065 |
1066 |

神经影像机器学习一般流程

1067 |

链接：https://www.processon.com/view/link/5fb4f37a1e085368771b301a
1068 | 微信二维码:
1069 |
1070 | 密码：F5tU

1071 |

文件说明

1072 | 1102 |

版本

1103 |

Python3.7

1104 |

依赖包

1105 |

os
1106 | scipy
1107 | pandas
1108 | numpy
1109 | matplotlib 1110 | seaborn
1111 | collections
1112 | scikit-learn
1113 | nilearn
1114 | pyecharts
1115 | jupyter

1116 |

安装方法

1117 |

切换到本目录，在终端使用如下命令安装所有依赖
1118 | pip install -r requirements.txt

1119 |

easylearn安装方法

1120 |

pip install eslearn

-------------------------------------------------------------------------------- /Machine_learning_in_neuroimaging_using_python_LiChao/README.md: -------------------------------------------------------------------------------- 1 | # Python在神经影像机器学习中的应用 2 | > 作者：黎超 3 | > Email: lichao19870617@163.com 4 | > easylearn为一站式机器学习图形界面软件，网址：https://github.com/lichao312214129/easylearn 5 | > easylearn微信交流群: 6 | > 7 | 8 | ### 神经影像机器学习一般流程 9 | 链接：https://www.processon.com/view/link/5fb4f37a1e085368771b301a 10 | 微信二维码: 11 | 12 | **密码：F5tU** 13 | 14 | ### 文件说明 15 | - *fc_triu.npy* 为145个人的功能连接矩阵，其中71个精神分裂症患者，74个正常对照。功能连接矩阵为246个节点间的时间序列的Pearson's相关矩阵，我只纳入了上三角部分，共30135（246*245/2）个特征。 [注：文件名一定要和targets.xlsx中的ID对应] 16 | 17 | - 若想获取 *fc_triu.npy* 的原始246*246的数据，请转到百度云盘下载链接：https://pan.baidu.com/s/177aIKoxuZWDUTMVxW5kw8Q 18 | 提取码：doey 19 | 20 | - *targets.xlsx* 为每个被试对应的ID/唯一识别码/唯一名称和类别，0为正常，1为精神分裂症 21 | 22 | - *BNA_subregions.xlsx* 为脑246分区的信息[http://atlas.brainnetome.org/] 23 | 24 | - *workshop.ipynb* 为演示代码的jupyter-n*otebook版本 25 | 26 | - *workshop.py* 为演示代码的普通版本 27 | 28 | - *requirements.txt* 为项目依赖的Python包 29 | 30 | - *easylearn操作视频.zip* 为使用easylearn图形界面完成精神分裂症诊断的操作视频 31 | 32 | - 注：Demo数据为COBRE数据集 33 | 34 | ### 版本 35 | Python3.7 36 | 37 | ### 依赖包 38 | os 39 | scipy 40 | pandas 41 | numpy 42 | matplotlib 43 | seaborn 44 | collections 45 | scikit-learn 46 | nilearn 47 | pyecharts 48 | jupyter 49 | 50 | #### 安装方法 51 | 切换到本目录，在终端使用如下命令安装所有依赖 52 | `pip install -r requirements.txt` 53 | 54 | #### easylearn安装方法 55 | `pip install eslearn` 56 | -------------------------------------------------------------------------------- /Machine_learning_in_neuroimaging_using_python_LiChao/classification_performances.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/coolspiderghy/Python-Workshop-SuZhou/0b040d4890f576f2519e771080cdb44eb8815be8/Machine_learning_in_neuroimaging_using_python_LiChao/classification_performances.pdf 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-------------------------------------------------------------------------------- https://raw.githubusercontent.com/coolspiderghy/Python-Workshop-SuZhou/0b040d4890f576f2519e771080cdb44eb8815be8/Machine_learning_in_neuroimaging_using_python_LiChao/fc_triu.npy -------------------------------------------------------------------------------- /Machine_learning_in_neuroimaging_using_python_LiChao/outputs.pickle: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/coolspiderghy/Python-Workshop-SuZhou/0b040d4890f576f2519e771080cdb44eb8815be8/Machine_learning_in_neuroimaging_using_python_LiChao/outputs.pickle -------------------------------------------------------------------------------- /Machine_learning_in_neuroimaging_using_python_LiChao/requirements.txt: -------------------------------------------------------------------------------- 1 | os 2 | scipy 3 | pandas 4 | numpy 5 | matplotlib 6 | seaborn 7 | collections 8 | scikit-learn 9 | nilearn 10 | pyecharts -------------------------------------------------------------------------------- /Machine_learning_in_neuroimaging_using_python_LiChao/szVShc.json: -------------------------------------------------------------------------------- 1 | { 2 | "data_loading": { 3 | "whole": { 4 | "modalities": { 5 | "fc": { 6 | "file": [ 7 | "F:/workshop/demo_data/data/sub-40000.mat", 8 | "F:/workshop/demo_data/data/sub-40001.mat", 9 | "F:/workshop/demo_data/data/sub-40002.mat", 10 | "F:/workshop/demo_data/data/sub-40003.mat", 11 | "F:/workshop/demo_data/data/sub-40004.mat", 12 | "F:/workshop/demo_data/data/sub-40005.mat", 13 | "F:/workshop/demo_data/data/sub-40006.mat", 14 | "F:/workshop/demo_data/data/sub-40007.mat", 15 | "F:/workshop/demo_data/data/sub-40008.mat", 16 | "F:/workshop/demo_data/data/sub-40009.mat", 17 | "F:/workshop/demo_data/data/sub-40010.mat", 18 | "F:/workshop/demo_data/data/sub-40011.mat", 19 | "F:/workshop/demo_data/data/sub-40012.mat", 20 | 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"F:/workshop/demo_data/data/sub-40136.mat", 141 | "F:/workshop/demo_data/data/sub-40137.mat", 142 | "F:/workshop/demo_data/data/sub-40138.mat", 143 | "F:/workshop/demo_data/data/sub-40139.mat", 144 | "F:/workshop/demo_data/data/sub-40140.mat", 145 | "F:/workshop/demo_data/data/sub-40141.mat", 146 | "F:/workshop/demo_data/data/sub-40142.mat", 147 | "F:/workshop/demo_data/data/sub-40143.mat", 148 | "F:/workshop/demo_data/data/sub-40144.mat", 149 | "F:/workshop/demo_data/data/sub-40145.mat", 150 | "F:/workshop/demo_data/data/sub-40146.mat", 151 | "F:/workshop/demo_data/data/sub-40147.mat" 152 | ], 153 | "mask": "" 154 | } 155 | }, 156 | "targets": "D:/My_Codes/Python-Workshop-SuZhou/Machine_learning_in_neuroimaging_using_python_LiChao/targets.xlsx", 157 | "covariates": "" 158 | } 159 | }, 160 | "feature_engineering": { 161 | "feature_preprocessing": { 162 | "StandardScaler()": {} 163 | }, 164 | "dimreduction": { 165 | "PCA()": { 166 | "n_components": { 167 | "value": "0.95" 168 | } 169 | } 170 | }, 171 | "feature_selection": { 172 | "RFECV()": { 173 | "step": { 174 | "value": "0.10" 175 | }, 176 | "cv": { 177 | "value": "5" 178 | }, 179 | "estimator": { 180 | "value": "SVC(kernel='linear')" 181 | }, 182 | "n_jobs": { 183 | "value": "-1" 184 | } 185 | } 186 | }, 187 | "unbalance_treatment": { 188 | "RandomOverSampler()": {} 189 | } 190 | }, 191 | "machine_learning": { 192 | "Classification": { 193 | "RidgeClassifier()": { 194 | "alpha": { 195 | "value": "1" 196 | } 197 | } 198 | } 199 | }, 200 | "model_evaluation": { 201 | "StratifiedKFold()": { 202 | "n_splits": { 203 | "value": "5" 204 | }, 205 | "shuffle": { 206 | "value": "True" 207 | }, 208 | "random_state": { 209 | "value": "0" 210 | } 211 | }, 212 | "Statistical_analysis": { 213 | "Binomial test": {} 214 | } 215 | } 216 | } -------------------------------------------------------------------------------- /Machine_learning_in_neuroimaging_using_python_LiChao/targets.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/coolspiderghy/Python-Workshop-SuZhou/0b040d4890f576f2519e771080cdb44eb8815be8/Machine_learning_in_neuroimaging_using_python_LiChao/targets.xlsx -------------------------------------------------------------------------------- /Machine_learning_in_neuroimaging_using_python_LiChao/workshop.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # coding: utf-8 3 | 4 | # In[59]: 5 | 6 | 7 | import os 8 | import scipy.io as sio 9 | import pandas as pd 10 | import numpy as np 11 | import matplotlib.pyplot as plt 12 | import seaborn as sns 13 | import seaborn as sns 14 | from collections import Counter 15 | from sklearn.model_selection import StratifiedShuffleSplit 16 | from sklearn.preprocessing import StandardScaler 17 | from sklearn.decomposition import PCA 18 | from sklearn.feature_selection import RFECV 19 | from sklearn.svm import LinearSVC 20 | from sklearn.linear_model import LogisticRegression, RidgeClassifier, LassoCV 21 | from sklearn import metrics 22 | from nilearn import plotting 23 | from pyecharts.charts import Graph 24 | from pyecharts import options as opts 25 | 26 | 27 | # ### ===========================输入=========================== 28 | 29 | # In[60]: 30 | 31 | 32 | data_file_raw = "F:/workshop/demo_data/data" # 如果下载了原始数据 33 | data_file_triu = "./fc_triu.npy" # 如果只有上三角向量 34 | label_file = "./targets.xlsx" 35 | 36 | 37 | # ### ===========================读取特征和标签=========================== 38 | 39 | # In[61]: 40 | 41 | 42 | # 读取保存数据的文件名 43 | files_name = os.listdir(data_file_raw) 44 | files = [os.path.join(data_file_raw, fn) for fn in files_name] # 获取所有功能连接的文件地址 45 | label = pd.read_excel(label_file) 46 | # print(f"files={files}") 47 | print(label) 48 | 49 | 50 | # In[62]: 51 | 52 | 53 | # 加载第一个功能连接， 查看数据形式 54 | onefc = sio.loadmat(files[0]) 55 | print(f"数据类型为：{type(onefc)}") 56 | # 查看字典的keys 57 | print(f"功能连接的keys为: {onefc.keys()}") 58 | # 第4个item是数据，我们查看数据的情况 59 | dd = onefc[list(onefc.keys())[3]] 60 | print(f"功能连接特征的原始形状为：{dd.shape}") 61 | plt.imshow(dd, cmap="jet") 62 | plt.colorbar() 63 | 64 | 65 | # In[63]: 66 | 67 | 68 | # 逐个读取每个被试的功能连接， 并将所有数据放入data 69 | # 因为功能连接是一个对称矩阵，即上三角和下三角是对称的，所以后续加载数据的时候，我们只保留上三角即可 70 | mask = np.ones(np.shape(dd)) 71 | mask = np.triu(mask, 1) 72 | mask = mask == 1 73 | plt.imshow(mask, cmap="gray") 74 | plt.colorbar() 75 | plt.show() 76 | 77 | 78 | # In[64]: 79 | 80 | 81 | # 如果下载了原始数据，加载并提取每个功能连接数据的上三角部分 82 | data = [] 83 | for file in files: 84 | dd = sio.loadmat(file) 85 | dd = dd[list(dd.keys())[3]] 86 | dd = dd[mask] # 用mask筛选上三角矩阵 87 | data.append(dd) 88 | data = np.array(data) # 将data转换为numpy.array 89 | print(f"###特征的形状：{data.shape}###") 90 | pd.DataFrame(data).head(5) 91 | 92 | 93 | # In[65]: 94 | 95 | 96 | # 如果只有fc_triu.npy，即上三角矩阵 97 | data = np.load(data_file_triu) 98 | print(f"###特征的形状：{data.shape}###") 99 | pd.DataFrame(data).head(5) 100 | 101 | 102 | # ### ===========================将每个被试特征与其标签对齐=========================== 103 | 104 | # In[66]: 105 | 106 | 107 | # 1.第一步提取文件名中的被试名 108 | files_name = pd.Series(files_name) 109 | files_id = files_name.str.findall(".*sub.?\d*") 110 | files_id = pd.DataFrame([fi[0] for fi in files_id]) 111 | # 2.将label按照文件名顺序重新排序 112 | label_sorted = pd.merge(files_id, label, left_on=0, right_on="__ID__") 113 | label_sorted = label_sorted["__Targets__"].values 114 | 115 | 116 | # In[67]: 117 | 118 | 119 | # 为了后续演示，我故意在特征中随机设置5000个缺失值 120 | np.seed = 666 121 | n_sub = len(data) 122 | id_nan = np.random.permutation(data.size)[:5000] 123 | data = data.reshape([-1,1]) 124 | data[id_nan,] = np.nan 125 | data = data.reshape(n_sub,-1) 126 | 127 | 128 | # ### =============================划分数据============================= 129 | 130 | # In[68]: 131 | 132 | 133 | np.random.seed(0) 134 | skf = StratifiedShuffleSplit(n_splits=1, test_size=0.4, random_state=666) 135 | skf_index = list(skf.split(data, label_sorted)) 136 | feature_train = data[skf_index[0][0],:] 137 | label_train = label_sorted[skf_index[0][0]] 138 | feature_other = data[skf_index[0][1],:] 139 | label_other = label_sorted[skf_index[0][1]] 140 | 141 | skf = StratifiedShuffleSplit(n_splits=1, test_size=0.5, random_state=666) 142 | skf_index = list(skf.split(feature_other, label_other)) 143 | feature_validation = feature_other[skf_index[0][0],:] 144 | label_validation = label_other[skf_index[0][0]] 145 | feature_test = feature_other[skf_index[0][1],:] 146 | label_test = label_other[skf_index[0][1]] 147 | 148 | # 检查各个数据中各类的数目, 即各类别是否平衡 149 | ct_tr = sorted(Counter(label_train).items()) 150 | ct_val = sorted(Counter(label_validation).items()) 151 | ct_te = sorted(Counter(label_test).items()) 152 | 153 | print(f"训练集中各个类别的数目为：{ct_tr}") 154 | print(f"验证集中各个类别的数目为：{ct_val}") 155 | print(f"测试集中各个类别的数目为：{ct_te}") 156 | 157 | # 画饼图，看各类比例 158 | plt.subplot(1,3,1) 159 | labels = ["SZ", "HC"] 160 | sizes = [ct[1] for ct in ct_tr] 161 | plt.pie(sizes,labels=labels,autopct='%1.1f%%',shadow=False,startangle=150, ) 162 | plt.title("Trainning dataset") 163 | 164 | plt.subplot(1,3,2) 165 | sizes = [ct[1] for ct in ct_val] 166 | plt.pie(sizes,labels=labels,autopct='%1.1f%%',shadow=False,startangle=150, ) 167 | plt.title("Validation dataset") 168 | 169 | plt.subplot(1,3,3) 170 | sizes = [ct[1] for ct in ct_te] 171 | plt.pie(sizes,labels=labels,autopct='%1.1f%%',shadow=False,startangle=150, ) 172 | plt.title("Test dataset") 173 | plt.show() 174 | 175 | 176 | # ### =============================数据检查与处理============================= 177 | 178 | # In[69]: 179 | 180 | 181 | # 检查训练集中缺失值 182 | nan_value = np.sum(np.isnan(feature_train), axis=0) 183 | print(f"所有特征中，最多的缺失值数量为：{np.max(nan_value)}") # 最大的缺失值 184 | 185 | 186 | # In[70]: 187 | 188 | 189 | # 缺失值不多，选择用均值填充缺失值 190 | feature_train = pd.DataFrame(feature_train) 191 | feature_validation = pd.DataFrame(feature_validation) 192 | feature_test = pd.DataFrame(feature_test) 193 | 194 | fill_value = feature_train.mean() # 只能用训练集的参数，因为实际应用中我们往往不能获取测试集的数据 195 | feature_train = pd.DataFrame(feature_train) 196 | feature_train.fillna(value=fill_value, inplace=True) 197 | feature_validation.fillna(value=fill_value, inplace=True) 198 | feature_test.fillna(value=fill_value, inplace=True) 199 | 200 | nan_value = np.sum(np.isnan(feature_train), axis=0) 201 | print(f"填充后，所有特征中，最多的缺失值数量为：{np.max(nan_value)}") # 最大的缺失值 202 | 203 | 204 | # In[71]: 205 | 206 | 207 | # 检查训练集特征数值为0的样本比例 208 | zero_value = np.sum(feature_train==0, axis=0) 209 | print(f"全为0的特征数量为: {np.max(zero_value)}") 210 | 211 | 212 | # In[72]: 213 | 214 | 215 | # 检查各个数据集的分布和相关性 216 | data_ = np.vstack([feature_train.mean(axis=0), feature_validation.mean(axis=0), feature_test.mean(axis=0)]) 217 | fig, axs = plt.subplots(1,2, figsize=(10,5)) 218 | sns.heatmap(np.corrcoef(data_), annot=True, ax=axs[0]) 219 | axs[0].axis('equal') 220 | axs[0].set_xticklabels(["Trainning dataset","Validation dataset","Test dataset"], rotation=45) 221 | axs[0].set_yticklabels(["Trainning dataset","Validation dataset","Test dataset"], rotation=0) 222 | sns.kdeplot(data_[0,:], ax=axs[1]) 223 | sns.kdeplot(data_[1,:], ax=axs[1]) 224 | sns.kdeplot(data_[2,:], ax=axs[1]) 225 | plt.title("Distribution", fontsize=20) 226 | plt.legend(["Training dataset", "Validation dataset", "Test dataset"]) 227 | plt.subplots_adjust(wspace=1) 228 | plt.show() 229 | 230 | 231 | # In[73]: 232 | 233 | 234 | # 规范化数据 235 | scaler = StandardScaler() 236 | feature_train_ = scaler.fit_transform(feature_train) 237 | 238 | feature_validation_ = scaler.transform(feature_validation) # 对验证集和测试集在规范化时，要用训练集的参数 239 | feature_test_ = scaler.transform(feature_test) 240 | 241 | pd.DataFrame(feature_train).head(5) 242 | 243 | 244 | # ### =============================特征工程：降维 + 特征选择============================= 245 | 246 | # In[74]: 247 | 248 | 249 | # 降维 250 | pca = PCA(n_components=0.95, random_state=666) 251 | feature_train_ = pca.fit_transform(feature_train_) 252 | 253 | feature_validation_ = pca.transform(feature_validation_) # 使用训练集的参数给验证集和测试集做降维 254 | feature_test_ = pca.transform(feature_test_) 255 | print(f"###降维后特征维度由{feature_train.shape[1]}变为{feature_train_.shape[1]}###") 256 | pd.DataFrame(feature_train_).head(5) 257 | 258 | 259 | # In[82]: 260 | 261 | 262 | # 递归特征消除筛选特征 263 | selector = RFECV(LinearSVC(random_state=666), step=5, cv=5, n_jobs=3) 264 | selector.fit(feature_train_, label_train) 265 | feature_train_ = selector.transform(feature_train_) 266 | 267 | # 对验证集和测试集做特征选择，要用训练集的参数 268 | feature_validation_ = selector.transform(feature_validation_) 269 | feature_test_ = selector.transform(feature_test_) 270 | print(f"###递归特征消除法选择特征后，特征维度为{feature_train_.shape[1]}###") 271 | print(f"被剔除的特征的编号为：\n{pd.DataFrame(np.where(1-selector.support_)).values}") 272 | pd.DataFrame(feature_train_).head(5) 273 | 274 | 275 | # ### ============================训练模型============================ 276 | 277 | # In[87]: 278 | 279 | 280 | # model = LogisticRegression(C=1, random_state=666) 281 | model = RidgeClassifier(random_state=666) 282 | model.fit(feature_train_, label_train) 283 | 284 | 285 | # ### ============================模型验证============================ 286 | # > 最好使用外部验证集 287 | 288 | # In[88]: 289 | 290 | 291 | pred_train_label = model.predict(feature_train_) 292 | pred_val_label = model.predict(feature_validation_) 293 | 294 | 295 | # In[89]: 296 | 297 | 298 | # 模型验证，以及根据验证情况调参 299 | acc_train = metrics.accuracy_score(label_train, pred_train_label) 300 | f1score_train = metrics.f1_score(label_train, pred_train_label) 301 | acc_validation = metrics.accuracy_score(label_validation, pred_val_label) 302 | f1score_validation = metrics.f1_score(label_validation, pred_val_label) 303 | print(f"acc_train = {acc_train:.3f}; f1score_train = {f1score_train}\nacc_validation = {acc_validation:.8f}; f1score_validaton = {f1score_validation}") 304 | 305 | 306 | # ### ============================最终的测试============================ 307 | # > 最好使用外部测试集 308 | 309 | # In[90]: 310 | 311 | 312 | pred_test_label = model.predict(feature_test_) 313 | pred_test_prob = model.decision_function(feature_test_) 314 | acc_test = metrics.accuracy_score(label_test, pred_test_label) 315 | f1score_test = metrics.f1_score(label_test, pred_test_label) 316 | print(f"acc_test = {acc_test:.8f}; f1score_test = {f1score_test}\n") 317 | 318 | 319 | # ### ============================结果可视化============================ 320 | 321 | # In[80]: 322 | 323 | 324 | # 获取权重 325 | wei = model.coef_ 326 | wei = (wei - wei.mean()) / wei.std() 327 | wei = selector.inverse_transform(wei) 328 | wei = pca.inverse_transform(wei) 329 | weight = np.zeros(mask.shape) 330 | weight[mask] = wei[0] 331 | weight = weight + weight.T 332 | 333 | # 只显示前0.2%的权重 334 | threshold = 99.8 335 | topperc = np.percentile(np.abs(weight), threshold) 336 | weight[np.abs(weight) < topperc] = 0 337 | 338 | # 获取MNI坐标 339 | coords_file = r"F:\workshop\demo_data\BNA_subregions.xlsx" 340 | coords_info = pd.read_excel(coords_file) 341 | 342 | # 获取MNI坐标 343 | coords = np.hstack([coords_info["lh.MNI(X,Y,Z)"].values, coords_info["rh.MNI(X,Y,Z)"].values]) 344 | label_idx = np.hstack([coords_info["Label ID.L"].values, coords_info["Label ID.R"].values]) 345 | sort_idx = np.argsort(label_idx) 346 | node_coords = coords[sort_idx] 347 | node_coords = pd.DataFrame([eval(nc) for nc in node_coords]).values 348 | 349 | # 获取node name 350 | name = coords_info["Left and Right Hemisphere"] 351 | name_L = ["L".join(name_.split("L(R)")) for name_ in name] 352 | name_R = ["R".join(name_.split("L(R)")) for name_ in name] 353 | node_name = np.hstack([name_L, name_R]) 354 | node_name = node_name[sort_idx] 355 | 356 | # 获取构成连接的节点名称 357 | id_mat = np.where(weight) 358 | node1 = node_name[id_mat[0]] 359 | node2 = node_name[id_mat[1]] 360 | node = np.hstack([node1, node2]) 361 | node = np.unique(node) 362 | 363 | # 显示连接网络 364 | weight_filter = weight[id_mat[0], id_mat[1]] 365 | wei_node = np.hstack([node1, node2]).T 366 | wei_node = np.vstack([wei_node, np.hstack([weight_filter, weight_filter])]).T 367 | 368 | nodes = [{"name": nd, "symbolSize": np.sum(np.abs(np.float64(wei_node[:,1][np.in1d(wei_node[:,0], str(nd))])))*20} for nd in node] 369 | links = [{"source": str(nd1), "target": str(nd2)} for (nd1, nd2) in zip(node1, node2)] 370 | graph= ( 371 | Graph() 372 | .add("", nodes, links, repulsion=100) 373 | .set_global_opts(title_opts=opts.TitleOpts(title=f"前{100-threshold:.2f}%的权重")) 374 | ) 375 | graph.render_notebook() 376 | 377 | 378 | # In[81]: 379 | 380 | 381 | # 矩阵显示 382 | plt.figure(figsize=(10,10)) 383 | plt.imshow(weight, cmap="RdBu_r") 384 | plt.colorbar() 385 | plt.show() 386 | 387 | # 脑图显示 388 | plotting.plot_connectome(weight, node_coords, node_size=0, annotate=True) 389 | plt.show() 390 | view = plotting.view_connectome(weight, node_coords) 391 | view 392 | 393 | 394 | # In[ ]: 395 | 396 | 397 | 398 | 399 | -------------------------------------------------------------------------------- /Machine_learning_in_neuroimaging_using_python_LiChao/一般流程.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/coolspiderghy/Python-Workshop-SuZhou/0b040d4890f576f2519e771080cdb44eb8815be8/Machine_learning_in_neuroimaging_using_python_LiChao/一般流程.png -------------------------------------------------------------------------------- /PsychoPy/Python在实验程序编写中的应用——Ryan.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/coolspiderghy/Python-Workshop-SuZhou/0b040d4890f576f2519e771080cdb44eb8815be8/PsychoPy/Python在实验程序编写中的应用——Ryan.pdf -------------------------------------------------------------------------------- /PsychoPy/ReadMe.md: -------------------------------------------------------------------------------- 1 | # Python在实验程序编写和设计中的应用，以PsychoPy为例 # 2 | >分享者：Ryan 3 | 4 | >微信公众号：Ryan聊心理 5 | ![](微信公众号.png) 6 | 7 | ## 文件说明 ## 8 | Python在实验程序编写中的应用——Ryan.pdf 9 | ———20201122工作坊分享PPT 10 | 11 | gonogo.psyexp 12 | ———使用PsychoPy Builder创建的GoNogo实验（PsychoPy工程文件） 13 | 14 | gonogo.py 15 | ———使用PsychoPy Builder创建的GoNogo实验（PsychoPy生成的Python文件） 16 | 17 | stimuli_list.xlsx 18 | ———运行使用PsychoPy Builder创建的GoNogo实验所需的刺激材料文件 19 | 20 | gonogo_test.py 21 | ———使用PsychoPy Coder创建的GoNogo实验 22 | 23 | stroop.psyexp 24 | ———使用PsychoPy Builder创建的Stroop实验 25 | 26 | ## 版本 ## 27 | Python 3.6 28 | 29 | PsychoPy 2020.2.4 30 | 31 | ## 安装 ## 32 | 如果已经安装好了Python，建议直接通过pip来安装PsychoPy，不另安装Standalone版的PsychoPy 33 | ### Python标准安装（推荐） ### 34 | pip install psychopy 35 | ### Python纯安装（后续手动安装依赖包-不推荐） ### 36 | pip install psychopy --no-deps 37 | 若只考虑使用PsychoPy的Builder模式，可直接安装PsychoPy的exe 38 | ### EXE直接安装（不推荐） ### 39 | Standalone版的PsychoPy 40 | >链接：https://pan.baidu.com/s/169nYLiGB6nfw7-Eh9fDh8Q 41 | > 42 | >提取码：c4bp 43 | 44 | ## 交流群 ## 45 | ![](群二维码.png) 46 | 47 | ## 招聘 ## 48 | >若感兴趣在企业的就业机会，可以打开链接详细了解 49 | 50 | >https://mp.weixin.qq.com/s/oi8pIErmhcox0lVPsi-spA -------------------------------------------------------------------------------- /PsychoPy/gonogo.psyexp: -------------------------------------------------------------------------------- 1 |  2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | 108 | 109 | 110 | 111 | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 127 | 128 | 129 | 130 | 131 | 132 | 133 | 134 | 135 | 136 | 137 | 138 | 139 | 140 | 141 | 142 | 143 | 144 | 145 | 146 | 147 | 148 | 149 | 150 | 151 | 152 | 153 | 154 | 155 | 156 | 157 | 158 | 159 | 160 | 161 | 162 | 163 | 164 | 165 | 166 | 167 | 168 | 169 | 170 | 171 | 172 | 173 | 174 | 175 | 176 | 177 | 178 | 179 | 180 | 181 | 182 | 183 | 184 | 185 | 186 | 187 | 188 | 189 | 190 | 191 | 192 | 193 | 194 | 195 | 196 | 197 | 198 | 199 | 200 | 201 | 202 | 203 | 204 | 205 | 206 | 207 | 208 | 209 | 210 | 211 | 212 | 213 | 214 | 215 | 216 | 217 | 218 | 219 | 220 | 221 | 222 | 223 | 224 | 225 | 226 | 227 | 228 | 229 | 230 | 231 | 232 | 233 | 234 | 235 | 236 | 237 | 238 | 239 | 240 | 241 | 242 | 243 | 244 | 245 | 246 | 247 | 248 | 249 | 250 | 251 | 252 | 253 | 254 | 255 | 256 | 257 | 258 | 259 | 260 | 261 | 262 | 263 | 264 | 265 | 266 | 267 | 268 | 269 | 270 | 271 | 272 | 273 | 274 | 275 | 276 | 277 | 278 | 279 | 280 | 281 | 282 | 283 | 284 | 285 | 286 | 287 | 288 | 289 | 290 | 291 | 292 | 293 | 294 | 295 | 296 | 297 | 298 | 299 | 300 | 301 | 302 | 303 | 304 | 305 | 306 | 307 | 308 | 309 | 310 | 311 | 312 | 313 | 314 | 315 | 316 | 317 | 318 | 319 | 320 | 321 | 322 | 323 | 324 | 325 | 326 | 327 | 328 | 329 | 330 | 331 | 332 | 333 | 334 | 335 | 336 | 337 | 338 | 339 | 340 | 341 | 342 | 343 | 344 | 345 | 346 | 347 | 348 | 349 | 350 | 351 | 352 | 353 | 354 | 355 | 356 | 357 | 358 | 359 | 360 | 361 | 362 | 363 | 364 | 365 | 366 | 367 | 368 | 369 | 370 | 371 | 372 | 373 | 374 | 375 | 376 | 377 | 378 | 379 | 380 | 381 | 382 | 383 | 384 | 385 | 386 | 387 | 388 | 389 | 390 | 391 | 392 | 393 | 394 | 395 | 396 | 397 | 398 | 399 | 400 | 401 | 402 | 403 | 404 | 405 | 406 | 407 | 408 | 409 | 410 | 411 | 412 | 413 | 414 | 415 | 416 | 417 | 418 | 419 | 420 | 421 | 422 | 423 | 424 | 425 | 426 | 427 | 428 | 429 | 430 | 431 | 432 | 433 | 434 | 435 | 436 | 437 | 438 | 439 | 440 | 441 | 442 | 443 | 444 | 445 | -------------------------------------------------------------------------------- /PsychoPy/gonogo.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | """ 4 | This experiment was created using PsychoPy3 Experiment Builder (v2020.2.6), 5 | on 十一月 21, 2020, at 15:37 6 | If you publish work using this script the most relevant publication is: 7 | 8 | Peirce J, Gray JR, Simpson S, MacAskill M, Höchenberger R, Sogo H, Kastman E, Lindeløv JK. (2019) 9 | PsychoPy2: Experiments in behavior made easy Behav Res 51: 195. 10 | https://doi.org/10.3758/s13428-018-01193-y 11 | 12 | """ 13 | 14 | from __future__ import absolute_import, division 15 | 16 | from psychopy import locale_setup 17 | from psychopy import prefs 18 | from psychopy import sound, gui, visual, core, data, event, logging, clock 19 | from psychopy.constants import (NOT_STARTED, STARTED, PLAYING, PAUSED, 20 | STOPPED, FINISHED, PRESSED, RELEASED, FOREVER) 21 | 22 | import numpy as np # whole numpy lib is available, prepend 'np.' 23 | from numpy import (sin, cos, tan, log, log10, pi, average, 24 | sqrt, std, deg2rad, rad2deg, linspace, asarray) 25 | from numpy.random import random, randint, normal, shuffle 26 | import os # handy system and path functions 27 | import sys # to get file system encoding 28 | 29 | from psychopy.hardware import keyboard 30 | 31 | 32 | 33 | # Ensure that relative paths start from the same directory as this script 34 | _thisDir = os.path.dirname(os.path.abspath(__file__)) 35 | os.chdir(_thisDir) 36 | 37 | # Store info about the experiment session 38 | psychopyVersion = '2020.2.6' 39 | expName = 'gonogo' # from the Builder filename that created this script 40 | expInfo = {'participant': '', 'session': '001'} 41 | dlg = gui.DlgFromDict(dictionary=expInfo, sortKeys=False, title=expName) 42 | if dlg.OK == False: 43 | core.quit() # user pressed cancel 44 | expInfo['date'] = data.getDateStr() # add a simple timestamp 45 | expInfo['expName'] = expName 46 | expInfo['psychopyVersion'] = psychopyVersion 47 | 48 | # Data file name stem = absolute path + name; later add .psyexp, .csv, .log, etc 49 | filename = _thisDir + os.sep + u'data/%s_%s_%s' % (expInfo['participant'], expName, expInfo['date']) 50 | 51 | # An ExperimentHandler isn't essential but helps with data saving 52 | thisExp = data.ExperimentHandler(name=expName, version='', 53 | extraInfo=expInfo, runtimeInfo=None, 54 | originPath='F:\\坚果云同步盘\\Ryan的同步文件\\201122Python分享\\Git\\gonogo.py', 55 | savePickle=True, saveWideText=True, 56 | dataFileName=filename) 57 | # save a log file for detail verbose info 58 | logFile = logging.LogFile(filename+'.log', level=logging.EXP) 59 | logging.console.setLevel(logging.WARNING) # this outputs to the screen, not a file 60 | 61 | endExpNow = False # flag for 'escape' or other condition => quit the exp 62 | frameTolerance = 0.001 # how close to onset before 'same' frame 63 | 64 | # Start Code - component code to be run after the window creation 65 | 66 | # Setup the Window 67 | win = visual.Window( 68 | size=[1024, 768], fullscr=False, screen=0, 69 | winType='pyglet', allowGUI=True, allowStencil=False, 70 | monitor='testMonitor', color=[0,0,0], colorSpace='rgb', 71 | blendMode='avg', useFBO=True, 72 | units='pix') 73 | # store frame rate of monitor if we can measure it 74 | expInfo['frameRate'] = win.getActualFrameRate() 75 | if expInfo['frameRate'] != None: 76 | frameDur = 1.0 / round(expInfo['frameRate']) 77 | else: 78 | frameDur = 1.0 / 60.0 # could not measure, so guess 79 | 80 | # create a default keyboard (e.g. to check for escape) 81 | defaultKeyboard = keyboard.Keyboard() 82 | 83 | # Initialize components for Routine "ins1" 84 | ins1Clock = core.Clock() 85 | ins1_text = visual.TextStim(win=win, name='ins1_text', 86 | text='If you see M, press SPACE, if see W, do nothing\n\nPress ENTER to start practice', 87 | font='Arial', 88 | pos=(0, 0), height=30, wrapWidth=None, ori=0, 89 | color='white', colorSpace='rgb', opacity=1, 90 | languageStyle='LTR', 91 | depth=0.0); 92 | key_resp_ins1 = keyboard.Keyboard() 93 | 94 | # Initialize components for Routine "practice" 95 | practiceClock = core.Clock() 96 | fix_text = visual.TextStim(win=win, name='fix_text', 97 | text='+', 98 | font='Arial', 99 | pos=(0, 0), height=50, wrapWidth=None, ori=0, 100 | color='white', colorSpace='rgb', opacity=1, 101 | languageStyle='LTR', 102 | depth=0.0); 103 | sti_text = visual.TextStim(win=win, name='sti_text', 104 | text='default text', 105 | font='Arial', 106 | pos=(0, 0), height=36, wrapWidth=None, ori=0, 107 | color='white', colorSpace='rgb', opacity=1, 108 | languageStyle='LTR', 109 | depth=-1.0); 110 | key_resp = keyboard.Keyboard() 111 | 112 | # Initialize components for Routine "ins2" 113 | ins2Clock = core.Clock() 114 | ins2_text = visual.TextStim(win=win, name='ins2_text', 115 | text='Press ENTER to start experiment', 116 | font='Arial', 117 | pos=(0, 0), height=30, wrapWidth=None, ori=0, 118 | color='white', colorSpace='rgb', opacity=1, 119 | languageStyle='LTR', 120 | depth=0.0); 121 | key_resp_ins2 = keyboard.Keyboard() 122 | 123 | # Initialize components for Routine "exp1" 124 | exp1Clock = core.Clock() 125 | fix1_text = visual.TextStim(win=win, name='fix1_text', 126 | text='+', 127 | font='Arial', 128 | pos=(0, 0), height=50, wrapWidth=None, ori=0, 129 | color='white', colorSpace='rgb', opacity=1, 130 | languageStyle='LTR', 131 | depth=0.0); 132 | sti1_text = visual.TextStim(win=win, name='sti1_text', 133 | text='default text', 134 | font='Arial', 135 | pos=(0, 0), height=36, wrapWidth=None, ori=0, 136 | color='white', colorSpace='rgb', opacity=1, 137 | languageStyle='LTR', 138 | depth=-1.0); 139 | key_resp_2 = keyboard.Keyboard() 140 | 141 | # Initialize components for Routine "rest" 142 | restClock = core.Clock() 143 | rest_text = visual.TextStim(win=win, name='rest_text', 144 | text='Rest for a while', 145 | font='Arial', 146 | pos=(0, 0), height=30, wrapWidth=None, ori=0, 147 | color='white', colorSpace='rgb', opacity=1, 148 | languageStyle='LTR', 149 | depth=0.0); 150 | 151 | # Initialize components for Routine "exp2" 152 | exp2Clock = core.Clock() 153 | fix2_text = visual.TextStim(win=win, name='fix2_text', 154 | text='+', 155 | font='Arial', 156 | pos=(0, 0), height=50, wrapWidth=None, ori=0, 157 | color='white', colorSpace='rgb', opacity=1, 158 | languageStyle='LTR', 159 | depth=0.0); 160 | si2_text = visual.TextStim(win=win, name='si2_text', 161 | text='default text', 162 | font='Arial', 163 | pos=(0, 0), height=36, wrapWidth=None, ori=0, 164 | color='white', colorSpace='rgb', opacity=1, 165 | languageStyle='LTR', 166 | depth=-1.0); 167 | key_resp_3 = keyboard.Keyboard() 168 | 169 | # Initialize components for Routine "finish" 170 | finishClock = core.Clock() 171 | finish_text = visual.TextStim(win=win, name='finish_text', 172 | text='This is the end of the experiment.\n\nThank you!', 173 | font='Arial', 174 | pos=(0, 0), height=30, wrapWidth=None, ori=0, 175 | color='white', colorSpace='rgb', opacity=1, 176 | languageStyle='LTR', 177 | depth=0.0); 178 | key_resp_finish = keyboard.Keyboard() 179 | 180 | # Create some handy timers 181 | globalClock = core.Clock() # to track the time since experiment started 182 | routineTimer = core.CountdownTimer() # to track time remaining of each (non-slip) routine 183 | 184 | # ------Prepare to start Routine "ins1"------- 185 | continueRoutine = True 186 | # update component parameters for each repeat 187 | key_resp_ins1.keys = [] 188 | key_resp_ins1.rt = [] 189 | _key_resp_ins1_allKeys = [] 190 | # keep track of which components have finished 191 | ins1Components = [ins1_text, key_resp_ins1] 192 | for thisComponent in ins1Components: 193 | thisComponent.tStart = None 194 | thisComponent.tStop = None 195 | thisComponent.tStartRefresh = None 196 | thisComponent.tStopRefresh = None 197 | if hasattr(thisComponent, 'status'): 198 | thisComponent.status = NOT_STARTED 199 | # reset timers 200 | t = 0 201 | _timeToFirstFrame = win.getFutureFlipTime(clock="now") 202 | ins1Clock.reset(-_timeToFirstFrame) # t0 is time of first possible flip 203 | frameN = -1 204 | 205 | # -------Run Routine "ins1"------- 206 | while continueRoutine: 207 | # get current time 208 | t = ins1Clock.getTime() 209 | tThisFlip = win.getFutureFlipTime(clock=ins1Clock) 210 | tThisFlipGlobal = win.getFutureFlipTime(clock=None) 211 | frameN = frameN + 1 # number of completed frames (so 0 is the first frame) 212 | # update/draw components on each frame 213 | 214 | # *ins1_text* updates 215 | if ins1_text.status == NOT_STARTED and tThisFlip >= 0.0-frameTolerance: 216 | # keep track of start time/frame for later 217 | ins1_text.frameNStart = frameN # exact frame index 218 | ins1_text.tStart = t # local t and not account for scr refresh 219 | ins1_text.tStartRefresh = tThisFlipGlobal # on global time 220 | win.timeOnFlip(ins1_text, 'tStartRefresh') # time at next scr refresh 221 | ins1_text.setAutoDraw(True) 222 | 223 | # *key_resp_ins1* updates 224 | waitOnFlip = False 225 | if key_resp_ins1.status == NOT_STARTED and tThisFlip >= 0.0-frameTolerance: 226 | # keep track of start time/frame for later 227 | key_resp_ins1.frameNStart = frameN # exact frame index 228 | key_resp_ins1.tStart = t # local t and not account for scr refresh 229 | key_resp_ins1.tStartRefresh = tThisFlipGlobal # on global time 230 | win.timeOnFlip(key_resp_ins1, 'tStartRefresh') # time at next scr refresh 231 | key_resp_ins1.status = STARTED 232 | # keyboard checking is just starting 233 | waitOnFlip = True 234 | win.callOnFlip(key_resp_ins1.clock.reset) # t=0 on next screen flip 235 | win.callOnFlip(key_resp_ins1.clearEvents, eventType='keyboard') # clear events on next screen flip 236 | if key_resp_ins1.status == STARTED and not waitOnFlip: 237 | theseKeys = key_resp_ins1.getKeys(keyList=['return'], waitRelease=False) 238 | _key_resp_ins1_allKeys.extend(theseKeys) 239 | if len(_key_resp_ins1_allKeys): 240 | key_resp_ins1.keys = _key_resp_ins1_allKeys[-1].name # just the last key pressed 241 | key_resp_ins1.rt = _key_resp_ins1_allKeys[-1].rt 242 | # a response ends the routine 243 | continueRoutine = False 244 | 245 | # check for quit (typically the Esc key) 246 | if endExpNow or defaultKeyboard.getKeys(keyList=["escape"]): 247 | core.quit() 248 | 249 | # check if all components have finished 250 | if not continueRoutine: # a component has requested a forced-end of Routine 251 | break 252 | continueRoutine = False # will revert to True if at least one component still running 253 | for thisComponent in ins1Components: 254 | if hasattr(thisComponent, "status") and thisComponent.status != FINISHED: 255 | continueRoutine = True 256 | break # at least one component has not yet finished 257 | 258 | # refresh the screen 259 | if continueRoutine: # don't flip if this routine is over or we'll get a blank screen 260 | win.flip() 261 | 262 | # -------Ending Routine "ins1"------- 263 | for thisComponent in ins1Components: 264 | if hasattr(thisComponent, "setAutoDraw"): 265 | thisComponent.setAutoDraw(False) 266 | thisExp.addData('ins1_text.started', ins1_text.tStartRefresh) 267 | thisExp.addData('ins1_text.stopped', ins1_text.tStopRefresh) 268 | # the Routine "ins1" was not non-slip safe, so reset the non-slip timer 269 | routineTimer.reset() 270 | 271 | # set up handler to look after randomisation of conditions etc 272 | prac_trials = data.TrialHandler(nReps=2, method='random', 273 | extraInfo=expInfo, originPath=-1, 274 | trialList=data.importConditions('stimuli_list.xlsx'), 275 | seed=None, name='prac_trials') 276 | thisExp.addLoop(prac_trials) # add the loop to the experiment 277 | thisPrac_trial = prac_trials.trialList[0] # so we can initialise stimuli with some values 278 | # abbreviate parameter names if possible (e.g. rgb = thisPrac_trial.rgb) 279 | if thisPrac_trial != None: 280 | for paramName in thisPrac_trial: 281 | exec('{} = thisPrac_trial[paramName]'.format(paramName)) 282 | 283 | for thisPrac_trial in prac_trials: 284 | currentLoop = prac_trials 285 | # abbreviate parameter names if possible (e.g. rgb = thisPrac_trial.rgb) 286 | if thisPrac_trial != None: 287 | for paramName in thisPrac_trial: 288 | exec('{} = thisPrac_trial[paramName]'.format(paramName)) 289 | 290 | # ------Prepare to start Routine "practice"------- 291 | continueRoutine = True 292 | routineTimer.add(1.800000) 293 | # update component parameters for each repeat 294 | sti_text.setText(sti) 295 | key_resp.keys = [] 296 | key_resp.rt = [] 297 | _key_resp_allKeys = [] 298 | # keep track of which components have finished 299 | practiceComponents = [fix_text, sti_text, key_resp] 300 | for thisComponent in practiceComponents: 301 | thisComponent.tStart = None 302 | thisComponent.tStop = None 303 | thisComponent.tStartRefresh = None 304 | thisComponent.tStopRefresh = None 305 | if hasattr(thisComponent, 'status'): 306 | thisComponent.status = NOT_STARTED 307 | # reset timers 308 | t = 0 309 | _timeToFirstFrame = win.getFutureFlipTime(clock="now") 310 | practiceClock.reset(-_timeToFirstFrame) # t0 is time of first possible flip 311 | frameN = -1 312 | 313 | # -------Run Routine "practice"------- 314 | while continueRoutine and routineTimer.getTime() > 0: 315 | # get current time 316 | t = practiceClock.getTime() 317 | tThisFlip = win.getFutureFlipTime(clock=practiceClock) 318 | tThisFlipGlobal = win.getFutureFlipTime(clock=None) 319 | frameN = frameN + 1 # number of completed frames (so 0 is the first frame) 320 | # update/draw components on each frame 321 | 322 | # *fix_text* updates 323 | if fix_text.status == NOT_STARTED and tThisFlip >= 0.4-frameTolerance: 324 | # keep track of start time/frame for later 325 | fix_text.frameNStart = frameN # exact frame index 326 | fix_text.tStart = t # local t and not account for scr refresh 327 | fix_text.tStartRefresh = tThisFlipGlobal # on global time 328 | win.timeOnFlip(fix_text, 'tStartRefresh') # time at next scr refresh 329 | fix_text.setAutoDraw(True) 330 | if fix_text.status == STARTED: 331 | # is it time to stop? (based on global clock, using actual start) 332 | if tThisFlipGlobal > fix_text.tStartRefresh + 0.5-frameTolerance: 333 | # keep track of stop time/frame for later 334 | fix_text.tStop = t # not accounting for scr refresh 335 | fix_text.frameNStop = frameN # exact frame index 336 | win.timeOnFlip(fix_text, 'tStopRefresh') # time at next scr refresh 337 | fix_text.setAutoDraw(False) 338 | 339 | # *sti_text* updates 340 | if sti_text.status == NOT_STARTED and tThisFlip >= 0.9-frameTolerance: 341 | # keep track of start time/frame for later 342 | sti_text.frameNStart = frameN # exact frame index 343 | sti_text.tStart = t # local t and not account for scr refresh 344 | sti_text.tStartRefresh = tThisFlipGlobal # on global time 345 | win.timeOnFlip(sti_text, 'tStartRefresh') # time at next scr refresh 346 | sti_text.setAutoDraw(True) 347 | if sti_text.status == STARTED: 348 | # is it time to stop? (based on global clock, using actual start) 349 | if tThisFlipGlobal > sti_text.tStartRefresh + 0.1-frameTolerance: 350 | # keep track of stop time/frame for later 351 | sti_text.tStop = t # not accounting for scr refresh 352 | sti_text.frameNStop = frameN # exact frame index 353 | win.timeOnFlip(sti_text, 'tStopRefresh') # time at next scr refresh 354 | sti_text.setAutoDraw(False) 355 | 356 | # *key_resp* updates 357 | waitOnFlip = False 358 | if key_resp.status == NOT_STARTED and tThisFlip >= 1.0-frameTolerance: 359 | # keep track of start time/frame for later 360 | key_resp.frameNStart = frameN # exact frame index 361 | key_resp.tStart = t # local t and not account for scr refresh 362 | key_resp.tStartRefresh = tThisFlipGlobal # on global time 363 | win.timeOnFlip(key_resp, 'tStartRefresh') # time at next scr refresh 364 | key_resp.status = STARTED 365 | # keyboard checking is just starting 366 | waitOnFlip = True 367 | win.callOnFlip(key_resp.clock.reset) # t=0 on next screen flip 368 | win.callOnFlip(key_resp.clearEvents, eventType='keyboard') # clear events on next screen flip 369 | if key_resp.status == STARTED: 370 | # is it time to stop? (based on global clock, using actual start) 371 | if tThisFlipGlobal > key_resp.tStartRefresh + 0.8-frameTolerance: 372 | # keep track of stop time/frame for later 373 | key_resp.tStop = t # not accounting for scr refresh 374 | key_resp.frameNStop = frameN # exact frame index 375 | win.timeOnFlip(key_resp, 'tStopRefresh') # time at next scr refresh 376 | key_resp.status = FINISHED 377 | if key_resp.status == STARTED and not waitOnFlip: 378 | theseKeys = key_resp.getKeys(keyList=['space'], waitRelease=False) 379 | _key_resp_allKeys.extend(theseKeys) 380 | if len(_key_resp_allKeys): 381 | key_resp.keys = _key_resp_allKeys[-1].name # just the last key pressed 382 | key_resp.rt = _key_resp_allKeys[-1].rt 383 | # was this correct? 384 | if (key_resp.keys == str(ans)) or (key_resp.keys == ans): 385 | key_resp.corr = 1 386 | else: 387 | key_resp.corr = 0 388 | # a response ends the routine 389 | continueRoutine = False 390 | 391 | # check for quit (typically the Esc key) 392 | if endExpNow or defaultKeyboard.getKeys(keyList=["escape"]): 393 | core.quit() 394 | 395 | # check if all components have finished 396 | if not continueRoutine: # a component has requested a forced-end of Routine 397 | break 398 | continueRoutine = False # will revert to True if at least one component still running 399 | for thisComponent in practiceComponents: 400 | if hasattr(thisComponent, "status") and thisComponent.status != FINISHED: 401 | continueRoutine = True 402 | break # at least one component has not yet finished 403 | 404 | # refresh the screen 405 | if continueRoutine: # don't flip if this routine is over or we'll get a blank screen 406 | win.flip() 407 | 408 | # -------Ending Routine "practice"------- 409 | for thisComponent in practiceComponents: 410 | if hasattr(thisComponent, "setAutoDraw"): 411 | thisComponent.setAutoDraw(False) 412 | prac_trials.addData('fix_text.started', fix_text.tStartRefresh) 413 | prac_trials.addData('fix_text.stopped', fix_text.tStopRefresh) 414 | prac_trials.addData('sti_text.started', sti_text.tStartRefresh) 415 | prac_trials.addData('sti_text.stopped', sti_text.tStopRefresh) 416 | # check responses 417 | if key_resp.keys in ['', [], None]: # No response was made 418 | key_resp.keys = None 419 | # was no response the correct answer?! 420 | if str(ans).lower() == 'none': 421 | key_resp.corr = 1; # correct non-response 422 | else: 423 | key_resp.corr = 0; # failed to respond (incorrectly) 424 | # store data for prac_trials (TrialHandler) 425 | prac_trials.addData('key_resp.keys',key_resp.keys) 426 | prac_trials.addData('key_resp.corr', key_resp.corr) 427 | if key_resp.keys != None: # we had a response 428 | prac_trials.addData('key_resp.rt', key_resp.rt) 429 | prac_trials.addData('key_resp.started', key_resp.tStartRefresh) 430 | prac_trials.addData('key_resp.stopped', key_resp.tStopRefresh) 431 | thisExp.nextEntry() 432 | 433 | # completed 2 repeats of 'prac_trials' 434 | 435 | 436 | # ------Prepare to start Routine "ins2"------- 437 | continueRoutine = True 438 | # update component parameters for each repeat 439 | key_resp_ins2.keys = [] 440 | key_resp_ins2.rt = [] 441 | _key_resp_ins2_allKeys = [] 442 | # keep track of which components have finished 443 | ins2Components = [ins2_text, key_resp_ins2] 444 | for thisComponent in ins2Components: 445 | thisComponent.tStart = None 446 | thisComponent.tStop = None 447 | thisComponent.tStartRefresh = None 448 | thisComponent.tStopRefresh = None 449 | if hasattr(thisComponent, 'status'): 450 | thisComponent.status = NOT_STARTED 451 | # reset timers 452 | t = 0 453 | _timeToFirstFrame = win.getFutureFlipTime(clock="now") 454 | ins2Clock.reset(-_timeToFirstFrame) # t0 is time of first possible flip 455 | frameN = -1 456 | 457 | # -------Run Routine "ins2"------- 458 | while continueRoutine: 459 | # get current time 460 | t = ins2Clock.getTime() 461 | tThisFlip = win.getFutureFlipTime(clock=ins2Clock) 462 | tThisFlipGlobal = win.getFutureFlipTime(clock=None) 463 | frameN = frameN + 1 # number of completed frames (so 0 is the first frame) 464 | # update/draw components on each frame 465 | 466 | # *ins2_text* updates 467 | if ins2_text.status == NOT_STARTED and tThisFlip >= 0.0-frameTolerance: 468 | # keep track of start time/frame for later 469 | ins2_text.frameNStart = frameN # exact frame index 470 | ins2_text.tStart = t # local t and not account for scr refresh 471 | ins2_text.tStartRefresh = tThisFlipGlobal # on global time 472 | win.timeOnFlip(ins2_text, 'tStartRefresh') # time at next scr refresh 473 | ins2_text.setAutoDraw(True) 474 | 475 | # *key_resp_ins2* updates 476 | waitOnFlip = False 477 | if key_resp_ins2.status == NOT_STARTED and tThisFlip >= 0.0-frameTolerance: 478 | # keep track of start time/frame for later 479 | key_resp_ins2.frameNStart = frameN # exact frame index 480 | key_resp_ins2.tStart = t # local t and not account for scr refresh 481 | key_resp_ins2.tStartRefresh = tThisFlipGlobal # on global time 482 | win.timeOnFlip(key_resp_ins2, 'tStartRefresh') # time at next scr refresh 483 | key_resp_ins2.status = STARTED 484 | # keyboard checking is just starting 485 | waitOnFlip = True 486 | win.callOnFlip(key_resp_ins2.clock.reset) # t=0 on next screen flip 487 | win.callOnFlip(key_resp_ins2.clearEvents, eventType='keyboard') # clear events on next screen flip 488 | if key_resp_ins2.status == STARTED and not waitOnFlip: 489 | theseKeys = key_resp_ins2.getKeys(keyList=['return'], waitRelease=False) 490 | _key_resp_ins2_allKeys.extend(theseKeys) 491 | if len(_key_resp_ins2_allKeys): 492 | key_resp_ins2.keys = _key_resp_ins2_allKeys[-1].name # just the last key pressed 493 | key_resp_ins2.rt = _key_resp_ins2_allKeys[-1].rt 494 | # a response ends the routine 495 | continueRoutine = False 496 | 497 | # check for quit (typically the Esc key) 498 | if endExpNow or defaultKeyboard.getKeys(keyList=["escape"]): 499 | core.quit() 500 | 501 | # check if all components have finished 502 | if not continueRoutine: # a component has requested a forced-end of Routine 503 | break 504 | continueRoutine = False # will revert to True if at least one component still running 505 | for thisComponent in ins2Components: 506 | if hasattr(thisComponent, "status") and thisComponent.status != FINISHED: 507 | continueRoutine = True 508 | break # at least one component has not yet finished 509 | 510 | # refresh the screen 511 | if continueRoutine: # don't flip if this routine is over or we'll get a blank screen 512 | win.flip() 513 | 514 | # -------Ending Routine "ins2"------- 515 | for thisComponent in ins2Components: 516 | if hasattr(thisComponent, "setAutoDraw"): 517 | thisComponent.setAutoDraw(False) 518 | thisExp.addData('ins2_text.started', ins2_text.tStartRefresh) 519 | thisExp.addData('ins2_text.stopped', ins2_text.tStopRefresh) 520 | # the Routine "ins2" was not non-slip safe, so reset the non-slip timer 521 | routineTimer.reset() 522 | 523 | # set up handler to look after randomisation of conditions etc 524 | exp1_trials = data.TrialHandler(nReps=3, method='random', 525 | extraInfo=expInfo, originPath=-1, 526 | trialList=data.importConditions('stimuli_list.xlsx'), 527 | seed=None, name='exp1_trials') 528 | thisExp.addLoop(exp1_trials) # add the loop to the experiment 529 | thisExp1_trial = exp1_trials.trialList[0] # so we can initialise stimuli with some values 530 | # abbreviate parameter names if possible (e.g. rgb = thisExp1_trial.rgb) 531 | if thisExp1_trial != None: 532 | for paramName in thisExp1_trial: 533 | exec('{} = thisExp1_trial[paramName]'.format(paramName)) 534 | 535 | for thisExp1_trial in exp1_trials: 536 | currentLoop = exp1_trials 537 | # abbreviate parameter names if possible (e.g. rgb = thisExp1_trial.rgb) 538 | if thisExp1_trial != None: 539 | for paramName in thisExp1_trial: 540 | exec('{} = thisExp1_trial[paramName]'.format(paramName)) 541 | 542 | # ------Prepare to start Routine "exp1"------- 543 | continueRoutine = True 544 | routineTimer.add(1.800000) 545 | # update component parameters for each repeat 546 | sti1_text.setText(sti) 547 | key_resp_2.keys = [] 548 | key_resp_2.rt = [] 549 | _key_resp_2_allKeys = [] 550 | # keep track of which components have finished 551 | exp1Components = [fix1_text, sti1_text, key_resp_2] 552 | for thisComponent in exp1Components: 553 | thisComponent.tStart = None 554 | thisComponent.tStop = None 555 | thisComponent.tStartRefresh = None 556 | thisComponent.tStopRefresh = None 557 | if hasattr(thisComponent, 'status'): 558 | thisComponent.status = NOT_STARTED 559 | # reset timers 560 | t = 0 561 | _timeToFirstFrame = win.getFutureFlipTime(clock="now") 562 | exp1Clock.reset(-_timeToFirstFrame) # t0 is time of first possible flip 563 | frameN = -1 564 | 565 | # -------Run Routine "exp1"------- 566 | while continueRoutine and routineTimer.getTime() > 0: 567 | # get current time 568 | t = exp1Clock.getTime() 569 | tThisFlip = win.getFutureFlipTime(clock=exp1Clock) 570 | tThisFlipGlobal = win.getFutureFlipTime(clock=None) 571 | frameN = frameN + 1 # number of completed frames (so 0 is the first frame) 572 | # update/draw components on each frame 573 | 574 | # *fix1_text* updates 575 | if fix1_text.status == NOT_STARTED and tThisFlip >= 0.4-frameTolerance: 576 | # keep track of start time/frame for later 577 | fix1_text.frameNStart = frameN # exact frame index 578 | fix1_text.tStart = t # local t and not account for scr refresh 579 | fix1_text.tStartRefresh = tThisFlipGlobal # on global time 580 | win.timeOnFlip(fix1_text, 'tStartRefresh') # time at next scr refresh 581 | fix1_text.setAutoDraw(True) 582 | if fix1_text.status == STARTED: 583 | # is it time to stop? (based on global clock, using actual start) 584 | if tThisFlipGlobal > fix1_text.tStartRefresh + 0.5-frameTolerance: 585 | # keep track of stop time/frame for later 586 | fix1_text.tStop = t # not accounting for scr refresh 587 | fix1_text.frameNStop = frameN # exact frame index 588 | win.timeOnFlip(fix1_text, 'tStopRefresh') # time at next scr refresh 589 | fix1_text.setAutoDraw(False) 590 | 591 | # *sti1_text* updates 592 | if sti1_text.status == NOT_STARTED and tThisFlip >= 0.9-frameTolerance: 593 | # keep track of start time/frame for later 594 | sti1_text.frameNStart = frameN # exact frame index 595 | sti1_text.tStart = t # local t and not account for scr refresh 596 | sti1_text.tStartRefresh = tThisFlipGlobal # on global time 597 | win.timeOnFlip(sti1_text, 'tStartRefresh') # time at next scr refresh 598 | sti1_text.setAutoDraw(True) 599 | if sti1_text.status == STARTED: 600 | # is it time to stop? (based on global clock, using actual start) 601 | if tThisFlipGlobal > sti1_text.tStartRefresh + 0.1-frameTolerance: 602 | # keep track of stop time/frame for later 603 | sti1_text.tStop = t # not accounting for scr refresh 604 | sti1_text.frameNStop = frameN # exact frame index 605 | win.timeOnFlip(sti1_text, 'tStopRefresh') # time at next scr refresh 606 | sti1_text.setAutoDraw(False) 607 | 608 | # *key_resp_2* updates 609 | waitOnFlip = False 610 | if key_resp_2.status == NOT_STARTED and tThisFlip >= 1.0-frameTolerance: 611 | # keep track of start time/frame for later 612 | key_resp_2.frameNStart = frameN # exact frame index 613 | key_resp_2.tStart = t # local t and not account for scr refresh 614 | key_resp_2.tStartRefresh = tThisFlipGlobal # on global time 615 | win.timeOnFlip(key_resp_2, 'tStartRefresh') # time at next scr refresh 616 | key_resp_2.status = STARTED 617 | # keyboard checking is just starting 618 | waitOnFlip = True 619 | win.callOnFlip(key_resp_2.clock.reset) # t=0 on next screen flip 620 | win.callOnFlip(key_resp_2.clearEvents, eventType='keyboard') # clear events on next screen flip 621 | if key_resp_2.status == STARTED: 622 | # is it time to stop? (based on global clock, using actual start) 623 | if tThisFlipGlobal > key_resp_2.tStartRefresh + 0.8-frameTolerance: 624 | # keep track of stop time/frame for later 625 | key_resp_2.tStop = t # not accounting for scr refresh 626 | key_resp_2.frameNStop = frameN # exact frame index 627 | win.timeOnFlip(key_resp_2, 'tStopRefresh') # time at next scr refresh 628 | key_resp_2.status = FINISHED 629 | if key_resp_2.status == STARTED and not waitOnFlip: 630 | theseKeys = key_resp_2.getKeys(keyList=['space'], waitRelease=False) 631 | _key_resp_2_allKeys.extend(theseKeys) 632 | if len(_key_resp_2_allKeys): 633 | key_resp_2.keys = _key_resp_2_allKeys[-1].name # just the last key pressed 634 | key_resp_2.rt = _key_resp_2_allKeys[-1].rt 635 | # was this correct? 636 | if (key_resp_2.keys == str(ans)) or (key_resp_2.keys == ans): 637 | key_resp_2.corr = 1 638 | else: 639 | key_resp_2.corr = 0 640 | # a response ends the routine 641 | continueRoutine = False 642 | 643 | # check for quit (typically the Esc key) 644 | if endExpNow or defaultKeyboard.getKeys(keyList=["escape"]): 645 | core.quit() 646 | 647 | # check if all components have finished 648 | if not continueRoutine: # a component has requested a forced-end of Routine 649 | break 650 | continueRoutine = False # will revert to True if at least one component still running 651 | for thisComponent in exp1Components: 652 | if hasattr(thisComponent, "status") and thisComponent.status != FINISHED: 653 | continueRoutine = True 654 | break # at least one component has not yet finished 655 | 656 | # refresh the screen 657 | if continueRoutine: # don't flip if this routine is over or we'll get a blank screen 658 | win.flip() 659 | 660 | # -------Ending Routine "exp1"------- 661 | for thisComponent in exp1Components: 662 | if hasattr(thisComponent, "setAutoDraw"): 663 | thisComponent.setAutoDraw(False) 664 | exp1_trials.addData('fix1_text.started', fix1_text.tStartRefresh) 665 | exp1_trials.addData('fix1_text.stopped', fix1_text.tStopRefresh) 666 | exp1_trials.addData('sti1_text.started', sti1_text.tStartRefresh) 667 | exp1_trials.addData('sti1_text.stopped', sti1_text.tStopRefresh) 668 | # check responses 669 | if key_resp_2.keys in ['', [], None]: # No response was made 670 | key_resp_2.keys = None 671 | # was no response the correct answer?! 672 | if str(ans).lower() == 'none': 673 | key_resp_2.corr = 1; # correct non-response 674 | else: 675 | key_resp_2.corr = 0; # failed to respond (incorrectly) 676 | # store data for exp1_trials (TrialHandler) 677 | exp1_trials.addData('key_resp_2.keys',key_resp_2.keys) 678 | exp1_trials.addData('key_resp_2.corr', key_resp_2.corr) 679 | if key_resp_2.keys != None: # we had a response 680 | exp1_trials.addData('key_resp_2.rt', key_resp_2.rt) 681 | exp1_trials.addData('key_resp_2.started', key_resp_2.tStartRefresh) 682 | exp1_trials.addData('key_resp_2.stopped', key_resp_2.tStopRefresh) 683 | thisExp.nextEntry() 684 | 685 | # completed 3 repeats of 'exp1_trials' 686 | 687 | 688 | # ------Prepare to start Routine "rest"------- 689 | continueRoutine = True 690 | routineTimer.add(5.000000) 691 | # update component parameters for each repeat 692 | # keep track of which components have finished 693 | restComponents = [rest_text] 694 | for thisComponent in restComponents: 695 | thisComponent.tStart = None 696 | thisComponent.tStop = None 697 | thisComponent.tStartRefresh = None 698 | thisComponent.tStopRefresh = None 699 | if hasattr(thisComponent, 'status'): 700 | thisComponent.status = NOT_STARTED 701 | # reset timers 702 | t = 0 703 | _timeToFirstFrame = win.getFutureFlipTime(clock="now") 704 | restClock.reset(-_timeToFirstFrame) # t0 is time of first possible flip 705 | frameN = -1 706 | 707 | # -------Run Routine "rest"------- 708 | while continueRoutine and routineTimer.getTime() > 0: 709 | # get current time 710 | t = restClock.getTime() 711 | tThisFlip = win.getFutureFlipTime(clock=restClock) 712 | tThisFlipGlobal = win.getFutureFlipTime(clock=None) 713 | frameN = frameN + 1 # number of completed frames (so 0 is the first frame) 714 | # update/draw components on each frame 715 | 716 | # *rest_text* updates 717 | if rest_text.status == NOT_STARTED and tThisFlip >= 0.0-frameTolerance: 718 | # keep track of start time/frame for later 719 | rest_text.frameNStart = frameN # exact frame index 720 | rest_text.tStart = t # local t and not account for scr refresh 721 | rest_text.tStartRefresh = tThisFlipGlobal # on global time 722 | win.timeOnFlip(rest_text, 'tStartRefresh') # time at next scr refresh 723 | rest_text.setAutoDraw(True) 724 | if rest_text.status == STARTED: 725 | # is it time to stop? (based on global clock, using actual start) 726 | if tThisFlipGlobal > rest_text.tStartRefresh + 5.0-frameTolerance: 727 | # keep track of stop time/frame for later 728 | rest_text.tStop = t # not accounting for scr refresh 729 | rest_text.frameNStop = frameN # exact frame index 730 | win.timeOnFlip(rest_text, 'tStopRefresh') # time at next scr refresh 731 | rest_text.setAutoDraw(False) 732 | 733 | # check for quit (typically the Esc key) 734 | if endExpNow or defaultKeyboard.getKeys(keyList=["escape"]): 735 | core.quit() 736 | 737 | # check if all components have finished 738 | if not continueRoutine: # a component has requested a forced-end of Routine 739 | break 740 | continueRoutine = False # will revert to True if at least one component still running 741 | for thisComponent in restComponents: 742 | if hasattr(thisComponent, "status") and thisComponent.status != FINISHED: 743 | continueRoutine = True 744 | break # at least one component has not yet finished 745 | 746 | # refresh the screen 747 | if continueRoutine: # don't flip if this routine is over or we'll get a blank screen 748 | win.flip() 749 | 750 | # -------Ending Routine "rest"------- 751 | for thisComponent in restComponents: 752 | if hasattr(thisComponent, "setAutoDraw"): 753 | thisComponent.setAutoDraw(False) 754 | thisExp.addData('rest_text.started', rest_text.tStartRefresh) 755 | thisExp.addData('rest_text.stopped', rest_text.tStopRefresh) 756 | 757 | # set up handler to look after randomisation of conditions etc 758 | exp2_trials = data.TrialHandler(nReps=3, method='random', 759 | extraInfo=expInfo, originPath=-1, 760 | trialList=data.importConditions('stimuli_list.xlsx'), 761 | seed=None, name='exp2_trials') 762 | thisExp.addLoop(exp2_trials) # add the loop to the experiment 763 | thisExp2_trial = exp2_trials.trialList[0] # so we can initialise stimuli with some values 764 | # abbreviate parameter names if possible (e.g. rgb = thisExp2_trial.rgb) 765 | if thisExp2_trial != None: 766 | for paramName in thisExp2_trial: 767 | exec('{} = thisExp2_trial[paramName]'.format(paramName)) 768 | 769 | for thisExp2_trial in exp2_trials: 770 | currentLoop = exp2_trials 771 | # abbreviate parameter names if possible (e.g. rgb = thisExp2_trial.rgb) 772 | if thisExp2_trial != None: 773 | for paramName in thisExp2_trial: 774 | exec('{} = thisExp2_trial[paramName]'.format(paramName)) 775 | 776 | # ------Prepare to start Routine "exp2"------- 777 | continueRoutine = True 778 | routineTimer.add(1.800000) 779 | # update component parameters for each repeat 780 | si2_text.setText(sti) 781 | key_resp_3.keys = [] 782 | key_resp_3.rt = [] 783 | _key_resp_3_allKeys = [] 784 | # keep track of which components have finished 785 | exp2Components = [fix2_text, si2_text, key_resp_3] 786 | for thisComponent in exp2Components: 787 | thisComponent.tStart = None 788 | thisComponent.tStop = None 789 | thisComponent.tStartRefresh = None 790 | thisComponent.tStopRefresh = None 791 | if hasattr(thisComponent, 'status'): 792 | thisComponent.status = NOT_STARTED 793 | # reset timers 794 | t = 0 795 | _timeToFirstFrame = win.getFutureFlipTime(clock="now") 796 | exp2Clock.reset(-_timeToFirstFrame) # t0 is time of first possible flip 797 | frameN = -1 798 | 799 | # -------Run Routine "exp2"------- 800 | while continueRoutine and routineTimer.getTime() > 0: 801 | # get current time 802 | t = exp2Clock.getTime() 803 | tThisFlip = win.getFutureFlipTime(clock=exp2Clock) 804 | tThisFlipGlobal = win.getFutureFlipTime(clock=None) 805 | frameN = frameN + 1 # number of completed frames (so 0 is the first frame) 806 | # update/draw components on each frame 807 | 808 | # *fix2_text* updates 809 | if fix2_text.status == NOT_STARTED and tThisFlip >= 0.4-frameTolerance: 810 | # keep track of start time/frame for later 811 | fix2_text.frameNStart = frameN # exact frame index 812 | fix2_text.tStart = t # local t and not account for scr refresh 813 | fix2_text.tStartRefresh = tThisFlipGlobal # on global time 814 | win.timeOnFlip(fix2_text, 'tStartRefresh') # time at next scr refresh 815 | fix2_text.setAutoDraw(True) 816 | if fix2_text.status == STARTED: 817 | # is it time to stop? (based on global clock, using actual start) 818 | if tThisFlipGlobal > fix2_text.tStartRefresh + 0.5-frameTolerance: 819 | # keep track of stop time/frame for later 820 | fix2_text.tStop = t # not accounting for scr refresh 821 | fix2_text.frameNStop = frameN # exact frame index 822 | win.timeOnFlip(fix2_text, 'tStopRefresh') # time at next scr refresh 823 | fix2_text.setAutoDraw(False) 824 | 825 | # *si2_text* updates 826 | if si2_text.status == NOT_STARTED and tThisFlip >= 0.9-frameTolerance: 827 | # keep track of start time/frame for later 828 | si2_text.frameNStart = frameN # exact frame index 829 | si2_text.tStart = t # local t and not account for scr refresh 830 | si2_text.tStartRefresh = tThisFlipGlobal # on global time 831 | win.timeOnFlip(si2_text, 'tStartRefresh') # time at next scr refresh 832 | si2_text.setAutoDraw(True) 833 | if si2_text.status == STARTED: 834 | # is it time to stop? (based on global clock, using actual start) 835 | if tThisFlipGlobal > si2_text.tStartRefresh + 0.1-frameTolerance: 836 | # keep track of stop time/frame for later 837 | si2_text.tStop = t # not accounting for scr refresh 838 | si2_text.frameNStop = frameN # exact frame index 839 | win.timeOnFlip(si2_text, 'tStopRefresh') # time at next scr refresh 840 | si2_text.setAutoDraw(False) 841 | 842 | # *key_resp_3* updates 843 | waitOnFlip = False 844 | if key_resp_3.status == NOT_STARTED and tThisFlip >= 1.0-frameTolerance: 845 | # keep track of start time/frame for later 846 | key_resp_3.frameNStart = frameN # exact frame index 847 | key_resp_3.tStart = t # local t and not account for scr refresh 848 | key_resp_3.tStartRefresh = tThisFlipGlobal # on global time 849 | win.timeOnFlip(key_resp_3, 'tStartRefresh') # time at next scr refresh 850 | key_resp_3.status = STARTED 851 | # keyboard checking is just starting 852 | waitOnFlip = True 853 | win.callOnFlip(key_resp_3.clock.reset) # t=0 on next screen flip 854 | win.callOnFlip(key_resp_3.clearEvents, eventType='keyboard') # clear events on next screen flip 855 | if key_resp_3.status == STARTED: 856 | # is it time to stop? (based on global clock, using actual start) 857 | if tThisFlipGlobal > key_resp_3.tStartRefresh + 0.8-frameTolerance: 858 | # keep track of stop time/frame for later 859 | key_resp_3.tStop = t # not accounting for scr refresh 860 | key_resp_3.frameNStop = frameN # exact frame index 861 | win.timeOnFlip(key_resp_3, 'tStopRefresh') # time at next scr refresh 862 | key_resp_3.status = FINISHED 863 | if key_resp_3.status == STARTED and not waitOnFlip: 864 | theseKeys = key_resp_3.getKeys(keyList=['space'], waitRelease=False) 865 | _key_resp_3_allKeys.extend(theseKeys) 866 | if len(_key_resp_3_allKeys): 867 | key_resp_3.keys = _key_resp_3_allKeys[-1].name # just the last key pressed 868 | key_resp_3.rt = _key_resp_3_allKeys[-1].rt 869 | # was this correct? 870 | if (key_resp_3.keys == str(ans)) or (key_resp_3.keys == ans): 871 | key_resp_3.corr = 1 872 | else: 873 | key_resp_3.corr = 0 874 | # a response ends the routine 875 | continueRoutine = False 876 | 877 | # check for quit (typically the Esc key) 878 | if endExpNow or defaultKeyboard.getKeys(keyList=["escape"]): 879 | core.quit() 880 | 881 | # check if all components have finished 882 | if not continueRoutine: # a component has requested a forced-end of Routine 883 | break 884 | continueRoutine = False # will revert to True if at least one component still running 885 | for thisComponent in exp2Components: 886 | if hasattr(thisComponent, "status") and thisComponent.status != FINISHED: 887 | continueRoutine = True 888 | break # at least one component has not yet finished 889 | 890 | # refresh the screen 891 | if continueRoutine: # don't flip if this routine is over or we'll get a blank screen 892 | win.flip() 893 | 894 | # -------Ending Routine "exp2"------- 895 | for thisComponent in exp2Components: 896 | if hasattr(thisComponent, "setAutoDraw"): 897 | thisComponent.setAutoDraw(False) 898 | exp2_trials.addData('fix2_text.started', fix2_text.tStartRefresh) 899 | exp2_trials.addData('fix2_text.stopped', fix2_text.tStopRefresh) 900 | exp2_trials.addData('si2_text.started', si2_text.tStartRefresh) 901 | exp2_trials.addData('si2_text.stopped', si2_text.tStopRefresh) 902 | # check responses 903 | if key_resp_3.keys in ['', [], None]: # No response was made 904 | key_resp_3.keys = None 905 | # was no response the correct answer?! 906 | if str(ans).lower() == 'none': 907 | key_resp_3.corr = 1; # correct non-response 908 | else: 909 | key_resp_3.corr = 0; # failed to respond (incorrectly) 910 | # store data for exp2_trials (TrialHandler) 911 | exp2_trials.addData('key_resp_3.keys',key_resp_3.keys) 912 | exp2_trials.addData('key_resp_3.corr', key_resp_3.corr) 913 | if key_resp_3.keys != None: # we had a response 914 | exp2_trials.addData('key_resp_3.rt', key_resp_3.rt) 915 | exp2_trials.addData('key_resp_3.started', key_resp_3.tStartRefresh) 916 | exp2_trials.addData('key_resp_3.stopped', key_resp_3.tStopRefresh) 917 | thisExp.nextEntry() 918 | 919 | # completed 3 repeats of 'exp2_trials' 920 | 921 | 922 | # ------Prepare to start Routine "finish"------- 923 | continueRoutine = True 924 | # update component parameters for each repeat 925 | key_resp_finish.keys = [] 926 | key_resp_finish.rt = [] 927 | _key_resp_finish_allKeys = [] 928 | # keep track of which components have finished 929 | finishComponents = [finish_text, key_resp_finish] 930 | for thisComponent in finishComponents: 931 | thisComponent.tStart = None 932 | thisComponent.tStop = None 933 | thisComponent.tStartRefresh = None 934 | thisComponent.tStopRefresh = None 935 | if hasattr(thisComponent, 'status'): 936 | thisComponent.status = NOT_STARTED 937 | # reset timers 938 | t = 0 939 | _timeToFirstFrame = win.getFutureFlipTime(clock="now") 940 | finishClock.reset(-_timeToFirstFrame) # t0 is time of first possible flip 941 | frameN = -1 942 | 943 | # -------Run Routine "finish"------- 944 | while continueRoutine: 945 | # get current time 946 | t = finishClock.getTime() 947 | tThisFlip = win.getFutureFlipTime(clock=finishClock) 948 | tThisFlipGlobal = win.getFutureFlipTime(clock=None) 949 | frameN = frameN + 1 # number of completed frames (so 0 is the first frame) 950 | # update/draw components on each frame 951 | 952 | # *finish_text* updates 953 | if finish_text.status == NOT_STARTED and tThisFlip >= 0.0-frameTolerance: 954 | # keep track of start time/frame for later 955 | finish_text.frameNStart = frameN # exact frame index 956 | finish_text.tStart = t # local t and not account for scr refresh 957 | finish_text.tStartRefresh = tThisFlipGlobal # on global time 958 | win.timeOnFlip(finish_text, 'tStartRefresh') # time at next scr refresh 959 | finish_text.setAutoDraw(True) 960 | 961 | # *key_resp_finish* updates 962 | waitOnFlip = False 963 | if key_resp_finish.status == NOT_STARTED and tThisFlip >= 0.0-frameTolerance: 964 | # keep track of start time/frame for later 965 | key_resp_finish.frameNStart = frameN # exact frame index 966 | key_resp_finish.tStart = t # local t and not account for scr refresh 967 | key_resp_finish.tStartRefresh = tThisFlipGlobal # on global time 968 | win.timeOnFlip(key_resp_finish, 'tStartRefresh') # time at next scr refresh 969 | key_resp_finish.status = STARTED 970 | # keyboard checking is just starting 971 | waitOnFlip = True 972 | win.callOnFlip(key_resp_finish.clock.reset) # t=0 on next screen flip 973 | win.callOnFlip(key_resp_finish.clearEvents, eventType='keyboard') # clear events on next screen flip 974 | if key_resp_finish.status == STARTED and not waitOnFlip: 975 | theseKeys = key_resp_finish.getKeys(keyList=['y', 'n', 'left', 'right', 'space', 'return'], waitRelease=False) 976 | _key_resp_finish_allKeys.extend(theseKeys) 977 | if len(_key_resp_finish_allKeys): 978 | key_resp_finish.keys = _key_resp_finish_allKeys[-1].name # just the last key pressed 979 | key_resp_finish.rt = _key_resp_finish_allKeys[-1].rt 980 | # a response ends the routine 981 | continueRoutine = False 982 | 983 | # check for quit (typically the Esc key) 984 | if endExpNow or defaultKeyboard.getKeys(keyList=["escape"]): 985 | core.quit() 986 | 987 | # check if all components have finished 988 | if not continueRoutine: # a component has requested a forced-end of Routine 989 | break 990 | continueRoutine = False # will revert to True if at least one component still running 991 | for thisComponent in finishComponents: 992 | if hasattr(thisComponent, "status") and thisComponent.status != FINISHED: 993 | continueRoutine = True 994 | break # at least one component has not yet finished 995 | 996 | # refresh the screen 997 | if continueRoutine: # don't flip if this routine is over or we'll get a blank screen 998 | win.flip() 999 | 1000 | # -------Ending Routine "finish"------- 1001 | for thisComponent in finishComponents: 1002 | if hasattr(thisComponent, "setAutoDraw"): 1003 | thisComponent.setAutoDraw(False) 1004 | thisExp.addData('finish_text.started', finish_text.tStartRefresh) 1005 | thisExp.addData('finish_text.stopped', finish_text.tStopRefresh) 1006 | # the Routine "finish" was not non-slip safe, so reset the non-slip timer 1007 | routineTimer.reset() 1008 | 1009 | # Flip one final time so any remaining win.callOnFlip() 1010 | # and win.timeOnFlip() tasks get executed before quitting 1011 | win.flip() 1012 | 1013 | # these shouldn't be strictly necessary (should auto-save) 1014 | thisExp.saveAsWideText(filename+'.csv', delim='auto') 1015 | thisExp.saveAsPickle(filename) 1016 | logging.flush() 1017 | # make sure everything is closed down 1018 | thisExp.abort() # or data files will save again on exit 1019 | win.close() 1020 | core.quit() 1021 | -------------------------------------------------------------------------------- /PsychoPy/gonogo_test.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Sun Jul 08 10:14:59 2018 4 | Modified on Sat Nov 21 15:15:15 2020 5 | 6 | @author: ryan 7 | """ 8 | 9 | from datetime import datetime 10 | from psychopy import core, gui, visual, event 11 | from random import shuffle 12 | 13 | data = {} 14 | data['Expname'] = 'Go/nogo Test' 15 | #Creat a string version of the current year/month/day hour/minute 16 | data['Expdate'] = datetime.now().strftime('%Y%m%d_%H%M') 17 | data['ParticipantID'] = '' 18 | data['Gender'] = '' 19 | 20 | #Set up input dialog 21 | #Use the 'fixed' argument to stop the user changing the 'expname' and 'expdate' parameter 22 | #Use the 'order' argument to set the order in which to display the fields 23 | dlg = gui.DlgFromDict(data, title='Input data', fixed=['Expname','Expdate'], 24 | order=['Expname','Expdate','ParticipantID','Gender']) 25 | 26 | #if participant quit, scripts end here 27 | if not dlg.OK: 28 | print("User cancelled the experiment") 29 | core.quit() 30 | 31 | filename = 'part_%s_%s.csv' % (data['ParticipantID'],data['Expdate']) 32 | f = open(filename, 'a+') 33 | 34 | #Print the title of data form 35 | f.write('trialID,stimuli,rt,acc\n') 36 | 37 | #set experiment window 38 | win = visual.Window([1024,768],fullscr = False, \ 39 | allowGUI =True, units = "pix", color = (0,0,0)) 40 | 41 | #set instructions of the experiment 42 | instruction1 = visual.TextStim(win, text = 'If you see M, press SPACE, if see W, do nothing', pos=(0, 88), color = (1,1,1)) 43 | instruction2 = visual.TextStim(win, text = 'Press ENTER to start practice', pos=(0, -88), color = (1,1,1)) 44 | instruction3 = visual.TextStim(win, text = 'Press ENTER to start experiment', pos=(0, -88), color = (1,1,1)) 45 | instruction4 = visual.TextStim(win, text = 'Rest for a while', pos=(0, -88), color = (1,1,1)) 46 | instruction5 = visual.TextStim(win, text = 'This is the end of the experiment. Thank you!', pos=(0, -88), color = (1,1,1)) 47 | 48 | #set fixations and stimuli of the experiment 49 | fix = visual.TextStim(win, text = '+', height=50, pos=(0, 0), color = (1,1,1)) 50 | 51 | sti = visual.TextStim(win, text = 'B', height=36, pos=(0, 0), color = (1,1,1)) 52 | 53 | #define a empty list for storing stimulis 54 | sti_list = [] 55 | 56 | #define a function for trial loop 57 | def trial(n_trial): 58 | #randomize the stimulus list 59 | shuffle(sti_list) 60 | for i in range(n_trial): 61 | core.wait(0.4) 62 | fix.draw() 63 | win.flip() 64 | core.wait(0.5) 65 | sti.setText(sti_list[i]) 66 | sti.draw() 67 | displaytime = win.flip() 68 | core.wait(0.1) 69 | win.flip() 70 | core.wait(0.8) 71 | keys = event.getKeys(keyList=['space'],timeStamped=True) 72 | 73 | #calculate rt 74 | if keys: 75 | rt = keys[0][1] - displaytime 76 | else: 77 | rt = "none" 78 | 79 | #calculate accuracy 80 | if sti_list[i] == "M": 81 | if not keys: 82 | acc = "0" 83 | else: 84 | acc = "1" 85 | elif sti_list[i] == "W": 86 | if not keys: 87 | acc = "1" 88 | else: 89 | acc = "0" 90 | 91 | #write into data file 92 | f.write(str(n_trial) + ',') 93 | f.write(sti_list[i] + ',') 94 | f.write(str(rt) + ',') 95 | f.write(acc + ',') 96 | f.write('\n') 97 | 98 | instruction1.draw() 99 | instruction2.draw() 100 | win.flip() 101 | event.waitKeys(keyList=['return']) 102 | core.wait(1.0) 103 | 104 | #set the practice stimulus list (fixed) 105 | sti_list = ["M","W","M","W","M","W","M","W","M","W","M","W","M","W"] 106 | 107 | #practice block 108 | trial(10) 109 | 110 | #mark between practice and experiment 111 | f.write('experiment starts below\n') 112 | 113 | #ready to run the experiment 114 | win.flip() 115 | instruction3.draw() 116 | win.flip() 117 | event.waitKeys(keyList=['return']) 118 | core.wait(1.0) 119 | 120 | #set the first experiment stimulus list (generate) 121 | sti_list = [] 122 | temp = ["M","W","W","W","W"] 123 | for i in range(3): 124 | sti_list.extend(temp) 125 | 126 | #first experiment block begin 127 | trial(15) 128 | 129 | #rest 130 | instruction4.draw() 131 | win.flip() 132 | core.wait(5.0) 133 | 134 | #second experiment block begin 135 | trial(15) 136 | 137 | #the end of the experiment, close the window and exit psychopy core 138 | instruction5.draw() 139 | win.flip() 140 | event.waitKeys() 141 | win.close() 142 | core.quit() -------------------------------------------------------------------------------- /PsychoPy/stimuli_list.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/coolspiderghy/Python-Workshop-SuZhou/0b040d4890f576f2519e771080cdb44eb8815be8/PsychoPy/stimuli_list.xlsx -------------------------------------------------------------------------------- /PsychoPy/stroop.psyexp: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 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-------------------------------------------------------------------------------- 1 | Python version is 2.7. 2 | For the dependence, please look into the beginning of the notebook. 3 | -------------------------------------------------------------------------------- /PythonPipeline_DataAnalysis_Psychology_HaiyangGeng/SE-QPE_40_demo1118.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/coolspiderghy/Python-Workshop-SuZhou/0b040d4890f576f2519e771080cdb44eb8815be8/PythonPipeline_DataAnalysis_Psychology_HaiyangGeng/SE-QPE_40_demo1118.xlsx -------------------------------------------------------------------------------- /PythonPipeline_DataAnalysis_Psychology_HaiyangGeng/SE_all_demo1118.xlsx: -------------------------------------------------------------------------------- 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https://raw.githubusercontent.com/coolspiderghy/Python-Workshop-SuZhou/0b040d4890f576f2519e771080cdb44eb8815be8/PythonPipeline_DataAnalysis_Psychology_HaiyangGeng/SE_scale_demo1118.xlsx -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Python-Workshop-SuZhou 2 | A place where all demo code and data are stored. 3 | 4 | For the the details of the conference and workshop, please go to [第七届全国脑电与脑成像研究与应用研讨会](http://www.cognscigd.cn/2020/11/16/%e7%ac%ac%e4%b8%83%e5%b1%8a%e5%85%a8%e5%9b%bd%e8%84%91%e7%94%b5%e4%b8%8e%e8%84%91%e6%88%90%e5%83%8f%e7%a0%94%e7%a9%b6%e4%b8%8e%e5%ba%94%e7%94%a8%e7%a0%94%e8%ae%a8%e4%bc%9a%ef%bc%88%e7%ac%ac%e4%b8%89/) 5 | 6 | The workshop will be hold on 1:30 pm 22th Nov, 2020, Fucui Hall, NanLin Hotel. 7 | 8 | 9 | | | | | 10 | |-----|-------------------------------------|-------------| 11 | | 报告人 | 主题 | 时间 | 12 | | 耿海洋 | Python在心理学（行为）数据分析中的应用，一个标准化流程 | 13:30-14:00 | 13 | | 夏涛 | Python在EEG数据分析中的应用，以MNE为例 | 14:00-14:30 | 14 | | 胡传鹏 | Python在计算建模中的应用，以HDDM为例 | 14:30-15:00 | 15 | | | 答疑 | 15:00-15:15 | 16 | | 孔祥祯 | Python在fMRI数据分析中的应用，以fMRIprep为例 | 15:15-15:45 | 17 | | 杨子鹏 | Python在实验程序编写和设计中的应用，以PsychoPy为例 | 15:45-16:15 | 18 | | 黎超 | Python在神经影像机器学习中的应用，以scikit-learn为例 | 16:15-16:45 | 19 | | | 答疑 | 16:45-17:00 | 20 | 21 | 22 | 23 | -------------------------------------------------------------------------------- /fMRIPrep/README.md: -------------------------------------------------------------------------------- 1 | # Neuroimaging in Python: fMRIPrep 2 | > Xiangzhen Kong, Zhejiang Univeristy 3 | > https://person.zju.edu.cn/konglab#lab 4 | 5 | ### Installation 6 | Two ways 7 | - [Mannually prepared enviroment](https://fmriprep.org/en/stable/installation.html#manually-prepared-environment-python-3-7) (Python 3.7+) 8 | - Use container technologies (RECOMMENDED), e.g., [Docker](https://fmriprep.org/en/stable/docker.html#run-docker) and [Singularity](https://fmriprep.org/en/stable/singularity.html#run-singularity) 9 | 10 | ### Usage Notes 11 | fMRIPrep can be ran via the command *fmriprep* directly, or via the command *docker run*, or via the *fmriprep-docker* wrapper. The *fmriprep-docker* accepts all of the typical options for fMRIPrep. To simplify the practice, we use *fmriprep-docker* for this course. 12 | - [Command-Line Arguments](https://fmriprep.org/en/stable/usage.html) 13 | 14 | ### Examples 15 | `docker run -rm hello-world` 16 | 17 | `python -m pip install --user --upgrade fmriprep-docker` 18 | 19 | `fmriprep-docker .\ds003030\ .\output\ participant --fs-licence-file .\license.txt` 20 | 21 | `fmriprep-docker .\ds003030\ .\output\ participant --fs-licence-file .\license.txt --fs-no-reconall` 22 | 23 | File *license.txt* is the FreeSurfer license file, which can be applied via this [link](https://surfer.nmr.mgh.harvard.edu/registration.html). When you receive the license file, put a copy to the main folder (i.e., the same folder as *output*, *ds003030_select*, and *README.md*). 24 | 25 | **To be updated ...** 26 | 27 | ### Links 28 | fMRIPrep https://fmriprep.org/en/stable/index.html 29 | 30 | Nipype https://nipype.readthedocs.io/en/latest/ 31 | -------------------------------------------------------------------------------- /fMRIPrep/ds003030_select/CHANGES: -------------------------------------------------------------------------------- 1 | 2 | 1.0.0 2020-07-27 3 | 4 | - Initial snapshot -------------------------------------------------------------------------------- /fMRIPrep/ds003030_select/dataset_description.json: -------------------------------------------------------------------------------- 1 | { 2 | "Name": "Flickering Checkerboard Demo Scans", 3 | "BIDSVersion": "1.1.1", 4 | "License": "CC0", 5 | "Authors": [ 6 | "C. Brock Kirwan" 7 | ], 8 | "DatasetDOI": "10.18112/openneuro.ds003030.v1.0.0" 9 | } -------------------------------------------------------------------------------- /fMRIPrep/ds003030_select/participants.tsv: -------------------------------------------------------------------------------- 1 | participant_id age sex 2 | sub-01 23 M 3 | sub-02 53 M 4 | -------------------------------------------------------------------------------- /fMRIPrep/ds003030_select/sub-01/anat/sub-01_T1w.json: -------------------------------------------------------------------------------- 1 | { 2 | "Modality": "MR", 3 | "MagneticFieldStrength": 3, 4 | "ImagingFrequency": 123.263, 5 | "Manufacturer": "Siemens", 6 | "ManufacturersModelName": "TrioTim", 7 | "InstitutionName": "Brigham_Young_University", 8 | "InstitutionalDepartmentName": "MRI", 9 | "InstitutionAddress": "University_Pkwy_1700_North_685_Provo_FCF749_UT_US_84601", 10 | "DeviceSerialNumber": "35002", 11 | "StationName": "MRC35002", 12 | "PatientPosition": "HFS", 13 | "ProcedureStepDescription": "Research_KIRWAN", 14 | "SoftwareVersions": "syngo_MR_B17", 15 | "MRAcquisitionType": "3D", 16 | "SeriesDescription": "t1_mpr_sag_iso_(mprage)", 17 | "ProtocolName": "t1_mpr_sag_iso_(mprage)", 18 | "ScanningSequence": "GR_IR", 19 | "SequenceVariant": "SP_MP_OSP", 20 | "ScanOptions": "IR_PFP", 21 | "SequenceName": "_tfl3d1_ns", 22 | "ImageType": ["ORIGINAL", "PRIMARY", "M", "ND", "NORM"], 23 | "SeriesNumber": 2, 24 | "AcquisitionTime": "10:23:50.052500", 25 | "AcquisitionNumber": 1, 26 | "SliceThickness": 1, 27 | "SAR": 0.0996595, 28 | "EchoTime": 0.00226, 29 | "RepetitionTime": 1.9, 30 | "InversionTime": 0.9, 31 | "FlipAngle": 9, 32 | "PartialFourier": 0.875, 33 | "BaseResolution": 256, 34 | "ShimSetting": [ 35 | -2768, 36 | -22810, 37 | 1838, 38 | -69, 39 | 10, 40 | 173, 41 | -426, 42 | 363 ], 43 | "TxRefAmp": 386.42, 44 | "PhaseResolution": 0.96, 45 | "PhaseOversampling": 0.2, 46 | "ReceiveCoilName": "HeadMatrix", 47 | "CoilString": "C:HEA;HEP", 48 | "PulseSequenceDetails": "%SiemensSeq%_tfl", 49 | "PercentPhaseFOV": 87.5, 50 | "PhaseEncodingSteps": 227, 51 | "AcquisitionMatrixPE": 215, 52 | "ReconMatrixPE": 224, 53 | "PixelBandwidth": 199, 54 | "DwellTime": 9.8e-06, 55 | "ImageOrientationPatientDICOM": [ 56 | 0, 57 | 1, 58 | 0, 59 | 0, 60 | 0, 61 | -1 ], 62 | "InPlanePhaseEncodingDirectionDICOM": "ROW", 63 | "ConversionSoftware": "dcm2niix", 64 | "ConversionSoftwareVersion": "v1.0.20190902" 65 | } 66 | -------------------------------------------------------------------------------- /fMRIPrep/ds003030_select/sub-01/anat/sub-01_T1w.nii.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/coolspiderghy/Python-Workshop-SuZhou/0b040d4890f576f2519e771080cdb44eb8815be8/fMRIPrep/ds003030_select/sub-01/anat/sub-01_T1w.nii.gz -------------------------------------------------------------------------------- /fMRIPrep/ds003030_select/sub-01/func/sub-01_task-FlickeringCheckerBoard_run-1_bold.json: -------------------------------------------------------------------------------- 1 | { 2 | "Modality": "MR", 3 | "MagneticFieldStrength": 3, 4 | "ImagingFrequency": 123.263, 5 | "Manufacturer": "Siemens", 6 | "ManufacturersModelName": "TrioTim", 7 | "InstitutionName": "Brigham_Young_University", 8 | "InstitutionalDepartmentName": "MRI", 9 | "InstitutionAddress": "University_Pkwy_1700_North_685_Provo_FCF749_UT_US_84601", 10 | "DeviceSerialNumber": "35002", 11 | "StationName": "MRC35002", 12 | "PatientPosition": "HFS", 13 | "ProcedureStepDescription": "Research_KIRWAN", 14 | "SoftwareVersions": "syngo_MR_B17", 15 | "MRAcquisitionType": "2D", 16 | "SeriesDescription": "act_fMRI_Demo", 17 | "ProtocolName": "fMRI_Demo", 18 | "ScanningSequence": "EP", 19 | "SequenceVariant": "SK", 20 | "ScanOptions": "FS", 21 | "SequenceName": "epfid2d1_64", 22 | "ImageType": [ 23 | "ORIGINAL", 24 | "PRIMARY", 25 | "FMRI", 26 | "NONE", 27 | "ND", 28 | "MOSAIC" 29 | ], 30 | "SeriesNumber": 4, 31 | "AcquisitionTime": "10:31:57.882500", 32 | "AcquisitionNumber": 1, 33 | "SliceThickness": 3, 34 | "SpacingBetweenSlices": 3, 35 | "SAR": 0.137259, 36 | "EchoTime": 0.028, 37 | "RepetitionTime": 2.5, 38 | "FlipAngle": 90, 39 | "PartialFourier": 1, 40 | "BaseResolution": 64, 41 | "ShimSetting": [ 42 | -2778, 43 | -22846, 44 | 2272, 45 | 554, 46 | 2, 47 | 135, 48 | -175, 49 | 424 50 | ], 51 | "TxRefAmp": 386.42, 52 | "PhaseResolution": 1, 53 | "ReceiveCoilName": "HeadMatrix", 54 | "CoilString": "C:HEA;HEP", 55 | "PulseSequenceDetails": "%CustomerSeq%_ep2d_bold_PMU", 56 | "PercentPhaseFOV": 100, 57 | "PhaseEncodingSteps": 64, 58 | "AcquisitionMatrixPE": 64, 59 | "ReconMatrixPE": 64, 60 | "BandwidthPerPixelPhaseEncode": 36.337, 61 | "EffectiveEchoSpacing": 0.000430002, 62 | "DerivedVendorReportedEchoSpacing": 0.000430002, 63 | "TotalReadoutTime": 0.0270902, 64 | "PixelBandwidth": 2894, 65 | "DwellTime": 2.7e-06, 66 | "PhaseEncodingDirection": "j-", 67 | "SliceTiming": [ 68 | 1.255, 69 | 0, 70 | 1.3175, 71 | 0.0625, 72 | 1.38, 73 | 0.125, 74 | 1.4425, 75 | 0.1875, 76 | 1.505, 77 | 0.25, 78 | 1.5675, 79 | 0.3125, 80 | 1.63, 81 | 0.375, 82 | 1.6925, 83 | 0.44, 84 | 1.7575, 85 | 0.5025, 86 | 1.82, 87 | 0.565, 88 | 1.8825, 89 | 0.6275, 90 | 1.945, 91 | 0.69, 92 | 2.0075, 93 | 0.7525, 94 | 2.07, 95 | 0.815, 96 | 2.1325, 97 | 0.8775, 98 | 2.195, 99 | 0.94, 100 | 2.2575, 101 | 1.0025, 102 | 2.32, 103 | 1.0675, 104 | 2.385, 105 | 1.13, 106 | 2.4475, 107 | 1.1925 108 | ], 109 | "ImageOrientationPatientDICOM": [ 110 | 1, 111 | 0, 112 | 0, 113 | 0, 114 | 1, 115 | 0 116 | ], 117 | "InPlanePhaseEncodingDirectionDICOM": "COL", 118 | "ConversionSoftware": "dcm2niix", 119 | "ConversionSoftwareVersion": "v1.0.20190902", 120 | "TaskName": "FlickeringCheckerBoard" 121 | } 122 | -------------------------------------------------------------------------------- /fMRIPrep/ds003030_select/sub-01/func/sub-01_task-FlickeringCheckerBoard_run-1_bold.nii.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/coolspiderghy/Python-Workshop-SuZhou/0b040d4890f576f2519e771080cdb44eb8815be8/fMRIPrep/ds003030_select/sub-01/func/sub-01_task-FlickeringCheckerBoard_run-1_bold.nii.gz -------------------------------------------------------------------------------- /fMRIPrep/ds003030_select/sub-01/func/sub-01_task-FlickeringCheckerBoard_run-1_events.tsv: -------------------------------------------------------------------------------- 1 | onset duration trial_type 2 | 0 20 Checkerboard 3 | 20 20 Music 4 | 40 20 Checkerboard 5 | 60 20 Music 6 | 80 20 Checkerboard 7 | 100 20 Music 8 | 120 20 Checkerboard 9 | 140 20 Music 10 | 160 20 Checkerboard 11 | 180 20 Music 12 | 200 20 Checkerboard 13 | 220 20 Music 14 | 240 20 Checkerboard 15 | 260 20 Music 16 | 280 20 Checkerboard 17 | 300 20 Music 18 | 320 20 Checkerboard 19 | 340 20 Music 20 | 360 20 Checkerboard 21 | 380 20 Music -------------------------------------------------------------------------------- /fMRIPrep/ds003030_select/sub-02/anat/sub-02_T1w.json: -------------------------------------------------------------------------------- 1 | { 2 | "Modality": "MR", 3 | "MagneticFieldStrength": 3, 4 | "ImagingFrequency": 123.261, 5 | "Manufacturer": "Siemens", 6 | "ManufacturersModelName": "TrioTim", 7 | "InstitutionName": "Brigham_Young_University", 8 | "InstitutionAddress": "University_Pkwy_1700_North_685_Provo_UT_US_84601", 9 | "DeviceSerialNumber": "35002", 10 | "StationName": "MININT-E41B04", 11 | "PatientPosition": "HFS", 12 | "ProcedureStepDescription": "Research_KIRWAN", 13 | "SoftwareVersions": "syngo_MR_B17", 14 | "MRAcquisitionType": "3D", 15 | "SeriesDescription": "t1_mpr_sag_iso_(mprage)", 16 | "ProtocolName": "t1_mpr_sag_iso_(mprage)", 17 | "ScanningSequence": "GR_IR", 18 | "SequenceVariant": "SP_MP_OSP", 19 | "ScanOptions": "IR_PFP", 20 | "SequenceName": "_tfl3d1_ns", 21 | "ImageType": ["ORIGINAL", "PRIMARY", "M", "ND", "NORM"], 22 | "SeriesNumber": 2, 23 | "AcquisitionTime": "15:17:3.095000", 24 | "AcquisitionNumber": 1, 25 | "SliceThickness": 1, 26 | "SAR": 0.086199, 27 | "EchoTime": 0.00226, 28 | "RepetitionTime": 1.9, 29 | "InversionTime": 0.9, 30 | "FlipAngle": 9, 31 | "PartialFourier": 0.875, 32 | "BaseResolution": 256, 33 | "ShimSetting": [ 34 | -2824, 35 | -22910, 36 | 1912, 37 | -115, 38 | -88, 39 | -35, 40 | -292, 41 | 395 ], 42 | "TxRefAmp": 388.099, 43 | "PhaseResolution": 0.96, 44 | "PhaseOversampling": 0.2, 45 | "ReceiveCoilName": "HeadMatrix", 46 | "PulseSequenceDetails": "%SiemensSeq%_tfl", 47 | "PercentPhaseFOV": 87.5, 48 | "PhaseEncodingSteps": 227, 49 | "AcquisitionMatrixPE": 215, 50 | "ReconMatrixPE": 224, 51 | "PixelBandwidth": 199, 52 | "ImageOrientationPatientDICOM": [ 53 | 0, 54 | 1, 55 | 0, 56 | -0.0471064, 57 | 0, 58 | -0.99889 ], 59 | "InPlanePhaseEncodingDirectionDICOM": "ROW", 60 | "ConversionSoftware": "dcm2niix", 61 | "ConversionSoftwareVersion": "v1.0.20190902" 62 | } 63 | -------------------------------------------------------------------------------- /fMRIPrep/ds003030_select/sub-02/anat/sub-02_T1w.nii.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/coolspiderghy/Python-Workshop-SuZhou/0b040d4890f576f2519e771080cdb44eb8815be8/fMRIPrep/ds003030_select/sub-02/anat/sub-02_T1w.nii.gz -------------------------------------------------------------------------------- /fMRIPrep/ds003030_select/sub-02/func/sub-02_task-FlickeringCheckerBoard_run-1_bold.json: -------------------------------------------------------------------------------- 1 | { 2 | "Modality": "MR", 3 | "MagneticFieldStrength": 3, 4 | "ImagingFrequency": 123.261, 5 | "Manufacturer": "Siemens", 6 | "ManufacturersModelName": "TrioTim", 7 | "InstitutionName": "Brigham_Young_University", 8 | "InstitutionalDepartmentName": "MRI", 9 | "InstitutionAddress": "University_Pkwy_1700_North_685_Provo_UT_US_84601", 10 | "DeviceSerialNumber": "35002", 11 | "StationName": "MININT-E41B04", 12 | "PatientPosition": "HFS", 13 | "ProcedureStepDescription": "Research_KIRWAN", 14 | "SoftwareVersions": "syngo_MR_B17", 15 | "MRAcquisitionType": "2D", 16 | "SeriesDescription": "act_fMRI_Demo", 17 | "ProtocolName": "fMRI_Demo", 18 | "ScanningSequence": "EP", 19 | "SequenceVariant": "SK", 20 | "ScanOptions": "FS", 21 | "SequenceName": "epfid2d1_64", 22 | "ImageType": [ 23 | "ORIGINAL", 24 | "PRIMARY", 25 | "FMRI", 26 | "NONE", 27 | "ND", 28 | "MOSAIC" 29 | ], 30 | "SeriesNumber": 4, 31 | "AcquisitionTime": "15:25:39.095000", 32 | "AcquisitionNumber": 1, 33 | "SliceThickness": 3, 34 | "SpacingBetweenSlices": 3, 35 | "SAR": 0.119467, 36 | "EchoTime": 0.028, 37 | "RepetitionTime": 2.5, 38 | "FlipAngle": 90, 39 | "PartialFourier": 1, 40 | "BaseResolution": 64, 41 | "ShimSetting": [ 42 | -2828, 43 | -22897, 44 | 2326, 45 | 408, 46 | -36, 47 | 118, 48 | -477, 49 | 244 50 | ], 51 | "TxRefAmp": 388.099, 52 | "PhaseResolution": 1, 53 | "ReceiveCoilName": "HeadMatrix", 54 | "CoilString": "C:HEA;HEP", 55 | "PulseSequenceDetails": "%CustomerSeq%_ep2d_bold_PMU", 56 | "PercentPhaseFOV": 100, 57 | "PhaseEncodingSteps": 64, 58 | "AcquisitionMatrixPE": 64, 59 | "ReconMatrixPE": 64, 60 | "BandwidthPerPixelPhaseEncode": 36.337, 61 | "EffectiveEchoSpacing": 0.000430002, 62 | "DerivedVendorReportedEchoSpacing": 0.000430002, 63 | "TotalReadoutTime": 0.0270902, 64 | "PixelBandwidth": 2894, 65 | "DwellTime": 2.7e-06, 66 | "PhaseEncodingDirection": "j-", 67 | "SliceTiming": [ 68 | 1.255, 69 | 0, 70 | 1.3175, 71 | 0.0625, 72 | 1.38, 73 | 0.125, 74 | 1.4425, 75 | 0.1875, 76 | 1.505, 77 | 0.25, 78 | 1.5675, 79 | 0.3125, 80 | 1.63, 81 | 0.3775, 82 | 1.695, 83 | 0.44, 84 | 1.7575, 85 | 0.5025, 86 | 1.82, 87 | 0.565, 88 | 1.8825, 89 | 0.6275, 90 | 1.945, 91 | 0.69, 92 | 2.0075, 93 | 0.7525, 94 | 2.07, 95 | 0.815, 96 | 2.1325, 97 | 0.8775, 98 | 2.195, 99 | 0.94, 100 | 2.2575, 101 | 1.005, 102 | 2.3225, 103 | 1.0675, 104 | 2.385, 105 | 1.13, 106 | 2.4475, 107 | 1.1925 108 | ], 109 | "ImageOrientationPatientDICOM": [ 110 | 1, 111 | 2e-16, 112 | 0, 113 | -2e-16, 114 | 0.987414, 115 | -0.158158 116 | ], 117 | "InPlanePhaseEncodingDirectionDICOM": "COL", 118 | "ConversionSoftware": "dcm2niix", 119 | "ConversionSoftwareVersion": "v1.0.20190902", 120 | "TaskName": "FlickeringCheckerBoard" 121 | } 122 | -------------------------------------------------------------------------------- /fMRIPrep/ds003030_select/sub-02/func/sub-02_task-FlickeringCheckerBoard_run-1_bold.nii.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/coolspiderghy/Python-Workshop-SuZhou/0b040d4890f576f2519e771080cdb44eb8815be8/fMRIPrep/ds003030_select/sub-02/func/sub-02_task-FlickeringCheckerBoard_run-1_bold.nii.gz -------------------------------------------------------------------------------- /fMRIPrep/ds003030_select/sub-02/func/sub-02_task-FlickeringCheckerBoard_run-1_events.tsv: -------------------------------------------------------------------------------- 1 | onset duration trial_type 2 | 0 20 Checkerboard 3 | 20 20 Music 4 | 40 20 Checkerboard 5 | 60 20 Music 6 | 80 20 Checkerboard 7 | 100 20 Music 8 | 120 20 Checkerboard 9 | 140 20 Music 10 | 160 20 Checkerboard 11 | 180 20 Music 12 | 200 20 Checkerboard 13 | 220 20 Music 14 | 240 20 Checkerboard 15 | 260 20 Music 16 | 280 20 Checkerboard 17 | 300 20 Music 18 | 320 20 Checkerboard 19 | 340 20 Music 20 | 360 20 Checkerboard 21 | 380 20 Music -------------------------------------------------------------------------------- /fMRIPrep/output/README.md: -------------------------------------------------------------------------------- 1 | . --------------------------------------------------------------------------------