Terminal rendition of the desktop Star Map / Memory Graph: learned skills and memories on a timeline, shared by `hermes journey` and the TUI `/journey` overlay via one size-aware Python renderer (agent/learning_graph_render.py). - TUI overlay mirrors /agents: static chart overview + selectable slice list → slice detail → single skill/memory body, with the shared inverse-row selection treatment and a pinned footer. - Reuse primitives: extract OverlayScrollbar into its own module (now shared with agentsOverlay), scroll the item body via ScrollBox, and unify both lists through one table-driven ListRow. - No animation/playback in the TUI — pure data; the renderer's reveal scrubber stays available in the CLI (`--play`, `--reveal`).
187 lines
6.7 KiB
Python
187 lines
6.7 KiB
Python
"""Behavior contracts for the terminal Star Map renderer.
|
|
|
|
Asserts invariants of the timeline layout, the ported age gradient + palette, and
|
|
the constellation scrubber — never a cell snapshot, which would be a
|
|
change-detector against layout tuning.
|
|
"""
|
|
|
|
from __future__ import annotations
|
|
|
|
from agent import learning_graph_render as render
|
|
|
|
LEAD_IN = render.LEAD_IN
|
|
|
|
|
|
def _payload(skills: int = 8, memories: int = 3, *, base_ts: int = 1_700_000_000):
|
|
nodes = []
|
|
for i in range(skills):
|
|
nodes.append(
|
|
{
|
|
"id": f"skill{i}",
|
|
"label": f"skill{i}",
|
|
"kind": "skill",
|
|
"timestamp": base_ts + i * 86400 * 20,
|
|
"category": "devops" if i % 2 else "research",
|
|
"useCount": i,
|
|
}
|
|
)
|
|
for j in range(memories):
|
|
nodes.append(
|
|
{
|
|
"id": f"memory:memory:{j}",
|
|
"label": f"mem {j}",
|
|
"kind": "memory",
|
|
"timestamp": base_ts + (skills + j) * 86400 * 20,
|
|
"category": "memory",
|
|
}
|
|
)
|
|
edges = [{"source": "skill0", "target": "skill1"}] if skills > 1 else []
|
|
return {
|
|
"nodes": nodes,
|
|
"edges": edges,
|
|
"clusters": [{"category": "devops", "count": skills}, {"category": "memory", "count": memories}],
|
|
"stats": {
|
|
"learned_skills": skills,
|
|
"memory_nodes": memories,
|
|
"related_edges": len(edges),
|
|
"memory_skill_edges": 0,
|
|
},
|
|
}
|
|
|
|
|
|
def _flatten(grid):
|
|
return "".join(run[0] for row in grid for run in row)
|
|
|
|
|
|
def _styles(grid):
|
|
return {run[1] for row in grid for run in row}
|
|
|
|
|
|
def test_recency_is_timed_and_bounded():
|
|
rec = render.compute_recency(_payload()["nodes"])
|
|
assert rec["timed"] is True
|
|
for ratio in rec["rec"].values():
|
|
assert LEAD_IN - 1e-9 <= ratio <= 1 + 1e-9
|
|
assert abs(min(rec["rec"].values()) - LEAD_IN) < 1e-9
|
|
assert abs(max(rec["rec"].values()) - 1.0) < 1e-9
|
|
|
|
|
|
def test_recency_ink_follows_age_gradient():
|
|
# Old quiet → recent bright (constants.ts AGE_GRADIENT), monotonic in between.
|
|
assert abs(render.recency_ink(0.0) - render.AGE_OLD_INK) < 1e-6
|
|
assert abs(render.recency_ink(1.0) - render.AGE_NEW_INK) < 1e-6
|
|
samples = [render.recency_ink(x / 10) for x in range(11)]
|
|
assert samples == sorted(samples)
|
|
|
|
|
|
def test_undated_graph_falls_back_to_ordinal():
|
|
nodes = [{"id": f"n{i}", "kind": "skill"} for i in range(5)]
|
|
rec = render.compute_recency(nodes)
|
|
assert rec["timed"] is False
|
|
assert len(set(rec["rec"].values())) == len(nodes)
|
|
|
|
|
|
def test_grid_runs_are_text_style_alpha():
|
|
# Runs are [text, style, alpha] with an optional 4th hex override for
|
|
# category-colored bars.
|
|
frame = render.render_graph(_payload(), cols=60, rows=20)
|
|
for row in frame["grid"]:
|
|
for run in row:
|
|
assert 3 <= len(run) <= 4
|
|
assert isinstance(run[0], str) and isinstance(run[1], str)
|
|
assert isinstance(run[2], (int, float)) and 0.0 <= run[2] <= 1.0
|
|
assert run[0] != ""
|
|
if len(run) == 4:
|
|
assert run[3] is None or isinstance(run[3], str)
|
|
|
|
|
|
def test_bars_render_skills_and_memories():
|
|
frame = render.render_graph(_payload(skills=10, memories=4), cols=72, rows=18, reveal=1.0)
|
|
flat = _flatten(frame["grid"])
|
|
# Skills draw as comet trails (━), memories anchor on diamonds (◆).
|
|
assert "━" in flat
|
|
assert render.MEMORY_GLYPH in flat
|
|
styles = _styles(frame["grid"])
|
|
assert render.STYLE_SKILL in styles
|
|
assert render.STYLE_MEMORY in styles
|
|
|
|
|
|
def test_run_alpha_follows_age_for_lit_stars():
|
|
# An all-skill, dated graph at full reveal: the newest star is brighter ink
|
|
# than the oldest (age gradient carried in the run alpha).
|
|
payload = _payload(skills=12, memories=0)
|
|
frame = render.render_graph(payload, cols=80, rows=20, reveal=1.0)
|
|
alphas = [run[2] for row in frame["grid"] for run in row if run[1] == render.STYLE_SKILL]
|
|
assert max(alphas) > min(alphas)
|
|
|
|
|
|
def test_reveal_monotonically_builds_up():
|
|
payload = _payload(skills=12, memories=5)
|
|
counts = [render.render_graph(payload, cols=60, rows=20, reveal=r)["visible"] for r in (0.0, 0.25, 0.5, 0.75, 1.0)]
|
|
assert counts == sorted(counts)
|
|
assert counts[-1] == len(payload["nodes"])
|
|
|
|
|
|
def test_empty_payload_renders_placeholder():
|
|
frame = render.render_graph({"nodes": []}, cols=40, rows=12)
|
|
assert frame["visible"] == 0
|
|
assert "no learning yet" in _flatten(frame["grid"])
|
|
|
|
|
|
def test_grid_fits_within_row_budget():
|
|
# The chart is a timeline of dated buckets + a trajectory row; it fills up to
|
|
# the row budget but never overflows it.
|
|
frame = render.render_graph(_payload(), cols=60, rows=14, reveal=1.0)
|
|
assert 0 < len(frame["grid"]) <= 14
|
|
|
|
|
|
def test_legend_counts_and_glyphs():
|
|
payload = _payload(skills=9, memories=4)
|
|
legend = render.build_legend(payload)
|
|
labels = {item["label"] for item in legend}
|
|
assert "skills (9)" in labels
|
|
assert "memories (4)" in labels
|
|
glyphs = {item["glyph"] for item in legend}
|
|
assert render.SKILL_GLYPH in glyphs and render.MEMORY_GLYPH in glyphs
|
|
|
|
|
|
def test_axis_labels_present_when_dated():
|
|
axis = render.axis_labels(_payload())
|
|
assert axis["start"] != "oldest" # dated → real dates
|
|
assert axis["end"] != "now"
|
|
|
|
|
|
def test_frames_play_through_grows_visibility():
|
|
payload = _payload(skills=10, memories=4)
|
|
out = render.render_frames(payload, cols=50, rows=16, frames=12)
|
|
assert out["count"] == len(payload["nodes"])
|
|
assert len(out["frames"]) == 12
|
|
assert out["frames"][0]["visible"] <= out["frames"][-1]["visible"]
|
|
assert out["frames"][-1]["visible"] == len(payload["nodes"])
|
|
assert "axis" in out
|
|
for fr in out["frames"]:
|
|
assert fr["grid"]
|
|
|
|
|
|
def test_frames_count_is_clamped():
|
|
payload = _payload(skills=3, memories=1)
|
|
assert len(render.render_frames(payload, cols=40, rows=12, frames=1)["frames"]) == 2
|
|
assert len(render.render_frames(payload, cols=40, rows=12, frames=9999)["frames"]) == 240
|
|
|
|
|
|
def test_format_date_handles_missing():
|
|
assert render.format_date(None) == "unknown"
|
|
assert render.format_date(0) == "unknown"
|
|
assert render.format_date(1_700_000_000) != "unknown"
|
|
|
|
|
|
def test_derive_palette_distinct_memory_hue():
|
|
pal = render.derive_palette("#FFD700", dark=True)
|
|
assert pal["skill"].startswith("#") and pal["memory"].startswith("#")
|
|
# Memory is a complement of the gold primary → clearly different ink.
|
|
assert pal["memory"].lower() != pal["skill"].lower()
|
|
|
|
|
|
def test_summary_reports_learning_totals():
|
|
lines = render.build_summary(_payload(skills=7, memories=2))
|
|
assert any("7 learned skills" in line and "2 memories" in line for line in lines)
|