feat(plot): create plot for line, histogram, and gen pareto distribution

src/anomalytics/plots/plot.py

@@ -0,0 +1,99 @@
+import typing
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import scipy.stats as stats
+
+
+def plot_line(
+    dataset: typing.Union[pd.DataFrame, pd.Series],
+    threshold: typing.Union[pd.DataFrame, pd.Series, float],
+    title: str,
+    xlabel: str,
+    ylabel: str,
+    is_threshold: bool = True,
+    plot_width: int = 13,
+    plot_height: int = 8,
+    plot_color: str = "black",
+    th_color: str = "red",
+    th_type: str = "dashed",
+    th_line_width: int = 2,
+    alpha: float = 0.8,
+):
+    fig = plt.figure(figsize=(plot_width, plot_height))
+    plt.plot(dataset.index, dataset.values, color=plot_color, alpha=alpha)
+
+    if is_threshold:
+        plt.axhline(threshold, c=th_color, ls=th_type, lw=th_line_width)
+    plt.title(title)
+    plt.xlabel(xlabel)
+    plt.ylabel(ylabel)
+
+    fig.legend(loc="upper left", shadow=True, fancybox=True)
+    plt.show()
+
+
+def plot_hist(
+    dataset: typing.Union[pd.DataFrame, pd.Series],
+    title: str,
+    xlabel: str,
+    ylabel: str,
+    bins: int = 50,
+    plot_width: int = 13,
+    plot_height: int = 8,
+    plot_color: str = "black",
+    alpha: float = 0.8,
+):
+    fig = plt.figure(figsize=(plot_width, plot_height))
+    plt.hist(dataset.values, bins=bins, color=plot_color, alpha=alpha)
+    plt.title(title)
+    plt.xlabel(xlabel)
+    plt.ylabel(ylabel)
+
+    fig.legend(loc="upper left", shadow=True, fancybox=True)
+    plt.show()
+
+
+def plot_gen_pareto(
+    dataset: typing.Union[pd.DataFrame, pd.Series],
+    title: str,
+    xlabel: str,
+    ylabel: str,
+    bins: int = 50,
+    plot_width: int = 13,
+    plot_height: int = 8,
+    plot_color: str = "black",
+    alpha: float = 0.8,
+    params: typing.Union[typing.Dict, None] = None,
+):
+    fig = plt.figure(figsize=(plot_width, plot_height))
+
+    nonzero_exceedences = [exceedence for exceedence in dataset if exceedence > 0]
+    if params:
+        param_label = f"\n{round(params['c'], 3)}\n{round(params['loc'], 3)}\n{round(params['scale'], 3)}\n"
+        overlay = np.linspace(
+            stats.genpareto.ppf(0.1, c=params["c"], loc=params["loc"], scale=params["scale"]),
+            stats.genpareto.ppf(0.999, c=params["c"], loc=params["loc"], scale=params["scale"]),
+            len(nonzero_exceedences),
+        )
+        plt.plot(
+            overlay,
+            stats.genpareto.pdf(overlay, c=params["c"], loc=params["loc"], scale=params["scale"]),
+            c="lime",
+            lw=2,
+            label=f"\nFitted Params:{param_label}",
+        )
+    plt.hist(
+        nonzero_exceedences,
+        bins=bins,
+        density=True,
+        alpha=alpha,
+        color=plot_color,
+        label=f"{len(nonzero_exceedences)}",
+    )
+    plt.xlabel(xlabel)
+    plt.ylabel(ylabel)
+    plt.title(title)
+    fig.legend(loc="upper right", shadow=True, fancybox=True)
+    plt.show()