Merge branch 'dev' of github.com:mrosol/NeuroKit into dev

.github/workflows/docs-build.yml

@@ -44,6 +44,7 @@ jobs:
                   pip install EMD-signal
                   pip install cvxopt
                   pip install ts2vg
+                  pip install pickleshare
                   pip install https://github.com/neuropsychology/neurokit/zipball/dev
 
             - name: Build documentation 📜

.github/workflows/docs-check.yml

@@ -38,6 +38,7 @@ jobs:
                   pip install EMD-signal
                   pip install cvxopt
                   pip install ts2vg
+                  pip install pickleshare
                   pip install https://github.com/neuropsychology/neurokit/zipball/dev
 
             - name: Build documentation 📜

.github/workflows/releasePR.yml

@@ -68,13 +68,13 @@ jobs:
               run: python setup.py sdist bdist_wheel
 
             - name: Publish distribution 📦 to Test PyPI
-              uses: pypa/gh-action-pypi-publish@master
+              uses: pypa/gh-action-pypi-publish@release/v1
               with:
                   password: ${{ secrets.TEST_PYPI_PASSWORD }}
                   repository_url: https://test.pypi.org/legacy/
 
             - name: Publish distribution 📦 to PyPI
               #if: startsWith(github.event.ref, 'refs/tags')
-              uses: pypa/gh-action-pypi-publish@master
+              uses: pypa/gh-action-pypi-publish@release/v1
               with:
                   password: ${{ secrets.PYPI_PASSWORD }}

AUTHORS.rst

@@ -52,6 +52,7 @@ Contributors
 * `Jannik Gut <https://github.com/rostro36>`_
 * `Nattapong Thammasan <https://github.com/Nattapong-OnePlanet>`_ *(OnePlanet, Netherlands)*
 * `Marek Sokol <https://github.com/sokolmarek>`_ *(Faculty of Biomedical Engineering of the CTU in Prague, Czech Republic)*
+* `Johannes Herforth <https://github.com/DerAndereJohannes>`_ *(University of Luxembourg, Luxembourg)*
 
 
 Thanks also to `Chuan-Peng Hu <https://github.com/hcp4715>`_, `@ucohen <https://github.com/ucohen>`_, `Anthony Gatti <https://github.com/gattia>`_, `Julien Lamour <https://github.com/lamourj>`_, `@renatosc <https://github.com/renatosc>`_, `Nicolas Beaudoin-Gagnon <https://github.com/Fegalf>`_ and `@rubinovitz <https://github.com/rubinovitz>`_ for their contribution in `NeuroKit 1 <https://github.com/neuropsychology/NeuroKit.py>`_.

NEWS.rst

@@ -1,6 +1,17 @@
 News
 =====
 
+0.2.8
+-------------------
+New Features
++++++++++++++
+
+* New feature `events_find()`: is now able to combine multiple digital input channels,
+  retrieve events from the combined events channel and differentiate between the inputs that
+  occur simultaneously.
+
+
+
 0.2.4
 -------------------
 Fixes

README.rst

@@ -16,9 +16,7 @@
 .. image:: https://codecov.io/gh/neuropsychology/NeuroKit/branch/master/graph/badge.svg
         :target: https://codecov.io/gh/neuropsychology/NeuroKit
 
-.. image:: https://api.codeclimate.com/v1/badges/517cb22bd60238174acf/maintainability
-        :target: https://codeclimate.com/github/neuropsychology/NeuroKit/maintainability
-        :alt: Maintainability
+
 
 
 **The Python Toolbox for Neurophysiological Signal Processing**
@@ -44,6 +42,7 @@ Quick Example
     # Compute relevant features
     results = nk.bio_analyze(processed_data, sampling_rate=100)
 
+
 And **boom** 💥 your analysis is done 😎
 
 Download

data/gudb/download_gudb.py

@@ -28,7 +28,7 @@
         # creating class which loads the experiment
         ecg_class = ecg_gudb_database.GUDb(participant, experiment)
 
-        # Chest Strap Data - only donwload if R-peaks annotations are available
+        # Chest Strap Data - only download if R-peaks annotations are available
         if ecg_class.anno_cs_exists:
 
             data = pd.DataFrame({"ECG": ecg_class.cs_V2_V1})
@@ -41,7 +41,7 @@
             anno = pd.DataFrame({"Rpeaks": ecg_class.anno_cs})
             anno["Participant"] = "GUDB_%.2i" %(participant)
             anno["Sampling_Rate"] = 250
-            anno["Database"] = "GUDB (" + experiment + ")"
+            anno["Database"] = "GUDB_" + experiment
 
             # Store with the rest
             dfs_ecg.append(data)

data/ludb/download_ludb.py

@@ -4,10 +4,8 @@
 The database consists of 200 10-second 12-lead ECG signal records representing different morphologies of the ECG signal. The ECGs were collected from healthy volunteers and patients, which had various cardiovascular diseases. The boundaries of P, T waves and QRS complexes were manually annotated by cardiologists for all 200 records.
 
 Steps:
-    1. In the command line, run 'pip install gsutil'
-    2. Then, 'gsutil -m cp -r gs://ludb-1.0.0.physionet.org D:/YOURPATH/NeuroKit/data/ludb'
-    This will download all the files in a folder named 'ludb-1.0.0.physionet.org' at the
-    destination you entered.
+    1. Download zipped data base from https://physionet.org/content/ludb/1.0.1/ 
+    2. Unzip the folder so that you have a `lobachevsky-university-electrocardiography-database-1.0.1/` folder'
     3. Run this script.
 """
 import pandas as pd
@@ -16,31 +14,33 @@
 import os
 
 
-files = os.listdir("./ludb-1.0.0.physionet.org/")
-
 dfs_ecg = []
 dfs_rpeaks = []
 
 
 for participant in range(200):
     filename = str(participant + 1)
 
-    data, info = wfdb.rdsamp("./ludb-1.0.0.physionet.org/" + filename)
+    data, info = wfdb.rdsamp(
+        "./lobachevsky-university-electrocardiography-database-1.0.1/data/" + filename
+    )
 
     # Get signal
     data = pd.DataFrame(data, columns=info["sig_name"])
     data = data[["i"]].rename(columns={"i": "ECG"})
-    data["Participant"] = "LUDB_%.2i" %(participant + 1)
+    data["Participant"] = "LUDB_%.2i" % (participant + 1)
     data["Sample"] = range(len(data))
-    data["Sampling_Rate"] = info['fs']
+    data["Sampling_Rate"] = info["fs"]
     data["Database"] = "LUDB"
 
     # Get annotations
-    anno = wfdb.rdann("./ludb-1.0.0.physionet.org/" + filename, 'atr_i')
+    anno = wfdb.rdann(
+        "./lobachevsky-university-electrocardiography-database-1.0.1/data/" + filename, "i"
+    )
     anno = anno.sample[np.where(np.array(anno.symbol) == "N")[0]]
     anno = pd.DataFrame({"Rpeaks": anno})
-    anno["Participant"] = "LUDB_%.2i" %(participant + 1)
-    anno["Sampling_Rate"] = info['fs']
+    anno["Participant"] = "LUDB_%.2i" % (participant + 1)
+    anno["Sampling_Rate"] = info["fs"]
     anno["Database"] = "LUDB"
 
     # Store with the rest

docs/codebook.rst

@@ -0,0 +1,88 @@
+Codebook
+========
+
+Here you can download the complete codebook which details the variables that you can compute using the NeuroKit package.
+
+.. raw:: html
+
+   <div style="text-align: center;">
+       <a href="_static/neurokit_codebook.csv" download="neurokit_codebook.csv">
+           <button style="background-color: #4CAF50; color: white; padding: 10px 20px; margin: 10px; border: none; cursor: pointer; width: 50%;">Download Codebook</button>
+       </a>
+   </div>
+
+This codebook contains detailed descriptions of all variables, their descriptions, and additional metadata.
+
+
+Codebook Table
+==============
+
+.. raw:: html
+
+   <style>
+    #csvDataTable {
+        width: 100%;
+        border-collapse: collapse;
+    }
+    #csvDataTable th, #csvDataTable td {
+        padding: 8px 12px;
+        border: 1px solid #ccc;
+        text-align: left;
+    }
+    </style>
+
+    <div id="csv-table">
+        <table id="csvDataTable">
+        </table>
+    </div>
+
+    <script>
+
+    function parseCSVLine(text) {
+        const cols = [];
+        let col = '';
+        let insideQuotes = false;
+
+        for (let i = 0; i < text.length; i++) {
+            const char = text[i];
+
+            if (insideQuotes && char === '"' && text[i + 1] == '"') {
+                i++;
+                col += char;
+                continue;
+            }
+
+            if (char === '"' && text[i - 1] !== '\\') {
+                insideQuotes = !insideQuotes;
+                continue;
+            }
+
+            if (char === ',' && !insideQuotes) {
+                cols.push(col);
+                col = '';
+            } else {
+                col += char;
+            }
+        }
+        cols.push(col);
+
+        return cols.map(field => field.replace(/""/g, '"')); // Replace escaped quotes
+    }
+
+    document.addEventListener("DOMContentLoaded", function() {
+        fetch('_static/neurokit_codebook.csv')
+            .then(response => response.text())
+            .then(csv => {
+                let lines = csv.trim().split('\n');
+                let html = '<tr><th>' + parseCSVLine(lines[0]).join('</th><th>') + '</th></tr>';
+                for (let i = 1; i < lines.length; i++) {
+                    html += '<tr><td>' + parseCSVLine(lines[i]).join('</td><td>') + '</td></tr>';
+                }
+                document.getElementById('csvDataTable').innerHTML = html;
+            })
+            .catch(error => console.error('Error loading the CSV file:', error));
+    });
+
+
+    </script>
+

docs/conf.py

@@ -21,7 +21,7 @@
 # add these directories to sys.path here. If the directory is relative to the
 # documentation root, use os.path.abspath to make it absolute, like shown here.
 #
-# sys.path.insert(0, os.path.abspath('.'))
+sys.path.insert(0, os.path.abspath('.'))
 sys.path.insert(0, os.path.abspath("../"))
 
 
@@ -69,6 +69,7 @@ def find_version():
     "sphinxemoji.sphinxemoji",
     "sphinx_copybutton",
     "myst_nb",
+    "directives.csv_codebook_directive",
 ]
 
 # Add any paths that contain templates here, relative to this directory.
@@ -131,8 +132,9 @@ def find_version():
     "use_issues_button": True,
     "path_to_docs": "docs/",
     "use_edit_page_button": True,
-    "logo_only": True,
+    # "logo_only": True,
     "show_toc_level": 1,
+    "navigation_with_keys": False,
 }