Applied math PhD with interests in data science, neuroscience, causality
mutable mode = 0;
mutable mode_m = 0;
mutable g_x = 0
mutable g_t = 0
mutable m_x = 0
mutable m_t = 0
gamma = 1/Math.sqrt(1-v_m*v_m);
mutable g_xp = 0;
mutable m_xp = 0;
mutable m_tp = 0;
g_x_func = (x) => {
return x - v_g*g_t;
}
m_x_func = (x,t) => {
return gamma*(x - v_m*t);
}
m_t_func = (x,t) => {
return gamma*(t - v_m*x);
}
g_x_inv = (x) => {
return x + v_g*g_t;
}
m_x_inv = (x,t) => {
return gamma*(x + v_m*t);
}
g_t_inv = (t) => {
return t
}
m_t_inv = (x,t) => {
return gamma*(t + v_m*x)
}
event_x_g = () => {
if (mode == 0) {
return g_x;
} else {
return g_x_inv(g_xp);
}
}
event_x_m = () => {
if (mode_m == 0) {
return m_x;
} else {
return m_x_inv(m_xp, m_tp);
}
}
event_t_m = () => {
if (mode_m == 0) {
return m_t;
} else {
return m_t_inv(m_xp, m_tp);
}
}
event_xp_g = () => {
if (mode == 1) {
return g_xp;
} else {
return g_x_func(g_x);
}
}
event_xp_m = () => {
if (mode_m == 1) {
return m_xp;
} else {
return m_x_func(m_x, m_t);
}
}
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