Conduit Basics

Ascent’s API is based on Conduit. Both mesh data and action descriptions are passed to Ascent as Conduit trees. The Conduit C++ and Python interfaces are very similar, with the C++ interface heavily influenced by the ease of use of Python. These examples provide basic knowledge about creating Conduit Nodes to use with Ascent. You can also find more introductory Conduit examples in Conduit’s Tutorial Docs .

Creating key-value entries

C++ Source

#include <iostream>
#include <vector>
#include "conduit.hpp"

using namespace conduit;

int main()
{
    // 
    // The core of Conduit's data model is `Node` object that
    // holds a dynamic hierarchical key value tree
    //
    // Here is a simple example that creates
    // a key value pair in a Conduit Node:
    //     
    Node n;
    n["key"] = "value";
    std::cout << n.to_yaml() << std::endl;
}

Output

key: "value"

Python Source

import conduit
import numpy as np

#
# The core of Conduit's data model is `Node` object that 
# holds a dynamic hierarchical key value tree
#
# Here is a simple example that creates
# a key value pair in a Conduit Node:
#
n = conduit.Node()
n["key"] = "value";
print(n.to_yaml())

Output

key: "value"

Creating a path hierarchy

C++ Source

#include <iostream>
#include <vector>
#include "conduit.hpp"

using namespace conduit;

int main()
{
    // 
    // Using hierarchical paths imposes a tree structure
    // 
    Node n;
    n["dir1/dir2/val1"] = 100.5;
    std::cout << n.to_yaml() << std::endl;
}

Output


dir1: 
  dir2: 
    val1: 100.5

Python Source

import conduit
import numpy as np

#
# Using hierarchical paths imposes a tree structure
#
n = conduit.Node()
n["dir1/dir2/val1"] = 100.5;
print(n.to_yaml()) 

Output

dir1: 
  dir2: 
    val1: 100.5

Setting array data

C++ Source

#include <iostream>
#include <vector>
#include "conduit.hpp"

using namespace conduit;

int main()
{
    //
    // Conduit's Node trees hold strings or bitwidth style numeric array leaves
    //
    // In C++: you can pass raw pointers to numeric arrays or
    // std::vectors with numeric values
    //
    // In python: numpy ndarrays are used for arrays
    //

    Node n;
    int *A = new int[10];
    A[0] = 0;
    A[1] = 1;
    for (int i = 2 ; i < 10 ; i++)
        A[i] = A[i-2]+A[i-1];
    n["fib"].set(A, 10);

    std::cout << n.to_yaml() << std::endl;

    delete [] A;
}

Output


fib: [0, 1, 1, 2, 3, 5, 8, 13, 21, 34]

Python Source

import conduit
import numpy as np

#
# Conduit's Node trees hold strings or bitwidth style numeric array leaves
#
# In C++: you can pass raw pointers to numeric arrays or 
#  std::vectors with numeric values
# 
# In python: numpy ndarrays are used for arrays
#
n = conduit.Node()
a = np.zeros(10, dtype=np.int32)

a[0] = 0
a[1] = 1
for i in range(2,10):
   a[i] = a[i-2] + a[i-1]

n["fib"].set(a);
print(n.to_yaml());

Output

fib: [0, 1, 1, 2, 3, 5, 8, 13, 21, 34]

Zero-copy vs deep copy of array data

C++ Source

#include <iostream>
#include <vector>
#include "conduit.hpp"

using namespace conduit;
using std::vector;

int main()
{
    //
    // Conduit supports zero copy, allowing a Conduit Node to describe and
    // point to externally allocated data.
    //
    // set_external() is method used to zero copy data into a Node
    //

    Node n;
    vector<int> A1(10);
    A1[0] = 0;
    A1[1] = 1;
    for (int i = 2 ; i < 10 ; i++)
        A1[i] = A1[i-2]+A1[i-1];
    n["fib_deep_copy"].set(A1);

    // create another array to demo difference 
    // between set and set_external
    vector<int> A2(10);

    A2[0] = 0;  
    A2[1] = 1; 
    for (int i = 2 ; i < 10 ; i++)
    {
        A2[i] = A2[i-2]+A2[i-1];
    }

    n["fib_shallow_copy"].set_external(A2);
    A1[9] = -1;
    A2[9] = -1;

    std::cout << n.to_yaml() << std::endl;
}

Output


fib_deep_copy: [0, 1, 1, 2, 3, 5, 8, 13, 21, 34]
fib_shallow_copy: [0, 1, 1, 2, 3, 5, 8, 13, 21, -1]

Python Source

import conduit
import numpy as np

#
# Conduit supports zero copy, allowing a Conduit Node to describe and
# point to externally allocated data.
# 
# set_external() is method used to zero copy data into a Node
#
n = conduit.Node()
a1 = np.zeros(10, dtype=np.int32)

a1[0] = 0
a1[1] = 1

for i in range(2,10):
   a1[i] = a1[i-2] + a1[i-1]


# create another array to demo difference 
# between set and set_external
a2 = np.zeros(10, dtype=np.int32) 

a2[0] = 0
a2[1] = 1

for i in range(2,10):
   a2[i] = a2[i-2] + a2[i-1]

n["fib_deep_copy"].set(a1);
n["fib_shallow_copy"].set_external(a2);

a1[-1] = -1
a2[-1] = -1

print(n.to_yaml())

Output

fib_deep_copy: [0, 1, 1, 2, 3, 5, 8, 13, 21, 34]
fib_shallow_copy: [0, 1, 1, 2, 3, 5, 8, 13, 21, -1]