add circuitpython code

1 files changed, 482 insertions, 0 deletions

diff --git a/circuitpython/extmod/ulab/docs/scipy-signal.ipynb b/circuitpython/extmod/ulab/docs/scipy-signal.ipynb
new file mode 100644
index 0000000..c4d5f85
--- /dev/null
+++ b/circuitpython/extmod/ulab/docs/scipy-signal.ipynb
@@ -0,0 +1,482 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-12T16:11:12.111639Z",
+     "start_time": "2021-01-12T16:11:11.914041Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pylab inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Notebook magic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-01-07T19:20:19.755153Z",
+     "start_time": "2022-01-07T19:20:19.745524Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.core.magic import Magics, magics_class, line_cell_magic\n",
+    "from IPython.core.magic import cell_magic, register_cell_magic, register_line_magic\n",
+    "from IPython.core.magic_arguments import argument, magic_arguments, parse_argstring\n",
+    "import subprocess\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-01-07T19:20:27.595871Z",
+     "start_time": "2022-01-07T19:20:27.565514Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "@magics_class\n",
+    "class PyboardMagic(Magics):\n",
+    "    @cell_magic\n",
+    "    @magic_arguments()\n",
+    "    @argument('-skip')\n",
+    "    @argument('-unix')\n",
+    "    @argument('-pyboard')\n",
+    "    @argument('-file')\n",
+    "    @argument('-data')\n",
+    "    @argument('-time')\n",
+    "    @argument('-memory')\n",
+    "    def micropython(self, line='', cell=None):\n",
+    "        args = parse_argstring(self.micropython, line)\n",
+    "        if args.skip: # doesn't care about the cell's content\n",
+    "            print('skipped execution')\n",
+    "            return None # do not parse the rest\n",
+    "        if args.unix: # tests the code on the unix port. Note that this works on unix only\n",
+    "            with open('/dev/shm/micropython.py', 'w') as fout:\n",
+    "                fout.write(cell)\n",
+    "            proc = subprocess.Popen([\"../micropython/ports/unix/micropython-2\", \"/dev/shm/micropython.py\"], \n",
+    "                                    stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n",
+    "            print(proc.stdout.read().decode(\"utf-8\"))\n",
+    "            print(proc.stderr.read().decode(\"utf-8\"))\n",
+    "            return None\n",
+    "        if args.file: # can be used to copy the cell content onto the pyboard's flash\n",
+    "            spaces = \"    \"\n",
+    "            try:\n",
+    "                with open(args.file, 'w') as fout:\n",
+    "                    fout.write(cell.replace('\\t', spaces))\n",
+    "                    printf('written cell to {}'.format(args.file))\n",
+    "            except:\n",
+    "                print('Failed to write to disc!')\n",
+    "            return None # do not parse the rest\n",
+    "        if args.data: # can be used to load data from the pyboard directly into kernel space\n",
+    "            message = pyb.exec(cell)\n",
+    "            if len(message) == 0:\n",
+    "                print('pyboard >>>')\n",
+    "            else:\n",
+    "                print(message.decode('utf-8'))\n",
+    "                # register new variable in user namespace\n",
+    "                self.shell.user_ns[args.data] = string_to_matrix(message.decode(\"utf-8\"))\n",
+    "        \n",
+    "        if args.time: # measures the time of executions\n",
+    "            pyb.exec('import utime')\n",
+    "            message = pyb.exec('t = utime.ticks_us()\\n' + cell + '\\ndelta = utime.ticks_diff(utime.ticks_us(), t)' + \n",
+    "                               \"\\nprint('execution time: {:d} us'.format(delta))\")\n",
+    "            print(message.decode('utf-8'))\n",
+    "        \n",
+    "        if args.memory: # prints out memory information \n",
+    "            message = pyb.exec('from micropython import mem_info\\nprint(mem_info())\\n')\n",
+    "            print(\"memory before execution:\\n========================\\n\", message.decode('utf-8'))\n",
+    "            message = pyb.exec(cell)\n",
+    "            print(\">>> \", message.decode('utf-8'))\n",
+    "            message = pyb.exec('print(mem_info())')\n",
+    "            print(\"memory after execution:\\n========================\\n\", message.decode('utf-8'))\n",
+    "\n",
+    "        if args.pyboard:\n",
+    "            message = pyb.exec(cell)\n",
+    "            print(message.decode('utf-8'))\n",
+    "\n",
+    "ip = get_ipython()\n",
+    "ip.register_magics(PyboardMagic)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## pyboard"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-05-07T07:35:35.126401Z",
+     "start_time": "2020-05-07T07:35:35.105824Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import pyboard\n",
+    "pyb = pyboard.Pyboard('/dev/ttyACM0')\n",
+    "pyb.enter_raw_repl()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-05-19T19:11:18.145548Z",
+     "start_time": "2020-05-19T19:11:18.137468Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "pyb.exit_raw_repl()\n",
+    "pyb.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-05-07T07:35:38.725924Z",
+     "start_time": "2020-05-07T07:35:38.645488Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%micropython -pyboard 1\n",
+    "\n",
+    "import utime\n",
+    "import ulab as np\n",
+    "\n",
+    "def timeit(n=1000):\n",
+    "    def wrapper(f, *args, **kwargs):\n",
+    "        func_name = str(f).split(' ')[1]\n",
+    "        def new_func(*args, **kwargs):\n",
+    "            run_times = np.zeros(n, dtype=np.uint16)\n",
+    "            for i in range(n):\n",
+    "                t = utime.ticks_us()\n",
+    "                result = f(*args, **kwargs)\n",
+    "                run_times[i] = utime.ticks_diff(utime.ticks_us(), t)\n",
+    "            print('{}() execution times based on {} cycles'.format(func_name, n, (delta2-delta1)/n))\n",
+    "            print('\\tbest: %d us'%np.min(run_times))\n",
+    "            print('\\tworst: %d us'%np.max(run_times))\n",
+    "            print('\\taverage: %d us'%np.mean(run_times))\n",
+    "            print('\\tdeviation: +/-%.3f us'%np.std(run_times))            \n",
+    "            return result\n",
+    "        return new_func\n",
+    "    return wrapper\n",
+    "\n",
+    "def timeit(f, *args, **kwargs):\n",
+    "    func_name = str(f).split(' ')[1]\n",
+    "    def new_func(*args, **kwargs):\n",
+    "        t = utime.ticks_us()\n",
+    "        result = f(*args, **kwargs)\n",
+    "        print('execution time: ', utime.ticks_diff(utime.ticks_us(), t), ' us')\n",
+    "        return result\n",
+    "    return new_func"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__END_OF_DEFS__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# scipy.signal\n",
+    "\n",
+    "Functions in the `signal` module can be called by prepending them by `scipy.signal.`. The module defines the following two functions:\n",
+    "\n",
+    "1. [scipy.signal.sosfilt](#sosfilt)\n",
+    "1. [scipy.signal.spectrogram](#spectrogram)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## sosfilt\n",
+    "\n",
+    "`scipy`: https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.sosfilt.html \n",
+    "\n",
+    "Filter data along one dimension using cascaded second-order sections.\n",
+    "\n",
+    "The function takes two positional arguments, `sos`, the filter segments of length 6, and the one-dimensional, uniformly sampled data set to be filtered. Returns the filtered data, or the filtered data and the final filter delays, if the `zi` keyword arguments is supplied. The keyword argument must be a float `ndarray` of shape `(n_sections, 2)`. If `zi` is not passed to the function, the initial values are assumed to be 0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-06-19T20:24:10.529668Z",
+     "start_time": "2020-06-19T20:24:10.520389Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "y:  array([0.0, 1.0, -4.0, 24.0, -104.0, 440.0, -1728.0, 6532.000000000001, -23848.0, 84864.0], dtype=float)\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%micropython -unix 1\n",
+    "\n",
+    "from ulab import numpy as np\n",
+    "from ulab import scipy as spy\n",
+    "\n",
+    "x = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n",
+    "sos = [[1, 2, 3, 1, 5, 6], [1, 2, 3, 1, 5, 6]]\n",
+    "y = spy.signal.sosfilt(sos, x)\n",
+    "print('y: ', y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-06-19T20:27:39.508508Z",
+     "start_time": "2020-06-19T20:27:39.498256Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "y:  array([4.0, -16.0, 63.00000000000001, -227.0, 802.9999999999999, -2751.0, 9271.000000000001, -30775.0, 101067.0, -328991.0000000001], dtype=float)\n",
+      "\n",
+      "========================================\n",
+      "zf:  array([[37242.0, 74835.0],\n",
+      "\t [1026187.0, 1936542.0]], dtype=float)\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%micropython -unix 1\n",
+    "\n",
+    "from ulab import numpy as np\n",
+    "from ulab import scipy as spy\n",
+    "\n",
+    "x = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n",
+    "sos = [[1, 2, 3, 1, 5, 6], [1, 2, 3, 1, 5, 6]]\n",
+    "# initial conditions of the filter\n",
+    "zi = np.array([[1, 2], [3, 4]])\n",
+    "\n",
+    "y, zf = spy.signal.sosfilt(sos, x, zi=zi)\n",
+    "print('y: ', y)\n",
+    "print('\\n' + '='*40 + '\\nzf: ', zf)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## spectrogram\n",
+    "\n",
+    "In addition to the Fourier transform and its inverse, `ulab` also sports a function called `spectrogram`, which returns the absolute value of the Fourier transform. This could be used to find the dominant spectral component in a time series. The arguments are treated in the same way as in `fft`, and `ifft`. This means that, if the firmware was compiled with complex support, the input can also be a complex array."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-12T16:12:06.573408Z",
+     "start_time": "2021-01-12T16:12:06.560558Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "original vector:\t array([0.0, 0.009775015390171337, 0.01954909674625918, ..., -0.5275140569487312, -0.5357931822978732, -0.5440211108893639], dtype=float64)\n",
+      "\n",
+      "spectrum:\t array([187.8635087634579, 315.3112063607119, 347.8814873399374, ..., 84.45888934298905, 347.8814873399374, 315.3112063607118], dtype=float64)\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%micropython -unix 1\n",
+    "\n",
+    "from ulab import numpy as np\n",
+    "from ulab import scipy as spy\n",
+    "\n",
+    "x = np.linspace(0, 10, num=1024)\n",
+    "y = np.sin(x)\n",
+    "\n",
+    "a = spy.signal.spectrogram(y)\n",
+    "\n",
+    "print('original vector:\\t', y)\n",
+    "print('\\nspectrum:\\t', a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As such, `spectrogram` is really just a shorthand for `np.sqrt(a*a + b*b)`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-12T16:13:36.726662Z",
+     "start_time": "2021-01-12T16:13:36.705036Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "spectrum calculated the hard way:\t array([187.8635087634579, 315.3112063607119, 347.8814873399374, ..., 84.45888934298905, 347.8814873399374, 315.3112063607118], dtype=float64)\n",
+      "\n",
+      "spectrum calculated the lazy way:\t array([187.8635087634579, 315.3112063607119, 347.8814873399374, ..., 84.45888934298905, 347.8814873399374, 315.3112063607118], dtype=float64)\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%micropython -unix 1\n",
+    "\n",
+    "from ulab import numpy as np\n",
+    "from ulab import scipy as spy\n",
+    "\n",
+    "x = np.linspace(0, 10, num=1024)\n",
+    "y = np.sin(x)\n",
+    "\n",
+    "a, b = np.fft.fft(y)\n",
+    "\n",
+    "print('\\nspectrum calculated the hard way:\\t', np.sqrt(a*a + b*b))\n",
+    "\n",
+    "a = spy.signal.spectrogram(y)\n",
+    "\n",
+    "print('\\nspectrum calculated the lazy way:\\t', a)"
+   ]
+  }
+ ],
+ "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": "3.8.5"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "382.797px"
+   },
+   "toc_section_display": true,
+   "toc_window_display": true
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}