Detailed Description

Configure a Dask connection and visualize the filling of a 1D and 2D histograms distributedly.

This tutorial showcases the process of setting up real-time data representation for distributed computations. By calling the LiveVisualize function, you can observe the canvas updating with the intermediate results of the histograms as the distributed computation progresses.

from dask.distributed import LocalCluster, Client
import ROOT
 
# Import the live visualization function
LiveVisualize = ROOT.RDF.Experimental.Distributed.LiveVisualize
 
# Point RDataFrame calls to Dask RDataFrame object
RDataFrame = ROOT.RDF.Experimental.Distributed.Dask.RDataFrame
 
# Function to create a Dask cluster and return the client
def create_connection():
    cluster = LocalCluster(n_workers=4, threads_per_worker=1, processes=True, memory_limit="2GiB")
    client = Client(cluster)
    return client
 
# Function to fit a Gaussian function to the plot
def fit_gaus(plot):
    plot.Fit("gaus")
 
if __name__ == "__main__":
    # Setup connection to a Dask cluster
    connection = create_connection()
 
    # Create an RDataFrame that will use Dask as a backend for computations
    num_entries = 100000000
    d = RDataFrame(num_entries, daskclient=connection, npartitions=30)
 
    # Define a gaussean distribution with a variable mean
    dd = d.Define("x", f"gRandom->Gaus(10*rdfentry_/{num_entries}, 2)")\
          .Define("y", f"gRandom->Gaus(10*rdfentry_/{num_entries}, 3)")\
 
    # Create a 1D and a 2D histogram using the defined columns
    h_normal_1d = dd.Histo1D(("normal_1d", "1D Histogram of a Normal Distribution",
                              100, -10, 20),
                              "x")
 
    h_normal_2d = dd.Histo2D(("normal_2d", "2D Histogram of a Normal Distribution",
                              100, -15, 25, 
                              100, -15, 25
                              ), "x", "y")
 
    # Apply LiveVisualize to the histograms. 
    # The `fit_gaus` function will be applied to the accumulating partial result 
    # of the 1D histogram. The 2D histogram will not be further modified, just drawn. 
    # Find more details about usage of LiveVisualize in the RDataFrame docs.
    LiveVisualize({h_normal_1d: fit_gaus, h_normal_2d: None})
    
    # Plot the histograms side by side on a canvas
    c = ROOT.TCanvas("distrdf003", "distrdf003", 1600, 400)
    c.Divide(2, 1)
    c.cd(1)
    h_normal_1d.Draw()
    c.cd(2)
    h_normal_2d.Draw()
 
    c.Update()

Date: August 2023

Author: Silia Taider

Definition in file distrdf003_live_visualization.py.