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Nov 05, 2025 — Nov 05, 2026
Commit Message Contributor Files Modified Lines Added Lines Removed Code Location Date
Section 1533 updated
Srinivasan Kannan
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Section 1423 updated
Srinivasan Kannan
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Section 1423 updated
Srinivasan Kannan
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---------------------------------------------------------------------------------------------------------------------------------------- 1558. (THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 6 November 2024 - N-Body gravitational acceleration and Barometric pressure analysis of date range 1 November 2024-31 December 2024 for Chennai location in NASA JPL Horizons API - 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 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 ---------------------------------------------------------------------------------------------------------------------------------------- (Disclaimer: Same as Disclaimer in Section 1548) 1558.1 GISWeatherAnalytics.py has been updated to compute gravitational accelerations of 3-body system Sun-Mercury-Venus for the date range 1 November 2024-31 December 2024: climate_analytics(datasource="precipitation_GaussianMixture",predict_EWE_params={'datefrom':(2024,11,1,17,30,00),'dateto':(2024,12,31,17,30,00),'loc':'@0','bodyconjunctions':"Sun-Mercury-Venus",'angularsepbounds':('0d','30d'),'longlat':['223@399']},precipitation_timeseries={"timeseries":nem_rainfall_timeseries,"averageseasonalrainfall":100,'forecast_timeseries':[10,2,12,25,30,1,2,3,4,10]}) 1558.2 Logs and Images for Variable gravity exerted by each body and Barometric Pressure for above date range are captured in: testlogs/GISWeatherAnalytics.BarometricPressure.Chennai.Mercury-Venus.2024-11-1#2024-12-31.jpg testlogs/GISWeatherAnalytics.BarometricPressure.Chennai.Sun-Mercury.2024-11-1#2024-12-31.jpg testlogs/GISWeatherAnalytics.BarometricPressure.Chennai.Sun-Venus.2024-11-1#2024-12-31.jpg testlogs/GISWeatherAnalytics.Gravity.Mercury-Venus.2024-11-1#2024-12-31.jpg testlogs/GISWeatherAnalytics.Gravity.Sun-Mercury.2024-11-1#2024-12-31.jpg testlogs/GISWeatherAnalytics.Gravity.Sun-Venus.2024-11-1#2024-12-31.jpg testlogs/GISWeatherAnalytics.log.6November2024 1558.3 Logs and Variable gravity plots earlier show a peak in Mercury's gravitational pull during 6/12/2024-16/12/2024 that could in turn affect all climate models (especially those exponentially depending on gravity) for this date range. 1558.4 Following are the angular separations for Mercury-Venus for above date range (Two different separations data from SkyCoord and SkyField) that could determine distances in N-Body gravitational acceleration equations (SkyCoord: 27 degrees on 5/11/2024-at closest 3 degrees on 18/11/2024-and again diverging to 27 degrees on 27/11/2024 - SkyField: 19 degrees on 2/11/2024-at closest 18 degrees on 11/11/2024-and again diverging to 59 degrees on 12/12/2024) : SkyCoord separation(): Angular separation of Mercury-Venus on 2024-11-05 17:30:00.000: [27d14m39.76342239s] SkyCoord separation(): Angular separation of Mercury-Venus on 2024-11-06 17:30:00.000: [25d46m46.84350477s] SkyCoord separation(): Angular separation of Mercury-Venus on 2024-11-07 17:30:00.000: [24d15m41.43258822s] SkyCoord separation(): Angular separation of Mercury-Venus on 2024-11-08 17:30:00.000: [22d41m06.82735694s] SkyCoord separation(): Angular separation of Mercury-Venus on 2024-11-09 17:30:00.000: [21d02m46.03173708s] SkyCoord separation(): Angular separation of Mercury-Venus on 2024-11-10 17:30:00.000: [19d20m22.05690177s] SkyCoord separation(): Angular separation of Mercury-Venus on 2024-11-11 17:30:00.000: [17d33m38.49601506s] SkyCoord separation(): Angular separation of Mercury-Venus on 2024-11-12 17:30:00.000: [15d42m20.64205924s] SkyCoord separation(): Angular separation of Mercury-Venus on 2024-11-13 17:30:00.000: [13d46m17.7653233s] SkyCoord separation(): Angular separation of Mercury-Venus on 2024-11-14 17:30:00.000: [11d45m28.12126211s] SkyCoord separation(): Angular separation of Mercury-Venus on 2024-11-15 17:30:00.000: [9d40m11.24414554s] SkyCoord separation(): Angular separation of Mercury-Venus on 2024-11-16 17:30:00.000: [7d31m43.13946945s] SkyCoord separation(): Angular separation of Mercury-Venus on 2024-11-17 17:30:00.000: [5d24m20.65303336s] SkyCoord separation(): Angular separation of Mercury-Venus on 2024-11-18 17:30:00.000: [3d34m40.1299015s] SkyCoord separation(): Angular separation of Mercury-Venus on 2024-11-19 17:30:00.000: [3d03m17.82588158s] SkyCoord separation(): Angular separation of Mercury-Venus on 2024-11-20 17:30:00.000: [4d34m56.25099287s] SkyCoord separation(): Angular separation of Mercury-Venus on 2024-11-21 17:30:00.000: [7d05m20.22437296s] SkyCoord separation(): Angular separation of Mercury-Venus on 2024-11-22 17:30:00.000: [9d59m32.33748587s] SkyCoord separation(): Angular separation of Mercury-Venus on 2024-11-23 17:30:00.000: [13d08m44.48668662s] SkyCoord separation(): Angular separation of Mercury-Venus on 2024-11-24 17:30:00.000: [16d30m21.1775406s] SkyCoord separation(): Angular separation of Mercury-Venus on 2024-11-25 17:30:00.000: [20d03m25.68011674s] SkyCoord separation(): Angular separation of Mercury-Venus on 2024-11-26 17:30:00.000: [23d47m30.84917091s] SkyCoord separation(): Angular separation of Mercury-Venus on 2024-11-27 17:30:00.000: [27d42m16.24456762s] -------------------------------------------------------------- SkyField separation_from(): Angular separations for Mercury-Venus : [(<Angle 19deg 16' 29.9">, ('2024', '11', '02', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Mercury-Venus : [(<Angle 19deg 05' 21.2">, ('2024', '11', '03', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Mercury-Venus : [(<Angle 18deg 55' 02.4">, ('2024', '11', '04', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Mercury-Venus : [(<Angle 18deg 45' 37.7">, ('2024', '11', '05', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Mercury-Venus : [(<Angle 18deg 37' 12.0">, ('2024', '11', '06', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Mercury-Venus : [(<Angle 18deg 29' 50.9">, ('2024', '11', '07', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Mercury-Venus : [(<Angle 18deg 23' 41.2">, ('2024', '11', '08', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Mercury-Venus : [(<Angle 18deg 18' 50.8">, ('2024', '11', '09', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Mercury-Venus : [(<Angle 18deg 15' 28.6">, ('2024', '11', '10', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Mercury-Venus : [(<Angle 18deg 13' 45.3">, ('2024', '11', '11', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Mercury-Venus : [(<Angle 18deg 13' 53.0">, ('2024', '11', '12', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Mercury-Venus : [(<Angle 18deg 16' 05.8">, ('2024', '11', '13', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Mercury-Venus : [(<Angle 18deg 20' 40.0">, ('2024', '11', '14', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Mercury-Venus : [(<Angle 18deg 27' 54.2">, ('2024', '11', '15', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Mercury-Venus : [(<Angle 18deg 38' 09.8">, ('2024', '11', '16', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Mercury-Venus : [(<Angle 18deg 51' 51.1">, ('2024', '11', '17', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Mercury-Venus : [(<Angle 19deg 09' 25.9">, ('2024', '11', '18', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Mercury-Venus : [(<Angle 19deg 31' 25.4">, ('2024', '11', '19', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Mercury-Venus : [(<Angle 19deg 58' 24.6">, ('2024', '11', '20', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Mercury-Venus : [(<Angle 20deg 31' 02.2">, ('2024', '11', '21', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Mercury-Venus : [(<Angle 21deg 10' 00.5">, ('2024', '11', '22', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Mercury-Venus : [(<Angle 21deg 56' 05.0">, ('2024', '11', '23', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Mercury-Venus : [(<Angle 22deg 50' 03.3">, ('2024', '11', '24', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Mercury-Venus : [(<Angle 23deg 52' 43.5">, ('2024', '11', '25', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Mercury-Venus : [(<Angle 25deg 04' 52.4">, ('2024', '11', '26', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Mercury-Venus : [(<Angle 26deg 27' 11.6">, ('2024', '11', '27', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Mercury-Venus : [(<Angle 28deg 00' 13.6">, ('2024', '11', '28', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Mercury-Venus : [(<Angle 29deg 44' 16.8">, ('2024', '11', '29', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Mercury-Venus : [(<Angle 31deg 39' 19.4">, ('2024', '11', '30', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Mercury-Venus : [(<Angle 33deg 44' 53.7">, ('2024', '12', '01', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Mercury-Venus : [(<Angle 36deg 00' 01.6">, ('2024', '12', '02', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Mercury-Venus : [(<Angle 38deg 23' 11.8">, ('2024', '12', '03', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Mercury-Venus : [(<Angle 40deg 52' 21.0">, ('2024', '12', '04', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Mercury-Venus : [(<Angle 43deg 24' 59.4">, ('2024', '12', '05', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Mercury-Venus : [(<Angle 45deg 58' 19.8">, ('2024', '12', '06', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Mercury-Venus : [(<Angle 48deg 29' 30.8">, ('2024', '12', '07', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Mercury-Venus : [(<Angle 50deg 55' 49.7">, ('2024', '12', '08', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Mercury-Venus : [(<Angle 53deg 14' 54.6">, ('2024', '12', '09', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Mercury-Venus : [(<Angle 55deg 24' 52.6">, ('2024', '12', '10', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Mercury-Venus : [(<Angle 57deg 24' 23.9">, ('2024', '12', '11', '17', '30', '00.000'))] SkyField separation_from(): Angular separations for Mercury-Venus : [(<Angle 59deg 12' 41.7">, ('2024', '12', '12', '17', '30', '00.000'))]
Srinivasan Kannan
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Section 1483 updated
Srinivasan Kannan
More... about 2 years ago
Section 1483 updated
Srinivasan Kannan
More... about 2 years ago
Section 1522 updated
K.Srinivasan
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Section 1483 updated
K.Srinivasan
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Section 1557 updated
K.Srinivasan
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Updated Requirements.txt
K.Srinivasan
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Section 1463 updated
K.Srinivasan
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Section 1548 updated
K.Srinivasan
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Section 1423 updated
Srinivasan Kannan
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Updated BestPractices.txt
Srinivasan Kannan
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ArXiv,DBLP,Google scholar profiles have been added and updated
Srinivasan Kannan
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Section 1557 updated
Srinivasan Kannan
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1557. (THEORY and FEATURE) Volatility from Higuchi Fractal Dimension of stockmarket 1-dimensional timeseries data (equally applicable to Music, Medical Imageing-ECG, Meteorology-Precipitation, Seismology-Tremors, NBody Gravitational Accelerations, Economics-Financial Markets timeseries) - fractal analysis of 4 stockquotes - 15 October 2024 - related to 690,691,692,1257,1315,1316,1327,1348,1375,1376,1437,1438 and all sections on Music Information Retrieval(MIR), Climate analytics and Weather Forecast, Computational Economics-Quantitative Finance and TimeSeries Analysis, Graphical Event Models (GEM), Causal Event Models (CEM),Chaos and Fractals, Polynomial Interpolation, EventNet causality, Granger causality
Srinivasan Kannan
More... about 2 years ago
------------------------------------------------------------------------------------------------------------------------------- 1557. (THEORY and FEATURE) Volatility from Higuchi Fractal Dimension of stockmarket 1-dimensional timeseries data (equally applicable to Music, Medical Imageing-ECG, Meteorology-Precipitation, Seismology-Tremors, NBody Gravitational Accelerations, Economics-Financial Markets timeseries) - fractal analysis of 4 stockquotes - 15 October 2024 - related to 690,691,692,1257,1315,1316,1327,1348,1375,1376,1437,1438 and all sections on Music Information Retrieval(MIR), Climate analytics and Weather Forecast, Computational Economics-Quantitative Finance and TimeSeries Analysis, Graphical Event Models (GEM), Causal Event Models (CEM),Chaos and Fractals, Polynomial Interpolation, EventNet causality, Granger causality ------------------------------------------------------------------------------------------------------------------------------- 1557.1 python-src/Streaming_StockMarketData.py has been updated to compute Higuchi Fractal Dimension within stockquote_Prophet_timeseries_forecast_model() Prophet stock market model for 4 stockquote tickers (2 years historical data): stockquote_Prophet_timeseries_forecast_model("AMZN") stockquote_Prophet_timeseries_forecast_model("MSFT") stockquote_Prophet_timeseries_forecast_model("GOOG") stockquote_Prophet_timeseries_forecast_model("TSLA") 1557.2 Function hfd has been imported from HiguchiFractalDimension implementation - https://github.com/inuritdino/HiguchiFractalDimension - and is invoked for Prophet stockquote timeseries. 1557.3 Following are the Higuchi Fractal Dimensions for above stocks and closest matching fractal dimensions are within parenthesis - source: https://en.wikipedia.org/wiki/List_of_fractals_by_Hausdorff_dimension - excerpts from testlogs/Streaming_StockMarketData.Prophet.log.15October2024 : AMZN - Higuchi Fractal Dimension of stock quote timeseries: 1.2748874096584635 (von Koch curve - dimension 1.2619) MSFT - Higuchi Fractal Dimension of stock quote timeseries: 1.253206242501465 (coastline of Great Britain - dimension 1.25) GOOG - Higuchi Fractal Dimension of stock quote timeseries: 1.3327119793041007 (Boundary of Brownian Motion - dimension 1.33) TSLA - Higuchi Fractal Dimension of stock quote timeseries: 1.7300695052077417 (Fractal percolation with 75% probability - dimension 1.7381) 1557.4 There seems to be very close resemblance between fluctuations of 4 stocks earlier and 4 natural fractals from 1557.3: 1557.4.1 AMZN is fractally a Koch curve, 1557.4.2 MSFT is fractally a coastline, 1557.4.3 GOOG is fractally a brownian motion (which is a remarkable find - Sections 1437.6 and 1438.7 - Bachelier's PhD Thesis which ushered in new field of Quantitative Finance - Theory of Speculation - Markets are difficult to predict by Efficient Market Hypothesis and follow Brownian motion random walks), 1557.4.4 TSLA is fractally a percolation. 1557.5 Relating stock quote timeseries to a known mathematical fractal of closest fractal dimension could reveal inner dynamics of the stock - Fractal dimension, in simplified terms, measures how much a timeseries deviates from topological dimension: Timeseries datasets are usually 1-dimensional topologically being set of points and extent of deviation of Fractal dimension from 1 measures volatility of the stock (stockdata fractally tends to higher dimensions - for example TSLA timeseries earlier is almost close to 2 dimensional rectangle having dimension 1.73).
Srinivasan Kannan
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Section 1285 updated
K.Srinivasan
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Section 1285 updated
K.Srinivasan
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Section 1323 updated
K.Srinivasan
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Section 1459 updated
K.Srinivasan
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Section 740 updated
K.Srinivasan
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Updated Requirements.txt
K.Srinivasan
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+1556. (THEORY and FEATURE) Analysis of Matiyasevich Python programs for Riemann Hypothesis 3 - Higuchi Fractal Dimension of whi le loop LHS-RHS ratio timeseries - related to 629,751,1191,1217,1354,1357,1443,1459,1477,1552,1553,1554 to all sections on Riema nn Zeta Function, Set partitions, Computational geometry, Program analysis/Software analytics/Scheduler Analytics,Master Theorem ,Busy Beaver Function, Fractal Hausdorff Dimension-Box counting dimension, Timeseries analysis ,Complement diophantines - 11 Oc tober 2024
K.Srinivasan
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------------------------------------------------------------------------------------------------------------------------------- 1556. (THEORY and FEATURE) Analysis of Matiyasevich Python programs for Riemann Hypothesis 3 - Higuchi Fractal Dimension of while loop LHS-RHS ratio timeseries - related to 629,751,1191,1217,1354,1357,1443,1459,1477,1552,1553,1554 to all sections on Riemann Zeta Function, Set partitions, Computational geometry, Program analysis/Software analytics/Scheduler Analytics,Master Theorem,Busy Beaver Function, Fractal Hausdorff Dimension-Box counting dimension, Timeseries analysis ,Complement diophantines - 11 October 2024 ------------------------------------------------------------------------------------------------------------------------------- 1556.1 python-src/MatiyasevichRH.py has been updated to import Higuchi Fractal Dimension C++-Python implementation from https://github.com/inuritdino/HiguchiFractalDimension . Higuchi Fractal Dimension is an algorithmic approximation of Hausdorff-Box counting dimension for timeseries data. 1556.2 Flag integerdivision has been set to True for maximum iterations of while loop set to 3000. The LHS/RHS ratio of the while loop is treated as an one dimensional evolving timeseries and its Higuchi Fractal Dimension (HFD) is computed after every iteration. 1556.3 Following are the excerpts of testlogs/MatiyasevichRH.log.11October2024 that show how Fractal dimension of while loop LHS/RHS ratio changes for first and last few iterations (of total 3000) - from 1.43 (iteration 1) to 1.86 (iteration 3000). Dimension greater than 1 - from section 1554.7 - implies this ratio set cannot be accepted by a BSS machine for semialgebraic sets with Hausdorff dimension 1 (unless the trend changes for higher iterations): Higuchi Fractal Dimension of the 1-dimensional timeseries: 1.437565898074131 Higuchi Fractal Dimension of the 1-dimensional timeseries: 1.5039262593966656 Higuchi Fractal Dimension of the 1-dimensional timeseries: 1.5253834724634125 Higuchi Fractal Dimension of the 1-dimensional timeseries: 1.5264950476444004 Higuchi Fractal Dimension of the 1-dimensional timeseries: 1.5715166483141845 Higuchi Fractal Dimension of the 1-dimensional timeseries: 1.6197836010225781 Higuchi Fractal Dimension of the 1-dimensional timeseries: 1.6546898583810052 Higuchi Fractal Dimension of the 1-dimensional timeseries: 1.6478550461203558 ..... Higuchi Fractal Dimension of the 1-dimensional timeseries: 1.8637742596549 Higuchi Fractal Dimension of the 1-dimensional timeseries: 1.8635089400569613 Higuchi Fractal Dimension of the 1-dimensional timeseries: 1.8635202285132875 Higuchi Fractal Dimension of the 1-dimensional timeseries: 1.863230884019129 Higuchi Fractal Dimension of the 1-dimensional timeseries: 1.8634088566394034 Higuchi Fractal Dimension of the 1-dimensional timeseries: 1.8635377739923928 Higuchi Fractal Dimension of the 1-dimensional timeseries: 1.8638247504442451 Higuchi Fractal Dimension of the 1-dimensional timeseries: 1.8634922101238023 Higuchi Fractal Dimension of the 1-dimensional timeseries: 1.8634850336594446 Higuchi Fractal Dimension of the 1-dimensional timeseries: 1.8634508938926084
K.Srinivasan
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Section 1553 updated
Srinivasan Kannan
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1555. (THEORY and FEATURE) GitHub Code Search - NeuronRain conceptual graph creation - related to 14,365,626,711,727,753,876,1127,1128,1129,1130,1204,1263,1269,1427,1442,1452,1532,1534,1547 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 - 7 October 2024
Srinivasan Kannan
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------------------------------------------------------------------------------------------------------------------------------------------- 1555. (THEORY and FEATURE) GitHub Code Search - NeuronRain conceptual graph creation - related to 14,365,626,711,727,753,876,1127,1128,1129,1130,1204,1263,1269,1427,1442,1452,1532,1534,1547 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 - 7 October 2024 ------------------------------------------------------------------------------------------------------------------------------------------- 1555.1 python-src/SocialNetworkAnalysis_PeopleAnalytics.py has been updated to import networkx, write_dot and operator for Conceptual Graph creation. NeuronRain Conceptual Graph was being manually created so far in http://www.krishna-iresearch.org/ ( https://neuronrain-documentation.readthedocs.io/en/latest/ ) based on theoretical connections between features implemented across all repositories of NeuronRain. In this code change NeuronRain conceptual graph is automatically created by parsing the related sections of design documents of NeuronRain repositories obtained by results of GitHub code search API. New DiGraph object neuronrainconceptualgraph has been instantiated. 1555.2 NeuronRain repository design notes are theoretical commentaries of features implemented and are dynamically updated which makes it quite like a non-linear book in which pages are not in sequential sorted order. Sections of the design documents are cluttered across multiple repositories which are headlined with the format: <Section number>. <Description> - related to <Comma separated list of related sections> - <Date> 1555.3 Function codesearch_statistics() has been enhanced to parse the section headlines from GitHub code search results of all design documents in NeuronRain repositories and populate the neuronrainconceptualgraph with edges of the form <section number>----<related section number>. Parsing earlier regex pattern is implemented by string find() and locating "related to" substring in section headline - comma separated list of related sections are translated to edges. 1555.4 NeuronRain conceptual graph is written as DOT file to GitHubCodeSearch_NeuronRainConceptualGraph.dot by write_dot() and Betweenness centrality of conceptual graph is computed by NetworkX betweenness_centrality() to infer most important sections of NeuronRain design. 1555.5 Following are the some of the prominent sections of NeuronRain design ranked by betweenness centralities - from testlogs/SocialNetworkAnalysis_PeopleAnalytics.log.7October2024: ..... Betweenness centrality of NeuronRain conceptual graph: [('752', 0.009119131221607664), ('1223', 0.008445783188341348), ('1380', 0.0056344596076106125), ('1383', 0.004786742883966264), ('1144', 0.00441364715837325), ('1214', 0.004329107102599974), ('1302', 0.0039606437088301515), ('1253', 0.003915478473554018), ('1254', 0.0037112698456815874), ('1259', 0.0036348363705989003), ('831', 0.0032379999444120183), ('1158', 0.0026134072035848086), ('1237', 0.0025859992402975807), ('1257', 0.0024291562053172996), ('1142', 0.0023076733032540604), ('1351', 0.0021227660579782653), ('740', 0.0021158175602434756), ('828', 0.0020845493204369216), ('1203', 0.0019195224992356653), ('1455', 0.0016582203816423778),....
Srinivasan Kannan
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Section 1411 updated
K.Srinivasan
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