Einarbeiten

FIR_phangl.ipynb

@@ -54,24 +54,26 @@
    "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=3000 #Sampleanzahl\n",
+    "fs=20000 #Samplingrate\n",
+    "f0=100 #Startfrequenz\n",
+    "f1=1000 #Stopfrequenz\n",
+    "t1=n/fs #Chirpdauer (Samples/Samplingrate)\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",
+    "t_chrip = np.linspace(0, t1, n) #Array mit Anzahl der Samples anlegen für Zeitachse\n",
+    "y_chrip = np.round(signal.chirp(t_chrip, f0=f0, f1=f1, t1=t1, method='linear')*(2**15-1)).astype(int) #Chirp erstellen, auf Ganzzahlen runden, auf 16 Bit Integer skalieren\n",
+    "\n",
+    "# Erste 4 Samples wegschneiden\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",
+    "# Doppelt so langes Signal mit abwechselnd Original- und invertierten Werten - Struktur für Symmetrie und 2-Kanal-Systeme\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",
+    "# Chirp in Header-Datei schreiben, welche über PCM eingelesen werden kann\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",
@@ -81,6 +83,88 @@
     "with open(\"pcm_chirp/include/chirp_data.h\", \"w\") as f:\n",
     "    f.write(file_str)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dc1c61df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ScyPyFIR Filter anlegen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dea7ae97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Vergleich und Plot\n",
+    "\n",
+    "%matplotlib widget\n",
+    "\n",
+    "b=signal.firwin(20, 100, fs=fs)\n",
+    "y_lfiltered = signal.lfilter(b, [1.0], y_chrip)\n",
+    "yf=fft(y_lfiltered)\n",
+    "xf = fftfreq(n, t1/n)[:n//2]\n",
+    "\n",
+    "cols = 1\n",
+    "rows = 3\n",
+    "\n",
+    "fig = plt.figure(1)\n",
+    "ax1 = fig.add_subplot(rows, cols, 1)\n",
+    "line1, = ax1.plot([0], \".-\", label = \"chrip\")\n",
+    "ax2 = fig.add_subplot(rows, cols, 2)\n",
+    "line2, = ax2.plot([0], label=\"chrip filtered signal.lfilter\")\n",
+    "ax3 = fig.add_subplot(rows, cols, 3)\n",
+    "line3, = ax3.plot([0], label=\"own fir implementation\")\n",
+    "\n",
+    "def update(numtaps = 40, f_cut=100):\n",
+    "    # Calculate the filter coefficients for given paramters\n",
+    "    b=signal.firwin(numtaps, f_cut, fs=fs)\n",
+    "    print(f\"Filter coeffs for {numtaps} tabs and {f_cut}Hz cutoff are:\\n\", b)\n",
+    "    \n",
+    "    bits=16\n",
+    "    if min(b)<0:\n",
+    "        bits=bits-1\n",
+    "    \n",
+    "    print(f\"Filter coeffs converted to Q1.{bits}bit int are :\\n\", \n",
+    "          \", \".join(\n",
+    "                np.array(np.array(np.round(b*(2**(bits)-1)), dtype=np.int32), dtype=str)\n",
+    "            )\n",
+    "        )\n",
+    "\n",
+    "    # plot the chirp\n",
+    "    line1.set_data(range(len(y_chrip)), y_chrip)\n",
+    "    ax1.set_xlim(0, len(y_chrip))\n",
+    "    ax1.set_ylim(min(y_chrip), max(y_chrip))\n",
+    "\n",
+    "    # Apply the coefficents with scipy function\n",
+    "    y_lfiltered = signal.lfilter(b, [1.0], y_chrip)\n",
+    "    line2.set_data(range(len(y_lfiltered)), y_lfiltered)\n",
+    "    ax2.set_xlim(0, len(y_lfiltered))\n",
+    "    ax2.set_ylim(min(y_lfiltered), max(y_lfiltered))\n",
+    "\n",
+    "    # yf=2.0/n*np.abs(fft(y_chrip[:n//2]))\n",
+    "    # xf = fftfreq(n, t1/n)[:n//2]\n",
+    "    data = simple_fir(b, y_chrip)\n",
+    "    line3.set_data(range(len(data)), data)\n",
+    "    ax3.set_xlim(0, len(data))\n",
+    "    ax3.set_ylim(min(data), max(data))\n",
+    "\n",
+    "    fig.canvas.draw_idle()\n",
+    "    # save coefficients to file\n",
+    "    with open(\"pcm_data_processing/include/coefficients.h\", \"w\") as f:\n",
+    "        f.write(\n",
+    "            \"#define NUMTAPS \" + str(numtaps) + \"\\n\" +\n",
+    "            \"#define COEFFICIENTS {\" + \",\".join(np.array(np.array(np.round(b*(2**(bits)-1)), dtype=np.int32), dtype=str)) +\"}\" \"\\n\"\n",
+    "        )\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "interact(update, numtaps=(0, 100,2), f_cut=(100,5000))"
+   ]
   }
  ],
  "metadata": {