def plot_ramachandran(predictions, title):
    phi_angles = predictions[:][:][0].flatten()
    phi_angles = list(map((lambda x: math.degrees(x)), phi_angles))
    psi_angles = predictions[:][:][1].flatten()
    psi_angles = list(map((lambda x: math.degrees(x)), psi_angles))
    colors = np.random.rand(len(psi_angles))
    fig = plt.figure()
    plt.xlim([(- 180), 180])
    plt.ylim([(- 180), 180])
    plt.title(title)
    plt.xlabel('phi')
    plt.ylabel('psi')
    plt.grid()
    plt.scatter(phi_angles, psi_angles, alpha=0.5, c=colors)
    fig.savefig((('./plots/' + title) + '.png'), bbox_inches='tight')
    plt.close()
def plot_ramachandran(predictions, title):
    phi_angles = predictions[:][:][0].flatten()
    phi_angles = list(map((lambda x: math.degrees(x)), phi_angles))
    psi_angles = predictions[:][:][1].flatten()
    psi_angles = list(map((lambda x: math.degrees(x)), psi_angles))
    colors = np.random.rand(len(psi_angles))
    fig = plt.figure()
    plt.xlim([(- 180), 180])
    plt.ylim([(- 180), 180])
    plt.title(title)
    plt.xlabel('phi')
    plt.ylabel('psi')
    plt.grid()
    plt.scatter(phi_angles, psi_angles, alpha=0.5, c=colors)
    fig.savefig((('./plots/' + title) + '.png'), bbox_inches='tight')
    plt.close()