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constrained-hmm: Start on data loading of CNC dataset
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‎handson/constrained-hmm/CNCMillSmartMichicanExplore.ipynb‎

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"cells": [
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"execution_count": 4,
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"id": "1af236bd-4f3c-4aef-81f6-1232772f5586",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas\n",
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"\n",
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"import os.path\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "775c0672-a0c5-4e0d-90f9-061564c9ec72",
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"metadata": {},
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"outputs": [],
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"source": [
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"dataset_path = './data/cnc-mill-toolwear'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "140539bb-172d-40b5-b5bf-69baa3eb7f1e",
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"metadata": {},
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"outputs": [
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>feedrate</th>\n",
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" <th>clamp_pressure</th>\n",
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"text/plain": [
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" feedrate clamp_pressure tool_condition machining_finalized \\\n",
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"experiment \n",
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"1 6 4.0 unworn yes \n",
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"2 20 4.0 unworn yes \n",
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"3 6 3.0 unworn yes \n",
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"4 6 2.5 unworn no \n",
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"5 20 3.0 unworn no \n",
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"6 6 4.0 worn yes \n",
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"7 20 4.0 worn no \n",
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"8 20 4.0 worn yes \n",
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"9 15 4.0 worn yes \n",
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"10 12 4.0 worn yes \n",
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"11 3 4.0 unworn yes \n",
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"12 3 3.0 unworn yes \n",
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"13 3 4.0 worn yes \n",
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"14 3 3.0 worn yes \n",
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"15 6 3.0 worn yes \n",
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"16 20 3.0 worn no \n",
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"17 3 2.5 unworn yes \n",
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"18 3 2.5 worn yes \n",
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"\n",
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" passed_visual_inspection \n",
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"experiment \n",
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"1 yes \n",
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"2 yes \n",
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"3 yes \n",
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"4 NaN \n",
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"5 NaN \n",
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"6 no \n",
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"7 NaN \n",
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"8 no \n",
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"9 no \n",
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"10 no \n",
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"11 yes \n",
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"12 yes \n",
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"13 yes \n",
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"14 yes \n",
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"15 yes \n",
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"16 NaN \n",
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"17 yes \n",
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"18 yes "
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"\n",
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"def load_experiments(dataset_path):\n",
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"\n",
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" metadata_path = os.path.join(dataset_path, 'train.csv')\n",
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" df = pandas.read_csv(metadata_path)\n",
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"\n",
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" df = df.drop(columns='material') # only \"wax\", not so useful\n",
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" df = df.rename(columns={'No': 'experiment'})\n",
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" df = df.set_index('experiment')\n",
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" categorical_columns = ['tool_condition', 'machining_finalized', 'passed_visual_inspection']\n",
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" for c in categorical_columns:\n",
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" df[c] = df[c].astype('category')\n",
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"\n",
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" return df\n",
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"\n",
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" \n",
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"experiments = load_experiments(dataset_path)\n",
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"experiments"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "486aa2b0-0e36-49d4-b025-e821d43b9cf6",
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"def load_timeseries(dataset_path):\n",
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"\n",
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" \"\"\"\n",
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" \n",
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" From the dataset README.txt -- \n",
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" Note: Some variables will not accurately reflect the operation of the CNC machine.\n",
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" This can usually be detected by\n",
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" when M1_CURRENT_FEEDRATE reads 50,\n",
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" when X1 ActualPosition reads 198,\n",
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" or when M1_CURRENT_PROGRAM_NUMBER does not read 0.\n",
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" The source of these errors has not been identified.\n",
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" \"\"\"\n",
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"\n",
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" \n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "7e7a6c95-176b-4291-97d6-de19880b72d8",
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"metadata": {},
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"outputs": [],
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"source": [
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"# TODO: plot time-series\n",
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"\n",
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"# TODO: plot each run in X-Y\n",
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"# maybe support overlay a variable, like spindle power?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d524d75e-6d18-4837-92e3-3d439cb42f62",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"display_name": "constrained-hmm",
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"language": "python",
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"name": "constrained-hmm"
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},
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"name": "ipython",
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.3"
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}
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},
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}

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