Move imports

Author: saisha-r

7-eeg/erp_avg_reref.ipynb

@@ -108,13 +108,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "c3cedbb5-8be2-456d-91b7-9b3c13f05ca7",
    "metadata": {},
    "outputs": [],
    "source": [
     "import mne\n",
     "mne.set_log_level('error')  # reduce extraneous MNE output\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "\n",
     "# Participant ID code\n",
     "p_id = 'sub-001'\n",
@@ -282,7 +284,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "6f3b434f",
    "metadata": {},
    "outputs": [
@@ -298,8 +300,6 @@
     }
    ],
    "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
     "# define the channels we want plots for\n",
     "channels = ['Fz', 'Cz', 'Pz', 'Oz']\n",
     "\n",
@@ -329,7 +329,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "47403f59",
    "metadata": {},
    "outputs": [
@@ -369,8 +369,6 @@
     }
    ],
    "source": [
-    "import numpy as np\n",
-    "\n",
     "# Specify times to plot at, as [min],[max],[stepsize]\n",
     "times = np.arange(0, np.max(epochs.times), 0.1)\n",
     "\n",

7-eeg/erp_group_stats.ipynb

@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "95dccf85-2fb3-4791-b0c9-cf71aecd369e",
    "metadata": {},
    "outputs": [],
@@ -39,7 +39,10 @@
     "mne.set_log_level('error')  # reduce extraneous MNE output\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "import glob"
+    "import glob\n",
+    "from scipy import stats\n",
+    "from mne.stats import spatio_temporal_cluster_1samp_test\n",
+    "from mne.channels import find_ch_adjacency, make_1020_channel_selections"
    ]
   },
   {
@@ -194,7 +197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "ea8c6e1a-f94e-4509-ad57-814dbbc10e81",
    "metadata": {},
    "outputs": [
@@ -207,20 +210,10 @@
     }
    ],
    "source": [
-    "from scipy import stats \n",
-    "\n",
     "t, pval = stats.ttest_1samp(y, 0)\n",
     "print('Difference t = ', str(round(t[0], 2)), 'p = ', str(round(pval[0], 4)))"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b6b9e108",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "id": "3d30a92c",
@@ -286,17 +279,8 @@
    "source": [
     "#### Prepare the Data for Mass Univariate Analysis\n",
     "\n",
-    "MNE provides a `spatio_temporal_cluster_1samp_test()` function which we import here. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "6fd14f1b-83a7-445c-a7eb-2c50891e3ecc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from mne.stats import spatio_temporal_cluster_1samp_test"
+    "MNE provides a `spatio_temporal_cluster_1samp_test()` function which we imported above using\n",
+    "```from mne.stats import spatio_temporal_cluster_1samp_test```."
    ]
   },
   {
@@ -454,7 +438,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "5e963988",
    "metadata": {},
    "outputs": [
@@ -480,8 +464,6 @@
     }
    ],
    "source": [
-    "from mne.channels import find_ch_adjacency, make_1020_channel_selections\n",
-    "\n",
     "# Create ROIs by checking channel labels\n",
     "selections = make_1020_channel_selections(diff_waves[0].info, midline=\"12z\")\n",
     "time_unit = dict(time_unit=\"s\")\n",