deephavendatalabs

from deephaven import InputTable, TableInputHandler
 
namespace = 'OMSExample'
tradeTableName = 'Trades'
 
# Create the input table if it doesn’t exist
if not db.hasTable(namespace, tradeTableName):
   InputTable.newInputTable(db, namespace, tradeTableName,
       TableInputHandler.c("Symbol", "java.lang.String"),
       TableInputHandler.cKey("Timestamp", "com.illumon.iris.db.tables.utils.DBDateTime"),

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orders = (
   db.i('FeedOS', 'EquityQuoteL1').where("Date = currentDateNy()")
   .update("rand = Math.random()", "Buy = rand > 0.9999", "Sell = rand < 0.0001")
   .where("Buy = true || Sell = true")
   .update("Shares = Buy ? (int) Math.round(Math.random() * 100) : (int) Math.round(Math.random() * -100)")
   .view("Date", "Timestamp", "Symbol = LocalCodeStr", "Bid", "Ask", "Shares")
)

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def unifrac(samp1, samp2, classes):

    t1 = origTable.where(f"!({samp1}==0 && {samp2}==0)")\
        .view(samp1, samp2, "Taxonomy=Silva_Taxonomy+`;`+Name")\
        .update("Taxonomy = (String)fixTaxonomy.call(Taxonomy)",\
            "TaxArr = (String[])getTaxArr.call(Taxonomy)",\
            "Len = TaxArr.length")
    
    for i, c in enumerate(classes):
        t1 = t1.update(f"{c} = Len<{i+1} ? null : TaxArr[{i}]")

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def jsd(samp1, samp2, pseudo):
    
    t1 = pseudo.update(f"m = ({samp1} + {samp2}) / 2",\
            f"kld1 = {samp1} * log({samp1} / m)",\
            f"kld2 = {samp2} * log({samp2} / m)")\
        .by(AggCombo(AggSum("kld1", "kld2")))\
        .update("divergence = (0.5*kld1) + (0.5*kld2)")
    
    return t1.getColumn("divergence").get(0)

deephavendatalabs

from deephaven import *

client = PersistentQueryTableHelper.getClientForPersistentQuery(3*60*1000, owner="noormalik", name="EarningsCallSentimentAnalysis")

callPredictions = PersistentQueryTableHelper.getPreemptiveTableFromPersistentQuery("callPredictionsPre", helper=client)

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# Run the following to get java details: R CMD javareconf
Sys.setenv(JAVA_HOME =
             '/Library/Java/JavaVirtualMachines/jdk1.8.0_251.jdk/Contents/Home/')
source(sprintf("%s/iris/.programfiles/%s/integrations/r/irisdb.R",home,system))

idb.init(devroot = sprintf("%s/iris/.programfiles/%s/",home,system), 
         workspace = sprintf("%s/iris/workspaces/%s/workspaces/r/",home,system), 
         propfile = "iris-common.prop", 
         userHome = home,
         keyfile = keyfile, 

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home <- "/Users/username"
system <- "Deephaven New"
keyfile <- sprintf("%s/priv.dh-demo.base64.txt",home)
workerHeapGB <- 4
jvmHeapGB <- 2
workerHost <- "dh-demo-query1.int.illumon.com"

deephavendatalabs

---
title: "StudentsPerformance"
output: html_document
---

# Introduction

This project uses a public, synthesized exam scores dataset from Kaggle to analyze
the average scores in each subject relative to the student's parents' level of
education and whether the student took a preparation course before the exam.

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# To see where the trend changes, we can add changepoints
add_changepoints_to_plot(fig1.gca(), m, forecast)

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def prophet_plot(df):
  
   m = Prophet()
   m.fit(df)
  
   # Make a forecast dataframe 1 year ahead
   future = m.make_future_dataframe(periods=365)
   forecast = m.predict(future)
  
   # Return forecasted data, & component breakdown of forecasts