diff --git a/README.md b/README.md
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diff --git a/main.py b/main.py
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+# Tool to fit discrete distributions to a set of data
+
+
+import numpy as np
+from scipy import stats
+import matplotlib.pyplot as plt
+
+
+def read_file(file_name: str) -> np.array:
+    return np.loadtxt(file_name, dtype=int)
+
+
+def show_data(sample_data: np.array, ax: plt.Axes = None, title: str = None) -> plt.Axes:
+    r"""
+    Always a good idea to look at the data before doing anything to it
+    :param sample_data:
+    :param ax:
+    :param title:
+    :return:
+    """
+    if ax is None:
+        _fig, ax = plt.subplots()
+
+    # just count the unique integers and plot as bars
+    labels, counts = np.unique(sample_data, return_counts=True)
+    ax.bar(labels, counts, align='center', color='C1')
+
+    if title:
+        ax.set_title(title)
+
+    # set axes labels
+    ax.set_xlabel('data')
+    ax.set_ylabel('frequency')
+
+    # non integer ticks are useless here
+    ax.xaxis.get_major_locator().set_params(integer=True)
+    ax.yaxis.get_major_locator().set_params(integer=True)
+
+    return ax
+
+
+def fit_uniform(sample_data: np.array):
+    """
+    The discrete uniform distribution describes a random variable
+    that has equal probabilities of being any integers in the
+    half open range [a,b)
+
+    :return:
+    """
+    uniform_dist = stats.randint
+
+    res = stats.fit(uniform_dist, sample_data,
+                    bounds={'low': (0, 4), 'high': (0, 4)})
+    res.plot()
+    plt.show()
+    print(res.params)
+    print(-np.sum(uniform_dist.logpmf(sample_data, *res.params)))
+
+    return res
+
+
+def fit_betabinom(sample_data: np.array):
+
+    betabinom_dist = stats.betabinom
+
+    res = stats.fit(betabinom_dist, sample_data, bounds={'n': (0, 4), 'a': (0, 4), 'b': (0, 4), 'loc': (0, 4)})
+
+    res.plot()
+    plt.show()
+    print(res.params)
+    print(-np.sum(betabinom_dist.logpmf(sample_data, *res.params)))
+
+
+def fit_zipf(sample_data: np.array):
+
+    zipf_dist = stats.zipf
+
+    res = stats.fit(zipf_dist, sample_data, bounds={'a': (0, 4)})
+
+    res.plot()
+    plt.show()
+    print(res.params)
+    print(-np.sum(zipf_dist.logpmf(sample_data, *res.params)))
+
+
+if __name__ == '__main__':
+    data = read_file('test_data.txt')
+    show_data(data)
+    plt.show()
+
+    fit_uniform(data)
+    fit_betabinom(data)
+    fit_zipf(data)
diff --git a/test_data.txt b/test_data.txt
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+1
+1
+2
+1
+3
+2
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