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[WIP] Add calculation for Ψ_CY using real data#305

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copilot/calculate-psi-cy-value
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[WIP] Add calculation for Ψ_CY using real data#305
Copilot wants to merge 3 commits intomainfrom
copilot/calculate-psi-cy-value

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Copilot AI commented Jan 1, 2026

Thanks for asking me to work on this. I will get started on it and keep this PR's description up to date as I form a plan and make progress.

Original prompt

Vamos a ejecutar el cálculo puro y riguroso de:

Ψ
𝐶
𝑌


1
,
1
log


1
,
1
+

2
,
1
log


2
,
1

1
,
1
+

2
,
1

1
Ψ
CY

=
h
1,1
+h
2,1
h
1,1
logh
1,1
+h
2,1
logh
2,1

−1

sobre una base real de datos CY (como CICY o Kreuzer–Skarke).

🔬 CÓDIGO PYTHON: Análisis de
Ψ
𝐶
𝑌
Ψ
CY

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

def compute_psi_cy(h11, h21):
"""Calcula la constante geométrica emergente Ψ_CY."""
h11 = np.asarray(h11)
h21 = np.asarray(h21)

# Evitar log(0)
h11_safe = np.where(h11 == 0, 1, h11)
h21_safe = np.where(h21 == 0, 1, h21)

numerator = h11 * np.log(h11_safe) + h21 * np.log(h21_safe)
denominator = h11 + h21
psi = numerator / denominator - 1
return psi

Ejemplo de carga de datos reales

Puedes sustituir este CSV por el que uses (CICY, Kreuzer–Skarke, etc.)

Debe tener columnas: 'h11', 'h21'

df = pd.read_csv("datos_CICY.csv") # o "kreuzer_skarke_data.csv"
df['Psi_CY'] = compute_psi_cy(df['h11'], df['h21'])
df['N'] = df['h11'] + df['h21']

Visualización: distribución de Ψ_CY

plt.figure(figsize=(10, 6))
plt.hist(df['Psi_CY'], bins=100, color='skyblue', edgecolor='k')
plt.axvline(np.mean(df['Psi_CY']), color='red', linestyle='--', label=f"Media ≈ {np.mean(df['Psi_CY']):.4f}")
plt.title("Distribución de Ψ_CY en variedades Calabi–Yau")
plt.xlabel("Ψ_CY")
plt.ylabel("Frecuencia")
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()

¿Se acumula cerca de 2.5773 u otra constante?

approx_count = ((df['Psi_CY'] > 2.55) & (df['Psi_CY'] < 2.60)).sum()
total = len(df)
print(f"Ψ_CY en [2.55, 2.60]: {approx_count} / {total} ({100 * approx_count / total:.2f}%)")

Exportar si deseas analizar en otros programas

df.to_csv("resultado_psi_cy.csv", index=False)

🎯 POSIBLES INTERPRETACIONES

Si Ψ_CY converge a ≈ 2.5773, será una constante geométrica emergente real y legítima.

Si Ψ_CY se dispersa sin patrón, entonces no hay razón para asignarle valor especial.

Si aparecen clusters, investigamos por qué: ¿topología? ¿simetrías? ¿regiones especiales del moduli?


✨ Let Copilot coding agent set things up for you — coding agent works faster and does higher quality work when set up for your repo.

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vercel bot commented Jan 1, 2026

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Copilot AI review requested due to automatic review settings January 1, 2026 19:29
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vercel bot commented Jan 24, 2026

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