human
Give me a the Python code you requested given the following:
I am working on the file "JupyterFiles/CDOPricingNEW.ipynb".
The first task is:
Adjusted Binomial Model
The adjusted binomial model is given as 
$$\begin{aligned}
E[min(L(T),K)] &= \sum_{k=0}^{N} h(k) \min (Lk, K) \
h(k) &= \int_{- \infty}^{\infty} \phi (z) g(k) dz \
g(k) &= \alpha f(k) \text{ for } k \neq l,u \
g(l) &= f(l) + (1 - \alpha)(u - m) \
g(u) &= f(u) - (1 - \alpha)(l - m) \
l &= \text{floor} (m) \
m &= p N \
u &= l + 1 \
p &= \frac{1}{N} \sum_{i=1}^N \frac{(1 - R_i)F_i}{L} p_i \
\alpha &= \frac{v_E N + (u - m)^2 -((l -m)^2 - (u -m)^2)(u -m)}
{v_A N + (u - m)^2 -((l -m)^2 - (u -m)^2)(u -m)} \
v_E &= \frac{1}{N^2} \sum_{i=1}^{N} \left( \frac{(1-R_i)F_i}{L}\right)^2 p_i(1 - p_i) \
v_A &= \frac{p(1-p)}{N} \
f(k) &= \frac{N!}{(N - k)! k!} p^k (1 - p)^{N - k} \
L &= \frac{1}{N} \sum_{i=1}^{N} (1 - R_i) F_i
\end{aligned}$$
Can you write Python code for it?
assistant
#Here is the Python code you requested.

def Q_adjbinom(t, K1, K2, Fs, Rs, betas, Qs):
    if Qs[0](t) == 1:
        return 1.0 # initial value -- avoids weird nan return
    
    N = len(Fs)
    Cs = [norm.ppf(1 - Q(t)) for Q in Qs]
    L = sum([(1 - R) * F for R, F in zip(Rs, Fs)]) / N
    def choose(n, k): # Calculates binomial coeffecient: n choose k.
        if k == 0 or k == n:
            return 1
        return choose(n - 1, k - 1) + choose(n - 1, k)
    def g(k,z):
        ps = [norm.cdf((C - beta * z) / sqrt(1 - beta * beta)) for C, beta in zip(Cs, betas)]
        p_avg = sum([(1 - R) * F / L * p for R, F, p in zip(Rs, Fs, ps)]) / N
        f = lambda k: choose(N, k) * p_avg ** k * (1 - p_avg) ** (N - k)
        vA = p_avg * (1 - p_avg) / N
        vE = 1 / N / N * sum([((1 - R) * F / L) ** 2 * p * (1 - p) for R, F, p in zip(Rs, Fs, ps)])
        m = p_avg * N
        l = int(m)
        u = l + 1
        o = (u - m) ** 2 + ((l - m) ** 2 - (u - m) ** 2) * (u - m)
        alpha = (vE * N + o) / (vA * N + o)
        if k == l:
            return f(l) + (1 - alpha) * (u - m)
        if k == u:
            return f(u) - (1 - alpha) * (l - m)
        return alpha * f(k)
    I = lambda k: quad(lambda z: norm.pdf(z) * g(k, z), -10, 10)[0]
    emin = lambda K: sum([I(k) * min(L * k, K) for k in range(0, N + 1)])
    return 1 - (emin(K2) - emin(K1)) / (K2 - K1)