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Analyzed about 4 hours ago. based on code collected 1 day ago.
Nov 05, 2025 — Nov 05, 2026
Commit Message Contributor Files Modified Lines Added Lines Removed Code Location Date
Updated Requirements.txt
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+1406. (THEORY and FEATURE) Social network analysis and People analytics - Opinion Mining - Aggregation of news articles based on a query - Google News scraper by Newspaper3k and GoogleNews libraries - 24 March 2023 - related to Section 1380 and all sections on Majority Voting, Approximate Majority,Election analytics-Psephology-Opinion polls,Reservoir Sampling,Topic detection and tracking,Social network analysis and People analytics from Twitter-Facebook-LinkedIn-PIPL streams
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-------------------------------------------------------------------------------------------------------------------------------------------- 1406. (THEORY and FEATURE) Social network analysis and People analytics - Opinion Mining - Aggregation of news articles based on a query - Google News scraper by Newspaper3k and GoogleNews libraries - 24 March 2023 - related to Section 1380 and all sections on Majority Voting, Approximate Majority,Election analytics-Psephology-Opinion polls,Reservoir Sampling,Topic detection and tracking,Social network analysis and People analytics from Twitter-Facebook-LinkedIn-PIPL streams -------------------------------------------------------------------------------------------------------------------------------------------- 1406.1 This commit implements a function opinion_mining() within new Python 3.x source file SocialNetworkAnalysis_OpinionMining.py for scraping Google News search results mostly from newspapers (print media) for a user query by GoogleNews Python API and aggregating the texts of the results by Newspaper3k library to get an approximate estimate of popular opinion on the queried topic. 1406.2 Earlier function is an auxiliary tool to sample the psychological trend of population reported in media for a queried topic(sentiment polarity of the news article could be measured by various sentiment analyzers implemented in NeuronRain - MarkovRandomFields,RecursiveGlossOverlap_BeliefPropagation,SentiWordNet) and is most useful in Election analytics-Opinion surveys-Psephology. 1406.3 Function opinion_mining() takes a query string, date range and maximum number of pages to scrape and invokes GoogleNews and Newspaper3k API and returns the results as Pandas DataFrame. An example query "Chennai Metropolitan Area" has been searched on Google News and newspaper articles have been aggregated as dataframe (detailed logs - testlogs/SocialNetworkAnalysis_OpinionMining.log.24March2023) 1406.4 This kind of opinion mining from streaming news feeds could serve as a representative sample of population, especially when size of electorate is huge and unknown, manual sampling is costly and some random sample has to be chosen for an opinion. GoogleNews aggregation implemented in this commit adds to Twitter-Facebook-PIPL-LinkedIn social media scrapers implemented few years ago (which often require developer accounts and API keys,get broken due to proprietary API changes,suffer from ratelimit and not suitable for largescale opinion mining)
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+1405. (THEORY and FEATURE) Recursive Lambda Function Growth - Tree Adjoining Grammar - CFG Parser for English Language from NLTK - related to 249,707,829,1133,1134,1204,1263,1269,1284,1285 and all sections on Intrinsic Merit of Texts, Question-Answering, Embedding in vectorspace, Machine Translation, Transformers Attention Model,Named Entity Recognition and Meaning Representation, Complexity models of Question-Answering (multiple choice,open-ended,adaptive and static) - LTF,PTF,Switching Circuits,TQBF,Markov Chains, Mildly Context Sensitive Grammars-Tree Adjoining Grammars for Natural Languages - 22 March 2023^M
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-------------------------------------------------------------------------------------------------------------------------------------------- 1405. (THEORY and FEATURE) Recursive Lambda Function Growth - Tree Adjoining Grammar - CFG Parser for English Language from NLTK - related to 249,707,829,1133,1134,1204,1263,1269,1284,1285 and all sections on Intrinsic Merit of Texts, Question-Answering, Embedding in vectorspace, Machine Translation, Transformers Attention Model,Named Entity Recognition and Meaning Representation, Complexity models of Question-Answering (multiple choice,open-ended,adaptive and static) - LTF,PTF,Switching Circuits,TQBF,Markov Chains, Mildly Context Sensitive Grammars-Tree Adjoining Grammars for Natural Languages - 22 March 2023 ------------------------------------------------------------------------------------------------------------------------------------------- 1405.1 Natural Languages are Mildly Context Sensitive (or CFG+ - computational power between CFG and CSG) and there have been multiple linguistic efforts - Chomsky Minimalistic Grammar, Tree Adjoining Grammar (TAG), XTAG - https://www.cis.upenn.edu/~xtag/, XMG-2 - https://github.com/spetitjean/XMG-2/wiki - to define mildly context sensitive grammar (MCSG) for natural languages. 1405.2 python-src/RecursiveLambdaFunctionGrowth.py has been updated to define a new function tree_adjoining_grammar_annotated(self,grammar,text) to parse parts of speech annotations (Noun, Verb, Noun Phrase, Verb Phrase, Pronoun, Determiner etc.,) for a natural language sentence based on NLTK Context Free Grammar parsers for English (Chart Parser and Recursive Descent Parser) - https://www.nltk.org/book/ch08.html. 1405.3 Following 3 grammars in NLTK book (https://www.nltk.org/book/ch08.html) are parsed by NLTK CFG parsers for 3 example sentences : groucho_grammar = nltk.CFG.fromstring(""" S -> NP VP PP -> P NP NP -> Det N | Det N PP | 'I' VP -> V NP | VP PP Det -> 'an' | 'my' N -> 'elephant' | 'pajamas' V -> 'shot' P -> 'in' """) grammar1 = nltk.CFG.fromstring(""" S -> NP VP VP -> V NP | V NP PP PP -> P NP V -> "saw" | "ate" | "walked" NP -> "John" | "Mary" | "Bob" | Det N | Det N PP Det -> "a" | "an" | "the" | "my" N -> "man" | "dog" | "cat" | "telescope" | "park" P -> "in" | "on" | "by" | "with" """) grammar2 = nltk.CFG.fromstring(""" S -> NP VP NP -> Det Nom | PropN Nom -> Adj Nom | N VP -> V Adj | V NP | V S | V NP PP PP -> P NP PropN -> 'Buster' | 'Chatterer' | 'Joe' Det -> 'the' | 'a' Adj -> 'angry' | 'frightened' | 'little' | 'tall' N -> 'bear' | 'squirrel' | 'tree' | 'fish' | 'log' V -> 'chased' | 'saw' | 'said' | 'thought' | 'was' | 'put' P -> 'on' """) lambdafn.tree_adjoining_grammar_annotated(groucho_grammar,"I shot an elephant in my pajamas") lambdafn.tree_adjoining_grammar_annotated(grammar1,"Mary saw Bob") lambdafn.tree_adjoining_grammar_annotated(grammar2,"the angry bear chased the frightened little squirrel") 1405.4 Earlier function tree_adjoining_grammar_annotated() accepts a text and a grammar which at present is restricted to CFG parsers of NLTK and could be extended to more generic mildly context sensitive tree adjoining grammars implemented by linguists (e.g XTAG, XMG-2).Text file python-src/RecursiveLambdaFunctionGrowth.txt has been updated to a text from news article https://www.thehindu.com/news/cities/chennai/government-issues-order-for-expanding-chennai-metropolitan-planning-area-to-5904-sq-km/article66041750.ece
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+1404. (JIRA,THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 21 March 2023 - Resolution of a JIRA issue and N-Body gravity predictive analysis of some extreme weather events between Mid-March to Mid-April 2023 - related to 148,172,313,445,446,463,1165,1171,1176,1266,1289,1316,1318,1327,1358,1359,1360,1361,1364,1367,1368,1388 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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------------------------------------------------------------------------------------------------------------------------------------------- 1404. (JIRA,THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 21 March 2023 - Resolution of a JIRA issue and N-Body gravity predictive analysis of some extreme weather events between Mid-March to Mid-April 2023 - related to 148,172,313,445,446,463,1165,1171,1176,1266,1289,1316,1318,1327,1358,1359,1360,1361,1364,1367,1368,1388 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 ------------------------------------------------------------------------------------------------------------------------------------------- 1404.1 python-src/image_pattern_mining/ImageNet/GISWeatherAnalytics.py has been updated to comment redundant Keras imports. 1404.2 Gaussian Ensemble Forecast function was not looping through all possible bodypairs of a bodyconjunction passed on as arguments which has been raised as a JIRA issue https://krishnairesearch.atlassian.net/browse/NEUR-21 and rectified in this update by looping through gmm_gravities which is returned from climate_analytics populated by a for loop appending tuples of gravity L2 norms for each pair. 1404.3 Gaussian Ensemble Forecast is computed for the date range of 13 March 2023 and 13 April 2023 for conjunctions Venus-Mercury-Jupiter: gaussian_ensemble_forecast_rainfall_timeseries(predEWEparams={'datefrom':(2023,3,13,17,30,00),'dateto':(2023,4,13,17,30,00),'loc':'@0','bodyconjunctions':"Venus-Mercury-Jupiter",'angularsepbounds':('0d','30d')},days=5,forecast_seasonal_rainfall=10,historic_training_timeseries=nem_rainfall_timeseries) 1404.4 Logs and Plots of Gravity L2 norms for the pairs Venus-Mercury, Venus-Jupiter and Mercury-Jupiter for this date range are in: testlogs/GISWeatherAnalytics.Mercury-Jupiter.Gravity.21March2023.jpg testlogs/GISWeatherAnalytics.Mercury-Venus.Gravity.21March2023.jpg testlogs/GISWeatherAnalytics.Venus-Jupiter.Gravity.21March2023.jpg testlogs/GISWeatherAnalytics.log.21March2023 1404.5 Notable in the above gravity plots is the abnormal peak of Mercury gravity (steep ebb and tide) during first week of April 2023 (maximum on 7 April 2023). Following are the matching dates for <30 degree angular separation Ephemeris Search for Mercury-Jupiter and Venus-Mercury which are prominent Sequence mined CARs from HURDAT2 dataset (and coincidentally mentioned in 1358.4) indicating heightened probability of rainfall activity potentially caused by gravity induced low pressure (Section 1361.13 - ECMWF buoyancy equations involving gravity constant): Angular separation of Mercury-Jupiter matches bounds for date: 2023-03-16 17:30:00.000 Angular separation of Mercury-Jupiter matches bounds for date: 2023-03-17 17:30:00.000 Angular separation of Mercury-Jupiter matches bounds for date: 2023-03-18 17:30:00.000 Angular separation of Mercury-Jupiter matches bounds for date: 2023-03-19 17:30:00.000 Angular separation of Mercury-Jupiter matches bounds for date: 2023-03-20 17:30:00.000 Angular separation of Mercury-Jupiter matches bounds for date: 2023-03-21 17:30:00.000 Angular separation of Mercury-Jupiter matches bounds for date: 2023-03-22 17:30:00.000 Angular separation of Mercury-Jupiter matches bounds for date: 2023-03-23 17:30:00.000 Angular separation of Mercury-Jupiter matches bounds for date: 2023-03-24 17:30:00.000 Angular separation of Mercury-Jupiter matches bounds for date: 2023-03-25 17:30:00.000 Angular separation of Mercury-Jupiter matches bounds for date: 2023-03-26 17:30:00.000 Angular separation of Venus-Mercury matches bounds for date: 2023-03-31 17:30:00.000 Angular separation of Venus-Mercury matches bounds for date: 2023-04-01 17:30:00.000 Angular separation of Venus-Mercury matches bounds for date: 2023-04-02 17:30:00.000 Angular separation of Venus-Mercury matches bounds for date: 2023-04-03 17:30:00.000 Angular separation of Venus-Mercury matches bounds for date: 2023-04-04 17:30:00.000 Angular separation of Venus-Mercury matches bounds for date: 2023-04-05 17:30:00.000 Angular separation of Venus-Mercury matches bounds for date: 2023-04-06 17:30:00.000 Angular separation of Venus-Mercury matches bounds for date: 2023-04-07 17:30:00.000 Angular separation of Venus-Mercury matches bounds for date: 2023-04-08 17:30:00.000 Angular separation of Venus-Mercury matches bounds for date: 2023-04-09 17:30:00.000 Angular separation of Venus-Mercury matches bounds for date: 2023-04-10 17:30:00.000 Angular separation of Venus-Mercury matches bounds for date: 2023-04-11 17:30:00.000 Angular separation of Venus-Mercury matches bounds for date: 2023-04-12 17:30:00.000
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Section 1285 updated
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Section 1398 updated
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NASA JPL DE421 Ephemeris
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Some utility scripts: 1) GitHubSSL.sh - GitHub SSL connection verification 2) reposync.sh - repository sync
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Section 1236 updated
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Section 1285 updated
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Section 1204 updated
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Section 1285 updated
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Section 1398 updated
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Section 1398 updated
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Updated Section 1285
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Updated Section 18
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Updated BestPractices.txt
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Updated index.rst from AstroInferDesign.txt
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Section 1137 updated
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Section 1398 updated
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Section 1285 updated
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Section 1375 updated
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Section 1398 updated
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Section 1398 updated
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+1401. (THEORY and FEATURE) Computational Geometric Integer Factorization - DMRC PySpark Cloud version - Iterative Binary Search implementation - related to 498,601,1226,1259,1303,1319,1321,1323,1324,1325,1355,1365,1366,1369,1379,1381,1384,1385 and all sections on Integer Factorization (DMRC MapReduce PySpark and NC HPC Rust implementations),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 - 21 February 2023^M
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------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 1401. (THEORY and FEATURE) Computational Geometric Integer Factorization - DMRC PySpark Cloud version - Iterative Binary Search implementation - related to 498,601,1226,1259,1303,1319,1321,1323,1324,1325,1355,1365,1366,1369,1379,1381,1384,1385 and all sections on Integer Factorization (DMRC MapReduce PySpark and NC HPC Rust implementations),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 - 21 February 2023 ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 1401.1 python-src/DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized.py has been updated to change function tilesearch_nonpersistent(y, numfactors=maxfactors, padding=1, binarysearch="iterative") to choose between recursive and iterative binary search implementations through flag binarysearch depending on which either binary_search_interval_nonpersistent() or iterative_binary_search_interval_nonpersistent() are called. 1401.2 New function iterative_binary_search_interval_nonpersistent(xl,yl,xr,yr,numfactors=maxfactors) has been defined in python-src/DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized.py (which so far depends only on recursive binary search and often prone to stack overflow redressed by setrecursionlimit()) which is a verbatim Python translated port of Rust High Performance Computing Rayon parallel iterator factorization NC implementation in https://github.com/shrinivaasanka/Grafit/blob/master/course_material/Programming/Rust/rasterization/src/main.rs 1401.3 Iterative binary search parallel factor point location has been made the default choice. Two example integers 999323828321 and 999999999999 have been factorized by iterative binary search point location and the factor points are in testlogs/DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized.log.21February2023
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Updated Section 1398
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