Examples for Plotter class

Ploter is the new api for partial wave plots.

First, we can build a simple config.

config_str = """

decay:
    A:
       - [R1, B]
       - [R2, C]
       - [R3, D]
    R1: [C, D]
    R2: [B, D]
    R3: [B, C]

particle:
    $top:
       A: { mass: 1.86, J: 0, P: -1}
    $finals:
       B: { mass: 0.494, J: 0, P: -1}
       C: { mass: 0.139, J: 0, P: -1}
       D: { mass: 0.139, J: 0, P: -1}
    R1: [ R1_a, R1_b ]
    R1_a: { mass: 0.7, width: 0.05, J: 1, P: -1}
    R1_b: { mass: 0.5, width: 0.05, J: 0, P: +1}
    R2: { mass: 0.824, width: 0.05, J: 0, P: +1}
    R3: { mass: 0.824, width: 0.05, J: 0, P: +1}


plot:
    mass:
        R1:
            display: "m(R1)"
        R2:
            display: "m(R2)"
"""

import matplotlib.pyplot as plt
import yaml

from tf_pwa.config_loader import ConfigLoader

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)

5.8%[▓▓>-----------------------------------------------] 0.57/9.90s eff: 90.000000%
94.2%[▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓>--] 1.93/2.05s eff: 4.824203%
99.6%[▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓>] 2.45/2.46s eff: 4.153819%
99.7%[▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓>] 2.92/2.92s eff: 4.113545%
99.8%[▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓>] 3.38/3.39s eff: 4.099741%
100.0%[▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓>] 3.83/3.83s eff: 4.090407%
100.0%[▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓] 3.83/3.83s  eff: 4.089721%

plotter can be created directly from config

plotter = config.get_plotter(datasets={"data": [data], "phsp": [phsp]})

Ploting all partial waves is simple.

plotter.plot_frame("m_R1")
plt.show()

Also we can plot other variables in data

from tf_pwa.data import data_index

m2 = config.get_data_index("mass", "R2")
m3 = config.get_data_index("mass", "R3")


def f(data):
    return data_index(data, m2) - data_index(data, m3)


plt.clf()
plotter.plot_var(f)
plt.xlabel("m(R2)+m(R3)")
plt.show()

There are 3 main parts in a Plotter

1. PlotAllData: datasets with weights There is three level: (1). idx: Datasets for combine fit (2). type: data, mc, or bg (3). observations and weights: weights are used for partial wave

2. Frame: function to get obsevations It is samilar to RooFit’s Frame.

3. Styles: Plot style for differenct componets

The plot process is as follow:

1. Plotter.plot_item, extra_plot_item, and hidden_plot_item provide the list of histograms for plotting.

2. Loop over all data to get the observations though frame.

3. Frame provide the binning, weights from datas. Their combination is histogram

4. Plot on the axis with style