Add Health Index example notebook

688d2f34 · VulcanixFR · 9cba6dfa · 688d2f34 · 688d2f34
Commit 688d2f34 authored 10 months ago by VulcanixFR
--- a/README.md
+++ b/README.md
@@ -7,9 +7,18 @@ python main.py

 (Windows users may replace `python` with `python.exe`)

-## The Database
+The required packages are :

-### Creating a Database
+```
+PySimpleGUI
+numpy
+pandas
+sqlite3
+seaborn
+scikit-learn
+PyWavelets
+openpyxl
+```

-### Editing a database

+Examples to use the tools in this repository are found in the `examples` folder.
--- a/examples/Plot-HealthIndex.ipynb
+++ b/examples/Plot-HealthIndex.ipynb
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Plotting the health index\n",
+    "\n",
+    "The Health index is computed using the database creation tool GUI.\n",
+    "This example relies on the `Robot3.sqlite` database."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Include Dependencies\n",
+    "\n",
+    "# Disable seaborn warnings\n",
+    "import warnings\n",
+    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.decomposition import PCA\n",
+    "\n",
+    "from typing import List, Dict, Tuple, Any\n",
+    "import re\n",
+    "\n",
+    "# Setup path to import tools.*\n",
+    "import sys\n",
+    "sys.path.append(\"../\")\n",
+    "# ---------------------\n",
+    "from tools.database import Database\n",
+    "from tools.processing import *\n",
+    "from tools.plots import *\n",
+    "from tools.predictive_indicators import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Getting the indicators from the Database\n",
+    "\n",
+    "This step has not been abstracted by the Database class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DB = Database(fr\"..\\db\\Robot3.sqlite\") # Change the path to the database if neeeded\n",
+    "data = pd.read_sql(QUERY_INDICATORS(3), DB.db).sort_values([ \"Date\", \"Axis\" ]) # Sort values \n",
+    "\n",
+    "# Focus on running @ 30% speed\n",
+    "focus = data[data[\"Speed\"] == 30]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting a single day\n",
+    "\n",
+    "This example plots the health index with the time scale in seconds.\n",
+    "The sample time of the index is the timestamp at which the run was started."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "day_data = focus[focus[\"Date\"] == \"2024-7-29\"]\n",
+    "\n",
+    "plt.figure()\n",
+    "sns.scatterplot(day_data, x=\"Time\", y=\"Health\", hue=\"Axis\")\n",
+    "plt.grid()\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "plt.figure()\n",
+    "sns.scatterplot(day_data, y=\"Health\", x=\"MeanTemperature\", hue=\"Axis\")\n",
+    "plt.grid()\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see on the plot the impact of the motor heat on the health index."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for a in day_data[\"Axis\"].unique():\n",
+    "    d = day_data[day_data[\"Axis\"] == a]\n",
+    "    print(a, np.corrcoef(d[\"Health\"], d[\"MeanTemperature\"])[0,1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also see that there is a correlation between the Health index and the motor temperature."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting multiple days\n",
+    "\n",
+    "To better take into account the temperature, we can look at a larger time scale.\n",
+    "\n",
+    "### Plotting according to the timestamp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure()\n",
+    "sns.scatterplot(focus, x=\"RunTime\", y=\"Health\", hue=\"Axis\")\n",
+    "plt.grid()\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The visualisation here is hard to read, we can try to plot a line instead."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure()\n",
+    "sns.lineplot(focus, x=\"RunTime\", y=\"Health\", hue=\"Axis\")\n",
+    "plt.grid()\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is again hard to determine a trend in the data with this graph. \n",
+    "\n",
+    "### Plotting according to the Date\n",
+    "\n",
+    "We can group the experiment per Date of sampling. We must keep in mind that \n",
+    "although the time scale will appear linear, there will be variable time gaps \n",
+    "between the points. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure()\n",
+    "sns.scatterplot(focus, x=\"Date\", y=\"Health\", hue=\"Axis\")\n",
+    "plt.grid()\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "plt.figure()\n",
+    "sns.lineplot(focus, x=\"Date\", y=\"Health\", hue=\"Axis\")\n",
+    "plt.grid()\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here since multiple data point exists for each date, seaborn drew the standard deviation of the data on top of each axis line.\n",
+    "\n",
+    "We can also use a boxplot to see the evolution per axis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure()\n",
+    "sns.boxplot(focus, x=\"Axis\", y=\"Health\", hue=\"Date\")\n",
+    "plt.grid()\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.10.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
+%% Cell type:markdown id: tags:
+
+# Plotting the health index
+
+The Health index is computed using the database creation tool GUI.
+This example relies on the `Robot3.sqlite` database.
+
+%% Cell type:code id: tags:
+
+``` python
+# Include Dependencies
+
+# Disable seaborn warnings
+import warnings
+warnings.simplefilter(action='ignore', category=FutureWarning)
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.decomposition import PCA
+
+from typing import List, Dict, Tuple, Any
+import re
+
+# Setup path to import tools.*
+import sys
+sys.path.append("../")
+# ---------------------
+from tools.database import Database
+from tools.processing import *
+from tools.plots import *
+from tools.predictive_indicators import *
+```
+
+%% Cell type:markdown id: tags:
+
+## Getting the indicators from the Database
+
+This step has not been abstracted by the Database class.
+
+%% Cell type:code id: tags:
+
+``` python
+DB = Database(fr"..\db\Robot3.sqlite") # Change the path to the database if neeeded
+data = pd.read_sql(QUERY_INDICATORS(3), DB.db).sort_values([ "Date", "Axis" ]) # Sort values
+
+# Focus on running @ 30% speed
+focus = data[data["Speed"] == 30]
+```
+
+%% Cell type:markdown id: tags:
+
+## Plotting a single day
+
+This example plots the health index with the time scale in seconds.
+The sample time of the index is the timestamp at which the run was started.
+
+%% Cell type:code id: tags:
+
+``` python
+day_data = focus[focus["Date"] == "2024-7-29"]
+
+plt.figure()
+sns.scatterplot(day_data, x="Time", y="Health", hue="Axis")
+plt.grid()
+plt.tight_layout()
+plt.show()
+
+plt.figure()
+sns.scatterplot(day_data, y="Health", x="MeanTemperature", hue="Axis")
+plt.grid()
+plt.tight_layout()
+plt.show()
+```
+
+%% Cell type:markdown id: tags:
+
+We can see on the plot the impact of the motor heat on the health index.
+
+%% Cell type:code id: tags:
+
+``` python
+for a in day_data["Axis"].unique():
+    d = day_data[day_data["Axis"] == a]
+    print(a, np.corrcoef(d["Health"], d["MeanTemperature"])[0,1])
+```
+
+%% Cell type:markdown id: tags:
+
+We can also see that there is a correlation between the Health index and the motor temperature.
+
+%% Cell type:markdown id: tags:
+
+## Plotting multiple days
+
+To better take into account the temperature, we can look at a larger time scale.
+
+### Plotting according to the timestamp
+
+%% Cell type:code id: tags:
+
+``` python
+plt.figure()
+sns.scatterplot(focus, x="RunTime", y="Health", hue="Axis")
+plt.grid()
+plt.tight_layout()
+plt.show()
+```
+
+%% Cell type:markdown id: tags:
+
+The visualisation here is hard to read, we can try to plot a line instead.
+
+%% Cell type:code id: tags:
+
+``` python
+plt.figure()
+sns.lineplot(focus, x="RunTime", y="Health", hue="Axis")
+plt.grid()
+plt.tight_layout()
+plt.show()
+```
+
+%% Cell type:markdown id: tags:
+
+It is again hard to determine a trend in the data with this graph.
+
+### Plotting according to the Date
+
+We can group the experiment per Date of sampling. We must keep in mind that
+although the time scale will appear linear, there will be variable time gaps
+between the points.
+
+%% Cell type:code id: tags:
+
+``` python
+plt.figure()
+sns.scatterplot(focus, x="Date", y="Health", hue="Axis")
+plt.grid()
+plt.tight_layout()
+plt.show()
+
+plt.figure()
+sns.lineplot(focus, x="Date", y="Health", hue="Axis")
+plt.grid()
+plt.tight_layout()
+plt.show()
+```
+
+%% Cell type:markdown id: tags:
+
+Here since multiple data point exists for each date, seaborn drew the standard deviation of the data on top of each axis line.
+
+We can also use a boxplot to see the evolution per axis.
+
+%% Cell type:code id: tags:
+
+``` python
+plt.figure()
+sns.boxplot(focus, x="Axis", y="Health", hue="Date")
+plt.grid()
+plt.tight_layout()
+plt.show()
+```