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-rw-r--r--BER Plots.ipynb363
-rw-r--r--decoder_prototype12_snr_calc.grc2568
-rw-r--r--decoder_prototype8_ber_working.grc22
-rw-r--r--gm_platform/fw/grid_scope.ipynb6
-rw-r--r--gr_sweep_results.json1
-rw-r--r--gr_sweep_results2.json1
-rw-r--r--grid_frequency_spectra.ipynb2
-rw-r--r--results_digitalocean.json1
-rw-r--r--results_digitalocean2.json1
-rw-r--r--results_digitalocean_undercommitted.json1
-rw-r--r--results_digitalocean_undercommitted2.json1
-rw-r--r--sweep_gr_sims.py118
12 files changed, 2997 insertions, 88 deletions
diff --git a/BER Plots.ipynb b/BER Plots.ipynb
index 4e71d1a..f51efe8 100644
--- a/BER Plots.ipynb
+++ b/BER Plots.ipynb
@@ -2,14 +2,18 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 105,
"metadata": {},
"outputs": [],
"source": [
"import math\n",
+ "import statistics\n",
+ "import json\n",
"\n",
"import numpy as np\n",
- "from matplotlib import pyplot as plt"
+ "from matplotlib import pyplot as plt\n",
+ "import matplotlib as mpl\n",
+ "import tqdm"
]
},
{
@@ -23,28 +27,323 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
- "from math import nan, inf\n",
- "data = {'dec_proto_am_ber_top.py': ([1.0, 1.2, 1.5, 1.8, 2.2, 2.7, 3.3, 3.9, 4.7, 5.6, 6.8, 8.2, 10.0, 12.0, 15.0, 18.0, 22.0, 27.0, 33.0, 39.0, 47.0, 56.0, 68.0, 82.0, 100.0, 120.0, 150.0, 180.0, 220.00000000000003, 270.0, 330.0, 390.0, 470.0, 560.0, 680.0, 819.9999999999999], [-1.1706362050026655, -1.193387892562896, -1.2494141100905836, -1.273546683602035, -1.3226867043413222, -1.3284842972643673, -1.4249085476621985, -2.4881654670462012, -2.9280282892286777, -1.8337596086785197, -3.4516299068927765, -3.6739503433927894, -3.85142894461751, -4.2109690103679895, -4.841764334589243, -5.121118910610676, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf], [0.000562024584446438, 0.002583366143280799, 0.003536123538459578, 0.0060136203314800725, 0.0017120634851061035, 0.01202664019209608, 0.009352711681458127, 0.010626429313400118, 0.0031605552412962345, 0.07580074150906693, 0.008303067934118849, 0.010968003992851543, 0.010921403354231309, 0.014436211616218221, 0.045257276108434545, 0.05063300417965297, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]), 'dec_proto_am_dc_ber_top.py': ([1.0, 1.2, 1.5, 1.8, 2.2, 2.7, 3.3, 3.9, 4.7, 5.6, 6.8, 8.2, 10.0, 12.0, 15.0, 18.0, 22.0, 27.0, 33.0, 39.0, 47.0, 56.0, 68.0, 82.0, 100.0, 120.0, 150.0, 180.0, 220.00000000000003, 270.0, 330.0, 390.0, 470.0, 560.0, 680.0, 819.9999999999999], [-1.208226392045617, -1.2001309534534812, -1.2082590111531317, -1.2057580375112593, -1.214704089694553, -1.231758143831406, -1.2328452042170934, -1.2342556988606688, -1.2555496906861663, -1.2633800823241472, -1.2801077286712825, -1.292429564986378, -1.2502315024699062, -1.2731027859982436, -1.3264964096914462, -1.350060076963517, -1.402916835230801, -1.6361557068303227, -1.3996004345826805, -2.025891115888953, -2.2259163050377957, -2.403329889470167, -2.5532801901852644, -2.6723825335502625, -2.7451475376985512, -2.7838943274880226, -2.7973828878928355, -2.8114503007382154, -2.7500487601808214, -2.7576294792325875, -2.7531131004032336, -2.771351588479543, -2.763352069271704, -2.7856492625232554, -2.8089246354122395, -2.805404500961304], [0.0006223969511333752, 0.001109700896962153, 0.00210398864758181, 0.0009171589283670842, 0.01005799259051457, 0.01198940071540007, 0.013730311872618627, 0.020358273695306007, 0.019376830251761356, 0.02698367824924875, 0.03015560422449139, 0.04189253434399468, 0.04626542022859063, 0.07217384274518368, 0.08584595043975161, 0.12539079396237413, 0.09791907379447246, 0.10581626829587948, 0.18250650933422224, 0.07591527055792387, 0.20120497031325296, 0.2529568393261202, 0.3140587593946733, 0.3626712973758648, 0.39454531783086805, 0.40694947364033235, 0.4101018950589088, 0.38136874448954844, 0.4108311426740005, 0.40839715897167816, 0.4083367927775933, 0.40823628264400785, 0.4080951641200549, 0.40959607776701595, 0.40969886669408834, 0.4099477409126599]), 'dec_proto_fm_ber_top.py': ([1.0, 1.2, 1.5, 1.8, 2.2, 2.7, 3.3, 3.9, 4.7, 5.6, 6.8, 8.2, 10.0, 12.0, 15.0, 18.0, 22.0, 27.0, 33.0, 39.0, 47.0, 56.0, 68.0, 82.0, 100.0, 120.0, 150.0, 180.0, 220.00000000000003, 270.0, 330.0, 390.0, 470.0, 560.0, 680.0, 819.9999999999999], [-1.3057961403392255, -1.3484294968657196, -1.4667961434461176, -2.8690875116735697, -1.6547222812660038, -1.3891625558026135, -3.5982019547373056, -3.771391890011728, -4.029223203659058, -4.187133187428117, -4.5257152915000916, -4.8291374538093805, -4.9882102105766535, -4.988903861492872, -4.977243402972817, -4.991583617404103, -4.978662932291627, -4.995597720146179, -4.980234434828162, -4.898328188806772, -4.886065758764744, -4.892892232164741, -4.887955756857991, -4.894121825695038, -4.874834077432752, -4.881909834221005, -4.885749246925116, -4.879474958404899, -4.893610496073961, -4.893589161336422, -4.900892127305269, -4.89244575984776, -4.886744260787964, -4.895636919885874, -4.909515650942922, -4.8994301706552505], [0.014213245118859085, 0.001330722343276248, 0.013951488821076687, 0.0041134580502828425, 0.038365233682153145, 0.030733212747131068, 0.0091992661239188, 0.010529797577944408, 0.014647350039240111, 0.014036738695564741, 0.0201667482688038, 0.03195929762792339, 0.050554225347760565, 0.05155121488079693, 0.05696637316379902, 0.05194819962648275, 0.04815391425232906, 0.04198674248536032, 0.0531488148233794, 0.043095657257340825, 0.05140641385191975, 0.047935496094956176, 0.05329373773860191, 0.05040869503181174, 0.05644083328947176, 0.053389328604204575, 0.05074839526504205, 0.053625197798602975, 0.047252304573416753, 0.051310379811370974, 0.046438087027853785, 0.05365724267638675, 0.0534321058650641, 0.04956836848859283, 0.04218369035098332, 0.05032427561533336])}"
+ "#from math import nan, inf\n",
+ "#data = {'dec_proto_am_ber_top.py': ([1.0, 1.2, 1.5, 1.8, 2.2, 2.7, 3.3, 3.9, 4.7, 5.6, 6.8, 8.2, 10.0, 12.0, 15.0, 18.0, 22.0, 27.0, 33.0, 39.0, 47.0, 56.0, 68.0, 82.0, 100.0, 120.0, 150.0, 180.0, 220.00000000000003, 270.0, 330.0, 390.0, 470.0, 560.0, 680.0, 819.9999999999999], [-1.1706362050026655, -1.193387892562896, -1.2494141100905836, -1.273546683602035, -1.3226867043413222, -1.3284842972643673, -1.4249085476621985, -2.4881654670462012, -2.9280282892286777, -1.8337596086785197, -3.4516299068927765, -3.6739503433927894, -3.85142894461751, -4.2109690103679895, -4.841764334589243, -5.121118910610676, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf], [0.000562024584446438, 0.002583366143280799, 0.003536123538459578, 0.0060136203314800725, 0.0017120634851061035, 0.01202664019209608, 0.009352711681458127, 0.010626429313400118, 0.0031605552412962345, 0.07580074150906693, 0.008303067934118849, 0.010968003992851543, 0.010921403354231309, 0.014436211616218221, 0.045257276108434545, 0.05063300417965297, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]), 'dec_proto_am_dc_ber_top.py': ([1.0, 1.2, 1.5, 1.8, 2.2, 2.7, 3.3, 3.9, 4.7, 5.6, 6.8, 8.2, 10.0, 12.0, 15.0, 18.0, 22.0, 27.0, 33.0, 39.0, 47.0, 56.0, 68.0, 82.0, 100.0, 120.0, 150.0, 180.0, 220.00000000000003, 270.0, 330.0, 390.0, 470.0, 560.0, 680.0, 819.9999999999999], [-1.208226392045617, -1.2001309534534812, -1.2082590111531317, -1.2057580375112593, -1.214704089694553, -1.231758143831406, -1.2328452042170934, -1.2342556988606688, -1.2555496906861663, -1.2633800823241472, -1.2801077286712825, -1.292429564986378, -1.2502315024699062, -1.2731027859982436, -1.3264964096914462, -1.350060076963517, -1.402916835230801, -1.6361557068303227, -1.3996004345826805, -2.025891115888953, -2.2259163050377957, -2.403329889470167, -2.5532801901852644, -2.6723825335502625, -2.7451475376985512, -2.7838943274880226, -2.7973828878928355, -2.8114503007382154, -2.7500487601808214, -2.7576294792325875, -2.7531131004032336, -2.771351588479543, -2.763352069271704, -2.7856492625232554, -2.8089246354122395, -2.805404500961304], [0.0006223969511333752, 0.001109700896962153, 0.00210398864758181, 0.0009171589283670842, 0.01005799259051457, 0.01198940071540007, 0.013730311872618627, 0.020358273695306007, 0.019376830251761356, 0.02698367824924875, 0.03015560422449139, 0.04189253434399468, 0.04626542022859063, 0.07217384274518368, 0.08584595043975161, 0.12539079396237413, 0.09791907379447246, 0.10581626829587948, 0.18250650933422224, 0.07591527055792387, 0.20120497031325296, 0.2529568393261202, 0.3140587593946733, 0.3626712973758648, 0.39454531783086805, 0.40694947364033235, 0.4101018950589088, 0.38136874448954844, 0.4108311426740005, 0.40839715897167816, 0.4083367927775933, 0.40823628264400785, 0.4080951641200549, 0.40959607776701595, 0.40969886669408834, 0.4099477409126599]), 'dec_proto_fm_ber_top.py': ([1.0, 1.2, 1.5, 1.8, 2.2, 2.7, 3.3, 3.9, 4.7, 5.6, 6.8, 8.2, 10.0, 12.0, 15.0, 18.0, 22.0, 27.0, 33.0, 39.0, 47.0, 56.0, 68.0, 82.0, 100.0, 120.0, 150.0, 180.0, 220.00000000000003, 270.0, 330.0, 390.0, 470.0, 560.0, 680.0, 819.9999999999999], [-1.3057961403392255, -1.3484294968657196, -1.4667961434461176, -2.8690875116735697, -1.6547222812660038, -1.3891625558026135, -3.5982019547373056, -3.771391890011728, -4.029223203659058, -4.187133187428117, -4.5257152915000916, -4.8291374538093805, -4.9882102105766535, -4.988903861492872, -4.977243402972817, -4.991583617404103, -4.978662932291627, -4.995597720146179, -4.980234434828162, -4.898328188806772, -4.886065758764744, -4.892892232164741, -4.887955756857991, -4.894121825695038, -4.874834077432752, -4.881909834221005, -4.885749246925116, -4.879474958404899, -4.893610496073961, -4.893589161336422, -4.900892127305269, -4.89244575984776, -4.886744260787964, -4.895636919885874, -4.909515650942922, -4.8994301706552505], [0.014213245118859085, 0.001330722343276248, 0.013951488821076687, 0.0041134580502828425, 0.038365233682153145, 0.030733212747131068, 0.0091992661239188, 0.010529797577944408, 0.014647350039240111, 0.014036738695564741, 0.0201667482688038, 0.03195929762792339, 0.050554225347760565, 0.05155121488079693, 0.05696637316379902, 0.05194819962648275, 0.04815391425232906, 0.04198674248536032, 0.0531488148233794, 0.043095657257340825, 0.05140641385191975, 0.047935496094956176, 0.05329373773860191, 0.05040869503181174, 0.05644083328947176, 0.053389328604204575, 0.05074839526504205, 0.053625197798602975, 0.047252304573416753, 0.051310379811370974, 0.046438087027853785, 0.05365724267638675, 0.0534321058650641, 0.04956836848859283, 0.04218369035098332, 0.05032427561533336])}"
]
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 54,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "75ad09f7b6df4c1aa68ef78b1e4ceb0a",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(IntProgress(value=0, max=380), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Will launch 450 simulation jobs in 38 batches of 12\n",
+ "Starting batch 1/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 2/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 3/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 4/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 5/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 6/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 7/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 8/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 9/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 10/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 11/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 12/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 13/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 14/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 15/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 16/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 17/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 18/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 19/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 20/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 21/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 22/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 23/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 24/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 25/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 26/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 27/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 28/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 29/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 30/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 31/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 32/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 33/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 34/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 35/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 36/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 37/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 38/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "import sweep_gr_sims\n",
+ "data = sweep_gr_sims.run_simulation(\n",
+ " amplitudes = [10**x for x in np.linspace(0, 2.5, 30)],\n",
+ " #simulations=['dec_proto_am_ber_top.py'],\n",
+ " duration=10.0,\n",
+ " forklimit=12,\n",
+ " repeat_runs=5,\n",
+ " tqdm=tqdm.tqdm_notebook)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 91,
"metadata": {},
"outputs": [],
"source": [
- "labels = {'dec_proto_am_ber_top.py': 'ASK',\n",
+ "with open('results_digitalocean2.json') as f:\n",
+ " data = json.loads(f.read())\n",
+ " for sim in list(data):\n",
+ " data[sim] = {\n",
+ " float(a): entry for a, entry in data[sim].items()\n",
+ " }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#with open('gr_sweep_results2.json', 'w') as f:\n",
+ "# f.write(json.dumps(data))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 73,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "labels = {\n",
" 'dec_proto_am_dc_ber_top.py': '\"DC\"',\n",
- " 'dec_proto_fm_ber_top.py': 'FSK'}"
+ " 'dec_proto_am_ber_top.py': 'ASK',\n",
+ " 'dec_proto_fm_ber_top.py': 'FSK'\n",
+ "}"
]
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 111,
"metadata": {
"scrolled": false
},
@@ -832,7 +1131,7 @@
{
"data": {
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\" 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\" width=\"900\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
@@ -840,31 +1139,47 @@
},
"metadata": {},
"output_type": "display_data"
- },
- {
- "data": {
- "text/plain": [
- "Text(0, 0.5, 'BER [dB]')"
- ]
- },
- "execution_count": 21,
- "metadata": {},
- "output_type": "execute_result"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(9, 5))\n",
"ax.set_title('BERs for basic modulation types')\n",
- "for sim, (ampls, bers, stdevs) in data.items():\n",
+ "if data.keys() - labels.keys():\n",
+ " raise ValueError(f'Unmatched simulation labels: {data.keys() - labels.keys()}')\n",
+ "for sim, label in labels.items():\n",
+ " d = data[sim]\n",
+ " ampls = np.array(sorted(list(d.keys())))\n",
+ " # We've left the gnuradio BER block at its default setting, a cutoff at -70dB BER,\n",
+ " # so we replace -inf with -7 here.\n",
+ " filter_inf = lambda l: [ x for x in l if math.isfinite(x) ] or [-7]\n",
+ " filter_nan = lambda l: [ x for x in l if math.isfinite(x) ] or [math.nan]\n",
+ " bers = np.array([ statistics.mean(filter_inf(d[a][0])) for a in ampls ])\n",
+ " #stdevs = [ statistics.stdev(filter_inf(d[a][0])) if len(filter_inf(d[a][0]))>1 else 0 for a in ampls ]\n",
+ " stdevs = np.array([ math.sqrt(statistics.mean([x**2 for x in filter_nan(d[a][1])] + [0])) for a in ampls ])\n",
+ " \n",
" # The Gnuradio BER block calculates bit error rate over whole bytes, but we only feed in bits casted\n",
" # to bytes. We correct for this by adding log10(8).\n",
- " bers = [(x + math.log10(8))*10 for x in bers]\n",
- " ax.errorbar(ampls, bers, yerr=stdevs, label=labels[sim])\n",
+ " # Also convert log10 values to dB.\n",
+ " bers = (bers + math.log10(8))*10\n",
+ " stdevs *= 10\n",
+ " #ax.errorbar(ampls, bers, yerr=stdevs, label=label)\n",
+ " p, = ax.plot(ampls, bers, label=label)\n",
+ " \n",
+ " ax.fill_between(ampls, bers-stdevs, bers+stdevs,\n",
+ " alpha=0.3, facecolor=p.get_color(), linewidth=0)\n",
"ax.grid()\n",
"ax.legend()\n",
"ax.set_xscale('log')\n",
"ax.set_xlabel('Amplitude Δf [mHz]')\n",
- "ax.set_ylabel('BER [dB]')"
+ "ax.set_ylabel('BER [dB]')\n",
+ "ax.set_ylim([-50, 0])\n",
+ "ber05 = 10*math.log10(0.5)\n",
+ "ax.axhline(ber05, linestyle='--', color='red')\n",
+ "bbox = {'facecolor': 'black', 'alpha': 0.8, 'pad': 2}\n",
+ "xform = mpl.transforms.blended_transform_factory(ax.transAxes, ax.transData)\n",
+ "ax.text(0.9, ber05, f'BER=0.5', transform=xform, color='white', bbox=bbox, ha='center', va='center')\n",
+ "\n",
+ "None"
]
}
],
diff --git a/decoder_prototype12_snr_calc.grc b/decoder_prototype12_snr_calc.grc
new file mode 100644
index 0000000..9839baf
--- /dev/null
+++ b/decoder_prototype12_snr_calc.grc
@@ -0,0 +1,2568 @@
+<?xml version='1.0' encoding='utf-8'?>
+<?grc format='1' created='3.7.13'?>
+<flow_graph>
+ <timestamp>Tue Feb 4 17:05:59 2020</timestamp>
+ <block>
+ <key>options</key>
+ <param>
+ <key>author</key>
+ <value></value>
+ </param>
+ <param>
+ <key>window_size</key>
+ <value>(3000, 1500)</value>
+ </param>
+ <param>
+ <key>category</key>
+ <value>[GRC Hier Blocks]</value>
+ </param>
+ <param>
+ <key>comment</key>
+ <value></value>
+ </param>
+ <param>
+ <key>description</key>
+ <value></value>
+ </param>
+ <param>
+ <key>_enabled</key>
+ <value>True</value>
+ </param>
+ <param>
+ <key>_coordinate</key>
+ <value>(8, 8)</value>
+ </param>
+ <param>
+ <key>_rotation</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>generate_options</key>
+ <value>qt_gui</value>
+ </param>
+ <param>
+ <key>hier_block_src_path</key>
+ <value>.:</value>
+ </param>
+ <param>
+ <key>id</key>
+ <value>dec_proto_fm_ber_gui_top</value>
+ </param>
+ <param>
+ <key>max_nouts</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>qt_qss_theme</key>
+ <value></value>
+ </param>
+ <param>
+ <key>realtime_scheduling</key>
+ <value></value>
+ </param>
+ <param>
+ <key>run_command</key>
+ <value>{python} -u {filename}</value>
+ </param>
+ <param>
+ <key>run_options</key>
+ <value>prompt</value>
+ </param>
+ <param>
+ <key>run</key>
+ <value>True</value>
+ </param>
+ <param>
+ <key>sizing_mode</key>
+ <value>fixed</value>
+ </param>
+ <param>
+ <key>thread_safe_setters</key>
+ <value></value>
+ </param>
+ <param>
+ <key>title</key>
+ <value></value>
+ </param>
+ <param>
+ <key>placement</key>
+ <value>(0,0)</value>
+ </param>
+ </block>
+ <block>
+ <key>variable</key>
+ <param>
+ <key>comment</key>
+ <value></value>
+ </param>
+ <param>
+ <key>_enabled</key>
+ <value>True</value>
+ </param>
+ <param>
+ <key>_coordinate</key>
+ <value>(8, 308)</value>
+ </param>
+ <param>
+ <key>_rotation</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>id</key>
+ <value>actual_sampling_rate</value>
+ </param>
+ <param>
+ <key>value</key>
+ <value>10</value>
+ </param>
+ </block>
+ <block>
+ <key>variable_tag_object</key>
+ <param>
+ <key>comment</key>
+ <value></value>
+ </param>
+ <param>
+ <key>_enabled</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>_coordinate</key>
+ <value>(48, 836)</value>
+ </param>
+ <param>
+ <key>_rotation</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>id</key>
+ <value>packet_time_est_tag</value>
+ </param>
+ <param>
+ <key>key</key>
+ <value>pmt.intern("start")</value>
+ </param>
+ <param>
+ <key>offset</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>src</key>
+ <value>pmt.intern("packet_vector_source")</value>
+ </param>
+ <param>
+ <key>value</key>
+ <value>pmt.from_double(0.0)</value>
+ </param>
+ </block>
+ <block>
+ <key>variable</key>
+ <param>
+ <key>comment</key>
+ <value></value>
+ </param>
+ <param>
+ <key>_enabled</key>
+ <value>True</value>
+ </param>
+ <param>
+ <key>_coordinate</key>
+ <value>(8, 236)</value>
+ </param>
+ <param>
+ <key>_rotation</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>id</key>
+ <value>pi</value>
+ </param>
+ <param>
+ <key>value</key>
+ <value>3.141592653589793</value>
+ </param>
+ </block>
+ <block>
+ <key>variable</key>
+ <param>
+ <key>comment</key>
+ <value></value>
+ </param>
+ <param>
+ <key>_enabled</key>
+ <value>True</value>
+ </param>
+ <param>
+ <key>_coordinate</key>
+ <value>(8, 452)</value>
+ </param>
+ <param>
+ <key>_rotation</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>id</key>
+ <value>samp_rate</value>
+ </param>
+ <param>
+ <key>value</key>
+ <value>actual_sampling_rate*sim_mul</value>
+ </param>
+ </block>
+ <block>
+ <key>variable</key>
+ <param>
+ <key>comment</key>
+ <value></value>
+ </param>
+ <param>
+ <key>_enabled</key>
+ <value>True</value>
+ </param>
+ <param>
+ <key>_coordinate</key>
+ <value>(8, 380)</value>
+ </param>
+ <param>
+ <key>_rotation</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>id</key>
+ <value>sim_mul</value>
+ </param>
+ <param>
+ <key>value</key>
+ <value>1e4</value>
+ </param>
+ </block>
+ <block>
+ <key>variable_tag_object</key>
+ <param>
+ <key>comment</key>
+ <value></value>
+ </param>
+ <param>
+ <key>_enabled</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>_coordinate</key>
+ <value>(248, 836)</value>
+ </param>
+ <param>
+ <key>_rotation</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>id</key>
+ <value>sync_tag</value>
+ </param>
+ <param>
+ <key>key</key>
+ <value>pmt.intern("sync")</value>
+ </param>
+ <param>
+ <key>offset</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>src</key>
+ <value>pmt.intern("correlate_access_code")</value>
+ </param>
+ <param>
+ <key>value</key>
+ <value>pmt.from_double(0.0)</value>
+ </param>
+ </block>
+ <block>
+ <key>analog_quadrature_demod_cf</key>
+ <param>
+ <key>alias</key>
+ <value></value>
+ </param>
+ <param>
+ <key>comment</key>
+ <value></value>
+ </param>
+ <param>
+ <key>affinity</key>
+ <value></value>
+ </param>
+ <param>
+ <key>_enabled</key>
+ <value>True</value>
+ </param>
+ <param>
+ <key>_coordinate</key>
+ <value>(1656, 284)</value>
+ </param>
+ <param>
+ <key>_rotation</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>gain</key>
+ <value>8</value>
+ </param>
+ <param>
+ <key>id</key>
+ <value>analog_quadrature_demod_cf_0</value>
+ </param>
+ <param>
+ <key>maxoutbuf</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>minoutbuf</key>
+ <value>0</value>
+ </param>
+ </block>
+ <block>
+ <key>analog_sig_source_x</key>
+ <param>
+ <key>amp</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>alias</key>
+ <value></value>
+ </param>
+ <param>
+ <key>comment</key>
+ <value></value>
+ </param>
+ <param>
+ <key>affinity</key>
+ <value></value>
+ </param>
+ <param>
+ <key>_enabled</key>
+ <value>True</value>
+ </param>
+ <param>
+ <key>freq</key>
+ <value>-1.1 * sim_mul</value>
+ </param>
+ <param>
+ <key>_coordinate</key>
+ <value>(960, 372)</value>
+ </param>
+ <param>
+ <key>_rotation</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>id</key>
+ <value>analog_sig_source_x_0</value>
+ </param>
+ <param>
+ <key>maxoutbuf</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>minoutbuf</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>offset</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>type</key>
+ <value>complex</value>
+ </param>
+ <param>
+ <key>samp_rate</key>
+ <value>samp_rate</value>
+ </param>
+ <param>
+ <key>waveform</key>
+ <value>analog.GR_COS_WAVE</value>
+ </param>
+ </block>
+ <block>
+ <key>parameter</key>
+ <param>
+ <key>alias</key>
+ <value></value>
+ </param>
+ <param>
+ <key>comment</key>
+ <value></value>
+ </param>
+ <param>
+ <key>_enabled</key>
+ <value>True</value>
+ </param>
+ <param>
+ <key>_coordinate</key>
+ <value>(200, 12)</value>
+ </param>
+ <param>
+ <key>_rotation</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>id</key>
+ <value>ber_file</value>
+ </param>
+ <param>
+ <key>label</key>
+ <value>BER data output file</value>
+ </param>
+ <param>
+ <key>short_id</key>
+ <value></value>
+ </param>
+ <param>
+ <key>hide</key>
+ <value>none</value>
+ </param>
+ <param>
+ <key>type</key>
+ <value>string</value>
+ </param>
+ <param>
+ <key>value</key>
+ <value>0</value>
+ </param>
+ </block>
+ <block>
+ <key>blocks_add_const_vxx</key>
+ <param>
+ <key>alias</key>
+ <value></value>
+ </param>
+ <param>
+ <key>comment</key>
+ <value></value>
+ </param>
+ <param>
+ <key>const</key>
+ <value>-50</value>
+ </param>
+ <param>
+ <key>affinity</key>
+ <value></value>
+ </param>
+ <param>
+ <key>_enabled</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>_coordinate</key>
+ <value>(640, 220)</value>
+ </param>
+ <param>
+ <key>_rotation</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>id</key>
+ <value>blocks_add_const_vxx_0</value>
+ </param>
+ <param>
+ <key>type</key>
+ <value>float</value>
+ </param>
+ <param>
+ <key>maxoutbuf</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>minoutbuf</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>vlen</key>
+ <value>1</value>
+ </param>
+ </block>
+ <block>
+ <key>blocks_add_const_vxx</key>
+ <param>
+ <key>alias</key>
+ <value></value>
+ </param>
+ <param>
+ <key>comment</key>
+ <value></value>
+ </param>
+ <param>
+ <key>const</key>
+ <value>1.0</value>
+ </param>
+ <param>
+ <key>affinity</key>
+ <value></value>
+ </param>
+ <param>
+ <key>_enabled</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>_coordinate</key>
+ <value>(608, 692)</value>
+ </param>
+ <param>
+ <key>_rotation</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>id</key>
+ <value>blocks_add_const_vxx_1</value>
+ </param>
+ <param>
+ <key>type</key>
+ <value>float</value>
+ </param>
+ <param>
+ <key>maxoutbuf</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>minoutbuf</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>vlen</key>
+ <value>1</value>
+ </param>
+ </block>
+ <block>
+ <key>blocks_add_const_vxx</key>
+ <param>
+ <key>alias</key>
+ <value></value>
+ </param>
+ <param>
+ <key>comment</key>
+ <value></value>
+ </param>
+ <param>
+ <key>const</key>
+ <value>-0.5</value>
+ </param>
+ <param>
+ <key>affinity</key>
+ <value></value>
+ </param>
+ <param>
+ <key>_enabled</key>
+ <value>2</value>
+ </param>
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+ <key>_coordinate</key>
+ <value>(1912, 284)</value>
+ </param>
+ <param>
+ <key>_rotation</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>id</key>
+ <value>blocks_add_const_vxx_2</value>
+ </param>
+ <param>
+ <key>type</key>
+ <value>float</value>
+ </param>
+ <param>
+ <key>maxoutbuf</key>
+ <value>0</value>
+ </param>
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+ <value>0</value>
+ </param>
+ <param>
+ <key>vlen</key>
+ <value>1</value>
+ </param>
+ </block>
+ <block>
+ <key>blocks_add_xx</key>
+ <param>
+ <key>alias</key>
+ <value></value>
+ </param>
+ <param>
+ <key>comment</key>
+ <value></value>
+ </param>
+ <param>
+ <key>affinity</key>
+ <value></value>
+ </param>
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+ <value>True</value>
+ </param>
+ <param>
+ <key>_coordinate</key>
+ <value>(832, 240)</value>
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+ <param>
+ <key>_rotation</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>id</key>
+ <value>blocks_add_xx_0</value>
+ </param>
+ <param>
+ <key>type</key>
+ <value>float</value>
+ </param>
+ <param>
+ <key>maxoutbuf</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>minoutbuf</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>num_inputs</key>
+ <value>2</value>
+ </param>
+ <param>
+ <key>vlen</key>
+ <value>1</value>
+ </param>
+ </block>
+ <block>
+ <key>blocks_file_source</key>
+ <param>
+ <key>begin_tag</key>
+ <value>pmt.PMT_NIL</value>
+ </param>
+ <param>
+ <key>alias</key>
+ <value></value>
+ </param>
+ <param>
+ <key>comment</key>
+ <value></value>
+ </param>
+ <param>
+ <key>affinity</key>
+ <value></value>
+ </param>
+ <param>
+ <key>_enabled</key>
+ <value>1</value>
+ </param>
+ <param>
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+ <value>/home/user/research/smart_meter_reset/gm_platform/fw/raw_freq.bin</value>
+ </param>
+ <param>
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+ <value>(224, 204)</value>
+ </param>
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+ <value>blocks_file_source_0</value>
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+ <key>maxoutbuf</key>
+ <value>0</value>
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+ </param>
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+ <param>
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+ <value>1</value>
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+ <block>
+ <key>blocks_float_to_complex</key>
+ <param>
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+ <value>blocks_float_to_complex_0</value>
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+ <key>minoutbuf</key>
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+ <value>1</value>
+ </param>
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+ <key>blocks_multiply_const_vxx</key>
+ <param>
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+ <value></value>
+ </param>
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+ <value>(448, 692)</value>
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+ <value>0</value>
+ </param>
+ <param>
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+ <value>blocks_multiply_const_vxx_1</value>
+ </param>
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+ <key>type</key>
+ <value>float</value>
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+ <value>0</value>
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+ </param>
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+ <key>vlen</key>
+ <value>1</value>
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+ <block>
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+ <param>
+ <key>bus_conns</key>
+ <value>[[0,],]</value>
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+ </param>
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+ <value>0</value>
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+ <value>0</value>
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+ <param>
+ <key>num_outputs</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>type</key>
+ <value>float</value>
+ </param>
+ <param>
+ <key>vlen</key>
+ <value>1</value>
+ </param>
+ </block>
+ <block>
+ <key>blocks_null_source</key>
+ <param>
+ <key>alias</key>
+ <value></value>
+ </param>
+ <param>
+ <key>bus_conns</key>
+ <value>[[0,],]</value>
+ </param>
+ <param>
+ <key>comment</key>
+ <value></value>
+ </param>
+ <param>
+ <key>affinity</key>
+ <value></value>
+ </param>
+ <param>
+ <key>_enabled</key>
+ <value>True</value>
+ </param>
+ <param>
+ <key>_coordinate</key>
+ <value>(800, 344)</value>
+ </param>
+ <param>
+ <key>_rotation</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>id</key>
+ <value>blocks_null_source_1</value>
+ </param>
+ <param>
+ <key>maxoutbuf</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>minoutbuf</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>num_outputs</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>type</key>
+ <value>float</value>
+ </param>
+ <param>
+ <key>vlen</key>
+ <value>1</value>
+ </param>
+ </block>
+ <block>
+ <key>blocks_repeat</key>
+ <param>
+ <key>alias</key>
+ <value></value>
+ </param>
+ <param>
+ <key>comment</key>
+ <value></value>
+ </param>
+ <param>
+ <key>affinity</key>
+ <value></value>
+ </param>
+ <param>
+ <key>_enabled</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>_coordinate</key>
+ <value>(272, 692)</value>
+ </param>
+ <param>
+ <key>_rotation</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>id</key>
+ <value>blocks_repeat_0</value>
+ </param>
+ <param>
+ <key>interp</key>
+ <value>10*5</value>
+ </param>
+ <param>
+ <key>maxoutbuf</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>minoutbuf</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>type</key>
+ <value>float</value>
+ </param>
+ <param>
+ <key>vlen</key>
+ <value>1</value>
+ </param>
+ </block>
+ <block>
+ <key>blocks_throttle</key>
+ <param>
+ <key>alias</key>
+ <value></value>
+ </param>
+ <param>
+ <key>comment</key>
+ <value></value>
+ </param>
+ <param>
+ <key>affinity</key>
+ <value></value>
+ </param>
+ <param>
+ <key>_enabled</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>_coordinate</key>
+ <value>(456, 220)</value>
+ </param>
+ <param>
+ <key>_rotation</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>id</key>
+ <value>blocks_throttle_0</value>
+ </param>
+ <param>
+ <key>ignoretag</key>
+ <value>True</value>
+ </param>
+ <param>
+ <key>maxoutbuf</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>minoutbuf</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>samples_per_second</key>
+ <value>samp_rate</value>
+ </param>
+ <param>
+ <key>type</key>
+ <value>float</value>
+ </param>
+ <param>
+ <key>vlen</key>
+ <value>1</value>
+ </param>
+ </block>
+ <block>
+ <key>blocks_throttle</key>
+ <param>
+ <key>alias</key>
+ <value></value>
+ </param>
+ <param>
+ <key>comment</key>
+ <value></value>
+ </param>
+ <param>
+ <key>affinity</key>
+ <value></value>
+ </param>
+ <param>
+ <key>_enabled</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>_coordinate</key>
+ <value>(536, 124)</value>
+ </param>
+ <param>
+ <key>_rotation</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>id</key>
+ <value>blocks_throttle_0_0</value>
+ </param>
+ <param>
+ <key>ignoretag</key>
+ <value>True</value>
+ </param>
+ <param>
+ <key>maxoutbuf</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>minoutbuf</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>samples_per_second</key>
+ <value>samp_rate</value>
+ </param>
+ <param>
+ <key>type</key>
+ <value>float</value>
+ </param>
+ <param>
+ <key>vlen</key>
+ <value>1</value>
+ </param>
+ </block>
+ <block>
+ <key>blocks_vco_f</key>
+ <param>
+ <key>amplitude</key>
+ <value>signal_strength*1e-3</value>
+ </param>
+ <param>
+ <key>alias</key>
+ <value></value>
+ </param>
+ <param>
+ <key>comment</key>
+ <value></value>
+ </param>
+ <param>
+ <key>affinity</key>
+ <value></value>
+ </param>
+ <param>
+ <key>_enabled</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>_coordinate</key>
+ <value>(760, 676)</value>
+ </param>
+ <param>
+ <key>_rotation</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>id</key>
+ <value>blocks_vco_f_0</value>
+ </param>
+ <param>
+ <key>maxoutbuf</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>minoutbuf</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>samp_rate</key>
+ <value>samp_rate</value>
+ </param>
+ <param>
+ <key>sensitivity</key>
+ <value>sim_mul*2*pi</value>
+ </param>
+ </block>
+ <block>
+ <key>blocks_vector_source_x</key>
+ <param>
+ <key>alias</key>
+ <value></value>
+ </param>
+ <param>
+ <key>comment</key>
+ <value></value>
+ </param>
+ <param>
+ <key>affinity</key>
+ <value></value>
+ </param>
+ <param>
+ <key>_enabled</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>_coordinate</key>
+ <value>(48, 676)</value>
+ </param>
+ <param>
+ <key>_rotation</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>id</key>
+ <value>blocks_vector_source_x_0_0_1_0</value>
+ </param>
+ <param>
+ <key>maxoutbuf</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>minoutbuf</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>type</key>
+ <value>float</value>
+ </param>
+ <param>
+ <key>repeat</key>
+ <value>True</value>
+ </param>
+ <param>
+ <key>tags</key>
+ <value>[packet_time_est_tag]</value>
+ </param>
+ <param>
+ <key>vlen</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>vector</key>
+ <value>[1,0]*(4*12)+[1,1,0,1,0,1,0,1]*12+[1,0,1,1,1,1,1,0,0,1]+[1,1,1,1,0,1,1,0,0,1]+[1,0,1,1,1,1,1,0,0,1]+[0,1,1,1,0,1,1,0,1,0]+[0,0,0,0,0,1,0,1,0,1,1,0,0,1,1,1,0,0,0,0]+[0]*128</value>
+ </param>
+ </block>
+ <block>
+ <key>low_pass_filter</key>
+ <param>
+ <key>beta</key>
+ <value>6.76</value>
+ </param>
+ <param>
+ <key>alias</key>
+ <value></value>
+ </param>
+ <param>
+ <key>comment</key>
+ <value></value>
+ </param>
+ <param>
+ <key>affinity</key>
+ <value></value>
+ </param>
+ <param>
+ <key>cutoff_freq</key>
+ <value>0.1 * sim_mul</value>
+ </param>
+ <param>
+ <key>decim</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>_enabled</key>
+ <value>True</value>
+ </param>
+ <param>
+ <key>type</key>
+ <value>fir_filter_ccf</value>
+ </param>
+ <param>
+ <key>_coordinate</key>
+ <value>(1312, 236)</value>
+ </param>
+ <param>
+ <key>_rotation</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>gain</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>id</key>
+ <value>low_pass_filter_0</value>
+ </param>
+ <param>
+ <key>interp</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>maxoutbuf</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>minoutbuf</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>samp_rate</key>
+ <value>samp_rate</value>
+ </param>
+ <param>
+ <key>width</key>
+ <value>0.05 * sim_mul</value>
+ </param>
+ <param>
+ <key>win</key>
+ <value>firdes.WIN_HAMMING</value>
+ </param>
+ </block>
+ <block>
+ <key>qtgui_freq_sink_x</key>
+ <param>
+ <key>autoscale</key>
+ <value>False</value>
+ </param>
+ <param>
+ <key>average</key>
+ <value>0.05</value>
+ </param>
+ <param>
+ <key>axislabels</key>
+ <value>True</value>
+ </param>
+ <param>
+ <key>bw</key>
+ <value>samp_rate</value>
+ </param>
+ <param>
+ <key>alias</key>
+ <value></value>
+ </param>
+ <param>
+ <key>fc</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>comment</key>
+ <value></value>
+ </param>
+ <param>
+ <key>ctrlpanel</key>
+ <value>False</value>
+ </param>
+ <param>
+ <key>affinity</key>
+ <value></value>
+ </param>
+ <param>
+ <key>_enabled</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>fftsize</key>
+ <value>8192</value>
+ </param>
+ <param>
+ <key>_coordinate</key>
+ <value>(1656, 180)</value>
+ </param>
+ <param>
+ <key>gui_hint</key>
+ <value></value>
+ </param>
+ <param>
+ <key>_rotation</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>grid</key>
+ <value>False</value>
+ </param>
+ <param>
+ <key>id</key>
+ <value>qtgui_freq_sink_x_0</value>
+ </param>
+ <param>
+ <key>legend</key>
+ <value>True</value>
+ </param>
+ <param>
+ <key>alpha1</key>
+ <value>1.0</value>
+ </param>
+ <param>
+ <key>color1</key>
+ <value>"blue"</value>
+ </param>
+ <param>
+ <key>label1</key>
+ <value></value>
+ </param>
+ <param>
+ <key>width1</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>alpha10</key>
+ <value>1.0</value>
+ </param>
+ <param>
+ <key>color10</key>
+ <value>"dark blue"</value>
+ </param>
+ <param>
+ <key>label10</key>
+ <value></value>
+ </param>
+ <param>
+ <key>width10</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>alpha2</key>
+ <value>1.0</value>
+ </param>
+ <param>
+ <key>color2</key>
+ <value>"red"</value>
+ </param>
+ <param>
+ <key>label2</key>
+ <value></value>
+ </param>
+ <param>
+ <key>width2</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>alpha3</key>
+ <value>1.0</value>
+ </param>
+ <param>
+ <key>color3</key>
+ <value>"green"</value>
+ </param>
+ <param>
+ <key>label3</key>
+ <value></value>
+ </param>
+ <param>
+ <key>width3</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>alpha4</key>
+ <value>1.0</value>
+ </param>
+ <param>
+ <key>color4</key>
+ <value>"black"</value>
+ </param>
+ <param>
+ <key>label4</key>
+ <value></value>
+ </param>
+ <param>
+ <key>width4</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>alpha5</key>
+ <value>1.0</value>
+ </param>
+ <param>
+ <key>color5</key>
+ <value>"cyan"</value>
+ </param>
+ <param>
+ <key>label5</key>
+ <value></value>
+ </param>
+ <param>
+ <key>width5</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>alpha6</key>
+ <value>1.0</value>
+ </param>
+ <param>
+ <key>color6</key>
+ <value>"magenta"</value>
+ </param>
+ <param>
+ <key>label6</key>
+ <value></value>
+ </param>
+ <param>
+ <key>width6</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>alpha7</key>
+ <value>1.0</value>
+ </param>
+ <param>
+ <key>color7</key>
+ <value>"yellow"</value>
+ </param>
+ <param>
+ <key>label7</key>
+ <value></value>
+ </param>
+ <param>
+ <key>width7</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>alpha8</key>
+ <value>1.0</value>
+ </param>
+ <param>
+ <key>color8</key>
+ <value>"dark red"</value>
+ </param>
+ <param>
+ <key>label8</key>
+ <value></value>
+ </param>
+ <param>
+ <key>width8</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>alpha9</key>
+ <value>1.0</value>
+ </param>
+ <param>
+ <key>color9</key>
+ <value>"dark green"</value>
+ </param>
+ <param>
+ <key>label9</key>
+ <value></value>
+ </param>
+ <param>
+ <key>width9</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>maxoutbuf</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>minoutbuf</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>name</key>
+ <value>""</value>
+ </param>
+ <param>
+ <key>nconnections</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>showports</key>
+ <value>True</value>
+ </param>
+ <param>
+ <key>freqhalf</key>
+ <value>True</value>
+ </param>
+ <param>
+ <key>tr_chan</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>tr_level</key>
+ <value>0.0</value>
+ </param>
+ <param>
+ <key>tr_mode</key>
+ <value>qtgui.TRIG_MODE_FREE</value>
+ </param>
+ <param>
+ <key>tr_tag</key>
+ <value>""</value>
+ </param>
+ <param>
+ <key>type</key>
+ <value>complex</value>
+ </param>
+ <param>
+ <key>update_time</key>
+ <value>0.1</value>
+ </param>
+ <param>
+ <key>wintype</key>
+ <value>firdes.WIN_BLACKMAN_hARRIS</value>
+ </param>
+ <param>
+ <key>label</key>
+ <value>Relative Gain</value>
+ </param>
+ <param>
+ <key>ymax</key>
+ <value>10</value>
+ </param>
+ <param>
+ <key>ymin</key>
+ <value>-140</value>
+ </param>
+ <param>
+ <key>units</key>
+ <value>dB</value>
+ </param>
+ </block>
+ <block>
+ <key>qtgui_time_sink_x</key>
+ <param>
+ <key>autoscale</key>
+ <value>False</value>
+ </param>
+ <param>
+ <key>axislabels</key>
+ <value>True</value>
+ </param>
+ <param>
+ <key>alias</key>
+ <value></value>
+ </param>
+ <param>
+ <key>comment</key>
+ <value></value>
+ </param>
+ <param>
+ <key>ctrlpanel</key>
+ <value>False</value>
+ </param>
+ <param>
+ <key>affinity</key>
+ <value></value>
+ </param>
+ <param>
+ <key>entags</key>
+ <value>True</value>
+ </param>
+ <param>
+ <key>_enabled</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>_coordinate</key>
+ <value>(2136, 260)</value>
+ </param>
+ <param>
+ <key>gui_hint</key>
+ <value></value>
+ </param>
+ <param>
+ <key>_rotation</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>grid</key>
+ <value>False</value>
+ </param>
+ <param>
+ <key>id</key>
+ <value>qtgui_time_sink_x_0</value>
+ </param>
+ <param>
+ <key>legend</key>
+ <value>True</value>
+ </param>
+ <param>
+ <key>alpha1</key>
+ <value>1.0</value>
+ </param>
+ <param>
+ <key>color1</key>
+ <value>"blue"</value>
+ </param>
+ <param>
+ <key>label1</key>
+ <value></value>
+ </param>
+ <param>
+ <key>marker1</key>
+ <value>-1</value>
+ </param>
+ <param>
+ <key>style1</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>width1</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>alpha10</key>
+ <value>1.0</value>
+ </param>
+ <param>
+ <key>color10</key>
+ <value>"blue"</value>
+ </param>
+ <param>
+ <key>label10</key>
+ <value></value>
+ </param>
+ <param>
+ <key>marker10</key>
+ <value>-1</value>
+ </param>
+ <param>
+ <key>style10</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>width10</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>alpha2</key>
+ <value>1.0</value>
+ </param>
+ <param>
+ <key>color2</key>
+ <value>"red"</value>
+ </param>
+ <param>
+ <key>label2</key>
+ <value></value>
+ </param>
+ <param>
+ <key>marker2</key>
+ <value>-1</value>
+ </param>
+ <param>
+ <key>style2</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>width2</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>alpha3</key>
+ <value>1.0</value>
+ </param>
+ <param>
+ <key>color3</key>
+ <value>"green"</value>
+ </param>
+ <param>
+ <key>label3</key>
+ <value></value>
+ </param>
+ <param>
+ <key>marker3</key>
+ <value>-1</value>
+ </param>
+ <param>
+ <key>style3</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>width3</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>alpha4</key>
+ <value>1.0</value>
+ </param>
+ <param>
+ <key>color4</key>
+ <value>"black"</value>
+ </param>
+ <param>
+ <key>label4</key>
+ <value></value>
+ </param>
+ <param>
+ <key>marker4</key>
+ <value>-1</value>
+ </param>
+ <param>
+ <key>style4</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>width4</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>alpha5</key>
+ <value>1.0</value>
+ </param>
+ <param>
+ <key>color5</key>
+ <value>"cyan"</value>
+ </param>
+ <param>
+ <key>label5</key>
+ <value></value>
+ </param>
+ <param>
+ <key>marker5</key>
+ <value>-1</value>
+ </param>
+ <param>
+ <key>style5</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>width5</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>alpha6</key>
+ <value>1.0</value>
+ </param>
+ <param>
+ <key>color6</key>
+ <value>"magenta"</value>
+ </param>
+ <param>
+ <key>label6</key>
+ <value></value>
+ </param>
+ <param>
+ <key>marker6</key>
+ <value>-1</value>
+ </param>
+ <param>
+ <key>style6</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>width6</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>alpha7</key>
+ <value>1.0</value>
+ </param>
+ <param>
+ <key>color7</key>
+ <value>"yellow"</value>
+ </param>
+ <param>
+ <key>label7</key>
+ <value></value>
+ </param>
+ <param>
+ <key>marker7</key>
+ <value>-1</value>
+ </param>
+ <param>
+ <key>style7</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>width7</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>alpha8</key>
+ <value>1.0</value>
+ </param>
+ <param>
+ <key>color8</key>
+ <value>"dark red"</value>
+ </param>
+ <param>
+ <key>label8</key>
+ <value></value>
+ </param>
+ <param>
+ <key>marker8</key>
+ <value>-1</value>
+ </param>
+ <param>
+ <key>style8</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>width8</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>alpha9</key>
+ <value>1.0</value>
+ </param>
+ <param>
+ <key>color9</key>
+ <value>"dark green"</value>
+ </param>
+ <param>
+ <key>label9</key>
+ <value></value>
+ </param>
+ <param>
+ <key>marker9</key>
+ <value>-1</value>
+ </param>
+ <param>
+ <key>style9</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>width9</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>name</key>
+ <value>"demod"</value>
+ </param>
+ <param>
+ <key>nconnections</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>size</key>
+ <value>19500</value>
+ </param>
+ <param>
+ <key>srate</key>
+ <value>samp_rate</value>
+ </param>
+ <param>
+ <key>stemplot</key>
+ <value>False</value>
+ </param>
+ <param>
+ <key>tr_chan</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>tr_delay</key>
+ <value>0.007</value>
+ </param>
+ <param>
+ <key>tr_level</key>
+ <value>0.1</value>
+ </param>
+ <param>
+ <key>tr_mode</key>
+ <value>qtgui.TRIG_MODE_TAG</value>
+ </param>
+ <param>
+ <key>tr_slope</key>
+ <value>qtgui.TRIG_SLOPE_POS</value>
+ </param>
+ <param>
+ <key>tr_tag</key>
+ <value>"start"</value>
+ </param>
+ <param>
+ <key>type</key>
+ <value>float</value>
+ </param>
+ <param>
+ <key>update_time</key>
+ <value>0.10</value>
+ </param>
+ <param>
+ <key>ylabel</key>
+ <value>Amplitude</value>
+ </param>
+ <param>
+ <key>yunit</key>
+ <value>""</value>
+ </param>
+ <param>
+ <key>ymax</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>ymin</key>
+ <value>-1</value>
+ </param>
+ </block>
+ <block>
+ <key>qtgui_time_sink_x</key>
+ <param>
+ <key>autoscale</key>
+ <value>True</value>
+ </param>
+ <param>
+ <key>axislabels</key>
+ <value>True</value>
+ </param>
+ <param>
+ <key>alias</key>
+ <value></value>
+ </param>
+ <param>
+ <key>comment</key>
+ <value></value>
+ </param>
+ <param>
+ <key>ctrlpanel</key>
+ <value>False</value>
+ </param>
+ <param>
+ <key>affinity</key>
+ <value></value>
+ </param>
+ <param>
+ <key>entags</key>
+ <value>True</value>
+ </param>
+ <param>
+ <key>_enabled</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>_coordinate</key>
+ <value>(1324, 16)</value>
+ </param>
+ <param>
+ <key>gui_hint</key>
+ <value></value>
+ </param>
+ <param>
+ <key>_rotation</key>
+ <value>90</value>
+ </param>
+ <param>
+ <key>grid</key>
+ <value>False</value>
+ </param>
+ <param>
+ <key>id</key>
+ <value>qtgui_time_sink_x_0_1</value>
+ </param>
+ <param>
+ <key>legend</key>
+ <value>True</value>
+ </param>
+ <param>
+ <key>alpha1</key>
+ <value>1.0</value>
+ </param>
+ <param>
+ <key>color1</key>
+ <value>"blue"</value>
+ </param>
+ <param>
+ <key>label1</key>
+ <value></value>
+ </param>
+ <param>
+ <key>marker1</key>
+ <value>-1</value>
+ </param>
+ <param>
+ <key>style1</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>width1</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>alpha10</key>
+ <value>1.0</value>
+ </param>
+ <param>
+ <key>color10</key>
+ <value>"blue"</value>
+ </param>
+ <param>
+ <key>label10</key>
+ <value></value>
+ </param>
+ <param>
+ <key>marker10</key>
+ <value>-1</value>
+ </param>
+ <param>
+ <key>style10</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>width10</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>alpha2</key>
+ <value>1.0</value>
+ </param>
+ <param>
+ <key>color2</key>
+ <value>"red"</value>
+ </param>
+ <param>
+ <key>label2</key>
+ <value></value>
+ </param>
+ <param>
+ <key>marker2</key>
+ <value>-1</value>
+ </param>
+ <param>
+ <key>style2</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>width2</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>alpha3</key>
+ <value>1.0</value>
+ </param>
+ <param>
+ <key>color3</key>
+ <value>"green"</value>
+ </param>
+ <param>
+ <key>label3</key>
+ <value></value>
+ </param>
+ <param>
+ <key>marker3</key>
+ <value>-1</value>
+ </param>
+ <param>
+ <key>style3</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>width3</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>alpha4</key>
+ <value>1.0</value>
+ </param>
+ <param>
+ <key>color4</key>
+ <value>"black"</value>
+ </param>
+ <param>
+ <key>label4</key>
+ <value></value>
+ </param>
+ <param>
+ <key>marker4</key>
+ <value>-1</value>
+ </param>
+ <param>
+ <key>style4</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>width4</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>alpha5</key>
+ <value>1.0</value>
+ </param>
+ <param>
+ <key>color5</key>
+ <value>"cyan"</value>
+ </param>
+ <param>
+ <key>label5</key>
+ <value></value>
+ </param>
+ <param>
+ <key>marker5</key>
+ <value>-1</value>
+ </param>
+ <param>
+ <key>style5</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>width5</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>alpha6</key>
+ <value>1.0</value>
+ </param>
+ <param>
+ <key>color6</key>
+ <value>"magenta"</value>
+ </param>
+ <param>
+ <key>label6</key>
+ <value></value>
+ </param>
+ <param>
+ <key>marker6</key>
+ <value>-1</value>
+ </param>
+ <param>
+ <key>style6</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>width6</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>alpha7</key>
+ <value>1.0</value>
+ </param>
+ <param>
+ <key>color7</key>
+ <value>"yellow"</value>
+ </param>
+ <param>
+ <key>label7</key>
+ <value></value>
+ </param>
+ <param>
+ <key>marker7</key>
+ <value>-1</value>
+ </param>
+ <param>
+ <key>style7</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>width7</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>alpha8</key>
+ <value>1.0</value>
+ </param>
+ <param>
+ <key>color8</key>
+ <value>"dark red"</value>
+ </param>
+ <param>
+ <key>label8</key>
+ <value></value>
+ </param>
+ <param>
+ <key>marker8</key>
+ <value>-1</value>
+ </param>
+ <param>
+ <key>style8</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>width8</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>alpha9</key>
+ <value>1.0</value>
+ </param>
+ <param>
+ <key>color9</key>
+ <value>"dark green"</value>
+ </param>
+ <param>
+ <key>label9</key>
+ <value></value>
+ </param>
+ <param>
+ <key>marker9</key>
+ <value>-1</value>
+ </param>
+ <param>
+ <key>style9</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>width9</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>name</key>
+ <value>"mod"</value>
+ </param>
+ <param>
+ <key>nconnections</key>
+ <value>3</value>
+ </param>
+ <param>
+ <key>size</key>
+ <value>19500</value>
+ </param>
+ <param>
+ <key>srate</key>
+ <value>samp_rate</value>
+ </param>
+ <param>
+ <key>stemplot</key>
+ <value>False</value>
+ </param>
+ <param>
+ <key>tr_chan</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>tr_delay</key>
+ <value>0.007</value>
+ </param>
+ <param>
+ <key>tr_level</key>
+ <value>0.1</value>
+ </param>
+ <param>
+ <key>tr_mode</key>
+ <value>qtgui.TRIG_MODE_TAG</value>
+ </param>
+ <param>
+ <key>tr_slope</key>
+ <value>qtgui.TRIG_SLOPE_POS</value>
+ </param>
+ <param>
+ <key>tr_tag</key>
+ <value>"start"</value>
+ </param>
+ <param>
+ <key>type</key>
+ <value>complex</value>
+ </param>
+ <param>
+ <key>update_time</key>
+ <value>0.10</value>
+ </param>
+ <param>
+ <key>ylabel</key>
+ <value>Amplitude</value>
+ </param>
+ <param>
+ <key>yunit</key>
+ <value>""</value>
+ </param>
+ <param>
+ <key>ymax</key>
+ <value>1</value>
+ </param>
+ <param>
+ <key>ymin</key>
+ <value>-1</value>
+ </param>
+ </block>
+ <block>
+ <key>parameter</key>
+ <param>
+ <key>alias</key>
+ <value></value>
+ </param>
+ <param>
+ <key>comment</key>
+ <value></value>
+ </param>
+ <param>
+ <key>_enabled</key>
+ <value>True</value>
+ </param>
+ <param>
+ <key>_coordinate</key>
+ <value>(8, 100)</value>
+ </param>
+ <param>
+ <key>_rotation</key>
+ <value>0</value>
+ </param>
+ <param>
+ <key>id</key>
+ <value>signal_strength</value>
+ </param>
+ <param>
+ <key>label</key>
+ <value>signal strength in mHz</value>
+ </param>
+ <param>
+ <key>short_id</key>
+ <value></value>
+ </param>
+ <param>
+ <key>hide</key>
+ <value>none</value>
+ </param>
+ <param>
+ <key>type</key>
+ <value>eng_float</value>
+ </param>
+ <param>
+ <key>value</key>
+ <value>10</value>
+ </param>
+ </block>
+ <connection>
+ <source_block_id>analog_quadrature_demod_cf_0</source_block_id>
+ <sink_block_id>blocks_add_const_vxx_2</sink_block_id>
+ <source_key>0</source_key>
+ <sink_key>0</sink_key>
+ </connection>
+ <connection>
+ <source_block_id>analog_sig_source_x_0</source_block_id>
+ <sink_block_id>blocks_multiply_xx_0</sink_block_id>
+ <source_key>0</source_key>
+ <sink_key>1</sink_key>
+ </connection>
+ <connection>
+ <source_block_id>blocks_add_const_vxx_0</source_block_id>
+ <sink_block_id>blocks_add_xx_0</sink_block_id>
+ <source_key>0</source_key>
+ <sink_key>0</sink_key>
+ </connection>
+ <connection>
+ <source_block_id>blocks_add_const_vxx_1</source_block_id>
+ <sink_block_id>blocks_vco_f_0</sink_block_id>
+ <source_key>0</source_key>
+ <sink_key>0</sink_key>
+ </connection>
+ <connection>
+ <source_block_id>blocks_add_const_vxx_2</source_block_id>
+ <sink_block_id>qtgui_time_sink_x_0</sink_block_id>
+ <source_key>0</source_key>
+ <sink_key>0</sink_key>
+ </connection>
+ <connection>
+ <source_block_id>blocks_add_xx_0</source_block_id>
+ <sink_block_id>blocks_float_to_complex_0</sink_block_id>
+ <source_key>0</source_key>
+ <sink_key>0</sink_key>
+ </connection>
+ <connection>
+ <source_block_id>blocks_file_source_0</source_block_id>
+ <sink_block_id>blocks_throttle_0</sink_block_id>
+ <source_key>0</source_key>
+ <sink_key>0</sink_key>
+ </connection>
+ <connection>
+ <source_block_id>blocks_float_to_complex_0</source_block_id>
+ <sink_block_id>blocks_multiply_xx_0</sink_block_id>
+ <source_key>0</source_key>
+ <sink_key>0</sink_key>
+ </connection>
+ <connection>
+ <source_block_id>blocks_float_to_complex_0</source_block_id>
+ <sink_block_id>qtgui_time_sink_x_0_1</sink_block_id>
+ <source_key>0</source_key>
+ <sink_key>2</sink_key>
+ </connection>
+ <connection>
+ <source_block_id>blocks_multiply_const_vxx_1</source_block_id>
+ <sink_block_id>blocks_add_const_vxx_1</sink_block_id>
+ <source_key>0</source_key>
+ <sink_key>0</sink_key>
+ </connection>
+ <connection>
+ <source_block_id>blocks_multiply_xx_0</source_block_id>
+ <sink_block_id>low_pass_filter_0</sink_block_id>
+ <source_key>0</source_key>
+ <sink_key>0</sink_key>
+ </connection>
+ <connection>
+ <source_block_id>blocks_multiply_xx_0</source_block_id>
+ <sink_block_id>qtgui_time_sink_x_0_1</sink_block_id>
+ <source_key>0</source_key>
+ <sink_key>0</sink_key>
+ </connection>
+ <connection>
+ <source_block_id>blocks_null_source_0</source_block_id>
+ <sink_block_id>blocks_throttle_0_0</sink_block_id>
+ <source_key>0</source_key>
+ <sink_key>0</sink_key>
+ </connection>
+ <connection>
+ <source_block_id>blocks_null_source_0_0</source_block_id>
+ <sink_block_id>blocks_add_xx_0</sink_block_id>
+ <source_key>0</source_key>
+ <sink_key>1</sink_key>
+ </connection>
+ <connection>
+ <source_block_id>blocks_null_source_1</source_block_id>
+ <sink_block_id>blocks_float_to_complex_0</sink_block_id>
+ <source_key>0</source_key>
+ <sink_key>1</sink_key>
+ </connection>
+ <connection>
+ <source_block_id>blocks_repeat_0</source_block_id>
+ <sink_block_id>blocks_multiply_const_vxx_1</sink_block_id>
+ <source_key>0</source_key>
+ <sink_key>0</sink_key>
+ </connection>
+ <connection>
+ <source_block_id>blocks_throttle_0</source_block_id>
+ <sink_block_id>blocks_add_const_vxx_0</sink_block_id>
+ <source_key>0</source_key>
+ <sink_key>0</sink_key>
+ </connection>
+ <connection>
+ <source_block_id>blocks_throttle_0_0</source_block_id>
+ <sink_block_id>blocks_add_xx_0</sink_block_id>
+ <source_key>0</source_key>
+ <sink_key>0</sink_key>
+ </connection>
+ <connection>
+ <source_block_id>blocks_vco_f_0</source_block_id>
+ <sink_block_id>blocks_add_xx_0</sink_block_id>
+ <source_key>0</source_key>
+ <sink_key>1</sink_key>
+ </connection>
+ <connection>
+ <source_block_id>blocks_vector_source_x_0_0_1_0</source_block_id>
+ <sink_block_id>blocks_repeat_0</sink_block_id>
+ <source_key>0</source_key>
+ <sink_key>0</sink_key>
+ </connection>
+ <connection>
+ <source_block_id>low_pass_filter_0</source_block_id>
+ <sink_block_id>analog_quadrature_demod_cf_0</sink_block_id>
+ <source_key>0</source_key>
+ <sink_key>0</sink_key>
+ </connection>
+ <connection>
+ <source_block_id>low_pass_filter_0</source_block_id>
+ <sink_block_id>qtgui_freq_sink_x_0</sink_block_id>
+ <source_key>0</source_key>
+ <sink_key>0</sink_key>
+ </connection>
+ <connection>
+ <source_block_id>low_pass_filter_0</source_block_id>
+ <sink_block_id>qtgui_time_sink_x_0_1</sink_block_id>
+ <source_key>0</source_key>
+ <sink_key>1</sink_key>
+ </connection>
+</flow_graph>
diff --git a/decoder_prototype8_ber_working.grc b/decoder_prototype8_ber_working.grc
index 9589ace..7a07fa7 100644
--- a/decoder_prototype8_ber_working.grc
+++ b/decoder_prototype8_ber_working.grc
@@ -38,7 +38,7 @@
</param>
<param>
<key>generate_options</key>
- <value>no_gui</value>
+ <value>qt_gui</value>
</param>
<param>
<key>hier_block_src_path</key>
@@ -46,7 +46,7 @@
</param>
<param>
<key>id</key>
- <value>dec_proto_fm_ber_top</value>
+ <value>dec_proto_fm_ber_gui_top</value>
</param>
<param>
<key>max_nouts</key>
@@ -448,7 +448,7 @@
</param>
<param>
<key>_enabled</key>
- <value>0</value>
+ <value>1</value>
</param>
<param>
<key>_coordinate</key>
@@ -699,7 +699,7 @@
</param>
<param>
<key>_enabled</key>
- <value>0</value>
+ <value>1</value>
</param>
<param>
<key>_coordinate</key>
@@ -805,7 +805,7 @@
</param>
<param>
<key>_enabled</key>
- <value>True</value>
+ <value>0</value>
</param>
<param>
<key>file</key>
@@ -1736,7 +1736,7 @@
</param>
<param>
<key>_enabled</key>
- <value>0</value>
+ <value>1</value>
</param>
<param>
<key>fftsize</key>
@@ -2019,7 +2019,7 @@
</param>
<param>
<key>_enabled</key>
- <value>0</value>
+ <value>1</value>
</param>
<param>
<key>_coordinate</key>
@@ -2258,7 +2258,7 @@
</param>
<param>
<key>_enabled</key>
- <value>0</value>
+ <value>1</value>
</param>
<param>
<key>_coordinate</key>
@@ -2625,7 +2625,7 @@
</param>
<param>
<key>_enabled</key>
- <value>0</value>
+ <value>1</value>
</param>
<param>
<key>_coordinate</key>
@@ -2992,7 +2992,7 @@
</param>
<param>
<key>_enabled</key>
- <value>0</value>
+ <value>1</value>
</param>
<param>
<key>_coordinate</key>
@@ -3371,7 +3371,7 @@
</param>
<param>
<key>value</key>
- <value>1</value>
+ <value>2</value>
</param>
</block>
<connection>
diff --git a/gm_platform/fw/grid_scope.ipynb b/gm_platform/fw/grid_scope.ipynb
index a04b2db..b5b3522 100644
--- a/gm_platform/fw/grid_scope.ipynb
+++ b/gm_platform/fw/grid_scope.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 92,
+ "execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
@@ -125,7 +125,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "e5ad4355e2a540f9a4c56bccad434a09",
+ "model_id": "ba042a79e76742dfae88bd0a5913cb1f",
"version_major": 2,
"version_minor": 0
},
@@ -3691,7 +3691,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.7.6"
}
},
"nbformat": 4,
diff --git a/gr_sweep_results.json b/gr_sweep_results.json
new file mode 100644
index 0000000..c8a64e1
--- /dev/null
+++ b/gr_sweep_results.json
@@ -0,0 +1 @@
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diff --git a/gr_sweep_results2.json b/gr_sweep_results2.json
new file mode 100644
index 0000000..d6e13b9
--- /dev/null
+++ b/gr_sweep_results2.json
@@ -0,0 +1 @@
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end of file
diff --git a/grid_frequency_spectra.ipynb b/grid_frequency_spectra.ipynb
index 706bd88..ba7c48d 100644
--- a/grid_frequency_spectra.ipynb
+++ b/grid_frequency_spectra.ipynb
@@ -4198,7 +4198,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.7.6"
}
},
"nbformat": 4,
diff --git a/results_digitalocean.json b/results_digitalocean.json
new file mode 100644
index 0000000..bc4336b
--- /dev/null
+++ b/results_digitalocean.json
@@ -0,0 +1 @@
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diff --git a/results_digitalocean2.json b/results_digitalocean2.json
new file mode 100644
index 0000000..11057bf
--- /dev/null
+++ b/results_digitalocean2.json
@@ -0,0 +1 @@
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diff --git a/results_digitalocean_undercommitted.json b/results_digitalocean_undercommitted.json
new file mode 100644
index 0000000..7842fda
--- /dev/null
+++ b/results_digitalocean_undercommitted.json
@@ -0,0 +1 @@
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diff --git a/results_digitalocean_undercommitted2.json b/results_digitalocean_undercommitted2.json
new file mode 100644
index 0000000..4e40780
--- /dev/null
+++ b/results_digitalocean_undercommitted2.json
@@ -0,0 +1 @@
+{"dec_proto_am_ber_top.py": {"1.0": [[-1.1648994982242584, -1.1648688218556345, -1.1648382265120745], [0.00024458697242414786, 0.00022943597423503547, 0.0001652929374350671]], "3.4333200182819943": [[-1.4425158193334937, -1.435760929249227, -1.4443270973861217], [0.007914668010232561, 0.003472683996040597, 0.009544519122921861]], "40.470899507597615": [[-Infinity, -Infinity, -Infinity], [NaN, NaN, NaN]], "2.2758459260747883": [[-1.3510851617902517, -1.3574173715896904, -1.357145563699305], [0.0030855487214924617, 0.007248303888866042, 0.007294614877006495]], "61.0540229658533": [[-Infinity, -Infinity, -Infinity], [NaN, NaN, NaN]], "5.179474679231212": [[-1.5079170451499522, -1.506855636369437, -1.5000451183877885], [0.009694203393816762, 0.012601544293732472, 0.008213616283050462]], "7.813707376518096": [[-3.6114922566339374, -3.613393370062113, -3.6143142357468605], [0.0065727723815408355, 0.009987106074416748, 0.008565613220097284]], "11.787686347935873": [[-4.19125415943563, -4.191602788865566, -4.184982007369399], [0.013540700022526555, 0.013271496289670094, 0.01148359721033321]], "26.826957952797258": [[-Infinity, -Infinity, -Infinity], [NaN, NaN, NaN]], "92.1055317689482": [[-Infinity, -Infinity, -Infinity], [NaN, NaN, NaN]], "1.5085907086001784": [[-1.1896418971009552, -1.190659907180816, -1.1924097444862127], [0.002437190717518311, 0.0017096762038300555, 0.0003089466369572302]], "316.22776601683796": [[-Infinity, -Infinity, -Infinity], [NaN, NaN, NaN]], "138.94954943731375": [[-Infinity, -Infinity, -Infinity], [NaN, NaN, NaN]], "17.78279410038923": [[-5.114153813570738, -5.111929476261139, -5.117537803947926], [0.022313657330737032, 0.022441321991869014, 0.022122817982635632]], "209.61799924531277": [[-Infinity, -Infinity, -Infinity], [NaN, NaN, NaN]]}, "dec_proto_am_dc_ber_top.py": {"11.787686347935873": [[-1.210616025608033, -1.2100331173278391, -1.209921614266932], [0.000514754588797331, 0.0004748800790474076, 0.00047599250659998853]], "61.0540229658533": [[-2.7781352987512946, -2.7781207719817758, -2.7785607799887657], [0.003035786030421982, 0.003028771499113924, 0.002619444590861458]], "17.78279410038923": [[-1.2106587942689657, -1.210781127680093, -1.210736510809511], [0.00029481089819583846, 0.0003601469612416894, 0.00034031152509585594]], "138.94954943731375": [[-3.771642465144396, -3.5519900312647223, -3.7895752200856805], [0.030650332848472073, 0.05316272756940253, 0.012115131144463649]], "1.0": [[-1.2058014213107526, -1.2058180342428386, -1.2057482143864036], [0.000413164804012497, 0.0004277855681768359, 0.00035083591033192917]], "2.2758459260747883": [[-1.205321069341153, -1.2040055501274765, -1.2040604911744595], [0.000295130148987318, 0.00021165740747961388, 0.00024700397857843045]], "26.826957952797258": [[-1.3171439138241112, -1.3563846265897155, -1.3305911961942911], [0.0016306585237914504, 0.01103094228781695, 0.011418765569306108]], "316.22776601683796": [[-3.804279121570289, -3.774571823887527, -3.7820643596351147], [0.014745320721172841, 0.034026608769865185, 0.03344346383742056]], "1.5085907086001784": [[-1.2070338143967092, -1.2069718702696264, -1.2071338566020131], [0.0005379093535752926, 0.0005093789340908418, 0.00048268633173964034]], "209.61799924531277": [[-3.796470208093524, -3.7755597569048405, -3.800564776174724], [0.032022262989861675, 0.03360546603615746, 0.0317213639546385]], "5.179474679231212": [[-1.2108109630644321, -1.2112391307018697, -1.2116083013825119], [0.0003201861235493567, 0.0006795252189129599, 0.0008755147852945613]], "3.4333200182819943": [[-1.2056334796361625, -1.2057312768884003, -1.2057073074392974], [0.0003430026507817074, 0.0003210191374809626, 0.0003196637664831918]], "40.470899507597615": [[-1.2721562357619405, -1.3670731694437563, -1.2732716351747513], [0.01305433935777301, 0.024618001872181865, 0.013077326671899049]], "92.1055317689482": [[-3.386830206029117, -3.506479388102889, -3.501045575365424], [0.03156821982166276, 0.014610748565135818, 0.013565850536715204]], "7.813707376518096": [[-1.2247387641109526, -1.2140266671776772, -1.2144211740233004], [0.0024209652777766343, 0.0009331732162983478, 0.0009457099868237327]]}, "dec_proto_fm_ber_top.py": {"26.826957952797258": [[-5.213533503934741, -5.211926465854049, -5.216977551579475], [0.02152168112819652, 0.02168737966732472, 0.021120579468670478]], "316.22776601683796": [[-5.102246003225446, -5.1297239530831575, -5.131846001371741], [0.027144947879606345, 0.010207332107130599, 0.009146095628715633]], "11.787686347935873": [[-5.2059867065399885, -5.193089090287685, -5.207498988136649], [0.024904442171588027, 0.03360182355305412, 0.022074142839662154]], "92.1055317689482": [[-5.111419413238764, -5.112058203667402, -5.116894083097577], [0.02580368193900204, 0.02217107294006117, 0.02150726277989941]], "17.78279410038923": [[-5.208709815517068, -5.208070281893015, -5.2144224382936954], [0.022628971827111224, 0.021857504209454693, 0.021447639587630445]], "138.94954943731375": [[-5.0934457667171955, -5.13145119510591, -5.107659179717302], [0.03309396384645255, 0.010558508678020077, 0.02360661097601443]], "7.813707376518096": [[-4.946145271882415, -4.9557805974036455, -4.95067036151886], [0.0292540603704563, 0.030460993329056895, 0.03252637825259364]], "40.470899507597615": [[-5.096907999366522, -5.11138217151165, -5.089819576591253], [0.031177870081210646, 0.021752914447845946, 0.034201844548127795]], "5.179474679231212": [[-4.112883938476443, -4.111411489546299, -4.111713128164411], [0.013561332721638869, 0.014851847496917212, 0.01368769670639725]], "1.0": [[-1.3530349847860634, -1.352895524352789, -1.3527899347245693], [0.0023998435516695287, 0.002186606876211801, 0.002265867176374771]], "3.4333200182819943": [[-3.6569942897185683, -3.6544545833021402, -3.6566532338038087], [0.008274804993531787, 0.00817880220046871, 0.008255747504550546]], "61.0540229658533": [[-5.110499389469624, -5.114741800352931, -5.111416533589363], [0.021797158567485035, 0.023712396376517203, 0.021809231449791158]], "2.2758459260747883": [[-3.164697186090052, -3.160753977485001, -3.161645512096584], [0.008018038070639048, 0.005375154661139067, 0.0055819490286239315]], "209.61799924531277": [[-5.11920659430325, -5.11182183586061, -5.114585341885686], [0.022291019110269678, 0.022475835929920834, 0.021959413357443256]], "1.5085907086001784": [[-1.4284238200634718, -1.4504670887254179, -1.4307382791303098], [0.017419075247542184, 0.027484309686763398, 0.015685477106804888]]}} \ No newline at end of file
diff --git a/sweep_gr_sims.py b/sweep_gr_sims.py
index f11ee64..661af56 100644
--- a/sweep_gr_sims.py
+++ b/sweep_gr_sims.py
@@ -2,10 +2,12 @@
import subprocess
import time
import struct
+import random
import math
import statistics
import tempfile
import itertools
+from collections import defaultdict
from os import path
import tqdm
@@ -22,52 +24,70 @@ SIMULATION_DURATION = 30.0 # seconds realtime
MAX_CONCURRENT_PROCESSES = 8
-with tempfile.TemporaryDirectory() as tmpdir:
-
- jobs = list(enumerate(itertools.product(SIMS, AMPLITUDES_MILLIHERTZ)))
- print(f'Will launch {len(jobs)} simulation jobs in {math.ceil(len(jobs)/MAX_CONCURRENT_PROCESSES):.0f} batches of {MAX_CONCURRENT_PROCESSES}')
-
- def start_processes(jobs):
- for i, (sim, ampl_mhz) in jobs:
- berfile = path.join(tmpdir, f'berfile_{i}')
- proc = subprocess.Popen(['/usr/bin/python2', sim,
- '--signal-strength', str(ampl_mhz),
- '--ber-file', berfile],
- stdin=subprocess.PIPE, stdout=subprocess.DEVNULL)
- yield proc, sim, ampl_mhz, berfile
-
- results = { sim: ([], [], []) for sim in SIMS }
- for n, i in enumerate(range(0, len(jobs), MAX_CONCURRENT_PROCESSES)):
- batch = jobs[i:][:MAX_CONCURRENT_PROCESSES]
- print(f'Starting batch {n}... ', end='')
- processes = list(start_processes(batch))
- print('done.')
-
- print('Waiting for simulation:')
- for _ in tqdm.trange(1000):
- time.sleep(SIMULATION_DURATION/1000)
-
- print('Terminating processes...', end='')
- for proc, *_ in processes:
- proc.communicate(b'\n', timeout=10)
-
- for proc, *_ in processes:
- proc.wait(5)
- print('done.')
-
- print('Processing simulation results')
- for _proc, sim, ampl_mhz, berfile in processes:
-
- with open(berfile, 'rb') as f:
- data = f.read()
-
- floats = struct.unpack(f'{len(data)//4}f', data)
- ber = statistics.mean(floats[-256:])
- stdev = statistics.stdev(floats[-256:])
-
- amplitudes, bers, stdevs = results[sim]
- amplitudes.append(ampl_mhz)
- bers.append(ber)
- stdevs.append(stdev)
-
-print(results)
+def run_simulation(
+ duration = SIMULATION_DURATION,
+ simulations = SIMS,
+ forklimit = MAX_CONCURRENT_PROCESSES,
+ amplitudes:'list(millihertz)' = AMPLITUDES_MILLIHERTZ,
+ terminate_timeout:'s' = 5.0,
+ communicate_timeout:'s' = 10.0,
+ repeat_runs = 1,
+ shuffle = False,
+ tqdm = tqdm.tqdm
+ ):
+ with tempfile.TemporaryDirectory() as tmpdir:
+
+ jobs = list(enumerate(itertools.product(simulations, amplitudes * repeat_runs)))
+ if shuffle:
+ random.shuffle(jobs)
+ nchunks = int(math.ceil(len(jobs)/forklimit))
+
+ def start_processes(jobs):
+ for i, (sim, ampl_mhz) in jobs:
+ berfile = path.join(tmpdir, f'berfile_{i}')
+ proc = subprocess.Popen(['/usr/bin/python2', sim,
+ '--signal-strength', str(ampl_mhz),
+ '--ber-file', berfile],
+ stdin=subprocess.PIPE, stdout=subprocess.DEVNULL)
+ yield proc, sim, ampl_mhz, berfile
+
+ results = { sim: defaultdict(lambda: ([], [])) for sim in simulations }
+ with tqdm(total = nchunks * duration, bar_format='{l_bar}{bar}| {elapsed}<{remaining}') as tq:
+ tq.write(f'Will launch {len(jobs)} simulation jobs in {nchunks} batches of {forklimit}')
+ for n, i in enumerate(range(0, len(jobs), forklimit)):
+ batch = jobs[i:][:forklimit]
+ tq.write(f'Starting batch {n+1}/{nchunks}...')
+ processes = list(start_processes(batch))
+ tq.write('done.')
+
+ tq.write('Waiting for simulation:')
+ for _ in range(100):
+ time.sleep(duration/100)
+ tq.update(duration/100)
+
+ tq.write('Terminating processes...')
+ for proc, *_ in processes:
+ proc.communicate(b'\n', timeout=communicate_timeout)
+
+ for proc, *_ in processes:
+ proc.wait(terminate_timeout)
+ tq.write('done.')
+
+ tq.write('Processing simulation results')
+ for _proc, sim, ampl_mhz, berfile in processes:
+
+ with open(berfile, 'rb') as f:
+ data = f.read()
+
+ floats = struct.unpack(f'{len(data)//4}f', data)
+ ber = statistics.mean(floats[-256:])
+ stdev = statistics.stdev(floats[-256:])
+
+ bers, stdevs = results[sim][ampl_mhz]
+ bers.append(ber)
+ stdevs.append(stdev)
+
+ return results
+
+if __name__ == '__main__':
+ print(run_simulation())
generated by cgit (git 2.34.1) at 2024-11-14 05:19:52 +0000