From b3b9164352b1fc276f0c1388be5e6c44f16906f2 Mon Sep 17 00:00:00 2001 From: Michal Hanus Date: Mon, 2 Jun 2025 09:46:16 +0200 Subject: final1 --- prilohy/web/20250526.csv | 67 + prilohy/web/20250527.csv | 97 + prilohy/web/20250528.csv | 135 + prilohy/web/Untitled.ipynb | 171 + prilohy/web/ltemgps.csv | 163 + prilohy/web/mapy.ipynb | 24774 ++++++++++++++++++++++++++++++++++++++ prilohy/web/mapy.ipynb.invalid | 0 prilohy/web/mapynb.ipynb | 24951 +++++++++++++++++++++++++++++++++++++++ prilohy/web/mqttagregator.py | 50 + prilohy/web/mqttdummy.py | 80 + prilohy/web/mqttschema.md | 14 + prilohy/web/nbiotgps.csv | 135 + prilohy/web/sav.bck | 1 + prilohy/web/save.bck | 1 + prilohy/web/save.csv | 554 + prilohy/web/savenb.csv | 1012 ++ prilohy/web/server.py | 67 + prilohy/web/tst | 0 18 files changed, 52272 insertions(+) create mode 100755 prilohy/web/20250526.csv create mode 100755 prilohy/web/20250527.csv create mode 100755 prilohy/web/20250528.csv create mode 100644 prilohy/web/Untitled.ipynb create mode 100755 prilohy/web/ltemgps.csv create mode 100644 prilohy/web/mapy.ipynb create mode 100644 prilohy/web/mapy.ipynb.invalid create mode 100644 prilohy/web/mapynb.ipynb create mode 100644 prilohy/web/mqttagregator.py create mode 100644 prilohy/web/mqttdummy.py create mode 100644 prilohy/web/mqttschema.md create mode 100755 prilohy/web/nbiotgps.csv create mode 100644 prilohy/web/sav.bck create mode 100644 prilohy/web/save.bck create mode 100644 prilohy/web/save.csv create mode 100644 prilohy/web/savenb.csv create mode 100644 prilohy/web/server.py create mode 100644 prilohy/web/tst (limited to 'prilohy/web') diff --git a/prilohy/web/20250526.csv b/prilohy/web/20250526.csv new file mode 100755 index 0000000..9f16425 --- /dev/null +++ b/prilohy/web/20250526.csv @@ -0,0 +1,67 @@ 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+2025-05-28T11:59:08.000Z,49.54429916,18.272564,470.88385009765625,6.432,,0.0,37,gps,1.0,0.9,1.3,42.8,,,,100,,1748433548000,2025-05-28T13:59:08.000+02:00,175510.05616132022,1748425387714,Default Profile,true diff --git a/prilohy/web/Untitled.ipynb b/prilohy/web/Untitled.ipynb new file mode 100644 index 0000000..0e52830 --- /dev/null +++ b/prilohy/web/Untitled.ipynb @@ -0,0 +1,171 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 46, + "id": "62087531-441f-4c1a-b182-053ad5e944ae", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "len 26\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f283310699e6411fac48d22e6f8cdd8f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FigureWidget({\n", + " 'data': [{'hovertemplate': ('temp=%{marker.size}
lati=%{' ... '%{marker.color}'),\n", + " 'lat': {'bdata': ('oS+9/bmcSEBQVMkPu5xIQKBF1QC8nE' ... 'y3nEhA2OTuSLycSECyG668wZxIQA=='),\n", + " 'dtype': 'f8'},\n", + " 'legendgroup': '',\n", + " 'lon': {'bdata': ('cayL22iQMEBxlTGCaZAwQEBK4mhrkD' ... 'cdkDBAn1sYhBqQMEBApnNNGpAwQA=='),\n", + " 'dtype': 'f8'},\n", + " 'marker': {'color': {'bdata': ('AAAAAAAAAABlc3ISjHuhv7AgzVg0nb' ... '3pVfK/iAuZW7wo879EZYnvQIzzvw=='),\n", + " 'dtype': 'f8'},\n", + " 'coloraxis': 'coloraxis',\n", + " 'opacity': 0.5,\n", + " 'size': {'bdata': ('AAAAAAAAPkAAAAAAAAA+QAAAAAAAAD' ... 'AAAD5AAAAAAAAAPkAAAAAAAAA+QA=='),\n", + " 'dtype': 'f8'},\n", + " 'sizemode': 'area',\n", + " 'sizeref': np.float64(0.3)},\n", + " 'mode': 'markers',\n", + " 'name': '',\n", + " 'showlegend': False,\n", + " 'subplot': 'map',\n", + " 'type': 'scattermap',\n", + " 'uid': '2f0753e7-5993-4855-bcc8-6a5dbc5e62ec'}],\n", + " 'layout': {'coloraxis': {'colorbar': {'title': {'text': 'rssi'}},\n", + " 'colorscale': [[0.0, 'rgb(0,0,131)'], [0.2,\n", + " 'rgb(0,60,170)'], [0.4,\n", + " 'rgb(5,255,255)'], [0.6,\n", + " 'rgb(255,255,0)'], [0.8,\n", + " 'rgb(250,0,0)'], [1.0, 'rgb(128,0,0)']]},\n", + " 'height': 800,\n", + " 'legend': {'itemsizing': 'constant', 'tracegroupgap': 0},\n", + " 'map': {'center': {'lat': np.float64(49.22425434846154), 'lon': np.float64(16.56396273223077)},\n", + " 'domain': {'x': [0.0, 1.0], 'y': [0.0, 1.0]},\n", + " 'style': 'open-street-map',\n", + " 'zoom': 13},\n", + " 'mapbox': {'center': {'lat': np.float64(49.22425434846154), 'lon': np.float64(16.56396273223077)},\n", + " 'style': 'open-street-map',\n", + " 'zoom': 13},\n", + " 'margin': {'t': 60},\n", + " 'template': '...',\n", + " 'width': 800}\n", + "})" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import dash\n", + "import time\n", + "import sys\n", + "import json\n", + "import pandas as pd\n", + "import plotly.express as px\n", + "\n", + "\n", + "df = pd.DataFrame()\n", + "dg = pd.DataFrame(columns=['lati', 'long', 'alti', 'rssi', 'snr', 'rttt', 'temp', 'humi', 'pres', 'voc'], dtype=float)\n", + "\n", + "with open('save.csv') as f:\n", + " for i, line in enumerate(f):\n", + " dg.loc[i] = pd.Series(json.loads(line))\n", + " savepos = f.tell()\n", + "\n", + "dg = dg.fillna(0.0)\n", + "\n", + "print('len', len(dg.index))\n", + "fig = px.scatter_map(dg, lat='lati', lon='long',color=\"rssi\", size='temp',\n", + " size_max=10, map_style=\"open-street-map\", color_continuous_scale='jet', zoom=13, height=800, width=800)\n", + "\n", + "f2 = go.FigureWidget(fig)\n", + "\n", + "f2" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "aee5d6bc-0590-4494-abd2-a1eaf28481b9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\"lati\": 49.232750116, \"long\": 16.565789814, \"alti\": NaN, \"rssi\": -8.493902113, \"snr\": 0.0, \"rttt\": NaN, \"temp\": 30.0, \"humi\": NaN, \"pres\": NaN, \"voc\": NaN}\n", + "\n", + "{\"lati\": 49.232391864, \"long\": 16.568253166, \"alti\": NaN, \"rssi\": -8.983527682, \"snr\": 0.0, \"rttt\": NaN, \"temp\": 30.0, \"humi\": NaN, \"pres\": NaN, \"voc\": NaN}\n", + "\n" + ] + } + ], + "source": [ + "#while True:\n", + "import time, threading\n", + "\n", + "def update():\n", + " global savepos\n", + " mi = max(dg.index)\n", + " with open('save.csv') as f:\n", + " f.seek(savepos)\n", + " for i, line in enumerate(f):\n", + " print(line)\n", + " dg.loc[mi+1+i] = pd.Series(json.loads(line)).fillna(0)\n", + " savepos = f.tell()\n", + "\n", + " f2.data[0]['lat'] = dg['lati']\n", + " f2.data[0]['lon'] = dg['long']\n", + " f2.data[0]['marker']['color'] = dg['rssi']\n", + " f2.data[0]['marker']['size'] = dg['temp']\n", + "\n", + " #time.sleep(1)\n", + " threading.Timer(1, update).start()\n", + "\n", + "update()" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "65a5202a-8cfc-456a-a39d-52a0b2d9f739", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "venv" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/prilohy/web/ltemgps.csv b/prilohy/web/ltemgps.csv new file mode 100755 index 0000000..6607871 --- /dev/null +++ b/prilohy/web/ltemgps.csv @@ -0,0 +1,163 @@ +time,lat,lon,elevation,accuracy,bearing,speed,satellites,provider,hdop,vdop,pdop,geoidheight,ageofdgpsdata,dgpsid,activity,battery,annotation,timestamp_ms,time_offset,distance,starttimestamp_ms,profile_name,battery_charging +2025-05-26T20:46:33.000Z,49.54409601,18.27256909,475.90447998046875,31.088001,23.1,0.71,44,gps,0.9,0.9,1.3,42.8,,,,48,,1748292393000,2025-05-26T22:46:33.000+02:00,0.0,1748292363134,Default Profile,true +2025-05-26T20:47:39.000Z,49.54405976,18.27248277,474.66473388671875,30.016,,0.0,45,gps,0.9,0.8,1.2,42.8,,,,50,,1748292459000,2025-05-26T22:47:39.000+02:00,7.418597706446831,1748292363134,Default Profile,true +2025-05-26T20:48:56.000Z,49.54405746,18.27247481,476.14727783203125,31.088001,,0.0,44,gps,1.1,0.8,1.4,42.8,,,,51,,1748292536000,2025-05-26T22:48:56.000+02:00,8.04728396614988,1748292363134,Default Profile,true +2025-05-26T20:50:03.000Z,49.54409822,18.27247813,477.41387939453125,28.944,,0.0,44,gps,1.3,0.9,1.6,42.8,,,,52,,1748292603000,2025-05-26T22:50:03.000+02:00,12.585914749528074,1748292363134,Default Profile,true +2025-05-26T20:51:37.000Z,49.54412451,18.272476,477.728515625,26.800001,,0.0,44,gps,1.3,0.9,1.6,42.8,,,,54,,1748292697000,2025-05-26T22:51:37.000+02:00,15.513266095550737,1748292363134,Default Profile,true +2025-05-26T20:52:44.000Z,49.54412692,18.27246656,479.0093994140625,33.232002,,0.0,44,gps,1.2,0.9,1.5,42.8,,,,56,,1748292764000,2025-05-26T22:52:44.000+02:00,16.245186411369236,1748292363134,Default Profile,true +2025-05-26T20:54:08.000Z,49.54402073,18.2724276,502.77001953125,28.944,107.2,0.39,44,gps,1.5,0.9,1.8,42.8,,,,57,,1748292848000,2025-05-26T22:54:08.000+02:00,28.38295655161574,1748292363134,Default Profile,true +2025-05-26T20:55:14.000Z,49.54409891,18.27245205,494.17559814453125,27.872002,,0.0,44,gps,1.1,0.9,1.4,42.8,,,,58,,1748292914000,2025-05-26T22:55:14.000+02:00,37.253357995048766,1748292363134,Default Profile,true +2025-05-26T20:56:21.000Z,49.54414081,18.27245704,490.5887451171875,39.664,,0.0,44,gps,1.4,0.9,1.6,42.8,,,,60,,1748292981000,2025-05-26T22:56:21.000+02:00,41.92631535776999,1748292363134,Default Profile,true +2025-05-26T20:57:29.000Z,49.5441287,18.27246457,491.5928955078125,34.304,,0.0,44,gps,1.2,0.8,1.4,42.8,,,,61,,1748293049000,2025-05-26T22:57:29.000+02:00,43.37835433618675,1748292363134,Default Profile,true +2025-05-26T20:58:35.000Z,49.5441171,18.27247092,494.050048828125,36.448,,0.0,44,gps,1.2,0.8,1.4,42.8,,,,63,,1748293115000,2025-05-26T22:58:35.000+02:00,44.747166456457,1748292363134,Default Profile,true +2025-05-26T20:59:48.000Z,49.54408421,18.27246402,498.05194091796875,22.512001,,0.0,44,gps,1.3,0.9,1.6,42.8,,,,64,,1748293188000,2025-05-26T22:59:48.000+02:00,48.43809606809598,1748292363134,Default Profile,true +2025-05-26T21:00:56.000Z,49.5440785,18.27246944,498.6219482421875,34.304,,0.0,44,gps,1.3,0.9,1.5,42.8,,,,65,,1748293256000,2025-05-26T23:00:56.000+02:00,49.18378419530161,1748292363134,Default Profile,true +2025-05-26T21:02:01.000Z,49.54409384,18.27246237,497.2977294921875,25.728,,0.0,45,gps,1.4,0.9,1.6,42.8,,,,67,,1748293321000,2025-05-26T23:02:01.000+02:00,50.964154868613896,1748292363134,Default Profile,true +2025-05-26T21:03:06.000Z,49.54409234,18.27247518,498.632568359375,26.800001,,0.0,45,gps,1.2,0.9,1.5,42.8,,,,68,,1748293386000,2025-05-26T23:03:06.000+02:00,51.903328795926065,1748292363134,Default Profile,true +2025-05-26T21:04:11.000Z,49.54409615,18.27247556,498.84649658203125,19.296001,,0.0,45,gps,1.2,0.9,1.5,42.8,,,,70,,1748293451000,2025-05-26T23:04:11.000+02:00,52.3278677014125,1748292363134,Default Profile,true +2025-05-26T21:07:20.000Z,49.54410204,18.27247533,498.1695556640625,22.512001,,0.0,45,gps,1.1,0.9,1.4,42.8,,,,70,,1748293640000,2025-05-26T23:07:20.000+02:00,52.98301601826462,1748292363134,Default Profile,false +2025-05-26T21:08:34.000Z,49.54410122,18.27247627,497.906494140625,36.448,,0.0,45,gps,1.1,0.9,1.4,42.8,,,,70,,1748293714000,2025-05-26T23:08:34.000+02:00,53.096653511141476,1748292363134,Default Profile,false +2025-05-26T21:09:49.000Z,49.54410871,18.27247447,496.670166015625,6.432,,0.0,46,gps,1.1,0.9,1.4,42.8,,,,70,,1748293789000,2025-05-26T23:09:49.000+02:00,53.93956836050118,1748292363134,Default Profile,false +2025-05-26T21:10:52.000Z,49.54413822,18.27246671,492.561767578125,16.08,,0.0,45,gps,0.8,0.7,1.1,42.8,,,,69,,1748293852000,2025-05-26T23:10:52.000+02:00,57.26835345555432,1748292363134,Default Profile,false +2025-05-26T21:11:55.000Z,49.54416519,18.27247002,490.1075439453125,17.152,,0.0,45,gps,1.8,0.9,2.0,42.8,,,,69,,1748293915000,2025-05-26T23:11:55.000+02:00,60.27677459287929,1748292363134,Default Profile,false +2025-05-26T21:12:59.000Z,49.5441887,18.27246539,488.7939453125,15.008,,0.0,45,gps,1.7,0.9,1.9,42.8,,,,68,,1748293979000,2025-05-26T23:12:59.000+02:00,62.91222452863944,1748292363134,Default Profile,false +2025-05-26T21:14:04.000Z,49.54419669,18.27247645,489.511474609375,30.016,,0.0,45,gps,1.6,0.9,1.9,42.8,,,,68,,1748294044000,2025-05-26T23:14:04.000+02:00,64.10642341026289,1748292363134,Default Profile,true +2025-05-26T21:15:08.000Z,49.54420345,18.27248096,489.3013916015625,20.368,,0.0,45,gps,1.6,0.9,1.9,42.8,,,,69,,1748294108000,2025-05-26T23:15:08.000+02:00,64.92550978233507,1748292363134,Default Profile,true +2025-05-26T21:16:11.000Z,49.54421047,18.27249085,489.2471923828125,19.296001,,0.0,44,gps,1.6,0.8,1.8,42.8,,,,70,,1748294171000,2025-05-26T23:16:11.000+02:00,65.9830976364981,1748292363134,Default Profile,true +2025-05-26T21:17:13.000Z,49.5438214,18.27308549,506.0565185546875,15.008,141.4,2.95,44,gps,1.1,0.8,1.3,42.8,,,,71,,1748294233000,2025-05-26T23:17:13.000+02:00,126.91225678981887,1748292363134,Default Profile,true +2025-05-26T21:18:16.000Z,49.54395677,18.27242086,469.2750244140625,16.08,297.7,4.3,27,gps,1.0,0.9,1.3,42.8,,,,72,,1748294296000,2025-05-26T23:18:16.000+02:00,177.1726409780048,1748292363134,Default Profile,true +2025-05-26T21:19:19.000Z,49.54473949,18.26899574,502.750732421875,17.152,,0.0,43,gps,0.9,0.7,1.1,42.8,,,,73,,1748294359000,2025-05-26T23:19:19.000+02:00,439.1731204949307,1748292363134,Default Profile,true +2025-05-26T21:20:21.000Z,49.5446657,18.26886853,474.455078125,13.936001,,0.0,43,gps,0.9,0.7,1.1,42.8,,,,74,,1748294421000,2025-05-26T23:20:21.000+02:00,451.48413458878736,1748292363134,Default Profile,true +2025-05-26T21:21:25.000Z,49.5471632,18.26589508,255.970458984375,27.872002,328.9,12.76,37,gps,1.0,0.8,1.3,42.8,,,,75,,1748294485000,2025-05-26T23:21:25.000+02:00,802.4034399270529,1748292363134,Default Profile,true +2025-05-26T21:22:28.000Z,49.55295479,18.25868271,490.9844970703125,16.08,326.8,13.44,43,gps,1.0,0.8,1.2,42.8,,,,75,,1748294548000,2025-05-26T23:22:28.000+02:00,1630.324506122788,1748292363134,Default Profile,true 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+2025-05-26T23:39:37.000Z,49.22454325,16.58158109,315.161865234375,39.664,233.9,1.35,40,gps,1.0,0.8,1.3,43.7,,,,83,,1748302777000,2025-05-27T01:39:37.000+02:00,166665.1787050348,1748292363134,Default Profile,false +2025-05-26T23:40:43.000Z,49.22422173,16.58170445,238.30755615234375,34.304,265.8,1.36,40,gps,1.2,0.8,1.4,43.7,,,,83,,1748302843000,2025-05-27T01:40:43.000+02:00,166702.03542480624,1748292363134,Default Profile,false +2025-05-26T23:42:14.000Z,49.2244247,16.58230941,317.84765625,31.088001,119.4,0.87,40,gps,2.1,1.0,2.4,43.7,,,,83,,1748302934000,2025-05-27T01:42:14.000+02:00,166751.4264975115,1748292363134,Default Profile,false diff --git a/prilohy/web/mapy.ipynb b/prilohy/web/mapy.ipynb new file mode 100644 index 0000000..be55975 --- /dev/null +++ b/prilohy/web/mapy.ipynb @@ -0,0 +1,24774 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 40, + "id": "9caeeb6a-144f-4c25-a191-15410836392e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "len: 450\n" + ] + }, + { + "data": { + "text/html": [ + "
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temppreshumigasrsrprsrqsnrenerestbandtxpdlplltemodetime
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" + ], + "text/plain": [ + " temp pres humi gas rsrp rsrq snr enerest band txp \\\n", + "104 13.11 97.045 79.304 39028.0 -89.0 14.0 3.0 8.0 20.0 6.0 \n", + "105 13.20 97.045 79.272 43610.0 -93.0 11.0 6.0 8.0 20.0 7.0 \n", + "106 13.25 97.045 79.187 46799.0 -90.0 19.0 10.0 8.0 20.0 7.0 \n", + "107 13.69 97.102 79.100 38666.0 -95.0 15.0 6.0 7.0 20.0 17.0 \n", + "108 13.83 97.118 78.338 46901.0 -91.0 16.0 3.0 7.0 20.0 17.0 \n", + "\n", + " dlpl ltemode time \n", + "104 105.0 1.0 1.748294e+09 \n", + "105 108.0 1.0 1.748294e+09 \n", + "106 105.0 1.0 1.748294e+09 \n", + "107 108.0 1.0 1.748294e+09 \n", + "108 109.0 1.0 1.748294e+09 " + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import time\n", + "import sys\n", + "import json\n", + "import numpy as np\n", + "import pandas as pd\n", + "import plotly.express as px\n", + "\n", + "cols = [ 'temp', 'pres', 'humi', 'gas', 'rsrp', 'rsrq', 'snr', 'enerest', 'band', 'txp', 'dlpl', 'ltemode', 'time']\n", + "\n", + "dg = pd.DataFrame(columns=cols, dtype=float)\n", + "\n", + "with open('save.csv') as f:\n", + " for i, line in enumerate(f):\n", + " dg.loc[i] = pd.Series(json.loads(line))\n", + "\n", + "dg['rsrp'] = dg['rsrp'] - 140 # to dBm\n", + "dg['snr'] = dg['snr'] - 24 # to dB\n", + "dg = dg.tail(450)\n", + "dg = dg.fillna(0.0)\n", + "\n", + "print('len: ', len(dg.index))\n", + "dg.head(5)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "9a698481-3cb6-46de-96d8-0db62ece9631", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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timelatlonelevationaccuracybearingspeedsatellitesproviderhdop...dgpsidactivitybatteryannotationtimestamp_mstime_offsetdistancestarttimestamp_msprofile_namebattery_charging
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31.748293e+0949.54409818.272478477.41387928.944000NaN0.00044gps1.3...NaNNaN52NaN17482926030002025-05-26T22:50:03.000+02:0012.5859151748292363134Default ProfileTrue
41.748293e+0949.54412518.272476477.72851626.800001NaN0.00044gps1.3...NaNNaN54NaN17482926970002025-05-26T22:51:37.000+02:0015.5132661748292363134Default ProfileTrue
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temppreshumigasrsrprsrqsnrenerestbandtxp...dgpsidactivitybatteryannotationtimestamp_mstime_offsetdistancestarttimestamp_msprofile_namebattery_charging
013.1197.04579.30439028.0-89.014.03.08.020.06.0...NaNNaN74NaN17482944210002025-05-26T23:20:21.000+02:00451.4841351748292363134Default ProfileTrue
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213.2597.04579.18746799.0-90.019.010.08.020.07.0...NaNNaN75NaN17482944850002025-05-26T23:21:25.000+02:00802.4034401748292363134Default ProfileTrue
313.6997.10279.10038666.0-95.015.06.07.020.017.0...NaNNaN75NaN17482944850002025-05-26T23:21:25.000+02:00802.4034401748292363134Default ProfileTrue
413.8397.11878.33846901.0-91.016.03.07.020.017.0...NaNNaN75NaN17482944850002025-05-26T23:21:25.000+02:00802.4034401748292363134Default ProfileTrue
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5 rows × 36 columns

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" + ], + "text/plain": [ + " temp pres humi gas rsrp rsrq snr enerest band txp ... \\\n", + "0 13.11 97.045 79.304 39028.0 -89.0 14.0 3.0 8.0 20.0 6.0 ... \n", + "1 13.20 97.045 79.272 43610.0 -93.0 11.0 6.0 8.0 20.0 7.0 ... \n", + "2 13.25 97.045 79.187 46799.0 -90.0 19.0 10.0 8.0 20.0 7.0 ... \n", + "3 13.69 97.102 79.100 38666.0 -95.0 15.0 6.0 7.0 20.0 17.0 ... \n", + "4 13.83 97.118 78.338 46901.0 -91.0 16.0 3.0 7.0 20.0 17.0 ... \n", + "\n", + " dgpsid activity battery annotation timestamp_ms \\\n", + "0 NaN NaN 74 NaN 1748294421000 \n", + "1 NaN NaN 74 NaN 1748294421000 \n", + "2 NaN NaN 75 NaN 1748294485000 \n", + "3 NaN NaN 75 NaN 1748294485000 \n", + "4 NaN NaN 75 NaN 1748294485000 \n", + "\n", + " time_offset distance starttimestamp_ms \\\n", + "0 2025-05-26T23:20:21.000+02:00 451.484135 1748292363134 \n", + "1 2025-05-26T23:20:21.000+02:00 451.484135 1748292363134 \n", + "2 2025-05-26T23:21:25.000+02:00 802.403440 1748292363134 \n", + "3 2025-05-26T23:21:25.000+02:00 802.403440 1748292363134 \n", + "4 2025-05-26T23:21:25.000+02:00 802.403440 1748292363134 \n", + "\n", + " profile_name battery_charging \n", + "0 Default Profile True \n", + "1 Default Profile True \n", + "2 Default Profile True \n", + "3 Default Profile True \n", + "4 Default Profile True \n", + "\n", + "[5 rows x 36 columns]" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.merge_asof(dg, gps, on='time', direction='nearest')\n", + "len(df.index)\n", + "\n", + "i = 0\n", + "def aproxpos(t):\n", + " global i\n", + " while i+2 < len(gps.index) and gps.at[i+1, 'time'] < t:\n", + " i += 1\n", + " t1, t2 = gps.at[i, 'time'], gps.at[i+1, 'time']\n", + " la1, la2 = gps.at[i, 'lat'], gps.at[i+1, 'lat']\n", + " lo1, lo2 = gps.at[i, 'lon'], gps.at[i+1, 'lon']\n", + " \n", + " la = (t-t1)*((la2-la1)/(t2-t1)) +la1\n", + " lo = (t-t1)*((lo2-lo1)/(t2-t1)) +lo1\n", + " return pd.Series([la, lo])\n", + "\n", + "df[['lat', 'lon']] = df['time'].apply(aproxpos)\n", + "df.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "5e753dcc-c84d-4b36-835f-9069eafb2c7d", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "speed=%{marker.size}
lat=%{lat}
lon=%{lon}
rsrp=%{marker.color}", + "lat": { + "bdata": 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+ "dtype": "f8" + }, + "legendgroup": "", + "lon": { + "bdata": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = px.scatter_map(df, lat='lat', lon='lon',color=\"humi\", opacity=1, size='battery', size_max=9,\n", + " map_style=\"open-street-map\", color_continuous_scale='jet', zoom=8)\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "8ceb16af-fa0c-4722-b5e4-840b6f9ee0f1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "a = dg['time'].diff()\n", + "plt.figure(figsize=(10, 5))\n", + "\n", + "plt.plot(a)\n", + "plt.grid()\n", + "plt.show()\n", + "plt.figure(figsize=(10, 5))\n", + "plt.hist(a, bins=100)\n", + "plt.grid()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "c0c837d4-d569-42da-a274-4dbf13d41efa", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('{\"lati\": 49.223027547, \"long\": 16.580739947, \"alti\": NaN, \"rssi\": -1.950746802, \"snr\": 0.0, \"rttt\": NaN, \"temp\": 30.0, \"humi\": NaN, \"pres\": NaN, \"voc\": NaN}',\n", + " lati 49.223028\n", + " long 16.580740\n", + " alti NaN\n", + " rssi -1.950747\n", + " snr 0.000000\n", + " rttt NaN\n", + " temp 30.000000\n", + " humi NaN\n", + " pres NaN\n", + " voc NaN\n", + " Name: 50, dtype: float64)" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import json\n", + "s = dg.loc[50]\n", + "js = json.dumps(s.to_dict())\n", + "ss = pd.Series(json.loads(js))\n", + "js, sss" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/prilohy/web/mapy.ipynb.invalid b/prilohy/web/mapy.ipynb.invalid new file mode 100644 index 0000000..e69de29 diff --git a/prilohy/web/mapynb.ipynb b/prilohy/web/mapynb.ipynb new file mode 100644 index 0000000..2a79c26 --- /dev/null +++ b/prilohy/web/mapynb.ipynb @@ -0,0 +1,24951 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 58, + "id": "9caeeb6a-144f-4c25-a191-15410836392e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "len: 938\n" + ] + }, + { + "data": { + "text/html": [ + "
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temppreshumigasrsrprsrqsnrenerestbandtxpdlplltemodetimetime2
93328.8496.30938.12646799.0-56.028.014.08.020.0-1.083.02.01.748434e+09(2025, 5, 28, 13, 59, 10, 2, 148, 1)
93428.8496.31138.26646694.0-56.029.014.08.020.0-1.083.02.01.748434e+09(2025, 5, 28, 13, 59, 18, 2, 148, 1)
93528.8596.31338.30746874.0-56.030.016.08.020.0-1.083.02.01.748434e+09(2025, 5, 28, 13, 59, 23, 2, 148, 1)
93628.7896.31138.68343794.0-57.031.016.08.020.0-1.084.02.01.748434e+09(2025, 5, 28, 13, 59, 28, 2, 148, 1)
93728.7596.31138.98345026.0-56.031.016.08.020.00.084.02.01.748434e+09(2025, 5, 28, 13, 59, 33, 2, 148, 1)
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" + ], + "text/plain": [ + " temp pres humi gas rsrp rsrq snr enerest band txp dlpl \\\n", + "0 0.00 98.318 38.619 5480.0 -57.0 28.0 5.0 8.0 20.0 3.0 85.0 \n", + "1 32.02 98.339 35.803 7416.0 -57.0 28.0 5.0 7.0 20.0 1.0 84.0 \n", + "2 31.54 98.349 35.465 10492.0 -59.0 26.0 2.0 7.0 20.0 1.0 87.0 \n", + "3 31.59 98.343 35.546 13663.0 -58.0 28.0 4.0 7.0 20.0 1.0 86.0 \n", + "4 31.57 98.341 35.417 16467.0 -59.0 28.0 4.0 7.0 20.0 1.0 87.0 \n", + "\n", + " ltemode time time2 \n", + "0 2.0 1.748426e+09 (2025, 5, 28, 11, 49, 16, 2, 148, 1) \n", + "1 2.0 1.748426e+09 (2025, 5, 28, 11, 49, 27, 2, 148, 1) \n", + "2 2.0 1.748426e+09 (2025, 5, 28, 11, 49, 37, 2, 148, 1) \n", + "3 2.0 1.748426e+09 (2025, 5, 28, 11, 49, 47, 2, 148, 1) \n", + "4 2.0 1.748426e+09 (2025, 5, 28, 11, 49, 57, 2, 148, 1) " + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import time\n", + "import sys\n", + "import json\n", + "import numpy as np\n", + "import pandas as pd\n", + "import plotly.express as px\n", + "import time\n", + "\n", + "cols = [ 'temp', 'pres', 'humi', 'gas', 'rsrp', 'rsrq', 'snr', 'enerest', 'band', 'txp', 'dlpl', 'ltemode', 'time']\n", + "\n", + "dg = pd.DataFrame(columns=cols, dtype=float)\n", + "\n", + "with open('savenb.csv') as f:\n", + " for i, line in enumerate(f):\n", + " dg.loc[i] = pd.Series(json.loads(line))\n", + "\n", + "dg['rsrp'] = dg['rsrp'] - 140 # to dBm\n", + "dg['snr'] = dg['snr'] - 24 # to dB\n", + "\n", + "dg = dg[dg['ltemode'] == 2].reset_index(drop=True)\n", + "\n", + "dg = dg.fillna(0.0)\n", + "\n", + "dg['time2'] = dg['time'].apply(time.localtime)\n", + "\n", + "print('len: ', len(dg.index))\n", + "display(dg.tail(5))\n", + "dg.head(5)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "9a698481-3cb6-46de-96d8-0db62ece9631", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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timelatlonelevationaccuracybearingspeedsatellitesproviderhdop...dgpsidactivitybatteryannotationtimestamp_mstime_offsetdistancestarttimestamp_msprofile_namebattery_charging
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11.748425e+0949.22778716.575137326.3611457.504000NaN0.00033gps0.8...NaNNaN67NaN17484254514412025-05-28T11:44:11.441+02:002.1603701748425387714Default ProfileTrue
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41.748426e+0949.22780116.575150325.92468310.720000NaN0.00038gps0.9...NaNNaN69NaN17484256350002025-05-28T11:47:15.000+02:004.2933991748425387714Default ProfileTrue
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" + ], + "text/plain": [ + " time lat lon elevation accuracy bearing speed \\\n", + "0 1.748425e+09 49.227804 16.575121 334.983032 9.648001 156.2 1.368 \n", + "1 1.748425e+09 49.227787 16.575137 326.361145 7.504000 NaN 0.000 \n", + "2 1.748426e+09 49.227791 16.575146 327.299805 7.504000 NaN 0.000 \n", + "3 1.748426e+09 49.227797 16.575143 327.650208 9.648001 NaN 0.000 \n", + "4 1.748426e+09 49.227801 16.575150 325.924683 10.720000 NaN 0.000 \n", + "\n", + " satellites provider hdop ... dgpsid activity battery annotation \\\n", + "0 42 gps 0.8 ... NaN NaN 66 NaN \n", + "1 33 gps 0.8 ... NaN NaN 67 NaN \n", + "2 31 gps 0.6 ... NaN NaN 68 NaN \n", + "3 38 gps 1.0 ... NaN NaN 68 NaN \n", + "4 38 gps 0.9 ... NaN NaN 69 NaN \n", + "\n", + " timestamp_ms time_offset distance starttimestamp_ms \\\n", + "0 1748425390888 2025-05-28T11:43:10.888+02:00 0.000000 1748425387714 \n", + "1 1748425451441 2025-05-28T11:44:11.441+02:00 2.160370 1748425387714 \n", + "2 1748425512441 2025-05-28T11:45:12.441+02:00 2.910257 1748425387714 \n", + "3 1748425573441 2025-05-28T11:46:13.441+02:00 3.633400 1748425387714 \n", + "4 1748425635000 2025-05-28T11:47:15.000+02:00 4.293399 1748425387714 \n", + "\n", + " profile_name battery_charging \n", + "0 Default Profile True \n", + "1 Default Profile True \n", + "2 Default Profile True \n", + "3 Default Profile True \n", + "4 Default Profile True \n", + "\n", + "[5 rows x 24 columns]" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gps = pd.read_csv('nbiotgps.csv')\n", + "gps['time'] = gps['time'].apply(lambda x: time.mktime(time.strptime(x[:-5], '%Y-%m-%dT%H:%M:%S')) +3600*2)\n", + "gps['speed'] = gps['speed'] * 3.6 # m/s -> km/s\n", + "gps.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "f3d732ee-3e78-4ac9-89f8-63c02abd9826", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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temppreshumigasrsrprsrqsnrenerestbandtxp...dgpsidactivitybatteryannotationtimestamp_mstime_offsetdistancestarttimestamp_msprofile_namebattery_charging
00.0098.31838.6195480.0-57.028.05.08.020.03.0...NaNNaN70NaN17484257576252025-05-28T11:49:17.625+02:007.3599821748425387714Default ProfileTrue
132.0298.33935.8037416.0-57.028.05.07.020.01.0...NaNNaN70NaN17484257576252025-05-28T11:49:17.625+02:007.3599821748425387714Default ProfileTrue
231.5498.34935.46510492.0-59.026.02.07.020.01.0...NaNNaN70NaN17484257576252025-05-28T11:49:17.625+02:007.3599821748425387714Default ProfileTrue
331.5998.34335.54613663.0-58.028.04.07.020.01.0...NaNNaN70NaN17484257576252025-05-28T11:49:17.625+02:007.3599821748425387714Default ProfileTrue
431.5798.34135.41716467.0-59.028.04.07.020.01.0...NaNNaN71NaN17484258184452025-05-28T11:50:18.445+02:008.5416911748425387714Default ProfileTrue
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" + ], + "text/plain": [ + " temp pres humi gas rsrp rsrq snr enerest band txp ... \\\n", + "0 0.00 98.318 38.619 5480.0 -57.0 28.0 5.0 8.0 20.0 3.0 ... \n", + "1 32.02 98.339 35.803 7416.0 -57.0 28.0 5.0 7.0 20.0 1.0 ... \n", + "2 31.54 98.349 35.465 10492.0 -59.0 26.0 2.0 7.0 20.0 1.0 ... \n", + "3 31.59 98.343 35.546 13663.0 -58.0 28.0 4.0 7.0 20.0 1.0 ... \n", + "4 31.57 98.341 35.417 16467.0 -59.0 28.0 4.0 7.0 20.0 1.0 ... \n", + "\n", + " dgpsid activity battery annotation timestamp_ms \\\n", + "0 NaN NaN 70 NaN 1748425757625 \n", + "1 NaN NaN 70 NaN 1748425757625 \n", + "2 NaN NaN 70 NaN 1748425757625 \n", + "3 NaN NaN 70 NaN 1748425757625 \n", + "4 NaN NaN 71 NaN 1748425818445 \n", + "\n", + " time_offset distance starttimestamp_ms \\\n", + "0 2025-05-28T11:49:17.625+02:00 7.359982 1748425387714 \n", + "1 2025-05-28T11:49:17.625+02:00 7.359982 1748425387714 \n", + "2 2025-05-28T11:49:17.625+02:00 7.359982 1748425387714 \n", + "3 2025-05-28T11:49:17.625+02:00 7.359982 1748425387714 \n", + "4 2025-05-28T11:50:18.445+02:00 8.541691 1748425387714 \n", + "\n", + " profile_name battery_charging \n", + "0 Default Profile True \n", + "1 Default Profile True \n", + "2 Default Profile True \n", + "3 Default Profile True \n", + "4 Default Profile True \n", + "\n", + "[5 rows x 37 columns]" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.merge_asof(dg, gps, on='time', direction='nearest')\n", + "len(df.index)\n", + "\n", + "i = 0\n", + "def aproxpos(t):\n", + " global i\n", + " while i+2 < len(gps.index) and gps.at[i+1, 'time'] < t:\n", + " i += 1\n", + " t1, t2 = gps.at[i, 'time'], gps.at[i+1, 'time']\n", + " la1, la2 = gps.at[i, 'lat'], gps.at[i+1, 'lat']\n", + " lo1, lo2 = gps.at[i, 'lon'], gps.at[i+1, 'lon']\n", + " \n", + " la = (t-t1)*((la2-la1)/(t2-t1)) +la1\n", + " lo = (t-t1)*((lo2-lo1)/(t2-t1)) +lo1\n", + " return pd.Series([la, lo])\n", + "\n", + "df[['lat', 'lon']] = df['time'].apply(aproxpos)\n", + "df.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "f865d9d4-cd00-429e-90b6-53e4b01237f8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "133" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "i" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "5e753dcc-c84d-4b36-835f-9069eafb2c7d", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "speed=%{marker.size}
lat=%{lat}
lon=%{lon}
rsrp=%{marker.color}", + "lat": { + "bdata": 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+ "dtype": "f8" + }, + "legendgroup": "", + "lon": { + "bdata": 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+ "dtype": "f8" + }, + "legendgroup": "", + "lon": { + "bdata": 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", + "dtype": "f8" + }, + "legendgroup": "", + "lon": { + "bdata": 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iYhATE+P1vtlsDklhhipdvTAZ3X2jI7Wdzb2M9YLlHHrNuYwNonkxIrmNzbmM9SQc5RxI+po/xVNbWwujUZqsyWSC3e7o95CdnY309HSsWrXK9XllZSUKCgqQk5OjdXZIBoe6JyIivdO8BuXGG2/EnDlzkJWVhT59+mDHjh2YN28efve73wFwROTTp0/H888/j27duiE7OxuzZs1CRkYGbrnlFq2zQ0RERE2Q5gHKq6++ilmzZuGBBx5ASUkJMjIy8Ic//AFPP/20a5nHHnsMNTU1uO+++1BeXo6RI0di+fLliI2N1To7RERE1ARpHqAkJCRg/vz5mD9/vuIyBoMBs2fPxuzZs7VePRERETUDnIuHiIiIdIcBChEREekOA5Qmps5qw9+/PYDthecjnRUiIqKQYYDSxLy17iheW3MYt72+IdJZISIiChkGKE3MwZLqSGeBiIgo5BigEBERke4wQCEiIiLdYYBCREREusMAhYiIiHSHAUoTI3CmPyIiagEYoBAREZHuMEAhIiIi3WGAQkRERLrDAKWJMRgMkc4CERFRyDFAaWLYSZaIiFoCBihERESkOwxQiIiISHcYoBAREZHuMEAhIiIi3WGAQkRERLrDAIWIiIh0hwEKERER6Q4DFCIiItIdBihERESkOwxQiIiISHcYoDQxHOieiIhaAgYoREREpDsMUJoYzmVMREQtAQMUIiIi0h0GKERERKQ7DFCaGHaSJSKiloABChEREekOA5Qmhp1kiYioJWCAQkRERLrDAIWIiIh0hwFKE8NOskRE1BIwQCEiIiLdYYBCREREusMAhYiIiHSHAQoRERHpDgMUIiIi0h0GKERERKQ7IQlQTp06hTvvvBNt27ZFXFwc+vXrh61bt7o+FwQBTz/9NDp06IC4uDjk5ubi0KFDocgKERERNUGaByjnz5/HFVdcAbPZjG+++QZ79+7FP/7xD7Rp08a1zMsvv4wFCxZg8eLFKCgoQKtWrTBu3DjU1dVpnR0iIiJqgqK0TvCll15CZmYmlixZ4novOzvb9VoQBMyfPx9PPfUUbr75ZgDAu+++i7S0NCxduhSTJk3SOkvkQeBwb0REpHOaByhfffUVxo0bh9tvvx1r165Fx44d8cADD+Dee+8FABw7dgxFRUXIzc11fScpKQnDhw/Hxo0bZQMUi8UCi8Xi+ruyshIAYLVaYbVaNcu7My0t09SaYLe7XgebT8HuDlDCva1NoYybA5Zz6LWEMhaEyJ0rxOtszmWsB+Es50DWoXmAcvToUSxatAgzZszAk08+iS1btuChhx5CdHQ0pkyZgqKiIgBAWlqa5HtpaWmuzzzNnTsXzz77rNf7K1asQHx8vNabgPz8fM3T1MqZM0Y4W+by8vKCSuNcaePTaCw9l3FzwnIOveZcxuXnTXDOoR6pcwXQvMtYT8JRzrW1taqX1TxAsdvtGDJkCF544QUAwMCBA7F7924sXrwYU6ZMCSrNmTNnYsaMGa6/KysrkZmZibFjxyIxMVGTfAOOyC4/Px9jxoyB2WzWLF0tfVu1CztKiwEAEyZMCCqNj4u34mBFWaPSCFZTKOPmgOUcei2hjJf8XABUVwAI/7kCaBllrAfhLGdnC4gamgcoHTp0QO/evSXv9erVC//9738BAOnp6QCA4uJidOjQwbVMcXExBgwYIJtmTEwMYmJivN43m80hKcxQpasFg9HdrznYPBqMhkan0Vh6LuPmhOUces25jA2GyJ8rnOturmWsJ+Eo50DS1/wpniuuuAIHDhyQvHfw4EF07twZgKPDbHp6OlatWuX6vLKyEgUFBcjJydE6OyTDAIP/hYiIiCJI8xqURx55BCNGjMALL7yAX/7yl9i8eTPefPNNvPnmmwAcEfn06dPx/PPPo1u3bsjOzsasWbOQkZGBW265RevskAw+xUNERHqneYAydOhQfPHFF5g5cyZmz56N7OxszJ8/H5MnT3Yt89hjj6Gmpgb33XcfysvLMXLkSCxfvhyxsbFaZ6f5YWxBREQtgOYBCgDccMMNuOGGGxQ/NxgMmD17NmbPnh2K1ZMfbOIhIiK941w8TQ1jCyIiagEYoDQ1bOIhIqIWgAFKC8ROskREpHcMUIiIiEh3GKC0QOwkS0REescApQViEw8REekdAxQiIiLSHQYoLRCbeIiISO8YoLRAbOIhIiK9Y4BCREREusMAhVq8I2ercd+7W7H7VEWks0JERBcxQKEW73fvbMGKvcW44dX1kc4KERFdxACFWrwTpbWRzgIREXlggEJERES6wwCFiIiIdIcBSgvEcVCIiEjvGKC0QBwHhYiI9I4BSgsnCAxWiIhIfxigtEBs4iEiIr1jgNLEaNE8wyYeIiLSOwYoLRxbeIiISI8YoDQxbJ4hIqKWgAFKE8PmGSIiagkYoLRwDHeIiEiPGKAQERGR7jBAISIiIt1hgNLEaN1JlgO1ERGRHjFAaWLYSZaIiFoCBigtHMMdIiLSIwYoREREpDsMUIiIiEh3GKC0cOwjS0REesQAhYiIiHSHAQoRERHpDgOUFo6PLRMRkR4xQCEiIiLdYYBCREREusMApYXjUzxERKRHDFCaGAYURETUEjBAISK/6hvseG7ZXnx/6Gyks0JELQQDlCbGoO1kxkSqvLvxON5efwy/eXtzpLNCRC1EyAOUF198EQaDAdOnT3e9V1dXh6lTp6Jt27Zo3bo1Jk6ciOLi4lBnhYiCdLKsNtJZIKIWJqQBypYtW/DGG2/gsssuk7z/yCOP4Ouvv8ann36KtWvX4vTp07jttttCmRVSwD4tRESkRyELUKqrqzF58mS89dZbaNOmjev9iooKvP3225g3bx6uvfZaDB48GEuWLMGGDRuwadOmUGWn2WBAQURELUHIApSpU6fi+uuvR25uruT9bdu2wWq1St7v2bMnsrKysHHjxlBlh4iIiJqQqFAk+tFHH2H79u3YsmWL12dFRUWIjo5GcnKy5P20tDQUFRXJpmexWGCxWFx/V1ZWAgCsViusVqtm+XampWWaWrPb7a7XweZTsLurYeqt9YgyhGQ3kKX3MtZrvgKldTlrsd81N3rfl7UgiKpsI7GdLaGM9SCc5RzIOjS/Mp08eRIPP/ww8vPzERsbq0mac+fOxbPPPuv1/ooVKxAfH6/JOsTy8/M1T1MrRUVGOCu+8vLygkrjXKk7jW+/XYEYk0aZC4C+yth9GARbpnqlVTkfP9H4/a650te+rK3y8yYAjkcHI/m7N+cy1pNwlHNtrfoO95oHKNu2bUNJSQkGDRrkes9ms2HdunV47bXX8O2336K+vh7l5eWSWpTi4mKkp6fLpjlz5kzMmDHD9XdlZSUyMzMxduxYJCYmapZ3q9WK/Px8jBkzBmazWbN0tfRN5S7sKnM88TRhwoSg0vi4eCsOVpQBAMaNG4v46PDWoOitjB/euML1Otgy1Ruty3nrsn34vugkgOZTRo2lx31Za0t+LgCqKwBE5ndvCWWsB+EsZ2cLiBqaX5lGjx6Nn376SfLe3XffjZ49e+Lxxx9HZmYmzGYzVq1ahYkTJwIADhw4gMLCQuTk5MimGRMTg5iYGK/3zWZzSAozVOlqwSAaCCXYPBqM7jSioswwm8MXoDjptYz1mKfG0KqcTSZ3NVtzK6PG0uu+rAUtzjdaaM5lrCfhKOdA0tf8ypSQkIC+fftK3mvVqhXatm3rev+ee+7BjBkzkJKSgsTERDz44IPIycnB5ZdfrnV2iIiIqAkK/60zgFdeeQVGoxETJ06ExWLBuHHj8Prrr0ciK0SkgsDn24kozMISoHz33XeSv2NjY7Fw4UIsXLgwHKsnH3jZISI1BEGQNPkQhRrn4iEiIiLdYYBCRH7xzpmIwo0BSgvHvgVEpAZPFRRuDFCIiIhIdxigtHC8KSIiNXiuoHBjgEJERES6wwCFiIj8Yn81CjcGKE2M1ucInnOIiEiPGKA0YbyjIaJw4dmGwo0BShMjHo6C8QkRETVXDFBaOgY5RKQCb4go3BigNGE8X2iDg6QSEekPA5QmhncxRBQJAm+JKMwYoDRhgXSSPVV+ATa79/I86RARkR4xQGkBVuwpwhUvrsb9722LdFaIqIli7S2FGwOUJkzt+eKt748CAPL3Fju+xxMNERHpHAOUJkyLQIPBCsuAiEiPGKC0AJ4XYD61QkREescApQkLtoOrOGBh5QERqcGaRgo3BijU4rFGiYhIfxigNGHB3tHwgkxEgeKQBBRuDFBaAF+nFU44SEQtUXltPQpLayOdDfKBAQoREfnV3O5lBszOx1V/W4PT5RcinRVSwAClCWtuJwwionDbebI80lkgBVGRzgCFztGz1fhoy0mUVlsk7/MpHiIKVHM9V/BGT78YoDRh/jqt3fjqetTU28KUGyKipoedf/WLTTzNmFJwwjsGChSf/KLmiudD/WKA0oQ196Hu536zD6P+tgYVF6whXQ+vvUT+Ndcn/prnVjUPDFCasOZ+YL2x9iiOl9bivU0nQrqe5l6OWmim1yYi0jEGKC1QU2tztdubVn6JmqPmehQ215qh5oABShOmxYHVFIIV/eeQiIi0xgCFiPxiJ1lqrhUNzXW7mgMGKE1YsMeV5IBsYQfn8t1nMPvrvbA102YjQRDw8vL9WLrzdEjXQdRcNIVa5JaK46A0MS3xYNLyenj/e9sBAP0zk3DzgI7aJawTBcfK8Pp3RwAA/8wJzToEgTUqLVIzPfUw3tYv1qA0YcEeWM31eAykVuRslcX/Qk3Q+Zr6kK/DzjM6NSPcnfWLAUoTYxCP2qHFOCiNT0IXztfUY9iclZj5+U8Bf5eVAf5xegRqrrW3zXOrmgcGKE1Mcz1J+KJmmz/YXIjSmnp8uLkwDDlq2XjHSU2duB8V+1TpFwOUJizoYEUHx2NxZR1W7y/myaGJMEgq7lrGb1ZjacC3e4pQZ21a81ntPlWBH38u1zzd5nSo2lkj2CQwQGnhInXSuWbe9/jdO1vx1S7/T5uoySM7bYZPc7pQ+fLwRzvwh/9sw1NLd0c6K6pZGmy44dX1uOm1H1Bb3xDp7OgWb4yaBgYoTVhTPsasNkfmvz90TpP0DOxJEjZNeb8LxMp9JQCAz7b9HOGcqFdXb3e9rq7TNkBpTj+7pD99c9qwZoYBShit2V+CBz/cgYra0E5+54+equi1CisaU4Oin9IIzpoD4d2v9LT/EAVDvA9zf9YvjoMSRne/swUAkBxnxnO39G10elocVk3h4FSTw5Zcf3L3Esd+lRgbhZGXtgv5+lpKDUpTFMpHwJtTs4jkqbTms1nNjuY1KHPnzsXQoUORkJCA1NRU3HLLLThw4IBkmbq6OkydOhVt27ZF69atMXHiRBQXF2udlbC5UB9YJ7ozFXUAgDqrrVET4enphHGh3hZUflTVfKhItzE1KM0luCm6uF+FgrgJLZBf2W4Xmlwn06bMJn46JYL50Lum+th8oNeapk7zAGXt2rWYOnUqNm3ahPz8fFitVowdOxY1NTWuZR555BF8/fXX+PTTT7F27VqcPn0at912m9ZZCYuVe4vR6+nlWLjmcEDfq7hgRe+nl+O2RRtClDNlWsc1haW16PX0cjz44Q5tEybdENe0BXKXftc7W9Dnr9+itLp5DoynN+IbHi2mc2iqF3J/7JLHjCOYkQA889Ue9Hp6OXaeLI90VsJG8wBl+fLluOuuu9CnTx/0798f77zzDgoLC7Ft2zYAQEVFBd5++23MmzcP1157LQYPHowlS5Zgw4YN2LRpk9bZCbnH//sjAOBv3x7ws6SbwQCsP3QOdgEB72xanzC0ODjf3XgcALDsxzMBf5edW5ueQPaZdQfPwmYXkLe7KHQZIhdxDUpznW9KC9I+sk2jnN7ZcBwAMH/lwchmJIxC3km2oqICAJCSkgIA2LZtG6xWK3Jzc13L9OzZE1lZWdi4cWOos6M5m87Cb0EQ8OXOUzh2rkZ5GY3X2dCIE6Fn08yOwvP47kCJdCAlNekw0PESsl0ziHRrLU33kVdjELvW4ZIqLPvxdEDNnnVWGz7b9nNQ0zAIgoCvdp3GweJq13ua1KBI1tHo5HQj0BqUk2W1WLrjlC6CPmMLGlMhpJ1k7XY7pk+fjiuuuAJ9+zo6hRYVFSE6OhrJycmSZdPS0lBUJH+XZbFYYLG4D9rKykoAgNVqhdWq3ZMLzrQCSVNcpar2e4LdDpvNfcIObH3uxwgd2y+NMb/adQZ/+kx5uHer1So5aTY0NDS6DOsb1G+L5+d2u13y3q2vO5q81sy40p1Hm81vuna7u21WzfZ4rjeQ7+qVXbCjweYuBwHabY94v6u3WhFostV19U22bA0Gg899Re6z3HnrAAAxvwFGdW+vaj1z/rcf/9lUiC5t45E/fWRAeczfW4KHPtwpec9S3/jzoyC4f/eGBu/zTagFc05WlW69Oz0155crX14DwLEf/2pIJ03zEjBB0L48QlTOvtalRkgDlKlTp2L37t1Yv359o9KZO3cunn32Wa/3V6xYgfj4+EalLSc/P1/1svVWE5zdLPPy8vws7Sju4uJibN9RBMCk8ntuxcVGOCu+Vq5chcRo6eefH3V/LicvLw/nz7vzvGr1aqTEqF69rGPH3esMZFsA4OTJk8jLOwHAeSfjKKMvvl3jen3k8BHk1R/ymc6+0waoK09Hmnv37kVe+Z6L6w3kN9QjxzaVlJRg+/ZiOMtBQGD7si/HRb9xfn4+WpkDy9vu/YeRV9fUqqYvnh4Fu8/9Qr6ML+7H321F7WF1d91fbXfsh8dLawPeD7864X3cr1m7FvsbeXosF50r5M434aLVfuxUYwVc++bu3cg7528OL8eyS3/YjYSSHzXNi3qOPJwtKQ7ZeUrrcpZTW1uretmQBSjTpk3DsmXLsG7dOnTq5I4409PTUV9fj/LyckktSnFxMdLT02XTmjlzJmbMmOH6u7KyEpmZmRg7diwSExM1y7PVakV+fj7GjBkDs1ndGfjJbauAi3etEyZM8LnswxtXAHCUwcB+6Xjn4I+qvif21fkdwPmzAIDRo0ejfYI0utj01V78UKw8sNSECRPwzs8FQJWj6e3aa65BRnKc6vV72ny8DBs3bpWkDwDnqi147L+7MWloJ4ztneb6vL6+Hr9bvNr1d1ZWJiZM6OP4rMEObFoJAMjJycGCPY7HZ7te2hXmjCR8uOUkXrqtr9c2A0DxhhNYeuKAJA9ynL9B7969MWFEZwDAI5tWuKp5A/kttFBnteHhj3/EtT3bB31n5tym1NRUDByQgSUX96vX9xoxfXx/jOvbodH53JG3H2vPOOY5ujY3F21bqbtSOfOW1ikLEyb0bnQ+QumFbw6gwS7g6et7AnDn3Wg0oji5J7YXluOV2/shymTEu5sKselIKcYnncF147zPF87vXtqtOyZc01XV+ufuWYuKekdNcaD74U/fHgROH5e8d8XIK9EzPSGgdDy9XbgJhTWOGmu5802oBXNOVuN8bT2e3PodAKBvnz6YMDzL5/LO3zOzU0dMmNBPcblFa4/icEkN/jaxL4zBtA2qyEOHDumYMGGApmmHqpzlOFtA1NA8QBEEAQ8++CC++OILfPfdd8jOzpZ8PnjwYJjNZqxatQoTJ04EABw4cACFhYXIycmRTTMmJgYxMd4HhtlsDklhBpKuuA+K2u8YDAaYokwBf8/5XaeoqCiv7xqMvqtgzWazJA2TTBqBmPz2VsnfzrT+tmIPvj9ciu8Pl+L4i9e7Pt9zuhIFZ915NBqNru9Y7O6mIpMpSrLMAxerr19ecQjzJw30yofJ5C7PqKgoyTbKEa9XLv/h8n+bTmL1gbNYfeAs7szJ9v8FH4wGI6Ki3OV2uNKIaR//hOMDfZ98VaVtlJZvoOVkaRDCXraBqKqzYskGR03e9NzuaNvafb4xGAx44ZuLwe9lGbipfwae+99+AEDqpQbc6ON8YTKaAjovOAVaVnL7uyGAdatJN8rcuHNFY2h9rjeZ3E1XRpP6cjL5WXbeSsfTnHcM74ycrm0bl0kFUSb5c5cWQnVN9VyHWpoHKFOnTsUHH3yAL7/8EgkJCa5+JUlJSYiLi0NSUhLuuecezJgxAykpKUhMTMSDDz6InJwcXH755VpnJ+SC7TMVqk6deunIdlbhsVJLg93jHXc5WETjZSh1LiytqcfZKgvatY6WnDzFpWkXAFMAxWswGCJWcOUaj/4aqu5zkpE3g+kkq5N5YSpqrYiLNiE6ShrIi7rYeHWEFJep5/DxFp0MSyF3HtK6k6xWvevLauqRFGeGSeMaBiV2u4DztfWSoFPw+Fysss6KaJMRsWYTPKnNcl1D6HaMltRJVvMeT4sWLUJFRQVGjRqFDh06uP59/PHHrmVeeeUV3HDDDZg4cSKuuuoqpKen4/PPP9c6K2ER7sHS/D9m7D8/4cixUrH4Kq96m6hDnsLJ9ftD5zB0zkq8ki/tzyA+ZvXQ014tLZ8CC+VWS/e7wNdUo4Mr+dkqC/rPXoExr6z1+szXAGfiC4JeH0mV24309oQh4JhpedBz+bhryeawrfOhj3Zg8PMrsfFIqes9cdGITxfVlgZc9swK5MxdJZtWuIIqX1pSgBKSJh5/YmNjsXDhQixcuFDr1Yed3i6GgZ6Twn0O81yd+FirF9WuiF/L5XHB6sOYMbaH7DoCHe47kiPyNmYk4XAS/EfGPunhOPn+kKPv1olS7056DaLg2DOv4mtSwMdXmAIauX1ei31L63GX/rPR0Yym1SShajjHZ3pz3RFXs4t4fxaX3d7Tjv4R5xVqNv01HYeDDmKksOFkgY0U7vOuv3EJ1JxAfS2z7MfT+OFw408eSidmz3WLjzVxUCJuCgp0Lh6li+EnW06qSCm8tLxwh/K8JakS13if33D4HL7eddrr/S92/IzNx8oCTm9H4Xl8vKXQK/D0lW+r6MMGm0cTj6QGxbczFRfw9vpjqvOqFbkgW1wL+fWu09jg57j+dOtJbDtxPuB1V9VZ8fb6YzhTccHvsp41o//d9jO2Hg/8Nw6GuPZD6Twqjj/kAjy1wUEoj0WtO9/qGScLjACDwbNNX1Admfu722/MHdvJslpM+8AxXL24Y2swgqmUEAcl4uYeNcTlJ1e1veloKR77b6QeD1Smg4oFVYJp4lFbM/XrfxUAAPp1TEKXdq0AOJoCHvl4F4DA90XnWDqZbeIxQjR5oq/8NEiaF6X7nuTQ9LNNty/eiJ/P+79Qa01uP3JeYI+erXZNQ6FUlpuPleHRz370WkZN36Onv9yDL3acwpIfjmH949f6yac7kZ0ny/GnT4P7jYMhPkeI8yE+X4h/aqvdjhijtB+K2uaVUB7WLamJhzUoOhDsXbTchaIxrRUlVaGbbM7JM8/iY80SQBOPJ393PkfPKo+sG0mhnH1WS8F0kg10vy6qdO9/hWXqx0pQcsIjDV/5Ft/Ze/V/UuivICcSwQkgfy5wXnjV5Ono2Wq/yyhZta9Y9XrEZXuiNLzHZJS4BkXym8r/qFab9/u+goNwNRW3nPCEAUpECAIw45Ndrr8DOY+LF/3je9vx9Je7FT9Xk4Yvf/1yN/743ragDjzlTrLK31HqgxLoRdxZnh9uLsRtr/+Aspp63bbbNpkAJYi+CJI7U4XyFwcxWheF2eTxpI7PGhT3Z1aP2rsGSR4FfLi5UHUe5q88hEc/3eV/wUaSO4c48+25PYFQqjl77LNdmPHxTgCBBaI2e/B5aSxx04h4XxDf0Ij3U6vXE4e+Z02XNBUFkK+tx8tw46vrVTevsQaFQqq4si7oC7B40Z0ny/HuxhPSeWs0OskLgoD/23gC3+wuwv6iqsC/r7IPipi4Wcciekwv0M5+zhPmzM9/wvbCcsxfeVC37bZadx4NVbgjbbNXtxY11yLxxVPrDqVmj2fNffZBETfxeNw5i38ju+DYrwLx6bafUVkX2iHE5X4TewABSiAlX1VnxSdbf8bnO06hqKIuoKeFItlZ2mRQqkFxvxYXlVwzs8lHcBDsU1OT/1WAn05VYKLKme31ei4LBQYoERBI1bMa9Y04ySutW3zXqHSC83WhkvvoRGmNV1oGhXFQauvdr9Uc+HbJRUS6/M6T5T7PwJHsma/1+TpUFTKSO2mV6/AXeNc32HG4RNS00Mi8N9ik6UUHUoPio4nH1yPIavkqipLKOlTWWXGmIrgm1rKaepRW13u97wwG6kUBl9rg8ly1Bedr6iX5Pnq2xuv7VpvdZ9BxuvyCZAyck2WRaQIDPDrJKjTxiM9P9TI1KL6Cg2BrQ73HhvKtBcUn7CQbCZ6DcwVUgyLzXn2DHTHOkWlVtfH4X0hawyO/zOvfHVGxMofvDpTgriVbfK9TdHL427cHXK/V3AFK74KkGf7x5wo8cUp/HWQB7dutQ9VkFEwtnb/A8tdvbcJWUbV2Y4O16R/vdD1SCng38ajuJOuxv9nsgV/g1aqotWLYC/JjbqhR32DHoOfk509x5tvqcSz7G8SwzmrDkOcdU06Ih8qf/K8CzLqhN34x2D0lQ4NdUByz6Pi5Goz6+3do2yoa22aNwc/na7H3jPphzrWmNJ6NXeFmTO68o7aJJ7B8Bbbvs4mHwqqxFxW5SL+xpJ1U5fMnDiL8+b8Nx2XfVxoHRazOqiZAcedR7o5Or0/LNJkmHoW+CL74a5rb6tHm3pi+EgAkwQkAmD1Hi/XZxKNcg6K0XCCUjqGDJYE3n4qdr/WuOXFyBohWH2O8OImzJ26OKquRpv/y8v2S39VmtytemNddHHem9GIaGw6Xyi8YJuJ4VVwM4tdWSV8kx2vxb+crOAj2WI6P9l9PIC5zPQwWFy6sQZFRWFaLL38sxt0juqCNj0nRLBoNZxxQJ1mZs4G0ice3P32yy3XCcCwvf2csTjOYAMrzG0onfaVxUMTqrP7LWdLpLQIdT5fvPoN/fX8M94zMRnJ8NL7dU4SrurfDjsJy1FltGN+vAwZltQHgGPb9rXXHMK5vGj7dpjyxYzDU3uHXWW14c91RXNszFX07JvlPN4ineAI9XysdT69/dxh/uKprwCdm73FQ3H+fq7bg3Q3H8cuhmSiqqMNra9yzZfsKlHafrvBYh7q8KF28Qnmx2Xr8PE6U1qJVtPtRWTXHhrjZ1TMAsjTYsXDNYdffwQZsQOiaIxtsdryx7igu1NvQITnW9b60iUd6vtheeB4bj5QiK8U9/bNzPxD/dr5+Ln9l6zzmRvdKRZ8M9zEXH21CtcXdDLZ8dxHO19bjjmGOObTOVlmw5Idjrs9bUAUKAxQ5k97ajLPV9ThQVIk3fjNEcbl/rjyk+FkgGjvio8Xqv7bD6b/b1V0Q1TTx+OTxHTW1PEptsRfq1QQo7teR6Ih3/3vbAThqBaJNRtTb7HhHVGv01vfHXGM9/HPlIbyx7iheWXlQLqlGURucvb3+GOblH8S8/IOqxqAI6imeAJ/QUfr9X15+AKkJsZKmBTU81yneLf761R7878cz+Gzbzzjt0ffDs5Os2P88amnUUtolo0IYoDj3v67tW4ny4f+HkDZzeC//L9FAdL6O60hdR9/bdEK2dtcoGQfF/b5NEHDbxbFzhnZp43rfuT+Kb658dZL11yn89TWHsWD1Ya9jrlVMFFDlnrvs/ve2AQByLmmLLu1a4aEPd2DjUXftE5t4WrizFzucbTjiu0oyf2+xJutrdBNPADUonpRWLb5Y+DphK6brkROlu1JxB9VahUDkgs5rUDwDTH+DzO08WR7CvKhbLtAns8RbqLZ8lQbDUuLrYnckiHE6PANVcfDuHL3UMzgBfDfxeFJ7rVAqs8ZebNT8FEdEYwApNvGIfuFAmowDGVDR1xhIWvbt2XdGft+W1lbJB8+7TrpryJznLPH+4KtDvb/jYufPFbLvx4kmJRT3fyqtcQQt4uAEYBMPXVRV14Bxr6zDfVddggWrD+HS9q1RWFaLP47qin+uOiQ7p4eng8VV+MN/tuHh0d0Ulwmsicf7vT2nKzD2lXUAgBQfTVKBEJ+kAukb8Pv/2wJBkG5Tlyf+p+q7FxRmvPUXKALScmlkV4aABfp4YWNOMMt+PI1/rDiIhb8ehF4dEnD3O9KOx3I56fLE//Dr4Vl44dZ+rvfET7jc++5WHCquwiNjuuOV/IN4ZEx33DygozRdQf61L+ITtpoLkCTQ1uB65XnBEP+ZkRyH4kr5Gbfvf2+b5jUbSoFBY4PpYMcI8iWQoOP2xRuVP/S4mHuNf+exTzW2YsBuFzBlyWbFeX4Ua1BEf4i3/Zmv9sBkNEiamH0FlOLf4q4lWzC6ZypOnq/Fx/floE2raMWa8lYx7gBFfGP47x+O49FPvTv3B1NOPxw+hye/+Alzb+uHEV3b+f+CTrAGxY8DxVX406e7cKK0Fqv2l+BQSTVmfLJLVXACAI9+ugvHztVg+sVBjeQEcvcg10HROSQ44N2pLRDilOv99GZXsnJfCVbtL1FdPmJqakqUSDvu+S9PLStZAm1Sasxd87QPduDYuRo8+OF2FJbV4rsDZyWfK12wPiiQDi4WY3Yf+vl7i3G8tBYPf7TT9b8n6X6nbnttAf4mjensLTsOiMd74kCyXesYn+kFUouihtLv0tj1BLrvKV0kxdmzqOiUHgxfTbVazLx89Fy1z0kI1TxmLLa/qAp7TldKaqB8xa2e27BqfwkOFlfjX+uPAlA+j0aLOnOLA5T//XgGR89pM9ru5H8V4ERpLX79VoEm6YULA5QQE3d+UhLJJ0x2niyHzS6gtNqC7w+6L3b+HrcDfEfy56rl706VHCyuwuny4Ifat6s44UiX167Q1V5kKuusmjXv1FntsuNmnFQ5RHxMVGCHflA1KKLdRk0RBToehJhccOu5TvFvHq4J6lzrVti0YJpPJekGXIOi1MTj9vP5xk8z4JW+IPi8AdHiePS3j5kURpKttai/MfK1DqVNqLzguAYoBZPiTsmqbtIieL0INzbxhJia4y7YkWS1MP3jnThQXIVFHmOaKM0sLGaANsfK4ZIqVxNVsMR3L2rKU8tiVHsXe9kzKzRcKzDpzU2Svy9YbXh19WGFpaVc4+aoJBkHReV3Av1NGlODUl3nfSPg3QfF/fp8bWhHdvXKi2INSuNqKwKtgVHKh/j3/eP72xuVJyfx/UuDXZAM2OZJixHw/e1iRoWRZD/eqn6Wc1/7sdJ5wPl0mppaolpVN7QtJ0JhDUqIqdmVIjn8MwCv4ATw7IOiEPlr0JvcYAB+0GB8BPEJVk15anmQa/X7BdTUJ7NsILVW0f5G6/Jcn+h1MJ1kQx2gyAXR3n1QInecKW1/Y/edQJ8AVCqCUJ+DGmyCVyd4cbOhFk08/sbnkY6DEtz6fO1DSmk692vFGhTRoVgT4BOLzR0DFJHvD53DOwfDXyRK+/zuUxWY/fVeVIju9sJ1jrWo6CSrRTdCAwxBtcN7tgUHejHUshwbexfsFEgx1MsEjf4uMuKTq9XPsoc9BhDz1cTz3qYT+M/G415pSPsF+VwdAN+dM4+drcEzX+1BkcJw8HLf9dwPInliVwokgm3isdrsmJu3D+t89LmQo7SPNLapyelCvQ3PL9vr1YRmtdt99kFZf+is7Pu7T1Xi8+NGVF7wX+PlbxsEwXEMLFh1CF//eNpvenJ87UOe/cGcnPummjK+ZeEPfpeJ9A1tODFAETlVXocdpdoWiZq7NqUL6g2vrse/fziGF/L2udMLUwNksJ1kA2UweA8trobnkzBKE34paezYM2JanTACuauzyLRV+8uH+HO5mVrFPtkqHS9H0kVW9EeNpQFPLd2NWV/ukQTSgEcTj4oykgTiHvv58j1FeGfDcfxZYWZguX3UM26MZNW4chNPcHl6b9MJvLHuKJ5btjeg74Wqs67TwjWH8a/1x/CLxRslNQM2m+BzwEXnOEKebl28CWvPGPHyCv9jBvk7T9kFR8fXefkH8cbao37Tk09DuZyeWrpb9v1zVY4HFyJR09rUMUAR6Z7WOujvZrdrJfu+ml3JLgg+d7p9Re75K8K1b4qr25Wq3rUaLyi4GhTPACXQJp6AV6lIq7vPgAIUmd/EXzk6PxcEwe+jpJ6/ubQPiijQEaXj2cFP2knW/7YdEtXaKG2KeCRXSY1Qg0yNkmcNSojuPAU/xy+gvE/agqx9C+YpOUC5r0ew+fCk9Bta7XZJrZ3nTNP+HCz2Pw6Ov1Ft7YKgwWPdgX/nQHEVBEFQrGkNtKm8BVWgMEARu7S9fJChhtKOr+aA2HCkFIOfX4mvd8lXOzqTqLhgRcGx8Dx9oKoPigaNPIKg/gLfJt7seu1Zg6LmsUExffZBUb+sXIDhrxztgoDlu89g0HP5WL2/xOey72w4LnniSKkGRTqHjTRPgQ7Udqi42nWhV7rgJ8RGXVyvHRMWrMcfL466KVceXjPvhuDMXmcD+s1eheyZeZiXr3yXr7T5Wj/O7I/Sft+YIeudDB6T3tlEv8mqfSWSUXgdzS3q01YzdpC/Pkw2u4BfvbHJ5zL+BFN7UXHBig82F/qYBymwNNlJtoVKjDOjZ1JgdxK9OiQC8PH4nop96amlu1FWU48HP9zhc7mPtxT6/FxL9aJ5URQPfA1qUOwydxZpifJjVEy95lLX64zkOGk6gY6DInrd2M3Q6iLT2EDHX61Ig13A/e9tx/laK34+73/a+wc/FFW7K/RBEdegeNbq+BrqXq42o8rSgKLKOq/viiXGOoLUHYXl2HemEt/sLoIgCLLV+55vyaXZrnXjBjbcctbo2u4Fq5Snvgh13w+1lAJFLYLszinxkout+LiY+flPkmXtghDQOtUEKP6aeIor61QN++BLsOW0s7DcxxNUgaXVkmpQ+Jixh/t72XHltaOR2CoW52vrMWyOYyr0xXcOwhWXtkPrmChU1jUgISYKlgY7DhZX4eaFP0iqTuusNhQcK8Pw7BRNm2SqZB6lDJW1MmOiWBps2HTUsV1mk1E2cLn8khRsOupdy/OLwZ3wmczEeI6Li7uQhnRug0/+kIN7392KVRfv8g/NGQ+bXUCs2YTObVvh3ne3SkZCBaQnXufr6Cj5PDrXqxVfJ62bB2Tgy53yNWN/HNUV91/dFf2fdTx+7BnkivejWLP/x4Ll+qVI8ilzMZx1Q2/8YnAntIo2wS4Ab6w9gn9crAlQaqLZ9XM5+nVKAiC9KHj2MfDV7Gb1CErbtY7BuWoLlvxwHDPH98S+M5WQ4wxQJGnZ5AOU3acrcLikGpemOppuxcFAVko81vx5lGt8jugoI2x2Af9cdcjVP2HxnYMU+0Y4Vat8WtkmCKhvsGPj0VIM7dIG8dFRsNrsWHNAviartNqCA0VVyOnaVrYJwNf+O6xLCtq0MuPbPY6pOPY/dx2GzVmJyroG1/d2n6pAfLQJWSnx2HCkFOUX5Ad4PPj8eBgMjmkoEmKi8If3trmm+EhNiMH3j1+DDYdLcfc7WxBrNkkunr4Cd7sQWE2ArzlwnPwF6GrW9+XUK9AjPQEmowGWBrtrGPrnlu3FOxuOBxQc/PTMWHy0+STm5O1Dg13wOv5Kqy04UFyleP748ZmxSIiJQpWlAXFmE/658hBeW3NYOtGhXcCGI6Xo2zERyfHajCKuJwxQPBgMjpoUs8mI1AT3TJjtE2KRcPHkmBTn+D8u2uTqCyHeaZ75ag8+2nIStwc4uZk/SnPVhMIaUY9053P8zy/bh/9sOoFbB3ZEP4UZcM2iwGFI5zbYeuI8AKCtwhD8dkHa/h1rNsFoNEgONrPJCOf12XnC8DWMufPu3FefA20HalM+MTr3FTld2sYjXjLTrPTzJ/77I5buPI07L8/C87f0gz/+TtByd3AxUUZJHsUjzIqJv/rU0t24pmcqOibHeQQoyk08nuXtWXPQMz0B6w9b8Oa6o0hPjMVb3x+TzUfri0084hvqC/U22UD0g4JCfFBQiMNzxiPKZJT8TlFGw8W7cgMSRPtsrGh8GLPJfwVzTYO6+jdBEDAv/yAWrz2C3F6p+NeUoViw6pBi8Dr+n9+jpMqC1ycPwoR+HbzT87GuKJMBw7PbugKUWLPJtS02u+PCeMOr6wEAj47rITuxnpNzlNOkOMf/4huD1jFRiIkyufYZm11aK+KvtiGQmkejBjUoamo/UlpFu24GxL+/czj6QM4bCbFmRF3sa2O12b22d9z8730ODeAMxp3/x0V75+HTbSfx+H9/wqWprbFyxtWq89ZUsInHj7/94jI8MKorBmUly37uDOzF+95HWxwD/3wqU2MQDOc65AKUF2/zf+FqrPKLT1f8Z9MJAMAXO04pzopsNBjw6f05mDQ0E09e38v1fqLChdruUYPi3Fala4PRVd4eHSBFf7vGHfA5ZoH7dWNDFc8T3zU92rtet46RvwfomByH2wZ1ks4P4pHO0osXr/c2qWva89ePQO4EHe0xomyUUSFA8SilQ8WOzpD1os6plgbp/im+Xnhum2eA0kYUwPrqy+HMr3hbaq0NPi9OJy6Oriu5QKiIK8Q37Vd3d/+mibGB39fZ7MA7GxxB18p9jlqT9y4eT3JKLs5uu2JPUcDrMhoM+G1OZ9w1ogsW3zkYgLsjps0u4KSoee+t7wN7mkUcKDhfO2s3bB6dUP01XwXSvKWmBsVfgKKmn41SICR3IyqmdDMUdfFE1mATPJo8BZ/ByeI7BynmQbyqry72WzxcEvhkmk0BAxQ/bh+Siceu66nY09q90yjsuFoMQHQxCc8q9Cu7tXNVX4dSqcz8Pkp3I0YDMLRLCl6ceBlSE9x9SZRqEt4vKMT2wvNe75sULpTOE4ilwY4nv/gJ3/zk6HgnLue6Bhu2nTjvs3lNuc+Q8vsvfrPfFaQBwN+/PYD/23Dc687opYmXuV4nyDRJAMDzt/aF2WSU1ATYBQENNjueWvoTlonGaTAYgKU7TuH3/7dFJiX1PtrsPWKmZ1NZlMfTFf/ZeBwvL9/vVZaL1zoG9xPX2jjncHlz3RFM+fdmzPhkp+szu+B4JPmxz3bhuwMlXk08saJAyVc/AefvUyeqMamtt8mOC+PkDKbEVexKlzvxYS4+5n85JNP1upVC0OmL3PGiZk6mpTtPY3vhedRZbfjzp7vw5Bc/YefJcry7UTm4MRgcF8ZnbuqD6/qmA3AH/J77fXmAI+qKdw9n0ODsH3L0bI1kLhx/4wP99as9kr89L/LOfcy5DkEQ8NLy/ZIxd/6x4gDe+cER+Mk9ySXmr2M4oBwIOX+rI2drMOPjna5xgk6UOv4WP10m5px0cvmeIsl51F9tzqCsNjJ5cPz/2bafUXBU3eCW1ZYGPKrwaL7Tsh9P4+mv9sJ5eHzz0xnM/PynkA4voRabeBrJqHDgO2nZB6XG48QdZTS4IvRQCixAcR/g4rtzX00dP8pMQz62Txo+3Fzo9T1n+idKa3Gi1FGFf/zF6yV3FXVWOyYu2qC4PgCK1SaCIP/49N4zla4T5p3Ds3CopBqvrXEMK//p/TmSZcVVw+ImHMkyF3ccg8EAgwGuGaA/33EK720qlNSaxJlNPiebVOuVld41E541KOLOiAYDMOtLx0UkM0XaKXnT0TLU1jd49UGps9rwQt5+r/XYBAFvfX8Un2z9GZ9s/RkFT46WfK6mjw3grrERB+sX6m0+x3U5WeaoMRDnVSk4uLJbO8xfeQhmk0GyTLxoxtkubVvJzoPki1zgq/bx0tte34B//XaIqw+X58SPnuQ6lJpEN1KNeaRYrgZFaTsC7Txeb7Mj1ugo5xpLA178xr0fmYwGyZQcd17eGUfP1bimdpgyogssGlxQFe6LXPvC+sOOAGzDkVJsenI0Hnh/O/acrsTSnadkv6c0K7a/spE7r4v3x1+9uQnHX7zeZxoAsPi7I35r8qd9sMORflcDboR7qoO+HRMxeXhnv+sIJQYojSRX7SYW6MBqVptdse3b884yymTUfFp4OaUyVZFKzSfik1WMyX1SV2rqUPr+NT1S8dn9ObikvbSGSGlzxRcAf51FAd+TpgkXnzAQ4KiajYs2SUbBrLI0SO48PWu2xLUQsQp9OsTLGA0G2C6OpXFcZvbSOLMpZP2PPGtQzKIztPipHLkO2tV1DZLAoNrSoNiR22qzS54c8rw7Uzt5obvDtnT8FV93e86yE5ehUoAyuHMKPn9gBDLbxGO/aPwhs9GI7/48CpYGO+bLBHr+yB0vgRy65SpGUnWSqwUwiM5TjdmXxOcb566j9IRNoE+81FltMJuMMBkNXuc6k9EgyXflhQbJ/Es1CkFqz/QEjOuTjn/6eMJKsh7FGhTp386nzfacduwjSpuqdC73VzshV6Zqh0sRX0NOlXs/recYk0XwyluFxy6mNGpzODFAaSS5PhFRRoNoUKzA0uv712/x6f05uKxTstdnXgGK0aCqI19jlV2sQTEZDa6TjlKbq/i4Et+dq71DFh+DQ7qkeKev4mRYp2JOF8WAUhBwz7vbsONis1NtvQ2f3T9C0pRRWl0vqb7+3TvSphfxb6K03WZJgALYAPx3+ym8LjMvkmcth5bMPmpQxBcAuf4CVZYGSbk8+tmPGJbt/ZsBjqYE8VNcnump3UbniV0cFNbW+wlQrA0X/3d/x9fJ3lm9frBYfDE2oIvCYIxq2OyC17lAzaOzTh9tVj/EgFyNhnNdNrvgc8h5f8R5dl7MlTYj0CaCMa+sQ2pCDL6eNtIr0DUZDJKO0OdqLJL0q+vk+yHldG3r6lyqhtJvonTe8fWkIODdZOq0cI33cS75nlwtmMx7nvvUgaIq3LxwPX4/8hL8eVwP2cD/kY93Yt2hc1g542qkKDy8oBfsg9JIrjsT0RVPvFMGMoEb4LgznJu3X1Ij4DzfeM7YajIaFA8ALdXW21Bb3yC5u1CqohQfRLFmI0b1aI/LL0lRHGnXk7+7BLk7X5tdkAQcFquaAEW+P4JdcLRVn6+14nytFZYGO7YcL5OUfVmNRXKB9Ox8Jw5QlGoGxH1snNv00nLvphEgtHNv+OqDIh4ZVq5fSFVdg9e2b1Y5kKBcbaAarhoUSRNPg+KM247Pba7lnNQ0r4gXaexxJnejoqYPipPzaTg15K6lzvecj1UHS5xn50VbaTsC3W/PVlmw53QlSmvqZWtQpMdgPapEy1TVWWUDFKPBoKqDrWt5hUBEKYkYP/utUqdzcf8a2e/J7G9q9pe/frUbdVa7q/lZLvBfuvM0ymrqsXTHKa/aX/G0I6G/svjHGpRGcvfuBlbtK0bHNnGSxy2DubbU1kvvTJ0nt8o6mT4oYWjiARy1BibnrT6gONCX+CAyGAx45+5hABwnHy3InWxOl1/Al6I24B8O+59AbfOxMry9/hiq6qySYKukyrtas7TGIukLc7q8TvHxUMAzSJO/exP/bv5OPCUalZ0ctU/xyPl4y0nFx8f98Rw1We3syluOn4fVZpcEJD/+XIFtPi7ge05XYuXeYsmFWc3axL+LZ9+cQB07VyPJ88myWtnqdy3I3Wk7L7wHiquw7qD8pHaBpm00+A5Q1qs4DuV8svUkiiulx6HJCFRZ3G0QZyrqsEy0Dy1ae0Q2H0ZDYL+Xv06yYoeKqxzHj4/DM9jzs9xx6JnUfzadkAQYNruAkkp3Zs7X1PvczzccKZXUeB6pNOCHI+7Ot6G7LVKPAUojOXeaKksD7vm/rZqkWVtvk70jrLZIGwlNRmNYOskCjjsWNQeb0slAbXW2v6Xk0n/oox2SO/nNx/3fxe85XYk9p70nWpObRKysph6pCe7g8Nmv9+BctfzAVp7Sk2Jl3xfXsoQpxpTlWYMSSLPDhwE0O3h6Y520nAPZj/9vw3HJiVmuWUxs24nz+P270mNTTRwmLgnxvh/MFA+zPSb1u37B9wGnoZZcLYDzAvuXL+QntFOdtjho83iKx9PRs979qdSQG5fF6FGD8uTnP0lqWT7fLt9J1WAwBLRPKy0rF7iMeWUdMhSOb6dga97ksuFZ6zfLY3LCOqtN8kDDQx/tkIzl5WnlvmKs3Ffs+ntfuRG//88OSXqRxiaeRlJT7RbIAQIAx0trUGtx7xwCHCNleg6EZTYZYA5XDUqNRVU/gUAOcDn+qt7lyntHYbmqtNX48VSF13vnqutRVWeV/O3Pq3cMxKwbeqNHWoLs556dZCPFuwYlMnkJZL2r9pV4HQuBUhNkiC/0gR7D/njWhmpJ7lgLpJnDZ9qSp3gu/h+GXcYAg6TM1A5ZbzAEdnwp/c5KSfjLR7B9BOXOg/62o8bSgApRZ+rvD52TnLcC1dhpAbTAGpRGUjPC4Z/Gdsee05WSybJ8sdoE3Pl2gevvQ8XVuGXhD17LmcL0mDEA/O4ddbVDSgeR2pYDf6Wp9YXC0y7RBHlO+XuLJRPnqXFj/wyfn4ufllGzD4WK12PGYejTpCYfvmw8WopDjRyYSk2Ri5cJpOkr0uTvvrVJ2yQTtIVj//1s+ynJuEq+fHjv5bjjLcekgEaDIaD8BdLEA/gPNLUM+P0lNflfBV7vbRQ12QTqw80n0alNvGQOtHBrOkedTqnZ/4Z2SUGrAHqSA9KRAS0NdtfjbJ3bxrvejwpTJ9lAKJ0I1d7F+O8kG2CGNKJVHxonk8dTPJHiOe29OUIX4kADgEA7n3tS10lWoQZFX4ecF7ljTavAXtJJ1k8fFK2p7YvVPc09NIHRENjxpTySrPo0xLS8gfRXznJBe1Uja0FC1U9KLdagNJKag3NolxTVtSfzfzUASfFm11M8raKjUCN6+qBfx2QMnbMSwMU+KEEeOddf1sGVp1d+1R+PfLxLdrkb+2e4OjRGm4x+53tRKg/NTpAe6Sy5eyjsdiGo/j/fP3YNblu0wSv4+MuEXpiTt0/y3jU92uO3I7rg7iWOR4o7Jsc16uAVN82Jy+zvt/fHn/2M/KglXwO1AcAl7VrhqMzYLHJ6pidgf5FjhM3sdq1wTOF7//1jDiYu2ih5zzNQ8uep63vhQFFV0NNJqLmmKnaSDWqNbv/67RAcL63B8/9z7GOZKXGuweTEfj08y++gbHLkLrJyx9+7vxuGk+drXf1SMlPisGjyYNc8PXLE11tnmlo1H/3nnmGIMhox7YPtrr4Uo7q3w3cHHZ1tB2Ul44nxvZAQG4WtJ867+mBMGprpml4EANq2dte0GA0GTQIocZl2aRuP2Tf3hf3i0P6+ape1rEHxtRnzftkfyfFmGGDAJe1b4VT5BVcfktPldXhK1F/lpv4Z+PXwLMzN24ddFwfJ/PdvB2Hzli0YOnQojCYTYkxG1DXYvGaNDzfWoDSSeKe57OIMr2LOoehbxairQbmsUxKu6ZGKa3um4dqeaRh+SVvX62t7pqG9qJrTbDL4vfNUGiDt8kvaul5f3T0V7VrLV59mJLs7WY3pneY3/4FWkXpT3wflkvatcE2PVIzu5T9fctISY2XnWJoyoovXe/06On4Xpxv6e0/gFgjxnZW4t/y1PVO9FwZCNl6BeDA9wLtT3+DObVSn9QvR5Jg3XiZfPlFGAwZ39h4rJdCawFE9UjFUZpwctdSsTdrEo92FJrd3mmQfG5gpX8b3XnmJzzwp8TVQm9hV3dtjQGay6+/YKBP6KkwCKpe287UW8cnArGRc2a09crq2xTWiY2BU93au11kp8RiWnYJeHRIlE7FOGpbleu054rEB2gRQ4vPOqB6puKp7e4y6eJ72Rcsabl/n0NsGdcK1PdNwTc9UdG7bCiO6tnNdMwZ6nOPG9E7D5Ze0lZTbld3aoVeygKu6tcM1PVIx4lLH93umJ2qW/2AwQGkk8U7TvnUM/nC1+6RySbtWWDBpIAAgPlq5sspsMuC6PukY3zdd9XghgOMOxt+dp1Jg1C21NW64rANye6WhTbzyMPTi2V0BYNHkQbikfSvZYAxQ7mui+imeAJp4xMPI/+P2/pLl1FxQoqOMkseA27WOwV0juiA6yoi3pwxB1/atMKZ3GnqmJ7guKDPH90TP9AT8fuQlePWOgUHPhSQ+cYnH9FAaGl/tSLz+eAaZ5ihpOXmWW6zZ5JpwzpmP4aJHE6/q3h5d27fCv+8agnF9HPvvoKxkTBnRBX07JqJda2lg5Rw06/XJ3pOhBSIhNgrj+6WjX8ck5PZKRdf2rXBlt3b+v3iRmiYepRoUX664tK3Pz+denNzTbDLinpHZGNy5DXK6en/HbDJIbkYA4N4rs/HRfTley3qSOwY9TxOv3jHQlQ8npZmspWkbvF77KpsFF9fjN11RWT90bTd0S22Nubf1k4wXFCc6h8aaTbhtYEeM6tEe/TomYdo1l6Jd6xj8aUwPSbrOqSQaSzLtgccx+sZvBnsu7qLlQJqev+uIrm3Rq0MiZozp7vN7nkMdOPN/y4COGJSVjHuvzNYsj1pjE08jiXfcNq2iMXN8L8wc38trOV8XmNgoExb72MmVRBkNfk+0SncP8dEmvPZr/xeJKI+29/H9OmB8vw44V23BkOdXei2vPKmi8jrETUz+ziXi8o4THXgTB3dC19TWrs7ESXFm2TmEPIkHUvvs/hzXaKGje6XJ1sz84equ+MPVXQE4mr9u7J+B4S+sRHFlYH0ilIaUF+fHbDK4Hp/WIkCJjzbhzss7I3+v+9FCr4HaPM6CsWYjruubLpn344sdP6Pg4oBs/3f3UMlvvubPo1yvlz14JSpqreg/e4UkDwAwoV8H3DEsEx/KTGCoRNz0lhAbhfjoKHz94EjX5x9tLpRMVueLmnhDMlCbZBwU+S+P75uORXcORpcn/gfAUcOX3bYVVl2cpG7ZgyMlNRSzbugNAJIxfJz+OWmgZALF//5xhKs26x+398effDQDyo8H4n7v+8euQWZKvNd2ed6MyJGrQRGnndsr1TVbc0yUETf1z8DC1Ydx4OKEjUrEOc5qG4/8GVcDAD7YdNz1vmdgMO9XA1yv/zyuB/48ThqcOPOmRfOyOIk4jwv+uD6O4+O6+etcTZxO4vJdNHkQPghgH/XOgzut52/pizsvVzdPjmeA4rxJiIs24fMHrgAAWK3BP+0TSqxBaSTxjuurJqJbmvKddozKYeA9Kc34K6Y0y6vSnbon8R3TQFF1sFJnSqWAyFcgJb7L8FuDIspPnEetlHjQMF+TE4qJD95gT2RXdmsf8HfE6xIPFGeQ3Km5t691bOMDlJgoo8eF1nubPf+OkbloZbdz78t+Hwv32E3EzWSBTgMhLifPiwQQ2MBSqjrJii6bwewb0SajZD1KQ67LBRRmk3SMI/ETLJ7TE6hJT0x87KuZlkGStuxTPO7PxWk4+zcNzfbfTKiUZ/H+qvacJU1Xm068RhW/o9w+FSWpATLJ7rdqidP3vLHwxXM0a1+1+XrTdHKqU+KdpnWM8kVxRNd2kjl6xJQmlPNHTfum0nwYnhd3JSajAf97aCTWHjyL3+Z0cb3v2TTgFMw1Xnyw+RufQhwAxXsc7G1FzQlqq1bFF8kkHwGmL3+9sbdknhk11HQKbRVtco1rkKBBDUp0lFFyofW8gALeAYXcvjkgMxlzbu2LzDbxXp95Ep/Y+3dKwlMXaw2CIZ6FV+5iIBfwPHad46765eXSwb/U7KbS+bX870+eWYoyGSSdSpUusA0ysws7949//XYIyi9YXTUeAPyOfST3sbhzu/gCJT6HqJmw0TPABaTHpDhAcab3+HU9cbbKgm/3uGvuvChsksnoPzDwxWjUpolHnIbSBV6u3MXlGx8dFVSQJZd+II/lKzXxNAWsQWkk8U7jryOsUpWc2on0PKnpZ6E0kZXaSD7KaECfjCQ8MOpSyUGhdMJWO4W8mPiOMNg+KI6/3ScOpdmWPYmfkAo2CEiIDTywUVNOrWK0rUGJjjJKAiO50Yo9i01p35w8vDOu6u6/5kh8gZkxtoekqUpcBGoGTvM395zczOGDs9rggVGXel081NxVi8tC/Fi42j3cbDJKprqIN8v/htYG73w7j7Xc3mmSzsfOdH2Re4pHPD+VOOhUMy2DmOw4KKKyFJ+TnPlMiDXjodHdfKarVKbi9IKtfdCmicd/TY7cPiUNUExBBVly6QfStyXWI5hpTC1OuDFAaSTxTtPKzwVOULhoqp1q3pO/Ay/KaFB8LNjzIAt0/BKlGoBgq8L95cP9ueiEKnOwd2rj6MU/8lJ1HSZrROMEBBNchYJzfozf5rgDWi36oESbjKqaBcWCbX50Ehep54lSTC648HT9ZekAgF4d5J8skDu8nBcEz4pLNT+15wzlgXwXcDSDioNApYuTVaYGxdc5wV/NqVwzq6VBPJOz6EIn2h/U1OrJDXWvNOKu+IbG33lBzfAEgdz5Ox82uK5vuiZNPGpqcuQ2UVy+MVFGxCkEqWqI0w/ksXzPsVgaEySFW0QDlIULF6JLly6IjY3F8OHDsXnz5khmJygBBSgK7wcboPirQYk1mxRnFFW7TuWhn7Vr4jEH0sTjp1Nf/iNXY/NfRksej/alxhL5+SY8vf/74djwxLWSx3G1qUEx+d1nPC/ywe6bTuKLpWewE2gflEtTE7Bp5mgsnTpC9nO55AK52/UkDlDUBN6e2xNlMkie0FK6qMjVcvq6Qw6qBkWhJlUc7KgJXqVD3TtrUOQ/D6SfhNLPIalBCaDvxPLpV6LgydHo2r61JgGKOAmlGgjZPiii8jWbjI1qXpEElgEel58/4D5m2MSjwscff4wZM2bgr3/9K7Zv347+/ftj3LhxKCkpiVSWgiLeJ/2NFqt0F5yWqO5i6pWen6YFX31bPA8mpVlpA71zD+ZkID5xJ8b5Xp/4ZCj3WGRctAmpCbGqm120enzXeTJuzLnQ+V2zyYiM5DhJuWjRByUtMcbvhTbao29RsM2PTuL9obVHE2iCKOhSe2eZnhQr23EXkK+hdF7UPB93VleD4n6tZiwNz47Z7VrHoK7Bd78ZQL4jsq9qeH8Bity+ckFh4jdxWs6XvjqYi4MfZ/Ag1+wDBNZPQrEGRdxEEsC+GBNlcp1XtRjCRk0Tj9w51LMJLV7leFj+8hBI8AdIz3NqntbSi4gFKPPmzcO9996Lu+++G71798bixYsRHx+Pf//735HKUlACqUFxPp4qlhRnxjM39QlonY9f1xNXd2+Pmy7O9/LkhJ6S4Z2dPGeyzO2VitxeaXjwWu+5FeQeOb66e3ufc8o8MKorxvROw2jRwErBzMuRnhSLf9zeH8OyU/Dnsd6PCoqJ00/3EdjdNqgjrureHo9f19P13uThWa7ZR9/67RAAwF+u74UhndtgUSPH5fjk/hwMykrGf//ofXc/y6Nj6MMK7fGe2yM+uYk7SQLAozKPVM65ta9sujcPyMCQzm0w59Z+kjtScROSU9f2rdFRNHqkrzJWw2g04K4RXXDrwI7o2l66j067phuGZ6fgpYn9kNsrFdf2TFUc0+G1Xw/0uy65GhlnYLLkrmGSQfnUNOeJAx7xfuf5zRdu6e3Ydy/+Jv+cNABDu7TBU9f3ws9ltX7Xc9ugjriyWzvXYHzXX9bB5/g6vpp4ci5pi9+NzPZ6v7xW/jFS8f7gHIH1/d8Px6CsZHx6v/eYKxdEfbacA6qJz4HiAR/F47j4qxlV+jnEgaHSzODhIK6JVsrH7Fu8j7/46ChMHp6F2wd3QnpSLMb0SkPvDom4/rIOXuP2XNK+FdISY/DyLy7zm59Ab2q7pbbGjf0zcM/I7IjO/RWoiDzFU19fj23btmHmzJmu94xGI3Jzc7Fx40av5S0WCywW9zgTlZWOeWmsVqumz2870wokTZuoj0eM0fd346OACX3TkLfb0Zv90HNjvdatxu+vyMLvr8gCBBusVhvuzsnCjf3SkPPSWslyl7SLx94zjrJKS4zBol8PUFxf5zYxOPTcWHSb5R6z4l+/Gehah5xHRrsDLuf3BLs94N+kbZwJN12WhpsuS5PNm5itwX2C7JISq7isEcDbvxkIQRDw0vL9AIABnRLxzA3ugMVqtSI9wYwPfz/U73r96ZPeCh/fO0w2nd8O74Tnlu0FADw5vgfuHtFZdl2XtGsleV+wu8u9e3t3gPL3iX1x84AMybT0zn3JOWy52LheqRjT23Ex2VPhvmDOGN1VNh9zbumNu97ZBgDI9lHGav1lvCPoaGiQzgsSbwbe+50jUBTsNrwxeQAAYF7+QclybVtFY1yv9n7z0WDz3k+Ngh1Wqx090+Lx8b3DXPupQRD8pldvdedXvKzdo9n0lsvSXCObWq1WTOiTigl9HOUtHotHaX0mOIYal2xLg/IcKpZ65Xy/e/dgAMrbZjRI8yEOwjq3cfzWPVLjFfflw6LxTK7t3hZWqxU2US1Rtmgk1y4pca7vNzR45+fm/h3w5a4zzozI5vlslXsKgC5tYoLaF38uc0+5sO+ZXESZjLj9zQLsPFkBAHjo2q5YsPoIAOXf6OhZ93antoqSXS61VRRMRoMrmHEu4zznWK1WdEmJxZcPXO76jnN/vOeKznjiOvdNh1z6p8rcc+10TDQHXBbzftFXMe1grn3BCmQdEQlQzp07B5vNhrQ06UBYaWlp2L9/v9fyc+fOxbPPPuv1/ooVKxAf7/9Rx0Dl5+erXtaxLzqKcfeW9Sjc5Xt5U6UBgAlRBgF5eXlB59HThQZ3Ppza1Z1CotmISqsB/RMuqFpfh3gTztQacEWaPcD8OdZtKTqMvLxDPpfx9M0336hei+Nc6Ejn590FyDvgc3HJesuP7ETe6Z2q16UtRx6sp/YgL2+P7GeZQomkzCvr3Z/t27LO9fr8xe3olWzEvnIjurR270tpcSYUX5DeIR3fsxV5xx2vS+vcaX63coXsnevJavcyG75TfyxoR7qfDEyuU7UvllU4ji2nOJPcMeZIO8FS7DfNc6KyEi8bU+VeT5xJ8Hm+6JZoxKFKIy5LCfR4Unam1p0vMV/rcO4XV6TKLeNIq/LYLuQV+T6BNZS6t92ZjvgcePbAVtfruPJjyMs76khbtC87pVz42ZVWUn2JbN4LRb/pqvxvfeZNyekydxorvl0OAEgXHO+ZDAJqTx302iZP58+60/B1vuqXbMTOMiM6xqs9vzvKJLrsKPLyjvhc8pTMdmgtkGtfsGpr/dcqOjWJcVBmzpyJGTNmuP6urKxEZmYmxo4di8RE7eYKsFqtyM/Px5gxY2A2q390tE3PUtRZbZKmDiXXWm0YvOM0ru7WzvXEiVaSu5UgLtqEpDgz9hVVYeLADNx5/gLWHzqHXw7ppOrRtAEjLmDV/rO4fXDHgPofXDKoCj+dqsQvBmUoVp9n9CvH0bNVqD3xE+rb94LBaMRV3dqha3v1w/sDQGL3szDA0QSlRnKPUtRYbK5ahEho17sMpdX1GN833euzSwdXYcfJCvxycEevskvuVow28WYMz05Bu16lqK5zb8cV11jx9Y9nML5vuqv9e8iVFvz7h+NIjjXBcuYQsnv1w00DpI+ptutehLatozHMx1w26d1Po0u7VuivMKVBKDnLqndGIr4/dA6/UrnvTgDQZedpdEyOw8HiKlzTo73XZGd9htfihyOl+MWgjqr6SKT2KEZKK7OkrMba7OhWcBKFpTXo2nDc5/li6JUWfLOnGLcNzNCsvxMAtO92BrX1NpiMBozq3g7f7CnGrQMyJP16xPqPuIDV+8/il4M7enVWbtNTul/5MrrBjv7bT+GKS9uis6jZManHOVhtAq7t0R59hlbg+Lka3DxA2jzcvof7/LS/qAq3DczA5LILPn+P3Pp6lFlW4Rejh+OyzODmXrrOLiB7+ykMyExC97QER7oNduRsP4WRl7ZFZps4dN1xGr07JKC3whNiY212dN9+GkO7tPF5vrryWiu+3HUG1/VJU5zfTKzjZfJlpbQdXbafQv9OSeiRnuB3+UAEe+0LhrMFRA2DoPTsawjV19cjPj4en332GW655RbX+1OmTEF5eTm+/PJLn9+vrKxEUlISKioqNA9Q8vLyMGHChJD/SC0Vyzg8WM6hxzIOPZZxeISznAO5fkekk2x0dDQGDx6MVatWud6z2+1YtWoVcnL8T4ZFREREzVvEmnhmzJiBKVOmYMiQIRg2bBjmz5+Pmpoa3H333ZHKEhEREelExAKUX/3qVzh79iyefvppFBUVYcCAAVi+fLlXx1kiIiJqeSLaSXbatGmYNm1aJLNAREREOsS5eIiIiEh3GKAQERGR7jBAISIiIt1hgEJERES6wwCFiIiIdIcBChEREekOAxQiIiLSHQYoREREpDsMUIiIiEh3IjqSbLCcEzAHMm2zGlarFbW1taisrOTMmSHCMg4PlnPosYxDj2UcHuEsZ+d123kd96VJBihVVVUAgMzMzAjnhIiIiAJVVVWFpKQkn8sYBDVhjM7Y7XacPn0aCQkJMBgMmqVbWVmJzMxMnDx5EomJiZqlS24s4/BgOYceyzj0WMbhEc5yFgQBVVVVyMjIgNHou5dJk6xBMRqN6NSpU8jST0xM5MEQYizj8GA5hx7LOPRYxuERrnL2V3PixE6yREREpDsMUIiIiEh3GKCIxMTE4K9//StiYmIinZVmi2UcHizn0GMZhx7LODz0Ws5NspMsERERNW+sQSEiIiLdYYBCREREusMAhYiIiHSHAQoRERHpDgMUkYULF6JLly6IjY3F8OHDsXnz5khnqUmYO3cuhg4dioSEBKSmpuKWW27BgQMHJMvU1dVh6tSpaNu2LVq3bo2JEyeiuLhYskxhYSGuv/56xMfHIzU1FY8++igaGhrCuSlNxosvvgiDwYDp06e73mMZa+PUqVO488470bZtW8TFxaFfv37YunWr63NBEPD000+jQ4cOiIuLQ25uLg4dOiRJo6ysDJMnT0ZiYiKSk5Nxzz33oLq6Otyboks2mw2zZs1CdnY24uLi0LVrVzz33HOSuVlYxoFbt24dbrzxRmRkZMBgMGDp0qWSz7Uq0x9//BFXXnklYmNjkZmZiZdffjl0GyWQIAiC8NFHHwnR0dHCv//9b2HPnj3CvffeKyQnJwvFxcWRzprujRs3TliyZImwe/duYefOncKECROErKwsobq62rXM/fffL2RmZgqrVq0Stm7dKlx++eXCiBEjXJ83NDQIffv2FXJzc4UdO3YIeXl5Qrt27YSZM2dGYpN0bfPmzUKXLl2Eyy67THj44Ydd77OMG6+srEzo3LmzcNdddwkFBQXC0aNHhW+//VY4fPiwa5kXX3xRSEpKEpYuXSrs2rVLuOmmm4Ts7GzhwoULrmWuu+46oX///sKmTZuE77//Xrj00kuFO+64IxKbpDtz5swR2rZtKyxbtkw4duyY8OmnnwqtW7cW/vnPf7qWYRkHLi8vT/jLX/4ifP755wIA4YsvvpB8rkWZVlRUCGlpacLkyZOF3bt3Cx9++KEQFxcnvPHGGyHZJgYoFw0bNkyYOnWq62+bzSZkZGQIc+fOjWCumqaSkhIBgLB27VpBEAShvLxcMJvNwqeffupaZt++fQIAYePGjYIgOA4uo9EoFBUVuZZZtGiRkJiYKFgslvBugI5VVVUJ3bp1E/Lz84Wrr77aFaCwjLXx+OOPCyNHjlT83G63C+np6cLf/vY313vl5eVCTEyM8OGHHwqCIAh79+4VAAhbtmxxLfPNN98IBoNBOHXqVOgy30Rcf/31wu9+9zvJe7fddpswefJkQRBYxlrwDFC0KtPXX39daNOmjeR88fjjjws9evQIyXawiQdAfX09tm3bhtzcXNd7RqMRubm52LhxYwRz1jRVVFQAAFJSUgAA27Ztg9VqlZRvz549kZWV5SrfjRs3ol+/fkhLS3MtM27cOFRWVmLPnj1hzL2+TZ06Fddff72kLAGWsVa++uorDBkyBLfffjtSU1MxcOBAvPXWW67Pjx07hqKiIkk5JyUlYfjw4ZJyTk5OxpAhQ1zL5Obmwmg0oqCgIHwbo1MjRozAqlWrcPDgQQDArl27sH79eowfPx4AyzgUtCrTjRs34qqrrkJ0dLRrmXHjxuHAgQM4f/685vlukpMFau3cuXOw2WySEzcApKWlYf/+/RHKVdNkt9sxffp0XHHFFejbty8AoKioCNHR0UhOTpYsm5aWhqKiItcycuXv/IyAjz76CNu3b8eWLVu8PmMZa+Po0aNYtGgRZsyYgSeffBJbtmzBQw89hOjoaEyZMsVVTnLlKC7n1NRUyedRUVFISUlhOQN44oknUFlZiZ49e8JkMsFms2HOnDmYPHkyALCMQ0CrMi0qKkJ2drZXGs7P2rRpo2m+GaCQpqZOnYrdu3dj/fr1kc5Ks3Ly5Ek8/PDDyM/PR2xsbKSz02zZ7XYMGTIEL7zwAgBg4MCB2L17NxYvXowpU6ZEOHfNwyeffIL3338fH3zwAfr06YOdO3di+vTpyMjIYBmTBJt4ALRr1w4mk8nriYfi4mKkp6dHKFdNz7Rp07Bs2TKsWbMGnTp1cr2fnp6O+vp6lJeXS5YXl296erps+Ts/a+m2bduGkpISDBo0CFFRUYiKisLatWuxYMECREVFIS0tjWWsgQ4dOqB3796S93r16oXCwkIA7nLyda5IT09HSUmJ5POGhgaUlZWxnAE8+uijeOKJJzBp0iT069cPv/nNb/DII49g7ty5AFjGoaBVmYb7HMIABUB0dDQGDx6MVatWud6z2+1YtWoVcnJyIpizpkEQBEybNg1ffPEFVq9e7VUFOHjwYJjNZkn5HjhwAIWFha7yzcnJwU8//SQ5QPLz85GYmOh1wWiJRo8ejZ9++gk7d+50/RsyZAgmT57ses0ybrwrrrjC6xH5gwcPonPnzgCA7OxspKenS8q5srISBQUFknIuLy/Htm3bXMusXr0adrsdw4cPD8NW6FttbS2MRumlx2QywW63A2AZh4JWZZqTk4N169bBarW6lsnPz0ePHj00b94BwMeMnT766CMhJiZGeOedd4S9e/cK9913n5CcnCx54oHk/fGPfxSSkpKE7777Tjhz5ozrX21trWuZ+++/X8jKyhJWr14tbN26VcjJyRFycnJcnzsfgR07dqywc+dOYfny5UL79u35CKwP4qd4BIFlrIXNmzcLUVFRwpw5c4RDhw4J77//vhAfHy+89957rmVefPFFITk5Wfjyyy+FH3/8Ubj55ptlH9ccOHCgUFBQIKxfv17o1q1bi34EVmzKlClCx44dXY8Zf/7550K7du2Exx57zLUMyzhwVVVVwo4dO4QdO3YIAIR58+YJO3bsEE6cOCEIgjZlWl5eLqSlpQm/+c1vhN27dwsfffSREB8fz8eMw+HVV18VsrKyhOjoaGHYsGHCpk2bIp2lJgGA7L8lS5a4lrlw4YLwwAMPCG3atBHi4+OFW2+9VThz5owknePHjwvjx48X4uLihHbt2gl/+tOfBKvVGuataTo8AxSWsTa+/vproW/fvkJMTIzQs2dP4c0335R8brfbhVmzZglpaWlCTEyMMHr0aOHAgQOSZUpLS4U77rhDaN26tZCYmCjcfffdQlVVVTg3Q7cqKyuFhx9+WMjKyhJiY2OFSy65RPjLX/4ieXSVZRy4NWvWyJ6Hp0yZIgiCdmW6a9cuYeTIkUJMTIzQsWNH4cUXXwzZNhkEQTR8HxEREZEOsA8KERER6Q4DFCIiItIdBihERESkOwxQiIiISHcYoBAREZHuMEAhIiIi3WGAQkRERLrDAIWIiIh0hwEKERER6Q4DFCIiItIdBihERESkOwxQiIiISHf+H8y/htXS0Hh1AAAAAElFTkSuQmCC", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "a = dg['time'].diff()\n", + "plt.plot(a)\n", + "plt.grid()\n", + "plt.show()\n", + "plt.hist(a, bins=100)\n", + "plt.grid()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "c0c837d4-d569-42da-a274-4dbf13d41efa", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('{\"lati\": 49.223027547, \"long\": 16.580739947, \"alti\": NaN, \"rssi\": -1.950746802, \"snr\": 0.0, \"rttt\": NaN, \"temp\": 30.0, \"humi\": NaN, \"pres\": NaN, \"voc\": NaN}',\n", + " lati 49.223028\n", + " long 16.580740\n", + " alti NaN\n", + " rssi -1.950747\n", + " snr 0.000000\n", + " rttt NaN\n", + " temp 30.000000\n", + " humi NaN\n", + " pres NaN\n", + " voc NaN\n", + " Name: 50, dtype: float64)" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import json\n", + "s = dg.loc[50]\n", + "js = json.dumps(s.to_dict())\n", + "ss = pd.Series(json.loads(js))\n", + "js, sss" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/prilohy/web/mqttagregator.py b/prilohy/web/mqttagregator.py new file mode 100644 index 0000000..71c9ca5 --- /dev/null +++ b/prilohy/web/mqttagregator.py @@ -0,0 +1,50 @@ +import json +import time +import numpy as np +import paho.mqtt.client as mqtt +import pandas as pd +import plotly.express as px + + +cols = ['temp', 'pres', 'humi', 'gas', 'rsrp', 'rsrq', 'snr', 'enerest', 'band', 'txp', 'dlpl', 'ltemode'] + +#dg = pd.DataFrame(columns=['lati', 'long', 'alti', 'rssi', 'snr', 'rttt', 'temp', 'humi', 'pres', 'voc'], dtype=float) +dg = pd.DataFrame(columns=cols, dtype=float) + +def on_connect(client, userdata, flags, reason_code): + client.subscribe("nrf/dk/#") + + +inx = 0 +def on_message(client, userdata, msg): + global inx + col = msg.topic[msg.topic.rfind('/')+1:] + if col == 'temp': + inx += 1 + val = msg.payload.decode('utf-8') + val = float(val) + dg.at[inx, col] = val + +mqttc = mqtt.Client() +mqttc.on_connect = on_connect +mqttc.on_message = on_message + +mqttc.connect("mikehanus.xyz", 1883, 60) + +mqttc.loop_start() + + +while True: + time.sleep(1) + if len(dg.index) > 1: + with open('save.csv', 'a') as f: + for inx in dg.index[:-1]: + s = dg.loc[inx] + d = s.to_dict() + d['time'] = int(time.time()) + sj = json.dumps(d) + print(sj) + f.write(sj + '\n') + dg.drop(index=inx, inplace=True) + +mqttc.loop_stop() diff --git a/prilohy/web/mqttdummy.py b/prilohy/web/mqttdummy.py new file mode 100644 index 0000000..5dae09d --- /dev/null +++ b/prilohy/web/mqttdummy.py @@ -0,0 +1,80 @@ +#nrf/id/pid +# +#nrf/id/lati +#nff/id/long +#nrf/id/alti +# +#nrf/id/rssi +#nrf/id/snr +#nrf/id/rttt - last round trip transmit time +# +#nrf/id/temp - bme temperature +#nrf/id/humi - bme humidity +#nrf/id/pres - bme barometric pressure +#nrf/id/voc - bme gas + +#mosquitto_pub -h mikehanus.xyz -p 1883 -u agr -P ATenKureHubouTlackaVKomore -t 'agr/test' -m kkk + +#['pid', 'lati', 'long', 'alti', 'rssi', 'snr', 'rttt', 'temp', 'humi', 'pres', 'voc '] + +import time +import numpy as np +import paho.mqtt.client as mqtt + +def on_connect(client, userdata, flags, reason_code): + print(f"Connected with result code {reason_code}") + client.subscribe("nrf/#") + +def on_message(client, userdata, msg): + print(msg.topic+" "+str(msg.payload)) + +mqttc = mqtt.Client() +mqttc.on_connect = on_connect +mqttc.on_message = on_message + +mqttc.connect("localhost", 1883, 60) + +mqttc.loop_start() + +nrfid = "tst" +pid = 0 +lati = 49.224426 +long = 16.564100 +#long = 16.550100 +alti = 266 + +temp = 30 +humi = 50.36 +pres = 100.001 +voc = 3 + +rssi = 0 +snr = 0 + +def publish_pid(ppid, top, val): + mqttc.publish(top, f"{int(ppid)}@{val:0.9f}") + + +while True: + + x = (pid/20) * np.sin(pid/5) + y = (pid/30) * np.cos(pid/5) + + #mqttc.publish(f"nrf/{nrfid}/pid", pid) + + publish_pid(pid, f"nrf/{nrfid}/lati", lati + y/1000) + publish_pid(pid, f"nrf/{nrfid}/long", long + x/1000) + #publish_pid(pid, f"nrf/{nrfid}/alti", alti) + + publish_pid(pid, f"nrf/{nrfid}/temp", temp) + #publish_pid(pid, f"nrf/{nrfid}/humi", humi) + #publish_pid(pid, f"nrf/{nrfid}/pres", pres) + #publish_pid(pid, f"nrf/{nrfid}/voc", voc) + + publish_pid(pid, f"nrf/{nrfid}/rssi", rssi - ((x*x+y*y)**0.5) ) + publish_pid(pid, f"nrf/{nrfid}/snr", snr) + + pid += 1 + time.sleep(1) + +mqttc.loop_stop() diff --git a/prilohy/web/mqttschema.md b/prilohy/web/mqttschema.md new file mode 100644 index 0000000..6709576 --- /dev/null +++ b/prilohy/web/mqttschema.md @@ -0,0 +1,14 @@ +nrf/id/pid + +nrf/id/lati +nff/id/long +nrf/id/alti + +nrf/id/rssi +nrf/id/snr +nrf/id/rttt - last round trip transmit time + +nrf/id/temp - bme temperature +nrf/id/humi - bme humidity +nrf/id/pres - bme barometric pressure +nrf/id/voc - bme gas diff --git a/prilohy/web/nbiotgps.csv b/prilohy/web/nbiotgps.csv new file mode 100755 index 0000000..a5b8759 --- /dev/null +++ b/prilohy/web/nbiotgps.csv @@ -0,0 +1,135 @@ +time,lat,lon,elevation,accuracy,bearing,speed,satellites,provider,hdop,vdop,pdop,geoidheight,ageofdgpsdata,dgpsid,activity,battery,annotation,timestamp_ms,time_offset,distance,starttimestamp_ms,profile_name,battery_charging +2025-05-28T09:43:10.888Z,49.2278037,16.57512088,334.9830322265625,9.648001,156.2,0.38,42,gps,0.8,0.8,1.1,,,,,66,,1748425390888,2025-05-28T11:43:10.888+02:00,0.0,1748425387714,Default Profile,true 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"gas": 45519.0, "rsrp": 84.0, "rsrq": 28.0, "snr": 38.0, "enerest": 8.0, "band": 20.0, "txp": -1.0, "dlpl": 83.0, "ltemode": 2.0, "time": 1748433535} +{"temp": 28.94, "pres": 96.313, "humi": 38.109, "gas": 46073.0, "rsrp": 84.0, "rsrq": 28.0, "snr": 38.0, "enerest": 8.0, "band": 20.0, "txp": -1.0, "dlpl": 83.0, "ltemode": 2.0, "time": 1748433540} +{"temp": 28.88, "pres": 96.313, "humi": 38.11, "gas": 46589.0, "rsrp": 84.0, "rsrq": 28.0, "snr": 37.0, "enerest": 8.0, "band": 20.0, "txp": -1.0, "dlpl": 84.0, "ltemode": 2.0, "time": 1748433545} +{"temp": 28.84, "pres": 96.309, "humi": 38.126, "gas": 46799.0, "rsrp": 84.0, "rsrq": 28.0, "snr": 38.0, "enerest": 8.0, "band": 20.0, "txp": -1.0, "dlpl": 83.0, "ltemode": 2.0, "time": 1748433550} +{"temp": 28.84, "pres": 96.311, "humi": 38.266, "gas": 46694.0, "rsrp": 84.0, "rsrq": 29.0, "snr": 38.0, "enerest": 8.0, "band": 20.0, "txp": -1.0, "dlpl": 83.0, "ltemode": 2.0, "time": 1748433558} +{"temp": 28.85, "pres": 96.313, "humi": 38.307, "gas": 46874.0, "rsrp": 84.0, "rsrq": 30.0, "snr": 40.0, "enerest": 8.0, "band": 20.0, "txp": -1.0, "dlpl": 83.0, "ltemode": 2.0, "time": 1748433563} +{"temp": 28.78, "pres": 96.311, "humi": 38.683, "gas": 43794.0, "rsrp": 83.0, "rsrq": 31.0, "snr": 40.0, "enerest": 8.0, "band": 20.0, "txp": -1.0, "dlpl": 84.0, "ltemode": 2.0, "time": 1748433568} +{"temp": 28.75, "pres": 96.311, "humi": 38.983, "gas": 45026.0, "rsrp": 84.0, "rsrq": 31.0, "snr": 40.0, "enerest": 8.0, "band": 20.0, "txp": 0.0, "dlpl": 84.0, "ltemode": 2.0, "time": 1748433573} diff --git a/prilohy/web/server.py b/prilohy/web/server.py new file mode 100644 index 0000000..2007c82 --- /dev/null +++ b/prilohy/web/server.py @@ -0,0 +1,67 @@ +import dash +import time +import sys +import json +from dash import dcc +from dash import html +from dash import dash_table +import pandas as pd +import plotly.express as px + + +df = pd.DataFrame() +dg = pd.DataFrame(columns=['lati', 'long', 'alti', 'rssi', 'snr', 'rttt', 'temp', 'humi', 'pres', 'voc'], dtype=float) + +with open('save.csv') as f: + for i, line in enumerate(f): + dg.loc[i] = pd.Series(json.loads(line)) + savepos = f.tell() + +dg = dg.fillna(0.0) + +print('len', len(dg.index)) +fig = px.scatter_map(dg, lat='lati', lon='long',color="rssi", opacity=0.5, size='temp', size_max=10, map_style="open-street-map", color_continuous_scale='jet', zoom=13, height=500) +#fig.show() + + +#app = dash.Dash(__name__) +#app.layout = html.Div([ +# html.H4('Dashboard'), +# #dcc.Interval('graph-update', interval = 10000, n_intervals = 0), +# dcc.Graph(id='map', figure=fig), +# +# #dash_table.DataTable( id = 'table', data = df.to_dict('records'), columns=[{"name": i, "id": i} for i in df.columns])]) +# ]) + +app = dash.Dash() +app.layout = html.Div([ + dcc.Interval('graph-update', interval = 10000, n_intervals = 0), + dcc.Graph(id='fig', figure=fig), +]) + +t = 0 + +@app.callback( + dash.dependencies.Output('fig','figure'), + [dash.dependencies.Input('graph-update', 'n_intervals')]) +def updateTable(n): + global dg + global savepos + global t + global fig + + print(time.time() - t) + t = time.time() + + mi = max(dg.index) + with open('save.csv') as f: + f.seek(savepos) + for i, line in enumerate(f): + print(line) + dg.loc[mi+1+i] = pd.Series(json.loads(line)).fillna(0) + savepos = f.tell() + + return fig + +if __name__ == '__main__': + app.run(debug=True, port=10451) diff --git a/prilohy/web/tst b/prilohy/web/tst new file mode 100644 index 0000000..e69de29 -- cgit v1.2.3