diff options
Diffstat (limited to '')
| -rw-r--r-- | notebooks/FIR_mehrere.ipynb | 66 |
1 files changed, 53 insertions, 13 deletions
diff --git a/notebooks/FIR_mehrere.ipynb b/notebooks/FIR_mehrere.ipynb index 0bf0e1b..1be4234 100644 --- a/notebooks/FIR_mehrere.ipynb +++ b/notebooks/FIR_mehrere.ipynb @@ -17,7 +17,7 @@ "source": [ "import numpy as np \n", "from numpy.fft import fft,ifft,fftshift\n", - "import matplotlib.pyplot as plt" + "import matplotlib.pyplot as plt\n" ] }, { @@ -93,7 +93,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_5041/4026507591.py:1: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n", + "/tmp/ipykernel_2862/4026507591.py:1: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n", " np.array(x)\n" ] }, @@ -191,36 +191,76 @@ }, { "cell_type": "code", - "execution_count": 11, - "id": "de264619", + "execution_count": 12, + "id": "efc93b69", + "metadata": {}, + "outputs": [], + "source": [ + "delay = 3.4\n", + "amplitude = .2\n", + "\n", + "d_int = int(np.floor(delay))\n", + "d_frac = delay - d_int\n", + "\n", + "h = np.concatenate([np.zeros(d_int-1), [amplitude], np.zeros(3)])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "43372541", + "metadata": {}, + "outputs": [], + "source": [ + "H_int = fft(h_int)\n", + "#H = H_int * np.exp(-1j*d_frac)\n", + "h = ifft(H_int)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "a32cb580", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.25" + "<StemContainer object of 3 artists>" ] }, - "execution_count": 11, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "sample_rate= 32e3\n", - "nsample= 4 \n", - "t = 1 / nsample " + "test = np.random.randint(0, 10, size = 20)\n", + "y = np.convolve(test, h)\n", + "\n", + "plt.stem(test, linefmt=\"C0-\")\n", + "plt.stem(y, linefmt='C1-')" ] }, { "cell_type": "code", "execution_count": null, - "id": "efc93b69", + "id": "e7b9d068", "metadata": {}, "outputs": [], - "source": [ - "f_tabs = np.exp((-2j*np.pi*t))" - ] + "source": [] } ], "metadata": { |