rename probability_plot to "plot"

statista/distributions.py

@@ -523,7 +523,7 @@ def confidence_interval(
         """
         pass
 
-    def probability_plot(
+    def plot(
         self,
         fig1size: tuple = (10, 5),
         xlabel: str = "Actual data",
@@ -1243,7 +1243,7 @@ def confidence_interval(
         else:
             return q_upper, q_lower
 
-    def probability_plot(
+    def plot(
         self,
         fig1_size: Tuple[float, float] = (10, 5),
         xlabel: str = "Actual data",
@@ -1279,13 +1279,26 @@ def probability_plot(
 
         Returns
         -------
-        Qth: [list]
-            theoretical-generated values based on the theoretical cdf calculated from
-            weibul or the distribution parameters.
-        q_upper: [list]
-            upper-bound coresponding to the confidence interval.
-        q_lower: [list]
-            lower-bound coresponding to the confidence interval.
+        Figure:
+            matplotlib figure object
+        Tuple[Axes, Axes]:
+            matplotlib plot axes
+
+        Examples
+        --------
+        - Instantiate the Gumbel class with the data and the parameters.
+
+            >>> import matplotlib.pyplot as plt
+            >>> data = np.loadtxt("examples/data/time_series2.txt")
+            >>> parameters = {"loc": 463.8040, "scale": 220.0724}
+            >>> gumbel_dist = Gumbel(data, parameters)
+
+        - to calculate the confidence interval, we need to provide the confidence level (`alpha`).
+
+            >>> fig, ax = gumbel_dist.plot()
+
+        .. image:: /_images/gumbel-confidence-interval.png
+            :align: center
         """
         # if no parameters are provided, take the parameters provided in the class initialization.
         if parameters is None:
@@ -2040,7 +2053,7 @@ def confidence_interval(
         else:
             return q_upper, q_lower
 
-    def probability_plot(
+    def plot(
         self,
         fig1_size=(10, 5),
         xlabel="Actual data",
@@ -3251,7 +3264,7 @@ def __init__(
 
     def __getattr__(self, name: str):
         """Delegate method calls to the subclass"""
-        # Retrieve the attribute or method from the animal object
+        # Retrieve the attribute or method from the distribution object
         try:
             # Retrieve the attribute or method from the subclasses
             attribute = getattr(self.distribution, name)

statista/eva.py

@@ -194,11 +194,11 @@ def ams_analysis(
         # get the Discharge coresponding to the return periods
         q_rp = dist.inverse_cdf(non_exceed_prop, param_dist)
 
-        # Gumbel.probability_plot method calculates the theoretical values
+        # Gumbel.plot method calculates the theoretical values
         # based on the Gumbel distribution
         # parameters, theoretical cdf (or weibul), and calculate the confidence interval
         if save_plots:
-            fig, _ = dist.probability_plot()
+            fig, _ = dist.plot()
             _, _, fig2, _ = dist.confidence_interval(
                 method=method, plot_figure=True, alpha=significance_level
             )

tests/test_distributions.py

@@ -2,7 +2,7 @@
 
 import matplotlib
 
-matplotlib.use("Agg")
+matplotlib.use("TkAgg")
 from typing import List, Dict
 
 import numpy as np
@@ -223,7 +223,7 @@ def test_confidence_interval(
         assert isinstance(fig, Figure)
         assert isinstance(ax, Axes)
 
-    def test_probability_plot(
+    def test_plot(
         self,
         time_series2: list,
         dist_estimation_parameters_ks: str,
@@ -233,13 +233,13 @@ def test_probability_plot(
         param = gum_dist_parameters[dist_estimation_parameters_ks]
         dist = Gumbel(time_series2, param)
         # test default parameters.
-        fig, ax = dist.probability_plot()
+        fig, ax = dist.plot()
         assert isinstance(fig, Figure)
         assert isinstance(ax[0], Axes)
         assert isinstance(ax[1], Axes)
         # test with the cdf parameter
         cdf_weibul = PlottingPosition.weibul(time_series2)
-        fig, ax = dist.probability_plot(cdf=cdf_weibul)
+        fig, ax = dist.plot(cdf=cdf_weibul)
         assert isinstance(fig, Figure)
         assert isinstance(ax[0], Axes)
         assert isinstance(ax[1], Axes)
@@ -386,7 +386,7 @@ def test_gev_confidence_interval(
         assert isinstance(fig, Figure)
         assert isinstance(ax, Axes)
 
-    def test_gev_probability_plot(
+    def test_gev_plot(
         self,
         time_series1: list,
         dist_estimation_parameters_ks: str,
@@ -396,13 +396,13 @@ def test_gev_probability_plot(
         param = gev_dist_parameters[dist_estimation_parameters_ks]
         dist = GEV(time_series1, param)
         # test default parameters.
-        fig, ax = dist.probability_plot()
+        fig, ax = dist.plot()
         assert isinstance(fig, Figure)
         assert isinstance(ax[0], Axes)
         assert isinstance(ax[1], Axes)
         # test with the cdf parameter
         cdf_weibul = PlottingPosition.weibul(time_series1)
-        fig, ax = dist.probability_plot(cdf=cdf_weibul)
+        fig, ax = dist.plot(cdf=cdf_weibul)
         assert isinstance(fig, Figure)
         assert isinstance(ax[0], Axes)
         assert isinstance(ax[1], Axes)