Add installation improvements and testing infrastructure

.github/workflows/tests.yml

@@ -0,0 +1,36 @@
+name: Tests
+
+on:
+  push:
+    branches: [ main, master ]
+  pull_request:
+    branches: [ main, master ]
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.8", "3.9", "3.10"]
+
+    steps:
+    - uses: actions/checkout@v3
+
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v4
+      with:
+        python-version: ${{ matrix.python-version }}
+
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install -r requirements.txt
+
+    - name: Install package
+      run: |
+        pip install -e .
+
+    - name: Run tests
+      run: |
+        pytest tests/ -v
+

.vscode/settings.json

@@ -0,0 +1,5 @@
+{
+    "python-envs.defaultEnvManager": "ms-python.python:conda",
+    "python-envs.defaultPackageManager": "ms-python.python:conda",
+    "python-envs.pythonProjects": []
+}

LFPAnalysis/lfp_preprocess_utils.py

@@ -1573,15 +1573,6 @@ def load_elec(elec_path=None, site='MSSM'):
             elec_data['manual'][elec_data['Notes'].str.lower().str.contains('cyst', na=False)] = 'oob'
             elec_data['manual'][elec_data['Notes'].str.lower().str.contains('bad', na=False)] = 'oob'
 
-
-
-
-
-
-
-
-
-
     return elec_data
 
 def make_mne_scalp(load_path=None, overwrite=True, return_data=False):
@@ -1605,17 +1596,6 @@ def make_mne_scalp(load_path=None, overwrite=True, return_data=False):
         whether to overwrite existing data for this person if it exists 
     return_data: bool 
         whether to actually return the data or just save it in the directory 
-    eeg_names : list
-        list of channel names that pertain to scalp EEG in case the hardcoded options don't work
-    resp_names : list 
-        list of channel names that pertain to respiration in case the hardcoded options don't work
-    ekg_names : list
-        list of channel names that pertain to the EKG in case the hardcoded options don't work
-    sync_name : str
-        provide the sync name in case the hardcoded options don't work
-    sync_type : str
-        what type of sync signal was used? options: ['photodiode', 'audio', 'ttl']
-
     Returns
     -------
     mne_data : mne object 
@@ -2515,7 +2495,7 @@ def compute_and_baseline_tfr(baseline_event, task_events, freqs, n_cycles, load_
 
                 iteration +=1
 
-        zpow = mne.time_frequency.EpochsTFR(event_epochs_reref.info, baseline_corrected_power, 
+        zpow = mne.time_frequency.EpochsTFRArray(event_epochs_reref.info, baseline_corrected_power, 
                                     temp_pow.times, freqs)
 
         zpow.metadata = event_epochs_reref.metadata

LFPAnalysisBook/module-01-00_BroadIntroduction.md

@@ -12,14 +12,37 @@ messing around with the code contained herein.
 
 [The origin of extracellular fields and currents — EEG, ECoG, LFP and spikes](https://www.nature.com/articles/nrn3241)
 
-
 [Advances in human intracranial electroencephalography research, guidelines and good practices](https://pubmed.ncbi.nlm.nih.gov/35792291/)
 
 [A tutorial review of functional connectivity analysis methods and their interpretational pitfalls](https://www.frontiersin.org/journals/systems-neuroscience/articles/10.3389/fnsys.2015.00175/full)
 
 ## Video resources: 
 
-[Mike X. Cohen describes time-frequency analyses in detail](https://www.youtube.com/watch?v=7ahrcB5HL0k&list=PLn0OLiymPak2BYu--bR0ADNBJsC4kuRWs)
+
+## Introductory Videos
+
+[Origin of EEG](https://youtu.be/Bmt89hHyxuM?si=fwcztI_cjwQ4fbyD)
+
+[Preprocessing basics](https://youtu.be/JMB9nZNGVyk?si=mg-VddKF-ZqNq6WH)
+
+[Time-domain (ERP)](https://youtu.be/iFWrVzLYop0?si=jgSZk5UQguJGt9PR)
+
+[Rhythm-based analyses](https://youtu.be/3hk4z3yrMzk?si=stSIsMaZH9vW3Ipg)
+
+[Interpreting time-frequency plots](https://youtu.be/s2MfmIx8wv4?si=7th8mEBbga4a0bE5)
+
+## Basic coding practice:
+
+[Python Novice Inflammation (Software Carpentry)](https://swcarpentry.github.io/python-novice-inflammation/)
+
+[Python Novice Gapminder (Software Carpentry)](https://swcarpentry.github.io/python-novice-gapminder/)
+
+[Case Studies in Python (Mark Kramer)](https://mark-kramer.github.io/Case-Studies-Python/01.html)
+
+## Statistics resources (not Python):
+
+[Introduction to Statistics (Statsthinking21)](https://statsthinking21.github.io/statsthinking21-core-site/)
+
 
 ## Code resources: 
 
@@ -29,6 +52,8 @@ The material in this JupyterBook will be most easily consumed if you have alread
 
 [Writing good research code](https://goodresearch.dev/)
 
+[More advanced practice for data analysis](https://neuraldatascience.io/intro.html)
+
 
 <!-- Whether you write your book's content in Jupyter Notebooks (`.ipynb`) or
 in regular markdown files (`.md`), you'll write in the same flavor of markdown

LFPAnalysisBook/module-01-01_LFPAnalysisIntroduction copy.md

@@ -4,5 +4,3 @@ The LFPAnalysis codebase is meant to help you get data from its raw, recorded fo
 
 You can find installation instructions here: [Repository](https://github.com/seqasim/LFPAnalysis).  
 
-
-