\n",
"\n"
],
"metadata": {
"id": "H7zTf9DPDIXS"
}
},
{
"cell_type": "markdown",
"source": [
"*^ A picture of the polar verity sense heart rate monitor ^*"
],
"metadata": {
"id": "naL5gEN2DaZ4"
}
},
{
"cell_type": "markdown",
"source": [
"The [Polar Verity Sense](https://www.polar.com/en/products/accessories/polar-verity-sense) is a $89 armband heart rate sensor that has often been reviewed as a viable alternative to ECG chest-strap style monitors. Portable and comfortable, it can be worn for exercises indoors, outdoors, and even in the pool.\n",
"\n",
"Beyond its accuracy as a heart rate monitor, the Verity Sense's utility lies in its interface with Polar's official app, which helps keep track of data collected from the sensor as well as providing plenty of useful visualizations and analytics. \n",
"\n",
"In the following sections, we will present what we have learned from using the Verity Sense and how to extract its data for visualizations and statistical analyses. With the wearipedia library, this process will only require your username and password!"
],
"metadata": {
"id": "XI6MGYdH9-pH"
}
},
{
"cell_type": "markdown",
"source": [
"We will be able to extract the following parameters (in bold are parameters we will be using in this notebook). Per Activity means one measurement through a single session of exercise. Per second means one measurement every second throughout an exercise.\n",
"\n",
"Parameter Name | Sampling Frequency \n",
"-------------------|-----------------\n",
"Calories | Per Activity\n",
"Duration | Per Activity\n",
"Sessions | Per day\n",
"Continuous Heart Rate | Per second \n"
],
"metadata": {
"id": "qfKAFMwf-cm2"
}
},
{
"cell_type": "markdown",
"source": [
"In this guide, we sequentially cover the following **nine** topics:\n",
"\n",
"1. **Set up**
"
],
"metadata": {
"id": "dN9IAHg5YeMB"
}
},
{
"cell_type": "markdown",
"source": [
"At the bottom (gray) zone is the amount of time spent with heart rate between 100-120 bpm, the next being 120-140 bpm, then 140-160 bpm, 160-180 bpm, and finally 180-200 bpm.\n",
"\n",
"We will want the following data:\n",
"\n",
"* The zones we want to plot\n",
"* Time in each heart rate zone\n",
"\n",
"Polar uses the following zones: 100-120 bpm, 120-140 bpm, 140-160 bpm, 160-180 bpm, 180-200 bpm, which is what we will be using as well. \n",
"\n",
"To get the time spent in each heart rate zone, we can use the pandas groupby function to aggregate the heart rates by zones, and then count the number of entries in each group. Since the continuous heart rate data has an entry for every second, this directly gives us the time spent in each heart rate zone."
],
"metadata": {
"id": "zLaER1dCQO23"
}
},
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"\n",
"ranges = [100,120,140,160,180,200]\n",
"z1 = hr_df.groupby(pd.cut(hr_df.bpm, ranges), as_index=False).count()\n",
"z2 = pd.to_datetime(z1['bpm'], unit='s')\n",
"z2 = pd.DataFrame(z2.dt.strftime('%H:%M:%S'))\n",
"display(z2['bpm'])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 121
},
"id": "dhTTlNxHnJTI",
"outputId": "bc71adbe-8e16-4375-ee3c-c0ad913d8f4a"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"0 00:05:56\n",
"1 00:27:52\n",
"2 00:12:57\n",
"3 00:06:35\n",
"4 00:03:57\n",
"Name: bpm, dtype: object"
]
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"z2 is exactly the heart rate zone data, as we wanted. Now we want to set up two more python arrays that we will use as the y-axis labels, shown here. This data is already available in our z2 series, so we can extract them as necessary."
],
"metadata": {
"id": "J3yqY-_zbHNE"
}
},
{
"cell_type": "code",
"source": [
"zones = ['1','2','3','4','5']\n",
"times = [e for e in z1['bpm']][::-1]\n",
"time_labels = [e for e in z2['bpm']]"
],
"metadata": {
"id": "FDnEkmZmNTuX"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"The zones array will be the left side y-axis label, and the times will be the actual data. We can place these in a pandas dataframe so it will be easier to plot using the python matplotlib library."
],
"metadata": {
"id": "oy4glSixQzZo"
}
},
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"\n",
"d = {\"zones\":zones, \"times\": times}\n",
"df1 = pd.DataFrame(data=d)\n",
"df1.set_index('zones', inplace=True)"
],
"metadata": {
"id": "RSzx9FzvQ5Xa"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"And now we can finally plot the data. We will be using the [horizontal bar plot](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.barh.html) from matplotlib, barh. Since we already have the data we need, we can use barh to directly plot the necessary bars, after which we can apply our desired styling to make the graph look like the one pictured."
],
"metadata": {
"id": "fMODQ5pbRejn"
}
},
{
"cell_type": "code",
"source": [
"from matplotlib import pyplot as plt, patches\n",
"import seaborn as sns\n",
"\n",
"sns.set_theme(style=\"dark\")\n",
"fig1, ax = plt.subplots(figsize=(16,6))\n",
"clrs = [\"#c0c8c8\", \"#46c7ee\", \"#6acc2b\", \"#f9bf1c\", \"#de0f5b\"][::-1] #colors array for the zones\n",
"\n",
"#plot the base graph\n",
"plt. margins(y=0)\n",
"plt.xlim(0, sum(i for i in df1[\"times\"])) #this sets the graph to have its width equal to the total session time\n",
"bar1 = ax.barh(df1.index[::-1], df1[\"times\"][::-1], align=\"center\", height=1., color = clrs[::-1]) #plot the bar graph\n",
"plt.xticks(color='w') #hide the x tick labels\n",
"plt.yticks(color='w')\n",
"ax.tick_params(axis='both', labelsize=25, pad=12.5)\n",
"\n",
"# Adding the times for each zone\n",
"for i in range(len(time_labels)):\n",
" plt.text(.97,0.15 + 0.15*i,time_labels[i],fontsize=18,transform=fig1.transFigure,\n",
" horizontalalignment='center', weight=300)\n",
"\n",
"#add colored backgrounds behind each zone\n",
"rect = patches.Rectangle((0, 0.805), 0.5, 0.194, color='#de0f5b')\n",
"rect2 = patches.Rectangle((0, 0.602), 0.5, 0.198, color='#f9bf1c')\n",
"rect3 = patches.Rectangle((0, 0.402), 0.5, 0.194, color='#6acc2b')\n",
"rect4 = patches.Rectangle((0, 0.202), 0.5, 0.194, color='#46c7ee')\n",
"rect5 = patches.Rectangle((0, 0.0), 0.5, 0.195, color='#c0c8c8')\n",
"\n",
"lax = fig1.add_axes([0.08,0.126,1.,0.752], anchor='NE', zorder=-1)\n",
"lax.add_patch(rect)\n",
"lax.add_patch(rect2)\n",
"lax.add_patch(rect3)\n",
"lax.add_patch(rect4)\n",
"lax.add_patch(rect5)\n",
"lax.axis('off')\n",
"\n",
"#add gray rectangles behind times \n",
"b1 = patches.Rectangle((0, 0.805), 0.5, 0.194, color='#eaeaf2')\n",
"b2 = patches.Rectangle((0, 0.602), 0.5, 0.194, color='#eaeaf2')\n",
"b3 = patches.Rectangle((0, 0.402), 0.5, 0.194, color='#eaeaf2')\n",
"b4 = patches.Rectangle((0, 0.202), 0.5, 0.194, color='#eaeaf2')\n",
"b5 = patches.Rectangle((0, 0.0), 0.5, 0.194, color='#eaeaf2')\n",
"rax = fig1.add_axes([.8,0.126,.42,0.752], anchor='NE', zorder=-1)\n",
"rax.add_patch(b1)\n",
"rax.add_patch(b2)\n",
"rax.add_patch(b3)\n",
"rax.add_patch(b4)\n",
"rax.add_patch(b5)\n",
"rax.axis('off')\n",
"\n",
"#set grid lines separating bars\n",
"ax.set_yticks([0.5,1.5,2.5,3.5,4.5], minor=True)\n",
"ax.yaxis.grid(True, which='minor')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 315
},
"id": "ln4Hmt2eMaHH",
"outputId": "bad029da-3015-48c0-f957-5158373ecb72"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"
"
],
"metadata": {
"id": "Lho0NmMPFfCN"
}
},
{
"cell_type": "markdown",
"source": [
"To start we want to extract the necessary data. This will include: \n",
"\n",
"\n",
"* Dates of exercises\n",
"* Durations of each exercise\n",
"* Average heart rates of each exercise\n",
"\n"
],
"metadata": {
"id": "RBGD5tOUg7zt"
}
},
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"durations = []\n",
"avg_rates = []\n",
"dates = []\n",
"day_array = list(df2['day'])\n",
"rate_array = list(df2['avg_rate'])\n",
"duration_array = list(df2['minutes'])\n",
"for i in range(len(df2['day'])):\n",
" \n",
" if datetime.strptime(day_array[i],'%Y-%M-%d') < (datetime.strptime(start_date,'%Y-%M-%d') + timedelta(days=30)) and datetime.strptime(day_array[i],'%Y-%M-%d') >= datetime.strptime(start_date,'%Y-%M-%d'):\n",
" dates.append(np.datetime64(day_array[i]))\n",
" durations.append(duration_array[i]*60000)\n",
" avg_rates.append(rate_array[i])"
],
"metadata": {
"id": "5FtAHv14hDyF"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Now that we have everything we need, we can move on to plotting the graph! \n",
"\n",
"To do this we will start by plotting a [matplotlib bar graph](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.bar.html), which will represent the durations of each exercise. Then we will overlay this plot with a second graph, the [matplotlib line plot](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html). This is all we need for the base graph!\n",
"\n",
"All that remains after is to add the necessary styling to achieve the graph pictured above."
],
"metadata": {
"id": "1wXrNnH2hD52"
}
},
{
"cell_type": "code",
"source": [
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import matplotlib.dates as mdates\n",
"\n",
"#set the style of the plot, initialize it\n",
"sns.set_style(\"whitegrid\")\n",
"fig, ax = plt.subplots(figsize=(18,8))\n",
"ax.set_facecolor('#f2f2f2')\n",
"\n",
"#before we begin, we need to add in a buffer at either end of the data so we can extend the graph\n",
"rbound = np.datetime64((pd.Timestamp(dates[len(dates)-1]) + pd.DateOffset(days=1)).strftime('%Y-%m-%d'))\n",
"lbound = np.datetime64((pd.Timestamp(dates[0]) - pd.DateOffset(days=1)).strftime('%Y-%m-%d'))\n",
"xdates = list(pd.date_range(start=lbound, end=rbound))\n",
"xdates.append(xdates[len(xdates)-1])\n",
"\n",
"#process and fill our data arrays so that they match dimensions of x axis\n",
"points = {np.datetime64(datetime.strftime(pd.to_datetime(date), '%Y-%m-%d')):(avg_rate,duration) for date, avg_rate, duration in zip(dates,avg_rates,durations)}\n",
"data1 = []\n",
"data2 = {}\n",
"for i in range(len(xdates)):\n",
" cdate = np.datetime64(datetime.strftime(xdates[i],'%Y-%m-%d'))\n",
" if i == 0:\n",
" added = points[np.datetime64(datetime.strftime(xdates[1],'%Y-%m-%d'))][0]\n",
" if cdate in points:\n",
" added = points[cdate][0]\n",
" data1.append(added)\n",
" data2[cdate] = points[cdate][1]/3600000\n",
" else:\n",
" data1.append(added)\n",
" data2[cdate] = 0\n",
"plt.xticks(rotation=45)\n",
"ax2 = ax.twinx()\n",
"\n",
"#plot the bar graph\n",
"ax.bar(data2.keys(),data2.values(), color ='#e71735', width=0.20)\n",
"ax.set_yticks(np.arange(0, max(data2.values())+0.5, 0.5))\n",
"\n",
"#plot the average heart rate\n",
"plt.plot(xdates, data1, color='#e71735', linestyle='dashed')\n",
"ax.xaxis.set_major_formatter(mdates.DateFormatter('%m-%d'))\n",
"\n",
"#clip the graph ends to get our desired look \n",
"axis = plt.axis()\n",
"plt.xlim(xdates[0], xdates[len(xdates)-2])\n",
"ax2.yaxis.grid(False)\n",
"\n",
"#hide ticks\n",
"ax2.tick_params(axis='y', length=0) \n",
"ax.tick_params(axis='y', length=0) \n",
"\n",
"print(points)"
],
"metadata": {
"id": "O8hVSOdCdnMn",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 531
},
"outputId": "3aadf707-1aaa-4775-85dc-9483d7da59dd"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"{numpy.datetime64('2022-03-11'): (148.60438074901958, 2880000), numpy.datetime64('2022-03-14'): (125.03413026104663, 2760000), numpy.datetime64('2022-03-27'): (90.8876123319256, 2700000), numpy.datetime64('2022-03-17'): (109.82817716867521, 2700000), numpy.datetime64('2022-05-15'): (134.32337357988095, 3000000), numpy.datetime64('2022-03-16'): (101.67126572452109, 3000000), numpy.datetime64('2022-03-28'): (89.30156282473392, 2880000), numpy.datetime64('2022-03-22'): (100.26106997019164, 3180000), numpy.datetime64('2022-03-13'): (77.30444661402656, 2760000), numpy.datetime64('2022-03-26'): (117.12185927449448, 2820000), numpy.datetime64('2022-04-30'): (100.18888458284101, 2700000), numpy.datetime64('2022-03-10'): (102.11270955167122, 3120000), numpy.datetime64('2022-04-20'): (100.23515912232084, 2940000), numpy.datetime64('2022-03-15'): (151.51392114667934, 3240000), numpy.datetime64('2022-05-12'): (122.15268124430676, 3240000), numpy.datetime64('2022-03-23'): (158.26683088729507, 2820000), numpy.datetime64('2022-04-15'): (131.07266595834122, 2760000), numpy.datetime64('2022-06-15'): (112.44969891174973, 2880000), numpy.datetime64('2022-05-14'): (133.4350159451914, 3180000), numpy.datetime64('2022-06-16'): (85.15969462663257, 2760000), numpy.datetime64('2022-05-26'): (91.9841307333696, 2700000), numpy.datetime64('2022-04-28'): (125.21753249314493, 2940000), numpy.datetime64('2022-05-27'): (107.13397021692715, 2760000), numpy.datetime64('2022-05-21'): (143.3458155760211, 3120000), numpy.datetime64('2022-04-25'): (151.4953087021972, 3360000), numpy.datetime64('2022-03-30'): (153.00516777290125, 3180000), numpy.datetime64('2022-05-29'): (103.10242645848972, 2760000), numpy.datetime64('2022-05-28'): (146.25693522751908, 3180000), numpy.datetime64('2022-04-10'): (86.58026740402435, 2940000), numpy.datetime64('2022-04-17'): (132.33100387749926, 2820000), numpy.datetime64('2022-03-29'): (119.68015580244948, 3360000), numpy.datetime64('2022-04-29'): (161.85980034725586, 2940000), numpy.datetime64('2022-03-21'): (139.88037913822555, 2820000), numpy.datetime64('2022-05-22'): (151.24360941968666, 2760000), numpy.datetime64('2022-05-16'): (126.88316898584463, 2880000), numpy.datetime64('2022-03-20'): (140.09253679690738, 2760000), numpy.datetime64('2022-04-11'): (125.91470056917359, 2760000), numpy.datetime64('2022-03-31'): (127.3760588913292, 3060000), numpy.datetime64('2022-05-23'): (147.5135913867359, 3000000), numpy.datetime64('2022-06-13'): (106.68748050611664, 2820000), numpy.datetime64('2022-04-12'): (109.3653383639486, 3360000), numpy.datetime64('2022-03-24'): (115.72448231096944, 3120000), numpy.datetime64('2022-04-19'): (83.08440428625981, 3240000), numpy.datetime64('2022-03-12'): (110.22994511488777, 3300000), numpy.datetime64('2022-05-30'): (87.58760730290345, 2880000), numpy.datetime64('2022-05-10'): (119.65725556045116, 3060000), numpy.datetime64('2022-04-16'): (126.39131742352002, 3000000), numpy.datetime64('2022-05-24'): (87.3043198208121, 2820000), numpy.datetime64('2022-03-19'): (153.789752043585, 3240000), numpy.datetime64('2022-05-25'): (114.18960726652924, 3000000), numpy.datetime64('2022-05-11'): (149.4561437472248, 3240000), numpy.datetime64('2022-05-13'): (132.18352710565964, 3240000), numpy.datetime64('2022-05-17'): (107.4544674966249, 2880000), numpy.datetime64('2022-06-11'): (110.7521333275521, 2820000), numpy.datetime64('2022-04-27'): (141.06358635016585, 2880000), numpy.datetime64('2022-06-09'): (74.70970628375426, 2940000), numpy.datetime64('2022-06-10'): (74.80748491633975, 2880000), numpy.datetime64('2022-03-25'): (120.97788470827379, 3180000), numpy.datetime64('2022-04-22'): (108.7103048662486, 2880000), numpy.datetime64('2022-05-31'): (127.00566093187098, 3120000), numpy.datetime64('2022-04-24'): (147.07278692696016, 3180000), numpy.datetime64('2022-05-20'): (157.0421130777547, 3000000), numpy.datetime64('2022-04-18'): (136.09561544189722, 3180000), numpy.datetime64('2022-06-12'): (106.69587741212065, 3300000), numpy.datetime64('2022-06-14'): (123.70767138220371, 3420000), numpy.datetime64('2022-04-23'): (111.68947949702499, 3540000), numpy.datetime64('2022-04-14'): (107.27921624551617, 3420000), numpy.datetime64('2022-04-21'): (128.67343246012445, 3240000), numpy.datetime64('2022-04-13'): (129.66215608850723, 3360000)}\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"
"
],
"metadata": {
"id": "15aeF03j3N61"
}
},
{
"cell_type": "markdown",
"source": [
"In this plot the line plot represents the heart rate (bpm) at every second of the exercise session, and the colored regions indicate the \"zone\" in which the heart rate (bpm) lies. We will need:\n",
"\n",
"\n",
"* Continuous heart rate data\n"
],
"metadata": {
"id": "hNntOOC0gqq3"
}
},
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"\n",
"heart_rts = list(hr_df[\"bpm\"]) \n",
"#we must add in '2000-01-10' as a dummy date to help with formatting concerns\n",
"times = list(hr_df[\"time\"]) "
],
"metadata": {
"id": "zH1L-IvOHlZK"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"We will now graph this using the matplotlib [line plot](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html) function, which will give us the base graph, after which we can focus on aesthetics."
],
"metadata": {
"id": "zFHHY6vOIYBG"
}
},
{
"cell_type": "code",
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import seaborn as sns\n",
"\n",
"sns.set_style(\"whitegrid\")\n",
"fig, ax = plt.subplots(figsize=(18,6))\n",
"ax.set_facecolor('#f2f2f2')\n",
"\n",
"ax = plt.gca()\n",
"plt.plot(times, heart_rts, color='#e71735')\n",
"\n",
"#set x axis values\n",
"xts = np.arange(min(times), max(times), timedelta(minutes=10))\n",
"ax.set_xticks(xts)\n",
"ax.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M:%S'))\n",
"\n",
"#set left y axis\n",
"yts = np.arange(100, 220, 20)\n",
"ax.set_yticks(yts)\n",
"\n",
"#ax.set_yscale('function', functions=(forward,inverse))\n",
"ax.axhspan(100, 120, facecolor='#e3e5e5', alpha=0.5)\n",
"ax.axhspan(120, 140, facecolor='#bee5f1', alpha=0.5)\n",
"ax.axhspan(140, 160, facecolor='#c9e7b6', alpha=0.5)\n",
"ax.axhspan(160, 180, facecolor='#f4e3b1', alpha=0.5)\n",
"ax.axhspan(180, 200, facecolor='#ecaec4', alpha=0.5)\n",
"\n",
"#right side y axis\n",
"f = lambda x: (x/200)*100\n",
"i = lambda x: (x/100)*200\n",
"ax2 = ax.secondary_yaxis(\"right\", functions=(f,i))\n",
"yticks = ax2.yaxis.get_major_ticks()\n",
"yticks[1].set_visible(False)\n",
"ax.tick_params(axis='y', colors='red') \n",
"ax2.tick_params(axis='y', colors='red', length=0) \n",
"\n",
"#cut off the edges\n",
"plt.xlim(times[0], times[len(times)-1])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 380
},
"id": "N2-eIqrsIXP2",
"outputId": "74ccee24-0515-49a6-ff87-b0b05b2cb1fa"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(738449.0, 738449.0401388889)"
]
},
"metadata": {},
"execution_count": 30
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"