diff --git a/chapters/01_getting_started.ipynb b/chapters/01_getting_started.ipynb index 8f58b4e..948010e 100644 --- a/chapters/01_getting_started.ipynb +++ b/chapters/01_getting_started.ipynb @@ -45,11 +45,12 @@ "```\n", "\n", "**Windows**\n", + "Make sure you are using **powershell**!\n", + "\n", "```bash\n", "powershell -ExecutionPolicy ByPass -c \"irm https://astral.sh/uv/install.ps1 | iex\"\n", "```\n", - "Warning: Maybe you need to have administrator permission to get this working. \n", - "Try that out if the plain run does not work.\n", + "\n", "\n", "That should be enough, but for more details you can follow detailed instructions [here](https://docs.astral.sh/uv/getting-started/installation/)." ] diff --git a/chapters/081_pandas.ipynb b/chapters/081_pandas.ipynb index 63fdfe1..2945452 100644 --- a/chapters/081_pandas.ipynb +++ b/chapters/081_pandas.ipynb @@ -56,145 +56,6 @@ "{{< video https://www.youtube.com/watch?v=_T8LGqJtuGc >}}" ] }, - { - "cell_type": "code", - "execution_count": 1, - "id": "7617b9b8-455a-44f0-9856-8b2e248d11a0", - "metadata": {}, - "outputs": [], - "source": [ - "import seaborn as sns" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "91cfdd53-1b65-4b02-b0f8-5bd227b6da4e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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subjecttimepointeventregionsignal
0s1318stimparietal-0.017552
1s514stimparietal-0.080883
2s1218stimparietal-0.081033
3s1118stimparietal-0.046134
4s1018stimparietal-0.037970
\n", - "
" - ], - "text/plain": [ - " subject timepoint event region signal\n", - "0 s13 18 stim parietal -0.017552\n", - "1 s5 14 stim parietal -0.080883\n", - "2 s12 18 stim parietal -0.081033\n", - "3 s11 18 stim parietal -0.046134\n", - "4 s10 18 stim parietal -0.037970" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = sns.load_dataset(\"fmri\")\n", - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "97350317-64f0-48cf-b73d-b14e2364ce87", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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4s1018stimparietal-0.037970
\n", + "
" + ], + "text/plain": [ + " subject timepoint event region signal\n", + "0 s13 18 stim parietal -0.017552\n", + "1 s5 14 stim parietal -0.080883\n", + "2 s12 18 stim parietal -0.081033\n", + "3 s11 18 stim parietal -0.046134\n", + "4 s10 18 stim parietal -0.037970" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = sns.load_dataset(\"fmri\")\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "135ed828-dd1e-48ad-b77e-70b801ab3400", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.lineplot(df, x=\"timepoint\", y=\"signal\", marker=\".\", hue=\"region\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9ab5b418-c0c6-4f9e-8791-881b4fe5dfbd", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1d827e29-842f-437e-9089-3305dda07970", + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 15, @@ -611,6 +766,7 @@ "source": [ "## Exercises\n", "1) Use numpy to create 4 arrays of numbers drawn from 4 different distributions: uniform, normal, lognormal and exponential. Each array should have of 100000 samples. Use matplotlib to plot a grid of 2x2 subplots, with one histogram on each subplot.\n", + "Each histogram should have a different color.\n", "Hint: Look at numpy's submodule `random`.\n", "\n", "2) Pick one interesting example of the [Seaborn gallery](http://seaborn.pydata.org/examples/index.html) and reproduce it on your computer. Change 1 or 2 parameters of the plot, for example, some color or order of variables, remove/add a variable. If you have own data with a similar shape, plot those! \n" diff --git a/chapters/lab_explore_data_solutions.ipynb b/chapters/lab_explore_data_solutions.ipynb index 6a0cec8..65ef641 100644 --- a/chapters/lab_explore_data_solutions.ipynb +++ b/chapters/lab_explore_data_solutions.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 1, "id": "f7cd8929-a0bf-457d-b05e-4aaaf2783d56", "metadata": {}, "outputs": [], @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 2, "id": "e061a4d5-66eb-4eb9-b699-48eb55fd90b5", "metadata": {}, "outputs": [], @@ -36,28 +36,21 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 3, "id": "75888c8d-0000-40c4-9409-05a2dfd45eba", "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "[PosixPath('../data/ds005420-download/sub-50'),\n", - " PosixPath('../data/ds005420-download/sub-40'),\n", - " PosixPath('../data/ds005420-download/sub-45'),\n", - " PosixPath('../data/ds005420-download/sub-9'),\n", - " PosixPath('../data/ds005420-download/sub-35'),\n", - " PosixPath('../data/ds005420-download/sub-16'),\n", - " PosixPath('../data/ds005420-download/CHANGES'),\n", - " PosixPath('../data/ds005420-download/sub-2'),\n", - " PosixPath('../data/ds005420-download/sub-36'),\n", - " PosixPath('../data/ds005420-download/sub-21')]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: '../data/ds005420-download'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m files \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miterdir\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m files[:\u001b[38;5;241m10\u001b[39m]\n", + "File \u001b[0;32m~/miniconda3/envs/course/lib/python3.10/pathlib.py:1048\u001b[0m, in \u001b[0;36mPath.iterdir\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1044\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21miterdir\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 1045\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Iterate over the files in this directory. Does not yield any\u001b[39;00m\n\u001b[1;32m 1046\u001b[0m \u001b[38;5;124;03m result for the special paths '.' and '..'.\u001b[39;00m\n\u001b[1;32m 1047\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1048\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_accessor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlistdir\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m:\n\u001b[1;32m 1049\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m..\u001b[39m\u001b[38;5;124m\"\u001b[39m}:\n\u001b[1;32m 1050\u001b[0m \u001b[38;5;66;03m# Yielding a path object for these makes little sense\u001b[39;00m\n\u001b[1;32m 1051\u001b[0m \u001b[38;5;28;01mcontinue\u001b[39;00m\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '../data/ds005420-download'" + ] } ], "source": [ @@ -73,86 +66,17 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "29339cd4-3f3f-4f34-8b01-a3caa8a519ec", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/fabrizio/miniconda3/envs/course/bin/python\n" - ] - } - ], + "outputs": [], "source": [ "!which python" ] }, { "cell_type": "code", - "execution_count": 15, - "id": "5e926c7f-e63d-435d-bb5e-7c333391fc95", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting mne\n", - " Downloading mne-1.8.0-py3-none-any.whl.metadata (21 kB)\n", - "Requirement already satisfied: decorator in /home/fdamicel/miniconda3/envs/course/lib/python3.10/site-packages (from mne) (5.1.1)\n", - "Requirement already satisfied: jinja2 in 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\u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m78.4/78.4 kB\u001b[0m \u001b[31m6.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hInstalling collected packages: tqdm, scipy, lazy-loader, pooch, mne\n", - "Successfully installed lazy-loader-0.4 mne-1.8.0 pooch-1.8.2 scipy-1.14.1 tqdm-4.66.5\n" - ] - } - ], - "source": [ - "# Add to pyproject.toml\n", - "!pip install mne" - ] - }, - { - "cell_type": "code", - "execution_count": 18, + "execution_count": 4, "id": "3d27edc6-05b1-4d33-a476-f76cc7dc6230", "metadata": {}, "outputs": [], @@ -163,7 +87,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 5, "id": "cb3dcf57-425e-41a6-b614-f1ec08c9ec9a", "metadata": {}, "outputs": [ @@ -172,9 +96,24 @@ "output_type": "stream", "text": [ "Extracting EDF parameters from /home/fdamicel/projects/python-course/data/ds005420-download/sub-1/eeg/sub-1_task-oa_eeg.edf...\n", - "EDF file detected\n", - "Setting channel info structure...\n", - "Creating raw.info structure...\n" + "EDF file detected\n" + ] + }, + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: '/home/fdamicel/projects/python-course/data/ds005420-download/sub-1/eeg/sub-1_task-oa_eeg.edf'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[5], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmne\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m content \u001b[38;5;241m=\u001b[39m \u001b[43mmne\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mio\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_raw_edf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m data \u001b[38;5;241m=\u001b[39m content\u001b[38;5;241m.\u001b[39mget_data()\n", + "File \u001b[0;32m~/miniconda3/envs/course/lib/python3.10/site-packages/mne/io/edf/edf.py:1689\u001b[0m, in \u001b[0;36mread_raw_edf\u001b[0;34m(input_fname, eog, misc, stim_channel, exclude, infer_types, include, preload, units, encoding, exclude_after_unique, verbose)\u001b[0m\n\u001b[1;32m 1687\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ext \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124medf\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 1688\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOnly EDF files are supported, got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mext\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 1689\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mRawEDF\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1690\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_fname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_fname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1691\u001b[0m \u001b[43m \u001b[49m\u001b[43meog\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43meog\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1692\u001b[0m \u001b[43m \u001b[49m\u001b[43mmisc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmisc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1693\u001b[0m \u001b[43m \u001b[49m\u001b[43mstim_channel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstim_channel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1694\u001b[0m \u001b[43m \u001b[49m\u001b[43mexclude\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexclude\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1695\u001b[0m \u001b[43m \u001b[49m\u001b[43minfer_types\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minfer_types\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1696\u001b[0m \u001b[43m \u001b[49m\u001b[43mpreload\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpreload\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1697\u001b[0m \u001b[43m \u001b[49m\u001b[43minclude\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minclude\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1698\u001b[0m \u001b[43m \u001b[49m\u001b[43munits\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43munits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1699\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1700\u001b[0m \u001b[43m \u001b[49m\u001b[43mexclude_after_unique\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexclude_after_unique\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1701\u001b[0m \u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1702\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m:12\u001b[0m, in \u001b[0;36m__init__\u001b[0;34m(self, input_fname, eog, misc, stim_channel, exclude, infer_types, preload, include, units, encoding, exclude_after_unique, verbose)\u001b[0m\n", + "File \u001b[0;32m~/miniconda3/envs/course/lib/python3.10/site-packages/mne/io/edf/edf.py:151\u001b[0m, in \u001b[0;36mRawEDF.__init__\u001b[0;34m(self, input_fname, eog, misc, stim_channel, exclude, infer_types, preload, include, units, encoding, exclude_after_unique, verbose)\u001b[0m\n\u001b[1;32m 149\u001b[0m logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExtracting EDF parameters from \u001b[39m\u001b[38;5;132;01m{\u001b[39;00minput_fname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m...\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 150\u001b[0m input_fname \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mabspath(input_fname)\n\u001b[0;32m--> 151\u001b[0m info, edf_info, orig_units \u001b[38;5;241m=\u001b[39m \u001b[43m_get_info\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 152\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_fname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 153\u001b[0m \u001b[43m \u001b[49m\u001b[43mstim_channel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 154\u001b[0m \u001b[43m \u001b[49m\u001b[43meog\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 155\u001b[0m \u001b[43m \u001b[49m\u001b[43mmisc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 156\u001b[0m \u001b[43m \u001b[49m\u001b[43mexclude\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 157\u001b[0m \u001b[43m \u001b[49m\u001b[43minfer_types\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 158\u001b[0m \u001b[43m \u001b[49m\u001b[43mpreload\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 159\u001b[0m \u001b[43m \u001b[49m\u001b[43minclude\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 160\u001b[0m \u001b[43m \u001b[49m\u001b[43mexclude_after_unique\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 161\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 162\u001b[0m logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCreating raw.info structure...\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 164\u001b[0m _validate_type(units, (\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;28mdict\u001b[39m), \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124munits\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/miniconda3/envs/course/lib/python3.10/site-packages/mne/io/edf/edf.py:537\u001b[0m, in \u001b[0;36m_get_info\u001b[0;34m(fname, stim_channel, eog, misc, exclude, infer_types, preload, include, exclude_after_unique)\u001b[0m\n\u001b[1;32m 534\u001b[0m eog \u001b[38;5;241m=\u001b[39m eog \u001b[38;5;28;01mif\u001b[39;00m eog \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m []\n\u001b[1;32m 535\u001b[0m misc \u001b[38;5;241m=\u001b[39m misc \u001b[38;5;28;01mif\u001b[39;00m misc \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m []\n\u001b[0;32m--> 537\u001b[0m edf_info, orig_units \u001b[38;5;241m=\u001b[39m \u001b[43m_read_header\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 538\u001b[0m \u001b[43m \u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexclude\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minfer_types\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minclude\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexclude_after_unique\u001b[49m\n\u001b[1;32m 539\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 541\u001b[0m \u001b[38;5;66;03m# XXX: `tal_ch_names` to pass to `_check_stim_channel` should be computed\u001b[39;00m\n\u001b[1;32m 542\u001b[0m \u001b[38;5;66;03m# from `edf_info['ch_names']` and `edf_info['tal_idx']` but 'tal_idx'\u001b[39;00m\n\u001b[1;32m 543\u001b[0m \u001b[38;5;66;03m# contains stim channels that are not TAL.\u001b[39;00m\n\u001b[1;32m 544\u001b[0m stim_channel_idxs, _ \u001b[38;5;241m=\u001b[39m _check_stim_channel(stim_channel, edf_info[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mch_names\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n", + "File \u001b[0;32m~/miniconda3/envs/course/lib/python3.10/site-packages/mne/io/edf/edf.py:511\u001b[0m, in \u001b[0;36m_read_header\u001b[0;34m(fname, exclude, infer_types, include, exclude_after_unique)\u001b[0m\n\u001b[1;32m 509\u001b[0m logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mext\u001b[38;5;241m.\u001b[39mupper()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m file detected\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 510\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ext \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbdf\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124medf\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 511\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read_edf_header\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 512\u001b[0m \u001b[43m \u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexclude\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minfer_types\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minclude\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexclude_after_unique\u001b[49m\n\u001b[1;32m 513\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 514\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m ext \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgdf\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 515\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _read_gdf_header(fname, exclude, include), \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m~/miniconda3/envs/course/lib/python3.10/site-packages/mne/io/edf/edf.py:805\u001b[0m, in \u001b[0;36m_read_edf_header\u001b[0;34m(fname, exclude, infer_types, include, exclude_after_unique)\u001b[0m\n\u001b[1;32m 802\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Read header information from EDF+ or BDF file.\"\"\"\u001b[39;00m\n\u001b[1;32m 803\u001b[0m edf_info \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mevents\u001b[39m\u001b[38;5;124m\"\u001b[39m: []}\n\u001b[0;32m--> 805\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrb\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m fid:\n\u001b[1;32m 806\u001b[0m fid\u001b[38;5;241m.\u001b[39mread(\u001b[38;5;241m8\u001b[39m) \u001b[38;5;66;03m# version (unused here)\u001b[39;00m\n\u001b[1;32m 808\u001b[0m \u001b[38;5;66;03m# patient ID\u001b[39;00m\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/home/fdamicel/projects/python-course/data/ds005420-download/sub-1/eeg/sub-1_task-oa_eeg.edf'" ] } ], @@ -186,33 +125,10 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "id": "a34ab989-3223-410b-ab3b-7a546f0f9be9", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1.05320562e-05, 1.09898195e-05, 9.76911736e-06, ...,\n", - " -2.51734823e-05, -2.41053679e-05, -1.35768116e-05],\n", - " [ 1.83455325e-06, 2.29231657e-06, 3.05525542e-06, ...,\n", - " -3.01675542e-07, -4.11636982e-06, -8.38882742e-06],\n", - " [ 1.98714103e-06, 2.44490434e-06, 2.75007988e-06, ...,\n", - " -7.32071302e-06, -6.40518639e-06, -3.04825542e-06],\n", - " ...,\n", - " [ 8.70100296e-06, 1.12949951e-05, 1.28208728e-05, ...,\n", - " 7.78547633e-06, 4.12336982e-06, -6.06851085e-07],\n", - " [ 5.19148422e-06, 6.56477416e-06, 6.71736193e-06, ...,\n", - " -2.43790434e-06, -1.67496548e-06, 1.37678994e-06],\n", - " [ 3.05525542e-06, 4.42854537e-06, 4.88630868e-06, ...,\n", - " 3.05525542e-06, 5.95442308e-06, 5.34407199e-06]])" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "data" ] diff --git a/docs/01_getting_started.html b/docs/01_getting_started.html index 127991e..54e1f07 100644 --- a/docs/01_getting_started.html +++ b/docs/01_getting_started.html @@ -390,9 +390,8 @@ 

curl -LsSf https://astral.sh/uv/install.sh | sh
-

Windows

+

Windows Make sure you are using powershell!

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
-

Warning: Maybe you need to have administrator permission to get this working. Try that out if the plain run does not work.

That should be enough, but for more details you can follow detailed instructions here.

diff --git a/docs/081_pandas.html b/docs/081_pandas.html index 0ebe356..89d37c2 100644 --- a/docs/081_pandas.html +++ b/docs/081_pandas.html @@ -20,44 +20,10 @@ margin: 0 0.8em 0.2em -1em; /* quarto-specific, see https://github.com/quarto-dev/quarto-cli/issues/4556 */ vertical-align: middle; } -/* CSS for syntax highlighting */ -pre > code.sourceCode { white-space: pre; position: relative; } -pre > code.sourceCode > span { line-height: 1.25; } -pre > code.sourceCode > span:empty { height: 1.2em; } -.sourceCode { overflow: visible; } -code.sourceCode > span { color: inherit; text-decoration: inherit; } -div.sourceCode { margin: 1em 0; } -pre.sourceCode { margin: 0; } -@media screen { -div.sourceCode { overflow: auto; } -} -@media print { -pre > code.sourceCode { white-space: pre-wrap; } -pre > code.sourceCode > span { text-indent: -5em; padding-left: 5em; } -} -pre.numberSource code - { counter-reset: source-line 0; } -pre.numberSource code > span - { position: relative; left: -4em; counter-increment: source-line; } -pre.numberSource code > span > a:first-child::before - { content: counter(source-line); - position: relative; left: -1em; text-align: right; vertical-align: baseline; - border: none; display: inline-block; - -webkit-touch-callout: none; -webkit-user-select: none; - -khtml-user-select: none; -moz-user-select: none; - -ms-user-select: none; user-select: none; - padding: 0 4px; width: 4em; - } -pre.numberSource { margin-left: 3em; padding-left: 4px; } -div.sourceCode - { } -@media screen { -pre > code.sourceCode > span > a:first-child::before { text-decoration: underline; } -} - + @@ -104,9 +70,6 @@ } - - - @@ -393,86 +356,6 @@

15  here

Here’s short introduction by the very author of the library:

-
-
import seaborn as sns
-
-
-
df = sns.load_dataset("fmri")
-df.head()
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- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
subjecttimepointeventregionsignal
0s1318stimparietal-0.017552
1s514stimparietal-0.080883
2s1218stimparietal-0.081033
3s1118stimparietal-0.046134
4s1018stimparietal-0.037970
- -
-
-
-
-
-
sns.lineplot(df, x="timepoint", y="signal", marker=".", hue="region")
-
-
-
-

-
-
-
-

15.1 Exercises

    diff --git a/docs/09_plotting.html b/docs/09_plotting.html index 55fbe6b..e11906d 100644 --- a/docs/09_plotting.html +++ b/docs/09_plotting.html @@ -57,7 +57,7 @@ - + @@ -104,6 +104,9 @@ } + + + @@ -597,12 +600,92 @@ 

    sns.set_theme(style="dark", font_scale=1.4)  # this has global effects
    +
    +
    import seaborn as sns
    +
    +
    +
    df = sns.load_dataset("fmri")
+df.head()
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    subjecttimepointeventregionsignal
    0s1318stimparietal-0.017552
    1s514stimparietal-0.080883
    2s1218stimparietal-0.081033
    3s1118stimparietal-0.046134
    4s1018stimparietal-0.037970
    + +
    +
    +
    +
    +
    +
    sns.lineplot(df, x="timepoint", y="signal", marker=".", hue="region")
    +
    +
    +
    +

    +
    +
    +
    +
    -
    plot_images(data)
    +
    plot_images(data)
    -

    +

    @@ -669,7 +752,7 @@

    16.3 Exercises

      -

    1. Use numpy to create 4 arrays of numbers drawn from 4 different distributions: uniform, normal, lognormal and exponential. Each array should have of 100000 samples. Use matplotlib to plot a grid of 2x2 subplots, with one histogram on each subplot. Hint: Look at numpy’s submodule random.

      2. +

    2. Use numpy to create 4 arrays of numbers drawn from 4 different distributions: uniform, normal, lognormal and exponential. Each array should have of 100000 samples. Use matplotlib to plot a grid of 2x2 subplots, with one histogram on each subplot. Each histogram should have a different color. Hint: Look at numpy’s submodule random.

    3. Pick one interesting example of the Seaborn gallery and reproduce it on your computer. Change 1 or 2 parameters of the plot, for example, some color or order of variables, remove/add a variable. If you have own data with a similar shape, plot those!

    diff --git a/docs/09_plotting_files/figure-html/cell-18-output-1.png b/docs/09_plotting_files/figure-html/cell-18-output-1.png new file mode 100644 index 0000000..c9c3f4e Binary files /dev/null and b/docs/09_plotting_files/figure-html/cell-18-output-1.png differ diff --git a/docs/09_plotting_files/figure-html/cell-16-output-1.png b/docs/09_plotting_files/figure-html/cell-21-output-1.png similarity index 100% rename from docs/09_plotting_files/figure-html/cell-16-output-1.png rename to docs/09_plotting_files/figure-html/cell-21-output-1.png diff --git a/docs/search.json b/docs/search.json index 18aa100..fb89d37 100644 --- a/docs/search.json +++ b/docs/search.json @@ -34,7 +34,7 @@ "href": "01_getting_started.html#install-uv", "title": "1  Getting Started", "section": "1.2 Install uv", - "text": "1.2 Install uv\nRun only one of these commands in your terminal - pick according to your operating system:\nLinux and macOS\ncurl -LsSf https://astral.sh/uv/install.sh | sh\nWindows\npowershell -ExecutionPolicy ByPass -c \"irm https://astral.sh/uv/install.ps1 | iex\"\nWarning: Maybe you need to have administrator permission to get this working. Try that out if the plain run does not work.\nThat should be enough, but for more details you can follow detailed instructions here.", + "text": "1.2 Install uv\nRun only one of these commands in your terminal - pick according to your operating system:\nLinux and macOS\ncurl -LsSf https://astral.sh/uv/install.sh | sh\nWindows Make sure you are using powershell!\npowershell -ExecutionPolicy ByPass -c \"irm https://astral.sh/uv/install.ps1 | iex\"\nThat should be enough, but for more details you can follow detailed instructions here.", "crumbs": [ "1  Getting Started" ] @@ -674,7 +674,7 @@ "href": "09_plotting.html#seaborn", "title": "16  Plotting", "section": "16.2 Seaborn", - "text": "16.2 Seaborn\n\n\n\n\n\nseaborn is a data visualization library built on top of matplotlib.\nIt implements a high-level interface for plotting statistical graphics. Seaborn integrates very well with pandas dataframes as input data and abstracts away some of the common data pre-processing steps.\nInstall seaborn:\nuv add seaborn\n\nimport seaborn as sns\n\nFor the sake of the example we can repeat the figure from above, but we will execute this one line of code that sets up some defaults for us:\n\nsns.set_theme(style=\"dark\", font_scale=1.4) # this has global effects\n\n\nplot_images(data)\n\n\n\n\n\n\n\n\nThere are many great plot examples in the example gallery.\nHere are a few interesting ones – click on figure to open documentation website with code.\n\n\nMultiple Regression\n\n\n\n\n\n\nTime Series\n\n\n\n\n\n\n\n\n\nHeat Scatter of Brain Networks Correlations\n\n\n\n\n\n\nAnnotated Heatmap\n\n\n\n\n\n\n\n\n\nSmall multiple time series\n\n\n\n\n\n\nScatterplot with categorical variables\n\n\n\n\n\n\n\n\n\nViolin Plot", + "text": "16.2 Seaborn\n\n\n\n\n\nseaborn is a data visualization library built on top of matplotlib.\nIt implements a high-level interface for plotting statistical graphics. Seaborn integrates very well with pandas dataframes as input data and abstracts away some of the common data pre-processing steps.\nInstall seaborn:\nuv add seaborn\n\nimport seaborn as sns\n\nFor the sake of the example we can repeat the figure from above, but we will execute this one line of code that sets up some defaults for us:\n\nsns.set_theme(style=\"dark\", font_scale=1.4) # this has global effects\n\n\nimport seaborn as sns\n\n\ndf = sns.load_dataset(\"fmri\")\ndf.head()\n\n\n\n\n\n\n\n\n\nsubject\ntimepoint\nevent\nregion\nsignal\n\n\n\n\n0\ns13\n18\nstim\nparietal\n-0.017552\n\n\n1\ns5\n14\nstim\nparietal\n-0.080883\n\n\n2\ns12\n18\nstim\nparietal\n-0.081033\n\n\n3\ns11\n18\nstim\nparietal\n-0.046134\n\n\n4\ns10\n18\nstim\nparietal\n-0.037970\n\n\n\n\n\n\n\n\n\nsns.lineplot(df, x=\"timepoint\", y=\"signal\", marker=\".\", hue=\"region\")\n\n\n\n\n\n\n\n\n\nplot_images(data)\n\n\n\n\n\n\n\n\nThere are many great plot examples in the example gallery.\nHere are a few interesting ones – click on figure to open documentation website with code.\n\n\nMultiple Regression\n\n\n\n\n\n\nTime Series\n\n\n\n\n\n\n\n\n\nHeat Scatter of Brain Networks Correlations\n\n\n\n\n\n\nAnnotated Heatmap\n\n\n\n\n\n\n\n\n\nSmall multiple time series\n\n\n\n\n\n\nScatterplot with categorical variables\n\n\n\n\n\n\n\n\n\nViolin Plot", "crumbs": [ "16  Plotting" ] @@ -684,7 +684,7 @@ "href": "09_plotting.html#exercises", "title": "16  Plotting", "section": "16.3 Exercises", - "text": "16.3 Exercises\n\nUse numpy to create 4 arrays of numbers drawn from 4 different distributions: uniform, normal, lognormal and exponential. Each array should have of 100000 samples. Use matplotlib to plot a grid of 2x2 subplots, with one histogram on each subplot. Hint: Look at numpy’s submodule random.\nPick one interesting example of the Seaborn gallery and reproduce it on your computer. Change 1 or 2 parameters of the plot, for example, some color or order of variables, remove/add a variable. If you have own data with a similar shape, plot those!", + "text": "16.3 Exercises\n\nUse numpy to create 4 arrays of numbers drawn from 4 different distributions: uniform, normal, lognormal and exponential. Each array should have of 100000 samples. Use matplotlib to plot a grid of 2x2 subplots, with one histogram on each subplot. Each histogram should have a different color. Hint: Look at numpy’s submodule random.\nPick one interesting example of the Seaborn gallery and reproduce it on your computer. Change 1 or 2 parameters of the plot, for example, some color or order of variables, remove/add a variable. If you have own data with a similar shape, plot those!", "crumbs": [ "16  Plotting" ] diff --git a/docs/sitemap.xml b/docs/sitemap.xml index d374c66..3d99706 100644 --- a/docs/sitemap.xml +++ b/docs/sitemap.xml @@ -6,7 +6,7 @@ https://fabridamicelli.github.io/python-course/01_getting_started.html - 2024-10-17T11:44:21.344Z + 2024-10-18T07:02:49.980Z https://fabridamicelli.github.io/python-course/010_executing_code.html @@ -62,11 +62,11 @@ https://fabridamicelli.github.io/python-course/081_pandas.html - 2024-10-16T15:51:10.495Z + 2024-10-18T07:03:19.911Z https://fabridamicelli.github.io/python-course/09_plotting.html - 2024-10-14T15:45:37.378Z + 2024-10-18T07:03:54.919Z https://fabridamicelli.github.io/python-course/10_tests.html