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Nov 05, 2025 — Nov 05, 2026
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+1368. (THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 13 December 2022 - Prediction of Extreme Weather Events from Gaussian Mixture Model - Multimodal Gaussian fit of the precipitation timeseries - Gaussian Ensemble Rainfall Forecast from Timeseries Partition Enumeration - related to 148,172,313,445,446,463,1165,1171,1176,1266,1289,1316,1318,1327,1358,1359,1360,1361,1364,1367 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, Astronomical algorithms, Multifractals, Timeseries analysis, Sequence Mining-Class Association Rules,Computational Geometry on Riemannian Manifolds-Spacetime, ECMWF equations, Integer Partitions and Partition number, Money changeing problem
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------------------------------------------------------------------------------------------------------------------------------ 1368. (THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 13 December 2022 - Prediction of Extreme Weather Events from Gaussian Mixture Model - Multimodal Gaussian fit of the precipitation timeseries - Gaussian Ensemble Rainfall Forecast from Timeseries Partition Enumeration - related to 148,172,313,445,446,463,1165,1171,1176,1266,1289,1316,1318,1327,1358,1359,1360,1361,1364,1367 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, Astronomical algorithms, Multifractals, Timeseries analysis, Sequence Mining-Class Association Rules,Computational Geometry on Riemannian Manifolds-Spacetime, ECMWF equations, Integer Partitions and Partition number, Money changeing problem ------------------------------------------------------------------------------------------------------------------------------ 1368.1 python-src/image_pattern_mining/ImageNet/GISWeatherAnalytics.py has been updated to import integer partition functions from SymPy and norm from SciPy. 1368.2 New function gaussian_ensemble_forecast_rainfall_timeseries(predEWEparams=None,days=5,forecast_seasonal_rainfall=20,historic_training_timeseries=None,maxiterations=100) has been defined to implement Gaussian Ensemble Forecast model mentioned in 1367.7 based on integer partitions described in 1352.6 which accepts predict_EWE() dictionary parameters, number of days to forecast (defaults to 5 days), remaining rainfall to be received in the season (forecast_seasonal_rainfall), past historic training rainfall timeseries (historic_training_timeseries) and maximum number of integer partitions to enumerate. 1368.3 Basic algorithm implemented in a loop is to enumerate all restricted k-parts integer partitions (k=number of days forecast) of remaining rainfall to be received as candidate forecast rainfall timeseries (assuming nature sticks to average normal rainfall thumbrule for a seasonal non-stationary rainfall timeseries) and estimate Gaussian Mixture Model N-Body gravities in climate_analytics() for "forecast_timeseries" set to candidate timeseries partition and find the forecast timeseries which maximizes the N-Body gravity for any solar system bodypair - in other words, candidate timeseries are the elements of Gaussian Ensemble from which best fit forecast timeseries probability distribution is chosen corresponding to maximum GMM gravity L2 norm thus establishing a correlation between rainfall timeseries peaks and N-Body gravity peaks. Number of k-part partitions of remaining rainfall are available from functions partition() filtered by "days" of forecast which is equal to number of parts per candidate partition. Partitions are enumerated in lexicographic order by SymPy next_lex(): if sum(candidate_partition_dict.values()) == days: gmm_gravity=climate_analytics(datasource="precipitation_GaussianMixture",predict_EWE_params=predEWEparams,precipitation_timeseries={"timeseries":historic_training_timeseries,"averageseasonalrainfall":forecast_seasonal_rainfall,'forecast_timeseries':candidate_partition_dict.values()}) ... gaussian_ensemble_forecast_rainfall_timeseries(predEWEparams={'datefrom':(2022,12,13,17,30,00),'dateto':(2022,12,31,17,30,00),'loc':'@0','bodyconjunctions':"Mercury-Jupiter",'angularsepbounds':('0d','30d')},days=5,forecast_seasonal_rainfall=10,historic_training_timeseries=nem_rainfall_timeseries) 1368.4 Gaussian Ensemble 5-day forecast by N-Body gravity model (Mercury-Jupiter pair) for remaining season of North-East Monsoon and Cyclones over Bay of Bengal for date range 13/12/2022 to 31/12/2022 (which contains Mercury gravity peak on 27/12/2022) has been captured in logs testlogs/GISWeatherAnalytics.log.13December2022 by a fictitious forecast_seasonal_rainfall value of 10 and the most probable timeseries distribution of 10 over 5 days is found as [4,3,1,1,1]: gaussian_ensemble_forecast_rainfall_timeseries(): Maximum probability rainfall timeseries = [4, 3, 1, 1, 1] 1368.5 Timeseries candidates are printed as Ferrer histograms in testlogs/GISWeatherAnalytics.log.13December2022 as visual representation of rainfall forecasts. 1368.6 Algorithm earlier is brute force and its complexity depends on partition number of an integer (remaining rainfall to be distributed over forecast days) which is computable in polynomial time by Hardy-Ramanujan-Rademacher formula.
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Section 1351 updated
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Section 1367 updated
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Section 1351 and 1367 updated
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------------------------------------------------------------------------------------------------------------------------------ 1367. (THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 12 December 2022 - Prediction of Extreme Weather Events from Gaussian Mixture Model - Multimodal Gaussian fit of the precipitation timeseries - Modes set from N-Body gravitations - related to 148,172,313,445,446,463,1165,1171,1176,1266,1289,1316,1318,1327,1358,1359,1360,1361,1364 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, Astronomical algorithms, Multifractals, Timeseries analysis, Sequence Mining-Class Association Rules,Computational Geometry on Riemannian Manifolds-Spacetime, ECMWF equations ------------------------------------------------------------------------------------------------------------------------------ 1367.1 python-src/EphemerisSearch.py has been updated to return L2 norms of N-Body gravitational acceleration vectors as tuples in predict_EWE() 1367.2 python-src/image_pattern_mining/ImageNet/GISWeatherAnalytics.py has been updated to import find_peaks from scipy.signal 1367.3 Function climate_analytics(datasource,date="",time="",predict_EWE_params=None,precipitation_timeseries=None) has been updated to change the argument precipitation_timeseries to a dictionary of key-value pairs - "timeseries","forecast_timeseries" and "averageseasonalrainfall" 1367.4 Two timeseries supplied by user - for training and forecast - are zipped to 2D array of ordered pairs. 1367.5 predict_EWE_params are now necessary from which N-Body conjunctions of solar system bodies (angular separation of 30 degrees) are obtained and for every pair of planets in the conjunction, predict_EWE() is invoked to get gravity L2 norms for the body pair. 1367.6 SciPy signal module has a dedicated method find_peaks() to infer the modes or peaks in a signal timeseries which is useful for wide range of disciplines (music, electrocardiogram and other medical imagery, stock market sensitive indices, commodity prices, graphical event models-GEM and CEM, telecom signals among others). Function find_peaks() is invoked on N-Body gravity L2 norm timeseries of the planetary pair for the required forecast daterange and a GaussianMixture model is fit for training timeseries (argument "timeseries") for n_components (modes) equal to peaks of training timeseries. This preprocessing gives means or average rainfall estimate for future GMMs. 1367.7 GaussianMixture models are then fit for forecast timeseries but for n_components (modes) equal to number of peaks of gravity L2 norms for a planet in the body pair and means_init equal to means computed for training timeseries in 1367.6. Fit GMMs are used to predict probability of a user supplied rainfall forecast timeseries (argument "forecast_timeseries"). Rationale is to fit a forecast GMM from past average rainfall history and correlate the peaks in future rainfall to peak N-Body gravity L2 norms which could be verified by the probability of a future precipitation timeseries - high probability implies correct forecast. Argument "averageseasonalrainfall" is at present used only for printing to logs but in future it could be used to plot the GMM forecast itself (sklearn.mixture.GaussianMixture - predict() and predict_proba() support only probability prediction of future timeseries points which is a forecast verfication and doesn't predict the future timeseries as such). Algorithm earlier is somewhat similar to Gaussian Ensemble Weather Forecast Models which choose the best fit from an ensemble of forecasts: gmm_predict_body1_fit = GaussianMixture(n_components=no_of_modes_body1,random_state=0,means_init=gmm_fit.means_).fit(precipitation_timeseries_2D) gmm_predict_body1_predict = gmm_predict_body1_fit.predict_proba(precipitation_timeseries_forecast_2D) gmm_predict_body2_fit = GaussianMixture(n_components=no_of_modes_body2,random_state=0,means_init=gmm_fit.means_).fit(precipitation_timeseries_2D) gmm_predict_body2_predict = gmm_predict_body2_fit.predict_proba(precipitation_timeseries_forecast_2D) print("climate_analytics(): number of N-Body gravity peaks (modes of predicted GMM for average seasonal rainfall ",precipitation_timeseries['averageseasonalrainfall'],") for bodypair ",bp," for daterange ",predict_EWE_params['datefrom'],"-",predict_EWE_params['dateto'],":",(no_of_modes_body1,no_of_modes_body2)) print("climate_analytics(): GMM probabilities of user supplied Forecast rainfall timeseries for bodypair ",bp," for daterange ",predict_EWE_params['datefrom'],"-",predict_EWE_params['dateto'],":",gmm_predict_body1_predict) print("climate_analytics(): GMM probabilities of user supplied Forecast rainfall timeseries for bodypair ",bp," for daterange ",predict_EWE_params['datefrom'],"-",predict_EWE_params['dateto'],":",gmm_predict_body2_predict) 1367.8 climate_analytics() is invoked for following 3 prominent body pairs - Mercury-Venus, Mercury-Jupiter and Sun-Moon - for user specified training timeseries and forecast timeseries on future date range 12 December 2022 to 31 December 2022 when Mercury's gravity again peaks around 27 December 2022 and probability of user supplied future rainfall timeseries is printed by GMM in image_pattern_mining/ImageNet/testlogs/GISWeatherAnalytics.log.12December2022 : climate_analytics(datasource="precipitation_GaussianMixture",predict_EWE_params={'datefrom':(2022,12,12,17,30,00),'dateto':(2022,12,31,17,30,00),'loc':'@0','bodyconjunctions':"Mercury-Jupiter",'angularsepbounds':('0d','30d')},precipitation_timeseries={"timeseries":nem_rainfall_timeseries,"averageseasonalrainfall":100,'forecast_timeseries':[10,2,12,25,30,1,2,3,4,10]}) climate_analytics(datasource="precipitation_GaussianMixture",predict_EWE_params={'datefrom':(2022,12,12,17,30,00),'dateto':(2022,12,31,17,30,00),'loc':'@0','bodyconjunctions':"Mercury-Venus",'angularsepbounds':('0d','30d')},precipitation_timeseries={"timeseries":nem_rainfall_timeseries,"averageseasonalrainfall":100,'forecast_timeseries':[10,2,12,25,30,1,2,3,4,10]}) climate_analytics(datasource="precipitation_GaussianMixture",predict_EWE_params={'datefrom':(2022,12,12,17,30,00),'dateto':(2022,12,31,17,30,00),'loc':'@0','bodyconjunctions':"Sun-Moon",'angularsepbounds':('0d','30d')},precipitation_timeseries={"timeseries":nem_rainfall_timeseries,"averageseasonalrainfall":100,'forecast_timeseries':[10,2,12,25,30,1,2,3,4,10]}) 1367.9 Following are the JPEG plots for N-Body gravity L2 norm timeseries for earlier date range: image_pattern_mining/ImageNet/testlogs/GISWeatherAnalytics.Mercury-Jupiter.Gravity.12December2022.jpg image_pattern_mining/ImageNet/testlogs/GISWeatherAnalytics.Mercury-Venus.Gravity.12December2022.jpg image_pattern_mining/ImageNet/testlogs/GISWeatherAnalytics.Sun-Moon.Gravity.12December2022.jpg 1367.10 PDEs and Equations of ECMWF or any other model involving constant gravity could be augmented by a function of N-Body gravity vectors of solar system that might increase the forecast accuracy, but the caveat is this kind of N-Body variable gravity augmentation is location and date specific and would exponentially blow-up the supercomputer simulations compared to constant gravity PDEs. 1367.11 NeuronRain Climate model goes beyond conventional wisdom of pollution induced climate vagaries and introduces variable gravity as an additional catalyst in determining weather. Going by similar line of reasoning gravitational effects on atmospheric carbon foot print and melting arctic-antartic ice sheets might need to be investigated in the context of global warming.
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Updated Requirements.txt
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1366. (THEORY and FEATURE) Computational Geometric Integer Factorization - Python 3.7.5 + Spark 3.3.0 Quadcore Benchmarks - Linux General Number Field Sieve Factorization (CADO-NFS) comparison - related to 498,601,1226,1259,1303,1319,1321,1323,1324,1325,1355,1365 and all sections on Integer Factorization,Computational Geometry,Set Partitions-to-Tile Cover reduction by Lagranges Four Square Theorem and ILP,Primality Testing,RSA Semiprimes,Almost-Square Bounding Boxes in Large Scale Visual Recognition,Pell's Equation,Brahmagupta's Chakravala,Approximate Factoring by Newton-Raphson Rayshooting queries,Algebraic Geometry of Computational Geometric Factorization,Quantum computation and Derandomization of BQP Factorization by Condorcet Jury Theorem Majority-BQPVoterSAT boolean composition - 9 December 2022
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------------------------------------------------------------------------------------------------------------------------------ 1366. (THEORY and FEATURE) Computational Geometric Integer Factorization - Python 3.7.5 + Spark 3.3.0 Quadcore Benchmarks - Linux General Number Field Sieve Factorization (CADO-NFS) comparison - related to 498,601,1226,1259,1303,1319,1321,1323,1324,1325,1355,1365 and all sections on Integer Factorization,Computational Geometry,Set Partitions-to-Tile Cover reduction by Lagranges Four Square Theorem and ILP,Primality Testing,RSA Semiprimes,Almost-Square Bounding Boxes in Large Scale Visual Recognition,Pell's Equation,Brahmagupta's Chakravala,Approximate Factoring by Newton-Raphson Rayshooting queries,Algebraic Geometry of Computational Geometric Factorization,Quantum computation and Derandomization of BQP Factorization by Condorcet Jury Theorem Majority-BQPVoterSAT boolean composition - 9 December 2022 ------------------------------------------------------------------------------------------------------------------------------ 1366.1 NeuronRain Computation Geometric Factorization has been benchmarked versus General Number Field Sieve CADO-NFS implementation which is presently the fastest subexponential time factorization algorithm known. 1366.2 General Number Field Sieve is the factorization choice for integers greater than 10^100 (or 333 bits). Huge 539 bits random integer 991028128192918281928128812891892819829189299102812819291828192812881289189281982918928192891892199102812819291828192812881289189281982918928192891892181928918921 has been factorized by DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized.py equivalent to a non-uniform polylogdepth Nick's class circuit of depth 57 and first 4 factors (2 pairs of small and huge factors) have been found in 25.43 seconds (25431624829 nanoseconds) and first factor pair was found in 6.6 seconds: spark-3.3.0-bin-hadoop3/bin/spark-submit DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized.py 991028128192918281928128812891892819829189299102812819291828192812881289189281982918928192891892199102812819291828192812881289189281982918928192891892181928918921 57 False False all False 2>&1 > testlogs/DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized.log.539bits_GNFS.9December2022 ('Factors are: (', 43, ',', 23047165771928332137863460764927739996027658118670065564926237042160029981146092626021585881206795327972391146321585879369332306727487974858795183532376323928347, ') (at ', 'Fri, 09 Dec 2022 08:17:52 GMT', ')') nanoseconds elapsed so far in finding all factors: 6607918873 ('Factors are: (', 2931, ',', 338119456906488666642145620229236717785462060424023479799327257868605011664715790828702897608970385227844701225461682979488669119509376635594743395391396086291, ') (at ', 'Fri, 09 Dec 2022 08:18:10 GMT', ')') nanoseconds elapsed so far in finding all factors: 25431624829 factors_accum._value: [23047165771928332137863460764927739996027658118670065564926237042160029981146092626021585881206795327972391146321585879369332306727487974858795183532376323928347, 43, 338119456906488666642145620229236717785462060424023479799327257868605011664715790828702897608970385227844701225461682979488669119509376635594743395391396086291, 2931] 1366.3 Random 539 bits integer above has been factorized again by CADO-NFS implementation for 4 server threads and computation has been interrupted in polynomial selection as ETA is printed as atleast 36 hours: Info:Polynomial Selection (size optimized): Marking workunit c160_polyselect1_8000-9000 as ok (0.9% => ETA Sun Dec 11 01:26:36 2022) 1366.4 Both computations from NeuronRain Computational Geometric Factorization and CADO-NFS have been captured in python-src/testlogs/DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized.log.539bits_GNFS.9December2022
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1365. (THEORY and FEATURE) Computational Geometric Integer Factorization - Python 3.7.5 + Spark 3.3.0 Quadcore Benchmarks - Linux Elliptic Curve Factorization gmp-ecm comparison - related to 498,601,1226,1259,1303,1319,1321,1323,1324,1325,1355 and all sections on Integer Factorization,Computational Geometry,Set Partitions-to-Tile Cover reduction by Lagranges Four Square Theorem and ILP,Primality Testing,RSA Semiprimes,Almost-Square Bounding Boxes in Large Scale Visual Recognition,Pell's Equation,Brahmagupta's Chakravala,Approximate Factoring by Newton-Raphson Rayshooting queries,Algebraic Geometry of Computational Geometric Factorization,Quantum computation and Derandomization of BQP Factorization by Condorcet Jury Theorem Majority-BQPVoterSAT boolean composition - 7 December 2022
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------------------------------------------------------------------------------------------------------------------------------ 1365. (THEORY and FEATURE) Computational Geometric Integer Factorization - Python 3.7.5 + Spark 3.3.0 Quadcore Benchmarks - Linux Elliptic Curve Factorization gmp-ecm comparison - related to 498,601,1226,1259,1303,1319,1321,1323,1324,1325,1355 and all sections on Integer Factorization,Computational Geometry,Set Partitions-to-Tile Cover reduction by Lagranges Four Square Theorem and ILP,Primality Testing,RSA Semiprimes,Almost-Square Bounding Boxes in Large Scale Visual Recognition,Pell's Equation,Brahmagupta's Chakravala,Approximate Factoring by Newton-Raphson Rayshooting queries,Algebraic Geometry of Computational Geometric Factorization,Quantum computation and Derandomization of BQP Factorization by Condorcet Jury Theorem Majority-BQPVoterSAT boolean composition - 7 December 2022 ------------------------------------------------------------------------------------------------------------------------------ 1365.1 Lenstra's Elliptic Curve Method Factorization (ECM) - https://en.wikipedia.org/wiki/Lenstra_elliptic-curve_factorization - is the fastest factorization algorithm known for small integers of size 60 bits and below and is based on elliptic curves. 1365.2 Huge 169 bits random integer 392093092302392039932839293283823823892838298392891 has been factorized by NeuronRain Computational Geometric Factorization in 26.75 seconds (26750878058 nanoseconds) on quadcore (4 PRAMs) and Python 3.7.5 + Spark 3.3.0.Following are the first pair of factors (small and huge) captured in python-src/testlogs/DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized.log.169bits_ECM.7December2022: ---------------------------------------------------- bin/spark-submit DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized.py 392093092302392039932839293283823823892838298392891 20 False False all False 2>&1 > testlogs/DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized.log.62bits_ECM.7December2022 ================================================= xl + intervalmidpoint = 36201005659901397833333883601128596056951186261 xr + intervalmidpoint = 36201005659901397833333883601128596056951186271 xl = 36201005659901397833333883601128596056951186256 yl = 10831 Factor point verification: (xl + intervalmidpoint)*yl == number_to_factorize = True Factor point verification: xl*yl == number_to_factorize = False ('Factors are: (', 10831, ',', 36201005659901397833333883601128596056951186261, ') (at ', 'Wed, 07 Dec 2022 06:59:43 GMT', ')') nanoseconds elapsed so far in finding all factors: 26750878058 ================================================= factors_accum._value: [36201005659901397833333883601128596056951186261, 10831] ---------------------------------------------------- 1365.3 ECM Ubuntu implementation by GMP-ECM - http://ecm.gforge.inria.fr/ - has been used to factorize same 169 integer 392093092302392039932839293283823823892838298392891 which found first factor in 1215.184 seconds (1215184 milliseconds) for B1=1000000000 (ECM requires a text file input which has been created as EllipticCurveFactorization.txt): -------------------------------------- ecm 1000000000 < EllipticCurveFactorization.txt GMP-ECM 7.0.4 [configured with GMP 6.1.2, --enable-asm-redc] [ECM] Input number is 392093092302392039932839293283823823892838298392891 (51 digits) Using B1=1000000000, B2=19071176724616, polynomial Dickson(30), sigma=1:204879369 Step 1 took 1215184ms ********** Factor found in step 1: 10964185373573 Found composite factor of 14 digits: 10964185373573 Prime cofactor 35761260772501610413094560738700549567 has 38 digits -------------------------------------- 1365.4 For a random integer above, computational geometric factorization is approximately 47 times faster though factors found are of different sizes. Lenstra's ECM is subexponential time and one of the three prominent factorization algorithms others being General Number Field Sieve and Polynomial Quadratic Sieve 1365.5 Depth of the polylogarithmic non-uniform NC circuit is set to 20 or runtime is O((logN)^20) which would require N/(logN)^20 PRAMs by definition of NC (or) 2^169 / 169^20 = 2^169/2^148 = 2^21 = 2097152 multicores-PRAMs
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1364. (THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 5 December 2022 - Prediction of Extreme Weather Events from Gaussian Mixture Model - Multimodal Gaussian fit of the precipitation timeseries - related to 148,172,313,445,446,463,1165,1171,1176,1266,1289,1316,1318,1327,1358,1359,1360,1361 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, Astronomical algorithms, Multifractals, Timeseries analysis, Sequence Mining-Class Association Rules,Computational Geometry on Riemannian Manifolds-Spacetime, ECMWF equations
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------------------------------------------------------------------------------------------------------------------------------ 1364. (THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 5 December 2022 - Prediction of Extreme Weather Events from Gaussian Mixture Model - Multimodal Gaussian fit of the precipitation timeseries - related to 148,172,313,445,446,463,1165,1171,1176,1266,1289,1316,1318,1327,1358,1359,1360,1361 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, Astronomical algorithms, Multifractals, Timeseries analysis, Sequence Mining-Class Association Rules,Computational Geometry on Riemannian Manifolds-Spacetime, ECMWF equations ------------------------------------------------------------------------------------------------------------------------------- 1364.1 python-src/image_pattern_mining/ImageNet/GISWeatherAnalytics.py has been updated to comment unnecessary imports and import GaussianMixture Model from scikit-learn. 1364.2 Function climate_analytics(datasource,date="",time="",predict_EWE_params=None,precipitation_timeseries=None) has been updated to include a clause for new datasource "precipitation_GaussianMixture" which instantiates Scikit Learn Gaussian Mixture Model and fits precipitation timeseries to a multimodal Gaussian at present restricted to 2 modes (bimodal Gaussian or mixture of 2 unimodal Gaussians) - number of modes or components should infact be equal to number of peak dates in N-Body Gravitational accelerations that would perturb every ECMWF partial differential equation involving constant gravity (g) including Barometric Height Formula and Gravity Wave Drag-Buoyancy (which constitutes 30% of ECMWF supercomputer simulations) making it a variable. 1364.3 GaussianMixture() object accepts 2D version of timeseries (ordered pairs of the form day-rainfall) and prints the means for each component of mixture model: climate_analytics(datasource="precipitation_GaussianMixture",precipitation_timeseries=nem_rainfall_timeseries) 1364.4 GMM computation is captured in testlogs/GISWeatherAnalytics.log.5December2022. Timeseries forecast example in Section 1352.6 and cloudbursts due to climate change in Section 1361.15 could be modelled by Gaussian Multimodal Mixture by setting means_init parameter to average normal seasonal rainfall and n_components parameter to number of peaks in N-Body gravity fluctuations computed by EphemerisSearch N-Body anaytics (for example from forecast made on 22 November 2022 - Section 1358 - Mercury's gravity on Earth peaks on two dates - 8 December 2022 and 27 December 2022 - which might potentially translate into a low pressure system).
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Updated Section 1361
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1363. (JIRA,THEORY and FEATURE) GIS Urban Sprawl Analytics - Overlay of GHSL R2022A BUILT-V Visualization and Chennai Metropolitan Area Map and its DBSCAN clustering - 2 December 2022 - 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 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, 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, Autonomous Driving, Expander Graphs
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------------------------------------------------------------------------------------------------------------------------------- 1363. (JIRA,THEORY and FEATURE) GIS Urban Sprawl Analytics - Overlay of GHSL R2022A BUILT-V Visualization and Chennai Metropolitan Area Map and its DBSCAN clustering - 2 December 2022 - 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 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, 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, Autonomous Driving, Expander Graphs ------------------------------------------------------------------------------------------------------------------------------- 1363.1 python-src/DBSCANClustering.py has been updated to introduce fraction of image as parameter to DBSCAN __init__() constructor. Image is sliced for the fraction specified before clustering. 1363.2 Neural network thresholding has been made similar to conventional thresholding and inequality has been reversed: if evaledthreshold < threshold: 1363.3 Function clustering(self,maxclusters=100000,neuralnetwork=False) now accepts maximum number of clusters as additional argument for limiting the runtime while processing huge images. 1363.4 Two versions of find_neighbours() with and without neuralnetwork have been made into if-else clause depending on neuralnetwork flag. Erroneous remove() from seedpoints array have been commented (DBSCAN pseudocode from https://en.wikipedia.org/wiki/DBSCAN has been exactly adhered to) 1363.5 In write_clustered_image(self,neuralnetwork=False,fraction=1) clause for undefined cluster labels (-1) has been introduced which was missing until now. 1363.6 DBSCAN clustering densities for BUILT-V overlay has been computed as below and logged to testlogs/DBSCANClustering.log.2December2022: dbscan3=DBSCAN("image_pattern_mining/ImageNet/testlogs/ChennaiMetropolitanAreaExpansion21October2022_GHSLR2022A_BUILT-V-overlay.jpg") dbscan3.clustering() dbscan3.write_clustered_image(neuralnetwork=False) 1363.7 find_neighbours() function has been implemented at present as a square of side 2*epsilon which results in somewhat tiled clustering in ChennaiMetropolitanAreaExpansion21October2022_GHSLR2022A_BUILT-V-overlay_dbscanclustered.jpg . Circular neighbourhood of area Pi*epsilon*epsilon could cluster better but approximation of circular vicinity on 2-D array is non-trivial (which reduces to rasterization of a circle). Cluster statistics for this image are: number of DBSCAN density clusters: 15684 size of image: 1272000 size of dbscandict: 719114 1363.8 This commit resolves JIRA issue: https://krishnairesearch.atlassian.net/browse/NEUR-17
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Section 1361 updated
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1362. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Overlay of GHSL R2022A BUILT-V Visualization and Chennai Metropolitan Area Map and its clustering - 30 November 2022 and 1 December 2022 - 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 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, 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, Autonomous Driving, Expander Graphs
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------------------------------------------------------------------------------------------------------------------------------- 1362. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Overlay of GHSL R2022A BUILT-V Visualization and Chennai Metropolitan Area Map and its clustering - 30 November 2022 and 1 December 2022 - 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 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, 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, Autonomous Driving, Expander Graphs ------------------------------------------------------------------------------------------------------------------------------- 1362.1 python-src/image_pattern_mining/ImageNet/GISUrbanSprawlAnalytics.py has been updated to perform segmentation and density clustering (DBSCAN) on overlay imagery of GHSL R2022A BUILT-V (https://ghsl.jrc.ec.europa.eu/visualisation.php#[email protected],80.05875,10z&v=301&ln=0&gr=ds&lv=00000000000000000000000010000000000000011111&lo=aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa&pg=V) and Chennai Metropolitan Area (testlogs/ChennaiMetropolitanAreaExpansion21October2022.jpeg) boundaries in the context of 2634% boom in periurban class 21 pixels found in Sections 1313,1320 and OSMNx Road Density rankings of periurban areas computed in 1350,1351. Overlay imagery is manually created from GIMP Layering tool by scaling and overlaying R2022A BUILT-V and testlogs/ChennaiMetropolitanAreaExpansion21October2022.jpeg after alpha channel coloring for opacity (transparency) 1362.2 DBSCAN clustering (neural network thresholding) and urban_sprawl_from_segments() are invoked for the overlay image to cluster the BUILT-V pixels in periurban regions of Chennai Metropolitan Area by density and segment by contours: #Chennai Metropolitan Area - GHSL R2022A BUILT-V overlay DBSCAN analysis dbscan1=DBSCANClustering.DBSCAN("testlogs/ChennaiMetropolitanAreaExpansion21October2022_GHSLR2022A_BUILT-V-overlay.jpg") dbscan1.clustering() dbscan1.write_clustered_image(neuralnetwork=True) seg14=ImageGraph_Keras_Theano.image_segmentation("testlogs/ChennaiMetropolitanAreaExpansion21October2022_GHSLR2022A_BUILT-V-overlay.jpg") urban_sprawl_from_segments("testlogs/ChennaiMetropolitanAreaExpansion21October2022_GHSLR2022A_BUILT-V-overlay.jpg",seg14,maximum_population_density=11422,sqkmtocontourarearatio=mapscale,legend=None,sqkmareatopopulationratio=6.22,voronoi_delaunay=False,number_of_clusters=5,maxiterations=5) 1362.3 DBSCAN clustering has been imported into GISUrbanSprawlAnalytics.py. Matplotlib was slowing down heavily allocating huge anon pages for heap (few GBs) which required some optimizations - (*) Overlay image had lot of sea pixels which were curtailed (*) fast style is used in matplotlib (*) shapely speedups have been disabled due to following slowdown warning: /usr/local/lib/python3.7/dist-packages/geopandas/_compat.py:115: UserWarning: The Shapely GEOS version (3.9.1-CAPI-1.14.2) is incompatible with the GEOS version PyGEOS was compiled with (3.10.1-CAPI-1.16.0). Conversions between both will be slow. shapely_geos_version, geos_capi_version_string 1362.4 python-src/image_pattern_mining/ImageNet/ImageGraph_Keras_Theano.py has been updated to do periodic garbage collection because of huge memory footprint. 1362.5 Functions image_segmentation_edge_detection(imagefile,plot="savefig"), contours_kmeans_clustering(imagefile,segment,number_of_clusters=3,maxiterations=3,plot="savefig"), image_segmentation_contours(imagefile1,plot="savefig") have been changed to choose between savefig() and show() in matlplotlib plots by parameter plot set to "savefig" or "show". The two functions have divergent codepaths and runtimes are different. 1362.6 Following are the affected logs,DOT file and images (Canny edge, kmeans, contour bounding circles, contour segments, watershed segments and DBSCAN neural network thresholding): testlogs/ChennaiMetropolitanAreaExpansion21October2022_GHSLR2022A_BUILT-V-overlay-cannyedgedetection.jpg testlogs/ChennaiMetropolitanAreaExpansion21October2022_GHSLR2022A_BUILT-V-overlay-contourkmeansclustered.jpg testlogs/ChennaiMetropolitanAreaExpansion21October2022_GHSLR2022A_BUILT-V-overlay-contourlabelled.jpg testlogs/ChennaiMetropolitanAreaExpansion21October2022_GHSLR2022A_BUILT-V-overlay-contoursegmented.jpg testlogs/ChennaiMetropolitanAreaExpansion21October2022_GHSLR2022A_BUILT-V-overlay.jpg testlogs/ChennaiMetropolitanAreaExpansion21October2022_GHSLR2022A_BUILT-V-overlay_neuralnetworkclustered.jpg testlogs/GISUrbanSprawlAnalytics.log.30November2022 testlogs/RemoteSensingGIS/ChennaiMetropolitanAreaExpansion21October2022_GHSLR2022A_BUILT-V-overlay_ImageNet_Keras_Theano_Segmentation_FaceGraph.dot testlogs/RemoteSensingGIS/ChennaiMetropolitanAreaExpansion21October2022_GHSLR2022A_BUILT-V-overlay_segmented.jpg 1362.7 DBSCAN neural network density thresholding - testlogs/ChennaiMetropolitanAreaExpansion21October2022_GHSLR2022A_BUILT-V-overlay_neuralnetworkclustered.jpg - shows marked concentration of built-up volume corresponding to 2634% boom in class 21 periurban pixels of GHSL R2022A SMOD raster (Sections 1313,1320) and high road densities (Sections 1350,1351).
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Section 1361 updated
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Section 1361 - MinedClassAssociationRules.json committed
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+1361. (THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 29 November 2022 - Prediction of Extreme Weather Events based on angular separations-NBody gravitational accelerations of solar system bodies from Sequence Mining - Arbitrary Celestial Conjunctions and Reading Sequence mined Class Association Rules from JSON - related to 148,172,313,445,446,463,1165,1171,1176,1266,1289,1316,1318,1327,1358,1359,1360 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, Astronomical algorithms, Multifractals, Timeseries analysis, Sequence Mining-Class Association Rules,Computational Geometry on Riemannian Manifolds-Spacetime, ECMWF equations
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----------------------------------------------------------------------------------------------------------------------------- 1361. (THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 29 November 2022 - Prediction of Extreme Weather Events based on angular separations-NBody gravitational accelerations of solar system bodies from Sequence Mining - Arbitrary Celestial Conjunctions and Reading Sequence mined Class Association Rules from JSON - related to 148,172,313,445,446,463,1165,1171,1176,1266,1289,1316,1318,1327,1358,1359,1360 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, Astronomical algorithms, Multifractals, Timeseries analysis, Sequence Mining-Class Association Rules,Computational Geometry on Riemannian Manifolds-Spacetime, ECMWF equations ----------------------------------------------------------------------------------------------------------------------------- 1361.1 GISWeatherAnalytics.py has been updated to change climate_analytics() to generalize celestial bodypairs to arbitrary celestial conjunctions by finding all possible pair combinations of a n-body conjunction to search in sky for a date range. Parameter "bodypair" has been changed to "bodyconjunctions" in predict_EWE_params and function itertools.combinations() has been invoked to compute all pairs of bodies on a tokenized "bodyconjunction" string parameter. 1361.2 predict_EWE() is invoked on every pair of the body pair combination computed earlier. New function sequence_mining_CAR_search(carjsonfile,pEWEparams,support=10.0,maxiterations=5) has been defined to read HURDAT2 Sequence Mined Class Association Rule JSON file populated in Section 1360 by json module. Every rule in JSON file is sifted for Solar system bodies (for ignoring any other strings and intercuspal rules) and valid (intracuspal) rules which have frequencies above support are searched by climate_analytics() by setting "bodyconjunctions" dictionary parameter. Number of iterations are limited by maxiterations. 1361.3 Functions sequence_mining_CAR_search() and climate_analytics() are called as below for new date range of 28 November 2022 to 8 December 2022 and angular separation of 30 degrees - First 5 valid rules in MinedClassAssociationRules.json are searched by sequence_mining_CAR_search() and 5 planetary conjunctions are searched by climate_analytics() : sequence_mining_CAR_search("../../MinedClassAssociationRules.json",pEWEparams={'datefrom':(2022,12,8,17,30,00),'dateto':(2022,12,27,17,30,00),'loc':'@0','angularsepbounds':('0d','30d')},maxiterations=5) climate_analytics(datasource="n-body-analytics",predict_EWE_params={'datefrom':(2022,11,28,17,30,00),'dateto':(2022,12,8,17,30,00),'loc':'@0','bodyconjunctions':"Venus-Sun-Mercury",'angularsepbounds':('0d','30d')}) climate_analytics(datasource="n-body-analytics",predict_EWE_params={'datefrom':(2022,11,28,17,30,00),'dateto':(2022,12,8,17,30,00),'loc':'@0','bodyconjunctions':"Venus-Mercury",'angularsepbounds':('0d','30d')}) climate_analytics(datasource="n-body-analytics",predict_EWE_params={'datefrom':(2022,11,28,17,30,00),'dateto':(2022,12,8,17,30,00),'loc':'@0','bodyconjunctions':"Mercury-Jupiter",'angularsepbounds':('0d','30d')}) climate_analytics(datasource="n-body-analytics",predict_EWE_params={'datefrom':(2022,11,28,17,30,00),'dateto':(2022,12,8,17,30,00),'loc':'@0','bodyconjunctions':"Jupiter-Venus",'angularsepbounds':('0d','30d')}) climate_analytics(datasource="n-body-analytics",predict_EWE_params={'datefrom':(2022,11,28,17,30,00),'dateto':(2022,12,8,17,30,00),'loc':'@0','bodyconjunctions':"Sun-Moon",'angularsepbounds':('0d','30d')}) 1361.4 Earlier ephemeris search for arbitrary bodies in spacetime (which includes Deep Field galaxies and Stars) is a computational geometric range query and point location problem on 3-Dimensional spacetime for retrieving celestial bodies within a 3-D bounding box of longitude-latitude-altitude on a date range. Spacetime is a pseudo-Riemannian Manifold (or a differentiable topological space) and existing computational geometric algorithms are mostly limited to terrestrial contexts involving euclidean geometry. 1361.5 Barometric height formula in 1359.10 which relates Pressure, Gravity, Temperature and Height and is the fundamental equation in ECMWF PDEs, has the unexpected implication for variable gravity in N-Body gravity climate model - As gravity increases in exponent of the formula, pressure exponentially decreases potentially causing severe low pressure troughs, a particularly important observation,implying even gravitational fluctuations due to distant solar system bodies (example: Pluto) can't be ignored as negligible. 1361.6 It is hereby disclaimed that N-Body climate model is still in drawing boards and thus experimental verification of it is a necessity (though it is prima-facie evidenced by Sequence Mining of HURDAT2 hurricane datasets and a classic 5000 year old Indian astronomy treatise - Brihat Samhita) - Following are the logs and gravity plots for sequence mined rules and new dateranges earlier which again show notable peaking of Mercury's gravity, slight increase on Moon's gravity and drop in Mars' gravity from 28 November 2022 to 8 December 2022 of all the bodies which should theoretically affect Barometric pressure on Oceanic Earth though pinpointing the location is difficult at present as N-Body gravity computation is global and not localized due to vectors() JPL Horizons API limitation (Section 1327): testlogs/GISWeatherAnalytics.Jupiter-Venus.Gravity.28November2022.jpg testlogs/GISWeatherAnalytics.Mars-Saturn.Gravity.28November2022.jpg testlogs/GISWeatherAnalytics.Mercury-Jupiter.Gravity.28November2022.jpg testlogs/GISWeatherAnalytics.Mercury-Venus.Gravity.28November2022.jpg testlogs/GISWeatherAnalytics.Moon-Mars.Gravity.28November2022.jpg testlogs/GISWeatherAnalytics.Moon-Saturn.Gravity.28November2022.jpg testlogs/GISWeatherAnalytics.Sun-Mercury.Gravity.28November2022.jpg testlogs/GISWeatherAnalytics.Sun-Moon.Gravity.28November2022.jpg testlogs/GISWeatherAnalytics.Venus-Sun.Gravity.28November2022.jpg testlogs/GISWeatherAnalytics.log.28November2022
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+1360. (THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 28 November 2022 - Prediction of Extreme Weather Events based on angular separations-NBody gravitational accelerations of solar system bodies from Sequence Mining - Python3.x upgrade of Sequence Mining (NeuronRain Green and Antariksh - GitHub and GitLab) and JSON persistence in NeuronRain Research (SourceForge) - related to 148,172,313,445,446,463,1165,1171,1176,1266,1289,1316,1318,1327,1358,1359 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, Astronomical algorithms, Multifractals, Timeseries analysis, Sequence Mining-Class Association Rules^M
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----------------------------------------------------------------------------------------------------------------------------- 1360. (THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 28 November 2022 - Prediction of Extreme Weather Events based on angular separations-NBody gravitational accelerations of solar system bodies from Sequence Mining - Python3.x upgrade of Sequence Mining (NeuronRain Green and Antariksh - GitHub and GitLab) and JSON persistence in NeuronRain Research (SourceForge) - related to 148,172,313,445,446,463,1165,1171,1176,1266,1289,1316,1318,1327,1358,1359 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, Astronomical algorithms, Multifractals, Timeseries analysis, Sequence Mining-Class Association Rules ------------------------------------------------------------------------------------------------------------------------------ 1360.1 SequenceMining.py versions in SourceForge,GitHub and GitLab have been upgraded to Python 3.x by 2to3 and autopep8 utilities. Python 2.7 version has been renamed as SequenceMining.py.2.7 1360.2 Sequence Mined Class Association Rules (in Zodiac format of 12*30 degrees) are persisted to MinedClassAssociationRules.json in NeuronRain Research SourceForge repository version of SequenceMining.py. 1360.3 Sequences (Astronomical Datasets) Mined for Length 7 in NeuronRain Research (SourceForge) version of SequenceMining.py are logged to testlogs/SequenceMining.log.length7.JSONCAR.28November2022 and also persisted in MinedClassAssociationRules.json readable by python-src/image_pattern_mining/ImageNet/GISWeatherAnalytics.py. 1360.4 Sequences (Generic Datasets) Mined for Length 4 in NeuronRain Green and Antariksh (GitHub and GitLab) versions of SequenceMining.py are logged to testlogs/SequenceMining.log.length4.JSONCAR.28November2022 1360.5 Length of the Mined Sequence has been made a parameter maxlength to doAprioriGSPSequenceMining(self,maxlength=5)
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Updated BestPractices.txt
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+1359. (THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 24 November 2022 - Prediction of Extreme Weather Events based on angular separations-NBody gravitational accelerations of solar system bodies from Sequence Mining - Graphics plot of Gravities - related to 148,172,313,445,446,463,1165,1171,1176,1266,1289,1316,1318,1327,1358 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, Astronomical algorithms, Multifractals, Timeseries analysis, Sequence Mining-Class Association Rules^M
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------------------------------------------------------------------------------------------------------------------------------- 1359. (THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 24 November 2022 - Prediction of Extreme Weather Events based on angular separations-NBody gravitational accelerations of solar system bodies from Sequence Mining - Graphics plot of Gravities - related to 148,172,313,445,446,463,1165,1171,1176,1266,1289,1316,1318,1327,1358 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, Astronomical algorithms, Multifractals, Timeseries analysis, Sequence Mining-Class Association Rules ------------------------------------------------------------------------------------------------------------------------------- 1359.1 python-src/EphemerisSearch.py has been changed to import SciPy L2 norm() and matplotlib plot() functions. 1359.2 Function predict_EWE(datefrom,dateto,loc,bodypair,angularsepbounds) has been changed to instantiate EphmerisSearch object and function extreme_weather_events_n_body_analytics() has been invoked to get details of the gravitational accelerations for every solar system body pair. Conjunctions of arbitrary number of solar system planets (some of the sequence mined rules contain 4 or 5 planets in HURDAT2 dataset example) could be searched by intersection of date ranges of pairwise angular separations. E.g Class association rule Sun+Moon+Mercury+Jupiter could be searched in Ephemeris by intersection of 30 degree angular separation dates for (1) Sun+Moon (2) Moon+Mercury (3) Mercury+Jupiter (4) Sun+Mercury (5) Sun+Jupiter (6) Moon+Jupiter (or) 4C2 = 4!/(2!2!) = 24/4 = 6 pairs. 1359.3 3D Gravitational acceleration vectors (x,y,z) computed for the pair of solar system bodies are plotted as L2 norms in matplotlib. From 1266.18 the vertical component z of the gravitational vectors is especially crucial as it could act on ocean surface and pull water vapor towards upper atmosphere by angular momentum decided by the aligning solar system bodies. As altitude of water vapor increases, pressure and temperature get lowered thus making a perfect recipe for storm surge (by Barometric Height Formula). 1359.4 Function extreme_weather_events_n_body_analytics(self,datesofEWEs=None,loc="@earth-moon",angularsep=False,maxiterations=200) has been updated to change datatype of gravity vectors to dictionary of planet-gravity_vector from present arrays. 1359.5 python-src/image_pattern_mining/ImageNet/GISWeatherAnalytics.py has been updated invoke predict_EWE() for new date range 8 December 2022 to 27 December 2022 and 3 pairs Venus-Mercury,Mercury-Jupiter and Jupiter-Venus: climate_analytics(datasource="n-body-analytics",predict_EWE_params={'datefrom':(2022,12,8,17,30,00),'dateto':(2022,12,27,17,30,00),'loc':'@0','bodypair':"Venus-Mercury",'angularsepbounds':('0d','30d')}) climate_analytics(datasource="n-body-analytics",predict_EWE_params={'datefrom':(2022,12,8,17,30,00),'dateto':(2022,12,27,17,30,00),'loc':'@0','bodypair':"Mercury-Jupiter",'angularsepbounds':('0d','30d')}) climate_analytics(datasource="n-body-analytics",predict_EWE_params={'datefrom':(2022,12,8,17,30,00),'dateto':(2022,12,27,17,30,00),'loc':'@0','bodypair':"Jupiter-Venus",'angularsepbounds':('0d','30d')}) 1359.6 Gravitational accelerations for 3 pairs have been plotted with legend in following JPG files and computations are in image_pattern_mining/ImageNet/testlogs/GISWeatherAnalytics.log.24November2022: image_pattern_mining/ImageNet/testlogs/GISWeatherAnalytics.Jupiter-Venus.Gravity.24November2022.jpg image_pattern_mining/ImageNet/testlogs/GISWeatherAnalytics.Mercury-Jupiter.Gravity.24November2022.jpg image_pattern_mining/ImageNet/testlogs/GISWeatherAnalytics.Venus-Mercury.Gravity.24November2022.jpg 1359.7 Gravity plots earlier show spike in pull of Mercury on Earth during the date range while Jupiter and Venus are static (abnormally high mercury gravity have been found earlier in Section 1271 and 1273). In theory, values of gravitational accelerations over ocean longitudes should peak (proportionately lowering pressure) during conjunctions of 2 or more solar system bodies and thus forming an one-to-one correspondence between Category of the storm and N-Body Gravity.
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Updated Section 1358
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Updated Section 1358
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