diff --git a/README.md b/README.md
index fcf4910..6800bf9 100644
--- a/README.md
+++ b/README.md
@@ -64,7 +64,6 @@ OPFython is based on the following structure, and you should pay attention to it
         - subgraph
     - math
         - distance
-        - distribution
         - general
         - random
     - models
diff --git a/data/boat.dat b/data/boat.dat
new file mode 100644
index 0000000..30ba743
Binary files /dev/null and b/data/boat.dat differ
diff --git a/data/boat.json b/data/boat.json
index 3e2cbde..52aa042 100644
--- a/data/boat.json
+++ b/data/boat.json
@@ -1,504 +1,804 @@
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-		},
-		{
+            "label": 3,
+            "features": [
+                1.738340973854065,
+                1.9195510149002075
+            ]
+        },
+        {
             "id": 73,
-			"label": 3,
-			"features": [-1.446955, 0.585442]
-		},
-		{
+            "label": 3,
+            "features": [
+                2.250093936920166,
+                -1.5932129621505737
+            ]
+        },
+        {
             "id": 74,
-			"label": 3,
-			"features": [-1.446955, 0.585442]
-		},
-		{
+            "label": 3,
+            "features": [
+                -2.923434019088745,
+                -1.4948740005493164
+            ]
+        },
+        {
             "id": 75,
-			"label": 3,
-			"features": [-1.446955, 0.585442]
-		},
-		{
+            "label": 3,
+            "features": [
+                -4.99771785736084,
+                -0.005394999869167805
+            ]
+        },
+        {
             "id": 76,
-			"label": 3,
-			"features": [-1.446955, 0.585442]
-		},
-		{
+            "label": 3,
+            "features": [
+                -4.554725170135498,
+                -0.7430559992790222
+            ]
+        },
+        {
             "id": 77,
-			"label": 3,
-			"features": [-1.446955, 0.585442]
-		},
-		{
+            "label": 3,
+            "features": [
+                3.1689229011535645,
+                1.5515300035476685
+            ]
+        },
+        {
             "id": 78,
-			"label": 3,
-			"features": [-1.446955, 0.585442]
-		},
-		{
+            "label": 3,
+            "features": [
+                2.2717700004577637,
+                1.9273630380630493
+            ]
+        },
+        {
             "id": 79,
-			"label": 3,
-			"features": [-1.446955, 0.585442]
-		},
-		{
+            "label": 3,
+            "features": [
+                4.550985813140869,
+                0.9768109917640686
+            ]
+        },
+        {
             "id": 80,
-			"label": 3,
-			"features": [-1.446955, 0.585442]
-		},
-		{
+            "label": 3,
+            "features": [
+                -1.5503400564193726,
+                1.970878005027771
+            ]
+        },
+        {
             "id": 81,
-			"label": 3,
-			"features": [-1.446955, 0.585442]
-		},
-		{
+            "label": 3,
+            "features": [
+                -4.405232906341553,
+                1.1377019882202148
+            ]
+        },
+        {
             "id": 82,
-			"label": 3,
-			"features": [-1.446955, 0.585442]
-		},
-		{
+            "label": 3,
+            "features": [
+                -3.4316511154174805,
+                -1.4357730150222778
+            ]
+        },
+        {
             "id": 83,
-			"label": 3,
-			"features": [-1.446955, 0.585442]
-		},
-		{
+            "label": 3,
+            "features": [
+                -0.5005350112915039,
+                2.068276882171631
+            ]
+        },
+        {
             "id": 84,
-			"label": 3,
-			"features": [-1.446955, 0.585442]
-		},
-		{
+            "label": 3,
+            "features": [
+                -2.4721639156341553,
+                -1.5898360013961792
+            ]
+        },
+        {
             "id": 85,
-			"label": 3,
-			"features": [-1.446955, 0.585442]
-		},
-		{
+            "label": 3,
+            "features": [
+                1.5083199739456177,
+                2.07338809967041
+            ]
+        },
+        {
             "id": 86,
-			"label": 3,
-			"features": [-1.446955, 0.585442]
-		},
-		{
+            "label": 3,
+            "features": [
+                -0.3002209961414337,
+                2.008028984069824
+            ]
+        },
+        {
             "id": 87,
-			"label": 3,
-			"features": [-1.446955, 0.585442]
-		},
-		{
+            "label": 3,
+            "features": [
+                0.4218739867210388,
+                -1.8202509880065918
+            ]
+        },
+        {
             "id": 88,
-			"label": 3,
-			"features": [-1.446955, 0.585442]
-		},
-		{
+            "label": 3,
+            "features": [
+                3.5519700050354004,
+                -1.250223994255066
+            ]
+        },
+        {
             "id": 89,
-			"label": 3,
-			"features": [-1.446955, 0.585442]
-		},
-		{
+            "label": 3,
+            "features": [
+                1.5598210096359253,
+                -1.8739399909973145
+            ]
+        },
+        {
             "id": 90,
-			"label": 3,
-			"features": [-1.446955, 0.585442]
-		},
-		{
+            "label": 3,
+            "features": [
+                -0.05125200003385544,
+                -1.954408049583435
+            ]
+        },
+        {
             "id": 91,
-			"label": 3,
-			"features": [-1.446955, 0.585442]
-		},
-		{
+            "label": 3,
+            "features": [
+                -1.7211699485778809,
+                1.9405139684677124
+            ]
+        },
+        {
             "id": 92,
-			"label": 3,
-			"features": [-1.446955, 0.585442]
-		},
-		{
+            "label": 3,
+            "features": [
+                -2.4832398891448975,
+                -1.5674279928207397
+            ]
+        },
+        {
             "id": 93,
-			"label": 3,
-			"features": [-1.446955, 0.585442]
-		},
-		{
+            "label": 3,
+            "features": [
+                -3.1551098823547363,
+                1.6419780254364014
+            ]
+        },
+        {
             "id": 94,
-			"label": 3,
-			"features": [-1.446955, 0.585442]
-		},
-		{
+            "label": 3,
+            "features": [
+                -1.744156002998352,
+                -1.6970750093460083
+            ]
+        },
+        {
             "id": 95,
-			"label": 3,
-			"features": [-1.446955, 0.585442]
-		},
-		{
+            "label": 3,
+            "features": [
+                2.612607955932617,
+                1.796733021736145
+            ]
+        },
+        {
             "id": 96,
-			"label": 3,
-			"features": [-1.446955, 0.585442]
-		},
-		{
+            "label": 3,
+            "features": [
+                2.99202299118042,
+                -1.589324951171875
+            ]
+        },
+        {
             "id": 97,
-			"label": 3,
-			"features": [-1.446955, 0.585442]
-		},
-		{
+            "label": 3,
+            "features": [
+                -1.2485530376434326,
+                -1.8398369550704956
+            ]
+        },
+        {
             "id": 98,
-			"label": 3,
-			"features": [-1.446955, 0.585442]
-		},
-		{
+            "label": 3,
+            "features": [
+                4.694591999053955,
+                -0.6377410292625427
+            ]
+        },
+        {
             "id": 99,
-			"label": 3,
-			"features": [-1.446955, 0.585442]
-		}
-	]
+            "label": 3,
+            "features": [
+                0.8488690257072449,
+                2.003649950027466
+            ]
+        }
+    ]
 }
\ No newline at end of file
diff --git a/data/boat.txt b/data/boat.txt
index 76d93cd..744ab16 100644
--- a/data/boat.txt
+++ b/data/boat.txt
@@ -97,4 +97,4 @@
 96 3 2.992023 -1.589325
 97 3 -1.248553 -1.839837
 98 3 4.694592 -0.637741
-99 3 0.848869 2.003650
+99 3 0.848869 2.003650
\ No newline at end of file
diff --git a/docs/api/opfython.math.distribution.rst b/docs/api/opfython.math.distribution.rst
deleted file mode 100644
index b27b12d..0000000
--- a/docs/api/opfython.math.distribution.rst
+++ /dev/null
@@ -1,7 +0,0 @@
-opfython.math.distribution
-===========================
-
-.. automodule:: opfython.math.distribution
-    :members:
-    :private-members:
-    :special-members:
\ No newline at end of file
diff --git a/docs/api/opfython.math.rst b/docs/api/opfython.math.rst
index a3c93b9..2905398 100644
--- a/docs/api/opfython.math.rst
+++ b/docs/api/opfython.math.rst
@@ -5,7 +5,6 @@ Just because we are computing stuff, it does not means that we do not need math.
 
 .. toctree::
     opfython.math.distance
-    opfython.math.distribution
     opfython.math.general
     opfython.math.random
 
diff --git a/examples/math/generate_distributions.py b/examples/math/generate_distributions.py
deleted file mode 100644
index 477208e..0000000
--- a/examples/math/generate_distributions.py
+++ /dev/null
@@ -1,9 +0,0 @@
-import opfython.math.distribution as d
-
-# Generating a Bernoulli distribution
-b = d.generate_bernoulli_distribution(prob=0.5, size=100)
-print(b)
-
-# Generating a Lévy distribution
-l = d.generate_levy_distribution(beta=0.1, size=100)
-print(l)
diff --git a/examples/utils/convert_from_opf.py b/examples/utils/convert_from_opf.py
new file mode 100644
index 0000000..deecf6b
--- /dev/null
+++ b/examples/utils/convert_from_opf.py
@@ -0,0 +1,13 @@
+import opfython.utils.converter as c
+
+# Defining the input OPF file
+opf_file = 'data/boat.dat'
+
+# Converting from .dat or .opf to .txt
+c.opf2txt(opf_file, output_file='out.txt')
+
+# Converting from .dat or .opf to .csv
+c.opf2csv(opf_file, output_file='out.csv')
+
+# Converting from .dat or .opf to .json
+c.opf2json(opf_file, output_file='out.json')
diff --git a/opfython/math/distribution.py b/opfython/math/distribution.py
deleted file mode 100644
index 1af2eeb..0000000
--- a/opfython/math/distribution.py
+++ /dev/null
@@ -1,73 +0,0 @@
-from math import gamma, pi, sin
-
-import numpy as np
-
-import opfython.math.random as r
-
-
-def generate_bernoulli_distribution(prob=0.0, size=1):
-    """Generates a Bernoulli distribution based on an input probability.
-
-    Args:
-        prob (float): Probability of distribution.
-        size (int): Size of array.
-
-    Returns:
-        A Bernoulli distribution n-dimensional array.
-
-    """
-
-    # Creating bernoulli array
-    bernoulli_array = np.zeros(size)
-
-    # Generating random number
-    r1 = r.generate_uniform_random_number(0, 1, size)
-
-    # For each dimension
-    for i in range(size):
-        # If random generated number if smaller than probability
-        if (r1[i] < prob):
-            # Mark as one
-            bernoulli_array[i] = 1
-        else:
-            # If not, mark as zero
-            bernoulli_array[i] = 0
-
-    return bernoulli_array
-
-
-def generate_levy_distribution(beta=0.1, size=1):
-    """Generates a n-dimensional array based on a Lévy distribution.
-
-    References:
-        X.-S. Yang and S. Deb. Computers & Operations Research.
-        Multiobjective Cuckoo Search for Design Optimization (2013).
-
-    Args:
-        beta (float): Skewness parameter.
-        size (int): Size of array.
-
-    Returns:
-        A Lévy distribution n-dimensional array.
-
-    """
-
-    # Calculates the equation's numerator
-    num = gamma(1 + beta) * sin(pi * beta / 2)
-
-    # Calculates the equation's denominator
-    den = gamma((1 + beta) / 2) * beta * (2 ** ((beta - 1) / 2))
-
-    # Calculates the sigma for further distribution generation
-    sigma = (num / den) ** (1 / beta)
-
-    # Calculates the 'u' distribution
-    u = r.generate_gaussian_random_number(size=size) * sigma
-
-    # Calculates the 'v' distribution
-    v = r.generate_gaussian_random_number(size=size)
-
-    # Finally, we can calculate the Lévy distribution
-    step = u / np.fabs(v) ** (1 / beta)
-
-    return step
diff --git a/opfython/utils/converter.py b/opfython/utils/converter.py
index 5f3d7f6..691895b 100644
--- a/opfython/utils/converter.py
+++ b/opfython/utils/converter.py
@@ -1,17 +1,208 @@
-def opf2txt():
-    pass
+import json as j
+import struct
 
-def txt2opf():
-    pass
+import numpy as np
 
-def opf2csv():
-    pass
+import opfython.utils.logging as l
 
-def csv2opf():
-    pass
+logger = l.get_logger(__name__)
 
-def opf2json():
-    pass
 
-def json2opf():
-    pass
\ No newline at end of file
+def opf2txt(opf_path, output_file=None):
+    """Converts a binary OPF file (.dat or .opf) to a .txt file.
+
+    Args:
+        opf_path (str): Path to the binary file.
+        output_file (str): The path to the output file.
+
+    """
+
+    logger.info(f'Converting file: {opf_path} ...')
+
+    # Defining header format
+    header_format = '<iii'
+
+    # Calculating size to be read
+    header_size = struct.calcsize(header_format)
+
+    # Opening input opf file
+    with open(opf_path, 'rb') as f:
+        # Reading binary data and unpacking to desired format
+        header_data = struct.unpack(header_format, f.read(header_size))
+
+        # Retrieving number of samples
+        n_samples = header_data[0]
+
+        # Retrieving number of features
+        n_features = header_data[2]
+
+        # Defining the file format for each subsequent line
+        file_format = '<ii'
+
+        # For every possible feature
+        for _ in range(n_features):
+            # Creates the desired file format
+            file_format += 'f'
+
+        # Calculates the size based on the file format
+        data_size = struct.calcsize(file_format)
+
+        # Creates an empty list to hold the samples
+        samples = []
+
+        # For every possible sample
+        for _ in range(n_samples):
+            # Reading binary data and unpacking to desired format
+            data = struct.unpack(file_format, f.read(data_size))
+
+            # Appending the data to list
+            samples.append(data)
+
+    # Closing opf file
+    f.close()
+
+    # Checking if there is an output file name
+    if not output_file:
+        # Defining output file
+        output_file = opf_path.split('.')[0] + '.txt'
+
+    # Saving output .txt file
+    np.savetxt(output_file, samples, delimiter=' ')
+
+    logger.info(f'File converted to {output_file}.')
+
+
+def opf2csv(opf_path, output_file=None):
+    """Converts a binary OPF file (.dat or .opf) to a .csv file.
+
+    Args:
+        opf_path (str): Path to the binary file.
+        output_file (str): The path to the output file.
+
+    """
+
+    logger.info(f'Converting file: {opf_path} ...')
+
+    # Defining header format
+    header_format = '<iii'
+
+    # Calculating size to be read
+    header_size = struct.calcsize(header_format)
+
+    # Opening input opf file
+    with open(opf_path, 'rb') as f:
+        # Reading binary data and unpacking to desired format
+        header_data = struct.unpack(header_format, f.read(header_size))
+
+        # Retrieving number of samples
+        n_samples = header_data[0]
+
+        # Retrieving number of features
+        n_features = header_data[2]
+
+        # Defining the file format for each subsequent line
+        file_format = '<ii'
+
+        # For every possible feature
+        for _ in range(n_features):
+            # Creates the desired file format
+            file_format += 'f'
+
+        # Calculates the size based on the file format
+        data_size = struct.calcsize(file_format)
+
+        # Creates an empty list to hold the samples
+        samples = []
+
+        # For every possible sample
+        for _ in range(n_samples):
+            # Reading binary data and unpacking to desired format
+            data = struct.unpack(file_format, f.read(data_size))
+
+            # Appending the data to list
+            samples.append(data)
+
+    # Closing opf file
+    f.close()
+
+    # Checking if there is an output file name
+    if not output_file:
+        # Defining output file
+        output_file = opf_path.split('.')[0] + '.csv'
+
+    # Saving output .txt file
+    np.savetxt(output_file, samples, delimiter=',')
+
+    logger.info(f'File converted to {output_file}.')
+
+
+def opf2json(opf_path, output_file=None):
+    """Converts a binary OPF file (.dat or .opf) to a .json file.
+
+    Args:
+        opf_path (str): Path to the binary file.
+        output_file (str): The path to the output file.
+
+    """
+
+    logger.info(f'Converting file: {opf_path} ...')
+
+    # Defining header format
+    header_format = '<iii'
+
+    # Calculating size to be read
+    header_size = struct.calcsize(header_format)
+
+    # Opening input opf file
+    with open(opf_path, 'rb') as f:
+        # Reading binary data and unpacking to desired format
+        header_data = struct.unpack(header_format, f.read(header_size))
+
+        # Retrieving number of samples
+        n_samples = header_data[0]
+
+        # Retrieving number of features
+        n_features = header_data[2]
+
+        # Defining the file format for each subsequent line
+        file_format = '<ii'
+
+        # For every possible feature
+        for _ in range(n_features):
+            # Creates the desired file format
+            file_format += 'f'
+
+        # Calculates the size based on the file format
+        data_size = struct.calcsize(file_format)
+
+        # Creating a JSON structure
+        json = {
+            'data': []
+        }
+
+        # For every possible sample
+        for _ in range(n_samples):
+            # Reading binary data and unpacking to desired format
+            data = struct.unpack(file_format, f.read(data_size))
+
+            # Appending the data to JSON structure
+            json['data'].append({
+                'id': data[0],
+                'label': data[1],
+                'features': list(data[2:])
+            })
+
+    # Closing opf file
+    f.close()
+
+    # Checking if there is an output file name
+    if not output_file:
+        # Defining output file
+        output_file = opf_path.split('.')[0] + '.json'
+
+    # Opening output file
+    with open(output_file, 'w') as f:
+        # Dumping JSON to file
+        j.dump(json, f)
+
+    logger.info(f'File converted to {output_file}.')
diff --git a/tests/opfython/math/test_distribution.py b/tests/opfython/math/test_distribution.py
deleted file mode 100644
index 11c2bce..0000000
--- a/tests/opfython/math/test_distribution.py
+++ /dev/null
@@ -1,16 +0,0 @@
-import pytest
-
-from opfython.math import distribution
-
-
-def test_generate_bernoulli_distribution():
-    bernoulli_array = distribution.generate_bernoulli_distribution(
-        prob=0.5, size=10)
-
-    assert bernoulli_array.shape == (10, )
-
-
-def test_generate_levy_distribution():
-    levy_array = distribution.generate_levy_distribution(beta=0.1, size=5)
-
-    assert levy_array.shape == (5, )
\ No newline at end of file