added discord notification for pushes

Unlimited-Research-Cooperative · Nov 27, 2023 · c72290e · c72290e
1 parent 6b5d28f
commit c72290e
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diff --git a/.github/workflows/discord_notification.yml b/.github/workflows/discord_notification.yml
@@ -0,0 +1,29 @@
+name: Send discord notification
+on: [push]
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v2
+
+    - name: Send Discord notification to Server 1
+      env:
+        DISCORD_WEBHOOK_ID: ${{ secrets.DISCORD_WEBHOOK_ID }}
+        DISCORD_WEBHOOK_TOKEN: ${{ secrets.DISCORD_WEBHOOK_TOKEN }}
+      run: |
+        COMMIT_MESSAGE=$(git log -1 --pretty=format:"%s")
+        COMMIT_AUTHOR=$(git log -1 --pretty=format:"%an")
+        COMMIT_DESCRIPTION=$(git log -1 --pretty=format:"%b")
+        curl -X POST -H "Content-Type: application/json" -d "{ \"content\": \"New commit by $COMMIT_AUTHOR! Check out the latest changes: https://github.com/Unlimited-Research-Cooperative/human-cortical-organoid-signal-analysis/commits/${{ github.sha }}\", \"embeds\": [ { \"title\": \"$COMMIT_MESSAGE\", \"description\": \"$COMMIT_DESCRIPTION\" } ] }" https://discord.com/api/webhooks/$DISCORD_WEBHOOK_ID/$DISCORD_WEBHOOK_TOKEN
+
+    - name: Send Discord notification to Server 2
+      env:
+        URC_WEBHOOK_ID: ${{ secrets.URC_WEBHOOK_ID }}
+        URC_WEBHOOK_TOKEN: ${{ secrets.URC_WEBHOOK_TOKEN }}
+      run: |
+        COMMIT_MESSAGE=$(git log -1 --pretty=format:"%s")
+        COMMIT_AUTHOR=$(git log -1 --pretty=format:"%an")
+        COMMIT_DESCRIPTION=$(git log -1 --pretty=format:"%b")
+        curl -X POST -H "Content-Type: application/json" -d "{ \"content\": \"New commit by $COMMIT_AUTHOR! Check out the latest changes: https://github.com/Unlimited-Research-Cooperative/human-cortical-organoid-signal-analysis/commits/${{ github.sha }}\", \"embeds\": [ { \"title\": \"$COMMIT_MESSAGE\", \"description\": \"$COMMIT_DESCRIPTION\" } ] }" https://discord.com/api/webhooks/$URC_WEBHOOK_ID/$URC_WEBHOOK_TOKEN
diff --git a/symbolic_dynamics_metric/.ipynb_checkpoints/symbolic_dynamics_metric-checkpoint.ipynb b/symbolic_dynamics_metric/.ipynb_checkpoints/symbolic_dynamics_metric-checkpoint.ipynb
@@ -17,6 +17,7 @@
    "source": [
     "import numpy as np\n",
     "import scipy.stats as stats\n",
+    "from collections import Counter\n",
     "\n",
     "def load_data(filepath):\n",
     "    # Load your electrophysiological data\n",
@@ -34,40 +35,26 @@
     "            symbols.append('H')  # High\n",
     "    return symbols\n",
     "\n",
-    "def analyze_patterns(symbolic_data):\n",
-    "    # Analyze patterns in symbolic sequences\n",
-    "    # Implement your specific pattern analysis logic here\n",
-    "    pass\n",
+    "def pattern_frequency(symbolic_sequence, pattern_length=3):\n",
+    "    # Count frequency of each pattern of given length\n",
+    "    patterns = [symbolic_sequence[i:i+pattern_length] for i in range(len(symbolic_sequence)-pattern_length+1)]\n",
+    "    pattern_counts = Counter(patterns)\n",
+    "    total_patterns = len(patterns)\n",
+    "    pattern_freq = {pattern: count/total_patterns for pattern, count in pattern_counts.items()}\n",
+    "    return pattern_freq\n",
     "\n",
-    "def quantify_changes(pre_data, post_data):\n",
-    "    # Existing entropy-based analysis\n",
-    "    pre_entropy = stats.entropy(pre_data)\n",
-    "    post_entropy = stats.entropy(post_data)\n",
-    "    entropy_change = post_entropy - pre_entropy\n",
+    "def analyze_patterns(pre_symbols, post_symbols):\n",
+    "    # Analyze changes in pattern frequencies\n",
+    "    pre_freq = pattern_frequency(pre_symbols)\n",
+    "    post_freq = pattern_frequency(post_symbols)\n",
     "\n",
-    "    # Additional metrics\n",
-    "    transfer_entropy_change = calculate_transfer_entropy(pre_data, post_data)\n",
-    "    fractal_dimension_change = calculate_higuchi_fractal_dimension(post_data) - calculate_higuchi_fractal_dimension(pre_data)\n",
-    "    mfdfa_change = perform_mfdfa(post_data) - perform_mfdfa(pre_data)\n",
-    "    phase_sync_change = calculate_phase_space_synchronization(pre_data, post_data)\n",
-    "    spectral_change = perform_spectral_analysis(post_data) - perform_spectral_analysis(pre_data)\n",
+    "    # Compute changes in frequencies\n",
+    "    pattern_changes = {pattern: post_freq.get(pattern, 0) - pre_freq.get(pattern, 0) for pattern in set(pre_freq) | set(post_freq)}\n",
+    "    return pattern_changes\n",
     "\n",
-    "    # Combine results\n",
-    "    results = {\n",
-    "        \"entropy_change\": entropy_change,\n",
-    "        \"transfer_entropy_change\": transfer_entropy_change,\n",
-    "        \"fractal_dimension_change\": fractal_dimension_change,\n",
-    "        \"mfdfa_change\": mfdfa_change,\n",
-    "        \"phase_sync_change\": phase_sync_change,\n",
-    "        \"spectral_change\": spectral_change\n",
-    "    }\n",
-    "    return results\n",
-    "\n",
-    "\n",
-    "def perform_paired_t_test(data_before, data_after):\n",
-    "    # Perform paired t-test\n",
-    "    t_statistic, p_value = stats.ttest_rel(data_before, data_after)\n",
-    "    return t_statistic, p_value\n",
+    "def quantify_changes(pattern_changes):\n",
+    "    # Convert changes in pattern frequencies to a numeric metric for t-test\n",
+    "    return np.mean(list(pattern_changes.values()))\n",
     "\n",
     "# Load data\n",
     "pre_stim_data = load_data('path_to_pre_stimulation_data.npy')\n",
@@ -81,11 +68,10 @@
     "post_symbols = symbolic_transformation(post_stim_data, thresholds)\n",
     "\n",
     "# Analyze patterns\n",
-    "analyze_patterns(pre_symbols)\n",
-    "analyze_patterns(post_symbols)\n",
+    "pattern_changes = analyze_patterns(pre_symbols, post_symbols)\n",
     "\n",
     "# Quantify changes for paired t-test\n",
-    "changes = quantify_changes(pre_symbols, post_symbols)\n",
+    "change_metric = quantify_changes(pattern_changes)\n",
     "\n",
     "# Perform paired t-test\n",
     "t_statistic, p_value = perform_paired_t_test(pre_stim_data, post_stim_data)\n",
@@ -96,7 +82,7 @@
     "if p_value < 0.05:\n",
     "    print(\"Significant changes observed in the cortical spheroids post-stimulation.\")\n",
     "else:\n",
-    "    print(\"No significant changes observed.\")\n"
+    "    print(\"No significant changes observed.\")"
    ]
   }
  ],

diff --git a/symbolic_dynamics_metric/symbolic_dynamics_metric.ipynb b/symbolic_dynamics_metric/symbolic_dynamics_metric.ipynb
@@ -17,6 +17,7 @@
    "source": [
     "import numpy as np\n",
     "import scipy.stats as stats\n",
+    "from collections import Counter\n",
     "\n",
     "def load_data(filepath):\n",
     "    # Load your electrophysiological data\n",
@@ -34,40 +35,26 @@
     "            symbols.append('H')  # High\n",
     "    return symbols\n",
     "\n",
-    "def analyze_patterns(symbolic_data):\n",
-    "    # Analyze patterns in symbolic sequences\n",
-    "    # Implement your specific pattern analysis logic here\n",
-    "    pass\n",
+    "def pattern_frequency(symbolic_sequence, pattern_length=3):\n",
+    "    # Count frequency of each pattern of given length\n",
+    "    patterns = [symbolic_sequence[i:i+pattern_length] for i in range(len(symbolic_sequence)-pattern_length+1)]\n",
+    "    pattern_counts = Counter(patterns)\n",
+    "    total_patterns = len(patterns)\n",
+    "    pattern_freq = {pattern: count/total_patterns for pattern, count in pattern_counts.items()}\n",
+    "    return pattern_freq\n",
     "\n",
-    "def quantify_changes(pre_data, post_data):\n",
-    "    # Existing entropy-based analysis\n",
-    "    pre_entropy = stats.entropy(pre_data)\n",
-    "    post_entropy = stats.entropy(post_data)\n",
-    "    entropy_change = post_entropy - pre_entropy\n",
+    "def analyze_patterns(pre_symbols, post_symbols):\n",
+    "    # Analyze changes in pattern frequencies\n",
+    "    pre_freq = pattern_frequency(pre_symbols)\n",
+    "    post_freq = pattern_frequency(post_symbols)\n",
     "\n",
-    "    # Additional metrics\n",
-    "    transfer_entropy_change = calculate_transfer_entropy(pre_data, post_data)\n",
-    "    fractal_dimension_change = calculate_higuchi_fractal_dimension(post_data) - calculate_higuchi_fractal_dimension(pre_data)\n",
-    "    mfdfa_change = perform_mfdfa(post_data) - perform_mfdfa(pre_data)\n",
-    "    phase_sync_change = calculate_phase_space_synchronization(pre_data, post_data)\n",
-    "    spectral_change = perform_spectral_analysis(post_data) - perform_spectral_analysis(pre_data)\n",
+    "    # Compute changes in frequencies\n",
+    "    pattern_changes = {pattern: post_freq.get(pattern, 0) - pre_freq.get(pattern, 0) for pattern in set(pre_freq) | set(post_freq)}\n",
+    "    return pattern_changes\n",
     "\n",
-    "    # Combine results\n",
-    "    results = {\n",
-    "        \"entropy_change\": entropy_change,\n",
-    "        \"transfer_entropy_change\": transfer_entropy_change,\n",
-    "        \"fractal_dimension_change\": fractal_dimension_change,\n",
-    "        \"mfdfa_change\": mfdfa_change,\n",
-    "        \"phase_sync_change\": phase_sync_change,\n",
-    "        \"spectral_change\": spectral_change\n",
-    "    }\n",
-    "    return results\n",
-    "\n",
-    "\n",
-    "def perform_paired_t_test(data_before, data_after):\n",
-    "    # Perform paired t-test\n",
-    "    t_statistic, p_value = stats.ttest_rel(data_before, data_after)\n",
-    "    return t_statistic, p_value\n",
+    "def quantify_changes(pattern_changes):\n",
+    "    # Convert changes in pattern frequencies to a numeric metric for t-test\n",
+    "    return np.mean(list(pattern_changes.values()))\n",
     "\n",
     "# Load data\n",
     "pre_stim_data = load_data('path_to_pre_stimulation_data.npy')\n",
@@ -81,11 +68,10 @@
     "post_symbols = symbolic_transformation(post_stim_data, thresholds)\n",
     "\n",
     "# Analyze patterns\n",
-    "analyze_patterns(pre_symbols)\n",
-    "analyze_patterns(post_symbols)\n",
+    "pattern_changes = analyze_patterns(pre_symbols, post_symbols)\n",
     "\n",
     "# Quantify changes for paired t-test\n",
-    "changes = quantify_changes(pre_symbols, post_symbols)\n",
+    "change_metric = quantify_changes(pattern_changes)\n",
     "\n",
     "# Perform paired t-test\n",
     "t_statistic, p_value = perform_paired_t_test(pre_stim_data, post_stim_data)\n",
@@ -96,7 +82,7 @@
     "if p_value < 0.05:\n",
     "    print(\"Significant changes observed in the cortical spheroids post-stimulation.\")\n",
     "else:\n",
-    "    print(\"No significant changes observed.\")\n"
+    "    print(\"No significant changes observed.\")"
    ]
   }
  ],