diff --git a/applications/lofar2/model/lofar2_station_sdp_firmware_model.ipynb b/applications/lofar2/model/lofar2_station_sdp_firmware_model.ipynb index 02fbcf3672e682874c938748f828ffb6125942ef..d2a083f35100dcc9cd4e425931285a0f4ea2d87f 100644 --- a/applications/lofar2/model/lofar2_station_sdp_firmware_model.ipynb +++ b/applications/lofar2/model/lofar2_station_sdp_firmware_model.ipynb @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 82, "id": "2b477516", "metadata": {}, "outputs": [], @@ -33,12 +33,12 @@ "id": "c2cc6c7a", "metadata": {}, "source": [ - "## 1 SDP Parameters" + "# 1 SDP Parameters" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 83, "id": "e1b6fa12", "metadata": {}, "outputs": [ @@ -52,8 +52,10 @@ } ], "source": [ - "# SDP\n", + "# General\n", "N_complex = 2\n", + "\n", + "# SDP\n", "N_fft = 1024\n", "N_sub = N_fft / N_complex\n", "f_adc = 200e6 # Hz\n", @@ -68,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 84, "id": "eb325c9c", "metadata": {}, "outputs": [ @@ -98,7 +100,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 85, "id": "3e71626f", "metadata": {}, "outputs": [ @@ -135,79 +137,7 @@ }, { "cell_type": "code", - "execution_count": 6, - "id": "def6eba7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Conclusion: G_fft_real_input_sine = 0.5\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 720x288 with 2 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# DFT of real sine input --> show that:\n", - "G_fft_real_input_sine = 0.5\n", - "G_fft_real_input_dc = 1.0\n", - "\n", - "# . DFT size\n", - "N_points = 1024\n", - "N_bins = N_points // 2 + 1 # positive frequency bins including DC and f_s/2\n", - "\n", - "# . select a bin\n", - "i_bin = 200 # bin index in range(N_bins)\n", - "\n", - "# . time and frequency axis\n", - "f_s = f_adc # sample frequency\n", - "f_s = 1 # normalized sample frequency\n", - "T_s = 1 / f_s # sample period\n", - "T_fft = N_points * T_s # DFT period\n", - "t_axis = np.linspace(0, T_fft, N_points, endpoint=False)\n", - "f_axis = np.linspace(0, f_s, N_points, endpoint=False)\n", - "f_axis_fft = f_axis - f_s/2 # fftshift axis\n", - "f_axis_rfft = f_axis[0:N_bins] # positive frequency bins\n", - "\n", - "f_bin = i_bin / N_points * f_s # bin frequency\n", - "\n", - "# . create sine at bin, use cos to see DC at i_bin = 0 \n", - "x = np.cos(2 * np.pi * f_bin * t_axis)\n", - "\n", - "# . DFT using complex input fft()\n", - "X_fft = np.fft.fftshift(np.fft.fft(x) / N_points)\n", - "\n", - "# . DFT using real input rfft()\n", - "X_rfft = np.fft.rfft(x) / N_points\n", - "\n", - "plt.figure(figsize=(10, 4))\n", - "plt.subplot(1, 2, 1)\n", - "plt.title('DFT of real sine using fft')\n", - "plt.plot(f_axis_fft, abs(X_fft))\n", - "plt.grid()\n", - "plt.subplot(1, 2, 2)\n", - "plt.title('DFT of real sine using rfft')\n", - "plt.plot(f_axis_rfft, abs(X_rfft))\n", - "plt.grid()\n", - "\n", - "print(\"Conclusion: G_fft_real_input_sine =\", G_fft_real_input_sine)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, + "execution_count": 86, "id": "0ec00484", "metadata": {}, "outputs": [ @@ -220,25 +150,26 @@ "subband_weight_re = 8192\n", "subband_weight_im = 0\n", "\n", - "G_subband = 0.994817 * 0.5 * 2**4 * 1.0 = 7.958536\n", - " . G_fir_dc = 0.994817\n", + "G_subband = 1 * 0.5 * 2**4 * 1.0 = 8.0 = 3.00 bits\n", + " . G_fir_dc = 1\n", " . G_fft_real_input_sine = 0.5\n", - " . W_sub_proc = 4.5\n", " . W_sub_gain = 4\n", " . subband_weight_gain = 1.0\n" ] } ], "source": [ - "# Subband filterbank (F_sub)\n", + "# Gain factor G_subband between subband and signal input in the subband filterbank (F_sub)\n", + "\n", "# . FIR filter DC gain\n", - "G_fir_dc = 0.994817\n", + "G_fir_dc = 0.994817 # actual gain of FIR filter in LOFAR\n", + "G_fir_dc = 1\n", "\n", "# . Signal level bit growth to accomodate processing gain of FFT\n", "W_sub_proc = np.log2(np.sqrt(N_sub))\n", "W_sub_gain = 4 # use W_sub_gain instead of W_sub_proc\n", "\n", - "# Subband equalizer (E_sub)\n", + "# . Subband equalizer (E_sub)\n", "subband_weight_gain = 1.0\n", "subband_weight_phase = 0\n", "subband_weight_re = int(subband_weight_gain * Unit_sub_weight * np.cos(subband_weight_phase))\n", @@ -250,20 +181,20 @@ "print(f\"subband_weight_im = {subband_weight_im:d}\")\n", "print()\n", "\n", - "# . Expected factor between subband amplitude and real signal input amplitude\n", + "# Expected factor from real signal input amplitude to subband amplitude\n", "G_subband = G_fir_dc * G_fft_real_input_sine * 2**W_sub_gain * subband_weight_gain\n", "\n", - "print(f\"G_subband = {G_fir_dc} * {G_fft_real_input_sine} * 2**{W_sub_gain} * {subband_weight_gain} = {G_subband}\")\n", + "print(f\"G_subband = {G_fir_dc} * {G_fft_real_input_sine} * 2**{W_sub_gain} * {subband_weight_gain} \\\n", + "= {G_subband} = {np.log2(G_subband):.2f} bits\")\n", "print(\" . G_fir_dc =\", G_fir_dc)\n", "print(\" . G_fft_real_input_sine =\", G_fft_real_input_sine)\n", - "print(\" . W_sub_proc =\", W_sub_proc)\n", "print(\" . W_sub_gain =\", W_sub_gain)\n", "print(\" . subband_weight_gain =\", subband_weight_gain)" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 101, "id": "4d197368", "metadata": {}, "outputs": [ @@ -271,54 +202,99 @@ "name": "stdout", "output_type": "stream", "text": [ - "beamlet_weight_gain = 1.0\n", - "beamlet_weight_phase = 0\n", - "beamlet_weight_re = 16384\n", - "beamlet_weight_im = 0\n", + "Same BF weight for all inputs:\n", + ". beamlet_weight_gain = 1.0\n", + ". beamlet_weight_phase = 0\n", + ". beamlet_weight_re = 16384\n", + ". beamlet_weight_im = 0\n", + "\n", + "N_ant_arr = [ 1 12 24 28 96]\n", + "\n", + "N_ant = 1 : bf_proc_coh = 1.00 = 0.0 bits\n", + "N_ant = 12 : bf_proc_coh = 12.00 = 3.6 bits\n", + "N_ant = 24 : bf_proc_coh = 24.00 = 4.6 bits\n", + "N_ant = 28 : bf_proc_coh = 28.00 = 4.8 bits\n", + "N_ant = 96 : bf_proc_coh = 96.00 = 6.6 bits\n", "\n", - "BF for coherent input:\n", - " . bf_proc = 10.00 for N_ant = 10\n", + "N_ant = 1 : bf_proc_incoh = 1.00 = 0.0 bits\n", + "N_ant = 12 : bf_proc_incoh = 3.46 = 1.8 bits\n", + "N_ant = 24 : bf_proc_incoh = 4.90 = 2.3 bits\n", + "N_ant = 28 : bf_proc_incoh = 5.29 = 2.4 bits\n", + "N_ant = 96 : bf_proc_incoh = 9.80 = 3.3 bits\n", "\n", - "BF for incoherent input:\n", - " . bf_proc = 3.16 for N_ant = 10\n", + "N_ant = 1 : G_beamlet_sum_coh = 8.00 * 1.00 * 1.0 = 8.00 = 3.0 bits\n", + "N_ant = 12 : G_beamlet_sum_coh = 8.00 * 12.00 * 1.0 = 96.00 = 6.6 bits\n", + "N_ant = 24 : G_beamlet_sum_coh = 8.00 * 24.00 * 1.0 = 192.00 = 7.6 bits\n", + "N_ant = 28 : G_beamlet_sum_coh = 8.00 * 28.00 * 1.0 = 224.00 = 7.8 bits\n", + "N_ant = 96 : G_beamlet_sum_coh = 8.00 * 96.00 * 1.0 = 768.00 = 9.6 bits\n", + "\n", + "N_ant = 1 : G_beamlet_sum_incoh = 8.00 * 1.00 * 1.0 = 8.00 = 3.0 bits\n", + "N_ant = 12 : G_beamlet_sum_incoh = 8.00 * 3.46 * 1.0 = 27.71 = 4.8 bits\n", + "N_ant = 24 : G_beamlet_sum_incoh = 8.00 * 4.90 * 1.0 = 39.19 = 5.3 bits\n", + "N_ant = 28 : G_beamlet_sum_incoh = 8.00 * 5.29 * 1.0 = 42.33 = 5.4 bits\n", + "N_ant = 96 : G_beamlet_sum_incoh = 8.00 * 9.80 * 1.0 = 78.38 = 6.3 bits\n", + "\n", + "N_ant = 1 : si_ampl_max = 2.000000 = 16384 = 14.0 bits\n", + "N_ant = 12 : si_ampl_max = 0.166667 = 1365 = 10.4 bits\n", + "N_ant = 24 : si_ampl_max = 0.083333 = 683 = 9.4 bits\n", + "N_ant = 28 : si_ampl_max = 0.071429 = 585 = 9.2 bits\n", + "N_ant = 96 : si_ampl_max = 0.020833 = 171 = 7.4 bits\n", "\n" ] } ], "source": [ - "# Digital beamformer (BF)\n", - "N_ant = 10\n", + "# Gain factor G_beamlet_sum between beamlet and signal input in the digital beamformer (BF)\n", + "# . coherent input is same signal (e.g. sine or noise) on all inputs\n", + "# . incoherent input is different signal (noise) on all inputs\n", "\n", - "# Assume all N_ant use same BF weight\n", + "# . Assume all N_ant use same BF weight\n", "beamlet_weight_gain = 1.0\n", "beamlet_weight_phase = 0\n", "beamlet_weight_re = int(beamlet_weight_gain * Unit_bf_weight * np.cos(beamlet_weight_phase))\n", "beamlet_weight_im = int(beamlet_weight_gain * Unit_bf_weight * np.sin(beamlet_weight_phase))\n", "\n", - "print(\"beamlet_weight_gain =\", beamlet_weight_gain)\n", - "print(\"beamlet_weight_phase =\", beamlet_weight_phase)\n", - "print(f\"beamlet_weight_re = {beamlet_weight_re:d}\")\n", - "print(f\"beamlet_weight_im = {beamlet_weight_im:d}\")\n", + "print(\"Same BF weight for all inputs:\")\n", + "print(f\". beamlet_weight_gain = {beamlet_weight_gain}\")\n", + "print(f\". beamlet_weight_phase = {beamlet_weight_phase}\")\n", + "print(f\". beamlet_weight_re = {beamlet_weight_re:d}\")\n", + "print(f\". beamlet_weight_im = {beamlet_weight_im:d}\")\n", "print()\n", "\n", - "si_types = [\"coherent\", \"incoherent\"]\n", - "for si_type in si_types:\n", - "\n", - " # . BF processing gain\n", - " if si_type == \"coherent\":\n", - " bf_proc = N_ant\n", - " else:\n", - " bf_proc = np.sqrt(N_ant)\n", - " \n", - " # . Normalize BF weights to get BF DC gain is 1.0\n", - " beamlet_weight_gain = 1 / bf_proc\n", + "N_ant_arr = np.array([1, 12, 24, 28, 96])\n", + "print(f\"N_ant_arr = {N_ant_arr}\")\n", + "print() \n", + "\n", + "# . BF processing gain for N_ant coherent inputs and for N_ant incoherent inputs\n", + "bf_proc_coh = N_ant_arr\n", + "bf_proc_coh_bits = np.log2(bf_proc_coh)\n", + "bf_proc_incoh = np.sqrt(N_ant_arr)\n", + "bf_proc_incoh_bits = np.log2(bf_proc_incoh)\n", + "for ni, na in enumerate(N_ant_arr):\n", + " print(f\"N_ant = {na:2d} : bf_proc_coh = {bf_proc_coh[ni]:5.2f} = {np.log2(bf_proc_coh[ni]):.1f} bits\")\n", + "print() \n", + "for ni, na in enumerate(N_ant_arr):\n", + " print(f\"N_ant = {na:2d} : bf_proc_incoh = {bf_proc_incoh[ni]:5.2f} = {np.log2(bf_proc_incoh[ni]):.1f} bits\")\n", + "print()\n", "\n", - " # . Expected factor between beamlet amplitude and real signal input amplitude\n", - " G_beamlet_sum = N_ant * beamlet_weight_gain * G_subband\n", + "# Expected factor from real signal input amplitude to beamlet amplitude\n", + "G_beamlet_sum_coh = G_subband * bf_proc_coh * beamlet_weight_gain\n", + "G_beamlet_sum_incoh = G_subband * bf_proc_incoh * beamlet_weight_gain\n", + "\n", + "for ni, na in enumerate(N_ant_arr):\n", + " print(f\"N_ant = {na:2d} : G_beamlet_sum_coh = {G_subband:.2f} * {bf_proc_coh[ni]:5.2f} * {beamlet_weight_gain} \\\n", + "= {G_beamlet_sum_coh[ni]:7.2f} = {np.log2(G_beamlet_sum_coh[ni]):.1f} bits\")\n", + "print() \n", + "for ni, na in enumerate(N_ant_arr):\n", + " print(f\"N_ant = {na:2d} : G_beamlet_sum_incoh = {G_subband:.2f} * {bf_proc_incoh[ni]:5.2f} * {beamlet_weight_gain} \\\n", + "= {G_beamlet_sum_incoh[ni]:6.2f} = {np.log2(G_beamlet_sum_incoh[ni]):.1f} bits\")\n", + "print()\n", "\n", - " print(f\"BF for {si_type} input:\")\n", - " print(f\" . bf_proc = {bf_proc:.2f} for N_ant = {N_ant}\")\n", - " print()\n" + "# Maximum signal input amplitude\n", + "si_ampl_max = 2**(W_beamlet_sum - 1) / G_beamlet_sum_coh\n", + "for ni, na in enumerate(N_ant_arr):\n", + " print(f\"N_ant = {na:2d} : si_ampl_max = {si_ampl_max[ni] / FS:f} = {si_ampl_max[ni]:6.0f} = {np.log2(si_ampl_max[ni]):5.1f} bits\")\n", + "print()" ] }, { @@ -326,12 +302,12 @@ "id": "d942fcc6", "metadata": {}, "source": [ - "## 2 Quantization model" + "# 2 Quantization model" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 88, "id": "f66c5028", "metadata": {}, "outputs": [ @@ -353,7 +329,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 89, "id": "a9fca052", "metadata": {}, "outputs": [ @@ -373,7 +349,7 @@ "# . The quantization noise power is q**2 * 1 / 12, so the standard deviation\n", "# of the quantization noise is q * sqrt(1 / 12) < q = one LSbit\n", "# . The quantization noise power is at a level of -10.79 dB or -1.8 bit.\n", - "# . The 0 dB power level corresponds to the power of one LSbit, so q**2 \n", + "# . The 0 dB power level or 0 bit level corresponds to the power of one LSbit, so q**2 \n", "P_quant = 1 / 12 # for W >> 1 [2]\n", "P_quant_dB = 10 * np.log10(P_quant)\n", "sigma_quant = np.sqrt(P_quant)\n", @@ -385,13 +361,13 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 90, "id": "d9972b6b", "metadata": {}, "outputs": [ { "data": { - "image/png": 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aEcldo7LAp5IJ5s2soO7lrTpdUkRy1qgs8BCcTbO1uY2XNjVFHUVEJCNGbYGfv/9i3GqmEZHcNGoLfOXYQk4+tkznw4tIzhq1BR6Ci4AsWbeTxj37oo4iIjLsRnWBr5lVSZfDwld0uqSI5J5RXeBPnTSO8UV51KuZRkRyUEYLvJmNM7M7zWyFmS03s3Myub4jlUwY80+o4OGV2+jq0umSIpJbMr0Hfz3wgLvPAk4Flmd4fUesdlYl23fv4/kNu6KOIiIyrDJW4M2sDJgH/BTA3fe5e2Om1jdY82ZWYIYuAiIiOSeTe/DTgG3Az81sqZndbGbFGVzfoIwvzue0yeOo0/nwIpJjLFM/1TezOcATwFx3f9LMrgea3P2rPaa7ErgSoKqqavaCBQsGtb6WlhZKSkoGNe/vVu3jnlXtXF9bRGmBDWoZR2IoWaMQp7xxygrxyhunrBCvvEPJWltbu9jd5/Q60t0z8gccBaxJu/1m4A/9zTN79mwfrLq6ukHP+9z6Rp/ypfv8t4vXD3oZR2IoWaMQp7xxyuoer7xxyuoer7xDyQo8433U1Iw10bj7ZmC9mVWHd50PvJSp9Q3FG48ppbykQM00IpJTUhle/qeB280sH1gNfCjD6xuURMKoqa7goZe20NHZRSo5qn8eICI5IqOVzN2Xufscdz/F3S93952ZXN9Q1FZXsmtvO8vWN0YdRURkWGhXNXTezHKSCdPpkiKSM1TgQ2Vj8pg9Zbyu8iQiOUMFPk1tdSUvbWpiS1Nr1FFERIZMBT5N7azgIiAP62waEckBKvBpqqvGcnRZodrhRSQnqMCnMTNqqit55JUG2ju7oo4jIjIkKvA91FRX0NLWwTNrsvaMThGRAVGB72HujHLykka9mmlEJOZU4HsoKUhx5rQJaocXkdhTge9FbXUlK7e08PrOPVFHEREZNBX4XtRUVwJQr9MlRSTGVOB7Mb2imMkTxqgdXkRiTQW+F2ZGbXUli1Ztp7W9M+o4IiKDogLfh9rqSva2d/LUazuijiIiMigq8H04+/iJFKQSOptGRGJLBb4PY/KTnDN9ovqlEZHYUoHvR211JasbdrOmYXfUUUREjpgKfD9qqoPeJXU2jYjEkQp8P6ZMLOb48mJdjFtEYkkF/jBqqit5fPV29u7T6ZIiEi8q8IdRO6uCfR1dPL66IeooIiJHRAX+MM6cNoExeUldq1VEYkcF/jAKUknmziin7uWtuHvUcUREBkwFfgBqZ1Xw+s69vLqtJeooIiIDpgI/AN29S6qZRkTiRAV+AI4dN4bqqrHqtkBEYkUFfoBqZlXw9JodtLR1RB1FRGRAVOAHqLa6kvZOZ9EqnS4pIvGgAj9As6eMZ2xBSt0WiEhsqMAPUF4ywXkzy6lbsU2nS4pILKjAH4Ha6ko2N7WyYnNz1FFERA5LBf4IzA97l9TZNCISB6nDTWBmVw9gObvd/b+HIU9Wqyot5I3HlFK/YhufqJkRdRwRkX4NZA/+C0AJMLafv2syFTDb1FZXsnjdTnbtaY86iohIvw67Bw/8wt3/rb8JzKx4mPJkvdpZFdxQt4pHVm3jklOOiTqOiEifDrsH7+5fHI5pcsVpk8czrihP3RaISNY74oOsZna2mT1gZvVm9s5MhMpmyYQxb2YFD6/cSleXTpcUkex12AJvZkf1uOtq4J3ARUC/TTe5qnZWBQ0t+3hh466oo4iI9Gkge/A/NrOvmVlheLsReBdBkW/KVLBsNm9mBWZQr2u1ikgWG0gb/OXAUuA+M/sH4LNAATARuDyD2bLWxJICTp00TufDi0hWG1AbvLv/HngbUAbcDax09x+4+2F3Yc0saWZLzey+oUXNLjXVFSxb38iO3fuijiIi0quBtMFfamZ1wAPAC8B7gMvMbIGZTR/AOq4Clg8tZvapra7EHRauVDONiGSngezBfwN4B/Bu4Dvu3uju1wBfBb7Z34xmNgm4GLh5qEGzzcnHljGxOF/NNCKStexwPSOa2SPAjUARcLm7XzLghZvdCfwHwa9dP9/bvGZ2JXAlQFVV1ewFCxYMPH2alpYWSkpKBjXvYP3kuTaWbevgh28pImE24PmiyDoUccobp6wQr7xxygrxyjuUrLW1tYvdfU6vI9293z+gHPg08DGg9HDTp813CfBf4XANcN/h5pk9e7YPVl1d3aDnHax7l23wKV+6z59Zs+OI5osi61DEKW+csrrHK2+csrrHK+9QsgLPeB81dSBdFTzo7mf0N4GZLellmrnApWZ2EVAIlJrZbe7+gQGsMxbmzawgYVD/8lZmTxkfdRwRkYMMpMC/wcye62e8EZxdcxB3/wrwFQAzqyFoosmZ4g5QVpTH7CnjqXt5K9e8tTrqOCIiBxlIgZ81gGk6hxokrmqqK/nun15ma1MrlaWFh59BRGSEDOSHTmsH8Pf6YZZR70dwcDZOaqsrAajX6ZIikmV0RachesPRY6kqLdDFuEUk66jAD5GZUVtdySOvNNDe2RV1HBGR/VTgh0FNdQXNrR0sWbsz6igiIvupwA+DuTPKSSWMOvUuKSJZRAV+GIwtzONNUyeoHV5EsooK/DCpnVXBis3NbGzcG3UUERFABX7Y7D9dUs00IpIlVOCHyYzKEo4dN0a9S4pI1lCBHyZmRu2sChataqCtY9T+sFdEsogK/DCqra5kz75Onn5Np0uKSPRU4IfROdMnkp9KqJlGRLKCCvwwKspPcfbxE1XgRSQrqMAPs9rqClZv28267XuijiIio5wK/DA70Luk9uJFJFoq8MNsankxUycWUbdCBV5EoqUCnwE11ZU89up2Wtt1uqSIREcFPgNqZ1XS1tHF46u3Rx1FREYxFfgMOGvaBArzEtSrmUZEIqQCnwGFeUnmTi+n7uVtuHvUcURklFKBz5CaWZWs27GH1Q27o44iIqOUCnyG1JxQAaCzaUQkMirwGTJ5QhEzK0vUfbCIREYFPoNqZ1Xy5Gvb2d3WEXUUERmFVOAzqKa6gvZOZ9GqhqijiMgopAKfQXOmTKCkIEX9SjXTiMjIU4HPoPxUgrkzJlK/YqtOlxSREacCn2G11ZVs3NXKyi0tUUcRkVFGBT7DasLeJdVHvIiMNBX4DDuqrJA3HF2q8+FFZMSpwI+A2uoKnlm7k6bW9qijiMgoogI/AmpnVdLZ5Tz6ik6XFJGRowI/Ak6fPI7SwpSaaURkRKnAj4BUMsG8EyqoX7mNri6dLikiI0MFfoTUVleyrbmNlzY1RR1FREYJFfgRMr9avUuKyMhSgR8h5SUFnDqpTN0WiMiIUYEfQTXVlSxdt5OWfWqHF5HMU4EfQTXVFXQ5vNDQGXUUERkFVOBH0CmTxjGhOJ9nG9Q/vIhkXsYKvJlNNrM6M3vJzF40s6syta64SCaM+SdU8Ny2Tl7aqLNpRCSzMrkH3wFc4+4nAmcDnzSzEzO4vlj40NypJAwuveFRvvunFbS2q7lGRDIjYwXe3Te5+5JwuBlYDhybqfXFxSmTxvEf5xVx2WnH8qO6V7no+kd4YvX2qGOJSA6ykbgQhZlNBRYCJ7l7U49xVwJXAlRVVc1esGDBoNbR0tJCSUnJEJOOjO6sLzR0csuLbTTsdWompXh3dT5FeRZ1vEPE8bmNizjljVNWiFfeoWStra1d7O5zeh3p7hn9A0qAxcDfHm7a2bNn+2DV1dUNet6Rlp51d1u7//vvX/RpX77P3/SNh/yBFzZFF6wPcX1u4yBOeeOU1T1eeYeSFXjG+6ipGT2LxszygN8Ct7v7XZlcV1wV5af4l0tO5O5PzGVCcT7/+xeL+fhti9na1Bp1NBGJuUyeRWPAT4Hl7n5dptaTK06dPI7ff/o8vvC2av6yYisXXPcwv3p6na7lKiKDlsk9+LnAFcBbzGxZ+HdRBtcXe3nJBJ+sncEfr3ozs44u5Uu/fZ73/eRJ1jTsjjqaiMRQJs+iedTdzd1PcffTwr/7M7W+XDK9ooQFHz2bb73zZF7YsIu3fX8hN9a/SkdnV9TRRCRG9EvWLJVIGO876zj+fM185p9QwXceWMFlP1rECxt2RR1NRGJCBT7LVZUWctM/zOHHHziDrc1tXHrDo3zr/uXs3acfSIlI/1TgY+LtJx3Nnz83n3fPmcxNC1fztu8vZNEqXeNVRPqmAh8jZUV5fPvvTuGXHz2LhMH7b36SL/zmWRr37Is6mohkIRX4GDp3ejkPfHYeH5s/nbuWbuCC6xbyh+c26ZRKETmICnxMFeYl+fI7ZvG7T87lqLICPvnLJXz01sVs2rU36mgikiVU4GPupGPLuOcTc/k/F83i0VXbuPC6hfziibV0dWlvXmS0U4HPAalkgivnTedPn53HKZPK+Oo9L/Cemx5n1daWqKOJSIRU4HPIlInF3P6Rs/i/7zqFlzc3c9H1j/DDv7zCvg79QEpkNFKBzzFmxrvnTObP18znwhOr+H8PreTSGx5l2frGqKOJyAhTgc9RlWML+dH7z+CmK2bTuKedd/7XIv7t9y+xu03XgxUZLVTgc9xb33gUD149j/efdRw/W/Qab/3PhTy8clvUsURkBKjAjwKlhXl84/KT+c3HzqEgL8E//uwprv7VMnbs1g+kRHKZCvwo8qapE7j/M2/m02+Zwb3PbuSC6x7md8s26AdSIjlKBX6UKcxLcs1bq7nvM+cxeUIRVy1YxodveZoNjfqBlEiuUYEfpWYdVcpdHz+Xr11yIk+s3sGF1z3MLYteo1M/kBLJGSrwo1gyYXz4vGk8+Ll5zJk6gWt//xLv+vFjrNzSHHU0ERkGKvDC5AlF/M+H3sR/vudU1jTs5uIfPMJ/PrSStg71OS8SZyrwAgQ/kHrn6ZP489Xzuejko7n+L69w8Q8eZfHaHVFHE5FBSkUdQLLLxJICrn/v6Vx+2rH8893P864fP870sgSP7VnOGceN44zjxlNZWhh1TBEZABV46VXtrEoevHo+Ny1czf2LX+WWRWu4aWHQp82k8WOYPWU8Zxw3ntlTxjPrqLGkkvoyKJJtVOClTyUFKa6+8ATOyNvIOee9mRc3NrFk7U6WrNvJE6u387tlGwEYk5fk1Mll+wv+6ceNZ0JxfsTpRUQFXgakIJXkjOOCvXYAd2fjrlYWr925v+jftHA1HeFplseXF3N6WPDPmDKOmZVjSSYsyocgMuqowMugmBnHjhvDsePGcOmpxwCwd18nz2/YFRT9dTupf3krv13yOhB8Gzj9uHH7i/5pk8dRNiYvyocgkvNU4GXYjMlPcua0CZw5bQIQ7OWv27Fnf8FfvLaRG/76Cl0OZjCzsiT4VhC250+vKMZMe/kiw0UFXjLGzJgysZgpE4v52zMmAdDS1sGz6xtZsnYni9ft5I8vbGbB0+sBGFeUx+mTx+0/gHvq5HEUF+gtKjJY+vTIiCopSDF3RjlzZ5QD0NXlrG5oYcnaxnAvfyd1LwfdGScs6FLhjCkHiv5xE4q0ly8yQCrwEqlEwphROZYZlWN595smA7BrTztL1+9kybpgT/+epRu57Yl1AJSX5B84eHvceE6ZVEZhXjLKhyCStVTgJeuUFeVRU11JTXUlAJ1dzsotzfv38Jeua+Shl7YAkEoYbzymdH87/p7dXbS2d6roi6ACLzGQTBhvOLqUNxxdyvvPmgLA9pY2lq5rZPG64DTNO55ax88XrQHgS488wLiiPI4qLaSytJCjSgvShgs5qqyQytICyosLSOjUTclhKvASSxNLCrjgxCouOLEKgPbOLlZsauae+qcYd/RUtjS3snlXG1ubW1mxqYmGljZ69oScShgVYwuoCgt/VWkBVWWFVI0NNgJV4X1jC3U6p8STCrzkhLxkgpMnlbF9Uh41NTMPGd/R2UVDyz42N7WyJfzbvKuVLU1tbGlq5dVtLSx6tYHm1kMvSl6cnwyL/YG9/6NKCw+6r6KkgPyUumuQ7KICL6NCKpngqLKgGPdnz74OtjS1sXlXK1ubg43A5qZWtja1sbmplafX7GBrUxv7OrsOmbe8JJ/KHnv/PTcE44vydBaQjBgVeJE0RfkpppWnmFZe3Oc07s6O3fv27/1vaWpN+2YQbByee72RhpZDL2qen0xQWXqgWWh3YxuP71nO2MIUJQUpxhbmUVKYYmxhirEFB4ZLClI6cCxHTAVe5AiZGRNLCphYUsCJx5T2Od2+ji62NrcesiHYGm4Elm9uomFXB49vWkNbx6HfCHrKTyYOKvjB/7xgY9BjA1Gafjuctnsa9fw5eqjAi2RIfirBpPFFTBpf1Oc09fX11NTU0NbRye62Tppb22lu7aC5tYOWtg6aW9vD/933BeNbwtsbGvceNM1Arqk7Ji+Z9i3h4I1ASfp9PTYc65u7WNOwm8K8JIV5CQpSSQpSCZ2JlMVU4EWyQFAsk0PqZtndaW3vCjYSbQc2Ai1t7TS1Hny7ubWD5nCj0NLazpamVlq652k79EDzfovqD7krP5WgMJWgICz8hakkBeH/gzYGeQkK84KNQmFeMhyfOHA7bdwh0+YlD6wjldC3kAFSgRfJEWbGmPwkY/KTVA5hOV1dzu59B3+LaG7t4KmlzzF95izaOoIfk7V2dNLW3nXgf3vngXHh8J59HezYfWCato5OWsP/7Z2H/7bRl1TCDt4whBuR9A1M085WfrNxCXkJIy8ZbBTyk70P5yWN/FSCVOLQ4bxUgryew6lg3t6Gu+fLhoPpKvAicpBEwhhbmHfo+f+bUtTMnjRs6+no7Nq/QTiwYTiwEQg2Cn2M63Xa7tudtLR00LjX2bWpifZOp6Ozi32dTntnFx2dXbR3eq9nQg2nvKQdusHoY8PQtbeVmprhz5DRAm9mbweuB5LAze7+7UyuT0TiIxXuPWeqx9Du4xt9cXc6u3x/se8u/O2dXeHfwcPBRqL34fZwA9LbcH/L3D88+C8z/cpYgTezJPAj4ELgdeBpM7vX3V/K1DpFRAbKzEgljVQSxhDtKaj19fUZWW4mj1ScCaxy99Xuvg9YAFyWwfWJiEgac8/MdwMzexfwdnf/SHj7CuAsd/9Uj+muBK4EqKqqmr1gwYJBra+lpYWSkpKhhR4hccoK8cobp6wQr7xxygrxyjuUrLW1tYvdfU6vI909I3/Auwja3btvXwHc0N88s2fP9sGqq6sb9LwjLU5Z3eOVN05Z3eOVN05Z3eOVdyhZgWe8j5qaySaaDcDktNuTwvtERGQEZLLAPw3MNLNpZpYPvBe4N4PrExGRNBk7i8bdO8zsU8CfCE6T/Jm7v5ip9YmIyMEyeh68u98P3J/JdYiISO/UoYOISI7K2GmSg2Fm24C1g5y9HGgYxjiZFKesEK+8ccoK8cobp6wQr7xDyTrF3St6G5FVBX4ozOwZ7+tc0CwTp6wQr7xxygrxyhunrBCvvJnKqiYaEZEcpQIvIpKjcqnA3xR1gCMQp6wQr7xxygrxyhunrBCvvBnJmjNt8CIicrBc2oMXEZE0KvAiIjkq9gXezH5mZlvN7IWosxyOmU02szoze8nMXjSzq6LO1BczKzSzp8zs2TDrv0adaSDMLGlmS83svqiz9MfM1pjZ82a2zMyeiTrP4ZjZODO708xWmNlyMzsn6ky9MbPq8Dnt/msys89Gnas/Zva58DP2gpndYWaFw7bsuLfBm9k8oAW41d1PijpPf8zsaOBod19iZmOBxcDlnoVXubLgisHF7t5iZnnAo8BV7v5ExNH6ZWZXA3OAUne/JOo8fTGzNcAcd4/FD3HM7H+AR9z95rDzwCJ3b4w4Vr/Cq8ptILgOxWB/QJlRZnYswWfrRHffa2a/Bu5391uGY/mx34N394XAjqhzDIS7b3L3JeFwM7AcODbaVL0Lu5puCW/mhX9ZvTdgZpOAi4Gbo86SS8ysDJgH/BTA3fdle3EPnQ+8mq3FPU0KGGNmKaAI2DhcC459gY8rM5sKnA48GXGUPoXNHcuArcBD7p61WUPfB74IdEWcYyAceNDMFodXNctm04BtwM/D5q+bzaw46lAD8F7gjqhD9MfdNwDfA9YBm4Bd7v7gcC1fBT4CZlYC/Bb4rLs3RZ2nL+7e6e6nEVys5Uwzy9omMDO7BNjq7oujzjJA57n7GcA7gE+GTY3ZKgWcAdzo7qcDu4EvRxupf2Ez0qXAb6LO0h8zG09wreppwDFAsZl9YLiWrwI/wsL27N8Ct7v7XVHnGYjw63gd8PaIo/RnLnBp2La9AHiLmd0WbaS+hXtuuPtW4G6Ci9Rnq9eB19O+wd1JUPCz2TuAJe6+Jeogh3EB8Jq7b3P3duAu4NzhWrgK/AgKD1z+FFju7tdFnac/ZlZhZuPC4THAhcCKSEP1w92/4u6T3H0qwVfzv7r7sO0JDSczKw4PshM2dbwVyNqzwNx9M7DezKrDu84Hsu7EgB7+nixvngmtA842s6KwPpxPcGxuWMS+wJvZHcDjQLWZvW5m/xR1pn7MJbj4+FvSTuO6KOpQfTgaqDOz5wguv/iQu2f1qYcxUgU8ambPAk8Bf3D3ByLOdDifBm4P3w+nAd+KNk7fwo3mhQR7w1kt/FZ0J7AEeJ6gJg9btwWxP01SRER6F/s9eBER6Z0KvIhIjlKBFxHJUSrwIiI5SgVeRCRHqcCLiOQoFXiJnbAvlBOjzjEUZvZBM9tmZoftGC3sYrrFzOaMRDbJHamoA4gcKXf/SNQZhsmv3P1Th5vI3WvNrH4E8kiO0R68ZK3wJ/1/CC868oKZvSe8v757b9bM/snMVoYXJ/mJmd0Q3n+Lmd1oZk+Y2WozqwkvDrPczG5JW8eNZvbMQC5qYmbfDi/W8pyZfc/MxprZa2H/QphZafdtM/tM2rQLBvBYx5jZgjDf3Wb2pPbYZai0By/Z7O3ARne/GPb3S76fmR0DfJWg46tm4K/As2mTjAfOIehV8F6CriI+AjxtZqe5+zLgn919R3hxiL+Y2Snu/lzPIGY2EXgnMMvd3czGuXtzuGd9MXAPQR84d7l7u5l9GZjm7m3dffocxseBPe7+BjM7heCn6yJDoj14yWbPAxea2XfM7M3uvqvH+DOBh919R9gTX8+uYX/vQV8czwNb3P15d+8CXgSmhtO828yWAEuBNwJ9te3vAlqBn5rZ3wJ7wvtvBj4UDn8I+Hk4/BxB3y0fADoG8FjnAbcBhBuYQzYyIkdKBV6ylruvJNg7fx74hpl97QgX0Rb+70ob7r6dMrNpwOeB8939FOAPQK/Xw3T3DoINyp3AJcAD4f2LgKlmVgMk3b27V8iLgR+F+Z8Or9YjMqJU4CVrhU0we9z9NuC7HNoH+dPAfDMbHxbQvzvCVZQSXLxil5lVEfQh3leWEqDM3e8HPgecmjb6VuCXhHvvZpYAJrt7HfAloAwoOUyWhcD7wvlPAk45wscicgjtVUg2Oxn4rpl1Ae0E7dT7ufsGM/sWQZe7Owj6q+/ZjNMnd3/WzJaG860HFvUz+VjgdxZc8d6Aq9PG3Q58gwP9jyeB28JjBgb8YADXML2R4JJ4ywn6A4/Llakki6m7YIk1Mytx95ZwD/5u4GfufvcIZ3gXcJm7X3EE83wQmNPXaZLhwdvPu/szvd0WGQg10UjcXWvBhcFfAF4jOJtlxJjZD4FvA/9+hLPuBd4x0B86AccTfIsRGTDtwYv0YGZ3E1wEOd2X3P1PUeQRGSwVeBGRHKUmGhGRHKUCLyKSo1TgRURylAq8iEiO+v8fVbF38RRfTQAAAABJRU5ErkJggg==\n", 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] @@ -403,16 +379,19 @@ } ], "source": [ - "# System noise\n", + "# Impact of quantization on the system noise\n", + "# The quantization noise has sigma_quant = 0.29 q, this increases the system noise.\n", + "# The system noise has sigma_sys = n * q. For n = 2 the quantization increases the\n", + "# total power by 2% (so sigma_sys increase by sqrt(2 %) is about 1 %).\n", "n = np.arange(1,9)\n", - "sigma_sys = n # = n * q, so n LSbits\n", + "sigma_sys = n # = n * q, so sigma of n LSbits\n", "P_sys = sigma_sys**2\n", "P_tot = P_sys + P_quant\n", "sigma_tot = np.sqrt(P_tot)\n", "\n", "plt.figure()\n", "plt.plot(n, (P_tot / P_sys - 1) * 100)\n", - "plt.title(\"Increase in total noise power due to sampling\")\n", + "plt.title(\"Increase in total noise power due to quantization\")\n", "plt.xlabel(\"sigma_sys [q]\")\n", "plt.ylabel(\"[%]\")\n", "plt.grid()" @@ -420,7 +399,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 91, "id": "be2d952f", "metadata": {}, "outputs": [ @@ -436,7 +415,7 @@ } ], "source": [ - "# FS sine\n", + "# Full scale (FS) sine\n", "P_fs_sine = FS**2 / 2\n", "P_fs_sine_dB = 10 * np.log10(P_fs_sine)\n", "print(\"W_adc = {W_adc} bits\")\n", @@ -447,7 +426,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 92, "id": "a9e7fabc", "metadata": {}, "outputs": [ @@ -461,7 +440,7 @@ } ], "source": [ - "# SNR\n", + "# Signal to noise ratio (SNR)\n", "SNR = P_fs_sine / P_quant\n", "SNR_dB = 10 * np.log10(SNR)\n", "\n", @@ -471,7 +450,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 93, "id": "92852a53", "metadata": {}, "outputs": [ @@ -479,32 +458,38 @@ "name": "stdout", "output_type": "stream", "text": [ - "Power at -50dBFS = 25.26 dB, so sigma = 18.3 q, ampl = 25.9 q\n", + "Power at -50dBFS = 25.26 dB corresponds to:\n", + " . sigma = 18.3 q\n", + " . Noise range 3 sigma = +-55 q\n", + " . Sine with amplitude A = = sigma * sqrt(2) = 25.9 q\n", "\n", "sigma = 16 q corresponds to:\n", " . Power = 24.08 dB, so at -51.2 dBFS\n", - " . Range 3 sigma = +-48 q\n", + " . Noise range 3 sigma = +-48 q\n", " . Sine with amplitude A = sigma * sqrt(2) = 22.6 q\n" ] } ], "source": [ - "# -50 dbFS level\n", + "# Signal level relative to FS sine\n", "Power_50dBFS = P_fs_sine_dB - 50 \n", "sigma_50dBFS = 10**(Power_50dBFS / 20)\n", "ampl_50dBFS = sigma_50dBFS * np.sqrt(2)\n", "\n", - "print(f\"Power at -50dBFS = {Power_50dBFS:.2f} dB, so sigma = {sigma_50dBFS:.1f} q, ampl = {ampl_50dBFS:.1f} q\")\n", + "print(f\"Power at -50dBFS = {Power_50dBFS:.2f} dB corresponds to:\")\n", + "print(f\" . sigma = {sigma_50dBFS:.1f} q\")\n", + "print(f\" . Noise range 3 sigma = +-{3 * sigma_50dBFS:.0f} q\")\n", + "print(f\" . Sine with amplitude A = = sigma * sqrt(2) = {ampl_50dBFS:.1f} q\")\n", "\n", - "# Assume sigma = 16 q\n", - "sigma = 16\n", - "Power = sigma**2\n", - "Power_dB = 10 * np.log10(Power)\n", + "# Assume signal with sigma = 16 q\n", + "sigma_16q = 16\n", + "Power_16q = sigma_16q**2\n", + "Power_16q_dB = 10 * np.log10(Power_16q)\n", "print()\n", - "print(f\"sigma = {sigma:.0f} q corresponds to:\")\n", - "print(f\" . Power = {Power_dB:.2f} dB, so at {Power_dB - P_fs_sine_dB:.1f} dBFS\")\n", - "print(f\" . Range 3 sigma = +-{3 * sigma:.0f} q\")\n", - "print(f\" . Sine with amplitude A = sigma * sqrt(2) = {np.sqrt(2) * sigma:.1f} q\")\n" + "print(f\"sigma = {sigma_16q:.0f} q corresponds to:\")\n", + "print(f\" . Power = {Power_16q_dB:.2f} dB, so at {Power_16q_dB - P_fs_sine_dB:.1f} dBFS\")\n", + "print(f\" . Noise range 3 sigma = +-{3 * sigma_16q:.0f} q\")\n", + "print(f\" . Sine with amplitude A = sigma * sqrt(2) = {np.sqrt(2) * sigma_16q:.1f} q\")\n" ] }, { @@ -512,12 +497,20 @@ "id": "77bb14cc", "metadata": {}, "source": [ - "## 3 Expected signal levels in the SDP FW" + "# 3 Expected signal levels in the SDP FW" + ] + }, + { + "cell_type": "markdown", + "id": "f7fff7a0", + "metadata": {}, + "source": [ + "## 3.1 Signal input power and DC level" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 94, "id": "a04af043", "metadata": {}, "outputs": [ @@ -532,26 +525,46 @@ } ], "source": [ - "# ADC power statistic (AST)\n", - "sigma = sigma_fs_sine\n", - "sigma_bits = np.log2(sigma)\n", - "P_ast = (sigma)**2 * N_int\n", - "print(f\"ADC sigma = {sigma:6.1f} q = {sigma_bits:4.1f} bits: P_ast = {P_ast:e}, uses {np.log2(P_ast):.1f} bits, is 0 dBFS = FS sine\")\n", - "\n", - "sigma = sigma_50dBFS\n", - "sigma_bits = np.log2(sigma)\n", + "# Signal input power statistic for ADC / WG (AST)\n", + "si_sigma = sigma_fs_sine\n", + "si_sigma_bits = np.log2(si_sigma)\n", + "P_ast = (si_sigma)**2 * N_int\n", + "print(f\"ADC sigma = {si_sigma:6.1f} q = {si_sigma_bits:4.1f} bits: P_ast = {P_ast:e}, uses {np.log2(P_ast):.1f} bits, is 0 dBFS = FS sine\")\n", + "\n", + "si_sigma = sigma_50dBFS\n", + "si_sigma_bits = np.log2(si_sigma)\n", + "P_ast = (si_sigma)**2 * N_int\n", + "print(f\"ADC sigma = {si_sigma:6.1f} q = {si_sigma_bits:4.1f} bits: P_ast = {P_ast:e}, uses {np.log2(P_ast):.1f} bits, is -50dBFS\")\n", + "\n", + "si_sigma = sigma_16q\n", + "si_sigma_bits = np.log2(si_sigma)\n", "P_ast = (sigma)**2 * N_int\n", - "print(f\"ADC sigma = {sigma:6.1f} q = {sigma_bits:4.1f} bits: P_ast = {P_ast:e}, uses {np.log2(P_ast):.1f} bits, is -50dBFS\")\n", + "print(f\"ADC sigma = {si_sigma:6.1f} q = {si_sigma_bits:4.1f} bits: P_ast = {P_ast:e}, uses {np.log2(P_ast):.1f} bits\")" + ] + }, + { + "cell_type": "markdown", + "id": "7ce94d23", + "metadata": {}, + "source": [ + "From measured P_ast and DC_ast to signal input sigma:\n", "\n", - "sigma = 16\n", - "sigma_bits = np.log2(sigma)\n", - "P_ast = (sigma)**2 * N_int\n", - "print(f\"ADC sigma = {sigma:6.1f} q = {sigma_bits:4.1f} bits: P_ast = {P_ast:e}, uses {np.log2(P_ast):.1f} bits\")" + "* si_rms = sqrt(P_ast / N_int)\n", + "* si_mean = DC_ast / N_int\n", + "* si_sigma = sqrt(si_rms^2 - si_mean^2)" + ] + }, + { + "cell_type": "markdown", + "id": "5cb555b6", + "metadata": {}, + "source": [ + "## 3.2 Subband statistics (SST)" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 95, "id": "0b2ac36c", "metadata": {}, "outputs": [ @@ -561,46 +574,55 @@ "text": [ "Signal input level --> Expected subband level and SST level:\n", "\n", - " ampl_si ampl_sub #bits SST #bits\n", - " 8192.0 65196.3 15.9925 8.301877e+14 49.6\n", - " 25.9 206.2 7.6877 8.301877e+09 33.0\n", - " 16.0 127.3 6.9925 3.166915e+09 31.6\n", - "\n", - "sigma_si sigma_sub #bits SST #bits\n", - " 22.6 180.1 7.4925 6.333830e+09 32.6\n" + " si_ampl sub_ampl #bits SST #bits\n", + " 8192.0 65536.0 16.0000 8.388608e+14 49.6\n", + " 25.9 207.2 7.6952 8.388608e+09 33.0\n", + " 22.6 181.0 7.5000 6.400000e+09 32.6\n" ] } ], "source": [ - "# Subband filterbank (F_sub)\n", - "ampl_sub_fs = FS * G_subband # subband amplitude for FS signal input sine\n", - "SST_fs = ampl_sub_fs**2 * N_int_sub\n", + "# SST for Subband filterbank (F_sub)\n", + "sub_ampl_fs = FS * G_subband # subband amplitude for FS signal input sine\n", + "SST_fs = sub_ampl_fs**2 * N_int_sub\n", "\n", - "ampl_sub_50dBFS = ampl_50dBFS * G_subband # subband amplitude -50dBFS signal input sine\n", - "SST_50dBFS = ampl_sub_50dBFS**2 * N_int_sub\n", + "sub_ampl_50dBFS = ampl_50dBFS * G_subband # subband amplitude -50dBFS signal input sine\n", + "SST_50dBFS = sub_ampl_50dBFS**2 * N_int_sub\n", "\n", - "ampl_si_16q = 16 # [q], so 16 q is 4 bits amplitude\n", - "ampl_sub_16q = ampl_si_16q * G_subband # subband amplitude for signal input sine with A = 16 q\n", - "SST_ampl_16q = ampl_sub_16q**2 * N_int_sub\n", - "\n", - "sigma_si_16q = 16 * np.sqrt(2) # [q], so A = 16 * sqrt(2) q for sigma = 16 q\n", - "sigma_sub_16q = sigma_si_16q * G_subband # subband sigma for arbitrary signal input with sigma = 16 q\n", - "SST_sigma_16q = sigma_sub_16q**2 * N_int_sub\n", + "si_ampl_s16q = sigma_16q * np.sqrt(2)\n", + "sub_ampl_s16q = si_ampl_s16q * G_subband # subband amplitude for signal input sine with sigma = 16 q\n", + "SST_ampl_s16q = sub_ampl_s16q**2 * N_int_sub\n", "\n", "print(\"Signal input level --> Expected subband level and SST level:\")\n", "print()\n", - "print(\" ampl_si ampl_sub #bits SST #bits\")\n", - "print(f\"{FS:8.1f} {ampl_sub_fs:10.1f} {np.log2(ampl_sub_fs):8.4f} {SST_fs:e} {np.log2(SST_fs):.1f}\")\n", - "print(f\"{ampl_50dBFS:8.1f} {ampl_sub_50dBFS:10.1f} {np.log2(ampl_sub_50dBFS):8.4f} {SST_50dBFS:e} {np.log2(SST_50dBFS):.1f}\")\n", - "print(f\"{ampl_si_16q:8.1f} {ampl_sub_16q:10.1f} {np.log2(ampl_sub_16q):8.4f} {SST_ampl_16q:e} {np.log2(SST_ampl_16q):.1f}\")\n", - "print()\n", - "print(\"sigma_si sigma_sub #bits SST #bits\")\n", - "print(f\"{sigma_si_16q:8.1f} {sigma_sub_16q:10.1f} {np.log2(sigma_sub_16q):8.4f} {SST_sigma_16q:e} {np.log2(SST_sigma_16q):.1f}\")" + "print(\" si_ampl sub_ampl #bits SST #bits\")\n", + "print(f\"{FS:8.1f} {sub_ampl_fs:10.1f} {np.log2(sub_ampl_fs):8.4f} {SST_fs:e} {np.log2(SST_fs):.1f}\")\n", + "print(f\"{ampl_50dBFS:8.1f} {sub_ampl_50dBFS:10.1f} {np.log2(sub_ampl_50dBFS):8.4f} {SST_50dBFS:e} {np.log2(SST_50dBFS):.1f}\")\n", + "print(f\"{si_ampl_s16q:8.1f} {sub_ampl_s16q:10.1f} {np.log2(sub_ampl_s16q):8.4f} {SST_ampl_s16q:e} {np.log2(SST_ampl_s16q):.1f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "c2c02740", + "metadata": {}, + "source": [ + "From measured SST to SSTq in q^2 units and subband amplitude in q units\n", + "\n", + "* SSTq = SST / N_int_sub / G_subband^2\n", + "* sub_ampl = sqrt(SSTq) # ampl real = ampl imag = std complex = sqrt(power complex)" + ] + }, + { + "cell_type": "markdown", + "id": "66d49365", + "metadata": {}, + "source": [ + "## 3.3 Beamlet statistics (BST)" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 96, "id": "f0b09a83", "metadata": {}, "outputs": [], @@ -610,10 +632,87 @@ "# . uses BF weights to form beamlets from sum of weighted subbands\n" ] }, + { + "cell_type": "markdown", + "id": "d2086ec5", + "metadata": {}, + "source": [ + "# Appendix 1: DFT of real input sine + DC" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "id": "def6eba7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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yzpQ0bJM0U9cAJyU5MckhwIuArcvG/BmjrTqSHM1ot/mNs4spSeuyAPUrns0nDdyqzVRVPQC8Gng/8AngoqraluSNSU5rhr0f+EKS64HLgZ+tqi+0FVqSJmH9kjQPE82ZqqpLgUuXPfa6sdsF/HTzI0kLo+v6Nbpop7umpCHzCuiSJElTsJmSpBYF90tJQ2czJUkt8rv5pOGzmZKktrlrSho0mylJapHfzScNn82UJEnSFGymJKlFiUf5pKGzmZKkFjn/XBo+mylJkqQp2ExJUosSv5tPGjqbKUmSpCnYTElSy9wxJQ2bzZQktcgroEvDt6HrAFq/K299gA2fuoNnnHR011EG7Yrtd3D7V+7l0V0HkbRPl//j7dyz60F+4ImP7TqKDkDumeqxt113Hy95x9Vdxxi8F7/9av7bhR/vOoZ6Kl4cYS5edv41vOo9H+06hg5QNlOS1DLP5pOGzWZKklrknClp+GymJEmSpmAzJUktCl4aQRo6mylJapGH+aThs5mSpJa5Z0oaNpspSWpRErspaeBspiRJkqZgMyVJLXICujR8NlOS1CYnoEuDZzMlSS1zz5Q0bDZTktSi4AR0aehspiRJkqZgMyVJLfLKCNLw2UxJkiRNwWZKklrkyXzS8E3UTCU5NckNSbYnOWc/456fpJJsnl1ESVq/ruuX380nDd+qzVSSg4FzgWcDJwNnJDl5hXGHAz8JXD3rkJK0HotSv5wzJQ3bJHumTgG2V9WNVbULuAA4fYVxvwS8Cbh3hvkkaRqd168Qym5KGrQNE4w5Brh57P4O4CnjA5I8GTiuqi5J8rP7WlCSs4GzATZt2sTS0tKaA7dh586dC5NlPfqYvY+Z+/o+6WvuGem8ft100y6gevk76ON7Z2lpqZe5oZ/rG/qbG2aXfZJmar+SHAS8BThztbFVdR5wHsDmzZtry5Yt0778TCwtLbEoWdbksksA+pW9x5k3btzYr9yN3r6/52Ae9esju26AG7f38nfQq/fOWG3pVe4x5p6/WWWf5DDfLcBxY/ePbR7b43DgCcBSks8A3wVsdRK6pAXQef1y/rk0fJM0U9cAJyU5MckhwIuArXuerKq7quroqjqhqk4ArgJOq6prW0ksSZNbiPrllClp2FZtpqrqAeDVwPuBTwAXVdW2JG9MclrbASVpvRaifnltBGnwJpozVVWXApcue+x1+xi7ZfpYkjQb1i9JbfMK6JLUoj37pcrrI0iDZTMlSZI0BZspSWrRnilT7piShstmSpJaFC+OIA2ezZQkzYE7pqThspmSpBY9fJjPdkoaKpspSZKkKdhMSVKLHro0QqcpJLXJZkqSWuQF0KXhs5mSpDlwypQ0XDZTktSiNLumygN90mDZTEmSJE3BZkqS5sDDfNJw2UxJkiRNwWZKklrk2XzS8NlMSVKL9nw3n4f5pOGymZIkSZqCzZQkteih7+bz0gjSYNlMSZIkTcFmSpJa9NB387ljShosmylJapFn80nDZzMlSXPgjqn5KHcBqgM2U5LUoocvjeAf+XlwNasLNlOSpMGwl1IXbKYkqUUPXxpB87DbXVPqgM2UJGkw7KXUBZspSZoD/8jPhxdHVRdspiSpRfE431zZtKoLNlOSJElTsJmSpBY9dAV0d03NhXum1IWJmqkkpya5Icn2JOes8PxPJ7k+yXVJ/irJ8bOPKklrZ/06sNi0qgurNlNJDgbOBZ4NnAyckeTkZcP+HthcVU8ELgbePOugkrRWi1C/Hpoy5d/4udjtelYHJtkzdQqwvapurKpdwAXA6eMDquryqrqnuXsVcOxsY0rSunRevx4+zKd58Erz6sKGCcYcA9w8dn8H8JT9jD8L+POVnkhyNnA2wKZNm1haWposZct27ty5MFnWo4/Z+5i5r++Tvuaekc7r16duuh+AK664gsMP6de3HvfxvfM3f/O37L7v7t7lhn6ub+hvbphd9kmaqYkleQmwGXjmSs9X1XnAeQCbN2+uLVu2zPLl121paYlFybIml10C0K/sPc68cePGfuVu9Pb9PWdt1a+b/u4z8IltPO1pT+OojYfOJuyc9Oq903xOn/6MZ/D3V1/Rn9xjerW+x/Q1N8wu+yTN1C3AcWP3j20e20uSZwGvBZ5ZVfdNnUySpmf9OtB4lE8dmGTO1DXASUlOTHII8CJg6/iAJE8C3gacVlW3zz6mJK1L5/XLa3bOl2fzqQurNlNV9QDwauD9wCeAi6pqW5I3JjmtGfYbwEbgj5N8LMnWfSxOkuZmEepXv2ZJ9dP4pHPP5lMXJpozVVWXApcue+x1Y7efNeNckjQTi1K/PMmsPePr1rP51AWvgC5JbWqO83n4qT27xxoo17K6YDMlSeq18QbKHVPqgs2UJLXooTlT/pFvzV6H+VzR6oDNlCSp1/ZqoOyl1AGbKUlqkZdGaN/4ninP5lMXbKYkqUVpDvQ5l2c+PMynLthMSZJ6ba+z+eyl1AGbKUlq0cOH+fwr35ZyypQ6ZjMlSeq1vS+NYDul+bOZkiZkkdZ67Lk0gm+f9pSH+dQxmylpQtZorUf8cr7WedFOdc1mSpLmwL/x7andY7dd0+qAzVRPechJ6oeHL43gZ7Yt4w2Uq1ldsJmSJPWaZ/OpazZTPeXW1/y5zrUuey6N4PunNZ7Np67ZTPWU5WL+XOfSYtrrbL4Oc+jAZTMlSS3yZL72uWdKXbOZ6ikLhtQPid/N1za/TkZds5mSJPWbE9DVMZupnrJgzJ/rXOvx0BXQfQe1xot2qms2U5KkXtv70gh2U5o/m6mecutr/lznWo94aYTWedFOdc1mqqfc+pL64aFmqtsYgzbeQO22m1IHbKYkSb3m2Xzqms1UT1kw5s9VrvXwu/na56pV12ymJEmDYWOlLthMSZOySGsdnDPVPs/mU9dspiRJvebZfOqazVRPWTDmz1WuafiZbc/ee6ak+bOZ6il3Zc+fa1zrsee7+XwHtWf8bD4vjaAu2ExJknrNr5NR1yZqppKcmuSGJNuTnLPC84cmubB5/uokJ8w8qfZiwZAm03X9emi/lJ/Z1uy9bl3Rmr9Vm6kkBwPnAs8GTgbOSHLysmFnAV+sqscDvwW8adZBJWmtrF8HCiegq1sbJhhzCrC9qm4ESHIBcDpw/diY04HXN7cvBt6aJDWDq9Tt+OI9vOwPrpl2Mft19z338MiP/nWrrzFrD46t2u97S7+yQz8zv+HKr/KW6/qXu83398bDNvCnr3x6K8uekU7r1+g1R/89653XcuiGfs2s6Ett3PXg7odu/9SFH2P3/ff2IvdyfVnfy/U1N8B/PP5BtsxgOZM0U8cAN4/d3wE8ZV9jquqBJHcBRwF3jA9KcjZwNsCmTZtYWlpa9cW/dO9ujsiuCWKu38ZDd3Nwvtrqa8xc4M5Dio2HHMQRPcq+6WvCg7t39yrzcYcfxFd2FY89rIfvE9p9fx/6QCb6HHeo0/oF8MC9uznlMcXug+5bT/5O9aY2boBHP+ZgHig47OBdPHhQT3Iv05v1vUxfcwPU/Q/MpIZN0kzNTFWdB5wHsHnz5tqyZctE/9/zTm0xFLC0tMSkWRaJuefL3Ae29dYvgCMP6+fvoK/vHXPPV19zw+yyT7LP+RbguLH7xzaPrTgmyQbgCOALU6eTpOlYvyS1bpJm6hrgpCQnJjkEeBGwddmYrcBLm9svAD44q/kGkjQF65ek1q16mK+ZQ/Bq4P3AwcDvV9W2JG8Erq2qrcA7gD9Ksh24k1HBkqROWb8kzcNEc6aq6lLg0mWPvW7s9r3AC2cbTZKmZ/2S1LZ+nacrSZK0YGymJEmSpmAzJUmSNAWbKUmSpCmkqzOAk3weuKmTF/+XjmbZ1Y57wtzzZe7pHV9Vj+k6xLTWUb8W6XewFuaeL3PP31qy77N+ddZMLZIk11bV5q5zrJW558vcWq++/g7MPV/mnr9ZZfcwnyRJ0hRspiRJkqZgMzVyXtcB1snc82VurVdffwfmni9zz99MsjtnSpIkaQrumZIkSZqCzZQkSdIUDshmKsnXJvnLJJ9q/vvo/Yx9VJIdSd46z4z7yLJq7iTfkeTKJNuSXJfkh7vI2mQ5NckNSbYnOWeF5w9NcmHz/NVJTugg5r8wQe6fTnJ9s37/KsnxXeRcbrXcY+Oen6SS9PJU5kU24Pf8v03y0SQPJHlBFxlXMtTPapJXJPmHJB9L8rdJTu4i53J9rTETrO8zk3y+Wd8fS/Kja36RqjrgfoA3A+c0t88B3rSfsf8LeA/w1j7kBr4ZOKm5/TjgNuDIDrIeDHwa+EbgEODjwMnLxrwS+L3m9ouACxdgHU+S+3uAr2lu/3hfcjfjDgc+BFwFbO4695B+Bv6ePwF4IvCHwAu6zryG3L38rAKPGrt9GnBZH3I34xaqxky4vs+c9m/8AblnCjgdeGdz+53A81YalOTfAJuAv5hPrFWtmruqPllVn2pu3wrcDnRxxelTgO1VdWNV7QIuYJR/3Pi/52Lge5NkjhlXsmruqrq8qu5p7l4FHDvnjCuZZH0D/BLwJuDeeYY7QAz5Pf+ZqroO2N1FwH0Y7Ge1qr48dveRwCKcKdbXGjNp7qkcqM3Upqq6rbn9WUYN016SHAT8JvCaeQZbxaq5xyU5hVEn/um2g63gGODmsfs7msdWHFNVDwB3AUfNJd2+TZJ73FnAn7eaaDKr5k7yZOC4qrpknsEOIAfKe35RDPazCpDkVUk+zeiIxH+dU7b96WuNmfR98vzmcPDFSY5b64tsWG+6RZfkA8DXr/DUa8fvVFUlWanrfyVwaVXtmOeG4wxy71nOY4E/Al5aVYu0NTkYSV4CbAae2XWW1TQbB29htDtbOqD06bO6R1WdC5yb5D8BvwC8tONI+9XzGvM+4L1VdV+SH2O09/jfrWUBg22mqupZ+3ouyeeSPLaqbmuajttXGPZU4LuTvBLYCBySZGdV7XPS3SzMIDdJHgVcAry2qq5qKepqbgHGu/tjm8dWGrMjyQbgCOAL84m3T5PkJsmzGDW4z6yq++aUbX9Wy3048ARgqdk4+Hpga5LTqurauaUctkG/5xfQUD+ry10A/G6riSbT1xqz6vquqvHP4NsZ7Q1cm64nh3XxA/wGe0/kfvMq489kMSagr5qb0WG9vwJ+quOsG4AbgRN5eNLfty0b8yr2nox70QKs40lyP4nRodOTus67ltzLxi+xAJNDh/Qz5Pf82NjzWZwJ6IP9rI7nBZ4LXNuH3MvGL0SNmXB9P3bs9n8Arlrz63T9D+1o5R7VNByfAj4AfG3z+Gbg7SuMX5RmatXcwEuA+4GPjf18R0d5nwN8silmr20eeyNwWnP7MOCPge3Ah4Fv7HodT5j7A8Dnxtbv1q4zT5J72diFKHRD+xnwe/47Gc01uZvRnrRtXWeeMHcvP6uMziLf1mS+nP00LYuUe9nYhakxE6zvX2vW98eb9f2v1/oafp2MJEnSFA7Us/kkSZJmwmZKkiRpCjZTkiRJU7CZkiRJmoLNlCRJ0hRspiRJkqZgMyVJkjSF/w8KlcSyweKhhAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "<Figure size 720x288 with 2 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# DFT of real sine input\n", + "# . Show that DFT of real sine with A = 1 yields bin phasor with amplitude 0.5. For DC at bin 0\n", + "# the real input gain is 1.0, because DC has only one phasor component of frequency 0.\n", + "G_fft_real_input_sine = 0.5\n", + "G_fft_real_input_dc = 1.0\n", + "\n", + "# . DFT size\n", + "N_points = 1024\n", + "N_bins = N_points // 2 + 1 # positive frequency bins including DC and f_s/2\n", + "\n", + "# . select a bin\n", + "i_bin = 200 # bin index in range(N_bins)\n", + "dc_bin = 0 # DC\n", + "\n", + "# . time and frequency axis\n", + "f_s = f_adc # sample frequency\n", + "f_s = 1 # normalized sample frequency\n", + "T_s = 1 / f_s # sample period\n", + "T_fft = N_points * T_s # DFT period\n", + "t_axis = np.linspace(0, T_fft, N_points, endpoint=False)\n", + "f_axis = np.linspace(0, f_s, N_points, endpoint=False)\n", + "f_axis_fft = f_axis - f_s/2 # fftshift axis\n", + "f_axis_rfft = f_axis[0:N_bins] # positive frequency bins\n", + "\n", + "f_bin = i_bin / N_points * f_s # bin frequency\n", + "\n", + "# . create sine at bin + DC, use cos to see DC at i_bin = 0 \n", + "s = np.cos(2 * np.pi * f_bin * t_axis)\n", + "dc = np.cos(2 * np.pi * dc_bin * t_axis) # equivalent to dc = 1\n", + "\n", + "x = s + dc\n", + "\n", + "# . DFT using complex input fft()\n", + "X_fft = np.fft.fftshift(np.fft.fft(x) / N_points)\n", + "\n", + "# . DFT using real input rfft()\n", + "X_rfft = np.fft.rfft(x) / N_points\n", + "\n", + "plt.figure(figsize=(10, 4))\n", + "plt.subplot(1, 2, 1)\n", + "plt.title('DFT of real sine using fft')\n", + "plt.plot(f_axis_fft, abs(X_fft))\n", + "plt.grid()\n", + "plt.subplot(1, 2, 2)\n", + "plt.title('DFT of real sine using rfft')\n", + "plt.plot(f_axis_rfft, abs(X_rfft))\n", + "plt.grid()" + ] + }, { "cell_type": "code", "execution_count": null, - "id": "8713e865", + "id": "2e386180", "metadata": {}, "outputs": [], "source": [] diff --git a/applications/lofar2/model/signal_statistics.ipynb b/applications/lofar2/model/signal_statistics.ipynb index 1c09874019802d28d0c03e95ebbe661b46761a37..fbd1d2772cd42bf0394cf85bcbffb3203318b1b3 100644 --- a/applications/lofar2/model/signal_statistics.ipynb +++ b/applications/lofar2/model/signal_statistics.ipynb @@ -12,9 +12,9 @@ "Purpose: Model the SNR of a beamformer and a correlator\n", "\n", "Status:\n", - "* coherent summator (= voltage beamformer): SNR of coherent input improves by the number of inputs N\n", - "* incoherent summator (= auto correlation, power beamformer): SNR does not improve, but accuracy of mean power measurement does improves by factor N, so the std of the mean power measurement reduces by N. Summing powers from N inputs (like in an incoherent beamformer) or summing N powers in time from 1 input (like in subband auto power statistics) is equivalent.\n", - "* correlator: SNR of coherent input improves by sqrt(N) for integration over N cross powers in time. Hence if the input SNR of the input signal is -20 dB (i.e. sigma_coh / sigma_sys = 0.1) then it takes integratiopn over N = 10000 cross powers in time to improve the SNR of the correlator output by a factor 100 = +20 dB to 0 dB.\n", + "* coherent summator (sums voltages, e.g. voltage beamformer): SNR of coherent input improves by the number of inputs N\n", + "* incoherent summator (sums powers, e.g. auto power statistics, power beamformer): SNR does not improve, but accuracy of mean power measurement does improve by factor N, so the std of the mean power measurement reduces by N. Summing powers from N inputs (like in an incoherent beamformer) or summing N powers in time from 1 input (like in subband auto power statistics) is equivalent.\n", + "* correlator: SNR of coherent input improves by sqrt(N) for integration over N cross powers in time. Hence if the input SNR of the input signal is -20 dB (i.e. sigma_coh / sigma_sys = 0.1) then it takes integration over N = 10000 cross powers in time to improve the SNR of the correlator output by a factor 100 = +20 dB to 0 dB.\n", "\n", "References:\n", "\n", @@ -23,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 17, "id": "2b477516", "metadata": {}, "outputs": [], @@ -39,25 +39,37 @@ "source": [ "## 1 Statistics basics:\n", "\n", - "* dc = mean # direct current\n", - "* sigma = std # standard deviation\n", - "* var = std**2 # variance\n", - "* mean power = var + mean**2\n", - "* rms = sqrt(mean power) = sqrt(var + mean**2)\n", + "Signal statistics\n", "\n", - "Signal with coherent or incoherent samples. With N samples, the std of their sum:\n", - " \n", - "* increases by N for coherent signals\n", - "* increases by sqrt(N) for incoherent signals\n", + "* dc = mean # direct current, average value of a signal\n", + "* sigma = std = sqrt(var) # standard deviation, measure for fluctuating portion of a signal\n", + "* var = std**2 # variance, power of the fluctuating portion of a signal\n", + "* power = var + mean**2\n", + "* rms = sqrt(power) = sqrt(var + mean**2)\n", + "\n", + "If mean = 0 then var = power and std = rms.\n", + "\n", + "For a complex signal (like subbands and beamlets):\n", + "\n", + "* power complex = power real + power imag = (std real)^2 + (std imag)^2\n", + "* power real = power imag = power complex / 2\n", + "* std real = std imag = std complex / sqrt(2)\n", + "* std complex = sqrt(power complex)\n", + "* ampl real = ampl imag = std complex = std real * sqrt(2) = std imag * sqrt(2)\n", + "\n", + "Coherent and incoherent signals. With S signals, the std of their sum signal:\n", + "\n", + "* increases by S for coherent signals\n", + "* increases by sqrt(S) for incoherent signals\n", "\n", - "Coherent averaging by summing the signal voltages improves the SNR of a signal by a factor N^2 / N = N, because the coherent signal power increases by a factor N^2, while the incoherent noise adds as powers, so the noise power increases by a factor N.\n", + "Coherent averaging by summing voltage signals improves the SNR of a signal by a factor N^2 / N = N, because the coherent signal power increases by a factor N^2, while the incoherent noise adds as powers, so the noise power increases by a factor N.\n", "\n", - "Incoherent averaging by summing the signal powers does not improve the SNR, because the phase information of the signal is lost in the powers. Incoherent averaging does reduce the std of the signal power estimate by a factor N, so incoherent averaging makes the signal power measurement more accurate." + "Incoherent averaging by summing power signals does not improve the SNR, because the phase information of the signal is lost in the powers. Incoherent averaging does reduce the std of the signal power estimate by a factor N, so incoherent averaging makes the signal power measurement more accurate." ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 18, "id": "9c55fb7b", "metadata": {}, "outputs": [], @@ -74,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 19, "id": "74edfe32", "metadata": {}, "outputs": [ @@ -82,9 +94,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "mean(si) = -0.206805, expected -0.2\n", + "mean(si) = -0.205130, expected -0.2\n", "std(si) = 0.500000, expected 0.5\n", - "rms(si) = 0.541081, expected 0.538516\n" + "rms(si) = 0.540443, expected 0.538516\n" ] } ], @@ -135,13 +147,13 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 20, "id": "89845ec3", "metadata": {}, "outputs": [ { "data": { - "image/png": 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vOHF/Sil3JWLL9sbHQ8aM9mdkJEREwE8/QUyM7XP/6CPOrl7t6mi9htM6tkTkKHDAGFPasak+trvGo9xpPXillEN8PLz0EpQqBeXL2z71ihXtCkq//w6NGtmiYP3769DHNObsUTRvANMcI2j2AC86eX/p5nI9+Hbt2rk6FKXc18mTdvz60qUQGgqVK9uRMcbYCo8lS/pMP7srODXBi0gUti8+bVxTED5LUtLdr36eioLwd1IP/siRIzz77LPExMSQmJjIl19+yZ9//smmTZsY4tjfmDFj2LZtG5999hnPPPMMBw8eJCkpiT59+vxTtkApj3TxIvz4I7zzji0jMG0atGnj6qh8jo49SoWdO3fy+uuvs337dnLmzPlPPfjw8PDrJnewRckaNmxIVFQUGzduJDg4mGeeeYYffviBhIQEACZMmEDHjh1ZtGgRhQsXZuPGjWzZsoVGjRqlZ/OUSjvbtsELL0BgoF0aLz4elizR5O4inlUu+Joz7YvnzrltPfgqVarQsWNHEhISaN68OcHBwYCt7z5//nzKli1LQkICFSpUwN/fn549e/L222/TtGlTateu7eQWKZXGLl2y5XnDwuwiGk8/DW3bQt26d/8tW90xPYNPhTupB1+nTh2WLl3KvffeywsvvMDkyZMBePnll5k4cSITJkzgxRftJYlSpUqxfv16KlSowAcffMAnn3yS9o1QylmWLYMKFeykpNatbUmB8eOhfn1N7i6mCT4V7qQe/N9//01gYCCvvPIKL7/8MuvXrwegWrVqHDhwgOnTp9O6dWsADh8+TNasWWnXrh2hoaH/PFcpt5aUZM/aH33UDoNcvBgmTYICBVwdmXLwrC4aF7lcD75jx46UK1eOzp078/rrr9/0NREREYSHh5MpUyayZ8/+zxk8wDPPPENUVBR58uQBbMGy0NBQ/Pz8yJQpE19++aVT26PUHdm3D1autOPYM2SAkSPh119tV8yXX0I6dJeq26MJ/haKFi3Kjh07/rV94sSJN33d888/z/PPP3/dx5YtW0aPHj3+ud+wYUMaNmx4V3Eq5VTffw/t2sG5c1e2Zcliu2JeeEGHOrop7aJJR9HR0ZQqVYosWbJQv359V4ej1K0lJ8PHH8N//gOlS8OaNbB5M6xfb8v5vviiJnc3pmfwd+l26sHnzp2bXbt2pVdoSt2Z5cth4ULYtcsm8x074PnnYdQoO0JGeQyPSPAikqqRK67gjvXgxUkVQpUP+OYbOxIG7LqmJUtCz5621ICb/h9UN+b2CT4gIIBTp06RL18+t03y7kREOHXqFAF6pqVu19df2wumNWrYWag5c7o6InWX3D7BBwUFcfDgQU6cOPGvxy5duuRziSw1bQ4ICCAoKCidIlJeYepU2w1TqxYsWAA+VIfem7l9gs+UKRPFihW77mMRERFUqlQpnSNyLV9ss3Kiw4dtjadvvrHj2efP1+TuRdw+wSul0tCiRXamaWKirfQ4bBjExdkJS6GhkDmzqyNUaUgTvFK+Yvx4e7E0pSeesBOWbrAspvJsmuCV8gVr1kDnzrY+zPTpkCmTvWl3jFfTBK+Utzt+HFq2hEKFYOZMyJ/f1RGpdKIJXilvdOaMnaC0axd89ZXtb4+M1OTuYzTBK+UtRGzp3sGDYe5cex/s7NPx40FHX/kcTfBKeao//7QLVcfE2JWT9u2DjRshb17o3RseecTWjyla1Pa3K5/j1ARvjNkHnAOSgEQRSbv1WZXyZbt2Qb16Nrnfd58d3pgjhy3b26EDZM3q6giVG0iPM/h6InIyHfajlG+4nNwTEmx99gcfdHVEyk1puWClPMkff1xJ7kuWaHL3cEnJSczePptJ+yY55f2NMysPGmP2AmcAAb4SkdHXeU4noBNAYGBgyMyZM1P9/rGxsWT3sXG8vthm8M12p2xzzs2bKTZxInnWrycuf342hoVx4QYlPDydtx/r+Hg/xo0PYvlG4XTAJi5m20nuAtF8/cZjZPa7/ZnE9erVW3fD7m8RcdoNuNfxsyCwEahzs+eHhITI7ViyZMltPd8b+GKbRXyz3UuWLBGJiBCpX18ERAoWFBk4UOT8eVeH5lTefKx/X3lGChY9JiDCvSvFP/9ByZAxSQoUuHjH7wmslRvkVKf2wYvIIcfP48aYOUBVYKkz96mUR4mOtnVhrh2fvno1wW++CZs2QWAgDBhgZ6LqxVO3ceqUnWqwe7e9HTgABw/CoUP22velS7bMT7ZsUOCeOGIz7+bvDQ9AwCWq9P6QsM6P8WiRwogY5s9fCzyS5jE6LcEbY7IBfiJyzvH7E8AnztqfUh4nMRHq1rXDHT//HN54wy6qER4OH3xAlly5bDGwl1+2658qlzt7FmbPhmnT4Lffrkw18PODe++1twcfhDx57PSDswnHiNyzha374uBYEMVqrmfiqNzUKXclFRoDOXMmOiVeZ57BBwJzHIt0ZASmi8giJ+5PKc8ydqwdt16pEvToATNm2EU2Fi+GVq1Y06EDjzRt6uoofc6BA3btk2rVbHl8Pz84etR+7o4aBRcu2MWuPvgAataEEiWgSJErhThFhF/2/EJ4ZDiL9ywmR5Ec9Ap5lTerv0lQzorp2hanJXgR2QM85Kz3V8qjnTljM8Sjj9rRMDNnwptvQmwsjB4NL79M4u+/uzpKnxIXB4MG2crJFy7YbYUK2ST+4492LlmbNtCli03+1y4wl5CUwKytswiPDGfjsY0Uyl6I/o/3p1NIJ3IH5E739oDOZFXKNT7+2Cb5IUNspmjdGpo0gfPnoXBhV0fntSIi7JomH3549YqEq1dDu3a2t6xFC/jkE7ve+Dff2Ne0bg3vvXf9qsqx8bGMXT+WwSsHs//sfsrmL8v4ZuNpU6EN/hn906tp16UJXqn0tm0bjBgBr7wCwcFXtufKZW/KKWbNskk8IcGekc+ZA2XKwKRJ8OqrcM89dj2Uhg3t88uXv7L++PUcjT3K8FXD+WLtF0RfiqZOkTp80eQLGpdsjJ9xjylGmuCVcrakJFi61FZz3LjRFgTLnh0+/dTVkfmML76Arl1tn3poqL1uXbWq/dL09dfw2GP2AyBfvlu/186TOxm4YiCTNk4iISmBlmVbElozlGpB1ZzfkNukCV4pZ9mwASZOtJnj6FG7rXhxqFHDDnksUMCl4fmC06fh/fftxdGnnrLJPEsWWLfOlsj/+mu7JG14OGS8RTZccWAF4ZHhzN0xl8wZMtMxuCNv1XiLkvlKpktb7oQmeKXS2pkz8PbbMGYM+PvDk0/a7/oNGmgXTBqws76uHqJ47QXP+Hj75//wQzvV4Nokft999ovUzp1Q8SYDW5Ilmfm75hO2PIzlB5aTJyAP79d+n65VuxKYPdAZzUtTmuCVSkvffGPHs588CT17Qp8+mtTT0B9/2EsXO3de2ZY5s50LFhhok/6hQ3DsmP29Xj0YOhQqVPj3e/n73zi5xyXGMXXTVAasGMCOkzsokqsIQxsNpWOljmTP7DllFDTBK5VWhg61p4qVK8PChbrARho6f96OYhk+3Ja3/+gje+YOdmTp8eM2qYP9swcFQZUq0Ljxv8/ubyb6UjSj1o5i6KqhHI09SqV7KjG95XRaPdiKjH6ely49L2KlXC0hwU5lrFYNypa124YNs8m9RQvbsasLbNy1Eyfgl1/g55/t6JZjx+yF0v/7v7RfK/zA2QMMWTmE0etHExsfS4PiDZjSYgr1i9XH3M4nhJvRBK/U7UhMtAtqXK56WqMGPPwwjBypyT2NXLyYgfffh4ED7eSjvHnh8cfh9dftvLC0tPnYZsIjw5mxZQYiwnPln6NXzV4E3xOctjtyEU3wSqVWUhJ07GiT+yef2MJf48Zpck8Dycnw11+2vsv771fl1Clo29ZO7n34YciQIe32JSJE7IsgLDKMRbsXkS1TNrpU6UKP6j0okrtI2u3IDWiCVyo1kpOhUyeYMsUWBnvvPbv9rbfsFb+SJdM2C3mZw4dt8l671t62brXFuHLlssMW//zT9rMDlCkTx/z5/lSvnrYxJCYn8t227wiPDGfdkXUUzFaQz+p9RucqncmbJW/a7sxNaIJX6laio+0UyB9/tOPuLid3sFfwypRxWWjuJDnZltH5+ms7xP/ee+1n3uLFdn4X2C89lSrZUaOJifZPe/481K5tJ/VWqgRnzqynevW6aRbX+fjzTIiawKAVg9gbvZdS+Uoxuulo2j/UnoCMAWm2H3ekCV6pm9m2DZo3h7177XTI115zdURuac8eOzv08iqCR47AggW2D71WLejXD554wg5XvNWEooiItInpxPkTjFg9ghFrRnD64mlqBNVg4BMD+U+Z/7hNKQFn0wSv1PXs2mX717/4wp52/vabPc1UVzl+3F6CGDjQDlscMwZeesl+sRGxly1uldDT2u7Tuxm0YhAToiZwKfESzUo3o3fN3tS6v1b6BuIGNMErdZmILTU4YICtHZMhAzRrZodABgW5OjqXu3TJnplfvi1cCFOn2rP05s3tn+m++64835j0Te5rDq0hLDKM2dtnk9EvI+0qtKNXzV6ULVA2/YJwM5rglRKx/Ql9+9orgMWK2T6FDh1sQXAfc/CgnTG6fbu97dljF8E4ceLq52XJAi++aIf/ly7tklBJlmQW/rmQ8Mhwfv/7d3L556J3zd50q9aNQjl879hdSxO88m1xcTZLzZhhE/v48faCqo8Od5w0yY43v3DBdrkUL25roIeE2LPze++1n3mFCtnHUtZUT0/xSfFM3zydAZED2HpiK0E5gxj4xEBefvhlcvq7KCg3pAle+a5Tp+z49T/+sKV7337b6xP7rl32ksK1PU7nz9tZohMn2slEgwfbSboBbjbIJCYuhtHrRjNk5RAOnTtEhYIVmNx8Ms+Vf45MGbz72N0JTfDKN8TH2+GNixfbM/WSJWHePPj7bztx6dlnXR2hU4nY4ft9+tj7990H1avb7fv2we7ddkHpPn3sSND0vjB6K4fPHWboyqGMWjeKmLgY6hWtx9hmY2lYoqFHlxJwNqcfRmNMBmAtcEhEdAVhlf4OHIBnnoGVK6FuXTsxacECyJ3bJvxHHnF1hE4VF2crME6ZYtcUrVbNrj2yapWtqFikCLRqZR+rW9fV0V5t24ltDIgcwNRNU0mSJFqVa0VozVBCCoe4OjSPkB6f028C2wHtGFPp78cf4YUXbJb79lv473/t9qQke/rqbqeqdyghAXbssKNbjh+3t9OnbWn6VavsAheffGLX+TYGunVzdcQ3JiIs/Xsp4ZHhzN81nywZs9AppBNv1XiL4nmKuzo8j+LUf93GmCDgSeBz4C1n7kupq+zfDz16wOzZdnHN776DUqWuPO4FZQWOH7d95cuW2cE/ly5d/bifn/2SUqCAvYb83HMuCTPVkpKTmLtjLn029GH70u3kz5qfvo/2pUvVLuTPmt/V4XkkI5eXRbn2AWNa3uyFIjL7lm9uzLfA/wE5gF7X66IxxnQCOgEEBgaGzLxcpS8VYmNjyZ7WdUPdnC+2GVLf7oyxsdz73XfcP3MmiPB3u3YceOYZJHPmdIgybd2szYcPB9C7d0WOHg2gVKlYHnzwLGXKnKNgwThy544nT54EsmZN/KdmujuLS4rjp2M/MevgLA5dPMQ9me/huSLP0TCwIQEZ3Owqr5Pczf/revXqrRORytd9UESuewMmOG4/AmeA7xy308D8G70uxeubAl84fq+bmteEhITI7ViyZMltPd8b+GKbRVLR7mPHRN55RyRHDruaW/PmInv3pkdoTnOjNq9fLxIYKJI3r0hkZPrGlJZOXTgln/7+qRQMLyj0RaqMriKztsySxb8tdnVo6e5u/l8Da+UGOfWGXTQi8iKAMeZnoJyIHHHcLwRMTMUHSy2gmTGmCRAA5DTGTBWRdql4rVKpc/asXWxzyBA7eLtVK3j3XVu5yoOdOwfbtuWkYEFbcTEuDlavtv3p48bZrpclS66sN+JJ9kXvY/CKwYzdMJYLCRdo/EBjetfqzaNFHsUYQ8SJCFeH6DVS0wd/3+Xk7nAMuP9WLxKRd4F3AYwxdbFdNJrcVdrYvdsObxw82F5NfPZZ+Phj102pvANJSXYY/q5ddmji5T7yKVOgd284fvzhf70mSxaoUwfGjvW86gkbjmwgPDKcWVtnYYyhbYW29KrZi/IFy7s6NK+VmgT/qzHmJ2CG4/6zwGLnhaTUDSQl2XVPJ0++Un+2USP43/88cv3Tzz6DH36wa4y2bXtlKbqVK+0Y9S5dtlCmTHmio+3Il8qV7fViT5qLJSL8sucXwiPDWbxnMTky56BH9R68Wf1NgnJ62CeUB7plgheRrsaYFkAdx6bRIjLndnYiIhFAxG1Hp5RDprNnbTJfvNgukzdoELRsaQdxe6BffrFfODp0gAkT4Jtv7ELS+/bZ+x06wNKlJ91uXHpqJSQlMGvrLMIjw9l4bCOFcxSm/+P9eTXkVXIF5HJ1eD7jlgneGNNfRN4G5lxnm1LOt24dIa++aleHGDvW1qP1ADEx9hJB1qy2ayVzZjs68/Bhe8ZerpytRuznZ3uYvGEybWx8LOPWj2PQykHsP7ufsvnLMr7ZeNpUaIN/Rn9Xh+dzUtNF0wC4Npk3vs42pdJWUpItNN6nj72quGyZ7adwcyK2Lvpbb11Zhu5a2bLZeVfZsqVvbM5yNPYow1cN58u1X3Lm0hlq31+bkU1G0qRkE59ZXMMd3TDBG2M6A68DxY0xm1I8lANY7uzAlI/btcvOQF2xAlq2ZF379tTygOR+5Ihd2WjBAqhf356VX7xoB/gkJNjPrKQkaNLEO1b623lyJwNXDGTyxsnEJ8XTomwLQmuGUj0ojRdUVXfkZmfw04GF2IlK76TYfk5ETjs1KuW7ROCrr+zpr7+/XVGiTRsSfv/d1ZH9Q8R+mciZ006ODQiANWvs8MUZM2wiHzYMunTBIyYa3YkVB1YQFhnGvB3zyJwhMy8Ev0DPGj0pma+kq0NTKdxsHPxZ4Kwx5gPgqIjEOYY7VjTGTBaR6PQJUfmMEyds//oPP9gFPMePtwXI3Uh0tC0fP3euvW8M5M9vQ8+SxQ7Df+89jxqtmWrJkswPO38gPDKc5QeWkycgD+/Xfp+uVbsSmD3Q1eGp60hNH/x3QGVjzAPAaGAe9uy+iTMDUz5m8WJo396OaR882FbDcrPT36goePppW2G4f387vHHHDrviUY0adhx7Li8cIBKXGMfUTVMZsGIAO07uoEiuIgxrNIyOlTqSLbOXXETwUqlJ8MkikuioTTNcRIYbYzY4OzDlIxIT7VJ5//uf7ZT+6SeoWNHVUV1lzx47KnPsWHu2/vvvULOmq6NyvuhL0YxaO4qhq4ZyNPYole6pxPSW02n1YCsy+nlHFU5vl5qjlGCMaQ10AJ5ybPOgqRbKrRw6ZMcGXrhgO7NXrbIzezp2tB3XLh5WsmOHHQGTmGi/QOzfb7tjMmSwK/n16wcFC7o0RKc7cPYAQ1YOYfT60cTGx9KgeAOmtJhC/WL1dXEND5OaBP8i8BrwuYjsNcYUA6Y4NyzllfbuhcceswtwZM1qM2iOHPZCatu2ro6Or7+2lwASE214SUm2X71XL3jzTShc2NUROtemY5sYEDmAGVtmICI8V/45etXsRfA9wa4OTd2h1Mxk3QZ0S3F/L9DfmUEpL7Rrlx03eP68HfpYpUq6h5CcbBfAyJLlylqj0dF2adaRI20VhJo1YdYst7u26zQiQsS+CMIiw1i0exHZMmWja5WudK/enSK5PXOWsLpCO9KUc4jYq5Fbt9rboEE2wy5ZAg89lK6hJCfbpN23r12t7zJjbJiXde8OYWGeVevlTiUmJzJ7+2zCloex7sg6CmYryGf1PqNzlc7kzZLX1eGpNKIJXqW9+Hg7lnD69CvbSpeGOXPStb6tiO0/79PHfsaUL28rCycn20sAycmQNy/ky2fDq1o13UJzmQsJF5iwYQIDVwxkb/ReSuYtyeimo2n/UHsCMvrG4hq+5GYzWTOKSGJ6BqO8wPnzdt3Tn36Ct9+Gp56yRVfy5EnXMJYtsyV3V6ywyXvGDLvutpuNvEw3J86fYOSakYxYPYJTF09RI6gGA58YSLPSzcjg5/nLF6rru9kZ/GrgYQBjzHAReSN9QlIe6/RpePJJuzJFOhYFi421I1/Wr4djx2wxr61b7UXRMWNsxQMvWVv7tv11+i8GrRjE+KjxXEq8RLPSzQitGUqt+2rpiBgfcLN/9imPfi1nB6I83Pbt8J//2H73b7+1K1k42YULdsRlWJidSVqkCNxzD5QoYcvtdu1qR8P4ojWH1hAWGcbs7bPJ6JeR9hXb07NGT8oW8MAloNQdu1mCv/5q3Epda8ECaN3aDk359Vd45BGn7Wr/fvjxR/j5Z7urc+dsVYOPP7aLZPgyEWHh7oWER4YTsS+CXP656F2zN92qdaNQjkKuDk+5wM0SfBlHFUkDlEhRUdIAIiLuNd1QpZ/ERNi0yXbFLF8O06bZNVDnzoX7b7ma4x05cyYTXbrYOmRJSfZsvXVrW93AiZ8nHiE+KZ4Zm2cwYMUAthzfQlDOIAY+MZCXH36ZnP45XR2ecqGbJXj9LqeulphoJyV9/LFdegjs3P1XXrH1Y5zQH7J3r12jtH//asTFwWuvwRtv2CqOvt6FHBMXw+h1oxmycgiHzh2ifMHyTGo+idblW5Mpgw+M9VS3dLNqkn9fu80Ykx84JSLafeNLLlyA776Dzz+3A8lDQuyCojVr2opbaZxpDx+GSZPsMnYbHFWPataMZty4/F5RQ/1uHT53mKErhzJq3Shi4mKoV7QeY5uNpWGJhnrhVF3lZsMkqwP9gNPAp9jyBPkBP2NMBxFZdLM3NsYEAEsBf8d+vhWRj9IqcJUO1q2zfSJff23XnytXDmbPhubN0zypi0BEhJ1ROneu7YapUQMGDLDXa/fv30KZMnXTdJ+eZtuJbQyIHMDUTVNJkiSeLvc0oTVDqVzY/RdCUa5xsy6aEcB7QC7gN6CxiKw0xpQBZgA3TfBAHPCYiMQaYzIBy4wxC0VkZVoErpzo9Gl4910YPdoW/2rVyo41rF3bKQPJjx6F11+386Dy5rVrfbz6qh0Nc9n+/Wm+W48gIizbv4ywyDDm75pPloxZ6BTSibdqvEXxPMVdHZ5yczdL8BlF5GcAY8wnlxOziOxIzddARzdOrONuJsdNu3bcmYidfdqjh03yPXvChx/apYvS0PnztgbMuXMQGWmLeV24YCs1vvnmlToxviwpOYmlJ5byzrh3WHVoFfmz5qfvo33pUrUL+bPmd3V4ykPcLMEnp/j94jWPpSpRG2MyAOuAB4CRIrLq9sJT6ebECXvaPGcOVKsGv/yS5jVjYmMhNNT2+qS8ilOrll3uzhtXQbpdFxMuMnnjZAauGMifp/+keJ7ifNHkC54Pfp6smXx0UL+6Y+ZG10uNMUnAeeywyCzAhcsPAQEikurL9MaY3MAc4A0R2XLNY52ATgCBgYEhM2fOTHXwsbGxZM+ePdXP9wbOaHO+ZcsoPXAgGc+fZ+9LL3Hg6adtAfQ0tHVrTv73v7IcORLAU08dpkSJWLJlSyJ37ngqVYq+Zc+Ptx/rmIQY5h2ex+xDs4lOiKZMjjI0L9Ccx4MeJ4PxrVIC3n6sr+du2lyvXr11InL9CzEiki434EOg182eExISIrdjyZIlt/V8b5CmbU5OFvnkExEQqVRJZPPmNHnbpCSRlStF+vUT6dBBJCRExM9PpEgRkYiIO3tPbz3We8/slW4LuknWz7MKfZEm05rIkr1LJDk52WvbfCu+2O67aTOwVm6QU51WocMYUwBIEJFoY0wWoAFaR959JCTYLpkJE+xsobFjIXPmu3rL7dth+HCYN88OdQRbD+bBB+01296907w732NtOLKB8MhwZm2dhTGGthXa0qtmL8oXLO/q0JQXcWYJpkLAJEc/vB8wS0TmO3F/KrX27rWTk3791V5E7dv3roY9itjPh8vrZDdqZIc2Nmpk50EpS0RYvGcxYZFhLN6zmByZc9Cjeg/erP4mQTmDXB2e8kJOS/Aisgmo5Kz3V3cgOtpOUBo+3JZXnDDBDn+8QyJ2idWePe2CGk88AZMnQ2BgmkXsFRKSEpi1dRYDVgwg6mgUhbIXol/9frxa+VVyB+R2dXjKi/loEVUftHChLbF46pRN6p9+ekfr0u3ZY0vw/v677ZKJjrbXY/v1syNkfLXe+vXExscydv1YBq8czP6z+ymbvyzjmo2jbYW2+Gf0d3V4ygdogvd2yck2mX/8MVSsaIc/Bgen6qUxMXYp1SNH7Jn63Ll2HY8MGWyVgtat7QJN9erZ1ZKUdSz2GMNXD+eLNV9w5tIZat9fm5FNRtKkZBP8jH4CqvSjCd5bJSXZWUT9+tlyvu3bw6hRqSoIlpRk66x/8IFN8pcFBdnPiZde8p1FqW/HzpM7GbhiIJM3TiY+KZ4WZVsQWjOU6kE+XsdYuYwmeG/z11+2KNgPP8DJk+Dvbwu8dO6cqgupa9bYio3r10ODBraEQOHCUKiQ/ZnGw+O9wooDKwiLDGPejnlkzpCZF4JfoGeNnpTMV9LVoSkfpwneW4jYM/RevWxHeLNmdoWlRo1SNTYxOhrefx++/NKuivT117YEjRYnvL5kSWb+rvmER4azbP8y8gTk4b3a7/FG1TcIzK5XmZV70ATvDXbtsuvT/fKLHcoybpztT0mFhAS7IHXv3rZaQbdu8MknOl79RuIS45i6aSoDVgxgx8kdFMlVhKGNhtKxUkeyZ/at2ZfK/WmC92R79tgLqJMnQ5Ys9gy+U6dUnXbv329Hw4wda6s5Vqliu+offjgd4vZA0ZeiGbV2FENXDeVo7FEq3VOJ6S2n0+rBVmT00/9Gyj3pv0xPJGIvnn74oR3P3r07vP02FCx4y5fu2mW76KdNswNsnnzS9rk3aqT969dz4OwBhqwcwuj1o4mNj+WJEk8wpcUU6herr4trKLenCd7DmIQE6NgRJk60neRDhtirnzeRnAx//GFP8GfNstddu3WzpXmLFEmXsD3O5mObCY8MZ8aWGYgIzzz4DL1r9Sb4nmBXh6ZUqmmC9ySnT1Px7bftOnapKDGwd6/tvZk0yf6eI4e9BtuzZ6pO9n2OiBCxL4KwyDAW7V5EtkzZ6FKlCz2q96BIbv0kVJ5HE7ynWLQIXn6ZXMeO2YzdocN1n3bhgj1LnzjRzjY1BurXt131LVo4ZV1sj5eYnMjs7bMJjwxn7eG1FMxWkM/qfUbnKp3JmyWvq8NT6o5pgnd3MTH2tHvMGChXjg19+hByneS+c6cd4jhxIpw9Cw88YMvOtG8P99+f/mF7ggsJF5iwYQIDVwxkb/ReSuUrxVdNv6LDQx0IyKjLSinPpwneXSUm2iEuffvC8eN2HOPHH3Nu5ZUlbePibPmAr76CJUsgUyb473/tnKbatXUM+42cOH+CEatHMHLNSE5dPEWNoBoMajiIZqWbaSkB5VU0wbujX36xV0F37LCZ+vvvoWrVq54yaZI9sT95EooVg//9z1571UqON/bX6b8YuGIgE6ImcCnxEs1KNyO0ZiiP3P+Iq0NTyik0wbuTxET46CObrUuVsqfnzZpddSqelGR46y0YPNjm/g8+gMcf1yqON7Pm0BrCI8P5bvt3ZPTLSLsK7ehVsxdlC5R1dWhKOZUmeHdx9Ci0aWP7Wjp2hBEj7OSlFE6dgnffrcCaNfDGGzBokB0Gr/5NRFi4eyHhkeFE7Isgl38uetfsTbdq3SiUo5Crw1MqXWh6cAc//QTPP28vqF5nEY5du2DoUHsBNT4+N6NH2wWZ1L/FJ8UzY/MMBqwYwJbjWwjKGcTAJwbyysOvkMM/h6vDUypdaYJ3pbg4eO89eyr+4IO2771CBQCOHYM5c+Cbb+C33+xyqW3aQJ0663jxxSouDtz9xMTFMHrdaIasHMKhc4coX7A8k5pPonX51mTKkMnV4SnlEprgXeH4cTsD6auvYPdu6NIFwsMhSxbOn7cleqdOtTNQS5e2NdhffdVeQI2IOO/q6N3K4XOHGbpyKKPWjSImLoa6ResyttlYGpZoqKUElM/TBJ+e4uJs9p482V5QrVnTlhp48knAzjZt0QI2bYIePWxPTfnyOtzxerad2MaAyAFM3TSVJEni6XJPE1ozlMqFK7s6NKXchtMSvDHmPmAyEAgIMFpEhjprf24vLs7WjvnhB1vat3NnKFfun4d//NFOTk1Otr83buzCWN2UiLBs/zLCIsOYv2s+WTJmoVNIJ96q8RbF8xR3dXhKuR1nnsEnAj1FZL0xJgewzhjzi4hsc+I+3VN8/JXk/sUXNrk7bNxoC0H+9JM9W58zx85CVVckJSex9MRS3h3/LisPriR/1vz0fbQvXap2IX/W/K4OTym35bQELyJHgCOO388ZY7YD9wK+leBPnrR9LT/++M/SeWfOwMKFNpl/9x3kzg0DB9queH9/VwfsPi4mXGTyxskMXDGQP0//SfE8xRnZZCQvBL9A1kxaVEepWzEi4vydGFMUWAqUF5GYax7rBHQCCAwMDJk5c2aq3zc2Npbs2d10FR0RCv76Kw+MGEHG2Fh2v/EGfz7ekv79y7B8eX6Skw158sTzxBNHadt2PzlyJKbqbd26zWnkbMJZ5h2ex5xDc4hOiKZ0jtI0L9CcBkENyGB8p2i9Lxzr6/HFdt9Nm+vVq7dORK5/8UlEnHoDsgPrgJa3em5ISIjcjiVLltzW89PNqVMiTZuKgEjVqiKbNsmJEyIhISIZM4q8/bbIypUiSUm3/9Zu2+Y0sPfMXum2oJtk/Tyr0BdpPLWx/LbnN0lOTvbqdt+IL7ZZxDfbfTdtBtbKDXKqU0fRGGMyAd8B00RktjP35Ta2b4ennoIDB+z49m7dOHQ0Aw3q2FEyc+f+M2hGOWw4soHwyHBmbZ2FMYY2FdrQq0YvKgRWcHVoSnk0Z46iMcA4YLuIDHLWftzKggXQurUtMRARgVSvwcyZEBpqJ6kuWgSPPurqIN2DiLB4z2LCIsNYvGcxOTLnoHv17rxZ7U3uy3Wfq8NTyis48wy+FtAe2GyMiXJse09EFjhxn65x6RL06WOvlD70EMybR+TB+3mrBqxaBcHBtiCkLmgNCUkJfLPtG8Ijw4k6GkWh7IXoV78fr1Z+ldwBuV0dnlJexZmjaJYB3j9FZ/16O4B961Z49VWi+wyk98fZGDMGChWC8ePtw76+oHVsfCzj1o9j0MpB7D+7n7L5yzKu2TjaVmiLf0YdOqSUM+hM1rvxzTe2QEzBgsiChcy+0IiulW0lgl697Fod2bK5OkjXOhZ7jOGrh/PFmi84c+kMte+vzcgmI2lSsokurqGUk2mCv1PffGP722vUYPfAebzxUV4WLYJKleyQd1/vjtl1ahcDIwcyaeMk4pPiaV6mOaE1Q6lxXw1Xh6aUz9AEfyccyT25eg0+rbaA/9XOQUCAXYSja1ffrtG+4sAKwiPDmbtjLpkzZOb5h56nZ82elMpXytWhKeVzfDgV3YGkJHsh9b33SKhSg+Z+C1gwMAcdOkD//nDPPa4O0DWSJZn5u+YTtjyM5QeWkycgD+/Xfp+uVbsSmF3XEFTKVTTBp9a+fXZRjqVLOd/ovzy6ZwKb9uZgyhRo187VwblGXGIcUzdNZcCKAew4uYMiuYowpOEQXnr4JbJn9q2ZiEq5I03wqfHbb9C8OQAbekyi8bT2xMUbfvnFN8e1R1+KZtTaUQxdNZSjsUcJvieYaS2n0apcK11cQyk3ogn+VjZsgObNiS9chK73/8CYwUUpVw6+/RbK+tiazQfOHmDoqqF8te4rYuNjebz440xuPpnHiz+ui2so5YY0wd/M3r3QuDHn/fPw0N+LOHLgXvr3t4txZPKhE9XNxzYTHhnOjC0zEBGeLf8svWr0olKhSq4OTSl1E5rgb+TYMWjYkLjzCVQ9v4TAGvfy2wy4/35XB5Y+RISIfRGERYaxaPcismXKRpcqXehRvQdFchdxdXhKqVTQBH8969ZBixYkHjvJY/GLCaxXlu+/B1+oYJqYnMjs7bMJjwxn7eG1FMxWkM/qfUbnKp3JmyWvq8NTSt0GTfDXmjIF6dSJGP8CPBb/BwUahjBnjq0f5s0uJFxgwoYJDFo5iD1n9lAyb0m+avoVHR7qQEDGAFeHp5S6A5rgL0tOhnffhbAwtuSvy2MnZ1H/2QJMnAgBXpzfTpw/wcg1IxmxegSnLp6ielB1whuE85/S/yGDn48X0FHKw2mCB0hIgJdegilTmJHrNV48PYz/G5SJ7t3BWweH/HX6LwatGMSEqAlcTLxIs9LNCK0ZyiP3P+Lq0JRSaUQTfGws0qoVZtEiPs74KV9kfp+ffjNeO759zaE1hEWGMXv7bDL6ZaR9xfb0rNGTsgV8bMynUj7AtxN8XBwJTZqR4Y/feYUx7K/3Mhsne1/JARFh4e6FhEeGE7Evglz+uQitGcqb1d6kUI5Crg5PKeUkvpvgk5OJb/simf9Ywgt+kyn3f+0Z3Qv8vKiCbXxSPDO3zCQ8Mpwtx7cQlDOIgU8M5JWHXyGHfw5Xh6eUcjKfTfBxPd/F/7sZfOD3P56d357GjV0dUdqJiYthzLoxDF45mEPnDlG+YHkmN5/Mc+Wf01ICSvkQn0zw8YNG4D8kjC9NZyp/+47XJPfD5w4zbNUwRq0dxdm4s9QrWo8xT42h0QONtJSAUj7I5xJ84tffkbFnN+byH/JOHU7zFp6f+Laf2M6AyAFM2TSFJEniv2X/S+9avalcuLKrQ1NKuZDTErwxZjzQFDguIuWdtZ/bkfz7HyS3actqqnN6+HQ6tvHccd4iwvIDywlbHsYPu34gS8YsdArpxFs13qJ4nuKuDk8p5QaceQY/ERgBTHbiPlJNtmzlUsNm7E8uysr3fuCtrlldHdIdSZIk5myfQ1hkGCsPriRflnz0fbQvXap2IX/W/K4OTynlRpyW4EVkqTGmqLPe/7acOcPZR5/iYlwAM59fxEef5XN1RLftYsJFJm+czGdrPuPgxYMUz1OcEY1H8GKlF8mayTM/rJRSzmVExHlvbhP8/Jt10RhjOgGdAAIDA0NmzpyZ6vePjY0l+60qgCUnU/j1Tyi6M5Luleby9ICsHjUUMiYhhnmH5zHn0BzOJJzhgawP0KZIG+oUqEMG47ldTLcrVcfay/him8E32303ba5Xr946Ebn+BTcRcdoNKApsSe3zQ0JC5HYsWbLkls853PVzEZCw+4fLhQu39fYutffMXum2oJtk+zyb0BdpPLWxLNm7RH777TdXh+YSqTnW3sYX2yzim+2+mzYDa+UGOdWrR9Gc/uZXCo7ow9wsrWm3ootHVITccGQD4ZHhzNo6C2MMbSq0oVeNXlQIrABAxL4I1waolPIYXpvgk06eQdq2Y5cpTbFfRlOosPsOhxQRFu9ZTHhkOL/s+YUcmXPQvXp3ulfvTlDOIFeHp5TyUM4cJjkDqAvkN8YcBD4SkXHO2t+1tjfpSZmEE6z4+Eea1nLP/rzE5ERmbZ1FeGQ4UUejKJS9EP3q9+PVyq+SOyC3q8NTSnk4Z46iae2s976VA+N+pvyaCXz7wDv8t8/DrgrjhmLjYxm3fhyDVw7m77N/UyZ/GcY1G0fbCm3xz+jv6vCUUl7C67poks7GkrFLJ3b5labWzx+5VT33Y7HHGL56OF+s+YIzl87wyP2PMKzxMJqWaoqf8aChPUopj+B1CX5T0/d4KG4/m/r8QcNi7rEU065TuxgYOZBJGycRnxRP8zLN6V2rN9WDqrs6NKWUF/OqBH8ychcVln3BwiKdafJxLVeHw4oDKwiPDGfujrlkzpCZF4Jf4K0ab1EqXylXh6aU8gFeleD/fuEjAgig7EzXdc0kSzLzd80nPDKcZfuXkScgD+/Xfp+uVbsSmD3QNUEppXyS1yT4Qws3EfLnTBYEv0eT6gXTff9xiXFM3TSVASsGsOPkDu7PdT9DGg7hpYdfIntm9xzFo5Tybl6T4I+9+iHZyMVDU3ql636jL0Uzau0ohq4aytHYowTfE8y0ltNoVa6VLq6hlHIpr0jwe2au5uED81hY61Mal8+TLvs8cPYAQ1YOYfT60cTGx9KgeAMmN5/M48Uf18U1lFJuwSsSfMybH3DS5Kfq1Dedvq/NxzYzYMUApm+ejojwzIPPEFozlEqFKjl930opdTs8PsGf/TuagDNHWN/gHZ4o6pyFpEWEiH0RhEeGs3D3QrJmysrrlV+nR40eFM1d1Cn7VEqpu+XxCT5Xkdz4n4ni/sTkNH/vxOREZm+fTdjyMNYdWUfBbAX5tN6ndK7cmXxZPa+mvFLKt3h8ggcIyJYBSLva6BcSLjBhwwQGrRzEnjN7KJm3JKOeHMXzwc8TkNE9Jk8ppdSteEWCTysnzp9g5JqRjFg9glMXT1E9qDoDGgygWelmZPDzncU1lFLeQRM88Nfpvxi0YhDjo8ZzKfESzUo3I7RmKLXuq6UjYpRSHsunE/yaQ2sIjwznu+3fkdEvI+0rtqdnjZ6ULVDW1aEppdRd87kELyIs2r2IsMgwIvZFkMs/F6E1Q+lWrRuFcxR2dXhKKZVmfCbBxyfFM3PLTMIjw9lyfAtBOYMY0GAAr4S8Qk7/nK4OTyml0pzXJ/iYuBjGrBvD4JWDOXTuEOULlmdy88k8W/5ZMmfI7OrwlFLKabw2wR8+d5hhq4Yxau0ozsadpV7Reox5agyNHmikF06VUj7B6xL89hPbGRA5gCmbppAkSfy37H8JrRlKlXuruDo0pZRKV05N8MaYRsBQ7CyksSLSzxn7ERGW7V9GeGQ4P+z6gSwZs9AppBM9qvegRN4SztilUkq5PacleGNMBmAk0AA4CKwxxnwvItvScj8xcTE0nNqQlQdXki9LPj569CO6VOlCgWwF0nI3SinlcZx5Bl8V2C0iewCMMTOB/wBpmuBz+uekRJ4StK/YnheCXyBrpqxp+fZKKeWxjIg4542NeRpoJCIvO+63B6qJSNdrntcJ6AQQGBgYMnPmzFTvIzY2luzZfWu1JF9sM/hmu32xzeCb7b6bNterV2+diFS+3mMuv8gqIqOB0QCVK1eWunXrpvq1ERER3M7zvYEvthl8s92+2GbwzXY7q81+af6OVxwC7ktxP8ixTSmlVDpwZoJfA5Q0xhQzxmQGngO+d+L+lFJKpeC0LhoRSTTGdAV+wg6THC8iW521P6WUUldzah+8iCwAFjhzH0oppa7PmV00SimlXEgTvFJKeSlN8Eop5aWcNtHpThhjTgB/38ZL8gMnnRSOu/LFNoNvttsX2wy+2e67aXMREblubRa3SvC3yxiz9kYzuLyVL7YZfLPdvthm8M12O6vN2kWjlFJeShO8Ukp5KU9P8KNdHYAL+GKbwTfb7YttBt9st1Pa7NF98EoppW7M08/glVJK3YAmeKWU8lIemeCNMY2MMTuNMbuNMe+4Oh5nMcbcZ4xZYozZZozZaox507E9rzHmF2PMn46feVwda1ozxmQwxmwwxsx33C9mjFnlOOZfOyqUehVjTG5jzLfGmB3GmO3GmBrefqyNMT0c/7a3GGNmGGMCvPFYG2PGG2OOG2O2pNh23WNrrGGO9m8yxjx8p/v1uASfYq3XxkA5oLUxppxro3KaRKCniJQDqgNdHG19B/hVREoCvzrue5s3ge0p7vcHBovIA8AZ4CWXROVcQ4FFIlIGeAjbfq891saYe4FuQGURKY+tOvsc3nmsJwKNrtl2o2PbGCjpuHUCvrzTnXpcgifFWq8iEg9cXuvV64jIERFZ7/j9HPY//L3Y9k5yPG0S0NwlATqJMSYIeBIY67hvgMeAbx1P8cY25wLqAOMARCReRKLx8mONrWibxRiTEcgKHMELj7WILAVOX7P5Rsf2P8BksVYCuY0xhe5kv56Y4O8FDqS4f9CxzasZY4oClYBVQKCIHHE8dBQIdFVcTjIE6A0kO+7nA6JFJNFx3xuPeTHgBDDB0TU11hiTDS8+1iJyCBgA7Mcm9rPAOrz/WF92o2ObZjnOExO8zzHGZAe+A7qLSEzKx8SOc/Wasa7GmKbAcRFZ5+pY0llG4GHgSxGpBJznmu4YLzzWebBnq8WAwkA2/t2N4ROcdWw9McH71FqvxphM2OQ+TURmOzYfu/yVzfHzuKvic4JaQDNjzD5s99tj2L7p3I6v8eCdx/wgcFBEVjnuf4tN+N58rB8H9orICRFJAGZjj7+3H+vLbnRs0yzHeWKC95m1Xh19z+OA7SIyKMVD3wPPO35/HpiX3rE5i4i8KyJBIlIUe2x/E5G2wBLgacfTvKrNACJyFDhgjCnt2FQf2IYXH2ts10x1Y0xWx7/1y2326mOdwo2O7fdAB8domurA2RRdObdHRDzuBjQBdgF/Ae+7Oh4ntvMR7Ne2TUCU49YE2yf9K/AnsBjI6+pYndT+usB8x+/FgdXAbuAbwN/V8TmhvcHAWsfxngvk8fZjDXwM7AC2AFMAf2881sAM7HWGBOy3tZdudGwBgx0p+BewGTvK6I72q6UKlFLKS3liF41SSqlU0ASvlFJeShO8Ukp5KU3wSinlpTTBK6WUl9IEr+6YMUaMMQNT3O9ljOmbRu890Rjz9K2fedf7aeWo3Lgklc9fYIzJncYxFE1ZZTDF9sLGmG+v95q73F+wMaZJWr+vcj+a4NXdiANaGmPyuzqQlFLMgkyNl4BXRKReap4sIk3EFgFzOhE5LCLO+JALxs6nUF5OE7y6G4nYtSR7XPvAtWfgxphYx8+6xpjfjTHzjDF7jDH9jDFtjTGrjTGbjTElUrzN48aYtcaYXY4aNZfrxIcbY9Y4amW/muJ9/zDGfI+dDXltPK0d77/FGNPfse1D7GSyccaY8GueX8gYs9QYE+V4TW3H9n2XP9CMMX2MXZdgmaOWeS/H9ghjTH9Hm3aleG1RR4zrHbeaN/vjpjyzN8a8YIyZbYxZZGz98LCUf1tjzGBj66r/aowpkCKOyo7f8ztizwx8AjzraNuzxphHHb9HOQqd5bhZXMpz3M6ZjlLXMxLYlDLhpMJDQFls+dQ9wFgRqWrsgiZvAN0dzyuKLQ9dAlhijHkA6ICdul3FGOMPLDfG/Ox4/sNAeRHZm3JnxpjC2BrjIdj64j8bY5qLyCfGmMeAXiKy9poY2wA/icjnxq5BkPWa96wC/NfRlkzAemwlxMsyOtrUBPgIW3flONBARC4ZY0piZzdWvo2/WzC2omgcsNMYM1xEDmCLdK0VkR6OD62PgK7XewMRiXc8p7KIdHW05Qegi4gsN7aw3aXbiEm5MT2DV3dFbHXLydiFG1Jrjdha93HY6diXE/RmbFK/bJaIJIvIn9gPgjLAE9g6HVHY0sn5sAsjAKy+Nrk7VAEixBa1SgSmYWuv3zRG4EXHNYUKYuvxp1QLmCcilxyP/XDN45cLw61L0aZMwBhjzGbsFPzbXajmVxE5KyKXsN9Siji2JwNfO36fiv1WcjuWA4OMMd2A3HKlVK/ycJrgVVoYgu3LzpZiWyKOf1/GGD8g5bJrcSl+T05xP5mrv1VeW0dDsHU63hCRYMetmIhc/oA4fzeNuGpHdoGGOtgqfhONMR1u8y0utymJK23qARzDnvVX5uq/ye2857Xve63Lf7d/jgEQcKM3FZF+wMtAFuw3ojK3GZdyU5rg1V0TkdPALK5eWm0ftksEoBn27PV2tTLG+Dn65YsDO4GfgM7GllHGGFPK2IUxbmY18KijHzoD0Br4/WYvMMYUAY6JyBjsylLXrou5HHjK2DVEswNNU9GeXMAREUkG2mOXqEsLflypvtgGWOb4fR9XjkHKi7XngH/62Y0xJURks4j0x35z0QTvJTTBq7QyEEg5mmYMNqluBGpwZ2fX+7HJeSHwmqNrYiy2e2K94wLkV9ziWpLYUqvvYMvQbgTWicitStDWBTYaYzYAz2Jr0qd8zzXYsq6bHPFtxq5IdDNfAM87/iZlSLtvHOeBqo6/x2PYi6hgV0vq7GhDymOzBCh3+SIr0N1xIXkTttrhwjSKS7mYVpNU6g4ZY7KLSKwxJiuwFOgkjjV00zmOWBHJnt77Ve5PR9EodedGG2PKYfu3J7kiuSt1M3oGr5RSXkr74JVSyktpgldKKS+lCV4ppbyUJnillPJSmuCVUspL/T/DJLw9D9A8DgAAAABJRU5ErkJggg==\n", + "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] @@ -153,7 +165,7 @@ }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] @@ -267,13 +279,13 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 21, "id": "8713e865", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] @@ -285,7 +297,7 @@ }, { "data": { - "image/png": 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\n", 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AFXe+1tZ4AV1tNTbfd1/Ye+/4eufPT7YPis8r3JaEX1UnA/uIyApAB1WNuQKONkc9Nu6msaktxPgbKdSTRLVCPXHTGBaJ0a/9zDPJ9Q4YkGxf0jmCnRDee6+wbA1CPUWFX0RWAY4DegMdxTNCVX+duTUORynUs3A2UqinGjH+Uj3+uGtczC6/O+64ccn2BW2M29/aCocckt6GGoV6hgGvAe8CdexaORqWehbORvT4S43xb789vPVW/rZSPf6oUE8UxexqbY2/2cyZA7ffHl2XzTWo51AP0FVVzyx+mMORAWlEsC30428kjz+KNMI/dmzhtrSNu0kx/ihsPP44fvaz/PQNxTz+cF113qvn3yJykoj0EpHV/JdN5SKyiog8KCIfisgHIvJdr/wzXqrnZ0Rk1TI/gyMt9Sg05fy46/Hz+NRz+0M1KXfkri+qtiIZJ7Jp7UraHx5tWyzGX0cev02NS4CrgFeB0d5rlGX9fweeUtUtgL7AB8D5wHOquinwnLfuqAW1atx99FHTb79c2kLjbiN5/JWM8acVSdvG3XI8/qQun1EDyMITyofDWHHUSPjPAvqoam9V3ch7bVyskIisDOwJ3AagqktUdQ5wCHCXd9hdwKGlGO7IgFoIzogRpoHr96GsH+XYUs/C6WL8Zhm8Dqpw0knw/PPF6/KvTbiet9822z4IZYz3j3v88fztWQr/HXeYuX/D4h6sa9ttC8stWhRdd/iGEKZGaZknYbJzpmUj4EvgDhHpi3lSOAPoqar+ALDpQM+owiJyMnAyQM+ePRnpT7ackpaWlpLLVpJa2tVv3jxWAsaMHs28xYvz9lXarjVefJFtgC9fe433A+fpNGcOuwFLly7l5YjzR9m1pyodgFdefpklq69eMZvjGOAto66Xv+/lF19k6WrJkdFun33Gzgl1lUItfl+dv/ySXYHFS5bwqnfuXRYvpivw1ujRzF2+3NjV3MyAW2+FW2817wN1+Db720a9+SYtc+eyxfTprA1MGD+eL0aOZOObbmIDYPI11/Dp0Ud/W361995j20Bdfj0vvfgiy1ZcseA7a2ppYY/QtuD5Xxg5ktauXfNs5Gc/A2Dh+uvTPbD540mT+CRkP+PHM2P99ekJTP7gA4Ie86wvv2QNYPL48SR50iNfeCF7r19VE1/AI8AE4CbgOv9lUW4HTGqHnb31vwN/AuaEjptdrK7+/ftrqTQ3N5dctpLU1K6ddzZDSV56qWBXxe165BFz7kGD8rd/+aXZvvrqkcUi7erUyZT5/PPMzbTCH5KTtG/atOL1jB+fXFcJWH+P//mP6h57ZHPSqVPNZ+jVK7etd2+zbcSInF3LluV/3tzQplw5f33UKLN+3HFm/YYbzPrZZ5v1K67It+GJJ6LrnjVL9eijC8/z9dfR197f1tJSaKP/2mST/PWLLy4sH3z9/vf564MGmeVFF0Uf779aW1N/FTkzGKURmmrj8f/Pe6XlM+AzVX3dW38QE8+fISK9VHWaiPQCZpZQt6McbOOllcDvGlcsrmmDTfz40kth2jT4xz/KP18p1Gv7w9y5JmTx299mV2c4LBMkHOpJW2dwfenSXBtR2BOO84xbW+Gee5LtiisXh21aB5+4UE+x9q5ahHpU9S4R6QZsoKrjbStW1ekiMlVENvfK7Q2M816Dgb94y6Glme4om3oS/lJssenO+Yc/mGWthL9eG3dPPTVaCNPy97/DzJlw2WU5kSwW409zMwxfmz/+EU4/Pbce7mMfJ5KVaNwN/4aL1VVCjF87dKASXTBs8vEfDIwFnvLWtxORRy3rPx0YIiLvANsBf8YI/r4iMhHYx1t31IJ6En6fUmyq58bRWjTuqrLmiBHJnuRXXxVue/ll+OabdOf6zW/gz3+Gr7+279VTjsc/a1b+etjDDwr/+usXP2c5Hn/aY8PC75NFD7eU2LQYXAzsBMwBUNWxkNgW8S2qOlZVd1DVbVX1UFWdrapfqereqrqpqu6jql+XaHv7YvLk0lISl0M9CX977dVTC49/2DC2/tOf4KKL7MtMmQK77w6nnFLaOY85pjrCHyZJ+D/7LPe+Eh5/mAqEerRCk8vb1LpUVeeGttVp4LINs+ee8Otf5yazqAa1EMyOXnQxTvhLiWfWs/DXwuP3veLPP7c/55w5Zvn22/bneeml3Psvvkj+HKWGetLmsUnr2Re79mlsLSb84acpmxh/hcba2DTuvi8iRwNNIrIp8GvglYpY08j4j94VusNHUkuPP9wwVk6Mv14bUKG2tiWJRvh6R8Xni7HHHvnrtjH+Snr8aQU+S4+/WH98/+YarjvJ46+Q8NuozOnA1sBi4B5gLvCbiljTyGQ1p6gN9dirx4V6qksWwl+szmB9Xv0rfPwx3HBD+jrj7CrX4y8nxh+2KTQmpoCvQ1Ft/0aRdMOokB4kevwi0gQ8oaoDgQsrYoHD4AthNdMo1JPw+/g2XXihyV0+1KLTVz0Lf40ad1MfkyT8y5bBRx/B5punP29I+PuffHK6+XCz8virEeMfMQLmzYOVVoreHxb+Z581y3rz+FV1OdDqpV9wVJJahARqKZjFPP4//9nk9LEhy8/xwAPJMzGlpRYefyntJUlPnBdeCFtsYTogxCFiFePvkHYS9LTCH3d8qaGed94xs2+Fw1pQeH0nToTDD4+va/bs6O11GuNvAd4VkWeABf5GdROxtH1qKfxZxviz/Bw/+UnpZZ9+Gnr3hs02y22rZa6erGL8r3hNelOnwsYJHfqS6ij1M1bb43/sMRg0KLf+/e8b0V97bejTByZNSrZt7Fg49tjoc82bF709KdRTQ+F/2Hs52hu1jC23x8bd/fYzy2AMux7i9zYkXcNu3cyyWB9/2+6caai28AcnVvF58UX40Y+gc2fo1Ckn1FGeugj85z/JNoepQXdOq5G7FTmzo3bUsnHXP2cWKRvCddYLv/pV7n0tY/xpPP6kUE85wh/6rbU2NdEhzXdf7NqER+6W27gbJ7Sq5np27Fhc+NMSzjBabn0W2Izc/VhEJodfFbHGUV2qJZiq8NRT5k8WFv4f/QjOP7/99uqplPDPmwff+55pdI2rr5QYf1SZ7l4OylKEP7RPw0K9666l1wnZd+dMEv4OHXLjUCBa+Evx0GfMiN1Vy+6cOwA7eq89MNk5Uz7LOOqSaoVIHnkEDjgArr22UPgffhiuuKL+hD+rOivVuDt0KLzwQvLoXF80evSAffZJPmdSfN73+N98M7dt2rTC4yz68RcI/6uvxhgfsjPuGmXZnXPpUnjwwfjjfI/fJyvhT6Dz3PDY2WwoaqWXYsF/fa6q1wI/qIg1jupQ7blqp041yylT4kM9QVtuvNGu3krG+LO6Nkm2vf46NDdXPtSzYAE891z0MT5Jwr+qNztqsI7ddos/b4JNBcJfDN+uuLrD9tqGdLbfvnD7X/8ab0eUxx/VKFurWe1SUjTGLyL9AqsdME8ANo3CjnqnmqEeMH+cuMbd4LGnnVZa/VlSDY9/l13McvTo9PWmid/bsP/+ZhnlsfqhHv8GAPDxx4X2WHTnTC38adujbLtz+hO7B4V/+vT4en2Pv1MnOzvqHBsBD94GlwFTgDL6vDlqTrUbd4MeaNy566U7p09ra2HDYRx+w19cPTbl0/DhhzBmTPH6br0Vbrkl3TnjBnAFl8XOm7QvbSik2G81fH3L6dWT1IYRbNxNIpgYro6x6dUzsBqGOGpALYU/6ZhS68+SYnUG9yfdJILHjRxpYu0zZkBwqsi09m+5Zbrjy8UX/GBKgqjv0qIff8kev22jbTnCvzBhhtknnjCD2IoJfxvBplfPGSKykhhuFZExIvL9ahjnqDDVEv6gIKQdWWlDPQh/HMF9V15p2jZeey3dudKSFLIods4kjz8o/J0729VZrHG3GGk9/jS/r3feyd9eLDPuhx82jvADP1PVecD3gdWBY3GTp7QP6tHjT0MtG3eD+20HLvnx4bRpC2ztANPb5uKL05fziQrFRPVbj4p1V6Jxt5jHP2qUabz2iTsunBkTYN9984+3SYkeJ/xduhQvW0fYCL/vAhwI3K2q7we2Odoy9RTjL8emtGXWXhsGFolgpknelXRs8DhfNLIYtRyHjbefdM5SPX7Lxt3WtB5zsd/L3/8ORx9deHyYcHdW36ashL9r1/gy//xn8XqDrLBCuuNLwEb4R4vI0xjhHy4iK+ImYmkf1LJXT5a2pC07Y4aJt5dTZ7GJRaI8Vd9LDncDrKcwV5LwF/P4gyG9Tz+FAw80Cc6CdpbauJt0c33lFbj7bnNd087nGzz+k0+KlylF+E85JX8ayGJUoeeQze33RMx8uZNVdaGIrA6cUFGrHJWl2r164mL8fv/+cm2pdYxf1UwCHnfcG29A377ZCH+xdAfldnu08fj9BupwfcGb6f/9Hzz5ZMH5Sgr1LFsGDz0Uf8ysWTB4MAwbls5bDnv8UYPSwsQJvz/ILY40N7y016gEbAZwtQIfA5uJyJ6YSVlWqbBdjmoQ8efvc911sN12lTlPWPg32CDRlqJUI8a/zz7wu9/F7/fPHxdXnzgRdt4Zzjij/FCPKpQ7krO11cz9Worw+x5/eDAYmH7xF1yQf57weSdMSP9dqcKQIXbH/ve/0UnWkupOa09Q+I88Mne9igm/TW+1nj3N0p+NDxh/5pnp7LPEplfPz4EXgOHAH73lxRWxxlFdIv786z3ySLp5V9OcxybGn4ZK9+MHI3KXX1643zbG/+WXZvnWWznRKDZFX5BFi0xKi2XLTFx9q63sy4btBPjtb41IxT052Mb405533jzYfHN6hAd+FaO1NTfYKmvKFf5f/jLnnSeFesBO+P26rrzy203TDj44nX2W2IR6zsDk6XlNVQeKyBbAnytijaO61GOMv1KNu62t6R63y43x+/iiGRz1mcbjv/xyuOQSWGUVUy4hoVdkXWHb/JTRcTefKIEKThFoc62nTDGvIIsWFS8XhR/qqQRz50Y/zSURjL937Ghey5YVF/404ZvddjOzdc2YYd9YnxKbf8IiVV0EICJdVPVDoMgcbI42QS169cSduxrCX4yZM+3rLNad0yeY7rgU4fcn70gaXJRE2LP3r0MpHj8Uv/HEMXFiaeVU0z0hpSU8pqIYK66Ye9+xI/zF69nup7WIw8bp8K+9iEmPscUW6WxLgY3wfyYiqwD/A54RkaGARfO3o26pduNumn78pcTrsxL+o46yr9M21BNMdxwX6rGxP+m6zZhh6r/oIthhh/z9cbbFedFRN4RgiKdXr+K2RmE7Ocn06fk2q5rG23ohOJ9uU5Npu1GNHtAWxCbU49dRhURvNikbfui9vVhEmoGVgacqapWjOtSyV08W3TrTNO5GHTNunPGkfbEMCkyxOm1DPVEefxrhtxGBt94yy0suiT9/mDjhj/KugwOkKk2vXvCd7+SfO6lHT7UJCn8w3l/Mo7f5Hnv0sD+2TKyCniKyu4icoKrPA68C61bWLEdFCE/23Oge/9Zbw447RtdTbDDSb35jd6yN8Cdh8+RhMXCqgDTC7/fFrxbvvpt7X2qIq1KsvHLufVD4f53BFOS+8FcytOVh06vnIuA8wO+r1Qk3EUvbY/hwWG01ePbZ3La23ribpldPKd0I43j7bbjzTru6g6Eev4EvaS6CUrC58YSJE/5XXoFnnsnfZjOitVKk6UlUDXxxhnzh/36R9GU237FfdxWesGw8/h8Cg4AFAKr6BbBiYglH/fHii2YZbMyq9QCuIPXQuBvkvPPy14NhlHDeF9tQjx8OsE0sBnaP/UnlgwI/YUL09jDXX5+/Xg3hD4ZQgpTaG6hc7rsvenuwV49NTx2/F1Xcd7T77rn3fsNxFZ6wbIR/iaoqoAAiUvlEEo7s8cUm6HXXMtSTZa+eUmP8PlH99IMePeRPcRgeRGXbnbMU4S+X44/PvR86NPc+KZwQvrG1tFQ+jUCciBab5zeJcmLlq60WvT1op03eIb/9KO43Mnhw7r0/0U0VbnY2wn+/iNwErCIiJwHPAjGzOzjqlqDw+9RTjN8njWfuPxrbeEhJ9f7ud+ker8PCbxvj90WjFOFPOiZp3xNP5N5vvHHufZLHH/x8qubaxHnkWRHXOFor4Y8rm1b4ixHsSXbllXD66fDjH5dfbxFsevVcLSL7AvMw/ff/T1WfKVLMUW/Ug/DbxPjTCP8aa5h8P/7o2CSK5biJEsLwHKwiZnL48E3CNtRjc3yYYuI1caL991hsvlgff+wA5OyvdNrhSnj8HTqUns4j7kYUvIZphD/8HV11lZlvOZjqYbXV4Lrr7OssA6tePZ7Q/wkzYne0iMQ8BznqlqjZkdpajP8//zH5WHz8WaxshL+YACxZUnjua64pPO666wpvEraNu379aRp3i31HY8YUNsYWswWSPf7gcf77Yv3Uy6USwr/GGqWXtfH4bWL8/vf3gx/kti1aBGefbZ7G0mYrzQibXj2/EJHpwDvAKGC0t3S0JYJed3hb0vFZnrvcGP+xx5rEWD6+F2ozwKeY8P/tb/Dee9E2hbelGXkb9PjjnmpsrnWS5//RR8XLB20B+zQIq6xilm0xxr/DDvDYY6WVjRPkUkM9wafHOpi0xcbys4FtVLWOhs85UpM21LN8eXbTzKWJ8Yf3L15cPIZfbowfckPvk2zx6ynV4/ePyzrGv6JlJztb4Q+ey29orPSUg5WI8Tc1wUEHwRFH5D8p2hB3o00b6vGvZdKxw4dXLh9RDDbPGR8BdTaKwpGatL16isXE0+Cf56OP0o/YPeggWHPN5DKV6M4ZV2+Ux//GG/F1BD3+YJ6c4FNKud05bXPQ236nUfa0RY/fr3PIkOhkbOutF182eN2DOXOybtwFMwbgwAOzqcsSG+G/AHhFRG4Skev8V6UNqzqvvgpXX11rKypHKR5/Kbz8cmHjp3+em29OL/zBAWdxZVpb4YMPTAqGOLIU/gsvzN/205/G1+HfJMKhnt69o4+/++7ofPdJ9tvmoE/znT7wQP5gpVoJ/7Bh5dfpe/5BunQxN+wTTijsugv5/5M//Sn3Pij2NjH+deszyYGN8N8EjABew8T3/Vf7Ytdd4Zxzam1F5aiG8E+fbgakBPsmh89TLMZvw4svmqns/BBPa6vJU7/11vFlshL+tI/kwX78QY8/eHMMnmfw4Oj5YbN4Akvj8Z97br6NtWrcLYekXDrLl5u8QLffDv37F5YNevzB305SqGfIENhlF/P+pJPMjF7ByYbqCJtnlU6qWvI0MCLShGkM/lxVDxKRjYD7gNUxN5BjVXVJUh2ODEjbq8dWJGbOhKefNl6vP8LTTxoWPnexcyYRLHfuufDZZ7luh+UO4LI5p8+SlD9Vv9tkMNQTvnm880502ZkzTaNzVJlSsP1Ov/iisEdMpT3+tL1bunfP5fE54QS4447CY4I3k3DILHg9o84dHEQV/O0E6wyXO/poM/jttdfMcWuvnfgRaonN1X5SRE4WkV4ispr/SnGOM4APAutXANeoah9gNmZOX0elSdurJ0okPvzQ/IGCoYjDDjO9bT7/PL8RN+rcSedMM7DL/9PGDYgqVt6WLIQ/6PH79YVFPO5JM+j5ZzG9ZNQI5TjCPaUq3bibdsKR4CCnW2LGkwZttmkrCcbyv/e93Ps4jz+KKAerDrER/qPw4vzkwjxW3TlFZD3gB8Ct3roAewEPeofcBRyayuK2wtKlcPLJ8EmdTF0Q1bh7yilmIEkUUcL/wgtm6feQmDfPxPQhfx5XG+FPGeqRoD2+F+3/CW2eIkoJlVTK47fNvvjhh7n3WYR6Jk8uvWylhT+cObYYwWto0/UySYijfrdNTcaDh2iPf7fdouuKCqkGqZPQj83I3Y3KqP9a4FxySd1WB+aoqu/yfEZMimcRORk4GaBnz56MHDmyJANaWlqsyg7wlqWeJ8xqr7/OtrfcwlfvvMO7EV0Fw3b553/++efpOWIECzbYgPlp51dNYPPPP6cXMH7iRNZtaeHbZrtzz2VkIDWxb8crL77IEn+AlEevCRPYHPjiiy+YMHIkm119Net4+15/5RVQZWdg4aJFvBH4bH0++wy//8S4cePYCli6bBnB4MHoUaOIiLR+y8LAaNIFc+awArB4+XK6ADOmT8ebppqRzc15f2D/83xw111s+Ze/MOqWWwhNVRLL0iVLCAc4Fs2bR5FJ9vKYPXMmqwIzZ81iUceObAB8NXEiqxcpN3LkSPZsbf3WM/vko4/YMMV5s+arOXOK2lwpZu26K2u88goA7//hD2z9pz8x8/PPWcvbP/L557/9noN8MXMmE7zfYY/x4wu+d///t8LHH7MjsGDhQt657z46trSwYORItvjyS9YGPhg3ji29Mm+/9x59ga8XL+adCK1Y+/PP2QKYMn8+U0L7OwwbBk1NtKbQGFv9So2qVuQFHATc6L0fADwOrAFMChyzPvBesbr69++vpdLc3Gx3oJ/ZPCseeMDU96Mf2dnln3/RouxtUVU94QRT5223qfbtmztH+Dz+tqlTC+u45Raz78QTzfqRR+aOHzfOvEB1s83yy516au64e+4xy1VWybfhtdfy10OvFx57LLe+2WZmudZaZvmTn+Qf/+abhZ/nkEPM8k9/SjxP3mvllQu39eplXx5U99jDLI88UvWcc8z78PX3XyK593vtle48lX4deGDtzh38Hh96yCwPPTR6f/B16qm538GYMdH1qqq+845Z33rr/N/tcceZ7XfemSszYYJZ/uUv0f+zZctU//538z/OAGv9igEYpVqoqZV8ftsNGCQiBwJdgZWAv2OSvXVU4/WvB3xeQRtqh9/wVGwuzmpR7BE0TFRoIZxdMtjgt2xZ7jE4KdRTYqw6MtTjx8rDdb78cvwUhEFbihF1DUoN9QR79UybFn1shw65c44Yke48laYSvW5KIc1kNrahHr8//y9+kb89KpvqppuajgVxU1A2NWUzKUuFSVQBMaxfSsWqeoGqrqeqvYEjgRGqegzQDBzuHTYYGBpTRW1IIwxJBIX/k0+S+5hndc4k/B/vLbfk52UvdnyQ8MQnwbjv8uW57UnCHxerLnINIoXfX4ZtjRrJWso1zkL4oxp3w9k9feq5QbBGOWUK8LuV2gh/UuPuXnvl3q+6qgkRnn56/jFB4b/nHjNJDZi++fVyPUok0XrvUaGMERSRnAecKSKTMDH/2zKuvzyymvHH7wO9wgpmsE5SH/MsR8nG4YvjK6/YjYb0bVq+HM4/33QtDAt/0OPffnvYZhvzPvwnCwpzlsIf5/FHCb9/zJtvJp4njyw9/mDjbtxvrJ7FpNq23Xhj9Hb/N2fzPQQ9/mB71cCB8PjjxcsPHGiW22xj0id/97vFy7QRbL7NMSKyY/HD4lHVkap6kPd+sqrupKp9VPXHqlpfc6uVM0Q8SJpQTzWEP63H69v09NNwxRXwy18WTm4e17fbxuMP25NG+P2GXl/4w2WjUhj4x6RJ2hX11FOq8D/5ZPEwVz0I/zHHRG+3sW3//bOz45e/zF9ffXXTvdX34m3GNQQ9/vXWgwu82WPXXjs/HXIcP/2pCcvtvLOdzW0Im1/azsCrIvKRiLwjIu+KSMyIkzpj++3ZPTxUOw7/R5LV5M5+PZdeWvzYanr8tvg2+cK1ZElyqCdIWCRsQj1FWGXs2NyKf3OOC/VEUUqoJ0pc0tbjh/i++qr+5o+NIu5mbiP8d9+drS1PPw3vv2/ez5pl0k8HQz1XXpmcJyncLuGHd+LSZURRx4OwysFG+PcDNsH0vz8Y01vn4EoalRljx9LRdmYl3wOIE/7hw43wHX64XZbJNDM62Yrh44/nRnlOmGBssqVU4fc/Z4cOyaGeIGGPPyq/e5gi13PLK66I3xn+bK+/XrgtiwFQtgRTRwcJT2kYppo2xhEn8DbCn3UD8L77mlQcQYKhnnPOgR0TghFhx2SffeDRR+GPf8zWzjZI0W9TVT/BdLvcy3u/0KZcm8MPycQJ/5NPmuVDD5llMeFP8+RgK/wHHwx9+5r3m2+e7tG6VOEP9gby//xphT947nvuiS5TTgN3+LNdcgmccQZMmRJ/TCWJy5ZZLDVwOTY+9VTpZYPECbzN91ONnj9+Kontt8/fHhzslmTPwQdXPv1EG8BmIpaLMA2yXoCMTsB/KmlU1VA1InXHHTnhzzrGb0OWoR6R/Hk8fcoVfpHSY/zBz+eP9E0Z40/EvykHueEG2Cgw9rCawl9q75xybNxvv9LLBomz3ca2oIfdo4fpkhrMePv88/FlBw2ys2/DDU145x//yN8e7l45eLC5+TsisfHcfwgMAhYAqOoX5Ebitm38YeJnnVU81JOGjz6Ce++1Pz7rSRjuu8/8gYP5TyoR6omL8ScJf62oRpdZn0oL/+abl1a/DXEev41tQQ97/nzTK+ass3LbkiZsD/Z9L9bdeMcdoWto/HTQ7h//2KRarvQE8W0YG+Ff4nXrVAARsZz1oY1RLNSThuD8mjZkJYxhcQs2fNkIXzBvT1SoJ4sYfxyVFuZqCn8le+c8/zz8/OeVq99G+HffPX/fww+b3DXFUjdHTTnob1M1uYSmTzeDpNLi2y0C99+fvnyDYfMLvV9EbsKMuD0JeBaISYfXhsky1FOsES94XPfudlMH2tRZru3nnpt7nxTq8ftA2/bqifIWA7l3gMoLc1sI9djQtWuu/qQJYEqlWKjnqKPMfAhBfvhDeOml4jc8v+3j3HNzjbKrrmqW8+eb0FzPntFli1EvI4vbCDaNu1djsmk+BGwO/J+qXl9pw6pOlh6/rQBfdZU59jqLCc122qn4MUlimlaMokI9/h/bv0ZxHv/o0ab7YriuJCot/H5m0WpQSeHv0iVXf1hoX3opvpxNv/WoOn2y+H422IDX//1vM7/xPfeY7LV772322TpLcYQ7HjgSsWncPRGYoqrnqOrZqvpMFeyqPlnG+KOEP+oP5QuizaCgiROLHxMW/mLn9wnmNvdJCvX4JHlZwTrrQfirSYcOsM46xY8rhaDHH/5Od9vN9GiKYrTlpHl+3ddem789o3DkN+utZ87Rpw/cdFOul05cGgtbnMefCptQzwbATSIyWUQeEJHTRWS7CttVfbL0+KPyiKyySvzxWcX4S/X4H3ywcNsjj5iJKfybUjDUE1V/mOA8BOWEWUqJ99YaEbsbdSl06ZK77lFiF54P2Md26sSoxGRg+tT36gXnnZe/PTzNZlr+8AczWvhnPyuvHhGTUuGBB8qrp0Gwycd/EYCIdANOAs7B5NlvX7dY3+PPqjtnGD+WGUWlhD9IsfhrWMRvuMEsp07NlU8akRsmePMrx+NPumHWK1E3yawI9maJEv6477kU4d9jj1w8f/XVzZSMYaImKk/D6qvDfzLoHS6SS6LmKIpNqOf3IvIk8DTQBzgbvp1Xo/3gx6uzStkQJilnTznC39oKkyaZ3h7lCH+cV37RRbnySQOzwgTDVzafL9jtz/Yc9YpI5Xr2BL+DNOfwhT/qhnTrrYX1q8KwYbnEZFHfw5pr2p/fUVfY/HIOw2TRfBZ4GBiqqjEJxdsBlRL+JA+wHOFfvtyEQwYMiA/1zJlTfNToqCKzaYbtX7w4ftpGyPf4bcQ7mIsnSD2MAUhL1NNRVqy1VnKoJw5f+KO6VAYTswU9/h49cqG28BPZV1+VN5Wjo6bY9OrpB+wDvAHsC7wrIgndB9o4tqGeLBsj04pb/8AkhUFRnTEj/zjfxvfeK17nLrsk7w8L2TXXRD/6+6T1+ONoi8JfqVDP9debetNMMu/jC3/SPAsA/fqZpZ8j50c/MstwTpzVVjM3BkebpGiMX0S2AfYAvgfsAEwFXkws1Jax9fgXLYrPyZKWtCN3x4zJvQ8K47vv5h9XbIRtGsKJ6ZLCSgAtLabMSy81pvBXwuP3Bd+P89tO2g65UGbwem61lfntBW096ijYbjvY0ptldtAgU6bcz+PX56gLbL7Nv2CmTbwO2FJVB6rq/1XWrBpiK/xpsm8WIythfPvt6GOyEP7gRCJgf7O6++72I/y2KXqj2kOgMLFYWsLCn2ZugE6dTD6foYEJ795/H8aPLxT1sEiXK/pz5th3J3VUBZtQz0HANcA8YHMRad+p7WyFv6Ulu3NmJYx+ymafLD3+Dh3yPX5bb7NLl/IaaKvZuLvWWsn7o+LjUcSFem67rfg5kvAF2Bf+NPn9RUwGz6h0IlmGpXr1yp/tCmDlle0HkDmqgk2vnu8BE4F/ADcCE0Rkz0obVjNsY/z1KPxxZBXqiZr+sBidO7cdj/+LL8ygoiCDB5vBRpAs/Kedlnsfd723396+bejccwv7zIc9/kWLosum/W36wn/55enKRfHZZ4VtTY66w0YR/gZ8X1XHA4jIZsC9QP/EUm2Vtuzxh/G95aw8/uAEI+1R+JuaCnvKNDXlrl8wa+Rbb5nt3/mOWQ+mr0gas2HLpZcWZni1Ff5w21Ox799mYiFb6mH6SEdRbL6lTr7oA6jqBExO/vZJGuFXNQmqyo33B8Ut/GceNy43fV+xsmH8OHwWE0+E/9A332xXrnPn8sI11Y7xhwWwQ4do4dxuu9zk8pB/TNKgM1uBjfrOwqGeOOEPYxuicjQMNsI/SkRuFZEB3usWoEin7zZMmsbdBx6A//0vP6tlKQTFLTwoZuutzcumbNy+LPKYlBoHfuwx4x2XyvLlyZ8/a8LC3NJi98QUPCZJ3E8+OX/93/8uPOaHP4wu63+Pfp75FVc0jedRs08FCQv/sGGF7UGOhsJG+H8JjAN+7b3GedvaJ2li/P5NIujxl+KhBmcmCoZQbLxDG+HPovGu1Ef4YNfTUli+vLwG0bRECb/NE1PwmKjr7TsHl16avz04S5ifktifXSouKd7225uUGrffDsceW3xilvA5DzggF6JyNCQ2uXoWi8gNwHNAKzBeVVP0I2tjpAn1RPVUyDLXj02IJMpj9MlyZq/wGIFqkUUf8nLP73vzf/sbnHlm9HFB4T/22Px9X31lBjxBvpiPGpU/GG+99UzDaFyK4eBkI8HG5CTaU9ZTR2bYDOD6AfAv4CNAgI1E5BeqGjHRaTugmPA3NRkxqIbw2wj3H/4Qvy/L+PiIEdnVlYZqC3+4K+KyZTnh32CD+HK+8P/854XTAvqiH6Z/qH/EY4/B8OHxk5G41MOOjLD5R/0VGKiqA1T1e8BATL/+9smyZck9Vvxka1G9es45p/wJJcK2hEnz5y/X47fN6FhJWlsrO7FJmMMOy18PevxJNyC/J005/dV79YLjj8+th29CTvgdGWEj/PNVdVJgfTJgMVdgHfPLX5qJoOOI8tr9UIc/TWJY+B97DK6+Gk4/PfncixbBr35lZ2e5wl+ux18PuVjmzUvv8Yc97jSIwK675tZthf+448zT12WX5bZdfDE0N5duy4EHmi6d/lPdhhuWXpfDEcCmg/coERkG3I+ZcP3HwJsichiAqj5cQfsqw7/+ZZb+HzkssAsX5npO+Hz+ef56uAvn7NlmOX48iZx1Ftx4I6vbzNBUa4+/Rw/4+uvy6siCtB6/TS+cvfeG556L3hf02m2Fv2vXwtmv/JTWYd57L//39fLL0dlJRczYidZWOOKI9L2bDjww3fGOhsHGleoKzMAkaRsAfAl0Aw4GDqqYZdXA7+YWnuzcpoE3bgDXlCnJ5bwp5jra9P2P8tjTCP/06eWNMUiaQyAL9tjD7ri0Hr/N8SeeGN8bJvjEsHx5Ln6f1NieZpDc1lvD+uvn1nfdFU49Nf74Dh1Si/7I5mZ44olUZRyNg02vnhOqYUjFmTTJ9KIIjj6Nw6aBNsuRu3GU6/FfdZWZQvHZZ0s7fyUH/hxxhBFYf4anJNIKv80TQpcuJqYe9YQW5/EnPUG5EauONkTj/Fo33dSknLWhHI+/FOJ6cURNdJG2gW/SpNJDPpVs3L3zTnu70oZ6bES4SxfYaafofUHhX7YsF5ZJsqOaDdAOR5k0jvCnwUb4n3suuQ99GlZeOXp7VCiklJ4dabI4Bqmk8HfubJ/v59FH09VtK/yXXRadynrvvXPvly83/fd/9zuTm97haAfE/kNE5AxvuVv1zKkTbAdx+X3b77rLZHFMIqsh8u1F+Dt0SDeRSBjfCw/P1bv99nbed+fOJoSz7baF+wYPNmkNwAj/qquam4Qf8vFnqXI42ihJrpEf27++GobUnKAIffONaYQVMb0tKpATvusXX8AVV6QvWEqmTdtkXmEqJfy7726WWYwsDjaKzp0Lr75a6PFHTQperP1i443NMtw19KuvzKxiDkcbJklFPhCRicA6IhJ0VwVQVY1wldowX36Ze79wYa5HxLHH2s1ZW4xQxsZ1HnustHraqsd/wAHwpDfY+667zDIL4Q/a6D8FhIU/qmdTMeHfbDPTPfO44/K3x43CdTjaELHCr6pHicjawHCg/Qc3g5NHLFyYG7z0/vvZ1B8KPzTZhpPCZC38w4bF9/fOUvg7djQDkD75JHctSg31DByYm8ovysZwqCfqWhcTfpHkdBgORxsmsRVMVaeral9gGrCi9/pCVT+phnFVZcaMXBgly3w7PvPn5yXM6ljqOUoR/qRQT9Io1yyFf+nSnO3++IRShH/xYnjmmdx6VOgrqXG3d2+zDH62XXaBs89ObwswY599spnvwOGoIm7qRZ8ZM3IDlkr1xpNYtizd5NhxNDXlxMuWJI8/yfPNsh//0qU5QfbbTPbbL7lM1OTmnTvn3/yiRD64bbPNzPgNn6g5Cl591Yx5KIEPLrwwm+/V4agiNt05/akXv6eqewL70R6TtC1caERFpDLCD4UjhEuhqcnYuOWW9mXeeCN+X5K4Z+nJRgn/eecZb7sU2+69F3bbrTC1BuRCPQ8+aCYpCWbBzHJyGoejjVKxqRdFZH0RaRaRcSLyfqB76Goi8oyITPSWGUxQmhEixuvPWvh9Icpi0JcvWGmE669/jd9XzVCPP/mHP0CqQ4fkxtJArP7dSy+FG2/M7TvwQNO7Jsrj98ttumlhvP+nPzXLLObFdTjaKJWcenEZcJaqbgXsApwmIlsB5wPPqeqmmMldzi/V+IrQvXv2MX6/obilpXgun2KUIvxJJAl/OVkuwyxdakbrPv10fpZJyzDJV7vtZrKq2uCLfdRN4fLLTcbPesg86nDUiIpNvaiq01R1jPd+PvABsC5wCOD15+Mu4NDUVleSbt3yPf4sZjDyRWb+/PJHf9pkikxDkrhn2XVxyRJzHfbdt3B7HL6A2+RXCuJfm6ibY4cOZq5ah6OBsZp6ERPn/1upJxGR3sD2wOtAT1Wd5u2aDkQmqhGRk4GTAXr27MnIkSNTn3eAtxw5cmTk+xdeeIFgK/WSpUtZ2q0bC6ZMYda4cWyV+ozRLOzYke7A2y+/TNOiRWwT3v/NN9jmwZy7YAGdFy1i6cKFRES3U/PyqFHEDc0eP3s2SbO5jr36alq7daOfxTSAC+bO5c2I73D7WbOISVjBN4sX0w1444ADaGlpif0NDPCW/v6dFi+mO/D6m2/yjddNN3xMViTZVUucXeloOLtUtaIvoAcwGjjMW58T2j+7WB39+/fXkjD+evz7hQtz70F1zTVV+/VTPegg1SFD8veV8+rXzywfesi8sqhz552zqaelJX7f//6Xe7/qqmZ5//351zJ4Pf3XrrsWbuvTJ/o78q9N8PXGG6rNzaqbbGLWJ0zQ5uZmu+9ZVXXTTc36hx/mtg0dqjp2bCm/okQS7aohzq50tFe7gFEaoakVTdImIp2Ah4AhmpuwZYaI9PL29wJmVtIGK/xGzGXLKtO4G4zxZ4VmNIl2ly6FU/z5+NvXXz+X06ZTJ5NqIiltwZ//XLhtrbWij/VDP8GJbjbfHAYMSDQ7j7PPzp/VLGqy8kGDoG9f+zodjnZMScIvIgmzTn97jAC3AR+oajBM9CjgZzQbDAwtxYZM8dMiz55dGOPPAj+mnKXwexO6pCJusFN40m+fbt1MmoVRo3Ii2qkTnHuu6Uppyx13wMMxE7Vddhl89hkEZyTzb8R+jL/YTe6qq+D6QEqpcLdRh8ORR6Lwi8h3ReRwEVnLW99WRO4BXraoezfgWGAvERnrvQ4E/gLs6+UB2sdbry3BfPiV9Piz6Mfvk3ZS9xEjovvli8T3EOrQAfbf33jrfv/3uL79fvfIqIRoxx8fP+dAUxOsu27+Nv8cpea498s54Xc4Iolt3BWRqzBTK44FzhOR4cDPgcuBnxWrWFVfwiR0i2LvmO21ISz8WXfn7NbNiGhLS3Zz2Ppz/NoycGC0xy+S85D/+tf8NMdB4fRvWiusUFjHN9+Yer74wswtUGo2UJ/wjShtWCsq1ONwOL4lqVfPD4DtVXWRN8hqKrCNqk6pimXVJJgaoFIDuHr0MOJ5+unZ1FlKmoA4b90Xyk02yd8enPPXF+OtIvo6+V1CN9oot021/FmpDjoIrrnGdCudNq348T4u1ONwJJIU6lmkqosAVHU2MLFdij7ke/zduqX3pm3o0cN4/OV6w+UQJ/y+qIeFMij8Dz8MQ4akG/E6cSK89lo6G4NceaWJ/0eFj5Lo1cssXfI0hyOSJOHfWEQe9V/ARqH19kNwQM+QIcbjH2UzODnlOaoxQbvP7bcXboubxMX3kJcvZ2RzM3z3u2Y9eCPo3RuOPjqdDX36wM47pysTpGPHwvi/DUOGwM03Rz+dOByOxFDPIaH1hIQvbZxgSML39seOzfYcfqinEkyenJsx6v77Tchmq63gZ6GmGFuPf599TMbKqOyYbYHVV4eTTqq1FQ5H3ZI0Ecvz1TSkauy/v91xlcjXk8bj339/eOqp9Oc5/HBzIws2bPqx92Ixfj+0c9FFcMIJ+TF7h8PRbkiabL1ZREbEvJ6rppGZ8vbbdsdVoi9/Go8/NFVjHn/8o4mfJxF8ivFnq/JDPeeck39suDG0qcmJvsPRjkmK8Z8NnBN6PQRsApmkiakNn0RMHvbFF4Xbau3xrxyXwQYzinaD0Bi6qNGyYFIT+w2y/ujW80MJUeulF8yQIWaeW4fDUVFihV9VR/svTL6dK4CjgFNUdcdqGZg5wRzzH39slvfck9vm54rP0uPv2jV9425SLvxvvins637BBYVdKKdNy3n7AKeeao4JZ938yU/Mcocd7O2rBEcf7ea5dTiqQGJ2ThHZD/g9sBi4TFWbq2JVtXjhBbMMdlvs2tUIa5Ye/9Zbw0cfZde4O2WKXVpm28bZQYOMty+SP+m8w+FolyTF+N8EbgLuA84F5opIP/9VLQMrii/8QcoddBTFttuaZGhJc9+G+fnP4/d17Ji9nZX43A6Hoy5J8vgXAC3A4cCPyE+/oMBeFbSrOkQJfyXYZhuT7Cz4ZBHmX/+CU07JrW+7rUk+Fm6I/e9/4dBDy7epuRneeaf8ehwOR5sjqTvngCraURuCqYDDZJVTB3KNq3GNp9deC7/4hZma8LXXcg3QZ59t4t7BQUx+PB5MuuSpU0uzacCAdKmPHQ5HuyEp1LOjiKwdWD9ORIaKyHUikuGcfA1CVDdSP7zipyRuboavvsrvsRPsex+eJtFv4HXJyBwORwqSWghvApYAiMiemPTJdwNzgZsrb1o7wRf1YcNy21byesNecgn885/wwx+a9a5dC3vc+MK/8sqFDc5+v/ykEJLD4XCESBL+JlX14x1HADer6kOq+gegT+VNqwLV6L4Y1S/dH5zVubOJ68fl0IHcvmXLCvf5Hn/UPofD4YghUfhFxFekvYERgX1FJ2lvE+y5Z/FjyuGDD+DEE837YO6YNBkufeFfujR+n/P4HQ5HCpKE/17geREZCnwDvAggIn0w4Z62T6WFP9jX/uabczeB8KjbJPxQz667Fu5zHr/D4SiBpJG7lwFnAXcCu3sztvtlMppNpMbsWOUByNddZ6ZA3GIL+zJNTfDWWzA0Ympi5/E7HI4SSAzZqGrBLBqqOqFy5lSZcC8ZgDXWyK4rZ3gCke7dzRSIXbqYPvq2mUK32y56u/P4HQ5HCViM+28HPPGE/bF+SGWttco75/z58bH8XXc1E55su21553Aev8PhKIHGEP6TT7Y/1u9bv+mmZrnGGsXL9O6dv37ZZSYbZ6VxHr/D4SiB9tE7pxjTp8O//53OMw5OID5rFqy3npn/1WfECBO6eeUV02Nn2jTYbDMzN63fL7/S9OkDL71UnZuMw+FoNzSGx3/OOfDTn8LgwcWP3W03s+zb1yz79IHhw+EQbybKHXc0QjtwoJlP9re/NeubbmpG0FZL9AH+8Q946CHo1z5y5jkcjurQGMJ/8cXR2/2c98FwzoknmieD/v1z277//Vw8/eijKzd3blq6d4fDDqu1FQ6Ho43RGMIf1XsHjKd+ww3wfGh6YZtc9w6Hw9FGaYwYfxKnnVZrCxwOh6OqONc2jr286Qb8HkF+D5rwlIcOh8PRxnDCH8faa5vGWj9n/YUXwgor5OfccTgcjjaIC/XYstpq6SZLdzgcjjrFefwOh8PRYDSmx//yy2amqyDvvgtjx9bEHIfD4agmjSn8USmOt9nGvBwOh6Od40I9DofD0WC0b4//jDOY+fbblJln0+FwONoV7Vv4r72WcSNHOuF3OByOAC7U43A4HA2GE36Hw+FoMGoi/CKyv4iMF5FJInJ+LWxwOByORqXqwi8iTcA/gAOArYCjRGSratvhcDgcjUotPP6dgEmqOllVlwD3AYfUwA6Hw+FoSERVq3tCkcOB/VX15976scDOqvqr0HEnAycD9OzZs/99991X0vlaWlroUYdTEzq70uHsSoezKx3t1a6BAweOVtUdCnaoalVfwOHArYH1Y4Ebksr0799fS6W5ubnkspXE2ZUOZ1c6nF3paK92AaM0QlNrEer5HFg/sL6et83hcDgcVaAWoZ6OwARgb4zgvwkcrarvJ5T5EvikxFOuAcwqsWwlcXalw9mVDmdXOtqrXRuq6prhjVUfuauqy0TkV8BwoAm4PUn0vTIFhtsiIqM0KsZVY5xd6XB2pcPZlY5Gs6smKRtUdRgwrBbndjgcjkbHjdx1OByOBqMRhP/mWhsQg7MrHc6udDi70tFQdlW9cdfhcDgctaURPH6Hw+FwBHDC73A4HA1GuxB+EbldRGaKyHsx+0VErvOygb4jIv3qxK4BIjJXRMZ6r/+rkl3ri0iziIwTkfdF5IyIY6p+zSztqvo1E5GuIvKGiLzt2fXHiGO6iMh/vev1uoj0rhO7jheRLwPX6+eVtitw7iYReUtEHo/YV/XrZWlXTa6XiEwRkXe9c46K2J/t/zFqOG9bewF7Av2A92L2Hwg8CQiwC/B6ndg1AHi8BterF9DPe78iZkDdVrW+ZpZ2Vf2aedegh/e+E/A6sEvomFOBf3nvjwT+Wyd2HU+RlCgVtO9M4J6o76sW18vSrppcL2AKsEbC/kz/j+3C41fVF4CvEw45BLhbDa8Bq4hIrzqwqyao6jRVHeO9nw98AKwbOqzq18zSrqrjXYMWb7WT9wr3ijgEuMt7/yCwt4hIHdhVE0RkPeAHwK0xh1T9elnaVa9k+n9sF8JvwbrA1MD6Z9SBoHh813tUf1JEtq72yb1H7O0x3mKQml6zBLugBtfMCw+MBWYCz6hq7PVS1WXAXGD1OrAL4EdeeOBBEVk/Yn8luBY4F2iN2V+T62VhF9TmeinwtIiMFpOZOEym/8dGEf56ZQwml0Zf4Hrgf9U8uYj0AB4CfqOq86p57iSK2FWTa6aqy1V1O0xSwZ1EZJtqnLcYFnY9BvRW1W2BZ8h52RVDRA4CZqrq6EqfKw2WdlX9ennsrqr9MBNUnSYie1byZI0i/HWZEVRV5/mP6mrSWHQSkTWqcW4R6YQR1yGq+nDEITW5ZsXsquU18845B2gG9g/t+vZ6iUlEuDLwVa3tUtWvVHWxt3or0L8K5uwGDBKRKZiJlvYSkf+EjqnF9SpqV42uF6r6ubecCTyCmbAqSKb/x0YR/keB47yW8V2Auao6rdZGicjaflxTRHbCfB8VFwvvnLcBH6jq32IOq/o1s7GrFtdMRNYUkVW8992AfYEPQ4c9Cgz23h8OjFCvVa6WdoXiwIMw7SYVRVUvUNX1VLU3puF2hKr+NHRY1a+XjV21uF4isoKIrOi/B74PhHsCZvp/rEmStqwRkXsxvT3WEJHPgIswDV2o6r8wCeEOBCYBC4ET6sSuw4Ffisgy4BvgyEr/+D12w0yA864XHwb4HbBBwLZaXDMbu2pxzXoBd4mZL7oDcL+qPi4il2AmungUc8P6t4hMwjToH1lhm2zt+rWIDAKWeXYdXwW7IqmD62VjVy2uV0/gEc+f6Qjco6pPicgpUJn/o0vZ4HA4HA1Go4R6HA6Hw+HhhN/hcDgaDCf8DofD0WA44Xc4HI4Gwwm/w+FwNBhO+B2pEREVkb8G1s8WkYszqvtOETk8i7qKnOfHIvKBiDRX+lxF7JiSxQA0EfmNiBznvS/5GorIFiLyqogsFpGzQ/v2F5HxYjJEnh/Yfp+IbFreJ3BUEyf8jlJYDBxWzRGzNngjQG05EThJVQdWyp5q4X3un2EyTpbL18CvgatD52gC/oFJKbAVcJSIbOXt/icm/42jjeCE31EKyzBzgf42vCPsbYpIi7ccICLPi8hQEZksIn8RkWPE5JN/V0Q2CVSzj4iMEpEJXn4VPxnZVSLyppgEWr8I1PuiiDwKjIuw5yiv/vdE5Apv2/8BuwO3ichVoeN7icgLYvKivycie3jb/+nZlJf33vPYL/eOHyUi/URkuIh85A/A8Wx8QUSe8Dzmf4lIwX9PRH7qXY+xInKT95mbvGv6nvc5Cq45sBcwxkt2Fq5zbzG5598VMz9EF2/7gSLyoZikYNeJl5teVWeq6pvA0lBVOwGTVHWyqi7BpDw4xNv3ovedtYsBoY2AE35HqfwDOEZEVk5Rpi9wCrAlZoTuZqq6EyYnyumB43pjhOYHwL9EpCvGQ5+rqjsCOwInichG3vH9gDNUdbPgyURkHeAKjDBuB+woIoeq6iXAKOAYVT0nZOPRwHAv8VlfYKy3/UJV3QHYFvieiGwbKPOpd/yLwJ2Y0cW7AMGJUXbyPuNWwCbAYSFbtwSOAHbz6loOHOPZva6qbqOq3wHuoJDdgILEY951uxM4wivbETPquStwE3CAqvYH1oyoM0xsdkhVbcWMKO1rUY+jDnDC7ygJL2vm3ZiwgC1vejn3FwMfAU9729/FiL3P/araqqoTgcnAFpj8JceJSeXwOiaFrx9XfkNVP444347ASFX90vOGh2Amx0m0ETjBa7P4jjcvAMBPRGQM8BawNUbAfR4NfI7XVXW+qn4JLBYvl45n42RVXQ7ci3niCLI3JiHYm95n3BvY2Pv8G4vI9SKyPxCVRbUX8GXE9s2Bj1V1grd+l/f5twAmB67ZvbFXw56ZwDoZ1OOoAu7RzFEO12LSJAe90GV4DoUXzugc2Lc48L41sN5K/m8xnEdEMTMPna6qw4M7RGQAsKAU46NQ1RfEpMT9AXCniPwN48mfDeyoqrNF5E6ga6BY8HOEP6P/uaI+UxAB7lLVC8I2iUhfYD/M09JPMPH8IN+E7KkExbJDdvXscLQBnMfvKBlV/Rq4HxOG8ZlCLpXtILykdCn5sYh08OL+GwPjgeGYMEUnABHZTEwmwyTewIRl1vAaJ48Cnk8qICIbAjNU9RZMCKofsBLm5jJXRHpiGjjTspOIbOTdDI8AXgrtfw44XETW8uxYTUQ29BrQO6jqQ8DvPXvCfAD0idg+HugtIv6+YzGffzzmKaK3t/0IC/vfBDb1PkNnTFK1RwP7N6Mwo6SjTnEev6Nc/gr8KrB+CzBURN4GnqI0b/xTjGivBJyiqotE5FZMOGiMiAgmtHFoUiWqOk1Mt8NmjEf9hKoOLXLuAcA5IrIUaAGOU9WPReQtTMrjqcDLJXymN4EbMALdjMm5HrR1nIj8HjMLUwdM4+ppGC/6jkBjcMETAWYu1n+HN3rX7QTgAa/h9U3MPLeLReRU4CkRWeBtB0zaa0z7x0pAq4j8BjPv8TwR+RXmBtwE3K6q73tlegLfqOr0Eq6Lowa47JwOR4XxwlFnq+pBFTzHI8C5XruIzfE9VLXFu4n+A5ioqteUeO7fAvNU9bZSyjuqjwv1OBztg/Mxjby2nOQ1Ir+Pmf3qpjLOPYfqTVHoyADn8TscDkeD4Tx+h8PhaDCc8DscDkeD4YTf4XA4Ggwn/A6Hw9FgOOF3OByOBuP/AVcM/F3EZYvfAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] @@ -297,7 +309,7 @@ }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] @@ -419,7 +431,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 22, "id": "470fd269", "metadata": {}, "outputs": [ @@ -432,7 +444,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] @@ -444,7 +456,7 @@ }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] @@ -460,7 +472,7 @@ "N_steps = 100\n", "\n", "N_min = 100\n", - "N_max = 100000000\n", + "N_max = 10000000\n", "n_incr = (N_max / N_min)**(1 / N_steps)\n", "N_samples_arr = []\n", "for s in range(N_steps + 1):\n", @@ -514,10 +526,10 @@ " # . the cross_coh_mean and cross_sys_mean become pow_coh, so constant > 0. Therefor it is\n", " # also possible to define relative cross_SNR using 1 divided by the error in cross_coh_mean\n", " # or the value of cross_incoh_mean, which both go to zero.\n", - " #cross_SNR = np.abs(cross_coh_mean / cross_incoh_mean)\n", + " cross_SNR = np.abs(cross_coh_mean / cross_incoh_mean)\n", " #cross_SNR = np.abs(1 / (cross_coh_mean - pow_coh))\n", " #cross_SNR = np.abs(1 / cross_incoh_mean)\n", - " cross_SNR = np.abs(cross_sys_mean / cross_incoh_mean)\n", + " #cross_SNR = np.abs(cross_sys_mean / cross_incoh_mean)\n", " #cross_SNR = np.abs(cross_sys_mean / (cross_sys_mean - cross_coh_mean))\n", " \n", " cross_SNR_dB = 10 * np.log10(cross_SNR)\n",