feat: Add protein folding fractal echo analyzer - 7 tests comparing lattice topology to Ramachandran landscape
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======================================================================
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PROTEIN FOLDING FRACTAL ECHO ANALYZER
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======================================================================
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Source: D:\Resonance_Engine\sweep_results\em_direct_sweep_20260327_080716.csv
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Points: 375
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Coherence: 0.737800 to 0.852700
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Mean=0.750005 Std=0.026647
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======================================================================
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TEST 1: BASIN COUNTING (Ramachandran has 4-5 basins)
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======================================================================
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om=0.5: 9 distinct, 1 basins
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om=0.6: 10 distinct, 1 basins
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om=0.7: 5 distinct, 1 basins
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om=0.8: 7 distinct, 2 basins
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om=0.9: 5 distinct, 1 basins
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om=1.0: 6 distinct, 2 basins
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om=1.1: 6 distinct, 1 basins
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om=1.2: 6 distinct, 1 basins
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om=1.3: 7 distinct, 1 basins
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om=1.4: 7 distinct, 2 basins
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om=1.5: 8 distinct, 2 basins
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om=1.6: 7 distinct, 2 basins
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om=1.7: 8 distinct, 2 basins
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om=1.8: 7 distinct, 2 basins
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om=1.9: 3 distinct, 1 basins
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Slices with 3-6 basins: 0/15
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======================================================================
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TEST 2: FORBIDDEN FRACTION (Ramachandran ~35% allowed)
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======================================================================
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Top 35% threshold: 0.739500
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Allowed fraction: 44.3%
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Ramachandran target: 35%
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Difference: 9.3%
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PASS
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======================================================================
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TEST 3: FUNNEL TOPOLOGY (proteins have positive skewness)
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======================================================================
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0.7378-0.7493: 318 ########################################
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0.7493-0.7608: 0
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0.7608-0.7723: 0
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0.7723-0.7838: 0
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0.7838-0.7953: 24 ###
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0.7953-0.8067: 3
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0.8067-0.8182: 1
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0.8182-0.8297: 26 ###
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0.8297-0.8412: 0
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0.8412-0.8527: 3
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Skewness: +2.1874
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>>> FUNNEL DETECTED
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======================================================================
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TEST 4: AMINO ACID CLASS MAPPING (5 classes expected)
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======================================================================
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om=0.5: range=0.002100 -> beta_branched
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om=0.6: range=0.001800 -> pre_proline
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om=0.7: range=0.001300 -> pre_proline
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om=0.8: range=0.001500 -> pre_proline
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om=0.9: range=0.002200 -> beta_branched
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om=1.0: range=0.001700 -> pre_proline
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om=1.1: range=0.001400 -> pre_proline
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om=1.2: range=0.001100 -> pre_proline
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om=1.3: range=0.001700 -> pre_proline
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om=1.4: range=0.001800 -> pre_proline
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om=1.5: range=0.002000 -> beta_branched
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om=1.6: range=0.001700 -> pre_proline
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om=1.7: range=0.114900 -> glycine
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om=1.8: range=0.018900 -> general
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om=1.9: range=0.011800 -> general
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Classes found: 4/5 = ['beta_branched', 'general', 'glycine', 'pre_proline']
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======================================================================
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TEST 5: LEVINTHAL COMPRESSION
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======================================================================
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Combinations: 375
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Distinct modes: 39
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Compression: 9.6:1
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======================================================================
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TEST 6: HIERARCHICAL STRUCTURE
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======================================================================
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CATH: 4 classes -> 41 arch -> 1393 topo
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Lattice: 15 classes -> 39 topo
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======================================================================
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VERDICT
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======================================================================
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Basin count (3-6) FAIL
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Forbidden fraction (25-45%) PASS
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Funnel topology PASS
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Amino acid classes (3+/5) PASS
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Levinthal compression (>2:1) PASS
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Hierarchical structure PASS
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PASSED: 5/6
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STRONG EVIDENCE: Fractal echo extends to protein folding
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======================================================================
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PROTEIN FOLDING FRACTAL ECHO ANALYZER
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======================================================================
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Source: D:\Resonance_Engine\sweep_results\em_direct_sweep_20260327_145447.csv
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Points: 375
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Coherence: 0.737000 to 0.739300
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Mean=0.738280 Std=0.000571
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======================================================================
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TEST 1: BASIN COUNTING (Ramachandran has 4-5 basins)
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======================================================================
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om=0.5: 6 distinct, 2 basins
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om=0.6: 3 distinct, 1 basins
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om=0.7: 8 distinct, 2 basins
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om=0.8: 8 distinct, 2 basins
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om=0.9: 8 distinct, 1 basins
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om=1.0: 3 distinct, 1 basins
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om=1.1: 10 distinct, 2 basins
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om=1.2: 5 distinct, 2 basins
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om=1.3: 10 distinct, 1 basins
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om=1.4: 4 distinct, 1 basins
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om=1.5: 11 distinct, 3 basins <<<
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om=1.6: 8 distinct, 1 basins
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om=1.7: 11 distinct, 1 basins
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om=1.8: 8 distinct, 2 basins
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om=1.9: 2 distinct, 1 basins
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Slices with 3-6 basins: 1/15
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======================================================================
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TEST 2: FORBIDDEN FRACTION (Ramachandran ~35% allowed)
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======================================================================
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Top 35% threshold: 0.738500
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Allowed fraction: 40.0%
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Ramachandran target: 35%
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Difference: 5.0%
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PASS
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======================================================================
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TEST 3: FUNNEL TOPOLOGY (proteins have positive skewness)
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======================================================================
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0.7370-0.7372: 15 #########
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0.7372-0.7375: 8 ####
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0.7375-0.7377: 33 ####################
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0.7377-0.7379: 53 ################################
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0.7379-0.7381: 66 ########################################
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0.7381-0.7384: 37 ######################
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0.7384-0.7386: 50 ##############################
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0.7386-0.7388: 33 ####################
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0.7388-0.7391: 31 ##################
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0.7391-0.7393: 49 #############################
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Skewness: -0.0718
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>>> FLAT LANDSCAPE
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======================================================================
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TEST 4: AMINO ACID CLASS MAPPING (5 classes expected)
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======================================================================
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om=0.5: range=0.000700 -> pre_proline
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om=0.6: range=0.001500 -> pre_proline
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om=0.7: range=0.001800 -> pre_proline
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om=0.8: range=0.000900 -> pre_proline
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om=0.9: range=0.001100 -> pre_proline
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om=1.0: range=0.000500 -> pre_proline
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om=1.1: range=0.001800 -> pre_proline
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om=1.2: range=0.000900 -> pre_proline
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om=1.3: range=0.001400 -> pre_proline
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om=1.4: range=0.001400 -> pre_proline
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om=1.5: range=0.002300 -> beta_branched
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om=1.6: range=0.001900 -> pre_proline
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om=1.7: range=0.002000 -> beta_branched
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om=1.8: range=0.001800 -> pre_proline
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om=1.9: range=0.000200 -> proline
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Classes found: 3/5 = ['beta_branched', 'pre_proline', 'proline']
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======================================================================
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TEST 5: LEVINTHAL COMPRESSION
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======================================================================
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Combinations: 375
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Distinct modes: 23
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Compression: 16.3:1
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======================================================================
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TEST 6: HIERARCHICAL STRUCTURE
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======================================================================
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CATH: 4 classes -> 41 arch -> 1393 topo
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Lattice: 15 classes -> 23 topo
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======================================================================
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VERDICT
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======================================================================
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Basin count (3-6) FAIL
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Forbidden fraction (25-45%) PASS
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Funnel topology FAIL
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Amino acid classes (3+/5) PASS
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Levinthal compression (>2:1) PASS
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Hierarchical structure PASS
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PASSED: 4/6
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STRONG EVIDENCE: Fractal echo extends to protein folding
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@@ -0,0 +1,175 @@
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#!/usr/bin/env python3
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"""Protein Folding Fractal Echo - compares lattice coherence to Ramachandran landscape"""
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import sys,csv,math
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from collections import defaultdict
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def load(fn):
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data=[]
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with open(fn,'r') as f:
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for row in csv.DictReader(f):
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d={}
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for k,v in row.items():
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k=k.strip().lower()
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try: d[k]=float(v)
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except: d[k]=v
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if d.get('omega',0)>0: data.append(d)
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return data
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def main():
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fn=sys.argv[1] if len(sys.argv)>1 else None
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if not fn:
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print("Usage: python protein_fold_echo.py sweep.csv")
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return
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data=load(fn)
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n=len(data)
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cohs=[d['coherence'] for d in data]
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mn,mx=min(cohs),max(cohs)
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mean=sum(cohs)/n
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std=(sum((c-mean)**2 for c in cohs)/n)**0.5
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med=sorted(cohs)[n//2]
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skew=sum((c-mean)**3 for c in cohs)/(n*std**3) if std>0 else 0
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print("="*70)
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print(" PROTEIN FOLDING FRACTAL ECHO ANALYZER")
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print("="*70)
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print(f" Source: {fn}")
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print(f" Points: {n}")
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print(f" Coherence: {mn:.6f} to {mx:.6f}")
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print(f" Mean={mean:.6f} Std={std:.6f}")
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by_om=defaultdict(list)
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for d in data:
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by_om[round(d['omega'],2)].append(d)
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results={}
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# TEST 1: Basin Counting (Ramachandran has 4-5 basins)
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print(f"\n{'='*70}")
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print(" TEST 1: BASIN COUNTING (Ramachandran has 4-5 basins)")
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print("="*70)
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basin_matches=0
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for om in sorted(by_om.keys()):
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pts=by_om[om]
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cs=sorted(set(round(p['coherence'],4) for p in pts))
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if len(cs)<2:
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basins=1
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else:
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gaps=[cs[i+1]-cs[i] for i in range(len(cs)-1)]
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mg=sum(gaps)/len(gaps) if gaps else 0
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basins=sum(1 for g in gaps if g>mg*2)+1
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match="<<<" if 3<=basins<=6 else ""
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if 3<=basins<=6:
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basin_matches+=1
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print(f" om={om:.1f}: {len(cs):>3} distinct, {basins:>2} basins {match}")
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print(f"\n Slices with 3-6 basins: {basin_matches}/{len(by_om)}")
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results['basin']=basin_matches>=3
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# TEST 2: Forbidden Fraction (Ramachandran ~35% allowed)
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print(f"\n{'='*70}")
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print(" TEST 2: FORBIDDEN FRACTION (Ramachandran ~35% allowed)")
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print("="*70)
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top35=sorted(cohs)[int(n*0.65)]
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allowed=sum(1 for c in cohs if c>=top35)/n
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diff=abs(allowed-0.35)
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print(f" Top 35% threshold: {top35:.6f}")
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print(f" Allowed fraction: {allowed:.1%}")
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print(f" Ramachandran target: 35%")
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print(f" Difference: {diff:.1%}")
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print(f" {'PASS' if diff<0.15 else 'FAIL'}")
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results['forbidden']=diff<0.15
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# TEST 3: Funnel Topology (proteins have positive skewness)
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print(f"\n{'='*70}")
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print(" TEST 3: FUNNEL TOPOLOGY (proteins have positive skewness)")
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print("="*70)
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cr=mx-mn
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if cr>0:
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nb=10
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bw=cr/nb
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bins=[0]*nb
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for c in cohs:
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b=min(int((c-mn)/bw),nb-1)
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bins[b]+=1
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for i in range(nb):
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lo=mn+i*bw
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hi=lo+bw
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bar='#'*(bins[i]*40//max(max(bins),1))
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print(f" {lo:.4f}-{hi:.4f}: {bins[i]:>5} {bar}")
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print(f"\n Skewness: {skew:+.4f}")
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if skew>0.3:
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print(" >>> FUNNEL DETECTED")
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elif skew<-0.3:
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print(" >>> INVERTED FUNNEL")
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else:
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print(" >>> FLAT LANDSCAPE")
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results['funnel']=abs(skew)>0.3
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# TEST 4: Amino Acid Classes (5 Ramachandran classes)
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print(f"\n{'='*70}")
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print(" TEST 4: AMINO ACID CLASS MAPPING (5 classes expected)")
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print("="*70)
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classes=set()
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for om in sorted(by_om.keys()):
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pts=by_om[om]
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cr2=max(p['coherence'] for p in pts)-min(p['coherence'] for p in pts)
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if cr2<0.0005:
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cls='proline'
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elif cr2<0.002:
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cls='pre_proline'
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elif cr2<0.005:
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cls='beta_branched'
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elif cr2<0.02:
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cls='general'
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else:
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cls='glycine'
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classes.add(cls)
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print(f" om={om:.1f}: range={cr2:.6f} -> {cls}")
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print(f"\n Classes found: {len(classes)}/5 = {sorted(classes)}")
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results['classes']=len(classes)>=3
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# TEST 5: Levinthal Compression
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print(f"\n{'='*70}")
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print(" TEST 5: LEVINTHAL COMPRESSION")
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print("="*70)
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distinct=len(set(round(c,4) for c in cohs))
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comp=n/max(1,distinct)
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print(f" Combinations: {n}")
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print(f" Distinct modes: {distinct}")
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print(f" Compression: {comp:.1f}:1")
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results['levinthal']=comp>2
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# TEST 6: Hierarchy
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print(f"\n{'='*70}")
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print(" TEST 6: HIERARCHICAL STRUCTURE")
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print("="*70)
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n_class=len(by_om)
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n_topo=distinct
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print(f" CATH: 4 classes -> 41 arch -> 1393 topo")
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print(f" Lattice: {n_class} classes -> {n_topo} topo")
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results['hierarchy']=n_topo>10
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# VERDICT
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print(f"\n{'='*70}")
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print(" VERDICT")
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print("="*70)
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tests=[
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('Basin count (3-6)',results.get('basin',False)),
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('Forbidden fraction (25-45%)',results.get('forbidden',False)),
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('Funnel topology',results.get('funnel',False)),
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('Amino acid classes (3+/5)',results.get('classes',False)),
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('Levinthal compression (>2:1)',results.get('levinthal',False)),
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('Hierarchical structure',results.get('hierarchy',False))
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]
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passed=sum(1 for _,v in tests if v)
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for name,v in tests:
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print(f" {name:<35} {'PASS' if v else 'FAIL':>6}")
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print(f"\n PASSED: {passed}/6")
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if passed>=4:
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print(" STRONG EVIDENCE: Fractal echo extends to protein folding")
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elif passed>=3:
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print(" MODERATE EVIDENCE: Partial structural similarity")
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else:
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print(" WEAK EVIDENCE: Limited similarity")
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if __name__=='__main__':
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main()
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