docs: introduce Mercury Notebook Deliverable Pattern

studies/202607_CTM_GenesysCX/tests/test_meters.py

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+"""Meter catalogue integrity."""
+
+from __future__ import annotations
+
+import pytest
+
+from tokencalc.defaults import DEFAULT_METERS, DEFAULT_PRICING
+from tokencalc.meters import Confidence, MeterType, TokenMeter, TokenPricing
+
+
+def test_all_spec_meters_present():
+    expected = {
+        # Voice / Bot
+        "Voice Bot", "Digital Bot",
+        # Virtual Agent
+        "Virtual Agent (legacy)", "Agentic Virtual Agent",
+        # Agent Copilot (named + concurrent)
+        "Agent Copilot [named]", "Agent Copilot [concurrent]",
+        # AI Quality / Analytics
+        "AI Scoring", "AI Summary & Insights",
+        # Speech & Text Analytics (named + concurrent)
+        "Speech & Text Analytics [named]", "Speech & Text Analytics [concurrent]",
+        # Routing
+        "Predictive Routing",
+        # Messaging
+        "Direct Messaging", "Social Listening", "Social Responses",
+        # Language
+        "AI Translate",
+        # Genesys Cloud Copilot
+        "Genesys Cloud Copilot",
+        # Email AI (rate TBD; Auto-Suggest is inside Agent Copilot)
+        "Email AI (Auto-Respond)",
+    }
+    assert expected == set(DEFAULT_METERS)
+
+
+def test_confirmed_rates():
+    m = DEFAULT_METERS
+    assert m["Voice Bot"].units_per_token == 17
+    assert m["Voice Bot"].tokens_per_unit == pytest.approx(0.0588, abs=1e-3)
+    assert m["Digital Bot"].units_per_token == 51
+    assert m["Agentic Virtual Agent"].tokens_per_unit == 1.2
+    assert m["AI Summary & Insights"].tokens_per_unit == 0.02
+    assert m["Direct Messaging"].units_per_token == 400
+    # Named variants
+    assert m["Speech & Text Analytics [named]"].tokens_per_unit == 30
+    assert m["Speech & Text Analytics [concurrent]"].tokens_per_unit == 45
+    assert m["Agent Copilot [named]"].tokens_per_unit == 40
+    assert m["Agent Copilot [concurrent]"].tokens_per_unit == 60
+    # AI Translate is now a confirmed consumption meter
+    assert m["AI Translate"].tokens_per_unit == 0.5
+    assert m["AI Translate"].units_per_token == 2
+    assert m["AI Translate"].confidence is Confidence.CONFIRMED
+    # New meters
+    assert m["AI Scoring"].units_per_token == 20
+    assert m["Predictive Routing"].units_per_token == 17
+    assert m["Genesys Cloud Copilot"].units_per_token == 20
+
+
+def test_unknown_meters_flagged():
+    unknown = {f for f, m in DEFAULT_METERS.items() if m.confidence is Confidence.UNKNOWN}
+    assert unknown == {"Email AI (Auto-Respond)"}
+    assert Confidence.UNKNOWN.icon == "🔴"
+    assert Confidence.CONFIRMED.icon == "🟢"
+
+
+def test_inverse_consistency_validated():
+    with pytest.raises(ValueError, match="not inverses"):
+        TokenMeter(
+            feature="Bad", meter_type=MeterType.PER_MINUTE,
+            units_per_token=10, tokens_per_unit=0.5,
+            confidence=Confidence.ESTIMATED, notes="",
+        )
+
+
+def test_every_confirmed_meter_has_source_url():
+    for m in DEFAULT_METERS.values():
+        if m.confidence is Confidence.CONFIRMED:
+            assert m.source_url, f"{m.feature} missing source URL"
+
+
+def test_pricing_effective_rate():
+    p = TokenPricing(region="US", list_rate_per_token=1.0,
+                     contracted_rate_per_token=0.85)
+    assert p.effective_rate(use_contracted=False) == 1.0
+    assert p.effective_rate(use_contracted=True) == 0.85
+    # no contracted rate → falls back to list
+    assert DEFAULT_PRICING["US"].effective_rate(use_contracted=True) == 1.0
+
+
+def test_all_regions_priced():
+    assert set(DEFAULT_PRICING) == {"US", "EU", "AU", "APAC"}