Welcome to TFPWA’s documentation!

TFPWA is a generic software package intended for Partial Wave Analysis (PWA). It is developed using TensorFlow2 and the calculation is accelerated by GPU. Users may modify the configuration file (in YML format) and write simple scripts to complete the whole analysis. A detailed configuration file sample (with all usable parameters) can be found here.

Contents:

For starters

Take the decay in config_sample.yml for example, we will generate a toy, and then use it to conduct an analysis.

First, we use the class ConfigLoader to load the configuration file, and thus building the amplitude expression.

from tf_pwa.config_loader import ConfigLoader
config = ConfigLoader("config_sample.yml")
amp = config.get_amplitude()

We can also use a json file to set the parameters in the amplitude formula config.set_params("parameters.json"). Otherwise, the parameters are set randomly.

Next, we can use functions in tf_pwa.applications to directly generate data samples. We need to provide the masses of A and B, C, D to generate the PhaseSpace MC, and then use it to generate the toy data.

from tf_pwa.applications import gen_mc, gen_data
PHSP = gen_mc(mother=4.6, daughters=[2.00698, 2.01028, 0.13957], number=100000, outfile="PHSP.dat")
data = gen_data(amp, Ndata=5000, mcfile="PHSP.dat", genfile="data.dat")

Now that we have updated data.dat and PHSP.dat, we’d better load the configuration file again, and then fit the data.

config = ConfigLoader("config_sample.yml")
fit_result = config.fit()

Fitting is the major part of an analysis, and it could also be the most time-consuming part. For this example (the complexity of the amplitude expression matters a lot), the time for fitting is about xxx (running on xxxGPU). Then we can step further to complete the analysis, like calculating the fit fractions.

errors = config.get_params_error(fit_result)
fit_result.save_as("final_parameters.json")
fit_frac, err_frac = config.cal_fitfractions()

We can use error_print in tf_pwa.utils to print the fitting parameters as well as the fit fractions.

from tf_pwa.utils import error_print
print("########## fitting parameters:")
for key, value in config.get_params().items():
   print(key, error_print(value, errors.get(key, None)))
print("########## fit fractions:")
for i in fit_frac:
   print(i, " : " + error_print(fit_frac[i], err_frac.get(i, None)))

If the plotting options are also provided in the config_sample.yml, we can also plot the distributions of variables indicated in the configuration file.

config.plot_partial_wave(fit_result, prefix="figure/")

The figures will be saved under path figure. Here are the three invariant mass pairs for example.

(three pictures here)

We can do a lot more using tf_pwa. For more examples, please see path tutorials.

Welcome to TFPWA’s documentation!

For starters

Indices and tables