#===============================================================================
# Copyright (c) 2015, Max Zwiessele
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#===============================================================================
import numpy as np
import GPy, os
from nose import SkipTest

from ..util.config import config

if config.get('plotting', 'library') != 'matplotlib':
    raise SkipTest("Matplotlib not installed, not testing plots")

try:
    from matplotlib import cbook, pyplot as plt
    from matplotlib.testing.compare import compare_images
    from matplotlib.testing.noseclasses import ImageComparisonFailure
except ImportError:
    raise SkipTest("Matplotlib not installed, not testing plots")

extensions = ['png']

def _image_directories():
    """
    Compute the baseline and result image directories for testing *func*.
    Create the result directory if it doesn't exist.
    """
    basedir = os.path.splitext(os.path.relpath(os.path.abspath(__file__)))[0]
    #module_name = __init__.__module__
    #mods = module_name.split('.')
    #basedir = os.path.join(*mods)
    result_dir = os.path.join(basedir, 'testresult')
    baseline_dir = os.path.join(basedir, 'baseline')
    if not os.path.exists(result_dir):
        cbook.mkdirs(result_dir)
    return baseline_dir, result_dir


def _sequenceEqual(a, b):
    assert len(a) == len(b), "Sequences not same length"
    for i, [x, y], in enumerate(zip(a, b)):
        assert x == y, "element not matching {}".format(i) 

def _notFound(path):
    raise IOError('File {} not in baseline')

def _image_comparison(baseline_images, extensions=['pdf','svg','ong'], tol=11):
    baseline_dir, result_dir = _image_directories()
    for num, base in zip(plt.get_fignums(), baseline_images):
        for ext in extensions:
            fig = plt.figure(num)
            fig.axes[0].set_axis_off()
            fig.set_frameon(False)
            fig.canvas.draw()
            fig.savefig(os.path.join(result_dir, "{}.{}".format(base, ext)), transparent=True, edgecolor='none', facecolor='none')
    for num, base in zip(plt.get_fignums(), baseline_images):
        for ext in extensions:
            #plt.close(num)
            actual = os.path.join(result_dir, "{}.{}".format(base, ext))
            expected = os.path.join(baseline_dir, "{}.{}".format(base, ext))
            def do_test():
                err = compare_images(expected, actual, tol, in_decorator=True)
                if err:
                    raise ImageComparisonFailure("Error between {} and {} is {:.5f}, which is bigger then the tolerance of {:.5f}".format(actual, expected, err['rms'], tol))
            yield do_test
    plt.close('all')
    
def test_plot():
    np.random.seed(11111)
    X = np.random.uniform(-2, 2, (40, 1))
    f = .2 * np.sin(1.3*X) + 1.3*np.cos(2*X)
    Y = f+np.random.normal(0, .1, f.shape)
    m = GPy.models.GPRegression(X, Y)
    m.optimize()
    m.plot_data()
    m.plot_mean()
    m.plot_confidence()
    m.plot_density()
    m.plot_errorbars_trainset()
    m.plot_samples()
    for do_test in _image_comparison(baseline_images=['gp_{}'.format(sub) for sub in ["data", "mean", 'conf', 'density', 'error', 'samples']], extensions=extensions):
        yield (do_test, )

def test_twod():
    np.random.seed(11111)
    X = np.random.uniform(-2, 2, (40, 2))
    f = .2 * np.sin(1.3*X[:,[0]]) + 1.3*np.cos(2*X[:,[1]])
    Y = f+np.random.normal(0, .1, f.shape)
    m = GPy.models.GPRegression(X, Y)
    m.optimize()
    m.plot_data()
    m.plot_mean()
    for do_test in _image_comparison(baseline_images=['gp_2d_{}'.format(sub) for sub in ["data", "mean"]], extensions=extensions):
        yield (do_test, )

def test_threed():
    np.random.seed(11111)
    X = np.random.uniform(-2, 2, (40, 2))
    f = .2 * np.sin(1.3*X[:,[0]]) + 1.3*np.cos(2*X[:,[1]])
    Y = f+np.random.normal(0, .1, f.shape)
    m = GPy.models.GPRegression(X, Y)
    m.optimize()
    m.plot_data(projection='3d')
    m.plot_mean(projection='3d')
    m.plot_samples(projection='3d', samples=1)
    m.plot_samples(projection='3d', plot_raw=False, samples=1)
    for do_test in _image_comparison(baseline_images=['gp_3d_{}'.format(sub) for sub in ["data", "mean", "samples", "samples_lik"]], extensions=extensions):
        yield (do_test, )

def test_sparse():
    np.random.seed(11111)
    X = np.random.uniform(-2, 2, (40, 1))
    f = .2 * np.sin(1.3*X) + 1.3*np.cos(2*X)
    Y = f+np.random.normal(0, .1, f.shape)
    m = GPy.models.SparseGPRegression(X, Y)
    m.optimize()
    m.plot_inducing()
    for do_test in _image_comparison(baseline_images=['sparse_gp_{}'.format(sub) for sub in ['inducing']], extensions=extensions):
        yield (do_test, )

def test_classification():
    np.random.seed(11111)
    X = np.random.uniform(-2, 2, (40, 1))
    f = .2 * np.sin(1.3*X) + 1.3*np.cos(2*X)
    Y = f+np.random.normal(0, .1, f.shape)
    m = GPy.models.GPClassification(X, Y>Y.mean())
    m.optimize()
    _, ax = plt.subplots()
    m.plot(plot_raw=False, apply_link=False, ax=ax)
    _, ax = plt.subplots()
    m.plot(plot_raw=True, apply_link=False, ax=ax)
    m.plot(plot_raw=True, apply_link=True)
    for do_test in _image_comparison(baseline_images=['gp_class_{}'.format(sub) for sub in ["likelihood", "raw", 'raw_link']], extensions=extensions):
        yield (do_test, )

 
def test_sparse_classification():
    np.random.seed(11111)
    X = np.random.uniform(-2, 2, (40, 1))
    f = .2 * np.sin(1.3*X) + 1.3*np.cos(2*X)
    Y = f+np.random.normal(0, .1, f.shape)
    m = GPy.models.SparseGPClassification(X, Y>Y.mean())
    m.optimize()
    m.plot(plot_raw=False, apply_link=False, samples_likelihood=3)
    m.plot(plot_raw=True, apply_link=False, samples=3)
    m.plot(plot_raw=True, apply_link=True, samples=3)
    for do_test in _image_comparison(baseline_images=['sparse_gp_class_{}'.format(sub) for sub in ["likelihood", "raw", 'raw_link']], extensions=extensions):
        yield (do_test, )

def test_gplvm():
    from ..examples.dimensionality_reduction import _simulate_matern
    from ..kern import RBF
    from ..models import GPLVM
    np.random.seed(11111)
    Q = 3
    _, _, Ylist = _simulate_matern(5, 1, 1, 100, num_inducing=5, plot_sim=False)
    Y = Ylist[0]
    k = RBF(Q, ARD=True)  # + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
    # k = kern.RBF(Q, ARD=True, lengthscale=10.)
    m = GPLVM(Y, Q, init="PCA", kernel=k)
    m.likelihood.variance = .1
    #m.optimize(messages=0)
    labels = np.random.multinomial(1, np.random.dirichlet([.3333333, .3333333, .3333333]), size=(m.Y.shape[0])).nonzero()[1]
    m.plot_latent()
    m.plot_scatter(projection='3d', labels=labels)
    m.plot_magnification(labels=labels)
    m.plot_steepest_gradient_map(resolution=7)
    for do_test in _image_comparison(baseline_images=['gplvm_{}'.format(sub) for sub in ["latent", "latent_3d", "magnification", 'gradient']], extensions=extensions):
        yield (do_test, )

def test_bayesian_gplvm():
    from ..examples.dimensionality_reduction import _simulate_matern
    from ..kern import RBF
    from ..models import BayesianGPLVM
    np.random.seed(11111)
    Q = 3
    _, _, Ylist = _simulate_matern(5, 1, 1, 100, num_inducing=5, plot_sim=False)
    Y = Ylist[0]
    k = RBF(Q, ARD=True)  # + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
    # k = kern.RBF(Q, ARD=True, lengthscale=10.)
    m = BayesianGPLVM(Y, Q, init="PCA", kernel=k)
    m.likelihood.variance = .1
    #m.optimize(messages=0)
    labels = np.random.multinomial(1, np.random.dirichlet([.3333333, .3333333, .3333333]), size=(m.Y.shape[0])).nonzero()[1]
    m.plot_inducing(projection='2d')
    m.plot_inducing(projection='3d')
    m.plot_scatter(projection='3d')
    m.plot_magnification(labels=labels)
    m.plot_steepest_gradient_map(resolution=7)
    for do_test in _image_comparison(baseline_images=['bayesian_gplvm_{}'.format(sub) for sub in ["inducing", "inducing_3d", "latent_3d", "magnification", 'gradient']], extensions=extensions):
        yield (do_test, )
        
if __name__ == '__main__':
    import nose
    nose.main()