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-0.13244195 -0.06134613\n", " -0.69865405 -0.6381467 0.81420724 -0.91070182 -0.53429543 -0.41588134\n", " -0.01960492 0.17289035 -0.01342005 -0.83176933 -0.51266509 0.68717677\n", " 0.2751774 0.2982981 0.34040651 0.52580604 -0.88378304 -0.26678323\n", " 0.07905487 -0.32308703 0.68895775 -0.03485498 0.53725518 0.70403103\n", " 0.00958297 0.81910449 0.17424788 0.7005486 -0.31881841 -0.00236608\n", " 0.06282208 -0.79004057 -0.20289499 0.83467535 0.26166448 -0.64498684\n", " -0.32228873 -0.61679398 -0.95035374 0.85492092 -0.10358534 -0.38492986\n", " 0.19695438 -0.98537109 -0.44395579 0.40606693 0.26753955]\n", " Counter Rollout CounterPlusRollout Timestamp\n", "0 37.270658 -59.474345 -22.203688 2025-01-01 00:00:00\n", "1 43.561734 -54.184658 -10.622924 2025-01-01 00:00:01\n", "2 42.158150 -43.826926 -1.668776 2025-01-01 00:00:02\n", "3 50.392926 -57.394165 -7.001239 2025-01-01 00:00:03\n", "4 54.925249 -56.055140 -1.129892 2025-01-01 00:00:04\n" ] } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "\n", "# Set random seed for reproducibility\n", "rng = np.random.default_rng(42)\n", "\n", "# Generate random walks starting at 0\n", "counter = np.zeros(300)\n", "rollout = np.zeros(300)\n", "\n", "# Generate random steps between -1 and 1\n", "counter_steps = rng.uniform(-1, 1, 299)\n", "rollout_steps = rng.uniform(-1, 1, 299)\n", "\n", "print(counter_steps)\n", "print(rollout_steps)\n", "\n", "# Calculate cumulative sum and scale to -100 to 100 range\n", "for i in range(1, 300):\n", " counter[i] = counter[i-1] + counter_steps[i-1]\n", " rollout[i] = rollout[i-1] + rollout_steps[i-1]\n", "\n", "# normalize values between -100 and 100, lowest value is -100, highest value is 100\n", "counter = (counter - min(counter)) / (max(counter) - min(counter)) * 200 - 100\n", "rollout = (rollout - min(rollout)) / (max(rollout) - min(rollout)) * 200 - 100\n", "\n", "# Create DataFrame\n", "df = pd.DataFrame({\n", " 'Counter': counter,\n", " 'Rollout': rollout,\n", " 'CounterPlusRollout': counter + rollout\n", "})\n", "\n", "# add a timestamp column\n", "df['Timestamp'] = pd.date_range(start='2025-01-01', periods=300, freq='1s')\n", "\n", "# Save to CSV\n", "df.to_csv('random_walks.csv', index=False)\n", "\n", "# Display first few rows\n", "print(df.head())\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "c61be7ab", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.5479121 -0.12224312 0.71719584 0.39473606 -0.8116453 0.9512447\n", " 0.5222794 0.57212861 -0.74377273 -0.09922812 -0.25840395 0.85352998\n", " 0.28773024 0.64552323 -0.1131716 -0.54552256 0.10916957 -0.87236549\n", " 0.65526234 0.2633288 0.51617548 -0.29094806 0.94139605 0.78624224\n", " 0.55676699 -0.61072258 -0.06655799 -0.91239247 -0.69142102 0.36609791\n", " 0.48952431 0.93501946 -0.34834928 -0.25908059 -0.06088838 -0.62105728\n", " -0.74015699 -0.04859015 -0.5461813 0.33962799 -0.12569616 0.66535639\n", " 0.4005302 -0.37526672 0.6645196 0.60952871 -0.22504324 -0.42334379\n", " 0.36499101]\n", "[-0.72049503 -0.6001836 -0.98527546 0.57384876 0.32970171 0.41033076\n", " 0.56145806 -0.08216845 0.13748239 -0.720406 -0.77093985 0.33680592\n", " -0.05780759 0.13047221 0.52999771 0.26943664 0.1071588 0.11841432\n", " -0.3920998 -0.93836433 -0.12656522 -0.57083065 -0.18294271 0.70680615\n", " -0.53212103 -0.88339452 -0.43723222 -0.41281248 0.32383303 0.1140643\n", " 0.56779642 0.32862708 -0.18722628 0.62804077 -0.66605416 -0.95457585\n", " -0.81990428 0.4447187 -0.07624554 -0.67745644 0.00208955 -0.69537579\n", " 0.39264075 -0.10768745 -0.23795755 -0.39697582 0.26056519 -0.27637478\n", " -0.82470016]\n", " Counter Rollout Square Timestamp\n", "0 -100.000000 100.000000 -37.646304 2025-01-01 00:00:00\n", "1 -79.942159 79.589040 -72.994086 2025-01-01 00:00:05\n", "2 -84.417207 62.586392 -30.252436 2025-01-01 00:00:10\n", "3 -58.162265 34.674447 7.555532 2025-01-01 00:00:15\n", "4 -43.711857 50.931053 26.035410 2025-01-01 00:00:20\n" ] } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "\n", "# Set random seed for reproducibility\n", "rng = np.random.default_rng(42)\n", "\n", "size = 50\n", "\n", "# Generate random walks starting at 0\n", "counter = np.zeros(size)\n", "rollout = np.zeros(size)\n", "square = np.zeros(size)\n", "\n", "# Generate random steps between -1 and 1\n", "counter_steps = rng.uniform(-1, 1, size-1)\n", "rollout_steps = rng.uniform(-1, 1, size-1)\n", "square_steps = rng.uniform(-1, 1, size-1)\n", "\n", "print(counter_steps)\n", "print(rollout_steps)\n", "\n", "# Calculate cumulative sum and scale to -100 to 100 range\n", "for i in range(1, size):\n", " counter[i] = counter[i-1] + counter_steps[i-1]\n", " rollout[i] = rollout[i-1] + rollout_steps[i-1]\n", " square[i] = square[i-1] + square_steps[i-1]\n", "\n", "# normalize values between -100 and 100, lowest value is -100, highest value is 100\n", "counter = (counter - min(counter)) / (max(counter) - min(counter)) * 200 - 100\n", "rollout = (rollout - min(rollout)) / (max(rollout) - min(rollout)) * 200 - 100\n", "square = (square - min(square)) / (max(square) - min(square)) * 200 - 100\n", "\n", "# Create DataFrame\n", "df = pd.DataFrame({\n", " 'Counter': counter,\n", " 'Rollout': rollout,\n", " 'Square': square\n", "})\n", "\n", "# add a timestamp column\n", "df['Timestamp'] = pd.date_range(start='2025-01-01', periods=size, freq='5s')\n", "\n", "# Save to CSV\n", "df.to_csv('random_walks_demo.csv', index=False)\n", "\n", "# Display first few rows\n", "print(df.head())\n" ] }, { "cell_type": "code", "execution_count": null, "id": "e7c8eeb1", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ab
timestamp
2025-01-0114
2025-01-0225
2025-01-0336
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" ], "text/plain": [ " a b\n", "timestamp \n", "2025-01-01 1 4\n", "2025-01-02 2 5\n", "2025-01-03 3 6" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "True\n" ] }, { "data": { "text/html": [ "
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ab
timestamp
2025-01-0336
\n", "
" ], "text/plain": [ " a b\n", "timestamp \n", "2025-01-03 3 6" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "3\n", "1\n" ] } ], "source": [ "from pandas import DataFrame\n", "\n", "a = DataFrame({\n", " \"a\": {\"2025-01-01\": 1, \"2025-01-02\": 2, \"2025-01-03\": 3},\n", " \"b\": {\"2025-01-01\": 4, \"2025-01-02\": 5, \"2025-01-03\": 6},\n", "})\n", "\n", "a.index.name = \"timestamp\"\n", "\n", "display(a)\n", "\n", "print(\"a\" in a.columns)\n", "\n", "b = a.tail(1)\n", "\n", "display(b)\n", "\n", "print(len(a))\n", "print(len(b))\n", "print(b.size)\n" ] }, { "cell_type": "code", "execution_count": null, "id": "f3374174", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "All elements in index are of the same type\n" ] } ], "source": [ "from pandas import DataFrame\n", "\n", "data = DataFrame({\n", " \"a\": {\"2025-01-01\": 1, \"2025-01-02\": 2, \"2025-01-03\": 3},\n", " \"b\": {\"2025-01-01\": 4, \"2025-01-02\": 5, \"2025-01-03\": 6},\n", "})\n", "\n", "index = data.index\n", "\n", "# Get type of first element of index\n", "index_type = type(index[0])\n", "\n", "print(index_type)\n", "\n", "# Check if all in index are of the same type\n", "if all(isinstance(i, index_type) for i in index):\n", " print(\"All elements in index are of the same type\")\n", "else:\n", " print(\"Elements in index are of different types\")\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "40e72c60", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 6, "id": "1fbb3788", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "test\n" ] } ], "source": [ "from unittest.mock import MagicMock\n", "from asyncua.ua.uaerrors import BadAlreadyExists\n", "\n", "mock1 = MagicMock(\n", " side_effect = Exception(\"test\")\n", ")\n", "\n", "mock2 = MagicMock(\n", " side_effect = BadAlreadyExists(\"test\")\n", ")\n", "\n", "try:\n", " mock1()\n", "except ValueError as e:\n", " try:\n", " mock2()\n", " except BadAlreadyExists as e:\n", " print(e)\n", "except Exception as e:\n", " print(e)\n" ] }, { "cell_type": "code", "execution_count": 7, "id": "771ab4ee", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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abtargetpredictiontimestamp
014712025-01-01
125822025-01-02
236932025-01-03
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" ], "text/plain": [ " a b target prediction timestamp\n", "0 1 4 7 1 2025-01-01\n", "1 2 5 8 2 2025-01-02\n", "2 3 6 9 3 2025-01-03" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pandas import DataFrame, merge\n", "\n", "retrain_dataset = DataFrame({\n", " 'a': {'2025-01-01': 1, '2025-01-02': 2, '2025-01-03': 3},\n", " 'b': {'2025-01-01': 4, '2025-01-02': 5, '2025-01-03': 6},\n", " 'c': {'2025-01-01': 7, '2025-01-02': 8, '2025-01-03': 9},\n", "})\n", "\n", "prediction_data = DataFrame({\n", " 'prediction': {'1': 1, '2': 2, '3': 3},\n", "})\n", "\n", "prediction_data.index = retrain_dataset.index\n", "\n", "prediction_data = merge(\n", " retrain_dataset, prediction_data, left_index=True, right_index=True, how='left')\n", "\n", "prediction_data.rename(columns={'c': 'target'}, inplace=True)\n", "\n", "prediction_data['timestamp'] = prediction_data.index\n", "\n", "prediction_data.reset_index(drop=True, inplace=True)\n", "\n", "display(prediction_data)" ] }, { "cell_type": "code", "execution_count": 4, "id": "486b95b3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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" ], "text/plain": [ " a b\n", "2025-01-01 1 4\n", "2025-01-02 2 5\n", "2025-01-03 3 6" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "{'index': ['2025-01-01', '2025-01-02', '2025-01-03'],\n", " 'columns': ['a', 'b'],\n", " 'data': [[1, 4], [2, 5], [3, 6]]}" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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index2025-01-012025-01-022025-01-03
columnsabNone
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" ], "text/plain": [ " 0 1 2\n", "index 2025-01-01 2025-01-02 2025-01-03\n", "columns a b None\n", "data [1, 4] [2, 5] [3, 6]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "3" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pandas import DataFrame\n", "\n", "data = DataFrame()\n", "\n", "display(data.to_dict(orient='records'))\n", "\n", "data = DataFrame({\n", " \"a\": {\"2025-01-01\": 1, \"2025-01-02\": 2, \"2025-01-03\": 3},\n", " \"b\": {\"2025-01-01\": 4, \"2025-01-02\": 5, \"2025-01-03\": 6},\n", "})\n", "\n", "display(data)\n", "\n", "data_list = data.to_dict('split')\n", "\n", "display(data_list)\n", "\n", "data_rec = DataFrame.from_dict(data_list, orient='index')\n", "\n", "display(data_rec)\n", "\n", "data.shape[0]" ] } ], "metadata": { "kernelspec": { "display_name": "venv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.13" } }, "nbformat": 4, "nbformat_minor": 5 }