Neues FIR File angelegt

FIR_phangl.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9c3a0b4a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0, 0, 0, 0, 0, 0, 0, 0, 0]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[0, 0, 0, 0, 0, 0, 0, 0, 0]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# FIR Filter anlegen\n",
+    "\n",
+    "from scipy import signal\n",
+    "from scipy.fft import fft, fftfreq\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from ipywidgets import interact\n",
+    "\n",
+    "\n",
+    "def fir_filter(taps, input):                    # taps, input sind 1d Eingabelisten mit Koeffizienten und Samples\n",
+    "    fir=[]                                      # Ausgabeliste anlegen\n",
+    "    for j in range(0, len(input) - len(taps)):  # Erste Samples (Koeffizientenzahl) zählen nicht zur Filterantwort\n",
+    "        fir_i=0\n",
+    "        for i in range (len(taps)):             # Durch Koeffizienten durchiterieren\n",
+    "            taps_i = taps[i]                    # taps_i ist Laufvariable\n",
+    "            fir_i += taps_i*input[j+i]          # fir_i ist Laufvariable für Filterergebnis - jeweiliger Koeffizient wird mit dem i-ten Input-Sample der reduzierten Liste j multipliziert\n",
+    "        fir.append(fir_i)                       # hänge Ergebnis an Ergebnisliste an\n",
+    "    return fir\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3f78fe4f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Chirp Generator\n",
+    "\n",
+    "n=3000 # number of samples to use for the chirp\n",
+    "fs=20000 # The sampling rate for the chrip\n",
+    "f0=100# the start frequency in Hz for the chirp\n",
+    "f1=1000 # the stop frequency of the chirp\n",
+    "t1=n/fs # the total length of the chirp in s\n",
+    "\n",
+    "t_chrip = np.linspace(0, t1, n)\n",
+    "# generate a chrip and scale to int16 (1 bit for sign)\n",
+    "y_chrip = np.round(signal.chirp(t_chrip, f0=f0, f1=f1, t1=t1, method='linear')*(2**15-1)).astype(int)\n",
+    "cutsamps = 45\n",
+    "y_chrip = y_chrip[cutsamps:]\n",
+    "t_chrip = t_chrip[cutsamps:]\n",
+    "\n",
+    "# Generate an array were the data is present in an interleaved format with the inverted signal \n",
+    "y_chrip_interleaved = np.empty((2*y_chrip.size), dtype=y_chrip.dtype)pa\n",
+    "y_chrip_interleaved[0::2] = y_chrip\n",
+    "y_chrip_interleaved[1::2] = -1*y_chrip[::-1]\n",
+    "\n",
+    "file_str= f\"#define CHIRP_DATA_SAMPLE_RATE {int(fs)}\\n\"\\\n",
+    "          \"#define CHIRP_DATA_LEN\"f\" {y_chrip.size}\" \"\\n\"\\\n",
+    "          \"#define CHIRP_DATA_INTERLEAVED_LEN\"f\" {y_chrip_interleaved.size}\" \"\\n\"\\\n",
+    "          \"#define CHIRP_DATA {\" + \",\".join(y_chrip.astype(str)) +\"}\\n\"\\\n",
+    "          \"#define CHIRP_DATA_INTERLEAVED_INVERTED {\" + \",\".join(y_chrip_interleaved.astype(str)) +\"}\" \"\\n\"\n",
+    "\n",
+    "with open(\"pcm_chirp/include/chirp_data.h\", \"w\") as f:\n",
+    "    f.write(file_str)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

requirements.txt

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+numpy
+matplotlib
+soundfile
+scipy
+ipywidgets
+numba
+ipympl