| Updated Requirements.txt |
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15 days ago
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| Section 1659 updated |
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21 days ago
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| Section 1659 updated |
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21 days ago
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| 1687. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Enterprise and Governance Decision Making from GIS - ORNL LandScan 2026 - Chennai metropolitan area population and growth estimate - 23 September 2026 - related to 823,1163,1164,1174,1175,1177,1179,1194,1211,1226,1234,1290,1291,1292,1295,1296,1297,1301,1302,1304,1312,1313,1320,1323,1326,1328,1342,1343,1349,1350,1351,1362,1363,1377,1386,1387,1395,1397,1398,1412,1414,1423,1448,1449,1462,1471,1472,1533,1559,1560,1608,1623,1630,1640,1665 and all sections on GIS Urban Sprawl Analytics, Merit of Large Scale Visuals, Gridded Population Estimators (SEDAC GPW4 and GHSL convex hull estimators), DBSCAN and Neural Network Urban Extent Learners, Computational Geometry, Intrinsic Merit Rankings, Verlhulste-R Ricker Population Estimation, Community detection, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Computational Geometry and Polygonal packing by Voronoi Tessellation, Four color theorem, Economic Merit, Computational Economics - GDP estimation from Urban sprawl demographics, electricity consumption, Poverty alleviation, Preferential Attachment, Automatic Delineation of Urban Sprawls, Cellular Automata-SLEUTH-Polya Urn-Dynamic Facegraph Urban Growth Models,Expander Graphs,Timeseries analysis,Graph Edit Distance and Earth mover distance, Face recognition,Graph Isomorphism (Exact, Approximate and Subgraph),Graph matching,Graph similarity,Image Search,Quadrilateral Mesh Deformation and Isomorphism,Maximum Common Subgraph Problem and Approximate Topological Matching |
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23 days ago
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| -------------------------------------------------------------------------------------------------- 1687. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Enterprise and Governance Decision Making from GIS - ORNL LandScan 2026 - Chennai metropolitan area population and growth estimate - 23 September 2026 - related to 823,1163,1164,1174,1175,1177,1179,1194,1211,1226,1234,1290,1291,1292,1295,1296,1297,1301,1302,1304,1312,1313,1320,1323,1326,1328,1342,1343,1349,1350,1351,1362,1363,1377,1386,1387,1395,1397,1398,1412,1414,1423,1448,1449,1462,1471,1472,1533,1559,1560,1608,1623,1630,1640,1665 and all sections on GIS Urban Sprawl Analytics, Merit of Large Scale Visuals, Gridded Population Estimators (SEDAC GPW4 and GHSL convex hull estimators), DBSCAN and Neural Network Urban Extent Learners, Computational Geometry, Intrinsic Merit Rankings, Verlhulste-R Ricker Population Estimation, Community detection, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Computational Geometry and Polygonal packing by Voronoi Tessellation, Four color theorem, Economic Merit, Computational Economics - GDP estimation from Urban sprawl demographics, electricity consumption, Poverty alleviation, Preferential Attachment, Automatic Delineation of Urban Sprawls, Cellular Automata-SLEUTH-Polya Urn-Dynamic Facegraph Urban Growth Models,Expander Graphs,Timeseries analysis,Graph Edit Distance and Earth mover distance, Face recognition,Graph Isomorphism (Exact, Approximate and Subgraph),Graph matching,Graph similarity,Image Search,Quadrilateral Mesh Deformation and Isomorphism,Maximum Common Subgraph Problem and Approximate Topological Matching -------------------------------------------------------------------------------------------------- 1687.1 python-src/image_pattern_mining/ImageNet/GISUrbanSprawlAnalytics.py has been updated to invoke urban_sprawl_from_raster() over Chennai metropolitan area bounding box for latest LandScan Global and Mosaic 2026 dataset testlogs/landscan-global-2026.tif downloaded from https://landscan.ornl.gov/ (new tabs added for Global 2026 and Mosaic datasets): r6data=urban_sprawl_from_raster(79.271851,12.439259,80.351257,13.568572,"testlogs/landscan-global-2026.tif",dt="ORNL LandScan 2026") 1687.2 Logs testlogs/GISUrbanSprawlAnalytics.log.23September2026 show 2026 population estimate for above rectangle as: urban_sprawl_from_raster(): urbansprawlstatisticsaggregate = [12355365] 1697.3 Earlier estimate in testlogs/GISUrbanSprawlAnalytics.log.5May2026 (dt.5 May 2026) for LandScan 2024 dataset landscan-global-2024.tif showed 2024 population estimate for same bounding box as: urban_sprawl_from_raster(): urbansprawlstatisticsaggregate = [11672212] which is a growth of 5.85% in 2 years between 2024-2026 or CAGR of 2.926% population addition per annum. 1697.4 Gini coefficient of landscan-global 2024.tif for same box was (from testlogs/GISUrbanSprawlAnalytics.log.5May2026): urbansprawl_gini_coefficient(): Gini Index of the dataset = -0.9285586767730634 1687.5 Gini coefficient of landscan-global-2026.tif for same box after 2 years is (from testlogs/GISUrbanSprawlAnalytics.log.23September2026) showing a slight increase: urbansprawl_gini_coefficient(): Gini Index of the dataset = -0.928559275827474 NOTE: Using bounding box to estimate population is less accurate compared to GeoJSON shape file clipping of raster and summing the datapoints within clipped region. |
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23 days ago
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| Section 1665 updated |
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23 days ago
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| Updated Requirements.txt |
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about 1 month ago
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| +1686. (THEORY and FEATURE) Google Finance Stock Quote Search - SerpAPI Google search integration - related to 690,691,692,1257,1315,1316,1327,1348,1375,1376,1437,1438,1557 and all sections on Computational Economics-Quantitative Finance and Stockmarket TimeSeries Analysis, Graphical Event Models (GEM), Causal Event Models (CEM),Chaos and Fractals, Polynomial Interpolation, EventNet causality, Granger causality - 16 September 2026 |
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about 1 month ago
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| -------------------------------------------------------------------------------------------------- 1686. (THEORY and FEATURE) Google Finance Stock Quote Search - SerpAPI Google search integration - related to 690,691,692,1257,1315,1316,1327,1348,1375,1376,1437,1438,1557 and all sections on Computational Economics-Quantitative Finance and Stockmarket TimeSeries Analysis, Graphical Event Models (GEM), Causal Event Models (CEM),Chaos and Fractals, Polynomial Interpolation, EventNet causality, Granger causality - 16 September 2026 -------------------------------------------------------------------------------------------------- 1686.1 New utility script GoogleSearchSerpApi.py has been added to NeuronRain AstroInfer repositories which implements a function google_finance_search_quote(apikey,ticker) that instantiates SerpApi Client object and searches the google_finance engine of SerpApi client for a Google Finance stock quote ticker. Python serpapi package installation with free SerpAPI API key are prerequisites. 1686.2 Results JSON from earlier stock quote search are persisted to testlogs/GoogleSearchSerpApi.json file by json.dump(). Earlier script is QAed by commandline teed to testlogs/GoogleSearchSerpApi.log.16September2026: python3.9 GoogleSearchSerpApi.py | tee testlogs/GoogleSearchSerpApi.log.16September2026 1686.3 JSON file testlogs/GoogleSearchSerpApi.json contains fields for timeseries graph of intraday price movement of the stock quote, knowledge graph and other vital statistics for the ticker symbol. |
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about 1 month ago
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| Section 1462 updated |
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about 1 month ago
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| Section 1685 updated |
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about 1 month ago
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| Gemini AI - summary - El Nino model and gravity |
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about 1 month ago
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| Section 1665 updated |
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about 1 month ago
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| Section 1665 updated |
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about 1 month ago
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| Finding a Convex Hull of points subject to Constraint Satisfaction - GIS airport site selection usecase |
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about 1 month ago
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| -------------------------------------------------------------------------------------------------- 1685. (THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 2 September 2026 - VARUNA N-Body Climate Model - N-Body gravity analysis of date range 1/10/2026-30/11/2026 for Mercury-Venus - related to 148,172,313,445,446,463,1165,1171,1176,1266,1289,1316,1318,1327,1358,1359,1360,1361,1364,1367,1368,1388,1404,1455,1456,1463,1479,1480,1515,1526,1527,1528,1548,1558,1561,1562,1567,1581,1590,1631,1638,1639 and all sections on Correlation between gravitational effects of Mined Celestial N-Body Patterns and terrestrial extreme weather events (or Gravitational effects on climate change), Drone natural obstacle avoidance-Drone autonomous online shopping delivery-Drone Electronic Voting Machine, Artificial Intelligence models for Climate Change-Adaptive Fourier Neural Operator-FourCastNet, Astronomical algorithms, Multifractals, Timeseries analysis, Sequence Mining-Class Association Rules, Global warming,Clausius-Clapeyron equation,Navier-Stokes equation -------------------------------------------------------------------------------------------------- (Disclaimer: Same as Disclaimer in Section 1548 - VARUNA N-Body model is a non-conventional meteorology model still in experimental stages) 1685.1 python-src/image_pattern_mining/ImageNet/GISWeatherAnalytics.py has been updated to compute N-Body gravitational forces of semisextile Mercury-Venus angular separation on Earth for the date range 1/10/2026-30/11/2026: climate_analytics(datasource="precipitation_GaussianMixture",predict_EWE_params={'datefrom':(2026,10,1,17,30,00),'dateto':(2026,11,30,17,30,00),'loc':'@0','bodyconjunctions':"Mercury-Venus",'angularsepbounds':('0d','30d'),'longlat':['223@399'],'seasurfacelonglat':(95,12)},precipitation_timeseries={"timeseries":nem_rainfall_timeseries,"averageseasonalrainfall":100,'forecast_timeseries':[10,2,12,25,30,1,2,3,4,10]},computepressuregradient=True,seasonaltemperature=25) 1685.2 Following are the datewise angular separations from GISWeatherAnalytics.log.2September2026 obtained from - grep "separation" GISWeatherAnalytics.log.2September2026 |grep "Venus-Mercury" : SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 08deg 35' 57.3">, ('2026', '10', '01', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 07deg 37' 31.3">, ('2026', '10', '02', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 06deg 43' 54.4">, ('2026', '10', '03', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 05deg 57' 53.4">, ('2026', '10', '04', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 05deg 23' 23.2">, ('2026', '10', '05', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 05deg 04' 58.3">, ('2026', '10', '06', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 05deg 06' 07.5">, ('2026', '10', '07', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 05deg 27' 03.5">, ('2026', '10', '08', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 06deg 04' 38.0">, ('2026', '10', '09', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 06deg 54' 25.8">, ('2026', '10', '10', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 07deg 52' 31.3">, ('2026', '10', '11', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 08deg 55' 59.1">, ('2026', '10', '12', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 10deg 02' 44.5">, ('2026', '10', '13', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 11deg 11' 16.9">, ('2026', '10', '14', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 12deg 20' 26.3">, ('2026', '10', '15', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 13deg 29' 14.6">, ('2026', '10', '16', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 14deg 36' 49.7">, ('2026', '10', '17', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 15deg 42' 22.7">, ('2026', '10', '18', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 16deg 45' 05.1">, ('2026', '10', '19', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 17deg 44' 08.1">, ('2026', '10', '20', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 18deg 38' 41.9">, ('2026', '10', '21', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 19deg 27' 55.6">, ('2026', '10', '22', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 20deg 10' 57.5">, ('2026', '10', '23', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 20deg 46' 56.6">, ('2026', '10', '24', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 21deg 15' 03.5">, ('2026', '10', '25', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 21deg 34' 33.1">, ('2026', '10', '26', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 21deg 44' 47.2">, ('2026', '10', '27', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 21deg 45' 18.4">, ('2026', '10', '28', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 21deg 35' 53.8">, ('2026', '10', '29', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 21deg 16' 39.7">, ('2026', '10', '30', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 20deg 48' 05.3">, ('2026', '10', '31', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 20deg 11' 05.6">, ('2026', '11', '01', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 19deg 27' 02.2">, ('2026', '11', '02', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 18deg 37' 41.5">, ('2026', '11', '03', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 17deg 45' 09.0">, ('2026', '11', '04', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 16deg 51' 41.7">, ('2026', '11', '05', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 15deg 59' 36.9">, ('2026', '11', '06', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 15deg 11' 02.0">, ('2026', '11', '07', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 14deg 27' 45.4">, ('2026', '11', '08', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 13deg 51' 10.2">, ('2026', '11', '09', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 13deg 22' 12.6">, ('2026', '11', '10', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 13deg 01' 23.3">, ('2026', '11', '11', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 12deg 48' 52.0">, ('2026', '11', '12', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 12deg 44' 31.9">, ('2026', '11', '13', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 12deg 48' 04.8">, ('2026', '11', '14', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 12deg 59' 03.9">, ('2026', '11', '15', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 13deg 16' 56.6">, ('2026', '11', '16', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 13deg 41' 06.1">, ('2026', '11', '17', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 14deg 10' 52.8">, ('2026', '11', '18', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 14deg 45' 36.1">, ('2026', '11', '19', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 15deg 24' 35.2">, ('2026', '11', '20', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 16deg 07' 10.6">, ('2026', '11', '21', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 16deg 52' 45.0">, ('2026', '11', '22', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 17deg 40' 44.2">, ('2026', '11', '23', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 18deg 30' 37.1">, ('2026', '11', '24', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 19deg 21' 56.1">, ('2026', '11', '25', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 20deg 14' 17.3">, ('2026', '11', '26', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 21deg 07' 19.8">, ('2026', '11', '27', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 22deg 00' 46.0">, ('2026', '11', '28', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 22deg 54' 20.9">, ('2026', '11', '29', '17', '30', '00.000'))] 1685.3 Corresponding N-Body Gravity L2 norm graphics plot for above 61 days range is at testlogs/GISWeatherAnalytics.Gravity.Mercury-Venus.2026-10-1#2026-11-30.jpg which shows a sharp spike in first week of November 2026. Navier-Stokes pressure gradients data in GISWeatherAnalytics.log.2September2026 for above date range shows corresponding spike during first week of November 2026 which is a prerequisite for creation of low pressure troughs. 1685.4 Following are the 61-day Navier-Stokes pressure gradients obtained from Mercury gravity L2 norms for above date range from GISWeatherAnalytics.log.2September2026, peaking in November 2026: pressure gradients: [0.6863541064898564, 0.6897621514219108, 0.6940231186151363, 0.6991551555156232, 0.705180085289406, 0.7121234659237101, 0.720014646481695, 0.7288868132354327, 0.7387770161336888, 0.7497261631889113, 0.7617789667483558, 0.7749838210827141, 0.7893925850986953, 0.8050602370666512, 0.8220443598629482, 0.8404044052081419, 0.8602006736705456, 0.8814929338992583, 0.904338590035417, 0.9287902913686537, 0.9548928646042913, 0.9826794391551036, 1.012166633651374, 1.0433486831867531, 1.0761904196700751, 1.1106190822503983, 1.1465150429928148, 1.18370169659284, 1.2219349901544734, 1.2608933589983855, 1.3001691698484827, 1.3392631133995776, 1.37758326927921, 1.4144507045512094, 1.449113372549162, 1.4807696612364256, 1.5086020998127898, 1.5318203726895283, 1.5497109190549723, 1.5616883082756439, 1.567341933846075, 1.566471142743836, 1.5591031551593022, 1.5454908310744628, 1.5260907543732933, 1.5015252903690524, 1.4725344567701317, 1.4399242443524798, 1.4045174773861377, 1.3671117963044772, 1.3284474098186334, 1.2891853976078766, 1.2498958828392548, 1.2110544735368418, 1.173044968759795, 1.1361663209227548, 1.1006420915079922, 1.0666310010387847, 1.034237558285558, 1.003522099438464] |
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about 1 month ago
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| +-------------------------------------------------------------------------------------------------- +1685. (THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 2 September 2026 - VARUNA N-Body Climate Model - N-Body gravity analysis of date range 1/10/2026-30/11/2026 for Mercury-Venus - related to 148,172,313,445,446,463,1165,1171,1176,1266,1289,1316,1318,1327,1358,1359,1360,1361,1364,1367,1368,1388,1404,1455,1456,1463,1479,1480,1515,1526,1527,1528,1548,1558,1561,1562,1567,1581,1590,1631,1638,1639 and all sections on Correlation between gravitational effects of Mined Celestial N-Body Patterns and terrestrial extreme weather events (or Gravitational effects on climate change), Drone natural obstacle avoidance-Drone autonomous online shopping delivery-Drone Electronic Voting Machine, Artificial Intelligence models for Climate Change-Adaptive Fourier Neural Operator-FourCastNet, Astronomical algorithms, Multifractals, Timeseries analysis, Sequence Mining-Class Association Rules, Global warming,Clausius-Clapeyron equation,Navier-Stokes equation +-------------------------------------------------------------------------------------------------- +(Disclaimer: Same as Disclaimer in Section 1548 - VARUNA N-Body model is a non-conventional meteorology model still in experimental stages) +1685.1 python-src/image_pattern_mining/ImageNet/GISWeatherAnalytics.py has been updated to compute N-Body gravitational forces of semisextile Mercury-Venus angular separation on Earth for the date range 1/10/2026-30/11/2026: + climate_analytics(datasource="precipitation_GaussianMixture",predict_EWE_params={'datefrom':(2026,10,1,17,30,00),'dateto':(2026,11,30,17,30,00),'loc':'@0','bodyconjunctions':"Mercury-Venus",'angularsepbounds':('0d','30d'),'longlat':['223@399'],'seasurfacelonglat':(95,12)},precipitation_timeseries={"timeseries":nem_rainfall_timeseries,"averageseasonalrainfall":100,'forecast_timeseries':[10,2,12,25,30,1,2,3,4,10]},computepressuregradient=True,seasonaltemperature=25) +1685.2 Following are the datewise angular separations from GISWeatherAnalytics.log.2September2026 obtained from - grep "separation" GISWeatherAnalytics.log.2September2026 |grep "Venus-Mercury" : +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 08deg 35' 57.3">, ('2026', '10', '01', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 07deg 37' 31.3">, ('2026', '10', '02', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 06deg 43' 54.4">, ('2026', '10', '03', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 05deg 57' 53.4">, ('2026', '10', '04', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 05deg 23' 23.2">, ('2026', '10', '05', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 05deg 04' 58.3">, ('2026', '10', '06', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 05deg 06' 07.5">, ('2026', '10', '07', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 05deg 27' 03.5">, ('2026', '10', '08', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 06deg 04' 38.0">, ('2026', '10', '09', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 06deg 54' 25.8">, ('2026', '10', '10', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 07deg 52' 31.3">, ('2026', '10', '11', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 08deg 55' 59.1">, ('2026', '10', '12', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 10deg 02' 44.5">, ('2026', '10', '13', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 11deg 11' 16.9">, ('2026', '10', '14', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 12deg 20' 26.3">, ('2026', '10', '15', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 13deg 29' 14.6">, ('2026', '10', '16', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 14deg 36' 49.7">, ('2026', '10', '17', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 15deg 42' 22.7">, ('2026', '10', '18', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 16deg 45' 05.1">, ('2026', '10', '19', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 17deg 44' 08.1">, ('2026', '10', '20', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 18deg 38' 41.9">, ('2026', '10', '21', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 19deg 27' 55.6">, ('2026', '10', '22', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 20deg 10' 57.5">, ('2026', '10', '23', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 20deg 46' 56.6">, ('2026', '10', '24', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 21deg 15' 03.5">, ('2026', '10', '25', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 21deg 34' 33.1">, ('2026', '10', '26', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 21deg 44' 47.2">, ('2026', '10', '27', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 21deg 45' 18.4">, ('2026', '10', '28', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 21deg 35' 53.8">, ('2026', '10', '29', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 21deg 16' 39.7">, ('2026', '10', '30', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 20deg 48' 05.3">, ('2026', '10', '31', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 20deg 11' 05.6">, ('2026', '11', '01', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 19deg 27' 02.2">, ('2026', '11', '02', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 18deg 37' 41.5">, ('2026', '11', '03', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 17deg 45' 09.0">, ('2026', '11', '04', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 16deg 51' 41.7">, ('2026', '11', '05', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 15deg 59' 36.9">, ('2026', '11', '06', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 15deg 11' 02.0">, ('2026', '11', '07', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 14deg 27' 45.4">, ('2026', '11', '08', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 13deg 51' 10.2">, ('2026', '11', '09', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 13deg 22' 12.6">, ('2026', '11', '10', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 13deg 01' 23.3">, ('2026', '11', '11', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 12deg 48' 52.0">, ('2026', '11', '12', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 12deg 44' 31.9">, ('2026', '11', '13', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 12deg 48' 04.8">, ('2026', '11', '14', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 12deg 59' 03.9">, ('2026', '11', '15', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 13deg 16' 56.6">, ('2026', '11', '16', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 13deg 41' 06.1">, ('2026', '11', '17', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 14deg 10' 52.8">, ('2026', '11', '18', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 14deg 45' 36.1">, ('2026', '11', '19', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 15deg 24' 35.2">, ('2026', '11', '20', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 16deg 07' 10.6">, ('2026', '11', '21', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 16deg 52' 45.0">, ('2026', '11', '22', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 17deg 40' 44.2">, ('2026', '11', '23', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 18deg 30' 37.1">, ('2026', '11', '24', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 19deg 21' 56.1">, ('2026', '11', '25', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 20deg 14' 17.3">, ('2026', '11', '26', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 21deg 07' 19.8">, ('2026', '11', '27', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 22deg 00' 46.0">, ('2026', '11', '28', '17', '30', '00.000'))] +SkyField separation_from(): Angular separations for Venus-Mercury : [(<Angle 22deg 54' 20.9">, ('2026', '11', '29', '17', '30', '00.000'))] +1685.3 Corresponding N-Body Gravity L2 norm graphics plot for above 61 days range is at testlogs/GISWeatherAnalytics.Gravity.Mercury-Venus.2026-10-1#2026-11-30.jpg which shows a sharp spike in first week of November 2026. Navier-Stokes pressure gradients data in GISWeatherAnalytics.log.2September2026 for above date range shows corresponding spike during first week of November 2026 which is a prerequisite for creation of low pressure troughs. +1685.4 Following are the 61-day Navier-Stokes pressure gradients obtained from Mercury gravity L2 norms for above date range from GISWeatherAnalytics.log.2September2026, peaking in November 2026: +pressure gradients: [0.6863541064898564, 0.6897621514219108, 0.6940231186151363, 0.6991551555156232, 0.705180085289406, 0.7121234659237101, 0.720014646481695, 0.7288868132354327, 0.7387770161336888, 0.7497261631889113, 0.7617789667483558, 0.7749838210827141, 0.7893925850986953, 0.8050602370666512, 0.8220443598629482, 0.8404044052081419, 0.8602006736705456, 0.8814929338992583, 0.904338590035417, 0.9287902913686537, 0.9548928646042913, 0.9826794391551036, 1.012166633651374, 1.0433486831867531, 1.0761904196700751, 1.1106190822503983, 1.1465150429928148, 1.18370169659284, 1.2219349901544734, 1.2608933589983855, 1.3001691698484827, 1.3392631133995776, 1.37758326927921, 1.4144507045512094, 1.449113372549162, 1.4807696612364256, 1.5086020998127898, 1.5318203726895283, 1.5497109190549723, 1.5616883082756439, 1.567341933846075, 1.566471142743836, 1.5591031551593022, 1.5454908310744628, 1.5260907543732933, 1.5015252903690524, 1.4725344567701317, 1.4399242443524798, 1.4045174773861377, 1.3671117963044772, 1.3284474098186334, 1.2891853976078766, 1.2498958828392548, 1.2110544735368418, 1.173044968759795, 1.1361663209227548, 1.1006420915079922, 1.0666310010387847, 1.034237558285558, 1.003522099438464] + +References: +----------- +1685.5 Precipitable Water Vapor Retrieval Based on GNSS Data and Its Application in Extreme Rainfall - https://www.mdpi.com/2072-4292/17/13/2301 - ".... The method for calculating PWV is based on the ideal gas law and the equation of state. The total water vapor content is obtained by calculating the water vapor density of each layer in the atmosphere and integrating it. The specific calculation formula is [37] PWV = 1/ρ(w)*Integral(q/g*dP) where g is the gravitational acceleration, measured in m/s^2 ; P is the pressure, measured in hPa; ρw is the density of liquid water, measured in kg/m 3 ; and q is the specific humidity, measured in kg/kg....." - Precipitable Water Vapor is an important determinant of rainfall (and crucial for agriculture forecasts) as opposed pressure gradient which influences wind velocity. In earlier equation, precipitable water vapor is inversely proportional to gravity though in Navier-Stokes equation, pressure gradient is directly proportional to gravity (which implies increased gravity is conducive for vigorous monsoon winds and storms and not much for rainfall per se). |
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about 1 month ago
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| Section 1438 updated |
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about 2 months ago
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about 2 months ago
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| Gemini AI - Traffic Origin-Destination matrix of Chennai metropolitan area by CUMTA |
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about 2 months ago
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| Section 1595 updated |
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about 2 months ago
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| Section 1646 updated |
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about 2 months ago
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| Section 1595 updated |
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about 2 months ago
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| Section 1595 updated |
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about 2 months ago
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| Section 1683 updated |
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about 2 months ago
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| Updated Requirements.txt |
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about 2 months ago
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| 1683. (THEORY and FEATURE) Social Network Analysis - Opensource Intelligence - new utility for python osint and SpiderFoot - related to 14,365,626,711,727,753,876,1127,1128,1129,1130,1204,1263,1269,1427,1442,1452,1532,1534,1547,1555,1571,1592,1605,1645,1657,1667,1681 and all sections on People Analytics, Intrinsic Merit Versus Fame Equilibrium, BKS Conjecture, Stability and Noise Sensitivity of Interview LTF/PTF,Peres Theorem,Unique Identification,Talent Analytics,Code search of opensource repositories,HR Analytics and Automated Recruitment,Question-Answering,Majority Voting,Condorcet Jury Theorem Variants,Margulis-Ruzzo Thresholds,Novelty Detection,Recommender Systems,AI Question-Answering, Partial Ordered Ranking, Fame-Merit equilibrium, Intrinsic Merit as Query Complexity,Software Analytics-Mythical Manmonth-CathedralAndBazaar,Birthday Paradox,Matching in Matrimony-Professional-Personal Social Media Network Profiles,Tree matching - 25 August 2026 |
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about 2 months ago
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| -------------------------------------------------------------------------------------------------- 1683. (THEORY and FEATURE) Social Network Analysis - Opensource Intelligence - new utility for python osint and SpiderFoot - related to 14,365,626,711,727,753,876,1127,1128,1129,1130,1204,1263,1269,1427,1442,1452,1532,1534,1547,1555,1571,1592,1605,1645,1657,1667,1681 and all sections on People Analytics, Intrinsic Merit Versus Fame Equilibrium, BKS Conjecture, Stability and Noise Sensitivity of Interview LTF/PTF,Peres Theorem,Unique Identification,Talent Analytics,Code search of opensource repositories,HR Analytics and Automated Recruitment,Question-Answering,Majority Voting,Condorcet Jury Theorem Variants,Margulis-Ruzzo Thresholds,Novelty Detection,Recommender Systems,AI Question-Answering, Partial Ordered Ranking, Fame-Merit equilibrium, Intrinsic Merit as Query Complexity,Software Analytics-Mythical Manmonth-CathedralAndBazaar,Birthday Paradox,Matching in Matrimony-Professional-Personal Social Media Network Profiles,Tree matching - 25 August 2026 ------------------------------------------------------------------------------------------------- 1683.1 New utility OSINT.py implements a function osint() that imports osint submodules installed from https://pypi.org/project/osint/ and invokes some functions on it to get data on URLs and ports. This function is QAed and logs are written to by commandline: venv/bin/python3.13 OSINT.py 2>&1 > osint_related/logs/OSINT.log.25August2026 1683.2 SpiderFoot support which was not feasible earlier due to installation errors has been resolved and SpiderFoot OSINT dashboard has been now enabled (which includes darkweb features). Additionally SpiderFoot python3.13 installation guide has been added within folder osint_related/ in text file osint_commandlines.txt that installs and starts a local SpiderFoot webserver. Modified requirements.txt for SpiderFoot installation has also been added at osint_related/requirements.txt (changes made for lxml and pyyaml versions) 1683.3 Sample Logs from SpiderFoot webserver dashboard IP:port vulnerability scan are at osint_related/logs/SpiderFoot-AC6CEE46.log.25August2026.csv 1683.4 SpiderFoot includes features for email,phone number and humanname extraction - https://asciinema.org/a/295947 and social media profile search - https://asciinema.org/a/295923 |
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about 2 months ago
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| Section 1646 updated |
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about 2 months ago
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| ChatGPT analysis of Matiyasevich Python Program for Riemann Hypothesis - Loop termination detection and Worst case execution time |
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about 2 months ago
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