NPM/numass-framework
4
0
Fork 1
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Added normalization to histogram.

Browse Source
This commit is contained in:
Alexander Nozik 2018-07-24 15:11:07 +03:00
parent 6c5dc8fbb5
commit 753555e13e
1 changed files with 19 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

21
numass-main/src/main/kotlin/inr/numass/tasks/NumassTasks.kt
View File

@ -44,6 +44,7 @@ import java.util.*
import java.util.concurrent.atomic.AtomicLong import java.util.concurrent.atomic.AtomicLong
import java.util.function.Predicate import java.util.function.Predicate
import java.util.stream.StreamSupport import java.util.stream.StreamSupport
import kotlin.collections.HashMap
import kotlin.collections.set import kotlin.collections.set
@ -277,7 +278,8 @@ val histogramTask = task("histogram") {
descriptor { descriptor {
value("plot", types = listOf(ValueType.BOOLEAN), defaultValue = false, info = "Show plot of the spectra") value("plot", types = listOf(ValueType.BOOLEAN), defaultValue = false, info = "Show plot of the spectra")
value("points", multiple = true, types = listOf(ValueType.NUMBER), info = "The list of point voltages to build histogram") value("points", multiple = true, types = listOf(ValueType.NUMBER), info = "The list of point voltages to build histogram")
value("binning", types = listOf(ValueType.NUMBER), info = "The binning of resulting histogram") value("binning", types = listOf(ValueType.NUMBER), defaultValue = 20, info = "The binning of resulting histogram")
value("normalize", types = listOf(ValueType.BOOLEAN), defaultValue = true, info = "If true reports the count rate in each bin, otherwise total count")
info = "Combine amplitude spectra from multiple sets, but with the same U" info = "Combine amplitude spectra from multiple sets, but with the same U"
} }
model { meta -> model { meta ->
@ -316,13 +318,28 @@ val histogramTask = task("histogram") {
names.add("U$u") names.add("U$u")
} }
val times: Map<Double, Double> = data.flatMap { it.value.points }
.filter { points == null || points.contains(it.voltage) }
.groupBy { it.voltage }
.mapValues {
it.value.sumByDouble { it.length.toMillis().toDouble() / 1000 }
}
val normalize = meta.getBoolean("normalize", true)
log.report("Combining spectra") log.report("Combining spectra")
val format = MetaTableFormat.forNames(names) val format = MetaTableFormat.forNames(names)
val table = buildTable(format) { val table = buildTable(format) {
aggregator.forEach { channel, counters -> aggregator.forEach { channel, counters ->
val values: MutableMap<String, Any> = HashMap() val values: MutableMap<String, Any> = HashMap()
values[NumassAnalyzer.CHANNEL_KEY] = channel values[NumassAnalyzer.CHANNEL_KEY] = channel
counters.forEach { u, counter -> values["U$u"] = counter.get() } counters.forEach { u, counter ->
if (normalize) {
values["U$u"] = counter.get().toDouble() / times[u]!!
} else {
values["U$u"] = counter.get()
}
}
format.names.forEach { format.names.forEach {
values.putIfAbsent(it, 0) values.putIfAbsent(it, 0)
} }