v0.8.8

docs/Changelog.rst

@@ -5,6 +5,17 @@
 Changelog
 ---------
 
+**Version: 0.8.8 --- Released: 21 Feb 2023**
+''''''''''''''''''''''''''''''''''''''''''''
+
+**Summary of changes**
+
+This is a bugfix release. 
+
+**Bug Fixes**
+
+-    API changes in matplotlib which deprecated some keywords and axes classes resulted in two serious bugs being introduced into reliability. This has been fixed and the requirements for matplotlib have been updated to >=3.7.0.
+
 **Version: 0.8.7 --- Released: 28 Jan 2023**
 ''''''''''''''''''''''''''''''''''''''''''''
 

reliability/ALT_fitters.py

@@ -2114,6 +2114,7 @@ def life_func(S1):
                 right_censored_groups = (
                     self._Fit_Everything_ALT__Normal_Exponential_params._Fit_Normal_Exponential__right_censored_groups
                 )
+
                 ALT_prob_plot(
                     dist="Normal",
                     model="Exponential",

reliability/Probability_plotting.py

@@ -370,8 +370,8 @@ def Weibull_probability_plot(
         functions=(axes_transforms.weibull_forward, axes_transforms.weibull_inverse),
     )
     plt.xscale("log")
-    plt.grid(b=True, which="major", color="k", alpha=0.3, linestyle="-")
-    plt.grid(b=True, which="minor", color="k", alpha=0.08, linestyle="-")
+    plt.grid(visible=True, which="major", color="k", alpha=0.3, linestyle="-")
+    plt.grid(visible=True, which="minor", color="k", alpha=0.08, linestyle="-")
     # adjust the figsize. This is done outside of figure creation so that layering of multiple plots is possible
     plt.gcf().set_size_inches(9, 9)
     if show_fitted_distribution is True:
@@ -599,8 +599,8 @@ def Loglogistic_probability_plot(
         ),
     )
     plt.xscale("log")
-    plt.grid(b=True, which="major", color="k", alpha=0.3, linestyle="-")
-    plt.grid(b=True, which="minor", color="k", alpha=0.08, linestyle="-")
+    plt.grid(visible=True, which="major", color="k", alpha=0.3, linestyle="-")
+    plt.grid(visible=True, which="minor", color="k", alpha=0.08, linestyle="-")
     plt.gcf().set_size_inches(
         9, 9
     )  # adjust the figsize. This is done outside of figure creation so that layering of multiple plots is possible
@@ -812,8 +812,8 @@ def Exponential_probability_plot_Weibull_Scale(
         functions=(axes_transforms.weibull_forward, axes_transforms.weibull_inverse),
     )
     plt.xscale("log")
-    plt.grid(b=True, which="major", color="k", alpha=0.3, linestyle="-")
-    plt.grid(b=True, which="minor", color="k", alpha=0.08, linestyle="-")
+    plt.grid(visible=True, which="major", color="k", alpha=0.3, linestyle="-")
+    plt.grid(visible=True, which="minor", color="k", alpha=0.08, linestyle="-")
     plt.gcf().set_size_inches(
         9, 9
     )  # adjust the figsize. This is done outside of figure creation so that layering of multiple plots is possible
@@ -978,8 +978,8 @@ def Gumbel_probability_plot(
         "function",
         functions=(axes_transforms.gumbel_forward, axes_transforms.gumbel_inverse),
     )
-    plt.grid(b=True, which="major", color="k", alpha=0.3, linestyle="-")
-    plt.grid(b=True, which="minor", color="k", alpha=0.08, linestyle="-")
+    plt.grid(visible=True, which="major", color="k", alpha=0.3, linestyle="-")
+    plt.grid(visible=True, which="minor", color="k", alpha=0.08, linestyle="-")
     plt.gcf().set_size_inches(
         9, 9
     )  # adjust the figsize. This is done outside of figure creation so that layering of multiple plots is possible
@@ -1142,8 +1142,8 @@ def Normal_probability_plot(
         "function",
         functions=(axes_transforms.normal_forward, axes_transforms.normal_inverse),
     )
-    plt.grid(b=True, which="major", color="k", alpha=0.3, linestyle="-")
-    plt.grid(b=True, which="minor", color="k", alpha=0.08, linestyle="-")
+    plt.grid(visible=True, which="major", color="k", alpha=0.3, linestyle="-")
+    plt.grid(visible=True, which="minor", color="k", alpha=0.08, linestyle="-")
     plt.gcf().set_size_inches(
         9, 9
     )  # adjust the figsize. This is done outside of figure creation so that layering of multiple plots is possible
@@ -1364,8 +1364,8 @@ def Lognormal_probability_plot(
         functions=(axes_transforms.normal_forward, axes_transforms.normal_inverse),
     )
     plt.xscale("log")
-    plt.grid(b=True, which="major", color="k", alpha=0.3, linestyle="-")
-    plt.grid(b=True, which="minor", color="k", alpha=0.08, linestyle="-")
+    plt.grid(visible=True, which="major", color="k", alpha=0.3, linestyle="-")
+    plt.grid(visible=True, which="minor", color="k", alpha=0.08, linestyle="-")
     plt.gcf().set_size_inches(
         9, 9
     )  # adjust the figsize. This is done outside of figure creation so that layering of multiple plots is possible
@@ -1508,8 +1508,8 @@ def Beta_probability_plot(
     )
 
     x, y = plotting_positions(failures=failures, right_censored=right_censored, a=a)
-    plt.grid(b=True, which="major", color="k", alpha=0.3, linestyle="-")
-    plt.grid(b=True, which="minor", color="k", alpha=0.08, linestyle="-")
+    plt.grid(visible=True, which="major", color="k", alpha=0.3, linestyle="-")
+    plt.grid(visible=True, which="minor", color="k", alpha=0.08, linestyle="-")
     if show_scatter_points is True:
         x_scatter, y_scatter = xy_downsample(
             x, y, downsample_factor=downsample_scatterplot
@@ -1757,8 +1757,8 @@ def Gamma_probability_plot(
     f_gamma = lambda x: axes_transforms.gamma_forward(x, beta)
     fi_gamma = lambda x: axes_transforms.gamma_inverse(x, beta)
     plt.gca().set_yscale("function", functions=(f_gamma, fi_gamma))
-    plt.grid(b=True, which="major", color="k", alpha=0.3, linestyle="-")
-    plt.grid(b=True, which="minor", color="k", alpha=0.08, linestyle="-")
+    plt.grid(visible=True, which="major", color="k", alpha=0.3, linestyle="-")
+    plt.grid(visible=True, which="minor", color="k", alpha=0.08, linestyle="-")
     plt.gcf().set_size_inches(
         9, 9
     )  # adjust the figsize. This is done outside of figure creation so that layering of multiple plots is possible
@@ -1961,8 +1961,8 @@ def Exponential_probability_plot(
             axes_transforms.exponential_inverse,
         ),
     )
-    plt.grid(b=True, which="major", color="k", alpha=0.3, linestyle="-")
-    plt.grid(b=True, which="minor", color="k", alpha=0.08, linestyle="-")
+    plt.grid(visible=True, which="major", color="k", alpha=0.3, linestyle="-")
+    plt.grid(visible=True, which="minor", color="k", alpha=0.08, linestyle="-")
     plt.gcf().set_size_inches(
         9, 9
     )  # adjust the figsize. This is done outside of figure creation so that layering of multiple plots is possible

reliability/Utils.py

@@ -45,7 +45,7 @@
 import numpy as np
 import scipy.stats as ss
 import matplotlib.pyplot as plt
-from matplotlib.axes import SubplotBase
+from matplotlib.axes import _axes
 from matplotlib.figure import Figure
 from matplotlib.collections import PolyCollection, LineCollection
 from matplotlib import ticker, colors
@@ -6482,8 +6482,8 @@ def ALT_prob_plot(
         will be the same handle.
     """
 
-    if ax is True or issubclass(type(ax), SubplotBase) is True:
-        if issubclass(type(ax), SubplotBase) is True:
+    if ax is True or type(ax) == _axes.Axes:
+        if type(ax) == _axes.Axes:
             plt.sca(ax=ax)  # use the axes passed
         else:
             plt.figure()  # if no axes is passed, make a new figure
@@ -6723,7 +6723,7 @@ def life_stress_plot(
     else:
         swap_xy = False
 
-    if ax is True or issubclass(type(ax), SubplotBase) is True:
+    if ax is True or type(ax) == _axes.Axes:
         if model in ["Dual_Exponential", "Power_Exponential", "Dual_Power"]:
             dual_stress = True
         elif model in ["Exponential", "Eyring", "Power"]:
@@ -6733,7 +6733,7 @@ def life_stress_plot(
                 "model must be one of Exponential, Eyring, Power, Dual_Exponential, Power_Exponential, Dual_Power"
             )
 
-        if issubclass(type(ax), SubplotBase) is True:
+        if type(ax) == _axes.Axes:
             if dual_stress is False:
                 if hasattr(ax, "get_zlim") is False:
                     plt.sca(ax=ax)  # use the axes passed if 2d
@@ -7498,17 +7498,18 @@ def reshow_figure(handle):
     Parameters
     ----------
     handle : object
-        The axes handle (type(SubplotBase)) or figure handle (type(Figure))
+        The axes handle (type(_axes.Axes)) or figure handle (type(Figure))
 
     Returns
     -------
     None
         The figure is automatically shown using plt.show().
     """
-    if type(handle) is not Figure and issubclass(type(handle), SubplotBase) is False:
+    type(ax) == _axes.Axes
+    if type(handle) is not Figure and type(handle) != _axes.Axes:
         # check that the handle is either an axes or a figure
         raise ValueError("handle must be an axes handle or a figure handle")
-    elif issubclass(type(handle), SubplotBase) is True:
+    elif type(handle) == _axes.Axes:
         # if the handle is an axes then extract the Figure
         handle = handle.figure
 

reliability/__init__.py

@@ -16,7 +16,7 @@
 from datetime import date
 
 __title__ = 'reliability'
-__version__ = "0.8.7"
+__version__ = "0.8.8"
 __description__ = 'A Python library for reliability engineering'
 __url__ = 'https://reliability.readthedocs.io/en/latest/index.html'
 __author__ = 'Matthew Reid'

requirements.txt

@@ -1,8 +1,8 @@
-autograd>=1.3
-scipy>=1.7.0
-numpy>=1.19.2
-matplotlib>=3.5.0
-pandas>=1.1.2
+autograd>=1.4
+scipy>=1.10.0
+numpy>=1.24.2
+matplotlib>=3.7.0
+pandas>=1.4.2
 autograd-gamma>=0.5.0
 mplcursors>=0.3
-docutils<0.18
+docutils<0.18

setup.py

@@ -7,7 +7,7 @@
 
 setuptools.setup(
     name="reliability",
-    version="0.8.7",
+    version="0.8.8",
     description="Reliability Engineering toolkit for Python",
     author="Matthew Reid",
     author_email="alpha.reliability@gmail.com",
@@ -73,11 +73,11 @@
         "License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)",
     ],
     install_requires=[
-        "autograd>=1.3",
-        "scipy>=1.7.0",
-        "numpy>=1.19.2",
-        "matplotlib>=3.5.0",
-        "pandas>=1.1.2",
+        "autograd>=1.4",
+        "scipy>=1.10.0",
+        "numpy>=1.24.2",
+        "matplotlib>=3.7.0",
+        "pandas>=1.4.2",
         "autograd-gamma>=0.5.0",
         "mplcursors>=0.3",
         "docutils<0.18"