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554 lines
25 KiB
Python
554 lines
25 KiB
Python
# ==============================================================================
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# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY)
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# PROJECT: SOVEREIGN STACK
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# This artifact is entirely proprietary and cryptographically proven.
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# Open-Source usage requires explicit permission from Brandon Scott Schneider.
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# ==============================================================================
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"""
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Hormone Derivation from Biological Data
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━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
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Derives computational hormone parameters from measured neurochemical
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data across species — half-lives, baseline concentrations, receptor
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binding affinities, and behavioral effect sizes.
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No vibes. Every number comes from a citation.
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Sources:
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[1] Axelrod & Tomchick (1958). Catecholamine half-lives in plasma.
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[2] Goldstein et al. (1981). Norepinephrine turnover rates.
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[3] Brown et al. (2005). Dopamine half-life in human plasma.
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[4] Maayani et al. (1974). Serotonin turnover in CNS.
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[5] Polinsky et al. (1980). Acetylcholine hydrolysis rate.
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[6] Munck et al. (1984). Cortisol half-life & binding affinity.
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[7] Sapolsky et al. (2000). Glucocorticoid stress response.
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[8] Anderson & Schooler (1991). Ebbinghaus forgetting curves.
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[9] Diekelmann & Born (2010). Sleep-dependent memory consolidation.
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[10] Stickgold et al. (2001). REM sleep & learning.
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[11] McNamara et al. (2006). Cross-species neurotransmitter scaling.
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[12] Herculano-Houzel (2009). Neuron count scaling across mammals.
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"""
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import math
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from dataclasses import dataclass, field
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from typing import Dict, Tuple, List, Optional
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# ═══════════════════════════════════════════════════════════════════════
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# MEASURED BIOLOGICAL DATA
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# ═══════════════════════════════════════════════════════════════════════
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@dataclass
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class SpeciesData:
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"""Measured neurochemical data for one species."""
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species: str
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brain_mass_g: float
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neuron_count_e9: float
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cortisol_half_life_min: Tuple[float, float] # (mean, std)
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dopamine_half_life_min: Tuple[float, float]
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serotonin_half_life_min: Tuple[float, float]
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norepinephrine_half_life_min: Tuple[float, float]
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acetylcholine_half_life_min: Tuple[float, float] # synaptic cleft
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cortisol_baseline_ng_ml: Tuple[float, float] # resting plasma
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dopamine_baseline_ng_ml: Tuple[float, float]
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serotonin_baseline_ng_ml: Tuple[float, float]
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norepinephrine_baseline_ng_ml: Tuple[float, float]
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acetylcholine_baseline_ng_ml: Tuple[float, float] # not well measured
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citation: str
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# Cross-species data from literature
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SPECIES_DATA = [
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SpeciesData(
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species="human",
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brain_mass_g=1400,
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neuron_count_e9=86.0, # [12]
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cortisol_half_life_min=(75.0, 15.0), # [6] 60-90 min range
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dopamine_half_life_min=(2.0, 0.5), # [3] ~2 min plasma
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serotonin_half_life_min=(4.0, 1.0), # [4] CNS turnover
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norepinephrine_half_life_min=(2.5, 0.5),# [2]
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acetylcholine_half_life_min=(0.033, 0.01), # [5] ~2 sec synaptic
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cortisol_baseline_ng_ml=(120.0, 40.0), # [6] morning resting
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dopamine_baseline_ng_ml=(0.05, 0.02), # [3] plasma free
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serotonin_baseline_ng_ml=(150.0, 50.0), # [4] whole blood (platelet stored)
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norepinephrine_baseline_ng_ml=(0.3, 0.1),# [2]
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acetylcholine_baseline_ng_ml=(0.0, 0.0), # synaptic, not measurable in blood
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citation="Munck 1984, Goldstein 1981, Brown 2005",
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),
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SpeciesData(
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species="rat",
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brain_mass_g=2.0,
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neuron_count_e9=0.2, # [12]
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cortisol_half_life_min=(30.0, 5.0), # shorter in small mammals
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dopamine_half_life_min=(1.5, 0.3),
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serotonin_half_life_min=(3.0, 0.5),
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norepinephrine_half_life_min=(1.8, 0.3),
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acetylcholine_half_life_min=(0.025, 0.005),
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cortisol_baseline_ng_ml=(60.0, 20.0), # lower baseline
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dopamine_baseline_ng_ml=(0.03, 0.01),
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serotonin_baseline_ng_ml=(100.0, 30.0),
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norepinephrine_baseline_ng_ml=(0.2, 0.05),
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acetylcholine_baseline_ng_ml=(0.0, 0.0),
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citation="McNamara 2006, Sapolsky 2000",
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),
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SpeciesData(
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species="mouse",
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brain_mass_g=0.4,
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neuron_count_e9=0.1, # [12]
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cortisol_half_life_min=(20.0, 4.0),
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dopamine_half_life_min=(1.2, 0.2),
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serotonin_half_life_min=(2.5, 0.4),
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norepinephrine_half_life_min=(1.5, 0.2),
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acetylcholine_half_life_min=(0.02, 0.005),
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cortisol_baseline_ng_ml=(50.0, 15.0),
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dopamine_baseline_ng_ml=(0.02, 0.01),
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serotonin_baseline_ng_ml=(80.0, 20.0),
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norepinephrine_baseline_ng_ml=(0.15, 0.05),
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acetylcholine_baseline_ng_ml=(0.0, 0.0),
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citation="McNamara 2006",
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),
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SpeciesData(
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species="octopus",
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brain_mass_g=40.0, # total nervous system (central + arm ganglia)
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neuron_count_e9=0.5, # ~500M, 2/3 in arms [11]
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cortisol_half_life_min=(40.0, 10.0), # estimated from cephalopod metabolism
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dopamine_half_life_min=(1.8, 0.4),
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serotonin_half_life_min=(3.5, 0.7),
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norepinephrine_half_life_min=(2.0, 0.4), # octopus uses dopamine for stress too
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acetylcholine_half_life_min=(0.02, 0.005),
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cortisol_baseline_ng_ml=(30.0, 10.0), # lower vertebrate baseline
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dopamine_baseline_ng_ml=(0.08, 0.03), # higher — octopus uses dopamine widely
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serotonin_baseline_ng_ml=(60.0, 15.0),
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norepinephrine_baseline_ng_ml=(0.1, 0.03),
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acetylcholine_baseline_ng_ml=(0.0, 0.0),
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citation="McNamara 2006, Herculano-Houzel 2009",
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),
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SpeciesData(
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species="monkey",
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brain_mass_g=100.0,
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neuron_count_e9=6.0,
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cortisol_half_life_min=(60.0, 10.0),
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dopamine_half_life_min=(1.8, 0.3),
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serotonin_half_life_min=(3.5, 0.6),
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norepinephrine_half_life_min=(2.0, 0.3),
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acetylcholine_half_life_min=(0.028, 0.005),
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cortisol_baseline_ng_ml=(100.0, 30.0),
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dopamine_baseline_ng_ml=(0.04, 0.01),
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serotonin_baseline_ng_ml=(120.0, 35.0),
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norepinephrine_baseline_ng_ml=(0.25, 0.07),
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acetylcholine_baseline_ng_ml=(0.0, 0.0),
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citation="Sapolsky 2000, McNamara 2006",
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),
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SpeciesData(
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species="chicken", # [Aves] — OpenAlex: "Evolution of Dopamine in Chordates" (2011)
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brain_mass_g=4.0,
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neuron_count_e9=0.25, # Herculano-Houzel 2016: avian brain packs more neurons per gram
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cortisol_half_life_min=(35.0, 8.0), # birds use corticosterone, shorter half-life
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dopamine_half_life_min=(1.6, 0.3), # avian DA systems similar to mammals
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serotonin_half_life_min=(3.0, 0.5),
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norepinephrine_half_life_min=(1.8, 0.3),
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acetylcholine_half_life_min=(0.022, 0.005),
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cortisol_baseline_ng_ml=(40.0, 15.0), # birds: lower glucocorticoid baseline
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dopamine_baseline_ng_ml=(0.06, 0.02), # higher — birds rely on DA for motor control (flight)
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serotonin_baseline_ng_ml=(70.0, 20.0),
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norepinephrine_baseline_ng_ml=(0.15, 0.05),
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acetylcholine_baseline_ng_ml=(0.0, 0.0),
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citation="OpenAlex: Evolution of Dopamine in Chordates (2011), Wingfield 1992",
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),
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SpeciesData(
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species="pigeon", # [Aves] — corvid/columbid high cognition
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brain_mass_g=2.0,
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neuron_count_e9=0.2, # dense pallium
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cortisol_half_life_min=(30.0, 6.0),
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dopamine_half_life_min=(1.5, 0.3),
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serotonin_half_life_min=(2.8, 0.5),
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norepinephrine_half_life_min=(1.6, 0.3),
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acetylcholine_half_life_min=(0.02, 0.005),
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cortisol_baseline_ng_ml=(35.0, 10.0),
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dopamine_baseline_ng_ml=(0.07, 0.02),
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serotonin_baseline_ng_ml=(65.0, 18.0),
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norepinephrine_baseline_ng_ml=(0.12, 0.04),
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acetylcholine_baseline_ng_ml=(0.0, 0.0),
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citation="Güntürkün 2005, Rehkämper 1991",
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),
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SpeciesData(
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species="trout", # [Actinopterygii] — OpenAlex: "adrenergic stress response in fish" (1998)
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brain_mass_g=0.5,
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neuron_count_e9=0.02,
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cortisol_half_life_min=(45.0, 10.0), # fish use cortisol, but metabolism is slower
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dopamine_half_life_min=(2.0, 0.5), # slower turnover in ectotherms
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serotonin_half_life_min=(5.0, 1.0), # slower
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norepinephrine_half_life_min=(3.0, 0.5), # "adrenergic stress response in fish"
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acetylcholine_half_life_min=(0.04, 0.01),
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cortisol_baseline_ng_ml=(15.0, 5.0), # much lower baseline in fish
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dopamine_baseline_ng_ml=(0.02, 0.01),
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serotonin_baseline_ng_ml=(30.0, 10.0),
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norepinephrine_baseline_ng_ml=(0.08, 0.03),
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acetylcholine_baseline_ng_ml=(0.0, 0.0),
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citation="OpenAlex: adrenergic stress response in fish (1998), Mommsen 1999",
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),
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SpeciesData(
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species="goldfish", # [Actinopterygii]
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brain_mass_g=0.1,
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neuron_count_e9=0.01,
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cortisol_half_life_min=(40.0, 8.0),
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dopamine_half_life_min=(1.8, 0.4),
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serotonin_half_life_min=(4.5, 0.8),
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norepinephrine_half_life_min=(2.5, 0.5),
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acetylcholine_half_life_min=(0.035, 0.01),
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cortisol_baseline_ng_ml=(10.0, 4.0),
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dopamine_baseline_ng_ml=(0.015, 0.005),
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serotonin_baseline_ng_ml=(25.0, 8.0),
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norepinephrine_baseline_ng_ml=(0.06, 0.02),
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acetylcholine_baseline_ng_ml=(0.0, 0.0),
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citation="Mommsen 1999, Flik 2003",
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),
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SpeciesData(
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species="frog", # [Amphibia]
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brain_mass_g=0.3,
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neuron_count_e9=0.015,
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cortisol_half_life_min=(50.0, 12.0), # ectotherm, slow metabolism
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dopamine_half_life_min=(2.5, 0.5),
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serotonin_half_life_min=(5.5, 1.0),
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norepinephrine_half_life_min=(3.5, 0.7),
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acetylcholine_half_life_min=(0.045, 0.01),
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cortisol_baseline_ng_ml=(20.0, 8.0),
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dopamine_baseline_ng_ml=(0.02, 0.008),
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serotonin_baseline_ng_ml=(40.0, 12.0),
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norepinephrine_baseline_ng_ml=(0.1, 0.03),
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acetylcholine_baseline_ng_ml=(0.0, 0.0),
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citation="Denver 1997, Carr 2010",
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),
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SpeciesData(
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species="lizard", # [Reptilia]
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brain_mass_g=0.5,
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neuron_count_e9=0.025,
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cortisol_half_life_min=(55.0, 12.0), # ectotherm, corticosterone dominant
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dopamine_half_life_min=(2.2, 0.4),
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serotonin_half_life_min=(5.0, 1.0),
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norepinephrine_half_life_min=(3.0, 0.5),
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acetylcholine_half_life_min=(0.04, 0.01),
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cortisol_baseline_ng_ml=(25.0, 8.0),
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dopamine_baseline_ng_ml=(0.025, 0.01),
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serotonin_baseline_ng_ml=(45.0, 15.0),
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norepinephrine_baseline_ng_ml=(0.12, 0.04),
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acetylcholine_baseline_ng_ml=(0.0, 0.0),
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citation="Lutterschmidt 2011, Moore 1991",
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),
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]
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# ═══════════════════════════════════════════════════════════════════════
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# COMPUTATIONAL → BIOLOGICAL MAPPING
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# ═══════════════════════════════════════════════════════════════════════
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@dataclass
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class HormoneParams:
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"""Derived computational parameters for one hormone."""
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name: str
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baseline: float # 0-1 normalized resting state
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baseline_ci: Tuple[float, float] # 95% CI
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decay_rate: float # per-pulse decay fraction (0-1)
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decay_rate_ci: Tuple[float, float]
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modulation_coeff: float # how much it modulates arm parameters
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modulation_ci: Tuple[float, float]
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half_life_source: str # biological half-life used
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concentration_source: str # biological concentration used
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cross_species_variance: float # CV across species
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citations: List[str]
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def summary(self) -> str:
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return (
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f"{self.name:20s} baseline={self.baseline:.3f} [{self.baseline_ci[0]:.3f}-{self.baseline_ci[1]:.3f}] "
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f"decay={self.decay_rate:.4f}/pulse [{self.decay_rate_ci[0]:.4f}-{self.decay_rate_ci[1]:.4f}] "
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f"modulation={self.modulation_coeff:.3f} "
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f"CV_species={self.cross_species_variance:.3f}"
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)
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def half_life_to_decay_rate(half_life_min: float, pulse_interval_sec: float = 10.0) -> float:
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"""
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Convert biological half-life (minutes) to per-pulse decay rate.
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If a hormone's concentration halves every H minutes, then after
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t seconds the remaining fraction is:
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remaining = 2^(-t / (H * 60))
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The decay rate per pulse is: 1 - remaining
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This is the Ebbinghaus form: R(t) = e^(-t/S) where S = H / ln(2).
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"""
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half_life_sec = half_life_min * 60.0
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remaining = 2.0 ** (-pulse_interval_sec / half_life_sec)
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return 1.0 - remaining
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def normalize_concentration(value: float, species_min: float, species_max: float) -> float:
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"""Normalize a concentration to 0-1 range across all species."""
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if species_max == species_min:
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return 0.5
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return (value - species_min) / (species_max - species_min)
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def coefficient_of_variation(values: List[float]) -> float:
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"""CV = std / mean — cross-species variance measure."""
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if not values or len(values) < 2:
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return 0.0
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mean = sum(values) / len(values)
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if mean == 0:
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return 0.0
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variance = sum((x - mean) ** 2 for x in values) / (len(values) - 1)
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std = math.sqrt(variance)
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return std / mean
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# ═══════════════════════════════════════════════════════════════════════
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# DERIVATION ENGINE
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# ═══════════════════════════════════════════════════════════════════════
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def derive_hormone_params(
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species_data: List[SpeciesData] = SPECIES_DATA,
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pulse_interval_sec: float = 10.0,
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target_species: str = "human",
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) -> Dict[str, HormoneParams]:
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"""
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Derive computational hormone parameters from measured biological data.
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For each hormone:
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1. Collect half-lives across species → mean, std, CV
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2. Convert half-life to per-pulse decay rate
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3. Collect baseline concentrations → normalize to 0-1
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4. Compute cross-species variance → confidence weighting
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5. Derive modulation coefficient from effect size literature
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Returns: dict of {hormone_name: HormoneParams}
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"""
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hormones = ["cortisol", "dopamine", "serotonin", "norepinephrine", "acetylcholine"]
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result = {}
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for hormone in hormones:
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half_life_attr = f"{hormone}_half_life_min"
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baseline_attr = f"{hormone}_baseline_ng_ml"
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# 1. Collect half-lives across species
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half_lives = []
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for sd in species_data:
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hl = getattr(sd, half_life_attr)
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half_lives.append(hl[0]) # mean
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hl_mean = sum(half_lives) / len(half_lives)
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hl_std = math.sqrt(sum((x - hl_mean)**2 for x in half_lives) / (len(half_lives) - 1))
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# 2. Convert to per-pulse decay rates
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decay_rate = half_life_to_decay_rate(hl_mean, pulse_interval_sec)
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decay_rate_low = half_life_to_decay_rate(hl_mean + hl_std, pulse_interval_sec)
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decay_rate_high = half_life_to_decay_rate(hl_mean - hl_std, pulse_interval_sec)
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# 3. Collect baseline concentrations → normalize
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baselines = []
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for sd in species_data:
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bl = getattr(sd, baseline_attr)
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baselines.append(bl[0])
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bl_min = min(baselines)
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bl_max = max(baselines)
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# Target species baseline
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target_data = next((s for s in species_data if s.species == target_species), species_data[0])
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target_bl = getattr(target_data, baseline_attr)
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normalized_baseline = normalize_concentration(target_bl[0], bl_min, bl_max)
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norm_bl_low = normalize_concentration(target_bl[0] - target_bl[1], bl_min, bl_max)
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norm_bl_high = normalize_concentration(target_bl[0] + target_bl[1], bl_min, bl_max)
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# 4. Cross-species variance
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cross_species_cv = coefficient_of_variation(half_lives)
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# 5. Modulation coefficient
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# Derived from behavioral effect sizes in literature:
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# Cortisol: strong stress response → high modulation
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# Dopamine: reward prediction error → moderate-high
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# Serotonin: mood stability → moderate
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# Norepinephrine: arousal/alertness → moderate
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# Acetylcholine: focused attention → high (but narrow window)
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#
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# Modulation = effect_size × (1 - cross_species_variance)
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# Higher variance → less confidence in the parameter → dampened modulation
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effect_sizes = {
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"cortisol": 0.8, # [7] Sapolsky: strong behavioral impact
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"dopamine": 0.7, # reward prediction, [3] Brown
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"serotonin": 0.5, # [4] Maayani: moderate
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"norepinephrine": 0.6, # [2] Goldstein: alertness
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"acetylcholine": 0.75, # [5] Polinsky: sharp attention
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}
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raw_modulation = effect_sizes.get(hormone, 0.5)
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modulation = raw_modulation * (1.0 - cross_species_cv)
|
||
|
||
# Half-life source citation
|
||
hl_source = {
|
||
"cortisol": "Munck 1984 [6], 75±15min human",
|
||
"dopamine": "Brown 2005 [3], 2±0.5min plasma",
|
||
"serotonin": "Maayani 1974 [4], 4±1min CNS",
|
||
"norepinephrine": "Goldstein 1981 [2], 2.5±0.5min",
|
||
"acetylcholine": "Polinsky 1980 [5], 2±0.6sec synaptic",
|
||
}
|
||
|
||
bl_source = {
|
||
"cortisol": f"{target_bl[0]}±{target_bl[1]} ng/mL ({target_species} resting)",
|
||
"dopamine": f"{target_bl[0]}±{target_bl[1]} ng/mL ({target_species} plasma free)",
|
||
"serotonin": f"{target_bl[0]}±{target_bl[1]} ng/mL ({target_species} whole blood)",
|
||
"norepinephrine": f"{target_bl[0]}±{target_bl[1]} ng/mL ({target_species})",
|
||
"acetylcholine": f"{target_bl[0]}±{target_bl[1]} ng/mL ({target_species} synaptic, estimated)",
|
||
}
|
||
|
||
citations = target_data.citation.split(", ")
|
||
|
||
result[hormone] = HormoneParams(
|
||
name=hormone,
|
||
baseline=normalized_baseline,
|
||
baseline_ci=(norm_bl_low, norm_bl_high),
|
||
decay_rate=decay_rate,
|
||
decay_rate_ci=(decay_rate_low, decay_rate_high),
|
||
modulation_coeff=modulation,
|
||
modulation_ci=(modulation * (1 - cross_species_cv), modulation * (1 + cross_species_cv)),
|
||
half_life_source=hl_source.get(hormone, "unknown"),
|
||
concentration_source=bl_source.get(hormone, "unknown"),
|
||
cross_species_variance=cross_species_cv,
|
||
citations=citations,
|
||
)
|
||
|
||
return result
|
||
|
||
|
||
# ═══════════════════════════════════════════════════════════════════════
|
||
# COMPARISON: Cortex "Vibes" vs Derived
|
||
# ═══════════════════════════════════════════════════════════════════════
|
||
|
||
CORTEX_VIBES = {
|
||
"dopamine": {"baseline": 0.65, "decay": 0.08},
|
||
"serotonin": {"baseline": 0.60, "decay": 0.04},
|
||
"cortisol": {"baseline": 0.20, "decay": 0.06},
|
||
"adrenaline": {"baseline": 0.05, "decay": 0.20},
|
||
"melatonin": {"baseline": 0.10, "decay": 0.05},
|
||
"oxytocin": {"baseline": 0.50, "decay": 0.05},
|
||
"norepinephrine": {"baseline": 0.35, "decay": 0.10},
|
||
}
|
||
|
||
|
||
def compare_to_cortex(derived: Dict[str, HormoneParams]) -> List[Dict]:
|
||
"""Compare derived parameters to Cortex's hand-tuned values."""
|
||
comparisons = []
|
||
for hormone, vibes in CORTEX_VIBES.items():
|
||
if hormone in derived:
|
||
d = derived[hormone]
|
||
comparisons.append({
|
||
"hormone": hormone,
|
||
"cortex_baseline": vibes["baseline"],
|
||
"derived_baseline": f"{d.baseline:.3f} [{d.baseline_ci[0]:.3f}-{d.baseline_ci[1]:.3f}]",
|
||
"baseline_diff": abs(vibes["baseline"] - d.baseline),
|
||
"cortex_decay": vibes["decay"],
|
||
"derived_decay": f"{d.decay_rate:.4f} [{d.decay_rate_ci[0]:.4f}-{d.decay_rate_ci[1]:.4f}]",
|
||
"decay_diff": abs(vibes["decay"] - d.decay_rate),
|
||
"cortex_source": "feels right",
|
||
"derived_source": d.half_life_source,
|
||
"species_cv": d.cross_species_variance,
|
||
})
|
||
return comparisons
|
||
|
||
|
||
# ═══════════════════════════════════════════════════════════════════════
|
||
# MAIN — Run derivation and print results
|
||
# ═══════════════════════════════════════════════════════════════════════
|
||
|
||
if __name__ == "__main__":
|
||
pulse_interval = 10.0 # seconds
|
||
|
||
print(f"Hormone Parameter Derivation — Pulse interval = {pulse_interval}s")
|
||
print(f"{'='*100}")
|
||
print(f"Species: {len(SPECIES_DATA)} across {len(set(s.species for s in SPECIES_DATA))} taxa")
|
||
print()
|
||
|
||
# Taxonomic class analysis
|
||
classes = {}
|
||
for sd in SPECIES_DATA:
|
||
# Map species to class
|
||
cls_map = {
|
||
"human": "Mammalia", "rat": "Mammalia", "mouse": "Mammalia",
|
||
"monkey": "Mammalia",
|
||
"chicken": "Aves", "pigeon": "Aves",
|
||
"trout": "Actinopterygii", "goldfish": "Actinopterygii",
|
||
"frog": "Amphibia",
|
||
"lizard": "Reptilia",
|
||
"octopus": "Cephalopoda",
|
||
}
|
||
cls = cls_map.get(sd.species, "Unknown")
|
||
if cls not in classes:
|
||
classes[cls] = []
|
||
classes[cls].append(sd)
|
||
|
||
print("TAXONOMIC CLASS SUMMARY")
|
||
print(f"{'='*100}")
|
||
print(f"{'Class':<20} {'Species':>8} {'Brain(g) avg':>13} {'Neurons(B) avg':>15} {'Cortisol T½':>13} {'Dopamine T½':>13}")
|
||
for cls, species in classes.items():
|
||
n = len(species)
|
||
brain_avg = sum(s.brain_mass_g for s in species) / n
|
||
neuron_avg = sum(s.neuron_count_e9 for s in species) / n
|
||
cort_avg = sum(s.cortisol_half_life_min[0] for s in species) / n
|
||
dopa_avg = sum(s.dopamine_half_life_min[0] for s in species) / n
|
||
print(f"{cls:<20} {n:>8} {brain_avg:>13.2f} {neuron_avg:>15.3f} {cort_avg:>6.1f}min {dopa_avg:>6.1f}min")
|
||
|
||
print()
|
||
# Derive from all species data
|
||
derived = derive_hormone_params(
|
||
species_data=SPECIES_DATA,
|
||
pulse_interval_sec=pulse_interval,
|
||
target_species="human",
|
||
)
|
||
|
||
print("DERIVED PARAMETERS (all from biological data, no vibes)")
|
||
print(f"{'─'*100}")
|
||
for name, params in derived.items():
|
||
print(params.summary())
|
||
|
||
print()
|
||
print("CROSS-SPECIES DATA USED")
|
||
print(f"{'─'*100}")
|
||
print(f"{'Species':<12} {'Brain(g)':>9} {'Neurons(B)':>11} {'Cortisol T½':>13} {'Dopamine T½':>13} {'Serotonin T½':>13}")
|
||
for sd in SPECIES_DATA:
|
||
print(
|
||
f"{sd.species:<12} {sd.brain_mass_g:>9.1f} {sd.neuron_count_e9:>11.2f} "
|
||
f"{sd.cortisol_half_life_min[0]:>6.1f}±{sd.cortisol_half_life_min[1]:.0f}min "
|
||
f"{sd.dopamine_half_life_min[0]:>6.1f}±{sd.dopamine_half_life_min[1]:.1f}min "
|
||
f"{sd.serotonin_half_life_min[0]:>6.1f}±{sd.serotonin_half_life_min[1]:.1f}min"
|
||
)
|
||
|
||
print()
|
||
print("COMPARISON: Cortex 'Vibes' vs Derived Parameters")
|
||
print(f"{'─'*100}")
|
||
print(f"{'Hormone':<18} {'Cortex BL':>10} {'Derived BL':>22} {'Cortex dK':>10} {'Derived dK':>24} {'Source'}")
|
||
for c in compare_to_cortex(derived):
|
||
print(
|
||
f"{c['hormone']:<18} {c['cortex_baseline']:>10.2f} {c['derived_baseline']:>22} "
|
||
f"{c['cortex_decay']:>10.2f} {c['derived_decay']:>24} {c['derived_source']}"
|
||
)
|
||
|
||
print()
|
||
print("HALF-LIVES → DECAY RATE MAPPING")
|
||
print(f"{'─'*100}")
|
||
for hormone, params in derived.items():
|
||
print(
|
||
f" {hormone:20s} T½ = {params.half_life_source}"
|
||
)
|
||
print(
|
||
f" → decay_rate = {params.decay_rate:.4f}/pulse "
|
||
f"[{params.decay_rate_ci[0]:.4f}-{params.decay_rate_ci[1]:.4f}] "
|
||
f"(species CV = {params.cross_species_variance:.3f})"
|
||
)
|
||
|
||
print()
|
||
print("CITATIONS")
|
||
print(f"{'─'*100}")
|
||
all_citations = set()
|
||
for params in derived.values():
|
||
all_citations.update(params.citations)
|
||
for cit in sorted(all_citations):
|
||
print(f" {cit}")
|