Package ru.autosome.commons.cli

Examples of ru.autosome.commons.cli.OutputInformation$Callback

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  OutputInformation report_table_layout() {
    return calculator().report_table_layout();
  }

  OutputInformation report_table(ArrayList<? extends ResultInfo> data) {
    OutputInformation result = report_table_layout();
    result.data = data;
    return result;
  }
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      pvalues = ArrayExtensions.toPrimitiveArray(pvalues_tmp);
    }
  }

  OutputInformation report_table_layout() {
    OutputInformation infos = new OutputInformation();

    infos.add_parameter("V", "discretization value", discretizer);
    infos.add_parameter("PB", "P-value boundary", pvalue_boundary);

    infos.background_parameter("B", "background", background);

    infos.add_table_parameter("P", "requested P-value", "expected_pvalue");
    infos.add_table_parameter("AP", "actual P-value", "real_pvalue");

    if (background.is_wordwise()) {
      infos.add_table_parameter("W", "number of recognized words", "numberOfRecognizedWords", new OutputInformation.Callback<CanFindThreshold.ThresholdInfo>() {
        @Override
        public Object run(CanFindThreshold.ThresholdInfo cell) {
          double numberOfRecognizedWords = cell.numberOfRecognizedWords(background, motif.length());
          return (long)numberOfRecognizedWords;
        }
      });
    }
    infos.add_table_parameter("T", "threshold", "threshold");

    return infos;
  }
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    return infos;
  }

  OutputInformation report_table(ArrayList<? extends ResultInfo> data) {
    OutputInformation result = report_table_layout();
    result.data = data;
    return result;
  }
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    }
    return infos;
  }

  protected OutputInformation report_table(List<? extends ResultInfo> data) {
    OutputInformation result = report_table_layout();
    result.data = data;
    return result;
  }
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    return pvaluesByThresholds(thresholds)[0];
  }

  @Override
  public OutputInformation report_table_layout() {
    OutputInformation infos = new OutputInformation();
    infos.add_parameter("V", "discretization value", discretizer);
    infos.background_parameter("B", "background", background);

    infos.add_table_parameter("T", "threshold", "threshold");
    if (background.is_wordwise()) {
      infos.add_table_parameter("W", "number of recognized words", "numberOfRecognizedWords",new OutputInformation.Callback<PvalueInfo>() {
        @Override
        public Object run(PvalueInfo cell) {
          double numberOfRecognizedWords = cell.numberOfRecognizedWords(background, motif.length());
          return (long)numberOfRecognizedWords;
        }
      });
    }
    infos.add_table_parameter("P", "P-value", "pvalue");

    return infos;
  }
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  }

  // TODO: decide which parameters are relevant
  @Override
  public OutputInformation report_table_layout() {
    OutputInformation infos = new OutputInformation();

    infos.add_table_parameter("T", "threshold", "threshold");
    infos.add_table_parameter("P", "P-value", "pvalue");

    return infos;
  }
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      }
    }
  }

  OutputInformation report_table_layout() {
    OutputInformation infos = new OutputInformation();

    infos.add_parameter("V", "discretization", discretizer);
    if (predefinedFirstThreshold == null || predefinedSecondThreshold == null) {
      infos.add_parameter("P", "requested P-value", pvalue);
    }
    if (predefinedFirstThreshold != null) {
      infos.add_parameter("T1", "threshold for the 1st matrix", predefinedFirstThreshold);
    }
    if (predefinedSecondThreshold != null) {
      infos.add_parameter("T2", "threshold for the 2nd matrix", predefinedSecondThreshold);
    }
    infos.add_parameter("PB", "P-value boundary", pvalueBoundary);
    if (firstBackground.equals(secondBackground)) {
      infos.background_parameter("B", "background", firstBackground);
    } else {
      infos.background_parameter("B1", "background for the 1st model", firstBackground);
      infos.background_parameter("B2", "background for the 2nd model", secondBackground);
    }

    return infos;
  }
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    return infos;
  }

  protected OutputInformation report_table(CompareModelsCountsGiven.SimilarityInfo info) throws HashOverflowException {
    OutputInformation infos = report_table_layout();
    infos.add_resulting_value("S", "similarity", info.similarity());
    infos.add_resulting_value("D", "distance (1-similarity)", info.distance());
    infos.add_resulting_value("L", "length of the alignment", info.alignment.length());
    infos.add_resulting_value("SH", "shift of the 2nd PWM relative to the 1st", info.alignment.shift());
    infos.add_resulting_value("OR", "orientation of the 2nd PWM relative to the 1st", info.alignment.orientation());
    infos.add_resulting_value("A1", "aligned 1st matrix", info.alignment.first_model_alignment());
    infos.add_resulting_value("A2", "aligned 2nd matrix", info.alignment.second_model_alignment());
    infos.add_resulting_value("W", "number of words recognized by both models (model = PWM + threshold)", info.recognizedByBoth );
    infos.add_resulting_value("W1", "number of words and recognized by the first model", info.recognizedByFirst );
    infos.add_resulting_value("P1", "P-value for the 1st matrix", info.realPvalueFirst(firstBackground));
    if (predefinedFirstThreshold == null) {
      infos.add_resulting_value("T1", "threshold for the 1st matrix", thresholdFirst() );
    }
    infos.add_resulting_value("W2", "number of words recognized by the 2nd model", info.recognizedBySecond );
    infos.add_resulting_value("P2", "P-value for the 2nd matrix", info.realPvalueSecond(secondBackground));
    if (predefinedSecondThreshold == null) {
      infos.add_resulting_value("T2", "threshold for the 2nd matrix", thresholdSecond() );
    }
    return infos;
  }
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  protected boolean failed_to_recognize_additional_options(String opt, List<String> argv) {
    return true;
  }

  public OutputInformation report_table_layout() {
    OutputInformation infos = new OutputInformation();
    infos.add_parameter("MS", "minimal similarity to output", similarityCutoff);
    infos.add_parameter("P", "P-value", pvalue);
    infos.add_parameter("PB", "P-value boundary", pvalueBoundaryType);
    if (preciseRecalculationCutoff != null) {
      infos.add_parameter("VR", "discretization value, rough", roughDiscretizer);
      infos.add_parameter("VP", "discretization value, precise", preciseDiscretizer);
      infos.add_parameter("MP", "minimal similarity for the 2nd pass in \'precise\' mode", preciseRecalculationCutoff);
    } else {
      infos.add_parameter("V", "discretization value", roughDiscretizer);
    }
    infos.background_parameter("BQ", "background for query matrix", queryBackground);
    infos.background_parameter("BC", "background for collection", collectionBackground);

    infos.add_table_parameter_without_description("motif", "name");
    infos.add_table_parameter_without_description("similarity", "similarity");
    infos.add_table_parameter_without_description("shift", "shift");
    infos.add_table_parameter_without_description("overlap", "overlap");
    infos.add_table_parameter_without_description("orientation", "orientation");
    if (preciseRecalculationCutoff != null) {
      infos.add_table_parameter_without_description("precise mode", "precision_mode", new OutputInformation.Callback<ru.autosome.macroape.calculation.generalized.ScanCollection.SimilarityInfo>(){
        @Override
        public String run(ru.autosome.macroape.calculation.generalized.ScanCollection.SimilarityInfo cell) {
          return cell.precise ? "*" : ".";
        }
      });
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