import numpy as np
import scipy.stats as sps
import pytest
from contextlib import contextmanager
from surmise.emulation import emulator
from surmise.calibration import calibrator
##############################################
#            Simple scenarios                #
##############################################


def balldropmodel_linear(x, theta):
    f = np.zeros((theta.shape[0], x.shape[0]))
    for k in range(0, theta.shape[0]):
        t = x[:, 0]
        h0 = x[:, 1] + theta[k, 0]
        vter = theta[k, 1]
        f[k, :] = h0 - vter * t
    return f.T


tvec = np.concatenate((np.arange(0.1, 4.3, 0.1), np.arange(0.1, 4.3, 0.1)))
h0vec = np.concatenate((25 * np.ones(42), 50 * np.ones(42)))
x = np.array([[0.1, 25.],
              [0.2, 25.],
              [0.3, 25.],
              [0.4, 25.],
              [0.5, 25.],
              [0.6, 25.],
              [0.7, 25.],
              [0.9, 25.],
              [1.1, 25.],
              [1.3, 25.],
              [2.0, 25.],
              [2.4, 25.],
              [0.1, 50.],
              [0.2, 50.],
              [0.3, 50.],
              [0.4, 50.],
              [0.5, 50.],
              [0.6, 50.],
              [0.7, 50.],
              [0.8, 50.],
              [0.9, 50.],
              [1.0, 50.],
              [1.2, 50.],
              [2.6, 50.],
              [2.9, 50.],
              [3.1, 50.],
              [3.3, 50.],
              [3.5, 50.],
              [3.7, 50.], ]).astype('object')
xv = x.astype('float')


class priorphys_lin:
    """ This defines the class instance of priors provided to the method. """
    def lpdf(theta):
        return (sps.norm.logpdf(theta[:, 0], 0, 5) +
        sps.gamma.logpdf(theta[:, 1], 2, 0, 10)).reshape((len(theta), 1))

    def rnd(n):
        return np.vstack((sps.norm.rvs(0, 5, size=n),
                          sps.gamma.rvs(2, 0, 10, size=n))).T


theta = priorphys_lin.rnd(50)
f = balldropmodel_linear(xv, theta)
f1 = f[0:15, :]
f2 = f[:, 0:25]
theta1 = theta[0:25, :]
x1 = x[0:15, :]
f0d = np.array(1)
theta0d = np.array(1)
x0d = np.array(1)

def balldroptrue(x):
    def logcosh(x):
        # preventing crashing
        s = np.sign(x) * x
        p = np.exp(-2 * s)
        return s + np.log1p(p) - np.log(2)
    t = x[:, 0]
    h0 = x[:, 1]
    vter = 20
    g = 9.81
    y = h0 - (vter ** 2) / g * logcosh(g * t / vter)
    return y

obsvar = 4*np.ones(x.shape[0])
y = balldroptrue(xv)
##############################################
# Unit tests to initialize an emulator class #
##############################################


@contextmanager
def does_not_raise():
    yield


@pytest.fixture
def cmdopt1(request):
    return request.config.getoption("--cmdopt1")


# tests for prediction class methods:

def test_accuracy(cmdopt1):
    emu = emulator(x=x, theta=theta, f=f, method=cmdopt1)
    theta_test = priorphys_lin.rnd(50)
    ftest = balldropmodel_linear(xv, theta_test)
    pred_test = emu.predict(x=x, theta=theta_test)
    
    print('\n')
    print('R2: (as close to one as possible)')
    rsq = 1 - np.mean((ftest - pred_test.mean())**2)/np.mean((ftest - np.mean(ftest))**2)
    print('test R2:', np.round(rsq, 2))
    
    print('RMSE : (as small as possible)')
    rmse = np.sqrt(np.mean((ftest - pred_test.mean())**2))    
    print('test rmse:', np.round(rmse, 2))
    
    print('mean((f-fhat)/sqrt(var)) (should be close to 0):' )
    print(np.round(np.mean((ftest - pred_test.mean())/np.sqrt(pred_test.var())), 2))

    print('mean((f-fhat)**2/var)(should be close to 1):' )
    print(np.round(np.mean((ftest - pred_test.mean())**2/pred_test.var()), 2))

    try:
        residstand = np.empty([50, pred_test.covxhalf().shape[2]])
        for k in range(0, 50):
            residstand[k,:] = np.linalg.pinv(pred_test.covxhalf()[:,k,:]) @ (ftest[:,k] -pred_test.mean()[:,k])
        print('average normalized value (should be close to 1)):' )
        print(np.mean(residstand ** 2))
    except:
        print('covxhalf is not provided.')
        




    

    
    
    