From 823661c87a6d88dfbaf6e92ac3c4142d3717e069 Mon Sep 17 00:00:00 2001
From: Eric Kooistra <kooistra@astron.nl>
Date: Wed, 31 Aug 2022 17:41:02 +0200
Subject: [PATCH] Almost done.
---
.../lofar2_station_sdp_firmware_model.ipynb | 947 +++++++++++++++---
.../lofar2/model/signal_statistics.ipynb | 101 +-
2 files changed, 874 insertions(+), 174 deletions(-)
diff --git a/applications/lofar2/model/lofar2_station_sdp_firmware_model.ipynb b/applications/lofar2/model/lofar2_station_sdp_firmware_model.ipynb
index d2a083f351..4365109bb1 100644
--- a/applications/lofar2/model/lofar2_station_sdp_firmware_model.ipynb
+++ b/applications/lofar2/model/lofar2_station_sdp_firmware_model.ipynb
@@ -7,7 +7,7 @@
"source": [
"# LOFAR2.0 Station SDP Firmware quantization model\n",
"\n",
- "Author: Eric Kooistra, 18 May 2022\n",
+ "Author: Eric Kooistra, Aug 2022\n",
"\n",
"Purpose: Model the expected signal levels in the SDP firmware and clarify calculations in [1].\n",
"\n",
@@ -19,7 +19,7 @@
},
{
"cell_type": "code",
- "execution_count": 82,
+ "execution_count": 23,
"id": "2b477516",
"metadata": {},
"outputs": [],
@@ -38,7 +38,7 @@
},
{
"cell_type": "code",
- "execution_count": 83,
+ "execution_count": 24,
"id": "e1b6fa12",
"metadata": {},
"outputs": [
@@ -54,10 +54,11 @@
"source": [
"# General\n",
"N_complex = 2\n",
+ "N_sidebands = 2\n",
"\n",
"# SDP\n",
- "N_fft = 1024\n",
- "N_sub = N_fft / N_complex\n",
+ "N_fft = 1024 # number of time points, number of frequency bins\n",
+ "N_sub = N_fft / N_sidebands # number of subbands, DC and positive frequeny bins\n",
"f_adc = 200e6 # Hz\n",
"f_sub = f_adc / N_fft\n",
"T_int = 1 # s\n",
@@ -70,7 +71,7 @@
},
{
"cell_type": "code",
- "execution_count": 84,
+ "execution_count": 25,
"id": "eb325c9c",
"metadata": {},
"outputs": [
@@ -100,7 +101,7 @@
},
{
"cell_type": "code",
- "execution_count": 85,
+ "execution_count": 26,
"id": "3e71626f",
"metadata": {},
"outputs": [
@@ -137,7 +138,7 @@
},
{
"cell_type": "code",
- "execution_count": 86,
+ "execution_count": 27,
"id": "0ec00484",
"metadata": {},
"outputs": [
@@ -145,26 +146,42 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "subband_weight_gain = 1.0\n",
+ "subband_weight_gain = 1.0 (Unit_sub_weight = 8192)\n",
"subband_weight_phase = 0\n",
"subband_weight_re = 8192\n",
"subband_weight_im = 0\n",
"\n",
- "G_subband = 1 * 0.5 * 2**4 * 1.0 = 8.0 = 3.00 bits\n",
+ "Coherent WG sine input:\n",
+ " G_subband_ampl = 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",
+ " . G_fft_real_input_sine = 0.5 = 0.5\n",
" . W_sub_gain = 4\n",
- " . subband_weight_gain = 1.0\n"
+ " . subband_weight_gain = 1.0\n",
+ "\n",
+ "Incoherent white noise input:\n",
+ " G_subband_sigma = 1 * 0.03125 * 2**4 * 1.0 = 0.5 = -1.00 bits\n",
+ " . G_fir_dc = 1\n",
+ " . G_fft_real_input_noise = 0.03125 = 0.03125\n",
+ " . W_sub_gain = 4\n",
+ " . subband_weight_gain = 1.0\n",
+ "\n"
]
}
],
"source": [
- "# Gain factor G_subband between subband and signal input in the subband filterbank (F_sub)\n",
+ "# Gain factor between subband level and signal input level in the subband filterbank (F_sub)\n",
+ "# . for coherent WG sine based on amplitude\n",
+ "# . for white noise based on std\n",
"\n",
"# . FIR filter DC gain\n",
- "G_fir_dc = 0.994817 # actual gain of FIR filter in LOFAR\n",
+ "G_fir_dc = 0.994817 # actual DC gain of FIR filter in LOFAR\n",
"G_fir_dc = 1\n",
"\n",
+ "# DFT gain for real input dc, sine and noise\n",
+ "G_fft_real_input_dc = 1 # coherent, one bin\n",
+ "G_fft_real_input_sine = 1 / N_sidebands # coherent, so proportional to 1/N\n",
+ "G_fft_real_input_noise = 1 / np.sqrt(N_fft) # incoherent, so proportional to 1/sqrt(N)\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",
@@ -175,27 +192,41 @@
"subband_weight_re = int(subband_weight_gain * Unit_sub_weight * np.cos(subband_weight_phase))\n",
"subband_weight_im = int(subband_weight_gain * Unit_sub_weight * np.sin(subband_weight_phase))\n",
"\n",
- "print(\"subband_weight_gain =\", subband_weight_gain)\n",
- "print(\"subband_weight_phase =\", subband_weight_phase)\n",
+ "print(f\"subband_weight_gain = {subband_weight_gain} (Unit_sub_weight = {Unit_sub_weight})\")\n",
+ "print(f\"subband_weight_phase = {subband_weight_phase}\")\n",
"print(f\"subband_weight_re = {subband_weight_re:d}\")\n",
"print(f\"subband_weight_im = {subband_weight_im:d}\")\n",
"print()\n",
"\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",
+ "G_subband_ampl = G_fir_dc * G_fft_real_input_sine * 2**W_sub_gain * subband_weight_gain\n",
+ "\n",
+ "print(\"Coherent WG sine input:\")\n",
+ "print(f\" G_subband_ampl = {G_fir_dc} * {G_fft_real_input_sine} * 2**{W_sub_gain} * {subband_weight_gain} \\\n",
+ "= {G_subband_ampl} = {np.log2(G_subband_ampl):.2f} bits\")\n",
+ "print(\" . G_fir_dc =\", G_fir_dc)\n",
+ "print(\" . G_fft_real_input_sine =\", G_fft_real_input_sine, \"=\", 1 / N_sidebands)\n",
+ "print(\" . W_sub_gain =\", W_sub_gain)\n",
+ "print(\" . subband_weight_gain =\", subband_weight_gain)\n",
+ "print()\n",
+ "\n",
+ "# Expected factor from real signal input white noise sigma to subband amplitude\n",
+ "G_subband_sigma = G_fir_dc * G_fft_real_input_noise * 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} \\\n",
- "= {G_subband} = {np.log2(G_subband):.2f} bits\")\n",
+ "print(\"Incoherent white noise input:\")\n",
+ "print(f\" G_subband_sigma = {G_fir_dc} * {G_fft_real_input_noise} * 2**{W_sub_gain} * {subband_weight_gain} \\\n",
+ "= {G_subband_sigma} = {np.log2(G_subband_sigma):.2f} bits\")\n",
"print(\" . G_fir_dc =\", G_fir_dc)\n",
- "print(\" . G_fft_real_input_sine =\", G_fft_real_input_sine)\n",
+ "print(\" . G_fft_real_input_noise =\", G_fft_real_input_noise, \"=\", 1 / np.sqrt(N_fft))\n",
"print(\" . W_sub_gain =\", W_sub_gain)\n",
- "print(\" . subband_weight_gain =\", subband_weight_gain)"
+ "print(\" . subband_weight_gain =\", subband_weight_gain)\n",
+ "print()\n"
]
},
{
"cell_type": "code",
- "execution_count": 101,
- "id": "4d197368",
+ "execution_count": 28,
+ "id": "ac73d7e3",
"metadata": {},
"outputs": [
{
@@ -203,42 +234,40 @@
"output_type": "stream",
"text": [
"Same BF weight for all inputs:\n",
- ". beamlet_weight_gain = 1.0\n",
+ ". beamlet_weight_gain = 1.0 (Unit_bf_weight = 16384)\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_ant_arr = [ 1 12 24 48 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",
+ "Gain from subband to beamlet:\n",
"\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_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 = 48 : bf_proc_coh = 48.00 = 5.6 bits\n",
+ " N_ant = 96 : bf_proc_coh = 96.00 = 6.6 bits\n",
"\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_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 = 48 : bf_proc_incoh = 6.93 = 2.8 bits\n",
+ " N_ant = 96 : bf_proc_incoh = 9.80 = 3.3 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",
+ "Gain from signal input to beamlet:\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_ant = 1 : G_beamlet_sum_ampl = 8.00 * 1.00 * 1.0 = 8.00 = 3.0 bits\n",
+ " N_ant = 12 : G_beamlet_sum_ampl = 8.00 * 12.00 * 1.0 = 96.00 = 6.6 bits\n",
+ " N_ant = 24 : G_beamlet_sum_ampl = 8.00 * 24.00 * 1.0 = 192.00 = 7.6 bits\n",
+ " N_ant = 48 : G_beamlet_sum_ampl = 8.00 * 48.00 * 1.0 = 384.00 = 8.6 bits\n",
+ " N_ant = 96 : G_beamlet_sum_ampl = 8.00 * 96.00 * 1.0 = 768.00 = 9.6 bits\n",
+ "\n",
+ " N_ant = 1 : G_beamlet_sum_sigma = 0.50 * 1.00 * 1.0 = 0.50 = -1.0 bits\n",
+ " N_ant = 12 : G_beamlet_sum_sigma = 0.50 * 3.46 * 1.0 = 1.73 = 0.8 bits\n",
+ " N_ant = 24 : G_beamlet_sum_sigma = 0.50 * 4.90 * 1.0 = 2.45 = 1.3 bits\n",
+ " N_ant = 48 : G_beamlet_sum_sigma = 0.50 * 6.93 * 1.0 = 3.46 = 1.8 bits\n",
+ " N_ant = 96 : G_beamlet_sum_sigma = 0.50 * 9.80 * 1.0 = 4.90 = 2.3 bits\n",
"\n"
]
}
@@ -255,45 +284,77 @@
"beamlet_weight_im = int(beamlet_weight_gain * Unit_bf_weight * np.sin(beamlet_weight_phase))\n",
"\n",
"print(\"Same BF weight for all inputs:\")\n",
- "print(f\". beamlet_weight_gain = {beamlet_weight_gain}\")\n",
+ "print(f\". beamlet_weight_gain = {beamlet_weight_gain} (Unit_bf_weight = {Unit_bf_weight})\")\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",
- "N_ant_arr = np.array([1, 12, 24, 28, 96])\n",
+ "# Use range of N_ant for number of signal inputs in the BF\n",
+ "N_ant_arr = np.array([1, 12, 24, 48, 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 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",
+ "\n",
+ "print(\"Gain from subband to beamlet:\")\n",
+ "print() \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(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(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 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",
+ "G_beamlet_sum_ampl = G_subband_ampl * bf_proc_coh * beamlet_weight_gain\n",
+ "\n",
+ "# Expected factor from real signal input sigma to beamlet sigma\n",
+ "G_beamlet_sum_sigma = G_subband_sigma * bf_proc_incoh * beamlet_weight_gain\n",
"\n",
+ "print(\"Gain from signal input to beamlet:\")\n",
+ "print() \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(f\" N_ant = {na:2d} : G_beamlet_sum_ampl = {G_subband_ampl:.2f} * \" \\\n",
+ " f\"{bf_proc_coh[ni]:5.2f} * {beamlet_weight_gain} \" \\\n",
+ " f\"= {G_beamlet_sum_ampl[ni]:7.2f} = {np.log2(G_beamlet_sum_ampl[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",
- "# Maximum signal input amplitude\n",
- "si_ampl_max = 2**(W_beamlet_sum - 1) / G_beamlet_sum_coh\n",
+ " print(f\" N_ant = {na:2d} : G_beamlet_sum_sigma = {G_subband_sigma:.2f} * \" \\\n",
+ " f\"{bf_proc_incoh[ni]:5.2f} * {beamlet_weight_gain} \" \\\n",
+ " f\"= {G_beamlet_sum_sigma[ni]:6.2f} = {np.log2(G_beamlet_sum_sigma[ni]):.1f} bits\")\n",
+ "print()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "id": "98f1917e",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "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 = 48 : si_ampl_max = 0.041667 = 341 = 8.4 bits\n",
+ "N_ant = 96 : si_ampl_max = 0.020833 = 171 = 7.4 bits\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Maximum coherent signal input amplitude\n",
+ "si_ampl_max = 2**(W_beamlet_sum - 1) / G_beamlet_sum_ampl\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(f\"N_ant = {na:2d} : si_ampl_max = {si_ampl_max[ni] / FS:f} \" \\\n",
+ " f\"= {si_ampl_max[ni]:6.0f} = {np.log2(si_ampl_max[ni]):5.1f} bits\")\n",
"print()"
]
},
@@ -307,7 +368,7 @@
},
{
"cell_type": "code",
- "execution_count": 88,
+ "execution_count": 30,
"id": "f66c5028",
"metadata": {},
"outputs": [
@@ -329,7 +390,7 @@
},
{
"cell_type": "code",
- "execution_count": 89,
+ "execution_count": 31,
"id": "a9fca052",
"metadata": {},
"outputs": [
@@ -361,7 +422,7 @@
},
{
"cell_type": "code",
- "execution_count": 90,
+ "execution_count": 32,
"id": "d9972b6b",
"metadata": {},
"outputs": [
@@ -399,7 +460,7 @@
},
{
"cell_type": "code",
- "execution_count": 91,
+ "execution_count": 33,
"id": "be2d952f",
"metadata": {},
"outputs": [
@@ -407,7 +468,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "W_adc = {W_adc} bits\n",
+ "W_adc = 14 bits\n",
"FS = 8192\n",
"sigma_fs_sine = 5792.6 q\n",
"P_fs_sine_dB = 75.26 dB = 12.5 bit\n"
@@ -418,7 +479,7 @@
"# 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",
+ "print(f\"W_adc = {W_adc} bits\")\n",
"print(\"FS =\", FS)\n",
"print(f\"sigma_fs_sine = {sigma_fs_sine:.1f} q\")\n",
"print(f\"P_fs_sine_dB = {P_fs_sine_dB:.2f} dB = {P_fs_sine_dB / P_bit_dB:.1f} bit\")"
@@ -426,7 +487,7 @@
},
{
"cell_type": "code",
- "execution_count": 92,
+ "execution_count": 34,
"id": "a9e7fabc",
"metadata": {},
"outputs": [
@@ -450,7 +511,7 @@
},
{
"cell_type": "code",
- "execution_count": 93,
+ "execution_count": 35,
"id": "92852a53",
"metadata": {},
"outputs": [
@@ -472,22 +533,23 @@
],
"source": [
"# Signal level relative to FS sine\n",
- "Power_50dBFS = P_fs_sine_dB - 50 \n",
- "sigma_50dBFS = 10**(Power_50dBFS / 20)\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 corresponds to:\")\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 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",
+ "power_16q = sigma_16q**2\n",
+ "power_16q_dB = 10 * np.log10(power_16q)\n",
+ "dBFS_16q = power_16q_dB - P_fs_sine_dB\n",
"print()\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\" . Power = {power_16q_dB:.2f} dB, so at {dBFS_16q:.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"
]
@@ -510,7 +572,7 @@
},
{
"cell_type": "code",
- "execution_count": 94,
+ "execution_count": 36,
"id": "a04af043",
"metadata": {},
"outputs": [
@@ -518,9 +580,9 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "ADC sigma = 5792.6 q = 12.5 bits: P_ast = 6.710886e+15, uses 52.6 bits, is 0 dBFS = FS sine\n",
- "ADC sigma = 18.3 q = 4.2 bits: P_ast = 6.710886e+10, uses 36.0 bits, is -50dBFS\n",
- "ADC sigma = 16.0 q = 4.0 bits: P_ast = 5.120000e+10, uses 35.6 bits\n"
+ "SI sigma = 5792.6 q = 12.5 bits: P_ast = 6.710886e+15,uses 52.6 bits, is 0 dBFS = FS sine\n",
+ "SI sigma = 18.3 q = 4.2 bits: P_ast = 6.710886e+10,uses 36.0 bits, is -50 dBFS\n",
+ "SI sigma = 16.0 q = 4.0 bits: P_ast = 5.120000e+10,uses 35.6 bits, is -51.2 dBFS\n"
]
}
],
@@ -529,17 +591,20 @@
"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",
+ "print(f\"SI sigma = {si_sigma:6.1f} q = {si_sigma_bits:4.1f} bits: P_ast = {P_ast:e},\\\n",
+ "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",
+ "print(f\"SI sigma = {si_sigma:6.1f} q = {si_sigma_bits:4.1f} bits: P_ast = {P_ast:e},\\\n",
+ "uses {np.log2(P_ast):.1f} bits, is -50 dBFS\")\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 = {si_sigma:6.1f} q = {si_sigma_bits:4.1f} bits: P_ast = {P_ast:e}, uses {np.log2(P_ast):.1f} bits\")"
+ "P_ast = (si_sigma)**2 * N_int\n",
+ "print(f\"SI sigma = {si_sigma:6.1f} q = {si_sigma_bits:4.1f} bits: P_ast = {P_ast:e},\\\n",
+ "uses {np.log2(P_ast):.1f} bits, is {dBFS_16q:.1f} dBFS\")"
]
},
{
@@ -547,7 +612,7 @@
"id": "7ce94d23",
"metadata": {},
"source": [
- "From measured P_ast and DC_ast to signal input sigma:\n",
+ "From measured P_ast and DC_ast to signal input sigma in q units:\n",
"\n",
"* si_rms = sqrt(P_ast / N_int)\n",
"* si_mean = DC_ast / N_int\n",
@@ -562,54 +627,252 @@
"## 3.2 Subband statistics (SST)"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "f842d856",
+ "metadata": {},
+ "source": [
+ "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)"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 95,
- "id": "0b2ac36c",
+ "execution_count": 37,
+ "id": "5ba30659",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Signal input level --> Expected subband level and SST level:\n",
+ "Coherent (WG sine) signal input level --> Expected subband level and SST level:\n",
"\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"
+ " si_ampl si_sigma sub_sigma = SST\n",
+ " sub_ampl\n",
+ " value #bits value #bits value #bits value dB #bits\n",
+ " 8192.0 13.0 5792.6 4.0 65536.0 16.0 8.388608e+14 149.2 49.6, at 0 dBFS (= FS sine)\n",
+ " 2048.0 11.0 1448.2 4.0 16384.0 14.0 5.242880e+13 137.2 45.6, at -24.1 dBFS (= FS / 4)\n",
+ " 25.9 4.7 18.3 4.2 207.2 7.7 8.388608e+09 99.2 33.0, at -50 dBFS (= FS / 316)\n",
+ " 22.6 4.5 16.0 4.0 181.0 7.5 6.400000e+09 98.1 32.6, at -51.2 dBFS (= FS / 362)\n"
]
}
],
"source": [
- "# SST for Subband filterbank (F_sub)\n",
- "sub_ampl_fs = FS * G_subband # subband amplitude for FS signal input sine\n",
+ "# Subband level and SST level for coherent (WG sine) input\n",
+ "# . use ampl real = ampl imag = std complex = sqrt(power complex) to calculate SST for sine input\n",
+ "si_ampl_fs = FS\n",
+ "si_ampl_fs_bits = np.log2(si_ampl_fs)\n",
+ "si_sigma_fs = si_ampl_fs / np.sqrt(2)\n",
+ "si_sigma_fs_bits = np.log2(si_sigma)\n",
+ "sub_ampl_fs = si_ampl_fs * G_subband_ampl # subband amplitude for FS signal input sine\n",
+ "sub_ampl_fs_bits = np.log2(sub_ampl_fs)\n",
"SST_fs = sub_ampl_fs**2 * N_int_sub\n",
+ "SST_fs_dB = 10 * np.log10(SST_fs)\n",
+ "SST_fs_bits = np.log2(SST_fs)\n",
+ "\n",
+ "si_ampl_fs4 = FS / 4\n",
+ "si_ampl_fs4_bits = np.log2(si_ampl_fs4)\n",
+ "si_sigma_fs4 = si_ampl_fs4 / np.sqrt(2)\n",
+ "si_sigma_fs4_bits = np.log2(si_sigma)\n",
+ "sub_ampl_fs4 = si_ampl_fs4 * G_subband_ampl # subband amplitude for FS signal input sine\n",
+ "sub_ampl_fs4_bits = np.log2(sub_ampl_fs4)\n",
+ "SST_fs4 = sub_ampl_fs4**2 * N_int_sub\n",
+ "SST_fs4_dB = 10 * np.log10(SST_fs4)\n",
+ "SST_fs4_bits = np.log2(SST_fs4)\n",
"\n",
- "sub_ampl_50dBFS = ampl_50dBFS * G_subband # subband amplitude -50dBFS signal input sine\n",
+ "si_ampl_50dBFS = ampl_50dBFS\n",
+ "si_ampl_50dBFS_bits = np.log2(si_ampl_50dBFS)\n",
+ "si_sigma_50dBFS = sigma_50dBFS\n",
+ "si_sigma_50dBFS_bits = np.log2(si_sigma_50dBFS)\n",
+ "sub_ampl_50dBFS = si_ampl_50dBFS * G_subband_ampl # subband amplitude -50dBFS signal input sine\n",
+ "sub_ampl_50dBFS_bits = np.log2(sub_ampl_50dBFS)\n",
"SST_50dBFS = sub_ampl_50dBFS**2 * N_int_sub\n",
+ "SST_50dBFS_dB = 10 * np.log10(SST_50dBFS)\n",
+ "SST_50dBFS_bits = np.log2(SST_50dBFS)\n",
"\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",
+ "si_ampl_s16q_bits = np.log2(si_ampl_s16q)\n",
+ "si_sigma_s16q = sigma_16q\n",
+ "si_sigma_s16q_bits = np.log2(sigma_16q) # = 16\n",
+ "sub_ampl_s16q = si_ampl_s16q * G_subband_ampl # subband amplitude for signal input sine with sigma = 16 q\n",
+ "sub_ampl_s16q_bits = np.log2(sub_ampl_s16q)\n",
+ "SST_s16q = sub_ampl_s16q**2 * N_int_sub\n",
+ "SST_s16q_dB = 10 * np.log10(SST_s16q)\n",
+ "SST_s16q_bits = np.log2(SST_s16q)\n",
+ "\n",
+ "print(\"Coherent (WG sine) signal input level --> Expected subband level and SST level:\")\n",
+ "print()\n",
+ "print(\" si_ampl si_sigma sub_sigma = SST\")\n",
+ "print(\" sub_ampl\")\n",
+ "print(\" value #bits value #bits value #bits value dB #bits\")\n",
+ "print(f\"{si_ampl_fs:9.1f} {si_ampl_fs_bits:6.1f} \" \\\n",
+ " f\"{si_sigma_fs:9.1f} {si_sigma_fs_bits:6.1f} \" \\\n",
+ " f\"{sub_ampl_fs:12.1f} {sub_ampl_fs_bits:6.1f} \" \\\n",
+ " f\"{SST_fs:15e} {SST_fs_dB:6.1f} {SST_fs_bits:6.1f}, \"\n",
+ " f\"at 0 dBFS (= FS sine)\")\n",
+ "print(f\"{si_ampl_fs4:9.1f} {si_ampl_fs4_bits:6.1f} \" \\\n",
+ " f\"{si_sigma_fs4:9.1f} {si_sigma_fs4_bits:6.1f} \" \\\n",
+ " f\"{sub_ampl_fs4:12.1f} {sub_ampl_fs4_bits:6.1f} \" \\\n",
+ " f\"{SST_fs4:15e} {SST_fs4_dB:6.1f} {SST_fs4_bits:6.1f}, \"\n",
+ " f\"at {20*np.log10(1 / 4**2):.1f} dBFS (= FS / 4)\")\n",
+ "print(f\"{si_ampl_50dBFS:9.1f} {si_ampl_50dBFS_bits:6.1f} \" \\\n",
+ " f\"{si_sigma_50dBFS:9.1f} {si_sigma_50dBFS_bits:6.1f} \" \\\n",
+ " f\"{sub_ampl_50dBFS:12.1f} {sub_ampl_50dBFS_bits:6.1f} \" \\\n",
+ " f\"{SST_50dBFS:15e} {SST_50dBFS_dB:6.1f} {SST_50dBFS_bits:6.1f}, \"\n",
+ " f\"at -50 dBFS (= FS / {10**(50/20):.0f})\")\n",
+ "print(f\"{si_ampl_s16q:9.1f} {si_ampl_s16q_bits:6.1f} \" \\\n",
+ " f\"{si_sigma_s16q:9.1f} {si_sigma_s16q_bits:6.1f} \" \\\n",
+ " f\"{sub_ampl_s16q:12.1f} {sub_ampl_s16q_bits:6.1f} \"\\\n",
+ " f\"{SST_s16q:15e} {SST_s16q_dB:6.1f} {SST_s16q_bits:6.1f}, \"\\\n",
+ " f\"at {dBFS_16q:.1f} dBFS (= FS / {10**(-dBFS_16q/20):.0f})\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "id": "5ec1330a",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Incoherent white noise input level --> Expected subband level and SST level:\n",
+ "\n",
+ " si_sigma sub_sigma sub_sigma_re = SST\n",
+ " sub_sigma_im\n",
+ " value #bits value #bits value #bits value dB #bits\n",
+ " 2048.0 11.0 1024.0 10.0 724.1 9.5 2.048000e+11 113.1 37.6\n",
+ " 16.0 4.0 8.0 3.0 5.7 2.5 1.250000e+07 71.0 23.6, at -51.2 dBFS\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Subband level and SST level for incoherent white noise input\n",
+ "# . the signal input power is equally distributed over all N_sub subbands\n",
+ "# . use std complex to calculate SST for noise input\n",
+ "# . use std real = std imag = std complex / sqrt(2) = sqrt(power complex / 2) to calculate subband level\n",
"\n",
- "print(\"Signal input level --> Expected subband level and SST level:\")\n",
+ "# si_std = FS / 4\n",
+ "ni_sigma_fs4 = FS / 4\n",
+ "ni_sigma_fs4_bits = np.log2(ni_sigma_fs4)\n",
+ "nsub_sigma_fs4 = ni_sigma_fs4 * G_subband_sigma\n",
+ "nsub_sigma_fs4_bits = np.log2(nsub_sigma_fs4)\n",
+ "nsub_sigma_re_fs4 = nsub_sigma_fs4 / np.sqrt(N_complex)\n",
+ "nsub_sigma_re_fs4_bits = np.log2(nsub_sigma_re_fs4)\n",
+ "nSST_fs4 = nsub_sigma_fs4**2 * N_int_sub\n",
+ "nSST_fs4_dB = 10 * np.log10(nSST_fs4)\n",
+ "nSST_fs4_bits = np.log2(nSST_fs4)\n",
+ "\n",
+ "# si_std = 16\n",
+ "ni_sigma_s16q = sigma_16q\n",
+ "ni_sigma_s16q_bits = np.log2(ni_sigma_s16q) # = 16\n",
+ "nsub_sigma_s16q = ni_sigma_s16q * G_subband_sigma\n",
+ "nsub_sigma_s16q_bits = np.log2(nsub_sigma_s16q)\n",
+ "nsub_sigma_re_s16q = nsub_sigma_s16q / np.sqrt(N_complex)\n",
+ "nsub_sigma_re_s16q_bits = np.log2(nsub_sigma_re_s16q)\n",
+ "nSST_s16q = nsub_sigma_s16q**2 * N_int_sub\n",
+ "nSST_s16q_dB = 10 * np.log10(nSST_s16q)\n",
+ "nSST_s16q_bits = np.log2(nSST_s16q)\n",
+ "\n",
+ "print(\"Incoherent white noise input level --> Expected subband level and SST level:\")\n",
"print()\n",
- "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}\")"
+ "print(\" si_sigma sub_sigma sub_sigma_re = SST\")\n",
+ "print(\" sub_sigma_im\")\n",
+ "print(\" value #bits value #bits value #bits value dB #bits\")\n",
+ "print(f\"{ni_sigma_fs4:9.1f} {ni_sigma_fs4_bits:6.1f} \" \\\n",
+ " f\"{nsub_sigma_fs4:10.1f} {nsub_sigma_fs4_bits:6.1f} \" \\\n",
+ " f\"{nsub_sigma_re_fs4:13.1f} {nsub_sigma_re_fs4_bits:6.1f} \" \\\n",
+ " f\"{nSST_fs4:15e} {nSST_fs4_dB:6.1f} {nSST_fs4_bits:6.1f}\")\n",
+ "print(f\"{ni_sigma_s16q:9.1f} {ni_sigma_s16q_bits:6.1f} \" \\\n",
+ " f\"{nsub_sigma_s16q:10.1f} {nsub_sigma_s16q_bits:6.1f} \" \\\n",
+ " f\"{nsub_sigma_re_s16q:13.1f} {nsub_sigma_re_s16q_bits:6.1f} \" \\\n",
+ " f\"{nSST_s16q:15e} {nSST_s16q_dB:6.1f} {nSST_s16q_bits:6.1f}, at {dBFS_16q:.1f} dBFS\")"
]
},
{
"cell_type": "markdown",
- "id": "c2c02740",
+ "id": "d6c867ae",
+ "metadata": {},
+ "source": [
+ "Conclusion:\n",
+ "* For FS sine input the subband amplitude is 16 bits, so including the sign bit this fits in W_subband = 18b. The one spare bit is to fit special test signals (e.g. first harmonic of FS square wave input)\n",
+ "* For XST the W_crosslet = 16b subband samples can only fit sine signal input <= 0.5 FS\n",
+ "* For sigma = FS / 4 white noise input the subband sigma uses 10 bits, so 9.5 bits for the subband real and imaginary parts. The 4 sigma just fits in FS and corresponds to 2 bits, so including the sign bit the 4 sigma range of the subband real and imag fits in 1 + 9.5 + 2 = 12.5 bits."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "id": "33f37393",
"metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "G_subband_ampl = 8.0 = 3.0 bits\n",
+ "G_subband_sigma = 0.5 = -1.0 bits\n",
+ "\n",
+ "sub_SST = 5.242880e+13\n",
+ ". sub_power = 268435456.0\n",
+ ". sub_ampl = 16384.0\n",
+ ". si_ampl = 2048.0 (si_ampl_exp = 2048.0)\n",
+ "\n",
+ "sub_SST = 2.048000e+11\n",
+ ". sub_power = 1048576.0\n",
+ ". sub_sigma = 1024.0\n",
+ ". sub_sigma_re = 724.0773439350246\n",
+ ". sub_sigma_im = 724.0773439350246\n",
+ ". si_sigma = 2048.0 (si_sigma_exp = 2048.0)\n"
+ ]
+ }
+ ],
"source": [
- "From measured SST to SSTq in q^2 units and subband amplitude in q units\n",
+ "# From measured SST to SSTq in q^2 units and subband and signal input\n",
+ "# . depends on whether the input was a coherent sine like signal or a white noise like signal\n",
+ "\n",
+ "# Fsub gain implementation factors\n",
+ "print(f\"G_subband_ampl = {G_subband_ampl} = {np.log2(G_subband_ampl)} bits\")\n",
+ "print(f\"G_subband_sigma = {G_subband_sigma} = {np.log2(G_subband_sigma)} bits\")\n",
+ "print()\n",
"\n",
- "* SSTq = SST / N_int_sub / G_subband^2\n",
- "* sub_ampl = sqrt(SSTq) # ampl real = ampl imag = std complex = sqrt(power complex)"
+ "# Coherent (WG sine) signal: from SST to subband amplitude and signal input amplitude in q units\n",
+ "sub_SST = SST_fs4 # SST in WG sine frequency subband for si_ampl = FS / 4 = 2048\n",
+ "si_ampl_exp = si_ampl_fs4\n",
+ "\n",
+ "sub_power = sub_SST / N_int_sub\n",
+ "sub_ampl = np.sqrt(sub_power) # ampl real = ampl imag = std complex = sqrt(power complex)\n",
+ "si_ampl = sub_ampl / G_subband_ampl\n",
+ "\n",
+ "print(f\"sub_SST = {sub_SST:e}\")\n",
+ "print(f\". sub_power = {sub_power}\")\n",
+ "print(f\". sub_ampl = {sub_ampl}\")\n",
+ "print(f\". si_ampl = {si_ampl} (si_ampl_exp = {si_ampl_exp})\")\n",
+ "print()\n",
+ "\n",
+ "# Incoherent white noise signal: from SST to subband sigma and signal input sigma in q units:\n",
+ "sub_SST = nSST_fs4 # SST in any subband for si_sigma = FS / 4 = 2048\n",
+ "si_sigma_exp = ni_sigma_fs4\n",
+ "\n",
+ "sub_power = sub_SST / N_int_sub\n",
+ "sub_sigma = np.sqrt(sub_power) # std complex = sqrt(power)\n",
+ "sub_sigma_re = sub_sigma / np.sqrt(N_complex)\n",
+ "sub_sigma_im = sub_sigma / np.sqrt(N_complex)\n",
+ "si_sigma = sub_sigma / G_subband_sigma\n",
+ "\n",
+ "print(f\"sub_SST = {sub_SST:e}\")\n",
+ "print(f\". sub_power = {sub_power}\")\n",
+ "print(f\". sub_sigma = {sub_sigma}\")\n",
+ "print(f\". sub_sigma_re = {sub_sigma_re}\")\n",
+ "print(f\". sub_sigma_im = {sub_sigma_im}\")\n",
+ "print(f\". si_sigma = {si_sigma} (si_sigma_exp = {si_sigma_exp})\")"
]
},
{
@@ -617,19 +880,216 @@
"id": "66d49365",
"metadata": {},
"source": [
- "## 3.3 Beamlet statistics (BST)"
+ "## 3.4 Crosslet statistics (XST)\n",
+ "\n",
+ "The crosslet statistics have W_crosslet = 16b, but use the same LSbit level as the subbands. The subbands have W_subband = 18b and the maximum subband sine amplitude is 16b. Therefore the maximum sine input for no XST overflow is A = 0.5. If subband_weight = 1.0 then the auto correlations of the XST are eqaul to the SST."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ba543d00",
+ "metadata": {},
+ "source": [
+ "## 3.5 Beamlet statistics (BST)"
]
},
{
"cell_type": "code",
- "execution_count": 96,
+ "execution_count": 40,
"id": "f0b09a83",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Coherent (WG sine) signal input level --> Expected beamlet level and BST level:\n",
+ "\n",
+ "N_ant si_ampl si_sigma beamlet_sigma = BST\n",
+ " beamlet_ampl\n",
+ " value #bits value #bits value #bits value dB #bits\n",
+ " 1 8192.0 13.0 5792.6 4.0 65536.0 16.0 8.388608e+14 149.2 49.6, at 0 dBFS (= FS sine)\n",
+ " 12 8192.0 13.0 5792.6 4.0 786432.0 19.6 1.207960e+17 170.8 56.7, at 0 dBFS (= FS sine)\n",
+ " 24 8192.0 13.0 5792.6 4.0 1572864.0 20.6 4.831838e+17 176.8 58.7, at 0 dBFS (= FS sine)\n",
+ " 48 8192.0 13.0 5792.6 4.0 3145728.0 21.6 1.932735e+18 182.9 60.7, at 0 dBFS (= FS sine)\n",
+ " 96 8192.0 13.0 5792.6 4.0 6291456.0 22.6 7.730941e+18 188.9 62.7, at 0 dBFS (= FS sine)\n",
+ "\n",
+ " 1 2048.0 11.0 1448.2 4.0 16384.0 14.0 5.242880e+13 137.2 45.6, at -24.1 dBFS (= FS / 4)\n",
+ " 12 2048.0 11.0 1448.2 4.0 196608.0 17.6 7.549747e+15 158.8 52.7, at -24.1 dBFS (= FS / 4)\n",
+ " 24 2048.0 11.0 1448.2 4.0 393216.0 18.6 3.019899e+16 164.8 54.7, at -24.1 dBFS (= FS / 4)\n",
+ " 48 2048.0 11.0 1448.2 4.0 786432.0 19.6 1.207960e+17 170.8 56.7, at -24.1 dBFS (= FS / 4)\n",
+ " 96 2048.0 11.0 1448.2 4.0 1572864.0 20.6 4.831838e+17 176.8 58.7, at -24.1 dBFS (= FS / 4)\n",
+ "\n",
+ " 1 25.9 4.7 18.3 4.2 207.2 7.7 8.388608e+09 99.2 33.0, at -50 dBFS (= FS / 316)\n",
+ " 12 25.9 4.7 18.3 4.2 2486.9 11.3 1.207960e+12 120.8 40.1, at -50 dBFS (= FS / 316)\n",
+ " 24 25.9 4.7 18.3 4.2 4973.8 12.3 4.831838e+12 126.8 42.1, at -50 dBFS (= FS / 316)\n",
+ " 48 25.9 4.7 18.3 4.2 9947.7 13.3 1.932735e+13 132.9 44.1, at -50 dBFS (= FS / 316)\n",
+ " 96 25.9 4.7 18.3 4.2 19895.3 14.3 7.730941e+13 138.9 46.1, at -50 dBFS (= FS / 316)\n",
+ "\n",
+ " 1 22.6 4.5 16.0 4.0 181.0 7.5 6.400000e+09 98.1 32.6, at -51.2 dBFS (= FS / 362)\n",
+ " 12 22.6 4.5 16.0 4.0 2172.2 11.1 9.216000e+11 119.6 39.7, at -51.2 dBFS (= FS / 362)\n",
+ " 24 22.6 4.5 16.0 4.0 4344.5 12.1 3.686400e+12 125.7 41.7, at -51.2 dBFS (= FS / 362)\n",
+ " 48 22.6 4.5 16.0 4.0 8688.9 13.1 1.474560e+13 131.7 43.7, at -51.2 dBFS (= FS / 362)\n",
+ " 96 22.6 4.5 16.0 4.0 17377.9 14.1 5.898240e+13 137.7 45.7, at -51.2 dBFS (= FS / 362)\n"
+ ]
+ }
+ ],
"source": [
"# Digital beamformer (BF)\n",
"# . is a coherent beamformer = voltage beamformer\n",
- "# . uses BF weights to form beamlets from sum of weighted subbands\n"
+ "# . uses BF weights to form beamlets from sum of weighted subbands\n",
+ "\n",
+ "# Beamlet_sum level and BST level for coherent (WG sine) input (similar as for SST)\n",
+ "beamlet_ampl_fs = si_ampl_fs * G_beamlet_sum_ampl # beamlet amplitude for FS signal input sine\n",
+ "beamlet_ampl_fs_bits = np.log2(beamlet_ampl_fs)\n",
+ "BST_fs = beamlet_ampl_fs**2 * N_int_sub\n",
+ "BST_fs_dB = 10 * np.log10(BST_fs)\n",
+ "BST_fs_bits = np.log2(BST_fs)\n",
+ "\n",
+ "beamlet_ampl_fs4 = si_ampl_fs4 * G_beamlet_sum_ampl # beamlet amplitude for FS signal input sine\n",
+ "beamlet_ampl_fs4_bits = np.log2(beamlet_ampl_fs4)\n",
+ "BST_fs4 = beamlet_ampl_fs4**2 * N_int_sub\n",
+ "BST_fs4_dB = 10 * np.log10(BST_fs4)\n",
+ "BST_fs4_bits = np.log2(BST_fs4)\n",
+ "\n",
+ "beamlet_ampl_50dBFS = si_ampl_50dBFS * G_beamlet_sum_ampl # beamlet amplitude -50dBFS signal input sine\n",
+ "beamlet_ampl_50dBFS_bits = np.log2(beamlet_ampl_50dBFS)\n",
+ "BST_50dBFS = beamlet_ampl_50dBFS**2 * N_int_sub\n",
+ "BST_50dBFS_dB = 10 * np.log10(BST_50dBFS)\n",
+ "BST_50dBFS_bits = np.log2(BST_50dBFS)\n",
+ "\n",
+ "beamlet_ampl_s16q = si_ampl_s16q * G_beamlet_sum_ampl # beamlet amplitude for signal input sine with sigma = 16 q\n",
+ "beamlet_ampl_s16q_bits = np.log2(beamlet_ampl_s16q)\n",
+ "BST_s16q = beamlet_ampl_s16q**2 * N_int_sub\n",
+ "BST_s16q_dB = 10 * np.log10(BST_s16q)\n",
+ "BST_s16q_bits = np.log2(BST_s16q)\n",
+ " \n",
+ "print(\"Coherent (WG sine) signal input level --> Expected beamlet level and BST level:\")\n",
+ "print()\n",
+ "print(\"N_ant si_ampl si_sigma beamlet_sigma = BST\")\n",
+ "print(\" beamlet_ampl\")\n",
+ "print(\" value #bits value #bits value #bits value dB #bits\")\n",
+ "for ni, na in enumerate(N_ant_arr):\n",
+ " print(f\"{na:5d} \" \\\n",
+ " f\"{si_ampl_fs:8.1f} {si_ampl_fs_bits:6.1f} \" \\\n",
+ " f\"{si_sigma_fs:9.1f} {si_sigma_fs_bits:6.1f} \" \\\n",
+ " f\"{beamlet_ampl_fs[ni]:12.1f} {beamlet_ampl_fs_bits[ni]:6.1f} \" \\\n",
+ " f\"{BST_fs[ni]:15e} {BST_fs_dB[ni]:6.1f} {BST_fs_bits[ni]:6.1f}, \" \\\n",
+ " f\"at 0 dBFS (= FS sine)\")\n",
+ "print()\n",
+ "for ni, na in enumerate(N_ant_arr):\n",
+ " print(f\"{na:5d} \" \\\n",
+ " f\"{si_ampl_fs4:8.1f} {si_ampl_fs4_bits:6.1f} \" \\\n",
+ " f\"{si_sigma_fs4:9.1f} {si_sigma_fs4_bits:6.1f} \" \\\n",
+ " f\"{beamlet_ampl_fs4[ni]:12.1f} {beamlet_ampl_fs4_bits[ni]:6.1f} \" \\\n",
+ " f\"{BST_fs4[ni]:15e} {BST_fs4_dB[ni]:6.1f} {BST_fs4_bits[ni]:6.1f}, \" \\\n",
+ " f\"at {20*np.log10(1 / 4**2):.1f} dBFS (= FS / 4)\")\n",
+ "print()\n",
+ "for ni, na in enumerate(N_ant_arr):\n",
+ " print(f\"{na:5d} \" \\\n",
+ " f\"{si_ampl_50dBFS:8.1f} {si_ampl_50dBFS_bits:6.1f} \" \\\n",
+ " f\"{si_sigma_50dBFS:9.1f} {si_sigma_50dBFS_bits:6.1f} \" \\\n",
+ " f\"{beamlet_ampl_50dBFS[ni]:12.1f} {beamlet_ampl_50dBFS_bits[ni]:6.1f} \" \\\n",
+ " f\"{BST_50dBFS[ni]:15e} {BST_50dBFS_dB[ni]:6.1f} {BST_50dBFS_bits[ni]:6.1f}, \" \n",
+ " f\"at -50 dBFS (= FS / {10**(50/20):.0f})\")\n",
+ "print()\n",
+ "for ni, na in enumerate(N_ant_arr):\n",
+ " print(f\"{na:5d} \" \\\n",
+ " f\"{si_ampl_s16q:8.1f} {si_ampl_s16q_bits:6.1f} \" \\\n",
+ " f\"{si_sigma_s16q:9.1f} {si_sigma_s16q_bits:6.1f} \" \\\n",
+ " f\"{beamlet_ampl_s16q[ni]:12.1f} {beamlet_ampl_s16q_bits[ni]:6.1f} \" \\\n",
+ " f\"{BST_s16q[ni]:15e} {BST_s16q_dB[ni]:6.1f} {BST_s16q_bits[ni]:6.1f}, \" \\\n",
+ " f\"at {dBFS_16q:.1f} dBFS (= FS / {10**(-dBFS_16q/20):.0f})\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "id": "06c7b393",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Incoherent white noise input level --> Expected beamlet level and BST level:\n",
+ "\n",
+ "N_ant si_sigma beamlet_sigma beamlet_sigma_re = BST\n",
+ " beamlet_sigma_im\n",
+ " value #bits value #bits value #bits value dB #bits\n",
+ " 1 2048.0 11.0 1024.0 10.0 724.1 9.5 2.048000e+11 113.1 37.6\n",
+ " 12 2048.0 11.0 3547.2 11.8 2508.3 11.3 2.457600e+12 123.9 41.2\n",
+ " 24 2048.0 11.0 5016.6 12.3 3547.2 11.8 4.915200e+12 126.9 42.2\n",
+ " 48 2048.0 11.0 7094.5 12.8 5016.6 12.3 9.830400e+12 129.9 43.2\n",
+ " 96 2048.0 11.0 10033.1 13.3 7094.5 12.8 1.966080e+13 132.9 44.2\n",
+ "\n",
+ " 1 16.0 4.0 8.0 3.0 5.7 2.5 1.250000e+07 71.0 23.6, at -51.2 dBFS\n",
+ " 12 16.0 4.0 27.7 4.8 19.6 4.3 1.500000e+08 81.8 27.2, at -51.2 dBFS\n",
+ " 24 16.0 4.0 39.2 5.3 27.7 4.8 3.000000e+08 84.8 28.2, at -51.2 dBFS\n",
+ " 48 16.0 4.0 55.4 5.8 39.2 5.3 6.000000e+08 87.8 29.2, at -51.2 dBFS\n",
+ " 96 16.0 4.0 78.4 6.3 55.4 5.8 1.200000e+09 90.8 30.2, at -51.2 dBFS\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Beamlet level and BST level for incoherent white noise input (similar as for SST)\n",
+ "\n",
+ "# si_std = FS / 4\n",
+ "nbeamlet_sigma_fs4 = ni_sigma_fs4 * G_beamlet_sum_sigma\n",
+ "nbeamlet_sigma_fs4_bits = np.log2(nbeamlet_sigma_fs4)\n",
+ "nbeamlet_sigma_re_fs4 = nbeamlet_sigma_fs4 / np.sqrt(N_complex)\n",
+ "nbeamlet_sigma_re_fs4_bits = np.log2(nbeamlet_sigma_re_fs4)\n",
+ "nBST_fs4 = nbeamlet_sigma_fs4**2 * N_int_sub\n",
+ "nBST_fs4_dB = 10 * np.log10(nBST_fs4)\n",
+ "nBST_fs4_bits = np.log2(nBST_fs4)\n",
+ "\n",
+ "# si_std = 16\n",
+ "nbeamlet_sigma_s16q = ni_sigma_s16q * G_beamlet_sum_sigma\n",
+ "nbeamlet_sigma_s16q_bits = np.log2(nbeamlet_sigma_s16q)\n",
+ "nbeamlet_sigma_re_s16q = nbeamlet_sigma_s16q / np.sqrt(N_complex)\n",
+ "nbeamlet_sigma_re_s16q_bits = np.log2(nbeamlet_sigma_re_s16q)\n",
+ "nBST_s16q = nbeamlet_sigma_s16q**2 * N_int_sub\n",
+ "nBST_s16q_dB = 10 * np.log10(nBST_s16q)\n",
+ "nBST_s16q_bits = np.log2(nBST_s16q)\n",
+ "\n",
+ "print(\"Incoherent white noise input level --> Expected beamlet level and BST level:\")\n",
+ "print()\n",
+ "print(\"N_ant si_sigma beamlet_sigma beamlet_sigma_re = BST\")\n",
+ "print(\" beamlet_sigma_im\")\n",
+ "print(\" value #bits value #bits value #bits value dB #bits\")\n",
+ "for ni, na in enumerate(N_ant_arr):\n",
+ " print(f\"{na:5d} \" \\\n",
+ " f\"{ni_sigma_fs4:9.1f} {ni_sigma_fs4_bits:6.1f} \" \\\n",
+ " f\"{nbeamlet_sigma_fs4[ni]:10.1f} {nbeamlet_sigma_fs4_bits[ni]:6.1f} \" \\\n",
+ " f\"{nbeamlet_sigma_re_fs4[ni]:16.1f} {nbeamlet_sigma_re_fs4_bits[ni]:6.1f} \" \\\n",
+ " f\"{nBST_fs4[ni]:15e} {nBST_fs4_dB[ni]:6.1f} {nBST_fs4_bits[ni]:6.1f}\")\n",
+ "print()\n",
+ "for ni, na in enumerate(N_ant_arr):\n",
+ " print(f\"{na:5d} \" \\\n",
+ " f\"{ni_sigma_s16q:9.1f} {ni_sigma_s16q_bits:6.1f} \" \\\n",
+ " f\"{nbeamlet_sigma_s16q[ni]:10.1f} {nbeamlet_sigma_s16q_bits[ni]:6.1f} \" \\\n",
+ " f\"{nbeamlet_sigma_re_s16q[ni]:16.1f} {nbeamlet_sigma_re_s16q_bits[ni]:6.1f} \" \\\n",
+ " f\"{nBST_s16q[ni]:15e} {nBST_s16q_dB[ni]:6.1f} {nBST_s16q_bits[ni]:6.1f}, at {dBFS_16q:.1f} dBFS\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8829fd41",
+ "metadata": {},
+ "source": [
+ "If subband_weight = 1.0 and beamlet_weight = 1.0 and N_ant = 1 then the BST are eqaul to the SST"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cdb20624",
+ "metadata": {},
+ "source": [
+ "## 3.6 Beamlet output\n",
+ "\n",
+ "The beamlet output is W_beamlet = 8 bit. The beamlet has a sign bit about 1 bit for the sigma and about 2 bits to fit a range of 4 sigma, so about 4 bits can carry the noise signal. The extra 4 bits are for some RFI and to fit differences in subband noise level due to the antenna and RCU2 band filter shape. The subband noise level can be equalized using the subband weights, to have more dynamic range for RFI the beamlet.\n",
+ "\n",
+ "Choosing FPGA_beamlet_output_scale_RW = 1 sqrt(N_ant) makes that the beamlet output level for noise input is equal to that of N_ant = 1 for all N_ant. The BST can be used to check whether the beamlet output will fit."
]
},
{
@@ -637,18 +1097,39 @@
"id": "d2086ec5",
"metadata": {},
"source": [
- "# Appendix 1: DFT of real input sine + DC"
+ "# Appendix 1: DFT of real input DC, sine and noise"
]
},
{
"cell_type": "code",
- "execution_count": 97,
+ "execution_count": 42,
"id": "def6eba7",
"metadata": {},
"outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The DFT of the sine plot shows:\n",
+ ". G_fft_real_input_dc = 1\n",
+ ". G_fft_real_input_sine = 0.5\n"
+ ]
+ },
{
"data": {
- "image/png": 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\n",
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ReG0EqfMspiRJkoZgMSVJNQo8zCd1ncWUJNXIo3xS91lMSVLdHJqSOs1iSpJq5MCU1H0WU5JUMwempG6zmJKkGkWExZTUcT0VUxFxXERcHxEbIuKMWV5/VkRcEhH/HBHXRMSrRx+qJPXP/CWpbgsWUxGxCDgbOB44Ejg5Io6c0ex9wPmZ+ULgJODPRh2oJPVrEvKXc6ak7utlZOpoYENm3pSZW4FzgdUz2iSwf3n/AOC20YUoSQNrPH95aQSp+xb30OYQYGPl8SbgmBltPgB8MSJ+FXgycOxsC4qI04HTAZYtW8bU1FSf4dZj8+bNExNLP9oaN9DKuNva322Ne0Qaz1933/0I27dvb+Vn0NZtx7jHq61xw+hi76WY6sXJwDmZ+YcR8VLgUxFxVGbuqDbKzDXAGoCVK1fmqlWrRrT64UxNTTEpsfSjlXFfdCFA++Kmpf1Ne+Meo1rz16dvuYp7N93Vys+grduOcY9XW+OG0cXey2G+W4EVlcfLy+eqTgPOB8jMy4B9gaVDRydJw5mI/OXZfFK39VJMXQkcERGHR8TeFBM0185o82/AjwNExI9QJKO7RhmoJA2g8fzlnCmp+xYspjJzG/AO4GLgOoqzXtZHxJkRcWLZ7N3AL0fEt4DPAqdmpjtjkho1CfnLWkrqvp7mTGXmOmDdjOfeX7l/LfDy0YYmScObhPzlvqXUbV4BXZJq5GE+qfsspiRJkoZgMSVJNQr8bT6p6yymJEmShmAxJUk1ivA6U1LXWUxJUo2cgC51n8WUJNXNoSmp0yymJKlG4WU7pc6zmJKkmjkwJXWbxZQk1ckJ6FLnWUxJUo08yCd1n8WUJNXNoSmp0yymJKlG4bURpM6zmJKkmjkwJXWbxZQk1chxKan7LKYkSZKGYDElSTXyt/mk7rOYkqQaeZhP6j6LKUmqWTo0JXWaxZQk1chLI0jdZzElSZI0BIspSapR4AR0qesspiSpTh7lkzrPYkqSauYEdKnbLKYkqUbh0JTUeRZTkiRJQ7CYkqQaeQV0qfsspiRJkoZgMSVJNXLGlNR9FlOSVCMvgC51X0/FVEQcFxHXR8SGiDhjjjY/FxHXRsT6iPjMaMOUpMFMQv7y0ghSty1eqEFELALOBl4FbAKujIi1mXltpc0RwK8DL8/M+yLi6XUFLEm9moT8FYQT0KWO62Vk6mhgQ2belJlbgXOB1TPa/DJwdmbeB5CZd442TEkaiPlLUu0WHJkCDgE2Vh5vAo6Z0eaHACLiUmAR8IHMvGjmgiLidOB0gGXLljE1NTVAyKO3efPmiYmlH22NG2hl3G3t77bGPSKN56/bv/coO3JHKz+Dtm47xj1ebY0bRhd7L8VUr8s5AlgFLAe+GhHPy8z7q40ycw2wBmDlypW5atWqEa1+OFNTU0xKLP1oZdwXXQjQvrhpaX/T3rjHqNb8dfG913DNXZta+Rm0ddsx7vFqa9wwuth7Ocx3K7Ci8nh5+VzVJmBtZj6Wmd8FbqBITpLUpInIX86Zkrqtl2LqSuCIiDg8IvYGTgLWzmjzeYq9OiJiKcWw+U2jC1OSBjIB+Ss8m0/quAWLqczcBrwDuBi4Djg/M9dHxJkRcWLZ7GLgnoi4FrgE+LXMvKeuoCWpF+YvSePQ05ypzFwHrJvx3Psr9xN4V3mTpInRdP4qLtrp0JTUZV4BXZIkaQgWU5JUo8BxKanrLKYkqUb+Np/UfRZTklQ3h6akTrOYkqQa+dt8UvdZTEmSJA3BYkqSahThUT6p6yymJKlGzj+Xus9iSpIkaQgWU5JUowh/m0/qOospSZKkIVhMSVLNHJiSus1iSpJq5BXQpe5b3HQAGtxlt21j8Y1384ojljYdSqdduuFu7nzwEZ7SdCCS5nTJd+7koa3b+cnnH9x0KNoDOTLVYh+75lFO+Ysrmg6j817/8Sv4r+d9q+kw1FLhxRHG4k3nXMnbP/NPTYehPZTFlCTVzLP5pG6zmJKkGjlnSuo+iylJkqQhWExJUo0CL40gdZ3FlCTVyMN8UvdZTElSzRyZkrrNYkqSahQRVlNSx1lMSZIkDcFiSpJq5AR0qfsspiSpTk5AlzrPYkqSaubIlNRtFlOSVKPACehS11lMSZIkDcFiSpJq5JURpO6zmJIkSRqCxZQk1ciT+aTu66mYiojjIuL6iNgQEWfM0+41EZERsXJ0IUrS4JrOX/42n9R9CxZTEbEIOBs4HjgSODkijpyl3X7AfwGuGHWQkjSISclfzpmSuq2XkamjgQ2ZeVNmbgXOBVbP0u63gA8Bj4wwPkkaRuP5KwjSakrqtMU9tDkE2Fh5vAk4ptogIl4ErMjMCyPi1+ZaUEScDpwOsGzZMqampvoOuA6bN2+emFgG0cbY2xhzW7eTtsY9Io3nr1tu2QpkKz+DNm47U1NTrYwb2tnf0N64YXSx91JMzSsi9gLOAk5dqG1mrgHWAKxcuTJXrVo17OpHYmpqikmJpS8XXQjQrthbHPOSJUvaFXeptdv3GIwjf31z6/Vw04ZWfgat2nYquaVVcVcY9/iNKvZeDvPdCqyoPF5ePjdtP+AoYCoibgZeAqx1ErqkCdB4/nL+udR9vRRTVwJHRMThEbE3cBKwdvrFzHwgM5dm5mGZeRhwOXBiZl5VS8SS1LuJyF9OmZK6bcFiKjO3Ae8ALgauA87PzPURcWZEnFh3gJI0qInIX14bQeq8nuZMZeY6YN2M594/R9tVw4clSaNh/pJUN6+ALkk1mh6XSq+PIHWWxZQkSdIQLKYkqUbTU6YcmJK6y2JKkmoUXhxB6jyLKUkaAwempO6ymJKkGu06zGc5JXWVxZQkSdIQLKYkqUY7L43QaBSS6mQxJUk18gLoUvdZTEnSGDhlSuouiylJqlGUQ1PpgT6psyymJEmShmAxJUlj4GE+qbsspiRJkoZgMSVJNfJsPqn7LKYkqUbTv83nYT6puyymJEmShmAxJUk12vnbfF4aQeosiylJkqQhWExJUo12/jafA1NSZ1lMSVKNPJtP6j6LKUkaAwemxiMdAlQDLKYkqUa7Lo3gH/lxsJvVBIspSVJnWEupCRZTklSjXZdG0DjscGhKDbCYkiR1hrWUmmAxJUlj4B/58fDiqGqCxZQk1Sg8zjdWFq1qgsWUJEnSECymJKlGO6+A7tDUWDgypSb0VExFxHERcX1EbIiIM2Z5/V0RcW1EXBMRX46IQ0cfqiT1z/y1Z7FoVRMWLKYiYhFwNnA8cCRwckQcOaPZPwMrM/P5wAXAh0cdqCT1axLy184pU/6NH4sd9rMa0MvI1NHAhsy8KTO3AucCq6sNMvOSzHyofHg5sHy0YUrSQBrPX7sO82kcvNK8mrC4hzaHABsrjzcBx8zT/jTg72Z7ISJOB04HWLZsGVNTU71FWbPNmzdPTCyDaGPsbYy5rdtJW+Mekcbz1423PAbApZdeyn57t+tXj9u47Xzta19nx6NbWhc3tLO/ob1xw+hi76WY6llEnAKsBF452+uZuQZYA7By5cpctWrVKFc/sKmpKSYllr5cdCFAu2JvccxLlixpV9yl1m7fY1ZX/rrlH2+G69bzspe9jKct2Wc0wY5Jq7ad8nv68le8gn++4tL2xF3Rqv6uaGvcMLrYeymmbgVWVB4vL5/bTUQcC7wXeGVmPjp0ZJI0PPPXnsajfGpAL3OmrgSOiIjDI2Jv4CRgbbVBRLwQ+BhwYmbeOfowJWkgjecvr9k5Xp7NpyYsWExl5jbgHcDFwHXA+Zm5PiLOjIgTy2a/DywB/iYiro6ItXMsTpLGZhLyV7tmSbVTddK5Z/OpCT3NmcrMdcC6Gc+9v3L/2BHHJUkjMSn5y5PM6lPtW8/mUxO8Arok1ak8zufhp/rsqBRQ9rKaYDElSWq1agHlwJSaYDElSTXaOWfKP/K12e0wnx2tBlhMSZJabbcCylpKDbCYkqQaeWmE+lVHpjybT02wmJKkGkV5oM+5POPhYT41wWJKktRqu53NZy2lBlhMSVKNdh3m8698XdIpU2qYxZQkqdV2vzSC5ZTGz2JK6pFJWoOYvjSCm0990sN8apjFlNQjc7QGEf44X+28aKeaZjElSWPg3/j65I7KfXtaDbCYaikPOUntsOvSCH5n61ItoOxmNcFiSpLUap7Np6ZZTLWUe1/jZ59rINOXRnD7qY1n86lpFlMtZboYP/tcmky7nc3XYBzac1lMSVKNPJmvfo5MqWkWUy1lwpDaIcLf5qubPyejpllMSZLazQnoapjFVEuZMMbPPtcgdl4B3S2oNl60U02zmJIktdrul0awmtL4WUy1lHtf42efaxDhpRFq50U71TSLqZZy70tqh53FVLNhdFq1gNphNaUGWExJklrNs/nUNIupljJhjJ9drkH423z1s2vVNIspSVJnWFipCRZTUq9M0hqAc6bq59l8aprFlCSp1TybT02zmGopE8b42eUaht/Z+uw+MiWNn8VUSzmUPX72uAYx/dt8bkH1qZ7N56UR1ASLKUlSq/lzMmpaT8VURBwXEddHxIaIOGOW1/eJiPPK16+IiMNGHql2Y8KQetN0/to5LuV3tja7960drfFbsJiKiEXA2cDxwJHAyRFx5IxmpwH3ZeazgT8CPjTqQCWpX+avPYUT0NWsxT20ORrYkJk3AUTEucBq4NpKm9XAB8r7FwB/GhGRI7hK3ab7HuJNf3nlsIuZ15aHHuLJ//QPta5j1LZXuvZVZ7UrdmhnzB+87GHOuqZ9cde5fS/ZdzGfe9vLa1n2iDSav4p1Fv+e9smr2Gdxu2ZWtCU3bt2+Y+f9d553NTsee6QVcc/Ulv6eqa1xA/zsodtZNYLl9FJMHQJsrDzeBBwzV5vM3BYRDwBPA+6uNoqI04HTAZYtW8bU1NSCK7//kR0cEFt7CHNwS/bZwaJ4uNZ1jFzAvXsnS/beiwNaFPuyJwXbd+xoVcwr9tuLB7cmB+/bwu2EerfvfbZFT9/jBjWavwC2PbKDow9Kduz16CDxN6o1uXExPOWgRWxL2HfRVrbv1ZK4Z2hNf8/Q1rgB8rFtI8lhvRRTI5OZa4A1ACtXrsxVq1b19P9++rgagwKmpqboNZZJYtzjZdx7tkHzF8CB+7bzM2jrtmPc49XWuGF0sfcy5nwrsKLyeHn53KxtImIxcABwz9DRSdJwzF+SatdLMXUlcEREHB4RewMnAWtntFkLvLG8/1rgK6OabyBJQzB/Sardgof5yjkE7wAuBhYBn8jM9RFxJnBVZq4F/gL4VERsAO6lSFiS1Cjzl6Rx6GnOVGauA9bNeO79lfuPAK8bbWiSNDzzl6S6tes8XUmSpAljMSVJkjQEiylJkqQhWExJkiQNIZo6Azgi7gJuaWTlj7eUGVc7bgnjHi/jHt6hmXlQ00EMa4D8NUmfQT+Me7yMe/z6iX3O/NVYMTVJIuKqzFzZdBz9Mu7xMm4Nqq2fgXGPl3GP36hi9zCfJEnSECymJEmShmAxVVjTdAADMu7xMm4Nqq2fgXGPl3GP30hid86UJEnSEByZkiRJGoLFlCRJ0hD2yGIqIp4aEV+KiBvLf58yT9v9I2JTRPzpOGOcI5YF446IF0TEZRGxPiKuiYifbyLWMpbjIuL6iNgQEWfM8vo+EXFe+foVEXFYA2E+Tg9xvysiri3798sRcWgTcc60UNyVdq+JiIyIVp7KPMk6vM3/h4j4p4jYFhGvbSLG2XT1uxoRvxIR346IqyPi6xFxZBNxztTWHNNDf58aEXeV/X11RLy575Vk5h53Az4MnFHePwP40DxtPwJ8BvjTNsQN/BBwRHn/mcDtwIENxLoI+FfgB4C9gW8BR85o8zbgo+X9k4DzJqCPe4n7x4Anlfff2pa4y3b7AV8FLgdWNh13l24d3+YPA54P/BXw2qZj7iPuVn5Xgf0r908ELmpD3GW7icoxPfb3qcP+jd8jR6aA1cAny/ufBH56tkYR8aPAMuCL4wlrQQvGnZk3ZOaN5f3bgDuBJq44fTSwITNvysytwLkU8VdV388FwI9HRIwxxtksGHdmXpKZD5UPLweWjznG2fTS3wC/BXwIeGScwe0hurzN35yZ1wA7mghwDp39rmbm9ysPnwxMwplibc0xvcY9lD21mFqWmbeX979HUTDtJiL2Av4QeM84A1vAgnFXRcTRFJX4v9Yd2CwOATZWHm8qn5u1TWZuAx4AnjaW6ObWS9xVpwF/V2tEvVkw7oh4EbAiMy8cZ2B7kD1lm58Unf2uAkTE2yPiXymOSPznMcU2n7bmmF63k9eUh4MviIgV/a5k8aDRTbqI+HvgGbO89N7qg8zMiJit6n8bsC4zN41zx3EEcU8v52DgU8AbM3OS9iY7IyJOAVYCr2w6loWUOwdnUQxnS3uUNn1Xp2Xm2cDZEfELwPuANzYc0rxanmO+AHw2Mx+NiLdQjB7/x34W0NliKjOPneu1iLgjIg7OzNvLouPOWZq9FPj3EfE2YAmwd0Rszsw5J92NwgjiJiL2By4E3puZl9cU6kJuBarV/fLyudnabIqIxcABwD3jCW9OvcRNRBxLUeC+MjMfHVNs81ko7v2Ao4CpcufgGcDaiDgxM68aW5Td1ultfgJ19bs607nAn9caUW/ammMW7O/MrH4HP04xGtifpieHNXEDfp/dJ3J/eIH2pzIZE9AXjJvisN6XgXc2HOti4CbgcHZN+nvujDZvZ/fJuOdPQB/3EvcLKQ6dHtF0vP3EPaP9FBMwObRLty5v85W25zA5E9A7+12txgucAFzVhrhntJ+IHNNjfx9cuf8zwOV9r6fpN9pQ5z6tLDhuBP4eeGr5/Erg47O0n5RiasG4gVOAx4CrK7cXNBTvq4EbymT23vK5M4ETy/v7An8DbAC+AfxA033cY9x/D9xR6d+1TcfcS9wz2k5EouvarcPb/Isp5ppsoRhJW990zD3G3crvKsVZ5OvLmC9hnqJlkuKe0XZickwP/f27ZX9/q+zv5/S7Dn9ORpIkaQh76tl8kiRJI2ExJUmSNASLKUmSpCFYTEmSJA3BYkqSJGkIFlOSJElDsJiSJEkawv8HXJQbSw0YlgQAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "<Figure size 720x288 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
"text/plain": [
"<Figure size 720x288 with 2 Axes>"
]
@@ -660,15 +1141,22 @@
}
],
"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",
+ "# Show DFT bin levels for real input\n",
+ "# . Sine with A = 1, so power A^2 / 2, yields phasor in both side bands, each with amplitude\n",
+ "# A / N_sidebands = 0.5, because both with equal power, such that P_sine = A^2/2. The factor\n",
+ "# is 1 / N and not 1 / sqrt(N)is because the input sine is coherent.\n",
+ "# . For DC at bin frequency 0 the real input gain is 1.0, because DC has only one phasor\n",
+ "# component of frequency 0.\n",
+ "# . For white noise the power in each bin is a factor N_fft less, so the std() is \n",
+ "# sqrt(N_fft) less, because the white noise is incoherent.\n",
+ "#\n",
+ "\n",
+ "# . Input level\n",
+ "ampl = 1.0\n",
+ "sigma = ampl / np.sqrt(2)\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_bins = N_fft // 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",
@@ -678,42 +1166,229 @@
"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",
+ "T_fft = N_fft * T_s # DFT period\n",
+ "t_axis = np.linspace(0, T_fft, N_fft, endpoint=False)\n",
+ "f_axis = np.linspace(0, f_s, N_fft, 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",
+ "bw_bin = f_s / N_fft # bin band width\n",
+ "f_bin = i_bin * bw_bin # 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",
+ "s = ampl * np.cos(2 * np.pi * f_bin * t_axis)\n",
+ "dc = ampl * np.cos(2 * np.pi * dc_bin * t_axis) # equivalent to dc = ampl\n",
+ "noise = np.random.randn(N_fft)\n",
+ "noise *= sigma / np.std(noise) # apply requested sigma\n",
"\n",
"x = s + dc\n",
+ "y = noise\n",
"\n",
"# . DFT using complex input fft()\n",
- "X_fft = np.fft.fftshift(np.fft.fft(x) / N_points)\n",
+ "S_fft = np.fft.fftshift(np.fft.fft(s) / N_fft)\n",
+ "X_fft = np.fft.fftshift(np.fft.fft(x) / N_fft)\n",
+ "Y_fft = np.fft.fftshift(np.fft.fft(y) / N_fft)\n",
"\n",
"# . DFT using real input rfft()\n",
- "X_rfft = np.fft.rfft(x) / N_points\n",
+ "S_rfft = np.fft.rfft(s) / N_fft\n",
+ "X_rfft = np.fft.rfft(x) / N_fft\n",
+ "Y_rfft = np.fft.rfft(y) / N_fft\n",
"\n",
+ "# Plot sine spectrum\n",
+ "# . DSB = double sideband\n",
+ "# . SSB = single sideband (= DC + positive frequencies)\n",
"plt.figure(figsize=(10, 4))\n",
"plt.subplot(1, 2, 1)\n",
- "plt.title('DFT of real sine using fft')\n",
+ "plt.title('DFT of real input sine using fft (DSB)')\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.title('DFT of real input sine using rfft (SSB)')\n",
"plt.plot(f_axis_rfft, abs(X_rfft))\n",
- "plt.grid()"
+ "plt.grid()\n",
+ "\n",
+ "# Plot noise spectrum\n",
+ "plt.figure(figsize=(10, 4))\n",
+ "plt.subplot(1, 2, 1)\n",
+ "plt.title('DFT of real input noise using fft (DSB)')\n",
+ "plt.plot(f_axis_fft, abs(Y_fft))\n",
+ "plt.grid()\n",
+ "plt.subplot(1, 2, 2)\n",
+ "plt.title('DFT of real input noise using rfft (SSB)')\n",
+ "plt.plot(f_axis_rfft, abs(Y_rfft))\n",
+ "plt.grid()\n",
+ "\n",
+ "print(\"The DFT of the sine plot shows:\")\n",
+ "print(f\". G_fft_real_input_dc = {G_fft_real_input_dc}\")\n",
+ "print(f\". G_fft_real_input_sine = {G_fft_real_input_sine}\")\n"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 43,
"id": "2e386180",
"metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "sine ampl = 1.0000\n",
+ "sine sigma = 0.7071 (= 0.7071)\n",
+ "sine power = 0.5000 (= 0.5000)\n",
+ "\n",
+ "sine bin ampl = 0.5000\n",
+ "sine bin re = 0.5000\n",
+ "sine bin im = 0.0000\n",
+ "sine bin std = 0.5000\n",
+ "sine bin power = 0.2500\n",
+ "sine all bins power = 0.5000\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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m3devdx2ZUkol48QJ2Vlfrpxs/nrxRanJatVKk6zzaLIVrOrWhT/+kKxl2TI5j2bAgKBdWly6VHYYPvKIhLpkicxoZc/uOrLQU7++LMG++ir8/LPMcg0cqD26lFJBZMYM6dszZIgkV+vWSeFpJBbipoAmW8EsKkoqqNeskS70Q4bIH/esWa4j+1d8vKx4VqkiO3knTJDzQsuVcx1ZaIuJkSPA1qyRnYuDB8uy7IIFriNTSkW03bsluWrYUOpCZs+GTz+Vw5/VBWmyFQpy55bdHXPmnOtS37WrHIzt0M8/yyzWiy/KgeurVknxu84e+0/+/NIXd/p0Ge4aNSQJO3bMdWRKqYgzebK84P/qK3kF+PffUmOsLkmTrVBSt66s1/XpI8uL5cvLOrnHTp6Uo6vq1JGmwLNmyU7DHDk8DyViNGoEy5dLjj1ihAz9b7+5jkopFRH275fZrHvvhaJFpUH3M89oMe5l8EuyZYxpaIxZY4xZb4x5+iLXu9sYY40xVfxxvxEpNlZ6BPzyi0zh1qsnZ0t5VMu1fLn0zHr1VanPWrZM2juowMuSRY4P++kn2aBaq5Yc/XP6tOvIlFJha9o0KRz98kt4/nk517BsWddRhZxUJ1vGmGjgbaARUBZobYz5z0gYYzIDjwG/p/Y+FdIjYOlS6N4d3nhDzpsK4I7FhAQpeK9SRc77mzZNOlVkzBiwu1QXULu2DH3btvK/r1Yt3bGolPKzkyehZ0/ZFp03r5xfOGCA9u+5Qv6Y2boRWG+t3WitPQVMAJolc73ngeGAtmv0l4wZJdGaPh3++UcyoY8/9ntfrj17ZBmrZ08pF1u2DBo39utdqMuUJQt88glMnCjtNSpWlBpVpZRKtXXr5CDXkSOld8/ChVKgq66Ysal8YjbGtAAaWms7+z5vB1S11nZLcp1KQH9r7d3GmLlAb2vtH8ncVhegC0DevHkrT5gwIVWxpURcXByZwuCMprT79lFm6FCyL1nC7vr1WdurF2f9MO20bFlWBg8uy5EjaXj00fXcccfOgBXAh8tYeG3v3nQMHVqapUuz07jxP3Tvvp506RJSdZs6FsFDxyK4hPt45J01i5IjRmBjYlj95JPsr1nTdUgXFGxjUa9evcXW2uTLpKy1qXoDWgAfJvm8HfBWks+jgLlAUd/nc4Eql7rdypUrWy/MmTPHk/vxxJkz1r7wgrVRUdYWL27tX39d8U0lJFj78svWRkdbW6KEtUuX+jHOCwirsfDY6dPW9utnLVhbsaK169en7vZ0LIKHjkVwCdvxOHHC2k6d5J9IrVrWbtvmOqJLCraxAP6wF8hp/LGMuAMolOTzgr6vJcoMXAfMNcZsBqoBU7VIPgCio6F/f6mgPnFCpoGvYHbw0CG46y7Z9Ni8ufRW1Rnk4JYmjbRhmzYNtmyRnlxTpriOSikVErZuhZtukm3lAwbIGYcFC7qOKqz4I9laBJQ0xhQzxqQFWgFTEy+01h621uay1ha11hYFFgBNbTLLiMpPbroJFi+WA0Fbt4YnnoAzZ1L0rcuXy7dNmwavvy5tVbJmDWy4yn8aN5bu/ddcIwnzU0/JMUpKKZWsxMOj162Dr7+WXTdaBO93qU62rLVngG7ADGAVMMlau8IYM9gY0zS1t6+uUL580tm3e3d47TVo0EAq3S9i6lSZDDtxQibHHntMG5SGoiJFpDNI167SJaRpUzh82HVUSqmgYq308GnQQBpnL1wo/yxUQPilz5a1drq1tpS1tri1dojva89aa6cmc926OqvlkZgY2a346adyzkuVKnLo3nmshaFDZcmwdGnZ4VujhvfhKv9Jl04OBX/nHZg5U5JobQ+hlALknLW2baU79Z13wu+/y3S4ChjtIB8J2rWTduPWSn+uadP+vej4cTl2sX9/WXH8+WcoUMBhrMqvHn5YOvzv2SPNaH/4wXVESimn9u6V0+7/9z944QWpFcmc2XVUYU+TrUhx/fUyTVy6NDRrBq+/zq6dljp1pFfTsGFyBp8e2B5+6taV2coCBeTs2HfecR2RUsqJlSuhalU5bmfyZHmVrbUintBkK5Lkzy/FWM2asarX+1S75gCrVlm+/loKqfUxF76KFZPJzdtvh0cflfFOSF0rLqVUKPnhB6kPOX5cngdatHAdUUTRZCvSZMzIzz0+p0a6xcQfPc1P5bpxR92jrqNSHsicWdpBPPywFM63aSMnciilwtwHH8i0dqFCUp91442uI4o4mmxFmAkToMFtUeQrloH5L/5E5UXvy2HWu3e7Dk15IDpazrQcPlz+Fm69FQ4edB2VUiogrJXT6rt0kV2H8+bJdmXlOU22IoS18PLLUgRftao85oo93VL6qqxcKYXzGza4DlN5wBh48kmpj12wQIZ+yxbXUSml/OrsWen/8vzz0KkTfPONHKqqnNBkKwIkJECvXvIEe++90gogRw7fhY0bS1O7gwdlPf/PP53GqrzTurX8LezcCdWqyQHjSqkwEB8P99wDo0ZBv37w4YfaqNQxTbbC3Jkz8qJm5EhpUjp+PMTGnnelatVkqis2FurU0f4AEaROHfj1V4iKko8XLHAdkVIqVQ4flvqsKVPkH/+QIbr7KQhoshXGEl/cfPIJPPccjBghT6rJKl1atqsVLSpb1iZP9jJU5dC110rClSMH3HKL5tpKhaydO+VV02+/ySvrHj1cR6R8NNkKU0ePQpMm8NVX8uLm2WdT8OKmQAE556VqVWjVSrI0FRGKFZOhv/pqWVn+5ZdcrkNSSl2OrVuhdm05KuLbb+V/uAoammyFoQMHZIZi7lzJly7rxU22bPD993DzzXD//XLmi4oIiW3YKleGQYOuZcwY1xEppVJkwwZJtPbulanpBg1cR6TOo8lWmNmzR2aRly6FL76A9u2v4EYyZpSdK02awCOPyGGlKiJkzy7H+1SqdJCOHeGtt1xHpJS6qNWrJdGKi5PNTtWquY5IJUOTrTCye7e0zNqwAaZPl1N5rlhsLHz5pRR99e4NgwdL/wgV9jJmhCFDltG8OXTvLsvQSqkgtGyZvLo+e1aWMipVch2RugDdCxomdu6Ulb+tW+G77+Txl2oxMdKMKUMGGDgQjh2TQxR1Z0vYS5vWMmmSlH307Cn/yx9/3HVUSql/LV4sXYnTp4fZs+Gaa1xHpC5Ck60wsGOHJFo7dki5Va1afrzxNGlg9GhJuF56SWa3hg/XhCsCxMRIl/k2beCJJ6SNyJNPuo5KKcWCBdLeIXt2SbSuvtp1ROoSNNkKcdu3nzttZ8YM6Qbud1FRcsYLSBv6qCh48UVNuCJA4uRmdLQcXn32LPTt6zoqpSLYwoVw222QO7fUaBUu7DoilQKabIWwrVsl0dq3TzqBB7Qu0hiplk6c2YqK0mZ5ESJNGhg7VhKufv1khuuZZ1xHpVQE+vNPSbRy5ZIarYIFXUekUkiTrRC1Y4ckWvv3y+4xTw5xT5zhSkiQma2oKDl3SxOusJcmjbQRiY6Wnm1RUdC/v+uolIogS5dKT5+sWWVGSxOtkKLJVgjavRvq15cZrR9+gBtu8PDOo6Kk91ZCgsxsRUdLe3oV9qKjpXzPWhgwQMr4evVyHZVSEWDZMkm0MmWCOXOgSBHXEanLpMlWiNm/X/rVbdsmS4eeJlqJoqLg/fcl4Ro8WD4fONBBIMpriQlXfLzsTkyfHrp2dR2VUmFs5Up5dR0bKzNaxYq5jkhdAU22QsihQ7LTd+1a6aMVkGL4lIqKgg8+kIRr0CBpztS7t8OAlFfSpIFx4yThevhhSbg6dHAdlVJhaPVq2WqeJo0kWiVKuI5IXSFNtkJEXJycD71smZx3ePPNriNCEq4PP4Tjx6FPH8iSBbp0cR2V8kDatDBpEjRtCp06yYvuli1dR6VUGNm0SWa0QBKtUqXcxqNSRZOtEHD8ONxxh+z4nTxZkq6gER0tW9Xi4mQ9KXNmaN3adVTKA7Gxkvg3aiS9uGJjU3lqgVJK7NwpNVrx8XJgaenSriNSqaTH9QS5kyfhrrvk8TZ2LNx5p+uIkpE2LXz+uZzP1a4dTJ3qOiLlkQwZYNo0qFIF7r1XmuoqpVLhwAGpF9mzR44Due461xEpP9BkK4idPQtt20qz0g8/DPIJo/Tp5fDqypXlWXf2bNcRKY9kzixJ1rXXyguD335zHZFSISqxXmTdOvj6a496+igvaLIVpKyFRx6RCaPXXpO6mKCXObO8EitZUtaT5s93HZHySLZsknAVKgSNG0ttoVLqMpw8Cc2bwx9/wMSJQVKYq/xFk60g9cwzMGqUHI0SUr2McuSQnhT588srtL//dh2R8kiePDL0GTJIk+tNm1xHpFSIOHNGli5mz5beKlr8GHY02QpCI0dKv9AHH5T3ISd/fum2mjGjPOtu3uw6IuWRIkUk4YqPl7KT3btdR6RUkEtIkH/2U6bIP//27V1HpAJAk60gM24c9OwJd98tjdpD9iScIkWk2Cw+XhKuvXtdR6Q8cu210gfun39kp+Lhw64jUipIWSv9CceMkX6FPXq4jkgFiCZbQeTbb+H++2Wp/rPPpKtCSLv2WtmqtnWrFPLExbmOSHmkWjX44gup3WrWTHJupdR5Xn4ZRoyAxx6TQ0dV2PJLsmWMaWiMWWOMWW+MeTqZyx83xqw0xvxtjJltjNGDnc4zbx7ccw9UrCi9i9Klcx2Rn9SsKcWeixdDixZw6pTriJRHGjaETz+Fn3+GVq2kLEUp5TNuHDz1lDw4XnsthJcxVEqkOtkyxkQDbwONgLJAa2NM2fOutgSoYq0tD3wOvJTa+w0ny5dDkyayk+u772RTX1hp2lSq/WfMgAcekBoFFRFat4Y33pBd7A89JKsmSkW8WbOgY0eoV0+WEKN0kSnc+aOD/I3AemvtRgBjzASgGbAy8QrW2jlJrr8AaOuH+w0LO3ZIXUv69FJYnDu364gC5IEHpFq6f3/ImxdeecV1RMoj3bpJf8bnn4fChfXMchXh/vxTGtKVLStF8WGzjKEuxh/JVgFgW5LPtwNVL3L9B4Dv/HC/Ie/IEemOcOgQ/PKL1JSHtb59YdcuePVVyJdPD66OIM89B9u2SQ1woUIh0jdOKX/buFH+6efIIcsYWbO6jkh5xNOzEY0xbYEqQJ0LXN4F6AKQN29e5s6dG/CY4uLiPLmf850+bejbtxwrVmTjxReXcejQQRyE4b3mzSm7bBl5+vRh5aFD7Lnlln8vcjUW6r8CMRZt2hiWLy/Hgw9mZ9++Zdx44wG/3n640sdFcLnS8Yg5fJjru3Uj5vhxlgwfzvG1a2HtWv8HGEFC6rFhrU3VG1AdmJHk875A32SudwuwCsiTktutXLmy9cKcOXM8uZ+kEhKsbd/eWrB2zBjP7969+Hhr69a1Nm1aa+fO/ffLLsZCJS9QY3H4sLUVK1qbKZO1ixcH5C7Cjj4ugssVjUdcnLVVq1obG2vtr7/6PaZIFWyPDeAPe4Gcxh9VeYuAksaYYsaYtEAr4P+dRGyMuR54H2hqrd3jh/sMac8+K7u0Bg+GDh1cR+NAunTw5ZdQvLgcT7FqleuIlEeyZJEWJzlySDcQ7Xerwt6ZM7LjcNEiGD9edmiriJPqZMtaewboBsxAZq4mWWtXGGMGG2Oa+q72MpAJmGyMWWqMmXqBmwt7H3wAL7wAnTvDgAGuo3Eoe3bpfJk2rdQwaKvxiHHVVVKuEh8vm0MO6GqiClfWQvfu0m/w7bflxaWKSH7Zb2qtnW6tLWWtLW6tHeL72rPW2qm+j2+x1ua11lb0vTW9+C2Gp+nT4eGH5QkmpLvD+0vRovJPaM8eaNKEqBMnXEekPFK2rLSD2LhRm56qMPbaa/Dee9JPq2tX19Eoh7S5h0f++EOallaoAJMmQRpPtyYEsRtukKn1P/+k7AsvwNmzriNSHqldW5bTf/0V2rXT9msqzHz5JfTpI//4hw51HY1yTJMtD2zeLE1L8+SRepVMmVxHFGSaNoWRI8n122/Qq5d2vowgLVtKy7XPP4d+/VxHo5SfLFwIbdtC1arwySfatFR52/ohEh0+LInWyZMwd660l1LJ6NaNbT//TKE334RixSTpUhHh8cdhwwYYPhxKlpT+t0qFrM2b4Y475J/9119Lx2oV8TTZCqAzZ+SV+5o1clJN6dKuIwpuG7p2pVBCAjzxhLQav/tu1yEpDxgjR/ps2CBlLcWKyWHsSoWcQ4dkm+2pU/LqOk8e1xGpIKFzmwFirRzkPmOGFMPrk0cKREXB2LEy9d62Lfz+u+uIlEfSpJFaxmuukRx79WrXESl1mU6fhhYtpFHpl19CmTKuI1JBRJOtAHnzTXjnHamP7NzZdTQhJH16mDoV8ueXbWpbt7qOSHkka1bZnJo2rUwO7N3rOiKlUsha2Wo+ezZ8+KEcMK1UEppsBcC330rJUfPmMGyY62hCUO7c8qx74oTUPhw96joi5ZGiRaXM5Z9/4M47tSWEChHDh8NHH8Ezz0Rop2p1KZps+dlff0mz4IoVYdw43YRyxcqWlXWlFSvgvvu0JUQEqVZNNnDNmyfF8ro5VQW1SZOgb1/5P/Xcc66jUUFKUwE/2rlTdh5mzQrffAMZM7qOKMTddptUTk+bJk0BVcS4914YMgT+9z851kqpoLRgAbRvDzfdJDNbEd+pWl2I7kb0k+PHpV3UwYPwyy9yJInyg0cekbMTX31VqqcffNB1RMojfftKrfGgQVCiBLRp4zoipZLYvl1qRQoUgClTIDbWdUQqiGmy5QcJCdIBe/FiqTe5/nrXEYWZESNg3TpJvIoX162dEcIYGDVK2hZ16iT1XHqGrwoKx4/LBp7jx6UoPlcu1xGpIKfLiH7Qr5/s9H3lFannVn6WJg1MnAilSklfgLVrXUekPJI2LXzxBRQpIpMImza5jkhFPGsl+1+yRI4au/Za1xGpEKDJVip9/LFsRHnoIW16HlCJfQHSpJHCuAMHXEekPJIzp+zwPXtWXswcOeI6IhXJiowbJy/+hg2THiVKpYAmW6kwb54kWfXrS18trY0MsGLF4KuvYMsWaR546pTriJRHSpaUTV+rV0vtlm5OVU5MmUKx0aOlbqRPH9fRqBCiydYV2roV7rpLljcmTYKYGNcRRYiaNWXXz5w5UsOlfQEixi23wMiRMsHZv7/raFTE+esvaNuWI2XKSDGhvrpWl0EL5K/AsWOy8zA+Hn76CXLkcB1RhGnbVqY4hgyRIzGeeMJ1RMojjz4qrdeGD5dSmXbtXEekIsKePfJPP3t2lj//PDV056G6TJpsXaaEBGkQvGyZvMLWw6UdGTxYEq4nn5QGqI0auY5IeWTkSBn6zp1lebFaNdcRqbB26pRszNmzB375hVNxca4jUiFIlxEv0+DBsjvqpZf0+d2pqChpM16+vLTs15OLI0ZMDEyeDIUKyQ7FbdtcR6TClrVSrvDrrzBmDFSp4joiFaI02boMkyfLaQz33w+PP+46GkXGjNLYLDb2XEdZFRFy5pRTGk6ckHZHx465jkiFpTfekBrR/v2hZUvX0agQpslWCi1ZIsuH1avDe+9pbWTQKFxYmpxt3ixnvJw54zoi5ZEyZWDCBKlb7tBBlviV8puZM+VVdfPmemaUSjVNtlJg92559Zwzpzyvp0vnOiL1/9SsKRnwDz9A796uo1EeatQIXn5Zlvb1DGDlN2vXykzWddfB2LFStqBUKmiB/CWcPAl33gn79klfrXz5XEekktWpk+xaeP11KFcOHnjAdUTKI716wfLlMvlQtqyu9qhUOnhQuuemSSNlCpkyuY5IhQFN1y/CWujaFebPl1psPfMwyL38MjRoAA8/LAWtKiIYA+++KxOc998Pf/zhOiIVss6ckQ03mzbJMkbRoq4jUmFCk62LGDFCNqA8+yzcc4/raNQlJZ6hWLSodJzdssV1RMoj6dLJc2OePLLk/88/riNSIalPH6nVeucdqFXLdTQqjGiydQHffSePu7vvhoEDXUejUix7dpg6VXrjNGsG2hMnYuTJI0N/+LDUNJ844ToiFVJGj5YyhB49pImbUn6kyVYyVq+WmeRy5WT5UGsjQ0zp0rJNbdkyWVfSbWoRo0IFGDcOFi2S50s9zUmlyK+/Ss1Igwbw6quuo1FhSNOI8xw8KC2b0qWT2siMGV1HpK5Iw4bntqnptu2I0rw5vPAC/O9/cqyPUhe1ZYuUHRQtKmUIaXTfmPI//atK4swZ2cm0ebOcc1ykiOuIVKr06iWzW889J1u4W7RwHZHySL9+skOxXz85Q/GOO1xHpIJSXJyUG5w6JWvQ2bO7jkiFKZ3ZSqJ3b5g1S1o21azpOhqVasbIYFavLl0vly51HZHyiDHS+LtSJbjvPkm8lPp/kh50O3GiHnSrAkqTLZ+PPpIDbnv2lJZNKkwkblPLkUPWh3fvdh2R8kiGDFIKkDmzzGzt2+c6IhVUnntO/je88grcdpvraFSY02QLqY18+GF5vL38sutolN/lyyfPuvv2SW3GyZOuI1IeKVAAvvoKdu6UVeRTp1xHpILCpElSy9mxo7zCVirA/JJsGWMaGmPWGGPWG2OeTubydMaYib7LfzfGFPXH/frDrl3puOsuKFZMNrBpbWSYqlQJPv4YfvsNHnlEt6lFkBtvlJnrn36SXf069BHuzz9ll3KNGtINVw+6VR5IdbJljIkG3gYaAWWB1saYsudd7QHgoLW2BDACCIo9QnFx0L9/uX9rI7Nlcx2RCqiWLWHAAOmnM3Kk62iUh9q0gaeegvffl36VKkLt2iUF8bly6UG3ylP+mNm6EVhvrd1orT0FTACanXedZsAnvo8/B+ob4/blREICtG8PmzdnZNIkuOYal9Eozzz3nBx2+cQTMGOG62iUh4YMgSZN4LHHYPZs19Eoz8XHy2P/wAF5dZ03r+uIlEcOHXJfs2lsKufUjTEtgIbW2s6+z9sBVa213ZJcZ7nvOtt9n2/wXWffebfVBegCkDdv3soTJkxIVWwXExcXzdNPl6d69e20abM3YPejUi4uLo5MHhz6Gn3iBNd360bs7t0sfucdThQuHPD7DDVejYXXjh2Lplu3Suzfn5Z33/2TAgWCv818uI6Fp6yl9LBh5Js5k+WDBrGvTp0rvikdj+CRkrE4e9bQt285du2K5aOPFhETE7g6gnr16i221lZJ9kJrbaregBbAh0k+bwe8dd51lgMFk3y+Ach1sdutXLmyDbRTp6z98cc5Ab8flTJz5szx7s42bbI2Vy5rS5Wy9uBB7+43RHg6Fh7bsMHaHDmsLVPG2kOHXEdzaeE8Fp55+WVrwdpBg1J9UzoewSMlY/H44zL0H3wQ+HiAP+wFchp/LCPuAAol+byg72vJXscYkwbICuz3w32nSkyM1kZGrKJFpWZj40Y5m+nMGdcRKY9cfTV8/jmsWwetW8PZs64jUgE1fTo8+STccw8884zraJSHxoyB116Dbt3cH3fpj2RrEVDSGFPMGJMWaAVMPe86U4EOvo9bAD/6skCl3KlVS6qlZ8yQf8YqYtSrB2++KQfOP/2f/dMqbKxcKS+mKlaUZ1496DZi/PYbPPQQ1K8PI0a4jsYPx/VYa88YY7oBM4BoYLS1doUxZjAypTYV+AgYa4xZDxxAEjKl3HvwQekgPWKEHOmjHW0jRteuMvSvvCJD36HDpb9HhZD9+6WRcWJ32wwZXEekPLJ1q+yFKFRIWqoFQ0snv4RgrZ0OTD/va88m+TgeuMcf96WU3732GqxaJc++11yjZzVFkNdfl6Hv0gVKlZKTnVQYOH0a7r0Xtm2DuXPlWVdFhGPH5DD6EyfkjOMcOVxHJHROVak0aeRstCJFpMP81q2uI1IeiYmByZPlufjOO+W5WYWBXr3gxx9h1CjNoCOItXIowNKl0qS87PkdPx3SZEspkJc/33wjvXiaNpWXRyoi5MwpbZeOH5dXxMePu45Ipcr778Pbb0svPV0bjigvvCAvnoYPh9tvdx3N/6fJllKJSpeWl0PLlsk/6YQE1xEpj5QtC+PHw5IlcpKLbt8JUXPnytazRo3kGVdFjC+/hGefhXbtoHdv19H8lyZbSiXVqBG89BJ88YUcVKsiRuPGMGyYvDJ+/nnX0ajLtnEj3H03lCghmXN0tOuIlEf++kuSrKpVZeU4GFs6BUGNvlJB5vHHYflyOdrn2mulP4+KCH36yNAPHChDf/fdriNSKXLkCNxxh0xJfvMNZM3qOiLlkT175LjLbNlgyhSIjXUdUfJ0Zkup8xkD770nhbUdOsjakooIxsgr46pV5ezUpUtdR6Qu6exZ6U67dq10qy1RwnVEyiOnTxtatIDdu6W7R/78riO6ME22lEpOunRSBJAzp7xs2r3bdUTKI7Gx8go5e3YZ+j17XEekLurpp6VL/Jtvws03u45GecRaGDmyJL/8AqNHQ5XkTyQMGppsKXUh+fLJNrV9+6QvwMmTriNSHsmfX14p790r3UB06IPUmDHSlfbRR6VPnooYb70F3357Ff36ycRmsNNkS6mLuf56+OQTmD9f/pnrNrWIUbkyfPwxzJsHjzyiQx905s0LrvNYlGd++EFaqdWosS9kNrNosqXUpdxzj+wpHjNGWo6riNGyJQwYIMsUI0e6jkb9a8sWmXIsXFjOY4mJcR2R8si6dXI4QJky0L//qpA57jJEwlTKsYEDZWta795ycLWKGM89J6vITzyhQx8U4uKkmO7kSdl5GCznsaiAO3xYek5HRUmFR4YMZ12HlGKabCmVElFRspxYrpxMd6xZ4zoi5ZGoKPj0UzmsWofesYQE2Sa6bJk0IC5d2nVEyiNnz8J998H69bLptFgx1xFdHk22lEqpjBmlajptWunpc/Cg64iURzJlklfSOvSODRwoW0VffRUaNnQdjfJQnz7nNp3Wres6msunyZZSl6NIEWkJsXmzTHOcOeM6IuURHXrHxo+Xw+86d4bHHnMdjfLQ++/LHogePUJ306kmW0pdrptugnffhVmzgvMQLhUwOvSOLFwInTpBrVpyyHQwnseiAmL2bOns0aiRTGiGKj2uR6kr8cADUjcycqSc6/Lgg64jUh5JOvTlysnnKoB27IDmzaXv3RdfyFquighr1kCLFlKaN2ECpAnhjEVntpS6Uq+8InUjjzwijV9UxHjlFWjQAB5+GH75xXU0YSwuTorkjh6VorncuV1HpDyyfz80aSJdPaZNgyxZXEeUOppsKXWl0qSBiRPlZVeLFrBqleuIlEcSh75YMWn3tHmz64jC0Nmz0KYN/PWXTGuUK+c6IuWRU6ek087WrfDVV1C0qOuIUk+TLaVSI0sWedmVLh00biznu6iIkD27TLacPi29f+LiXEcUZp56Sn7Br78ujy0VEayVGeOffpJmwjVquI7IPzTZUiq1ihSRJ4WdO6W2JD7edUTKI9dcIzNcK1ZAq1YyGaP84P33pRq6Wzfo3t11NMpDr74qSdaAATKxGS402VLKH6pWlc6Xv/0mFdN6kF7EuO22xENx5bw2lUqzZp3bfqZnHkaUr7+GJ5+UE9Kee851NP6lyZZS/nLPPTB0KPzvfzB4sOtolIcefhgef1waLr7xhutoQtjKlVL/WLZs6G8/U5dl6VLpEF+lihxDGypnHqaU/iUr5U9PPw1r18KgQVCiRHjNg6uLeukl2LgRevaUgt6mTV1HFGL27JHarAwZwmP7mUqxnTtl02mOHDK7lSGD64j8L8xyR6UcM0bqTerUkSaM8+a5jkh5JDoaxo2DypWhdWtYvNh1RCHkxAk5XHr3bql/LFzYdUTKI3Fx0uLhwAEZ+vz5XUcUGJpsKeVvadNK88UiRaRgfsMG1xEpj2TMCN98A7lyySv1bdtcRxQCEhKgY0dYsECy1RtucB2R8siZM7KxZOlS2Why/fWuIwocTbaUCoScOaViOiFBXrYdOuQ6IuWRfPlk6I8dk1WxI0dcRxTkBg2SZ9rhw6VpmYoI1spZh99+KxtMmjRxHVFgabKlVKCULCknF2/YIB36Tp1yHZHyyHXXweefS733vffqodUX9Mkn8PzzsoO3Tx/X0SgPvfKKnDPap49sMAl3mmwpFUh16sBHH8GPP0oNl7aEiBgNGsiTyYwZ0i5Kh/48M2dC585Qvz68844eLh1BJk2SFg/33gvDhrmOxhu6G1GpQGvXTs6dGDBACn+HDnUdkfLIgw/KxObw4TLR+cQTriMKEkuXnmvxoIdLR5R586B9e6hZUyY2w63Fw4VosqWUF/r1k2rpF1+EQoUiY95cAZJbb9woyyWFC0s7toi2dSvcfjtkzQrTp8t7FRHWrZOWKIULS4uH2FjXEXknVTmlMSaHMWaWMWad7332ZK5T0Rgz3xizwhjztzGmZWruU6mQZMy5KtBu3WSPs4oIUVHyCr5GDWjbVs58i1gHD0pn+OPH4bvvoEAB1xEpj+zdK0MfFSVDnzOn64i8ldoJvKeB2dbaksBs3+fnOw60t9ZeCzQEXjfGZEvl/SoVetKkka7YlSvLfucFC1xHpDySPr3k18WLSzupZctcR+TAyZNw550yvTFliuwiUBHhxAmZ0dqxQ1qjFC/uOiLvpTbZagZ84vv4E6D5+Vew1q611q7zffwPsAfIncr7VSo0Zcwo3bHz55dGTOvWuY5IeSRHDvj+e/kTaNQownpwJSTA/ffLtN6YMVCvnuuIlEfOnJEmv7//Dp99BtWquY7IjdQmW3mttTt9H+8C8l7sysaYG4G0gHZ5VJErTx551rVWnnX37HEdkfJI4cIy9EePQsOGsqoWEZ5+WmZ1hw+XA/BURLAWHnlE6rPeeCOy26gZe4n9yMaYH4B8yVzUH/jEWpstyXUPWmv/U7fluyw/MBfoYK1Ndv3EGNMF6AKQN2/eyhMmTEjBj5A6cXFxZMqUKeD3oy4t0sYiy8qVVOjVi2NXX83S114jIX161yH9K9LGwmtLlmTjqafKU6bMEV5++W/Spk244HVDfSwKfPklJd98kx3Nm7OuR4+Qb/EQ6uPhpdGjizJ2bFHatt3CAw9s8vvtB9tY1KtXb7G1tkqyF1prr/gNWAPk932cH1hzgetlAf4EWqT0titXrmy9MGfOHE/uR11aRI7FV19ZGxVlbePG1p465Tqaf0XkWHhswgRrwdq77rL2zJkLXy+kx2L8eGuNsbZZs4v/kCEkpMfDQ2+/LX/fDzxgbUJCYO4j2MYC+MNeIKdJ7TLiVKCD7+MOwNfnX8EYkxaYAnxqrf08lfenVHhp1gzeflvOrOjUSWpbVERo2RJGjJBDBh57LAybns6cKQ2VatWC8ePlpG4VET7/XDZd33EHvPdeyE9m+kVq+2wNAyYZYx4AtgD3AhhjqgBdrbWdfV+rDeQ0xtzv+777rbVLU3nfSoWHrl1h3z545hnZDz1ihP53ihA9e8oOrVdegauuknZsYWHBAtl5WLasbMMMoiVyFVhz5kCbNlC9upTppdFunkAqky1r7X6gfjJf/wPo7Pt4HDAuNfejVNjr318SrpEjIXdu+VxFhOHDYdcuGfIcOST3DmkrV8oJ3Pnzy24AbVoaMZYuhebNoUQJafGQIYPriIKH5pxKBQNj4LXXYP9+OdYnVy546CHXUSkPREXB6NFw+LDs3MqaVbbKh6QtW+DWW+X4nZkzIV9ye6tUONq4UTZXZ8kiOXaOHK4jCi6abCkVLBKfdQ8elON8cuTQs10iREwMTJwoT1bt28sTVuPGrqO6THv3SqIVFwc//wxXX+06IuWRHTvgllvg1CmYPVtOJFP/X4QcAalUiIiJgUmT5JTWNm1g1izXESmPJHaZr1BBzmgOqWN9jh6V8w63bpWmveXLu45IeWTvXmjQQKogvv9eyvTUf2mypVSwyZBBCh7KlJEi499/dx2R8kjiEkzRorKTa/Fi1xGlwIkTsqt2yRLZhnbTTa4jUh45fBhuuw02bZJ/WTfc4Dqi4KXJllLBKFs2mDFDal4aNpTKUxURcuWSCc0cOWTot24N4irjkyelLfjcuXIMT8itfaordeyYDPfy5dK+pE4d1xEFN022lApW+fJJAUTmzDJPv2KF64iURwoWlIQrOhp69y7Pli2uI0rG6dNSyf/99/D++9C2reuIlEfi42XSff58Oe+wUSPXEQU/TbaUCmZFisCPP0otV/36sHat64iUR0qWlMnNEyeiuflm2L7ddURJnD0rlfxTpsihdw8+6Doi5ZHTp6FVK3kx8OGHuocnpTTZUirYlSghM1wJCXDzzbLHWkWEChVg+PC/2btXhv6ff1xHhPwddu587mDp7t1dR6Q8cvYsdOwoB0uPHCkfq5TRZEupUFCmDPzwgxQj168P27a5jkh5pGzZo3z/PezcKQnXrl0Og7FWzmEZMwYGDoQnn3QYjPJSYqL12WcwZAj06OE6otCiyZZSoaJ8eWkUeeCAPOvu3Ok6IuWRGjVg+nTJsevXhz17HARhLfTpA+++K0nWwIEOglAuJE5mjh0LgweH0bFSHtJkS6lQUrkyfPedJFr168Pu3a4jUh6pVUvOK9+0SRpI7tvn4Z1bC089Ba++KsuGw4bp+Z0RIiFBSvLGjIFBg+QIV3X5NNlSKtTUqCHPulu2QN26OsMVQerWlX5G69bJBtUDBzy4U2uhd294+WU5T+j11zXRihAJCXJq2OjR8OyzOpmZGppsKRWK6tSRGa5t2+TjHTtcR6Q8Ur8+fPWVnPcc8ITLWujVS87t7N4d3npLjpVSYS8hQU4N+/BDOa510CDXEYU2fdQoFapq15beALt2ScK1davriJRHbrtNui4sXw716gWohstaqYIeORJ69pT3OqMVERISZBJz1Cipzxo8WIc+tTTZUiqU1awpRfN790rCtXmz64iUR26/XY4hXLdOlhf92hYiIQEefVRmsnr3lpktfbaNCGfOwP33S5/avn3hhRd06P1Bky2lQl21atKH69AhSbi0D1fEaNBAGrhv2yYTnX6Z3ExcP3r3XSmKf+klfbaNEKdOyaEAY8fC889Liwcdev/QZEupcFClinSaj4uThGv1atcRKY/Uri3dvPftk483bEjFjZ0+LZ3hE9ePXnxRn20jRHy8HHP5+ecykTlggA69P2mypVS4uP56mDNHnjBr1YLFi11HpDxSrdq5XLt27SvMtU+cgLvvlq6VQ4fq+lEEOXYMmjSRXm7vvSd7IpR/abKlVDgpXx5++QUyZpTK6blzXUekPFKpkgz32bOScP3552V885EjcprwtGnwzjtSrKOJVkQ4dEg2XMyZA598Iq0elP9psqVUuClZEubNg0KFoGFDmDrVdUTKI9ddBz//DBkyyGry7Nkp+KZ9++REgnnzZFbr4YcDHqcKDv/8I4n5woUwcSK0a+c6ovClyZZS4ahAAXnWrVBBCjE+/dR1RMojpUrBb79B0aKyY3HSpItceft2ebZdsUKad7Vu7VGUyrXVq6F6dTmRYPp0aNHCdUThTZMtpcJVzpxyeHWdOtChgxy1Yq3rqJQHrrpKcu0bb4RWreDtt5O50rJl8my7fbtsaWzc2PM4lRu//w433SRF8T/9JMc/qcDSZEupcJY5sxzt06KF9Evq0UOKelTYy55dWrDdcQd06yZn2v2ba//4ozzbnj0rWVmdOk5jVd6ZPl1WjbNlkxnQSpVcRxQZNNlSKtzFxkpBxhNPSJPKu+6S7Ucq7KVPD198AZ06yebCTp3g1Jj/SS1foUKwYAFUrOg6TOWRMWOgaVMoXVpK9IoXdx1R5NBkS6lIEBUFr7wCb74pO87q1YPdu11HpTyQJo2cbzfwWcuYMXBrx6s4ULUR/PorFC7sOjzlgYQEaZvWseO5Tcp587qOKrJosqVUJOnW7dyhetWrw5o1riNSHjBnTjNoV1fG0Yb5UTWpvvsr1u3N5jos5YHjx6FlS+lP26WLLCNmzuw6qsijyZZSkaZpU3lpe+yYdMOcNct1RCqQ9u2DW2+FUaNo07cIs+em4cBBQ7VqUq6lwtfOnXJu5hdfyP6Y996DmBjXUUUmTbaUikQ33ij1Oom9uEaM0J2K4Wj5chnr+fPlwLuhQ7mplmHBAsiTR3ahffKJ6yBVIPz1F1StKl09pkyBxx/XPrUuabKlVKQqVky2IzVrJv+JO3aUveAqPEydKkvFifv727b996LixWXoa9WC+++Hnj3llCcVHiZMgBo1ZLPpL7/IQ1y5pcmWUpEsUyY5eXbQIJniqFtX1h5U6EpIkLMNmzeXbWeLFskUx3myZ5f2Wj17wsiRMsuleyZC25kzsum4dWtp6bB4sbZ2CBaabCkV6aKiYOBASbqWL4fKlcn611+uo1JX4uBBSbL695dn3J9/ltMELiAmRlaQP/tMcrLKlaXhpQo9e/dKad5rr8k+mNmzIV8+11GpRKlKtowxOYwxs4wx63zvs1/kulmMMduNMW+l5j6VUgFy991S25M5MxUffxyGDZNZEhUaEqcxvv8e3ngDxo2TRlspcN99MvRp08rpPaNGaQlfKFm4UBLl+fNlgvrNN2UsVfBI7czW08Bsa21JYLbv8wt5HtC9L0oFs3LlYNEi9tauDX37Svvx/ftdR6UuxlrZZpa0SKd798uuhq5QAf74Q/owPfSQTIwdPhygmJVfJCRI+7yaNWWCet48aN/edVQqOalNtpoBiXtZPgGaJ3clY0xlIC8wM5X3p5QKtCxZWPnss3Kg3g8/wPXXy85FFXyOHJHC94cfhvr1YcmSZOuzUipHDunDNHSorCpXqiTLiyr47Nkjx1n26SPdXJYs0fqsYGZsKuaKjTGHrLXZfB8b4GDi50muEwX8CLQFbgGqWGu7XeD2ugBdAPLmzVt5woQJVxxbSsXFxZEpU6aA34+6NB2L4JE4FpnXrKHsoEHE7tnD5vbt2dq2LTY62nV4EeVCj4ssy5ZRZuhQYvfsYVPHjmy97z6Z3vCT5cuz8MILZdm3Ly1dumykRYvt/rz5kBUM/6f+/DMbQ4eW4ciRGB59dD1Nm/4TkW0dgmEskqpXr95ia22VZC+01l70DfgBWJ7MWzPg0HnXPZjM93cDnvR9fD/w1qXu01pL5cqVrRfmzJnjyf2oS9OxCB7/bywOHrS2TRtrwdqqVa1du9ZVWBHpP4+LU6esHTDA2qgoa6++2trffgvYfR84YO2dd8rQ33abtdu2BeyuQobL/1MnTlj75JPWGmNt6dLW/vWXs1CCQrA9ZwB/2AvkNJd8nWKtvcVae10yb18Du40x+QF87/ckcxPVgW7GmM3AK0B7Y8ywFKWJSin3smWTYusJE+R4n4oVtYLalTVr4Kab5FTp9u1h6VLppRUg2bNL9/F335VSsOuug08/1aF3YfFiKYJ/6SV44AGprytf3nVUKqVSOyk8Fejg+7gD8PX5V7DWtrHWFrbWFgV6A59aay9WSK+UCkYtW8KyZfLk/tBD0KQJbNvmOqrIcOYMDB8uVezr1sHkyfDxx54ccmcMdO0qHcmvuw46dIA774RduwJ+1wo4dUo6s1StCocOSU3dBx9AxoyuI1OXI7XJ1jCggTFmHVKPNQzAGFPFGPNhaoNTSgWZggVh5kzpgjl3LpQtC2+9JbvgVEBkXL9enmmffloqoleuhBYtPI+jRAlpRP/KK9Jd4rrrpM2AznIFzsKFctrS4MGyO3T5cmjUyHVU6kqkKtmy1u631ta31pb0LTce8H39D2tt52SuP8ZeoDheKRUioqKgRw/5z1+zprQZuOkmOYRN+c/x49C/P5W7doUdO2R74BdfOO1UGR0tHcqXLIFSpeSon3r1YNUqZyGFpcOH4dFH5Zz4PXvkbMOxY2VZV4Um3VuilLoyxYrBd9/Js8C6ddIi4sknpR2BunLWSlJVpgwMHcqe+vVlNuvuu11H9q8yZeDXX6V07++/ZXVzwAA4ccJ1ZKHNWimNLF1aWqd17w6rV8uhACq0abKllLpyxkifp9WroV07WWMqVUrqibT7/OVbtUrOXGnRQjYm/PQTq/v2lQZYQSYqCh58UIa+VSsYMkSSsAkTdGnxSvz5J9x8sywXFiggS4gjR0KWLK4jU/6gyZZSKvVy5YKPPpJniOLFoVMnKTb59VfXkYWG3btlGqN8edlm9uabsv2sdm3XkV1SnjyyQ3HOHFnmat1amtnPn+86stCwY4csx1apIivzb78t51NWruw6MuVPmmwppfynShVJsD77TLar1aoFt98uL9vVfx0+DM88Iwnqu+9C586wdq2cJJwmjevoLkvdupInjh4NW7ZIwnXvvTLzpf7r4EEZ+pIlZTbwySdh/Xp45BGpjVPhRZMtpZR/GSMnG69dK+0KEl+mt2ghtUcK4uJkybV4cemZ1aSJLCG++y7kzu06uisWHQ0dO0oJ36BB0qagbFlo00aTrkQHD8Kzz0LRojL0zZrJ72bYMMia1XV0KlA02VJKBUaGDPJyfeNGaRQ0c6b0C2jRQhKwSLR/v2QhhQvLoXaVKsl00IQJMsURJjJmlCHftEn+BL7+WpKu++6TpbJItG/fuSTr+eelNO/vv2H8ePmaCm+abCmlAitrVkkwNm6Evn1h9mzZ016nDnz7bWQU0q9bBz17QpEi8NxzUou1YIEkoGFcnJM7t8zYbN4MTz0FU6dCuXKSaHz3XWQM/YoV0KULFCr0/5OsyZPld6EigyZbSilv5MolW9a2boXXXpPkq0kT6ZY5bJgUiYeTs2fhm2+gYUPZofn229J6fdky+OoraVQaIXLlghdflFquoUMlAbn9drj2WtkLsH+/6wj969Qp6Y11220ymTtunHTeX7lSk6xIpcmWUspbmTNDr16SbI0fL7M9ffvKS/+WLSVBOXXKdZRXbsUK6fZetCg0bSrJ1eDBcrTR2LHy7BuhcuaUod60SfZQZM4s/XGvukraR8ycGdqHEaxYIU1fCxaEu+6SJdMhQ2To33tPWmOoyBRa212UUuEjJkaeYVu1kuLwUaOkh8CkSdJD4O675bLateW6wWzNGilMGj9eDoeOjpYZrREjpAI62OP3WNq0Ur91331y5uLo0TL7M3Ei5M0rE4AtWshKczBvyrRWEqzE5v7Ll8tQN20q3U9uvTW441fe0T8DpZR7ZcpIYjJ8OMyaJUnL+PHw4YfS1bFBA1l3athQpkFcO35cGkl9/70UIq1dK1+vUkU6UbZqJQ2o1CVVqCC/spdekl/l5MmSc7/3nsyENWwow3/LLdLs07Xjx6OZNk3+TL//XobeGOly8sYbMvQhvKFUBYgmW0qp4JE2rRy23LixJDQzZ0r/gOnTZeoApF1CzZryVr26nG0SyJkja2UdaOlSSbB+/hkWLYLTp+V+b74ZHnsM7rhDlkLVFUmXDu65R96OH4cZM+DLL+VP4LPP5Dply8oxnDfcID1zy5YN7MyRtVJiuHChDPn8+TB/fk3OnoXYWJl569VLjtNxeGSlCgGabCmlglOGDPIs1ry5POstWybPvPPmyVa2Tz+V68XESMJ13XXyvnBhSXoKFYL8+SFTJpl6uBBr5dn94EFpxLp5sxQVbdokDZCWLpXLQJ7Zq1SRZ9g6dWQ6I3PmwP4eIlCGDLKUeOedsmNx2TL44Qd5mzRJVpwTr1e6NFxzjexBKFVKZr/y5pW3bNkuPvQAx47JYc+7d8POndJYdM0ambFatUpaNoC8Drj+emjZchsPPFCEGjUk4VIqJTTZUkoFP2PkKJvy5aF3b0mQ1q+XKYfly+Vt/nxZekzuezNlkqQofXr53oQEeR8fL4lUcgX52bPLTskWLaBiRXmrUEGaSCnPREXJr71CBSk+Txz6RYvkbdUq6aKR3JmMMTEy9LGxMnOWLh2cOSPDHh8vOXZyh2fnzSuJW7Nm0pnjhhvkTy9tWpg7dxN16xbx5odXYUOTLaVU6DFGmoCe3wg0Pl4Om9u2DbZvl5mqo0fhyBF5f+KEPHsbI+/TppVDnrNnl/e5c0OxYrKTUNt5B6WkQ3/ffee+Hh8vG1x37pRh371bZqyOHz+XXJ08KQlYYvIVGytDnjgTli8fXH21zIgp5U+abCmlwkdsrNR0FS/uOhLlsdhYqeEqW9Z1JEr9l/bZUkoppZQKIE22lFJKKaUCSJMtpZRSSqkA0mRLKaWUUiqANNlSSimllAogTbaUUkoppQJIky2llFJKqQDSZEsppZRSKoCMPf98gyBhjNkLbPHgrnIB+zy4H3VpOhbBQ8cieOhYBBcdj+ARbGNRxFqbO7kLgjbZ8oox5g9rbRXXcSgdi2CiYxE8dCyCi45H8AilsdBlRKWUUkqpANJkSymllFIqgDTZglGuA1D/0rEIHjoWwUPHIrjoeASPkBmLiK/ZUkoppZQKJJ3ZUkoppZQKIE22lFJKKaUCKGKTLWNMQ2PMGmPMemPM067jCXfGmELGmDnGmJXGmBXGmMd8X89hjJlljFnne5/d93VjjHnDNz5/G2Mquf0Jwo8xJtoYs8QYM833eTFjzO++3/lEY0xa39fT+T5f77u8qNPAw5AxJpsx5nNjzGpjzCpjTHV9bLhhjOnl+x+13Bgz3hgTq48NbxhjRhtj9hhjlif52mU/DowxHXzXX2eM6eDiZzlfRCZbxpho4G2gEVAWaG2MKes2qrB3BnjCWlsWqAY86vudPw3MttaWBGb7PgcZm5K+ty7Au96HHPYeA1Yl+Xw4MMJaWwI4CDzg+/oDwEHf10f4rqf8ayTwvbW2NFABGRd9bHjMGFMA6AFUsdZeB0QDrdDHhlfGAA3P+9plPQ6MMTmAgUBV4EZgYGKC5lJEJlvIAKy31m601p4CJgDNHMcU1qy1O621f/o+Poo8mRRAfu+f+K72CdDc93Ez4FMrFgDZjDH5vY06fBljCgKNgQ99nxvgZuBz31XOH4vEMfocqO+7vvIDY0xWoDbwEYC19pS19hD62HAlDZDeGJMGyADsRB8bnrDW/gwcOO/Ll/s4uA2YZa09YK09CMzivwmc5yI12SoAbEvy+Xbf15QHfFPt1wO/A3mttTt9F+0C8vo+1jEKrNeBJ4EE3+c5gUPW2jO+z5P+vv8dC9/lh33XV/5RDNgLfOxb1v3QGJMRfWx4zlq7A3gF2IokWYeBxehjw6XLfRwE5eMjUpMt5YgxJhPwBdDTWnsk6WVW+pBoL5IAM8Y0AfZYaxe7jkUBMpNSCXjXWns9cIxzSyWAPja84ltuaoYkwFcBGQmCWRElQvlxEKnJ1g6gUJLPC/q+pgLIGBODJFqfWWu/9H15d+ISiO/9Ht/XdYwCpybQ1BizGVlCvxmpGcrmWzqB///7/ncsfJdnBfZ7GXCY2w5st9b+7vv8cyT50seG924BNllr91prTwNfIo8XfWy4c7mPg6B8fERqsrUIKOnbYZIWKYCc6jimsOarY/gIWGWtfS3JRVOBxN0iHYCvk3y9vW/HSTXgcJKpZJUK1tq+1tqC1tqiyN/+j9baNsAcoIXvauePReIYtfBdPyRfXQYja+0uYJsx5hrfl+oDK9HHhgtbgWrGmAy+/1mJY6GPDXcu93EwA7jVGJPdN1N5q+9rTkVsB3ljzO1I3Uo0MNpaO8RtROHNGHMT8AuwjHN1Qv2Quq1JQGFgC3CvtfaA7x/dW8gU/nGgo7X2D88DD3PGmLpAb2ttE2PM1chMVw5gCdDWWnvSGBMLjEXq7A4Aray1Gx2FHJaMMRWRzQppgY1AR+TFsD42PGaMeQ5oieygXgJ0Rmp+9LERYMaY8UBdIBewG9lV+BWX+TgwxnRCnl8AhlhrP/bwx0hWxCZbSimllFJeiNRlRKWUUkopT2iypZRSSikVQJpsKaWUUkoFkCZbSimllFIBpMmWUkoppVQAabKllFJKKRVAmmwppZRSSgXQ/wGXKGPZjumT0QAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<Figure size 720x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Log amplitude, sigma and power of the sine bins\n",
+ "# . For sine input the bin is a constant phasor when the sine frequency corresponds to\n",
+ "# the center of the bin and a rotating phasor when the sine frequency is off center,\n",
+ "# the phasor then rotates with the delta frequency = bin frequency - sine frequency.\n",
+ "# . The input sine has ampl = A and power A**2 / 2. \n",
+ "# . The real input results in N_sidebands = 2 bins.\n",
+ "# . The amplitude of the phasor is equal to the std() of a rotating phasor.\n",
+ "# . The amplitude of the bin phasor is A/2, so the bin phasor A/2 exp(jwt) has power\n",
+ "# (A/2)**2\n",
+ "# . The total power in the N_sidebands = 2 bins is eual to the input power as expected\n",
+ "\n",
+ "# . input sine\n",
+ "sin_std = np.std(s)\n",
+ "sine_power = np.sum(s**2) / N_fft\n",
+ "print(f\"sine ampl = {ampl:.4f}\")\n",
+ "print(f\"sine sigma = {sin_std:.4f} (= {sigma:.4f})\")\n",
+ "print(f\"sine power = {sin_std**2:.4f} (= {sine_power:.4f})\")\n",
+ "print()\n",
+ "\n",
+ "# . fft bin\n",
+ "bin_ampl = np.max(np.abs(S_rfft))\n",
+ "bin_re = np.max(S_rfft.real)\n",
+ "bin_im = np.max(S_rfft.imag)\n",
+ "bin_power = bin_ampl**2\n",
+ "bins_power = bin_power * N_sidebands\n",
+ "\n",
+ "# . Model bin_std using a rotating phasor with bw_bin frequency to have one complete\n",
+ "# period in t_axis\n",
+ "bin_phasor = bin_ampl * np.exp(2 * np.pi * 1j * bw_bin * t_axis)\n",
+ "bin_std = np.std(bin_phasor)\n",
+ "\n",
+ "plt.figure(figsize=(10, 4))\n",
+ "plt.title('Bin phasor to model bin_std')\n",
+ "plt.plot(t_axis, bin_phasor.real, 'r', t_axis, bin_phasor.imag, 'b')\n",
+ "plt.grid()\n",
+ "\n",
+ "print(f\"sine bin ampl = {bin_ampl:.4f}\")\n",
+ "print(f\"sine bin re = {bin_re:.4f}\")\n",
+ "print(f\"sine bin im = {bin_im:.4f}\")\n",
+ "print(f\"sine bin std = {bin_std:.4f}\")\n",
+ "print(f\"sine bin power = {bin_power:.4f}\")\n",
+ "print(f\"sine all bins power = {bins_power:.4f}\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "id": "97e9a32d",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "noise sigma = 0.7071 (= 0.7071)\n",
+ "noise power = 0.5000 (= 0.5001)\n",
+ "\n",
+ "N_fft = 1024\n",
+ "sqrt(N_fft) = 32.0\n",
+ "sigma / std(Y_fft) = 31.996921\n",
+ "sigma / std(Y_rfft) = 32.018608\n",
+ "\n",
+ "noise bin std (fft) = 0.022099\n",
+ "noise bin std (rfft) = 0.022084\n",
+ "noise bin.re std = 0.015340\n",
+ "noise bin.im std = 0.015887\n",
+ "noise bin power = 0.000488\n",
+ "noise bin.re power + bin.im power = 0.000488\n",
+ "noise bins power = 0.499419 (= 0.500120)\n",
+ "\n",
+ "The ratio of real input noise std and DFT bin noise std shows:\n",
+ ". G_fft_real_input_noise = 0.03125 = (1 / sqrt(1024))\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Log sigma and power of the noise bins (incoherent signal input)\n",
+ "\n",
+ "# . input noise\n",
+ "noise_std = np.std(y)\n",
+ "noise_power = np.sum(y**2) / N_fft\n",
+ "print(f\"noise sigma = {noise_std:.4f} (= {sigma:.4f})\")\n",
+ "print(f\"noise power = {noise_std**2:.4f} (= {noise_power:.4f})\")\n",
+ "print()\n",
+ "\n",
+ "# . fft bin\n",
+ "# . The white noise will appear equally in all bins, therefore the bin_std can\n",
+ "# be modelled by averaging over all bins. This however does cause small \n",
+ "# differences in fft input and output power, due to the DC bin (?)\n",
+ "bin_std = np.std(Y_rfft)\n",
+ "bin_power = bin_std**2\n",
+ "bin_re_std = np.std(Y_rfft.real)\n",
+ "bin_re_power = bin_re_std**2\n",
+ "bin_im_std = np.std(Y_rfft.imag)\n",
+ "bin_im_power = bin_im_std**2\n",
+ "bins_power = bin_power * N_fft\n",
+ "\n",
+ "print(f\"N_fft = {N_fft}\")\n",
+ "print(f\"sqrt(N_fft) = {np.sqrt(N_fft)}\")\n",
+ "print(f\"sigma / std(Y_fft) = {sigma / np.std(Y_fft):f}\")\n",
+ "print(f\"sigma / std(Y_rfft) = {sigma / bin_std:f}\")\n",
+ "print()\n",
+ "print(f\"noise bin std (fft) = {np.std(Y_fft):f}\")\n",
+ "print(f\"noise bin std (rfft) = {bin_std:f}\")\n",
+ "print(f\"noise bin.re std = {bin_re_std:f}\")\n",
+ "print(f\"noise bin.im std = {bin_im_std:f}\")\n",
+ "print(f\"noise bin power = {bin_power:f}\")\n",
+ "print(f\"noise bin.re power + bin.im power = {bin_re_power + bin_im_power:f}\")\n",
+ "print(f\"noise bins power = {bins_power:f} (= {noise_power:f})\")\n",
+ "\n",
+ "print()\n",
+ "print(\"The ratio of real input noise std and DFT bin noise std shows:\")\n",
+ "print(f\". G_fft_real_input_noise = {G_fft_real_input_noise} = (1 / sqrt({N_fft}))\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6d9f356a",
+ "metadata": {},
+ "source": [
+ "Conclusion:\n",
+ "* For coherent sine input is easiest to calculate power via the amplitude of the single bin phasor\n",
+ "* For incoherent white noise input is easiest to calculate power via the std of all bins"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "17aee663",
+ "metadata": {},
"outputs": [],
"source": []
}
diff --git a/applications/lofar2/model/signal_statistics.ipynb b/applications/lofar2/model/signal_statistics.ipynb
index fbd1d2772c..e1b8219cfa 100644
--- a/applications/lofar2/model/signal_statistics.ipynb
+++ b/applications/lofar2/model/signal_statistics.ipynb
@@ -7,14 +7,19 @@
"source": [
"# Signal statistics for beamformer and correlator\n",
"\n",
- "Author: Eric Kooistra, 18 May 2022\n",
+ "Author: Eric Kooistra, Aug 2022\n",
"\n",
"Purpose: Model the SNR of a beamformer and a correlator\n",
"\n",
- "Status:\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",
+ "Description:\n",
+ "* SNR: This model shows two different SNR measures, one regarding the 'coherent' SNR of the coherent signal versus the incoherent signal (e.g. in a voltage beamformer, in a correlator) and one regarding the 'incoherent' SNR of the power measurement itself, that indicates the accuracy if the measured power (e.g. in power statistics, in a powers beamformer). The 'coherent' SNR makes use of phase information of the input voltage signals. The 'incoherent' SNR is based on input powers, so the input phase information is lost, and therefore the 'incoherent' SNR can only improve the accuracy of the mean power measurement.\n",
+ "* Coherent summator (sums voltages, e.g. voltage beamformer): The 'coherent' SNR of coherent input versus the incoherent input improves by the number of inputs N.\n",
+ "* Incoherent summator (sums powers, e.g. auto power statistics, power beamformer): The 'coherent' SNR of coherent input versus incoherent input does not improve, because the coherent phase information is lost in the powers. However, the accuracy of mean power measurement, so the 'incoherent' SNR, does improve by factor N, because the std of the mean power measurement reduces by N.\n",
+ "* Correlator: The 'coherent' SNR of coherent input versus the incoherent input improves by sqrt(N) for integration over N cross powers in time. The mean correlation of the coherent input is constant and non-zero. The mean correlation of the incoherent input is zero. The power of the mean correlation of the incoherent input reduces by N, so the std of the mean correlation of the incoherent input reduces by sqrt(N). For example, 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",
+ "Remarks:\n",
+ "* Summing powers from N inputs (like in an incoherent array beamformer = IAB) or summing N powers in time from 1 input (like in auto power statistics of subbands = SST or of beamlets = BST) is equivalent.\n",
+ "* The field of view of a voltage beamformer reduces by a factor N, to accomodate for the 'coherent' SNR improvement. The field of view of the incoherent array power beam (IAB) is the same as the field of view of one input, because the 'coherent' SNR of the IAB does not improve.\n",
"\n",
"References:\n",
"\n",
@@ -23,7 +28,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 1,
"id": "2b477516",
"metadata": {},
"outputs": [],
@@ -62,14 +67,14 @@
"* increases by S for coherent signals\n",
"* increases by sqrt(S) for incoherent signals\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",
+ "Coherent averaging by summing voltage signals improves the 'coherent' 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 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."
+ "Incoherent averaging by summing power signals does not improve the 'coherent' 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 does inprove the 'incoherent' SNR, so it makes the signal power measurement more accurate."
]
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 2,
"id": "9c55fb7b",
"metadata": {},
"outputs": [],
@@ -86,7 +91,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 3,
"id": "74edfe32",
"metadata": {},
"outputs": [
@@ -94,9 +99,9 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "mean(si) = -0.205130, expected -0.2\n",
+ "mean(si) = -0.199964, expected -0.2\n",
"std(si) = 0.500000, expected 0.5\n",
- "rms(si) = 0.540443, expected 0.538516\n"
+ "rms(si) = 0.538503, expected 0.538516\n"
]
}
],
@@ -125,7 +130,7 @@
"\n",
"Two types:\n",
"\n",
- "1. Coherent summation in voltage beamformer (e.g. digital BF in LOFAR2 Station, TAB in ARTS)\n",
+ "1. Coherent summation in voltage beamformer (e.g. digital BF in LOFAR2 Station, tied array beamformer = TAB in ARTS)\n",
"2. Incoherent summation in power statistics (e.g. SST, BST), power beamformer (e.g. IAB in ARTS)"
]
},
@@ -142,18 +147,18 @@
"2. Incoherent signal, add up as power\n",
"\n",
"In the voltage beamformer the sky signal in the beamlet direction adds coherently and the sky\n",
- "signals from other directions and the signals from the receivers noise add incoherently. Hence the SNR of the beamlet signal improves by factor S/sqrt(S) = sqrt(S)."
+ "signals from other directions and the signals from the receivers noise add incoherently. Hence the 'coherent' SNR of the beamlet signal improves by factor S/sqrt(S) = sqrt(S)."
]
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 4,
"id": "89845ec3",
"metadata": {},
"outputs": [
{
"data": {
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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -165,7 +170,7 @@
},
{
"data": {
- "image/png": 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/Oucgeb8ObyDvkpIkgmOjcqcDHxCGUf9WmkE552pc0n4dffqEPh3z53vy6ILarYHEnsb6CPh5uuE452pWsf06vNbR5bWbQCR9GTgPaIyXN7Mt0gvLOVdTiu3X0dgYah2uS0vSBnIdcBYwHUg4bKZzrtNraeFL55wDixZBjx7FjZo7Zky6sbmakCSBLDGz+1KPJCLpdmCbaHU94D0z2zlHufnA+4Sk9qnlmvDdOVeaqMaxdqbGkTR5+G2rbiVJAmmVdDFwF7A8s9HMnk0jIDM7MrMs6VfAkgLFm83s7TTicK5bK6ZPB3i/jm4qSQLZPfoa/w/fgKHlD2cVSQKOSPs8zrlIppF84cJkY1dlGtK91tFtJXkKq7kSgeTwFeAtM3s5z34DHpRkwNVmNq5yoTnXxSTtz1FXBytXwsCBnjQcsnb+05B0do7NS4DpZjajpJNKk4GNcuwabWZ3R2V+B8w1s1/leY8BZva6pA2Bh4AzzOzRHOVGACMAGhoamiZMmFBKyLS1tVFfX1/SsWnyuIrjceX2paOOYu233ipYZkXv3sweNYpFw4ZVKKr8qn298qnVuKBjsTU3N0/P2c5sZgVfwK3AHOBX0Ws2YVysZ4Aftnd8KS9CzegtYNOE5c8DRrVXrqmpyUrV2tpa8rFp8riK43HlIZmFG1KrvVZC2NfYaDZ+fHVjjKn69cqjVuMy61hswDTL8ZmapCf6psCuZnaOmZ0DNAEbAnsDx5eUzto3DPi7mb2Wa6ekvpLWzSwDXwVmpRSLc11Tpjd5j/wfA8sbGsItK+9J7nJIkkA2JPb0FfAJ0GBmy7K2l9NRwG3xDZI2kXRvtNoAPC7peeBp4B4zuz+lWJzrGuIJY4MN4LvfDb3KM/WNbH36MO+kkyoepus8kjyF1QI8JenuaP1g4NboP/+/pRGUmR2fY9sbwIHR8jxgpzTO7VyXlN1I/s47uctlNZIvGjCA7SoXpetkkjyF9QtJ9wFfjjadYmbTomWv0zrXGSTt17FyZXhlTJ2aWkiu88t7C0tSv+jrZ4F5wC3Ra160zTlXy+K3rJLOEjhwYKohua6lUA3kVuAgwhhY8RukitZ9MEXnalXSfh1xPoaVK1LeBGJmB0VfN69cOM65skhyy6pnT+jXD9591zsGupK0+xSWpC9HDeZIOkbSpZK8nutcLVu4MP8+KQw/csMN8Pbb/piuK1mSx3h/R5jWdifgHOAVQluIc65WxNs7+vXLP5ZVY6MnDFc2SRLIp1FPxOHAWDP7LbBuumE55xLLtHdk+nS8/36oZfTqtXo5b+NwZZYkgbwv6VzgGOAeST2AnumG5ZxL7Cc/WbO9wwzWXTfUODK3rHy4dVdmSRLIkYQe5yea2T8JQ5tcnGpUzrnc4reqBg2CsWPzt3e8+264VeW3rFxKknQk/CdwaWx9IXBzmkE553LIfjR3wQI444z85b1Ph0tZkhqIc64W5Hs0d731QvtGnLd3uArwBOJcZ5HvVtWSJaF9w9s7XIUlSiCS1pG0TdrBOOdi4u0djY2w9tq5yw0cGJKFt3e4CkvSkfBgYAZwf7S+s6RJKcflXPeW/WjuwoWwbNmac3f4rSpXRUlqIOcBXwTeA7Awja0Pb+JcmvK1d6y/vt+qcjUjyXwgn5jZEknxbYUnUnfOlW7Fivyj5777bhh+xLkakKQG8qKk/wTqJG0l6TfAEx09saTDJb0oaaWkwVn7zpU0V9JsSV/Lc/zmkp6Kyt0uqVeucs7VtKidY5+hQ0ON4oc/hF13zV/eH811NSRJAjkD2J7QmfA2YCnwgzKcexZwKPBofKOk7QhT2m4P7A9cKakux/EXApeZ2ZbAYuDEMsTkXOXE2jmUaee4+GJ44w047TR/NNfVvHYTiJl9aGajzWw3MxscLX/U0ROb2UtmNjvHruHABDNbbmb/AOYS2mD+TeF+2lDgzmjTTcA3OxqTcxWVr52jT5/Qw9wfzXU1TpZv1M5MAelPrNnmsQSYBlzd0WQiaSowKjNNrqSxwJNmNj5avw64z8zujB2zQVRmy2h9s6jMDjnefwQwAqChoaFpwoQJJcXZ1tZGfX19ScemyeMqTi3Ftc/QoaHmkcUkHpkypQoRramWrlecx1W8jsTW3Nw83cwGZ29P0og+D/gc4fYVhLGx3ge2Bq4Bjs13oKTJwEY5do02s7sTnLvDzGwcMA5g8ODBNmTIkJLeZ+rUqZR6bJo8ruLUTFxPPhkeyV2xYo1dGjiwNmKkhq5XFo+reGnEliSB7Glmu8XW/yTpGTPbTdKLhQ40s2ElxPQ6sFlsfdNoW9w7wHqS1jKzT/OUca42tLSE21ULF8Jmm0FzM9x6a3gkt60NPopV4r2dw3UiSRrR6+MzEEbLmXrQxynENAk4SlJvSZsDWwFPxwtE85O0AodFm44DKlKjca4ouToE3nQTbLcdzJkD114LjY2Yt3O4TihJAjkHeFxSa9Re8RgwKprm9qZSTyzpEEmvAXsQ5hl5AMDMXgTuAP5G6P1+mpmtiI65V9Im0Vv8CDhb0lygP3BdqbE4l5p8DeWLF4caSDQEySNTpvgQJK7TSTKc+72StgK2jTbNjjWcX17qic1sIjAxz74xwBr1eDM7MLY8j6yns5yrOfkGQHz11crG4VwKkrSBQLiNtA2wNrCTJMzM5wRxrpA5c8K0ssuXr7nPOwS6LiDJYIr/A/wmejUDFwHfSDku5zqX7JFzjzkGdtoprPvc5K6LStIGchiwL/BPMzsB2An4TKpROdeZ5Goob2mBbbeFuXPh+uu9Q6DrkpLcwlpmZislfSqpH7CI1R+zda57y9dQ/u67sMkmIVl4wnBdUJIEMk3SeoROg9OBNuCvaQblXKeRqXHk4g3lrotL8hTWyGjxKkn3A/3M7IV0w3KuE3j4Yfjxj0MSycUbyl0Xl6QR/eHMspnNN7MX4tuc6/LiDeSDBsEvfgH77QfDhsGiRaH9w0fOdd1Q3hqIpLWBPsAGktYHMjNK9QMGVCA256ov00CeaeNYsAB+9jOor4fLL4dTToHevWHvvVcNVzJwYEge3u7hurhCt7BOJsz7sQnwbGz7UmBsijE5VzsKTS175pmr1r2h3HVDeROImV0BXCHpDDP7TQVjcq525Gsgf+21ysbhXA0qdAvr0Gjx9djyv5nZXalF5Vy1mcENN+Tf7w3kzhW8hXVwgX0GeAJxXVNbG4wcCbfcEkbN/cc/YNmyVfu9gdw5oPAtrBMqGYhzNWHWLDj8cJg9G37+89AGMmGCN5A7l0O7/UAkfQb4H2DvaNMjwPlmtiTNwJyrKDO48UY47TTo1w8mT4ahQ8M+byB3LqckY2FdT5jC9ojotRQocHPYuU4i3r9j3XXhu9+FPfeEGTNWJQ/nXF5JhjL5vJl9K7b+c0kzUorHucrI7t/xwQfQsyccdxxstFF1Y3Ouk0hSA1kmaa/MiqQvA8sKlG+XpMMlvShppaTBse37SZouaWb0Nee/gZLOk/S6pBnR68Bc5ZzL6yc/WbN/xyefwE9/Wp14nOuEktRATgVuitpCABYT5iDviFnAocDVWdvfBg42szck7QA8QP5e75eZ2SUdjMN1Rw8/nL9/R77tzrk1JEkgM81sp2god8xsaUdPamYvAUjK3v5cbPVFYB1Jvc0sx5RuzhXpjTf4j1/8AqZMgbXWgk8/XbOM9+9wLjFZvpFEMwWkhcD9wO3AFGvvgGJOLk0FRpnZtBz7DgNOMbNhOfadBxxPaNCfBpxjZovznGMEMAKgoaGhacKECSXF2tbWRn19fUnHpsnjap9WrGDAxIkMuuEG9MknLDz6aD5qaGDryy+nLjbd7IrevZk9ahSLhq3xK5e6WrpecR5XcWo1LuhYbM3NzdPNbPAaO8ys4IswoOIRhI6D8wnjYO2V4LjJhFtV2a/hsTJTgcE5jt0eeIXQgJ/rvRuAOkIbzhjg+vbiMTOampqsVK2trSUfmyaPqx2PP262445mYHbAAfbX8eNX7Rs/3qyx0UwKX+P7KqxmrlcWj6s4tRqXWcdiA6ZZjs/UJPOBfAjcAdwRjcp7BaEvSF07x5X0b5ykTYGJwHfM7JU87/1WrPw1wJ9LOZfrYlpaVnX4GzAAttgCHn0UNtsM7roLvvlNPnrkkVXlvX+Hcx2SpA0ESfsARwL7E24ZHZFGMNHMh/cAPzazvxQot7GZvRmtHkKo2bjuLPux3NdeC6+DDgo9yfv2rW58znVBSSaUmk8Y1v0x4AtmdoSZ/aEjJ5V0iKTXgD2AeyQ9EO06HdgS+FnsEd0No2OujT3ye1H0qO8LQDNwVkficV1AvmHXZ8705OFcSpLUQHa0Mjx5FWdmEwm3qbK3XwBckOeYk2LLx5YzHtfJLVwYJnrKt885l4p2ayDlTh7OlU1bW+j4t802+cv4Y7nOpSZJT3TnasvKlWHgw623hgsugEMOgSuu8HnJnauwvAlE0pnR1y9XLhzn2vHoo7DbbnDCCaF28cQTcOut8P3vw7hx0NgIUvg6bpw/ZeVcigrVQDLzgfh0tq765s2Dww6DffaBRYvCU1dPPAF77LGqzNFHw/z5oYYyf74nD+dSVqgR/SVJLwObRE87ZQgwM9sx3dBctxTvyzFwIPz3f8PLL8Pll4fhR84/H845Z83bVc65iis0I+G3JW1EGNDwG5ULyXVb2X05FiyA730vLB93XGjPGJBvbE3nXKUVfIzXzP4J7CSpF7B1tHm2mX2SemSu+8nXl2OjjUKjuXOupiSZ0nYf4GbCOFgCNpN0nJk9mnJsrrvJ12fjrbdyb3fOVVWSjoSXAl81s9kAkrYGbgOa0gzMdTMzZ4Y2jk9yVG69L4dzNSlJP5CemeQBYGZzgJ7pheS6lZUrQx+O3XaDddaB3r1X3+99OZyrWUkSyLRoHKoh0esawoCKznXMm2/CgQfCD34A++0Xnra67jrvy+FcJ5F0StvTgO9H648BV6YWkeseJk2CE08Mw5FceSWcckpIGj7EunOdRpL5QJYT2kEuTT8c1+V9+GHox3HVVbDTTqEX+XbbVTsq51wJfCwsVznPPQdNTSF5nHMOPPWUJw/nOjFPIC59K1fCJZfA7rvDkiXw0ENhPbvB3DnXqZSUQCR16LlKSYdLelHSytgkUUgaJGlZbDKpq/Ic/1lJD0l6Ofq6fkficSl6/XX46lfhv/4rzA44cyYMK2m2Y+dcjSmYQCTtIemw2KyAO0q6Fcg73WxCs4BDgVydEV8xs52j1yl5jv8x8LCZbQU8HK27WnPXXbDjjvDXv8I118Af/gD9+1c7KudcmRQazv1i4HrgW4RpZy8AHgSeArbqyEnN7KV435ISDAduipZvAr7ZkXhcmbW1sc3FF8O3vgWbbx7aPk46KTxl5ZzrMgo9hfV1YBcz+yi6RfQqsIOZzU85ps0lPQcsBf7bzB7LUabBzN6Mlv8JNKQckyskPoJuQwOYsdGiRXDuuXDeedCrV7UjdM6lQGaWe4f0rJntGlt/zsx2SfzG0mRgoxy7RpvZ3VGZqcAoM5sWrfcG6s3sHUlNwB+B7bOn1ZX0npmtF1tfbGY520EkjQBGADQ0NDRNmDAh6bewmra2Nurr60s6Nk3VjmvDyZPZ5pJLqFu+/N/bDHj58MN5Y+TIqsWVT7WvVz4eV3E8ruJ1JLbm5ubpZjZ4jR1mlvMFvAdMir1WW893XDEvYCowuNj9wGxg42h5Y8IIwe2er6mpyUrV2tpa8rFpqnpcjY1msMZrWUNDdePKo+rXKw+PqzgeV/E6EhswzXJ8pha6hTU8a/1XJSSuokj6HPCuma2QtAWhrWVejqKTgOOAX0Zf7047NpfD+++HOTty6L1oUYWDcc5VWqEJpR5J66SSDiFMlfs5QgP9DDP7GrA3cL6kT4CVwClm9m50zLXAVRZud/0SuEPSicAC4Ii0YnU5mMEdd8DZZ+ctsnzDDVm7giE55yovbwKR1Eq4nZ2Lmdm+pZ7UzCYCE3Ns/wPwhzzHnBRbfgco+fyuA/7+dzj9dHj4YdhllzCD4EUXrT4RVJ8+zDvpJLyPuXNdW6FbWKNybPsS8EPA7090N21tcMEFcOml0Lcv/Pa3cPLJUFcHW265+jzmY8awaMAATyDOdXGFbmFNzyxHsxL+FFibcFvpvgrE5mqBWegAeNZZ8NprcPzxcOGFsOGGq8rkGkF36tRKRumcq4KCo/FK+hrw38ByYIyZtVYkKlcb5syBM86ABx8MI+dOmABf/nK1o3LO1YhCbSDPEBq5Lwb+Gm37d78QM3s29ehcdXzwAfzv/8LFF4dZAn/9azj11DDlrHPORQp9InwAtAGHEYYziY9DYcDQFONy1WAGf/xjmCFw4UI49tjQQL5Rrv6gzrnurlAbyJAKxuGqbe5c+P734b77YIcd4JFHYO+9qx2Vc66GFRpMcTdJG8XWvyPpbkm/lvTZyoTnUvfhh/Czn8H228Pjj8Nll8Gzz3rycM61q9Bw7lcDHwNI2pvQee9mYAkwLv3QXOomTQqJ4xe/gMMPh9mzw+2rnj2rHZlzrhMo1AZSl+kFDhwJjMt09JM0I/XIXHrmzYMzz4Q//zkkkKlTYZ99qh2Vc66TKVQDqZOUSTD7AlNi+/xxnM5o2TL4+c/DPORTp4ZpZZ97zpOHc64khRLBbcAjkt4GlgGPAUjaknAby9Wy+BwdAwfCYYfBxImh9nHUUSF5DBhQ7Sidc51Yoaewxkh6mDBc+oPRkL4Qai1nVCI4V6KWljBGVWZ8qgUL4Fe/go03DmNYDfUnsJ1zHVfwVpSZPZlj25z0wnFlMXr06oMbZvTs6cnDOVc2hdpAXGe1cGHu7a++Wtk4nHNdmjeGdyUrVoRbVXmmKWbgwMrG45zr0rwG0lXMng177QU/+hEMHhzGsIrr0wfGjKlObM65LqkqCUTS4ZJelLRS0uDY9qMlzYi9VkraOcfx50l6PVbuwIp+A7VkxYrQe3znnUMSufVWePppuOYaaGwEKXwdN27NIdedc64DqnULaxZwKKG3+7+ZWQvQAiDpC8AfzWxGnve4zMwuSTPImjd3LjufdRbMnAkHHwxXXx2etILcc3Q451wZVaUGYmYvmdnsdop9G5hQiXg6nZUr4Te/gR13pH7ePLj5Zrj77lXJwznnKkCWr8G1EieXpgKjzGxajn2vAMPNbFaOfecBxwNLgWnAOWa2OM85RgAjABoaGpomTCgtJ7W1tVFfX1/SseW09htvsO1FF7He88/zzu6789ypp7JWY2O1w1pDrVyvbB5XcTyu4tRqXNCx2Jqbm6eb2eA1dphZKi9gMuFWVfZreKzMVGBwjmN3B2YWeO8GoI5QgxoDXJ8kpqamJitVa2tryceWxcqVZr/7nVnfvmb9+pldd53ZypXVjysPj6s4HldxPK7idSQ2YJrl+ExNrQ3EzIZ14PCjCEOp5HvvtzLLkq4B/tyBc9W+BQvgpJNg8mTYbz+49lp/JNc5V3U19xivpB7AERRo/5AUv9l/CKFm0/WYhWTxhS/Ak0+GRvIHHvDk4ZyrCdV6jPcQSa8BewD3SHogtntv4FUzm5d1zLWxR34vkjRT0gtAM3BWRQKvpNdegwMOgO99L/TrmDkzjG8ltX+sc85VQFUe4zWzicDEPPumAl/Ksf2k2PKxqQVXbWbhqaozz4RPPoGxY+HUU6FHzVUWnXPdnA9lUkveeANOPjlM9PSVr8ANN8DnP1/tqJxzLif/t7YWmIUh2HfYITSUX3ZZmPDJk4dzroZ5Aqm2t96CQw+FY46BbbeF558P85L7LSvnXI3zT6lquv32MCf5fffBxRfDY4/B1ltXOyrnnEvEE0g1/OtfcMQRYWrZLbYI85KPGgV1ddWOzDnnEvMEUml33RVqHXffDf/3f/DEE/Af/1HtqJxzrmj+FFalvPMOnHEG3HYb7LorTJkSGs2dc66T8hpIJUyaFGodv/89nH9+6FXuycM518l5DSRNixeHJ6puvhl22gnuvz9M/OScc12A10DScu+9oZbR0gI//WmYJdCTh3OuC/EEUm5LlsCJJ8LXvw7rrw9PPRVuW/XqVe3InHOurDyBlNNDD4WRc2+8Ec49F6ZPh6amakflnHOp8ARSDu+/D6ecAl/9KvTtC3/9K/zv/0Lv3tWOzDnnUuMJpKOmTAm1jnHjQmfAZ5+FL36x2lE551zqPIGUqq0NTj8d9t03tG88/ngYjmSddaodmXPOVYQnkFI8+mh4LPfKK8NjujNmwJ57Vjsq55yrqKolEEkXS/q7pBckTZS0XmzfuZLmSpot6Wt5jt9c0lNRudslpfOYU0sLDBrEPkOHhqlk998fhgwJ+6ZODUOv9+mTyqmdc66WVbMG8hCwg5ntCMwBzgWQtB1wFLA9sD9wpaRcowxeCFxmZlsCi4ETyx5hS0uYRnbBAmQGr74a5iQfNgxeeAH23rvsp3TOuc6iagnEzB40s0+j1SeBTaPl4cAEM1tuZv8A5gKrtUpLEjAUuDPadBPwzbIHOXo0fPjhmtvnzAlPWznnXDcmM6t2DEj6E3C7mY2XNBZ40szGR/uuA+4zsztj5TeIymwZrW8WlVljgClJI4ARAA0NDU0TJkxIHNc+Q4eGmkcWk3hkypRivsXUtLW1UV9fX+0w1uBxFcfjKo7HVbyOxNbc3DzdzAavscPMUnsBk4FZOV7DY2VGAxNZlczGAsfE9l8HHJb1vhsAc2PrmwGz2ounqanJitLYaBYmnF391dhY3PukqLW1tdoh5ORxFcfjKo7HVbyOxAZMsxyfqakOpmhmwwrtl3Q8cBCwbxQkwOtRQsjYNNoW9w6wnqS1LNwGy1Wm48aMCW0g8dtYffqE7c45181V8yms/YEfAt8ws3hDwyTgKEm9JW0ObAU8HT82SjatwGHRpuOAu8se5NFHhw6CjY2YBI2NYf3oo8t+Kuec62yq+RTWWGBd4CFJMyRdBWBmLwJ3AH8D7gdOM7MVAJLulbRJdPyPgLMlzQX6E251ld/RR8P8+aHNY/58Tx7OORep2nwgFjWA59k3BljjPpGZHRhbnkfW01nOOecqx3uiO+ecK4knEOeccyXxBOKcc64knkCcc86VpCZ6oleKpH8BC0o8fAPg7TKGUy4eV3E8ruJ4XMWp1bigY7E1mtnnsjd2qwTSEZKmWa6u/FXmcRXH4yqOx1WcWo0L0onNb2E555wriScQ55xzJfEEkty4ageQh8dVHI+rOB5XcWo1LkghNm8Dcc45VxKvgTjnnCuJJxDnnHMl8QRCGFpe0mxJcyX9OMf+3pJuj/Y/JWlQbN+50fbZkr5W4bjOlvQ3SS9IelhSY2zfimiU4xmSJlU4ruMl/St2/pNi+46T9HL0Oq7CcV0Wi2mOpPdi+1K5XpKul7RI0qw8+yXp11HML0jaNbYvzWvVXlxHR/HMlPSEpJ1i++ZH22dImlbhuIZIWhL7Wf0stq/gzz/luP4rFtOs6Pfps9G+NK/XZpJao8+BFyWdmaNMer9juWaZ6k4voA54BdgC6AU8D2yXVWYkcFW0fBRh+l2A7aLyvYHNo/epq2BczUCfaPnUTFzRelsVr9fxwNgcx34WmBd9XT9aXr9ScWWVPwO4vgLXa29gV/LMmAkcCNwHCPgS8FTa1yphXHtmzgcckIkrWp8PbFCl6zUE+HNHf/7ljiur7MHAlApdr42BXaPldYE5Of4eU/sd8xpIGBJ+rpnNM7OPgQnA8Kwyw4GbouU7gX0lKdo+wcyWm9k/gLmUb4j5duMys1ZbNRnXk4SZGdOW5Hrl8zXgITN718wWAw8B+1cprm8Dt5Xp3HmZ2aPAuwWKDAdutuBJwkybG5PutWo3LjN7IjovVO53K8n1yqcjv5fljqsiv1sAZvammT0bLb8PvAQMyCqW2u+YJ5BwsV+Nrb/Gmj+Af5exMIXuEsIkVkmOTTOuuBMJ/2VkrC1pmqQnJX2zTDEVE9e3ourynZIyUxTXxPWKbvVtDkyJbU7rerUnX9xpXqtiZf9uGfCgpOmSRlQhnj0kPS/pPknbR9tq4npJ6kP4EP5DbHNFrpfCrfVdgKeydqX2O1a1CaVc+Ug6BhgM7BPb3Ghmr0vaApgiaaaZvVKhkP4E3GZmyyWdTKi9Da3QuZM4CrjTopkuI9W8XjVLUjMhgewV27xXdK02JMwo+vfoP/RKeJbws2qTdCDwR8K017XiYOAvZhavraR+vSTVE5LWD8xsaTnfuxCvgcDrwGax9U2jbTnLSFoL+AzwTsJj04wLScOA0YS55ZdntpvZ69HXecBUwn8mFYnLzN6JxXIt0JT02DTjijmKrFsMKV6v9uSLO81rlYikHQk/v+Fm9k5me+xaLQImUsGZQc1sqZm1Rcv3Aj0lbUANXK9Iod+tVK6XpJ6E5NFiZnflKJLe71gaDTud6UWohc0j3NLINL5tn1XmNFZvRL8jWt6e1RvR51G+RvQkce1CaDjcKmv7+kDvaHkD4GXK1KCYMK6NY8uHAE/aqka7f0TxrR8tf7ZScUXltiU0aqoS1yt6z0HkbxT+Oqs3cD6d9rVKGNdAQpvenlnb+wLrxpafAPavYFwbZX52hA/ihdG1S/TzTyuuaP9nCO0kfSt1vaLv/Wbg8gJlUvsdK9vF7cwvwlMKcwgfxqOjbecT/qsHWBv4ffQH9TSwRezY0dFxs4EDKhzXZOAtYEb0mhRt3xOYGf0RzQROrHBc/we8GJ2/Fdg2dux3o+s4FzihknFF6+cBv8w6LrXrRfhv9E3gE8I95hOBU4BTov0CfhvFPBMYXKFr1V5c1wKLY79b06LtW0TX6fnoZzy6wnGdHvvdepJYgsv1869UXFGZ4wkP1cSPS/t67UVoY3kh9rM6sFK/Yz6UiXPOuZJ4G4hzzrmSeAJxzjlXEk8gzjnnSuIJxDnnXEk8gTjnnCuJJxBXVpJM0q9i66MknVem975R0mHleK92znO4pJcktSYsf6+k9cocw6BcI79K2kTSneU8V/S+O0c9u4s5Zh1Jj0iqyxdvEe81RtKrktqytuccCVvSFyTdWOr5XHl4AnHlthw4NOodXDOiEQSSOhH4npk1JylsZgea2XslBVYkM3vDzNJIojsT+g8U47vAXbb6kDCl+hO5e2ifCCw2sy2By4ALAcxsJrCppIFlOLcrkScQV26fEuZePit7R3YNIvPfZjTHwyOS7pY0T9IvFeajeDqaR+HzsbcZFg16OEfSQdHxdZIulvRMNIDjybH3fUxhfo+/5Yjn29H7z5J0YbTtZ4TOWddJujir/MaSHtWqOR++Em2fn0mYkn6qMCfF45JukzQq2j5V0oXR9zQnduygKMZno9eehS5u/D99hXlX7pJ0v8J8DhfFr63C/CcvKswV87lYHIOj5Q2i2HsROlweGX1vR0raR6vmt3hO0ro5wjkauDtHjGtLuiG6ts8pjKeFpD6S7lCYu2JiVKMYDGBmT5rZmznOkW8kbAhJ56hC18ulyxOIS8NvgaMlfaaIY3Yi9J79D+BYYGsz+yKhR/QZsXKDCP+pfh24StLahP9Sl5jZbsBuwPckbR6V3xU408y2jp9M0iaE/2aHEv773k3SN83sfGAacLSZ/VdWjP8JPGBmO0fxzsh6z92Ab0X7DiAMcBm3VvQ9/QD4n2jbImA/M9sVOBL4daGLlMPO0XFfICSAzNhGfQm9x7cHHomdbw0Whj//GWE+mZ3N7HZgFHBa9L1+BViW9b32IozIMD/HW54W3ta+QBja/Kbo5zSSUJvYDvgpq8ZIKyTfSNgQfk5fSfAeLiWeQFzZWRgN9Gbg+0Uc9oyFuQ2WE4ZceDDaPpOQNDLuMLOVZvYyYeyjbYGvAt+RNIMwlHV/Vo3Q+rSFuVqy7QZMNbN/RR9MLYRJgwrGCJyg0KbzBQvzL8R9GbjbzD6K9v0pa39moLvpse+pJ3CNpJmE4XK2ayeGbA+b2RIz+4hQy2qMtq8Ebo+Wx7P6aLpJ/AW4VNL3gfWiaxS3AfBenmP3is6Jmf0dWABsHW2fEG2fRRh+oyMWAZt08D1cB3gCcWm5nFAz6Bvb9inR75ykHoRB7zKWx5ZXxtZXsvq0A9lj7xhhrJ8zov+edzazzc0sk4A+6Mg3sdqJwhDcexNGLL1R0neKfIvM97SCVd/TWYTxzHYi1Fh65TguyXtmv2+2zHX798+AMMZb7sJmvwROAtYB/iJp26wiywodX0b5RsImOv+yPMe5CvAE4lJhYT6EOwhJJGM+q25bfIPw33exDpfUI2oX2YIwiOUDwKkKw1ojaWtJfQu9CWFQzH2idoA6wq2WRwodoDAR1Vtmdg3h1tquWUX+AhwctQHUAwcl+H4+A7xpZisJt+7qEhyTRA8g0970n8Dj0fJ8Vv0M4o3x7xOmRAVA0ufNbKaZXUioea2WQCzMYFcX3ZrK9hihfQRJWxNG9p1NuD5HRNu3I9x2a88k4LhYvFNs1QB+WwMlP/nlOs4TiEvTrwi3OjKuIXxoPw/sQWm1g4WED//7CKONfkT4MP8b8GzUwHw17UyWFjXY/pgwWvDzwHQzW6NBOMsQ4HlJzxHaHa7Ies9nCB94L0TxzSTcsy/kSuC46JpsS/lqTB8AX4yux1BCIznAJYRk+xyr/2xage0yjejAD6IHBV4gjEAbn5Ew40Fy3xq7EugR3Za7HTg+ujV5JfA5SX8DLiCMTrsEQNJFkl4D+kh6Tase/b4O6C9pLnA24WeW0Qzck/ySuHLz0XidKyNJ9RZmy+sDPAqMsGjO6grH0WZm9SmfY1fgLDM7NmH5OqCnmX0U1SAnA9tEjfjFnrs3oca4V472GVchPqWtc+U1Lro9szZwUzWSR6WY2bOSWiXVJewL0gdojW41ChhZSvKIDAR+7MmjurwG4pxzriTeBuKcc64knkCcc86VxBOIc865kngCcc45VxJPIM4550ry/+O8wNrzyTIGAAAAAElFTkSuQmCC\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -240,9 +245,7 @@
"metadata": {},
"source": [
"**Conclusion:**\n",
- "The voltage beamformer improves the SNR of the beamlet signal by factor S, because the coherent signal power increases by S^2 while the incoherent noise power increases by S. For very weak astronomical signals this SNR improvement is not enough to make them appear above the system noise, so then additional beamforming is needed or integration in time using a correlator is needed.\n",
- "\n",
- "The averaging over N_samples in time does not improve the SNR of the beamlet signal, but it does improve the accuracy of the SNR measurement by a factor N_samples. Hence this model shows two different SNR measures, one regarding the SNR of the coherent beamlet signal (depending on S) and one regarding the SNR measurement itself (depending on N_samples)."
+ "The voltage beamformer improves the 'coherent' SNR of the beamlet signal by factor S, because the coherent signal power increases by S^2 while the incoherent noise power increases by S. For very weak astronomical signals this 'coherent' SNR improvement is not enough to make them appear above the system noise, so then additional voltage beamforming is needed or integration in time using a correlator is needed."
]
},
{
@@ -258,7 +261,7 @@
"id": "fd6ffb94",
"metadata": {},
"source": [
- "Incoherent summation of powers from S inputs is equivalent to incoherent summation of S powers in time from a single input. Incoherent summation does not improve the SNR of the signal, but it does improve the accuracy of the power measurement by a factor S. Hence instead of measuring with one dish for S intervals it is equivalent to sum the powers of S dishes for 1 interval. Hence the field of view of the summed array power beam is the same as the field of view of one dish."
+ "Incoherent summation of powers from S inputs is equivalent to incoherent summation of S powers in time from a single input. Incoherent summation does not improve the 'coherent' SNR of the signal, but it does improve the accuracy of the power measurement by a factor S. Hence instead of measuring with one dish for S intervals it is equivalent to sum the powers of S dishes for 1 interval. Hence the field of view of the summed incoherent array power beam (IAB) is the same as the field of view of one signal input."
]
},
{
@@ -279,13 +282,13 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 5,
"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>"
]
@@ -297,7 +300,7 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -309,7 +312,7 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -409,7 +412,10 @@
"metadata": {},
"source": [
"**Conclusion:**\n",
- "The summation of power values does not improve the SNR, but it does improve the accuracy of the power measurement. Therefore the SNR for the auto correlation is defined as the accuracy of the mean power measurement. This SNR improves by N."
+ "The summation of power values does not improve the 'coherent' SNR, but it does improve the 'incoherent' SNR, so the accuracy of the power measurement. Therefore the SNR for the auto correlation is defined as the accuracy of the mean power measurement. This 'incoherent' SNR improves by N, and applies to:\n",
+ "* subband statistics (SST), averaging powers in time\n",
+ "* beamlet statistics (SST), averaging powers in time\n",
+ "* incoherent array (power) beamformer (IAB), averaging powers in space"
]
},
{
@@ -420,18 +426,9 @@
"### 3.2 Cross powers"
]
},
- {
- "cell_type": "markdown",
- "id": "4fc1cbf5",
- "metadata": {},
- "source": [
- "**Conclusion:**\n",
- "The expected coherent cross power is pow_coh and the measurement of cross_coh_mean = pow_coh becomes more accurate when N_samples increases. The incoherent cross power is cross_incoh_mean and goes to zero. The SNR of the coherent correlator is proportional to 1 / cross_incoh_mean. Dividing by almost zero causes the SNR to fluctuate, but in general the SNR of the coherent signal improves by sqrt(N_samples)."
- ]
- },
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 6,
"id": "470fd269",
"metadata": {},
"outputs": [
@@ -444,7 +441,7 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -456,7 +453,7 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -553,6 +550,34 @@
"plt.grid()"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "48b355ae",
+ "metadata": {},
+ "source": [
+ "The expected cross correlation power is the power of the coherent input, because the other terms are uncorrelated and become zero:\n",
+ "\n",
+ "E{(x+a)(y+a)} = E{xy} + E{xa} + E{ya} + E{a^2} = sigma_a^2 = var(a)\n",
+ "\n",
+ "where:\n",
+ "* x = sA_incoh\n",
+ "* y = sB_incoh\n",
+ "* a = si_coh\n",
+ "\n",
+ "The std of E{xy} is a measure of how close E{xy} is to zero. The var of E{xy} reduces with N_samples, so the std(E{xy}) reduces with sqrt(N_samples). Similar for E{xa} and E{ya}. The var(a) is constant and the std of E{xy} has the same (power) units as E{xy}, so therefore the 'coherent' SNR of the correlator depends on the accuracy of E{xy}, and therefore the 'coherent' SNR improves with sqrt(N_samples). This agrees with cross_SNR in the simulation.\n",
+ "\n",
+ "Note that var(E{xy}) is not E{xyxy}."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4fc1cbf5",
+ "metadata": {},
+ "source": [
+ "**Conclusion:**\n",
+ "The expected coherent cross power is pow_coh. The measurement of cross_coh_mean = pow_coh becomes more accurate when N_samples increases. The incoherent cross power is cross_incoh_mean and goes to zero. The cross power is a power statistics, but the two inputs are voltages so their phase information is preserved and therefore the correlator also has a 'coherent' SNR improvement. The 'coherent' SNR of the coherent correlator is proportional to 1 / cross_incoh_mean. Dividing by almost zero causes the 'coherent' SNR to fluctuate, but on average the 'coherent' SNR of the coherent signal improves by sqrt(N_samples)."
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
--
GitLab