set up project structure with uv

.gitignore

@@ -0,0 +1,4 @@
+.venv
+__pycache__
+*.pyc
+.pytest_cache

.python-version

@@ -0,0 +1 @@
+3.12

.vscode/settings.json

@@ -0,0 +1,7 @@
+{
+    "files.exclude": {
+        "**/__pycache__/": true,
+        ".venv/": true,
+        ".pytest_cache/": true
+    }
+}

app.py

@@ -1,14 +1,32 @@
 from .calc_metrics import calc_token_metrics, load_model
 from .visualize import visualize_batch
+from .sparky import Sparky
 
 model, tokenizer = load_model('gpt2')
 
 texts = [
     "I am a language model.",
-    "The quick brown fox jumped over the lazy dog.",    # Short
-    "In a shocking turn of events, the seemingly impossible task", # Long, truncated
-    "In a shocking turn of table, the seemingly impossible task", # Long, truncated
-    "Hello"                   # Tiny!
+    "In a shocking turn of events, the seemingly impossible task",
+    "In a shocking turn of table, the seemingly impossible task",
+    "Hello",
 ]
 metrics = calc_token_metrics(texts, model, tokenizer)
-visualize_batch(metrics)
+
+n = len(seq)
+surp = surp[:n]
+ent = ent[:n]
+vis = Sparky(chars_per_line=60)
+vis.visualize(seq, [
+    # Base metrics
+    # mu.EntropySeries(surp, vocab_size=vocab_size, label='Surprisal'),
+    # mu.EntropySeries(ent, vocab_size=vocab_size, label='Entropy'),
+
+    # Surprise-surprise as two series, with negative values mirrored around the baseline
+    mu.Series((surp - ent)/math.log(vocab_size), label="S₂"),
+    mu.Series(-(surp - ent)/math.log(vocab_size), label="-S₂", dasharray='3'),
+
+    # Earlier attempts at relative surprisal
+    # mu.Series(surp / ent / 2, label='ERS'),
+    # mu.Series(torch.log(surp / (ent+1) + 1), label='ERS'),
+    # mu.Series(torch.log(torch.log(surp / (ent+1) + 1) + 1), label='ERS'),
+])

pyproject.toml

@@ -0,0 +1,28 @@
+[project]
+name = "sparky"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "numpy>=2.2.2",
+]
+
+[dependency-groups]
+dev = [
+  "pytest>=8.3.4",
+  "ruff>=0.9.3",
+]
+
+[project.optional-dependencies]
+transformers = [
+    "torch>=2.5.1",
+    "transformers>=4.48.1",
+]
+kde = [
+    "scipy>=1.15.1",
+]
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"

src/__init__.py

@@ -1 +0,0 @@
-

src/sparky/__init__.py

@@ -0,0 +1,3 @@
+__all__ = ["Sparky", "Series", "EntropySeries"]
+from .sparky import Sparky
+from .series import Series, EntropySeries

src/{calc_metrics.py → sparky/inference/calc_metrics.py}

@@ -1,7 +1,8 @@
 import torch
 from torch import Tensor
 import torch.nn.functional as F
-from transformers import GPT2LMHeadModel, GPT2Tokenizerimport torch
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+import torch
 
 
 def load_model(name='gpt2'):

src/{visualize.py → sparky/inference/visualize.py}

@@ -1,12 +1,12 @@
 import math
 
-from .decompose import select
-from .sparky import Sparky
+from ..utils.decompose import select
+from ..sparky import Sparky
+from ..series import Series
 
 
 def visualize_batch(metrics: dict) -> None:
     """Draw token-level metrics for each sequence in a batch: sparklines, KDE plots"""
-    import ml_utils as mu
     tokens, surprisals, entropies, vocab_size = select(metrics, 'tok_str, tok_surp, tok_ent, vocab_size')
     for seq, surp, ent in zip(tokens, surprisals, entropies):
         n = len(seq)
@@ -19,8 +19,8 @@ def visualize_batch(metrics: dict) -> None:
             # mu.EntropySeries(ent, vocab_size=vocab_size, label='Entropy'),
 
             # Surprise-surprise as two series, with negative values mirrored around the baseline
-            mu.Series((surp - ent)/math.log(vocab_size), label="S₂"),
-            mu.Series(-(surp - ent)/math.log(vocab_size), label="-S₂", dasharray='3'),
+            Series((surp - ent)/math.log(vocab_size), label="S₂"),
+            Series(-(surp - ent)/math.log(vocab_size), label="-S₂", dasharray='3'),
 
             # Earlier attempts at relative surprisal
             # mu.Series(surp / ent / 2, label='ERS'),

src/{kde_plot.py → sparky/kde_plot.py}

@@ -4,6 +4,9 @@ import numpy as np
 from scipy import stats
 import torch
 
+from .series import Series
+from .utils.dom import Element
+
 
 class KDEPlot:
     """A multi-series KDE plot handler."""
@@ -89,4 +92,4 @@ class KDEPlot:
             stroke=color,
             stroke_width=self.stroke_width,
             fill="none",
-            style="mix-blend-mode: var(--blend-mode);")
+            style="mix-blend-mode: var(--blend-mode);")

src/{series.py → sparky/series.py}

@@ -1,5 +1,4 @@
 from dataclasses import dataclass
-from functools import lru_cache
 import math
 import torch
 

src/{sparkline.py → sparky/sparkline.py}

@@ -1,10 +1,13 @@
-from math import isclose
+from math import isclose, isnan
 from typing import Optional, NamedTuple
 import xml.etree.ElementTree as ET
-import numpy as np
-from scipy import stats
 import torch
 
+from .utils.dom import Element, id_sequence
+from .utils.decompose import sliceable
+from .series import Series
+
+
 @sliceable  
 class TokenBB(NamedTuple):
     # All positions relative to token start
@@ -64,7 +67,7 @@ class Sparkline:
             knot1 = (x+first-cp_dx1, x+first)
             knot2 = (x+last-cp_dx2, x+last)
             
-            if math.isnan(v):
+            if isnan(v):
                 is_drawing = False
             else:
                 y = h - h * v
@@ -103,7 +106,7 @@ class Sparkline:
         start, end = window
         w = sum(span.width for span in spans[start:end])
 
-        clip = Element(parent, "clipPath", id=f"clip-{next(ids)}")
+        clip = Element(parent, "clipPath", id=f"clip-{next(id_sequence)}")
         # clip.set('id', f"clip-{next(ids)}")
         Element(clip, "rect", x=0, y=-h, width=w, height=h*2)
 

src/{sparky.py → sparky/sparky.py}

@@ -1,13 +1,11 @@
-import math
-from math import isclose
-from typing import NamedTuple, Optional
-import torch
-from IPython.display import HTML, display
 import xml.etree.ElementTree as ET
-import numpy as np
-from scipy import stats
 
-from theming import svg_theme_toggle
+from .utils.dom import Element
+from .series import Series
+from .sparkline import Sparkline
+from .kde_plot import KDEPlot
+from .theming import svg_theme_toggle
+from .token_bb import TokenBB
 
 
 class Sparky:
@@ -165,7 +163,7 @@ class Sparky:
         # Add light/dark support
         svg_theme_toggle(
             svg,
-            toggle_pos=(width-20,20),
+            toggle_pos=(width-20, 20),
             theme_vars={
                 'col-series-1': ('#ef4444', '#ff7878'),
                 'col-series-2': '#3b82f6',
@@ -177,6 +175,4 @@ class Sparky:
                 'blend-mode': ('multiply', 'screen'),
             })
 
-        # Convert to string and display
-        svg_str = ET.tostring(svg, encoding='unicode')
-        display(HTML(svg_str))
+        return ET.tostring(svg, encoding='unicode')

src/{token_bb.py → sparky/token_bb.py}

@@ -1,7 +1,7 @@
 from math import isclose
 from typing import NamedTuple
 
-from .sliceable import sliceable
+from .utils.decompose import sliceable
 
 
 @sliceable

src/{dom.py → sparky/utils/dom.py}

@@ -22,3 +22,6 @@ def gen_ids():
     seed = randbytes(4).hex()
     while True:
         yield f"{seed}-{next(counter):x}"
+
+
+id_sequence = gen_ids()

{src → tests}/test_decompose.py

@@ -1,3 +1,5 @@
+from sparky.utils.decompose import select, selecta, sliceable
+
 def test_sliceable():
     from collections import namedtuple
     Foo = sliceable(namedtuple('Foo', 'a,b,c,d'))
@@ -60,4 +62,4 @@ def test_selecta():
     assert (1, 3) == selecta(data, "a, c")
     
     # Missing keys return None
-    assert (1, None, 3) == selecta(data, "a x c")
+    assert (1, None, 3) == selecta(data, "a x c")

uv.lock

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