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Moving to java 9. Finally!

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This commit is contained in:
Alexander Nozik 2017-11-07 07:56:59 +03:00
parent b175c3af74
commit 964fc2ceba
3 changed files with 173 additions and 0 deletions
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3
numass-core/src/main/java/inr/numass/data/api/NumassAnalyzer.java
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@ -4,6 +4,7 @@ import hep.dataforge.meta.Meta;
import hep.dataforge.tables.*;
import hep.dataforge.values.Value;
import hep.dataforge.values.Values;
import inr.numass.data.analyzers.SmartAnalyzer;
import java.util.HashMap;
import java.util.Map;
@ -19,6 +20,8 @@ import static inr.numass.data.api.NumassPoint.HV_KEY;
* Created by darksnake on 06-Jul-17.
*/
public interface NumassAnalyzer {
static NumassAnalyzer DEFAULT_ANALYZER = new SmartAnalyzer();
short MAX_CHANNEL = 10000;
/**

32
numass-main/src/main/kotlin/inr/numass/actions/AnalyzeDataAction.kt Normal file
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@ -0,0 +1,32 @@
package inr.numass.actions
import hep.dataforge.actions.OneToOneAction
import hep.dataforge.context.Context
import hep.dataforge.description.TypedActionDef
import hep.dataforge.description.ValueDef
import hep.dataforge.description.ValueDefs
import hep.dataforge.meta.Laminate
import hep.dataforge.tables.Table
import hep.dataforge.values.ValueType.NUMBER
import hep.dataforge.values.ValueType.STRING
import inr.numass.data.api.NumassAnalyzer
import inr.numass.data.api.NumassSet
/**
* The action performs the readout of data and collection of count rate into a table
* Created by darksnake on 11.07.2017.
*/
@TypedActionDef(name = "numass.analyze", inputType = NumassSet::class, outputType = Table::class)
@ValueDefs(
ValueDef(name = "window.lo", type = arrayOf(NUMBER, STRING), def = "0", info = "Lower bound for window"),
ValueDef(name = "window.up", type = arrayOf(NUMBER, STRING), def = "10000", info = "Upper bound for window")
)
class AnalyzeDataAction : OneToOneAction<NumassSet, Table>() {
override fun execute(context: Context, name: String, input: NumassSet, inputMeta: Laminate): Table {
//TODO add processor here
val analyzer = NumassAnalyzer.DEFAULT_ANALYZER
val res = analyzer.analyzeSet(input, inputMeta)
output(context, name) { stream -> NumassUtils.write(stream, inputMeta, res) }
return res
}
}

138
numass-main/src/main/kotlin/inr/numass/actions/TimeSpectrumAction.kt Normal file
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@ -0,0 +1,138 @@
package inr.numass.actions
import hep.dataforge.actions.OneToOneAction
import hep.dataforge.context.Context
import hep.dataforge.description.*
import hep.dataforge.kodex.buildMeta
import hep.dataforge.kodex.configure
import hep.dataforge.maths.histogram.UnivariateHistogram
import hep.dataforge.meta.Laminate
import hep.dataforge.plots.PlotPlugin
import hep.dataforge.plots.data.DataPlot
import hep.dataforge.tables.Table
import hep.dataforge.tables.XYAdapter
import hep.dataforge.values.ValueType
import inr.numass.data.analyzers.TimeAnalyzer
import inr.numass.data.api.NumassAnalyzer
import inr.numass.data.api.NumassPoint
/**
* Plot time analysis graphics
*/
@ValueDefs(
ValueDef(name = "normalize", type = arrayOf(ValueType.BOOLEAN), def = "true", info = "Normalize t0 dependencies"),
ValueDef(name = "t0", type = arrayOf(ValueType.NUMBER), def = "30e3", info = "The default t0 in nanoseconds"),
ValueDef(name = "window.lo", type = arrayOf(ValueType.NUMBER), def = "500", info = "Lower boundary for amplitude window"),
ValueDef(name = "window.up", type = arrayOf(ValueType.NUMBER), def = "10000", info = "Upper boundary for amplitude window"),
ValueDef(name = "binNum", type = arrayOf(ValueType.NUMBER), def = "1000", info = "Number of bins for time histogram"),
ValueDef(name = "binSize", type = arrayOf(ValueType.NUMBER), info = "Size of bin for time histogram. By default is defined automatically")
)
@NodeDefs(
NodeDef(name = "histogram", info = "Configuration for histogram plots"),
NodeDef(name = "plot", info = "Configuration for stat plots")
)
@TypedActionDef(name = "numass.timeSpectrum", inputType = NumassPoint::class, outputType = Table::class)
class TimeSpectrumAction : OneToOneAction<NumassPoint, Table>() {
private val analyzer = TimeAnalyzer();
override fun execute(context: Context, name: String, input: NumassPoint, inputMeta: Laminate): Table {
val log = getLog(context, name);
val t0 = inputMeta.getDouble("t0", 30e3);
val loChannel = inputMeta.getInt("window.lo", 500);
val upChannel = inputMeta.getInt("window.up", 10000);
val pm = context.getFeature(PlotPlugin::class.java);
val trueCR = analyzer.analyze(input, buildMeta {
"t0" to t0
"window.lo" to loChannel
"window.up" to upChannel
}).getDouble("cr")
log.report("The expected count rate for 30 us delay is $trueCR")
val binNum = inputMeta.getInt("binNum", 1000);
val binSize = inputMeta.getDouble("binSize", 1.0 / trueCR * 10 / binNum * 1e6)
val histogram = UnivariateHistogram.buildUniform(0.0, binSize * binNum, binSize)
.fill(analyzer
.getEventsWithDelay(input, inputMeta)
.mapToDouble { it.value / 1000.0 }
).asTable()
//.histogram(input, loChannel, upChannel, binSize, binNum).asTable();
log.report("Finished histogram calculation...");
if (inputMeta.getBoolean("plotHist", true)) {
val histPlot = pm.getPlotFrame(getName(), "histogram");
histPlot.configure {
node("xAxis") {
"axisTitle" to "delay"
"axisUnits" to "us"
}
node("yAxis") {
"type" to "log"
}
}
val histogramPlot = DataPlot(name)
.configure {
"showLine" to true
"showSymbol" to false
"showErrors" to false
"connectionType" to "step"
node("@adapter") {
"y.value" to "count"
}
}.apply { configure(inputMeta.getMetaOrEmpty("histogram")) }
.fillData(histogram)
histPlot.add(histogramPlot)
}
if (inputMeta.getBoolean("plotStat", true)) {
val statPlot = DataPlot(name).configure {
"showLine" to true
"thickness" to 4
"title" to "${name}_${input.voltage}"
}.apply {
configure(inputMeta.getMetaOrEmpty("plot"))
}
pm.getPlotFrame(getName(), "stat-method").add(statPlot)
(1..100).map { 1000 * it }.map { t ->
val result = analyzer.analyze(input, buildMeta {
"t0" to t
"window.lo" to loChannel
"window.up" to upChannel
})
val norm = if (inputMeta.getBoolean("normalize", true)) {
trueCR
} else {
1.0
}
statPlot.append(
XYAdapter.DEFAULT_ADAPTER.buildXYDataPoint(
t / 1000.0,
result.getDouble("cr") / norm,
result.getDouble(NumassAnalyzer.COUNT_RATE_ERROR_KEY) / norm
)
)
}
}
return histogram;
}
}