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Nov 05, 2025 — Nov 05, 2026
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------------------------------------------------------------------------------------------------------------------------------ 1287. (THEORY and FEATURE) Merit of Large Scale Visuals - Archaeology - Rebus Decipherment of Indus Pashupathi Seal - 9 August 2022 - related to 1203,1232,1233,1236,1237,1240 and all sections on Merit of Large Scale Visuals, Physique Recognition From Quadrilateral mesh isomorphism,Topological Face and Handwriting recognition, Archaeology-Topological Script Recognition, Product Homotopy, Homeomorphism and Topological Deformations, People Analytics, Computational Geometry, Graph Edit Distance and Earth mover distance, Graph Isomorphism (Exact, Approximate and Subgraph),Graph matching,Graph similarity,Image Search,Quadrilateral Mesh Deformation and Isomorphism,Maximum Common Subgraph Problem and Approximate Topological Matching -------------------------------------------------------------------------------------------------------------------------------- 1287.1 ImageGraph_Keras_Theano.py has been updated to rebus decipher Indus Pashupathi seal or Yogi seal in testlogs/yogic-seal_0.jpg by ResNet50 and ResNet50V2 image object search and predictions (interpreted as Lord Shiva in Yogic posture by John Marshall) as below: ('Predictions - ', 'ResNet50', ':', [[('n02892201', 'brass', 0.7663543), ('n02910353', 'buckle', 0.09748204), ('n04192698', 'shield', 0.05866886), ('n03903868', 'pedestal', 0.014400147), ('n03929855', 'pickelhaube', 0.010885257)]]) ('Predictions - ', 'ResNet50V2', ':', [[('n02883205', 'bow_tie', 1.0), ('n15075141', 'toilet_tissue', 0.0), ('n02317335', 'starfish', 0.0), ('n02389026', 'sorrel', 0.0), ('n02364673', 'guinea_pig', 0.0)]]) 1287.2 Following are the sorted PageRanks of the textgraph vertices deep-learnt from the predictions for Pashupathi seal image through ResNet50: [('brass', 0.2087566573359813), ('pickelhaube', 0.09785508542625027), ('shield', 0.06616892202346995), ('base', 0.05032584032207979), ('buckle', 0.05032584032207979), ('support', 0.026561217769994573), ('foundation', 0.026561217769994573), ('fasten', 0.026561217769994573), ('buckles', 0.026561217769994573), ('structure', 0.023920704153096214), ('covering', 0.023920704153096214), ('protective', 0.023920704153096214), ('helmet', 0.021808293259577533), ('worn', 0.021808293259577533), ('spiked', 0.021808293259577533), ('German', 0.021808293259577533), ('soldiers', 0.021808293259577533), ('usually', 0.01995993372774868), ('means', 0.01995993372774868), ('mouthpiece', 0.01995993372774868), ('blown', 0.01995993372774868), ('cup-shaped', 0.01995993372774868), ('wind', 0.01995993372774868), ('variable', 0.01995993372774868), ('instrument', 0.01995993372774868), ('consists', 0.01995993372774868), ('tube', 0.01995993372774868), ('funnel-shaped', 0.01995993372774868), ('length', 0.01995993372774868)] 1287.3 Seat of the posture is identified by ["base","support","structure","pedestal","foundation"]. Posture is identified by "buckles" (resembling folded legs of Mulabandhasana). Crescent Moon Crown is identified by ["pickelhaube","helmet","worn","spiked"]. Martial nature of the seal is deciphered by ["soldiers","shield","protective","instrument","brass"] perhaps indicative of a warfare culture. 1287.4 There are some out of place vertices namely "German" originating from "pickelhaube" which is German military helmet (Crown in seal is matched to German helmet by decode_predictions()). Apart from earlier figurine motif decipherment, ["funnel-shaped","cup-shaped","wind","tube"] seem to indicate rebus decipherment of the script on seal - coincidentally funnel-shaped,cup-shaped,wind and tube are related to respiratory tract and breathing exercises. Possible automated rebus decipherment from earlier predictions is (which is in no way a decipherment claim but could assist a manual decipherment): the script on seal might be a description on modus operandi of martial (weaponry) respiratory ritual (which could be yogic posture). 1287.5 It can be observed from earlier ResNet50 (Version 1 and 2) predictions and the logs testlogs/ImageGraph_Keras_Theano.log.PashupathiSeal.9August2022 that ResNet50 produces more meaningful, contextually relevant predictions than ResNet50V2 (except "starfish" for fish shaped script on seal). 1287.6 Apart from rebus decipherment, Pashupathi seal is image segmented to get Chain approximation contours,Voronoi-Delaunay,Watershed segments,Canny Edge Detection imagery and FaceGraph DOT below: testlogs/RemoteSensingGIS/yogic-seal_0_ImageNet_Keras_Theano_Segmentation_FaceGraph.dot testlogs/RemoteSensingGIS/yogic-seal_0_segmented.jpg testlogs/yogic-seal_0-cannyedgedetection.jpg testlogs/yogic-seal_0-contourlabelled.jpg testlogs/yogic-seal_0-contoursegmented.jpg
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Section 1286 updated
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+------------------------------------------------------------------------------------------------------------------------------------------- +1286. (THEORY) Economic Merit and People Analytics - Mechanism Design of Multiround Majority Voting derived from Simultaneous Ascending +Auction - related to 400,790,791 and all sections on Majority Voting, Auction Design, Mechanism Design, Economic Merit, People Analytics, +Quantitate Majority Circuit - 8 August 2022 +-------------------------------------------------------------------------------------------------------------------------------------------
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Updated BestPractices.txt
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------------------------------------------------------------------------------------------------------------------------------- 1285. (THEORY and FEATURE) NeuronRainApps - Wikipedia Textgraph Question-Answering Bot - WordNet Walk on answer textgraph - 4 August 2022 - related to 249,707,829,1133,1134,1204,1263,1269,1284 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 -------------------------------------------------------------------------------------------------------------------------------
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------------------------------------------------------------------------------------------------------------------------------- 1285. (THEORY and FEATURE) NeuronRainApps - Wikipedia Textgraph Question-Answering Bot - WordNet Walk on answer textgraph - 4 August 2022 - related to 249,707,829,1133,1134,1204,1263,1269,1284 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 ------------------------------------------------------------------------------------------------------------------------------- 1285.1 In continuation of earlier commit, QuestionAnswering.py has been refactored and enhanced to choose edges of high Query-Key-Values degree attention edges learnt from wikipedia summary answer textgraph and automatically write an answer by concatenating all WordNet walks between vertices of those high degree attention edges. 1285.2 AIQuestionAnswering() as a new function within QuestionAnswering.py has been supplied with lot of configurable parameters (questionfraction=5,maxanswers=1,keywordsearch=False,wsheading=False,answersliced=1,answerfraction=20) to respectively (*) choose fraction of question textgraph cores (*) restrict maximum number of answers per question (*) to choose between dense subgraph core number keyword search of question textgraph and direct question search without question textgraph (*) to choose between wikipedia summary and page headings for a search() query (*) choose only a slice of sentences from wikipedia summary() (*) choose Fraction of sorted Query-Key-Values degree attention for answer WordNet walk. 1285.3 rlfg_transformers_attention_model() function of RecursiveLambdaFunctionGrowth.py returns Query-Key-Values and Degree Attention as tuple 1285.4 Query-Key-Values attention for answer textgraph are stored in a dictionary of edges-attention, sorted descending and onlyanswerfraction of those edges are chosen to be part of answer generated by concatenating WordNet walks between all pairs of vertices of the chosen edges connected by "is a" relation (WordNetPath.path_between() is used for WordNet walk between vertices - Section 249). 1285.5 Some array overflow errors in Grafit/course_material/NeuronRain/LinuxKernelAndCloud/code/Transformers_PerceptronAndGradient.py which is part of Transformers implementation Grafit/course_material/NeuronRain/LinuxKernelAndCloud/code/Transformers.py have been resolved. 1285.6 An excerpt of answer written based on WordNet walk for question "Which is the largest city in the world" (logs have been withleld because of huge sizes (GBs) of Transformer Training iteration logs and only a minimum slice (1 sentence) of wikipedia summary() is used to minimize runtime). Bot generated WordNet walk answer simulates a deep learning human thought process in grasping a question on urban areas, less readability notwithstanding. Though Wikipedia summary() does a reasonably great job of providing very relevant natural language answers to natural language questions, it is not always reliable due to rare exceptions in which case an alternative answer source might have to be resorted to for answer textgraph creation. Question textgraph-Answer textgraph duo is a graph theoretic Meaning Representation,a drastic departure from conventional natural language question-answer pair and is useful in inferring first order logic sentences from lambda functions computed by Recursive Lambda Function Growth algorithm Beta Reduction and multilingual question-answering by mapping textgraph vertices in one language to another (Example: Earlier question is answered in First Order Logic and its equivalent lambda function as "There exist x,y - x and y are cities - For all y, x !=y and x is larger than y") ... Bot generated answer from textgraph: classified_ad is a(n) newspaper_ad.newspaper_ad is a(n) ad.ad is a(n) promotion.promotion is a(n) message.message is a(n) communication.communication is a(n) act.act is a(n) legal_document.legal_document is a(n) document.document is a(n) writing.writing is a(n) written_communication.written_communication is a(n) communication.city is a(n) municipality.municipality is a(n) administrative_district.administrative_district is a(n) urban_area.urban_area is a(n) district.district is a(n) geographical_area.geographical_area is a(n) region.region is a(n) location.location is a(n) object.object is a(n) physical_entity.physical_entity is a(n) entity.entity is a(n) act.act is a(n) legal_document.legal_document is a(n) document.document is a(n) writing.writing is a(n) written_communication.written_communication is a(n) communication.communication is a(n) abstraction.abstraction is a(n) entity.state is a(n) political_unit.political_unit is a(n) unit.unit is a(n) organization.organization is a(n) social_group.social_group is a(n) group.group is a(n) abstraction.abstraction is a(n) act.act is a(n) legal_document.legal_document is a(n) document.document is a(n) writing.writing is a(n) written_communication.written_communication is a(n) communication.communication is a(n) abstraction.use is a(n) activity. ...
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Section 1245 updated
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Section 1284 updated
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+-------------------------------------------------------------------------------------------------------------------------------------------- +1284. (THEORY and FEATURE) NeuronRainApps - Wikipedia Textgraph Question-Answering Bot - related to 1133,1134,1204,1263,1269 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 +--------------------------------------------------------------------------------------------------------------------------------------------
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1284. (THEORY and FEATURE) NeuronRainApps - Wikipedia Textgraph Question-Answering Bot - related to 1133,1134,1204,1263,1269 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 -------------------------------------------------------------------------------------------------------------------------------------------- 1284.1 New Question-Answering Bot python implementation QuestionAnswering.py has been added to NeuronRain AstroInfer repositories within NeuronRainApps/QuestionAnswering/ which (*) takes questions from users as commandline arguments, (*) parses the question into a question textgraph, (*) computes dense subgraphs and core numbers of prominent vertices in question textgraph, (*) searches Wikipedia Corpus through wikipedia python API for top percentile core number vertices in question textgraph to get matching wikipedia pages, (*) for each matching wikipedia page gets the summary as the text "Answer" to the question, (*) parses the answer into an answer textgraph and (*) computes the degree attention of edges of answer textgraph and learns Query-Key-Value weights (by Perceptrons Gradient Descent) through rlfg_transformers_attention_model() function in RecursiveLambdaFunctionGrowth.py 1284.2 The summary() function of wikipedia python API which wraps MediaWiki PyWikiBot interface of Wikipedia, provides a crisp natural language summary for the question keywords simulating a GPT3-like background intelligence which could be a Turing test. 1284.3 Transformers degree attention learnt for answer textgraph could be helpful in cherrypicking prominent and relevant edges of the textgraph vertices of which are highly interdependent thus aiding a traversal of the answer textgraph to print a natural language answer simulating summary() function of MediaWiki API. 1284.4 Following are 5 questions asked through commandline chosen from varied disciplines - Geography,Physics,History,Sports,Archaeology - and the answers from summary() and answer textgraph degree attention are printed to logs - Last two questions are sourced from OpenAI GPT3 QA examples - https://beta.openai.com/examples/default-qa: python3.7 QuestionAnswering.py "which is the largest country in the world" 2>&1 > testlogs/QuestionAnswering.log1.2August2022 python3.7 QuestionAnswering.py "Define Theory of General Relativity" 2>&1 > testlogs/QuestionAnswering.log2.2August2022 python3.7 QuestionAnswering.py "Who is the Father of Indian Freedom Struggle" 2>&1 > testlogs/QuestionAnswering.log3.2August2022 GPT3 - python3.7 QuestionAnswering.py "Where were the 1992 Olympics held" 2>&1 > testlogs/QuestionAnswering.log4.2August2022 GPT3 - python3.7 QuestionAnswering.py "Where is the Valley of Kings?" 2>&1 > testlogs/QuestionAnswering.log5.2August2022 1284.5 Relevance of answers to questions is along expected lines and multiple answers are allowed per question based on wikipedia matches. For Question 5 earlier following edges have top percentile Query-Key-Value weights laying emphasis on these words in a hypothetical natural language answer (a sentence containing discovery, exploration and land). An example natural language answer could be obtained from edges below by connecting the vertices by a WordNet path between them: Query weight for textgraph edge ( discovery , exploration ): 2.0132 Key weight for textgraph edge ( discovery , exploration ): 5.0504 Value weight for textgraph edge ( discovery , exploration ): 8.0876 Query weight for textgraph edge ( discovery , land ): 3.0338 Key weight for textgraph edge ( discovery , land ): 6.2636 Value weight for textgraph edge ( discovery , land ): 9.4934 1284.6 Earlier Bot implementation could have sourced answers from Google search but wikipedia corpus has better open API access compared to Google search code for which has since been commented.
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Section 1223 updated
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Section 1273 updated
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Section 1245 updated
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Section 1245 updated
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Section 24 split into Section 24 and 1274-1283
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Section 1245 updated
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1273. (THEORY and FEATURE) Astronomy and Cosmology Datasets Analytics - Wilkinson Microwave Anisotropy Probe-Cosmic Microwave Background (WMAP CMB) and N-Body gravitational accelerations from arbitrary vantage points - 29 July 2022 - related to 148,731,1206,1236,1255,1266,1267,1268,1270,1271,1272 and all sections on Astronomical Dataset Mining, Archaeoastronomical Dating, Sequence Mining, String analytics, Sequence Alignment, Visual Planetarium Search, Archaeology, Climate and Extreme Weather Event Analytics, Computational Astrophysics, N-Body problem, Merit of Large Scale Visuals, Astronomical algorithms, Histogram analytics
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----------------------------------------------------------------------------------------------------------------------------- 1273. (THEORY and FEATURE) Astronomy and Cosmology Datasets Analytics - Wilkinson Microwave Anisotropy Probe-Cosmic Microwave Background (WMAP CMB) and N-Body gravitational accelerations from arbitrary vantage points - 29 July 2022 - related to 148,731,1206,1236,1255,1266,1267,1268,1270,1271,1272 and all sections on Astronomical Dataset Mining, Archaeoastronomical Dating, Sequence Mining, String analytics, Sequence Alignment, Visual Planetarium Search, Archaeology, Climate and Extreme Weather Event Analytics, Computational Astrophysics, N-Body problem, Merit of Large Scale Visuals, Astronomical algorithms, Histogram analytics ----------------------------------------------------------------------------------------------------------------------------- 1273.1 extreme_weather_events_n_body_analytics() function in EphemerisSearch.py has been changed to add vantage point of the observation in NASA JPL Horizons Ephemeris as configurable parameter loc which defaults to "@earth-moon" barycenter and is invoked for location earth denoted by "@0". By changing loc to any other solar system body, N-Body gravitational accelerations for other planets could be found. 1273.2 N-Body gravitational accelerations exerted on Earth during Extreme Weather Event days have been recomputed for location Earth instead of Earth-Moon barycenter and the Mercury-Venus high gravity phenomenon remains (Coincidentally GSP Sequence Mining analysis in Section 148 had found Mercury-Venus pattern in Seismic events earlier). 1273.3 New function WMAP_CMB_analytics() has been defined to read Cosmic Microwave Background temperatures data from AstroML WMAP dataset which are footprints of primordial universe before stars-galaxies formation (375000 years after BigBang and Inflation from quantum singularity or 13.77 billion years before present, which stretched the spacetime leaving microwave radiation as traces) and plots the spectrum using HealPy (tool for spherical coordinates) in two images: python-src/testlogs/WMAP_CMB_1.jpg and python-src/testlogs/WMAP_CMB_2.jpg.
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Updated Section 1245
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1272. (THEORY and FEATURE) Astronomy and Cosmology Datasets Analytics - Hubble eXtreme Deep Field imagery RGB analysis and Distance similarit y of N-Body gravitational accelerations - 27 July 2022 - related to 731,1206,1236,1255,1266,1267,1268,1270,1271 and all sections on Astronomic al Dataset Mining, Archaeoastronomical Dating, Sequence Mining, String analytics, Sequence Alignment, Visual Planetarium Search, Archaeology, Climate and Extreme Weather Event Analytics, Computational Astrophysics, N-Body problem, Merit of Large Scale Visuals, Astronomical algorithms , Histogram analytics
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------------------------------------------------------------------------------------------------------------------------------- 1272. (THEORY and FEATURE) Astronomy and Cosmology Datasets Analytics - Hubble eXtreme Deep Field imagery RGB analysis and Distance similarity of N-Body gravitational accelerations - 27 July 2022 - related to 731,1206,1236,1255,1266,1267,1268,1270,1271 and all sections on Astronomical Dataset Mining, Archaeoastronomical Dating, Sequence Mining, String analytics, Sequence Alignment, Visual Planetarium Search, Archaeology, Climate and Extreme Weather Event Analytics, Computational Astrophysics, N-Body problem, Merit of Large Scale Visuals, Astronomical algorithms, Histogram analytics ------------------------------------------------------------------------------------------------------------------------------- 1272.1 Pairwise Hausdorff Distance similarity measures of N-Body gravitational accelerations on days of Extreme Weather Events have been computed within extreme_weather_events_n_body_analytics() function of EphemerisSearch.py 1272.2 Function hubble_deep_field_RGB_analytics() has been updated to compute RGB channel histograms of Hubble eXtreme Deep Field image (HXDF 2012) from Scikit-Image (skimage) and plot the Histogram and RGB Channel graphics in following files(reference: 1272.5): testlogs/skimage_HXDFChannels.jpg testlogs/skimage_HXDFHistograms.jpg 1272.3 RGB channel histogram analysis of Hubble Ultra Deep Field (HUDF in 1270) has been updated to plot Histogram graphics in file testlogs/HubbleUltraDeepField_heic0611bHistograms.jpg. Marked deviation in shapes of RGB histogram bins in testlogs/skimage_HXDFChannels.jpg (HXDF) and testlogs/HubbleUltraDeepField_heic0611bHistograms.jpg (HUDF) could have contributed to RGB ratio asymmetry in 1270. 1272.4 Following are some excerpts of logs testlogs/EphemerisSearch.log.27July2022 (almost close-enough Pairwise gravity similarities and Mercury-Venus abnormal gravity vectors): ... extreme_weather_events_n_body_analytics(): Pairwise Directed hausdorff Distance of Gravitational Acceleration: (0.027206568989690528, 3, 3) extreme_weather_events_n_body_analytics(): Pairwise Directed hausdorff Distance of Gravitational Acceleration: (0.02284149980887304, 3, 3) extreme_weather_events_n_body_analytics(): Pairwise Directed hausdorff Distance of Gravitational Acceleration: (0.01760670123773365, 3, 3) extreme_weather_events_n_body_analytics(): Pairwise Directed hausdorff Distance of Gravitational Acceleration: (0.031958191350966725, 3, 3) extreme_weather_events_n_body_analytics(): Pairwise Directed hausdorff Distance of Gravitational Acceleration: (0.033769298297935034, 3, 3) ... Mercury (199) 2455631.5 ... 1.205450513238685 -0.0203938188627328 extreme_weather_events_n_body_analytics(): gravity of Mercury at Earth-Moon Barycenter on (2011, 3, 11, 5, 46, 23) : [-0.04248091 -0.04133797 0.00052057] Mercury (199) 2454598.5 ... 0.882339348179382 -0.02192844068446965 extreme_weather_events_n_body_analytics(): gravity of Mercury at Earth-Moon Barycenter on (2008, 5, 12, 6, 27, 59) : [ 0.04121007 -0.00578165 -0.004254 ] Mercury (199) 2453365.5 ... 0.9338643038989636 0.02207439343425398 extreme_weather_events_n_body_analytics(): gravity of Mercury at Earth-Moon Barycenter on (2004, 12, 26, 0, 58, 52) : [ 0.04213787 -0.01067853 -0.00473918] ... Venus (299) 2455631.5 ... 1.122263766864372 0.006774280431450814 extreme_weather_events_n_body_analytics(): gravity of Venus at Earth-Moon Barycenter on (2011, 3, 11, 5, 46, 23) : [ 3.89399938e-03 1.05719009e-02 -7.99002829e-05] Venus (299) 2454598.5 ... 1.712807970350092 0.001579916105722341 extreme_weather_events_n_body_analytics(): gravity of Venus at Earth-Moon Barycenter on (2008, 5, 12, 6, 27, 59) : [-0.00944266 -0.00621917 0.00045984] Venus (299) 2453365.5 ... 1.518303955991236 0.003995808610124593 extreme_weather_events_n_body_analytics(): gravity of Venus at Earth-Moon Barycenter on (2004, 12, 26, 0, 58, 52) : [ 0.00875022 0.0072093 -0.00040642]
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Section 1174 updated
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Section 1271 updated
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1271. (THEORY and FEATURE) Astronomy Dataset Analytics - Computation of N-Body Gravitational Acceleration for Solar System bodies on days of Extreme Weather Events (Earthquakes and Hurricanes) - 25 July 2022 - related to 731,1206,1236,1255,1266,1267,1268,1270 and all sections on Astronomical Dataset Mining, Archaeoastronomical Dating, Sequence Mining, String analytics, Sequence Alignment, Visual Planetarium Search, Archaeology, Climate and Extreme Weather Event Analytics, Computational Astrophysics, N-Body problem, Merit of Large Scale Visuals, Astronomical algorithms
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(THEORY and FEATURE) Astronomy Dataset Analytics - Computation of N-Body Gravitational Acceleration for Solar System bodies on days of Extreme Weather Events (Earthquakes and Hurricanes) - 25 July 2022 - related to 731,1206,1236,1255,1266,1267,1268,1270 and all sections on Astronomical Dataset Mining, Archaeoastronomical Dating, Sequence Mining, String analytics, Sequence Alignment, Visual Planetarium Search, Archaeology, Climate and Extreme Weather Event Analytics, Computational Astrophysics, N-Body problem, Merit of Large Scale Visuals, Astronomical algorithms ------------------------------------------------------------------------------------------------------------------------------- 1271.1 This update implements long pending N-Body Gravitational Acceleration computation for Solar system bodies with respect to observations from Earth for dates of Extreme Weather Events (Earthquakes of high magnitude and Category4-Category5 Hurricanes) thus fulfilling the original goal of NeuronRain which was initially astronomy software. 1271.2 New function n_body_gravitational_acceleration() has been implemented to compute gravitational acceleration of solar system bodies by considering AstroPy JPL Horizons N-Body simulation Jupyter notebook (function a_t() for vectorial acceleration) from https://amor.cms.hu-berlin.de/~rodrigus/Subsites/Jupyter%20Notebooks/Solar_System_N-Body_Simulation.html as reference. 1271.3 Function n_body_gravitational_acceleration() is invoked within extreme_weather_events_n_body_analytics() iteratively for Horizons Ephemeris data as Vectors on dates of Extreme Weather Events (JPL Horizons Ephemeris Data Format - r is the Vector of 3D location positions of planets, m is the Vector of planetary masses and v is the Vector of velocities of planets on that day as observed from Earth-Moon barycenter which is center of mass of Earth-Moon situated within Earth and hence seismically sensitive). ===================================================================== 1271.4 An example Planetwise gravitational pull (3D components of acceleration) on Earth-Moon Barycenter (Detailed logs: testlogs/EphemerisSearch.log.25July2022) during Hurricane Katrina (Costliest Cat5 Storm Ever to hit US Eastern Seaboard - 23 August 2005): extreme_weather_events_n_body_analytics(): gravity of Sun at Earth-Moon Barycenter on (2005, 8, 23, 1, 0, 0) : [-4.59023763e-06 -9.57552083e-06 1.33988806e-07] extreme_weather_events_n_body_analytics(): gravity of Moon at Earth-Moon Barycenter on (2005, 8, 23, 1, 0, 0) : [-5.00998359e-03 2.89038032e-03 -1.69038687e-08] extreme_weather_events_n_body_analytics(): gravity of Mars at Earth-Moon Barycenter on (2005, 8, 23, 1, 0, 0) : [-3.06720042e-03 4.51175455e-05 7.63092623e-05] extreme_weather_events_n_body_analytics(): gravity of Mercury at Earth-Moon Barycenter on (2005, 8, 23, 1, 0, 0) : [-0.04877074 -0.0265718 0.00230507] extreme_weather_events_n_body_analytics(): gravity of Jupiter at Earth-Moon Barycenter on (2005, 8, 23, 1, 0, 0) : [ 1.81639208e-04 8.30369383e-05 -4.40933772e-06] extreme_weather_events_n_body_analytics(): gravity of Venus at Earth-Moon Barycenter on (2005, 8, 23, 1, 0, 0) : [ 0.00495958 0.01013055 -0.00014766] extreme_weather_events_n_body_analytics(): gravity of Saturn at Earth-Moon Barycenter on (2005, 8, 23, 1, 0, 0) : [ 3.80719163e-05 -6.09938497e-05 -4.53335739e-07] extreme_weather_events_n_body_analytics(): gravity of Uranus at Earth-Moon Barycenter on (2005, 8, 23, 1, 0, 0) : [-1.37254726e-05 5.37648172e-06 1.97793457e-07] extreme_weather_events_n_body_analytics(): gravity of Neptune at Earth-Moon Barycenter on (2005, 8, 23, 1, 0, 0) : [-4.74401544e-06 4.54907508e-06 1.56547622e-08] extreme_weather_events_n_body_analytics(): gravity of Pluto at Earth-Moon Barycenter on (2005, 8, 23, 1, 0, 0) : [ 6.90750144e-07 6.08469083e-06 -8.50610615e-07] ===================================================================== 1271.5 Another example Planetwise gravitational pull on Earth-Moon Barycenter during Sumatra Boxing Day Earthquake (Subduction of India-Burma plates - 9.1-9.3 magnitude - Duration of 8 minutes - 26 December 2004 - Syzygy Day): extreme_weather_events_n_body_analytics(): gravity of Sun at Earth-Moon Barycenter on (2004, 12, 26, 0, 58, 52) : [-8.49202750e-06 -6.83465500e-06 4.02245707e-07] extreme_weather_events_n_body_analytics(): gravity of Moon at Earth-Moon Barycenter on (2004, 12, 26, 0, 58, 52) : [ 4.74835374e-04 -6.08346634e-03 -1.11752400e-06] extreme_weather_events_n_body_analytics(): gravity of Mars at Earth-Moon Barycenter on (2004, 12, 26, 0, 58, 52) : [ 1.84618951e-03 1.52572942e-03 -1.31875566e-05] extreme_weather_events_n_body_analytics(): gravity of Mercury at Earth-Moon Barycenter on (2004, 12, 26, 0, 58, 52) : [ 0.04213648 -0.01067818 -0.00473903] extreme_weather_events_n_body_analytics(): gravity of Jupiter at Earth-Moon Barycenter on (2004, 12, 26, 0, 58, 52) : [ 1.98371660e-04 2.24260649e-05 -4.53163289e-06] extreme_weather_events_n_body_analytics(): gravity of Venus at Earth-Moon Barycenter on (2004, 12, 26, 0, 58, 52) : [ 0.00874993 0.00720906 -0.00040641] extreme_weather_events_n_body_analytics(): gravity of Saturn at Earth-Moon Barycenter on (2004, 12, 26, 0, 58, 52) : [ 2.83958262e-05 -6.65696263e-05 2.86980912e-08] extreme_weather_events_n_body_analytics(): gravity of Uranus at Earth-Moon Barycenter on (2004, 12, 26, 0, 58, 52) : [-1.34855345e-05 5.99274945e-06 1.96999535e-07] extreme_weather_events_n_body_analytics(): gravity of Neptune at Earth-Moon Barycenter on (2004, 12, 26, 0, 58, 52) : [-4.62705846e-06 4.66464089e-06 1.05755462e-08] extreme_weather_events_n_body_analytics(): gravity of Pluto at Earth-Moon Barycenter on (2004, 12, 26, 0, 58, 52) : [ 8.51278012e-07 6.10016758e-06 -8.98726252e-07]
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Section 1269 updated
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+------------------------------------------------------------------------------------------------------------------------------ +1270. (THEORY and FEATURE) Astronomy Dataset Analytics - NASA JPL Horizons Ephemeris Lookup Service support and Hubble Ultra Deep Field imagery RGB analytics - 20 July 2022 - related to 731,1206,1236,1255,1266,1267,1268 and all sections on Astronomical Dataset Mining, Archaeoastronomical Dating, Sequence Mining, String analytics, Sequence Alignment, Visual Planetarium Search, Archaeology, Climate and Extreme Weather Event Analytics, Computational Astrophysics, N-Body problem, Merit of Large Scale Visuals, Astronomical algorithms +------------------------------------------------------------------------------------------------------------------------------
Krishna iResearch - NeuronRain - K.Srinivasan
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------------------------------------------------------------------------------------------------------------------------------ 1270. (THEORY and FEATURE) Astronomy Dataset Analytics - NASA JPL Horizons Ephemeris Lookup Service support and Hubble Ultra Deep Field imagery RGB analytics - 20 July 2022 - related to 731,1206,1236,1255,1266,1267,1268 and all sections on Astronomical Dataset Mining, Archaeoastronomical Dating, Sequence Mining, String analytics, Sequence Alignment, Visual Planetarium Search, Archaeology, Climate and Extreme Weather Event Analytics, Computational Astrophysics, N-Body problem, Merit of Large Scale Visuals, Astronomical algorithms ------------------------------------------------------------------------------------------------------------------------------ 1270.1 EphemerisSearch.py has been updated to define two new functions jplhorizons_ephemeris() and hubble_deep_field_RGB_analytics() which respectively integrate NASA JPL Horizons Online Ephemeris Lookup API Service and split Hubble Deep Field imagery into RGB Channel for Red-shift distance analysis of Galaxies. 1270.2 jplhorizons_ephemeris() wraps the AstroPy AstroQuery NASA JPL Horizons Ephemeris API lookup service ephemerides() for a celestial body id, location (longitude-latitude-elevation) and epochs(range of dates for ephemeris). 1270.3 jplhorizons_ephemeris() is invoked within sky_on_datetime() if a boolean flag jplhorizons is set to True and the arguments are passed on as heterogeneous array jplhorizonsdata (id is number or name of body,location and epochs are dictionaries). 1270.4 hubble_deep_field_RGB_analytics() function takes an imagename which could be either a postcard that can be looked-up by ESAHubble.get_postcard() or name of a Hubble Space Telescope Deep Field image already downloaded. Image file is opened in Pillow Image and split into Red-Green-Blue channel ndarrays. Each of RGB channel ndarrays are flattened and histograms are computed per color channel. 1270.5 Percentage of Pixel colors are obtained from RGB histograms which provide a clue to the distribution and distance of the galaxies in Deep Field. Reddest galaxies are farthest away (gravitational red shift) compared to Green and Blue channel galaxies. Each of RGB channel data are written to FITS files analyzable by fv tool of NASA GSFC. FITS files created are at present removed because of huge sizes. 1270.6 Sample computations of hubble_deep_field_RGB_analytics() for 13 billion light years old Hubble Ultra Deep Field Imagery HubbleUltraDeepField_heic0611b.jpg (containing primordial light 800 million years after BigBang and Inflation) from https://esahubble.org/images/heic0611b/ and sky_on_datetime()-jplhorizons_ephemeris() for solar system bodies identified by numeric id(s) have been captured (for a date range) at testlogs/EphemerisSearch.log.20July2022. Hubble postcard RGB analysis for J6FL25S4Q postcard id has been commented. 1270.7 Following are the percentage of RGB galaxies by histogram analysis showing slightly higher number of primordial red galaxies than blue and green: ('Percentage of Blue Galaxies (Closer) :', 0.0026145) ('Percentage of Green Galaxies (Closer) :', 0.0011199375) ('Percentage of Red Galaxies (Farthest - Red Shift):', 0.0032260625)
Krishna iResearch - NeuronRain - K.Srinivasan
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Section 1265,1269 updated
Krishna iResearch - NeuronRain - K.Srinivasan
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Section 1269 updated
Krishna iResearch - NeuronRain - K.Srinivasan
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