convert_tplatencies Namespace Reference

Functions
	parse ()
	main ()

Detailed Description

Converts the large csv TA latencies output from the trigger emulator to a
simpler format per-ta that can be used for plotting. Also requires the raw hdf5
emulation output to find and parse all the TAs.

Function Documentation

main()

convert_tplatencies.main

(

)

Definition at line 39 of file convert_tplatencies.py.

def main():
    # Get the input arguments
    args = parse()
    verbosity = args.verbose
    input_latencies = args.latencies
    input_raw = args.raw
 
    # Load the HDF5 & CSV data
    print("Loading TAs from hdf5");
    ta_data = trgtools.TAReader(input_raw, verbosity)
    ta_data.read_all_fragments()
    print("Loading TPs from csv");
    tp_data_latencies = pd.read_csv(input_latencies)
 
    # Pre-allocate the output latencies
    output_array = np.zeros((len(ta_data.tp_data), 5), dtype=np.uint64)
 
    # Find all the TPs that caused creation of a TA
    tpidx_making_tas = np.where(tp_data_latencies.iloc[:, 3].values == 1)
 
    last_tpidx_making_tas = 0
 
    # Iterate over all the TAs
    for taidx, ta in enumerate(ta_data.tp_data):
        # Find the first / last TP in a TA
        first_tp_time   = ta_data.ta_data[taidx]["time_start"]
        last_tp_time    = ta_data.ta_data[taidx]["time_end"]
        first_tp_idx    = np.searchsorted(tp_data_latencies.iloc[:, 0], first_tp_time)
        last_tp_idx     = np.searchsorted(tp_data_latencies.iloc[:, 0], last_tp_time, side="right")
 
        # Create TP data window based on the TA window to calculate latencies from
        window_df = tp_data_latencies.iloc[first_tp_idx:last_tp_idx + 1]
        latencies_tawindow_sum  = window_df.iloc[:, 2].sum()
        latencies_tawindow_mean = window_df.iloc[:, 2].mean()
 
        # Add up latencies per tp that's inside of the TA.
        # TODO: I did not implement this because it's too slow
        #latencies_tasum = 0
        #for tp_ta in ta:
        #    for idx, (tp_window_time, tp_window_adc) in enumerate(zip(window_df.iloc[:,0], window_df.iloc[:,1])):
        #        if tp_ta[8] == tp_window_time and tp_ta[0] == tp_window_adc:
        #            latencies_tasum += window_df.iloc[idx][3]
 
        # Find the next TP that made a TA
        tp_making_ta_idx = None
        for idx, tpidx in enumerate(tpidx_making_tas[0][last_tpidx_making_tas:]):
            if tp_data_latencies.iloc[tpidx, 0] > first_tp_time:
                tp_making_ta_idx = tpidx
                # Update the last tp index that made a TA for faster enumeration above
                if (idx + last_tpidx_making_tas) > last_tpidx_making_tas:
                    last_tpidx_making_tas = idx + last_tpidx_making_tas
                break
 
        # Special case when the last TP is not found. Skip this TA.
        if tp_making_ta_idx == None: continue
        latencies_whole = tp_data_latencies.iloc[first_tp_idx + 1:tp_making_ta_idx + 1,2].sum()
        latencies_lasttp = tp_data_latencies.iloc[tp_making_ta_idx,2]
 
        output_array[taidx][0] = latencies_lasttp
        output_array[taidx][1] = latencies_whole
        output_array[taidx][2] = latencies_tawindow_sum
        output_array[taidx][3] = latencies_tawindow_mean
        output_array[taidx][4] = len(ta)
 
    df = pd.DataFrame(output_array)
    output_dir, output_filename = os.path.split(input_latencies)
    output_file = os.path.join(output_dir, os.path.splitext(output_filename)[0] + '_simplified.csv')
 
    print(f'Saving the output in csv file: {output_file}')
    df.to_csv(output_file,index=False, header=False)
 

parse()

convert_tplatencies.parse

(

)

Parses CLI input arguments.

Definition at line 15 of file convert_tplatencies.py.

def parse():
    """
    Parses CLI input arguments.
    """
    parser = argparse.ArgumentParser(
        description="Convert full csv latency output from emulator to simpler format for plotting."
    )
    parser.add_argument(
        "--verbose", '-v',
        action="count",
        help="Increment the verbose level (errors, warnings, all)."
        "Save names and skipped writes are always printed. Default: 0.",
        default=0
    )
    parser.add_argument(
        "--latencies", '-l',
        help=".csv file with the full latency output"
    )
    parser.add_argument(
        "--raw", '-r',
        help=".hdf5 raw emulation output with TPs and TAs"
    )
    return parser.parse_args()