Chunking bug with empty instruction

[mhliu0001]

Strax complains about data not being continuous with an empty instruction. Here is how to reproduce it:

import os
import shutil
import unittest
import tempfile
import timeout_decorator
import fuse
from _utils import build_random_csv_input

TIMEOUT = 240


class TestDetectorPhysicsCsv(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        cls.temp_dir = tempfile.TemporaryDirectory()

        cls.test_context = fuse.context.full_chain_context(
            output_folder=cls.temp_dir.name, run_without_proper_corrections=True
        )

        cls.input_file = os.path.join(cls.temp_dir.name, "test.csv")

        cls.test_context.set_config(
            {
                "path": cls.temp_dir.name,
                "file_name": "test.csv",
            }
        )

        cls.run_number = "TestRun_00000"

    @classmethod
    def tearDownClass(cls):
        cls.temp_dir.cleanup()

    def setUp(self):
        detectorphysics_instructions = build_random_csv_input(1)
        detectorphysics_instructions["zp"] = 1
        detectorphysics_instructions.to_csv(self.input_file, index=False)

        assert os.path.exists(self.input_file)

    def tearDown(self):
        # self.temp_dir.cleanup()
        shutil.rmtree(self.temp_dir.name)
        os.makedirs(self.temp_dir.name)

    @timeout_decorator.timeout(
        TIMEOUT, exception_message="PMTResponseAndDAQ with empty input timed out"
    )
    def test_PMTResponseAndDAQ_CsvInput(self):
        self.test_context.make(self.run_number, "raw_records")


if __name__ == "__main__":
    unittest.main()

def build_random_csv_input(n):
    """Build a random input with electron ionization.
    
    This should be used in micro_physics.ChunkInput.
    Returns a Pandas Dataframe.
    
    Args:
        n (int): number of instructions to generate
    """
    df = pd.DataFrame()
    r = np.sqrt(np.random.uniform(0, 250000, n))
    t = np.random.uniform(-np.pi, np.pi, n)
    df["xp"] = r * np.cos(t)
    df["yp"] = r * np.sin(t)
    df["zp"] = np.random.uniform(-1500, -10, n)
    df["xp_pri"] = df["xp"]
    df["yp_pri"] = df["yp"]
    df["zp_pri"] = df["zp"]
    df["time"] = np.zeros(n)
    df["ed"] = np.random.uniform(1, 100, n)
    df["type"] = "e-"
    df["trackid"] = 0
    df["parenttype"] = "None"
    df["parentid"] = 0
    df["creaproc"] = "None"
    df["edproc"] = "eIoni"
    df["eventid"] = np.arange(n)
    
    return df

In this code I build a random csv input with a single interaction higher than the liquid level. Then, interactions_in_roi will be empty.

This is a rare case, but it might be necessary to look at how fuse produce chunks.