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Analyzed 1 day ago. based on code collected 2 days ago.
Nov 05, 2025 — Nov 05, 2026
Commit Message Contributor Files Modified Lines Added Lines Removed Code Location Date
Section 1630 updated
Srinivasan Kannan
More... 12 months ago
Section 868 updated
Srinivasan Kannan
More... 12 months ago
1644. (THEORY and FEATURE) Time series analysis - Least Squares Curve Fit for timeseries - related to 690,691,692,1257,1315,1316,1327,1348,1367,1375,1376,1436,1437,1438,1489 and all sections on Computational Economics-Quantitative Finance and TimeSeries Analysis, Graphical Event Models (GEM), Causal Event Models (CEM), Chain Event Graphs(CEG), Chaos and Fractals, EventNet causality, Multifractal Detrended Fluctuation Analysis (MFDFA),Fame-Merit in the context of quantitative finance - 20 November 2025
Srinivasan Kannan
More... 12 months ago
------------------------------------------------------------------------------------------------------------------------------- 1644. (THEORY and FEATURE) Time series analysis - Least Squares Curve Fit for timeseries - related to 690,691,692,1257,1315,1316,1327,1348,1367,1375,1376,1436,1437,1438,1489 and all sections on Computational Economics-Quantitative Finance and TimeSeries Analysis, Graphical Event Models (GEM), Causal Event Models (CEM), Chain Event Graphs(CEG), Chaos and Fractals, EventNet causality, Multifractal Detrended Fluctuation Analysis (MFDFA),Fame-Merit in the context of quantitative finance - 20 November 2025 ------------------------------------------------------------------------------------------------------------------------------- 1644.1 python-src/Streaming_TimeSeriesData.py has been updated to import SciPy non-linear least squares curve_fit() function and matplotlib. 1644.2 Two functions least_squares_func() and timeseries_least_squares_model() have been defined to fit an arbitrary timeseries data to a function defined within least_squares_func() through non-linear least squares curve fit function curve_fit() from SciPy. Though error band in least squares fit could be high, trend in data is reasonably accurate especially for chaotic financial timeseries. 1644.3 Curve fit Plot and logs for a random timeseries have been added to repositories: testlogs/Streaming_TimeSeriesData.LeastSquares.20November2025.jpg testlogs/Streaming_TimeSeriesData.log.20November2025
Srinivasan Kannan
More... 12 months ago
Section 1630 updated
Srinivasan Kannan
More... 12 months ago
1643. (THEORY and FEATURE) Music Information Retrieval(MIR) - Learning the Longest Repeated Notes Subsequence in a music notes sequence - 20 November 2025 - related to 3,110,134,298,1223,1224,1257,1258,1261,1305,1306,1307,1308,1309,1310,1311,1315,1353,1432,1433,1434,1438,1439,1440,1441,1458,1478,1524,1535,1628 and all sections on Music search-Music discovery, Intrinsic Merit of Music, MIR(Music Information Retrieval), Music Similarity and Clustering, AI Music Synthesis from summation of damped sinusoids, Additive Synthesis, fMRI connectomes - OpenNeuro - Psychology of music, Weighted automata, Chaos, Deep Learning, Dynamic Time Warping-Music Timeseries analysis, Music Evoked Autobiographical Memories, Waveshaping, ThoughtNet, Majority Voting (Enjoyable music) Vs Intrinsic (Quality music) - Condorcet Jury Theorem variants,Computational Learning Theory, Boolean Function Composition, Longest Common Subsequence, Suffix Trees - Longest Repeated Substring, MultiFractal Detrended Fluctuation Analysis of Non-stationary timeseries, Ramsey Theory-Van Der Waerden Theorem for coloring of sequences, Pumping Lemma, Normalized Compression Distance-Kolmogorov complexity, Merit of Large Scale Visuals, Recurrent Neural Networks,Neo-Riemannian Theory of Music,Attack-Decay-Sustain-Release model
Srinivasan Kannan
More... 12 months ago
---------------------------------------------------------------------------------------------------------------------------------------------- 1643. (THEORY and FEATURE) Music Information Retrieval(MIR) - Learning the Longest Repeated Notes Subsequence in a music notes sequence - 20 November 2025 - related to 3,110,134,298,1223,1224,1257,1258,1261,1305,1306,1307,1308,1309,1310,1311,1315,1353,1432,1433,1434,1438,1439,1440,1441,1458,1478,1524,1535,1628 and all sections on Music search-Music discovery, Intrinsic Merit of Music, MIR(Music Information Retrieval), Music Similarity and Clustering, AI Music Synthesis from summation of damped sinusoids, Additive Synthesis, fMRI connectomes - OpenNeuro - Psychology of music, Weighted automata, Chaos, Deep Learning, Dynamic Time Warping-Music Timeseries analysis, Music Evoked Autobiographical Memories, Waveshaping, ThoughtNet, Majority Voting (Enjoyable music) Vs Intrinsic (Quality music) - Condorcet Jury Theorem variants,Computational Learning Theory, Boolean Function Composition, Longest Common Subsequence, Suffix Trees - Longest Repeated Substring, MultiFractal Detrended Fluctuation Analysis of Non-stationary timeseries, Ramsey Theory-Van Der Waerden Theorem for coloring of sequences, Pumping Lemma, Normalized Compression Distance-Kolmogorov complexity, Merit of Large Scale Visuals, Recurrent Neural Networks,Neo-Riemannian Theory of Music,Attack-Decay-Sustain-Release model ---------------------------------------------------------------------------------------------------------------------------------------------- 1643.1 python-src/StringSearch_LongestRepeatedSubstring.py has been updated to remove hardcoded pattern file name and replace it with SuffixArray constructor initializer. 1643.2 python-src/music_pattern_mining/AudioToBitMatrix.py has been updated to import SuffixArray from StringSearch_LongestRepeatedSubstring 1643.3 New function def longest_repeated_notes_sequence(clip,clipslice=1000) has been defined that accepts a music clip as argument, gets the notes sequence in the music and learns the Longest Repeated Subsequence of notes within the music by instantiating a SuffixArray object for music notes and invoking longest_repeated_substring() on SuffixArray object. Music has periodicities of notes and Longest Repeated Subsequence of notes within the music is the obvious notion to capture the pattern in music. 1643.4 Earlier function includes a clipslice argument to configure the number of notes processed by SuffixArray. 1643.5 Longest Repeated Subsequence is learnt for JS Bach's Musicological Offering: longest_repeated_notes_sequence("testlogs/JSBach_Musicological_Offering.mp4") 1643.6 Learning Longest repeated subsequence of notes in music complements the Weighted Finite Automaton-Recurrent Neural Network (WFA-RNN) based music synthesizer implementation. In ideal sense, Minimum Description Length or Kolmogorov complexity of music notes sequence is the best measure of music structure but is undecidable and Longest Repeated Subsequence is an approximation of minimum description length.
Srinivasan Kannan
More... 12 months ago
Section 1639 updated
Srinivasan Kannan
More... 12 months ago
Section 744 updated
Srinivasan Kannan
More... about 1 year ago
Section 385 updated
Srinivasan Kannan
More... about 1 year ago
Section 1133 updated
Srinivasan Kannan
More... about 1 year ago
Section 1639 updated
Srinivasan Kannan
More... about 1 year ago
Section 1630 updated
Srinivasan Kannan
More... about 1 year ago
Section 1639 updated
Srinivasan Kannan
More... about 1 year ago
Section 440 updated
Srinivasan Kannan
More... about 1 year ago
Section 440 updated
Srinivasan Kannan
More... about 1 year ago
Section 1639 updated
Srinivasan Kannan
More... about 1 year ago
1640. (THEORY and FEATURE) GIS Urban Sprawl Analytics - WorldPop Population Estimates - India - Spatial Distribution of Population (2015-2030) 2025 - Global 2 Constrained Raster Dataset - Population Estimate of Chennai Metropolitan Area Bounding Box (for year 2025) - 14 October 2025 - related to 823,1163,1164,1174,1175,1177,1179,1194,1211,1226,1234,1290,1291,1292,1295,1296,1297,1301,1302,1304,1312,1313,1320,1323,1326,1328,1342,1343,1349,1350,1351,1362,1363,1377,1386,1387,1395,1397,1398,1412,1414,1423,1448,1449,1462,1471,1472,1533,1559,1560,1608,1623,1630 and all sections on GIS Urban Sprawl Analytics, Merit of Large Scale Visuals, Gridded Population Estimators (SEDAC GPW4 and GHSL convex hull estimators), DBSCAN and Neural Network Urban Extent Learners, Computational Geometry, Intrinsic Merit Rankings, Verlhulste-R Ricker Population Estimation, Community detection, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Computational Geometry and Polygonal packing by Voronoi Tessellation, Four color theorem, Economic Merit, Computational Economics - GDP estimation from Urban sprawl demographics, electricity consumption, Poverty alleviation, Preferential Attachment, Automatic Delineation of Urban Sprawls, Cellular Automata-SLEUTH-Polya Urn-Dynamic Facegraph Urban Growth Models,Expander Graphs,Timeseries analysis,Graph Edit Distance and Earth mover distance, Face recognition,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
Srinivasan Kannan
More... about 1 year ago
-------------------------------------------------------------------------------------------------------------------------------------------- 1640. (THEORY and FEATURE) GIS Urban Sprawl Analytics - WorldPop Population Estimates - India - Spatial Distribution of Population (2015-2030) 2025 - Global 2 Constrained Raster Dataset - Population Estimate of Chennai Metropolitan Area Bounding Box (for year 2025) - 14 October 2025 - related to 823,1163,1164,1174,1175,1177,1179,1194,1211,1226,1234,1290,1291,1292,1295,1296,1297,1301,1302,1304,1312,1313,1320,1323,1326,1328,1342,1343,1349,1350,1351,1362,1363,1377,1386,1387,1395,1397,1398,1412,1414,1423,1448,1449,1462,1471,1472,1533,1559,1560,1608,1623,1630 and all sections on GIS Urban Sprawl Analytics, Merit of Large Scale Visuals, Gridded Population Estimators (SEDAC GPW4 and GHSL convex hull estimators), DBSCAN and Neural Network Urban Extent Learners, Computational Geometry, Intrinsic Merit Rankings, Verlhulste-R Ricker Population Estimation, Community detection, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Computational Geometry and Polygonal packing by Voronoi Tessellation, Four color theorem, Economic Merit, Computational Economics - GDP estimation from Urban sprawl demographics, electricity consumption, Poverty alleviation, Preferential Attachment, Automatic Delineation of Urban Sprawls, Cellular Automata-SLEUTH-Polya Urn-Dynamic Facegraph Urban Growth Models,Expander Graphs,Timeseries analysis,Graph Edit Distance and Earth mover distance, Face recognition,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 -------------------------------------------------------------------------------------------------------------------------------------------- 1640.1 python-src/image_pattern_mining/ImageNet/GISUrbanSprawlAnalytics.py has been updated to change the WorldPop dataset to recently released Global 2 Demographic Data Public release R2025A V1 - testlogs/ind_pop_2025_CN_1km_R2025A_UA_v1.tif - from earlier testlogs/ind_pop_2025_CN_1km_R2024B_UA_v1.tif which has been marked obsolete (Global 1): r2data=urban_sprawl_from_raster(79.271851,12.439259,80.351257,13.568572,"testlogs/ind_pop_2025_CN_1km_R2025A_UA_v1.tif",dt="WorldPop population estimates 2025") 1640.2 Revised population aggregate has been logged to python-src/image_pattern_mining/ImageNet/testlogs/GISUrbanSprawlAnalytics.log.14October2025 1640.3 From python-src/image_pattern_mining/ImageNet/testlogs/GISUrbanSprawlAnalytics.log.14October2025 aggregate population living only in built pixels of earlier bounding box is 9361964 and from 1630.6 inaccuracies in built area population estimation algorithms could be as high as 50% (for every person estimated in urban cell, 1 person is omitted) which could double the estimate to 9361964*2 = 18723928 dwelling within earlier bounding box: urban_sprawl_from_raster(): urbansprawlstatisitcsaggregate = [9361964.] 1640.4 Earlier dataset is for 1km resolution and better estimate requires 100m resolution dataset (700 MB) - https://data.worldpop.org/GIS/Population/Global_2015_2030/R2025A/2025/IND/v1/100m/constrained/ind_pop_2025_CN_100m_R2025A_v1.tif
Srinivasan Kannan
More... about 1 year ago
Section 1639 updated
Srinivasan Kannan
More... about 1 year ago
Section 1639 updated
Srinivasan Kannan
More... about 1 year ago
1639. (THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 12 October 2025 - VARUNA N-Body Climate Model - Implementation of General Form of Navier-Stokes Pressure Gradient - N-Body gravity analysis of date range 1/12/2025-31/12/2025 for Sun-Mercury-Venus - related to 148,172,313,445,446,463,1165,1171,1176,1266,1289,1316,1318,1327,1358,1359,1360,1361,1364,1367,1368,1388,1404,1455,1456,1463,1479,1480,1515,1526,1527,1528,1548,1558,1561,1562,1567,1581,1590,1631 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,Clausius-Clapeyron equation,Navier-Stokes equation
Srinivasan Kannan
More... about 1 year ago
---------------------------------------------------------------------------------------------------------------------------------------- 1639. (THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 12 October 2025 - VARUNA N-Body Climate Model - Implementation of General Form of Navier-Stokes Pressure Gradient - N-Body gravity analysis of date range 1/12/2025-31/12/2025 for Sun-Mercury-Venus - related to 148,172,313,445,446,463,1165,1171,1176,1266,1289,1316,1318,1327,1358,1359,1360,1361,1364,1367,1368,1388,1404,1455,1456,1463,1479,1480,1515,1526,1527,1528,1548,1558,1561,1562,1567,1581,1590,1631 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,Clausius-Clapeyron equation,Navier-Stokes equation ---------------------------------------------------------------------------------------------------------------------------------------- (Disclaimer: Same as Disclaimer in Section 1548)
Srinivasan Kannan
More... about 1 year ago
Section 1638 updated
Srinivasan Kannan
More... about 1 year ago
Section 1624 updated
Srinivasan Kannan
More... about 1 year ago
Section 1638 updated
Srinivasan Kannan
More... about 1 year ago
Section 1638 updated
Srinivasan Kannan
More... about 1 year ago
Section 1638 updated
Srinivasan Kannan
More... about 1 year ago
Section 1638 updated
Srinivasan Kannan
More... about 1 year ago
Section 1203 updated
Srinivasan Kannan
More... about 1 year ago