# combile problem

**URL:** <https://mne.discourse.group/t/combile-problem/11334>\
**Category:** Support & Discussions\
**Tags:** list-archive\
**Created:** [July 2, 2025, 7:22am UTC](https://mne.discourse.group/t/combile-problem/11334 "2025-07-02T07:22:33Z")\
**Posts on this page:** 3\
**Page:** 1

<div class="post-metadata">

**Author:** ![Grit1979](https://yyz2.discourse-cdn.com/free1/user_avatar/mne.discourse.group/grit1979/32/4374_2.png) [@Grit1979](https://mne.discourse.group/u/Grit1979)\
**Post date:** [July 2, 2025, 7:22am UTC](https://mne.discourse.group/t/combile-problem/11334/1 "2025-07-02T07:22:33Z")

</div>

In my python source cIode,I have read from a txt data files(64 channel data:include 62 eegs and two eog(Heog,Veog),and I transform it into matrix ,then I transforms it into numpy array,finally I use the library MNE and MNE\_Connectivity, designed a GUI by PyQt5.When I used Nuitka to combile and package the source code, it showed that the packaging was successful. However, the compiled exe. program couldn’t run normally,while the source code can run normally. The console output captured indicated that there was a problem with the initialization of the **init** () file.my combile instruction as follows:python -m nuitka --mingw64 --standalone --jobs=2 --windows-disable-console --show-progress --enable-plugin=pyqt5 --plugin-enable=numpy --nofollow-imports --include-package=mne,mne\_connectivity --follow-import-to=gui --output-dir=out brain\_connectivity\_analysis1.0.0.py

- Nuitka version: e.g. 2.0.4
- 
  - MNE version: e.g. 1.6.0

- 
  - MNE\_CONNECTIVITY version: e.g. 0.5.0

- operating system: e.g. Windows 11  
the output:  
D:\ERPMaster\Data3422.txt  
64  
512  
112.5  
120.0  
14.0  
35.0  
(64, 330145)  
Creating RawArray with float64 data, n\_channels=64, n\_times=330145

Traceback (most recent call last):  
File “D:\ERPMaster\out\brain\_connectivity\_analysisV1.0.0.dist\brain\_connectivity\_analysisV1.0.0.py”, line 486, in on\_btnElectrodeConn\_clicked  
File “D:\ERPMaster\out\brain\_connectivity\_analysisV1.0.0.dist\brain\_connectivity\_analysisV1.0.0.py”, line 421, in brain\_sensor\_connectivity  
File “D:\ERPMaster\out\brain\_connectivity\_analysisV1.0.0.dist\brain\_connectivity\_analysisV1.0.0.py”, line 74, in crop\_the\_data  
File “”, line 12, in set\_montage  
File “D:\ERPMaster\out\brain\_connectivity\_analysisV1.0.0.dist\mne\channels\_fiff\meas\_info.py”, line 427, in set\_montage  
File “D:\ERPMaster\out\brain\_connectivity\_analysisV1.0.0.dist\mne\io\array\array.py”, line 89, in **init**  
File “”, line 12, in **init**  
File “D:\ERPMaster\out\brain\_connectivity\_analysisV1.0.0.dist\mne\io\base.py”, line 310, in **init**  
self.\_init\_kwargs = \_get\_argvalues()  
File “D:\ERPMaster\out\brain\_connectivity\_analysisV1.0.0.dist\mne\utils\misc.py”, line 343, in \_get\_argvalues  
params[arg] = values[arg]  
KeyError: ‘self’

---

<div class="post-metadata">

**Author:** ![Grit1979](https://yyz2.discourse-cdn.com/free1/user_avatar/mne.discourse.group/grit1979/32/4374_2.png) [@Grit1979](https://mne.discourse.group/u/Grit1979)\
**Post date:** [July 3, 2025, 6:08am UTC](https://mne.discourse.group/t/combile-problem/11334/2 "2025-07-03T06:08:48Z")

</div>

It’s too complicated

---

<div class="post-metadata">

**Author:** ![Grit1979](https://yyz2.discourse-cdn.com/free1/user_avatar/mne.discourse.group/grit1979/32/4374_2.png) [@Grit1979](https://mne.discourse.group/u/Grit1979)\
**Post date:** [July 3, 2025, 6:11am UTC](https://mne.discourse.group/t/combile-problem/11334/3 "2025-07-03T06:11:11Z")

</div>

the source code:

# -_- coding: utf-8 -_-

import sys  
from PyQt5.QtWidgets import QApplication,QWidget,QMessageBox  
from PyQt5.QtCore import pyqtSlot,pyqtSignal  
import os  
import os.path as op  
import numpy as np  
import re  
import mne  
import mne\_connectivity  
from mne.beamformer import make\_lcmv, apply\_lcmv\_epochs  
from mne\_connectivity import envelope\_correlation  
from mne\_connectivity.viz import plot\_sensors\_connectivity

from mne\_connectivity import spectral\_connectivity\_epochs  
from mne.datasets import fetch\_fsaverage

from gui import connectivity\_analysis\_gui  
##import connectivity\_analysis\_gui

##import warnings  
##warnings.filterwarnings(“ignore”, category=DeprecationWarning)

def read\_data\_into\_matrix(eeg\_path, chan\_n):  
“”"  
使用正则表达式从指定文件中读取浮点数数据并构造矩阵。

```
参数:
    eeg_path: 要读取的文件路径
    skip_rows: 要跳过的行数，默认为65
    chan_n: 矩阵的行数
    sample_n: 矩阵的列数

返回:
    一个形状为 (chan_n, sample_n) 的 NumPy 数组
"""
data = []
skip_rows = 65

float_pattern = re.compile(r'-?\d+\.\d+')  

with open(eeg_path, 'r') as file:
    for _ in range(skip_rows):
        next(file)
    
    for line in file:
        floats = float_pattern.findall(line)
        data.extend(map(float, floats))

total_len = len(data)

if total_len % chan_n != 0 and total_len < chan_n:
    raise ValueError(f"文件中的数据不足。")

data = data[:total_len]

sample_n = int(total_len / chan_n)
data_matrix = np.array(data).reshape((chan_n, sample_n))

return data_matrix

```

def crop\_the\_data(eeg\_path, chan\_n, sfreq, crop\_tmin, crop\_tmax):  
data = read\_data\_into\_matrix(eeg\_path, chan\_n)  
print(data.shape)

```
ch_names = ['Fp1', 'Fp2', 'PO5', 'PO6', 'Fpz', 'AF3', 'AF4', 'AF7', 'AF8', 'F1', 'F2', 'F3', 'F4', 'F5',
 'F6', 'F7', 'F8', 'Fz', 'FC1', 'FC2', 'FC3', 'FC4', 'FC5', 'FC6', 'FCz', 'FT7', 'FT8', 'C1',
 'C2', 'C3', 'C4', 'C5', 'C6', 'Cz', 'T7', 'T8', 'CP1', 'CP2', 'CP3', 'CP4', 'CP5', 'CP6',
 'CPz', 'TP7', 'TP8', 'P1', 'P2', 'P3', 'P4', 'P5', 'P6', 'P7', 'P8', 'Pz', 'PO3', 'PO4',
  'PO7', 'PO8', 'POz', 'O1', 'O2', 'Oz', 'HEOG', 'VEOG']
ch_types = ['eeg'] * 62 + ['eog'] * 2
info = mne.create_info(ch_names=ch_names, ch_types=ch_types, sfreq=sfreq)
info.set_montage('standard_1005')
info['description'] = 'My custom dataset'
scalings = {'eeg': 0.0001, 'eog': 0.0001}

raw = mne.io.RawArray(data, info)
raw.apply_function(lambda x: x * 1e-6, channel_wise=False)
print(raw.info)
print(raw.info['ch_names'])
#raw.plot(show_scrollbars=False, show_scalebars=False)

raw.plot(n_channels=chan_n, scalings=scalings, title='Data from matrix', show=True, block=True)
raw.plot_sensors(ch_type='eeg', show_names='True')

eeg_data = raw.pick_types(meg=False, eeg=True, eog=False, exclude='bads')
eeg_data.crop(crop_tmin, crop_tmax)

nchan = eeg_data.info['nchan']
print(f"EEG通道数量：{nchan}")

print(eeg_data.info)
print(eeg_data.info['ch_names'])

return eeg_data

```

def crop\_the\_data1(eeg\_path, chan\_n, sfreq, crop\_tmin, crop\_tmax):

```
#第66行开始是数据行
# 示例：假设文件名是 "Data331.txt"，第66行开始是数据行
data = read_data_into_matrix(eeg_path, chan_n)
print(data.shape) # 应输出 (64, 339749)

ch_names = ['Fp1', 'Fp2', 'PO5', 'PO6', 'Fpz', 'AF3', 'AF4', 'AF7', 'AF8', 'F1', 'F2', 'F3', 'F4', 'F5',
 'F6', 'F7', 'F8', 'Fz', 'FC1', 'FC2', 'FC3', 'FC4', 'FC5', 'FC6', 'FCz', 'FT7', 'FT8', 'C1',
 'C2', 'C3', 'C4', 'C5', 'C6', 'Cz', 'T7', 'T8', 'CP1', 'CP2', 'CP3', 'CP4', 'CP5', 'CP6',
 'CPz', 'TP7', 'TP8', 'P1', 'P2', 'P3', 'P4', 'P5', 'P6', 'P7', 'P8', 'Pz', 'PO3', 'PO4',
  'PO7', 'PO8', 'POz', 'O1', 'O2','Oz', 'HEOG', 'VEOG']
# 确保 ch_types 与 ch_names 长度一致，前62个为'eeg'，后两个为'eog'
ch_types = ['eeg'] * 62 + ['eog'] * 2
info = mne.create_info(ch_names=ch_names,ch_types=ch_types,sfreq=sfreq)
info.set_montage('standard_1005')
info['description'] = 'My custom dataset'
scalings = {'eeg':0.0001,'eog':0.0001}

raw = mne.io.RawArray(data, info)
raw.apply_function(lambda x: x * 1e-6, channel_wise=False)
print(raw.info)
print(raw.info['ch_names'])

eeg_data = raw.pick_types(meg=False, eeg=True, eog=False, exclude='bads')
eeg_data.crop(crop_tmin, crop_tmax)

# 对原始数据进行带通滤波
eeg_data.filter(l_freq=1.0, h_freq=40.0) # 带通滤波，1-40 Hz

# 获取 EEG 类型数据的通道数量
nchan = eeg_data.info['nchan']
print(f"EEG通道数量：{nchan}")

print(eeg_data.info)
print(eeg_data.info['ch_names'])

return eeg_data

```

def load\_local\_bst\_resting\_data(local\_data\_path):  
“”"  
加载本地的 bst\_resting 数据集。

```
参数:
    local_data_path: 数据集的本地存储路径，例如 'D:\\ERPMaster\\mne_data\\data'

返回:
    数据集的路径
"""
if not op.exists(local_data_path):
    raise FileNotFoundError(f"指定的本地路径不存在: {local_data_path}")

# 检查数据文件是否存在
bst_resting_path = op.join(local_data_path, 'MNE-brainstorm-data', 'bst_resting')
if not op.exists(bst_resting_path):
    raise FileNotFoundError(f"数据文件未找到在指定的本地路径: {bst_resting_path}")

return local_data_path

```

def source\_connectivity\_glass(mri\_path, eeg\_path, chan\_n, sfreq, crop\_tmin, crop\_tmax, low, high):  
# 加载本地数据路径  
data\_path = load\_local\_bst\_resting\_data(mri\_path)

```
# 指向正确的 subjects 目录
subjects_dir = op.join(data_path, 'MNE-brainstorm-data', 'bst_resting', 'subjects')
subject1 = 'bst_resting'
print(subjects_dir)

# 指向正确的 fsaverage 目录
default_dir=op.dirname(mri_path)
print(default_dir)
fs_dir = fetch_fsaverage(default_dir,verbose=True)

subjects_dir = op.dirname(fs_dir)

# The files live in:
subject = 'fsaverage'
trans = 'fsaverage' # MNE has a built-in fsaverage transformation
##src = op.join(fs_dir, 'bem', 'fsaverage-vol-5-src.fif')#容积源空间
##src1 = op.join(fs_dir, 'bem', 'fsaverage-ico-5-src.fif')#非容积源空间
bem = op.join(fs_dir, 'bem', 'fsaverage-5120-5120-5120-bem-sol.fif')#边界元模型

print("边界元模型的目录：",bem)

##############################################################################
# 加载EEG数据
raw = crop_the_data1(eeg_path, chan_n, sfreq, crop_tmin, crop_tmax)
print(raw.ch_names)

# 设置电极位置
montage = mne.channels.make_standard_montage('standard_1005')
raw.set_montage(montage)
raw.set_eeg_reference(projection=True)
new_events = mne.make_fixed_length_events(raw, id=1, duration=2.0, overlap=0.5)

##############################################################################
"""
====================================================
Compute envelope correlations in volume source space
====================================================

Compute envelope correlations of orthogonalized activity
:footcite:`HippEtAl2012,KhanEtAl2018` in source space using resting state
CTF data in a volume source space.
"""

# Authors: Eric Larson <larson.eric.d@gmail.com>
# Sheraz Khan <sheraz@khansheraz.com>
# Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
# 数据预处理
raw1 = raw.copy()
raw1.load_data().resample(80)

raw1.apply_proj()
cov = mne.compute_raw_covariance(raw1)

raw1.filter(low, high)
events = mne.make_fixed_length_events(raw1, duration=5.)
epochs = mne.Epochs(raw1, events=events, tmin=0, tmax=5.,
                    baseline=None, reject=dict(eeg=200e-6), preload=True)
data_cov = mne.compute_covariance(epochs)
print("计算完相关数据相关性及分段相关性!")

del raw1

##############################################################################
# Compute the forward and inverse
# -------------------------------

# This source space is really far too coarse, but we do this for speed
# considerations here

# 计算前向模型和滤波器
pos = 15. # 1.5 cm is very broad, done here for speed!

# 加载本地数据路径
data_path = load_local_bst_resting_data(mri_path)

# 指向正确的 subjects 目录
subjects_dir1 = op.join(data_path, 'MNE-brainstorm-data', 'bst_resting', 'subjects')
print(subjects_dir1)

# 指向正确的 fsaverage 目录
default_dir=op.dirname(mri_path)
print(default_dir)
fs_dir = fetch_fsaverage(default_dir,verbose=True)

subjects_dir = op.dirname(fs_dir)
subject = 'bst_resting'

# The files live in:
subject = 'bst_resting'
trans = 'fsaverage'
bem = op.join(fs_dir, 'bem', 'fsaverage-5120-5120-5120-bem-sol.fif')#边界元模型
src = mne.setup_volume_source_space('bst_resting', pos, bem=bem,
                                    subjects_dir=subjects_dir1, verbose=True)
fwd = mne.make_forward_solution(epochs.info, trans, src, bem)
filters = make_lcmv(epochs.info, fwd, data_cov, 0.05, cov,
                    pick_ori='max-power', weight_norm='nai')
print("计算完正向解和LCMV空间滤波!")
del fwd, data_cov, cov

# 计算相关性和程度
epochs.apply_hilbert()
stcs = apply_lcmv_epochs(epochs, filters, return_generator=True)
corr = envelope_correlation(stcs, verbose=True)
del stcs, epochs, filters
print("完成希尔伯特滤波，LCMV分段滤波及包络相关性连接度!")

corr = corr.combine()
degree = mne_connectivity.degree(corr, 0.15)
stc = mne.VolSourceEstimate(degree, [src[0]['vertno']], 0, 1, 'bst_resting')
brain = stc.plot(
    src, clim=dict(kind='percent', lims=[75, 85, 95]), colormap='gnuplot',
    subjects_dir=subjects_dir1, mode='glass_brain')

```

def source\_volume\_connecitivity(mri\_path, eeg\_path, chan\_n, sfreq, crop\_tmin, crop\_tmax, low, high):  
# 加载本地数据路径  
data\_path = load\_local\_bst\_resting\_data(mri\_path)

```
# 指向正确的 subjects 目录
subjects_dir = op.join(data_path, 'MNE-brainstorm-data', 'bst_resting', 'subjects')
subject1 = 'bst_resting'
print(subjects_dir)

# 指向正确的 fsaverage 目录
default_dir=op.dirname(mri_path)
print(default_dir)
fs_dir = fetch_fsaverage(default_dir,verbose=True)

subjects_dir = op.dirname(fs_dir)

# The files live in:
subject = 'fsaverage'
trans = 'fsaverage' # MNE has a built-in fsaverage transformation
##src = op.join(fs_dir, 'bem', 'fsaverage-vol-5-src.fif')#容积源空间
##src1 = op.join(fs_dir, 'bem', 'fsaverage-ico-5-src.fif')#非容积源空间
bem = op.join(fs_dir, 'bem', 'fsaverage-5120-5120-5120-bem-sol.fif')#边界元模型
print("边界元模型的目录：",bem)

##############################################################################
# Load the data
#提供弹窗选择目录下的文件或输入文件名，以获得脑电数据
raw = crop_the_data1(eeg_path, chan_n, sfreq, crop_tmin, crop_tmax)
print(raw.ch_names)

# 设置电极位置
montage = mne.channels.make_standard_montage('standard_1005')
raw.set_montage(montage)
raw.set_eeg_reference(projection=True)
new_events = mne.make_fixed_length_events(raw, id=1, duration=2.0, overlap=0.5)

##############################################################################
"""
====================================================
Compute envelope correlations in volume source space
====================================================

Compute envelope correlations of orthogonalized activity
:footcite:`HippEtAl2012,KhanEtAl2018` in source space using resting state
CTF data in a volume source space.
"""

# Authors: Eric Larson <larson.eric.d@gmail.com>
# Sheraz Khan <sheraz@khansheraz.com>
# Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
# 数据预处理
raw1 = raw.copy()
raw1.load_data().resample(80)

raw1.apply_proj()
cov = mne.compute_raw_covariance(raw1)

##############################################################################
# Now we band-pass filter our data and create epochs.

raw1.filter(low, high)
events = mne.make_fixed_length_events(raw1, duration=5.)
epochs = mne.Epochs(raw1, events=events, tmin=0, tmax=5.,
                    baseline=None, reject=dict(eeg=200e-6), preload=True)
data_cov = mne.compute_covariance(epochs)
del raw1
print("计算完数据相关性及分段相关性!")

##############################################################################
# Compute the forward and inverse
# -------------------------------

# This source space is really far too coarse, but we do this for speed
# considerations here
# 计算前向模型和滤波器
pos = 15. # 1.5 cm is very broad, done here for speed!

# 加载本地数据路径
data_path = load_local_bst_resting_data(mri_path)

# 指向正确的 subjects 目录
subjects_dir1 = op.join(data_path, 'MNE-brainstorm-data', 'bst_resting', 'subjects')
print(subjects_dir1)

# 指向正确的 fsaverage 目录
default_dir=op.dirname(mri_path)
print(default_dir)
fs_dir = fetch_fsaverage(default_dir,verbose=True)

subjects_dir = op.dirname(fs_dir)
subject = 'bst_resting'

# The files live in:
trans = 'fsaverage'
bem = op.join(fs_dir, 'bem', 'fsaverage-5120-5120-5120-bem-sol.fif')#边界元模型
src = mne.setup_volume_source_space('bst_resting', pos, bem=bem,
                                    subjects_dir=subjects_dir1, verbose=True)
fwd = mne.make_forward_solution(epochs.info, trans, src, bem)
filters = make_lcmv(epochs.info, fwd, data_cov, 0.05, cov,
                    pick_ori='max-power', weight_norm='nai')
del fwd, data_cov, cov
print("计算完正向解和LCMV空间滤波!")

# 计算相关性和程度
##############################################################################
# Compute label time series and do envelope correlation
# -----------------------------------------------------

epochs.apply_hilbert() # faster to do in sensor space
stcs = apply_lcmv_epochs(epochs, filters, return_generator=True)
corr = envelope_correlation(stcs, verbose=True)
del stcs, epochs, filters
print("完成希尔伯特滤波，LCMV分段滤波及包络相关性连接度!")

# average over epochs
corr = corr.combine()

##############################################################################
# Compute the degree and plot it
# ------------------------------

degree = mne_connectivity.degree(corr, 0.15)
stc = mne.VolSourceEstimate(degree, [src[0]['vertno']], 0, 1, 'bst_resting')

lims = [75, 85, 95]

kwargs = dict(src=src, subject=subject1, subjects_dir=subjects_dir1,
              initial_time=0, verbose=True)
stc.plot(mode='stat_map', clim=dict(kind='percent', pos_lims=lims), **kwargs)

```

def brain\_sensor\_connectivity(eeg\_path, chan\_n, sfreq, crop\_tmin, crop\_tmax, low, high):  
raw = crop\_the\_data(eeg\_path, chan\_n, sfreq, crop\_tmin, crop\_tmax)  
print(raw.ch\_names)

```
montage = mne.channels.make_standard_montage('standard_1005')
raw.set_montage(montage)
raw.set_eeg_reference(projection=True)
new_events = mne.make_fixed_length_events(raw, id=1, duration=2.0, overlap=0.5)
epochs = mne.Epochs(raw, new_events)

event_id = 1  
tmin, tmax = -0.2, 1.5

sfreq = raw.info['sfreq']
tmin = 0.0

raw.pick(['eeg'])  
con = spectral_connectivity_epochs(
    epochs, method='wpli', mode='multitaper', sfreq=sfreq, fmin=low, fmax=high,
    faverage=True, tmin=tmin, mt_adaptive=False, n_jobs=1)

plot_sensors_connectivity(
    epochs.info,
    con.get_data(output='dense')[:, :, 0]) 

```

class QmyWidget(QWidget):

```
numberSignal = pyqtSignal(int)

def __init__ (self, parent=None):
    super(). __init__ (parent)
    self.ui = connectivity_analysis_gui.Ui_Form()
    self.ui.setupUi(self)
    self.ui.eeg_path.setText("D:\\ERPMaster\\Data3422.txt")
    self.ui.mri_path.setText("D:\\ERPMaster\\mne_data\\data")

    
@pyqtSlot()
def on_btnElectrodeConn_clicked(self):
    s1 = self.ui.eeg_path.text()
    eeg_path = str(s1)
    print(eeg_path)
    s3 = self.ui.ch_n.text()
    chan_n = int(s3)
    print(chan_n)
    s4 = self.ui.sfreq.text()
    sfreq = int(s4)
    print(sfreq)
    s5 = self.ui.crop_tmin.text()
    crop_tmin = str(s5)
    print(crop_tmin)
    crop_tmin = float(crop_tmin[0:])
    s6 = self.ui.crop_tmax.text()
    crop_tmax = str(s6)
    print(crop_tmax)
    crop_tmax = float(crop_tmax[0:])
    s7 = self.ui.low.text()
    low = str(s7)
    low = float(low[0:])
    print(low)
    s8 = self.ui.high.text()
    high = str(s8)
    high = float(high[0:])
    print(high)
    brain_sensor_connectivity(eeg_path, chan_n, sfreq, crop_tmin, crop_tmax, low, high)

@pyqtSlot()
def on_btnGlassTrans_clicked(self):
    s1 = self.ui.eeg_path.text()
    eeg_path = str(s1)
    s2 = self.ui.mri_path.text()
    mri_path = str(s2)
    s3 = self.ui.ch_n.text()
    chan_n = int(s3)
    print(chan_n)
    s4 = self.ui.sfreq.text()
    sfreq = int(s4)
    print(sfreq)
    s5 = self.ui.crop_tmin.text()
    crop_tmin = str(s5)
    print(crop_tmin)
    crop_tmin = float(crop_tmin[0:])
    s6 = self.ui.crop_tmax.text()
    crop_tmax = str(s6)
    print(crop_tmax)
    crop_tmax = float(crop_tmax[0:])
    s7 = self.ui.low.text()
    low = str(s7)
    low = float(low[0:])
    print(low)
    s8 = self.ui.high.text()
    high = str(s8)
    high = float(high[0:])
    print(high)
    source_connectivity_glass(mri_path, eeg_path, chan_n, sfreq, crop_tmin, crop_tmax, low, high)
    

@pyqtSlot()
def on_btnMRISlice_clicked(self):
    s1 = self.ui.eeg_path.text()
    eeg_path = str(s1)
    s2 = self.ui.mri_path.text()
    mri_path = str(s2)
    s3 = self.ui.ch_n.text()
    chan_n = int(s3)
    print(chan_n)
    s4 = self.ui.sfreq.text()
    sfreq = int(s4)
    print(sfreq)
    s5 = self.ui.crop_tmin.text()
    crop_tmin = str(s5)
    print(crop_tmin)
    crop_tmin = float(crop_tmin[0:])
    s6 = self.ui.crop_tmax.text()
    crop_tmax = str(s6)
    print(crop_tmax)
    crop_tmax = float(crop_tmax[0:])
    s7 = self.ui.low.text()
    low = str(s7)
    low = float(low[0:])
    print(low)
    s8 = self.ui.high.text()
    high = str(s8)
    high = float(high[0:])
    print(high)
    source_volume_connecitivity(mri_path, eeg_path, chan_n, sfreq, crop_tmin, crop_tmax, low, high)

```

if **name** == “ **main** ”:  
app = QApplication(sys.argv)  
myWindow = QmyWidget()  
myWindow.show()  
n = app.exec()  
sys.exit(n)