Go straight to the Jupyter notebook. To look under the hood, keep reading.
A previous post showed how to check valid data values once you’ve created an
Ocre object. To see how many valid values OCRE includes for denomination, for example, we can use the
hasDenomination function to find only issues with valid data values for that property, and apply the
denominationList to that result to get a list of unique values.
This brief summary goes all the way from loading OCRE data to showing how many distinct denominations it records:
import edu.holycross.shot.nomisma._ val ocreCex = "https://raw.githubusercontent.com/neelsmith/nomisma/master/cex/ocre-cite-ids.cex" val ocre = OcreSource.fromUrl(ocreCex) println("Number of valid values for denomnination: " + ocre.hasDenomination.denominationList.size)
Number of valid values for denomination: 71
How often? The
Seventy one seems like a lot of denominations. How often does each denomination appear in one of OCRE’s 50,000+ issues?
Ocre class includes a function to create a
Histogram object for a named property. If you’re in to Scala, you’ll appreciate that the
Histogram class is parameterized by type: it computes and works with frequencies of objects of any class you specifiy. Here, we’ll just work with histograms of strings, but we could equally easily construct histograms of
OcreIssues, for example.
The histogram has a Vector of
Frequencys, each of which has one value of the specified type and an integer count for it. The histogram’s
sorted function sorts the histogram by count from highest to lowest value, so we can look at the first and last of the sorted entries to see the most and least common values in OCRE for denomination.
import edu.holycross.shot.histoutils._ val denominationHisto: edu.holycross.shot.histoutils.Histogram[String] = ocre.histogram("denomination").sorted println("Entries in histogram of denominations: " + denominationHisto.size) println("Most frequent denomination: " + denominationHisto.frequencies.head) println("Least frequent denomination: " + denominationHisto.frequencies.last)
Entries in histogram of denominations: 71 Most frequent denomination: Frequency(antoninianus,9472) Least frequent denomination: Frequency(4-denarius,1)
This is helpful, but a visualization as a bar graph would be easier to interpret. Since our notebook is using the almond.sh kernel, we can do that in Scala with the plotly library.
// Import plotly, set default height as recommended // by almond.sh for use in Jupyter notebooks import plotly._, plotly.element._, plotly.layout._, plotly.Almond._ repl.pprinter() = repl.pprinter().copy(defaultHeight = 3)
Plotly can construct a bar graph from two parallel lists of values for x and y values. We can use the
count properties of our histogram’s
Frequency objects for x and y respectively.
val denominationValues = denominationHisto.frequencies.map(_.item) val denominationCounts = denominationHisto.frequencies.map(_.count) val denominationPlot = Seq( Bar(x = denominationValues, y = denominationCounts) ) plot(denominationPlot)
Your output should include a graph that looks like this (although in the Jupyter notebook, it will be interactive):
The notebook to accompany this blog post repeats this process to visualize a histogram of geographic regions. It is
ocre/Frequencies_ocre.ipynb in the notebook github repository, or you can read it on