示例#1
0
def test_chain_phsp():
    dic = yaml.full_load(config_text)
    config = ConfigLoader(dic)
    data = config.generate_toy(10)
    data.mass_hist("(C, F, G, H)").draw()
    import matplotlib.pyplot as plt

    plt.savefig("chain_phsp.png")
示例#2
0
config = ConfigLoader(yaml.full_load(config_str))

# %%
# We set parameters to a blance value. And we can generate some toy data and calclute the weights
#

input_params = {
    "A->R1_a.BR1_a->C.D_total_0r": 6.0,
    "A->R1_b.BR1_b->C.D_total_0r": 1.0,
    "A->R2.CR2->B.D_total_0r": 2.0,
    "A->R3.DR3->B.C_total_0r": 1.0,
}
config.set_params(input_params)

data = config.generate_toy(1000)
phsp = config.generate_phsp(10000)

# You can also fit the data fit to the data
fit_result = config.fit([data], [phsp])
err = config.get_params_error(fit_result, [data], [phsp])

# %%
# we can see that thre fit results consistant with inputs, the first one is fixed.

for var in input_params:
    print(
        f"in: {input_params[var]} => out: {fit_result.params[var]} +/- {err.get(var, 0.)}"
    )

# %%