from pymc import deterministic, Normal, Uniform, Model
from pymc.distributions import truncated_normal_like
import pymc
import numpy as np
from grade_model2 import *
import pydot_ng as pydot
from pymc.ScipyDistributions import stochastic_from_scipy_dist, stochastic_from_dist
from scipy.stats import truncnorm
import matplotlib.pyplot as plt
from distributions import TruncNormal
test = TruncNormal('test', 0, 1, 0.5, 0.2, 0.5)
x = np.linspace(-1,2, 100)
plt.hist([test.random() for i in xrange(1000)])
# plt.hist([test.random(0,1,0.5, 0.2) for i in xrange(1000)])
num_students = 120
num_assignments = 4
num_questions_pr_handin = 20
num_graders_pr_handin = 7
students = []
assignments = []
handins = []
def create_model():
all_vars = []
sigma_qd = Uniform('sigma_qd', 0,0.15)
