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|---|---|---|---|
| 1 | /// @begin_doc_example{cpp} | ||
| 2 | /// @file iguana_ex_cpp_dataframes.cc | ||
| 3 | /// @brief Example using HIPO data frames with Iguana. Requires ROOT. | ||
| 4 | /// @par Usage | ||
| 5 | /// ```bash | ||
| 6 | /// iguana_ex_cpp_dataframes [HIPO_FILE] [NUM_EVENTS] [INTERACTIVE_MODE] | ||
| 7 | /// | ||
| 8 | /// HIPO_FILE the HIPO file to analyze | ||
| 9 | /// | ||
| 10 | /// NUM_EVENTS the number of events to analyze; | ||
| 11 | /// set to zero to analyze all events | ||
| 12 | /// | ||
| 13 | /// INTERACTIVE_MODE if 'true', plot will be drawn interactively, | ||
| 14 | /// otherwise it will be saved to a PNG file | ||
| 15 | /// (default = false) | ||
| 16 | /// ``` | ||
| 17 | /// @end_doc_example | ||
| 18 | |||
| 19 | #include <hipo4/RHipoDS.hxx> | ||
| 20 | #include <iguana/algorithms/clas12/EventBuilderFilter/Algorithm.h> | ||
| 21 | |||
| 22 | #include <TApplication.h> | ||
| 23 | #include <TCanvas.h> | ||
| 24 | |||
| 25 | /// main function | ||
| 26 | 1 | int main(int argc, char** argv) | |
| 27 | { | ||
| 28 | |||
| 29 | // parse arguments | ||
| 30 | int argi = 1; | ||
| 31 |
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1 | char const* in_file = argc > argi ? argv[argi++] : "data.hipo"; |
| 32 |
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1 | int const num_events = argc > argi ? std::stoi(argv[argi++]) : 100; |
| 33 |
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1 | bool const interactive_mode = argc > argi ? std::string(argv[argi++]) == "true" : false; |
| 34 | |||
| 35 | // iguana algorithms | ||
| 36 | 1 | iguana::clas12::EventBuilderFilter algo_eventbuilder_filter; | |
| 37 |
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2 | algo_eventbuilder_filter.SetOption<std::vector<int>>("pids", {11, 211, -211}); |
| 38 |
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1 | algo_eventbuilder_filter.Start(); |
| 39 | |||
| 40 | // enable interactive mode | ||
| 41 |
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1 | auto app = interactive_mode ? new TApplication("app", &argc, argv) : nullptr; |
| 42 | |||
| 43 | // enable multi-threading | ||
| 44 | // ROOT::EnableImplicitMT(); | ||
| 45 | |||
| 46 | // open the HIPO file | ||
| 47 |
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1 | auto frame_init = MakeHipoDataFrame(in_file).Range(0, num_events); |
| 48 | |||
| 49 | // print the column names | ||
| 50 | 1 | fmt::print("DATAFRAME COLUMNS:\n"); | |
| 51 |
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299 | for(auto const& column_name : frame_init.GetColumnNames()) |
| 52 | 299 | fmt::print(" - {}\n", column_name); | |
| 53 | |||
| 54 | // run algorithms | ||
| 55 | // FIXME: we want to be able to apply an Iguana Filter as an RDataFrame Filter to a set of columns; | ||
| 56 | // this example chain filters only `REC::Particle::pid`, whereas ideally we want all of `REC::Particle`'s columns | ||
| 57 | // to be filtered | ||
| 58 | auto frame_filtered = frame_init | ||
| 59 |
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2 | .Define( // define a filter column, type std::deque<bool> (to avoid std::vector<bool>) |
| 60 | "REC_Particle_EventBuilderFilter", | ||
| 61 | 100 | [&](std::vector<int> const& pids) | |
| 62 |
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200 | { return algo_eventbuilder_filter.Filter(pids); }, |
| 63 | {"REC_Particle_pid"}) | ||
| 64 |
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2 | .Define( // apply the filtering column to `REC_Particle_pid` |
| 65 | "REC_Particle_pid_good", | ||
| 66 | 100 | [](std::vector<int> const& pids, std::deque<bool>& filter) | |
| 67 | { | ||
| 68 | std::vector<int> result; | ||
| 69 |
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802 | for(std::deque<bool>::size_type i = 0; i < filter.size(); i++) { |
| 70 |
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702 | if(filter.at(i)) |
| 71 |
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193 | result.push_back(pids.at(i)); |
| 72 | } | ||
| 73 | 100 | return result; | |
| 74 | }, | ||
| 75 |
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1 | {"REC_Particle_pid", "REC_Particle_EventBuilderFilter"}); |
| 76 | |||
| 77 | // draw | ||
| 78 |
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1 | auto hist = frame_filtered.Histo1D({"pid_filter", "PDG", 6000, -3000, 3000}, "REC_Particle_pid_good"); |
| 79 | |||
| 80 | // write or hold open | ||
| 81 |
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1 | auto canv = new TCanvas("canv", "canv", 1600, 1200); |
| 82 |
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1 | canv->SetGrid(1, 1); |
| 83 |
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1 | hist->Draw(); |
| 84 |
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1 | if(interactive_mode) { |
| 85 | ✗ | fmt::print("\n\nShowing plots interactively;\npress ^C to exit.\n\n"); | |
| 86 | ✗ | app->Run(); | |
| 87 | } | ||
| 88 | else { | ||
| 89 |
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1 | canv->SaveAs("out-iguana-dataframe-example.png"); |
| 90 | } | ||
| 91 | |||
| 92 | return 0; | ||
| 93 | 1 | } | |
| 94 |