# Random Numbers Ecosystem in Julia – The Natural Side

If we keenly observe, **randomness** can be effectively derived from **nature**. Despite being explainable by scientific phenomena, the behavior of most matter on earth is random(though it may depend on the conditions around). Eg: The fluttering of leaves on a tree though justifiable by physics, is random to the human eye, the movement of players on a football field is again quite random though influenced by strategy/position of the ball, etc.

In the last article, we talked about the **mathematical/algorithmic** approach towards random number generation, hence **pseudo**. Pseudo random number generators are quiet good for general applications. But they have once shortcoming. If a third person is aware of the **seed** value that the RNG is initialized with, he/she can easily predict the sequence of numbers that might be given out by the RNG. This can cause a serious lapse in the application stages.

### True Random Number Generators(TRNGs)

To fix this problem, one way out is using the **TRNGs(True Random Number Generators)** instead of **PRNGs**.

But the generation of **TRNGs** tend to be very slow, and the user might have to wait before getting the desired number of random values. Also their performance/effectiveness depends very much on the **source**. eg: The initial value might not be predictable but we might end up with a consistent pattern/stream of random numbers.

Other way is to seed the **PRNG** with a value generated by the **TRNG**. This way our seed value, hence the final output tends to become more **secure** since the **seed** value too is random. There are many **TRNG** sources available. One of the most random sources is the **atmospheric noise** caused to to thundering, **space debris** etc. This approach is quite effecting and mostly used.

### Tunneling nature’s randomness into computers

Generating random numbers from the nature is a classical yet effective approach. **Random.org** is an organisation that generates randomness from atmospheric noise. You can check the above link and use the services they offer. But what if we are running a **simulation** that needs autonomous and continuous input of random numbers at a particular step? We just can’t go to the **random.org** website every time and copy the numbers. Fortunately **Random.org** offers an **API**, that allows developers to write **clients** for their interface. The **API client** will then directly make an **HTTP call** to the Random.org servers and fetch the required data.

### TRNGs in Julia

Coming to **Julia**, there is provision to exploit **JSON-RPC** requests and **HTTP** calls(though the **HTTP.jl** and **JSON.jl** respectively). But manually making **POST** requests, everytime we need random numbers might become tiresome. Fortunately the **Randomorg_API.jl** packages handles this for us. The link to the package with a detailed readme is available here.

Currently the package supports the following functions:

#### generateIntegers() Function

This method generates true random **integers** within a user-defined range.

Syntax:

generateIntegers(n, min, max, replacement)Parameters:

n:It specifies how many random integers you need.min:It is the lower boundary for the range from which the random numbers will be picked.max:It is the upper boundary for the range from which the random numbers will be picked.replacement:It specifies whether the random numbers should be picked with replacement. The default (true) will cause the numbers to be picked with replacement, i.e., the resulting numbers may contain duplicate values. If you want the numbers picked to be unique, set this value tofalse.

**Example:**

## Python3

`# import the package` `using Randomorg_API` `# generate 4 integers from 1-1000` `generateIntegers(` `4` `, ` `1` `, ` `1000` `, true)` |

**Output:**

#### generateIntegerSequences() Function

This method generates uniform or multiform **sequences** of true random integers within user-defined range. **Syntax:**

generateIntegerSequences(n, length, min, max, replacement)

**Example:**

## Python3

`# generate 2 integer sequqnces` `# length 4 and in the interval [-100 100]` `generateIntegerSequences(` `2` `, ` `4` `, ` `-` `100` `, ` `100` `, true)` |

**Output:**

#### generateDecimalFractions() Function

This method generates true random **decimal fractions** from a uniform distribution across

the [0, 1] interval with a user-defined number of decimal places.

Syntax:

generateDecimalFractions(n, decimalPlaces, replacement)Parameters:decimalPlaces:The number of decimal places to use.

**Example:**

## Python3

`# generate 4 float values` `# with 3 decimal places each` `generateDecimalFractions(` `4` `, ` `3` `, true)` |

**Output:**

#### generateGaussians() Function

This method generates true random numbers from a **Gaussian distribution** (also known as

a normal distribution).

Syntax:

generateGaussians(n, mean, standardDeviation, significantDigits)Parameters:

meanThe distribution’s mean.standardDeviationThe distribution’s standard deviation.

The form uses a **Box-Muller Transform** to generate the **Gaussian distribution** from uniformly distributed numbers.**Example:**

## Python3

`# generate 4 random numbers` `# belonging to a normal-distribution` `# with mean 0 and standard-deviation 4.5` `# and 2 significant digits` `generateGaussians(` `4` `, ` `0` `, ` `4.5` `, ` `2` `)` |

**Output:**

#### generateStrings() Function

This method generates true random **strings**.

Syntax:

generateStrings(n, length, characters, replacement)Parameters:

characters:A string that contains the set of characters that are allowed to occur in the random strings.length:The length of each string.

**Example:**

## Python3

`# generate 4 random strings` `# each of length 4, (a-j)` `# without any repetition` `generateStrings(` `4` `, ` `4` `, ` `"abcdefghij"` `, false)` |

**Output:**

#### generateUUIDs() Function

This method generates **version 4** true random **Universally Unique Identifiers(UUIDs)** **Syntax:**

generateUUIDs(n)

**Example:**

## Python3

`# generate 4 random UUIDs` `generateUUIDs(` `4` `)` |

**Output:**

#### generateBlobs() Function

This method generates **Binary Large Objects(BLOBs)** containing true random data.

Syntax:generateBlobs(n, size, format)

**Example:**

## Python3

`# generate 4 random data blobs` `# belonging to a normal-distribution` `# of 50 bits each of base64 type` `generateBlobs(` `4` `, ` `56` `, ` `"base64"` `)` |

**Output:**

There are many such services online, eg: the **HotBits** service of **FermiLab** uses radioactivity, world renowned **Cloudflare** uses a large number of **lava-lamps** and **image-processing** for random number generation.

From the previous article you might recall, we talked about the **hardware** based random number generator on **Intel** chips, viz. **RDRAND**. One might wonder if we can directly communicate with **RDRAND** to get **processor** generated entropy. In Julia we exploit this method with this package. It is a nicely written module and you might consider trying it out.

Attention reader! Don’t stop learning now. Get hold of all the important DSA concepts with the **DSA Self Paced Course** at a student-friendly price and become industry ready. To complete your preparation from learning a language to DS Algo and many more, please refer **Complete Interview Preparation Course****.**

In case you wish to attend **live classes **with experts, please refer **DSA Live Classes for Working Professionals **and **Competitive Programming Live for Students**.