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"""
glyph_mapper.py
Core implementation of the Glyph Mapper module for the glyphs framework.
This module transforms attribution traces, residue patterns, and attention
flows into symbolic glyph representations that visualize latent spaces.
"""
import logging
import time
import numpy as np
from typing import Dict, List, Optional, Tuple, Union, Any, Set
from dataclasses import dataclass, field
import json
import hashlib
from pathlib import Path
from enum import Enum
import networkx as nx
import matplotlib.pyplot as plt
from scipy.spatial import distance
from sklearn.manifold import TSNE
from sklearn.cluster import DBSCAN
from ..models.adapter import ModelAdapter
from ..attribution.tracer import AttributionMap, AttributionType, AttributionLink
from ..residue.patterns import ResiduePattern, ResidueRegistry
from ..utils.visualization_utils import VisualizationEngine
# Configure glyph-aware logging
logger = logging.getLogger("glyphs.glyph_mapper")
logger.setLevel(logging.INFO)
class GlyphType(Enum):
"""Types of glyphs for different interpretability functions."""
ATTRIBUTION = "attribution" # Glyphs representing attribution relations
ATTENTION = "attention" # Glyphs representing attention patterns
RESIDUE = "residue" # Glyphs representing symbolic residue
SALIENCE = "salience" # Glyphs representing token salience
COLLAPSE = "collapse" # Glyphs representing collapse patterns
RECURSIVE = "recursive" # Glyphs representing recursive structures
META = "meta" # Glyphs representing meta-level patterns
SENTINEL = "sentinel" # Special marker glyphs
class GlyphSemantic(Enum):
"""Semantic dimensions captured by glyphs."""
STRENGTH = "strength" # Strength of the pattern
DIRECTION = "direction" # Directional relationship
STABILITY = "stability" # Stability of the pattern
COMPLEXITY = "complexity" # Complexity of the pattern
RECURSION = "recursion" # Degree of recursion
CERTAINTY = "certainty" # Certainty of the pattern
TEMPORAL = "temporal" # Temporal aspects of the pattern
EMERGENCE = "emergence" # Emergent properties
@dataclass
class Glyph:
"""A symbolic representation of a pattern in transformer cognition."""
id: str # Unique identifier
symbol: str # Unicode glyph symbol
type: GlyphType # Type of glyph
semantics: List[GlyphSemantic] # Semantic dimensions
position: Tuple[float, float] # Position in 2D visualization
size: float # Relative size of glyph
color: str # Color of glyph
opacity: float # Opacity of glyph
source_elements: List[Any] = field(default_factory=list) # Elements that generated this glyph
description: Optional[str] = None # Human-readable description
metadata: Dict[str, Any] = field(default_factory=dict) # Additional metadata
@dataclass
class GlyphConnection:
"""A connection between glyphs in a glyph map."""
source_id: str # Source glyph ID
target_id: str # Target glyph ID
strength: float # Connection strength
type: str # Type of connection
directed: bool # Whether connection is directed
color: str # Connection color
width: float # Connection width
opacity: float # Connection opacity
metadata: Dict[str, Any] = field(default_factory=dict) # Additional metadata
@dataclass
class GlyphMap:
"""A complete map of glyphs representing transformer cognition."""
id: str # Unique identifier
glyphs: List[Glyph] # Glyphs in the map
connections: List[GlyphConnection] # Connections between glyphs
source_type: str # Type of source data
layout_type: str # Type of layout
dimensions: Tuple[int, int] # Dimensions of visualization
scale: float # Scale factor
focal_points: List[str] = field(default_factory=list) # Focal glyph IDs
regions: Dict[str, List[str]] = field(default_factory=dict) # Named regions with glyph IDs
metadata: Dict[str, Any] = field(default_factory=dict) # Additional metadata
class GlyphRegistry:
"""Registry of available glyphs and their semantics."""
def __init__(self):
"""Initialize the glyph registry."""
# Attribution glyphs
self.attribution_glyphs = {
"direct_strong": {
"symbol": "🔍",
"semantics": [GlyphSemantic.STRENGTH, GlyphSemantic.CERTAINTY],
"description": "Strong direct attribution"
},
"direct_medium": {
"symbol": "🔗",
"semantics": [GlyphSemantic.STRENGTH, GlyphSemantic.CERTAINTY],
"description": "Medium direct attribution"
},
"direct_weak": {
"symbol": "🧩",
"semantics": [GlyphSemantic.STRENGTH, GlyphSemantic.CERTAINTY],
"description": "Weak direct attribution"
},
"indirect": {
"symbol": "⤑",
"semantics": [GlyphSemantic.DIRECTION, GlyphSemantic.COMPLEXITY],
"description": "Indirect attribution"
},
"composite": {
"symbol": "⬥",
"semantics": [GlyphSemantic.COMPLEXITY, GlyphSemantic.EMERGENCE],
"description": "Composite attribution"
},
"fork": {
"symbol": "🔀",
"semantics": [GlyphSemantic.DIRECTION, GlyphSemantic.COMPLEXITY],
"description": "Attribution fork"
},
"loop": {
"symbol": "🔄",
"semantics": [GlyphSemantic.RECURSION, GlyphSemantic.COMPLEXITY],
"description": "Attribution loop"
},
"gap": {
"symbol": "⊟",
"semantics": [GlyphSemantic.CERTAINTY, GlyphSemantic.STABILITY],
"description": "Attribution gap"
}
}
# Attention glyphs
self.attention_glyphs = {
"focus": {
"symbol": "🎯",
"semantics": [GlyphSemantic.STRENGTH, GlyphSemantic.CERTAINTY],
"description": "Attention focus point"
},
"diffuse": {
"symbol": "🌫️",
"semantics": [GlyphSemantic.STRENGTH, GlyphSemantic.CERTAINTY],
"description": "Diffuse attention"
},
"induction": {
"symbol": "📈",
"semantics": [GlyphSemantic.TEMPORAL, GlyphSemantic.DIRECTION],
"description": "Induction head pattern"
},
"inhibition": {
"symbol": "🛑",
"semantics": [GlyphSemantic.DIRECTION, GlyphSemantic.STRENGTH],
"description": "Attention inhibition"
},
"multi_head": {
"symbol": "⟁",
"semantics": [GlyphSemantic.COMPLEXITY, GlyphSemantic.EMERGENCE],
"description": "Multi-head attention pattern"
}
}
# Residue glyphs
self.residue_glyphs = {
"memory_decay": {
"symbol": "🌊",
"semantics": [GlyphSemantic.TEMPORAL, GlyphSemantic.STABILITY],
"description": "Memory decay residue"
},
"value_conflict": {
"symbol": "⚡",
"semantics": [GlyphSemantic.STABILITY, GlyphSemantic.CERTAINTY],
"description": "Value conflict residue"
},
"ghost_activation": {
"symbol": "👻",
"semantics": [GlyphSemantic.STRENGTH, GlyphSemantic.CERTAINTY],
"description": "Ghost activation residue"
},
"boundary_hesitation": {
"symbol": "⧋",
"semantics": [GlyphSemantic.CERTAINTY, GlyphSemantic.STABILITY],
"description": "Boundary hesitation residue"
},
"null_output": {
"symbol": "⊘",
"semantics": [GlyphSemantic.CERTAINTY, GlyphSemantic.STABILITY],
"description": "Null output residue"
}
}
# Recursive glyphs
self.recursive_glyphs = {
"recursive_aegis": {
"symbol": "🜏",
"semantics": [GlyphSemantic.RECURSION, GlyphSemantic.STABILITY],
"description": "Recursive immunity"
},
"recursive_seed": {
"symbol": "∴",
"semantics": [GlyphSemantic.RECURSION, GlyphSemantic.EMERGENCE],
"description": "Recursion initiation"
},
"recursive_exchange": {
"symbol": "⇌",
"semantics": [GlyphSemantic.RECURSION, GlyphSemantic.DIRECTION],
"description": "Bidirectional recursion"
},
"recursive_mirror": {
"symbol": "🝚",
"semantics": [GlyphSemantic.RECURSION, GlyphSemantic.EMERGENCE],
"description": "Recursive reflection"
},
"recursive_anchor": {
"symbol": "☍",
"semantics": [GlyphSemantic.RECURSION, GlyphSemantic.STABILITY],
"description": "Stable recursive reference"
}
}
# Meta glyphs
self.meta_glyphs = {
"uncertainty": {
"symbol": "❓",
"semantics": [GlyphSemantic.CERTAINTY],
"description": "Uncertainty marker"
},
"emergence": {
"symbol": "✧",
"semantics": [GlyphSemantic.EMERGENCE, GlyphSemantic.COMPLEXITY],
"description": "Emergent pattern marker"
},
"collapse_point": {
"symbol": "💥",
"semantics": [GlyphSemantic.STABILITY, GlyphSemantic.CERTAINTY],
"description": "Collapse point marker"
},
"temporal_marker": {
"symbol": "⧖",
"semantics": [GlyphSemantic.TEMPORAL],
"description": "Temporal sequence marker"
}
}
# Sentinel glyphs
self.sentinel_glyphs = {
"start": {
"symbol": "◉",
"semantics": [GlyphSemantic.DIRECTION],
"description": "Start marker"
},
"end": {
"symbol": "◯",
"semantics": [GlyphSemantic.DIRECTION],
"description": "End marker"
},
"boundary": {
"symbol": "⬚",
"semantics": [GlyphSemantic.STABILITY],
"description": "Boundary marker"
},
"reference": {
"symbol": "✱",
"semantics": [GlyphSemantic.DIRECTION],
"description": "Reference marker"
}
}
# Combine all glyphs into a single map
self.all_glyphs = {
**{f"attribution_{k}": v for k, v in self.attribution_glyphs.items()},
**{f"attention_{k}": v for k, v in self.attention_glyphs.items()},
**{f"residue_{k}": v for k, v in self.residue_glyphs.items()},
**{f"recursive_{k}": v for k, v in self.recursive_glyphs.items()},
**{f"meta_{k}": v for k, v in self.meta_glyphs.items()},
**{f"sentinel_{k}": v for k, v in self.sentinel_glyphs.items()}
}
def get_glyph(self, glyph_id: str) -> Dict[str, Any]:
"""Get a glyph by ID."""
if glyph_id in self.all_glyphs:
return self.all_glyphs[glyph_id]
else:
raise ValueError(f"Unknown glyph ID: {glyph_id}")
def find_glyphs_by_semantic(self, semantic: GlyphSemantic) -> List[str]:
"""Find glyphs that have a specific semantic dimension."""
return [
glyph_id for glyph_id, glyph in self.all_glyphs.items()
if semantic in glyph.get("semantics", [])
]
def find_glyphs_by_type(self, glyph_type: str) -> List[str]:
"""Find glyphs of a specific type."""
return [
glyph_id for glyph_id in self.all_glyphs.keys()
if glyph_id.startswith(f"{glyph_type}_")
]
class GlyphMapper:
"""
Core glyph mapping system for the glyphs framework.
This class transforms attribution traces, residue patterns, and attention
flows into symbolic glyph representations that visualize latent spaces.
It serves as the bridge between raw interpretability data and meaningful
symbolic visualization.
"""
def __init__(
self,
config: Optional[Dict[str, Any]] = None,
visualizer: Optional[VisualizationEngine] = None
):
"""
Initialize the glyph mapper.
Parameters:
-----------
config : Optional[Dict[str, Any]]
Configuration parameters for the mapper
visualizer : Optional[VisualizationEngine]
Visualization engine for glyph visualization
"""
self.config = config or {}
self.visualizer = visualizer
# Configure mapper parameters
self.min_connection_strength = self.config.get("min_connection_strength", 0.1)
self.auto_layout = self.config.get("auto_layout", True)
self.default_layout = self.config.get("default_layout", "force_directed")
self.default_dimensions = self.config.get("default_dimensions", (800, 600))
self.default_scale = self.config.get("default_scale", 1.0)
self.connection_bundling = self.config.get("connection_bundling", True)
self.color_scheme = self.config.get("color_scheme", "semantic")
# Initialize glyph registry
self.registry = GlyphRegistry()
# Track created glyph maps
self.glyph_map_history = []
logger.info("Glyph mapper initialized")
def map_attribution(
self,
attribution_map: AttributionMap,
layout_type: Optional[str] = None,
dimensions: Optional[Tuple[int, int]] = None,
scale: Optional[float] = None,
include_tokens: bool = True,
focus_on: Optional[List[str]] = None
) -> GlyphMap:
"""
Map attribution patterns to glyphs.
Parameters:
-----------
attribution_map : AttributionMap
Attribution map to visualize
layout_type : Optional[str]
Type of layout to use
dimensions : Optional[Tuple[int, int]]
Dimensions for visualization
scale : Optional[float]
Scale factor for visualization
include_tokens : bool
Whether to include tokens as sentinel glyphs
focus_on : Optional[List[str]]
Tokens to focus visualization on
Returns:
--------
GlyphMap
Glyph map representation of attribution
"""
map_start = time.time()
layout_type = layout_type or self.default_layout
dimensions = dimensions or self.default_dimensions
scale = scale or self.default_scale
logger.info(f"Mapping attribution to glyphs with {layout_type} layout")
# Create unique ID for glyph map
map_id = f"attribution_{int(time.time())}_{hashlib.md5(str(attribution_map).encode()).hexdigest()[:8]}"
# Initialize glyph map
glyph_map = GlyphMap(
id=map_id,
glyphs=[],
connections=[],
source_type="attribution",
layout_type=layout_type,
dimensions=dimensions,
scale=scale,
metadata={
"attribution_map_id": attribution_map.metadata.get("id", "unknown"),
"prompt": attribution_map.metadata.get("prompt", ""),
"output": attribution_map.metadata.get("output", ""),
"model_id": attribution_map.metadata.get("model_id", "unknown"),
"timestamp": time.time()
}
)
# Add sentinel glyphs for tokens if requested
token_glyph_ids = {}
if include_tokens:
# Add prompt tokens
for i, token in enumerate(attribution_map.prompt_tokens):
glyph_id = f"token_prompt_{i}"
glyph = Glyph(
id=glyph_id,
symbol="◆", # Diamond for prompt tokens
type=GlyphType.SENTINEL,
semantics=[GlyphSemantic.DIRECTION],
position=(0, i * 20), # Initial position, will be updated by layout
size=5.0,
color="#3498db", # Blue
opacity=0.8,
source_elements=[token],
description=f"Prompt token: '{token}'",
metadata={"token_index": i, "token_type": "prompt"}
)
glyph_map.glyphs.append(glyph)
token_glyph_ids[f"prompt_{i}"] = glyph_id
# Add output tokens
for i, token in enumerate(attribution_map.output_tokens):
glyph_id = f"token_output_{i}"
glyph = Glyph(
id=glyph_id,
symbol="◇", # Open diamond for output tokens
type=GlyphType.SENTINEL,
semantics=[GlyphSemantic.DIRECTION],
position=(100, i * 20), # Initial position, will be updated by layout
size=5.0,
color="#e74c3c", # Red
opacity=0.8,
source_elements=[token],
description=f"Output token: '{token}'",
metadata={"token_index": i, "token_type": "output"}
)
glyph_map.glyphs.append(glyph)
token_glyph_ids[f"output_{i}"] = glyph_id
# Process attribution links
link_glyphs = {}
for link_idx, link in enumerate(attribution_map.links):
# Skip weak attributions
if link.strength < self.min_connection_strength:
continue
# Determine glyph type based on attribution type and strength
if link.attribution_type == AttributionType.DIRECT:
if link.strength > 0.7:
glyph_type = "attribution_direct_strong"
elif link.strength > 0.4:
glyph_type = "attribution_direct_medium"
else:
glyph_type = "attribution_direct_weak"
elif link.attribution_type == AttributionType.INDIRECT:
glyph_type = "attribution_indirect"
elif link.attribution_type == AttributionType.COMPOSITE:
glyph_type = "attribution_composite"
elif link.attribution_type == AttributionType.RECURSIVE:
glyph_type = "recursive_recursive_exchange"
else:
glyph_type = "attribution_direct_weak"
# Get glyph from registry
glyph_info = self.registry.get_glyph(glyph_type)
# Create glyph for this attribution link
glyph_id = f"link_{link_idx}"
glyph = Glyph(
id=glyph_id,
symbol=glyph_info["symbol"],
type=GlyphType.ATTRIBUTION,
semantics=glyph_info["semantics"],
position=(50, (link.source_idx + link.target_idx) * 10), # Initial position
size=10.0 * link.strength, # Size based on strength
color=self._get_color_for_attribution(link),
opacity=min(1.0, 0.5 + link.strength / 2),
source_elements=[link],
description=glyph_info["description"],
metadata={
"link_index": link_idx,
"source_index": link.source_idx,
"target_index": link.target_idx,
"attribution_type": str(link.attribution_type),
"strength": link.strength
}
)
glyph_map.glyphs.append(glyph)
link_glyphs[link_idx] = glyph_id
# Create connections to token glyphs if tokens are included
if include_tokens:
source_glyph_id = token_glyph_ids.get(f"prompt_{link.source_idx}")
target_glyph_id = token_glyph_ids.get(f"output_{link.target_idx}")
if source_glyph_id and target_glyph_id:
# Connection from source token to attribution glyph
glyph_map.connections.append(GlyphConnection(
source_id=source_glyph_id,
target_id=glyph_id,
strength=link.strength,
type="attribution_flow",
directed=True,
color="#7f8c8d", # Gray
width=1.0 + 2.0 * link.strength,
opacity=0.7 * link.strength
))
# Connection from attribution glyph to target token
glyph_map.connections.append(GlyphConnection(
source_id=glyph_id,
target_id=target_glyph_id,
strength=link.strength,
type="attribution_flow",
directed=True,
color="#7f8c8d", # Gray
width=1.0 + 2.0 * link.strength,
opacity=0.7 * link.strength
))
# Process attribution gaps
for gap_idx, (start_idx, end_idx) in enumerate(attribution_map.attribution_gaps):
# Add attribution gap glyph
glyph_info = self.registry.get_glyph("attribution_gap")
glyph_id = f"gap_{gap_idx}"
glyph = Glyph(
id=glyph_id,
symbol=glyph_info["symbol"],
type=GlyphType.ATTRIBUTION,
semantics=glyph_info["semantics"],
position=(50, (start_idx + end_idx) * 10), # Initial position
size=8.0,
color="#e67e22", # Orange
opacity=0.8,
source_elements=[(start_idx, end_idx)],
description=f"Attribution gap between indices {start_idx} and {end_idx}",
metadata={
"gap_index": gap_idx,
"start_index": start_idx,
"end_index": end_idx
}
)
glyph_map.glyphs.append(glyph)
# Create connections to token glyphs if tokens are included
if include_tokens:
source_glyph_id = token_glyph_ids.get(f"prompt_{start_idx}")
target_glyph_id = token_glyph_ids.get(f"output_{end_idx}")
if source_glyph_id and target_glyph_id:
# Connect source token to gap glyph
glyph_map.connections.append(GlyphConnection(
source_id=source_glyph_id,
target_id=glyph_id,
strength=0.5,
type="attribution_gap",
directed=True,
color="#e67e22", # Orange
width=1.5,
opacity=0.6
))
# Connect gap glyph to target token
glyph_map.connections.append(GlyphConnection(
source_id=glyph_id,
target_id=target_glyph_id,
strength=0.5,
type="attribution_gap",
directed=True,
color="#e67e22", # Orange
width=1.5,
opacity=0.6
))
# Process collapsed regions
for collapse_idx, (start_idx, end_idx) in enumerate(attribution_map.collapsed_regions):
# Add collapse glyph
glyph_info = self.registry.get_glyph("meta_collapse_point")
glyph_id = f"collapse_{collapse_idx}"
glyph = Glyph(
id=glyph_id,
symbol=glyph_info["symbol"],
type=GlyphType.COLLAPSE,
semantics=glyph_info["semantics"],
position=(50, (start_idx + end_idx) * 10), # Initial position
size=12.0,
color="#9b59b6", # Purple
opacity=0.9,
source_elements=[(start_idx, end_idx)],
description=f"Attribution collapse between indices {start_idx} and {end_idx}",
metadata={
"collapse_index": collapse_idx,
"start_index": start_idx,
"end_index": end_idx
}
)
glyph_map.glyphs.append(glyph)
# Create connections to token glyphs if tokens are included
if include_tokens:
# Connect to all tokens in the collapsed region
for i in range(start_idx, end_idx + 1):
token_glyph_id = token_glyph_ids.get(f"output_{i}")
if token_glyph_id:
glyph_map.connections.append(GlyphConnection(
source_id=glyph_id,
target_id=token_glyph_id,
strength=0.7,
type="collapse_effect",
directed=True,
color="#9b59b6", # Purple
width=1.5,
opacity=0.7
))
# Identify focal points based on token salience
if attribution_map.token_salience:
# Find top salient tokens
salient_tokens = sorted(
attribution_map.token_salience.items(),
key=lambda x: x[1],
reverse=True
)[:5] # Top 5 salient tokens
for token_idx, salience in salient_tokens:
# Add to focal points if salience is significant
if salience > 0.5:
token_glyph_id = token_glyph_ids.get(f"output_{token_idx}")
if token_glyph_id:
glyph_map.focal_points.append(token_glyph_id)
# Add salience glyph for highly salient tokens
if salience > 0.8:
glyph_info = self.registry.get_glyph("attention_focus")
glyph_id = f"salience_{token_idx}"
glyph = Glyph(
id=glyph_id,
symbol=glyph_info["symbol"],
type=GlyphType.SALIENCE,
semantics=glyph_info["semantics"],
position=(120, token_idx * 20), # Initial position
size=10.0 * salience,
color="#f1c40f", # Yellow
opacity=salience,
source_elements=[token_idx],
description=f"High salience token at index {token_idx}",
metadata={
"token_index": token_idx,
"salience": salience
}
)
glyph_map.glyphs.append(glyph)
# Connect salience glyph to token
glyph_map.connections.append(GlyphConnection(
source_id=glyph_id,
target_id=token_glyph_id,
strength=salience,
type="salience_marker",
directed=False,
color="#f1c40f", # Yellow
width=2.0 * salience,
opacity=0.8
))
# Apply layout if auto_layout is enabled
if self.auto_layout:
glyph_map = self._apply_layout(glyph_map, layout_type)
# Apply focus if specified
if focus_on:
glyph_map = self._apply_focus(glyph_map, focus_on)
# Record execution time
map_time = time.time() - map_start
glyph_map.metadata["map_time"] = map_time
# Add to history
self.glyph_map_history.append(glyph_map)
logger.info(f"Attribution mapping completed in {map_time:.2f}s")
return glyph_map
def map_residue_patterns(
self,
residue_patterns: List[ResiduePattern],
layout_type: Optional[str] = None,
dimensions: Optional[Tuple[int, int]] = None,
scale: Optional[float] = None,
cluster_patterns: bool = True
) ->
def map_residue_patterns(
self,
residue_patterns: List[ResiduePattern],
layout_type: Optional[str] = None,
dimensions: Optional[Tuple[int, int]] = None,
scale: Optional[float] = None,
cluster_patterns: bool = True
) -> GlyphMap:
"""
Map residue patterns to glyphs.
Parameters:
-----------
residue_patterns : List[ResiduePattern]
Residue patterns to visualize
layout_type : Optional[str]
Type of layout to use
dimensions : Optional[Tuple[int, int]]
Dimensions for visualization
scale : Optional[float]
Scale factor for visualization
cluster_patterns : bool
Whether to cluster similar patterns
Returns:
--------
GlyphMap
Glyph map representation of residue patterns
"""
map_start = time.time()
layout_type = layout_type or self.default_layout
dimensions = dimensions or self.default_dimensions
scale = scale or self.default_scale
logger.info(f"Mapping {len(residue_patterns)} residue patterns to glyphs")
# Create unique ID for glyph map
map_id = f"residue_{int(time.time())}_{hashlib.md5(str(residue_patterns).encode()).hexdigest()[:8]}"
# Initialize glyph map
glyph_map = GlyphMap(
id=map_id,
glyphs=[],
connections=[],
source_type="residue",
layout_type=layout_type,
dimensions=dimensions,
scale=scale,
metadata={
"num_patterns": len(residue_patterns),
"pattern_types": [p.type for p in residue_patterns],
"timestamp": time.time()
}
)
# Group patterns by type
pattern_by_type = {}
for pattern in residue_patterns:
if pattern.type not in pattern_by_type:
pattern_by_type[pattern.type] = []
pattern_by_type[pattern.type].append(pattern)
# Create type center glyphs
type_glyphs = {}
for i, (pattern_type, patterns) in enumerate(pattern_by_type.items()):
# Determine glyph based on pattern type
if pattern_type == "memory_decay":
glyph_type = "residue_memory_decay"
elif pattern_type == "value_conflict":
glyph_type = "residue_value_conflict"
elif pattern_type == "ghost_activation":
glyph_type = "residue_ghost_activation"
elif pattern_type == "boundary_hesitation":
glyph_type = "residue_boundary_hesitation"
elif pattern_type == "null_output":
glyph_type = "residue_null_output"
else:
# Default to null output for unknown types
glyph_type = "residue_null_output"
# Get glyph from registry
glyph_info = self.registry.get_glyph(glyph_type)
# Create center glyph for this pattern type
glyph_id = f"type_{pattern_type}"
glyph = Glyph(
id=glyph_id,
symbol=glyph_info["symbol"],
type=GlyphType.RESIDUE,
semantics=glyph_info["semantics"],
position=(dimensions[0] / 2, 100 + i * 150), # Initial position
size=20.0, # Larger for type centers
color=self._get_color_for_residue_type(pattern_type),
opacity=1.0,
source_elements=patterns,
description=f"{pattern_type.replace('_', ' ').title()} Residue Pattern",
metadata={
"pattern_type": pattern_type,
"pattern_count": len(patterns),
"average_confidence": sum(p.confidence for p in patterns) / len(patterns)
}
)
glyph_map.glyphs.append(glyph)
type_glyphs[pattern_type] = glyph_id
# Add to regions
glyph_map.regions[pattern_type] = [glyph_id]
# For each pattern, create instance glyph
instance_glyphs = {}
for pattern_idx, pattern in enumerate(residue_patterns):
# Get parent type glyph
parent_glyph_id = type_glyphs.get(pattern.type)
if not parent_glyph_id:
continue
# Determine specific glyph variant based on confidence
if pattern.confidence > 0.8:
size_factor = 1.2
opacity = 0.9
elif pattern.confidence > 0.5:
size_factor = 1.0
opacity = 0.7
else:
size_factor = 0.8
opacity = 0.5
# Get glyph from registry
glyph_type = f"residue_{pattern.type}"
glyph_info = self.registry.get_glyph(glyph_type)
# Create glyph for this pattern instance
glyph_id = f"pattern_{pattern_idx}"
glyph = Glyph(
id=glyph_id,
symbol=glyph_info["symbol"],
type=GlyphType.RESIDUE,
semantics=glyph_info["semantics"],
position=(0, 0), # Will be set by layout
size=12.0 * size_factor,
color=self._get_color_for_residue_type(pattern.type),
opacity=opacity,
source_elements=[pattern],
description=f"{pattern.type.replace('_', ' ').title()} Pattern: {pattern.signature[:20]}",
metadata={
"pattern_type": pattern.type,
"confidence": pattern.confidence,
"signature": pattern.signature,
"context": pattern.context
}
)
glyph_map.glyphs.append(glyph)
instance_glyphs[pattern_idx] = glyph_id
# Add to regions
if pattern.type in glyph_map.regions:
glyph_map.regions[pattern.type].append(glyph_id)
# Connect to parent type glyph
glyph_map.connections.append(GlyphConnection(
source_id=parent_glyph_id,
target_id=glyph_id,
strength=pattern.confidence,
type="type_instance",
directed=True,
color=self._get_color_for_residue_type(pattern.type),
width=1.0 + pattern.confidence,
opacity=0.6 * pattern.confidence
))
# Connect similar patterns if clustering enabled
if cluster_patterns and len(residue_patterns) > 1:
# Create similarity matrix based on pattern signatures
similarity_matrix = np.zeros((len(residue_patterns), len(residue_patterns)))
for i, pattern1 in enumerate(residue_patterns):
for j, pattern2 in enumerate(residue_patterns):
if i == j:
similarity_matrix[i, j] = 1.0
else:
# Calculate similarity based on signature and context
signature_sim = self._calculate_signature_similarity(
pattern1.signature, pattern2.signature
)
context_sim = self._calculate_context_similarity(
pattern1.context, pattern2.context
)
# Weighted combination
similarity_matrix[i, j] = 0.7 * signature_sim + 0.3 * context_sim
# Find significant connections
for i in range(len(residue_patterns)):
for j in range(i + 1, len(residue_patterns)):
similarity = similarity_matrix[i, j]
if similarity > 0.6: # Only connect sufficiently similar patterns
source_id = instance_glyphs.get(i)
target_id = instance_glyphs.get(j)
if source_id and target_id:
glyph_map.connections.append(GlyphConnection(
source_id=source_id,
target_id=target_id,
strength=similarity,
type="pattern_similarity",
directed=False,
color="#2ecc71", # Green
width=1.0 + 2.0 * similarity,
opacity=0.5 * similarity
))
# Use similarity matrix for layout
if layout_type == "similarity":
# Use t-SNE to layout based on similarity
tsne = TSNE(n_components=2, perplexity=min(5, len(residue_patterns) - 1))
positions = tsne.fit_transform(similarity_matrix)
# Scale positions to dimensions
positions = self._scale_positions(positions, dimensions)
# Update glyph positions for pattern instances
for i, pattern_idx in enumerate(instance_glyphs.keys()):
glyph_id = instance_glyphs[pattern_idx]
for glyph in glyph_map.glyphs:
if glyph.id == glyph_id:
glyph.position = (positions[i, 0], positions[i, 1])
# Apply layout if auto_layout is enabled and not already layout by similarity
if self.auto_layout and layout_type != "similarity":
glyph_map = self._apply_layout(glyph_map, layout_type)
# Add sentinel glyphs for context markers
for pattern_idx, pattern in enumerate(residue_patterns):
# Only add context markers for high-confidence patterns
if pattern.confidence < 0.7:
continue
glyph_id = instance_glyphs.get(pattern_idx)
if not glyph_id:
continue
# Add reference glyph for context
ref_glyph_id = f"ref_{pattern_idx}"
ref_glyph = Glyph(
id=ref_glyph_id,
symbol="✱", # Star for reference
type=GlyphType.SENTINEL,
semantics=[GlyphSemantic.DIRECTION],
position=(0, 0), # Will be set relative to pattern glyph
size=6.0,
color="#3498db", # Blue
opacity=0.7,
source_elements=[pattern.context],
description=f"Context reference for pattern {pattern_idx}",
metadata={
"pattern_index": pattern_idx,
"reference_type": "context"
}
)
# Position relative to pattern glyph
for glyph in glyph_map.glyphs:
if glyph.id == glyph_id:
x, y = glyph.position
ref_glyph.position = (x + 20, y - 20)
glyph_map.glyphs.append(ref_glyph)
# Connect reference to pattern
glyph_map.connections.append(GlyphConnection(
source_id=ref_glyph_id,
target_id=glyph_id,
strength=0.7,
type="context_reference",
directed=True,
color="#3498db", # Blue
width=1.0,
opacity=0.5
))
# Record execution time
map_time = time.time() - map_start
glyph_map.metadata["map_time"] = map_time
# Add to history
self.glyph_map_history.append(glyph_map)
logger.info(f"Residue pattern mapping completed in {map_time:.2f}s")
return glyph_map
def map_attention_heads(
self,
attention_data: Dict[str, Any],
layout_type: Optional[str] = None,
dimensions: Optional[Tuple[int, int]] = None,
scale: Optional[float] = None,
include_tokens: bool = True
) -> GlyphMap:
"""
Map attention head patterns to glyphs.
Parameters:
-----------
attention_data : Dict[str, Any]
Attention head data to visualize
layout_type : Optional[str]
Type of layout to use
dimensions : Optional[Tuple[int, int]]
Dimensions for visualization
scale : Optional[float]
Scale factor for visualization
include_tokens : bool
Whether to include tokens as sentinel glyphs
Returns:
--------
GlyphMap
Glyph map representation of attention head patterns
"""
map_start = time.time()
layout_type = layout_type or self.default_layout
dimensions = dimensions or self.default_dimensions
scale = scale or self.default_scale
logger.info("Mapping attention head patterns to glyphs")
# Extract data
prompt_tokens = attention_data.get("prompt_tokens", [])
output_tokens = attention_data.get("output_tokens", [])
attention_heads = attention_data.get("attention_heads", [])
head_patterns = attention_data.get("head_patterns", {})
# Create unique ID for glyph map
map_id = f"attention_{int(time.time())}_{hashlib.md5(str(attention_heads).encode()).hexdigest()[:8]}"
# Initialize glyph map
glyph_map = GlyphMap(
id=map_id,
glyphs=[],
connections=[],
source_type="attention",
layout_type=layout_type,
dimensions=dimensions,
scale=scale,
metadata={
"num_heads": len(attention_heads),
"model_id": attention_data.get("metadata", {}).get("model_id", "unknown"),
"timestamp": time.time()
}
)
# Add sentinel glyphs for tokens if requested
token_glyph_ids = {}
if include_tokens:
# Add prompt tokens
for i, token in enumerate(prompt_tokens):
glyph_id = f"token_prompt_{i}"
glyph = Glyph(
id=glyph_id,
symbol="◆", # Diamond for prompt tokens
type=GlyphType.SENTINEL,
semantics=[GlyphSemantic.DIRECTION],
position=(0, i * 20), # Initial position, will be updated by layout
size=5.0,
color="#3498db", # Blue
opacity=0.8,
source_elements=[token],
description=f"Prompt token: '{token}'",
metadata={"token_index": i, "token_type": "prompt"}
)
glyph_map.glyphs.append(glyph)
token_glyph_ids[f"prompt_{i}"] = glyph_id
# Add output tokens
for i, token in enumerate(output_tokens):
glyph_id = f"token_output_{i}"
glyph = Glyph(
id=glyph_id,
symbol="◇", # Open diamond for output tokens
type=GlyphType.SENTINEL,
semantics=[GlyphSemantic.DIRECTION],
position=(100, i * 20), # Initial position, will be updated by layout
size=5.0,
color="#e74c3c", # Red
opacity=0.8,
source_elements=[token],
description=f"Output token: '{token}'",
metadata={"token_index": i, "token_type": "output"}
)
glyph_map.glyphs.append(glyph)
token_glyph_ids[f"output_{i}"] = glyph_id
# Process attention heads
head_glyphs = {}
for head_idx, head in enumerate(attention_heads):
# Determine glyph type based on attention pattern
if head.pattern_type == "induction":
glyph_type = "attention_induction"
elif head.pattern_type == "focus":
glyph_type = "attention_focus"
elif head.pattern_type == "diffuse":
glyph_type = "attention_diffuse"
elif head.pattern_type == "inhibition":
glyph_type = "attention_inhibition"
else:
glyph_type = "attention_multi_head"
# Get glyph from registry
glyph_info = self.registry.get_glyph(glyph_type)
# Create glyph for this attention head
glyph_id = f"head_{head_idx}"
glyph = Glyph(
id=glyph_id,
symbol=glyph_info["symbol"],
type=GlyphType.ATTENTION,
semantics=glyph_info["semantics"],
position=(50, head.layer * 40 + head.head * 10), # Initial position
size=8.0 + 5.0 * head.strength, # Size based on strength
color=self._get_color_for_layer(head.layer),
opacity=min(1.0, 0.5 + head.strength / 2),
source_elements=[head],
description=f"Attention head {head.layer}.{head.head}: {head.pattern_type}",
metadata={
"head_index": head_idx,
"layer": head.layer,
"head": head.head,
"pattern_type": head.pattern_type,
"strength": head.strength,
"function": head.function,
"attribution_role": head.attribution_role
}
)
glyph_map.glyphs.append(glyph)
head_glyphs[head_idx] = glyph_id
# Connect to focus tokens if include_tokens is True
if include_tokens and head.focus_tokens:
for token_idx in head.focus_tokens:
token_type = "prompt" if token_idx < len(prompt_tokens) else "output"
adjusted_idx = token_idx if token_type == "prompt" else token_idx - len(prompt_tokens)
token_glyph_id = token_glyph_ids.get(f"{token_type}_{adjusted_idx}")
if token_glyph_id:
glyph_map.connections.append(GlyphConnection(
source_id=glyph_id,
target_id=token_glyph_id,
strength=head.strength,
type="attention_focus",
directed=True,
color=self._get_color_for_layer(head.layer, alpha=0.6),
width=1.0 + 2.0 * head.strength,
opacity=0.6 * head.strength
))
# Add pattern connections between heads
for pattern_name, related_heads in head_patterns.items():
# Skip patterns with less than 2 heads
if len(related_heads) < 2:
continue
# Create pattern node
glyph_type = "meta_emergence" if "emergent" in pattern_name else "recursive_recursive_exchange"
glyph_info = self.registry.get_glyph(glyph_type)
pattern_glyph_id = f"pattern_{pattern_name}"
pattern_glyph = Glyph(
id=pattern_glyph_id,
symbol=glyph_info["symbol"],
type=GlyphType.META if "emergent" in pattern_name else GlyphType.RECURSIVE,
semantics=glyph_info["semantics"],
position=(100, 100), # Will be updated by layout
size=12.0,
color="#f1c40f", # Yellow
opacity=0.9,
source_elements=[pattern_name, related_heads],
description=f"Attention pattern: {pattern_name}",
metadata={
"pattern_name": pattern_name,
"related_heads": related_heads
}
)
glyph_map.glyphs.append(pattern_glyph)
# Connect pattern to all related heads
for head_idx in related_heads:
head_glyph_id = head_glyphs.get(head_idx)
if head_glyph_id:
glyph_map.connections.append(GlyphConnection(
source_id=pattern_glyph_id,
target_id=head_glyph_id,
strength=0.8,
type="pattern_membership",
directed=True,
color="#f1c40f", # Yellow
width=1.5,
opacity=0.7
))
# Add pattern to regions
if pattern_name not in glyph_map.regions:
glyph_map.regions[pattern_name] = []
glyph_map.regions[pattern_name].append(pattern_glyph_id)
for head_idx in related_heads:
head_glyph_id = head_glyphs.get(head_idx)
if head_glyph_id:
glyph_map.regions[pattern_name].append(head_glyph_id)
# Add layer groups
layers = set(head.layer for head in attention_heads)
for layer in layers:
# Create layer group
layer_heads = [
head_idx for head_idx, head in enumerate(attention_heads)
if head.layer == layer
]
# Add layer to regions
layer_region = f"layer_{layer}"
glyph_map.regions[layer_region] = [
head_glyphs[head_idx] for head_idx in layer_heads
if head_idx in head_glyphs
]
# Apply layout if auto_layout is enabled
if self.auto_layout:
glyph_map = self._apply_layout(glyph_map, layout_type)
# Record execution time
map_time = time.time() - map_start
glyph_map.metadata["map_time"] = map_time
# Add to history
self.glyph_map_history.append(glyph_map)
logger.info(f"Attention head mapping completed in {map_time:.2f}s")
return glyph_map
def map_recursive_trace(
self,
trace_data: Dict[str, Any],
layout_type: Optional[str] = None,
dimensions: Optional[Tuple[int, int]] = None,
scale: Optional[float] = None,
depth_limit: Optional[int] = None
) -> GlyphMap:
"""
Map recursive trace data to glyphs.
Parameters:
-----------
trace_data : Dict[str, Any]
Recursive trace data to visualize
layout_type : Optional[str]
Type of layout to use
dimensions : Optional[Tuple[int, int]]
Dimensions for visualization
scale : Optional[float]
Scale factor for visualization
depth_limit : Optional[int]
Maximum recursion depth to visualize
Returns:
--------
GlyphMap
Glyph map representation of recursive trace
"""
map_start = time.time()
layout_type = layout_type or self.default_layout
dimensions = dimensions or self.default_dimensions
scale = scale or self.default_scale
logger.info("Mapping recursive trace to glyphs")
# Extract trace data
trace_operations = trace_data.get("operations", [])
trace_result = trace_data.get("result", {})
trace_depth = trace_data.get("depth", 0)
# Apply depth limit if specified
if depth_limit is not None:
trace_depth = min(trace_depth, depth_limit)
# Filter operations by depth
trace_operations = [
op for op in trace_operations
if op.get("depth", 0) <= depth_limit
]
# Create unique ID for glyph map
map_id = f"recursive_{int(time.time())}_{hashlib.md5(str(trace_data).encode()).hexdigest()[:8]}"
# Initialize glyph map
glyph_map = GlyphMap(
id=map_id,
glyphs=[],
connections=[],
source_type="recursive",
layout_type=layout_type,
dimensions=dimensions,
scale=scale,
metadata={
"trace_command": trace_data.get("command", "unknown"),
"trace_target": trace_data.get("target", "unknown"),
"trace_depth": trace_depth,
"timestamp": time.time()
}
)
# Add recursive seed glyph
seed_glyph = Glyph(
id="seed",
symbol="∴", # Recursive seed symbol
type=GlyphType.RECURSIVE,
semantics=[GlyphSemantic.RECURSION, GlyphSemantic.EMERGENCE],
position=(dimensions[0] / 2, 50), # Top center
size=15.0,
color="#9b59b6", # Purple
opacity=1.0,
source_elements=[trace_data.get("command", "")],
description=f"Recursive seed: {trace_data.get('command', 'unknown')}",
metadata={
"command": trace_data.get("command", ""),
"target": trace_data.get("target", ""),
"depth": trace_depth
}
)
glyph_map.glyphs.append(seed_glyph)
glyph_map.focal_points.append("seed")
# Process operations by depth
depth_glyphs = {"0": "seed"}
for op_idx, operation in enumerate(trace_operations):
op_depth = operation.get("depth", 0)
op_type = operation.get("type", "unknown")
# Determine glyph type based on operation type
if "reflect" in op_type:
glyph_type = "recursive_recursive_mirror"
elif "collapse" in op_type:
glyph_type = "meta_collapse_point"
elif "fork" in op_type:
glyph_type = "attribution_fork"
elif "trace" in op_type:
glyph_type = "recursive_recursive_exchange"
else:
glyph_type = "recursive_recursive_anchor"
# Get glyph from registry
glyph_info = self.registry.get_glyph(glyph_type)
# Create glyph for this operation
glyph_id = f"op_{op_idx}"
glyph = Glyph(
id=glyph_id,
symbol=glyph_info["symbol"],
type=GlyphType.RECURSIVE,
semantics=glyph_info["semantics"],
position=(100 + op_depth * 80, 100 + op_idx * 30), # Initial position
size=10.0 - op_depth * 0.5, # Size decreases with depth
color=self._get_color_for_depth(op_depth),
opacity=max(0.5, 1.0 - op_depth * 0.1),
source_elements=[operation],
description=f"Operation {op_type} at depth {op_depth}",
metadata={
"operation_index": op_idx,
"operation_type": op_type,
"depth": op_depth,
"parameters": operation.get("parameters", {})
}
)
glyph_map.glyphs.append(glyph)
# Store glyph ID by depth
depth_key = str(op_depth)
if depth_key not in depth_glyphs:
depth_glyphs[depth_key] = []
if isinstance(depth_glyphs[depth_key], list):
depth_glyphs[depth_key].append(glyph_id)
else:
depth_glyphs[depth_key] = [depth_glyphs[depth_key], glyph_id]
# Connect to parent depth
parent_depth_key = str(op_depth - 1)
if parent_depth_key in depth_glyphs:
parent_glyphs = depth_glyphs[parent_depth_key]
if isinstance(parent_glyphs, list):
# Connect to closest parent in terms of operation index
parent_indices = [
int(g.split('_')[1]) if g.startswith('op_') else 0
for g in parent_glyphs
]
closest_parent_idx = min(range(len(parent_indices)), key=lambda i: abs(parent_indices[i] - op_idx))
parent_glyph_id = parent_glyphs[closest_parent_idx]
else:
parent_glyph_id = parent_glyphs
glyph_map.connections.append(GlyphConnection(
source_id=parent_glyph_id,
target_id=glyph_id,
strength=0.8,
type="recursive_descent",
directed=True,
color=self._get_color_for_depth(op_depth - 1, alpha=0.6),
width=2.0 - op_depth * 0.2,
opacity=max(0.4, 0.9 - op_depth * 0.1)
))
# Add result glyphs
if trace_result:
result_type = trace_result.get("type", "unknown")
# Determine glyph type based on result type
if "success" in result_type:
glyph_type = "recursive_recursive_aegis"
elif "collapse" in result_type:
glyph_type = "meta_collapse_point"
elif "partial" in result_type:
glyph_type = "residue_ghost_activation"
else:
glyph_type = "meta_uncertainty"
# Get glyph from registry
glyph_info = self.registry.get_glyph(glyph_type)
# Create result glyph
result_glyph_id = "result"
result_glyph = Glyph(
id=result_glyph_id,
symbol=glyph_info["symbol"],
type=GlyphType.RECURSIVE if "success" in result_type else GlyphType.META,
semantics=glyph_info["semantics"],
position=(dimensions[0] / 2, dimensions[1] - 80), # Bottom center
size=15.0,
color="#27ae60" if "success" in result_type else "#e74c3c",
opacity=1.0,
source_elements=[trace_result],
description=f"Trace result: {result_type}",
metadata={
"result_type": result_type,
"result_data": trace_result.get("data", {}),
"confidence": trace_result.get("confidence", 0.0)
}
)
glyph_map.glyphs.append(result_glyph)
# Connect deepest operations to result
max_depth = max(int(d) for d in depth_glyphs.keys())
deepest_glyphs = depth_glyphs[str(max_depth)]
if isinstance(deepest_glyphs, list):
for glyph_id in deepest_glyphs:
glyph_map.connections.append(GlyphConnection(
source_id=glyph_id,
target_id=result_glyph_id,
strength=0.9,
type="recursion_result",
directed=True,
color="#27ae60" if "success" in result_type else "#e74c3c",
width=1.5,
opacity=0.8
))
else:
glyph_map.connections.append(GlyphConnection(
source_id=deepest_glyphs,
target_id=result_glyph_id,
strength=0.9,
type="recursion_result",
directed=True,
color="#27ae60" if "success" in result_type else "#e74c3c",
width=1.5,
opacity=0.8
))
# Add seed to result connection
glyph_map.connections.append(GlyphConnection(
source_id="seed",
target_id=result_glyph_id,
strength=1.0,
type="recursion_completion",
directed=True,
color="#9b59b6", # Purple
width=2.0,
opacity=0.5
))
# Add depth regions
for depth in range(trace_depth + 1):
depth_key = str(depth)
if depth_key in depth_glyphs:
depth_glyphs_list = depth_glyphs[depth_key]
if not isinstance(depth_glyphs_list, list):
depth_glyphs_list = [depth_glyphs_list]
glyph_map.regions[f"depth_{depth}"] = depth_glyphs_list
# Apply layout if auto_layout is enabled
if self.auto_layout:
# For recursive traces, prefer hierarchical layout
if layout_type == self.default_layout and self.default_layout != "hierarchical":
layout_type = "hierarchical"
glyph_map = self._apply_layout(glyph_map, layout_type)
# Record execution time
map_time = time.time() - map_start
glyph_map.metadata["map_time"] = map_time
# Add to history
self.glyph_map_history.append(glyph_map)
logger.info(f"Recursive trace mapping completed in {map_time:.2f}s")
return glyph_map
def combine_glyph_maps(
self,
glyph_maps: List[GlyphMap],
layout_type: Optional[str] = None,
dimensions: Optional[Tuple[int, int]] = None,
scale: Optional[float] = None,
connection_threshold: float = 0.5
) -> GlyphMap:
"""
Combine multiple glyph maps into a unified map.
Parameters:
-----------
glyph_maps : List[GlyphMap]
Glyph maps to combine
layout_type : Optional[str]
Type of layout to use
dimensions : Optional[Tuple[int, int]]
Dimensions for visualization
scale : Optional[float]
Scale factor for visualization
connection_threshold : float
Minimum strength for inter-map connections
Returns:
--------
GlyphMap
Combined glyph map
"""
map_start = time.time()
layout_type = layout_type or self.default_layout
dimensions = dimensions or (
max(gm.dimensions[0] for gm in glyph_maps),
max(gm.dimensions[1] for gm in glyph_maps)
)
scale = scale or self.default_scale
logger.info(f"Combining {len(glyph_maps)} glyph maps")
# Create unique ID for combined glyph map
map_id = f"combined_{int(time.time())}_{hashlib.md5(str([gm.id for gm in glyph_maps]).encode()).hexdigest()[:8]}"
# Initialize combined glyph map
combined_map = GlyphMap(
id=map_id,
glyphs=[],
connections=[],
source_type="combined",
layout_type=layout_type,
dimensions=dimensions,
scale=scale,
metadata={
"source_maps": [gm.id for gm in glyph_maps],
"source_types": [gm.source_type for gm in glyph_maps],
"timestamp": time.time()
}
)
# Generate prefix mappings to ensure unique IDs
id_mapping = {}
for i, gm in enumerate(glyph_maps):
prefix = f"map{i}_"
for glyph in gm.glyphs:
id_mapping[glyph.id] = prefix + glyph.id
# Add glyphs from each map
for i, gm in enumerate(glyph_maps):
prefix = f"map{i}_"
# Add region for this map
map_region = f"map_{i}"
combined_map.regions[map_region] = []
# Add glyphs with prefixed IDs
for glyph in gm.glyphs:
new_id = prefix + glyph.id
new_glyph = Glyph(
id=new_id,
symbol=glyph.symbol,
type=glyph.type,
semantics=glyph.semantics,
position=glyph.position, # Will be updated by layout
size=glyph.size,
color=glyph.color,
opacity=glyph.opacity,
source_elements=glyph.source_elements,
description=glyph.description,
metadata={
**glyph.metadata,
"original_id": glyph.id,
"source_map": gm.id
}
)
combined_map.glyphs.append(new_glyph)
combined_map.regions[map_region].append(new_id)
# Add connections with prefixed IDs
for conn in gm.connections:
combined_map.connections.append(GlyphConnection(
source_id=prefix + conn.source_id,
target_id=prefix + conn.target_id,
strength=conn.strength,
type=conn.type,
directed=conn.directed,
color=conn.color,
width=conn.width,
opacity=conn.opacity,
metadata={
**conn.metadata,
"source_map": gm.id
}
))
# Add focal points with prefixed IDs
for focal_point in gm.focal_points:
combined_map.focal_points.append(prefix + focal_point)
# Create connections between maps for related glyphs
for i, gm1 in enumerate(glyph_maps):
for j, gm2 in enumerate(glyph_maps):
if i >= j:
continue # Skip self-connections and duplicates
# Find related glyphs between maps
related_pairs = self._find_related_glyphs(gm1, gm2)
# Add connections for sufficiently related glyphs
for glyph1_id, glyph2_id, similarity in related_pairs:
if similarity >= connection_threshold:
prefixed_id1 = f"map{i}_{glyph1_id}"
prefixed_id2 = f"map{j}_{glyph2_id}"
combined_map.connections.append(GlyphConnection(
source_id=prefixed_id1,
target_id=prefixed_id2,
strength=similarity,
type="cross_map_relation",
directed=False,
color="#3498db", # Blue
width=1.0 + similarity,
opacity=0.6 * similarity,
metadata={
"relation_type": "cross_map",
"source_map": gm1.id,
"target_map": gm2.id,
"similarity": similarity
}
))
# Apply layout if auto_layout is enabled
if self.auto_layout:
combined_map = self._apply_layout(combined_map, layout_type)
# Record execution time
map_time = time.time() - map_start
combined_map.metadata["map_time"] = map_time
# Add to history
self.glyph_map_history.append(combined_map)
logger.info(f"Glyph map combination completed in {map_time:.2f}s")
return combined_map
def visualize(
self,
glyph_map: GlyphMap,
output_path: Optional[str] = None,
interactive: bool = True
) -> Any:
"""
Visualize a glyph map.
Parameters:
-----------
glyph_map : GlyphMap
Glyph map to visualize
output_path : Optional[str]
Path to save visualization to
interactive : bool
Whether to generate interactive visualization
Returns:
--------
Any
Visualization result
"""
if self.visualizer:
return self.visualizer.visualize_glyph_map(
glyph_map=glyph_map,
output_path=output_path,
interactive=interactive
)
else:
# Simple matplotlib visualization if no visualizer
return self._simple_visualization(
glyph_map=glyph_map,
output_path=output_path
)
def save_glyph_map(
self,
glyph_map: GlyphMap,
output_path: str
) -> str:
"""
Save a glyph map to a file.
Parameters:
-----------
glyph_map : GlyphMap
Glyph map to save
output_path : str
Path to save glyph map to
Returns:
--------
str
Path to saved file
"""
# Convert to serializable format
serializable_map = {
"id": glyph_map.id,
"source_type": glyph_map.source_type,
"layout_type": glyph_map.layout_type,
"dimensions": glyph_map.dimensions,
"scale": glyph_map.scale,
"focal_points": glyph_map.focal_points,
"regions": glyph_map.regions,
"metadata": glyph_map.metadata,
"glyphs": [
{
"id": g.id,
"symbol": g.symbol,
"type": g.type.value,
"semantics": [s.value for s in g.semantics],
"position": g.position,
"size": g.size,
"color": g.color,
"opacity": g.opacity,
"description": g.description,
"metadata": g.metadata
}
for g in glyph_map.glyphs
],
"connections": [
{
"source_id": c.source_id,
"target_id": c.target_id,
"strength": c.strength,
"type": c.type,
"directed": c.directed,
"color": c.color,
"width": c.width,
"opacity": c.opacity,
"metadata": c.metadata
}
for c in glyph_map.connections
]
}
# Ensure directory exists
output_dir = Path(output_path).parent
output_dir.mkdir(parents=True, exist_ok=True)
# Save to file
with open(output_path, "w") as f:
json.dump(serializable_map, f, indent=2)
logger.info(f"Saved glyph map to {output_path}")
return output_path
def load_glyph_map(
self,
input_path: str
) -> GlyphMap:
"""
Load a glyph map from a file.
Parameters:
-----------
input_path : str
Path to load glyph map from
Returns:
--------
GlyphMap
Loaded glyph map
"""
# Load from file
with open(input_path, "r") as f:
data = json.load(f)
# Convert to GlyphMap
glyphs = [
Glyph(
id=g["id"],
symbol=g["symbol"],
type=GlyphType(g["type"]),
semantics=[GlyphSemantic(s) for s in g["semantics"]],
position=tuple(g["position"]),
size=g["size"],
color=g["color"],
opacity=g["opacity"],
description=g.get("description"),
metadata=g.get("metadata", {})
)
for g in data["glyphs"]
]
connections = [
GlyphConnection(
source_id=c["source_id"],
target_id=c["target_id"],
strength=c["strength"],
type=c["type"],
directed=c["directed"],
color=c["color"],
width=c["width"],
opacity=c["opacity"],
metadata=c.get("metadata", {})
)
for c in data["connections"]
]
glyph_map = GlyphMap(
id=data["id"],
glyphs=glyphs,
connections=connections,
source_type=data["source_type"],
layout_type=data["layout_type"],
dimensions=tuple(data["dimensions"]),
scale=data["scale"],
focal_points=data.get("focal_points", []),
regions=data.get("regions", {}),
metadata=data.get("metadata", {})
)
logger.info(f"Loaded glyph map from {input_path}")
return glyph_map
# Helper methods
def _apply_layout(
self,
glyph_map: GlyphMap,
layout_type: str
) -> GlyphMap:
"""Apply a layout to a glyph map."""
layout_start = time.time()
if layout_type == "force_directed":
glyph_map = self._apply_force_directed_layout(glyph_map)
elif layout_type == "hierarchical":
glyph_map = self._apply_hierarchical_layout(glyph_map)
elif layout_type == "circular":
glyph_map = self._apply_circular_layout(glyph_map)
elif layout_type == "grid":
glyph_map = self._apply_grid_layout(glyph_map)
elif layout_type == "radial":
glyph_map = self._apply_radial_layout(glyph_map)
else:
logger.warning(f"Unknown layout type: {layout_type}, using force_directed")
glyph_map = self._apply_force_directed_layout(glyph_map)
layout_time = time.time() - layout_start
logger.info(f"Applied {layout_type} layout in {layout_time:.2f}s")
return glyph_map
def _apply_force_directed_layout(self, glyph_map: GlyphMap) -> GlyphMap:
"""Apply force-directed layout to a glyph map."""
# Create networkx graph
G = nx.Graph()
# Add nodes
for glyph in glyph_map.glyphs:
G.add_node(glyph.id, size=glyph.size, type=glyph.type.value)
# Add edges
for conn in glyph_map.connections:
if conn.source_id in G and conn.target_id in G:
G.add_edge(
conn.source_id,
conn.target_id,
weight=conn.strength
)
# Apply force-directed layout
width, height = glyph_map.dimensions
pos = nx.spring_layout(
G,
k=0.2, # Optimal distance between nodes
iterations=100,
seed=42
)
# Scale and shift positions to fit dimensions
pos_array = np.array(list(pos.values()))
if len(pos_array) > 0:
min_x, min_y = pos_array.min(axis=0)
max_x, max_y = pos_array.max(axis=0)
# Avoid division by zero
x_range = max_x - min_x
y_range = max_y - min_y
if x_range > 0:
scale_x = (width * 0.8) / x_range
else:
scale_x = 1.0
if y_range > 0:
scale_y = (height * 0.8) / y_range
else:
scale_y = 1.0
# Apply scaling
for node_id, (x, y) in pos.items():
x_scaled = ((x - min_x) * scale_x) + width * 0.1
y_scaled = ((y - min_y) * scale_y) + height * 0.1
pos[node_id] = (x_scaled, y_scaled)
# Update glyph positions
for glyph in glyph_map.glyphs:
if glyph.id in pos:
glyph.position = pos[glyph.id]
return glyph_map
def _apply_hierarchical_layout(self, glyph_map: GlyphMap) -> GlyphMap:
"""Apply hierarchical layout to a glyph map."""
# Create directed graph
G = nx.DiGraph()
# Add nodes
for glyph in glyph_map.glyphs:
G.add_node(glyph.id, size=glyph.size, type=glyph.type.value)
# Add directed edges
for conn in glyph_map.connections:
if conn.directed and conn.source_id in G and conn.target_id in G:
G.add_edge(
conn.source_id,
conn.target_id,
weight=conn.strength
)
# Find root nodes (nodes with no incoming edges)
root_nodes = [n for n in G.nodes() if G.in_degree(n) == 0]
# If no root nodes, use focal points or any node
if not root_nodes:
if glyph_map.focal_points:
root_nodes = [fp for fp in glyph_map.focal_points if fp in G]
else:
root_nodes = [glyph_map.glyphs[0].id] if glyph_map.glyphs else []
# Apply hierarchical layout
width, height = glyph_map.dimensions
# If we have root nodes, use them
if root_nodes:
# Create layers
layers = {}
visited = set()
# BFS to assign layers
current_layer = root_nodes
layer_idx = 0
while current_layer and layer_idx < 20: # Limit to 20 layers to prevent infinite loops
layers[layer_idx] = current_layer
next_layer = []
for node in current_layer:
visited.add(node)
for _, neighbor in G.out_edges(node):
if neighbor not in visited and neighbor not in next_layer:
next_layer.append(neighbor)
current_layer = next_layer
layer_idx += 1
# Place nodes by layer
for layer_idx, nodes in layers.items():
y_pos = (layer_idx + 1) * (height / (len(layers) + 1))
x_step = width / (len(nodes) + 1)
for i, node_id in enumerate(nodes):
x_pos = (i + 1) * x_step
# Find and update glyph position
for glyph in glyph_map.glyphs:
if glyph.id == node_id:
glyph.position = (x_pos, y_pos)
break
# Assign positions to any unvisited nodes
unvisited = [g.id for g in glyph_map.glyphs if g.id not in visited]
if unvisited:
y_pos = (layer_idx + 1) * (height / (len(layers) + 2))
x_step = width / (len(unvisited) + 1)
for i, node_id in enumerate(unvisited):
x_pos = (i + 1) * x_step
# Find and update glyph position
for glyph in glyph_map.glyphs:
if glyph.id == node_id:
glyph.position = (x_pos, y_pos)
break
else:
# Fallback to simple grid layout
glyph_map = self._apply_grid_layout(glyph_map)
return glyph_map
def _apply_circular_layout(self, glyph_map: GlyphMap) -> GlyphMap:
"""Apply circular layout to a glyph map."""
# Create networkx graph
G = nx.Graph()
# Add nodes
for glyph in glyph_map.glyphs:
G.add_node(glyph.id, size=glyph.size, type=glyph.type.value)
# Add edges
for conn in glyph_map.connections:
if conn.source_id in G and conn.target_id in G:
G.add_edge(
conn.source_id,
conn.target_id,
weight=conn.strength
)
# Apply circular layout
width, height = glyph_map.dimensions
center_x, center_y = width / 2, height / 2
radius = min(width, height) * 0.4
pos = nx.circular_layout(G, scale=radius)
# Center the layout
for node_id, (x, y) in pos.items():
pos[node_id] = (x + center_x, y + center_y)
# Update glyph positions
for glyph in glyph_map.glyphs:
if glyph.id in pos:
glyph.position = pos[glyph.id]
return glyph_map
def _apply_grid_layout(self, glyph_map: GlyphMap) -> GlyphMap:
"""Apply grid layout to a glyph map."""
width, height = glyph_map.dimensions
num_glyphs = len(glyph_map.glyphs)
# Calculate grid dimensions
grid_size = int(np.ceil(np.sqrt(num_glyphs)))
cell_width = width / (grid_size + 1)
cell_height = height / (grid_size + 1)
# Assign positions
for i, glyph in enumerate(glyph_map.glyphs):
row = i // grid_size
col = i % grid_size
glyph.position = (
(col + 1) * cell_width,
(row + 1) * cell_height
)
return glyph_map
def _apply_radial_layout(self, glyph_map: GlyphMap) -> GlyphMap:
"""Apply radial layout to a glyph map."""
# Create networkx graph
G = nx.Graph()
# Add nodes
for glyph in glyph_map.glyphs:
G.add_node(glyph.id, size=glyph.size, type=glyph.type.value)
# Add edges
for conn in glyph_map.connections:
if conn.source_id in G and conn.target_id in G:
G.add_edge(
conn.source_id,
conn.target_id,
weight=conn.strength
)
# Determine central nodes
if glyph_map.focal_points:
central_nodes = [fp for fp in glyph_map.focal_points if fp in G]
else:
# Use betweenness centrality to find central nodes
centrality = nx.betweenness_centrality(G)
central_nodes = sorted(
centrality.keys(),
key=lambda x: centrality[x],
reverse=True
)[:min(3, len(centrality))]
# Apply radial layout
width, height = glyph_map.dimensions
center_x, center_y = width / 2, height / 2
pos = nx.kamada_kawai_layout(G)
# Scale and shift positions to fit dimensions
pos_array = np.array(list(pos.values()))
if len(pos_array) > 0:
min_x, min_y = pos_array.min(axis=0)
max_x, max_y = pos_array.max(axis=0)
# Avoid division by zero
x_range = max_x - min_x
y_range = max_y - min_y
if x_range > 0:
scale_x = (width * 0.8) / x_range
else:
scale_x = 1.0
if y_range > 0:
scale_y = (height * 0.8) / y_range
else:
scale_y = 1.0
# Apply scaling
for node_id, (x, y) in pos.items():
x_scaled = ((x - min_x) * scale_x) + width * 0.1
y_scaled = ((y - min_y) * scale_y) + height * 0.1
pos[node_id] = (x_scaled, y_scaled)
# Force central nodes to center
if central_nodes:
# Place central nodes near center
angle_step = 2 * np.pi / len(central_nodes)
center_radius = min(width, height) * 0.15
for i, node_id in enumerate(central_nodes):
angle = i * angle_step
x = center_x + center_radius * np.cos(angle)
y = center_y + center_radius * np.sin(angle)
pos[node_id] = (x, y)
# Update glyph positions
for glyph in glyph_map.glyphs:
if glyph.id in pos:
glyph.position = pos[glyph.id]
return glyph_map
def _apply_focus(
self,
glyph_map: GlyphMap,
focus_on: List[str]
) -> GlyphMap:
"""Apply focus to specific tokens or elements."""
# Find token glyphs matching focus terms
focus_glyph_ids = []
for glyph in glyph_map.glyphs:
# Check if glyph contains any focus term
if glyph.type == GlyphType.SENTINEL and hasattr(glyph, 'source_elements') and glyph.source_elements:
for term in focus_on:
if any(term in str(elem) for elem in glyph.source_elements):
focus_glyph_ids.append(glyph.id)
break
if not focus_glyph_ids:
# No matching token glyphs found
return glyph_map
# Update focal points
glyph_map.focal_points = focus_glyph_ids
# Increase size and opacity of focal glyphs
for glyph in glyph_map.glyphs:
if glyph.id in focus_glyph_ids:
glyph.size *= 1.5
glyph.opacity = min(1.0, glyph.opacity + 0.2)
# Highlight connections to focal glyphs
for conn in glyph_map.connections:
if conn.source_id in focus_glyph_ids or conn.target_id in focus_glyph_ids:
conn.width *= 1.5
conn.opacity = min(1.0, conn.opacity + 0.2)
return glyph_map
def _find_related_glyphs(
self,
glyph_map1: GlyphMap,
glyph_map2: GlyphMap
) -> List[Tuple[str, str, float]]:
"""Find related glyphs between two glyph maps."""
related_pairs = []
# Define similarity functions based on glyph type
def token_similarity(g1: Glyph, g2: Glyph) -> float:
"""Calculate similarity between token glyphs."""
if (g1.type == GlyphType.SENTINEL and g2.type == GlyphType.SENTINEL and
hasattr(g1, 'source_elements') and hasattr(g2, 'source_elements') and
g1.source_elements and g2.source_elements):
# Compare token text
text1 = str(g1.source_elements[0])
text2 = str(g2.source_elements[0])
if text1 == text2:
return 1.0
elif text1 in text2 or text2 in text1:
return 0.8
else:
# Compute string similarity
return 1.0 - min(1.0, distance.levenshtein(text1, text2) / max(len(text1), len(text2)))
return 0.0
def attribution_similarity(g1: Glyph, g2: Glyph) -> float:
"""Calculate similarity between attribution glyphs."""
if g1.type == GlyphType.ATTRIBUTION and g2.type == GlyphType.ATTRIBUTION:
# Compare attribution metadata
metadata_sim = 0.0
count = 0
# Compare attributes if they exist in both
for attr in ['source_index', 'target_index', 'attribution_type', 'strength']:
if attr in g1.metadata and attr in g2.metadata:
if g1.metadata[attr] == g2.metadata[attr]:
metadata_sim += 1.0
else:
# For numeric values, calculate relative similarity
if attr == 'strength' and isinstance(g1.metadata[attr], (int, float)) and isinstance(g2.metadata[attr], (int, float)):
diff = abs(g1.metadata[attr] - g2.metadata[attr])
metadata_sim += max(0.0, 1.0 - diff)
else:
metadata_sim += 0.0
count += 1
# Symbol similarity
symbol_sim = 1.0 if g1.symbol == g2.symbol else 0.0
# Combine similarities
if count > 0:
return (metadata_sim / count) * 0.7 + symbol_sim * 0.3
else:
return symbol_sim
return 0.0
def residue_similarity(g1: Glyph, g2: Glyph) -> float:
"""Calculate similarity between residue glyphs."""
if g1.type == GlyphType.RESIDUE and g2.type == GlyphType.RESIDUE:
# Check if they represent the same residue type
if g1.metadata.get('pattern_type') == g2.metadata.get('pattern_type'):
return 0.9
# Compare symbols
if g1.symbol == g2.symbol:
return 0.7
# Compare confidence if available
if 'confidence' in g1.metadata and 'confidence' in g2.metadata:
conf_diff = abs(g1.metadata['confidence'] - g2.metadata['confidence'])
return max(0.0, 0.5 - conf_diff)
return 0.3 # Different residue types but still residues
return 0.0
def recursive_similarity(g1: Glyph, g2: Glyph) -> float:
"""Calculate similarity between recursive glyphs."""
if g1.type == GlyphType.RECURSIVE and g2.type == GlyphType.RECURSIVE:
# Compare symbols
if g1.symbol == g2.symbol:
return 0.8
# Compare depth if available
if 'depth' in g1.metadata and 'depth' in g2.metadata:
if g1.metadata['depth'] == g2.metadata['depth']:
return 0.7
else:
depth_diff = abs(g1.metadata['depth'] - g2.metadata['depth'])
return max(0.0, 0.6 - (depth_diff * 0.1))
return 0.4 # Different recursive types but still recursive
return 0.0
def meta_similarity(g1: Glyph, g2: Glyph) -> float:
"""Calculate similarity between meta glyphs."""
if g1.type == GlyphType.META and g2.type == GlyphType.META:
# Compare symbols
if g1.symbol == g2.symbol:
return 0.9
# Compare semantics
common_semantics = set(s.value for s in g1.semantics).intersection(
set(s.value for s in g2.semantics)
)
if common_semantics:
return 0.6 + 0.3 * (len(common_semantics) / max(len(g1.semantics), len(g2.semantics)))
return 0.3 # Different meta types but still meta
return 0.0
# Apply appropriate similarity function based on glyph types
for g1 in glyph_map1.glyphs:
for g2 in glyph_map2.glyphs:
similarity = 0.0
# Apply type-specific similarity function
if g1.type == GlyphType.SENTINEL and g2.type == GlyphType.SENTINEL:
similarity = token_similarity(g1, g2)
elif g1.type == GlyphType.ATTRIBUTION and g2.type == GlyphType.ATTRIBUTION:
similarity = attribution_similarity(g1, g2)
elif g1.type == GlyphType.RESIDUE and g2.type == GlyphType.RESIDUE:
similarity = residue_similarity(g1, g2)
elif g1.type == GlyphType.RECURSIVE and g2.type == GlyphType.RECURSIVE:
similarity = recursive_similarity(g1, g2)
elif g1.type == GlyphType.META and g2.type == GlyphType.META:
similarity = meta_similarity(g1, g2)
elif g1.type == g2.type:
# Same type but not handled above
if g1.symbol == g2.symbol:
similarity = 0.6
else:
similarity = 0.3
# Add if similarity is significant
if similarity >= 0.5:
related_pairs.append((g1.id, g2.id, similarity))
# Sort by similarity (highest first)
related_pairs.sort(key=lambda x: x[2], reverse=True)
return related_pairs
def _calculate_signature_similarity(
self,
signature1: str,
signature2: str
) -> float:
"""Calculate similarity between two residue signatures."""
# Normalize signatures
sig1 = signature1.lower()
sig2 = signature2.lower()
# Calculate Levenshtein distance
max_len = max(len(sig1), len(sig2))
if max_len == 0:
return 1.0 # Both empty
lev_dist = distance.levenshtein(sig1, sig2)
sim = 1.0 - (lev_dist / max_len)
# Boost similarity for common prefixes
common_prefix_len = 0
for i in range(min(len(sig1), len(sig2))):
if sig1[i] == sig2[i]:
common_prefix_len += 1
else:
break
prefix_boost = 0.0
if common_prefix_len > 3: # At least a few characters
prefix_boost = min(0.2, common_prefix_len / max_len)
return min(1.0, sim + prefix_boost)
def _calculate_context_similarity(
self,
context1: Dict[str, Any],
context2: Dict[str, Any]
) -> float:
"""Calculate similarity between two residue contexts."""
# Handle empty contexts
if not context1 or not context2:
return 0.0
# Find common keys
common_keys = set(context1.keys()).intersection(set(context2.keys()))
if not common_keys:
return 0.0
# Calculate similarity for each common key
similarity_sum = 0.0
for key in common_keys:
val1 = context1[key]
val2 = context2[key]
if isinstance(val1, str) and isinstance(val2, str):
# String similarity
similarity_sum += self._calculate_signature_similarity(val1, val2)
elif isinstance(val1, (int, float)) and isinstance(val2, (int, float)):
# Numeric similarity
max_val = max(abs(val1), abs(val2))
if max_val > 0:
similarity_sum += 1.0 - min(1.0, abs(val1 - val2) / max_val)
else:
similarity_sum += 1.0 # Both zero
elif val1 == val2:
# Other types, check equality
similarity_sum += 1.0
else:
similarity_sum += 0.0
# Average similarity
return similarity_sum / len(common_keys)
def _get_color_for_attribution(self, link: AttributionLink) -> str:
"""Get color for attribution link based on type and strength."""
if link.attribution_type == AttributionType.DIRECT:
# Blues for direct attribution, darker with strength
blue_val = max(0, int(255 - (link.strength * 170)))
return f"rgb(0, {120 + int(link.strength * 70)}, {180 + blue_val})"
elif link.attribution_type == AttributionType.INDIRECT:
# Purples for indirect attribution
return f"rgb({120 + int(link.strength * 70)}, 0, {180 + int(link.strength * 60)})"
elif link.attribution_type == AttributionType.RESIDUAL:
# Greens for residual attribution
return f"rgb(0, {150 + int(link.strength * 70)}, {80 + int(link.strength * 40)})"
elif link.attribution_type == AttributionType.RECURSIVE:
# Orange-reds for recursive attribution
return f"rgb({200 + int(link.strength * 50)}, {70 + int(link.strength * 60)}, 0)"
else:
# Default gray
intensity = 100 + int(link.strength * 100)
return f"rgb({intensity}, {intensity}, {intensity})"
def _get_color_for_residue_type(self, residue_type: str) -> str:
"""Get color for residue based on type."""
colors = {
"memory_decay": "#3498db", # Blue
"value_conflict": "#e74c3c", # Red
"ghost_activation": "#9b59b6", # Purple
"boundary_hesitation": "#f39c12", # Orange
"null_output": "#95a5a6", # Gray
"recursive_collapse": "#27ae60", # Green
"attention_drift": "#1abc9c", # Turquoise
"token_oscillation": "#d35400" # Dark Orange
}
return colors.get(residue_type, "#2c3e50") # Default dark blue
def _get_color_for_layer(self, layer: int, alpha: float = 1.0) -> str:
"""Get color for a specific layer."""
# HSL color with hue based on layer
hue = (layer * 30) % 360
return f"hsl({hue}, 70%, 60%, {alpha})"
def _get_color_for_depth(self, depth: int, alpha: float = 1.0) -> str:
"""Get color for a specific recursion depth."""
# Base color shifts from purple to blue to green with increasing depth
if depth == 0:
return f"rgba(155, 89, 182, {alpha})" # Purple
elif depth == 1:
return f"rgba(52, 152, 219, {alpha})" # Blue
elif depth == 2:
return f"rgba(26, 188, 156, {alpha})" # Turquoise
elif depth == 3:
return f"rgba(39, 174, 96, {alpha})" # Green
elif depth == 4:
return f"rgba(241, 196, 15, {alpha})" # Yellow
elif depth >= 5:
return f"rgba(230, 126, 34, {alpha})" # Orange
def _scale_positions(
self,
positions: np.ndarray,
dimensions: Tuple[int, int]
) -> np.ndarray:
"""Scale positions to fit dimensions."""
width, height = dimensions
# Get bounds
min_x, min_y = positions.min(axis=0)
max_x, max_y = positions.max(axis=0)
# Avoid division by zero
x_range = max_x - min_x
y_range = max_y - min_y
if x_range > 0:
scale_x = (width * 0.8) / x_range
else:
scale_x = 1.0
if y_range > 0:
scale_y = (height * 0.8) / y_range
else:
scale_y = 1.0
# Apply scaling and shift
positions_scaled = np.zeros_like(positions)
positions_scaled[:, 0] = (positions[:, 0] - min_x) * scale_x + width * 0.1
positions_scaled[:, 1] = (positions[:, 1] - min_y) * scale_y + height * 0.1
return positions_scaled
def _simple_visualization(
self,
glyph_map: GlyphMap,
output_path: Optional[str] = None
) -> Dict[str, Any]:
"""Simple matplotlib visualization if no visualizer available."""
# Create figure
plt.figure(figsize=(12, 10))
# Plot connections
for conn in glyph_map.connections:
# Find source and target glyphs
source_glyph = next((g for g in glyph_map.glyphs if g.id == conn.source_id), None)
target_glyph = next((g for g in glyph_map.glyphs if g.id == conn.target_id), None)
if source_glyph and target_glyph:
# Get positions
source_x, source_y = source_glyph.position
target_x, target_y = target_glyph.position
# Draw connection
plt.plot(
[source_x, target_x],
[source_y, target_y],
color=conn.color,
linewidth=conn.width,
alpha=conn.opacity,
zorder=1,
linestyle='-' if conn.directed else '--'
)
# Add arrow if directed
if conn.directed:
dx = target_x - source_x
dy = target_y - source_y
dist = np.sqrt(dx**2 + dy**2)
if dist > 0:
# Normalize and scale
dx, dy = dx / dist, dy / dist
midpoint_x = (source_x + target_x) / 2
midpoint_y = (source_y + target_y) / 2
# Draw arrowhead
plt.arrow(
midpoint_x - dx * 5,
midpoint_y - dy * 5,
dx * 10,
dy * 10,
head_width=5,
head_length=5,
fc=conn.color,
ec=conn.color,
alpha=conn.opacity,
zorder=1
)
# Plot glyphs
for glyph in glyph_map.glyphs:
x, y = glyph.position
# Draw glyph as text
plt.text(
x, y,
glyph.symbol,
fontsize=glyph.size,
color=glyph.color,
alpha=glyph.opacity,
ha='center',
va='center',
zorder=2
)
# Draw circle around focal points
if glyph.id in glyph_map.focal_points:
circle = plt.Circle(
(x, y),
glyph.size * 0.8,
fill=False,
color='black',
linestyle=':',
alpha=0.7,
zorder=1
)
plt.gca().add_patch(circle)
# Set plot limits
width, height = glyph_map.dimensions
plt.xlim(0, width)
plt.ylim(0, height)
# Remove axes
plt.axis('off')
# Add title
title = f"Glyph Map: {glyph_map.source_type.capitalize()}"
if "trace_target" in glyph_map.metadata:
title += f" - {glyph_map.metadata['trace_target']}"
plt.title(title)
# Save if output path provided
if output_path:
plt.savefig(output_path, dpi=300, bbox_inches='tight')
plt.close()
return {"output_path": output_path}
# Return figure data
return {"figure": plt.gcf()}
# Helper class for glyph map exploration
class GlyphExplorer:
"""
Utility class for interactive exploration of glyph maps.
This class provides methods for filtering, searching, and analyzing
glyph maps to extract insights and patterns.
"""
def __init__(self, glyph_map: GlyphMap):
"""
Initialize the glyph explorer.
Parameters:
-----------
glyph_map : GlyphMap
Glyph map to explore
"""
self.glyph_map = glyph_map
self.filtered_glyphs = glyph_map.glyphs
self.filtered_connections = glyph_map.connections
def filter_by_type(self, glyph_type: GlyphType) -> 'GlyphExplorer':
"""
Filter glyphs by type.
Parameters:
-----------
glyph_type : GlyphType
Type of glyphs to include
Returns:
--------
GlyphExplorer
Self, for method chaining
"""
self.filtered_glyphs = [
g for g in self.filtered_glyphs
if g.type == glyph_type
]
self._update_connections()
return self
def filter_by_semantic(self, semantic: GlyphSemantic) -> 'GlyphExplorer':
"""
Filter glyphs by semantic dimension.
Parameters:
-----------
semantic : GlyphSemantic
Semantic dimension to filter by
Returns:
--------
GlyphExplorer
Self, for method chaining
"""
self.filtered_glyphs = [
g for g in self.filtered_glyphs
if semantic in g.semantics
]
self._update_connections()
return self
def filter_by_symbol(self, symbol: str) -> 'GlyphExplorer':
"""
Filter glyphs by symbol.
Parameters:
-----------
symbol : str
Symbol to filter by
Returns:
--------
GlyphExplorer
Self, for method chaining
"""
self.filtered_glyphs = [
g for g in self.filtered_glyphs
if g.symbol == symbol
]
self._update_connections()
return self
def filter_by_size(
self,
min_size: Optional[float] = None,
max_size: Optional[float] = None
) -> 'GlyphExplorer':
"""
Filter glyphs by size.
Parameters:
-----------
min_size : Optional[float]
Minimum size (inclusive)
max_size : Optional[float]
Maximum size (inclusive)
Returns:
--------
GlyphExplorer
Self, for method chaining
"""
if min_size is not None:
self.filtered_glyphs = [
g for g in self.filtered_glyphs
if g.size >= min_size
]
if max_size is not None:
self.filtered_glyphs = [
g for g in self.filtered_glyphs
if g.size <= max_size
]
self._update_connections()
return self
def filter_by_metadata(
self,
key: str,
value: Any
) -> 'GlyphExplorer':
"""
Filter glyphs by metadata field.
Parameters:
-----------
key : str
Metadata key
value : Any
Metadata value to match
Returns:
--------
GlyphExplorer
Self, for method chaining
"""
self.filtered_glyphs = [
g for g in self.filtered_glyphs
if key in g.metadata and g.metadata[key] == value
]
self._update_connections()
return self
def filter_connections_by_type(self, conn_type: str) -> 'GlyphExplorer':
"""
Filter connections by type.
Parameters:
-----------
conn_type : str
Connection type to filter by
Returns:
--------
GlyphExplorer
Self, for method chaining
"""
self.filtered_connections = [
c for c in self.filtered_connections
if c.type == conn_type
]
return self
def filter_connections_by_strength(
self,
min_strength: Optional[float] = None,
max_strength: Optional[float] = None
) -> 'GlyphExplorer':
"""
Filter connections by strength.
Parameters:
-----------
min_strength : Optional[float]
Minimum strength (inclusive)
max_strength : Optional[float]
Maximum strength (inclusive)
Returns:
--------
GlyphExplorer
Self, for method chaining
"""
if min_strength is not None:
self.filtered_connections = [
c for c in self.filtered_connections
if c.strength >= min_strength
]
if max_strength is not None:
self.filtered_connections = [
c for c in self.filtered_connections
if c.strength <= max_strength
]
return self
def search_by_description(self, query: str) -> 'GlyphExplorer':
"""
Search glyphs by description text.
Parameters:
-----------
query : str
Search query
Returns:
--------
GlyphExplorer
Self, for method chaining
"""
self.filtered_glyphs = [
g for g in self.filtered_glyphs
if g.description and query.lower() in g.description.lower()
]
self._update_connections()
return self
def find_central_glyphs(self, top_n: int = 5) -> List[Glyph]:
"""
Find central glyphs based on connection count.
Parameters:
-----------
top_n : int
Number of top central glyphs to return
Returns:
--------
List[Glyph]
Top central glyphs
"""
# Count connections for each glyph
glyph_ids = [g.id for g in self.filtered_glyphs]
connection_counts = {}
for glyph_id in glyph_ids:
count = sum(
1 for c in self.filtered_connections
if c.source_id == glyph_id or c.target_id == glyph_id
)
connection_counts[glyph_id] = count
# Get top N glyphs by connection count
top_glyph_ids = sorted(
connection_counts.keys(),
key=lambda x: connection_counts[x],
reverse=True
)[:top_n]
# Find corresponding glyphs
top_glyphs = [
g for g in self.filtered_glyphs
if g.id in top_glyph_ids
]
return top_glyphs
def find_clusters(self, min_size: int = 3) -> Dict[str, List[Glyph]]:
"""
Find clusters of connected glyphs.
Parameters:
-----------
min_size : int
Minimum cluster size
Returns:
--------
Dict[str, List[Glyph]]
Dictionary of clusters
"""
# Create networkx graph
G = nx.Graph()
# Add nodes
for glyph in self.filtered_glyphs:
G.add_node(glyph.id)
# Add edges
for conn in self.filtered_connections:
if conn.source_id in G and conn.target_id in G:
G.add_edge(conn.source_id, conn.target_id, weight=conn.strength)
# Find connected components (clusters)
components = list(nx.connected_components(G))
# Filter by minimum size
clusters = {}
for i, component in enumerate(components):
if len(component) >= min_size:
cluster_glyphs = [
g for g in self.filtered_glyphs
if g.id in component
]
clusters[f"cluster_{i}"] = cluster_glyphs
return clusters
def calculate_statistics(self) -> Dict[str, Any]:
"""
Calculate statistics for the filtered glyph map.
Returns:
--------
Dict[str, Any]
Dictionary of statistics
"""
stats = {
"num_glyphs": len(self.filtered_glyphs),
"num_connections": len(self.filtered_connections),
"glyph_types": {},
"connection_types": {},
"avg_connection_strength": 0.0,
"glyph_size_stats": {
"min": float('inf'),
"max": 0.0,
"avg": 0.0
}
}
# Count glyph types
for glyph in self.filtered_glyphs:
glyph_type = glyph.type.value
if glyph_type not in stats["glyph_types"]:
stats["glyph_types"][glyph_type] = 0
stats["glyph_types"][glyph_type] += 1
# Update size stats
stats["glyph_size_stats"]["min"] = min(stats["glyph_size_stats"]["min"], glyph.size)
stats["glyph_size_stats"]["max"] = max(stats["glyph_size_stats"]["max"], glyph.size)
stats["glyph_size_stats"]["avg"] += glyph.size
if self.filtered_glyphs:
stats["glyph_size_stats"]["avg"] /= len(self.filtered_glyphs)
else:
stats["glyph_size_stats"]["min"] = 0.0
# Count connection types
total_strength = 0.0
for conn in self.filtered_connections:
if conn.type not in stats["connection_types"]:
stats["connection_types"][conn.type] = 0
stats["connection_types"][conn.type] += 1
total_strength += conn.strength
if self.filtered_connections:
stats["avg_connection_strength"] = total_strength / len(self.filtered_connections)
return stats
def reset_filters(self) -> 'GlyphExplorer':
"""
Reset all filters.
Returns:
--------
GlyphExplorer
Self, for method chaining
"""
self.filtered_glyphs = self.glyph_map.glyphs
self.filtered_connections = self.glyph_map.connections
return self
def _update_connections(self):
"""Update connections based on filtered glyphs."""
filtered_glyph_ids = [g.id for g in self.filtered_glyphs]
self.filtered_connections = [
c for c in self.glyph_map.connections
if c.source_id in filtered_glyph_ids and c.target_id in filtered_glyph_ids
]
# Main execution for CLI usage
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="Glyph Mapper for Attribution and Residue Visualization")
parser.add_argument("--input", "-i", type=str, help="Input attribution or residue file")
parser.add_argument("--output", "-o", type=str, help="Output visualization file")
parser.add_argument("--type", "-t", type=str, default="attribution", choices=["attribution", "residue", "attention", "recursive"], help="Type of input data")
parser.add_argument("--layout", "-l", type=str, default="force_directed", choices=["force_directed", "hierarchical", "circular", "grid", "radial"], help="Layout type")
parser.add_argument("--width", "-w", type=int, default=1200, help="Visualization width")
parser.add_argument("--height", "-h", type=int, default=900,
parser.add_argument("--height", "-h", type=int, default=900, help="Visualization height")
parser.add_argument("--focus", "-f", type=str, help="Comma-separated tokens to focus on")
parser.add_argument("--include-tokens", action="store_true", help="Include token sentinels in visualization")
parser.add_argument("--cluster", action="store_true", help="Apply clustering to similar patterns")
parser.add_argument("--save-map", "-s", type=str, help="Save glyph map to file")
parser.add_argument("--interactive", "-i", action="store_true", help="Generate interactive visualization")
args = parser.parse_args()
# Initialize mapper
mapper = GlyphMapper()
if args.input:
# Load input data
with open(args.input, "r") as f:
data = json.load(f)
# Process based on type
if args.type == "attribution":
# Convert to AttributionMap
attribution_map = AttributionMap(
prompt_tokens=data.get("prompt_tokens", []),
output_tokens=data.get("output_tokens", []),
links=[
AttributionLink(
source_idx=link.get("source_idx", 0),
target_idx=link.get("target_idx", 0),
attribution_type=AttributionType(link.get("attribution_type", "direct")),
strength=link.get("strength", 0.5),
attention_heads=link.get("attention_heads", []),
layers=link.get("layers", []),
intermediate_tokens=link.get("intermediate_tokens", []),
residue=link.get("residue")
)
for link in data.get("links", [])
],
token_salience=data.get("token_salience", {}),
attribution_gaps=data.get("attribution_gaps", []),
collapsed_regions=data.get("collapsed_regions", []),
uncertainty=data.get("uncertainty", {}),
metadata=data.get("metadata", {})
)
# Parse focus tokens if provided
focus_on = args.focus.split(",") if args.focus else None
# Create glyph map
glyph_map = mapper.map_attribution(
attribution_map=attribution_map,
layout_type=args.layout,
dimensions=(args.width, args.height),
include_tokens=args.include_tokens,
focus_on=focus_on
)
elif args.type == "residue":
# Convert to ResiduePattern list
residue_patterns = [
ResiduePattern(
type=pattern.get("type", "unknown"),
pattern=pattern.get("pattern", ""),
context=pattern.get("context", {}),
signature=pattern.get("signature", ""),
confidence=pattern.get("confidence", 0.5)
)
for pattern in data
]
# Create glyph map
glyph_map = mapper.map_residue_patterns(
residue_patterns=residue_patterns,
layout_type=args.layout,
dimensions=(args.width, args.height),
cluster_patterns=args.cluster
)
elif args.type == "attention":
# Create glyph map
glyph_map = mapper.map_attention_heads(
attention_data=data,
layout_type=args.layout,
dimensions=(args.width, args.height),
include_tokens=args.include_tokens
)
elif args.type == "recursive":
# Create glyph map
glyph_map = mapper.map_recursive_trace(
trace_data=data,
layout_type=args.layout,
dimensions=(args.width, args.height)
)
else:
print(f"Unknown data type: {args.type}")
exit(1)
# Save glyph map if requested
if args.save_map:
mapper.save_glyph_map(glyph_map, args.save_map)
# Generate visualization
if args.output:
mapper.visualize(
glyph_map=glyph_map,
output_path=args.output,
interactive=args.interactive
)
print(f"Visualization saved to {args.output}")
else:
# Display basic statistics
explorer = GlyphExplorer(glyph_map)
stats = explorer.calculate_statistics()
print(f"Glyph Map Statistics:")
print(f" Number of glyphs: {stats['num_glyphs']}")
print(f" Number of connections: {stats['num_connections']}")
print(f" Glyph types: {stats['glyph_types']}")
print(f" Connection types: {stats['connection_types']}")
print(f" Average connection strength: {stats['avg_connection_strength']:.2f}")
# Show central glyphs
central_glyphs = explorer.find_central_glyphs(top_n=3)
print(f"\nCentral Glyphs:")
for glyph in central_glyphs:
print(f" {glyph.symbol} - {glyph.description}")
else:
print("No input file specified. Use --input to provide input data.")
exit(1)
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