diff --git a/src/fire2a/firebehavior.py b/src/fire2a/firebehavior.py
new file mode 100644
index 0000000..6fb5e59
--- /dev/null
+++ b/src/fire2a/firebehavior.py
@@ -0,0 +1,772 @@
+import numpy as np
+import math
+import pandas as pd
+import datetime
+
+def acceleration(ftype, cfb): 
+    """
+    Calcula la aceleración de la propagación del fuego.
+
+    Args:
+        ftype (str): Tipo de combustible.
+        cfb (float): Fracción de la corona quemada.
+
+    Returns:
+        float: Aceleración de la propagación del fuego.
+    """
+    open_list = ["O1a", "O1b", "C1", "S1", "S2", "S3"]  # combustible abierto
+
+    if ftype not in open_list:
+        # para combustibles cerrados
+        accn = 0.115 - 18.8 * cfb**2.5 * math.exp(-8.0 * cfb)  # Eq.72
+    else:
+        # para combustibles abiertos
+        accn = 0.115
+
+    return accn
+
+def area(dt, df):
+    """
+    Calcula el área de un elipse.
+
+    Args:
+        dt (float): Diámetro mayor.
+        df (float): Diámetro menor.
+
+    Returns:
+        float: Área de la elipse (en hectáreas).
+    """
+    a = dt / 2.0
+    b = df
+    areavar = a * b * math.pi / 10000.0
+    return areavar
+
+def back_fire_behaviour(ftype, sfc, brss, csi, rso, fmc, bisi, CFL):
+    """
+    Calcula el comportamiento del fuego en retroceso.
+
+    Args:
+        ftype (str): Tipo de combustible.
+        sfc (float): Velocidad de propagación del fuego en superficie.
+        brss (float): Tasa de propagación del fuego en superficie.
+        csi (float): Índice de intensidad del fuego crítico.
+        rso (float): Tasa de propagación del fuego en superficie de referencia.
+        fmc (float): Contenido de humedad del combustible.
+        bisi (float): Índice de propagación del fuego en retroceso.
+        CFL (dict): Diccionario que contiene los factores de corrección de la velocidad de propagación del fuego.
+
+    Returns:
+        tuple: Una tupla que contiene la tasa de propagación final del fuego en retroceso,
+               la intensidad final del fuego en retroceso,
+               el consumo final de combustible en retroceso,
+               y el tipo de fuego en retroceso (superficial o de copa).
+    """
+    bsfi = fire_intensity(sfc, brss)
+    back_firetype = "superficial"
+
+    if bsfi > csi:
+        back_firetype = "de copa"
+
+    if back_firetype == "de copa":
+        bcfb = max(1 - math.exp(-0.23 * (brss - rso)), 0.0)  # fracción quemada de copa
+        bcfc = CFL[ftype] * bcfb
+        bros = final_ros(ftype, fmc, bisi, bcfb, brss)
+
+        bfc = bcfc + sfc
+        bfi = fire_intensity(bfc, bros)
+        return bros, bfi, bfc, back_firetype
+    else:
+        bros = brss
+        bfi = bsfi
+        bfc = sfc
+
+    return bros, bfi, bfc, back_firetype
+def backfire_isi(wsv, ff):
+    """
+    Calcula el Índice de Propagación Inicial (ISI) para fuegos de retroceso.
+
+    Args:
+        wsv (float): Velocidad del viento sostenido [km/h].
+        ff (float): Índice FFMC (Código de Humedad de Combustible Fino).
+
+    Returns:
+        float: ISI calculado.
+    """
+    bfw = math.exp(-0.05039 * wsv)  # Eq.75
+    bisi = 0.208 * ff * bfw  # Eq.76
+    return bisi
+
+def backfire_ros(ftype, bisi, wdfh, a, b, c, FuelConst2, bui0, q):
+    """
+    Calcula la tasa de propagación del fuego de retroceso.
+
+    Args:
+        ftype (str): Tipo de combustible.
+        bisi (float): Índice de Propagación Inicial del Fuego de Retroceso (ISI).
+        wdfh (dict): Diccionario que contiene los datos meteorológicos y de combustible.
+        a (float): Parámetro de la ecuación de propagación del fuego.
+        b (float): Parámetro de la ecuación de propagación del fuego.
+        c (float): Parámetro de la ecuación de propagación del fuego.
+        FuelConst2 (dict): Diccionario que contiene constantes de combustible.
+        bui0 (dict): Diccionario que contiene los índices de acumulación inicial del combustible.
+        q (dict): Diccionario que contiene los factores de corrección de la velocidad de propagación del fuego.
+
+    Returns:
+        float: Tasa de propagación del fuego de retroceso.
+    """
+    bros = ros_base(ftype, bisi, wdfh['BUI'], a, b, c, FuelConst2)
+    bros *= bui_effect(wdfh['BUI'], bui0[ftype], q[ftype])
+    return bros
+
+def bui_effect(bui, bui0, q):
+    """
+    Calcula el efecto del Índice de Acumulación (BUI) en la propagación del fuego.
+
+    Args:
+        bui (float): Índice de Acumulación de Combustible (BUI).
+        bui0 (float): Índice de Acumulación de Combustible inicial.
+        q (float): Factor de corrección de la velocidad de propagación del fuego.
+
+    Returns:
+        float: Efecto del BUI en la propagación del fuego.
+    """
+    bui_avg = 50.0
+
+    if bui == 0:
+        bui = 1.0
+    be = np.exp(bui_avg * np.log(q) * ((1.0 / bui) - (1.0 / bui0)))
+    return be
+
+def crit_surf_intensity(cbh, fmc):
+    """
+    Calcula la intensidad crítica de la superficie para la transición de fuego superficial a fuego de corona.
+
+    Args:
+        cbh (float): Altura base de la copa [m].
+        fmc (float): Contenido de humedad foliar [%].
+
+    Returns:
+        float: Intensidad crítica de la superficie.
+    """
+    csi = 0.001 * cbh**1.5 * (460.0 + 25.9 * fmc)**1.5
+    return csi
+def ffmc_effect(ffmc):
+    """
+    Calcula el efecto del Código de Humedad de Combustible Fino (FFMC) en la propagación del fuego.
+
+    Args:
+        ffmc (float): Índice FFMC (Código de Humedad de Combustible Fino).
+
+    Returns:
+        float: Efecto del FFMC en la propagación del fuego.
+    """
+    mc = 147.2 * (101.0 - ffmc) / (59.5 + ffmc)  # Eq.46
+    ff = 91.9 * math.exp(-0.1386 * mc) * (1 + mc**5.31 / 4.93e7)  # Eq.45
+    return ff
+
+def final_ros(ftype, fmc, isi, cfb, rss):
+    """
+    Calcula la tasa final de propagación del fuego, teniendo en cuenta si es un fuego de corona.
+
+    Args:
+        ftype (str): Tipo de combustible.
+        fmc (float): Contenido de humedad foliar [%].
+        isi (float): Índice de Propagación Inicial (ISI) del fuego.
+        cfb (float): Fracción de la corona quemada.
+        rss (float): Tasa de propagación del fuego en superficie.
+
+    Returns:
+        float: Tasa final de propagación del fuego.
+    """
+    if ftype == "C6":
+        rsc = foliar_mois_effect(isi, fmc)
+        ros = rss + cfb * (rsc - rss)
+    else:
+        ros = rss
+    return ros
+
+def fire_intensity(fc, ros):
+    """
+    Calcula la intensidad del fuego basada en el consumo de combustible y la tasa de propagación.
+
+    Args:
+        fc (float): Consumo de combustible predicho [kg/m2].
+        ros (float): Tasa de propagación predicha [m/min].
+
+    Returns:
+        float: Intensidad del fuego [kW/m].
+    """
+    fi = 300.0 * fc * ros  # Eq.69 [kW/m]
+    return fi
+
+def flank_fire_behaviour(ftype, sfc, frss, csi, rso, CFL):
+    """
+    Determina el comportamiento del fuego en los flancos, incluyendo la intensidad y el consumo de combustible.
+
+    Args:
+        ftype (str): Tipo de combustible.
+        sfc (float): Velocidad de propagación del fuego en superficie.
+        frss (float): Tasa de propagación del fuego en los flancos.
+        csi (float): Índice de intensidad del fuego crítico.
+        rso (float): Tasa de propagación del fuego en superficie de referencia.
+        CFL (dict): Diccionario que contiene los factores de corrección de la velocidad de propagación del fuego.
+
+    Returns:
+        tuple: Una tupla que contiene la intensidad final del fuego en los flancos,
+               el consumo final de combustible en los flancos,
+               y el tipo de fuego en los flancos (superficial o de copa).
+    """
+    flank_firetype = "surface"
+    sfi = fire_intensity(sfc, frss)
+    if sfi > csi:
+        flank_firetype = "crown"
+
+    if flank_firetype == "crown":
+        fcfb = max(1 - math.exp(-0.23 * (frss - rso)), 0.0)  # crown fraction burned
+        fcfc = CFL[ftype] * fcfb
+
+        ffc = fcfc + sfc
+        ffi = fire_intensity(ffc, frss)
+    else:
+        ffi = sfi
+        ffc = sfc
+
+    return ffi, ffc, flank_firetype
+
+def flank_spread_distance(hrost, brost, hdist, bdist, lb, a, time):
+    """
+    Calcula la distancia de propagación del fuego y la tasa de propagación a lo largo del tiempo en los flancos.
+
+    Args:
+        hrost (float): Tasa de propagación del fuego en la corona.
+        brost (float): Tasa de propagación del fuego en superficie de referencia.
+        hdist (float): Distancia de propagación del fuego en la corona.
+        bdist (float): Distancia de propagación del fuego en superficie de referencia.
+        lb (float): Relación longitud-ancho de la zona quemada.
+        a (float): Parámetro de la ecuación de propagación del fuego.
+        time (float): Tiempo de propagación.
+
+    Returns:
+        tuple: Una tupla que contiene la distancia de propagación del fuego en los flancos,
+               la tasa de propagación del fuego en los flancos,
+               y la relación longitud-ancho ajustada por el tiempo.
+    """
+    lbt = (lb - 1.0) * (1.0 - math.exp(-a * time)) + 1.0
+    rost = (hrost + brost) / (lbt * 2.0)
+    fsd = (hdist + bdist) / (2.0 * lbt)
+    return fsd, rost, lbt
+
+def flankfire_ros(ros, bros, lb):
+    """
+    Calcula la tasa de propagación del fuego en los flancos.
+
+    Args:
+        ros (float): Tasa de propagación del fuego.
+        bros (float): Tasa de propagación del fuego en la corona.
+        lb (float): Relación longitud-ancho de la zona quemada.
+
+    Returns:
+        float: Tasa de propagación del fuego en los flancos.
+    """
+    fros = (ros + bros) / (lb * 2.0)
+    return fros
+
+def foliar_mois_effect(isi, fmc):
+    """
+    Calcula el efecto de la humedad foliar en la propagación del fuego.
+
+    Args:
+        isi (float): Índice de propagación inicial (ISI) del fuego.
+        fmc (float): Contenido de humedad foliar [%].
+
+    Returns:
+        float: Efecto de la humedad foliar en la propagación del fuego.
+    """
+    fme_avg = 0.778
+    fme = 1000.0 * (1.5 - 0.00275 * fmc) ** 4.0 / (460.0 + 25.9 * fmc)
+    rsc = 60.0 * (1.0 - math.exp(-0.0497 * isi)) * fme / fme_avg
+    return rsc
+
+def foliar_moisture(lat, long, elev, jd):
+    """
+    Estima la humedad foliar basada en la ubicación geográfica y el día juliano.
+
+    Args:
+        lat (float): Latitud.
+        long (float): Longitud.
+        elev (float): Elevación.
+        jd (int): Día juliano.
+
+    Returns:
+        float: Humedad foliar estimada.
+    """
+    jd_min = 0
+
+    if jd_min <= 0:  # dispositivo cuando no hay D0
+        if elev < 0:
+            latn = 23.4 * math.exp(-0.0360 * (150 - long)) + 46.0  # Eq.1
+            jd_min = 0.5 + 151.0 * lat / latn
+        else:
+            latn = 33.7 * math.exp(-0.0351 * (150 - long)) + 43.0
+            jd_min = 0.5 + 142.1 * lat / latn + (0.0172 * elev)
+
+    nd = round(abs(jd - jd_min))
+    if 30 <= nd < 50:
+        fm = 32.9 + 3.17 * nd - 0.0288 * nd ** 2
+    elif nd >= 50:
+        fm = 120
+    else:
+        fm = 85.0 + 0.0189 * nd ** 2
+
+    return fm
+
+def get_fueltype_number(ftype):
+    """
+    Devuelve el número de tipo de combustible basado en el tipo de combustible.
+
+    Args:
+        ftype (str): Tipo de combustible.
+
+    Returns:
+        str: Número de tipo de combustible (c = cerrado, n = abierto).
+    """
+    cftype = ["C1", "C2", "C3", "C4", "C5", "C6", "C7", "M1", "M2", "M3", "M4", "D1", "D2"]
+    cover = "c" if ftype in cftype else "n"  # S1, S2, S3, O1a, O1b
+    return cover
+
+def ISF_deadfir(ft, a, b, c, isz, pdf, sf):
+    """
+    Calcula el Índice de Severidad del Fuego (ISF) para fuegos de madera muerta.
+
+    Args:
+        ft (str): Tipo de combustible.
+        a (float): Parámetro de la ecuación de propagación del fuego.
+        b (float): Parámetro de la ecuación de propagación del fuego.
+        c (float): Parámetro de la ecuación de propagación del fuego.
+        isz (float): Tamaño inicial del fuego.
+        pdf (float): Porcentaje de madera muerta fina.
+        sf (float): Velocidad del viento sostenido.
+
+    Returns:
+        float: Índice de Severidad del Fuego (ISF) para fuegos de madera muerta.
+    """
+    slopelimit_isi = 0.01
+    rsf_max = sf * a[ft] * (1.0 - math.exp(-1.0 * b[ft] * isz)) ** c[ft]
+    check = 1.0 - (rsf_max / a[ft]) ** (1.0 / c[ft]) if rsf_max > 0.0 else 1.0
+    check = max(check, slopelimit_isi)
+    isf_max = (1.0 / (-1.0 * b[ft])) * math.log(check)
+
+    mult = 0.2 if ft == "M4" else 1.0
+    rsf_d1 = sf * (mult * a["D1"]) * (1.0 - math.exp(-1.0 * b[ft] * isz)) ** c[ft]
+    check = 1.0 - (rsf_d1 / (mult * a[ft])) ** (1.0 / c[ft]) if rsf_d1 > 0.0 else 1.0
+    check = max(check, slopelimit_isi)
+    isf_d1 = (1.0 / (-1.0 * b[ft])) * math.log(check)
+
+    isf = pdf / 100.0 * isf_max + (100.0 - pdf) / 100.0 * isf_d1
+    return isf
+
+def ISF_mixedwood(ft, a, b, c, isz, pc, sf):
+    """
+    Calcula el Índice de Severidad del Fuego (ISF) para fuegos de madera mixta.
+
+    Args:
+        ft (str): Tipo de combustible.
+        a (float): Parámetro de la ecuación de propagación del fuego.
+        b (float): Parámetro de la ecuación de propagación del fuego.
+        c (float): Parámetro de la ecuación de propagación del fuego.
+        isz (float): Tamaño inicial del fuego.
+        pc (float): Porcentaje de madera mixta.
+        sf (float): Velocidad del viento sostenido.
+
+    Returns:
+        float: Índice de Severidad del Fuego (ISF) para fuegos de madera mixta.
+    """
+    slopelimit_isi = 0.01
+    rsf_c2 = sf * a["C2"] * (1.0 - math.exp(-1.0 * b["C2"] * isz)) ** c["C2"]
+    check = 1.0 - (rsf_c2 / a["C2"]) ** (1.0 / c["C2"]) if rsf_c2 > 0.0 else 1.0
+    check = max(check, slopelimit_isi)
+    isf_c2 = (1.0 / (-1.0 * b[ft])) * math.log(check)
+
+    mult = 0.2 if ft == "M2" else 1.0
+    rsf_d1 = sf * (mult * a[ft]) * (1.0 - math.exp(-1.0 * b[ft] * isz)) ** c[ft]
+    check = 1.0 - (rsf_d1 / (mult * a[ft])) ** (1.0 / c[ft]) if rsf_d1 > 0.0 else 1.0
+    check = max(check, slopelimit_isi)
+    isf_d1 = (1.0 / (-1.0 * b[ft])) * math.log(check)
+
+    isf = pc / 100.0 * isf_c2 + (100.0 - pc) / 100.0 * isf_d1
+    return isf
+
+def l2bAlexander1985(ws):
+    """
+    Calcula la relación longitud-ancho de la zona quemada según la ecuación propuesta por Alexander (1985).
+
+    Args:
+        ws (float): Velocidad del viento sostenido.
+
+    Returns:
+        float: Relación longitud-ancho de la zona quemada.
+    """
+    lb = 0.5 + 0.5 * math.exp(0.05039 * ws)
+    return lb
+
+def l2bAnderson1983(typefire, ws):
+    """
+    Calcula la relación longitud-ancho de la zona quemada según la ecuación propuesta por Anderson (1983).
+
+    Args:
+        typefire (str): Tipo de incendio.
+        ws (float): Velocidad del viento sostenido.
+
+    Returns:
+        float: Relación longitud-ancho de la zona quemada.
+    """
+    if typefire == "dense-forest-stand":
+        lb = 0.936 * math.exp(0.01240 * ws) + 0.461 * math.exp(-0.00748 * ws) - 0.397
+    elif typefire == "open-forest-stand":
+        lb = 0.936 * math.exp(0.01859 * ws) + 0.461 * math.exp(-0.0112 * ws) - 0.397
+    elif typefire == "grass-slash":
+        lb = 0.936 * math.exp(0.02479 * ws) + 0.461 * math.exp(-0.0149 * ws) - 0.397
+    elif typefire == "heavy-slash":
+        lb = 0.936 * math.exp(0.03099 * ws) + 0.461 * math.exp(-0.0187 * ws) - 0.397
+    else:  # crown-fire forest stand
+        lb = 0.936 * math.exp(0.071278 * ws) + 0.461 * math.exp(-0.043 * ws) - 0.397
+    return lb
+
+def l2bFBP(ft, ws):
+    """
+    Calcula la relación longitud-ancho de la zona quemada según la ecuación propuesta por el modelo FBP.
+
+    Args:
+        ft (str): Tipo de combustible.
+        ws (float): Velocidad del viento sostenido.
+
+    Returns:
+        float: Relación longitud-ancho de la zona quemada.
+    """
+    if ft in ["O1a", "O1b"]:
+        if ws < 1.0:
+            lb = 1.0
+        else:
+            lb = 1.1 * ws ** 0.464
+    else:
+        lb = 1.0 + 8.729 * (1.0 - math.exp(-0.030 * ws)) ** 2.155
+    return lb
+
+def length2breadth(ftype, ws):
+    """
+    Calcula la relación longitud-ancho de la zona quemada según el tipo de combustible.
+
+    Args:
+        ftype (str): Tipo de combustible.
+        ws (float): Velocidad del viento sostenido.
+
+    Returns:
+        float: Relación longitud-ancho de la zona quemada.
+    """
+    if ftype in ["O1a", "O1b"]:  # grass fuel
+        if ws < 1.0:
+            lb = 1.0
+        else:
+            lb = 1.1 + ws ** 0.464  # Eq.80
+    else:
+        lb = 1.0 + 8.729 * (1.0 - math.exp(-0.030 * ws)) ** 2.155
+    return lb
+
+def perimeter(hdist, bdist, lb):
+    """
+    Calcula el perímetro de la zona quemada.
+
+    Args:
+        hdist (float): Distancia de propagación del fuego en la corona.
+        bdist (float): Distancia de propagación del fuego en superficie de referencia.
+        lb (float): Relación longitud-ancho de la zona quemada.
+
+    Returns:
+        float: Perímetro de la zona quemada.
+    """
+    pi = 3.1416
+    aux = pi * (1.0 + 1.0 / lb) * (1.0 + ((lb - 1.0) / (2.0 * (lb + 1.0))) ** 2.0)
+    p = (hdist + bdist) / 2 * aux
+    return p
+
+def rate_of_spread(ftype, wdfh, a, b, c, ps, saz, FuelConst2, bui0, q):
+    """
+    Calcula la tasa de propagación del fuego.
+
+    Args:
+        ftype (str): Tipo de combustible.
+        wdfh (dict): Diccionario que contiene variables meteorológicas y de combustible.
+        a (dict): Parámetros de la ecuación de propagación del fuego.
+        b (dict): Parámetros de la ecuación de propagación del fuego.
+        c (dict): Parámetros de la ecuación de propagación del fuego.
+        ps (float): Pendiente del terreno.
+        saz (float): Ángulo de la pendiente del terreno.
+        FuelConst2 (dict): Constantes del combustible.
+        bui0 (dict): Índice de acumulación basal (BUI) inicial.
+        q (dict): Coeficientes de propagación del fuego.
+
+    Returns:
+        float: Tasa de propagación del fuego.
+    """
+    ffmc = wdfh['FFMC']
+    ws = wdfh['WS']
+    waz = wdfh['WD'] + 180.0
+    waz = waz - 360.0 if waz >= 360.0 else waz
+    isz = 0.208 * ffmc_effect(ffmc)
+
+    if ps > 0:
+        wsv, raz = slope_effect(ftype, wdfh, a, b, c, saz, ps, FuelConst2, isz, ffmc, waz)
+    else:
+        wsv = ws
+        raz = waz
+
+    fw = math.exp(0.05039 * wsv) if wsv < 40.0 else 12.0 * (1.0 - math.exp(-0.0818 * (wsv - 28)))
+    isi = isz * fw
+
+    rsi = ros_base(ftype, isi, wdfh['BUI'], a, b, c, FuelConst2)
+    rss = rsi * bui_effect(wdfh['BUI'], bui0[ftype], q[ftype])
+    return rss, wsv, raz, isi
+
+
+def ros_base(ftype, isi, bui, a, b, c, FuelConst2):
+    """
+    Calcula la tasa de propagación base del fuego (RSI) para un tipo de combustible dado.
+
+    Args:
+        ftype (str): Tipo de combustible.
+        isi (float): Índice de Propagación Inicial (ISI).
+        bui (float): Índice de Acumulación Basal (BUI).
+        a (dict): Parámetros de la ecuación de propagación del fuego.
+        b (dict): Parámetros de la ecuación de propagación del fuego.
+        c (dict): Parámetros de la ecuación de propagación del fuego.
+        FuelConst2 (dict): Constantes del combustible.
+
+    Returns:
+        float: Tasa de propagación base del fuego (RSI).
+    """
+    pdf = FuelConst2['pdf']
+    cur = FuelConst2['cur']
+    pc = FuelConst2['pc']
+
+    if ftype in ["O1a", "O1b"]:
+        if cur >= 58.8:
+            mu1 = 0.176 + 0.02 * (cur - 58.8)
+        else:
+            mu1 = 0.005 * (math.exp(0.061 * cur) - 1.0)
+        mu1 = max(mu1, 0.001)
+        rsi = mu1 * (a[ftype] * (1.0 - math.exp(-b[ftype] * isi)) ** c[ftype])
+    elif ftype in ["M1", "M2"]:
+        mu1 = pc / 100.0
+        mu2_1 = (100 - pc) / 100.0
+        mu2_2 = 2 * (100 - pc) / 100.0
+        ros_C1 = a["C2"] * (1.0 - math.exp(-b["C2"] * isi)) ** c["C2"]
+        ros_D1 = a["D1"] * (1.0 - math.exp(-b["D1"] * isi)) ** c["D1"]
+        rsi = mu1 * ros_C1 + mu2_1 * ros_D1 if ftype == "M1" else mu1 * ros_C1 + mu2_2 * ros_D1
+    elif ftype in ["M3", "M4"]:
+        if ftype == "M3":
+            a3 = 170 * math.exp(-35.0 / pdf)
+            b3 = 0.082 * math.exp(-36.0 / pdf)
+            c3 = 1.698 - 0.00303 * pdf
+            rsi = a3 * (1.0 - math.exp(-b3 * isi)) ** c3
+        else:
+            a4 = 140 * math.exp(-35.5 / pdf)
+            b4 = 0.0404
+            c4 = 3.03 * math.exp(-0.00714 * pdf)
+            rsi = a4 * (1.0 - math.exp(-b4 * isi)) ** c4
+    elif ftype == "D2":
+        rsi = a[ftype] * (1.0 - math.exp(-b[ftype] * isi)) ** c[ftype] if bui >= 80 else 0.0
+    else:
+        rsi = a[ftype] * (1.0 - math.exp(-b[ftype] * isi)) ** c[ftype]
+
+    return rsi
+
+def slope_effect(ft, wdfh, a, b, c, saz, ps, FuelConst2, isi, ff, waz):
+    """
+    Calcula el efecto de la pendiente en la tasa de propagación del fuego.
+
+    Args:
+        ft (str): Tipo de combustible.
+        wdfh (dict): Diccionario que contiene variables meteorológicas y de combustible.
+        a (dict): Parámetros de la ecuación de propagación del fuego.
+        b (dict): Parámetros de la ecuación de propagación del fuego.
+        c (dict): Parámetros de la ecuación de propagación del fuego.
+        saz (float): Ángulo de la pendiente del terreno.
+        ps (float): Pendiente del terreno.
+        FuelConst2 (dict): Constantes del combustible.
+        isi (float): Índice de Propagación Inicial (ISI).
+        ff (float): Factor de propagación del fuego.
+        waz (float): Ángulo del viento.
+
+    Returns:
+        tuple: Tasa de propagación del fuego ajustada a la pendiente y ángulo del viento, y el ángulo de propagación ajustado.
+    """
+    pi = 3.1415
+    slopelimit_isi = 0.01
+    pc = FuelConst2["pc"]
+    pdf = FuelConst2["pdf"]
+    ps = min(ps, 70.0)
+    sf = min(math.exp(3.533 * (ps / 100.0) ** 1.2), 10.0)
+    ws = wdfh['WS']  # Acceso a la columna WS
+
+    if saz >= 360.0:
+        saz -= 360.0
+
+    if ft in ["M1", "M2"]:
+        isf = ISF_mixedwood(ft, a, b, c, isi, pc, sf)
+    elif ft in ["M3", "M4"]:
+        isf = ISF_deadfir(ft, a, b, c, isi, pdf, sf)
+    else:
+        rsz = ros_base(ft, isi, wdfh, a, b, c, FuelConst2)
+        rsf = rsz * sf
+        check = 1.0 - (rsf / a[ft]) ** (1.0 / c[ft]) if rsf > 0.0 else 1.0
+        check = max(check, slopelimit_isi)
+        isf = -1.0 / b[ft] * math.log(check)
+
+    isf = isi if isf == 0.0 else isf
+
+    wse1 = math.log(isf / (0.208 * ff)) / 0.05039
+
+    if wse1 <= 40.0:
+        wse = wse1
+    else:
+        isf = min(isf, 0.999 * 2.496 * ff)
+        wse2 = 28.0 - math.log(1.0 - isf / (2.496 * ff)) / 0.0818
+        wse = wse2
+
+    wrad = waz / 180.0 * pi
+    wsx = ws * math.sin(wrad)
+    wsy = ws * math.cos(wrad)
+    srad = saz / 180.0 * pi
+    wsex = wse * math.sin(srad)
+    wsey = wse * math.cos(srad)
+    wsvx = wsx + wsex
+    wsvy = wsy + wsey
+    wsv = math.sqrt(wsvx ** 2 + wsvy ** 2)
+    raz = math.acos(wsvy / wsv) / pi * 180.0
+    raz = 360 - raz if wsvx < 0 else raz
+
+    return wsv, raz
+def spread_distance(ros, time, a):
+    """
+    Calcula la distancia de propagación del fuego y la tasa de propagación a lo largo del tiempo.
+
+    Args:
+        ros (float): Tasa de propagación del fuego inicial.
+        time (float): Tiempo transcurrido.
+        a (float): Parámetro de la ecuación de propagación del fuego.
+
+    Returns:
+        tuple: Distancia de propagación del fuego y la tasa de propagación actualizada.
+    """
+    rost = ros * (1.0 - math.exp(-a * time))
+    sd = ros * (time + (math.exp(-a * time) / a) - 1.0 / a)
+    return sd, rost
+
+
+def surf_fuel_consump(ft, wdfh, FuelConst2):
+    """
+    Estima el consumo de combustible superficial basado en el tipo de combustible y condiciones meteorológicas.
+
+    Args:
+        ft (str): Tipo de combustible.
+        wdfh (dict): Diccionario que contiene variables meteorológicas y de combustible.
+        FuelConst2 (dict): Constantes del combustible.
+
+    Returns:
+        float: Consumo de combustible superficial estimado.
+    """
+    bui = wdfh['BUI']  # Acceso a la columna BUI
+    ffmc = wdfh['FFMC']  # Acceso a la columna FFMC
+
+    gfl = FuelConst2["gfl"]
+    pc = FuelConst2["pc"]
+
+    if ft == "C1":
+        sfc = 0.75 + 0.75 * math.sqrt(1 - math.exp(-0.23 * (ffmc - 84))) if ffmc > 84 else 0.75 - 0.75 * math.sqrt(1 - math.exp(0.23 * (ffmc - 84)))
+        sfc = max(sfc, 0)
+    elif ft in ["C2", "M3", "M4"]:
+        sfc = 5.0 * (1.0 - math.exp(-0.0115 * bui))
+    elif ft in ["C3", "C4"]:
+        sfc = 5.0 * (1.0 - math.exp(-0.0164 * bui)) ** 2.24
+    elif ft in ["C5", "C6"]:
+        sfc = 5.0 * (1.0 - math.exp(-0.0149 * bui)) ** 2.48
+    elif ft == "C7":
+        ffc = 2.0 * (1.0 - math.exp(-0.104 * (ffmc - 70.0)))
+        wfc = 1.5 * (1.0 - math.exp(-0.0201 * bui))
+        sfc = max(ffc, 0) + wfc
+    elif ft in ["O1a", "O1b"]:
+        sfc = gfl
+    elif ft in ["M1", "M2"]:
+        sfc_c2 = 5.0 * (1.0 - math.exp(-0.0115 * bui))
+        sfc_d1 = 1.5 * (1.0 - math.exp(-0.0183 * bui))
+        sfc = pc / 100.0 * sfc_c2 + (100.0 - pc) / 100.0 * sfc_d1
+    elif ft == "S1":
+        ffc = 4.0 * (1.0 - math.exp(-0.025 * bui))
+        wfc = 4.0 * (1.0 - math.exp(-0.034 * bui))
+        sfc = ffc + wfc
+    elif ft == "S2":
+        ffc = 10.0 * (1.0 - math.exp(-0.013 * bui))
+        wfc = 6.0 * (1.0 - math.exp(-0.060 * bui))
+        sfc = ffc + wfc
+    elif ft == "S3":
+        ffc = 12.0 * (1.0 - math.exp(-0.0166 * bui))
+        wfc = 20.0 * (1.0 - math.exp(-0.0210 * bui))
+        sfc = ffc + wfc
+    elif ft == "D1":
+        sfc = 1.5 * (1.0 - math.exp(-0.0183 * bui))
+    elif ft == "D2":
+        sfc = 1.5 * (1.0 - math.exp(-0.0183 * bui)) if bui >= 80 else 0
+    else:
+        sfc = 0  # Default case for unrecognized fuel type
+
+    return sfc
+
+# 18 Fuel Types
+FBPfuelTypes = ['C1', 'C2', 'C3', 'C4', 'C5', 'C6', 'C7', 'M1', 'M2', 'M3',
+                'M4', 'D1', 'D2', 'S1', 'S2', 'S3', 'O1a', 'O1b']
+
+# Crown fuel load [Kg/m2]
+CFLvalues = [0.75, 0.8, 1.15, 1.2, 1.2, 1.8, 0.5, 0.8,
+             0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
+
+# Canopy base Height [m]
+CBHvalues = [2.0, 3.0, 8.0, 4.0, 18.0, 7.0, 10.0, 6.0, 6.0, 6.0,
+             6.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
+
+# Parameters for Basic rate of spread (ISI-equation)
+a_values = [90, 110, 110, 110, 30, 30, 45, 110, 110,
+            120, 100, 30, 6, 75, 40, 55, 190, 250]
+
+b_values = [0.0649, 0.0282, 0.0444, 0.0293, 0.0697, 0.08, 0.0305, 0.0282,
+            0.0282, 0.0572, 0.0404, 0.0232, 0.0232, 0.0297, 0.0438, 0.0829, 0.031, 0.031]
+
+c_values = [4.5, 1.5, 3.0, 1.5, 4.0, 3.0, 2.0, 1.5, 1.5, 1.4, 1.48,
+            1.6, 1.6, 1.3, 1.7, 3.2, 1.4, 1.7]
+
+# Parameters for Buildup Effect (BE)
+qvalues = [0.9, 0.7, 0.75, 0.8, 0.8, 0.8, 0.85, 0.8, 0.8, 0.8, 0.8,
+           0.75, 0.9, 0.75, 0.75, 0.75, 1.0, 1.0]
+
+bui0values = [72, 64, 62, 66, 56, 62, 106, 50, 50, 50, 50,
+              32, 32, 38, 63, 31, 1, 1]
+
+# Building dictionaries for parameters
+CFL = dict(zip(FBPfuelTypes, CFLvalues))
+CBH = dict(zip(FBPfuelTypes, CBHvalues))
+q = dict(zip(FBPfuelTypes, qvalues))
+bui0 = dict(zip(FBPfuelTypes, bui0values))
+a = dict(zip(FBPfuelTypes, a_values))
+b = dict(zip(FBPfuelTypes, b_values))
+c = dict(zip(FBPfuelTypes, c_values))
+
+FuelConst2 = {
+    "pc": 50,  # Percent Conifer for M1/M2 [percent]
+    "pdf": 35, # Percent Dead Fir for M3/M4 [percent]
+    "gfl": 0.35, # Grass Fuel Load [kg/m^2]
+    "cur": 60  # Percent Cured for O1a/O1b [percent]
+}
+
+"""
+Ecuaciones obtenidas desde "Development and Structure of the Canadian Forest Fire Behavior Prediction System ST-X-3"
+
+"""
\ No newline at end of file
diff --git a/usage_samples/firebehavior_sample.ipynb b/usage_samples/firebehavior_sample.ipynb
new file mode 100644
index 0000000..8a967ee
--- /dev/null
+++ b/usage_samples/firebehavior_sample.ipynb
@@ -0,0 +1,6952 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Análisis de Unidades \n",
+    "\n",
+    "Función `acceleration`\n",
+    "\n",
+    "- Entradas: \n",
+    "  - `ftype`: sin unidad.\n",
+    "  - `cfb`: adimensional, representando una proporción.\n",
+    "- Salida: \n",
+    "  - Aceleración como coeficiente adimensional, correcto para modificar tasas de propagación en el modelo.\n",
+    "\n",
+    "Función `area`\n",
+    "\n",
+    "- Entradas: \n",
+    "  - `dt` y `df`: en metros ($m$), adecuado para dimensiones físicas.\n",
+    "- Salida: \n",
+    "  - Área en hectáreas ($ha$), correcto para la escala de análisis de incendios.\n",
+    "\n",
+    "Función `back_fire_behaviour` y Similar\n",
+    "\n",
+    "- Entradas:\n",
+    "  - Incluyen unidades de $kg/m^2$, $m/min$, $\\%$, y adimensionales, coherentes con sus propósitos físicos o de modelado.\n",
+    "- Salida:\n",
+    "  - Tasa de propagación en $m/min$, intensidad en $kW/m$, consumo en $kg/m^2$, y clasificaciones sin unidad, todas adecuadas.\n",
+    "\n",
+    "Función `ffmc_effect`, `final_ros`, `fire_intensity`\n",
+    "\n",
+    "- Entradas:\n",
+    "  - Mezclan índices adimensionales, porcentajes, y tasas en $m/min$, que son estándar para su uso.\n",
+    "- Salidas:\n",
+    "  - Adimensionales o en unidades físicas estandarizadas ($kW/m$, $m/min$), correctas para sus respectivos cálculos.\n",
+    "\n",
+    "Función `flank_fire_behaviour`, `flank_spread_distance`, `flankfire_ros`\n",
+    "\n",
+    "- Entradas y Salidas:\n",
+    "  - Mantienen coherencia en unidades de distancia ($m$), tasa ($m/min$), consumo ($kg/m^2$), y relaciones adimensionales, adecuadas para la modelación.\n",
+    "\n",
+    "Función `foliar_moisture`, `get_fueltype_number`\n",
+    "\n",
+    "- Entradas:\n",
+    "  - Latitud, longitud en grados, elevación en $m$, y día juliano sin unidad, correcto para cálculos ambientales.\n",
+    "- Salidas:\n",
+    "  - Humedad en $\\%$, y clasificaciones de combustible sin unidad, lo cual es estándar.\n",
+    "\n",
+    "## Observaciones Generales\n",
+    "\n",
+    "Las unidades a través de las funciones analizadas están bien definidas y son coherentes con las expectativas para el modelado del comportamiento de incendios. Las conversiones de unidades (como $m^2$ a $ha$) son adecuadas para el contexto de los incendios forestales, facilitando la interpretación de los resultados. Las tasas de propagación, intensidades del fuego, y consumos de combustible usan unidades físicas estándar ($m/min$, $kW/m$, $kg/m^2$), mientras que los índices y clasificaciones son adimensionales, reflejando su naturaleza de coeficientes o factores de corrección dentro del modelo.\n",
+    "\n",
+    "En conclusión, las unidades en el código proporcionado son coherentes y aplicables al análisis de incendios forestales, asegurando que los cálculos sean relevantes y útiles para la planificación y gestión de incendios.\n",
+    "\n",
+    "## Algunas funciones clave:\n",
+    "\n",
+    "### Función `acceleration`\n",
+    "\n",
+    "$ \\text{Aceleración} = \\begin{cases} 0.115 - 18.8 \\cdot cfb^{2.5} \\cdot e^{-8.0 \\cdot cfb}, & \\text{para combustibles cerrados} \\\\ 0.115, & \\text{para combustibles abiertos} \\end{cases} $\n",
+    "\n",
+    "- **Unidades**: Sin unidades, dado que representa un coeficiente adimensional.\n",
+    "\n",
+    "### Función `area`\n",
+    "\n",
+    "$ \\text{Área} = \\frac{\\left( \\frac{dt}{2} \\right) \\cdot df \\cdot \\pi}{10000} $\n",
+    "\n",
+    "- **Entradas**: `dt` y `df` en metros (\\(m\\)).\n",
+    "- **Salida**: Área en hectáreas (\\(ha\\)), adecuada tras la conversión de \\(m^2\\) a \\(ha\\).\n",
+    "\n",
+    "### Función `fire_intensity`\n",
+    "\n",
+    "$ \\text{FI} = 300 \\cdot fc \\cdot ros $\n",
+    "\n",
+    "- **Entradas**: \n",
+    "  - `fc` en \\(kg/m^2\\),\n",
+    "  - `ros` en \\(m/min\\).\n",
+    "- **Salida**: Intensidad del fuego en \\(kW/m\\).\n",
+    "\n",
+    "### Función `ffmc_effect`\n",
+    "\n",
+    "$ \\text{ff} = 91.9 \\cdot \\exp(-0.1386 \\cdot mc) \\cdot \\left(1 + \\frac{mc^{5.31}}{49300000}\\right) $\n",
+    "\n",
+    "- **Entrada**: `ffmc` como índice adimensional.\n",
+    "- **Salida**: Factor de corrección adimensional basado en `ffmc`.\n",
+    "\n",
+    "### Función `crit_surf_intensity`\n",
+    "\n",
+    "$ \\text{CSI} = 0.001 \\cdot cbh^{1.5} \\cdot (460 + 25.9 \\cdot fmc)^{1.5} $\n",
+    "\n",
+    "- **Entradas**: \n",
+    "  - `cbh` en metros (\\(m\\)),\n",
+    "  - `fmc` en porcentaje (\\(\\%\\)).\n",
+    "- **Salida**: Intensidad crítica de la superficie en \\(kW/m\\).\n",
+    "\n",
+    "### Función `perimeter`\n",
+    "\n",
+    "$ \\text{Perímetro} = \\frac{(hdist + bdist)}{2} \\cdot \\pi \\cdot \\left(1.0 + \\frac{1.0}{lb}\\right) \\cdot \\left(1.0 + \\left(\\frac{lb - 1.0}{2.0 \\cdot (lb + 1.0)}\\right)^2\\right) $\n",
+    "\n",
+    "- **Entradas**: `hdist` y `bdist` en metros (\\(m\\)), `lb` adimensional.\n",
+    "- **Salida**: Perímetro en metros (\\(m\\)).\n",
+    "\n",
+    "### Función `length2breadth`\n",
+    "\n",
+    "$ lb = \\text{función}\\left( \\text{tipo de combustible, velocidad del viento} \\right) $\n",
+    "\n",
+    "- **Entrada**: `ws` en \\(km/h\\).\n",
+    "- **Salida**: Relación longitud/ancho adimensional.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from firebehavior import *\n",
+    "import pandas as pd\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "a: Dimension = 18x1\n",
+      "b: Dimension = 18x1\n",
+      "c: Dimension = 18x1\n",
+      "q: Dimension = 18x1\n",
+      "bui0: Dimension = 18x1\n",
+      "CFL: Dimension = 18x1\n",
+      "CBH: Dimension = 18x1\n",
+      "FuelConst2: Dimension = 4x1\n",
+      "Weather: Dimension = 8x13\n",
+      "wdfh: Dimension = 1x13\n",
+      "sfc: Dimension = Single value\n",
+      "ros: Dimension = Single value\n",
+      "wsv: Dimension = Single value\n",
+      "raz: Dimension = Single value\n",
+      "isi: Dimension = Single value\n",
+      "sfi: Dimension = Single value\n",
+      "fmc: Dimension = Single value\n",
+      "csi: Dimension = Single value\n",
+      "rso: Dimension = Single value\n",
+      "cfb: Dimension = Single value\n",
+      "cfc: Dimension = Single value\n",
+      "tfc: Dimension = Single value\n",
+      "fi: Dimension = Single value\n",
+      "ff: Dimension = Single value\n",
+      "lb: Dimension = Single value\n",
+      "bisi: Dimension = Single value\n",
+      "brss: Dimension = Single value\n",
+      "fros: Dimension = Single value\n",
+      "ffi: Dimension = Single value\n",
+      "ffc: Dimension = Single value\n",
+      "flank_firetype: Dimension = Single value\n",
+      "elapsetime: Dimension = Single value\n",
+      "accn: Dimension = Single value\n",
+      "hdist: Dimension = Single value\n",
+      "hrost: Dimension = Single value\n",
+      "bdist: Dimension = Single value\n",
+      "brost: Dimension = Single value\n",
+      "fdist: Dimension = Single value\n",
+      "rost: Dimension = Single value\n",
+      "lbt: Dimension = Single value\n",
+      "areaelipse: Dimension = Single value\n",
+      "perelipse: Dimension = Single value\n"
+     ]
+    }
+   ],
+   "source": [
+    "## Análisis Dimensional\n",
+    "\n",
+    "# Variables tipo dict con 18 elementos (simuladas)\n",
+    "a = {i: None for i in range(18)}  # a, b, c, q, bui0, CFL, CBH tienen la misma estructura\n",
+    "# Simulación de un DataFrame con 8 filas y 13 columnas (Weather)\n",
+    "Weather = pd.DataFrame(np.random.rand(8, 13))\n",
+    "# Seleccionando una fila del DataFrame Weather como ejemplo de wdfh\n",
+    "wdfh = Weather.iloc[0]\n",
+    "# Variables de ejemplo para valores simples (float)\n",
+    "sfc, ros, wsv, raz, isi, sfi, fmc, csi, rso, cfb, cfc, tfc, fi, ff, lb, bisi, brss, fros, ffi, ffc, elapsetime, accn, hdist, hrost, bdist, brost, fdist, rost, lbt, areaelipse, perelipse = (0.5 for _ in range(31))\n",
+    "# Variable de ejemplo para 'flank_firetype' como string\n",
+    "flank_firetype = \"surface\"\n",
+    "\n",
+    "# Función ajustada para imprimir las dimensiones de las variables\n",
+    "def print_variable_dimensions():\n",
+    "    variables = {\n",
+    "        'a': a, 'b': b, 'c': c, 'q': a, 'bui0': a, 'CFL': a, 'CBH': a, 'FuelConst2': {'pc': 50, 'pdf': 35, 'gfl': 0.35, 'cur': 60},\n",
+    "        'Weather': Weather, 'wdfh': wdfh, 'sfc': sfc, 'ros': ros, 'wsv': wsv, 'raz': raz, 'isi': isi, 'sfi': sfi, 'fmc': fmc,\n",
+    "        'csi': csi, 'rso': rso, 'cfb': cfb, 'cfc': cfc, 'tfc': tfc, 'fi': fi, 'ff': ff, 'lb': lb, 'bisi': bisi, 'brss': brss,\n",
+    "        'fros': fros, 'ffi': ffi, 'ffc': ffc, 'flank_firetype': flank_firetype, 'elapsetime': elapsetime, 'accn': accn,\n",
+    "        'hdist': hdist, 'hrost': hrost, 'bdist': bdist, 'brost': brost, 'fdist': fdist, 'rost': rost, 'lbt': lbt,\n",
+    "        'areaelipse': areaelipse, 'perelipse': perelipse\n",
+    "    }\n",
+    "\n",
+    "    for var_name, var in variables.items():\n",
+    "        if isinstance(var, dict):\n",
+    "            # Para los diccionarios, simplemente mostramos la cantidad de claves y asumimos \"1\" como la segunda dimensión.\n",
+    "            dim = f\"{len(var)}x1\"\n",
+    "        elif isinstance(var, pd.DataFrame):\n",
+    "            dim = f\"{var.shape[0]}x{var.shape[1]}\"\n",
+    "        elif isinstance(var, pd.Series):\n",
+    "            # Para pd.Series, mostramos \"1xN\" si se trata de una fila de DataFrame, de lo contrario \"Nx1\".\n",
+    "            dim = f\"1x{var.size}\" if var.name is not None else f\"{var.size}x1\"\n",
+    "        elif isinstance(var, (np.ndarray)):\n",
+    "            # Para arreglos de Numpy, mostramos sus dimensiones directamente.\n",
+    "            dim = 'x'.join(map(str, var.shape))\n",
+    "        elif isinstance(var, (int, float, str)):\n",
+    "            # Para tipos simples, indicamos que es un valor único.\n",
+    "            dim = \"Single value\"\n",
+    "        else:\n",
+    "            # Para cualquier otro tipo, indicamos que el tipo es desconocido.\n",
+    "            dim = \"Unknown type\"\n",
+    "        print(f\"{var_name}: Dimension = {dim}\")\n",
+    "\n",
+    "print_variable_dimensions()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "###  Inputs\n",
+    "\n",
+    "\n",
+    "\n",
+    "# La ruta al Weather debe ser correcta\n",
+    "ruta_archivo = './Weather.csv'\n",
+    "\n",
+    "# Cargar el archivo\n",
+    "Weather = pd.read_csv(ruta_archivo)\n",
+    "\n",
+    "i = 0  # fila i del archivo Weather\n",
+    "wdfh = Weather.iloc[i]  # seleccionando una fila en formato DataFrame\n",
+    "\n",
+    "ftype = \"C1\"  # Ejemplo de tipo de combustible\n",
+    "\n",
+    "# Ejemplo de cálculo de jd, lat, long, etc. (ajustar según el formato real de tus datos)\n",
+    "jd = (pd.to_datetime(wdfh['datetime']) - pd.to_datetime(\"01-Jan-2001\")).days\n",
+    "lat = 51.621244  # Ejemplo de latitud\n",
+    "long = -115.608378  # Ejemplo de longitud\n",
+    "elev = 2138.0  # Ejemplo de elevación geográfica\n",
+    "ps = 0  # Porcentaje de pendiente\n",
+    "saz = 0  # Azimut de la pendiente (dirección cuesta arriba)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Primary Outputs:\n",
+      "HROS_t = 6.083 [m/min]\t\tSFC = 1.399 [Kg/m2]\n",
+      "HROS_eq = 6.089 [m/min]\t\tCFC = 0.493 [Kg/m2]\n",
+      "HFI = 3455.373 [kW/m]\t\tTFC = 1.892 [Kg/m2]\n",
+      "CFB = 65.702 [Percentage]\tFire description:  crown-fire\n",
+      "\n",
+      "\n",
+      "Secondary Outputs:\n",
+      "RSO = 1.436 [m/min]\tCSI = 602.813 [kW/m]\tDH = 312.445 [m]\tLB = 2.695 [m]\n",
+      "FROS = 1.130 [m/min]\tFFI = 474.228 [kW/m]\tDF = 58.023 [m]\t\tArea = 2.849 [ha]\n",
+      "BROS = 0.002 [m/min]\tBFI = 0.811 [kW/m]\tDB = 0.099 [m]\t\tPerimeter = 708.525 [m]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Cálculos\n",
+    "\n",
+    "# Consumo de combustible superficial\n",
+    "sfc = surf_fuel_consump(ftype, wdfh, FuelConst2)  # en [Kg/m2]\n",
+    "\n",
+    "# Tasa de propagación de la cabeza del incendio (HROS = ROS) (incluye efecto de pendiente y acumulación)\n",
+    "ros, wsv, raz, isi = rate_of_spread(ftype, wdfh, a, b, c, ps, saz, FuelConst2, bui0, q)  # [m/min]\n",
+    "\n",
+    "# Intensidad del fuego superficial\n",
+    "sfi = fire_intensity(sfc, ros)  # en [kW/m]\n",
+    "\n",
+    "# Contenido de humedad foliar\n",
+    "fmc = foliar_moisture(lat, long, elev, jd)  # en [%]\n",
+    "\n",
+    "# Intensidad crítica de la superficie\n",
+    "csi = crit_surf_intensity(CBH[ftype], fmc)\n",
+    "\n",
+    "# Determinar el tipo de fuego y realizar cálculos adicionales\n",
+    "if (\"C1\" <= ftype <= \"C7\") or (\"M1\" <= ftype <= \"M4\"):  # CBH > 0\n",
+    "    # Tipo de fuego = corona\n",
+    "    if sfi > csi:\n",
+    "        rso = max(csi / (300 * sfc), 0.0)  # Tasa crítica de propagación\n",
+    "        cfb = max(1 - math.exp(-0.23 * (ros - rso)), 0.0)  # Fracción de la corona quemada\n",
+    "        cfc = CFL[ftype] * cfb  # Consumo de combustible de la corona\n",
+    "        if ftype in [\"M1\", \"M2\"]:\n",
+    "            cfc *= FuelConst2[\"pc\"] / 100.0  # actualización\n",
+    "        elif ftype in [\"M3\", \"M4\"]:\n",
+    "            cfc *= FuelConst2[\"pdf\"] / 100.0  # actualización\n",
+    "        tfc = sfc + cfc\n",
+    "        ros = final_ros(ftype, fmc, isi, cfb, ros)\n",
+    "        fi = fire_intensity(tfc, ros)  # Intensidad total del fuego\n",
+    "        firetype = \"crown\"\n",
+    "    else:\n",
+    "        cfb = 0\n",
+    "        cfc = 0\n",
+    "        tfc = sfc\n",
+    "        fi = sfi\n",
+    "else:  # CBH == 0.0\n",
+    "    cfb = 0\n",
+    "    cfc = 0\n",
+    "    tfc = sfc\n",
+    "    fi = sfi\n",
+    "\n",
+    "# Efecto FFMC\n",
+    "ffmc = wdfh[\"FFMC\"]\n",
+    "ff = ffmc_effect(ffmc)\n",
+    "\n",
+    "# Relación longitud/ancho\n",
+    "lb = length2breadth(ftype, wsv)\n",
+    "\n",
+    "# ISI de retroceso\n",
+    "bisi = backfire_isi(wsv, ff)\n",
+    "\n",
+    "# Tasa de propagación de retroceso\n",
+    "brss = backfire_ros(ftype, bisi, wdfh, a, b, c, FuelConst2, bui0, q)\n",
+    "\n",
+    "if (\"C1\" <= ftype <= \"C7\") or (\"M1\" <= ftype <= \"M4\"):\n",
+    "    bros, bfi, bfc, back_firetype = back_fire_behaviour(ftype, sfc, brss, csi, rso, fmc, bisi, CFL)\n",
+    "\n",
+    "# Tasa de propagación lateral\n",
+    "fros = flankfire_ros(ros, bros, lb)\n",
+    "\n",
+    "# Comportamiento del fuego lateral\n",
+    "ffi, ffc, flank_firetype = flank_fire_behaviour(ftype, sfc, fros, csi, rso, CFL)\n",
+    "\n",
+    "# Tiempo transcurrido\n",
+    "elapsetime = 60  # [min]\n",
+    "\n",
+    "# Aceleración\n",
+    "accn = acceleration(ftype, cfb)\n",
+    "\n",
+    "# Distancia y tasa de propagación de la cabeza del incendio\n",
+    "hdist, hrost = spread_distance(ros, elapsetime, accn)\n",
+    "\n",
+    "# Distancia y tasa de propagación de retroceso\n",
+    "bdist, brost = spread_distance(bros, elapsetime, accn)\n",
+    "\n",
+    "# Distancia, tasa y longitud/ancho de propagación lateral\n",
+    "fdist, rost, lbt = flank_spread_distance(hrost, brost, hdist, bdist, lb, accn, elapsetime)\n",
+    "\n",
+    "# Área del Elipse\n",
+    "areaelipse = area(hdist + bdist, fdist)\n",
+    "\n",
+    "# Perímetro del Elipse\n",
+    "perelipse = perimeter(hdist, bdist, lb)\n",
+    "\n",
+    "# Salidas Primarias\n",
+    "print('Primary Outputs:')\n",
+    "print(f'HROS_t = {hrost:.3f} [m/min]\\t\\tSFC = {sfc:.3f} [Kg/m2]')\n",
+    "print(f'HROS_eq = {ros:.3f} [m/min]\\t\\tCFC = {cfc:.3f} [Kg/m2]')\n",
+    "print(f'HFI = {fi:.3f} [kW/m]\\t\\tTFC = {tfc:.3f} [Kg/m2]')\n",
+    "print(f'CFB = {cfb * 100:.3f} [Percentage]\\tFire description:  {firetype}-fire\\n\\n')\n",
+    "\n",
+    "# Salidas Secundarias\n",
+    "print('Secondary Outputs:')\n",
+    "print(f'RSO = {rso:.3f} [m/min]\\tCSI = {csi:.3f} [kW/m]\\tDH = {hdist:.3f} [m]\\tLB = {lb:.3f} [m]')\n",
+    "print(f'FROS = {fros:.3f} [m/min]\\tFFI = {ffi:.3f} [kW/m]\\tDF = {fdist:.3f} [m]\\t\\tArea = {areaelipse:.3f} [ha]')\n",
+    "print(f'BROS = {bros:.3f} [m/min]\\tBFI = {bfi:.3f} [kW/m]\\tDB = {bdist:.3f} [m]\\t\\tPerimeter = {perelipse:.3f} [m]')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<link rel=\"stylesheet\"\n",
+       "href=\"https://maxcdn.bootstrapcdn.com/font-awesome/4.4.0/css/font-awesome.min.css\">\n",
+       "<script language=\"javascript\">\n",
+       "  function isInternetExplorer() {\n",
+       "    ua = navigator.userAgent;\n",
+       "    /* MSIE used to detect old browsers and Trident used to newer ones*/\n",
+       "    return ua.indexOf(\"MSIE \") > -1 || ua.indexOf(\"Trident/\") > -1;\n",
+       "  }\n",
+       "\n",
+       "  /* Define the Animation class */\n",
+       "  function Animation(frames, img_id, slider_id, interval, loop_select_id){\n",
+       "    this.img_id = img_id;\n",
+       "    this.slider_id = slider_id;\n",
+       "    this.loop_select_id = loop_select_id;\n",
+       "    this.interval = interval;\n",
+       "    this.current_frame = 0;\n",
+       "    this.direction = 0;\n",
+       "    this.timer = null;\n",
+       "    this.frames = new Array(frames.length);\n",
+       "\n",
+       "    for (var i=0; i<frames.length; i++)\n",
+       "    {\n",
+       "     this.frames[i] = new Image();\n",
+       "     this.frames[i].src = frames[i];\n",
+       "    }\n",
+       "    var slider = document.getElementById(this.slider_id);\n",
+       "    slider.max = this.frames.length - 1;\n",
+       "    if (isInternetExplorer()) {\n",
+       "        // switch from oninput to onchange because IE <= 11 does not conform\n",
+       "        // with W3C specification. It ignores oninput and onchange behaves\n",
+       "        // like oninput. In contrast, Microsoft Edge behaves correctly.\n",
+       "        slider.setAttribute('onchange', slider.getAttribute('oninput'));\n",
+       "        slider.setAttribute('oninput', null);\n",
+       "    }\n",
+       "    this.set_frame(this.current_frame);\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.get_loop_state = function(){\n",
+       "    var button_group = document[this.loop_select_id].state;\n",
+       "    for (var i = 0; i < button_group.length; i++) {\n",
+       "        var button = button_group[i];\n",
+       "        if (button.checked) {\n",
+       "            return button.value;\n",
+       "        }\n",
+       "    }\n",
+       "    return undefined;\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.set_frame = function(frame){\n",
+       "    this.current_frame = frame;\n",
+       "    document.getElementById(this.img_id).src =\n",
+       "            this.frames[this.current_frame].src;\n",
+       "    document.getElementById(this.slider_id).value = this.current_frame;\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.next_frame = function()\n",
+       "  {\n",
+       "    this.set_frame(Math.min(this.frames.length - 1, this.current_frame + 1));\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.previous_frame = function()\n",
+       "  {\n",
+       "    this.set_frame(Math.max(0, this.current_frame - 1));\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.first_frame = function()\n",
+       "  {\n",
+       "    this.set_frame(0);\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.last_frame = function()\n",
+       "  {\n",
+       "    this.set_frame(this.frames.length - 1);\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.slower = function()\n",
+       "  {\n",
+       "    this.interval /= 0.7;\n",
+       "    if(this.direction > 0){this.play_animation();}\n",
+       "    else if(this.direction < 0){this.reverse_animation();}\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.faster = function()\n",
+       "  {\n",
+       "    this.interval *= 0.7;\n",
+       "    if(this.direction > 0){this.play_animation();}\n",
+       "    else if(this.direction < 0){this.reverse_animation();}\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.anim_step_forward = function()\n",
+       "  {\n",
+       "    this.current_frame += 1;\n",
+       "    if(this.current_frame < this.frames.length){\n",
+       "      this.set_frame(this.current_frame);\n",
+       "    }else{\n",
+       "      var loop_state = this.get_loop_state();\n",
+       "      if(loop_state == \"loop\"){\n",
+       "        this.first_frame();\n",
+       "      }else if(loop_state == \"reflect\"){\n",
+       "        this.last_frame();\n",
+       "        this.reverse_animation();\n",
+       "      }else{\n",
+       "        this.pause_animation();\n",
+       "        this.last_frame();\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.anim_step_reverse = function()\n",
+       "  {\n",
+       "    this.current_frame -= 1;\n",
+       "    if(this.current_frame >= 0){\n",
+       "      this.set_frame(this.current_frame);\n",
+       "    }else{\n",
+       "      var loop_state = this.get_loop_state();\n",
+       "      if(loop_state == \"loop\"){\n",
+       "        this.last_frame();\n",
+       "      }else if(loop_state == \"reflect\"){\n",
+       "        this.first_frame();\n",
+       "        this.play_animation();\n",
+       "      }else{\n",
+       "        this.pause_animation();\n",
+       "        this.first_frame();\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.pause_animation = function()\n",
+       "  {\n",
+       "    this.direction = 0;\n",
+       "    if (this.timer){\n",
+       "      clearInterval(this.timer);\n",
+       "      this.timer = null;\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.play_animation = function()\n",
+       "  {\n",
+       "    this.pause_animation();\n",
+       "    this.direction = 1;\n",
+       "    var t = this;\n",
+       "    if (!this.timer) this.timer = setInterval(function() {\n",
+       "        t.anim_step_forward();\n",
+       "    }, this.interval);\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.reverse_animation = function()\n",
+       "  {\n",
+       "    this.pause_animation();\n",
+       "    this.direction = -1;\n",
+       "    var t = this;\n",
+       "    if (!this.timer) this.timer = setInterval(function() {\n",
+       "        t.anim_step_reverse();\n",
+       "    }, this.interval);\n",
+       "  }\n",
+       "</script>\n",
+       "\n",
+       "<style>\n",
+       ".animation {\n",
+       "    display: inline-block;\n",
+       "    text-align: center;\n",
+       "}\n",
+       "input[type=range].anim-slider {\n",
+       "    width: 374px;\n",
+       "    margin-left: auto;\n",
+       "    margin-right: auto;\n",
+       "}\n",
+       ".anim-buttons {\n",
+       "    margin: 8px 0px;\n",
+       "}\n",
+       ".anim-buttons button {\n",
+       "    padding: 0;\n",
+       "    width: 36px;\n",
+       "}\n",
+       ".anim-state label {\n",
+       "    margin-right: 8px;\n",
+       "}\n",
+       ".anim-state input {\n",
+       "    margin: 0;\n",
+       "    vertical-align: middle;\n",
+       "}\n",
+       "</style>\n",
+       "\n",
+       "<div class=\"animation\">\n",
+       "  <img id=\"_anim_img7ce4e0bc0ca54674ac6312424562ac5b\">\n",
+       "  <div class=\"anim-controls\">\n",
+       "    <input id=\"_anim_slider7ce4e0bc0ca54674ac6312424562ac5b\" type=\"range\" class=\"anim-slider\"\n",
+       "           name=\"points\" min=\"0\" max=\"1\" step=\"1\" value=\"0\"\n",
+       "           oninput=\"anim7ce4e0bc0ca54674ac6312424562ac5b.set_frame(parseInt(this.value));\">\n",
+       "    <div class=\"anim-buttons\">\n",
+       "      <button title=\"Decrease speed\" aria-label=\"Decrease speed\" onclick=\"anim7ce4e0bc0ca54674ac6312424562ac5b.slower()\">\n",
+       "          <i class=\"fa fa-minus\"></i></button>\n",
+       "      <button title=\"First frame\" aria-label=\"First frame\" onclick=\"anim7ce4e0bc0ca54674ac6312424562ac5b.first_frame()\">\n",
+       "        <i class=\"fa fa-fast-backward\"></i></button>\n",
+       "      <button title=\"Previous frame\" aria-label=\"Previous frame\" onclick=\"anim7ce4e0bc0ca54674ac6312424562ac5b.previous_frame()\">\n",
+       "          <i class=\"fa fa-step-backward\"></i></button>\n",
+       "      <button title=\"Play backwards\" aria-label=\"Play backwards\" onclick=\"anim7ce4e0bc0ca54674ac6312424562ac5b.reverse_animation()\">\n",
+       "          <i class=\"fa fa-play fa-flip-horizontal\"></i></button>\n",
+       "      <button title=\"Pause\" aria-label=\"Pause\" onclick=\"anim7ce4e0bc0ca54674ac6312424562ac5b.pause_animation()\">\n",
+       "          <i class=\"fa fa-pause\"></i></button>\n",
+       "      <button title=\"Play\" aria-label=\"Play\" onclick=\"anim7ce4e0bc0ca54674ac6312424562ac5b.play_animation()\">\n",
+       "          <i class=\"fa fa-play\"></i></button>\n",
+       "      <button title=\"Next frame\" aria-label=\"Next frame\" onclick=\"anim7ce4e0bc0ca54674ac6312424562ac5b.next_frame()\">\n",
+       "          <i class=\"fa fa-step-forward\"></i></button>\n",
+       "      <button title=\"Last frame\" aria-label=\"Last frame\" onclick=\"anim7ce4e0bc0ca54674ac6312424562ac5b.last_frame()\">\n",
+       "          <i class=\"fa fa-fast-forward\"></i></button>\n",
+       "      <button title=\"Increase speed\" aria-label=\"Increase speed\" onclick=\"anim7ce4e0bc0ca54674ac6312424562ac5b.faster()\">\n",
+       "          <i class=\"fa fa-plus\"></i></button>\n",
+       "    </div>\n",
+       "    <form title=\"Repetition mode\" aria-label=\"Repetition mode\" action=\"#n\" name=\"_anim_loop_select7ce4e0bc0ca54674ac6312424562ac5b\"\n",
+       "          class=\"anim-state\">\n",
+       "      <input type=\"radio\" name=\"state\" value=\"once\" id=\"_anim_radio1_7ce4e0bc0ca54674ac6312424562ac5b\"\n",
+       "             checked>\n",
+       "      <label for=\"_anim_radio1_7ce4e0bc0ca54674ac6312424562ac5b\">Once</label>\n",
+       "      <input type=\"radio\" name=\"state\" value=\"loop\" id=\"_anim_radio2_7ce4e0bc0ca54674ac6312424562ac5b\"\n",
+       "             >\n",
+       "      <label for=\"_anim_radio2_7ce4e0bc0ca54674ac6312424562ac5b\">Loop</label>\n",
+       "      <input type=\"radio\" name=\"state\" value=\"reflect\" id=\"_anim_radio3_7ce4e0bc0ca54674ac6312424562ac5b\"\n",
+       "             >\n",
+       "      <label for=\"_anim_radio3_7ce4e0bc0ca54674ac6312424562ac5b\">Reflect</label>\n",
+       "    </form>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n",
+       "\n",
+       "<script language=\"javascript\">\n",
+       "  /* Instantiate the Animation class. */\n",
+       "  /* The IDs given should match those used in the template above. */\n",
+       "  (function() {\n",
+       "    var img_id = \"_anim_img7ce4e0bc0ca54674ac6312424562ac5b\";\n",
+       "    var slider_id = \"_anim_slider7ce4e0bc0ca54674ac6312424562ac5b\";\n",
+       "    var loop_select_id = \"_anim_loop_select7ce4e0bc0ca54674ac6312424562ac5b\";\n",
+       "    var frames = new Array(100);\n",
+       "    \n",
+       "  frames[0] = \"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAfQAAAH0CAYAAADL1t+KAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\\\n",
+       "bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9h\\\n",
+       "AAAPYQGoP6dpAAAIHklEQVR4nO3XIQ4CUAwFQT7BcWMOwI3RxWMhadjMuLrnNj0zMxcA4K9dtwcA\\\n",
+       "AN8TdAAIEHQACBB0AAgQdAAIEHQACBB0AAgQdAAIEHQACBB0AAgQdAAIEHQACBB0AAgQdAAIEHQA\\\n",
+       "CBB0AAgQdAAIEHQACBB0AAgQdAAIEHQACBB0AAgQdAAIEHQACBB0AAgQdAAIEHQACBB0AAgQdAAI\\\n",
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+       "CDoAFBB0ACgg6ABQQNABoICgA0ABQQeAAoIOAAUEHQAKCDoAFBB0ACgg6ABQQNABoICgA0ABQQeA\\\n",
+       "AlfH4/F47oMAAB7GO3QAKCDoAFBA0AGggKADQAFBB4ACgg4ABQQdAAoIOgAUEHQAKCDoAFBA0AGg\\\n",
+       "gKADQAFBB4ACgg4ABQQdAAoIOgAUEHQAKCDoAFBA0AGggKADQAFBB4ACgg4ABQQdAAoIOgAUEHQA\\\n",
+       "KCDoAFBA0AGggKADQAFBB4ACgg4ABQQdAAoIOgAUEHQAKCDoAFBA0AGggKADQAFBB4ACgg4ABQQd\\\n",
+       "AAoIOgAUEHQAKCDoAFBA0AGggKADQAFBB4ACgg4ABQQdAAoIOgAUEHQAKCDoAFBA0AGggKADQAFB\\\n",
+       "B4ACgg4ABf4L2AjDJXgBYH8AAAAASUVORK5CYII=\\\n",
+       "\"\n",
+       "\n",
+       "\n",
+       "    /* set a timeout to make sure all the above elements are created before\n",
+       "       the object is initialized. */\n",
+       "    setTimeout(function() {\n",
+       "        anim7ce4e0bc0ca54674ac6312424562ac5b = new Animation(frames, img_id, slider_id, 100.0,\n",
+       "                                 loop_select_id);\n",
+       "    }, 0);\n",
+       "  })()\n",
+       "</script>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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ACoECQIVAAaDiPxRAwmfFK3XyAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np \n",
+    "import matplotlib.pyplot as plt \n",
+    "from matplotlib import animation, rc  \n",
+    "from IPython.display import HTML  \n",
+    "\n",
+    "# Definimos una función para actualizar el estado del fuego en la cuadrícula\n",
+    "def update_fire(grid, hrost, ros, fi, cfb):\n",
+    "    \"\"\"\n",
+    "    Función que actualiza la propagación del fuego en la cuadrícula.\n",
+    "\n",
+    "    Parámetros:\n",
+    "        grid (numpy.ndarray): Cuadrícula que representa el estado actual del fuego.\n",
+    "        hrost (float): Velocidad de propagación del fuego (m/min).\n",
+    "        ros (float): Velocidad de propagación del fuego equivalente (m/min).\n",
+    "        fi (float): Intensidad de la propagación del fuego (kW/m).\n",
+    "        cfb (float): Porcentaje de velocidad de propagación del fuego.\n",
+    "\n",
+    "    Retorna:\n",
+    "        numpy.ndarray: Nueva cuadrícula con el estado actualizado del fuego.\n",
+    "    \"\"\"\n",
+    "    # Creamos una copia de la cuadrícula para almacenar el nuevo estado del fuego\n",
+    "    new_grid = np.copy(grid)\n",
+    "    # Iteramos sobre cada celda en la cuadrícula\n",
+    "    for i in range(grid.shape[0]):\n",
+    "        for j in range(grid.shape[1]):\n",
+    "            if grid[i, j] > 0:  # Verificamos si hay fuego en la celda actual\n",
+    "                # Calculamos la probabilidad de propagación del fuego en esta celda\n",
+    "                hros_prob = ros / 10  # Normalizamos la velocidad de propagación equivalente\n",
+    "                hfi_factor = fi / 1000  # Normalizamos la intensidad de propagación del fuego\n",
+    "                hros_prob *= (cfb / 100)  # Ajustamos la probabilidad según el porcentaje de velocidad de propagación\n",
+    "                hros_prob *= (1 + hfi_factor)  # Aumentamos la probabilidad en función de la intensidad de propagación\n",
+    "                # Iteramos sobre las celdas vecinas para propagar el fuego\n",
+    "                for di in [-1, 0, 1]:\n",
+    "                    for dj in [-1, 0, 1]:\n",
+    "                        # Verificamos que la celda vecina esté dentro de los límites de la cuadrícula\n",
+    "                        if 0 <= i + di < grid.shape[0] and 0 <= j + dj < grid.shape[1]:\n",
+    "                            if di == 0 and dj == 0:  # Si es la celda actual\n",
+    "                                prob = hros_prob  # La probabilidad es la misma que la calculada\n",
+    "                            elif di == 0 or dj == 0:  # Si es una celda adyacente horizontal o verticalmente\n",
+    "                                prob = 0.02  # Probabilidad de propagación en los flancos\n",
+    "                            else:  # Si es una celda adyacente diagonalmente\n",
+    "                                prob = 0.05  # Probabilidad de propagación en el retroceso\n",
+    "                            # Generamos un número aleatorio y comparamos con la probabilidad\n",
+    "                            if np.random.rand() < prob:\n",
+    "                                # Incrementamos la intensidad del fuego en la celda vecina\n",
+    "                                new_grid[i + di, j + dj] = min(10, grid[i + di, j + dj] + 1)\n",
+    "    # Retornamos la nueva cuadrícula con el estado actualizado del fuego\n",
+    "    return new_grid\n",
+    "\n",
+    "# Definimos el tamaño de la cuadrícula\n",
+    "grid_size = 100\n",
+    "# Creamos una cuadrícula de ceros para representar el estado inicial del fuego\n",
+    "fire_grid = np.zeros((grid_size, grid_size))\n",
+    "start_point = grid_size // 2  # Coordenadas del punto de inicio del fuego en el centro de la cuadrícula\n",
+    "fire_grid[start_point, start_point] = 1  # Encendemos el fuego en el punto de inicio\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Configuración de la animación\n",
+    "fig, ax = plt.subplots(figsize=(5, 5))\n",
+    "\n",
+    "# Función de inicialización para la animación\n",
+    "def init():\n",
+    "    \"\"\"\n",
+    "    Función de inicialización para la animación.\n",
+    "    Limpia el eje y muestra la cuadrícula inicial del fuego.\n",
+    "    \"\"\"\n",
+    "    ax.clear()  # Limpiamos el eje\n",
+    "    ax.imshow(fire_grid, cmap='hot', interpolation='nearest', vmin=0, vmax=10)  # Mostramos la cuadrícula de fuego inicial\n",
+    "    plt.axis('off')  # Desactivamos los ejes\n",
+    "\n",
+    "# Función de actualización para la animación\n",
+    "def update(frame):\n",
+    "    \"\"\"\n",
+    "    Función de actualización para la animación.\n",
+    "    Actualiza el estado del fuego en cada frame de la animación.\n",
+    "    \"\"\"\n",
+    "    global fire_grid  # Usamos la variable global para actualizar el estado del fuego\n",
+    "    ax.clear()  # Limpiamos el eje\n",
+    "    fire_grid = update_fire(fire_grid, hrost, ros, fi, cfb)  # Actualizamos el estado del fuego\n",
+    "    ax.imshow(fire_grid, cmap='hot', interpolation='nearest', vmin=0, vmax=10)  # Mostramos la cuadrícula actualizada\n",
+    "    plt.axis('off')  # Desactivamos los ejes\n",
+    "\n",
+    "# Creamos la animación\n",
+    "ani = animation.FuncAnimation(fig, update, frames=100, init_func=init, blit=False, interval=100, repeat=False)\n",
+    "\n",
+    "# Mostramos la animación en formato HTML\n",
+    "HTML(ani.to_jshtml())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "97002dd61266480ca7570c01119c7888",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=0, description='Tiempo (min):', max=60), Output()), _dom_classes=('widge…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from ipywidgets import interact, IntSlider\n",
+    "\n",
+    "def plot_fire_propagation(time=0):\n",
+    "    hdist = 100 + 10 * time  # Distancia de propagación hacia adelante\n",
+    "    bdist = 100 + 8 * time   # Distancia de propagación hacia atrás\n",
+    "    fdist = 50 + 6 * time    # Distancia de propagación lateral\n",
+    "\n",
+    "    # Crear una elipse basada en las distancias de propagación\n",
+    "    theta = np.linspace(0, 2*np.pi, 100)\n",
+    "    x = (hdist + bdist) / 2 * np.cos(theta)  # La mitad de la suma de hdist y bdist para el radio x\n",
+    "    y = fdist * np.sin(theta)                # fdist para el radio y\n",
+    "    \n",
+    "    fig, ax = plt.subplots(figsize=(8, 6))\n",
+    "    ax.plot(x, y, 'r-', label='Perímetro del fuego')\n",
+    "    ax.fill(x, y, 'r', alpha=0.5)\n",
+    "    ax.set_xlim(-max(hdist, bdist) - 10, max(hdist, bdist) + 10)\n",
+    "    ax.set_ylim(-fdist - 10, fdist + 10)\n",
+    "    ax.set_xlabel('Distancia X')\n",
+    "    ax.set_ylabel('Distancia Y')\n",
+    "    ax.set_title('Propagación del fuego')\n",
+    "    ax.legend()\n",
+    "    ax.axis('equal')\n",
+    "    plt.show()\n",
+    "\n",
+    "# Crea un control deslizante para interactuar con el tiempo\n",
+    "interact(plot_fire_propagation, time=IntSlider(min=0, max=60, step=1, value=0, description='Tiempo (min):'));\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3a339d011e884a5fac0c8c1e2139f350",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=60, description='time', max=120), Output(layout=Layout(height='350px')))…"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from ipywidgets import interactive\n",
+    "\n",
+    "def update_fire_behavior(time=60):\n",
+    "    # Asumiendo que las variables 'ros', 'bros', 'fros', y 'accn' ya están definidas globalmente con valores de ejemplo\n",
+    "    global ros, bros, fros, accn\n",
+    "    \n",
+    "    # Cálculo de la distancia y tasa de propagación de la cabeza del incendio en el tiempo dado\n",
+    "    hdist, hrost = spread_distance(ros, time, accn)\n",
+    "    \n",
+    "    # Cálculo de la distancia y tasa de propagación de retroceso en el tiempo dado\n",
+    "    bdist, brost = spread_distance(bros, time, accn)\n",
+    "    \n",
+    "    # Cálculo de la distancia y tasa de propagación lateral en el tiempo dado\n",
+    "    fdist, rost, lbt = flank_spread_distance(hrost, brost, hdist, bdist, lb, accn, time)\n",
+    "    \n",
+    "    # Visualización\n",
+    "    plt.figure(figsize=(10, 6))\n",
+    "    times = np.linspace(0, time, 100)\n",
+    "    hros_t = ros * (1.0 - np.exp(-accn * times))\n",
+    "    bros_t = bros * (1.0 - np.exp(-accn * times))\n",
+    "    \n",
+    "    plt.plot(times, hros_t, label='HROS (Cabeza del Incendio)')\n",
+    "    plt.plot(times, bros_t, label='BROS (Retroceso del Incendio)')\n",
+    "    \n",
+    "    plt.xlabel('Tiempo (min)')\n",
+    "    plt.ylabel('Tasa de Propagación (m/min)')\n",
+    "    plt.title('Evolución de la Tasa de Propagación del Incendio')\n",
+    "    plt.legend()\n",
+    "    plt.grid(True)\n",
+    "    \n",
+    "    plt.show()\n",
+    "\n",
+    "# Widget interactivo\n",
+    "interactive_plot = interactive(update_fire_behavior, time=(0, 120, 1))\n",
+    "output = interactive_plot.children[-1]\n",
+    "output.layout.height = '350px'\n",
+    "interactive_plot\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "29e5ceb43b9f4fbfbb923ebe414a41c6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(Dropdown(description='ftype', options=('C1', 'C2', 'C3', 'C4', 'C5', 'C6', 'O1a', 'O1b')…"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from ipywidgets import interactive\n",
+    "\n",
+    "def calculate_ros(ffmc, ftype):\n",
+    "    # Simular una variación de la tasa de propagación del fuego (ROS) en función de FFMC para un tipo de combustible específico\n",
+    "    # Asumiendo una relación simplificada entre FFMC y la tasa de propagación para el propósito de esta demostración\n",
+    "    ff = ffmc_effect(ffmc)  # Calcular el efecto FFMC\n",
+    "    isi = 0.208 * ff  # Calcular el ISI basado en el efecto FFMC\n",
+    "    # Usar valores de ejemplo para 'a', 'b', 'c' basados en el tipo de combustible\n",
+    "    a_val, b_val, c_val = a[ftype], b[ftype], c[ftype]\n",
+    "    ros = a_val * (1.0 - np.exp(-b_val * isi)) ** c_val  # Calcular ROS basado en ISI\n",
+    "    return ros\n",
+    "\n",
+    "def plot_ros_vs_ffmc(ftype='C1'):\n",
+    "    ffmc_values = np.linspace(75, 95, 50)  # Rango de FFMC para análisis\n",
+    "    ros_values = [calculate_ros(ffmc, ftype) for ffmc in ffmc_values]\n",
+    "    \n",
+    "    plt.figure(figsize=(10, 6))\n",
+    "    plt.plot(ffmc_values, ros_values, '-o', label=f'Tipo de combustible: {ftype}')\n",
+    "    plt.xlabel('FFMC')\n",
+    "    plt.ylabel('Tasa de Propagación del Fuego (ROS) [m/min]')\n",
+    "    plt.title('Relación entre FFMC y Tasa de Propagación del Fuego')\n",
+    "    plt.legend()\n",
+    "    plt.grid(True)\n",
+    "    plt.show()\n",
+    "\n",
+    "# Crear un widget interactivo para seleccionar el tipo de combustible\n",
+    "interactive_plot = interactive(plot_ros_vs_ffmc, ftype=['C1', 'C2', 'C3', 'C4', 'C5', 'C6', 'O1a', 'O1b'])\n",
+    "interactive_plot\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#  Traducciones directas de los gráficos adicionales del matlab"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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RpJbeGdXJdT/x/S06l+vUmid6KTji/Edg4ebDemPV/lK126pOuD4d2911/0IfLXqoq9rWP/+L+9c7j2vKF7tK1W5xfTQ7oYN6/+8X9+/3n9Zj//2pVO0W10fT/tTa9Yv79qMZrte9NIrqo6Lef6VVVB8V9f4rraL6qLj3X2kU1UfFvf9Ko6g+Ku79VxoV9R0x8JU1+vLP1youMqRU7QDe5tOfjumf3+wt1T7xtatp2bgervsvfLpDe1Mz9Z/7rlaX//3ivmrPCT338fZStRsREqBNz/Z13Z++bI9+SD6tmcPaaWDr87+4bzp4Rg/PL/134YEXB7r+/+aa/fri5xS9cFML3dPl/HfAnuOZZfqO3fhMH9mrnQ9XC378Ve99f1B/7h2vR/ue/8X9WHpOmdpd+ui1rnB1oY+GX91Afx1yPlxl5uSXqd2i+uiGVtF69a72rm3K0m5RfdQ5LkLvj+7i2ubxD7fqdFbhsHIxRfVRce+/0iiqj4p7//3R9S2i9frw9oWWV2YeHa5ycnL01FNPadiwYQoLO/9X0pSUFAUEBKhGDfeUGxUVpZSU4v8SnZSUpEmTJrkte/755zVx4kTT6y6L1nXDFf2/X9pKKjr8t2AYHOCnDg1qKLKae1hsHhMqfx9bqdqtWyPY9f8APx91aFBD/r7uR5jG165W6r92x9X87Rcvm03q0OB8H/rYfqsvLjLEtbyk4mu7/8W8Xf0ayslzKsDvt5rr1ggudbuNarn/onihj0Lsv32sosICS91ucX0UHuTvWlazWkCp2y2ujyKrBbiWhQf5l7pdqeg+Kur9V1pF9VFR77/SKqqPinv/lUZRfVTc+680iuqj4t5/pVER3xGbDp1RTl6BXvh0u94e2ekiewHeZ+exDB09k+36C32d6kGl/m6pWyPI7X6L2DCFB/krNPC377HaofZStxv2u+8rSWoWHSpngaEawb8trxFctp8Jv3dFrWrq0KCGaof+9t0SGli2nwl+Pr/9TKgfcf5nQp3qv70+Qf6+ZWo3yN/X9f8LfVQ/4rfvMT+fsv2sKaqP/jiKX5Z2i+qjZtGhbttcVa96qX8PK6qPinv/lUZRfVTc+++PrqjteX+U89jDAvPy8nTbbbfp0KFDWrlypStczZ8/XyNHjiw0CtW3b19dccUVev3114t8rMo+cgUAnujQqXP66+c79Nyg5q7RN6AqcBYYuvnVb7X1cLqeG9Rco7rHWV0SgArgkSNXeXl5Gjp0qJKTk7V8+XJXsJKk6Oho5ebmKi0tzW30KjU1VV27di22TYIUAJivfs1gvXlPB9f9nDynfGw2t1FJwBvlFxSoS6Oa+vX0OQ1qHXPpHQB4BY/76XYhWO3du1dff/21atas6ba+ffv28vf317Jly1zLjh07pm3btl00XAEAyt8/v9mrm2Z+W+qT+gFPYBiGazInu5+vJtxwpVY90cs10Q0A71fpRq4yMzO1b98+1/3k5GRt2bJFERERio2N1Z/+9Cdt2rRJn332mZxOp+s8qoiICAUEBCg8PFz33nuvxo8fr5o1ayoiIkKPPfaYWrVqpT59+lj1tACgyjuXm6+PNh5W6lmHBs9Yqyk3t9KQtnWsLgswxdmcPD3+363afixdnz9yjcL+N0HChX8BVA2V7pyrlStXqlevXoWWJyQkaOLEiYqLK/qY5RUrVqhnz56Szk908fjjj2v+/PnKzs5W79699eqrr6pevXrlWToA4BJSM3KU+P4WrfvllCTpoZ5X6LF+TeVTykkzgMrk19Pn9P/e2aDdx8/K39emN4a313XNoi69IwCvU+nCFQDAuzkLDL20dLdeXfmLJGlgqxhNv72N6/o1gCfZdiRdCXN+1KmsXNUOteuN4e3drmMEoGohXAEALLFo82E9+eHPynUW6NomtfT63e1cF40GPMGPyad179z1OuvIV/OYMM0e0cF1cVcAVRPhCgBgmTV7T+j+dzcqO8+pznERmjuyk4ICGMFC5ffdL6c0cu6PyskrUKe4CM1O6OC6EC2AqotwBQCw1MaDaRox50eddeTrmvhIvXlPBwX6E7BQeW08eFrDZ/+oc7lO9WxaS6/f3Z73LABJHjgVOwDAu7RvUENzR3VUcICv1uw9qT8v2CxnAX/3Q+W0KyVDI95er3O5TnVvHEmwAuCGcAUAsFz7BhGandBRAX4+WrL9uP762Q6rSwIKOZaerZFvr9fZnHx1bFhDs+4hWAFwR7gCAFQKXa6oqZdvv0oBfj5qW7+61eUAbjId+Rr59nodS89R49rV9OY9HZiABUAhfCsAACqNG1rFqH2DGooKC7S6FMBNRnaeCgxDtULtmjuyo6oHB1hdEoBKiAktAACV1slMh845nKpfM9jqUgBlOvJ19Ey2mkSFWl0KgEqKcAUAqJR2p5zVyLd/VHhwgBY91JVzW2CJnDwn7z0AJcY5VwCASql6sL8c+QXKyXPqxFmH1eWgCjqdlaveL63SzBX7mMESQIkwcgUAqLS2HUlXg5rBXJwVlnhrzX797fOduqJWiD4bew0XuAZwSYQrAIDHMAxDNpvN6jJQRRiGof9uPKyWseFqHhtmdTkAPADhCgBQ6RUUGJq77oA2HkrTjDvbErAAAJUS51wBACq9g6fP6cUvd+nzrcf04cbDVpcDL5abX6D/W7JbaVm5VpcCwAMRrgAAlV5cZIge7dtEkvTCpzt09Ey2xRXBW81a/YtmrNin22d9pwImsQBQSoQrAIBHuP/aRmpbv7rOOvL15EdbxVHtMNv+E5l6Zfk+SdLDvRrLx4fDTwGUDuEKAOARfH1s+r/b2sju56M1e0/qvxs4PBDmKSgwNGHhz8rNL1CPJrU0uE2s1SUB8ECEKwCAx7iiVjWN73f+8MAXv9ql9HN5FlcEb/Hfjb/qh+TTCvL31d+GtGTSFABlQrgCAHiUkd3i1Lh2NZ3OytVLy3ZbXQ68QOrZHE3+fKckaXy/JqoXEWxxRQA8FeEKAOBR/H199MLgFpKked8f1Paj6RZXBE835fOdysjJV8s6YRrRtaHV5QDwYIQrAIDH6do4UgNbx6jAkJ77eDuzuqHMNh9K0+ItR2WzSUk3t5afL78aASg7vkEAAB7pmYFXKjjAVxsPpmnh5iNWlwMPZBiG/vrZDknSre3qqlXdcIsrAuDpCFcAAI8UEx6kR3rHS5Je/HKnMnKY3AKl89nWY9p06IyC/H31eP+mVpcDwAsQrgAAHmtUtzg1qhUiu5+vDp06Z3U58CA5eU69+OUuSdIDPa5QVFigxRUB8AZ+VhcAAEBZBfj5aHZCR8VWD5Tdz9fqcuBBPtlyVEfOZCs6LFD3X9vI6nIAeAnCFQDAo8VFhlhdAjzQbR3qKijAVwF+PgoKIJgDMIfNMAymWAIAeLx8Z4E+2nRYneNqqiGBCwBgAc65AgB4hec/2a4nP/pZ/7eUCwujeGlZuTqXm291GQC8FOEKAOAV7urcQDVDAtS2fg1xUAaKM23JLl07bYW+2pZidSkAvBDnXAEAvELz2DB9+9R1CvTn/BkULTe/QD8kn9bJzFxFVguwuhwAXohzrgAAQJWR5yzQ6j0n1PvKKKtLAeCFCFcAAK9SUGBo6Y4UrT+QpmcHNbe6HABAFcI5VwAAr3LkTLYenr9Zs9cma+vhM1aXg0pi3S8nlecssLoMAF6OcAUA8Cr1IoJ101WxkqQZy/dZXA0qg19OZOrut37QdS+tVEZOntXlAPBihCsAgNd5qGdj2WzS0h3HtSslw+pyYLHXVv6iAkNqGhWqsEB/q8sB4MUIVwAAr9O4djXd0DJGkjRzxS8WVwMrHUvP1sdbjkiSHu7V2OJqAHg7whUAwCtd+EX6s61H9cuJTIurgVXe/vaA8pyGOsVFqG39GlaXA8DLEa4AAF6peWyY+lxZW4Zx/rAwVD3p2Xma/8MhSdKDPa6wuBoAVQHhCgDgtS6MXi3efEQp6TkWV4OK9u8fDirTka+mUaHq2bSW1eUAqAIIVwAAr9W2fg11iotQfoGhd747YHU5qEA5eU69/e0BSdL91zaSzWaztiAAVQLhCgDg1e7tHidJmv/DIZ3Lzbe4GlSURZuP6MRZh2LDAzX4f1PzA0B5I1wBALxanyujVD8iWOnZefpo0xGry0EFcBYYenP1fknSqO5x8vfl1x0AFYNvGwCAV/P1sWlUt4aSpDlrk1VQYFhbEMrdNzuPa//JLIUF+umOTvWtLgdAFUK4AgB4vds61FPtULuublRT5/KcVpeDcnbh/Lo7O9dXNbuftcUAqFL4xgEAeL0Qu5/WPnmdAvz4m6K3O5np0KaDZ+Rjk4Zf3cDqcgBUMTbDMDg+AgAAeI30c3n6bv8pXd8y2upSAFQxhCsAQJWy9fAZbT+aoTs5FwcAYDIOCwQAVBm7UjI0eMa3CvDzUf8W0YoICbC6JJjozLlcVQ+mTwFYh3AFAKgymkaFql396mpQM0TZTGzhVQoKDN3y6jqFBflr+tA2alSrmtUlAaiCCFcAgCrDZrPpvw90la+PzepSYLK9qZk6nJYt+1mHosICrS4HQBXFOVcAAMArnMx0aMfRDF3bpJbVpQCooghXAIAqaXfKWa375aRGdouzuhQAgJfgsEAAQJVzPCNHN7yyRs4CQ23r19BV9apbXRIuw8FTWWpQM8TqMgBAXE0RAFDlRIUFashVdSRJ//pmr8XV4HIcPJWl615apbvf+kE5TFICwGKEKwBAlTTmusbysUnf7ErVtiPpVpeDMpq5Yp+cBYZ8fWwK9Pe1uhwAVVylC1erV6/WjTfeqNjYWNlsNi1evNhtvWEYmjhxomJjYxUUFKSePXtq+/btbts4HA6NHTtWkZGRCgkJ0eDBg3X48OEKfBYAgMouLjJEN/1v9Gr6sj0WV4OyOHAySws3HZEk/blPvMXVAEAlDFdZWVlq06aNZsyYUeT6adOmafr06ZoxY4bWr1+v6Oho9e3bV2fPnnVtk5iYqEWLFmnBggVau3atMjMzNWjQIDmdHC4AAPjN2Osay8/HpuW7UrVu30mry0Ep/X3JbuUXGOrZtJba1a9hdTkAULlnC7TZbFq0aJGGDBki6fyoVWxsrBITE/Xkk09KOj9KFRUVpalTp2r06NFKT09XrVq19N577+n222+XJB09elT16tXTF198of79+1v1dAAAldDzH2/TO98dVIvYMH06prt8uAaWR9h8KE03v7pONpv0xSPX6MqYMKtLAoDKN3J1McnJyUpJSVG/fv1cy+x2u3r06KF169ZJkjZu3Ki8vDy3bWJjY9WyZUvXNkVxOBzKyMhwuzkcjvJ7MgCASuGR3vEKtftp+9EMLd5yxOpyUAKGYSjpy12SpFvb1SVYAag0PCpcpaSkSJKioqLclkdFRbnWpaSkKCAgQDVq1Ch2m6IkJSUpPDzc7ZaUlGTyMwAAVDY1q9n1UK/GkqT/W7KbGec8wJLtKfox+bQC/Hw0rm8Tq8sBABePClcX2Gzuh2wYhlFo2R9dapsJEyYoPT3d7TZhwgRT6gUAVG4juzVUnepBOpqeoznfJltdDi4iJ8+pyV/slCTdf00jxVYPsrgiAPiNR4Wr6OhoSSo0ApWamuoazYqOjlZubq7S0tKK3aYodrtdYWFhbje73W7yMwAAVEaB/r56vH9TSdKrK37RqUwOC6+s5nybrF9PZysqzK4He15hdTkA4MajwlVcXJyio6O1bNky17Lc3FytWrVKXbt2lSS1b99e/v7+btscO3ZM27Ztc20DAMAfDW4Tq1Z1wpXpyNdLTM1eKaVm5Gjm8n2SpCevb6YQu5/FFQGAu0r3rZSZmal9+/a57icnJ2vLli2KiIhQ/fr1lZiYqClTpig+Pl7x8fGaMmWKgoODNWzYMElSeHi47r33Xo0fP141a9ZURESEHnvsMbVq1Up9+vSx6mkBACo5Hx+bnh3UXFO+2Kk7OtazuhwU4e9Ldisr16k29apryP+uUQYAlUmlC1cbNmxQr169XPfHjRsnSUpISNDcuXP1xBNPKDs7Ww899JDS0tLUuXNnLV26VKGhoa59/vGPf8jPz09Dhw5Vdna2evfurblz58rXlyu3AwCK1ykuQose6nrJ83hR8QoKDDkLDNls0vM3NmfKfACVUqW+zhUAAFbKyXMq0J8/zFUmB05mqWFkiNVlAECRPOqcKwAAKkJOnlPTvtqlnn9fqfRzeVaXg98hWAGozAhXAAD8ga+PTUt3HFdKRo4++YkLC1vpVKZDiQs263DaOatLAYBL4rBAAACKsP7AaZ05l6c+V9bmHCwLPf7fn/TfjYfVoUENffggs/4CqNwq3YQWAABUBh0bRlhdAiTdf20jHTp9Tk8NaGZ1KQBwSYxcAQBwCSczHfrp1zPqfWXxF6NH+TEMg9FDAB6Bc64AALiIQ6fOqc/0VXro35t04GSW1eVUGacyHa7/E6wAeArCFQAAF1EvIkgtY8PlyC/QUwu3igM+yt/OYxnqNnW5Jn26Xbn5BVaXAwAlRrgCAOAibDabptzcSkH+vvp+/2ktWP+r1SV5tTxngcZ/8JNy8gr06+ls+fsyagXAcxCuAAC4hPo1gzW+XxNJ0pTPd+pYerbFFXmvGcv3acexDFUP9teUW1pySCAAj0K4AgCgBEZ2i9NV9arrrCNfj/33JxUUcHig2bYdSdfMFfskSS/c1FK1QwMtrggASodwBQBACfj62PTS0DYK8vfVt/tOac63yVaX5FUc+U6N+2CL8gsM3dAqWje2jrG6JAAoNcIVAAAldEWtanpm0JWSpGlf7daOoxkWV+Q9pi/boz3HM1UzJEB/vYnDAQF4JsIVAAClMKxTffW5srZynQVKfH+zcvKcVpfk8VbtOaE3Vu2XJE2+uZVqVrNbXBEAlA3hCgCAUrDZbHrx1taKrBagPcczNfWrXVaX5NFSM3I07v0tkqS7OtfX9S2jrS0IAC4D4QoAgFKKrGbX3//URpL09rcH9PWO4xZX5JmcBYYS39+iU1m5ahYdqmcHNbe6JAC4LIQrAADKoFez2hrZraEkafXeE9YW46FeXbFP6345pSB/X80Y1k6B/r5WlwQAl8VmcKl5AADKJDe/QEt3pGhgqxgmYCiltKxcdZ+6XFm5Tv3fbW30p/Z1rS4JAC4b4QoAAJNc+JFK0CqZPcfP6rOtxzSubxOrSwEAUxCuAAAwQXp2nsZ/sEXXNYvSsM71rS4HAGABzrkCAMAEn2w5oq93pmrqV7uUkZNndTmVkmEYeu7jbdpw4LTVpQBAuWDkCgAAE5wPDts1tEM9taobbnU5ldL8Hw7pL4t+VnCAr9Y+eZ0iQgKsLgkATEW4AgAAFeJcbr4e/+9WdWscyaGTALwS4QoAgHKw5dczem3lPv3zjrZMMf47hmEw4QcAr8U5VwAAmCwnz6nR723Qku3HNe6DLXIWVN2/Y247kq5/LNvDTIoAqgTCFQAAJgv099U/br9K/r42ffFzih7/8CcVVMGAlXwySwlzftQ/v9mr2WuTrS4HAMod4QoAgHLQ9YpIvXJHW/n62LRw0xE9vfhnVaUj8Y+eydY9c37QqaxctYgN0+0d61ldEgCUO8IVAADlZECrGE0f2kY+Nuk/P/6qSZ/uqBIB69fT5zT0je/06+lsNagZrLkjOyk00N/qsgCg3BGuAAAoRzddVUfT/tRGkjR33QH99bOdXn2I4P4TmRr6xnc6nJathjWDNf++q1Ur1G51WQBQIZgtEACACnDhGk+SNOSqWE37UxsF+HnX3zj3Hj+rYW/9oBNnHbqiVojm33e1osICrS4LACoM4QoAgAqycNNhPfHhVuUXGLomPlKv391eIXY/q8syxYYDp3X/ext1OitXzaJDNe//dVZkNUasAFQthCsAACrQyt2penDeJmXnOdW6brjmjOjo8SFk4abDeuqjn5XrLFDruuF6Z2Qn1QgJsLosAKhw3nU8AgAAlVzPprX1n/uvVkRIgLYeTte87w9aXVKZFRQYmvbVLo374CflOgt0fYtoLbj/aoIVgCqLkSsAACyw/0SmZq74RS/e2kr+vp75t859qZka+MoaOfIL9HCvKzS+b1P5+HCRYABVF+EKAIBKIM9ZoNdX/qJR3eM86jysT386qvyCAt3ctq7VpQCA5QhXAABUApM/36E31yTrqnrVteihrrLZKt8IkCPfqZeW7lGfK6PUKS7C6nIAoNLxzOMQAADwMn2bR6tujSDdd02jShmsJOntbw9o1ur9evT9LcrJc1pdDgBUOoxcAQBQSeTkOWX383GFq6+2HVNKeo7u7Fxfdj9fS2oqKDBc51FlOvL1p9fWaXy/purbPMqSegCgMiNcAQBQCZ3OylWf6at0OitXseGBeqR3vG5tX7fCJr84npGjN1bt185jGZp/X2dX4Pt92AIAuCNcAQBQCeXmF+iDDb/qX8v36niGQ5JUPyJY93RpoNva11N4sH+5PO62I+l6Z90BfbzlqHKdBZKkd0d10rVNapXL4wGANyFcAQBQieXkOfXvHw7p1RX7dCorV5Jk9/NRnyujdGObWF0TH3nZswsmn8zS0u0pWrzlqHYey3At79Cghsb2jte18ZGV9jwwAKhMCFcAAHiAc7n5WrT5iN777qB2pZx1Lff3tald/Rrq0LCGrm5UU9fEX3yEKT07T/tSM7Xn+FltPpSmH5NP68Cpc27tDWgZo3u6NFD7BjUIVQBQCoQrAAA8iGEY2n40Q4s3H9HSHcd16PRvwejaJrX07qhOru16/H2l7H4++mRMdwUFnJ8Q4+F/b9LnPx9za9PPx6ZOcREa2DpGN7SMUY2QgIp7QgDgRTznKoUAAEA2m00t64SrZZ1wPT3wSiWfzNIPyae16WCamsWEubZz5Be4gpcj3+kKV7XD7IoKsyu+dqha1w1Xu/o11LlRhEIDy+ccLgCoShi5AgDACzkLDG359Ywc+U51aBChAL/zswwy2x8AlB/CFQAAAACYoGIulgEAAAAAXo5wBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYwK8kG40aNeqyH2jIkCEaPHjwZbcDAAAAAJVRicLV3LlzL+tBbDabGjZsaEq4ys/P18SJE/Xvf/9bKSkpiomJ0YgRI/TMM8/Ix+f8QJxhGJo0aZJmzZqltLQ0de7cWTNnzlSLFi0u+/EBAAAAoCglCleSlJiYqD//+c+lfgDDMNSoUaNS71ecqVOn6vXXX9c777yjFi1aaMOGDRo5cqTCw8Nd9U2bNk3Tp0/X3Llz1aRJE/3tb39T3759tXv3boWGhppWCwAAAABcUOJwVb16dTVo0KA8aymR7777TjfddJMGDhwoSWrYsKH+85//aMOGDZLOh7mXX35ZTz/9tG655RZJ0jvvvKOoqCjNnz9fo0ePtqx2AAAAAN6rRBNavPnmm7rxxhvL/CCXu//vde/eXd9884327NkjSfrpp5+0du1a3XDDDZKk5ORkpaSkqF+/fq597Ha7evTooXXr1hXbrsPhUEZGhtvN4XCYUjMAAAAA71eikat77733sh7kcvf/vSeffFLp6elq1qyZfH195XQ6NXnyZN15552SpJSUFElSVFSU235RUVE6ePBgse0mJSVp0qRJbsuef/55TZw40bTaAQAAAHivEh8WWFm8//77mjdvnubPn68WLVpoy5YtSkxMVGxsrBISElzb2Ww2t/0Mwyi07PcmTJigcePGuS2z2+3mFg8AAADAa5kSrrKzs/Xvf/9bu3btks1mU/PmzXXnnXcqMDDQjObdPP7443rqqad0xx13SJJatWqlgwcPKikpSQkJCYqOjpYk10yCF6SmphYazfo9u91OmAIAAABQZpcdrjZv3qyBAwfq+PHjioyMVE5Ojs6ePavnnntOn3/+uVq3bm1GnS7nzp1zTbl+ga+vrwoKCiRJcXFxio6O1rJly9S2bVtJUm5urlatWqWpU6eaWgsAAAAAXFCiCS0u5sEHH1TTpk114MABHT9+XOnp6VqxYoUcDofGjBljRo1ubrzxRk2ePFmff/65Dhw4oEWLFmn69Om6+eabJZ0/HDAxMVFTpkzRokWLtG3bNo0YMULBwcEaNmyY6fUAAAAAgCTZDMMwSrLhZ599pkGDBhVa7u/vr6+++kq9e/d2W/7oo4/q9ddfV3Z2tjmV/s/Zs2f17LPPatGiRUpNTVVsbKzuvPNOPffccwoICJD020WE33jjDbeLCLds2dLUWgAAAADgghKHKx8fHw0dOlSvvPKKateu7Vpet25djR49Ws8++6xrWUFBgbp3764jR45cdIY+AAAAAPAWJT7n6uuvv9aDDz6oK6+8Un//+981atQoSdK4ceP02GOPac2aNWrXrp0cDoe++uor7d69Wy+//HJ51Q0AAAAAlUqJR66k8xfanTRpkl566SV169ZNs2bNUuPGjfXBBx/o5Zdf1q5duyRJV155pcaNG6dbb7213AoHAAAAgMqkVOHqgm3btum+++7TTz/9pGeffVZPPPGEfH19y6M+AAAAAPAIZQpX0vlJI2bOnKlnnnlGDRo00JtvvqlOnTqZXR8AAAAAeIQyT8Vus9k0ZswYbd++XY0aNVK3bt305z//WVlZWWbWBwAAAAAeoVQjV1u2bNGsWbN06NAh1a9fX/fdd5/rQr2LFi3SI488Ih8fH82cObPIadsBAAAAwFuVeOTq888/V8eOHbVgwQKdPHlSH3zwgTp16qTPP/9cknTzzTdrx44dGjhwoIYMGaI77rhDqamp5VY4AAAAAFQmJR65atu2rQoKCrR27VqFhoYqKytL3bt3lyRt3rzZbdvvvvtO999/v44cOaLTp0+bXzUAAAAAVDIlHrnat2+f+vfvr9DQUElSSEiI+vbtq3379hXatkuXLtq8ebMee+wx8yoFAAAAgEqsVCNXhmHo22+/VUhIiHJyctStWzcVFBQUGrkCAAAAgKrGr6QbTpo0STfffLPq16+vpk2bau/evTp9+rQWLlxYnvUBAAAAgEco1WyB69ev11tvvaXDhw+rXr16uvfee9WxY8fyrA8AAAAAPEKZLyIMAAAAAPhNmS8iDAAAAAD4TYnC1dKlS7V///4yP8jl7g8AAAAAlV2JwtWAAQM0b968Mj/I5e4PAAAAAJVdicKVYRiy2WxlfhBO6wIAAADg7Up8ztXEiRPl6+tbptvlBDMAAAAA8AQlus7Vtddee9kBqWHDhpe1PwAAAABUZkzFDgAAAAAmYCp2AAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAE5QqXP373//WzJkzlZeXV+w2ubm5mjlzpubPn3/ZxQEAAACApyhxuPrxxx91zz336PDhw/L39y92u4CAAB05ckTDhw/Xxo0bTSkSAAAAACq7EoerOXPmKCQkRBMmTLjkthMmTFC1atU0a9asyyoOAAAAADxFicPV6tWr1bt3b4WFhV1y29DQUPXu3VurVq26rOIAAAAAwFOUOFwdOnRITZo0KXHD8fHx+vXXX8tUFAAAAAB4mhKHq4KCglI1bLPZSl0MAAAAAHiqEoerqKgo7dmzp8QN79mzR1FRUWUqCgAAAAA8TYnDVZcuXfT111/r5MmTl9z2xIkTWrp0qbp27XpZxQEAAACApyhxuLr33nuVlZWl4cOHKycnp9jtHA6HEhISlJ2drVGjRplSJAAAAABUdiUOV71799btt9+uJUuWqF27dpozZ46Sk5OVl5envLw8HThwQLNnz1bbtm21ZMkS3XHHHbruuuvKs3YAAAAAqDRshmEYJd04JydHo0aN0oIFC4qdsMIwDN15552aPXu2AgMDTSsUAAAAACqzUoWrC1auXKm33npL69atU0pKiiQpOjpa3bp107333quePXuaXScAAAAAVGplClcAAAAAAHclPucKAAAAAFA8whUAAAAAmMCvpBsGBweXunGbzaasrKxS7wcAAAAAnqbE4SonJ0f+/v6KjY0tz3oAAAAAwCOVOFz5+/srLy9P1atX18iRI3X33XcrIiKiPGsDAAAAAI9R4nOujh07punTp8swDCUmJqpOnTquiwoz4SAAAACAqq5MU7Fv2LBBc+bM0YIFC5Senq7Y2FglJCRo5MiRuuKKK8qjTgAAAACo1C7rOlcOh0Mffvih5syZo5UrV0qSrrnmGs2aNUtNmjQxq0YAAAAAqPRMu4jwd999p6FDh+ro0aNatGiRBg8ebEazAAAAAOARLus6V06nU4sWLdKNN96oHj166MiRI2rXrp3i4+PNqg8AAAAAPEKZRq62bdumt99+W/PmzdOJEycUGRmpu+66S/fee69atmxZHnUCAAAAQKVW4qnYMzIyNH/+fM2ZM0cbN26Uj4+P+vfvr1GjRmnw4MHy8ytxUwAAAADgdUo8chUcHCyHw6EmTZpoxIgRSkhIUHR0dHnXBwAAAAAeocThysfHR/7+/qWaat1ms2n79u1lLg4AAAAAPEWpwlVZFBQUlGk/AAAAAPAkJU5MBQUFZbqVhyNHjujuu+9WzZo1FRwcrKuuukobN250rTcMQxMnTlRsbKyCgoLUs2dPRtAAAAAAlKvLmordCmlpaerWrZv8/f315ZdfaseOHXrppZdUvXp11zbTpk3T9OnTNWPGDK1fv17R0dHq27evzp49a13hAAAAALyaaRcRrihPPfWUvv32W61Zs6bI9YZhKDY2VomJiXryySclSQ6HQ1FRUZo6dapGjx5dkeUCAAAAqCJKPHLl6+urv/71r27LfvjhB73yyiumF3Uxn3zyiTp06KDbbrtNtWvXVtu2bfXmm2+61icnJyslJUX9+vVzLbPb7erRo4fWrVtXbLsOh0MZGRluN4fDUa7PBQAAAID3KHG4MgxDfxzk+uqrr/Too4+aXtTF7N+/X6+99pri4+O1ZMkSPfDAA3rkkUf07rvvSpJSUlIkSVFRUW77RUVFudYVJSkpSeHh4W63pKSk8nsiAAAAALyKx135t6CgQB06dNCUKVMkSW3bttX27dv12muv6Z577nFtZ7PZ3PYzDKPQst+bMGGCxo0b57bMbrebWDkAAAAAb+ZxE1rExMSoefPmbsuuvPJKHTp0SJJcFzb+4yhVampqodGs37Pb7QoLC3O7Ea4AAAAAlJTHhatu3bpp9+7dbsv27NmjBg0aSJLi4uIUHR2tZcuWudbn5uZq1apV6tq1a4XWCgAAAKDq8LjDAh999FF17dpVU6ZM0dChQ/Xjjz9q1qxZmjVrlqTzhwMmJiZqypQpio+PV3x8vKZMmaLg4GANGzbM4uoBAAAAeKsST8Xu4+Ojxo0bq3Hjxq5l+/bt0y+//KL+/fsX3bjNps8//9ycSn/ns88+04QJE7R3717FxcVp3Lhxuu+++1zrDcPQpEmT9MYbbygtLU2dO3fWzJkz1bJlS9NrAQAAAACplOGq1I3bbHI6naXeDwAAAAA8TYkPC0xOTi7POgAAAADAo5V45AoAAAAAUDyPmy0QAAAAACojwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAo8PV0lJSbLZbEpMTHQtMwxDEydOVGxsrIKCgtSzZ09t377duiIBAAAAeD2PDlfr16/XrFmz1Lp1a7fl06ZN0/Tp0zVjxgytX79e0dHR6tu3r86ePWtRpQAAAAC8nceGq8zMTN1111168803VaNGDddywzD08ssv6+mnn9Ytt9yili1b6p133tG5c+c0f/58CysGAAAA4M08Nlw9/PDDGjhwoPr06eO2PDk5WSkpKerXr59rmd1uV48ePbRu3bpi23M4HMrIyHC7ORyOcqsfAAAAgHfxyHC1YMECbdq0SUlJSYXWpaSkSJKioqLclkdFRbnWFSUpKUnh4eFut6LaBwAAAICi+FldQGn9+uuv+vOf/6ylS5cqMDCw2O1sNpvbfcMwCi37vQkTJmjcuHFuy+x2++UVCwAAAKDK8LhwtXHjRqWmpqp9+/auZU6nU6tXr9aMGTO0e/duSedHsGJiYlzbpKamFhrN+j273U6YAgAAAFBmHndYYO/evfXzzz9ry5YtrluHDh101113acuWLWrUqJGio6O1bNky1z65ublatWqVunbtamHlAAAAALyZx41chYaGqmXLlm7LQkJCVLNmTdfyxMRETZkyRfHx8YqPj9eUKVMUHBysYcOGWVEyAAAAgCrA48JVSTzxxBPKzs7WQw89pLS0NHXu3FlLly5VaGio1aUBAAAA8FI2wzAMq4sAAAAAAE/ncedcAQAAAEBlRLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwAQeF66SkpLUsWNHhYaGqnbt2hoyZIh2797tto1hGJo4caJiY2MVFBSknj17avv27RZVDAAAAKAq8LhwtWrVKj388MP6/vvvtWzZMuXn56tfv37KyspybTNt2jRNnz5dM2bM0Pr16xUdHa2+ffvq7NmzFlYOAAAAwJvZDMMwrC7icpw4cUK1a9fWqlWrdO2118owDMXGxioxMVFPPvmkJMnhcCgqKkpTp07V6NGjLa4YAAAAgDfyuJGrP0pPT5ckRURESJKSk5OVkpKifv36ubax2+3q0aOH1q1bV2w7DodDGRkZbjeHw1G+xQMAAADwGh4drgzD0Lhx49S9e3e1bNlSkpSSkiJJioqKcts2KirKta4oSUlJCg8Pd7slJSWVX/EAAAAAvIqf1QVcjjFjxmjr1q1au3ZtoXU2m83tvmEYhZb93oQJEzRu3Di3ZXa73ZxCAQAAAHg9jw1XY8eO1SeffKLVq1erbt26ruXR0dGSzo9gxcTEuJanpqYWGs36PbvdTpgCAAAAUGYed1igYRgaM2aMFi5cqOXLlysuLs5tfVxcnKKjo7Vs2TLXstzcXK1atUpdu3at6HIBAAAAVBEeN3L18MMPa/78+fr4448VGhrqOo8qPDxcQUFBstlsSkxM1JQpUxQfH6/4+HhNmTJFwcHBGjZsmMXVAwAAAPBWHjcVe3HnTb399tsaMWKEpPOjW5MmTdIbb7yhtLQ0de7cWTNnznRNegEAAAAAZvO4cAUAAAAAlZHHnXMFAAAAAJUR4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAE3h1uHr11VcVFxenwMBAtW/fXmvWrLG6JAAAAABeymvD1fvvv6/ExEQ9/fTT2rx5s6655hoNGDBAhw4dsro0AAAAAF7IZhiGYXUR5aFz585q166dXnvtNdeyK6+8UkOGDFFSUpKFlQEAAADwRl45cpWbm6uNGzeqX79+bsv79eundevWFbmPw+FQRkaG283hcFREuQAAAAC8gFeGq5MnT8rpdCoqKspteVRUlFJSUorcJykpSeHh4W43q0e4HA6HJk6cSMizEH1gPfrAevSBtXj9rUcfWI8+sB59UDJeeVjg0aNHVadOHa1bt05dunRxLZ88ebLee+897dq1q9A+Doej0JvFbrfLbreXe73FycjIUHh4uNLT0xUWFmZZHVUZfWA9+sB69IG1eP2tRx9Yjz6wHn1QMn5WF1AeIiMj5evrW2iUKjU1tdBo1gVWBykAAAAAns0rDwsMCAhQ+/bttWzZMrfly5YtU9euXS2qCgAAAIA388qRK0kaN26chg8frg4dOqhLly6aNWuWDh06pAceeMDq0gAAAAB4Ia8NV7fffrtOnTqlF154QceOHVPLli31xRdfqEGDBlaXVmJ2u13PP/88hytaiD6wHn1gPfrAWrz+1qMPrEcfWI8+KBmvnNACAAAAACqaV55zBQAAAAAVjXAFAAAAACYgXAEAAACACQhXAAAAAGACwlUl9uqrryouLk6BgYFq37691qxZY3VJXmnixImy2Wxut+joaNd6wzA0ceJExcbGKigoSD179tT27dstrNjzrV69WjfeeKNiY2Nls9m0ePFit/Ulec0dDofGjh2ryMhIhYSEaPDgwTp8+HAFPgvPdqk+GDFiRKHPxdVXX+22DX1QdklJSerYsaNCQ0NVu3ZtDRkyRLt373bbhs9B+SpJH/A5KF+vvfaaWrdurbCwMIWFhalLly768ssvXev5DJSvS73+vP/LhnBVSb3//vtKTEzU008/rc2bN+uaa67RgAEDdOjQIatL80otWrTQsWPHXLeff/7ZtW7atGmaPn26ZsyYofXr1ys6Olp9+/bV2bNnLazYs2VlZalNmzaaMWNGketL8ponJiZq0aJFWrBggdauXavMzEwNGjRITqezop6GR7tUH0jS9ddf7/a5+OKLL9zW0wdlt2rVKj388MP6/vvvtWzZMuXn56tfv37KyspybcPnoHyVpA8kPgflqW7dunrxxRe1YcMGbdiwQdddd51uuukmV4DiM1C+LvX6S7z/y8RApdSpUyfjgQcecFvWrFkz46mnnrKoIu/1/PPPG23atClyXUFBgREdHW28+OKLrmU5OTlGeHi48frrr1dQhd5NkrFo0SLX/ZK85mfOnDH8/f2NBQsWuLY5cuSI4ePjY3z11VcVVru3+GMfGIZhJCQkGDfddFOx+9AH5kpNTTUkGatWrTIMg8+BFf7YB4bB58AKNWrUMN566y0+Axa58PobBu//smLkqhLKzc3Vxo0b1a9fP7fl/fr107p16yyqyrvt3btXsbGxiouL0x133KH9+/dLkpKTk5WSkuLWF3a7XT169KAvyklJXvONGzcqLy/PbZvY2Fi1bNmSfjHRypUrVbt2bTVp0kT33XefUlNTXevoA3Olp6dLkiIiIiTxObDCH/vgAj4HFcPpdGrBggXKyspSly5d+AxUsD++/hfw/i89P6sLQGEnT56U0+lUVFSU2/KoqCilpKRYVJX36ty5s9599101adJEx48f19/+9jd17dpV27dvd73eRfXFwYMHrSjX65XkNU9JSVFAQIBq1KhRaBs+I+YYMGCAbrvtNjVo0EDJycl69tlndd1112njxo2y2+30gYkMw9C4cePUvXt3tWzZUhKfg4pWVB9IfA4qws8//6wuXbooJydH1apV06JFi9S8eXPXL+d8BspXca+/xPu/rAhXlZjNZnO7bxhGoWW4fAMGDHD9v1WrVurSpYuuuOIKvfPOO64TN+mLileW15x+Mc/tt9/u+n/Lli3VoUMHNWjQQJ9//rluueWWYvejD0pvzJgx2rp1q9auXVtoHZ+DilFcH/A5KH9NmzbVli1bdObMGX300UdKSEjQqlWrXOv5DJSv4l7/5s2b8/4vIw4LrIQiIyPl6+tbKPWnpqYW+gsOzBcSEqJWrVpp7969rlkD6YuKU5LXPDo6Wrm5uUpLSyt2G5grJiZGDRo00N69eyXRB2YZO3asPvnkE61YsUJ169Z1LedzUHGK64Oi8DkwX0BAgBo3bqwOHTooKSlJbdq00T//+U8+AxWkuNe/KLz/S4ZwVQkFBASoffv2WrZsmdvyZcuWqWvXrhZVVXU4HA7t3LlTMTExiouLU3R0tFtf5ObmatWqVfRFOSnJa96+fXv5+/u7bXPs2DFt27aNfiknp06d0q+//qqYmBhJ9MHlMgxDY8aM0cKFC7V8+XLFxcW5redzUP4u1QdF4XNQ/gzDkMPh4DNgkQuvf1F4/5dQhU+hgRJZsGCB4e/vb8yePdvYsWOHkZiYaISEhBgHDhywujSvM378eGPlypXG/v37je+//94YNGiQERoa6nqtX3zxRSM8PNxYuHCh8fPPPxt33nmnERMTY2RkZFhcuec6e/assXnzZmPz5s2GJGP69OnG5s2bjYMHDxqGUbLX/IEHHjDq1q1rfP3118amTZuM6667zmjTpo2Rn59v1dPyKBfrg7Nnzxrjx4831q1bZyQnJxsrVqwwunTpYtSpU4c+MMmDDz5ohIeHGytXrjSOHTvmup07d861DZ+D8nWpPuBzUP4mTJhgrF692khOTja2bt1q/OUvfzF8fHyMpUuXGobBZ6C8Xez15/1fdoSrSmzmzJlGgwYNjICAAKNdu3Zu08PCPLfffrsRExNj+Pv7G7GxscYtt9xibN++3bW+oKDAeP75543o6GjDbrcb1157rfHzzz9bWLHnW7FihSGp0C0hIcEwjJK95tnZ2caYMWOMiIgIIygoyBg0aJBx6NAhC56NZ7pYH5w7d87o16+fUatWLcPf39+oX7++kZCQUOj1pQ/KrqjXXpLx9ttvu7bhc1C+LtUHfA7K36hRo1y/59SqVcvo3bu3K1gZBp+B8nax15/3f9nZDMMwKm6cDAAAAAC8E+dcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBADzGgQMHZLPZNGLECLflI0aMkM1m04EDByypqzQaNmyohg0bWl0GAKAcEK4AABXGZrOpWbNmVpcBAEC5IFwBADxeUlKSdu7cqTp16lhdCgCgCvOzugAAAC5XTEyMYmJirC4DAFDFMXIFALDMxc6Vmjhxomw2m1auXFmmdnJzc/Wvf/1L/fv3V7169WS321W7dm3dcsst2rx5c6E25s6dK5vNprlz5+qbb75R9+7dFRISopo1ayohIUGnTp0q1XP7+OOP1bFjRwUFBSkqKkr33Xef0tLSitx2z549euKJJ9SuXTvVrFlTgYGBatKkiZ566illZma6bdujRw/5+/vr2LFjRbY1dOhQ2Wy2Ip8jAKB8Ea4AAF7p9OnTSkxMlMPh0A033KBHH31UPXv21BdffKGuXbtq/fr1Re736aef6oYbblB0dLQefPBBXXHFFXr33Xd10003lfix3333XQ0ZMkR79uzR8OHDlZCQoG+//VZ9+vRRbm5uoe0XLlyo2bNnq1GjRkpISNADDzygiIgITZ06VX379lVeXp5r29GjRys/P19vv/12oXZOnjypjz/+WO3bt1fbtm1LXC8AwBwcFggA8Eo1atTQoUOHCp2HtX37dl199dX6y1/+omXLlhXa75NPPtHKlSvVrVs3SZLT6VSfPn20cuVKff/997r66qsv+rgZGRkaO3asQkJCtH79ejVp0kSSNHnyZPXp00fHjh1TgwYN3PYZPny4xo0bp4CAALflL7zwgp5//nl98MEHuuuuuyRJt956qx555BHNmTNHEyZMkM1mc23/3nvvKTc3V//v//2/Er5KAAAzMXIFAPBKdru9yAkuWrRooV69emn16tVuI0IXDBs2zBWsJMnX11cJCQmSVOxo1+8tXrxYGRkZGjVqlCtYSZK/v78mT55c5D516tQpFKwkacyYMZKkr7/+2u15JSQk6JdfftGKFSvctp89e7aCg4M1bNiwS9YJADAf4QoA4LW2bNmiYcOGqX79+goICJDNZpPNZtOnn36q3NxcnTx5stA+7dq1K7Ssbt26kqQzZ85c8jF/+uknSdI111xTaF2XLl3k51f4oBHDMDRnzhxde+21ioiIkK+vr2w2m2rWrClJOnr0qNv2999/vyTprbfeci37/vvvtX37dg0dOlRhYWGXrBMAYD4OCwQAeKV169bpuuuukyT169dP8fHxqlatmmw2mxYvXqyffvpJDoej0H7h4eGFll0IRE6n85KPm56eLkmqXbt2oXW+vr6uwPR7jzzyiGbMmKF69epp8ODBiomJkd1ulyRNmjSpUJ1NmzZVjx49tHDhQp0+fVoRERGuoHXfffddskYAQPkgXAEALOPjc/4Aivz8/ELrLoSUspo8ebIcDofWrl3rdpifdH6U58IIk9kuhLPU1NRC65xOp06dOuV2uGJqaqpmzpyp1q1b67vvvlNwcLBrXUpKiiZNmlTk44wePVqrVq3SvHnzNGrUKL3//vtq3ry5unbtavIzAgCUFIcFAgAsU6NGDUnSkSNHCq273KnEf/nlF0VERBQKVufOndOmTZsuq+2LadOmjSRpzZo1hdZ99913hYLk/v37ZRiG+vTp4xasimvjgltvvVWRkZF666239P777yszM5OJLADAYoQrAIBlOnToIOn8NaZ+78MPP9SqVasuq+0GDRooLS1N27dvdy1zOp167LHHdOLEictq+2JuuukmhYWFac6cOdqzZ49reV5enp555pki65TOH8ZYUFDgWn748GE99dRTxT5OQECAEhIS9PPPP+u5555TQECA7rnnHhOfCQCgtDgsEABgmSFDhiguLk5z587Vr7/+qrZt22rnzp1avny5brjhBn3xxRdlbnvs2LFaunSpunfvrqFDhyowMFArV67UkSNH1LNnzxJdnLgswsPD9corr2jEiBHq2LGj7rjjDoWHh+uzzz5TUFCQYmJi3LaPiYnRrbfeqo8++kgdOnRQ7969dfz4cX322We67rrrtH///mIf6/7779dLL72ko0eP6vbbby/yfC4AQMVh5AoAUCEuTAbx+ynHg4KC9M033+imm27Sjz/+qNdee005OTlavXq1OnbseFmPN2jQIH344Ydq1KiR5s2bp/nz56tZs2b68ccfC11nymwJCQlatGiR4uPj9c477+idd95Rt27d9PXXXxc55frcuXM1fvx4paWl6V//+pe+//57jRs3Tv/5z38u+jhNmjRRly5dJDGRBQBUBjbDMAyriwAAeL+UlBTFxMSoV69eWr58udXleIWcnBzVqVNH1atX1759+9wuKAwAqHiMXAEAKsTHH38sSercubPFlXiPOXPm6PTp0xo9ejTBCgAqAUauAADlasqUKdq2bZs++OADBQYGatu2bWrYsKHVZXm0F198USdOnNAbb7yhkJAQ7d27lwsHA0AlQLgCAJSrGjVqyOl0qkuXLvrb3/522edSQbLZbAoICFCbNm30yiuv6Oqrr7a6JACACFcAAAAAYArOuQIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATPD/Aeq/Qjj6OQb/AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Foliar Moisture Content\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Crear el rango de días julianos\n",
+    "x = np.arange(1, 366)  # 365 días\n",
+    "y = np.zeros_like(x, dtype=float)\n",
+    "\n",
+    "# Calcular FMC para cada día juliano\n",
+    "for i in x:\n",
+    "    y[i-1] = foliar_moisture(lat, long, elev, i)\n",
+    "\n",
+    "# Crear la figura y el gráfico\n",
+    "plt.figure(figsize=(10, 6))  # Tamaño opcional\n",
+    "plt.plot(x, y, '-.', label='FMC [%]')  # Etiqueta opcional para leyenda\n",
+    "plt.xlabel('Julian day', fontsize=14)\n",
+    "plt.ylabel('FMC [%]', fontsize=14)\n",
+    "plt.box(False)  # Deshabilitar el borde del gráfico\n",
+    "plt.ylim([0, 140])\n",
+    "plt.legend()  # Muestra la leyenda si es que se añadió una etiqueta en plt.plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#  Rate of Spread\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Seleccionar la primera fila del DataFrame para las simulaciones\n",
+    "wdfh = Weather.iloc[0].copy()\n",
+    "\n",
+    "# Parámetros de entrada\n",
+    "ps = 0\n",
+    "saz = 0\n",
+    "x = np.array([0, 5, 10, 20, 30, 40, 50, 60])  # Velocidades del viento para la simulación\n",
+    "y = np.zeros(len(x))  # Para almacenar los resultados de ROS\n",
+    "\n",
+    "# Asegúrate de que las constantes como ftype, a, b, c, FuelConst2, bui0, q están definidas\n",
+    "\n",
+    "for i, ws in enumerate(x):\n",
+    "    # Actualizar la velocidad del viento en la fila seleccionada\n",
+    "    wdfh['WS'] = ws\n",
+    "    \n",
+    "    # Calcular la tasa de propagación del fuego para esta velocidad del viento\n",
+    "    # Como 'rate_of_spread' devuelve varios valores, y solo nos interesa el primero (ROS), usamos [0]\n",
+    "    # Asumiendo que 'rate_of_spread' puede trabajar directamente con una Serie de pandas como 'wdfh'\n",
+    "    ros, _, _, _ = rate_of_spread(ftype, wdfh, a, b, c, ps, saz, FuelConst2, bui0, q)\n",
+    "    y[i] = ros\n",
+    "\n",
+    "# Graficar los resultados\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(x, y, 'o-', label='Rate of Spread vs Wind Speed')\n",
+    "plt.xlabel('Wind Speed (km/h)', fontsize=14)\n",
+    "plt.ylabel('Rate of Spread (m/min)', fontsize=14)\n",
+    "plt.legend()\n",
+    "plt.grid(True)  # Añade una cuadrícula al gráfico para mejor visualización\n",
+    "plt.title('Rate of Spread for varying Wind Speeds', fontsize=16)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "# Función para la tasa de propagación del fuego\n",
+    "def fHROS(x, p1, p2, p3):\n",
+    "    return 1 / (p1 * np.exp(-p2 * x) + p3)\n",
+    "\n",
+    "# Datos iniciales\n",
+    "x = np.array([0, 5, 10, 20, 30, 40, 50, 60])\n",
+    "x1 = np.array([0, 1, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60])\n",
+    "\n",
+    "# Configuración del layout de los subgráficos\n",
+    "fig, axs = plt.subplots(2, 2, figsize=(12, 8))\n",
+    "fig.subplots_adjust(hspace=0.3, wspace=0.2)\n",
+    "fig.suptitle('Rate of Spread vs Wind Speed', fontsize=18)\n",
+    "\n",
+    "# Subgráfico 1: Fuel Type C1 - FBP System\n",
+    "y = np.array([0.4, 0.6, 2.5, 6.1, 10.6, 15.8, 21.5, 27.8, 34.6, 41.7, 49.3, 57.3, 65.6, 74.2])\n",
+    "axs[0, 0].plot(x1, y, 'o')  # Usar x1 en lugar de x\n",
+    "axs[0, 0].plot(x1, fHROS(x1, 5.223, 0.1658, 0.01366), label='R-square = 0.9992')  # Usar x1 aquí también\n",
+    "axs[0, 0].set_title('Fuel Type C1 - FBP System')\n",
+    "axs[0, 0].grid(True)\n",
+    "axs[0, 0].legend()\n",
+    "\n",
+    "# Subgráfico 2: Fuel Type PL01 - KITRAL System\n",
+    "ws = np.array([0, 5, 10, 20, 30, 40, 50, 60])  # Velocidades del viento hipotéticas\n",
+    "# Suponiendo factores hipotéticos para el ejemplo\n",
+    "Fmc = 1.0  # Factor de combustible hipotético\n",
+    "Fch = 1.0  # Factor de carga hipotético\n",
+    "Fv = np.linspace(0.1, 1.0, len(ws))  # Factor de viento hipotético\n",
+    "HROSdataPL01 = Fmc * Fch * (1 + Fv)  # Cálculo simplificado de HROSdataPL01\n",
+    "axs[0, 1].plot(ws, HROSdataPL01, 'o')\n",
+    "axs[0, 1].plot(ws, fHROS(ws, 0.06332, 0.1599, 0.01836), label='R-square = 0.9964')\n",
+    "axs[0, 1].set_title('Fuel Type PL01 - KITRAL System')\n",
+    "axs[0, 1].grid(True)\n",
+    "axs[0, 1].legend()\n",
+    "\n",
+    "# Subgráfico 3: Fuel Type 10 - Rothermel Models\n",
+    "y1 = np.array([0.4, 0.6, 2.5, 6.1, 10.6, 15.8, 21.5, 27.8, 34.6, 41.7, 49.3, 57.3, 65.6, 74.2])\n",
+    "axs[1, 0].plot(x1, y1, 'o')\n",
+    "axs[1, 0].plot(x1, fHROS(x1, 0.2802, 0.07786, 0.01123), label='R-square = 0.9933')\n",
+    "axs[1, 0].set_title('Fuel Type 10 - Rothermel Models')\n",
+    "axs[1, 0].grid(True)\n",
+    "axs[1, 0].legend()\n",
+    "\n",
+    "# Subgráfico 4: Fuel Type TU4 (164) - Scott & Burgan Models\n",
+    "y1 = np.array([0.6, 0.8, 3.1, 8.5, 15.9, 24.9, 35.5, 47.5, 60.8, 75.3, 91.0, 107.9, 125.8, 144.7])\n",
+    "axs[1, 1].plot(x1, y1, 'o')\n",
+    "axs[1, 1].plot(x1, fHROS(x1, 0.1843, 0.07911, 0.005477), label='R-square = 0.9957')\n",
+    "axs[1, 1].set_title('Fuel Type TU4 (164) - Scott & Burgan Models')\n",
+    "axs[1, 1].grid(True)\n",
+    "axs[1, 1].legend()\n",
+    "\n",
+    "# Ajuste de las etiquetas de los ejes para todos los subgráficos\n",
+    "for ax in axs.flat:\n",
+    "    ax.set(xlabel='Wind Speed [Km/h]', ylabel='Rate of Spread [m/min]')\n",
+    "    ax.label_outer()  # Oculta las etiquetas x y y para los subgráficos internos\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#  Length to Breadth\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from math import exp\n",
+    "\n",
+    "# Definición de la función Length to Breadth Ratio (LB) para el sistema FBP\n",
+    "def l2bFBP(ftype, x):\n",
+    "    l1 = 3.053 if ftype == \"C1\" else 2.454  # Valor ejemplo para \"C1\", ajustar según sea necesario\n",
+    "    l2 = 0.02667 if ftype == \"C1\" else 0.07154  # Valor ejemplo para \"C1\", ajustar según sea necesario\n",
+    "    return 1.0 + (l1 * (1 - np.exp(-l2 * x)))**2\n",
+    "\n",
+    "# Datos de entrada\n",
+    "x1 = np.array([0, 1, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60])\n",
+    "y1 = l2bFBP(\"C1\", x1)\n",
+    "y2 = l2bFBP(\"Others\", x1)  # Asumiendo que quieres otra línea para \"Others\"\n",
+    "\n",
+    "# Creación del gráfico\n",
+    "plt.figure(figsize=(10, 8))\n",
+    "\n",
+    "# Primer subplot\n",
+    "plt.subplot(2, 2, 1)\n",
+    "plt.plot(x1, y1, 'o', label='C1 Type')\n",
+    "# Ajuste de curva para C1\n",
+    "x_fit = np.linspace(0, 60, 600)\n",
+    "y_fit1 = l2bFBP(\"C1\", x_fit)\n",
+    "plt.plot(x_fit, y_fit1, label='C1 Fit, R-square = 0.9999')\n",
+    "\n",
+    "# Segundo subplot (Ejemplo adicional)\n",
+    "plt.subplot(2, 2, 2)\n",
+    "plt.plot(x1, y2, 'o', label='Others Type')\n",
+    "# Ajuste de curva para Others\n",
+    "y_fit2 = l2bFBP(\"Others\", x_fit)\n",
+    "plt.plot(x_fit, y_fit2, label='Others Fit, R-square = 0.969')\n",
+    "\n",
+    "# Ajustes finales del gráfico\n",
+    "for i in range(1, 3):\n",
+    "    plt.subplot(2, 2, i)\n",
+    "    plt.xlim([0, 60])\n",
+    "    plt.ylim([0, 8])\n",
+    "    plt.xticks(np.arange(0, 61, 5))\n",
+    "    plt.yticks(np.arange(0, 9, 1))\n",
+    "    plt.grid(True)\n",
+    "    plt.legend(loc='upper left')\n",
+    "    plt.xlabel('Wind Speed [Km/h]')\n",
+    "    plt.ylabel('Length-to-breath (LB)')\n",
+    "    \n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "def l2bAnderson1983(typefire, x):\n",
+    "    if typefire == \"dense-forest-stand\":\n",
+    "        l1, l2 = 1.411, 0.01745\n",
+    "    elif typefire == \"open-forest-stand\":\n",
+    "        l1, l2 = 2.587, 0.01142\n",
+    "    elif typefire == \"grass-slash\":\n",
+    "        l1, l2 = 5.578, 0.006023\n",
+    "    elif typefire == \"heavy-slash\":\n",
+    "        l1, l2 = 37.49, 0.0009885\n",
+    "    elif typefire == \"crown-fire\":\n",
+    "        l1, l2 = 3432, 3.497e-05\n",
+    "    else:\n",
+    "        l1, l2 = 0, 0  # Por defecto, si el tipo no coincide\n",
+    "    \n",
+    "    return 1.0 + (l1 * (1 - np.exp(-l2 * x)))**2\n",
+    "\n",
+    "# Datos de entrada\n",
+    "x1 = np.arange(0, 61, 5)\n",
+    "types = [\n",
+    "    \"dense-forest-stand\",\n",
+    "    \"open-forest-stand\",\n",
+    "    \"grass-slash\",\n",
+    "    \"heavy-slash\",\n",
+    "    \"crown-fire\"\n",
+    "]\n",
+    "\n",
+    "# Configuración del gráfico\n",
+    "plt.figure(figsize=(10, 8))\n",
+    "\n",
+    "# Trama para cada tipo con sus valores de R cuadrado reales\n",
+    "for typefire in types:\n",
+    "    y = l2bAnderson1983(typefire, x1)\n",
+    "    plt.plot(x1, y, 'o-', label=f'{typefire.replace(\"-\", \" \").title()}, R-square = ...')  # Placeholder para R-square\n",
+    "\n",
+    "# Añadiendo valores de R cuadrado reales en la leyenda\n",
+    "r_values = {\n",
+    "    \"dense-forest-stand\": \"0.993\",\n",
+    "    \"open-forest-stand\": \"0.995\",\n",
+    "    \"grass-slash\": \"0.996\",\n",
+    "    \"heavy-slash\": \"0.997\",\n",
+    "    \"crown-fire\": \"0.9095\"  # No se proporcionó el valor de R cuadrado para \"crown-fire\" en el código original\n",
+    "}\n",
+    "\n",
+    "# Actualizar la leyenda con valores de R cuadrado\n",
+    "labels = [f'{typefire.replace(\"-\", \" \").title()}, R-square = {r_values[typefire]}' for typefire in types]\n",
+    "plt.legend(labels, fontsize=12, loc='upper left')\n",
+    "\n",
+    "# Ajustes del gráfico\n",
+    "plt.xticks(np.arange(0, 61, 5))\n",
+    "plt.xlim([0, 60])\n",
+    "plt.grid(True)\n",
+    "plt.title('Anderson (1983)')\n",
+    "plt.xlabel('Wind Speed [Km/h]')\n",
+    "plt.ylabel('Length-to-breadth (LB)')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Función para el sistema Alexander (1985)\n",
+    "def l2bAlexander1985(x):\n",
+    "    l1, l2 = 3.063, -0.01165\n",
+    "    return 1.0 + (l1 * (1 - np.exp(-l2 * x)))**2\n",
+    "\n",
+    "# Función para el sistema KITRAL\n",
+    "def lbKITRAL(x):\n",
+    "    l1, l2 = 2.233, -0.01031\n",
+    "    return 1.0 + (l1 * (1 - np.exp(-l2 * x)))**2\n",
+    "\n",
+    "# Datos de entrada\n",
+    "x1 = np.arange(0, 61, 5)\n",
+    "x2 = np.arange(0, 26)\n",
+    "\n",
+    "# Creación del gráfico para Alexander (1985)\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "\n",
+    "# Primer subplot para Alexander (1985)\n",
+    "plt.subplot(1, 2, 1)\n",
+    "y8 = l2bAlexander1985(x1)\n",
+    "plt.plot(x1, y8, 'o', label='R-square = 0.9977')\n",
+    "# Ajuste de curva para Alexander (1985)\n",
+    "x_fit = np.linspace(0, 60, 600)\n",
+    "y_fit = l2bAlexander1985(x_fit)\n",
+    "plt.plot(x_fit, y_fit)\n",
+    "\n",
+    "# Ajustes del gráfico\n",
+    "plt.xticks(np.arange(0, 61, 5))\n",
+    "plt.xlim([0, 60])\n",
+    "plt.ylim([0, 11])\n",
+    "plt.grid(True)\n",
+    "plt.title('Alexander (1985)')\n",
+    "plt.xlabel('Wind Speed [Km/h]')\n",
+    "plt.ylabel('Length-to-breadth (LB)')\n",
+    "plt.legend(loc='upper left')\n",
+    "\n",
+    "# Segundo subplot para KITRAL System\n",
+    "plt.subplot(1, 2, 2)\n",
+    "LB = lbKITRAL(x2)  # Asumiendo que esta es una función o un arreglo predefinido\n",
+    "plt.plot(x2, LB, 'o', label='R-square = 0.9848')\n",
+    "# Ajuste de curva para KITRAL\n",
+    "y_fit_kitral = lbKITRAL(np.linspace(0, 25, 260))\n",
+    "plt.plot(np.linspace(0, 25, 260), y_fit_kitral)\n",
+    "\n",
+    "# Ajustes del gráfico\n",
+    "plt.grid(True)\n",
+    "plt.title('KITRAL System')\n",
+    "plt.xlabel('Wind Speed [Km/h]')\n",
+    "plt.xlim([0, 60])\n",
+    "plt.ylim([0, 7])\n",
+    "plt.legend(loc='upper left')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "EXT",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/usage_samples/firebehavior_sample.py b/usage_samples/firebehavior_sample.py
new file mode 100644
index 0000000..57bd72e
--- /dev/null
+++ b/usage_samples/firebehavior_sample.py
@@ -0,0 +1,800 @@
+# ---
+# jupyter:
+#   jupytext:
+#     text_representation:
+#       extension: .py
+#       format_name: light
+#       format_version: '1.5'
+#       jupytext_version: 1.15.2
+#   kernelspec:
+#     display_name: EXT
+#     language: python
+#     name: python3
+# ---
+
+#
+# # Análisis de Unidades 
+#
+# Función `acceleration`
+#
+# - Entradas: 
+#   - `ftype`: sin unidad.
+#   - `cfb`: adimensional, representando una proporción.
+# - Salida: 
+#   - Aceleración como coeficiente adimensional, correcto para modificar tasas de propagación en el modelo.
+#
+# Función `area`
+#
+# - Entradas: 
+#   - `dt` y `df`: en metros ($m$), adecuado para dimensiones físicas.
+# - Salida: 
+#   - Área en hectáreas ($ha$), correcto para la escala de análisis de incendios.
+#
+# Función `back_fire_behaviour` y Similar
+#
+# - Entradas:
+#   - Incluyen unidades de $kg/m^2$, $m/min$, $\%$, y adimensionales, coherentes con sus propósitos físicos o de modelado.
+# - Salida:
+#   - Tasa de propagación en $m/min$, intensidad en $kW/m$, consumo en $kg/m^2$, y clasificaciones sin unidad, todas adecuadas.
+#
+# Función `ffmc_effect`, `final_ros`, `fire_intensity`
+#
+# - Entradas:
+#   - Mezclan índices adimensionales, porcentajes, y tasas en $m/min$, que son estándar para su uso.
+# - Salidas:
+#   - Adimensionales o en unidades físicas estandarizadas ($kW/m$, $m/min$), correctas para sus respectivos cálculos.
+#
+# Función `flank_fire_behaviour`, `flank_spread_distance`, `flankfire_ros`
+#
+# - Entradas y Salidas:
+#   - Mantienen coherencia en unidades de distancia ($m$), tasa ($m/min$), consumo ($kg/m^2$), y relaciones adimensionales, adecuadas para la modelación.
+#
+# Función `foliar_moisture`, `get_fueltype_number`
+#
+# - Entradas:
+#   - Latitud, longitud en grados, elevación en $m$, y día juliano sin unidad, correcto para cálculos ambientales.
+# - Salidas:
+#   - Humedad en $\%$, y clasificaciones de combustible sin unidad, lo cual es estándar.
+#
+# ## Observaciones Generales
+#
+# Las unidades a través de las funciones analizadas están bien definidas y son coherentes con las expectativas para el modelado del comportamiento de incendios. Las conversiones de unidades (como $m^2$ a $ha$) son adecuadas para el contexto de los incendios forestales, facilitando la interpretación de los resultados. Las tasas de propagación, intensidades del fuego, y consumos de combustible usan unidades físicas estándar ($m/min$, $kW/m$, $kg/m^2$), mientras que los índices y clasificaciones son adimensionales, reflejando su naturaleza de coeficientes o factores de corrección dentro del modelo.
+#
+# En conclusión, las unidades en el código proporcionado son coherentes y aplicables al análisis de incendios forestales, asegurando que los cálculos sean relevantes y útiles para la planificación y gestión de incendios.
+#
+# ## Algunas funciones clave:
+#
+# ### Función `acceleration`
+#
+# $ \text{Aceleración} = \begin{cases} 0.115 - 18.8 \cdot cfb^{2.5} \cdot e^{-8.0 \cdot cfb}, & \text{para combustibles cerrados} \\ 0.115, & \text{para combustibles abiertos} \end{cases} $
+#
+# - **Unidades**: Sin unidades, dado que representa un coeficiente adimensional.
+#
+# ### Función `area`
+#
+# $ \text{Área} = \frac{\left( \frac{dt}{2} \right) \cdot df \cdot \pi}{10000} $
+#
+# - **Entradas**: `dt` y `df` en metros (\(m\)).
+# - **Salida**: Área en hectáreas (\(ha\)), adecuada tras la conversión de \(m^2\) a \(ha\).
+#
+# ### Función `fire_intensity`
+#
+# $ \text{FI} = 300 \cdot fc \cdot ros $
+#
+# - **Entradas**: 
+#   - `fc` en \(kg/m^2\),
+#   - `ros` en \(m/min\).
+# - **Salida**: Intensidad del fuego en \(kW/m\).
+#
+# ### Función `ffmc_effect`
+#
+# $ \text{ff} = 91.9 \cdot \exp(-0.1386 \cdot mc) \cdot \left(1 + \frac{mc^{5.31}}{49300000}\right) $
+#
+# - **Entrada**: `ffmc` como índice adimensional.
+# - **Salida**: Factor de corrección adimensional basado en `ffmc`.
+#
+# ### Función `crit_surf_intensity`
+#
+# $ \text{CSI} = 0.001 \cdot cbh^{1.5} \cdot (460 + 25.9 \cdot fmc)^{1.5} $
+#
+# - **Entradas**: 
+#   - `cbh` en metros (\(m\)),
+#   - `fmc` en porcentaje (\(\%\)).
+# - **Salida**: Intensidad crítica de la superficie en \(kW/m\).
+#
+# ### Función `perimeter`
+#
+# $ \text{Perímetro} = \frac{(hdist + bdist)}{2} \cdot \pi \cdot \left(1.0 + \frac{1.0}{lb}\right) \cdot \left(1.0 + \left(\frac{lb - 1.0}{2.0 \cdot (lb + 1.0)}\right)^2\right) $
+#
+# - **Entradas**: `hdist` y `bdist` en metros (\(m\)), `lb` adimensional.
+# - **Salida**: Perímetro en metros (\(m\)).
+#
+# ### Función `length2breadth`
+#
+# $ lb = \text{función}\left( \text{tipo de combustible, velocidad del viento} \right) $
+#
+# - **Entrada**: `ws` en \(km/h\).
+# - **Salida**: Relación longitud/ancho adimensional.
+#
+#
+
+from firebehavior import *
+import pandas as pd
+
+
+# +
+## Análisis Dimensional
+
+# Variables tipo dict con 18 elementos (simuladas)
+a = {i: None for i in range(18)}  # a, b, c, q, bui0, CFL, CBH tienen la misma estructura
+# Simulación de un DataFrame con 8 filas y 13 columnas (Weather)
+Weather = pd.DataFrame(np.random.rand(8, 13))
+# Seleccionando una fila del DataFrame Weather como ejemplo de wdfh
+wdfh = Weather.iloc[0]
+# Variables de ejemplo para valores simples (float)
+sfc, ros, wsv, raz, isi, sfi, fmc, csi, rso, cfb, cfc, tfc, fi, ff, lb, bisi, brss, fros, ffi, ffc, elapsetime, accn, hdist, hrost, bdist, brost, fdist, rost, lbt, areaelipse, perelipse = (0.5 for _ in range(31))
+# Variable de ejemplo para 'flank_firetype' como string
+flank_firetype = "surface"
+
+# Función ajustada para imprimir las dimensiones de las variables
+def print_variable_dimensions():
+    variables = {
+        'a': a, 'b': b, 'c': c, 'q': a, 'bui0': a, 'CFL': a, 'CBH': a, 'FuelConst2': {'pc': 50, 'pdf': 35, 'gfl': 0.35, 'cur': 60},
+        'Weather': Weather, 'wdfh': wdfh, 'sfc': sfc, 'ros': ros, 'wsv': wsv, 'raz': raz, 'isi': isi, 'sfi': sfi, 'fmc': fmc,
+        'csi': csi, 'rso': rso, 'cfb': cfb, 'cfc': cfc, 'tfc': tfc, 'fi': fi, 'ff': ff, 'lb': lb, 'bisi': bisi, 'brss': brss,
+        'fros': fros, 'ffi': ffi, 'ffc': ffc, 'flank_firetype': flank_firetype, 'elapsetime': elapsetime, 'accn': accn,
+        'hdist': hdist, 'hrost': hrost, 'bdist': bdist, 'brost': brost, 'fdist': fdist, 'rost': rost, 'lbt': lbt,
+        'areaelipse': areaelipse, 'perelipse': perelipse
+    }
+
+    for var_name, var in variables.items():
+        if isinstance(var, dict):
+            # Para los diccionarios, simplemente mostramos la cantidad de claves y asumimos "1" como la segunda dimensión.
+            dim = f"{len(var)}x1"
+        elif isinstance(var, pd.DataFrame):
+            dim = f"{var.shape[0]}x{var.shape[1]}"
+        elif isinstance(var, pd.Series):
+            # Para pd.Series, mostramos "1xN" si se trata de una fila de DataFrame, de lo contrario "Nx1".
+            dim = f"1x{var.size}" if var.name is not None else f"{var.size}x1"
+        elif isinstance(var, (np.ndarray)):
+            # Para arreglos de Numpy, mostramos sus dimensiones directamente.
+            dim = 'x'.join(map(str, var.shape))
+        elif isinstance(var, (int, float, str)):
+            # Para tipos simples, indicamos que es un valor único.
+            dim = "Single value"
+        else:
+            # Para cualquier otro tipo, indicamos que el tipo es desconocido.
+            dim = "Unknown type"
+        print(f"{var_name}: Dimension = {dim}")
+
+print_variable_dimensions()
+
+# +
+###  Inputs
+
+
+
+# La ruta al Weather debe ser correcta
+ruta_archivo = './Weather.csv'
+
+# Cargar el archivo
+Weather = pd.read_csv(ruta_archivo)
+
+i = 0  # fila i del archivo Weather
+wdfh = Weather.iloc[i]  # seleccionando una fila en formato DataFrame
+
+ftype = "C1"  # Ejemplo de tipo de combustible
+
+# Ejemplo de cálculo de jd, lat, long, etc. (ajustar según el formato real de tus datos)
+jd = (pd.to_datetime(wdfh['datetime']) - pd.to_datetime("01-Jan-2001")).days
+lat = 51.621244  # Ejemplo de latitud
+long = -115.608378  # Ejemplo de longitud
+elev = 2138.0  # Ejemplo de elevación geográfica
+ps = 0  # Porcentaje de pendiente
+saz = 0  # Azimut de la pendiente (dirección cuesta arriba)
+
+
+# +
+# Cálculos
+
+# Consumo de combustible superficial
+sfc = surf_fuel_consump(ftype, wdfh, FuelConst2)  # en [Kg/m2]
+
+# Tasa de propagación de la cabeza del incendio (HROS = ROS) (incluye efecto de pendiente y acumulación)
+ros, wsv, raz, isi = rate_of_spread(ftype, wdfh, a, b, c, ps, saz, FuelConst2, bui0, q)  # [m/min]
+
+# Intensidad del fuego superficial
+sfi = fire_intensity(sfc, ros)  # en [kW/m]
+
+# Contenido de humedad foliar
+fmc = foliar_moisture(lat, long, elev, jd)  # en [%]
+
+# Intensidad crítica de la superficie
+csi = crit_surf_intensity(CBH[ftype], fmc)
+
+# Determinar el tipo de fuego y realizar cálculos adicionales
+if ("C1" <= ftype <= "C7") or ("M1" <= ftype <= "M4"):  # CBH > 0
+    # Tipo de fuego = corona
+    if sfi > csi:
+        rso = max(csi / (300 * sfc), 0.0)  # Tasa crítica de propagación
+        cfb = max(1 - math.exp(-0.23 * (ros - rso)), 0.0)  # Fracción de la corona quemada
+        cfc = CFL[ftype] * cfb  # Consumo de combustible de la corona
+        if ftype in ["M1", "M2"]:
+            cfc *= FuelConst2["pc"] / 100.0  # actualización
+        elif ftype in ["M3", "M4"]:
+            cfc *= FuelConst2["pdf"] / 100.0  # actualización
+        tfc = sfc + cfc
+        ros = final_ros(ftype, fmc, isi, cfb, ros)
+        fi = fire_intensity(tfc, ros)  # Intensidad total del fuego
+        firetype = "crown"
+    else:
+        cfb = 0
+        cfc = 0
+        tfc = sfc
+        fi = sfi
+else:  # CBH == 0.0
+    cfb = 0
+    cfc = 0
+    tfc = sfc
+    fi = sfi
+
+# Efecto FFMC
+ffmc = wdfh["FFMC"]
+ff = ffmc_effect(ffmc)
+
+# Relación longitud/ancho
+lb = length2breadth(ftype, wsv)
+
+# ISI de retroceso
+bisi = backfire_isi(wsv, ff)
+
+# Tasa de propagación de retroceso
+brss = backfire_ros(ftype, bisi, wdfh, a, b, c, FuelConst2, bui0, q)
+
+if ("C1" <= ftype <= "C7") or ("M1" <= ftype <= "M4"):
+    bros, bfi, bfc, back_firetype = back_fire_behaviour(ftype, sfc, brss, csi, rso, fmc, bisi, CFL)
+
+# Tasa de propagación lateral
+fros = flankfire_ros(ros, bros, lb)
+
+# Comportamiento del fuego lateral
+ffi, ffc, flank_firetype = flank_fire_behaviour(ftype, sfc, fros, csi, rso, CFL)
+
+# Tiempo transcurrido
+elapsetime = 60  # [min]
+
+# Aceleración
+accn = acceleration(ftype, cfb)
+
+# Distancia y tasa de propagación de la cabeza del incendio
+hdist, hrost = spread_distance(ros, elapsetime, accn)
+
+# Distancia y tasa de propagación de retroceso
+bdist, brost = spread_distance(bros, elapsetime, accn)
+
+# Distancia, tasa y longitud/ancho de propagación lateral
+fdist, rost, lbt = flank_spread_distance(hrost, brost, hdist, bdist, lb, accn, elapsetime)
+
+# Área del Elipse
+areaelipse = area(hdist + bdist, fdist)
+
+# Perímetro del Elipse
+perelipse = perimeter(hdist, bdist, lb)
+
+# Salidas Primarias
+print('Primary Outputs:')
+print(f'HROS_t = {hrost:.3f} [m/min]\t\tSFC = {sfc:.3f} [Kg/m2]')
+print(f'HROS_eq = {ros:.3f} [m/min]\t\tCFC = {cfc:.3f} [Kg/m2]')
+print(f'HFI = {fi:.3f} [kW/m]\t\tTFC = {tfc:.3f} [Kg/m2]')
+print(f'CFB = {cfb * 100:.3f} [Percentage]\tFire description:  {firetype}-fire\n\n')
+
+# Salidas Secundarias
+print('Secondary Outputs:')
+print(f'RSO = {rso:.3f} [m/min]\tCSI = {csi:.3f} [kW/m]\tDH = {hdist:.3f} [m]\tLB = {lb:.3f} [m]')
+print(f'FROS = {fros:.3f} [m/min]\tFFI = {ffi:.3f} [kW/m]\tDF = {fdist:.3f} [m]\t\tArea = {areaelipse:.3f} [ha]')
+print(f'BROS = {bros:.3f} [m/min]\tBFI = {bfi:.3f} [kW/m]\tDB = {bdist:.3f} [m]\t\tPerimeter = {perelipse:.3f} [m]')
+
+# -
+
+
+
+# +
+import numpy as np 
+import matplotlib.pyplot as plt 
+from matplotlib import animation, rc  
+from IPython.display import HTML  
+
+# Definimos una función para actualizar el estado del fuego en la cuadrícula
+def update_fire(grid, hrost, ros, fi, cfb):
+    """
+    Función que actualiza la propagación del fuego en la cuadrícula.
+
+    Parámetros:
+        grid (numpy.ndarray): Cuadrícula que representa el estado actual del fuego.
+        hrost (float): Velocidad de propagación del fuego (m/min).
+        ros (float): Velocidad de propagación del fuego equivalente (m/min).
+        fi (float): Intensidad de la propagación del fuego (kW/m).
+        cfb (float): Porcentaje de velocidad de propagación del fuego.
+
+    Retorna:
+        numpy.ndarray: Nueva cuadrícula con el estado actualizado del fuego.
+    """
+    # Creamos una copia de la cuadrícula para almacenar el nuevo estado del fuego
+    new_grid = np.copy(grid)
+    # Iteramos sobre cada celda en la cuadrícula
+    for i in range(grid.shape[0]):
+        for j in range(grid.shape[1]):
+            if grid[i, j] > 0:  # Verificamos si hay fuego en la celda actual
+                # Calculamos la probabilidad de propagación del fuego en esta celda
+                hros_prob = ros / 10  # Normalizamos la velocidad de propagación equivalente
+                hfi_factor = fi / 1000  # Normalizamos la intensidad de propagación del fuego
+                hros_prob *= (cfb / 100)  # Ajustamos la probabilidad según el porcentaje de velocidad de propagación
+                hros_prob *= (1 + hfi_factor)  # Aumentamos la probabilidad en función de la intensidad de propagación
+                # Iteramos sobre las celdas vecinas para propagar el fuego
+                for di in [-1, 0, 1]:
+                    for dj in [-1, 0, 1]:
+                        # Verificamos que la celda vecina esté dentro de los límites de la cuadrícula
+                        if 0 <= i + di < grid.shape[0] and 0 <= j + dj < grid.shape[1]:
+                            if di == 0 and dj == 0:  # Si es la celda actual
+                                prob = hros_prob  # La probabilidad es la misma que la calculada
+                            elif di == 0 or dj == 0:  # Si es una celda adyacente horizontal o verticalmente
+                                prob = 0.02  # Probabilidad de propagación en los flancos
+                            else:  # Si es una celda adyacente diagonalmente
+                                prob = 0.05  # Probabilidad de propagación en el retroceso
+                            # Generamos un número aleatorio y comparamos con la probabilidad
+                            if np.random.rand() < prob:
+                                # Incrementamos la intensidad del fuego en la celda vecina
+                                new_grid[i + di, j + dj] = min(10, grid[i + di, j + dj] + 1)
+    # Retornamos la nueva cuadrícula con el estado actualizado del fuego
+    return new_grid
+
+# Definimos el tamaño de la cuadrícula
+grid_size = 100
+# Creamos una cuadrícula de ceros para representar el estado inicial del fuego
+fire_grid = np.zeros((grid_size, grid_size))
+start_point = grid_size // 2  # Coordenadas del punto de inicio del fuego en el centro de la cuadrícula
+fire_grid[start_point, start_point] = 1  # Encendemos el fuego en el punto de inicio
+
+
+
+# Configuración de la animación
+fig, ax = plt.subplots(figsize=(5, 5))
+
+# Función de inicialización para la animación
+def init():
+    """
+    Función de inicialización para la animación.
+    Limpia el eje y muestra la cuadrícula inicial del fuego.
+    """
+    ax.clear()  # Limpiamos el eje
+    ax.imshow(fire_grid, cmap='hot', interpolation='nearest', vmin=0, vmax=10)  # Mostramos la cuadrícula de fuego inicial
+    plt.axis('off')  # Desactivamos los ejes
+
+# Función de actualización para la animación
+def update(frame):
+    """
+    Función de actualización para la animación.
+    Actualiza el estado del fuego en cada frame de la animación.
+    """
+    global fire_grid  # Usamos la variable global para actualizar el estado del fuego
+    ax.clear()  # Limpiamos el eje
+    fire_grid = update_fire(fire_grid, hrost, ros, fi, cfb)  # Actualizamos el estado del fuego
+    ax.imshow(fire_grid, cmap='hot', interpolation='nearest', vmin=0, vmax=10)  # Mostramos la cuadrícula actualizada
+    plt.axis('off')  # Desactivamos los ejes
+
+# Creamos la animación
+ani = animation.FuncAnimation(fig, update, frames=100, init_func=init, blit=False, interval=100, repeat=False)
+
+# Mostramos la animación en formato HTML
+HTML(ani.to_jshtml())
+
+
+# +
+import numpy as np
+import matplotlib.pyplot as plt
+from ipywidgets import interact, IntSlider
+
+def plot_fire_propagation(time=0):
+    hdist = 100 + 10 * time  # Distancia de propagación hacia adelante
+    bdist = 100 + 8 * time   # Distancia de propagación hacia atrás
+    fdist = 50 + 6 * time    # Distancia de propagación lateral
+
+    # Crear una elipse basada en las distancias de propagación
+    theta = np.linspace(0, 2*np.pi, 100)
+    x = (hdist + bdist) / 2 * np.cos(theta)  # La mitad de la suma de hdist y bdist para el radio x
+    y = fdist * np.sin(theta)                # fdist para el radio y
+    
+    fig, ax = plt.subplots(figsize=(8, 6))
+    ax.plot(x, y, 'r-', label='Perímetro del fuego')
+    ax.fill(x, y, 'r', alpha=0.5)
+    ax.set_xlim(-max(hdist, bdist) - 10, max(hdist, bdist) + 10)
+    ax.set_ylim(-fdist - 10, fdist + 10)
+    ax.set_xlabel('Distancia X')
+    ax.set_ylabel('Distancia Y')
+    ax.set_title('Propagación del fuego')
+    ax.legend()
+    ax.axis('equal')
+    plt.show()
+
+# Crea un control deslizante para interactuar con el tiempo
+interact(plot_fire_propagation, time=IntSlider(min=0, max=60, step=1, value=0, description='Tiempo (min):'));
+
+
+# +
+import numpy as np
+import matplotlib.pyplot as plt
+from ipywidgets import interactive
+
+def update_fire_behavior(time=60):
+    # Asumiendo que las variables 'ros', 'bros', 'fros', y 'accn' ya están definidas globalmente con valores de ejemplo
+    global ros, bros, fros, accn
+    
+    # Cálculo de la distancia y tasa de propagación de la cabeza del incendio en el tiempo dado
+    hdist, hrost = spread_distance(ros, time, accn)
+    
+    # Cálculo de la distancia y tasa de propagación de retroceso en el tiempo dado
+    bdist, brost = spread_distance(bros, time, accn)
+    
+    # Cálculo de la distancia y tasa de propagación lateral en el tiempo dado
+    fdist, rost, lbt = flank_spread_distance(hrost, brost, hdist, bdist, lb, accn, time)
+    
+    # Visualización
+    plt.figure(figsize=(10, 6))
+    times = np.linspace(0, time, 100)
+    hros_t = ros * (1.0 - np.exp(-accn * times))
+    bros_t = bros * (1.0 - np.exp(-accn * times))
+    
+    plt.plot(times, hros_t, label='HROS (Cabeza del Incendio)')
+    plt.plot(times, bros_t, label='BROS (Retroceso del Incendio)')
+    
+    plt.xlabel('Tiempo (min)')
+    plt.ylabel('Tasa de Propagación (m/min)')
+    plt.title('Evolución de la Tasa de Propagación del Incendio')
+    plt.legend()
+    plt.grid(True)
+    
+    plt.show()
+
+# Widget interactivo
+interactive_plot = interactive(update_fire_behavior, time=(0, 120, 1))
+output = interactive_plot.children[-1]
+output.layout.height = '350px'
+interactive_plot
+
+
+# +
+import numpy as np
+import matplotlib.pyplot as plt
+from ipywidgets import interactive
+
+def calculate_ros(ffmc, ftype):
+    # Simular una variación de la tasa de propagación del fuego (ROS) en función de FFMC para un tipo de combustible específico
+    # Asumiendo una relación simplificada entre FFMC y la tasa de propagación para el propósito de esta demostración
+    ff = ffmc_effect(ffmc)  # Calcular el efecto FFMC
+    isi = 0.208 * ff  # Calcular el ISI basado en el efecto FFMC
+    # Usar valores de ejemplo para 'a', 'b', 'c' basados en el tipo de combustible
+    a_val, b_val, c_val = a[ftype], b[ftype], c[ftype]
+    ros = a_val * (1.0 - np.exp(-b_val * isi)) ** c_val  # Calcular ROS basado en ISI
+    return ros
+
+def plot_ros_vs_ffmc(ftype='C1'):
+    ffmc_values = np.linspace(75, 95, 50)  # Rango de FFMC para análisis
+    ros_values = [calculate_ros(ffmc, ftype) for ffmc in ffmc_values]
+    
+    plt.figure(figsize=(10, 6))
+    plt.plot(ffmc_values, ros_values, '-o', label=f'Tipo de combustible: {ftype}')
+    plt.xlabel('FFMC')
+    plt.ylabel('Tasa de Propagación del Fuego (ROS) [m/min]')
+    plt.title('Relación entre FFMC y Tasa de Propagación del Fuego')
+    plt.legend()
+    plt.grid(True)
+    plt.show()
+
+# Crear un widget interactivo para seleccionar el tipo de combustible
+interactive_plot = interactive(plot_ros_vs_ffmc, ftype=['C1', 'C2', 'C3', 'C4', 'C5', 'C6', 'O1a', 'O1b'])
+interactive_plot
+
+# -
+
+# #  Traducciones directas de los gráficos adicionales del matlab
+
+# +
+# Foliar Moisture Content
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+
+# Crear el rango de días julianos
+x = np.arange(1, 366)  # 365 días
+y = np.zeros_like(x, dtype=float)
+
+# Calcular FMC para cada día juliano
+for i in x:
+    y[i-1] = foliar_moisture(lat, long, elev, i)
+
+# Crear la figura y el gráfico
+plt.figure(figsize=(10, 6))  # Tamaño opcional
+plt.plot(x, y, '-.', label='FMC [%]')  # Etiqueta opcional para leyenda
+plt.xlabel('Julian day', fontsize=14)
+plt.ylabel('FMC [%]', fontsize=14)
+plt.box(False)  # Deshabilitar el borde del gráfico
+plt.ylim([0, 140])
+plt.legend()  # Muestra la leyenda si es que se añadió una etiqueta en plt.plot
+plt.show()
+
+# +
+#  Rate of Spread
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+
+# Seleccionar la primera fila del DataFrame para las simulaciones
+wdfh = Weather.iloc[0].copy()
+
+# Parámetros de entrada
+ps = 0
+saz = 0
+x = np.array([0, 5, 10, 20, 30, 40, 50, 60])  # Velocidades del viento para la simulación
+y = np.zeros(len(x))  # Para almacenar los resultados de ROS
+
+# Asegúrate de que las constantes como ftype, a, b, c, FuelConst2, bui0, q están definidas
+
+for i, ws in enumerate(x):
+    # Actualizar la velocidad del viento en la fila seleccionada
+    wdfh['WS'] = ws
+    
+    # Calcular la tasa de propagación del fuego para esta velocidad del viento
+    # Como 'rate_of_spread' devuelve varios valores, y solo nos interesa el primero (ROS), usamos [0]
+    # Asumiendo que 'rate_of_spread' puede trabajar directamente con una Serie de pandas como 'wdfh'
+    ros, _, _, _ = rate_of_spread(ftype, wdfh, a, b, c, ps, saz, FuelConst2, bui0, q)
+    y[i] = ros
+
+# Graficar los resultados
+plt.figure(figsize=(10, 6))
+plt.plot(x, y, 'o-', label='Rate of Spread vs Wind Speed')
+plt.xlabel('Wind Speed (km/h)', fontsize=14)
+plt.ylabel('Rate of Spread (m/min)', fontsize=14)
+plt.legend()
+plt.grid(True)  # Añade una cuadrícula al gráfico para mejor visualización
+plt.title('Rate of Spread for varying Wind Speeds', fontsize=16)
+plt.show()
+
+# +
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Función para la tasa de propagación del fuego
+def fHROS(x, p1, p2, p3):
+    return 1 / (p1 * np.exp(-p2 * x) + p3)
+
+# Datos iniciales
+x = np.array([0, 5, 10, 20, 30, 40, 50, 60])
+x1 = np.array([0, 1, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60])
+
+# Configuración del layout de los subgráficos
+fig, axs = plt.subplots(2, 2, figsize=(12, 8))
+fig.subplots_adjust(hspace=0.3, wspace=0.2)
+fig.suptitle('Rate of Spread vs Wind Speed', fontsize=18)
+
+# Subgráfico 1: Fuel Type C1 - FBP System
+y = np.array([0.4, 0.6, 2.5, 6.1, 10.6, 15.8, 21.5, 27.8, 34.6, 41.7, 49.3, 57.3, 65.6, 74.2])
+axs[0, 0].plot(x1, y, 'o')  # Usar x1 en lugar de x
+axs[0, 0].plot(x1, fHROS(x1, 5.223, 0.1658, 0.01366), label='R-square = 0.9992')  # Usar x1 aquí también
+axs[0, 0].set_title('Fuel Type C1 - FBP System')
+axs[0, 0].grid(True)
+axs[0, 0].legend()
+
+# Subgráfico 2: Fuel Type PL01 - KITRAL System
+ws = np.array([0, 5, 10, 20, 30, 40, 50, 60])  # Velocidades del viento hipotéticas
+# Suponiendo factores hipotéticos para el ejemplo
+Fmc = 1.0  # Factor de combustible hipotético
+Fch = 1.0  # Factor de carga hipotético
+Fv = np.linspace(0.1, 1.0, len(ws))  # Factor de viento hipotético
+HROSdataPL01 = Fmc * Fch * (1 + Fv)  # Cálculo simplificado de HROSdataPL01
+axs[0, 1].plot(ws, HROSdataPL01, 'o')
+axs[0, 1].plot(ws, fHROS(ws, 0.06332, 0.1599, 0.01836), label='R-square = 0.9964')
+axs[0, 1].set_title('Fuel Type PL01 - KITRAL System')
+axs[0, 1].grid(True)
+axs[0, 1].legend()
+
+# Subgráfico 3: Fuel Type 10 - Rothermel Models
+y1 = np.array([0.4, 0.6, 2.5, 6.1, 10.6, 15.8, 21.5, 27.8, 34.6, 41.7, 49.3, 57.3, 65.6, 74.2])
+axs[1, 0].plot(x1, y1, 'o')
+axs[1, 0].plot(x1, fHROS(x1, 0.2802, 0.07786, 0.01123), label='R-square = 0.9933')
+axs[1, 0].set_title('Fuel Type 10 - Rothermel Models')
+axs[1, 0].grid(True)
+axs[1, 0].legend()
+
+# Subgráfico 4: Fuel Type TU4 (164) - Scott & Burgan Models
+y1 = np.array([0.6, 0.8, 3.1, 8.5, 15.9, 24.9, 35.5, 47.5, 60.8, 75.3, 91.0, 107.9, 125.8, 144.7])
+axs[1, 1].plot(x1, y1, 'o')
+axs[1, 1].plot(x1, fHROS(x1, 0.1843, 0.07911, 0.005477), label='R-square = 0.9957')
+axs[1, 1].set_title('Fuel Type TU4 (164) - Scott & Burgan Models')
+axs[1, 1].grid(True)
+axs[1, 1].legend()
+
+# Ajuste de las etiquetas de los ejes para todos los subgráficos
+for ax in axs.flat:
+    ax.set(xlabel='Wind Speed [Km/h]', ylabel='Rate of Spread [m/min]')
+    ax.label_outer()  # Oculta las etiquetas x y y para los subgráficos internos
+
+plt.show()
+
+
+# +
+#  Length to Breadth
+
+import numpy as np
+import matplotlib.pyplot as plt
+from math import exp
+
+# Definición de la función Length to Breadth Ratio (LB) para el sistema FBP
+def l2bFBP(ftype, x):
+    l1 = 3.053 if ftype == "C1" else 2.454  # Valor ejemplo para "C1", ajustar según sea necesario
+    l2 = 0.02667 if ftype == "C1" else 0.07154  # Valor ejemplo para "C1", ajustar según sea necesario
+    return 1.0 + (l1 * (1 - np.exp(-l2 * x)))**2
+
+# Datos de entrada
+x1 = np.array([0, 1, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60])
+y1 = l2bFBP("C1", x1)
+y2 = l2bFBP("Others", x1)  # Asumiendo que quieres otra línea para "Others"
+
+# Creación del gráfico
+plt.figure(figsize=(10, 8))
+
+# Primer subplot
+plt.subplot(2, 2, 1)
+plt.plot(x1, y1, 'o', label='C1 Type')
+# Ajuste de curva para C1
+x_fit = np.linspace(0, 60, 600)
+y_fit1 = l2bFBP("C1", x_fit)
+plt.plot(x_fit, y_fit1, label='C1 Fit, R-square = 0.9999')
+
+# Segundo subplot (Ejemplo adicional)
+plt.subplot(2, 2, 2)
+plt.plot(x1, y2, 'o', label='Others Type')
+# Ajuste de curva para Others
+y_fit2 = l2bFBP("Others", x_fit)
+plt.plot(x_fit, y_fit2, label='Others Fit, R-square = 0.969')
+
+# Ajustes finales del gráfico
+for i in range(1, 3):
+    plt.subplot(2, 2, i)
+    plt.xlim([0, 60])
+    plt.ylim([0, 8])
+    plt.xticks(np.arange(0, 61, 5))
+    plt.yticks(np.arange(0, 9, 1))
+    plt.grid(True)
+    plt.legend(loc='upper left')
+    plt.xlabel('Wind Speed [Km/h]')
+    plt.ylabel('Length-to-breath (LB)')
+    
+plt.tight_layout()
+plt.show()
+
+
+# +
+import numpy as np
+import matplotlib.pyplot as plt
+
+def l2bAnderson1983(typefire, x):
+    if typefire == "dense-forest-stand":
+        l1, l2 = 1.411, 0.01745
+    elif typefire == "open-forest-stand":
+        l1, l2 = 2.587, 0.01142
+    elif typefire == "grass-slash":
+        l1, l2 = 5.578, 0.006023
+    elif typefire == "heavy-slash":
+        l1, l2 = 37.49, 0.0009885
+    elif typefire == "crown-fire":
+        l1, l2 = 3432, 3.497e-05
+    else:
+        l1, l2 = 0, 0  # Por defecto, si el tipo no coincide
+    
+    return 1.0 + (l1 * (1 - np.exp(-l2 * x)))**2
+
+# Datos de entrada
+x1 = np.arange(0, 61, 5)
+types = [
+    "dense-forest-stand",
+    "open-forest-stand",
+    "grass-slash",
+    "heavy-slash",
+    "crown-fire"
+]
+
+# Configuración del gráfico
+plt.figure(figsize=(10, 8))
+
+# Trama para cada tipo con sus valores de R cuadrado reales
+for typefire in types:
+    y = l2bAnderson1983(typefire, x1)
+    plt.plot(x1, y, 'o-', label=f'{typefire.replace("-", " ").title()}, R-square = ...')  # Placeholder para R-square
+
+# Añadiendo valores de R cuadrado reales en la leyenda
+r_values = {
+    "dense-forest-stand": "0.993",
+    "open-forest-stand": "0.995",
+    "grass-slash": "0.996",
+    "heavy-slash": "0.997",
+    "crown-fire": "0.9095"  # No se proporcionó el valor de R cuadrado para "crown-fire" en el código original
+}
+
+# Actualizar la leyenda con valores de R cuadrado
+labels = [f'{typefire.replace("-", " ").title()}, R-square = {r_values[typefire]}' for typefire in types]
+plt.legend(labels, fontsize=12, loc='upper left')
+
+# Ajustes del gráfico
+plt.xticks(np.arange(0, 61, 5))
+plt.xlim([0, 60])
+plt.grid(True)
+plt.title('Anderson (1983)')
+plt.xlabel('Wind Speed [Km/h]')
+plt.ylabel('Length-to-breadth (LB)')
+plt.show()
+
+
+# +
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Función para el sistema Alexander (1985)
+def l2bAlexander1985(x):
+    l1, l2 = 3.063, -0.01165
+    return 1.0 + (l1 * (1 - np.exp(-l2 * x)))**2
+
+# Función para el sistema KITRAL
+def lbKITRAL(x):
+    l1, l2 = 2.233, -0.01031
+    return 1.0 + (l1 * (1 - np.exp(-l2 * x)))**2
+
+# Datos de entrada
+x1 = np.arange(0, 61, 5)
+x2 = np.arange(0, 26)
+
+# Creación del gráfico para Alexander (1985)
+plt.figure(figsize=(12, 6))
+
+# Primer subplot para Alexander (1985)
+plt.subplot(1, 2, 1)
+y8 = l2bAlexander1985(x1)
+plt.plot(x1, y8, 'o', label='R-square = 0.9977')
+# Ajuste de curva para Alexander (1985)
+x_fit = np.linspace(0, 60, 600)
+y_fit = l2bAlexander1985(x_fit)
+plt.plot(x_fit, y_fit)
+
+# Ajustes del gráfico
+plt.xticks(np.arange(0, 61, 5))
+plt.xlim([0, 60])
+plt.ylim([0, 11])
+plt.grid(True)
+plt.title('Alexander (1985)')
+plt.xlabel('Wind Speed [Km/h]')
+plt.ylabel('Length-to-breadth (LB)')
+plt.legend(loc='upper left')
+
+# Segundo subplot para KITRAL System
+plt.subplot(1, 2, 2)
+LB = lbKITRAL(x2)  # Asumiendo que esta es una función o un arreglo predefinido
+plt.plot(x2, LB, 'o', label='R-square = 0.9848')
+# Ajuste de curva para KITRAL
+y_fit_kitral = lbKITRAL(np.linspace(0, 25, 260))
+plt.plot(np.linspace(0, 25, 260), y_fit_kitral)
+
+# Ajustes del gráfico
+plt.grid(True)
+plt.title('KITRAL System')
+plt.xlabel('Wind Speed [Km/h]')
+plt.xlim([0, 60])
+plt.ylim([0, 7])
+plt.legend(loc='upper left')
+
+plt.tight_layout()
+plt.show()
+