from etrics.Utilities import Timing
with Timing("time to print", True):
    print("hello world")

from etrics import quantreg
from etrics.quantreg import main, main_2
main()

main_2()

import numpy as np
from scipy.stats.distributions import norm

def createData(theta, form, N):
    beta = theta["beta"]
    X = np.matrix([np.repeat(1, N), norm.rvs(loc=4, scale=2, size=N), norm.rvs(loc=5, scale=1, size=N)])
    eps = np.matrix(norm.rvs(loc=0, scale=1, size=N))
    return [X.T * np.matrix(beta).T + eps.T, X.T]

def estimateModel(data):
    import statsmodels.regression.linear_model as model
    res = model.OLS(data[0], data[1]).fit()
    return res.params

from etrics.Simulation import Simulation, Progress, onWarning, Results

x = Simulation("/tmp/statefile.tmp")
x.AddStatistics({"95th Quantile": lambda x: [np.percentile(x, 95, axis=0)]}, type=Results.Original)
x.SetIdentifiedParameters([1,2,3], ("beta{} "*3).format(1,2,3).split())
x.SetStructuralParameters({"beta":[1,2,3]}, None)
x.SetSamplingParameters(N=1000)
x.SetEstimationParameters()
x.Generating += createData
x.Estimating += estimateModel
x.PreEstimation += Progress
x.Warning += onWarning
x.SetWritingOptions("/tmp/table.tmp")
x.Simulate(100)

res1 = x.GetResults(Results.Original)
res2 = x.GetResults(Results.Bias)

print(res2)

from IPython.display import Latex
with open("/tmp/table.tmp", 'r') as t:
    print(t.read())