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"# ALPACA Kernel & Updated ALPACA Firmware\n",
"#### `matplotlib`, `numpy`, and live plotting\n",
"\n",
"This notebook outlines two new tools at your disposal for doing the final assignment:\n",
"- New Jupyter Notebook Kernel: *The Alpaca Kernel*\n",
"- Updated *Alpaca Firmware*\n",
"\n",
"After installing both tools, you'll be able to do the following things:\n",
"\n",
"## Features:\n",
"\n",
"### 1. Plotting data directly from the Alpaca\n",
"**Before**, plotting data was done like so:\n",
"```python\n",
"%use micropython\n",
"pico.store_data(\"general_data.txt\", samples=[xx, yy])\n",
"\n",
"%use micropython\n",
"%fetchfile --binary \"general_data.txt\" \"data_picopiX.txt\"\n",
"\n",
"%use python3\n",
"samples=np.loadtxt(\"data_picopiX.txt\")\n",
"xx, yy = (samples[0,:], samples[1,:])\n",
"plt.plot(xx, yy)\n",
"```\n",
"\n",
"**Now**, plotting can be done *directly* from MicroPython **(NEW!)**:\n",
"```python\n",
"%use micropython\n",
"plt.plot(xx,yy)\n",
"```\n",
"------------------\n",
"\n",
"### 2. Using `numpy` directly from the Alpaca\n",
"**Before**, numpy wasn't available for MicroPython. This meant that lists had to be modified using `for` loops (slow and ugly):\n",
"```python\n",
"%use micropython\n",
"my_list = [1, 2, 3]\n",
"\n",
"for ii in range(len(my_list)):\n",
" my_list[ii] = my_list[ii] * 3\n",
"\n",
"print(my_list)\n",
"[3, 6, 9]\n",
"```\n",
"\n",
"**Now**, `numpy` can be used *directly* from MicroPython **(NEW!)**:\n",
"```python\n",
"%use micropython\n",
"import numpy as np\n",
"my_array = np.array(my_list)\n",
"my_array * 3\n",
"\n",
"print(my_array)\n",
"array([3.0, 6.0, 9.0], dtype=float32)\n",
"```\n",
"------------------\n",
"\n",
"### 3. Live plotting\n",
" **(NEW!)** Plot data as it comes in rather than after your measurement. This might be useful if you are performing a very long measurement and want to check if everthing is going alright.\n",
"\n",
"\n",
"\n",
"
![](\"resource\\asnlib\\public\\msedge_oRfTDroDwi.gif\")
\n",
"