messing around with bifurcation analysis, sLORETA, finished spectral …

…analysis
Metaverse-Crowdsource · Aug 9, 2023 · 88e87d5 · 88e87d5
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diff --git a/.ipynb_checkpoints/Bifurcation Analysis-checkpoint.ipynb b/.ipynb_checkpoints/Bifurcation Analysis-checkpoint.ipynb
diff --git a/.ipynb_checkpoints/Data Loading and Variable Assigning-checkpoint.ipynb b/.ipynb_checkpoints/Data Loading and Variable Assigning-checkpoint.ipynb
@@ -5,7 +5,16 @@
    "execution_count": 2,
    "id": "f3abfd51-c111-42f3-9ea4-c017fc1f6e30",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/vincent/miniconda3/lib/python3.10/site-packages/antropy/fractal.py:197: NumbaDeprecationWarning: \u001b[1mThe 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\u001b[0m\n",
+      "  @jit((types.Array(types.float64, 1, \"C\", readonly=True), types.int32))\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import nolds\n",
@@ -46,7 +55,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "bc64aca4-ae85-4599-8d78-ec9c147f9362",
    "metadata": {
     "tags": []
@@ -142,7 +151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 4,
    "id": "2e9b777a-1875-4fed-9879-2c90eea896f1",
    "metadata": {},
    "outputs": [
@@ -235,10 +244,10 @@
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.sources.Source at 0x7faf172704c0>"
+       "<graphviz.sources.Source at 0x7fab34746620>"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -271,7 +280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "id": "36ae53a4-fab6-4393-92bb-fcbbf415aa0b",
    "metadata": {},
    "outputs": [],
@@ -390,7 +399,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "id": "370695e1-1312-4ae2-b5d7-d8454add55b5",
    "metadata": {},
    "outputs": [
@@ -478,6 +487,39 @@
     "print(eeg_df.tail())"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "070ef93a-0e51-449c-b27d-974d07593915",
+   "metadata": {},
+   "source": [
+    "# Make the eeg df a npy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "6cccb986-4630-4cf7-abb8-18b5c320863a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Extract EEG channel names (excluding 'Time')\n",
+    "eeg_channel_names = eeg_df.columns[:-1]\n",
+    "\n",
+    "# Create a dictionary to store EEG data for each channel\n",
+    "eeg_data_dict = {}\n",
+    "\n",
+    "# Populate the dictionary with EEG data\n",
+    "for channel in eeg_channel_names:\n",
+    "    eeg_data_dict[channel] = eeg_df[channel].values\n",
+    "\n",
+    "# Convert the dictionary values to a numpy array\n",
+    "eeg_data_array = np.array([eeg_data_dict[channel] for channel in eeg_channel_names]).T\n",
+    "\n",
+    "# Save the numpy array with EEG data as a single .npy file\n",
+    "save_path = '/home/vincent/AAA_projects/MVCS/Neuroscience/eeg_data_with_channels.npy'\n",
+    "np.save(save_path, eeg_data_array)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 17,
@@ -1629,7 +1671,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.13"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

diff --git a/.ipynb_checkpoints/PyCoBi Bifurcation with Auto-07p-checkpoint.ipynb b/.ipynb_checkpoints/PyCoBi Bifurcation with Auto-07p-checkpoint.ipynb
diff --git a/.ipynb_checkpoints/Spectral Analysis-checkpoint.ipynb b/.ipynb_checkpoints/Spectral Analysis-checkpoint.ipynb
diff --git a/.ipynb_checkpoints/Visualization-checkpoint.ipynb b/.ipynb_checkpoints/Visualization-checkpoint.ipynb
diff --git a/Bifurcation Analysis.ipynb b/Bifurcation Analysis.ipynb
diff --git a/Data Loading and Variable Assigning.ipynb b/Data Loading and Variable Assigning.ipynb
@@ -5,7 +5,16 @@
    "execution_count": 2,
    "id": "f3abfd51-c111-42f3-9ea4-c017fc1f6e30",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/vincent/miniconda3/lib/python3.10/site-packages/antropy/fractal.py:197: NumbaDeprecationWarning: \u001b[1mThe 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\u001b[0m\n",
+      "  @jit((types.Array(types.float64, 1, \"C\", readonly=True), types.int32))\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import nolds\n",
@@ -46,7 +55,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "bc64aca4-ae85-4599-8d78-ec9c147f9362",
    "metadata": {
     "tags": []
@@ -142,7 +151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 4,
    "id": "2e9b777a-1875-4fed-9879-2c90eea896f1",
    "metadata": {},
    "outputs": [
@@ -235,10 +244,10 @@
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.sources.Source at 0x7faf172704c0>"
+       "<graphviz.sources.Source at 0x7fab34746620>"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -271,7 +280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "id": "36ae53a4-fab6-4393-92bb-fcbbf415aa0b",
    "metadata": {},
    "outputs": [],
@@ -390,7 +399,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "id": "370695e1-1312-4ae2-b5d7-d8454add55b5",
    "metadata": {},
    "outputs": [
@@ -478,6 +487,39 @@
     "print(eeg_df.tail())"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "070ef93a-0e51-449c-b27d-974d07593915",
+   "metadata": {},
+   "source": [
+    "# Make the eeg df a npy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "6cccb986-4630-4cf7-abb8-18b5c320863a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Extract EEG channel names (excluding 'Time')\n",
+    "eeg_channel_names = eeg_df.columns[:-1]\n",
+    "\n",
+    "# Create a dictionary to store EEG data for each channel\n",
+    "eeg_data_dict = {}\n",
+    "\n",
+    "# Populate the dictionary with EEG data\n",
+    "for channel in eeg_channel_names:\n",
+    "    eeg_data_dict[channel] = eeg_df[channel].values\n",
+    "\n",
+    "# Convert the dictionary values to a numpy array\n",
+    "eeg_data_array = np.array([eeg_data_dict[channel] for channel in eeg_channel_names]).T\n",
+    "\n",
+    "# Save the numpy array with EEG data as a single .npy file\n",
+    "save_path = '/home/vincent/AAA_projects/MVCS/Neuroscience/eeg_data_with_channels.npy'\n",
+    "np.save(save_path, eeg_data_array)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 17,