remove print during import

Author: dungscout96

.gitignore

@@ -4,3 +4,4 @@ python_temp.mat
 python_temp_reformated.mat
 *.asv
 .venv/
+build/

Dockerfile

@@ -47,4 +47,7 @@ RUN chown -R octave:octave /home/octave/
 USER octave
 WORKDIR /home/octave
 
-VOLUME ["/source"]
+VOLUME ["/source"]
+
+#https://wiki.ubuntu.com/DashAsBinSh
+#RUN rm /bin/sh && ln -s /bin/bash /bin/sh

dist/eegprep-0.0.2-py3-none-any.whl

@@ -0,0 +1,59 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sys.path.insert(0, '/Users/dtyoung/Documents/EEGLAB/eegprep/src/')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import eegprep"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

dist/eegprep-0.0.2.tar.gz

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "eegprep"
-version = "0.0.2"
+version = "0.0.3"
 authors = [
   { name="Arnaud Delorme", email="[email protected]" },
   { name="Young Truong", email="[email protected]" },

dist/eegprep-0.0.3-py3-none-any.whl

@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: eegprep
-Version: 0.0.2
+Version: 0.0.3
 Summary: EEG preprocessing pipeline on Python
 Author-email: Arnaud Delorme <[email protected]>, Young Truong <[email protected]>
 License: GNU General Public License
@@ -40,7 +40,7 @@ Requires-Dist: oct2py>=5.5.0
 Requires-Dist: torch>=1.10.0
 Requires-Dist: h5py>=3.3.0
 
-## how to duplicate a function
+## How to convert a function from MATLAB to Python
 
 1. Get a MATLAB file to load and process an EEG file
 
@@ -54,3 +54,10 @@ Requires-Dist: h5py>=3.3.0
 
 6. Write the function to compare (see example) and the helper Python function to load the file (note that there could be a general Python helper function)
 
+## Install package
+Packaging was done following the tutorial: https://packaging.python.org/en/latest/tutorials/packaging-projects/ with setuptools
+
+To install the package, run:
+```
+pip install eegprep==0.0.2
+```

notebooks/test.ipynb

@@ -1,5 +1,5 @@
-from ICL_feature_extractor import ICL_feature_extractor
-from pop_loadset import pop_loadset
+from .ICL_feature_extractor import ICL_feature_extractor
+from .pop_loadset import pop_loadset
 import sys
 
 # check if a parameter is present and if it is assign eeglab_file_path to it

pyproject.toml

@@ -1 +1,3 @@
-from .iclabel import iclabel
+from .iclabel import iclabel
+from .pop_saveset import pop_saveset
+from .pop_loadset import pop_loadset

src/eegprep.egg-info/PKG-INFO

@@ -50,12 +50,12 @@ def test_eeg_autocorr():
     psdmed = eeg_autocorr(EEG, 100)
 
     # print information about psdmed
-    print(psdmed.shape)
+    # print(psdmed.shape)
 
     #print(psdmed)
 
 
     #assert psdmed.shape == (10, 100)
     #assert np.all(np.isfinite(psdmed))
 
-test_eeg_autocorr()
+# test_eeg_autocorr()

src/eegprep/ICL_feature_extractor_compare_helper.py

@@ -1,5 +1,5 @@
-from eeg_autocorr import eeg_autocorr
-from pop_loadset import pop_loadset
+from .eeg_autocorr import eeg_autocorr
+from .pop_loadset import pop_loadset
 
 eeglab_file_path = './eeglab_data_with_ica_tmp.set'
 EEG = pop_loadset(eeglab_file_path)

src/eegprep/__init__.py

@@ -1,6 +1,6 @@
-from eeg_autocorr_fftw import eeg_autocorr_fftw
-from pop_loadset import pop_loadset
-from pop_reref import pop_reref
+from .eeg_autocorr_fftw import eeg_autocorr_fftw
+from .pop_loadset import pop_loadset
+from .pop_reref import pop_reref
 import sys
 
 # check if a parameter is present and if it is assign eeglab_file_path to it

src/eegprep/eeg_autocorr.py

@@ -55,7 +55,7 @@ def test_eeg_autocorr_welch():
     psdmed = eeg_autocorr_welch(EEG, 100)
 
     # print information about psdmed
-    print(psdmed.shape)
-    print(psdmed)
+    # print(psdmed.shape)
+    # print(psdmed)
 
 # test_eeg_autocorr_welch()

src/eegprep/eeg_autocorr_compare_helper.py

@@ -1,5 +1,5 @@
-from eeg_autocorr_welch import eeg_autocorr_welch
-from pop_loadset import pop_loadset
+from .eeg_autocorr_welch import eeg_autocorr_welch
+from .pop_loadset import pop_loadset
 
 eeglab_file_path = './eeglab_data_with_ica_tmp.set'
 EEG = pop_loadset(eeglab_file_path)

src/eegprep/eeg_autocorr_fftw_compare_helper.py

@@ -1,6 +1,6 @@
 import numpy as np
 import os
-from pop_loadset import pop_loadset
+from .pop_loadset import pop_loadset
 
 def eeg_checkset(EEG):
 

src/eegprep/eeg_autocorr_welch.py

@@ -1,10 +1,10 @@
-from eeg_autocorr import eeg_autocorr
-from pop_loadset import pop_loadset
+from .eeg_autocorr import eeg_autocorr
+from .pop_loadset import pop_loadset
 import mne
 import tempfile
 import os
 from mne.export import export_raw
-from pop_saveset import pop_saveset # in development
+from .pop_saveset import pop_saveset # in development
 
 # write a funtion that converts a MNE raw object to an EEGLAB set file
 def eeg_eeglab2mne(EEG):

src/eegprep/eeg_autocorr_welch_compare_helper.py

@@ -1,5 +1,5 @@
-from eeg_autocorr import eeg_autocorr
-from pop_loadset import pop_loadset
+from .eeg_autocorr import eeg_autocorr
+from .pop_loadset import pop_loadset
 import mne
 import tempfile
 import os

src/eegprep/eeg_checkset.py

@@ -1,5 +1,5 @@
-from eeg_rpsd import eeg_rpsd
-from pop_loadset import pop_loadset
+from .eeg_rpsd import eeg_rpsd
+from .pop_loadset import pop_loadset
 import sys
 
 if len(sys.argv) > 1:

src/eegprep/eeg_eeglab2mne.py

@@ -1,6 +1,6 @@
 from oct2py import Oct2Py, get_log
-from pop_saveset import pop_saveset
-from pop_loadset import pop_loadset
+from .pop_saveset import pop_saveset
+from .pop_loadset import pop_loadset
 import logging
 import os
 import sys

src/eegprep/eeg_mne2eeglab.py

@@ -15,9 +15,9 @@ def iclabel(EEG, algorithm='default'):
     features[0] = np.single(np.concatenate([features[0],-features[0],features[0][:, ::-1, :, :],-features[0][:, ::-1, :, :]], axis=3))
     features[1] = np.single(np.tile(features[1], (1, 1, 1, 4)))
     features[2] = np.single(np.tile(features[2], (1, 1, 1, 4)))
-    print('Feature 0 shape:', features[0].shape)
-    print('Feature 1 shape:', features[1].shape)
-    print('Feature 2 shape:', features[2].shape)
+    # print('Feature 0 shape:', features[0].shape)
+    # print('Feature 1 shape:', features[1].shape)
+    # print('Feature 2 shape:', features[2].shape)
 
     # Load the ICLabelNet model
     model = ICLabelNet('netICL.mat')

src/eegprep/eeg_rpsd_compare_helper.py

@@ -1,6 +1,6 @@
 from iclabel import iclabel
-from pop_loadset import pop_loadset
-from pop_saveset import pop_saveset
+from .pop_loadset import pop_loadset
+from .pop_saveset import pop_saveset
 import sys
 
 # check if there are 2 arguments otherwise issue an error
@@ -19,7 +19,7 @@
 # EEG['trials'] = 1
 
 # Print the loaded data
-print('Data shape:', EEG['data'].shape)
+# print('Data shape:', EEG['data'].shape)
 EEG = iclabel(EEG, 'default')
 
 # save dataset

src/eegprep/eeglabcompat.py

@@ -25,10 +25,10 @@ def __init__(self, mat_path):
         super().__init__()
         iclabel_matlab = scipy.io.loadmat(mat_path)
         params = iclabel_matlab['params'][0]
-        i = 11
-        print('shape of param', i, torch.tensor(params[i][1]).shape)
+        # i = 11
+        # print('shape of param', i, torch.tensor(params[i][1]).shape)
         self.discriminator_image_layer1_conv = torch.nn.Conv2d(in_channels=1, out_channels=128, kernel_size=4, stride=2, padding=1, dilation=1)
-        print(self.discriminator_image_layer1_conv.weight.shape)
+        # print(self.discriminator_image_layer1_conv.weight.shape)
         self.discriminator_image_layer1_conv.weight = torch.nn.Parameter(torch.tensor(params[0][1]).permute(3, 2, 0, 1))
         self.discriminator_image_layer1_conv.bias = torch.nn.Parameter(torch.tensor(params[1][1]).squeeze())
         self.discriminator_image_layer1_relu = torch.nn.LeakyReLU(0.2)
@@ -83,7 +83,7 @@ def forward(self, image, psdmed, autocorr):
         x_image = self.discriminator_image_layer2_relu(x_image)
         x_image = self.discriminator_image_layer3_conv(x_image)
         x_image = self.discriminator_image_layer3_relu(x_image)
-        print('x_image', x_image.shape)
+        # print('x_image', x_image.shape)
 
         x_psdmed = self.discriminator_psdmed_layer1_conv_conv(psdmed)
         x_psdmed = self.discriminator_psdmed_layer1_conv_relu(x_psdmed)
@@ -94,7 +94,7 @@ def forward(self, image, psdmed, autocorr):
         x_psdmed = self.discriminator_psdmed_reshape(x_psdmed)
         x_psdmed = self.discriminator_psdmed_concat1([x_psdmed]*4)
         x_psdmed = self.discriminator_psdmed_concat2([x_psdmed]*4)
-        print('x_psdmed', x_psdmed.shape)
+        # print('x_psdmed', x_psdmed.shape)
 
         x_autocorr = self.discriminator_autocorr_layer1_conv_conv(autocorr)
         x_autocorr = self.discriminator_autocorr_layer1_conv_relu(x_autocorr)
@@ -105,11 +105,11 @@ def forward(self, image, psdmed, autocorr):
         x_autocorr = self.discriminator_autocorr_reshape(x_autocorr)
         x_autocorr = self.discriminator_autocorr_concat1([x_autocorr]*4)
         x_autocorr = self.discriminator_autocorr_concat2([x_autocorr]*4)
-        print('x_autocorr', x_autocorr.shape)
+        # print('x_autocorr', x_autocorr.shape)
 
         x = self.discriminator_concat([x_image, x_psdmed, x_autocorr])
         x = self.discriminator_conv(x)
-        print('x', x.shape)
+        # print('x', x.shape)
         # subtract max value to avoid overflow
         x = x - torch.max(x, dim=1, keepdim=True).values
         x = self.discriminator_softmax(x)

src/eegprep/iclabel.py

@@ -1,5 +1,5 @@
-from pop_reref import pop_reref
-from pop_loadset import pop_loadset
+from .pop_reref import pop_reref
+from .pop_loadset import pop_loadset
 import sys
 import os
 import scipy.io

src/eegprep/iclabel_helper.py

@@ -1,7 +1,7 @@
-from ICL_feature_extractor import ICL_feature_extractor
-from pop_loadset import pop_loadset
-from pop_saveset import pop_saveset
-from pop_reref import pop_reref
+from .ICL_feature_extractor import ICL_feature_extractor
+from .pop_loadset import pop_loadset
+from .pop_saveset import pop_saveset
+from .pop_reref import pop_reref
 import sys
 
 # check if a parameter is present and if it is assign eeglab_file_path to it

src/eegprep/iclabel_net.py

@@ -2,8 +2,8 @@
 import os
 from scipy.io import savemat
 import tempfile # need to do something with tempfile
-from pop_loadset import pop_loadset
-from pop_saveset import pop_saveset
+from .pop_loadset import pop_loadset
+from .pop_saveset import pop_saveset
 from oct2py import octave as eeglab
 
 def pop_resample( EEG, freq): # 2 additional parameters in MATLAB (never used)

src/eegprep/pop_reref_compare_helper.py

@@ -183,7 +183,7 @@ def pop_saveset2(EEG, file_name):
     # # Step 4: Save the EEGLAB dataset as a .mat file
     savemat(file_name, eeglab_dict)
 
-from pop_loadset import pop_loadset
+from .pop_loadset import pop_loadset
 
 def test_pop_saveset():
     file_path = './eeglab_data_with_ica_tmp.set'

src/eegprep/pop_reref_helper.py

@@ -1,5 +1,5 @@
-from topoplot import topoplot
-from pop_loadset import pop_loadset
+from .topoplot import topoplot
+from .pop_loadset import pop_loadset
 import sys
 
 print('Topoplot compare helper')