| -------------------------------------------------------------------------------------------------------------------------------------------- 1573. (THEORY and FEATURE) Nachiketas Formal LLM - Dynamically configured Sentence PoS Dictionary retrieved by inorder traversal of grammar trees from treebank - Changes to Wikipedia Question-Answering algorithm for better accuracy 12 - related to 2,249,707,829,1133,1134,1204,1263,1269,1284,1285,1405,1413,1455,1468,1469,1536,1537,1538,1539,1540,1541,1542,1543,1550,1568,1572 and all sections on Intrinsic Merit of Texts, Recursive Lambda Function Growth and Recursive Gloss Overlap textgraph algorithms, 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, Large Language Models, Mildly Context Sensitive Grammars-Tree Adjoining Grammars for Natural Languages,Text restoration-Vowelless text (de)compression - 5 January 2025 -------------------------------------------------------------------------------------------------------------------------------------------- 1573.1 python-src/NeuronRainApps/QuestionAnswering/QuestionAnswering.py has been updated to import NLTK treebank and defaultdict. Function def WikipediaRLFGTransformersQuestionAnswering(question,questionfraction=1,maxanswers=1,keywordsearch=False,wsheading=True,answerslice=1,answerfraction=1,bothvertices_intersection=True,sentence_type="xtag_node34_triplets",number_of_random_walks=10,number_of_words_per_sentence=5,number_of_cores_per_random_walk=5,std_sentence_PoS_dict={"ADJ":[],"PROPN":[],"NOUN":[],"AUX":[],"ADP":[],"ADV":[],"VERB":[],"DET":[],"PRON":[],"CCONJ":[],"NUM":[],"SYM":[],"X":[]},blanks=False,perplexity_algorithm="WordNet",treenode_type="PoS") has been updated to add 2 new arguments perplexity_algorithm and treenode_type : (*) perplexity_algorithm is either "pyplexity" or "WordNet" which separates clauses for pyplexity or WordNet (*) treenode_type is either "PoS" (tree nodes are names of part-of-speech) or "tag" (grammar tree nodes are names from XTAG-PSG-PCFG-CCG-DependencyGrammar trees from a treebank) 1573.2 Function def make_sentence(wordnetsynsets,sentence_type="xtag_node34_triplets",standard_sentence_PoS_dict={"ADJ":[],"PROPN":[],"NOUN":[],"AUX":[],"ADP":[],"ADV":[],"VERB":[],"DET":[],"PRON":[],"CCONJ":[],"NUM":[],"SYM":[],"X":[]},markblanks=False,edgelabels=None,enable_frege_projective_dependency_grammar=True,treenode_type="PoS") has been updated to add argument treenode_type and standard_sentence_PoS_dict is populated either from Part-of-speech names or Grammar tree node names from a treebank. 1573.3 New function create_sentence_PoS_dict_from_treebank(dataset="wsj_0001.mrg") has been defined which (*) takes name of a dataset from NLTK grammar treebank sentences (*) iterates through grammar trees of the sentences (*) gets inorder traversal of those grammar trees by pos() function (*) extracts the grammar tree nodes from inorder traversal (*) populates the standard_sentence_PoS_dict from inorder traversal tree nodes (which was assumed to exist in earlier revisions and populated manually). 1573.4 WikipediaRLFGTransformersQuestionAnswering() is invoked (*) once for manually set std_sentence_PoS_dict and (*) in a loop for all sentence_PoS_dicts obtained from create_sentence_PoS_dict_from_treebank() for dataset wsj_0001.mrg which is one of 199 Penn Treebank datasets constituting 10% of Penn Treebank - Full Penn Treebank dataset is behind paywall. 1573.5 Following are some of the phrases synthesized for query python3.9 QuestionAnswering.py "what is electricity made of" logged to testlogs/QuestionAnswering.log.5January2025 - phrase "phenomenon moving physical electrons associated" epitomizes the answer - phrases synthesized from treebank grammar tree inorder traversals are longer than manually set sentence_PoS_dict template (It is noted hereby that comprehensive sentence synthesis from all possible randomwalks of entire textgraph and grammar trees from entire Penn Treebank is the best benchmark to ascertain meaningfulness of answer to a query - only a small fraction of textgraph randomwalks have been tried so far due to heavy computational complexity and limited computing resources involved): phenomenon moving stationary protons associated --- 0.0027210884353741495 phenomenon moving stationary electrons associated --- 0.0031397174254317113 phenomenon moving physical protons associated --- 0.0027210884353741495 phenomenon moving physical electrons associated --- 0.0031397174254317113 |
|
More...
|
almost 2 years ago
|
| Section 1215 updated |
|
More...
|
almost 2 years ago
|
| Section 1560 updated |
|
More...
|
almost 2 years ago
|
| 1572. (THEORY and FEATURE) Nachiketas Formal LLM - NLTK Frege Probabilistic Projective Dependency Grammar parsing from Penn TreeBank - Changes to Wikipedia Question-Answering algorithm for better accuracy 11 - related to 2,249,707,829,1133,1134,1204,1263,1269,1284,1285,1405,1413,1455,1468,1469,1536,1537,1538,1539,1540,1541,1542,1543,1550,1568 and all sections on Intrinsic Merit of Texts, Recursive Lambda Function Growth and Recursive Gloss Overlap textgraph algorithms, 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, Large Language Models, Mildly Context Sensitive Grammars-Tree Adjoining Grammars for Natural Languages,Text restoration-Vowelless text (de)compression - 2 January 2025 |
|
More...
|
almost 2 years ago
|
| -------------------------------------------------------------------------------------------------------------------------------------------- 1572. (THEORY and FEATURE) Nachiketas Formal LLM - NLTK Frege Probabilistic Projective Dependency Grammar parsing from Penn TreeBank - Changes to Wikipedia Question-Answering algorithm for better accuracy 11 - related to 2,249,707,829,1133,1134,1204,1263,1269,1284,1285,1405,1413,1455,1468,1469,1536,1537,1538,1539,1540,1541,1542,1543,1550,1568 and all sections on Intrinsic Merit of Texts, Recursive Lambda Function Growth and Recursive Gloss Overlap textgraph algorithms, 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, Large Language Models, Mildly Context Sensitive Grammars-Tree Adjoining Grammars for Natural Languages,Text restoration-Vowelless text (de)compression - 2 January 2025 -------------------------------------------------------------------------------------------------------------------------------------------- 1572.1 python-src/NeuronRainApps/QuestionAnswering/QuestionAnswering.py has been updated to import NLTK classes conll_data2, DependencyGraph, ProjectiveDependencyParser, ProbabilisticProjectiveDependencyParser and Penn TreeBank - dependency_treebank 1572.2 Function def make_sentence(wordnetsynsets,sentence_type="xtag_node34_triplets",standard_sentence_PoS_dict={"ADJ":[],"PROPN":[],"NOUN":[],"AUX":[],"ADP":[],"ADV":[],"VERB":[],"DET":[],"PRON":[],"CCONJ":[],"NUM":[],"SYM":[],"X":[]},markblanks=False,edgelabels=None,enable_frege_projective_dependency_grammar=True) has been updated to add a boolean argument enable_frege_projective_dependency_grammar for optional Frege Projective Dependency Parsing mentioned in 1568.9. 1572.3 Subclause for "textgraph_random_walk" has been introduced which based on flag enable_frege_projective_dependency_grammar: (*) reads Penn TreeBank Dependency Grammar parsed sentences (*) Converts them to CoNLL data format (*) Array of Dependency Graphs are comprehended from CoNLL data (*) Lexicon of all possible words is created from leaves of Projective Dependency graphs as trees (*) ProbabilisticProjectiveDependencyGraph object is instantiated and trained by Penn TreeBank dependency graphs (*) Set of words in textgraph randomwalks are curtailed to words in Dependency Graph Lexicon created earlier (*) Curtailed set of words in textgraph randomwalk is parsed by ProbabilisticProjectiveDependencyParser which might or might not return any meaningful parsed sentence (as accuracy depends on dependency graphs training dataset) - this code at present has been implemented as a stub for future enhancement when a massive and comprehensive dependency graph sentences treebank is available that might work in most iterations. 1572.4 For example query python3.9 QuestionAnswering.py "describe how stock exchanges operate" logged to testlogs/QuestionAnswering.Frege.2January2025 no parsed sentence is returned while following are the most meaningful phrases synthesized through sentence PoS dict template ranked by perplexity (which is assumed to be derived from some XTAG-PCFG-CCG tree): WordNet-ConceptNet TextGraph Random Walk - Bot generated answers ranked by pyplexity perplexity: {5011.538790088545: 'equity capital raised', 28764.139384991136: 'equity capital entitling', 43256.231962472855: 'equity interest raised', 69845.51733320243: 'equity capital entitling raised', 75562.8320780802: 'equity ownership raised', 80184.48196783279: 'holders capital raised', 94849.82066975773: 'equity interest entitling raised', 131548.2357660783: 'stock ownership entitling raised', .... 1572.5 ProjectiveDependencyParser code has been commented which is non-probabilistic and does not support train() API. |
|
More...
|
almost 2 years ago
|
| Section 1568 updated |
|
More...
|
almost 2 years ago
|
| Section 1215 updated |
|
More...
|
almost 2 years ago
|
| Section 1215 updated |
|
More...
|
almost 2 years ago
|
| 1571. (THEORY and FEATURE) Social Network Analysis - LinkedIn JSON datasource added - related to 14,365,626,711,727,753,876,1127,1128,1129,1130,1204,1263,1269,1427,1442,1452,1532,1534,1547,1555 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 - 31 December 2024 |
|
More...
|
almost 2 years ago
|
| ------------------------------------------------------------------------------------------------------------------------------------------- 1571. (THEORY and FEATURE) Social Network Analysis - LinkedIn JSON datasource added - related to 14,365,626,711,727,753,876,1127,1128,1129,1130,1204,1263,1269,1427,1442,1452,1532,1534,1547,1555 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 - 31 December 2024 ------------------------------------------------------------------------------------------------------------------------------------------- 1571.1 NeuronRain Conceptual Graph DOT file python-src/GitHubCodeSearch_NeuronRainConceptualGraph.dot has been updated by SocialNetworkAnalysis_PeopleAnalytics.py GitHub CodeSearch API calls. 1571.2 python-src/SocialNetworkAnalysis_PeopleAnalytics.OpenSource_SLOC has been updated by cloc analysis of GitHub NeuronRain repositories 1571.3 python-src/SocialNetworkAnalysis_PeopleAnalytics.py has been updated to include additional clause for LinkedIn Profile JSON datasource similar to PIPL JSON data format and linkedin json data is dumped. 1571.4 parse_sloc() reads updated python-src/SocialNetworkAnalysis_PeopleAnalytics.OpenSource_SLOC data and domainspecificdict["opensource_sloc"] is set from it. 1571.5 Example JSON profile parsing is logged to testlogs/SocialNetworkAnalysis_PeopleAnalytics.log.31December2024 |
|
More...
|
almost 2 years ago
|
| Section 1215 updated |
|
More...
|
almost 2 years ago
|
| Section 1568 updated |
|
More...
|
almost 2 years ago
|
| Section 1568 updated |
|
More...
|
almost 2 years ago
|
| Section 1462 updated |
|
More...
|
almost 2 years ago
|
| Section 1568 updated |
|
More...
|
almost 2 years ago
|
| Section 1449 updated |
|
More...
|
almost 2 years ago
|
| Section 1567 updated |
|
More...
|
almost 2 years ago
|
| 1568. (THEORY and FEATURE) Nachiketas Formal LLM - Phrase Structure Grammar (PSG) Noun Phrase(NP)-Verb Phrase(VP) segregation - Changes to Wikipedia Question-Answering algorithm for better accuracy 10 - related to 2,249,707,829,1133,1134,1204,1263,1269,1284,1285,1405,1413,1455,1468,1469,1536,1537,1538,1539,1540,1541,1542,1543,1550 and all sections on Intrinsic Merit of Texts, Recursive Lambda Function Growth and Recursive Gloss Overlap textgraph algorithms, 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, Large Language Models, Mildly Context Sensitive Grammars-Tree Adjoining Grammars for Natural Languages,Text restoration-Vowelless text (de)compression - 26 December 2024 |
|
More...
|
almost 2 years ago
|
| -------------------------------------------------------------------------------------------------------------------------------------------- 1568. (THEORY and FEATURE) Nachiketas Formal LLM - Phrase Structure Grammar (PSG) Noun Phrase(NP)-Verb Phrase(VP) segregation - Changes to Wikipedia Question-Answering algorithm for better accuracy 10 - related to 2,249,707,829,1133,1134,1204,1263,1269,1284,1285,1405,1413,1455,1468,1469,1536,1537,1538,1539,1540,1541,1542,1543,1550 and all sections on Intrinsic Merit of Texts, Recursive Lambda Function Growth and Recursive Gloss Overlap textgraph algorithms, 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, Large Language Models, Mildly Context Sensitive Grammars-Tree Adjoining Grammars for Natural Languages,Text restoration-Vowelless text (de)compression - 26 December 2024 -------------------------------------------------------------------------------------------------------------------------------------------- 1568.1 Nachiketas unsupervised Formal LLM python-src/NeuronRainApps/QuestionAnswering/QuestionAnswering.py has been updated to cast OrderedDict of synthesized sentences ranked by pyplexity and wordnet perplexity to dict(). 1568.2 Synthesized sentences (which are mostly phrases) ranked by pyplexity perplexity are segregated into Noun Phrases(NP) and Verb Phrases(VP) of Chomsky Phrase Structure Grammar (PSG) as mentioned in 1550.6 by simple classifier that looks for VERB part-of-speech in Spacy PoS tagging of a synthesized phrase which is appended to VerbPhrases array and NounPhrases array otherwise. 1568.3 All possible Noun phrase-Verb phrase (NP-VP) combinations are synthesized to get larger PSG sentences by nested loop iterating over NPs and VPs. 1568.4 Both normally synthesized phrases and PSG sentences are returned as tuple. 1568.5 For query "Question: Explain Riemann hypothesis and Riemann zeta function" following NPs and VPs are synthesized which are then permuted to obtain all possible larger PSG sentences (sentences are dominated by Greek alphabet Zeta as understood by Nachiketas Formal LLM by textgraph randomwalks) - from python-src/NeuronRainApps/QuestionAnswering/testlogs/QuestionAnswering.log.PSG.26December2024: Noun Phrases: ['6th zeta', 'Greek zeta', 'alphabet zeta'] Verb Phrases: ['6th element associated another', 'Greek element associated another', 'alphabet element associated another', '6th function associated another', 'Greek function associated another', 'alphabet function associated another', '6th domain given another', '6th function given another', 'Greek domain given another', 'Greek function given another', '6th range given another', 'alphabet domain given another', '6th range associated another', 'alphabet function given another', 'Greek range given another', 'Greek range associated another', 'alphabet range given another', 'alphabet range associated another', '6th domain associated another', 'Greek domain associated another', '6th relation associated another', 'alphabet domain associated another', 'Greek relation associated another', '6th relation given another', 'Greek relation given another', 'alphabet relation associated another', 'alphabet relation given another', '6th element given another', 'Greek element given another', '6th letter associated another', 'Greek letter associated another', '6th letter given another', 'Greek letter given another', 'alphabet element given another', 'alphabet letter associated another', 'alphabet letter given another', '6th zeta associated another', 'Greek zeta associated another', '6th alphabet associated another', '6th zeta given another', 'Greek alphabet associated another', 'Greek zeta given another', '6th alphabet given another', 'Greek alphabet given another', 'alphabet zeta associated another', '6th range mathematics another', 'Greek range mathematics another', '6th function mathematics another', 'alphabet alphabet associated another', 'alphabet zeta given another', 'Greek function mathematics another', 'alphabet alphabet given another', 'alphabet range mathematics another', 'alphabet function mathematics another', '6th domain mathematics another', 'Greek domain mathematics another', '6th relation mathematics another', 'alphabet domain mathematics another', 'Greek relation mathematics another', 'alphabet relation mathematics another', '6th element mathematics another', 'Greek element mathematics another', '6th letter mathematics another', 'Greek letter mathematics another', 'alphabet element mathematics another', 'alphabet letter mathematics another', '6th zeta mathematics another', 'Greek zeta mathematics another', '6th alphabet mathematics another', 'Greek alphabet mathematics another', 'alphabet zeta mathematics another', 'alphabet alphabet mathematics another'] |
|
More...
|
almost 2 years ago
|
| Section 1567 updated |
|
More...
|
almost 2 years ago
|
| LinkedIn dataset updated for SocialNetworkAnalysis_PeopleAnalytics.py: - ID verified LinkedIn profile and connections - JSON resume format of LinkedIn profile from chrome browser extension https://github.com/joshuatz/linkedin-to-jsonresume |
|
More...
|
almost 2 years ago
|
| 1567. (THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 19 December 2024 - VARUNA N-Body Climate Model - Pressure Gradient computation has been made optional - related to 148,172,313,445,446,463,1165,1171,1176,1266,1289,1316,1318,1327,1358,1359,1360,1361,1364,1367,1368,1388,1404,1455,1456,1463,1479,1480,1515,1526,1527,1528,1548,1558,1561,1562 and all sections on Correlation between gravitational effects of Mined Celestial N-Body Patterns and terrestrial extreme weather events (or Gravitational effects on climate change), Drone natural obstacle avoidance-Drone autonomous online shopping delivery-Drone Electronic Voting Machine, Artificial Intelligence models for Climate Change-Adaptive Fourier Neural Operator-FourCastNet, Astronomical algorithms, Multifractals, Timeseries analysis, Sequence Mining-Class Association Rules, Global warming |
|
More...
|
almost 2 years ago
|
| ---------------------------------------------------------------------------------------------------------------------------------------- 1567. (THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 19 December 2024 - VARUNA N-Body Climate Model - Pressure Gradient computation has been made optional - related to 148,172,313,445,446,463,1165,1171,1176,1266,1289,1316,1318,1327,1358,1359,1360,1361,1364,1367,1368,1388,1404,1455,1456,1463,1479,1480,1515,1526,1527,1528,1548,1558,1561,1562 and all sections on Correlation between gravitational effects of Mined Celestial N-Body Patterns and terrestrial extreme weather events (or Gravitational effects on climate change), Drone natural obstacle avoidance-Drone autonomous online shopping delivery-Drone Electronic Voting Machine, Artificial Intelligence models for Climate Change-Adaptive Fourier Neural Operator-FourCastNet, Astronomical algorithms, Multifractals, Timeseries analysis, Sequence Mining-Class Association Rules, Global warming ---------------------------------------------------------------------------------------------------------------------------------------- 1567.1 python-src/image_pattern_mining/ImageNet/GISWeatherAnalytics.py has been updated to include a boolean argument computepressuregradient to make Navier-Stokes pressure gradient computation optional due to unavailability of Sea-Surface Temperature data for all recent dates after 2016 and limit it to postfacto analysis of past extreme weather events between 1981 and 2016: def climate_analytics(datasource,date="",time="",predict_EWE_params=None,precipitation_timeseries=None,computepressuregradient=False) 1567.2 Function invocation of pressure_gradient() has been made optional based on truth value of computepressuregradient. Pressure gradients are computed for daterange 24/12/2011-26/12/2011 and longitude-latitude bounding box slice over Andaman sea (95 to 96 degree east, 12 to 13 degree north) when Cyclone Thane was formed and rapidly intensified due to Madden-Julian Oscillation - https://en.wikipedia.org/wiki/Cyclone_Thane - 956 millibar: climate_analytics(datasource="precipitation_GaussianMixture",predict_EWE_params={'datefrom':(2016,12,24,17,30,00),'dateto':(2016,12,26,17,30,00),'loc':'@0','bodyconjunctions':"Sun-Mercury-Venus",'angularsepbounds':('0d','30d'),'longlat':['223@399'],'seasurfacelonglat':(95,12)},precipitation_timeseries={"timeseries":nem_rainfall_timeseries,"averageseasonalrainfall":100,'forecast_timeseries':[10,2,12,25,30,1,2,3,4,10]},computepressuregradient=True) 1567.3 Following are approximate pressure gradients - products of SeaSurfaceTemperature and VariableGravity - ranging from 0.003 to 12.04 for longitude-latitude points over earlier bounding box slice from testlogs/GISWeatherAnalytics.PressureGradient.log.19December2024 - locations of peak pressure gradient 12.04 could be likely contributors to the storm low pressure: pressure gradients: [0.003182402452428039, 0.0031820852612691602, 0.003181979746000642, 0.003181768070110281, 0.003181662554841763, 0.0031815567168965828, 0.0031815567168965828, 0.0031815567168965828, 0.0031815567168965828, 0.0031815567168965828, 0.0031815567168965828, 0.0031814508789514023, 0.003181239525737704, 0.0031811336877925235, 0.0031808164966336447, 0.003180499305474766, 0.0031801821143158868, 0.003179864923157008, 0.0031795477319981292, 0.0031793363787844305, 0.003182158503342032, 0.003181947018778569, 0.0031818411150582393, 0.0031816296304947762, 0.0031816296304947762, 0.0031815237267744465, 0.0031815237267744465, 0.0031815237267744465, 0.0031816296304947762, 0.0031816296304947762, 0.0031816296304947762, 0.0031816296304947762, 0.0031815237267744465, 0.0031813122422109834, 0.003181100434770324, 0.003180783046486531, 0.003180571561923068, 0.0031801482699189455, 0.0031798308816351527, 0.0031796193970716896] pressure gradients: [12.046497907556635, 12.045297228921909, 12.04489781700578, 12.044096550287183, 12.043697138371053, 12.043296505011755, 12.043296505011755, 12.043296505011755, 12.043296505011755, 12.043296505011755, 12.043296505011755, 12.042895871652458, 12.04209582637703, 12.041695193017732, 12.040494514383006, 12.03929383574828, 12.038093157113554, 12.036892478478828, 12.035691799844102, 12.034891754568674, 11.691030281823261, 11.690253302162132, 11.689864219217323, 11.689087239556194, 11.689087239556194, 11.688698156611384, 11.688698156611384, 11.688698156611384, 11.689087239556194, 11.689087239556194, 11.689087239556194, 11.689087239556194, 11.688698156611384, 11.687921176950255, 11.687143011060636, 11.685976948454696, 11.685199968793567, 11.683644823242819, 11.68247876063688, 11.68170178097575] pressure gradients: [0.003182402452428039, 0.0031820852612691602, 0.003181979746000642, 0.003181768070110281, 0.003181662554841763, 0.0031815567168965828, 0.0031815567168965828, 0.0031815567168965828, 0.0031815567168965828, 0.0031815567168965828, 0.0031815567168965828, 0.0031814508789514023, 0.003181239525737704, 0.0031811336877925235, 0.0031808164966336447, 0.003180499305474766, 0.0031801821143158868, 0.003179864923157008, 0.0031795477319981292, 0.0031793363787844305, 0.003182158503342032, 0.003181947018778569, 0.0031818411150582393, 0.0031816296304947762, 0.0031816296304947762, 0.0031815237267744465, 0.0031815237267744465, 0.0031815237267744465, 0.0031816296304947762, 0.0031816296304947762, 0.0031816296304947762, 0.0031816296304947762, 0.0031815237267744465, 0.0031813122422109834, 0.003181100434770324, 0.003180783046486531, 0.003180571561923068, 0.0031801482699189455, 0.0031798308816351527, 0.0031796193970716896] pressure gradients: [3.376784369746692, 3.376447804474532, 3.3763358443077, 3.3761112392023724, 3.3759992790355398, 3.375886976482876, 3.375886976482876, 3.375886976482876, 3.375886976482876, 3.375886976482876, 3.375886976482876, 3.3757746739302124, 3.375550411210716, 3.3754381086580523, 3.375101543385892, 3.3747649781137325, 3.3744284128415725, 3.3740918475694124, 3.3737552822972523, 3.373531019577756, 3.3752304711659273, 3.375006155142061, 3.374893825896522, 3.3746695098726556, 3.3746695098726556, 3.374557180627116, 3.374557180627116, 3.374557180627116, 3.3746695098726556, 3.3746695098726556, 3.3746695098726556, 3.3746695098726556, 3.374557180627116, 3.37433286460325, 3.374108206112172, 3.373771560842766, 3.3735472448189, 3.3730982703039554, 3.3727616250345496, 3.3725373090106836] pressure gradients: [12.046497907556635, 12.045297228921909, 12.04489781700578, 12.044096550287183, 12.043697138371053, 12.043296505011755, 12.043296505011755, 12.043296505011755, 12.043296505011755, 12.043296505011755, 12.043296505011755, 12.042895871652458, 12.04209582637703, 12.041695193017732, 12.040494514383006, 12.03929383574828, 12.038093157113554, 12.036892478478828, 12.035691799844102, 12.034891754568674, 11.691030281823261, 11.690253302162132, 11.689864219217323, 11.689087239556194, 11.689087239556194, 11.688698156611384, 11.688698156611384, 11.688698156611384, 11.689087239556194, 11.689087239556194, 11.689087239556194, 11.689087239556194, 11.688698156611384, 11.687921176950255, 11.687143011060636, 11.685976948454696, 11.685199968793567, 11.683644823242819, 11.68247876063688, 11.68170178097575] pressure gradients: [3.376784369746692, 3.376447804474532, 3.3763358443077, 3.3761112392023724, 3.3759992790355398, 3.375886976482876, 3.375886976482876, 3.375886976482876, 3.375886976482876, 3.375886976482876, 3.375886976482876, 3.3757746739302124, 3.375550411210716, 3.3754381086580523, 3.375101543385892, 3.3747649781137325, 3.3744284128415725, 3.3740918475694124, 3.3737552822972523, 3.373531019577756, 3.3752304711659273, 3.375006155142061, 3.374893825896522, 3.3746695098726556, 3.3746695098726556, 3.374557180627116, 3.374557180627116, 3.374557180627116, 3.3746695098726556, 3.3746695098726556, 3.3746695098726556, 3.3746695098726556, 3.374557180627116, 3.37433286460325, 3.374108206112172, 3.373771560842766, 3.3735472448189, 3.3730982703039554, 3.3727616250345496, 3.3725373090106836] 1567.4 Following are the gravity L2 norm plots for Sun-Mercury-Venus 3-body pairwise and there is a marked dip in Mercury's gravity L2 norm during above daterange: testlogs/GISWeatherAnalytics.Gravity.Mercury-Venus.2011-12-24#2011-12-26.jpg testlogs/GISWeatherAnalytics.Gravity.Sun-Mercury.2011-12-24#2011-12-26.jpg testlogs/GISWeatherAnalytics.Gravity.Sun-Venus.2011-12-24#2011-12-26.jpg |
|
More...
|
almost 2 years ago
|
| Section 1566 updated |
|
More...
|
almost 2 years ago
|
| Updated Requirements.txt |
|
More...
|
almost 2 years ago
|
| Section 1566 updated |
|
More...
|
almost 2 years ago
|
| 1566. (THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 18 December 2024 - VARUNA N-Body Climate Model - Approximation of Pressure Gradient from Variable gravity and Sea Surface Temperature dataset - related to 148,172,313,445,446,463,1165,1171,1176,1266,1289,1316,1318,1327,1358,1359,1360,1361,1364,1367,1368,1388,1404,1455,1456,1463,1479,1480,1515,1526,1527,1528,1548,1558,1561,1562 and all sections on Correlation between gravitational effects of Mined Celestial N-Body Patterns and terrestrial extreme weather events (or Gravitational effects on climate change), Drone natural obstacle avoidance-Drone autonomous online shopping delivery-Drone Electronic Voting Machine, Artificial Intelligence models for Climate Change-Adaptive Fourier Neural Operator-FourCastNet, Astronomical algorithms, Multifractals, Timeseries analysis, Sequence Mining-Class Association Rules, Global warming |
|
More...
|
almost 2 years ago
|
| ---------------------------------------------------------------------------------------------------------------------------------------- 1566. (THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 18 December 2024 - VARUNA N-Body Climate Model - Approximation of Pressure Gradient from Variable gravity and Sea Surface Temperature dataset - related to 148,172,313,445,446,463,1165,1171,1176,1266,1289,1316,1318,1327,1358,1359,1360,1361,1364,1367,1368,1388,1404,1455,1456,1463,1479,1480,1515,1526,1527,1528,1548,1558,1561,1562 and all sections on Correlation between gravitational effects of Mined Celestial N-Body Patterns and terrestrial extreme weather events (or Gravitational effects on climate change), Drone natural obstacle avoidance-Drone autonomous online shopping delivery-Drone Electronic Voting Machine, Artificial Intelligence models for Climate Change-Adaptive Fourier Neural Operator-FourCastNet, Astronomical algorithms, Multifractals, Timeseries analysis, Sequence Mining-Class Association Rules, Global warming ---------------------------------------------------------------------------------------------------------------------------------------- 1566.1 python-src/image_pattern_mining/ImageNet/GISWeatherAnalytics.py has been updated to import xarray, astropy Time, zarr and s3fs for reading sea surface temperature AWS dataset from https://surftemp.github.io/sst-data-tutorials/ 1566.2 New function pressure_gradient(variablegravities=[],seasurfacelonglat=None,datefrom=None,dateto=None) has been implemented to approximately estimate Navier-Stokes Equation Pressure gradient in Section 1562 as product of Variable gravity and Sea Surface Temperature (which is directly proportional to extent of precipitable water vapour added to atmosphere turning density of air into a variable one). This function iterates through a daterange and computes the product of daily sea surface temperature (for each points in a rectangular longitude-latitude bounding box) obtained from earlier AWS S3 filesystem at s3://surftemp-sst/data/sst.zarr and variable gravity of a celestial body for each date in the range. 1566.3 Postfacto analysis of Cyclone Vardah (https://en.wikipedia.org/wiki/Cyclones_Vardah_and_ARB_02 - 959 millibar) for date range 5/12/2016 and 7/12/2016 is performed and pressure gradient data for this daterange has been computed over a bounding box on Andaman Sea in GISWeatherAnalytics.PressureGradient.log.18December2024 - date range has been made as narrow as possible since AWS S3FS SST dataset access is slow: 6climate_analytics(datasource="precipitation_GaussianMixture",predict_EWE_params={'datefrom':(2016,12,5,17,30,00),'dateto':(2016,12,7,17,30,00),'loc':'@0','bodyconjunctions':"Sun-Mercury-Venus",'angularsepbounds':('0d','30d'),'longlat':['223@399'],'seasurfacelonglat':(95,12)},precipitation_timeseries={"timeseries":nem_rainfall_timeseries,"averageseasonalrainfall":100,'forecast_timeseries':[10,2,12,25,30,1,2,3,4,10]}) 1566.4 Following are the gravity plots for above daterange (and some other dates): testlogs/GISWeatherAnalytics.Gravity.Mercury-Venus.2016-12-5#2016-12-7.jpg testlogs/GISWeatherAnalytics.Gravity.Sun-Mercury.2016-12-18#2016-12-19.jpg testlogs/GISWeatherAnalytics.Gravity.Sun-Mercury.2016-12-18#2016-12-20.jpg testlogs/GISWeatherAnalytics.Gravity.Sun-Mercury.2016-12-5#2016-12-10.jpg testlogs/GISWeatherAnalytics.Gravity.Sun-Mercury.2016-12-5#2016-12-7.jpg testlogs/GISWeatherAnalytics.Gravity.Sun-Mercury.2024-12-18#2024-12-20.jpg testlogs/GISWeatherAnalytics.Gravity.Sun-Venus.2016-12-5#2016-12-7.jpg testlogs/GISWeatherAnalytics.PressureGradient.log.18December2024 1566.5 As evident from plots, Mercury's N-Body gravity had a sharp increase on above dates and following are the pressure gradients for above date ranges at multiple points within the bounding box which show similar ascending trend: pressure gradients: [3.403155119402356, 3.403155119402356, 3.4032677377154026, 3.4033803560284492, 3.40349263099298, 3.4038301425836046, 3.4040553792096975, 3.404280272487275, 3.4045055091133682, 3.4046177840778995, 3.4045055091133682, 3.4045055091133682, 3.4043928908003216, 3.404280272487275, 3.404167997522744, 3.404280272487275, 3.404280272487275, 3.4045055091133682, 3.4048430207039924, 3.4051805322946165, 3.4035373725735423, 3.4035373725735423, 3.403650025881262, 3.4037626791889815, 3.4039876423492146, 3.4042126055094477, 3.4044379121248873, 3.4046628752851205, 3.4047755285928405, 3.4048878384453536, 3.4047755285928405, 3.4047755285928405, 3.4046628752851205, 3.404550221977401, 3.404550221977401, 3.4046628752851205, 3.4047755285928405, 3.4050004917530736, 3.405225798368513, 3.405450761528746] pressure gradients: [0.0032777296722085267, 0.0032777296722085267, 0.0032778381399019923, 0.003277946607595458, 0.0032780547445947356, 0.0032783798169809444, 0.0032785967523678754, 0.003278813357060619, 0.0032790302924475497, 0.003279138429446828, 0.0032790302924475497, 0.0032790302924475497, 0.0032789218247540842, 0.003278813357060619, 0.003278705220061341, 0.003278813357060619, 0.003278813357060619, 0.0032790302924475497, 0.0032793553648337585, 0.003279680437219968, 0.00327957508556243, 0.00327957508556243, 0.003279683635856098, 0.0032797921861497663, 0.0032800089557910857, 0.003280225725432405, 0.003280442826019742, 0.0032806595956610613, 0.0032807681459547295, 0.0032808763653023806, 0.0032807681459547295, 0.0032807681459547295, 0.0032806595956610613, 0.0032805510453673926, 0.0032805510453673926, 0.0032806595956610613, 0.0032807681459547295, 0.003280984915596049, 0.0032812020161833854, 0.0032814187858247047] pressure gradients: [11.467698312833956, 11.467698312833956, 11.468077805626148, 11.468457298418338, 11.468835634220307, 11.469972955606657, 11.470731941191037, 11.471489769785197, 11.472248755369577, 11.472627091171548, 11.472248755369577, 11.472248755369577, 11.471869262577387, 11.471489769785197, 11.471111433983227, 11.471489769785197, 11.471489769785197, 11.472248755369577, 11.473386076755927, 11.474523398142278, 11.795920962553142, 11.795920962553142, 11.796311394438726, 11.79670182632431, 11.797481499754367, 11.798261173184422, 11.799042036955592, 11.799821710385647, 11.800212142271233, 11.800601383815703, 11.800212142271233, 11.800212142271233, 11.799821710385647, 11.799431278500062, 11.799431278500062, 11.799821710385647, 11.800212142271233, 11.800991815701288, 11.801772679472458, 11.802552352902513] pressure gradients: [0.0032777296722085267, 0.0032777296722085267, 0.0032778381399019923, 0.003277946607595458, 0.0032780547445947356, 0.0032783798169809444, 0.0032785967523678754, 0.003278813357060619, 0.0032790302924475497, 0.003279138429446828, 0.0032790302924475497, 0.0032790302924475497, 0.0032789218247540842, 0.003278813357060619, 0.003278705220061341, 0.003278813357060619, 0.003278813357060619, 0.0032790302924475497, 0.0032793553648337585, 0.003279680437219968, 0.00327957508556243, 0.00327957508556243, 0.003279683635856098, 0.0032797921861497663, 0.0032800089557910857, 0.003280225725432405, 0.003280442826019742, 0.0032806595956610613, 0.0032807681459547295, 0.0032808763653023806, 0.0032807681459547295, 0.0032807681459547295, 0.0032806595956610613, 0.0032805510453673926, 0.0032805510453673926, 0.0032806595956610613, 0.0032807681459547295, 0.003280984915596049, 0.0032812020161833854, 0.0032814187858247047] pressure gradients: [3.403155119402356, 3.403155119402356, 3.4032677377154026, 3.4033803560284492, 3.40349263099298, 3.4038301425836046, 3.4040553792096975, 3.404280272487275, 3.4045055091133682, 3.4046177840778995, 3.4045055091133682, 3.4045055091133682, 3.4043928908003216, 3.404280272487275, 3.404167997522744, 3.404280272487275, 3.404280272487275, 3.4045055091133682, 3.4048430207039924, 3.4051805322946165, 3.4035373725735423, 3.4035373725735423, 3.403650025881262, 3.4037626791889815, 3.4039876423492146, 3.4042126055094477, 3.4044379121248873, 3.4046628752851205, 3.4047755285928405, 3.4048878384453536, 3.4047755285928405, 3.4047755285928405, 3.4046628752851205, 3.404550221977401, 3.404550221977401, 3.4046628752851205, 3.4047755285928405, 3.4050004917530736, 3.405225798368513, 3.405450761528746] pressure gradients: [11.467698312833956, 11.467698312833956, 11.468077805626148, 11.468457298418338, 11.468835634220307, 11.469972955606657, 11.470731941191037, 11.471489769785197, 11.472248755369577, 11.472627091171548, 11.472248755369577, 11.472248755369577, 11.471869262577387, 11.471489769785197, 11.471111433983227, 11.471489769785197, 11.471489769785197, 11.472248755369577, 11.473386076755927, 11.474523398142278, 11.795920962553142, 11.795920962553142, 11.796311394438726, 11.79670182632431, 11.797481499754367, 11.798261173184422, 11.799042036955592, 11.799821710385647, 11.800212142271233, 11.800601383815703, 11.800212142271233, 11.800212142271233, 11.799821710385647, 11.799431278500062, 11.799431278500062, 11.799821710385647, 11.800212142271233, 11.800991815701288, 11.801772679472458, 11.802552352902513] pressure gradients: [3.403155119402356, 3.403155119402356, 3.4032677377154026, 3.4033803560284492, 3.40349263099298, 3.4038301425836046, 3.4040553792096975, 3.404280272487275, 3.4045055091133682, 3.4046177840778995, 3.4045055091133682, 3.4045055091133682, 3.4043928908003216, 3.404280272487275, 3.404167997522744, 3.404280272487275, 3.404280272487275, 3.4045055091133682, 3.4048430207039924, 3.4051805322946165, 3.4035373725735423, 3.4035373725735423, 3.403650025881262, 3.4037626791889815, 3.4039876423492146, 3.4042126055094477, 3.4044379121248873, 3.4046628752851205, 3.4047755285928405, 3.4048878384453536, 3.4047755285928405, 3.4047755285928405, 3.4046628752851205, 3.404550221977401, 3.404550221977401, 3.4046628752851205, 3.4047755285928405, 3.4050004917530736, 3.405225798368513, 3.405450761528746] |
|
More...
|
almost 2 years ago
|
| Section 1565 updated |
|
More...
|
almost 2 years ago
|
| Section 1565 updated |
|
More...
|
almost 2 years ago
|
